From ef28514af80e0b88c9ebc73c467b143e1b1a2789 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Thu, 10 Oct 2019 21:47:19 -0400 Subject: [PATCH 0001/1072] Update README.md --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index e69de29b..27f5521f 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,7 @@ +# Bedshift + +Install with: `pip install --user .` + +Run with: `bedshift -b BEDFILE` + +See: `bedshift --help` for parameters. From 2d8303881d9e4ad706709daf4bc5a4b38a35218d Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 11 Oct 2019 08:10:21 -0400 Subject: [PATCH 0002/1072] add license --- LICENSE.txt | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 LICENSE.txt diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 00000000..1b78bad2 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,9 @@ +Copyright 2017 Nathan Sheffield + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. From a46afb034c69ed65fe4efdb29e33e9cc571a8000 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Fri, 11 Oct 2019 16:15:37 -0400 Subject: [PATCH 0003/1072] moved perturbation.py into bedshift.py --- bedshift/bedshift.py | 153 +++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 148 insertions(+), 5 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 4a0879aa..5da390a1 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -1,12 +1,19 @@ """ Computing configuration representation """ +from __future__ import division, print_function import argparse import logmuse import logging import os +import pandas as pd +import numpy as np +import random from . import __version__ _LOGGER = logging.getLogger(__name__) +chroms = ['chr'+str(num) for num in list(range(1, 23)) + ['X', 'Y']] +chrom_lens = [247249719, 242951149, 199501827, 191273063, 180857866, 170899992, 158821424, 146274826, 140273252, 135374737, 134452384, 132349534, 114142980, 106368585, 100338915, 88827254, 78774742, 76117153, 63811651, 62435964, 46944323, 49691432, 154913754, 57772954] + class _VersionInHelpParser(argparse.ArgumentParser): def format_help(self): @@ -45,7 +52,15 @@ def build_argparser(): parser.add_argument( "-a", "--addrate", type=float, default=0.0, help="Addrate parameter") - + + parser.add_argument( + "--addmean", type=float, default=320.0, + help="Mean add region length") + + parser.add_argument( + "--addstdev", type=float, default=30.0, + help="Stdev add length") + parser.add_argument( "-p", "--shiftrate", type=float, default=0.0, help="Shift prob.") @@ -55,10 +70,16 @@ def build_argparser(): help="Mean shift") parser.add_argument( - "-s", "--stdev", type=float, default=1.0, - help="Stdev") + "-s", "--stdev", type=float, default=150.0, + help="Stdev shift") + parser.add_argument( + "-c", "--cutrate", type=float, default=0.0, + help="Cut prob.") + parser.add_argument( + "--mergerate", type=float, default=0.0, + help="Merge prob.") return parser @@ -66,6 +87,87 @@ def build_argparser(): +def shift(df, row, mean, stdev): + # row = random.randint(0, df.shape[0] - 1) + + theshift = int(np.random.normal(mean, stdev)) + + start = df.iloc[row][1] + end = df.iloc[row][2] + + ''' + if random.random() < 0.5: # shift the start value + df.at[row, 1] = start + theshift + else: # shift the end value + df.at[row, 2] = end + theshift + + if end < start or start > end: + return drop(df, row) + ''' + + df.at[row, 1] = start + theshift + df.at[row, 2] = end + theshift + df.at[row, 3] = 1.0 + + return df + + +def drop(df, row=None): + if not row or row >= df.shape[0]: + row = random.randint(0, df.shape[0] - 1) + df = df.drop(row, axis=0) + return df + + +def cut(df, row, mean, stdev): + # row = random.randint(0, df.shape[0] - 1) + + chrom = df.iloc[row][0] + start = df.iloc[row][1] + end = df.iloc[row][2] + + if end-start < 0: + print('ERROR: the end value of a region is less than the start value') + exit(1) + thecut = int(np.random.normal((start+end)/2, (end - start)/4)) + if thecut <= start: + thecut = start + 1 + if thecut >= end: + thecut = end - 1 + + df = drop(df, row) + new_segs = pd.DataFrame([[chrom, start, thecut, 2.0], [chrom, thecut, end, 2.0]]) + df = df.append(new_segs, ignore_index=True) + + # adjust the cut regions using the shift function + df = shift(df, df.shape[0]-1, mean, stdev) + return shift(df, df.shape[0]-2, mean, stdev) + + +def add(df, mean, stdev): + index = random.randrange(len(chroms)) + chrom = chroms[index] + start = random.randint(1, chrom_lens[index]) + end = min(start + max(int(np.random.normal(mean, stdev)), 20), chrom_lens[index]) + return df.append(pd.DataFrame([[chrom, start, end, 3.0]]), ignore_index=True) + + +def merge(df, row): + tempdf = df.sort_values([0, 1]).reset_index(drop=True) + + if row >= tempdf.shape[0] - 1: + print("df bounds exceeded") + return df + # ensure the selected two regions are the same chromosome + # row = random.randint(0, df.shape[0] - 2) + while tempdf.iloc[row][0] != tempdf.iloc[row+1][0]: + row = random.randint(0, df.shape[0] - 2) + + return df.append([[tempdf.iloc[row][0], tempdf.iloc[row][1], tempdf.iloc[row+1][2], 4.0]], ignore_index=True) + + + + def main(): """ Primary workflow """ @@ -79,26 +181,67 @@ def main(): msg = """Params: droprate: {droprate} addrate: {addrate} + addmean: {addmean} + addstdev: {addstdev} shiftrate: {shiftrate} mean: {mean} stdev: {stdev} + cutrate: {cutrate} + mergerate: {mergerate} """ _LOGGER.info(msg.format( droprate=args.droprate, addrate=args.addrate, + addmean=args.addmean, + addstdev=args.addstdev, shiftrate=args.shiftrate, mean=args.mean, - stdev=args.stdev)) + stdev=args.stdev, + cutrate=args.cutrate, + mergerate=args.mergerate)) if not args.bedfile: parser.print_help() _LOGGER.error("No bedfile given") sys.exit(1) + df = pd.read_csv(args.bedfile, sep='\t', header=None, usecols=[0,1,2]) + df[3] = 0 + rows = df.shape[0] + for i in range(rows): + if random.random() < args.droprate: + df = drop(df, i) + if random.random() < args.addrate: + df = add(df, args.addmean, args.addstdev) + if random.random() < args.shiftrate: + df = shift(df, i, args.mean, args.stdev) + if random.random() < args.cutrate: + df = cut(df, i, args.mean, args.stdev) + if random.random() < args.mergerate: + df = merge(df, i) + + ''' + if 0 <= x < 0.2: + df = shift(df) + elif 0.2 <= x < 0.4: + df = delete(df) + elif 0.4 <= x < 0.6: + df = cut(df) + elif 0.6 <= x < 0.8: + df = create(df) + else: + df = merge(df) + ''' + + df.to_csv('changed_{}'.format(os.path.basename(args.bedfile)), sep='\t', header=False, index=False) + + if __name__ == '__main__': try: sys.exit(main()) except KeyboardInterrupt: _LOGGER.error("Program canceled by user!") - sys.exit(1) \ No newline at end of file + sys.exit(1) + + From 74798af9bb4947e2c4dbe888a349316784a483a3 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Thu, 24 Oct 2019 15:12:35 -0400 Subject: [PATCH 0004/1072] fixed bad iloc bug and changed how rows are modified - Took a closer look at pandas iloc and loc to find this issue. iloc selects the position of the row in the dataframe. loc selects the row number from the index. the difference is that when a row is deleted, its position is replaced by the next row (so all rows are shifted by 1), but loc acts like a set which doesn't change other rows. - Modifications are now performed one at a time across all of the rows of the dataframe. This is one of the ways in which all specified modifications can be performed without changing the user's input probabilities/parameters. --- bedshift/bedshift.py | 177 +++++++++++++++++++++---------------------- 1 file changed, 87 insertions(+), 90 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 5da390a1..b42d8ff6 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -11,6 +11,8 @@ from . import __version__ _LOGGER = logging.getLogger(__name__) + + chroms = ['chr'+str(num) for num in list(range(1, 23)) + ['X', 'Y']] chrom_lens = [247249719, 242951149, 199501827, 191273063, 180857866, 170899992, 158821424, 146274826, 140273252, 135374737, 134452384, 132349534, 114142980, 106368585, 100338915, 88827254, 78774742, 76117153, 63811651, 62435964, 46944323, 49691432, 154913754, 57772954] @@ -66,11 +68,11 @@ def build_argparser(): help="Shift prob.") parser.add_argument( - "-m", "--mean", type=float, default=0.0, + "--shiftmean", type=float, default=0.0, help="Mean shift") parser.add_argument( - "-s", "--stdev", type=float, default=150.0, + "--shiftstdev", type=float, default=150.0, help="Stdev shift") parser.add_argument( @@ -78,8 +80,12 @@ def build_argparser(): help="Cut prob.") parser.add_argument( - "--mergerate", type=float, default=0.0, - help="Merge prob.") + "-m", "--mergerate", type=float, default=0.0, + help="Merge prob. WARNING: will likely create regions that are thousands of base pairs long") + + parser.add_argument( + "-o", "--outputfile", type=str, + help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed") return parser @@ -88,22 +94,10 @@ def build_argparser(): def shift(df, row, mean, stdev): - # row = random.randint(0, df.shape[0] - 1) - theshift = int(np.random.normal(mean, stdev)) - start = df.iloc[row][1] - end = df.iloc[row][2] - - ''' - if random.random() < 0.5: # shift the start value - df.at[row, 1] = start + theshift - else: # shift the end value - df.at[row, 2] = end + theshift - - if end < start or start > end: - return drop(df, row) - ''' + start = df.loc[row][1] + end = df.loc[row][2] df.at[row, 1] = start + theshift df.at[row, 2] = end + theshift @@ -112,59 +106,52 @@ def shift(df, row, mean, stdev): return df -def drop(df, row=None): - if not row or row >= df.shape[0]: - row = random.randint(0, df.shape[0] - 1) - df = df.drop(row, axis=0) - return df +def drop(df, row): + return df.drop(row) -def cut(df, row, mean, stdev): - # row = random.randint(0, df.shape[0] - 1) - - chrom = df.iloc[row][0] - start = df.iloc[row][1] - end = df.iloc[row][2] +def cut(df, row, meanshift, stdevshift): + chrom = df.loc[row][0] + start = df.loc[row][1] + end = df.loc[row][2] if end-start < 0: - print('ERROR: the end value of a region is less than the start value') - exit(1) - thecut = int(np.random.normal((start+end)/2, (end - start)/4)) + _LOGGER.error('ERROR: the end value of a region is less than the start value') + sys.exit(1) + thecut = int(np.random.normal((start+end)/2, (end - start)/6)) if thecut <= start: - thecut = start + 1 + thecut = start + 10 if thecut >= end: - thecut = end - 1 + thecut = end - 10 - df = drop(df, row) new_segs = pd.DataFrame([[chrom, start, thecut, 2.0], [chrom, thecut, end, 2.0]]) - df = df.append(new_segs, ignore_index=True) - # adjust the cut regions using the shift function - df = shift(df, df.shape[0]-1, mean, stdev) - return shift(df, df.shape[0]-2, mean, stdev) + new_segs = shift(new_segs, 0, meanshift, stdevshift) + new_segs = shift(new_segs, 1, meanshift, stdevshift) + new_segs[3] = 2.0 + df.loc[row] = new_segs.loc[0] + return df.append(new_segs.loc[1], ignore_index=True) def add(df, mean, stdev): - index = random.randrange(len(chroms)) - chrom = chroms[index] - start = random.randint(1, chrom_lens[index]) - end = min(start + max(int(np.random.normal(mean, stdev)), 20), chrom_lens[index]) - return df.append(pd.DataFrame([[chrom, start, end, 3.0]]), ignore_index=True) + chrom_index = random.randrange(len(chroms)) + chrom_num = chroms[chrom_index] + start = random.randint(1, chrom_lens[chrom_index]) + end = min(start + max(int(np.random.normal(mean, stdev)), 20), chrom_lens[chrom_index]) + return df.append(pd.DataFrame([[chrom_num, start, end, 3.0]]), ignore_index=True) def merge(df, row): - tempdf = df.sort_values([0, 1]).reset_index(drop=True) - - if row >= tempdf.shape[0] - 1: - print("df bounds exceeded") + # check if the regions being merged are on the same chromosome + if row + 1 not in df.index or df.loc[row][0] != df.loc[row+1][0]: return df - # ensure the selected two regions are the same chromosome - # row = random.randint(0, df.shape[0] - 2) - while tempdf.iloc[row][0] != tempdf.iloc[row+1][0]: - row = random.randint(0, df.shape[0] - 2) - - return df.append([[tempdf.iloc[row][0], tempdf.iloc[row][1], tempdf.iloc[row+1][2], 4.0]], ignore_index=True) + chrom = df.loc[row][0] + start = df.loc[row][1] + end = df.loc[row+1][2] + df = drop(df, row) + df.loc[row+1] = [chrom, start, end, 4.0] + return df @@ -179,16 +166,19 @@ def main(): _LOGGER.info("welcome to bedshift") _LOGGER.info("Shifting file: '{}'".format(args.bedfile)) msg = """Params: - droprate: {droprate} - addrate: {addrate} - addmean: {addmean} - addstdev: {addstdev} - shiftrate: {shiftrate} - mean: {mean} - stdev: {stdev} - cutrate: {cutrate} - mergerate: {mergerate} - """ + drop rate: {droprate} + add rate: {addrate} + add mean length: {addmean} + add stdev: {addstdev} + shift rate: {shiftrate} + shift mean distance: {shiftmean} + shift stdev: {shiftstdev} + cut rate: {cutrate} + merge rate: {mergerate} + outputfile: {outputfile} + """ + + outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) if not args.outputfile else args.outputfile _LOGGER.info(msg.format( droprate=args.droprate, @@ -196,45 +186,52 @@ def main(): addmean=args.addmean, addstdev=args.addstdev, shiftrate=args.shiftrate, - mean=args.mean, - stdev=args.stdev, + shiftmean=args.shiftmean, + shiftstdev=args.shiftstdev, cutrate=args.cutrate, - mergerate=args.mergerate)) + mergerate=args.mergerate, + outputfile=args.outputfile)) if not args.bedfile: parser.print_help() _LOGGER.error("No bedfile given") sys.exit(1) + if args.addrate < 0 or args.shiftrate < 0 or args.cutrate < 0 or args.mergerate < 0 or args.addrate > 1 or args.shiftrate > 1 or args.cutrate > 1 or args.mergerate > 1: + parser.print_help() + _LOGGER.error("Rate must be between 0 and 1") + sys.exit(1) + df = pd.read_csv(args.bedfile, sep='\t', header=None, usecols=[0,1,2]) - df[3] = 0 + df[3] = 0 # column indicating which modifications were made rows = df.shape[0] - for i in range(rows): - if random.random() < args.droprate: - df = drop(df, i) + _LOGGER.info('The bedfile contains {} rows'.format(rows)) + df = df.sort_values([0, 1]).reset_index(drop=True) + + # unmodified rows display a 0 + for _ in range(rows): if random.random() < args.addrate: - df = add(df, args.addmean, args.addstdev) + df = add(df, args.addmean, args.addstdev) # added rows display a 3 + for i in range(rows): if random.random() < args.shiftrate: - df = shift(df, i, args.mean, args.stdev) + df = shift(df, i, args.shiftmean, args.shiftstdev) # shifted rows display a 1 + for i in range(rows): if random.random() < args.cutrate: - df = cut(df, i, args.mean, args.stdev) + df = cut(df, i, args.shiftmean, args.shiftstdev) # cut rows display a 2 + df.reset_index(inplace=True, drop=True) + i = 0 + while i < rows: if random.random() < args.mergerate: - df = merge(df, i) - - ''' - if 0 <= x < 0.2: - df = shift(df) - elif 0.2 <= x < 0.4: - df = delete(df) - elif 0.4 <= x < 0.6: - df = cut(df) - elif 0.6 <= x < 0.8: - df = create(df) - else: - df = merge(df) - ''' - - df.to_csv('changed_{}'.format(os.path.basename(args.bedfile)), sep='\t', header=False, index=False) + df = merge(df, i) # merged rows display a 4 + i += 1 + i += 1 + df.reset_index(inplace=True, drop=True) + for i in range(rows): + if random.random() < args.droprate: + df = drop(df, i) + + _LOGGER.info('The output bedfile located in {} has {} rows'.format(outfile, df.shape[0])) + df.to_csv(outfile, sep='\t', header=False, index=False) if __name__ == '__main__': From 475177f68166ce7a177e1016699c4160efb6a560 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Thu, 31 Oct 2019 21:32:06 -0400 Subject: [PATCH 0005/1072] make bedshift class to work in python scripts --- bedshift/_version.pyc | Bin 0 -> 180 bytes bedshift/bedshift.py | 115 ++++++++++++++++++++++-------------------- 2 files changed, 59 insertions(+), 56 deletions(-) create mode 100644 bedshift/_version.pyc diff --git a/bedshift/_version.pyc b/bedshift/_version.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fba5b7f5defe6e1ddf3db23fc86ac8098709f609 GIT binary patch literal 180 zcmZSn%*%CU;)2*@1}I z__EZZ;>`TK_;?K= end: + thecut = end - 10 - if end-start < 0: - _LOGGER.error('ERROR: the end value of a region is less than the start value') - sys.exit(1) - thecut = int(np.random.normal((start+end)/2, (end - start)/6)) - if thecut <= start: - thecut = start + 10 - if thecut >= end: - thecut = end - 10 - - new_segs = pd.DataFrame([[chrom, start, thecut, 2.0], [chrom, thecut, end, 2.0]]) - # adjust the cut regions using the shift function - new_segs = shift(new_segs, 0, meanshift, stdevshift) - new_segs = shift(new_segs, 1, meanshift, stdevshift) - new_segs[3] = 2.0 - df.loc[row] = new_segs.loc[0] - return df.append(new_segs.loc[1], ignore_index=True) - - -def add(df, mean, stdev): - chrom_index = random.randrange(len(chroms)) - chrom_num = chroms[chrom_index] - start = random.randint(1, chrom_lens[chrom_index]) - end = min(start + max(int(np.random.normal(mean, stdev)), 20), chrom_lens[chrom_index]) - return df.append(pd.DataFrame([[chrom_num, start, end, 3.0]]), ignore_index=True) - - -def merge(df, row): - # check if the regions being merged are on the same chromosome - if row + 1 not in df.index or df.loc[row][0] != df.loc[row+1][0]: - return df + new_segs = pd.DataFrame([[chrom, start, thecut, 2.0], [chrom, thecut, end, 2.0]]) + # adjust the cut regions using the shift function + new_segs = self.shift(new_segs, 0, meanshift, stdevshift) + new_segs = self.shift(new_segs, 1, meanshift, stdevshift) + new_segs[3] = 2.0 + df.loc[row] = new_segs.loc[0] + return df.append(new_segs.loc[1], ignore_index=True) - chrom = df.loc[row][0] - start = df.loc[row][1] - end = df.loc[row+1][2] - df = drop(df, row) - df.loc[row+1] = [chrom, start, end, 4.0] - return df + + def add(self, df, mean, stdev): + chrom_index = random.randrange(len(chroms)) + chrom_num = chroms[chrom_index] + start = random.randint(1, chrom_lens[chrom_index]) + end = min(start + max(int(np.random.normal(mean, stdev)), 20), chrom_lens[chrom_index]) + return df.append(pd.DataFrame([[chrom_num, start, end, 3.0]]), ignore_index=True) + + + def merge(self, df, row): + # check if the regions being merged are on the same chromosome + if row + 1 not in df.index or df.loc[row][0] != df.loc[row+1][0]: + return df + + chrom = df.loc[row][0] + start = df.loc[row][1] + end = df.loc[row+1][2] + df = self.drop(df, row) + df.loc[row+1] = [chrom, start, end, 4.0] + return df @@ -208,27 +209,29 @@ def main(): _LOGGER.info('The bedfile contains {} rows'.format(rows)) df = df.sort_values([0, 1]).reset_index(drop=True) + bedshift = Bedshift() + # unmodified rows display a 0 for _ in range(rows): if random.random() < args.addrate: - df = add(df, args.addmean, args.addstdev) # added rows display a 3 + df = bedshift.add(df, args.addmean, args.addstdev) # added rows display a 3 for i in range(rows): if random.random() < args.shiftrate: - df = shift(df, i, args.shiftmean, args.shiftstdev) # shifted rows display a 1 + df = bedshift.shift(df, i, args.shiftmean, args.shiftstdev) # shifted rows display a 1 for i in range(rows): if random.random() < args.cutrate: - df = cut(df, i, args.shiftmean, args.shiftstdev) # cut rows display a 2 + df = bedshift.cut(df, i, args.shiftmean, args.shiftstdev) # cut rows display a 2 df.reset_index(inplace=True, drop=True) i = 0 while i < rows: if random.random() < args.mergerate: - df = merge(df, i) # merged rows display a 4 + df = bedshift.merge(df, i) # merged rows display a 4 i += 1 i += 1 df.reset_index(inplace=True, drop=True) for i in range(rows): if random.random() < args.droprate: - df = drop(df, i) + df = bedshift.drop(df, i) _LOGGER.info('The output bedfile located in {} has {} rows'.format(outfile, df.shape[0])) df.to_csv(outfile, sep='\t', header=False, index=False) From 6a909c71605fb7dd38d10e12c34838923a7969cd Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Fri, 1 Nov 2019 02:35:26 -0400 Subject: [PATCH 0006/1072] add a readDF function to Bedshift class --- bedshift/bedshift.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index e451a8c7..c936bed9 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -94,6 +94,12 @@ def build_argparser(): class Bedshift(object): + def readDF(self, bedfile_path): + df = pd.read_csv(bedfile_path, sep='\t', header=None, usecols=[0,1,2]) + df[3] = 0 # column indicating which modifications were made + return df.sort_values([0, 1]).reset_index(drop=True) + + def shift(self, df, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) @@ -203,13 +209,9 @@ def main(): _LOGGER.error("Rate must be between 0 and 1") sys.exit(1) - df = pd.read_csv(args.bedfile, sep='\t', header=None, usecols=[0,1,2]) - df[3] = 0 # column indicating which modifications were made - rows = df.shape[0] - _LOGGER.info('The bedfile contains {} rows'.format(rows)) - df = df.sort_values([0, 1]).reset_index(drop=True) - bedshift = Bedshift() + df = bedshift.readDF(args.bedfile) + rows = df.shape[0] # unmodified rows display a 0 for _ in range(rows): @@ -233,7 +235,7 @@ def main(): if random.random() < args.droprate: df = bedshift.drop(df, i) - _LOGGER.info('The output bedfile located in {} has {} rows'.format(outfile, df.shape[0])) + _LOGGER.info('The output bedfile located in {} has {} lines. The original bedfile had {} lines.'.format(outfile, df.shape[0], rows)) df.to_csv(outfile, sep='\t', header=False, index=False) From fdf6ad41fd98eb3d24150d0b491cedf37c6a39f5 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Fri, 1 Nov 2019 15:42:40 -0400 Subject: [PATCH 0007/1072] Create MANIFEST.in --- MANIFEST.in | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 MANIFEST.in diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 00000000..a92a303c --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,3 @@ +include README.md +include LICENSE.txt +include requirements/* From c2dfac01b7370b6e64a7fb33a69bca15d44ae837 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Sun, 3 Nov 2019 16:24:01 -0500 Subject: [PATCH 0008/1072] added script to perturb multiple .bed files easily --- bedshift/bedscript.sh | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 bedshift/bedscript.sh diff --git a/bedshift/bedscript.sh b/bedshift/bedscript.sh new file mode 100644 index 00000000..1377baab --- /dev/null +++ b/bedshift/bedscript.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +# Change NUM_BEDFILES, BEDFILE, and OUTPUT_FILE. +# Other parameters are set to the default values. Edit as needed. +# To run, execute ./bedscript.sh in Terminal. + +NUM_BEDFILES=NUMBER +for (( c=1; c<=$NUM_BEDFILES; c++ )) +do + BEDFILE=PATH/TO/ORIGINAL/FILE.bed + DROP_RATE=0.0 + + ADD_RATE=0.0 + ADD_MEAN=320.0 + ADD_STDEV=30.0 + + SHIFT_RATE=0.05 + SHIFT_MEAN=0.0 + SHIFT_STDEV=50.0 + + CUT_RATE=0.0 + MERGE_RATE=0.0 + OUTPUT_FILE=PATH/TO/PERTURBED/FILE.bed + + bedshift --bedfile $BEDFILE --droprate $DROP_RATE --addrate $ADD_RATE --addmean $ADD_MEAN --addstdev $ADD_STDEV --shiftrate $SHIFT_RATE --shiftmean $SHIFT_MEAN --shiftstdev $SHIFT_STDEV --cutrate $CUT_RATE --mergerate $MERGE_RATE --outputfile $OUTPUT_FILE +done From 663443f9a1710c125c3e059322c340f5f1cb5011 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Sun, 3 Nov 2019 21:42:30 -0500 Subject: [PATCH 0009/1072] Update bedscript.sh --- bedshift/bedscript.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bedshift/bedscript.sh b/bedshift/bedscript.sh index 1377baab..bdbc888c 100644 --- a/bedshift/bedscript.sh +++ b/bedshift/bedscript.sh @@ -14,9 +14,9 @@ do ADD_MEAN=320.0 ADD_STDEV=30.0 - SHIFT_RATE=0.05 + SHIFT_RATE=0.0 SHIFT_MEAN=0.0 - SHIFT_STDEV=50.0 + SHIFT_STDEV=150.0 CUT_RATE=0.0 MERGE_RATE=0.0 From 89343cc6cfb955799e2cc9f8cffd046a0aae80c7 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Sun, 3 Nov 2019 21:43:31 -0500 Subject: [PATCH 0010/1072] Rename bedscript.sh to bedshift.sh --- bedshift/{bedscript.sh => bedshift.sh} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename bedshift/{bedscript.sh => bedshift.sh} (100%) diff --git a/bedshift/bedscript.sh b/bedshift/bedshift.sh similarity index 100% rename from bedshift/bedscript.sh rename to bedshift/bedshift.sh From dcfe545cc1bb45c7546ee5d5b0fe869c0ebc18ec Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Sun, 3 Nov 2019 21:45:24 -0500 Subject: [PATCH 0011/1072] Update bedshift.sh --- bedshift/bedshift.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bedshift/bedshift.sh b/bedshift/bedshift.sh index bdbc888c..9bd91219 100644 --- a/bedshift/bedshift.sh +++ b/bedshift/bedshift.sh @@ -7,7 +7,7 @@ NUM_BEDFILES=NUMBER for (( c=1; c<=$NUM_BEDFILES; c++ )) do - BEDFILE=PATH/TO/ORIGINAL/FILE.bed + BEDFILE=PATH/TO/ORIGINAL/FILE%c.bed DROP_RATE=0.0 ADD_RATE=0.0 @@ -20,7 +20,7 @@ do CUT_RATE=0.0 MERGE_RATE=0.0 - OUTPUT_FILE=PATH/TO/PERTURBED/FILE.bed + OUTPUT_FILE=PATH/TO/PERTURBED/FILE%c.bed bedshift --bedfile $BEDFILE --droprate $DROP_RATE --addrate $ADD_RATE --addmean $ADD_MEAN --addstdev $ADD_STDEV --shiftrate $SHIFT_RATE --shiftmean $SHIFT_MEAN --shiftstdev $SHIFT_STDEV --cutrate $CUT_RATE --mergerate $MERGE_RATE --outputfile $OUTPUT_FILE done From a693d86f9274aa2e3198527c372911c78cc07e3d Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Sun, 3 Nov 2019 21:45:47 -0500 Subject: [PATCH 0012/1072] Update bedshift.sh --- bedshift/bedshift.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bedshift/bedshift.sh b/bedshift/bedshift.sh index 9bd91219..51afe7a6 100644 --- a/bedshift/bedshift.sh +++ b/bedshift/bedshift.sh @@ -7,7 +7,7 @@ NUM_BEDFILES=NUMBER for (( c=1; c<=$NUM_BEDFILES; c++ )) do - BEDFILE=PATH/TO/ORIGINAL/FILE%c.bed + BEDFILE=PATH/TO/ORIGINAL/FILE$c.bed DROP_RATE=0.0 ADD_RATE=0.0 @@ -20,7 +20,7 @@ do CUT_RATE=0.0 MERGE_RATE=0.0 - OUTPUT_FILE=PATH/TO/PERTURBED/FILE%c.bed + OUTPUT_FILE=PATH/TO/PERTURBED/FILE$c.bed bedshift --bedfile $BEDFILE --droprate $DROP_RATE --addrate $ADD_RATE --addmean $ADD_MEAN --addstdev $ADD_STDEV --shiftrate $SHIFT_RATE --shiftmean $SHIFT_MEAN --shiftstdev $SHIFT_STDEV --cutrate $CUT_RATE --mergerate $MERGE_RATE --outputfile $OUTPUT_FILE done From d080d2643121b02999f05ce8020c035a50cdd653 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Sun, 3 Nov 2019 21:48:39 -0500 Subject: [PATCH 0013/1072] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 27f5521f..c98d7efb 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,6 @@ Install with: `pip install --user .` -Run with: `bedshift -b BEDFILE` +Run with: `bedshift -b BEDFILE` or `./bedshift.sh` if running bedshift on multiple bedfiles with a set of parameters, which are editable in bedshift.sh. See: `bedshift --help` for parameters. From 5a694b51beb46e21409d62d047fe80b330c2d401 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Mon, 4 Nov 2019 16:56:30 -0500 Subject: [PATCH 0014/1072] move more functionality into Bedshift class; add tests --- bedshift/_version.py | 2 +- bedshift/bedshift.py | 130 +- tests/py_output.bed | 12878 ++++++++++++++++++++++++++++++++++++++ tests/pypackage_test.py | 11 + tests/sh_output.bed | 10167 ++++++++++++++++++++++++++++++ tests/shell_test.sh | 3 + tests/test.bed | 10000 +++++++++++++++++++++++++++++ 7 files changed, 33150 insertions(+), 41 deletions(-) create mode 100644 tests/py_output.bed create mode 100755 tests/pypackage_test.py create mode 100644 tests/sh_output.bed create mode 100755 tests/shell_test.sh create mode 100644 tests/test.bed diff --git a/bedshift/_version.py b/bedshift/_version.py index f102a9ca..485f44ac 100644 --- a/bedshift/_version.py +++ b/bedshift/_version.py @@ -1 +1 @@ -__version__ = "0.0.1" +__version__ = "0.1.1" diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index c936bed9..dd379394 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -100,7 +100,14 @@ def readDF(self, bedfile_path): return df.sort_values([0, 1]).reset_index(drop=True) - def shift(self, df, row, mean, stdev): + def check_rate(self, rates): + for rate in rates: + if rate < 0 or rate > 1: + _LOGGER.error("Rate must be between 0 and 1") + sys.exit(1) + + + def __shift(self, df, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) start = df.loc[row][1] @@ -113,11 +120,11 @@ def shift(self, df, row, mean, stdev): return df - def drop(self, df, row): + def __drop(self, df, row): return df.drop(row) - def cut(self, df, row, meanshift, stdevshift): + def __cut(self, df, row): chrom = df.loc[row][0] start = df.loc[row][1] end = df.loc[row][2] @@ -132,15 +139,17 @@ def cut(self, df, row, meanshift, stdevshift): thecut = end - 10 new_segs = pd.DataFrame([[chrom, start, thecut, 2.0], [chrom, thecut, end, 2.0]]) + ''' may add in later, this makes the api confusing! # adjust the cut regions using the shift function - new_segs = self.shift(new_segs, 0, meanshift, stdevshift) - new_segs = self.shift(new_segs, 1, meanshift, stdevshift) + new_segs = self.__shift(new_segs, 0, meanshift, stdevshift) + new_segs = self.__shift(new_segs, 1, meanshift, stdevshift) + ''' new_segs[3] = 2.0 df.loc[row] = new_segs.loc[0] return df.append(new_segs.loc[1], ignore_index=True) - def add(self, df, mean, stdev): + def __add(self, df, mean, stdev): chrom_index = random.randrange(len(chroms)) chrom_num = chroms[chrom_index] start = random.randint(1, chrom_lens[chrom_index]) @@ -148,7 +157,7 @@ def add(self, df, mean, stdev): return df.append(pd.DataFrame([[chrom_num, start, end, 3.0]]), ignore_index=True) - def merge(self, df, row): + def __merge(self, df, row): # check if the regions being merged are on the same chromosome if row + 1 not in df.index or df.loc[row][0] != df.loc[row+1][0]: return df @@ -156,11 +165,74 @@ def merge(self, df, row): chrom = df.loc[row][0] start = df.loc[row][1] end = df.loc[row+1][2] - df = self.drop(df, row) + df = self.__drop(df, row) df.loc[row+1] = [chrom, start, end, 4.0] return df + def add(self, df, addrate, addmean, addstdev): + self.check_rate([addrate]) + rows = df.shape[0] + for _ in range(rows): + if random.random() < addrate: + df = self.__add(df, addmean, addstdev) # added rows display a 3 + return df + + + def shift(self, df, shiftrate, shiftmean, shiftstdev): + self.check_rate([shiftrate]) + rows = df.shape[0] + for i in range(rows): + if random.random() < shiftrate: + df = self.__shift(df, i, shiftmean, shiftstdev) # shifted rows display a 1 + return df + + + def cut(self, df, cutrate): + self.check_rate([cutrate]) + rows = df.shape[0] + for i in range(rows): + if random.random() < cutrate: + df = self.__cut(df, i) # cut rows display a 2 + return df.reset_index(drop=True) + + + def merge(self, df, mergerate): + self.check_rate([mergerate]) + rows = df.shape[0] + i = 0 + while i < rows: + if random.random() < mergerate: + df = self.__merge(df, i) # merged rows display a 4 + i += 1 + i += 1 + return df.reset_index(drop=True) + + + def drop(self, df, droprate): + self.check_rate([droprate]) + rows = df.shape[0] + for i in range(rows): + if random.random() < droprate: + df = self.__drop(df, i) + return df + + + def all_perturbations(self, df, addrate, addmean, addstdev, shiftrate, shiftmean, shiftstdev, cutrate, mergerate, droprate): + self.check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) + df = self.add(df, addrate, addmean, addstdev) + df = self.shift(df, shiftrate, shiftmean, shiftstdev) + df = self.cut(df, cutrate) + df = self.merge(df, mergerate) + df = self.drop(df, droprate) + return df + + + def write_csv(self, df, outfile_name): + df.to_csv(outfile_name, sep='\t', header=False, index=False) + + + def main(): """ Primary workflow """ @@ -204,39 +276,17 @@ def main(): _LOGGER.error("No bedfile given") sys.exit(1) - if args.addrate < 0 or args.shiftrate < 0 or args.cutrate < 0 or args.mergerate < 0 or args.addrate > 1 or args.shiftrate > 1 or args.cutrate > 1 or args.mergerate > 1: - parser.print_help() - _LOGGER.error("Rate must be between 0 and 1") - sys.exit(1) - bedshift = Bedshift() - df = bedshift.readDF(args.bedfile) - rows = df.shape[0] - - # unmodified rows display a 0 - for _ in range(rows): - if random.random() < args.addrate: - df = bedshift.add(df, args.addmean, args.addstdev) # added rows display a 3 - for i in range(rows): - if random.random() < args.shiftrate: - df = bedshift.shift(df, i, args.shiftmean, args.shiftstdev) # shifted rows display a 1 - for i in range(rows): - if random.random() < args.cutrate: - df = bedshift.cut(df, i, args.shiftmean, args.shiftstdev) # cut rows display a 2 - df.reset_index(inplace=True, drop=True) - i = 0 - while i < rows: - if random.random() < args.mergerate: - df = bedshift.merge(df, i) # merged rows display a 4 - i += 1 - i += 1 - df.reset_index(inplace=True, drop=True) - for i in range(rows): - if random.random() < args.droprate: - df = bedshift.drop(df, i) - - _LOGGER.info('The output bedfile located in {} has {} lines. The original bedfile had {} lines.'.format(outfile, df.shape[0], rows)) - df.to_csv(outfile, sep='\t', header=False, index=False) + bedshifter = Bedshift() + bedshifter.check_rate([args.addrate, args.shiftrate, args.cutrate, args.mergerate, args.droprate]) + + df = bedshifter.readDF(args.bedfile) + original_rows = df.shape[0] + df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) + + _LOGGER.info('The output bedfile located in {} has {} lines. The original bedfile had {} lines.'.format(outfile, df.shape[0], original_rows)) + + bedshifter.write_csv(df, outfile) if __name__ == '__main__': diff --git a/tests/py_output.bed b/tests/py_output.bed new file mode 100644 index 00000000..27db045a --- /dev/null +++ b/tests/py_output.bed @@ -0,0 +1,12878 @@ +chr1 1057472 1057792 0.0 +chr1 1280014 1280334 0.0 +chr1 1307559 1307676 0.0 +chr1 1376423 1376511 0.0 +chr1 1837836 1837877 2.0 +chr1 1875443 1875763 0.0 +chr1 1891508 1891828 0.0 +chr1 1976238 2106074 4.0 +chr1 2126604 2126924 0.0 +chr1 2313240 2313560 0.0 +chr1 2345848 2479697 4.0 +chr1 3236554 3236874 0.0 +chr1 3341407 3341727 1.0 +chr1 3369791 3369911 0.0 +chr1 3400447 3408082 4.0 +chr1 3481804 3482124 0.0 +chr1 3531706 3532026 0.0 +chr1 3612043 3641155 4.0 +chr1 4712650 4712970 0.0 +chr1 5570053 5570151 0.0 +chr1 5805886 5806206 0.0 +chr1 6035303 6035623 1.0 +chr1 6094534 6265604 4.0 +chr1 6305157 6305477 0.0 +chr1 6306771 6307091 1.0 +chr1 6403604 6474696 4.0 +chr1 6535684 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77551667 77551755 2.0 +chrY 827714 827813 2.0 +chr5 84408375 84408606 2.0 +chr20 40940498 40940733 2.0 diff --git a/tests/pypackage_test.py b/tests/pypackage_test.py new file mode 100755 index 00000000..bb5f36f8 --- /dev/null +++ b/tests/pypackage_test.py @@ -0,0 +1,11 @@ +#!/usr/bin/python + +from bedshift import bedshift + +bedshifter = bedshift.Bedshift() + +df = bedshifter.readDF('test.bed') +original_rows = df.shape[0] +df = bedshifter.all_perturbations(df, 0.3, 320.0, 20.0, 0.3, -10.0, 120.0, 0.1, 0.11, 0.03) + +bedshifter.write_csv(df, 'py_output.bed') diff --git a/tests/sh_output.bed b/tests/sh_output.bed new file mode 100644 index 00000000..3a64b9d8 --- /dev/null +++ b/tests/sh_output.bed @@ -0,0 +1,10167 @@ +chr1 1057450 1057770 1.0 +chr1 1280014 1307676 4.0 +chr1 1875738 1876058 1.0 +chr1 1891508 1891828 0.0 +chr1 1976320 1976640 0.0 +chr1 2105773 2126818 4.0 +chr1 2313240 2313560 0.0 +chr1 2345847 2346167 0.0 +chr1 2479476 3236639 4.0 +chr1 3341278 3341598 0.0 +chr1 3407541 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74.3590462797138 -1 4.70433072024128 160 +chr8 146095093 146095413 . 475 . 74.3586323300679 -1 4.70433072024128 160 +chr11 29181168 29181488 . 475 . 74.3558995150116 -1 4.70433072024128 160 +chr10 106057558 106057878 . 475 . 74.3537614685376 -1 4.70433072024128 160 +chr12 4958213 4958533 . 475 . 74.350483861627 -1 4.70433072024128 160 +chr11 75863226 75863546 . 474 . 74.3401770320196 -1 4.70433072024128 160 +chr19 11607157 11607477 . 474 . 74.3391065508268 -1 4.70433072024128 160 +chr17 17338746 17339066 . 474 . 74.3173971383879 -1 4.70433072024128 160 +chr18 77288799 77289119 . 474 . 74.3052224469662 -1 4.70433072024128 160 +chr1 33520764 33521084 . 474 . 74.302668973784 -1 4.70433072024128 160 +chr7 27183386 27183706 . 474 . 74.2987188321004 -1 4.70433072024128 160 +chr19 38393239 38393559 . 474 . 74.2758991516509 -1 4.70433072024128 160 +chr1 116681703 116682023 . 474 . 74.2751556741457 -1 4.70433072024128 160 +chr1 204098171 204098491 . 474 . 74.2719691246269 -1 4.70433072024128 160 +chr5 172822266 172822586 . 474 . 74.2692790506651 -1 4.70433072024128 160 +chr3 125978315 125978635 . 474 . 74.2638732968426 -1 4.70433072024128 160 +chr4 113242983 113243303 . 474 . 74.2630036236132 -1 4.70433072024128 160 +chr1 6949982 6950302 . 474 . 74.2622807599546 -1 4.70433072024128 160 +chr22 41939791 41940111 . 474 . 74.2563915102656 -1 4.70433072024128 160 +chr5 177503150 177503470 . 474 . 74.2555668249828 -1 4.70433072024128 160 +chr6 16763303 16763623 . 474 . 74.2541718867599 -1 4.70433072024128 160 +chr7 48194412 48194732 . 474 . 74.25385434571 -1 4.70433072024128 160 +chr9 117501121 117501441 . 474 . 74.250837779769 -1 4.70433072024128 160 +chr1 21581710 21582030 . 474 . 74.2507375192106 -1 4.70433072024128 160 +chr7 120497596 120497916 . 474 . 74.2386734629629 -1 4.70433072024128 160 +chr9 86198097 86198417 . 474 . 74.2350998858396 -1 4.70433072024128 160 +chr1 52234676 52234996 . 474 . 74.2347685828422 -1 4.70433072024128 160 +chr21 34499662 34499982 . 474 . 74.234462903259 -1 4.70433072024128 160 +chr15 72518999 72519319 . 474 . 74.222940414842 -1 4.70433072024128 160 +chr5 146956546 146956866 . 474 . 74.2128840219193 -1 4.70433072024128 160 +chr9 126306315 126306635 . 474 . 74.2065808927473 -1 4.70433072024128 160 +chr2 219122440 219122760 . 474 . 74.2061119351742 -1 4.70433072024128 160 +chr15 65127989 65128309 . 474 . 74.1979215874717 -1 4.70433072024128 160 +chr21 47573147 47573467 . 474 . 74.1947651377319 -1 4.70433072024128 160 +chr5 176736696 176737016 . 474 . 74.1926976557762 -1 4.70433072024128 160 +chr7 100492400 100492720 . 474 . 74.192477446202 -1 4.70433072024128 160 +chr20 39591647 39591967 . 473 . 74.1892383444762 -1 4.70433072024128 160 +chr11 122893859 122894179 . 473 . 74.1857881162358 -1 4.70433072024128 160 +chr17 46114322 46114642 . 473 . 74.1801817138709 -1 4.70433072024128 160 +chr10 28721107 28721427 . 473 . 74.1759655729176 -1 4.70433072024128 160 +chr5 152876367 152876687 . 473 . 74.1752334931972 -1 4.70433072024128 160 +chr1 17980283 17980603 . 473 . 74.1523784442192 -1 4.70433072024128 160 +chr4 169506674 169506994 . 473 . 74.1520196936993 -1 4.70433072024128 160 +chr1 153769717 153770037 . 473 . 74.1436280547435 -1 4.70433072024128 160 +chr7 75649477 75649797 . 473 . 74.1416242745786 -1 4.70433072024128 160 +chrX 102719713 102720033 . 473 . 74.1349293608953 -1 4.70433072024128 160 +chr15 59785101 59785421 . 473 . 74.1327327195467 -1 4.70433072024128 160 +chr3 131513631 131513951 . 473 . 74.127451142067 -1 4.70433072024128 160 +chr4 8919756 8920076 . 473 . 74.1191147349499 -1 4.70433072024128 160 +chr12 133191475 133191795 . 473 . 74.1052877972232 -1 4.70433072024128 160 +chr12 68616928 68617248 . 473 . 74.0995409761985 -1 4.70433072024128 160 +chr15 74604126 74604446 . 473 . 74.0556786002974 -1 4.70433072024128 160 +chr17 36762648 36762968 . 473 . 74.0548392681538 -1 4.70433072024128 160 +chr12 117804844 117805164 . 473 . 74.0511157701516 -1 4.70433072024128 160 +chr12 92537824 92538144 . 473 . 74.0504074380544 -1 4.70433072024128 160 +chr2 39955999 39956319 . 473 . 74.0419944732815 -1 4.70433072024128 160 +chr12 117592986 117593306 . 472 . 74.0278235655343 -1 4.70433072024128 160 +chr8 121820880 121821200 . 472 . 74.0209432046368 -1 4.70433072024128 160 +chr11 65835736 65836056 . 472 . 74.0123947973832 -1 4.70433072024128 160 +chr2 55276123 55276443 . 472 . 74.0095464353727 -1 4.70433072024128 160 +chr8 117778442 117778762 . 472 . 74.0085724890565 -1 4.70433072024128 160 +chr19 52164295 52164615 . 472 . 74.0039554668459 -1 4.70433072024128 160 +chr11 15841244 15841564 . 472 . 73.9997502509056 -1 4.70433072024128 160 +chr12 14409483 14409803 . 472 . 73.9967909558286 -1 4.70433072024128 160 +chr14 88082425 88082745 . 472 . 73.993208074346 -1 4.70433072024128 160 +chr16 4556431 4556751 . 472 . 73.9896421323815 -1 4.70433072024128 160 +chr6 35995130 35995450 . 472 . 73.9768498780231 -1 4.70433072024128 160 +chr12 12186293 12186613 . 472 . 73.969485660754 -1 4.70433072024128 160 +chr18 52901877 52902197 . 472 . 73.9653225287818 -1 4.70433072024128 160 +chr2 63715280 63715600 . 472 . 73.9608128886957 -1 4.70433072024128 160 +chr17 61934434 61934754 . 472 . 73.9485117790666 -1 4.70433072024128 160 +chr11 120823537 120823857 . 472 . 73.9437088484417 -1 4.70433072024128 160 +chr17 4118661 4118981 . 472 . 73.9339891100689 -1 4.70433072024128 160 +chr11 132182529 132182849 . 472 . 73.9254491460257 -1 4.70433072024128 160 +chr1 68225996 68226316 . 472 . 73.9225773467074 -1 4.70433072024128 160 +chr8 25946218 25946538 . 472 . 73.9036891546841 -1 4.70433072024128 160 +chr1 183204151 183204471 . 472 . 73.8982468421946 -1 4.70433072024128 160 +chr12 1202372 1202692 . 472 . 73.8971832011824 -1 4.70433072024128 160 +chr1 24716580 24716900 . 472 . 73.8957247802703 -1 4.70433072024128 160 +chr12 2692242 2692562 . 472 . 73.8915616678503 -1 4.70433072024128 160 +chr1 156675756 156676076 . 472 . 73.8800485037721 -1 4.70433072024128 160 +chr22 33056211 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. 62.9613716897737 -1 4.70433072024128 160 +chr19 10360785 10361105 . 402 . 62.9592124383753 -1 4.70433072024128 160 +chr12 44394193 44394513 . 402 . 62.9408175970991 -1 4.70433072024128 160 From 075de5e4a91adbe909726e8e91d983fc8bb4523e Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Tue, 5 Nov 2019 13:26:39 -0500 Subject: [PATCH 0015/1072] add main test script --- .gitignore | 2 + README.md | 31 + run-tests.sh | 4 + tests/py_output.bed | 12878 -------------------------------------- tests/pypackage_test.py | 3 + tests/sh_output.bed | 10167 ------------------------------ tests/shell_test.sh | 2 +- 7 files changed, 41 insertions(+), 23046 deletions(-) create mode 100644 .gitignore create mode 100755 run-tests.sh delete mode 100644 tests/py_output.bed delete mode 100644 tests/sh_output.bed diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..d237af58 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +tests/sh_output.bed +tests/py_output.bed diff --git a/README.md b/README.md index c98d7efb..e507714f 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,37 @@ Install with: `pip install --user .` +## Command line + Run with: `bedshift -b BEDFILE` or `./bedshift.sh` if running bedshift on multiple bedfiles with a set of parameters, which are editable in bedshift.sh. See: `bedshift --help` for parameters. + +## Python + +```py +from bedshift import bedshift + +bedshifter = bedshift.Bedshift() + +df = bedshifter.readDF('test.bed') +df = bedshifter.all_perturbations(df, addrate=0.3, addmean=320.0, addstdev=20.0, shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, cutrate=0.1, mergerate=0.11, droprate=0.03) +# can also run single operations: shift, add, cut, merge, drop + +bedshifter.write_csv(df, 'output_file.bed') +``` + + + + +## Development + +test changes: + +``` +pip install --user . + +./run-tests.sh +``` + +if the tests complete, then bedshift is working properly diff --git a/run-tests.sh b/run-tests.sh new file mode 100755 index 00000000..d9088b7f --- /dev/null +++ b/run-tests.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +./tests/pypackage_test.py +./tests/shell_test.sh diff --git a/tests/py_output.bed b/tests/py_output.bed deleted file mode 100644 index 27db045a..00000000 --- a/tests/py_output.bed +++ /dev/null @@ -1,12878 +0,0 @@ -chr1 1057472 1057792 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-chr4 86704725 86704798 2.0 -chr21 2016134 2016358 2.0 -chr19 40926208 40926322 2.0 -chrX 146049598 146049710 2.0 -chr21 9211770 9211880 2.0 -chr1 201854362 201854477 2.0 -chr3 192679035 192679158 2.0 -chr10 32312944 32313120 2.0 -chr19 3603165 3603319 2.0 -chrX 142654575 142654678 2.0 -chr15 66453706 66453901 2.0 -chr22 30448709 30448852 2.0 -chr6 13002506 13002680 2.0 -chr5 101530343 101530448 2.0 -chr7 77762140 77762250 2.0 -chr18 38307241 38307427 2.0 -chr2 161592239 161592370 2.0 -chr15 43875167 43875275 2.0 -chr16 58617838 58617945 2.0 -chr22 14967208 14967333 2.0 -chr11 83556398 83556678 2.0 -chr14 98943427 98943681 2.0 -chr13 77551667 77551755 2.0 -chrY 827714 827813 2.0 -chr5 84408375 84408606 2.0 -chr20 40940498 40940733 2.0 diff --git a/tests/pypackage_test.py b/tests/pypackage_test.py index bb5f36f8..37add803 100755 --- a/tests/pypackage_test.py +++ b/tests/pypackage_test.py @@ -1,7 +1,10 @@ #!/usr/bin/python +import os, sys from bedshift import bedshift 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-chr19 12433931 12433997 2.0 -chr21 41219062 41219232 2.0 -chr11 106533950 106534132 2.0 -chr12 98916585 98916767 2.0 -chr22 47262445 47262681 2.0 -chr4 73662444 73662655 2.0 -chr22 19901115 19901226 2.0 -chr2 127010089 127010255 2.0 -chr3 54048224 54048330 2.0 -chr14 59112640 59112935 2.0 -chr2 221413109 221413271 2.0 -chr19 50050709 50050915 2.0 -chr5 28355380 28355596 2.0 -chr8 24796348 24796501 2.0 -chr5 105503302 105503368 2.0 -chr7 132524332 132524483 2.0 -chr11 28562434 28562588 2.0 -chrX 113562173 113562392 2.0 -chr5 18940118 18940299 2.0 -chr9 25618079 25618222 2.0 -chr8 5182276 5182443 2.0 -chrX 57464400 57464630 2.0 -chr14 40278689 40278837 2.0 -chr15 38370142 38370226 2.0 -chr8 5354029 5354217 2.0 -chrX 141225161 141225191 2.0 -chr6 128921801 128921842 2.0 -chrX 132810182 132810355 2.0 -chr19 4459660 4459860 2.0 -chr7 111737574 111737777 2.0 -chr5 24824733 24824917 2.0 -chr8 140805659 140805808 2.0 -chr6 68181717 68181779 2.0 -chr7 19062205 19062328 2.0 -chr3 3367007 3367192 2.0 -chr17 11854523 11854631 2.0 -chr8 56841842 56841975 2.0 -chr19 3718497 3718682 2.0 -chr3 163998196 163998349 2.0 -chr2 97299772 97299978 2.0 -chr18 52374290 52374425 2.0 -chr1 117906908 117907010 2.0 -chr4 45865502 45865632 2.0 -chr21 9352563 9352787 2.0 -chr4 58243215 58243383 2.0 -chr9 60554085 60554184 2.0 -chr17 21036155 21036359 2.0 -chr11 83620828 83620976 2.0 -chr13 27352912 27352990 2.0 -chr7 20421821 20422042 2.0 -chr7 153427280 153427467 2.0 -chr14 72885915 72886040 2.0 -chrY 5178518 5178690 2.0 -chr21 9013278 9013477 2.0 -chr18 26709762 26709898 2.0 -chrX 116477254 116477396 2.0 -chr5 98697785 98698002 2.0 -chr8 73584082 73584216 2.0 -chrY 24022549 24022635 2.0 -chr22 23124716 23124918 2.0 -chr9 60039339 60039496 2.0 -chr15 6520582 6520863 2.0 -chr10 100235019 100235199 2.0 -chr18 63170241 63170390 2.0 -chrY 846177 846313 2.0 -chr6 139468898 139469021 2.0 -chr16 6617209 6617396 2.0 -chrY 27274688 27274737 2.0 -chrY 47784212 47784369 2.0 -chr14 39605584 39605700 2.0 -chr2 167907750 167907853 2.0 -chr8 28950211 28950451 2.0 -chr5 156491381 156491555 2.0 -chr17 70141668 70141901 2.0 -chr21 10770473 10770635 2.0 -chr18 56475488 56475652 2.0 -chr20 45335809 45335968 2.0 -chrY 3750046 3750098 2.0 -chr7 58395109 58395264 2.0 -chr20 8581933 8582049 2.0 -chr10 104559205 104559380 2.0 -chr18 15271480 15271604 2.0 -chr8 18077494 18077587 2.0 -chr3 21562592 21562782 2.0 -chr6 97532475 97532645 2.0 -chr22 45456031 45456227 2.0 -chr19 57678445 57678531 2.0 -chr13 100126378 100126518 2.0 -chr11 8082196 8082347 2.0 -chr10 13796465 13796585 2.0 -chr8 31083896 31084120 2.0 -chr7 93197522 93197662 2.0 -chr15 89258623 89258797 2.0 -chr21 19444038 19444309 2.0 -chr12 117421814 117421968 2.0 -chr2 15613260 15613459 2.0 -chr17 48048365 48048411 2.0 -chr4 61754339 61754543 2.0 diff --git a/tests/shell_test.sh b/tests/shell_test.sh index c15fdc5e..9db13ca4 100755 --- a/tests/shell_test.sh +++ b/tests/shell_test.sh @@ -1,3 +1,3 @@ #!/bin/bash -bedshift --bedfile test.bed --droprate 0.1 --addrate 0.2 --addmean 320.0 --addstdev 30.0 --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile sh_output.bed +bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addmean 320.0 --addstdev 30.0 --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output.bed From a716a153f6278e62de0651139da4caf381db7755 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Tue, 5 Nov 2019 13:50:22 -0500 Subject: [PATCH 0016/1072] make python parameters optional --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index dd379394..66195abc 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -218,7 +218,7 @@ def drop(self, df, droprate): return df - def all_perturbations(self, df, addrate, addmean, addstdev, shiftrate, shiftmean, shiftstdev, cutrate, mergerate, droprate): + def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0): self.check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) df = self.add(df, addrate, addmean, addstdev) df = self.shift(df, shiftrate, shiftmean, shiftstdev) From 5d5cdfd2c5a0cc45162b1b62df4da5f684f36447 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Tue, 5 Nov 2019 18:29:43 -0500 Subject: [PATCH 0017/1072] remove pyc files --- .gitignore | 1 + bedshift/_version.pyc | Bin 180 -> 0 bytes 2 files changed, 1 insertion(+) delete mode 100644 bedshift/_version.pyc diff --git a/.gitignore b/.gitignore index d237af58..01045141 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,3 @@ tests/sh_output.bed tests/py_output.bed +*.pyc diff --git a/bedshift/_version.pyc b/bedshift/_version.pyc deleted file mode 100644 index fba5b7f5defe6e1ddf3db23fc86ac8098709f609..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 180 zcmZSn%*%CU;)2*@1}I z__EZZ;>`TK_;?K Date: Thu, 21 Nov 2019 15:00:16 -0500 Subject: [PATCH 0018/1072] change chromosome indexing --- bedshift/bedshift.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 66195abc..c14aab30 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -15,7 +15,7 @@ chroms = ['chr'+str(num) for num in list(range(1, 23)) + ['X', 'Y']] -chrom_lens = [247249719, 242951149, 199501827, 191273063, 180857866, 170899992, 158821424, 146274826, 140273252, 135374737, 134452384, 132349534, 114142980, 106368585, 100338915, 88827254, 78774742, 76117153, 63811651, 62435964, 46944323, 49691432, 154913754, 57772954] +chrom_lens = [-1, 247249719, 242951149, 199501827, 191273063, 180857866, 170899992, 158821424, 146274826, 140273252, 135374737, 134452384, 132349534, 114142980, 106368585, 100338915, 88827254, 78774742, 76117153, 63811651, 62435964, 46944323, 49691432, 154913754, 57772954] class _VersionInHelpParser(argparse.ArgumentParser): @@ -150,7 +150,7 @@ def __cut(self, df, row): def __add(self, df, mean, stdev): - chrom_index = random.randrange(len(chroms)) + chrom_index = random.randrange(1, len(chroms)+1) chrom_num = chroms[chrom_index] start = random.randint(1, chrom_lens[chrom_index]) end = min(start + max(int(np.random.normal(mean, stdev)), 20), chrom_lens[chrom_index]) From 73ccdf2b15c653a11a3a5178cdc2922d4645120e Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Wed, 4 Dec 2019 13:20:33 -0500 Subject: [PATCH 0019/1072] started mkdocs --- .gitignore | 4 +-- bedshift/bedshift.py | 21 ++++++++++----- docs/README.md | 39 +++++++++++++++++++++++++++ {tests => examples}/pypackage_test.py | 0 {tests => examples}/shell_test.sh | 0 {tests => examples}/test.bed | 0 mkdocs.yml | 9 +++++++ run-tests.sh | 4 +-- 8 files changed, 67 insertions(+), 10 deletions(-) create mode 100644 docs/README.md rename {tests => examples}/pypackage_test.py (100%) rename {tests => examples}/shell_test.sh (100%) rename {tests => examples}/test.bed (100%) create mode 100644 mkdocs.yml diff --git a/.gitignore b/.gitignore index 01045141..448b24ea 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,3 @@ -tests/sh_output.bed -tests/py_output.bed +examples/sh_output.bed +examples/py_output.bed *.pyc diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index c14aab30..3b509333 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -14,7 +14,8 @@ _LOGGER = logging.getLogger(__name__) -chroms = ['chr'+str(num) for num in list(range(1, 23)) + ['X', 'Y']] +chroms = [-1] + ['chr'+str(num) for num in list(range(1, 23))] + ['X', 'Y'] + chrom_lens = [-1, 247249719, 242951149, 199501827, 191273063, 180857866, 170899992, 158821424, 146274826, 140273252, 135374737, 134452384, 132349534, 114142980, 106368585, 100338915, 88827254, 78774742, 76117153, 63811651, 62435964, 46944323, 49691432, 154913754, 57772954] @@ -94,9 +95,13 @@ def build_argparser(): class Bedshift(object): + def __init__(self): + self.original_regions = -1 + def readDF(self, bedfile_path): df = pd.read_csv(bedfile_path, sep='\t', header=None, usecols=[0,1,2]) df[3] = 0 # column indicating which modifications were made + self.original_regions = df.shape[0] return df.sort_values([0, 1]).reset_index(drop=True) @@ -106,6 +111,11 @@ def check_rate(self, rates): _LOGGER.error("Rate must be between 0 and 1") sys.exit(1) + def pick_random_chrom(self): + chrom_index = random.randint(1, len(chroms)-1) + chrom_num = chroms[chrom_index] + chrom_len = chrom_lens[chrom_index] + return chrom_num, chrom_len def __shift(self, df, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) @@ -150,10 +160,9 @@ def __cut(self, df, row): def __add(self, df, mean, stdev): - chrom_index = random.randrange(1, len(chroms)+1) - chrom_num = chroms[chrom_index] - start = random.randint(1, chrom_lens[chrom_index]) - end = min(start + max(int(np.random.normal(mean, stdev)), 20), chrom_lens[chrom_index]) + chrom_num, chrom_len = self.pick_random_chrom() + start = random.randint(1, chrom_len) + end = min(start + max(int(np.random.normal(mean, stdev)), 20), chrom_len) return df.append(pd.DataFrame([[chrom_num, start, end, 3.0]]), ignore_index=True) @@ -230,7 +239,7 @@ def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, shift def write_csv(self, df, outfile_name): df.to_csv(outfile_name, sep='\t', header=False, index=False) - + print("Original bedfile had {} regions. Perturbed bedfile has {} regions".format(self.original_regions, df.shape[0])) diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 00000000..909938e8 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,39 @@ +# Bedshift + +Bedshift is a tool for randomly perturbing bedfiles. Different kinds of perturbations supported are region shifts, drops, adds, cuts, and merges. This tool is particularly useful for creating test datasets for various tasks, since there often is no ground truth dataset to compare to. By perturbing a file, analysis can be done on both the perturbed file and the original file and be compared. + +## Installing + +This package is not available on pypi yet, but is available for local install by cloning the repository. + +``` +git clone https://github.com/databio/bedshift.git +cd bedshift +pip install . +``` + +## Usage + +The package is available for use both as a command line interface and a python package. + +The following examples will shift 10% of the regions and add 10% new regions in `examples/test.bed`. The output is located at `bedshifted_test.bed`. + +CLI: + +``` +bedshift -h +bedshift -b examples/test.bed -p 0.1 -a 0.1 +``` + +Python: + +```py +from bedshift import bedshift + +bedshifter = bedshift.Bedshift() + +df = bedshifter.readDF('examples/test.bed') +df = bedshifter.shift(df, shiftrate=0.1, shiftmean=0.0, shiftstdev=120.0) +df = bedshifter.add(df, addrate=0.1, addmean=320.0, addstdev=20.0) +bedshifter.write_csv(df, 'output_file.bed') +``` diff --git a/tests/pypackage_test.py b/examples/pypackage_test.py similarity index 100% rename from tests/pypackage_test.py rename to examples/pypackage_test.py diff --git a/tests/shell_test.sh b/examples/shell_test.sh similarity index 100% rename from tests/shell_test.sh rename to examples/shell_test.sh diff --git a/tests/test.bed b/examples/test.bed similarity index 100% rename from tests/test.bed rename to examples/test.bed diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..3d4d85e6 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,9 @@ +site_name: Bedshift +# site_logo: img/logo_caravel_dark.svg +site_url: http://code.databio.org/bedshift/ +repo_url: http://github.com/databio/bedshift +theme: databio + +nav: + - Introduction: README.md + diff --git a/run-tests.sh b/run-tests.sh index d9088b7f..08f04a4c 100755 --- a/run-tests.sh +++ b/run-tests.sh @@ -1,4 +1,4 @@ #!/bin/bash -./tests/pypackage_test.py -./tests/shell_test.sh +./examples/pypackage_test.py +./examples/shell_test.sh From 49380c1d972433f5b63bafd2b181a49c68d21ca5 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Fri, 6 Dec 2019 13:01:24 -0500 Subject: [PATCH 0020/1072] add mkdocs requirements --- .gitignore | 1 + requirements/requirements-doc.txt | 2 ++ 2 files changed, 3 insertions(+) create mode 100644 requirements/requirements-doc.txt diff --git a/.gitignore b/.gitignore index 448b24ea..9939595e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ examples/sh_output.bed examples/py_output.bed *.pyc +site/ diff --git a/requirements/requirements-doc.txt b/requirements/requirements-doc.txt new file mode 100644 index 00000000..a8e26967 --- /dev/null +++ b/requirements/requirements-doc.txt @@ -0,0 +1,2 @@ +https://github.com/databio/mkdocs-databio/archive/master.zip +https://github.com/databio/bedshift/archive/master.zip From 980ea42d3f3d1e69382dc164a2ad802a02dfc9e6 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Fri, 6 Dec 2019 13:08:37 -0500 Subject: [PATCH 0021/1072] change function naming of bedfile io --- README.md | 4 ++-- bedshift/bedshift.py | 24 ++++++++++++++++++++---- docs/README.md | 4 ++-- examples/pypackage_test.py | 4 ++-- 4 files changed, 26 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index e507714f..e2367e91 100644 --- a/README.md +++ b/README.md @@ -15,11 +15,11 @@ from bedshift import bedshift bedshifter = bedshift.Bedshift() -df = bedshifter.readDF('test.bed') +df = bedshifter.read_bed('test.bed') df = bedshifter.all_perturbations(df, addrate=0.3, addmean=320.0, addstdev=20.0, shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, cutrate=0.1, mergerate=0.11, droprate=0.03) # can also run single operations: shift, add, cut, merge, drop -bedshifter.write_csv(df, 'output_file.bed') +bedshifter.write_bed(df, 'output_file.bed') ``` diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 3b509333..0462a86a 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -98,7 +98,7 @@ class Bedshift(object): def __init__(self): self.original_regions = -1 - def readDF(self, bedfile_path): + def read_bed(self, bedfile_path): df = pd.read_csv(bedfile_path, sep='\t', header=None, usecols=[0,1,2]) df[3] = 0 # column indicating which modifications were made self.original_regions = df.shape[0] @@ -228,6 +228,22 @@ def drop(self, df, droprate): def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0): + ''' + Perform all five perturbations in the order of add, shift, cut, merge, drop. + + :param df: the dataframe to perturb. + :param addrate: the rate (as a proportion of the total number of regions) to add regions + :param addmean: the mean length of added regions + :param addstdev: the standard deviation of the length of added regions + :param shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) + :param shiftmean: the mean shift distance + :param shiftstdev: the standard deviation of the shift distance + :param cutrate: the rate to cut regions into two separate regions + :param mergerate: the rate to merge two regions into one + :param droprate: the rate to drop/remove regions + :return pandas.DataFrame: the new dataframe after all perturbations + ''' + self.check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) df = self.add(df, addrate, addmean, addstdev) df = self.shift(df, shiftrate, shiftmean, shiftstdev) @@ -237,7 +253,7 @@ def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, shift return df - def write_csv(self, df, outfile_name): + def write_bed(self, df, outfile_name): df.to_csv(outfile_name, sep='\t', header=False, index=False) print("Original bedfile had {} regions. Perturbed bedfile has {} regions".format(self.original_regions, df.shape[0])) @@ -289,13 +305,13 @@ def main(): bedshifter = Bedshift() bedshifter.check_rate([args.addrate, args.shiftrate, args.cutrate, args.mergerate, args.droprate]) - df = bedshifter.readDF(args.bedfile) + df = bedshifter.read_bed(args.bedfile) original_rows = df.shape[0] df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) _LOGGER.info('The output bedfile located in {} has {} lines. The original bedfile had {} lines.'.format(outfile, df.shape[0], original_rows)) - bedshifter.write_csv(df, outfile) + bedshifter.write_bed(df, outfile) if __name__ == '__main__': diff --git a/docs/README.md b/docs/README.md index 909938e8..09013958 100644 --- a/docs/README.md +++ b/docs/README.md @@ -32,8 +32,8 @@ from bedshift import bedshift bedshifter = bedshift.Bedshift() -df = bedshifter.readDF('examples/test.bed') +df = bedshifter.read_bed('examples/test.bed') df = bedshifter.shift(df, shiftrate=0.1, shiftmean=0.0, shiftstdev=120.0) df = bedshifter.add(df, addrate=0.1, addmean=320.0, addstdev=20.0) -bedshifter.write_csv(df, 'output_file.bed') +bedshifter.write_bed(df, 'output_file.bed') ``` diff --git a/examples/pypackage_test.py b/examples/pypackage_test.py index 37add803..6343ec9a 100755 --- a/examples/pypackage_test.py +++ b/examples/pypackage_test.py @@ -7,8 +7,8 @@ bedshifter = bedshift.Bedshift() -df = bedshifter.readDF('test.bed') +df = bedshifter.read_bed('test.bed') original_rows = df.shape[0] df = bedshifter.all_perturbations(df, 0.3, 320.0, 20.0, 0.3, -10.0, 120.0, 0.1, 0.11, 0.03) -bedshifter.write_csv(df, 'py_output.bed') +bedshifter.write_bed(df, 'py_output.bed') From c13ec3023ef7ff997bf6da985ca378da8257fc53 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Thu, 12 Dec 2019 16:52:14 -0500 Subject: [PATCH 0022/1072] add documentation --- bedshift/bedshift.py | 96 +++++++++++++++++++++++++++++++++++--------- mkdocs.yml | 13 +++++- 2 files changed, 89 insertions(+), 20 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 0462a86a..1b951d91 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -94,24 +94,38 @@ def build_argparser(): class Bedshift(object): + """ + The bedshift object with methods to perturb regions + """ def __init__(self): self.original_regions = -1 def read_bed(self, bedfile_path): + """ + Read in a bedfile to pandas DataFrame format + + :param str bedfile_path: the path to the bedfile + """ + df = pd.read_csv(bedfile_path, sep='\t', header=None, usecols=[0,1,2]) df[3] = 0 # column indicating which modifications were made self.original_regions = df.shape[0] return df.sort_values([0, 1]).reset_index(drop=True) - def check_rate(self, rates): + def __check_rate(self, rates): for rate in rates: if rate < 0 or rate > 1: _LOGGER.error("Rate must be between 0 and 1") sys.exit(1) def pick_random_chrom(self): + """ + Utility function to pick a random chromosome + + :return str, float chrom_num, chrom_len: chromosome number and length + """ chrom_index = random.randint(1, len(chroms)-1) chrom_num = chroms[chrom_index] chrom_len = chrom_lens[chrom_index] @@ -180,7 +194,16 @@ def __merge(self, df, row): def add(self, df, addrate, addmean, addstdev): - self.check_rate([addrate]) + """ + Add regions + + :param pandas.DataFrame df: the dataframe to perturb. + :param float addrate: the rate to add regions + :param float addmean: the mean length of added regions + :param float addstdev: the standard deviation of the length of added regions + :return pandas.DataFrame: the new dataframe after adds + """ + self.__check_rate([addrate]) rows = df.shape[0] for _ in range(rows): if random.random() < addrate: @@ -189,7 +212,16 @@ def add(self, df, addrate, addmean, addstdev): def shift(self, df, shiftrate, shiftmean, shiftstdev): - self.check_rate([shiftrate]) + """ + Shift regions + + :param pandas.DataFrame df: the dataframe to perturb. + :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) + :param float shiftmean: the mean shift distance + :param float shiftstdev: the standard deviation of the shift distance + :return pandas.DataFrame: the new dataframe after shifts + """ + self.__check_rate([shiftrate]) rows = df.shape[0] for i in range(rows): if random.random() < shiftrate: @@ -198,7 +230,14 @@ def shift(self, df, shiftrate, shiftmean, shiftstdev): def cut(self, df, cutrate): - self.check_rate([cutrate]) + """ + Cut regions to create two new regions + + :param pandas.DataFrame df: the dataframe to perturb. + :param float cutrate: the rate to cut regions into two separate regions + :return pandas.DataFrame: the new dataframe after cuts + """ + self.__check_rate([cutrate]) rows = df.shape[0] for i in range(rows): if random.random() < cutrate: @@ -207,7 +246,15 @@ def cut(self, df, cutrate): def merge(self, df, mergerate): - self.check_rate([mergerate]) + """ + Merge two regions into one new region + + :param pandas.DataFrame df: the dataframe to perturb. + :param float mergerate: the rate to merge two regions into one + :return pandas.DataFrame: the new dataframe after merges + """ + + self.__check_rate([mergerate]) rows = df.shape[0] i = 0 while i < rows: @@ -219,7 +266,14 @@ def merge(self, df, mergerate): def drop(self, df, droprate): - self.check_rate([droprate]) + """ + Drop regions + + :param pandas.DataFrame df: the dataframe to perturb. + :param float droprate: the rate to drop/remove regions + :return pandas.DataFrame: the new dataframe after drops + """ + self.__check_rate([droprate]) rows = df.shape[0] for i in range(rows): if random.random() < droprate: @@ -231,20 +285,20 @@ def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, shift ''' Perform all five perturbations in the order of add, shift, cut, merge, drop. - :param df: the dataframe to perturb. - :param addrate: the rate (as a proportion of the total number of regions) to add regions - :param addmean: the mean length of added regions - :param addstdev: the standard deviation of the length of added regions - :param shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) - :param shiftmean: the mean shift distance - :param shiftstdev: the standard deviation of the shift distance - :param cutrate: the rate to cut regions into two separate regions - :param mergerate: the rate to merge two regions into one - :param droprate: the rate to drop/remove regions + :param pandas.DataFrame df: the dataframe to perturb. + :param float addrate: the rate (as a proportion of the total number of regions) to add regions + :param float addmean: the mean length of added regions + :param float addstdev: the standard deviation of the length of added regions + :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) + :param float shiftmean: the mean shift distance + :param float shiftstdev: the standard deviation of the shift distance + :param float cutrate: the rate to cut regions into two separate regions + :param float mergerate: the rate to merge two regions into one + :param float droprate: the rate to drop/remove regions :return pandas.DataFrame: the new dataframe after all perturbations ''' - self.check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) + self.__check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) df = self.add(df, addrate, addmean, addstdev) df = self.shift(df, shiftrate, shiftmean, shiftstdev) df = self.cut(df, cutrate) @@ -254,6 +308,12 @@ def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, shift def write_bed(self, df, outfile_name): + """ + Write a pandas dataframe back into bedfile format + + :param pandas.DataFrame df: A dataframe containing regions like a bedfile + :param str outfile_name: The name of the output bedfile + """ df.to_csv(outfile_name, sep='\t', header=False, index=False) print("Original bedfile had {} regions. Perturbed bedfile has {} regions".format(self.original_regions, df.shape[0])) @@ -303,7 +363,7 @@ def main(): bedshifter = Bedshift() - bedshifter.check_rate([args.addrate, args.shiftrate, args.cutrate, args.mergerate, args.droprate]) + bedshifter.__check_rate([args.addrate, args.shiftrate, args.cutrate, args.mergerate, args.droprate]) df = bedshifter.read_bed(args.bedfile) original_rows = df.shape[0] diff --git a/mkdocs.yml b/mkdocs.yml index 3d4d85e6..300ed2c4 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -3,7 +3,16 @@ site_name: Bedshift site_url: http://code.databio.org/bedshift/ repo_url: http://github.com/databio/bedshift theme: databio +# paper_link: https://doi.org/10.1101/698704 nav: - - Introduction: README.md - + - Introduction: + - Overview: README.md + - Reference: + - Python API: autodoc_build/bedshift.md + +plugins: +- databio: + autodoc_build: "docs/autodoc_build" +- search + From 9939bf1e191fc8be66ef39d97db6672e3b980ff6 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Thu, 19 Dec 2019 13:48:29 -0500 Subject: [PATCH 0023/1072] improve performance a little; organize code --- bedshift/_version.py | 2 +- bedshift/bedshift.py | 140 ++++++++++++++++++++----------------------- 2 files changed, 65 insertions(+), 77 deletions(-) diff --git a/bedshift/_version.py b/bedshift/_version.py index 485f44ac..d3ec452c 100644 --- a/bedshift/_version.py +++ b/bedshift/_version.py @@ -1 +1 @@ -__version__ = "0.1.1" +__version__ = "0.2.0" diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 1b951d91..76f019d7 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -111,7 +111,7 @@ def read_bed(self, bedfile_path): df = pd.read_csv(bedfile_path, sep='\t', header=None, usecols=[0,1,2]) df[3] = 0 # column indicating which modifications were made self.original_regions = df.shape[0] - return df.sort_values([0, 1]).reset_index(drop=True) + return df.astype({1: 'int64', 2: 'int64', 3: 'int64'}).sort_values([0, 1]).reset_index(drop=True) def __check_rate(self, rates): @@ -131,67 +131,6 @@ def pick_random_chrom(self): chrom_len = chrom_lens[chrom_index] return chrom_num, chrom_len - def __shift(self, df, row, mean, stdev): - theshift = int(np.random.normal(mean, stdev)) - - start = df.loc[row][1] - end = df.loc[row][2] - - df.at[row, 1] = start + theshift - df.at[row, 2] = end + theshift - df.at[row, 3] = 1.0 - - return df - - - def __drop(self, df, row): - return df.drop(row) - - - def __cut(self, df, row): - chrom = df.loc[row][0] - start = df.loc[row][1] - end = df.loc[row][2] - - if end-start < 0: - _LOGGER.error('ERROR: the end value of a region is less than the start value') - sys.exit(1) - thecut = int(np.random.normal((start+end)/2, (end - start)/6)) - if thecut <= start: - thecut = start + 10 - if thecut >= end: - thecut = end - 10 - - new_segs = pd.DataFrame([[chrom, start, thecut, 2.0], [chrom, thecut, end, 2.0]]) - ''' may add in later, this makes the api confusing! - # adjust the cut regions using the shift function - new_segs = self.__shift(new_segs, 0, meanshift, stdevshift) - new_segs = self.__shift(new_segs, 1, meanshift, stdevshift) - ''' - new_segs[3] = 2.0 - df.loc[row] = new_segs.loc[0] - return df.append(new_segs.loc[1], ignore_index=True) - - - def __add(self, df, mean, stdev): - chrom_num, chrom_len = self.pick_random_chrom() - start = random.randint(1, chrom_len) - end = min(start + max(int(np.random.normal(mean, stdev)), 20), chrom_len) - return df.append(pd.DataFrame([[chrom_num, start, end, 3.0]]), ignore_index=True) - - - def __merge(self, df, row): - # check if the regions being merged are on the same chromosome - if row + 1 not in df.index or df.loc[row][0] != df.loc[row+1][0]: - return df - - chrom = df.loc[row][0] - start = df.loc[row][1] - end = df.loc[row+1][2] - df = self.__drop(df, row) - df.loc[row+1] = [chrom, start, end, 4.0] - return df - def add(self, df, addrate, addmean, addstdev): """ @@ -201,14 +140,21 @@ def add(self, df, addrate, addmean, addstdev): :param float addrate: the rate to add regions :param float addmean: the mean length of added regions :param float addstdev: the standard deviation of the length of added regions - :return pandas.DataFrame: the new dataframe after adds + :return pandas.DataFrame: the new dataframe object after adds """ self.__check_rate([addrate]) rows = df.shape[0] - for _ in range(rows): - if random.random() < addrate: - df = self.__add(df, addmean, addstdev) # added rows display a 3 - return df + num_add = int(rows * addrate) + new_regions = {0: [], 1: [], 2: [], 3: []} + for _ in range(num_add): + chrom_num, chrom_len = self.pick_random_chrom() + start = random.randint(1, chrom_len) + end = min(start + max(int(np.random.normal(addmean, addstdev)), 20), chrom_len) + new_regions[0].append(chrom_num) + new_regions[1].append(start) + new_regions[2].append(end) + new_regions[3].append(3) + return df.append(pd.DataFrame(new_regions), ignore_index=True) def shift(self, df, shiftrate, shiftmean, shiftstdev): @@ -219,7 +165,7 @@ def shift(self, df, shiftrate, shiftmean, shiftstdev): :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) :param float shiftmean: the mean shift distance :param float shiftstdev: the standard deviation of the shift distance - :return pandas.DataFrame: the new dataframe after shifts + :return pandas.DataFrame: the original dataframe after shifts """ self.__check_rate([shiftrate]) rows = df.shape[0] @@ -228,6 +174,18 @@ def shift(self, df, shiftrate, shiftmean, shiftstdev): df = self.__shift(df, i, shiftmean, shiftstdev) # shifted rows display a 1 return df + def __shift(self, df, row, mean, stdev): + theshift = int(np.random.normal(mean, stdev)) + + start = df.loc[row][1] + end = df.loc[row][2] + + df.at[row, 1] = start + theshift + df.at[row, 2] = end + theshift + df.at[row, 3] = 1 + + return df + def cut(self, df, cutrate): """ @@ -244,6 +202,26 @@ def cut(self, df, cutrate): df = self.__cut(df, i) # cut rows display a 2 return df.reset_index(drop=True) + def __cut(self, df, row): + chrom = df.loc[row][0] + start = df.loc[row][1] + end = df.loc[row][2] + + thecut = int(np.random.normal((start+end)/2, (end - start)/6)) + if thecut <= start: + thecut = start + 10 + if thecut >= end: + thecut = end - 10 + + new_segs = pd.DataFrame([[chrom, start, thecut, 2], [chrom, thecut, end, 2]]) + ''' may add in later, this makes the api confusing! + # adjust the cut regions using the shift function + new_segs = self.__shift(new_segs, 0, meanshift, stdevshift) + new_segs = self.__shift(new_segs, 1, meanshift, stdevshift) + ''' + df.loc[row] = new_segs.loc[0] + return df.append(new_segs.loc[1], ignore_index=True) + def merge(self, df, mergerate): """ @@ -264,6 +242,18 @@ def merge(self, df, mergerate): i += 1 return df.reset_index(drop=True) + def __merge(self, df, row): + # check if the regions being merged are on the same chromosome + if row + 1 not in df.index or df.loc[row][0] != df.loc[row+1][0]: + return df + + chrom = df.loc[row][0] + start = df.loc[row][1] + end = df.loc[row+1][2] + df = self.__drop(df, row) + df.loc[row+1] = [chrom, start, end, 4] + return df + def drop(self, df, droprate): """ @@ -277,8 +267,8 @@ def drop(self, df, droprate): rows = df.shape[0] for i in range(rows): if random.random() < droprate: - df = self.__drop(df, i) - return df + df = df.drop(i) + return df.reset_index(drop=True) def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0): @@ -314,9 +304,9 @@ def write_bed(self, df, outfile_name): :param pandas.DataFrame df: A dataframe containing regions like a bedfile :param str outfile_name: The name of the output bedfile """ - df.to_csv(outfile_name, sep='\t', header=False, index=False) - print("Original bedfile had {} regions. Perturbed bedfile has {} regions".format(self.original_regions, df.shape[0])) - + df.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') + # print("Original bedfile had {} regions. Perturbed bedfile has {} regions".format(self.original_regions, df.shape[0])) + print('The output bedfile located in {} has {} lines. The original bedfile had {} lines.'.format(outfile_name, df.shape[0], self.original_regions)) def main(): @@ -363,13 +353,11 @@ def main(): bedshifter = Bedshift() - bedshifter.__check_rate([args.addrate, args.shiftrate, args.cutrate, args.mergerate, args.droprate]) df = bedshifter.read_bed(args.bedfile) - original_rows = df.shape[0] df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) - _LOGGER.info('The output bedfile located in {} has {} lines. The original bedfile had {} lines.'.format(outfile, df.shape[0], original_rows)) + # _LOGGER.info('The output bedfile located in {} has {} lines. The original bedfile had {} lines.'.format(outfile, df.shape[0], original_rows)) bedshifter.write_bed(df, outfile) From 0c79b2c4fa8c216e227c65b94ccaa3e405980758 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Sat, 21 Dec 2019 11:38:46 -0500 Subject: [PATCH 0024/1072] try autodoc --- mkdocs.yml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index 300ed2c4..12237b1d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -12,7 +12,9 @@ nav: - Python API: autodoc_build/bedshift.md plugins: -- databio: - autodoc_build: "docs/autodoc_build" -- search + - databio: + autodoc_build: "docs/autodoc_build" + autodoc_package: "bedshift" + no_top_level: true + - search From badd11fdb88e9ac4471a9d97ee79658534cbfbb9 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Sat, 21 Dec 2019 11:59:11 -0500 Subject: [PATCH 0025/1072] try adding _all__ for autodoc --- bedshift/bedshift.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 76f019d7..06e68120 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -1,4 +1,4 @@ -""" Computing configuration representation """ +""" Perturb regions in bedfiles """ from __future__ import division, print_function import argparse @@ -13,6 +13,8 @@ _LOGGER = logging.getLogger(__name__) +__all__ = ["Bedshift"] + chroms = [-1] + ['chr'+str(num) for num in list(range(1, 23))] + ['X', 'Y'] From 90192375c35ddd54635a9b8264d7ccb3f9f10a2c Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Wed, 22 Jan 2020 15:41:02 -0500 Subject: [PATCH 0026/1072] implement add from file functionality --- .gitignore | 4 +-- bedshift/bedshift.py | 57 +++++++++++++++++++++++++++++--------- docs/README.md | 2 +- examples/pypackage_test.py | 7 +++-- examples/shell_test.sh | 2 ++ run-tests.sh | 2 +- 6 files changed, 54 insertions(+), 20 deletions(-) diff --git a/.gitignore b/.gitignore index 9939595e..884966ca 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,4 @@ -examples/sh_output.bed -examples/py_output.bed +examples/sh_output* +examples/py_output* *.pyc site/ diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 06e68120..07b07e95 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -1,6 +1,6 @@ """ Perturb regions in bedfiles """ -from __future__ import division, print_function +from __future__ import division, print_function, absolute_import import argparse import logmuse import logging @@ -9,7 +9,7 @@ import pandas as pd import numpy as np import random -from _version import __version__ +from bedshift._version import __version__ _LOGGER = logging.getLogger(__name__) @@ -67,9 +67,12 @@ def build_argparser(): "--addstdev", type=float, default=30.0, help="Stdev add length") + parser.add_argument( + "--addfile", type=str, help="Add regions from a bedfile") + parser.add_argument( "-p", "--shiftrate", type=float, default=0.0, - help="Shift prob.") + help="Shift probability") parser.add_argument( "--shiftmean", type=float, default=0.0, @@ -81,11 +84,11 @@ def build_argparser(): parser.add_argument( "-c", "--cutrate", type=float, default=0.0, - help="Cut prob.") + help="Cut probability") parser.add_argument( "-m", "--mergerate", type=float, default=0.0, - help="Merge prob. WARNING: will likely create regions that are thousands of base pairs long") + help="Merge probability. WARNING: will likely create regions that are thousands of base pairs long") parser.add_argument( "-o", "--outputfile", type=str, @@ -113,7 +116,7 @@ def read_bed(self, bedfile_path): df = pd.read_csv(bedfile_path, sep='\t', header=None, usecols=[0,1,2]) df[3] = 0 # column indicating which modifications were made self.original_regions = df.shape[0] - return df.astype({1: 'int64', 2: 'int64', 3: 'int64'}).sort_values([0, 1]).reset_index(drop=True) + return df.astype({1: 'int64', 2: 'int64', 3: 'int64'}).sort_values([0, 1, 2]).reset_index(drop=True) def __check_rate(self, rates): @@ -138,7 +141,7 @@ def add(self, df, addrate, addmean, addstdev): """ Add regions - :param pandas.DataFrame df: the dataframe to perturb. + :param pandas.DataFrame df: the dataframe to perturb :param float addrate: the rate to add regions :param float addmean: the mean length of added regions :param float addstdev: the standard deviation of the length of added regions @@ -158,6 +161,29 @@ def add(self, df, addrate, addmean, addstdev): new_regions[3].append(3) return df.append(pd.DataFrame(new_regions), ignore_index=True) + def add_from_file(self, df, fp, addrate): + """ + Add regions from another bedfile to this perturbed bedfile + + :param pandas.DataFrame df: the dataframe to perturb + :param float addrate: the rate to add regions + :param str fp: the filepath to the other bedfile + """ + + self.__check_rate([addrate]) + rows = df.shape[0] + num_add = int(rows * addrate) + new_regions = {0: [], 1: [], 2: [], 3: []} + with open(fp, 'r') as f: + for region in f: + if random.random() < addrate: + region = region.split('\t') + new_regions[0].append(region[0]) + new_regions[1].append(int(region[1])) + new_regions[2].append(int(region[2])) + new_regions[3].append(3) + return df.append(pd.DataFrame(new_regions), ignore_index=True) + def shift(self, df, shiftrate, shiftmean, shiftstdev): """ @@ -252,7 +278,7 @@ def __merge(self, df, row): chrom = df.loc[row][0] start = df.loc[row][1] end = df.loc[row+1][2] - df = self.__drop(df, row) + df = df.drop(row) df.loc[row+1] = [chrom, start, end, 4] return df @@ -273,7 +299,7 @@ def drop(self, df, droprate): return df.reset_index(drop=True) - def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0): + def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, addfile=None, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0): ''' Perform all five perturbations in the order of add, shift, cut, merge, drop. @@ -291,7 +317,10 @@ def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, shift ''' self.__check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) - df = self.add(df, addrate, addmean, addstdev) + if addfile: + df = self.add_from_file(df, addfile, addrate) + else: + df = self.add(df, addrate, addmean, addstdev) df = self.shift(df, shiftrate, shiftmean, shiftstdev) df = self.cut(df, cutrate) df = self.merge(df, mergerate) @@ -306,9 +335,9 @@ def write_bed(self, df, outfile_name): :param pandas.DataFrame df: A dataframe containing regions like a bedfile :param str outfile_name: The name of the output bedfile """ + df.sort_values([0,1,2], inplace=True) df.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') - # print("Original bedfile had {} regions. Perturbed bedfile has {} regions".format(self.original_regions, df.shape[0])) - print('The output bedfile located in {} has {} lines. The original bedfile had {} lines.'.format(outfile_name, df.shape[0], self.original_regions)) + print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.'.format(outfile_name, df.shape[0], self.original_regions)) def main(): @@ -326,6 +355,7 @@ def main(): add rate: {addrate} add mean length: {addmean} add stdev: {addstdev} + add from file: {addfile} shift rate: {shiftrate} shift mean distance: {shiftmean} shift stdev: {shiftstdev} @@ -341,6 +371,7 @@ def main(): addrate=args.addrate, addmean=args.addmean, addstdev=args.addstdev, + addfile=args.addfile, shiftrate=args.shiftrate, shiftmean=args.shiftmean, shiftstdev=args.shiftstdev, @@ -357,7 +388,7 @@ def main(): bedshifter = Bedshift() df = bedshifter.read_bed(args.bedfile) - df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) + df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.addfile, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) # _LOGGER.info('The output bedfile located in {} has {} lines. The original bedfile had {} lines.'.format(outfile, df.shape[0], original_rows)) diff --git a/docs/README.md b/docs/README.md index 09013958..208ab8d0 100644 --- a/docs/README.md +++ b/docs/README.md @@ -12,7 +12,7 @@ cd bedshift pip install . ``` -## Usage +## Quickstart The package is available for use both as a command line interface and a python package. diff --git a/examples/pypackage_test.py b/examples/pypackage_test.py index 6343ec9a..edd81f37 100755 --- a/examples/pypackage_test.py +++ b/examples/pypackage_test.py @@ -1,5 +1,3 @@ -#!/usr/bin/python - import os, sys from bedshift import bedshift @@ -9,6 +7,9 @@ df = bedshifter.read_bed('test.bed') original_rows = df.shape[0] -df = bedshifter.all_perturbations(df, 0.3, 320.0, 20.0, 0.3, -10.0, 120.0, 0.1, 0.11, 0.03) +df = bedshifter.all_perturbations(df, addrate=0.3, addmean=320.0, addstdev=20.0, addfile=None, shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, cutrate=0.1, mergerate=0.11, droprate=0.03) bedshifter.write_bed(df, 'py_output.bed') + +df = bedshifter.all_perturbations(df, addrate=0.3, addmean=320.0, addstdev=20.0, addfile='py_output.bed', shiftrate=0.3, shiftmean=100.0, shiftstdev=30.0) +bedshifter.write_bed(df, 'py_output2.bed') diff --git a/examples/shell_test.sh b/examples/shell_test.sh index 9db13ca4..89908fa2 100755 --- a/examples/shell_test.sh +++ b/examples/shell_test.sh @@ -1,3 +1,5 @@ #!/bin/bash bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addmean 320.0 --addstdev 30.0 --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output.bed + +bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output2.bed diff --git a/run-tests.sh b/run-tests.sh index 08f04a4c..4e44e2c4 100755 --- a/run-tests.sh +++ b/run-tests.sh @@ -1,4 +1,4 @@ #!/bin/bash -./examples/pypackage_test.py +python3 ./examples/pypackage_test.py ./examples/shell_test.sh From 6a1b28672b492b80c14706fcca37843e1c17af54 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Wed, 22 Jan 2020 16:13:49 -0500 Subject: [PATCH 0027/1072] fix chromosome X and Y naming --- bedshift/bedshift.py | 2 +- examples/shell_test.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 07b07e95..73cd57e7 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -16,7 +16,7 @@ __all__ = ["Bedshift"] -chroms = [-1] + ['chr'+str(num) for num in list(range(1, 23))] + ['X', 'Y'] +chroms = [-1] + ['chr'+str(num) for num in list(range(1, 23))] + ['chrX', 'chrY'] chrom_lens = [-1, 247249719, 242951149, 199501827, 191273063, 180857866, 170899992, 158821424, 146274826, 140273252, 135374737, 134452384, 132349534, 114142980, 106368585, 100338915, 88827254, 78774742, 76117153, 63811651, 62435964, 46944323, 49691432, 154913754, 57772954] diff --git a/examples/shell_test.sh b/examples/shell_test.sh index 89908fa2..ada54b71 100755 --- a/examples/shell_test.sh +++ b/examples/shell_test.sh @@ -2,4 +2,4 @@ bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addmean 320.0 --addstdev 30.0 --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output.bed -bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output2.bed +bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed From 9ad1d4050dbb5d51ce19028199250d764213b765 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Fri, 24 Jan 2020 20:17:11 -0500 Subject: [PATCH 0028/1072] fix addrate of add from file --- bedshift/bedshift.py | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 73cd57e7..4b6cf205 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -114,6 +114,12 @@ def read_bed(self, bedfile_path): """ df = pd.read_csv(bedfile_path, sep='\t', header=None, usecols=[0,1,2]) + + # if there is 'chrom', 'start', 'stop' in the table, move them to header + if not str(df.iloc[0, 1]).isdigit(): + df.columns = df.iloc[0] + df = df[1:] + df[3] = 0 # column indicating which modifications were made self.original_regions = df.shape[0] return df.astype({1: 'int64', 2: 'int64', 3: 'int64'}).sort_values([0, 1, 2]).reset_index(drop=True) @@ -175,13 +181,19 @@ def add_from_file(self, df, fp, addrate): num_add = int(rows * addrate) new_regions = {0: [], 1: [], 2: [], 3: []} with open(fp, 'r') as f: + lines = 0 + for region in f: + lines += 1 + f.seek(0) + addrate_newfile = num_add / lines for region in f: - if random.random() < addrate: + if random.random() < addrate_newfile: region = region.split('\t') - new_regions[0].append(region[0]) - new_regions[1].append(int(region[1])) - new_regions[2].append(int(region[2])) - new_regions[3].append(3) + if str(region[1]).isdigit(): + new_regions[0].append(region[0]) + new_regions[1].append(int(region[1])) + new_regions[2].append(int(region[2])) + new_regions[3].append(3) return df.append(pd.DataFrame(new_regions), ignore_index=True) From 9a8244edb4e971d5300e5956833561bf2179702b Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Wed, 5 Feb 2020 15:56:22 -0500 Subject: [PATCH 0029/1072] change shift flag to -s --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 4b6cf205..47311356 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -71,7 +71,7 @@ def build_argparser(): "--addfile", type=str, help="Add regions from a bedfile") parser.add_argument( - "-p", "--shiftrate", type=float, default=0.0, + "-s", "--shiftrate", type=float, default=0.0, help="Shift probability") parser.add_argument( From ea0a0d61690c0a6127bb737ec0d03344c84ada9f Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Wed, 19 Feb 2020 17:11:09 -0500 Subject: [PATCH 0030/1072] add repeat functionality --- .gitignore | 1 + bedshift/bedshift.py | 29 +++++++++++++++++++++-------- examples/shell_test.sh | 2 ++ 3 files changed, 24 insertions(+), 8 deletions(-) diff --git a/.gitignore b/.gitignore index 884966ca..9beed11f 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ examples/sh_output* examples/py_output* *.pyc site/ +*.bed diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 47311356..25fa323e 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -94,6 +94,9 @@ def build_argparser(): "-o", "--outputfile", type=str, help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed") + parser.add_argument( + "-r", "--repeat", type=int, default=1, + help="the number of times to repeat the operation") return parser @@ -247,7 +250,7 @@ def __cut(self, df, row): start = df.loc[row][1] end = df.loc[row][2] - thecut = int(np.random.normal((start+end)/2, (end - start)/6)) + thecut = (start + end) // 2 # int(np.random.normal((start+end)/2, (end - start)/6)) if thecut <= start: thecut = start + 10 if thecut >= end: @@ -329,11 +332,11 @@ def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, addfi ''' self.__check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) + df = self.shift(df, shiftrate, shiftmean, shiftstdev) if addfile: df = self.add_from_file(df, addfile, addrate) else: df = self.add(df, addrate, addmean, addstdev) - df = self.shift(df, shiftrate, shiftmean, shiftstdev) df = self.cut(df, cutrate) df = self.merge(df, mergerate) df = self.drop(df, droprate) @@ -374,6 +377,7 @@ def main(): cut rate: {cutrate} merge rate: {mergerate} outputfile: {outputfile} + repeat: {repeat} """ outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) if not args.outputfile else args.outputfile @@ -389,7 +393,8 @@ def main(): shiftstdev=args.shiftstdev, cutrate=args.cutrate, mergerate=args.mergerate, - outputfile=args.outputfile)) + outputfile=args.outputfile, + repeat=args.repeat)) if not args.bedfile: parser.print_help() @@ -400,11 +405,19 @@ def main(): bedshifter = Bedshift() df = bedshifter.read_bed(args.bedfile) - df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.addfile, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) - - # _LOGGER.info('The output bedfile located in {} has {} lines. The original bedfile had {} lines.'.format(outfile, df.shape[0], original_rows)) - - bedshifter.write_bed(df, outfile) + if args.repeat == 1: + df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.addfile, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) + bedshifter.write_bed(df, outfile) + elif args.repeat > 1: + for i in range(args.repeat): + repeat_df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.addfile, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) + modified_outfile = outfile.rsplit("/") + modified_outfile[-1] = "gen" + str(i+1) + "_" + modified_outfile[-1] + modified_outfile = "/".join(modified_outfile) + bedshifter.write_bed(repeat_df, modified_outfile) + else: + _LOGGER.error("repeats specified is less than 1") + sys.exit(1) if __name__ == '__main__': diff --git a/examples/shell_test.sh b/examples/shell_test.sh index ada54b71..de606ea5 100755 --- a/examples/shell_test.sh +++ b/examples/shell_test.sh @@ -3,3 +3,5 @@ bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addmean 320.0 --addstdev 30.0 --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output.bed bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed + +bedshift --bedfile $(dirname "$0")/test.bed --addrate 0.1 --cutrate 0.5 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed -r 10 From f465fadcee575ef7a203892faf1907d158207021 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Fri, 21 Feb 2020 14:51:36 -0500 Subject: [PATCH 0031/1072] ensure shift does not go past the length of a chromosome --- bedshift/bedshift.py | 22 +++++++++++++--------- examples/shell_test.sh | 2 +- 2 files changed, 14 insertions(+), 10 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 25fa323e..91a69d36 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -16,9 +16,9 @@ __all__ = ["Bedshift"] -chroms = [-1] + ['chr'+str(num) for num in list(range(1, 23))] + ['chrX', 'chrY'] +chroms = {num: 'chr'+str(num) for num in list(range(1, 23))} + {'X': 'chrX', 'Y': 'chrY'} -chrom_lens = [-1, 247249719, 242951149, 199501827, 191273063, 180857866, 170899992, 158821424, 146274826, 140273252, 135374737, 134452384, 132349534, 114142980, 106368585, 100338915, 88827254, 78774742, 76117153, 63811651, 62435964, 46944323, 49691432, 154913754, 57772954] +chrom_lens = {'chr1': 247249719, 'chr2': 242951149, 'chr3': 199501827, 'chr4': 191273063, 'chr5': 180857866, 'chr6': 170899992, 'chr7': 158821424, 'chr8': 146274826, 'chr9': 140273252, 'chr10': 135374737, 'chr11': 134452384, 'chr12': 132349534, 'chr13': 114142980, 'chr14': 106368585, 'chr15': 100338915, 'chr16': 88827254, 'chr17': 78774742, 'chr18': 76117153, 'chr19': 63811651, 'chr20': 62435964, 'chr21': 46944323, 'chr22': 49691432, 'chrX': 154913754, 'chrY': 57772954] class _VersionInHelpParser(argparse.ArgumentParser): @@ -138,12 +138,11 @@ def pick_random_chrom(self): """ Utility function to pick a random chromosome - :return str, float chrom_num, chrom_len: chromosome number and length + :return str, float chrom_str, chrom_len: chromosome number and length """ - chrom_index = random.randint(1, len(chroms)-1) - chrom_num = chroms[chrom_index] - chrom_len = chrom_lens[chrom_index] - return chrom_num, chrom_len + chrom_str = random.choice(list(chroms.values())) + chrom_len = chrom_lens[chrom_str] + return chrom_str, chrom_len def add(self, df, addrate, addmean, addstdev): @@ -161,10 +160,10 @@ def add(self, df, addrate, addmean, addstdev): num_add = int(rows * addrate) new_regions = {0: [], 1: [], 2: [], 3: []} for _ in range(num_add): - chrom_num, chrom_len = self.pick_random_chrom() + chrom_str, chrom_len = self.pick_random_chrom() start = random.randint(1, chrom_len) end = min(start + max(int(np.random.normal(addmean, addstdev)), 20), chrom_len) - new_regions[0].append(chrom_num) + new_regions[0].append(chrom_str) new_regions[1].append(start) new_regions[2].append(end) new_regions[3].append(3) @@ -223,8 +222,13 @@ def __shift(self, df, row, mean, stdev): start = df.loc[row][1] end = df.loc[row][2] + if start + theshift < 0 || \ + end + theshift > chrom_lens(df.loc[row][0]) : + return df + df.at[row, 1] = start + theshift df.at[row, 2] = end + theshift + df.at[row, 3] = 1 return df diff --git a/examples/shell_test.sh b/examples/shell_test.sh index de606ea5..e9202552 100755 --- a/examples/shell_test.sh +++ b/examples/shell_test.sh @@ -4,4 +4,4 @@ bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addme bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed -bedshift --bedfile $(dirname "$0")/test.bed --addrate 0.1 --cutrate 0.5 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed -r 10 +bedshift --bedfile $(dirname "$0")/test.bed --addrate 0.1 --cutrate 0.5 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed -r 3 From d6674fc084b0805aedd3337ef0bd7c1adfeab669 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Fri, 8 May 2020 01:34:16 -0400 Subject: [PATCH 0032/1072] improve performance --- bedshift/bedshift.py | 74 +++++++++++++++++++++++++------------------- 1 file changed, 43 insertions(+), 31 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 91a69d36..d75faf3b 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -16,9 +16,10 @@ __all__ = ["Bedshift"] -chroms = {num: 'chr'+str(num) for num in list(range(1, 23))} + {'X': 'chrX', 'Y': 'chrY'} +chroms = {num: 'chr'+str(num) for num in list(range(1, 23))} +chroms.update({'X': 'chrX', 'Y': 'chrY'}) -chrom_lens = {'chr1': 247249719, 'chr2': 242951149, 'chr3': 199501827, 'chr4': 191273063, 'chr5': 180857866, 'chr6': 170899992, 'chr7': 158821424, 'chr8': 146274826, 'chr9': 140273252, 'chr10': 135374737, 'chr11': 134452384, 'chr12': 132349534, 'chr13': 114142980, 'chr14': 106368585, 'chr15': 100338915, 'chr16': 88827254, 'chr17': 78774742, 'chr18': 76117153, 'chr19': 63811651, 'chr20': 62435964, 'chr21': 46944323, 'chr22': 49691432, 'chrX': 154913754, 'chrY': 57772954] +chrom_lens = {'chr1': 247249719, 'chr2': 242951149, 'chr3': 199501827, 'chr4': 191273063, 'chr5': 180857866, 'chr6': 170899992, 'chr7': 158821424, 'chr8': 146274826, 'chr9': 140273252, 'chr10': 135374737, 'chr11': 134452384, 'chr12': 132349534, 'chr13': 114142980, 'chr14': 106368585, 'chr15': 100338915, 'chr16': 88827254, 'chr17': 78774742, 'chr18': 76117153, 'chr19': 63811651, 'chr20': 62435964, 'chr21': 46944323, 'chr22': 49691432, 'chrX': 154913754, 'chrY': 57772954} class _VersionInHelpParser(argparse.ArgumentParser): @@ -134,6 +135,7 @@ def __check_rate(self, rates): _LOGGER.error("Rate must be between 0 and 1") sys.exit(1) + def pick_random_chrom(self): """ Utility function to pick a random chromosome @@ -222,13 +224,13 @@ def __shift(self, df, row, mean, stdev): start = df.loc[row][1] end = df.loc[row][2] - if start + theshift < 0 || \ - end + theshift > chrom_lens(df.loc[row][0]) : + if start + theshift < 0 or \ + end + theshift > chrom_lens[df.loc[row][0]]: + # shifting out of bounds check return df df.at[row, 1] = start + theshift df.at[row, 2] = end + theshift - df.at[row, 3] = 1 return df @@ -243,10 +245,18 @@ def cut(self, df, cutrate): :return pandas.DataFrame: the new dataframe after cuts """ self.__check_rate([cutrate]) + if cutrate == 0: + return df rows = df.shape[0] - for i in range(rows): - if random.random() < cutrate: - df = self.__cut(df, i) # cut rows display a 2 + cut_rows = random.sample(list(range(rows)), int(rows * cutrate)) + new_row_list = [] + to_drop = [] + for row in cut_rows: + drop_row, new_regions = self.__cut(df, row) # cut rows display a 2 + new_row_list.extend(new_regions) + to_drop.append(drop_row) + df = df.drop(to_drop) + df = df.append(new_row_list, ignore_index=True) return df.reset_index(drop=True) def __cut(self, df, row): @@ -260,14 +270,13 @@ def __cut(self, df, row): if thecut >= end: thecut = end - 10 - new_segs = pd.DataFrame([[chrom, start, thecut, 2], [chrom, thecut, end, 2]]) ''' may add in later, this makes the api confusing! # adjust the cut regions using the shift function new_segs = self.__shift(new_segs, 0, meanshift, stdevshift) new_segs = self.__shift(new_segs, 1, meanshift, stdevshift) ''' - df.loc[row] = new_segs.loc[0] - return df.append(new_segs.loc[1], ignore_index=True) + + return row, [{0: chrom, 1: start, 2: thecut, 3: 2}, {0: chrom, 1: thecut, 2: end, 3: 2}] def merge(self, df, mergerate): @@ -280,26 +289,30 @@ def merge(self, df, mergerate): """ self.__check_rate([mergerate]) + if mergerate == 0: + return df rows = df.shape[0] - i = 0 - while i < rows: - if random.random() < mergerate: - df = self.__merge(df, i) # merged rows display a 4 - i += 1 - i += 1 + merge_rows = random.sample(list(range(rows)), int(rows * mergerate)) + to_add = [] + to_drop = [] + for row in merge_rows: + drop_row, add_row = self.__merge(df, row) + if add_row: + to_add.append(add_row) + to_drop.extend(drop_row) + df = df.drop(to_drop) + df = df.append(to_add, ignore_index=True) return df.reset_index(drop=True) def __merge(self, df, row): # check if the regions being merged are on the same chromosome if row + 1 not in df.index or df.loc[row][0] != df.loc[row+1][0]: - return df + return [], None chrom = df.loc[row][0] start = df.loc[row][1] end = df.loc[row+1][2] - df = df.drop(row) - df.loc[row+1] = [chrom, start, end, 4] - return df + return [row, row+1], {0: chrom, 1: start, 2: end, 3: 4} def drop(self, df, droprate): @@ -312,9 +325,8 @@ def drop(self, df, droprate): """ self.__check_rate([droprate]) rows = df.shape[0] - for i in range(rows): - if random.random() < droprate: - df = df.drop(i) + drop_rows = random.sample(list(range(rows)), int(rows * droprate)) + df = df.drop(drop_rows) return df.reset_index(drop=True) @@ -370,15 +382,15 @@ def main(): _LOGGER.info("welcome to bedshift") _LOGGER.info("Shifting file: '{}'".format(args.bedfile)) msg = """Params: - drop rate: {droprate} - add rate: {addrate} - add mean length: {addmean} - add stdev: {addstdev} - add from file: {addfile} shift rate: {shiftrate} - shift mean distance: {shiftmean} - shift stdev: {shiftstdev} + shift mean distance: {shiftmean} + shift stdev: {shiftstdev} + add rate: {addrate} + add mean length: {addmean} + add stdev: {addstdev} + add regions from file: {addfile} cut rate: {cutrate} + drop rate: {droprate} merge rate: {mergerate} outputfile: {outputfile} repeat: {repeat} From dc404f2cb12c933dea9c10c48ea475c708bcaae6 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 8 May 2020 17:03:46 +0000 Subject: [PATCH 0033/1072] comply with lucidoc --- bedshift/__init__.py | 3 +- docs/autodoc_build/bedshift.md | 218 +++++++++++++++++++++++++++++++++ 2 files changed, 220 insertions(+), 1 deletion(-) create mode 100644 docs/autodoc_build/bedshift.md diff --git a/bedshift/__init__.py b/bedshift/__init__.py index a972cbb5..2222220b 100644 --- a/bedshift/__init__.py +++ b/bedshift/__init__.py @@ -1,9 +1,10 @@ # Project configuration, particularly for logging. import logmuse +from .bedshift import Bedshift from ._version import __version__ -__classes__ = [] +__classes__ = ["Bedshift"] __all__ = __classes__ + [] logmuse.init_logger("bedshift") diff --git a/docs/autodoc_build/bedshift.md b/docs/autodoc_build/bedshift.md new file mode 100644 index 00000000..dae56e55 --- /dev/null +++ b/docs/autodoc_build/bedshift.md @@ -0,0 +1,218 @@ + + + + + +# Package `bedshift` Documentation + +## Class `Bedshift` +The bedshift object with methods to perturb regions + + +```python +def __init__(self) +``` + +Initialize self. See help(type(self)) for accurate signature. + + + +```python +def add(self, df, addrate, addmean, addstdev) +``` + +Add regions +#### Parameters: + +- `df` (`pandas.DataFrame`): the dataframe to perturb +- `addrate` (`float`): the rate to add regions +- `addmean` (`float`): the mean length of added regions +- `addstdev` (`float`): the standard deviation of the length of added regions + + +#### Returns: + +- `pandas.DataFrame`: the new dataframe object after adds + + + + +```python +def add_from_file(self, df, fp, addrate) +``` + +Add regions from another bedfile to this perturbed bedfile +#### Parameters: + +- `df` (`pandas.DataFrame`): the dataframe to perturb +- `addrate` (`float`): the rate to add regions +- `fp` (`str`): the filepath to the other bedfile + + + + +```python +def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, addfile=None, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0) +``` + +Perform all five perturbations in the order of add, shift, cut, merge, drop. +#### Parameters: + +- `df` (`pandas.DataFrame`): the dataframe to perturb. +- `addrate` (`float`): the rate (as a proportion of the total number of regions) to add regions +- `addmean` (`float`): the mean length of added regions +- `addstdev` (`float`): the standard deviation of the length of added regions +- `shiftrate` (`float`): the rate to shift regions (both the start and end are shifted by the same amount) +- `shiftmean` (`float`): the mean shift distance +- `shiftstdev` (`float`): the standard deviation of the shift distance +- `cutrate` (`float`): the rate to cut regions into two separate regions +- `mergerate` (`float`): the rate to merge two regions into one +- `droprate` (`float`): the rate to drop/remove regions + + +#### Returns: + +- `pandas.DataFrame`: the new dataframe after all perturbations + + + + +```python +def cut(self, df, cutrate) +``` + +Cut regions to create two new regions +#### Parameters: + +- `df` (`pandas.DataFrame`): the dataframe to perturb. +- `cutrate` (`float`): the rate to cut regions into two separate regions + + +#### Returns: + +- `pandas.DataFrame`: the new dataframe after cuts + + + + +```python +def drop(self, df, droprate) +``` + +Drop regions +#### Parameters: + +- `df` (`pandas.DataFrame`): the dataframe to perturb. +- `droprate` (`float`): the rate to drop/remove regions + + +#### Returns: + +- `pandas.DataFrame`: the new dataframe after drops + + + + +```python +def merge(self, df, mergerate) +``` + +Merge two regions into one new region +#### Parameters: + +- `df` (`pandas.DataFrame`): the dataframe to perturb. +- `mergerate` (`float`): the rate to merge two regions into one + + +#### Returns: + +- `pandas.DataFrame`: the new dataframe after merges + + + + +```python +def pick_random_chrom(self) +``` + +Utility function to pick a random chromosome +#### Returns: + +- `str, float chrom_str, chrom_len`: chromosome number and length + + + + +```python +def read_bed(self, bedfile_path) +``` + +Read in a bedfile to pandas DataFrame format +#### Parameters: + +- `bedfile_path` (`str`): the path to the bedfile + + + + +```python +def shift(self, df, shiftrate, shiftmean, shiftstdev) +``` + +Shift regions +#### Parameters: + +- `df` (`pandas.DataFrame`): the dataframe to perturb. +- `shiftrate` (`float`): the rate to shift regions (both the start and end are shifted by the same amount) +- `shiftmean` (`float`): the mean shift distance +- `shiftstdev` (`float`): the standard deviation of the shift distance + + +#### Returns: + +- `pandas.DataFrame`: the original dataframe after shifts + + + + +```python +def write_bed(self, df, outfile_name) +``` + +Write a pandas dataframe back into bedfile format +#### Parameters: + +- `df` (`pandas.DataFrame`): A dataframe containing regions like a bedfile +- `outfile_name` (`str`): The name of the output bedfile + + + + + + + +*Version Information: `bedshift` v0.2.0, generated by `lucidoc` v0.4.2* \ No newline at end of file From f98a2cae51735edcc7ddcdea45de021163417f98 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Mon, 11 May 2020 17:28:13 -0400 Subject: [PATCH 0034/1072] change bedshift import in docs --- README.md | 2 +- bedshift/bedshift.py | 2 +- docs/README.md | 2 +- examples/pypackage_test.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index e2367e91..b4b84071 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ See: `bedshift --help` for parameters. ## Python ```py -from bedshift import bedshift +import bedshift bedshifter = bedshift.Bedshift() diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index d75faf3b..3b049420 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -33,7 +33,7 @@ def build_argparser(): """ Builds argument parser. - :return argparse.ArgumentParser + :return: argparse.ArgumentParser """ banner = "%(prog)s - randomize BED files" diff --git a/docs/README.md b/docs/README.md index 208ab8d0..49dcfed3 100644 --- a/docs/README.md +++ b/docs/README.md @@ -28,7 +28,7 @@ bedshift -b examples/test.bed -p 0.1 -a 0.1 Python: ```py -from bedshift import bedshift +import bedshift bedshifter = bedshift.Bedshift() diff --git a/examples/pypackage_test.py b/examples/pypackage_test.py index edd81f37..28138e27 100755 --- a/examples/pypackage_test.py +++ b/examples/pypackage_test.py @@ -1,5 +1,5 @@ import os, sys -from bedshift import bedshift +import bedshift os.chdir(sys.path[0]) From 6418acecc87d23399acea262de8421038f579336 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Mon, 18 May 2020 16:19:24 -0400 Subject: [PATCH 0035/1072] update dependencies --- .gitignore | 1 + bedshift/bedshift.py | 1 - requirements/requirements-all.txt | 2 ++ 3 files changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 9beed11f..b6294c43 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ examples/py_output* *.pyc site/ *.bed +.DS_Store \ No newline at end of file diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index d75faf3b..e5dc83ba 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -1,6 +1,5 @@ """ Perturb regions in bedfiles """ -from __future__ import division, print_function, absolute_import import argparse import logmuse import logging diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index 6e33e56c..baabbc3e 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -1 +1,3 @@ logmuse +pandas +numpy \ No newline at end of file From 9f831a3d846f5afaecb8b82b47f0fac9c3c5bae6 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Tue, 19 May 2020 12:57:27 -0400 Subject: [PATCH 0036/1072] packaging for pypi --- bedshift.egg-info/PKG-INFO | 56 + bedshift.egg-info/SOURCES.txt | 15 + bedshift.egg-info/dependency_links.txt | 1 + bedshift.egg-info/entry_points.txt | 3 + bedshift.egg-info/requires.txt | 3 + bedshift.egg-info/top_level.txt | 1 + build/lib/bedshift/__init__.py | 10 + build/lib/bedshift/_version.py | 1 + build/lib/bedshift/bedshift.py | 445 + dist/bedshift-0.2.0-py3-none-any.whl | Bin 0 -> 7579 bytes dist/bedshift-0.2.0.tar.gz | Bin 0 -> 7310 bytes examples/test.bed | 10000 ----------------------- setup.py | 10 +- {examples => tests}/pypackage_test.py | 0 {examples => tests}/shell_test.sh | 0 15 files changed, 540 insertions(+), 10005 deletions(-) create mode 100644 bedshift.egg-info/PKG-INFO create mode 100644 bedshift.egg-info/SOURCES.txt create mode 100644 bedshift.egg-info/dependency_links.txt create mode 100644 bedshift.egg-info/entry_points.txt create mode 100644 bedshift.egg-info/requires.txt create mode 100644 bedshift.egg-info/top_level.txt create mode 100644 build/lib/bedshift/__init__.py create mode 100644 build/lib/bedshift/_version.py create mode 100644 build/lib/bedshift/bedshift.py create mode 100644 dist/bedshift-0.2.0-py3-none-any.whl create mode 100644 dist/bedshift-0.2.0.tar.gz delete mode 100644 examples/test.bed rename {examples => tests}/pypackage_test.py (100%) rename {examples => tests}/shell_test.sh (100%) diff --git a/bedshift.egg-info/PKG-INFO b/bedshift.egg-info/PKG-INFO new file mode 100644 index 00000000..ca62e70a --- /dev/null +++ b/bedshift.egg-info/PKG-INFO @@ -0,0 +1,56 @@ +Metadata-Version: 2.1 +Name: bedshift +Version: 0.2.0 +Summary: BED file perturbations +Home-page: https://bedshift.databio.org +Author: Aaron Gu +Author-email: ag5ym@virginia.edu +License: BSD2 +Description: # Bedshift + + Install with: `pip install --user .` + + ## Command line + + Run with: `bedshift -b BEDFILE` or `./bedshift.sh` if running bedshift on multiple bedfiles with a set of parameters, which are editable in bedshift.sh. + + See: `bedshift --help` for parameters. + + ## Python + + ```py + from bedshift import bedshift + + bedshifter = bedshift.Bedshift() + + df = bedshifter.read_bed('test.bed') + df = bedshifter.all_perturbations(df, addrate=0.3, addmean=320.0, addstdev=20.0, shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, cutrate=0.1, mergerate=0.11, droprate=0.03) + # can also run single operations: shift, add, cut, merge, drop + + bedshifter.write_bed(df, 'output_file.bed') + ``` + + + + + ## Development + + test changes: + + ``` + pip install --user . + + ./run-tests.sh + ``` + + if the tests complete, then bedshift is working properly + +Keywords: BED file,perturbation,bioinformatics,region set +Platform: UNKNOWN +Classifier: Development Status :: 4 - Beta +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: Python :: 2.7 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Topic :: System :: Distributed Computing +Description-Content-Type: text/markdown diff --git a/bedshift.egg-info/SOURCES.txt b/bedshift.egg-info/SOURCES.txt new file mode 100644 index 00000000..93011a90 --- /dev/null +++ b/bedshift.egg-info/SOURCES.txt @@ -0,0 +1,15 @@ +LICENSE.txt +MANIFEST.in +README.md +setup.py +bedshift/__init__.py +bedshift/_version.py +bedshift/bedshift.py +bedshift.egg-info/PKG-INFO +bedshift.egg-info/SOURCES.txt +bedshift.egg-info/dependency_links.txt +bedshift.egg-info/entry_points.txt +bedshift.egg-info/requires.txt +bedshift.egg-info/top_level.txt +requirements/requirements-all.txt +requirements/requirements-doc.txt \ No newline at end of file diff --git a/bedshift.egg-info/dependency_links.txt b/bedshift.egg-info/dependency_links.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/bedshift.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/bedshift.egg-info/entry_points.txt b/bedshift.egg-info/entry_points.txt new file mode 100644 index 00000000..c008846a --- /dev/null +++ b/bedshift.egg-info/entry_points.txt @@ -0,0 +1,3 @@ +[console_scripts] +bedshift = bedshift.bedshift:main + diff --git a/bedshift.egg-info/requires.txt b/bedshift.egg-info/requires.txt new file mode 100644 index 00000000..b972df1c --- /dev/null +++ b/bedshift.egg-info/requires.txt @@ -0,0 +1,3 @@ +logmuse +pandas +numpy diff --git a/bedshift.egg-info/top_level.txt b/bedshift.egg-info/top_level.txt new file mode 100644 index 00000000..566eef66 --- /dev/null +++ b/bedshift.egg-info/top_level.txt @@ -0,0 +1 @@ +bedshift diff --git a/build/lib/bedshift/__init__.py b/build/lib/bedshift/__init__.py new file mode 100644 index 00000000..2222220b --- /dev/null +++ b/build/lib/bedshift/__init__.py @@ -0,0 +1,10 @@ +# Project configuration, particularly for logging. + +import logmuse +from .bedshift import Bedshift +from ._version import __version__ + +__classes__ = ["Bedshift"] +__all__ = __classes__ + [] + +logmuse.init_logger("bedshift") diff --git a/build/lib/bedshift/_version.py b/build/lib/bedshift/_version.py new file mode 100644 index 00000000..d3ec452c --- /dev/null +++ b/build/lib/bedshift/_version.py @@ -0,0 +1 @@ +__version__ = "0.2.0" diff --git a/build/lib/bedshift/bedshift.py b/build/lib/bedshift/bedshift.py new file mode 100644 index 00000000..e5dc83ba --- /dev/null +++ b/build/lib/bedshift/bedshift.py @@ -0,0 +1,445 @@ +""" Perturb regions in bedfiles """ + +import argparse +import logmuse +import logging +import os +import sys +import pandas as pd +import numpy as np +import random +from bedshift._version import __version__ + +_LOGGER = logging.getLogger(__name__) + +__all__ = ["Bedshift"] + + +chroms = {num: 'chr'+str(num) for num in list(range(1, 23))} +chroms.update({'X': 'chrX', 'Y': 'chrY'}) + +chrom_lens = {'chr1': 247249719, 'chr2': 242951149, 'chr3': 199501827, 'chr4': 191273063, 'chr5': 180857866, 'chr6': 170899992, 'chr7': 158821424, 'chr8': 146274826, 'chr9': 140273252, 'chr10': 135374737, 'chr11': 134452384, 'chr12': 132349534, 'chr13': 114142980, 'chr14': 106368585, 'chr15': 100338915, 'chr16': 88827254, 'chr17': 78774742, 'chr18': 76117153, 'chr19': 63811651, 'chr20': 62435964, 'chr21': 46944323, 'chr22': 49691432, 'chrX': 154913754, 'chrY': 57772954} + + +class _VersionInHelpParser(argparse.ArgumentParser): + def format_help(self): + """ Add version information to help text. """ + return "version: {}\n".format(__version__) + \ + super(_VersionInHelpParser, self).format_help() + + +def build_argparser(): + """ + Builds argument parser. + + :return argparse.ArgumentParser + """ + + banner = "%(prog)s - randomize BED files" + additional_description = "\n..." + + parser = _VersionInHelpParser( + description=banner, + epilog=additional_description) + + parser.add_argument( + "-V", "--version", + action="version", + version="%(prog)s {v}".format(v=__version__)) + + parser.add_argument( + "-b", "--bedfile", required=True, + help="File path to bed file.") + + parser.add_argument( + "-d", "--droprate", type=float, default=0.0, + help="Droprate parameter") + + parser.add_argument( + "-a", "--addrate", type=float, default=0.0, + help="Addrate parameter") + + parser.add_argument( + "--addmean", type=float, default=320.0, + help="Mean add region length") + + parser.add_argument( + "--addstdev", type=float, default=30.0, + help="Stdev add length") + + parser.add_argument( + "--addfile", type=str, help="Add regions from a bedfile") + + parser.add_argument( + "-s", "--shiftrate", type=float, default=0.0, + help="Shift probability") + + parser.add_argument( + "--shiftmean", type=float, default=0.0, + help="Mean shift") + + parser.add_argument( + "--shiftstdev", type=float, default=150.0, + help="Stdev shift") + + parser.add_argument( + "-c", "--cutrate", type=float, default=0.0, + help="Cut probability") + + parser.add_argument( + "-m", "--mergerate", type=float, default=0.0, + help="Merge probability. WARNING: will likely create regions that are thousands of base pairs long") + + parser.add_argument( + "-o", "--outputfile", type=str, + help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed") + + parser.add_argument( + "-r", "--repeat", type=int, default=1, + help="the number of times to repeat the operation") + + return parser + + +class Bedshift(object): + """ + The bedshift object with methods to perturb regions + """ + + def __init__(self): + self.original_regions = -1 + + def read_bed(self, bedfile_path): + """ + Read in a bedfile to pandas DataFrame format + + :param str bedfile_path: the path to the bedfile + """ + + df = pd.read_csv(bedfile_path, sep='\t', header=None, usecols=[0,1,2]) + + # if there is 'chrom', 'start', 'stop' in the table, move them to header + if not str(df.iloc[0, 1]).isdigit(): + df.columns = df.iloc[0] + df = df[1:] + + df[3] = 0 # column indicating which modifications were made + self.original_regions = df.shape[0] + return df.astype({1: 'int64', 2: 'int64', 3: 'int64'}).sort_values([0, 1, 2]).reset_index(drop=True) + + + def __check_rate(self, rates): + for rate in rates: + if rate < 0 or rate > 1: + _LOGGER.error("Rate must be between 0 and 1") + sys.exit(1) + + + def pick_random_chrom(self): + """ + Utility function to pick a random chromosome + + :return str, float chrom_str, chrom_len: chromosome number and length + """ + chrom_str = random.choice(list(chroms.values())) + chrom_len = chrom_lens[chrom_str] + return chrom_str, chrom_len + + + def add(self, df, addrate, addmean, addstdev): + """ + Add regions + + :param pandas.DataFrame df: the dataframe to perturb + :param float addrate: the rate to add regions + :param float addmean: the mean length of added regions + :param float addstdev: the standard deviation of the length of added regions + :return pandas.DataFrame: the new dataframe object after adds + """ + self.__check_rate([addrate]) + rows = df.shape[0] + num_add = int(rows * addrate) + new_regions = {0: [], 1: [], 2: [], 3: []} + for _ in range(num_add): + chrom_str, chrom_len = self.pick_random_chrom() + start = random.randint(1, chrom_len) + end = min(start + max(int(np.random.normal(addmean, addstdev)), 20), chrom_len) + new_regions[0].append(chrom_str) + new_regions[1].append(start) + new_regions[2].append(end) + new_regions[3].append(3) + return df.append(pd.DataFrame(new_regions), ignore_index=True) + + def add_from_file(self, df, fp, addrate): + """ + Add regions from another bedfile to this perturbed bedfile + + :param pandas.DataFrame df: the dataframe to perturb + :param float addrate: the rate to add regions + :param str fp: the filepath to the other bedfile + """ + + self.__check_rate([addrate]) + rows = df.shape[0] + num_add = int(rows * addrate) + new_regions = {0: [], 1: [], 2: [], 3: []} + with open(fp, 'r') as f: + lines = 0 + for region in f: + lines += 1 + f.seek(0) + addrate_newfile = num_add / lines + for region in f: + if random.random() < addrate_newfile: + region = region.split('\t') + if str(region[1]).isdigit(): + new_regions[0].append(region[0]) + new_regions[1].append(int(region[1])) + new_regions[2].append(int(region[2])) + new_regions[3].append(3) + return df.append(pd.DataFrame(new_regions), ignore_index=True) + + + def shift(self, df, shiftrate, shiftmean, shiftstdev): + """ + Shift regions + + :param pandas.DataFrame df: the dataframe to perturb. + :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) + :param float shiftmean: the mean shift distance + :param float shiftstdev: the standard deviation of the shift distance + :return pandas.DataFrame: the original dataframe after shifts + """ + self.__check_rate([shiftrate]) + rows = df.shape[0] + for i in range(rows): + if random.random() < shiftrate: + df = self.__shift(df, i, shiftmean, shiftstdev) # shifted rows display a 1 + return df + + def __shift(self, df, row, mean, stdev): + theshift = int(np.random.normal(mean, stdev)) + + start = df.loc[row][1] + end = df.loc[row][2] + + if start + theshift < 0 or \ + end + theshift > chrom_lens[df.loc[row][0]]: + # shifting out of bounds check + return df + + df.at[row, 1] = start + theshift + df.at[row, 2] = end + theshift + df.at[row, 3] = 1 + + return df + + + def cut(self, df, cutrate): + """ + Cut regions to create two new regions + + :param pandas.DataFrame df: the dataframe to perturb. + :param float cutrate: the rate to cut regions into two separate regions + :return pandas.DataFrame: the new dataframe after cuts + """ + self.__check_rate([cutrate]) + if cutrate == 0: + return df + rows = df.shape[0] + cut_rows = random.sample(list(range(rows)), int(rows * cutrate)) + new_row_list = [] + to_drop = [] + for row in cut_rows: + drop_row, new_regions = self.__cut(df, row) # cut rows display a 2 + new_row_list.extend(new_regions) + to_drop.append(drop_row) + df = df.drop(to_drop) + df = df.append(new_row_list, ignore_index=True) + return df.reset_index(drop=True) + + def __cut(self, df, row): + chrom = df.loc[row][0] + start = df.loc[row][1] + end = df.loc[row][2] + + thecut = (start + end) // 2 # int(np.random.normal((start+end)/2, (end - start)/6)) + if thecut <= start: + thecut = start + 10 + if thecut >= end: + thecut = end - 10 + + ''' may add in later, this makes the api confusing! + # adjust the cut regions using the shift function + new_segs = self.__shift(new_segs, 0, meanshift, stdevshift) + new_segs = self.__shift(new_segs, 1, meanshift, stdevshift) + ''' + + return row, [{0: chrom, 1: start, 2: thecut, 3: 2}, {0: chrom, 1: thecut, 2: end, 3: 2}] + + + def merge(self, df, mergerate): + """ + Merge two regions into one new region + + :param pandas.DataFrame df: the dataframe to perturb. + :param float mergerate: the rate to merge two regions into one + :return pandas.DataFrame: the new dataframe after merges + """ + + self.__check_rate([mergerate]) + if mergerate == 0: + return df + rows = df.shape[0] + merge_rows = random.sample(list(range(rows)), int(rows * mergerate)) + to_add = [] + to_drop = [] + for row in merge_rows: + drop_row, add_row = self.__merge(df, row) + if add_row: + to_add.append(add_row) + to_drop.extend(drop_row) + df = df.drop(to_drop) + df = df.append(to_add, ignore_index=True) + return df.reset_index(drop=True) + + def __merge(self, df, row): + # check if the regions being merged are on the same chromosome + if row + 1 not in df.index or df.loc[row][0] != df.loc[row+1][0]: + return [], None + + chrom = df.loc[row][0] + start = df.loc[row][1] + end = df.loc[row+1][2] + return [row, row+1], {0: chrom, 1: start, 2: end, 3: 4} + + + def drop(self, df, droprate): + """ + Drop regions + + :param pandas.DataFrame df: the dataframe to perturb. + :param float droprate: the rate to drop/remove regions + :return pandas.DataFrame: the new dataframe after drops + """ + self.__check_rate([droprate]) + rows = df.shape[0] + drop_rows = random.sample(list(range(rows)), int(rows * droprate)) + df = df.drop(drop_rows) + return df.reset_index(drop=True) + + + def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, addfile=None, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0): + ''' + Perform all five perturbations in the order of add, shift, cut, merge, drop. + + :param pandas.DataFrame df: the dataframe to perturb. + :param float addrate: the rate (as a proportion of the total number of regions) to add regions + :param float addmean: the mean length of added regions + :param float addstdev: the standard deviation of the length of added regions + :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) + :param float shiftmean: the mean shift distance + :param float shiftstdev: the standard deviation of the shift distance + :param float cutrate: the rate to cut regions into two separate regions + :param float mergerate: the rate to merge two regions into one + :param float droprate: the rate to drop/remove regions + :return pandas.DataFrame: the new dataframe after all perturbations + ''' + + self.__check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) + df = self.shift(df, shiftrate, shiftmean, shiftstdev) + if addfile: + df = self.add_from_file(df, addfile, addrate) + else: + df = self.add(df, addrate, addmean, addstdev) + df = self.cut(df, cutrate) + df = self.merge(df, mergerate) + df = self.drop(df, droprate) + return df + + + def write_bed(self, df, outfile_name): + """ + Write a pandas dataframe back into bedfile format + + :param pandas.DataFrame df: A dataframe containing regions like a bedfile + :param str outfile_name: The name of the output bedfile + """ + df.sort_values([0,1,2], inplace=True) + df.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') + print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.'.format(outfile_name, df.shape[0], self.original_regions)) + + +def main(): + """ Primary workflow """ + + parser = logmuse.add_logging_options(build_argparser()) + args, remaining_args = parser.parse_known_args() + global _LOGGER + _LOGGER = logmuse.logger_via_cli(args) + + _LOGGER.info("welcome to bedshift") + _LOGGER.info("Shifting file: '{}'".format(args.bedfile)) + msg = """Params: + shift rate: {shiftrate} + shift mean distance: {shiftmean} + shift stdev: {shiftstdev} + add rate: {addrate} + add mean length: {addmean} + add stdev: {addstdev} + add regions from file: {addfile} + cut rate: {cutrate} + drop rate: {droprate} + merge rate: {mergerate} + outputfile: {outputfile} + repeat: {repeat} + """ + + outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) if not args.outputfile else args.outputfile + + _LOGGER.info(msg.format( + droprate=args.droprate, + addrate=args.addrate, + addmean=args.addmean, + addstdev=args.addstdev, + addfile=args.addfile, + shiftrate=args.shiftrate, + shiftmean=args.shiftmean, + shiftstdev=args.shiftstdev, + cutrate=args.cutrate, + mergerate=args.mergerate, + outputfile=args.outputfile, + repeat=args.repeat)) + + if not args.bedfile: + parser.print_help() + _LOGGER.error("No bedfile given") + sys.exit(1) + + + bedshifter = Bedshift() + + df = bedshifter.read_bed(args.bedfile) + if args.repeat == 1: + df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.addfile, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) + bedshifter.write_bed(df, outfile) + elif args.repeat > 1: + for i in range(args.repeat): + repeat_df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.addfile, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) + modified_outfile = outfile.rsplit("/") + modified_outfile[-1] = "gen" + str(i+1) + "_" + modified_outfile[-1] + modified_outfile = "/".join(modified_outfile) + bedshifter.write_bed(repeat_df, modified_outfile) + else: + _LOGGER.error("repeats specified is less than 1") + sys.exit(1) + + +if __name__ == '__main__': + try: + sys.exit(main()) + except KeyboardInterrupt: + _LOGGER.error("Program canceled by user!") + sys.exit(1) + + diff --git a/dist/bedshift-0.2.0-py3-none-any.whl b/dist/bedshift-0.2.0-py3-none-any.whl new file mode 100644 index 0000000000000000000000000000000000000000..10da00c218f2e179303055460857f975a9c365d5 GIT binary patch literal 7579 zcmaKx1yoeszy609x}~Mdp@uF2X^@5?MLLIW5T!$hmTr;m1_h+M8)WEikOmRt;{E;K zzwhh4_nou$I_Jz<=d+)+&)&~I>-(rFA|Vq3001;VLsqq#xnkNx)WhAvL;LlwsfD>K z)Y{UG!^p_m!P?Ep2<+sg93_E)$b|*?<|R`|A_J#>bpl*UVw{?rdHy-3mi79LXC5#* zMZV+UxcKzEJUfm;5NyM$_-u91#^N(W$!?GYVO&?A5Dq}3ReB#~EAJH=3lENf9|tZD zQCf3tgZ1k-?ECBGMM!i2x+FX17Q9t5xk<<2T9&j4+r`r~agXs#@TjdJ0)CT7 zv#Zr8!eJ8TcmM$8*&ieE*N{ArHXqwMuJ@dL@#~hApedd?kyfZS&%toArov9u$4%nJ 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160 -chr9 17063581 17063901 . 402 . 62.9941276957876 -1 4.70433072024128 160 -chr9 5600531 5600851 . 402 . 62.989584485335 -1 4.70433072024128 160 -chr7 99510760 99511080 . 402 . 62.983911429443 -1 4.70433072024128 160 -chr15 90762869 90763189 . 402 . 62.9798274255038 -1 4.70433072024128 160 -chr13 45968136 45968456 . 402 . 62.9790557293635 -1 4.70433072024128 160 -chr19 10535007 10535327 . 402 . 62.9777267019555 -1 4.70433072024128 160 -chr14 102783190 102783510 . 402 . 62.9759383417967 -1 4.70433072024128 160 -chr9 36995847 36996167 . 402 . 62.9736557997164 -1 4.70433072024128 160 -chr4 91236271 91236591 . 402 . 62.973509196248 -1 4.70433072024128 160 -chr19 3472009 3472329 . 402 . 62.9718358693825 -1 4.70433072024128 160 -chr17 66462303 66462623 . 402 . 62.9619446326814 -1 4.70433072024128 160 -chr2 27165494 27165814 . 402 . 62.9613716897737 -1 4.70433072024128 160 -chr19 10360785 10361105 . 402 . 62.9592124383753 -1 4.70433072024128 160 -chr12 44394193 44394513 . 402 . 62.9408175970991 -1 4.70433072024128 160 diff --git a/setup.py b/setup.py index b1bc2123..04c3c64a 100644 --- a/setup.py +++ b/setup.py @@ -33,7 +33,7 @@ name=PACKAGE, packages=[PACKAGE], version=version, - description="BED file shifter", + description="BED file perturbations", long_description=long_description, long_description_content_type='text/markdown', classifiers=[ @@ -44,16 +44,16 @@ "Programming Language :: Python :: 3.6", "Topic :: System :: Distributed Computing" ], - keywords="docker, containers, reproducibility, bioinformatics, workflow", + keywords="BED file, perturbation, bioinformatics, region set", url="https://bedshift.databio.org", - author=u"Nathan Sheffield", - author_email=u"nathan@code.databio.org", + author=u"Aaron Gu", + author_email=u"ag5ym@virginia.edu", license="BSD2", entry_points={ "console_scripts": [ 'bedshift = bedshift.bedshift:main' ], - }, + }, package_data={"bedshift": [os.path.join("bedshift", "*")]}, include_package_data=True, test_suite="tests", diff --git a/examples/pypackage_test.py b/tests/pypackage_test.py similarity index 100% rename from examples/pypackage_test.py rename to tests/pypackage_test.py diff --git a/examples/shell_test.sh b/tests/shell_test.sh similarity index 100% rename from examples/shell_test.sh rename to tests/shell_test.sh From dca441901a33b110ca1b0615dd3a6d5290df806b Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Wed, 20 May 2020 13:24:32 -0400 Subject: [PATCH 0037/1072] improve usability of python API --- README.md | 12 +- bedshift/_version.py | 2 +- bedshift/bedshift.py | 236 ++++++++++++++++++++++++---------------- docs/README.md | 12 +- run-tests.sh | 5 +- setup.py | 1 - tests/pypackage_test.py | 20 ++-- 7 files changed, 168 insertions(+), 120 deletions(-) diff --git a/README.md b/README.md index e2367e91..586db403 100644 --- a/README.md +++ b/README.md @@ -13,13 +13,15 @@ See: `bedshift --help` for parameters. ```py from bedshift import bedshift -bedshifter = bedshift.Bedshift() - -df = bedshifter.read_bed('test.bed') -df = bedshifter.all_perturbations(df, addrate=0.3, addmean=320.0, addstdev=20.0, shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, cutrate=0.1, mergerate=0.11, droprate=0.03) +bedshifter = bedshift.Bedshift('tests/test.bed') +bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, + shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, + cutrate=0.1, + mergerate=0.11, + droprate=0.03) # can also run single operations: shift, add, cut, merge, drop -bedshifter.write_bed(df, 'output_file.bed') +bedshifter.to_bed('test_output.bed') ``` diff --git a/bedshift/_version.py b/bedshift/_version.py index d3ec452c..5becc17c 100644 --- a/bedshift/_version.py +++ b/bedshift/_version.py @@ -1 +1 @@ -__version__ = "0.2.0" +__version__ = "1.0.0" diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index e5dc83ba..d4133528 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -18,7 +18,30 @@ chroms = {num: 'chr'+str(num) for num in list(range(1, 23))} chroms.update({'X': 'chrX', 'Y': 'chrY'}) -chrom_lens = {'chr1': 247249719, 'chr2': 242951149, 'chr3': 199501827, 'chr4': 191273063, 'chr5': 180857866, 'chr6': 170899992, 'chr7': 158821424, 'chr8': 146274826, 'chr9': 140273252, 'chr10': 135374737, 'chr11': 134452384, 'chr12': 132349534, 'chr13': 114142980, 'chr14': 106368585, 'chr15': 100338915, 'chr16': 88827254, 'chr17': 78774742, 'chr18': 76117153, 'chr19': 63811651, 'chr20': 62435964, 'chr21': 46944323, 'chr22': 49691432, 'chrX': 154913754, 'chrY': 57772954} +chrom_lens = {'chr1': 247249719, + 'chr2': 242951149, + 'chr3': 199501827, + 'chr4': 191273063, + 'chr5': 180857866, + 'chr6': 170899992, + 'chr7': 158821424, + 'chr8': 146274826, + 'chr9': 140273252, + 'chr10': 135374737, + 'chr11': 134452384, + 'chr12': 132349534, + 'chr13': 114142980, + 'chr14': 106368585, + 'chr15': 100338915, + 'chr16': 88827254, + 'chr17': 78774742, + 'chr18': 76117153, + 'chr19': 63811651, + 'chr20': 62435964, + 'chr21': 46944323, + 'chr22': 49691432, + 'chrX': 154913754, + 'chrY': 57772954} class _VersionInHelpParser(argparse.ArgumentParser): @@ -106,12 +129,9 @@ class Bedshift(object): The bedshift object with methods to perturb regions """ - def __init__(self): - self.original_regions = -1 - - def read_bed(self, bedfile_path): + def __init__(self, bedfile_path): """ - Read in a bedfile to pandas DataFrame format + Read in a .bed file to pandas DataFrame format :param str bedfile_path: the path to the bedfile """ @@ -125,7 +145,17 @@ def read_bed(self, bedfile_path): df[3] = 0 # column indicating which modifications were made self.original_regions = df.shape[0] - return df.astype({1: 'int64', 2: 'int64', 3: 'int64'}).sort_values([0, 1, 2]).reset_index(drop=True) + self.bed = df.astype({1: 'int64', 2: 'int64', 3: 'int64'}) \ + .sort_values([0, 1, 2]).reset_index(drop=True) + self.original_bed = self.bed + + + def reset_bed(self): + """ + Reset the stored bedfile to the state before perturbations + """ + + self.bed = self.original_bed def __check_rate(self, rates): @@ -146,18 +176,17 @@ def pick_random_chrom(self): return chrom_str, chrom_len - def add(self, df, addrate, addmean, addstdev): + def add(self, addrate, addmean, addstdev): """ Add regions - :param pandas.DataFrame df: the dataframe to perturb :param float addrate: the rate to add regions :param float addmean: the mean length of added regions :param float addstdev: the standard deviation of the length of added regions - :return pandas.DataFrame: the new dataframe object after adds + :return int: the number of regions added """ self.__check_rate([addrate]) - rows = df.shape[0] + rows = self.bed.shape[0] num_add = int(rows * addrate) new_regions = {0: [], 1: [], 2: [], 3: []} for _ in range(num_add): @@ -168,19 +197,20 @@ def add(self, df, addrate, addmean, addstdev): new_regions[1].append(start) new_regions[2].append(end) new_regions[3].append(3) - return df.append(pd.DataFrame(new_regions), ignore_index=True) + self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) + return len(new_regions) - def add_from_file(self, df, fp, addrate): + def add_from_file(self, fp, addrate): """ Add regions from another bedfile to this perturbed bedfile - :param pandas.DataFrame df: the dataframe to perturb :param float addrate: the rate to add regions :param str fp: the filepath to the other bedfile + :return int: the number of regions added """ self.__check_rate([addrate]) - rows = df.shape[0] + rows = self.bed.shape[0] num_add = int(rows * addrate) new_regions = {0: [], 1: [], 2: [], 3: []} with open(fp, 'r') as f: @@ -197,72 +227,70 @@ def add_from_file(self, df, fp, addrate): new_regions[1].append(int(region[1])) new_regions[2].append(int(region[2])) new_regions[3].append(3) - return df.append(pd.DataFrame(new_regions), ignore_index=True) + self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) + return len(new_regions) - - def shift(self, df, shiftrate, shiftmean, shiftstdev): + def shift(self, shiftrate, shiftmean, shiftstdev): """ Shift regions - :param pandas.DataFrame df: the dataframe to perturb. :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) :param float shiftmean: the mean shift distance :param float shiftstdev: the standard deviation of the shift distance - :return pandas.DataFrame: the original dataframe after shifts + :return int: the number of regions shifted """ self.__check_rate([shiftrate]) - rows = df.shape[0] - for i in range(rows): - if random.random() < shiftrate: - df = self.__shift(df, i, shiftmean, shiftstdev) # shifted rows display a 1 - return df + rows = self.bed.shape[0] + shift_rows = random.sample(list(range(rows)), int(rows * shiftrate)) + for row in shift_rows: + self.__shift(row, shiftmean, shiftstdev) # shifted rows display a 1 + return len(shift_rows) - def __shift(self, df, row, mean, stdev): + def __shift(self, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) - start = df.loc[row][1] - end = df.loc[row][2] + start = self.bed.loc[row][1] + end = self.bed.loc[row][2] if start + theshift < 0 or \ - end + theshift > chrom_lens[df.loc[row][0]]: + end + theshift > chrom_lens[self.bed.loc[row][0]]: # shifting out of bounds check - return df - - df.at[row, 1] = start + theshift - df.at[row, 2] = end + theshift - df.at[row, 3] = 1 + return - return df + self.bed.at[row, 1] = start + theshift + self.bed.at[row, 2] = end + theshift + self.bed.at[row, 3] = 1 - def cut(self, df, cutrate): + def cut(self, cutrate): """ Cut regions to create two new regions - :param pandas.DataFrame df: the dataframe to perturb. :param float cutrate: the rate to cut regions into two separate regions - :return pandas.DataFrame: the new dataframe after cuts + :return int: the number of regions cut """ self.__check_rate([cutrate]) if cutrate == 0: - return df - rows = df.shape[0] + return 0 + rows = self.bed.shape[0] cut_rows = random.sample(list(range(rows)), int(rows * cutrate)) new_row_list = [] to_drop = [] for row in cut_rows: - drop_row, new_regions = self.__cut(df, row) # cut rows display a 2 + drop_row, new_regions = self.__cut(row) # cut rows display a 2 new_row_list.extend(new_regions) to_drop.append(drop_row) - df = df.drop(to_drop) - df = df.append(new_row_list, ignore_index=True) - return df.reset_index(drop=True) + self.bed = self.bed.drop(to_drop) + self.bed = self.bed.append(new_row_list, ignore_index=True) + self.bed = self.bed.reset_index(drop=True) + return len(cut_rows) - def __cut(self, df, row): - chrom = df.loc[row][0] - start = df.loc[row][1] - end = df.loc[row][2] + def __cut(self, row): + chrom = self.bed.loc[row][0] + start = self.bed.loc[row][1] + end = self.bed.loc[row][2] + # choose where to cut the region thecut = (start + end) // 2 # int(np.random.normal((start+end)/2, (end - start)/6)) if thecut <= start: thecut = start + 10 @@ -278,62 +306,67 @@ def __cut(self, df, row): return row, [{0: chrom, 1: start, 2: thecut, 3: 2}, {0: chrom, 1: thecut, 2: end, 3: 2}] - def merge(self, df, mergerate): + def merge(self, mergerate): """ Merge two regions into one new region - :param pandas.DataFrame df: the dataframe to perturb. :param float mergerate: the rate to merge two regions into one - :return pandas.DataFrame: the new dataframe after merges + :return int: number of regions merged """ self.__check_rate([mergerate]) if mergerate == 0: - return df - rows = df.shape[0] + return 0 + rows = self.bed.shape[0] merge_rows = random.sample(list(range(rows)), int(rows * mergerate)) to_add = [] to_drop = [] for row in merge_rows: - drop_row, add_row = self.__merge(df, row) + drop_row, add_row = self.__merge(row) if add_row: to_add.append(add_row) to_drop.extend(drop_row) - df = df.drop(to_drop) - df = df.append(to_add, ignore_index=True) - return df.reset_index(drop=True) + self.bed = self.bed.drop(to_drop) + self.bed = self.bed.append(to_add, ignore_index=True) + self.bed = self.bed.reset_index(drop=True) + return len(merge_rows) - def __merge(self, df, row): + def __merge(self, row): # check if the regions being merged are on the same chromosome - if row + 1 not in df.index or df.loc[row][0] != df.loc[row+1][0]: + if row + 1 not in self.bed.index or self.bed.loc[row][0] != self.bed.loc[row+1][0]: return [], None - chrom = df.loc[row][0] - start = df.loc[row][1] - end = df.loc[row+1][2] + chrom = self.bed.loc[row][0] + start = self.bed.loc[row][1] + end = self.bed.loc[row+1][2] return [row, row+1], {0: chrom, 1: start, 2: end, 3: 4} - def drop(self, df, droprate): + def drop(self, droprate): """ Drop regions - :param pandas.DataFrame df: the dataframe to perturb. :param float droprate: the rate to drop/remove regions - :return pandas.DataFrame: the new dataframe after drops + :return int: the number of rows dropped """ self.__check_rate([droprate]) - rows = df.shape[0] + rows = self.bed.shape[0] drop_rows = random.sample(list(range(rows)), int(rows * droprate)) - df = df.drop(drop_rows) - return df.reset_index(drop=True) - - - def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, addfile=None, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0): + self.bed = self.bed.drop(drop_rows) + self.bed = self.bed.reset_index(drop=True) + return len(drop_rows) + + + def all_perturbations(self, + addrate=0.0, addmean=320.0, addstdev=30.0, + addfile=None, + shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, + cutrate=0.0, + mergerate=0.0, + droprate=0.0): ''' - Perform all five perturbations in the order of add, shift, cut, merge, drop. + Perform all five perturbations in the order of shift, add, cut, merge, drop. - :param pandas.DataFrame df: the dataframe to perturb. :param float addrate: the rate (as a proportion of the total number of regions) to add regions :param float addmean: the mean length of added regions :param float addstdev: the standard deviation of the length of added regions @@ -343,31 +376,32 @@ def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, addfi :param float cutrate: the rate to cut regions into two separate regions :param float mergerate: the rate to merge two regions into one :param float droprate: the rate to drop/remove regions - :return pandas.DataFrame: the new dataframe after all perturbations + :return int: the number of total regions perturbed ''' self.__check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) - df = self.shift(df, shiftrate, shiftmean, shiftstdev) + n = 0 + n += self.shift(shiftrate, shiftmean, shiftstdev) if addfile: - df = self.add_from_file(df, addfile, addrate) + n += self.add_from_file(addfile, addrate) else: - df = self.add(df, addrate, addmean, addstdev) - df = self.cut(df, cutrate) - df = self.merge(df, mergerate) - df = self.drop(df, droprate) - return df + n += self.add(addrate, addmean, addstdev) + n += self.cut(cutrate) + n += self.merge(mergerate) + n += self.drop(droprate) + return n - def write_bed(self, df, outfile_name): + def to_bed(self, outfile_name): """ - Write a pandas dataframe back into bedfile format + Write a pandas dataframe back into BED file format - :param pandas.DataFrame df: A dataframe containing regions like a bedfile - :param str outfile_name: The name of the output bedfile + :param str outfile_name: The name of the output BED file """ - df.sort_values([0,1,2], inplace=True) - df.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') - print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.'.format(outfile_name, df.shape[0], self.original_regions)) + self.bed.sort_values([0,1,2], inplace=True) + self.bed.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') + print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.' \ + .format(outfile_name, self.bed.shape[0], self.original_regions)) def main(): @@ -417,19 +451,29 @@ def main(): sys.exit(1) - bedshifter = Bedshift() - - df = bedshifter.read_bed(args.bedfile) + bedshifter = Bedshift(args.bedfile) if args.repeat == 1: - df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.addfile, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) - bedshifter.write_bed(df, outfile) + n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, + args.addfile, + args.shiftrate, args.shiftmean, args.shiftstdev, + args.cutrate, + args.mergerate, + args.droprate) + bedshifter.to_bed(outfile) elif args.repeat > 1: for i in range(args.repeat): - repeat_df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.addfile, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) + + n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, + args.addfile, + args.shiftrate, args.shiftmean, args.shiftstdev, + args.cutrate, + args.mergerate, + args.droprate) modified_outfile = outfile.rsplit("/") - modified_outfile[-1] = "gen" + str(i+1) + "_" + modified_outfile[-1] + modified_outfile[-1] = "rep" + str(i+1) + "_" + modified_outfile[-1] modified_outfile = "/".join(modified_outfile) - bedshifter.write_bed(repeat_df, modified_outfile) + bedshifter.to_bed(modified_outfile) + bedshifter.reset_bed() else: _LOGGER.error("repeats specified is less than 1") sys.exit(1) diff --git a/docs/README.md b/docs/README.md index 208ab8d0..1e5e8130 100644 --- a/docs/README.md +++ b/docs/README.md @@ -22,7 +22,7 @@ CLI: ``` bedshift -h -bedshift -b examples/test.bed -p 0.1 -a 0.1 +bedshift -b tests/test.bed -p 0.1 -a 0.1 ``` Python: @@ -30,10 +30,8 @@ Python: ```py from bedshift import bedshift -bedshifter = bedshift.Bedshift() - -df = bedshifter.read_bed('examples/test.bed') -df = bedshifter.shift(df, shiftrate=0.1, shiftmean=0.0, shiftstdev=120.0) -df = bedshifter.add(df, addrate=0.1, addmean=320.0, addstdev=20.0) -bedshifter.write_bed(df, 'output_file.bed') +bedshifter = bedshift.Bedshift('tests/test.bed') +bedshifter.shift(shiftrate=0.1, shiftmean=0.0, shiftstdev=120.0) +bedshifter.add(addrate=0.1, addmean=320.0, addstdev=20.0) +bedshifter.to_bed('tests/test_output.bed') ``` diff --git a/run-tests.sh b/run-tests.sh index 4e44e2c4..667e5cc2 100755 --- a/run-tests.sh +++ b/run-tests.sh @@ -1,4 +1,5 @@ #!/bin/bash -python3 ./examples/pypackage_test.py -./examples/shell_test.sh +pip install . +python3 ./tests/pypackage_test.py +./tests/shell_test.sh diff --git a/setup.py b/setup.py index 04c3c64a..c22257f4 100644 --- a/setup.py +++ b/setup.py @@ -39,7 +39,6 @@ classifiers=[ "Development Status :: 4 - Beta", "License :: OSI Approved :: BSD License", - "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: System :: Distributed Computing" diff --git a/tests/pypackage_test.py b/tests/pypackage_test.py index edd81f37..158f9f2a 100755 --- a/tests/pypackage_test.py +++ b/tests/pypackage_test.py @@ -3,13 +3,17 @@ os.chdir(sys.path[0]) -bedshifter = bedshift.Bedshift() +bedshifter = bedshift.Bedshift('test.bed') -df = bedshifter.read_bed('test.bed') -original_rows = df.shape[0] +bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, + shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, + cutrate=0.1, + mergerate=0.11, + droprate=0.03) +bedshifter.to_bed('py_output.bed') +bedshifter.reset_bed() -df = bedshifter.all_perturbations(df, addrate=0.3, addmean=320.0, addstdev=20.0, addfile=None, shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, cutrate=0.1, mergerate=0.11, droprate=0.03) -bedshifter.write_bed(df, 'py_output.bed') - -df = bedshifter.all_perturbations(df, addrate=0.3, addmean=320.0, addstdev=20.0, addfile='py_output.bed', shiftrate=0.3, shiftmean=100.0, shiftstdev=30.0) -bedshifter.write_bed(df, 'py_output2.bed') +bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, + addfile='py_output.bed', + shiftrate=0.3, shiftmean=100.0, shiftstdev=30.0) +bedshifter.to_bed('py_output2.bed') From 448a17c1469e2539e3a40f79da209759e935a408 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Thu, 21 May 2020 15:20:41 -0400 Subject: [PATCH 0038/1072] update pypi package --- bedshift.egg-info/PKG-INFO | 19 ++- build/lib/bedshift/_version.py | 2 +- build/lib/bedshift/bedshift.py | 238 ++++++++++++++++----------- dist/bedshift-0.2.0-py3-none-any.whl | Bin 7579 -> 0 bytes dist/bedshift-0.2.0.tar.gz | Bin 7310 -> 0 bytes dist/bedshift-1.0.0-py3-none-any.whl | Bin 0 -> 7633 bytes dist/bedshift-1.0.0.tar.gz | Bin 0 -> 7400 bytes 7 files changed, 152 insertions(+), 107 deletions(-) delete mode 100644 dist/bedshift-0.2.0-py3-none-any.whl delete mode 100644 dist/bedshift-0.2.0.tar.gz create mode 100644 dist/bedshift-1.0.0-py3-none-any.whl create mode 100644 dist/bedshift-1.0.0.tar.gz diff --git a/bedshift.egg-info/PKG-INFO b/bedshift.egg-info/PKG-INFO index ca62e70a..ecc52db1 100644 --- a/bedshift.egg-info/PKG-INFO +++ b/bedshift.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: bedshift -Version: 0.2.0 +Version: 1.0.0 Summary: BED file perturbations Home-page: https://bedshift.databio.org Author: Aaron Gu @@ -19,15 +19,17 @@ Description: # Bedshift ## Python ```py - from bedshift import bedshift - - bedshifter = bedshift.Bedshift() - - df = bedshifter.read_bed('test.bed') - df = bedshifter.all_perturbations(df, addrate=0.3, addmean=320.0, addstdev=20.0, shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, cutrate=0.1, mergerate=0.11, droprate=0.03) + import bedshift + + bedshifter = bedshift.Bedshift('tests/test.bed') + bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, + shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, + cutrate=0.1, + mergerate=0.11, + droprate=0.03) # can also run single operations: shift, add, cut, merge, drop - bedshifter.write_bed(df, 'output_file.bed') + bedshifter.to_bed('test_output.bed') ``` @@ -49,7 +51,6 @@ Keywords: BED file,perturbation,bioinformatics,region set Platform: UNKNOWN Classifier: Development Status :: 4 - Beta Classifier: License :: OSI Approved :: BSD License -Classifier: Programming Language :: Python :: 2.7 Classifier: Programming Language :: Python :: 3.5 Classifier: Programming Language :: Python :: 3.6 Classifier: Topic :: System :: Distributed Computing diff --git a/build/lib/bedshift/_version.py b/build/lib/bedshift/_version.py index d3ec452c..5becc17c 100644 --- a/build/lib/bedshift/_version.py +++ b/build/lib/bedshift/_version.py @@ -1 +1 @@ -__version__ = "0.2.0" +__version__ = "1.0.0" diff --git a/build/lib/bedshift/bedshift.py b/build/lib/bedshift/bedshift.py index e5dc83ba..535a7e63 100644 --- a/build/lib/bedshift/bedshift.py +++ b/build/lib/bedshift/bedshift.py @@ -18,7 +18,30 @@ chroms = {num: 'chr'+str(num) for num in list(range(1, 23))} chroms.update({'X': 'chrX', 'Y': 'chrY'}) -chrom_lens = {'chr1': 247249719, 'chr2': 242951149, 'chr3': 199501827, 'chr4': 191273063, 'chr5': 180857866, 'chr6': 170899992, 'chr7': 158821424, 'chr8': 146274826, 'chr9': 140273252, 'chr10': 135374737, 'chr11': 134452384, 'chr12': 132349534, 'chr13': 114142980, 'chr14': 106368585, 'chr15': 100338915, 'chr16': 88827254, 'chr17': 78774742, 'chr18': 76117153, 'chr19': 63811651, 'chr20': 62435964, 'chr21': 46944323, 'chr22': 49691432, 'chrX': 154913754, 'chrY': 57772954} +chrom_lens = {'chr1': 247249719, + 'chr2': 242951149, + 'chr3': 199501827, + 'chr4': 191273063, + 'chr5': 180857866, + 'chr6': 170899992, + 'chr7': 158821424, + 'chr8': 146274826, + 'chr9': 140273252, + 'chr10': 135374737, + 'chr11': 134452384, + 'chr12': 132349534, + 'chr13': 114142980, + 'chr14': 106368585, + 'chr15': 100338915, + 'chr16': 88827254, + 'chr17': 78774742, + 'chr18': 76117153, + 'chr19': 63811651, + 'chr20': 62435964, + 'chr21': 46944323, + 'chr22': 49691432, + 'chrX': 154913754, + 'chrY': 57772954} class _VersionInHelpParser(argparse.ArgumentParser): @@ -32,7 +55,7 @@ def build_argparser(): """ Builds argument parser. - :return argparse.ArgumentParser + :return: argparse.ArgumentParser """ banner = "%(prog)s - randomize BED files" @@ -106,12 +129,9 @@ class Bedshift(object): The bedshift object with methods to perturb regions """ - def __init__(self): - self.original_regions = -1 - - def read_bed(self, bedfile_path): + def __init__(self, bedfile_path): """ - Read in a bedfile to pandas DataFrame format + Read in a .bed file to pandas DataFrame format :param str bedfile_path: the path to the bedfile """ @@ -125,7 +145,17 @@ def read_bed(self, bedfile_path): df[3] = 0 # column indicating which modifications were made self.original_regions = df.shape[0] - return df.astype({1: 'int64', 2: 'int64', 3: 'int64'}).sort_values([0, 1, 2]).reset_index(drop=True) + self.bed = df.astype({1: 'int64', 2: 'int64', 3: 'int64'}) \ + .sort_values([0, 1, 2]).reset_index(drop=True) + self.original_bed = self.bed + + + def reset_bed(self): + """ + Reset the stored bedfile to the state before perturbations + """ + + self.bed = self.original_bed def __check_rate(self, rates): @@ -146,18 +176,17 @@ def pick_random_chrom(self): return chrom_str, chrom_len - def add(self, df, addrate, addmean, addstdev): + def add(self, addrate, addmean, addstdev): """ Add regions - :param pandas.DataFrame df: the dataframe to perturb :param float addrate: the rate to add regions :param float addmean: the mean length of added regions :param float addstdev: the standard deviation of the length of added regions - :return pandas.DataFrame: the new dataframe object after adds + :return int: the number of regions added """ self.__check_rate([addrate]) - rows = df.shape[0] + rows = self.bed.shape[0] num_add = int(rows * addrate) new_regions = {0: [], 1: [], 2: [], 3: []} for _ in range(num_add): @@ -168,19 +197,20 @@ def add(self, df, addrate, addmean, addstdev): new_regions[1].append(start) new_regions[2].append(end) new_regions[3].append(3) - return df.append(pd.DataFrame(new_regions), ignore_index=True) + self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) + return len(new_regions) - def add_from_file(self, df, fp, addrate): + def add_from_file(self, fp, addrate): """ Add regions from another bedfile to this perturbed bedfile - :param pandas.DataFrame df: the dataframe to perturb :param float addrate: the rate to add regions :param str fp: the filepath to the other bedfile + :return int: the number of regions added """ self.__check_rate([addrate]) - rows = df.shape[0] + rows = self.bed.shape[0] num_add = int(rows * addrate) new_regions = {0: [], 1: [], 2: [], 3: []} with open(fp, 'r') as f: @@ -197,72 +227,70 @@ def add_from_file(self, df, fp, addrate): new_regions[1].append(int(region[1])) new_regions[2].append(int(region[2])) new_regions[3].append(3) - return df.append(pd.DataFrame(new_regions), ignore_index=True) + self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) + return len(new_regions) - - def shift(self, df, shiftrate, shiftmean, shiftstdev): + def shift(self, shiftrate, shiftmean, shiftstdev): """ Shift regions - :param pandas.DataFrame df: the dataframe to perturb. :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) :param float shiftmean: the mean shift distance :param float shiftstdev: the standard deviation of the shift distance - :return pandas.DataFrame: the original dataframe after shifts + :return int: the number of regions shifted """ self.__check_rate([shiftrate]) - rows = df.shape[0] - for i in range(rows): - if random.random() < shiftrate: - df = self.__shift(df, i, shiftmean, shiftstdev) # shifted rows display a 1 - return df + rows = self.bed.shape[0] + shift_rows = random.sample(list(range(rows)), int(rows * shiftrate)) + for row in shift_rows: + self.__shift(row, shiftmean, shiftstdev) # shifted rows display a 1 + return len(shift_rows) - def __shift(self, df, row, mean, stdev): + def __shift(self, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) - start = df.loc[row][1] - end = df.loc[row][2] + start = self.bed.loc[row][1] + end = self.bed.loc[row][2] if start + theshift < 0 or \ - end + theshift > chrom_lens[df.loc[row][0]]: + end + theshift > chrom_lens[self.bed.loc[row][0]]: # shifting out of bounds check - return df - - df.at[row, 1] = start + theshift - df.at[row, 2] = end + theshift - df.at[row, 3] = 1 + return - return df + self.bed.at[row, 1] = start + theshift + self.bed.at[row, 2] = end + theshift + self.bed.at[row, 3] = 1 - def cut(self, df, cutrate): + def cut(self, cutrate): """ Cut regions to create two new regions - :param pandas.DataFrame df: the dataframe to perturb. :param float cutrate: the rate to cut regions into two separate regions - :return pandas.DataFrame: the new dataframe after cuts + :return int: the number of regions cut """ self.__check_rate([cutrate]) if cutrate == 0: - return df - rows = df.shape[0] + return 0 + rows = self.bed.shape[0] cut_rows = random.sample(list(range(rows)), int(rows * cutrate)) new_row_list = [] to_drop = [] for row in cut_rows: - drop_row, new_regions = self.__cut(df, row) # cut rows display a 2 + drop_row, new_regions = self.__cut(row) # cut rows display a 2 new_row_list.extend(new_regions) to_drop.append(drop_row) - df = df.drop(to_drop) - df = df.append(new_row_list, ignore_index=True) - return df.reset_index(drop=True) + self.bed = self.bed.drop(to_drop) + self.bed = self.bed.append(new_row_list, ignore_index=True) + self.bed = self.bed.reset_index(drop=True) + return len(cut_rows) - def __cut(self, df, row): - chrom = df.loc[row][0] - start = df.loc[row][1] - end = df.loc[row][2] + def __cut(self, row): + chrom = self.bed.loc[row][0] + start = self.bed.loc[row][1] + end = self.bed.loc[row][2] + # choose where to cut the region thecut = (start + end) // 2 # int(np.random.normal((start+end)/2, (end - start)/6)) if thecut <= start: thecut = start + 10 @@ -278,62 +306,67 @@ def __cut(self, df, row): return row, [{0: chrom, 1: start, 2: thecut, 3: 2}, {0: chrom, 1: thecut, 2: end, 3: 2}] - def merge(self, df, mergerate): + def merge(self, mergerate): """ Merge two regions into one new region - :param pandas.DataFrame df: the dataframe to perturb. :param float mergerate: the rate to merge two regions into one - :return pandas.DataFrame: the new dataframe after merges + :return int: number of regions merged """ self.__check_rate([mergerate]) if mergerate == 0: - return df - rows = df.shape[0] + return 0 + rows = self.bed.shape[0] merge_rows = random.sample(list(range(rows)), int(rows * mergerate)) to_add = [] to_drop = [] for row in merge_rows: - drop_row, add_row = self.__merge(df, row) + drop_row, add_row = self.__merge(row) if add_row: to_add.append(add_row) to_drop.extend(drop_row) - df = df.drop(to_drop) - df = df.append(to_add, ignore_index=True) - return df.reset_index(drop=True) + self.bed = self.bed.drop(to_drop) + self.bed = self.bed.append(to_add, ignore_index=True) + self.bed = self.bed.reset_index(drop=True) + return len(merge_rows) - def __merge(self, df, row): + def __merge(self, row): # check if the regions being merged are on the same chromosome - if row + 1 not in df.index or df.loc[row][0] != df.loc[row+1][0]: + if row + 1 not in self.bed.index or self.bed.loc[row][0] != self.bed.loc[row+1][0]: return [], None - chrom = df.loc[row][0] - start = df.loc[row][1] - end = df.loc[row+1][2] + chrom = self.bed.loc[row][0] + start = self.bed.loc[row][1] + end = self.bed.loc[row+1][2] return [row, row+1], {0: chrom, 1: start, 2: end, 3: 4} - def drop(self, df, droprate): + def drop(self, droprate): """ Drop regions - :param pandas.DataFrame df: the dataframe to perturb. :param float droprate: the rate to drop/remove regions - :return pandas.DataFrame: the new dataframe after drops + :return int: the number of rows dropped """ self.__check_rate([droprate]) - rows = df.shape[0] + rows = self.bed.shape[0] drop_rows = random.sample(list(range(rows)), int(rows * droprate)) - df = df.drop(drop_rows) - return df.reset_index(drop=True) - - - def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, addfile=None, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0): + self.bed = self.bed.drop(drop_rows) + self.bed = self.bed.reset_index(drop=True) + return len(drop_rows) + + + def all_perturbations(self, + addrate=0.0, addmean=320.0, addstdev=30.0, + addfile=None, + shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, + cutrate=0.0, + mergerate=0.0, + droprate=0.0): ''' - Perform all five perturbations in the order of add, shift, cut, merge, drop. + Perform all five perturbations in the order of shift, add, cut, merge, drop. - :param pandas.DataFrame df: the dataframe to perturb. :param float addrate: the rate (as a proportion of the total number of regions) to add regions :param float addmean: the mean length of added regions :param float addstdev: the standard deviation of the length of added regions @@ -343,31 +376,32 @@ def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, addfi :param float cutrate: the rate to cut regions into two separate regions :param float mergerate: the rate to merge two regions into one :param float droprate: the rate to drop/remove regions - :return pandas.DataFrame: the new dataframe after all perturbations + :return int: the number of total regions perturbed ''' self.__check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) - df = self.shift(df, shiftrate, shiftmean, shiftstdev) + n = 0 + n += self.shift(shiftrate, shiftmean, shiftstdev) if addfile: - df = self.add_from_file(df, addfile, addrate) + n += self.add_from_file(addfile, addrate) else: - df = self.add(df, addrate, addmean, addstdev) - df = self.cut(df, cutrate) - df = self.merge(df, mergerate) - df = self.drop(df, droprate) - return df + n += self.add(addrate, addmean, addstdev) + n += self.cut(cutrate) + n += self.merge(mergerate) + n += self.drop(droprate) + return n - def write_bed(self, df, outfile_name): + def to_bed(self, outfile_name): """ - Write a pandas dataframe back into bedfile format + Write a pandas dataframe back into BED file format - :param pandas.DataFrame df: A dataframe containing regions like a bedfile - :param str outfile_name: The name of the output bedfile + :param str outfile_name: The name of the output BED file """ - df.sort_values([0,1,2], inplace=True) - df.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') - print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.'.format(outfile_name, df.shape[0], self.original_regions)) + self.bed.sort_values([0,1,2], inplace=True) + self.bed.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') + print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.' \ + .format(outfile_name, self.bed.shape[0], self.original_regions)) def main(): @@ -417,19 +451,29 @@ def main(): sys.exit(1) - bedshifter = Bedshift() - - df = bedshifter.read_bed(args.bedfile) + bedshifter = Bedshift(args.bedfile) if args.repeat == 1: - df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.addfile, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) - bedshifter.write_bed(df, outfile) + n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, + args.addfile, + args.shiftrate, args.shiftmean, args.shiftstdev, + args.cutrate, + args.mergerate, + args.droprate) + bedshifter.to_bed(outfile) elif args.repeat > 1: for i in range(args.repeat): - repeat_df = bedshifter.all_perturbations(df, args.addrate, args.addmean, args.addstdev, args.addfile, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, args.droprate) + + n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, + args.addfile, + args.shiftrate, args.shiftmean, args.shiftstdev, + args.cutrate, + args.mergerate, + args.droprate) modified_outfile = outfile.rsplit("/") - modified_outfile[-1] = "gen" + str(i+1) + "_" + modified_outfile[-1] + modified_outfile[-1] = "rep" + str(i+1) + "_" + modified_outfile[-1] modified_outfile = "/".join(modified_outfile) - bedshifter.write_bed(repeat_df, modified_outfile) + bedshifter.to_bed(modified_outfile) + bedshifter.reset_bed() else: _LOGGER.error("repeats specified is less than 1") sys.exit(1) diff --git a/dist/bedshift-0.2.0-py3-none-any.whl b/dist/bedshift-0.2.0-py3-none-any.whl deleted file mode 100644 index 10da00c218f2e179303055460857f975a9c365d5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7579 zcmaKx1yoeszy609x}~Mdp@uF2X^@5?MLLIW5T!$hmTr;m1_h+M8)WEikOmRt;{E;K zzwhh4_nou$I_Jz<=d+)+&)&~I>-(rFA|Vq3001;VLsqq#xnkNx)WhAvL;LlwsfD>K 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Different kinds of perturbations supported are region shifts, drops, adds, cuts, and merges. This tool is particularly useful for creating test datasets for various tasks, since there often is no ground truth dataset to compare to. By perturbing a file, analysis can be done on both the perturbed file and the original file and be compared. +Bedshift is a tool for randomly perturbing BED file regions. The perturbations supported on regions are shift, drop, add, cut, and merge. This tool is particularly useful for creating test datasets for various tasks, since there often is no ground truth dataset to compare to. By perturbing a file, a pipeline or analysis can be run on both the perturbed file and the original file, then be compared. ## Installing -This package is not available on pypi yet, but is available for local install by cloning the repository. +The package is available for public download on PyPi. ``` -git clone https://github.com/databio/bedshift.git -cd bedshift -pip install . +pip install bedshift ``` ## Quickstart -The package is available for use both as a command line interface and a python package. +The package is available for use both as a command line interface and a python package. To get started, type on the command line: + +``` +bedshift -h +``` The following examples will shift 10% of the regions and add 10% new regions in `examples/test.bed`. The output is located at `bedshifted_test.bed`. CLI: ``` -bedshift -h -bedshift -b tests/test.bed -p 0.1 -a 0.1 +bedshift -b tests/test.bed -s 0.1 -a 0.1 ``` Python: diff --git a/docs/evaluation_guide.md b/docs/evaluation_guide.md new file mode 100644 index 00000000..4eda5f9b --- /dev/null +++ b/docs/evaluation_guide.md @@ -0,0 +1,91 @@ +# Using bedshift to create an evaluation dataset for similarity scores + +## Generate different files + +Bedshift perturbations include add, drop, shift, cut, and merge. Using any of these perturbations, or combinations of them, you can generate a set of files that are slightly perturbed from the original file. Assuming that the original file is called `original.bed`, and you want 100 files of added regions and 100 files of dropped regions: + +``` +bedshift -b original.bed -a 0.1 -r 100 +bedshift -b original.bed -d 0.3 -r 100 +``` + +The output file will be in `bedshifted_original.bed`. + + +## Evaluating a similarity score + +This is when the bedshifted file will be put to use. The 100 repetitions of add and drop will be compared against the original file using the similarity score of your choice. The output of the similarity score should reflect the degree of change specified to bedshift. In very general terms, the pseudocode should be like this: + +``` +for each bedshift_file in bedshift_output: + score = similarity_score(bedshift_file, original_file, ...) + add score to score_list +avg_similarity_score = mean(score_list) +``` + +You can repeat this for each of the similarity scores and each of the perturbation combinations, and then compare the results. This way, you can get an accurate understanding of whether your similarity score reflects added regions, dropped regions, and more. + + +## Using a PEP to quickly submit multiple bedshift jobs + +Using a [Portable Encapsulated Project](http://pep.databio.org/en/latest/) (PEP), creating multiple combinations of bedshift files becomes faster and more organized. The PEP consists of a sample table containing the perturbation parameters and a config file. Here is what the `sample_table.csv` may look like: + +| sample_name | add | drop | shift | cut | merge | +|-------------|-----|------|------|------|-------| +| add1 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | +| add2 | 0.2 | 0.0 | 0.0 | 0.0 | 0.0 | +| add3 | 0.3 | 0.0 | 0.0 | 0.0 | 0.0 | +| drop-shift1 | 0.0 | 0.1 | 0.2 | 0.0 | 0.0 | +| drop-shift2 | 0.0 | 0.2 | 0.2 | 0.0 | 0.0 | +| drop-cut | 0.0 | 0.3 | 0.0 | 0.4 | 0.0 | +| shift-merge | 0.0 | 0.0 | 0.4 | 0.0 | 0.4 | + +And here is what the `project_config.yaml` file looks like: + +``` +pep_version: 2.0.0 +sample_table: "sample_table.csv" +sample_modifiers: + append: + file: "original.bed" + repeat: 100 +``` + +Now the project is described neatly in two files. The `sample_modifiers` in the config file just adds extra columns to the sample table in post-processing and makes the project more configurable, instead of having to repeat the same parameter. The columns added are the file which bedshift is to be performed on, and the number of repetitions that bedshift should create. + + +The PEP describes the project, but the tool that submits the project jobs is called [looper](http://looper.databio.org/en/latest/). In one line of code, it will interpret the PEP and form commands to be submitted to your processor or a computing cluster. To use looper, you will need to add a few lines to your `project_config.yaml`: + +``` +pep_version: 2.0.0 +sample_table: "sample_table.csv" +looper: + output_dir: "looper_output/" +sample_modifiers: + append: + pipeline_interfaces: "pipeline_interface.yaml" + file: "/project/shefflab/resources/regions/LOLACore/hg19/encode_tfbs/regions/wgEncodeAwgTfbsUwHek293CtcfUniPk.narrowPeak" + repeat: 100 +``` + +You will also need to create a `pipeline_interface.yaml` that describes how to form commands: + +``` +pipeline_name: bedshift_run +pipeline_type: sample +command_template: > + bedshift -b {sample.file} -a {sample.add} -d {sample.drop} -s {sample.shift} -c {sample.cut} -m {sample.merge} -r {sample.repeat} -o {sample.sample_name}.bed +compute: + mem: 4000 + cores: 1 + time: "01:00:00" +``` + +After all of this, the command to run looper and submit the jobs is: + +``` +looper run project_config.yaml +``` + +Soon, you should see bedshift files appear in the `looper_output` folder. The BED file names will correspond to the sample names from `sample_table.csv`. + diff --git a/mkdocs.yml b/mkdocs.yml index 12237b1d..f47a852a 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -8,6 +8,8 @@ theme: databio nav: - Introduction: - Overview: README.md + - How-to Guides: + - Evaluating Similarity Scores: evaluation_guide.md - Reference: - Python API: autodoc_build/bedshift.md From 854be44b8625ee52852d13603ee6de6bbc7d4c08 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Thu, 9 Jul 2020 20:11:19 -0400 Subject: [PATCH 0040/1072] add back test.bed file --- .gitignore | 1 + tests/test.bed | 10000 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 10001 insertions(+) create mode 100644 tests/test.bed diff --git a/.gitignore b/.gitignore index b6294c43..e6dd39dd 100644 --- a/.gitignore +++ b/.gitignore @@ -3,4 +3,5 @@ examples/py_output* *.pyc site/ *.bed +!tests/test.bed .DS_Store \ No newline at end of file diff --git a/tests/test.bed b/tests/test.bed new file mode 100644 index 00000000..0191284d --- /dev/null +++ b/tests/test.bed @@ -0,0 +1,10000 @@ +chr16 57683001 57683273 . 1000 . 348.60214448824 -1 4.70433072024128 141 +chr6 53036699 53036974 . 1000 . 346.176651534705 -1 4.70433072024128 144 +chr10 76995732 76995979 . 1000 . 310.108500714016 -1 4.70433072024128 132 +chr12 54773504 54773736 . 1000 . 310.086438316725 -1 4.70433072024128 115 +chr3 52481114 52481353 . 1000 . 307.439597272129 -1 4.70433072024128 110 +chr1 17036345 17036564 . 1000 . 305.457551478902 -1 4.70433072024128 107 +chr12 58299186 58299434 . 1000 . 304.51947917175 -1 4.70433072024128 126 +chr17 17259432 17259658 . 1000 . 298.437380062858 -1 4.70433072024128 119 +chr17 36823867 36824096 . 1000 . 288.161887548839 -1 4.70433072024128 109 +chr16 4304077 4304332 . 1000 . 287.865197551482 -1 4.70433072024128 131 +chr14 50328828 50329070 . 1000 . 285.279029981494 -1 4.70433072024128 115 +chr1 114889180 114889434 . 1000 . 283.732905372961 -1 4.70433072024128 127 +chr9 36008855 36009095 . 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4.70433072024128 160 +chr1 154298703 154299023 . 471 . 73.735166635437 -1 4.70433072024128 160 +chr16 1244646 1244966 . 470 . 73.7155129052692 -1 4.70433072024128 160 +chr1 165083556 165083876 . 470 . 73.7154274856514 -1 4.70433072024128 160 +chr2 238384091 238384411 . 470 . 73.7118218571884 -1 4.70433072024128 160 +chr3 42922514 42922834 . 470 . 73.709280841775 -1 4.70433072024128 160 +chr16 20397099 20397419 . 470 . 73.7031168398267 -1 4.70433072024128 160 +chr16 9254000 9254320 . 470 . 73.7028876598812 -1 4.70433072024128 160 +chr5 153847255 153847575 . 470 . 73.7014036947578 -1 4.70433072024128 160 +chr17 67605701 67606021 . 470 . 73.6996152668361 -1 4.70433072024128 160 +chr20 31044787 31045107 . 470 . 73.6978248997453 -1 4.70433072024128 160 +chr14 65007017 65007337 . 470 . 73.6887154409194 -1 4.70433072024128 160 +chr12 76552541 76552861 . 470 . 73.6743128081829 -1 4.70433072024128 160 +chr19 43427312 43427632 . 470 . 73.6728733272737 -1 4.70433072024128 160 +chr4 84031556 84031876 . 470 . 73.66912281581 -1 4.70433072024128 160 +chr15 99619967 99620287 . 470 . 73.6523872785516 -1 4.70433072024128 160 +chr12 50170146 50170466 . 470 . 73.6513553928755 -1 4.70433072024128 160 +chr12 77015290 77015610 . 470 . 73.6401211557903 -1 4.70433072024128 160 +chr17 62359359 62359679 . 470 . 73.6379384160201 -1 4.70433072024128 160 +chr9 13034202 13034522 . 470 . 73.6377703849148 -1 4.70433072024128 160 +chr11 43966340 43966660 . 470 . 73.6289324532417 -1 4.70433072024128 160 +chr4 7721330 7721650 . 470 . 73.6252391104674 -1 4.70433072024128 160 +chr9 79626844 79627164 . 470 . 73.6224784935771 -1 4.70433072024128 160 +chr4 150185355 150185675 . 470 . 73.6206957948983 -1 4.70433072024128 160 +chr9 7052615 7052935 . 470 . 73.6109795186496 -1 4.70433072024128 160 +chr17 47634757 47635077 . 470 . 73.6028809464784 -1 4.70433072024128 160 +chr10 29593534 29593854 . 470 . 73.5977450504962 -1 4.70433072024128 160 +chr2 11724389 11724709 . 470 . 73.5896823463611 -1 4.70433072024128 160 +chr5 39080731 39081051 . 470 . 73.5830237949582 -1 4.70433072024128 160 +chr7 2508579 2508899 . 470 . 73.5810448139977 -1 4.70433072024128 160 +chr1 32879579 32879899 . 470 . 73.5732960080082 -1 4.70433072024128 160 +chr2 134965206 134965526 . 470 . 73.566591407322 -1 4.70433072024128 160 +chr17 76172650 76172970 . 470 . 73.5663051039756 -1 4.70433072024128 160 +chr11 44115846 44116166 . 469 . 73.55995023167 -1 4.70433072024128 160 +chr9 131790751 131791071 . 469 . 73.5592999928328 -1 4.70433072024128 160 +chr2 13147038 13147358 . 469 . 73.5493792891307 -1 4.70433072024128 160 +chr2 231712633 231712953 . 469 . 73.5475502429601 -1 4.70433072024128 160 +chr15 76622651 76622971 . 469 . 73.543548027357 -1 4.70433072024128 160 +chr4 158899932 158900252 . 469 . 73.539884819296 -1 4.70433072024128 160 +chr15 69217243 69217563 . 469 . 73.5343835963275 -1 4.70433072024128 160 +chr17 27230031 27230351 . 469 . 73.5298952843573 -1 4.70433072024128 160 +chr19 9900847 9901167 . 469 . 73.5250489783667 -1 4.70433072024128 160 +chr11 112151326 112151646 . 469 . 73.520600295078 -1 4.70433072024128 160 +chr1 206652869 206653189 . 469 . 73.5080898002783 -1 4.70433072024128 160 +chr16 66512637 66512957 . 469 . 73.5069809700083 -1 4.70433072024128 160 +chr14 37074162 37074482 . 469 . 73.5018828818907 -1 4.70433072024128 160 +chr14 59495293 59495613 . 469 . 73.4946493429977 -1 4.70433072024128 160 +chr17 33446801 33447121 . 469 . 73.4927515894202 -1 4.70433072024128 160 +chr2 237252217 237252537 . 469 . 73.4898322169028 -1 4.70433072024128 160 +chr1 219559194 219559514 . 469 . 73.4844177391242 -1 4.70433072024128 160 +chr20 56169414 56169734 . 469 . 73.4560380885693 -1 4.70433072024128 160 +chr6 5136038 5136358 . 469 . 73.4555124873193 -1 4.70433072024128 160 +chr4 55099473 55099793 . 469 . 73.451143873049 -1 4.70433072024128 160 +chr4 177968513 177968833 . 469 . 73.4476815059242 -1 4.70433072024128 160 +chr6 108371093 108371413 . 469 . 73.4447726026659 -1 4.70433072024128 160 +chr2 179171118 179171438 . 469 . 73.4226644171559 -1 4.70433072024128 160 +chr3 58809969 58810289 . 469 . 73.4184220907349 -1 4.70433072024128 160 +chr11 72296749 72297069 . 468 . 73.4093751063527 -1 4.70433072024128 160 +chr14 75380707 75381027 . 468 . 73.4064284034976 -1 4.70433072024128 160 +chr11 12068916 12069236 . 468 . 73.4045248240068 -1 4.70433072024128 160 +chr9 118353803 118354123 . 468 . 73.3945412890568 -1 4.70433072024128 160 +chr11 124628262 124628582 . 468 . 73.3840098388017 -1 4.70433072024128 160 +chr7 157179931 157180251 . 468 . 73.3801062757578 -1 4.70433072024128 160 +chr11 127215756 127216076 . 468 . 73.3779965439743 -1 4.70433072024128 160 +chr21 43237621 43237941 . 468 . 73.3748924864839 -1 4.70433072024128 160 +chr16 88503833 88504153 . 468 . 73.3730009405968 -1 4.70433072024128 160 +chr16 28992896 28993216 . 468 . 73.3721573770732 -1 4.70433072024128 160 +chr22 37957758 37958078 . 468 . 73.3683959431305 -1 4.70433072024128 160 +chr18 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73.2245494849422 -1 4.70433072024128 160 +chr20 50745057 50745377 . 467 . 73.2180310506007 -1 4.70433072024128 160 +chr19 11236793 11237113 . 467 . 73.2102274695393 -1 4.70433072024128 160 +chr12 58087419 58087739 . 467 . 73.2022734842661 -1 4.70433072024128 160 +chr8 143897775 143898095 . 467 . 73.1978704524319 -1 4.70433072024128 160 +chr8 27283707 27284027 . 467 . 73.1902747492765 -1 4.70433072024128 160 +chr15 59249589 59249909 . 467 . 73.1897883494134 -1 4.70433072024128 160 +chr10 126077829 126078149 . 467 . 73.1884003812922 -1 4.70433072024128 160 +chr17 40374192 40374512 . 467 . 73.1820463857611 -1 4.70433072024128 160 +chr15 71735825 71736145 . 467 . 73.177296923796 -1 4.70433072024128 160 +chr12 16132079 16132399 . 467 . 73.1678652146862 -1 4.70433072024128 160 +chr19 14169778 14170098 . 467 . 73.1673003020695 -1 4.70433072024128 160 +chr12 1692665 1692985 . 467 . 73.1546766844069 -1 4.70433072024128 160 +chr16 67706631 67706951 . 467 . 73.1537968056613 -1 4.70433072024128 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69.6923566014784 -1 4.70433072024128 160 +chr19 1064893 1065213 . 445 . 69.6890704757222 -1 4.70433072024128 160 +chr2 24149849 24150169 . 445 . 69.684708356188 -1 4.70433072024128 160 +chr19 15740123 15740443 . 445 . 69.6838203213469 -1 4.70433072024128 160 +chr8 70468517 70468837 . 445 . 69.6757235262971 -1 4.70433072024128 160 +chr13 114519921 114520241 . 445 . 69.6676481728655 -1 4.70433072024128 160 +chr7 149580540 149580860 . 444 . 69.6520036015269 -1 4.70433072024128 160 +chr3 124407716 124408036 . 444 . 69.6455611068035 -1 4.70433072024128 160 +chr17 41957753 41958073 . 444 . 69.6420277179612 -1 4.70433072024128 160 +chr2 153046440 153046760 . 444 . 69.6272570427959 -1 4.70433072024128 160 +chr9 73099514 73099834 . 444 . 69.6262906923207 -1 4.70433072024128 160 +chr10 6711250 6711570 . 444 . 69.6203408558283 -1 4.70433072024128 160 +chr11 66311199 66311519 . 444 . 69.6170810762826 -1 4.70433072024128 160 +chr4 84692302 84692622 . 444 . 69.6155356881168 -1 4.70433072024128 160 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4.70433072024128 160 +chr5 139619422 139619742 . 402 . 63.0294846456816 -1 4.70433072024128 160 +chr19 19471866 19472186 . 402 . 63.0275027880762 -1 4.70433072024128 160 +chr13 111997137 111997457 . 402 . 63.0235684577798 -1 4.70433072024128 160 +chr1 179675500 179675820 . 402 . 63.0106435416256 -1 4.70433072024128 160 +chr18 39878746 39879066 . 402 . 63.0105893167878 -1 4.70433072024128 160 +chr6 2791436 2791756 . 402 . 63.0079209418752 -1 4.70433072024128 160 +chr8 43135935 43136255 . 402 . 62.9961790811846 -1 4.70433072024128 160 +chr17 1812003 1812323 . 402 . 62.9951145175291 -1 4.70433072024128 160 +chr9 17063581 17063901 . 402 . 62.9941276957876 -1 4.70433072024128 160 +chr9 5600531 5600851 . 402 . 62.989584485335 -1 4.70433072024128 160 +chr7 99510760 99511080 . 402 . 62.983911429443 -1 4.70433072024128 160 +chr15 90762869 90763189 . 402 . 62.9798274255038 -1 4.70433072024128 160 +chr13 45968136 45968456 . 402 . 62.9790557293635 -1 4.70433072024128 160 +chr19 10535007 10535327 . 402 . 62.9777267019555 -1 4.70433072024128 160 +chr14 102783190 102783510 . 402 . 62.9759383417967 -1 4.70433072024128 160 +chr9 36995847 36996167 . 402 . 62.9736557997164 -1 4.70433072024128 160 +chr4 91236271 91236591 . 402 . 62.973509196248 -1 4.70433072024128 160 +chr19 3472009 3472329 . 402 . 62.9718358693825 -1 4.70433072024128 160 +chr17 66462303 66462623 . 402 . 62.9619446326814 -1 4.70433072024128 160 +chr2 27165494 27165814 . 402 . 62.9613716897737 -1 4.70433072024128 160 +chr19 10360785 10361105 . 402 . 62.9592124383753 -1 4.70433072024128 160 +chr12 44394193 44394513 . 402 . 62.9408175970991 -1 4.70433072024128 160 From be35ca17720b5b6f395aa9f119ad48da580eb855 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Fri, 10 Jul 2020 13:32:10 -0400 Subject: [PATCH 0041/1072] modify add from file function --- bedshift/bedshift.py | 59 ++++++++++++++++++++++++-------------------- tests/shell_test.sh | 4 +-- 2 files changed, 34 insertions(+), 29 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 535a7e63..321c8a6d 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -129,21 +129,14 @@ class Bedshift(object): The bedshift object with methods to perturb regions """ - def __init__(self, bedfile_path): + def __init__(self, bedfile_path, delimiter='\t'): """ Read in a .bed file to pandas DataFrame format :param str bedfile_path: the path to the bedfile """ - df = pd.read_csv(bedfile_path, sep='\t', header=None, usecols=[0,1,2]) - - # if there is 'chrom', 'start', 'stop' in the table, move them to header - if not str(df.iloc[0, 1]).isdigit(): - df.columns = df.iloc[0] - df = df[1:] - - df[3] = 0 # column indicating which modifications were made + df = self.read_bed(bedfile_path, delimiter=delimiter) self.original_regions = df.shape[0] self.bed = df.astype({1: 'int64', 2: 'int64', 3: 'int64'}) \ .sort_values([0, 1, 2]).reset_index(drop=True) @@ -200,7 +193,7 @@ def add(self, addrate, addmean, addstdev): self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) return len(new_regions) - def add_from_file(self, fp, addrate): + def add_from_file(self, fp, addrate, delimiter='\t'): """ Add regions from another bedfile to this perturbed bedfile @@ -212,23 +205,14 @@ def add_from_file(self, fp, addrate): self.__check_rate([addrate]) rows = self.bed.shape[0] num_add = int(rows * addrate) - new_regions = {0: [], 1: [], 2: [], 3: []} - with open(fp, 'r') as f: - lines = 0 - for region in f: - lines += 1 - f.seek(0) - addrate_newfile = num_add / lines - for region in f: - if random.random() < addrate_newfile: - region = region.split('\t') - if str(region[1]).isdigit(): - new_regions[0].append(region[0]) - new_regions[1].append(int(region[1])) - new_regions[2].append(int(region[2])) - new_regions[3].append(3) - self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) - return len(new_regions) + + df = self.read_bed(fp, delimiter=delimiter) + add_rows = random.sample(list(range(df.shape[0])), num_add) + add_df = df.loc[add_rows].reset_index(drop=True) + add_df[3] = pd.Series([3] * add_df.shape[0]) + self.bed = self.bed.append(add_df, ignore_index=True) + return num_add + def shift(self, shiftrate, shiftmean, shiftstdev): """ @@ -404,6 +388,25 @@ def to_bed(self, outfile_name): .format(outfile_name, self.bed.shape[0], self.original_regions)) + def read_bed(self, bedfile_path, delimiter='\t'): + """ + Read a BED file into pandas dataframe + + :param str bedfile_path: The path to the BED file + """ + + df = pd.read_csv(bedfile_path, sep=delimiter, header=None, usecols=[0,1,2]) + + # if there is 'chrom', 'start', 'stop' in the table, move them to header + if not str(df.iloc[0, 1]).isdigit(): + df.columns = df.iloc[0] + df = df[1:] + + df[3] = 0 # column indicating which modifications were made + return df + + + def main(): """ Primary workflow """ @@ -460,6 +463,7 @@ def main(): args.mergerate, args.droprate) bedshifter.to_bed(outfile) + print(str(n) + " regions changed") elif args.repeat > 1: for i in range(args.repeat): @@ -473,6 +477,7 @@ def main(): modified_outfile[-1] = "rep" + str(i+1) + "_" + modified_outfile[-1] modified_outfile = "/".join(modified_outfile) bedshifter.to_bed(modified_outfile) + print(str(n) + " regions changed") bedshifter.reset_bed() else: _LOGGER.error("repeats specified is less than 1") diff --git a/tests/shell_test.sh b/tests/shell_test.sh index e9202552..4fe1794a 100755 --- a/tests/shell_test.sh +++ b/tests/shell_test.sh @@ -2,6 +2,6 @@ bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addmean 320.0 --addstdev 30.0 --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output.bed -bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed +bedshift --bedfile $(dirname "$0")/test.bed --shiftrate 0.4 --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed -bedshift --bedfile $(dirname "$0")/test.bed --addrate 0.1 --cutrate 0.5 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed -r 3 +bedshift --bedfile $(dirname "$0")/test.bed --addrate 0.1 --cutrate 0.5 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output3.bed -r 3 From 3966ab1f3300479bd7b56fbf51bdf7b67af8c76e Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Fri, 10 Jul 2020 15:37:00 -0400 Subject: [PATCH 0042/1072] fix reset_bed dataframe copy --- bedshift/bedshift.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 321c8a6d..e8cab819 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -140,7 +140,7 @@ def __init__(self, bedfile_path, delimiter='\t'): self.original_regions = df.shape[0] self.bed = df.astype({1: 'int64', 2: 'int64', 3: 'int64'}) \ .sort_values([0, 1, 2]).reset_index(drop=True) - self.original_bed = self.bed + self.original_bed = self.bed.copy(deep=True) def reset_bed(self): @@ -148,7 +148,7 @@ def reset_bed(self): Reset the stored bedfile to the state before perturbations """ - self.bed = self.original_bed + self.bed = self.original_bed.copy(deep=True) def __check_rate(self, rates): From c86a8d7bc655e23caa82a46bd46a4ac963f4144f Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Wed, 29 Jul 2020 19:24:25 -0400 Subject: [PATCH 0043/1072] repo cleanup --- .github/workflows/python-publish.yml | 31 ++ .gitignore | 5 +- bedshift.egg-info/PKG-INFO | 57 --- bedshift.egg-info/SOURCES.txt | 15 - bedshift.egg-info/dependency_links.txt | 1 - bedshift.egg-info/entry_points.txt | 3 - bedshift.egg-info/requires.txt | 3 - bedshift.egg-info/top_level.txt | 1 - build/lib/bedshift/__init__.py | 10 - build/lib/bedshift/_version.py | 1 - build/lib/bedshift/bedshift.py | 489 ------------------------- dist/bedshift-1.0.0-py3-none-any.whl | Bin 7633 -> 0 bytes dist/bedshift-1.0.0.tar.gz | Bin 7400 -> 0 bytes {bedshift => extras}/bedshift.sh | 0 14 files changed, 35 insertions(+), 581 deletions(-) create mode 100644 .github/workflows/python-publish.yml delete mode 100644 bedshift.egg-info/PKG-INFO delete mode 100644 bedshift.egg-info/SOURCES.txt delete mode 100644 bedshift.egg-info/dependency_links.txt delete mode 100644 bedshift.egg-info/entry_points.txt delete mode 100644 bedshift.egg-info/requires.txt delete mode 100644 bedshift.egg-info/top_level.txt delete mode 100644 build/lib/bedshift/__init__.py delete mode 100644 build/lib/bedshift/_version.py delete mode 100644 build/lib/bedshift/bedshift.py delete mode 100644 dist/bedshift-1.0.0-py3-none-any.whl delete mode 100644 dist/bedshift-1.0.0.tar.gz rename {bedshift => extras}/bedshift.sh (100%) diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml new file mode 100644 index 00000000..3bd2eb89 --- /dev/null +++ b/.github/workflows/python-publish.yml @@ -0,0 +1,31 @@ +# This workflows will upload a Python Package using Twine when a release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries + +name: Upload Python Package + +on: + release: + types: [created] + +jobs: + deploy: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.x' + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine + - name: Build and publish + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + python setup.py sdist bdist_wheel + twine upload dist/* \ No newline at end of file diff --git a/.gitignore b/.gitignore index e6dd39dd..12b73c74 100644 --- a/.gitignore +++ b/.gitignore @@ -4,4 +4,7 @@ examples/py_output* site/ *.bed !tests/test.bed -.DS_Store \ No newline at end of file +.DS_Store +build/ +bedshift.egg-info/ +dist/ \ No newline at end of file diff --git a/bedshift.egg-info/PKG-INFO b/bedshift.egg-info/PKG-INFO deleted file mode 100644 index ecc52db1..00000000 --- a/bedshift.egg-info/PKG-INFO +++ /dev/null @@ -1,57 +0,0 @@ -Metadata-Version: 2.1 -Name: bedshift -Version: 1.0.0 -Summary: BED file perturbations -Home-page: https://bedshift.databio.org -Author: Aaron Gu -Author-email: ag5ym@virginia.edu -License: BSD2 -Description: # Bedshift - - Install with: `pip install --user .` - - ## Command line - - Run with: `bedshift -b BEDFILE` or `./bedshift.sh` if running bedshift on multiple bedfiles with a set of parameters, which are editable in bedshift.sh. - - See: `bedshift --help` for parameters. - - ## Python - - ```py - import bedshift - - bedshifter = bedshift.Bedshift('tests/test.bed') - bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, - shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, - cutrate=0.1, - mergerate=0.11, - droprate=0.03) - # can also run single operations: shift, add, cut, merge, drop - - bedshifter.to_bed('test_output.bed') - ``` - - - - - ## Development - - test changes: - - ``` - pip install --user . - - ./run-tests.sh - ``` - - if the tests complete, then bedshift is working properly - -Keywords: BED file,perturbation,bioinformatics,region set -Platform: UNKNOWN -Classifier: Development Status :: 4 - Beta -Classifier: License :: OSI Approved :: BSD License -Classifier: Programming Language :: Python :: 3.5 -Classifier: Programming Language :: Python :: 3.6 -Classifier: Topic :: System :: Distributed Computing -Description-Content-Type: text/markdown diff --git a/bedshift.egg-info/SOURCES.txt b/bedshift.egg-info/SOURCES.txt deleted file mode 100644 index 93011a90..00000000 --- a/bedshift.egg-info/SOURCES.txt +++ /dev/null @@ -1,15 +0,0 @@ -LICENSE.txt -MANIFEST.in -README.md -setup.py -bedshift/__init__.py -bedshift/_version.py -bedshift/bedshift.py -bedshift.egg-info/PKG-INFO -bedshift.egg-info/SOURCES.txt -bedshift.egg-info/dependency_links.txt -bedshift.egg-info/entry_points.txt -bedshift.egg-info/requires.txt -bedshift.egg-info/top_level.txt -requirements/requirements-all.txt -requirements/requirements-doc.txt \ No newline at end of file diff --git a/bedshift.egg-info/dependency_links.txt b/bedshift.egg-info/dependency_links.txt deleted file mode 100644 index 8b137891..00000000 --- a/bedshift.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/bedshift.egg-info/entry_points.txt b/bedshift.egg-info/entry_points.txt deleted file mode 100644 index c008846a..00000000 --- a/bedshift.egg-info/entry_points.txt +++ /dev/null @@ -1,3 +0,0 @@ -[console_scripts] -bedshift = bedshift.bedshift:main - diff --git a/bedshift.egg-info/requires.txt b/bedshift.egg-info/requires.txt deleted file mode 100644 index b972df1c..00000000 --- a/bedshift.egg-info/requires.txt +++ /dev/null @@ -1,3 +0,0 @@ -logmuse -pandas -numpy diff --git a/bedshift.egg-info/top_level.txt b/bedshift.egg-info/top_level.txt deleted file mode 100644 index 566eef66..00000000 --- a/bedshift.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -bedshift diff --git a/build/lib/bedshift/__init__.py b/build/lib/bedshift/__init__.py deleted file mode 100644 index 2222220b..00000000 --- a/build/lib/bedshift/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Project configuration, particularly for logging. - -import logmuse -from .bedshift import Bedshift -from ._version import __version__ - -__classes__ = ["Bedshift"] -__all__ = __classes__ + [] - -logmuse.init_logger("bedshift") diff --git a/build/lib/bedshift/_version.py b/build/lib/bedshift/_version.py deleted file mode 100644 index 5becc17c..00000000 --- a/build/lib/bedshift/_version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = "1.0.0" diff --git a/build/lib/bedshift/bedshift.py b/build/lib/bedshift/bedshift.py deleted file mode 100644 index 535a7e63..00000000 --- a/build/lib/bedshift/bedshift.py +++ /dev/null @@ -1,489 +0,0 @@ -""" Perturb regions in bedfiles """ - -import argparse -import logmuse -import logging -import os -import sys -import pandas as pd -import numpy as np -import random -from bedshift._version import __version__ - -_LOGGER = logging.getLogger(__name__) - -__all__ = ["Bedshift"] - - -chroms = {num: 'chr'+str(num) for num in list(range(1, 23))} -chroms.update({'X': 'chrX', 'Y': 'chrY'}) - -chrom_lens = {'chr1': 247249719, - 'chr2': 242951149, - 'chr3': 199501827, - 'chr4': 191273063, - 'chr5': 180857866, - 'chr6': 170899992, - 'chr7': 158821424, - 'chr8': 146274826, - 'chr9': 140273252, - 'chr10': 135374737, - 'chr11': 134452384, - 'chr12': 132349534, - 'chr13': 114142980, - 'chr14': 106368585, - 'chr15': 100338915, - 'chr16': 88827254, - 'chr17': 78774742, - 'chr18': 76117153, - 'chr19': 63811651, - 'chr20': 62435964, - 'chr21': 46944323, - 'chr22': 49691432, - 'chrX': 154913754, - 'chrY': 57772954} - - -class _VersionInHelpParser(argparse.ArgumentParser): - def format_help(self): - """ Add version information to help text. """ - return "version: {}\n".format(__version__) + \ - super(_VersionInHelpParser, self).format_help() - - -def build_argparser(): - """ - Builds argument parser. - - :return: argparse.ArgumentParser - """ - - banner = "%(prog)s - randomize BED files" - additional_description = "\n..." - - parser = _VersionInHelpParser( - description=banner, - epilog=additional_description) - - parser.add_argument( - "-V", "--version", - action="version", - version="%(prog)s {v}".format(v=__version__)) - - parser.add_argument( - "-b", "--bedfile", required=True, - help="File path to bed file.") - - parser.add_argument( - "-d", "--droprate", type=float, default=0.0, - help="Droprate parameter") - - parser.add_argument( - "-a", "--addrate", type=float, default=0.0, - help="Addrate parameter") - - parser.add_argument( - "--addmean", type=float, default=320.0, - help="Mean add region length") - - parser.add_argument( - "--addstdev", type=float, default=30.0, - help="Stdev add length") - - parser.add_argument( - "--addfile", type=str, help="Add regions from a bedfile") - - parser.add_argument( - "-s", "--shiftrate", type=float, default=0.0, - help="Shift probability") - - parser.add_argument( - "--shiftmean", type=float, default=0.0, - help="Mean shift") - - parser.add_argument( - "--shiftstdev", type=float, default=150.0, - help="Stdev shift") - - parser.add_argument( - "-c", "--cutrate", type=float, default=0.0, - help="Cut probability") - - parser.add_argument( - "-m", "--mergerate", type=float, default=0.0, - help="Merge probability. WARNING: will likely create regions that are thousands of base pairs long") - - parser.add_argument( - "-o", "--outputfile", type=str, - help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed") - - parser.add_argument( - "-r", "--repeat", type=int, default=1, - help="the number of times to repeat the operation") - - return parser - - -class Bedshift(object): - """ - The bedshift object with methods to perturb regions - """ - - def __init__(self, bedfile_path): - """ - Read in a .bed file to pandas DataFrame format - - :param str bedfile_path: the path to the bedfile - """ - - df = pd.read_csv(bedfile_path, sep='\t', header=None, usecols=[0,1,2]) - - # if there is 'chrom', 'start', 'stop' in the table, move them to header - if not str(df.iloc[0, 1]).isdigit(): - df.columns = df.iloc[0] - df = df[1:] - - df[3] = 0 # column indicating which modifications were made - self.original_regions = df.shape[0] - self.bed = df.astype({1: 'int64', 2: 'int64', 3: 'int64'}) \ - .sort_values([0, 1, 2]).reset_index(drop=True) - self.original_bed = self.bed - - - def reset_bed(self): - """ - Reset the stored bedfile to the state before perturbations - """ - - self.bed = self.original_bed - - - def __check_rate(self, rates): - for rate in rates: - if rate < 0 or rate > 1: - _LOGGER.error("Rate must be between 0 and 1") - sys.exit(1) - - - def pick_random_chrom(self): - """ - Utility function to pick a random chromosome - - :return str, float chrom_str, chrom_len: chromosome number and length - """ - chrom_str = random.choice(list(chroms.values())) - chrom_len = chrom_lens[chrom_str] - return chrom_str, chrom_len - - - def add(self, addrate, addmean, addstdev): - """ - Add regions - - :param float addrate: the rate to add regions - :param float addmean: the mean length of added regions - :param float addstdev: the standard deviation of the length of added regions - :return int: the number of regions added - """ - self.__check_rate([addrate]) - rows = self.bed.shape[0] - num_add = int(rows * addrate) - new_regions = {0: [], 1: [], 2: [], 3: []} - for _ in range(num_add): - chrom_str, chrom_len = self.pick_random_chrom() - start = random.randint(1, chrom_len) - end = min(start + max(int(np.random.normal(addmean, addstdev)), 20), chrom_len) - new_regions[0].append(chrom_str) - new_regions[1].append(start) - new_regions[2].append(end) - new_regions[3].append(3) - self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) - return len(new_regions) - - def add_from_file(self, fp, addrate): - """ - Add regions from another bedfile to this perturbed bedfile - - :param float addrate: the rate to add regions - :param str fp: the filepath to the other bedfile - :return int: the number of regions added - """ - - self.__check_rate([addrate]) - rows = self.bed.shape[0] - num_add = int(rows * addrate) - new_regions = {0: [], 1: [], 2: [], 3: []} - with open(fp, 'r') as f: - lines = 0 - for region in f: - lines += 1 - f.seek(0) - addrate_newfile = num_add / lines - for region in f: - if random.random() < addrate_newfile: - region = region.split('\t') - if str(region[1]).isdigit(): - new_regions[0].append(region[0]) - new_regions[1].append(int(region[1])) - new_regions[2].append(int(region[2])) - new_regions[3].append(3) - self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) - return len(new_regions) - - def shift(self, shiftrate, shiftmean, shiftstdev): - """ - Shift regions - - :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) - :param float shiftmean: the mean shift distance - :param float shiftstdev: the standard deviation of the shift distance - :return int: the number of regions shifted - """ - self.__check_rate([shiftrate]) - rows = self.bed.shape[0] - shift_rows = random.sample(list(range(rows)), int(rows * shiftrate)) - for row in shift_rows: - self.__shift(row, shiftmean, shiftstdev) # shifted rows display a 1 - return len(shift_rows) - - def __shift(self, row, mean, stdev): - theshift = int(np.random.normal(mean, stdev)) - - start = self.bed.loc[row][1] - end = self.bed.loc[row][2] - - if start + theshift < 0 or \ - end + theshift > chrom_lens[self.bed.loc[row][0]]: - # shifting out of bounds check - return - - self.bed.at[row, 1] = start + theshift - self.bed.at[row, 2] = end + theshift - self.bed.at[row, 3] = 1 - - - def cut(self, cutrate): - """ - Cut regions to create two new regions - - :param float cutrate: the rate to cut regions into two separate regions - :return int: the number of regions cut - """ - self.__check_rate([cutrate]) - if cutrate == 0: - return 0 - rows = self.bed.shape[0] - cut_rows = random.sample(list(range(rows)), int(rows * cutrate)) - new_row_list = [] - to_drop = [] - for row in cut_rows: - drop_row, new_regions = self.__cut(row) # cut rows display a 2 - new_row_list.extend(new_regions) - to_drop.append(drop_row) - self.bed = self.bed.drop(to_drop) - self.bed = self.bed.append(new_row_list, ignore_index=True) - self.bed = self.bed.reset_index(drop=True) - return len(cut_rows) - - def __cut(self, row): - chrom = self.bed.loc[row][0] - start = self.bed.loc[row][1] - end = self.bed.loc[row][2] - - # choose where to cut the region - thecut = (start + end) // 2 # int(np.random.normal((start+end)/2, (end - start)/6)) - if thecut <= start: - thecut = start + 10 - if thecut >= end: - thecut = end - 10 - - ''' may add in later, this makes the api confusing! - # adjust the cut regions using the shift function - new_segs = self.__shift(new_segs, 0, meanshift, stdevshift) - new_segs = self.__shift(new_segs, 1, meanshift, stdevshift) - ''' - - return row, [{0: chrom, 1: start, 2: thecut, 3: 2}, {0: chrom, 1: thecut, 2: end, 3: 2}] - - - def merge(self, mergerate): - """ - Merge two regions into one new region - - :param float mergerate: the rate to merge two regions into one - :return int: number of regions merged - """ - - self.__check_rate([mergerate]) - if mergerate == 0: - return 0 - rows = self.bed.shape[0] - merge_rows = random.sample(list(range(rows)), int(rows * mergerate)) - to_add = [] - to_drop = [] - for row in merge_rows: - drop_row, add_row = self.__merge(row) - if add_row: - to_add.append(add_row) - to_drop.extend(drop_row) - self.bed = self.bed.drop(to_drop) - self.bed = self.bed.append(to_add, ignore_index=True) - self.bed = self.bed.reset_index(drop=True) - return len(merge_rows) - - def __merge(self, row): - # check if the regions being merged are on the same chromosome - if row + 1 not in self.bed.index or self.bed.loc[row][0] != self.bed.loc[row+1][0]: - return [], None - - chrom = self.bed.loc[row][0] - start = self.bed.loc[row][1] - end = self.bed.loc[row+1][2] - return [row, row+1], {0: chrom, 1: start, 2: end, 3: 4} - - - def drop(self, droprate): - """ - Drop regions - - :param float droprate: the rate to drop/remove regions - :return int: the number of rows dropped - """ - self.__check_rate([droprate]) - rows = self.bed.shape[0] - drop_rows = random.sample(list(range(rows)), int(rows * droprate)) - self.bed = self.bed.drop(drop_rows) - self.bed = self.bed.reset_index(drop=True) - return len(drop_rows) - - - def all_perturbations(self, - addrate=0.0, addmean=320.0, addstdev=30.0, - addfile=None, - shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, - cutrate=0.0, - mergerate=0.0, - droprate=0.0): - ''' - Perform all five perturbations in the order of shift, add, cut, merge, drop. - - :param float addrate: the rate (as a proportion of the total number of regions) to add regions - :param float addmean: the mean length of added regions - :param float addstdev: the standard deviation of the length of added regions - :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) - :param float shiftmean: the mean shift distance - :param float shiftstdev: the standard deviation of the shift distance - :param float cutrate: the rate to cut regions into two separate regions - :param float mergerate: the rate to merge two regions into one - :param float droprate: the rate to drop/remove regions - :return int: the number of total regions perturbed - ''' - - self.__check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) - n = 0 - n += self.shift(shiftrate, shiftmean, shiftstdev) - if addfile: - n += self.add_from_file(addfile, addrate) - else: - n += self.add(addrate, addmean, addstdev) - n += self.cut(cutrate) - n += self.merge(mergerate) - n += self.drop(droprate) - return n - - - def to_bed(self, outfile_name): - """ - Write a pandas dataframe back into BED file format - - :param str outfile_name: The name of the output BED file - """ - self.bed.sort_values([0,1,2], inplace=True) - self.bed.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') - print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.' \ - .format(outfile_name, self.bed.shape[0], self.original_regions)) - - -def main(): - """ Primary workflow """ - - parser = logmuse.add_logging_options(build_argparser()) - args, remaining_args = parser.parse_known_args() - global _LOGGER - _LOGGER = logmuse.logger_via_cli(args) - - _LOGGER.info("welcome to bedshift") - _LOGGER.info("Shifting file: '{}'".format(args.bedfile)) - msg = """Params: - shift rate: {shiftrate} - shift mean distance: {shiftmean} - shift stdev: {shiftstdev} - add rate: {addrate} - add mean length: {addmean} - add stdev: {addstdev} - add regions from file: {addfile} - cut rate: {cutrate} - drop rate: {droprate} - merge rate: {mergerate} - outputfile: {outputfile} - repeat: {repeat} - """ - - outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) if not args.outputfile else args.outputfile - - _LOGGER.info(msg.format( - droprate=args.droprate, - addrate=args.addrate, - addmean=args.addmean, - addstdev=args.addstdev, - addfile=args.addfile, - shiftrate=args.shiftrate, - shiftmean=args.shiftmean, - shiftstdev=args.shiftstdev, - cutrate=args.cutrate, - mergerate=args.mergerate, - outputfile=args.outputfile, - repeat=args.repeat)) - - if not args.bedfile: - parser.print_help() - _LOGGER.error("No bedfile given") - sys.exit(1) - - - bedshifter = Bedshift(args.bedfile) - if args.repeat == 1: - n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, - args.shiftrate, args.shiftmean, args.shiftstdev, - args.cutrate, - args.mergerate, - args.droprate) - bedshifter.to_bed(outfile) - elif args.repeat > 1: - for i in range(args.repeat): - - n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, - args.shiftrate, args.shiftmean, args.shiftstdev, - args.cutrate, - args.mergerate, - args.droprate) - modified_outfile = outfile.rsplit("/") - modified_outfile[-1] = "rep" + str(i+1) + "_" + modified_outfile[-1] - modified_outfile = "/".join(modified_outfile) - bedshifter.to_bed(modified_outfile) - bedshifter.reset_bed() - else: - _LOGGER.error("repeats specified is less than 1") - sys.exit(1) - - -if __name__ == '__main__': - try: - sys.exit(main()) - except KeyboardInterrupt: - _LOGGER.error("Program canceled by user!") - sys.exit(1) - - diff --git a/dist/bedshift-1.0.0-py3-none-any.whl b/dist/bedshift-1.0.0-py3-none-any.whl deleted file mode 100644 index 4a1a14bf4a662073e85245fcd7efef9922db0fb9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7633 zcmaKx1yoeqzsHB}Zl!x@M7o7x=eQi1SyQnOPb&}Y*RPgVP!C%> zYfo-7GdpKHPct(vS3i|lX-q_3Y{0P}sFVz}NaK5ovYN&;vot5)oY262{lcdRB`!mu z`|t#I_N^*EiBgEmo?S^|W7yuRnX!C7RGK)ccR&~yAl4!K1$DQ`7oCj{S1^zp50@mX zt)bQK`3uf3TUBL9^Z@!aM_@bt1_gN9Wwe1UYYOk4o60R4VA$E2!S3W|dR6R`j^vM^ z{=fPGQ4Q)PQ>S3^2i>IqN58j~yN8{N^Mn4VIBq3Qwy}}%s5reE9rY(Od=q@?n}~ql zB%%kBG>{k$!v_E;p8ahk{uq)6(hjAA%NG3fLqMOLG%alIR93ML`VKSxX&kCb)oZ0p zG=Eg-H|!b0kx+#piTRVjzI*5TKVtv6LypI1F#=13K2egnfTHh`DR?VP@RD>ZkzPEJ@z{foHXBnNd&g#>6k<)@^+c z`%VGrf&XX_Zy{H|7S!<|xG79}s zzRC?Jtw>u(o=2+OyJLM37wM-m=xMYoQ5jpx+;W@%B7iFwmo>y@V{92vjJ)1*K6vdv zr3eYfk2;JJOD0dl?7aXx@xgnzEvxHeI6|vtcn#;A~%|?dIi>ms` zK$OHLnl@<>Dv_hM`;`_qShd`nxkG|d0+>iUeN7m#OKL(oxE;?z>~-N?2Ji}Q8_va_JE35ZIzsH zJFkhwHHQu5q8IAD!n-WKxe|PlAxC*@=x99%Vr^>g-Jtx*HyDJ$`T-oO)OC{RH2~|C zC3BFB^+RnhyU$3m9&}ExC!3;reZP4)T-{+wOpa_JiT;c@jm8gQT+kpr@x=Ucd7;Va zjaChkno;vae0;bSlRob!3N@LN<8tr!GCA2eJZ}>t6bkP*_wLZ|zCUx;Su5OdiSX}u zsV!lmbJp77$J%^vUBuowO<{-?4qXV#ioQR`w-(d?nM~C(h+q$g-Q#p3>~22NA=#a5 zTKHL#gpE8%t2Bk(O}awj12l6g=;fBP6}h8h(}lcPk^9LmjD;cX$m#2iXl1638b6H@ zNXIN5#ihC8A$gQ1E_eSRGIg$QF0)amk`)d&BMU=M%CCLtLnOH7wS zp+nE1B|cQ+pK_EFBuVx{9%E+z!!?5{#9Y!LkV?+N&1YyRVmINXSW^`JhG@z{ZJUQR zSnyaoMC!erjFWySG`#|uXx-s|x;yJr-ELC1%o9m#6VP8E z+B7JXSvA$RxzwK-(gSiIpzGb{2v*sTf7!2O}v;*x8;A%ngmB|?B@yN)uCq45DQctz&lb?EEjX=e?qkFo~S;-y)`?oA; 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z>V?r0*9mQB8HWkWh>9z?uy|Qv{X8%5U`JhSu@vyYlw3@a`*wFP$Jj)t&N@rZ(J$H{K0tVOq^pcauz2SRzZQzW(ys-*B=5jAoLlU(Ik%ooSZENxz#*ic zgpW=viO;;=qCe?Q=sWz3)5_-oMLw8}4&W5BD>RBx&=GzJ#}DulZ+6nDG&t_#L8b~B z&<6OA;}Lz}BazV#rJPi4nf4k#tH^kcmcxTsT~5Xdzsq3q*_c^D_ng1JsK|uu^1l^j z^^=8#?1*^&3BgqayQ?pn$+p%po)p8$s#KG}XVB9M-DN?X zUr4;y=_v-tO&?<@@L2FUqbi^}I;`=+a=1nJE+)pxHhrN(&J*IN_~5pEe6JNJ)qR55 z(b_Y*cd_V(7Q__<^vq?P12Xi7LivK1XCF<&4`NC6#9ytXq2|HT4xyhk5W5h+4}|FJ zmd*rRi|L_A9#Y#E1aPyR>Jmo1*dCF6s@|34M Date: Thu, 30 Jul 2020 12:31:33 -0400 Subject: [PATCH 0044/1072] parse chrom.sizes file --- bedshift/bedshift.py | 73 ++++++++++++++++++++++++++++---------------- 1 file changed, 46 insertions(+), 27 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index e8cab819..cdf20fe8 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -15,33 +15,44 @@ __all__ = ["Bedshift"] -chroms = {num: 'chr'+str(num) for num in list(range(1, 23))} -chroms.update({'X': 'chrX', 'Y': 'chrY'}) - -chrom_lens = {'chr1': 247249719, - 'chr2': 242951149, - 'chr3': 199501827, - 'chr4': 191273063, - 'chr5': 180857866, - 'chr6': 170899992, - 'chr7': 158821424, - 'chr8': 146274826, - 'chr9': 140273252, - 'chr10': 135374737, - 'chr11': 134452384, - 'chr12': 132349534, - 'chr13': 114142980, - 'chr14': 106368585, - 'chr15': 100338915, - 'chr16': 88827254, - 'chr17': 78774742, - 'chr18': 76117153, - 'chr19': 63811651, - 'chr20': 62435964, - 'chr21': 46944323, - 'chr22': 49691432, - 'chrX': 154913754, - 'chrY': 57772954} +# chroms = {num: 'chr'+str(num) for num in list(range(1, 23))} +# chroms.update({'X': 'chrX', 'Y': 'chrY'}) + +# chrom_lens = {'chr1': 247249719, +# 'chr2': 242951149, +# 'chr3': 199501827, +# 'chr4': 191273063, +# 'chr5': 180857866, +# 'chr6': 170899992, +# 'chr7': 158821424, +# 'chr8': 146274826, +# 'chr9': 140273252, +# 'chr10': 135374737, +# 'chr11': 134452384, +# 'chr12': 132349534, +# 'chr13': 114142980, +# 'chr14': 106368585, +# 'chr15': 100338915, +# 'chr16': 88827254, +# 'chr17': 78774742, +# 'chr18': 76117153, +# 'chr19': 63811651, +# 'chr20': 62435964, +# 'chr21': 46944323, +# 'chr22': 49691432, +# 'chrX': 154913754, +# 'chrY': 57772954} + +chrom_lens = {} + +def read_chromsizes(fp): + with open(fp) as f: + for line in f: + line = line.split('\t') + chrom = line[0] + size = line[1] + chrom_lens[chrom] = size + class _VersionInHelpParser(argparse.ArgumentParser): @@ -74,6 +85,11 @@ def build_argparser(): "-b", "--bedfile", required=True, help="File path to bed file.") + parser.add_argument( + "-l", "--chrom_lengths", type=str, required=True, + help="chrom.sizes file" + ) + parser.add_argument( "-d", "--droprate", type=float, default=0.0, help="Droprate parameter") @@ -418,6 +434,7 @@ def main(): _LOGGER.info("welcome to bedshift") _LOGGER.info("Shifting file: '{}'".format(args.bedfile)) msg = """Params: + chrom.sizes file: {chromsizes} shift rate: {shiftrate} shift mean distance: {shiftmean} shift stdev: {shiftstdev} @@ -435,6 +452,7 @@ def main(): outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) if not args.outputfile else args.outputfile _LOGGER.info(msg.format( + chromsizes=args.chrom_lengths, droprate=args.droprate, addrate=args.addrate, addmean=args.addmean, @@ -453,6 +471,7 @@ def main(): _LOGGER.error("No bedfile given") sys.exit(1) + read_chromsizes(args.chrom_lengths) bedshifter = Bedshift(args.bedfile) if args.repeat == 1: From 7a24239776b0e69206c6ce006ebbc3524e6e2452 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Thu, 30 Jul 2020 13:14:58 -0400 Subject: [PATCH 0045/1072] read from chrom.sizes file --- bedshift/_version.py | 2 +- bedshift/bedshift.py | 78 ++++++++++++++--------------------------- run-tests.sh | 2 +- tests/pypackage_test.py | 10 +++--- tests/shell_test.sh | 6 ++-- 5 files changed, 36 insertions(+), 62 deletions(-) diff --git a/bedshift/_version.py b/bedshift/_version.py index 5becc17c..5c4105cd 100644 --- a/bedshift/_version.py +++ b/bedshift/_version.py @@ -1 +1 @@ -__version__ = "1.0.0" +__version__ = "1.0.1" diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index cdf20fe8..976ef27c 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -15,34 +15,6 @@ __all__ = ["Bedshift"] -# chroms = {num: 'chr'+str(num) for num in list(range(1, 23))} -# chroms.update({'X': 'chrX', 'Y': 'chrY'}) - -# chrom_lens = {'chr1': 247249719, -# 'chr2': 242951149, -# 'chr3': 199501827, -# 'chr4': 191273063, -# 'chr5': 180857866, -# 'chr6': 170899992, -# 'chr7': 158821424, -# 'chr8': 146274826, -# 'chr9': 140273252, -# 'chr10': 135374737, -# 'chr11': 134452384, -# 'chr12': 132349534, -# 'chr13': 114142980, -# 'chr14': 106368585, -# 'chr15': 100338915, -# 'chr16': 88827254, -# 'chr17': 78774742, -# 'chr18': 76117153, -# 'chr19': 63811651, -# 'chr20': 62435964, -# 'chr21': 46944323, -# 'chr22': 49691432, -# 'chrX': 154913754, -# 'chrY': 57772954} - chrom_lens = {} def read_chromsizes(fp): @@ -50,11 +22,10 @@ def read_chromsizes(fp): for line in f: line = line.split('\t') chrom = line[0] - size = line[1] + size = int(line[1]) chrom_lens[chrom] = size - class _VersionInHelpParser(argparse.ArgumentParser): def format_help(self): """ Add version information to help text. """ @@ -145,13 +116,16 @@ class Bedshift(object): The bedshift object with methods to perturb regions """ - def __init__(self, bedfile_path, delimiter='\t'): + def __init__(self, bedfile_path, chrom_sizes, delimiter='\t'): """ Read in a .bed file to pandas DataFrame format - :param str bedfile_path: the path to the bedfile + :param str bedfile_path: the path to the BED file + :param str chrom_sizes: the path to the chrom.sizes file + :param str delimiter: the delimiter used in the BED file """ + read_chromsizes(chrom_sizes) df = self.read_bed(bedfile_path, delimiter=delimiter) self.original_regions = df.shape[0] self.bed = df.astype({1: 'int64', 2: 'int64', 3: 'int64'}) \ @@ -180,7 +154,7 @@ def pick_random_chrom(self): :return str, float chrom_str, chrom_len: chromosome number and length """ - chrom_str = random.choice(list(chroms.values())) + chrom_str = random.choice(list(chrom_lens.keys())) chrom_len = chrom_lens[chrom_str] return chrom_str, chrom_len @@ -249,11 +223,12 @@ def shift(self, shiftrate, shiftmean, shiftstdev): def __shift(self, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) + chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] end = self.bed.loc[row][2] if start + theshift < 0 or \ - end + theshift > chrom_lens[self.bed.loc[row][0]]: + end + theshift > chrom_lens[chrom]: # shifting out of bounds check return @@ -357,12 +332,12 @@ def drop(self, droprate): return len(drop_rows) - def all_perturbations(self, - addrate=0.0, addmean=320.0, addstdev=30.0, - addfile=None, - shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, - cutrate=0.0, - mergerate=0.0, + def all_perturbations(self, + addrate=0.0, addmean=320.0, addstdev=30.0, + addfile=None, + shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, + cutrate=0.0, + mergerate=0.0, droprate=0.0): ''' Perform all five perturbations in the order of shift, add, cut, merge, drop. @@ -471,26 +446,25 @@ def main(): _LOGGER.error("No bedfile given") sys.exit(1) - read_chromsizes(args.chrom_lengths) - bedshifter = Bedshift(args.bedfile) + bedshifter = Bedshift(args.bedfile, args.chrom_lengths) if args.repeat == 1: - n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, - args.shiftrate, args.shiftmean, args.shiftstdev, - args.cutrate, - args.mergerate, + n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, + args.addfile, + args.shiftrate, args.shiftmean, args.shiftstdev, + args.cutrate, + args.mergerate, args.droprate) bedshifter.to_bed(outfile) print(str(n) + " regions changed") elif args.repeat > 1: for i in range(args.repeat): - n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, - args.shiftrate, args.shiftmean, args.shiftstdev, - args.cutrate, - args.mergerate, + n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, + args.addfile, + args.shiftrate, args.shiftmean, args.shiftstdev, + args.cutrate, + args.mergerate, args.droprate) modified_outfile = outfile.rsplit("/") modified_outfile[-1] = "rep" + str(i+1) + "_" + modified_outfile[-1] diff --git a/run-tests.sh b/run-tests.sh index 667e5cc2..46ae5400 100755 --- a/run-tests.sh +++ b/run-tests.sh @@ -1,5 +1,5 @@ #!/bin/bash -pip install . +pip install . --user python3 ./tests/pypackage_test.py ./tests/shell_test.sh diff --git a/tests/pypackage_test.py b/tests/pypackage_test.py index b0266b82..df506d2a 100755 --- a/tests/pypackage_test.py +++ b/tests/pypackage_test.py @@ -3,17 +3,17 @@ os.chdir(sys.path[0]) -bedshifter = bedshift.Bedshift('test.bed') +bedshifter = bedshift.Bedshift('test.bed', chrom_sizes="/project/shefflab/genomes/hg38/fasta/default/hg38.chrom.sizes") bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, - cutrate=0.1, - mergerate=0.11, + cutrate=0.1, + mergerate=0.11, droprate=0.03) bedshifter.to_bed('py_output.bed') bedshifter.reset_bed() -bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, - addfile='py_output.bed', +bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, + addfile='py_output.bed', shiftrate=0.3, shiftmean=100.0, shiftstdev=30.0) bedshifter.to_bed('py_output2.bed') diff --git a/tests/shell_test.sh b/tests/shell_test.sh index 4fe1794a..1fff584d 100755 --- a/tests/shell_test.sh +++ b/tests/shell_test.sh @@ -1,7 +1,7 @@ #!/bin/bash -bedshift --bedfile $(dirname "$0")/test.bed --droprate 0.1 --addrate 0.2 --addmean 320.0 --addstdev 30.0 --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output.bed +bedshift --bedfile $(dirname "$0")/test.bed -l /project/shefflab/genomes/hg38/fasta/default/hg38.chrom.sizes --droprate 0.1 --addrate 0.2 --addmean 320.0 --addstdev 30.0 --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output.bed -bedshift --bedfile $(dirname "$0")/test.bed --shiftrate 0.4 --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed +bedshift --bedfile $(dirname "$0")/test.bed -l /project/shefflab/genomes/hg38/fasta/default/hg38.chrom.sizes --shiftrate 0.4 --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed -bedshift --bedfile $(dirname "$0")/test.bed --addrate 0.1 --cutrate 0.5 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output3.bed -r 3 +bedshift --bedfile $(dirname "$0")/test.bed -l /project/shefflab/genomes/hg38/fasta/default/hg38.chrom.sizes --addrate 0.1 --cutrate 0.5 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output3.bed -r 3 From ae7ddced112adeb4f1e86cc138e684cc20111669 Mon Sep 17 00:00:00 2001 From: Aaron Gu Date: Thu, 30 Jul 2020 13:19:52 -0400 Subject: [PATCH 0046/1072] update docs --- README.md | 4 ++-- docs/README.md | 6 +++--- docs/evaluation_guide.md | 6 +++--- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 632de704..90db7446 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ Install from local repository: `pip install .` ## Command line -Run with: `bedshift -b BEDFILE` or `./bedshift.sh` if running bedshift on multiple bedfiles with a set of parameters, which are editable in bedshift.sh. +Run with: `bedshift -l hg38.chrom.sizes -b BEDFILE` See `bedshift -h` for parameters. @@ -17,7 +17,7 @@ See `bedshift -h` for parameters. ```py import bedshift -bedshifter = bedshift.Bedshift('tests/test.bed') +bedshifter = bedshift.Bedshift('tests/test.bed', 'hg38.chrom.sizes') bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, cutrate=0.1, diff --git a/docs/README.md b/docs/README.md index fb7d2cec..11d42a33 100644 --- a/docs/README.md +++ b/docs/README.md @@ -18,12 +18,12 @@ The package is available for use both as a command line interface and a python p bedshift -h ``` -The following examples will shift 10% of the regions and add 10% new regions in `examples/test.bed`. The output is located at `bedshifted_test.bed`. +The following examples will shift 10% of the regions and add 10% new regions in `examples/test.bed`. The -l argument is the file in which chromosome sizes are located. The output is located at `bedshifted_test.bed`. CLI: ``` -bedshift -b tests/test.bed -s 0.1 -a 0.1 +bedshift -l hg38.chrom.sizes -b tests/test.bed -s 0.1 -a 0.1 ``` Python: @@ -31,7 +31,7 @@ Python: ```py import bedshift -bedshifter = bedshift.Bedshift('tests/test.bed') +bedshifter = bedshift.Bedshift('tests/test.bed', 'hg38.chrom.sizes') bedshifter.shift(shiftrate=0.1, shiftmean=0.0, shiftstdev=120.0) bedshifter.add(addrate=0.1, addmean=320.0, addstdev=20.0) bedshifter.to_bed('tests/test_output.bed') diff --git a/docs/evaluation_guide.md b/docs/evaluation_guide.md index 4eda5f9b..f4d04ea9 100644 --- a/docs/evaluation_guide.md +++ b/docs/evaluation_guide.md @@ -5,8 +5,8 @@ Bedshift perturbations include add, drop, shift, cut, and merge. Using any of these perturbations, or combinations of them, you can generate a set of files that are slightly perturbed from the original file. Assuming that the original file is called `original.bed`, and you want 100 files of added regions and 100 files of dropped regions: ``` -bedshift -b original.bed -a 0.1 -r 100 -bedshift -b original.bed -d 0.3 -r 100 +bedshift -l hg38.chrom.sizes -b original.bed -a 0.1 -r 100 +bedshift -l hg38.chrom.sizes -b original.bed -d 0.3 -r 100 ``` The output file will be in `bedshifted_original.bed`. @@ -74,7 +74,7 @@ You will also need to create a `pipeline_interface.yaml` that describes how to f pipeline_name: bedshift_run pipeline_type: sample command_template: > - bedshift -b {sample.file} -a {sample.add} -d {sample.drop} -s {sample.shift} -c {sample.cut} -m {sample.merge} -r {sample.repeat} -o {sample.sample_name}.bed + bedshift -l hg38.chrom.sizes -b {sample.file} -a {sample.add} -d {sample.drop} -s {sample.shift} -c {sample.cut} -m {sample.merge} -r {sample.repeat} -o {sample.sample_name}.bed compute: mem: 4000 cores: 1 From 34a0c8fb06924928644acb26fa9c070d6075636c Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 6 Aug 2020 14:19:45 -0400 Subject: [PATCH 0047/1072] fix params output formatting --- bedshift/bedshift.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 976ef27c..6d62c85d 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -409,19 +409,19 @@ def main(): _LOGGER.info("welcome to bedshift") _LOGGER.info("Shifting file: '{}'".format(args.bedfile)) msg = """Params: - chrom.sizes file: {chromsizes} - shift rate: {shiftrate} - shift mean distance: {shiftmean} - shift stdev: {shiftstdev} - add rate: {addrate} - add mean length: {addmean} - add stdev: {addstdev} - add regions from file: {addfile} - cut rate: {cutrate} - drop rate: {droprate} - merge rate: {mergerate} - outputfile: {outputfile} - repeat: {repeat} + chrom.sizes file: {chromsizes} + shift rate: {shiftrate} + shift mean distance: {shiftmean} + shift stdev: {shiftstdev} + add rate: {addrate} + add mean length: {addmean} + add stdev: {addstdev} + add regions from file: {addfile} + cut rate: {cutrate} + drop rate: {droprate} + merge rate: {mergerate} + outputfile: {outputfile} + repeat: {repeat} """ outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) if not args.outputfile else args.outputfile From 117aa41876ee7d73dde05f874eb4dfcbf4619869 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 6 Aug 2020 14:43:12 -0400 Subject: [PATCH 0048/1072] indent with 2 spaces on params output --- bedshift/bedshift.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 6d62c85d..8d661d08 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -58,7 +58,7 @@ def build_argparser(): parser.add_argument( "-l", "--chrom_lengths", type=str, required=True, - help="chrom.sizes file" + help="TSV text file with one row per chromosomes indicating chromosome sizes" ) parser.add_argument( @@ -409,20 +409,20 @@ def main(): _LOGGER.info("welcome to bedshift") _LOGGER.info("Shifting file: '{}'".format(args.bedfile)) msg = """Params: - chrom.sizes file: {chromsizes} - shift rate: {shiftrate} - shift mean distance: {shiftmean} - shift stdev: {shiftstdev} - add rate: {addrate} - add mean length: {addmean} - add stdev: {addstdev} - add regions from file: {addfile} - cut rate: {cutrate} - drop rate: {droprate} - merge rate: {mergerate} - outputfile: {outputfile} - repeat: {repeat} - """ + chrom.sizes file: {chromsizes} + shift rate: {shiftrate} + shift mean distance: {shiftmean} + shift stdev: {shiftstdev} + add rate: {addrate} + add mean length: {addmean} + add stdev: {addstdev} + add regions from file: {addfile} + cut rate: {cutrate} + drop rate: {droprate} + merge rate: {mergerate} + outputfile: {outputfile} + repeat: {repeat} +""" outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) if not args.outputfile else args.outputfile From 113b56b1d755f0adb9175e9f1d484d52af9468e5 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 6 Aug 2020 14:44:03 -0400 Subject: [PATCH 0049/1072] print version in welcome --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 8d661d08..b889d320 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -406,7 +406,7 @@ def main(): global _LOGGER _LOGGER = logmuse.logger_via_cli(args) - _LOGGER.info("welcome to bedshift") + _LOGGER.info("Welcome to bedshift version {}".format(__version__)) _LOGGER.info("Shifting file: '{}'".format(args.bedfile)) msg = """Params: chrom.sizes file: {chromsizes} From 85715cb4389968ebcc4271ffa8a23c6862602cea Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 6 Aug 2020 14:53:00 -0400 Subject: [PATCH 0050/1072] implement refgenie connection. See #2 --- bedshift/bedshift.py | 38 +++++++++++++++++++++++++++++--------- 1 file changed, 29 insertions(+), 9 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index b889d320..117921ea 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -57,10 +57,14 @@ def build_argparser(): help="File path to bed file.") parser.add_argument( - "-l", "--chrom_lengths", type=str, required=True, + "-l", "--chrom_lengths", type=str, required=False, help="TSV text file with one row per chromosomes indicating chromosome sizes" ) + parser.add_argument( + "-g", "--genome", type=str, required=False, + help="Refgenie genome identifier (used for chrom sizes).") + parser.add_argument( "-d", "--droprate", type=float, default=0.0, help="Droprate parameter") @@ -408,15 +412,36 @@ def main(): _LOGGER.info("Welcome to bedshift version {}".format(__version__)) _LOGGER.info("Shifting file: '{}'".format(args.bedfile)) + + if not args.bedfile: + parser.print_help() + _LOGGER.error("No BED file given") + sys.exit(1) + + if not args.chrom_lengths: + if not args.genome: + _LOGGER.error("You must provide either chrom sizes or a refgenie genome.") + sys.exit(1) + else: + import refgenconf + rgc = refgenconf.RefGenConf(refgenconf.select_genome_config()) + args.chrom_lengths = rgc.seek(args.genome, "fasta", "", "chrom_sizes") + + + + + msg = """Params: chrom.sizes file: {chromsizes} - shift rate: {shiftrate} + shift: + shift rate: {shiftrate} shift mean distance: {shiftmean} shift stdev: {shiftstdev} - add rate: {addrate} + add: + rate: {addrate} add mean length: {addmean} add stdev: {addstdev} - add regions from file: {addfile} + add file: {addfile} cut rate: {cutrate} drop rate: {droprate} merge rate: {mergerate} @@ -441,11 +466,6 @@ def main(): outputfile=args.outputfile, repeat=args.repeat)) - if not args.bedfile: - parser.print_help() - _LOGGER.error("No bedfile given") - sys.exit(1) - bedshifter = Bedshift(args.bedfile, args.chrom_lengths) if args.repeat == 1: From 7cfe5c660399a33240427a99f64d25e6ea721ca9 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 6 Aug 2020 14:53:59 -0400 Subject: [PATCH 0051/1072] fix argparser indentation --- bedshift/bedshift.py | 70 ++++++++++++++++++++++---------------------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 117921ea..a07a5869 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -44,73 +44,73 @@ def build_argparser(): additional_description = "\n..." parser = _VersionInHelpParser( - description=banner, - epilog=additional_description) + description=banner, + epilog=additional_description) parser.add_argument( - "-V", "--version", - action="version", - version="%(prog)s {v}".format(v=__version__)) + "-V", "--version", + action="version", + version="%(prog)s {v}".format(v=__version__)) parser.add_argument( - "-b", "--bedfile", required=True, - help="File path to bed file.") + "-b", "--bedfile", required=True, + help="File path to bed file.") parser.add_argument( - "-l", "--chrom_lengths", type=str, required=False, - help="TSV text file with one row per chromosomes indicating chromosome sizes" - ) + "-l", "--chrom_lengths", type=str, required=False, + help="TSV text file with one row per chromosomes indicating chromosome sizes" + ) parser.add_argument( - "-g", "--genome", type=str, required=False, - help="Refgenie genome identifier (used for chrom sizes).") + "-g", "--genome", type=str, required=False, + help="Refgenie genome identifier (used for chrom sizes).") parser.add_argument( - "-d", "--droprate", type=float, default=0.0, - help="Droprate parameter") + "-d", "--droprate", type=float, default=0.0, + help="Droprate parameter") parser.add_argument( - "-a", "--addrate", type=float, default=0.0, - help="Addrate parameter") + "-a", "--addrate", type=float, default=0.0, + help="Addrate parameter") parser.add_argument( - "--addmean", type=float, default=320.0, - help="Mean add region length") + "--addmean", type=float, default=320.0, + help="Mean add region length") parser.add_argument( - "--addstdev", type=float, default=30.0, - help="Stdev add length") + "--addstdev", type=float, default=30.0, + help="Stdev add length") parser.add_argument( - "--addfile", type=str, help="Add regions from a bedfile") + "--addfile", type=str, help="Add regions from a bedfile") parser.add_argument( - "-s", "--shiftrate", type=float, default=0.0, - help="Shift probability") + "-s", "--shiftrate", type=float, default=0.0, + help="Shift probability") parser.add_argument( - "--shiftmean", type=float, default=0.0, - help="Mean shift") + "--shiftmean", type=float, default=0.0, + help="Mean shift") parser.add_argument( - "--shiftstdev", type=float, default=150.0, - help="Stdev shift") + "--shiftstdev", type=float, default=150.0, + help="Stdev shift") parser.add_argument( - "-c", "--cutrate", type=float, default=0.0, - help="Cut probability") + "-c", "--cutrate", type=float, default=0.0, + help="Cut probability") parser.add_argument( - "-m", "--mergerate", type=float, default=0.0, - help="Merge probability. WARNING: will likely create regions that are thousands of base pairs long") + "-m", "--mergerate", type=float, default=0.0, + help="Merge probability. WARNING: will likely create regions that are thousands of base pairs long") parser.add_argument( - "-o", "--outputfile", type=str, - help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed") + "-o", "--outputfile", type=str, + help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed") parser.add_argument( - "-r", "--repeat", type=int, default=1, - help="the number of times to repeat the operation") + "-r", "--repeat", type=int, default=1, + help="the number of times to repeat the operation") return parser From bdefd18fe28989aef4fb71127cef839e9d73c05b Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 6 Aug 2020 14:55:07 -0400 Subject: [PATCH 0052/1072] it's typical to use hyphehs, not underscores, in CLI options --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index a07a5869..fc9d97ed 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -57,7 +57,7 @@ def build_argparser(): help="File path to bed file.") parser.add_argument( - "-l", "--chrom_lengths", type=str, required=False, + "-l", "--chrom-lengths", type=str, required=False, help="TSV text file with one row per chromosomes indicating chromosome sizes" ) From d3db3beae0f1743cf01475cf7bb52cfd3b632d51 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 6 Aug 2020 14:57:44 -0400 Subject: [PATCH 0053/1072] version bump to 1.1.0 --- bedshift/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/_version.py b/bedshift/_version.py index 5c4105cd..98e28b87 100644 --- a/bedshift/_version.py +++ b/bedshift/_version.py @@ -1 +1 @@ -__version__ = "1.0.1" +__version__ = "1.1.0-dev" From 50b2d399acc3b375fda07471f8da88d9ed0ab739 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 6 Aug 2020 14:58:48 -0400 Subject: [PATCH 0054/1072] add changelog --- docs/changelog.md | 9 +++++++++ mkdocs.yml | 3 ++- 2 files changed, 11 insertions(+), 1 deletion(-) create mode 100644 docs/changelog.md diff --git a/docs/changelog.md b/docs/changelog.md new file mode 100644 index 00000000..5a9664f9 --- /dev/null +++ b/docs/changelog.md @@ -0,0 +1,9 @@ +# Changelog + +This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html) and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) format. + +## [1.1.0] - unreleased + +- Added ability to specify chrom sizes file, or refgenie genome. + +## [1.0.0] - ? diff --git a/mkdocs.yml b/mkdocs.yml index f47a852a..2cbf8e8c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -12,7 +12,8 @@ nav: - Evaluating Similarity Scores: evaluation_guide.md - Reference: - Python API: autodoc_build/bedshift.md - + - Changelog: changelog.md + plugins: - databio: autodoc_build: "docs/autodoc_build" From 4e94b830e13e7d470bac269d44c01a955637c6ac Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 6 Aug 2020 15:02:38 -0400 Subject: [PATCH 0055/1072] improve msg in case of missing refgenie --- bedshift/bedshift.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index fc9d97ed..1307cf0d 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -423,9 +423,14 @@ def main(): _LOGGER.error("You must provide either chrom sizes or a refgenie genome.") sys.exit(1) else: - import refgenconf - rgc = refgenconf.RefGenConf(refgenconf.select_genome_config()) - args.chrom_lengths = rgc.seek(args.genome, "fasta", "", "chrom_sizes") + try: + import refgenconf + rgc = refgenconf.RefGenConf(refgenconf.select_genome_config()) + args.chrom_lengths = rgc.seek(args.genome, "fasta", "", "chrom_sizes") + except ModuleNotFoundError: + _LOGGER.error("You must have package refgenconf installed to use a refgenie genome") + sys.exit(1) + From 308070ee30ad63488afecfb98557e973c70983b0 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 6 Aug 2020 15:33:58 -0400 Subject: [PATCH 0056/1072] use None for tag --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 1307cf0d..2db55fe4 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -426,7 +426,7 @@ def main(): try: import refgenconf rgc = refgenconf.RefGenConf(refgenconf.select_genome_config()) - args.chrom_lengths = rgc.seek(args.genome, "fasta", "", "chrom_sizes") + args.chrom_lengths = rgc.seek(args.genome, "fasta", None, "chrom_sizes") except ModuleNotFoundError: _LOGGER.error("You must have package refgenconf installed to use a refgenie genome") sys.exit(1) From 66ad174a37a36a56d516a844814412255fa7c88b Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Thu, 6 Aug 2020 17:12:53 -0400 Subject: [PATCH 0057/1072] fix #5 changed regions reporting bug --- bedshift/bedshift.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 1307cf0d..8cd0087c 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -20,7 +20,7 @@ def read_chromsizes(fp): with open(fp) as f: for line in f: - line = line.split('\t') + line = line.strip().split('\t') chrom = line[0] size = int(line[1]) chrom_lens[chrom] = size @@ -179,13 +179,14 @@ def add(self, addrate, addmean, addstdev): for _ in range(num_add): chrom_str, chrom_len = self.pick_random_chrom() start = random.randint(1, chrom_len) - end = min(start + max(int(np.random.normal(addmean, addstdev)), 20), chrom_len) + # ensure chromosome length is not exceeded + end = min(start + int(np.random.normal(addmean, addstdev)), chrom_len) new_regions[0].append(chrom_str) new_regions[1].append(start) new_regions[2].append(end) new_regions[3].append(3) self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) - return len(new_regions) + return num_add def add_from_file(self, fp, addrate, delimiter='\t'): """ From 9b178666ca2b04fc0d06cc4f052e3b99b6f07e1f Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Thu, 6 Aug 2020 17:36:02 -0400 Subject: [PATCH 0058/1072] improve error message logging --- bedshift/bedshift.py | 39 +++++++++++++++++++++++++++------------ run-tests.sh | 2 +- tests/hg38.chrom.sizes | 24 ++++++++++++++++++++++++ tests/pypackage_test.py | 2 +- tests/shell_test.sh | 6 +++--- 5 files changed, 56 insertions(+), 17 deletions(-) create mode 100644 tests/hg38.chrom.sizes diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 1307cf0d..adb33411 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -18,12 +18,16 @@ chrom_lens = {} def read_chromsizes(fp): - with open(fp) as f: - for line in f: - line = line.split('\t') - chrom = line[0] - size = int(line[1]) - chrom_lens[chrom] = size + try: + with open(fp) as f: + for line in f: + line = line.split('\t') + chrom = line[0] + size = int(line[1]) + chrom_lens[chrom] = size + except FileNotFoundError: + _LOGGER.error("fasta file path {} invalid".format(fp)) + sys.exit(1) class _VersionInHelpParser(argparse.ArgumentParser): @@ -173,6 +177,8 @@ def add(self, addrate, addmean, addstdev): :return int: the number of regions added """ self.__check_rate([addrate]) + if addrate == 0: + return 0 rows = self.bed.shape[0] num_add = int(rows * addrate) new_regions = {0: [], 1: [], 2: [], 3: []} @@ -197,6 +203,8 @@ def add_from_file(self, fp, addrate, delimiter='\t'): """ self.__check_rate([addrate]) + if addrate == 0: + return 0 rows = self.bed.shape[0] num_add = int(rows * addrate) @@ -218,6 +226,8 @@ def shift(self, shiftrate, shiftmean, shiftstdev): :return int: the number of regions shifted """ self.__check_rate([shiftrate]) + if shiftrate == 0: + return 0 rows = self.bed.shape[0] shift_rows = random.sample(list(range(rows)), int(rows * shiftrate)) for row in shift_rows: @@ -329,6 +339,8 @@ def drop(self, droprate): :return int: the number of rows dropped """ self.__check_rate([droprate]) + if droprate == 0: + return 0 rows = self.bed.shape[0] drop_rows = random.sample(list(range(rows)), int(rows * droprate)) self.bed = self.bed.drop(drop_rows) @@ -389,8 +401,11 @@ def read_bed(self, bedfile_path, delimiter='\t'): :param str bedfile_path: The path to the BED file """ - - df = pd.read_csv(bedfile_path, sep=delimiter, header=None, usecols=[0,1,2]) + try: + df = pd.read_csv(bedfile_path, sep=delimiter, header=None, usecols=[0,1,2]) + except FileNotFoundError: + _LOGGER.error("BED file path {} invalid".format(bedfile_path)) + sys.exit(1) # if there is 'chrom', 'start', 'stop' in the table, move them to header if not str(df.iloc[0, 1]).isdigit(): @@ -433,9 +448,6 @@ def main(): - - - msg = """Params: chrom.sizes file: {chromsizes} shift: @@ -454,7 +466,10 @@ def main(): repeat: {repeat} """ - outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) if not args.outputfile else args.outputfile + if args.outputfile: + outfile = args.outputfile + else: + outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) _LOGGER.info(msg.format( chromsizes=args.chrom_lengths, diff --git a/run-tests.sh b/run-tests.sh index 46ae5400..667e5cc2 100755 --- a/run-tests.sh +++ b/run-tests.sh @@ -1,5 +1,5 @@ #!/bin/bash -pip install . --user +pip install . python3 ./tests/pypackage_test.py ./tests/shell_test.sh diff --git a/tests/hg38.chrom.sizes b/tests/hg38.chrom.sizes new file mode 100644 index 00000000..f86e3d10 --- /dev/null +++ b/tests/hg38.chrom.sizes @@ -0,0 +1,24 @@ +chr1 1248956422 +chr2 242193529 +chr3 198295559 +chr4 190214555 +chr5 181538259 +chr6 170805979 +chr7 159345973 +chr8 145138636 +chr9 138394717 +chr10 133797422 +chr11 135086622 +chr12 133275309 +chr13 114364328 +chr14 107043718 +chr15 101991189 +chr16 90338345 +chr17 83257441 +chr18 80373285 +chr19 58617616 +chr20 64444167 +chr21 46709983 +chr22 50818468 +chrX 156040895 +chrY 57227415 diff --git a/tests/pypackage_test.py b/tests/pypackage_test.py index df506d2a..374a144a 100755 --- a/tests/pypackage_test.py +++ b/tests/pypackage_test.py @@ -3,7 +3,7 @@ os.chdir(sys.path[0]) -bedshifter = bedshift.Bedshift('test.bed', chrom_sizes="/project/shefflab/genomes/hg38/fasta/default/hg38.chrom.sizes") +bedshifter = bedshift.Bedshift('test.bed', chrom_sizes="hg38.chrom.sizes") bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, diff --git a/tests/shell_test.sh b/tests/shell_test.sh index 1fff584d..1a3f75e3 100755 --- a/tests/shell_test.sh +++ b/tests/shell_test.sh @@ -1,7 +1,7 @@ #!/bin/bash -bedshift --bedfile $(dirname "$0")/test.bed -l /project/shefflab/genomes/hg38/fasta/default/hg38.chrom.sizes --droprate 0.1 --addrate 0.2 --addmean 320.0 --addstdev 30.0 --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output.bed +bedshift --bedfile $(dirname "$0")/test.bed -l $(dirname "$0")/hg38.chrom.sizes --droprate 0.1 --addrate 0.2 --addmean 320.0 --addstdev 30.0 --shiftrate 0.3 --shiftmean 0.0 --shiftstdev 150.0 --cutrate 0.1 --mergerate 0.2 --outputfile $(dirname "$0")/sh_output.bed -bedshift --bedfile $(dirname "$0")/test.bed -l /project/shefflab/genomes/hg38/fasta/default/hg38.chrom.sizes --shiftrate 0.4 --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed +bedshift --bedfile $(dirname "$0")/test.bed -l $(dirname "$0")/hg38.chrom.sizes --shiftrate 0.4 --droprate 0.1 --addrate 0.2 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output2.bed -bedshift --bedfile $(dirname "$0")/test.bed -l /project/shefflab/genomes/hg38/fasta/default/hg38.chrom.sizes --addrate 0.1 --cutrate 0.5 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output3.bed -r 3 +bedshift --bedfile $(dirname "$0")/test.bed -l $(dirname "$0")/hg38.chrom.sizes --addrate 0.1 --cutrate 0.5 --addfile $(dirname "$0")/test.bed --outputfile $(dirname "$0")/sh_output3.bed -r 3 From 50ca58a9674ad950770d9b063355a3fd76ce598f Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Thu, 6 Aug 2020 17:57:39 -0400 Subject: [PATCH 0059/1072] chromsizes only required for add and shift --- .gitignore | 3 ++- bedshift/bedshift.py | 40 +++++++++++++++++++++++++++------------- 2 files changed, 29 insertions(+), 14 deletions(-) diff --git a/.gitignore b/.gitignore index 12b73c74..d20dee45 100644 --- a/.gitignore +++ b/.gitignore @@ -7,4 +7,5 @@ site/ .DS_Store build/ bedshift.egg-info/ -dist/ \ No newline at end of file +dist/ +bedshifted* \ No newline at end of file diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index d3ede2d7..8febd401 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -124,7 +124,7 @@ class Bedshift(object): The bedshift object with methods to perturb regions """ - def __init__(self, bedfile_path, chrom_sizes, delimiter='\t'): + def __init__(self, bedfile_path, chrom_sizes=None, delimiter='\t'): """ Read in a .bed file to pandas DataFrame format @@ -133,7 +133,8 @@ def __init__(self, bedfile_path, chrom_sizes, delimiter='\t'): :param str delimiter: the delimiter used in the BED file """ - read_chromsizes(chrom_sizes) + if chrom_sizes: + read_chromsizes(chrom_sizes) df = self.read_bed(bedfile_path, delimiter=delimiter) self.original_regions = df.shape[0] self.bed = df.astype({1: 'int64', 2: 'int64', 3: 'int64'}) \ @@ -179,6 +180,10 @@ def add(self, addrate, addmean, addstdev): self.__check_rate([addrate]) if addrate == 0: return 0 + if len(chrom_lens) == 0: + _LOGGER.error("chrom.sizes file must be specified when adding regions") + sys.exit(1) + rows = self.bed.shape[0] num_add = int(rows * addrate) new_regions = {0: [], 1: [], 2: [], 3: []} @@ -205,9 +210,12 @@ def add_from_file(self, fp, addrate, delimiter='\t'): self.__check_rate([addrate]) if addrate == 0: return 0 + if len(chrom_lens) == 0: + _LOGGER.error("chrom.sizes file must be specified when adding regions") + sys.exit(1) + rows = self.bed.shape[0] num_add = int(rows * addrate) - df = self.read_bed(fp, delimiter=delimiter) add_rows = random.sample(list(range(df.shape[0])), num_add) add_df = df.loc[add_rows].reset_index(drop=True) @@ -228,6 +236,10 @@ def shift(self, shiftrate, shiftmean, shiftstdev): self.__check_rate([shiftrate]) if shiftrate == 0: return 0 + if len(chrom_lens) == 0: + _LOGGER.error("chrom.sizes file must be specified when shifting regions") + sys.exit(1) + rows = self.bed.shape[0] shift_rows = random.sample(list(range(rows)), int(rows * shiftrate)) for row in shift_rows: @@ -433,18 +445,20 @@ def main(): _LOGGER.error("No BED file given") sys.exit(1) - if not args.chrom_lengths: - if not args.genome: + if args.chrom_lengths: + pass + elif args.genome: + try: + import refgenconf + rgc = refgenconf.RefGenConf(refgenconf.select_genome_config()) + args.chrom_lengths = rgc.seek(args.genome, "fasta", None, "chrom_sizes") + except ModuleNotFoundError: + _LOGGER.error("You must have package refgenconf installed to use a refgenie genome") + sys.exit(1) + else: + if args.addrate > 0 or args.shiftrate > 0: _LOGGER.error("You must provide either chrom sizes or a refgenie genome.") sys.exit(1) - else: - try: - import refgenconf - rgc = refgenconf.RefGenConf(refgenconf.select_genome_config()) - args.chrom_lengths = rgc.seek(args.genome, "fasta", None, "chrom_sizes") - except ModuleNotFoundError: - _LOGGER.error("You must have package refgenconf installed to use a refgenie genome") - sys.exit(1) From 40a1aafebd07d1e84f0f19f0e45f79043000f27e Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Fri, 7 Aug 2020 21:53:44 -0400 Subject: [PATCH 0060/1072] initial docs --- docs/README.md | 2 +- docs/bedshift_all.md | 33 +++++++++++++++++++++++++++++++++ docs/generate_random.md | 28 ++++++++++++++++++++++++++++ extras/bedshift2.sh | 19 +++++++++++++++++++ mkdocs.yml | 2 ++ 5 files changed, 83 insertions(+), 1 deletion(-) create mode 100644 docs/bedshift_all.md create mode 100644 docs/generate_random.md create mode 100755 extras/bedshift2.sh diff --git a/docs/README.md b/docs/README.md index fb7d2cec..84e4c876 100644 --- a/docs/README.md +++ b/docs/README.md @@ -18,7 +18,7 @@ The package is available for use both as a command line interface and a python p bedshift -h ``` -The following examples will shift 10% of the regions and add 10% new regions in `examples/test.bed`. The output is located at `bedshifted_test.bed`. +The following examples will shift 10% of the regions and add 10% new regions in `examples/test.bed`. The -l argument is the file in which chromosome sizes are located, and is only required for adding and/or shifting regions. The output is located at `bedshifted_test.bed`. CLI: diff --git a/docs/bedshift_all.md b/docs/bedshift_all.md new file mode 100644 index 00000000..56ffc380 --- /dev/null +++ b/docs/bedshift_all.md @@ -0,0 +1,33 @@ +# How to bedshift all files in a directory + +## Using shell + +Assuming you are bedshifting all files in the current working directory using the same parameter, use the following shell script (changing parameters as needed), which iterates over files in the directory and applies bedshift: + +``` +#!/bin/bash +for filename in *.bed; do + CHROM_LENGTHS=hg38.chrom.sizes + BEDFILE=filename + DROP_RATE=0.3 + + ADD_RATE=0.2 + ADD_MEAN=320.0 + ADD_STDEV=30.0 + + SHIFT_RATE=0.2 + SHIFT_MEAN=0.0 + SHIFT_STDEV=150.0 + + CUT_RATE=0.0 + MERGE_RATE=0.0 + + bedshift --bedfile $BEDFILE --chrom-lengths $CHROM_LENGTHS --droprate $DROP_RATE --addrate $ADD_RATE --addmean $ADD_MEAN --addstdev $ADD_STDEV --shiftrate $SHIFT_RATE --shiftmean $SHIFT_MEAN --shiftstdev $SHIFT_STDEV --cutrate $CUT_RATE --mergerate $MERGE_RATE +done +``` + +## Using Python + +```py + +``` diff --git a/docs/generate_random.md b/docs/generate_random.md new file mode 100644 index 00000000..8ade38fe --- /dev/null +++ b/docs/generate_random.md @@ -0,0 +1,28 @@ +# Generate a random BED file + +To generate a random BED file, you need to start with is a BED file with one region. On the command line: + +``` +touch random.bed +``` + +Add in any region, which you will delete later. It is easier to add a region on chromosome 1 so it will be at the top of the BED file after perturbations. + +``` +chr1 1 100 +``` + +The next step is to construct a bedshift command to add regions to this file. We will need to specify the rate of add, which is going to be the number of the new regions we want in this case. Let's try generating 1,000 new regions. Don't forget to have a chromosome sizes file, which is required for adding regions. + +``` +bedshift -b random.bed -l hg38.chrom.sizes -a 1000 +``` + +The printed output should say that 1000 regions changed. Finally, go into the output at bedshifted_random.bed and delete the original region. If you specified repeats and have many output files that need to have the original region deleted, here is a handy command to delete the first line of every BED file. + +``` +find . -name "*.bed" -exec sed -i '.bak' '1d' {} \; +``` + +This `find` command will find all BED files and execute a `sed` command to remove the first line. The `sed` command will operate in place and create `.bak` backup files, which can be removed later. + diff --git a/extras/bedshift2.sh b/extras/bedshift2.sh new file mode 100755 index 00000000..d78c1264 --- /dev/null +++ b/extras/bedshift2.sh @@ -0,0 +1,19 @@ +#!/bin/bash +for filename in *.bed; do + CHROM_LENGTHS=../tests/hg38.chrom.sizes + BEDFILE=filename + DROP_RATE=0.3 + + ADD_RATE=0.2 + ADD_MEAN=320.0 + ADD_STDEV=30.0 + + SHIFT_RATE=0.2 + SHIFT_MEAN=0.0 + SHIFT_STDEV=150.0 + + CUT_RATE=0.0 + MERGE_RATE=0.0 + + bedshift --bedfile $BEDFILE --chrom-lengths $CHROM_LENGTHS --droprate $DROP_RATE --addrate $ADD_RATE --addmean $ADD_MEAN --addstdev $ADD_STDEV --shiftrate $SHIFT_RATE --shiftmean $SHIFT_MEAN --shiftstdev $SHIFT_STDEV --cutrate $CUT_RATE --mergerate $MERGE_RATE +done \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index f47a852a..cdb29444 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -10,6 +10,8 @@ nav: - Overview: README.md - How-to Guides: - Evaluating Similarity Scores: evaluation_guide.md + - Generating a Random BED File: generate_random.md + - Bedshifting an Entire Folder: bedshift_all.md - Reference: - Python API: autodoc_build/bedshift.md From 47a5f2f87a99c637d663b372c57e416ca1568efa Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Mon, 10 Aug 2020 17:47:21 -0400 Subject: [PATCH 0061/1072] bedshift directory docs --- docs/bedshift_all.md | 17 ++++++++++++++++- extras/bedshift2.sh | 2 +- extras/bedshift_files.py | 14 ++++++++++++++ tests/hg38.chrom.sizes | 24 ++++++++++++++++++++++++ 4 files changed, 55 insertions(+), 2 deletions(-) create mode 100644 extras/bedshift_files.py create mode 100644 tests/hg38.chrom.sizes diff --git a/docs/bedshift_all.md b/docs/bedshift_all.md index 56ffc380..e409334b 100644 --- a/docs/bedshift_all.md +++ b/docs/bedshift_all.md @@ -8,7 +8,7 @@ Assuming you are bedshifting all files in the current working directory using th #!/bin/bash for filename in *.bed; do CHROM_LENGTHS=hg38.chrom.sizes - BEDFILE=filename + BEDFILE=$filename DROP_RATE=0.3 ADD_RATE=0.2 @@ -28,6 +28,21 @@ done ## Using Python +In Python, you need the `os` library to get the filenames in a directory. After that, running bedshift is as easy as passing parameters. + ```py +import bedshift +import os + + +datafolder = '.' +files = os.listdir() +for file in files: + if file.endswith('.bed'): + # you may also pass in a chrom.sizes file as the + # second argument if you are adding or shifting regions + b = bedshift.Bedshift(file) + b.all_perturbations(cutrate=0.3, droprate=0.2) + b.to_bed('bedshifted_' + file) ``` diff --git a/extras/bedshift2.sh b/extras/bedshift2.sh index d78c1264..ff1adc4f 100755 --- a/extras/bedshift2.sh +++ b/extras/bedshift2.sh @@ -1,7 +1,7 @@ #!/bin/bash for filename in *.bed; do CHROM_LENGTHS=../tests/hg38.chrom.sizes - BEDFILE=filename + BEDFILE=$filename DROP_RATE=0.3 ADD_RATE=0.2 diff --git a/extras/bedshift_files.py b/extras/bedshift_files.py new file mode 100644 index 00000000..cd9b2510 --- /dev/null +++ b/extras/bedshift_files.py @@ -0,0 +1,14 @@ +import bedshift +import os + + +datafolder = '.' + +files = os.listdir() +for file in files: + if file.endswith('.bed'): + # you may also pass in a chrom.sizes file as the + # second argument if you are adding or shifting regions + b = bedshift.Bedshift(file) + b.all_perturbations(cutrate=0.3, droprate=0.2) + b.to_bed('bedshifted_' + file) \ No newline at end of file diff --git a/tests/hg38.chrom.sizes b/tests/hg38.chrom.sizes new file mode 100644 index 00000000..f86e3d10 --- /dev/null +++ b/tests/hg38.chrom.sizes @@ -0,0 +1,24 @@ +chr1 1248956422 +chr2 242193529 +chr3 198295559 +chr4 190214555 +chr5 181538259 +chr6 170805979 +chr7 159345973 +chr8 145138636 +chr9 138394717 +chr10 133797422 +chr11 135086622 +chr12 133275309 +chr13 114364328 +chr14 107043718 +chr15 101991189 +chr16 90338345 +chr17 83257441 +chr18 80373285 +chr19 58617616 +chr20 64444167 +chr21 46709983 +chr22 50818468 +chrX 156040895 +chrY 57227415 From 45326b4c3ca5490b1758260d9aa3554a8061b51e Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Mon, 10 Aug 2020 21:20:12 -0400 Subject: [PATCH 0062/1072] edit docs --- docs/bedshift_all.md | 7 ++----- docs/generate_random.md | 14 ++++---------- mkdocs.yml | 6 +++--- 3 files changed, 9 insertions(+), 18 deletions(-) diff --git a/docs/bedshift_all.md b/docs/bedshift_all.md index e409334b..96188b5c 100644 --- a/docs/bedshift_all.md +++ b/docs/bedshift_all.md @@ -28,16 +28,13 @@ done ## Using Python -In Python, you need the `os` library to get the filenames in a directory. After that, running bedshift is as easy as passing parameters. +In Python, you need the `os` library to get the filenames in a directory. Then you loop through the filenames and apply bedshift. ```py import bedshift import os - -datafolder = '.' - -files = os.listdir() +files = os.listdir('/path/to/data/') for file in files: if file.endswith('.bed'): # you may also pass in a chrom.sizes file as the diff --git a/docs/generate_random.md b/docs/generate_random.md index 8ade38fe..c2e078bf 100644 --- a/docs/generate_random.md +++ b/docs/generate_random.md @@ -1,24 +1,18 @@ # Generate a random BED file -To generate a random BED file, you need to start with is a BED file with one region. On the command line: +To generate a random BED file, you need to start with is a BED file with one region, which you will delete later. (It will appear at the top of the BED files, so it will be easier to delete later.) On the command line: ``` -touch random.bed +echo "chr1\t1\t1000" > random.bed ``` -Add in any region, which you will delete later. It is easier to add a region on chromosome 1 so it will be at the top of the BED file after perturbations. - -``` -chr1 1 100 -``` - -The next step is to construct a bedshift command to add regions to this file. We will need to specify the rate of add, which is going to be the number of the new regions we want in this case. Let's try generating 1,000 new regions. Don't forget to have a chromosome sizes file, which is required for adding regions. +The next step is to construct a bedshift command to add regions to this file. We will need to specify the rate of add, which in this case is going to be the number of the new regions we want. Let's try generating 1,000 new regions. Don't forget to specify a chromosome sizes file, which is required for adding regions. ``` bedshift -b random.bed -l hg38.chrom.sizes -a 1000 ``` -The printed output should say that 1000 regions changed. Finally, go into the output at bedshifted_random.bed and delete the original region. If you specified repeats and have many output files that need to have the original region deleted, here is a handy command to delete the first line of every BED file. +The printed output should say that 1000 regions changed. Finally, go into the output at bedshifted_random.bed and delete the original region. If you specified repeats and have many output files that need to have the original region deleted, here is a handy command to delete the first line of every BED file. (Warning: make sure there are no other BED files in the folder before using this command.) ``` find . -name "*.bed" -exec sed -i '.bak' '1d' {} \; diff --git a/mkdocs.yml b/mkdocs.yml index cdb29444..c20d4554 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -9,9 +9,9 @@ nav: - Introduction: - Overview: README.md - How-to Guides: - - Evaluating Similarity Scores: evaluation_guide.md - - Generating a Random BED File: generate_random.md - - Bedshifting an Entire Folder: bedshift_all.md + - Generate a Random BED File: generate_random.md + - Bedshift All BED Files in Folder: bedshift_all.md + - Evaluate Similarity Scores: evaluation_guide.md - Reference: - Python API: autodoc_build/bedshift.md From 86af67afa155f931cd7992e00ef2bb42a9208a1d Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Mon, 10 Aug 2020 21:26:53 -0400 Subject: [PATCH 0063/1072] update docs to use chrom lengths argument --- docs/evaluation_guide.md | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/docs/evaluation_guide.md b/docs/evaluation_guide.md index 4eda5f9b..2ebf5b1b 100644 --- a/docs/evaluation_guide.md +++ b/docs/evaluation_guide.md @@ -5,11 +5,11 @@ Bedshift perturbations include add, drop, shift, cut, and merge. Using any of these perturbations, or combinations of them, you can generate a set of files that are slightly perturbed from the original file. Assuming that the original file is called `original.bed`, and you want 100 files of added regions and 100 files of dropped regions: ``` -bedshift -b original.bed -a 0.1 -r 100 +bedshift -b original.bed -l hg38.chrom.sizes -a 0.1 -r 100 bedshift -b original.bed -d 0.3 -r 100 ``` -The output file will be in `bedshifted_original.bed`. +Don't forget the add and shift operations require a chrom.sizes file. The output file will be in `bedshifted_original.bed`. ## Evaluating a similarity score @@ -17,8 +17,8 @@ The output file will be in `bedshifted_original.bed`. This is when the bedshifted file will be put to use. The 100 repetitions of add and drop will be compared against the original file using the similarity score of your choice. The output of the similarity score should reflect the degree of change specified to bedshift. In very general terms, the pseudocode should be like this: ``` -for each bedshift_file in bedshift_output: - score = similarity_score(bedshift_file, original_file, ...) +for each bedshift_file in folder: + score = SimilarityScore(bedshift_file, original_file, ...) add score to score_list avg_similarity_score = mean(score_list) ``` @@ -28,7 +28,7 @@ You can repeat this for each of the similarity scores and each of the perturbati ## Using a PEP to quickly submit multiple bedshift jobs -Using a [Portable Encapsulated Project](http://pep.databio.org/en/latest/) (PEP), creating multiple combinations of bedshift files becomes faster and more organized. The PEP consists of a sample table containing the perturbation parameters and a config file. Here is what the `sample_table.csv` may look like: +Using a [Portable Encapsulated Project](http://pep.databio.org/en/latest/) (PEP), creating multiple combinations of bedshift files becomes faster and more organized. The PEP consists of a sample table containing the perturbation parameters and a config file. Here is what the `sample_table.csv` may look like. Each row specifies the arguments for a bedshift command. | sample_name | add | drop | shift | cut | merge | |-------------|-----|------|------|------|-------| @@ -51,10 +51,9 @@ sample_modifiers: repeat: 100 ``` -Now the project is described neatly in two files. The `sample_modifiers` in the config file just adds extra columns to the sample table in post-processing and makes the project more configurable, instead of having to repeat the same parameter. The columns added are the file which bedshift is to be performed on, and the number of repetitions that bedshift should create. +Now the project is described neatly in two files. The `sample_modifiers` in the config file just adds extra columns to the sample table in post-processing and makes the project more configurable, instead of having to repeat the same parameter in the `sample_table.csv`. In this example, the `sample_modifiers` append two columns with the file which bedshift is to be performed on, and the number of repetitions that bedshift should create. - -The PEP describes the project, but the tool that submits the project jobs is called [looper](http://looper.databio.org/en/latest/). In one line of code, it will interpret the PEP and form commands to be submitted to your processor or a computing cluster. To use looper, you will need to add a few lines to your `project_config.yaml`: +The PEP describes the project, but the tool that submits the project jobs is called [looper](http://looper.databio.org/en/latest/). In one line of code, it will interpret the PEP and form commands to be submitted to your processor or computing cluster. To use looper, you will need to add a few lines to your `project_config.yaml`: ``` pep_version: 2.0.0 @@ -64,7 +63,7 @@ looper: sample_modifiers: append: pipeline_interfaces: "pipeline_interface.yaml" - file: "/project/shefflab/resources/regions/LOLACore/hg19/encode_tfbs/regions/wgEncodeAwgTfbsUwHek293CtcfUniPk.narrowPeak" + file: "original.bed" repeat: 100 ``` @@ -74,11 +73,11 @@ You will also need to create a `pipeline_interface.yaml` that describes how to f pipeline_name: bedshift_run pipeline_type: sample command_template: > - bedshift -b {sample.file} -a {sample.add} -d {sample.drop} -s {sample.shift} -c {sample.cut} -m {sample.merge} -r {sample.repeat} -o {sample.sample_name}.bed + bedshift -b {sample.file} -l hg38.chrom.sizes -a {sample.add} -d {sample.drop} -s {sample.shift} -c {sample.cut} -m {sample.merge} -r {sample.repeat} -o {sample.sample_name}.bed compute: mem: 4000 cores: 1 - time: "01:00:00" + time: "00:10:00" ``` After all of this, the command to run looper and submit the jobs is: From 6c2fe990e5eb49950368fefe57433e0965da4278 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Fri, 7 Aug 2020 17:24:05 -0400 Subject: [PATCH 0064/1072] allow rate to be greater than 1 --- bedshift/bedshift.py | 32 +++++++++++++++++++------------- docs/changelog.md | 6 +++++- 2 files changed, 24 insertions(+), 14 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 8febd401..3bff4cb3 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -150,11 +150,10 @@ def reset_bed(self): self.bed = self.original_bed.copy(deep=True) - def __check_rate(self, rates): - for rate in rates: - if rate < 0 or rate > 1: - _LOGGER.error("Rate must be between 0 and 1") - sys.exit(1) + def __check_rate(self, rate): + if rate < 0 or rate > 1: + _LOGGER.error("Rate must be between 0 and 1") + sys.exit(1) def pick_random_chrom(self): @@ -177,7 +176,9 @@ def add(self, addrate, addmean, addstdev): :param float addstdev: the standard deviation of the length of added regions :return int: the number of regions added """ - self.__check_rate([addrate]) + if addrate < 0: + _LOGGER.error("Rate must be greater than or equal to 0") + sys.exit(1) if addrate == 0: return 0 if len(chrom_lens) == 0: @@ -206,8 +207,9 @@ def add_from_file(self, fp, addrate, delimiter='\t'): :param str fp: the filepath to the other bedfile :return int: the number of regions added """ - - self.__check_rate([addrate]) + if addrate < 0: + _LOGGER.error("Rate must be greater than or equal to 0") + sys.exit(1) if addrate == 0: return 0 if len(chrom_lens) == 0: @@ -217,6 +219,8 @@ def add_from_file(self, fp, addrate, delimiter='\t'): rows = self.bed.shape[0] num_add = int(rows * addrate) df = self.read_bed(fp, delimiter=delimiter) + if num_add > df.shape[0]: + num_add = df.shape[0] add_rows = random.sample(list(range(df.shape[0])), num_add) add_df = df.loc[add_rows].reset_index(drop=True) add_df[3] = pd.Series([3] * add_df.shape[0]) @@ -233,7 +237,7 @@ def shift(self, shiftrate, shiftmean, shiftstdev): :param float shiftstdev: the standard deviation of the shift distance :return int: the number of regions shifted """ - self.__check_rate([shiftrate]) + self.__check_rate(shiftrate) if shiftrate == 0: return 0 if len(chrom_lens) == 0: @@ -270,7 +274,7 @@ def cut(self, cutrate): :param float cutrate: the rate to cut regions into two separate regions :return int: the number of regions cut """ - self.__check_rate([cutrate]) + self.__check_rate(cutrate) if cutrate == 0: return 0 rows = self.bed.shape[0] @@ -315,7 +319,7 @@ def merge(self, mergerate): :return int: number of regions merged """ - self.__check_rate([mergerate]) + self.__check_rate(mergerate) if mergerate == 0: return 0 rows = self.bed.shape[0] @@ -350,7 +354,7 @@ def drop(self, droprate): :param float droprate: the rate to drop/remove regions :return int: the number of rows dropped """ - self.__check_rate([droprate]) + self.__check_rate(droprate) if droprate == 0: return 0 rows = self.bed.shape[0] @@ -382,7 +386,6 @@ def all_perturbations(self, :return int: the number of total regions perturbed ''' - self.__check_rate([addrate, shiftrate, cutrate, mergerate, droprate]) n = 0 n += self.shift(shiftrate, shiftmean, shiftstdev) if addfile: @@ -418,6 +421,9 @@ def read_bed(self, bedfile_path, delimiter='\t'): except FileNotFoundError: _LOGGER.error("BED file path {} invalid".format(bedfile_path)) sys.exit(1) + except: + _LOGGER.error("file {} could not be read".format(bedfile_path)) + sys.exit(1) # if there is 'chrom', 'start', 'stop' in the table, move them to header if not str(df.iloc[0, 1]).isdigit(): diff --git a/docs/changelog.md b/docs/changelog.md index 5a9664f9..9637ca6f 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -6,4 +6,8 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm - Added ability to specify chrom sizes file, or refgenie genome. -## [1.0.0] - ? +## [1.0.0] - released May 20, 2020 + +- Add, shift, drop, cut, and merge perturbations. +- Basic documentation +- Basic tests From ee86948f4985302b9bdde5b75c48a10695a9cf55 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 20 Feb 2021 17:29:50 -0500 Subject: [PATCH 0065/1072] add unittests --- README.md | 4 +-- bedshift/bedshift.py | 2 +- run-tests.sh | 5 --- tests/__init__.py | 0 tests/pypackage_test.py | 19 ----------- tests/test.py | 72 +++++++++++++++++++++++++++++++++++++++++ 6 files changed, 75 insertions(+), 27 deletions(-) delete mode 100755 run-tests.sh create mode 100644 tests/__init__.py delete mode 100755 tests/pypackage_test.py create mode 100755 tests/test.py diff --git a/README.md b/README.md index 90db7446..b588591f 100644 --- a/README.md +++ b/README.md @@ -32,10 +32,10 @@ bedshifter.to_bed('test_output.bed') ## Development -test changes: +Run tests (from this directory): ``` -./run-tests.sh +python -m unittest ``` Double check the output files to see if the regions make sense. diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 443f620a..3ad45e49 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -475,7 +475,7 @@ def main(): shift rate: {shiftrate} shift mean distance: {shiftmean} shift stdev: {shiftstdev} - add: + add: rate: {addrate} add mean length: {addmean} add stdev: {addstdev} diff --git a/run-tests.sh b/run-tests.sh deleted file mode 100755 index 667e5cc2..00000000 --- a/run-tests.sh +++ /dev/null @@ -1,5 +0,0 @@ -#!/bin/bash - -pip install . -python3 ./tests/pypackage_test.py -./tests/shell_test.sh diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/pypackage_test.py b/tests/pypackage_test.py deleted file mode 100755 index 374a144a..00000000 --- a/tests/pypackage_test.py +++ /dev/null @@ -1,19 +0,0 @@ -import os, sys -import bedshift - -os.chdir(sys.path[0]) - -bedshifter = bedshift.Bedshift('test.bed', chrom_sizes="hg38.chrom.sizes") - -bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, - shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, - cutrate=0.1, - mergerate=0.11, - droprate=0.03) -bedshifter.to_bed('py_output.bed') -bedshifter.reset_bed() - -bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, - addfile='py_output.bed', - shiftrate=0.3, shiftmean=100.0, shiftstdev=30.0) -bedshifter.to_bed('py_output2.bed') diff --git a/tests/test.py b/tests/test.py new file mode 100755 index 00000000..f708b602 --- /dev/null +++ b/tests/test.py @@ -0,0 +1,72 @@ +import unittest +from bedshift import bedshift + + +class TestBedshift(unittest.TestCase): + def setUp(self): + self.bs = bedshift.Bedshift('tests/test.bed', chrom_sizes="tests/hg38.chrom.sizes") + + def test_add(self): + added = self.bs.add(0.1, 100, 20) + self.assertEqual(added, 1000) + self.bs.reset_bed() + + def test_add_high_rate(self): + added = self.bs.add(1.23, 500, 123) + self.assertEqual(added, 12300) + self.bs.reset_bed() + + def test_add_from_file(self): + added = self.bs.add_from_file("tests/test.bed", 0.25) + self.assertEqual(added, 2500) + self.bs.reset_bed() + + def test_drop(self): + dropped = self.bs.drop(0.315) + self.assertEqual(dropped, 3150) + self.bs.reset_bed() + + def test_shift(self): + shifted = self.bs.shift(0.129, 200, 30) + self.assertEqual(shifted, 1290) + self.bs.reset_bed() + + def test_cut(self): + cut = self.bs.cut(0.909) + self.assertEqual(cut, 9090) + self.bs.reset_bed() + + def test_merge(self): + merged = self.bs.merge(0.2) + self.assertEqual(merged, 2000) + self.bs.reset_bed() + + def test_combo(self): + _ = self.bs.drop(0.4) + _ = self.bs.add(0.2, 200, 10) + self.assertEqual(len(self.bs.bed), 7200) + self.bs.reset_bed() + + def test_all_perturbations1(self): + perturbed = self.bs.all_perturbations( + addrate=0.5, addmean=320.0, addstdev=20.0, + shiftrate=0.23, shiftmean=-10.0, shiftstdev=120.0, + cutrate=0.12, + droprate=0.42) + self.assertEqual(perturbed, 16156) + self.assertEqual(len(self.bs.bed), 9744) + self.bs.reset_bed() + + def test_all_perturbations2(self): + perturbed = self.bs.all_perturbations( + addrate=0.3, addmean=320.0, addstdev=20.0, + shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, + cutrate=0.1, + mergerate=0.11, + droprate=0.03) + # merge sometimes merges more or less than expected because it depends + # if the randomly chosen regions are adjacent + self.assertAlmostEqual(perturbed, 9250, places=-2) + + def test_to_bed(self): + self.bs.to_bed('py_output.bed') From bb9cfcfaf375093e76f97986a03f1e7c6ccc8387 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 27 Feb 2021 20:45:25 -0500 Subject: [PATCH 0066/1072] fix #11 improve shift performance --- bedshift/bedshift.py | 42 +++++++++++++++++++++++------------------- docs/changelog.md | 5 ++++- 2 files changed, 27 insertions(+), 20 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 3ad45e49..c0128ae4 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -150,7 +150,7 @@ def reset_bed(self): self.bed = self.original_bed.copy(deep=True) - def __check_rate(self, rate): + def _check_rate(self, rate): if rate < 0 or rate > 1: _LOGGER.error("Rate must be between 0 and 1") sys.exit(1) @@ -238,7 +238,7 @@ def shift(self, shiftrate, shiftmean, shiftstdev): :param float shiftstdev: the standard deviation of the shift distance :return int: the number of regions shifted """ - self.__check_rate(shiftrate) + self._check_rate(shiftrate) if shiftrate == 0: return 0 if len(chrom_lens) == 0: @@ -247,26 +247,30 @@ def shift(self, shiftrate, shiftmean, shiftstdev): rows = self.bed.shape[0] shift_rows = random.sample(list(range(rows)), int(rows * shiftrate)) + new_row_list = [] + to_drop = [] for row in shift_rows: - self.__shift(row, shiftmean, shiftstdev) # shifted rows display a 1 + drop_row, new_region = self._shift(row, shiftmean, shiftstdev) # shifted rows display a 1 + if drop_row and new_region: + new_row_list.append(new_region) + to_drop.append(drop_row) + self.bed = self.bed.drop(to_drop) + self.bed = self.bed.append(new_row_list, ignore_index=True) + self.bed = self.bed.reset_index(drop=True) return len(shift_rows) - def __shift(self, row, mean, stdev): + def _shift(self, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] end = self.bed.loc[row][2] - if start + theshift < 0 or \ - end + theshift > chrom_lens[chrom]: - # shifting out of bounds check - return - - self.bed.at[row, 1] = start + theshift - self.bed.at[row, 2] = end + theshift - self.bed.at[row, 3] = 1 + if start + theshift < 0 or end + theshift > chrom_lens[chrom]: + # check if the region is shifted out of chromosome length bounds + return None, None + return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: 1} def cut(self, cutrate): """ @@ -275,7 +279,7 @@ def cut(self, cutrate): :param float cutrate: the rate to cut regions into two separate regions :return int: the number of regions cut """ - self.__check_rate(cutrate) + self._check_rate(cutrate) if cutrate == 0: return 0 rows = self.bed.shape[0] @@ -283,7 +287,7 @@ def cut(self, cutrate): new_row_list = [] to_drop = [] for row in cut_rows: - drop_row, new_regions = self.__cut(row) # cut rows display a 2 + drop_row, new_regions = self._cut(row) # cut rows display a 2 new_row_list.extend(new_regions) to_drop.append(drop_row) self.bed = self.bed.drop(to_drop) @@ -291,7 +295,7 @@ def cut(self, cutrate): self.bed = self.bed.reset_index(drop=True) return len(cut_rows) - def __cut(self, row): + def _cut(self, row): chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] end = self.bed.loc[row][2] @@ -320,7 +324,7 @@ def merge(self, mergerate): :return int: number of regions merged """ - self.__check_rate(mergerate) + self._check_rate(mergerate) if mergerate == 0: return 0 rows = self.bed.shape[0] @@ -328,7 +332,7 @@ def merge(self, mergerate): to_add = [] to_drop = [] for row in merge_rows: - drop_row, add_row = self.__merge(row) + drop_row, add_row = self._merge(row) if add_row: to_add.append(add_row) to_drop.extend(drop_row) @@ -337,7 +341,7 @@ def merge(self, mergerate): self.bed = self.bed.reset_index(drop=True) return len(merge_rows) - def __merge(self, row): + def _merge(self, row): # check if the regions being merged are on the same chromosome if row + 1 not in self.bed.index or self.bed.loc[row][0] != self.bed.loc[row+1][0]: return [], None @@ -355,7 +359,7 @@ def drop(self, droprate): :param float droprate: the rate to drop/remove regions :return int: the number of rows dropped """ - self.__check_rate(droprate) + self._check_rate(droprate) if droprate == 0: return 0 rows = self.bed.shape[0] diff --git a/docs/changelog.md b/docs/changelog.md index 9637ca6f..697fecae 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -1,10 +1,13 @@ # Changelog -This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html) and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) format. +This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html) and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) format. ## [1.1.0] - unreleased - Added ability to specify chrom sizes file, or refgenie genome. +- Add --yaml_config option +- Improve performance of perturbations +- Add --drop-from-file and --add-from-file options ## [1.0.0] - released May 20, 2020 From 7b0b787f0c8c4f1e1abd7ee0d7359add20f75bd6 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 28 Feb 2021 12:01:31 -0500 Subject: [PATCH 0067/1072] fix #10; add selects chromosome proportional to chromosome lengths --- bedshift/bedshift.py | 59 +++++++++++++++++++++++++------------------- 1 file changed, 33 insertions(+), 26 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index c0128ae4..cb7873a4 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -15,21 +15,6 @@ __all__ = ["Bedshift"] -chrom_lens = {} - -def read_chromsizes(fp): - try: - with open(fp) as f: - for line in f: - line = line.strip().split('\t') - chrom = line[0] - size = int(line[1]) - chrom_lens[chrom] = size - except FileNotFoundError: - _LOGGER.error("fasta file path {} invalid".format(fp)) - sys.exit(1) - - class _VersionInHelpParser(argparse.ArgumentParser): def format_help(self): """ Add version information to help text. """ @@ -133,8 +118,9 @@ def __init__(self, bedfile_path, chrom_sizes=None, delimiter='\t'): :param str delimiter: the delimiter used in the BED file """ + self.chrom_lens = {} if chrom_sizes: - read_chromsizes(chrom_sizes) + self._read_chromsizes(chrom_sizes) df = self.read_bed(bedfile_path, delimiter=delimiter) self.original_regions = df.shape[0] self.bed = df.astype({1: 'int64', 2: 'int64', 3: 'int64'}) \ @@ -142,6 +128,22 @@ def __init__(self, bedfile_path, chrom_sizes=None, delimiter='\t'): self.original_bed = self.bed.copy(deep=True) + def _read_chromsizes(self, fp): + try: + with open(fp) as f: + for line in f: + line = line.strip().split('\t') + chrom = line[0] + size = int(line[1]) + self.chrom_lens[chrom] = size + except FileNotFoundError: + _LOGGER.error("fasta file path {} invalid".format(fp)) + sys.exit(1) + + total_len = sum(self.chrom_lens.values()) + self.chrom_weights = [chrom_len / total_len for chrom_len in self.chrom_lens.values()] + + def reset_bed(self): """ Reset the stored bedfile to the state before perturbations @@ -156,15 +158,15 @@ def _check_rate(self, rate): sys.exit(1) - def pick_random_chrom(self): + def pick_random_chroms(self, n): """ Utility function to pick a random chromosome :return str, float chrom_str, chrom_len: chromosome number and length """ - chrom_str = random.choice(list(chrom_lens.keys())) - chrom_len = chrom_lens[chrom_str] - return chrom_str, chrom_len + chrom_strs = random.choices(list(self.chrom_lens.keys()), weights=self.chrom_weights, k=n) + chrom_lens = [self.chrom_lens[chrom_str] for chrom_str in chrom_strs] + return zip(chrom_strs, chrom_lens) def add(self, addrate, addmean, addstdev): @@ -181,15 +183,15 @@ def add(self, addrate, addmean, addstdev): sys.exit(1) if addrate == 0: return 0 - if len(chrom_lens) == 0: + if len(self.chrom_lens) == 0: _LOGGER.error("chrom.sizes file must be specified when adding regions") sys.exit(1) rows = self.bed.shape[0] num_add = int(rows * addrate) new_regions = {0: [], 1: [], 2: [], 3: []} - for _ in range(num_add): - chrom_str, chrom_len = self.pick_random_chrom() + random_chroms = self.pick_random_chroms(num_add) + for chrom_str, chrom_len in random_chroms: start = random.randint(1, chrom_len) # ensure chromosome length is not exceeded end = min(start + int(np.random.normal(addmean, addstdev)), chrom_len) @@ -200,6 +202,7 @@ def add(self, addrate, addmean, addstdev): self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) return num_add + def add_from_file(self, fp, addrate, delimiter='\t'): """ Add regions from another bedfile to this perturbed bedfile @@ -213,7 +216,7 @@ def add_from_file(self, fp, addrate, delimiter='\t'): sys.exit(1) if addrate == 0: return 0 - if len(chrom_lens) == 0: + if len(self.chrom_lens) == 0: _LOGGER.error("chrom.sizes file must be specified when adding regions") sys.exit(1) @@ -241,7 +244,7 @@ def shift(self, shiftrate, shiftmean, shiftstdev): self._check_rate(shiftrate) if shiftrate == 0: return 0 - if len(chrom_lens) == 0: + if len(self.chrom_lens) == 0: _LOGGER.error("chrom.sizes file must be specified when shifting regions") sys.exit(1) @@ -259,6 +262,7 @@ def shift(self, shiftrate, shiftmean, shiftstdev): self.bed = self.bed.reset_index(drop=True) return len(shift_rows) + def _shift(self, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) @@ -266,12 +270,13 @@ def _shift(self, row, mean, stdev): start = self.bed.loc[row][1] end = self.bed.loc[row][2] - if start + theshift < 0 or end + theshift > chrom_lens[chrom]: + if start + theshift < 0 or end + theshift > self.chrom_lens[chrom]: # check if the region is shifted out of chromosome length bounds return None, None return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: 1} + def cut(self, cutrate): """ Cut regions to create two new regions @@ -295,6 +300,7 @@ def cut(self, cutrate): self.bed = self.bed.reset_index(drop=True) return len(cut_rows) + def _cut(self, row): chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] @@ -341,6 +347,7 @@ def merge(self, mergerate): self.bed = self.bed.reset_index(drop=True) return len(merge_rows) + def _merge(self, row): # check if the regions being merged are on the same chromosome if row + 1 not in self.bed.index or self.bed.loc[row][0] != self.bed.loc[row+1][0]: From 471ae914e5544ade59d50c147c708ac4733b2c45 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Fri, 5 Mar 2021 02:24:13 +0900 Subject: [PATCH 0068/1072] drop_from_file and yaml_config --- bedshift/bedshift.py | 261 +++++++++++++++++++++++++++++++++++++++---- 1 file changed, 237 insertions(+), 24 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index c0128ae4..f5b5683e 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -8,6 +8,8 @@ import pandas as pd import numpy as np import random +import yaml +import pybedtools from bedshift._version import __version__ _LOGGER = logging.getLogger(__name__) @@ -108,6 +110,9 @@ def build_argparser(): "-m", "--mergerate", type=float, default=0.0, help="Merge probability. WARNING: will likely create regions that are thousands of base pairs long") + parser.add_argument( + "--dropfile", type=str, help="Drop regions from a bedfile") + parser.add_argument( "-o", "--outputfile", type=str, help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed") @@ -115,6 +120,10 @@ def build_argparser(): parser.add_argument( "-r", "--repeat", type=int, default=1, help="the number of times to repeat the operation") + + parser.add_argument( + "-y", "--yaml_config", type=str, + help="run yaml configuration file") return parser @@ -132,7 +141,7 @@ def __init__(self, bedfile_path, chrom_sizes=None, delimiter='\t'): :param str chrom_sizes: the path to the chrom.sizes file :param str delimiter: the delimiter used in the BED file """ - + self.bedfile_path = bedfile_path if chrom_sizes: read_chromsizes(chrom_sizes) df = self.read_bed(bedfile_path, delimiter=delimiter) @@ -315,7 +324,6 @@ def _cut(self, row): return row, [{0: chrom, 1: start, 2: thecut, 3: 2}, {0: chrom, 1: thecut, 2: end, 3: 2}] - def merge(self, mergerate): """ Merge two regions into one new region @@ -368,6 +376,48 @@ def drop(self, droprate): self.bed = self.bed.reset_index(drop=True) return len(drop_rows) + def _find_intersection(self, fp, threshold=1): + """ + find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file + + :param str fp: the filepath to the other bedfile containing regions to be compared to the reference bedfile + :param int threshold: the number of base pairs overlap needed for a match + :return dataframe intersect_ref: the dataframe consisting of matching regions, where regions are + """ + reference_bed = pybedtools.example_bedtool(os.path.abspath(self.bedfile_path)) + comparison_bed = pybedtools.example_bedtool(os.path.abspath(fp)) + + intersect_ref = reference_bed.intersect(comparison_bed, u=True).to_dataframe() + return intersect_ref[['chrom', 'start', 'end']] + + def drop_from_file(self, fp, droprate, delimiter='\t'): + """ + drop regions from another bedfile to this perturbed bedfile + + :param float droprate: the rate to drop regions + :param str fp: the filepath to the other bedfile containing regions to be dropped + :return int: the number of regions dropped + """ + if droprate < 0: + _LOGGER.error("Rate must be greater than or equal to 0") + sys.exit(1) + if droprate == 0: + return 0 + + rows = self.bed.shape[0] + num_drop = int(rows * droprate) + drop_bed = self.read_bed(fp, delimiter=delimiter) + drop_rows = drop_bed.shape[0] + + if num_drop >= drop_rows: + print("Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format(num_drop, drop_rows, drop_rows)) + num_drop = drop_rows + + intersect_regions = self._find_intersection(fp) + + rows2drop = random.sample(list(range(drop_rows)), num_drop) + self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index(drop=True) + return num_drop def all_perturbations(self, addrate=0.0, addmean=320.0, addstdev=30.0, @@ -375,19 +425,26 @@ def all_perturbations(self, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, - droprate=0.0): + droprate=0.0, + dropfile=None, + yaml=None, + bedshifter=None): ''' Perform all five perturbations in the order of shift, add, cut, merge, drop. :param float addrate: the rate (as a proportion of the total number of regions) to add regions :param float addmean: the mean length of added regions :param float addstdev: the standard deviation of the length of added regions + :param float addfile: the file containing regions to be added :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) :param float shiftmean: the mean shift distance :param float shiftstdev: the standard deviation of the shift distance :param float cutrate: the rate to cut regions into two separate regions :param float mergerate: the rate to merge two regions into one :param float droprate: the rate to drop/remove regions + :param float dropfile: the file containing regions to be dropped + :param string yaml: the yaml_config filepath + :param string bedshifter: Bedshift instance :return int: the number of total regions perturbed ''' @@ -399,7 +456,11 @@ def all_perturbations(self, n += self.add(addrate, addmean, addstdev) n += self.cut(cutrate) n += self.merge(mergerate) - n += self.drop(droprate) + if dropfile: + n += self.drop_from_file(dropfile, droprate) + n +=self.drop(droprate) + if yaml: + n += handle_yaml(bedshifter, yaml) return n @@ -414,7 +475,6 @@ def to_bed(self, outfile_name): print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.' \ .format(outfile_name, self.bed.shape[0], self.original_regions)) - def read_bed(self, bedfile_path, delimiter='\t'): """ Read a BED file into pandas dataframe @@ -422,7 +482,7 @@ def read_bed(self, bedfile_path, delimiter='\t'): :param str bedfile_path: The path to the BED file """ try: - df = pd.read_csv(bedfile_path, sep=delimiter, header=None, usecols=[0,1,2]) + df = pd.read_csv(bedfile_path, sep=delimiter, header=None, usecols=[0,1,2], engine='python') except FileNotFoundError: _LOGGER.error("BED file path {} invalid".format(bedfile_path)) sys.exit(1) @@ -438,7 +498,152 @@ def read_bed(self, bedfile_path, delimiter='\t'): df[3] = 0 # column indicating which modifications were made return df +def _print_sample_config(): + """ + bedshift_operations: + - add: + rate: 0.1 + mean: 100 + stdev: 20 + - add_from_file: + file: tests/test.bed + rate: 0.1 + delimiter: \t + - drop_from_file: + file: tests/test.bed + rate: 0.1 + - add_from_file: + file: tests/test.bed + rate: 0.2 + - cut: + rate: 0.2 + - shift: + rate: 0.3 + mean: 100 + stdev: 200 + - merge: + rate: 0.15 + - drop: + rate: 0.30 + """ + print(_print_sample_config.__doc__) + print("No changes made.") + +def _read_from_yaml(fp): + """ + Loads yaml config data + + :param float fp: the path to the configuration file for multiple add_from_file and drop_from_file + :return int: loaded yaml data + """ + with open(fp, "r") as yaml_file: + config_data = yaml.load(yaml_file, Loader=yaml.FullLoader) + print("Loaded configuration settings from {}".format(fp)) + return config_data + +def handle_yaml(bedshifter, yaml_fp): + data = _read_from_yaml(yaml_fp) + operations = [operation for operation in data["bedshift_operations"]] + num_changed = 0 + + for operation in operations: + ##### add ##### + if set(['add', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): + rate = operation['rate'] + mean = operation['mean'] + std = operation['stdev'] + num_added = bedshifter.add(rate, mean, std) + num_changed += num_added + print("\t{} regions added.".format(num_added)) + + ##### add_from_file with no delimiter provided ##### + elif set(['add_from_file', 'file', 'rate']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + add_rate = operation['rate'] + num_added = bedshifter.add_from_file(fp, add_rate) + num_changed += num_added + print("\t{} regions added from {}.".format(num_added, fp)) + else: + print ("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### add_from_file with delimiter provided ##### + elif set(['add_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + add_rate = operation['rate'] + delimiter = operation['delimiter'] + num_added = bedshifter.add_from_file(fp, add_rate, delimiter) + num_changed += num_added + print("\t{} regions added from {}.".format(num_added, fp)) + else: + print ("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### drop ##### + elif set(['drop', 'rate']) == set(list(operation.keys())): + rate = operation['rate'] + num_dropped = bedshifter.drop(rate) + num_changed += num_dropped + print("\t{} regions dropped.".format(num_dropped)) + + ##### drop_from_file with no delimiter provided ##### + elif set(['drop_from_file', 'file', 'rate']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + drop_rate = operation['rate'] + num_dropped = bedshifter.drop_from_file(fp, drop_rate) + num_changed += num_dropped + print("\t{} regions dropped from {}.".format(num_dropped, fp)) + else: + print ("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### drop_from_file with delimiter provided ##### + elif set(['drop_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + drop_rate = operation['rate'] + delimiter = operation['delimiter'] + num_dropped = bedshifter.drop_from_file(fp, drop_rate, delimiter) + num_changed += num_dropped + print("\t{} regions dropped from {}.".format(num_dropped, fp)) + else: + print ("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### shift ##### + elif set(['shift', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): + rate = operation['rate'] + mean = operation['mean'] + std = operation['stdev'] + num_shifted = bedshifter.shift(rate, mean, std) + num_changed += num_shifted + print("\t{} regions shifted.".format(num_shifted)) + + ##### cut ##### + elif set(['cut', 'rate']) == set(list(operation.keys())): + rate = operation['rate'] + num_cut = bedshifter.cut(rate) + num_changed += num_cut + print("\t{} regions cut.".format(num_cut)) + + ##### merge ##### + elif set(['merge', 'rate']) == set(list(operation.keys())): + rate = operation['rate'] + num_merged = bedshifter.merge(rate) + num_changed += num_merged + print("\t{} regions merged.".format(num_merged)) + + else: + print("Invalid settings entered in the config file. Please refer to the example below.") + _print_sample_config() + sys.exit(1) + + return num_changed + def main(): """ Primary workflow """ @@ -471,8 +676,6 @@ def main(): _LOGGER.error("You must provide either chrom sizes or a refgenie genome.") sys.exit(1) - - msg = """Params: chrom.sizes file: {chromsizes} shift: @@ -486,9 +689,11 @@ def main(): add file: {addfile} cut rate: {cutrate} drop rate: {droprate} + drop regions from file: {dropfile} merge rate: {mergerate} outputfile: {outputfile} repeat: {repeat} + yaml_config: {yaml_config} """ if args.outputfile: @@ -497,8 +702,10 @@ def main(): outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) _LOGGER.info(msg.format( + bedfile=args.bedfile, chromsizes=args.chrom_lengths, droprate=args.droprate, + dropfile=args.dropfile, addrate=args.addrate, addmean=args.addmean, addstdev=args.addstdev, @@ -509,34 +716,42 @@ def main(): cutrate=args.cutrate, mergerate=args.mergerate, outputfile=args.outputfile, - repeat=args.repeat)) + repeat=args.repeat, + yaml_config=args.yaml_config)) bedshifter = Bedshift(args.bedfile, args.chrom_lengths) if args.repeat == 1: n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, - args.shiftrate, args.shiftmean, args.shiftstdev, - args.cutrate, - args.mergerate, - args.droprate) + args.addfile, + args.shiftrate, args.shiftmean, args.shiftstdev, + args.cutrate, + args.mergerate, + args.droprate, + args.dropfile, + args.yaml_config, + bedshifter) + print("\t" + str(n) + " regions changed in total.\n") bedshifter.to_bed(outfile) - print(str(n) + " regions changed") + elif args.repeat > 1: for i in range(args.repeat): - n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, - args.shiftrate, args.shiftmean, args.shiftstdev, - args.cutrate, - args.mergerate, - args.droprate) + args.addfile, + args.shiftrate, args.shiftmean, args.shiftstdev, + args.cutrate, + args.mergerate, + args.droprate, + args.dropfile, + args.yaml_config, + bedshifter) + print("\t" + str(n) + " regions changed in total.\n") modified_outfile = outfile.rsplit("/") modified_outfile[-1] = "rep" + str(i+1) + "_" + modified_outfile[-1] modified_outfile = "/".join(modified_outfile) bedshifter.to_bed(modified_outfile) - print(str(n) + " regions changed") bedshifter.reset_bed() + else: _LOGGER.error("repeats specified is less than 1") sys.exit(1) @@ -548,5 +763,3 @@ def main(): except KeyboardInterrupt: _LOGGER.error("Program canceled by user!") sys.exit(1) - - From 6654f9e2cad37674ff13212a3f22d59da4dfab3b Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Fri, 5 Mar 2021 02:24:56 +0900 Subject: [PATCH 0069/1072] drop_from_file and yaml unittests --- tests/test.py | 171 ++++++++++++++++++++++++++++++-------------------- 1 file changed, 103 insertions(+), 68 deletions(-) diff --git a/tests/test.py b/tests/test.py index f708b602..55d9faa6 100755 --- a/tests/test.py +++ b/tests/test.py @@ -1,72 +1,107 @@ import unittest from bedshift import bedshift - +from bedshift.bedshift import handle_yaml class TestBedshift(unittest.TestCase): - def setUp(self): - self.bs = bedshift.Bedshift('tests/test.bed', chrom_sizes="tests/hg38.chrom.sizes") - - def test_add(self): - added = self.bs.add(0.1, 100, 20) - self.assertEqual(added, 1000) - self.bs.reset_bed() - - def test_add_high_rate(self): - added = self.bs.add(1.23, 500, 123) - self.assertEqual(added, 12300) - self.bs.reset_bed() - - def test_add_from_file(self): - added = self.bs.add_from_file("tests/test.bed", 0.25) - self.assertEqual(added, 2500) - self.bs.reset_bed() - - def test_drop(self): - dropped = self.bs.drop(0.315) - self.assertEqual(dropped, 3150) - self.bs.reset_bed() - - def test_shift(self): - shifted = self.bs.shift(0.129, 200, 30) - self.assertEqual(shifted, 1290) - self.bs.reset_bed() - - def test_cut(self): - cut = self.bs.cut(0.909) - self.assertEqual(cut, 9090) - self.bs.reset_bed() - - def test_merge(self): - merged = self.bs.merge(0.2) - self.assertEqual(merged, 2000) - self.bs.reset_bed() - - def test_combo(self): - _ = self.bs.drop(0.4) - _ = self.bs.add(0.2, 200, 10) - self.assertEqual(len(self.bs.bed), 7200) - self.bs.reset_bed() - - def test_all_perturbations1(self): - perturbed = self.bs.all_perturbations( - addrate=0.5, addmean=320.0, addstdev=20.0, - shiftrate=0.23, shiftmean=-10.0, shiftstdev=120.0, - cutrate=0.12, - droprate=0.42) - self.assertEqual(perturbed, 16156) - self.assertEqual(len(self.bs.bed), 9744) - self.bs.reset_bed() - - def test_all_perturbations2(self): - perturbed = self.bs.all_perturbations( - addrate=0.3, addmean=320.0, addstdev=20.0, - shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, - cutrate=0.1, - mergerate=0.11, - droprate=0.03) - # merge sometimes merges more or less than expected because it depends - # if the randomly chosen regions are adjacent - self.assertAlmostEqual(perturbed, 9250, places=-2) - - def test_to_bed(self): - self.bs.to_bed('py_output.bed') + def setUp(self): + self.bs = bedshift.Bedshift('tests/test.bed', chrom_sizes="tests/hg38.chrom.sizes") + + def test_add(self): + added = self.bs.add(0.1, 100, 20) + self.assertEqual(added, 1000) + self.bs.reset_bed() + + def test_add_high_rate(self): + added = self.bs.add(1.23, 500, 123) + self.assertEqual(added, 12300) + self.bs.reset_bed() + + def test_add_from_file(self): + added = self.bs.add_from_file("tests/test.bed", 0.25) + self.assertEqual(added, 2500) + self.bs.reset_bed() + + def test_drop(self): + dropped = self.bs.drop(0.315) + self.assertEqual(dropped, 3150) + self.bs.reset_bed() + + def test_shift(self): + shifted = self.bs.shift(0.129, 200, 30) + self.assertEqual(shifted, 1290) + self.bs.reset_bed() + + def test_cut(self): + cut = self.bs.cut(0.909) + self.assertEqual(cut, 9090) + self.bs.reset_bed() + + def test_merge(self): + merged = self.bs.merge(0.2) + self.assertEqual(merged, 2000) + self.bs.reset_bed() + + def test_combo(self): + _ = self.bs.drop(0.4) + _ = self.bs.add(0.2, 200, 10) + self.assertEqual(len(self.bs.bed), 7200) + self.bs.reset_bed() + + def test_drop_from_file(self): + dropped = self.bs.drop_from_file("tests/test.bed", 0.25) + self.assertEqual(dropped, 2500) + self.bs.reset_bed() + + def test_drop_from_file_high_rate(self): + dropped = self.bs.drop_from_file("tests/test.bed", 1) + self.assertEqual(dropped, 10000) + self.bs.reset_bed() + + def test_drop_from_file_zero_rate(self): + dropped = self.bs.drop_from_file("tests/test.bed", 0) + self.assertEqual(dropped, 0) + self.bs.reset_bed() + + def test_handle_yaml(self): + yamled = handle_yaml(bedshifter=self.bs, yaml_fp="tests/bedshift_analysis.yaml") + self.bs.reset_bed() + + added = self.bs.add(addrate=0.1, addmean=100, addstdev=20) + f_added_10 = self.bs.add_from_file(fp="tests/test.bed", addrate=0.1) + f_dropped_15 = self.bs.drop_from_file(fp="tests/test.bed", droprate=0.1) + f_added_20 = self.bs.add_from_file(fp="tests/test.bed", addrate=0.2) + cut = self.bs.cut(cutrate=0.20) + shifted = self.bs.shift(shiftrate=0.30, shiftmean=100, shiftstdev=200) + merged = self.bs.merge(mergerate=0.15) + # dropped = self.bs.drop(droprate=0.30) + # print("dropped", dropped) + + total = added+f_added_10+f_dropped_15+f_added_20+cut+shifted+merged#+dropped + + self.assertEqual(yamled, total) + self.bs.reset_bed() + + def test_all_perturbations1(self): + perturbed = self.bs.all_perturbations( + addrate=0.5, addmean=320.0, addstdev=20.0, + shiftrate=0.23, shiftmean=-10.0, shiftstdev=120.0, + cutrate=0.12, + droprate=0.42) + self.assertEqual(perturbed, 16156) + self.assertEqual(len(self.bs.bed), 9744) + self.bs.reset_bed() + + def test_all_perturbations2(self): + perturbed = self.bs.all_perturbations( + addrate=0.3, addmean=320.0, addstdev=20.0, + shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, + cutrate=0.1, + mergerate=0.11, + droprate=0.03) + # merge sometimes merges more or less than expected because it depends + # if the randomly chosen regions are adjacent + self.assertAlmostEqual(perturbed, 9250, places=-2) + + def test_to_bed(self): + self.bs.to_bed('py_output.bed') + From 24f0e0cbe2b5acf9b228e9a02e536a13a5ba1413 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Fri, 5 Mar 2021 20:05:39 +0900 Subject: [PATCH 0070/1072] drop --- bedshift/bedshift.py | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index f5b5683e..a71935d3 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -359,7 +359,6 @@ def _merge(self, row): end = self.bed.loc[row+1][2] return [row, row+1], {0: chrom, 1: start, 2: end, 3: 4} - def drop(self, droprate): """ Drop regions @@ -376,18 +375,22 @@ def drop(self, droprate): self.bed = self.bed.reset_index(drop=True) return len(drop_rows) - def _find_intersection(self, fp, threshold=1): + def _find_intersection(self, fp): """ find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file :param str fp: the filepath to the other bedfile containing regions to be compared to the reference bedfile - :param int threshold: the number of base pairs overlap needed for a match :return dataframe intersect_ref: the dataframe consisting of matching regions, where regions are """ reference_bed = pybedtools.example_bedtool(os.path.abspath(self.bedfile_path)) comparison_bed = pybedtools.example_bedtool(os.path.abspath(fp)) - intersect_ref = reference_bed.intersect(comparison_bed, u=True).to_dataframe() + try: + intersect_ref = reference_bed.intersect(comparison_bed, u=True).to_dataframe() + except ValueError: + print("No interection found between two files.") + sys.exit(1) + return intersect_ref[['chrom', 'start', 'end']] def drop_from_file(self, fp, droprate, delimiter='\t'): @@ -414,8 +417,8 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): num_drop = drop_rows intersect_regions = self._find_intersection(fp) - - rows2drop = random.sample(list(range(drop_rows)), num_drop) + rows2drop = random.sample(list(range(len(intersect_regions))), num_drop) + self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index(drop=True) return num_drop @@ -458,7 +461,8 @@ def all_perturbations(self, n += self.merge(mergerate) if dropfile: n += self.drop_from_file(dropfile, droprate) - n +=self.drop(droprate) + else: + n +=self.drop(droprate) if yaml: n += handle_yaml(bedshifter, yaml) return n From 42da2c645bd8291c11c7458f43be36a526c470ae Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Fri, 5 Mar 2021 20:06:20 +0900 Subject: [PATCH 0071/1072] drop_from_file bug fixes From 3579c9bbf395e4a7ac4430f4fe6c3008582250b8 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Thu, 11 Mar 2021 23:34:22 +0900 Subject: [PATCH 0072/1072] pyranges instead of pybedtools --- bedshift/bedshift.py | 308 ++++++++++++++++++++++--------------------- 1 file changed, 159 insertions(+), 149 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index a71935d3..66186cf0 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -9,7 +9,8 @@ import numpy as np import random import yaml -import pybedtools +import pyranges as pr +from pyranges.methods.coverage import _number_overlapping from bedshift._version import __version__ _LOGGER = logging.getLogger(__name__) @@ -375,23 +376,27 @@ def drop(self, droprate): self.bed = self.bed.reset_index(drop=True) return len(drop_rows) - def _find_intersection(self, fp): + def _find_overlap(self, fp): """ find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file :param str fp: the filepath to the other bedfile containing regions to be compared to the reference bedfile - :return dataframe intersect_ref: the dataframe consisting of matching regions, where regions are + :return dataframe intersection: the dataframe consisting of matching regions """ - reference_bed = pybedtools.example_bedtool(os.path.abspath(self.bedfile_path)) - comparison_bed = pybedtools.example_bedtool(os.path.abspath(fp)) - + reference_pr = self.read_bed(self.bedfile_path) + comparison_pr = self.read_bed(fp) + reference_pr.columns = ['Chromosome', 'Start', 'End', 'modifications'] + comparison_pr.columns = ['Chromosome', 'Start', 'End', 'modifications'] + reference_pr = pr.PyRanges(reference_pr) + comparison_pr = pr.PyRanges(comparison_pr) try: - intersect_ref = reference_bed.intersect(comparison_bed, u=True).to_dataframe() + intersection = reference_pr.overlap(comparison_pr, how='first').as_df() + intersection = intersection.drop(['modifications'], axis=1) + intersection.columns = ['chrom', 'start', 'end'] except ValueError: print("No interection found between two files.") sys.exit(1) - - return intersect_ref[['chrom', 'start', 'end']] + return intersection def drop_from_file(self, fp, droprate, delimiter='\t'): """ @@ -415,8 +420,7 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): if num_drop >= drop_rows: print("Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format(num_drop, drop_rows, drop_rows)) num_drop = drop_rows - - intersect_regions = self._find_intersection(fp) + intersect_regions = self._find_overlap(fp) rows2drop = random.sample(list(range(len(intersect_regions))), num_drop) self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index(drop=True) @@ -464,7 +468,7 @@ def all_perturbations(self, else: n +=self.drop(droprate) if yaml: - n += handle_yaml(bedshifter, yaml) + n += self.handle_yaml(bedshifter, yaml) return n @@ -502,151 +506,157 @@ def read_bed(self, bedfile_path, delimiter='\t'): df[3] = 0 # column indicating which modifications were made return df -def _print_sample_config(): - """ - bedshift_operations: - - add: - rate: 0.1 - mean: 100 - stdev: 20 - - add_from_file: - file: tests/test.bed - rate: 0.1 - delimiter: \t - - drop_from_file: - file: tests/test.bed - rate: 0.1 - - add_from_file: - file: tests/test.bed - rate: 0.2 - - cut: - rate: 0.2 - - shift: - rate: 0.3 - mean: 100 - stdev: 200 - - merge: - rate: 0.15 - - drop: - rate: 0.30 - """ - print(_print_sample_config.__doc__) - print("No changes made.") + def _print_sample_config(self): + """ + bedshift_operations: + - add: + rate: 0.1 + mean: 100 + stdev: 20 + - add_from_file: + file: tests/test.bed + rate: 0.1 + delimiter: \t + - drop_from_file: + file: tests/test.bed + rate: 0.1 + - add_from_file: + file: tests/test.bed + rate: 0.2 + - cut: + rate: 0.2 + - shift: + rate: 0.3 + mean: 100 + stdev: 200 + - merge: + rate: 0.15 + - drop: + rate: 0.30 + """ + print(_print_sample_config.__doc__) + print("No changes made.") -def _read_from_yaml(fp): - """ - Loads yaml config data + def _read_from_yaml(self, fp): + """ + Loads yaml config data - :param float fp: the path to the configuration file for multiple add_from_file and drop_from_file - :return int: loaded yaml data - """ - with open(fp, "r") as yaml_file: - config_data = yaml.load(yaml_file, Loader=yaml.FullLoader) - print("Loaded configuration settings from {}".format(fp)) - return config_data - -def handle_yaml(bedshifter, yaml_fp): - data = _read_from_yaml(yaml_fp) - operations = [operation for operation in data["bedshift_operations"]] - num_changed = 0 - - for operation in operations: - ##### add ##### - if set(['add', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): - rate = operation['rate'] - mean = operation['mean'] - std = operation['stdev'] - num_added = bedshifter.add(rate, mean, std) - num_changed += num_added - print("\t{} regions added.".format(num_added)) - - ##### add_from_file with no delimiter provided ##### - elif set(['add_from_file', 'file', 'rate']) == set(list(operation.keys())): - fp = operation['file'] - if os.path.isfile(fp): - add_rate = operation['rate'] - num_added = bedshifter.add_from_file(fp, add_rate) - num_changed += num_added - print("\t{} regions added from {}.".format(num_added, fp)) - else: - print ("File \'{}\' does not exist.".format(fp)) - sys.exit(1) + :param float fp: the path to the configuration file + :return int: loaded yaml data + """ + with open(fp, "r") as yaml_file: + config_data = yaml.load(yaml_file, Loader=yaml.FullLoader) + print("Loaded configuration settings from {}".format(fp)) + return config_data - ##### add_from_file with delimiter provided ##### - elif set(['add_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): - fp = operation['file'] - if os.path.isfile(fp): - add_rate = operation['rate'] - delimiter = operation['delimiter'] - num_added = bedshifter.add_from_file(fp, add_rate, delimiter) - num_changed += num_added - print("\t{} regions added from {}.".format(num_added, fp)) - else: - print ("File \'{}\' does not exist.".format(fp)) - sys.exit(1) + def handle_yaml(self, bedshifter, yaml_fp): + """ + Performs operations provided in the yaml config file in the order they were provided. - ##### drop ##### - elif set(['drop', 'rate']) == set(list(operation.keys())): - rate = operation['rate'] - num_dropped = bedshifter.drop(rate) - num_changed += num_dropped - print("\t{} regions dropped.".format(num_dropped)) - - ##### drop_from_file with no delimiter provided ##### - elif set(['drop_from_file', 'file', 'rate']) == set(list(operation.keys())): - fp = operation['file'] - if os.path.isfile(fp): - drop_rate = operation['rate'] - num_dropped = bedshifter.drop_from_file(fp, drop_rate) + :param str bedshifter: the current instance of Bedshift + :param float yaml_fp: the path to the configuration file + :return int: the number of total regions perturbed + """ + data = self._read_from_yaml(yaml_fp) + operations = [operation for operation in data["bedshift_operations"]] + num_changed = 0 + + for operation in operations: + ##### add ##### + if set(['add', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): + rate = operation['rate'] + mean = operation['mean'] + std = operation['stdev'] + num_added = bedshifter.add(rate, mean, std) + num_changed += num_added + print("\t{} regions added.".format(num_added)) + + ##### add_from_file with no delimiter provided ##### + elif set(['add_from_file', 'file', 'rate']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + add_rate = operation['rate'] + num_added = bedshifter.add_from_file(fp, add_rate) + num_changed += num_added + print("\t{} regions added from {}.".format(num_added, fp)) + else: + print ("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### add_from_file with delimiter provided ##### + elif set(['add_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + add_rate = operation['rate'] + delimiter = operation['delimiter'] + num_added = bedshifter.add_from_file(fp, add_rate, delimiter) + num_changed += num_added + print("\t{} regions added from {}.".format(num_added, fp)) + else: + print ("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### drop ##### + elif set(['drop', 'rate']) == set(list(operation.keys())): + rate = operation['rate'] + num_dropped = bedshifter.drop(rate) num_changed += num_dropped - print("\t{} regions dropped from {}.".format(num_dropped, fp)) - else: - print ("File \'{}\' does not exist.".format(fp)) - sys.exit(1) + print("\t{} regions dropped.".format(num_dropped)) + + ##### drop_from_file with no delimiter provided ##### + elif set(['drop_from_file', 'file', 'rate']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + drop_rate = operation['rate'] + num_dropped = bedshifter.drop_from_file(fp, drop_rate) + num_changed += num_dropped + print("\t{} regions dropped from {}.".format(num_dropped, fp)) + else: + print ("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### drop_from_file with delimiter provided ##### + elif set(['drop_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + drop_rate = operation['rate'] + delimiter = operation['delimiter'] + num_dropped = bedshifter.drop_from_file(fp, drop_rate, delimiter) + num_changed += num_dropped + print("\t{} regions dropped from {}.".format(num_dropped, fp)) + else: + print ("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### shift ##### + elif set(['shift', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): + rate = operation['rate'] + mean = operation['mean'] + std = operation['stdev'] + num_shifted = bedshifter.shift(rate, mean, std) + num_changed += num_shifted + print("\t{} regions shifted.".format(num_shifted)) + + ##### cut ##### + elif set(['cut', 'rate']) == set(list(operation.keys())): + rate = operation['rate'] + num_cut = bedshifter.cut(rate) + num_changed += num_cut + print("\t{} regions cut.".format(num_cut)) + + ##### merge ##### + elif set(['merge', 'rate']) == set(list(operation.keys())): + rate = operation['rate'] + num_merged = bedshifter.merge(rate) + num_changed += num_merged + print("\t{} regions merged.".format(num_merged)) - ##### drop_from_file with delimiter provided ##### - elif set(['drop_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): - fp = operation['file'] - if os.path.isfile(fp): - drop_rate = operation['rate'] - delimiter = operation['delimiter'] - num_dropped = bedshifter.drop_from_file(fp, drop_rate, delimiter) - num_changed += num_dropped - print("\t{} regions dropped from {}.".format(num_dropped, fp)) else: - print ("File \'{}\' does not exist.".format(fp)) + print("Invalid settings entered in the config file. Please refer to the example below.") + _print_sample_config() sys.exit(1) - - ##### shift ##### - elif set(['shift', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): - rate = operation['rate'] - mean = operation['mean'] - std = operation['stdev'] - num_shifted = bedshifter.shift(rate, mean, std) - num_changed += num_shifted - print("\t{} regions shifted.".format(num_shifted)) - - ##### cut ##### - elif set(['cut', 'rate']) == set(list(operation.keys())): - rate = operation['rate'] - num_cut = bedshifter.cut(rate) - num_changed += num_cut - print("\t{} regions cut.".format(num_cut)) - - ##### merge ##### - elif set(['merge', 'rate']) == set(list(operation.keys())): - rate = operation['rate'] - num_merged = bedshifter.merge(rate) - num_changed += num_merged - print("\t{} regions merged.".format(num_merged)) - - else: - print("Invalid settings entered in the config file. Please refer to the example below.") - _print_sample_config() - - sys.exit(1) - - return num_changed + + return num_changed def main(): From cf720d82d8dee4b62745a2c4be44ed9fd53ea2a1 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 11 Mar 2021 12:00:16 -0500 Subject: [PATCH 0073/1072] Use string index. Fix #19 --- bedshift/bedshift.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index c0128ae4..59013f4d 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -265,8 +265,9 @@ def _shift(self, row, mean, stdev): chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] end = self.bed.loc[row][2] - - if start + theshift < 0 or end + theshift > chrom_lens[chrom]: + _LOGGER.debug("Chrom lengths: {}".format(str(chrom_lens))) + _LOGGER.debug("chrom: {}".format(str(chrom))) + if start + theshift < 0 or end + theshift > chrom_lens[str(chrom)]: # check if the region is shifted out of chromosome length bounds return None, None From eb46656abd3de90452f02ab3882e7ae6906585b0 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 11 Mar 2021 12:03:20 -0500 Subject: [PATCH 0074/1072] Fix #18 --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index c0128ae4..109940df 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -508,7 +508,7 @@ def main(): shiftstdev=args.shiftstdev, cutrate=args.cutrate, mergerate=args.mergerate, - outputfile=args.outputfile, + outputfile=outfile, repeat=args.repeat)) From 6049aebd3ab7ca02bc6b9d28690c112dbec11d65 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 11 Mar 2021 12:10:33 -0500 Subject: [PATCH 0075/1072] add some test files --- bedshift/bedshift.py | 3 +-- tests/chrom_sizes_1 | 1 + tests/chrom_sizes_2 | 1 + 3 files changed, 3 insertions(+), 2 deletions(-) create mode 100644 tests/chrom_sizes_1 create mode 100644 tests/chrom_sizes_2 diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 68799fcc..78d44b12 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -265,8 +265,7 @@ def _shift(self, row, mean, stdev): chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] end = self.bed.loc[row][2] - _LOGGER.debug("Chrom lengths: {}".format(str(chrom_lens))) - _LOGGER.debug("chrom: {}".format(str(chrom))) + if start + theshift < 0 or end + theshift > chrom_lens[str(chrom)]: # check if the region is shifted out of chromosome length bounds return None, None diff --git a/tests/chrom_sizes_1 b/tests/chrom_sizes_1 new file mode 100644 index 00000000..9e0256aa --- /dev/null +++ b/tests/chrom_sizes_1 @@ -0,0 +1 @@ +1 1000 diff --git a/tests/chrom_sizes_2 b/tests/chrom_sizes_2 new file mode 100644 index 00000000..c63a4587 --- /dev/null +++ b/tests/chrom_sizes_2 @@ -0,0 +1 @@ +1 100 From c9cf4448015254a3c7b49a7f0ed12db7b10d2892 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 11 Mar 2021 15:21:20 -0500 Subject: [PATCH 0076/1072] add bedplot.R script --- bedplot.R | 45 +++++++++++++++++++++++++++++++++++++++++++++ tests/chrom_sizes_1 | 2 +- 2 files changed, 46 insertions(+), 1 deletion(-) create mode 100644 bedplot.R diff --git a/bedplot.R b/bedplot.R new file mode 100644 index 00000000..c4762298 --- /dev/null +++ b/bedplot.R @@ -0,0 +1,45 @@ +library("data.table") +library("ggplot2") + +# This script produces visualizations of bedshift results (perturbed bed files) using R. + +# Load in the files and process them, returning a table with all the regions. +bedshiftread = function(startfile, randfiles){ + files = c(randfiles, startfile) + nfiles = length(files) + regionslist = lapply (files, fread) + rowsperfile = sapply(regionslist, NROW) + regionstable = rbindlist(regionslist, fill=TRUE) + regionstable[,fileid:=rep(seq_len(nfiles), rowsperfile)] + regionstable[,file:="random"] + + starfileregions = seq(from=NROW(regionstable)+1-rowsperfile[length(rowsperfile)], to=NROW(regionstable)) + regionstable[starfileregions,file:="original"] + return(regionstable) +} + +# Plot the results of the bedshiftread function +bedshiftplot = function(regionstable) { + ggplot(regionstable, + aes(xmin=V2, xmax=V3, ymin=fileid, ymax=fileid+0.75, fill=file)) + + geom_rect() + + theme_classic() + + scale_fill_manual(values=c("black", "gray")) + + xlab("Genome") + + ylab("Files") + + theme(axis.text.y = element_blank(), axis.ticks.y = element_blank()) +} + +# Provide the original file (the one that's being perturbed) +# and the filenames of all randomized files. + +# Run the randomization with a command like this: +# bedshift --verbosity 5 -b tests/simple_1.bed -d .3 -l tests/chrom_sizes_1 -r 10 + +startfile = "tests/simple_1.bed" +randfiles = paste0("rep", 1:10, "_bedshifted_simple_1.bed") + +pdf("drop_H.pdf", width=6, height=2) +regionstable = bedshiftread(startfile, randfiles) +bedshiftplot(regionstable) +dev.off() diff --git a/tests/chrom_sizes_1 b/tests/chrom_sizes_1 index 9e0256aa..407f3917 100644 --- a/tests/chrom_sizes_1 +++ b/tests/chrom_sizes_1 @@ -1 +1 @@ -1 1000 +1 10000 From 1303b1d0d84e5e7ba99366fafb230eb14d93418d Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 13 Mar 2021 17:31:19 -0600 Subject: [PATCH 0077/1072] reduce size of test.bed; fix #21 --- tests/test.bed | 20000 +++++++++++++++++++++++------------------------ 1 file changed, 10000 insertions(+), 10000 deletions(-) diff --git a/tests/test.bed b/tests/test.bed index 0191284d..8ec73b70 100644 --- a/tests/test.bed +++ b/tests/test.bed @@ -1,10000 +1,10000 @@ -chr16 57683001 57683273 . 1000 . 348.60214448824 -1 4.70433072024128 141 -chr6 53036699 53036974 . 1000 . 346.176651534705 -1 4.70433072024128 144 -chr10 76995732 76995979 . 1000 . 310.108500714016 -1 4.70433072024128 132 -chr12 54773504 54773736 . 1000 . 310.086438316725 -1 4.70433072024128 115 -chr3 52481114 52481353 . 1000 . 307.439597272129 -1 4.70433072024128 110 -chr1 17036345 17036564 . 1000 . 305.457551478902 -1 4.70433072024128 107 -chr12 58299186 58299434 . 1000 . 304.51947917175 -1 4.70433072024128 126 -chr17 17259432 17259658 . 1000 . 298.437380062858 -1 4.70433072024128 119 -chr17 36823867 36824096 . 1000 . 288.161887548839 -1 4.70433072024128 109 -chr16 4304077 4304332 . 1000 . 287.865197551482 -1 4.70433072024128 131 -chr14 50328828 50329070 . 1000 . 285.279029981494 -1 4.70433072024128 115 -chr1 114889180 114889434 . 1000 . 283.732905372961 -1 4.70433072024128 127 -chr9 36008855 36009095 . 1000 . 280.807702801907 -1 4.70433072024128 122 -chr2 91815094 91815295 . 1000 . 272.341481173969 -1 4.70433072024128 121 -chr9 135897807 135898028 . 1000 . 271.474182847492 -1 4.70433072024128 98 -chr19 19285892 19286130 . 1000 . 267.97918872509 -1 4.70433072024128 120 -chr3 195577808 195578049 . 1000 . 264.106095560477 -1 4.70433072024128 118 -chr15 40566969 40567171 . 1000 . 262.521748053189 -1 4.70433072024128 106 -chr12 57632931 57633148 . 1000 . 262.374322071965 -1 4.70433072024128 100 -chr16 19897657 19897872 . 1000 . 262.054388718535 -1 4.70433072024128 113 -chr2 178029373 178029587 . 1000 . 261.8071215607 -1 4.70433072024128 102 -chr6 32383276 32383499 . 1000 . 261.025327673144 -1 4.70433072024128 119 -chr6 73121954 73122191 . 1000 . 258.69499107358 -1 4.70433072024128 108 -chr17 39818986 39819236 . 1000 . 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13998950 13999270 +chr17 74001687 74002007 +chr16 2552963 2553283 +chr7 129917867 129918187 +chr7 4671279 4671599 +chr9 36862149 36862469 +chr17 36648297 36648617 +chr5 125570309 125570629 +chr6 150155682 150156002 +chr2 46877700 46878020 +chr11 113644640 113644960 +chr22 35420058 35420378 +chr5 91854037 91854357 +chr17 53813203 53813523 +chr1 179783921 179784241 +chr12 89918491 89918811 +chr21 47300374 47300694 +chr17 72858386 72858706 +chr6 137736781 137737101 +chr2 85154153 85154473 +chr2 180942065 180942385 +chr8 101859683 101860003 +chr11 19764270 19764590 +chr6 35383021 35383341 +chr20 48806827 48807147 +chr3 57029471 57029791 +chr5 141524910 141525230 +chr7 140353243 140353563 +chr6 41755165 41755485 +chr5 168133592 168133912 +chr11 65133734 65134054 +chr12 69751279 69751599 +chr8 133887735 133888055 +chr9 133557938 133558258 +chr9 125227308 125227628 +chr17 38518840 38519160 +chr20 58100577 58100897 +chr11 82952419 82952739 +chr6 160559261 160559581 +chr12 62517498 62517818 +chr13 76259505 76259825 +chr5 139619422 139619742 +chr19 19471866 19472186 +chr13 111997137 111997457 +chr1 179675500 179675820 +chr18 39878746 39879066 +chr6 2791436 2791756 +chr8 43135935 43136255 +chr17 1812003 1812323 +chr9 17063581 17063901 +chr9 5600531 5600851 +chr7 99510760 99511080 +chr15 90762869 90763189 +chr13 45968136 45968456 +chr19 10535007 10535327 +chr14 102783190 102783510 +chr9 36995847 36996167 +chr4 91236271 91236591 +chr19 3472009 3472329 +chr17 66462303 66462623 +chr2 27165494 27165814 +chr19 10360785 10361105 +chr12 44394193 44394513 From 2ca7cbbdec85d3b54ce417111726d25e0d7a7f4b Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 13 Mar 2021 17:41:35 -0600 Subject: [PATCH 0078/1072] fix shifted regions reporting; fix #22 --- bedshift/bedshift.py | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 3469ee9a..1c4e9147 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -106,7 +106,7 @@ def build_argparser(): parser.add_argument( "-r", "--repeat", type=int, default=1, help="the number of times to repeat the operation") - + parser.add_argument( "-y", "--yaml_config", type=str, help="run yaml configuration file") @@ -262,15 +262,22 @@ def shift(self, shiftrate, shiftmean, shiftstdev): shift_rows = random.sample(list(range(rows)), int(rows * shiftrate)) new_row_list = [] to_drop = [] + num_shifted = 0 + invalid_shifted = 0 for row in shift_rows: drop_row, new_region = self._shift(row, shiftmean, shiftstdev) # shifted rows display a 1 if drop_row and new_region: + num_shifted += 1 new_row_list.append(new_region) to_drop.append(drop_row) + else: + invalid_shifted += 1 self.bed = self.bed.drop(to_drop) self.bed = self.bed.append(new_row_list, ignore_index=True) self.bed = self.bed.reset_index(drop=True) - return len(shift_rows) + if invalid_shifted > 0: + _LOGGER.warning(f"{invalid_shifted} regions were prevented from being shifted outside of chromosome boundaries. Reported regions shifted will be less than expected.") + return num_shifted def _shift(self, row, mean, stdev): @@ -423,7 +430,7 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): num_drop = int(rows * droprate) drop_bed = self.read_bed(fp, delimiter=delimiter) drop_rows = drop_bed.shape[0] - + if num_drop >= drop_rows: print("Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format(num_drop, drop_rows, drop_rows)) num_drop = drop_rows @@ -634,7 +641,7 @@ def handle_yaml(self, bedshifter, yaml_fp): else: print ("File \'{}\' does not exist.".format(fp)) sys.exit(1) - + ##### shift ##### elif set(['shift', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): rate = operation['rate'] @@ -662,9 +669,9 @@ def handle_yaml(self, bedshifter, yaml_fp): print("Invalid settings entered in the config file. Please refer to the example below.") _print_sample_config() sys.exit(1) - + return num_changed - + def main(): """ Primary workflow """ From 36abcef1b2f9643382560b7edf8aa5dd890af2b6 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 13 Mar 2021 18:03:50 -0600 Subject: [PATCH 0079/1072] cleanup code, fix tests, add bedshift_analysis.yaml file --- bedshift/bedshift.py | 58 +++++++++++++++--------------------- tests/bedshift_analysis.yaml | 25 ++++++++++++++++ tests/test.py | 12 ++++---- 3 files changed, 55 insertions(+), 40 deletions(-) create mode 100644 tests/bedshift_analysis.yaml diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 1c4e9147..dc351725 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -1,16 +1,17 @@ """ Perturb regions in bedfiles """ import argparse -import logmuse import logging import os import sys -import pandas as pd -import numpy as np import random import yaml + +import logmuse +import pandas as pd +import numpy as np import pyranges as pr -from pyranges.methods.coverage import _number_overlapping + from bedshift._version import __version__ _LOGGER = logging.getLogger(__name__) @@ -548,7 +549,7 @@ def _print_sample_config(self): - drop: rate: 0.30 """ - print(_print_sample_config.__doc__) + print(self._print_sample_config.__doc__) print("No changes made.") def _read_from_yaml(self, fp): @@ -667,7 +668,7 @@ def handle_yaml(self, bedshifter, yaml_fp): else: print("Invalid settings entered in the config file. Please refer to the example below.") - _print_sample_config() + self._print_sample_config() sys.exit(1) return num_changed @@ -724,6 +725,10 @@ def main(): yaml_config: {yaml_config} """ + if args.repeat < 1: + _LOGGER.error("repeats specified is less than 1") + sys.exit(1) + if args.outputfile: outfile = args.outputfile else: @@ -749,40 +754,25 @@ def main(): bedshifter = Bedshift(args.bedfile, args.chrom_lengths) - if args.repeat == 1: + for i in range(args.repeat): n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, - args.shiftrate, args.shiftmean, args.shiftstdev, - args.cutrate, - args.mergerate, - args.droprate, - args.dropfile, - args.yaml_config, - bedshifter) + args.addfile, + args.shiftrate, args.shiftmean, args.shiftstdev, + args.cutrate, + args.mergerate, + args.droprate, + args.dropfile, + args.yaml_config, + bedshifter) print("\t" + str(n) + " regions changed in total.\n") - bedshifter.to_bed(outfile) - - elif args.repeat > 1: - for i in range(args.repeat): - n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, - args.shiftrate, args.shiftmean, args.shiftstdev, - args.cutrate, - args.mergerate, - args.droprate, - args.dropfile, - args.yaml_config, - bedshifter) - print("\t" + str(n) + " regions changed in total.\n") + if args.repeat == 1: + bedshifter.to_bed(outfile) + else: modified_outfile = outfile.rsplit("/") modified_outfile[-1] = "rep" + str(i+1) + "_" + modified_outfile[-1] modified_outfile = "/".join(modified_outfile) bedshifter.to_bed(modified_outfile) - bedshifter.reset_bed() - - else: - _LOGGER.error("repeats specified is less than 1") - sys.exit(1) + bedshifter.reset_bed() if __name__ == '__main__': diff --git a/tests/bedshift_analysis.yaml b/tests/bedshift_analysis.yaml new file mode 100644 index 00000000..607dd204 --- /dev/null +++ b/tests/bedshift_analysis.yaml @@ -0,0 +1,25 @@ +bedshift_operations: + - add: + rate: 0.1 + mean: 100 + stdev: 20 + - add_from_file: + file: tests/test.bed + rate: 0.1 + delimiter: \t + - drop_from_file: + file: tests/test.bed + rate: 0.1 + - add_from_file: + file: tests/test.bed + rate: 0.2 + - cut: + rate: 0.2 + - shift: + rate: 0.3 + mean: 100 + stdev: 200 + - merge: + rate: 0.15 + # - drop: + # rate: 0.30 \ No newline at end of file diff --git a/tests/test.py b/tests/test.py index 55d9faa6..3d5188e1 100755 --- a/tests/test.py +++ b/tests/test.py @@ -1,6 +1,6 @@ import unittest from bedshift import bedshift -from bedshift.bedshift import handle_yaml + class TestBedshift(unittest.TestCase): def setUp(self): @@ -28,7 +28,7 @@ def test_drop(self): def test_shift(self): shifted = self.bs.shift(0.129, 200, 30) - self.assertEqual(shifted, 1290) + self.assertAlmostEqual(shifted, 1290, places=-2) self.bs.reset_bed() def test_cut(self): @@ -63,7 +63,7 @@ def test_drop_from_file_zero_rate(self): self.bs.reset_bed() def test_handle_yaml(self): - yamled = handle_yaml(bedshifter=self.bs, yaml_fp="tests/bedshift_analysis.yaml") + yamled = self.bs.handle_yaml(bedshifter=self.bs, yaml_fp="tests/bedshift_analysis.yaml") self.bs.reset_bed() added = self.bs.add(addrate=0.1, addmean=100, addstdev=20) @@ -78,7 +78,7 @@ def test_handle_yaml(self): total = added+f_added_10+f_dropped_15+f_added_20+cut+shifted+merged#+dropped - self.assertEqual(yamled, total) + self.assertAlmostEqual(yamled, total, places=-2) self.bs.reset_bed() def test_all_perturbations1(self): @@ -87,8 +87,8 @@ def test_all_perturbations1(self): shiftrate=0.23, shiftmean=-10.0, shiftstdev=120.0, cutrate=0.12, droprate=0.42) - self.assertEqual(perturbed, 16156) - self.assertEqual(len(self.bs.bed), 9744) + self.assertAlmostEqual(perturbed, 16156, places=-2) + self.assertAlmostEqual(len(self.bs.bed), 9744, places=-2) self.bs.reset_bed() def test_all_perturbations2(self): From 6202d711ae183dd55b370cde4bb454755abb9d3d Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 13 Mar 2021 18:04:49 -0600 Subject: [PATCH 0080/1072] add pyranges to requirements --- docs/changelog.md | 3 ++- requirements/requirements-all.txt | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 697fecae..035e64a4 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -7,7 +7,8 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm - Added ability to specify chrom sizes file, or refgenie genome. - Add --yaml_config option - Improve performance of perturbations -- Add --drop-from-file and --add-from-file options +- Add --dropfile, --addfile, and --shiftfile options +- Improved testing framework ## [1.0.0] - released May 20, 2020 diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index baabbc3e..85cb8d88 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -1,3 +1,4 @@ logmuse pandas -numpy \ No newline at end of file +numpy +pyranges \ No newline at end of file From e0dad29b974d3fcb0e695d2d02690a9026b8e29c Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 14 Mar 2021 14:52:40 -0500 Subject: [PATCH 0081/1072] add small test --- .gitignore | 17 ++++++++++++----- bedshift/bedshift.py | 4 ++-- tests/hg38.chrom.sizes | 2 +- tests/small_test.bed | 1 + tests/test.py | 16 ++++++++++++++++ 5 files changed, 32 insertions(+), 8 deletions(-) create mode 100644 tests/small_test.bed diff --git a/.gitignore b/.gitignore index d20dee45..506f42bb 100644 --- a/.gitignore +++ b/.gitignore @@ -1,11 +1,18 @@ -examples/sh_output* -examples/py_output* *.pyc -site/ + +# BED files +bedshifted* *.bed !tests/test.bed -.DS_Store +!tests/small_test.bed +examples/sh_output* +examples/py_output* + +# Python build +site/ build/ bedshift.egg-info/ dist/ -bedshifted* \ No newline at end of file + +# MacOS +.DS_Store diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index dc351725..7f52a3d2 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -144,7 +144,7 @@ def _read_chromsizes(self, fp): with open(fp) as f: for line in f: line = line.strip().split('\t') - chrom = line[0] + chrom = str(line[0]) size = int(line[1]) self.chrom_lens[chrom] = size except FileNotFoundError: @@ -267,7 +267,7 @@ def shift(self, shiftrate, shiftmean, shiftstdev): invalid_shifted = 0 for row in shift_rows: drop_row, new_region = self._shift(row, shiftmean, shiftstdev) # shifted rows display a 1 - if drop_row and new_region: + if drop_row is not None and new_region is not None: num_shifted += 1 new_row_list.append(new_region) to_drop.append(drop_row) diff --git a/tests/hg38.chrom.sizes b/tests/hg38.chrom.sizes index f86e3d10..63962a61 100644 --- a/tests/hg38.chrom.sizes +++ b/tests/hg38.chrom.sizes @@ -1,4 +1,4 @@ -chr1 1248956422 +chr1 248956422 chr2 242193529 chr3 198295559 chr4 190214555 diff --git a/tests/small_test.bed b/tests/small_test.bed new file mode 100644 index 00000000..d080f0ed --- /dev/null +++ b/tests/small_test.bed @@ -0,0 +1 @@ +chr16 57683001 57683273 \ No newline at end of file diff --git a/tests/test.py b/tests/test.py index 3d5188e1..40411668 100755 --- a/tests/test.py +++ b/tests/test.py @@ -1,4 +1,6 @@ import unittest +import os + from bedshift import bedshift @@ -104,4 +106,18 @@ def test_all_perturbations2(self): def test_to_bed(self): self.bs.to_bed('py_output.bed') + self.assertTrue(os.path.exists('tests/py_output.bed')) + + def test_small_file(self): + bs_small = bedshift.Bedshift('tests/small_test.bed', chrom_sizes="tests/hg38.chrom.sizes") + shifted = bs_small.shift(0.3, 50, 50) + self.assertEqual(shifted, 0) + shifted = bs_small.shift(1.0, 50, 50) + self.assertEqual(shifted, 1) + added = bs_small.add(0.2, 100, 50) + self.assertEqual(added, 0) + added = bs_small.add(1.0, 100, 50) + self.assertEqual(added, 1) + added = bs_small.add(2.0, 100, 50) + self.assertEqual(added, 4) From 1e0ec488212e4829de3f5dd1a4a81aaf150c4bbc Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Tue, 16 Mar 2021 12:54:20 +0900 Subject: [PATCH 0082/1072] Update bedshift_analysis.yaml --- tests/bedshift_analysis.yaml | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/tests/bedshift_analysis.yaml b/tests/bedshift_analysis.yaml index 607dd204..25f7b042 100644 --- a/tests/bedshift_analysis.yaml +++ b/tests/bedshift_analysis.yaml @@ -3,15 +3,17 @@ bedshift_operations: rate: 0.1 mean: 100 stdev: 20 - - add_from_file: - file: tests/test.bed - rate: 0.1 - delimiter: \t - drop_from_file: file: tests/test.bed rate: 0.1 + delimiter: \t + - shift_from_file: + file: bedshifted_test.bed + rate: 0.5 + mean: 100 + stdev: 200 - add_from_file: - file: tests/test.bed + file: tests/small_test.bed rate: 0.2 - cut: rate: 0.2 @@ -19,7 +21,7 @@ bedshift_operations: rate: 0.3 mean: 100 stdev: 200 + - drop: + rate: 0.30 - merge: rate: 0.15 - # - drop: - # rate: 0.30 \ No newline at end of file From ff8c59ff4734d5f401e60db18ccddb731fdc839c Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Tue, 16 Mar 2021 15:04:52 +0900 Subject: [PATCH 0083/1072] Update test_handle_yaml unittest --- tests/test.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/tests/test.py b/tests/test.py index 40411668..7e1a64c1 100755 --- a/tests/test.py +++ b/tests/test.py @@ -69,16 +69,21 @@ def test_handle_yaml(self): self.bs.reset_bed() added = self.bs.add(addrate=0.1, addmean=100, addstdev=20) - f_added_10 = self.bs.add_from_file(fp="tests/test.bed", addrate=0.1) - f_dropped_15 = self.bs.drop_from_file(fp="tests/test.bed", droprate=0.1) - f_added_20 = self.bs.add_from_file(fp="tests/test.bed", addrate=0.2) + f_drop_10 = self.bs.from_from_file(fp="tests/test.bed", addrate=0.2) + f_shift_30 = self.bs.shift_from_file(fp="bedshifted_test.bed", + shiftrate=0.50, + shiftmean=100, + shiftstdev=200) + f_added_20 = self.bs.add_from_file(fp="tests/test.bed", addrate=0.1) cut = self.bs.cut(cutrate=0.20) shifted = self.bs.shift(shiftrate=0.30, shiftmean=100, shiftstdev=200) + dropped = self.bs.drop(droprate=0.30) merged = self.bs.merge(mergerate=0.15) - # dropped = self.bs.drop(droprate=0.30) - # print("dropped", dropped) - total = added+f_added_10+f_dropped_15+f_added_20+cut+shifted+merged#+dropped + total = added+f_drop_10+f_shift_30+f_added_20+cut+shifted+dropped+merged + + self.assertAlmostEqual(yamled, total, places=-2) + self.bs.reset_bed() self.assertAlmostEqual(yamled, total, places=-2) self.bs.reset_bed() From ad0fe8d512accbd93bcc47ea6ffd8629ad18a3ae Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Tue, 16 Mar 2021 15:24:32 +0900 Subject: [PATCH 0084/1072] Fix typos for test_handle_yaml --- tests/test.py | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/tests/test.py b/tests/test.py index 7e1a64c1..3d3a4703 100755 --- a/tests/test.py +++ b/tests/test.py @@ -69,21 +69,18 @@ def test_handle_yaml(self): self.bs.reset_bed() added = self.bs.add(addrate=0.1, addmean=100, addstdev=20) - f_drop_10 = self.bs.from_from_file(fp="tests/test.bed", addrate=0.2) + f_drop_10 = self.bs.drop_from_file(fp="tests/test.bed", droprate=0.1) f_shift_30 = self.bs.shift_from_file(fp="bedshifted_test.bed", - shiftrate=0.50, + shiftrate=0.30, shiftmean=100, shiftstdev=200) - f_added_20 = self.bs.add_from_file(fp="tests/test.bed", addrate=0.1) - cut = self.bs.cut(cutrate=0.20) - shifted = self.bs.shift(shiftrate=0.30, shiftmean=100, shiftstdev=200) - dropped = self.bs.drop(droprate=0.30) + f_added_20 = self.bs.add_from_file(fp="tests/small_test.bed", addrate=0.2) + cut = self.bs.cut(cutrate=0.2) + dropped = self.bs.drop(droprate=0.3) + shifted = self.bs.shift(shiftrate=0.05, shiftmean=100, shiftstdev=200) merged = self.bs.merge(mergerate=0.15) - total = added+f_drop_10+f_shift_30+f_added_20+cut+shifted+dropped+merged - - self.assertAlmostEqual(yamled, total, places=-2) - self.bs.reset_bed() + total = added+f_drop_10+f_shift_30+f_added_20+cut+dropped+shifted+merged self.assertAlmostEqual(yamled, total, places=-2) self.bs.reset_bed() From 7ce475257f87446596269bbe9c57cee585a232a1 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Fri, 19 Mar 2021 21:41:11 +0900 Subject: [PATCH 0085/1072] add shift_from_file --- bedshift/bedshift.py | 179 ++++++++++++++++++++++++++++++------------- 1 file changed, 126 insertions(+), 53 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 7f52a3d2..1591cf4c 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -6,12 +6,10 @@ import sys import random import yaml - import logmuse import pandas as pd import numpy as np import pyranges as pr - from bedshift._version import __version__ _LOGGER = logging.getLogger(__name__) @@ -89,6 +87,9 @@ def build_argparser(): "--shiftstdev", type=float, default=150.0, help="Stdev shift") + parser.add_argument( + "--shiftfile", type=str, help="Shift regions from a bedfile") + parser.add_argument( "-c", "--cutrate", type=float, default=0.0, help="Cut probability") @@ -159,7 +160,6 @@ def reset_bed(self): """ Reset the stored bedfile to the state before perturbations """ - self.bed = self.original_bed.copy(deep=True) @@ -243,7 +243,7 @@ def add_from_file(self, fp, addrate, delimiter='\t'): return num_add - def shift(self, shiftrate, shiftmean, shiftstdev): + def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=None): """ Shift regions @@ -260,7 +260,8 @@ def shift(self, shiftrate, shiftmean, shiftstdev): sys.exit(1) rows = self.bed.shape[0] - shift_rows = random.sample(list(range(rows)), int(rows * shiftrate)) + if shift_rows == None: + shift_rows = random.sample(list(range(rows)), int(rows * shiftrate)) new_row_list = [] to_drop = [] num_shifted = 0 @@ -284,17 +285,42 @@ def shift(self, shiftrate, shiftmean, shiftstdev): def _shift(self, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) - chrom = str(self.bed.loc[row][0]) + chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] end = self.bed.loc[row][2] - - if start + theshift < 0 or end + theshift > self.chrom_lens[chrom]: + _LOGGER.debug("Chrom lengths: {}".format(str(self.chrom_lens))) + _LOGGER.debug("chrom: {}".format(str(chrom))) + if start + theshift < 0 or end + theshift > self.chrom_lens[str(chrom)]: # check if the region is shifted out of chromosome length bounds return None, None return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: 1} + def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): + self._check_rate(shiftrate) + if shiftrate == 0: + return 0 + if len(self.chrom_lens) == 0: + _LOGGER.error("chrom.sizes file must be specified when shifting regions") + sys.exit(1) + + rows = self.bed.shape[0] + num_shift = int(rows * shiftrate) + shift_bed = self.read_bed(fp, delimiter=delimiter) + + intersect_regions = self._find_overlap(fp) + try: + rows2shift = random.sample(list(range(len(intersect_regions))), num_shift) + return self.shift(shiftrate, shiftmean, shiftstdev, rows2shift) + except ValueError: + bedname = os.path.basename(self.bedfile_path) + shift_file_name = os.path.basename(fp) + print("The number of overlapping regions between "+ + "{} and {} is {} but the shift ratio provided is trying to shift {} regions."\ + .format(bedname, shift_file_name, len(intersect_regions), num_shift)) + sys.exit(1) + def cut(self, cutrate): """ Cut regions to create two new regions @@ -391,31 +417,41 @@ def drop(self, droprate): self.bed = self.bed.reset_index(drop=True) return len(drop_rows) - def _find_overlap(self, fp): - """ - find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file - :param str fp: the filepath to the other bedfile containing regions to be compared to the reference bedfile - :return dataframe intersection: the dataframe consisting of matching regions + def _find_overlap(self, fp, reference=None): """ - reference_pr = self.read_bed(self.bedfile_path) - comparison_pr = self.read_bed(fp) - reference_pr.columns = ['Chromosome', 'Start', 'End', 'modifications'] - comparison_pr.columns = ['Chromosome', 'Start', 'End', 'modifications'] - reference_pr = pr.PyRanges(reference_pr) - comparison_pr = pr.PyRanges(comparison_pr) - try: - intersection = reference_pr.overlap(comparison_pr, how='first').as_df() - intersection = intersection.drop(['modifications'], axis=1) - intersection.columns = ['chrom', 'start', 'end'] - except ValueError: - print("No interection found between two files.") - sys.exit(1) + find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file. + """ + if reference is None: + reference_bed = self.original_bed + else: + if isinstance(reference, pd.DataFrame): + reference_bed = reference + elif isinstance(reference, str): + reference_bed = self.read_bed(reference) + else: + raise Exception("unsupported input type: {}".format(type(reference))) + if isinstance(fp, pd.DataFrame): + comparison_bed = fp + elif isinstance(fp, str): + comparison_bed = self.read_bed(fp) + else: + raise Exception("unsupported input type: {}".format(type(reference))) + reference_bed.columns = ['Chromosome', 'Start', 'End', 'modifications'] + comparison_bed.columns = ['Chromosome', 'Start', 'End', 'modifications'] + reference_pr = pr.PyRanges(reference_bed) + comparison_pr = pr.PyRanges(comparison_bed) + intersection = reference_pr.overlap(comparison_pr, how='first').as_df() + if len(intersection) == 0: + raise Exception("no intersection found between {} and {}".format(reference_bed, comparison_bed)) + intersection = intersection.drop(['modifications'], axis=1) + intersection.columns = ['chrom', 'start', 'end'] return intersection + def drop_from_file(self, fp, droprate, delimiter='\t'): """ - drop regions from another bedfile to this perturbed bedfile + drop regions that overlap between the reference bedfile and the provided bedfile. :param float droprate: the rate to drop regions :param str fp: the filepath to the other bedfile containing regions to be dropped @@ -441,10 +477,12 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index(drop=True) return num_drop + def all_perturbations(self, addrate=0.0, addmean=320.0, addstdev=30.0, addfile=None, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, + shiftfile=None, cutrate=0.0, mergerate=0.0, droprate=0.0, @@ -461,6 +499,7 @@ def all_perturbations(self, :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) :param float shiftmean: the mean shift distance :param float shiftstdev: the standard deviation of the shift distance + :param float shiftfile: the file containing regions to be shifted :param float cutrate: the rate to cut regions into two separate regions :param float mergerate: the rate to merge two regions into one :param float droprate: the rate to drop/remove regions @@ -469,9 +508,11 @@ def all_perturbations(self, :param string bedshifter: Bedshift instance :return int: the number of total regions perturbed ''' - n = 0 - n += self.shift(shiftrate, shiftmean, shiftstdev) + if shiftfile: + n+= self.shift_from_file(shiftfile, shiftrate, shiftmean, shiftstdev) + else: + n += self.shift(shiftrate, shiftmean, shiftstdev) if addfile: n += self.add_from_file(addfile, addrate) else: @@ -498,6 +539,7 @@ def to_bed(self, outfile_name): print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.' \ .format(outfile_name, self.bed.shape[0], self.original_regions)) + def read_bed(self, bedfile_path, delimiter='\t'): """ Read a BED file into pandas dataframe @@ -521,6 +563,7 @@ def read_bed(self, bedfile_path, delimiter='\t'): df[3] = 0 # column indicating which modifications were made return df + def _print_sample_config(self): """ bedshift_operations: @@ -528,42 +571,40 @@ def _print_sample_config(self): rate: 0.1 mean: 100 stdev: 20 - - add_from_file: - file: tests/test.bed - rate: 0.1 - delimiter: \t - drop_from_file: file: tests/test.bed rate: 0.1 + delimiter: \t + - shift_from_file: + file: bedshifted_test.bed + rate: 0.3 + mean: 100 + stdev: 200 - add_from_file: - file: tests/test.bed + file: tests/small_test.bed rate: 0.2 - cut: rate: 0.2 + - drop: + rate: 0.30 - shift: - rate: 0.3 + rate: 0.05 mean: 100 stdev: 200 - merge: rate: 0.15 - - drop: - rate: 0.30 """ print(self._print_sample_config.__doc__) print("No changes made.") def _read_from_yaml(self, fp): - """ - Loads yaml config data - - :param float fp: the path to the configuration file - :return int: loaded yaml data - """ + # Loads yaml config data with open(fp, "r") as yaml_file: config_data = yaml.load(yaml_file, Loader=yaml.FullLoader) print("Loaded configuration settings from {}".format(fp)) return config_data + def handle_yaml(self, bedshifter, yaml_fp): """ Performs operations provided in the yaml config file in the order they were provided. @@ -642,7 +683,7 @@ def handle_yaml(self, bedshifter, yaml_fp): else: print ("File \'{}\' does not exist.".format(fp)) sys.exit(1) - + ##### shift ##### elif set(['shift', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): rate = operation['rate'] @@ -652,6 +693,35 @@ def handle_yaml(self, bedshifter, yaml_fp): num_changed += num_shifted print("\t{} regions shifted.".format(num_shifted)) + ##### shift_from_file ##### + elif set(['shift_from_file', 'file', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + rate = operation['rate'] + mean = operation['mean'] + std = operation['stdev'] + num_shifted = bedshifter.shift_from_file(fp, rate, mean, std) + num_changed += num_shifted + print("\t{} regions shifted from {}.".format(num_shifted, fp)) + else: + print ("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### shift_from_file with delimiter provided ##### + elif set(['shift_from_file', 'file', 'rate', 'mean', 'stdev', 'delimiter']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + rate = operation['rate'] + mean = operation['mean'] + std = operation['stdev'] + delimiter = operation['delimiter'] + num_shifted = bedshifter.shift_from_file(fp, rate, mean, std, delimiter) + num_changed += num_shifted + print("\t{} regions shifted from {}.".format(num_shifted, fp)) + else: + print ("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + ##### cut ##### elif set(['cut', 'rate']) == set(list(operation.keys())): rate = operation['rate'] @@ -670,7 +740,7 @@ def handle_yaml(self, bedshifter, yaml_fp): print("Invalid settings entered in the config file. Please refer to the example below.") self._print_sample_config() sys.exit(1) - + return num_changed @@ -711,6 +781,7 @@ def main(): shift rate: {shiftrate} shift mean distance: {shiftmean} shift stdev: {shiftstdev} + shift regions from file: {shiftfile} add: rate: {addrate} add mean length: {addmean} @@ -746,6 +817,7 @@ def main(): shiftrate=args.shiftrate, shiftmean=args.shiftmean, shiftstdev=args.shiftstdev, + shiftfile=args.shiftfile, cutrate=args.cutrate, mergerate=args.mergerate, outputfile=outfile, @@ -756,14 +828,15 @@ def main(): bedshifter = Bedshift(args.bedfile, args.chrom_lengths) for i in range(args.repeat): n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, - args.shiftrate, args.shiftmean, args.shiftstdev, - args.cutrate, - args.mergerate, - args.droprate, - args.dropfile, - args.yaml_config, - bedshifter) + args.addfile, + args.shiftrate, args.shiftmean, args.shiftstdev, + args.shiftfile, + args.cutrate, + args.mergerate, + args.droprate, + args.dropfile, + args.yaml_config, + bedshifter) print("\t" + str(n) + " regions changed in total.\n") if args.repeat == 1: bedshifter.to_bed(outfile) From 64d6fa00ef078f2640d4c2a642839f6fbb48084b Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 20 Mar 2021 11:39:46 -0500 Subject: [PATCH 0086/1072] fix #26 --- .gitignore | 1 + bedshift/bedshift.py | 9 ++++----- tests/header_test.bed | 4 ++++ tests/test.py | 6 +++++- 4 files changed, 14 insertions(+), 6 deletions(-) create mode 100644 tests/header_test.bed diff --git a/.gitignore b/.gitignore index 506f42bb..c791e63e 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ bedshifted* *.bed !tests/test.bed !tests/small_test.bed +!tests/header_test.bed examples/sh_output* examples/py_output* diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 1591cf4c..059c171d 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -555,9 +555,8 @@ def read_bed(self, bedfile_path, delimiter='\t'): _LOGGER.error("file {} could not be read".format(bedfile_path)) sys.exit(1) - # if there is 'chrom', 'start', 'stop' in the table, move them to header + # if there is a header line in the table, remove it if not str(df.iloc[0, 1]).isdigit(): - df.columns = df.iloc[0] df = df[1:] df[3] = 0 # column indicating which modifications were made @@ -683,7 +682,7 @@ def handle_yaml(self, bedshifter, yaml_fp): else: print ("File \'{}\' does not exist.".format(fp)) sys.exit(1) - + ##### shift ##### elif set(['shift', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): rate = operation['rate'] @@ -704,7 +703,7 @@ def handle_yaml(self, bedshifter, yaml_fp): num_changed += num_shifted print("\t{} regions shifted from {}.".format(num_shifted, fp)) else: - print ("File \'{}\' does not exist.".format(fp)) + print("File \'{}\' does not exist.".format(fp)) sys.exit(1) ##### shift_from_file with delimiter provided ##### @@ -740,7 +739,7 @@ def handle_yaml(self, bedshifter, yaml_fp): print("Invalid settings entered in the config file. Please refer to the example below.") self._print_sample_config() sys.exit(1) - + return num_changed diff --git a/tests/header_test.bed b/tests/header_test.bed new file mode 100644 index 00000000..604ff6ea --- /dev/null +++ b/tests/header_test.bed @@ -0,0 +1,4 @@ +chrom start end +chr16 57683001 57683273 +chr1 10000 50000 +chr2 123456 234567 \ No newline at end of file diff --git a/tests/test.py b/tests/test.py index 3d3a4703..2f8bad88 100755 --- a/tests/test.py +++ b/tests/test.py @@ -8,6 +8,10 @@ class TestBedshift(unittest.TestCase): def setUp(self): self.bs = bedshift.Bedshift('tests/test.bed', chrom_sizes="tests/hg38.chrom.sizes") + def test_read_bed(self): + reader = bedshift.Bedshift('tests/header_test.bed') + self.assertTrue(list(reader.bed.columns), [0,1,2,3]) + def test_add(self): added = self.bs.add(0.1, 100, 20) self.assertEqual(added, 1000) @@ -70,7 +74,7 @@ def test_handle_yaml(self): added = self.bs.add(addrate=0.1, addmean=100, addstdev=20) f_drop_10 = self.bs.drop_from_file(fp="tests/test.bed", droprate=0.1) - f_shift_30 = self.bs.shift_from_file(fp="bedshifted_test.bed", + f_shift_30 = self.bs.shift_from_file(fp="tests/bedshifted_test.bed", shiftrate=0.30, shiftmean=100, shiftstdev=200) From 0861174a06c4e218f5752e3e606144fd9c3e5911 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Sun, 21 Mar 2021 17:33:02 +0900 Subject: [PATCH 0087/1072] add bedshifted_test.py --- tests/bedshifted_test.bed | 7746 +++++++++++++++++++++++++++++++++++++ 1 file changed, 7746 insertions(+) create mode 100644 tests/bedshifted_test.bed diff --git a/tests/bedshifted_test.bed b/tests/bedshifted_test.bed new file mode 100644 index 00000000..a12553cd --- /dev/null +++ b/tests/bedshifted_test.bed @@ -0,0 +1,7746 @@ +chr1 1057472 1057792 0 +chr1 1280014 1837925 4 +chr1 1307759 205225637 4 +chr1 1376528 1376616 1 +chr1 1837836 1875763 4 +chr1 1891508 1891828 4 +chr1 2313442 2313762 1 +chr1 2479800 2480120 1 +chr1 3236554 3236874 0 +chr1 3400447 3400767 0 +chr1 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153029402 153029722 0 +chrX 153212916 153213076 2 +chrX 153212921 153213081 1 +chrX 153218901 153279397 4 +chrX 153316698 153317018 0 +chrX 153536723 153537043 0 +chrX 153763163 153763325 0 +chrX 153943402 153943562 2 +chrY 4055823 4055906 3 +chrY 10358489 10358547 2 +chrY 10358547 10358605 2 +chrY 26266811 26266918 3 +chrY 27811821 27811885 2 +chrY 33291667 33291761 3 +chrY 45782319 45782444 3 +chrY 55739083 55739152 3 From b5ef5789ebad8fc05fe9ecfc31ab7f9eff7214be Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 21 Mar 2021 12:00:45 -0500 Subject: [PATCH 0088/1072] use deep copy in _find_overlap --- bedshift/bedshift.py | 16 ++++++++-------- tests/bedshift_analysis.yaml | 2 +- tests/test.py | 8 ++++---- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 1591cf4c..ea942066 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -137,7 +137,7 @@ def __init__(self, bedfile_path, chrom_sizes=None, delimiter='\t'): self.original_regions = df.shape[0] self.bed = df.astype({1: 'int64', 2: 'int64', 3: 'int64'}) \ .sort_values([0, 1, 2]).reset_index(drop=True) - self.original_bed = self.bed.copy(deep=True) + self.original_bed = self.bed.copy() def _read_chromsizes(self, fp): @@ -160,7 +160,7 @@ def reset_bed(self): """ Reset the stored bedfile to the state before perturbations """ - self.bed = self.original_bed.copy(deep=True) + self.bed = self.original_bed.copy() def _check_rate(self, rate): @@ -423,16 +423,16 @@ def _find_overlap(self, fp, reference=None): find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file. """ if reference is None: - reference_bed = self.original_bed + reference_bed = self.original_bed.copy() else: if isinstance(reference, pd.DataFrame): - reference_bed = reference + reference_bed = reference.copy() elif isinstance(reference, str): reference_bed = self.read_bed(reference) else: raise Exception("unsupported input type: {}".format(type(reference))) if isinstance(fp, pd.DataFrame): - comparison_bed = fp + comparison_bed = fp.copy() elif isinstance(fp, str): comparison_bed = self.read_bed(fp) else: @@ -445,7 +445,7 @@ def _find_overlap(self, fp, reference=None): if len(intersection) == 0: raise Exception("no intersection found between {} and {}".format(reference_bed, comparison_bed)) intersection = intersection.drop(['modifications'], axis=1) - intersection.columns = ['chrom', 'start', 'end'] + intersection.columns = [0, 1, 2] return intersection @@ -683,7 +683,7 @@ def handle_yaml(self, bedshifter, yaml_fp): else: print ("File \'{}\' does not exist.".format(fp)) sys.exit(1) - + ##### shift ##### elif set(['shift', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): rate = operation['rate'] @@ -740,7 +740,7 @@ def handle_yaml(self, bedshifter, yaml_fp): print("Invalid settings entered in the config file. Please refer to the example below.") self._print_sample_config() sys.exit(1) - + return num_changed diff --git a/tests/bedshift_analysis.yaml b/tests/bedshift_analysis.yaml index 25f7b042..68c85c9f 100644 --- a/tests/bedshift_analysis.yaml +++ b/tests/bedshift_analysis.yaml @@ -8,7 +8,7 @@ bedshift_operations: rate: 0.1 delimiter: \t - shift_from_file: - file: bedshifted_test.bed + file: tests/bedshifted_test.bed rate: 0.5 mean: 100 stdev: 200 diff --git a/tests/test.py b/tests/test.py index 3d3a4703..ba9a09f3 100755 --- a/tests/test.py +++ b/tests/test.py @@ -70,14 +70,14 @@ def test_handle_yaml(self): added = self.bs.add(addrate=0.1, addmean=100, addstdev=20) f_drop_10 = self.bs.drop_from_file(fp="tests/test.bed", droprate=0.1) - f_shift_30 = self.bs.shift_from_file(fp="bedshifted_test.bed", - shiftrate=0.30, + f_shift_30 = self.bs.shift_from_file(fp="tests/bedshifted_test.bed", + shiftrate=0.50, shiftmean=100, shiftstdev=200) f_added_20 = self.bs.add_from_file(fp="tests/small_test.bed", addrate=0.2) cut = self.bs.cut(cutrate=0.2) + shifted = self.bs.shift(shiftrate=0.3, shiftmean=100, shiftstdev=200) dropped = self.bs.drop(droprate=0.3) - shifted = self.bs.shift(shiftrate=0.05, shiftmean=100, shiftstdev=200) merged = self.bs.merge(mergerate=0.15) total = added+f_drop_10+f_shift_30+f_added_20+cut+dropped+shifted+merged @@ -107,7 +107,7 @@ def test_all_perturbations2(self): self.assertAlmostEqual(perturbed, 9250, places=-2) def test_to_bed(self): - self.bs.to_bed('py_output.bed') + self.bs.to_bed('tests/py_output.bed') self.assertTrue(os.path.exists('tests/py_output.bed')) def test_small_file(self): From e51e4a4fd81684ca8c6aaa68b764b973edb175e7 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 20 Mar 2021 12:23:04 -0500 Subject: [PATCH 0089/1072] move args to arguments.py; use self._precheck before perturbations; use _LOGGER instead of print --- bedshift/arguments.py | 122 +++++++++++++++++++++++ bedshift/bedshift.py | 223 +++++++----------------------------------- 2 files changed, 160 insertions(+), 185 deletions(-) create mode 100644 bedshift/arguments.py diff --git a/bedshift/arguments.py b/bedshift/arguments.py new file mode 100644 index 00000000..9b61d041 --- /dev/null +++ b/bedshift/arguments.py @@ -0,0 +1,122 @@ +import argparse +from bedshift._version import __version__ + + + +class _VersionInHelpParser(argparse.ArgumentParser): + def format_help(self): + """ Add version information to help text. """ + return "version: {}\n".format(__version__) + \ + super(_VersionInHelpParser, self).format_help() + + +def build_argparser(): + """ + Builds argument parser. + + :return: argparse.ArgumentParser + """ + + banner = "%(prog)s - randomize BED files" + additional_description = "\n..." + + parser = _VersionInHelpParser( + description=banner, + epilog=additional_description) + + parser.add_argument( + "-V", "--version", + action="version", + version="%(prog)s {v}".format(v=__version__)) + + parser.add_argument( + "-b", "--bedfile", required=True, + help="File path to bed file.") + + parser.add_argument( + "-l", "--chrom-lengths", type=str, required=False, + help="TSV text file with one row per chromosomes indicating chromosome sizes") + + parser.add_argument( + "-g", "--genome", type=str, required=False, + help="Refgenie genome identifier (used for chrom sizes).") + + parser.add_argument( + "-d", "--droprate", type=float, default=0.0, + help="Droprate parameter") + + parser.add_argument( + "-a", "--addrate", type=float, default=0.0, + help="Addrate parameter") + + parser.add_argument( + "--addmean", type=float, default=320.0, + help="Mean add region length") + + parser.add_argument( + "--addstdev", type=float, default=30.0, + help="Stdev add length") + + parser.add_argument( + "--addfile", type=str, help="Add regions from a bedfile") + + parser.add_argument( + "-s", "--shiftrate", type=float, default=0.0, + help="Shift probability") + + parser.add_argument( + "--shiftmean", type=float, default=0.0, + help="Mean shift") + + parser.add_argument( + "--shiftstdev", type=float, default=150.0, + help="Stdev shift") + + parser.add_argument( + "--shiftfile", type=str, help="Shift regions from a bedfile") + + parser.add_argument( + "-c", "--cutrate", type=float, default=0.0, + help="Cut probability") + + parser.add_argument( + "-m", "--mergerate", type=float, default=0.0, + help="Merge probability. WARNING: will likely create regions that are thousands of base pairs long") + + parser.add_argument( + "--dropfile", type=str, help="Drop regions from a bedfile") + + parser.add_argument( + "-o", "--outputfile", type=str, + help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed") + + parser.add_argument( + "-r", "--repeat", type=int, default=1, + help="the number of times to repeat the operation") + + parser.add_argument( + "-y", "--yaml_config", type=str, + help="run yaml configuration file") + + return parser + +param_msg = """Params: + chrom.sizes file: {chromsizes} + shift: + shift rate: {shiftrate} + shift mean distance: {shiftmean} + shift stdev: {shiftstdev} + shift regions from file: {shiftfile} + add: + rate: {addrate} + add mean length: {addmean} + add stdev: {addstdev} + add file: {addfile} + cut rate: {cutrate} + drop rate: {droprate} + drop regions from file: {dropfile} + merge rate: {mergerate} + outputfile: {outputfile} + repeat: {repeat} + yaml_config: {yaml_config} +""" \ No newline at end of file diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 61c9fdb0..7caeea42 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -1,6 +1,5 @@ """ Perturb regions in bedfiles """ -import argparse import logging import os import sys @@ -11,111 +10,13 @@ import numpy as np import pyranges as pr from bedshift._version import __version__ +from bedshift import arguments _LOGGER = logging.getLogger(__name__) __all__ = ["Bedshift"] -class _VersionInHelpParser(argparse.ArgumentParser): - def format_help(self): - """ Add version information to help text. """ - return "version: {}\n".format(__version__) + \ - super(_VersionInHelpParser, self).format_help() - - -def build_argparser(): - """ - Builds argument parser. - - :return: argparse.ArgumentParser - """ - - banner = "%(prog)s - randomize BED files" - additional_description = "\n..." - - parser = _VersionInHelpParser( - description=banner, - epilog=additional_description) - - parser.add_argument( - "-V", "--version", - action="version", - version="%(prog)s {v}".format(v=__version__)) - - parser.add_argument( - "-b", "--bedfile", required=True, - help="File path to bed file.") - - parser.add_argument( - "-l", "--chrom-lengths", type=str, required=False, - help="TSV text file with one row per chromosomes indicating chromosome sizes" - ) - - parser.add_argument( - "-g", "--genome", type=str, required=False, - help="Refgenie genome identifier (used for chrom sizes).") - - parser.add_argument( - "-d", "--droprate", type=float, default=0.0, - help="Droprate parameter") - - parser.add_argument( - "-a", "--addrate", type=float, default=0.0, - help="Addrate parameter") - - parser.add_argument( - "--addmean", type=float, default=320.0, - help="Mean add region length") - - parser.add_argument( - "--addstdev", type=float, default=30.0, - help="Stdev add length") - - parser.add_argument( - "--addfile", type=str, help="Add regions from a bedfile") - - parser.add_argument( - "-s", "--shiftrate", type=float, default=0.0, - help="Shift probability") - - parser.add_argument( - "--shiftmean", type=float, default=0.0, - help="Mean shift") - - parser.add_argument( - "--shiftstdev", type=float, default=150.0, - help="Stdev shift") - - parser.add_argument( - "--shiftfile", type=str, help="Shift regions from a bedfile") - - parser.add_argument( - "-c", "--cutrate", type=float, default=0.0, - help="Cut probability") - - parser.add_argument( - "-m", "--mergerate", type=float, default=0.0, - help="Merge probability. WARNING: will likely create regions that are thousands of base pairs long") - - parser.add_argument( - "--dropfile", type=str, help="Drop regions from a bedfile") - - parser.add_argument( - "-o", "--outputfile", type=str, - help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed") - - parser.add_argument( - "-r", "--repeat", type=int, default=1, - help="the number of times to repeat the operation") - - parser.add_argument( - "-y", "--yaml_config", type=str, - help="run yaml configuration file") - - return parser - - class Bedshift(object): """ The bedshift object with methods to perturb regions @@ -163,10 +64,19 @@ def reset_bed(self): self.bed = self.original_bed.copy() - def _check_rate(self, rate): - if rate < 0 or rate > 1: - _LOGGER.error("Rate must be between 0 and 1") - sys.exit(1) + def _precheck(self, rate, requiresChromLens=False, isAdd=False): + if isAdd: + if rate < 0: + _LOGGER.error("Rate must be between 0 and 1") + sys.exit(1) + else: + if rate < 0 or rate > 1: + _LOGGER.error("Rate must be between 0 and 1") + sys.exit(1) + if requiresChromLens: + if len(self.chrom_lens) == 0: + _LOGGER.error("chrom.sizes file must be specified when shifting regions") + sys.exit(1) def pick_random_chroms(self, n): @@ -189,14 +99,7 @@ def add(self, addrate, addmean, addstdev): :param float addstdev: the standard deviation of the length of added regions :return int: the number of regions added """ - if addrate < 0: - _LOGGER.error("Rate must be greater than or equal to 0") - sys.exit(1) - if addrate == 0: - return 0 - if len(self.chrom_lens) == 0: - _LOGGER.error("chrom.sizes file must be specified when adding regions") - sys.exit(1) + self._precheck(addrate, requiresChromLens=True, isAdd=True) rows = self.bed.shape[0] num_add = int(rows * addrate) @@ -222,14 +125,7 @@ def add_from_file(self, fp, addrate, delimiter='\t'): :param str fp: the filepath to the other bedfile :return int: the number of regions added """ - if addrate < 0: - _LOGGER.error("Rate must be greater than or equal to 0") - sys.exit(1) - if addrate == 0: - return 0 - if len(self.chrom_lens) == 0: - _LOGGER.error("chrom.sizes file must be specified when adding regions") - sys.exit(1) + self._precheck(addrate, requiresChromLens=True, isAdd=True) rows = self.bed.shape[0] num_add = int(rows * addrate) @@ -252,12 +148,7 @@ def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=None): :param float shiftstdev: the standard deviation of the shift distance :return int: the number of regions shifted """ - self._check_rate(shiftrate) - if shiftrate == 0: - return 0 - if len(self.chrom_lens) == 0: - _LOGGER.error("chrom.sizes file must be specified when shifting regions") - sys.exit(1) + self._precheck(shiftrate, requiresChromLens=True) rows = self.bed.shape[0] if shift_rows == None: @@ -298,12 +189,7 @@ def _shift(self, row, mean, stdev): def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): - self._check_rate(shiftrate) - if shiftrate == 0: - return 0 - if len(self.chrom_lens) == 0: - _LOGGER.error("chrom.sizes file must be specified when shifting regions") - sys.exit(1) + self._precheck(shiftrate) rows = self.bed.shape[0] num_shift = int(rows * shiftrate) @@ -316,7 +202,7 @@ def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): except ValueError: bedname = os.path.basename(self.bedfile_path) shift_file_name = os.path.basename(fp) - print("The number of overlapping regions between "+ + _LOGGER.error("The number of overlapping regions between "+ "{} and {} is {} but the shift ratio provided is trying to shift {} regions."\ .format(bedname, shift_file_name, len(intersect_regions), num_shift)) sys.exit(1) @@ -328,9 +214,8 @@ def cut(self, cutrate): :param float cutrate: the rate to cut regions into two separate regions :return int: the number of regions cut """ - self._check_rate(cutrate) - if cutrate == 0: - return 0 + self._precheck(cutrate) + rows = self.bed.shape[0] cut_rows = random.sample(list(range(rows)), int(rows * cutrate)) new_row_list = [] @@ -373,9 +258,8 @@ def merge(self, mergerate): :return int: number of regions merged """ - self._check_rate(mergerate) - if mergerate == 0: - return 0 + self._precheck(mergerate) + rows = self.bed.shape[0] merge_rows = random.sample(list(range(rows)), int(rows * mergerate)) to_add = [] @@ -408,9 +292,8 @@ def drop(self, droprate): :param float droprate: the rate to drop/remove regions :return int: the number of rows dropped """ - self._check_rate(droprate) - if droprate == 0: - return 0 + self._precheck(droprate) + rows = self.bed.shape[0] drop_rows = random.sample(list(range(rows)), int(rows * droprate)) self.bed = self.bed.drop(drop_rows) @@ -469,7 +352,7 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): drop_rows = drop_bed.shape[0] if num_drop >= drop_rows: - print("Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format(num_drop, drop_rows, drop_rows)) + _LOGGER.warning("Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format(num_drop, drop_rows, drop_rows)) num_drop = drop_rows intersect_regions = self._find_overlap(fp) rows2drop = random.sample(list(range(len(intersect_regions))), num_drop) @@ -536,7 +419,7 @@ def to_bed(self, outfile_name): """ self.bed.sort_values([0,1,2], inplace=True) self.bed.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') - print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.' \ + _LOGGER.info('The output bedfile located in {} has {} regions. The original bedfile had {} regions.' \ .format(outfile_name, self.bed.shape[0], self.original_regions)) @@ -600,7 +483,7 @@ def _read_from_yaml(self, fp): # Loads yaml config data with open(fp, "r") as yaml_file: config_data = yaml.load(yaml_file, Loader=yaml.FullLoader) - print("Loaded configuration settings from {}".format(fp)) + _LOGGER.info("Loaded configuration settings from {}".format(fp)) return config_data @@ -624,7 +507,6 @@ def handle_yaml(self, bedshifter, yaml_fp): std = operation['stdev'] num_added = bedshifter.add(rate, mean, std) num_changed += num_added - print("\t{} regions added.".format(num_added)) ##### add_from_file with no delimiter provided ##### elif set(['add_from_file', 'file', 'rate']) == set(list(operation.keys())): @@ -633,9 +515,8 @@ def handle_yaml(self, bedshifter, yaml_fp): add_rate = operation['rate'] num_added = bedshifter.add_from_file(fp, add_rate) num_changed += num_added - print("\t{} regions added from {}.".format(num_added, fp)) else: - print ("File \'{}\' does not exist.".format(fp)) + _LOGGER.error("File \'{}\' does not exist.".format(fp)) sys.exit(1) ##### add_from_file with delimiter provided ##### @@ -646,9 +527,8 @@ def handle_yaml(self, bedshifter, yaml_fp): delimiter = operation['delimiter'] num_added = bedshifter.add_from_file(fp, add_rate, delimiter) num_changed += num_added - print("\t{} regions added from {}.".format(num_added, fp)) else: - print ("File \'{}\' does not exist.".format(fp)) + _LOGGER.error("File \'{}\' does not exist.".format(fp)) sys.exit(1) ##### drop ##### @@ -656,7 +536,6 @@ def handle_yaml(self, bedshifter, yaml_fp): rate = operation['rate'] num_dropped = bedshifter.drop(rate) num_changed += num_dropped - print("\t{} regions dropped.".format(num_dropped)) ##### drop_from_file with no delimiter provided ##### elif set(['drop_from_file', 'file', 'rate']) == set(list(operation.keys())): @@ -665,9 +544,8 @@ def handle_yaml(self, bedshifter, yaml_fp): drop_rate = operation['rate'] num_dropped = bedshifter.drop_from_file(fp, drop_rate) num_changed += num_dropped - print("\t{} regions dropped from {}.".format(num_dropped, fp)) else: - print ("File \'{}\' does not exist.".format(fp)) + _LOGGER.error("File \'{}\' does not exist.".format(fp)) sys.exit(1) ##### drop_from_file with delimiter provided ##### @@ -678,9 +556,8 @@ def handle_yaml(self, bedshifter, yaml_fp): delimiter = operation['delimiter'] num_dropped = bedshifter.drop_from_file(fp, drop_rate, delimiter) num_changed += num_dropped - print("\t{} regions dropped from {}.".format(num_dropped, fp)) else: - print ("File \'{}\' does not exist.".format(fp)) + _LOGGER.error("File \'{}\' does not exist.".format(fp)) sys.exit(1) ##### shift ##### @@ -690,7 +567,6 @@ def handle_yaml(self, bedshifter, yaml_fp): std = operation['stdev'] num_shifted = bedshifter.shift(rate, mean, std) num_changed += num_shifted - print("\t{} regions shifted.".format(num_shifted)) ##### shift_from_file ##### elif set(['shift_from_file', 'file', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): @@ -701,9 +577,8 @@ def handle_yaml(self, bedshifter, yaml_fp): std = operation['stdev'] num_shifted = bedshifter.shift_from_file(fp, rate, mean, std) num_changed += num_shifted - print("\t{} regions shifted from {}.".format(num_shifted, fp)) else: - print("File \'{}\' does not exist.".format(fp)) + _LOGGER.error("File \'{}\' does not exist.".format(fp)) sys.exit(1) ##### shift_from_file with delimiter provided ##### @@ -716,9 +591,8 @@ def handle_yaml(self, bedshifter, yaml_fp): delimiter = operation['delimiter'] num_shifted = bedshifter.shift_from_file(fp, rate, mean, std, delimiter) num_changed += num_shifted - print("\t{} regions shifted from {}.".format(num_shifted, fp)) else: - print ("File \'{}\' does not exist.".format(fp)) + _LOGGER.error("File \'{}\' does not exist.".format(fp)) sys.exit(1) ##### cut ##### @@ -726,17 +600,15 @@ def handle_yaml(self, bedshifter, yaml_fp): rate = operation['rate'] num_cut = bedshifter.cut(rate) num_changed += num_cut - print("\t{} regions cut.".format(num_cut)) ##### merge ##### elif set(['merge', 'rate']) == set(list(operation.keys())): rate = operation['rate'] num_merged = bedshifter.merge(rate) num_changed += num_merged - print("\t{} regions merged.".format(num_merged)) else: - print("Invalid settings entered in the config file. Please refer to the example below.") + _LOGGER.error("Invalid settings entered in the config file. Please refer to the example below.") self._print_sample_config() sys.exit(1) @@ -746,7 +618,7 @@ def handle_yaml(self, bedshifter, yaml_fp): def main(): """ Primary workflow """ - parser = logmuse.add_logging_options(build_argparser()) + parser = logmuse.add_logging_options(arguments.build_argparser()) args, remaining_args = parser.parse_known_args() global _LOGGER _LOGGER = logmuse.logger_via_cli(args) @@ -774,26 +646,7 @@ def main(): _LOGGER.error("You must provide either chrom sizes or a refgenie genome.") sys.exit(1) - msg = """Params: - chrom.sizes file: {chromsizes} - shift: - shift rate: {shiftrate} - shift mean distance: {shiftmean} - shift stdev: {shiftstdev} - shift regions from file: {shiftfile} - add: - rate: {addrate} - add mean length: {addmean} - add stdev: {addstdev} - add file: {addfile} - cut rate: {cutrate} - drop rate: {droprate} - drop regions from file: {dropfile} - merge rate: {mergerate} - outputfile: {outputfile} - repeat: {repeat} - yaml_config: {yaml_config} -""" + msg = arguments.param_msg if args.repeat < 1: _LOGGER.error("repeats specified is less than 1") @@ -836,7 +689,7 @@ def main(): args.dropfile, args.yaml_config, bedshifter) - print("\t" + str(n) + " regions changed in total.\n") + _LOGGER.info("\t" + str(n) + " regions changed in total.\n") if args.repeat == 1: bedshifter.to_bed(outfile) else: From 8345d026cd8273d25b736b71c7c3bb4a50b66e11 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 20 Mar 2021 13:12:19 -0500 Subject: [PATCH 0090/1072] move yaml handler to separate file --- bedshift/BedshiftYAMLHandler.py | 179 +++++++++++++++++++++++++++++++ bedshift/bedshift.py | 180 ++------------------------------ tests/test.py | 44 ++++---- 3 files changed, 208 insertions(+), 195 deletions(-) create mode 100644 bedshift/BedshiftYAMLHandler.py diff --git a/bedshift/BedshiftYAMLHandler.py b/bedshift/BedshiftYAMLHandler.py new file mode 100644 index 00000000..ac6defd9 --- /dev/null +++ b/bedshift/BedshiftYAMLHandler.py @@ -0,0 +1,179 @@ +import os +import sys +import yaml +import logging + + +class BedshiftYAMLHandler(object): + def __init__(self, bedshifter, yaml_fp, logger=None): + self.bedshifter = bedshifter + self.yaml_fp = yaml_fp + if logger is not None: + self._LOGGER = logger + else: + self._LOGGER = logging.getLogger("BedshiftYAMLHandler") + + + def _print_sample_config(self): + """ + bedshift_operations: + - add: + rate: 0.1 + mean: 100 + stdev: 20 + - drop_from_file: + file: tests/test.bed + rate: 0.1 + delimiter: \t + - shift_from_file: + file: bedshifted_test.bed + rate: 0.3 + mean: 100 + stdev: 200 + - add_from_file: + file: tests/small_test.bed + rate: 0.2 + - cut: + rate: 0.2 + - drop: + rate: 0.30 + - shift: + rate: 0.05 + mean: 100 + stdev: 200 + - merge: + rate: 0.15 + """ + print(self._print_sample_config.__doc__) + print("No changes made.") + + def _read_from_yaml(self, fp): + # Loads yaml config data + with open(fp, "r") as yaml_file: + config_data = yaml.load(yaml_file, Loader=yaml.FullLoader) + self._LOGGER.info("Loaded configuration settings from {}".format(fp)) + return config_data + + + def handle_yaml(self): + """ + Performs operations provided in the yaml config file in the order they were provided. + """ + data = self._read_from_yaml(self.yaml_fp) + operations = [operation for operation in data["bedshift_operations"]] + num_changed = 0 + + for operation in operations: + ##### add ##### + if set(['add', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): + rate = operation['rate'] + mean = operation['mean'] + std = operation['stdev'] + num_added = self.bedshifter.add(rate, mean, std) + num_changed += num_added + + ##### add_from_file with no delimiter provided ##### + elif set(['add_from_file', 'file', 'rate']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + add_rate = operation['rate'] + num_added = self.bedshifter.add_from_file(fp, add_rate) + num_changed += num_added + else: + self._logger.error("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### add_from_file with delimiter provided ##### + elif set(['add_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + add_rate = operation['rate'] + delimiter = operation['delimiter'] + num_added = self.bedshifter.add_from_file(fp, add_rate, delimiter) + num_changed += num_added + else: + self._logger.error("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### drop ##### + elif set(['drop', 'rate']) == set(list(operation.keys())): + rate = operation['rate'] + num_dropped = self.bedshifter.drop(rate) + num_changed += num_dropped + + ##### drop_from_file with no delimiter provided ##### + elif set(['drop_from_file', 'file', 'rate']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + drop_rate = operation['rate'] + num_dropped = self.bedshifter.drop_from_file(fp, drop_rate) + num_changed += num_dropped + else: + self._LOGGER.error("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### drop_from_file with delimiter provided ##### + elif set(['drop_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + drop_rate = operation['rate'] + delimiter = operation['delimiter'] + num_dropped = self.bedshifter.drop_from_file(fp, drop_rate, delimiter) + num_changed += num_dropped + else: + self._LOGGER.error("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### shift ##### + elif set(['shift', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): + rate = operation['rate'] + mean = operation['mean'] + std = operation['stdev'] + num_shifted = self.bedshifter.shift(rate, mean, std) + num_changed += num_shifted + + ##### shift_from_file ##### + elif set(['shift_from_file', 'file', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + rate = operation['rate'] + mean = operation['mean'] + std = operation['stdev'] + num_shifted = self.bedshifter.shift_from_file(fp, rate, mean, std) + num_changed += num_shifted + else: + self._LOGGER.error("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### shift_from_file with delimiter provided ##### + elif set(['shift_from_file', 'file', 'rate', 'mean', 'stdev', 'delimiter']) == set(list(operation.keys())): + fp = operation['file'] + if os.path.isfile(fp): + rate = operation['rate'] + mean = operation['mean'] + std = operation['stdev'] + delimiter = operation['delimiter'] + num_shifted = self.bedshifter.shift_from_file(fp, rate, mean, std, delimiter) + num_changed += num_shifted + else: + self._LOGGER.error("File \'{}\' does not exist.".format(fp)) + sys.exit(1) + + ##### cut ##### + elif set(['cut', 'rate']) == set(list(operation.keys())): + rate = operation['rate'] + num_cut = self.bedshifter.cut(rate) + num_changed += num_cut + + ##### merge ##### + elif set(['merge', 'rate']) == set(list(operation.keys())): + rate = operation['rate'] + num_merged = self.bedshifter.merge(rate) + num_changed += num_merged + + else: + self._LOGGER.error("Invalid settings entered in the config file. Please refer to the example below.") + self._print_sample_config() + sys.exit(1) + + return num_changed diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 7caeea42..7d7f504c 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -4,13 +4,14 @@ import os import sys import random -import yaml import logmuse import pandas as pd import numpy as np import pyranges as pr + from bedshift._version import __version__ from bedshift import arguments +from bedshift import BedshiftYAMLHandler _LOGGER = logging.getLogger(__name__) @@ -391,9 +392,11 @@ def all_perturbations(self, :param string bedshifter: Bedshift instance :return int: the number of total regions perturbed ''' + if yaml: + return BedshiftYAMLHandler.BedshiftYAMLHandler(bedshifter, yaml).handle_yaml() n = 0 if shiftfile: - n+= self.shift_from_file(shiftfile, shiftrate, shiftmean, shiftstdev) + n += self.shift_from_file(shiftfile, shiftrate, shiftmean, shiftstdev) else: n += self.shift(shiftrate, shiftmean, shiftstdev) if addfile: @@ -405,9 +408,7 @@ def all_perturbations(self, if dropfile: n += self.drop_from_file(dropfile, droprate) else: - n +=self.drop(droprate) - if yaml: - n += self.handle_yaml(bedshifter, yaml) + n += self.drop(droprate) return n @@ -446,175 +447,6 @@ def read_bed(self, bedfile_path, delimiter='\t'): return df - def _print_sample_config(self): - """ - bedshift_operations: - - add: - rate: 0.1 - mean: 100 - stdev: 20 - - drop_from_file: - file: tests/test.bed - rate: 0.1 - delimiter: \t - - shift_from_file: - file: bedshifted_test.bed - rate: 0.3 - mean: 100 - stdev: 200 - - add_from_file: - file: tests/small_test.bed - rate: 0.2 - - cut: - rate: 0.2 - - drop: - rate: 0.30 - - shift: - rate: 0.05 - mean: 100 - stdev: 200 - - merge: - rate: 0.15 - """ - print(self._print_sample_config.__doc__) - print("No changes made.") - - def _read_from_yaml(self, fp): - # Loads yaml config data - with open(fp, "r") as yaml_file: - config_data = yaml.load(yaml_file, Loader=yaml.FullLoader) - _LOGGER.info("Loaded configuration settings from {}".format(fp)) - return config_data - - - def handle_yaml(self, bedshifter, yaml_fp): - """ - Performs operations provided in the yaml config file in the order they were provided. - - :param str bedshifter: the current instance of Bedshift - :param float yaml_fp: the path to the configuration file - :return int: the number of total regions perturbed - """ - data = self._read_from_yaml(yaml_fp) - operations = [operation for operation in data["bedshift_operations"]] - num_changed = 0 - - for operation in operations: - ##### add ##### - if set(['add', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): - rate = operation['rate'] - mean = operation['mean'] - std = operation['stdev'] - num_added = bedshifter.add(rate, mean, std) - num_changed += num_added - - ##### add_from_file with no delimiter provided ##### - elif set(['add_from_file', 'file', 'rate']) == set(list(operation.keys())): - fp = operation['file'] - if os.path.isfile(fp): - add_rate = operation['rate'] - num_added = bedshifter.add_from_file(fp, add_rate) - num_changed += num_added - else: - _LOGGER.error("File \'{}\' does not exist.".format(fp)) - sys.exit(1) - - ##### add_from_file with delimiter provided ##### - elif set(['add_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): - fp = operation['file'] - if os.path.isfile(fp): - add_rate = operation['rate'] - delimiter = operation['delimiter'] - num_added = bedshifter.add_from_file(fp, add_rate, delimiter) - num_changed += num_added - else: - _LOGGER.error("File \'{}\' does not exist.".format(fp)) - sys.exit(1) - - ##### drop ##### - elif set(['drop', 'rate']) == set(list(operation.keys())): - rate = operation['rate'] - num_dropped = bedshifter.drop(rate) - num_changed += num_dropped - - ##### drop_from_file with no delimiter provided ##### - elif set(['drop_from_file', 'file', 'rate']) == set(list(operation.keys())): - fp = operation['file'] - if os.path.isfile(fp): - drop_rate = operation['rate'] - num_dropped = bedshifter.drop_from_file(fp, drop_rate) - num_changed += num_dropped - else: - _LOGGER.error("File \'{}\' does not exist.".format(fp)) - sys.exit(1) - - ##### drop_from_file with delimiter provided ##### - elif set(['drop_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): - fp = operation['file'] - if os.path.isfile(fp): - drop_rate = operation['rate'] - delimiter = operation['delimiter'] - num_dropped = bedshifter.drop_from_file(fp, drop_rate, delimiter) - num_changed += num_dropped - else: - _LOGGER.error("File \'{}\' does not exist.".format(fp)) - sys.exit(1) - - ##### shift ##### - elif set(['shift', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): - rate = operation['rate'] - mean = operation['mean'] - std = operation['stdev'] - num_shifted = bedshifter.shift(rate, mean, std) - num_changed += num_shifted - - ##### shift_from_file ##### - elif set(['shift_from_file', 'file', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): - fp = operation['file'] - if os.path.isfile(fp): - rate = operation['rate'] - mean = operation['mean'] - std = operation['stdev'] - num_shifted = bedshifter.shift_from_file(fp, rate, mean, std) - num_changed += num_shifted - else: - _LOGGER.error("File \'{}\' does not exist.".format(fp)) - sys.exit(1) - - ##### shift_from_file with delimiter provided ##### - elif set(['shift_from_file', 'file', 'rate', 'mean', 'stdev', 'delimiter']) == set(list(operation.keys())): - fp = operation['file'] - if os.path.isfile(fp): - rate = operation['rate'] - mean = operation['mean'] - std = operation['stdev'] - delimiter = operation['delimiter'] - num_shifted = bedshifter.shift_from_file(fp, rate, mean, std, delimiter) - num_changed += num_shifted - else: - _LOGGER.error("File \'{}\' does not exist.".format(fp)) - sys.exit(1) - - ##### cut ##### - elif set(['cut', 'rate']) == set(list(operation.keys())): - rate = operation['rate'] - num_cut = bedshifter.cut(rate) - num_changed += num_cut - - ##### merge ##### - elif set(['merge', 'rate']) == set(list(operation.keys())): - rate = operation['rate'] - num_merged = bedshifter.merge(rate) - num_changed += num_merged - - else: - _LOGGER.error("Invalid settings entered in the config file. Please refer to the example below.") - self._print_sample_config() - sys.exit(1) - - return num_changed - - def main(): """ Primary workflow """ diff --git a/tests/test.py b/tests/test.py index 74d27a9e..8f3c2d12 100755 --- a/tests/test.py +++ b/tests/test.py @@ -2,6 +2,7 @@ import os from bedshift import bedshift +from bedshift import BedshiftYAMLHandler class TestBedshift(unittest.TestCase): @@ -68,27 +69,6 @@ def test_drop_from_file_zero_rate(self): self.assertEqual(dropped, 0) self.bs.reset_bed() - def test_handle_yaml(self): - yamled = self.bs.handle_yaml(bedshifter=self.bs, yaml_fp="tests/bedshift_analysis.yaml") - self.bs.reset_bed() - - added = self.bs.add(addrate=0.1, addmean=100, addstdev=20) - f_drop_10 = self.bs.drop_from_file(fp="tests/test.bed", droprate=0.1) - f_shift_30 = self.bs.shift_from_file(fp="tests/bedshifted_test.bed", - shiftrate=0.50, - shiftmean=100, - shiftstdev=200) - f_added_20 = self.bs.add_from_file(fp="tests/small_test.bed", addrate=0.2) - cut = self.bs.cut(cutrate=0.2) - shifted = self.bs.shift(shiftrate=0.3, shiftmean=100, shiftstdev=200) - dropped = self.bs.drop(droprate=0.3) - merged = self.bs.merge(mergerate=0.15) - - total = added+f_drop_10+f_shift_30+f_added_20+cut+dropped+shifted+merged - - self.assertAlmostEqual(yamled, total, places=-2) - self.bs.reset_bed() - def test_all_perturbations1(self): perturbed = self.bs.all_perturbations( addrate=0.5, addmean=320.0, addstdev=20.0, @@ -127,3 +107,25 @@ def test_small_file(self): added = bs_small.add(2.0, 100, 50) self.assertEqual(added, 4) + +class TestBedshiftYAMLHandler(unittest.TestCase): + def test_handle_yaml(self): + yamled = self.bs.handle_yaml(bedshifter=self.bs, yaml_fp="tests/bedshift_analysis.yaml") + self.bs.reset_bed() + + added = self.bs.add(addrate=0.1, addmean=100, addstdev=20) + f_drop_10 = self.bs.drop_from_file(fp="tests/test.bed", droprate=0.1) + f_shift_30 = self.bs.shift_from_file(fp="tests/bedshifted_test.bed", + shiftrate=0.50, + shiftmean=100, + shiftstdev=200) + f_added_20 = self.bs.add_from_file(fp="tests/small_test.bed", addrate=0.2) + cut = self.bs.cut(cutrate=0.2) + shifted = self.bs.shift(shiftrate=0.3, shiftmean=100, shiftstdev=200) + dropped = self.bs.drop(droprate=0.3) + merged = self.bs.merge(mergerate=0.15) + + total = added+f_drop_10+f_shift_30+f_added_20+cut+dropped+shifted+merged + + self.assertAlmostEqual(yamled, total, places=-2) + self.bs.reset_bed() From 4b65eec3b98ca4932ca9794ab1171e116f5d1b96 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 21 Mar 2021 13:00:18 -0500 Subject: [PATCH 0091/1072] foo --- tests/test.py | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/tests/test.py b/tests/test.py index 8f3c2d12..e3189fc2 100755 --- a/tests/test.py +++ b/tests/test.py @@ -110,22 +110,23 @@ def test_small_file(self): class TestBedshiftYAMLHandler(unittest.TestCase): def test_handle_yaml(self): - yamled = self.bs.handle_yaml(bedshifter=self.bs, yaml_fp="tests/bedshift_analysis.yaml") - self.bs.reset_bed() + bedshifter = bedshift.Bedshift('tests/test.bed', chrom_sizes="tests/hg38.chrom.sizes") + yamled = BedshiftYAMLHandler.BedshiftYAMLHandler(bedshifter=bedshifter, yaml_fp="tests/bedshift_analysis.yaml").handle_yaml() + bedshifter.reset_bed() - added = self.bs.add(addrate=0.1, addmean=100, addstdev=20) - f_drop_10 = self.bs.drop_from_file(fp="tests/test.bed", droprate=0.1) - f_shift_30 = self.bs.shift_from_file(fp="tests/bedshifted_test.bed", + added = bedshifter.add(addrate=0.1, addmean=100, addstdev=20) + f_drop_10 = bedshifter.drop_from_file(fp="tests/test.bed", droprate=0.1) + f_shift_30 = bedshifter.shift_from_file(fp="tests/bedshifted_test.bed", shiftrate=0.50, shiftmean=100, shiftstdev=200) - f_added_20 = self.bs.add_from_file(fp="tests/small_test.bed", addrate=0.2) - cut = self.bs.cut(cutrate=0.2) - shifted = self.bs.shift(shiftrate=0.3, shiftmean=100, shiftstdev=200) - dropped = self.bs.drop(droprate=0.3) - merged = self.bs.merge(mergerate=0.15) + f_added_20 = bedshifter.add_from_file(fp="tests/small_test.bed", addrate=0.2) + cut = bedshifter.cut(cutrate=0.2) + shifted = bedshifter.shift(shiftrate=0.3, shiftmean=100, shiftstdev=200) + dropped = bedshifter.drop(droprate=0.3) + merged = bedshifter.merge(mergerate=0.15) total = added+f_drop_10+f_shift_30+f_added_20+cut+dropped+shifted+merged self.assertAlmostEqual(yamled, total, places=-2) - self.bs.reset_bed() + bedshifter.reset_bed() From 7c5225242b2c32699166049d3322dee96f434abb Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 20 Mar 2021 13:31:30 -0500 Subject: [PATCH 0092/1072] add chrom sizes test --- tests/hg19.chrom.sizes | 93 ++++++++++++++++++++++++++++++++++++++++++ tests/test.py | 4 ++ 2 files changed, 97 insertions(+) create mode 100644 tests/hg19.chrom.sizes diff --git a/tests/hg19.chrom.sizes b/tests/hg19.chrom.sizes new file mode 100644 index 00000000..ae5e9d32 --- /dev/null +++ b/tests/hg19.chrom.sizes @@ -0,0 +1,93 @@ +chr1 249250621 +chr2 243199373 +chr3 198022430 +chr4 191154276 +chr5 180915260 +chr6 171115067 +chr7 159138663 +chr8 146364022 +chr9 141213431 +chr10 135534747 +chr11 135006516 +chr12 133851895 +chr13 115169878 +chr14 107349540 +chr15 102531392 +chr16 90354753 +chr17 81195210 +chr18 78077248 +chr19 59128983 +chr20 63025520 +chr21 48129895 +chr22 51304566 +chrX 155270560 +chrY 59373566 +chrM 16571 +chr1_gl000191_random 106433 +chr1_gl000192_random 547496 +chr4_ctg9_hap1 590426 +chr4_gl000193_random 189789 +chr4_gl000194_random 191469 +chr6_apd_hap1 4622290 +chr6_cox_hap2 4795371 +chr6_dbb_hap3 4610396 +chr6_mann_hap4 4683263 +chr6_mcf_hap5 4833398 +chr6_qbl_hap6 4611984 +chr6_ssto_hap7 4928567 +chr7_gl000195_random 182896 +chr8_gl000196_random 38914 +chr8_gl000197_random 37175 +chr9_gl000198_random 90085 +chr9_gl000199_random 169874 +chr9_gl000200_random 187035 +chr9_gl000201_random 36148 +chr11_gl000202_random 40103 +chr17_ctg5_hap1 1680828 +chr17_gl000203_random 37498 +chr17_gl000204_random 81310 +chr17_gl000205_random 174588 +chr17_gl000206_random 41001 +chr18_gl000207_random 4262 +chr19_gl000208_random 92689 +chr19_gl000209_random 159169 +chr21_gl000210_random 27682 +chrUn_gl000211 166566 +chrUn_gl000212 186858 +chrUn_gl000213 164239 +chrUn_gl000214 137718 +chrUn_gl000215 172545 +chrUn_gl000216 172294 +chrUn_gl000217 172149 +chrUn_gl000218 161147 +chrUn_gl000219 179198 +chrUn_gl000220 161802 +chrUn_gl000221 155397 +chrUn_gl000222 186861 +chrUn_gl000223 180455 +chrUn_gl000224 179693 +chrUn_gl000225 211173 +chrUn_gl000226 15008 +chrUn_gl000227 128374 +chrUn_gl000228 129120 +chrUn_gl000229 19913 +chrUn_gl000230 43691 +chrUn_gl000231 27386 +chrUn_gl000232 40652 +chrUn_gl000233 45941 +chrUn_gl000234 40531 +chrUn_gl000235 34474 +chrUn_gl000236 41934 +chrUn_gl000237 45867 +chrUn_gl000238 39939 +chrUn_gl000239 33824 +chrUn_gl000240 41933 +chrUn_gl000241 42152 +chrUn_gl000242 43523 +chrUn_gl000243 43341 +chrUn_gl000244 39929 +chrUn_gl000245 36651 +chrUn_gl000246 38154 +chrUn_gl000247 36422 +chrUn_gl000248 39786 +chrUn_gl000249 38502 diff --git a/tests/test.py b/tests/test.py index e3189fc2..cc545dd7 100755 --- a/tests/test.py +++ b/tests/test.py @@ -13,6 +13,10 @@ def test_read_bed(self): reader = bedshift.Bedshift('tests/header_test.bed') self.assertTrue(list(reader.bed.columns), [0,1,2,3]) + def test_read_chrom_sizes(self): + self.bs._read_chromsizes("tests/hg19.chrom.sizes") + self.assertEqual(len(self.bs.chrom_lens), 93) + def test_add(self): added = self.bs.add(0.1, 100, 20) self.assertEqual(added, 1000) From 9483483e8bd53f1cb3ea29aa8726dcc7e7f33dd3 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 20 Mar 2021 13:46:40 -0500 Subject: [PATCH 0093/1072] report changed regions using chars --- bedshift/bedshift.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 7d7f504c..12a11cb6 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -36,8 +36,8 @@ def __init__(self, bedfile_path, chrom_sizes=None, delimiter='\t'): if chrom_sizes: self._read_chromsizes(chrom_sizes) df = self.read_bed(bedfile_path, delimiter=delimiter) - self.original_regions = df.shape[0] - self.bed = df.astype({1: 'int64', 2: 'int64', 3: 'int64'}) \ + self.original_num_regions = df.shape[0] + self.bed = df.astype({0: 'object', 1: 'int64', 2: 'int64', 3: 'object'}) \ .sort_values([0, 1, 2]).reset_index(drop=True) self.original_bed = self.bed.copy() @@ -113,7 +113,7 @@ def add(self, addrate, addmean, addstdev): new_regions[0].append(chrom_str) new_regions[1].append(start) new_regions[2].append(end) - new_regions[3].append(3) + new_regions[3].append('A') self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) return num_add @@ -135,7 +135,7 @@ def add_from_file(self, fp, addrate, delimiter='\t'): num_add = df.shape[0] add_rows = random.sample(list(range(df.shape[0])), num_add) add_df = df.loc[add_rows].reset_index(drop=True) - add_df[3] = pd.Series([3] * add_df.shape[0]) + add_df[3] = pd.Series(['A'] * add_df.shape[0]) self.bed = self.bed.append(add_df, ignore_index=True) return num_add @@ -186,7 +186,7 @@ def _shift(self, row, mean, stdev): # check if the region is shifted out of chromosome length bounds return None, None - return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: 1} + return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: 'S'} def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): @@ -249,7 +249,7 @@ def _cut(self, row): new_segs = self.__shift(new_segs, 1, meanshift, stdevshift) ''' - return row, [{0: chrom, 1: start, 2: thecut, 3: 2}, {0: chrom, 1: thecut, 2: end, 3: 2}] + return row, [{0: chrom, 1: start, 2: thecut, 3: 'C'}, {0: chrom, 1: thecut, 2: end, 3: 'C'}] def merge(self, mergerate): """ @@ -258,7 +258,6 @@ def merge(self, mergerate): :param float mergerate: the rate to merge two regions into one :return int: number of regions merged """ - self._precheck(mergerate) rows = self.bed.shape[0] @@ -284,7 +283,7 @@ def _merge(self, row): chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] end = self.bed.loc[row+1][2] - return [row, row+1], {0: chrom, 1: start, 2: end, 3: 4} + return [row, row+1], {0: chrom, 1: start, 2: end, 3: 'M'} def drop(self, droprate): """ @@ -421,7 +420,7 @@ def to_bed(self, outfile_name): self.bed.sort_values([0,1,2], inplace=True) self.bed.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') _LOGGER.info('The output bedfile located in {} has {} regions. The original bedfile had {} regions.' \ - .format(outfile_name, self.bed.shape[0], self.original_regions)) + .format(outfile_name, self.bed.shape[0], self.original_num_regions)) def read_bed(self, bedfile_path, delimiter='\t'): @@ -443,7 +442,7 @@ def read_bed(self, bedfile_path, delimiter='\t'): if not str(df.iloc[0, 1]).isdigit(): df = df[1:] - df[3] = 0 # column indicating which modifications were made + df[3] = '-' # column indicating which modifications were made return df From 3abc5e2b06bad76ce9c5230df19bc423f2b8857e Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 20 Mar 2021 14:05:02 -0500 Subject: [PATCH 0094/1072] add docstrings --- bedshift/BedshiftYAMLHandler.py | 13 ++++++---- bedshift/bedshift.py | 42 ++++++++++++++++++++++++++------- 2 files changed, 42 insertions(+), 13 deletions(-) diff --git a/bedshift/BedshiftYAMLHandler.py b/bedshift/BedshiftYAMLHandler.py index ac6defd9..48d1c51d 100644 --- a/bedshift/BedshiftYAMLHandler.py +++ b/bedshift/BedshiftYAMLHandler.py @@ -6,6 +6,13 @@ class BedshiftYAMLHandler(object): def __init__(self, bedshifter, yaml_fp, logger=None): + """ + Handles Bedshift perturbations from yaml files + + :param bedshift.Bedshift bedshifter: a Bedshift object + :param str yaml_fp: path to yaml file + :param logging.logger logger: logger object + """ self.bedshifter = bedshifter self.yaml_fp = yaml_fp if logger is not None: @@ -44,11 +51,9 @@ def _print_sample_config(self): - merge: rate: 0.15 """ - print(self._print_sample_config.__doc__) - print("No changes made.") + self._LOGGER.info(self._print_sample_config.__doc__) def _read_from_yaml(self, fp): - # Loads yaml config data with open(fp, "r") as yaml_file: config_data = yaml.load(yaml_file, Loader=yaml.FullLoader) self._LOGGER.info("Loaded configuration settings from {}".format(fp)) @@ -172,7 +177,7 @@ def handle_yaml(self): num_changed += num_merged else: - self._LOGGER.error("Invalid settings entered in the config file. Please refer to the example below.") + self._LOGGER.error("\n\nInvalid settings entered in the config file. Please refer to the example below.\n\n") self._print_sample_config() sys.exit(1) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 12a11cb6..3efe2a1a 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -43,6 +43,11 @@ def __init__(self, bedfile_path, chrom_sizes=None, delimiter='\t'): def _read_chromsizes(self, fp): + """ + Read chromosome sizes file + + :param str fp: path to the chrom sizes file + """ try: with open(fp) as f: for line in f: @@ -66,14 +71,19 @@ def reset_bed(self): def _precheck(self, rate, requiresChromLens=False, isAdd=False): - if isAdd: - if rate < 0: - _LOGGER.error("Rate must be between 0 and 1") - sys.exit(1) - else: - if rate < 0 or rate > 1: - _LOGGER.error("Rate must be between 0 and 1") - sys.exit(1) + """ + Check if the rate of perturbation is too high or low + + :param float rate: the rate of perturbation + :param bool requiresChromLens: check if the perturbation requires a chromosome lengths file + :param bool isAdd: if True, do a special check for the add rate + """ + if isAdd and rate < 0: + _LOGGER.error("Rate must be between 0 and 1") + sys.exit(1) + elif rate < 0 or rate > 1: + _LOGGER.error("Rate must be between 0 and 1") + sys.exit(1) if requiresChromLens: if len(self.chrom_lens) == 0: _LOGGER.error("chrom.sizes file must be specified when shifting regions") @@ -190,6 +200,16 @@ def _shift(self, row, mean, stdev): def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): + """ + Shift regions that overlap the specified file's regions + + :param str fp: the file on which to find overlaps + :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) + :param float shiftmean: the mean shift distance + :param float shiftstdev: the standard deviation of the shift distance + :param str delimiter: the delimiter used in fp + :return int: the number of regions shifted + """ self._precheck(shiftrate) rows = self.bed.shape[0] @@ -303,7 +323,11 @@ def drop(self, droprate): def _find_overlap(self, fp, reference=None): """ - find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file. + Find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file. + + :param str fp: path to file, or pandas DataFrame, for comparison + :param str reference: path to file, or pandas DataFrame, for reference + :return pd.DataFrame: a DataFrame of overlapping regions """ if reference is None: reference_bed = self.original_bed.copy() From 0297ab045c1a36a96e77bb4464b15ce11d1078f5 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sat, 20 Mar 2021 14:14:31 -0500 Subject: [PATCH 0095/1072] add check rate test --- bedshift/bedshift.py | 14 ++++++++------ tests/test.py | 4 ++++ 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 3efe2a1a..3966cee3 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -78,12 +78,14 @@ def _precheck(self, rate, requiresChromLens=False, isAdd=False): :param bool requiresChromLens: check if the perturbation requires a chromosome lengths file :param bool isAdd: if True, do a special check for the add rate """ - if isAdd and rate < 0: - _LOGGER.error("Rate must be between 0 and 1") - sys.exit(1) - elif rate < 0 or rate > 1: - _LOGGER.error("Rate must be between 0 and 1") - sys.exit(1) + if isAdd: + if rate < 0: + _LOGGER.error("Rate must be greater than 0") + sys.exit(1) + else: + if rate < 0 or rate > 1: + _LOGGER.error("Rate must be between 0 and 1") + sys.exit(1) if requiresChromLens: if len(self.chrom_lens) == 0: _LOGGER.error("chrom.sizes file must be specified when shifting regions") diff --git a/tests/test.py b/tests/test.py index cc545dd7..03beed0a 100755 --- a/tests/test.py +++ b/tests/test.py @@ -22,6 +22,10 @@ def test_add(self): self.assertEqual(added, 1000) self.bs.reset_bed() + def test_check_rate(self): + self.assertRaises(SystemExit, self.bs.shift, -0.1, 250, 250) + self.assertRaises(SystemExit, self.bs.cut, 1.5) + def test_add_high_rate(self): added = self.bs.add(1.23, 500, 123) self.assertEqual(added, 12300) From 493c1927e333abe9bfae3e00c369c4ff03393cf4 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 21 Mar 2021 15:29:56 -0500 Subject: [PATCH 0096/1072] fix precheck for add from file --- bedshift/bedshift.py | 56 ++++++++++++++++++++------------------------ 1 file changed, 26 insertions(+), 30 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 3966cee3..2dea5d45 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -138,7 +138,7 @@ def add_from_file(self, fp, addrate, delimiter='\t'): :param str fp: the filepath to the other bedfile :return int: the number of regions added """ - self._precheck(addrate, requiresChromLens=True, isAdd=True) + self._precheck(addrate, requiresChromLens=False, isAdd=True) rows = self.bed.shape[0] num_add = int(rows * addrate) @@ -323,6 +323,31 @@ def drop(self, droprate): return len(drop_rows) + def drop_from_file(self, fp, droprate, delimiter='\t'): + """ + drop regions that overlap between the reference bedfile and the provided bedfile. + + :param float droprate: the rate to drop regions + :param str fp: the filepath to the other bedfile containing regions to be dropped + :return int: the number of regions dropped + """ + self._precheck(droprate) + + rows = self.bed.shape[0] + num_drop = int(rows * droprate) + drop_bed = self.read_bed(fp, delimiter=delimiter) + drop_rows = drop_bed.shape[0] + + if num_drop >= drop_rows: + _LOGGER.warning("Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format(num_drop, drop_rows, drop_rows)) + num_drop = drop_rows + intersect_regions = self._find_overlap(fp) + rows2drop = random.sample(list(range(len(intersect_regions))), num_drop) + + self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index(drop=True) + return num_drop + + def _find_overlap(self, fp, reference=None): """ Find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file. @@ -358,35 +383,6 @@ def _find_overlap(self, fp, reference=None): return intersection - def drop_from_file(self, fp, droprate, delimiter='\t'): - """ - drop regions that overlap between the reference bedfile and the provided bedfile. - - :param float droprate: the rate to drop regions - :param str fp: the filepath to the other bedfile containing regions to be dropped - :return int: the number of regions dropped - """ - if droprate < 0: - _LOGGER.error("Rate must be greater than or equal to 0") - sys.exit(1) - if droprate == 0: - return 0 - - rows = self.bed.shape[0] - num_drop = int(rows * droprate) - drop_bed = self.read_bed(fp, delimiter=delimiter) - drop_rows = drop_bed.shape[0] - - if num_drop >= drop_rows: - _LOGGER.warning("Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format(num_drop, drop_rows, drop_rows)) - num_drop = drop_rows - intersect_regions = self._find_overlap(fp) - rows2drop = random.sample(list(range(len(intersect_regions))), num_drop) - - self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index(drop=True) - return num_drop - - def all_perturbations(self, addrate=0.0, addmean=320.0, addstdev=30.0, addfile=None, From dea8e545ae503d3dd0597f894db934067ff9b36a Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 21 Mar 2021 15:50:09 -0500 Subject: [PATCH 0097/1072] only call perturbation function if rate > 0 --- bedshift/bedshift.py | 33 +++++++++++++++++++-------------- 1 file changed, 19 insertions(+), 14 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 2dea5d45..ad5422fa 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -416,20 +416,25 @@ def all_perturbations(self, if yaml: return BedshiftYAMLHandler.BedshiftYAMLHandler(bedshifter, yaml).handle_yaml() n = 0 - if shiftfile: - n += self.shift_from_file(shiftfile, shiftrate, shiftmean, shiftstdev) - else: - n += self.shift(shiftrate, shiftmean, shiftstdev) - if addfile: - n += self.add_from_file(addfile, addrate) - else: - n += self.add(addrate, addmean, addstdev) - n += self.cut(cutrate) - n += self.merge(mergerate) - if dropfile: - n += self.drop_from_file(dropfile, droprate) - else: - n += self.drop(droprate) + if shiftrate > 0: + if shiftfile: + n += self.shift_from_file(shiftfile, shiftrate, shiftmean, shiftstdev) + else: + n += self.shift(shiftrate, shiftmean, shiftstdev) + if addrate > 0: + if addfile: + n += self.add_from_file(addfile, addrate) + else: + n += self.add(addrate, addmean, addstdev) + if cutrate > 0: + n += self.cut(cutrate) + if mergerate > 0: + n += self.merge(mergerate) + if droprate > 0: + if dropfile: + n += self.drop_from_file(dropfile, droprate) + else: + n += self.drop(droprate) return n From 184eab5961aaec79fcb0635542fbb96c69b602ba Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 21 Mar 2021 15:57:59 -0500 Subject: [PATCH 0098/1072] fix merged regions reporting --- bedshift/bedshift.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index ad5422fa..41b5bd3e 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -288,19 +288,19 @@ def merge(self, mergerate): to_drop = [] for row in merge_rows: drop_row, add_row = self._merge(row) - if add_row: + if drop_row is not None and add_row is not None: to_add.append(add_row) - to_drop.extend(drop_row) + to_drop.extend(drop_row) self.bed = self.bed.drop(to_drop) self.bed = self.bed.append(to_add, ignore_index=True) self.bed = self.bed.reset_index(drop=True) - return len(merge_rows) + return len(to_drop) def _merge(self, row): # check if the regions being merged are on the same chromosome if row + 1 not in self.bed.index or self.bed.loc[row][0] != self.bed.loc[row+1][0]: - return [], None + return None, None chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] From 1c0d11ccdfff10bad2b4592c472a367b2101db0f Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 21 Mar 2021 21:20:27 -0500 Subject: [PATCH 0099/1072] add test files; send warning instead of error in from_file --- .gitignore | 2 ++ bedshift/bedshift.py | 59 ++++++++++++++++++------------------------- tests/from_file.bed | 4 +++ tests/small_test2.bed | 7 +++++ tests/test.py | 4 +-- 5 files changed, 40 insertions(+), 36 deletions(-) create mode 100644 tests/from_file.bed create mode 100644 tests/small_test2.bed diff --git a/.gitignore b/.gitignore index c791e63e..5f7d71dd 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,8 @@ bedshifted* *.bed !tests/test.bed !tests/small_test.bed +!tests/small_test2.bed +!tests/from_file.bed !tests/header_test.bed examples/sh_output* examples/py_output* diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 41b5bd3e..722e0b30 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -88,7 +88,7 @@ def _precheck(self, rate, requiresChromLens=False, isAdd=False): sys.exit(1) if requiresChromLens: if len(self.chrom_lens) == 0: - _LOGGER.error("chrom.sizes file must be specified when shifting regions") + _LOGGER.error("chrom.sizes file must be specified") sys.exit(1) @@ -143,9 +143,11 @@ def add_from_file(self, fp, addrate, delimiter='\t'): rows = self.bed.shape[0] num_add = int(rows * addrate) df = self.read_bed(fp, delimiter=delimiter) - if num_add > df.shape[0]: - num_add = df.shape[0] - add_rows = random.sample(list(range(df.shape[0])), num_add) + dflen = len(df) + if num_add > dflen: + _LOGGER.warning("Number of regions to be added ({}) is larger than the provided bedfile size ({}). Adding {} regions.".format(num_add, dflen, dflen)) + num_add = dflen + add_rows = random.sample(list(range(dflen)), num_add) add_df = df.loc[add_rows].reset_index(drop=True) add_df[3] = pd.Series(['A'] * add_df.shape[0]) self.bed = self.bed.append(add_df, ignore_index=True) @@ -172,7 +174,7 @@ def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=None): invalid_shifted = 0 for row in shift_rows: drop_row, new_region = self._shift(row, shiftmean, shiftstdev) # shifted rows display a 1 - if drop_row is not None and new_region is not None: + if drop_row is not None and new_region: num_shifted += 1 new_row_list.append(new_region) to_drop.append(drop_row) @@ -192,8 +194,6 @@ def _shift(self, row, mean, stdev): chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] end = self.bed.loc[row][2] - _LOGGER.debug("Chrom lengths: {}".format(str(self.chrom_lens))) - _LOGGER.debug("chrom: {}".format(str(chrom))) if start + theshift < 0 or end + theshift > self.chrom_lens[str(chrom)]: # check if the region is shifted out of chromosome length bounds return None, None @@ -212,23 +212,19 @@ def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): :param str delimiter: the delimiter used in fp :return int: the number of regions shifted """ - self._precheck(shiftrate) + self._precheck(shiftrate, requiresChromLens=True) rows = self.bed.shape[0] num_shift = int(rows * shiftrate) - shift_bed = self.read_bed(fp, delimiter=delimiter) intersect_regions = self._find_overlap(fp) - try: - rows2shift = random.sample(list(range(len(intersect_regions))), num_shift) - return self.shift(shiftrate, shiftmean, shiftstdev, rows2shift) - except ValueError: - bedname = os.path.basename(self.bedfile_path) - shift_file_name = os.path.basename(fp) - _LOGGER.error("The number of overlapping regions between "+ - "{} and {} is {} but the shift ratio provided is trying to shift {} regions."\ - .format(bedname, shift_file_name, len(intersect_regions), num_shift)) - sys.exit(1) + interlen = len(intersect_regions) + if num_shift > interlen: + _LOGGER.warning("Number of regions to be shifted ({}) is larger than the provided bedfile size ({}). Shifting {} regions.".format(num_shift, interlen, interlen)) + num_shift = interlen + rows2shift = random.sample(list(range(interlen)), num_shift) + return self.shift(shiftrate, shiftmean, shiftstdev, rows2shift) + def cut(self, cutrate): """ @@ -287,10 +283,10 @@ def merge(self, mergerate): to_add = [] to_drop = [] for row in merge_rows: - drop_row, add_row = self._merge(row) - if drop_row is not None and add_row is not None: + drop_rows, add_row = self._merge(row) + if drop_rows and add_row: to_add.append(add_row) - to_drop.extend(drop_row) + to_drop.extend(drop_rows) self.bed = self.bed.drop(to_drop) self.bed = self.bed.append(to_add, ignore_index=True) self.bed = self.bed.reset_index(drop=True) @@ -336,14 +332,13 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): rows = self.bed.shape[0] num_drop = int(rows * droprate) drop_bed = self.read_bed(fp, delimiter=delimiter) - drop_rows = drop_bed.shape[0] - - if num_drop >= drop_rows: - _LOGGER.warning("Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format(num_drop, drop_rows, drop_rows)) - num_drop = drop_rows - intersect_regions = self._find_overlap(fp) - rows2drop = random.sample(list(range(len(intersect_regions))), num_drop) + intersect_regions = self._find_overlap(drop_bed) + interlen = len(intersect_regions) + if num_drop > interlen: + _LOGGER.warning("Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format(num_drop, interlen, interlen)) + num_drop = interlen + rows2drop = random.sample(list(range(interlen)), num_drop) self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index(drop=True) return num_drop @@ -353,7 +348,7 @@ def _find_overlap(self, fp, reference=None): Find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file. :param str fp: path to file, or pandas DataFrame, for comparison - :param str reference: path to file, or pandas DataFrame, for reference + :param str reference: path to file, or pandas DataFrame, for reference. If None, then defaults to the original BED file provided to the Bedshift constructor :return pd.DataFrame: a DataFrame of overlapping regions """ if reference is None: @@ -499,10 +494,6 @@ def main(): except ModuleNotFoundError: _LOGGER.error("You must have package refgenconf installed to use a refgenie genome") sys.exit(1) - else: - if args.addrate > 0 or args.shiftrate > 0: - _LOGGER.error("You must provide either chrom sizes or a refgenie genome.") - sys.exit(1) msg = arguments.param_msg diff --git a/tests/from_file.bed b/tests/from_file.bed new file mode 100644 index 00000000..151d15d6 --- /dev/null +++ b/tests/from_file.bed @@ -0,0 +1,4 @@ +chr1 2100 2550 +chr15 12345600 12400000 +chrY 500 1100 +chr16 57682000 57683001 \ No newline at end of file diff --git a/tests/small_test2.bed b/tests/small_test2.bed new file mode 100644 index 00000000..5ad16f3e --- /dev/null +++ b/tests/small_test2.bed @@ -0,0 +1,7 @@ +chr16 57683001 57683273 +chr1 2000 2500 +chr1 2600 2777 +chr2 100000 200000 +chr2 200000 210000 +chr15 12345678 12345789 +chrY 1000 1200 diff --git a/tests/test.py b/tests/test.py index 03beed0a..9da08edc 100755 --- a/tests/test.py +++ b/tests/test.py @@ -53,7 +53,7 @@ def test_cut(self): def test_merge(self): merged = self.bs.merge(0.2) - self.assertEqual(merged, 2000) + self.assertAlmostEqual(merged, 4000, places=-3) self.bs.reset_bed() def test_combo(self): @@ -96,7 +96,7 @@ def test_all_perturbations2(self): droprate=0.03) # merge sometimes merges more or less than expected because it depends # if the randomly chosen regions are adjacent - self.assertAlmostEqual(perturbed, 9250, places=-2) + self.assertAlmostEqual(perturbed, 9400, places=-3) def test_to_bed(self): self.bs.to_bed('tests/py_output.bed') From 19a3f7d9557e573f8b5c61350be7c29f07769e9b Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 21 Mar 2021 22:27:56 -0500 Subject: [PATCH 0100/1072] add from valid regions --- bedshift/bedshift.py | 61 +++++++++++++++++++++++++++++++++----------- tests/test.py | 6 +++++ 2 files changed, 52 insertions(+), 15 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 7f52a3d2..17b4c009 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -77,6 +77,9 @@ def build_argparser(): parser.add_argument( "--addfile", type=str, help="Add regions from a bedfile") + parser.add_argument( + "--valid_regions", type=str, help="valid regions in which regions can be randomly added") + parser.add_argument( "-s", "--shiftrate", type=float, default=0.0, help="Shift probability") @@ -173,6 +176,7 @@ def pick_random_chroms(self, n): """ Utility function to pick a random chromosome + :param str n: the number of random chromosomes to pick :return str, float chrom_str, chrom_len: chromosome number and length """ chrom_strs = random.choices(list(self.chrom_lens.keys()), weights=self.chrom_weights, k=n) @@ -180,7 +184,7 @@ def pick_random_chroms(self, n): return zip(chrom_strs, chrom_lens) - def add(self, addrate, addmean, addstdev): + def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter='\t'): """ Add regions @@ -201,15 +205,31 @@ def add(self, addrate, addmean, addstdev): rows = self.bed.shape[0] num_add = int(rows * addrate) new_regions = {0: [], 1: [], 2: [], 3: []} - random_chroms = self.pick_random_chroms(num_add) - for chrom_str, chrom_len in random_chroms: - start = random.randint(1, chrom_len) - # ensure chromosome length is not exceeded - end = min(start + int(np.random.normal(addmean, addstdev)), chrom_len) - new_regions[0].append(chrom_str) - new_regions[1].append(start) - new_regions[2].append(end) - new_regions[3].append(3) + if valid_bed: + valid_regions = self.read_bed(valid_bed, delimiter) + valid_regions[3] = valid_regions[2] - valid_regions[1] + total_bp = valid_regions[3].sum() + valid_regions[4] = valid_regions[3].apply(lambda x: x / total_bp) + add_rows = random.choices(list(range(len(valid_regions))), weights=list(valid_regions[4]), k=num_add) + for row in add_rows: + data = valid_regions.loc[row] + chrom = data[0] + start = random.randint(data[1], data[2]) + end = start + int(np.random.normal(addmean, addstdev)) + new_regions[0].append(chrom) + new_regions[1].append(start) + new_regions[2].append(end) + new_regions[3].append('A') + else: + random_chroms = self.pick_random_chroms(num_add) + for chrom_str, chrom_len in random_chroms: + start = random.randint(1, chrom_len) + # ensure chromosome length is not exceeded + end = min(start + int(np.random.normal(addmean, addstdev)), chrom_len) + new_regions[0].append(chrom_str) + new_regions[1].append(start) + new_regions[2].append(end) + new_regions[3].append(3) self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) return num_add @@ -339,6 +359,7 @@ def _cut(self, row): return row, [{0: chrom, 1: start, 2: thecut, 3: 2}, {0: chrom, 1: thecut, 2: end, 3: 2}] + def merge(self, mergerate): """ Merge two regions into one new region @@ -375,6 +396,7 @@ def _merge(self, row): end = self.bed.loc[row+1][2] return [row, row+1], {0: chrom, 1: start, 2: end, 3: 4} + def drop(self, droprate): """ Drop regions @@ -413,6 +435,7 @@ def _find_overlap(self, fp): sys.exit(1) return intersection + def drop_from_file(self, fp, droprate, delimiter='\t'): """ drop regions from another bedfile to this perturbed bedfile @@ -441,9 +464,10 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index(drop=True) return num_drop + def all_perturbations(self, addrate=0.0, addmean=320.0, addstdev=30.0, - addfile=None, + addfile=None, valid_regions=None, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, @@ -457,14 +481,15 @@ def all_perturbations(self, :param float addrate: the rate (as a proportion of the total number of regions) to add regions :param float addmean: the mean length of added regions :param float addstdev: the standard deviation of the length of added regions - :param float addfile: the file containing regions to be added + :param string addfile: the file containing regions to be added + :param string valid_regions: the file containing regions where new regions can be added :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) :param float shiftmean: the mean shift distance :param float shiftstdev: the standard deviation of the shift distance :param float cutrate: the rate to cut regions into two separate regions :param float mergerate: the rate to merge two regions into one :param float droprate: the rate to drop/remove regions - :param float dropfile: the file containing regions to be dropped + :param string dropfile: the file containing regions to be dropped :param string yaml: the yaml_config filepath :param string bedshifter: Bedshift instance :return int: the number of total regions perturbed @@ -475,7 +500,7 @@ def all_perturbations(self, if addfile: n += self.add_from_file(addfile, addrate) else: - n += self.add(addrate, addmean, addstdev) + n += self.add(addrate, addmean, addstdev, valid_regions) n += self.cut(cutrate) n += self.merge(mergerate) if dropfile: @@ -498,6 +523,7 @@ def to_bed(self, outfile_name): print('The output bedfile located in {} has {} regions. The original bedfile had {} regions.' \ .format(outfile_name, self.bed.shape[0], self.original_regions)) + def read_bed(self, bedfile_path, delimiter='\t'): """ Read a BED file into pandas dataframe @@ -521,6 +547,7 @@ def read_bed(self, bedfile_path, delimiter='\t'): df[3] = 0 # column indicating which modifications were made return df + def _print_sample_config(self): """ bedshift_operations: @@ -552,6 +579,7 @@ def _print_sample_config(self): print(self._print_sample_config.__doc__) print("No changes made.") + def _read_from_yaml(self, fp): """ Loads yaml config data @@ -564,6 +592,7 @@ def _read_from_yaml(self, fp): print("Loaded configuration settings from {}".format(fp)) return config_data + def handle_yaml(self, bedshifter, yaml_fp): """ Performs operations provided in the yaml config file in the order they were provided. @@ -716,6 +745,7 @@ def main(): add mean length: {addmean} add stdev: {addstdev} add file: {addfile} + valid regions: {valid_regions} cut rate: {cutrate} drop rate: {droprate} drop regions from file: {dropfile} @@ -743,6 +773,7 @@ def main(): addmean=args.addmean, addstdev=args.addstdev, addfile=args.addfile, + valid_regions=args.valid_regions, shiftrate=args.shiftrate, shiftmean=args.shiftmean, shiftstdev=args.shiftstdev, @@ -756,7 +787,7 @@ def main(): bedshifter = Bedshift(args.bedfile, args.chrom_lengths) for i in range(args.repeat): n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, + args.addfile, args.valid_regions, args.shiftrate, args.shiftmean, args.shiftstdev, args.cutrate, args.mergerate, diff --git a/tests/test.py b/tests/test.py index 3d3a4703..df86bff4 100755 --- a/tests/test.py +++ b/tests/test.py @@ -18,6 +18,12 @@ def test_add_high_rate(self): self.assertEqual(added, 12300) self.bs.reset_bed() + def test_add_valid_regions(self): + added = self.bs.add(0.5, 2000, 1000, valid_bed='tests/small_test.bed', delimiter='\t') + self.assertEqual(added, 5000) + self.bs.to_bed('tests/add_valid_test.bed') + self.bs.reset_bed() + def test_add_from_file(self): added = self.bs.add_from_file("tests/test.bed", 0.25) self.assertEqual(added, 2500) From fbb81c742a9d9016b1d22175b58723573b31de72 Mon Sep 17 00:00:00 2001 From: Aaron Gu <32209687+aaron-gu@users.noreply.github.com> Date: Mon, 22 Mar 2021 20:04:32 -0500 Subject: [PATCH 0101/1072] small change in handle_yaml docstring --- bedshift/BedshiftYAMLHandler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/BedshiftYAMLHandler.py b/bedshift/BedshiftYAMLHandler.py index 48d1c51d..6f20fea7 100644 --- a/bedshift/BedshiftYAMLHandler.py +++ b/bedshift/BedshiftYAMLHandler.py @@ -62,7 +62,7 @@ def _read_from_yaml(self, fp): def handle_yaml(self): """ - Performs operations provided in the yaml config file in the order they were provided. + Performs perturbations provided in the yaml config file in the order they were provided. """ data = self._read_from_yaml(self.yaml_fp) operations = [operation for operation in data["bedshift_operations"]] From 41530f28639980316036d0861605121b2f83e21f Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Mon, 22 Mar 2021 20:21:18 -0500 Subject: [PATCH 0102/1072] use hyphen instead of underscore in argparser --- bedshift/bedshift.py | 5 +++-- docs/changelog.md | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 17b4c009..347bcad2 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -78,7 +78,8 @@ def build_argparser(): "--addfile", type=str, help="Add regions from a bedfile") parser.add_argument( - "--valid_regions", type=str, help="valid regions in which regions can be randomly added") + "--valid-regions", type=str, dest='valid_regions', + help="valid regions in which regions can be randomly added") parser.add_argument( "-s", "--shiftrate", type=float, default=0.0, @@ -112,7 +113,7 @@ def build_argparser(): help="the number of times to repeat the operation") parser.add_argument( - "-y", "--yaml_config", type=str, + "-y", "--yaml-config", dest='yaml_config', type=str, help="run yaml configuration file") return parser diff --git a/docs/changelog.md b/docs/changelog.md index 035e64a4..55d94931 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -5,7 +5,7 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm ## [1.1.0] - unreleased - Added ability to specify chrom sizes file, or refgenie genome. -- Add --yaml_config option +- Add --yaml-config option - Improve performance of perturbations - Add --dropfile, --addfile, and --shiftfile options - Improved testing framework From 16486f6c94493bb822110e4fecb73bdd83168256 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Mon, 22 Mar 2021 20:47:38 -0500 Subject: [PATCH 0103/1072] fix merge issues --- bedshift/arguments.py | 8 ++++++-- bedshift/bedshift.py | 22 +++++++++++----------- tests/test.py | 3 ++- 3 files changed, 19 insertions(+), 14 deletions(-) diff --git a/bedshift/arguments.py b/bedshift/arguments.py index 19760317..15621b2f 100644 --- a/bedshift/arguments.py +++ b/bedshift/arguments.py @@ -75,6 +75,9 @@ def build_argparser(): "--shiftstdev", type=float, default=150.0, help="Stdev shift") + parser.add_argument( + "--shiftfile", type=str, help="Shift regions from a bedfile") + parser.add_argument( "-c", "--cutrate", type=float, default=0.0, help="Cut probability") @@ -107,15 +110,16 @@ def build_argparser(): shift rate: {shiftrate} shift mean distance: {shiftmean} shift stdev: {shiftstdev} + shift regions from file: {shiftfile} add: rate: {addrate} add mean length: {addmean} add stdev: {addstdev} - add file: {addfile} + add regions from file: {addfile} valid regions: {valid_regions} cut rate: {cutrate} drop rate: {droprate} - drop regions from file: {dropfile} + drop regions from file: {dropfile} merge rate: {mergerate} outputfile: {outputfile} repeat: {repeat} diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 3c7ed8ff..3185a788 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -111,6 +111,8 @@ def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter='\t'): :param float addrate: the rate to add regions :param float addmean: the mean length of added regions :param float addstdev: the standard deviation of the length of added regions + :param str valid_bed: the file with valid regions where new regions can be added + :param str delimiter: the delimiter used in valid_bed :return int: the number of regions added """ self._precheck(addrate, requiresChromLens=True, isAdd=True) @@ -407,30 +409,29 @@ def all_perturbations(self, mergerate=0.0, droprate=0.0, dropfile=None, - yaml=None, - bedshifter=None): + yaml=None): ''' Perform all five perturbations in the order of shift, add, cut, merge, drop. :param float addrate: the rate (as a proportion of the total number of regions) to add regions :param float addmean: the mean length of added regions :param float addstdev: the standard deviation of the length of added regions - :param string addfile: the file containing regions to be added - :param string valid_regions: the file containing regions where new regions can be added + :param str addfile: the file containing regions to be added + :param str valid_regions: the file containing regions where new regions can be added :param float shiftrate: the rate to shift regions (both the start and end are shifted by the same amount) :param float shiftmean: the mean shift distance :param float shiftstdev: the standard deviation of the shift distance - :param float shiftfile: the file containing regions to be shifted + :param str shiftfile: the file containing regions to be shifted :param float cutrate: the rate to cut regions into two separate regions :param float mergerate: the rate to merge two regions into one :param float droprate: the rate to drop/remove regions - :param string dropfile: the file containing regions to be dropped - :param string yaml: the yaml_config filepath - :param string bedshifter: Bedshift instance + :param str dropfile: the file containing regions to be dropped + :param str yaml: the yaml_config filepath + :param bedshift.Bedshift bedshifter: Bedshift instance :return int: the number of total regions perturbed ''' if yaml: - return BedshiftYAMLHandler.BedshiftYAMLHandler(bedshifter, yaml).handle_yaml() + return BedshiftYAMLHandler.BedshiftYAMLHandler(self, yaml).handle_yaml() n = 0 if shiftrate > 0: if shiftfile: @@ -559,8 +560,7 @@ def main(): args.mergerate, args.droprate, args.dropfile, - args.yaml_config, - bedshifter) + args.yaml_config) _LOGGER.info("\t" + str(n) + " regions changed in total.\n") if args.repeat == 1: bedshifter.to_bed(outfile) diff --git a/tests/test.py b/tests/test.py index e2fe8cd8..ec7edf52 100755 --- a/tests/test.py +++ b/tests/test.py @@ -142,5 +142,6 @@ def test_handle_yaml(self): total = added+f_drop_10+f_shift_30+f_added_20+cut+dropped+shifted+merged - self.assertAlmostEqual(yamled, total, places=-2) + # yamled and total both should be around 16750, but can vary by over 100 + self.assertAlmostEqual(yamled, total, places=-3) bedshifter.reset_bed() From 75e2ecc799faf92b63e7b502bdcd4476c80c8973 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 21 Mar 2021 15:14:31 -0500 Subject: [PATCH 0104/1072] add from file doc page --- README.md | 14 +++++++++----- docs/changelog.md | 1 + docs/from_file.md | 31 +++++++++++++++++++++++++++++++ mkdocs.yml | 3 ++- 4 files changed, 43 insertions(+), 6 deletions(-) create mode 100644 docs/from_file.md diff --git a/README.md b/README.md index b588591f..e9f7831b 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,11 @@ Install from local repository: `pip install .` ## Command line -Run with: `bedshift -l hg38.chrom.sizes -b BEDFILE` +Run with: + +``` +bedshift -l hg38.chrom.sizes -b BEDFILE +``` See `bedshift -h` for parameters. @@ -18,10 +22,10 @@ See `bedshift -h` for parameters. import bedshift bedshifter = bedshift.Bedshift('tests/test.bed', 'hg38.chrom.sizes') -bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, - shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, - cutrate=0.1, - mergerate=0.11, +bedshifter.all_perturbations(addrate=0.3, addmean=320.0, addstdev=20.0, + shiftrate=0.3, shiftmean=-10.0, shiftstdev=120.0, + cutrate=0.1, + mergerate=0.11, droprate=0.03) # can also run single operations: shift, add, cut, merge, drop diff --git a/docs/changelog.md b/docs/changelog.md index 55d94931..9e19cd48 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -8,6 +8,7 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm - Add --yaml-config option - Improve performance of perturbations - Add --dropfile, --addfile, and --shiftfile options +- Add --add_valid option - Improved testing framework ## [1.0.0] - released May 20, 2020 diff --git a/docs/from_file.md b/docs/from_file.md new file mode 100644 index 00000000..689f96cf --- /dev/null +++ b/docs/from_file.md @@ -0,0 +1,31 @@ +# Shift, Add, and Drop From File + +"From file" means that the regions selected to shift, add, or drop are specified from a provided file. These features provide the ability to finely control what regions are perturbed. For example, if you have a BED file specifying exon regions and you want to add only exons, you can use `--addfile`. + +## Add from file example + +``` +bedshift -b mydata.bed -a 0.07 --addfile exons.bed +``` + +Specifying `--addfile` with `-a` add rate will increase the size of `mydata.bed` by 7% with new regions selected from `exons.bed`. + +## Shift from file example + +Shift from file first calculates which regions overlap between the specified `--shiftfile` and `--bedfile`, then selects which regions to shift among those overlaps. + +``` +bedshift -b mydata.bed -s 0.42 --shiftmean 5 --shiftstdev 5 --shiftfile snp.bed +``` + +In this example, we only want to shift regions that are SNPs. The number of shifted regions is 42% of the total regions in `mydata.bed`. Notice here that unlike `--addfile`, we still have to specify the shift mean and standard deviation. This is because `--shiftfile` tells which regions to shift, but not by how much. + +## Drop from file example + +Drop from file, like shift from file, calculates overlaps between the specified `--dropfile` and `--bedfile`, then selects regions from those overlaps to drop. + +``` +bedshift -b mydata.bed -d 0.4 -dropfile snp.bed +``` + +This command will drop regions that overlap with SNPs. The number of dropped regions is 40% of the total regions in `mydata.bed`. diff --git a/mkdocs.yml b/mkdocs.yml index f30ff99f..5d5e6e6d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -12,10 +12,11 @@ nav: - Generate a Random BED File: generate_random.md - Bedshift All BED Files in Folder: bedshift_all.md - Evaluate Similarity Scores: evaluation_guide.md + - \"From File\ Features": from_file.md - Reference: - Python API: autodoc_build/bedshift.md - Changelog: changelog.md - + plugins: - databio: autodoc_build: "docs/autodoc_build" From 5c41c12902cef34f99b933dbefac2f42955be5da Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Sun, 21 Mar 2021 15:26:01 -0500 Subject: [PATCH 0105/1072] add yaml config doc --- docs/yaml_config.md | 30 ++++++++++++++++++++++++++++++ mkdocs.yml | 1 + 2 files changed, 31 insertions(+) create mode 100644 docs/yaml_config.md diff --git a/docs/yaml_config.md b/docs/yaml_config.md new file mode 100644 index 00000000..72a87a6d --- /dev/null +++ b/docs/yaml_config.md @@ -0,0 +1,30 @@ +# Use a YAML File to Specify Perturbations + +Sometimes the default settings of bedshift does not allow enough control over perturbations. For example, the order of perturbations is fixed as shift, add, cut, merge, drop, so if you wanted to change the order you would have to specify multiple commands. +The same problem arises when you want to run multiple "add from file" commands - there is just no way to do it using a single command. + +This is why we created the YAML config file perturbation option. In the YAML file, users can specify as many perturbations as they want, along with the parameters specific to each perturbation. An example of a YAML config file follows: + +``` +bedshift_operations: + - add_from_file: + file: exons.bed + rate: 0.2 + - add_from_file: + file: snp.bed + rate: 0.05 + - shift_from_file: + file: exons.bed + rate: 0.4 + mean: 100 + stdev: 85 + - shift_from_file: + file: snp.bed + rate: 0.4 + mean: 2 + stdev: 1 + - merge: + rate: 0.15 +``` + +The order of perturbations is run in the same order they are specified. So in this example, we add from two different files, then also shift those regions that were just added. Finally we perform a merge at 15% rate. diff --git a/mkdocs.yml b/mkdocs.yml index 5d5e6e6d..1bb92aa4 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -13,6 +13,7 @@ nav: - Bedshift All BED Files in Folder: bedshift_all.md - Evaluate Similarity Scores: evaluation_guide.md - \"From File\ Features": from_file.md + - Use YAML to Specify Perturbations: yaml_config.md - Reference: - Python API: autodoc_build/bedshift.md - Changelog: changelog.md From 3b9c7fb3903ae6b35371a89c2d3bc672f7392914 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Mon, 22 Mar 2021 21:16:56 -0500 Subject: [PATCH 0106/1072] add add-valid doc page --- docs/add_valid.md | 9 +++++++++ mkdocs.yml | 3 ++- 2 files changed, 11 insertions(+), 1 deletion(-) create mode 100644 docs/add_valid.md diff --git a/docs/add_valid.md b/docs/add_valid.md new file mode 100644 index 00000000..79f61ce0 --- /dev/null +++ b/docs/add_valid.md @@ -0,0 +1,9 @@ +# Add Random Regions Only in Valid Regions + +Using the basic `--add` option, regions are added randomly onto any chromosome at any location, without any regard for non-coding regions. For use cases of Bedshift more rooted in biology, this effect is not desirable. The `--add-valid` option gives the user the ability to specify a BED file indicating areas where it is valid to add regions. Thus, if an `--add-valid` file has only coding regions, then regions will be randomly added only in those areas. Here is an example: + +``` +bedshift -b mydata.bed -a 0.5 --add-valid coding.bed --addmean 500 --addstdev 200 +``` + +`coding.bed` contains large regions of the genome which are coding. Added regions can be anywhere inside of those regions. In addition, the method considers the size of the valid regions in deciding where the new regions will be added, so the smaller valid regions will contain proportionally less new regions than the larger valid regions. \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 1bb92aa4..54dc32f6 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -12,8 +12,9 @@ nav: - Generate a Random BED File: generate_random.md - Bedshift All BED Files in Folder: bedshift_all.md - Evaluate Similarity Scores: evaluation_guide.md - - \"From File\ Features": from_file.md + - From-file Features: from_file.md - Use YAML to Specify Perturbations: yaml_config.md + - Add Regions in Valid Regions: add_valid.md - Reference: - Python API: autodoc_build/bedshift.md - Changelog: changelog.md From a600a90cf1205bde3ce6601978d75e90b2a743e8 Mon Sep 17 00:00:00 2001 From: nsheff Date: Tue, 23 Mar 2021 17:59:54 -0400 Subject: [PATCH 0107/1072] reindex -- does this fix #32? --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 3185a788..8c59f316 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -167,7 +167,7 @@ def add_from_file(self, fp, addrate, delimiter='\t'): _LOGGER.warning("Number of regions to be added ({}) is larger than the provided bedfile size ({}). Adding {} regions.".format(num_add, dflen, dflen)) num_add = dflen add_rows = random.sample(list(range(dflen)), num_add) - add_df = df.loc[add_rows].reset_index(drop=True) + add_df = df.reindex([add_rows]).reset_index(drop=True) add_df[3] = pd.Series(['A'] * add_df.shape[0]) self.bed = self.bed.append(add_df, ignore_index=True) return num_add From 9ea3ec58c9d69fae7d8da6e20e1964501bd6d76b Mon Sep 17 00:00:00 2001 From: nsheff Date: Tue, 23 Mar 2021 18:00:54 -0400 Subject: [PATCH 0108/1072] apply black linter --- bedshift/BedshiftYAMLHandler.py | 112 ++++++++------ bedshift/arguments.py | 117 ++++++++------ bedshift/bedshift.py | 266 +++++++++++++++++++------------- 3 files changed, 287 insertions(+), 208 deletions(-) diff --git a/bedshift/BedshiftYAMLHandler.py b/bedshift/BedshiftYAMLHandler.py index 6f20fea7..2d7ecdd6 100644 --- a/bedshift/BedshiftYAMLHandler.py +++ b/bedshift/BedshiftYAMLHandler.py @@ -20,7 +20,6 @@ def __init__(self, bedshifter, yaml_fp, logger=None): else: self._LOGGER = logging.getLogger("BedshiftYAMLHandler") - def _print_sample_config(self): """ bedshift_operations: @@ -59,7 +58,6 @@ def _read_from_yaml(self, fp): self._LOGGER.info("Loaded configuration settings from {}".format(fp)) return config_data - def handle_yaml(self): """ Performs perturbations provided in the yaml config file in the order they were provided. @@ -70,114 +68,128 @@ def handle_yaml(self): for operation in operations: ##### add ##### - if set(['add', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): - rate = operation['rate'] - mean = operation['mean'] - std = operation['stdev'] + if set(["add", "rate", "mean", "stdev"]) == set(list(operation.keys())): + rate = operation["rate"] + mean = operation["mean"] + std = operation["stdev"] num_added = self.bedshifter.add(rate, mean, std) num_changed += num_added ##### add_from_file with no delimiter provided ##### - elif set(['add_from_file', 'file', 'rate']) == set(list(operation.keys())): - fp = operation['file'] + elif set(["add_from_file", "file", "rate"]) == set(list(operation.keys())): + fp = operation["file"] if os.path.isfile(fp): - add_rate = operation['rate'] + add_rate = operation["rate"] num_added = self.bedshifter.add_from_file(fp, add_rate) num_changed += num_added else: - self._logger.error("File \'{}\' does not exist.".format(fp)) + self._logger.error("File '{}' does not exist.".format(fp)) sys.exit(1) ##### add_from_file with delimiter provided ##### - elif set(['add_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): - fp = operation['file'] + elif set(["add_from_file", "file", "rate", "delimiter"]) == set( + list(operation.keys()) + ): + fp = operation["file"] if os.path.isfile(fp): - add_rate = operation['rate'] - delimiter = operation['delimiter'] + add_rate = operation["rate"] + delimiter = operation["delimiter"] num_added = self.bedshifter.add_from_file(fp, add_rate, delimiter) num_changed += num_added else: - self._logger.error("File \'{}\' does not exist.".format(fp)) + self._logger.error("File '{}' does not exist.".format(fp)) sys.exit(1) ##### drop ##### - elif set(['drop', 'rate']) == set(list(operation.keys())): - rate = operation['rate'] + elif set(["drop", "rate"]) == set(list(operation.keys())): + rate = operation["rate"] num_dropped = self.bedshifter.drop(rate) num_changed += num_dropped ##### drop_from_file with no delimiter provided ##### - elif set(['drop_from_file', 'file', 'rate']) == set(list(operation.keys())): - fp = operation['file'] + elif set(["drop_from_file", "file", "rate"]) == set(list(operation.keys())): + fp = operation["file"] if os.path.isfile(fp): - drop_rate = operation['rate'] + drop_rate = operation["rate"] num_dropped = self.bedshifter.drop_from_file(fp, drop_rate) num_changed += num_dropped else: - self._LOGGER.error("File \'{}\' does not exist.".format(fp)) + self._LOGGER.error("File '{}' does not exist.".format(fp)) sys.exit(1) ##### drop_from_file with delimiter provided ##### - elif set(['drop_from_file', 'file', 'rate', 'delimiter']) == set(list(operation.keys())): - fp = operation['file'] + elif set(["drop_from_file", "file", "rate", "delimiter"]) == set( + list(operation.keys()) + ): + fp = operation["file"] if os.path.isfile(fp): - drop_rate = operation['rate'] - delimiter = operation['delimiter'] - num_dropped = self.bedshifter.drop_from_file(fp, drop_rate, delimiter) + drop_rate = operation["rate"] + delimiter = operation["delimiter"] + num_dropped = self.bedshifter.drop_from_file( + fp, drop_rate, delimiter + ) num_changed += num_dropped else: - self._LOGGER.error("File \'{}\' does not exist.".format(fp)) + self._LOGGER.error("File '{}' does not exist.".format(fp)) sys.exit(1) ##### shift ##### - elif set(['shift', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): - rate = operation['rate'] - mean = operation['mean'] - std = operation['stdev'] + elif set(["shift", "rate", "mean", "stdev"]) == set(list(operation.keys())): + rate = operation["rate"] + mean = operation["mean"] + std = operation["stdev"] num_shifted = self.bedshifter.shift(rate, mean, std) num_changed += num_shifted ##### shift_from_file ##### - elif set(['shift_from_file', 'file', 'rate', 'mean', 'stdev']) == set(list(operation.keys())): - fp = operation['file'] + elif set(["shift_from_file", "file", "rate", "mean", "stdev"]) == set( + list(operation.keys()) + ): + fp = operation["file"] if os.path.isfile(fp): - rate = operation['rate'] - mean = operation['mean'] - std = operation['stdev'] + rate = operation["rate"] + mean = operation["mean"] + std = operation["stdev"] num_shifted = self.bedshifter.shift_from_file(fp, rate, mean, std) num_changed += num_shifted else: - self._LOGGER.error("File \'{}\' does not exist.".format(fp)) + self._LOGGER.error("File '{}' does not exist.".format(fp)) sys.exit(1) ##### shift_from_file with delimiter provided ##### - elif set(['shift_from_file', 'file', 'rate', 'mean', 'stdev', 'delimiter']) == set(list(operation.keys())): - fp = operation['file'] + elif set( + ["shift_from_file", "file", "rate", "mean", "stdev", "delimiter"] + ) == set(list(operation.keys())): + fp = operation["file"] if os.path.isfile(fp): - rate = operation['rate'] - mean = operation['mean'] - std = operation['stdev'] - delimiter = operation['delimiter'] - num_shifted = self.bedshifter.shift_from_file(fp, rate, mean, std, delimiter) + rate = operation["rate"] + mean = operation["mean"] + std = operation["stdev"] + delimiter = operation["delimiter"] + num_shifted = self.bedshifter.shift_from_file( + fp, rate, mean, std, delimiter + ) num_changed += num_shifted else: - self._LOGGER.error("File \'{}\' does not exist.".format(fp)) + self._LOGGER.error("File '{}' does not exist.".format(fp)) sys.exit(1) ##### cut ##### - elif set(['cut', 'rate']) == set(list(operation.keys())): - rate = operation['rate'] + elif set(["cut", "rate"]) == set(list(operation.keys())): + rate = operation["rate"] num_cut = self.bedshifter.cut(rate) num_changed += num_cut ##### merge ##### - elif set(['merge', 'rate']) == set(list(operation.keys())): - rate = operation['rate'] + elif set(["merge", "rate"]) == set(list(operation.keys())): + rate = operation["rate"] num_merged = self.bedshifter.merge(rate) num_changed += num_merged else: - self._LOGGER.error("\n\nInvalid settings entered in the config file. Please refer to the example below.\n\n") + self._LOGGER.error( + "\n\nInvalid settings entered in the config file. Please refer to the example below.\n\n" + ) self._print_sample_config() sys.exit(1) diff --git a/bedshift/arguments.py b/bedshift/arguments.py index 15621b2f..decd3fe2 100644 --- a/bedshift/arguments.py +++ b/bedshift/arguments.py @@ -5,8 +5,10 @@ class _VersionInHelpParser(argparse.ArgumentParser): def format_help(self): """ Add version information to help text. """ - return "version: {}\n".format(__version__) + \ - super(_VersionInHelpParser, self).format_help() + return ( + "version: {}\n".format(__version__) + + super(_VersionInHelpParser, self).format_help() + ) def build_argparser(): @@ -19,87 +21,102 @@ def build_argparser(): banner = "%(prog)s - randomize BED files" additional_description = "\n..." - parser = _VersionInHelpParser( - description=banner, - epilog=additional_description) + parser = _VersionInHelpParser(description=banner, epilog=additional_description) parser.add_argument( - "-V", "--version", + "-V", + "--version", action="version", - version="%(prog)s {v}".format(v=__version__)) + version="%(prog)s {v}".format(v=__version__), + ) - parser.add_argument( - "-b", "--bedfile", required=True, - help="File path to bed file.") + parser.add_argument("-b", "--bedfile", required=True, help="File path to bed file.") parser.add_argument( - "-l", "--chrom-lengths", type=str, required=False, - help="TSV text file with one row per chromosomes indicating chromosome sizes") + "-l", + "--chrom-lengths", + type=str, + required=False, + help="TSV text file with one row per chromosomes indicating chromosome sizes", + ) parser.add_argument( - "-g", "--genome", type=str, required=False, - help="Refgenie genome identifier (used for chrom sizes).") + "-g", + "--genome", + type=str, + required=False, + help="Refgenie genome identifier (used for chrom sizes).", + ) parser.add_argument( - "-d", "--droprate", type=float, default=0.0, - help="Droprate parameter") + "-d", "--droprate", type=float, default=0.0, help="Droprate parameter" + ) parser.add_argument( - "-a", "--addrate", type=float, default=0.0, - help="Addrate parameter") + "-a", "--addrate", type=float, default=0.0, help="Addrate parameter" + ) parser.add_argument( - "--addmean", type=float, default=320.0, - help="Mean add region length") + "--addmean", type=float, default=320.0, help="Mean add region length" + ) - parser.add_argument( - "--addstdev", type=float, default=30.0, - help="Stdev add length") + parser.add_argument("--addstdev", type=float, default=30.0, help="Stdev add length") - parser.add_argument( - "--addfile", type=str, help="Add regions from a bedfile") + parser.add_argument("--addfile", type=str, help="Add regions from a bedfile") parser.add_argument( - "--valid-regions", type=str, dest='valid_regions', - help="valid regions in which regions can be randomly added") + "--valid-regions", + type=str, + dest="valid_regions", + help="valid regions in which regions can be randomly added", + ) parser.add_argument( - "-s", "--shiftrate", type=float, default=0.0, - help="Shift probability") + "-s", "--shiftrate", type=float, default=0.0, help="Shift probability" + ) - parser.add_argument( - "--shiftmean", type=float, default=0.0, - help="Mean shift") + parser.add_argument("--shiftmean", type=float, default=0.0, help="Mean shift") - parser.add_argument( - "--shiftstdev", type=float, default=150.0, - help="Stdev shift") + parser.add_argument("--shiftstdev", type=float, default=150.0, help="Stdev shift") - parser.add_argument( - "--shiftfile", type=str, help="Shift regions from a bedfile") + parser.add_argument("--shiftfile", type=str, help="Shift regions from a bedfile") parser.add_argument( - "-c", "--cutrate", type=float, default=0.0, - help="Cut probability") + "-c", "--cutrate", type=float, default=0.0, help="Cut probability" + ) parser.add_argument( - "-m", "--mergerate", type=float, default=0.0, - help="Merge probability. WARNING: will likely create regions that are thousands of base pairs long") + "-m", + "--mergerate", + type=float, + default=0.0, + help="Merge probability. WARNING: will likely create regions that are thousands of base pairs long", + ) - parser.add_argument( - "--dropfile", type=str, help="Drop regions from a bedfile") + parser.add_argument("--dropfile", type=str, help="Drop regions from a bedfile") parser.add_argument( - "-o", "--outputfile", type=str, - help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed") + "-o", + "--outputfile", + type=str, + help="output file name (including extension). if not specified, will default to bedshifted_{originalname}.bed", + ) parser.add_argument( - "-r", "--repeat", type=int, default=1, - help="the number of times to repeat the operation") + "-r", + "--repeat", + type=int, + default=1, + help="the number of times to repeat the operation", + ) parser.add_argument( - "-y", "--yaml-config", dest='yaml_config', type=str, - help="run yaml configuration file") + "-y", + "--yaml-config", + dest="yaml_config", + type=str, + help="run yaml configuration file", + ) return parser @@ -124,4 +141,4 @@ def build_argparser(): outputfile: {outputfile} repeat: {repeat} yaml_config: {yaml_config} -""" \ No newline at end of file +""" diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 8c59f316..f3982190 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -23,7 +23,7 @@ class Bedshift(object): The bedshift object with methods to perturb regions """ - def __init__(self, bedfile_path, chrom_sizes=None, delimiter='\t'): + def __init__(self, bedfile_path, chrom_sizes=None, delimiter="\t"): """ Read in a .bed file to pandas DataFrame format @@ -37,11 +37,13 @@ def __init__(self, bedfile_path, chrom_sizes=None, delimiter='\t'): self._read_chromsizes(chrom_sizes) df = self.read_bed(bedfile_path, delimiter=delimiter) self.original_num_regions = df.shape[0] - self.bed = df.astype({0: 'object', 1: 'int64', 2: 'int64', 3: 'object'}) \ - .sort_values([0, 1, 2]).reset_index(drop=True) + self.bed = ( + df.astype({0: "object", 1: "int64", 2: "int64", 3: "object"}) + .sort_values([0, 1, 2]) + .reset_index(drop=True) + ) self.original_bed = self.bed.copy() - def _read_chromsizes(self, fp): """ Read chromosome sizes file @@ -51,7 +53,7 @@ def _read_chromsizes(self, fp): try: with open(fp) as f: for line in f: - line = line.strip().split('\t') + line = line.strip().split("\t") chrom = str(line[0]) size = int(line[1]) self.chrom_lens[chrom] = size @@ -60,8 +62,9 @@ def _read_chromsizes(self, fp): sys.exit(1) total_len = sum(self.chrom_lens.values()) - self.chrom_weights = [chrom_len / total_len for chrom_len in self.chrom_lens.values()] - + self.chrom_weights = [ + chrom_len / total_len for chrom_len in self.chrom_lens.values() + ] def reset_bed(self): """ @@ -69,7 +72,6 @@ def reset_bed(self): """ self.bed = self.original_bed.copy() - def _precheck(self, rate, requiresChromLens=False, isAdd=False): """ Check if the rate of perturbation is too high or low @@ -91,7 +93,6 @@ def _precheck(self, rate, requiresChromLens=False, isAdd=False): _LOGGER.error("chrom.sizes file must be specified") sys.exit(1) - def pick_random_chroms(self, n): """ Utility function to pick a random chromosome @@ -99,12 +100,13 @@ def pick_random_chroms(self, n): :param str n: the number of random chromosomes to pick :return str, float chrom_str, chrom_len: chromosome number and length """ - chrom_strs = random.choices(list(self.chrom_lens.keys()), weights=self.chrom_weights, k=n) + chrom_strs = random.choices( + list(self.chrom_lens.keys()), weights=self.chrom_weights, k=n + ) chrom_lens = [self.chrom_lens[chrom_str] for chrom_str in chrom_strs] return zip(chrom_strs, chrom_lens) - - def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter='\t'): + def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter="\t"): """ Add regions @@ -125,7 +127,11 @@ def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter='\t'): valid_regions[3] = valid_regions[2] - valid_regions[1] total_bp = valid_regions[3].sum() valid_regions[4] = valid_regions[3].apply(lambda x: x / total_bp) - add_rows = random.choices(list(range(len(valid_regions))), weights=list(valid_regions[4]), k=num_add) + add_rows = random.choices( + list(range(len(valid_regions))), + weights=list(valid_regions[4]), + k=num_add, + ) for row in add_rows: data = valid_regions.loc[row] chrom = data[0] @@ -134,7 +140,7 @@ def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter='\t'): new_regions[0].append(chrom) new_regions[1].append(start) new_regions[2].append(end) - new_regions[3].append('A') + new_regions[3].append("A") else: random_chroms = self.pick_random_chroms(num_add) for chrom_str, chrom_len in random_chroms: @@ -144,12 +150,11 @@ def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter='\t'): new_regions[0].append(chrom_str) new_regions[1].append(start) new_regions[2].append(end) - new_regions[3].append('A') + new_regions[3].append("A") self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) return num_add - - def add_from_file(self, fp, addrate, delimiter='\t'): + def add_from_file(self, fp, addrate, delimiter="\t"): """ Add regions from another bedfile to this perturbed bedfile @@ -164,15 +169,18 @@ def add_from_file(self, fp, addrate, delimiter='\t'): df = self.read_bed(fp, delimiter=delimiter) dflen = len(df) if num_add > dflen: - _LOGGER.warning("Number of regions to be added ({}) is larger than the provided bedfile size ({}). Adding {} regions.".format(num_add, dflen, dflen)) + _LOGGER.warning( + "Number of regions to be added ({}) is larger than the provided bedfile size ({}). Adding {} regions.".format( + num_add, dflen, dflen + ) + ) num_add = dflen add_rows = random.sample(list(range(dflen)), num_add) add_df = df.reindex([add_rows]).reset_index(drop=True) - add_df[3] = pd.Series(['A'] * add_df.shape[0]) + add_df[3] = pd.Series(["A"] * add_df.shape[0]) self.bed = self.bed.append(add_df, ignore_index=True) return num_add - def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=None): """ Shift regions @@ -192,7 +200,9 @@ def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=None): num_shifted = 0 invalid_shifted = 0 for row in shift_rows: - drop_row, new_region = self._shift(row, shiftmean, shiftstdev) # shifted rows display a 1 + drop_row, new_region = self._shift( + row, shiftmean, shiftstdev + ) # shifted rows display a 1 if drop_row is not None and new_region: num_shifted += 1 new_row_list.append(new_region) @@ -203,10 +213,11 @@ def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=None): self.bed = self.bed.append(new_row_list, ignore_index=True) self.bed = self.bed.reset_index(drop=True) if invalid_shifted > 0: - _LOGGER.warning(f"{invalid_shifted} regions were prevented from being shifted outside of chromosome boundaries. Reported regions shifted will be less than expected.") + _LOGGER.warning( + f"{invalid_shifted} regions were prevented from being shifted outside of chromosome boundaries. Reported regions shifted will be less than expected." + ) return num_shifted - def _shift(self, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) @@ -217,10 +228,9 @@ def _shift(self, row, mean, stdev): # check if the region is shifted out of chromosome length bounds return None, None - return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: 'S'} - + return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: "S"} - def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): + def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter="\t"): """ Shift regions that overlap the specified file's regions @@ -239,12 +249,15 @@ def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): intersect_regions = self._find_overlap(fp) interlen = len(intersect_regions) if num_shift > interlen: - _LOGGER.warning("Number of regions to be shifted ({}) is larger than the provided bedfile size ({}). Shifting {} regions.".format(num_shift, interlen, interlen)) + _LOGGER.warning( + "Number of regions to be shifted ({}) is larger than the provided bedfile size ({}). Shifting {} regions.".format( + num_shift, interlen, interlen + ) + ) num_shift = interlen rows2shift = random.sample(list(range(interlen)), num_shift) return self.shift(shiftrate, shiftmean, shiftstdev, rows2shift) - def cut(self, cutrate): """ Cut regions to create two new regions @@ -259,7 +272,7 @@ def cut(self, cutrate): new_row_list = [] to_drop = [] for row in cut_rows: - drop_row, new_regions = self._cut(row) # cut rows display a 2 + drop_row, new_regions = self._cut(row) # cut rows display a 2 new_row_list.extend(new_regions) to_drop.append(drop_row) self.bed = self.bed.drop(to_drop) @@ -267,27 +280,30 @@ def cut(self, cutrate): self.bed = self.bed.reset_index(drop=True) return len(cut_rows) - def _cut(self, row): chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] end = self.bed.loc[row][2] # choose where to cut the region - thecut = (start + end) // 2 # int(np.random.normal((start+end)/2, (end - start)/6)) + thecut = ( + start + end + ) // 2 # int(np.random.normal((start+end)/2, (end - start)/6)) if thecut <= start: thecut = start + 10 if thecut >= end: thecut = end - 10 - ''' may add in later, this makes the api confusing! + """ may add in later, this makes the api confusing! # adjust the cut regions using the shift function new_segs = self.__shift(new_segs, 0, meanshift, stdevshift) new_segs = self.__shift(new_segs, 1, meanshift, stdevshift) - ''' - - return row, [{0: chrom, 1: start, 2: thecut, 3: 'C'}, {0: chrom, 1: thecut, 2: end, 3: 'C'}] + """ + return row, [ + {0: chrom, 1: start, 2: thecut, 3: "C"}, + {0: chrom, 1: thecut, 2: end, 3: "C"}, + ] def merge(self, mergerate): """ @@ -312,17 +328,18 @@ def merge(self, mergerate): self.bed = self.bed.reset_index(drop=True) return len(to_drop) - def _merge(self, row): # check if the regions being merged are on the same chromosome - if row + 1 not in self.bed.index or self.bed.loc[row][0] != self.bed.loc[row+1][0]: + if ( + row + 1 not in self.bed.index + or self.bed.loc[row][0] != self.bed.loc[row + 1][0] + ): return None, None chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] - end = self.bed.loc[row+1][2] - return [row, row+1], {0: chrom, 1: start, 2: end, 3: 'M'} - + end = self.bed.loc[row + 1][2] + return [row, row + 1], {0: chrom, 1: start, 2: end, 3: "M"} def drop(self, droprate): """ @@ -339,9 +356,7 @@ def drop(self, droprate): self.bed = self.bed.reset_index(drop=True) return len(drop_rows) - - - def drop_from_file(self, fp, droprate, delimiter='\t'): + def drop_from_file(self, fp, droprate, delimiter="\t"): """ drop regions that overlap between the reference bedfile and the provided bedfile. @@ -358,13 +373,18 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): intersect_regions = self._find_overlap(drop_bed) interlen = len(intersect_regions) if num_drop > interlen: - _LOGGER.warning("Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format(num_drop, interlen, interlen)) + _LOGGER.warning( + "Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format( + num_drop, interlen, interlen + ) + ) num_drop = interlen rows2drop = random.sample(list(range(interlen)), num_drop) - self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index(drop=True) + self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index( + drop=True + ) return num_drop - def _find_overlap(self, fp, reference=None): """ Find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file. @@ -388,29 +408,39 @@ def _find_overlap(self, fp, reference=None): comparison_bed = self.read_bed(fp) else: raise Exception("unsupported input type: {}".format(type(reference))) - reference_bed.columns = ['Chromosome', 'Start', 'End', 'modifications'] - comparison_bed.columns = ['Chromosome', 'Start', 'End', 'modifications'] + reference_bed.columns = ["Chromosome", "Start", "End", "modifications"] + comparison_bed.columns = ["Chromosome", "Start", "End", "modifications"] reference_pr = pr.PyRanges(reference_bed) comparison_pr = pr.PyRanges(comparison_bed) - intersection = reference_pr.overlap(comparison_pr, how='first').as_df() + intersection = reference_pr.overlap(comparison_pr, how="first").as_df() if len(intersection) == 0: - raise Exception("no intersection found between {} and {}".format(reference_bed, comparison_bed)) - intersection = intersection.drop(['modifications'], axis=1) + raise Exception( + "no intersection found between {} and {}".format( + reference_bed, comparison_bed + ) + ) + intersection = intersection.drop(["modifications"], axis=1) intersection.columns = [0, 1, 2] return intersection - - def all_perturbations(self, - addrate=0.0, addmean=320.0, addstdev=30.0, - addfile=None, valid_regions=None, - shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, - shiftfile=None, - cutrate=0.0, - mergerate=0.0, - droprate=0.0, - dropfile=None, - yaml=None): - ''' + def all_perturbations( + self, + addrate=0.0, + addmean=320.0, + addstdev=30.0, + addfile=None, + valid_regions=None, + shiftrate=0.0, + shiftmean=0.0, + shiftstdev=150.0, + shiftfile=None, + cutrate=0.0, + mergerate=0.0, + droprate=0.0, + dropfile=None, + yaml=None, + ): + """ Perform all five perturbations in the order of shift, add, cut, merge, drop. :param float addrate: the rate (as a proportion of the total number of regions) to add regions @@ -429,7 +459,7 @@ def all_perturbations(self, :param str yaml: the yaml_config filepath :param bedshift.Bedshift bedshifter: Bedshift instance :return int: the number of total regions perturbed - ''' + """ if yaml: return BedshiftYAMLHandler.BedshiftYAMLHandler(self, yaml).handle_yaml() n = 0 @@ -454,28 +484,36 @@ def all_perturbations(self, n += self.drop(droprate) return n - def to_bed(self, outfile_name): """ Write a pandas dataframe back into BED file format :param str outfile_name: The name of the output BED file """ - self.bed.sort_values([0,1,2], inplace=True) - self.bed.to_csv(outfile_name, sep='\t', header=False, index=False, float_format='%.0f') - _LOGGER.info('The output bedfile located in {} has {} regions. The original bedfile had {} regions.' \ - .format(outfile_name, self.bed.shape[0], self.original_num_regions)) - + self.bed.sort_values([0, 1, 2], inplace=True) + self.bed.to_csv( + outfile_name, sep="\t", header=False, index=False, float_format="%.0f" + ) + _LOGGER.info( + "The output bedfile located in {} has {} regions. The original bedfile had {} regions.".format( + outfile_name, self.bed.shape[0], self.original_num_regions + ) + ) - - def read_bed(self, bedfile_path, delimiter='\t'): + def read_bed(self, bedfile_path, delimiter="\t"): """ Read a BED file into pandas dataframe :param str bedfile_path: The path to the BED file """ try: - df = pd.read_csv(bedfile_path, sep=delimiter, header=None, usecols=[0,1,2], engine='python') + df = pd.read_csv( + bedfile_path, + sep=delimiter, + header=None, + usecols=[0, 1, 2], + engine="python", + ) except FileNotFoundError: _LOGGER.error("BED file path {} invalid".format(bedfile_path)) sys.exit(1) @@ -487,7 +525,7 @@ def read_bed(self, bedfile_path, delimiter='\t'): if not str(df.iloc[0, 1]).isdigit(): df = df[1:] - df[3] = '-' # column indicating which modifications were made + df[3] = "-" # column indicating which modifications were made return df @@ -512,10 +550,13 @@ def main(): elif args.genome: try: import refgenconf + rgc = refgenconf.RefGenConf(refgenconf.select_genome_config()) args.chrom_lengths = rgc.seek(args.genome, "fasta", None, "chrom_sizes") except ModuleNotFoundError: - _LOGGER.error("You must have package refgenconf installed to use a refgenie genome") + _LOGGER.error( + "You must have package refgenconf installed to use a refgenie genome" + ) sys.exit(1) msg = arguments.param_msg @@ -527,52 +568,61 @@ def main(): if args.outputfile: outfile = args.outputfile else: - outfile = 'bedshifted_{}'.format(os.path.basename(args.bedfile)) - - _LOGGER.info(msg.format( - bedfile=args.bedfile, - chromsizes=args.chrom_lengths, - droprate=args.droprate, - dropfile=args.dropfile, - addrate=args.addrate, - addmean=args.addmean, - addstdev=args.addstdev, - addfile=args.addfile, - valid_regions=args.valid_regions, - shiftrate=args.shiftrate, - shiftmean=args.shiftmean, - shiftstdev=args.shiftstdev, - shiftfile=args.shiftfile, - cutrate=args.cutrate, - mergerate=args.mergerate, - outputfile=outfile, - repeat=args.repeat, - yaml_config=args.yaml_config)) - + outfile = "bedshifted_{}".format(os.path.basename(args.bedfile)) + + _LOGGER.info( + msg.format( + bedfile=args.bedfile, + chromsizes=args.chrom_lengths, + droprate=args.droprate, + dropfile=args.dropfile, + addrate=args.addrate, + addmean=args.addmean, + addstdev=args.addstdev, + addfile=args.addfile, + valid_regions=args.valid_regions, + shiftrate=args.shiftrate, + shiftmean=args.shiftmean, + shiftstdev=args.shiftstdev, + shiftfile=args.shiftfile, + cutrate=args.cutrate, + mergerate=args.mergerate, + outputfile=outfile, + repeat=args.repeat, + yaml_config=args.yaml_config, + ) + ) bedshifter = Bedshift(args.bedfile, args.chrom_lengths) for i in range(args.repeat): - n = bedshifter.all_perturbations(args.addrate, args.addmean, args.addstdev, - args.addfile, args.valid_regions, - args.shiftrate, args.shiftmean, args.shiftstdev, - args.shiftfile, - args.cutrate, - args.mergerate, - args.droprate, - args.dropfile, - args.yaml_config) + n = bedshifter.all_perturbations( + args.addrate, + args.addmean, + args.addstdev, + args.addfile, + args.valid_regions, + args.shiftrate, + args.shiftmean, + args.shiftstdev, + args.shiftfile, + args.cutrate, + args.mergerate, + args.droprate, + args.dropfile, + args.yaml_config, + ) _LOGGER.info("\t" + str(n) + " regions changed in total.\n") if args.repeat == 1: bedshifter.to_bed(outfile) else: modified_outfile = outfile.rsplit("/") - modified_outfile[-1] = "rep" + str(i+1) + "_" + modified_outfile[-1] + modified_outfile[-1] = "rep" + str(i + 1) + "_" + modified_outfile[-1] modified_outfile = "/".join(modified_outfile) bedshifter.to_bed(modified_outfile) bedshifter.reset_bed() -if __name__ == '__main__': +if __name__ == "__main__": try: sys.exit(main()) except KeyboardInterrupt: From 3b8e433f66a2f8fc30fcecda54e21a812b0afeda Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Tue, 23 Mar 2021 19:49:20 -0500 Subject: [PATCH 0109/1072] revert reindex -- does this fix #32? --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index f3982190..ae1f749d 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -176,7 +176,7 @@ def add_from_file(self, fp, addrate, delimiter="\t"): ) num_add = dflen add_rows = random.sample(list(range(dflen)), num_add) - add_df = df.reindex([add_rows]).reset_index(drop=True) + add_df = df.loc[add_rows].reset_index(drop=True) add_df[3] = pd.Series(["A"] * add_df.shape[0]) self.bed = self.bed.append(add_df, ignore_index=True) return num_add From 9b40941f81d43922b83fe511a819e755d7b72b0a Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Tue, 23 Mar 2021 19:59:39 -0500 Subject: [PATCH 0110/1072] reset index after deleting header row --- bedshift/bedshift.py | 2 +- tests/test.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index ae1f749d..67df0a6b 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -523,7 +523,7 @@ def read_bed(self, bedfile_path, delimiter="\t"): # if there is a header line in the table, remove it if not str(df.iloc[0, 1]).isdigit(): - df = df[1:] + df = df[1:].reset_index(drop=True) df[3] = "-" # column indicating which modifications were made return df diff --git a/tests/test.py b/tests/test.py index ec7edf52..ed1466c4 100755 --- a/tests/test.py +++ b/tests/test.py @@ -12,6 +12,7 @@ def setUp(self): def test_read_bed(self): reader = bedshift.Bedshift('tests/header_test.bed') self.assertTrue(list(reader.bed.columns), [0,1,2,3]) + self.assertTrue(list(reader.bed.index), [0,1,2]) def test_read_chrom_sizes(self): self.bs._read_chromsizes("tests/hg19.chrom.sizes") From 8fe3e90d83e7c11291cc49c8ab99a8851ce344c4 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Thu, 25 Mar 2021 02:56:35 +0900 Subject: [PATCH 0111/1072] fix bugs in shift_from_file and drop_from_file --- bedshift/bedshift.py | 77 ++++++++++++++++++++++++++------------------ 1 file changed, 46 insertions(+), 31 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index f3982190..98e7f410 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -181,7 +181,8 @@ def add_from_file(self, fp, addrate, delimiter="\t"): self.bed = self.bed.append(add_df, ignore_index=True) return num_add - def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=None): + + def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=[]): """ Shift regions @@ -193,16 +194,14 @@ def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=None): self._precheck(shiftrate, requiresChromLens=True) rows = self.bed.shape[0] - if shift_rows == None: + if len(shift_rows) == 0: shift_rows = random.sample(list(range(rows)), int(rows * shiftrate)) new_row_list = [] to_drop = [] num_shifted = 0 invalid_shifted = 0 for row in shift_rows: - drop_row, new_region = self._shift( - row, shiftmean, shiftstdev - ) # shifted rows display a 1 + drop_row, new_region = self._shift(row, shiftmean, shiftstdev) # shifted rows display a 1 if drop_row is not None and new_region: num_shifted += 1 new_row_list.append(new_region) @@ -213,11 +212,10 @@ def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=None): self.bed = self.bed.append(new_row_list, ignore_index=True) self.bed = self.bed.reset_index(drop=True) if invalid_shifted > 0: - _LOGGER.warning( - f"{invalid_shifted} regions were prevented from being shifted outside of chromosome boundaries. Reported regions shifted will be less than expected." - ) + _LOGGER.warning(f"{invalid_shifted} regions were prevented from being shifted outside of chromosome boundaries. Reported regions shifted will be less than expected.") return num_shifted + def _shift(self, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) @@ -228,9 +226,10 @@ def _shift(self, row, mean, stdev): # check if the region is shifted out of chromosome length bounds return None, None - return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: "S"} + return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: 'S'} + - def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter="\t"): + def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): """ Shift regions that overlap the specified file's regions @@ -247,16 +246,24 @@ def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter="\t"): num_shift = int(rows * shiftrate) intersect_regions = self._find_overlap(fp) - interlen = len(intersect_regions) + rows2shift = intersect_regions.sample(frac=shiftrate) + indices2shift = [] + for _, r in rows2shift.iterrows(): + chrom = r[0] + start = r[1] + end = r[2] + index = (self.bed[(self.bed[0] == chrom) & (self.bed[1] == start)].index.tolist()) + + if len(index) > 0: + indices2shift.append(index[0]) + + interlen = len(indices2shift) if num_shift > interlen: - _LOGGER.warning( - "Number of regions to be shifted ({}) is larger than the provided bedfile size ({}). Shifting {} regions.".format( - num_shift, interlen, interlen - ) - ) - num_shift = interlen - rows2shift = random.sample(list(range(interlen)), num_shift) - return self.shift(shiftrate, shiftmean, shiftstdev, rows2shift) + _LOGGER.warning("Number of regions to be shifted ({}) is larger than the desired number of regions to be shifted: ({}). Shifting {} regions.".format(num_shift, interlen, interlen)) + num_shift = len(indices2shift) + + return self.shift(shiftrate, shiftmean, shiftstdev, indices2shift) + def cut(self, cutrate): """ @@ -356,7 +363,8 @@ def drop(self, droprate): self.bed = self.bed.reset_index(drop=True) return len(drop_rows) - def drop_from_file(self, fp, droprate, delimiter="\t"): + + def drop_from_file(self, fp, droprate, delimiter='\t'): """ drop regions that overlap between the reference bedfile and the provided bedfile. @@ -371,20 +379,27 @@ def drop_from_file(self, fp, droprate, delimiter="\t"): drop_bed = self.read_bed(fp, delimiter=delimiter) intersect_regions = self._find_overlap(drop_bed) - interlen = len(intersect_regions) + + rows2drop = intersect_regions.sample(frac=droprate) + indices2drop = [] + for _, r in rows2drop.iterrows(): + chrom = r[0] + start = r[1] + end = r[2] + index = (self.bed[(self.bed[0] == chrom) & (self.bed[1] == start)].index.tolist()) + + if len(index) > 0: + indices2drop.append(index[0]) + + interlen = len(indices2drop) if num_drop > interlen: - _LOGGER.warning( - "Number of regions to be dropped ({}) is larger than the provided bedfile size ({}). Dropping {} regions.".format( - num_drop, interlen, interlen - ) - ) - num_drop = interlen - rows2drop = random.sample(list(range(interlen)), num_drop) - self.bed = self.bed.drop(intersect_regions.index[rows2drop]).reset_index( - drop=True - ) + _LOGGER.warning("Number of regions to be dropped ({}) is larger than the desired number of regions to be dropped: ({}). Dropping {} regions.".format(num_drop, interlen, interlen)) + num_drop = len(indices2drop) + + self.bed = self.bed.drop(indices2drop) return num_drop + def _find_overlap(self, fp, reference=None): """ Find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file. From 24fcd1f7c393a40c220c127bfb6088a62c2cc41e Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Wed, 24 Mar 2021 20:53:38 -0500 Subject: [PATCH 0112/1072] fix #34 --- bedshift/bedshift.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index f3982190..401335dc 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -494,11 +494,6 @@ def to_bed(self, outfile_name): self.bed.to_csv( outfile_name, sep="\t", header=False, index=False, float_format="%.0f" ) - _LOGGER.info( - "The output bedfile located in {} has {} regions. The original bedfile had {} regions.".format( - outfile_name, self.bed.shape[0], self.original_num_regions - ) - ) def read_bed(self, bedfile_path, delimiter="\t"): """ @@ -611,7 +606,11 @@ def main(): args.dropfile, args.yaml_config, ) - _LOGGER.info("\t" + str(n) + " regions changed in total.\n") + _LOGGER.info( + "REGION COUNT | original: {}\tnew: {}\tchanged: {}\t\noutput file: {}".format( + bedshifter.original_num_regions, bedshifter.bed.shape[0], str(n), outfile + ) + ) if args.repeat == 1: bedshifter.to_bed(outfile) else: From 8087f6086e1a45e26a545e93bfee42e834d49b79 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Wed, 24 Mar 2021 21:01:26 -0500 Subject: [PATCH 0113/1072] change bedshifted_test file --- .gitignore | 1 + tests/bedshift_analysis.yaml | 2 +- tests/bedshifted_test.bed | 7746 ---------------------------------- tests/test.py | 2 +- tests/test2.bed | 500 +++ 5 files changed, 503 insertions(+), 7748 deletions(-) delete mode 100644 tests/bedshifted_test.bed create mode 100644 tests/test2.bed diff --git a/.gitignore b/.gitignore index 5f7d71dd..06dba87f 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ bedshifted* *.bed !tests/test.bed +!tests/test2.bed !tests/small_test.bed !tests/small_test2.bed !tests/from_file.bed diff --git a/tests/bedshift_analysis.yaml b/tests/bedshift_analysis.yaml index 68c85c9f..895308ba 100644 --- a/tests/bedshift_analysis.yaml +++ b/tests/bedshift_analysis.yaml @@ -8,7 +8,7 @@ bedshift_operations: rate: 0.1 delimiter: \t - shift_from_file: - file: tests/bedshifted_test.bed + file: tests/test2.bed rate: 0.5 mean: 100 stdev: 200 diff --git a/tests/bedshifted_test.bed b/tests/bedshifted_test.bed deleted file mode 100644 index a12553cd..00000000 --- a/tests/bedshifted_test.bed +++ /dev/null @@ -1,7746 +0,0 @@ -chr1 1057472 1057792 0 -chr1 1280014 1837925 4 -chr1 1307759 205225637 4 -chr1 1376528 1376616 1 -chr1 1837836 1875763 4 -chr1 1891508 1891828 4 -chr1 2313442 2313762 1 -chr1 2479800 2480120 1 -chr1 3236554 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-chrX 152820261 152820581 0 -chrX 152934764 152935084 0 -chrX 153029402 153029722 0 -chrX 153212916 153213076 2 -chrX 153212921 153213081 1 -chrX 153218901 153279397 4 -chrX 153316698 153317018 0 -chrX 153536723 153537043 0 -chrX 153763163 153763325 0 -chrX 153943402 153943562 2 -chrY 4055823 4055906 3 -chrY 10358489 10358547 2 -chrY 10358547 10358605 2 -chrY 26266811 26266918 3 -chrY 27811821 27811885 2 -chrY 33291667 33291761 3 -chrY 45782319 45782444 3 -chrY 55739083 55739152 3 diff --git a/tests/test.py b/tests/test.py index ec7edf52..008eabcb 100755 --- a/tests/test.py +++ b/tests/test.py @@ -130,7 +130,7 @@ def test_handle_yaml(self): added = bedshifter.add(addrate=0.1, addmean=100, addstdev=20) f_drop_10 = bedshifter.drop_from_file(fp="tests/test.bed", droprate=0.1) - f_shift_30 = bedshifter.shift_from_file(fp="tests/bedshifted_test.bed", + f_shift_30 = bedshifter.shift_from_file(fp="tests/test2.bed", shiftrate=0.50, shiftmean=100, shiftstdev=200) diff --git a/tests/test2.bed 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+chr9 131901407 131901727 - +chr17 42193108 42193198 - +chr12 6574760 6574897 - +chr8 121963451 121963771 - +chr9 77075186 77075510 A +chr3 46671529 46671715 - +chr7 122651549 122651844 A +chr9 133741793 133742113 - +chr14 75774507 75774827 - +chr16 36516476 36516828 A +chr9 134631013 134631333 - +chr9 71669135 71669455 - +chr2 237981009 237981329 - +chr1 30170858 30171178 - +chr1 152025059 152025379 - +chr1 33761095 33761415 - +chr2 38830569 38830889 - +chr5 148608624 148608944 - +chr7 134966256 134966576 - +chr18 45275651 45275766 - +chr11 125218779 125218889 - +chr4 114199503 114199823 - +chr6 22768086 22768387 A +chr20 45901593 45901913 - +chr1 239113000 239113306 A +chr16 85693365 85693685 - +chr2 30934552 30934712 - +chr10 99436448 99436768 - +chr3 187397436 187397756 - +chr14 98980252 98980554 A +chr1 15650045 15650365 - +chr9 127670785 127671079 A +chr2 1595693 1596013 - +chr15 72961550 72961844 A +chr22 43176092 43176412 - +chr1 30035028 30035371 A +chr11 116661975 116662295 - +chr4 100471291 100471650 A +chr5 104040677 104040936 A +chr12 40563820 40564177 A +chr5 107179168 107179514 A +chr17 74500566 74500908 A +chr10 121249390 121249522 - +chr3 47563603 47563923 - +chr6 44623239 44623559 - +chr9 116870292 116870474 - +chr11 20132445 20132765 - +chr6 41888986 41889306 - +chr2 200353554 200353874 - +chr13 21286753 21287073 - +chr1 94979690 94980010 - +chr3 52035515 52035835 - +chr9 132048592 132048861 A +chrX 109656392 109656712 - +chr6 145955180 145955500 - +chr13 66163548 66163894 A +chr2 71221976 71222296 - +chr3 58809969 58810289 - +chr4 100326702 100327022 - +chrX 102719713 102720033 - +chr6 160011083 160011383 A +chr14 80331024 80331371 A +chr11 59074478 59074783 A +chr14 76853608 76853803 - +chr16 40748955 40749291 A +chr9 100149325 100149509 - +chr6 2209506 2209826 - +chr19 48794521 48794841 - +chr13 78784910 78785203 A +chr15 99753443 99753763 - +chr21 29889313 29889633 - +chr17 25981746 25982066 - +chr9 123518128 123518448 - +chr2 84530076 84530396 - +chr3 140730380 140730700 - +chr3 133537684 133538004 - +chr6 88032145 88032339 - +chr22 22292531 22292641 - +chr9 27529788 27529925 - +chr16 22199933 22200253 - +chr11 109816958 109817278 - +chr7 13137998 13138346 A +chr7 39493375 39493695 - +chr8 23331791 23332111 - +chr12 121668104 121668424 - +chr2 236688074 236688394 - +chr11 73000616 73000765 - +chr2 216501571 216501888 A +chr3 178051653 178051949 A +chr20 4573239 4573559 - +chr3 115692428 115692665 A +chr14 104808526 104808841 A +chr11 130894961 130895281 - +chr3 56528872 56528969 - +chr17 44079893 44080213 - +chr17 28348790 28349110 - +chr7 50460442 50460748 A From c096ddd336920fad7fb80fc642f5b0f7793d7108 Mon Sep 17 00:00:00 2001 From: Hyun Jae Cho Date: Thu, 25 Mar 2021 16:51:26 +0900 Subject: [PATCH 0114/1072] fixed shift_from_file and drop_from_file to use merge Also included fixes in PR #35 --- bedshift/bedshift.py | 74 +++++++++++++++++++++++++------------------- 1 file changed, 42 insertions(+), 32 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 98e7f410..e0e85687 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -44,6 +44,7 @@ def __init__(self, bedfile_path, chrom_sizes=None, delimiter="\t"): ) self.original_bed = self.bed.copy() + def _read_chromsizes(self, fp): """ Read chromosome sizes file @@ -66,12 +67,14 @@ def _read_chromsizes(self, fp): chrom_len / total_len for chrom_len in self.chrom_lens.values() ] + def reset_bed(self): """ Reset the stored bedfile to the state before perturbations """ self.bed = self.original_bed.copy() + def _precheck(self, rate, requiresChromLens=False, isAdd=False): """ Check if the rate of perturbation is too high or low @@ -93,6 +96,7 @@ def _precheck(self, rate, requiresChromLens=False, isAdd=False): _LOGGER.error("chrom.sizes file must be specified") sys.exit(1) + def pick_random_chroms(self, n): """ Utility function to pick a random chromosome @@ -106,6 +110,7 @@ def pick_random_chroms(self, n): chrom_lens = [self.chrom_lens[chrom_str] for chrom_str in chrom_strs] return zip(chrom_strs, chrom_lens) + def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter="\t"): """ Add regions @@ -154,6 +159,7 @@ def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter="\t"): self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) return num_add + def add_from_file(self, fp, addrate, delimiter="\t"): """ Add regions from another bedfile to this perturbed bedfile @@ -176,7 +182,7 @@ def add_from_file(self, fp, addrate, delimiter="\t"): ) num_add = dflen add_rows = random.sample(list(range(dflen)), num_add) - add_df = df.reindex([add_rows]).reset_index(drop=True) + add_df = df.loc[add_rows].reset_index(drop=True) add_df[3] = pd.Series(["A"] * add_df.shape[0]) self.bed = self.bed.append(add_df, ignore_index=True) return num_add @@ -246,23 +252,23 @@ def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): num_shift = int(rows * shiftrate) intersect_regions = self._find_overlap(fp) - rows2shift = intersect_regions.sample(frac=shiftrate) - indices2shift = [] - for _, r in rows2shift.iterrows(): - chrom = r[0] - start = r[1] - end = r[2] - index = (self.bed[(self.bed[0] == chrom) & (self.bed[1] == start)].index.tolist()) - - if len(index) > 0: - indices2shift.append(index[0]) - - interlen = len(indices2shift) + original_colnames = self.bed.columns + intersect_regions.columns = [str(col) for col in intersect_regions.columns] + self.bed.columns = [str(col) for col in self.bed.columns] + indices_of_overlap_regions = self.bed.reset_index().merge(intersect_regions)['index'] + self.bed.columns = [int(col) for col in self.bed.columns] + + interlen = len(indices_of_overlap_regions) if num_shift > interlen: - _LOGGER.warning("Number of regions to be shifted ({}) is larger than the desired number of regions to be shifted: ({}). Shifting {} regions.".format(num_shift, interlen, interlen)) - num_shift = len(indices2shift) + _LOGGER.warning("Desired regions shifted ({}) is greater than the number of overlaps found ({}). Shifting {} regions.".format(num_shift, interlen, interlen)) + num_shift = len(indices_of_overlap_regions) + + elif interlen > num_shift: + indices_of_overlap_regions = indices_of_overlap_regions.sample(n=num_shift) - return self.shift(shiftrate, shiftmean, shiftstdev, indices2shift) + indices_of_overlap_regions = indices_of_overlap_regions.to_list() + + return self.shift(shiftrate, shiftmean, shiftstdev, indices_of_overlap_regions) def cut(self, cutrate): @@ -287,6 +293,7 @@ def cut(self, cutrate): self.bed = self.bed.reset_index(drop=True) return len(cut_rows) + def _cut(self, row): chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] @@ -312,6 +319,7 @@ def _cut(self, row): {0: chrom, 1: thecut, 2: end, 3: "C"}, ] + def merge(self, mergerate): """ Merge two regions into one new region @@ -335,6 +343,7 @@ def merge(self, mergerate): self.bed = self.bed.reset_index(drop=True) return len(to_drop) + def _merge(self, row): # check if the regions being merged are on the same chromosome if ( @@ -348,6 +357,7 @@ def _merge(self, row): end = self.bed.loc[row + 1][2] return [row, row + 1], {0: chrom, 1: start, 2: end, 3: "M"} + def drop(self, droprate): """ Drop regions @@ -379,24 +389,21 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): drop_bed = self.read_bed(fp, delimiter=delimiter) intersect_regions = self._find_overlap(drop_bed) + original_colnames = self.bed.columns + intersect_regions.columns = [str(col) for col in intersect_regions.columns] + self.bed.columns = [str(col) for col in self.bed.columns] + indices_of_overlap_regions = self.bed.reset_index().merge(intersect_regions)['index'] + self.bed.columns = [int(col) for col in self.bed.columns] - rows2drop = intersect_regions.sample(frac=droprate) - indices2drop = [] - for _, r in rows2drop.iterrows(): - chrom = r[0] - start = r[1] - end = r[2] - index = (self.bed[(self.bed[0] == chrom) & (self.bed[1] == start)].index.tolist()) - - if len(index) > 0: - indices2drop.append(index[0]) - - interlen = len(indices2drop) + interlen = len(indices_of_overlap_regions) if num_drop > interlen: - _LOGGER.warning("Number of regions to be dropped ({}) is larger than the desired number of regions to be dropped: ({}). Dropping {} regions.".format(num_drop, interlen, interlen)) - num_drop = len(indices2drop) + _LOGGER.warning("Desired regions dropped ({}) is greater than the number of overlaps found ({}). Dropping {} regions.".format(num_drop, interlen, interlen)) + num_drop = len(indices_of_overlap_regions) + elif interlen > num_drop: + indices_of_overlap_regions = indices_of_overlap_regions.sample(n=num_drop) + indices_of_overlap_regions = indices_of_overlap_regions.to_list() - self.bed = self.bed.drop(indices2drop) + self.bed = self.bed.drop(indices_of_overlap_regions) return num_drop @@ -438,6 +445,7 @@ def _find_overlap(self, fp, reference=None): intersection.columns = [0, 1, 2] return intersection + def all_perturbations( self, addrate=0.0, @@ -499,6 +507,7 @@ def all_perturbations( n += self.drop(droprate) return n + def to_bed(self, outfile_name): """ Write a pandas dataframe back into BED file format @@ -515,6 +524,7 @@ def to_bed(self, outfile_name): ) ) + def read_bed(self, bedfile_path, delimiter="\t"): """ Read a BED file into pandas dataframe @@ -538,7 +548,7 @@ def read_bed(self, bedfile_path, delimiter="\t"): # if there is a header line in the table, remove it if not str(df.iloc[0, 1]).isdigit(): - df = df[1:] + df = df[1:].reset_index(drop=True) df[3] = "-" # column indicating which modifications were made return df From 2f2dce86b2d62e70f0b679e02e71c868f5421e43 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 25 Mar 2021 12:05:39 -0400 Subject: [PATCH 0115/1072] simplify repetition output. See #40 --- bedshift/bedshift.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 4938b756..80f89050 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -589,6 +589,7 @@ def main(): ) bedshifter = Bedshift(args.bedfile, args.chrom_lengths) + _LOGGER.info(f"Generating {args.repeat} repetitions...") for i in range(args.repeat): n = bedshifter.all_perturbations( args.addrate, @@ -606,13 +607,20 @@ def main(): args.dropfile, args.yaml_config, ) - _LOGGER.info( - "REGION COUNT | original: {}\tnew: {}\tchanged: {}\t\noutput file: {}".format( - bedshifter.original_num_regions, bedshifter.bed.shape[0], str(n), outfile - ) - ) + if int((100 * i) / args.repeat) in [5, 25, 50, 75, 100]: + pct_finished = int(100 * i / args.repeat) + _LOGGER.info(f"{pct_finished}% finished") + if args.repeat == 1: bedshifter.to_bed(outfile) + _LOGGER.info( + "REGION COUNT | original: {}\tnew: {}\tchanged: {}\t\noutput file: {}".format( + bedshifter.original_num_regions, + bedshifter.bed.shape[0], + str(n), + outfile, + ) + ) else: modified_outfile = outfile.rsplit("/") modified_outfile[-1] = "rep" + str(i + 1) + "_" + modified_outfile[-1] From 1e68f5a3e56805f17d260ee496ec86018df0f35d Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 25 Mar 2021 12:41:33 -0400 Subject: [PATCH 0116/1072] fix pct calc --- bedshift/bedshift.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 80f89050..f4ea9622 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -607,8 +607,9 @@ def main(): args.dropfile, args.yaml_config, ) - if int((100 * i) / args.repeat) in [5, 25, 50, 75, 100]: - pct_finished = int(100 * i / args.repeat) + + pct_finished = int(100 * (i+i) / args.repeat) + if int(pct_finished in [5, 25, 50, 75, 100]: _LOGGER.info(f"{pct_finished}% finished") if args.repeat == 1: From 3f5ad0d0d2d0b2d066070ff1c4b6ae7adb760c1d Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 25 Mar 2021 14:16:23 -0400 Subject: [PATCH 0117/1072] fix bug --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index f4ea9622..c557bedd 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -609,7 +609,7 @@ def main(): ) pct_finished = int(100 * (i+i) / args.repeat) - if int(pct_finished in [5, 25, 50, 75, 100]: + if int(pct_finished) in [5, 25, 50, 75, 100]: _LOGGER.info(f"{pct_finished}% finished") if args.repeat == 1: From e376073ec972cd610dbb4e0ca0b1fcb69b365d12 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 25 Mar 2021 15:09:50 -0400 Subject: [PATCH 0118/1072] progress message --- bedshift/bedshift.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index c557bedd..5832bf2f 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -589,7 +589,7 @@ def main(): ) bedshifter = Bedshift(args.bedfile, args.chrom_lengths) - _LOGGER.info(f"Generating {args.repeat} repetitions...") + _LOGGER.info(f"Generating {args.repeat} repetitions... asdfasfd") for i in range(args.repeat): n = bedshifter.all_perturbations( args.addrate, @@ -607,8 +607,7 @@ def main(): args.dropfile, args.yaml_config, ) - - pct_finished = int(100 * (i+i) / args.repeat) + pct_finished = int((100 * (i+1)) / args.repeat) if int(pct_finished) in [5, 25, 50, 75, 100]: _LOGGER.info(f"{pct_finished}% finished") From 85bad5dac30deb1e42b634581e56d901dbe29740 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 25 Mar 2021 15:11:29 -0400 Subject: [PATCH 0119/1072] remove extra text --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 5832bf2f..cb56e676 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -589,7 +589,7 @@ def main(): ) bedshifter = Bedshift(args.bedfile, args.chrom_lengths) - _LOGGER.info(f"Generating {args.repeat} repetitions... asdfasfd") + _LOGGER.info(f"Generating {args.repeat} repetitions...") for i in range(args.repeat): n = bedshifter.all_perturbations( args.addrate, From 76db57d96cedd38550a3c32627910b96435c2eda Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 25 Mar 2021 16:48:05 -0400 Subject: [PATCH 0120/1072] black --- bedshift/bedshift.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index cb56e676..a1c90884 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -607,7 +607,7 @@ def main(): args.dropfile, args.yaml_config, ) - pct_finished = int((100 * (i+1)) / args.repeat) + pct_finished = int((100 * (i + 1)) / args.repeat) if int(pct_finished) in [5, 25, 50, 75, 100]: _LOGGER.info(f"{pct_finished}% finished") From f1a0a43d10f0ce855a1b712c788ecf58dd52b182 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Thu, 25 Mar 2021 22:30:50 -0500 Subject: [PATCH 0121/1072] version bump to 1.1.0 --- bedshift/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/_version.py b/bedshift/_version.py index 98e28b87..6849410a 100644 --- a/bedshift/_version.py +++ b/bedshift/_version.py @@ -1 +1 @@ -__version__ = "1.1.0-dev" +__version__ = "1.1.0" From 6e9a89630c45f5342426a26641ec7a50295405c0 Mon Sep 17 00:00:00 2001 From: nsheff Date: Wed, 31 Mar 2021 08:45:43 -0400 Subject: [PATCH 0122/1072] change outfile name. Fix #41 --- bedshift/bedshift.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index a1c90884..a1e99085 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -622,10 +622,14 @@ def main(): ) ) else: - modified_outfile = outfile.rsplit("/") - modified_outfile[-1] = "rep" + str(i + 1) + "_" + modified_outfile[-1] - modified_outfile = "/".join(modified_outfile) - bedshifter.to_bed(modified_outfile) + basename = os.path.basename(outfile) + rep = str(i + 1) + modified_outfile_path = f"{basename}_rep_{rep}" + + # modified_outfile = outfile.rsplit("/") + # modified_outfile[-1] = "rep" + str(i + 1) + "_" + modified_outfile[-1] + # modified_outfile = "/".join(modified_outfile) + bedshifter.to_bed(modified_outfile_path) bedshifter.reset_bed() From 4c5a1db6a2dcdda9146bc229d9cd7b9af4539529 Mon Sep 17 00:00:00 2001 From: nsheff Date: Wed, 31 Mar 2021 08:53:53 -0400 Subject: [PATCH 0123/1072] Fix #41 --- README.md | 2 +- bedshift/bedshift.py | 72 ++++++++++++++++++++++---------------------- 2 files changed, 37 insertions(+), 37 deletions(-) diff --git a/README.md b/README.md index e9f7831b..61025e48 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ Install from local repository: `pip install .` Run with: ``` -bedshift -l hg38.chrom.sizes -b BEDFILE +bedshift -l tests/hg38.chrom.sizes -b tests/test.bed ``` See `bedshift -h` for parameters. diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index fa042554..257532cc 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -1,13 +1,14 @@ """ Perturb regions in bedfiles """ import logging -import os -import sys -import random import logmuse -import pandas as pd +import math import numpy as np +import os +import pandas as pd import pyranges as pr +import random +import sys from bedshift._version import __version__ from bedshift import arguments @@ -44,7 +45,6 @@ def __init__(self, bedfile_path, chrom_sizes=None, delimiter="\t"): ) self.original_bed = self.bed.copy() - def _read_chromsizes(self, fp): """ Read chromosome sizes file @@ -67,14 +67,12 @@ def _read_chromsizes(self, fp): chrom_len / total_len for chrom_len in self.chrom_lens.values() ] - def reset_bed(self): """ Reset the stored bedfile to the state before perturbations """ self.bed = self.original_bed.copy() - def _precheck(self, rate, requiresChromLens=False, isAdd=False): """ Check if the rate of perturbation is too high or low @@ -96,7 +94,6 @@ def _precheck(self, rate, requiresChromLens=False, isAdd=False): _LOGGER.error("chrom.sizes file must be specified") sys.exit(1) - def pick_random_chroms(self, n): """ Utility function to pick a random chromosome @@ -110,7 +107,6 @@ def pick_random_chroms(self, n): chrom_lens = [self.chrom_lens[chrom_str] for chrom_str in chrom_strs] return zip(chrom_strs, chrom_lens) - def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter="\t"): """ Add regions @@ -159,7 +155,6 @@ def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter="\t"): self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) return num_add - def add_from_file(self, fp, addrate, delimiter="\t"): """ Add regions from another bedfile to this perturbed bedfile @@ -187,7 +182,6 @@ def add_from_file(self, fp, addrate, delimiter="\t"): self.bed = self.bed.append(add_df, ignore_index=True) return num_add - def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=[]): """ Shift regions @@ -207,7 +201,9 @@ def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=[]): num_shifted = 0 invalid_shifted = 0 for row in shift_rows: - drop_row, new_region = self._shift(row, shiftmean, shiftstdev) # shifted rows display a 1 + drop_row, new_region = self._shift( + row, shiftmean, shiftstdev + ) # shifted rows display a 1 if drop_row is not None and new_region: num_shifted += 1 new_row_list.append(new_region) @@ -218,10 +214,11 @@ def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=[]): self.bed = self.bed.append(new_row_list, ignore_index=True) self.bed = self.bed.reset_index(drop=True) if invalid_shifted > 0: - _LOGGER.warning(f"{invalid_shifted} regions were prevented from being shifted outside of chromosome boundaries. Reported regions shifted will be less than expected.") + _LOGGER.warning( + f"{invalid_shifted} regions were prevented from being shifted outside of chromosome boundaries. Reported regions shifted will be less than expected." + ) return num_shifted - def _shift(self, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) @@ -232,10 +229,9 @@ def _shift(self, row, mean, stdev): # check if the region is shifted out of chromosome length bounds return None, None - return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: 'S'} + return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: "S"} - - def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): + def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter="\t"): """ Shift regions that overlap the specified file's regions @@ -255,12 +251,18 @@ def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): original_colnames = self.bed.columns intersect_regions.columns = [str(col) for col in intersect_regions.columns] self.bed.columns = [str(col) for col in self.bed.columns] - indices_of_overlap_regions = self.bed.reset_index().merge(intersect_regions)['index'] + indices_of_overlap_regions = self.bed.reset_index().merge(intersect_regions)[ + "index" + ] self.bed.columns = [int(col) for col in self.bed.columns] interlen = len(indices_of_overlap_regions) if num_shift > interlen: - _LOGGER.warning("Desired regions shifted ({}) is greater than the number of overlaps found ({}). Shifting {} regions.".format(num_shift, interlen, interlen)) + _LOGGER.warning( + "Desired regions shifted ({}) is greater than the number of overlaps found ({}). Shifting {} regions.".format( + num_shift, interlen, interlen + ) + ) num_shift = len(indices_of_overlap_regions) elif interlen > num_shift: @@ -270,7 +272,6 @@ def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): return self.shift(shiftrate, shiftmean, shiftstdev, indices_of_overlap_regions) - def cut(self, cutrate): """ Cut regions to create two new regions @@ -293,7 +294,6 @@ def cut(self, cutrate): self.bed = self.bed.reset_index(drop=True) return len(cut_rows) - def _cut(self, row): chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] @@ -319,7 +319,6 @@ def _cut(self, row): {0: chrom, 1: thecut, 2: end, 3: "C"}, ] - def merge(self, mergerate): """ Merge two regions into one new region @@ -343,7 +342,6 @@ def merge(self, mergerate): self.bed = self.bed.reset_index(drop=True) return len(to_drop) - def _merge(self, row): # check if the regions being merged are on the same chromosome if ( @@ -357,7 +355,6 @@ def _merge(self, row): end = self.bed.loc[row + 1][2] return [row, row + 1], {0: chrom, 1: start, 2: end, 3: "M"} - def drop(self, droprate): """ Drop regions @@ -373,8 +370,7 @@ def drop(self, droprate): self.bed = self.bed.reset_index(drop=True) return len(drop_rows) - - def drop_from_file(self, fp, droprate, delimiter='\t'): + def drop_from_file(self, fp, droprate, delimiter="\t"): """ drop regions that overlap between the reference bedfile and the provided bedfile. @@ -392,12 +388,18 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): original_colnames = self.bed.columns intersect_regions.columns = [str(col) for col in intersect_regions.columns] self.bed.columns = [str(col) for col in self.bed.columns] - indices_of_overlap_regions = self.bed.reset_index().merge(intersect_regions)['index'] + indices_of_overlap_regions = self.bed.reset_index().merge(intersect_regions)[ + "index" + ] self.bed.columns = [int(col) for col in self.bed.columns] - + interlen = len(indices_of_overlap_regions) if num_drop > interlen: - _LOGGER.warning("Desired regions dropped ({}) is greater than the number of overlaps found ({}). Dropping {} regions.".format(num_drop, interlen, interlen)) + _LOGGER.warning( + "Desired regions dropped ({}) is greater than the number of overlaps found ({}). Dropping {} regions.".format( + num_drop, interlen, interlen + ) + ) num_drop = len(indices_of_overlap_regions) elif interlen > num_drop: indices_of_overlap_regions = indices_of_overlap_regions.sample(n=num_drop) @@ -406,7 +408,6 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): self.bed = self.bed.drop(indices_of_overlap_regions) return num_drop - def _find_overlap(self, fp, reference=None): """ Find intersecting regions between the reference bedfile and the comparison file provided in the yaml config file. @@ -445,7 +446,6 @@ def _find_overlap(self, fp, reference=None): intersection.columns = [0, 1, 2] return intersection - def all_perturbations( self, addrate=0.0, @@ -507,7 +507,6 @@ def all_perturbations( n += self.drop(droprate) return n - def to_bed(self, outfile_name): """ Write a pandas dataframe back into BED file format @@ -519,7 +518,6 @@ def to_bed(self, outfile_name): outfile_name, sep="\t", header=False, index=False, float_format="%.0f" ) - def read_bed(self, bedfile_path, delimiter="\t"): """ Read a BED file into pandas dataframe @@ -647,9 +645,11 @@ def main(): ) ) else: - basename = os.path.basename(outfile) - rep = str(i + 1) - modified_outfile_path = f"{basename}_rep_{rep}" + basename, ext = os.path.splitext(os.path.basename(outfile)) + digits = int(math.log10(args.repeat)) + 1 + + rep = str(i + 1).zfill(digits) + modified_outfile_path = f"{basename}_rep{rep}{ext}" # modified_outfile = outfile.rsplit("/") # modified_outfile[-1] = "rep" + str(i + 1) + "_" + modified_outfile[-1] From 4726135b5945a5f55a2fe2ffe59ef0631c1b3b31 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 1 Apr 2021 10:07:11 -0400 Subject: [PATCH 0124/1072] improve reporting of output progress --- bedshift/bedshift.py | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 257532cc..468c35e9 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -584,9 +584,9 @@ def main(): sys.exit(1) if args.outputfile: - outfile = args.outputfile + outfile_base = args.outputfile else: - outfile = "bedshifted_{}".format(os.path.basename(args.bedfile)) + outfile_base = "bedshifted_{}".format(os.path.basename(args.bedfile)) _LOGGER.info( msg.format( @@ -605,7 +605,7 @@ def main(): shiftfile=args.shiftfile, cutrate=args.cutrate, mergerate=args.mergerate, - outputfile=outfile, + outputfile=outfile_base, repeat=args.repeat, yaml_config=args.yaml_config, ) @@ -613,6 +613,9 @@ def main(): bedshifter = Bedshift(args.bedfile, args.chrom_lengths) _LOGGER.info(f"Generating {args.repeat} repetitions...") + + pct_reports = [int(x*args.repeat/100) for x in [5, 25, 50, 75, 100]] + for i in range(args.repeat): n = bedshifter.all_perturbations( args.addrate, @@ -630,12 +633,8 @@ def main(): args.dropfile, args.yaml_config, ) - pct_finished = int((100 * (i + 1)) / args.repeat) - if int(pct_finished) in [5, 25, 50, 75, 100]: - _LOGGER.info(f"{pct_finished}% finished") - if args.repeat == 1: - bedshifter.to_bed(outfile) + bedshifter.to_bed(outfile_base) _LOGGER.info( "REGION COUNT | original: {}\tnew: {}\tchanged: {}\t\noutput file: {}".format( bedshifter.original_num_regions, @@ -645,19 +644,25 @@ def main(): ) ) else: - basename, ext = os.path.splitext(os.path.basename(outfile)) + basename, ext = os.path.splitext(os.path.basename(outfile_base)) + dirname = os.path.dirname(outfile_base) digits = int(math.log10(args.repeat)) + 1 rep = str(i + 1).zfill(digits) - modified_outfile_path = f"{basename}_rep{rep}{ext}" + modified_outfile_path = os.path.join(dirname, f"{basename}_rep{rep}{ext}") # modified_outfile = outfile.rsplit("/") # modified_outfile[-1] = "rep" + str(i + 1) + "_" + modified_outfile[-1] # modified_outfile = "/".join(modified_outfile) bedshifter.to_bed(modified_outfile_path) + pct_finished = int((100 * (i + 1)) / args.repeat) + if i+1 in pct_reports: + _LOGGER.info(f"Rep {i+1}. Finished: {pct_finished}%. Output file: {modified_outfile_path}") + bedshifter.reset_bed() + if __name__ == "__main__": try: sys.exit(main()) From a803ab25b102ea988ce7d9cdc7b0cbbeed9a7318 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Fri, 2 Apr 2021 16:45:56 -0500 Subject: [PATCH 0125/1072] update changelog --- bedshift/bedshift.py | 32 ++++++++++++++++++-------------- docs/changelog.md | 6 ++++-- 2 files changed, 22 insertions(+), 16 deletions(-) diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index 468c35e9..3b99d84b 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -118,7 +118,10 @@ def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter="\t"): :param str delimiter: the delimiter used in valid_bed :return int: the number of regions added """ - self._precheck(addrate, requiresChromLens=True, isAdd=True) + if valid_bed: + self._precheck(addrate, requiresChromLens=False, isAdd=True) + else: + self._precheck(addrate, requiresChromLens=True, isAdd=True) rows = self.bed.shape[0] num_add = int(rows * addrate) @@ -314,10 +317,13 @@ def _cut(self, row): new_segs = self.__shift(new_segs, 1, meanshift, stdevshift) """ - return row, [ - {0: chrom, 1: start, 2: thecut, 3: "C"}, - {0: chrom, 1: thecut, 2: end, 3: "C"}, - ] + return ( + row, + [ + {0: chrom, 1: start, 2: thecut, 3: "C"}, + {0: chrom, 1: thecut, 2: end, 3: "C"}, + ], + ) def merge(self, mergerate): """ @@ -614,7 +620,7 @@ def main(): bedshifter = Bedshift(args.bedfile, args.chrom_lengths) _LOGGER.info(f"Generating {args.repeat} repetitions...") - pct_reports = [int(x*args.repeat/100) for x in [5, 25, 50, 75, 100]] + pct_reports = [int(x * args.repeat / 100) for x in [5, 25, 50, 75, 100]] for i in range(args.repeat): n = bedshifter.all_perturbations( @@ -640,7 +646,7 @@ def main(): bedshifter.original_num_regions, bedshifter.bed.shape[0], str(n), - outfile, + outfile_base, ) ) else: @@ -650,19 +656,17 @@ def main(): rep = str(i + 1).zfill(digits) modified_outfile_path = os.path.join(dirname, f"{basename}_rep{rep}{ext}") - - # modified_outfile = outfile.rsplit("/") - # modified_outfile[-1] = "rep" + str(i + 1) + "_" + modified_outfile[-1] - # modified_outfile = "/".join(modified_outfile) bedshifter.to_bed(modified_outfile_path) + pct_finished = int((100 * (i + 1)) / args.repeat) - if i+1 in pct_reports: - _LOGGER.info(f"Rep {i+1}. Finished: {pct_finished}%. Output file: {modified_outfile_path}") + if i + 1 in pct_reports: + _LOGGER.info( + f"Rep {i+1}. Finished: {pct_finished}%. Output file: {modified_outfile_path}" + ) bedshifter.reset_bed() - if __name__ == "__main__": try: sys.exit(main()) diff --git a/docs/changelog.md b/docs/changelog.md index 9e19cd48..b8ee3333 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -2,14 +2,16 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html) and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) format. -## [1.1.0] - unreleased +## [1.1.0] - released Apr 2, 2021 - Added ability to specify chrom sizes file, or refgenie genome. -- Add --yaml-config option +- Add perturbation will create regions proportional to chromosome size - Improve performance of perturbations - Add --dropfile, --addfile, and --shiftfile options - Add --add_valid option +- Add --yaml-config option - Improved testing framework +- Improved logging messages ## [1.0.0] - released May 20, 2020 From 9738edcb4281f91aa61ff46120fedaa092238135 Mon Sep 17 00:00:00 2001 From: hyunjaecho94 Date: Sat, 3 Apr 2021 16:39:09 +0900 Subject: [PATCH 0126/1072] added seed --- bedshift/arguments.py | 7 +++ bedshift/bedshift.py | 138 ++++++++++++++++++++++++++---------------- 2 files changed, 94 insertions(+), 51 deletions(-) diff --git a/bedshift/arguments.py b/bedshift/arguments.py index decd3fe2..7425d882 100644 --- a/bedshift/arguments.py +++ b/bedshift/arguments.py @@ -118,6 +118,12 @@ def build_argparser(): help="run yaml configuration file", ) + parser.add_argument( + "--seed", + default=None, + help="an integer-valued seed for allowing reproducible perturbations", + ) + return parser @@ -141,4 +147,5 @@ def build_argparser(): outputfile: {outputfile} repeat: {repeat} yaml_config: {yaml_config} + seed: {seed} """ diff --git a/bedshift/bedshift.py b/bedshift/bedshift.py index e0e85687..56bb6f0d 100644 --- a/bedshift/bedshift.py +++ b/bedshift/bedshift.py @@ -1,13 +1,14 @@ """ Perturb regions in bedfiles """ import logging -import os -import sys -import random import logmuse -import pandas as pd +import math import numpy as np +import os +import pandas as pd import pyranges as pr +import random +import sys from bedshift._version import __version__ from bedshift import arguments @@ -44,7 +45,6 @@ def __init__(self, bedfile_path, chrom_sizes=None, delimiter="\t"): ) self.original_bed = self.bed.copy() - def _read_chromsizes(self, fp): """ Read chromosome sizes file @@ -67,14 +67,12 @@ def _read_chromsizes(self, fp): chrom_len / total_len for chrom_len in self.chrom_lens.values() ] - def reset_bed(self): """ Reset the stored bedfile to the state before perturbations """ self.bed = self.original_bed.copy() - def _precheck(self, rate, requiresChromLens=False, isAdd=False): """ Check if the rate of perturbation is too high or low @@ -96,7 +94,6 @@ def _precheck(self, rate, requiresChromLens=False, isAdd=False): _LOGGER.error("chrom.sizes file must be specified") sys.exit(1) - def pick_random_chroms(self, n): """ Utility function to pick a random chromosome @@ -110,7 +107,6 @@ def pick_random_chroms(self, n): chrom_lens = [self.chrom_lens[chrom_str] for chrom_str in chrom_strs] return zip(chrom_strs, chrom_lens) - def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter="\t"): """ Add regions @@ -122,7 +118,10 @@ def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter="\t"): :param str delimiter: the delimiter used in valid_bed :return int: the number of regions added """ - self._precheck(addrate, requiresChromLens=True, isAdd=True) + if valid_bed: + self._precheck(addrate, requiresChromLens=False, isAdd=True) + else: + self._precheck(addrate, requiresChromLens=True, isAdd=True) rows = self.bed.shape[0] num_add = int(rows * addrate) @@ -159,7 +158,6 @@ def add(self, addrate, addmean, addstdev, valid_bed=None, delimiter="\t"): self.bed = self.bed.append(pd.DataFrame(new_regions), ignore_index=True) return num_add - def add_from_file(self, fp, addrate, delimiter="\t"): """ Add regions from another bedfile to this perturbed bedfile @@ -187,7 +185,6 @@ def add_from_file(self, fp, addrate, delimiter="\t"): self.bed = self.bed.append(add_df, ignore_index=True) return num_add - def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=[]): """ Shift regions @@ -207,7 +204,9 @@ def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=[]): num_shifted = 0 invalid_shifted = 0 for row in shift_rows: - drop_row, new_region = self._shift(row, shiftmean, shiftstdev) # shifted rows display a 1 + drop_row, new_region = self._shift( + row, shiftmean, shiftstdev + ) # shifted rows display a 1 if drop_row is not None and new_region: num_shifted += 1 new_row_list.append(new_region) @@ -218,10 +217,11 @@ def shift(self, shiftrate, shiftmean, shiftstdev, shift_rows=[]): self.bed = self.bed.append(new_row_list, ignore_index=True) self.bed = self.bed.reset_index(drop=True) if invalid_shifted > 0: - _LOGGER.warning(f"{invalid_shifted} regions were prevented from being shifted outside of chromosome boundaries. Reported regions shifted will be less than expected.") + _LOGGER.warning( + f"{invalid_shifted} regions were prevented from being shifted outside of chromosome boundaries. Reported regions shifted will be less than expected." + ) return num_shifted - def _shift(self, row, mean, stdev): theshift = int(np.random.normal(mean, stdev)) @@ -232,10 +232,9 @@ def _shift(self, row, mean, stdev): # check if the region is shifted out of chromosome length bounds return None, None - return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: 'S'} + return row, {0: chrom, 1: start + theshift, 2: end + theshift, 3: "S"} - - def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): + def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter="\t"): """ Shift regions that overlap the specified file's regions @@ -255,12 +254,18 @@ def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): original_colnames = self.bed.columns intersect_regions.columns = [str(col) for col in intersect_regions.columns] self.bed.columns = [str(col) for col in self.bed.columns] - indices_of_overlap_regions = self.bed.reset_index().merge(intersect_regions)['index'] + indices_of_overlap_regions = self.bed.reset_index().merge(intersect_regions)[ + "index" + ] self.bed.columns = [int(col) for col in self.bed.columns] interlen = len(indices_of_overlap_regions) if num_shift > interlen: - _LOGGER.warning("Desired regions shifted ({}) is greater than the number of overlaps found ({}). Shifting {} regions.".format(num_shift, interlen, interlen)) + _LOGGER.warning( + "Desired regions shifted ({}) is greater than the number of overlaps found ({}). Shifting {} regions.".format( + num_shift, interlen, interlen + ) + ) num_shift = len(indices_of_overlap_regions) elif interlen > num_shift: @@ -270,7 +275,6 @@ def shift_from_file(self, fp, shiftrate, shiftmean, shiftstdev, delimiter='\t'): return self.shift(shiftrate, shiftmean, shiftstdev, indices_of_overlap_regions) - def cut(self, cutrate): """ Cut regions to create two new regions @@ -293,7 +297,6 @@ def cut(self, cutrate): self.bed = self.bed.reset_index(drop=True) return len(cut_rows) - def _cut(self, row): chrom = self.bed.loc[row][0] start = self.bed.loc[row][1] @@ -314,11 +317,13 @@ def _cut(self, row): new_segs = self.__shift(new_segs, 1, meanshift, stdevshift) """ - return row, [ - {0: chrom, 1: start, 2: thecut, 3: "C"}, - {0: chrom, 1: thecut, 2: end, 3: "C"}, - ] - + return ( + row, + [ + {0: chrom, 1: start, 2: thecut, 3: "C"}, + {0: chrom, 1: thecut, 2: end, 3: "C"}, + ], + ) def merge(self, mergerate): """ @@ -343,7 +348,6 @@ def merge(self, mergerate): self.bed = self.bed.reset_index(drop=True) return len(to_drop) - def _merge(self, row): # check if the regions being merged are on the same chromosome if ( @@ -357,7 +361,6 @@ def _merge(self, row): end = self.bed.loc[row + 1][2] return [row, row + 1], {0: chrom, 1: start, 2: end, 3: "M"} - def drop(self, droprate): """ Drop regions @@ -373,8 +376,7 @@ def drop(self, droprate): self.bed = self.bed.reset_index(drop=True) return len(drop_rows) - - def drop_from_file(self, fp, droprate, delimiter='\t'): + def drop_from_file(self, fp, droprate, delimiter="\t"): """ drop regions that overlap between the reference bedfile and the provided bedfile. @@ -392,12 +394,18 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): original_colnames = self.bed.columns intersect_regions.columns = [str(col) for col in intersect_regions.columns] self.bed.columns = [str(col) for col in self.bed.columns] - indices_of_overlap_regions = self.bed.reset_index().merge(intersect_regions)['index'] + indices_of_overlap_regions = self.bed.reset_index().merge(intersect_regions)[ + "index" + ] self.bed.columns = [int(col) for col in self.bed.columns] - + interlen = len(indices_of_overlap_regions) if num_drop > interlen: - _LOGGER.warning("Desired regions dropped ({}) is greater than the number of overlaps found ({}). Dropping {} regions.".format(num_drop, interlen, interlen)) + _LOGGER.warning( + "Desired regions dropped ({}) is greater than the number of overlaps found ({}). Dropping {} regions.".format( + num_drop, interlen, interlen + ) + ) num_drop = len(indices_of_overlap_regions) elif interlen > num_drop: indices_of_overlap_regions = indices_of_overlap_regions.sample(n=num_drop) @@ -406,6 +414,14 @@ def drop_from_file(self, fp, droprate, delimiter='\t'): self.bed = self.bed.drop(indices_of_overlap_regions) return num_drop + def set_seed(self, seednum): + try: + seednum = int(seednum) + random.seed(seednum) + np.random.seed(seednum) + except ValueError: + _LOGGER.error("Seed should be an integer, not {}.".format(type(seednum))) + sys.exit(1) def _find_overlap(self, fp, reference=None): """ @@ -445,7 +461,6 @@ def _find_overlap(self, fp, reference=None): intersection.columns = [0, 1, 2] return intersection - def all_perturbations( self, addrate=0.0, @@ -462,6 +477,7 @@ def all_perturbations( droprate=0.0, dropfile=None, yaml=None, + seed=None, ): """ Perform all five perturbations in the order of shift, add, cut, merge, drop. @@ -481,8 +497,11 @@ def all_perturbations( :param str dropfile: the file containing regions to be dropped :param str yaml: the yaml_config filepath :param bedshift.Bedshift bedshifter: Bedshift instance + :param int seed: a seed for allowing reproducible perturbations :return int: the number of total regions perturbed """ + if seed: + self.set_seed(seed) if yaml: return BedshiftYAMLHandler.BedshiftYAMLHandler(self, yaml).handle_yaml() n = 0 @@ -505,8 +524,8 @@ def all_perturbations( n += self.drop_from_file(dropfile, droprate) else: n += self.drop(droprate) - return n + return n def to_bed(self, outfile_name): """ @@ -518,12 +537,6 @@ def to_bed(self, outfile_name): self.bed.to_csv( outfile_name, sep="\t", header=False, index=False, float_format="%.0f" ) - _LOGGER.info( - "The output bedfile located in {} has {} regions. The original bedfile had {} regions.".format( - outfile_name, self.bed.shape[0], self.original_num_regions - ) - ) - def read_bed(self, bedfile_path, delimiter="\t"): """ @@ -591,9 +604,9 @@ def main(): sys.exit(1) if args.outputfile: - outfile = args.outputfile + outfile_base = args.outputfile else: - outfile = "bedshifted_{}".format(os.path.basename(args.bedfile)) + outfile_base = "bedshifted_{}".format(os.path.basename(args.bedfile)) _LOGGER.info( msg.format( @@ -612,13 +625,18 @@ def main(): shiftfile=args.shiftfile, cutrate=args.cutrate, mergerate=args.mergerate, - outputfile=outfile, + outputfile=outfile_base, repeat=args.repeat, yaml_config=args.yaml_config, + seed=args.seed ) ) bedshifter = Bedshift(args.bedfile, args.chrom_lengths) + _LOGGER.info(f"Generating {args.repeat} repetitions...") + + pct_reports = [int(x * args.repeat / 100) for x in [5, 25, 50, 75, 100]] + for i in range(args.repeat): n = bedshifter.all_perturbations( args.addrate, @@ -635,15 +653,33 @@ def main(): args.droprate, args.dropfile, args.yaml_config, + args.seed ) - _LOGGER.info("\t" + str(n) + " regions changed in total.\n") if args.repeat == 1: - bedshifter.to_bed(outfile) + bedshifter.to_bed(outfile_base) + _LOGGER.info( + "REGION COUNT | original: {}\tnew: {}\tchanged: {}\t\noutput file: {}".format( + bedshifter.original_num_regions, + bedshifter.bed.shape[0], + str(n), + outfile_base, + ) + ) else: - modified_outfile = outfile.rsplit("/") - modified_outfile[-1] = "rep" + str(i + 1) + "_" + modified_outfile[-1] - modified_outfile = "/".join(modified_outfile) - bedshifter.to_bed(modified_outfile) + basename, ext = os.path.splitext(os.path.basename(outfile_base)) + dirname = os.path.dirname(outfile_base) + digits = int(math.log10(args.repeat)) + 1 + + rep = str(i + 1).zfill(digits) + modified_outfile_path = os.path.join(dirname, f"{basename}_rep{rep}{ext}") + bedshifter.to_bed(modified_outfile_path) + + pct_finished = int((100 * (i + 1)) / args.repeat) + if i + 1 in pct_reports: + _LOGGER.info( + f"Rep {i+1}. Finished: {pct_finished}%. Output file: {modified_outfile_path}" + ) + bedshifter.reset_bed() From f244994b49243e7efda22d5ba5c5b19606843fb9 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 10:16:58 -0400 Subject: [PATCH 0127/1072] update pypi and paper links in docs --- mkdocs.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index 54dc32f6..c82fa3da 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -3,7 +3,8 @@ site_name: Bedshift site_url: http://code.databio.org/bedshift/ repo_url: http://github.com/databio/bedshift theme: databio -# paper_link: https://doi.org/10.1101/698704 +pypi_name: bedshift +paper_link: http://10.1101/2020.11.11.378554v1 nav: - Introduction: From a8603837356669f38d5a182857dee41479799866 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 10:20:36 -0400 Subject: [PATCH 0128/1072] Update mkdocs.yml --- mkdocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index c82fa3da..e9d81a21 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -4,7 +4,7 @@ site_url: http://code.databio.org/bedshift/ repo_url: http://github.com/databio/bedshift theme: databio pypi_name: bedshift -paper_link: http://10.1101/2020.11.11.378554v1 +paper_link: https://doi.org/10.1101/2020.11.11.378554 nav: - Introduction: From d8fe50f7143513611ae2df66667a961128dd7d1c Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 10:41:03 -0400 Subject: [PATCH 0129/1072] Update requirements-all.txt --- requirements/requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index 85cb8d88..4e15ff85 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -1,4 +1,4 @@ logmuse pandas numpy -pyranges \ No newline at end of file +pyranges>=0.0.95 From af44975a10be0751e73ced0ccf401fc93b9ce23f Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 10:48:55 -0400 Subject: [PATCH 0130/1072] add cython for pyranges? --- requirements/requirements-all.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index 85cb8d88..fcd7a041 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -1,4 +1,5 @@ +Cython logmuse pandas numpy -pyranges \ No newline at end of file +pyranges From 4bbfff5a35039974485b77a245ef2776f630fc9f Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 10:54:09 -0400 Subject: [PATCH 0131/1072] cleaning up pyranges requirements --- requirements/requirements-all.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index 4e15ff85..e30d7103 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -1,4 +1,6 @@ +Cython logmuse pandas numpy pyranges>=0.0.95 +sorted_nearest>=0.0.31 From 0851239fbe1f3c961fe0c1958adb96f11d8cba7b Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 11:26:55 -0400 Subject: [PATCH 0132/1072] Create pyproject.toml --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..c753eee1 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,2 @@ +[build-system] +requires = ["cython", "setuptools", "wheel"] From ea7af24e47aa59028bdc4390f0f987e2c6c74308 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 11:33:55 -0400 Subject: [PATCH 0133/1072] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index c753eee1..51202400 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,2 +1,2 @@ [build-system] -requires = ["cython", "setuptools", "wheel"] +requires = ["cython"] From bdb24fd329cfd96503b012abf511018299ae479e Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 11:37:29 -0400 Subject: [PATCH 0134/1072] Update requirements-all.txt --- requirements/requirements-all.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index e30d7103..e780a7ff 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -1,4 +1,4 @@ -Cython +cython logmuse pandas numpy From 773e42180e3ccaffd3724c57435f7299e2239308 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 11:37:43 -0400 Subject: [PATCH 0135/1072] Update requirements-doc.txt --- requirements/requirements-doc.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements/requirements-doc.txt b/requirements/requirements-doc.txt index a8e26967..e615851c 100644 --- a/requirements/requirements-doc.txt +++ b/requirements/requirements-doc.txt @@ -1,2 +1,3 @@ +cython https://github.com/databio/mkdocs-databio/archive/master.zip https://github.com/databio/bedshift/archive/master.zip From b6cb2568099f4cdbfdd4e7b63085fd918afed8a2 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 12:09:45 -0400 Subject: [PATCH 0136/1072] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 51202400..6af09102 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,2 +1,2 @@ [build-system] -requires = ["cython"] +requires = ["cython", "setuptools", "wheel"] From 4acc2269b1098c28376f0ab2f45a67252bc2b777 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 12:21:16 -0400 Subject: [PATCH 0137/1072] add include pyproject.toml to manifest --- MANIFEST.in | 1 + 1 file changed, 1 insertion(+) diff --git a/MANIFEST.in b/MANIFEST.in index a92a303c..7647470a 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,4 @@ include README.md include LICENSE.txt include requirements/* +include pyproject.toml From c1b158005e27d820f29ddc9fcd2b345e3d180995 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 12:31:22 -0400 Subject: [PATCH 0138/1072] Update requirements-doc.txt --- requirements/requirements-doc.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements-doc.txt b/requirements/requirements-doc.txt index e615851c..93a2b59e 100644 --- a/requirements/requirements-doc.txt +++ b/requirements/requirements-doc.txt @@ -1,3 +1,3 @@ -cython +https://github.com/nsheff/sorted_nearest/archive/refs/heads/master.zip https://github.com/databio/mkdocs-databio/archive/master.zip https://github.com/databio/bedshift/archive/master.zip From bf2946910044ad5f366c651804e801193c3e3865 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 13:11:56 -0400 Subject: [PATCH 0139/1072] Update changelog.md --- docs/changelog.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index b8ee3333..ab5594cc 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -2,7 +2,13 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html) and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) format. -## [1.1.0] - released Apr 2, 2021 + +## [1.1.1] - 2021-04-15 + +- Updated documentation +- Fixed dependencies packaging for building documentation + +## [1.1.0] - 2021-04-02 - Added ability to specify chrom sizes file, or refgenie genome. - Add perturbation will create regions proportional to chromosome size @@ -13,7 +19,7 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm - Improved testing framework - Improved logging messages -## [1.0.0] - released May 20, 2020 +## [1.0.0] - 2020-05-20 - Add, shift, drop, cut, and merge perturbations. - Basic documentation From 2da8845a253a42ac65121b69114b5a3b5a982ced Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Wed, 14 Apr 2021 13:12:58 -0400 Subject: [PATCH 0140/1072] Update _version.py --- bedshift/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bedshift/_version.py b/bedshift/_version.py index 6849410a..a82b376d 100644 --- a/bedshift/_version.py +++ b/bedshift/_version.py @@ -1 +1 @@ -__version__ = "1.1.0" +__version__ = "1.1.1" From 115b1552e20b92e9497c8e67f0b331cf55bbc0b9 Mon Sep 17 00:00:00 2001 From: nsheff Date: Wed, 14 Apr 2021 13:19:20 -0400 Subject: [PATCH 0141/1072] rebuild autodoc --- docs/autodoc_build/bedshift.md | 73 +++++++++++++++++++--------------- 1 file changed, 42 insertions(+), 31 deletions(-) diff --git a/docs/autodoc_build/bedshift.md b/docs/autodoc_build/bedshift.md index dae56e55..cef81cc5 100644 --- a/docs/autodoc_build/bedshift.md +++ b/docs/autodoc_build/bedshift.md @@ -34,21 +34,26 @@ The bedshift object with methods to perturb regions ```python -def __init__(self) +def __init__(self, bedfile_path, chrom_sizes, delimiter='\t') ``` -Initialize self. See help(type(self)) for accurate signature. +Read in a .bed file to pandas DataFrame format +#### Parameters: + +- `bedfile_path` (`str`): the path to the BED file +- `chrom_sizes` (`str`): the path to the chrom.sizes file +- `delimiter` (`str`): the delimiter used in the BED file + ```python -def add(self, df, addrate, addmean, addstdev) +def add(self, addrate, addmean, addstdev) ``` Add regions #### Parameters: -- `df` (`pandas.DataFrame`): the dataframe to perturb - `addrate` (`float`): the rate to add regions - `addmean` (`float`): the mean length of added regions - `addstdev` (`float`): the standard deviation of the length of added regions @@ -56,33 +61,36 @@ Add regions #### Returns: -- `pandas.DataFrame`: the new dataframe object after adds +- `int`: the number of regions added ```python -def add_from_file(self, df, fp, addrate) +def add_from_file(self, fp, addrate, delimiter='\t') ``` Add regions from another bedfile to this perturbed bedfile #### Parameters: -- `df` (`pandas.DataFrame`): the dataframe to perturb - `addrate` (`float`): the rate to add regions - `fp` (`str`): the filepath to the other bedfile +#### Returns: + +- `int`: the number of regions added + + ```python -def all_perturbations(self, df, addrate=0.0, addmean=320.0, addstdev=30.0, addfile=None, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0) +def all_perturbations(self, addrate=0.0, addmean=320.0, addstdev=30.0, addfile=None, shiftrate=0.0, shiftmean=0.0, shiftstdev=150.0, cutrate=0.0, mergerate=0.0, droprate=0.0) ``` -Perform all five perturbations in the order of add, shift, cut, merge, drop. +Perform all five perturbations in the order of shift, add, cut, merge, drop. #### Parameters: -- `df` (`pandas.DataFrame`): the dataframe to perturb. - `addrate` (`float`): the rate (as a proportion of the total number of regions) to add regions - `addmean` (`float`): the mean length of added regions - `addstdev` (`float`): the standard deviation of the length of added regions @@ -96,61 +104,58 @@ Perform all five perturbations in the order of add, shift, cut, merge, drop. #### Returns: -- `pandas.DataFrame`: the new dataframe after all perturbations +- `int`: the number of total regions perturbed ```python -def cut(self, df, cutrate) +def cut(self, cutrate) ``` Cut regions to create two new regions #### Parameters: -- `df` (`pandas.DataFrame`): the dataframe to perturb. - `cutrate` (`float`): the rate to cut regions into two separate regions #### Returns: -- `pandas.DataFrame`: the new dataframe after cuts +- `int`: the number of regions cut ```python -def drop(self, df, droprate) +def drop(self, droprate) ``` Drop regions #### Parameters: -- `df` (`pandas.DataFrame`): the dataframe to perturb. - `droprate` (`float`): the rate to drop/remove regions #### Returns: -- `pandas.DataFrame`: the new dataframe after drops +- `int`: the number of rows dropped ```python -def merge(self, df, mergerate) +def merge(self, mergerate) ``` Merge two regions into one new region #### Parameters: -- `df` (`pandas.DataFrame`): the dataframe to perturb. - `mergerate` (`float`): the rate to merge two regions into one #### Returns: -- `pandas.DataFrame`: the new dataframe after merges +- `int`: number of regions merged @@ -168,25 +173,32 @@ Utility function to pick a random chromosome ```python -def read_bed(self, bedfile_path) +def read_bed(self, bedfile_path, delimiter='\t') ``` -Read in a bedfile to pandas DataFrame format +Read a BED file into pandas dataframe #### Parameters: -- `bedfile_path` (`str`): the path to the bedfile +- `bedfile_path` (`str`): The path to the BED file + + + + +```python +def reset_bed(self) +``` +Reset the stored bedfile to the state before perturbations ```python -def shift(self, df, shiftrate, shiftmean, shiftstdev) +def shift(self, shiftrate, shiftmean, shiftstdev) ``` Shift regions #### Parameters: -- `df` (`pandas.DataFrame`): the dataframe to perturb. - `shiftrate` (`float`): the rate to shift regions (both the start and end are shifted by the same amount) - `shiftmean` (`float`): the mean shift distance - `shiftstdev` (`float`): the standard deviation of the shift distance @@ -194,20 +206,19 @@ Shift regions #### Returns: -- `pandas.DataFrame`: the original dataframe after shifts +- `int`: the number of regions shifted ```python -def write_bed(self, df, outfile_name) +def to_bed(self, outfile_name) ``` -Write a pandas dataframe back into bedfile format +Write a pandas dataframe back into BED file format #### Parameters: -- `df` (`pandas.DataFrame`): A dataframe containing regions like a bedfile -- `outfile_name` (`str`): The name of the output bedfile +- `outfile_name` (`str`): The name of the output BED file @@ -215,4 +226,4 @@ Write a pandas dataframe back into bedfile format -*Version Information: `bedshift` v0.2.0, generated by `lucidoc` v0.4.2* \ No newline at end of file +*Version Information: `bedshift` v1.1.0-dev, generated by `lucidoc` v0.4.2* \ No newline at end of file From b8ea3a09c23b82c2b4120087655656cba5362ce8 Mon Sep 17 00:00:00 2001 From: aaron-gu Date: Wed, 12 May 2021 19:53:02 -0500 Subject: [PATCH 0142/1072] add example repository to docs --- docs/README.md | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index 41299743..ab7eb575 100644 --- a/docs/README.md +++ b/docs/README.md @@ -18,7 +18,7 @@ The package is available for use both as a command line interface and a python p bedshift -h ``` -The following examples will shift 10% of the regions and add 10% new regions in `examples/test.bed`. The -l argument is the file in which chromosome sizes are located, and is only required for adding and/or shifting regions. The output is located at `bedshifted_test.bed`. +The following examples will shift 10% of the regions and add 10% new regions in `examples/test.bed`. The -l argument is the file in which chromosome sizes are located, and is only required for adding and/or shifting regions. The output is located at `bedshifted_test.bed`. CLI: @@ -36,3 +36,14 @@ bedshifter.shift(shiftrate=0.1, shiftmean=0.0, shiftstdev=120.0) bedshifter.add(addrate=0.1, addmean=320.0, addstdev=20.0) bedshifter.to_bed('tests/test_output.bed') ``` + +## Example Repository + +If you're looking to use Bedshift in your own experiment, we created an [example repository](https://github.com/databio/bedshift_analysis) containing working code to: + +1. Produce a large dataset of Bedshift files +2. Run a pipeline on the dataset and obtain results +3. Aggregate and visualize the results + +It integrates the [PEP](http://pep.databio.org/en/latest/) and [looper](http://looper.databio.org/en/latest/) workflow allowing you to easily +run the project out of the box. From 59c1b2b13aeda2b19a85762818ec8acf81b5c19a Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Mon, 24 May 2021 09:03:04 -0400 Subject: [PATCH 0143/1072] Update requirements-doc.txt --- requirements/requirements-doc.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements/requirements-doc.txt b/requirements/requirements-doc.txt index 93a2b59e..a8e26967 100644 --- a/requirements/requirements-doc.txt +++ b/requirements/requirements-doc.txt @@ -1,3 +1,2 @@ -https://github.com/nsheff/sorted_nearest/archive/refs/heads/master.zip https://github.com/databio/mkdocs-databio/archive/master.zip https://github.com/databio/bedshift/archive/master.zip From d7bc0a1bcc69e77272067707355b94ee4895d7dc Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Mon, 24 May 2021 09:04:42 -0400 Subject: [PATCH 0144/1072] Update requirements-doc.txt --- requirements/requirements-doc.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements/requirements-doc.txt b/requirements/requirements-doc.txt index a8e26967..8ccf52b4 100644 --- a/requirements/requirements-doc.txt +++ b/requirements/requirements-doc.txt @@ -1,2 +1,3 @@ +sorted_nearest https://github.com/databio/mkdocs-databio/archive/master.zip https://github.com/databio/bedshift/archive/master.zip From c12e549793d298f08c96d3ef71b7fce6331db0e1 Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Mon, 24 May 2021 10:32:56 -0400 Subject: [PATCH 0145/1072] Update requirements-doc.txt --- requirements/requirements-doc.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements-doc.txt b/requirements/requirements-doc.txt index 93a2b59e..8ccf52b4 100644 --- a/requirements/requirements-doc.txt +++ b/requirements/requirements-doc.txt @@ -1,3 +1,3 @@ -https://github.com/nsheff/sorted_nearest/archive/refs/heads/master.zip +sorted_nearest https://github.com/databio/mkdocs-databio/archive/master.zip https://github.com/databio/bedshift/archive/master.zip From 51f50366f686fd121c0fc740bbb918d8238b9704 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 12 May 2023 12:37:39 -0400 Subject: [PATCH 0146/1072] Revert "Merge pull request #14 from databio/dev_Julia_820" This reverts commit e0eea232106723d40c1334bf7b58b5cac43cc664, reversing changes made to 18db5b50a8866623efd03f8071dc3a163f44db84. --- .gitignore | 2 +- docs/likelihood/assess.md | 118 --------- docs/likelihood/consensus-peaks.md | 122 ++------- gitk/assess/cli.py | 47 +--- gitk/assess/distance.py | 250 +++++-------------- gitk/assess/intersection.py | 81 +++--- gitk/assess/likelihood.py | 214 +++++----------- gitk/assess/recovered.py | 151 +++++++++++ gitk/assess/utils.py | 23 ++ gitk/cli.py | 32 ++- gitk/hmm/cli.py | 15 +- gitk/hmm/hmm.py | 46 ++-- gitk/hmm/models.py | 8 +- gitk/likelihood/build_model.py | 137 ++++------ gitk/likelihood/cli.py | 62 +---- gitk/likelihood/universe_flexible.py | 104 ++++---- gitk/likelihood/universe_hard.py | 39 +-- gitk/region2vec/main.py | 4 +- gitk/region2vec/region2vec_train.py | 4 +- gitk/region2vec/region_shuffling.py | 4 +- gitk/tokenization/hard_tokenization_batch.py | 2 +- gitk/tokenization/main.py | 2 +- gitk/tokenization/split_file.py | 2 +- gitk/utils.py | 13 - mkdocs.yml | 1 - requirements/requirements-all.txt | 1 - tests/consesnus/cli_test.sh | 9 +- 27 files changed, 567 insertions(+), 926 deletions(-) delete mode 100644 docs/likelihood/assess.md create mode 100644 gitk/assess/recovered.py diff --git a/.gitignore b/.gitignore index 156ecf83..06e8058a 100644 --- a/.gitignore +++ b/.gitignore @@ -156,7 +156,7 @@ cython_debug/ # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea name. +# option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ gitk/__pycache__ diff --git a/docs/likelihood/assess.md b/docs/likelihood/assess.md deleted file mode 100644 index 61b90ad6..00000000 --- a/docs/likelihood/assess.md +++ /dev/null @@ -1,118 +0,0 @@ -# How to assess universe fit to collection of files? -Given a universe it is important to assess it fit to collection of data. -We will use here universes produced [eriler](consensus-peaks.md). - - -## Base pair - level overlap measure -First test checks how much of our file is present in the universe and how much -additional information is present in the univers. We can check that with: - -``` -gitk assess intersection --raw_data_folder tests/consesnus/raw/\ - --file_list tests/consesnus/file_list.txt \ - --universe tests/consenus/universe/universe.bed \ - --save_to_file \ - --folder_out tests/consesnus/results/intersection/ \ - --pref test \ - --no_workers 1 -``` -Where: - -- ``--raw_data_folder``, takes the path to folder with files from the collection -- ``--file_list``, takes the path to file with list of files -- ``--universe``, takes the path to file with the assessed universe -- ``--save_to_file``, is a flag that specifies whether to out put table with each row -containing file name, number of bp in univers but not in file, number of bp in file -but not the univers, and number of bp both in univers and file -- ``--folder_out``, takes the path to folder in which put the output file -- ``--pref``, takes a prefix of output file name -- ``--no_workers``, takes the number of workers that should be used - -Similarly, we can do it in python: - -``` -from gitk.assess.intersection import run_intersection - -F_10 = run_intersection("test/consesnus/raw/", - "tests/consesnus/file_list.txt", - "tests/consenus/universe/universe.bed", - no_workers=1) -``` -where we can directly return F10 score of the universe if we don't specify that we want to save metrics to the file. - -## Region boundary distance measure -Next, we can calculate the distance between region in file and the nearest region in the universe: -``` - gitk assess distance --raw_data_folder tests/consesnus/raw/\ - --file_list tests/consesnus/file_list.txt \ - --universe tests/consenus/universe/universe.bed \ - --save_to_file \ - --folder_out tests/consesnus/results/distance/ \ - --pref test_flex \ - --npool 1 \ - --save_each \ - --flexible -``` - -Where: - -- ``--raw_data_folder``, takes the path to folder with files from the collection -- ``--file_list``, takes the path to file with list of files -- ``--universe``, takes the path to file with the assessed universe -- ``--save_to_file``, is a flag that specifies whether to out put table with each row -containing file name, median of the distances -- ``--folder_out``, takes the path to folder in which put the output file -- ``--pref``, takes a prefix of output file name -- ``--no_workers``, takes the number of workers that should be used -- ``--save_each``, is a flag that specifies whether to save for each peak in the file -distance to the closest peak in the universe -- ``--flexible``, is a flag that specifies whether we should treat the univers as -a flexible one - -We can also calculate the distance between region in the universe and the nearest region in file. We use for that with the same command as before with additional flag ```--universe_to_file```. - - -Both presented distance measures can be done using python, which will result in matrix where first column is file names and the second one is median of distances. - -``` -from gitk.assess.distance import run_distance - -d_median = run_distance("tests/consesnus/raw", - "tests/consesnus/file_list.txt", - "tests/consesnus/universe/universe.bed", - npool=2) -``` -Additionally, we can directly calculate the closeness score using: - -``` -from gitk.assess.distance import get_closeness_score - -closeness_score = get_closeness_score("tests/consesnus/raw", - "tests/consesnus/file_list.txt", - "tests/consesnus/universe/universe.bed", - no_workers=2) -``` - -## Universe likelihood - -We can also calculate the likelihood of universe given collection of file. For that we -will need [likelihood model](consensus-peaks.md#making-likelihood-model-). We can do it -either for hard universe: - -``` -from gitk.assess.likelihood import hard_universe_likelihood - -lh_hard = hard_universe_likelihood("tests/consesnus/lh_model.tar", - "tests/consenus/universe/universe.bed", - "tests/consesnus/coverage", "all") -``` - -or with taking into account universe flexibility: - -``` -from gitk.assess.likelihood import likelihood_flexible_universe - -lh_flexible = likelihood_flexible_universe("tests/consesnus/lh_model.tar", - "tests/consenus/universe/universe.bed", - "tests/consesnus/coverage", "all") -``` \ No newline at end of file diff --git a/docs/likelihood/consensus-peaks.md b/docs/likelihood/consensus-peaks.md index dea92282..f361c233 100644 --- a/docs/likelihood/consensus-peaks.md +++ b/docs/likelihood/consensus-peaks.md @@ -1,126 +1,40 @@ -# How to create consensus peak set from a collection of BED files? - -We will start with simple example of how to creat a consensus peak set from -collection of files. Example data that will be used can be found in -```tests/consenus/raw``` . +# How to create consensus peaks from a set of BED files ## Data preprocessing -First step of analysis is creating three tracks with genome coverage by peaks, -their starts and ends. To do that we have to: -1. install [uniwig](https://github.com/databio/uniwig/tree/smoothing), make sure to use branch dev -2. use [create_unsorted.sh](https://github.com/databio/uniwig/blob/smoothing/create_unsorted.sh) to make three bigWig: - - {prefix}_start.bw - with smoothed coverage of genome by starts - - {prefix}_core.bw - with coverage of genome by peaks - - {prefix}_start.bw - with smoothed coverage of genome by ends -In this tutorial we will use prefix "all" as it is a default prefix in -```gitk``` module -## Coverage universe -We will start by making a coverage universe with cutoff that results in maximum -likelihood universe. We can do it through CLI: +1. install [uniwig](https://github.com/databio/uniwig/tree/smoothing), make sure to use branch smoothing +2. use [create_unsorted.sh](https://github.com/databio/uniwig/blob/smoothing/create_unsorted.sh) to make three bigWig from your files +## Cut-off universe +Make cut-off universe from coverage using: ``` - gitk lh universe_hard --coverage_file tests/consenus/coverage/all_core.bw \ - --fout tests/consenus/universe/universe.bed + gitk lh universe_hard --coverage_file coverage.bw \ + --fout universe.bed ``` - Where: - -- ```--coverage_file```, takes the path to bigWig file with genome coverage by collection +- ```--coverage_file```, takes the path to bigWig file with cverage track - ```--fout```, takes the path to output file -Or we can import it directly into python: -``` -from gitk.likelihood.universe_hard import main as cutoff - -cutoff("tests/consenus/coverage/all_core.bw", - fout="tests/consenus/universe/universe.bed") -``` - -Depending on the task we can also smooth the output universe by setting ``--merge`` -flag with the distance beloved witch peaks should be merged together and -``--filter_size`` with minimum size of peak that should be part of the universe. We can also not use the maximum likelihood cut-off and instead of it use user defined cutoff. For that we have to set ``--cutoff`` . If we set it to 1 we get union universe, and when to number of files we will get intersection universe. - ## Maximum likelihood universe -Another type of universe that we can make is maximum likelihood flexible universe. To make it first we have to have a likelihood model of genome coverage by collection of files. - -#### Making likelihood model: -To make a likelihood model we can use this CLI: +Make likelihood model from coverage tracks using: ``` -gitk lh build_model --model_folder tests/consenus/model.tar \ - --coverage_folder tests/consenus/coverage/ \ - --file_no x +gitk lh build_model --model_folder model.tar \ + --file_no x \ + --coverage_folder coverage/ ``` - Where: - - ```--model_folder```, takes the name of tar archive that will contain the likelihood model - ```--file_no```, number of files used in analysis - ```--coverage_folder``` path to folder with coverage tracks +- ```--coverage_prefix``` prefix used in uniwig for making files -Or, we can do it directly in python: - +Use likelihood model to make a maximum likelihood universe ``` -from gitk.likelihood.build_model import main - -main("tests/consenus/model.tar", "tests/consesnus/coverage", - "all", - file_no=4) +gitk lh universe_flexible --model_folder model.tar \ + --output_file universe.bed ``` - -#### Making universe: -Now that we have the model we make the universe: - -``` -gitk lh universe_flexible --model_folder tests/consenus/model.tar \ - --output_file tests/consenus/universe/universe.bed \ - --cov_folder tests/consesnus/coverage -``` - Where: - -- ```--model_folder```, takes the name of tar archive that contains the likelihood model -- ```--output_file```, takes the path to output file -- ```--cov_folder``` path to folder with coverage tracks - -Similarly, we can do it in python: - -``` -from gitk.likelihood.universe_flexible import main - -main("tests/consesnus/model.tar", - "/home/hee6jn/Documents/gitk/tests/consesnus/coverage", - "all", - "tests/consenus/universe/universe.bed") -``` - -## HMM -Another approach to making flexible universes is using Hidden Markov Models. -We can do it for example with: - -``` -gitk hmm --out_file tests/consenus/universe/universe.bed \ - --cov_folder tests/consenus/coverage/ \ - --normlaize \ - --save_max_cove -``` - -Where: - -- ```--out_file```, takes the path to output file -- ```--cov_folder```, path to folder with coverage tracks -- ```--coverage_prefix``` prefix used in uniwig for making files, default is "all" -- ```--not_normlaize```, is a flag that specifies whether not to normalize tracks before running HMM -- ```--save_max_cove```, is a flag that specifies whether to save maximum coverage of each output peak - -Similarly, we can do it in python: - -``` -from gitk.hmm.hmm import run_hmm_save_bed - -run_hmm_save_bed("tests/consenus/coverage/", - "tests/consenus/universe/universe.bed", - save_max_cove=True) -``` \ No newline at end of file +- ```--model_folder```, takes the name of tar archive that contains the likelihood model +- ```--output_file```, takes the path to output file \ No newline at end of file diff --git a/gitk/assess/cli.py b/gitk/assess/cli.py index fea15df0..6b3ee7db 100644 --- a/gitk/assess/cli.py +++ b/gitk/assess/cli.py @@ -5,49 +5,22 @@ def build_subparser(parser): :return argparse.ArgumentParser """ - parser.add_argument( - "--raw_data_folder", help="folder with raw data", type=str, required=True - ) - parser.add_argument( - "--file_list", - help="list of files that need to be assessed", - type=str, - required=True, - ) - parser.add_argument("--universe", help="universe file", type=str, required=True) - parser.add_argument( - "--no_workers", help="number of core that should be used", default=4, type=int - ) - parser.add_argument( - "--save_to_file", - help="if save statistics for each BED file to a file", - action="store_true", - ) - parser.add_argument( - "--folder_out", help="folder to which save the statistic", type=str - ) - parser.add_argument("--pref", help="statistic file prefix", type=str) + parser.add_argument("--raw_data_folder", type=str, required=True) + parser.add_argument("--file_list", type=str, required=True) + parser.add_argument("--universe", type=str, required=True) + parser.add_argument("--npool", default=4, type=int) + parser.add_argument("--save_to_file", action="store_true") + parser.add_argument("--folder_out", type=str) + parser.add_argument("--pref", type=str) return parser def build_subparser_distance(parser): parser = build_subparser(parser) - parser.add_argument( - "--flexible", - help="if calculate the distance taking into account that the universe is flexible", - action="store_true", - ) - parser.add_argument( - "--save_each", - help="if save distance for each peak in each file ", - action="store_true", - ) - parser.add_argument( - "--universe_to_file", - help="if calculate distance from universe to file", - action="store_true", - ) + parser.add_argument("--flexible", action="store_true") + parser.add_argument("--save_each", action="store_true") + return parser diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index f5d71618..d0e4071b 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -2,19 +2,18 @@ # -*- coding: utf-8 -*- import os +from typing import List, Any + import numpy as np +import argparse from multiprocessing import Pool -from .utils import process_db_line, prep_data, check_if_uni_sorted +from .utils import process_db_line, chrom_cmp_bigger, prep_data, check_if_uni_sorted from ..utils import natural_chr_sort import tempfile def flexible_distance(r, q): - """Calculate distance between region and flexible region from flexible universe - :param [int, int] r: region from flexible universe - :param int q: analysed region - :return int: distance - """ + """Calculate region distance for univers""" if r[0] <= q <= r[1]: return 0 else: @@ -22,31 +21,24 @@ def flexible_distance(r, q): def distance(r, q): - """Calculate distance between region and region from hard universe - :param [int] r: region from hard universe - :param int q: analysed region - :return int: distance - """ + """Calculate distance for hard universe""" return abs(r[0] - q) -def asses(db, db_que, i, current_chrom, unused_db, pos_index, flexible, uni_to_file): +def asses(db, db_que, i, current_chrom, unused_db, pos_index, flexible): """ Calculate distance from given peak to the closest region in universe :param file db: universe file - :param list db_que: que of last three positions in universe + :param list db_que: que of three last positions in universe :param int i: analysed position from the query :param str current_chrom: current analysed chromosome from query :param list unused_db: list of positions from universe that were not compared to query - :param list pos_index: which indexes from universe BED file line should be used in analysis + :param list pos_index: which indexes from universe region use to calculate distance :param bool flexible: whether the universe if flexible :return int: peak distance to universe """ if flexible: - if uni_to_file: - dist_to_db_que = [flexible_distance(i, j[0]) for j in db_que] - else: - dist_to_db_que = [flexible_distance(j, i) for j in db_que] + dist_to_db_que = [flexible_distance(j, i) for j in db_que] else: dist_to_db_que = [distance(j, i) for j in db_que] min_pos = np.argmin(dist_to_db_que) @@ -61,10 +53,7 @@ def asses(db, db_que, i, current_chrom, unused_db, pos_index, flexible, uni_to_f db_que[:-1] = db_que[1:] db_que[-1] = pos if flexible: - if uni_to_file: - dist_to_db_que = [flexible_distance(i, j[0]) for j in db_que] - else: - dist_to_db_que = [flexible_distance(j, i) for j in db_que] + dist_to_db_que = [flexible_distance(j, i) for j in db_que] else: dist_to_db_que = [distance(j, i) for j in db_que] min_pos = np.argmin(dist_to_db_que) @@ -82,23 +71,21 @@ def process_line( start, pos_index, flexible, - uni_to_file, ): """ Calculate distance from new peak to universe :param file db: universe file - :param str q_chrom: new peak's chromosome + :param str q_chrom: on which chromosome id the new peak :param str current_chrom: chromosome that was analysed so far :param list unused_db: list of positions from universe that were not compared to query :param list db_que: que of three last positions in universe :param list dist: list of all calculated distances :param bool waiting: whether iterating through file, without calculating distance, if present chromosome not present in universe - :param int start: analysed position from the query - :param list pos_index: which indexes from universe region use to calculate distance - :param bool flexible: whether the universe if flexible - :param bool uni_to_file: whether to calculate distance from universe to file - :return bool, str: if iterating through chromosome not present in universe; current chromosome in query + :param start: analysed position from the query + :param pos_index: which indexes from universe region use to calculate distance + :param flexible: whether the universe if flexible + :return: if iterating through chromosome not present in universe; current chromosome in query """ if q_chrom != current_chrom: # change chromosome @@ -140,39 +127,42 @@ def process_line( if len(db_que) == 0: waiting = True if not waiting: - res = asses( - db, - db_que, - start, - current_chrom, - unused_db, - pos_index, - flexible, - uni_to_file, - ) + res = asses(db, db_que, start, current_chrom, unused_db, pos_index, flexible) dist.append(res) return waiting, current_chrom -def calc_distance_main( - q, - db_start, - db_end, +def calc_distance( + db_file, + q_folder, q_file, - flexible, - save_each, - folder_out, - pref, - uni_to_file=False, + flexible=False, + save_each=False, + folder_out=None, + pref=None, ): """ - Maine function for calculating distance between regions in file q to regions in database + For given file calculate distance to the nearst region from universe + :param str db_file: path to universe + :param str q_folder: path to folder containing query files + :param str q_file: query file + :param boolean flexible: whether the universe if flexible + :param bool save_each: whether to save calculated distances for each file + :param str folder_out: output folder + :param str pref: prefix used as the name of the folder + containing calculated distance for each file + :return str, int, int: file name; median od distance of starts to + starts in universe; median od distance of ends to ends in universe """ + q = tempfile.NamedTemporaryFile() + prep_data(q_folder, q_file, q) + db_start = open(db_file) db_que_start = [] current_chrom_start = "chr0" dist_start = [] unused_db_start = [] waiting_start = False + db_end = open(db_file) db_que_end = [] current_chrom_end = "chr0" dist_end = [] @@ -180,19 +170,13 @@ def calc_distance_main( waiting_end = False pos_start = [1] pos_end = [2] - if flexible and not uni_to_file: + if flexible: pos_start = [1, 6] pos_end = [7, 2] for i in q: - if not uni_to_file: - i = i.decode("utf-8") - i = i.split("\t") - if uni_to_file and flexible: - start = [int(i[1]), int(i[6])] - end = [int(i[7]), int(i[2])] - else: - start = int(i[1]) - end = int(i[2]) + i = i.decode("utf-8").split("\t") + start = int(i[1]) + end = int(i[2]) q_chrom = i[0] res_start = process_line( db_start, @@ -205,7 +189,6 @@ def calc_distance_main( start, pos_start, flexible, - uni_to_file, ) (waiting_start, current_chrom_start) = res_start res_end = process_line( @@ -219,123 +202,41 @@ def calc_distance_main( end, pos_end, flexible, - uni_to_file, ) (waiting_end, current_chrom_end) = res_end - q.close() + tmp_file.close() if save_each: with open(os.path.join(folder_out, pref, q_file), "w") as f: for i, j in zip(dist_start, dist_end): f.write(f"{i}\t{j}\n") if not dist_start: print(f"File {q_file} doesn't contain any chromosomes present in universe") - return q_file, None - dist = dist_start + dist_end - return q_file, np.median(dist) - - -def calc_distance_file( - db_file, - q_folder, - q_file, - flexible=False, - save_each=False, - folder_out=None, - pref=None, -): - """ - For given file calculate distances to the nearst region from universe - :param str db_file: path to universe - :param str q_folder: path to folder containing query files - :param str q_file: query file - :param boolean flexible: whether the universe if flexible - :param bool save_each: whether to save calculated distances for each file - :param str folder_out: output name - :param str pref: prefix used as the name of the name - containing calculated distance for each file - :return str, int, int: file name; median od distance of starts to - starts in universe; median od distance of ends to ends in universe - """ - q = tempfile.NamedTemporaryFile() - prep_data(q_folder, q_file, q) - db_start = open(db_file) - db_end = open(db_file) - return calc_distance_main( - q, - db_start, - db_end, - q_file, - flexible, - save_each, - folder_out, - pref, - ) - - -def calc_distance_uni( - universe, - q_folder, - q_file, - flexible=False, - save_each=False, - folder_out=None, - pref=None, -): - """ - For given universe calculate distances to the nearst region from combined collection - :param str universe: path to universe - :param str q_folder: path to folder containing query files - :param str q_file: query file - :param boolean flexible: whether the universe if flexible - :param bool save_each: whether to save calculated distances for each file - :param str folder_out: output name - :param str pref: prefix used as the name of the name - containing calculated distance for each file - :return str, int, int: file name; median od distance of starts to - starts in universe; median od distance of ends to ends in universe - """ - q = tempfile.NamedTemporaryFile() - prep_data(q_folder, q_file, q) - db_start = open(q.name) - db_end = open(q.name) - uni = open(universe) - return calc_distance_main( - uni, - db_start, - db_end, - q_file, - flexible, - save_each, - folder_out, - pref, - uni_to_file=True, - ) + return q_file, None, None + return q_file, np.median(dist_start), np.median(dist_end) def run_distance( folder, file_list, universe, - no_workers, + npool, flexible=False, save_to_file=False, folder_out=None, pref=None, save_each=False, - uni_to_file=False, ): """ - For group of files calculate distances to the nearest region in universe - :param str folder: path to name containing query files + For group of files calculate distance to the nearest region in universe + :param str folder: path to folder containing query files :param str file_list: path to file containing list of query files :param str universe: path to universe file - :param int no_workers: number of parallel processes + :param int npool: number of parallel processes :param bool flexible: whether the universe if flexible :param bool save_to_file: whether to save median of calculated distances for each file - :param str folder_out: output name + :param str folder_out: output folder :param str pref: prefix used for saving :param bool save_each: whether to save calculated distances for each file - :param uni_to_file: whether to calculate distance from universe to file :return float; float: mean of median distances from starts in query to the nearest starts in universe; mean of median distances from ends in query to the nearest ends in universe """ @@ -345,53 +246,28 @@ def run_distance( if folder_out: os.makedirs(folder_out, exist_ok=True) if save_each: - os.makedirs(os.path.join(folder_out, pref), exist_ok=True) - if uni_to_file: - dist_function = calc_distance_uni - else: - dist_function = calc_distance_file - if no_workers <= 1: + os.makedirs(os.path.join(folder_out, pref)) + if npool <= 1: for i in files: - r = dist_function( + r = calc_distance( universe, folder, i, flexible, save_each, folder_out, pref ) res.append(r) else: - with Pool(no_workers) as p: + with Pool(npool) as p: args = [ (universe, folder, f, flexible, save_each, folder_out, pref) for f in files ] - res = p.starmap(dist_function, args) + res = p.starmap(calc_distance, args) if save_to_file: - file_out = os.path.join(folder_out, pref + "_data.tsv") - with open(file_out, "w") as o: - o.write("file\tmedian_dist\n") + fout = os.path.join(folder_out, pref + "_data.tsv") + with open(fout, "w") as o: + o.write("file\tmedian_dist_start\tmedian_dist_end\n") for r in res: - o.write(f"{r[0]}\t{r[1]}\n") + o.write(f"{r[0]}\t{r[1]}\t{r[2]}\n") else: res = np.array(res) - return res - - -def get_closeness_score(folder, file_list, universe, no_workers, flexible=False): - file_to_uni = run_distance( - folder, - file_list, - universe, - no_workers, - flexible=flexible, - uni_to_file=False, - ) - - uni_to_file = run_distance( - folder, - file_list, - universe, - no_workers, - flexible=flexible, - uni_to_file=True, - ) - me = (file_to_uni[:, 1].astype("float") + uni_to_file[:, 1].astype("float")) / 2 - cs = 11 / (me + 10) / 1.1 - return np.mean(cs) + res = res[:, 1:] + res = res.astype("float") + return np.mean([np.nanmedian(res[:, 0]), np.nanmedian(res[:, 1])]) diff --git a/gitk/assess/intersection.py b/gitk/assess/intersection.py index a897984b..e56c1a09 100644 --- a/gitk/assess/intersection.py +++ b/gitk/assess/intersection.py @@ -10,15 +10,31 @@ def chrom_cmp(a, b): - """Return smaller chromosome name""" - c = natural_chr_sort(a, b) - if c > 0: - return b, True, False + """Natural chromosome names comparison""" + # com = natural_chr_sort(a, b) + # if com < 0: + # return a, False, True + # if com == 0: + # return a, False, False + # if com > 0: + # return b, True, False + ac = a.replace("chr", "") + ac = ac.split("_")[0] + bc = b.replace("chr", "") + bc = bc.split("_")[0] + if bc.isnumeric() and ac.isnumeric() and bc != ac: + if int(bc) < int(ac): + return b, True, False + else: + return a, False, True else: - return a, False, True + if b < a: + return b, True, False + else: + return a, False, True -def relationship_helper(region_a, region_b, only_in, overlap): +def relationship_helper(region_a, region_b, only_in, overlap, start_a, start_b): """For two region calculate their overlap; for earlier region calculate how many base pair only in it""" if region_b[0] <= region_a[1]: @@ -27,19 +43,15 @@ def relationship_helper(region_a, region_b, only_in, overlap): overlap += region_b[1] - region_b[0] start_a, start_b = region_b[1], region_b[1] inside_b, inside_a = False, True - return only_in, inside_a, inside_b, overlap, start_a, start_b - elif region_b[1] > region_a[1]: overlap += region_a[1] - region_b[0] start_a, start_b = region_a[1], region_a[1] inside_a, inside_b = False, True - return only_in, inside_a, inside_b, overlap, start_a, start_b - elif region_b[0] > region_a[1]: only_in += region_a[1] - region_a[0] inside_a, inside_b = False, True start_a, start_b = region_a[1], region_b[0] - return only_in, inside_a, inside_b, overlap, start_a, start_b + return only_in, inside_a, inside_b, overlap, start_a, start_b def relationship( @@ -56,7 +68,7 @@ def relationship( waiting_q, ): """ - Check mutual position of two regions and calculate intersection and difference of two regions + Check mutual position and calculate intersection and difference of two regions :param list region_d: region from universe :param list region_q: region from query :param int only_in_d: number of base pair only in universe @@ -79,18 +91,19 @@ def relationship( inside_q, inside_d = False, True else: if region_d[0] <= region_q[0]: - res = relationship_helper(region_d, region_q, only_in_d, overlap) + res = relationship_helper( + region_d, region_q, only_in_d, overlap, start_d, start_q + ) (only_in_d, inside_d, inside_q, overlap, start_d, start_q) = res if region_d[0] > region_q[0]: - res = relationship_helper(region_q, region_d, only_in_q, overlap) + res = relationship_helper( + region_q, region_d, only_in_q, overlap, start_q, start_d + ) (only_in_q, inside_q, inside_d, overlap, start_q, start_d) = res return only_in_d, only_in_q, inside_d, inside_q, overlap, start_d, start_q -def read_in_new_line(region, start, chrom, inside, waiting, lines, c_chrom, not_e): - """ - Read in a new line from query or universe file - """ +def read_in_new_line(region, start, chrom, inside, waiting, lines, cchrom, not_e): if not inside: if not waiting: line = lines.readline() @@ -99,7 +112,7 @@ def read_in_new_line(region, start, chrom, inside, waiting, lines, c_chrom, not_ line = line.strip("\n") if line != "": region, start, chrom = process_line(line) - if chrom != c_chrom: + if chrom != cchrom: waiting = True else: not_e = False @@ -111,7 +124,7 @@ def calc_stats(db, folder, query): """ Difference and overlap of two files on base pair level :param str db: path to universe file - :param str folder: path to name with query file + :param str folder: path to folder with query file :param str query: query file name :return str; int; int; int: file name; bp only in universe; bp only in query; overlap in bp """ @@ -177,22 +190,16 @@ def calc_stats(db, folder, query): def run_intersection( - folder, - file_list, - universe, - no_workers, - save_to_file=False, - folder_out=None, - pref=None, + folder, file_list, universe, npool, save_to_file=False, folder_out=None, pref=None ): """ Calculate the base pair intersection of universe and group of files - :param str folder: path to name containing query files + :param str folder: path to folder containing query files :param str file_list: path to file containing list of query files :param str universe: path to universe file - :param int no_workers: number of parallel processes + :param int npool: number of parallel processes :param str save_to_file: whether to save median of calculated distances for each file - :param str folder_out: output name + :param str folder_out: output folder :param str pref: prefix used for saving :return float; float: mean of fractions of intersection of file and universe divided by universe size; mean of fractions of intersection of file and universe divided by file size @@ -200,19 +207,21 @@ def run_intersection( check_if_uni_sorted(universe) if save_to_file: os.makedirs(folder_out, exist_ok=True) + # os.mkdir("tmp") files = open(file_list).read().split("\n")[:-1] res = [] - if no_workers <= 1: + if npool <= 1: for i in files: r = calc_stats(universe, folder, i) res.append(r) else: - with Pool(no_workers) as p: + with Pool(npool) as p: args = [(universe, folder, f) for f in files] res = p.starmap(calc_stats, args) + # os.rmdir("tmp") if save_to_file: - file_out = os.path.join(folder_out, pref + "_data.tsv") - with open(file_out, "w") as o: + fout = os.path.join(folder_out, pref + "_data.tsv") + with open(fout, "w") as o: o.write("file\tunivers/file\tfile/universe\tuniverse&file\n") for r in res: o.write(f"{r[0]}\t{r[1]}\t{r[2]}\t{r[3]}\n") @@ -222,5 +231,5 @@ def run_intersection( res = res.astype("float") recall = res[:, 2] / (res[:, 2] + res[:, 1]) precision = res[:, 2] / (res[:, 2] + res[:, 0]) - f_10 = (1 + 10**2) * (precision * recall) / ((10**2 * precision) + recall) - return np.mean(f_10) + F_10 = (1 + 10**2) * (precision * recall) / ((10**2 * precision) + recall) + return np.median(F_10) diff --git a/gitk/assess/likelihood.py b/gitk/assess/likelihood.py index f2999b63..ec1bc754 100644 --- a/gitk/assess/likelihood.py +++ b/gitk/assess/likelihood.py @@ -1,42 +1,16 @@ import numpy as np import os from .utils import check_if_uni_sorted -from ..utils import read_chromosome_from_bw from ..likelihood.build_model import ModelLH -class LhModel: - def __init__(self, model, cove): - """ - Object with combined information about lh model and coverage - :param ndarray model: lh model array - :param ndarray cove: coverage array - """ - self.model = model - self.cove = cove - - def __getitem__(self, sliced): - return self.model[self.cove[sliced[0]], sliced[1]] - - -def calc_likelihood_hard( - universe, - chroms, - model_lh, - coverage_folder, - coverage_prefix, - name, - s_index, - e_index=None, -): +def calc_likelihood_hard(universe, chroms, model_lh, name, s_index, e_index=None): """ Calculate likelihood of universe for given type of model To be used with binomial model :param str universe: path to universe file - :param list chroms: list of chromosomes present in model - :param ModelLH model_lh: likelihood model - :param coverage_prefix: prefix used in uniwig for creating coverage - :param coverage_folder: path to a folder with genome coverage by tracks + :param list chroms: list of chromosomes present in likelihood model + :param str model_folder: path to folder with model :param str name: suffix of model file name, which contains information about model type :param int s_index: from which position in univers line take assess region @@ -45,12 +19,12 @@ def calc_likelihood_hard( end position :return float: likelihood of univers for given model """ - current_chrom = "" + curent_chrom = "" missing_chrom = "" empty_start = 0 res = 0 e = 0 - prob_array, cove_array = None, None + prob_array = None with open(universe) as uni: for i in uni: e += 1 @@ -59,22 +33,17 @@ def calc_likelihood_hard( if i[0] == missing_chrom: pass else: - if i[0] != current_chrom: + if i[0] != curent_chrom: if i[0] in chroms: - model_lh.clear_chrom(current_chrom) + model_lh.clear_chrom(curent_chrom) if e != 1: - res += np.sum(prob_array[cove_array[empty_start:], 0]) + res += np.sum(prob_array[empty_start:, 0]) - current_chrom = i[0] - model_lh.read_chrom_track(current_chrom, name) - prob_model = model_lh[current_chrom] - cove_array = read_chromosome_from_bw( - os.path.join( - coverage_folder, f"{coverage_prefix}_{name}.bw" - ), - current_chrom, - ) - prob_array = LhModel(prob_model[name], cove_array) + curent_chrom = i[0] + model_lh.read_chrom_track(curent_chrom, name) + prob_array = model_lh.chromosomes_models[curent_chrom].models[ + name + ] empty_start = 0 else: print(f"Chromosome {i[0]} missing from model") @@ -93,53 +62,39 @@ def calc_likelihood_hard( return res -def hard_universe_likelihood(model, universe, coverage_folder, coverage_prefix): +def hard_universe_likelihood(model_folder, universe): """ Calculate likelihood of hard universe based on core, start, end coverage model - :param str model: path to file containing model + :param str model_folder: path to folder containing model :param str universe: path to universe - :param coverage_prefix: prefix used in uniwig for creating coverage - :param coverage_folder: path to a folder with genome coverage by tracks :return float: likelihood """ check_if_uni_sorted(universe) - model_lh = ModelLH(model) + model_lh = ModelLH(model_folder) chroms = model_lh.chromosomes_list - s = calc_likelihood_hard( - universe, chroms, model_lh, coverage_folder, coverage_prefix, "start", 1 - ) - e = calc_likelihood_hard( - universe, chroms, model_lh, coverage_folder, coverage_prefix, "end", 2 - ) - c = calc_likelihood_hard( - universe, chroms, model_lh, coverage_folder, coverage_prefix, "core", 1, 2 - ) + s = calc_likelihood_hard(universe, chroms, model_lh, "start", 1) + e = calc_likelihood_hard(universe, chroms, model_lh, "end", 2) + c = calc_likelihood_hard(universe, chroms, model_lh, "core", 1, 2) return sum([s, e, c]) -def likelihood_only_core(model_file, universe, coverage_folder, coverage_prefix): +def likelihood_only_core(model_folder, universe, core="core"): """ - Calculate likelihood of universe based only on core coverage model - :param str model_file: path to name containing model + Calculate likelihood of universe based on core coverage model + :param str model_folder: path to folder containing model :param str universe: path to universe - :param coverage_prefix: prefix used in uniwig for creating coverage - :param coverage_folder: path to a folder with genome coverage by tracks + :param str core: model file name :return float: likelihood """ check_if_uni_sorted(universe) - model_lh = ModelLH(model_file) + model_lh = ModelLH(model_folder) chroms = model_lh.chromosomes_list - c = calc_likelihood_hard( - universe, chroms, model_lh, coverage_folder, coverage_prefix, "core", 1, 2 - ) + c = calc_likelihood_hard(universe, chroms, model_folder, core, 1, 2) return c def background_likelihood(start, end, model_start, model_cove, model_end): - """ - Calculate likelihood of background for given region - """ res = np.sum(model_start[start:end, 0]) res += np.sum(model_cove[start:end, 0]) res += np.sum(model_end[start:end, 0]) @@ -147,16 +102,6 @@ def background_likelihood(start, end, model_start, model_cove, model_end): def weigh_livelihood(start, end, model_process, model_cove, model_out, reverse): - """ - Calculate weighted likelihood of flexible part of the region - :param int start: start of the region - :param int end: end of the region - :param array model_process: model for analysed type of flexible region - :param array model_cove: model for coverage - :param array model_out: model for flexible region that is not being analysed - :param bool reverse: if model_process corespondents to end we have to reverse the weighs - :return float: likelihood of flexible part of the region - """ e_w = 1 / (end - start) # weights for processed model c_w = np.linspace( start=e_w, stop=1, num=(end - start) @@ -172,59 +117,29 @@ def weigh_livelihood(start, end, model_process, model_cove, model_out, reverse): def flexible_peak_likelihood( - start_s, start_e, end_s, end_e, model_start, model_cove, model_end + startS, startE, endS, endE, model_start, model_cove, model_end ): - """ - Likelihood of flexible peak - """ # core part of the peak - res = np.sum(model_cove[start_e:end_s, 1]) - res += np.sum(model_start[start_e:end_s, 0]) - res += np.sum(model_end[start_e:end_s, 0]) + res = np.sum(model_cove[startE:endS, 1]) + res += np.sum(model_start[startE:endS, 0]) + res += np.sum(model_end[startE:endS, 0]) # start part of the peak - res += weigh_livelihood(start_s, start_e, model_start, model_cove, model_end, False) + res += weigh_livelihood(startS, startE, model_start, model_cove, model_end, False) # end part of the peak - res += weigh_livelihood(end_s, end_e, model_end, model_cove, model_start, True) + res += weigh_livelihood(endS, endE, model_end, model_cove, model_start, True) return res -def read_coverage(cove_folder, cove_prefix, current_chrom): - cove_start = read_chromosome_from_bw( - os.path.join(cove_folder, f"{cove_prefix}_start.bw"), - current_chrom, - ) - cove_core = read_chromosome_from_bw( - os.path.join(cove_folder, f"{cove_prefix}_core.bw"), - current_chrom, - ) - cove_end = read_chromosome_from_bw( - os.path.join(cove_folder, f"{cove_prefix}_end.bw"), - current_chrom, - ) - cove = {"start": cove_start, "core": cove_core, "end": cove_end} - return cove - - -def likelihood_flexible_universe( - model_file, universe, cove_folder, cove_prefix, save_peak_input=False -): - """ - Likelihood of given universe under the model - :param str model_file: path to file with lh model - :param str universe: path to universe - :param cove_folder: path to a folder with genome coverage by tracks - :param cove_prefix: prefix used in uniwig for creating coverage - :param bool save_peak_input: whether to save universe with each peak lh - :return float: lh of the flexible universe - """ - current_chrom = "" +def likelihood_flexible_universe(model_folder, universe, save_peak_input=False): + curent_chrom = "" missing_chrom = "" empty_start = 0 res = 0 check_if_uni_sorted(universe) - model_lh = ModelLH(model_file) + model_lh = ModelLH(model_folder) chroms = model_lh.chromosomes_list - output = [] + if save_peak_input: + output = [] e = 0 # number of processed chromosomes with open(universe) as uni: for line in uni: @@ -234,65 +149,58 @@ def likelihood_flexible_universe( if i[0] == missing_chrom: pass else: - if i[0] != current_chrom: + if i[0] != curent_chrom: if i[0] in chroms: - model_lh.clear_chrom(current_chrom) + model_lh.clear_chrom(curent_chrom) if e != 0: # if we read any chromosomes add to result background # likelihood of part of the genome after the last region res += background_likelihood( empty_start, - chr_size, - prob_start, - prob_core, - prob_end, + len(model_start), + model_start, + model_core, + model_end, ) - current_chrom = i[0] + curent_chrom = i[0] e += 1 - model_lh.read_chrom(current_chrom) - models_current = model_lh[current_chrom] - cove_current = read_coverage( - cove_folder, cove_prefix, current_chrom - ) - chr_size = len(cove_current["start"]) - prob_start = LhModel( - models_current["start"], cove_current["start"] - ) - prob_core = LhModel( - models_current["core"], cove_current["core"] - ) - prob_end = LhModel(models_current["end"], cove_current["end"]) + model_lh.read_chrom(curent_chrom) + model_start = model_lh.chromosomes_models[curent_chrom].models[ + "start" + ] + model_core = model_lh.chromosomes_models[curent_chrom].models[ + "core" + ] + model_end = model_lh.chromosomes_models[curent_chrom].models[ + "end" + ] else: print(f"Chromosome {i[0]} missing from model") missing_chrom = i[0] res += background_likelihood( - empty_start, - peak_start_s, - prob_start, - prob_core, - prob_end, + empty_start, peak_start_s, model_start, model_core, model_end ) peak_likelihood = flexible_peak_likelihood( peak_start_s, peak_start_e, peak_end_s, peak_end_e, - prob_start, - prob_core, - prob_end, + model_start, + model_core, + model_end, ) res += peak_likelihood if save_peak_input: - background = background_likelihood( - peak_start_s, peak_end_e, prob_start, prob_core, prob_end + backgroung = background_likelihood( + peak_start_s, peak_end_e, model_start, model_core, model_end ) - contribution = peak_likelihood - background + contribution = peak_likelihood - backgroung output.append("{}\t{}\n".format(line.strip("\n"), contribution)) empty_start = peak_end_e res += background_likelihood( - empty_start, chr_size, prob_start, prob_core, prob_end + empty_start, len(model_start), model_start, model_core, model_end ) if save_peak_input: print("saving") diff --git a/gitk/assess/recovered.py b/gitk/assess/recovered.py new file mode 100644 index 00000000..d2e6da67 --- /dev/null +++ b/gitk/assess/recovered.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os +from .utils import chrom_cmp_bigger, process_db_line, prep_data, check_if_uni_sorted +import numpy as np +from multiprocessing import Pool + + +def process_region( + start, start_region, q_chrom, start_region_chrom, db, b_index, e_index +): + """ + For given position check if it is covered by universe flexible region + :param int start: position to analyse from query peak + :param list start_region: last analysed region from universe + :param str q_chrom: chromosome of query peak + :param str start_region_chrom: chromosome of universe region + :param file db: universe file + :param int b_index: index of region beginning in line from universe + :param int e_index: index of region ending in line from universe + :return: whether peak is covered; current region from universe; current chromosome of region from universe + """ + while chrom_cmp_bigger(q_chrom, start_region_chrom): + dn = db.readline().strip("\n") + if dn == "": + break + start_region, start_region_chrom = process_db_line(dn, [b_index, e_index]) + while start > start_region[1] and q_chrom == start_region_chrom: + dn = db.readline().strip("\n") + if dn == "": + break + start_region, start_region_chrom = process_db_line(dn, [b_index, e_index]) + if start_region[0] <= start < start_region[1] and q_chrom == start_region_chrom: + return True, start_region, start_region_chrom + return False, start_region, start_region_chrom + + +def calc_no_retrieve(db_file, q_folder, q_file): + """ + Calculate percent of strats and ends covered by flexible universe for given file + :param str db_file: path to universe file + :param str q_folder: path to folder containing query files + :param str q_file: file name + :return: file name; number of peaks in file; number of peaks with start covered by universe; + percent of peaks with start covered by universe; number of peaks with end covered by universe; + percent of peaks with end covered by universe; number of peaks with at least on end covered by universe; + percent of peaks with at least on end covered by universe; number of peaks with both ends covered by universe; + percent of peaks with both ends covered by universe; + """ + prep_data(q_folder, q_file) + q = open(os.path.join("tmp", q_file + "_sorted"), "r") + db_start = open(db_file) + d_start = db_start.readline().strip("\n") + start_region, start_region_chrom = process_db_line(d_start, [1, 6]) + db_end = open(db_file) + d_end = db_end.readline().strip("\n") + end_region, end_region_chrom = process_db_line(d_end, [1, 6]) + res_start = 0 + res_end = 0 + res_or = 0 + res_and = 0 + q_len = 0 + for i in q: + q_len += 1 + i = i.split("\t") + start = int(i[1]) + end = int(i[2]) + q_chrom = i[0] + start_out = process_region( + start, start_region, q_chrom, start_region_chrom, db_start, 1, 6 + ) + (found_start, start_region, start_region_chrom) = start_out + end_out = process_region( + end, end_region, q_chrom, end_region_chrom, db_end, 7, 2 + ) + (found_end, end_region, end_region_chrom) = end_out + if found_start: + res_start += 1 + if found_end: + res_end += 1 + if found_start or found_end: + res_or += 1 + if found_start and found_end: + res_and += 1 + os.remove(os.path.join("tmp", q_file + "_sorted")) + return ( + q_file, + q_len, + res_start, + res_start / q_len * 100, + res_end, + res_end / q_len * 100, + res_or, + res_or / q_len * 100, + res_and, + res_and / q_len * 100, + ) + + +def run_recovered( + q_folder, file_list, db_file, npool, save_to_file=False, folder_out=None, pref=None +): + """ + Calculate percent of strats and ends covered by flexible universe for set of files + :param str q_folder: path to folder containing query files + :param str file_list: path to file containing list of query files + :param str db_file: path to universe file + :param int npool: number of parallel processes + :param bool save_to_file: whether to save median of calculated distances for each file + :param str folder_out: output folder + :param str pref: prefix used for saving + :return: mean of percent of strats covered by flexible universe; + mean of percent of ends covered by flexible universe; + mean of percent of regions with at least one end covered by flexible universe; + mean of percent of regions with both ends covered by flexible universe + """ + check_if_uni_sorted(db_file) + if folder_out: + os.makedirs(folder_out, exist_ok=True) + os.mkdir("tmp") + files = open(file_list).read().split("\n")[:-1] + res = [] + if npool <= 1: + for i in files: + r = calc_no_retrieve(db_file, q_folder, i) + res.append(r) + else: + with Pool(npool) as p: + args = [(db_file, q_folder, f) for f in files] + res = p.starmap(calc_no_retrieve, args) + os.rmdir("tmp") + if save_to_file: + fout = os.path.join(folder_out, pref + "_data.tsv") + with open(fout, "w") as o: + header = [ + "file", + "peak_no", + "peak_start_percent", + "peak_end_percent", + "peak_or_percent", + "peak_and_percent", + ] + o.write("\t".join(header) + "\n") + for r in res: + o.write(f"{r[0]}\t{r[1]}\t{r[3]}\t{r[5]}\t{r[7]}\t{r[9]}\n") + else: + res = np.array(res) + res = res[:, [1, 3, 5, 7, 9]] + res = res.astype("float") + return np.mean(res, axis=0) diff --git a/gitk/assess/utils.py b/gitk/assess/utils.py index 1a45c32d..6ea8b6d6 100644 --- a/gitk/assess/utils.py +++ b/gitk/assess/utils.py @@ -2,6 +2,11 @@ import subprocess import shlex import os +import tempfile + + +def help(f): + f.write(b"Welcome to geeksforgeeks") def prep_data(folder, file, tmp_file): @@ -40,6 +45,24 @@ def process_line(line): return pos, start, chrom +def chrom_cmp_bigger(a, b): + """Natural check if chromosomes name is bigger""" + ac = a.replace("chr", "") + ac = ac.split("_")[0] + bc = b.replace("chr", "") + bc = bc.split("_")[0] + if bc.isnumeric() and ac.isnumeric() and bc != ac: + if int(bc) < int(ac): + return True + else: + return False + else: + if b < a: + return True + else: + return False + + def process_db_line(dn, pos_index): """Helper for reading in universe bed file line""" dn = dn.split("\t") diff --git a/gitk/cli.py b/gitk/cli.py index c3e8a073..05fee7d3 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -77,13 +77,12 @@ def main(test_args=None): args.raw_data_folder, args.file_list, args.universe, - args.no_workers, + args.npool, args.flexible, args.save_to_file, args.folder_out, args.pref, args.save_each, - args.universe_to_file, ) if args.subcommand == "intersection": @@ -93,7 +92,20 @@ def main(test_args=None): args.raw_data_folder, args.file_list, args.universe, - args.no_workers, + args.npool, + args.save_to_file, + args.folder_out, + args.pref, + ) + + if args.subcommand == "recovered": + from .assess.recovered import run_recovered + + run_recovered( + args.raw_data_folder, + args.file_list, + args.universe, + args.npool, args.save_to_file, args.folder_out, args.pref, @@ -105,7 +117,7 @@ def main(test_args=None): from .likelihood.build_model import main main( - args.model_file, + args.model_folder, args.coverage_folder, args.coverage_prefix, args.file_no, @@ -115,15 +127,17 @@ def main(test_args=None): from .likelihood.universe_hard import main main( - args.coverage_file, args.fout, args.merge, args.filter_size, args.cutoff + args.coverage_file, + args.fout, + args.merge, + args.filter_size, + args.cut_off, ) if args.subcommand == "universe_flexible": from .likelihood.universe_flexible import main - main( - args.model_file, args.cov_folder, args.coverage_prefix, args.output_file - ) + main(args.model_folder, args.output_file) if args.command == "hmm": from .hmm.hmm import run_hmm_save_bed @@ -132,7 +146,7 @@ def main(test_args=None): coverage_folder=args.cov_folder, out_file=args.out_file, prefix=args.coverage_prefix, - normalize=args.not_normalize, + normalize=args.normalize, save_max_cove=args.save_max_cove, ) diff --git a/gitk/hmm/cli.py b/gitk/hmm/cli.py index abf05238..cc1b2a2f 100644 --- a/gitk/hmm/cli.py +++ b/gitk/hmm/cli.py @@ -11,24 +11,15 @@ def build_subparser(parser): parser.add_argument( "--cov_folder", type=str, help="path to coverage folder", required=True ) - parser.add_argument( - "--not_normalize", - help="if not to normalize coverage signal before using HMM", - action="store_false", - ) + parser.add_argument("--normalize", action="store_true") parser.add_argument( "--save_max_cove", help="if present saves maximum coverage for each peak", action="store_true", ) parser.add_argument( - "--lambdas", type=str, help="lambdas matrix use to set emissions" - ) - parser.add_argument( - "--coverage_prefix", - help="prefixed used for making coverage files", - default="all", - type=str, + "--lambdas", type=str, help="lambdas matrix used to set emissions" ) + parser.add_argument("--coverage_prefix", default="all", type=str) return parser diff --git a/gitk/hmm/hmm.py b/gitk/hmm/hmm.py index 8196416f..ed9e5dfc 100644 --- a/gitk/hmm/hmm.py +++ b/gitk/hmm/hmm.py @@ -20,7 +20,7 @@ transmat = [ [1 - 1e-10, 1e-10, 0, 0], [0, 1 - 1e-6, 1e-6, 0], - [0, 0, 1 - 1e-10, 1e-10], + [0, 0, 1 - 1e-6, 1e-6], [0.1, 0, 0, 0.9], ] @@ -38,7 +38,7 @@ def norm(track, mode): important_val_unique_sort = np.sort(important_val_unique) if mode == "ends": n = 0.1 - else: + if mode == "core": n = 0.085 bs = 0 # what fraction of the distribution was used for normalization val = {} # for each unique value in track holds the corresponding quantile @@ -50,7 +50,7 @@ def norm(track, mode): track[track != 0] = [val[i] for i in important_val] -def process_bigwig(file, seq, p, chrom, chrom_size, normalize=True, mode=None): +def process_bigwig(file, seq, p, chrom, chrom_size, normalize=False, mode=None): """Preprocess bigWig file""" if pyBigWig.numpy: track = file.values(chrom, 0, chrom_size, numpy=True) @@ -64,7 +64,7 @@ def process_bigwig(file, seq, p, chrom, chrom_size, normalize=True, mode=None): seq[:, p] = track -def read_data(start, core, end, chrom, normalize=True): +def read_data(start, core, end, chrom, normalize=False): """ Read in and preprocess data :param str start: path to file with start coverage @@ -90,12 +90,8 @@ def read_data(start, core, end, chrom, normalize=True): def find_full_full_pos(seq, gap_size=1000, area_size=500): - """Look for nonzero positions in coverage matrix, when most of the positions are zero - :param ndarray seq: vector with information about non-zero positions - :param int gap_size: size of minium gap between non-zero positions that are separated - :param int area_size: size of the area around non-zero positions to be included in the result - :return list: list of starts of non-zero regions and list of ends of non-zero regions - """ + """Look for nonzero positions in coverage matrix, + when most of the positions are zero""" size = len(seq) seq = np.argwhere(seq >= 1).flatten() starts, ends = [], [] @@ -112,12 +108,8 @@ def find_full_full_pos(seq, gap_size=1000, area_size=500): def find_full_empty_pos(seq, gap_size=10000, area_size=1000): - """Look for nonzero positions in coverage matrix, when most of the positions are nonzero - :param ndarray seq: vector with information about non-zero positions - :param int gap_size: size of minium gap between non-zero positions that are separated - :param int area_size: size of the area around non-zero positions to be included in the result - :return list: list of starts of non-zero regions and list of ends of non-zero regions - """ + """Look for nonzero positions in coverage matrix, + when most of the positions are nonzero""" size = len(seq) seq = np.argwhere(seq == 0).flatten() starts, ends = [], [] @@ -165,12 +157,13 @@ def ana_region(region, start_s): def predictions_to_bed(states, chrom, bedname, save_max_cove=False, cove_file=None): """ Save HMM prediction into a file - :param ndarray states: result of HMM prediction + :param array states: result of HMM prediction :param str chrom: which chromosome is being analysed :param str bedname: path to the output file - :param bool save_max_cove: whether to save the maximum peak coverage to output - file, can result in nonstandard bed file - :param str cove_file: file with core coverage, require for saving maximum peak coverage + :param bool save_max_cove: whether to save the maximum peak + coverage to output file, can result in nonstandard bed file + :param str cove_file: file with core coverage, require for + saving maximum peak coverage """ ind = np.argwhere(states != 3) ind = ind.flatten() @@ -236,7 +229,7 @@ def split_predict(seq, empty_starts, empty_ends, model): return hmm_predictions -def run_hmm(start, core, end, chrom, normalize=True): +def run_hmm(start, core, end, chrom, normalize=False): """Make HMM prediction for given chromosome""" chrom_size, seq = read_data(start, core, end, chrom, normalize=normalize) empty_starts, empty_ends = find_full(seq) @@ -250,16 +243,19 @@ def run_hmm_save_bed( coverage_folder, out_file, prefix="all", - normalize=True, + normalize=False, save_max_cove=False, ): """ Create HMM based univers from coverage - :param str coverage_folder: path to name with coverage files - :param str prefix: prefix of coverage files + :param coverage_folder: path to folder with coverage files + :param str start: start coverage file name + :param str end: end coverage file name + :param str core: core coverage file name :param str out_file: path to the output file with universe :param bool normalize: whether to normalize file - :param bool save_max_cove: whether to save the maximum peak coverage + :param bool save_max_cove: whether to save the maximum + peak coverage """ if os.path.isfile(out_file): raise Exception(f"File : {out_file} exists") diff --git a/gitk/hmm/models.py b/gitk/hmm/models.py index a7791e5b..596b7f53 100644 --- a/gitk/hmm/models.py +++ b/gitk/hmm/models.py @@ -50,7 +50,7 @@ def __init__( """ :param lambdas_matrix: Lambda values for emission probabilities - :param out_folder: name to which save parameter matrix + :param out_folder: folder to which save parameter matrix """ Model.__init__( self, @@ -98,7 +98,7 @@ def __init__( :param means_matrix: mean values for emission probabilities :param covars_matrix: covariance values for emission probabilities - :param out_folder: name to which save parameter matrix + :param out_folder: folder to which save parameter matrix """ Model.__init__( self, @@ -150,7 +150,7 @@ def __init__( :param failures_matrix: number of failures for emission probabilities :param prob_matrix: success probability for emission probabilities - :param out_folder: name to which save parameter matrix + :param out_folder: folder to which save parameter matrix """ Model.__init__( self, @@ -199,7 +199,7 @@ def __init__( :param alfa_matrix: alfa values for emission probabilities :param beta_matrix: beta values for emission probabilities - :param out_folder: name to which save parameter matrix + :param out_folder: folder to which save parameter matrix """ Model.__init__( self, diff --git a/gitk/likelihood/build_model.py b/gitk/likelihood/build_model.py index 73362138..c20e0089 100644 --- a/gitk/likelihood/build_model.py +++ b/gitk/likelihood/build_model.py @@ -3,7 +3,7 @@ import numpy as np import os -from ..utils import timer_func, read_chromosome_from_bw +from ..utils import timer_func import pyBigWig import tarfile import tempfile @@ -12,22 +12,32 @@ WRONG_UNIWIG = False -def model_binomial(folder_in, in_file, chrom, file_out, file_no=None): - """Create binomial likelihood model - first column - likelihood of background - second column - likelihood of coverage""" +def model_binomial(folder_in, in_file, chrom, file_out, file_no=None, start=0): + """ "Create binomial likelihood model + First column likelihood of background + Second column likelihood of coverage""" in_file = os.path.join(folder_in, in_file) - distr_cov = read_chromosome_from_bw(in_file, chrom) - chrom_size = len(distr_cov) + bw = pyBigWig.open(in_file) + chrom_size = bw.chroms(chrom) + if pyBigWig.numpy: + distr_cov = bw.values(chrom, start, chrom_size, numpy=True) + else: + distr_cov = bw.values(chrom, start, chrom_size) + distr_cov = np.array(distr_cov) + distr_cov[np.isnan(distr_cov)] = 0 + if WRONG_UNIWIG and ("cove" not in in_file): + distr_cov = np.pad(distr_cov[WINDOW_SIZE:], (0, WINDOW_SIZE)) no_possible = file_no * len(distr_cov) # number of possible spots covered no_cov = np.sum(distr_cov) # number of spots covered - cov_uniq = np.unique(distr_cov).astype(np.uint16) - prob_array = np.zeros((int(np.max(cov_uniq)) + 1, 2)) - for i in cov_uniq: - prob_array[i] = [ - np.log10((file_no - i) / (no_possible - no_cov) + 1e-10), - np.log10((i / no_cov) + 1e-10), - ] + no_ncov = np.subtract(no_possible, no_cov) # number of spots uncovered + distr_ncov = np.subtract( + file_no, distr_cov + ) # for each position in how many files is empty + cov = distr_cov / no_cov + ncov = distr_ncov / no_ncov + p_cov = np.log10(cov + 1e-10) + p_ncov = np.log10(ncov + 1e-10) + prob_array = np.vstack((p_ncov, p_cov)).T header = f"{chrom}_{chrom_size}" r = {header: prob_array} np.savez_compressed(file_out, **r) @@ -35,10 +45,6 @@ def model_binomial(folder_in, in_file, chrom, file_out, file_no=None): class ChromosomeModel: def __init__(self, folder, chrom): - """ - :param str folder: file with the model - :param str chrom: of which chromosome is the model - """ self.folder = folder self.chromosome = chrom self.start_file = f"{self.chromosome}_start" @@ -51,42 +57,32 @@ def __init__(self, folder, chrom): } self.models = {} - def __getitem__(self, item): - return self.models[item] - - def make_model(self, coverage_folder, coverage_prefix, file_no): - """ - Make a lh model of given chromosome from coverage files - :param str coverage_folder: path to name with coverage files - :param str coverage_prefix: prefixed used for making coverage files - :param int file_no: number of files from which model is being created - """ + def make_model( + self, coverage_folder, coverage_start, coverage_end, coverage_core, file_no + ): model_binomial( coverage_folder, - f"{coverage_prefix}_start.bw", + coverage_start, self.chromosome, os.path.join(self.folder, self.start_file), file_no, ) model_binomial( coverage_folder, - f"{coverage_prefix}_core.bw", + coverage_core, self.chromosome, os.path.join(self.folder, self.core_file), file_no, ) model_binomial( coverage_folder, - f"{coverage_prefix}_end.bw", + coverage_end, self.chromosome, os.path.join(self.folder, self.end_file), file_no, ) def read(self): - """ - Read model - """ model_folder = tarfile.open(self.folder, "r") for f in self.files: file = model_folder.extractfile(self.files[f]) @@ -95,9 +91,6 @@ def read(self): model_folder.close() def read_track(self, track): - """ - Read specific track from model - """ model_folder = tarfile.open(self.folder, "r") file = model_folder.extractfile(self.files[track]) values = np.load(file) @@ -106,40 +99,18 @@ def read_track(self, track): class ModelLH: - def __init__(self, file): - """ - Likelihood model class - :param str file: file containing the model - """ - self.name = file + def __init__(self, folder): + self.folder = folder self.chromosomes_list = [] self.chromosomes_models = {} - if os.path.exists(self.name): - if tarfile.is_tarfile(self.name): - files = tarfile.open(self.name, "r") + if os.path.exists(self.folder): + if tarfile.is_tarfile(self.folder): + files = tarfile.open(self.folder, "r") chroms = files.getnames() self.chromosomes_list = list(set([i.split("_")[0] for i in chroms])) - def __getitem__(self, item): - return self.chromosomes_models[item] - - def make(self, coverage_folder, coverage_prefix, file_no, force=False): - """ - Make lh model for all chromosomes - :param str coverage_folder: folder with coverage files - :param str coverage_prefix: prefixed used for making coverage files - :param int file_no: number of file from which model is being made - :param bool force: if overwrite an existing model - """ - if os.path.exists(self.name): - if not force: - print( - "Model already exists. If you want to overwrite it use force argument" - ) - return - else: - print("Overwriting existing model") - tar_arch = tarfile.open(self.name, "w") + def make(self, coverage_folder, coverage_prefix, file_no): + tar_arch = tarfile.open(self.folder, "w") temp_dir = tempfile.TemporaryDirectory() bw_start = pyBigWig.open( os.path.join(coverage_folder, f"{coverage_prefix}_start.bw") @@ -151,7 +122,9 @@ def make(self, coverage_folder, coverage_prefix, file_no, force=False): chrom_model = ChromosomeModel(temp_dir.name, c) chrom_model.make_model( coverage_folder, - coverage_prefix, + f"{coverage_prefix}_start.bw", + f"{coverage_prefix}_core.bw", + f"{coverage_prefix}_end.bw", file_no, ) for f in chrom_model.files: @@ -164,34 +137,32 @@ def make(self, coverage_folder, coverage_prefix, file_no, force=False): tar_arch.close() def read_chrom(self, chrom): - """ - Read into model specific chromosome - """ - self.chromosomes_models[chrom] = ChromosomeModel(self.name, chrom) + self.chromosomes_models[chrom] = ChromosomeModel(self.folder, chrom) self.chromosomes_models[chrom].read() def read_chrom_track(self, chrom, track): - """ - Read into model specific track for chromosome - """ - self.chromosomes_models[chrom] = ChromosomeModel(self.name, chrom) + self.chromosomes_models[chrom] = ChromosomeModel(self.folder, chrom) self.chromosomes_models[chrom].read_track(track) def clear_chrom(self, chrom): - """ - Clear model for given chromosome - """ self.chromosomes_models[chrom] = None @timer_func -def main(model_file, coverage_folder, coverage_prefix, file_no=None, force=False): +def main( + model_folder, + coverage_folder, + coverage_prefix, + file_no=None, +): """ Crate likelihood models for all chromosomes - :param str model_file: output name + :param str model_folder: output folder :param str coverage_folder: folder with coverage files - :param str coverage_prefix: prefix used for making coverage files + :param str coverage_start: file with coverage of start without extension + :param str coverage_end: file with coverage of end without extension + :param str coverage_core: file with coverage of core without extension :param int file_no: number of files used for making coverage tracks """ - model = ModelLH(model_file) - model.make(coverage_folder, coverage_prefix, file_no, force) + model = ModelLH(model_folder) + model.make(coverage_folder, coverage_prefix, file_no) diff --git a/gitk/likelihood/cli.py b/gitk/likelihood/cli.py index bfcc1722..577da807 100644 --- a/gitk/likelihood/cli.py +++ b/gitk/likelihood/cli.py @@ -5,67 +5,27 @@ def build_subparser_model(parser): :return argparse.ArgumentParser """ - parser.add_argument( - "--model_file", help="path to file with lh model", required=True, type=str - ) - parser.add_argument( - "--file_no", help="number of files used to make the model", type=int - ) - parser.add_argument( - "--coverage_folder", help="path to coverage folder", required=True, type=str - ) - parser.add_argument( - "--coverage_prefix", - help="prefix used when making coverage files", - default="all", - type=str, - ) + parser.add_argument("--model_folder", required=True, type=str) + parser.add_argument("--file_no", type=int) + parser.add_argument("--coverage_folder", required=True, type=str) + parser.add_argument("--coverage_prefix", default="all", type=str) return parser def build_subparser_universe_hard(parser): - parser.add_argument( - "--merge", - help="distance between output peaks that should be merged into one in output universe", - default=0, - type=int, - ) - parser.add_argument( - "--filter_size", - help="minimal siez of the region in the universe", - default=0, - type=int, - ) - parser.add_argument( - "--fout", help="output file with the universe", required=True, type=str - ) - parser.add_argument( - "--coverage_file", help="path to core coverage file", required=True, type=str - ) - parser.add_argument( - "--cutoff", help="cutoff value used for making universe", type=int - ) + parser.add_argument("--merge", default=0, type=int) + parser.add_argument("--filter_size", default=0, type=int) + parser.add_argument("--fout", required=True, type=str) + parser.add_argument("--coverage_file", required=True, type=str) + parser.add_argument("--cut_off", type=int) return parser def build_subparser_universe_flexible(parser): - parser.add_argument( - "--output_file", help="path to output, universe file", required=True, type=str - ) - parser.add_argument( - "--model_file", help="path to lh model file", required=True, type=str - ) - parser.add_argument( - "--coverage_prefix", - help="prefixed used for making coverage files", - default="all", - type=str, - ) - parser.add_argument( - "--cov_folder", type=str, help="path to coverage folder", required=True - ) + parser.add_argument("--output_file", required=True, type=str) + parser.add_argument("--model_folder", required=True, type=str) return parser diff --git a/gitk/likelihood/universe_flexible.py b/gitk/likelihood/universe_flexible.py index 391ae206..29953262 100644 --- a/gitk/likelihood/universe_flexible.py +++ b/gitk/likelihood/universe_flexible.py @@ -4,40 +4,32 @@ import numpy as np import os from functools import cmp_to_key -from ..utils import natural_chr_sort, timer_func, read_chromosome_from_bw -from ..hmm.hmm import predictions_to_bed, find_full +from ..utils import natural_chr_sort, timer_func +from ..hmm.hmm import predictions_to_bed, find_full_full_pos, find_full_empty_pos from .build_model import ModelLH from numba import njit @njit -def process_part( - cove, - model_start=np.array([[]]), - model_core=np.array([[]]), - model_end=np.array([[]]), -): +def process_part(model): """ Finding ML path through matrix using dynamic programing - :param ndarray cove: coverage tracks - :param ndarray model_start: lh model for starts - :param ndarray model_core: lh model for core - :param ndarray model_end: lh model for ends - :return ndarray: ML path through matrix + :param array mat: fragment of likelihood model to be processed + :return array: ML path through matrix """ - mat = np.zeros((len(cove), 4)) + mat = np.zeros((len(model), 4)) (N, M) = mat.shape + background = [0, 2, 4] for i in range(N): - sb = model_start[cove[i, 0], 0] - cb = model_core[cove[i, 1], 0] - eb = model_end[cove[i, 2], 0] - s = model_start[cove[i, 0], 1] - c = model_core[cove[i, 1], 1] - e = model_end[cove[i, 2], 1] - mat[i, 0] = s + cb + eb - mat[i, 1] = sb + c + eb - mat[i, 2] = sb + cb + e - mat[i, 3] = sb + cb + eb + for k in background: + mat[i, 3] += model[i, k] + for j in range(M - 1): + back = background[:] + back.remove(2 * j) + mat[i, j] = model[i, 2 * j + 1] + for k in back: + mat[i, j] += model[i, k] + for i in range(1, N): for j in range(M): mat[i, j] += max(mat[i - 1, j], mat[i - 1, j - 1]) @@ -52,54 +44,46 @@ def process_part( return path -def make_ml_flexible_universe(model_lh, cove_folder, cove_prefix, chrom, file_out): +def make_ml_flexible_universe(model_lh, chrom, fout): """ Make ML flexible universe per chromosome - :param ModelLH model_lh: lh model - :param str cove_folder: path to a folder with genome coverage by tracks - :param str cove_prefix: prefix used in uniwig for creating coverage + :param str folderin: input folder with likelihood models :param str chrom: chromosome to be processed - :param str file_out: output file with the universe + :param str fout: output file with the universe """ model_lh.read_chrom(chrom) - chrom_model = model_lh[chrom] - start = read_chromosome_from_bw( - os.path.join(cove_folder, f"{cove_prefix}_start.bw"), chrom - ) - core = read_chromosome_from_bw( - os.path.join(cove_folder, f"{cove_prefix}_core.bw"), chrom - ) - end = read_chromosome_from_bw( - os.path.join(cove_folder, f"{cove_prefix}_end.bw"), chrom + chrom_model = model_lh.chromosomes_models[chrom] + model = np.hstack( + ( + chrom_model.models["start"], + chrom_model.models["core"], + chrom_model.models["end"], + ) ) - cove = np.zeros((len(start), 3), dtype=np.uint16) - cove[:, 0] = start - cove[:, 1] = core - cove[:, 2] = end - full_start, full_end = find_full(cove) - path = np.full(len(cove), 3, dtype=np.uint8) + model_lh.clear_chrom(chrom) + seq = np.where(np.sum(model[:, [1, 3, 5]], axis=1) > -30, 1, 0).astype(np.uint8) + full_pos_no = np.sum(seq) + if full_pos_no < len(seq) - full_pos_no: + full_start, full_end = find_full_full_pos(seq) + else: + full_start, full_end = find_full_empty_pos(seq) + path = np.full(len(model), 3, dtype=np.uint8) for s, e in zip(full_start, full_end): - res = process_part( - cove[s:e], - chrom_model["start"], - chrom_model["core"], - chrom_model["end"], - ) + res = process_part(model[s:e]) path[s:e] = res - predictions_to_bed(path, chrom, file_out) + predictions_to_bed(path, chrom, fout) -def main(folder_in, cove_folder, cove_prefix, file_out): +@timer_func +def main(folderin, fout): """ Make ML flexible universe - :param str folder_in: input name with likelihood models - :param str file_out: output file with the universe - :param str cove_folder: path to a folder with genome coverage by tracks - :param str cove_prefix: prefix used in uniwig for creating coverage + :param str folderin: input folder with likelihood models + :param str fout: output file with the universe """ - if os.path.isfile(file_out): - raise Exception(f"File : {file_out} exists") - lh_model = ModelLH(folder_in) + if os.path.isfile(fout): + raise Exception(f"File : {fout} exists") + lh_model = ModelLH(folderin) chroms = sorted(lh_model.chromosomes_list, key=cmp_to_key(natural_chr_sort)) for C in chroms: - make_ml_flexible_universe(lh_model, cove_folder, cove_prefix, C, file_out) + make_ml_flexible_universe(lh_model, C, fout) diff --git a/gitk/likelihood/universe_hard.py b/gitk/likelihood/universe_hard.py index 3e204cb3..91812745 100644 --- a/gitk/likelihood/universe_hard.py +++ b/gitk/likelihood/universe_hard.py @@ -3,18 +3,19 @@ import numpy as np import os +from time import time from functools import cmp_to_key import pyBigWig from ..utils import natural_chr_sort, timer_func -def get_uni(file, chrom, cutoff=None): +def get_uni(file, chrom, cut_off=None): """For each position check if coverage is bigger than cut-off; if cut-off not provided calculate value that gives maximum likelihood universe :param str file: coverage file :param str chrom: chromosome to analyse - :param int cutoff: base pairs with values grater of equal to cut-off can be included in universe + :param int cut_off: base pairs with values grater of equal to cut-off can be included in universe :return:""" file = pyBigWig.open(file) if pyBigWig.numpy: @@ -24,23 +25,23 @@ def get_uni(file, chrom, cutoff=None): track = np.array(track) track[np.isnan(track)] = 0 track = track.astype(np.uint16) - if cutoff is None: - cutoff = np.sum(track) / len(track) - inter_pos = track >= cutoff + if cut_off is None: + cut_off = np.sum(track) / len(track) + inter_pos = track >= cut_off file.close() return inter_pos -def save_simple(file_out, inter_pos, chrom): +def save_simple(fout, inter_pos, chrom): """ Save cut-off universe to a file without any processing - :param str file_out: output file + :param str fout: output file :param bool vector inter_pos: whether each position should be included in universe :param str chrom: chromosome to analyse """ inter_pos_uni = np.argwhere(inter_pos) start = inter_pos_uni[0][0] - with open(file_out, "a") as f: + with open(fout, "a") as f: for i in range(1, len(inter_pos_uni)): if inter_pos_uni[i] - inter_pos_uni[i - 1] != 1: end = inter_pos_uni[i - 1][0] + 1 @@ -50,10 +51,10 @@ def save_simple(file_out, inter_pos, chrom): f.write(f"{chrom}\t{start}\t{end}\n") -def marge_filter(file_out, inter_pos, chrom, merge_dist=100, size_flt=1000): +def marge_filter(fout, inter_pos, chrom, merge_dist=100, size_flt=1000): """ Save cut-off universe to a file with filtering region size and merging close regions - :param file_out: output file + :param fout: output file :param bool vector inter_pos: whether each position should be included in universe :param str chrom: chromosome to analyse :param int merge_dist: regions closer than merge_dist will be merged into one @@ -61,7 +62,7 @@ def marge_filter(file_out, inter_pos, chrom, merge_dist=100, size_flt=1000): """ inter_pos_uni = np.argwhere(inter_pos) start = inter_pos_uni[0][0] - with open(file_out, "a") as f: + with open(fout, "a") as f: for i in range(1, len(inter_pos_uni)): if inter_pos_uni[i] - inter_pos_uni[i - 1] >= merge_dist: end = inter_pos_uni[i - 1][0] + 1 @@ -73,17 +74,17 @@ def marge_filter(file_out, inter_pos, chrom, merge_dist=100, size_flt=1000): f.write(f"{chrom}\t{start}\t{end}\n") -def main(file, file_out, merge=0, filter_size=0, cutoff=None): +def main(file, fout, merge=0, filter_size=0, cut_off=None): """ Creat cut-off universe based on coverage track :param str file: path to coverage file without extension :param int merge: regions closer than this value will be merged into one :param int filter_size: regions smaller than this value will not be reported - :param str file_out: output file - :param int cutoff: base pairs with coverage equal to or greater than this value will be included in the universe + :param str fout: output file + :param int cut_off: base pairs with coverage equal to or greater than this value will be included in the universe """ - if os.path.isfile(file_out): - raise Exception(f"File : {file_out} exists") + if os.path.isfile(fout): + raise Exception(f"File : {fout} exists") bw_start = pyBigWig.open(file) chroms = bw_start.chroms() bw_start.close() @@ -92,8 +93,8 @@ def main(file, file_out, merge=0, filter_size=0, cutoff=None): chroms = {i: chroms[i] for i in chroms_key} for chrom in chroms: if chroms[chrom] > 0: - inter_pos = get_uni(file, chrom, cutoff) + inter_pos = get_uni(file, chrom, cut_off) if merge == 0 and filter_size == 0: - save_simple(file_out, inter_pos, chrom) + save_simple(fout, inter_pos, chrom) else: - marge_filter(file_out, inter_pos, chrom, merge, filter_size) + marge_filter(fout, inter_pos, chrom, merge, filter_size) diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 4c9278ab..6e4fafb1 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -14,8 +14,8 @@ def __init__(self, **kwargs): def region2vec( - token_folder, # path to the name of tokenized files - save_dir, # name to save the training results + token_folder, # path to the folder of tokenized files + save_dir, # folder to save the training results file_list=None, # specifies which files from token_folder are used for training num_shufflings=1000, # Number of shuffled datasets or number of training epochs num_processes=10, # Maximum number of parallel processes diff --git a/gitk/region2vec/region2vec_train.py b/gitk/region2vec/region2vec_train.py index 4e022a4a..efc7441d 100644 --- a/gitk/region2vec/region2vec_train.py +++ b/gitk/region2vec/region2vec_train.py @@ -30,10 +30,10 @@ def main(args): save_dir = args.save_dir data_folder = os.path.join( save_dir, "shuffled_datasets" - ) # shuffled datasets are stored in the shuffled_datasets name + ) # shuffled datasets are stored in the shuffled_datasets folder model_dir = os.path.join( save_dir, "models" - ) # model snapshots are stored in the models name + ) # model snapshots are stored in the models folder os.makedirs(save_dir, exist_ok=True) os.makedirs(model_dir, exist_ok=True) utils.set_log_path(save_dir) # specify the path to the log file diff --git a/gitk/region2vec/region_shuffling.py b/gitk/region2vec/region_shuffling.py index 272750eb..4b603b2d 100644 --- a/gitk/region2vec/region_shuffling.py +++ b/gitk/region2vec/region_shuffling.py @@ -136,7 +136,9 @@ def main(args): parser.add_argument("--file_list", help="path to a file list") parser.add_argument("--tokenization_mode", help="tokenization mode") parser.add_argument("--tokenization_folder", help="path to the tokenized regions") - parser.add_argument("--save_dir", help="parent name to generated shuffled datasets") + parser.add_argument( + "--save_dir", help="parent folder to generated shuffled datasets" + ) parser.add_argument( "--pool", type=int, diff --git a/gitk/tokenization/hard_tokenization_batch.py b/gitk/tokenization/hard_tokenization_batch.py index 4a9450e2..54c0bb2a 100644 --- a/gitk/tokenization/hard_tokenization_batch.py +++ b/gitk/tokenization/hard_tokenization_batch.py @@ -50,7 +50,7 @@ def generate_tokens( not_covered = all_set - existing_set number = len(not_covered) if number == 0: - print("Use the existing name {}".format(token_folder), flush=True) + print("Use the existing folder {}".format(token_folder), flush=True) return 0 else: print( diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index c22fb76d..e7b8075a 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -57,7 +57,7 @@ def hard_tokenization_main( return 1 elif len(existing_set) > 0: print( - "Folder {} exists with incomplete tokenized files. Please empty/delete the name first".format( + "Folder {} exists with incomplete tokenized files. Please empty/delete the folder first".format( dst_folder ) ) diff --git a/gitk/tokenization/split_file.py b/gitk/tokenization/split_file.py index dadb16d0..66661c33 100644 --- a/gitk/tokenization/split_file.py +++ b/gitk/tokenization/split_file.py @@ -18,7 +18,7 @@ def split_file(file_path, dest_folder, num_parts): Split a list of files into a specified non-overlapping batches file_path: path to a list of files - dest_folder: name to store file splits + dest_folder: folder to store file splits num_parts: number of parts needed """ if os.path.exists(dest_folder): diff --git a/gitk/utils.py b/gitk/utils.py index fd9f76cd..9ac5176a 100644 --- a/gitk/utils.py +++ b/gitk/utils.py @@ -1,6 +1,4 @@ from time import time -import pyBigWig -import numpy as np def natural_chr_sort(a, b): @@ -33,14 +31,3 @@ def wrap_func(*args, **kwargs): return result return wrap_func - - -def read_chromosome_from_bw(file, chrom): - bw = pyBigWig.open(file) - chrom_size = bw.chroms(chrom) - if pyBigWig.numpy: - cove = bw.values(chrom, 0, chrom_size, numpy=True) - else: - cove = bw.values(chrom, 0, chrom_size) - cove = np.array(cove) - return cove.astype(np.uint16) diff --git a/mkdocs.yml b/mkdocs.yml index 9e9baafa..3af949a7 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -9,7 +9,6 @@ nav: - Introduction: README.md - How-to guides: - Create consensus peaks: likelihood/consensus-peaks.md - - Assess universe fit to collection: likelihood/assess.md - Reference: - API: autodoc_build/gitk.md - Support: support.md diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index e47fde50..9b7ba6b1 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -11,4 +11,3 @@ seaborn tqdm umap-learn ubiquerg -scipy diff --git a/tests/consesnus/cli_test.sh b/tests/consesnus/cli_test.sh index c46c669b..6d25d0af 100644 --- a/tests/consesnus/cli_test.sh +++ b/tests/consesnus/cli_test.sh @@ -1,12 +1,12 @@ #!/bin/bash -#pip install --user --upgrade . - rm -r tests/consesnus/results mkdir tests/consesnus/results # make lh model -gitk lh build_model --model_file tests/consesnus/results/lh_model.tar --file_no 4 --coverage_folder tests/consesnus/coverage/ +gitk lh build_model --model_folder tests/consesnus/results/lh_model.tar \ + --file_no 4 \ + --coverage_folder tests/consesnus/coverage/ mkdir tests/consesnus/results/universe/ # make cut-off universe @@ -24,7 +24,8 @@ gitk lh universe_hard --coverage_file tests/consesnus/coverage/all_core.bw \ --fout tests/consesnus/results/universe/cut_off_c1_m100_f300.bed # make ML flexible universe -gitk lh universe_flexible --model_file tests/consesnus/results/lh_model.tar --output_file tests/consesnus/results/universe/ML_flexible.bed +gitk lh universe_flexible --model_folder tests/consesnus/results/lh_model.tar \ + --output_file tests/consesnus/results/universe/ML_flexible.bed # make HMM universe gitk hmm --out_file tests/consesnus/results/universe/hmm_raw.bed --cov_folder tests/consesnus/coverage/ From 81c847efb1c02d922bd0e2b69bf3f9e0f83a80fc Mon Sep 17 00:00:00 2001 From: Nathan Sheffield Date: Fri, 12 May 2023 16:36:22 -0400 Subject: [PATCH 0147/1072] Update .github/workflows/run-pytest.yml --- .github/workflows/run-pytest.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/run-pytest.yml b/.github/workflows/run-pytest.yml index 11cf1a05..c4570a6b 100644 --- a/.github/workflows/run-pytest.yml +++ b/.github/workflows/run-pytest.yml @@ -9,7 +9,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - python-version: [3.8, "3.10"] + python-version: ["3.8", "3.10"] os: [ubuntu-latest] steps: From 68033b0e012162b28d058307c881503a13ca7fc6 Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Fri, 12 May 2023 16:59:10 -0400 Subject: [PATCH 0148/1072] Update cli.py Change underscores to hyphens --- gitk/eval/cli.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/gitk/eval/cli.py b/gitk/eval/cli.py index f4b92524..4834b8cb 100644 --- a/gitk/eval/cli.py +++ b/gitk/eval/cli.py @@ -5,9 +5,9 @@ def build_subparser_gdst(parser): :return argparse.ArgumentParser """ - parser.add_argument("--model_path", required=True, type=str) - parser.add_argument("--embed_type", required=True, type=str) - parser.add_argument("--num_samples", default=10000, type=int) + parser.add_argument("--model-path", required=True, type=str) + parser.add_argument("--embed-type", required=True, type=str) + parser.add_argument("--num-samples", default=10000, type=int) return parser @@ -18,10 +18,10 @@ def build_subparser_npt(parser): :return argparse.ArgumentParser """ - parser.add_argument("--model_path", required=True, type=str) - parser.add_argument("--embed_type", required=True, type=str) + parser.add_argument("--model-path", required=True, type=str) + parser.add_argument("--embed-type", required=True, type=str) parser.add_argument("--K", required=True, type=int) - parser.add_argument("--num_samples", default=1000, type=int) + parser.add_argument("--num-samples", default=1000, type=int) return parser @@ -32,13 +32,13 @@ def build_subparser_cct_tss(parser): :return argparse.ArgumentParser """ - parser.add_argument("--model_path", required=True, type=str) - parser.add_argument("--embed_type", required=True, type=str) - parser.add_argument("--save_folder", required=True, type=str) - parser.add_argument("--Rscript_path", required=True, type=str) + parser.add_argument("--model-path", required=True, type=str) + parser.add_argument("--embed-type", required=True, type=str) + parser.add_argument("--save-folder", required=True, type=str) + parser.add_argument("--Rscript-path", required=True, type=str) parser.add_argument("--assembly", required=True, type=str) parser.add_argument("--threshold", default=0.0001, type=float) - parser.add_argument("--num_samples", default=1000, type=int) + parser.add_argument("--num-samples", default=1000, type=int) return parser From 0579d8618ae2603b0fde01d32ef9504494ee0af1 Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Fri, 12 May 2023 17:30:06 -0400 Subject: [PATCH 0149/1072] Update gitk/eval/cct.py --- gitk/eval/cct.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gitk/eval/cct.py b/gitk/eval/cct.py index e1af8749..1c67e34a 100644 --- a/gitk/eval/cct.py +++ b/gitk/eval/cct.py @@ -87,7 +87,7 @@ def clustering_batch(batch, K, save_folder, seed=0, num_workers=10): worker_func = clustering with mp.Pool(processes=num_workers) as pool: all_processes = [] - for i, (path, embed_type) in enumerate(enumerate(batch)): + for i, (path, embed_type) in enumerate(batch): folder = os.path.join(save_folder, "model_{}".format(i)) os.makedirs(folder, exist_ok=True) process = pool.apply_async(worker_func, (path, embed_type, K, folder)) From 29ec03d449bda8769b819167137c79438edfb184 Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Fri, 12 May 2023 17:30:48 -0400 Subject: [PATCH 0150/1072] Update gitk/eval/cct.py --- gitk/eval/cct.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gitk/eval/cct.py b/gitk/eval/cct.py index 1c67e34a..90e0b150 100644 --- a/gitk/eval/cct.py +++ b/gitk/eval/cct.py @@ -70,7 +70,7 @@ def region2tuple(x): f.write("{}\t{}\t{}\n".format(chr_name, start, end)) -def clustering(model_path, embed_type, K, save_folder, seed=0): +def clustering(model_path, embed_type, n_clusters, save_folder, seed=0): np.random.seed(seed) embeds, vocab = load_genomic_embeddings(model_path, embed_type) From ffe76bcc0126fdffffadc01f83d0a8b3a47b2d8d Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Fri, 12 May 2023 17:31:02 -0400 Subject: [PATCH 0151/1072] Update gitk/eval/cct.py --- gitk/eval/cct.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gitk/eval/cct.py b/gitk/eval/cct.py index 90e0b150..c22a0696 100644 --- a/gitk/eval/cct.py +++ b/gitk/eval/cct.py @@ -74,7 +74,7 @@ def clustering(model_path, embed_type, n_clusters, save_folder, seed=0): np.random.seed(seed) embeds, vocab = load_genomic_embeddings(model_path, embed_type) - clustering = KMeans(n_clusters=K, random_state=seed, n_init="auto").fit(embeds) + clustering = KMeans(n_clusters=n_clusters, random_state=seed, n_init="auto").fit(embeds) labels = clustering.labels_ cluster_idxes = np.sort(np.unique(labels)) for c in cluster_idxes: From c10a178de769eac5e9df31eaa5501e5a3e48a1a1 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Tue, 16 May 2023 20:36:33 -0400 Subject: [PATCH 0152/1072] work on projection --- .vscode/settings.json | 10 +- gitk/scembed/__init__.py | 1 + gitk/scembed/cli.py | 8 +- gitk/scembed/const.py | 8 +- gitk/scembed/models.py | 31 ++++ gitk/scembed/projection.py | 172 +++++++++++++++++++ gitk/scembed/scembed.py | 11 +- gitk/scembed/utils.py | 135 ++++++++++++++- requirements/requirements-all.txt | 1 + tests/integration/scembed_train_in_chunks.py | 2 + tests/test_projector.py | 101 +++++++++++ tests/test_scembed.py | 8 +- tests/test_utils.py | 5 +- 13 files changed, 464 insertions(+), 29 deletions(-) create mode 100644 gitk/scembed/models.py create mode 100644 gitk/scembed/projection.py create mode 100644 tests/test_projector.py diff --git a/.vscode/settings.json b/.vscode/settings.json index 5259ece3..49867c48 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -1,11 +1,11 @@ { + "[python]": { + "editor.defaultFormatter": "ms-python.black-formatter" + }, + "python.formatting.provider": "none", "python.testing.pytestArgs": [ "tests" ], "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true, - "[python]": { - "editor.defaultFormatter": "ms-python.black-formatter" - }, - "python.formatting.provider": "none" + "python.testing.pytestEnabled": true } \ No newline at end of file diff --git a/gitk/scembed/__init__.py b/gitk/scembed/__init__.py index 880b30b5..e04e2290 100644 --- a/gitk/scembed/__init__.py +++ b/gitk/scembed/__init__.py @@ -1,3 +1,4 @@ from .const import * from .scembed import * from .utils import * +from .projection import * diff --git a/gitk/scembed/cli.py b/gitk/scembed/cli.py index 179769ff..310d9272 100644 --- a/gitk/scembed/cli.py +++ b/gitk/scembed/cli.py @@ -7,12 +7,8 @@ from ._version import __version__ from .argparser import build_argparser from .const import * -from .scembed import ( - convert_anndata_to_documents, - load_scanpy_data, - shuffle_documents, - train, -) +from .scembed import (convert_anndata_to_documents, load_scanpy_data, + shuffle_documents, train) def main(): diff --git a/gitk/scembed/const.py b/gitk/scembed/const.py index a3221f87..60a41cb4 100644 --- a/gitk/scembed/const.py +++ b/gitk/scembed/const.py @@ -1,4 +1,4 @@ -""" Constants for scembed """ +from pathlib import Path __author__ = ["Nathan LeRoy", "Jason Smith", "Erfaneh Gharavi"] __email__ = "nleroy@virginia.edu" @@ -24,3 +24,9 @@ CHR_KEY = "chr" START_KEY = "start" END_KEY = "end" + +MODEL_CACHE_DIR = str(Path.home() / ".scembed") +MODEL_HUB_URL = "http://big.databio.org/scembed/models" +MODEL_CONFIG_FILE_NAME = "model.yaml" + +DEFAULT_BEDTOOLS_PATH = "bedtools" diff --git a/gitk/scembed/models.py b/gitk/scembed/models.py new file mode 100644 index 00000000..c5b7cb3d --- /dev/null +++ b/gitk/scembed/models.py @@ -0,0 +1,31 @@ +from typing import List, Optional +from pydantic import BaseModel + + +class ModelParameter(BaseModel): + embedding_dim: Optional[int] + description: Optional[str] + + +class Maintainer(BaseModel): + name: Optional[str] + email: Optional[str] + + +class ModelCard(BaseModel): + path_to_weights: str + path_to_universe: str + name: str + reference: str + description: Optional[str] + tags: Optional[List[str]] + datasets: Optional[List[str]] + model_parameters: Optional[List[ModelParameter]] + model_architecture: Optional[str] + maintainers: Optional[List[Maintainer]] + + +class Universe(BaseModel): + reference: str + regions: List[str] + description: Optional[str] diff --git a/gitk/scembed/projection.py b/gitk/scembed/projection.py new file mode 100644 index 00000000..b73a467c --- /dev/null +++ b/gitk/scembed/projection.py @@ -0,0 +1,172 @@ +import logging +import os +from typing import Dict, List + +import numpy as np +import scanpy as sc +import yacman +from tqdm import tqdm + +from .const import MODEL_CACHE_DIR, MODEL_HUB_URL, MODULE_NAME, MODEL_CONFIG_FILE_NAME +from .utils import ( + load_scembed_model, + check_model_exists_on_hub, + download_remote_model, + load_universe_file, + generate_var_conversion_map, +) +from .models import ModelCard, Universe + +_LOGGER = logging.getLogger(MODULE_NAME) + + +class Projector: + def __init__(self, path_to_model: str, no_cache: bool = False): + """ + Initialize a projector object. + + :param str path_to_model: The path to the model. This can be a local path or a registry name ont he model hub. + :param bool no_cache: If True, will not use the cache directory. + """ + self.path_to_model = path_to_model + self.model_config: ModelCard = None + self.model: Dict[str, np.ndarray] = None + self.universe: Universe = None + self.no_cache = no_cache + self._load_model() + + def _make_cache_dir(self): + """ + Make the cache directory if it doesn't exist. + """ + _LOGGER.debug(f"Making cache directory: {MODEL_CACHE_DIR}") + if not os.path.exists(MODEL_CACHE_DIR): + os.mkdir(MODEL_CACHE_DIR) + + def _load_local_model(self, path: str): + """ + Will load in a local model from the path_to_model attribute. It + is assumed that this is a pickel-dumped scembed.SCEmbed() model using + the model.save_model() method. + + The `path` parameter should be a path to a config `yaml` file that + contains the path to the model. + + :param str path: The path to the model. + """ + _LOGGER.debug(f"Loading local model from {path}") + # read yaml file + yacmap = yacman.YacAttMap(filepath=path) + self.model_config = ModelCard(**yacmap.to_dict()["model"]) + + # load in the scEmbed model, build path to weights. It sits + # next to the config file. + _model = load_scembed_model( + os.path.join(os.path.dirname(path), self.model_config.path_to_weights) + ) + + # load in the universe + path_to_universe = os.path.join( + os.path.dirname(path), self.model_config.path_to_universe + ) + universe_regions = load_universe_file(path_to_universe) + self.universe = Universe( + reference=self.model_config.reference, regions=universe_regions + ) + + # we only want the region2vec dictionary from this + self.model = _model.region2vec + + def _load_remote_model(self, registry: str): + """ + Load a model from the model-hub using the reigstry path. + """ + # look for model in cache, should be at cache/registry + local_model_path = os.path.join( + MODEL_CACHE_DIR, registry, MODEL_CONFIG_FILE_NAME + ) + + # if exists and no_cache is False, load it + if os.path.exists(local_model_path) and not self.no_cache: + self._load_local_model(local_model_path) + return + + # check model exists in the hub first + if not check_model_exists_on_hub(registry): + raise ValueError( + f"Model {registry} does not exist in the model-hub. Please check the registry at {MODEL_HUB_URL}." + ) + + # download model + download_remote_model(registry, MODEL_CACHE_DIR) + + # load model once downloaded + self._load_local_model(local_model_path) + + def _load_model(self): + """ + Load the model from the path_to_model attribute. + + Order of operations: + 1. look for local model - this should be a path to a model.yaml + 2. look for model in cache + 3. download model from big.databio.org + """ + # first just check if this is a local model + if os.path.exists(self.path_to_model): + self._load_local_model(self.path_to_model) + # else, assume its on the model-hub + else: + self._load_remote_model(self.path_to_model) + + def convert_to_universe(self, adata: sc.AnnData) -> sc.AnnData: + """ + Converts the conesnsus peak set (.var) attributes of the AnnData object + to a universe representation. This is done through interval overlap + analysis. + """ + # ensure adata has chr, start, and end + if not all([x in adata.var.columns for x in ["chr", "start", "end"]]): + raise ValueError( + "AnnData object must have `chr`, `start`, and `end` columns in .var" + ) + + # create list of regions from adata + query_set: List[str] = adata.var.apply( + lambda x: f"{x['chr']}_{x['start']}_{x['end']}", axis=1 + ).tolist() + universe_set = self.universe.regions + + # generate conversion map + _map = generate_var_conversion_map(query_set, universe_set) + + # create a new DataFrame with the updated values + updated_var = adata.var.copy() + + for i, row in tqdm(adata.var.iterrows(), total=adata.var.shape[0]): + region = f"{row['chr']}_{row['start']}_{row['end']}" + if region not in _map: + continue + + # if it is, change the region to the universe region, + # grab the first for now + universe_region = _map[region][0] + chr, start, end = universe_region.split("_") + + updated_var.at[i, "chr"] = chr + updated_var.at[i, "start"] = start + updated_var.at[i, "end"] = end + + # create a boolean mask of columns to keep + columns_to_keep = np.array( + [ + region in _map + for region in updated_var.apply( + lambda x: f"{x['chr']}_{x['start']}_{x['end']}", axis=1 + ) + ] + ) + + # update adata with the new DataFrame and filtered columns + adata = adata[:, columns_to_keep] + adata.var = updated_var[columns_to_keep] diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index e37b10d2..1ad78729 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -12,14 +12,9 @@ from .const import * from .exceptions import * -from .utils import ( - LearningRateScheduler, - ScheduleType, - convert_anndata_to_documents, - remove_regions_below_min_count, - shuffle_documents, -) - +from .utils import (LearningRateScheduler, ScheduleType, + convert_anndata_to_documents, + remove_regions_below_min_count, shuffle_documents) _GENSIM_LOGGER = getLogger("gensim") _LOGGER = getLogger(MODULE_NAME) diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index b4216f35..55ece56b 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -1,12 +1,16 @@ -from enum import Enum +import os +import subprocess +import shutil from collections import Counter +from concurrent.futures import ThreadPoolExecutor +from enum import Enum from logging import getLogger from random import shuffle -from concurrent.futures import ThreadPoolExecutor -from typing import Union, List, Dict, TYPE_CHECKING +from typing import TYPE_CHECKING, Dict, List, Union import pandas as pd import scanpy as sc +import pybedtools from tqdm import tqdm if TYPE_CHECKING: @@ -264,3 +268,128 @@ def load_scembed_model(path: str) -> "SCEmbed": with open(path, "rb") as f: return pickle.load(f) + + +def check_model_exists_on_hub(registry: str) -> bool: + """ + Check the model hub for the existing model registry. Registry + is of the name /. + + Looks for the existence of model.yaml at {MODEL_HUB_URL}/{registry_name}/model.yaml + """ + # check if model exists in the hub + url = f"{MODEL_HUB_URL}/{registry}/model.yaml" + cmd = f"curl -s -o /dev/null -w '%{{http_code}}' {url}" + result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE) + return result.stdout.decode("utf-8") == "200" + + +def download_remote_model(registry: str, path: str, overwrite: bool = True) -> None: + """ + Download a model from the model hub. Overwrites any existing. + + :param str registry: the registry name of the model to download + :param str path: the path to download the model to, this is the folder that will contain registry/model.yaml + """ + if os.path.exists(path) and overwrite: + _LOGGER.debug("Removing existing model.") + shutil.rmtree(path) + + cmd = f"wget -r -np -nH --cut-dirs=2 -P {path} {MODEL_HUB_URL}/{registry}" + result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE) + if result.returncode != 0: + raise ValueError("Could not download model from model-hub.") + + +def load_universe_file(path: str) -> List[str]: + """ + Loads a bed file into a list of regions. Assumes + the bed file is of the form chr start end (tab-separated). + """ + with open(path, "r") as f: + regions = [line.strip().split("\t") for line in f.readlines()] + regions = [f"{r[0]}_{r[1]}_{r[2]}" for r in regions] + return regions + + +def generate_var_conversion_map( + a: List[str], b: List[str], path_to_bedtools: str = None, fraction: float = 1.0e-9 +) -> Dict[str, List[str]]: + """ + Find the regions that overlap between two lists of regions using bedtools. for each region in + a, if it overlaps a region in b, report region from a and the region from b that it overlapped. + + i.e. the result of the bedtools operation is a bed file with 6 columns, + chr_a\tstart_a\tend_a\tchr_b\tstart_b\tend_b. The first three columns are the region from a, the last three + columns are the region from b. + + Where chr_start_end (a) -- Overlaps --> chr_start_end (b) + + This occurs in three steps: + 1. write both a and b to a temporary file, + 2. use bedtools to find the overlaps and dump to a temporary file, + 3. read in the temporary file and parse the results. + + The function returns a dictionary of the form: + + Dict[str, List[str]] + + Where a_region and b_region are strings of the form chr_start_end and any and all regions + from a overlap any and all regions from b. + + :param List[str] a: the first list of regions + :param List[str] b: the second list of regions + :param str path_to_bedtools: the path to the bedtools executable + :param float fraction: the fraction of the region that must overlap to be considered an overlap + """ + # write a and b to temp files in cache + a_file = os.path.join(MODEL_CACHE_DIR, "a.bed") + b_file = os.path.join(MODEL_CACHE_DIR, "b.bed") + with open(a_file, "w") as f: + # split each region into chr start end + a = [region.split("_") for region in a] + a = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in a] + f.writelines(a) + + with open(b_file, "w") as f: + b = [region.split("_") for region in b] + b = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in b] + f.writelines(b) + + # run bedtools + if path_to_bedtools is None: + path_to_bedtools = DEFAULT_BEDTOOLS_PATH + + overlaps_file = os.path.join(MODEL_CACHE_DIR, "ab_tokens" + ".bed") + + with open(overlaps_file, "w") as f_target: + cmd = f"{path_to_bedtools} intersect -a {a_file} -b {b_file} -wa -wb -f {fraction}" + subprocess.run(cmd, shell=True, stdout=f_target) + + # read in the and create a dictionary for mapping, + # each key is a region from a, each value is a list of regions from b that it overlaps + olaps = {} + with open(overlaps_file, "r") as f: + # create list of tuples + for line in f.readlines(): + line = line.strip() + # region_a + region_a = line.split("\t")[:3] + region_a = "_".join(region_a) + + # region_b + region_b = line.split("\t")[3:6] + region_b = "_".join(region_b) + + # add to dictionary + if region_a not in olaps: + olaps[region_a] = [region_b] + else: + olaps[region_a].append(region_b) + + # remove temp files + os.remove(a_file) + os.remove(b_file) + os.remove(overlaps_file) + + return olaps diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index 9b7ba6b1..2f675173 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -11,3 +11,4 @@ seaborn tqdm umap-learn ubiquerg +yacman \ No newline at end of file diff --git a/tests/integration/scembed_train_in_chunks.py b/tests/integration/scembed_train_in_chunks.py index 98781451..38a81a04 100644 --- a/tests/integration/scembed_train_in_chunks.py +++ b/tests/integration/scembed_train_in_chunks.py @@ -1,7 +1,9 @@ # %% # import libraries import logging + import scanpy as sc + from gitk import scembed # set to DEBUG to see more info diff --git a/tests/test_projector.py b/tests/test_projector.py new file mode 100644 index 00000000..7d822ae7 --- /dev/null +++ b/tests/test_projector.py @@ -0,0 +1,101 @@ +import logging +import os +import sys + +import pytest + +# add parent directory to path +sys.path.append("../") + +from gitk import scembed + + +def test_model_exists(): + """ + Test that the model exists on the model-hub. + """ + exists = scembed.utils.check_model_exists_on_hub("databio/scatlas") + assert exists + + +def test_model_doesnt_exist(): + """ + Test that the model exists on the model-hub. + """ + exists = scembed.utils.check_model_exists_on_hub("databio/scatlas_doesnt_exist") + assert not exists + + +@pytest.mark.skip +def test_download_model(): + registry = "databio/scatlas" + scembed.utils.download_remote_model(registry, scembed.const.MODEL_CACHE_DIR) + config_file = os.path.join( + scembed.const.MODEL_CACHE_DIR, registry, scembed.const.MODEL_CONFIG_FILE_NAME + ) + assert os.path.exists(config_file) + + +@pytest.mark.skip +def test_load_local_model(): + registry = "databio/scatlas" + config_file = os.path.join( + scembed.const.MODEL_CACHE_DIR, registry, scembed.const.MODEL_CONFIG_FILE_NAME + ) + projector = scembed.Projector(config_file) + assert isinstance(projector.model_config, scembed.models.ModelCard) + assert isinstance(projector.model, dict) + assert isinstance(projector.universe, scembed.models.Universe) + + +@pytest.mark.skip +def test_init_projector(): + registry = "databio/scatlas" + projector = scembed.Projector(registry) + assert isinstance(projector.model_config, scembed.models.ModelCard) + assert isinstance(projector.model, dict) + assert isinstance(projector.universe, scembed.models.Universe) + + +@pytest.mark.skip +def test_tokenization(): + """ + Tokenize the top into the bottom + + chr1: 1000 2000 3000 4000 5000 6000 + A |--------| |--------| |--------| + B |--------| |--------| |----| |--------| + + chr1: 1000 2000 3000 4000 5000 6000 + A |--------| |--------| |--------| + C |--------| |---------------| + + """ + regions_a = [ + "chr1_1000_2000", + "chr1_3000_4000", + "chr1_5000_6000", + ] + regions_b = [ + "chr1_1500_2500", + "chr1_3500_4500", + "chr1_4800_5050", + "chr1_5500_6050", + ] + regions_c = ["chr1_1500_2500", "chr1_3500_5500"] + + var_map_ab = scembed.utils.generate_var_conversion_map(regions_a, regions_b) + var_map_ac = scembed.utils.generate_var_conversion_map(regions_a, regions_c) + assert len(var_map_ab) == 3 + assert len(var_map_ac) == 3 + + +def test_convert_to_universe(): + registry = "databio/scatlas" + projector = scembed.Projector(registry) + + path_to_data = "/Users/nathanleroy/uva/lab/code/scEmbed-benchmarking/input/buenrostro2018/buenrostro2018_annotated.h5ad" + adata = scembed.load_scanpy_data(path_to_data) + + # convert to universe + adata_converted = projector.convert_to_universe(adata) diff --git a/tests/test_scembed.py b/tests/test_scembed.py index 21fc2706..5d3292fc 100644 --- a/tests/test_scembed.py +++ b/tests/test_scembed.py @@ -1,15 +1,15 @@ +import logging import os import sys +from itertools import chain + import pytest import scanpy as sc -import logging -from itertools import chain # add parent directory to path sys.path.append("../") -from gitk import scembed -from gitk import utils +from gitk import scembed, utils # set to DEBUG to see more info logging.basicConfig(level=logging.INFO) diff --git a/tests/test_utils.py b/tests/test_utils.py index 9b270faf..af2c1469 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,9 +1,10 @@ +import logging import os import sys +from itertools import chain + import pytest import scanpy as sc -import logging -from itertools import chain # add parent directory to path sys.path.append("../") From 40ad3acc7b261354f29962b5b171f60715933e96 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Tue, 16 May 2023 20:37:56 -0400 Subject: [PATCH 0153/1072] update tests --- tests/test_projector.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_projector.py b/tests/test_projector.py index 7d822ae7..447f4077 100644 --- a/tests/test_projector.py +++ b/tests/test_projector.py @@ -90,6 +90,7 @@ def test_tokenization(): assert len(var_map_ac) == 3 +@pytest.mark.skip def test_convert_to_universe(): registry = "databio/scatlas" projector = scembed.Projector(registry) From e6c2afb901452658c765e825c9e671a03bf99ac1 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 17 May 2023 10:01:57 -0400 Subject: [PATCH 0154/1072] finish projector unit tests --- gitk/scembed/projection.py | 56 ++++++++++++++++++++++++++------ gitk/scembed/utils.py | 32 ++++++++++++++++++- tests/test_projector.py | 65 +++++++++++++++++++++++++++++--------- 3 files changed, 128 insertions(+), 25 deletions(-) diff --git a/gitk/scembed/projection.py b/gitk/scembed/projection.py index b73a467c..7feb5036 100644 --- a/gitk/scembed/projection.py +++ b/gitk/scembed/projection.py @@ -14,6 +14,7 @@ download_remote_model, load_universe_file, generate_var_conversion_map, + anndata_to_regionsets, ) from .models import ModelCard, Universe @@ -143,13 +144,18 @@ def convert_to_universe(self, adata: sc.AnnData) -> sc.AnnData: # create a new DataFrame with the updated values updated_var = adata.var.copy() + # find the regions that overlap with the universe + # use dynamic programming to create a boolean mask of columns to keep + columns_to_keep = [] for i, row in tqdm(adata.var.iterrows(), total=adata.var.shape[0]): region = f"{row['chr']}_{row['start']}_{row['end']}" if region not in _map: + columns_to_keep.append(False) continue # if it is, change the region to the universe region, # grab the first for now + # TODO - this is a simplification, we should be able to handle multiple universe_region = _map[region][0] chr, start, end = universe_region.split("_") @@ -157,16 +163,48 @@ def convert_to_universe(self, adata: sc.AnnData) -> sc.AnnData: updated_var.at[i, "start"] = start updated_var.at[i, "end"] = end - # create a boolean mask of columns to keep - columns_to_keep = np.array( - [ - region in _map - for region in updated_var.apply( - lambda x: f"{x['chr']}_{x['start']}_{x['end']}", axis=1 - ) - ] - ) + columns_to_keep.append(True) # update adata with the new DataFrame and filtered columns adata = adata[:, columns_to_keep] adata.var = updated_var[columns_to_keep] + + return adata + + def get_embedding(self, region: str) -> np.ndarray: + """ + Get the embedding for a region. + """ + return self.model[region] + + def project(self, adata: sc.AnnData) -> sc.AnnData: + """ + Project the AnnData object into the model space. This is done in two steps: + + 1. Convert the consensus peaks to a universe representation + 2. Project the universe representation into the model space using the + model. + + """ + adata_converted = self.convert_to_universe(adata) + + # convert each row to a region set + region_sets = anndata_to_regionsets(adata_converted) + + # convert each region set to a vector by averaging the region + # vectors in the model + cell_embeddings = [] + for region_set in tqdm(region_sets, total=len(region_sets)): + cell_embeddings.append( + np.mean( + [ + self.get_embedding(region) + for region in region_set + if region in self.model # ignore regions not in the model + ], + axis=0, + ) + ) + + adata.obs["embedding"] = cell_embeddings + return adata diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 55ece56b..ee2d603b 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -10,7 +10,7 @@ import pandas as pd import scanpy as sc -import pybedtools +import numpy as np from tqdm import tqdm if TYPE_CHECKING: @@ -393,3 +393,33 @@ def generate_var_conversion_map( os.remove(overlaps_file) return olaps + + +# This method should only be used in the Projector. It +# assmes that the AnnData.var attribute has chr, start, and end +def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: + """ + Converts an AnnData object to a list of lists of regions. This + is done by taking each cell and creating a list of all regions + that have a value greater than 0. + + *Note: this method requires that the sc.AnnData object have + chr, start, and end in `.var` attributes* + """ + # Extract the arrays for chr, start, and end + chr_values = adata.var["chr"].values + start_values = adata.var["start"].values + end_values = adata.var["end"].values + + # Perform the comparison using numpy operations + positive_values = adata.X > 0 + + regions = [] + for i in tqdm(range(adata.shape[0]), total=adata.shape[0]): + regions.append( + [ + f"{chr_values[j]}_{start_values[j]}_{end_values[j]}" + for j in np.where(positive_values[i])[0] + ] + ) + return regions diff --git a/tests/test_projector.py b/tests/test_projector.py index 447f4077..0b433747 100644 --- a/tests/test_projector.py +++ b/tests/test_projector.py @@ -1,8 +1,9 @@ -import logging import os import sys import pytest +import scanpy as sc +import numpy as np # add parent directory to path sys.path.append("../") @@ -10,6 +11,16 @@ from gitk import scembed +@pytest.fixture +def adata(): + return sc.read_h5ad("tests/data/buenrostro.h5ad") + + +@pytest.fixture +def projector(): + return scembed.Projector("databio/scatlas") + + def test_model_exists(): """ Test that the model exists on the model-hub. @@ -26,7 +37,7 @@ def test_model_doesnt_exist(): assert not exists -@pytest.mark.skip +@pytest.mark.skip # projector is too large to test in CI def test_download_model(): registry = "databio/scatlas" scembed.utils.download_remote_model(registry, scembed.const.MODEL_CACHE_DIR) @@ -36,7 +47,7 @@ def test_download_model(): assert os.path.exists(config_file) -@pytest.mark.skip +@pytest.mark.skip # projector is too large to test in CI def test_load_local_model(): registry = "databio/scatlas" config_file = os.path.join( @@ -48,16 +59,13 @@ def test_load_local_model(): assert isinstance(projector.universe, scembed.models.Universe) -@pytest.mark.skip -def test_init_projector(): - registry = "databio/scatlas" - projector = scembed.Projector(registry) +# @pytest.mark.skip # projector is too large to test in CI +def test_init_projector(projector): assert isinstance(projector.model_config, scembed.models.ModelCard) assert isinstance(projector.model, dict) assert isinstance(projector.universe, scembed.models.Universe) -@pytest.mark.skip def test_tokenization(): """ Tokenize the top into the bottom @@ -91,12 +99,39 @@ def test_tokenization(): @pytest.mark.skip -def test_convert_to_universe(): - registry = "databio/scatlas" - projector = scembed.Projector(registry) - - path_to_data = "/Users/nathanleroy/uva/lab/code/scEmbed-benchmarking/input/buenrostro2018/buenrostro2018_annotated.h5ad" - adata = scembed.load_scanpy_data(path_to_data) - +def test_convert_to_universe(projector, adata): # convert to universe adata_converted = projector.convert_to_universe(adata) + + # ensure all chr_start_end in the new adata are in the universe + def check_region_in_universe(r): + assert f"{r['chr']}_{r['start'] }_{r['end']}" in projector.universe.regions + + adata_converted.var.apply( + lambda r: check_region_in_universe(r), + axis=1, + ) + + +def test_anndata_to_regionsets(adata): + region_sets = scembed.utils.anndata_to_regionsets(adata) + + # assert the sum of the clipped row in the matrix equals + # the number of regions in the region set + x_clipped = adata.X.clip(max=1) + for i in range(adata.shape[0]): + assert len(region_sets[i]) == int(x_clipped[i, :].sum()) + + +@pytest.mark.skip # projector is too large to test in CI +def test_projection(projector, adata): + adata_projected = projector.project(adata) + + # assert the embeddings are there and the shape is correct + assert "embedding" in adata_projected.obs + + embeddings = np.array(adata_projected.obs["embedding"].to_numpy().tolist()) + assert embeddings.shape == ( + adata.shape[0], + projector.model_config.model_parameters[0].embedding_dim, + ) From 724aa6a274aa5c86f9b42eafc1d085a9f068bf3c Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 17 May 2023 11:35:19 -0400 Subject: [PATCH 0155/1072] underscore to hyphen --- gitk/assess/cli.py | 8 ++++---- gitk/hmm/cli.py | 8 ++++---- gitk/likelihood/cli.py | 18 +++++++++--------- 3 files changed, 17 insertions(+), 17 deletions(-) diff --git a/gitk/assess/cli.py b/gitk/assess/cli.py index 6b3ee7db..a746fb28 100644 --- a/gitk/assess/cli.py +++ b/gitk/assess/cli.py @@ -5,11 +5,11 @@ def build_subparser(parser): :return argparse.ArgumentParser """ - parser.add_argument("--raw_data_folder", type=str, required=True) - parser.add_argument("--file_list", type=str, required=True) + parser.add_argument("--raw-data-folder", type=str, required=True) + parser.add_argument("--file-list", type=str, required=True) parser.add_argument("--universe", type=str, required=True) parser.add_argument("--npool", default=4, type=int) - parser.add_argument("--save_to_file", action="store_true") + parser.add_argument("--save-to-file", action="store_true") parser.add_argument("--folder_out", type=str) parser.add_argument("--pref", type=str) @@ -19,7 +19,7 @@ def build_subparser(parser): def build_subparser_distance(parser): parser = build_subparser(parser) parser.add_argument("--flexible", action="store_true") - parser.add_argument("--save_each", action="store_true") + parser.add_argument("--save-each", action="store_true") return parser diff --git a/gitk/hmm/cli.py b/gitk/hmm/cli.py index cc1b2a2f..ff7fedb9 100644 --- a/gitk/hmm/cli.py +++ b/gitk/hmm/cli.py @@ -6,20 +6,20 @@ def build_subparser(parser): """ parser.add_argument( - "--out_file", type=str, help="path to result file", required=True + "--out-file", type=str, help="path to result file", required=True ) parser.add_argument( - "--cov_folder", type=str, help="path to coverage folder", required=True + "--cov-folder", type=str, help="path to coverage folder", required=True ) parser.add_argument("--normalize", action="store_true") parser.add_argument( - "--save_max_cove", + "--save-max-cove", help="if present saves maximum coverage for each peak", action="store_true", ) parser.add_argument( "--lambdas", type=str, help="lambdas matrix used to set emissions" ) - parser.add_argument("--coverage_prefix", default="all", type=str) + parser.add_argument("--coverage-prefix", default="all", type=str) return parser diff --git a/gitk/likelihood/cli.py b/gitk/likelihood/cli.py index 577da807..672cb69d 100644 --- a/gitk/likelihood/cli.py +++ b/gitk/likelihood/cli.py @@ -5,27 +5,27 @@ def build_subparser_model(parser): :return argparse.ArgumentParser """ - parser.add_argument("--model_folder", required=True, type=str) - parser.add_argument("--file_no", type=int) - parser.add_argument("--coverage_folder", required=True, type=str) - parser.add_argument("--coverage_prefix", default="all", type=str) + parser.add_argument("--model-folder", required=True, type=str) + parser.add_argument("--file-no", type=int) + parser.add_argument("--coverage-folder", required=True, type=str) + parser.add_argument("--coverage-prefix", default="all", type=str) return parser def build_subparser_universe_hard(parser): parser.add_argument("--merge", default=0, type=int) - parser.add_argument("--filter_size", default=0, type=int) + parser.add_argument("--filter-size", default=0, type=int) parser.add_argument("--fout", required=True, type=str) - parser.add_argument("--coverage_file", required=True, type=str) - parser.add_argument("--cut_off", type=int) + parser.add_argument("--coverage-file", required=True, type=str) + parser.add_argument("--cut-off", type=int) return parser def build_subparser_universe_flexible(parser): - parser.add_argument("--output_file", required=True, type=str) - parser.add_argument("--model_folder", required=True, type=str) + parser.add_argument("--output-file", required=True, type=str) + parser.add_argument("--model-folder", required=True, type=str) return parser From 0a0e5feafaa22de91602f2ea63586dcce6e7d738 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 17 May 2023 11:57:15 -0400 Subject: [PATCH 0156/1072] improved function names --- gitk/assess/intersection.py | 44 +++++++++++++++++++------------------ 1 file changed, 23 insertions(+), 21 deletions(-) diff --git a/gitk/assess/intersection.py b/gitk/assess/intersection.py index e56c1a09..382567e0 100644 --- a/gitk/assess/intersection.py +++ b/gitk/assess/intersection.py @@ -11,13 +11,6 @@ def chrom_cmp(a, b): """Natural chromosome names comparison""" - # com = natural_chr_sort(a, b) - # if com < 0: - # return a, False, True - # if com == 0: - # return a, False, False - # if com > 0: - # return b, True, False ac = a.replace("chr", "") ac = ac.split("_")[0] bc = b.replace("chr", "") @@ -34,9 +27,14 @@ def chrom_cmp(a, b): return a, False, True -def relationship_helper(region_a, region_b, only_in, overlap, start_a, start_b): +def relationship_helper(region_a, region_b, only_in, overlap): """For two region calculate their overlap; for earlier region - calculate how many base pair only in it""" + calculate how many base pair only in it + :param [int, int] region_a: region that starts first + :param [int, int] region_b: region that starts second + :param int only_in: number of positions only in a so far + :param int overlap: number of overlapping so far + :return:""" if region_b[0] <= region_a[1]: only_in += region_b[0] - region_a[0] if region_b[1] <= region_a[1]: @@ -54,7 +52,7 @@ def relationship_helper(region_a, region_b, only_in, overlap, start_a, start_b): return only_in, inside_a, inside_b, overlap, start_a, start_b -def relationship( +def two_region_intersection_diff( region_d, region_q, only_in_d, @@ -91,14 +89,10 @@ def relationship( inside_q, inside_d = False, True else: if region_d[0] <= region_q[0]: - res = relationship_helper( - region_d, region_q, only_in_d, overlap, start_d, start_q - ) + res = relationship_helper(region_d, region_q, only_in_d, overlap) (only_in_d, inside_d, inside_q, overlap, start_d, start_q) = res if region_d[0] > region_q[0]: - res = relationship_helper( - region_q, region_d, only_in_q, overlap, start_q, start_d - ) + res = relationship_helper(region_q, region_d, only_in_q, overlap) (only_in_q, inside_q, inside_d, overlap, start_q, start_d) = res return only_in_d, only_in_q, inside_d, inside_q, overlap, start_d, start_q @@ -120,7 +114,7 @@ def read_in_new_line(region, start, chrom, inside, waiting, lines, cchrom, not_e return region, start, chrom, waiting, not_e -def calc_stats(db, folder, query): +def calc_diff_intersection(db, folder, query): """ Difference and overlap of two files on base pair level :param str db: path to universe file @@ -148,7 +142,7 @@ def calc_stats(db, folder, query): else: c_chrom, waiting_d, waiting_q = chrom_cmp(chrom_d, chrom_q) while not_end_d or not_end_q: - res = relationship( + regions_stats = two_region_intersection_diff( [start_d, pos_d[1]], [start_q, pos_q[1]], only_in_d, @@ -161,7 +155,15 @@ def calc_stats(db, folder, query): waiting_d, waiting_q, ) - (only_in_d, only_in_q, inside_d, inside_q, overlap, start_d, start_q) = res + ( + only_in_d, + only_in_q, + inside_d, + inside_q, + overlap, + start_d, + start_q, + ) = regions_stats new_d = read_in_new_line( pos_d, start_d, chrom_d, inside_d, waiting_d, lines_db, c_chrom, not_end_d ) @@ -212,12 +214,12 @@ def run_intersection( res = [] if npool <= 1: for i in files: - r = calc_stats(universe, folder, i) + r = calc_diff_intersection(universe, folder, i) res.append(r) else: with Pool(npool) as p: args = [(universe, folder, f) for f in files] - res = p.starmap(calc_stats, args) + res = p.starmap(calc_diff_intersection, args) # os.rmdir("tmp") if save_to_file: fout = os.path.join(folder_out, pref + "_data.tsv") From 73c4e67e2eea1db3ad0dad047d2dcb8703d564ba Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 17 May 2023 12:27:46 -0400 Subject: [PATCH 0157/1072] code clean up --- gitk/assess/distance.py | 2 +- gitk/hmm/const.py | 8 +++++ gitk/hmm/hmm.py | 13 ++------ gitk/hmm/models.py | 55 ++++++++++++---------------------- gitk/likelihood/build_model.py | 5 ---- 5 files changed, 30 insertions(+), 53 deletions(-) create mode 100644 gitk/hmm/const.py diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index d0e4071b..73422b65 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -204,7 +204,7 @@ def calc_distance( flexible, ) (waiting_end, current_chrom_end) = res_end - tmp_file.close() + q.close() if save_each: with open(os.path.join(folder_out, pref, q_file), "w") as f: for i, j in zip(dist_start, dist_end): diff --git a/gitk/hmm/const.py b/gitk/hmm/const.py new file mode 100644 index 00000000..0b2b6e96 --- /dev/null +++ b/gitk/hmm/const.py @@ -0,0 +1,8 @@ +TRANSMAT = [ + [1 - 1e-10, 1e-10, 0, 0], + [0, 1 - 1e-6, 1e-6, 0], + [0, 0, 1 - 1e-6, 1e-6], + [0.1, 0, 0, 0.9], +] + +LAMBDAS = [[3, 1, 0.0001], [0.05, 2, 0.05], [0.0001, 1, 3], [1e-4, 1e-3, 1e-4]] diff --git a/gitk/hmm/hmm.py b/gitk/hmm/hmm.py index ed9e5dfc..11774e88 100644 --- a/gitk/hmm/hmm.py +++ b/gitk/hmm/hmm.py @@ -1,6 +1,7 @@ import numpy as np import os from .models import PoissonModel +from .const import TRANSMAT, LAMBDAS import pyBigWig from scipy.stats import nbinom from functools import cmp_to_key @@ -17,15 +18,6 @@ 2 -> end 3 -> background""" -transmat = [ - [1 - 1e-10, 1e-10, 0, 0], - [0, 1 - 1e-6, 1e-6, 0], - [0, 0, 1 - 1e-6, 1e-6], - [0.1, 0, 0, 0.9], -] - -lambdas = [[3, 1, 0.0001], [0.05, 2, 0.05], [0.0001, 1, 3], [1e-4, 1e-3, 1e-4]] - def norm(track, mode): """Normalize the coverage track depending on track type. @@ -233,8 +225,7 @@ def run_hmm(start, core, end, chrom, normalize=False): """Make HMM prediction for given chromosome""" chrom_size, seq = read_data(start, core, end, chrom, normalize=normalize) empty_starts, empty_ends = find_full(seq) - model = PoissonModel(transmat, lambdas, save_matrix=False) - model = model.make() + model = PoissonModel(TRANSMAT, LAMBDAS, save_matrix=False).model() hmm_predictions = split_predict(seq, empty_starts, empty_ends, model) return hmm_predictions, model diff --git a/gitk/hmm/models.py b/gitk/hmm/models.py index 596b7f53..6a7439b6 100644 --- a/gitk/hmm/models.py +++ b/gitk/hmm/models.py @@ -26,11 +26,6 @@ def __init__(self, trans_matrix, init_para, para, save_matrix): self.trans_matrix = trans_matrix self.start_matrix = np.array([0.01, 0.01, 0.97, 0.01]) - @abstractmethod - def make(self): - """Abstract method for defining the model""" - pass - def save_tras(self, out_folder): np.savetxt(os.path.join(out_folder, "trans_matrix.csv"), self.trans_matrix) @@ -66,9 +61,7 @@ def __init__( os.path.join(out_folder, "lambdas_matrix.csv"), self.lambdas_matrix ) - def make(self): - """Initialize HMM model""" - model = hmm.PoissonHMM( + self.model = hmm.PoissonHMM( n_components=self.state_no, verbose=True, init_params=self.init_para, @@ -76,11 +69,10 @@ def make(self): ) if "t" not in self.init_para: - model.transmat_ = self.trans_matrix + self.model.transmat_ = self.trans_matrix if "l" not in self.init_para: - model.lambdas_ = self.lambdas_matrix - model.startprob_ = self.start_matrix - return model + self.model.lambdas_ = self.lambdas_matrix + self.model.startprob_ = self.start_matrix class GaussianModel(Model): @@ -116,9 +108,7 @@ def __init__( ) np.savetxt(os.path.join(out_folder, "means_matrix.csv"), self.means_matrix) - def make(self): - """Initialize HMM model""" - model = hmm.GaussianHMM( + self.model = hmm.GaussianHMM( n_components=self.state_no, verbose=True, init_params=self.init_para, @@ -126,13 +116,12 @@ def make(self): ) if "t" not in self.init_para: - model.transmat_ = self.trans_matrix + self.model.transmat_ = self.trans_matrix if "m" not in self.init_para: - model.means_ = self.means_matrix + self.model.means_ = self.means_matrix if "c" not in self.init_para: - model.covars_ = self.covars_matrix - model.startprob_ = self.start_matrix - return model + self.model.covars_ = self.covars_matrix + self.model.startprob_ = self.start_matrix class NBModel(Model): @@ -168,20 +157,17 @@ def __init__( ) np.savetxt(os.path.join(out_folder, "prob_matrix.csv"), self.prob_matrix) - def make(self): - """Initialize HMM model""" - model = NBHMM( + self.model = NBHMM( n_components=self.state_no, verbose=True, init_params=self.init_para, params=self.para, ) - model.transmat_ = self.trans_matrix - model.failures_ = self.failures_matrix - model.prob_ = self.prob_matrix - model.startprob_ = self.start_matrix - return model + self.model.transmat_ = self.trans_matrix + self.model.failures_ = self.failures_matrix + self.model.prob_ = self.prob_matrix + self.model.startprob_ = self.start_matrix class BetaModel(Model): @@ -215,17 +201,14 @@ def __init__( np.savetxt(os.path.join(out_folder, "alfa_matrix.csv"), self.alfa_matrix) np.savetxt(os.path.join(out_folder, "beta_matrix.csv"), self.beta_matrix) - def make(self): - """Initialize HMM model""" - model = BetaHMM( + self.model = BetaHMM( n_components=self.state_no, verbose=True, init_params=self.init_para, params=self.para, ) - model.transmat_ = self.trans_matrix - model.alfa_ = self.alfa_matrix - model.beta_ = self.beta_matrix - model.startprob_ = self.start_matrix - return model + self.model.transmat_ = self.trans_matrix + self.model.alfa_ = self.alfa_matrix + self.model.beta_ = self.beta_matrix + self.model.startprob_ = self.start_matrix diff --git a/gitk/likelihood/build_model.py b/gitk/likelihood/build_model.py index c20e0089..313f80a6 100644 --- a/gitk/likelihood/build_model.py +++ b/gitk/likelihood/build_model.py @@ -8,9 +8,6 @@ import tarfile import tempfile -WINDOW_SIZE = 25 -WRONG_UNIWIG = False - def model_binomial(folder_in, in_file, chrom, file_out, file_no=None, start=0): """ "Create binomial likelihood model @@ -25,8 +22,6 @@ def model_binomial(folder_in, in_file, chrom, file_out, file_no=None, start=0): distr_cov = bw.values(chrom, start, chrom_size) distr_cov = np.array(distr_cov) distr_cov[np.isnan(distr_cov)] = 0 - if WRONG_UNIWIG and ("cove" not in in_file): - distr_cov = np.pad(distr_cov[WINDOW_SIZE:], (0, WINDOW_SIZE)) no_possible = file_no * len(distr_cov) # number of possible spots covered no_cov = np.sum(distr_cov) # number of spots covered no_ncov = np.subtract(no_possible, no_cov) # number of spots uncovered From 06fe3f875fad3948112b12e30fbad250929f206b Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 17 May 2023 12:29:36 -0400 Subject: [PATCH 0158/1072] import sorted --- gitk/assess/distance.py | 12 ++- gitk/assess/intersection.py | 6 +- gitk/assess/likelihood.py | 6 +- gitk/assess/recovered.py | 151 --------------------------- gitk/assess/utils.py | 10 +- gitk/hmm/custom_distribution.py | 4 +- gitk/hmm/hmm.py | 13 +-- gitk/hmm/models.py | 8 +- gitk/likelihood/build_model.py | 8 +- gitk/likelihood/universe_flexible.py | 9 +- gitk/likelihood/universe_hard.py | 6 +- 11 files changed, 47 insertions(+), 186 deletions(-) delete mode 100644 gitk/assess/recovered.py diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index 73422b65..751a2bce 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -1,15 +1,17 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- +import argparse import os -from typing import List, Any +import tempfile +from multiprocessing import Pool +from typing import Any, List import numpy as np -import argparse -from multiprocessing import Pool -from .utils import process_db_line, chrom_cmp_bigger, prep_data, check_if_uni_sorted + from ..utils import natural_chr_sort -import tempfile +from .utils import (check_if_uni_sorted, chrom_cmp_bigger, prep_data, + process_db_line) def flexible_distance(r, q): diff --git a/gitk/assess/intersection.py b/gitk/assess/intersection.py index 382567e0..9c5e5361 100644 --- a/gitk/assess/intersection.py +++ b/gitk/assess/intersection.py @@ -1,12 +1,14 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -from .utils import process_line, prep_data, check_if_uni_sorted import os +import tempfile from multiprocessing import Pool + import numpy as np + from ..utils import natural_chr_sort -import tempfile +from .utils import check_if_uni_sorted, prep_data, process_line def chrom_cmp(a, b): diff --git a/gitk/assess/likelihood.py b/gitk/assess/likelihood.py index ec1bc754..afee0149 100644 --- a/gitk/assess/likelihood.py +++ b/gitk/assess/likelihood.py @@ -1,7 +1,9 @@ -import numpy as np import os -from .utils import check_if_uni_sorted + +import numpy as np + from ..likelihood.build_model import ModelLH +from .utils import check_if_uni_sorted def calc_likelihood_hard(universe, chroms, model_lh, name, s_index, e_index=None): diff --git a/gitk/assess/recovered.py b/gitk/assess/recovered.py deleted file mode 100644 index d2e6da67..00000000 --- a/gitk/assess/recovered.py +++ /dev/null @@ -1,151 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import os -from .utils import chrom_cmp_bigger, process_db_line, prep_data, check_if_uni_sorted -import numpy as np -from multiprocessing import Pool - - -def process_region( - start, start_region, q_chrom, start_region_chrom, db, b_index, e_index -): - """ - For given position check if it is covered by universe flexible region - :param int start: position to analyse from query peak - :param list start_region: last analysed region from universe - :param str q_chrom: chromosome of query peak - :param str start_region_chrom: chromosome of universe region - :param file db: universe file - :param int b_index: index of region beginning in line from universe - :param int e_index: index of region ending in line from universe - :return: whether peak is covered; current region from universe; current chromosome of region from universe - """ - while chrom_cmp_bigger(q_chrom, start_region_chrom): - dn = db.readline().strip("\n") - if dn == "": - break - start_region, start_region_chrom = process_db_line(dn, [b_index, e_index]) - while start > start_region[1] and q_chrom == start_region_chrom: - dn = db.readline().strip("\n") - if dn == "": - break - start_region, start_region_chrom = process_db_line(dn, [b_index, e_index]) - if start_region[0] <= start < start_region[1] and q_chrom == start_region_chrom: - return True, start_region, start_region_chrom - return False, start_region, start_region_chrom - - -def calc_no_retrieve(db_file, q_folder, q_file): - """ - Calculate percent of strats and ends covered by flexible universe for given file - :param str db_file: path to universe file - :param str q_folder: path to folder containing query files - :param str q_file: file name - :return: file name; number of peaks in file; number of peaks with start covered by universe; - percent of peaks with start covered by universe; number of peaks with end covered by universe; - percent of peaks with end covered by universe; number of peaks with at least on end covered by universe; - percent of peaks with at least on end covered by universe; number of peaks with both ends covered by universe; - percent of peaks with both ends covered by universe; - """ - prep_data(q_folder, q_file) - q = open(os.path.join("tmp", q_file + "_sorted"), "r") - db_start = open(db_file) - d_start = db_start.readline().strip("\n") - start_region, start_region_chrom = process_db_line(d_start, [1, 6]) - db_end = open(db_file) - d_end = db_end.readline().strip("\n") - end_region, end_region_chrom = process_db_line(d_end, [1, 6]) - res_start = 0 - res_end = 0 - res_or = 0 - res_and = 0 - q_len = 0 - for i in q: - q_len += 1 - i = i.split("\t") - start = int(i[1]) - end = int(i[2]) - q_chrom = i[0] - start_out = process_region( - start, start_region, q_chrom, start_region_chrom, db_start, 1, 6 - ) - (found_start, start_region, start_region_chrom) = start_out - end_out = process_region( - end, end_region, q_chrom, end_region_chrom, db_end, 7, 2 - ) - (found_end, end_region, end_region_chrom) = end_out - if found_start: - res_start += 1 - if found_end: - res_end += 1 - if found_start or found_end: - res_or += 1 - if found_start and found_end: - res_and += 1 - os.remove(os.path.join("tmp", q_file + "_sorted")) - return ( - q_file, - q_len, - res_start, - res_start / q_len * 100, - res_end, - res_end / q_len * 100, - res_or, - res_or / q_len * 100, - res_and, - res_and / q_len * 100, - ) - - -def run_recovered( - q_folder, file_list, db_file, npool, save_to_file=False, folder_out=None, pref=None -): - """ - Calculate percent of strats and ends covered by flexible universe for set of files - :param str q_folder: path to folder containing query files - :param str file_list: path to file containing list of query files - :param str db_file: path to universe file - :param int npool: number of parallel processes - :param bool save_to_file: whether to save median of calculated distances for each file - :param str folder_out: output folder - :param str pref: prefix used for saving - :return: mean of percent of strats covered by flexible universe; - mean of percent of ends covered by flexible universe; - mean of percent of regions with at least one end covered by flexible universe; - mean of percent of regions with both ends covered by flexible universe - """ - check_if_uni_sorted(db_file) - if folder_out: - os.makedirs(folder_out, exist_ok=True) - os.mkdir("tmp") - files = open(file_list).read().split("\n")[:-1] - res = [] - if npool <= 1: - for i in files: - r = calc_no_retrieve(db_file, q_folder, i) - res.append(r) - else: - with Pool(npool) as p: - args = [(db_file, q_folder, f) for f in files] - res = p.starmap(calc_no_retrieve, args) - os.rmdir("tmp") - if save_to_file: - fout = os.path.join(folder_out, pref + "_data.tsv") - with open(fout, "w") as o: - header = [ - "file", - "peak_no", - "peak_start_percent", - "peak_end_percent", - "peak_or_percent", - "peak_and_percent", - ] - o.write("\t".join(header) + "\n") - for r in res: - o.write(f"{r[0]}\t{r[1]}\t{r[3]}\t{r[5]}\t{r[7]}\t{r[9]}\n") - else: - res = np.array(res) - res = res[:, [1, 3, 5, 7, 9]] - res = res.astype("float") - return np.mean(res, axis=0) diff --git a/gitk/assess/utils.py b/gitk/assess/utils.py index 6ea8b6d6..b14e9c80 100644 --- a/gitk/assess/utils.py +++ b/gitk/assess/utils.py @@ -1,12 +1,8 @@ -from subprocess import Popen, PIPE -import subprocess -import shlex import os +import shlex +import subprocess import tempfile - - -def help(f): - f.write(b"Welcome to geeksforgeeks") +from subprocess import PIPE, Popen def prep_data(folder, file, tmp_file): diff --git a/gitk/hmm/custom_distribution.py b/gitk/hmm/custom_distribution.py index ffd9d80a..f625f864 100644 --- a/gitk/hmm/custom_distribution.py +++ b/gitk/hmm/custom_distribution.py @@ -3,9 +3,9 @@ import numpy as np -from sklearn.utils import check_random_state -from scipy.stats import nbinom, beta from hmmlearn.base import BaseHMM +from scipy.stats import beta, nbinom +from sklearn.utils import check_random_state def _check_and_set_n_features(model, X): diff --git a/gitk/hmm/hmm.py b/gitk/hmm/hmm.py index 11774e88..f4e3b186 100644 --- a/gitk/hmm/hmm.py +++ b/gitk/hmm/hmm.py @@ -1,14 +1,15 @@ -import numpy as np import os -from .models import PoissonModel -from .const import TRANSMAT, LAMBDAS +from functools import cmp_to_key +from logging import getLogger + +import numpy as np import pyBigWig from scipy.stats import nbinom -from functools import cmp_to_key -from ..utils import natural_chr_sort -from logging import getLogger from ..const import PKG_NAME +from ..utils import natural_chr_sort +from .const import LAMBDAS, TRANSMAT +from .models import PoissonModel _LOGGER = getLogger(PKG_NAME) diff --git a/gitk/hmm/models.py b/gitk/hmm/models.py index 6a7439b6..4cbedfae 100644 --- a/gitk/hmm/models.py +++ b/gitk/hmm/models.py @@ -1,12 +1,14 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -from hmmlearn import hmm -import numpy as np import os -from .custom_distribution import NBHMM, BetaHMM from abc import ABC, abstractmethod +import numpy as np +from hmmlearn import hmm + +from .custom_distribution import NBHMM, BetaHMM + class Model(ABC): """Abstract class of HMM models""" diff --git a/gitk/likelihood/build_model.py b/gitk/likelihood/build_model.py index 313f80a6..3ed75a39 100644 --- a/gitk/likelihood/build_model.py +++ b/gitk/likelihood/build_model.py @@ -1,13 +1,15 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -import numpy as np import os -from ..utils import timer_func -import pyBigWig import tarfile import tempfile +import numpy as np +import pyBigWig + +from ..utils import timer_func + def model_binomial(folder_in, in_file, chrom, file_out, file_no=None, start=0): """ "Create binomial likelihood model diff --git a/gitk/likelihood/universe_flexible.py b/gitk/likelihood/universe_flexible.py index 29953262..e7344b2d 100644 --- a/gitk/likelihood/universe_flexible.py +++ b/gitk/likelihood/universe_flexible.py @@ -1,13 +1,16 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -import numpy as np import os from functools import cmp_to_key + +import numpy as np +from numba import njit + +from ..hmm.hmm import (find_full_empty_pos, find_full_full_pos, + predictions_to_bed) from ..utils import natural_chr_sort, timer_func -from ..hmm.hmm import predictions_to_bed, find_full_full_pos, find_full_empty_pos from .build_model import ModelLH -from numba import njit @njit diff --git a/gitk/likelihood/universe_hard.py b/gitk/likelihood/universe_hard.py index 91812745..bc870824 100644 --- a/gitk/likelihood/universe_hard.py +++ b/gitk/likelihood/universe_hard.py @@ -1,11 +1,13 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -import numpy as np import os -from time import time from functools import cmp_to_key +from time import time + +import numpy as np import pyBigWig + from ..utils import natural_chr_sort, timer_func From 90fbe4ba2e730a43919f352f19cab52323a8c09d Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 17 May 2023 12:34:34 -0400 Subject: [PATCH 0159/1072] lint --- gitk/assess/distance.py | 3 +-- gitk/likelihood/universe_flexible.py | 3 +-- gitk/utils.py | 3 +++ 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index 751a2bce..bf5cc8a1 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -10,8 +10,7 @@ import numpy as np from ..utils import natural_chr_sort -from .utils import (check_if_uni_sorted, chrom_cmp_bigger, prep_data, - process_db_line) +from .utils import check_if_uni_sorted, prep_data, process_db_line def flexible_distance(r, q): diff --git a/gitk/likelihood/universe_flexible.py b/gitk/likelihood/universe_flexible.py index e7344b2d..ffdd423b 100644 --- a/gitk/likelihood/universe_flexible.py +++ b/gitk/likelihood/universe_flexible.py @@ -7,8 +7,7 @@ import numpy as np from numba import njit -from ..hmm.hmm import (find_full_empty_pos, find_full_full_pos, - predictions_to_bed) +from ..hmm.hmm import find_full_empty_pos, find_full_full_pos, predictions_to_bed from ..utils import natural_chr_sort, timer_func from .build_model import ModelLH diff --git a/gitk/utils.py b/gitk/utils.py index d695bacf..856771a0 100644 --- a/gitk/utils.py +++ b/gitk/utils.py @@ -1,5 +1,7 @@ import scanpy as sc from time import time +import numpy as np +import pyBigWig def natural_chr_sort(a, b): @@ -33,6 +35,7 @@ def wrap_func(*args, **kwargs): return wrap_func + def read_chromosome_from_bw(file, chrom): bw = pyBigWig.open(file) chrom_size = bw.chroms(chrom) From d63090ab2d10b7d332d53603324e64ed5097cdf3 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 17 May 2023 12:35:02 -0400 Subject: [PATCH 0160/1072] Examples of projection --- gitk/scembed/projection.py | 3 ++ gitk/scembed/utils.py | 42 +++++++++++++++++++ tests/integration/make_annadata.py | 19 +++++++++ tests/integration/project_with_atlas.py | 56 +++++++++++++++++++++++++ tests/test_scembed.py | 15 +++++++ 5 files changed, 135 insertions(+) create mode 100644 tests/integration/make_annadata.py create mode 100644 tests/integration/project_with_atlas.py diff --git a/gitk/scembed/projection.py b/gitk/scembed/projection.py index 7feb5036..34a28a43 100644 --- a/gitk/scembed/projection.py +++ b/gitk/scembed/projection.py @@ -186,14 +186,17 @@ def project(self, adata: sc.AnnData) -> sc.AnnData: model. """ + _LOGGER.info("Step 1/3: Converting consensus peaks to universe representation") adata_converted = self.convert_to_universe(adata) # convert each row to a region set + _LOGGER.info("Step 2/3: Converting universe representation to region sets") region_sets = anndata_to_regionsets(adata_converted) # convert each region set to a vector by averaging the region # vectors in the model cell_embeddings = [] + _LOGGER.info("Step 3/3: Projecting region sets into model space") for region_set in tqdm(region_sets, total=len(region_sets)): cell_embeddings.append( np.mean( diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index ee2d603b..5188ea53 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -423,3 +423,45 @@ def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: ] ) return regions + + +def barcode_mtx_peaks_to_anndata( + barcodes_path: str, mtx_path: str, peaks_path: str, transpose: bool = True +) -> sc.AnnData: + """ + This function will take three files: + + 1. a barcodes file (.tsv) + 2. a matrix file (.mtx) + 3. a peaks file (.bed) + + And turn them into an AnnData object. It will attach the peaks + as a .var attribute (chr, start, end) and the barcodes as a .obs attribute (index) + + :param str barcodes_path: the path to the barcodes file + :param str mtx_path: the path to the matrix file + :param str peaks_path: the path to the peaks file + :param bool transpose: whether or not to transpose the matrix + + :return sc.AnnData: an AnnData object + """ + from scipy.io import mmread + + # load the barcodes + barcodes = pd.read_csv(barcodes_path, sep="\t", header=None) + barcodes.columns = ["barcode"] + barcodes.index = barcodes["barcode"] + + # load the mtx + mtx = mmread(mtx_path).toarray() + if transpose: + mtx = mtx.T + + # load the peaks + peaks = pd.read_csv(peaks_path, sep="\t", header=None) + peaks.columns = ["chr", "start", "end"] + + # create the AnnData object + adata = sc.AnnData(X=mtx, obs=barcodes, var=peaks) + + return adata diff --git a/tests/integration/make_annadata.py b/tests/integration/make_annadata.py new file mode 100644 index 00000000..1d71ac5d --- /dev/null +++ b/tests/integration/make_annadata.py @@ -0,0 +1,19 @@ +# %% +import os +from gitk import scembed + +folder = "/Users/nathanleroy/Desktop/pbmc" + +path_to_barcodes = os.path.join(folder, "barcodes.tsv") +path_to_mtx = os.path.join(folder, "matrix.mtx") +path_to_peaks = os.path.join(folder, "peaks.bed") + + +# %% +adata = scembed.utils.barcode_mtx_peaks_to_anndata( + path_to_barcodes, path_to_mtx, path_to_peaks +) + +# %% + +adata.write_h5ad(os.path.join(folder, "pbmc.h5ad")) diff --git a/tests/integration/project_with_atlas.py b/tests/integration/project_with_atlas.py new file mode 100644 index 00000000..68c0c34b --- /dev/null +++ b/tests/integration/project_with_atlas.py @@ -0,0 +1,56 @@ +# %% +import os +import scanpy as sc +from gitk import scembed + +folder = "/Users/nathanleroy/Desktop/pbmc" + +pbmc = sc.read_h5ad(os.path.join(folder, "pbmc.h5ad")) + +# %% +projector = scembed.Projector("databio/scatlas") +pbmc = projector.project(pbmc) + + +# %% +import numpy as np +import umap + +reducer = umap.UMAP( + n_components=2, + random_state=42, +) + +embeddings = np.array(pbmc.obs["embedding"].tolist()) +umap_embedding = reducer.fit_transform(embeddings) + +# %% +# create dataframe with umap embeddings and original labels +import pandas as pd + +umap_df = pd.DataFrame(umap_embedding, columns=["umap1", "umap2"]) + +# %% +import seaborn as sns +import matplotlib.pyplot as plt + +plt.rcParams["figure.dpi"] = 200 + +_, ax = plt.subplots(figsize=(6, 6)) +sns.scatterplot( + data=umap_df, + x="umap1", + y="umap2", + palette="tab20", + linewidth=0, + s=10, + alpha=0.8, + ax=ax, +) + +# increase font size +ax.legend(fontsize=10) +ax.set_xlabel("UMAP 1", fontsize=16) +ax.set_ylabel("UMAP 2", fontsize=16) +ax.set_title("PBMCs", fontsize=20) +ax.tick_params(labelsize=14) diff --git a/tests/test_scembed.py b/tests/test_scembed.py index 5d3292fc..1d4b6c9e 100644 --- a/tests/test_scembed.py +++ b/tests/test_scembed.py @@ -157,3 +157,18 @@ def count_regions(chunk: sc.AnnData): assert isinstance(model.region2vec, dict) # assert len(model.region2vec) == total_regions # ignore for now, since we're not sure if this is correct + + +@pytest.mark.skip(reason="Uses large, local data") +def test_create_anndata_from_files(): + folder = "/Users/nathanleroy/Desktop/GSE158398_RAW" + + path_to_barcodes = os.path.join(folder, "lymphneg_barcodes.tsv") + path_to_mtx = os.path.join(folder, "lymphneg_matrix.mtx") + path_to_peaks = os.path.join(folder, "lymphneg_peaks.bed") + + adata = scembed.utils.barcode_mtx_peaks_to_anndata( + path_to_barcodes, path_to_mtx, path_to_peaks + ) + + assert isinstance(adata, sc.AnnData) From a642f338f41db1bd7289c992e0bcb00fafaa67d6 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 17 May 2023 12:39:38 -0400 Subject: [PATCH 0161/1072] lint --- gitk/scembed/cli.py | 8 ++++++-- gitk/scembed/scembed.py | 10 +++++++--- gitk/utils.py | 1 + 3 files changed, 14 insertions(+), 5 deletions(-) diff --git a/gitk/scembed/cli.py b/gitk/scembed/cli.py index 310d9272..179769ff 100644 --- a/gitk/scembed/cli.py +++ b/gitk/scembed/cli.py @@ -7,8 +7,12 @@ from ._version import __version__ from .argparser import build_argparser from .const import * -from .scembed import (convert_anndata_to_documents, load_scanpy_data, - shuffle_documents, train) +from .scembed import ( + convert_anndata_to_documents, + load_scanpy_data, + shuffle_documents, + train, +) def main(): diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index 1ad78729..99e75d1d 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -12,9 +12,13 @@ from .const import * from .exceptions import * -from .utils import (LearningRateScheduler, ScheduleType, - convert_anndata_to_documents, - remove_regions_below_min_count, shuffle_documents) +from .utils import ( + LearningRateScheduler, + ScheduleType, + convert_anndata_to_documents, + remove_regions_below_min_count, + shuffle_documents, +) _GENSIM_LOGGER = getLogger("gensim") _LOGGER = getLogger(MODULE_NAME) diff --git a/gitk/utils.py b/gitk/utils.py index d695bacf..904a467d 100644 --- a/gitk/utils.py +++ b/gitk/utils.py @@ -33,6 +33,7 @@ def wrap_func(*args, **kwargs): return wrap_func + def read_chromosome_from_bw(file, chrom): bw = pyBigWig.open(file) chrom_size = bw.chroms(chrom) From 0f375326fd92e56d0ea2d45ca4ab90d6483b753e Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 17 May 2023 12:44:34 -0400 Subject: [PATCH 0162/1072] add req --- requirements/requirements-all.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index 2f675173..bfc92d16 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -11,4 +11,5 @@ seaborn tqdm umap-learn ubiquerg -yacman \ No newline at end of file +yacman +pydantic \ No newline at end of file From e5137c4b08aa3f71623772d16808f080fb53edd0 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 17 May 2023 12:46:57 -0400 Subject: [PATCH 0163/1072] skip test with bedtools requirement --- tests/test_projector.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_projector.py b/tests/test_projector.py index 0b433747..b5e0d74a 100644 --- a/tests/test_projector.py +++ b/tests/test_projector.py @@ -66,6 +66,7 @@ def test_init_projector(projector): assert isinstance(projector.universe, scembed.models.Universe) +@pytest.mark.skip(reason="bedtools isnt installed in CI") def test_tokenization(): """ Tokenize the top into the bottom From 7dcb0ab6ae643ec2923a0a7d83c7b78c912c51bc Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 17 May 2023 17:37:14 -0400 Subject: [PATCH 0164/1072] add examples --- examples/scembed/projection.ipynb | 64 +++++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 examples/scembed/projection.ipynb diff --git a/examples/scembed/projection.ipynb b/examples/scembed/projection.ipynb new file mode 100644 index 00000000..b0800b34 --- /dev/null +++ b/examples/scembed/projection.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Installation\n", + "Make sure that you have `gitk` installed:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install gitk" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting data\n", + "The data used in this example is available from [10X genomics](https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-atac-v2-chromium-controller-2-standard)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p pbmc\n", + "!wget \"big.databio.org/scembed/data/pbmc.h5ad\" \"pbmc/pbmc.h5ad\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import scanpy as sc\n", + "from gitk import scembed\n", + "\n", + "folder = \"/Users/nathanleroy/Desktop/pbmc\"\n", + "\n", + "pbmc = sc.read_h5ad(os.path.join(folder, \"pbmc.h5ad\"))" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 8c35e615dda83df776357eac0e4f862a526c74d1 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Thu, 18 May 2023 11:44:44 -0400 Subject: [PATCH 0165/1072] new HMM model --- gitk/hmm/const.py | 2 +- gitk/hmm/hmm.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/gitk/hmm/const.py b/gitk/hmm/const.py index 0b2b6e96..34f56a79 100644 --- a/gitk/hmm/const.py +++ b/gitk/hmm/const.py @@ -5,4 +5,4 @@ [0.1, 0, 0, 0.9], ] -LAMBDAS = [[3, 1, 0.0001], [0.05, 2, 0.05], [0.0001, 1, 3], [1e-4, 1e-3, 1e-4]] +LAMBDAS = [[4, 1, 0.0001], [0.5, 3, 0.5], [0.0001, 1, 4], [1e-4, 1e-3, 1e-4]] diff --git a/gitk/hmm/hmm.py b/gitk/hmm/hmm.py index f4e3b186..3d8a4948 100644 --- a/gitk/hmm/hmm.py +++ b/gitk/hmm/hmm.py @@ -226,7 +226,7 @@ def run_hmm(start, core, end, chrom, normalize=False): """Make HMM prediction for given chromosome""" chrom_size, seq = read_data(start, core, end, chrom, normalize=normalize) empty_starts, empty_ends = find_full(seq) - model = PoissonModel(TRANSMAT, LAMBDAS, save_matrix=False).model() + model = PoissonModel(TRANSMAT, LAMBDAS, save_matrix=False).model hmm_predictions = split_predict(seq, empty_starts, empty_ends, model) return hmm_predictions, model From ae7a5b486c517729168804114993330eb5ea0945 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Thu, 18 May 2023 14:26:09 -0400 Subject: [PATCH 0166/1072] fixed typo --- gitk/cli.py | 8 ++------ gitk/likelihood/cli.py | 2 +- gitk/likelihood/universe_hard.py | 16 ++++++++-------- 3 files changed, 11 insertions(+), 15 deletions(-) diff --git a/gitk/cli.py b/gitk/cli.py index 680603d6..db81445f 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -128,11 +128,7 @@ def main(test_args=None): from .likelihood.universe_hard import main main( - args.coverage_file, - args.fout, - args.merge, - args.filter_size, - args.cut_off, + args.coverage_file, args.fout, args.merge, args.filter_size, args.cutoff ) if args.subcommand == "universe_flexible": @@ -147,7 +143,7 @@ def main(test_args=None): coverage_folder=args.cov_folder, out_file=args.out_file, prefix=args.coverage_prefix, - normalize=args.normalize, + normalize=args.not_normalize, save_max_cove=args.save_max_cove, ) diff --git a/gitk/likelihood/cli.py b/gitk/likelihood/cli.py index 672cb69d..855db13a 100644 --- a/gitk/likelihood/cli.py +++ b/gitk/likelihood/cli.py @@ -18,7 +18,7 @@ def build_subparser_universe_hard(parser): parser.add_argument("--filter-size", default=0, type=int) parser.add_argument("--fout", required=True, type=str) parser.add_argument("--coverage-file", required=True, type=str) - parser.add_argument("--cut-off", type=int) + parser.add_argument("--cutoff", type=int) return parser diff --git a/gitk/likelihood/universe_hard.py b/gitk/likelihood/universe_hard.py index bc870824..15fbee1a 100644 --- a/gitk/likelihood/universe_hard.py +++ b/gitk/likelihood/universe_hard.py @@ -11,13 +11,13 @@ from ..utils import natural_chr_sort, timer_func -def get_uni(file, chrom, cut_off=None): +def get_uni(file, chrom, cutoff=None): """For each position check if coverage is bigger than cut-off; if cut-off not provided calculate value that gives maximum likelihood universe :param str file: coverage file :param str chrom: chromosome to analyse - :param int cut_off: base pairs with values grater of equal to cut-off can be included in universe + :param int cutoff: base pairs with values grater of equal to cut-off can be included in universe :return:""" file = pyBigWig.open(file) if pyBigWig.numpy: @@ -27,9 +27,9 @@ def get_uni(file, chrom, cut_off=None): track = np.array(track) track[np.isnan(track)] = 0 track = track.astype(np.uint16) - if cut_off is None: - cut_off = np.sum(track) / len(track) - inter_pos = track >= cut_off + if cutoff is None: + cutoff = np.sum(track) / len(track) + inter_pos = track >= cutoff file.close() return inter_pos @@ -76,14 +76,14 @@ def marge_filter(fout, inter_pos, chrom, merge_dist=100, size_flt=1000): f.write(f"{chrom}\t{start}\t{end}\n") -def main(file, fout, merge=0, filter_size=0, cut_off=None): +def main(file, fout, merge=0, filter_size=0, cutoff=None): """ Creat cut-off universe based on coverage track :param str file: path to coverage file without extension :param int merge: regions closer than this value will be merged into one :param int filter_size: regions smaller than this value will not be reported :param str fout: output file - :param int cut_off: base pairs with coverage equal to or greater than this value will be included in the universe + :param int cutoff: base pairs with coverage equal to or greater than this value will be included in the universe """ if os.path.isfile(fout): raise Exception(f"File : {fout} exists") @@ -95,7 +95,7 @@ def main(file, fout, merge=0, filter_size=0, cut_off=None): chroms = {i: chroms[i] for i in chroms_key} for chrom in chroms: if chroms[chrom] > 0: - inter_pos = get_uni(file, chrom, cut_off) + inter_pos = get_uni(file, chrom, cutoff) if merge == 0 and filter_size == 0: save_simple(fout, inter_pos, chrom) else: From 7158bafa49e6682c362a8a8e99424fdf28e301a1 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Thu, 18 May 2023 14:26:30 -0400 Subject: [PATCH 0167/1072] add docs --- docs/likelihood/assess.md | 118 ++++++++++++++++++++++++++ docs/likelihood/consensus-peaks.md | 128 ++++++++++++++++++++++++----- 2 files changed, 225 insertions(+), 21 deletions(-) create mode 100644 docs/likelihood/assess.md diff --git a/docs/likelihood/assess.md b/docs/likelihood/assess.md new file mode 100644 index 00000000..b5c53c93 --- /dev/null +++ b/docs/likelihood/assess.md @@ -0,0 +1,118 @@ +# How to assess universe fit to collection of files? +Given a universe it is important to assess it fit to collection of data. +We will use here universes produced [eriler](consensus-peaks.md). + + +## Base pair - level overlap measure +First test checks how much of our file is present in the universe and how much +additional information is present in the univers. We can check that with: + +``` +gitk assess intersection --raw-data-folder tests/consesnus/raw/\ + --file-list tests/consesnus/file_list.txt \ + --universe tests/consenus/universe/universe.bed \ + --save-to-file \ + --folder-out tests/consesnus/results/intersection/ \ + --pref test \ + --no-workers 1 +``` +Where: + +- ``--raw-data-folder``, takes the path to folder with files from the collection +- ``--file-list``, takes the path to file with list of files +- ``--universe``, takes the path to file with the assessed universe +- ``--save-to-file``, is a flag that specifies whether to out put table with each row +containing file name, number of bp in univers but not in file, number of bp in file +but not the univers, and number of bp both in univers and file +- ``--folder-out``, takes the path to folder in which put the output file +- ``--pref``, takes a prefix of output file name +- ``--no-workers``, takes the number of workers that should be used + +Similarly, we can do it in python: + +``` +from gitk.assess.intersection import run_intersection + +F_10 = run_intersection("test/consesnus/raw/", + "tests/consesnus/file_list.txt", + "tests/consenus/universe/universe.bed", + no_workers=1) +``` +where we can directly return F10 score of the universe if we don't specify that we want to save metrics to the file. + +## Region boundary distance measure +Next, we can calculate the distance between region in file and the nearest region in the universe: +``` + gitk assess distance --raw-data-folder tests/consesnus/raw/\ + --file-list tests/consesnus/file_list.txt \ + --universe tests/consenus/universe/universe.bed \ + --save-to-file \ + --folder-out tests/consesnus/results/distance/ \ + --pref test-flex \ + --npool 1 \ + --save-each \ + --flexible +``` + +Where: + +- ``--raw-data-folder``, takes the path to folder with files from the collection +- ``--file-list``, takes the path to file with list of files +- ``--universe``, takes the path to file with the assessed universe +- ``--save-to-file``, is a flag that specifies whether to out put table with each row +containing file name, median of the distances +- ``--folder-out``, takes the path to folder in which put the output file +- ``--pref``, takes a prefix of output file name +- ``--no-workers``, takes the number of workers that should be used +- ``--save-each``, is a flag that specifies whether to save for each peak in the file +distance to the closest peak in the universe +- ``--flexible``, is a flag that specifies whether we should treat the univers as +a flexible one + +We can also calculate the distance between region in the universe and the nearest region in file. We use for that with the same command as before with additional flag ```--universe-to-file```. + + +Both presented distance measures can be done using python, which will result in matrix where first column is file names and the second one is median of distances. + +``` +from gitk.assess.distance import run_distance + +d_median = run_distance("tests/consesnus/raw", + "tests/consesnus/file_list.txt", + "tests/consesnus/universe/universe.bed", + npool=2) +``` +Additionally, we can directly calculate the closeness score using: + +``` +from gitk.assess.distance import get_closeness_score + +closeness_score = get_closeness_score("tests/consesnus/raw", + "tests/consesnus/file_list.txt", + "tests/consesnus/universe/universe.bed", + no_workers=2) +``` + +## Universe likelihood + +We can also calculate the likelihood of universe given collection of file. For that we +will need [likelihood model](consensus-peaks.md#making-likelihood-model-). We can do it +either for hard universe: + +``` +from gitk.assess.likelihood import hard_universe_likelihood + +lh_hard = hard_universe_likelihood("tests/consesnus/lh_model.tar", + "tests/consenus/universe/universe.bed", + "tests/consesnus/coverage", "all") +``` + +or with taking into account universe flexibility: + +``` +from gitk.assess.likelihood import likelihood_flexible_universe + +lh_flexible = likelihood_flexible_universe("tests/consesnus/lh_model.tar", + "tests/consenus/universe/universe.bed", + "tests/consesnus/coverage", "all") +``` \ No newline at end of file diff --git a/docs/likelihood/consensus-peaks.md b/docs/likelihood/consensus-peaks.md index f361c233..a731151a 100644 --- a/docs/likelihood/consensus-peaks.md +++ b/docs/likelihood/consensus-peaks.md @@ -1,40 +1,126 @@ -# How to create consensus peaks from a set of BED files +# How to create consensus peak set from a collection of BED files? + +We will start with simple example of how to creat a consensus peak set from +collection of files. Example data that will be used can be found in +```tests/consenus/raw``` . ## Data preprocessing +First step of analysis is creating three tracks with genome coverage by peaks, +their starts and ends. To do that we have to: +1. install [uniwig](https://github.com/databio/uniwig/tree/smoothing), make sure to use branch dev +2. use [create_unsorted.sh](https://github.com/databio/uniwig/blob/smoothing/create_unsorted.sh) to make three bigWig: + - {prefix}_start.bw - with smoothed coverage of genome by starts + - {prefix}_core.bw - with coverage of genome by peaks + - {prefix}_start.bw - with smoothed coverage of genome by ends -1. install [uniwig](https://github.com/databio/uniwig/tree/smoothing), make sure to use branch smoothing -2. use [create_unsorted.sh](https://github.com/databio/uniwig/blob/smoothing/create_unsorted.sh) to make three bigWig from your files +In this tutorial we will use prefix "all" as it is a default prefix in +```gitk``` module +## Coverage universe +We will start by making a coverage universe with cutoff that results in maximum +likelihood universe. We can do it through CLI: -## Cut-off universe -Make cut-off universe from coverage using: ``` - gitk lh universe_hard --coverage_file coverage.bw \ - --fout universe.bed + gitk lh universe_hard --coverage-file tests/consenus/coverage/all_core.bw \ + --fout tests/consenus/universe/universe.bed ``` + Where: -- ```--coverage_file```, takes the path to bigWig file with cverage track + +- ```--coverage_file```, takes the path to bigWig file with genome coverage by collection - ```--fout```, takes the path to output file +Or we can import it directly into python: +``` +from gitk.likelihood.universe-hard import main as cutoff + +cutoff("tests/consenus/coverage/all_core.bw", + fout="tests/consenus/universe/universe.bed") +``` + +Depending on the task we can also smooth the output universe by setting ``--merge`` +flag with the distance beloved witch peaks should be merged together and +``--filter-size`` with minimum size of peak that should be part of the universe. We can also not use the maximum likelihood cut-off and instead of it use user defined cutoff. For that we have to set ``--cutoff`` . If we set it to 1 we get union universe, and when to number of files we will get intersection universe. + ## Maximum likelihood universe -Make likelihood model from coverage tracks using: +Another type of universe that we can make is maximum likelihood flexible universe. To make it first we have to have a likelihood model of genome coverage by collection of files. + +#### Making likelihood model: +To make a likelihood model we can use this CLI: ``` -gitk lh build_model --model_folder model.tar \ - --file_no x \ - --coverage_folder coverage/ +gitk lh build_model --model-folder tests/consenus/model.tar \ + --coverage-folder tests/consenus/coverage/ \ + --file-no x ``` + Where: -- ```--model_folder```, takes the name of tar archive that will contain the likelihood model -- ```--file_no```, number of files used in analysis -- ```--coverage_folder``` path to folder with coverage tracks -- ```--coverage_prefix``` prefix used in uniwig for making files -Use likelihood model to make a maximum likelihood universe +- ```--model-folder```, takes the name of tar archive that will contain the likelihood model +- ```--file-no```, number of files used in analysis +- ```--coverage-folder``` path to folder with coverage tracks + +Or, we can do it directly in python: + ``` -gitk lh universe_flexible --model_folder model.tar \ - --output_file universe.bed +from gitk.likelihood.build_model import main + +main("tests/consenus/model.tar", "tests/consesnus/coverage", + "all", + file_no=4) ``` + +#### Making universe: +Now that we have the model we make the universe: + +``` +gitk lh universe_flexible --model-folder tests/consenus/model.tar \ + --output-file tests/consenus/universe/universe.bed \ + --cov-folder tests/consesnus/coverage +``` + Where: -- ```--model_folder```, takes the name of tar archive that contains the likelihood model -- ```--output_file```, takes the path to output file \ No newline at end of file + +- ```--model-folder```, takes the name of tar archive that contains the likelihood model +- ```--output-file```, takes the path to output file +- ```--cov-folder``` path to folder with coverage tracks + +Similarly, we can do it in python: + +``` +from gitk.likelihood.universe_flexible import main + +main("tests/consesnus/model.tar", + "/home/hee6jn/Documents/gitk/tests/consesnus/coverage", + "all", + "tests/consenus/universe/universe.bed") +``` + +## HMM +Another approach to making flexible universes is using Hidden Markov Models. +We can do it for example with: + +``` +gitk hmm --out-file tests/consenus/universe/universe.bed \ + --cov-folder tests/consenus/coverage/ \ + --normlaize \ + --save-max-cove +``` + +Where: + +- ```--out-file```, takes the path to output file +- ```--cov-folder```, path to folder with coverage tracks +- ```--coverage-prefix``` prefix used in uniwig for making files, default is "all" +- ```--not-normlaize```, is a flag that specifies whether not to normalize tracks before running HMM +- ```--save-max-cove```, is a flag that specifies whether to save maximum coverage of each output peak + +Similarly, we can do it in python: + +``` +from gitk.hmm.hmm import run_hmm_save_bed + +run_hmm_save_bed("tests/consenus/coverage/", + "tests/consenus/universe/universe.bed", + save_max_cove=True) +``` \ No newline at end of file From e120fd459f1c8ccb584764c6b765262ac98a70df Mon Sep 17 00:00:00 2001 From: Julia820 Date: Thu, 18 May 2023 14:26:59 -0400 Subject: [PATCH 0168/1072] default normalization --- gitk/hmm/cli.py | 6 +++++- gitk/hmm/hmm.py | 8 ++++---- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/gitk/hmm/cli.py b/gitk/hmm/cli.py index ff7fedb9..037bbb05 100644 --- a/gitk/hmm/cli.py +++ b/gitk/hmm/cli.py @@ -11,7 +11,11 @@ def build_subparser(parser): parser.add_argument( "--cov-folder", type=str, help="path to coverage folder", required=True ) - parser.add_argument("--normalize", action="store_true") + parser.add_argument( + "--not_normalize", + help="if not to normalize coverage signal before using HMM", + action="store_false", + ) parser.add_argument( "--save-max-cove", help="if present saves maximum coverage for each peak", diff --git a/gitk/hmm/hmm.py b/gitk/hmm/hmm.py index 3d8a4948..3d65a12d 100644 --- a/gitk/hmm/hmm.py +++ b/gitk/hmm/hmm.py @@ -43,7 +43,7 @@ def norm(track, mode): track[track != 0] = [val[i] for i in important_val] -def process_bigwig(file, seq, p, chrom, chrom_size, normalize=False, mode=None): +def process_bigwig(file, seq, p, chrom, chrom_size, normalize=True, mode=None): """Preprocess bigWig file""" if pyBigWig.numpy: track = file.values(chrom, 0, chrom_size, numpy=True) @@ -57,7 +57,7 @@ def process_bigwig(file, seq, p, chrom, chrom_size, normalize=False, mode=None): seq[:, p] = track -def read_data(start, core, end, chrom, normalize=False): +def read_data(start, core, end, chrom, normalize=True): """ Read in and preprocess data :param str start: path to file with start coverage @@ -222,7 +222,7 @@ def split_predict(seq, empty_starts, empty_ends, model): return hmm_predictions -def run_hmm(start, core, end, chrom, normalize=False): +def run_hmm(start, core, end, chrom, normalize=True): """Make HMM prediction for given chromosome""" chrom_size, seq = read_data(start, core, end, chrom, normalize=normalize) empty_starts, empty_ends = find_full(seq) @@ -235,7 +235,7 @@ def run_hmm_save_bed( coverage_folder, out_file, prefix="all", - normalize=False, + normalize=True, save_max_cove=False, ): """ From a6abbaba5698b9c62a10c5dc701fafe428594a82 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 18 May 2023 15:52:42 -0400 Subject: [PATCH 0169/1072] add example tutorial --- .gitignore | 3 + examples/scembed/projection.ipynb | 285 ++++++++++++++++++++++++++++-- gitk/scembed/projection.py | 6 +- gitk/scembed/utils.py | 9 +- 4 files changed, 284 insertions(+), 19 deletions(-) diff --git a/.gitignore b/.gitignore index 46ae98dc..a018973c 100644 --- a/.gitignore +++ b/.gitignore @@ -175,3 +175,6 @@ tests/data/buenrostro_metadata.tsv # integration test stuff tests/integration/buenrostro2018.model + +# examples +examples/scembed/pbmc/ \ No newline at end of file diff --git a/examples/scembed/projection.ipynb b/examples/scembed/projection.ipynb index b0800b34..3eb1897d 100644 --- a/examples/scembed/projection.ipynb +++ b/examples/scembed/projection.ipynb @@ -5,8 +5,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Installation\n", - "Make sure that you have `gitk` installed:" + "# Getting Started With Cell Embeddings from scATAC-seq data\n", + "## Overview\n", + "A *cell embedding* is a low-dimensional representation of single-cell in an scATAC-seq workflow. Think of it as a single vector of dimensionality ~100 that summarizes the epigenetic state of a cell. Cell embeddings are useful for a variety of tasks, including visualization, clustering, and differential accessibility analysis. In this tutorial, we will learn how to use pretrained models from `scEmbed` to generate cell embeddings from scATAC-seq data. We will then use these embeddings to visualize the data and perform clustering.\n", + "\n", + "Overwhlemingly, scATAC-seq datasets are represented and manipulated as *binary accessibilty matrices*. Breifly, these are matrices where each row represents a cell and each column represents a genomic region. The value of each entry is 1 if the region is accessible in the cell and 0 otherwise. For example, the following matrix represents a hypothetical dataset with 3 cells and 4 genomic regions:\n", + "\n", + "| Cell | Region 1 | Region 2 | Region 3 | Region 4 |\n", + "|------|----------|----------|----------|----------|\n", + "| 1 | 1 | 0 | 1 | 0 |\n", + "| 2 | 0 | 1 | 1 | 0 |\n", + "| 3 | 1 | 1 | 0 | 1 |\n", + "\n", + "These matrices are typically incredibly sparse, with most cells only having accessibilty at a small fraction of the genome. They are also extremely high-dimensional; matrices with > 1M features is common. This binary accessibility matrix is the input to `scEmbed`. We use [`scanpy`](https://github.com/scverse/scanpy) and their [`AnnData`](https://github.com/scverse/anndata) objects. These objects are a standard way of representing single-cell data in Python. We will use the `AnnData` object to represent our binary accessibility matrix.\n", + "\n", + "## Installation\n", + "First, we need to install `scEmbed`. `scEmbed` is a part of the `gitk` package. We recommend using virtual environments for this:" ] }, { @@ -15,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install gitk" + "%pip install gitk" ] }, { @@ -23,39 +37,280 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Getting data\n", - "The data used in this example is available from [10X genomics](https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-atac-v2-chromium-controller-2-standard)." + "## Loading Data\n", + "For this tutorial we've curated a dataset already for you to use. It comes from [10X genomics](https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-atac-v2-chromium-controller-2-standard). \n", + "\n", + "*Note: when using a pre-trained model, `scEmbed` requires that your `AnnData` object have `chr`, `start`, and `end` values on the `.var` attribute of the object. This is because we use interval overlap analysis to find similar regions and recast the feature-space of the new data we are embedding*.\n", + "\n", + "An example of how to do this is below:\n", + "\n", + "```python\n", + "import scanpy as sc\n", + "import pandas as pd\n", + "\n", + "adata = sc.read_h5ad(\"path/to/your/data.h5ad\")\n", + "\n", + "peaks = pd.read_csv(\"/path/to/your/peaks.bed\", sep=\"\\t\", header=None)\n", + "peaks.columns = [\"chr\", \"start\", \"end\"]\n", + "adata.var = peaks\n", + "```\n", + "\n", + "The data we are using in this example already has this complete:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-05-18 14:44:11-- http://big.databio.org/scembed/data/pbmc.h5ad\n", + "Resolving big.databio.org (big.databio.org)... 128.143.223.179\n", + "Connecting to big.databio.org (big.databio.org)|128.143.223.179|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 13570752714 (13G) [application/octet-stream]\n", + "Saving to: â€pbmc/pbmc.h5ad’\n", + "\n", + "pbmc/pbmc.h5ad 100%[===================>] 12.64G 56.0MB/s in 4m 19s \n", + "\n", + "2023-05-18 14:48:31 (49.9 MB/s) - â€pbmc/pbmc.h5ad’ saved [13570752714/13570752714]\n", + "\n" + ] + } + ], + "source": [ + "%mkdir -p pbmc\n", + "%wget \"http://big.databio.org/scembed/data/pbmc.h5ad\" -O \"pbmc/pbmc.h5ad\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "!mkdir -p pbmc\n", - "!wget \"big.databio.org/scembed/data/pbmc.h5ad\" \"pbmc/pbmc.h5ad\"" + "## Generating Embeddings\n", + "Now that we have the data, we can generate embeddings. This occurs in two steps:\n", + "\n", + "1. Create a `Projector()`\n", + "2. Project some data\n", + "\n", + "If this is your first time through the tutorial, the model will need time to download. This will take a few minutes. If you've already run this tutorial, the model will be cached and this step will be much faster." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "import os\n", + "# read in the data\n", "import scanpy as sc\n", - "from gitk import scembed\n", "\n", - "folder = \"/Users/nathanleroy/Desktop/pbmc\"\n", + "pbmc = sc.read_h5ad(\"pbmc/pbmc.h5ad\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# create projector\n", + "from gitk.scembed import Projector\n", + "\n", + "model = Projector(\"databio/scatlas\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", + "KI270727.1\t52092\t52991\n", + "\n", + "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", + "KI270727.1\t52092\t52991\n", + "\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 26226.77it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [03:07<00:00, 54.66it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:39<00:00, 103.15it/s]\n" + ] + } + ], + "source": [ + "# create embeddings (takes a few minutes)\n", + "pbmc = model.project(pbmc)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "barcode\n", + "AAACGAAAGAGAGGTA-1 [0.028793463, -0.07469678, -0.06822055, 0.1305...\n", + "AAACGAAAGCAGGAGG-1 [0.0026508372, -0.08474362, -0.06303765, 0.133...\n", + "AAACGAAAGGAAGAAC-1 [0.040292475, -0.080502935, -0.08098125, 0.149...\n", + "AAACGAAAGTCGACCC-1 [0.020774242, -0.07915346, -0.08313269, 0.1635...\n", + "AAACGAACAAGCACTT-1 [0.007883045, -0.0827978, -0.07880802, 0.13779...\n", + "Name: embedding, dtype: object" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# view embeddings\n", + "pbmc.obs['embedding'].head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyzing Embeddings\n", + "Pretty fast, huh? We can now analyze the embeddings. First, let's visualize them. We'll use `umap`. We'll also color the cells by their cluster assignment. We'll use `leiden` clustering for this. This occurs in three steps:\n", + "\n", + "1. `scanpy.pp.neighbors()` to compute the nearest neighbors of each cell\n", + "2. `scanpy.tl.leiden()` to compute the UMAP embedding and associated cluster assignments\n", + "3. `umap.Umap()` to compute the UMAP embedding\n", + "\n", + "We can then run visualizations with seaborn and matplotlib. We'll color the cells by their cluster assignment." + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# add embeddings to pbmc object as obsm attribute\n", + "pbmc.obsm['embedding'] = np.array(\n", + " [emb['embedding'] for _, emb in pbmc.obs['embedding'].reset_index().drop(columns=['barcode']).iterrows()]\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], + "source": [ + "# run leiden clustering on embeddings\n", + "sc.pp.neighbors(pbmc, use_rep='embedding')\n", + "sc.tl.leiden(pbmc)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [], + "source": [ + "import umap\n", + "\n", + "reducer = umap.UMAP(n_components=2)\n", + "umap_embedding = reducer.fit_transform(pbmc.obsm['embedding'])\n", "\n", - "pbmc = sc.read_h5ad(os.path.join(folder, \"pbmc.h5ad\"))" + "# attach umaps back to pbmc object\n", + "pbmc.obsm['umap'] = umap_embedding\n", + "pbmc.obs['umap1'] = umap_embedding[:, 0]\n", + "pbmc.obs['umap2'] = umap_embedding[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'UMAP 2')" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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KVkMlJycrNTXVqq5x48YOjVG8/dGjRyscFwAA9vi3by95e0sFBXafF2RmKmXu3NLH6dBBsf9+2tnhAQAAwE22xac7bSVqcTn/f+Zqt+ZhLhkfVR8rUmuo3NxcmzpvBy/b8PHxsSoXOnB7MgAAjgho21YNpk1TxE03KXjgQKtnptBQFRyIL32MCy5Qvff+46IIAQAA4GrHk3MrfCZqaY4k5uh4sm3OBJBYkVpjRUZG2tQdO3bMoVWpxVeglvV8VQAAyiPowh4KurCH8o8d076lS4vqjbS0UvsG9OihhtP+K1OxXwICAACg6th7LNNt88RG+JXeEDUOK1JrKF9fX9WpU8eqrviZqaX5+ZybjyWpadOmFY4LAIDSeEVFyfuc/4eZQ0NL7ZO9dq0Snv63K8MCAACAC6Vm5Ssx3T2XQSWm5ysty/6RUqjZSKTWYJdeeqlV+e2331bBec6eK+7XX3/VH3/8UeJ4AACcK+WrBUqYPFnpy1dUaByzr68azpqp8JEjFXHzzar/34+lMqw0TV20SPkJCRWaGwAAAJ5x5LRrt/QXd9jN86FqIJFag40ePdqqvGPHDt19992yWEo+tHnfvn268cYbreqaN2+uiy66yOkxAgCqh+QvvlDCU08p5bPPdeSee5S5Zk2FxvNt2FB1npus2KeeVGDHjgobMqT0Tj4+MgcFVWheAAAAeEZyhntXiCZnumf1K6oWEqk12OWXX65+/fpZ1f33v//VJZdcop9//tlmdWpiYqLeeOMNde3aVceOHbN69tJLL8nLy8vlMQMAqqbsLVv/KRiGsrdsqfCYiXM+0Z7uPbS7cxelLl5s28DbW/r//zeZ/PxUd8pr8irDMQAAAACoXAzDUIqbE5spGfkyDMOtc6Ly47KpGm7evHnq2bOnDhw4UFT3+++/a8CAAQoODlbjxo0VEBCgxMRE7d+/3+4/Io888oiGDx/uzrABAFVMUM+eSl2w4EzBy0uBPXpUaLycPXt18qWXSm5UUCB5ecm7Th0F9++voC5dKjQnAAAAPKOg0FB+oXuTmvmFhgoshny8TG6dF5UbidQaLjY2Vr/++qtuueUW/fLLL1bPMjIytH379vP29fHx0fPPP6/HHnvMxVECAKq6sCGDZQ4MVM6O7Qq6+GIFXnBBhcZLmjOnbA0LC1WQkKCUuXOVsXy5mnyzSF4hIRWaGwAAAO5l8dDCUItFEptvcQ629kP169fXzz//rC+++EJ9+/aV2Vzy2yIsLEx33XWXtm/frscff1wmE7+dAQCULqR/P9W+/34FOmFlqDkw0OE+BQkJytm1q8JzAwAAwL3MHko7lJIeQQ3EilRIksxms0aMGKERI0YoPT1dGzZs0P79+5WSkqKcnByFhoYqMjJSHTp0UJs2bUpNtgIA4Eq1brpRqd9+K0tyctk7+fjIt2FD1wUFAAAAl/D2MsnHy+TW7f0+XiZ5eyqDi0qLRCpshISEqF+/fjYXUQEAUFH5CQnK/esv+bdpI++oqHKP49uokaLuuEMnX31VKsslAGaz6rz4gnxiYso9JwAAADzDZDIpPMhHp9Ly3DZneLAPO3Bhg0QqAABwi+xt23Rw7DgZWVnyiohQo/99Lt8GDco1Vs6ePTr52mulJ1H9/BR65RWKHDdO/i1blmsuAAAAeF5EsLdbE6kRQT5umwtVB/uzAQCAW6R8tUBGVpYkqTA5WWlLljg8hlFYqGP/ekLxw0f8/+n/JTOZTEpbuEgHbxmj7C1bHJ4PAAAAlUO9KH+3zlffzfOhaiCRCgAA3MKnTqxV2Tu2jsNjpH3/g1IXLpSRn1+m9kZOjiTJkpqqU+/+x+H5AAAAUDmEBfooMsQ9q0QjQ3wUGsgmbtjiXQEAANyi1q23Kv/4cWVv3abgXhcr7JqrHR7Dkp1lVTYHB8uSkVGmviZftmcBAABUZS3qBumPPSlumQf/WLVqlbKzs23qt27dalXOycnRsmXL7I5Rt25dtWnTxiXxuROJVAAA4BZmX1/VefbZCo0RNmiQUr6cr5xt2+RVq5a8o6OVu3t36R1NJkU//niF5gYAAIBnxUb4qV6kv44k5rhsjnqR/oqN8HPZ+FXRTTfdpIMHD5ba7sSJExo4cKDdZ2PGjNGsWbOcHJn7kUgFAABVhjkoSI3mzVV+QoK8IyOVsWqVjj74kFRYWGK/wF695Ne4sZuiBAAAgKt0aBSi02l5yskv/bx8R/n7mNWhUYjTx0X1wRmpAACgSjHy8+UTGytzYKBCBw5Us6U/KfK++0rs4xUS7KboAAAA4Ep+Pmb1bB0uHy+TU8f18TKpZ+tw+fmQKsP58e4AAABVxqn33teezl20p3sPpf/8syTJp25d+UTXLrFfQVKyLHl57ggRAAAALhYW6KPebSPk76Skp7+PWb3bRigskDP17YmPj5dhGBX6qA7b+iUSqQAAoIrIP3ZMp997TzIMGdnZOj75OUnSyTfe1PF/P1Ni3+w1a7S3azdlrlnjjlABAADgYmGBPurfIVL1Iv0rNE69SH/17xBJEhVlQiIVAABUDSaTTdkwDCXNm1em7kZenk689poLAgMAAIAn+PmY1a15mC5qGa7IEMcSoZEhPrqoZbi6NQ9jOz/KjMumAABAleBTp45qP3C/Tv3nPZn8/RXz739r32WXy8jMtG0cECCf6GjlHzokGUZRtcmLb30AAACqm9gIP8VG+Cktq0CHT+coOTNfKRn5yi/85/tAHy+TwoN9FBHko/pR/goN5PtCOI53DQAAqDKi7rpLtcaPlwxDR+66WwWHD9tvmJ2t8GHD5Fu/no49/W8ZWVkyh4Up9qkn3RswAAAA3CY00FttG5y5ZNQwDBVYDFksktkseZtNMhXf4QQ4iEQqAABwufyTJ5W5erX8GjVSQKdOFRrL7OurzLXrlLl6dclzHj2qqDsmKHTQIBkFBTJ5820PAABATWEymeTjZZK8PB0JqhN+ogAAAC6Vf/KkDlx3nQpPnZZMJtV99RWFXXVVhcb0Cg8r8bnJx0f+Hdr/UyaJCgAAAKCCOE0XAAC4VObq1WeSqJJkGEr9ZnGZ+uUdPKjkL75Q9vbtNs/8W7aUOSrSfkd/fxn5+Tr+72eU+u2S8oYNAAAAAFZIpAIAAJfybdhQOuc8Kt/GjUvtk/v33zow7Dodf2aS4q+/QekrVti0qTXuVvudc3LO/NcwlPrNonLFDAAAAADFkUgFAAAuFXjBBar7yssK6tVLETffrOiHHyq1T8Yvv8qSmXmmYLEo/YcfbNqEDx1S6jh+ZUjaAgAAAEBZcGAYAABwubCrr1bY1VeXub1fs6ZWZd+mzYo+T/v+eyXOmq38gwdLHCPk6qtV+6HSk7YAAAAAUBYkUgEAQKUTfMklin1usjJW/CL/Nm0UOf5WGfn5OvX++0qc+lGZxig4cVxmf38XRwoAAACgpiCRCgAAKqWIkSMVMXJkUfnYk08pdVHZzzzN3rBRRx54UHVefEFewcGuCBEAAABADcIZqQAAoErIXLPGsQ4FBUr/8Ued/s97rgkIAAAAQI1CIhUAAFQJAZ0v+Kfg7S1zUJBkMpXaryAx0YVRAQAAAKgp2NoPAACqhLovv6ykli1VmJys8BEjFH/zzVKmUWIfU0CAIm680U0RAgAAAKjOSKQCAIAqweTnp7Bhw+QVHq6cP/+UJTnlvG3NQUGqdeedCrtqqHxjYtwXJAAAAIBqi0QqAACo9Cx5eTo84Q5lrVkjr8hIeUVF2TYymSTjzApVS2amTr/xhpJnz1b9Dz9UQPt2bo4YAAAAQHXDGakAAKDSO/XOu8r6/8umChMTlX/4sG0js+23NYWnT+vka6/ZHTP/2DGlLV2q/GPHnBorAAAAgOqJRCoAAKjUTr37rpKmT7eq823UyKZd2JAh8mvVyiahauTl2bTN2b1b+4depaP33a/9Q69Szu7dTo0ZAAAAQPVDIhUAAFRqqYu/tSr7xMWp3rvvKPDCC/+pNJkUfsP1Kjh5UrJYrNoHXXyxnTEXy5KZKenMMQCpixc7P3AAAAAA1QqJVAAA4DaW7Gydevc/Spj0bJlXgfo2afxPwc9PDefNk2+9egq66KJ/6g1DyZ//T4UpKTb9g3peZFPnExdXYhkAAAAAiuOyKQAA4BaFGRk69uhEZfzyiyQp7Ycf1PT77+Rdq1aJ/eq+8opOvv66ChOTVGvsGPnEREuSvGvXtmqXtnixvKOizqxK/X9h149UYNeuNmNG3HCDChISlLlunYK6d1fEDTdU8NUBAAAAqO5IpAIAAJfL2rBBh++8S5aMjKI6S2qq8uIPlppI9Y6IUN0XX5QkGYahU/95T5m//y7/jh0UetVQpX3z/9vyLRZZ8vOt+ubu3GV3TJPZrOhHHqnAKwIAAABQ07C1HwAAuNzpDz6wSqJKkndsrHzi6irtp5+UtWlzmcZJXbRIp99/X9lbtyp5zifyjomVTKai52Z/f6v2luxsFSQnyyh2bioAAAAAOIpEKgAAcDlTYKBVOWzYMNWfOUPx19+go/c/oIM33qjEGTNLHSf/yFGrcuZvv8m/XTuZ/P3lHRurulNek3+7dkXPC06f1l8X9dT+IUOVf86WfwAAAABwFIlUAADgcjGPPy6/1q1lDgpSxC03q+5LLyrp4/+q4PjxojbJ8+aVOk7ooCtlDg09UzCblbtnj3K2b5fZz09NFn6twA4drC6xsvz/5VN5+/cradZsp74mAAAAADULZ6QCAACX861fX02+XmBVl3vggFXZHBxU4hjZf/6pwuQUNZ4/Xzm7d+n4s5NVmJQkSSpMTVXe0aPyb9lSJrNZhp3+Ji9+fwwAAACg/PiJAgAAeERA27bW5c5dzts2ceYsxV83XIdvu01HHnpIgV26yCs8vOi5KTBQhybcoSP3P6CYZ56RKTBQJn9/+TZuLEnyb99etW691SWvAwAAAEDNwIpUAADgdvnHjsmSk2Ndd+Twedsnf/pp0ee5f/6pvy7pKxUUFNUZWVkysrKUsXy5/Fo0V8uNGyRJJpNJRn6+TD4+zn0BAAAAAGocEqkAAMCt8uLjdWDk9bKkpVnVB3bpKklKXbxY2Vu3KbhPbwX36SNJMgdZX1Z1bhK1OEtamkwmU1GZJCoAAAAAZyCRCgAAXM6SlaWsrVvl37y50pevsEqi+jZpolpjxih85AilfLVACU89JUlKnjtXDWbNUlCP7grs3kO5e/8qfSJfX4WPGuWqlwEAAADUSIZhKD4+Xtu3b9eRI0eUkpIiPz8/RUREqHnz5urWrZv8/f09HabLkUgFAABOlXfkqLK3bVPyZ58pd/du+bVupZxt22Xk5EgmkyLG3GLVPnTIYAV06qT9Vw5S3uFztvcbhrI3b1JQj+5K++mnMk6ep7z9B+TfvLkTXxEAAABQ8yQnJ2vhwoX64YcftHz5cp0+ffq8bX18fDR48GA9+OCDuuSSS9wYpXuRSAUAAE6TvX27Dt4yRkZ29j9169b/08AwlLb4W8U89ZQyViyXf5s2irr9dh0cO0558fHWg5nNCuze/czneXl25zP5+MjIz3fyqwAAAABqtnvuuUfTpk1T3nm+Dy8uPz9fCxcu1MKFC3XLLbfoP//5j0JDQ10cpfuRSAUAAE6T+vXXVklUe0y+vqp182j5t22rY08+qZSFi2zOMQ0dMkThI0YosHNnSZJ3bIwKU1JsxjKKlb2ioxUy4NKKvAQAAACgxlu7dq3dJKqXl5fq1KmjmJgY5efn6+DBg0pNTbVqM2fOHO3evVs///yzgoOD3RWyW5g9HQAAAKg+fOLiSmngo7g3XpeRn69D48crPz5ehadPqyAhQSY/P0lSyMCBqvvaqwrq0b2oW3Cf82wPKrYatfDkSR1/7vkKvQYAAAAA/wgPD9fdd9+tJUuWKDk5WYcPH9aGDRu0detWJSYmasWKFerdu7dVn3Xr1mns2LGeCdiFSKQCAACnqXXLLQq99hrJ/P/fYpitv9UI7NhRgZ076+Tbb9usXA295mo1XbZMce++I1OxfrUfuF8xTz4p3zKcfZryv/8pe8efFXodAAAAQE3XqFEjTZs2TceOHdP777+vQYMGKSQkxKqNl5eX+vbtqxUrVmjChAlWz7766iutWLHCnSG7HIlUAADgNJlr1ylr1WrJYjlTcfa//+/sitWcnTtt+ga0ayffenEymUw2z0xeXvKpW0d5+/ZZP/C2f0qRyYfTiwAAAGo0w5By0qTMxDP/NYofCoWSTJ48WXv27NH48eMVEBBQansvLy998MEH6tq1q1X9tGnTXBWiR/BTBgAAcIrClBQdue++85+R6uOjmCefkCSF9L9UWX+sOVNvNiv8+usVPnx4ieOnLv7W9hvgggKbdiGDBsm/ZUuH4wcAAEAVd+JPaft86ehGKWGrlJPyzzP/cKlORymui9R+hBTTxlNRVgmDBw92uI+Xl5cee+wxjRw5sqjuxx9/dGZYHkciFQAAOEVBcnKJF00FXXihvMLCJEm1bh4tn7g45cXHK6R/P/k2aiTDMJTy9ULlHTqo0CuulH/LFpIkw2JR8iefKO/gwVJj8GnQQHFvvO6cFwQAAICqYe+P0u9vS4dWn79NTop04NczH7+/KTXoKfV6SGpxmbuirBGKn5WamJiorKwsBQYGeigi5yKRCgAAKiz3r7908PYJJbbJ3rbNqhzSv59VOXHqVJ16511JUvLsOWr8zSJ5BQfrwIiRyj98uKidd506Kjh1yno1qsmkoL59VfelF+0eDQAAAIBqKCtJ+m6itGO+430PrZbmrT6zOvXK16TAWs6PrwaKiIiwqUtNTSWRCgAAcNbJN95U4fHjJbYx+/mV+Dxz9R9Fn1uyspS9datytm23SqJKUkFCgm1nw1DmihXK27dP3t26lT1wAAAAVE3Hd0hzh0vpdr43dMT2L6X436XRX0kxbZ0TWw129OhRm7rIyEgPROIaXDYFAAAqrpTD+02+vqrz0kt2n2X89puS5nwi3+bN/mnv5yf/Nm1kycp0KIzcv/c71B4AAABV0PEd0qzBFU+inpWeIM0cdOaMVVTIypUrrcoNGzaUr6+vh6JxPlakAgCACqv98EPK2blTBadOKejii5WzZ48KT58ueh47ebKCe11s0y/p07k68cILkiRzWJii7r1HhckpCh0yWH6NG6vWmDFKX75ChYmJkslUYsLWHBamoF69nP/iAAAAUHlkJZ1ZiXruRVLOkJMifXqddNdqtvlXwIwZM6zKgwYN8lAkrkEiFQAAVJh/y5Zq9usvsmRlySs4WJbsbGWuW6fcv/5SQJs2CurZ026/9KVLiz63pKbKt2Ejhd07pKjO5O8v7zp1ziRSS1n1Gjn+VvnWi3POCwIAAEDl9N1E561ELS49Qfr+Mem6aa4Zv5r77rvv9Ntvv1nVjR071jPBuAhb+wEAgFOYzGZ5BQerMD1d2Vu3yr95c0Xddtt5k6iS5N+q1TkDmFSQnFRUTJrzif4eMFC5O3bYny8gwKrs26BBxV4AAAAAKre9P5bvYilHbP/yzDxwSFJSku644w6rumuuuUbdu3f3UESuwYpUAABQIWk//KBT7/5H5qAgRT/ysBKe/rfyjxyRyd9fETfdqLTvv5dXSKjqvvyS/Nu0sepb+5GHlbFypfL275cMQydfeVVJ02eo4OTJUlegGtnZ8m3SRIXJyQq54nKFXH65K18mAAAAPO33t90zz6p3pBZ8b1lWFotFo0eP1pEjR4rqwsLC9O6773owKtcgkQoAAMqtIDFRxyY+JiM/X5J09NGJRWejGjk5SpoxUzIMFShBx/71hJp8s8iqv9nXV5bMcy6UslhUcOKE3bl86tWTV2SkcrZu/Wf+lBRFjBih2vffJ5PJ5ORXBwAAgErjxJ/SodXumevgKunETimmTeltoYkTJ+r777+3qvvoo49Uv359D0XkOmztBwAA5VaYllaURJXOrBK1cs6q0sKUFLtjWDIySp3HJy5OzZYtVczjj1n3TUpS4scfK2n27LIHDQAAgKpnu4u39Bfn6iMEqol3331Xb775plXdY489puuvv95DEbkWK1IBAEC5+TZqpJDLL1f6j2fOkYq6+25ZMjOU9uNP8mveXEZBgTKWLZPMZkXdc0+558k/fly72rZTcN++Mvn5ycjNtXqeFx9fkZcBAACAyu7oxuo9XxU0b948Pfjgg1Z1Y8eO1SuvvOKZgNyARCoAACg3k8mkuLffUs727TIHBcmvaVNJUu3775ckGYah3L17ZQ4Klm+9OLtjhF59tVLmzTtTCAiQzq5qPffzwkJJUsbPPytk4EClL136zwDe3godPMT5Lw4AAACVg2FICVtLb+dMx7acmZfjo+z69ttvNWbMGBnn7EAbNmyYpk2bVq2P3CKRCgAAKsRkMimgQ4fzPvNv2bLE/jH/elzZmzYpd/du6dyVpsWPCfh/PnF1rcqBXbsq6MIejgUNAACAqiM3XcpJce+cOSlSXobkF+LeeauAFStWaMSIESooKCiqGzhwoD777DN5eXl5MDLXI5EKAACcJv/4caV+s1jetSIUNmyYTObSj2NPnD7jTBJVkiyWEtv6NGwo/2JJW5tzWQEAAFC9FOaX3sYVCvIkP89MXVmtXbtWV111lXJycorqevbsqa+//lq+vr4ejMw9SKQCAACnKExPV/yoG1WQkCBJytm5U7HPPFNyn4wMJU6dWuY5IifcrpB+/eTftq1y/vxTMpsVMWZMheIGAABAJefl45l5vat/YtAR27Zt05VXXqmMcy6LveCCC/Tdd98pKCjIg5G5T+nLRAAAAMog96+/ipKokpTx28pS+xSmpNpcHFWS4089rdMffyyvqKgzFRaLMpYvdzhWAAAAVCF+IZJ/uHvn9A+XfIPdO2cltmfPHg0cOFDJyclFda1bt9aPP/6osLAwD0bmXiRSAQCAU/g2aiRzaGhROaBD+1L7+MTVVfAll1jV+bVpLXN4+Hn7JH44VZm//lpUTluyREZenuMBAwAAoGowmaQ6Hd07Z91OXDT1/w4ePKgBAwbo5MmTRXWNGzfW0qVLVbt2bQ9G5n4kUgEAgFN416qlhrNnKXzkSEXedafqvPBCqX1MJpPqvf+edM55Srk7dyly3FipjAfVe0dHy1QDzmMCAACo0eK6VO/5KqmEhARdeumlOnLkSFFdXFycfv75Z8XFxXkwMs8gkQoAAJzGv3Vr1XlusqIfeEDmwMAy9TF5e8u/WbOisldYmGqNHq1my5erwexZks85Z2LZWRVQcPKkDlx/gywOHBEAAACAKqb9cPfO187N81VCSUlJGjhwoP7++++iutq1a2vp0qVq3LixByPzHBKpAADA4+q99x+FDh6s4P79VX/af2UOCpJPTLSCevRQvXfeVmCPHgodPFgRN99s29kwlLN1qxL+9YT7AwcAAIB7xLSVGvR0z1wNL5Zi2rhnrkoqPT1dV1xxhf7888+iuvDwcP30009q3bq1ByPzLG9PBwAAAOBTt67i3ni9qFyYlqa0H36QV2iYQi6/TCH9+0uSsrZtU/KcOXbHyDt40C2xAgAAwEN6PSjNW+36eS5+0PVzVHJXXXWV1q9fb1X38MMP6/Tp01q2bJlDY3Xp0kURERHODM9jSKQCAIBKxZKbq4Ojb1bu3r2SpIjRoxX79FOSpIyl5/+mLXzECLfEBwAAAA9pcfmZLfc75rtujvYjpBaXuW78KuKXX36xqXvmmWfKNdaKFSvUt2/figVUSZBIBQAAlUre/v1FSVRJSv3uO8U89aRMJpN8mzaxaht5910y+fgooFMnBV90kbtDBQAAgLsNmiIdXCWlJzh/7JA60pWvOX9cVBskUgEAQKXiHRsrc0iILOnpkiRLUpIOT7hD9T94X+HXXKPCxCRlrl2jwC5dFTnhdpnsXEAFAACAaiqwljT6K2nmICknxXnj+oefGTewlvPGRLXDZVMAAKBS8Y6IUIPp0+QVFVVUl7lypdJXrJAkRY6/VQ0+/lhRd0wgiQoAAFATxbSVxn13ZgWpM4TUOTN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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams['figure.dpi'] = 300\n", + "\n", + "_, ax = plt.subplots(figsize=(4, 4))\n", + "sns.scatterplot(\n", + " data=pbmc.obs,\n", + " x='umap1',\n", + " y='umap2',\n", + " hue='leiden',\n", + " palette='tab20',\n", + " linewidth=0,\n", + " s=1,\n", + " ax=ax\n", + ")\n", + "\n", + "# legend to upper right\n", + "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "ax.set_title(\"10X genomics PBMC\")\n", + "ax.set_xlabel(\"UMAP 1\")\n", + "ax.set_ylabel(\"UMAP 2\")" ] } ], "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" }, "orig_nbformat": 4 }, diff --git a/gitk/scembed/projection.py b/gitk/scembed/projection.py index 34a28a43..94bc672e 100644 --- a/gitk/scembed/projection.py +++ b/gitk/scembed/projection.py @@ -177,7 +177,7 @@ def get_embedding(self, region: str) -> np.ndarray: """ return self.model[region] - def project(self, adata: sc.AnnData) -> sc.AnnData: + def project(self, adata: sc.AnnData, key_added: str = "embedding") -> sc.AnnData: """ Project the AnnData object into the model space. This is done in two steps: @@ -185,6 +185,8 @@ def project(self, adata: sc.AnnData) -> sc.AnnData: 2. Project the universe representation into the model space using the model. + :param adata: AnnData object to project + :param key_added: Key to add to the .obsm attribute of the AnnData object """ _LOGGER.info("Step 1/3: Converting consensus peaks to universe representation") adata_converted = self.convert_to_universe(adata) @@ -209,5 +211,5 @@ def project(self, adata: sc.AnnData) -> sc.AnnData: ) ) - adata.obs["embedding"] = cell_embeddings + adata.obsm[key_added] = np.array(cell_embeddings) return adata diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 5188ea53..e2edd5e6 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -426,7 +426,11 @@ def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: def barcode_mtx_peaks_to_anndata( - barcodes_path: str, mtx_path: str, peaks_path: str, transpose: bool = True + barcodes_path: str, + mtx_path: str, + peaks_path: str, + transpose: bool = True, + sparse: bool = True, ) -> sc.AnnData: """ This function will take three files: @@ -442,6 +446,7 @@ def barcode_mtx_peaks_to_anndata( :param str mtx_path: the path to the matrix file :param str peaks_path: the path to the peaks file :param bool transpose: whether or not to transpose the matrix + :param bool sparse: whether or not the matrix is sparse :return sc.AnnData: an AnnData object """ @@ -453,7 +458,7 @@ def barcode_mtx_peaks_to_anndata( barcodes.index = barcodes["barcode"] # load the mtx - mtx = mmread(mtx_path).toarray() + mtx = mmread(mtx_path) if transpose: mtx = mtx.T From 6046baaf0ea18383296b6666c6d127c6767661da Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 18 May 2023 16:14:29 -0400 Subject: [PATCH 0170/1072] update tutorial --- examples/scembed/projection.ipynb | 32 +++++++++++++++---------------- 1 file changed, 15 insertions(+), 17 deletions(-) diff --git a/examples/scembed/projection.ipynb b/examples/scembed/projection.ipynb index 3eb1897d..1e4da4bd 100644 --- a/examples/scembed/projection.ipynb +++ b/examples/scembed/projection.ipynb @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -139,42 +139,40 @@ "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", "KI270727.1\t52092\t52991\n", "\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 26226.77it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [03:07<00:00, 54.66it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:39<00:00, 103.15it/s]\n" + "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 26181.45it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [02:59<00:00, 56.92it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:43<00:00, 99.10it/s] \n" ] } ], "source": [ "# create embeddings (takes a few minutes)\n", + "# there will be three progress bars:\n", + "# 1. converting dataset to universe, \n", + "# 2. creating documents, and \n", + "# 3. projecting\n", "pbmc = model.project(pbmc)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "barcode\n", - "AAACGAAAGAGAGGTA-1 [0.028793463, -0.07469678, -0.06822055, 0.1305...\n", - "AAACGAAAGCAGGAGG-1 [0.0026508372, -0.08474362, -0.06303765, 0.133...\n", - "AAACGAAAGGAAGAAC-1 [0.040292475, -0.080502935, -0.08098125, 0.149...\n", - "AAACGAAAGTCGACCC-1 [0.020774242, -0.07915346, -0.08313269, 0.1635...\n", - "AAACGAACAAGCACTT-1 [0.007883045, -0.0827978, -0.07880802, 0.13779...\n", - "Name: embedding, dtype: object" + "(10246, 100)" ] }, - "execution_count": 13, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# view embeddings\n", - "pbmc.obs['embedding'].head()" + "# insepct the results\n", + "pbmc.obsm['embedding'].shape" ] }, { From 4a55312680371149b8ece99acb1cf7531c747cab Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 18 May 2023 16:15:32 -0400 Subject: [PATCH 0171/1072] tutorial update --- examples/scembed/projection.ipynb | 36 +++++++------------------------ 1 file changed, 8 insertions(+), 28 deletions(-) diff --git a/examples/scembed/projection.ipynb b/examples/scembed/projection.ipynb index 1e4da4bd..9bce1b08 100644 --- a/examples/scembed/projection.ipynb +++ b/examples/scembed/projection.ipynb @@ -29,7 +29,9 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install gitk" + "%pip install gitk\n", + "# or if using local version:\n", + "# %pip install ." ] }, { @@ -192,31 +194,9 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# add embeddings to pbmc object as obsm attribute\n", - "pbmc.obsm['embedding'] = np.array(\n", - " [emb['embedding'] for _, emb in pbmc.obs['embedding'].reset_index().drop(columns=['barcode']).iterrows()]\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" - ] - } - ], "source": [ "# run leiden clustering on embeddings\n", "sc.pp.neighbors(pbmc, use_rep='embedding')\n", @@ -225,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -242,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -251,13 +231,13 @@ "Text(0, 0.5, 'UMAP 2')" ] }, - "execution_count": 163, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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AeFnUaafVOKBJkmQyKfmN1xU2erRk/vuruNWqyOPH1fs8R37+/obTqYr0rUo/5WRtO+MMbRk/QRXpW92Xg3p0V0DHju52yODBniUEWBv98wAAALR1zCQFAAAAvKzo2/kKSEiQAgLkyM529wempcmZn6+U559TxebNKl25UiH9+yu4Z886n1WxaZMcxcXutiU6Wrvvvlv2XbslSY7cXOW99546/OfOquvh4Up9/z0VfPa5AmLbKWryZGWnpCj31VdliY6usYcpAAAACEkBAAAAryr6/ntlP/mku20KDparvFySVLllizJvvEkyWxR5wvEK6tbtoM/b99ZbHsvnHfn50oEzS1UVnB7I2qGD4q68wt1OuPEGxV93rUwWS+N/IAAAAANguT0AAADgRba/Z3j+w1VZqbBqe4CW/f67+7OztFTOioo6n2cOj6j9gskkS1ycIo4/XrGXXHzQughIAQAA6kZICgAAAHhRxLixnjM7nU6V/PKLZDJVtU0mhR19lCQp97XXtXHwEdp4xBDlf/pprc+Lu/oqhQ0fLkt0tAI6dXL3x5xztnos/UFJzzwtc2hoC/00AAAAxsByewAAAMCLrO3bK+E/d2r3zbfs76yokCwWRZ54oqImT1b4scfIUVCgrMcek1wuyWbTnvvuV9Spp8r0T5j6N0tkpFJeeVmS5HI6VfLjMpmsVoUddWRr/lgAAABtGjNJAQAAAC+ztm9fs9PhUNGCBQodPKiqbTLtn10qyWQy1QhIqzOZzQoffiwBKQAAgJcRkgIAAABeZMvO1o5LL6v1mquiQvacHElVM0Tb33mHZLXKFBKijg/c35plAgAA4AAstwcAAAC8aPsFF0iVlbVeC+zaVdaOHd3tduedp5izzpLMZpnMzF8AAADwFb6JAQAAAE1QuW2bsp95VnkffSSX0+nut23bXuv48JNPVudZ78tRUCCXw+HuNwUEEJACAAD4GDNJAQAAgEay5+Ro29nnyJGfL0mq3LpN7W+9RcVLltR5T/Hcudq6fLnsWVmyJier88w3ZE1MbKWKAQAAUB/+yRoAAABopPL1690BqSSVLFsmSdr7yCP13mfPypIk2XbuVO5rr9W4XjB3rnbdcafy58zxvG/fPpWtWStneXkzKwcAAEBtmEkKAAAANFJQ9+4yh4XJWVIiSQoZOECSZMvKbvhDAjy/ihfOn69dt9wqSSr4+GOZrFZFnXKKylat0o7LLpezuFiB3boq9b33ZImM9MrPAQAAgCrMJAUAAAAaydqhg1LeelMx552n+Ok3qcMdd6h46VK5CgvruMEqa69eChk4UDKZFNynj+KuuMJjSPmatR7tsj/WSJJyZ74pZ3GxJKly8xYVfvW1938gAAAAg2MmKQAAANAEIX36KKRPH3e75MdldQ+22WTbsEH20FB1evwxRU2YUGNI2LHHVC3Bdzgkk0nhI4ZLkizRUR7jLNHRXqkfAAAA+zGTFAAAAPCCsGOPOegYV2mpdt/5H7mczpr3Dx2qzm+/pfgbblDKzJkKHzFCkhR//fUKGz5cAe3bq93UCxVx/Div1w4AAGB0zCQFAAAAmsnlcinv/fcbNtZmk5xOyVxzvkLooEEKHTTIoy8gJkYpr7zslToBAABQO2aSAgAAAM1k27FDxQu+a9BYc3CwZDK1cEUAAABoDEJSAAAAoJmyX3ixwWOdxcUqXriwBasBAABAYxGSAgAAAM1QvGSJCj/9tFH31LYnKQAAAHyHkBQAAABoBkdR0UHHmEJCZLJaJUnhY8coYvToli4LAAAAjcDBTQAAAEAzRIweLVNQoFwVlbVetyYlqduC+XI5nXKWlMgSEdHKFQIAAOBgCEkBAACAJnA5ncqf87FKf/lFCgyS6ghJ/znF3mQ2E5ACAAD4KUJSg3C5XNq2bZvWrFmjjIwM5efnKygoSDExMerevbuGDBmi4OBgX5cJAABwSHA5HNp+0cUq+/XXg441H+Q7lsvlUv6sWSpft07ho0YrYvQo2TIzZQoOVkBsrLdKBgAAQD0ISduwvLw8ffrpp/r666/1/fffKycnp86xVqtVEydO1A033KDjjjuuFasEAAA49JSvWdOggFSSYi+7rN7reW+/rb3/e1CSlD97jiLGjVPRt99KFos63HuPYqZMaXa9AAAAqB8HN7VR//rXv9ShQwddcskl+vDDD+sNSCXJZrPp008/1ciRIzV16lQVFha2UqUAAACHHlNIaIPGJfz734o65eR6x5St/mN/w+WqCkglyeFQ1mOPN7VEAAAANAIhaRu1fPlyVVbW3BfLYrEoKSlJgwcPVr9+/RQVFVVjzFtvvaVx48apuLi4NUoFAAA45LhstoMPMpkUfeopBx0WNvzY/Y2AAPceppJkDm1YGAsAAIDmISQ1gOjoaE2bNk3z5s1TXl6edu7cqd9++02rV69Wbm6uFi5cqOHDh3vc88svv+iiiy7yTcEAAAB+zto+QaaD7efucqn0t98O+qzoSZOU9MLzirvuWqW+/546zLhX5qgoBXTsqE4PPeSligEAAFAf9iRtw1JTU/Wf//xH5557rkJCQmodY7FYNHLkSC1cuFDTpk3Tyy+/7L42Z84cLVy4UKNGjWqtkgEAAPyOs7xczrIyBcTEuPsC4uMVe/nlynn22XrvtWVlNegdEaNGKeLv71whhx/OPqQAAACtjJmkbdSMGTO0ceNGXXrppXUGpAeyWCx6/vnndcQRR3j0v/rqqy1VIgAAgN8rWbZMm4Ydo01HD9OuO+509xctXqKc55476P1777tfOS+/0pIlAgAAwAsISduoiRMnKjAwsFH3WCwW3XrrrR5933zzjTfLAgAAOKTsffQxOUtLJUkFH3+sslWrlPXkU8q48krJ6WzQM7Kfflp7H3xI9ry8liwVAAAAzUBICg/V9ybNzc1V6d+/GAAAABiNKcBzd6rSVauVe8D2RA3icGjfm28q89rrvFgZAAAAvImQFB5iDthr6x8FBQU+qAQAAMD3OvznTgXEx0sBAYo660xlP/645HI16Vlla9d6uToAAAB4CyEpPGRmZtboi42N9UElAAAAvhfSv7+6/7BEvVavUtRJJ8tlszXqfnNUlPtz+HHHebs8AAAAeAmn28PDDz/84NHu3Llzo/c2BQAAaGvK165VxjXXePSZgoLkqqhwt8NPOEHFB+znbu3cWV0+/ED5H38ic0iIok+b3Gr1AgAAoHEISeHh9ddf92hPmDDBq8/PyspSdnZ2o+7ZvHmzV2sAAABorKxHH5Oz2hZEBwakkmSJjFBAQoLsWVmSpPDhw2WJilLsxRe1VpkAAABoIkJSuH355ZdasmSJR99FF13k1Xc8//zzmjFjhlefCQAA0OLMB9+lqmD2HHVbMF8Fn38uS1ycok8/vRUKAwAAgDewJykkSfv27dOVV17p0Tdp0iQNHTrURxUBAAD4j4TbbpUpKKj+QS6XLPHxirv6akUef7wyb7hRWyZMVPZzz7VOkQAAAGgyZpJCTqdT559/vjIyMtx9UVFReuaZZ3xYFQAAgH/IefkVlSxbpqCePVX+xx91jjOFhsj8917uWU8+qaJvv626/9n/U8jhhyt8xIhWqRcAAACNR0gK3XLLLfrqq688+l566SUlJyd7/V3Tpk3TlClTGnXP5s2bNWnSJK/XAgAAcDAFn32m7CeeaNDY0MFHuD/bs3M8rtkbuSc7AAAAWhchqcE988wzeqLaF/9bb71VZ511Vou8LyEhQQkJCS3ybAAAAG9x2e0yBQSoYtu2Bt8Tf/117s/tzjtXJUuXylVRocDOnRU+enQLVAkAAABvISQ1sPfee0833HCDR99FF12khx56yDcFAQAA+JjL6dTuf/9bBZ/PlTUxUdFTpzbsRotF+XPmqOKvTYo+bbLChg1T16++VOXODAX36SNLeFjLFg4AAIBmISQ1qC+++EJTp06Vy+Vy95122ml69dVXZTKZfFgZAACA7xQvWqyCzz6XJNkyMpTTwKX2cjiU//4s5b8/Sy67TTFnnilrp06ydurUgtUCAADAWzjd3oAWLlyoKVOmyG63u/vGjRun999/XxaLxYeVAQAA+JbTbvNouyorG/2MovkLvFUOAAAAWgkhqcEsX75cp5xyisrLy919w4YN0yeffKLAv09jBQAAMKqieV/ub1gskrnxK2xKfvhBebM+cLedZWWy5+V5ozwAAAC0EEJSA/njjz80fvx4FRcXu/sGDhyoL7/8UmFh7JMFAACMzbZ3r4q++WZ/h8Mh2ex131CPgi/mSpKKFy/WX8OO0aajh2n3vfd6oUoAAAC0BEJSg9i4caPGjRunvANmMRx22GH65ptvFBUV5cPKAAAA/IMlPFymJq6sCR0xXDLv/2od1CVNkrT34UfkKiuTJOXP+kDl69c3v1AAAAB4HQc3GcD27ds1duxYZWVlufu6dOmi+fPnKz4+3oeVAQAA+J7Lbte+d96Rffcetbv8cuU+/7x0wOGWDRF6xBGKOe005X/8sQKTkpUw/SZJkinA8+u2yWr1Wt0AAADwHkLSNm737t0aM2aMMjIy3H2JiYn67rvvlJiY6MPKAAAA/MPehx5W3jvvSJLM4eEKHztGxY08fKngo9lqd9FUJf3f/8l8wGzUDvfcrYzrrpejoEBxV1yuoG7dvFo7AAAAvIOQtA3bt2+fxo0bpy1btrj74uPjNX/+fHXp0sWHlQEAAPiP0hUr3J+dxcUq/u77Rj/DtnOn9t7/gEp++klJzz4rk6nqwKfQwYPV48elcjkcMlksXqsZAAAA3sWepG1UUVGRTjzxRP3555/uvujoaH377bc67LDDfFgZAACAfwk78kj3Z1NwsOR0NvlZxQu+04Z+/bXn/gc8+glIAQAA/BszSduoU045Rb/++qtH30033aScnBwtWNC45WODBw9WTEyMN8sDAADwGwm33KzA1M6y7dota2In7bnn3uY90GZT3rvvKuL44xV25FCv1AgAAICWRUjaRi1atKhG3913392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" ] From 8fdb56e6eab22bb6e140cba75ed6b08e637816db Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 18 May 2023 16:42:59 -0400 Subject: [PATCH 0172/1072] update title --- examples/scembed/projection.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/scembed/projection.ipynb b/examples/scembed/projection.ipynb index 9bce1b08..95fd5ce1 100644 --- a/examples/scembed/projection.ipynb +++ b/examples/scembed/projection.ipynb @@ -57,7 +57,9 @@ "adata.var = peaks\n", "```\n", "\n", - "The data we are using in this example already has this complete:" + "**The data we are using in this example already has this completed for you.**\n", + "\n", + "### Retreive preprocessed data:" ] }, { From 44f300fde8961498dcbe47bae67feeadccaa1291 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Thu, 18 May 2023 16:55:12 -0400 Subject: [PATCH 0173/1072] fixed typo --- gitk/assess/cli.py | 3 +-- gitk/likelihood/cli.py | 2 +- tests/consesnus/cli_test.sh | 18 +++++++++--------- 3 files changed, 11 insertions(+), 12 deletions(-) diff --git a/gitk/assess/cli.py b/gitk/assess/cli.py index a746fb28..cb0bb3e0 100644 --- a/gitk/assess/cli.py +++ b/gitk/assess/cli.py @@ -10,7 +10,7 @@ def build_subparser(parser): parser.add_argument("--universe", type=str, required=True) parser.add_argument("--npool", default=4, type=int) parser.add_argument("--save-to-file", action="store_true") - parser.add_argument("--folder_out", type=str) + parser.add_argument("--folder-out", type=str) parser.add_argument("--pref", type=str) return parser @@ -36,6 +36,5 @@ def build_mode_parser(parser): subparsers[k] = sp.add_parser(k, description=v, help=v) subparsers["distance"] = build_subparser_distance(subparsers["distance"]) subparsers["intersection"] = build_subparser(subparsers["intersection"]) - subparsers["recovered"] = build_subparser(subparsers["recovered"]) return parser diff --git a/gitk/likelihood/cli.py b/gitk/likelihood/cli.py index 855db13a..772ac354 100644 --- a/gitk/likelihood/cli.py +++ b/gitk/likelihood/cli.py @@ -33,7 +33,7 @@ def build_subparser_universe_flexible(parser): def build_subparser(parser): sp = parser.add_subparsers(dest="subcommand") msg_by_cmd = { - "build_model": "Asses based on distance", + "build_model": "Assess based on distance", "universe_hard": "Making cut-off universe", "universe_flexible": "Making ML flexible universe", } diff --git a/tests/consesnus/cli_test.sh b/tests/consesnus/cli_test.sh index 6d25d0af..c1f9ad0c 100644 --- a/tests/consesnus/cli_test.sh +++ b/tests/consesnus/cli_test.sh @@ -4,14 +4,14 @@ rm -r tests/consesnus/results mkdir tests/consesnus/results # make lh model -gitk lh build_model --model_folder tests/consesnus/results/lh_model.tar \ - --file_no 4 \ - --coverage_folder tests/consesnus/coverage/ +gitk lh build_model --model-folder tests/consesnus/results/lh_model.tar \ + --file-no 4 \ + --coverage-folder tests/consesnus/coverage/ mkdir tests/consesnus/results/universe/ # make cut-off universe -gitk lh universe_hard --coverage_file tests/consesnus/coverage/all_core.bw \ - --fout tests/consesnus/results/universe/cut_off_ML.bed +gitk lh universe_hard --coverage-file tests/consesnus/coverage/all_core.bw \ + --fout tests/consesnus/results/cut_off.bed gitk lh universe_hard --coverage_file tests/consesnus/coverage/all_core.bw \ --cut_off 2 \ @@ -28,7 +28,7 @@ gitk lh universe_flexible --model_folder tests/consesnus/results/lh_model.tar \ --output_file tests/consesnus/results/universe/ML_flexible.bed # make HMM universe -gitk hmm --out_file tests/consesnus/results/universe/hmm_raw.bed --cov_folder tests/consesnus/coverage/ +gitk hmm --out-file tests/consesnus/results/universe/hmm_raw.bed --cov-folder tests/consesnus/coverage/ gitk hmm --out_file tests/consesnus/results/universe/hmm_norm.bed --cov_folder tests/consesnus/coverage/ --normlaize --save_max_cove @@ -45,8 +45,8 @@ gitk hmm --out_file tests/consesnus/results/universe/hmm_norm.bed --cov_folder t --save_to_file --folder_out tests/consesnus/results/distance/ \ --pref test_flex --npool 1 --save_each --flexible -gitk assess intersection --raw_data_folder tests/consesnus/raw/\ - --file_list tests/consesnus/file_list.txt \ +gitk assess intersection --raw-data_folder tests/consesnus/raw/\ + --file-list tests/consesnus/file_list.txt \ --universe tests/consesnus/results/universe/ML_flexible.bed \ - --save_to_file --folder_out tests/consesnus/results/intersection/ \ + --save-to-file --folder_out tests/consesnus/results/intersection/ \ --pref test --npool 1 From 336891b8780d600da920974a254f0d73f39d08dc Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 19 May 2023 09:31:49 -0400 Subject: [PATCH 0174/1072] fixed transition matrix --- gitk/hmm/const.py | 2 +- gitk/hmm/hmm.py | 25 ++++++++++++++++--------- 2 files changed, 17 insertions(+), 10 deletions(-) diff --git a/gitk/hmm/const.py b/gitk/hmm/const.py index 34f56a79..d8388081 100644 --- a/gitk/hmm/const.py +++ b/gitk/hmm/const.py @@ -1,7 +1,7 @@ TRANSMAT = [ [1 - 1e-10, 1e-10, 0, 0], [0, 1 - 1e-6, 1e-6, 0], - [0, 0, 1 - 1e-6, 1e-6], + [0, 0, 1 - 1e-10, 1e-10], [0.1, 0, 0, 0.9], ] diff --git a/gitk/hmm/hmm.py b/gitk/hmm/hmm.py index 3d65a12d..26e720b4 100644 --- a/gitk/hmm/hmm.py +++ b/gitk/hmm/hmm.py @@ -83,8 +83,12 @@ def read_data(start, core, end, chrom, normalize=True): def find_full_full_pos(seq, gap_size=1000, area_size=500): - """Look for nonzero positions in coverage matrix, - when most of the positions are zero""" + """Look for nonzero positions in coverage matrix, when most of the positions are zero + :param ndarray seq: vector with information about non-zero positions + :param int gap_size: size of minium gap between non-zero positions that are separated + :param int area_size: size of the area around non-zero positions to be included in the result + :return list: list of starts of non-zero regions and list of ends of non-zero regions + """ size = len(seq) seq = np.argwhere(seq >= 1).flatten() starts, ends = [], [] @@ -101,8 +105,12 @@ def find_full_full_pos(seq, gap_size=1000, area_size=500): def find_full_empty_pos(seq, gap_size=10000, area_size=1000): - """Look for nonzero positions in coverage matrix, - when most of the positions are nonzero""" + """Look for nonzero positions in coverage matrix, when most of the positions are nonzero + :param ndarray seq: vector with information about non-zero positions + :param int gap_size: size of minium gap between non-zero positions that are separated + :param int area_size: size of the area around non-zero positions to be included in the result + :return list: list of starts of non-zero regions and list of ends of non-zero regions + """ size = len(seq) seq = np.argwhere(seq == 0).flatten() starts, ends = [], [] @@ -150,13 +158,12 @@ def ana_region(region, start_s): def predictions_to_bed(states, chrom, bedname, save_max_cove=False, cove_file=None): """ Save HMM prediction into a file - :param array states: result of HMM prediction + :param ndarray states: result of HMM prediction :param str chrom: which chromosome is being analysed :param str bedname: path to the output file - :param bool save_max_cove: whether to save the maximum peak - coverage to output file, can result in nonstandard bed file - :param str cove_file: file with core coverage, require for - saving maximum peak coverage + :param bool save_max_cove: whether to save the maximum peak coverage to output + file, can result in nonstandard bed file + :param str cove_file: file with core coverage, require for saving maximum peak coverage """ ind = np.argwhere(states != 3) ind = ind.flatten() From 7b3a972172b888a3e4598d9c9642bd4428640672 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 19 May 2023 11:01:48 -0400 Subject: [PATCH 0175/1072] change to subsampled data --- examples/scembed/projection.ipynb | 37 +++++++++++++++++-------------- 1 file changed, 20 insertions(+), 17 deletions(-) diff --git a/examples/scembed/projection.ipynb b/examples/scembed/projection.ipynb index 95fd5ce1..66a6c8b6 100644 --- a/examples/scembed/projection.ipynb +++ b/examples/scembed/projection.ipynb @@ -29,9 +29,9 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install gitk\n", + "!pip install gitk\n", "# or if using local version:\n", - "# %pip install ." + "!pip install ." ] }, { @@ -64,30 +64,33 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "--2023-05-18 14:44:11-- http://big.databio.org/scembed/data/pbmc.h5ad\n", + "--2023-05-19 10:58:16-- http://big.databio.org/scembed/data/pbmc_sampled.h5ad\n", "Resolving big.databio.org (big.databio.org)... 128.143.223.179\n", "Connecting to big.databio.org (big.databio.org)|128.143.223.179|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", - "Length: 13570752714 (13G) [application/octet-stream]\n", + "Length: 687117706 (655M) [application/octet-stream]\n", "Saving to: â€pbmc/pbmc.h5ad’\n", "\n", - "pbmc/pbmc.h5ad 100%[===================>] 12.64G 56.0MB/s in 4m 19s \n", + "pbmc/pbmc.h5ad 100%[===================>] 655.29M 7.35MB/s in 82s \n", "\n", - "2023-05-18 14:48:31 (49.9 MB/s) - â€pbmc/pbmc.h5ad’ saved [13570752714/13570752714]\n", + "2023-05-19 10:59:37 (8.02 MB/s) - â€pbmc/pbmc.h5ad’ saved [687117706/687117706]\n", "\n" ] } ], "source": [ - "%mkdir -p pbmc\n", - "%wget \"http://big.databio.org/scembed/data/pbmc.h5ad\" -O \"pbmc/pbmc.h5ad\"" + "!mkdir -p pbmc\n", + "!wget \"http://big.databio.org/scembed/data/pbmc_sampled.h5ad\" -O \"pbmc/pbmc.h5ad\"\n", + "\n", + "# or use the full dataset (careful, it's 12GB!):\n", + "# %wget \"http://big.databio.org/scembed/data/pbmc.h5ad\" -O \"pbmc/pbmc.h5ad\"" ] }, { @@ -106,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -118,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -143,9 +146,9 @@ "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", "KI270727.1\t52092\t52991\n", "\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 26181.45it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [02:59<00:00, 56.92it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:43<00:00, 99.10it/s] \n" + "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 24910.45it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 512/512 [00:08<00:00, 57.31it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 512/512 [00:05<00:00, 96.74it/s] \n" ] } ], @@ -166,7 +169,7 @@ { "data": { "text/plain": [ - "(10246, 100)" + "(512, 100)" ] }, "execution_count": 6, @@ -239,7 +242,7 @@ }, { "data": { - "image/png": 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" ] From d38560ebb54e74f04828d1b19bbb2bce6ce49a8d Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 19 May 2023 12:13:34 -0400 Subject: [PATCH 0176/1072] add distance from universe to file --- gitk/assess/cli.py | 46 ++++-- gitk/assess/distance.py | 312 +++++++++++++++++++++++++--------------- 2 files changed, 232 insertions(+), 126 deletions(-) diff --git a/gitk/assess/cli.py b/gitk/assess/cli.py index cb0bb3e0..2816dc9e 100644 --- a/gitk/assess/cli.py +++ b/gitk/assess/cli.py @@ -5,21 +5,49 @@ def build_subparser(parser): :return argparse.ArgumentParser """ - parser.add_argument("--raw-data-folder", type=str, required=True) - parser.add_argument("--file-list", type=str, required=True) - parser.add_argument("--universe", type=str, required=True) - parser.add_argument("--npool", default=4, type=int) - parser.add_argument("--save-to-file", action="store_true") - parser.add_argument("--folder-out", type=str) - parser.add_argument("--pref", type=str) + parser.add_argument( + "--raw-data-folder", help="folder with raw data", type=str, required=True + ) + parser.add_argument( + "--file-list", + help="list of files that need to be assessed", + type=str, + required=True, + ) + parser.add_argument("--universe", help="universe file", type=str, required=True) + parser.add_argument( + "--no-workers", help="number of core that should be used", default=4, type=int + ) + parser.add_argument( + "--save-to-file", + help="if save statistics for each BED file to a file", + action="store_true", + ) + parser.add_argument( + "--folder-out", help="folder to which save the statistic", type=str + ) + parser.add_argument("--pref", help="statistic file prefix", type=str) return parser def build_subparser_distance(parser): parser = build_subparser(parser) - parser.add_argument("--flexible", action="store_true") - parser.add_argument("--save-each", action="store_true") + parser.add_argument( + "--flexible", + help="if calculate the distance taking into account that the universe is flexible", + action="store_true", + ) + parser.add_argument( + "--save-each", + help="if save distance for each peak in each file ", + action="store_true", + ) + parser.add_argument( + "--universe-to-file", + help="if calculate distance from universe to file", + action="store_true", + ) return parser diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index bf5cc8a1..d5440c8f 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -1,11 +1,9 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -import argparse import os import tempfile from multiprocessing import Pool -from typing import Any, List import numpy as np @@ -13,35 +11,53 @@ from .utils import check_if_uni_sorted, prep_data, process_db_line -def flexible_distance(r, q): - """Calculate region distance for univers""" - if r[0] <= q <= r[1]: +def flexible_distance_between_two_regions(region, query): + """Calculate distance between region and flexible region from flexible universe + :param [int, int] region: region from flexible universe + :param int query: analysed region + :return int: distance + """ + if region[0] <= query <= region[1]: return 0 else: - return min(abs(r[0] - q), abs(r[1] - q)) + return min(abs(region[0] - query), abs(region[1] - query)) -def distance(r, q): - """Calculate distance for hard universe""" - return abs(r[0] - q) +def distance_between_two_regions(region, query): + """Calculate distance between region and region from hard universe + :param [int] region: region from hard universe + :param int query: analysed region + :return int: distance + """ + return abs(region[0] - query) -def asses(db, db_que, i, current_chrom, unused_db, pos_index, flexible): +def distance_to_closest_region( + db, db_queue, i, current_chrom, unused_db, pos_index, flexible, uni_to_file +): """ Calculate distance from given peak to the closest region in universe :param file db: universe file - :param list db_que: que of three last positions in universe + :param list db_queue: queue of three last positions in universe :param int i: analysed position from the query :param str current_chrom: current analysed chromosome from query :param list unused_db: list of positions from universe that were not compared to query :param list pos_index: which indexes from universe region use to calculate distance :param bool flexible: whether the universe if flexible + :param bool uni_to_file: whether calculate distance from universe to file :return int: peak distance to universe """ if flexible: - dist_to_db_que = [flexible_distance(j, i) for j in db_que] + if uni_to_file: + dist_to_db_que = [ + flexible_distance_between_two_regions(i, j[0]) for j in db_queue + ] + else: + dist_to_db_que = [ + flexible_distance_between_two_regions(j, i) for j in db_queue + ] else: - dist_to_db_que = [distance(j, i) for j in db_que] + dist_to_db_que = [distance_between_two_regions(j, i) for j in db_queue] min_pos = np.argmin(dist_to_db_que) while min_pos == 2: d = db.readline().strip("\n") @@ -51,46 +67,47 @@ def asses(db, db_que, i, current_chrom, unused_db, pos_index, flexible): if pos_chrom != current_chrom: unused_db.append([pos, pos_chrom]) return dist_to_db_que[min_pos] - db_que[:-1] = db_que[1:] - db_que[-1] = pos + db_queue[:-1] = db_queue[1:] + db_queue[-1] = pos if flexible: - dist_to_db_que = [flexible_distance(j, i) for j in db_que] + if uni_to_file: + dist_to_db_que = [ + flexible_distance_between_two_regions(i, j[0]) for j in db_queue + ] + else: + dist_to_db_que = [ + flexible_distance_between_two_regions(j, i) for j in db_queue + ] else: - dist_to_db_que = [distance(j, i) for j in db_que] + dist_to_db_que = [distance_between_two_regions(j, i) for j in db_queue] min_pos = np.argmin(dist_to_db_que) return dist_to_db_que[min_pos] -def process_line( +def read_in_new_universe_regions( db, q_chrom, current_chrom, unused_db, - db_que, - dist, + db_queue, waiting, - start, pos_index, - flexible, ): """ - Calculate distance from new peak to universe + Read in new universe regions closest to the peak :param file db: universe file - :param str q_chrom: on which chromosome id the new peak + :param str q_chrom: new peak's chromosome :param str current_chrom: chromosome that was analysed so far :param list unused_db: list of positions from universe that were not compared to query - :param list db_que: que of three last positions in universe - :param list dist: list of all calculated distances + :param list db_queue: que of three last positions in universe :param bool waiting: whether iterating through file, without calculating distance, if present chromosome not present in universe - :param start: analysed position from the query - :param pos_index: which indexes from universe region use to calculate distance - :param flexible: whether the universe if flexible - :return: if iterating through chromosome not present in universe; current chromosome in query + :param list pos_index: which indexes from universe region use to calculate distance + :return bool, str: if iterating through chromosome not present in universe; current chromosome in query """ if q_chrom != current_chrom: # change chromosome - db_que.clear() + db_queue.clear() # clean up the que if len(unused_db) == 0: d = db.readline().strip("\n") @@ -108,43 +125,41 @@ def process_line( current_chrom = q_chrom if current_chrom == unused_db[-1][1]: waiting = False - db_que.append(unused_db[-1][0]) + db_queue.append(unused_db[-1][0]) unused_db.clear() elif natural_chr_sort(unused_db[-1][1], current_chrom) == 1: # chrom present in file not in DB waiting = True return waiting, current_chrom - while len(db_que) < 3: + while len(db_queue) < 3: d = db.readline().strip("\n") if d == "": break d_start, d_start_chrom = process_db_line(d, pos_index) if d_start_chrom == current_chrom: - db_que.append(d_start) + db_queue.append(d_start) elif natural_chr_sort(d_start_chrom, current_chrom) == 1: unused_db.append([d_start, d_start_chrom]) waiting = True return waiting, current_chrom - if len(db_que) == 0: + if len(db_queue) == 0: waiting = True - if not waiting: - res = asses(db, db_que, start, current_chrom, unused_db, pos_index, flexible) - dist.append(res) return waiting, current_chrom -def calc_distance( - db_file, +def calc_distance_between_two_files( q_folder, + universe, q_file, - flexible=False, - save_each=False, - folder_out=None, - pref=None, + flexible, + save_each, + folder_out, + pref, + uni_to_file=False, ): """ - For given file calculate distance to the nearst region from universe - :param str db_file: path to universe + Maine function for calculating distance between regions in file query to regions in database + :param str universe: path to universe :param str q_folder: path to folder containing query files :param str q_file: query file :param boolean flexible: whether the universe if flexible @@ -152,92 +167,127 @@ def calc_distance( :param str folder_out: output folder :param str pref: prefix used as the name of the folder containing calculated distance for each file + :param uni_to_file: whether to calculate distance from universe to file :return str, int, int: file name; median od distance of starts to starts in universe; median od distance of ends to ends in universe """ - q = tempfile.NamedTemporaryFile() - prep_data(q_folder, q_file, q) - db_start = open(db_file) - db_que_start = [] - current_chrom_start = "chr0" - dist_start = [] - unused_db_start = [] - waiting_start = False - db_end = open(db_file) - db_que_end = [] - current_chrom_end = "chr0" - dist_end = [] - unused_db_end = [] - waiting_end = False - pos_start = [1] - pos_end = [2] - if flexible: - pos_start = [1, 6] - pos_end = [7, 2] - for i in q: - i = i.decode("utf-8").split("\t") - start = int(i[1]) - end = int(i[2]) - q_chrom = i[0] - res_start = process_line( - db_start, - q_chrom, - current_chrom_start, - unused_db_start, - db_que_start, - dist_start, - waiting_start, - start, - pos_start, - flexible, - ) - (waiting_start, current_chrom_start) = res_start - res_end = process_line( - db_end, - q_chrom, - current_chrom_end, - unused_db_end, - db_que_end, - dist_end, - waiting_end, - end, - pos_end, - flexible, - ) - (waiting_end, current_chrom_end) = res_end - q.close() + + query = tempfile.NamedTemporaryFile() + prep_data(q_folder, q_file, query) + if uni_to_file: + db_start_name = query.name + db_end_name = query.name + query = open(universe) + else: + db_start_name = universe + db_end_name = universe + with open(db_start_name) as db_start, open(db_end_name) as db_end: + db_queue_start = [] + current_chrom_start = "chr0" + dist_start = [] + unused_db_start = [] + waiting_start = False + db_queue_end = [] + current_chrom_end = "chr0" + dist_end = [] + unused_db_end = [] + waiting_end = False + pos_start = [1] + pos_end = [2] + if flexible and not uni_to_file: + pos_start = [1, 6] + pos_end = [7, 2] + for i in query: + if not uni_to_file: + i = i.decode("utf-8") + i = i.split("\t") + if uni_to_file and flexible: + start = [int(i[1]), int(i[6])] + end = [int(i[7]), int(i[2])] + else: + start = int(i[1]) + end = int(i[2]) + q_chrom = i[0] + result_start = read_in_new_universe_regions( + db_start, + q_chrom, + current_chrom_start, + unused_db_start, + db_queue_start, + waiting_start, + pos_start, + ) + (waiting_start, current_chrom_start) = result_start + if not waiting_start: + result = distance_to_closest_region( + db_start, + db_queue_start, + start, + current_chrom_start, + unused_db_start, + pos_start, + flexible, + uni_to_file, + ) + dist_start.append(result) + result_end = read_in_new_universe_regions( + db_end, + q_chrom, + current_chrom_end, + unused_db_end, + db_queue_end, + waiting_end, + pos_end, + ) + (waiting_end, current_chrom_end) = result_end + if not waiting_end: + res = distance_to_closest_region( + db_end, + db_queue_end, + start, + current_chrom_end, + unused_db_end, + pos_end, + flexible, + uni_to_file, + ) + dist_start.append(res) + query.close() if save_each: with open(os.path.join(folder_out, pref, q_file), "w") as f: for i, j in zip(dist_start, dist_end): f.write(f"{i}\t{j}\n") if not dist_start: print(f"File {q_file} doesn't contain any chromosomes present in universe") - return q_file, None, None - return q_file, np.median(dist_start), np.median(dist_end) + return q_file, None + dist = dist_start + dist_end + return q_file, np.median(dist) def run_distance( folder, file_list, universe, - npool, + no_workers, flexible=False, save_to_file=False, folder_out=None, pref=None, save_each=False, + uni_to_file=False, ): """ For group of files calculate distance to the nearest region in universe :param str folder: path to folder containing query files :param str file_list: path to file containing list of query files :param str universe: path to universe file - :param int npool: number of parallel processes + :param int no_workers: number of parallel processes :param bool flexible: whether the universe if flexible :param bool save_to_file: whether to save median of calculated distances for each file :param str folder_out: output folder :param str pref: prefix used for saving :param bool save_each: whether to save calculated distances for each file + :param uni_to_file: whether to calculate distance from universe to file :return float; float: mean of median distances from starts in query to the nearest starts in universe; mean of median distances from ends in query to the nearest ends in universe """ @@ -245,30 +295,58 @@ def run_distance( files = open(file_list).read().split("\n")[:-1] res = [] if folder_out: - os.makedirs(folder_out, exist_ok=True) - if save_each: - os.makedirs(os.path.join(folder_out, pref)) - if npool <= 1: + os.makedirs(os.path.join(folder_out, pref), exist_ok=True) + if no_workers <= 1: for i in files: - r = calc_distance( - universe, folder, i, flexible, save_each, folder_out, pref + r = calc_distance_between_two_files( + universe, folder, i, flexible, save_each, folder_out, pref, uni_to_file ) res.append(r) else: - with Pool(npool) as p: + with Pool(no_workers) as p: args = [ - (universe, folder, f, flexible, save_each, folder_out, pref) + ( + universe, + folder, + f, + flexible, + save_each, + folder_out, + pref, + uni_to_file, + ) for f in files ] - res = p.starmap(calc_distance, args) + res = p.starmap(calc_distance_between_two_files, args) if save_to_file: - fout = os.path.join(folder_out, pref + "_data.tsv") - with open(fout, "w") as o: - o.write("file\tmedian_dist_start\tmedian_dist_end\n") + file_out = os.path.join(folder_out, pref + "_data.tsv") + with open(file_out, "w") as o: + o.write("file\tmedian_dist\n") for r in res: - o.write(f"{r[0]}\t{r[1]}\t{r[2]}\n") + o.write(f"{r[0]}\t{r[1]}\n") else: res = np.array(res) - res = res[:, 1:] - res = res.astype("float") - return np.mean([np.nanmedian(res[:, 0]), np.nanmedian(res[:, 1])]) + return res + + +def get_closeness_score(folder, file_list, universe, no_workers, flexible=False): + file_to_uni = run_distance( + folder, + file_list, + universe, + no_workers, + flexible=flexible, + uni_to_file=False, + ) + + uni_to_file = run_distance( + folder, + file_list, + universe, + no_workers, + flexible=flexible, + uni_to_file=True, + ) + me = (file_to_uni[:, 1].astype("float") + uni_to_file[:, 1].astype("float")) / 2 + cs = 11 / (me + 10) / 1.1 + return np.mean(cs) From 0bfcca09fa2b8a8bdf0ad34f99309f6d0177dce4 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 19 May 2023 12:20:40 -0400 Subject: [PATCH 0177/1072] improve documentation --- gitk/assess/intersection.py | 59 +++++++++++++++++++------------------ 1 file changed, 30 insertions(+), 29 deletions(-) diff --git a/gitk/assess/intersection.py b/gitk/assess/intersection.py index 9c5e5361..96605d34 100644 --- a/gitk/assess/intersection.py +++ b/gitk/assess/intersection.py @@ -12,21 +12,12 @@ def chrom_cmp(a, b): - """Natural chromosome names comparison""" - ac = a.replace("chr", "") - ac = ac.split("_")[0] - bc = b.replace("chr", "") - bc = bc.split("_")[0] - if bc.isnumeric() and ac.isnumeric() and bc != ac: - if int(bc) < int(ac): - return b, True, False - else: - return a, False, True + """Return smaller chromosome name""" + c = natural_chr_sort(a, b) + if c > 0: + return b, True, False else: - if b < a: - return b, True, False - else: - return a, False, True + return a, False, True def relationship_helper(region_a, region_b, only_in, overlap): @@ -43,15 +34,17 @@ def relationship_helper(region_a, region_b, only_in, overlap): overlap += region_b[1] - region_b[0] start_a, start_b = region_b[1], region_b[1] inside_b, inside_a = False, True + return only_in, inside_a, inside_b, overlap, start_a, start_b elif region_b[1] > region_a[1]: overlap += region_a[1] - region_b[0] start_a, start_b = region_a[1], region_a[1] inside_a, inside_b = False, True + return only_in, inside_a, inside_b, overlap, start_a, start_b elif region_b[0] > region_a[1]: only_in += region_a[1] - region_a[0] inside_a, inside_b = False, True start_a, start_b = region_a[1], region_b[0] - return only_in, inside_a, inside_b, overlap, start_a, start_b + return only_in, inside_a, inside_b, overlap, start_a, start_b def two_region_intersection_diff( @@ -68,7 +61,7 @@ def two_region_intersection_diff( waiting_q, ): """ - Check mutual position and calculate intersection and difference of two regions + Check mutual position of two regions and calculate intersection and difference of two regions :param list region_d: region from universe :param list region_q: region from query :param int only_in_d: number of base pair only in universe @@ -99,7 +92,10 @@ def two_region_intersection_diff( return only_in_d, only_in_q, inside_d, inside_q, overlap, start_d, start_q -def read_in_new_line(region, start, chrom, inside, waiting, lines, cchrom, not_e): +def read_in_new_line(region, start, chrom, inside, waiting, lines, c_chrom, not_e): + """ + Read in a new line from query or universe file + """ if not inside: if not waiting: line = lines.readline() @@ -108,7 +104,7 @@ def read_in_new_line(region, start, chrom, inside, waiting, lines, cchrom, not_e line = line.strip("\n") if line != "": region, start, chrom = process_line(line) - if chrom != cchrom: + if chrom != c_chrom: waiting = True else: not_e = False @@ -194,14 +190,20 @@ def calc_diff_intersection(db, folder, query): def run_intersection( - folder, file_list, universe, npool, save_to_file=False, folder_out=None, pref=None + folder, + file_list, + universe, + no_workers, + save_to_file=False, + folder_out=None, + pref=None, ): """ Calculate the base pair intersection of universe and group of files :param str folder: path to folder containing query files :param str file_list: path to file containing list of query files :param str universe: path to universe file - :param int npool: number of parallel processes + :param int no_workers: number of parallel processes :param str save_to_file: whether to save median of calculated distances for each file :param str folder_out: output folder :param str pref: prefix used for saving @@ -211,21 +213,20 @@ def run_intersection( check_if_uni_sorted(universe) if save_to_file: os.makedirs(folder_out, exist_ok=True) - # os.mkdir("tmp") - files = open(file_list).read().split("\n")[:-1] + with open(file_list) as f: + files = f.read().split("\n")[:-1] res = [] - if npool <= 1: + if no_workers <= 1: for i in files: r = calc_diff_intersection(universe, folder, i) res.append(r) else: - with Pool(npool) as p: + with Pool(no_workers) as p: args = [(universe, folder, f) for f in files] res = p.starmap(calc_diff_intersection, args) - # os.rmdir("tmp") if save_to_file: - fout = os.path.join(folder_out, pref + "_data.tsv") - with open(fout, "w") as o: + file_out = os.path.join(folder_out, pref + "_data.tsv") + with open(file_out, "w") as o: o.write("file\tunivers/file\tfile/universe\tuniverse&file\n") for r in res: o.write(f"{r[0]}\t{r[1]}\t{r[2]}\t{r[3]}\n") @@ -235,5 +236,5 @@ def run_intersection( res = res.astype("float") recall = res[:, 2] / (res[:, 2] + res[:, 1]) precision = res[:, 2] / (res[:, 2] + res[:, 0]) - F_10 = (1 + 10**2) * (precision * recall) / ((10**2 * precision) + recall) - return np.median(F_10) + f_10 = (1 + 10**2) * (precision * recall) / ((10**2 * precision) + recall) + return np.median(f_10) From fa2f1f11a5377a00a871f77bbad6ce5a7192ffa8 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 19 May 2023 12:31:57 -0400 Subject: [PATCH 0178/1072] new lh model --- gitk/assess/likelihood.py | 213 +++++++++++++++++++++++++++----------- 1 file changed, 150 insertions(+), 63 deletions(-) diff --git a/gitk/assess/likelihood.py b/gitk/assess/likelihood.py index afee0149..b7123a35 100644 --- a/gitk/assess/likelihood.py +++ b/gitk/assess/likelihood.py @@ -1,18 +1,42 @@ +import numpy as np import os +from .utils import check_if_uni_sorted +from ..utils import read_chromosome_from_bw +from ..likelihood.build_model import ModelLH -import numpy as np -from ..likelihood.build_model import ModelLH -from .utils import check_if_uni_sorted +class LhModel: + def __init__(self, model, cove): + """ + Object with combined information about lh model and coverage + :param ndarray model: lh model array + :param ndarray cove: coverage array + """ + self.model = model + self.cove = cove + + def __getitem__(self, sliced): + return self.model[self.cove[sliced[0]], sliced[1]] -def calc_likelihood_hard(universe, chroms, model_lh, name, s_index, e_index=None): +def calc_likelihood_hard( + universe, + chroms, + model_lh, + coverage_folder, + coverage_prefix, + name, + s_index, + e_index=None, +): """ Calculate likelihood of universe for given type of model To be used with binomial model :param str universe: path to universe file - :param list chroms: list of chromosomes present in likelihood model - :param str model_folder: path to folder with model + :param list chroms: list of chromosomes present in model + :param ModelLH model_lh: likelihood model + :param coverage_prefix: prefix used in uniwig for creating coverage + :param coverage_folder: path to a folder with genome coverage by tracks :param str name: suffix of model file name, which contains information about model type :param int s_index: from which position in univers line take assess region @@ -21,12 +45,12 @@ def calc_likelihood_hard(universe, chroms, model_lh, name, s_index, e_index=None end position :return float: likelihood of univers for given model """ - curent_chrom = "" + current_chrom = "" missing_chrom = "" empty_start = 0 res = 0 e = 0 - prob_array = None + prob_array, cove_array = None, None with open(universe) as uni: for i in uni: e += 1 @@ -35,17 +59,22 @@ def calc_likelihood_hard(universe, chroms, model_lh, name, s_index, e_index=None if i[0] == missing_chrom: pass else: - if i[0] != curent_chrom: + if i[0] != current_chrom: if i[0] in chroms: - model_lh.clear_chrom(curent_chrom) + model_lh.clear_chrom(current_chrom) if e != 1: res += np.sum(prob_array[empty_start:, 0]) - curent_chrom = i[0] - model_lh.read_chrom_track(curent_chrom, name) - prob_array = model_lh.chromosomes_models[curent_chrom].models[ - name - ] + current_chrom = i[0] + model_lh.read_chrom_track(current_chrom, name) + prob_model = model_lh[current_chrom] + cove_array = read_chromosome_from_bw( + os.path.join( + coverage_folder, f"{coverage_prefix}_{name}.bw" + ), + current_chrom, + ) + prob_array = LhModel(prob_model[name], cove_array) empty_start = 0 else: print(f"Chromosome {i[0]} missing from model") @@ -64,39 +93,53 @@ def calc_likelihood_hard(universe, chroms, model_lh, name, s_index, e_index=None return res -def hard_universe_likelihood(model_folder, universe): +def hard_universe_likelihood(model, universe, coverage_folder, coverage_prefix): """ Calculate likelihood of hard universe based on core, start, end coverage model - :param str model_folder: path to folder containing model + :param str model: path to file containing model :param str universe: path to universe + :param coverage_prefix: prefix used in uniwig for creating coverage + :param coverage_folder: path to a folder with genome coverage by tracks :return float: likelihood """ check_if_uni_sorted(universe) - model_lh = ModelLH(model_folder) + model_lh = ModelLH(model) chroms = model_lh.chromosomes_list - s = calc_likelihood_hard(universe, chroms, model_lh, "start", 1) - e = calc_likelihood_hard(universe, chroms, model_lh, "end", 2) - c = calc_likelihood_hard(universe, chroms, model_lh, "core", 1, 2) + s = calc_likelihood_hard( + universe, chroms, model_lh, coverage_folder, coverage_prefix, "start", 1 + ) + e = calc_likelihood_hard( + universe, chroms, model_lh, coverage_folder, coverage_prefix, "end", 2 + ) + c = calc_likelihood_hard( + universe, chroms, model_lh, coverage_folder, coverage_prefix, "core", 1, 2 + ) return sum([s, e, c]) -def likelihood_only_core(model_folder, universe, core="core"): +def likelihood_only_core(model_file, universe, coverage_folder, coverage_prefix): """ - Calculate likelihood of universe based on core coverage model - :param str model_folder: path to folder containing model + Calculate likelihood of universe based only on core coverage model + :param str model_file: path to name containing model :param str universe: path to universe - :param str core: model file name + :param coverage_prefix: prefix used in uniwig for creating coverage + :param coverage_folder: path to a folder with genome coverage by tracks :return float: likelihood """ check_if_uni_sorted(universe) - model_lh = ModelLH(model_folder) + model_lh = ModelLH(model_file) chroms = model_lh.chromosomes_list - c = calc_likelihood_hard(universe, chroms, model_folder, core, 1, 2) + c = calc_likelihood_hard( + universe, chroms, model_lh, coverage_folder, coverage_prefix, "core", 1, 2 + ) return c def background_likelihood(start, end, model_start, model_cove, model_end): + """ + Calculate likelihood of background for given region + """ res = np.sum(model_start[start:end, 0]) res += np.sum(model_cove[start:end, 0]) res += np.sum(model_end[start:end, 0]) @@ -104,6 +147,16 @@ def background_likelihood(start, end, model_start, model_cove, model_end): def weigh_livelihood(start, end, model_process, model_cove, model_out, reverse): + """ + Calculate weighted likelihood of flexible part of the region + :param int start: start of the region + :param int end: end of the region + :param array model_process: model for analysed type of flexible region + :param array model_cove: model for coverage + :param array model_out: model for flexible region that is not being analysed + :param bool reverse: if model_process corespondents to end we have to reverse the weighs + :return float: likelihood of flexible part of the region + """ e_w = 1 / (end - start) # weights for processed model c_w = np.linspace( start=e_w, stop=1, num=(end - start) @@ -119,29 +172,56 @@ def weigh_livelihood(start, end, model_process, model_cove, model_out, reverse): def flexible_peak_likelihood( - startS, startE, endS, endE, model_start, model_cove, model_end + start_s, start_e, end_s, end_e, model_start, model_cove, model_end ): # core part of the peak - res = np.sum(model_cove[startE:endS, 1]) - res += np.sum(model_start[startE:endS, 0]) - res += np.sum(model_end[startE:endS, 0]) + res = np.sum(model_cove[start_e:end_s, 1]) + res += np.sum(model_start[start_e:end_s, 0]) + res += np.sum(model_end[start_e:end_s, 0]) # start part of the peak - res += weigh_livelihood(startS, startE, model_start, model_cove, model_end, False) + res += weigh_livelihood(start_s, start_e, model_start, model_cove, model_end, False) # end part of the peak - res += weigh_livelihood(endS, endE, model_end, model_cove, model_start, True) + res += weigh_livelihood(end_s, end_e, model_end, model_cove, model_start, True) return res -def likelihood_flexible_universe(model_folder, universe, save_peak_input=False): - curent_chrom = "" +def read_coverage(cove_folder, cove_prefix, current_chrom): + cove_start = read_chromosome_from_bw( + os.path.join(cove_folder, f"{cove_prefix}_start.bw"), + current_chrom, + ) + cove_core = read_chromosome_from_bw( + os.path.join(cove_folder, f"{cove_prefix}_core.bw"), + current_chrom, + ) + cove_end = read_chromosome_from_bw( + os.path.join(cove_folder, f"{cove_prefix}_end.bw"), + current_chrom, + ) + cove = {"start": cove_start, "core": cove_core, "end": cove_end} + return cove + + +def likelihood_flexible_universe( + model_file, universe, cove_folder, cove_prefix, save_peak_input=False +): + """ + Likelihood of given universe under the model + :param str model_file: path to file with lh model + :param str universe: path to universe + :param cove_folder: path to a folder with genome coverage by tracks + :param cove_prefix: prefix used in uniwig for creating coverage + :param bool save_peak_input: whether to save universe with each peak lh + :return float: lh of the flexible universe + """ + current_chrom = "" missing_chrom = "" empty_start = 0 res = 0 check_if_uni_sorted(universe) - model_lh = ModelLH(model_folder) + model_lh = ModelLH(model_file) chroms = model_lh.chromosomes_list - if save_peak_input: - output = [] + output = [] e = 0 # number of processed chromosomes with open(universe) as uni: for line in uni: @@ -151,58 +231,65 @@ def likelihood_flexible_universe(model_folder, universe, save_peak_input=False): if i[0] == missing_chrom: pass else: - if i[0] != curent_chrom: + if i[0] != current_chrom: if i[0] in chroms: - model_lh.clear_chrom(curent_chrom) + model_lh.clear_chrom(current_chrom) if e != 0: # if we read any chromosomes add to result background # likelihood of part of the genome after the last region res += background_likelihood( empty_start, - len(model_start), - model_start, - model_core, - model_end, + chr_size, + prob_start, + prob_core, + prob_end, ) - curent_chrom = i[0] + current_chrom = i[0] e += 1 - model_lh.read_chrom(curent_chrom) - model_start = model_lh.chromosomes_models[curent_chrom].models[ - "start" - ] - model_core = model_lh.chromosomes_models[curent_chrom].models[ - "core" - ] - model_end = model_lh.chromosomes_models[curent_chrom].models[ - "end" - ] + model_lh.read_chrom(current_chrom) + models_current = model_lh[current_chrom] + cove_current = read_coverage( + cove_folder, cove_prefix, current_chrom + ) + chr_size = len(cove_current["start"]) + prob_start = LhModel( + models_current["start"], cove_current["start"] + ) + prob_core = LhModel( + models_current["core"], cove_current["core"] + ) + prob_end = LhModel(models_current["end"], cove_current["end"]) else: print(f"Chromosome {i[0]} missing from model") missing_chrom = i[0] res += background_likelihood( - empty_start, peak_start_s, model_start, model_core, model_end + empty_start, + peak_start_s, + prob_start, + prob_core, + prob_end, ) peak_likelihood = flexible_peak_likelihood( peak_start_s, peak_start_e, peak_end_s, peak_end_e, - model_start, - model_core, - model_end, + prob_start, + prob_core, + prob_end, ) res += peak_likelihood if save_peak_input: - backgroung = background_likelihood( - peak_start_s, peak_end_e, model_start, model_core, model_end + background = background_likelihood( + peak_start_s, peak_end_e, prob_start, prob_core, prob_end ) - contribution = peak_likelihood - backgroung + contribution = peak_likelihood - background output.append("{}\t{}\n".format(line.strip("\n"), contribution)) empty_start = peak_end_e res += background_likelihood( - empty_start, len(model_start), model_start, model_core, model_end + empty_start, chr_size, prob_start, prob_core, prob_end ) if save_peak_input: print("saving") From 66a4708304ebf1896f544ac31122618a0bc8f00d Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 19 May 2023 12:45:38 -0400 Subject: [PATCH 0179/1072] switch to compressed pickle objects --- gitk/scembed/scembed.py | 8 +++++--- gitk/scembed/utils.py | 6 ++++-- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index 99e75d1d..e7cbfaea 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -1,3 +1,4 @@ +import gzip import pickle from logging import getLogger from typing import Dict, List, Union @@ -90,7 +91,7 @@ def save_model(self, filepath: str, **kwargs): Instead, we will just use pickle to dump the object. """ - with open(filepath, "wb") as f: + with gzip.open(filepath, "wb") as f: pickle.dump(self, f) def load_model(self, filepath: str, **kwargs): @@ -100,8 +101,9 @@ def load_model(self, filepath: str, **kwargs): Instead we will just use pickle to load the object. Override the current object. """ - with open(filepath, "rb") as f: - self = pickle.load(f) + with gzip.open(filepath, "rb") as f: + obj = pickle.load(f) + self.__dict__.update(obj.__dict__) def train( self, diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index e2edd5e6..1b894985 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -265,9 +265,11 @@ def load_scembed_model(path: str) -> "SCEmbed": :param str path: The path to the model. """ import pickle + import gzip - with open(path, "rb") as f: - return pickle.load(f) + with gzip.open(path, "rb") as f: + model = pickle.load(f) + return model def check_model_exists_on_hub(registry: str) -> bool: From 9a2c706f61ce87d331382342358d2db6576f6255 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 19 May 2023 12:50:41 -0400 Subject: [PATCH 0180/1072] new lh model --- gitk/cli.py | 27 ++---- gitk/likelihood/build_model.py | 135 ++++++++++++++++----------- gitk/likelihood/cli.py | 63 ++++++++++--- gitk/likelihood/universe_flexible.py | 104 ++++++++++++--------- gitk/likelihood/universe_hard.py | 25 +++-- 5 files changed, 214 insertions(+), 140 deletions(-) diff --git a/gitk/cli.py b/gitk/cli.py index db81445f..735a4685 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -78,7 +78,7 @@ def main(test_args=None): args.raw_data_folder, args.file_list, args.universe, - args.npool, + args.no_workers, args.flexible, args.save_to_file, args.folder_out, @@ -93,20 +93,7 @@ def main(test_args=None): args.raw_data_folder, args.file_list, args.universe, - args.npool, - args.save_to_file, - args.folder_out, - args.pref, - ) - - if args.subcommand == "recovered": - from .assess.recovered import run_recovered - - run_recovered( - args.raw_data_folder, - args.file_list, - args.universe, - args.npool, + args.no_workers, args.save_to_file, args.folder_out, args.pref, @@ -118,7 +105,7 @@ def main(test_args=None): from .likelihood.build_model import main main( - args.model_folder, + args.model_file, args.coverage_folder, args.coverage_prefix, args.file_no, @@ -127,14 +114,14 @@ def main(test_args=None): if args.subcommand == "universe_hard": from .likelihood.universe_hard import main - main( - args.coverage_file, args.fout, args.merge, args.filter_size, args.cutoff - ) + main(args.coverage_file, args.fout, args.merge, args.filter_size, args.cutoff) if args.subcommand == "universe_flexible": from .likelihood.universe_flexible import main - main(args.model_folder, args.output_file) + main( + args.model_file, args.cov_folder, args.coverage_prefix, args.output_file + ) if args.command == "hmm": from .hmm.hmm import run_hmm_save_bed diff --git a/gitk/likelihood/build_model.py b/gitk/likelihood/build_model.py index 3ed75a39..0411b676 100644 --- a/gitk/likelihood/build_model.py +++ b/gitk/likelihood/build_model.py @@ -8,33 +8,25 @@ import numpy as np import pyBigWig -from ..utils import timer_func +from ..utils import timer_func, read_chromosome_from_bw -def model_binomial(folder_in, in_file, chrom, file_out, file_no=None, start=0): - """ "Create binomial likelihood model - First column likelihood of background - Second column likelihood of coverage""" +def model_binomial(folder_in, in_file, chrom, file_out, file_no=None): + """Create binomial likelihood model + first column - likelihood of background + second column - likelihood of coverage""" in_file = os.path.join(folder_in, in_file) - bw = pyBigWig.open(in_file) - chrom_size = bw.chroms(chrom) - if pyBigWig.numpy: - distr_cov = bw.values(chrom, start, chrom_size, numpy=True) - else: - distr_cov = bw.values(chrom, start, chrom_size) - distr_cov = np.array(distr_cov) - distr_cov[np.isnan(distr_cov)] = 0 + distr_cov = read_chromosome_from_bw(in_file, chrom) + chrom_size = len(distr_cov) no_possible = file_no * len(distr_cov) # number of possible spots covered no_cov = np.sum(distr_cov) # number of spots covered - no_ncov = np.subtract(no_possible, no_cov) # number of spots uncovered - distr_ncov = np.subtract( - file_no, distr_cov - ) # for each position in how many files is empty - cov = distr_cov / no_cov - ncov = distr_ncov / no_ncov - p_cov = np.log10(cov + 1e-10) - p_ncov = np.log10(ncov + 1e-10) - prob_array = np.vstack((p_ncov, p_cov)).T + cov_uniq = np.unique(distr_cov).astype(np.uint16) + prob_array = np.zeros((int(np.max(cov_uniq)) + 1, 2)) + for i in cov_uniq: + prob_array[i] = [ + np.log10((file_no - i) / (no_possible - no_cov) + 1e-10), + np.log10((i / no_cov) + 1e-10), + ] header = f"{chrom}_{chrom_size}" r = {header: prob_array} np.savez_compressed(file_out, **r) @@ -42,6 +34,10 @@ def model_binomial(folder_in, in_file, chrom, file_out, file_no=None, start=0): class ChromosomeModel: def __init__(self, folder, chrom): + """ + :param str folder: file with the model + :param str chrom: of which chromosome is the model + """ self.folder = folder self.chromosome = chrom self.start_file = f"{self.chromosome}_start" @@ -54,32 +50,42 @@ def __init__(self, folder, chrom): } self.models = {} - def make_model( - self, coverage_folder, coverage_start, coverage_end, coverage_core, file_no - ): + def __getitem__(self, item): + return self.models[item] + + def make_model(self, coverage_folder, coverage_prefix, file_no): + """ + Make a lh model of given chromosome from coverage files + :param str coverage_folder: path to name with coverage files + :param str coverage_prefix: prefixed used for making coverage files + :param int file_no: number of files from which model is being created + """ model_binomial( coverage_folder, - coverage_start, + f"{coverage_prefix}_start.bw", self.chromosome, os.path.join(self.folder, self.start_file), file_no, ) model_binomial( coverage_folder, - coverage_core, + f"{coverage_prefix}_core.bw", self.chromosome, os.path.join(self.folder, self.core_file), file_no, ) model_binomial( coverage_folder, - coverage_end, + f"{coverage_prefix}_end.bw", self.chromosome, os.path.join(self.folder, self.end_file), file_no, ) def read(self): + """ + Read model + """ model_folder = tarfile.open(self.folder, "r") for f in self.files: file = model_folder.extractfile(self.files[f]) @@ -88,6 +94,9 @@ def read(self): model_folder.close() def read_track(self, track): + """ + Read specific track from model + """ model_folder = tarfile.open(self.folder, "r") file = model_folder.extractfile(self.files[track]) values = np.load(file) @@ -96,18 +105,40 @@ def read_track(self, track): class ModelLH: - def __init__(self, folder): - self.folder = folder + def __init__(self, file): + """ + Likelihood model class + :param str file: file containing the model + """ + self.name = file self.chromosomes_list = [] self.chromosomes_models = {} - if os.path.exists(self.folder): - if tarfile.is_tarfile(self.folder): - files = tarfile.open(self.folder, "r") + if os.path.exists(self.name): + if tarfile.is_tarfile(self.name): + files = tarfile.open(self.name, "r") chroms = files.getnames() self.chromosomes_list = list(set([i.split("_")[0] for i in chroms])) - def make(self, coverage_folder, coverage_prefix, file_no): - tar_arch = tarfile.open(self.folder, "w") + def __getitem__(self, item): + return self.chromosomes_models[item] + + def make(self, coverage_folder, coverage_prefix, file_no, force=False): + """ + Make lh model for all chromosomes + :param str coverage_folder: folder with coverage files + :param str coverage_prefix: prefixed used for making coverage files + :param int file_no: number of file from which model is being made + :param bool force: if overwrite an existing model + """ + if os.path.exists(self.name): + if not force: + print( + "Model already exists. If you want to overwrite it use force argument" + ) + return + else: + print("Overwriting existing model") + tar_arch = tarfile.open(self.name, "w") temp_dir = tempfile.TemporaryDirectory() bw_start = pyBigWig.open( os.path.join(coverage_folder, f"{coverage_prefix}_start.bw") @@ -119,9 +150,7 @@ def make(self, coverage_folder, coverage_prefix, file_no): chrom_model = ChromosomeModel(temp_dir.name, c) chrom_model.make_model( coverage_folder, - f"{coverage_prefix}_start.bw", - f"{coverage_prefix}_core.bw", - f"{coverage_prefix}_end.bw", + coverage_prefix, file_no, ) for f in chrom_model.files: @@ -134,32 +163,34 @@ def make(self, coverage_folder, coverage_prefix, file_no): tar_arch.close() def read_chrom(self, chrom): - self.chromosomes_models[chrom] = ChromosomeModel(self.folder, chrom) + """ + Read into model specific chromosome + """ + self.chromosomes_models[chrom] = ChromosomeModel(self.name, chrom) self.chromosomes_models[chrom].read() def read_chrom_track(self, chrom, track): - self.chromosomes_models[chrom] = ChromosomeModel(self.folder, chrom) + """ + Read into model specific track for chromosome + """ + self.chromosomes_models[chrom] = ChromosomeModel(self.name, chrom) self.chromosomes_models[chrom].read_track(track) def clear_chrom(self, chrom): + """ + Clear model for given chromosome + """ self.chromosomes_models[chrom] = None @timer_func -def main( - model_folder, - coverage_folder, - coverage_prefix, - file_no=None, -): +def main(model_file, coverage_folder, coverage_prefix, file_no=None, force=False): """ Crate likelihood models for all chromosomes - :param str model_folder: output folder + :param str model_file: output name :param str coverage_folder: folder with coverage files - :param str coverage_start: file with coverage of start without extension - :param str coverage_end: file with coverage of end without extension - :param str coverage_core: file with coverage of core without extension + :param str coverage_prefix: prefix used for making coverage files :param int file_no: number of files used for making coverage tracks """ - model = ModelLH(model_folder) - model.make(coverage_folder, coverage_prefix, file_no) + model = ModelLH(model_file) + model.make(coverage_folder, coverage_prefix, file_no, force) diff --git a/gitk/likelihood/cli.py b/gitk/likelihood/cli.py index 772ac354..9417b642 100644 --- a/gitk/likelihood/cli.py +++ b/gitk/likelihood/cli.py @@ -5,28 +5,67 @@ def build_subparser_model(parser): :return argparse.ArgumentParser """ - parser.add_argument("--model-folder", required=True, type=str) - parser.add_argument("--file-no", type=int) - parser.add_argument("--coverage-folder", required=True, type=str) - parser.add_argument("--coverage-prefix", default="all", type=str) + parser.add_argument( + "--model-file", help="path to file with lh model", required=True, type=str + ) + parser.add_argument( + "--file-no", help="number of files used to make the model", type=int + ) + parser.add_argument( + "--coverage-folder", help="path to coverage folder", required=True, type=str + ) + parser.add_argument( + "--coverage-prefix", + help="prefix used when making coverage files", + default="all", + type=str, + ) return parser def build_subparser_universe_hard(parser): - parser.add_argument("--merge", default=0, type=int) - parser.add_argument("--filter-size", default=0, type=int) - parser.add_argument("--fout", required=True, type=str) - parser.add_argument("--coverage-file", required=True, type=str) - parser.add_argument("--cutoff", type=int) + parser.add_argument( + "--merge", + help="distance between output peaks that should be merged into one in output universe", + default=0, + type=int, + ) + parser.add_argument( + "--filter-size", + help="minimal siez of the region in the universe", + default=0, + type=int, + ) + parser.add_argument( + "--fout", help="output file with the universe", required=True, type=str + ) + parser.add_argument( + "--coverage-file", help="path to core coverage file", required=True, type=str + ) + parser.add_argument( + "--cutoff", help="cutoff value used for making universe", type=int + ) return parser def build_subparser_universe_flexible(parser): - parser.add_argument("--output-file", required=True, type=str) - parser.add_argument("--model-folder", required=True, type=str) - + parser.add_argument( + "--output-file", help="path to output, universe file", required=True, type=str + ) + parser.add_argument( + "--model-file", help="path to lh model file", required=True, type=str + ) + parser.add_argument( + "--coverage-prefix", + help="prefixed used for making coverage files", + default="all", + type=str, + ) + parser.add_argument( + "--cov-folder", type=str, help="path to coverage folder", required=True + ) return parser diff --git a/gitk/likelihood/universe_flexible.py b/gitk/likelihood/universe_flexible.py index ffdd423b..ff74481b 100644 --- a/gitk/likelihood/universe_flexible.py +++ b/gitk/likelihood/universe_flexible.py @@ -7,30 +7,40 @@ import numpy as np from numba import njit -from ..hmm.hmm import find_full_empty_pos, find_full_full_pos, predictions_to_bed -from ..utils import natural_chr_sort, timer_func +from ..utils import natural_chr_sort, timer_func, read_chromosome_from_bw +from ..hmm.hmm import predictions_to_bed, find_full from .build_model import ModelLH @njit -def process_part(model): +def process_part( + cove, + model_start=np.array([[]]), + model_core=np.array([[]]), + model_end=np.array([[]]), +): """ Finding ML path through matrix using dynamic programing - :param array mat: fragment of likelihood model to be processed - :return array: ML path through matrix + :param ndarray cove: coverage tracks + :param ndarray model_start: lh model for starts + :param ndarray model_core: lh model for core + :param ndarray model_end: lh model for ends + :return ndarray: ML path through matrix """ - mat = np.zeros((len(model), 4)) + mat = np.zeros((len(cove), 4)) (N, M) = mat.shape background = [0, 2, 4] for i in range(N): - for k in background: - mat[i, 3] += model[i, k] - for j in range(M - 1): - back = background[:] - back.remove(2 * j) - mat[i, j] = model[i, 2 * j + 1] - for k in back: - mat[i, j] += model[i, k] + start_b = model_start[cove[i, 0], 0] + core_b = model_core[cove[i, 1], 0] + end_b = model_end[cove[i, 2], 0] + start = model_start[cove[i, 0], 1] + core = model_core[cove[i, 1], 1] + end = model_end[cove[i, 2], 1] + mat[i, 0] = start + core_b + end_b + mat[i, 1] = start_b + core + end_b + mat[i, 2] = start_b + core_b + end + mat[i, 3] = start_b + core_b + end_b for i in range(1, N): for j in range(M): @@ -46,46 +56,54 @@ def process_part(model): return path -def make_ml_flexible_universe(model_lh, chrom, fout): +def make_ml_flexible_universe(model_lh, cove_folder, cove_prefix, chrom, file_out): """ Make ML flexible universe per chromosome - :param str folderin: input folder with likelihood models + :param ModelLH model_lh: lh model + :param str cove_folder: path to a folder with genome coverage by tracks + :param str cove_prefix: prefix used in uniwig for creating coverage :param str chrom: chromosome to be processed - :param str fout: output file with the universe + :param str file_out: output file with the universe """ model_lh.read_chrom(chrom) - chrom_model = model_lh.chromosomes_models[chrom] - model = np.hstack( - ( - chrom_model.models["start"], - chrom_model.models["core"], - chrom_model.models["end"], - ) + chrom_model = model_lh[chrom] + start = read_chromosome_from_bw( + os.path.join(cove_folder, f"{cove_prefix}_start.bw"), chrom + ) + core = read_chromosome_from_bw( + os.path.join(cove_folder, f"{cove_prefix}_core.bw"), chrom + ) + end = read_chromosome_from_bw( + os.path.join(cove_folder, f"{cove_prefix}_end.bw"), chrom ) - model_lh.clear_chrom(chrom) - seq = np.where(np.sum(model[:, [1, 3, 5]], axis=1) > -30, 1, 0).astype(np.uint8) - full_pos_no = np.sum(seq) - if full_pos_no < len(seq) - full_pos_no: - full_start, full_end = find_full_full_pos(seq) - else: - full_start, full_end = find_full_empty_pos(seq) - path = np.full(len(model), 3, dtype=np.uint8) + cove = np.zeros((len(start), 3), dtype=np.uint16) + cove[:, 0] = start + cove[:, 1] = core + cove[:, 2] = end + full_start, full_end = find_full(cove) + path = np.full(len(cove), 3, dtype=np.uint8) for s, e in zip(full_start, full_end): - res = process_part(model[s:e]) - path[s:e] = res - predictions_to_bed(path, chrom, fout) + res = process_part( + cove[s:e], + chrom_model["start"], + chrom_model["core"], + chrom_model["end"], + ) + + predictions_to_bed(path, chrom, file_out) -@timer_func -def main(folderin, fout): +def main(folder_in, cove_folder, cove_prefix, file_out): """ Make ML flexible universe - :param str folderin: input folder with likelihood models - :param str fout: output file with the universe + :param str folder_in: input name with likelihood models + :param str file_out: output file with the universe + :param str cove_folder: path to a folder with genome coverage by tracks + :param str cove_prefix: prefix used in uniwig for creating coverage """ - if os.path.isfile(fout): - raise Exception(f"File : {fout} exists") - lh_model = ModelLH(folderin) + if os.path.isfile(file_out): + raise Exception(f"File : {file_out} exists") + lh_model = ModelLH(folder_in) chroms = sorted(lh_model.chromosomes_list, key=cmp_to_key(natural_chr_sort)) for C in chroms: - make_ml_flexible_universe(lh_model, C, fout) + make_ml_flexible_universe(lh_model, cove_folder, cove_prefix, C, file_out) diff --git a/gitk/likelihood/universe_hard.py b/gitk/likelihood/universe_hard.py index 15fbee1a..5257de47 100644 --- a/gitk/likelihood/universe_hard.py +++ b/gitk/likelihood/universe_hard.py @@ -3,7 +3,6 @@ import os from functools import cmp_to_key -from time import time import numpy as np import pyBigWig @@ -34,16 +33,16 @@ def get_uni(file, chrom, cutoff=None): return inter_pos -def save_simple(fout, inter_pos, chrom): +def save_simple(file_out, inter_pos, chrom): """ Save cut-off universe to a file without any processing - :param str fout: output file + :param str file_out: output file :param bool vector inter_pos: whether each position should be included in universe :param str chrom: chromosome to analyse """ inter_pos_uni = np.argwhere(inter_pos) start = inter_pos_uni[0][0] - with open(fout, "a") as f: + with open(file_out, "a") as f: for i in range(1, len(inter_pos_uni)): if inter_pos_uni[i] - inter_pos_uni[i - 1] != 1: end = inter_pos_uni[i - 1][0] + 1 @@ -53,10 +52,10 @@ def save_simple(fout, inter_pos, chrom): f.write(f"{chrom}\t{start}\t{end}\n") -def marge_filter(fout, inter_pos, chrom, merge_dist=100, size_flt=1000): +def marge_filter(file_out, inter_pos, chrom, merge_dist=100, size_flt=1000): """ Save cut-off universe to a file with filtering region size and merging close regions - :param fout: output file + :param file_out: output file :param bool vector inter_pos: whether each position should be included in universe :param str chrom: chromosome to analyse :param int merge_dist: regions closer than merge_dist will be merged into one @@ -64,7 +63,7 @@ def marge_filter(fout, inter_pos, chrom, merge_dist=100, size_flt=1000): """ inter_pos_uni = np.argwhere(inter_pos) start = inter_pos_uni[0][0] - with open(fout, "a") as f: + with open(file_out, "a") as f: for i in range(1, len(inter_pos_uni)): if inter_pos_uni[i] - inter_pos_uni[i - 1] >= merge_dist: end = inter_pos_uni[i - 1][0] + 1 @@ -76,17 +75,17 @@ def marge_filter(fout, inter_pos, chrom, merge_dist=100, size_flt=1000): f.write(f"{chrom}\t{start}\t{end}\n") -def main(file, fout, merge=0, filter_size=0, cutoff=None): +def main(file, file_out, merge=0, filter_size=0, cutoff=None): """ Creat cut-off universe based on coverage track :param str file: path to coverage file without extension :param int merge: regions closer than this value will be merged into one :param int filter_size: regions smaller than this value will not be reported - :param str fout: output file + :param str file_out: output file :param int cutoff: base pairs with coverage equal to or greater than this value will be included in the universe """ - if os.path.isfile(fout): - raise Exception(f"File : {fout} exists") + if os.path.isfile(file_out): + raise Exception(f"File : {file_out} exists") bw_start = pyBigWig.open(file) chroms = bw_start.chroms() bw_start.close() @@ -97,6 +96,6 @@ def main(file, fout, merge=0, filter_size=0, cutoff=None): if chroms[chrom] > 0: inter_pos = get_uni(file, chrom, cutoff) if merge == 0 and filter_size == 0: - save_simple(fout, inter_pos, chrom) + save_simple(file_out, inter_pos, chrom) else: - marge_filter(fout, inter_pos, chrom, merge, filter_size) + marge_filter(file_out, inter_pos, chrom, merge, filter_size) From 3bad8c4b73be06098396a1aef96895295a120d38 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 19 May 2023 13:13:10 -0400 Subject: [PATCH 0181/1072] working ml flex --- gitk/cli.py | 5 ++++- gitk/likelihood/cli.py | 6 +++++- gitk/likelihood/universe_flexible.py | 2 +- gitk/utils.py | 1 + tests/consesnus/cli_test.sh | 7 ++++--- 5 files changed, 15 insertions(+), 6 deletions(-) diff --git a/gitk/cli.py b/gitk/cli.py index 735a4685..88f5907d 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -109,12 +109,15 @@ def main(test_args=None): args.coverage_folder, args.coverage_prefix, args.file_no, + args.force, ) if args.subcommand == "universe_hard": from .likelihood.universe_hard import main - main(args.coverage_file, args.fout, args.merge, args.filter_size, args.cutoff) + main( + args.coverage_file, args.fout, args.merge, args.filter_size, args.cutoff + ) if args.subcommand == "universe_flexible": from .likelihood.universe_flexible import main diff --git a/gitk/likelihood/cli.py b/gitk/likelihood/cli.py index 9417b642..ea201d6a 100644 --- a/gitk/likelihood/cli.py +++ b/gitk/likelihood/cli.py @@ -20,7 +20,11 @@ def build_subparser_model(parser): default="all", type=str, ) - + parser.add_argument( + "--force", + help="if overwrite existing model", + action="store_true", + ) return parser diff --git a/gitk/likelihood/universe_flexible.py b/gitk/likelihood/universe_flexible.py index ff74481b..eecf4553 100644 --- a/gitk/likelihood/universe_flexible.py +++ b/gitk/likelihood/universe_flexible.py @@ -29,7 +29,6 @@ def process_part( """ mat = np.zeros((len(cove), 4)) (N, M) = mat.shape - background = [0, 2, 4] for i in range(N): start_b = model_start[cove[i, 0], 0] core_b = model_core[cove[i, 1], 0] @@ -89,6 +88,7 @@ def make_ml_flexible_universe(model_lh, cove_folder, cove_prefix, chrom, file_ou chrom_model["core"], chrom_model["end"], ) + path[s:e] = res predictions_to_bed(path, chrom, file_out) diff --git a/gitk/utils.py b/gitk/utils.py index 856771a0..48ba4039 100644 --- a/gitk/utils.py +++ b/gitk/utils.py @@ -44,6 +44,7 @@ def read_chromosome_from_bw(file, chrom): else: cove = bw.values(chrom, 0, chrom_size) cove = np.array(cove) + cove[np.isnan(cove)] = 0 return cove.astype(np.uint16) diff --git a/tests/consesnus/cli_test.sh b/tests/consesnus/cli_test.sh index c1f9ad0c..0b14749b 100644 --- a/tests/consesnus/cli_test.sh +++ b/tests/consesnus/cli_test.sh @@ -4,7 +4,7 @@ rm -r tests/consesnus/results mkdir tests/consesnus/results # make lh model -gitk lh build_model --model-folder tests/consesnus/results/lh_model.tar \ +gitk lh build_model --model-file tests/consesnus/results/lh_model.tar \ --file-no 4 \ --coverage-folder tests/consesnus/coverage/ @@ -24,8 +24,9 @@ gitk lh universe_hard --coverage_file tests/consesnus/coverage/all_core.bw \ --fout tests/consesnus/results/universe/cut_off_c1_m100_f300.bed # make ML flexible universe -gitk lh universe_flexible --model_folder tests/consesnus/results/lh_model.tar \ - --output_file tests/consesnus/results/universe/ML_flexible.bed +gitk lh universe_flexible --model-file tests/consesnus/results/lh_model.tar \ + --output-file tests/consesnus/results/ML_flexible.bed \ + --cov-folder tests/consesnus/coverage/ # make HMM universe gitk hmm --out-file tests/consesnus/results/universe/hmm_raw.bed --cov-folder tests/consesnus/coverage/ From f18f296661be0953f7a8e6ff97d7204235458600 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 19 May 2023 14:26:08 -0400 Subject: [PATCH 0182/1072] fixed order of arguments --- gitk/assess/distance.py | 4 ++-- tests/consesnus/cli_test.sh | 20 ++++++++++---------- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index d5440c8f..8c06de7c 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -148,8 +148,8 @@ def read_in_new_universe_regions( def calc_distance_between_two_files( - q_folder, universe, + q_folder, q_file, flexible, save_each, @@ -251,7 +251,7 @@ def calc_distance_between_two_files( flexible, uni_to_file, ) - dist_start.append(res) + dist_end.append(res) query.close() if save_each: with open(os.path.join(folder_out, pref, q_file), "w") as f: diff --git a/tests/consesnus/cli_test.sh b/tests/consesnus/cli_test.sh index 0b14749b..569c4f4a 100644 --- a/tests/consesnus/cli_test.sh +++ b/tests/consesnus/cli_test.sh @@ -29,16 +29,16 @@ gitk lh universe_flexible --model-file tests/consesnus/results/lh_model.tar \ --cov-folder tests/consesnus/coverage/ # make HMM universe -gitk hmm --out-file tests/consesnus/results/universe/hmm_raw.bed --cov-folder tests/consesnus/coverage/ +gitk hmm --out-file tests/consesnus/results/hmm_raw.bed --cov-folder tests/consesnus/coverage/ gitk hmm --out_file tests/consesnus/results/universe/hmm_norm.bed --cov_folder tests/consesnus/coverage/ --normlaize --save_max_cove # assessment - gitk assess distance --raw_data_folder tests/consesnus/raw/\ - --file_list tests/consesnus/file_list.txt \ - --universe tests/consesnus/results/universe/ML_flexible.bed \ - --save_to_file --folder_out tests/consesnus/results/distance/ \ - --pref test --npool 1 --save_each + gitk assess distance --raw-data-folder tests/consesnus/raw/\ + --file-list tests/consesnus/file_list.txt \ + --universe tests/consesnus/results/ML_flexible.bed \ + --save-to-file --folder-out tests/consesnus/results/distance/ \ + --pref test --no-workers 1 --save-each gitk assess distance --raw_data_folder tests/consesnus/raw/\ --file_list tests/consesnus/file_list.txt \ @@ -46,8 +46,8 @@ gitk hmm --out_file tests/consesnus/results/universe/hmm_norm.bed --cov_folder t --save_to_file --folder_out tests/consesnus/results/distance/ \ --pref test_flex --npool 1 --save_each --flexible -gitk assess intersection --raw-data_folder tests/consesnus/raw/\ +gitk assess intersection --raw-data-folder tests/consesnus/raw/\ --file-list tests/consesnus/file_list.txt \ - --universe tests/consesnus/results/universe/ML_flexible.bed \ - --save-to-file --folder_out tests/consesnus/results/intersection/ \ - --pref test --npool 1 + --universe tests/consesnus/results/ML_flexible.bed \ + --save-to-file --folder-out tests/consesnus/results/intersection/ \ + --pref test --no-workers 1 From f5afddecb7829821f5c543ccccd64eab12e340e5 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Mon, 22 May 2023 11:38:08 -0400 Subject: [PATCH 0183/1072] fix bug in regionset creation --- gitk/scembed/utils.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 1b894985..88f4cb35 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -272,6 +272,19 @@ def load_scembed_model(path: str) -> "SCEmbed": return model +def load_scembed_model_deprecated(path: str) -> "SCEmbed": + """ + Load a scembed model from disk. THIS IS DEPRECATED AND WILL BE REMOVED IN THE FUTURE. + + :param str path: The path to the model. + """ + import pickle + + with open(path, "rb") as f: + model = pickle.load(f) + return model + + def check_model_exists_on_hub(registry: str) -> bool: """ Check the model hub for the existing model registry. Registry @@ -416,6 +429,9 @@ def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: # Perform the comparison using numpy operations positive_values = adata.X > 0 + if not isinstance(positive_values, np.ndarray): + positive_values = positive_values.toarray() + regions = [] for i in tqdm(range(adata.shape[0]), total=adata.shape[0]): regions.append( From 138345ed7dff6ee8925580251a331aae7cdf8f51 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Mon, 22 May 2023 13:47:13 -0400 Subject: [PATCH 0184/1072] fix embedding dimension size in embeddings_to_csv --- gitk/scembed/scembed.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index e7cbfaea..30766068 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -283,7 +283,8 @@ def embeddings_to_csv(self, output_path: str): for row in list(embeddings_df.iterrows()): row_dict = row[1].to_dict() new_row_dict = { - f"embedding_dim_{i}": row_dict["embedding"][i] for i in range(100) + f"embedding_dim_{i}": row_dict["embedding"][i] + for i in range(self.vector_size) } if "id" in row_dict: new_row_dict["id"] = row_dict["id"] From b14af41b7c6335ed2e360cb78ec9e6d99eea6854 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Mon, 22 May 2023 16:32:14 -0400 Subject: [PATCH 0185/1072] revert gzip --- gitk/scembed/utils.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 88f4cb35..572bef66 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -265,9 +265,8 @@ def load_scembed_model(path: str) -> "SCEmbed": :param str path: The path to the model. """ import pickle - import gzip - with gzip.open(path, "rb") as f: + with open(path, "rb") as f: model = pickle.load(f) return model From 43eefde1f8c953f5d3640fe3d402017e1776b258 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 24 May 2023 12:24:52 -0400 Subject: [PATCH 0186/1072] make model download code more robust --- examples/scembed/projection.ipynb | 60 ++++++++++++++++++++----------- gitk/scembed/__init__.py | 2 +- gitk/scembed/models.py | 1 + gitk/scembed/projection.py | 36 +++++++++++-------- gitk/scembed/utils.py | 12 ++++--- tests/test_projector.py | 6 ++-- 6 files changed, 73 insertions(+), 44 deletions(-) diff --git a/examples/scembed/projection.ipynb b/examples/scembed/projection.ipynb index 66a6c8b6..993c009a 100644 --- a/examples/scembed/projection.ipynb +++ b/examples/scembed/projection.ipynb @@ -64,23 +64,23 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "--2023-05-19 10:58:16-- http://big.databio.org/scembed/data/pbmc_sampled.h5ad\n", + "--2023-05-23 15:52:17-- http://big.databio.org/scembed/data/pbmc.h5ad\n", "Resolving big.databio.org (big.databio.org)... 128.143.223.179\n", "Connecting to big.databio.org (big.databio.org)|128.143.223.179|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", - "Length: 687117706 (655M) [application/octet-stream]\n", + "Length: 13570752714 (13G) [application/octet-stream]\n", "Saving to: â€pbmc/pbmc.h5ad’\n", "\n", - "pbmc/pbmc.h5ad 100%[===================>] 655.29M 7.35MB/s in 82s \n", + "pbmc/pbmc.h5ad 100%[===================>] 12.64G 43.5MB/s in 5m 17s \n", "\n", - "2023-05-19 10:59:37 (8.02 MB/s) - â€pbmc/pbmc.h5ad’ saved [687117706/687117706]\n", + "2023-05-23 15:57:34 (40.8 MB/s) - â€pbmc/pbmc.h5ad’ saved [13570752714/13570752714]\n", "\n" ] } @@ -90,7 +90,7 @@ "!wget \"http://big.databio.org/scembed/data/pbmc_sampled.h5ad\" -O \"pbmc/pbmc.h5ad\"\n", "\n", "# or use the full dataset (careful, it's 12GB!):\n", - "# %wget \"http://big.databio.org/scembed/data/pbmc.h5ad\" -O \"pbmc/pbmc.h5ad\"" + "# !wget \"http://big.databio.org/scembed/data/pbmc.h5ad\" -O \"pbmc/pbmc.h5ad\"" ] }, { @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -146,9 +146,9 @@ "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", "KI270727.1\t52092\t52991\n", "\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 24910.45it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 512/512 [00:08<00:00, 57.31it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 512/512 [00:05<00:00, 96.74it/s] \n" + "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 26462.93it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [02:57<00:00, 57.81it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:43<00:00, 98.64it/s] \n" ] } ], @@ -163,16 +163,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(512, 100)" + "(10246, 100)" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -199,9 +199,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], "source": [ "# run leiden clustering on embeddings\n", "sc.pp.neighbors(pbmc, use_rep='embedding')\n", @@ -210,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -227,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -236,13 +254,13 @@ "Text(0, 0.5, 'UMAP 2')" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] diff --git a/gitk/scembed/__init__.py b/gitk/scembed/__init__.py index e04e2290..ceb7dc09 100644 --- a/gitk/scembed/__init__.py +++ b/gitk/scembed/__init__.py @@ -1,4 +1,4 @@ from .const import * +from .projection import * from .scembed import * from .utils import * -from .projection import * diff --git a/gitk/scembed/models.py b/gitk/scembed/models.py index c5b7cb3d..7cb992dc 100644 --- a/gitk/scembed/models.py +++ b/gitk/scembed/models.py @@ -1,4 +1,5 @@ from typing import List, Optional + from pydantic import BaseModel diff --git a/gitk/scembed/projection.py b/gitk/scembed/projection.py index 94bc672e..111be31b 100644 --- a/gitk/scembed/projection.py +++ b/gitk/scembed/projection.py @@ -7,16 +7,16 @@ import yacman from tqdm import tqdm -from .const import MODEL_CACHE_DIR, MODEL_HUB_URL, MODULE_NAME, MODEL_CONFIG_FILE_NAME +from .const import MODEL_CACHE_DIR, MODEL_CONFIG_FILE_NAME, MODEL_HUB_URL, MODULE_NAME +from .models import ModelCard, Universe from .utils import ( - load_scembed_model, + anndata_to_regionsets, check_model_exists_on_hub, download_remote_model, - load_universe_file, generate_var_conversion_map, - anndata_to_regionsets, + load_scembed_model, + load_universe_file, ) -from .models import ModelCard, Universe _LOGGER = logging.getLogger(MODULE_NAME) @@ -197,19 +197,27 @@ def project(self, adata: sc.AnnData, key_added: str = "embedding") -> sc.AnnData # convert each region set to a vector by averaging the region # vectors in the model + # TODO: what do we do if there are no region overlaps? i.e. the regionset + # representation of the cell is [] cell_embeddings = [] _LOGGER.info("Step 3/3: Projecting region sets into model space") for region_set in tqdm(region_sets, total=len(region_sets)): - cell_embeddings.append( - np.mean( - [ - self.get_embedding(region) - for region in region_set - if region in self.model # ignore regions not in the model - ], - axis=0, - ) + embedding = np.mean( + [ + self.get_embedding(region) + for region in region_set + if region in self.model # ignore regions not in the model + ], + axis=0, ) + # make sure not nan before appending, + # otherwise append np.zeros + if np.isnan(embedding).any(): + embedding = np.zeros( + self.model_config.model_parameters[0].embedding_dim + ) + + cell_embeddings.append(embedding) adata.obsm[key_added] = np.array(cell_embeddings) return adata diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 572bef66..06a36c26 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -1,6 +1,7 @@ import os -import subprocess import shutil +import subprocess +import urllib.request from collections import Counter from concurrent.futures import ThreadPoolExecutor from enum import Enum @@ -8,9 +9,9 @@ from random import shuffle from typing import TYPE_CHECKING, Dict, List, Union +import numpy as np import pandas as pd import scanpy as sc -import numpy as np from tqdm import tqdm if TYPE_CHECKING: @@ -305,11 +306,12 @@ def download_remote_model(registry: str, path: str, overwrite: bool = True) -> N :param str registry: the registry name of the model to download :param str path: the path to download the model to, this is the folder that will contain registry/model.yaml """ - if os.path.exists(path) and overwrite: + path_to_model = os.path.join(path, registry) + if os.path.exists(path_to_model) and overwrite: _LOGGER.debug("Removing existing model.") - shutil.rmtree(path) + shutil.rmtree(path_to_model) - cmd = f"wget -r -np -nH --cut-dirs=2 -P {path} {MODEL_HUB_URL}/{registry}" + cmd = f"wget -r -np -nH --cut-dirs=2 -P {path} -l 1 {MODEL_HUB_URL}/{registry}" result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE) if result.returncode != 0: raise ValueError("Could not download model from model-hub.") diff --git a/tests/test_projector.py b/tests/test_projector.py index b5e0d74a..5f5bd5af 100644 --- a/tests/test_projector.py +++ b/tests/test_projector.py @@ -39,7 +39,7 @@ def test_model_doesnt_exist(): @pytest.mark.skip # projector is too large to test in CI def test_download_model(): - registry = "databio/scatlas" + registry = "databio/scatlas_v2" scembed.utils.download_remote_model(registry, scembed.const.MODEL_CACHE_DIR) config_file = os.path.join( scembed.const.MODEL_CACHE_DIR, registry, scembed.const.MODEL_CONFIG_FILE_NAME @@ -49,7 +49,7 @@ def test_download_model(): @pytest.mark.skip # projector is too large to test in CI def test_load_local_model(): - registry = "databio/scatlas" + registry = "databio/scatlas_v2" config_file = os.path.join( scembed.const.MODEL_CACHE_DIR, registry, scembed.const.MODEL_CONFIG_FILE_NAME ) @@ -59,7 +59,7 @@ def test_load_local_model(): assert isinstance(projector.universe, scembed.models.Universe) -# @pytest.mark.skip # projector is too large to test in CI +@pytest.mark.skip # projector is too large to test in CI def test_init_projector(projector): assert isinstance(projector.model_config, scembed.models.ModelCard) assert isinstance(projector.model, dict) From da314972d937b7e402fb53aa2a356a7d564e0a21 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 24 May 2023 12:31:40 -0400 Subject: [PATCH 0187/1072] merged assess cli --- gitk/assess/cli.py | 34 ++++++++++++- gitk/assess/distance.py | 21 +++----- gitk/assess/intersection.py | 42 +++++++++------- gitk/cli.py | 98 ++++++++++++++++++++++++++++++++----- tests/consesnus/cli_test.sh | 22 +++------ 5 files changed, 155 insertions(+), 62 deletions(-) diff --git a/gitk/assess/cli.py b/gitk/assess/cli.py index 2816dc9e..4b64062f 100644 --- a/gitk/assess/cli.py +++ b/gitk/assess/cli.py @@ -4,7 +4,33 @@ def build_subparser(parser): :return argparse.ArgumentParser """ - + parser.add_argument( + "--overlap", + help="if calculate base-level overlap score", + action="store_true", + ) + parser.add_argument( + "--distance", + help="if calculate distance from region in query to nearest region in the universe", + action="store_true", + ) + parser.add_argument( + "--distance-universe-to-file", + help="if calculate distance from region in the universe to nearest region query", + action="store_true", + ) + parser.add_argument( + "--distance-flexible", + help="if calculate distance from region in query to nearest region in the universe taking into account " + "universe flexibility ", + action="store_true", + ) + parser.add_argument( + "--distance-flexible-universe-to-file", + help="if calculate distance from region in the universe to nearest region in query taking into account " + "universe flexibility ", + action="store_true", + ) parser.add_argument( "--raw-data-folder", help="folder with raw data", type=str, required=True ) @@ -28,6 +54,12 @@ def build_subparser(parser): ) parser.add_argument("--pref", help="statistic file prefix", type=str) + parser.add_argument( + "--save-each", + help="if save distance for each peak in each file ", + action="store_true", + ) + return parser diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index 8c06de7c..61efa60d 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -6,6 +6,7 @@ from multiprocessing import Pool import numpy as np +import pandas as pd from ..utils import natural_chr_sort from .utils import check_if_uni_sorted, prep_data, process_db_line @@ -171,16 +172,16 @@ def calc_distance_between_two_files( :return str, int, int: file name; median od distance of starts to starts in universe; median od distance of ends to ends in universe """ - query = tempfile.NamedTemporaryFile() prep_data(q_folder, q_file, query) if uni_to_file: db_start_name = query.name db_end_name = query.name - query = open(universe) + q = open(universe) else: db_start_name = universe db_end_name = universe + q = query with open(db_start_name) as db_start, open(db_end_name) as db_end: db_queue_start = [] current_chrom_start = "chr0" @@ -197,7 +198,7 @@ def calc_distance_between_two_files( if flexible and not uni_to_file: pos_start = [1, 6] pos_end = [7, 2] - for i in query: + for i in q: if not uni_to_file: i = i.decode("utf-8") i = i.split("\t") @@ -244,7 +245,7 @@ def calc_distance_between_two_files( res = distance_to_closest_region( db_end, db_queue_end, - start, + end, current_chrom_end, unused_db_end, pos_end, @@ -270,7 +271,6 @@ def run_distance( universe, no_workers, flexible=False, - save_to_file=False, folder_out=None, pref=None, save_each=False, @@ -283,7 +283,6 @@ def run_distance( :param str universe: path to universe file :param int no_workers: number of parallel processes :param bool flexible: whether the universe if flexible - :param bool save_to_file: whether to save median of calculated distances for each file :param str folder_out: output folder :param str pref: prefix used for saving :param bool save_each: whether to save calculated distances for each file @@ -318,15 +317,7 @@ def run_distance( for f in files ] res = p.starmap(calc_distance_between_two_files, args) - if save_to_file: - file_out = os.path.join(folder_out, pref + "_data.tsv") - with open(file_out, "w") as o: - o.write("file\tmedian_dist\n") - for r in res: - o.write(f"{r[0]}\t{r[1]}\n") - else: - res = np.array(res) - return res + return pd.DataFrame(res) def get_closeness_score(folder, file_list, universe, no_workers, flexible=False): diff --git a/gitk/assess/intersection.py b/gitk/assess/intersection.py index 96605d34..53988aa4 100644 --- a/gitk/assess/intersection.py +++ b/gitk/assess/intersection.py @@ -6,6 +6,7 @@ from multiprocessing import Pool import numpy as np +import pandas as pd from ..utils import natural_chr_sort from .utils import check_if_uni_sorted, prep_data, process_line @@ -194,9 +195,6 @@ def run_intersection( file_list, universe, no_workers, - save_to_file=False, - folder_out=None, - pref=None, ): """ Calculate the base pair intersection of universe and group of files @@ -211,8 +209,6 @@ def run_intersection( mean of fractions of intersection of file and universe divided by file size """ check_if_uni_sorted(universe) - if save_to_file: - os.makedirs(folder_out, exist_ok=True) with open(file_list) as f: files = f.read().split("\n")[:-1] res = [] @@ -224,17 +220,25 @@ def run_intersection( with Pool(no_workers) as p: args = [(universe, folder, f) for f in files] res = p.starmap(calc_diff_intersection, args) - if save_to_file: - file_out = os.path.join(folder_out, pref + "_data.tsv") - with open(file_out, "w") as o: - o.write("file\tunivers/file\tfile/universe\tuniverse&file\n") - for r in res: - o.write(f"{r[0]}\t{r[1]}\t{r[2]}\t{r[3]}\n") - else: - res = np.array(res) - res = res[:, 1:] - res = res.astype("float") - recall = res[:, 2] / (res[:, 2] + res[:, 1]) - precision = res[:, 2] / (res[:, 2] + res[:, 0]) - f_10 = (1 + 10**2) * (precision * recall) / ((10**2 * precision) + recall) - return np.median(f_10) + return pd.DataFrame(res) + + +def get_f_10_score( + folder, + file_list, + universe, + no_workers, +): + res = run_intersection( + folder, + file_list, + universe, + no_workers, + ) + res = np.array(res) + res = res[:, 1:] + res = res.astype("float") + recall = res[:, 2] / (res[:, 2] + res[:, 1]) + precision = res[:, 2] / (res[:, 2] + res[:, 0]) + f_10 = (1 + 10**2) * (precision * recall) / ((10**2 * precision) + recall) + return np.mean(f_10) diff --git a/gitk/cli.py b/gitk/cli.py index 88f5907d..6ff3b9e5 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -1,11 +1,12 @@ +import os.path from typing import Dict import logmuse import sys -import os +import pandas as pd from ubiquerg import VersionInHelpParser -from .assess.cli import build_mode_parser as assess_subparser +from .assess.cli import build_subparser as assess_subparser from .eval.cli import build_subparser as eval_subparser from .hmm.cli import build_subparser as hmm_subparser from .likelihood.cli import build_subparser as likelihood_subparser @@ -70,33 +71,104 @@ def main(test_args=None): _LOGGER.info(f"Command: {args.command}") if args.command == "assess": - _LOGGER.info(f"Subcommand: {args.subcommand}") - if args.subcommand == "distance": + asses_results = [] + if args.distance: from .assess.distance import run_distance - run_distance( + r_distance = run_distance( args.raw_data_folder, args.file_list, args.universe, args.no_workers, - args.flexible, - args.save_to_file, + False, args.folder_out, - args.pref, + args.pref + "_dist_file_to_universe", args.save_each, + False, ) + r_distance.columns = ["file", "median_dist_file_to_universe"] + print(r_distance) + asses_results.append(r_distance) - if args.subcommand == "intersection": - from .assess.intersection import run_intersection + if args.distance_flexible: + from .assess.distance import run_distance - run_intersection( + r_distance_flex = run_distance( args.raw_data_folder, args.file_list, args.universe, args.no_workers, - args.save_to_file, + True, args.folder_out, - args.pref, + args.pref + "_dist_file_to_universe_flex", + args.save_each, + False, + ) + r_distance_flex.columns = ["file", "median_dist_file_to_universe_flex"] + print(r_distance_flex) + asses_results.append(r_distance_flex) + + if args.distance_universe_to_file: + from .assess.distance import run_distance + + r_distance_utf = run_distance( + args.raw_data_folder, + args.file_list, + args.universe, + args.no_workers, + False, + args.folder_out, + args.pref + "_dist_universe_to_file", + args.save_each, + True, + ) + r_distance_utf.columns = ["file", "median_dist_universe_to_file"] + print(r_distance_utf) + asses_results.append(r_distance_utf) + + if args.distance_flexible_universe_to_file: + from .assess.distance import run_distance + + r_distance_utf_flex = run_distance( + args.raw_data_folder, + args.file_list, + args.universe, + args.no_workers, + True, + args.folder_out, + args.pref + "median_dist_universe_to_file_flex", + args.save_each, + True, + ) + r_distance_utf_flex.columns = ["file", "median_dist_universe_to_file_flex"] + print(r_distance_utf_flex) + asses_results.append(r_distance_utf_flex) + + if args.overlap: + from .assess.intersection import run_intersection + + r_overlap = run_intersection( + args.raw_data_folder, + args.file_list, + args.universe, + args.no_workers, + ) + r_overlap.columns = [ + "file", + "univers/file", + "file/universe", + "universe&file", + ] + print(r_overlap) + asses_results.append(r_overlap) + if len(asses_results) == 0: + raise Exception("No assessment method was provided") + if args.save_to_file: + df = asses_results[0] + for i in asses_results[1:]: + df = pd.merge(df, i, on="file") + df.to_csv( + os.path.join(args.folder_out, args.pref + "_data.csv"), index=False ) if args.command == "lh": diff --git a/tests/consesnus/cli_test.sh b/tests/consesnus/cli_test.sh index 569c4f4a..6ff1bde0 100644 --- a/tests/consesnus/cli_test.sh +++ b/tests/consesnus/cli_test.sh @@ -34,20 +34,14 @@ gitk hmm --out-file tests/consesnus/results/hmm_raw.bed --cov-folder tests/conse gitk hmm --out_file tests/consesnus/results/universe/hmm_norm.bed --cov_folder tests/consesnus/coverage/ --normlaize --save_max_cove # assessment - gitk assess distance --raw-data-folder tests/consesnus/raw/\ + gitk assess --raw-data-folder tests/consesnus/raw/\ --file-list tests/consesnus/file_list.txt \ --universe tests/consesnus/results/ML_flexible.bed \ - --save-to-file --folder-out tests/consesnus/results/distance/ \ - --pref test --no-workers 1 --save-each + --save-to-file --folder-out tests/consesnus/results/ \ + --pref test --no-workers 1 --save-each \ + --overlap \ + --distance \ + --distance-universe-to-file\ + --distance-flexible\ + --distance-flexible-universe-to-file - gitk assess distance --raw_data_folder tests/consesnus/raw/\ - --file_list tests/consesnus/file_list.txt \ - --universe tests/consesnus/results/universe/ML_flexible.bed \ - --save_to_file --folder_out tests/consesnus/results/distance/ \ - --pref test_flex --npool 1 --save_each --flexible - -gitk assess intersection --raw-data-folder tests/consesnus/raw/\ - --file-list tests/consesnus/file_list.txt \ - --universe tests/consesnus/results/ML_flexible.bed \ - --save-to-file --folder-out tests/consesnus/results/intersection/ \ - --pref test --no-workers 1 From f95bf87a9047d1b10fb02d07678be33074cb46ab Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 24 May 2023 13:16:27 -0400 Subject: [PATCH 0188/1072] update assess tutorial --- docs/likelihood/assess.md | 80 +++++++++++++++++---------------------- gitk/assess/distance.py | 4 +- 2 files changed, 36 insertions(+), 48 deletions(-) diff --git a/docs/likelihood/assess.md b/docs/likelihood/assess.md index b5c53c93..ae228b00 100644 --- a/docs/likelihood/assess.md +++ b/docs/likelihood/assess.md @@ -1,20 +1,18 @@ # How to assess universe fit to collection of files? Given a universe it is important to assess it fit to collection of data. -We will use here universes produced [eriler](consensus-peaks.md). - - -## Base pair - level overlap measure -First test checks how much of our file is present in the universe and how much -additional information is present in the univers. We can check that with: +We will use here universes produced [eriler](consensus-peaks.md). We can do it both from CLI and directly in python script. Both overlap and distance based assessments can be run using: ```gitk assess ...``` with appropriate flags that can be combined. ``` -gitk assess intersection --raw-data-folder tests/consesnus/raw/\ - --file-list tests/consesnus/file_list.txt \ - --universe tests/consenus/universe/universe.bed \ - --save-to-file \ - --folder-out tests/consesnus/results/intersection/ \ - --pref test \ - --no-workers 1 + gitk assess --assessmnet-method1 \ + --assessmnet-method2 \ + --... + --raw-data-folder tests/consesnus/raw/\ + --file-list tests/consesnus/file_list.txt \ + --universe tests/consenus/universe/universe.bed \ + --save-to-file \ + --folder-out tests/consesnus/results/intersection/ \ + --pref test \ + --no-workers 1 ``` Where: @@ -22,57 +20,46 @@ Where: - ``--file-list``, takes the path to file with list of files - ``--universe``, takes the path to file with the assessed universe - ``--save-to-file``, is a flag that specifies whether to out put table with each row -containing file name, number of bp in univers but not in file, number of bp in file -but not the univers, and number of bp both in univers and file +containing file name and results of chosen metrics - ``--folder-out``, takes the path to folder in which put the output file - ``--pref``, takes a prefix of output file name - ``--no-workers``, takes the number of workers that should be used +- ``--save-each``, is a flag that specifies whether to save between the closest peaks to file + +## Base-level overlap measure +First test checks how much of our file is present in the universe and how much +additional information is present in the univers. We can check that by adding ```--overlap``` to ```gitk assess ...```. In the result files it will output columns with: number of bp in univers but not in file, number of bp in file but not the univers, and number of bp both in univers and file -Similarly, we can do it in python: +We can also us it directly in python: ``` from gitk.assess.intersection import run_intersection -F_10 = run_intersection("test/consesnus/raw/", +run_intersection("test/consesnus/raw/", "tests/consesnus/file_list.txt", "tests/consenus/universe/universe.bed", no_workers=1) ``` -where we can directly return F10 score of the universe if we don't specify that we want to save metrics to the file. +or we can calculate F10 score of the universe using: -## Region boundary distance measure -Next, we can calculate the distance between region in file and the nearest region in the universe: -``` - gitk assess distance --raw-data-folder tests/consesnus/raw/\ - --file-list tests/consesnus/file_list.txt \ - --universe tests/consenus/universe/universe.bed \ - --save-to-file \ - --folder-out tests/consesnus/results/distance/ \ - --pref test-flex \ - --npool 1 \ - --save-each \ - --flexible ``` +from gitk.assess.intersection import get_f_10_score -Where: - -- ``--raw-data-folder``, takes the path to folder with files from the collection -- ``--file-list``, takes the path to file with list of files -- ``--universe``, takes the path to file with the assessed universe -- ``--save-to-file``, is a flag that specifies whether to out put table with each row -containing file name, median of the distances -- ``--folder-out``, takes the path to folder in which put the output file -- ``--pref``, takes a prefix of output file name -- ``--no-workers``, takes the number of workers that should be used -- ``--save-each``, is a flag that specifies whether to save for each peak in the file -distance to the closest peak in the universe -- ``--flexible``, is a flag that specifies whether we should treat the univers as -a flexible one +get_f_10_score("test/consesnus/raw/", + "tests/consesnus/file_list.txt", + "tests/consenus/universe/universe.bed", + no_workers=1) +``` -We can also calculate the distance between region in the universe and the nearest region in file. We use for that with the same command as before with additional flag ```--universe-to-file```. +## Region boundary distance measure +Next, we can calculate the distance between query and universe. To do tat we can choose from : + - ```distance``` - calculates distance from region in query to the nearest region in the universe + - ```distance-universe-to-file```- calculates distance from region in query to the nearest region in the universe accounting for universe flexibility + - ```distance-flexible``` - calculates distance from region in universe to the nearest region in the query + - ```distance-flexible-universe-to-file``` - calculates distance from region in universe to the nearest region in the query accounting for universe flexibility -Both presented distance measures can be done using python, which will result in matrix where first column is file names and the second one is median of distances. +All presented distance measures can be done using python, which will result in matrix where first column is file names and the second one is median of distances. ``` from gitk.assess.distance import run_distance @@ -93,6 +80,7 @@ closeness_score = get_closeness_score("tests/consesnus/raw", no_workers=2) ``` + ## Universe likelihood We can also calculate the likelihood of universe given collection of file. For that we diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index 61efa60d..bc2ae0b8 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -338,6 +338,6 @@ def get_closeness_score(folder, file_list, universe, no_workers, flexible=False) flexible=flexible, uni_to_file=True, ) - me = (file_to_uni[:, 1].astype("float") + uni_to_file[:, 1].astype("float")) / 2 - cs = 11 / (me + 10) / 1.1 + me = (10 * file_to_uni[1] + uni_to_file[1]) / 11 + cs = 1001 / (me + 1000) / 1.001 return np.mean(cs) From 69cc674f386d5765b8c4ea132b7e26aa9fa509cd Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Wed, 24 May 2023 16:20:37 -0400 Subject: [PATCH 0189/1072] formatting --- .vscode/settings.json | 2 +- gitk/cli.py | 4 +- gitk/eval/README.md | 8 +- gitk/eval/cct.py | 71 ++++++----------- gitk/eval/gdst.py | 80 +++----------------- gitk/eval/npt.py | 65 +++++----------- gitk/eval/permutation.R | 1 - gitk/eval/utils.py | 35 ++++++++- gitk/region2vec/main.py | 8 +- gitk/region2vec/region2vec_train.py | 39 ++++------ gitk/region2vec/region_shuffling.py | 7 +- gitk/region2vec/utils.py | 30 ++++---- gitk/tokenization/hard_tokenization_batch.py | 20 ++--- gitk/tokenization/main.py | 36 ++++----- gitk/tokenization/split_file.py | 2 +- gitk/tokenization/utils.py | 30 ++++---- 16 files changed, 157 insertions(+), 281 deletions(-) diff --git a/.vscode/settings.json b/.vscode/settings.json index 5259ece3..cb1e2a13 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -7,5 +7,5 @@ "[python]": { "editor.defaultFormatter": "ms-python.black-formatter" }, - "python.formatting.provider": "none" + "python.formatting.provider": "black" } \ No newline at end of file diff --git a/gitk/cli.py b/gitk/cli.py index 680603d6..6237feb7 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -175,9 +175,9 @@ def main(test_args=None): ) print(npt["SNPR"][0]) if args.subcommand == "cct-tss": - from gitk.eval.cct import get_scctss + from gitk.eval.cct import get_scc_tss - scctss = get_scctss( + scctss = get_scc_tss( args.model_path, args.embed_type, args.save_folder, diff --git a/gitk/eval/README.md b/gitk/eval/README.md index 79477e00..e3d6eb35 100644 --- a/gitk/eval/README.md +++ b/gitk/eval/README.md @@ -32,7 +32,7 @@ model_path1 = '/path/to/the/region2vec/model1/' model_path2 = '/path/to/the/region2vec/model2/' batch = [(model_path1, 'region2vec'), (model_path2, 'base')] # (model_path, embed_type) gds_arr = get_gds_batch(batch, num_samples=1000, seed=42, num_workers=2) # set num_workers > 1 to enable multiprocessing -print("Model1: {}, GDS:{:.4f}".format(gds_arr[0][0],gds_arr[0][1])) +print(f"Model1: {gds_arr[0][0]}, GDS:{gds_arr[0][1]:.4f}") ``` Run GDST 20 times for the two models @@ -76,7 +76,7 @@ print(result_list[1]["SNPR"][0]) # SNPRs for model2 K = 1000 resolution = 100 # increase the number of neighboring regions by resolution every time npr_results = npt_eval(batch, K, num_samples=1000, num_workers=10, num_runs=20, resolution=resolution) -print("Model: {}".format(snpr_results[0][0])) +print("Model: {snpr_results[0][0]}") print(snpr_results[0][1].shape) # (20,10) print(snpr_results[0][1][:,0]) # results from 20 runs when num_neighborhs=100 print(snpr_results[0][1][:,1]) # results from 20 runs when num_neighborhs=200 @@ -99,7 +99,7 @@ assembly = 'hg19' num_samples = 1000 K_arr = [5, 20, 40, 60, 100] threshold = 0.0001 # significance threshold, the smaller the more significant -scores = get_scctss(model_path, embed_type, save_folder, Rscript_path, assembly, K_arr, num_samples, threshold) +scores = get_scc_tss(model_path, embed_type, save_folder, Rscript_path, assembly, K_arr, num_samples, threshold) print(scores) # scroes for each K in K_arr # process a batch of two models @@ -108,7 +108,7 @@ model_path2 = '/path/to/the/region2vec/model2/' batch = [(model_path1, 'region2vec'), (model_path2, 'base')] # (model_path, embed_type) # since we have more than one models, we can rank them based on the average scores over different Ks -scores_batch, avg_ranks = get_scctss_batch(batch, save_folder, Rscript_path, assembly, K_arr, num_samples, threshold) +scores_batch, avg_ranks = get_scc_tss_batch(batch, save_folder, Rscript_path, assembly, K_arr, num_samples, threshold) # average ranks after running clustering_significance_test_batch num_runs times avg_ranks_arr = cct_tss_eval(batch, save_folder, Rscript_path, assembly, K_arr, num_samples, threshold, num_runs=20) diff --git a/gitk/eval/cct.py b/gitk/eval/cct.py index c22a0696..4da4ce90 100644 --- a/gitk/eval/cct.py +++ b/gitk/eval/cct.py @@ -10,7 +10,7 @@ import matplotlib.pyplot as plt from matplotlib.lines import Line2D import multiprocessing as mp -from gitk.eval.utils import load_genomic_embeddings +from gitk.eval.utils import load_genomic_embeddings, region2tuple import subprocess @@ -20,9 +20,9 @@ def random_annotate_points( cluster_labels = np.unique(labels) # sorted positions = np.arange(len(labels)) if cluster_labels[0] == -1: - print("Number of clusters: {}".format(len(cluster_labels) - 1)) + print(f"Number of clusters: {len(cluster_labels) - 1}") else: - print("Number of clusters: {}".format(len(cluster_labels))) + print(f"Number of clusters: {len(cluster_labels)}") clusters = [positions[labels == c] for c in cluster_labels] ratios = np.array([len(clusters[i]) / len(labels) for i in range(len(clusters))]) annotate_arr = [] @@ -54,27 +54,22 @@ def assign_color_by_size(cmap_name, labels): def get_cluster_regions(cluster_idx, labels, vocab, path): - def region2tuple(x): - eles = x.split(":") - chr_name = eles[0].strip() - start, end = eles[1].split("-") - start, end = int(start.strip()), int(end.strip()) - return chr_name, start, end - positions = np.arange(len(labels)) indices = positions[labels == cluster_idx] regions = [region2tuple(vocab[i]) for i in indices] os.makedirs(path, exist_ok=True) - with open(os.path.join(path, "cluster_{}.bed".format(cluster_idx)), "w") as f: + with open(os.path.join(path, f"cluster_{cluster_idx}.bed"), "w") as f: for chr_name, start, end in regions: - f.write("{}\t{}\t{}\n".format(chr_name, start, end)) + f.write(f"{chr_name}\t{start}\t{end}\n") def clustering(model_path, embed_type, n_clusters, save_folder, seed=0): np.random.seed(seed) embeds, vocab = load_genomic_embeddings(model_path, embed_type) - clustering = KMeans(n_clusters=n_clusters, random_state=seed, n_init="auto").fit(embeds) + clustering = KMeans(n_clusters=n_clusters, random_state=seed, n_init="auto").fit( + embeds + ) labels = clustering.labels_ cluster_idxes = np.sort(np.unique(labels)) for c in cluster_idxes: @@ -83,25 +78,12 @@ def clustering(model_path, embed_type, n_clusters, save_folder, seed=0): pickle.dump(labels, f) -def clustering_batch(batch, K, save_folder, seed=0, num_workers=10): - worker_func = clustering - with mp.Pool(processes=num_workers) as pool: - all_processes = [] - for i, (path, embed_type) in enumerate(batch): - folder = os.path.join(save_folder, "model_{}".format(i)) - os.makedirs(folder, exist_ok=True) - process = pool.apply_async(worker_func, (path, embed_type, K, folder)) - all_processes.append(process) - for process in all_processes: - process.get() - - def cal_significance_val(pvals, threshold): num = (pvals < threshold).sum() + (pvals > 1 - threshold).sum() return num / len(pvals) -def get_scctss( +def get_scc_tss( model_path, embed_type, save_folder, @@ -114,13 +96,13 @@ def get_scctss( seed=0, ): for K in K_arr: - target_folder = os.path.join(save_folder, "Kmeans_{}".format(K)) + target_folder = os.path.join(save_folder, f"Kmeans_{K}") clustering(model_path, embed_type, K, target_folder, seed=0) curr_folder = os.path.dirname(os.path.abspath(__file__)) subprocess.call( [ Rscript_path, - "{}/permutation.R".format(curr_folder), + f"{curr_folder}/permutation.R", "--assembly", assembly, "--num_workers", @@ -133,10 +115,10 @@ def get_scctss( ) scores = [] for K in K_arr: - target_folder = os.path.join(save_folder, "Kmeans_{}".format(K)) + target_folder = os.path.join(save_folder, f"Kmeans_{K}") tmp_files = glob.glob(os.path.join(target_folder, "cluster_*.bed")) - for tmp in tmp_files: - os.remove(tmp) + # for tmp in tmp_files: + # os.remove(tmp) with open(os.path.join(target_folder, "pvals.txt"), "r") as f: pvals = f.readlines() pvals = np.array([float(p.strip()) for p in pvals]) @@ -145,15 +127,13 @@ def get_scctss( print(model_path) print( "(K, CCSI): " - + " ".join( - ["({},{:.6f})".format(K_arr[i], scores[i]) for i in range(len(K_arr))] - ) + + " ".join([f"({K_arr[i]},{scores[i]:.6f})" for i in range(len(K_arr))]) ) print("\n") return scores -def get_scctss_batch( +def get_scc_tss_batch( batch, save_folder, Rscript_path, @@ -166,8 +146,8 @@ def get_scctss_batch( ): scores_batch = [] for i, (model_path, embed_type) in enumerate(batch): - target_folder = os.path.join(save_folder, "model_{}".format(i)) - scores = get_scctss( + target_folder = os.path.join(save_folder, f"model_{i}") + scores = get_scc_tss( model_path, embed_type, target_folder, @@ -181,8 +161,7 @@ def get_scctss_batch( ) scores_batch.append(scores) scores_batch = np.array(scores_batch) - avg_ranks = rankdata(-scores_batch, method="average", axis=0).mean(axis=1) - return scores_batch, avg_ranks + return scores_batch def cct_tss_eval( @@ -196,10 +175,9 @@ def cct_tss_eval( num_runs=20, num_workers=10, ): - avg_ranks_arr = [] for seed in range(num_runs): - target_folder = os.path.join(save_folder, "cct_tss_seed{}".format(seed)) - scores_batch, avg_ranks = get_scctss_batch( + target_folder = os.path.join(save_folder, f"cct_tss_seed{seed}") + scores_batch = get_scc_tss_batch( batch, target_folder, Rscript_path, @@ -210,16 +188,13 @@ def cct_tss_eval( num_workers, seed, ) - avg_ranks_arr.append(avg_ranks) - result_path = os.path.join(save_folder, "cct_tss_seed{}.pickle".format(seed)) + result_path = os.path.join(save_folder, f"cct_tss_seed{seed}.pickle") scores_batch = [ (batch[i][0], scores_batch[i]) for i in range(len(scores_batch)) ] with open(result_path, "wb") as f: pickle.dump(scores_batch, f) - avg_ranks_arr = np.vstack(avg_ranks_arr) - avg_ranks_arr = [(batch[i][0], avg_ranks_arr[:, i]) for i in range(num_runs)] - return avg_ranks_arr + return scores_batch def cct_tss_plot(avg_ranks_arr, row_labels=None, legend_pos=(0.25, 0.6), filename=None): diff --git a/gitk/eval/gdst.py b/gitk/eval/gdst.py index c49cab38..6a1805ae 100644 --- a/gitk/eval/gdst.py +++ b/gitk/eval/gdst.py @@ -6,20 +6,13 @@ import random import glob import time -import time import multiprocessing as mp import argparse from gensim.models import Word2Vec -import matplotlib.pyplot as plt -import matplotlib -from matplotlib.lines import Line2D -from matplotlib.patches import Patch from sklearn.metrics import r2_score -from gitk.eval.utils import load_genomic_embeddings +from gitk.eval.utils import load_genomic_embeddings, Timer, genome_distance from sklearn.linear_model import LinearRegression -matplotlib.rcParams["svg.fonttype"] = "none" -matplotlib.rcParams["text.usetex"] = False _log_path = None GENOME_DIST_SCALAR = 1e10 @@ -37,25 +30,6 @@ def log(obj, filename="log.txt"): f.write("\n") -func_gdist = lambda u, v: float(u[1] < v[1]) * max(v[0] - u[1] + 1, 0) + float( - u[1] >= v[1] -) * max(u[0] - v[1] + 1, 0) - - -class Timer: - def __init__(self): - self.o = time.time() - - def measure(self, p=1): - x = (time.time() - self.o) / float(p) - x = int(x) - if x >= 3600: - return "{:.1f}h".format(x / 3600) - if x >= 60: - return "{}m".format(round(x / 60)) - return "{}s".format(x) - - def sample_from_vocab(vocab, num_samples, seed=42): chr_probs = {} region_dict = {} @@ -88,13 +62,9 @@ def sample_from_vocab(vocab, num_samples, seed=42): continue sel_indexes = np.random.choice(len(regions), 2, replace=False) r1, r2 = regions[sel_indexes[0]], regions[sel_indexes[1]] - gdist = func_gdist(r1, r2) + gdist = genome_distance(r1, r2) sampled_regions.append( - ( - "{}:{}-{}".format(sel_chr, r1[0], r1[1]), - "{}:{}-{}".format(sel_chr, r2[0], r2[1]), - gdist, - ) + (f"{sel_chr}:{r1[0]}-{r1[1]}", f"{sel_chr}:{r2[0]}-{r2[1]}", gdist,) ) count += 1 return sampled_regions @@ -106,11 +76,11 @@ def remap_name(name): def convert_position(pos): if pos // 1e6 > 0: - return "{:.4f} MB".format(pos / 1e6) + return f"{pos / 1e6:.4f} MB" elif pos // 1e3 > 0: - return "{:.4f} KB".format(pos / 1e3) + return f"{pos / 1e3:.4f} KB" else: - return "{:.4f} B".format(pos) + return f"{pos:.4f} B" def get_gds_results(save_paths): @@ -207,8 +177,7 @@ def get_gds_batch( all_processes = [] for i, (path, embed_type) in enumerate(batch): process = pool.apply_async( - get_gds, - (path, embed_type, num_samples, seed, queue, i, dist), + get_gds, (path, embed_type, num_samples, seed, queue, i, dist), ) all_processes.append(process) @@ -229,11 +198,9 @@ def gds_eval( ): results_seeds = [] for seed in range(num_runs): - print("----------------Run {}----------------".format(seed)) + print(f"----------------Run {seed}----------------") save_path = ( - os.path.join(save_folder, "gds_eval_seed{}".format(seed + 42)) - if save_folder - else None + os.path.join(save_folder, f"gds_eval_seed{seed}") if save_folder else None ) result_list = get_gds_batch( batch, num_samples, seed, save_path, num_workers, dist @@ -250,33 +217,6 @@ def gds_eval( std_gds = [np.array(r).std() for r in gds_res] models = [t[0] for t in batch] for i in range(len(mean_gds)): - print( - "{}\n GDS (std): {:.4f} ({:.4f}) \n".format( - batch[i][0], mean_gds[i], std_gds[i] - ) - ) + print(f"{batch[i][0]}\n GDS (std): {mean_gds[i]:.4f} ({std_gds[i]:.4f}) \n") gds_arr = [(batch[i][0], gds_res[i]) for i in range(len(batch))] return gds_arr - - -def plot_gds_arr(gds_arr, row_labels, filename=None): - # yerr = None - data = [g[1] for g in gds_arr] - mean_gds = [(i, np.mean(np.array(d))) for i, d in enumerate(data)] - mean_gds = sorted(mean_gds, key=lambda x: -x[1]) - indexes = [t[0] for t in mean_gds] - mean_gds = [t[1] for t in mean_gds] - std_gds = [np.array(data[i]).std() for i in indexes] - sem_gds = [g / np.sqrt(len(data[0])) for g in std_gds] - row_labels = [row_labels[i] for i in indexes] - - br1 = np.arange(len(mean_gds)) - fig, ax = plt.subplots(figsize=(10, 6)) - ax.bar(br1, mean_gds, yerr=sem_gds) - ax.set_xticks(np.arange(len(mean_gds))) - ax.set_xticklabels(row_labels) - _ = plt.setp(ax.get_xticklabels(), rotation=-20, ha="left", rotation_mode="anchor") - ax.set_ylabel("GDS") - ax.yaxis.grid(True) - if filename: - fig.savefig(filename, bbox_inches="tight") diff --git a/gitk/eval/npt.py b/gitk/eval/npt.py index e35681b9..f849b41d 100644 --- a/gitk/eval/npt.py +++ b/gitk/eval/npt.py @@ -8,31 +8,11 @@ from gensim.models import Word2Vec import time import multiprocessing as mp -from gitk.eval.utils import load_genomic_embeddings +from gitk.eval.utils import load_genomic_embeddings, Timer, genome_distance import matplotlib.pyplot as plt from matplotlib.lines import Line2D -class Timer: - def __init__(self): - self.o = time.time() - - def measure(self, p=1): - x = (time.time() - self.o) / float(p) - x = int(x) - if x >= 3600: - return "{:.1f}h".format(x / 3600) - if x >= 60: - return "{}m".format(round(x / 60)) - return "{}s".format(x) - - -# function calculating the chromosome distance between two regions -func_rdist = lambda u, v: float(u[1] < v[1]) * max(v[0] - u[1] + 1, 0) + float( - u[1] >= v[1] -) * max(u[0] - v[1] + 1, 0) - - def get_topk_embed(i, K, embed, dist="cosine"): """ Return the indices for the most similar K regions to the i-th region @@ -73,7 +53,7 @@ def find_Kneighbors(region_array, index, K): right_idx = min(index + K, len(region_array) - 1) rdist_arr = [] for idx in range(left_idx, right_idx + 1): - rdist_arr.append(func_rdist(qregion, region_array[idx])) + rdist_arr.append(genome_distance(qregion, region_array[idx])) rdist_arr = np.array(rdist_arr) Kneighbors_idx = np.argsort(rdist_arr)[1 : K + 1] Kneighbors_idx = Kneighbors_idx + left_idx @@ -95,13 +75,13 @@ def calculate_overlap_bins( if len(Kindices) == 0: return 0 str_kregions = [ - "{}:{}-{}".format(chromo, *region_array[k]) for k in Kindices + f"{chromo}:{region_array[k][0]}-{region_array[k][1]}" for k in Kindices ] # sorted in ascending order _Krdist_global_indices = np.array([region2index[r] for r in str_kregions]) if same_chromo: chr_regions = [ - "{}:{}-{}".format(chromo, *region_array[k]) + f"{chromo}:{region_array[k][0]}-{region_array[k][1]}" for k in range(len(region_array)) ] chr_global_indices = np.array([region2index[r] for r in chr_regions]) @@ -111,7 +91,9 @@ def calculate_overlap_bins( [chr_global_indices[i] for i in _Kedist_local_indices] ) else: - idx = region2index["{}:{}-{}".format(chromo, *region_array[local_idx])] + idx = region2index[ + f"{chromo}:{region_array[local_idx][0]}-{region_array[local_idx][1]}" + ] _Kedist_global_indices, _ = get_topk_embed(idx, K, embed_rep, dist) bin_overlaps = [] @@ -277,15 +259,13 @@ def get_snpr( count = count + 1 snprs = cal_snpr(avg_ratio, avg_ratio_ref) - ratio_msg = " ".join(["{:.6f}".format(r) for r in avg_ratio]) - ratio_ref_msg = " ".join(["{:.6f}".format(r) for r in avg_ratio_ref]) - snprs_msg = " ".join(["{:.6f}".format(r) for r in snprs]) + ratio_msg = " ".join([f"{r:.6f}" for r in avg_ratio]) + ratio_ref_msg = " ".join([f"{r:.6f}" for r in avg_ratio_ref]) + snprs_msg = " ".join([f"{r:.6f}" for r in snprs]) print(model_path) # print( - # "[seed={}] K={}\n[{}]: {}\n[Random]: {}\n[SNPR] {}".format( - # seed, K, embed_type, ratio_msg, ratio_ref_msg, snprs_msg - # ) + # f"[seed={seed}] K={K}\n[{embed_type}]: {ratio_msg}\n[Random]: {ratio_ref_msg}\n[SNPR] {snprs_msg}" # ) result = { "K": K, @@ -325,7 +305,7 @@ def get_snpr_batch( dist="cosine", save_path=None, ): - print("Total number of models: {}".format(len(batch))) + print(f"Total number of models: {len(batch)}") result_list = [] for index, (path, embed_type) in enumerate(batch): result = get_snpr( @@ -342,7 +322,7 @@ def get_snpr_batch( # def get_snpr_batch( # batch, K, num_samples=100, num_workers=10, seed=0, resolution=10, dist='cosine', save_path=None # ): -# print("Total number of models: {}".format(len(batch))) +# print(f"Total number of models: {len(batch)}") # result_list = [] # with mp.Pool( # processes=num_workers @@ -375,11 +355,9 @@ def npt_eval( results_seeds = [] assert resolution < K, "resolution < K" for seed in range(num_runs): - print("----------------Run {}----------------".format(seed)) + print(f"----------------Run {seed}----------------") save_path = ( - os.path.join(save_folder, "npt_eval_seed{}".format(seed)) - if save_folder - else None + os.path.join(save_folder, f"npt_eval_seed{seed}") if save_folder else None ) result_list = get_snpr_batch( batch, @@ -405,17 +383,8 @@ def npt_eval( snpr_arr = snpr_results[i] avg_snprs = snpr_arr.mean(axis=0) std_snprs = snpr_arr.std(axis=0) - print( - "{}\nSNPRs:{}\n".format( - paths[i], - " ".join( - [ - "{:.4f}({:.4f})".format(m, s) - for m, s in zip(avg_snprs, std_snprs) - ] - ), - ) - ) + msg = " ".join([f"{m:.4f}({s:.4f})" for m, s in zip(avg_snprs, std_snprs)]) + print(f"{paths[i]}\nSNPRs:{msg}\n") snpr_results = [(paths[i], snpr_results[i], resolution) for i in range(len(batch))] return snpr_results diff --git a/gitk/eval/permutation.R b/gitk/eval/permutation.R index 90bc0369..b767554b 100644 --- a/gitk/eval/permutation.R +++ b/gitk/eval/permutation.R @@ -106,4 +106,3 @@ out <- foreach (i=1:num_path) %dopar% { cat(format(pvals, nsmall=6),sep='\n',file=save_path) # } } - diff --git a/gitk/eval/utils.py b/gitk/eval/utils.py index cb7fc5b2..7e13e5d5 100644 --- a/gitk/eval/utils.py +++ b/gitk/eval/utils.py @@ -3,12 +3,31 @@ import numpy as np import time import argparse -import time import multiprocessing as mp import glob from gensim.models import Word2Vec +class Timer: + def __init__(self): + self.o = time.time() + + def measure(self, p=1): + x = (time.time() - self.o) / float(p) + x = int(x) + if x >= 3600: + return f"{x / 3600:.1f}h" + if x >= 60: + return f"{round(x / 60)}m" + return f"{x}s" + + +def genome_distance(u, v): + return float(u[1] < v[1]) * max(v[0] - u[1] + 1, 0) + float(u[1] >= v[1]) * max( + u[0] - v[1] + 1, 0 + ) + + class BaseEmbeddings: def __init__(self, embeddings, vocab): self.embeddings = embeddings @@ -20,7 +39,7 @@ def get_bin_embeddings(universe_file, tokenized_files): with open(universe_file, "r") as f: for line in f: eles = line.strip().split("\t") - region = "{}:{}-{}".format(*eles) + region = f"{eles[0]}:{eles[1]}-{eles[2]}" vocab.append(region) vocab_dict = {v: i for i, v in enumerate(vocab)} print("vocab size is", len(vocab)) @@ -29,7 +48,7 @@ def get_bin_embeddings(universe_file, tokenized_files): with open(token_file, "r") as f: for line in f: eles = line.strip().split("\t") - region = "{}:{}-{}".format(*eles) + region = f"{eles[0]}:{eles[1]}-{eles[2]}" if region in vocab_dict: bin_embeds[vocab_dict[region]][i] = 1 bin_embed_obj = BaseEmbeddings(bin_embeds, vocab) @@ -107,4 +126,12 @@ def write_vocab(vocab, file_name): s, e = eles[1].split("-") s = s.strip() e = e.strip() - f.write("{}\t{}\t{}\n".format(chr, s, e)) + f.write(f"{chr}\t{s}\t{e}\n") + + +def region2tuple(x): + eles = x.split(":") + chr_name = eles[0].strip() + start, end = eles[1].split("-") + start, end = int(start.strip()), int(end.strip()) + return chr_name, start, end diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index dbee1795..a5ac83d9 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -53,7 +53,7 @@ def region2vec( training_processes = [] num_sent_processes = min(int(np.ceil(num_processes / 2)), 4) nworkers = min(num_shufflings, num_sent_processes) - utils.log("num_sent_processes: {}".format(nworkers)) + utils.log(f"num_sent_processes: {nworkers}") if nworkers <= 1: sent_gen_args = Namespace( tokenization_folder=token_folder, @@ -117,8 +117,4 @@ def region2vec( p.join() os.remove(file_list_path) elapsed_time = timer.t() - start_time - print( - "[Training] {}/{}".format( - utils.time_str(elapsed_time), utils.time_str(timer.t()) - ) - ) + print(f"[Training] {utils.time_str(elapsed_time)}/{utils.time_str(timer.t())}") diff --git a/gitk/region2vec/region2vec_train.py b/gitk/region2vec/region2vec_train.py index efc7441d..47dc8385 100644 --- a/gitk/region2vec/region2vec_train.py +++ b/gitk/region2vec/region2vec_train.py @@ -50,8 +50,8 @@ def main(args): msg_model += "hierarchical softmax\033[00m" else: hs = 0 - msg_model += "negative sampling with {} negative samples\033[00m".format( - args.neg_samples + msg_model += ( + f"negative sampling with {args.neg_samples} negative samples\033[00m" ) if not os.path.exists(args.resume): vocab_update = False @@ -72,9 +72,7 @@ def main(args): utils.log(msg_model) else: utils.log( - "\033[91mResuming {}, make sure the model configurations are consistent\033[00m".format( - args.resume - ) + f"\033[91mResuming {args.resume}, make sure the model configurations are consistent\033[00m" ) model = Word2Vec.load(args.resume) vocab_update = True @@ -91,17 +89,15 @@ def main(args): ) run_timer = utils.Timer() - utils.log("[{}] Start training".format(datetime.datetime.now().strftime("%x-%X"))) - utils.log( - "[{}] Building vocabulary".format(datetime.datetime.now().strftime("%x-%X")) - ) + cur_time = datetime.datetime.now().strftime("%x-%X") + utils.log(f"[{cur_time}] Start training") + utils.log(f"[{cur_time}] Building vocabulary") dset = find_dataset(data_folder) sentences = LineSentence(dset) # create sentence iterator model.build_vocab(sentences, update=vocab_update) # prepare the model vocabulary + cur_time = datetime.datetime.now().strftime("%x-%X") utils.log( - "[{}]\033[93m Vocabulary size is {}\033[00m".format( - datetime.datetime.now().strftime("%x-%X"), len(model.wv.index_to_key) - ) + f"[{cur_time}]\033[93m Vocabulary size is {len(model.wv.index_to_key)}\033[00m" ) build_vocab_time = run_timer.t() min_loss = 1.0e100 @@ -109,7 +105,7 @@ def main(args): # start training for sidx in range(args.num_shuffle): epoch_timer = utils.Timer() - msg = "[Shuffling {:>4d}] ".format(sidx + 1) + msg = f"[Shuffling {sidx + 1:>4d}] " dset = find_dataset(data_folder) dname = dset.split("/")[-1] dst_name = os.path.join(data_folder, dname + "using") @@ -136,17 +132,11 @@ def main(args): model.save(os.path.join(model_dir, "word2vec_best.pt")) model.save(os.path.join(model_dir, "word2vec_latest.pt")) if args.save_freq > 0 and (sidx + 1) % args.save_freq == 0: - model.save(os.path.join(model_dir, "word2vec_{}.pt".format(sidx + 1))) + model.save(os.path.join(model_dir, f"word2vec_{sidx + 1}.pt")) est_time = (run_timer.t() - build_vocab_time) / ( sidx + 1 ) * args.num_shuffle + build_vocab_time - msg += "loss {:>12.4f} lr {:>5.4f} vocab_size {:>12d} ({}/{})".format( - loss, - lr_scheduler.lr, - len(model.wv.index_to_key), - utils.time_str(epoch_timer.t()), - utils.time_str(est_time), - ) + msg += f"loss {loss:>12.4f} lr {lr_scheduler.lr:>5.4f} vocab_size {len(model.wv.index_to_key):>12d} ({utils.time_str(epoch_timer.t())}/{utils.time_str(est_time)})" utils.log(msg) lr_scheduler.step() @@ -154,13 +144,12 @@ def main(args): pickle.dump(loss_all, f) elasped_time = run_timer.t() + cur_time = datetime.datetime.now().strftime("%x-%X") utils.log( - "[{}] Training finished, training Time {}".format( - datetime.datetime.now().strftime("%x-%X"), utils.time_str(elasped_time) - ) + f"[{cur_time}] Training finished, training Time {utils.time_str(elasped_time)}" ) # remove intermediate datasets - os.system("rm -rf {}".format(data_folder)) # remove the generated shuffled datasets + os.system(f"rm -rf {data_folder}") # remove the generated shuffled datasets if __name__ == "__main__": diff --git a/gitk/region2vec/region_shuffling.py b/gitk/region2vec/region_shuffling.py index fb7eef88..7a3bb684 100644 --- a/gitk/region2vec/region_shuffling.py +++ b/gitk/region2vec/region_shuffling.py @@ -103,9 +103,7 @@ def main(args): dataset = MatrixDataset(matrix) pool = args.pool utils.log( - "[{}] Creating shuffled datasets in \033[93m{}\033[00m (at most {} datasets coexist)".format( - worker_id, DATA_FOLDER, pool - ) + f"[{worker_id}] Creating shuffled datasets in \033[93m{DATA_FOLDER}\033[00m" ) for i in range(pool): @@ -142,8 +140,7 @@ def main(args): # delete the used dataset and generate a new dataset in the same foler sel_file = files[random.randint(0, len(files) - 1)] fname = sel_file.split("/")[-1][:-4] - # print('[',datetime.datetime.now(),']','Find used dataset {}'.format(fname)) - os.system("rm -f {}".format(sel_file)) # delete the dataset + os.system(f"rm -f {sel_file}") # delete the dataset dpath = os.path.join(DATA_FOLDER, fname + "creating") with open(dpath, "w") as f: pass diff --git a/gitk/region2vec/utils.py b/gitk/region2vec/utils.py index 579dfcd9..ead2a9a7 100644 --- a/gitk/region2vec/utils.py +++ b/gitk/region2vec/utils.py @@ -7,35 +7,35 @@ def prRed(skk): - print("\033[91m{}\033[00m".format(skk)) + print(f"\033[91mskk}\033[00m") def prGreen(skk): - print("\033[92m{}\033[00m".format(skk)) + print(f"\033[92m{skk}\033[00m") def prYellow(skk): - print("\033[93m{}\033[00m".format(skk)) + print(f"\033[93m{skk}\033[00m") def prLightPurple(skk): - print("\033[94m{}\033[00m".format(skk)) + print(f"\033[94m{skk}\033[00m") def prPurple(skk): - print("\033[95m{}\033[00m".format(skk)) + print(f"\033[95m{skk}\033[00m") def prCyan(skk): - print("\033[96m{}\033[00m".format(skk)) + print(f"\033[96m{skk}\033[00m") def prLightGray(skk): - print("\033[97m{}\033[00m".format(skk)) + print(f"\033[97m{skk}\033[00m") def prBlack(skk): - print("\033[98m{}\033[00m".format(skk)) + print(f"\033[98m{skk}\033[00m") _log_path = None @@ -59,10 +59,10 @@ def t(self): def time_str(t): if t >= 3600: - return "{:.2f}h".format(t / 3600) + return f"{t / 3600:.2f}h" if t >= 60: - return "{:.2f}m".format(t / 60) - return "{:.2f}s".format(t) + return f"{t / 60:.2f}m" + return f"{t:.2f}s" def timed_response(prompt, wait_time, default): @@ -71,12 +71,12 @@ def timed_response(prompt, wait_time, default): if i: ans = sys.stdin.readline().strip() if ans not in ["y", "n"]: - print("\033[91m{}\033[00m".format(default)) + print(f"\033[91m{default}\033[00m") return default else: return ans else: - print("\033[91m{}\033[00m".format(default)) + print(f"\033[91m{default}\033[00m") return default @@ -120,9 +120,9 @@ def step(self): def ensure_dir(path, default="y"): if os.path.exists(path): if default == "y": - prompt = "\033[91m{} exists,remove?([y]/n):\033[00m ".format(path) + prompt = f"\033[91m{path} exists,remove?([y]/n):\033[00m " else: - prompt = "\033[91m{} exists,remove?(y/[n]):\033[00m ".format(path) + prompt = f"\033[91m{path} exists,remove?(y/[n]):\033[00m " ans = timed_response(prompt, 5, default) if ans != "n": shutil.rmtree(path) diff --git a/gitk/tokenization/hard_tokenization_batch.py b/gitk/tokenization/hard_tokenization_batch.py index 2ac2ef36..6c6cb3c0 100644 --- a/gitk/tokenization/hard_tokenization_batch.py +++ b/gitk/tokenization/hard_tokenization_batch.py @@ -12,15 +12,11 @@ def bedtool_tokenization( temp = os.path.join(target_folder, f + "_sorted") target = os.path.join(target_folder, f) with open(temp, "w") as f_temp: - subprocess.run( - shlex.split("sort -k1,1V -k2,2n {}".format(fname)), stdout=f_temp - ) + subprocess.run(shlex.split(f"sort -k1,1V -k2,2n {fname}"), stdout=f_temp) with open(target, "w") as f_target: subprocess.run( shlex.split( - "{} intersect -a {} -b {} -u -f {} ".format( - bedtool_path, universe, temp, fraction - ) + f"{bedtool_path} intersect -a {universe} -b {temp} -u -f {fraction} " ), stdout=f_target, ) @@ -37,7 +33,7 @@ def generate_tokens( with open(universe, "r") as f: for _ in f: usize += 1 - print("\033[93mUniverse size is {}\033[00m".format(usize)) + print(f"\033[93mUniverse size is {usize}\033[00m") all_set = [] with open(file_list, "r") as fin: @@ -52,13 +48,11 @@ def generate_tokens( not_covered = all_set - existing_set number = len(not_covered) if number == 0: - print("Use the existing folder {}".format(token_folder), flush=True) + print(f"Use the existing folder {token_folder}", flush=True) return 0 else: print( - "Folder {} exists with {} files not processed. Continue...".format( - token_folder, number - ), + f"Folder {token_folder} exists with {number} files not processed. Continue...", flush=True, ) else: @@ -73,7 +67,7 @@ def generate_tokens( def main(args): local_timer = utils.Timer() - print("Entering hard tokenization. Results stored in {}".format(args.token_folder)) + print(f"Entering hard tokenization. Results stored in {args.token_folder}") status = generate_tokens( args.data_folder, args.token_folder, @@ -85,7 +79,7 @@ def main(args): if status < 0: return tokenization_time = local_timer.t() - print("Hard tokenization takes {}".format(utils.time_str(tokenization_time))) + print(f"Hard tokenization takes {utils.time_str(tokenization_time)}") if __name__ == "__main__": diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index e7b8075a..39b49cd1 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -33,17 +33,17 @@ def hard_tokenization_main( files = os.listdir(src_folder) file_count = len(files) if file_count == 0: - print("No files in {}".format(src_folder)) + print(f"No files in {src_folder}") return 0 os.makedirs(dst_folder, exist_ok=True) if file_list is None: # use all bed files in data_folder # generate a file list file_list = files - print("Use all ({}) bed files in {}".format(file_count, src_folder)) + print(f"Use all ({file_count}) bed files in {src_folder}") else: file_number = len(file_list) - print("{} bed files in total, use {} of them".format(file_count, file_number)) + print(f"{file_count} bed files in total, use {file_number} of them") # check whether all files in file_list are tokenized number = -1 @@ -57,9 +57,7 @@ def hard_tokenization_main( return 1 elif len(existing_set) > 0: print( - "Folder {} exists with incomplete tokenized files. Please empty/delete the folder first".format( - dst_folder - ) + f"Folder {dst_folder} exists with incomplete tokenized files. Please empty/delete the folder first" ) return 0 @@ -77,15 +75,15 @@ def hard_tokenization_main( if not os.path.isfile(bedtools_path): # Download bedtools bedtool_url = "https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary" - print("Downloading bedtools from \n{}".format(bedtool_url)) + print(f"Downloading bedtools from \n{bedtool_url}") response = requests.get(bedtool_url) with open(bedtools_path, "wb") as f: f.write(response.content) - print("bedtools is saved in \n{}".format(bedtools_path)) - subprocess.run(shlex.split("chmod +x {}".format(bedtools_path))) - subprocess.run(shlex.split("{} --version".format(bedtools_path))) + print(f"bedtools is saved in \n{bedtools_path}") + subprocess.run(shlex.split(f"chmod +x {bedtools_path}")) + subprocess.run(shlex.split(f"{bedtools_path} --version")) - print("Tokenizing {} bed files ...".format(len(file_list))) + print(f"Tokenizing {len(file_list)} bed files ...") file_count = len(file_list) # split the file_list into several subsets for each worker to process in parallel @@ -106,10 +104,10 @@ def hard_tokenization_main( split_file(file_list_path, dest_folder, nworkers) args_arr = [] for n in range(nworkers): - temp_token_folder = os.path.join(dst_folder, "batch_{}".format(n)) + temp_token_folder = os.path.join(dst_folder, f"batch_{n}") tokenization_args = Namespace( data_folder=src_folder, - file_list=os.path.join(dest_folder, "split_{}.txt".format(n)), + file_list=os.path.join(dest_folder, f"split_{n}.txt"), token_folder=temp_token_folder, universe=universe_file, bedtools_path=bedtools_path, @@ -131,15 +129,7 @@ def hard_tokenization_main( ) shutil.rmtree(param.token_folder) os.remove(file_list_path) - print( - "Tokenization complete {}/{} bed files".format( - len(os.listdir(dst_folder)), file_count - ) - ) + print(f"Tokenization complete {len(os.listdir(dst_folder))}/{file_count} bed files") elapsed_time = timer.t() - start_time - print( - "[Tokenization] {}/{}".format( - utils.time_str(elapsed_time), utils.time_str(timer.t()) - ) - ) + print(f"[Tokenization] {utils.time_str(elapsed_time)}/{utils.time_str(timer.t())}") return 1 diff --git a/gitk/tokenization/split_file.py b/gitk/tokenization/split_file.py index 66661c33..ea803277 100644 --- a/gitk/tokenization/split_file.py +++ b/gitk/tokenization/split_file.py @@ -41,7 +41,7 @@ def split_file(file_path, dest_folder, num_parts): num_arr = [num_per_file] * (num_parts - 1) + [last_file_num] pos = 0 for index in range(num_parts): - fname = os.path.join(dest_folder, "split_{}.txt".format(index)) + fname = os.path.join(dest_folder, f"split_{index}.txt") with open(fname, "w") as fout: for _ in range(num_arr[index]): fout.write(list_arr[pos]) diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index f3c0e740..a908390f 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -7,35 +7,35 @@ def prRed(skk): - print("\033[91m{}\033[00m".format(skk)) + print(f"\033[91m{skk}\033[00m") def prGreen(skk): - print("\033[92m{}\033[00m".format(skk)) + print(f"\033[92m{skk}\033[00m") def prYellow(skk): - print("\033[93m{}\033[00m".format(skk)) + print(f"\033[93m{skk}\033[00m") def prLightPurple(skk): - print("\033[94m{}\033[00m".format(skk)) + print(f"\033[94m{skk}\033[00m") def prPurple(skk): - print("\033[95m{}\033[00m".format(skk)) + print(f"\033[95m{skk}\033[00m") def prCyan(skk): - print("\033[96m{}\033[00m".format(skk)) + print(f"\033[96m{skk}\033[00m") def prLightGray(skk): - print("\033[97m{}\033[00m".format(skk)) + print(f"\033[97m{skk}\033[00m") def prBlack(skk): - print("\033[98m{}\033[00m".format(skk)) + print(f"\033[98m{skk}\033[00m") _log_path = None @@ -59,10 +59,10 @@ def t(self): def time_str(t): if t >= 3600: - return "{:.2f}h".format(t / 3600) + return f"{t / 3600:.2f}h" if t >= 60: - return "{:.2f}m".format(t / 60) - return "{:.2f}s".format(t) + return f"{t / 60:.2f}m" + return f"{t:.2f}s" def timed_response(prompt, wait_time, default): @@ -71,12 +71,12 @@ def timed_response(prompt, wait_time, default): if i: ans = sys.stdin.readline().strip() if ans not in ["y", "n"]: - print("\033[91m{}\033[00m".format(default)) + print(f"\033[91m{default}\033[00m") return default else: return ans else: - print("\033[91m{}\033[00m".format(default)) + print(f"\033[91m{default}\033[00m") return default @@ -119,9 +119,9 @@ def step(self): def ensure_dir(path, default="y"): if os.path.exists(path): if default == "y": - prompt = "\033[91m{} exists,remove?([y]/n):\033[00m ".format(path) + prompt = f"\033[91m{path} exists,remove?([y]/n):\033[00m " else: - prompt = "\033[91m{} exists,remove?(y/[n]):\033[00m ".format(path) + prompt = f"\033[91m{path} exists,remove?(y/[n]):\033[00m " ans = timed_response(prompt, 5, default) if ans != "n": shutil.rmtree(path) From a7792278c476bb195f5258b30e961ca19fc4316e Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Thu, 25 May 2023 00:51:28 -0400 Subject: [PATCH 0190/1072] add cli support --- gitk/cli.py | 37 +++++++++ gitk/eval/cct.py | 83 +++----------------- gitk/eval/cli.py | 78 ++++++++++++++---- gitk/eval/const.py | 1 + gitk/eval/gdst.py | 38 +++++---- gitk/eval/npt.py | 13 +-- gitk/eval/permutation.R | 7 +- gitk/eval/utils.py | 15 ++-- gitk/region2vec/README.md | 2 +- gitk/region2vec/cli.py | 69 ++++++++++++++++ gitk/region2vec/const.py | 1 + gitk/region2vec/main.py | 3 +- gitk/region2vec/region2vec_train.py | 65 ++++++++------- gitk/region2vec/region_shuffling.py | 28 ++++--- gitk/region2vec/utils.py | 11 +-- gitk/tokenization/cli.py | 29 +++++++ gitk/tokenization/hard_tokenization_batch.py | 37 +++++++-- gitk/tokenization/main.py | 62 ++++++++------- gitk/tokenization/split_file.py | 15 +++- gitk/tokenization/utils.py | 9 ++- 20 files changed, 393 insertions(+), 210 deletions(-) create mode 100644 gitk/eval/const.py create mode 100644 gitk/region2vec/cli.py create mode 100644 gitk/region2vec/const.py create mode 100644 gitk/tokenization/cli.py diff --git a/gitk/cli.py b/gitk/cli.py index edc0ea21..5db99246 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -7,6 +7,8 @@ from .assess.cli import build_mode_parser as assess_subparser from .eval.cli import build_subparser as eval_subparser +from .region2vec.cli import build_subparser as region2vec_subparser +from .tokenization.cli import build_subparser as tokenization_subparser from .hmm.cli import build_subparser as hmm_subparser from .likelihood.cli import build_subparser as likelihood_subparser from .scembed.argparser import build_argparser as scembed_subparser @@ -37,6 +39,8 @@ def build_argparser(): "lh": "Compute likelihood", "assess": "Assess a universe", "scembed": "Embed single-cell data as region vectors", + "region2vec": "Train a region2vec model", + "tokenize": "Tokenize BED files", "eval": "Evaluate a set of region embeddings", } @@ -50,6 +54,8 @@ def build_argparser(): subparsers["assess"] = assess_subparser(subparsers["assess"]) subparsers["lh"] = likelihood_subparser(subparsers["lh"]) subparsers["scembed"] = scembed_subparser(subparsers["scembed"]) + subparsers["region2vec"] = region2vec_subparser(subparsers["region2vec"]) + subparsers["tokenize"] = tokenization_subparser(subparsers["tokenize"]) subparsers["eval"] = eval_subparser(subparsers["eval"]) return parser @@ -143,6 +149,37 @@ def main(test_args=None): _LOGGER.info("Running scembed") pass # scembed_main(test_args) + + if args.command == "region2vec": + from .region2vec import region2vec + region2vec( + token_folder=args.token_folder, + save_dir=args.save_dir, + num_shufflings=args.num_shuffle, + num_processes=args.nworkers, + embedding_dim=args.embed_dim, + context_win_size=args.context_len, + save_freq=args.save_freq, + resume_path=args.resume, + train_alg=args.train_alg, + min_count=args.min_count, + neg_samples=args.neg_samples, + init_lr=args.init_lr, + min_lr=args.min_lr, + lr_scheduler=args.lr_mode, # How to decay the learning rate. Select from linear and milestone + milestones=args.milestones, # Specify only when lr_scheduler=milestone. At each given epoch, the learning rate will be multiplied by 0.1 + seed=args.seed, # random seed + ) + if args.command == "tokenize": + from .tokenization import hard_tokenization + hard_tokenization( + src_folder=args.data_folder, + dst_folder=args.token_folder, + universe_file=args.universe, + fraction=args.fraction, + num_workers=args.nworkers, + bedtools_path=args.bedtools_path + ) if args.command == "eval": if args.subcommand == "gdst": from gitk.eval.gdst import get_gds diff --git a/gitk/eval/cct.py b/gitk/eval/cct.py index 4da4ce90..8ce6374b 100644 --- a/gitk/eval/cct.py +++ b/gitk/eval/cct.py @@ -1,17 +1,19 @@ -import pickle -import numpy as np import glob +import multiprocessing as mp import os -import time -from sklearn.cluster import KMeans -from tqdm import tqdm +import pickle import shutil -from scipy.stats import rankdata +import subprocess +import time + import matplotlib.pyplot as plt +import numpy as np from matplotlib.lines import Line2D -import multiprocessing as mp -from gitk.eval.utils import load_genomic_embeddings, region2tuple -import subprocess +from scipy.stats import rankdata +from sklearn.cluster import KMeans +from tqdm import tqdm + +from .utils import load_genomic_embeddings, region2tuple def random_annotate_points( @@ -195,66 +197,3 @@ def cct_tss_eval( with open(result_path, "wb") as f: pickle.dump(scores_batch, f) return scores_batch - - -def cct_tss_plot(avg_ranks_arr, row_labels=None, legend_pos=(0.25, 0.6), filename=None): - mean_rank = [t[1].mean() for t in avg_ranks_arr] - std_rank = [t[1].std() for t in avg_ranks_arr] - mean_rank_tuple = [(i, r) for i, r in enumerate(mean_rank)] - mean_rank_tuple = sorted(mean_rank_tuple, key=lambda x: x[1]) - indexes = [t[0] for t in mean_rank_tuple] - - if row_labels is None: - row_labels = [t[0] for t in avg_ranks_arr] - - mean_rank = [mean_rank[i] for i in indexes] - std_rank = [std_rank[i] for i in indexes] - row_labels = [row_labels[i] for i in indexes] - - cmap = plt.get_cmap("Set1") - cmaplist = [cmap(i) for i in range(9)] - fig, ax = plt.subplots(figsize=(10, 6)) - ax.set_xticks(list(range(1, len(mean_rank) + 1))) - - ax.errorbar( - range(1, len(mean_rank) + 1), - mean_rank, - yerr=std_rank, - fmt="o", - ms=10, - mfc=cmaplist[1], - mec=cmaplist[8], - ecolor=cmaplist[2], - elinewidth=3, - capsize=5, - ) - ax.set_xticklabels(row_labels) - ax.set_ylabel("CCSI Rank") - _ = plt.setp( - ax.get_xticklabels(), rotation=-15, ha="left", va="top", rotation_mode="anchor" - ) - patches = [ - Line2D( - [0], - [0], - marker="o", - linestyle="", - color=cmaplist[1], - markersize=12, - mec=cmaplist[8], - ), - Line2D([0], [0], color=cmaplist[2], lw=4), - ] - legend = ax.legend( - labels=["CCSI average rank", "CCSI rank standard deviation"], - handles=patches, - bbox_to_anchor=legend_pos, - loc="center left", - borderaxespad=0, - fontsize=12, - frameon=True, - ) - ax.grid("on") - ax.set_ylim(ax.get_ylim()[::-1]) - if filename: - fig.savefig(filename, bbox_inches="tight") diff --git a/gitk/eval/cli.py b/gitk/eval/cli.py index 4834b8cb..5c126e6f 100644 --- a/gitk/eval/cli.py +++ b/gitk/eval/cli.py @@ -5,9 +5,21 @@ def build_subparser_gdst(parser): :return argparse.ArgumentParser """ - parser.add_argument("--model-path", required=True, type=str) - parser.add_argument("--embed-type", required=True, type=str) - parser.add_argument("--num-samples", default=10000, type=int) + parser.add_argument( + "--model-path", + required=True, + type=str, + help="path to a Region2Vec model or a Base model", + ) + parser.add_argument( + "--embed-type", required=True, type=str, help="region2vec or base" + ) + parser.add_argument( + "--num-samples", + default=10000, + type=int, + help="number of samples used in calculation", + ) return parser @@ -18,10 +30,24 @@ def build_subparser_npt(parser): :return argparse.ArgumentParser """ - parser.add_argument("--model-path", required=True, type=str) - parser.add_argument("--embed-type", required=True, type=str) - parser.add_argument("--K", required=True, type=int) - parser.add_argument("--num-samples", default=1000, type=int) + parser.add_argument( + "--model-path", + required=True, + type=str, + help="path to a region2vec model or a Base model", + ) + parser.add_argument( + "--embed-type", required=True, type=str, help="region2vec or base" + ) + parser.add_argument( + "--K", required=True, type=int, help="number of nearest regions" + ) + parser.add_argument( + "--num-samples", + default=1000, + type=int, + help="number of samples used in calculation", + ) return parser @@ -32,13 +58,37 @@ def build_subparser_cct_tss(parser): :return argparse.ArgumentParser """ - parser.add_argument("--model-path", required=True, type=str) - parser.add_argument("--embed-type", required=True, type=str) - parser.add_argument("--save-folder", required=True, type=str) - parser.add_argument("--Rscript-path", required=True, type=str) - parser.add_argument("--assembly", required=True, type=str) - parser.add_argument("--threshold", default=0.0001, type=float) - parser.add_argument("--num-samples", default=1000, type=int) + parser.add_argument( + "--model-path", + required=True, + type=str, + help="path to a region2vec model or a Base model", + ) + parser.add_argument( + "--embed-type", required=True, type=str, help="region2vec or base" + ) + parser.add_argument( + "--save-folder", + required=True, + type=str, + help="path to the folder that saves intermediate results", + ) + parser.add_argument( + "--Rscript-path", required=True, type=str, help="path to Rscript" + ) + parser.add_argument("--assembly", required=True, type=str, help="hg19 or hg38") + parser.add_argument( + "--threshold", + default=0.0001, + type=float, + help="threshold that determines a significant cluster", + ) + parser.add_argument( + "--num-samples", + default=1000, + type=int, + help="number of samples used in calculation", + ) return parser diff --git a/gitk/eval/const.py b/gitk/eval/const.py new file mode 100644 index 00000000..fef7c457 --- /dev/null +++ b/gitk/eval/const.py @@ -0,0 +1 @@ +GENOME_DIST_SCALAR = 1e10 diff --git a/gitk/eval/gdst.py b/gitk/eval/gdst.py index 6a1805ae..5114371d 100644 --- a/gitk/eval/gdst.py +++ b/gitk/eval/gdst.py @@ -1,20 +1,22 @@ -import pickle import os +import pickle os.environ["OPENBLAS_NUM_THREADS"] = "1" -import numpy as np -import random +import argparse import glob -import time import multiprocessing as mp -import argparse +import random +import time + +import numpy as np from gensim.models import Word2Vec -from sklearn.metrics import r2_score -from gitk.eval.utils import load_genomic_embeddings, Timer, genome_distance from sklearn.linear_model import LinearRegression +from sklearn.metrics import r2_score + +from .const import * +from .utils import Timer, cosine_distance, genome_distance, load_genomic_embeddings _log_path = None -GENOME_DIST_SCALAR = 1e10 def set_log_path(path): @@ -64,7 +66,11 @@ def sample_from_vocab(vocab, num_samples, seed=42): r1, r2 = regions[sel_indexes[0]], regions[sel_indexes[1]] gdist = genome_distance(r1, r2) sampled_regions.append( - (f"{sel_chr}:{r1[0]}-{r1[1]}", f"{sel_chr}:{r2[0]}-{r2[1]}", gdist,) + ( + f"{sel_chr}:{r1[0]}-{r1[1]}", + f"{sel_chr}:{r2[0]}-{r2[1]}", + gdist, + ) ) count += 1 return sampled_regions @@ -107,23 +113,14 @@ def get_gds( seed=42, queue=None, worker_id=None, - dist="cosine", ): - if dist == "cosine": - dist_func = ( - lambda x, y: (1 - ((x / np.linalg.norm(x)) * (y / np.linalg.norm(y))).sum()) - / 2 - ) - elif dist == "euclidean": - dist_func = lambda x, y: np.linalg.norm(x - y) - embed_rep, vocab = load_genomic_embeddings(path, embed_type) regions = sample_from_vocab(vocab, num_samples, seed) region2idx = {r: i for i, r in enumerate(vocab)} gdist_arr = [r[2] / GENOME_DIST_SCALAR for r in regions] edist_arr = np.array( [ - dist_func(embed_rep[region2idx[t[0]]], embed_rep[region2idx[t[1]]]) + cosine_distance(embed_rep[region2idx[t[0]]], embed_rep[region2idx[t[1]]]) for t in regions ] ) @@ -177,7 +174,8 @@ def get_gds_batch( all_processes = [] for i, (path, embed_type) in enumerate(batch): process = pool.apply_async( - get_gds, (path, embed_type, num_samples, seed, queue, i, dist), + get_gds, + (path, embed_type, num_samples, seed, queue, i, dist), ) all_processes.append(process) diff --git a/gitk/eval/npt.py b/gitk/eval/npt.py index f849b41d..8dea3a63 100644 --- a/gitk/eval/npt.py +++ b/gitk/eval/npt.py @@ -1,17 +1,18 @@ -import pickle import os +import pickle os.environ["OPENBLAS_NUM_THREADS"] = "1" -import numpy as np -import time import argparse -from gensim.models import Word2Vec -import time import multiprocessing as mp -from gitk.eval.utils import load_genomic_embeddings, Timer, genome_distance +import time + import matplotlib.pyplot as plt +import numpy as np +from gensim.models import Word2Vec from matplotlib.lines import Line2D +from .utils import Timer, genome_distance, load_genomic_embeddings + def get_topk_embed(i, K, embed, dist="cosine"): """ diff --git a/gitk/eval/permutation.R b/gitk/eval/permutation.R index b767554b..b45af85c 100644 --- a/gitk/eval/permutation.R +++ b/gitk/eval/permutation.R @@ -1,3 +1,6 @@ +packages <- c("optparse", "GenomicDistributions", "foreach", "doParallel") +install.packages(setdiff(packages, rownames(installed.packages()))) + suppressPackageStartupMessages(library("optparse")) suppressPackageStartupMessages(library("GenomicDistributions")) suppressPackageStartupMessages(library(foreach)) @@ -84,9 +87,9 @@ option_list = list( help="dataset file name", metavar="character"), make_option("--assembly", type="character", default="hg19", help="hg19 or hg38", metavar="character"), - make_option("--num_workers", type="integer",default=10, + make_option("--num-workers", type="integer",default=10, help="number of parallel processes", metavar="number of processes"), - make_option("--num_samples", type="integer",default=1000, + make_option("--num-samples", type="integer",default=1000, help="number of samples", metavar="number of samples") ); diff --git a/gitk/eval/utils.py b/gitk/eval/utils.py index 7e13e5d5..95a6d673 100644 --- a/gitk/eval/utils.py +++ b/gitk/eval/utils.py @@ -1,10 +1,11 @@ -import pickle -import os -import numpy as np -import time import argparse -import multiprocessing as mp import glob +import multiprocessing as mp +import os +import pickle +import time + +import numpy as np from gensim.models import Word2Vec @@ -28,6 +29,10 @@ def genome_distance(u, v): ) +def cosine_distance(x, y): + return (1 - ((x / np.linalg.norm(x)) * (y / np.linalg.norm(y))).sum()) / 2 + + class BaseEmbeddings: def __init__(self, embeddings, vocab): self.embeddings = embeddings diff --git a/gitk/region2vec/README.md b/gitk/region2vec/README.md index b0ce0d59..19a329b3 100644 --- a/gitk/region2vec/README.md +++ b/gitk/region2vec/README.md @@ -1,5 +1,5 @@ # region2vec -`region2vec` will generate embedding vectors for a given region set (universe) from a set of raw bed files. The program will first map all raw regions to the given region set. Then, it will concatenate all regions in a bed file in random orders into a sentence. The generated sentences will be used for word2vec training. +`region2vec` will generate embedding vectors for a given region set (universe) from a set of raw bed files. The program will first map all raw regions to the given region set. Then, it will concatenate all regions in a bed file in random orders into a sentence. The generated sentences will be used for Region2Vec training. ## Requirements diff --git a/gitk/region2vec/cli.py b/gitk/region2vec/cli.py new file mode 100644 index 00000000..fab2bb42 --- /dev/null +++ b/gitk/region2vec/cli.py @@ -0,0 +1,69 @@ +def build_subparser(parser): + """ + Builds argument parser. + + :return argparse.ArgumentParser + """ + parser.add_argument("--token-folder", type=str, help="path to tokenized files") + parser.add_argument( + "--num-shuffle", type=int, help="number of shufflings/training epochs" + ) + parser.add_argument("--embed-dim", type=int, help="embedding dimension") + parser.add_argument("--context-len", type=int, help="Context window size (half)") + parser.add_argument("--nworkers", type=int, default=10, help="number of workers") + parser.add_argument( + "--save-freq", + type=int, + default=-1, + help="Save a model after the given number of training epochs. If -1, then only save the best and latest models", + ) + parser.add_argument( + "--save-dir", type=str, help="path to the folder that saves the training result" + ) + parser.add_argument( + "--resume", + type=str, + default="", + help="path to a trained model. If specified, the model will be used to initialize the region2vec embeddings", + ) + parser.add_argument( + "--train-alg", + type=str, + default="cbow", + help="training algorithm, select from [cbow, skip-gram]", + ) + parser.add_argument( + "--min-count", + type=int, + default=5, + help="threshold for filtering out regions with low frequency in the internal vocabulary", + ) + parser.add_argument( + "--neg-samples", + type=int, + default=5, + help="number of noise words in negative sampling, usually between 5-20", + ) + parser.add_argument( + "--init-lr", type=float, default=0.1, help="initial learning rate" + ) + parser.add_argument("--milestones", nargs="+", type=int, default=[100, 200]) + parser.add_argument( + "--lr-mode", + type=str, + default="linear", + choices=["milestone", "linear"], + help="type of learning rate scheduler, milestone or linear", + ) + parser.add_argument( + "--update-vocab", + type=str, + default="once", + help="[every] update at every epoch; [once] Update once since the vocabulary does not change", + ) + parser.add_argument( + "--min-lr", type=float, default=1.0e-6, help="minimum learning rate" + ) + parser.add_argument("--seed", type=int, default=42, help="random seed") + + return parser diff --git a/gitk/region2vec/const.py b/gitk/region2vec/const.py new file mode 100644 index 00000000..cc9cd08c --- /dev/null +++ b/gitk/region2vec/const.py @@ -0,0 +1 @@ +MAX_WAIT_TIME = 10800 diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index a5ac83d9..707453f2 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -1,8 +1,7 @@ -import numpy as np - import multiprocessing import os +import numpy as np from gitk.region2vec import utils from gitk.region2vec.region2vec_train import main as region2_train from gitk.region2vec.region_shuffling import main as sent_gen diff --git a/gitk/region2vec/region2vec_train.py b/gitk/region2vec/region2vec_train.py index 47dc8385..0ed51368 100644 --- a/gitk/region2vec/region2vec_train.py +++ b/gitk/region2vec/region2vec_train.py @@ -1,14 +1,17 @@ -import os -import glob -import time -import datetime import argparse +import datetime +import glob +import logging +import os import pickle +import random +import time + from gensim.models import Word2Vec from gensim.models.word2vec import LineSentence -from gitk.region2vec import utils -import logging -import random + +from . import utils +from .const import * logging.basicConfig( format="%(asctime)s : %(levelname)s : %(message)s", level=logging.ERROR @@ -17,11 +20,16 @@ def find_dataset(data_folder): train_pattern = os.path.join(data_folder, "pool*[0-9]") + count = 0 while True: dsets = glob.glob(train_pattern) if len(dsets) == 0: print("No available dataset, waiting for generation...", end="\r") time.sleep(1) + count += 1 + if count == MAX_WAIT_TIME: + print("Wait time exceeds MAX_WAIT_TIME, exit") + return -1 else: return dsets[random.randint(0, len(dsets) - 1)] @@ -55,9 +63,6 @@ def main(args): ) if not os.path.exists(args.resume): vocab_update = False - # model = Word2Vec(size=args.embed_dim, alpha=args.init_lr, window=args.context_len, min_count=args.min_count, - # seed=args.seed, workers=args.nworkers, sg=train_alg, negative=args.neg_samples, hs=hs) - # lasest model = Word2Vec( vector_size=args.embed_dim, alpha=args.init_lr, @@ -93,6 +98,8 @@ def main(args): utils.log(f"[{cur_time}] Start training") utils.log(f"[{cur_time}] Building vocabulary") dset = find_dataset(data_folder) + if dset == -1: + return sentences = LineSentence(dset) # create sentence iterator model.build_vocab(sentences, update=vocab_update) # prepare the model vocabulary cur_time = datetime.datetime.now().strftime("%x-%X") @@ -107,6 +114,8 @@ def main(args): epoch_timer = utils.Timer() msg = f"[Shuffling {sidx + 1:>4d}] " dset = find_dataset(data_folder) + if dset == -1: + return dname = dset.split("/")[-1] dst_name = os.path.join(data_folder, dname + "using") os.rename(dset, dst_name) # change to file name to pool%dusing @@ -129,10 +138,10 @@ def main(args): if loss < min_loss: min_loss = loss - model.save(os.path.join(model_dir, "word2vec_best.pt")) - model.save(os.path.join(model_dir, "word2vec_latest.pt")) + model.save(os.path.join(model_dir, "region2vec_best.pt")) + model.save(os.path.join(model_dir, "region2vec_latest.pt")) if args.save_freq > 0 and (sidx + 1) % args.save_freq == 0: - model.save(os.path.join(model_dir, f"word2vec_{sidx + 1}.pt")) + model.save(os.path.join(model_dir, f"region2vec_{sidx + 1}.pt")) est_time = (run_timer.t() - build_vocab_time) / ( sidx + 1 ) * args.num_shuffle + build_vocab_time @@ -154,45 +163,47 @@ def main(args): if __name__ == "__main__": parser = argparse.ArgumentParser(description="Gene Embedding") - parser.add_argument("--num_shuffle", type=int, help="number of shuffled datasets") - parser.add_argument("--embed_dim", type=int, help="embedding dimension") - parser.add_argument("--context_len", type=int, help="window size") + parser.add_argument("--num-shuffle", type=int, help="number of shuffled datasets") + parser.add_argument("--embed-dim", type=int, help="embedding dimension") + parser.add_argument("--context-len", type=int, help="window size") parser.add_argument("--nworkers", type=int, help="number of workers") - parser.add_argument("--save_freq", type=int, default=0, help="save frequency") - parser.add_argument("--save_dir", help="path to save the training result") + parser.add_argument("--save-freq", type=int, default=0, help="save frequency") + parser.add_argument( + "--save-dir", help="path to the folder that saves the training result" + ) parser.add_argument("--resume", help="path to a saved model") parser.add_argument( - "--train_alg", help="training algorithm, select from [cbow, skip-gram]" + "--train-alg", help="training algorithm, select from [cbow, skip-gram]" ) parser.add_argument( - "--min_count", type=int, help="threshold of pruning the internal vocabulary" + "--min-count", type=int, help="threshold of pruning the internal vocabulary" ) parser.add_argument( - "--neg_samples", + "--neg-samples", type=int, help="number of noise words in negative sampling, usually between 5-20", ) parser.add_argument( - "--hier_softmax", + "--hier-softmax", default=False, action="store_true", help="if given, hierarchical softmax will be used", ) - parser.add_argument("--init_lr", type=float, help="initial learning rate") + parser.add_argument("--init-lr", type=float, help="initial learning rate") parser.add_argument("--milestones", nargs="+", type=int, default=[100, 200]) parser.add_argument( - "--lr_mode", + "--lr-mode", type=str, choices=["milestone", "linear"], - help="milestone or linear", + help="type of learning rate scheduler, milestone or linear", ) parser.add_argument( - "--update_vocab", + "--update-vocab", type=str, default="once", help="[every] update at every epoch; [once] Update once since the vocabulary does not change", ) - parser.add_argument("--min_lr", type=float, help="minimum learning rate") + parser.add_argument("--min-lr", type=float, help="minimum learning rate") parser.add_argument("--seed", type=int, help="random seed") args = parser.parse_args() main(args) diff --git a/gitk/region2vec/region_shuffling.py b/gitk/region2vec/region_shuffling.py index 7a3bb684..8f4f13b3 100644 --- a/gitk/region2vec/region_shuffling.py +++ b/gitk/region2vec/region_shuffling.py @@ -1,13 +1,13 @@ +import argparse +import datetime +import glob import os -import numpy as np +import pickle import random -import glob import time -import datetime -import argparse -import os + +import numpy as np from gitk.region2vec import utils -import pickle class BEDDataset: @@ -157,11 +157,13 @@ def main(args): if __name__ == "__main__": parser = argparse.ArgumentParser(description="Sentence Generation") - parser.add_argument("--file_list", help="path to a file list") - parser.add_argument("--tokenization_mode", help="tokenization mode") - parser.add_argument("--tokenization_folder", help="path to the tokenized regions") + parser.add_argument("--file-list", help="path to a file list") + parser.add_argument("--tokenization-mode", help="tokenization mode") parser.add_argument( - "--save_dir", help="parent folder to generated shuffled datasets" + "--tokenization-folder", help="path to the folder that saves tokenized regions" + ) + parser.add_argument( + "--save-dir", help="parent folder to generated shuffled datasets" ) parser.add_argument( "--pool", @@ -170,13 +172,13 @@ def main(args): help="maximum number of shuffled datasets before consuming one", ) parser.add_argument( - "--worker_id", + "--worker-id", type=int, default=0, - help="maximum number of shuffled datasets before consuming one", + help="used in the parallel mode", ) parser.add_argument( - "--number", type=int, default=1000, help="total number of shuffled datasets" + "--number", type=int, default=1000, help="number of shuffling the whole dataset" ) args = parser.parse_args() diff --git a/gitk/region2vec/utils.py b/gitk/region2vec/utils.py index ead2a9a7..a49cd8fc 100644 --- a/gitk/region2vec/utils.py +++ b/gitk/region2vec/utils.py @@ -1,13 +1,14 @@ -import time -import sys -import select import os -import numpy as np +import select import shutil +import sys +import time + +import numpy as np def prRed(skk): - print(f"\033[91mskk}\033[00m") + print(f"\033[91m{skk}\033[00m") def prGreen(skk): diff --git a/gitk/tokenization/cli.py b/gitk/tokenization/cli.py new file mode 100644 index 00000000..a491ad2f --- /dev/null +++ b/gitk/tokenization/cli.py @@ -0,0 +1,29 @@ +def build_subparser(parser): + """ + Builds argument parser. + + :return argparse.ArgumentParser + """ + parser.add_argument( + "--data-folder", type=str, help="Path to the folder that stores BED files" + ) + parser.add_argument( + "--token-folder", type=str, help="Folder that stores tokenized files" + ) + # parameters for hard tokenization + parser.add_argument("--universe", type=str, help="Path to a universe file") + parser.add_argument("--nworkers", type=int, default=10, help="number of workers") + parser.add_argument( + "--bedtools-path", + type=str, + default="", + help="Path to the bedtools binary. If not provided, bedtools will be automatically downloaded", + ) + parser.add_argument( + "--fraction", + type=float, + default=1.0e-9, + help="A parameter for bedtools.intersect", + ) + + return parser diff --git a/gitk/tokenization/hard_tokenization_batch.py b/gitk/tokenization/hard_tokenization_batch.py index 6c6cb3c0..100841b8 100644 --- a/gitk/tokenization/hard_tokenization_batch.py +++ b/gitk/tokenization/hard_tokenization_batch.py @@ -1,8 +1,9 @@ import argparse import os -from gitk.tokenization import utils -import subprocess import shlex +import subprocess + +from gitk.tokenization import utils def bedtool_tokenization( @@ -85,23 +86,43 @@ def main(args): if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( - "--data_folder", type=str, default="/scratch/gz5hp/encode3/cell/datasets" + "--data-folder", + type=str, + default="/scratch/gz5hp/encode3/cell/datasets", + help="path to the folder that stores BED files", ) parser.add_argument( - "--file_list", type=str, default="/home/gz5hp/encode3_proj/all_file_list.txt" + "--file-list", + type=str, + default="/home/gz5hp/encode3_proj/all_file_list.txt", + help="list of BED files that will be tokenized", ) parser.add_argument( - "--token_folder", type=str, default="/scratch/gz5hp/encode3/cell/tokens" + "--token-folder", + type=str, + default="/scratch/gz5hp/encode3/cell/tokens", + help="folder that stores tokenized files", ) # parameters for hard tokenization parser.add_argument( - "--universe", type=str, default="/home/gz5hp/encode3_proj/GRCh38-universe.bed" + "--universe", + type=str, + default="/home/gz5hp/encode3_proj/GRCh38-universe.bed", + help="path to a universe file", + ) + parser.add_argument( + "--bedtools-path", + type=str, + default="/scratch/gz5hp/genomes/bedtools", + help="path to the bedtools binary", ) parser.add_argument( - "--bedtools_path", type=str, default="/scratch/gz5hp/genomes/bedtools" + "--fraction", + type=float, + default=1.0e-9, + help="a parameter for bedtools.intersect", ) - parser.add_argument("--fraction", type=float, default=1.0e-9) args = parser.parse_args() if os.path.exists(args.file_list): diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 39b49cd1..037528f4 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -1,21 +1,22 @@ -import numpy as np -import shutil import argparse -import subprocess +import glob import json - import multiprocessing -import shlex import os -from queue import Queue -import yaml import random +import shlex +import shutil +import subprocess import sys +from queue import Queue + +import gitk.region2vec.utils as utils +import numpy as np import requests -import glob -from .hard_tokenization_batch import main as hard_tokenization +import yaml from gitk.tokenization.split_file import split_file -import gitk.region2vec.utils as utils + +from .hard_tokenization_batch import main as hard_tokenization class Namespace: @@ -24,7 +25,13 @@ def __init__(self, **kwargs): def hard_tokenization_main( - src_folder, dst_folder, universe_file, fraction=1e-9, file_list=None, num_workers=10 + src_folder, + dst_folder, + universe_file, + fraction=1e-9, + file_list=None, + num_workers=10, + bedtools_path="", ): timer = utils.Timer() start_time = timer.t() @@ -66,22 +73,23 @@ def hard_tokenization_main( f.write(file) f.write("\n") - # Download bedtools for tokenization - bedtools_folder = os.path.join( - os.path.dirname(os.path.abspath(__file__)), "bedtools" - ) - os.makedirs(bedtools_folder, exist_ok=True) - bedtools_path = os.path.join(bedtools_folder, "bedtools") - if not os.path.isfile(bedtools_path): - # Download bedtools - bedtool_url = "https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary" - print(f"Downloading bedtools from \n{bedtool_url}") - response = requests.get(bedtool_url) - with open(bedtools_path, "wb") as f: - f.write(response.content) - print(f"bedtools is saved in \n{bedtools_path}") - subprocess.run(shlex.split(f"chmod +x {bedtools_path}")) - subprocess.run(shlex.split(f"{bedtools_path} --version")) + if bedtools_path == "": + # Download bedtools for tokenization + bedtools_folder = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "bedtools" + ) + os.makedirs(bedtools_folder, exist_ok=True) + bedtools_path = os.path.join(bedtools_folder, "bedtools") + if not os.path.isfile(bedtools_path): + # Download bedtools + bedtool_url = "https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary" + print(f"Downloading bedtools from \n{bedtool_url}") + response = requests.get(bedtool_url) + with open(bedtools_path, "wb") as f: + f.write(response.content) + print(f"bedtools is saved in \n{bedtools_path}") + subprocess.run(shlex.split(f"chmod +x {bedtools_path}")) + subprocess.run(shlex.split(f"{bedtools_path} --version")) print(f"Tokenizing {len(file_list)} bed files ...") diff --git a/gitk/tokenization/split_file.py b/gitk/tokenization/split_file.py index ea803277..88c14729 100644 --- a/gitk/tokenization/split_file.py +++ b/gitk/tokenization/split_file.py @@ -1,5 +1,5 @@ -import os import argparse +import os def get_file_rows(file_path): @@ -52,9 +52,16 @@ def split_file(file_path, dest_folder, num_parts): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument("--file_path", default="") - parser.add_argument("--dest_folder", default="") - parser.add_argument("--num_parts", type=int, default=5) + parser.add_argument("--file-path", default="", help="path to a file list") + parser.add_argument( + "--dest-folder", default="", help="where to store the split files" + ) + parser.add_argument( + "--num-parts", + type=int, + default=5, + help="split the original file list to the specified parts", + ) args = parser.parse_args() split_file(args.file_path, args.dest_folder, args.num_parts) diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index a908390f..4c2f964e 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -1,9 +1,10 @@ -import time -import sys -import select import os -import numpy as np +import select import shutil +import sys +import time + +import numpy as np def prRed(skk): From a84a7ad01dd5f24a0c00fcb09d724199dfd60a34 Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Thu, 25 May 2023 00:56:34 -0400 Subject: [PATCH 0191/1072] minor fix --- gitk/cli.py | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/gitk/cli.py b/gitk/cli.py index 5db99246..92a87301 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -8,7 +8,7 @@ from .assess.cli import build_mode_parser as assess_subparser from .eval.cli import build_subparser as eval_subparser from .region2vec.cli import build_subparser as region2vec_subparser -from .tokenization.cli import build_subparser as tokenization_subparser +from .tokenization.cli import build_subparser as tokenization_subparser from .hmm.cli import build_subparser as hmm_subparser from .likelihood.cli import build_subparser as likelihood_subparser from .scembed.argparser import build_argparser as scembed_subparser @@ -149,18 +149,19 @@ def main(test_args=None): _LOGGER.info("Running scembed") pass # scembed_main(test_args) - + if args.command == "region2vec": from .region2vec import region2vec + region2vec( - token_folder=args.token_folder, + token_folder=args.token_folder, save_dir=args.save_dir, num_shufflings=args.num_shuffle, - num_processes=args.nworkers, + num_processes=args.nworkers, embedding_dim=args.embed_dim, context_win_size=args.context_len, save_freq=args.save_freq, - resume_path=args.resume, + resume_path=args.resume, train_alg=args.train_alg, min_count=args.min_count, neg_samples=args.neg_samples, @@ -172,13 +173,14 @@ def main(test_args=None): ) if args.command == "tokenize": from .tokenization import hard_tokenization + hard_tokenization( - src_folder=args.data_folder, - dst_folder=args.token_folder, - universe_file=args.universe, - fraction=args.fraction, - num_workers=args.nworkers, - bedtools_path=args.bedtools_path + src_folder=args.data_folder, + dst_folder=args.token_folder, + universe_file=args.universe, + fraction=args.fraction, + num_workers=args.nworkers, + bedtools_path=args.bedtools_path, ) if args.command == "eval": if args.subcommand == "gdst": From 2ef26087bf430a2f861386f1c3790eb91bc61d3c Mon Sep 17 00:00:00 2001 From: Julia820 Date: Thu, 25 May 2023 17:26:29 -0400 Subject: [PATCH 0192/1072] fixed making folders in assess distance --- gitk/assess/distance.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index bc2ae0b8..a4b21508 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -293,7 +293,7 @@ def run_distance( check_if_uni_sorted(universe) files = open(file_list).read().split("\n")[:-1] res = [] - if folder_out: + if save_each: os.makedirs(os.path.join(folder_out, pref), exist_ok=True) if no_workers <= 1: for i in files: From 62daceffb3586a68e2a58e83e635ab71ac2429c3 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy <41063083+nleroy917@users.noreply.github.com> Date: Fri, 26 May 2023 07:20:30 -0400 Subject: [PATCH 0193/1072] Update gitk/scembed/projection.py Co-authored-by: Guangtao Zheng --- gitk/scembed/projection.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gitk/scembed/projection.py b/gitk/scembed/projection.py index 94bc672e..a9336054 100644 --- a/gitk/scembed/projection.py +++ b/gitk/scembed/projection.py @@ -26,7 +26,7 @@ def __init__(self, path_to_model: str, no_cache: bool = False): """ Initialize a projector object. - :param str path_to_model: The path to the model. This can be a local path or a registry name ont he model hub. + :param str path_to_model: The path to the model. This can be a local path or a registry name on the model hub. :param bool no_cache: If True, will not use the cache directory. """ self.path_to_model = path_to_model From 5936b1ef6b5672392d58846711b512b6e9bb3ac6 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 26 May 2023 11:56:21 -0400 Subject: [PATCH 0194/1072] new assessment cli interface --- gitk/assess/assess.py | 206 ++++++++++++++++++++++++++++++++++++ gitk/assess/distance.py | 23 ---- gitk/assess/intersection.py | 21 ---- gitk/cli.py | 115 +++----------------- 4 files changed, 222 insertions(+), 143 deletions(-) create mode 100644 gitk/assess/assess.py diff --git a/gitk/assess/assess.py b/gitk/assess/assess.py new file mode 100644 index 00000000..10410f52 --- /dev/null +++ b/gitk/assess/assess.py @@ -0,0 +1,206 @@ +from .distance import run_distance +from .intersection import run_intersection +from .likelihood import hard_universe_likelihood, likelihood_flexible_universe +import pandas as pd +import os +import numpy as np +import warnings + + +def run_all_assessment_methods( + raw_data_folder, + file_list, + universe, + no_workers, + folder_out, + pref, + save_each, + overlap=False, + distance_f_t_u=False, + distance_f_t_u_flex=False, + distance_u_t_f=False, + distance_u_t_f_flex=False, +): + if not all( + [ + overlap, + distance_f_t_u, + distance_f_t_u_flex, + distance_u_t_f, + distance_u_t_f_flex, + ] + ): + raise AttributeError("Choose at least one assessment method") + if not all( + [ + distance_f_t_u, + distance_f_t_u_flex, + distance_u_t_f, + distance_u_t_f_flex, + ] + ): + warnings.warn("Unused argument: save_each") + asses_results = [] + if overlap: + r_overlap = run_intersection( + raw_data_folder, + file_list, + universe, + no_workers, + ) + r_overlap.columns = [ + "file", + "univers/file", + "file/universe", + "universe&file", + ] + asses_results.append(r_overlap) + if distance_f_t_u: + r_distance = run_distance( + raw_data_folder, + file_list, + universe, + no_workers, + False, + folder_out, + pref + "_dist_file_to_universe", + save_each, + False, + ) + r_distance.columns = ["file", "median_dist_file_to_universe"] + asses_results.append(r_distance) + if distance_f_t_u_flex: + r_distance_flex = run_distance( + raw_data_folder, + file_list, + universe, + no_workers, + True, + folder_out, + pref + "_dist_file_to_universe_flex", + save_each, + False, + ) + r_distance_flex.columns = ["file", "median_dist_file_to_universe_flex"] + asses_results.append(r_distance_flex) + + if distance_u_t_f: + r_distance_utf = run_distance( + raw_data_folder, + file_list, + universe, + no_workers, + False, + folder_out, + pref + "_dist_universe_to_file", + save_each, + True, + ) + r_distance_utf.columns = ["file", "median_dist_universe_to_file"] + asses_results.append(r_distance_utf) + if distance_u_t_f_flex: + r_distance_utf_flex = run_distance( + raw_data_folder, + file_list, + universe, + no_workers, + True, + folder_out, + pref + "median_dist_universe_to_file_flex", + save_each, + True, + ) + r_distance_utf_flex.columns = ["file", "median_dist_universe_to_file_flex"] + asses_results.append(r_distance_utf_flex) + df = asses_results[0] + for i in asses_results[1:]: + df = pd.merge(df, i, on="file") + df.to_csv(os.path.join(folder_out, pref + "_data.csv"), index=False) + + +def get_closeness_score(folder, file_list, universe, no_workers, flexible=False): + file_to_uni = run_distance( + folder, + file_list, + universe, + no_workers, + flexible=flexible, + uni_to_file=False, + ) + + uni_to_file = run_distance( + folder, + file_list, + universe, + no_workers, + flexible=flexible, + uni_to_file=True, + ) + me = (10 * file_to_uni[1] + uni_to_file[1]) / 11 + cs = 1001 / (me + 1000) / 1.001 + return np.mean(cs) + + +def get_closeness_score_from_assessment_file(file, cs_each_file=False): + df = pd.read_csv(file, index_col=(0)) + df = df[["median_dist_file_to_universe", "median_dist_universe_to_file"]] + df["CS"] = ( + df["median_dist_universe_to_file"] + 10 * df["median_dist_file_to_universe"] + ) / 11 + df["CS"] = (1001 / (df["CS"] + 1000)) / 1.001 + if cs_each_file: + return df + else: + return df["CS"].mean() + + +def get_f_10_score( + folder, + file_list, + universe, + no_workers, +): + res = run_intersection( + folder, + file_list, + universe, + no_workers, + ) + res = np.array(res) + res = res[:, 1:] + res = res.astype("float") + recall = res[:, 2] / (res[:, 2] + res[:, 1]) + precision = res[:, 2] / (res[:, 2] + res[:, 0]) + f_10 = (1 + 10**2) * (precision * recall) / ((10**2 * precision) + recall) + return np.mean(f_10) + + +def get_f_10_score_from_assessment_file(file, f10_each_file=False): + df = pd.read_csv(file, index_col=(0)) + r = df["A&U/A"] + p = df["A&U/U"] + df["F_10"] = (1 + 10**2) * (p * r) / ((10**2 * p) + r) + if f10_each_file: + return df["F_10"] + else: + return df["F_10"].mean() + + +def get_likelihood( + model_file, + universe, + cove_folder, + cove_prefix, + flexible=False, + save_peak_input=False, +): + if flexible: + lh = likelihood_flexible_universe( + model_file, universe, cove_folder, cove_prefix, save_peak_input + ) + else: + if save_peak_input: + warnings.warn("Unused argument: save_peak_input") + lh = hard_universe_likelihood(model_file, universe, cove_folder, cove_prefix) + + return lh diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index a4b21508..a000b357 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -318,26 +318,3 @@ def run_distance( ] res = p.starmap(calc_distance_between_two_files, args) return pd.DataFrame(res) - - -def get_closeness_score(folder, file_list, universe, no_workers, flexible=False): - file_to_uni = run_distance( - folder, - file_list, - universe, - no_workers, - flexible=flexible, - uni_to_file=False, - ) - - uni_to_file = run_distance( - folder, - file_list, - universe, - no_workers, - flexible=flexible, - uni_to_file=True, - ) - me = (10 * file_to_uni[1] + uni_to_file[1]) / 11 - cs = 1001 / (me + 1000) / 1.001 - return np.mean(cs) diff --git a/gitk/assess/intersection.py b/gitk/assess/intersection.py index 53988aa4..a580e05a 100644 --- a/gitk/assess/intersection.py +++ b/gitk/assess/intersection.py @@ -221,24 +221,3 @@ def run_intersection( args = [(universe, folder, f) for f in files] res = p.starmap(calc_diff_intersection, args) return pd.DataFrame(res) - - -def get_f_10_score( - folder, - file_list, - universe, - no_workers, -): - res = run_intersection( - folder, - file_list, - universe, - no_workers, - ) - res = np.array(res) - res = res[:, 1:] - res = res.astype("float") - recall = res[:, 2] / (res[:, 2] + res[:, 1]) - precision = res[:, 2] / (res[:, 2] + res[:, 0]) - f_10 = (1 + 10**2) * (precision * recall) / ((10**2 * precision) + recall) - return np.mean(f_10) diff --git a/gitk/cli.py b/gitk/cli.py index 6ff3b9e5..01b72d17 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -71,105 +71,22 @@ def main(test_args=None): _LOGGER.info(f"Command: {args.command}") if args.command == "assess": - asses_results = [] - if args.distance: - from .assess.distance import run_distance - - r_distance = run_distance( - args.raw_data_folder, - args.file_list, - args.universe, - args.no_workers, - False, - args.folder_out, - args.pref + "_dist_file_to_universe", - args.save_each, - False, - ) - r_distance.columns = ["file", "median_dist_file_to_universe"] - print(r_distance) - asses_results.append(r_distance) - - if args.distance_flexible: - from .assess.distance import run_distance - - r_distance_flex = run_distance( - args.raw_data_folder, - args.file_list, - args.universe, - args.no_workers, - True, - args.folder_out, - args.pref + "_dist_file_to_universe_flex", - args.save_each, - False, - ) - r_distance_flex.columns = ["file", "median_dist_file_to_universe_flex"] - print(r_distance_flex) - asses_results.append(r_distance_flex) - - if args.distance_universe_to_file: - from .assess.distance import run_distance - - r_distance_utf = run_distance( - args.raw_data_folder, - args.file_list, - args.universe, - args.no_workers, - False, - args.folder_out, - args.pref + "_dist_universe_to_file", - args.save_each, - True, - ) - r_distance_utf.columns = ["file", "median_dist_universe_to_file"] - print(r_distance_utf) - asses_results.append(r_distance_utf) - - if args.distance_flexible_universe_to_file: - from .assess.distance import run_distance - - r_distance_utf_flex = run_distance( - args.raw_data_folder, - args.file_list, - args.universe, - args.no_workers, - True, - args.folder_out, - args.pref + "median_dist_universe_to_file_flex", - args.save_each, - True, - ) - r_distance_utf_flex.columns = ["file", "median_dist_universe_to_file_flex"] - print(r_distance_utf_flex) - asses_results.append(r_distance_utf_flex) - - if args.overlap: - from .assess.intersection import run_intersection - - r_overlap = run_intersection( - args.raw_data_folder, - args.file_list, - args.universe, - args.no_workers, - ) - r_overlap.columns = [ - "file", - "univers/file", - "file/universe", - "universe&file", - ] - print(r_overlap) - asses_results.append(r_overlap) - if len(asses_results) == 0: - raise Exception("No assessment method was provided") - if args.save_to_file: - df = asses_results[0] - for i in asses_results[1:]: - df = pd.merge(df, i, on="file") - df.to_csv( - os.path.join(args.folder_out, args.pref + "_data.csv"), index=False - ) + from .assess.assess import run_all_assessment_methods + + run_all_assessment_methods( + args.raw_data_folder, + args.file_list, + args.universe, + args.no_workers, + args.folder_out, + args.pref, + args.save_each, + args.overlap, + args.distance, + args.distance_flexible, + args.distance_universe_to_file, + args.distance_flexible_universe_to_file, + ) if args.command == "lh": _LOGGER.info(f"Subcommand: {args.subcommand}") From 0c5a42c0e119404ea1e57dd0bcf2ede442c99c7d Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 26 May 2023 12:27:54 -0400 Subject: [PATCH 0195/1072] add check if universe flexible --- gitk/assess/cli.py | 41 ++-------------------------------- gitk/assess/distance.py | 47 ++++++++++++++++++++++----------------- gitk/assess/likelihood.py | 3 ++- gitk/assess/utils.py | 8 +++++++ 4 files changed, 38 insertions(+), 61 deletions(-) diff --git a/gitk/assess/cli.py b/gitk/assess/cli.py index 4b64062f..73203175 100644 --- a/gitk/assess/cli.py +++ b/gitk/assess/cli.py @@ -22,13 +22,13 @@ def build_subparser(parser): parser.add_argument( "--distance-flexible", help="if calculate distance from region in query to nearest region in the universe taking into account " - "universe flexibility ", + "universe flexibility ", action="store_true", ) parser.add_argument( "--distance-flexible-universe-to-file", help="if calculate distance from region in the universe to nearest region in query taking into account " - "universe flexibility ", + "universe flexibility ", action="store_true", ) parser.add_argument( @@ -61,40 +61,3 @@ def build_subparser(parser): ) return parser - - -def build_subparser_distance(parser): - parser = build_subparser(parser) - parser.add_argument( - "--flexible", - help="if calculate the distance taking into account that the universe is flexible", - action="store_true", - ) - parser.add_argument( - "--save-each", - help="if save distance for each peak in each file ", - action="store_true", - ) - parser.add_argument( - "--universe-to-file", - help="if calculate distance from universe to file", - action="store_true", - ) - - return parser - - -def build_mode_parser(parser): - sp = parser.add_subparsers(dest="subcommand") - msg_by_cmd = { - "distance": "Asses based on distance", - "intersection": "Asses based on coverage", - "recovered": "Asses based on percent of recovered starts, ends", - } - subparsers = {} - for k, v in msg_by_cmd.items(): - subparsers[k] = sp.add_parser(k, description=v, help=v) - subparsers["distance"] = build_subparser_distance(subparsers["distance"]) - subparsers["intersection"] = build_subparser(subparsers["intersection"]) - - return parser diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index a000b357..9d1a01bc 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -9,7 +9,12 @@ import pandas as pd from ..utils import natural_chr_sort -from .utils import check_if_uni_sorted, prep_data, process_db_line +from .utils import ( + check_if_uni_sorted, + prep_data, + process_db_line, + check_if_uni_flexible, +) def flexible_distance_between_two_regions(region, query): @@ -25,7 +30,7 @@ def flexible_distance_between_two_regions(region, query): def distance_between_two_regions(region, query): - """Calculate distance between region and region from hard universe + """Calculate distance between region in database and region from the query :param [int] region: region from hard universe :param int query: analysed region :return int: distance @@ -37,10 +42,10 @@ def distance_to_closest_region( db, db_queue, i, current_chrom, unused_db, pos_index, flexible, uni_to_file ): """ - Calculate distance from given peak to the closest region in universe - :param file db: universe file - :param list db_queue: queue of three last positions in universe - :param int i: analysed position from the query + Calculate distance from given peak to the closest region in database + :param file db: database file + :param list db_queue: queue of three last positions in database + :param i: analysed position from the query :param str current_chrom: current analysed chromosome from query :param list unused_db: list of positions from universe that were not compared to query :param list pos_index: which indexes from universe region use to calculate distance @@ -193,21 +198,17 @@ def calc_distance_between_two_files( dist_end = [] unused_db_end = [] waiting_end = False - pos_start = [1] - pos_end = [2] + start_index = [1] + end_index = [2] if flexible and not uni_to_file: - pos_start = [1, 6] - pos_end = [7, 2] + start_index = [1, 6] + end_index = [7, 2] for i in q: if not uni_to_file: i = i.decode("utf-8") i = i.split("\t") - if uni_to_file and flexible: - start = [int(i[1]), int(i[6])] - end = [int(i[7]), int(i[2])] - else: - start = int(i[1]) - end = int(i[2]) + start = [int(i[ind]) for ind in start_index] + end = [int(i[ind]) for ind in end_index] q_chrom = i[0] result_start = read_in_new_universe_regions( db_start, @@ -216,7 +217,7 @@ def calc_distance_between_two_files( unused_db_start, db_queue_start, waiting_start, - pos_start, + start_index, ) (waiting_start, current_chrom_start) = result_start if not waiting_start: @@ -226,7 +227,7 @@ def calc_distance_between_two_files( start, current_chrom_start, unused_db_start, - pos_start, + start_index, flexible, uni_to_file, ) @@ -238,7 +239,7 @@ def calc_distance_between_two_files( unused_db_end, db_queue_end, waiting_end, - pos_end, + end_index, ) (waiting_end, current_chrom_end) = result_end if not waiting_end: @@ -248,7 +249,7 @@ def calc_distance_between_two_files( end, current_chrom_end, unused_db_end, - pos_end, + end_index, flexible, uni_to_file, ) @@ -291,7 +292,11 @@ def run_distance( mean of median distances from ends in query to the nearest ends in universe """ check_if_uni_sorted(universe) - files = open(file_list).read().split("\n")[:-1] + if flexible: + check_if_uni_flexible(universe) + print(no_workers) + with open(file_list) as f: + files = f.read().split("\n")[:-1] res = [] if save_each: os.makedirs(os.path.join(folder_out, pref), exist_ok=True) diff --git a/gitk/assess/likelihood.py b/gitk/assess/likelihood.py index b7123a35..1393841c 100644 --- a/gitk/assess/likelihood.py +++ b/gitk/assess/likelihood.py @@ -1,6 +1,6 @@ import numpy as np import os -from .utils import check_if_uni_sorted +from .utils import check_if_uni_sorted, check_if_uni_flexible from ..utils import read_chromosome_from_bw from ..likelihood.build_model import ModelLH @@ -219,6 +219,7 @@ def likelihood_flexible_universe( empty_start = 0 res = 0 check_if_uni_sorted(universe) + check_if_uni_flexible(universe) model_lh = ModelLH(model_file) chroms = model_lh.chromosomes_list output = [] diff --git a/gitk/assess/utils.py b/gitk/assess/utils.py index b14e9c80..8b50e65e 100644 --- a/gitk/assess/utils.py +++ b/gitk/assess/utils.py @@ -32,6 +32,14 @@ def check_if_uni_sorted(universe): raise Exception("Universe not sorted") +def check_if_uni_flexible(universe): + with open(universe) as u: + l = u.readline() + l = l.split("\t") + if len(l) < 6: + raise Exception("Universe is not flexible") + + def process_line(line): """Helper for reading in bed file line""" line = line.split("\t")[:3] From 714993d6268b030385e418aedd17a538457251e1 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Mon, 29 May 2023 08:40:08 -0400 Subject: [PATCH 0196/1072] add changes discussed during code review --- gitk/scembed/const.py | 2 +- gitk/scembed/scembed.py | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/gitk/scembed/const.py b/gitk/scembed/const.py index 60a41cb4..a64ee25f 100644 --- a/gitk/scembed/const.py +++ b/gitk/scembed/const.py @@ -9,7 +9,7 @@ DEFAULT_EPOCHS = 100 DEFAULT_GENSIM_EPOCHS = 1 DEFAULT_MIN_COUNT = 10 -DEAFULT_N_SHUFFLES = 10 +DEAFULT_N_SHUFFLES = 1 # 1 is sufficient for most cases DEFAULT_WINDOW_SIZE = 5 DEFAULT_EMBEDDING_SIZE = 100 DEFAULT_EPOCHS = 10 diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index 30766068..1d0f8a35 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -128,6 +128,8 @@ def train( :param float min_lr: The minimum learning rate. :param Union[str, ScheduleType] lr_schedule: The learning rate schedule to use. """ + # force to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205683978) + n_shuffles = 1 if not isinstance(data, sc.AnnData) and not isinstance(data, str): raise TypeError( @@ -202,7 +204,7 @@ def train( super().train( self.region_sets, total_examples=len(self.region_sets), - epochs=gensim_epochs or 1, # use the epochs passed in or just one + epochs=1, # for to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205692089) callbacks=self.callbacks, compute_loss=report_loss, start_alpha=current_lr, From b5a206c7194493352d1cffe3e1d06bfb29ee9f81 Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Thu, 1 Jun 2023 17:18:50 -0400 Subject: [PATCH 0197/1072] add rct, ctt --- gitk/cli.py | 67 +++++++--- gitk/eval/README.md | 174 ++++++++++++------------ gitk/eval/cct.py | 199 ---------------------------- gitk/eval/cli.py | 130 +++++++++++++++--- gitk/eval/const.py | 3 + gitk/eval/ctt.py | 115 ++++++++++++++++ gitk/eval/gdst.py | 46 +++---- gitk/eval/npt.py | 47 +------ gitk/eval/permutation.R | 111 ---------------- gitk/eval/rct.py | 112 ++++++++++++++++ gitk/region2vec/README.md | 21 ++- gitk/region2vec/main.py | 1 + gitk/region2vec/region_shuffling.py | 1 + gitk/tokenization/README.md | 7 + gitk/tokenization/main.py | 3 +- 15 files changed, 522 insertions(+), 515 deletions(-) delete mode 100644 gitk/eval/cct.py create mode 100644 gitk/eval/ctt.py delete mode 100644 gitk/eval/permutation.R create mode 100644 gitk/eval/rct.py diff --git a/gitk/cli.py b/gitk/cli.py index 92a87301..415bc66a 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -167,9 +167,9 @@ def main(test_args=None): neg_samples=args.neg_samples, init_lr=args.init_lr, min_lr=args.min_lr, - lr_scheduler=args.lr_mode, # How to decay the learning rate. Select from linear and milestone - milestones=args.milestones, # Specify only when lr_scheduler=milestone. At each given epoch, the learning rate will be multiplied by 0.1 - seed=args.seed, # random seed + lr_scheduler=args.lr_mode, + milestones=args.milestones, + seed=args.seed, ) if args.command == "tokenize": from .tokenization import hard_tokenization @@ -184,34 +184,63 @@ def main(test_args=None): ) if args.command == "eval": if args.subcommand == "gdst": - from gitk.eval.gdst import get_gds + from gitk.eval.gdst import get_gdst_score - gds = get_gds(args.model_path, args.embed_type, args.num_samples) - print(gds) + gdst_score = get_gdst_score( + args.model_path, args.embed_type, args.num_samples, args.seed + ) + print(gdst_score) if args.subcommand == "npt": - from gitk.eval.npt import get_snpr + from gitk.eval.npt import get_npt_score - npt = get_snpr( + npt_score = get_npt_score( args.model_path, args.embed_type, args.K, args.num_samples, - resolution=args.K, + args.seed, + args.K, + num_workers=args.num_workers, + ) + print(npt_score["SNPR"][0]) + if args.subcommand == "ctt": + from gitk.eval.ctt import get_ctt_score + + ctt_score = get_ctt_score( + args.model_path, + args.embed_type, + args.seed, + args.num_samples, + args.num_workers, ) - print(npt["SNPR"][0]) - if args.subcommand == "cct-tss": - from gitk.eval.cct import get_scc_tss + print(ctt_score) + if args.subcommand == "rct": + from gitk.eval.rct import get_rct_score - scctss = get_scc_tss( + rct_score = get_rct_score( args.model_path, args.embed_type, - args.save_folder, - args.Rscript_path, - args.assembly, - num_samples=args.num_samples, - threshold=args.threshold, + args.bin_path, + args.out_dim, + args.cv_num, + args.seed, + args.num_workers, ) - print(scctss) + print(rct_score) + if args.subcommand == "bin-gen": + from gitk.eval.utils import get_bin_embeddings + import glob, pickle + + if os.path.exists(args.file_name): + print(f"{args.file_name} exists!") + return + token_files = glob.glob(os.path.join(args.token_folder, "*")) + bin_embed = get_bin_embeddings(args.universe, token_files) + os.makedirs(os.path.dirname(args.file_name), exist_ok=True) + with open(args.file_name, "wb") as f: + pickle.dump(bin_embed, f) + print(f"binary embeddings saved to {args.file_name}") + return diff --git a/gitk/eval/README.md b/gitk/eval/README.md index e3d6eb35..8ef057bf 100644 --- a/gitk/eval/README.md +++ b/gitk/eval/README.md @@ -1,6 +1,7 @@ # Evaluation of Genomic Region Embeddings -## Create a Base Embedding Object +## Preparations +### Create a Base Embedding Object Given a set of genomic region embeddings `embeddings` and the corresponding regions `vocab`, use `BaseEmbeddings` to create an `base` embedding object. ``` from gitk.eval.utils import BaseEmbeddings @@ -9,108 +10,105 @@ base_obj = BaseEmbeddings(embeddings, vocab) with open("base_embed.pt", "wb") as f: pickle.dump(base_obj, f) ``` -## Genome Distance Scaling Test (GDST) -GDST evaluates how significant a set of genomic region embeddings preserves the biological knowledge that close regions on the genome tend to have similar biological functions and distant regions on the genome tend to have different biological functions. We assume that embedding distance reflects the function similarity between two genomic regions. The code uses a set of embeddings as input and outputs a GDS value showing how much the embedding distance scales the corresponding genome distance. - -``` -from gitk.eval.gdst import * -import numpy as np - -# evaluate a single model -model_path = "/path/to/a/region2vec/model/" -gds = get_gds(model_path, embed_type="region2vec", num_samples=1000, seed=42) -print("Genome distance scaling: ", gds) -``` -Or use the command line +### Generate Binary Embeddings +```python +from gitk.eval.utils import get_bin_embeddings +universe_file = "/path/to/universe.bed" +token_files = ["file1.bed", "file2.bed"] +bin_embed = get_bin_embeddings(universe_file, token_files) ``` -gitk eval gdst --model_path /path/to/a/region2vec/model/ --embed_type region2vec +Or use command line: +```bash +gitk eval bin-gen --universe /path/to/universe.bed --token-folder /path/to/tokenized/folder --file-name bin_embed.pickle ``` +## Statistical Tests +### Cluster Tendency Test (CTT) +CTT analyzes how well a set of region embeddings can be clustered. CTT score lies between 0 and 1. A larger CTT score indicates a greater tendency for the embeddings being evaluated to have clusters. When the embeddings are uniformly distributed, the score is 0.5. For evenly spaced embeddings, the score approaches 0. -Process a batch of two models (can be more than two models) +```python +from gitk.eval.ctt import get_ctt_score, ctt_eval +path = "/path/to/a/region2vec/model/" +embed_type = "region2vec" +ctt_score = get_ctt_score(path, embed_type, seed=42, num_data=10000, num_workers=10) +print(ctt_score) + +# evaluate a batch of models and run CTT for 5 times with different random seeds +batch = [(path, embed_type)] +ctt_score_arr = ctt_eval(batch, num_runs=5, num_data=10000,num_workers=10) +print(f"Model: {ctt_score_arr[0][0]}\n CTT scores:{ctt_score_arr[0][1]}") # CTT scores for the 1st model in the batch ``` -model_path1 = '/path/to/the/region2vec/model1/' -model_path2 = '/path/to/the/region2vec/model2/' -batch = [(model_path1, 'region2vec'), (model_path2, 'base')] # (model_path, embed_type) -gds_arr = get_gds_batch(batch, num_samples=1000, seed=42, num_workers=2) # set num_workers > 1 to enable multiprocessing -print(f"Model1: {gds_arr[0][0]}, GDS:{gds_arr[0][1]:.4f}") + +Or use the command line +```bash +gitk eval ctt --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` +### Reconstruction Test (RCT) +RCT evaluates how well an embedding of a region preserves the region’s occurrence information in the training data. The best RCT score is 1. + +```python +from gitk.eval.rct import get_rct_score, rct_eval +path = "/path/to/a/region2vec/model/" +embed_type = "region2vec" +bin_path = "/path/to/a/binary/embedding/for/the/same/tokenized/files/" +# set out_dim to -1 use all the dimensions of the binary embeddings. Set out_dim to a small positive number to reduce computational complexity. +rct_score = get_rct_score(path, embed_type, bin_path, out_dim=-1, cv_num=5, seed=42, num_workers=10) +print(rct_score) -Run GDST 20 times for the two models +# evaluate a batch of models and run RCT for 5 times with different random seeds +batch = [(path, embed_type, bin_path)] +rct_score_arr = rct_eval(batch, num_runs=5, cv_num=5, out_dim=-1, num_workers=10) +print(f"Model: {rct_score_arr[0][0]}\n RCT scores:{rct_score_arr[0][1]}") # RCT scores for the 1st model in the batch ``` -row_labels = ['model1-region2vec', 'model2-base'] -gds_arr = gds_eval(batch, num_runs=20, num_samples=1000, num_workers=10) -print(gds_arr[0]) + +Or use the command line +```bash +gitk eval rct --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` +To change the learning setting, go to the definition of `get_rct_score` in `gitk/eval/rct.py` and change the constructor of `MLPRegressor`. +## Biological Tests +### Genome Distance Scaling Test (GDST) +GDST calculates a score measuring how much the embedding distance between two regions scales the corresponding genome distance. -## Neighborhood Preserving Test (NPT) -Evaluate how significant genomic region embeddings preserve their neighboring regions on the genome against random embeddings. The code output the significance of neighborhood preserving ratio (SNPR) for a set of region embeddings. +```python +from gitk.eval.gdst import get_gdst_score, gdst_eval +path = "/path/to/a/region2vec/model/" +embed_type = "region2vec" +gdst_score = get_gdst_score(path, embed_type, num_samples=10000,seed=42) +print(gdst_score) -``` -from gitk.eval.npt import * -model_path = '/path/to/a/region2vec/model/' -embed_type = 'region2vec' -K = 50 -resolution = 10 -result = get_snpr( - model_path, embed_type, K, num_samples=1000, seed=0, resolution=resolution, num_workers=10 -) -print(result["SNPR"]) # an array of SNPRs when the number of neighbors is 10, 20, 30, 40, 50, respectively +# evaluate a batch of models and run GDST for 5 times with different random seeds +batch = [(path,embed_type)] +gdst_score_arr = gdst_eval(batch, num_runs=5, num_samples=10000) ``` -Or use the command line (the output will be the result when resolution=K) -``` -gitk eval npt --model_path /path/to/a/region2vec/model/ --embed_type region2vec --K 50 --num_samples 1000 +Or use the command line +```bash +gitk eval gdst --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` -Process a batch of models -``` -model_path1 = '/path/to/the/region2vec/model1/' -model_path2 = '/path/to/the/region2vec/model2/' -batch = [(model_path1, 'region2vec'), (model_path2, 'base')] # (model_path, embed_type) -result_list = get_snpr_batch(batch, K, num_samples=1000, seed=0) -print(result_list[0]["SNPR"][0]) # SNPRs for model1 -print(result_list[1]["SNPR"][0]) # SNPRs for model2 - -# Run the genome distance test 20 times for the two models, setting save_folder will save the result for each run -K = 1000 -resolution = 100 # increase the number of neighboring regions by resolution every time -npr_results = npt_eval(batch, K, num_samples=1000, num_workers=10, num_runs=20, resolution=resolution) -print("Model: {snpr_results[0][0]}") -print(snpr_results[0][1].shape) # (20,10) -print(snpr_results[0][1][:,0]) # results from 20 runs when num_neighborhs=100 -print(snpr_results[0][1][:,1]) # results from 20 runs when num_neighborhs=200 -``` -## Constrastive Clusters Test - Transcription Start Sites (CCT-TSS) -CCT-TSS evaluates how well a set of genomic region embeddings can separate regions relating to transcription start sites from those are not. This test involves clustering. -Since we do not know a priori the true number of clusters for a set of region embeddings, we specify `K_arr` to test clusters in different sizes. The code gives the significance of contrastive clusters relating to transcription start sites (SCC-TSS) for a set of region embeddings. +### Neighborhood Preserving Test (NPT) +NPT evaluates how significant genomic region embeddings preserve their neighboring regions on the genome against random embeddings. The code output the NPT score for a set of region embeddings. + +```python +from gitk.eval.npt import get_npt_score, npt_eval +path = "/path/to/a/region2vec/model/" +embed_type = "region2vec" +K = 10 +# If resolution = K gives NPT for K neighbors +# If resolution < K, gives NPT for [resolution, resolution*2, ...] neighbors +resolution = K +npt_score = get_npt_score(path, embed_type, K, num_samples=100, seed=0, resolution=resolution,num_workers=10) +print(npt_score['SNPR']) -The test requires running R scripts with the `GenomicDistributions`, `doParallel` and `optparse` packages. +# evaluate a batch of models and run NPT for 5 times with different random seeds +batch = [(path, embed_type)] +npt_score_arr = npt_eval(batch, K, num_samples=100, num_workers=10, num_runs=5, resolution=resolution) +print(f"Model: {npt_score_arr[0][0]}\n NPT scores: {npt_score_arr[0][1]}") # NPT scores for the 1st model in the batch +``` + +Or use the command line (the output will be the result when resolution=K) +```bash +gitk eval npt --model-path /path/to/a/region2vec/model/ --embed-type region2vec --K 50 --num-samples 1000 ``` -from gitk.eval.cct import * - -# process a single model (a set of genomic region embeddings) -model_path = '/path/to/a/region2vec/model/' -embed_type = 'region2vec' -save_folder = '/path/to/cst/results/' -Rscript_path = '/path/to/Rscript/' -assembly = 'hg19' -num_samples = 1000 -K_arr = [5, 20, 40, 60, 100] -threshold = 0.0001 # significance threshold, the smaller the more significant -scores = get_scc_tss(model_path, embed_type, save_folder, Rscript_path, assembly, K_arr, num_samples, threshold) -print(scores) # scroes for each K in K_arr - -# process a batch of two models -model_path1 = '/path/to/the/region2vec/model1/' -model_path2 = '/path/to/the/region2vec/model2/' -batch = [(model_path1, 'region2vec'), (model_path2, 'base')] # (model_path, embed_type) - -# since we have more than one models, we can rank them based on the average scores over different Ks -scores_batch, avg_ranks = get_scc_tss_batch(batch, save_folder, Rscript_path, assembly, K_arr, num_samples, threshold) - -# average ranks after running clustering_significance_test_batch num_runs times -avg_ranks_arr = cct_tss_eval(batch, save_folder, Rscript_path, assembly, K_arr, num_samples, threshold, num_runs=20) - -``` \ No newline at end of file diff --git a/gitk/eval/cct.py b/gitk/eval/cct.py deleted file mode 100644 index 8ce6374b..00000000 --- a/gitk/eval/cct.py +++ /dev/null @@ -1,199 +0,0 @@ -import glob -import multiprocessing as mp -import os -import pickle -import shutil -import subprocess -import time - -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.lines import Line2D -from scipy.stats import rankdata -from sklearn.cluster import KMeans -from tqdm import tqdm - -from .utils import load_genomic_embeddings, region2tuple - - -def random_annotate_points( - labels, num_per_cluster=10, min_num_per_cluster=1, max_font_size=20, min_font_size=8 -): - cluster_labels = np.unique(labels) # sorted - positions = np.arange(len(labels)) - if cluster_labels[0] == -1: - print(f"Number of clusters: {len(cluster_labels) - 1}") - else: - print(f"Number of clusters: {len(cluster_labels)}") - clusters = [positions[labels == c] for c in cluster_labels] - ratios = np.array([len(clusters[i]) / len(labels) for i in range(len(clusters))]) - annotate_arr = [] - for i, c in enumerate(cluster_labels): - num = len(clusters[i]) - annotate_num = max( - min(int(ratios[i] / ratios.max() * num_per_cluster), num), - min(num, min_num_per_cluster), - ) - fsize = max(int(ratios[i] / ratios.max() * max_font_size), min_font_size) - indices = np.random.permutation(num)[0:annotate_num] - pos = clusters[i][indices] - annotate_arr.extend([(p, c, fsize) for p in pos]) - return annotate_arr - - -def assign_color_by_size(cmap_name, labels): - cluster_labels = np.unique(labels) # sorted - cluster_sizes = [(c, (labels == c).sum()) for c in cluster_labels] - cluster_sizes = sorted(cluster_sizes, key=lambda x: -x[1]) - color_mapping = {c: i for i, (c, s) in enumerate(cluster_sizes) if c != -1} - cmap = plt.get_cmap(cmap_name) - colors = cmap(np.linspace(0, 1, len(color_mapping))) - # add outlier color - color_mapping[-1] = len(color_mapping) - colors = np.vstack([colors, [0.0, 0, 0, 1.0]]) - label_colors = [colors[color_mapping[l]] for l in labels] - return label_colors - - -def get_cluster_regions(cluster_idx, labels, vocab, path): - positions = np.arange(len(labels)) - indices = positions[labels == cluster_idx] - regions = [region2tuple(vocab[i]) for i in indices] - os.makedirs(path, exist_ok=True) - with open(os.path.join(path, f"cluster_{cluster_idx}.bed"), "w") as f: - for chr_name, start, end in regions: - f.write(f"{chr_name}\t{start}\t{end}\n") - - -def clustering(model_path, embed_type, n_clusters, save_folder, seed=0): - np.random.seed(seed) - embeds, vocab = load_genomic_embeddings(model_path, embed_type) - - clustering = KMeans(n_clusters=n_clusters, random_state=seed, n_init="auto").fit( - embeds - ) - labels = clustering.labels_ - cluster_idxes = np.sort(np.unique(labels)) - for c in cluster_idxes: - get_cluster_regions(c, labels, vocab, save_folder) - with open(os.path.join(save_folder, "labels.pickle"), "wb") as f: - pickle.dump(labels, f) - - -def cal_significance_val(pvals, threshold): - num = (pvals < threshold).sum() + (pvals > 1 - threshold).sum() - return num / len(pvals) - - -def get_scc_tss( - model_path, - embed_type, - save_folder, - Rscript_path, - assembly, - K_arr=[5, 20, 40], - num_samples=1000, - threshold=0.0001, - num_workers=10, - seed=0, -): - for K in K_arr: - target_folder = os.path.join(save_folder, f"Kmeans_{K}") - clustering(model_path, embed_type, K, target_folder, seed=0) - curr_folder = os.path.dirname(os.path.abspath(__file__)) - subprocess.call( - [ - Rscript_path, - f"{curr_folder}/permutation.R", - "--assembly", - assembly, - "--num_workers", - str(num_workers), - "--path", - save_folder, - "--num_samples", - str(num_samples), - ] - ) - scores = [] - for K in K_arr: - target_folder = os.path.join(save_folder, f"Kmeans_{K}") - tmp_files = glob.glob(os.path.join(target_folder, "cluster_*.bed")) - # for tmp in tmp_files: - # os.remove(tmp) - with open(os.path.join(target_folder, "pvals.txt"), "r") as f: - pvals = f.readlines() - pvals = np.array([float(p.strip()) for p in pvals]) - score = cal_significance_val(pvals, threshold) - scores.append(score) - print(model_path) - print( - "(K, CCSI): " - + " ".join([f"({K_arr[i]},{scores[i]:.6f})" for i in range(len(K_arr))]) - ) - print("\n") - return scores - - -def get_scc_tss_batch( - batch, - save_folder, - Rscript_path, - assembly, - K_arr=[5, 20, 40, 60, 100], - num_samples=1000, - threshold=0.0001, - num_workers=10, - seed=0, -): - scores_batch = [] - for i, (model_path, embed_type) in enumerate(batch): - target_folder = os.path.join(save_folder, f"model_{i}") - scores = get_scc_tss( - model_path, - embed_type, - target_folder, - Rscript_path, - assembly, - K_arr, - num_samples, - threshold, - num_workers, - seed, - ) - scores_batch.append(scores) - scores_batch = np.array(scores_batch) - return scores_batch - - -def cct_tss_eval( - batch, - save_folder, - Rscript_path, - assembly, - K_arr=[5, 20, 40, 60, 100], - num_samples=1000, - threshold=0.0001, - num_runs=20, - num_workers=10, -): - for seed in range(num_runs): - target_folder = os.path.join(save_folder, f"cct_tss_seed{seed}") - scores_batch = get_scc_tss_batch( - batch, - target_folder, - Rscript_path, - assembly, - K_arr, - num_samples, - threshold, - num_workers, - seed, - ) - result_path = os.path.join(save_folder, f"cct_tss_seed{seed}.pickle") - scores_batch = [ - (batch[i][0], scores_batch[i]) for i in range(len(scores_batch)) - ] - with open(result_path, "wb") as f: - pickle.dump(scores_batch, f) - return scores_batch diff --git a/gitk/eval/cli.py b/gitk/eval/cli.py index 5c126e6f..3ad30b91 100644 --- a/gitk/eval/cli.py +++ b/gitk/eval/cli.py @@ -20,6 +20,12 @@ def build_subparser_gdst(parser): type=int, help="number of samples used in calculation", ) + parser.add_argument( + "--seed", + default=42, + type=int, + help="random seed", + ) return parser @@ -48,11 +54,22 @@ def build_subparser_npt(parser): type=int, help="number of samples used in calculation", ) - + parser.add_argument( + "--seed", + default=42, + type=int, + help="random seed", + ) + parser.add_argument( + "--num-workers", + default=10, + type=int, + help="number of parllel processes", + ) return parser -def build_subparser_cct_tss(parser): +def build_subparser_ctt(parser): """ Builds argument parser. @@ -67,27 +84,105 @@ def build_subparser_cct_tss(parser): parser.add_argument( "--embed-type", required=True, type=str, help="region2vec or base" ) + parser.add_argument( - "--save-folder", + "--num-samples", + default=10000, + type=int, + help="number of samples used in calculation", + ) + parser.add_argument( + "--seed", + default=42, + type=int, + help="random seed", + ) + parser.add_argument( + "--num-workers", + default=10, + type=int, + help="number of parllel processes", + ) + + return parser + + +def build_subparser_rct(parser): + """ + Builds argument parser. + + :return argparse.ArgumentParser + """ + parser.add_argument( + "--model-path", required=True, type=str, - help="path to the folder that saves intermediate results", + help="path to a region2vec model or a Base model", ) parser.add_argument( - "--Rscript-path", required=True, type=str, help="path to Rscript" + "--bin-path", + required=True, + type=str, + help="path to a set of binary embedding", ) - parser.add_argument("--assembly", required=True, type=str, help="hg19 or hg38") parser.add_argument( - "--threshold", - default=0.0001, - type=float, - help="threshold that determines a significant cluster", + "--embed-type", + required=True, + type=str, + help="region2vec or base for model-path", ) + parser.add_argument( - "--num-samples", - default=1000, + "--cv-num", + default=5, type=int, - help="number of samples used in calculation", + help="number of folds in cross-validation", + ) + parser.add_argument( + "--seed", + default=42, + type=int, + help="random seed", + ) + parser.add_argument( + "--out-dim", + default=-1, + type=int, + help="Used when the binary embeddings are very high-dimensional, i.e., there are many training files. Default -1 represents using all the dimensions", + ) + parser.add_argument( + "--num-workers", + default=10, + type=int, + help="number of parllel processes", + ) + + return parser + + +def build_subparser_bingen(parser): + """ + Builds argument parser. + + :return argparse.ArgumentParser + """ + parser.add_argument( + "--universe", + required=True, + type=str, + help="path to a universe file", + ) + parser.add_argument( + "--token-folder", + required=True, + type=str, + help="path to a folder storing tokenized files", + ) + parser.add_argument( + "--file-name", + required=True, + type=str, + help="name of the generated binary embeddings", ) return parser @@ -98,13 +193,16 @@ def build_subparser(parser): msg_by_cmd = { "gdst": "Genome distance scaling test", "npt": "Neighorhood preserving test", - "cct-tss": "Contrastive clusters test on transcription start sites", + "ctt": "Cluster tendency test", + "rct": "Reconstruction test", + "bin-gen": "Generate binary embeddings", } subparsers = {} for k, v in msg_by_cmd.items(): subparsers[k] = sp.add_parser(k, description=v, help=v) subparsers["gdst"] = build_subparser_gdst(subparsers["gdst"]) subparsers["npt"] = build_subparser_npt(subparsers["npt"]) - subparsers["cct-tss"] = build_subparser_cct_tss(subparsers["cct-tss"]) - + subparsers["ctt"] = build_subparser_ctt(subparsers["ctt"]) + subparsers["rct"] = build_subparser_rct(subparsers["rct"]) + subparsers["bin-gen"] = build_subparser_bingen(subparsers["bin-gen"]) return parser diff --git a/gitk/eval/const.py b/gitk/eval/const.py index fef7c457..ed67b7d7 100644 --- a/gitk/eval/const.py +++ b/gitk/eval/const.py @@ -1 +1,4 @@ GENOME_DIST_SCALAR = 1e10 +CTT_QUANTILE_MAX = 0.95 +CTT_QUANTILE_MIN = 0.05 +CTT_TEST_RATIO = 0.1 diff --git a/gitk/eval/ctt.py b/gitk/eval/ctt.py new file mode 100644 index 00000000..3a7bd304 --- /dev/null +++ b/gitk/eval/ctt.py @@ -0,0 +1,115 @@ +import os +import pickle +from typing import Union + +os.environ["OPENBLAS_NUM_THREADS"] = "1" +import argparse +import glob +import multiprocessing as mp +import random +import time + +import numpy as np +import pandas as pd +from sklearn.decomposition import PCA +from sklearn.neighbors import NearestNeighbors + +from .const import * +from .utils import (Timer, cosine_distance, genome_distance, + load_genomic_embeddings) + + +def explained_variance(bin_path, dim): + bin_embed, _ = load_genomic_embeddings(bin_path, "base") + pca_obj = PCA(n_components=dim).fit(bin_embed) + ratio = pca_obj.explained_variance_ratio_.sum() + return ratio + + +def get_ctt_score( + path, + embed_type, + seed=42, + num_data=10000, + num_workers=10, +): + """Implementation of hopkins' test. A score between 0 and 1, a score around 0.5 express a + uniform distribution, a score around 0 indicate an evenely spaced distribution, and a score tending to 1 express a high cluster tendency. + """ + np.random.seed(seed) + data, vocab = load_genomic_embeddings(path, embed_type) + num_ori, dimension = data.shape + if num_data < num_ori: + num = num_data + else: + num = num_ori + data = data[np.random.choice(num_ori, num)] + if num < 100: + raise Exception(f"Number of samples ({num}) is too small") + num_samples = int(num * CTT_TEST_RATIO) + + sel_indexes = np.random.choice(num, num_samples) + data_sample = data[sel_indexes] + neigh = NearestNeighbors(n_neighbors=2, n_jobs=num_workers).fit(data) + sample_dist, _ = neigh.kneighbors(data_sample) + sample_dist_to_nn = sample_dist[:, 1] + + max_vals = np.quantile(data, CTT_QUANTILE_MAX, axis=0) + min_vals = np.quantile(data, CTT_QUANTILE_MIN, axis=0) + random_points = np.random.uniform(min_vals, max_vals, (num_samples, dimension)) + + random_dist, _ = neigh.kneighbors(random_points, n_neighbors=1) + + random_dist_to_nn = random_dist[:, 0] + + x = sum(sample_dist_to_nn**2) + y = sum(random_dist_to_nn**2) + + if x + y == 0: + raise Exception("The denominator of the hopkins statistics is zero") + + return y / (x + y) + + +def get_ctt_batch(batch, seed=42, num_data=10000, save_path=None, num_workers=10): + ctt_arr = [] + for path, embed_type in batch: + ctt = get_ctt_score(path, embed_type, seed, num_data, num_workers) + # print(f"{'/'.join(path.split('/')[-3:])}: {ctt:.4f}") + ctt_arr.append((path, ctt)) + if save_path: + os.makedirs(os.path.dirname(save_path), exist_ok=True) + with open(save_path, "wb") as f: + pickle.dump(ctt_arr, f) + return ctt_arr + + +def ctt_eval( + batch, + num_runs=20, + num_data=10000, + save_folder=None, + num_workers=10, +): + results_seeds = [] + for seed in range(num_runs): + print(f"----------------Run {seed}----------------") + save_path = ( + os.path.join(save_folder, f"ctt_eval_seed{seed}") if save_folder else None + ) + result_list = get_ctt_batch(batch, seed, num_data, save_path, num_workers) + results_seeds.append(result_list) + + ctt_res = [[] for i in range(len(batch))] + for results in results_seeds: + for i, res in enumerate(results): + ctt_res[i].append(res[1]) + assert res[0] == batch[i][0], "key == batch[i][0]" + + mean_ctt = [np.array(r).mean() for r in ctt_res] + std_ctt = [np.array(r).std() for r in ctt_res] + models = [t[0] for t in batch] + for i in range(len(mean_ctt)): + print(f"{batch[i][0]}\n CTT (std): {mean_ctt[i]:.4f} ({std_ctt[i]:.4f}) \n") + ctt_arr = [(batch[i][0], ctt_res[i]) for i in range(len(batch))] + return ctt_arr diff --git a/gitk/eval/gdst.py b/gitk/eval/gdst.py index 5114371d..8ee6dc5b 100644 --- a/gitk/eval/gdst.py +++ b/gitk/eval/gdst.py @@ -11,25 +11,10 @@ import numpy as np from gensim.models import Word2Vec from sklearn.linear_model import LinearRegression -from sklearn.metrics import r2_score from .const import * -from .utils import Timer, cosine_distance, genome_distance, load_genomic_embeddings - -_log_path = None - - -def set_log_path(path): - global _log_path - _log_path = path - - -def log(obj, filename="log.txt"): - print(obj) - if _log_path is not None: - with open(os.path.join(_log_path, filename), "a") as f: - f.write(obj) - f.write("\n") +from .utils import (Timer, cosine_distance, genome_distance, + load_genomic_embeddings) def sample_from_vocab(vocab, num_samples, seed=42): @@ -89,7 +74,7 @@ def convert_position(pos): return f"{pos:.4f} B" -def get_gds_results(save_paths): +def get_gdst_results(save_paths): with open(save_paths[0], "rb") as f: results = pickle.load(f) num = len(results) @@ -106,7 +91,7 @@ def get_gds_results(save_paths): return gds_arr -def get_gds( +def get_gdst_score( path, embed_type, num_samples=10000, @@ -151,13 +136,13 @@ def writer_multiprocessing(save_path, num, q): return results -def get_gds_batch( - batch, num_samples=10000, seed=42, save_path=None, num_workers=1, dist="cosine" +def get_gdst_score_batch( + batch, num_samples=10000, seed=42, save_path=None, num_workers=1 ): if num_workers <= 1: gds_arr = [] for path, embed_type in batch: - gds = get_gds(path, embed_type, num_samples, seed, dist=dist) + gds = get_gdst_score(path, embed_type, num_samples, seed) print(path, gds) gds_arr.append((path, gds)) if save_path: @@ -174,8 +159,8 @@ def get_gds_batch( all_processes = [] for i, (path, embed_type) in enumerate(batch): process = pool.apply_async( - get_gds, - (path, embed_type, num_samples, seed, queue, i, dist), + get_gdst_score, + (path, embed_type, num_samples, seed, queue, i), ) all_processes.append(process) @@ -186,11 +171,10 @@ def get_gds_batch( return gds_arr -def gds_eval( +def gdst_eval( batch, num_runs=20, num_samples=1000, - dist="cosine", save_folder=None, num_workers=10, ): @@ -198,10 +182,10 @@ def gds_eval( for seed in range(num_runs): print(f"----------------Run {seed}----------------") save_path = ( - os.path.join(save_folder, f"gds_eval_seed{seed}") if save_folder else None + os.path.join(save_folder, f"gdst_eval_seed{seed}") if save_folder else None ) - result_list = get_gds_batch( - batch, num_samples, seed, save_path, num_workers, dist + result_list = get_gdst_score_batch( + batch, num_samples, seed, save_path, num_workers ) results_seeds.append(result_list) @@ -215,6 +199,8 @@ def gds_eval( std_gds = [np.array(r).std() for r in gds_res] models = [t[0] for t in batch] for i in range(len(mean_gds)): - print(f"{batch[i][0]}\n GDS (std): {mean_gds[i]:.4f} ({std_gds[i]:.4f}) \n") + print( + f"{batch[i][0]}\n GDST score (std): {mean_gds[i]:.4f} ({std_gds[i]:.4f}) \n" + ) gds_arr = [(batch[i][0], gds_res[i]) for i in range(len(batch))] return gds_arr diff --git a/gitk/eval/npt.py b/gitk/eval/npt.py index 8dea3a63..76c22e1e 100644 --- a/gitk/eval/npt.py +++ b/gitk/eval/npt.py @@ -143,7 +143,7 @@ def init_worker(embed_rep, ref_embed, region2index): var_dict["region2vec_index"] = region2index -def get_snpr( +def get_npt_score( model_path, embed_type, K, @@ -158,7 +158,6 @@ def get_snpr( If num_samples == 0, all regions are used in calculation """ - timer = Timer() embed_rep, regions_r2v = load_genomic_embeddings(model_path, embed_type) region2index = {r: i for i, r in enumerate(regions_r2v)} @@ -263,21 +262,14 @@ def get_snpr( ratio_msg = " ".join([f"{r:.6f}" for r in avg_ratio]) ratio_ref_msg = " ".join([f"{r:.6f}" for r in avg_ratio_ref]) snprs_msg = " ".join([f"{r:.6f}" for r in snprs]) - print(model_path) - - # print( - # f"[seed={seed}] K={K}\n[{embed_type}]: {ratio_msg}\n[Random]: {ratio_ref_msg}\n[SNPR] {snprs_msg}" - # ) result = { "K": K, - "AvgENPR": avg_ratio, - "AvgRNPR": avg_ratio_ref, + "Avg_qNPR": avg_ratio, + "Avg_rNPR": avg_ratio_ref, "SNPR": snprs, "Resolution": resolution, "Path": model_path, } - elapsed_time = timer.measure() - print("Elapsed time:", elapsed_time) return result @@ -296,7 +288,7 @@ def writer_multiprocessing(save_path, num, q): return results -def get_snpr_batch( +def get_npt_score_batch( batch, K, num_samples=100, @@ -306,10 +298,9 @@ def get_snpr_batch( dist="cosine", save_path=None, ): - print(f"Total number of models: {len(batch)}") result_list = [] for index, (path, embed_type) in enumerate(batch): - result = get_snpr( + result = get_npt_score( path, embed_type, K, num_samples, seed, resolution, dist, num_workers ) result_list.append(result) @@ -320,29 +311,6 @@ def get_snpr_batch( return result_list -# def get_snpr_batch( -# batch, K, num_samples=100, num_workers=10, seed=0, resolution=10, dist='cosine', save_path=None -# ): -# print(f"Total number of models: {len(batch)}") -# result_list = [] -# with mp.Pool( -# processes=num_workers -# ) as pool: -# all_processes = [] -# for index, (path, embed_type) in enumerate(batch): -# process = pool.apply_async( -# get_snpr, (path, embed_type, K, num_samples, seed, resolution, dist, 0) -# ) -# all_processes.append(process) -# for process in all_processes: -# result_list.append(process.get()) -# if save_path: -# os.makedirs(os.path.dirname(save_path), exist_ok=True) -# with open(save_path, "wb") as f: -# pickle.dump(result_list, f) -# return result_list - - def npt_eval( batch, K, @@ -354,13 +322,13 @@ def npt_eval( save_folder=None, ): results_seeds = [] - assert resolution < K, "resolution < K" + assert resolution <= K, "resolution <= K" for seed in range(num_runs): print(f"----------------Run {seed}----------------") save_path = ( os.path.join(save_folder, f"npt_eval_seed{seed}") if save_folder else None ) - result_list = get_snpr_batch( + result_list = get_npt_score_batch( batch, K, num_samples=num_samples, @@ -379,7 +347,6 @@ def npt_eval( snpr_results[i].append(result["SNPR"]) paths[i] = key snpr_results = [np.array(v) for v in snpr_results] - print(snpr_results[0].shape) for i in range(len(batch)): snpr_arr = snpr_results[i] avg_snprs = snpr_arr.mean(axis=0) diff --git a/gitk/eval/permutation.R b/gitk/eval/permutation.R deleted file mode 100644 index b45af85c..00000000 --- a/gitk/eval/permutation.R +++ /dev/null @@ -1,111 +0,0 @@ -packages <- c("optparse", "GenomicDistributions", "foreach", "doParallel") -install.packages(setdiff(packages, rownames(installed.packages()))) - -suppressPackageStartupMessages(library("optparse")) -suppressPackageStartupMessages(library("GenomicDistributions")) -suppressPackageStartupMessages(library(foreach)) -suppressPackageStartupMessages(library(doParallel)) - -get_cluster_median <- function(fpath){ - query_data = rtracklayer::import(fpath) - queryList = GRangesList(cluster=query_data) - TSSdist = calcFeatureDistRefTSS(queryList, "hg19") - abs_dist = lapply(TSSdist,abs)[['cluster']] - return (c(median(abs_dist), length(query_data))) -} -lappend <- function (lst, ...){ -lst <- c(lst, list(...)) - return(lst) -} -get_cluster_tss <- function(cluster_folder, assembly="hg19"){ - cluster_files = list.files(cluster_folder, pattern="cluster.*bed$") - num_clusters = length(cluster_files) - tss_arr = list() - - for (i in 1:num_clusters){ - queryFile = file.path(cluster_folder,cluster_files[i]) - query_data = rtracklayer::import(queryFile) - queryList = GRangesList(cluster=query_data) - TSSdist = calcFeatureDistRefTSS(queryList, assembly) - abs_dist = lapply(TSSdist,abs)[['cluster']] - tss_arr <- lappend(tss_arr,abs_dist) - } - return (tss_arr) -} -random_median <- function(array, sample_size){ - sample_arr = sample(array,sample_size,FALSE) - return (median(sample_arr)) -} - -perm_test <- function(perm_num, array, size, pos){ - median_arr = replicate(perm_num,random_median(array,size)) - pval = sum(median_arr < pos)/perm_num - return (pval) -} - -find_peak <- function(tss_arr, ldist, rdist){ - a = density(unlist(tss_arr)) - x = unlist(a[1]) - y = unlist(a[2]) - idx = which.max(y) - peak_pos = x[idx] - if (peak_pos > ldist & peak_pos < rdist){ - return (TRUE) - } else { - return (FALSE) - } -} - - -get_significance_vals <- function(path, assembly, num_replicates=10000){ - # calculate the tss of all regions in all the clusters - cluster_tss = get_cluster_tss(path, assembly) - num_clusters <- length(cluster_tss) - # get cluster size - csize_arr = rep(0,times=num_clusters) - for (i in 1:num_clusters){ - s = length(unlist(cluster_tss[i])) - csize_arr[i] <- s - } - # merge tss from all clusters - all_tss = c() - for (i in 1:num_clusters){ - c_tss = unlist(cluster_tss[i]) - all_tss = c(all_tss,c_tss) - } - pval_arr = rep(0, num_clusters) - for (i in 1:num_clusters){ - median_tss = median(unlist(cluster_tss[i])) - pval = perm_test(num_replicates, all_tss, csize_arr[i], median_tss) - pval_arr[i] = pval - } - return (pval_arr) -} - -option_list = list( - make_option("--path", type="character", default="/bigtemp/gz5hp/genomes/tfbs_experiments/tbfs_clustering_results/KMeans_20K_seed0", - help="dataset file name", metavar="character"), - make_option("--assembly", type="character", default="hg19", - help="hg19 or hg38", metavar="character"), - make_option("--num-workers", type="integer",default=10, - help="number of parallel processes", metavar="number of processes"), - make_option("--num-samples", type="integer",default=1000, - help="number of samples", metavar="number of samples") -); - -opt_parser = OptionParser(option_list=option_list) -opt = parse_args(opt_parser) - -registerDoParallel(opt$num_workers) - -pattern = file.path(opt$path, 'Kmeans_*') -paths = Sys.glob(pattern) -num_path <- length(paths) -out <- foreach (i=1:num_path) %dopar% { - folder <- paths[i] - save_path <- file.path(folder, 'pvals.txt') - # if (!file.exists(save_path)){ - pvals <- get_significance_vals(folder, opt$assembly, opt$num_samples) - cat(format(pvals, nsmall=6),sep='\n',file=save_path) - # } -} diff --git a/gitk/eval/rct.py b/gitk/eval/rct.py new file mode 100644 index 00000000..90d9c5d6 --- /dev/null +++ b/gitk/eval/rct.py @@ -0,0 +1,112 @@ +import argparse +import glob +import multiprocessing as mp +import os +import pickle +import random +import time + +import numpy as np +import sklearn.neural_network as nn +from gensim.models import Word2Vec +from sklearn.compose import TransformedTargetRegressor +from sklearn.model_selection import KFold, cross_val_score, train_test_split +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler + +from ..utils import timer_func +from .utils import (Timer, cosine_distance, genome_distance, + load_genomic_embeddings) + + +def get_rct_score( + path, embed_type, bin_path, out_dim=-1, cv_num=5, seed=42, num_workers=10 +): + embed_rep, vocab = load_genomic_embeddings(path, embed_type) + embed_bin, vocab_bin = load_genomic_embeddings(bin_path, "base") + region2idx = {r: i for i, r in enumerate(vocab)} + region2idx_bin = {r: i for i, r in enumerate(vocab_bin)} + # align embed_bin with embed_rep + if out_dim <= 0: + embed_bin = np.array([embed_bin[region2idx_bin[v]] for v in vocab]) + else: + bin_dim = embed_bin.shape[1] + out_dim = min(bin_dim, out_dim) + sel_dims = np.random.choice(bin_dim, out_dim) + embed_bin = np.array([embed_bin[region2idx_bin[v]][sel_dims] for v in vocab]) + + regressor = nn.MLPRegressor( + hidden_layer_sizes=(200), + activation="relu", + solver="adam", + alpha=0.0001, # regularizer strength + batch_size="auto", + learning_rate_init=0.001, + max_iter=200, + shuffle=True, + random_state=seed, + tol=0.0001, + verbose=False, + early_stopping=False, + validation_fraction=0.1, + n_iter_no_change=10, + ) + model_in = make_pipeline(StandardScaler(), regressor) + model = TransformedTargetRegressor(regressor=model_in, transformer=StandardScaler()) + + kf = KFold(n_splits=cv_num, shuffle=True, random_state=seed) + if num_workers > cv_num: + num_workers = cv_num + score = cross_val_score( + model, embed_rep, embed_bin, cv=kf, n_jobs=num_workers, verbose=0 + ) + return score.mean() + + +def reconstruction_batch( + batch, cv_num, out_dim=-1, seed=42, save_path=None, num_workers=10 +): + rct_arr = [] + for path, embed_type, bin_path in batch: + score = get_rct_score( + path, embed_type, bin_path, out_dim, cv_num, seed, num_workers + ) + rct_arr.append((path, score)) + if save_path: + os.makedirs(os.path.dirname(save_path), exist_ok=True) + with open(save_path, "wb") as f: + pickle.dump(rct_arr, f) + return rct_arr + + +def rct_eval( + batch, + num_runs=5, + cv_num=5, + out_dim=-1, + save_folder=None, + num_workers=10, +): + results_seeds = [] + for seed in range(num_runs): + print(f"----------------Run {seed}----------------") + save_path = ( + os.path.join(save_folder, f"rct_eval_seed{seed}") if save_folder else None + ) + result_list = reconstruction_batch( + batch, cv_num, out_dim, seed, save_path, num_workers + ) + results_seeds.append(result_list) + + rct_res = [[] for i in range(len(batch))] + for results in results_seeds: + for i, res in enumerate(results): + rct_res[i].append(res[1]) + assert res[0] == batch[i][0], "key == batch[i][0]" + mean_rct = [np.array(r).mean() for r in rct_res] + std_rct = [np.array(r).std() for r in rct_res] + models = [t[0] for t in batch] + for i in range(len(mean_rct)): + print(f"{batch[i][0]}\n RCT (std): {mean_rct[i]:.4f} ({std_rct[i]:.4f}) \n") + rct_arr = [(batch[i][0], rct_res[i]) for i in range(len(batch))] + return rct_arr diff --git a/gitk/region2vec/README.md b/gitk/region2vec/README.md index 19a329b3..5d083e40 100644 --- a/gitk/region2vec/README.md +++ b/gitk/region2vec/README.md @@ -1,15 +1,9 @@ -# region2vec -`region2vec` will generate embedding vectors for a given region set (universe) from a set of raw bed files. The program will first map all raw regions to the given region set. Then, it will concatenate all regions in a bed file in random orders into a sentence. The generated sentences will be used for Region2Vec training. - - -## Requirements -- python>=3.6 -All other required packages are listed in `requirements.txt` - +# Region2Vec +`Region2Vec` will generate embedding vectors for a given region set (universe) from a set of raw bed files. The program will first map all raw regions to the given region set. Then, it will concatenate all regions in a bed file in random orders into a sentence. The generated sentences will be used for Region2Vec training. ## Usage -1. Prepare a set of bed files in `src_folder`. [Optional] If only a subset of files will be used, specify a list of those files as `file_list`. By default, the program will use all the files in the folder to train a region2vec model. +1. Prepare a set of bed files in `src_folder`. [Optional] If only a subset of files will be used, specify a list of those files as `file_list`. By default, the program will use all the files in the folder to train a Region2Vec model. 2. Prepare a universe file `universe_file`. 3. Create a token folder which will be used to store tokenized files `dst_folder`. 5. Run the following command @@ -24,12 +18,17 @@ universe_file = '/path/to/universe_file' # must run tokenization first status = hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) -if status: # if hard_tokenization is successful, then run region2vec training +if status: # if hard_tokenization is successful, then run Region2Vec training save_dir = '/path/to/training/results' region2vec(dst_folder, save_dir, num_shufflings=1000) ``` For customized settings, please go and check the parameters used in `main.py`. -For training a region2vec model, the parameters, `init_lr`, `window_size`, `num_shufflings`, `embedding_dim`, are frequently tuned in experiments. +For training a Region2Vec model, the parameters, `init_lr`, `window_size`, `num_shufflings`, `embedding_dim`, are frequently tuned in experiments. + +For command line usage, type `gitk region2vec --help` for details. We give a simple usage below +```bash +gitk region2vec --token-folder /path/to/token/folder --save-dir ./region2vec_model --num-shuffle 10 --embed-dim 100 --context-len 50 +``` diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 707453f2..ebc3d406 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -2,6 +2,7 @@ import os import numpy as np + from gitk.region2vec import utils from gitk.region2vec.region2vec_train import main as region2_train from gitk.region2vec.region_shuffling import main as sent_gen diff --git a/gitk/region2vec/region_shuffling.py b/gitk/region2vec/region_shuffling.py index 8f4f13b3..6268a55d 100644 --- a/gitk/region2vec/region_shuffling.py +++ b/gitk/region2vec/region_shuffling.py @@ -7,6 +7,7 @@ import time import numpy as np + from gitk.region2vec import utils diff --git a/gitk/tokenization/README.md b/gitk/tokenization/README.md index edaf4ea7..9cbc5cb7 100644 --- a/gitk/tokenization/README.md +++ b/gitk/tokenization/README.md @@ -20,3 +20,10 @@ hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) Note that we use the `intersect` function of `bedtools` to do tokenization. If you want to switch to different tools, you can override the `bedtool_tokenization` function in `hard_tokenization_batch.py` and provide the path to your tool by specifying the input argument `bedtools_path`. The `fraction` argument specifies the minimum overlap required as a fraction of some region in the universe (default: 1E-9,i.e. 1bp; maximum 1.0). A raw region will be mapped into a universe region when an overlap is above the threshold. The bedtools (version 2.30.0) will be automatically downloaded from https://github.com/arq5x/bedtools2/releases to the `bedtools` folder. + +Command line usage +```bash +gitk tokenize --data-folder /folder/with/raw/BED/files --token-folder ./tokens --universe /universe/file +``` + +For more details, type `gitk tokenize --help`. diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 037528f4..8175f3cb 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -10,10 +10,11 @@ import sys from queue import Queue -import gitk.region2vec.utils as utils import numpy as np import requests import yaml + +import gitk.region2vec.utils as utils from gitk.tokenization.split_file import split_file from .hard_tokenization_batch import main as hard_tokenization From 881babfafd9681e6c6c261b992e0d027b1b6e01c Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Thu, 1 Jun 2023 17:28:38 -0400 Subject: [PATCH 0198/1072] code reformatting --- gitk/eval/ctt.py | 3 +-- gitk/eval/gdst.py | 3 +-- gitk/eval/rct.py | 3 +-- 3 files changed, 3 insertions(+), 6 deletions(-) diff --git a/gitk/eval/ctt.py b/gitk/eval/ctt.py index 3a7bd304..f205431d 100644 --- a/gitk/eval/ctt.py +++ b/gitk/eval/ctt.py @@ -15,8 +15,7 @@ from sklearn.neighbors import NearestNeighbors from .const import * -from .utils import (Timer, cosine_distance, genome_distance, - load_genomic_embeddings) +from .utils import Timer, cosine_distance, genome_distance, load_genomic_embeddings def explained_variance(bin_path, dim): diff --git a/gitk/eval/gdst.py b/gitk/eval/gdst.py index 8ee6dc5b..15476270 100644 --- a/gitk/eval/gdst.py +++ b/gitk/eval/gdst.py @@ -13,8 +13,7 @@ from sklearn.linear_model import LinearRegression from .const import * -from .utils import (Timer, cosine_distance, genome_distance, - load_genomic_embeddings) +from .utils import Timer, cosine_distance, genome_distance, load_genomic_embeddings def sample_from_vocab(vocab, num_samples, seed=42): diff --git a/gitk/eval/rct.py b/gitk/eval/rct.py index 90d9c5d6..f3e17a25 100644 --- a/gitk/eval/rct.py +++ b/gitk/eval/rct.py @@ -15,8 +15,7 @@ from sklearn.preprocessing import StandardScaler from ..utils import timer_func -from .utils import (Timer, cosine_distance, genome_distance, - load_genomic_embeddings) +from .utils import Timer, cosine_distance, genome_distance, load_genomic_embeddings def get_rct_score( From a6f829462e894ec32f10603a74ce0f20daf049cb Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Thu, 1 Jun 2023 17:34:05 -0400 Subject: [PATCH 0199/1072] add docs --- docs/eval/evaluation.md | 114 ++++++++++++++++++++++++++++++ docs/region2vec/region2vec.md | 34 +++++++++ docs/tokenization/tokenization.md | 29 ++++++++ 3 files changed, 177 insertions(+) create mode 100644 docs/eval/evaluation.md create mode 100644 docs/region2vec/region2vec.md create mode 100644 docs/tokenization/tokenization.md diff --git a/docs/eval/evaluation.md b/docs/eval/evaluation.md new file mode 100644 index 00000000..8ef057bf --- /dev/null +++ b/docs/eval/evaluation.md @@ -0,0 +1,114 @@ +# Evaluation of Genomic Region Embeddings + +## Preparations +### Create a Base Embedding Object +Given a set of genomic region embeddings `embeddings` and the corresponding regions `vocab`, use `BaseEmbeddings` to create an `base` embedding object. +``` +from gitk.eval.utils import BaseEmbeddings +import pickle +base_obj = BaseEmbeddings(embeddings, vocab) +with open("base_embed.pt", "wb") as f: + pickle.dump(base_obj, f) +``` +### Generate Binary Embeddings +```python +from gitk.eval.utils import get_bin_embeddings +universe_file = "/path/to/universe.bed" +token_files = ["file1.bed", "file2.bed"] +bin_embed = get_bin_embeddings(universe_file, token_files) +``` +Or use command line: +```bash +gitk eval bin-gen --universe /path/to/universe.bed --token-folder /path/to/tokenized/folder --file-name bin_embed.pickle +``` +## Statistical Tests +### Cluster Tendency Test (CTT) +CTT analyzes how well a set of region embeddings can be clustered. CTT score lies between 0 and 1. A larger CTT score indicates a greater tendency for the embeddings being evaluated to have clusters. When the embeddings are uniformly distributed, the score is 0.5. For evenly spaced embeddings, the score approaches 0. + +```python +from gitk.eval.ctt import get_ctt_score, ctt_eval +path = "/path/to/a/region2vec/model/" +embed_type = "region2vec" +ctt_score = get_ctt_score(path, embed_type, seed=42, num_data=10000, num_workers=10) +print(ctt_score) + +# evaluate a batch of models and run CTT for 5 times with different random seeds +batch = [(path, embed_type)] +ctt_score_arr = ctt_eval(batch, num_runs=5, num_data=10000,num_workers=10) +print(f"Model: {ctt_score_arr[0][0]}\n CTT scores:{ctt_score_arr[0][1]}") # CTT scores for the 1st model in the batch +``` + +Or use the command line +```bash +gitk eval ctt --model-path /path/to/a/region2vec/model/ --embed-type region2vec +``` +### Reconstruction Test (RCT) +RCT evaluates how well an embedding of a region preserves the region’s occurrence information in the training data. The best RCT score is 1. + +```python +from gitk.eval.rct import get_rct_score, rct_eval +path = "/path/to/a/region2vec/model/" +embed_type = "region2vec" +bin_path = "/path/to/a/binary/embedding/for/the/same/tokenized/files/" +# set out_dim to -1 use all the dimensions of the binary embeddings. Set out_dim to a small positive number to reduce computational complexity. +rct_score = get_rct_score(path, embed_type, bin_path, out_dim=-1, cv_num=5, seed=42, num_workers=10) +print(rct_score) + +# evaluate a batch of models and run RCT for 5 times with different random seeds +batch = [(path, embed_type, bin_path)] +rct_score_arr = rct_eval(batch, num_runs=5, cv_num=5, out_dim=-1, num_workers=10) +print(f"Model: {rct_score_arr[0][0]}\n RCT scores:{rct_score_arr[0][1]}") # RCT scores for the 1st model in the batch +``` + +Or use the command line +```bash +gitk eval rct --model-path /path/to/a/region2vec/model/ --embed-type region2vec +``` +To change the learning setting, go to the definition of `get_rct_score` in `gitk/eval/rct.py` and change the constructor of `MLPRegressor`. + +## Biological Tests +### Genome Distance Scaling Test (GDST) +GDST calculates a score measuring how much the embedding distance between two regions scales the corresponding genome distance. + +```python +from gitk.eval.gdst import get_gdst_score, gdst_eval +path = "/path/to/a/region2vec/model/" +embed_type = "region2vec" +gdst_score = get_gdst_score(path, embed_type, num_samples=10000,seed=42) +print(gdst_score) + +# evaluate a batch of models and run GDST for 5 times with different random seeds +batch = [(path,embed_type)] +gdst_score_arr = gdst_eval(batch, num_runs=5, num_samples=10000) +``` + +Or use the command line +```bash +gitk eval gdst --model-path /path/to/a/region2vec/model/ --embed-type region2vec +``` + + +### Neighborhood Preserving Test (NPT) +NPT evaluates how significant genomic region embeddings preserve their neighboring regions on the genome against random embeddings. The code output the NPT score for a set of region embeddings. + +```python +from gitk.eval.npt import get_npt_score, npt_eval +path = "/path/to/a/region2vec/model/" +embed_type = "region2vec" +K = 10 +# If resolution = K gives NPT for K neighbors +# If resolution < K, gives NPT for [resolution, resolution*2, ...] neighbors +resolution = K +npt_score = get_npt_score(path, embed_type, K, num_samples=100, seed=0, resolution=resolution,num_workers=10) +print(npt_score['SNPR']) + +# evaluate a batch of models and run NPT for 5 times with different random seeds +batch = [(path, embed_type)] +npt_score_arr = npt_eval(batch, K, num_samples=100, num_workers=10, num_runs=5, resolution=resolution) +print(f"Model: {npt_score_arr[0][0]}\n NPT scores: {npt_score_arr[0][1]}") # NPT scores for the 1st model in the batch +``` + +Or use the command line (the output will be the result when resolution=K) +```bash +gitk eval npt --model-path /path/to/a/region2vec/model/ --embed-type region2vec --K 50 --num-samples 1000 +``` diff --git a/docs/region2vec/region2vec.md b/docs/region2vec/region2vec.md new file mode 100644 index 00000000..5d083e40 --- /dev/null +++ b/docs/region2vec/region2vec.md @@ -0,0 +1,34 @@ +# Region2Vec +`Region2Vec` will generate embedding vectors for a given region set (universe) from a set of raw bed files. The program will first map all raw regions to the given region set. Then, it will concatenate all regions in a bed file in random orders into a sentence. The generated sentences will be used for Region2Vec training. + + +## Usage +1. Prepare a set of bed files in `src_folder`. [Optional] If only a subset of files will be used, specify a list of those files as `file_list`. By default, the program will use all the files in the folder to train a Region2Vec model. +2. Prepare a universe file `universe_file`. +3. Create a token folder which will be used to store tokenized files `dst_folder`. +5. Run the following command +``` +from gitk.tokenization import hard_tokenization +from gitk.region2vec import region2vec + +src_folder = '/path/to/raw/bed/files' +dst_folder = '/path/to/tokenized_files' +universe_file = '/path/to/universe_file' + +# must run tokenization first +status = hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) + +if status: # if hard_tokenization is successful, then run Region2Vec training + save_dir = '/path/to/training/results' + region2vec(dst_folder, save_dir, num_shufflings=1000) + +``` +For customized settings, please go and check the parameters used in `main.py`. +For training a Region2Vec model, the parameters, `init_lr`, `window_size`, `num_shufflings`, `embedding_dim`, are frequently tuned in experiments. + +For command line usage, type `gitk region2vec --help` for details. We give a simple usage below +```bash +gitk region2vec --token-folder /path/to/token/folder --save-dir ./region2vec_model --num-shuffle 10 --embed-dim 100 --context-len 50 +``` + + diff --git a/docs/tokenization/tokenization.md b/docs/tokenization/tokenization.md new file mode 100644 index 00000000..9cbc5cb7 --- /dev/null +++ b/docs/tokenization/tokenization.md @@ -0,0 +1,29 @@ +# Tokenization +## Introduction +In training word embeddings, we need to first tokenize each word such that words in different forms are represented by one word. For example, "orange", "oranges" and "Orange" are all mapped to "orange" since they essentially convey the same meaning. This can reduce the vocabulary size and also improve the quality of learned embeddings.
+Similary, before running region2vec, we need to run tokenization on regions. First, we need to provide a universe, the "vocabulary" in the setting of genomic regions. The universe is a BED file, containing representative regions. With the given universe, we represent (tokenize) raw regions with the regions in the universe. + +For hard tokenization, if the overlap between a raw region in a bed file and a region in the universe exceeds a certain amount, then we use the region in the universe to represent this raw region; otherwise, we ignore this raw region. This is a "zeor or one" process. After hard tokenization, each bed file will contain regions all from the universe, and the number of regions will be smaller or equal to the original number. + + +## Usage +For hard tokenization, run +``` +from gitk.tokenization import hard_tokenization + +src_folder = '/path/to/raw/bed/files' +dst_folder = '/path/to/tokenized_files' +universe_file = '/path/to/universe_file' +hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) + +``` +Note that we use the `intersect` function of `bedtools` to do tokenization. If you want to switch to different tools, you can override the `bedtool_tokenization` function in `hard_tokenization_batch.py` and provide the path to your tool by specifying the input argument `bedtools_path`. The `fraction` argument specifies the minimum overlap required as a fraction of some region in the universe (default: 1E-9,i.e. 1bp; maximum 1.0). A raw region will be mapped into a universe region when an overlap is above the threshold. + +The bedtools (version 2.30.0) will be automatically downloaded from https://github.com/arq5x/bedtools2/releases to the `bedtools` folder. + +Command line usage +```bash +gitk tokenize --data-folder /folder/with/raw/BED/files --token-folder ./tokens --universe /universe/file +``` + +For more details, type `gitk tokenize --help`. From 395d1348dec95757ad16af6e22978560221ac498 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Tue, 6 Jun 2023 11:49:39 -0400 Subject: [PATCH 0200/1072] new hmm model --- gitk/hmm/const.py | 2 +- gitk/hmm/hmm.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/gitk/hmm/const.py b/gitk/hmm/const.py index d8388081..a81a4c8f 100644 --- a/gitk/hmm/const.py +++ b/gitk/hmm/const.py @@ -5,4 +5,4 @@ [0.1, 0, 0, 0.9], ] -LAMBDAS = [[4, 1, 0.0001], [0.5, 3, 0.5], [0.0001, 1, 4], [1e-4, 1e-3, 1e-4]] +LAMBDAS = [[5, 3, 0.0001], [0.25, 3, 0.25], [0.0001, 3, 5], [1e-4, 1e-3, 1e-4]] diff --git a/gitk/hmm/hmm.py b/gitk/hmm/hmm.py index 26e720b4..edf7238f 100644 --- a/gitk/hmm/hmm.py +++ b/gitk/hmm/hmm.py @@ -32,7 +32,7 @@ def norm(track, mode): if mode == "ends": n = 0.1 if mode == "core": - n = 0.085 + n = 0.2 bs = 0 # what fraction of the distribution was used for normalization val = {} # for each unique value in track holds the corresponding quantile for i in important_val_unique_sort: From 15e495a72c9121d97a0d22ef0eef2b5a400049c7 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Tue, 6 Jun 2023 11:51:03 -0400 Subject: [PATCH 0201/1072] fixed index in assess distance --- gitk/assess/distance.py | 32 +++++++++++++++++--------------- 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index 9d1a01bc..95c529bb 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -60,10 +60,10 @@ def distance_to_closest_region( ] else: dist_to_db_que = [ - flexible_distance_between_two_regions(j, i) for j in db_queue + flexible_distance_between_two_regions(j, i[0]) for j in db_queue ] else: - dist_to_db_que = [distance_between_two_regions(j, i) for j in db_queue] + dist_to_db_que = [distance_between_two_regions(j, i[0]) for j in db_queue] min_pos = np.argmin(dist_to_db_que) while min_pos == 2: d = db.readline().strip("\n") @@ -82,10 +82,10 @@ def distance_to_closest_region( ] else: dist_to_db_que = [ - flexible_distance_between_two_regions(j, i) for j in db_queue + flexible_distance_between_two_regions(j, i[0]) for j in db_queue ] else: - dist_to_db_que = [distance_between_two_regions(j, i) for j in db_queue] + dist_to_db_que = [distance_between_two_regions(j, i[0]) for j in db_queue] min_pos = np.argmin(dist_to_db_que) return dist_to_db_que[min_pos] @@ -198,17 +198,20 @@ def calc_distance_between_two_files( dist_end = [] unused_db_end = [] waiting_end = False - start_index = [1] - end_index = [2] + start_index_q, start_index_db = [1], [1] + end_index_q, end_index_db = [2], [2] + if flexible and uni_to_file: + start_index_q = [1, 6] + end_index_q = [7, 2] if flexible and not uni_to_file: - start_index = [1, 6] - end_index = [7, 2] + start_index_db = [1, 6] + end_index_db = [7, 2] for i in q: if not uni_to_file: i = i.decode("utf-8") i = i.split("\t") - start = [int(i[ind]) for ind in start_index] - end = [int(i[ind]) for ind in end_index] + start = [int(i[ind]) for ind in start_index_q] + end = [int(i[ind]) for ind in end_index_q] q_chrom = i[0] result_start = read_in_new_universe_regions( db_start, @@ -217,7 +220,7 @@ def calc_distance_between_two_files( unused_db_start, db_queue_start, waiting_start, - start_index, + start_index_db, ) (waiting_start, current_chrom_start) = result_start if not waiting_start: @@ -227,7 +230,7 @@ def calc_distance_between_two_files( start, current_chrom_start, unused_db_start, - start_index, + start_index_db, flexible, uni_to_file, ) @@ -239,7 +242,7 @@ def calc_distance_between_two_files( unused_db_end, db_queue_end, waiting_end, - end_index, + end_index_db, ) (waiting_end, current_chrom_end) = result_end if not waiting_end: @@ -249,7 +252,7 @@ def calc_distance_between_two_files( end, current_chrom_end, unused_db_end, - end_index, + end_index_db, flexible, uni_to_file, ) @@ -294,7 +297,6 @@ def run_distance( check_if_uni_sorted(universe) if flexible: check_if_uni_flexible(universe) - print(no_workers) with open(file_list) as f: files = f.read().split("\n")[:-1] res = [] From 9615761f459b3c29a3d93962f99ab4631778f1f3 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Sun, 11 Jun 2023 10:57:20 -0400 Subject: [PATCH 0202/1072] projection tests --- examples/scembed/projection.ipynb | 78 +++++-- examples/scembed/projection2.ipynb | 356 +++++++++++++++++++++++++++++ 2 files changed, 417 insertions(+), 17 deletions(-) create mode 100644 examples/scembed/projection2.ipynb diff --git a/examples/scembed/projection.ipynb b/examples/scembed/projection.ipynb index 993c009a..f983a9b6 100644 --- a/examples/scembed/projection.ipynb +++ b/examples/scembed/projection.ipynb @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -121,19 +121,63 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs Ă— n_vars = 10246 Ă— 165434\n", + " obs: 'barcode'\n", + " var: 'chr', 'start', 'end'\n", + " obsm: 'embedding'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# inspect \n", + "pbmc" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# create projector\n", "from gitk.scembed import Projector\n", "\n", - "model = Projector(\"databio/scatlas\")" + "model = Projector(\"/Users/nathanleroy/.scembed/databio/scatlas/model.yaml\")" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "974599" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -146,9 +190,9 @@ "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", "KI270727.1\t52092\t52991\n", "\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 26462.93it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [02:57<00:00, 57.81it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:43<00:00, 98.64it/s] \n" + "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 25235.34it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [03:04<00:00, 55.67it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:59<00:00, 86.10it/s] \n" ] } ], @@ -163,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -172,7 +216,7 @@ "(10246, 100)" ] }, - "execution_count": 4, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -199,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -228,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -245,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -254,15 +298,15 @@ "Text(0, 0.5, 'UMAP 2')" ] }, - "execution_count": 7, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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5+5hMJk2aNMmsbvr06ZyVCgDlZDKZ9PRFT5ttxS8yiiRJxSqWr4flDaKH0w/rmhbX6NJGl6p7THer4wb7/p04rRdYT4uuWyQvj7LvlxzVepQkqVdcL/183c/6bcRvuqTh+f9NAAAAAFztVEpehc9EteXYmVydSsmr1DlQfZFIraW+//57s/LYsWPt6jdgwAA1adKkpHzq1CmtWbPGqbEBQG3SLqqdPhz0oQY0GKABDQaYPetUp5Oahja16BPoE6g3+r+huOA4q2Mm5yaXfL4+Yb2CfYL1zoB3rLZ9uOvDGtBwgNVnAAAAQFWy70RWjZoH1Q+J1FooMzNTy5cvN6u79NJL7eprMpk0aNAgs7offvjBabEBQG10cdzFenfgu3p34Lt6pNsjah7WXJc0vESP9XhM31/9vf43wPwolkUHFmnBgQXqUqeLzbHbRLSRl4eX+sT1kbeHt9mz29repjHtxzjzpQAAAACVIi27QGcyXHMZ1JmMAqVnF7pkLlQvZe/zQ421c+dOFRT8/c2nSZMmio2Ntbv/xRdfrI8//rikvGXLFmeGBwC1TkZ+hoK8g2QymTS63WiNbjfa7Hm4f7hZ+WjmUT258km1i2yn1/q9ph2JO7TrzC6tT1hf0sbD5KEesT3UOKSxXlr7ktpEtFFBsfn/eLaOaF15LwoAAABwomNJlbulv7SjSblq1/D8dxOg9iGRWgvt3r3brNy2bdsyWlpXun3p8QAA9skqyNKdv92prYlb1SS0iaZdOk11AuqUPF92dJleXveyPEwe6h7T3SxRKkk7z+zUhbEX6vLGlys5N1n/XflfLT9+dsdBsVGs9afWa83Js8eveJgsN6EUG9xICgAAgOohJdO1K0RTslyz+hXVC4nUWmjv3r1m5QYNGjjUv3T7w4cPKzc3V35+fhWODQBqk7n75mpr4lZJ0qG0Q/rf5v/pSPoRJeUkaVSrUfrflv8ppzBHknQy66RFf0+Tp4J9zl4sFeEXoed7P69+X/WTobOXAJ67uEqyTJo2DG6owY0GV8rrAgAAAJzJMAylujixmZpZIMMwZDKZbDdGrUEitRY6ffq0Wbl+/foO9Y+JiZGXl5cKC8/+Nqi4uFhnzpxRXJz1S08cjS0xMdGhPvv376/wvABQFfwW/5uyCs8ebP/qhlfNnpXelm+SSQ93fVieHp4ldeF+4fr3Bf/Wu5velaeHp5qHNteelD0W80T6RWr+1fMtzkwFAAAAqqLCIkMFRYZL5ywoMlRYbMjbk0Qq/kYitRbKzMw0KwcGBjrU32Qyyd/fXxkZGWWOWV4ffPCBJk+e7JSxAKCqG9lypJYeXaqNCRvVIryF9qfY/4uht/q9pUsaX2JRP67DOPWr30/vb3lf2YXZujX2VkX4RygzP1Ozd89WhF+EXu/3OklUAAAAVBvFrs2h/j1vsSRPm81Qi5BIrYVKJz3LsyW/shKpAFCbBHgHaOblM5VbmCs/Lz+N/WWs1p1aZ7Nfs9Bm6tuwb5nP/7PyP9qTfHYl6s6knfptxG8K8A7QA10fcFboAAAAgMt4uGlRqIflNQOo5XhL1EK5ueY33fn4+Dg8hq+vr1k5JyenQjEBQG3m53X2F1rvDnhXIT4hFs99PM5+n/Y0eerm1jfr8yGfn3dFaXxafMnn6fnpSs5Ndm7AAAAAgAt5eZpcvsXe29MkL3dlcFFlsSK1Fiq9AjU/P9/hMfLy8s47ZnndfffdGjlypEN99u/fr6uvvtop8wOAKy04sEDLjy1X+8j2Gt1utA6lH1J6frpZm+taXKfb2t2mrae3qn1Ue7UIb1HyLCErQS+sfUFncs9odNvRurTxpSoqLlKUf5SOZR6TJLWLbKd6QfVc+roAAAAAZzKZTAoL9FZiuuP5i/IKC/LmoilYIJFaCwUFBZmVS69QtUfpFailxyyvOnXqqE6dOk4ZCwCqsuXHluvJlU9Kkn6J/0VeHl4a2nSogn2ClZF/9uiU3nG99UyvZyRJTUObWozx9OqntfrEaknSw8seludyT/WM7VmSRJWkUN9QeZjYgAIAAIDqLTzIy6WJ1PBA7hSAJRKptVDppGdWVpZD/Q3DqLREKgDUFnuT95qVlx9brkYhjTR18FTN2T1HYb5hurvz3TqZeVLp+elqGd7S4jfiJzJPmJWLjCKtPrnarM7TxOn4AAAAqP7qR/lp34lsl83XIMo5O29Rs5BIrYVKr/g8duxYGS2tS0hIUGFhYUnZw8NDUVFRTokNAGqL3nG9NXXbVOUVnT0q5c+Tf+rPk39qaNOhernPy5Kk7/d/r0mrJ6nIKFLD4IYK8g5S5zqddf8F9yvAO0CjWo/Sy+tethjb39NfOUU5ivaP5oIpAAAA1AihAd6KDPbWmYyCSp8rMthbIQGkzGCJd0Ut1KpVK7PykSNHHOpfun2jRo2cdkYqANQGBUUFWnhwoZqGNlWkX6RWnlhZ8mzRwUWadNEkbU/crufXPK8io0iSdCTj7PfeXcm7lFOYo2cvflY3t7lZPh4+enbNs2bj5xSd3TXQq14vtQxv6aJXBQAAAFSulvUC9efeVJfMg7NWrVpl9YLxrVu3mpVzc3P1+++/Wx2jXr16atu2baXE52okUmuh1q1bm5V37drlUP/du3efdzwAwPl9suMTfb7rc6vPIvwilJabpnsW36PcIutnWO9L2Vfy+chWI+Xr6asp26ZIhnQ082jJs1PZp5wbOAAAAOBGseG+qh/pp2NnHL/rxV71I/0UG+5baeNXNzfffLMOHz5ss11CQoIGDx5s9dno0aM1c+ZMJ0fmHtw+UQu1a9dO3t5/H5ocHx+vkydP2t1/1apVZuXOnTs7KzQAqBWOZhwt89mghoN0MvtkmUlUSbqiyRUlnx9OP6zpO6braMZRHc08Kn8vf0mSl8lLN7S6QZJUVFyk2btn68W1L2rL6S3OeREAAACAG3RsHCw/78pJZ/l5e6hj4+BKGRs1A4nUWig4OFh9+/Y1q/vtt9/s6msYhsVS7WHDhjktNgCoDYY1GyZvj7O/0PL1NP9td5vINmoV3kpNQ5uW1I1sOVL96vdTu8h2erbXsxrdbrQkKTE7UTctukkH0g6UtM0pzNFb/d/SvKvmaXCjs78Rfn/L+3p53cv6Ys8XGvfrOMWnxVfyKwQAAAAqh6+3h3q1CZO3p8l2Ywd4e5rUq02YfCspSYuaga39tdTw4cO1ePHikvInn3yi2267zWa/pUuX6tChQyXlmJgY9ezZs1JiBICa6sK6F2rusLnal7JP7SLbadqOadqYsFGB3oE6nXVaxUaxPrviMy09ulQRfhHqW7+v1XF2JO1Qen66RX2biDaKC44rKW9J3FLyeV5RnnYn71bj0MbOflkAAACAS4QGeKtPu3Ct3p2q3ILiCo/n9//J2dAAb9uNa5n4+Hh3h1ClkGavpUaNGqXAwL8PT16+fLmWLFly3j6GYWjy5Mlmdbfffrs8PHgbAYCjmoY1VUJ2gv699N9KzU1VQVGBdp3ZpQ+3fagH/3hQob6hurr51WUmUSWpZURLeZgsvwevT1hvVr6w7oUlnwd4BahDVAfnvRAAAADADUIDvDWwY6TqR1bs8uv6kX4a2DGSJCrsQgaslqpTp47uvfdes7px48bpxIkTZfZ56aWXtHz58pJyaGioJk6cWGkxAkBNtuHUBr2+4XXtT92vJUeX6ETW399/NyVssmuMuKA4Xd/yeov6D7d8aFYe33G8Xuj9gsZ3HK/PrvhM9YPrVyx4AAAAoArw9fZQ9xahuqhVmCKDHUuERgZ766JWYereIpTt/LAbW/urqFWrViknJ8eifuvWrWbl3NxcizNLz6lXr57atm1b5hyPPvqoPv30U506dfZW50OHDqlXr1569913NWzYMJlMZ88bOXbsmJ5//nlNnTrVrP+TTz6piIgIh14XANR2xUaxcgtzdTr7tFm9n6dfyQVTXWK62BwnMTtRT6x8QkfTj6pVeCvtTdlb8uxM7hmL9sObDa9g5AAAAEDVFBvuq9hwX6VnF+poUq5SsgqUmlmggiKjpI23p0lhQd4KD/RWgyg/hQSQEoPjeNdUUTfffLMOHz5ss11CQoIGDx5s9dno0aM1c+bMMvtGREToq6++0mWXXabc3LM/vB8+fFhXXXWVwsLC1KRJE6WmpurIkSMqKioy63vVVVfpkUcesf8FAQC0I2mH7ll8j5Jzk9Wvfj81CG6goxlH5WHy0IV1L9Sak2uUW5SrXUm79NaGt+Tn7SfDMPTH0T/UJLSJnrrwKQX7nL1F9NX1r2rtybVnB84yn6dJSBPXvjAAAACgCggJ8FK7hkGSzh5PWFhsqLhY8vCQvDxMJQvGgPIikVrL9e3bV4sWLdLIkSOVnJxcUp+amqrNmzdb7XPTTTdp+vTpfAMCAAe9vuF1Jeee/V677Ngy+Xj66OJ6F2vViVX649gfJe0yCjI0fed0s767k3cr0DtQT1/0tCQpJS/F7HmYb5hS81Ll5eGle7uYH90CAAAA1DYmk0nenibJ092RoCbhEAho4MCB2rVrlyZMmKCAgIAy23Xp0kXz5s3T7Nmz5evr68IIAaBmyi/K1+bT1n9pZc3WxK16d9O72pG0Q7e3u12+nme/F7eNbKuvr/xa7w54V/OGzVO/Bv0qK2QAAAAAqLVMhmEYtpuhtsjJydHq1au1e/dupaamysfHR3FxcerZs6eaN2/u7vCs2rlzp9q3b19S3rFjh9q1a+fGiADAuh1JOzT+t/HKyM8oqYsJiFFCdoJD43iaPDVv+DwF+wTrdPZptQxvKR9PH2eHCwAAgDJU159DCwsL9ddff5nVtWjRQl5ebFhG1VcV3r/8TYEZf39/XXLJJbrkkkvcHQoA1Djto9pr5aiVemvjW5r31zzFBcWpW51umrVnltX2HvJQsYot6ouMIi09slTjOo5TnYA6lR02AAAAAEAkUgEAcCkPk4ce7vawHu72sLYlbtPNP95cZtt/JlG9TF4qNApLyo1DG1dmmAAAAACAUjgjFQAAN8grytOdv91pV9sesT30/MXPy+P//9nuHN1ZgxoNqszwAAAAAAClkEgFAMANVh9frcyCTLvaHss4piFNh+jiuIslSVsSt+iNDW+ooLhAM3bM0PNrnte2xG2SpPi0eI1cOFK9v+yt9za/V2nxAwAAAEBtw9Z+AADcIMwvzO62Z3LO6FTWKa04vqKk7os9X6iwuFCzdp89X3XBgQX6dvi3enndy9qTvEeSNHXbVPWO663OdTo7M3QAAAAAqJVYkQoAgBt0qdNFEzpNUKBXoEJ8QhTqE1pm2/bR7RXiG6JA78CSurqBdbU1cWtJOacwR3tT9iqjIMOsr72rXgEAAAAA50ciFQAAF8gvytdXe77SrF2zlJl/Nrl5d+e7NbjxYKXnpystP81qvwjfCD3Y5UEFegfqqQufUoBXgLw9vNWrXi9dVO+iknbBPsFqH9leEzpNkJ+nnySpbURbXRBzQeW/OAAAAACoBdjaDwCACzz0x0NadmyZJGnuvrlqF9VOgd6BWnhgodX2JplkyFByXrJu//V2/Xzdz1p0cJGyC7MlSXP2zNHnV3yuBsENdCLzhC5vcrliAmMUExij0e1Ga+q2qdqVvEv3L7lfUwZNkaeHp8teKwAAAADURCRSAQCoZIZhmJ1veiDtgA6kHTh/HxklnxcUF2h74nZl5Jtv28/Iz9DVza+26Dvvr3kln685uUYH0g6oZXjLckYPAAAAAJDY2g8AQKUzmUxqHdG6/P1lUovwFhrfcbx8PX0lST1ie+jCuhdabR8TEFPyuY+HjyL8Iso9NwAAAADgLFakAgDgAu9f8r4eW/6Y1p9ab7ba1B4dozuqYUhDNQxpqJ+v+1nJuclqGtpUXh7W/xl/pe8ren7N80rPT9f4juMV5R/ljJcAAAAAALUaiVQAAFwgyj9KxzKOmSVRb2p9kzYkbNC+lH3n7Zuam6orv7tSYb5hCvIO0oaEDQrzDdPUwVPVLKyZRftGIY308aUfO/01AAAAAEBtRiIVAAAXCfQONCsvPrxYAd4BNvsdzjh89r86XFKXkJ2gfy/5txZdu8i5QQIAAAAArCKRCgCAExmGoanbpmrtybXqUqeL7u1yrzxMHsrIz1BqXqpZ24ScBCmn/HOl5adVLFgAAAAAgN1IpAIA4ETz98/X+1velyRtSNigKP8o3dTmJv1v8/+UmJPo1LmahTbTGxveUPfY7upZt2fJRVTWbDm9RftS9qln3Z5qFNLIqXEAAAAAQG1AIhUAACc6mnHUajkpJ6nCY8cExOiNfm/op0M/6af4n7Tp9CZtOr1JM3fOVMeojppx+Qz5ePpY9Ft8ZLEe+uMhFRvFCvQO1Jwhc9Q0rGmF4wEAAACA2sTD3QEAAFCTXNb4MgV4nT331M/TT5c3uVySVD+ofoXHfqDrA+pUp5Oua3mdknOTzZ5tS9qmrYlbrfb7Nf5XFRvFkqSsgiytOL6iwrEAAAAAQG3DilQAAJyoVUQrzRs+T9uTtqttZFs1CmmktLw0fb7rc6vtL653sQ6mHVSIT4j2puw979hf7P5CmfmZGt5suGICYpSQnVDyzMPkoUj/SKv9moc1Nyu3CGvh4KsCAAAAAJBIBQDAyeoH11f94L9XoOYV5anQKLRo1yaijV7s86Ii/CK058wejfxh5HnH3Za0TduStqlOQB3NvHymPtn+idYnrJevp69ua3ubmoZa365/R/s7VFhcqN3JuzWgwQD1iutVsRcIAAAAALUQiVQAACpZnYA6ahneUvtS9pnVX9HkCkX4RUiS/kr9y+yZt4e3bm5zsz7f9bmKjCKzZwfTDmpgw4Ga1GuSWX16Xrre3vi2DJOhcR3GKS4oTpLk6eGpCZ0nOPtlAQAAAECtwhmpAAC4wPUtr7eom7p1asnnob6hZs+CfYL1cLeHdf8F95vVe5m81L9+f4uxcgpzdMW3V+ibv77R3H1zdc331+hE5gnnBA8AAAAAIJEKAEBlS8hK0KoTq+Tr6WtW7+flV/J5p+hOivKLKikPaTJEknRj6xt1Yd0LJUlR/lH6+NKPdSb3jH469JOyCrJUWHz2yID1p9YrPT+9pH9OYY5GLRqlg2kHK+11AQAAAEBtwtZ+AAAq2R2/3KEjGUcs6h/r8VjJ56G+oZozdI4WHVqkKP8oDW82XNLZZOvHl36s3MJc+Xn5acrWKXp/y/uSJJNMMmQoyi9Kj/d83GL8lNwUXTX/KoX4hOilPi+pb/2+lfQKAQAAAKDmI5EKAEAlO5ZxzKzcIbKDHuj6gHrU7WFWXzeorsZ1GCdJOpNzRh9t+0j5xfm6o90dahDSQJL0/f7vS9obMiRJSblJevbPZ1UvsJ5OZFlu50/PT9d9i+/TI90f0a1tb1VaXpq+2vuVPEweGtVqlIJ8gpz6egEAAADULIZhKD4+Xtu3b9exY8eUmpoqX19fhYeHq0WLFurevbv8/PxsD1TNkUgFAKCSNQ1rqv2p+0vK4zuNt0iilnbfkvu0PWm7JGnV8VVadM0ieXt6q1FoIx3LPGbRPj0/3Wxrf2nFKtar619VpF+kZu6cqd3JuyVJK46t0KdXfFqelwUAAACgBktJSdH8+fP1888/a8mSJUpKSiqzrbe3t4YOHaoHHnhA/fr1c2GUrsUZqQAAVJI3N7ypHrN7qLC4UJ2jO6tRcCM9eMGD6t+g/3n7GYahXWd2lZRPZp1Ucm6yJOn5i5/X0CZDFeEXUa6YNp3eVJJEPVfOL8ov11gAAAAAaqZ77rlHsbGxuuOOO/T111+fN4kqSQUFBZo/f7769++v0aNHKz297EUe1RkrUgEAqASbEjZpxs4ZkqT49HhdWPdC/XDtD3b1NZlM6lO/j/44+ockqW1kW0X5n72IKso/Si/3fVmGYehQ+iEVFhXq1fWvau2ptXaNXTegrpqHNS9ZIdsusp18PH0ce3EAAAAAarS1a9cqP99ywYWnp6fq1q2rmJgYFRQU6PDhw0pLSzNr89lnn2nPnj1avHixgoJq1jFiJFIBAKiAguICfffXd8opzNHVza9WqG+oJCm3MNesXemyLW/0e0Pz989XXlGerml+jTw9PM2em0wmNQ1tKkn6cPCH+vPEnwr0DtSPB3/U1/u+LnPcXnG9dFWLq/TZrs/kafLU6LajHYoLAAAAQO0SFhamm266SUOHDlWfPn0UHBxc8qyoqEgrVqzQ008/rRUrVpTUr1u3TmPGjNHcuXPdEXKlIZEKAEA5rT+1Xvcsvkc5hTmSpPn75+vrYV/L28NbPev2VL/6/bTs2DIFeAXo3i73OjS2j6ePrm91vV1tvT281bd+X0lSp+hO8vPy0/z9883OTI30i9RjPR5Tm8g2kqSHuj7kUDwAAAAAapfGjRvrqaee0k033SR/f3+rbTw9PdW/f38tXbpUd999tz766KOSZ/PmzdPSpUs1YMAAV4Vc6UyGYRjuDgKoiJ07d6p9+/Yl5R07dqhdu3ZujAhAbTHw64FKzEk0q4vwjdAr/V7RhXUvlGEYOpF1QqE+oQrycc+WluyCbGUVZCk6IFoFRQX6cOuHOpB6QJc3uVxXNLnCLTEBAABUd9X159DCwkL99ddfZnUtWrSQl1cNXGdnGFJehlRUIHl6S77Bksnk7qiqjUWLFmnw4MHy8bH/GLCioiJdeOGF2rBhQ0ndTTfdpNmzZzslpqrw/q2Bf1MAAHCNcxdAmdXlJevJlU9q8cjFMplMiguKc0NkfwvwDlCAd4Ak6cOtH+rj7R9LkpYeXarYwFh1qdPFneEBAAAAzpOwU9o+Vzq+UTq5VcpN/fuZX5hUt5MU11XqMFKKaeuuKKuFoUOHOtzH09NTjz76qK6//u+ddb/88oszw3I7EqkAAJRDQXGBiowiq8/ObfWvav5K+fu3t4YM7U/dTyIVAAAA1d++X6SVb0tHVpfdJjdVOrTs7MfKN6WGvaTeD0otL3VVlLVCnz59zMpnzpxRdna2AgIC3BSRc3m4OwAAAKqj3IKyL4+qqhc4Xdr47/9JDPYJ1kV1L3JjNAAAAEAFZSdLc8dKc64/fxLVmiOrpTkjpXnjzo4DpwgPD7eoS0tLc0MklYMVqQAAlMOExRPKfNapTicXRmK/Yc2GqW5gXR1MO6iL6l2k+sH13R0SAAAAUD6ndkizR0gZJys2zvZvpPiV0i3zpJiqf85tVXf8+HGLusjISDdEUjlYkQoAQDmcyDxhtb5VeCt1iq6aiVRJ6hbbTde3ul4Nghu4OxQAAACgfE7tkGYOrXgS9ZyMk9KMIWfPWEWFrFixwqzcqFEjhy6squpIpAIAUA4jW440K1/V/Cq9O+BdfXbFZ/L38ndTVAAAAEANl518diXqPy+ScobcVGnWdWzzr6Dp06eblYcMGeKmSCoHW/sBALBTUXGR5uyZo8TsRI1uN1oxATH69fCvuqzxZbqmxTXuDg8AAACo+X6c6LyVqKVlnJR+elS6blrljF/D/fjjj1q+fLlZ3ZgxY9wTTCUhkQoAgJ3G/jpWGxM2SpK+2POFfhvxm65tea2bowIAAABqiX2/SDvmVu4c27+ROoyUWl5WufPUMMnJybrzzjvN6q6++mr16NHDTRFVDrb2AwBgh2KjuCSJKkm5Rbn6Of5nN0YEAAAA1DIr33bNPKvecc08NURxcbFuueUWHTt2rKQuNDRU7777rhujqhwkUgEAsIOHyUM+nuaHpDcObuyeYAAAAIDaJmGndGS1a+Y6vEpK2OWauWqAiRMn6qeffjKrmzp1qho0qHkX3JJIBQDAToGegWblcP9wN0UCAAAA1DLbK3lLf2mVfYRADfHuu+/qzTffNKt79NFHdcMNN7gpospFIhUAADvVD6lf8rm3h7ci/CLcGA0AAABQixzfaLtNdZ6vGpozZ44eeOABs7oxY8bo5Zdfdk9ALkAiFQAAO73U5yX1rNtTbSLa6LW+ryk6INrdIQEAAAA1n2FIJ7e6ds4TW87OC6t++OEHjR49WsY/vkbXXnutpk2bJpP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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -273,7 +317,7 @@ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", - "plt.rcParams['figure.dpi'] = 300\n", + "plt.rcParams['figure.dpi'] = 200\n", "\n", "_, ax = plt.subplots(figsize=(4, 4))\n", "sns.scatterplot(\n", @@ -289,7 +333,7 @@ "\n", "# legend to upper right\n", "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", - "ax.set_title(\"10X genomics PBMC\")\n", + "ax.set_title(\"10X genomics PBMC (10X Matrix)\")\n", "ax.set_xlabel(\"UMAP 1\")\n", "ax.set_ylabel(\"UMAP 2\")" ] diff --git a/examples/scembed/projection2.ipynb b/examples/scembed/projection2.ipynb new file mode 100644 index 00000000..b3b4be6c --- /dev/null +++ b/examples/scembed/projection2.ipynb @@ -0,0 +1,356 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Started With Cell Embeddings from scATAC-seq data\n", + "## Overview\n", + "A *cell embedding* is a low-dimensional representation of single-cell in an scATAC-seq workflow. Think of it as a single vector of dimensionality ~100 that summarizes the epigenetic state of a cell. Cell embeddings are useful for a variety of tasks, including visualization, clustering, and differential accessibility analysis. In this tutorial, we will learn how to use pretrained models from `scEmbed` to generate cell embeddings from scATAC-seq data. We will then use these embeddings to visualize the data and perform clustering.\n", + "\n", + "Overwhlemingly, scATAC-seq datasets are represented and manipulated as *binary accessibilty matrices*. Breifly, these are matrices where each row represents a cell and each column represents a genomic region. The value of each entry is 1 if the region is accessible in the cell and 0 otherwise. For example, the following matrix represents a hypothetical dataset with 3 cells and 4 genomic regions:\n", + "\n", + "| Cell | Region 1 | Region 2 | Region 3 | Region 4 |\n", + "|------|----------|----------|----------|----------|\n", + "| 1 | 1 | 0 | 1 | 0 |\n", + "| 2 | 0 | 1 | 1 | 0 |\n", + "| 3 | 1 | 1 | 0 | 1 |\n", + "\n", + "These matrices are typically incredibly sparse, with most cells only having accessibilty at a small fraction of the genome. They are also extremely high-dimensional; matrices with > 1M features is common. This binary accessibility matrix is the input to `scEmbed`. We use [`scanpy`](https://github.com/scverse/scanpy) and their [`AnnData`](https://github.com/scverse/anndata) objects. These objects are a standard way of representing single-cell data in Python. We will use the `AnnData` object to represent our binary accessibility matrix.\n", + "\n", + "## Installation\n", + "First, we need to install `scEmbed`. `scEmbed` is a part of the `gitk` package. We recommend using virtual environments for this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install gitk\n", + "# or if using local version:\n", + "!pip install ." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading Data\n", + "For this tutorial we've curated a dataset already for you to use. It comes from [10X genomics](https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-atac-v2-chromium-controller-2-standard). \n", + "\n", + "*Note: when using a pre-trained model, `scEmbed` requires that your `AnnData` object have `chr`, `start`, and `end` values on the `.var` attribute of the object. This is because we use interval overlap analysis to find similar regions and recast the feature-space of the new data we are embedding*.\n", + "\n", + "An example of how to do this is below:\n", + "\n", + "```python\n", + "import scanpy as sc\n", + "import pandas as pd\n", + "\n", + "adata = sc.read_h5ad(\"path/to/your/data.h5ad\")\n", + "\n", + "peaks = pd.read_csv(\"/path/to/your/peaks.bed\", sep=\"\\t\", header=None)\n", + "peaks.columns = [\"chr\", \"start\", \"end\"]\n", + "adata.var = peaks\n", + "```\n", + "\n", + "**The data we are using in this example already has this completed for you.**\n", + "\n", + "### Retreive preprocessed data:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-05-23 15:52:17-- http://big.databio.org/scembed/data/pbmc.h5ad\n", + "Resolving big.databio.org (big.databio.org)... 128.143.223.179\n", + "Connecting to big.databio.org (big.databio.org)|128.143.223.179|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 13570752714 (13G) [application/octet-stream]\n", + "Saving to: â€pbmc/pbmc.h5ad’\n", + "\n", + "pbmc/pbmc.h5ad 100%[===================>] 12.64G 43.5MB/s in 5m 17s \n", + "\n", + "2023-05-23 15:57:34 (40.8 MB/s) - â€pbmc/pbmc.h5ad’ saved [13570752714/13570752714]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir -p pbmc\n", + "!wget \"http://big.databio.org/scembed/data/pbmc_sampled.h5ad\" -O \"pbmc/pbmc.h5ad\"\n", + "\n", + "# or use the full dataset (careful, it's 12GB!):\n", + "# !wget \"http://big.databio.org/scembed/data/pbmc.h5ad\" -O \"pbmc/pbmc.h5ad\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating Embeddings\n", + "Now that we have the data, we can generate embeddings. This occurs in two steps:\n", + "\n", + "1. Create a `Projector()`\n", + "2. Project some data\n", + "\n", + "If this is your first time through the tutorial, the model will need time to download. This will take a few minutes. If you've already run this tutorial, the model will be cached and this step will be much faster." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# read in the data\n", + "import scanpy as sc\n", + "\n", + "pbmc = sc.read_h5ad(\"/Volumes/data/genomics/10x/10kpbmcsV2_atac/pbmc.h5ad\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs Ă— n_vars = 9221 Ă— 191125\n", + " obs: 'sample', 'barcode'\n", + " var: 'chr', 'start', 'end'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pbmc" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# create projector\n", + "from gitk.scembed import Projector\n", + "\n", + "model = Projector(\"/Users/nathanleroy/.scembed/databio/scatlas/model.yaml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "974599" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|â–â–â–â–â–â–â–â–â–â–| 191125/191125 [00:07<00:00, 25967.15it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 9221/9221 [02:20<00:00, 65.48it/s] \n", + "100%|â–â–â–â–â–â–â–â–â–â–| 9221/9221 [01:20<00:00, 113.88it/s]\n" + ] + } + ], + "source": [ + "# create embeddings (takes a few minutes)\n", + "# there will be three progress bars:\n", + "# 1. converting dataset to universe, \n", + "# 2. creating documents, and \n", + "# 3. projecting\n", + "pbmc = model.project(pbmc)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9221, 100)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# insepct the results\n", + "pbmc.obsm['embedding'].shape" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyzing Embeddings\n", + "Pretty fast, huh? We can now analyze the embeddings. First, let's visualize them. We'll use `umap`. We'll also color the cells by their cluster assignment. We'll use `leiden` clustering for this. This occurs in three steps:\n", + "\n", + "1. `scanpy.pp.neighbors()` to compute the nearest neighbors of each cell\n", + "2. `scanpy.tl.leiden()` to compute the UMAP embedding and associated cluster assignments\n", + "3. `umap.Umap()` to compute the UMAP embedding\n", + "\n", + "We can then run visualizations with seaborn and matplotlib. We'll color the cells by their cluster assignment." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], + "source": [ + "# run leiden clustering on embeddings\n", + "sc.pp.neighbors(pbmc, use_rep='embedding')\n", + "sc.tl.leiden(pbmc)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import umap\n", + "\n", + "reducer = umap.UMAP(n_components=2)\n", + "umap_embedding = reducer.fit_transform(pbmc.obsm['embedding'])\n", + "\n", + "# attach umaps back to pbmc object\n", + "pbmc.obsm['umap'] = umap_embedding\n", + "pbmc.obs['umap1'] = umap_embedding[:, 0]\n", + "pbmc.obs['umap2'] = umap_embedding[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'UMAP 2')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams['figure.dpi'] = 200\n", + "\n", + "_, ax = plt.subplots(figsize=(4, 4))\n", + "sns.scatterplot(\n", + " data=pbmc.obs,\n", + " x='umap1',\n", + " y='umap2',\n", + " hue='leiden',\n", + " palette='tab20',\n", + " linewidth=0,\n", + " s=1,\n", + " ax=ax\n", + ")\n", + "\n", + "# legend to upper right\n", + "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "ax.set_title(\"10X genomics PBMC\")\n", + "ax.set_xlabel(\"UMAP 1\")\n", + "ax.set_ylabel(\"UMAP 2\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f34ff3e87e4ea9b1454363a2010fd572d00c3b39 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Mon, 12 Jun 2023 11:31:53 -0400 Subject: [PATCH 0203/1072] filtering universes --- gitk/assess/assess.py | 54 ++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 51 insertions(+), 3 deletions(-) diff --git a/gitk/assess/assess.py b/gitk/assess/assess.py index 10410f52..6898ae57 100644 --- a/gitk/assess/assess.py +++ b/gitk/assess/assess.py @@ -1,6 +1,10 @@ from .distance import run_distance +from .utils import check_if_uni_flexible from .intersection import run_intersection -from .likelihood import hard_universe_likelihood, likelihood_flexible_universe +from .likelihood import ( + hard_universe_likelihood, + likelihood_flexible_universe, +) import pandas as pd import os import numpy as np @@ -21,7 +25,7 @@ def run_all_assessment_methods( distance_u_t_f=False, distance_u_t_f_flex=False, ): - if not all( + if not any( [ overlap, distance_f_t_u, @@ -31,7 +35,7 @@ def run_all_assessment_methods( ] ): raise AttributeError("Choose at least one assessment method") - if not all( + if not any( [ distance_f_t_u, distance_f_t_u_flex, @@ -204,3 +208,47 @@ def get_likelihood( lh = hard_universe_likelihood(model_file, universe, cove_folder, cove_prefix) return lh + + +def filter_universe( + universe, + universe_filtered, + min_size=0, + min_coverage=0, + filter_lh=False, + model_file=None, + cove_folder=None, + cove_prefix=None, + lh_cutoff=0, +): + if filter_lh: + check_if_uni_flexible(universe) + if not all([model_file, cove_folder, cove_prefix]): + miss_args = [] + if not model_file: + miss_args.append("model_file") + if not cove_folder: + miss_args.append("cove_folder") + if not cove_prefix: + miss_args.append("cove_prefix") + raise ValueError( + "Missing {} for peak likelihood calculations.".format( + ",".join(miss_args) + ) + ) + likelihood_flexible_universe( + model_file, universe, cove_folder, cove_prefix, True + ) + universe = universe + "_peak_likelihood" + with open(universe) as uni: + with open(universe_filtered, "w+") as uni_flt: + for i in uni: + j = i.split("\t") + j[1], j[2], j[4] = int(j[1]), int(j[2]), int(j[4]) + if j[2] - j[1] > min_size: + if j[4] > min_coverage: + if filter_lh: + if int(j[0]) > lh_cutoff: + uni_flt.write(i) + else: + uni_flt.write(i) From ed0e7c942563fcd8959c5e1fe2a922699d67cfc7 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 09:32:34 -0400 Subject: [PATCH 0204/1072] working model --- gitk/likelihood/universe_cutoff_flexible.py | 156 ++++++++++++++++++++ 1 file changed, 156 insertions(+) create mode 100644 gitk/likelihood/universe_cutoff_flexible.py diff --git a/gitk/likelihood/universe_cutoff_flexible.py b/gitk/likelihood/universe_cutoff_flexible.py new file mode 100644 index 00000000..edfd7fce --- /dev/null +++ b/gitk/likelihood/universe_cutoff_flexible.py @@ -0,0 +1,156 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os +from functools import cmp_to_key + +import numpy as np +import pyBigWig + +from ..utils import natural_chr_sort, timer_func + + +def check_if_line_correct(line): + line = line.split("\t") + if not int(line[1]) < int(line[6]) < int(line[7]) < int(line[2]): + print(line) + raise Exception("wrong line format") + + +def ana_region(reg, start_s, starts, ends, track_val): + core_s, core_e = [], [] + core_pos = np.argwhere(reg == 2).flatten() + + if len(core_pos) == 0: + return "empty" + else: + core_s.append(core_pos[0] + start_s) + if core_pos[0] == 0: + core_s[0] += 10 + for i in range(1, len(core_pos)): + if core_pos[i] - core_pos[i - 1] >= 100: + core_e.append(core_pos[i - 1] + start_s) + min_point = np.argmin(track_val[core_pos[i - 1] : core_pos[i]]) + min_point = min_point + core_pos[i - 1] + ends.append(int(min_point) + start_s) + starts.append(ends[-1] + 1) + core_s.append(core_pos[i] + start_s) + core_e.append(core_pos[-1] + start_s) + if core_pos[-1] == len(reg) - 1: + core_e[-1] -= 10 + return core_s, core_e, starts, ends + + +def save_regions(inter_pos, chrom, bedname, track): + ind = np.argwhere(inter_pos != 0) + ind = ind.flatten() + start_s = ind[0] + to_file = [] + line = chrom + "\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n" + for i in range(1, len(ind)): + if ind[i] - ind[i - 1] != 1: + end_e = ind[i - 1] + region = inter_pos[start_s : end_e + 1] + res = ana_region(region, start_s, [start_s], [], track[start_s : end_e + 1]) + if res != "empty": + save_start_e, save_end_s, save_start_s, save_end_e = res + save_end_e = save_end_e + [end_e] + for a, b, c, d in zip( + save_start_e, save_end_s, save_start_s, save_end_e + ): + if a != b: + val = 0 + li = line.format( + int(c), + int(d) + 1, + "universe", + val, + ".", + int(a), + int(b), + "0,0,255", + ) + check_if_line_correct(li) + to_file.append(li) + start_s = ind[i] + end_e = ind[-1] + region = inter_pos[start_s : end_e + 1] + res = ana_region(region, start_s, [start_s], [], track[start_s : end_e + 1]) + if res != "empty": + save_start_e, save_end_s, save_start_s, save_end_e = res + save_end_e = save_end_e + [end_e] + for a, b, c, d in zip(save_start_e, save_end_s, save_start_s, save_end_e): + val = 0 + li = line.format( + int(c), + int(d) + 1, + "universe", + val, + ".", + int(a), + int(b), + "0,0,255", + ) + check_if_line_correct(li) + to_file.append(li) + with open(bedname, "w") as f: + f.writelines(to_file) + + +def get_uni(file, chrom, bedname): + """For each position check if coverage is bigger than cut-off; + if cut-off not provided calculate value that gives + maximum likelihood universe + :param str file: coverage file + :param str chrom: chromosome to analyse + :param int cutoff: base pairs with values grater of equal to cut-off can be included in universe + :return:""" + file = pyBigWig.open(file) + if pyBigWig.numpy: + track = file.values(chrom, 0, file.chroms(chrom), numpy=True) + else: + track = file.values(chrom, 0, file.chroms(chrom)) + track = np.array(track) + track[np.isnan(track)] = 0 + track = track.astype(np.uint16) + cutoff = np.sum(track) / len(track) + track_non_zero_sort = np.sort(track[track != 0]) + + cutoff = np.round(cutoff) + pos = np.where(track_non_zero_sort == cutoff)[0] + + f, l = pos[0] / len(track_non_zero_sort), pos[-1] / len(track_non_zero_sort) + q_cutoff = np.mean([f, l]) + lower = np.quantile(track_non_zero_sort, q_cutoff - 0.2) + upper = np.quantile(track_non_zero_sort, q_cutoff + 0.2) + inter_pos = np.zeros(len(track), dtype=np.uint8) + inter_pos[track >= lower] = 1 + inter_pos[track > upper] = 2 + inter_pos = inter_pos[:7110285] + save_regions(inter_pos, chrom, bedname, track) + + +def main(file, file_out, merge=0, filter_size=0, cutoff=None): + """ + Creat cut-off universe based on coverage track + :param str file: path to coverage file without extension + :param int merge: regions closer than this value will be merged into one + :param int filter_size: regions smaller than this value will not be reported + :param str file_out: output file + :param int cutoff: base pairs with coverage equal to or greater than this value will be included in the universe + """ + if os.path.isfile(file_out): + raise Exception(f"File : {file_out} exists") + bw_start = pyBigWig.open(file) + chroms = bw_start.chroms() + bw_start.close() + chroms_key = list(chroms.keys()) + chroms_key = sorted(chroms_key, key=cmp_to_key(natural_chr_sort)) + chroms = {i: chroms[i] for i in chroms_key} + for chrom in chroms: + if chroms[chrom] > 0: + inter_pos = get_uni(file, chrom, cutoff) + if merge == 0 and filter_size == 0: + save_simple(file_out, inter_pos, chrom) + else: + marge_filter(file_out, inter_pos, chrom, merge, filter_size) From 984e8af8e45966da3e204d8f6f5138aab43ff3ee Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 09:36:09 -0400 Subject: [PATCH 0205/1072] new hmm model --- gitk/assess/assess.py | 2 +- gitk/hmm/const.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/gitk/assess/assess.py b/gitk/assess/assess.py index 6898ae57..3acc597b 100644 --- a/gitk/assess/assess.py +++ b/gitk/assess/assess.py @@ -248,7 +248,7 @@ def filter_universe( if j[2] - j[1] > min_size: if j[4] > min_coverage: if filter_lh: - if int(j[0]) > lh_cutoff: + if int(j[9].strip("\n")) > lh_cutoff: uni_flt.write(i) else: uni_flt.write(i) diff --git a/gitk/hmm/const.py b/gitk/hmm/const.py index a81a4c8f..9fcab822 100644 --- a/gitk/hmm/const.py +++ b/gitk/hmm/const.py @@ -5,4 +5,4 @@ [0.1, 0, 0, 0.9], ] -LAMBDAS = [[5, 3, 0.0001], [0.25, 3, 0.25], [0.0001, 3, 5], [1e-4, 1e-3, 1e-4]] +LAMBDAS = [[5, 3, 0.0001], [0.1, 3, 0.1], [0.0001, 3, 5], [1e-4, 1e-3, 1e-4]] From 006b4650259beb0d63ac4e7fbddcf8400c48524b Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 09:40:19 -0400 Subject: [PATCH 0206/1072] change hmm to universe --- gitk/cli.py | 18 +++++++----------- gitk/{hmm => universe}/README.md | 0 gitk/{hmm => universe}/__init__.py | 0 gitk/{hmm => universe}/cli.py | 0 gitk/{hmm => universe}/const.py | 0 gitk/{hmm => universe}/custom_distribution.py | 0 gitk/{hmm => universe}/hmm.py | 0 gitk/{hmm => universe}/models.py | 0 setup.py | 2 +- 9 files changed, 8 insertions(+), 12 deletions(-) rename gitk/{hmm => universe}/README.md (100%) rename gitk/{hmm => universe}/__init__.py (100%) rename gitk/{hmm => universe}/cli.py (100%) rename gitk/{hmm => universe}/const.py (100%) rename gitk/{hmm => universe}/custom_distribution.py (100%) rename gitk/{hmm => universe}/hmm.py (100%) rename gitk/{hmm => universe}/models.py (100%) diff --git a/gitk/cli.py b/gitk/cli.py index 01b72d17..9eddf2a5 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -1,14 +1,12 @@ -import os.path from typing import Dict import logmuse import sys -import pandas as pd from ubiquerg import VersionInHelpParser from .assess.cli import build_subparser as assess_subparser from .eval.cli import build_subparser as eval_subparser -from .hmm.cli import build_subparser as hmm_subparser +from .universe.cli import build_subparser as hmm_subparser from .likelihood.cli import build_subparser as likelihood_subparser from .scembed.argparser import build_argparser as scembed_subparser @@ -34,7 +32,7 @@ def build_argparser(): # Individual subcommands msg_by_cmd = { - "hmm": "Use an HMM to build a consensus peak set.", + "universe": "Use an HMM to build a consensus peak set.", "lh": "Compute likelihood", "assess": "Assess a universe", "scembed": "Embed single-cell data as region vectors", @@ -47,7 +45,7 @@ def build_argparser(): subparsers[k] = sp.add_parser(k, description=v, help=v) # build up subparsers for modules - subparsers["hmm"] = hmm_subparser(subparsers["hmm"]) + subparsers["universe"] = hmm_subparser(subparsers["universe"]) subparsers["assess"] = assess_subparser(subparsers["assess"]) subparsers["lh"] = likelihood_subparser(subparsers["lh"]) subparsers["scembed"] = scembed_subparser(subparsers["scembed"]) @@ -102,21 +100,21 @@ def main(test_args=None): ) if args.subcommand == "universe_hard": - from .likelihood.universe_hard import main + from gitk.universe.universe_hard import main main( args.coverage_file, args.fout, args.merge, args.filter_size, args.cutoff ) if args.subcommand == "universe_flexible": - from .likelihood.universe_flexible import main + from gitk.universe.universe_flexible import main main( args.model_file, args.cov_folder, args.coverage_prefix, args.output_file ) - if args.command == "hmm": - from .hmm.hmm import run_hmm_save_bed + if args.command == "universe": + from .universe.hmm import run_hmm_save_bed run_hmm_save_bed( coverage_folder=args.cov_folder, @@ -127,8 +125,6 @@ def main(test_args=None): ) if args.command == "scembed": - from .scembed.scembed import main as scembed_main - _LOGGER.info("Running scembed") pass # scembed_main(test_args) diff --git a/gitk/hmm/README.md b/gitk/universe/README.md similarity index 100% rename from gitk/hmm/README.md rename to gitk/universe/README.md diff --git a/gitk/hmm/__init__.py b/gitk/universe/__init__.py similarity index 100% rename from gitk/hmm/__init__.py rename to gitk/universe/__init__.py diff --git a/gitk/hmm/cli.py b/gitk/universe/cli.py similarity index 100% rename from gitk/hmm/cli.py rename to gitk/universe/cli.py diff --git a/gitk/hmm/const.py b/gitk/universe/const.py similarity index 100% rename from gitk/hmm/const.py rename to gitk/universe/const.py diff --git a/gitk/hmm/custom_distribution.py b/gitk/universe/custom_distribution.py similarity index 100% rename from gitk/hmm/custom_distribution.py rename to gitk/universe/custom_distribution.py diff --git a/gitk/hmm/hmm.py b/gitk/universe/hmm.py similarity index 100% rename from gitk/hmm/hmm.py rename to gitk/universe/hmm.py diff --git a/gitk/hmm/models.py b/gitk/universe/models.py similarity index 100% rename from gitk/hmm/models.py rename to gitk/universe/models.py diff --git a/setup.py b/setup.py index 5d3ff6c4..144c36bc 100755 --- a/setup.py +++ b/setup.py @@ -28,7 +28,7 @@ PACKAGE_NAME, "gitk.assess", "gitk.eval", - "gitk.hmm", + "gitk.universe", "gitk.likelihood", "gitk.scembed", ], From 196168322e51183696a90915938d759c4df065ab Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 09:40:30 -0400 Subject: [PATCH 0207/1072] move lh uni to universe --- gitk/{likelihood => universe}/universe_flexible.py | 6 +++--- gitk/{likelihood => universe}/universe_hard.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) rename gitk/{likelihood => universe}/universe_flexible.py (95%) rename gitk/{likelihood => universe}/universe_hard.py (98%) diff --git a/gitk/likelihood/universe_flexible.py b/gitk/universe/universe_flexible.py similarity index 95% rename from gitk/likelihood/universe_flexible.py rename to gitk/universe/universe_flexible.py index eecf4553..eca1df1c 100644 --- a/gitk/likelihood/universe_flexible.py +++ b/gitk/universe/universe_flexible.py @@ -7,9 +7,9 @@ import numpy as np from numba import njit -from ..utils import natural_chr_sort, timer_func, read_chromosome_from_bw -from ..hmm.hmm import predictions_to_bed, find_full -from .build_model import ModelLH +from gitk.utils import natural_chr_sort, timer_func, read_chromosome_from_bw +from gitk.universe.hmm import predictions_to_bed, find_full +from gitk.likelihood.build_model import ModelLH @njit diff --git a/gitk/likelihood/universe_hard.py b/gitk/universe/universe_hard.py similarity index 98% rename from gitk/likelihood/universe_hard.py rename to gitk/universe/universe_hard.py index 5257de47..255dd905 100644 --- a/gitk/likelihood/universe_hard.py +++ b/gitk/universe/universe_hard.py @@ -7,7 +7,7 @@ import numpy as np import pyBigWig -from ..utils import natural_chr_sort, timer_func +from gitk.utils import natural_chr_sort, timer_func def get_uni(file, chrom, cutoff=None): From ee83e390c123e9daaeb65b7538c145d5f7f16567 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 09:45:35 -0400 Subject: [PATCH 0208/1072] new lh cli --- gitk/cli.py | 48 +++++++++++++++--------------- gitk/likelihood/cli.py | 67 +----------------------------------------- 2 files changed, 25 insertions(+), 90 deletions(-) diff --git a/gitk/cli.py b/gitk/cli.py index 9eddf2a5..f6efb564 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -87,31 +87,31 @@ def main(test_args=None): ) if args.command == "lh": - _LOGGER.info(f"Subcommand: {args.subcommand}") - if args.subcommand == "build_model": - from .likelihood.build_model import main - - main( - args.model_file, - args.coverage_folder, - args.coverage_prefix, - args.file_no, - args.force, - ) - - if args.subcommand == "universe_hard": - from gitk.universe.universe_hard import main - - main( - args.coverage_file, args.fout, args.merge, args.filter_size, args.cutoff - ) - - if args.subcommand == "universe_flexible": - from gitk.universe.universe_flexible import main + # _LOGGER.info(f"Subcommand: {args.subcommand}") + # if args.subcommand == "build_model": + from .likelihood.build_model import main + + main( + args.model_file, + args.coverage_folder, + args.coverage_prefix, + args.file_no, + args.force, + ) - main( - args.model_file, args.cov_folder, args.coverage_prefix, args.output_file - ) + # if args.subcommand == "universe_hard": + # from gitk.universe.universe_hard import main + # + # main( + # args.coverage_file, args.fout, args.merge, args.filter_size, args.cutoff + # ) + # + # if args.subcommand == "universe_flexible": + # from gitk.universe.universe_flexible import main + # + # main( + # args.model_file, args.cov_folder, args.coverage_prefix, args.output_file + # ) if args.command == "universe": from .universe.hmm import run_hmm_save_bed diff --git a/gitk/likelihood/cli.py b/gitk/likelihood/cli.py index ea201d6a..cec5039b 100644 --- a/gitk/likelihood/cli.py +++ b/gitk/likelihood/cli.py @@ -1,4 +1,4 @@ -def build_subparser_model(parser): +def build_subparser(parser): """ Builds argument parser. @@ -27,68 +27,3 @@ def build_subparser_model(parser): ) return parser - -def build_subparser_universe_hard(parser): - parser.add_argument( - "--merge", - help="distance between output peaks that should be merged into one in output universe", - default=0, - type=int, - ) - parser.add_argument( - "--filter-size", - help="minimal siez of the region in the universe", - default=0, - type=int, - ) - parser.add_argument( - "--fout", help="output file with the universe", required=True, type=str - ) - parser.add_argument( - "--coverage-file", help="path to core coverage file", required=True, type=str - ) - parser.add_argument( - "--cutoff", help="cutoff value used for making universe", type=int - ) - - return parser - - -def build_subparser_universe_flexible(parser): - parser.add_argument( - "--output-file", help="path to output, universe file", required=True, type=str - ) - parser.add_argument( - "--model-file", help="path to lh model file", required=True, type=str - ) - parser.add_argument( - "--coverage-prefix", - help="prefixed used for making coverage files", - default="all", - type=str, - ) - parser.add_argument( - "--cov-folder", type=str, help="path to coverage folder", required=True - ) - return parser - - -def build_subparser(parser): - sp = parser.add_subparsers(dest="subcommand") - msg_by_cmd = { - "build_model": "Assess based on distance", - "universe_hard": "Making cut-off universe", - "universe_flexible": "Making ML flexible universe", - } - subparsers = {} - for k, v in msg_by_cmd.items(): - subparsers[k] = sp.add_parser(k, description=v, help=v) - subparsers["build_model"] = build_subparser_model(subparsers["build_model"]) - subparsers["universe_hard"] = build_subparser_universe_hard( - subparsers["universe_hard"] - ) - subparsers["universe_flexible"] = build_subparser_universe_flexible( - subparsers["universe_flexible"] - ) - - return parser From 72e52c17cb220626ab11a8c50ed71ef671ac27cc Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 10:49:18 -0400 Subject: [PATCH 0209/1072] new universe module --- gitk/cli.py | 68 +++++--- .../{universe_hard.py => cc_universe.py} | 3 +- gitk/universe/ccf_universe.py | 152 ++++++++++++++++++ gitk/universe/cli.py | 83 ++++++++-- gitk/universe/{hmm.py => hmm_universe.py} | 2 +- .../{universe_flexible.py => ml_universe.py} | 8 +- tests/consesnus/cli_test.sh | 60 ++++--- 7 files changed, 311 insertions(+), 65 deletions(-) rename gitk/universe/{universe_hard.py => cc_universe.py} (96%) create mode 100644 gitk/universe/ccf_universe.py rename gitk/universe/{hmm.py => hmm_universe.py} (99%) rename gitk/universe/{universe_flexible.py => ml_universe.py} (93%) diff --git a/gitk/cli.py b/gitk/cli.py index f6efb564..7806d096 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -6,7 +6,7 @@ from .assess.cli import build_subparser as assess_subparser from .eval.cli import build_subparser as eval_subparser -from .universe.cli import build_subparser as hmm_subparser +from .universe.cli import build_mode_parser as universe_subparser from .likelihood.cli import build_subparser as likelihood_subparser from .scembed.argparser import build_argparser as scembed_subparser @@ -45,7 +45,7 @@ def build_argparser(): subparsers[k] = sp.add_parser(k, description=v, help=v) # build up subparsers for modules - subparsers["universe"] = hmm_subparser(subparsers["universe"]) + subparsers["universe"] = universe_subparser(subparsers["universe"]) subparsers["assess"] = assess_subparser(subparsers["assess"]) subparsers["lh"] = likelihood_subparser(subparsers["lh"]) subparsers["scembed"] = scembed_subparser(subparsers["scembed"]) @@ -99,30 +99,48 @@ def main(test_args=None): args.force, ) - # if args.subcommand == "universe_hard": - # from gitk.universe.universe_hard import main - # - # main( - # args.coverage_file, args.fout, args.merge, args.filter_size, args.cutoff - # ) - # - # if args.subcommand == "universe_flexible": - # from gitk.universe.universe_flexible import main - # - # main( - # args.model_file, args.cov_folder, args.coverage_prefix, args.output_file - # ) - if args.command == "universe": - from .universe.hmm import run_hmm_save_bed - - run_hmm_save_bed( - coverage_folder=args.cov_folder, - out_file=args.out_file, - prefix=args.coverage_prefix, - normalize=args.not_normalize, - save_max_cove=args.save_max_cove, - ) + _LOGGER.info(f"Subcommand: {args.subcommand}") + if args.subcommand == "hmm": + from .universe.hmm_universe import hmm_universe + + hmm_universe( + coverage_folder=args.coverage_folder, + out_file=args.output_file, + prefix=args.coverage_prefix, + normalize=args.not_normalize, + save_max_cove=args.save_max_cove, + ) + + if args.subcommand == "ml": + from .universe.ml_universe import ml_universe + + ml_universe( + model_file=args.model_file, + cove_folder=args.coverage_folder, + cove_prefix=args.coverage_prefix, + file_out=args.output_file, + ) + + if args.subcommand == "cc": + from .universe.cc_universe import cc_universe + + cc_universe( + cove=args.coverage_folder, + cove_prefix=args.coverage_prefix, + file_out=args.output_file, + merge=args.merge, + filter_size=args.filter_size, + cutoff=args.cutoff, + ) + if args.subcommand == "ccf": + from .universe.ccf_universe import ccf_universe + + ccf_universe( + cove=args.coverage_folder, + cove_prefix=args.coverage_prefix, + file_out=args.output_file, + ) if args.command == "scembed": _LOGGER.info("Running scembed") diff --git a/gitk/universe/universe_hard.py b/gitk/universe/cc_universe.py similarity index 96% rename from gitk/universe/universe_hard.py rename to gitk/universe/cc_universe.py index 255dd905..f61cb305 100644 --- a/gitk/universe/universe_hard.py +++ b/gitk/universe/cc_universe.py @@ -75,7 +75,7 @@ def marge_filter(file_out, inter_pos, chrom, merge_dist=100, size_flt=1000): f.write(f"{chrom}\t{start}\t{end}\n") -def main(file, file_out, merge=0, filter_size=0, cutoff=None): +def cc_universe(cove, cove_prefix, file_out, merge=0, filter_size=0, cutoff=None): """ Creat cut-off universe based on coverage track :param str file: path to coverage file without extension @@ -86,6 +86,7 @@ def main(file, file_out, merge=0, filter_size=0, cutoff=None): """ if os.path.isfile(file_out): raise Exception(f"File : {file_out} exists") + file = os.path.join(cove, f"{cove_prefix}_core.bw") bw_start = pyBigWig.open(file) chroms = bw_start.chroms() bw_start.close() diff --git a/gitk/universe/ccf_universe.py b/gitk/universe/ccf_universe.py new file mode 100644 index 00000000..c883e427 --- /dev/null +++ b/gitk/universe/ccf_universe.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os +from functools import cmp_to_key + +import numpy as np +import pyBigWig + +from gitk.utils import natural_chr_sort, timer_func + + +def check_if_line_correct(line): + line = line.split("\t") + if not int(line[1]) < int(line[6]) < int(line[7]) < int(line[2]): + print(line) + raise Exception("wrong line format") + + +def ana_region(reg, start_s, starts, ends, track_val): + core_s, core_e = [], [] + core_pos = np.argwhere(reg == 2).flatten() + + if len(core_pos) == 0: + return "empty" + else: + core_s.append(core_pos[0] + start_s) + if core_pos[0] == 0: + core_s[0] += 10 + for i in range(1, len(core_pos)): + if core_pos[i] - core_pos[i - 1] >= 100: + core_e.append(core_pos[i - 1] + start_s) + min_point = np.argmin(track_val[core_pos[i - 1] : core_pos[i]]) + min_point = min_point + core_pos[i - 1] + ends.append(int(min_point) + start_s) + starts.append(ends[-1] + 1) + core_s.append(core_pos[i] + start_s) + core_e.append(core_pos[-1] + start_s) + if core_pos[-1] == len(reg) - 1: + core_e[-1] -= 10 + return core_s, core_e, starts, ends + + +def save_regions(inter_pos, chrom, bedname, track): + ind = np.argwhere(inter_pos != 0) + ind = ind.flatten() + start_s = ind[0] + to_file = [] + line = chrom + "\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n" + for i in range(1, len(ind)): + if ind[i] - ind[i - 1] != 1: + end_e = ind[i - 1] + region = inter_pos[start_s : end_e + 1] + res = ana_region(region, start_s, [start_s], [], track[start_s : end_e + 1]) + if res != "empty": + save_start_e, save_end_s, save_start_s, save_end_e = res + save_end_e = save_end_e + [end_e] + for a, b, c, d in zip( + save_start_e, save_end_s, save_start_s, save_end_e + ): + if a != b: + val = 0 + li = line.format( + int(c), + int(d) + 1, + "universe", + val, + ".", + int(a), + int(b), + "0,0,255", + ) + check_if_line_correct(li) + to_file.append(li) + start_s = ind[i] + end_e = ind[-1] + region = inter_pos[start_s : end_e + 1] + res = ana_region(region, start_s, [start_s], [], track[start_s : end_e + 1]) + if res != "empty": + save_start_e, save_end_s, save_start_s, save_end_e = res + save_end_e = save_end_e + [end_e] + for a, b, c, d in zip(save_start_e, save_end_s, save_start_s, save_end_e): + val = 0 + li = line.format( + int(c), + int(d) + 1, + "universe", + val, + ".", + int(a), + int(b), + "0,0,255", + ) + check_if_line_correct(li) + to_file.append(li) + with open(bedname, "a") as f: + f.writelines(to_file) + + +def get_uni(file, chrom, bedname): + """For each position check if coverage is bigger than cut-off; + if cut-off not provided calculate value that gives + maximum likelihood universe + :param str file: coverage file + :param str chrom: chromosome to analyse + :param int cutoff: base pairs with values grater of equal to cut-off can be included in universe + :return:""" + file = pyBigWig.open(file) + if pyBigWig.numpy: + track = file.values(chrom, 0, file.chroms(chrom), numpy=True) + else: + track = file.values(chrom, 0, file.chroms(chrom)) + track = np.array(track) + track[np.isnan(track)] = 0 + track = track.astype(np.uint16) + cutoff = np.sum(track) / len(track) + track_non_zero_sort = np.sort(track[track != 0]) + + cutoff = np.round(cutoff) + pos = np.where(track_non_zero_sort == cutoff)[0] + + f, l = pos[0] / len(track_non_zero_sort), pos[-1] / len(track_non_zero_sort) + q_cutoff = np.mean([f, l]) + lower = np.quantile(track_non_zero_sort, max(0, q_cutoff - 0.2)) + upper = np.quantile(track_non_zero_sort, min(1, q_cutoff + 0.2)) + inter_pos = np.zeros(len(track), dtype=np.uint8) + inter_pos[track >= lower] = 1 + inter_pos[track > upper] = 2 + save_regions(inter_pos, chrom, bedname, track) + + +def ccf_universe(cove, cove_prefix, file_out): + """ + Creat cut-off universe based on coverage track + :param str file: path to coverage file without extension + :param int merge: regions closer than this value will be merged into one + :param int filter_size: regions smaller than this value will not be reported + :param str file_out: output file + :param int cutoff: base pairs with coverage equal to or greater than this value will be included in the universe + """ + if os.path.isfile(file_out): + raise Exception(f"File : {file_out} exists") + file = os.path.join(cove, f"{cove_prefix}_core.bw") + bw = pyBigWig.open(file) + chroms = bw.chroms() + bw.close() + chroms_key = list(chroms.keys()) + chroms_key = sorted(chroms_key, key=cmp_to_key(natural_chr_sort)) + chroms = {i: chroms[i] for i in chroms_key} + for chrom in chroms: + if chroms[chrom] > 0: + get_uni(file, chrom, file_out) diff --git a/gitk/universe/cli.py b/gitk/universe/cli.py index 037bbb05..c5e18e82 100644 --- a/gitk/universe/cli.py +++ b/gitk/universe/cli.py @@ -1,18 +1,8 @@ -def build_subparser(parser): - """ - Builds argument parser. - - :return argparse.ArgumentParser - """ +def build_subparser_hmm(parser): + parser = build_subparser(parser) parser.add_argument( - "--out-file", type=str, help="path to result file", required=True - ) - parser.add_argument( - "--cov-folder", type=str, help="path to coverage folder", required=True - ) - parser.add_argument( - "--not_normalize", + "--not-normalize", help="if not to normalize coverage signal before using HMM", action="store_false", ) @@ -21,9 +11,72 @@ def build_subparser(parser): help="if present saves maximum coverage for each peak", action="store_true", ) + + return parser + + +def build_subparser_ml(parser): + parser = build_subparser(parser) + parser.add_argument( + "--model-file", help="path to lh model file", required=True, type=str + ) + return parser + + +def build_subparser_cc(parser): + parser = build_subparser(parser) + parser.add_argument( + "--merge", + help="distance between output peaks that should be merged into one in output universe", + default=0, + type=int, + ) parser.add_argument( - "--lambdas", type=str, help="lambdas matrix used to set emissions" + "--filter-size", + help="minimal siez of the region in the universe", + default=0, + type=int, ) - parser.add_argument("--coverage-prefix", default="all", type=str) + parser.add_argument( + "--cutoff", help="cutoff value used for making universe", type=int + ) + + return parser + + +def build_subparser(parser): + parser.add_argument( + "--output-file", help="path to output, universe file", required=True, type=str + ) + parser.add_argument( + "--coverage-folder", + help="path to core coverage folder", + required=True, + type=str, + ) + parser.add_argument( + "--coverage-prefix", + help="prefixed used for making coverage files", + default="all", + type=str, + ) + + return parser + +def build_mode_parser(parser): + sp = parser.add_subparsers(dest="subcommand") + msg_by_cmd = { + "cc": "Making coverage cut-off universe", + "ccf": "Making coverage cut-off flexible universe", + "ml": "Making ML universe", + "hmm": "Making HMM universe", + } + subparsers = {} + for k, v in msg_by_cmd.items(): + subparsers[k] = sp.add_parser(k, description=v, help=v) + subparsers["cc"] = build_subparser_cc(subparsers["cc"]) + subparsers["ccf"] = build_subparser(subparsers["ccf"]) + subparsers["ml"] = build_subparser_ml(subparsers["ml"]) + subparsers["hmm"] = build_subparser_hmm(subparsers["hmm"]) return parser diff --git a/gitk/universe/hmm.py b/gitk/universe/hmm_universe.py similarity index 99% rename from gitk/universe/hmm.py rename to gitk/universe/hmm_universe.py index edf7238f..b5af3526 100644 --- a/gitk/universe/hmm.py +++ b/gitk/universe/hmm_universe.py @@ -238,7 +238,7 @@ def run_hmm(start, core, end, chrom, normalize=True): return hmm_predictions, model -def run_hmm_save_bed( +def hmm_universe( coverage_folder, out_file, prefix="all", diff --git a/gitk/universe/universe_flexible.py b/gitk/universe/ml_universe.py similarity index 93% rename from gitk/universe/universe_flexible.py rename to gitk/universe/ml_universe.py index eca1df1c..050e92c0 100644 --- a/gitk/universe/universe_flexible.py +++ b/gitk/universe/ml_universe.py @@ -8,7 +8,7 @@ from numba import njit from gitk.utils import natural_chr_sort, timer_func, read_chromosome_from_bw -from gitk.universe.hmm import predictions_to_bed, find_full +from gitk.universe.hmm_universe import predictions_to_bed, find_full from gitk.likelihood.build_model import ModelLH @@ -93,17 +93,17 @@ def make_ml_flexible_universe(model_lh, cove_folder, cove_prefix, chrom, file_ou predictions_to_bed(path, chrom, file_out) -def main(folder_in, cove_folder, cove_prefix, file_out): +def ml_universe(model_file, cove_folder, cove_prefix, file_out): """ Make ML flexible universe - :param str folder_in: input name with likelihood models + :param str model_file: input name with likelihood models :param str file_out: output file with the universe :param str cove_folder: path to a folder with genome coverage by tracks :param str cove_prefix: prefix used in uniwig for creating coverage """ if os.path.isfile(file_out): raise Exception(f"File : {file_out} exists") - lh_model = ModelLH(folder_in) + lh_model = ModelLH(model_file) chroms = sorted(lh_model.chromosomes_list, key=cmp_to_key(natural_chr_sort)) for C in chroms: make_ml_flexible_universe(lh_model, cove_folder, cove_prefix, C, file_out) diff --git a/tests/consesnus/cli_test.sh b/tests/consesnus/cli_test.sh index 6ff1bde0..b5fdadee 100644 --- a/tests/consesnus/cli_test.sh +++ b/tests/consesnus/cli_test.sh @@ -10,35 +10,57 @@ gitk lh build_model --model-file tests/consesnus/results/lh_model.tar \ mkdir tests/consesnus/results/universe/ # make cut-off universe -gitk lh universe_hard --coverage-file tests/consesnus/coverage/all_core.bw \ - --fout tests/consesnus/results/cut_off.bed +gitk universe cc --coverage-folder tests/consesnus/coverage/ \ + --output-file tests/consesnus/results/universe/cc_universe.bed -gitk lh universe_hard --coverage_file tests/consesnus/coverage/all_core.bw \ - --cut_off 2 \ - --fout tests/consesnus/results/universe/cut_off_c2.bed +gitk universe ccf --coverage-folder tests/consesnus/coverage/ \ + --output-file tests/consesnus/results/universe/ccf_universe.bed -gitk lh universe_hard --coverage_file tests/consesnus/coverage/all_core.bw \ - --cut_off 1 \ - --merge 100 \ - --filter_size 300 \ - --fout tests/consesnus/results/universe/cut_off_c1_m100_f300.bed -# make ML flexible universe - -gitk lh universe_flexible --model-file tests/consesnus/results/lh_model.tar \ - --output-file tests/consesnus/results/ML_flexible.bed \ - --cov-folder tests/consesnus/coverage/ +gitk universe ml --model-file tests/consesnus/results/lh_model.tar \ + --output-file tests/consesnus/results/universe/ml_universe.bed\ + --coverage-folder tests/consesnus/coverage/ # make HMM universe -gitk hmm --out-file tests/consesnus/results/hmm_raw.bed --cov-folder tests/consesnus/coverage/ +gitk universe hmm --output-file tests/consesnus/results/universe/hmm_universe.bed --coverage-folder tests/consesnus/coverage/ -gitk hmm --out_file tests/consesnus/results/universe/hmm_norm.bed --cov_folder tests/consesnus/coverage/ --normlaize --save_max_cove # assessment gitk assess --raw-data-folder tests/consesnus/raw/\ --file-list tests/consesnus/file_list.txt \ - --universe tests/consesnus/results/ML_flexible.bed \ + --universe tests/consesnus/results/universe/cc_universe.bed\ + --save-to-file --folder-out tests/consesnus/results/ \ + --pref cc --no-workers 1 --save-each \ + --overlap \ + --distance \ + --distance-universe-to-file + + gitk assess --raw-data-folder tests/consesnus/raw/\ + --file-list tests/consesnus/file_list.txt \ + --universe tests/consesnus/results/universe/ccf_universe.bed \ + --save-to-file --folder-out tests/consesnus/results/ \ + --pref ccf --no-workers 1 --save-each \ + --overlap \ + --distance \ + --distance-universe-to-file\ + --distance-flexible\ + --distance-flexible-universe-to-file + + gitk assess --raw-data-folder tests/consesnus/raw/\ + --file-list tests/consesnus/file_list.txt \ + --universe tests/consesnus/results/universe/ml_universe.bed \ + --save-to-file --folder-out tests/consesnus/results/ \ + --pref ml --no-workers 1 --save-each \ + --overlap \ + --distance \ + --distance-universe-to-file\ + --distance-flexible\ + --distance-flexible-universe-to-file + + gitk assess --raw-data-folder tests/consesnus/raw/\ + --file-list tests/consesnus/file_list.txt \ + --universe tests/consesnus/results/universe/hmm_universe.bed \ --save-to-file --folder-out tests/consesnus/results/ \ - --pref test --no-workers 1 --save-each \ + --pref hmm --no-workers 1 --save-each \ --overlap \ --distance \ --distance-universe-to-file\ From 8cc2808ed4013457d9e17223b4f9f0a1c8ca0e3b Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 11:03:32 -0400 Subject: [PATCH 0210/1072] moved finding full, saving to utils --- gitk/likelihood/universe_cutoff_flexible.py | 156 -------------------- gitk/universe/hmm_universe.py | 138 +---------------- gitk/universe/ml_universe.py | 4 +- gitk/universe/utils.py | 142 ++++++++++++++++++ tests/consesnus/cli_test.sh | 8 +- 5 files changed, 149 insertions(+), 299 deletions(-) delete mode 100644 gitk/likelihood/universe_cutoff_flexible.py create mode 100644 gitk/universe/utils.py diff --git a/gitk/likelihood/universe_cutoff_flexible.py b/gitk/likelihood/universe_cutoff_flexible.py deleted file mode 100644 index edfd7fce..00000000 --- a/gitk/likelihood/universe_cutoff_flexible.py +++ /dev/null @@ -1,156 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import os -from functools import cmp_to_key - -import numpy as np -import pyBigWig - -from ..utils import natural_chr_sort, timer_func - - -def check_if_line_correct(line): - line = line.split("\t") - if not int(line[1]) < int(line[6]) < int(line[7]) < int(line[2]): - print(line) - raise Exception("wrong line format") - - -def ana_region(reg, start_s, starts, ends, track_val): - core_s, core_e = [], [] - core_pos = np.argwhere(reg == 2).flatten() - - if len(core_pos) == 0: - return "empty" - else: - core_s.append(core_pos[0] + start_s) - if core_pos[0] == 0: - core_s[0] += 10 - for i in range(1, len(core_pos)): - if core_pos[i] - core_pos[i - 1] >= 100: - core_e.append(core_pos[i - 1] + start_s) - min_point = np.argmin(track_val[core_pos[i - 1] : core_pos[i]]) - min_point = min_point + core_pos[i - 1] - ends.append(int(min_point) + start_s) - starts.append(ends[-1] + 1) - core_s.append(core_pos[i] + start_s) - core_e.append(core_pos[-1] + start_s) - if core_pos[-1] == len(reg) - 1: - core_e[-1] -= 10 - return core_s, core_e, starts, ends - - -def save_regions(inter_pos, chrom, bedname, track): - ind = np.argwhere(inter_pos != 0) - ind = ind.flatten() - start_s = ind[0] - to_file = [] - line = chrom + "\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n" - for i in range(1, len(ind)): - if ind[i] - ind[i - 1] != 1: - end_e = ind[i - 1] - region = inter_pos[start_s : end_e + 1] - res = ana_region(region, start_s, [start_s], [], track[start_s : end_e + 1]) - if res != "empty": - save_start_e, save_end_s, save_start_s, save_end_e = res - save_end_e = save_end_e + [end_e] - for a, b, c, d in zip( - save_start_e, save_end_s, save_start_s, save_end_e - ): - if a != b: - val = 0 - li = line.format( - int(c), - int(d) + 1, - "universe", - val, - ".", - int(a), - int(b), - "0,0,255", - ) - check_if_line_correct(li) - to_file.append(li) - start_s = ind[i] - end_e = ind[-1] - region = inter_pos[start_s : end_e + 1] - res = ana_region(region, start_s, [start_s], [], track[start_s : end_e + 1]) - if res != "empty": - save_start_e, save_end_s, save_start_s, save_end_e = res - save_end_e = save_end_e + [end_e] - for a, b, c, d in zip(save_start_e, save_end_s, save_start_s, save_end_e): - val = 0 - li = line.format( - int(c), - int(d) + 1, - "universe", - val, - ".", - int(a), - int(b), - "0,0,255", - ) - check_if_line_correct(li) - to_file.append(li) - with open(bedname, "w") as f: - f.writelines(to_file) - - -def get_uni(file, chrom, bedname): - """For each position check if coverage is bigger than cut-off; - if cut-off not provided calculate value that gives - maximum likelihood universe - :param str file: coverage file - :param str chrom: chromosome to analyse - :param int cutoff: base pairs with values grater of equal to cut-off can be included in universe - :return:""" - file = pyBigWig.open(file) - if pyBigWig.numpy: - track = file.values(chrom, 0, file.chroms(chrom), numpy=True) - else: - track = file.values(chrom, 0, file.chroms(chrom)) - track = np.array(track) - track[np.isnan(track)] = 0 - track = track.astype(np.uint16) - cutoff = np.sum(track) / len(track) - track_non_zero_sort = np.sort(track[track != 0]) - - cutoff = np.round(cutoff) - pos = np.where(track_non_zero_sort == cutoff)[0] - - f, l = pos[0] / len(track_non_zero_sort), pos[-1] / len(track_non_zero_sort) - q_cutoff = np.mean([f, l]) - lower = np.quantile(track_non_zero_sort, q_cutoff - 0.2) - upper = np.quantile(track_non_zero_sort, q_cutoff + 0.2) - inter_pos = np.zeros(len(track), dtype=np.uint8) - inter_pos[track >= lower] = 1 - inter_pos[track > upper] = 2 - inter_pos = inter_pos[:7110285] - save_regions(inter_pos, chrom, bedname, track) - - -def main(file, file_out, merge=0, filter_size=0, cutoff=None): - """ - Creat cut-off universe based on coverage track - :param str file: path to coverage file without extension - :param int merge: regions closer than this value will be merged into one - :param int filter_size: regions smaller than this value will not be reported - :param str file_out: output file - :param int cutoff: base pairs with coverage equal to or greater than this value will be included in the universe - """ - if os.path.isfile(file_out): - raise Exception(f"File : {file_out} exists") - bw_start = pyBigWig.open(file) - chroms = bw_start.chroms() - bw_start.close() - chroms_key = list(chroms.keys()) - chroms_key = sorted(chroms_key, key=cmp_to_key(natural_chr_sort)) - chroms = {i: chroms[i] for i in chroms_key} - for chrom in chroms: - if chroms[chrom] > 0: - inter_pos = get_uni(file, chrom, cutoff) - if merge == 0 and filter_size == 0: - save_simple(file_out, inter_pos, chrom) - else: - marge_filter(file_out, inter_pos, chrom, merge, filter_size) diff --git a/gitk/universe/hmm_universe.py b/gitk/universe/hmm_universe.py index b5af3526..82c11e4f 100644 --- a/gitk/universe/hmm_universe.py +++ b/gitk/universe/hmm_universe.py @@ -10,6 +10,7 @@ from ..utils import natural_chr_sort from .const import LAMBDAS, TRANSMAT from .models import PoissonModel +from .utils import predictions_to_bed, find_full _LOGGER = getLogger(PKG_NAME) @@ -82,143 +83,6 @@ def read_data(start, core, end, chrom, normalize=True): return chrom_size, seq -def find_full_full_pos(seq, gap_size=1000, area_size=500): - """Look for nonzero positions in coverage matrix, when most of the positions are zero - :param ndarray seq: vector with information about non-zero positions - :param int gap_size: size of minium gap between non-zero positions that are separated - :param int area_size: size of the area around non-zero positions to be included in the result - :return list: list of starts of non-zero regions and list of ends of non-zero regions - """ - size = len(seq) - seq = np.argwhere(seq >= 1).flatten() - starts, ends = [], [] - if seq[0] > gap_size: - starts.append(int(seq[0] - area_size)) - else: - starts.append(0) - for e in range(1, len(seq)): - if seq[e] - seq[e - 1] > gap_size: - ends.append(int(seq[e - 1] + area_size)) - starts.append(int(seq[e] - area_size)) - ends.append(min(int(seq[-1] + area_size), size)) - return starts, ends - - -def find_full_empty_pos(seq, gap_size=10000, area_size=1000): - """Look for nonzero positions in coverage matrix, when most of the positions are nonzero - :param ndarray seq: vector with information about non-zero positions - :param int gap_size: size of minium gap between non-zero positions that are separated - :param int area_size: size of the area around non-zero positions to be included in the result - :return list: list of starts of non-zero regions and list of ends of non-zero regions - """ - size = len(seq) - seq = np.argwhere(seq == 0).flatten() - starts, ends = [], [] - gap_len = 0 - gap_start = 0 - looking_for_first = True - for e in range(1, len(seq)): - if seq[e] - seq[e - 1] == 1: - gap_len += 1 - else: - if gap_len >= gap_size: - starts.append(gap_start) - ends.append(seq[e - 1]) - looking_for_first = False - elif looking_for_first: - starts.append(gap_start) - ends.append(seq[e - 1]) - looking_for_first = False - gap_len = 1 - gap_start = seq[e] - starts_res = [max(0, i - area_size) for i in ends] - end_res = [i + area_size for i in starts[1:]] + [size] - if not starts_res: - starts_res = [0] - return starts_res, end_res - - -def find_full(seq): - """Look for nonzero positions in coverage matrix""" - seq = np.sum(seq, axis=1, dtype=np.uint8) - full_pos_no = np.sum(seq >= 1) - if full_pos_no < len(seq) - full_pos_no: - return find_full_full_pos(seq) - else: - return find_full_empty_pos(seq) - - -def ana_region(region, start_s): - """Helper for saving HMM prediction into a file""" - start_e = start_s + np.where(region == 1)[0][0] - end_s = start_s + np.where(region == 2)[0][0] - return start_e, end_s - - -def predictions_to_bed(states, chrom, bedname, save_max_cove=False, cove_file=None): - """ - Save HMM prediction into a file - :param ndarray states: result of HMM prediction - :param str chrom: which chromosome is being analysed - :param str bedname: path to the output file - :param bool save_max_cove: whether to save the maximum peak coverage to output - file, can result in nonstandard bed file - :param str cove_file: file with core coverage, require for saving maximum peak coverage - """ - ind = np.argwhere(states != 3) - ind = ind.flatten() - start_s = ind[0] - to_file = [] - line = chrom + "\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n" - if save_max_cove: - coverage = pyBigWig.open(cove_file) - for i in range(1, len(ind)): - if ind[i] - ind[i - 1] != 1: - end_e = ind[i - 1] - region = states[start_s : end_e + 1] - res = ana_region(region, start_s) - save_start_e, save_end_s = res - val = 0 - if save_max_cove: - val = coverage.stats(chrom, int(start_s), int(end_e) + 1, type="max") - val = int(val[0]) - to_file.append( - line.format( - start_s, - end_e + 1, - "universe", - val, - ".", - save_start_e, - save_end_s, - "0,0,255", - ) - ) - start_s = ind[i] - if states[ind[-1]] == 2: - region = states[start_s : ind[-1] + 1] - res = ana_region(region, start_s) - save_start_e, save_end_s = res - val = 0 - if save_max_cove: - val = coverage.stats(chrom, int(start_s), int(ind[-1]) + 1, type="max") - val = int(val[0]) - to_file.append( - line.format( - start_s, - ind[-1] + 1, - "universe", - val, - ".", - save_start_e, - save_end_s, - "0,0,255", - ) - ) - with open(bedname, "a") as f: - f.writelines(to_file) - - def split_predict(seq, empty_starts, empty_ends, model): """Make model prediction only for regions containing nonzero positions""" diff --git a/gitk/universe/ml_universe.py b/gitk/universe/ml_universe.py index 050e92c0..2d466113 100644 --- a/gitk/universe/ml_universe.py +++ b/gitk/universe/ml_universe.py @@ -7,8 +7,8 @@ import numpy as np from numba import njit -from gitk.utils import natural_chr_sort, timer_func, read_chromosome_from_bw -from gitk.universe.hmm_universe import predictions_to_bed, find_full +from ..utils import natural_chr_sort, timer_func, read_chromosome_from_bw +from .utils import predictions_to_bed, find_full from gitk.likelihood.build_model import ModelLH diff --git a/gitk/universe/utils.py b/gitk/universe/utils.py new file mode 100644 index 00000000..f9eae805 --- /dev/null +++ b/gitk/universe/utils.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import numpy as np +import pyBigWig + + +def ana_region(region, start_s): + """Helper for saving HMM prediction into a file""" + start_e = start_s + np.where(region == 1)[0][0] + end_s = start_s + np.where(region == 2)[0][0] + return start_e, end_s + + +def predictions_to_bed(states, chrom, bedname, save_max_cove=False, cove_file=None): + """ + Save HMM prediction into a file + :param ndarray states: result of HMM prediction + :param str chrom: which chromosome is being analysed + :param str bedname: path to the output file + :param bool save_max_cove: whether to save the maximum peak coverage to output + file, can result in nonstandard bed file + :param str cove_file: file with core coverage, require for saving maximum peak coverage + """ + ind = np.argwhere(states != 3) + ind = ind.flatten() + start_s = ind[0] + to_file = [] + line = chrom + "\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n" + if save_max_cove: + coverage = pyBigWig.open(cove_file) + for i in range(1, len(ind)): + if ind[i] - ind[i - 1] != 1: + end_e = ind[i - 1] + region = states[start_s : end_e + 1] + res = ana_region(region, start_s) + save_start_e, save_end_s = res + val = 0 + if save_max_cove: + val = coverage.stats(chrom, int(start_s), int(end_e) + 1, type="max") + val = int(val[0]) + to_file.append( + line.format( + start_s, + end_e + 1, + "universe", + val, + ".", + save_start_e, + save_end_s, + "0,0,255", + ) + ) + start_s = ind[i] + if states[ind[-1]] == 2: + region = states[start_s : ind[-1] + 1] + res = ana_region(region, start_s) + save_start_e, save_end_s = res + val = 0 + if save_max_cove: + val = coverage.stats(chrom, int(start_s), int(ind[-1]) + 1, type="max") + val = int(val[0]) + to_file.append( + line.format( + start_s, + ind[-1] + 1, + "universe", + val, + ".", + save_start_e, + save_end_s, + "0,0,255", + ) + ) + with open(bedname, "a") as f: + f.writelines(to_file) + + +def find_full_full_pos(seq, gap_size=1000, area_size=500): + """Look for nonzero positions in coverage matrix, when most of the positions are zero + :param ndarray seq: vector with information about non-zero positions + :param int gap_size: size of minium gap between non-zero positions that are separated + :param int area_size: size of the area around non-zero positions to be included in the result + :return list: list of starts of non-zero regions and list of ends of non-zero regions + """ + size = len(seq) + seq = np.argwhere(seq >= 1).flatten() + starts, ends = [], [] + if seq[0] > gap_size: + starts.append(int(seq[0] - area_size)) + else: + starts.append(0) + for e in range(1, len(seq)): + if seq[e] - seq[e - 1] > gap_size: + ends.append(int(seq[e - 1] + area_size)) + starts.append(int(seq[e] - area_size)) + ends.append(min(int(seq[-1] + area_size), size)) + return starts, ends + + +def find_full_empty_pos(seq, gap_size=10000, area_size=1000): + """Look for nonzero positions in coverage matrix, when most of the positions are nonzero + :param ndarray seq: vector with information about non-zero positions + :param int gap_size: size of minium gap between non-zero positions that are separated + :param int area_size: size of the area around non-zero positions to be included in the result + :return list: list of starts of non-zero regions and list of ends of non-zero regions + """ + size = len(seq) + seq = np.argwhere(seq == 0).flatten() + starts, ends = [], [] + gap_len = 0 + gap_start = 0 + looking_for_first = True + for e in range(1, len(seq)): + if seq[e] - seq[e - 1] == 1: + gap_len += 1 + else: + if gap_len >= gap_size: + starts.append(gap_start) + ends.append(seq[e - 1]) + looking_for_first = False + elif looking_for_first: + starts.append(gap_start) + ends.append(seq[e - 1]) + looking_for_first = False + gap_len = 1 + gap_start = seq[e] + starts_res = [max(0, i - area_size) for i in ends] + end_res = [i + area_size for i in starts[1:]] + [size] + if not starts_res: + starts_res = [0] + return starts_res, end_res + + +def find_full(seq): + """Look for nonzero positions in coverage matrix""" + seq = np.sum(seq, axis=1, dtype=np.uint8) + full_pos_no = np.sum(seq >= 1) + if full_pos_no < len(seq) - full_pos_no: + return find_full_full_pos(seq) + else: + return find_full_empty_pos(seq) diff --git a/tests/consesnus/cli_test.sh b/tests/consesnus/cli_test.sh index b5fdadee..af0eb2b1 100644 --- a/tests/consesnus/cli_test.sh +++ b/tests/consesnus/cli_test.sh @@ -29,7 +29,7 @@ gitk universe hmm --output-file tests/consesnus/results/universe/hmm_universe.be --file-list tests/consesnus/file_list.txt \ --universe tests/consesnus/results/universe/cc_universe.bed\ --save-to-file --folder-out tests/consesnus/results/ \ - --pref cc --no-workers 1 --save-each \ + --pref cc --no-workers 1 \ --overlap \ --distance \ --distance-universe-to-file @@ -38,7 +38,7 @@ gitk universe hmm --output-file tests/consesnus/results/universe/hmm_universe.be --file-list tests/consesnus/file_list.txt \ --universe tests/consesnus/results/universe/ccf_universe.bed \ --save-to-file --folder-out tests/consesnus/results/ \ - --pref ccf --no-workers 1 --save-each \ + --pref ccf --no-workers 1 \ --overlap \ --distance \ --distance-universe-to-file\ @@ -49,7 +49,7 @@ gitk universe hmm --output-file tests/consesnus/results/universe/hmm_universe.be --file-list tests/consesnus/file_list.txt \ --universe tests/consesnus/results/universe/ml_universe.bed \ --save-to-file --folder-out tests/consesnus/results/ \ - --pref ml --no-workers 1 --save-each \ + --pref ml --no-workers 1 \ --overlap \ --distance \ --distance-universe-to-file\ @@ -60,7 +60,7 @@ gitk universe hmm --output-file tests/consesnus/results/universe/hmm_universe.be --file-list tests/consesnus/file_list.txt \ --universe tests/consesnus/results/universe/hmm_universe.bed \ --save-to-file --folder-out tests/consesnus/results/ \ - --pref hmm --no-workers 1 --save-each \ + --pref hmm --no-workers 1 \ --overlap \ --distance \ --distance-universe-to-file\ From 6d767a38ba00feafbbc2a23e0f708cb9ed1915d8 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 11:31:27 -0400 Subject: [PATCH 0211/1072] fix in ccf when cutoff eq 0 --- gitk/universe/ccf_universe.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gitk/universe/ccf_universe.py b/gitk/universe/ccf_universe.py index c883e427..b74d4bc3 100644 --- a/gitk/universe/ccf_universe.py +++ b/gitk/universe/ccf_universe.py @@ -98,6 +98,7 @@ def save_regions(inter_pos, chrom, bedname, track): def get_uni(file, chrom, bedname): + print(chrom) """For each position check if coverage is bigger than cut-off; if cut-off not provided calculate value that gives maximum likelihood universe @@ -116,7 +117,7 @@ def get_uni(file, chrom, bedname): cutoff = np.sum(track) / len(track) track_non_zero_sort = np.sort(track[track != 0]) - cutoff = np.round(cutoff) + cutoff = max([1, np.round(cutoff)]) pos = np.where(track_non_zero_sort == cutoff)[0] f, l = pos[0] / len(track_non_zero_sort), pos[-1] / len(track_non_zero_sort) From 8fbe9537909dc11a54eeb9a56f12a827575c444e Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 12:01:45 -0400 Subject: [PATCH 0212/1072] fix if cutoff not in track --- gitk/universe/ccf_universe.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/gitk/universe/ccf_universe.py b/gitk/universe/ccf_universe.py index b74d4bc3..2a7f8379 100644 --- a/gitk/universe/ccf_universe.py +++ b/gitk/universe/ccf_universe.py @@ -98,7 +98,6 @@ def save_regions(inter_pos, chrom, bedname, track): def get_uni(file, chrom, bedname): - print(chrom) """For each position check if coverage is bigger than cut-off; if cut-off not provided calculate value that gives maximum likelihood universe @@ -119,7 +118,11 @@ def get_uni(file, chrom, bedname): cutoff = max([1, np.round(cutoff)]) pos = np.where(track_non_zero_sort == cutoff)[0] - + if len(pos) == 0: + uniq_val = np.unique(track_non_zero_sort) + dist = np.absolute(uniq_val - cutoff) + cutoff = uniq_val[dist.argmin()] + pos = np.where(track_non_zero_sort == cutoff)[0] f, l = pos[0] / len(track_non_zero_sort), pos[-1] / len(track_non_zero_sort) q_cutoff = np.mean([f, l]) lower = np.quantile(track_non_zero_sort, max(0, q_cutoff - 0.2)) From 7fe5c89abf43cb3163f53af3eaac67b75fc70c07 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 12:25:01 -0400 Subject: [PATCH 0213/1072] add assess log --- gitk/assess/assess.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/gitk/assess/assess.py b/gitk/assess/assess.py index 3acc597b..81bee5c1 100644 --- a/gitk/assess/assess.py +++ b/gitk/assess/assess.py @@ -9,6 +9,10 @@ import os import numpy as np import warnings +from logging import getLogger +from ..const import PKG_NAME + +_LOGGER = getLogger(PKG_NAME) def run_all_assessment_methods( @@ -59,6 +63,7 @@ def run_all_assessment_methods( "universe&file", ] asses_results.append(r_overlap) + _LOGGER.info("DONE: Overlap") if distance_f_t_u: r_distance = run_distance( raw_data_folder, @@ -73,6 +78,7 @@ def run_all_assessment_methods( ) r_distance.columns = ["file", "median_dist_file_to_universe"] asses_results.append(r_distance) + _LOGGER.info("DONE: Distance file to universe") if distance_f_t_u_flex: r_distance_flex = run_distance( raw_data_folder, @@ -87,6 +93,7 @@ def run_all_assessment_methods( ) r_distance_flex.columns = ["file", "median_dist_file_to_universe_flex"] asses_results.append(r_distance_flex) + _LOGGER.info("DONE: Flexible distance file to universe") if distance_u_t_f: r_distance_utf = run_distance( @@ -102,6 +109,7 @@ def run_all_assessment_methods( ) r_distance_utf.columns = ["file", "median_dist_universe_to_file"] asses_results.append(r_distance_utf) + _LOGGER.info("DONE: Distance universe to file") if distance_u_t_f_flex: r_distance_utf_flex = run_distance( raw_data_folder, @@ -116,6 +124,8 @@ def run_all_assessment_methods( ) r_distance_utf_flex.columns = ["file", "median_dist_universe_to_file_flex"] asses_results.append(r_distance_utf_flex) + _LOGGER.info("DONE: Flexible distance universe to file") + df = asses_results[0] for i in asses_results[1:]: df = pd.merge(df, i, on="file") From 446cdca3f01233d5151f42216bb8820d18583b6b Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 14:54:20 -0400 Subject: [PATCH 0214/1072] fix ccf --- gitk/universe/ccf_universe.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/gitk/universe/ccf_universe.py b/gitk/universe/ccf_universe.py index 2a7f8379..3f592942 100644 --- a/gitk/universe/ccf_universe.py +++ b/gitk/universe/ccf_universe.py @@ -20,24 +20,25 @@ def check_if_line_correct(line): def ana_region(reg, start_s, starts, ends, track_val): core_s, core_e = [], [] core_pos = np.argwhere(reg == 2).flatten() - if len(core_pos) == 0: return "empty" else: core_s.append(core_pos[0] + start_s) if core_pos[0] == 0: - core_s[0] += 10 + core_s[0] += 1 for i in range(1, len(core_pos)): - if core_pos[i] - core_pos[i - 1] >= 100: + if core_pos[i] - core_pos[i - 1] >= 50: core_e.append(core_pos[i - 1] + start_s) min_point = np.argmin(track_val[core_pos[i - 1] : core_pos[i]]) min_point = min_point + core_pos[i - 1] ends.append(int(min_point) + start_s) starts.append(ends[-1] + 1) core_s.append(core_pos[i] + start_s) + if core_s[-1] == starts[-1]: + core_s[-1] += 1 core_e.append(core_pos[-1] + start_s) if core_pos[-1] == len(reg) - 1: - core_e[-1] -= 10 + core_e[-1] -= 1 return core_s, core_e, starts, ends @@ -151,6 +152,7 @@ def ccf_universe(cove, cove_prefix, file_out): chroms_key = list(chroms.keys()) chroms_key = sorted(chroms_key, key=cmp_to_key(natural_chr_sort)) chroms = {i: chroms[i] for i in chroms_key} + chroms = {"chr5": chroms["chr5"]} for chrom in chroms: if chroms[chrom] > 0: get_uni(file, chrom, file_out) From 2cb68dc67cbe6cdc17a9b77f7f28b49b52c5a5b7 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 14 Jun 2023 16:37:13 -0400 Subject: [PATCH 0215/1072] add functions for visualizing projection --- .gitignore | 4 +- examples/scembed/projection2.ipynb | 18 ++--- gitk/scembed/const.py | 4 + gitk/scembed/projection.py | 119 ++++++++++++++++++++++++++++- gitk/scembed/scembed.py | 105 ++++++++++++++++++++++++- gitk/scembed/utils.py | 79 ++++++++++++------- 6 files changed, 289 insertions(+), 40 deletions(-) diff --git a/.gitignore b/.gitignore index a018973c..98441d0c 100644 --- a/.gitignore +++ b/.gitignore @@ -177,4 +177,6 @@ tests/data/buenrostro_metadata.tsv tests/integration/buenrostro2018.model # examples -examples/scembed/pbmc/ \ No newline at end of file +examples/scembed/pbmc/ +examples/scembed/buenrostro +examples/scembed/atlas \ No newline at end of file diff --git a/examples/scembed/projection2.ipynb b/examples/scembed/projection2.ipynb index b3b4be6c..57e87119 100644 --- a/examples/scembed/projection2.ipynb +++ b/examples/scembed/projection2.ipynb @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -164,7 +164,7 @@ "974599" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -182,9 +182,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|â–â–â–â–â–â–â–â–â–â–| 191125/191125 [00:07<00:00, 25967.15it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 9221/9221 [02:20<00:00, 65.48it/s] \n", - "100%|â–â–â–â–â–â–â–â–â–â–| 9221/9221 [01:20<00:00, 113.88it/s]\n" + "100%|â–â–â–â–â–â–â–â–â–â–| 191125/191125 [00:07<00:00, 24548.46it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 9221/9221 [02:25<00:00, 63.58it/s] \n", + "100%|â–â–â–â–â–â–â–â–â–â–| 9221/9221 [01:32<00:00, 100.14it/s]\n" ] } ], @@ -281,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -290,13 +290,13 @@ "Text(0, 0.5, 'UMAP 2')" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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G//vf/7apj9ZyNqirTc1fv21TgfDJgXVLt0GDBmm7d+9u9VhrgzpN0+yz3r6+vg7PV1RU2PdUdurUyeH5ll73jBkznP5ZEkKIi41kvxRCOOwlcnFxceqck8sNVFRUOH29JUuWUFJS4vBcbYmE061+9spZs2a12N6ZNvX3sF1//fVOZd+rny10zZo1zbZ1c3NjypQpzbapv++xqf1cK1eutN93Zq/gLbfcYv8+ZGRk2PfuNWXKlCnNlp1wd3cnISHB/njmzJnN9hcfH4+Hhweg9kGe/DPirGXLltnvX3rppXTr1q1N/URHR9vvL1682GE/19nyt7/9jbvvvtt+u+OOO5g6dSpRUVG88sor9nYhISEOj0/Fpk2bGDp0KE8//bR9r21r3XTTTQAUFRXxzTff2J//5ptv7HsAa9s4q/7Pg5eXV5vGJYQQFypJlCKEwM3NzX7f2ZTsVVVVDo/d3d2dOi87O5v777+/wfPz589n9OjR+Pj4ONWPMzRNY9euXfbHgwcPbvGc+Ph4goKCyM3NbbLN+vXr7fdXrlxJcnKyU2Op1Via//o6d+6MyWRqtk1gYKD9fnFxcYPjaWlpZGdn2x8PGzasxTEGBweTmJjIgQMHAJWIpUuXLk2279GjR4t9+vv72+93797dqfa1wVNxcTHe3t4tnnOyDRs22O+PGTOm1efXmjx5Mp6enpSVldnfi7lz53L55ZfTt29fDAZDm/t2ljP13vr378+HH37oVEKTlStXOpTMAPWzWVRUxP79+/n000959dVXKS0t5fHHH2f//v18+OGHrR73TTfdxJNPPommaSxevJhrr73W4fXodLpWB3X1fxZKS0tbPSYhhLiQSVAnhHD4q7ezM24nt3P2L+d33303eXl5gPqQn5eXR2ZmJqmpqTz66KO8+uqrTo66ZUVFRQ5Bav2Zl+ZERUU1G9Slp6fb7//444+tHldLtdicyWBYP+izWCwNjufk5Njvu7u7O10HLDY21h7UNfceODvO+hksW9u+tXX4amVlZdnvx8fHt6kPUIHz22+/bc+gmZKSwpNPPsmTTz6Jl5cXgwcPZvTo0UyZMoU+ffq0+TqtYTAY8PHxISoqioEDBzJz5kwmTpzYplpttXQ6HX5+fgwdOpShQ4dyxRVXMHHiRKxWKx999BHjx49nzpw5reozLi6OESNGsHr1an7++Wf79+Tnn38GYOTIkcTGxraqz4CAAPv9+hk/hRBCSPFxIQSOsz71PxA3JzMz0+Fx/Q9cTfn666/5/PPPAfXhdOHChSxYsMB+/PXXX29xaWJrnPzX/NqlfS1pKUAtKipq85hAFcNuzql8QK9V/7W3pkRE/bYtLX9s7ThPx+tyxulcpnfdddexadMmZsyY4RBIl5aWsnz5cp544gn69u3LgAEDWL169SldqzHHjh1DU/vf0TQNi8VCfn4+u3bt4p133mHSpEmn/X0dN26cfWYN4Pnnn29TP7WBoMVi4aOPPuKjjz6y/wGitUEi4BAEnjhxQmbrhBCiHgnqhBB07tzZfj87O7vRel0nO3HihP1+QEBAizNBhYWFzJ8/3/74vvvus880TJs2DVDLwObNm9dgaWdbnfyB3tk9UWVlZc0erx/4LFmyxOFDt7O3M63+a2/p9dRXv21blj6eD073Mr0+ffqwZMkSsrOz+eabb3j44YcZOnSoQ5C3detWxowZY/+jRXs3YcIE+/29e/c2+COOM66++mr7suzFixfz3nvvAWrm+Oqrr251fyNGjLDft9lsbNmypdV9CCHEhUqCOiEEnTt3tifI0DSNHTt2tHjOtm3b7Pe7du3aYvsHHniAjIwMQBW//vvf/24/9uqrr9qX5h04cICnn366NcNvkq+vr8MH79TUVKfOa6ld/eLhbfmwezbUD7IrKipaXEpZq37SlaCgoNM9rLOi/vfn2LFjp61fPz8/pk6dyr/+9S/WrVtHbm4uCxcupEOHDoCagZ0/f36rkgadr8LDwx0e1/8jjrN8fHzsf7DZsWMHO3fuBGD69Olt+oPBkCFDHGbbP/roo1b3IYQQFyoJ6oQQuLm5MWTIEPvj+hkjm7Jq1Sr7/fqZHRvzyy+/sHDhQvvjN9980+HDWUREBP/617/sj5977jl2797tzNCbpdPp6NWrl/3xxo0bWzzn+PHjDvvRGlM/4cratWvbPsAzKDIykpCQEPvjdevWtXhObm4uSUlJ9sf9+vU7I2M70+r/LK9YseKMXcfHx4ebb76ZFStW2LOA5ubmOiTSaa9OntVua3ba2bNnO/WcM9zc3Lj55pvtjz/++OPz9o8qQghxtklQJ4QA1F/Pay1atKjZtikpKSxfvrzRc09WVlbGbbfdZn986623NhoEzps3j1GjRgEqQcatt97a5nTq9dXP9OdMFr8PPvigxTZXXHGF/f6SJUuc3od4ttXP/NjS97S2Te17HhER4bAstz2ZNGmS/f7y5cvZv3//Gb1ex44dHTJ7nq8/D61RfyYe1B8J2mL8+PGEhYXZH4eHh3PZZZe1eVwPPvigPZlOaWmpw++W1qhN2CKEEBcKCeqEEIBKXFC7V+zgwYO8/fbbTbb905/+ZE/2MXTo0GZndP785z/bl/RFRETw73//u9F2Op2Ot956y15eYdOmTfzvf/9ry0txUL8+25o1a5rd85SSktLk+OobNGiQPVisqKjgpptucroURHV1dYvZL0+X22+/3X7/q6++4qeffmqybXJyMs8884zDuWcrscnpNmjQIIYPHw6o5cSzZ89u0946Z5esWq1W+9JiwGGGtD0qLi7mnXfesT/u2rVrg+WYzjIYDKxevZrNmzezefNmfv/991MqBREfH88//vEP++PvvvuOW265pdEMsI0pKyvjnnvucfi3IYQQFwIJ6oQQgPog+sADD9gf33vvvXz22WcObcxmM48++igff/yx/bl//vOfTfa5bt06h4LIr7zySrNp7RMTE/nb3/5mf/zXv/7VqRpwzenWrRs33HCD/fGcOXMcxl9r586djBs3jqKiomYLatdasGCBPRnJL7/8wqhRo5pd3pmUlMTf//53YmNjz9qSzTFjxjjMWs2cObPRoHbr1q2MGzfOniY+Ojqae++996yM8Uz53//+Z/8+btmypdnvT2ZmJv/+978bZHl8+OGHGTVqFIsXL24yhX5eXh7z5s2zB3U+Pj5O1QQ8Xx06dIjJkyeTlpZmf67+74W2SEhIYMCAAQwYMMChGH1bPfTQQw6F7BctWkTfvn35+uuvm/zjSnp6Oi+++CLx8fG8/PLLZyVZkRBCnE1Sp06Idmry5MkO9dLAMWnHli1bGq2dtXTpUiIiIhrt8/HHH2ft2rWsWLGCiooKrr32Wp5++mn69etHZWUlv//+u8OMxFNPPcXo0aMb7auqqoq5c+fal/NdffXVzS7TrPXQQw/x6aefsmPHDsrKyrj99ttZtmxZi+c156WXXmLDhg0cPXqUiooKbrjhBp544gmGDBmCi4sLBw4cYP369WiaxsyZM8nJybHvGWxqL1GPHj34+OOPufbaaykvL2fjxo0MGTKEjh070q9fPwICAqisrCQ7O5tdu3Y5fEg+mxYuXMjw4cM5cuQIpaWlXHPNNXTq1InBgwfj4uLCvn372Lhxo/1DrqenJx9//DF+fn7nZLynS79+/XjnnXe4+eabsVgsbN++nSFDhtC5c2f69u2Lr68vRUVF7Nu3jz179mCz2bjvvvsc+tA0jdWrV7N69WoMBgNdunSha9eu+Pv7U1FRQVpaGmvXrnUIJP7973/bMz6ej1566SW++OILh+c0TaOkpIR9+/axbds2h4DnyiuvdJjtPh/odDo++eQTHnjgAfts/p49e5gxYwaenp4MHjyY8PBwPD09yc7O5tixY+zatcvhdbXXzK5CCNEkTQjRLsXExGhAq2/Hjh1rtt/CwkLtmmuuabYPk8mkPfPMM8328+c//9nePiAgQMvKynL6tW3ZskUzGAz28xcvXuz0uU1JTk7W+vTp0+zrmjZtmlZcXKwNGzbM/tz27dub7XfHjh1a//79nX7/Y2NjG+1z5cqV9jajR4926jXV77c5mZmZ2tixY1scW0JCgrZp06Zm+5ozZ469/cKFC1sc4+jRo+3tV65c2WL7+j/XTf2sOtOm1vLly7W4uDinvjePPfaYw7l33323099Xb29v7c0332zx9bXk2LFjrfr36oy2/J4wGo3aI488olVXVzs91oqKijaPsaKiok2v+4cfftD69u3r9Ovy9/fXHnvsMa2oqKjNYxVCiPORzNQJIRz4+vry6aefMm/ePN577z3Wr19PRkYGJpOJ6OhoJkyYwNy5c5stY7B9+3aHpWz/+c9/WrXPqH///jzwwAP2Pu6//34mTpzYYi285nTo0IHNmzezcOFCPv74Y/bs2UNRURFhYWH07t2bm2++mRkzZqDT6cjPz7ef19KMVe/evdmyZQs///wzX3/9NWvXriU9PZ3CwkJcXV0JDg6mc+fODB48mAkTJjB06NCzvlctNDSU5cuXs2zZMj799FPWrFlDZmYmZrOZkJAQ+vbty/Tp05k1a5ZDCYgLwdixYzl48CCffPIJ33//PVu2bCE7O5uqqip8fX1JSEhg6NChzJgxg5EjRzqcu2DBAubPn8+vv/7Khg0b2Lt3LydOnKCkpASj0UhgYCDdu3dn/Pjx3HTTTe16L52npyf+/v50796dUaNGMXv2bKKios71sFo0efJkJk2axOrVq/npp59YtWoVaWlp5ObmUl1djZ+fH9HR0QwcOJBLL72UKVOmOLW8Wggh2hudpsnCciGEqFVeXo6vry8WiwVPT0+Ki4vbnM5dCCGEEOJskE8qQghRz5IlS+yZ9Pr16ycBnRBCCCHOe/JpRQghahQUFPDXv/7V/rh+1kwhhBBCiPOVBHVCiIvCtddeyxdffEFlZWWjx9euXcvw4cPtJRQiIyO58cYbz+YQhRBCCCHaRPbUCSEuCrGxsSQnJ+Pl5UXfvn2Ji4vD3d2dgoICtm3bxuHDh+1tTSYTP/zwA5dddtk5HLEQQgghhHMkqBNCXBRqg7qWhIeHs3jxYsaNG3cWRiWEEEIIceokqBNCXBSOHTvGV199xerVqzly5Ai5ubnk5eVhMpkICgqib9++TJw4kdmzZ5/XxaOFEEIIIU4mQZ0QQgghhBBCtGOSKEUIIYQQQggh2jEJ6oQQQgghhBCiHZOgTgghhBBCCCHaMQnqhBBCCCGEEKIdk6BOCCGEEEIIIdoxCeqEEEIIIYQQoh0znusBXKwqKyvZvXs3AMHBwRiN8q0QQgghhDifWCwWcnJyAOjZsydubm7neERCNE4iiXNk9+7dDBo06FwPQwghhBBCOGHTpk0MHDjwnF3fZrNRWlpKcXEx1dXVWK3WczYW0XoGgwEPDw/8/PzOyB8HJKgTQgghhBDiPFZSUkJaWhqapp3roYg2slgsVFVVUVBQgK+vL+Hh4eh0utPWvwR150hwcLD9/qZNmwgPDz+HoxFCCCGEECfLyMiwr6yq/9ntbGosoNPpdBgMhnMyHtE2FovFfr+oqAgXFxeCgoJOW/8S1J0j9ffQhYeHExUVdQ5HI4QQQgghmnMu8h/YbDaHgM7Ly4uAgAA8PDxO6yyPOPOsViuFhYVkZ2cDkJOTg4+PDy4uLqelf8l+KYQQQgghxHmotLTUIaCLiorC09NTArp2yGAwEBgYSGBgoP250tLS09a/BHVCCCGEEEKch4qLi+33AwICJJi7APj4+Njvl5WVnbZ+JagTQgghhBDiPFRdXQ2oPXQeHh7neDTidHB1dbUH57Xf39NBgjohhBBCCCHOQ7VlCwwGg8zSXSDqJ7mx2WynrV8J6oQQQgghhBCiHZOgTgghhBBCCCHaMQnqhBBCCCGEEKIdk6BOCCGEEEIIIdoxCeqEEEIIIYQQoh2ToE4IIYQQQggh2jEJ6oQQQgghhBDiJMnJyTz44IN06dIFT09PAgICGDhwIM8//zzl5eXnengOjOd6AEIIIYQQQghxPvnuu++YNWsWxcXF9ufKy8vZsmULW7Zs4e233+aHH34gISHhHI6yjszUCSGEEEIIIUSN7du3c+2111JcXIyXlxfPPPMM69atY/ny5cybNw+ApKQkLr/8ckpKSs7xaBWZqRNCCCGEEEI4raTSTGZRJWXVVjxdDIT5uuHtZjrXwzpt7rvvPioqKjAajfz8888MHTrUfmzs2LF06tSJRx55hKSkJF544QWefPLJczfYGhLUCSGEEEIIIZqlaRrrj+bx/vpkft6XhdWm2Y8Z9DomdA9l1pAYhsYHotPpzuFIT82mTZtYvXo1AHPnznUI6Go9+OCDLFy4kP379/PSSy/x2GOPYTKd26BWll8KIYQQQgghmrQnrYgJ//2dG97ayI97Mh0COgCrTWPp7kxueGsjE/77O3vSis7RSE/d119/bb9/yy23NNpGr9cze/ZsAAoLC1m5cuXZGFqzJKgTQgghhBBCNGr1oRyueWM9SVmlTrVPyirlmjfWs/pQzhke2ZmxZs0aADw9Penfv3+T7UaPHm2/v3bt2jM+rpZIUCeEEEIIIYRoYE9aEbe/v5XyamurziuvtnL7+1vb5Yzd/v37AUhISMBobHqnWpcuXRqccy5JUCeEEEIIIYRwoGkaD3y2o9UBXa3yaisPfrYTTdNabnyeqKysJDc3F4CoqKhm2/r7++Pp6QlASkrKGR9bSySoE0IIIYQQQjhYfzTP6SWXTTmYVcKGo/mnaURnXv3yBF5eXi22rw3qSktP7X06HSSoE0IIIYQQQjj4YEPyedXP2VBZWWm/7+Li0mJ7V1dXACoqKs7YmJwlQZ0QQgghhBDCrqTSzE97s05LX8v2ZlJSaT4tfZ1pbm5u9vvV1dUttq+qqgLA3d39jI3JWRLUCSGEEEIIIewyiyoblC1oK6tNI6u4suWG5wFvb2/7fWeWVJaVlQHOLdU80ySoE0IIIYQQQtiVtTE5SlNKq05vf2eKm5sbgYGBAKSmpjbbtqCgwB7URUdHn/GxtUSCOiGEYrPCjsWw5jkobD/r34UQQghxenm6GE5rf16up7e/M6lbt24AHD58GIvF0mS7AwcO2O937dr1jI+rJRLUCSGUygLI3Q+VhZC161yPRgghhBDnSJivGwa97rT0ZdTrCPVxa7nheWLEiBGAWlq5devWJtutWrXKfn/48OFnfFwtkaBOiPagMAXyjpzZa7gHQtQQ8I2BqMFn9lpCCCGEOG95u5mY0D30tPQ1oXsY3m6m09LX2TB9+nT7/YULFzbaxmazsXjxYgD8/PwYM2bM2RhasySoE+J8l5MELw+AlwfC8bVn7jo6HXSZBgPvAM+QM3cdIYQQQpz3Zg2JOa/6OVsGDRrEyJEjAXjnnXdYv359gzYvvPAC+/fvB+C+++7DZDr3QasEdUKc70ozwVIJmhWKmt+0K4QQQghxOgyNDyQx9NSyOnYO9WZIfMBpGtHZ89JLL+Hu7o7FYmH8+PH885//ZMOGDaxcuZLbb7+dRx55BIDExEQefPDBczxaxXiuByCEaEHcKJj2ClSXQc+rz/VohBBCCHER0Ol0vHhNH655Yz3lbciG6eFi4IVreqPTnZ69eWdT3759+fTTT5k1axbFxcX85S9/adAmMTGRH374waEMwrkkM3VCtAd9Z8Hg20Ev/2SFEEIIcXb0iPTljZv649HKbJgeLgbeuKk/PSJ9z9DIzrwpU6awa9cu7r//fhITE/Hw8MDPz48BAwbw3HPPsX37dhISEs71MO1kpk4I0YCmaexOLqWsykKfWB/c21EqYiGEEEKcPiM7BfPZ7UN54LMdJGW1XJC7c6g3L1zTu10HdLViYmJ48cUXefHFF8/1UFokQZ0QooGicgtHMssBOOZRQbfoU1tTL4QQQoj2q0ekLz/9cRQbjubz/obj/LQ3C6tNsx836nVM6B7GrCExDIkPaJdLLts7CeqEEA14uRnx9zRSWmUl1M/lXA9HCCGEEOeYTqdjaMdAhnYMpKTSTFZxJaVVVrxcDYT6uLWrsgUXIgnqhBANGA06LukZeK6HIYQQQojzkLebSYK484xkXRBCCCGEEEKIduyiDep0Op1Tt0suueRcD1UIIYQQQgghmnTRBnVCnPc0G2hay+2EEEIIIcRF7aLfU3fnnXcyf/78Jo97enqexdEIUaM0C7a+CQYXGDgfXM+PwpZCCCGEEOL8c9EHdSEhIfTo0eNcD0MIR4XHwVyubiXp4Nr5XI9ICCGEEEKcpy76oE6I81JYbyg6AUZXCEhosllKbgX7U8uIDXEnMUJmlYUQQgghLkayp06IllgtcGIDVJW0/lxLFZxYA0Upzp9js0LOQUicAp2ngt7QZNMjGeWUVVpJSi9r/diEEEIIIcQFQYI6IVry/R/h3QmweHrrzz30IyT9ANveBqvZ+eu9MQo+vrbFph3DPfB0M5AYLrN0QgghhBAXq4s+qPv888/p1q0bHh4eeHt706lTJ+bMmcPKlSvP9dDE+aIks+ZrRuvPdakJtozuoHPyn1veUcevzYgOcmd8nyASIxsGdbkVuVhsFmdH6sBm0yivsrbpXCGEEEIIcXZd9Hvq9u3b5/D48OHDHD58mMWLFzN9+nQWLVqEr69vq/tNTU1t9nhGRhsCBHFuTF0AOz+GxAlOn1JltnE4o4wAvxGE908Az+Bml1E6mPYybP8Auk5xePpo4VHWZ6znivgr8HVt/mfy7d1v89K2lxgUNoh3Jrzj9LhrrT1QQG6xmS6RnnSN9mr1+UIIIYQQ4uy5aIM6Dw8Ppk6dyqWXXkqXLl3w8vIiJyeHVatW8frrr5OXl8fXX3/NtGnT+OWXXzCZTK3qPzo6+gyNXJx1PuEw8oFWnbI/tZRjWRXogMkDYnAxtmJSPCAOLn1c3S/PV8lSXDy59edbyanIYWvWVl685MVmu9iRvQOAbdnb+Cb9Yy4LmYKH0fngrLBUzfBlFlYRFeSGt/tF+6tCCCGEEOK8d9F+UktLS8PPz6/B85dddhn33HMPkyZNYvv27axatYrXXnuNe++99+wPUpwXbJqN1Xm/UGQuYHTQBHxN/i2e41MTBHm4GjDodW27cPI6WDwN3HzhjjV4u3iTU5GDj4tPi6c+NOAh3EwuGD0sZFancrziCN28ezt96UGJvhxILSW/1MLK3flM6BuEq8mJwPTISvhsDkT0gVlfgqF1fwwRQgghhBCtd9EGdY0FdLVCQ0P54osv6NKlC2azmQULFrQ6qEtJaT7bYUZGBoMGDWpVn+LcyK/O4UDpLgAOlu5hkP/IFs+JD/Mg2NcFNxd924O6jJ1grYayHCg4zqKJi9iXt49BJn+oLgcXjyZPjfWN5Znh/2BZ9ldYNDMd3ONbdelQP1cKyyzkl5aiaZrzJx74AaqK4NgqtQfRr0OrrqtpGv/Y+A+OFh3lb0P/Rgef1p0vhBBCCHExumiDupbEx8dz2WWXsXTpUg4fPkx6ejoRERFOnx8VFXUGRyfOJj9TIFFuMRRZConzSHT6vFNesthvDhSngWcIdBiCPzD88DpY+TSE9oQ71zR7uqvBjWnh17f58okRHni46vFyMzo3Swcw5E7IPwoRfVsd0AEcKTzCJwc/AeCLQ1/wQP+Wl72WWUpx1bth1MuvMyGEEEJcnORTUDO6devG0qVLAbVcszVBnbhwGPVGLg+75sxepLocqorBO6zuORcPGP+0Y7uc/epr3iFVP8/Qwj/hbYthx0cw8iHoNK5VQ9LpdEQHubfqHAI7wk1L1P2jq+DQzzD4DvBTe0yzi6rYcriYQG8Tgzr5otM5zmJ28OnAkPAhHC08yrgOLY/3QMluVuUtw88UwMyIORh08itNCCGEEBcf+QTUjJM/cApxRlSVwGvDoCgV+lwLg26D8H6Ntx3/DPhGQfyYlgO63MOw9GGwVMKqZ50P6rL3qiWfHYaBwaV1r6WWpsHH14O5DAqOw3UfApCaW0mV2UZ6fhVVZhtuLo4ZQV0MLrw1/i2nL5NTrcpNFJrzqbZV497SeyKEEEIIcQGST0DNqF/uQGbpxBlTngeFJ9T9nANw5NemgzqfcLjs/5zr97t7VUCHDnq1XMgcgIp82PUhoIFmgfjWze7Z6XQQ0gXStkJIV/vTHcM8KK20EuBlahDQtcUAv2Ho0RPiGo67oek9hkIIIYQQFzIJ6ppw7NgxfvnlFwA6duxIZGTkOR6RuGD5x8K0V+DwL+AfDZEDnTuv4CiUZoHFDPnHoM8NjtkmgztD8lroOBYGzXOuT6MbmDzUDJt7QKtfioObl0JRCgR1sj/l62liVPdT7Lced4MnwwMvPW39CSGEEOLilp2dzaZNm9i0aRObN29m8+bN5OXlATBnzhwWLVp0bgfYhIsyqPvuu++YNGkSRmPjLz8rK4urrrqK6upqAObPn382hyfai+NrYPfnMPBWCOt5an31naVujcgvMXMip4KYEHf8vWqCtqpi2PYuVBbBlg9Bs0JZNox6uO7EyS/AoNshoBWZL00eMPSPUF0GXqHquaJUWP5/ENkfBt/udFc2oyvHLZG45lcSGeDWsEFZDhz/DYK6qMQvQgghhBDnWGho6LkeQptclEHdPffcg9ls5qqrrmLo0KHExsbi7u5Obm4uv/32G2+88Qa5ubkAjBgxgrvuuuscj1icl76cByXpmDN2Yrrtt7rnq0vVvrTARHD3p6jczPajJfh5Gukd693qvZpbjxRRWmklr9TMpb0C1ZN6k9rvpjeo4uTmcnDzczxRr1dLIFvLxUvdaq35D+z6VN26TgEf55YiH8+qYOfxEgDG9jTg63lSzbrDyyBnH2TuhJDuoGtFgXYhhBBCiDOsQ4cOdOnShZ9//vlcD6VFF2VQB5Cens6CBQtYsGBBk22uuuoq3n77bVxdXc/iyER7URjcCb+SdD4vO8aQwqPE+9XMiO39HPKSwCMYhj3A8awKCkrNFJSa6RTugadb6/7Z+XuZKK204u9Z7zyTOwy5FyoLYfB9ajZNr8H+JdBxArh4qnaaBqtfgPJ8GPvXZmvbNSluNGxdBKHdwSPI6dPcXFSQZtCDydhIwOYfp4I6v9jmA7r0rZC7X70uz+DWjV0IIYQQp53ZYqOi2obFpmHU63B30Tf+f3079MQTTzBw4EAGDhxIaGgox48fJy4u7lwPq0UXZVD33nvvsWrVKtavX8/Ro0fJzc2luLgYLy8voqOjGTZsGHPmzGHo0KHneqjibEpeBymbYMAt4ObbYvM1w2/lv+ZDZBmNvFGWWRfU1WaMNKqvUUFuZBZW4edpwsPVyeQgWbvVDFxgIv07+tAt2ovcqnRmfjsXfzd/XhrzEh5ufnWzcy4esOG/bKjM5c/b/02vsIH8Z8x/0CevgxV/V238Y1q1fNLOOwgufw4SJthfk13GLvjpLxA3CkY/4nAoIsCNsT0NmIz6xl93hxEQ3l+9zpOl71AFzLteAe9fCVWlMPxuuPSp1o9fCCGEEKdM0zRyi80czSonI78Krd4xHRAe4Ep8qAdBPqZ2nUH+qafa52eNizKoGz16NKNHjz7XwxDnk6pSeH+GyhZZmAxX/KfFUybFXU5+ZSFuRjeGRQ6rO9D9apW90lcV3w70dmFCXydmmKxmOPQDVBSomT6AgfPR+Ubj4Wpg5eEVHCw4CMDu3N0MDh9cd66bL7gHsqxwN7nmElakrCC3IpeQgI6qeHlVsSoI3lo2Kxz8HtDU/rfeNzkeX/8yHF+tboPmgbu/w+EGSy5PZmqkDp6mweKpar/g0VVQUaieL8tr/fibU5qpCqWH9218HEIIIYQAoLDMzJbDRZRUWBs9rgHp+VWk51fh7W5gQIIvfi19BhCn1UUZ1AnRgMGFTb7BfKkv51oXA00UFHA8RW9gdvfZdU9omipJ4B8HwV2bPrEpWbsgdWPdY53eIZvlhNgJ/Jr8K/5u/vQO7u14rtENht7P9XkTObbleXoH9ybEI0Qd++MusFbXzT5m74GkpWrZY9fpzdei0xvUfrec/RDSo+Hx7jMgaRnEjmy4p6+tdDoViFYWqZp8Jg+wVECniaen/1pb31ZZPotOQM/rTm/fQgghxAUiu7CKDUlFWG1ay42Bkgorv+8tYEiiLyF+soXpbJGgTggAowv/FxFNcmkqSRVH+Kotffz8VzVzFT0E5v7U+vN9o9WMkc4Anaeq/WsmD0jbDEFdCPMM4/3J7zd9vt5A5+AevDfpPUjbBCseh9De0H2m40xUynqoLIDMAqgugX5zmx9Xrxsbfz59h8qQ+eiJVr/UFt36C2TvV+/FtvfUc0Upp/caJjcV1MksnRBCCNGowjJzqwK6WlabxoakIkZ195cZu7NEgjohagyKGEpy0ueOyxpPtv97tddr+B/B96Tahbk1SyZzD7ZtAJ4hMPIxdV9fswdt86sqmPGLhQGN7IerLlNfaxOj1MraDTYLZO1UQV190cPU7JTNojJ1tkXKJnhnvLp/y1KIGdZ8+8bYrHWv82Tu/nV9TnxW1ePrN6dtY23KgDugJAP8W1HyQQghhLhIaJrGlsOtD+hqWW0aWw8XM7ZXQLveY9deSFAnRI0nhj7Bff3uw9e1iSQp5gr4fI4KhioK4aq3HI9f/gJsfgcST2GZ4MlBjq7mn6i+kX+qpVkq6NM0GHgneIfXHYuvKcjdWP23kO4w9AGKU9bxVuZaIg58zPVdrm/dOKtLoXaLdFUbAsNPb4ID36v3bMAfmm875M7W9+8MFy8I7NRyOyGEEOIilFtsbnIPnbOKKyzkFpsJ9m1mq4c4LSSoE6KeJgM6AIOrSuufsRMiG9l159cBLjvNGZN63wQFR8C/o3pcXbNcUKeH8ly1Vw6gPMcxqPOLbX5Zpbs//8vfzm/pewjLq2RM1FjCvFpRbLPjWLj2Q9BskDi+5fZWM5Tlgk+4CkIPLlXnHvyx5aBOCCGEEGfd0azy09LPsaxyCerOAgnqhHCWXg9zf1HByclLL88Uk3tdgpIjv8CxFRDcHXrPUslYEiaq4KixJCYtGBgyhATbjbjqPcjKdSPMq+VzACjLVktMY4bAjo9h9xfQc2bz5yycBKmb4bK/w/B7YcpLcOAHGP2nVo9bCCGEEGeW2WIjI7/qtPSVnl+F2WK7YOrYna8kqBOiNYyuZy+gO1nBMfW1sOarTg+xzZTmsNmgPBv2fQneEdBlusosWePSmEtZlpeN2Qomg5P18wB2LIaKPLXUdE9NSpmgThDeu/H2Voua3QRI26K+9p2lbo0pzwVXX4fMn02xWDVKKy34ehhlvb4QQghxmlRU22jbTrqGNKDCLEHdmSZBnbho2DQbvyb/SgefDnQJ6HKuh+M0s8WG0aBD1/kKOLEWQns5HC+vslJUZibU3xV9bWDzzV2w/UPocx14+UNxKsSMAo9A+3lGg44xPQMprrAS5ufEsgjNBoeWqvICoJZSolN70+r124DBCFcvgsO/wvD76p63mhsGbkeXw9FfVRA6+J4Wh7RmXz4FZRY6hnnQK9a75dcghBBCiBZZ2pgcpcn+rKe3P9GQBHXiovHe3vd4ceuLuBpcWXbVMoLcg871kFqUnFPBtiPF+HsaGd0jHF33qx2O2zSN3/bkU2W2OQY2+78DNMg+CMGTVZB0UmFwAE83I26uODfLVXRCBZUAEQOhy1To9wdw9QbvsObP7XK5utXa/i7kHVZ9RA2pe740S30ty1ZBpK75v+qVVakN3GWVlpbHL4QQQginGPWnd/WL0SCrac40CerERUOHzv619n6raDY4+J0qit11Orj6nJ6BWS1q9su14UxTbrFKhFJQZsFs1XAxnjRuDWw1f02z1f+r2uUvwq7PYMT9EDO0yUuvy1/B7uKtjMmsJHHF65AwDq55z348I7+S7KJqOkV44uEZCh7BqrZd1GCVkTOoDdkjbVbIP6IGn3fIMahLvFwVMQ/s1GJABzC0iz9ZBVXEhkitOSGEEOJ0cXfRo4PTsgRTpwN3kyy9PNMkqBMXjdndZxPtHU20TzSB7s0sFzxZeR5U5Kv9dKkb1HPp0RA35tQHVV0Gb4xW++Wu/RA6O5ZD6BrlBRoE+bjgUn8t+oqnIWsf+knPMqp7BPmlZqIC3eqO95zZcvISIKVc7c9zP/iLKlOw72tVusHkjtWmsfFQEZoG1RaNgZ18YdgDKnvlqexf0xug21WQewDiLnU85uYLiZOd7irAy0SA16kVNc0vNePpasBV/sMRQgghADAZ9YQHuJJ+GpKlRPi7yn66s0CCOnHR0Ov0XBpzacsN66sug40LwFoFHSeAdyRUFkJgYrOnVVRbqbbY8PVoIeAoyVSzVQAn1jcI6jxcDfRPOKnMQu5h+P15dT8gDp8Jz+Dj0bZ/ysMCx7KvZCcuox4l32MxHpFDcTOpWS+9Dnw9jBSWWfDzqtd/WwO6/CPg5g8eARDeT93OsYNpZexLKcXdRc/4PkHoT/NyEyGEEKK9ig/1OC1BXVyox2kYjWiJBHXiolFhqcBF74Lh5ALfzdFsqtg4gGaFwXe3fJ1qK7/uzMNi1RjUyZfI+jNooALFIz+DeyDEjoIJ/4SkH1WAV5gCftFQeAKOrICuU1UQVJ9fNEQNhOz90KnxGnFllRYsVg1fz+aDymj3OKI1b1LL1vHtgH64G1241laNSe+CTqdjdPcAKs02PFxb8Z41JmU9HPxW1fob/jC4eJ5af405thq+ugM6DIYr31YlKFpQUa325FWZbdg0DX1bluUKIYQQF6AgHxPe7oZTKkDu424kyOfUVtScbWvWrOHw4cP2x7m5ufb7hw8fZtGiRQ7tb7755rM0suZJUCcuXKlb4cu5ENaDdcNu5+7f7iPSK5JPrvgET5OTQYWrNwy4TSXuCOvr1Clmi2bP8lRdlAUBHRxnt1LWQdomdT8oEfrPgZ//qoJGgCvfgA9mqlpwB36AGz93vIDRFW79tcllkKWVFpbvzMOmQXSQG/mlZrpFeREV5NagLTYLbH6VKHMFg31d2RCsx6KZMaGyYer1OjwMZrBawdBEhsySTDj0C2x4FbrPgNGPNGxjqay7nu0MJTXZ9anK8rknFSb8o+XkLUCXKHeqjBl08I7AaJClIUIIIUQtnU7HgARfft9bgLUN2TANeh39E3zaXcmht99+m/fee6/RY2vXrmXt2rUOz0lQJ8SZtvtztVet4Bi7Q6O5Nyebg0WFZJZl0tGvo/P9+HZQNyf5eBgZlOiL5/I/4bdsEfSZBdNfqWvgFws6g9o/5uanlnMGJUDOQYgaoNqYagIwYyOBWK0mfkmaLRq1v3vT8yux2uBIZnnjQV09oW6RTAgZi7uhXsBbmAzb3lbjGHS3GnN9eUfg9ZFgLgc0yD3UeFAXM0qVPvAMVn1oNji6QgWy8ePUPrtTNfBWyN4H0YM5YaigsGgL3bz7YNQ3/WtufeEvJFn3kl4ezgy/JurmCSGEEBcpP08TQxJ92ZBU1KrAzqDXMSTRF78WVgyJ00eCOnHh6ncTpGyEsB7MLjfjVlxCgW9vvKxO1GQ7RZEBbpBbU2j7xDrHg4GdYPTjKnukZoMtb0CnS2DkH6HXDarNrK8gZQPEX9Lqa/t7mRiU6Eu12UaV2caJ3Eo6hjexnl1vhIHzoSSd0OBu6jGwPn09n+//iGtxZ7BmgPSd8HwCxAyHWUvqljaWZoG5TN13D1DveaPXMUBYb/VeJK8GoztkbFXHPEMgvK9TJQyaFdEH5q2gxFLMstQ30dAwa9X09xvW5CkVtnKHr0IIIYRwFOLnyqju/mw5XOTUUkwfdyP9E3zabUC3aNGiBkss2wMJ6sSFK7Q73LYSAPfdX7C30Iek+NvxOqFjXIB25pcDXPFf2LoQ+tzY8JjRVX212gCdCnrq15HzDHSs69ZKkQF1s3Jdoryab+wRpG71PLPxGZKLkzli9OKb0Eugsgys1XB0pZpZrN3n5xUKvW+AwI4w/I+q0Hh9VrNaJmp0UTNzyavqjtUu5/QIgk2vQGkm9JoFQZ3b9JprmXQmXPSuVNkq8TQ0/9ovCZzI4bL9RLvHN3rcZtPQUH9xFEIIIS5Wfp4mLu0VSG6xmaNZ5WTkVzmUO9DpVJbLuFAPgnxM7W7J5YVAgjpxceg5kwpjBhRBpVnVXTnjv26iB6pbcwwmGDQfSjIguNuZHpGSe1DVxQvt3XAJZ+EJKM5gROQIkouTGeYRwR6vSegG30ZXl7+gjx3mmLhl4WQVjPW8pmFAl38U3qrJNnrrr+BaL8By9YUBt4PJXS3dLE5Vz+clnXJQ52Zw5+qIWyi3lhHsGtpsWw+jF718G36P0kvTuXfFfZir3ZgV9X+M7xVVl8n0VGcUhRBCiHZIp9MR7OtCsK8LZouNCrMNi1XDaNDhbtJL2YJzTII6cdHomRCKd3YFwT4m9OfTX5AamSk7Y4pSYMeiusdhferul+XCa8OhqphHJz3LfTN+Irfany2Hi6EI/DrMIHLYNY791c441n7NPgA/PgyR/SEgTtX3A0jfrurm+XaALe9BVhJYqtXspNENOo6HknToMOK0vExPoxeexhZmKJvxW8pvHCw4AMCxsj0UlIapoG7n+6q+XpfpENlCwC6EEEJcoExGCeLON/LdEBckq63hmm9Xk57OkZ4EeJ/5PXXnLYNL3SxTSRb8pwe8OgzK81XR8eoyCOkMJSdw3/oGAeVJdKlaj3t1Dr7rnobKIsf+5v6siqZf/oJ6vPE1VY9Oq4S8/dBvNgy+A7pOUcfLC2H1v2HPl7Dpjbp+4saAqw/seE8lt6mVtVeNra32fQu/PgUVBa06bVzMOPqH9qdf4AhGxwwiOshNzdDl7K/7KoQQQghxnpCZOtGu5VbksiljEyOiRuDj4gPA2rS13LfyPhL8Elg8aTEuRSfUEsHIwWq5Y62qUsw//Y20cjfSez/AoMQAjIaTZvCy90PmHug+ve5cm02VIChOhcn/Bq+QhgNrotyAA5tVtTmbS/m8QmHwParEwL7v1MwdQNpW6HQZ3LQE0rdAZR5Yq/E89DldNSudsz9A36GnCrzq8w6DrleoWb7qMug2DTJ21L32kQ+ATzT88IBa2hkYozJgVpVA3Oi6fswVqtQDqL13fW+GrYvUed7h5P9hE0XVRjoEuzfY35ZTVM2WI0UEeJkY1Mm3bh1/SRZ8PkcFYZYqmPgPp9+mEI8QFk1cdNKzOug6HXIOQMdxTvclhBBCCHGmSVAn2oXjRce549c78Hbx5u3xb+PrqlLr3738bvbm7WVE5AheG/caAGvS1lBlrWJv3l5yCo8SueN99cHeXK6W+dXa+TGmbW8TC6R6DyA/fDIhfq51x6tK4O3LoLoE9n4FM99VpQbStsKGmhIFYb1g1EOOgy1Jh61vqwLbA+5ovNB20QnY9g64eMOgu9Tesla9IWuhKBV6Xt18ke2KQijPU4lManmFqb1wyWshtCeE9YC4UepY/CUQO1JlpnTxgQNfQ1UR+uH3qrIEVaWweLoKBmd9AWE91dLKdyeC3gS3/QbzfoP0rSo5in8cHPsdttXUe9GGQZ+Z0H8eBNQbk8ldBd0ZW6HgMOz7AgqOA2CuLGdNUgVWDcoqrfSI8XZ4ial5lVRW20jPr6LSbMPdpFd784xu4BOl3uuQLq17f5sSOUjdhBBCCCHOIxLUiXZhbfpa0krTANiTu4fhkcMB0GpyL2laXQ6m2d1mk1WeRZeALkT6xKgP9+ZyFUDVF9kfzeRJtcETl/BuBHifnHpXVzfjdPAH+PERmPo/CO4MId2gOA06jmk42PwjKhGJpQK+uxcOLlPFsPvPqWtTcFRlk6zIU4XN/WJafhOOrVRBT0hv+PBaVeOtJBMytqsSAVf8p66+Haig9NWhKsic9ir0rcnCaa6EE+vV/aAEmPG643X0hrrAxfceqCxQwRGoOnBpm9X95X+HGz+DnKSa4uKV6rUHJUDkgLr+wnqp4LEsGxInQ2g3tQzTkAq+UXXtuk5XAV15nlrmOfoRcPNBF9oXQ4kOq0XDZGw4+xkf6k5xuYUAbxPuLgY4sRaSvldB5tyf1CxgYCvqEgohhBBCtDMS1Il2YVLcJDakb8DbxZuBYXUJKl659BU2ZGxgZORI+3PhXuG8eMmLdScPuU+l4a9fQDx9O3xxC7qYYbhe8x6D6s+maRqsWwDWKvjDz7Bwojpfq9mn5+YD89c3Pdjwfirgc/GEDfNUcLf7c8egLnKQCnJcfR3HZbNAWY6q3VZbkHv9K2pPWO5+FZzqjGopqMWqlgLu/Uq163qFYxmEqhK17BRUxstaJjeY/jocWQ6j/9T06wD1Guq/N6E9UHlDtZob0OMqVaTc6Aqdxjfsw90P7lxT93jbYvj2SvXa790GnvWSxPS6CXL2qQQurt4w6mGMwJgqK6WVFoJ9Gu6H9DWZGR1rBa+arJy1+yk1mwo2NVvzr7ERRzLL2ZNcQkyIO33ifFo+QQghhBDiHJKgTrQLAW4BLLh0QYPng9yDuCL+iuZPdvVpuBdszxIoOI6lOAtLbgpuEfWW5x36GX55XN33i1FLClM3Q5cWrlPLxRN6XqfuT3hGXevk4MnkAd2vaXjurg9VdsWwvtDjGrXM8qe/OLaJGQm3/0567j5OWCsYfGwVOpM7RJ2UjdEnAq5epJKNDL3b8Vjva9WttVw81DLUw8vVfrnyPLCZ1ayas0qz1dfqErUPr35Q5xWqbvXZrHhUZ+PhHdJwn2JFgcrYWZIJ138MiRMgZoQKvDUdvDVWtZm5EHpc6fQQ0/IqsWmQmlspQZ0QQgghznsS1ImLU9+bqE7fw/JO/6LqhA9D3KsI86/ZTxcQDy5eatYsqJNKzR8Q17brDJyrbgDL/w8ydsKkf6nlgJoNUjaovWcRNcsVy3PV14qar/4x4OYH1aVqBlGzQnhfzAHxXL/iNvIr87ll1K08MOAB1V6zqXa1s3zdpqlba2TvVTN8HUbUlSqozycSSrPUXrmiw1CaA0Pvg5DudW2s1aA3Np4EZtg9KsgO7KheX0v2fQGZOyC4O/Se5XisJFPNigKk71BBnU6vZvpyD9VlvayfUdMJXaO8OJhWprJeCiGEEEKc5ySoExcOS7UKJlxbqE9Wmg1bF6G3VBKa8RPJUVdTWGauC+qCOsEfd6tlfF7Bp2dsBcdh9QtUuIaweV8hxuACBnsfxZD0nTruFqCCyZ43QNYutYQT1J6zP+5Sr+3zmyF5DRz6GVvsMMxWMwBV1irVtqoENr2ilhwOuA28I1o/zspCNVuIpoLahAkN26z4OxxfDXmHwOQK2Qch7wT8YZk6nrkL9n6qrj/gjroAs5bRFQbf5vyYynMcv9YX0lWVU8g/BkPuAMBsM/PL8V9IwJXEGW+ooG/IfKcvtz+1lLJKKwM7+eJqkqovQgghhDj/SVAnLgzl+fDGaDWDdNMSiK0pYr3yH3BwqUpUUpvh8es74fCvGIE+GVsxDZxDxzCPur5yD8HxNWqv2OniEwmxI8nQdybPFAWF1RR6eBGAHht69mVCd38NvXc4eIc7vi5XH3DzhbzD6rnkNbhuW8jiIU+yx1bOxNiJ6vnSTKiqqSNXmNy2oM7opmYpq0saFkRP3wqpGyFqACSvgw7DIHNnzbWz69oVHFYzhsWpaj+hSyuKgFcVq6Qv9UtPdL8GMrZBaO/Gzxl4q8PDN3a+wRu73sDdZmN5lT/ed6xp/LxGFJWZOZBaBoCnq4Gu0W0vYC6EEEIIcbZIUCcuDAXHVep6gJSNEDsCzVLNi7veIMVk5C/rFxBSG9S5+dacpEPf40p6npQin4WTVRKT5HVw1VsNr2Uuh+TVKl1/YGKzw0oq3cvx8sP09xtK4M3fE1ZlJTmpEKNBh09EJw5V3cbRHDMVRR7EVFjx8aj3T3LbYvj2XojoA7cuh6sXqqQomhmKkknQLCQMqrdXLqCjKjtgqaib6WstoxsM/aNa7ul5Uv29I7+ooNErDJ7IU/vbcg/Bjg+h+4y6drFjwGpWCWBaE9BlbIe9n4N7AAy5VxVKBzWOhIlOd2PQqZlBA6DLOwxWCxia+FVXWahq7AV0BJ0ODzcDXm4GKqqtBPtexEXqhRBCCNGuSFAnLgyR/WDs46p22wC1h+1A0REW+akkFwkhkdjDn2mvqlm7be+rIMZmc6z1VlszzqXe7F19uz+G/MNw/DcY/bhKetIITdNYlbsMGzasmpVJoVfi4WpgTM9Ae5vQiAgKiw/Sr/B9DDt8YeCdddc/sQHQ1D686jKIGQZu3rD7I3Xc76Q0/To9dJrk3PvVHJOHulmq1B4/7wgVwEUOgJT1ENG/LmFJUCcY96Tj+e7+0KMVSVh+eAj2fwsDZgMaVOSrMgSGtgVVt/W6jUS3IOKPrsFr1LSmAzprNWx8GcxlED8O4i+lqMzCwAQffDxM6PUNyycIIYQQQpyPJKgT7Vtekpo5C+3doAh4jE8MXQO6klqSyoheN9cdMLmpGaa0LerW+xrITwL/eDXTNfcXVfKg49jGr2mprLtvtcDJ5e1q6HQ6otzjOFFxhA7ujSda8fU0MSgwFwqq1Z6x+jXrLvmzCtRiR6psjgDZe+znbi85Tl+A5N9VcpOEiWr28HTZ8gaUZqhsm50m1wQ+405f/7WS10DvK8FcAuH9ITChbjZVs0HSUjCXQuepTQbQ9Rn0Bi7tcjV0ubr5hhm71KwmgKWK9PxKNiYVodPB2J6BjrOmQgghhBDnMfnUItqv4jTYvlDdt9kgwnHJoU4P8zsOI27bd/imbYWQPnUH/eNUdkbvcCg6ruq45R5U+8b63gKdm1nu1+M62PGBmsFyaz7d/aTQK7FqFgy6Jv6pFaVC+h7w6aAyQfpG1x3zi4ZpLzs0r8o5irbhbXYEx7Ak8RL6ahocWgZoaubwdAZ1lTWZI8vzW2hXBFvfVFk3+89TM3WgMk/m7Fe17U4uKVFf/9lQkgLoVLKX1A3q3PhL1bLalLWqnXekCjBPhblCBcB6E7w7ETz8Yfg90PEyLPkWQL0MW71i9kIIIYQQ5zsJ6kT7ZTCBzqDS/DeSev9I2QFi139ATMohrEe3Q58/1C0bPLFeZXcsSgG/ePVBX7OpmbK8JPDvqOqveYXZ+ys3l1NYVUiEuRp+e17NEOqNoLNBWG8ISFANS7NUYGNwgaSfMHiHQXgTST4+naVmBQPiYdDt8NMQNePYq5EadoDrni/AUk1sQTpzet6iXk/EAMjYCmX5KiI5uZZbW/W5Wb0XUYMbHqsuVYGR0VUlS6moCfwKj9UFdTvfVzN92XtUNs56bDaNpPQyDHodCQNuQ/f9vWqZaXg+eAao70tAgqpZ5xEI1eXqe+Ks0hw4tgoSxqni57X2faGKm7v6qO93aTa4+IDBRHSQEb1Oh8mow8+zielXIYQQQojzkAR1ov3yDIHBd6u9X7VLFuuJdo9jf3hHYlMOkRPeibD6wc6AP0DWHoi/BKKHqLpmez9TgZ5nCKz/j/rQ3/cPEJhApaWSiV9OpKCqgBfir2V8dYnq58C34BumZvlG/QWOrYQjP6s+TP7w/X0q+LlnC/jHNnwNNjU7RP5R+O2fKnHHuv81GdQx+k+w4TXChswnLKi7CoQOrYDt76vjgYlUdrqUY2sfx2j0JGHYU+icCfKO/a7KJwTE1z3nF9Po+0reYdixEEye0O9WOPqret4rTNWSq6EZ3NCB2rd4kpS8SvbXZJn0MVYTuv1DdcDFXQV1rr7gEYTN5E7JwLn4GHzR6VtRXuCjq1WwnHAZzPqi7nl9za88ozv84ScoyYCuU4Ca5bJN1aXTNPjpMVXv7vIXVGF3IYQQQojzhAR1on2rN5MGUFhmxsWox8PVgK/Jn0GTPiZrWBIB3nFqH11lkUrJHzsc7tpYd6LJDfrMrunkuJr9A5XaH9idu5uCKrUc8f2Sg4y/4r8qAAtOgJR1KtMjqJk+UDNXxtolh5oKEBsz6V+w6HIVNPS4Cg79AoOaqOFWWQTBiaoenMld7edbdIUqA6DTq4DFN5qMzS/QdeXbAOQF9CCwaxMBYq0v58Huz1SQdl/NrNuuT6HHTAjt1rB9Sbp6PdUldSUUAEJ72WdM80uqWcs0PAKsjOoW2GDboY+7Eb0O9Hodnj7elIx6EI/j6zBc9n8Q3ksFwnoDy7O/5Wj5Qbp49WR0UDNLYs2Van9kRF9w8VTvJzR837tdBaE9wTcGXL0b9tOUzF2w4ZWa19kDxj4GwIHMYm5/fytR/u68M2cgbiZDM50IIYQQQpwZEtSJC0ZaXiWbDhVh0OsY1zsQjx3voF+3gNChd0EnE7w2HKxVcM1i6DZNnZS1V808dZlSlwHTLxZ6XA/WSrWsEkjwS8DL5EWZuYzrOl8HHS+vu3Ds6LrU/Z0mg4s3BCWq5YJeIWrvXf0ZsPpihsFdm1UQGdy5+Re4fSGUZUFQVxWA6vTg5qeCun43w6gHwTcKv9//D5tOh4YOL58W9tgdX6sCOlBJQ6xVsGSemgE8+CPMX9/wnKjBKmOkqw8EdlIZO8ty2FYWQ9rmbHrHelNeZcVihWKrgdJqPf4nJbL09zIxsV8wOh0sS/6ev6Z8QfeQ7nwQ2R9DvWLledUqSM6tzqZZS26F/d9B/BiY/TXc8Bkc/Q0Sxzu2M7hASI/m+2pMYILas1lwDDrV9blsTybJeeUk55VzILOEPtF+re9bCCGEEOIUSVAnLhiVZjUrY7VpWKwabHpD1a7b9IaambNWqYYVheprWS68dakKZsY9CSPur+ssrJdD3/5u/vwy8xdKzaWEeTrODjokAcncASdWQ0UeBCSQHTGezIJqOlZa8XSrN4uT9L1actllKgQlNP2irNV1s3C1yyh1NcGnXg/zVkD2PpUhsyYo9Q/sgU1bjiGwE4bIgQAcSitBd3w1Hbr1xsVcCMdXQsQg8A6rKV9QCRP+qfbDhXaHnANNB6JGV8fSCd7haF5hnNiUjaZBSmomQ/TrsXr2RvPpgJ9n479mXE1qvJszN6OhsTdvL6XmUnxdfe1txgZfzqHSfXQxdYBjv6lZNo/Ahp3VFj+v/eodCr1bUVahBTajB+lX/0Cknzu6tS/Blnfhsqe4ql8U64/kEenvTo+I5pPmCCGEEOL8t2XLFpYuXcqaNWvYt28fOTk5mEwmIiIiGD58OHPnzmXEiBHnepgNSFAnLhhxIe7oAHcXvUpHP/w+WP+K2jO17mW45C9q31jkAHhlCHgG1S3Tc4KXixdeLRXTzj+kvuYloWkaGw4WYbVplFdZGcJ6WHI7xAyF4DgVpKVuVDODjSlOU1klDa5q72DfP6iZoqB6M3peweA12vG8cU+h736lml0CisrNWFb8k65HXsGyJgRG3gXlWVD5C4x6DO7apPb2BcSpMgZefnDpn2HgXU6/Nzqdjp5+haTlltK5YB0GWzrdfQqh5/wWz72z953YNBsDQgc4BHQAIa7hhLiGq3pyJWkq6crgmoqDK55WheYn/QuuelsVZu9yReMXsVTDDw+oJbNT/gceAU6/NoCHvtjJkm1pXN3Dh+cP/w0AzSsE/xGP88ltQ5zbtyiEEEKI89qoUaNYvXp1g+erq6s5dOgQhw4dYtGiRcyePZu33noLF5e21dQ9EySoExcMvV5HfFi9Omb9Zqvbu5PgxDo1w/PIUVj9gkq1nwNMf03tweoy5fQMImESoIfKInSbXsbH/RoKylFB5uav1R60pGXQ9X8qsArvr87bs0TNuA27t65MQtEJNVNnrYajK1QWxw4jYePrqo9xT6mi6yfT6SCij/2hh4sBD5vaG6i3lEJ4HzixBiIHwcY34Mc/QfcZcPXCuiDX5AlGFyjOUElg4ka3mFWzo2cBHVJ+JEcXhm3XN+irSiB8AEQPcmhXVG7G1ajHzUXNXEZ4hvNM3JVqyWpTXL2hhLp9cCWZ8Pvz6v6GV2HqAhXEN+X46rpkMjEjYMgdzb6Wk+1OVXsHd2VbVVBdcIxtQddxYmcekQGuDEr0czyhOE3NxvrHQ8fLWnUtIYQQQpwb6enpAERERHD11VczcuRIOnTogNVqZf369bzwwgukpaWxePFizGYzH3300TkecR0J6sSFY8fHcHSlyhAZWC/9fewIFdTF1kyVx42G/rPqkpM0Ug6BohSVACW8LwQ2E2ycTKeD/MP2RCsjg/ZSkTgKLzcjmO5RSUZiR0HfOXXnFJ6AL/4AaGCzwjg1E0REf1WQvLoc0mqSuhg94Ke/qPurX4DrPmxxSCajnuhr/4l1R08M0QNUIBh3iTq4drq67sGl6nFoH/U+Vq+AxCnw5uiaEg0Bav/f1YtUKYnGWGGTbSgVOk8iitPUcweXOgR1KbkVbDlcjMmg47I+QWoJ5s9/hfUvqwQkd65tvO+eN0BxKvhEqceewdBpAqRuqtsf2ZyIvhDSTS297Tim5fYnef7q3nyxNYVrBkRDxHqwVlN8oByqLBSVWxqecGKNSrhTeBw6jFCJbYQQQogLRWUxFKdDdZn647hPy7V724MuXbrwj3/8g6uuugqDwTH52ZAhQ7jpppsYPnw4SUlJfPzxx9xxxx2MGjXqHI3WkQR14sJQXQ7fzFfZDm1WmPlO3bGxj8GQO+vqp+UfBPeaXzy1+9NqJf2EtvcrdN4hgBkKjsLwR9T+O+9Qx7bZe+HQUpX1MWGCeq4ivy5zpps/hrCeKqADCOsBc75rOHY3P1UEvSQdgrvUPW+zQkBntVQwd7967BUKHYZC+jaHpYblllK+zfwEq2bhirBr8TX5O1xC7+YFQxrJqnnp4yrgSLgUfnwUSjMhbUfN69unioHXvq4D36ukMr5RkJOkAqX6ZQZy9mO1hFDi34mUDtcQXb0P+tzgcLnyKrXv0WzVMFtsKqgrPKEOFqU0XWfPYHIsrK43wI2fNWzXFI+AxpO+OKlPtJ9jEhS9O/3ijSTnVBLdWBmEsN6QdwgCOjZa0kEIIYRodzRNrXzZ9BYc+KHu8w6ousFdr4CBt6p9/u10W8L333/f7PGgoCBeeOEFpkxRK7y++OILCeqEOK1M7hA9GE5sgLhG/nHV30MV2lMV1Q5MbDDrZPtyHvqqIgqDBuLbZSA6/3j46Fo4/AuMfhTG/LmuceoGFewk/67q3GXtUl8Ta0oUdBjeMGhsjJsP3LUByvMck5MsnKyCt1EPw4iHVcBakgEd+qqbS90MY3Z1BkUWVXIhvTKlQVDXgGaDXR+pmaTLnoRt78PG19SxzpermnrxY+Dm71UWzEO/QEhXtUTyzUtUiv/Bd8KkZ9U5+ceg8ASDbElkVJUSFBMDWhCU5wJ1M50J4R7odeDpZsDLvebXz+R/Q1hP6Di26f8Efv83ZOyA8U83Xu/vHPD1NNGrqSLlQV1g9F/P7oCEEEKIMyV9B3x1h9q+0hjNCvu+UbfgrjDjdYetIBeSMWPqVvwcOXLkHI7EkQR14sJgrlCFpgfNU0sqmxPaS90aURw0EL+0X8nwG4LH8L/gYtTBl3eqg6ueU8lVBs1Tj6OHqdpxoT1h90eqRl3eIRjUcnIQu8O/Qs5BGDAXAuIxW2xUVNvwcTdAbpJqk72/rmQCqJIJFfnw/YNQkguDbyPKLY5Ez+5YNAvxni2URgAwl0POXnU/a5eadUOnAqar3lJLKQAi+6vb2HoBSlFqzdeUuue+uRuS1+DmF0Pc/Mdg06tgsTrOrgEGvY5OEZ6OY/EOhdGPND3WwhRY8featuEw+fnmX5vNCru/UAFy9MDm2wohhBCieUdWwCezVDkjZ+TsV3+Yvu4D9QfbC0xVVZX9/slLNM8lCerE+a84DY78DMHdVI20xqx9CVY9q2bGogaCX4c2Xcpw48dsOZyKf0gYLu4eagbKvrxAUwlNaoO64K7qBiozY1m22nt2MqtFBYSaDS55tG52sCgVPrxG9V9VgnXUn1ixO5/yKiu9w2zEX/o4FKXB4Nvr+nL1gX7z4D9dwWqGTW9CYEeMCZcyJniy8y/UxQvixkDBcbXnyztcJRBx82l5/9esL+DwcpWEppZ/LCSvURk0Xbxg+MPq/dLpVcbOmsLobeIVqr6nGbtU4N6U8nz4cKYKAsuyVQHz+3aCb2TbriuEEEJc7NJ3tC6gq2UuU+fdsvSCm7FbtWqV/X7Xrl3P4UgcSVAnzn9Hf1XLJfMOQeTAxpc0+seor+4BdRkS28Dbw4UBveotgQyIU7NU+74BDRWUNabXLJVQxCus4bHdn8Hv/wIgJWAU2d59qLbYqC6tYqSrD/rKAvAOw2rTqKhWAWT4V1OhKAmG3KX2sNXnFQzXfaL2EOYdgu/+CPfvdu4FWi0quUhoD+h4UmHuk/cMlueqYt2uJ218rp292/kpfHsPdJ4IMxfCoFvrglydDtCpzJk7Fqn7A+9sW2BndIFbfwWbzXEP38lObIC0rXWPDSYVTAohhBCi9TRNLblsbUBXy1wGX98Jd65rt3vsTmaz2Xj22Wftj6+55ppzOBpH8olHnP9CuqvgJbhb03vU+tygUvV7hdQlRDldhv8RtCr1y8lW1XibigK1ty6gk6qDt20xrH8Vht0NBYfA4ILV4MrWqs5oVZU1J3lxcNovdPUpgch+uABDEv3IL6nGraqmiHZJeuPX6zQOel+nZijjRzfepjHf3wfbP1B72O5Y03S7vEOwfaF6v0sKoOvl0PmkmcD936qC7vu+VbOQEX0b9mOzqgxZyZvAPRiiBqj3KaSnKnoeNcgxaMzeC4d/hNDeDUsBNBfQgXofYoZCRREMvVvN7p0cqIIqEZH0vQr4Ol2ukq5omlqS6uLZsP156lBWCZ9sTmFq7wh610/iIoQQQpwOx1c3vYfOWdn74PgaiBt5esZ0jv3nP/9h06ZNAFx55ZX079//HI+ojgR14vwXMUDVc2vprzyh3drUvcVqw2hoLmDQVHFuUAFBY47+qvamZe2GsF6w+kW17HDNf2H4fBg4G71/Av5eLuSXmOlevQ4vXRme0deAV13hyjB/V8L8XWH213D0N+h7U4NLlVlK+T7rU2xdo7li2D68PVuxvDBrn/qauQfyjzomZqmvskC9bs0KR36FHR/ALT+qsga1ht8HWXtU0N3UjFhINyjNV9kyf3kKRt4DmkUlmUGDsizodWNd+5T1KmFM8u+tr+9mqYTomsDS0w+CmyhFkbkL0jar+/4Jaox7PoWsnRAzGjpNbN11z5GHv9jFjpRCft2fxaqHW1+mQQghhGjW5rdPXz8XQFC3atUqHn1UrdgKCQnhtddeO8cjcuREaj4hzgNnaNr+YFoZ323OYVNSYdONDC4QNUTdL81svE1AJ1SikTi1l6vfLDUb1m2qCloG3oFuwFxGdfPncs+1JFZvIqJqL76ujdQ4AzXrNeJ+0rVqUorrJSQpzaT8xApKq/IothSSbito3QvuPr3mjgZleapkwQcz1Ybmkqy6duH9Ie5SOLZeFfpG1zBwy96v9uQd/AFWPtP0NbtMUed7h6gg0TuibnauPA+KTlBhLWdFzg9sDg1UReJj2pAe2NUHvMJVWuWAhLrnS7Mgc4eaNQS139Lkodr71ATEBUfV18Kjrb/uOdIlTC0z7hza9uXGQgghRKMqi2F/8+n9nbb/O9VfO7Z3715mzJiBxWLBzc2Nzz//nJCQkHM9LAcyUycuatlFajllVlETM3AAyx5V+9Ci+kLhMRUc6E/KdhQ5QM3Q6U1gqYCkZZC5G/KOwIgHILATALpjv+Oy9EHwDIRxT6jgAqAwWZUXiBxkT1RyqOAQ131/HVbNysKJC+kb1Au2vEmwpYKJAaHsi4oj1rNT617wkLvA4KpKPEQPhANLVbkGUIXCB9yi7usN0HGcKtGQtEyVMqhXRBxQM1w6o5p5+/15VQIhdnjDa/a5QRUlPfqLeo96XAuWKtj0CpRmwN7P2N9tOIfK1CxibP+bCHZtZG9iSwwmGHyPChxrA1BLFWx+TS0Tjc2EhIngGQKjHlPHa5fzdp+pAr/oYY12fT76x4yezBsVT0yAx7keihBCiAtNcbpjHbpToVlVSaZ2Wpz82LFjjB8/noKCAgwGA5988sl5U5uuPgnqxEWtZ4w3SWllRNYWkF79Auz4CMY8Bj2uVIlFNtRMr1cUqqLmJwd0tQw1yyj1pnr177SaGxSUmjFt/wqvinxVkiC0t2pis8D2d9XSzrIcFWAAORU5VNtUsJmZdwBsetWvpYJIzwQiQ66g1QxGGHJH3eOYYRAzXO0n6zS+YXvPIOg7q/G+ogfC1e/CZ3NUEOXu13i7Iyvh0M8w+I66hDZGVzWrWZ4D3lGEu0Vj1JnwNHjh01yNvR8fhi2LIHE8XPthw+M6nQo0HR7XzvLWW5hw8t7MwER1OxUlmfDTYyoAvuRPp9aXE/R6HR2DvVpuKIQQQrRWdRuTozSlqvT09neWpKenM27cONLT09HpdLz77rtMmzbtXA+rURLUiYtL+lY49AOE9YPOV+DnaWJQol/d8TX/hapi+OVv6hdanxtViv7qUgjoCOGNJAM5mcEE130Kuz9VBchdvSmvsvL73nw8fK5meGQSHjH96gIcnV4l6KiodvgrVt8qF34piyAtrA99M/ZD2i5VfiCgo1ruWZatfknW7IurslXxa/a32LAyLngq7gYnZnDc/VS64bbqNg3uXKtm/4ISGh7XNPjkRpVkpiAZrv+o7pjRCw6ugMpKwntex80d7kGPHl1zS20PLlPB78EfnRufwQUGzldLMD2CYcPrqmZOU/vtTsXG12HPF+p+t2kQ0uX0X0MIIYQ4G0534jDX9vdHyNzcXC677DKOHlVbMxYsWMDs2bNbOOvckT114uKSvlUVKk/bVPdcdRkk/QCZO2H4veAZDEUn4Nu7IWUj3LMVZn0J13/i2JfNqoI9YMH2BUz5agq/pfymjrn5wMB59uQtuqpSelb+RifjMbIu/xDG/72uH50eBt2tknSgV/XngOrl/yQsewP9dr2JmZqZRKMbBHWB6hLYuAC2vQXpWwBIqzhOauVx0itTSC4/3Pjr378EVv2fWmp4uoR2bzygAzVLFtpd3Q/rob5m74PcAyoLZ84B2PIuVJdh0BmaD+g2vKa+V55B0GMmfDYbTmxseXyewaqEw/f3w7I/wfszWvf6QNUL3Pim+tqU+EvA6K4Sx7SxTqIQQghxXvCJUHvUTwe9UdXDbUeKioqYMGEC+/aprSHPPvssd9111zkeVfNkpk5ceBrb81Yr7hI4aoHQPnXPHVuusi6ig5F/Vmn235+hAijvsLpbfZoGW16H4lToPJV3d7+LRbPw0f6PuCT6EtUmc7daWll0HPe0TcTbT00EBjr2V5EPyTXFLCsKoMfV2LpMwZq2irzIywgaOBdS10PKOhX0RQ5QZQQALGqJZoRbB8JcI7FWFxO99TPw3wm962XP1DRI2wJokLFD7Zc7G8L7qD2J+76GzpNg32fq+W5XQHWRmtFa+Qx4BEHiRBVMh/WEK/7rmCBn3csqsUpIN5UZtDRLFXCft8K5cdQuD21LyYvPblI18HZ/pmrmNSb+EvhzqlriKoQQQrRnbj7Q9QpVp/dUdbmiXe2nKy8v5/LLL2fbtm0APPbYY/zpT2d+W8Wpkk8f4sKSsx92faj+IjTg9oYZGwMTwbeDmimqKlYZEL0j1DGPABXIxY9Ws3Mm94bBXC2bxV5DzpJ/nEuCr2Nn0W9clXCtOp62Fd4epwKpS+6vOUkHJjd0fjEN+3PxVrM85jL49SnIT8Z/1ENYh80hRF8T2JRmqL1vaZtU2v1+c9U+v/A+ALgZ3JkWfgPsfB8sVvVe1Aa4G99Qs4odL1MFwWNratuZKyBju1rC2Vjh9NNhf81/CDkH4cS6uvcisj9kbgc0VfJg/SuQvkO9d2lbVcmE+iUXht8Lm96EoXdB8jrY8SEkNFL2oDwfvp6vipZPfhHyDrHTy8KJwSMZ2mMSQRGNJHNpSW22zhYK21drerYdLESng/4dfVoolSGEEEKcxwbeenqCuoG3nnofZ0l1dTUzZsxg7dq1ANx33308/fTT53hUzpGgTlxY8pJUlqXiVBXEuPk1bLP3c8jZp4KYIfepOngBCSoTZW2Ck4C4xvuvLoePr4XiDLj0MajMpzTtMP+3dwn7E+4hyq8mYKgqrZtJs9V8dQ+EYfc3XkDdxQsG3QULBqhZu4LjABhqArrj2RWUeU+is20ZxoiaBCv+8dDYpFP8peraQV1UQHdkJfz4iDo27VUYWC9RyoGvVX09kyeM/mvjr7kxJenqGj5RDY9pmsqa5R2uCoaPexK+f0AFRF2n1WSnNIB3pAqwi5KhNAd6Xane94Ke4B+rjmlaXTbLwberG6jkLZP/DS6N7Bs88D0k1ey5yzsCWXvwiu1K+iVXscOsY9zeVOhzsz3LqFOufR+S16vi5s3IyK8io0BlVI0KrCYy0M35a5wGmqbx5Ld7OZxTyj9n9KJDoGTGFEII0UaxIyG466kVIA/pBrEjTt+YzrDrr7+en3/+GYCxY8cyd+5c9uzZ02R7FxcXEhPPwD79NpCgTrRPx36DqiJImKBm12rFjFK1UDa/C/svhZnvQs5eQINuM1XWRU1lo7R/BUjdqJY2xl/aoEZatcXGvhOleLgZSLTshmO/qwNb34XwHvjuWIyuupRuqe/h7ncLZOxSM2MD56nskrYKyN6jApXGArqD36rln3Fj4Jr3VPAwaJ79cHGFhe1HiwF3DGFX0yU6sPn3xjsC+sype+zXQQWN1moIOqkEQm1gY2pF8FGcCpteBTTod6tK3FLf0odUodGeV8NVb6uSBn1uaNhPWR54RqrvS6/ZsGuxKgcxYA70vxVsZtjwMlTkqWWkJ2enbCygA7UMMihRfa/NFQAEVFowYSC+sFBdtyhZBb3OcvVWGTdbEOzrgpebAb1eR5AtAzb9oJLaJExw/lqnICmrlPfWJwPw4aZk/jyp61m5rhBCiAuQTgczXle1bM1tyIZp8oTpr52xWsNnwpIlS+z3V6xYQa9evZptHxMTw/Hjx8/wqJwjQZ1od7Yf+pa+yevVAze/uqWEoPZLmbwhTa2D5pe/QUTNB9vgbip7ZY9r1F+d6gcjGdtU0JOxo0FQdzSznGPZKjgI7dYT3x4z1UxVTXZDXeIESNuK16j5oNep/WFJy9QG4/F/h71fQ+JUiB7c+Auq/QtY5i7QjGrv2a5P4aYl4B+Lq1GPq8FGlUXD5+cHYMbfIDiR1IpkMkoO0e/YfgzWajI6TeCHzA2MjxlPB596iToCO8J9O9VePJ+TNionToHg7q3bwGypprZMA5aqhsdTt9R83dzwWO4h8I1SweQHV6r3u+sUGHpnXZvCo5B/CNz8oSwLG2DNP4TJ2ZIDfh3g7s1119v/Hf49ruIPHgGQ/xl4uKpZzjPAw9XAZX2C1IPdP0FxmrrFXqKCzNPJalElHsrz1P5DjwBigzwY1jGQw9mlTOh+hpbTCiGEuHhE9IHrPoBPZrUusDN5qvMi+pypkYmTSFAn2p0/b/k3i/z7EWBww8W3kSyDHvVmsjoMA5eaH3P/eLUXzlJVV5qgskhlVEyYCGmbIWZkg+4CvE3odeDmYsDdw13VqgNVU67oBIT2rKtRB5AwTtVlixsJy/8PNrwKbr7wYJIKdJbMU/vJrlmsliF2nqquvf1jtVes1nf3gXc4ruOf4TK3jVi+fwT3qiz47DC2btP4Kd6dsNJyDCUqA+dPWxbwUvY6fjr+E59P+dzxRXgGOT4uy1OJRkK7qcLo1WVgqXSc9WxKQLyaObNZ1bKKk015CbYugt7XOT6/8p+w6lm1B/D2Vep6AOZKtWy2lslDFQh388PccRxLtF0U6w8wobwrHTxaGYwFdYKRD9Q97je3deefivB+qqh8YOLpD+gAkteqzKGg/mAw9C5cjQY+mjfk9F9LCCHExavjWFX+6Ks7nFuKGdJNzdC1w4BOq7+Kq52RoE60O/HBPZiUtoK7et3Orf5xKrjISwKfSJXQIm6kKkxtLldLAGun/W1WVQagLAu6TFep5zf8VwUXXa4EswWSN6j14/WE+LoyeUAwBp0Ovb7eEgLPYHU7mX8kDLtNFSGvVuUJ0BnUOPZ8CSUZat9XSSb4RqrrBXeFrR+otnqjCj6P/qYeB3TENOJ+TLn74MBSyNqNftUBfDu9SIa7mXz/CAL0nhzK3wFAkNZCco7KInh1iKpzN/VliB8F295W+wkH39v4PsSTBTcSzNWK6AMR/1UJS76/Xy2FHHKnKmUAkJuk9hne+Lna79d1Clgr1fsV2EkF3DVBcllUXwprZl0zqlJbH9SdDWlbVEmMoETocV3dz1tQF5VNtSVFJ2Dfl2p/YrerGl+i25iwnhDcRc3UxV/S5uELIYQQLYroA/PXw/E1sPkt2P+92vNeS29UWS4H3qr20LWjJZcXCgnqRLvz8qUvU1BZQKB7zYxc0veQukEFIyNqUs52vaLhiTazCmRALYkLSKhbPnjgB1jzH3XfPxY6jnE41dSaLIauvuqDuckDRv1R/XKzVMKqf0H3GSq4iRqoArr6bvhMBXKrX1BLMU2egKbONxhhzF9UKYOvboeYYUwPv4kSawn+8ep9eCL/KFduXUL34z9B9AcqmUhjqkqhPBeiB0D+QQiMVXvPClJUoOlMUNcEq82KTbNhMpjUDGXtTFLHsTDxWfXeJoxTCVT8OkD/env/ejccr58pgCH+l1BgzqWXT/82j6vNtixUy3IHzlNjbszaFyEgRiWc6TRZzcq2Rvo29XNZlq2ykzr7/nsEwF1O1OkTQgghTgedTv3hPG6kyl9QkqE+U7h6qW0c7ahswYVIgjrR7uh1+rqADtSHblB7xjSt6b8OGd2g53UU5GTjFj0Qdw9f6DNbBTg2HaxboGaLfCIbP99ZHYarWZfKApUhsvNk+GeUmjksOAZTFzSeGj9rF2RshJ7TYMQD0GmcGnNtRs6Dy9SSyYcPg96AEfA31L0PrkD/inL1HlQ3s+7dN1JtfM7eAVjV+3dkrRqbqy+MfAh8owFIKU6h0lpJJ/9OTfdXI7cil+t/uJ6S6hLemfAO3Wuzf+qNqt/0rbDrMzVTGD+6+c7q6e07sOVGJ6sohKSf1HXql6WwVKnre4W03MehX+H7P6r7nkHQ46rG22UlQe5h8I+rK31Qn82i6vS5+zeYBQYgor9K3OITpd4nIYQQ4nzn5iNB3HlGgjrR/nWeCr4xNdklm5/uT9Z1YltpKKYDVVzWx4Zr/QyI92xTy/5OTibSFmmbIWOr2hs29H71gT97rwq4XhkMLp4wfwP4RdedU5apvlYWwpAr6oI5gMw9qpQCqCBhYCN7wwLiYc53kHcIelzd/Pi6ToGiY1BdAt5RaiYxfiS4ecD2d2H0ExwtPsbV316N2Wbm9cteZ1jEsGa7PFp4lMya17Ajewfdrea68VqrYMdHUJoJ295T5QiM9fYh7vpcLWMc+RB4BasC7IXH1TLPtuxHWzJP7WsM6wl3rFHPWarg9REqecr016DP9c334RMBBle1vKSxvZu1AuNVAJl3DFY9rZb1drtSBdYfXw8urhCcAOjUz8LJS3Z9o1VpDSGEEEKINpKgTrR/RleIGtTk4ZyiajRNI8TPlUqzmj0yWzUsVg3XenET/o0UBW9MXhJUlai9X03tf6opTE55nvp66y+q+PbvzwOaqqFXke8Y1CVMUkXI/eMdAzpQSxtM7mo20iOg6bHpdVBwEDYtgGEPNuyn1rf3we7PVBCcm6z2ZJXWjNnVF3Q6iquKqbapWdC8ijy1xCJ5LXQY0ugSwwFhA5jbYy6FVYVM6zgNOkxUgaxvtHpvB8yFw7+orI3HflczkaBq1C2Zp94XmxUu/zdsfUvNdIb2hp7XNbhWi2q/L/W/P5VFKqBDU8XNWwrqQrvBvdvUmJr72YgcoII6rxCwlEP6ZugyVWVgPbYKgjupoM7g4lwiGiGEEEKIVpKgTrQb2YVVZPz+AWEV+wm94s/g2UK9NiC3uJo1+wsAGNbFj4RwD4x6HZ5uBjzdDC1f9MhKVXNtwC1qL1hpJmxfhApALBDVSJkCcwUcWgHVxdBhsGrn4glHVsDBpYAOJvwDwns7nufqDZ0mqfuVhZC8Wu37C+4KVYUwqGb/mVsjSzdrVeSrr9Ulalll/aDOZlWzTgYX2P+dek7ToDC17nG/OXCJKk7eJ6QPz49+ntLqUi6Pvxw+ug4O/QQxI+CWHxpcWq/T88f+f6x7InkDHF6u7ne+XM2k1u5hTF5bF9S5+UBggkpg4xeplsPWzriay9S6/dolHsUZ4BXa9P62Wle+CYd+wRI9kqKSavy9TOi9QtSy07StMOph1c5qbjrwBVV+oSWjH1F7JbHC0eU1Rd+Nat9k16kqS2q3q9VMamPLboUQQgghTpEEdaJdsNo09h84wqhtD6JDw+ptxDD52Vb3Y9Dr6BjeRNHqxvz4J8g9CNn71ayNwUWVIbBZ6gp3n2zXZyoYBDXLpK8tqRCrvrr7Qa9rmr/uoR/VHrvUjTDmKRUo1s461e5Va0zHcWpcPlEqkKxlqYJNr6hC3joD9LsBsg+qoM7kAXGXQOZOtSyz3mzSxNiJABSUmtEXFuALKmB0hltNwhhNU0FZ0QnoUVMAfsj8unZGV7VEcvNrUJ4Nuz+G/rfBrk/g+0dg5b9VQpBVz8H6l6HbNFUOoqVr95zJmj35FJQWEBviTt94H1VmobbUwtJHYNMbMOJB6DZZLeFtLJupM7xCIH0LdJmm3ntQBd2vfb9t/QkhhBBCtIIEdaJdSM2rpNDqSZlHNJ7lKRgi+zh1XpCPCyO7+aNpEOzr0vIJJ0ucoIK6xAnsLt5KmaWE/oPuxGQxg18TS/Ii+oKLlwqOEifVPd9zJoR2B49A8mw+lGZXEB3k5lgmoZZ3hArqvMJUEBk5SM34aFaIH9v0eI1uEH9pw+eriqA8R93XbODiBtd+AM/Fqud6XQdzvlH3v75LLRuc8hIkqL6OHk+jsNszBOWsodf4mTiVqDh6INy+GtDUNd+doJ6f9qraN1efyU3VFyzPVss/zWWQskkFzyUZauYyZZNqm9JIUfMmVFSrdMsVVdaGBw/+qL4WHVUlBUyeMOovzpcUqKdg3wqMBUl4p6yH0X9t2KAsB3IPQFjvumQqVSWwfaF6b/re0vqsmUIIIYQQNSSoE+2Cv6cJg4s7q0d/z+h4PR7B0S2fVCPIpw3BXK3xf4exfyXbms+6DFVHzt3fg95+Te/hI7wXPJSkZsRMJ+2hCulKRbWV1dtz0TSoNNvoHFkzo1acBlXFarll7GgI7VW3XE9vUPu0nFFZBKlbIGZ43fU9Q9TSzoKjUFWm9u25+kL0EJWZMWZo3bk7aurl7fxYBXUFx+iX+TZW9Ozrfgs6v2aShpwsrIf6mntIZRa1mZvOltXzeihJA+9IWPs8ePqpoLrnNar8wRUvYtv0Jpt9Auj121O4e4aqYOjkRCo2CxxdAQYTwxKHkVlkpkNwI7Oqk5+HbYtV4e7iE+o9di5cdZBRUMWG6mHoPQYxVv8rjS6w3Pm+CqrzDkK/W9Vz+YehNEPdz9qlvveBnVSxciGEEEKIVpCgTpz/NBs+6b8y2asSEiahN7mqJX27P1ZBSveZah9TU5b9BTa+ruq8jXqo9dcvTsMbC+5WqNJrBJrrffDXNDixRu1Xix1VN8tTf+njSfQ6HQadDoumYTToYOMbatbINxQ8A6DrlRA5UKXAb4sPZqpArfsMuHpR3fMxo9Tts9nw419g9KPQ9xqI7g3mYtXGzReG3wdHV8Gg26A4HVa/iM5ahtE7lF7hWquGYrNp7DpegsUWQu/bVmOq3WdYo8xcxvxf55NXmceCsQuI84tTB1x91DLPvjdA1+nqubCefNF5JOl7PmKwb1dVBqA0A/xiHS+auROOq+Wvvp6h+Kb8Dlme0O8mx3adJ6qbzaJmY30i21Qstbom+Y5NZ8TStYmsoy6eKqgz1fxcVOSr+oo6g1r2WXAMcvereotjnmrTbKEQQgghLl4S1InzX94hOL4KPajilqE9VVbF7N3qeOau5oO6vV+pZYub3lLLE4feVffhvaoECk+oZZGNydwBez7FHT3X62xYdDrc40ohqOZ47gE4tFTdd/eDsD4tvhxXk54xvQIor7IS4mGD1x9RB4ISoMt4oHWBUwMVBTVfCxs/fmy1+np8NfS9Ts1QGer9Krjs/9RXmxU+vVEFnK7ecNMSCOjYeJ/V5eDScK9iVlE1x7IrAPD0CCExOpH66Wn25e1jW/Y2AH5L+Y24A79A8hq45M+qP1/HGdlQj1DeLE9jiFsIPSOH4+nTyIytV5iaFdTp4chy+KlmOaR/rCqYejK9UZUhaEpVCax4WpU4GH6f4/OrX6CDfxzEX4OLUYe/TxPZLfvMgeLUugC0JF3VLQSI7K++Z7n7wTNUAjohhBBCtJoEdeL85xWmUv1bq9WH/J0fQOExcPNXM0sdhtubVpltFJSaCfF1qdurNvEfsOp5FQj+/BgEdVLL+jQN3rpUzdKMeUxlMTxZbWFzbJgCEjHp9GoWrZZHoEqeotnAo4kkG1YLLH1QZW6c8hL4hOPlZsTLreafX+fJcPhXGDhPBR2BnZsvot6SGz5VNdq6X9n48emvwd4l0OcGKEwGlwDocZ1a/lm7hy/nILw7UdWXA5WdsqmyEZvegqUPQcdLVeBXj7+nEQ9XPZWWataWf83BTC+mh99oP947uDc3xU6iY1UFowJ6whc1+wFdvGD6qw0uNTp6NOFXfIiXyQtPr4jGx+MTCf3nwZY3VPISnUF9j+oXIW+Nze+omV5QmT+j+qv7G16DNf9BB8TMHwwBzfxhweimMpnWCuoKHUaon5uQnioDZ1hvKT4uhBBCiDaRoE6cd/anlFJWZaVnjDeuJr0K3Eb8SX0ANpjqsj+6+cGA2xzOXbU3n7JKK24uegZ38iPA26SWIfrFqCBFb6jLQmk1q6AG1GzfwR9V2YL6Ke5DeqnleRVFcOL3mn1h9ZJueIbA8IdVENZUuvq0rbB1kbr/wVUqZX/vq6AkR80QXf9xXRBXeBxW/Z96zYPualtds8COEHinur/3K0AH3afXHa9ddrjuRVjxnCo8XlUAOg3C+kKPa+DEhrryCIPvgEufaPxalkoVkIJKrmKz1uxNU9xcDEzoG8yPmV9hrSwmr7oCTdPQ1QSsLgYXHnGNBHOmKtjeYahKiJLQSLKXGon+zuw502qKhkfAtYvAp4MqBG6tUgXaa38GnBHZXxUh9wh0PC+0h5pV8wxR2S9bQ2+AxMsdn/MIarzt6VBVomaU3QNVhlQhhBBCXFAkqBPnlcIyMwfSygDwcjPQJcpLHdAboHbhXu9ZVGQewi00oUFaC7NFBXyV1TYOHMtiWIKb+rAc2Q/+uFt9CK/NvGh0gRs/V/vHtn+garUNuh0m/0sdL82BN0appXHja5YkalbHoA7UrFJzQrtBRD8oSlWzhQA7v4DSbOBzldAkumYWLP+oSiZSnqsKl/tEAip7Y2peJeH+rni5O/nP9tAv8PnNNWP8sq4uHMDuL2D5s+pa6oWpL7WBXI+rYONrqpTD1kVqlu7A99DpUjUj5x2uxrjxZfAOgs6ToNuMuoDOZgV0qp7cxjcZv/VdDvW7HK/eV9sDurr3zxvIVPvo/rAMLNXqe9NWlkoozYLEKTWZQwfC/u/VMl6A42taF9TFjVSJb4xudYlnyvLgxHq4/AXoeXWjAf2B1FIKysz0jPGum5U9HaqKYc9nqnRF92uar7NXK3WDWkoM6g8J3uGnbzxCCCGEOOckqBPnFU9XA15uBiqqrU1mrdyZZuNoVgTR1TYGJDgeG9U9gC2HiyguqyYyfyVsPAzDH1GJKrxDG3YWN0otqdvyrnpcu88JIP+I2vsEqs5bt6vUkk+PgNa9KFdvuK2mbt3Pf4WDy8DdG8rzVcBZP8CIGqyCJXd/e0AHsOVwEbklZpJzKhjXO0jNVmq2uhp4oPr7+nbw8IMh99SkztepGcCTM07u+bJuaeXUlyF+FOQlkevbgRdW/5muAV2Z3fNqWP5/Kmj98WEVZKash+ydMPBusJSrPtx9YMBcCO+n+ss9BO+MV0seb/0Vfv8XhrIcuux0hSGPN3x/es9S73NtfTdnAjpzJaxfoM7pc73jsX1L1H5LNz81wwvQ6TKsvW4kyXMYrsGTiLf3U6GCIn0Lvwrd/Rwfr3pO1bjT6VWB8ZOCuooqK/tT1R8n3F3K6RPXRMbPtsjaBQVH1P3IQSpjZkv8O6pi9u7+4N7Kn18hhBBCnPckqBPnFZNRz7jegWhpm9EnfaNqrp2UnCO/VM0u5ZeYoSgNyrJVbTjA22hmTHSJmvE6uk99WG+uWDeo2aQ/LFNLDntcVfd89GAY81c169P/5qaXV7bG2Mdhz1dq1mj0n2DE/Y5FzF081fLHk7iaVPIMV6NeLaXb9Iqakeo/ry74O/obeAeDuy/s/wrG/A1u/10FdWE9HTsc8QBUFqsZttqskH4xvLPpOb4/+j3fH/2ecVcuIyJqoHr/f3lcBYK+kWom7cgK6D8HOo5Xy1NDe9f1nbKpbsYvfbvKorllIQz4Q+PvicEFKorh3d7gG6X25TWTPRSAzW+r5CUAWxeq5aHRgx0DNH29GSyTO8eH/ZsDx0sg1YKvbzWBVUdVqQE3Xxh8b+uWutaWavCPbXSm1tVFT6C3icIyM2F+rg2On5KgLmqpqqlhIpkmBcTDJX9TQWhb92oKIYQQ4rwlQZ04b5gtNgrKzAR6u2A49ANYqzly+CgVYSF0ifRS6f+BPnE+HM+uoINbIbzUTy0hHP0ojPgjLL1fLZGLHwMJk9VeOmeCseDO6lafTgejH3Zi4BWweLoK1K7/pG4pZWOs1aoQNaiZL1Mj9dMa0T/Bl7gSM/5eRig6poqJg9qDVxvUdRwDR36ueT1d1dfwXo13GD0QrnsfDnwDST9Ap8mg0zEobBCfHPiEjp5hBBrd1UwmwMx3VVbMn/4CeSdg1GMqQIgb07Dv7jNUSQWDCyROhG5TG09CU56vAjA3H7WfsThV3bL3q4Ljbn4Ns1WaK2Hf1yqgQQdokLIRFl1el6il6wy1xNDXsZ6ej7sRnQ6Meh0eLgbIPqEC/ooCtaSxhaDOqlmptFbgafSCfrNVCYuKHKjIA1PdrCplueh//RujgjqjDbun4XLTU+URBEPvb/159fY6CiGEEOLCIkGdOG+s3V9AQZmFUlJI9OxCYnk+u2y9IL0cV6OeThFq9sbfy4S/lwmO7qjbE3Z4ucrYuO8r9dhcoWq+RQ2FvCPgFar2tjWmogDNxYe0QjNuJn3ri5XnH4WUDer+6hfUB/4u9ZJgZGxXs30xNXXsZn0JqZth4FynL2HQ6wj2rRmXf5wqTl5dDhH96xq5+8PU16C61LlANnUj5CWpW8QA8AplTIcxrI+diUtVCfqkH6B3vdpuvlFwzeKW+3XxUFk+AXIPw5dzVfHwq96pW1p5fA18cCUY3dVsYt8b1R41v2jI2q0KoGcfhJuXOgamP/8VNr+lXuvta+CbO1WSG1AJaUDN1jVSoiDY14UJfYMw6HW4GPUq+6S5QgVJns0nOtE0ja8zPiS3OovhAZfSw6cflKSo5CN6o0qW41qzxHLDq2qPJqBLnAjBZ6mYeFGKKsTewmsRQgghxIVHCiKJ80a1RSXrSC05wX1Jr1Leew6uBtCh4evZyN8fYkeo2TUXT4gbpoo718o7qr5m7oD3p8Obo6Egue54ZSFseQu+/AM8F8vxHxaw+VARa/YVUFJhad3AQ7rBkPkqG2LSMvjkBrUEEdQs0N7PIHkVbFoAvz8NtkoY+YBa9tcWOj0kTIRuVzacXdLpnAroyiyl/ORRwLoQHzS/GId9Vm4uXuh1NbNgyetVllBQX/d8Sv7G//Hihmc4XnS85bHu+QIydsD+byGzpq5gdSms/5/ap1hZqIJi3yiY/TVMXQDFxyFmEHQa0zAJSO1jvRFCusDYv9Yd8wmHVf9qdjjuegsuv/xFFaTXGaHLNIeSGE2xaGbyqrMByK7KUE82VU+uwzA1SxnYSdW2Oxty9sHmV2HD/9RyZCGEEEJcVGSmTpw3hnbx483NX/Fp+j+xYua5LX/mxd2/Y60sxlX3ZxjsWL4AvR4G3QJFJ9ResqsXw9d3qD1el/wJyjKhME21tVnVrEzuIbVHLGU9rHsZ8o+D3og+Zx+EqJhIX10COXshuLuaEWqJTgcT/6myTX44UwUctYFV0Qn14V+zqQAG1DLNjpedrretTQ6U7uK4OQ18jCSETyOkfvDU71a1DPLru9Uyyj43qppx+YchcwcBQEXBbv5RnMyb499s/kLdr1RZRf061O1Dqy6DwFiI7g8hPSD+EsdzvCNVHcLYkSpoN1fULVO97P9U2YPwXlBVqJZcjn4Ujq5UyzCzn1FLPptadvrdH2HnR+p+9EC1VNQJJr0L44KnkFZ5gr6+g2vOH6aCYXf/ulk6UFlG/3RclUEwnKVfsWZV4B3NWq+2ohBCCCFao7i4mKVLl7J582a2bNlCWloaOTk5VFRU4OfnR7du3Zg8eTJz584lMDDwXA/XgQR14uwqTlMzVI0kl/B2N3LviOn8lPUOGeVp5FZkYixNxWizqCCjMT2vh6w9arbMPUCVKKjPZgPPUDVjsvz/4OAP0G8OjLy/bgbKxYuYqCA6/NoHS8dxmHRDoThFzfINurvx61rNUJYFXuF1e5U6XQbzVqqZw9r9eWmbVEBXG9gBxF7SqresAc2GPatlfXlHVHBRW7KhGVHusewu3oqX0YfAlf+Dbe/DuL/BoHlq/EGdoTRTNS6pmZnyjVYBq83CULdgPAObWM5ay1IJQQlw51rH571C1fet43jI2AMr/q6SxhhrEor0vVktV/WOgC1vqsC463SV6dFgUnv09n6mlrWG9YYxf1azth/OVDN+zZUrqF2qiQ7CezfdLnk1HP5JlXLoPBWAeM/OxHvW23ep09XtXTxZS4leTrfQ3ipY9gqpyyIqhBBCiFbZtGkT119/faPHcnJyWLVqFatWreL555/ngw8+YMKECWd5hE2ToE6cPakb4cDXKqAb9mCjiSlMehOvj3+VX47/wqS4SRD8Pez8BGJGNuwPVDKNmBFNX1Ovh54z1f2v7lBfs/epfWk3fQVb31MzUb/9E52lHNPBb6FTTXIQk0fNuDeoD/gRAyFxsnpu5/uQfwjC+6paYbUi+zleP2oIVBZASE/1Qd8jCIJbCIaaYrPC/iWQuVMFb4Pmq9mg1A2QtQ+W/knNaF3/kUoUU5+lqi5oAkJdI7i5wz3qwftxUF0Cuz5VQV2t6z+FpB9B7wL/66eyWLp6Q0UBQ8OHMLb/7U2PNXWT+l77doABtzcMQMP7qvqAv/5NPfaNhgG3qPsGF/XYWq0COjQoOKqCulolJwWccSPh0RMq46W+mVXl456E35+H3terjJBNydqlZr0yd9mDuvPazsXq5zFmpMqOKYQQQog2iY6OZsyYMfTv35/o6GjCw8Ox2WykpqbyxRdfsGTJEnJzc5k6dSqbNm2id+9m/kh8FklQJ86e2uWH5nL1gb2JbIPxvvHc3rsmYNj+gSrYvewRSNzu/LWKUtQ16pdDmLlQpeXvN1s9PrYSDBqc+B1GPghVpdBpPPSdA4XJddkTM7apWaf0LXVBXW3K/oqC5scR2lPdQAVlm9+G9N3Q+1rnX0utnH1qLKACxdJM9TqP/AxpOwFNvbd7PoW40XV7vvZ+rs6LG6Nmx0522VOw4yMY9ZDj8+ZyiB4CSx9SNfvWLYA7VkHeIdxDm1jeWKvgCKChFZ1A99NfILgL9J0Fm95SgWf/OSqocveH6nKsIV3554anySrL4omhTxDsEayCu+4zKSk4QFpoLHHWSlwNNT8z3WdC+ta62njgELQ2qdNl6taSjhPUPsjwfk23Kc1RM88RfVruz0nvb0jmh13pPDi+MwNjW1FPrraeYu1XIYQQQrTamDFjOHHiRJPHr7nmGr7++mtmzJhBdXU1Tz31FEuWLDmLI2yaBHXi7Ikbo7IdeoeBqw8Wq8bBtFLcXAx0DPNo/JzYESqoi21ipg6gLBfW/AeiBqg9UsXpsPk1+H/2zju8rcJq4z9tW7Itee9tx46dvfcihJWwIWGHPUrZLS2UtrSlX0uZLbtQwoawIUACWWTvvRPHe+8lyZrfH0eOt+MMAoH7ex49lqW7dKU499U5533xglclVUFTKMQNl1srrfNOap3k3F37Wdtz7QOdA+KhcCPUFsCISmlvHHQVVOyC6Hbba8XrlSpP50Drbe/CNz5r//1fi3X/eU92te3vCXU7V05TJASnSNYcQPwI+b2pFGJHdDTxqD7Y9rM7UTfs2o7mKvlrxMzkncsBr5jAuFqkiqfWSkZa+Q4YfK0ItPpiaftrN5dXHJnNtrxFmKpymZK/DYDF1duZf/gLrq5vJEBVysAh96O7ewd4XNyz9k8sL1oOwGXfWQnf+ZVUWM99ggXuzTQ0rqTAXcaMiAtlB4ExkHF0E5JGm4uCShuxoX5YTLqjLn+E0DQJmt/0GjhskDiu4/MtjfDiODElOfeJjhXOPtDkakSv1qNXdxSif/1yDw63h38vOchbN47u+wYHXSmfx7ixx3QcCgoKCgoKx4XLDvZ6+QJdo5fRmmPJe/2JotEcPf7nwgsvJCMjg/3797Ny5cpTcFR9QxF1CqcOjV6yvXzkVVg5UGIFINikIySwm4vucx+HKb8DYy9Vi2V/h61vQXMJ1OX4qmnipInHCcv+BjOfblt+86syo5d+tlwEdwo378KO+eLeCLDnCxh1IwRGy60zbocIyuZKadsLimtzQDTHitjS6CVrDWDT//ou6sLSpQXRWg1Zl8i2YobJcehN0pLptHWtWGVdLJW6+B5cHnd+JLEDACNvkmqifzBHzmHSBDGCATGYafK1PNbnw6Y3Yd3zYl5yw8Ijmyx0W/ldzWbOsFmZrFKhMsfzRM1miv39qdBouUJdS5AthzSTtAquK1kDQLDGj+GVeeCywfb3YOtbaC6/H4z+qFW9/KHd+REcWgyTfgOhbe/n5pwGapuclNS0cOaQsO7Xba6CJY9KNXHsr9oeX/QQ7Hhfvoj4XUFbHAPIeW6t1tb3MO/ZA3nWg3xb8Tn+GiOXxVyPn6Ytq/CiobF8sb2EWYOO0TUzOEVuCgoKCgoKPxRe3zhE0TrpHmr1CgC5JgnPkrGT4JSuYxc/MwID5ctwu93+Ix9JG4qoU/jRCDJKGLROo8Jo6GUOqjdBB2J4kfMdBEVJELS9HpKmwsFvoGQHDJzTtmxjqa81ENj5ASRM7jbTrAMDLxNRp9FDv6O07rU0tBmMLP0L5K+H67+RKmLqNPjVRglHX/0s5CyD4XN73x5AQynkrRRXx/7duDW2F5fdhZmHZbbNWTWUSpWpfXZaq6mHSiPPgYiW2W9LZS7jnLZlI7IlR07rB7s+E0EHULJV/tj7/oiPiRrFH0Y9RJPLijvxHLT+wZy14yXe3P0GCcmD8BhDiCrYC/3luH5nzuIbaxG3x0zGlDgDlv8fNFVA5T5mfvU6ZXcsJN6/B9HicsDHNwMeaRltl6UX4KehtslJgH8vgnDtc7DFt076WWLuAhCcKD/NsV2jFQIi4Mr5EtNwjFW6GkcVXrxY3c3Y3NYOou6flw7in5cepbVVQUFBQUHhVNNQLOMczeXdP+/1SMdIxS7pJsq+DIJiT+0xniL279/Ptm3bAMjM/OnMsau8Xq/3xz6IXyJFRUXEx8cDUFhYSFzcL9Oxzu5wo1Gr0GlPMDKx+jAUrpQ4g+zLpZ2wpUlcFNtVbnBaYdXj0jaQvwFsjTDjr1It682xsDYPdKY+OUuS971ksy39h+zv4v/CoMu7X9ZaA4eXi+Dzt3S/zPNjoHIvZM6EOe8cff/tKd8pLpFJkySX7fkx0lqZOE6EyoUvSiWxYJ28fls1bHodcldJxe/6b9rETWc+nAu7PxUxOPsdyPSJP3sdbHiBQlUyO/ymERvmz5Dkdpb/C+6VCiUqmHAvDJoDNfugbBvOtBnkWyzE+ifiX3EQVjwB/c+HQZf1/Bo/uUVMXqBLK6TX66XB6iLQX4ta3cO3hgcWwftXiWvmrd93/ByUbIOQ5OPPFOwGp8fB1vr1BGktZAYOPGnbVVBQUFA4+fzY12sHDx7E5XKh1WpJT08/+go/BNUHYcfbxxaZo9HDoKs7jrOcxlitVoqLi/nyyy95/PHHKS8Xcfv2229z1VVXHfP2foj3VanUKfw4uF1QsAa/yAFHr8R1x6HFIg5Sp8GsZyE0RW4lm2D3B5A0TYw4DJ2iE3RGmPQQrHgSKg9JZem92d0LJrcTDn4LGgOf0MATG59gZupMHhr9UO/HljRZbmo/EY8DLul52feugMJ14lbZfqbPaYPXz4XqQ91n5S19THLZznlcQrhbqc2Vebe40RAQBfs+B2ez3OLGi8slQL4vZmDHfJhwDySMEdOXXe+CqxmsVfJ8wbqeRd2Mx+RbuNSpkDa97fG6AnA0UuCXgsMNeeU2BicFomptxVC1/tnxwqqnpGp52yrIvIBvy+ZTVLWKCEM0F0VfDbPfkkXXPCfRFdP/LDOZ7Tn4nfyMG0nDkNkEer1H9qVSqTCbdFC+G+ZfJ69l9jtSLW2l31nwYK60WXbOlTuJJiit6NR6RgX3seVWQUFBQUHhx6Sh+NgFHcjyO96G4becthW7efPmcf311/f4/O9+9zuuvPLKU3hEvaOIOoVTSpm9mI11q0jO3cWARf+WPLQ7Nxz7hra+LeJh8zwRF63ibf8CcLeI02RIN5b7m16Hhb+Xys+4X8u8HcicWofl/gcL7qN1rmx/XAoTWuqZsexZCB4B/ToZjlTnSOtBWLtvW0b3YvnfitctPz2ujo/X5EKJz+ly0GyIHw0Jo2HRw/LHccXj8ty65+H8/4jpSvFmqM8Fl1XaTEfeJjlqJZsgrL8Ecl/ymjh77l8oc3+pU+F/54i75ey3JHIhxA0Z50p1qv/MtmPa+yWU7ZK5M78gaUs867FOr8cjrpFAP20eTpee2OZtqMpHQVQ2VO6X+cfO56CpHCKzONI20L5/oCYXvn1Y7htDu+7z/H/Djvm8GJ3IC++N58zEM3lqylMdl9nzuXzTWH1QIi06R0+0N4rpC06rCGadCTJmdjSmUVBQUFBQ+Dng9UrL5bEKulbcDll/zN0/qxm7IUOG8MorrzBy5Mgf+1A6oIg6hVPK1vp1lNgLKI0KJBtQtVaE+sCivEU8svoRJsRO4KlRt0DVIREl7atxUUOgdDO0Wu47rCLyWqtduz8RE44dH8I5/5DHtP4w89mOOzvwLe2VxazKYmKcTkI8HpmVay/q8lbDG+fL8td/LVWv1sDxozHnXTi0REw6dn8KGedJC2RTMYy+TUw4xt4BlkQJ6V77nKyXOEFaK7MuAKcdXjsLWuohZTLEZLd9K5Z1CWRe2BaQ3prZN/F++Vm8BQrEpMTx5uVUXLGUuJHRUtFc/wq8dTFMfUjcIOdfK6/LZZect31fiuC0JLS9HrfziK1+eNN2prANNMC6dXDhy1C5T84/SAi8Rg/RQ+R9BKaHz6LAdpg4/6S2bQZGySxf1YHuXVD7z4L+s1i/cC4AG8q6+ZJg0GzIXSHnMeoktDyWbJGKKMixHc1sR0FBQUFB4XSj9nDPM3R9pblcuoh6y4b9iXLhhRcyYsQIAGw2Gzk5OcyfP59PP/2UK664gmeeeYaZM2ceZSunDkXUKZxSUk0ZlNgLSfFLRDXjMUiZ0ud1v837FpvLxnf532Gb8Bj+t6/qulD/C+UGYgry8kSx/b/uCxFbk38nz2VfLCYlEdkQFA3mTj3y0x4GtYaywAjuackhXWXg0cM7paI3sN18V20evHMpeH2VtsYyKN4gVZzwbLGab0dds5PNhxowm7QMTw1CFRAhQuvJTGl5HH0bBIWBrQZXWBLa4dfC1tekghY5AFCBJR6u/KBNzLocbUYeUYNg/P0dWzbVvZiEfP2bI3f1zjrqDqwjLsXXG/7dIyLgVj4h4jMgUiqAYenwwVXSGpqzTIRsK1qDHCNeOSaXXX6P8UU/ZM70vQdemPTbLu2Ofhp/+gV0Mq7R+Ut7pssO+h6iL4AHRz7IG3ve4Jwk32yfx0PF1oVUaWJITUvEkDpBBH5jMViSej4nnVn8KOz/RhxAfeKTkFSp0umM0uaqoKCgoKDwc6No3cnbzmko6iwWCxaL5cjvI0eOZM6cObz11ltcd911XHDBBbz22mvMnTv3RzvG9iiiTuGU0i9gAOmmbPZU76F55AxMul7MSVop3gJf3s3D4WnUR41iQuxE/LXduDx2pjZXWgwBSreLqEsaD0lfti0T30PpPGogzHmHNzc+zu49i9kN3HjTQpLMSR2XqzoorXggzolZF8C2N8DroaXyMHlFTURYDAQHiOgqqLTRYHPRYHORGWciwM/3T9DrAX0AaFR4W5q4uWodm4q/4u/OGs71eiQPbchcuH+fRBe0FzdaPdyyTFoj02e0CaWWRqnuBcXC+LvlMUczfHC1BGdfNg/KpNrUGDKIZlMSIUPbOV0OvQZ2zhcjE0Mg3LHOdz7dbSK2Na6hPSnToWInJJ8h+Xb+IWI2AiIwJ/mEZOtxHl4Om9+AETf0HO+gVvcq6AD664P5hykbgrMAcG/7gLX28Xg0BjT79pDhbJYFG45B1LldMvcHEpzeKuoCo2HyH/q2DQUFBQUFhdMNl11iC04Glbtlez+DHDuAa665hgULFjB//nzuvPNOzj//fEJCjsMf4iSjiDqFU85Tm59i3u559Avux8fnf3z0FTbPg7IdhJTt4NX79kllrRMtTg8rdtfgdHuZkBVMkL9WHB6n/1lyyIZec1zHel7yeawsWkl6cDpxgd04XgVEtt2P6C8946nTQQXbVZMoLmrmYKmVmSMjAPAzNbCg8lkyLQO40HCtrKfRwU2L4dC30FhAs6uZ9S3SlrrCUcO5MeNFRHQ2TCnZJC2YydOkrbB9GyTAmn/D+pflfng/6HeOCOScpfLY/q/E/v/QYgLH3UWgnxm+uFP+6J7/HzjvCbm14m+R27Y3IeNMOa+z/iPPHVwMi34HmbNg+p8gZVr3J7Q6B147U8RlUCxc+ILMLtbkiJnJtZ93+/72ifevlLnCxAlw/Veo/c0Ya4poCkjF1JgDuWvEIMUYAwk9ZPa1UnUIvv0DxI+S8PX9X/ctfkJBQUFBQeHngL2+Yw7dieD1SOTTz0TUAVxwwQXMnz+f5uZmFi5c+JMwTFFEncIpp7ipGIDSplK87ZwKO+C00pC/E1dAPKq0ywjOWwlxI7s6H/qoa3bSZBfTkcp6h4g6EMv8EyA7LJsvL/qy5wUis2HI1dKKOMwn0oLiYMhc/HbuB8Bf3zZbNz9nHhtqFrKhZiHXjziPMH9fIHZoKgReC7s/IECt4/exg9hQsYVbhv4KzO1aFuwN8MZMaCiBYXPEZMTtgNSzu7pUWhJkrk/nL3N6IOcwc6ZkwGVfJMu05tBteVPMUEDE12Xzune+jB8nwdupZ7bl4i37q1QtVz0N0x6Rylpn1vwHCtYfMaVpsjkJ2PyGVL9qcuSYnsoUEdUadt7K9/+Cit1iimNu56K1+t+w/iV5nzW+0HVfK6qq/7lMCdiCzVFEkD4TVu6S/1hC+zD/tuZZOPCN3B442PV4fgoUbRbjlwGXdnXtVFBQUFBQOBGO1xylJ1wtJ3d7PzLh4W0RV/n5+T/ikbShXAkonHIeGv0Q6ZZ0xsWO617QAcW717HBng2VXlD1Y9Dlq0mN6rn9LjxIT1KEP06Xh/gwP1xuLw6XB6Ohl3my5kqpdkUOFCHWC3aHm7pmFxFmfce8M7UGLngONr4qFbCQFKnWNZQy8JNxxJoHEBQUBINFLI2IHMGHBz6kX3A/zHrJPmsp2Qv5qzEMvhj6XQDvX8GVHhdXznkPAiM7HkjFXmklBV80gBc2vgELfgtTHoIpD7Ytm3EujNosAeJ+vpw4nR/2i9/E2uImJFDftmxDkbwWlVqET8kWqfKd/fdO+98nQmL0XR2FW/QAcamM7C9GKNteF9OUIXOlwnh4uVS+gMrE82nwBrE3/R7Ojq9CmzQWxt0Nz/rMbda9IIYxyRPk9+qcNpfSoE6OmxtekXbKxX+GS18H1+3YEkazMu9bhkUOI8zZgO6dS+QYzv2HmJtojLDySZlf7CmbsN85EvcQN1IcN0+ASmslT25+kjRLGjcNvOmEtnWExnJ4/Wz5T7e+CCY9cHK2q6CgoKCgAGJkdjLRGk7u9n5kiouLj9wPCAjoZclThyLqFE45Yf5h3K4Ngb1fgcoAIWlw4Ct5Mv1c0OhoVvlEiE/0OZy9twCo1SqGpsg6bo+XpTuqabK7GZIcSHJkD2Jwz8dQnw8Vu2C8b87L44HiTeJG2SqEgO9312JtcZMc2SlIG6S69bXvolprkIqdRo9K5SW0bhvUt4mfs5PPZmLcRPw0fmjUGuqbnXyf6w/eaUxZ9A+C0sdI/hxAzhIY0qmcHzcCRt4klbqRt4MpHNb5Klel2zouawyFzAugqQwSJwPgcntZurOGFqeHAYkBpEebpCVi40ti+tLaaqExSH5be6w18PIkMRuZ8nuY8ru25yY9CMmTIWaYRCY0+P7Y1RyE2FEQ7AvwdtqpGvUH9jVa8NOrUSekyXLBCRIavuEV+X3pX+HGRXI/KFYMYKoOSC5heyY9IO2bjib48DoYcwd/qlzFN3nfkGJO4fOIMyUuorkSooZB8lR4bqS8TqddDHG6I/NceLjspFgwv733bb46LJ/vMxLOINmcfEzru9xe1h+ow+HyMCrdgslPI0JdoxdRp+vDfKmCgoKCgsKx4Gdu+6L3RFGpxQ/gZ8SHH3545P7AgSfBVfskoIg6hVOL1yMXomXb5PfSbfJ70TporoZNb8CQq0kdcAGOg0Xk1GqweV2srlhFcMh4okxHdxp0ub1HWjHrra6eFzRFiKgztZXQWfQQrH9RnCZvl4Bur9eLy+05su0uBMVxxPFx7XMi6kyhcP4Lkic37BoxOinZDJGDMbUzF2m0u3Fr5KK82S+WIHOcCBePCzQa2PkuZFzQVlFSa+C8Jzvuf/ZbcGCRtC12Jnlqh1/dHi9Ol7wWu6P1D7VK/uCa40REGsxwxh+7GpNU54igAyhY2/E5jT/krIDGKhh1E0QMkPc13OdkGZwId+8Aj4tMYyjhjU6CipeiXrUTRt0qAvrcf0HeKsmRa99iqfOD21aKaUlrm6HbJedi+Fwo2ii5hU4rrHwC16S5ADg9TjFfsVbJTGLccLDV4fa3oLHWYAuKoFc5dJIydUZHjebtPW+TEJTQp89vZ2qaHFTUSxtMSa1dhLgpFG75XoR42hkn5TgVFBQUFBSOoPWD8Cz54vtECc8+bebp5s2bx5w5c/Dz6/l4n376ab7+Wpy/k5OTmTixB5O3U4wi6hROHTmLIXepzGQlT4WaQ5AwDnQBYg+f/4208OWuQjPgYlJT4sjdXs27ub8j17ad1w8EsfqK1UfdjUGnZlS6mZomJ/1ienHX7H+hGGa0b69rrTDVFYigSpiIyhzPxKwQqhsdxDn2weGGjlEM297iSKZde+OUIXPkBrDheWlxrNwL4+47skhsiIHmKDU0lhG15QNY/0ex/B98OWx5VRYKiofEiWKtv+MDGHk9qL0QPRxSzoC06WLosellEYMpZ8GyxyB2uGTMATht0FSOITiJcZnB1FmdJEf4RJshEEb9Cuy1EJoh21B1bVstMRgJ8zOjt9d3NZ5Z+1xbqHi/s2DQVV3Pt78FavNQqdSEqd0w/0qZCbTViojU+sENC8XMJXFc1/V9gm5HwadsbNlDdt5Bxox/Ci54XgLAv/kNpM/g0QmPMqlgCcMihlJDMyFjbpPzbq/H62fm/QtuQmetIyzUn2m7P4WUqXJsPeH1npDAGxc7jrVXrkWn1vXYbtwbIQF6oix6WlxeYkPa/ScTlia37qjYLXELwcdWFVRQUFBQUDhC3JiTI+rixpz4Nk4Rf/7zn7n//vu55JJLmDBhAqmpqQQEBNDY2MjOnTt55513WL1arkX1ej2vvPIKGk0voz6nEEXUKZw6KncDXvk54UFIbRfgPekh0Jll7ipFWgX99RqmDw7lycMitGK94C3ZjCpqsLSf9YS1htj97xKbMhl0vZTEVeqOIgykChY9GBryxFnS3gAjbyPIqCWodCW8dZG8hsvfgqzzRfxVHZR1/YNh9jtHjoEPrgaPG2a/LdXAhqKOVUFApVKRkRQOnhD4IlcerDoglSVjuBjG+CVRV2kjbsOrqB0NsPVdyDob8leKqAOoLxTzEoAd78KhxXIbdq1s67/TpAJ29j8IH3M74eZOvfIBkXIr3ixB6sYQuHm5VISAebvm8eTmJ+nffwQfjH8cVWcx0fq6dMaeZ9D2fQXvXyXn6eZlEBABRgtovLDuWRh7r7R7tMYGlO2QdtawDPndaYdPbuJgvzhcwaEcjE1gTNUBaGkSx8x75T+eQOD81PP5sPh16mpqGFnnYVhVHdTlohp6A8bARCoMes5Z+AKU7BZRd+1n3R/zptfhq/slquKy17tfpg/oT2A2QatRMTYz+OgLtlK6FXbPB1Qw5i4lR09BQUFB4fgITgFT5IkFkJsiT7svGGtqavjvf//Lf//73x6XiYuL43//+x/Tp08/hUfWO4qoUzh1pJ+Ls2A9tvARdOmsdruk33rsXTC5LRDbX6/hvuH38vymJ3g7bDyqPR+Je2LajM5baOOr+2H3J5KP9tvDx1ZlCYiQOa1d70PZdghNB2Dd+5cwZt9iHMAhvY4stwP2fAHzrwVjMIy7E/qf3zaHl7MU8n1VxUOLKYq9gLzmgVgs0QxwOaBwHUQPaVterZFA8ZxlMPpWEUfj7sPucLNsaxUebwO2Ka+Rsf1RGHY1eOwQPaztuEP7SeXO6wJ9MGz7AGKGygW90wYqL4SmQNnO3l9//lqZT3M0QdV+MEnFbE+NZNUcbMjFGZxAF4nSWCo/nVaaq0uo8/cjOsSAuv25rz4EeEV8Oq1wwyJYeL88Z68TZ6xW8VO2A3a9J/dH3g7mBJl13PslIxrT2D7sTDIdJnDkwme3icj/1QaZnavNwzPgIhpd9QDU+/m2aRBjmllRc2h2NxKoWiiPu509n499C6SauPeLE67YtcfmtrKtfgORhhhSTP36vmLeKhHNEf17Xqa1yqpSIW3BfTuepZVfoVXrmBZ2Ljr1SR6QV1BQUFA4/VCpIPsy2PzK8blhavSy/kn6v/NUsGjRIr766itWr17NoUOHKC8vp7q6Gn9/fyIiIhgyZAgzZ87k8ssvx2jsPT/3VKOIOoVTgtfrJc8Vy17PWbQUeQiuq2ZQYhAhgToJyX5+jFSyAHQGmNYW7Hx+2vmcnzQDVv1Twit9lvU90lo1MoYe/x+SAXOg/8VHRIbLN0PmVqn4fWI6H1ZsQ+/1SOuitQYssbD/M3A2SfUsdZrkpdmqwRRKbqWdKk8INeUtDFh/N+z5DKIHwa0r2/aZPElu7VCpVNKy5/WiSR4P43poP9XoIPtSub/2eZlhG32btCxW5kC6r/qVdWnvr3vo1dKqaIqA+LZ2ifuG30eIXwhjosd0X3UaPheKt+AOz2RpSSguTz2DDYdJWXEbhPWTCuaoW0S4WRIgMgsqD8ChFVINTRgn57IVrW8fKjWodTS5GthntDEwaRyJ1noSE2+WaIItvpZPrxfqCuGdS8DjQmut5uwhl1BqL2RAdDZENRxxONWqtZjVwTDnXTj0HY3JY6ix5hDvn4xaJaY2To+TFdWLMA4ezyhA0//8k/qf0qa6Vexp3I4KFdf6/Qo/TR/MTnZ+BB/fKJ/JuV+BygNRQ7s6ikUNEvMUnbFrJboHcq0HKLLnAVBszyfJmH6Mr0hBQUFB4WdJUCwMuhp2vH1swk6jl/WCYo++7E+IjIwMMjIyuO+++46+8E8MRdQpnBIKq+xsy2088nttk4vNOfVkJwQQ7SlD1SroQJwnO6P1g1F3grVSqlK9cfb/Qf+ZYnZSuVfMWSKyj/2g24mXkmFX8c2Wt1kQYCRLZ0bvFSMWRt0k1bw6Xwtm7WHgDGlfnPIA5HwL+ctIzx6E06UjLswPNu71LXv0XBODTs3UgSE0291EWvpYPVn+D3G0XP2suDjqjIgZigr0gfDieKl2XvNp14qPv0Vm1DoRZYrid6PauV16XFId++LXsPtzCSm/4Rs8bg9sqQK8BOV+KUYetXnSUmqvh4GXQUgyNJZJNfCsx6R9ddzdHXcYlikVOrUOAqNZWf4JBbYcdk07l7kJv253YAPAkoA3PAtVeD85Jo8LcpYSN+YO4vx9OXsGX/ti7WGoL4DY0RAYiXPwbD4ueokWj51h5rGMDJ4Ah5ZQV7mFnEgP3kA1wRf8iczAQR2Pr7kaVvxLhHlnh9I+EKqLoNbawMa8neibw5ibPffoKzma5afXAwcXgMcpLqhZF3ezg2MTZfH+yQTrwtCpdEQZeo/3UFBQUFD4hRGaDsNvgd0f9q0V0xQpFbrTTNCd7iiiTuGU4O+sIsJVTIW2ra9aZa1k/QE3g5NiSTnncSjdKbb2MYOPLNNkd3G4zEZMiIGwguVQsk2Cpo0hPe/MaZUKhb0Gtr1BYel2PjMHM33CI/QP7aVtrRcun/4vSD2fwOJV5JojcNkcaA8tEdfHqX+A6gOSgZbYrtIWGC2VJq2RqIKPiHI0QvJ1MPFeCfq2xMFXD8BZf2+rTHVDoL+WQP9j+Kc66hbY9i5E9IMVf4fsy+WPcdVeyZ8r9w09f3i9tH1e+jqEdxTKFY12wkyGjpl8rRSslgiK8CzY+o60J+6YD4nj0bnsTM5OpMHqIiTteqjdBGHp0m761f2gD4BbV8BrZ4qwPO8pOPfx7l+HOaHtrs4CNjBrO82WbXkL6gpQ1RWwt/5a+g+/XtxLDy2W1xo7vG1ZVwtsfV1En61OjHLw4vEJdLfXJdXDty8hHC8jxl7M/uxxxPgl0IWVT8p+UIlpTjtH076QFTSEN6vf40BlDk9WPsmFqRdi8bP0vtKwa8UF1RgGJeugpf6kuYkFas1cHnv9SdmWgoKCgsLPkKBYGHM31OZC0Vqo3NMx7kClFpfLuDEyQ3catVz+XFBEncIpITznHYJbmtkaeCEqRz1ZjrXUqSPRe+0E5ABDr5BZsk5sy22kst5BYWUz5311I+CVFszkyTIz9t0jEsR80UtgiQeXA16aKH90Jv8Wqg/zJ+shNnr9WPDNXBZdvb5t482V4hIYNUgcF6sPSxthTwIreSITkicyAeDLu+HAd/J4YxmE95dbe8IyxRCmqQK2vkaFJp6Sw/Wk9ruMwBYrfP0bEUQpk6H/rBM/ya2c8QhMuAdW/h0cjVC+XS7+876HHF+7p1onVUyQ+cN2mXP/9/lmXl5bxrn6rbxw56UQ0alyWrUf8EL1fpjxN9j7uVSNnhkIeAm69nOCkicB6XDjt7LOqqflp8sugspWJ783lsqsXN4yiBsrLavdMDZ4KhkBAwjqLOoGXY519wdUBYdzSNtA/+yLRDAHxUBoJzMXtUZEpb0OfAJKp9ZzQfSVVLVUkBaQCQ1lcq5cNhyEMDv2xiMtmR2IGQqopOro3+6YWhrhw7li3nLZPDFw6YEzEqbzbd53DI0YSqA+sMfljqBSwUBf+2xEf1hwN+z+BsxJ0s6qoKCgoKDwQ6JSQUiK3Fx26QpytcgYgCHotIkt+LmiiDqFU4POiLalnpGBeWLRX5ME/nEY8xeCDXZtWos3YiADMxM7rGY2aqmsd2A2amVOrDYPSrZKSLU5QVrpAHZ+CBPvA5dNnCABmqtg8NXEL9nERiDe5YY1z8C4e+T5He9IG0HBalj+hHzj5GiS7bRSsF5m/bIuAnW7i/uRN0H+GmkVNXfTXvDZHbD/G5j5tLhkxo5iQ/0InDY9TXmNTEieJK2OGr1PIPSBA4ugcr9UM2vz4Y1Z4hZ5w6IjLpVHMARK1bAuXyIkijfAprfFNAUk+y3pLGgshgE+oeD1QOE6zLX7gEg2OZIgb2VXUZd2FuTqpaU1eqj8cX9vNgB79TpWHfiACyIyiTBFiJnM8n/A4Dlw/n9k2bjhcNWHULEXRt4oA9i2GihY1b2oczlQbZ5HaEgKpHdymcpbhdHWRFyLDX9dfzGfuWcXHFwoQfLtUWulYuloAnP8kYdD9RGE6iPkF3McWy99gSe+/x07yr7ln7lncG7KuV2PadBlMv/oF9Qx/DtvtVQJQT5rI25oc+/sxJmJZ3LG1Wd0LxqPRl2+fL5AXEUVUaegoKCgcCrR+iki7ieGIuoUTg3Db5YMuOBkqZjEjcLotEHdHuobmsnXZeOo8yPb6+3gmDgwMZCkCH9MBg2kr5GWvUW+7DUVkDQRmsohc6Y85mfGO/s99pc7cceNon9SGH9sKmf2gU9JjRgA1na94H5BIur8Q6WC09LQ0ViiJhdeP0eqaefWiJhqpXw3eFrEFbFwI8SPbHvO7YRtvmiDnR9C9oXQ/yKC99ZSUe8g2KQDrxPmLpBqnroPF/V1hfDeHBFeLQ2gBuIGw8FlIiIGz+66Tvo5MtTsdoIhpG0mS6WWilL8SJjwTrvXtAMOfMkdcaCvOUD/2hWg8wWaN1dLRS1lirRgDL66bb2YISKSbDXcER9LVdkKvl34B25N+zvTNvwbdcVuWPUUPJjXtk7aGXJrrpY3Uq0Vgdgd656HxX+W4/71FpyWWKocFUQYotEYxD1UrTUS7u8Taq9Ok0qt3iRifNydUtn6+jfyZcD4e+DMR7vf16HFZC5/kkEuN7v81UQYI7pdrNnuYtUhDVqNlQn9DRh0vvcwcZyIvYZicFth+5tSrTV08XuV4z4eQQfyRcDgK6C+qPv3XkFBQUFBQeEXhSLqFE4NOv+u7XA6fxh5O/YDW9DXQWK4rqMFvo8j82R6k9zOfw7SZ8jFc3BSl+XLIiazt7YOKjwEBtpJGHkzWU4rWMtkDqyVQVdDY4m4ImbMFHHYfgZLrZWb2w0aPfuLmymqsjMw3EZE5XbIOg8OLAW8UnVa/Cg0V8Alr0mY9oFFMO6uI5sbm2nB7vBgbDgA232ujcNv6Zjf4vHIzFtYPyjZJO2TKWfI6/azSEVLowVnvbT2qTTw2e1gChORBFS1lGPSBuLv9sL6f1NrqyZgyFx0k34rImD3p1LR7Oxi6WcR4YSKm5xvg6ZWTExyV8Lnv5Lq0OAr4aIXO71BUZIR5/US+dUVVFXvJlAbRqPNjXXgXAIaS8VVszPFm2HeTJmBjB7QMZy09jBU7ZOWTJNPWOkDQB/AwopPKbEXkGrMZProW6SSaI5vq1a25vU5mmHb2yKwrv0MDvpaQfd/TdOU+9hQt4pIQwzZQe3E5Ion8S/bxW90RmZfs4Qkc1LX4wbWH6jH2iKzeFUNDmJDfd9W+gXBdV9C0XrY95mY1BxLRl3Zdtj/pZj8ZF7Y83IanbQcKygoKCgoKCigiDqFU4HXK5WW2jw453EI7GizHtlvGGd2WuXzQ5/z6NpHmZYwjScmP9HxSX+LmEb0QKC/Bp1GhcfrJcjo+4iPu1uqVu0vsDV6sCTJfXOc3NpjiYdblsvcV9oZ7N9QjtsDBZV2IlRqqZpNegCq90HOEjjga4fb/SlMvF9u7VCrVBgNmnbB6SqpWoIImJoc2LNATE76nQ0RKfKcPgASJ8Kv1sv8XkgSbHoFWprB3eJb/wCkncHOhs2sqVmKURPAHPN5LKzdw8O120hr2MkHFy1Ap9HJvF1tngjjDq83CcbeT5WrinJDAJraQjJjhsAbM9uWaWnouI61Br64U9pQz/gjr531GnsqD+BpSsBk0GNKuArGtBN0FXtkuDppkghhp1Uery2EyHYOk9velNfWWAYRg+HG72ROLiAca71UHK3uJlm2UwwEc96DJY9KNbJij1TPAM59Ar55EKoOYJ1/BQfHT+Vg8x6SjekYtQGyzJAroXIf6iFX9ijoADweLwA6jap7V9K40SI0j3XGoHQLOJuheCNkXKAMmisoKCgoKCj0CUXUKZw49nr49g+SDzftka4XoqXbYfUzcj88E6b+/qibXHzwU5weJ9/lLcI94R9oNH3/qAb4aTlrWBheL+i17drbjqVi0kpk1pF5pbRoE4WVNqJi4yDpNrn4DsuEDS9IaHl4P9AYIOuC3rcZmg4jbpUqmzle2iO3vy1tnsWbZZnqgxA3SGbgAn0zewERcgMY/4Dv+IZKhXG4OBcG529mTk0dG0KdqCu/Y5ujHi9wqLmURmcjIZoQCM+QWzfkeCt4v+AN5h/+Cq8XXonqT1v9TC3mM+35+CYRtPu+gvSzMSWMYmRvM4K73hdTFUcjDLwKcldIIPqZf+0w50ZzpVS99i6AL+6F1OkSf2CrZUb4BeRZD5EW0M6YxuWA968UcXv5G2LQ4vFI1c4UJl8s6IxgEPEWUnoAFdMI1UdgaJ8RN+waqSDrA3p+DcCofhaKqu3Eh/mhrTsEhWsgdlTHSnDgsTliAjIH6bRBxABF0CkoKCgoKCj0GUXUKZw4W98Wx0GA1DMgaXzH50NTxVSjrgBSp/Zpk7c5DDitNqZYbWjqC8Vl8BjQaY5zVqkXsuIDyIpvvdhvJ0AGXiHzaBN/J4Hn7XE0w7uzRXjNfqctOqC1Qgig1uAxReBpqkI76QGo2O/L2csWwac39XxQIzra0MdW5KPyephoNaFpOMStgal4jaEM7nchIX69xED4sLmttLiceLxSiao3hfrcIO2Ap6P5SG0+WKvafu9Uge0WSxLUHJSWU60eLn6l++V2fy37s/uyDXOWwAuLwRRBsL+FYEuCnM9Wqg/CIZ8b6e7PZOZMrRZBBzLTt+Qv4lQ58HK0Q67k+oTxaFU6CXdvZdcnEvANcMO3kDC628MLMmrJMvo+C9u+ltnMpoqOou44qA8MYWtKCvH+EaSe0JaOn4q6Fppb3CSG+3cfaaGgoKCgoKDwk0MRdQonTvwY0JkkO667CpAhEO5YI4KgL6YgQPbQ63kpZxXEjOxYwemNA9+KM+aY20RMfXi97PuyeUcqNMdCvdXJ1pwGLCYdg5MDO178t8c/GJImy/3Cjb7IhYnye+l2KFgrM2O7P4YpXauUjs/vY5n5WuwBUYxLCCN8YKcW0XZYnVZKmkpIC+40n+hDlTQVynfglzwDDn5LRE0O94x7mNdyv2Rx/mKmJ07vdr1WsgKHMCdVQ6ZxMIEaCzOqSn2CDjCYIbotQ5B5M6G+gMdikllqMvJQwyHOCE7sfsOtDJ0rlaiehKrDKgL4qg9lBq42H3a8L+9jSwM0V9LiqGdXbCSRpSuJS/A17oZnwtBrZAZw6DVdt9tcLT+dNglK9zOja32uoRSaykQIthqaqDRdjtHucLM1txF/vZrBSe0+DxHZkFsBESfuQLm+ZhVF9dUc0O4jKTkdjUpz5Dmnx4FOfRzV5mOg2e5i9b462Z/bS7+Y7t+nZYe+RPfpbUToAul386o28aygoKCgoKDwo6CIOoUTJ244PJjrMxbR9LxcHwUdAAlj4L7dUJcHa54QYTfwCp+RRzc0V4s7pN4oOWQhKRJ4DRI90G9G9+t1xtEs7YAxw8irsFPb7KK22UVajJEAv6P8cyneLKHaeGV2cPStEDsChl0JfgHgagRbbcdcM7cLa846rONF7FU3Ogg393Dh3lTBZ++cxQKamTD6Hu4Ycoe0KeZ9L6ImYoC0KcYMh7xV8N2fwevh+5ZK/te0C7VKzbLLl7VV7BrLJMi6XWurWqUmK2AQWemDpP0vvERaa90tItjbv4dqNS7gfYMbXI18lvMZZySe0fs5Uql7FnQeN/x3KlTug7P+D6b6XE7H3Qn+IbD6WWhpYEOYlj3xiag9O7nOMwm92iCfuwue63m/0x4GS4IINz9z2+PN1fDCGPnMnP+ctF/etFQiHyKzO2wiv9JOWa3MMMaH+REa6HufUs8UM5vjdbIsWCPGNUmTUdcMILjeCIbaDoJuaeVXHGzewzDzWEYGd5/ldzLQqFVo1SpcHi8Gbc+vp2Dbm1zX3AQ00bL7EwyjbvnBjklBQUFBQUHh6Jz8HjWFXyZaQ5ugK94Iuz444kJobXFTWGXD5fb0soEeKN0GLfWUVjez83ANdoe7++X0Ruh/Noy8BvwDIOMcuYBPniQCsa+8Oxv+dxZ8fgfxoX4YDRpiQgwSqXA0PB5A2hZZ9DA0VUqL4bC58pjOKOcJJP9u4e+hNhfLmCvJLp9PkrGRlChjz5v/+gGuLD3MS+UV5NXlyoMHv4HSzXK+vV6+2D+fxv8MhQ/azEkiAsUAJjYgFpPOJ6hWPQ1PZnQ0QQFxx3wqC55Ih+ocMSeJHyXPdXYvnfs12ul/5M6k8xgQms11Wdcd/Ry10lwJW/4Hh75te8xpg+pDcr98d9vjUQMhMBp2fQTb3yNII0HdJm0AGlUfv5fSm6SC27md0tncZv7SWCYhqhp1t9mDkRY9Bp0ai0lLkH+n/R6voKs9DAe+hMOLYfvb+HlFcJqQNt4KawXVtmqKbHkAFNnzjm8/fcRPr2HaoFAmZQeTGOHf43Jjxt7PocBQqswxGDLO+0GPqQNV+8WFFSRvcP+XULbt1O1fQUFBQUHhJ4pSqVM4uThtsPdTwCsXutmXsWpPLc0tbuJCDYxMtxx1E4VVNqoanGTEmjDGjcbVWM56zyy8lW5avE2MSDN3XGH5P8RxMn6Y/K7zB3MctVd+R2GVnaSaSoLqV0C/s8QKHqSq1lgOmZ2CpRtL5WdDKSGBes4a2ve2Mm/cCFTD58LmebKf1gv9mOGS7aYPEGEH8OF1sq/SHXD9V/RzO+HLX0GBHpKnQfbFXbavtiQAYPQP4TFDrFzUWpKgai/oTOyp3ME/V/+Zc6y1skLWRTDsGkanTuXrxrsI9QvFoPGJyqJNvvOwpWNbbPEWiXkAKFwv85DnPweHl3eNJXC3gL2KW1Fx6/AHIaqrQYrb62Zp5Vc0uRuYFnYeZp2vSlm4Wmbrag5C3CiJUzAEwOy3pcrYLgoCkKqh79gH1zqIG3EdAZqgDtWs48KSAFd+KAYrI26AvZ/IfKR/CIz/jSzjccOuT7AEJ3Hu8JG9b689Xq9sS2sQQ53u8AuWCrfHBdX7GTa4kUJzGNHBBnZW7uS6hdehVWv5z4ynafTWMNA8vPvtnERMfhpMfr2f14zYMXD/4bYHmsokgiNqqHzWfwjKd8DO9wAVjP41FG+AonVQqIKQtKOa2ygoKCgoKPycUUSdwslFa5BogPoiCO5s9dBxJs3r9bKiehFVjgqmhJ1NqD4Cp8vDpkNSOfF4vAxPi0Yz8hYCd9TQYHNhNvk+sq4WCSIPioEV/5KLYmOoCLfCTfDKFDYNe5tmh4rM76dDSzWM+RVM/R1U7JNQcY8Lzv9Px3iE2e/AvgUw6PJjetn7iprYW9RM6oC/MCh9BoSktuWmAQRESetlfQGYEygyWohrLMUeloYfSFXMFCxCsGgtZM5qE6CtnPlXiMxCW7pZjDlqctouoFvqiWssw+tn4a4oJw/GTCfpzP+T+AcgPrDTXOKMv4pbafqMji2V/c4SJ02PU1w8HVZpKW2uAHstTLi3bVlDgE+QuGXerjP2eqrsxRTXbsOp03OoeS/DLb54gfBsqcL6WUAf2LZOxjly64xKBTctlpnJ9BmEavs4W1a+G/Z/LUHdnSMrWkmfLjfZUaefwPqX2gLvLQlwwyL53B1139ulggow8o7uZ0P9gyUvccc7YAjAaA4jw9eeml+Wj9PjlJvDy/TYWXi9XtbXrsDqamJsyFT8ND1X004aVYfgrQtl3nDuApmd7czuj6CxWCpp4+7v+vzJwNvp99bPvjHs2GIjFBQUFBQUfoYook7hpOB0OyluKpZsrxG3iWujr9VwYlYwVY0OooM7Xng1uOrY17QTgH2NOxkfegZajQqLSUtds4uQQBE1KpWKUf3MgJdAfx24XfDKFMkgO+9JGHunVOrG3A6JE+B9aQM0p+2j2S8DtdcpO3S3wNqnxfgjLFVcJjsTkSk3EKOVnCVwwfPQf1avr7+4RmatSmodDBrWTTtaSyOsexbcLVQkTWaWfxOxcdFk6aP5/RvXEVzwDQy+VObbcpZD3ga4eVlHweV2Qu4qOLwMhlwBYRmSvacPAFcLQWH9WXDxApqdzW0ibtt70tI44R4xG2klJAVmPdP1OLWGjo/b6iSyAqC5quOypggYe5+IY1O4POayQ5mvOjXvfMKcVq5zO6kPicZ789y2dfVBsO5VOS/GaBg8u9fzC0hLpNcJ9hoRyZ3JWQZf/BqSJrQFc787R4R03mq49jP2Ne5kb+N2BptHkuI0ijhrPweadTGE94e6Qnhlqoj71pZZEAfXgnUwoGsltQutVVGVuqtAb09YBkz5o1T2Di2Rz19wEucknUOFtQKDxsD4GHGULW8pYVv9egCC9aEMMXfvznlSObwM6gvlfvFmSO+cKom8H43F3b8vJ4uoQdLOrPWXdtzAaAjtJ7+rlf/KFBQUFBR+2Sj/E3bDgw8+yOOPP37k92XLljFlypQf74BOA25bfBsbyjZwXdZ1PDDygQ4Xwv4GDfGGjhWFmkYHWq2JVGMmVfZi0hc+Bc1/Q3X1x0zODsHp9mLQiaDZVdDIwRIrkWY94/oHyxxUpU+QlWwV0XXmo20bH3wl5K5gZLyK5vhoNGnfycVoymTY+IIsM/4e8AuBjLO7f0GOZtj9idzf9fFRRd2AhAByyqwk9TSH5HaKAAMsai2xQUkUNZUwSZvN3uAkxuV+BoZgmTWryQdVkZhntJqK5H0Puz6EbT4bf2utVCe0fjD+txKErjUQAm1GKFWH4LPb5L5GB1N+1/2xFW+Q9rnkM7qamPhb4Lov5Dx3F/juHyyvbeMLYulvjoeaQ+Laaa+jVS5ZakrBo5UK7sbXJP6gxRdX0DojdTQqdsOOt0UkRQ6HpIkQEN72/Pb3RHxsfw/O+rtUlIJiRNT5qjqb6lbT7G7E9c1vYddyyDgPrni3bRu2eghJh28egpIt8jl7uAQCIiW2wz9YguH7Qnh/GHm7nIujiR21FpY+Bisel33cuweN3sgNA27osFiwLpQgrQWbu5loQx9dYU+UAZdI+60hqGvQeytZF4sDrP/RYzNOiM5trK1upQoKCgoKCr9wFFHXiW3btvHUU0/92Idx2nGw9qD8rDt41GULq2xsOtSARg3TB5+LsfBtOLhKnsxfjbr/LAxqFc12NxX1LdTUNgIaaqurwaYSoXHp/8TVcuJ9XXdw0YuANNAFAPi1q74NvhqsNRLD0Nu3+3qTBKnnLOs639UNkRYDkRZDzwsYQ8TO31aLPmYEnyVNYemuMqxWHdGGNTDgUhj7K2ll1Jvk4rm9wKraL+2Okf3B5RLh2kpPVSBTqIiRpgqI6N/9MtYamYFsaYL1r0rbZ+yQtnMQniVGM72Zzdhr2yo5jqa2dWf8TUScoxkSxort/XtXwv6vZLYw6zywN0D2hR02V2TLo8nVSL+AbNTtDUhcUg3F64FvfgP+oXDnhrbnR90iIix5YluL4DWfQNkuiJVZtP6Bg9jVsIXYWl/EQckWEdvb3oTCDbD9QxGAE+4TF87Bc2S5/rOOKuzb0+iqZ1v9emL9EkkxJfRtpcq98tNpl9fYDQaNH3Nib8KLV86N0wbWSgiK/+HCyo0hMMf3ZYLLLm2/lqSOFU6Vuq1aq6CgoKCgoHDKUURdOzweD7fccgsul4uIiAgqKip+7EM6bXh66tMsLVjKnIw5Rx6ranCwu6CJqGADGbFtAsXhlOEYtwfcHi9kzoQ9n0vVKaUtnHzV3hqsLR5CqCM5fyEmaz5NYZcQkDlJhEAnMdAnjiUcetIDcusFr9dLXoUNgKQI/45ZdnsXSMba2F+L62JovyNP2W0umq0ixlwpM2DCRW3rzXy6647Sz5Fq3aCrIHJg347fPxju3CjCydJDVUdvkupKzgoo3QVaHTQcllZAlQqGXCfVEa8Xqg/I3KKxk3mMKQKSpsjcXfq58jMotkMVpaDShqbaTmxYGuxH4hEGXyn7D00/slyds4avyz/CixeX18mAoGFt+4n2BYqveU5aQTWd5qjiRsCt33d9fe0cL4dbxjHcbwDE5IF/lLSlNhRDbY60qXo9IlCTJ8HIjlWyY2FD7UoONe8lp347SQ0RYjOcfbmY+PREVAa4mkWA95KrqFKpUKGS92TDc+IymzQV0voY23EibH5V2iyjh0P2pT/8/hQUFBQUFBT6hCLq2vHvf/+bjRs3kpmZyUUXXcT//d///diHdNowPHI4wyPbOfN53DQcWIXObmRPUzLpMUbUPsGTHOWPRgP+eg2B/logVCoqnbA7pFrRrAkl3g+2Jz7EvgaY4fRIa+aOD8WOfvj13WfgedyQt0yCpJMmH7/tfC+U1bawLVfaCA06NTEh7YTGgnuknbKxDK79HD67XSorF72MVmtGq1Hhatdm2ivmeCrTz+Iva/9CaE4oj4x5BE1vmYCt+Jk75rJ1RmuAMfeAKRY+v5MjBiEOKxhMUs20N8C3D0FLncwiTvhdV3GSdlbbfWNoh6dKauxszhHzm/EjHyJi0GwITpYYik5oVFo0Kg0ur0vy59pTvEXO5zn/gvixkDKl4/ML7oX9C2HybyFxnAjH7ljxOGz8r3wuLnheKpqRA6GhAkp2gM4EYd2Hu/eVCEM0h5r30r/FH3XVPnmwci/EDOt5pYxZ0qaZ2EOLY2f2LZCgdo3uSHzID46z2fez6dTsrzNFm+TfUcwwuPDFjv/uG0qlMn0seZgKCgoKCgo/ExRR56OgoIBHHnkEgJdeeolly5b9yEd0mlO4mpS670hGxZ64m48IOgC1SkVSRM95bA1WFxsO1qFWgccLoWYDzphbobAJt1dcMclfC5/cJCvoTW1tcu2p2AWHl8h9UwREZHdd5gTRadtel0bdqf0tfYbMwKXPkJmkPZ/L43u/wG/4XM4YFEqL00NwQC8mGu349NCnLC9aDoDdZecfk/7Rt4P0uMSq39UC/S/u2NaZs1RuY+6AB/Pgndmw52up3l02DyyJkqe35S28aKiMPxdz5PcY0ibAvi/gwGKpuB1xj+yK3hdirQL0Og1YsqUi5nZ2bB31egn06rk0Zi42dzNRbgPsnC+tsi6HuHB63XIxP/LGjjtxO2HT/+T+gvtkZ9d8JnOUnQlJkZ+GQNj0Mgy9FgZeCRqTzDOOOP4KXSsDg4aTaszA4HJDw5vyekM6u8F2ImqI3PpK3irY8yWYY2DIiR9znxgyVyq2x3KcJ5Nt70j8RNWBtkB5kM/ouhcg+yL53CooKCgoKPzCUESdj1/96lc0NTVx3XXXMXnyZEXUnSg+m3qVRk92clvlpsHqEqfG3AUy5xSWxtbDDRRW2RiYGEhypJHiGjuNNgkZH5wUeMR8xKBTE+Cnwd+gkWqQxiDW+4E9mFCYIkCt637ex+uFdS/KxfaYO/r+7f6m/8GG/8LIm6Aml8Cc7+lvGkedORtnyDCwDGhb9sIX4LynQOcns2vRQ6RSlzoNAGNzAcbdn0DWhZIHdxQmxE7g+a3P48HDgfIteL1e9hc34/J46R/hRWMI6L4aWXMISrfK/ZA0qXKBuIi+d4XMSdUXw2Wvw+Xz4MA3kH4WBEXLcr4L552ZD5KTNBdjTTUztr+LauWT0FAGuSvgN4d6PO6wID3TBoagUqkIMmqlvXHTyyLQBl0tYtvtktD3kq2YL3kV84CLYf6VsOcrEaHXfN62wUUPS4zF3K8hKJoGqwtri4fIyb9DtesjXxslUiHtDnsDDLwM/PzBbZP8M0uSRDhkXXDU96GvGLUB8hd29K9P2jY7MO4uicmIHgJh6Udd/KQQECm3H4uh18jsY8yQjhERhet9Pzf+KIeloKCgoPDL4KdspqiIOmD+/PksWLCAkJAQnnjiiR/7cE4fWhqlgmIK7fpc9FCZvTIEHpmtqm50sGJ3LSqvh8lr/0OwsxgeOEBhlQ23B4qq7SRHGokP86OizoHRoCYpwh+1rwLWwVkyvJ/Mi7ns0kZXndNVGAVGw8TfAaqu7YL7v4ZFv5f75ri+z+etfEpEyZJHwV6PAchkBwDevUFw354j0QHNzmb+tu5vGDQGHhr9EPrO814f3yiunLs/hdtW9b7f4s1k1RfzvC6RT2t3cUXFXspr7ewtaiarZTWaAxuxh6bgN/TmtnXcTjj4FbicYIqUXL/3roTU6XDJf/Go1ajD0qFsJ0T4Zg0DI2H43I77HnM7xI3GsUOEm1NnFhFsjhVRlzSh92Mv3Ij5o+shMlvCxUu3iqAD2PeZL4BbB8W+QPTDyyUyoMXX4uewymfsxu9g10dSkbHVQOF67OmzWL6rGrcHBmXeTerU38OO+dDSyJrQOB775DxMOhP/mvwvEoMSpbq1+E+y3eHXgjkRYn+YWACnx8n3hd+TGZJJXGAPGXkngjkWLn7l5G/3p0zsMLh9ddfHZz4NG1+FQX2IxviJUlpvI6eimXGpoUf+5ikoKCgo/HT4qZsp/uKHD+rq6rj77rsB+Oc//0lYWNhR1lAApAry7BB4MgNyV3a/jDlewqV9OJwyI+dVqXHozSLIXA4GJgYSHqSnf5yYQwT4aZkcb2Pkvj+h3vVhz8cQnCgX/s+Pklvx5q7L6Izdm1MEJ4kxi8Yg9/vKqFvkdcWP8j3QdvGl8rqlAuhjUd4iFhxegLp4I5VbXmmz8G+l1eY+MBq+fxw+uVkqep2pK4DXzoL516C11XJbXQMjzKkEGvXoNCpCPcUAeOtycXocbetV7oWi9VC2BZKnQMku2f6ez9hYtpEx747hqugI6m9awWLL9SzeXoXN4e7+dccNY/CMSxmUFMjEARGoBl0FM5+Fe3fDpa/3fL68HomGqC+CAwuhNl+yBFs/F44myF8u0QTn/EtcQCf6wqsveEnEZswQPDoj1qgMmPqQWOwPmgP9zsJL2ynXVu+RnLdBl8PIG/lk87PcrQsn2lrDW3vekoVCUsAYildjYGdKf5YkxmL/5jfwVLa09J5Entn8DPcuv5ervr4Kp8d5Uret0InowXD+f47+BcNPFLvTzaz/rOLq19bz7JKjOwgrKCgoKJxaOpsp/hT5xVfqfvvb31JWVsb48eO58cYbj75CHykqKur1+dLS0pO2rx+F2nyw+sKoy3eJjXx7vF747o9QcxjO/RcExRAd4seINC+qugIiq3yVqU2vkTz+LpIjO83YLf87bH1bssFSp4kdfnc0lUvVxws0VUJjObx1kTx29SdSzeiOyGy4e7scZ2ubYWcOfiftliNvgrQz5LHxd8kNRETu+RxWPyuVpqs+Br82x8fhkcPJMsXyR8sgaW/MWy5mGK1c+r82S/03fW1/ehPMfKbTgaiO2NV/Zy1kSWp/ll+yEJO1hLOb5rEv0ECVW0+x2cIUVTvzFHOcBJODWN5PfVjaM7MuYHXxamwuGzvqDrLTqTnS7lrV4CA+rHuHRp1WTWpU6/ukE3OR+iL49xAxpbluAWx6Dba+jSc4mZVj3qPB6mKMIY7wobNlZi00VV7LhAdhxzsynxXhc/McfYvcQCrAZTslYB7Yv/oPrMjMYIRlPMMv/d+RY/IHJmWHYK/IJeqdc+RcXvA8DL6ShxxaQgwGRlgGsS9hmu88xMA9OylrzmVN/Tdg3UcYZQxuKIKdH0Li2O4/C8eBy+Nq++k9ysIKR8fRDO/Oln/zs9+Rav3PBLfHS3OL/BtssCtfACgoKCj81DgdzBR/0aJu5cqVvPrqq2i1Wl566aWOdvQnSHz8KQoG/rFIGA1n/Z/Y1w+7ruvzZTthzb/lfkR/mPYHABEMBotUauz1EJLc/fbjR4uo0+jh6QFw1YeyT7ezo9FH5rlyEV+wHlY+AXEjJaQa4OAiCczuKY+up1m8Vr75rYjS6pyOeWitxA6HqEESMh2RCTFDOzydGJTIBxd9KbbzzZUyt9UenZ+4NFbuFTHY0tT9haolHm5awnsbnmR+3RbSjeGybl0+WreVAXVWygZfTGJINpqGEnBaISxDzE4m+lpMVWqZQ7z6YwCuaC6noLGAZHMyo+Oz2OIUd8qozll7Xg/s+1wEfP9L2vLfWilYB7V5ANgXP4jfnoUAWFUB1FgBtJRqUggPt8O4+zuuO+iqLi+1uKkYP5eT0Dcvki8OgpPBZWdvpMxElreUdFknOEAHLRoRliAV4NVPE7L7K1BrCZn5L8bFjGtbQW8iWJOKuTkYm8dKTNgIiPOKeD8eGkokKy5yYIeZxvtG3MeAsAFkh2Wja2cI4/a4WV64nGRLMinmlOPb54+BtUacVPviuvpDULod8nxdAfsWQHg3GZWtNFfDG7OkOn7tZ32aWf0xMRm0fHDrGLYX1XPpsB+gVVdBQUHhJNPkaKLcWo7VacWoMxJpjCSg9Yvknxmni5niL1bUORwObrnlFrxeL/feey8DBgw4+koKHRl7R8/PhaZKS1RNHqSd2fG5wEi4c5OIuu6s410OmX2KGgxl2+Wxg4ugcKUIlqHXd3QSTJoIX/xaBEhNroREW6ugahdseB5G3YnNCXkVNiKDDYQczW1y4e9h0+sQN1xEXfqZ8N2fRCRNuLvjshodDLmi521pdDD6LnA78Gr96PZrg/D+cN2X0FLfvZ29oxl2fMBloUNIHX4zGdUF0pIZOwK2vQ0OO1HmbLA1wqaX5DwMmCPnr4cYh0hTJE9NaesLH93P0nWhmhyppBX7BG3Jxo7RBQAZ54jYttWgN8dTExxJcF0FAeZgMiLU1FvdpKrVEHt5z+fIx8ayjdz87c2ku718WJMHQE7yAFLPf5vh1hzyrIcYFDSi40oeN871L/KPyrU4xlzO76MmYRw4h5pv/kEI4FHrUGd0NT/x0/gzJ+4mvF4vqgQVTO2ySI8caNrNuprlpAX0Z1zAKNj0ojiM2moguW1DBo2BWaldA8tf2fkKL2x7AZPOxHeXfkegz1ToJ0fBOljyF8mRDEuG/BVitDOsY0dDXrmV/OoGsmJDCDfrf7jjiR0hrbeNZWJ00xvFm498udO451v2xc9mZFJI7+v8yAyKszAozvJjH4aCgoJCj3i9XjaWbeT9/e+ztGApbm/byIZGpWFawjTmZMxhZNTIk1oo+bE5XcwUf7Gi7u9//zv79u0jISGBP/3pTyd9+4WFhb0+X1payqhRo3pd5rTD65X2N61Bqmm3rpDfD3wF+7+UYGq1BmuLm4OVBsLMcXTbHFm8CXZ8IPeTJ0vrZfZFsPcjeayxpKOoa64SISMHIWYcez6Gkk3SquVxsjXXRnmdg8PlVs4b0a4X+tASn6X/7dKuCLJvlw2cdnikGja8DIsekueMZhg29+jnIn8lVO+HtLPBYGFb2SI2eHPJDBjEpHqdmJYMuORIWyXRQ+Rn9QGpfsaObrP73/wGrH0OLTAq41wxeQmIgsvfhK3vyTLxY2DAhW0DZkfOx3FgrYK8FXL+8EpV1evtPhJCb4Ir50PuMnL0LWwfOJDxlslEG5Noi3k/SkXUR2FjIW6vm31qWD3sXOLriimLiifBbSfRmEqisZtqy65PWLHyb8z3VfKGpZzFRWo16yJvIGxwNN6wdEZ3Z+Tj43j+09nftAubx8qexu2MCxjdJpwLN0BTNQzsPZTb43tvPF4P3h+xL9PqbmZf407i/ZMIN3TzHq18CvJXQ8FamPkveayxa9v4itoF2Iz5VJUP5RJzz9EWJ4xWLy3LfSFlMgy+AretgYu/j+Jg01p+c1YGv5p6YvmDCgoKCr9U9lTv4eFVD3Oornu3a7fXzXf53/Fd/nekWdJ4bMJjZIVmdbvs6cTpZKb4ixR1+/btO9IL+5///AeTyXSUNY6duLhfWAtNa45Y+W64/A3IPE8eL9vRVukJjAFzEntKtRRW2cmtsBE5wkBds5MthxsIC9QxNCUIVdQgSBgr81S2WnHPPPAtpJ0jxhqxncRw3HAITYfqg2J8ApB6prRdWpJB64fJTwxETH7tPvIeN7x/pc/Sv0iOG2DG32DbuzDhXtBopX1UpRaxWnMIcr6F1Bndn4ey7RJ43lQuv+cuhw2vkzt4AN6IOOoKv4fPnwe8VDQWERGZDZV7oN954ha67Q0RZK4WSPHN8cUMAY0Bp86fB2wHGRYUyHVut7RlGkPFoj8yW4TeiFulmhne/6hvmd3hRqNRodN0qubt/0LOZSs6E8SNgqAePtN+Fuh/EenAiRjrn596PvUt9QTqA8kKzSB467skNCCfoc7veSvBiQxwuol0uXEagxlqSgCPh5SoAHJVM8mMO/n/toeYR+H0OEgzZVJNM/aBs4g5uAbVImnNICRZWnN74NZBt5JiTiHVkkqQPqj7hdxOWP2MnPveKuInwKrqxeRaD7CjYSNzE7qJXRh4qQi6/rMg8wIoWgvhXTsagus3cuaybyiP3w39fkBRdyxoDXDRS9haXBT89TvAQ2Vjy499VAo/cRrtTppaXESbu58rbo/V4WLd4WqGJ4Zg9u9b1qiCwunKmpI13LPsHmwuW5+WP1R3iLkL5/LM1Gc6jj+cZpxuZoq/SFH39NNP43A4SElJwWq18v7773dZZteuXUfuL126lLIyybyaNWvWDyICT3uaKqB0m9w/vLxN1FkSxH3S6YB354C1lphz3qKQYQT5a9GopS2y2e6m2e4mOyEQgyEALnsDnuwnF/RlO2Dfl5C2GPp1054IcMV7sOBemfvJWw1J4/FmnM+mQ/U0lFczLDWQhDB/Av3bfeTVGtAH0KSPQKcJ4Mg02ZAr5dZK6jS49lNxkmypg9xl8nrqS2RmT+fXtmze9yLo1FoRgj4zkdGbK9g+ehZZ4VPwql9G5XHy+LbneKKfr42sYA1kXyqi1GU7EgMByNzdfXsofHEUTxYc5JHwMLjiWzH9uGubCMAAXw6fJbFPb1dFXQtr9teh16oxB26kuXAZCWnnkpk4FQKiRdSZE0BrhOp9sPczMTRp7yS66hnY+labI2UrzZVwYIFETWTMAv/gPh2TVq3l+gHXyy/WajmHHhfoeunRjx9F5B2b+E6thY2vonplGmScR+YV75IZ17aey+Ok2F5ApCEGP83RL9iOsPhRqVad888jM5Px/snE+yfT7GriveL/4va6GB8Ry4DW6Axjz5XB1td5TvI5ve93x3xY+je5H5F5JNvwZGLSBPp+9nB+B10ut1Yyzu92sTMOHsBYXkhUeRGc45IvQn4iBBi0vHPTaLYX1TNn5M98zlnhhKhtdjDjmRVUN7Xw0tXDmZHde4fB3e9v47s95YxIDOaj20/fi1YFhaOxp3rPMQm6VmwuG/csu4d5Z887bSt2P5SZ4g/FT+d/31NIS4t8Y3v48GGuuKKXeSgff/3rX4/cz83NVURddxxe2na/39lt900RMPFhEWar/gNAjH0vZ4+egUGrRqVSkRzpT73VRVigDoPOVzUyhUPGuXjzVuF1teA2RqDrzewgNE2qCh4XrHwSAsNpKj1MUZNciBcd2MPAYUMAaDi0Aat/LFHR0RQHjmTD0GfRuxuZ7vS07b+V0q3QVAZJUyTKYPubEpMwf64EnzeVwxmPHFm8OiINs6MebfxEiREAuGo+MQe+JSbtKgiKwXrDQu78+mo2GnRcptUwWhsIsSNlu2PvgZaGrlUxlZqUpmoA/qIKg7KtYs/fzm2Tkm0SvJ114VEvrOsbmvB6ocXpITR3CeM1eioPfcMHmsP0Cx/A0Nj7pQJXvlPaSM1xUv1oz6qnwV4H616kMfUCmu1uPst/nU/3vs2dpmRmGeOkajjytl6PBcDp8qDVqKQd0u0EVDDmXqmiBsV0XWHT/6Q9duRNULgeVWiatD8C5H7PzrWPsC0mgkGW0Qw2j2J51UJyrPuI0IRwUdwNbW2vvWGtgVVP4QVWlHxMrWoPk0PPJlgvos2LF6+v3dUT1g9+tUHaUXtyXD0GKkKj2HLGHFIKD9HPkgh7v5QvLfqdJcZAJ4FxIVNJNWUQrD/6N48ba1dxqHkPo4InkWrK7PDcSt1ZjPFuYIdpHBN/QoKulRFJIYz4ic/T/VLYX9bIG2vzmDUohrGpvX/5caqpamo5Us3dX9Z4VFHXZBd326YW1w9+bAoKPxZer5eHVz18zIKuFZvLxsOrHuaT8z857WbsfkgzxR+Kn97/wAo/fVwO2PUxRGaJGQocCdxGowevS1obW13y1Bqpcpz9D2lzHHUz/vo2B73QQD1nDOr0H7xaDVe8x+FSK7sOV+FRa5niCaTHmo9KBUOugj2fSQDxjncJcDuI97RQj4WEXQ9Cvy9o2fI+QUsfIQA1rsAYmkb8HQCHJhCHq5Oos9fB7vnYVUZa6prY6n8G2si7GZ2oRrf6RWkNNYWLe6VGzyZXFf8t+oD0iGQuiojmiASNHtx2ngBj3AjmnvsKQyq20S/rGgnfbsUQ1LFKd2SlEDj3CdjxHrrITJl3S5wEeCUWwc8Cb1wgIqg2Fyb9pvvz5LTB2udJKd+Lw5mIv72MunipXNn8/Khz1bCtfj1DE32B3NFDZZautfLYnnG/hq1v0zLyTpbtrMHt8bKnuo4SRz0fkC+irt1rW1+6HrfX3aUVo7DKxqZDDVi0LUx2LUK95XVpvb3oZRjcLkx60+vyues3A771CenC9fKeqzQw5x15z8p3UdV0GKs3iD3Vqxn86jWMikghJDKKwes+h/DX4abFUmGtPACbXxchnNAphNw/GLIvpq56F/vCzNBSwr6mHYwNETOUAG0gF0ZcRmPFFpINGWA+eYYnG/TVFMenURyfQb/QVIkHaa4UR9jznpb5shPB60W1/B9E2evgjD/BUTa3o2ETLq+T3Q1bu4i6960juLXlVfw9Gvae2FEp/Mz5w2c72ZhXy+I95Wx4+CfSqusjPTKQxy8dRH51MzdM6MGVuR3PXjGERbvLmZoRzj8X7mPZvgr+NCv7JydWFRROhI1lG3ucoesrh+oOsal8EyOjRp6ko/rhOV3NFH+Rom7evHnMmzev12X+/Oc/8+ijjwKwbNkypkyZ8sMf2OnC9/+ElU/g1Rmx/WoXRkuoGJkERIhBSf5yqWJlzBSDjfJdYmwy5vZj3lWAvwaPxoBOo8KvcxUNoC5fqnP6QMl3O98Xo7DlVVQ1OYzwrIZ1L0tboiEQVYMEdavxoG4sItXixBtqIsBP29aa2VgKm/8LGgM1HjMrA6/B6/TidUt+VJXDQvTta8SB0i9QqnfAH+s2U9hcSn5NMZcnXNvr65oUN4lJcT20kgIlTSW8vONlRkSOID0iET+NkaRRN0O/M+HA1+LEufZp39JesDex0aDnmXAz0xv2cX1PG17/Eiz9KxogW62DgEiY/T0OZxMOdRNBDWvJCOj0x8vRDO9fLaY3s98W91KASQ/ApAfwOtx4t0pm4djo8RS61nNt9vVgjJfIh1XPsLFmDzfVSrj3y9NfZlxsm7CrapDzWucy4FzyGAZnrTxRsJayhAsprW0hLdpI4MLf+wxsrBIc73HJHCJILuHOj6DqAACjNy+lOrY/Awr2QWMZQc1VDLVlofK4oXwnNc8O4Nfpg7i7po5R+Zth92dwfydJolLBZa8T5HWTWPE5Nc6qLoIm/Os/E77rI0gcD9d/3eP72YGVT8GG/9Iy6WH0I67C4fJyuMxKuFlPWJCoqyRjGsX2fBJNvinFsXeK62O/s09c0AHkfg/f/0Puh6bBqJt7XXyoeQwHm3czMKjrrODD5/UnLMBw1MpGK4U1ViKCDBi0P1I0gsKPxuA4Cxvzahkcb/mxD6VbLh/R9xbdiEA/rhmTiN3p5sXlOQC8uTZPEXUKPyve3991POm4trPv/dNK1P3QZoo/FL9IUadwgmhlhsyNhiW76khPMsj8UvwYyF0iF94eX4Dud4/Amv9AzDC4pasFrK3FjcvjbRNUy/4uYmnGY2AKJdJiYMaQMLQaVdfWyIYi2PQy4IXdX0mr5+y3JX9uyPXgaJQK1qjbpKKm80d/5h+w6i1oCtdgsESi7X8OmfpO7bTVB+U1uGw06LPwqLSgggB3DUY/HWGB4aCNkbbAylYhoCLCL4zC5lIyVcEklBVCSlsGWZ2zhqqWcpJN/dCojn4x+/y25/ki5ws+PfgpN4+fg16r45LoawmzJMGoOyTIvNwX9+Bxw+4v+F+QgR1+BnbVbGKu19t9q0BICqACfwvcuFjaKnV+6AkjDUgL6ubbqNwVkO8Liz/4LQy7psPTfnoNk7JDaLK7iAudwBUpifDCWDG16X8+7P0CtcEAMSIG1Wo1VO2H3R9CSBoZ6ZficbYQuu6vIugGXCJtjJMfZMO+egLqdpNX5GDg4Nmw/QMYeo20Ibqdcvx7P5cqVlC0VGu3vo0xYjCXZvwOtEvgwGYIikQVNxJqC8BWR0hTJdbKfeSFDmZUTaQYzfSARqXh7LDzReR1zmhrNcRpqjjaW9rGhv+yP/x89rjPJGJfHQadmsIqOwdLm5k5IgK1WsWAoGFkBQ5B3VodTRwLNy/p+z6ORlg/+ffiaILYYUddfJhlDMMsY7p9Li0ikH9dNrjb5zrz/LJD/GvRfgbHmfn8zgnHdMgKpz9/mJnFDROSiQzyO/rCpwl+Og3XjElk2f4KLlfmNhV+RjQ5mlhasPToC/aBJQVLaHI0nRY5dqfCTPGHQhF1CsfOxPvxRg1keUkoLm0Atc2+mQK1RtwX6wskIw0kuBsk86wTzXY3S3ZU4/Z4GZthIcq2R6qAIJW9ydJCaPLrdCFdsRe+/YOvXc5nCa9SQdEmeGUa3LNDZsr8LL5ttQt4NgRinPH73l9f1BA4tBDwovZlsKRpDjPAuw5VxhWgbScuwzIgeToYQ3khrB+7N7/E4IWPwtbvYNxdMPUhXBoNn5a+jcPTwiDHSMaGTOl9/8DQiKF8mfMlqcEp6DRaNGjQqdtVaOLHQUsjFXlL8XM7CbTXc77an92mIKanX4jKVi0X7J0Dz7MugF9vlhBpvRF2fyCtlVmXdp2ZayV5stzcDhFT3RBct4VgRzOETBKjHEeTPFG6AwKiGG6r5fXB9+ONypZv6z6/DUzBUL4dY/rZDM8Ih8jfQN3lkNR2sR/nWsOQtZfI+3DF+zDr2a47v2OdtGsmTpC23VnPtD3X7yzodxYN1hLyareSpQLt4VV8bwxgjjmcy/2iYEgqjP9tz2/Gni/gs9tFaN60WMx/WrnoZdj5YZsxUF+Y9ADVNdEAVDc6SY82AuCv13QY9VP3kDF4UgiKgXt2SrXTcJL+k/W4pf2385ck7dhdUg/A3rJG3B4vGvVPf0ZB4eQSYzkGo6LThL9eeHq0ZikoHAvl1vIOOXQngtvrpsJacVqIutPZTFERdQrHjlqNKuNsBka2UOZrjTtCQKTcWjn3CYkESO8qBhwuD26PiDJri1vEV0gqNJbijB/H2t0ypzWmnwV/Qztht/pZOLRYbrd+L1WS/Usg23dhfSIZbQAtjVCyC29AGPoRNzPREkxY0FhgbNdl876H3MWg1mL0qBlpMPue8MKaZ6VVbsqDqHyx40cuYd1OMWEJipVbJy7tdynTE6YToA+g1lmFXm3ArGs3e6fWQuIkbt39Moca8/l1/8ncYhnEOePvlny7NU9KtTTrUojp1DLXajhTvInlhd9jUesYEjUYInq4MPG3wHVf9Hy+6vJh32dyX+cvFvhb3pJ2wYtelHnKhhJGHPoaKg5AxDCozgM84PFA6zmzxMutHUMSDEeENU5rx/1W7JYZztB0SO65lRVgWf1yypzF7M5I4IozljMZpNp5aJFUnnsStI3l8OH1MifqaBIzmvaizhwLE+6R+1/eI4Ym5z7e0Q20MyNvZIDNhV9JM9EhfkQHG4gOMWDy07RVV1c+JZ/vGX/tNR7hhNCdxGqJxw0bnpPKZdalUpnvhofPyyIi0I/JGeGKoPspU3NIOiHixhzpzDit8Xpg1wdQXwjZl0Hw0WfmFBR+6Vg7/597gjQ7m0/q9n4oTmczRUXUKRw7Lgd8eguR9UVEXvwK+KV0eLq42k5lg4N+MSaM5lg444/dbiY4QMeItCBanF6SIv1BZYSZT8O7s+HrB6gb8i5urZGyuhaSI9sJx8yZsOdzmWMyhsPaFyE0BYITZLbuBGeOvAcWojr8PSogKvtySEg56jq4HbD+dXGwvOJ9+ORmEYd7F6BNmcKFsVdR7aggyeibkcr5FgpWgVoHkx7uVlRYfJXGI8HQFXth/zdiBLPod1C6jYQQM4eAveEpMNX3h8VaJRUYkMpJD3zZmMNDNRtRo+Ij7ug5Yy5/Lax/UVobu6vU+Znlws/tkJY+/2C46buOm6jbSr2mhqzKErRNZXDhC7B3AWRfKNb9heslNqBTG6Q6bjhc8ym0NFIQPoPCvbVkxJoIK18K+74AfzOMvF3iF3rBrAumrKWYGLdR2mtD0yFxMgTFy1xeT6JOpRKR7HJJdmJ7Z9f2eDywxZcxuP393kUdEOSvZViq+cjvFlO7nCunDZbIPC+rn5WQ+Z8irZ+zgCj5nDWVA16oz+9R1MVa/Pnz+T23uir8BHBaYes8mVNtaZLZ6NOdlkYo3yH3y7Yqok5BoQ8YdcajL3QMmHSnTxvj6coP2N9zevPnP/8Zr1csyxWTlE6Ubofdn0LRRtj5cYenXG4vGw/Wk1tuY09h01E3FR/mT1q0EXVrhSL3e3DZ0FXtIUFbTmigjujgThfc/WfCw6Vw9UcS0Lzjfdj3DRhCYMh1ElR+qN38Uc4yWPSwzOodBau7mU+CaimLSsKROplS8wiabL1YVidNhoFXgkcjeXFh/SDjHLhjvTiCVu6Fj2/EUn6QVFNm2zxdq4jQ6Lu6SvbEe1fAkkdxPTNQWgJrC/inTcMTk5/gbxP+1racMQyG3QhZl0D8WBFbVjEyoWgz/CsNXpuB2tfOqVKpUffWErHwdyKiv7yn4+P1RZC7UqIFRv0axv9GKq2daHDWsUiTx9owIztiE6SSG5IC4+8SMbjyCchbCete7H7/qdPAz8KOnGoq6h3s3rYN3pkDW96VSpr66N9NTQo9i0tDL2FSzgHY+j/JHFSp5Hj9zD2vGBAhs6Bz3oPrv+n5CwO1WlwkY0fA+LuPejy9ovOHAZfKcR1FHB6Nw80H+KrsQwqsuSd2TJ1pKoO1z8C6f0tVR2+Sz1vsKEg54+Tu64fAZYftb8G2N0VEK7Sh1rW10La2sJ/u+Jkhbqx0RcSOPvryCgoKRBoj++QB0Be0Ki0RxoiTsq0fmnnz5h25/u/p1t48ZdmyZUceT0pK+vEOHKVSp3A8RA2EtDPloj6rXSBxUxkaXQBmk5a6ZhchAboeN2F1Wvn31n9jMVi4ddCtbW1no26BmsMQms6QMd0bM3SgfeuiywXle+FdX6D3dQtkPuv9K+Xb59o8sb3vhWpHBVUGL4smX0Sc35k05LnRaWo4e1gYWk034kulFsv/wXOkihPnc3cyx0pFcft7EJokF77mBGn/iR4CydPkd1OEVIL6QmAUtro8/L1uPIBHrcFv5M2cldRN9axVXHk9sOEFaC6HtHPg8AoxFWmu5LzAf2Ge/iIhWhOpQUlwYBHs+ADG3AFxI9q2lTZdguXT2l2s2xvgpQkS6wAQkQW3rgDA5rbS6KonwiBzY7oNr6IPs9Fi8MMUN7GjCDOGQca5ULBOqnbdUVcAb11EbNafyI+7jNictwCvjFMmTIbAbnLsOqFWqQnVhUj1AUToglSd6wvbWlK7I6I/5foU8g7WkxJpJNzcg7CbcE9bK+aJculrJ2Uza2uW0eRuwOpuIsF4EqsTTlvbuWydn4wZ3rXV96dK1X6o3OO7vxeij24W84tBo4PRd0k8SDet4actmef3+nSTzUVlg4PYUD/0WuX7bgWFAH0A0xKm8V3+d0df+ChMS5h2WszTne4ook7h2NH5SZWsPcUbYe8nqPSBTB5zLw70+Ol7/obn00Of8s5eEVjDI4e3Wd0GRsFl8/p+LGNul9a9xnLJNGtfjdPopBoT0R+KN0NkDzNjzVXw3Z8gLI3Y8Xdz1oECkta8SWPUOhYPeQOv94gdS0da56vKt0s7pVrXcf7popdg5A1QmyMVvU0vg7sFmkrBkgx4unwTvq1iG39Y/QcGhA3g7xP+3tEs46qPaNnwMgdW/4tvjAayz3uOWamzej8/HrdU6dxOCexOnATRgyA4CSIHMqHmoFQs/JfA2tegsQTqCju2T57xCEy8X4xVWnE7wdGu375yHziacNlq+KjmE6xaGBM8hUhtPBHr/svlrkZssYMJvbpTfp4vj7BXtP6gMzJ09x8ZmmgE9155bNRNMKz36IgO+Flg+C1gqxYzHIB3LhF3z3F3yfxaD2zPa6TZ7qbR5mL64KOHdfeIxwPWaggIP/5tHAOppgx2NGwixZTR9cnNb8CWN+W9zTz32DYcnAwDrpDPc2TfnC9/UgSnSNuo1wvBvQj6Xyp6U6+GNz9HVu6txe7wUFnvYFQ/yzGtW9vsYE9pA6OTQ7r/8k9B4TRlTsackyLq5mTOOQlHo3A0FFGncHKw1chPRyPqza/gN/Q6wNLj4gPCBuCn8SNAH0BSUNLx71droOXSdyiqthPhrycwJEXa5NRaiB8ly8z9GhqKpRpjr/MZY7Qb/l/7Auz9DFqaUPc7m6SqSgACKrcwIjUQs0mPrtN/1FVr3qOoqJTkmqWYp/9aHlSpoHOMQNwouXnc0mrpbpHKxi6fkNHoITzryOJf5HxBfkM++Q353DPsHqJM7bK/DAFYJt6PdegVzLLXkh3Wh9kkjQ4GXwPLHoPdn8ttxNVgCMDRWIm6Jhet1y1Co//Z8nzKlA6b8Hg9LChcjMXtZpLGDMlTwBQq5in5q6GuCJLGg38w7vlXY58wAdCyr3YP1664iesCdNztTsQ4+MajH293BITD7aslPzBhzFEz1XrFkii3Vsp8Dlblvp+2WpmL7DRjF2nRc7jMRqSlh9m7vvLRXGllnXAfTP/hs2/GhExhdPBkKlpK2Vi7iqzAwZi0vpD0pX+Vqu33/zx2UQeSQQh4vV4arE7Mxj5WnE8UtxN2vCV/cwZeBYHRx74NQyCMOcE2WYWfFVqfcY9Gc+wGPpe8uIbDVc1cNzaRRy9QnDAVfj6MjBpJmiXthALI0yxpjIgccfQFFU4YRdQpnBySpkBLA5RugeYymSWL78Yt0sfg8MEsn70crVqLQXMMF8peT5cZtC2H6ymrdWA0qDlraDgkjuu4js5PBF35Ltj5LugDYOy9MrsEEGCBkddCbZHkns34G5hCUfU7h/jw7geFN3uHY00wUxc0gCmxI6VNyRgugqAzLQ2ACkb9SmaRtH7S+uVxw/y5MP1RmcMDLul3CTsrd5Ic2B8/b0i3+44JiCEm4Ogth0cIy4CUaSLY/C2g9cPqF8fSA6BxZTLVlIdf/QHQ+8HEe2DMXbKewwruFr4o/p5HVj8CwPvFpWSPuRemPSwCK6Fji6zBFMnZiz9g85hZlPtpUatUvO6nIn3qb5iVcHHfj7kzwYly6wG728ZX5R/i8jg5J/ISgnSWvm139luw7ysYeRPsXwgfXAXmeLhtpVz4l++BDS8zOPtiBoyadOKOjXm+vL+dH1Edm8Fys4MwfSSTQmegclrly4ieTFuOE5VKxTcVH9PisVPtqOTsyIvkiWHXSvV26NUntP3b3t7Mot3l3HdmP+46o0e7nZNHc4WY3QBU7Do+Uaeg0ImJWcHUNruI6Km9uhfqbJLLWmN1nuzDUlD4UVGpVDw24THmLpyLzXXs88f+Wn8em/BY97m5CicdpU9A4eSgNUDmBSIgLEkdqk+tlNZYWbl+D9sOSiXMpDMdm6Cry4flfxGDBlfLkYdbq2idq2ntqWpw0FJXAngllLx1DgjanCLjR8iFfFgaXPC8GLIAlG2Dg193MFSwhAQT69xPkrGWg0V1uEKzO0Y5tNJQDKv/JbemUrHRL94o4ehb50Plfsk585Edms1jI+Yx1v9XfL+7Frvj5GTEMHwu3L4GfrUJpv2F5gE34HSDXeVPqS1IWi5BZsway6CpEv49FJ7oh7muGACd14vR6xVxvPZ5cHdjIHPhS7jO+Tvl5iAwuDg7fQzTMsZhb9otba7Hw+p/w9+iJJuwldo8WHCfuIECZS3FVDnKqXPVUGA9TL2ztm/bTpoAZ/+fiP6ijfJZqM2VcwDw1f2weR58fNOJC7pN/5OKqEYP9QXszfuEKkc5+5p20FizB1b9A1Y/LtXkk0yQ1gKAWWcRofpEhryuoFiZ8TwB1uZUA7Am5zjf32MlIAqih8rfGWUWTuEk4afXEB1sOK5/5+/cNJpHZmbx1wsUZ1eFnx9ZoVk8M/UZ/LXHljHpr/XnmanPkBXa9XrwdOenaqaoiDqFk4dGD0PmSgB5N46C+h3zmNj4FsHF3+BpOQ7HuZpD0r7YXA7WyiMPD00JItKix+P1Ut3o6LJaXoWNlXtqWVzXH2f8ZMi6DEztZpqyLxVHtCxxGlyYt5Dhbw3njsV34LXVwa75kL8SNr0Cyx+F0q2MygxjgHcL23ST2FVt5EBJD/krdbkiFDxOKNkCdXlQulkqjqNugfgxtIy4Ha+3bWqv9ZKiyxdbbidUH+g1pqAnlhQsYZ2nUdomdf6EW/wZmBhARqyJhCETAD3UlsHiv8FzI6F4k1QV3Q6mtrh5d8q/+ShyGsmZF8L+r2HRQ/B4kuSytUerJyxmAkavFoPdyhX5OZzp0TBqzWew+fVjPm4Adn0ELhvsmN/22LePwKbXYP614HET65dIijGDBP8UDjTv5v3iV9lUu7rDZppcjSypXMCO+k3d72fsr6Rid86/ICwdt9eNt9UwptUA50RobfV0y7f56UX5mDVBpBozCbA2yOfEaQVrzYnvqxOzomZzSfS1jAmeAjlL5L21VkP5Lr549S888tmuo22iR566fAgXDY3lkZmn6D9utQayL5e/M8bQU7NPhZ8ddqebwpqec7g8Hi+2Pn6p1j86iBsnJGMxnlicjoLCT5VxMeOYd/Y80ixpfVo+zZLGvLPnMS5m3NEXVjhpKO2XCqcMs7MEVBBh24fa1QyGY/vWh9hR0nrlZ4HANlc2l9tLeZ2IubxyG6GBHf9jdboljNzp1eNInI7Or9PH3pzQIedsacFSHB4HK4tXYsWDyd8is1bNvhyusm2oooeii8xCV2PHgRFri4sGq4sgY9u2y2pbyCkJIlmTSoyqRMxSGorkwn3b65Axi+1Jd3C4zEaCq4HhaSKE06KNGP00BBg0Hc1m9nwI5TvlWEfe3rdz5nGz59sH+SbnExYFmHjvvPcYEDbAt59WI4QAcQVd9yLs/kTaRc1xcOZfpWI14gYGGgIgcSrU5MHOTwCPZD9990cJG29HoDaIq2Jugk9vQ93iz6BN6yQg/qzJvR+rtQo2vypfDoy4RdpkAaY9Inlt7U1RYofD3i+kyqTWoEPDmRHnk1tuZYn9Jfw8HqodFR02v71+A4ea93KoeS8ppn4EaIM67t8QANFZ0NJAQc1WFjUsI3hAGheO2om2GxfAkho7RdV20qNNBPfi9HqEqQ+J2UzcKAjPJNI/mDmthiluJ7TUg9YocQ/HwbrD1azJqebasYmEBXSsgOvUesIMvkry4Cske7BqH5V1jbxvm8DWzUX89cIBNLoaqHaUE++f0mcr6+lZkUzP6qZKrfDzo3IP5HwnldLEST/20Rw3Xq+Xi19Yw57SBn53Tia3Te5oluNwebj4xdXsK23kP1cM5ZyBSouvgkJWaBafnP8Jm8o38d6+91hasBS3t+2LD61Ky7SEaczJnMOIyBFKy+WPgCLqFE4Z2kFXwva38I/KApPPQdDtpLo+nwqvk/6h/XvfgCEQBl7R9WGdmqQIf6oaHCRGdBWKqVFG9Bo1RoMGU2dB1w03DbyJJkcTY9xgWvs0ZF0q5hoVu6Bit8wPArqMGZzhcLM1t5HCqhbK6hycNzz8yB+yrYcbsBNJo2ESKv1BgvRRmMbeC8v/LDuqL6DaI66E1Y1tsxgqlYrYED+6ULpTWgRTjsGVbvv7ZK37L08A5TodWrWWiroWDHq1GFt4PCIyDQEw4kapFgXFSGxF1MCO2yrfATvfhyn3wM4voDYfBvriIw4thvUvw4gbIOMc1IbAjvERHrdUWDrTWApb3wJbPWSeLcIGJGYg3Pd5qM2T6lX7WcoJ98CgyztWXIEduXVcanNgdtdjTw2Cty6SitQFz5NSX8VeHQT7ReCv6Wb2sbFEqsFAccNOPLipdlRg3f08QQdXwsynIaEt42pLTgNOt5cWh4eJ2d3MPzqtcHipzHzFDJfP/Iy/dV0OxNAmrYdg8z7gdHuY+/oG7E4PBdXNPDNnaM8Lm8LgincBOHioCufiAzw0JBaP18NnpW9jdTczMGg440KmHffxKJxkmsokCDy0b9+S/2Dkr5RjObz0tBZ1TreXQxXSgr+npKHL87VWB7uK5fE1OdWKqFNQ8KFSqRgZNZKRUSNpcjRRYa2g2dmMSWciwhihxBb8yCiiTuG4yW/IJ0gfRLBfcN9WiMyCGf/X9rvbifvlSQRX7OGJsFBGzPgXl/TrGLbs9Xr79G3P0JSgHp9Tq1Tdir2e6Bfcjxcm/hNW/l1ETk0ORA2GhAlya4efXoPJIGJD0+k4NRoVOLy0qIysc41Av6uaszNVaAbMEfGQPJUhLhO55VYSw9uOz+lx8p8t/8Ftq+buoEz08eNFGGx4TWYJzUl9fi0ERgEqPBodf5z6DHpXIqsP16FWwfSBFkzvnAMlW+Gil0Ukjft1z9uqzQW80v55yzIwtJ1z76KHUVXug6oDkHEOXq+XwsZCYgJi0Kq13Qu6+gLY+KLYyu/+DE9gNA3BsaDRYQltZ7ix4l/QVA5r/g0DL217PKjNLGZNzVLyrTnEBA/B3FSPCvCv2A85S2WBty8luqmMG4bPRTXzge4/U0FxEDkQ7A0MCpuG1baNUEwErb5Qnt/8egdRF2HWU1zTQkRPjph5y6HQ1wIanAL+ffx30kfcXjdl9iI8eInSxxIXbORQRROJoX0X/ePSwhiXJl+weLyeI9+6ur3dzEsq/DhYa2D9c5ILmHXpj5sFGDtKuhZ+KrOMzRUiNCMGyDx3D7y/oYCXvs9h7rgk5o5PRq9V89I1w1h1sJqbJ3XNb4wM8uPPs7LYXlTP7VN+GpEX1hY3NU3O4577U1A42QToAxQR9xNDEXUKx8WivEU88P0DWAwWPr/wc0L8undqpGwb2OshYXzH0GkAez3qir2ogGEtLZRZyzo8vW5/HWV1LQxLCSIh/BhbNTtR0lTC39b9jSRzEr8Z8RtUKhVbK7ayJH8JszNmEx8U33EFQ6BUTuryIXFir9sekBBIuNmAxaTtIBZGppnZW9SEx6OnssGJ19kC616mOO5SDnsmk2L1IzZER0hAx/nD5YXLeX23zJ9lBRdynrVKwoCDYiWYPbzTxYutFvZ/DqZISD+n43NpZ8Ada1HrjKQHJ5JbLjMkXi947fVQvBmPSotnwQPYKwoImP5Azy80eaqvkhfXQdABLPLXMwPYFZbIIOAv6/7CRwc+YnrCdJ6e+jRuj5fVe2tpsLoY3c8iAd6tZjcqFehMFIWG8o2hCLBxXksRcf5J8vyoW8RkZMSNYtDiaoHxdx8Rim6vm50NmwEIsRRB4GXQUACxYyHne6nUVR8GQN1c1TasaKuTVs/W/D21FgZeCYAJmGaK5bvKL3jrqt8zdeNy4oZcJcutfBJWPcOocb/GNeE3aHuyQA+MA1Qi5nyZX16vly31a7G6mxkdPAm9uu9GQS2eFppdjYToRYQtr/qGQ817AQjSmvn1ZeHEMZbh8VG9baZH1Co1F0RdSXlLCammzOPahsIPgNctM7jQZup0XNvxdjOoe4xED5XbT4X9X0LNITzlOznQ/z4yo7r/cu+1VbnkVVv578pc5o4XETctM5JpmT23Dbcu91Nhxe4abA4PCeF+DE/tOrOuoKCgoIg6heOioEFCvuta6qhrqete1DWWwq4P5L5KA4kdq1yYwlBd8Byl+z7HmzaeGwe05Zh5PF5Ka+Wiv7S25YRF3fz981lZvJKVxSu5IPVCMkL6cc+ye6ix13Cg9gCvzHil60pJR5kB86FWq4gObrs4ry3bzr7Fv0edOYtxI27H7fFSVNFE8M55aHCxu8FCs8eJ3dnUbZtlZnAmZoMZj6uF/jozhKSJgLllubQiRvvCnt0OaWssWgtV++UWMxxMER03GNHW1poU4Y9Oq8ZfryYgUA8zn8G55J8YbKWoN/8P26R78W83x+fxethYtwqP182o4IlosjpWUlv5s5+T3ybFMzo8kv8Cu6rEeGNj2UZAvmVubTHdeKiewcmBxIamw6CrwGmH8Q+iVzURsu85huxcTbhxL8z6j4ihSQ/I7cC38K6v3dMcD4PkvkalYUjQKPJtOWQHDUXlnyQXnrs/FaF21v9JfEXOEhgsoo38tfDmBWLoc9sqCOx6cWdzN5NrPQA6DTkz7iEuTMR9476VVIfNIG7Hx2gn/7bb81Foy+Vb+xJGREUy0DIatUbmPMtbitlUJ9U7s9bCIHPfDFhcHhcfFc+jyd3A+JAzGBA0DKu9ivHrFuJRa1g/YhoN6npizRHAsYm6nQ2b2VC7kqzAwYwNnkKw/udtPuJ0ebA63KcuV8+Hw+Xh2z1lDIgxkxR2DC3UpnAYfrPMsEYOPPrynXHaYNNLsv6wG+XLoZ8LlkSoOcSaShNXL1rJ81cO47xBXVslb5qYzMvfH+a6cUmn/hhPEq1WWu08tRQUFBQ6oIg6ltQwoQABAABJREFUhePimqxr8OIlITCBFHMPxg56k2SyuVrAv53oczTDgQVS7RlyJdFDr+byTquq1SqyE43kVtSSZt8OtRniIJmzCNCAJQX6zQDAWnoI+6oX0A+YSUD/7ueApsRP4eODHzM5/FL2HDBTZa4l3ZLO+rL1RPolUVJjJ6azwHI7peXPP1hmxXKWiYFIWO9ZXDUfXsPY6nwqctdRP/BKzAYzupKP2L3lGcYYo4ifeS8HKyE+rJu5OSA+KJ4lly2hvtlFebWThsgAgkAESKugs9fB+v/IMfY7T86zKRKO0gqrUqmIC2233xHXc6i6jthdH5CfdBX9O7X15FsPsa1+PQCh+gj6BXSy7LbWQH0Rj014jO/yv+P67OsB6B/Sn301+6h31JNXn0eSOYl+MUYOlVppcXrYmdcogjaiLag3au07XLboRd9vOyDtLBg8u21fwUmgM0m1ItT3mfO4obGM0SGTGY1PhDeUwH/PkC8V8Eql7oaFENUuFLh0m89JtUKqn92IOqM2gMFBoyhvKWZAkLSbeb1eVgx6DodXR422lJ6a0Kryl3Dmxi9ICEiGwDIwhkFIGmZdCCZNIC0eGxGGvmcNurxOrG6ZAapzijvm9Ao3/vvEybMxJpuDCSnEG49eXejc0nyoaS/65hoGfHg9eLVw/TdH/Yyfrng8XpburMHa4mZgYkA7s6Afnn8u3Mdrq3IxGTRcPDSOWyalEB/ScbaztN7G3xbsJT0ygHum92t7IvgEqkbN5fI5B2n7/jmJupTp5BqyufY/UqkvqeveVXn2yARmj0zo9rnThUlZIVQ3Orr+P6WgoKDgQxF1CseFn9aPWwbd0vtChiAYe5/MYLU3tCjZJG2ZIHl2lu5DpV/e/2cGbfmA0IZGWX/qb8U9MncNFG+Dy+ZB9kXYP7+fkLLlOA98CA8XdrutIRFDWDlnJRsO1FFc00JFvYPnpr3A1uLDlJZaWH+gnolZasKC2jlnbn0LlvxF7lfnwNrnJFz8rq2+WbVOVB+C6gNowtKhOp9KvwD6aY00O5u5fNe/qY8K4+6aKm4KV9E/qBHemCrbu/4bcNRDXYEEtuv8MWgMbD/cSJPdTVWTm6kDO1VPmqvEiKOVKX+Sn43lYjKy5j8w4GLIuqDHtwfA6rRybcXbRMSlMDzUyt+0HVNOQvThGNR+eL1ewvWdhI/TBi+Og8ZSzjjzr5wx8R9Hnjoz8UwWHF5AfGA8EUapHGYnBNJsd1Nc04LN4aGivoUIc7v2w1bLf5DKbminWZbwfnDPDhFyrSLsg2tg/1cw7i6Y8Vd5rGSbmJ60kj6j6wsfdi3UF4lpSOLYrs87bTDvPMZUHoAr3wd9W/VTrTOAw4MmtPsvM1ZUfUv2d08RWl2M1/8wqhHXHHHy9NcYuSLuZjxeNzp1H+3PPS78CjdykXoQeYFGBgbJTJV/wgRxgtXoGJ99D+N7CWdv5e11+fz5i92cPziGp2YPAWC4ZTyl+bsJbPJl++Wv/vFEnaNJTDjMcT/I3Ja7nU19k/0kZUD2kdYKS3OLm7fW5VPZ2MJL13Scj5u3Oo+vdpbCTjhvUCRlmvU4PA4mhJ6J4RhadTtgToD48eBo+HHn8X4gkmOjeeHqERTVWrl2bNKPfTg/GCY/DSa/E+tYUVBQ+HmjiDqFHxZDoNzaE5wCGoM4LnZuFWxHUVMRI1qvhNxOaakr2yGCDiR4OvsiWkKzoGw5Vkt/OkwabHxNhFhr/hiQGReAFzG58NPpSbEkU1ZWJ2Ndzgb45gkJHx95kzjNqbXiTFi80XccDjkWjxus1XhM4bQ4PfjrVLD9TfA4CU6aRcmI20mNG45Oo8PqsmJXqcELTelnQEAE7FsgweQAheugZL3MzjgaIPNCAIKMWprsbgL9u/lnGpIKqTOoay4jV6NmKMDyf+Jc+SxNgWkE121nZdEKdjtLuar/VQTu+kxEo8EPjMEw6BrQ6NBpdATpQimy72OY/Wya7W5Mfm3tl2ZdMFfH3YYX0Kk7tas5bW0h3RtegfF3HXlqYtxEVl+xGr1aj6adSUpKlJHimhZUKtB3EpBM/5NU1w4vk3PR2DZj6XB52J7biF6rZ6BnK+pt78h7VOR7X4raZc+lnylzeE4bnPkXMHbTGqw3wVmPdX28ldp8KJZv/zn4rYSUl+1EtedzpmRfRa0umqhuDFIcHgd7m7YTERJGaHUxqoRxMO7+DiYpGpWmz3EBABSuhUPfEAaEjbkHNL5v6sMz4IGD1Da7KKx1k6h3Yjb13lL46fYcXB4vC3aWHhF1CcZkEkY+CtV2cFhhQPcttqeE3KXSTlykgpD0rn87ThCdVs3YDAs1jU5So7txQP0BefCcDIYkWHhx+SH2ljYyILbr/NekfuG8sTaP1PAAVP4V7KneDkCUIZbsoOOcZVOpIWPmiRz6T56zBxzfHKmCgoLCzwlF1Cn0jeoDYmXtHyIuiIkTpUpwPJjjYcofAVWvg/v/mPgPFkV+QrlDTWS/c+UiNmIgFGyR2TJfblnUpX+nfsxcAqKSOm5g7fPSWrf2+SOiLsioZXQ/Cza3le+rFmLRhTBlwFA0ahVB6x+H9b72v6RJkDwJ7trGztV/Z5+zjouCLkI76nawxMOr06FoI6umfUe1PpHs+ABSjRFomoopcVjwGEYT4y8XpGaDmdfPnsf+2v3MSvVlumVfDIe/lwpO6jSo3iMtle3aJ0emm0XU+XUjAFQqisLSuWT1w4wPnk1hoorzC9dTH5hJWcRUVA07+XVoAO5tz1NftZ8HV/5P1kudDNHZcPAbiB9LmS2APw54k7yaciz6KJbtqubMwWEYdG2CS4u6e/dKYwiE9ZNZPk1XgeOv7fqtcliQnjMGh6IGAtR22PMNJI6XUPTAKKm4FayRz1Zym2V6QaVkwgFEb36CiMoVIvoueVWy9Ubd2raTqoNwxh9PTBBEZMKE+8TNc5SvIv3BNThrc/HmrSTmhkXdrqZX6xkUNIKK4X4k9bscv/RZHV0vPW5Y/YzcH39P9+e1M62fCa2fVHbbo9WzKbeBJrub6kZH14puO0rtRWQP2UOtK5yLh3Rq59PqJbLhxyYoHlgn7araH6bNLNJiILInx9IfEINWw/mDYzg7O4ryBnuX1kuA8Wlh7H70bDRqFTZ3M0H1FpweBzF+p3fr4M+Vzfk1fL6thCtHJ/Ro0vLm2jz+8c0+5oxM4I+zsk7xESooKPySUESdwhFsDjcVdQ6igg0dLuppaYBtb/gc2NSAR9qkusmM6zMqde/Pe70kf3Y3t+WvFrv9VsdHnR/csabjplQqzPHd2FmPuxPW+Cp1ndjZsJl9TTsBSIhJJUgfCjFDpe0vKPZIe1+tXwBza9bh8LgoTb2Iu1ot7Uu340VFrVaG8muanCQPvZnVO/KpdpsZoFWzfFcNAX4ahqUGMTB8IAPD25kc+Fvg8jfafh99l7hYtrPpV6tUBPlrpX11y//A2QxD5uIxhrGnoIlau5ehQWczMXQO7iaoiptGaEQBReEXsGPA1YTtv4VyaxlxllTQB0q7ZliazKUVraW5poRN6osAFckhcdQ2uXC6vDiqCzC0CuTcZZDzLcSMgO5MUq58H3Z+BP3P7/Gt7ExQa+Xx/VulYhkzVExgynbBOp+7ZVO5zMb5yYVSuFmHXqtCp1FhdlfK+uY4SJkst1ZWPAFL/wqh6fCrDaA+yuesN8b9WkSXn9R/G4MTuDzQTYWmjOcLljMmdoK8ZwHhHVYbGzIVdq0FRyMc/AoGXyeVR0OQvN7Wlt7QtKO2xwIQOYB9rlnkWys5U2+i8ysy+yq67YPvu0Ov1hMf2cLs8wo4L3J0r8v+aEQPFWMgrZ9UyH+G6LXqbgVdK6129f4aE1fE3XyqDkvhOLjrvW0U19nYWVzPp3eM73aZz7YWY3W4+WRrkSLqFBQUflAUUadwhHX766hrdhEepGdCVrvqgkaPV+uPytmMSxeI1lkPgX03eTguWhog93u5f/DbjvlkfWXEDXLrhihDHGrUBGktGDUmPj/0OcGmYCY9cFAs7nX+eLweDlh3MTCmP5uLdhJkaTdDdeafUO34kFF+eymzjCE9xojOoGXssDScLi+55TZqm5zUNjlJjzYetS0Onb/cfNQ1O9mZ30h4kJ5MYxk0+GYFq/ZRFjiSg6VWQE+KORWXx4Ha20KApxaVRsOQFDNYEvk44yPKmsvICMmAjMtF1AUnSYB4+Xb0Wg3+WjU2h4eUCCO2vDcIKFxG4KbDbDzrOyrqHQxz1xINULm3++MOSYEeHCCPSmukgatFzHP+d7YIIXM89J/VIbrBbNRxbmuwe8bXEjUR0007WnWO7wQWSKus+ijVHpdDqlTtqHPW0Fi2ifg3rgGtAW75Hjb9jxKVhyINgIcVu19n1OL/Q12wTgLFO+f7haRA2XYIiIHV/xJzl+E3i9hsrbb1MUi6ylbF1cvuosXdwr0t1dwwoONnemS6maxObbPdEaqP4NKYubi9LsIMPVu5/+ic5JZLBYWTTZ3VwRfbS0gK+3/2zjrMjevs4j8xLTMzg5mZHUPIjsOMDacpt2mT9mvTNqW0SRpmZoccchIzM6zXy15mEPN8f1wtecFrCFbHjx5rpZnRaDTS3HPf856jp67TRkHc0BEDdy3I4pGvylg5LmFE27a7PHglCb36Gx6euWzw3HJoOSom61JmnHgdP/zw4zsFP6nzowdynxRyQK6pUktJ0vXU1zdjlIezaIwaXXD/6gR1e2DPCzDmMkgcmU37sFCoIXGyMP6Ydufpb8/YAG9dI6olFzxDkj6Vq5NuRyFT8l7Ze/xuy+8AeH3aX8nb8SyWmKkcHT2XfeZtTE0fyxU5VzM/vo/hxpTbYMptxAJ9DbRVCjkqBSSEa6hvtxOgVQzeE3cClDVYaTW6aDW6SBuXhDpmjKjUxYwhWBJVK0kCi7yWp0pXoZUrmKtNQhuVD7seh7yVBMeNJ1jjG2wE9OldzL8A4iegCkpgPmpcHgm9RgHWrdD0Ca7YSdS2CcJVq8wgNtLy9WRTrXhCVK7S54nKrVINTkQI+vS7oPEgxI7qWbzHsVEfBg374fFZkHcezLy7d5uL/g+C48WARHUcobN1CLeK7h67Q2/D2zdAwkS4Zg3IFdg8Ft6ufwG35GJaZiGFR3ZCxTrY8h+yZXJuL5xHvcfOuPAEZDs/FNup3jaQ1MVMAGUQRGZCnXAPxdIC8RPgLp8hjGFk0QEy3z8A+YA6nTguASM8x85EXEFxrZnKJhu5iQZSor7ZvjQ//Pgu4FfvHOTjQ43EBGnZ8LO5JIYNbWAyKyuSWVmRQz7fF3WdNs55aBMOt5c3bppKXtzgks6vBR3HoM7Xm1zyqZ/U+eHH9xB+UudHD6Zkh9BqdBIVPNCVT2sIplMhQ6OSoxxsMPr+7dB0CKo2wu27T39nDq+GGt9guL0MZ2QmH5R/QFZoFoWRhbjcXjotbsIDVcgHsNBBUPwhVG8V96u3QubCHvfBIF+QtkquQr//NSj9DEPpZ1QFTkYWJUMhUzIhfAy8uAKaj2Bb+RJbjSnIZDAtJ7S/VNWHYIOKhWNESHRZRxl/2v4nCiMLuXv83QOWHQzx4VoaOxxEBKlRqVRQ0GvtbwDOGicGCVMc15Hgbmd+7RHCss+F5gOCCL99oyA88387cONyJYRl0OZsZm3zBwSpQlkUdQ6K8x+DMZeh2vg3csoeoiVyJhmaEpjTn7CUdpRidVsZHTm690GHEQ6/IaSd+asGBs0PBn1YT18kADd8Bc1HIH0+PDpFuInO/c3glcAtDwli13gIZvy4tzfTEAHz7hm4fMtReHKekBBf97mINyj/Usgia7aBvQv0YXglCa8kXBHdGfMhbimMuhj2vwq1u7ikci/rFl5Jc1gc9hWPoqvYKAhoX1jb4Yk5ouq48A+QtVxUI7uJ8QjJXDfCdeG8suwVqk3VzE2ce1LrdkOSJN4pfQeFXMF5Geed0ja6UdFkw+HyUtlkOyOk7r73D/P6zhp+vTSHK37A7oXfC9TtgJI1wnk0Z+SS6v81BGqVPf8nhZ/4O2B3ebjm2Z0cbTLyu+X5nDd28FiJkkYTbRYnAAfrOr9ZUtfdR9xytLeP2A8//PhewU/q/OiBRiUnPnxwuVpylI7QABUalRzV8a6FAPHjBamLn3BKry1JEuWNVuQyGWkxepCcQo6o1ELcWB7d/yhPHXwKjULDF6u+YG+Jmw6Lm6RILePTg6GzSgymY0YPbj6RdZaIKNAEiQpgH8xNmMcdGY+AR49GUYlUtAa8buZu/TWamdegMoShMnaI8GqguaKILr2Q0rQYnSSoOuCju0U0w+L7RR+XuVlUmlJn83Lxy+xq2sWupl1cmnMpMYYTO7XFhmpYPvE4Z1CPG8/bNyBrOYJixRMQO4oIXQQ3zP1r7zINWfDJrwUh2vh30VfY16ijD8otR+l0t9PpbqfD2SYkeVmLYOM/yC17iNymN+DsB/utU9ZRxoUfXIhbcvPwvIeZnTibrfVbie6sIa3dJ32MnyTcOU8Wocni5naIuAGA9srBlx17OTQdhvzzhjXb6UFrqegDBfjst5CzFGb+RMg+k6b1VO8MygDOibmELncHGcm5IJPjdHuRrvwEzcvnEFi1kbP37oGr7oMEoOC4hEWXHQ68LjLwuv9OGrzX5mSQGZpJZuipxwx8Wf0l9229D4BAdQDzkxac8rZy4g1UNtvIijszGW/v7KnF5vLw3mefc0X2fCFd/V9EV52oqH+bvYTd+Y0Nu781Uvfrdw+ysbSF+88vZGbmyCpcpwOr001Dl530yIARr/PH8wpZUhjL6ISQES1/uL6LrRVtAPzsrf2cMzpu0MnIWVmR3DY3A6vTw7ljBhI/SZK4+4397D7Wwd9XjWZS6iDOvqeDBfee2e354Ycf3yj8pM6PEWNYI4az/y0qKkP02m1v2M5/9/2XZWnLuDD7uIGw20Fdp8TBY2b01loMJBNtboBJV0FADIQkYVCJAaRGoUEpV2J1iEFzq9GJrasN3e4nRRXGZYbkWce/vHCsvGnDoPvm8UpEKrORFNAYlUXMuCuR7XqaUIMc2o9COxA9WsxeNhURmz+V6HY1MhlEB6th0/NwdI3Y2KhVEDdOhF93VcPkmzlrzPl8Wf0loyJGESnJ4Z2bICQJ5v1m2ON9PDprjhJS9A4Azj2vol42auBCseNgxk+g9RZIHd6hNCsgnzr7MYIlLWH1RyElShCktDmiehWWKsKKbR09xNBmacItuQEwu8y8XfI29229D41czYdJ5xJjiO4JN+50tXOgaxcp+gyS9CcxUFdq4LK3oGoTTBrCKKJghbiNFNlLYd5vRXWuwne7bZfIOjwO0do4ohHnsdnu5quD7Xi9EjPPfoGw5s2QMnPg9lvLICQR00e/I3Df40hKPbKz/ybkyN8BROgjfPJqGSW2A8zn1EldWoxeTLycIfxmeiDvfLmZ292rYW/j4NXlHzo2/QvW3gcJk+D6z7+9/UibBxVfQMyYb+XlzQ43r2yvBuCNXbXDkjq3x0tD1+AuoiOFxytx9kObKG+x8OulOdw4a2STUWqlnLnZg8fxHKzt4tZX9pAeaeDxKyagVsopjA8hPdJAeYuFxFD9kOoShVzGTxcPYvrlQ4vJwbt7RRTOO3tqzzyp88MPP77X8JM6P84MZDLhRgh0WVwUNzYRFxJMsE5HkF7J4wceZ0/zHo60H+HCjBXCvt8QAZsehLX3oh/3YxLd8Uw48FO8e+Phgieg+ZCITgCuy7uavK5WkiMLMKgM6DR2HG43VoeXrSUm5oLoOhrEWh+gqtnKkaPHyDetJWnKsp5qgNcrcaTWQnSImkCdksxYA7Kwu8DeiTNmHCpXK15VIPuaNCRO+yNRwWrUb17FtCqfK6dyAcQU4lUZsAWk0CVPIU6ShBwRwN7F5NjJrL/IZ/ry1f1w4DVxP2fp4GYffeH1iCiJgGiMumSMsecQbD6KO2sVQ4r4MhfCz0qH3awkSchdQZwTtgLFf0aDpRnm3gOzfybcQp0mUEhgbhA9YanzofkIhc+v4r9aJaZ5v2FJ6lJeKX4FAI/kxTXuGghMBGsrOExsNW+g2lZBY+MmkoqOiQrp1FuGf7/dSJ0pbmcKcjnM+ilE50P1NlqTCtnnPkiqxUu6YehBlGXf+7gRlTaTR0dY7tkDF/r8Xtj8IJ2ZF9PoCSUHaImIx5C/DMN3xMFxdORo7px2Ey2uRuKCYk+8wjeIi+aM46KGB6DdBHn/o5K/7kzE+r3wzBLxfbz4VYjM+mb3IyxD3L4lBGiU3DAzlY2lrVwxJXnYZW94YRdfHW3hhpmp/GbZqblKOtweqtutAJQ2mU9pG8fjo4MNVLdbqW63cqTByOjEENRKOWvvns3heiOpEade4Y4K0nLV1GR2V3dwySR/zIUffvjRH35S58eZgeTtiSl4cuf7vHDsD4SqYrk15TEWFMazLHUZRW1FXJG8BB4aK9wJlzwApWJWOuzwMwSOvRoAuakegpMgubdRW7bzSaat/bPo1bp9D1HB4XRaRMXI7lGK11cHQcLgVu11bQ7GHvglMS3roOwZuGMvAA0dDsobxUU9IVwr+uNCkqiZ8wi7yowEBsiwOCS8HVDb2cm5BUooek9s9NBbkDEfPrwbuctCs6GAgzVuzlFXwvirQBPcv9K0+T+w/q8iNiEsdWQys6K3oHEfhGeRMOZqSpc/hsdtRKOEli4nkYP0P44E+6tMVDbZiAyQo0/7CSZ9EuMcBwl02aDqS8ha2JtN6LDD5gegsQhcFma6AFUEyGRcknMJwZpg4gxxJAYmQlcN7HoMgJTsaVQDGVVqDnqyyf7yL6gLL+g1bXE7oaUYovJA0eenqKtGTBIE9XGLK10LtnYoXDUyueUg6HC20Z6QRtrPytje+Rm19nKOOY4NS+qi9v2LfO0mPPooEif9oufxOls11bZyCgLHEdh0GABN825Kp39ARUomNUntqJtf5eL469ApegdxRab91NuqmRg6g2DV4LLY4WC2u+myuIkN1Yysl7QPLk29lhpbJQm6lJN+3a8VKi1cufrb3otvF4v+CIYoMTH2xe/FY0fXfPOk7juAkRK0g3VG3/9dA56zOT385M19WJ0e/nnhGMIMg/9O6tVKnrhyAjsq27luRuqgy5wsLpyQwPaKNkqbTVzw2Baev2YS0zIikMlkFMQP7ZLZjZp2K1vL21hcEEOwbuCk0O/PLTgj++mHH3788OAndX6cPpoPw8FXxSB8/A3UO44i4aXdVYfZ3YnTHcvKrJWszFoJJR9Cpy/kuGKdMJLY9E8ovABVexUgE/K2kONmIbvt/uUqUKjITwogOUpLm9FJeMMWZGZAF0ZLl5P9VUaiQzQUJvdao+ckGPDofE3nfSzTgw1K4SQJhPSJHWg3uQAw2aWex5QKmTC5mPlTOLYFptwsnpC8AMjwEhmsxvvaJXw6agytUWoWYCQW3+uWftb7Xm7bNTw5cdtF1dHcBJ11oA5GLpORHWzGu/2/IHnZoFvF+HEFp+SuabYJMxCjXaIl/nwAqqOnkl+zBWp9BjWFl8PTi0EXBIXnQmgiTLgGAuOFCQsgl8lZnra8d8NOc8/xyNWkEhU2gw0RNoiQQUAMhbo+cqE3roCST2DUxVjO+TsfNb1JpMXG3OpaQEZdztkYdSFk223IXxYZeTaXCd346076/bbaWljxwfmYnRYeTF5Ogk5JbYReEBxzs8jay1wIEf371mRzfkXWpn9B5lX9bGE/a34Xp+Sky9XBWcv+DjueRJe9hIVxUewz5lJj3ozT68DpdfaQOqfXyca2z3qO27zIZSf1HjxeiXWH2nG5JTJi9f3O75FAq9CRGXCKOVmWFqhaDxHZEF144uW/JnS5Oigy7SNVn0WMdnCzie8lQlNg+T/FREfTYfG9L1w14tV3VbXz0rZjrJqQyPSMiK9vP79DeOiSsXx8qIErpw6s6G0qa2XNwUYAPj7UwGWTh676zc2OGlJKeSpICNXz+3PzOefhzQDsqGpn2gg+kyc3VPC3z46iUcox2d18WdzMY1eMP2P75Ycffvzw4Sd1fpw+2o4KF8GuY+Cy8uuZPyJgt4d4fToLk7OJCe0jiYybAIXni0rdwj+IQfTFL4vnnjkLkHA3FVPdaCUkQEVYgI9ojb9aGJEExeEJiKW6yUqIQUVylB5qRAM6GgMVTVZMNg8mm5W8xABqWu2U1FtIj9aTfsWTUHkFJPSauQRolSwZF4lEb+gvQHa8AQlhHmN1eJABuYm+Rvrje36u+wxqdxKfuYzk9mJMConqREEOysxHeP/oR3glLzfM/x3KLf+B/BXDE7qKL6BiLUQVQNl6KFsLKW0w65fg6ELu62kL8HYJnuEwDcz26qwSldPg48hxwwEo/Ywx+ZdTFWAgJkRDSb0Fs81DTISBqtpIkmQK5NogQaBdViHFVBogbgykLxJxE0MhMheyz4Pdz8C7txJ8/lPoNcFYHR5Cpl7SvyLXnSnXXk6dvZoOVxsBLqfvSYn/2/xbNtubeHj0bcyQy1F4vbx67EOuHHsVypG4a/ZBlbGKDpuY0S9qK+FHAWnkEYNqwvnw0gVQ9jmebY+z9+wNxIdpiA3zGQblLOVgeCLPHHqaRZXhLEldAkCYOpJGRx1h6kgxIF/8JwC0wMTwSehVKoJUof2qcSqZilh1LI3OxlOvlkknXuRrQdkn0FIkqsZR+T1V+QFoLRNkPSBKyAfVZzbyYEPbZ9TbqymzHOGKxBFKeb9PUKrhgqdPerXfvneYIw1G9lR3suHnp+aQ+n3D1PRwpqYPLkKfkBxKYXwwVqebWd+A2Uo3DtR2cskT2wjWqbhlTjptZidX9XF03VXVztt7arlkUhKjjjNZ+eBAPU63F49HfMkHc1U+WRTVG+myuXqOU2WrhVte3kN0kIbHLh+PVjV8tqUffvjx/YKf1Plx+kieDU6rqK5pAokA/jCzl/h4vFIvYdKFQ0QaBMdAVxUV7njq2uzkJgYQMe+3sPEfHMn9FWVVJhRyWDIustdtM10MVg5WGKlstiGTwbIJkaicJvG8w0xKko4ui5voEDUKuYzyBisWu4fSBgvpsZG40xfidHvpO9SUy2WiulS1EfBC8iy0agVjUoP4ZE8LNqeowOnUCl4sepG3St7iplE3sTRtqdhAWCqEpaICaDlMUMpMChtbaE0ajdns4OF9D4vDNO0/LFj5AurB3EP7ottFsqMcLK3ivt3XoxeeBeZ2aDrMqBg76jf/LVw5z/orTPlR7/p7ngJkMOEmUUF1GIXZycurwNxIQO1OCi59HYCIIEHSKhqt7O+K5qD+RqbmRhARoocr3hHka+wVA0K6h4Yc9om+QWXRO8yf8VNcLhe6vU9BpQYm+iptq56Fw+/C6EtJ1icSrAyl1tBJSVoWCq+ezXUiB84YGMd/Z1zAttL1HDCVkVG/hVkJfcxw2spBGyzkalv/K3oCx13Rb4/GR03g0txLqDXXsCrzcuioQtVtqONzvrTKg6hptdPY4WA5tdB5DFf8RNa2v0dmcgxPFj3aQ+qWx1yIyW0kRDXQqEAhU5IRkMfOjk2Y3UYKgsYBINv5FOes+SnutDkor/zpCI+lgMPlZU9FFxFBamLD1CSGD52L9bUgNFWQupDk/oTO1iEmcyLzBNkv/gCai8StYR8kTzujuxGmiqDeXk2oylf5qFoPDXshfYGYBPkfxdS0cI40GJmadvo5hKeFPjL8kcJkd1HVaqUwYXhp4q6qduq77CwvjD2h9DjUoOaD27/5nLVdVR1YnB4sTg8zMyMHkM6fvrmfqjYr+2u6WHNn/57hny7K5rH15SwpiCEyUMPsrNOrHpY1mznn4U24vRKPXDqOZaNiWXOwgSMNRo40CNnqxBS/0YoffgwG2QjbPGbPns26deu+3p05CfhJnR+nD304jL4cgGpjNS8feZm5SXOZEjuFqmYbeyuMRAapmZEXKi76XjdWWQAep5yDrSa8EhTXWpiRNx1SpqOqNUOtBaVCPujF22ITzpeSBF0WNxGjLoeWwxA9mmidhkVjfZXBxkOkK12UaBNJi9bj8Up8ebANi91DQriW/KQAEboNorer7GNxXxcG0cJZ0qBRYHN6MchFBempg0/Rbm/n+aLne0ldH1gT5rHJMQO5QsWiiDjqrcfQK/XkBczC0ZbHWmMbC0aHD0/sspZD9Ubh/GluAa0B5vaxmq7aAp3VqOUakdUGQsraTeq8bt+Ckrj/3HKo3gJL/w5BsaJPLnCgWUaQXolcBnKlFoPOdwzT5ojbcGgqgiMfwOiLRSRBTCFkLhaxBHnnolTIUB5+Dz79lVg+NEX0IsYUihvQXcuVkNij7uLihFU8qNJitnew3GShLPUiXmsqIkqpJScsh6ZOB/srTWQYvyLtqxsEqdMGiQrwpn8NIHUymYxfTfp17wOJfWIGznkIRl9CA1nIm93Mtr4Fe4R0y+JsQ68VezcpYVzPKgqZclBChySBy8J+806KzQcASNKlEaQKEZ8RoKzcCB53/6rlCSDIpjgH0xz7kAdPGrYK5pHcuLwutIozRP6SZghnVeVxkSe7nxCmR7FjIf9CKFgJRz+BwGgRczIY2iuFMUjO8oEB8SfAtLB55AWOEccToPIrYcFfvfl/mtT97uw87lyQOWgP1jeGzirY+6xw3J14i3CxPQEkSeK8RzZT3mLh9nkZ/GTR4P2tx9osXPTENjxeiS6rk7lZMZQ3WkmP0ZPgi+FZW9TEExsqWDEunpXjE2gy2qlosXD7q3sZmxTC01dN7KfG+DqwakICpc0mQvVqJg/iTDk2KZSqNivjkkMGPHcyIeUjgcvjxSOJqp/NJeT2Z4+K47OiJmKCNBSOoL/PDz/8+H7BT+r8OD1IXmyN+zjqNpEfP40/7/gzm+o28X75+2y9dCvNnYKAtRidomKn1GAuvIkvy2V4uuREh6hpNbpIiOgd3GXHGwgLVBGoVQ68CDceInPj72gZ9R9UKiUBGjnUHhSVhL55bO0V8MQcUrwuUs5/HGIvxuHyYrGLi1ttmx2Tzc28Ub6ZVH24kBsigT4CPG6cH/yMME8ScV3lpHV8Bel7uCLvCt4qeYtLci4Z9HC0eMOxSEZwQ5vJRagmkQcnv4tcUlHf5sXh8uJyS5hsTrosbpKjdAPfY1CcCBtvPgSaAIgfDfqQ3udXPAUH3xT9bc1HRLD6rD4B3RHZMPpKMWOuj+gJca868DIbJl7IZWH3o0gcaCgTEaTmrHGRyOWgUpzEbPvrl4njXbkerlkDKi3OS17iiGkfURo9sSDInlwljG6CEwZuo243k9W5HFLWUhAoHEHnJ8+HNT+DHU+QqQtl/Y+LkKu0yGVyttd2YnF4sNSXis/M3ikkugffOvngXKUG0ueSBaSGN6Da1djzVIg2hkkhSbQ5W5iaMAJZ26HXoWk/uRFpHAyREawMpc3ZjFwmJ2D+vaKfMnPxSRE6gOgQNWUNctSdZYSsvQKK58GFzw+6rNPr4K365zG7jSyOOp9k/SlkBg4G1QiklCFJcN2nQz/v9cLTi4S744RrYfm/TmoXZDIZoeo+1Y+kGdCwBxImU9pgwebwkptoOLnzdzBIErSXiu+P7vtRzfhWCR2I+BOPU3y21taeaJPh4PFK1HfaAWEQMhQUchkKuQyPV0KjVFBUY8Zi9+BwmXtI3T8/L6GowciOqnYeXFtKo9FOflwQXTYX64620GJyEBN8cpMIJ4tArYo/rxgkasaHf6wazS/OyiE66MSEdzh4vBKH67vIjApEpx5cQpkbG8TL102m3epkWWEsTq+Do5613HGhnFnhi1ENlufqhx9+9MPNN9/MLbcMLfU3GM5MXuuZgp/U+XF6qPiS23f+ie2OVpalLCE3LJdNdZvIDc8FRB+axeEhSKfs8ZlwaCLwSB0AxIdpmZbT3wVQJpMRFey76BW/L2SI+auEjLB2B1GNX7CkfSbyC55CtbsIPrtHOE3evktI6eRKUQmRPNRHzUfpNBCF6FGYmBnM4WoTVocXlbIPmQqIhhk+YqQOgLo9FDkTqUy6FFmsi8QNn6K2tnF9cC7XL3ttyPy32DANsR0a5HKIDtH4KoMKQvQycuL1BOlVqJQy1u7vwCuB1eGhYCjDi8h8yF0hyFlEnxnspMniBtB4SLhxGhuoWvkokYZokekXmSskl9v+DVnzaXQYucV9jJoDjxI56wGWDHFBH0kfh8crUdNqJzRASbBeBcGJgtQFJ/Yss7NjI4dMe/B4PWwrOcxDcx8m+I49gtgdb6l/8C14+zpSVQZSb9sJhj6Dwe7eOZkCpUIljoWxgbSoUMx2D5rcxeDeCRkLhOxy4e9PuP/Ho9nRwP6unaQbckjzKGH3qyKUfMadkDKLQq8bhUzRT44hSVLP30ZXJ9s61hGhjmFcZxUAIZXbuKroA/au+hOfud9DJ9dzWeJNKFY+ddL7BxCoU3LWuEh4aAm4jSKgfQhY3GZMbtE/2OSoP3OkbjCMv7FXfjli9DYGHqjt5LnNVZw3Nv7UqhTpCyB9AR1mF4cOtQOgVctPPxi9ah2UfwZKHcz4xYiqTidCl9XF4fouJqaGnT7p/C4iYYqQi+vCRkToAJQKOS9cN4ktZW1cPmVoi/6EUD0f3DaDZpOdmZmRHK0zU1JvJTlSVKK7bC7GJ4dypNGIJEGTSRBFg1rBjIwIxiSGfO2ErhsbSlpICNWRNkiYuVwuOyP7cc/qQ7y6o5pJqWG8cdPUIZfra9Dy7J7NEH4UgBR95rCuv3744YdAVFQUBQXfHxWIn9T5cXqQPDR5bAA0WBr5y+wHWJG5gmhDNAAWi41Oi5tOnw17XLiW8EA149ODcHkkkiKHucDZu6B2q7h/8HWY/hMYdRE0HkSjDoCMOVC/UzzvtsHmv4lq3aRbITKLuovWsaMzBjww5/CXhGZOJiFcmIO0mZy9JizdUPe5CEflEajdBIBesqK85CUoXo2xswJV/U50U+4adJfVSjlTskN6/g7WK7HYPSRTQVrJ+xCSgnv0NSgVMpxuCZXMNfT7l8kgfuLQzwNUfAWSl5e7DvOX984lKTCJ1ee8i8raIhw0XVaIzMASP47WPX9D7fWQZIiDz34LHpcgQX0GrO+VvYeExHkZ59Fma0Ov0qNT+iR8G/8BraUU5/yMmkYjs7dfglejRr7yMZj8I9j5FHxwJyz7J3qf46PNZae6vZmtZbVMTcki2DBINcHSIv53WQWZ6osFv4ekKRA7GhQqQeC3PERk7jnMv+hFeHyF6N2ytgtSdwrY1r6OBkctdfZjpNUrRZ8YwO4XqZ1yOZ80vYNeGYBOoSdUGY7VY6bOXs3ciKVkBORywLiLSmspldZSMnOWEnhsO+x9E43LgcdtBTW4JTeSJPnCFE8DV7wjZJyD5eX5EKoOZ3rYfDpd7YwKmjDkcmcEutD+FfITQS6H6z7vkV/e++Ru2tx1JHm/wtSYwJLolchPsicLQK9RoFXJcbq9hA52jp0sPD7DHq+7x831dHHRE1spbjRxyaQk/ryikHazi8omK0kRulOOJvlOQR0AhRef9GoTU8JG1NuVHRNIdoyYAMuODyA7vvf3+sLHtnK0ycS87EgajQ7GJIagVMi4ZnrqsLlw2yva+NU7B5mUGsafVxSOuI9mKDy7uZLff1CEXq1g48/nEh5w+pMBg6G2Q1Q1h6tu9oXL4+WRj7u49GwVOpWSGM0PyDnWj28NTq8Di9uES3KhkqkwKANFW4gf3xr8pO5/HfX74LXLRJ/T5W/1RgeMFGkL+Jfk5IvOEpbnigt6QmCvvE5btRaZdyIgQ9O8B8KFcUKSb4aVDX+DHU+J0OuJ18ORD6FoNUy7A2IKRKXG6wa178KsNvSXbM242zcr7IH2o8Kp0dIC2mDkUdnQ2QWSF1nTPlBZIWs5SoWM6JAT/PCotKSfcweRNjc6VSRypZy9NZ9zfcPnBLRoeXPUpUTpIk+YmTYxMxiL3UNAxQbhENpRjtJrZ15ENebK7URUdULcTwb2Knm9IqQ8MAbS5w39ArN/Dh4n5Wo7dByk3lyPrfRDVHU76dCoOZyZz4y6VtKjxrNmxRokSSKsahts+Y9YP26M6IUD1tes557N9wBQY6rh6YNPE6mP5O1z3iaosx6++AMAkVIEFlkqOmcLjLoMSj4AuV4YtgCMvZKxCVPQy4L4966HuCr5j9hMEWwv6WLR2IHW3q0F53PQWsv0uOmo++Zy7XkJPr9HBLRfIkxdqNkh/q/dCS0l0FEp/g6IHvIQubwuzO4uQtWD24on6FJpcNSSqEuF/Amw4wkw1sHkm6i31+DBg8ndhcndRbOjoWe9Y7ZyMgJySdKlUWw+SIQ6Cn1EPkSOEiHspgYmZ11CmK2EKGMnyo1/Eb2aOecOvqOdx8SxDMuEjMWDLxOSBOOuHPK9dqPbnOU7CZ+xEMCUtHDK3UfQad3U2quweEwEKk++10ejkrNwTAReSTqxEdFIkDZfmDoFxpz8b+IQaLcIothmFlXW/ZVGOi1uWo0uFg/yvfBj5Gj1HdPKNguVrVbKms1s//V8Qo/Lp3O6vf3Oj9d31VDRaqGi1UKARsk9y0dece6yuXhp2zHGJYX2GKJ09665PRJu76lZ1a452MDDX5ZxyeSkIQPY/7yikDd31bIwb+jfvb5QKeTMy0jh5Xf0/O7sPH78SjEer8Q/Lxrz7ct2/fheQZIk6u01HDbtpcpaitRHeSFDRqo+k7zAscRpE097ksSPk4ef1P2vo/gjMNaCsZaGkv2EZk5Ee7xGv/OYz4giZOD6cgUZmWeTMcTmQ2KTWPDa+UgqHYFXvjRwgZ1PC+OOXc8JUvfujwQxMzfDVe/D5DtEr0bM6MFfQKkWphgep7BdV+p6Qr1jw7TMyPagPPgSIWWrwTNLmJCcBIJ8GXB7mvbwkqsVJ17a3VZqyj4mqqVEmEfkrexdQZL6ET05EoGmYojIBVODqGqodOjcbeg8tWC1wN+zhQX8VR+IQSTA7mfho7vFfUOkCGovWDFwB8PS4IJnuMPeSXDR84yJHENQRy0AepcL2eH3YMdnsOGfRNy5H/Sh7DAnMVoVihIXii0PiQpYwUrCdeEoZOKzb7W14pE8NFoaabW2EhScICSgHVVEjp6PQz+amvatJKj0ovgUmgSBsdgDImnVBZAAZAfn8d/oBew2QzWg1w4u+bzi02uoNdeyUqviPnzmM04rfHinIPTlXwpHz6hcWPYPQboKLoB9L4lqLsDcXw35Gb7f+AqtzmbGBU9lYuhAR7xxIVMoDBqPSu4b3NyxF3dLOdbDn5AnJWAx5KOSqam2VxCiCiNem0yDvYaxwVMASNKncV3SXf0vYNlnAeIHNjdwFJQ+LSqRdTuHJnU1WwSZNNZBypyTlvxJPlOE79OF9Bdn5VBtDOGAdT2R6phTInTdUCpknH4p1Ae5EuLPbJXzxesms7G0hfPHiipJeKCaToub8ED/oHokaDba2VHVztzsKAya/kOX7mNrdXr49xelBOmU/aTkDreHVY9t5UiDkccuH8/8XEGGLpucxCeHGrE6PTy3peqkSN0DnxTz8vZq1Eo5e3+7EINGyY0z04gO1JIaaSA66NRklv9dV0ZRg5F/ry0dktQlhOr58cKTC6Z/8GLRq/zhgXo+K2oChLnMyvGD9Dj74ccgaHE08VXrGjpcrYM+LyFRYS2hwlpCqCqCuRFLidSMbOLBjzMDP6n7X8fYy6FmO42yOLYZkwgu7uw1DwERRF28WlSSpv20t2I2UsSPJ+CWz8UgSTOwx4DZv4Bdz8CMH4u/02ZD8Yc0h09Fa3MTZIgUpOZEUKgh+5wBD0eGGkDqgvr94lZwIcQO3cg+GDrsHVz/2fW4vC4KIwqZnzSf8Q6HkGU1Hewlda2l8OQ8Ma5c9RwkTobND4KlQZBiJDA3cOzgNiIzZqOXJHj3FmHyYe+Eqk1QeIHYlrbP4NbSAnte4GhcPqHaUKL0A62uQ7Qh3DnuTvFHlBGjRstOeRNZpb4MP6e1pw+rRR7LJ3M3EtW5k6k7roGv7oeClRREFLD63NW0m12YOkOIySkkMMzEftsWPMpxZN6yDTxOZCotiUDtuQ9zrLmYBE0HyuRp7IzN4/rPrkex5hIemPUAWQ4HSW9dy1iZgrRlzxCUPTiZcXjEftnsHb0PKlSiN8fSDLFjICKb1WWrOdp+lItn/5Rfrr0Zu8vKf8NTiQ3PgegCXF4nW9u/Qi3XMCl0FnKZHEmS6HSJ7Xa62nq373FB3XYwREN4Zi+h88H1/EqCzJV4NvyeuRe9ANkL+j0/Ori/LPaERCpljnBpjBrm3IsdJyZQwjJOmtC1O1t4v/E11HIN58Ve1iN/PZOQJImDx8zYnB7GpAadkRwtgKSgGJKCLjoj2/ouo698EGBUSiDZ8QbUyu8PCf82cfnT2ylpMrOsMJZHLutfic6LCyIvLgiAWVkRJIbq0at7hzctJgcHasUE0MbS1h5SNz45jH9fPJb71xxhWeFAR+DhEBciKriRAZqeHkmlQn7aJOnSSck8uLaEywchdM0mOxtLWpmXEzWgCjlSTE4NJy82CI9XYlrGtxyB4cf3BrW2Kj5tXo1bGqZlpA86XK283/gqi6POO/Vc1u8A3nzzTd544w2qqqpQKBTExMQwbdo0rr76aubO/e5lgvpJ3f86QpPhqvcxfvock/feTn3+rUAfuZ/TLP73OPtY5Z8kdCGDPtxhdtGRcBFJY69C2W0ccNFLbNxXTatDS2BJJwtGj1CW5PWA1wVKLZIkUdFkQ6WQCZln/HgkuQqnJpyDLYGMjvT2Zt8NgT9s/QN7mvZw77R7yQrNwqAy0OnoZHHKYq7Kv0rYd1d+BdF9KogV64Q5CcCuJ2HdX4RcUG2ASVchARIyjpm0NNc5mJi2QMgGrW0QMwoyF/Vuq/ACCE7Ec3g18qqN7EydzHUfXECgOpD3z3ufCN1xx6V0LXz+W8g7F6b8iCBLF/N1sbDwGoicJKpcvirg5KwQGjocxLsasGlD0I29vGczKcEpHKtpp93sIlE5C4v+Q5odDez0bCIzIA/kvtlnj4sEWS1k5fYQkFZbK17Ji1fycve6u1HKFbyhDSDDbiE0OACGsBN/ZvEz7FpzO2dtfB4cOljyF0Hqbt4CbaWQNJUGSyO/3SyyD721uzhkrgFg06SrWDVZZL6VNHzGEYeIEYjXJZOoS0Umk7EkagU19qoeV01AGGFUfimMV6b/vD+J3vk0WrOQdSq8Dtj7Yk/l7ZQRlg5hJwjLjsiGmb88pc032GtxeO04vHbanM3odam9Tx75QBD30ZfA9DtweV2AhEp+coPCdrOL8kbRwxNisJEd/91y/TptNB0CpxHiJ8M35Ax42sTYYRJGUuHZZ0wm+l1Ft5zR7R2+x3F8sujP67K66LA6SYkwkBCq51dLcjhY18UNs9L6Lb8wL3rEMkaAn7yxn/f21XHvOfm8f9t0ksL0Z0by68Olk5O4dPLgpjHXP7+LA7VdzMyM4MXrBjoYjwSRgZoBGXl++DEcWhxNJ0XouuGWXHzavJpzYi753lbsioqK+v1dVlZGWVkZL7zwAueddx7PPfccwcHfnXgQP6nzA4DMXb9G5rIQpXPinjGnl2SlzBZW5oaogX1fpwCHy0tFk5UQg5KdpUY8XgmTzc3oVDHLikyGxhAMDkdvhlw3nFZhlR1y3AXP44Idj4C1BQovoVqWzoEqEUiu1yiIyJhPwzUH2FXtxGPVEdXh6O3pAyH1NPT2x7Xb23mz5E0A3ip5iz/N+BNvnv0mtaZaJsT4JFkhKTD2mv77MfpiKH6fToeC9Ql/JWP/jeQDDiRfDpuM3epFtCniGdW0ATLPhps2CAmhYSB5tUSN56umVLzhd9Gu3iB21WnG7DQPJHXb/isCn1uOQuJ4YccOeGInsCNoBY52G9NbnkEl8xBReAnOcBPnb3sDR1Isz+ctpa8PWny4hg6LS1iFBxSwr2s72QHHuT8del1kA4amwfgbADgrdhqWKb/lSPtR3ih5gxS5DvWi+yB2kjA66QOXw4LKZQJNMCnBKaQ0Vgrpau2O3oUCIsUNCNYEkxCQQJ25jtlhBTQ17sEuk7EgqlciF12xDXWMBpVMSZiqt7obp0siTnfcOaPxnW9Kjajy9kX5l8gQHo1SZB7yyTcN+GxOG6Wfw8e/gJylsOiPp725zIA8mh0NqOUa4rXHzfBveVicGxv+jmnSVbzT8CIeyc25sZcSrh55wHGQTkmQXonN6SHqh2Ds0RemBjj4su8PGSQO7Sj4ncK+58BULyYExlz9be/NaWFbRRtPbqjggvEJLBmkavbSdZPZWt7Gwvz+g8Pdxzqo7bCyfFRcTzyM0e5i4b/W02xy8OBFYzhvbDw3zT4zLrAfHqjD7ZX4w/uHSIsM5PWbppzU+qv31nHv+4dZWhjLn1cUntS6Lo8gtPIzJLE+2mjCoFGQEDqCuBI//ichSRJfta45aULXDbfkYl3rGi6Iu/p71Rqg1+s555xzmD9/Pjk5OQQEBNDS0sL69et57LHHaGtrY/Xq1Zx77rl8/vnnqFTfDRm9n9T5AYAsazEcfodi/Qyq9rQytzAMg1YpZJOJU0VVas9TIuB31GVDbsdsd1Pbaic+XEug3CYkbuFZECwG1YdrzBxrtiED1Co5Hq/UUzXbXdZFQ4eDUSmBZMQaCDb0OT09LnhiNrSWwLJ/wsTrep9zWcAiegToqEIeJi7echlsrP+C8HYFId4JoFKiV8l6nOa8koTl7bsIPPQcjLkcznsEgDBtGBdkXcDupt2szBTSyhhDDDGGmOEPoiYQrvyATTub8HrgH9E5REilVOsCeC5uAtrwbMa/chkFdiu6kCiYcTY0HwRTI6TOBZUOq8NDXZud2DANZpsHl0fMTs+MPQuZykpiYCIpwSkDX3vCNaKqlXeusJev3wW6MIyyMBo7zMS6K1HZBdEz1e2gTKWk3S4s4IvaisgO66V1GbEGMmJ9VZiSDsZ4UiCuN0i62liNs+UgGch73Sqr1iEr+5QLQlKRptxDXlguZ9ftR91eAeqgfqTu5++uJNzj4hch+aAJgcqtYjuBsUM6WOpVet49913MLkFopyXNFv2TSb2z1RHaWK6srECWOhe5so/U12WDT34lzuXF94s+zITJEBgnKnR9KhydrnY0c3+OTqHCmbGc6uglRAWrOePzcDufFn2CWx6C+feddG7d8VDLNcyNXNrvMUmS2ND2Ga1zljJnnZXwtCV0uNqwe4VbbYuj6aRInUopZ/6oH6hcS6UT5N7j7CX83yecmifHdwp//riY/TWd7K/tGpTUxYXoBkgb6zptXPT4VtxeiVazk+tmiAp1l9VFi888pbxFqE0qWy18uL+e5aPjhnXEPBESw/SUNVtweeFok4mvips5f9zIJZfv7K2jy+bizV013H9+ATKZjCMNRp7eVMmywljm5gz+ndxf00lJowmZjAHVxlPBV8XNXPv8TjRKOZ/eNYvk8BMfk/01nfzt06PMyIzgR2eIJPvx3Ua9vWbIHrqRot3VSoO9ZuDk6ncYdXV1hISEDHh84cKF3H777SxZsoS9e/eyfv16Hn30Ue64445vficHgZ/U+SGw6lmOzXyQ0io7eCSMrQ0YEnpzx2gv6///ENhV2kWHxU1du4P5fCICtKs3w5x7ATD4qm96jYIZeSFY7F4iglR4JYnqVpEtVNduZ2p2r016ZZOVo2V1LG4rFzYIjQf7v6g2RPTTmeop007gYLmRIJ2CNvVm/m/7PQQoQvh5xlsARAZpesJaW758ktDitwHwHttCXwHNvVPv7bnfYXahUsoI0Pq+Lp3HoOhtQVTzVooKX+0uEQg+7koSIxKpaLKxKOkKvgqyMy96PFpf35184b3o9jwPU24VfXSHRUUQmQwyl7CrrIs2k4vqFjvzRoWRm2DA45VIjQwgPfqq3h2UJFrrjlF9rI7UuGBCc8/ub3M/UxiHBHsl4sNc1HQo2NPVjkYmZ0vrXq4dfxc3FN6AzW1jaVp/MtB74DfCKxf2/r3sHzDxep459AwbG75kkS6WG8ddTyhAl5BDYqxBBqzMvgBaK8HWPkAWtsFYxp3BPkMCRxdUbQavz0L+0DtQsJLBoFVq0XZXi9PmDFxg7LUoHMaBFvuHVwvjGYDUmYL4Qr9cPYBySzFrWz5AK9dx4Yr/cqDcSX21GbVSxrIJIyc/J0JzpwNv5hVEt5cjy1562oSuH4y1cPAV0Ediyj+bYvMBUEDxefcxPXw+iZLEuOCpuCUXGYbcM/e633G4vC7sbjuB6iEyIbUhMPXHIgYk4ASTN98ljLla/CZH5HzbewIMrPwcquvC4fYyPvnEsReL8qI5UNt5UlJIpVyGUiHD7ZXQ9JFAJobpeeiSsZQ0mbnRR4Bue2UPh+uNfFrUyIe3Dy8//MvHxTy9qYJb52Zw3YxUDtZ1MSE5DLVSjuK4yI0/flTE2aPjetUtJ8Atc9KxOtzMzIrgpe3VzMmK5P8+LGJLeRufFzWx/95Fg67Xanbgm+PD7vSM6LWGQ5PRjiSB3eWl0+oieZj5Gq9X4pInt7Gjsh0J2FTWyhVTkgcY1vjxw0ORae8Z2c5h077vFakbjNB1Izo6mrfeeoucnBxcLhcPPfSQn9T58d1DQlQQpsYqFMYqYkp2QfSvewfkmUvh2AZhyT4MdBoFHRY3erUcFL4Lubb3gp4dbyAmRI1eo0CllKP3+UHIgIKkABo6HAOCg2ta7dgUQewY/SDhnXsIGv9TBgyxfXKptpJOwIHZ7iHI5z7m8Fpxeu2o5Rriwnwv6DATtfHnyJCw6BJRLnuIwawp6tvtbC/pQi6D+aPDBbGr3y2kntYWyFgkZvbfvl7Y69fsYPSNXzEqJRCZLJqLOc5Br2BFr4ul2yGOjb1TVKno7bHRqOTIZDJyEgLosrjYWNRBaICKUSm+QenR99nTnoNFkUFnWTHzUhkUcrmMSVkhFLhGcdmaZqqN1Tw05goUcgV3jDvBj5BKBz1CRIRkMHU2d+37mEJbAx/k5hIY4pP6ZS4ROVUROb3unxNvFrK20P6zyveNuoX1h1/hLK+O0MwlkLsMyr8S/YhZQ1j5DwWHSQQeh6WKPqjBMtMSJoI+XFTq4sYOfN6HLlcnAHavDYfXjkYlfh61qjPXX2Wxu9lc3AlMoGDFOjJPNyT7eDQdFDl7tg4CHE7S9Nm0OpvICBAETiaTDeoA+kOGzW3jog8vosZYwz/m/IN5SUNEhAzm7vtdhyYQYoc+p880mixNKOSKgfJvhJPi9S/sQqdS8NmPZ9Flc3HOw5vwSvDsNROZmz38xMitczP40ez0HgnlSBAdpOWD22ZQ32Vn9nHh9ctHxfX7OzZYx+F6I7HBJ+49/OhgPS6PxEcHGthQ0sKe6k7OGxPHgxeP5b+XjeXO1/fhcns52mTGIw1eKH10XTnv7q1j5bj+0s8paeG8dfM0rnh6OxtLW8mODuSsghi2lLf15PUVNxopbTKzpCAGpUKO0e5CkuD/zs1Ho1Kw4Dji+8bOGtqtolI50mD7VRMScXm8hBrUjE4MGXZZk93N9kqh7FDKZczPHehA6scPD06vg0pr6RnZVqW1BKfX8YPJsUtLS2PhwoWsWbOGsrIy6uvriYuLO/GKXzP830o/eqCQyygI6YTWbaAyCBOJbgTFQ+Elw2/A3sWEBOiMDSXEoALZEkEC9f0vtoMGUAOZcYZBB7m5CQEcrjZRH7OY+pjFpDv1A0mdD4XJgejUCqJD1ESHLCZOH4Wz7hgdHjnxMQHEhPoqPWoDspQZSMc2I5/7CzQZgw90HS7Rw+CVRPYQIKzOu6pFb1/3zH9MIUanHEvS+RisLurbHSREaDnSuReVXMWYqDEDN67UwNS7cNgsoAlGA0zICKbd5CK0TzB6ZZONdrOLdrOLjFi96DU01hLmCcIiDyHcVgbM7t3up7+Bo2tg3m8hZSYERKJX6XnnnHdwe92oFCPUfidMgBu/gs0PweF3oK0cdj1LSHMxK4GVE3ySRgB9BOSef9zBs4gqWcaCfmTqrLE3cZYiDN65Hg6uhlu2warnkNxOJLlKVExdNkF6hzDZAYRk879TRZXwvEdhzKXiMaUO5HIOthxkW8M2VmSuIPynpYBMhF8P9XZ1yezs3AgIN8lRKVkkhGsJ0vveo+QV+9XXAdbr7j0GI4BCLkMpF5UFdfsReGGFIJ2XvnFmzDniJohKsiEKeUAMCwN9jrBuh+hXDEkeNtPvh4hWWyuVXcL4Zm/z3qFJnR/DYn/Lfq7+5GpUchVvLH9jgAy8ySSUFjaXh06rC6fHQ3dUm22ElaWTIXTdyIwOJDN6iApsHzx86VgO13dREB8svsdV6yAwFm/UaOTHve5vlubx8vZjXDM9hd9/IIwS2nw5g+lRgXx4+0zsLg8fHWhgdGLIACL1yaFG/vpJMSCqftfNSO2p5G0pb0Uuk6FX96pWfrwwi7NHx/Ls5ioeX1/Ov78oxer0cNeCTO5akMWPXtzNlvK2QQ1Sdh/r4OdvC4OoAI1yUNfM4/HHD4v44EA99yzLG0B+B0OwXsV9Z+exraKdXyzJOS35qh/fH1jcpn45dKcDCQmL24xa/cMgdQB5eXmsWbMGEHJNP6nz4zsBk83N3gojgTol6sAInvC0MD9+NOecjK26qR52PooCCJ94M8h9J3fQ6WfgRAarmVMYTv2RHVgdHuJjJ4knbB2iMiNJcOB1UBvQ557dW80CRkWPpV2fj9MtERPa5/3IZHD1h8hcdnSq/gYwXknC6xW5V8mRWmQy0CjlgqiCkF1OvavfOl1Ln+LLwx2ADHVRB063RGlzG78+eC0yZLw450FGW43C5bJP5crokLOuyAW0MLsgjGC9qqfnr+f9h0FVuxOd1oWuuwcq93zG12ynQLYf7ZgLehf2uGHrw+L+OzeATAFXfwSJE5HJZCMndN2IGwuB0YAkevYW/h6KP4TIbOHoeDxcNiG3DYqHL/4olt36X/hFZf/lepxUJZA8eLwS64pMmG1upiTLiX55Jljb4Yp3ICILKjdA5sIel8pDxj1sbf+KrLxRzN5aAw37RbXv4JsQmoK08I/ctO9+TC4TRzuO8vfZfz/hW5X3EeB6JS/yYxuJcHRB+iJAA7ufFK6nWcsgaQY07IWityA4GZSBsPMpmHYb5J9Pq9FJl9VNSpSu30BVq1Ywb1Q4dpeH8HX3C5OcsrXCrCfo5CzVB4UhEib+aODjJR9B/U5BeGff03/C5geOxMBEfjXpV5R0lHB1/tXf9u58rbA63dzz7iGQwZ/OK+yRmo8UtR1Wmoz2HgfJvqg2VuP2unF73dSb6weQuosmJOLxSkQEaChMEN/TZ6+ZiM3pYelJxgV8HdCqFKRHBnDbK3u5KOQQC/RH8Uowa0sldy2fyAV9+vXOKojhrAIhw00KM/DqjmoCNAo6rU5C9Oqe7Q0VX9BXCjovJ4pWs4M7Xt2LWilnU5mIVnn+mklcOCGRCb5j/faeOl7eXg2AzqfY2H2sg48PNmD1keLu/0uaTCjlMtIiA4gK1BCgUWJ1ukkOH5nhyfNbq3B5JF7bWc3Zo0c2EL16eipXTx9CEuLHDxKuUzRHGXp7zjO6vW8b30XjFz+p84OqZhttJhdtJhdrSh9iW+N2NjTv5uyciwY/abc+Aofehnn3QLpv1tvW0TtQt3UIE4pThCRJuL1S/9nP1mLi6t4V901hsO4FQV7GXCb24V2fQ+E1n0Byr3Od0epmw+EOJGBCRhCJEcdJb44jdC6Pl3UH27E4PATplBhtbsanBxEbNrzzZ5PRRXfwsXAmk5ArxA+YhISnah24XNBa3M+23mRz4/FNZ5ttHoL1A0lXvXSApoitALQ5ryJMHYk8MA5Z3vn026uuWmHAUXghVG8Rf+MWOYARmf2rXrYO2PO0MIcYd11v9cnWLshUWHqvjHLqbYJ0xI6CnGXiNhS2/hucXeJYBPgIaHD8wOXGXALaICGLjMrFZndjtIrzp7m1k2hTg1iubo8wOWk8AFlnwaWvA1BhOYoXL+VZ45ityBaVqINviHU6KpG9cRlRBVMxuUzEGnwDykPviCrWlFsGzYGL0ERzdszFOL0OUlxqKHtBPKENhsTpvX2DHVWC1LWViOpdZyUc/gQ6KmDdX3BkncvmIx14JdH7kp/Uv4pg0CowaBViPzqqIGHSmSF0w6GbzJ8sqR8OXo/oLXV0Qd4Fg0tfvyO4NPfSb3sXThpri5p4dH05F01M5MIJiSdeAfi8qIl39tYBMDsrknPHDPLdGwJtZgdLHtyIyeHmzysKuWRS//6XJalLaLG1oFVomRY/bcD6SoWcK6em9HvsRJLLbxprDjbyeVETumiJ+YVQb9fQ7lTwxZGmHlJncbjRqhQ9kzHpkQbe3lNLp9VFZauV/1wyuNz1/f31rC1q4ta5GczNieK1G6egVSkYkxgi+tGqOvotr1bKmZ0uVCwmu4sPD9QDEBus5YkrxvP0pkpW76tnU1krr90whdJmMwvzotlW0calT25DIZfx3q0zyIsL4qufzsHu8pAYNjJSd/fCbD48UM+Ns/xmJ34MDZXszDo6qmQ/LNfkvnEH34UqHfhJnR9AQriW+jY7gXolc4Nns71xC7MTZg89C7H29yJIedO/ekldZB5kLe+9PwIYrW7UShna42aTNxV10GpyMSY1kNRocZEqMxo4bLiNVNcBRql0UCVkclRu9AV2y4R8TXsaznX7X8O25Tmk0f8GWRBdPpKxr9KEyy2RFjP4BdPtkWg1uVDIQadWMCM3BJvTS4ghisDwR1Ar1IwztkLN5p7K5ZfVX/LG0Te4NPdS8hLHg+QlTtkKnqgBFvux2kQUXTsIkIex54gMu7OFaZl6IkqeFTmCY68R5g5rfiZkl9pg+MUxQb43/A32vyKWm3AVuK2QcRaUrwWbL4y7Yb+IrQiMgd2PC7OIzCWQPEs8HxwPFzw98I2Xr4XWI6JyFZomyGDR+5AxWwz4z/oLjL4UooSJg8PjoOjL3xEfnErUpB/1I4cBWiX5iQEYbW4ykiJgwe+FK+iEa+CAj6xJvflUE0JnsLdzKxmGPEgtgG2PiSfkSjG5oNDw4owHqFBIFEYUQnMxvOWLoJDJYfqdg36WcVrf4NlpFr2Slhb48s+i8jj1ZlGpS/FJXVPnidcKSwdVGGx7BMZejlwuqrxOtzR4fpW1XYTSx42DK98bdD/OODKXQnimmGw5U1U6Yy00+proG/ZCml/WeCbx4BclHKozUt1uHTGpG58cSrwvFHtCysBq23Cwu71YXaIS1G4ZOKOulCu5tuDak9rmt4Fmk53PDjcxPzdqQP/cjIwIUsL1lMjS6Ry7jE0lRqZldXLngkwAPthfz52v7SUvLoh3b5mOSiF6m0P1ajqtLsIGCfxuMTmQIfGTN/bh8kg0dNmID9Fx/rgEpqQJ95FRCcFsLW9DBvzfuQWkRRmYmh6Ow+1h9d46PF6JmnbhSnvJpCQKE0KYlxvN6n31BOtUZEQFMNm3rV1VYsLI65HotIrPKTJQwyvbj/F/H4pB5kvXTx602tplc3Hry3tweby8cO0kwgN+OFI4P848DMpAZMjOiARTjhxDX1fq7zkqKyv5/PPPAUhPTyc+fuQTaF8n/KTOD0IDVCweJ2YMp3EJq7IvQCUfZoZmwjVC5jamT7SBTAZJ04d/IUmCPS+AUkNN3LnsKjcik8GcVC9mjxq3QkdCuJZWkyj5t3Q5SY1Ug6mBOrMGr0yiVjeWUcExsPTvQu42+hJInws3rReDTJdFvI6PkAbplczKDx0ovxwMu54hKEDDIutzdOozaUq6kJJ6K26PxNF6y5CkrsXopKnT128Ro0enUaLzvdSsBB8xigWSZ/TYpT+w8wHqzHVkKJdQEGxllnIjsuKdQsp3nHwuQZfM1Um3Y7R6WV/fCUBrawcRJjGzS1uJIHXhGeLv8Aw+PfYZW2jhHkM4KnunCGav/EI8r48AUzO0VwlSYn1VfC4hSVDgc9B0O/rtg8cr0W50EuquQRkQKUxRurdXvVmQOm0wKA2w53X2Tv8Rb237A6uyVjFGIypVH354Eyv3imqrOzQDZeaCfq+RFW8Qn525CWbc1fvEZW9C5XpRqfMhTptIXESUkJiammDFExA/HkIShTlL9XYCjY2MzvI5yelCxf7ZuyA0ZdDPsR/UATDtp4IYH/MR2rGXC7fTbpiaYOsTEFMIZ/8HZv8MABUwb1Q4FruHiI7d8N5LMP4a0aMIsP6vsP0xQa6Sp/dk8X2tkCvOvENiYKz43O2dEDWyiRw/Ro6V4xI41lbCqiEkfoMhIVTP5l+eGrmOD9Hx4rWTqGi1jJhEDoV2k5Nd5UZCDEomZgSfUKbk8DjQKAb+Pu8+1sG+mk4umphIwBDGHDanhz9+VMSagw3MzIyk0WhnR2U7r+8M5oPb+/dKJ4XrWfezuT1/Xzw1iov7RBJuq2jDK8GhOiOdVheRgWKf3rl5GkcajeTEBLH8oY20m508ceV4ihvN/PrdgyjlMsYkhrCzqoP6Tjs7qzrYUt7Gjt+I37hfLcllWWEs4QY18X0y4R7+soyHvixDpZCxakICnVYXF09KZENJC9nRgXz+41mEGtT9yNfSwhgeuGAUGqWcaRm9hjX/+aIMm68H/KvilkFJ3abSVjaVCXv6L4ubWXXc5/zIV2W8tbuWuxdmjViW6ccPF2q5hlR9JhXWktPeVoo+83tjkvLBBx+wZMkSlMrBf3OamppYuXIlTqcY991yyy3f5O4NCz+p82MAhiN0TrcX1Vl/Qbbkr8NvpKsGqtaLHrJux8yi1fCBcFzsPDcXiEHySigPvkAETtbqr0Tv8DBe30izKpXsxAA48Aq0HmFyaB47g5aR3B0anjhJ3ACMDbDxH+DshJh8Ians49IZFjjCkv/0O6FUNL2GeFsJiQ9ArZRTUmchdQhCBxAeqCLUoMTtldBr5BTVmEmK1PZGIAC0lsG6PwsCOvZyFqUs4s0jq0nXTcHm9OLqrpp1/98XnVUoq7cQGjuO7PgErA4PqYlh4JooKkoxPjnQwj/A6ItxBSfyyzdm45bcyHJmcl/CHyBxopBbuu0QlAjjRkHzUajZCm7fD7apCQougqJ3hUzU64bkmaAOYE+5EX3DRiKdm4WJzoyfQ/xkUanrzrCTK+C6z8Bt53cfXkiVsYoj7Ud455x3ALBrDHgBLyDpQ3reXou1hZvX3oyExKMOA1GH38Mz+Ud4F/9RnItBsSLY/XjU7YYiX6Xr8DuQuQgenSYkhqZG8fi1n0LSFNEXeNsusHVCZNbQ50BfKFSQsxz3rqdxKEDpcfZ3SN37IjTsE7eZd0NYb6+kTq0Q/UzP3Sry6Or3wc2bxZPR+eL/kCThXvh9hULdEzzvx5nHNdNTuWa4HiavF9zHGfecJqZlRPQjCqeK6lY7FrsHi91DQZJXmDsNgScOPMFDex/inPRz+NOMP/U8bnG4ueypbdhdXqrbLPz+3IJB1//n50d7etHe31/PDN/+6wfpJ5QkCa80tCHLrXMzsLk8jEsK7SF0AKEGNdPSI9hS1sqhOqNY9uW9HGu3AuAEfjQ7neeuCefhr8p4dF0509JFZc3jlfjF2wcobTLx91Uir3NPdQe/efdQzz7qVAp+uzyPIK2KN3bW8PO3DyCXQV5cEP+9dDz4ChxGuwurwzMo6b5qWjL/+aKMxDAdl00Z3Dp+ekY4k1LCcHm9zM4eOJn0+PpyjHY3f/u0uMd504//beQFjj0jpC4/cMzp78w3hNtvvx2Xy8XKlSuZOnUqKSkp6HQ6WltbWbduHY8//jitrWJyZMaMGdx66+D5ut8G/KTOjxGjpM7C4Roz0SFqpuWcoH+mzNdj1F7WS7ACokV1QqYgO0pGfbOccMsRAqROJEApOQmr/gClrYWkgBjQ3Ql20Yeg9XQxM29wOZFl7V8xdA/uw9NOPTw4Z5moptTtgBhx8U2N1pMa4oV9LwMTB7XEVyvlzCkUF/DP9rXi6azDWVbHmLF5YIgS0sZ194s+xENvQ/4K7h5/Nz8e92MOV5tpNTqR4s6Hzr091RRJknpnt49+CKY6ZF3HyPPlz1G6Fj76BaTMgNFXiMdkMojORwUURBSwr2UfmQnTIXc5bq8badqPUckUvfLO4DjY6/uxVgfArJ/BnpdArRTGN6Z6aD4COeeyt7WM8tZ3uV0fQIJcKeSVuefRYp3Og3seJKs5i6vyrxL7oNIxLnocVcYqxkf1hpZfuPBBtkWPITU0k9j43qiHnY07OdpxFIAdZpirC+Cd1ACcNY9wdsxFRGliofgjIVscc1mvg2XsGEibKyp7OWdD2edg7UOKZXJQ9uk6DIjq7fM7Dha3mS8a3kLTWcs8/RRU6WKG3RmWxAvnXolHBnnFzzAzMldUAwFGXShiHmIKhavkYEiaIkhdYh/HunFXQuosUTE9rqfTDz9GBI8bnlkE9XthxZM+Cfp3BylROjrMLkIMKnTq4YnBxlohpd9Ut6nf40qFjGCdCrvLMaxMMMwgnlPKZVw7PZU7FmSyrbyNian9rxedVifnPbKZNrOTl2+YzKiEkAHbigvR8c8Lxwz5WhNTw1g1PoF2i5OiBkHuksL0XDElmXk5UchkMn5xVg5XT02hpsPCnz4qYlp6BG/trgXgrd21/GppLi9tO8YR3/r/vWwshfEhBGnFZKrDLSSw3RXDV3Yc45dLcmkzO1j84AY6rC5mZERgd3n468pRpPicKG+ek8HNczKG3HeAEL2aN340dcjnL5qYyJMbK6lut/Hs5qozEnLux/cbcdpEQlURpxVAHqaKIFZ7etX/bxr19fU89NBDPPTQQ0Mus3LlSp566ik0mu9OBdJP6vwYMVqMotTcahyBg1HlVjjyARSuEDbz1ZtF2PMt20GhRB2WxuJ4wBMMx9zY1RFMD09FWR4PtpZeo5XCS4RJho9kdcNsd9NmdKFXK6iUjWIictxhmaim/RSC+8uVPF6JyiYrQTolUSG9Xz63R2JPeRcer8S49GAcGGmXm0jKWobc13fUbnah2/BXdDv+I4jPz8qHHYgHKR2M3XIeGmc7HJsKmQtg2k9EWPahd4Tczpf9J5PJKEjuU6kJXwjAtqOdNHY4GJMWREqUTvRCmeogPAuqN0HlOji2AyzNokK17B+g7z+AefasZzE6jYRpw6gz13HZR5fhlty8uORFUoN9s//7Xu1dwWmGmu1CmmhuAUOEqHZtfgzpi/t5IjUZh9eJRT2ahwuvBJ9U6vnDz/N++fsAzEucR2KQ+OG+b+p93DnuTsK0vfulUqiYNnZgZWdmwkzmJM4BCWYnn0Nn1SdYtVqQXDR3FRPlaoDX+hhdjPORWLUerlwtzi+XHWveORTtfpQuuYLx8+8nJDAOYkcPeL3BUGE9SoOnBQI1NH75WxJ9pE4hU6DzyjErvARazaI6N/F6QQ7jx8Mde4bf8LmPwIL7BpLJE0hAj1nL+bL1I+I0iSyKOu+757LlccOR94XsMur0ZJ0N9lo2tn1GnDaJGeELTryCH0JGXLdb3K9c/90gdQ6TqG4rtYQYVMwtHCbNug9+MuEnPH/4eZanLe/3uEap4MPbZ1LZamFiytCTiDfPSWdCSijJYXqifNmkx+e4AZQ0malqE5W1bRVtg5K6E0GlkPM3X7WtqN7I50VNrBwf3xO2DrDuaDM3vrAbjyTh8Ursr+1kUV40Zc3mHknjqvGJ7D7WwaSUMJYUxPb7fl82WYR6P7+liuJGE4+tr8Du9LBqQgKtZnHtXV/SAsAbu2r4+VmDf/9azQ6q262MSzqxgZEkSdy/5gjFDUZ0KgU2lzAK88MPmUzG3IilvN/4Ku5TcMNUylTMiVj63buGDYPnn3+e9evXs3XrVioqKmhtbcVoNBIQEEBiYiLTpk3jqquuYurUoSdIvi34v7V+jBiFyYGU1Ft6AryP1Jpp6nBQmBJI+PESx4p14HFCSylUrIXabaJyMuseWt0WmloPkx+RL6pGafPoaWfPXwVpC3udGg1RkL4APC54bjk0HICLXmBjRz52l5eYUDVNscv4KGImE/LiiAkeKEUqqbNQXGdBBizOAl35h5C1mGZPBHXtonesps3CJueLOLx2xgVPZWLoDCqbrOyrNKEKvZ5FqudRawzDZol1WV047Q5UbrN4wGXDYm1mb91GZoy7EgpWiqrdMD9uXkmioUPsU2OHQ5C6jMWQOlccq/X/By4rRGWCyy4qdfqBFUylXNlDqI60HaHNLipY2xu3UuU8QqI+jYzofOisQorMw+20Yy+4isC8+VC8BiIzBIGRvMgkL5OicrCovOR3ueDJeeK9XPAMk2In8XLxy6QEpRDZJ49QJpP1I3TDIVAdyEPzemfDAtIXMK76dRwdpWRXfAE5KwWJ9DgE2ewLSys8Oh2sbZQuuodrAiTAzW8lExeOkNABJOvSKXasRdNRQ7Sid78VMgUrU27BVLWWyIonwbxRGMv4XDhPCJnPBbToPdj/Gky7HZIHOgcejwrLUZxeB1W2MhxeO1rFiQOTv1Gs/ytseEBMdNx95LQMiopM++hwtdHhamNiyAw0Cn/18oQwhMPyf0H1Npj50xGtUtZs5tfvHCQ3NpD7zsk/s4Os9jK2b30Ap1zFzHl/OSlZ8ZioMYPneCIMQFQKGUv+vZF2i5OXrp9M1iCZdN2h3V6vxI/f2MfB2i7+ceFoxvYhNBOSQ7lxVhotJscJewYrWy0cqO1kcX4MWtXgv/l5cUHkxQ0877dWtOH09Jo65cYEDZCOTk0PZ32f3r6KFjN/+biYiSlh3DArjRXjElhaGEve7z4B4Lmtx/iiuBmA6enhSMCxNmtP7MLxsDk9LHlwAy1mJ4mhOp69ZhI2p4fSZhNnj44bkKt3tMnEkxtF7Mxlk5M4d0w8k1JH9vvtxw8fkZpoFkedx6fNq0+K2CllKhZHnUek5vuVjTp79mxmz5594gW/g/CTOj9GjCC9kgkZIn/I45UorrUAUNZgHUjqFtwHe1+GmT8Bra8hQBuC2eNg5fsrabe386tJvxpoNS6TD0pSMNb1Ol4Wr0EeI/qSNEo5cwvDcHtDB+6DDxpf5o9SKUPxwa1Q+RnsHkX4desJ1ivxeCXajC4CjIvwBG3BIwn5i93XdO6WafFc+ArEZA5rCX+s2U6b28DGiS8Q3LmLDcodbG3ZSoJaYkbCrBH1vshlMsakBtLY6SAnoc/y3ZLJbgfIgCjRvzYCzE6czeW5l+PyuggIUlFiOUyZ5Qhpq55F3lbOEUcCR+ttKGyw2KtAk3+uWDFxMrQcwSFXMC5zDB455NaK3hWqt8OeF5nVdIit536EOjCmp7p5upDJZEyUJUHrfpGzFxQHF70A2lBIEjLGRnsda1veJ6XLwgyz6J/LdtgZFzUOm9vWa1AzQgSpQliV/Qtor4AJ/Su9WoUObfrZEPg3IfU8nlh6vSInURcs+v/Cs2hzqKhptZMapSPYoIKPfiKcNM3NcMMXJ9yfUcETMHtMxGoT+hG6knoLDpeX3IQAlIqBg/J/7f4X75W9x53j7uT8zPMHPD8k+pgLjQg9y54+McgNGEWLo5F4XdIpEzqL20ytvYpkXfp3jwB/XZhwrbiNEC9vP8aOqnZ2VLVzzfRUIduzdUD5l5A6RxDFU8S+2s1c3ypiVx6q/JQ5OSdXObQ43Pz1k2JC9Gp+vCCzh3A6XF72HeuiuNEEwPqyatT6ADYcksiPC2Z8cv8qVF2njff2CQOpd/fW9SN1crmMXy/NPeG+uDxeVvx3Mx1WF1dNTR6yl28oXDcjlQM1new61sGElDDuPXtoEyGPV+KBT4v5oqiJshYLnxU1EaJXURAfTIfVyZS08B7zltpO4Y5p0Ch54soJQ24TRO97p1UMvms6bDy7uZK3dtficHupabf1uH12IznMwJjEEMqaTaRGGBiXFHLC97m3uoPfvXeYaenh/GoEx9WP7zcSdCmcE3MJX7WuGZEUM0wVwZyIpd87Qvd9h5/U+TEyeJxQvwsC4yEkGYVcRlKklqZOJ4kRgwzExl0pbt0IzwRNEHaXCaND9BI0W5tH/vqhKYIgNuyHyTcxKyCMdouLmBDNkE3vAJ0WFy1GJzkJBlKidKiP+pZVB6BRyUUQtNPDx3taURBCmncuE0MTaGi302F2khqtIzZUgy5kxpCv0Y3ECC21rTbaQ8fRHjqO4hY5Xt1BLsvtdQn1ShJmm4cAnQK5TEZVVxUB6gAidL1EITVa3xPlgMsKZZ+JPLfkmZC5TIRIp4x8FkklV/GLSb8AYG/bXhosLcQEhCNXaiA6D0WdhYT6Dyk8+hdcrefzYEQ4QZogfpR9BWU17VQHTEDmXQ9yBwGJs2GsV/SyvX0dAFqFChb9cdDXfuHwCzxx8Akuz72cH40eJBS7L9wOEZKt1IjYBW0w1W4rv/voUlLlWn4bPh553BhQaqiwHsXiMXM4AMYt/B16czvaKbfyvCTBvlegqwEMg89i98DcAofegvT5veYpYcP0kFz5vpACJx0nudjzPHx4FyCDsRdBwgR2yc/H6vDQaXaJfsvsJcJhNHvJ8PvkQ7g6ijBVBKXmIqLUcSTpU2kzOTlcLarAOrWCjFi9IJk1WyG6AMIyeOPoG5hdZt4te3fkpK7iS+FkmjAVspefeHmAWT8Xhi+RuacXIwLE6ZK4OOH609rGJ83v0OpsIkGbwrKYVae1rR8qlo+K5eODjeTGBhIf6iO+b14tVBWJU+C6T0952/Logh7rc0XAyAdxJU0mYoO1vL27lhe2HgNgalo4U9PD8UoS6w61YXV4+cm8HA63tKJKWMenbVa+OhbFnz6KYsevF/TL9kwI1XHxxEQO1nUNW41ze7x02VxoVQocbm+/qAIZvaHCm+o2sa/ZPWQlcTBEBWqJCdbh8rSztbwNk91DsL53wsvp9qJSyJDJZGwoaeHx9RWAMG9JCNHxs7cOoFHKMWiUtFuc5McFccH4BAI0SraUt3Hr3Ax2VrUTZlCTHjm4RXywXsWz107kvvcOY3Z6WJwXw3v76nG4vT2TnH2hUytYfet05vztK/740RGq26384QRk9vktVRys6+JgXRe3zMkYNGPVjx8WIjXRrIq7mgZ7DYdNe6m0lvaLO5AjJ0WfSX7gGGK1id8ryeUPBX5S58eJ4bbDvhehs0LkgM38Nah0jE8PHvk2DEKaF6HU8N8F/6Wko4QLsy88uf2Y/Qs4th6cRnQaBfHdjmq126F+N1sCwtlmq+Py3MuJ0osepqJqM01dTho7HOQmBMAFz0DVJmFg4YNGJSc5Ukur0UlWZAwKmZL9VR3YnF6C9RJjUkc2aA0NUDE1J5SNh9uRgNvH3ilesw92lXZR1+4gIVyLRbuXO768gwBVAO+e+y7RhuMGQ5IEJR9Dwy7xd1gGxE8QtxOhrUQQ8ajeC7PJ5qamPI5IaSVTIkN6Hs+K0+PseBeNo5nXjr7GSy3icx0XlA5OPUZFDIs3bEFt2UdU5Hi4+GXRxxaaKsK848YNuRvvlr1Ll6OLt0vfHkDq3il9h/0t+7ll9C3ivTfuE4S1+71G5fP2rn+y297MbuDSgBQy3XZQasgLHEOro5kwdQS6afPpcHQSqgmE928X5EkdCD+vAGX/6m2DuQGdUkeINgTeuwVKPxM9mD8+fOJjqgsRBic+1NqqMCgCCe0mNXIFKJSAjBCDEqvDQ0iAb6BzzkOw7F++58Eredle/Sby9koKC25Fr+ovKfNIbg6ZRL9esXk/CdoUtle20Wl3EabTEGLw/XQffV8YEjUfgtn3cNvY2/ig/AOuLzwJktR8SFSAmw+NnNQplJB37shfow+8Xgn5MBMxpwI5YqB6pqrFP0SMTw5j26/n93/QK1QJ+NQJp4pR0eN5YckLuLwuJsZMHHbZFpODf60tod3s5JPDjaSE6/nHhWPQqxUEapWkRwmFQrvZidUhlAkzMiK5cUECL9ZsAcCgcxOgUaJS9j+PZDIZf1k5iuHw+s5qfvPuIdxeCa1Kjtvj5YVrJ/e4fioVclbfMp2Vb/6YJs12/rZrIy8vffmEx2BvdQe/fe8QU1LDe2SeU9PDCdarMNpdqBVydla1c93zu0gJ17P61ulkxwQSEaDB4fLw2k1T2Frexh8/OgJAflwQG0tbmZYe3uOCumpCIu/tq+PO1/ahVsj55M4ZvLVHhM3/eGFWP1nljIxI1v5kTs/fH9w+g6pWC3P6uF6u3lvH9sp27pifQUyQFqNdZLN22U4ssVs5PoEdle1MTY/wE7r/IchkMuJ0ScTpknB6HVjcZlySE5VMjUEZ8L2JLfihwk/q/DgxSj8WhA5Eb9MwfWXd6HJ08fiBx8kJy+Gc9HP6PTc1bipT44ZoMHXZRMP9YC6FxzZA5VfifnCicA8EKP8Ul9PCHcVP4JA8NFoaeWDWAwBEh2ho6nIS3W2QogmA7LP6bVYmk5GfFMja/a3sKO1iigxiQzVUNNmIPVG23XEIDVCxfGIUMhmDzlIZbeKiabK5qXfVIiERIXlRlX8GybMhuI8VdcmHvYQOmSAqQEWjlfoOB3kJhsHjGjqPwd5nxf1Rl/UQO3t7Ix6vuPhanb09HzKZDM3su+ALIwUZs9A1foZOqSPFIydi7x10BI8m3FOPzNgA8nKxktoAt2wDhxE6qqDsS5B5wdElgq59ZjA3j76Z5w8/P4DAt9nauHfLvYCoJN4z5R7x3pVacY4FxgKwMGURH1d8QKpcR3L+pSJrDghRhXFOrIg5+OO2P/L60ddZmbmS+3Q+6a42eMB5urluM7d+cSsGlYF3znmHaKeQdJ3KgLbItJ+NbZ+hlCm5OOd6DNd+KvZdDoRnMUmpw+b09jj/Hejaxe7OzeQHjWNS6EyaLFWMev0nGKwm6pvr0S/+T7/tK2RKRgdNosZWQX7gWB75qox/fF5CgEbJ+p/NITzA97kHJQpS5wu1vyz3sn6V4REhc4kw4IkffjB+JrD1aAeNHU7GpAb2VqPPAM6KXkG9vYYEXYqQFKoDhpVK++HDqueFa2z6cdl2TUWiYh6ePuJNjbSa9dj6cl7xRRAA1HfayYkJZPc9C1HIZaiV4jtz5TM7sDm8LCuIY9mESNRyOcujL6TF0UhBYTzpc0LQq09uCLOvppOnNlbg9orqQrfEfvexjn5RDknhehYVRPB+uYcZcSdWaQC8uPUYh+qMHKozctu8DF66XkjFd1W1c9lT2wnSqVhaEIPT7aWkyUx9p42MqEC2/moeXklCo1SQGxNEQqiOxDA9OTFBNBrtPWHy3egmXk6Pl7te38+Bui4AylrMmO1ufn9OPpmD9B2mRhhIjeiV9HfZXNz9xj68Eni8Xh64YDSv3jCFreWtnD/uxNmIMzMj2fKr+Sdczo8fLtRyDWq1n8R9l+AndX6cGGpftUllgEm39vZ3DYOnDj7Fi0UvAjAxeiKxAbEnfh2HGR6bDp3VoqKWf5x8LCAaQW4CQGWgoquCB3Y8wA36JMZJepINsZSYa8kI6bV1To/VkxKtG1aiCaJ3w+mWiGjbiuvLfYzOLqBw8krkpyAfGK4KMTEjmJpWO0mROnSai3B6nZzX0URYcxEY62HGL3oXtvbRrasDQKVDkiQOVJmQgGIZTMsZ5LOQi2oRSNCdOdh4kMhn5jEmbiXuCTeQGHFcdS1jAWQsoAD4yvUrlHKlCAOe9VMiOo/BpL/BkQ+hYEXvOiottByBpxeJAO0xvj4afTgEJsK+l1mUdy6LlvWZ5TY3Q2cNQbGjyArNoqyzjLFRvpiIwFiYdY/o1/JVXAoiCvjswq+GO9zsbtrd+/+574kBalTeAFJX2VWJR/JgdBppsbUQPfoCCIyAiOzehUyNSA37KdVNxitTkhVvQN5WDvtfFe/dlzHn9oqZbK/kFT2YfSq/+I5+32yuEvMhnJKTEvMhJoXOJFwZjNxhByDU5WUwTAmbzRSEzNblEZEPHq+EUt6nGpV5FiROPb28u/BMcfuaIUkSTR3Cva+x03FGSZ1OoSfdkC0iQ96+XnymN60XxORrwsHaLooaujhvbDwa5Yknur6TMITjyF9FU5eDpG5RQcV6eOFcQYpvXA/RZzZYfnxyKM9uriQpzMDSgmimpEdgGCRY3OJ0c6zdSnFLZw/Ri9HGE6ONh5MQiXRjT3UHKx/dgiRBVKAGuQwajQ7kMjhvrHClfGNXDW/vruW2eRn8acafuGfKPeiUg/doHmuzsLW8jaWjYgnSqlg5PoHtle1MSQsnWNc7oXCgtguH20uLycH0jAiMdjcZUQFkRInvbHd1rbbDyh8/PEJeXBBnFYjr5fGEDuCySUk8/EUpTSYHJoebQK0SSYLPDjcB8MzmSv68YvhqJYBBrSAnJogjjcae3sTsmECyY77H2Zl++PE/Dj+p8+PESFsg5HCGyF6CNwxsTg85oTnIkBEfEE+wZoRXYGurqPqAyF86ntRFjxLVueL3YO+zrHa1sLl+M5vZzNoL1vKjlmmYXCZWZK7ot9pQhK7L4mJvhZGQABWjUwKZpthB9M6rhEL8kBx53rlCllb2hbCvDxTySJvDw6Yj7QDMyAsTIdMjRLBBRbBBxdb2ryhrO8Ks0EzCPrxPEJA5P+u/cO75ol9KHSDeu1yBDEgKU+BqPkpcUPZgLwFB8YJ8W1qgeDUodRCUDB4nqTWvQv3bkLSz11bf6/YRQQGDyjeba++C3c+BqUEQpfm/Hfha3cYt9i5RYZO8EJIC79wM1Vvg4Ftw1wGxjMMsnCotzagW/5nXl7+OxWXpf36MoAp8PH439Xe8VfIW52ecLzLs0mYLw5Ntr4rBfcZiAFZlryLM3EKWFzIM8VBwMUTmiagIEHLXpxYg66pBkXQ5h/N+h0GrIPHdm6Bul7Dwv03IQwuCxqFV6AhSBhOkCjnhPk4Inc7+rp3kBorBllobDlevgdpd6Pr2ng6B2+dnkhppICs6cKDUSXsKI9y+cDtFNEZMYW8w+nHwSl6qrGWEqyMJVp3YIn0wyGQyxqYH0djhICf+zAVm90P1dnEOthwRuYZBI5hMOgV02Vxc+PhWbC4Pla1Wfrnk9GIdvk1c+Pg29td08pOFWdw+P1P8biAJ+bat/Yy/3tLCWPb8diF6tbKHrA2GF66dxIbSVpYXntxn2GVzcdHjW2k1O3nxuknkxgp5tNPtRfK1//x5RSEzMkV+XHZ0IIlh4ny8f80ROq0uXJ+XMDMzEsk7dLX30ie3U9dpY2NpK49cNo7pGRFs/mVvxdPp9qKUy7h4UiJ1nTbCDGoW5cewKH/wXt9nNlXxyeFGPjncSHSgBpPDjd3l4Zrpqf1Ir1wu48mrJvDW7lpWjU8kPcqA2yNx52t72VvTyZKCkR0vpULOe7dNx2hzDZsDeCqoaDEjk8kwqBU8tr6CSalhQzp1+uGHH2cOflLnx4khk4n+qRGgvNHKgSoTSvlEnp71PgWJ0UPOdOJ2wJvXgLEWLnhWSH3OeRiaDsP0uwZfx9oGXUK6c3bsaN5UBVAYUcjB1oPcvf5uAFKDU3urP8OgstlGh8VNh8VNZqyB6CBxAZeBGOAqVPDebcLaPzQV7twHQGtjPWa7qJC11lWTmDqyY9MXh4x78eKhuO59MuxCPkP4cQ5i2hAhjfPB6RYEapzjc7AfgOpdEHfn4C8QFC+C3+2dQKeo6BReIEiWxykGvKEpcOgNaNwrjEmON1+xdQpChyRCyEHEKPTJ6XvXWk3bvNu5Knwc8tYiHG4bJreN6PA0QeqUKmgrFZUgt0NI4wBM9SjlypET/mEwNmrswM+7ZguYG8QtdR4oVGjkKpaa2wWJLV0jpKmJfeIFJEn0CgJqjwWFHAJ1SmGeUrern4mKXCYnK2BwAjQYUvSZpOiPq4YlToLESXS62tnT8hFJ+nQyDIOTA5VCzvljTyyJOmnYu+Dz38Lu58XkwU+KB6367erczN6ubajlGi5P+BEq+Ymr9YMhOVJHcuTX6E45824RfRE39msjdCCCrjUqOTaXB8NJTOp81+D1ShT7QrC7w7QpWCm+B2qDiEz5GhCiP/H5kxxu4Irwkyf/RxqMPW6ZG0tbekjdlLRwnr1mIi63l/m5YoLussnJ/dZdUhDDm7tqyY8L5s9rjvD4hgounZzE/ecXDnidbsORwYxHtlW0cfWzO4gL1vHebdO5bHISlzy5jXf21PLqDVPYXN5KRmQASoW8Z//mZEfyyo5jBGtV/OKdgz3bsjo9A7LoRiWEDMjZe/aaSSdzmADxu9JN6FrNDoJ1qgFxB8fj1R3VlDSZuGt+1qC9dPtqOln56BZkwOzsSL440szzW6vYf+8iAgapyPrhhx9nDv5vmB9nFB1mIUtzeyVqm3SMSx7GorxmBxirBcHY95wIGB97WY/0DgBzo3BEDEmBtPmCHIRlgNdDZuZytuZfDB4Xm4rfBCDP4ST64GqYnn5CV77ECOHeGWJQotfIRa/dZW+LQX/mIrGQy9b/fyDGvI+4xjZkkkSMNhAYmtS5vW7KO8tJC05D1afHZ2LoDMrLXmf0gU2iApqzDFJmDtyAz2q+y+pi/aEOZDI4S+FEBSK7byhIEtRtF/cN0RCaBovuh/AU0IVDvE9+2eoja4feAn0URPUhlqHJsPIpQeim3wlf3S/yycZeAec+zP6W/fxuy+8AyA2IZ7rLjB54ZuNvuW3sHYBbyN++/D+Y8VOIHQWXvy0cTCdeN/S+Aw3tdo7UWkiO1JEeO0KZnrlJDOiDk4SZjKlOVOq6j7tMLiSeXTXCxfV4yOVwzcdQvYWonJUs1gSKAdt5j8K02yDy66nGbO/YQJW1lHJLMSHyCD6t+pRpcdN6Q+K78fm9UP4FLPkbJJ+B0FOvG3Y8DM0+kxi5sv93rw+6Hc6k7lLHdxWBMXD2v7/2lzFolHxw2wzKW8zMyow88QrfUcjlMp64cgLrjjZz3Qzf+SaTwfirTn5jpiY4ukb8dgYP8v36hjAhOZTLJifRanaw8rjesLnZ/Xu17S4Pt768h2aTg4cvHYskiWvX6ztrROQDsMEX9G13eXB7pR5i8vqNU9lb3cGsrIGf/7aKNuwuLxWtFo61WXnkqzKajA6acHDTi7vZW9OJXAZeCf7vvAKumJLMrKxIPrlzFvP+sa7ftpLCBv/9azM7CDOoT8ph0Ob08MSGChLDdKzoc2xe21HNL985SF5sEO/fNh3lEMSurNnEr3yEUy6D3y4fOLHVYnLg8fUsRgeK639GZADaYaqyfvjhx5mBn9T5cXKo2ymiDVLnQkT/Qa4kSZjtvaYT0SHq4XvZOssgfSZ43CC5oWG3sNYO6nMhPrZJVJzayyBhipg9HnccISh6mxlNB3ku/izGbH8JRf2DSO3HkKVOg9ixQxpAhAeqWTz2uMyxzAXi/70vwa5nRSxD6sx+hEuVvZjJNfcK57j8Pwz9/oDfbf4dH1R8wJzEOf0CtscET2KMbTNIQbDyX5A2Z+DK5kbY/SSodFjSru25ULYmn0NsdB6EDWNiIHl7qk4ExokBe80mcFvBZO2diU+eBTseg9q9ICng7Af7b6ewT95U6ef9/o/SRRGgCsDmtiGPymdLexktlkYO2dtF2HzO2fDln4TpQvl6+EWVkEamiYqgo70U+auXoNQEI7vsLdD1yvpK6kx0Wb0cqTX3krqDb8HmB2Hi9TD+6v77aWmG7Q8J05PRVwhZ5YxBSNj4G8FpFlXQwRCVA1E59BMjKZRwfJC5pRV2Pi0qGSnTB9/WEPBKXl458goKuYJLci4hXptElbWUGG0Cv9/ye9bVriM+IJ5PVn7Su5LDLN47wI4nzgypk7yiaps0QbyPsVcNmaU4IWQ6YaoIwtVRp1yl+7ohSdI3aqGdGKYncYgB9/cJs7MimT0IMTlpvHUNHNssqqQ3rjv97Z0ilAo5fxqksgbgcHtQK+Q958mB2q6eUO+PDjaQFinO//hQHfedncfLO6q5cEIi9Z02zn5oE1anh/9cMqbHyOR4KaXJ7qKkycxlk5OobreSFKanID6YRfnRfHyoEaVcRmyIlr01gtAB1LZbe9Z/d29dz+My4J8Xjh7UtOSBT4r577pylo2K5ZFLh3YfPh7PbaniX2tLAMiPCyY7JpDyFjP//Fw8VtxoxOL0EKyTU9xoZGNJKyvGxfdU88qazT3b6mu21RcL86J5YOUokMGFExK5dV4G4Qb1kETRDz/8OHPwkzo/+sPjgv0vClfD2PGiOpAwCZb8RTxf9onITqv4cgCpc3uknkpdQriGiZkhw79W9wAyMFaQOm2YqCr1RVS+sFoPTe1xVBwAn2nFeG0Eki4UXFZKrREEmiDG9BGyuPFCRmiIPKHJiyRJeCVQfPprIU0z1sC1n4q+paMfCiJV+jGoVTD2hn5SxMFQZawC4Jjx2MAnZ/xY3IBWoxOZjP4B6u3l4li7rMSo2shJiGVX81YeK93C1QVXktrt9DgY5ApBftsreklteBY07BGVrO5jmTyLxq2PEOWVkBesHPa9sPh+2PYIjBHuirEBsXx4/ofY3DYSAhOwRo9nT9Hr/N3YJDL1EqdCxSZB6vThA8Kti/b8jbEtpeKP6u29rqS2dlI61mJRTiY5rM/x2PgPaC6C9Q8MJHUeV6+Lpds+zHFRDk3oTgaf/gYOvCb6FX9ZPSA6YShUWyt5o+xVnt/7KgDxAfHMSphFpiEPtVzDtjLRfxioPk4CqQmAcVeJoOixx7lbuuzivY8g3L4fFGoYf5M4x2PHDvvdUMgUZAacGcMMr+TtiR44U0TskHEPW9q/JMOQy7zIZae9PT9OAd2/KaoTE92vioUk78qpyczLGZhr192PdiajLzaWtnDd87tIDtPz3m3T0auV1HZYiQvREqhVsawwluRwA3Ozo4gN0RGgUfY4Ym4sbaHNIkx+bnl5Dy6PxFNXTmBBXv99X/XYVoobTVwzPYUfzU7vmdQ8f2wC45PC0GsUBGiULC1swuby0Gx0cPW0lJ71V++r67kvAXb34MRpR2V7v/9HivRIg+im0KsJD1AjSRIvbKmi2eQA4J5luT0mL1c8vYMWk4Ndx9p5/AoRozM+OYz4EB1mh7vffh+PCyf2ZgQOZvbihx9+fD3wkzo/+sNUB+2+gXbRu1C3W9ym3yl6VOImQN0OMQg8DiqlnNEpgb6w7xMbqpC7AmLGisrcIISt3eTkUHM0MVk/I2s4Y4W8ldCUDR4HNnkIuzuiaQ2bTIrrEHaCSTryHor6HRCcDBOHDsB2ur2sO9iO3eVhaewYlMc2i1Bqa5uIU2gtFu/dRyJprxCyxmHwxxl/5IPyDzgr5awhl2nudLC5uFMckgwHap2NNF0m7H8TvHZInoY8NI1QuZt/H74Hh9dKvbmeJxc/DkdWCwllxtKB1aTgpP4RCdGFooLVx4zk/p1/5VXXUZZMOp8HUvvIPzuPCQIUFM/+lv38csMvyQ3P5W+rnkfRZ/1wXXjPfb1Kz4zR1/Tfh8V/EtLSQQw4ypOyiIxNRakNJSZxonjN4ESwdZLsPECy8wAYzgd8M9UTrhXEbuJxGWxeL9TugrgpEJoEMWMGP9AdVSKYPGe5kIKeDrpNZoITTso6f33bJ1jpQi6To5ApiDWIvi+NQkwO3Dv1XpalLiM/YpB+vXP+M/Cxrlp4fLYgstesGXgOgAhMVyjFZ388guLE7RvC1vZ1HDDuZHTQRMLUEaxv/ZREXSpnRa848crDoDsEt9Jaeob21I+TxqrnoHIjJE874aL/91ERFS1CmthN6qxONwdqu5DJ4PKntqNSyHn35mlkx55euH03tpa34XR7KW02U9dhIz5UBH17vBLnjw0n2de/lxkdyKG6Lj473MiqCYkkhumZnh7BXQsyqWq1sHpfPQBNpoGTR41G8VhRvZGzHtyAXCZj9a3TKYgPJilckF2Lw83c7KhBHT9/NDudZzZVkh0TSHpkwAD5aDfuOyefZzdXce6Ygd9dj1dCflykTpPRzu/eO0SH1cXTV05gbFIo60qa+dmbByiMDybMoCY/Logrpqb0rBNuUNNicvQzUYkM1LD5l/O+8aq4H374MTL4SZ0f/RGUAFGFolIXkgZHP+OrwLPZvqmT62cEE+Gyiv627lBrcxPN1eW4Q9OJi40mLUZPWkyfmdq3r4fij2D5v2D0xf1fS64cYKVusrnZXtKJVqVALoc2k4s2k4uMWP3Qs7Yqvagmrvs9eredgMA0WmVyml3B1GjSSbT6LPFt7cIQAklUPY67KFnsHiwOUe1pmPJHEhPfFNXD2LFi3dZiIWXUBIPHLqSig6C03kKnxU1+UgBpwWncOW4IMxOA5mJkJhcgej22tH+FS92Mo6yUO3a/LpbRRYJcgdXhIF6bQ4V1Dzmh+UJy2N03V/7J4AP643Gcu+SBFlEZOtB6oPfBthJfzp0MJt3Cmoo11JprqTXXUm+pJzEwkRFDrhDy1UEwL+Vqai4WVSp2PS3kponTRQB29tmiAhXbR1o06QZxo7e3SyaTwbr7YcPfxDI3rBvwufbgvdugaqOQ1t68RYSJnwBlDVaKasykRusoTO5TPStcAcExwhn2JAY38dokrEFmrph0Hiq5mszQ/ue/WqFmWvyJB8U9aC7ujb6o39vvHKhstWBpPEpBo+g3ZfyNIzY8+rpwzFrm+78co7sTL16O2crxSG4UslO/HE0MmcHerm0i1uAbgsNjp85eTbwuGc0PKHD3lMPhNYGQs3REi56VH8Nj68tZ3Ee+eN1zu9ha0UZ6pAGXR8Ll8fCfL0t55LLxJ78vg+Dq6Sk0dtlJjwogMzoQr1ciPy6IA7VdjEsK6bfsTS/upq7Txq5jHbxywxTkchlKuYyyFjN3zs8kIkDNirEJTPrTWlrMDu5Zlst1M9J44dpJbCxtJcKgZntlO15JotPa2/tc1mzm/P9uBuDdW6aTEdV/8nN8cihfFjczOTWsH8E6HgXxwfzjwt7vepPRzpVP78Du8tBqdhAVpGX1LdN5Y1cNa4800WVz9ZjHRAZqmJcbzdqiZtxeiX21nRz5w1loVf2vDa/fOJWHvizlg/11/DtIw53zs/B6pZ7t++GHH989+End/zhsTg9aVW+PAXIl7akXoFLKOVpnQX7OZm58ZjOusgraO9p4INoXht24D5Jn0n7wUzbLFoMRJqntxIf3+bH3ekQfFBIcXj2Q1B2HenM9re0aTDYPJpuH8EAVKoWMmFDNyAYZUQXQsIeEADvW+o3klfyTxsi5tMy/g9jQg2BshPduFct2m5P0QWiAipx4A1anh+jkAsjuU83JWARx44ST49H3QaYQ/Ug+eLwSh6vNeLwSVc3CVEWjkjEqZZhZ5urt8OxZRCJj5oVr6IxMZa2pCRky1ltruC19PvLWEsg7D4CU4od4saOJmtG/IX/SxcJgpjuPrtuWH2hxNKKUKQlVRwz6sn1x79R7eaPkDZanLe990OP23ZHAZWdl1koOtBwgJzyHhIA+M8dNBwWhNkSK8yEiV9wfIcLUEYR172O3A6jD93/i0MSmorOCqz+5Gq1Sy0tLXyKqvar3ycYDED+E82losiB1xnr492i4ebOotA2D2lYbHq9EdYuNwjglNB4SVb4DLwmjEaUGQi7tt06rowm9woBeObBaPS9yGZHqWPZ0bSE7YPC+n5NC+jyY9TPRczfqop6HK1rMnPXvjTjdXp4ZE8q8yK4R5Ut+3ZgWNp8i0z7yg8agVxjwSB6SdGmnRehA5Jct0Z5APnyG8UnzuzQ6aknQprAsZtU3+tpfF65/fidfHW3hzysKuXDCSUzenCR+flYOP12U3e93vcUsJIBBWhXBOhUmu4tzxgjDFZPdxUNflvHs5krm5UT1yAFPBlGBWv550Ziev+VyGa9cP5knN1YOICmJYTrqOm0khemRJIk/fVTEU5uqAIgI0PDcNZPYW93RI1t8ckMFBrWSCyckMiohBEkSxFitlDMjs/d3+GijCZMvPLy40UhssJY7X9uHzeXm3xeP5T9flPJ5UROfFzVxwfjEIeNyXtxaxb+/KPUFmseSHmXgaJOp5/nKVgv7ajq5/+MjSBJE+qptMpkg1AC3zcvA7vJgdbq58PGt/PG8gn6OmsF6Fa/sqBZ9hGvLuHN+Fje9uJvPjzRx85x0fnGcI6cfXx8UCgVutxu3243H40Gh+P467voh4PV68XhEEeFMfp5+Uvc/jHf31vKTN/YzJjGEN68fh0Kp4miDg6IaM9FSHTavhg5ZOAkheirbLGTFR4IuTphs+EiEXBsMdi/I5AOJl1wBZ/1FOKLN+umw+7K+Zj13fHUH8bo07sx8FKtdRpvJxeiUwP6Vv244zPDcUhFUftnbkDBeyDBzVxBp7yTii0nILM3U59xIaEQEqBeISoZcBUjCeGXtPYIwxfUOEHITfQNxp5nK4mPUuiLJSQohMlgNCi20lfkcKfvvTl2bnfJG0fCu18ixOb1EBp9g9t7a1pPzFqE0ERERTbt3Jm+XvsUlOdchz+pjUmJpRb7ufoKA/PqtEBQAucthyp2CpKj1IElU2yr5uPlt5MhZGXdVL2kaArnhudw79V7xx4a/if63xX+C8DwhdTzyGVk3beDV5a/2X7FhLxx+A5CJ6q6xBmp3wPQ+n7PTDFXrhQQ0+gQEZty1gjDHnXiwtqdxJx2ODnDAkYOvELX8H2CqF5W35Klw6HWxnbB0JEnimUPP4PA4uCnvbBSmWihbJ6IeOquxeLXUb19NEpVoMmYL8t73+CQGUFJnISlKB69dJtwnC1ZC0jgw1rHDXMO+A09wXcF1KOQKik0HWN/2KVq5jovjr++RVfbFP7b+h91Nu7lj7B1MCfPFSHg9ULsVlHoxeTBSyOUw754BD5vs7p4IjNb4xTAmSvSufstI0qeSpO+tFi6J/maJ2OlCkiR2dG7E7DbilsSkjqe7l/O7DFMTrL4ZDBEiNmaQHlCXx8sXxc1IEnxxpOmMkDqT3YXHKw0aYXD89eLxK8bzeVETZ4+OIzZIi8Pt7SE11z+/i+2+/rHPippwuD1nJPD9hW3H+PcXpchk8MXds0mLFL//z10zidImM3lxQRxtMvUQOoNa0XNcRieEkB8XRHmzmUajg1++cxC5TMaFExORyWSsGuT4Lc6P5ta56UiSIFdfHW1h7RERGv7p4Ubm50bx8aFGpmdEoFbIeHBtCVanh7sXZvWrpL247RitZietZicPf1XGmjtnsDAvGpfbS027lfJWCze+KPoHazps3DArlazoQMYnhxKoFXLxpDA9caFaXtwqIoJe2naMBy4I6be/qREGDtcbiQvR8tvVh/jct6+7qzpO+9j7MXLo9XocDjGB0NnZSXh4+AnW8OO7DrPZ3KM40unOXN+pn9T9D2NzWRteCfZWd2L+8i8EBxiwx15PvKuESY41eJGzJfQqVt86jTaL03fB65U3tZmc1IfMZrLShDowkoiQ40iMyw7ZS2DKIH1sLUWCGCTNwKiOp7rNjFfyUmMtIya2g5qacDweiSD9EKdo61FhjQ9QtlaQOhBTkbpQZLftAGs7+eF9HCLjxuL40Q6UjXtQvHO9WNZtH0gkPG54eSV6qxpz/p84IlcSGRwmSGTDfhh9iQji7mNUEmIQVUW35MSs28vY1EJiQwYaAPRDzlI47zFBfjMXAjApaiqTogZxNtSFQfJ04S4HuIpWo3rnRpGnlzJJGM1kLMYRIWScXry4vM6B2xkKbeXw5R/F/SCfW2Z7pfjb1CgyBLvhtPRWKWWyXmOE44PpK9ZC7XZAJnoPhzPyCEro73o6DJZEjGWfyYxWkpjWUs27NV9Skj+bm0bdRMi+F0Q2XVc1Tfl3sKOino3VJezuWsNVySsxRGWJHrysZZA8jaO7DpCiNqPxepGObUR2HKmLDtEQ3X1ed9X4/q+FCU+wsfwjbtkiCFVqcCoLkxdidovZcofXjktyoqE/qfN4PRxqFZbg/SSvDXtEdAeAPkxInEeIUnMR6/+fvbMOj+M8v/a9zIIVM1tk2TIzxhiww9ygw0kDbUppU04pSZukSds0nIbRSRqynThmRlmyZTHTSlot43x/vPJKa0mGxKXvp+NLl9a7szOzo9mZ97zPec6xfEa6Lpsl8Sux+210aTfxgwujiJalc/GkNOE/3g9Jkvjzvj9jcVn4zuTvDDVl+S9ATacdk1ZFnOk/J2u0+/s42LeHNF0WqTqRZ9bpbWOfVUiex0dModg0kQz9iftq/ytw8E0xIQFQeuWwbrsqhZxfnT+WLw93cPdZY8JeO9Ri5XefHmFuXiyr5pza523sdnLO4xvxBoK8ecuMIblqxyMnzkjOvIFryOAqVV9/dStSp+LeRXlnhNDtru/GHxATHxqFPFRBA9CqFJSkigxNh8dPSpQOm9vH89dPYVKGuO7L5TL++e051HY5WPqnDXj9QWKMJ66GKxVy7l86UOGammlmUkY0Do+f+fnxpETpWDE+BYVcxleVnfxpregRzYo1cMXUgf7o2+fn8uiaStr73ExIjyIv3sTfrxH3sauf2U51lwOPP0idRUw07qjt5rLJ6SFCB8IJ8xihizdphs3BfOe2mexp6GF8ahTLHtsAiL/Bz1eeekbnKL45oqKi6OkRRLqjo4NAIEBERAQajWa0t/F/DMFgELvdTltbW+g5k+nM3YP/paSurq6O999/n+rqahQKBYWFhaxcuZLExMRTeu8NN9yATCZj3bp1/8rd/D+Lby/MwxcIMs3UQaTSC24vRbFeLB45tIAMiVljTMj0aiKHmWndUWnF7QvSYzIwN+24ikQwCM+cBe1lsOTXIudrMCreA6+doKuX9bKLMQYn893iR4iMcjI+oZCimCBBafhgV0D0uU27TVTqJl4z9HVddJhFPkBLt5vtNQayLC5KkUTFLTJz6Hubd0P9NhKAzOa30Rb+QHyk3iZ8qig0HpsI9x6ECL2S5ZPi+M2Oh3hj++tEaaL48tIvUcpP8hUrveLErx+DXI71ytd45JlJpHo9TOmtYoLPAY3bIKlAVOq8DnJb65CME1BHZpKgPYEBRu2Xwvwlcx5kzhdELrEErM2iVyyuUFSz4ovCCZ27F7Y9Jkhd3jnCDdQQJ0xOIlKwufx0WL2kxmjRGPsrQ/oYIVMcDs4uKHuToC6K73bu4Ki1mt/P/T2Fx4LY2w/B+7dD4lg47wmQy+kmjWtKnyKn6TU6Sy/jwXXCOEUlV3Ff9BhB6qJzONzswO/VszTuZspsa+hOmYzB2gKl1wlXVSAuOYV6ZzFGbw+qYcx/APDYhAvppS9DxQdQcgnIlSTGFqFVaPEGvXzR8AVnpZ9FaeRUVHIV0apYjMqh0tvPOt9jceFs7DYf90+8f+AFbTQgE2T6eHJ8EtQ4KwlIfmqdlQSkAPutO6hyVCCLh7NTS4ZURPZ07OHpA08DgoxeW/w1Msn+hVhT3s7NL+/CqFGy9r55JPyH+ne2dH9BrfMo5bZ93JB+NzKZjEiVmSiVGYffRqY+j0Tt18tjc/vdKOQKVPJTN9n5RshbCjufAX2siBwYAVdNyxgSyA3w1PpqNlR2sqGyk6umZYwoCxyMmi5HiIwdbrUxLjUKrz/Ir/9ZTkCS+PE5RUP6uEbC09+axBeHO1g2NvGMnA9bqrq48pntyGSQG2+kqsPO9985wKf3zA1bTpIkbnhhF1aXj5WlySFCdwzBoER5Sx9PXjmB5CgdxcmRp7UfkXoV79wWLjU/5piZG2/ErFfj8PrpcXiZ/Ks1FCRG8Oy1kzlnXBJzx8RhNgy9L//+4nFc/cx2AlIQo0bF4TYbays6WPXSTt66dWBbln65K8BDF5QwI2do9UerUjAzR6g9frlyLG/uauRb0zNDgemj+PdAq9USGRmJ1SraEywWCxaLBZlMNirF/B9DIBAIy3vV6XQYDKfpXH0C/MtI3Y9//GP+8Ic/4Pf7w56/9957ufPOO/nlL3+JRjPyLKzD4WD9+vWjsxD/QqTH6Hns8gkiWLvaDTozKk8Pieu+DQmFyOb9cGTJlr2dTHk9h6V0Ig3DnEYBD3QeEY/bRGWips2J2xckP8WAIiYfWncTMI9B6ndlnpk8i7xkcXKrlHLRs3TwTWHaUnwJaCJERUMbLQwfYjNBrQCXhaAxEbcviF4z8gWu1yHOxVrzYgrOfgKtVgfj+nth2vaJDL6MuWJ7OjNIAQrnrYAEPcGgxPpFX2H1qpiYInH8sKfnwBu8s/txOqMFkZKPEOI8GC5PgJ1VVtRKOZNzI1EqBs71dfXr+MOuP7A0cyn3ThKxB2qFms1xGXS4Ovh5wQom6BIhIU8YFCRPAVcXstY9jJEpYN5Pht3muoZ1SJLEopZ9okrZsluQOpUObt1E/e6/sb7hC87uKieudrvIpMuYCaYEMMSL3rdjkQFyxcD50Z+Zt/lAJy5vkLYeD/nphfxO/irqQCcPSgE0w11uWvdCXyPyvkaaO3ZR57PySd0nIVLn2fUsmtZ90LoPZt2LVZ/Brqo+UE5ENncuKXEyUo2ptDhahGNk5lIRUq/SkV11hLSdf8YfmcSa+AVEa6Jg8rlhm09LjiEp4SwU8kXDX2u8Dtj2R/EdKb4U5n0v9FJeZBYvpp9LV189D9Z9TlnBFYyLG8f4yKlD1+O0QEcZzkAjmTGppKRkkB4xyJk0Jhdm3ifkwdrTGxhOjJyBP+gjXZ+NQqYgRZdBuW0fZnUsesXQm0VWZBZJhiR6Pb1MiB95gH+6cPjt7OzdSJw6keKIr7/eeosDSRISUovd+x8jddGqWGo5SrQqJnRuaOQaLku5UcQyWCqhZR2kzwLlqe/jIcshbvj0BgwqA2+c+wZx+n9DeHncGLh7/9d++7LiRNaWtzN3TNwpETqAuXmx3L80H6fXz8oJ4rq4tqKdF7eKeJdJGdHDVoeGQ5pZz7UnsND/tKyVl7bWc93MzCHZccPB01+hkySI6rfwN2mHXp9kMhlmgxqry0eMYeh45ZlNNTz08WHUSjmbvrfglD7LqSIlSseFk1J4ZmMtj607iscfZFNVF/k/+RS9SoHLH+Dhi8dz0aTwY5gcpeOL784P/f/yp7eyraYbl0/IhI+229h4tIupWWae31yHWikjJ/7kE0nz8+OZf1x4+5ptu3l3XyurFo5n0piR5bp9bh8f7GtheraZ3Pj/PmXA/wKSkpJQq9V0dnaGnpMkacgYexT/O9DpdKSnp59RnvMvIXU/+MEP+MMf/hDGRo/B7XbzyCOP8Mknn/DOO+8wZsyYYdYwin8rVDooWCkef/UH0TdmqYIZ34bqNSLmYMx5ouIColdqx5MUBn1kZy5FnTFv+HVe9jLUboCZ38Zi87K/TkjTNCo5OcUXQ8FKVAoVc+0+HJ4AKebwm2bAWo+iXQxEvK07USv1/RI1Gf6Jt7LZOw23biEzOqrZ05FET7/j5Jjk4Wc9cpP0+AMSJp0CbUJ4da+1qoy+QAw5VV+g3PmscLuMK4BUIWnxBSSsPjXIwOLXDyF1v9/5Wz5S+qHXikFl5E/z/3TSKl1ztxuLTTijddu9xA/qwXvjyBs025t56dBLIVKnU+p4a8VbtNpbBYEpbIYdfwZ3j5BfDq6MDWOzv6V5C/d8eQ8AT028nzkyuZBO9tSGXBHvqn2HWkcrfQEdd3VXizd++QuIyxWkJmkCFJwvyI4+Dp5ZRDAihV0lf8DhV4RmmTusXtoPBtjVXkWXt4GF6Ys5K+OsoQchYRx0lCFpo8nXyJBba1mZ038uOiy84WlhjkrJUbWaCVoDJqUclUKGLyBh0CrQq7S8t/I97D47sbr+/sH+eIy03b+Eug8F+Zxxk6iCDQOlQi56NNsOir/34GPndwsZMYhzonotBH2sUcrQ2duZ7XWDNoHrYyeSE3WCMPiDr4KthfOMcWzLHce4iGEc/fQnN7YZDnGahDCjjkx9Ljdk3I0cxbA3C7PWzMcXfkxACqBRnIa8MeiHyo8gGCQwZjlVrmqi1THEa8R5t8+6nSP2Mo5QRoY+F6Py6w3evjUjA39QIilSS1Hyf64iMCV6NnnGIoyKofsg9zlFnqcUFOfImFPPxjvQeQCn34nT76TaWn1SUtdmdROpU52YTHkd8OkPRUV8ya9POTfxVHHe+GTOG3960RcymYw7FuSGPVeSEkmsUUNQkihNix7hnaeP33xymHqLk/Y+9ymRugX58Tx77WTkchnTs2LYXmthYsbw+/PubTM53GZjatbQXFB5//fr+AgBgIrWPirbbZxTkiQqKsdVzDcd7eK2V3YzNjmSmbkxPP1VDTfNzebbZw044gb7Y+qUMhkTsmPYXmNBApz9BO2p9VUsKkoIZcsNhyeumMia8nbm54vz7PKnt2Jx+MiI0fPFd+ejVclJitTRZnVj0ipDUQsn7VsM+vnhxw10edW0frCX9787Mqn76epDvLe3mVijmp0PjDCBNooTQiaTERsbS0REBHa7HYfDgdfrJXjsJBnF/wQUCgU6nQ6TyYTBYDjj34UzTuoOHjzIww8/DEBcXBwPPvggCxYswOPxsH79eh577DEaGho4dOgQs2bN4pNPPmHy5NN3shrFvwjjLhUBx5GpIotr2x/F8zoz5K8YtKAg7BolI1u65y8XP4DeG0CllOEPSETo+k+7/sFztFFFtHHoTaldJSHplOj9QTr0CvKD/b1bciW9QRPdcjXIZLQYJ2HtELNVvXbfkPU43AEO1NuI0isZlxk+0Hx458NsbdrN1Ul/BKUMv8FCceYRYdqRMSu0nEYlZ1JOBJY+LwXeHXCoVwzkjhGI6Dz+evQrFJLE/fFBXAHXCAd4AEnRWho63aiV8tDnt7S34QmquKrwKjqcHSzNXBr2HrPWjFnbP7gwJopMNqel/3eXqBjoYzneySUQlJD5o1DJNPglL6nqCHBWiBcr3oWZ3wEg2pBEraOVpsyZYJ4Mrh7orz7itIjfqdPE76/+AE07kbMTZ8SV9EaVkpekBxkcbXEiQ0G2sQCDX8b4+BHiFowJMONeZMAvJIkt1c+z1foxMtVSsl++AZO9hquSEsnVxjJHG4VWqWBxaSxefxBT/3mkVWrRDlcpSRoPFR8Kklt0CaSc4DrzjwuhcbsIVj//qYHn9TEw7ipxbPUxcOAVADb07Getq5VPsi4mSqbk2hn3guoEEop+SaVGG8s8pxFq34acxaEKJwE/bH1CkOxpt4y8nlPEydwklXIlytO9/Hce7u+RhAa1j/XKBhQouCrtFnQKA0naVA7Z9hKtiqXD08x7rV+SocthbuySk6w4HBqlglvnnYAg/xsRpRo6kAeEk6jaJNxaj012nSJW5qykureaCHUEUxOHqeoOwup9zdzzxj7SovV8ds/ckYndofdgz4vicdY8YaL0NVHVYUOlkIdy284k0sx6tv9ITO4cT3JA5LvtbujhggkpGIfJcRsOvkCQFeOTeXpDDSvGD5XDrt7XzAtb6rhhVlYYMT2rcKDn+fgK1DEcbLJicXhGfP3G2VlkxBhIN+vD+j+tTh8XPrUFly/AV0c6+bislZw4I49fMYHd9T0sLU5kTXkbNrefrTUW6rrs2Dx+3tnTRKROxZbqLu5fWsD3luXT0O2g1+njoQvG8uLWOt7d00x+gold9T1Udzp4fUcDt8zLIRCU2F5joSApIkyWGWfScOW0AVVAf5GSeouTHbUWLpuSzqdlrdz+yh4MGiXLihMZk2DkoU8OsyA/nueumzL8gZcpmJ/o5u0GNfMzT2z0oO8/b/Vq5Sih+4ZQq9WYzWbM5hGuTaP4P40zTur++te/EgwGMZvNbN26lezsgabqCRMmcOutt/K9732PJ598EovFwllnncXq1auZP3/+md6VUXwdRGfADZ+Ix8EARGUJZ8HYwoFlnBZIGC/6yo4N8E8CnUrOknHRBJGjHWZg4vMH2V3dB8CknAhUSjkxulQ+ysrDHXBytnk8qGOFiYTaiNlylPMd72NRpmFwphOZt5AOqy8k33R7A+yt7UOnViBDoq3HQ1uPh/Q4HQat2H5QCvJS+Uuo5TrcATtahQmPPgXO+xOc9aDY1iCkx+lIV7bC7v4eT2MCZIgMttvGXIHswGcA/CxmBtOThs+wGwyDVsHCcWJAuK5+HVX7/sGqfR/iU0WQc8UXvH/++ydegVwBYwds7Kn9QlQNOsvF70GB7juO9tLWE8NvS98jJaWPrI5BIc2DAsqfPOtJDnYdZFLCpAEL/PYykYmXMTt8+0UroewdpMhUtOnjiPQryYjXYdQq0AScKA68xgVeC5z/NqhOLk+TdvyNsjgrkiTncO9OpBgzM/0WNjZ3IA80IzvyGRSfj0YlH7nXcjAmXof0xa+Q2Ts48MWDjPvWx0MWCUhiMkDRXSOeqPgQzntsoFrn6BBy3JSpoWMaDPio9NtxSAHKcxYwM3mgT6Wuw0VXn5eiNGO4FHjcVdDXBBFp8NUvRNWr7ssBUlf2Nqz9mXgckwu5w1Q1/9OITBUSaCmI2xQHrgYUMgVyxOfMNuRznS4LpUzJmo7VOAN2Kuz7mRVzFgrZ/2d9Hwq1cJ719InrwGlAr9Lz4+lDHUuHQ1mzFUmCxh4nvS4vOvUIg+e0aWLiTamB5NLT2p/B2Fpt4apntqGUy/nwrtnkJw5fbfUFgjy4+hA2t49fX1BywkrR8RiOzB1b52VPb8Xm9lPeYuU3F44bdrnB+N7b+3lzVxP3L83nyK+WD7vMo2sqqbc4+aOr8rSqjTWdIlPOH5R49NLxXDhMELhMJmNxkfj7d9o8XPn3bXgDQf569cTQ52zsEW0Hh1r6uOa5HTT3uPiiooPvLy/gowOtWBxe3P4gkzOiuWRyGt9/RxgoaZQK7l+az9qKDgD+uLaSteUduH0Brp2RSVufG4vdy+RMcZ/6yftlvLqjAbkM3rxlBomRWuJNWtTK8Gvlw5eM46aXdiMBL22tY8X4FHbX9RDslzy/tbuJrFgDkgRfVXaGyOLPPyxnQUE8P1hecOzD8/AtF/MrRy/aCFEF9AWC9Di8QyIiVs3OYnq2menZX0+NMIpRjOLUcMZJ3YYNG5DJZNx7771hhO4YdDodTzzxBNOnT2fVqlXYbDbOPvts3nzzTc499+vPLo7izKPPI7FDdSH6JAXToqMIDcv2vyh6jHx2SBvGqfF4+N2w40nUnj6CpdcTVGYMMW9o6/XQ2uMJPU6L1aFRaLkoeUAmGZQkqp1xKD0ysrrK8aOgU5aEq7OTtPQekrL6b9iuHlpbe2nrEZW9cRlGFHIZkQYlOvXADU4uk3PTuJtYV7+OFncV2YYJuLxBMeCWj0AaDAmgi+awvYX17Vs5PzaPREMisux5YmAV8HHWwl/DKfTUDXyuIPdvuJ9LeywoJB8KrwW1vRHIHLpwbx10VwsyPdhQw90HulhBws3ZYYQOwOMV07MqmZ7imDQwpOLw2vDpzUTlDUjHjGojDp+DVype4crCK4U8L2GsaD6pWQteG+QuF+uPGwN3bEMGHE9h89rfg739UQkTvnVKJEUWDDJp3wZqMwuJic9h7ZQ5RFqLufy9/spZ5+GTriMMGiNdah1xXhef2Ws5fojY5+vlvdZ/ECTI+QULid79hhikO7ogQkgKfXueoUPuIqG3BmXJVTD7B8glid86bsAb8JJv7neD7TqKv3EX+52zCCo0yGXgM++nwVnDTPNCUnTpwgEUhNtq235IHNR3Zs4RREGugqihRhX/FdBGwewfABIFyDC6s4hQRoXFNqjlYiKgJHIy9oCNDH3O/3+E7hhUuiHfszONOxbkIklQnBJBUuQJthWbB/dXIcx2Tv3aczw6bG6CEngDQbodI7vnbqrq4rUdwjnxZEHZpwq5TIZBrcTm9oe5NJ4IxwjPuor2IVLPY7h0chpPb6ihIMHENc/t4K6FuUzJPHGVw+7xs6e+h0B/C8mxfrQTYXd9N0c77AAcbO5j9Z2zqO6wMzYlkl9/XEF+gokP97cAoFTIyIo1sLAgnrd2N5EUqePt22YSDEqs3tfM9tpuPilrZV9jD9OyzJS39NHc4wrtR323g6/uX4AvEAyZzdR0iW0HJbj+hZ3Y3H4mZUQPMWJZVJTILfNyeHlrHcvGJnHWI+tp7/OwqDCePQ292Nw+bpydxeaqLs4qTMDrD/LzD8s50m7jSLuNexcPch9VqEKETpIkLv7LFvY3WfnpeUXMz4/nwdVl6NUKPi9vJ1qv5vN7w41oRjGKUZxZnHFS19AgLvQLFpy4afiqq64iJSWFlStXYrPZuOiii3jppZe47LLLTvi+Ufz70GJxh4LArU4/5mMSSX0cWBtAP7wkZQgcnUK+Bhw9dJCjWj3zis0h+RxAXISaCJ0SfyDI3po+9tXaGJdhosPqISdRj9mkprHLTVmDuHEZMxbT7U3nsCRcDKNkkZhAbGfb42QG/XQZV2I35JIeryM7UY9MJqPH7kOvUYQqPXeZCrkrPZKqhJk09/jxaCq5/p2fsFhl5srS2yHzuJuQ2gAz7+eOtxfR0baOfdYa/rr4r6Kqd+Pnw358u9vPjkorWpWcqWOiwgxRQJDLCfETeMe/jaXmCeQkz+MHLc9TduA7PDr/UWanzKbZ4ganhZQjfweC0HUYpvYHqUuScBrtqoQ534HJNw/Zhyl5UTR3u0N9i+WOZq468CgA/4jOFD16QH1fPfetvw8JiYAUYFWJcJbE2iAqgSDI4/HH5XjkLRZue7ooSBnUP+bug1cvFdXe2Hyxz+c/JXrZpt3KpIpkJkVncCRCBV3V2CJjca74I/reFpghHFTb3S0cth+kwFRCgmaEmXe/Byo/pXrRA3R89TBXyKLA1Sv2px9d3g7cQSGT7Zx6FdFun9iPiAFzoDVxKhq1KjL8vSyDUPUyK3Igaw2/F55ZhNLdy6Sc69iZ9yPMJiWf9VvfH7LtEaTuGApWDvSwHkPaFGFkIVeB8QQ9Vk6LMAuKH/ufyZ2TyQAZMiBNlzXiYsnatLAJmVF8PUTp1fz43KIRXw9IfvZZd6BT6CkylfKPbfVYXT5umpM9pEJzKlgxPhmXN4BOrRjWDfEYxqVEkh1roM/tP+FypwOFXMYHd86ios3G7NxTq+j86vyxvLunmVvnDZ1Abu9z09zr4o4FudyxIJfiBz/F4Q3g8QV445YZ+AJB3tvbTG68kYnpA/10Tq+fpX/cQHPvgIT+nd1Nw7qCAvzqo3LWVLTzg2UFnDc+GZ8/yNLiRCJ1KnL6c++evFJkT14+NY0dNd2YtEo6bW5quuzoVHLuXdyf+yqX8epN0/nuW/t4e3czDd0unrhiIuPTonjyyyr2NPQSqVNx7YxMFHIZCvnAhMkfLy1l0aNf4fAGUPRLHA+1WJEkaYjk8QfLC/jB8gIq22088nklACUpUTx11SQ8/gAmrYqrp2cQDEpc8fdtoWDzSyenjthn5/GLaiTAlioL22u62Xi0K/R6t8OLxe4l1vifiykZxSj+f8cZJ3VutzAVOJUwvfnz57Nu3TqWL1+OxWLh6quvxuFwcMMNN5zp3RrF10BqrJa2Xi96tZyowXlxE1eByyKqVqeCiFTIXIDTaqHWk0+S6xC27jGYUgb6H7RqBWeNj2HbkR5ae7yAxIF6G/6AhMMdYH5JDAaNAplMNKVroxIwqmPgqJUY5xEMr/0C0qbQN/F2KlULSPLXMCVNDikDA47KZgeHGu3o1HKm5kVSeWAf09ZcjAyJ3LM95E69iVvXPMcuZyv7aOPK1t2QNn1AhngMMhlJhiQ6nB0kGk7elN/S7cHq9GMFeh0+YiOGmhg8vfhp+rx9RGujaXO0sfPt57jSkImi6nPaNePYUeUClMxUpJEQqAdb68CbpaCIIgAR8dBZCZEporLl6oHcRRi0ijADmQ5HO/6gkB62Hf04ROpMahMmtYlimZrz+yz9FaXxok9PGyXs/Vt2CsfN4yIA+px+9Bq5MB4xZ8MVrwhJ2CAiRfNuaNgqHneJwQR7/yHIlFwOxecDIg3RoDCiU+jRZ4ZPHqy3fEqvz0K7p4VLU64feKF+K7x/q9i3hHGw/xWmayL6q2+9wsnzmNspkKHPYVzEFIJSkOzoWXDJUMMflykWfF249Ccx7OgfXKXEGUiYFIsqYKdYMYEGVw0FxpPLyAARKzES1v8OyldDyQrRx9W2D2bdP/Ly/wHUOo7S7G5gYuQ0NAodazs+oM/fy1lx52FW//8ru/q0rBWdWsm8MeFkXJIk/EEJleLrV85OhgrbAXb1iuzKxlY9P35fyKqj9KoRSciJIJPJuHxQHhqIz9HV58OoU4R6+mKMmjCHxTOF+AjtEOneiXB2SRJnlwyd3Ohz+1j2pw30OH38fEUx187M5JxxSby3tzm0/F/WV/PomkrUSjmbv78w1BNnc/tpsYb3RNdbnDy4uoxxqZEi97Ef/kCQZzaJPM9nNtVi0ChZMT45TI4aDEp4+ytq8SYtr+5oYEu1hdRoHU09YjvbaywhGafV5eP9vaKiV5oaxfi0KEBUbRfkx5Nq1oUqmYe7D7O5eTPn555PUlQMO3+8iJ11PSSaNLy2s5H5+XFhhK7b4eWml3YRCEo8fc0kxiSY+MXKYuq6nFw/OxO1Uh42GbCxqisU+m5QK4adYGiwOLnlH7uJ0qn4xYpifvtpBWsr2pEQbRfj06KYmmkmI8Ywopx3FKMYxZnBGSd1ZrOZjo4OWlpamDDh5LbWkydPZv369SxZsoTW1lZuuukmHA4HCxcuPNO7NorThFGrZP7Y42QqlZ+LwfiUVWEGKU5PgF1VVjQqYc8f1jchk0HuElT+IBO2v0qC5xBS9TZI+WHYqiVpYACkkENCpJrmbg9x/a6QsRFqlpTGClKnVmDSKVliUKL9/B3kzTuheSeHUr9Nm6qIZlUByfY9yOrWC8t+wOkV0hWPL8jhZjt9XhUBuQZl0B3KtDs7Yyl72nezTBMv+qkatrDPEMXjex5nYfpCri66GhAk7GjvUcbGjD3hMexz+ZHLZEQZlOjUimENYcTnVRCtFfuQaEjkweJVXNLbBj4fDssuoAgkUEpekKshRZCRn2/9ORWWCn5x/mMUdNVCwAdPThFulj31QBAuehZKLg7b3ry4CfzNZ8KoiaCkeUDWaNaaWX3+aox7X0Tr7BJuo4nj+yuU34Utj4pqaO26MFJ3pNlBeaMdo1bBovExyBo2w9F/CkI863sDweMKJaRPF06UxnjoOAwTvzXsMUnVZQ77fIImmV6fJbxKV/YuvH0Dxwx8MPQTQblSGKYgg+xw0qaQKZhhns/2ng283PQUU6JmMzZiYtgyi+MvoNZZSbY+f9h9AYTT4Kp10HYAWf7ZqN68Go58zOxFP4PZ94Yv23EIempEPqDmFJ0dJQm++q0g772loIsEXYwwNDr8T5h+e3iW4EngCjhpdtWTpssKk05+E3R62vm8830AGp3VLIo7jzpXFQDVjsOY1eH9mJst62h21zM7ZjHJ2pFd8/7b8cnBVm57ZQ8ggpon9Tso2tw+zn9yMy29bl68YeqwzokgYiAO9O0iRZtBun7kyudIiFKZkSFDJVeTGR2NSaPE5QuQHXucRX3QLyThpyELP4YjzQ4qmhzIZDA9P4LEqG8mOfUHgmLiZxjY3D5ufGEXPU4vf79mMpmxX8+sxe0LhMLEO21C1v/7i8fz+4sHDJuOmXeoFcJR9xgSIrQ8ccUE9jb0cKilD4vdg1Gj5KX+KIZYoyZknKJUyLltfg5ry9uxu/3sru9hd103F/fHDLh9Ac5/cjPVnXb+ctUkFhbEs7VaGE519Hm4dV4OR9r6wuIaNEo5sUYNbX1uZuaGV0GLkiP454FWfrK6jCVFCWz130W3u5uyrjL+uOCP6AdNLvxsxdCA8C3VXeyuFyHWGyu7uGhSKtecQDqbG2/EbFDj8Pj5y9WTiBhGFrumop2KVlGhS4rU0ucekKpePyuL7y0rGPKeUYxiFP8anHFSV1BQQEdHB5s3b+acc07N5rm4uJgNGzawaNEi6uvrueeee7jqqqvO9K6N4gTosftQKGQDzpTDLlQPr10mBpc+F8wbqBQ0WwbZ8x/4lLjkNCERG4TKFieGY+aUxw0unJ4AbT1uGrpEpbcozURukh6fPygy6/pxfA6dQasUjp1VayBlErHRJtrsDmI0bmSNm8RCkRkQnUVxmhGNSk5Vi4P2Xh9qUwrl535OSawbWfo0KHuHFe/ezMKEYprjCwEZGOL5087H2d21k13tuzkv8xIi9Rr0Kj3j4wY5OvbUwr4XRRD3pJtBocIfkPiqrBt/QCIvSc/YjFOfpbxk3I2w7XHwOTDEprEg0Qht+4mKXQ4xwvK62dbIu0ffBeC9+Cp+OO+H8N6tYgXWJkIERxpqeSwL+piZ2U9y4krCXovVxYq+r+o1ouJ1DHKF6Bus3wgxBcIJMqYANj+GPWIlRM3G6QkQlEAR9A1sWwpCd61w6LM3QPpEyF0aItuDcaT7CEnGJCLUwxCeoB/kSubFLGVK1Cz0ikED147ygc8rV8HUW2DcZaICeBLCc8R2ELmzG/XOX0D+NTD2wtBrEaqo4XPnjoc5S/wA1Hw18HswqfN7RLTBMRv84kuGrmc4yGSCuJWvhpxlkDpJkNbfZ4sqZE8dXP3Oqa0L+LT9XTq8raTpsqg8OI6t1RZ+cm4R3ZrtNLnrmBOz+ITSyuGgGPR91josmJvfICdrDH2BPvKM4bP7nqCHMpsgQp93vM9lKTegGyZP738Bx/qZ5DIxGD+GeouT6k4HIAbSI5G6bT3rqXJUcKhvD9dn3H3a/YepukyuTL0ZpUyFVqFj/f3z8fiDJA8mXr11sOc5UcGeeudp9wF6/eJ75fYFuOzpbXz53flDeqIH44vD7aze18Kq2dmUpIZnLn50oIV7Xt/H5MxoXl01fch69jb0sqNOVIbWVrSzas5QWeWpIN6k5aUbpnK4zRbm/DgYN87OIj/RRJ/Lx/LHNlKQaOLpayajUsg5d1wy544bmDQq+elnocc/fPcA3z5rDOeXpnDXa3uxury8fOM0PjvUxq/+WR72vk6bh8Nttv7j0sGiogSmZcewrcbCjbMz+f7yAcJT0drHp2VtXDwplTEJRtr63Hx8sHUIKXp3TxPdDi9v7Gpk4oxYut3dA3EuJ8GcvDjm5MXiD0gsKDh5+0RKlI7N31+IPxgM63McLOk8uySRT8taidKruXBiCu/va0aS4Mpp6WHxDKMYxSj+9TjjpG7mzJl89dVXvPPOOzz00EOn/L6cnBw2btzIokWLqKys5JVXXjnTuzaKEdDW42HrkV5kMlhYEkOEfoTTQm2kPv0KevU55JvSGTzHn2TWUN/pQutuI7prK/RsH0LqNCoZ+zULsKizGDeuiGO3CI8vyLoDFvwBCaVCRjAoEW0U+6A6ri/EE3DzZdcnyGUyFsSejUquhsxZcF85AHlARoIela0RepSg0IQsxxVyse5jls4ZcTrGZgwiZkfXQNCPsXU/q9Q9/H3ZcxTEF1FsmsdBy24KTbPpsQWI1Atzk7CAccsREbje1ySkqUYhyzw2ZDltF2e1EWZ9FwJeUBuJAoicFbZIsiGZ5ZnLqegq49yoQuFWuujnwo0va45Yh7MbCs4eun6VXljw+xzCCAURe9Da4yHaoMKQOU9Uk45Hxlzxs+cZYdjSWQHlqxmr3ohuwXPEFs4UVdqMuSJEWx8nBpN/mwvdNYJgFS0HYxKu979Do8tA4vgFRBQt4MVDL/LwrodJMabwwfkfoB4se636DOrWQ+p0ZAUrMRyfgTbz22DvELbuQR/UbYClvz6lQz01ei7q7Q+QfegrKPsKsuaCYdAgqa8ZnJ0QXxKSWY4Idy+seBwqP4VZ94S/plAJMmZvA9NQ6/UTYumvh36e5FKRA5l8ekHfUj/5dbgk/rRWyPWe3VxJwVSRCXnYdvC0SZ1ZHce5CZfT0/A5OS3VKIJ9LIq4Rshvj4NGriFFm06zuwFP0E2zq4FcY+Ewa/3vx4KCeN6+dQZalYKxKQMEZmxKJN8+K4/Gbiffmj6yDNKsigMqiFSZkfP1ZJpG5cAESMxw/Uo9teI74eoWVfbI06uMFqUZWXe4jff2N9Pj8hKQJOTBoOizNSUNCV6//60DWBxeWnpdvHXrTGxuH89uqqUkJZIvD3fiD0psq+nG5vYTqR8gCpXtNr792h50KgVFyRFh5OjrYGZuLDNzY/mqspO/rK/i0slpYQ6WMpmMOXlx/PzDQ7Ra3bRa3TT1uMgapjo4Pi2KTVVdqBUyWq0eHvm8kpQoHWsr2gH4pKyVGTkxfGt6Bp+Xt3P7K7t54oqJpJn1/Gh5AU+ur+bVHQ2UpEby2k3TcHoDGDRKfIEgHn8Qo0bJLS/vpqHbydYaC4n9ElS9eui9+JZ5OfQ4vZxVGI9XepAJ+iZ+MHXZKR2TSJ2Kl28UjtVVHTb2NfawID9+SM+dLxDE7RO9dUJyO3Ddq+qwc9nftqJTK3j39pkkRep461ZhxrKluotg/9za1CxzaNJjFKMYxb8HZ5zULVu2jN/85jdUVVWxdu1aFi1adMrvTU1NZePGjSxZsoT9+/ef6V0bxQjw9bMcSRLSmJHgVkWxp/BBQAZmHYOTx4xaJYvGx0JdGdh8YA6Xfri8AXodfjISTeSnTEc1KNYgEJQIBMSdIDdJT26ifgiZO4Y6VxX1/bKudFs1ZimbuEh12E1JrZSLaIY5PxKD8H5yUN/porLFCUB+1S8p3L0LLn4OEvorCXO+S3vPUexdR3jDo8NpbwPglkkXM960CK1aQVqcjkd3P8oLZS9wY8mN3D3xbiF5bD0ESr0oFrWVQa4gdSlGD1pbLXn6ZOA0+wkU6qH9fINfliv4/exfw6bfQP0WWPuQqKZe+jLkDv3efVb3GWvr17KqZJVwbZxxL/icoroIHKy3UdvuQqOSs3xi7InzhGLyBakzJIBciSZgpygjGo4FqMsV4PfDiysgeaIgkQAJJTD7+4CcPcqZdGTNps7SxRKg0dYIQLuzHXfAHU7qDr0vSHdXDSSUCqIV8EHSOCGd1UaIKAKNSVTtpqw65cNcYCqB7Ivg0FdCtqoZ9Hfy2GDXX0WVMKcHsk5gANVbB7v/DjIFLPtN6LiGIJND8WWw7wVo3jEga/26uPpd0VsZNXwlYiRMipzBF52fsqFxLQVFEbTVLebcknQMEVNodNUNkaCeKlJ0aaRkXgK+T8CUPCyhO4bFcStZ2/khcGLDlf8FTB7BSfG+fvOLE2FC1DSyDWMwKIxnNr/L0QUf3SPOwcU/FwY72kjR33yaUCpk3Lwgi9goNZMzooVEvvxd0VtrSoZpd4UtPyMnho8OtDIjR0yMPL7uKH/fWItCLuPNW6Zj9/iYnh0TRugAttd20+sSksk7F+aSGPnNpMEWu4e/rK/m8/J2GrqdVHXYh40luGpaBoea+yhMMpEZox/y+tF2GzvrLKjkMi6bnMr7+1pw+wI8t7mW6Vlm+tx+FhUmcP6Tm7H0O4Y29bj4wTIX6TF6Lp+Wzm8+ERL3PfU9XDE1HYNGid3j59zHN9Lc6+LpayaTZtbR0O2kucfFt6ZnsLI0mQmDDFyOYWqWmXdvn8W7e5q4703Rlzw3vZu5x/V0nghddg/nPbEZly/Aj88pDFVEJUli9b4WfvvpYSx2D09fM5kFx2X07ajtFp/TISI3FhZouf+t/WytsXDHglzUCjn+YJCEU+yNPNJm44UttZw7LplZp2iQM4pRjGJ4nHFSN3v2bFJTU2ltbeVnP/vZaZE6EIHl69ev5+yzz2br1q1nevdGMQxSY7RIkrh5m01q/IEg7b1eYiPUYXlgKqUco1aJ3R0IOWFaHT40qkHZc5nzIXXGEDJS1eqksV9amZuoo8cexKRToFTI0WsUzCyMwu4KkBmvO6G0J0WbQbQqBhlyamoNVLh7yUnUh0LFJUnC7QuiVcmRHSczEm6bQbwBJxnNXyBzNYt8sIQHxQLmLBLyV5DQ8CA4jyBVfAx55xGhV7BEs0G4eLou4ouGL5CQWNewTpC6vS/Dul8MbEiuhPsO0+Q0kND2GcmBaoIH1LDgZ6dVsutz+jncZCchSkNGvG7Ia5sregjK3CySVGh8Tug8AkDHrg+IHzM08PmBTQ/gCXjo8/bxt8V/E4RiMKk4ptbst/E+IZR6GH8dHHpXEB4QwdTHMrJcPXDwDbA2igrdrZuEmUv2vJD8SxMliK/GIP52d024ixhdDLMM6UQcvwst+8HeCXVb4S8zOZQ1Brshkon/rEe16EHIO1tkdJ1idW4IJl0LOQtFVVc5qNoxuA9JPsLlsuuwCA8Pegfkpm5riNSFuc9Z64XRiccqKh1x36BCpVCdNqEDKLPtxYuLwpQs1te8wrYf/gyDygAkDImmOG1oIsKzE0daTKHlnMRTlJ/+G+EP+nl87+P4Aj7umXSPiPT4FyNSNXTg/o2x/zWRuQhQfCEUD/TUSpLEw58fEeYf5xURbxp58O31B9lQ2UlJamR4xdFrD/89CE9cMSEsu+5YiHm8SUNhUgR/+9bkYbd1fmkyext68PgC1HY6mJrpxzAogPzLwx3saejhxtlZROlHnuw6hj+tPcrL2+qRAVqlnIJEE9c/v4P7FueHZKFlzVY+OtDKQxeWkBtvHHY9Tb0uPP0S1Je3N5IXb+Roh531Rzr56v75ZMQY2N/Yi0GjxOLwEmNQs6Q4gTSzuM45PH6MWiUub4CWXiczfrOOhy8ZT0KEhjqLmGTcXdfDs9dO4YKnNlPRauPB1WXsfVBcwztsbjZWdrGwIJ7oQaHiWbEGlHIZCrmM1OjTk9QGJSkU1+AdNJH78rZ6Hlx9KPT/3XU9Q0jdeeOT2FXXjU6tYE5eHFanj7d2NwGC8K29bx7eQJDceCNddg/ffm0vWpWCx6+YMGyg/IOry9he282a8nZ2/XjxaX2OUYxiFOE446ROJpOFYg2+LiIjI/nyyy9pbW09+cKjOGW4vQHaer0kRWvCyJoMSKcOVFFAEruq+mjt8RBlULKgZKBRWyGXsXBcDD5/EK1aQUOni93VfagUMhaXxg6sUzl0IBQXoaa6zYlJp6Sm3UV1m4tIvTIUvh3ft5v4zgrQLTmhPMioNHFpyg1IksQ/WzoBCZ9/4Ka0r9ZGXYeLjBglE2P7ROZX/6A8NkKNObmOH6z/DpGKXubHF7IpKpZnP7qCb+dewIzuJhyWFtRKI8jkVGun0VbWzdx0D/KWXWIDLbv53pTv8Ub5P7g8+3wApOhsysd8B4c+nXHlv0CrVhJQaOm0+ohQxECgWsjuTnMmvqLJTku3h+ZuD2mx2jCy22714PYFATUPe/08MGkVDe54lO37KEu8lkWShHzQ9vyBIHdk/ZVgQIE6qnrY7ZVkmoiJUBFtVA1fNfDaxQx9ezls/YuQXV35JsT05+IlDurN2/MMqNVQcgEoDELimRjeuzdx5kwy7T4iDeIyZFJFcJm6GHPdJ9C8F2Z+Z6D/Z8oq+PIhKDqfruoP2TRDSEo1gSClhz/EfvgT9It/iXykIOiAV0g4VTqqtNOpaHaSnaCjONUAVWtF5l505tD3qQ0iOsLVLaqTx6O3TvRSApRcKcilQg0xuTj8dla3vYo74GKqaQFjzeNEpdJSKZYxD5+r9a9GlmEMze56Wno6uXjMxf2EbhQAG5o28HzZ8wAUmAtYmbvyJO/4L0X2AjGpYIiDxHAZ/KGWPp78UlwDsuOMJ6wo/vzDQ7yyvUH0Vv1gkHlZ4QXQmiFiSY6DTCYLc3+8enoG07LMxJu0w8oJj8GkVfHr80uY9Ks1/PNgG3UWB79YKfbd6vJx00u78Acluh1efn1ByYjrOYbiZCFLVchlXDYljX9sbyAQFNfFZ6+bAsBdr+2ltsvBthoL798xa9j1FCaayE80caS/N66tz82UzGiKkiJIN+t5f28z97yxD71KzhNXTGD52MQwI5gjbbaQacvmatEv+Mc1lbx920x+fE4h1Z0ObpidhVal4JySJCpabSEjFoCbXtrN/sZeZuXG8MqqgWkXlUKOPygcVj/Y38KdC3KHNaDxBYI88UUVSrmMOxfkIgEPf3aEvHgjF05I4bpZA5XywVd9hVzGtTMzsbl9Yf10Jq2KRy8rDf0/Ui/npjlZbKm2cPX0DNIHVTvXlLezpd8YZnNVF0uLh7pFT0iPZnttN6Vp/4LJjVGM4v8YzjipO1NQq9VkZPyXhvD+j2JbpZUeu48Gk4q5xYMkQ83b4fBqUYmY+R2C/TN4wWGUmAq5DGdAwuPwiaBuwBeQ8AeCaFRyaqw1AGRHhje4J0ZrOG9KPHIZ7DhqBYQ5CiAqG0c+AiSxD6XXjvgZAkGJzRU9ONwBxmeZ8Pol0mMHZpotNiF/6e7qgvoXRF9Y7kC/wbz0GTx7zjNolVq0kdn8/u0ltDpayVAng1xNXdQ8qhZ8G2QygnI12H141GZ0MflIjk7c5mJm+Z2MqzhK5I5VsOozrImzqMwWFRdT/hwK0yNp6FPQZHGCeibmvCnExZ9i/MMgJESpaen2EKlX4vAEwnL90mN1rKvaSZOjlh22TTyQ+BMMS4ooq7eTEa0JI3QAvQ4/UYo0UEBx7Bj8gf7j6AkwIz+KaKMKhVxGWqwgURW2/TS66phimEh0417RpxfwQVeFcHAEYfxhiIXrPhaVp9hBDf3HqlvpM0TlZhiSKJfLBuIdJIkjdZ3IOiyYAcnvQhb0Af2kbvIN4gcwVC9B59uNWyHHHJmDe9OLGIMB9rXupPT2vcMfzJbd0LgFAFuUGX8ghYZON8U1T8L63wgJ572HBIlz9sDuZyFjlth/Q/yAm+YxBPzCyVOuRAyFJDGZkTEntEiXtx2bX5zrj5c9wr1FPyLDlIt+/PBun98Uvb5u9AoDavmJq0tFpvEUmcYPm23/fx1joscQrYnGH/RTHDPUPfB/Bolj+8PIhyIz1kB+gonGHidz8k4sdzsWdu32BcIrzhrT8D23IyAv4dSk53KZaAMA8PgHXBR1KgXJUUKe+O6eJpYUJw6JkDgeF01K5f29zWyr7ebFrfUsH5vImvJ2lhQPXItz4gzUdjlCeXLD4Y2dTSFCp1LIuH1+DrfNH5iQ6bILd02XP0hxcsQQYjUnL47b5+fQZffw5i5R0TqW1Xa8EcydC/NYNSc7rBft2Fze8dd0pUKGXCbCxv+09ihtVje/vUgYWz2zsYbH1h7lulmZ5MYbeXyd6J0tSDSRHKUL7ceC/Pgwp+qrp2fg9gVZX9nBpZPSuPHFnZQ1W3n00lLOnzB8H7DLG2BBQTz3Lc4PRV4cw/z8OIqTI9CqFEwbwSzoB8sLuH5W5mh+3ShGcQbwX0vqRnHmcezarThe3njMHVGSQJKYnBtJS7eH+MhBEhdbG3z6A/pip/CF/mIkCSZlm0LmIzZXgGpbOdd8IgKHX17+MiXHuSq6vUEcHj/jM01E6JQkRPWvXyYXLoudh4QZxQlgc/lDLps2V4CitPCb8cTsCOo6nGQ0rRNPeIbKg4piBtz4Lsq7iFcPv4pXHwvuPlI0fTSrNOhlbhKCR5GSJqDTapBKr2V9WTe9R/1ESg6s014nvekdJrUewNh6kPhgAd3KVOJS0sCgRrI5+481GMxJUPGWqNAUXgTxpzZYzIzX4/NLfHG0jL98/Bzn5c/lmrHCFVajknP+xALeP3qY84ufxOH2E2NSM+/4CIp+mI0q0mK1uH1B0mK12Fx+uu3iOLb1eMKiFvxBHxssIkhdZm9ncZPIYaL0OtBEQt4iyF8JUWmQID6LR5uApdtNvEmBcu/TQn6pNMJH98PGx+H2rYIE7n0ZMmaGB5IDlL9NYeseqpSlHNLOoyA/B8UIlv+6nCVc/uzv8XeVo5fpaFLrSHXbqQy6KD1u2Q6rB5szQKYpFYVcBUoNyWlp2LoUZMbrYJ/4O+Fzi/5CtQFevRiadoEpgZqVH2OVx1OcbhzIb9r+NHzyPZGpd8kLopIX9Iuq8CAkaAYGQWatmaO1Rg4HupiUE0F63HFyqa6j8OHdEFcA5zxy2lXd8r59bOxeg0kZyaXJ16OUDx+d8Z9Cm7uZjZY1JGvTmBVz1im9xxNwIyGhVXwzC/3TQaoplbWXrEVC+rdIL/8TMGqUfHbvXIJBaUSpu9XpwxcM8ouVY5maaWZKlvmM9fy5fQHe3t3IhPRo/AGJNLMec7+ssM7iDBFJ2aC6kVop58UbprLg4fW4fEE+PtDK7NxYntlYg0YpD6s2gYhGOPeJTTT1uEiO1LJyQgrfX1YwJIj7qasmUdNlJy9+ZNI5Lz+OF7bUkhKt442bZ4RJQgGunZmJRqUgNVpH9jDkUCGXhRws40xaajrs/HzlyPeA481FnrlmMq/uaMAfkOhxeEMSzDHxJq6cls4/tgllVGOPM/Sed/Y0Y/P4eXt3E89eOwWtUo7bH+TX/6zgrdtmMCcvljqLg+LkCBq7naSZ9dR2Obj9H7uIM2l54OxCEiN13P3GPgC21VhGJHW3v7KbL490srAgnuf6K6AgKoQ9Dh/v3zHrpFmNp9p/N4pRjOLEGCV1/4cwPT+KTqs3RNYO1NnosfsozZxMZIlJhEvrolGDGPAOxo6n4dB7eKMakKaL/gyHJ0ig3+pq65Fe9JEQkMQNudvdHfZ2rz/Ilwct+AISJRlGCiOtohqiiYApt0LJ5Sfc90BQor3XQ5ReSXSEgh++fwAJiddunh7mVmY2qTGb1BB7HvTW4Uko4URDs1vG38It428J9UFFaaNY0n0U9goJFmlmoAhJAqtTSGjsimgIgiN5NtQ9hXL/a0w3xiP/TmVowNDSI2ZvgxLolQER4g3i9ymSOuiPKLO8wmH7Fg7v3sLF+Reg7zcdSTQkclb8tyhrsNPW1s2SCbFhwbGDIZfLmJQTgYSY8dWq5GQn6HB4AkP69ZRyFSnaDFrcDaRp00FWT1ATyUfdZcRkz2FWylCZ0qaKHvqcflIi5UztE7PAtOwTZKe3XhiOrP0p7HtVuHJ+v070hB1Dj6jwZqjb8U68CIX2xJcm9fgr6VrzAGrJjXr6zfzd3cKECeHmKC5vgC2He5Ek8KZEUzj3AZDJSVCoSDg2Wb/gAXC0AwE4spo92WPJ9PVXC4HqDh92uSs8vL3yE/Hq4Y/F/yOGH+xoFVqy9HnUOaqYHD2HgEV8pr7+8ygMu1+A+s3iZ9qtQg56Gujxie+b3d+HT/Kh5L+L1JXb9tHt66Tb18mkqJkDRK2jQlRK85bAhKtDy1t9Pbzb8hJBgqxIvJI4zelXur8u1CcwJ/r/CSMRugaLk3Me34gnEOT1m6cPCSP/plj5580cabeFqkxJkVrW3z8fjVLBLz8SLsZqhTyU9XYMWbEG7lqYy666Hm6YncWH+1tCBiSZsYYwyWKb1U19f7/aitIUVAo5b+1q5JLJ4dJ+tVJOQaKYPNrT0MN7e5q5fGoaxckDTqalaVGh/rbhoFLIT+hwCtBpc/PRgRae/LKqf5+SWT5MaPpwiNKr+dtXNdg9fmq7HDx+hXC7feKLqhChA7hpUNXv3kV5PL2hhiunpVOUHMGt83L407qj1Hc7qety8vKN0/j4YCu3v7IHlVLOJ3fP4epnttNqdVPRZmfD0U08dEEJv7mwhJ113dx1gmiCY+YwXXYPwaBEZYeNrFgD976xj48PtnFOSRJPXvX1zJdGMYpRnB5GSd3/IaiVclJixIyYyxOguk3c9Go63EzIPkmPQs5C2PYXzGo3Grz4ZGqSotXI5VDeKPKYIuRp/Hq2MKqYlxYuzXF7g/j7CaAvIEH3UfC7xI+9DaIyAfiy82PqXFXMjVlCjmFAznewzkZthwudWo5SG6S1zwUIC+XhLKiJTONXFS/yxvrbuXHsjdwz6Z6wlz2+IDUtVmKNcuJiIkW1sD+AHJVB/F+SQC0IlFwuY9qYKDqtHuIjNXTbfaTHFeH7VEIFOCU3LY5D5BtFD0hmnI4+p5/UGK3on8pZgq/9ML6kmQz1VxsZecl6lmUvoPzARqYlTUWn1LGjdQeNtkZW5K7A7xPVtkAgyBB/E0slNG6D1Gn4ovJYX9aNx+VkjrmRyJwpjM8avhLW5mgjR1XC8oSLRG5W7Bzeq/knP9v6M2TIeOu8t4R75iAE+/+2fpkScpaCrVnk9cXmC/dLYzxY+nv5/G5hLtJxSMgVI1Kg6CJo2YMqbTqqfkLn8Dl4ct+TJBmS+FZRuGTxH3o1F3pdKCWJ+H2vc9NVb4p+tUFQyGUoFTJ8fkn0ew7T64lSAymlwrQk4OWw/QAHFl7I5PoGxhTfQ7A9FoU/SIxpEEla+BPRT1h08n6rJfHnA1DvrKZe006klM6YlGHO1+ILhMNnfOFA3t1pYHLUTNRyNXGaRHSKE59hASlApb2MCGU0KbqhA/Y651F8QR+5hsJvXJ1xegJoVHLyjSW0e1pI1qaFV96+/LUw9Kj4kC+TTNR4apkTsxiNXItXEoPFHl/Xv5XUjQSvP8iuo1aQweTcyBEnUP7XUd/twOYREw9V7XYmDuPA+E3Q1CvuO8euV112Dx5/EI1SEVKRFCZHDOsq+p0lA9cdp9ePWiHvNwoJP+fzEkz8+JxCarocqBXykPzw7d1NxJk0PHzJ+FBFzO7xc+8b+9h0tAuXL8C+xl4+vGv2Gfu8j62t5I/90SHHsKO2m5x4I7lxRr779n4ONffx8CXjw3L9AkGJD/e34PD4QmYmm6o6Q6+7fAOTQ/kJxjBSu6Q4kcxYAwebrLh9Aa6ZmUllh414k5ZJGdGUNVu5+/W9SIjz2mLz0GZ1h+3j0Q4bPz2vmCtOQuqfvHIin5S1snxsEj//8BAvbq1nRnYMPU7x/a3pcpz4ADXvAFsLZC8SE36jGMUovjZGSd3/UWjVcpLNGnrsPtJiT0H6kDkbfthEZ58fz+FekKDb7ic/xYhRq6S1x8OYFAMRuhXDvn1bpaiYROgV5CcbwD9Z2NJrIsTdvW49weTJVDpEv1aVvSKM1B1z6gpKMDsvhosnpeLyBk6YZbSpWYSPb2zeOITUHarppL4H5JKfcwrtKKMGVVsiUmgdexl/P/g3IqrfD703KVpDUrQgBon9v61Lf8bOSC/tcSmMC3pCq0iN1ZI66LjW6qexjyKUtTIWRwYG3EKPh98tetf6bfVlMhnn55/HtPhFZMYZaHG0cPOamwlIAfq8fVzb1oiuto4IVwOacW+CatBA6MhHIl/N2YF93N3Y3QFm7rqVSMtmGHsxXPxs2Ka9/iBbqur43q4rcAWc/G7O7zg7+2xQGzBpowBQypXolUNJw8yCaPbUWGnv9XIgcRLjxs0XLyz51cBC0VnQuF2Eg5e/CwE3uHtAZQJrK8y4XUQf+L2gVPP64dd5ufxlACYlTAqTzdq8Nt40x3KdKwAp45GcFo402fH4ghSlG1Ep5KiVcs4aF4PLExDV25Ew7iqRtxdbwGR/E4fke9HOuhS1oYDFyRKSdJxkOWUiXPHayOs7Ds6Ag8863kNCYlzEZNTK8FiEemc1hrg0Yu87NMIaTg6NQsuU6FMbiB7s28X2ng3IkHFFyk2YVAMDyTZ3M591vA+ATCYnd9B38HRR1ergYL2dSL2SBSVpXJF609CFchdBxUdI2Qs46j6KhES14wjL4i9gatRcApI/7DrwTdDqbsKkjMR4fMbhKaKtx0O7VQxUO3q9Yd/v/58wOzeWH59TiN3jH1Fy903wx0tL+etX1RQkmnh1RyMmrZL6LgclqVE8ceUENlR2MiM75qTrmZAezYbvLUAhlxFnGjphc6xfbU15O3KZ6MvbXisq2hdMSGFythatUsumo8J58RgG5w2eCaw/0hF6HKVT0evy8dLWOp7fUseD5xXx7p5mAN7e3RgidV5/kAdXl/H6zsawdXU7fASCEgq5jMyYgckhtz/IPa/v5dcXlGDQKHH7Alz0ly3Y3H7KWqwsLU7ki8MdpEbrcfkCVHfaxeQqcPOcbKZmx/CbC0t4cWs9dZ12XP4g5S19Qz6L3ePn26/txe0L8PgVE4g1akgz67l5bg4A1Z2O/t92Xr5xGqv3NZ/4HHL3QsV74rFcCWPOPb2DO4pRjCIMo6Tu/yhkMlF5CkPAB+VvC6fAoouHZmj5nMTvepQppNCZfSnpcWJQkxKjDVUAh0NrjweHW8gy5TKZkP2ojWIwHfTD+l9A0Ifc0cW01HnUO6sYHzk1bB3jMyOINakxm1RoVUoePn8MlL0Bh/fjHnMJ5W0B4mgjTi/RrckkIUrDA9Me4L2q97ii4Ioh+2SQuQAdOsmO3OmEqPAbz0v1n/BW8wZo3sCFeReSHjH8bGWkLonC6T8jzW8jz1g07DJA6AYakKRQOGsILfvgzWsgLh9SSkSI+YQbwCxulFsqenB5g3TbAuSla9AoNDj9TkxqE3KNkczmd0SVUXGc5C6uEOo7cRpT2V/7BmnxS4jw9Q9erE1D9rGpfA/VXS7cAVEF7XQNzAovyVzCY873sNmNeBwmOK7IZ9AqQsY5HVYPw2byLfsNxOaJCQJrLbQfAFcvWBuweRV8ubuH4sZHyTnyBJz/JEXrH8VokEhUR5NkCJcqrSpZxWp9HLWoyVJHYImaSsVhW2hfcpPEuatTK4Y07x+PoNqIPFUE8o4hkjHGAXmsXCYLt4T7GlDJVOgVBhwBO5HKQVWPgJ/y1s/Y6C9HjoLLU1dhUg5fPXX6nDy0/SEUcgU/mvajb9TvpZGL76pSpkRxXEyDSq5CjpwgQTQnMVwZCZIkcdh+gJbeSEBHn8tPIAjK4f4Mk66D8VcgU2qY0bebOmcVEyOnI5PJmBA17bS33elpY691O9n6MWGB5mV9e9jcvQ6NXMsVKTehUZw+IYuPVBPV79QaF/m/KdHceLSTf2yrp7rTwf1L84d1I5TJZEMMPM4klhQnsqQ4ke+9LSTp3Q4fVz6zneLkCK6ennFaoeOnkmW3uCiBDd9bgNXp487X9qJXK/Cqypn/xn3E6+P5+6JXGZsSgdcX5DcXljAxY+A7+vmhNp7dVMvV0zM4b/zAfq3e18ye+h7uWJh7wkiI657fwf4mK6nROjJjDGyq6gKg/3aA0+PnwgkplLVYuXiSkIZ6/UHOeXwjRzvC+8EVchn3LR4TmmAKDJJm1Fuc1FucGDRKfn1BCTIZoT42tVLOlmoLbl+Qqg67kNeWJFHX5USllHFrPyG7fGo6ZxUmMOXXawFo7BYVVavTR3lrH1OzzGyu6uKLw4Kkri1vHyLNfeiCEl7b2cCSogTyE02hXsIRoTKAPg6cXRB5ZmW+oxjF/0WMkrpRDKC7Sgy0AV48GzRmmHYz5C8Xz237C7a9q7ElnU1uYQdKxfAD0OMxuEf6eGMTZHJRlXJ1gzaC0siplB5H6EA4fWXo+6BpDySWij4oi8hl66w5SLctilLny6zVX4NDbiU9TsucnDnMSZ0zZF0A+XkZJFTvxCBzIU8cVOEI+KCjggUpc1hdvZr86PwhhGLI55Mp6fV34wjYMCmHn+XNTdKjVsoxahXoNceNbis+EH1nATck9Luq9bWESJ1aKcflDaJWyojVxfL2irfZbd1MT6CZQ+PnU5wyEcw5eJRqfr35QXxBHz+Z/hP0ecshfTauPxYy323jsfQCJl/yFNR8BeMuDd+HL35N9obfE2MqoG/yr4iNtXF5Yf8yPhc8v5y57ZVsmfQ3GnUzBWnqOAwNW6DkEtCYKM0yUdfuIithUCXP7xVB5HIF6M0w97vieWmasEXf/Qx4+9CrJZba/84B03hyAh4oX80MSxObLSCTtSFrOSAy7vqhUqi4eMxA9pbJF0SrcuALBMNMX06Gemc1n3esJlYdz4qkK4TcFESPpezMyOtUcjWXJF+PM2AnWj3IbfD9W5G8NTBjOSAxVD87gLUNa1ldvRqAWcmzWJI5co/PyVBoGk+UKgZD80H0z50HYy+CGXcAEKOO56LkawlIfuI0Qwf8p4I6ZxUbLJ8jV+sojF1JtjkOpeIEzLhfFlsSMYmSiEkjL3cK2NGzgSZ3PY2umjBS5wqIAaov6CUgDdPTeArQqhVhES//a6jtcnDtcztCk0rPbaodltT9u3DXwjx21vVQ2+XA6w+yraabxm7XaZG6U0VqtJ7UaPjyu/MBeGrfU/glPy2OFn60ehNlzUp+el4Rk46TfD78+REq2+0097pCpK7b4eWeN/YhSeAPSiPGK7h9Ab6q7ESSICvGwJ0Lc9nT0IPTG2BKZjSLChO4dmbmEGOUg829IUJXnBxBnFFDc6+L7ywZw7KxA/eiyRnRfH9ZPhlmA7e/ugeA2k4H7X1ufvz+QaZmRrO8JIlzSpLodflosDjIiDFQ1B/1cPeioX1yUXoVxv5gdLvHz2s7GnhuUy1HO+xcMyOD+xaPYVJGNG5fgHn5Q91H02P0fP8kRG5fYy9/WlvJ8rGJXDYlHaZ/W5hoHT+JPIpRjOK0MUrqRjGAyHTQmoVpRVcNePYJK/gfiGZsKb6QnaV/os+UT1uXjAWnqMyJj9QwtzgauUw2dMAtk8PUO8HZiVefwu4jPcgQph6q43tWDr0lerXaDuAffx12YzGR9KBOyAObqCoF+k/p4JBy2HHoPkpUQhqYjiNsb18PFR8ytfgCtlyx5ZQ+32cd7+EOurB4Ozg74eJhl5HLZEPNZ46h9Cqo3woxOSDXQd1GWnqVNHlLyEs2MLsoml67L2T/n2ZKY11PJ17JS6WjnOJsYTDxVd3nvFclpCzTdr/J+fN/CTkLiXaLCtYKlw+OvC/63I7PZOuqBCDC3UBOMlg83lAYOT110LIXJZDZtxl16hKRd/HC2eC0CKfI858iPlJDvFEGzTtBShHRAC+cB9oIuHm96KtDBN+2WDxE6JVEjL8aKj9GceSfKMo/YaI+VpCMRT8HWzvyph0gBaB1fxipOx4alZwlE2KRJGnYrCax4SB8+n048okgta0HsBVNI2iGDm8rzoBDVMoq3oXmXZC79LRs208EjUI7tDpkqaKoZS86dSTGc58Kk0Fi74COcsicC3I5E+InEKeLQyFXMC5u3DfenyRtKmy6DZp3Q1tZiNQBmAcTz68Bg9KIHDmGoJOp1o9Qe+Ig6gJB7Aehz9fLP9vfQiVTcU7ipSftBTwVpOmyaXLXk6YLrzRNiJyOTqHHrI5Drxyhb8fvBluruA7KT1zd/V+EXq1Aq1Lg9AYwG9RcfRJzj3810sx61tw7lz0NvWw62snfN9ZywSnIPdusboxa5bBB1qeKqwqvwuqxkh6Rzs9fUQNB1h/p5PrjXDRXlqbw5JdVYftl1CjJjRMB5OPTokbchlal4Bcrilm9r4VNVV1sru7ir1dPIkqvpiQlkg8PtPDuniZe3FLP2SVJIZL11q4BFcXDl4ynMGno5GlZs5WVT24mKEm8umo6D5xdyJrydr6zdAy3v7KH3fU9ACRF6fAFJN7c1cjt83PC+u7cvgC//mcFjd1OZubGcMOsLFQKOdF6FXaPnz63n5+uPoSxv8fZYvcSpVfzzm0zR/zMbl+A/Y29jEuNGlEh8eiaSjZUdrK5qkuQOrkSTpBfOIpRjOLUMfpNGsUA1AaYfT+s+yWYEsEUL/qH+tGWuAR7dxsANo8wGxkcYj4SatudOD0B8ocziAARMB2ZTluni7Ye0bPS1usJZaaFYIiDtoOw7TmkjU+xY/rbxGflUxofQUyMGWy3Msfppktpoo96Pq7ZwpLMJSjlSqhZB7VfCmMOfayQmcoUMONe0A+afbfU9P+uRpIkmixu9BoFMcf1ZHVYPeyp7iM2Qk2kwYzb00yUrQ/8e8KO2anAbsigd8V7JJk1KF6/HBp2sOesP+PrFgHjc4vNxEeFS+GmR8/jiL2MiVEzQs+NixtHijEFX28Dk+y9cOANKL2SYNY85LVfkWHvdyTtaxm6E8t+C+ZsDifk8cCWBwBQN2ziqmnfF+Yds++DrkrSl98FkRpRVVLpAUv4DGv1WmjYKG7U2njw2sRPR0WI1B1ucnCk2YFSIWPZhFhUYy+D8g9BCqBwtMP8H/aTxR1inbmLQhl1J4KQJfVXhIJBcHSI8xjg7Ruh7O2Bhbc+BX4XhX2NKAqmkb3/czSzomDqTf05fBJ0lp8xUjcsLnga2YHXyS6+EDSDJheCAXh6AfQ1wax7YPHPSVMa+eLcN8W5C7T3enB7g6THaQfMTOo2Q82XMOUmMJ2CsUjp1SJIvuSSYV+2On1E6JSnbZYSr0ni0pQb0NRvRd27EXobIGXKkMiHJlcdff5eADo8rWToc05rO4Ph9Dn53c7foVVouWfyPeiOi0FQypWMjRDfy+Nt7UPY9Tdh2pQyDQrP/9r78t+KhAgt62/MoNflYUxB6X96dwBQKuRMzTIzNcvMfYNMUEbCmvJ2bnl5F7FGDZ/fO5co/deTwUZqIvnhtB8CYLy4mU/L2rhzYe6Q5e5YkMsdCwae33S0i8p2G2/fNgOPL0j8MFb8W6q7eHZjLZdMTuVbMzJJitSx6qVdSJIgelOzzDzw3kFe2d6AUi7DH5So7rSHSN2iwgTe29vMxPRoxvTn+zX1OHlhcx0rS5MpSY2iz+ULOU9bXT5umpvNTXPFREZqlC5E6p7fXIdJq8Tm9uPxBcJI3UcHWnl5Wz0A6ys70auVXD09g8evmMAfPjvCjloL54xL4qY52by6o56rp518EuDu1/fy2aF2JmVE89sLS4bNJ1xWnMiWqi6Wjz2xAmYUoxjF6WOU1I1iKM76CRSfJ3rWQFTuorPRqeUE+6sNgaBEV59X9NL11oPXAfFDe8qsDh/7akWlSKmQj0zsEH0qOrWQGh5pdpBi1iKXy5AkiYomBwHjMooTepDvfR0VbiJsR3C4xQ1XKZPA3oJRG01b0EFFlY4Oj57nbS9y0/gbhaxUCkD7fsiY379FaSCj7xgufhYOvgUll1Ld5uRgvR2ZDBaPj8WgHZh5bOx04/IGaexysyT9ItwVr2F+725BZu7YIapufg+8fhVYquDSFyFp/JDPHGzYyd6jdroMReQk6hi39CEwJZFgkGjyEp4VOAgFpnEUmMIrNomGRD696FPY/jeQvQdzhNRRaRAyGTmIAWvi0P0gIgkW/ZRoRxvGg4/j8DlIDUii6hYzBhb9NHx5mQxWrRUVtJyFA88fI3hKLZReKfLX9GbIHJDBDhlOyxVw9qOg1EN8gei766wUhipBn+i70pymK9o7N8Ch92DGnbD011AupIsSMmTxxRDwIFmO0q2PobBsozBq2fYXmHoTgTHnIm/bhyxz/ult80Twe+HNb4mK6MXPQ3KpiC0468GhywYD4Bah5bi6BdHY/mdAgkk30adOZcvhXrGoJAm5azAAr1wssvYs1XDJ8yffp/GXiZ9j8LmgfgukTuaRDW088UUV541P5ol+C/XTQaQqGhImiEkYbdTQijiQbcinwVWDSq4mRfvN+mk+qf2Ed4++C8CM5BnMT5s/ZBlJkvhn+5u0ups5K+4csg3HkQhvfw+T1/aN9uW/Fj21xB99iXiAeCOYh5KY/yYcaOrl2U21rCxNZmFBAq1WF7//9DBBCTpsHrrsnmFJndXlo8HiDHOSHA7PbarlyyMdfH9ZAX+5ehKV7TZc3sCIFaaOPjfXPb8Df1Ci1+kdkYQ+9HEFZc19bK2xkBSpY1FRAo9dXkqfy8cvPypHrZQzrn/fFHIZWqWcxYNksIuKEjj8y2VhEw8r/7wZi8PL81vq2P3jRczMjeWpqybi9QdZNjZcQvvIpeOZkmXmx++XAQPX20hduEqmNC0Sk0aJzeNHBqRGi4mQCenRPHfdFG5/ZQ8VrX28vbuRf2xr4PUdjXxw5yyKkkc+rh02YRa2u76HZY9t5L3bZzIuNSpsmSunpXPF1LQzlns4ilGMYgCjpG4Uw0NtAmRCHqkSA/Uog4r5Y80crLehVMgE4XB0iBluJGGukhzeE6NVK9Co5Hh9QSL1A6ebJEl4j1nN90OnVpAUraGm3YXNFcDjD6JTK2jv9XKkuT82IeNyMmY24QooiSg6j8xEI4GghKNyAxFNnwMynKnXoZZHkqorpE/W7zyWuwwaN0PKdJETp9KJXj7DcX0B8YWhgbaiXfThyGUgP64gmZ2ox+byExOhxqDWYAgc68Ua1BvVVQlVa8TjQ++Hkzq/G/a8gHzvP5jdeoj1M95CSpxKfSCZffE/JN6gZm6eAbsrwMF6G2PTjad+E5x2i/g5hnMehtTJglgljj3hWxMNiXx4wUc4Kv9JhrMbUqdj9ViJUEcM3b4pcaASdgyZ84R8TR8jnE1XPC6e99rB3o7HmIE/ECQ3UU9mgm6g16qjHI58LAxcZt4tCM8tG8DTB+nTT+1zD0Zjf5WvcTsAfbmziaj8EmuEGWnVaqJ+X4QMqGnbRd3Yi5kiU8CMO2h1N/Gxdz36OCMXRiSdMOMwDN214OiCtCnDv245CpWfisfl7wtSNxKUag5+6ykanVWMTVhMiqsHRX/+I+4+FFpZKOMrFOorVwh30Y5DYkLh6+C9WwT5TZvOVq8g8Vuru77eukAQudnfH/FlrULHsoQLv/76B2FC/ASiNFGoFWoKzYXDLuOrWE3e7j/jHDuDekP1UFI34Qbxd0r+383Uquqws6GykxWlycQajz97B0nST9C/+d+CX35Uzs66HjYd7WL3TxbzxBdVoV4ztUJG7jCB4f5AkHOf2Ehjt4vvLhnDnQuHz1fz+AP8oj8Tz6CuIjfeyJ+/rKI4OYJ/fltMQB1sslLTZeeckiSUCjlddk/oCB4LAAchB31s3VEmZ0Rz0aRUlhQlUtbch9Mb4N439vHFd+ezsjSFN3c2hvb/9vk51HQ62F7bjQfhkNnt8BKpU6GQy4Zca+X9/5ckKRRvcPYIOXdKhZyrp2dgNqj53aeHQ3l9G4520WZ1h8xlcuNN7HlwMe1WNy5fgOSogep2eWtfyBDl2Lb9QYm3djXx4HkRvLS1HrlcxtXT0sP29fHLJ/DHNZW8u7eZQFCi1+kbdh9HCd0oRvGvwb+M1LW2tvLoo4/y6aefUl9fTyAQIDk5mQULFnDPPfdQVDSyU+Ao/gtgzoHp/ZWnQfLEaKOKucWDmsk9MlG1kaRhjSU0KjmLx8cQCEphNv6bKnro6vNRkmEMORUCjEk24AtIRBtVoRlTk06JWikjGITICAMs+RU6oMjnBKWMnVVWZJ0yJgMBmYrclHi6/A48sh7OL+qXlsUVip9jSBi+uX0wshKEuYlaKRsyexttVDF/kGmCb9xV1MiLMBs1xMX2z4DHF4l+OUs1TLiaTquXbrtXrLdxK/TVQ84cZH0tFCfKiUk3sb2yl6AEbT3ekBQVRK7g1OPdSk8EazOsvgMikuG8x2H6baf81lhdLLHjrwXgr/v/ypP7nmRh2kJ+O/tRDtbbiNApKUg9QeUsOgtXwEFZzyZSdBkka1Jhx1Pg7sEaNZUqv+jJ6Ojz4nD7mZ4fRXzlZ6LK1LiNt3c/wcT0BWSb80LRDqcCm9+KM+AgQZMMF/wVDrwp5JRA+4qH+GfNy7j0UVwok2ObcTvt2//My5EmZuTMYsq5TwHQ2rsVv+Snz99Ln7+XOMUwRhLObqj+XJCo1GniWP9lptj/C54Or34dQ1yBOBe6jorfJ4A/6GNL4AhooLH3U6JVMVxceAFyKQgJY7G6mwikbiJVnUdq7CASuWqNIJcnIe4jwiUkW7h7+enKYp7dVMPK0hQCUoC1HR/Q6+9hUdy5xKjjT7ye/wCyo7JZf+l6ZDIZ8hEMbtTv3U6+z4HZG0RZPAzZNCUNW1H8X8K1z+2gudfFluounrn2uAmG6GyYuEqoE2JGDpM+XTzxxVEOt9r44dkFQ/Livglm5MSys66HmblCcjwty8xr2xuQgILE4a8LvoBER5+oFu1p6OVou21YCaBGqcBsUNPt8BKhU1LdKchWbZeDYFCi2+nl4r9uweMPsq3awqRMM629rpDksWRQ7MFj6yp5bUcjr+1ooCgpgm+flUdjt5O3djdRmDzQD7ekOIFPD8WjUsiYnx/PusMdUCteUyvlTPzlGmbmxPDqTdPp6HPzj231zBkTx5RMMx/eNYsn11ezfGximNtmq9XFZ4fauGhCCp12L1c/sx29Rsl3Fo9BpZDj9Q8oUQoSTfQ4vby8rY7zxidTkBiBSiEn1qTh/Cc3c7TDzpNXTmTZ2ETGJkdydkkizb1ufrWymPve3E+f28dFk1L5tKyNn34g4leSI7WcVTgg9U4z63n0slJK06P4aH8L5a19zB0jJk7ruhy8v6+Zc0qShv2bjGIUo/jm+JeQus2bN7NixQp6e3sBMbsEUFNTQ01NDS+++CLPPvssV1999b9i86M4UzCeQl+OIQ6m3Cbkl7HDy1FUSjmDhR+SJGGxiRm8rj4fuf3jqF53Lwe7DjItaxpqxcBMqEGrYOmEOECYYEiSxK79R+lyyogy7cWrmEe3qhCbPBqN3sRMUxQLx0YBJ2i6DwaErM2YIIjrMHB5Auyt6cMflJhVED3ExrzD2cFjex6jKKaIEt15VLtTkXtgeWZQBBPLFXC+IAu+QJAtuzoJSuDyBimNSBEkWKaEcx8nvlBIGAtSjHh9ffQ4wh36ZDIZ75S9RLatjXF5K1GMcKxDOPC66K8CmPAtyJhx4uVHwK62XQDsbNtJdauT3sajFOy+BX9ULMpvvQXa4aU4W7q/pMpRwYG+nVyXdicKvwsJqAtkAqBRyuhzis/Y0eslfsbtYG3kn64m3t7/N8779JegiRTVuuiBXg6nJ0BrjwezScXOo1aCQYk5RdHIlG7ean4Bn+Rlfuxy8rPmQtbc0PvyjEVocm+mpVXP/kqYMOPHuMddxHmOFhZnLA4tV2Qqpc9vxaSMGNn9sfYLIeNt3w8J4wSZ8/cH97q6h3/PoHPheJTb9uPw2yiNnIZKrkIpV5Gpz6XeWY2EhNXXgz+9FLVcnH+7rVvp9DXT7W9nKoMG7mrD1yd0ABf+XUhWxyylxBzJny4XskuLt4M6VxUA1Y4j/5WkDkBxMnOTzFlw9HPi8i4A9am7WDZ2O7ni79tQKeS8cfP0Yfuo/lsQpVfR3Osi4jipXQjmr9+3OByeWl/FI58LkyWTTslvL/zmJj7HkBSp5dZ52dx9liCgK0tTeHzdUao7HZi0w38+nVrBC9dP5eWtdXxc1samo12svW8e6THhZNPrD9LbH469/kgnE9OjuXlOFgsLE5DLZchlMpRyGR7gjV2NvLazkZvnZlOQaCIhQhuWZTcxPZrXdog8uSuf2cam7y/kdxeN486FuWEkN0qv5rnrxPf1yS+r2Fpt4cppaczPj+e17Q2093Wys66bb7+2hzqLkwNNVp7dVMvBny0lMVLHL1eGf7clSWLe77/EG5D41UcVfH9ZAS39AeK3vSLcMO9dlEdjj4sF+XG8sKWOK/++jR6nj88OtbP2PtEv3O3wcrhNSI631VhYNjYRtVLOU1cNqG7W3DfQWxyUJJRyCAThT2uPMiXLTMRxf4/mXhc76nrYUdfDkqIEsuOM3PPGPvY19vLPA61h6xvFKEZx5nDGSV1vby+XXHIJPT09oediYmJQqVS0t7cjSRI+n49Vq1ZRWlrK2LHfYBAyijOHYHCoxvBUEZF6wpddngAbDnUTBOYWRWPQKpmcE8Fj+/6Iy95OdtoDxOvjWfX5Ko70HOHc7HP5zZzfhK1DyPSEZMPtC9LkjgQ51AaTCZo+YqziXOyeOPIyTtG5r/wtaNsPMfkw4bphF3F6A6F8uT6XnwrbNna17+KaomuI1cXyfNnzfFD9AR9Uf8DfZk8HjGhU8vCg6n4oZDK0KjlOb1BEGsSMgTk/AoWKPq+cTbs7USlkzC02M78khpZuN16/hFwmYXMF6FUcRFn+ORMMaQT2vwjzfz40l24wxiyH3S+IykPiyauSI+H+KffzwqEXWJq5lHidhNSxjghHNTiqhetl7llD3hMIBkJ5bEY0yCVg4k3Yu5po7kjG3LOX0ua/01J6P30RBdT5NvOHba9x5Yzr2N68mTF7X0YjSSKY1lIVRuq2HenF6vRj1CpwuAMY7dUon38Ad3ox/uJMAFwBx5B9KreU4/PJaO3UAz5qO5yMzyyhJC782GgVOubHLjvxQTHnQOseITNVakQP4FVvg7VRmI8Mwo6ejXR525llPkv0mR2HTk8bGy2fA6CWaxgfKQZ9S+MvwOm3s79vJ0na1BChA8gzFNHlaSP3BLmIXwumxGErutGqWHINhVh9PeQZxDbtbj9OT4D4yK+XZWfxdlDvrGaMcezXDgM/bVzxhjin9OaTLjoYW2ssNPWI7Mbd9T0sH0H29t+AV1dN50BzL9OyYihv6SMhQkPMEBnmN8Nfv6rmYLOVH51dSPWgLLUpmad3XE+EA029/PDdgwDEmbTcOFu4Uhr7ycMxR8ZjeHzdUb480sGPzylkRk4MRztsfFzWhi8QxBsIhJZ7c2cjH5e1cvdZefzh4vG8sKWOg81WPj3UxvPXTWF6f+i52aBm9Z2z2d/Uy4/fO4jLFyQtWsen98zleFwyOY2y5j5e3FqH0xvA4fXz5ZEOxqdGDXsvAHh+cy1ddi9lzX08dME4cuKMRBsE0ftgf2touZRoHX/47DBGrYrb5uWIjNd+BKWB/FN/UKI0PZLlYxPx+oN8cbgDCciNN7K1xsLPPiyns7/fDSArdkAdkxyl4+crijnYbOW2+Scn/eNSo7h70Rge+bySg81WdtV1s7AgfAJ4enYMz2+qIyNGT1KkkHWmRuvY19gb6t0bxShGceZxxknds88+S1tbGzKZjIsvvpjf/va3ZGWJC3JXVxe/+93veOSRR/D5fDz88MO88MILZ3oXRnG6aNgO/7gIIlPhxs+FBX31GuhrhvxzQ457AH1OP31OP8kxmpDW/mTotvtw9gdTd9l8GLRK+mS1fNr8GgBvVY7hjtI7sPvEAKHXdWKjAp1aQU6MRF1vDy79EaKUMkpr/iyCzOMuhchTMHbor6jsd2dTv6OdsekmshN0QlbntsKYc4gxGRifaRIuZ9ESF795N/6gnx53D7+c9UsmJ07m9cOvkxWVxaTMVJwJcvQaRdiN3OMLsqemTxC2rCCew58Q1S5BwtUhU5EuqxOPL4jHB70OHwlRGpLN4dWAOmsy+4PipuxQqNlU9znLspePKDUjoQjuOXjSw1BhqUCn1JEZmSmeCHhF5UkbBUC+OX+AYG98hNjDvxOPcxZCxiwALC4LH1S+w8wA/K19C+s69/LA5O9xqcVBpKcbWdcLMOkmDKYkknxWxux6lMiu7UR2bIMfNXPee09S11dHl6uL989/n/2ZS/Ec+hiNzhxuwgKolOLYGjRyoo0qkhs+QNO+B037HpZPWE2fMZICt0b8DfuriDtad7Dq81XIZDJ+WPw0UbJsUmO+QbUlaQLEjxUV3mPfgWHIrc1nZa91GwAH+3YzO3o+7HkO7K0w7ltgzsagMKKV6/AE3UOiBPRKIzPMC4ast8BUQoHp6xP1Y+j1dbPfuoMMfS6Z+lwhy2vYLD5X2gws3g78QT8J2mTOijs39D6PL8iXB7vxB6Qh8ulTxSft7+II2Gh1N3FO4vDum2cccvlpEzqA5WMT2VDZiUohZ0HBcVVKjx12PC36ZYc5B04Fdc6j6OQGErSnns9mdfkwaZRhg3yASL2KOXlxvLq9gR+9d5BYo4YvvztvxMrW6aKpx8lvPzkMQKxBzf1LC9CrlUzKiOb8U4gjOFUkRGiJ0quwuf3kxQ9IvV+8fgo763qYlTtQaXX7Ajy6RlQLn9lYy6QMM75+2aHEAPEB+PH7ZXgDQYISvHTDVCZmRHP1M9vxBYL8bUM1vkCQGTkxPPB+GUaNkl+tHMuEtCharW5m5Q58P4+02ciI0Ycy5n54dgF5CUaKkiP48xdVvLS1nlijmu0/WjQssbtjQS6v7Wjgpv6Q95w4I9OzYnh3T3NometnZvLB/hb+8pVwZM6NN4blCirkMn59QTGPrati+dgkpmTGMCVTHJeHPzvMh/tb+f1nR0I9dWaDmoJEI/cvLaD4OLOTa2dmnuJfRuCyyWlsrOxCr1EwLUts02L3sLainXlj4lmQH88fLhnHd97cx7XP7eC1m6fzx8tKuWlONvkjSGdHMYpRfHOccVL3ySefADBnzhzeeOONsNdiY2P5wx/+gM1m4+mnnw4tO4r/MGrWC8e3zgrorgZzlpCYATREQ8FKAHz+IF+VdeMPSsxoqSDRcxiyF0HM8C5qHp+4sSZGa8iI0xKUIMUsZo0zIzIpNBfSZGtiVrIgB3eOeZhNTVuYYFogbMc9VmGwMUwlcFxeIsVSHO0eIzE+GQT7pW0+F1gqwZQM6hP0fRVfCq17abSMIRCEJoubbF0X1K0Xryu1ULCC7EQhnwkEA6Sb0qmx1pBmTONbH38Lq9fK6+e+Tm5ULgq5YliDxmaLm7YeQcZSfXUkOo6AAyH9jEwTz8dq6erz4nAHaOvxEBuhHjIQyIzM5NJlf+FwZxk3fvVd+urfocfby6V5V1DR5MCkU4SHfp8CNjRt4I51d6CSq3h7xdtkG1Nh25/A1QvFF0PScYYRMXnIkUSP5YV/B2sdlL/LL3r28EXvEV6Ua7AGvQSR+LJ5I5fK+wdBPTXgtSNX6pme6IDJF8CnO6BAEIVzs8/lubLnOC/nPFRyFZNTZkHKrIFj6Gqg29dJoXEc08dE0W33EWNSi+qt4UpoXwMxeaTFz4Q9L8LH3xWD94mXQ/El2Hw2JCQkSSI7WcbUpKGhuacEWwvUrheELvHkMjO90kiyNp0uT5sgTe5e6O1vouk8BOZs9Eojl6eswi/5MPy7Klb92Nq9ngZXNUcdFazKuFdUro9+DIBFo+Id53okJJbFXxgWNyBJUigH0j9owHw6MCpNOAI2jMqhGVz/bTBpVfz5yhHMU9b/Brb+WRDh7xwBw+ll/B22HeQry6fIkHFpyg1EqU5OOp/eUM1DHx9m3pg4Xrxh6rDLNPWIgXyP04vTGzhjpC7epGV8aiQVrTbmjokjMVLLL88Xapsnv6yivLWPB84uDDPd+DpIiNDy1XcX4PT5Q5UeEBLGxUWiKhQISuxr7GFMgomLJ6Wy/khHKE/ujV1CDqmQyzANquotL0nkw/0tTM6IAkTFyqBRUNnuosPmodXqZnZuLB/sE7EvCSYNdy8aQ3bcwMX9t58c5q9fVTM+LYrVd4jrlFalCOX+vbtH5MwNF5Va3Wlnd30Pl01JG5KJ19jjDLWng6gKWxxCIqqUyzjYZOWzQ23cvzQ/dEyunJbJldMyh2zn3T3NISkmQIRWycffnhMySRkOvU4vOrUCjfLEMuZ9jb18crCV3108Lqzid/fr+9hU1UWsUc3rN89gc1UX/iDsqOvG6vJhNqhPmOs3ilGM4pvjjJO6srIyZDIZt99++4jL3HXXXTz99NN0dXXR1dVFbOw3C7sdxTfE5OsFoYvKgKRSMWMfnS0qdYPNRSDkjxzf+QVIPqj7clhS1+vw8VVZNzIZzB8bw8Sc8JlBvUrPLyY8R2WLA61X3BjSTRlMi47DoFUg8zlg22OiV2kYV00AhUxBsjYNNBIojOBzCLJU+SHozDgm3YtOrRgymy12IBZyFjPW6KDR4qEgxQh6nSCCXjs0bYO0GSF3TIVcwWvnvEanq5NmWzNP7HsCEL1m+eZ84dLYUwuxBUKS14/4SDV6jQKVQkZ0Sg7Y0/st3gdm5dVKORnxOvaUN5HT+RZSgwRTbhlSVYjVxYImmsdjJrPF3Y5aoaaq1Ul1mxjAxUSoidCd+le619MLgC/ow+61C0Ls6gUksLXB8SqzohXw7b3Qsg86D4OtAbw2YrxCmhar0HBbVDFrVArunHgXOK1Q+U8hU1Tp4c1roOJDmH4HPGgJBTzfMv4Wbhl/C8PBFXDwcftbBAniDDiYFj2XhMGZfUnj4K7dA/+3iN4vnD3gc0N7GWeVXsPv5/4etVzN1KThB8GnhKpPhTtiV8UpkTqFTMF5iceZpmTME5W6tIEAX41Ci4Z/f59WoiaFBle1MJYBQdZlCpDJ8at1SE4xuvQFvWHv06oVzC6Kxu4KkBb79fb7nIRL6PZ2jdy3eIbQ47XgDXpOqwp2Wojqj2LQxwpH3dPEMRdAWf+/U8HWagsg+p9Gwp0LczFpVaEesDMFtVLO6jtn4wsEUSnkfHG4nd99coQ5Y2J5ZqOYsEiM0PKTc8OlwZIk4fYFR4wLOIYdtd28ur2ey6emMz07hkhGJqM/++AQL2+rD3OsPAabW/Trjk+NDOtrWzE+mdX7Wvjzl9WsGJ9CZqyBLrs4v2XAOSVJxJsGri9DHUQJmarUDJKeDsaPzylicoaZ8Wnh8stgUOKSv26l2+Fld10Pv7s4/BrywuY6JAly4gysGJ/C3zfW9O+Dmr9ePYmL/7oVAINaGSLSAC5vAK1KHuYoefWMDF7YXEen3YMkCfJ6IkK3prydW/+xm5QoHR/fPeeEwe63/2M3LVY3+xp7eeOWgV5ttVKoRrrsXr7z1n4eu6yUPpefKVlmzAb1SKsbxShGcQZxxkndsV66MWPGjLjM4Nd6enpGSd1/GsZ4uOSFgf/LFDDppiGLqZRy5o810+fyI+uYDG17IXECNq+NO9fdid1n5/GFj5NiTMHuCoiZSkn030Toh55qDV1uAkFo7HJTkGqkJMNIUrRGLOvrFTlvcPLcqMrP4Kt+WeD0m0Gp5KA0nqp9FhIi1cwsHNrL5PQEqDtaSX7HG2TqIulOv5ydliYmZS9Gfvg9sdBxGXZ6lZ4MVQYJ+gSWZCzB6rWyNHOpeHHPc+BoF1WccQPuhkadkqUTBp3fU0TPUjAoUd3iwO7209nnIy1GS7ysnQ5vM9tsnZzTeQBl8hQe2fUIOnkEl2TdRHqsntiOCmLV0UxSR0P2ClptYjCiVcvRnkIQ/DFIksSk6MX8YpqKCL2acXH9A4yxl4qK1EgZbZ1H4O3rxOMr34CIVH6UOZezjXG8c+hF/tC0gRhDIsUxxRCDyMU7ZmDR1E++mncNPHcSKGRK1HINUa1HyDjaCrPGgt5Mt7ub3+34HUmGJO6eePfAgGb+D4TsUq0DXSRkC0nc8qzlp3xsWns8VLc6yUrUkTJYBmseI0hdzMjXtjDUbgRdVHhPY95J+vX+jZgQNY1801i08n4yEpkOs+4HmZwEjYn5CujytpGhzxVZkfb9gIwi03hiTGpivkFhUSVXh4jW+q5PaHTVMidmiahoniH0+rp5u+UFggRZEnc+WYYz5/gYwrRbIG2aqLqrT1+Gmm8ci06uR6fQh3ou7R4/NrcvrEI1GD88u5AovZolRSMbWenVylPqj/q6OBal8czGWo6026jtsjM2JYLKdjtz8sLv5/5AkIv+upWyZit/uqyU88aPTLAfeO8gRzvsHGiy8sV35wPieFz192209bl54fqpFCaJ6m5rfyWqvc89ZD1/+9YkPjvUxuVTBOmu7XJQ3WGn0y6W9fqD9LmFWdfLN07l3T1NGDUqrp6eQZxJQ16CEa9fCsltA0GJNeVt5MQZWTEuiU1HuwgEJa78+1aeu25qSIYJomo3nBRVJgN1/3HTqOS8uKUOu8fPzXOzUSnkXDgxhbd2N3H9rCyunp6Bxx/g7d1NfG9ZAeNSoyhOjuBwm40ZOQPS039sq+cnq8uYlB7NL88fGzo2t8/P5fb5ubyzu4kPD7RwV3+wutXlY1uNhRk5MWHmJvsaewgEJRq6nXT0uTHGDZWduLwBttVayIw10GJ1kxsfvszjV+HLHE0AAQAASURBVEzgyqe3caDZSlaMnsxYA3/91tDJ2FGMYhT/OpxxUufz+ZDJZKjVI8/MqFSqsOVH8Z9Hn8vPoQY7cRFqcpPCZXySJFHf6UajkpMUrcGkU4J5BRSsAGBf00b2dAi3ra/WP8CVs39OSkw6Do8RmQySoodv1C9OM1LT5iS7f3symWzAYVJlhtJrwWmB1JNUV6LSQKERJKzwApBDb282OKDX6RcGFgf+IWbTS68DhYqjLQ70ljIUQQ9BeztXfHw1La4Oriu8nsXquThkERQH9BwThx1pdmB3+RmbYUKr0vLI/EeO24l+zczxYeYjoK7DRVnDwExvfaeLxZMns/St+2n39bGr+j0mOZt5q/ItAOSOQs42x1FoTifQeYReWSy66m0k5c9j+aQ4FHLZQOYbQDDIvneu5ne2MmbmnMtd8x4K2/6RZgcVTQ5UsnHMHTw2TCwVP11VosdSddzsbmg2WCaO55hlKIHJwHtV7+EhiFYp3iNJErLB5O3Cp6HsHZiyKvTU/s79/GHnH5iVMovbxg816VDLNcwznkX6ml8g93vA6YGVT/J25dt8XCukgmelnzVgeKKLxjrzDp7Y+wRpGjPXRgwdXEmSdMKcpEP1NmzuAE5PIJzUZcwWEQYnMqgJreQ9eOs6Icu7bavI3fsPQJIk9ll34JU8TI6aiUIWfsnXK44jIoPcTHdbN2PzW/EGPWTq89hoWRN6z5kiX83uBo7YRUhypf3QGSV1vqCPIOL76A16TrL0N8CJMgdPAen67NDjPrePpX/cQFufm6eunDisKcuYBBN/vOybbfNM4cpp6dRbnKwoTeb7ywrwB4IoFQOTS+19buxuP/sbewHYXNV1QlI3MyeGox12pg8iLodb+9jfZAXgyyMdIeLy0IVjmbAriqmZZs5/cjMtvS6ev34KxcmRjEuNCoVe29w+Vvx5Eza3n3sX5fHbC0uINqgZlxpFbZeDTw628e6eZnqcPg42W7lnUR6//KiC0rQo5ufHIZPJ+Mv6Kh7+vBKDWsGiogRcPmG+sqW6m8fXHeV7ywqG/TwOjx+H10+8SYtMJuO9O2ZyoMmKTinnmud3AhBn0nDp5DR+vnIsPx/kcPm9ZQVh6/3wztm4/QH06oHv8MajnUgS7KrvYfljG3n+uilhfZ8XTUrF6Qvwq39WcPdZeTy9oYYt1RZm5cbwyqqB/M+b5mRjd/vJjTeGSU0H4+7X9/J5eTuTMqJZc+9cco5bzqhR8vZtM6nqsDMm4QStD6MYxSj+ZRgNHx8FAJXNDtp6PLT1eMiI06JSDtyY6ztd7K0R1bIFJWaiDOGD2jTNOAqMM/EGbMxVJ0NnObKoDPJTTjxznSJvJSVDB8YR+kiGse0/1GCjudtDSbqRpGMD7oRiuHufIFSRov+uNNZPTbtTDMo7dgt5pKcPZ287+zoNxDoOkOsTRNQfmU5vh7D/b+uspDpGVNrijuwiYuoC+px+yhsFAdOqFRSnD3PDmnCD6B07Xq56PFzdWJuO0ODMBOTIZaLKlpOoR6FSYzLE09XrRK82URpXil6pR4WBeHUGcush7F4Ha/S3ApCz90XGJeejOT4AHKCvmX90bKPMaKCs7kNunPlj9KoBsu7zCslRICghHXoLZt8LQJOtiV2vX8D5zZUEUyYhv+mLsNX+wXqAnqxiLi3+FqWpk8Ne+8n0n5ClnkmUbAxvlX/Eb3f/lGlJ03jyrCcFicqaI34G4cVDL7K/cz/7O/dzXfF16JTh1Ym9HXtZ9en1vC+X4Uu/Ck30bFKAqYlT0Sv1xOvjB0xe+vFqxau8cUT0885InsGYaEGoJEli8+Feuvq8TM6JJPWYdNByVBjExBdDxyFS+6o4op5CaswwlZLBhM7ZBV1HIHH80P5NX3/1IBgQ6z4O3oCXO9bdQZ21jl/N+QVTE6afciCv1deDDBkRqqiw5yVJos/fi0kZGTLQaXbXs6N3AwB6uR6FXEmKNiNUFeqwelAr5aHvtC/o5bD9IHHqxFAUTZAgRqUJOWKdBsWZ6f0LSkE+axdVcY1cy7iIySd5x+khTpPA8viLcAddIddOEARvZ88mjEoTuYYiJKR/nwPnSdBt94YqUBWtff9xp81Om4fH1lUyLiWKS6ekDXn93HHJnDtugKQNJnQ7aru56pltaJUK7ls8hupOO3csODFp//nKsdy7eAxR+oFJ4dK0KK6YmkZ7n4eLJw70V8ebtNyxIJc9DT3s6yeNXx7uGGICEpQI5cttOtrFHy8vDUkyv/vWfnbX94Tmqo602Xh9ZyOH22wcbrNx11l5vLWrkZe21ofWlRKlRaOU4+k3YylKGugLXVfRjk6tQK2Q88jnRyhrtuL0BXnmmsksKIgnKVJHUqSOeosDo0aJxx8ge1BfWk2nHa1KMWxPolwuCyN0AFdPz6CitY+GbiGBH+xwCfDj9w/yyvYGJAn+/EUVwf7vtLu/373D5kavVhKlV4cRyuFwTNLq8PhDOXP7G3tRKeQU9efxqZUDj0cxilH8+zFK6kYBCDOTJoubuAh1eMWHAa28XMaQ1wD8fiVXpvwCAJP0KaSEh956fEEO1tswaBQUpvUPfpt3QcU74vHEG8F88hl6SZKobBH9YzXtrgFSByJkGyH13FFpRaOSM21MlNhf5VToawJ9HFV9EWxq3sAC94fINIJMqs05PLf0OfZ07GFF/HRqNn2AR24kbaK4yek1coxaBU5PgLje7eALCoOYwYNwbaRwRRyM3jqo+hwSxg70UO15lkhXN3mKXDbrZnGeYie6rU9B+gwktYGb0x/FlaDFE72TPHMeGy7fgMsj4a74goSeXQTjphHd14M1oCPOflCYb1gbxLYHE47IVFYkTGOP7RAzU2aFEToAp7URSCSIgqCtE54/By56hs+rPyTfLpxBpfby/lB58Tn9QT8vlb8EgM1azhPH/4GCKmKCYmD+cd06vEEvG5s34qv+HHX3UchdPiQn67zs89jdvpvZKbNDhE6SJHySF7VcQ521Dq8U4KGC61iSfB8ACxw+SuNL2XLFFhQNW2H/G7hLLiGoUKFX6RkfNx6lXEmCPoFEwwDh9QckOq2CYLX1egSpszbA3ufEAiVXQU8VBd7tFHi3QcIPxfPNO6FhE6TPDj+39z4vXFQtR4fGYoy/XFQ59THDZsfVWmvZ1iqcMf9S/jhelZ05MYvZ2baTH2z8AeNix/HI/EeGuJu2u1v4oE24xl6QdDWxmoEy61eWTzliLyNHX8Ci+PMAiFRFo5Fr8Ut+6pxVtHgaMSoiuCrtFhq7XOyq6gOCuBO+ZEHSPGqcRzjYtxvF/2PvvMPjKM+u/5vtVVr13iVLtuXeewMXsCmh956QQIBUSEIIKaST5E0gJEAI2BTTTTPdvfciW7Kt3rt2V9vLzPfHrFaSdyXbwSR5v1fnuvaSvTszOzM7O/uc5z73OYKKy1NvpMvfTp6hCLVCwzUZdyAgYFZHzyY8VygEBQalEVvAx2jTeFJ10d0TnYFeTjqOk2MoiHAIPROStWlUOI7S6WsL9+8d7z1EWa88obOnZxsAl6Vd/6X3950NchON/P6qCVS2O7hrfv6ZVzhL7Kruwu0LRrp3ngFPbqzkxV31QD0LipPOqT+vqsOBPyjhDwaYlhvPfUvOTv46kNAdbbTR0OPimmlZ/OCtMv742Sl+eXnpoAmQCZkWrp+RTYvVzVVTI4lnrF7NG3fP5tqnd7K3rocfvHWUNXfMACAnwcD+uh70aiUuX5Bks4bUGB0alYKxaTGkxeh4YkMlAVEiK07PX66fxNV/24UvKGLUKClKNuMLygTpo7IW7n5Rvq6Kko2cau+PVjnWbGNRSTK9Hj//2FbD2PRYtnx/Ef6gGD6nOyo7ufEfu9GoFKy/b96QFbOBeGpTFfXdbmJ0shPqjqpOchIM5CUaMelUoc8O1EqBK6ZksqREDj1fUpLM5pMd3P78XuIMaj56YD6JJi2fl7dxuMHKHXPziTUMnrz9n2sn8mFZK4tD19C2U/L+KgQozYhlQqaFn1069qwnp0YwghGcf3xppO62227DaDxzj8GZlhMEgc8///x87toIoiAzQUdGvDbqDTk9XsfCUtns4/R8IIC8FAOBoETa/l8Se/BJaNs9KGi5utVFQ6cntC0tsUa1bBbRh+7qIUldICihVMjXgSAIFGcYaeryUJAa3emxuduLLRRqbXX6SYzRhJwQZclfms1H7bGDPOc4RapyPGr9WA5ZxzMnI4mxY8YCMP6iTH63/3Feq9zLjxJ+RII+gQsmJCDWbUNZ+Zn8RglFYMkd/qTWbJDdDm11/aQuFHS+2rabl+v/xMaAgqfaa6H9OH51LJ4l9yIAkicBV9CBRR2P1gBMWgbuaSgNCWS2uumpc3B8ym9JPfo3BNEH7k4ouqj/vQWB+Ve+wobT9wno9nTzfvP7TE64E53kQHXoFUjOhz1/5laFCkqv4kj7MUqm34dywPWgUqi4c9ydbKzfyHUl10VsV6dRkp+qp8vu5+6sr6Iu9zIzdQaa2s2ABHVbeKLuQ944+Qb3TrqXK0ddyaLsRSzKHmzb/1H7W9S7q5kZt5BVBavo8nRhJhucoFT09/MoHe2w+lJaBYl7Tv6Ty005zBt/G7PzLmTLNVvQKXWo28rkPL0J16HWmhifY6bD7mNUXxVZUCF3JUqgVMlGJn43xGSBNjTjXLsZ3F1Qt2UwqeurKkYzyBAEGHt5lLMvoyiuiCtHXcme9p2MSSuiy9cOwPqa9bS72vms/jM6XB2kGAf3TTmDjrCk0HVaHl/fNvr+AphVsVyf+TVEKcjO7o3gBZVCHqyJIaWwV9OKXWpld88WsvWyI59G0BCrtpCg7XcKPb0yeD5wedqNWP3dJGuHrkht6vyIRk8t5R2buW7r5wgBH1z3ipyrdwZs7/6cSmc5GkHLrdnflCXemjQUKFEr1HhF+b5kD9jOitS5Ag4O2feQqs0g3xipJDgfuHLK8LmfZ8JHZS28vq+RO+flM6sggcMNVq57ZheSBH+7cQrLS8+evE7KtrB6p2y0ETtUoHkIQVGiqsNBfqIRlVLBFZMz6XJ42Xqqk9uf38vtc3KJN2losXlYNT79jE6ILTY3Vzy1A19QZHK2hfIWO+Utdu5bUjio31CpEPjl5cNHfIxJj2FUiol9dVYO1PWwv66HKTlx/PaK8RSnmHllTx0FSSZ+emkpd76wD19ApMnqRqEQuGt+Ps9tq6Ghx82TG6sw6ZR0O0U8AZFDjVbq33dSmGzicIMsERUEyIo3hEndBaOTuXVOHs9ureZ3H5/AGxBRCLDrh0uIN2pYs6uOrDg9Hb1eREmuonU6fOQnwTuHmjjWbOcbCwuwGDQ0dLtIMGnCFTttaMLV4w/iC0qsO9TMukPNxOhUTMmJ54KSZPbV93DttCyumy73F143PRtJkrjjhX0ERYlOh49WmwelIPDVNfsJihJ2T4BHLxk76Bwmx+gGRR84vHLrjCjBkUYbRxptzMiLJyfByLjMf23ip9fjP29OrSMYwf9FfGmkbt++fcO+3kcehlvuTL0vIzi/GO5cx5mGvtEqFQIlmSb4YLv8RPXm/hclkSTrbk4xGoNWhUEX6rEqWArWekCE7NkR2wRot3rZecKKQatk4bh4alrdWJ1+ZhRbhnR5zIjX0dztRadWRN3npFgNP118N4/vd7LBNJsY7wwIQIfNJ/cKAod7ynnppCzfG5MwhjvHyRlnyrgcUKiRNEYOdZrpqe9ickFMhByVgAfKXpMzrJQaOmMy+OEnX2VZ7jKumHwn2OqoPvhHsMFhjQopLhfB24vG1cUYxRGqDNnkppoH25srlGEnTptTJq29PgVBpR6V6JN7CgdCDMDhNXIlKnViOJYCZNnb+u632Gbbxo2jLkGVUgLJcgVNIQZAEBg/4x4ovBCANquX5m4vfk0dnoCHP096gGxTdtTzPyG3T3qTwJSMELFXvQMtB8FWz2vNH9Hjd/DmyTe5ctSVUbfR5KkP/a1jQuw07hwnE3Kby49aKWcBytvVgsZIteDlDnMBFxkyCFZtgNSpmPVxMjl7fqXsZtpRARc/TkGagYKBPaMx6TD9HlkiGReyGB93GmHNmg31W2U31IGYfAfYG2Sn2HOEQlDwk1k/oc3TTKWzgjFm2ajm2uJrqeypZHzS+AhCB5BnKGJewlIUKAb1YgEsTFxBRW8ZRabBEuC+4PJ5CUvJM44iJUSgspN0KBWwwfkOSEHavc1cmnYdqbpMYlSWMPk7G0iSxGH7Xvyij8mWWSiFSCOcaPdzrVJHinJ4Z0qtQq5kJDZVINTL1U1b+RucKJrAaPMEzMPEIvT1DAYJsKnzQxYkLidDn83NWd9AISg54TiKKInkG86u53GvdTsVjiOUcYBbdDlolf9+19Iz4cfvHKOj10uHw8u7984dlCc6VBi2KImIUnDQZ25z+ZmRl8C+hy/EpFWFFRtD4TuvHWLdoWaWlCQzLS+ehcVJ3Lu4iGe21uD2B3lqc1XY5v+jsla2Pbh42O0JCGG35UnZcVhdfiZkWUgxRz/n++t6ePTdY8wuTOAHKyJl8L/8yniW/nELTl+Q9w43MyUnDpVSwUfHWqntcmN1B8iMM4QNpzodXipa7Ty4vISNFe1UtPZid/t57pbp/H1zJd0uP7trulEoBK54agf+oMQts3O4fFImxSlmfvJuGUqFwE9WjcXlC/LCjtqwZDMlRodJq+L57bU8tr4chQCffmsBj64ag1mnZnpePG12Dw+8eghJko210uP0/Oy94+QnGvnogfloVIqw8YovKJGfZEQpCJxqd2D3yAHoFr0aq9vP3zZXc9ucvHBVsK9/EGR5a2lGLB5/kLRYHY09bvKT5O/NI++U8fGxVn56yViWlw6eeFlemsYT10+iqcfNP7fXkmTWcu8rBwG4ZmomapWCm2bmnnUu3UNvHmHt3ga+Nj+fH1w0dBuDKEr8ZUMlvmCQBy4YFZ7kG8EIRvAlkbq+XowR/B/DRb+HPX+HCQMGxfYmEts+YyUbEJIWIyhDAb0qLcy4Z9jNdfb6ESVweILYnAGOhfraehzdXDAhEW0Ut0ejTsnC0uGznpIMSfx63q8RRYlDtb0EAmJ/fxVyFaXQUkiHu4PZ6bOhYj3+D39IbdHXiJ39ICa9mtrDsstrbZubifkDBr8+F2z5DdjrISYVSq/hoSNPsLtlNwfaD3DFqCsguZQfzPgRv9r6ND/SWxDSbLKcM66A4rhcMjwBmru9uLzBfgIzAGOzTWjUChJjNHRJV7CzfgOL0qcwaG7U3iTn9YEcz5C7IBwonqhP5Pnlz1Nrr5VdIad+Xc7aqt0sE9Gxl8sVqxD2nLIRCErUe1qpqHiWzNZfyjl+d2+DxCLw9oKzA+JyYYBc0O9oY+fRNZSOuZr47lPg6uIey1jeEnu5c/ydRENQlJhmXEqHWMNky4xBr8VqJFh3F9gaZdOVuFz42lZmHnuNPVb5WAVJgv1Pw9wH5apoX0RF6Nij4jQzlb5qTJo2kzzjKHnSIdrEg1ovO2FKopzvaEod2gEx6A/LYwNBicoWJ2a9ioyE9LALpCiJKDRBHl/y2yErV8LBNYxpL4e53x70vN0VoMdmZGriQvl74WiTpbkD+lJVCtUgIxJBEMhM1DNTN5sd3RspMJaEtuXkgPUwy3KXoVKo8Ik+TjmOkarLJEETPeOvyVPH7h55MsesiqHEPNiuvd5Vw6cd64jXJHFJ6nVRSd9QmJNwAfXuaprS87DlTCFWMPBhrBObbRddvnZWpFwx5Loz4xbiCbo56TzGSecxSmOmkKRNQdt6FE68R2naRBj9lbPelyRNChVArDr+nEhvNGxu2Mye1j3cMvYWkg3nJoscDkvHpPDS7nouHC1PCozLjOWNu2fj9gWZWxQpX/UE3bzVsgZ30MmS+Ctoa4lBqYQ7X9pDryfA87dNY17RmbMdT4Us/ndWd/F5RTsv7Khl5w+W8KOLR/PavgbKmmzhXq6i5DNLC1NjdbxzzxyaetwsGZ0cEZNwOl7YUcvRJhtHm2zcs6hwkLtj33veODObY832cNUKoDQ9htpOJ7fOzqOj18uhUMVNlKDZ6qYkNYZnbp7Khop2lpemcu/LB9hb20NGqO+tx+kLx+YUJpuZGKpA/vbKCQD4gyKr/rKNJqub1BgdN8/O4YYZORg0KhJM8oSLXq3ErFNx64Dsuhidmqw4Aw09LlrtHp7dJkdG1HY5ufm5PSiEwf18P101lsc/OSFvT6PE7QsyLTeOzyvayU8yDaq0xhs13DYnlz013TyySj6vOrWS9ffPo93uoTDZjChKrNlVhyTBa/saI0gdEO6n/NqCAjZWtHPb87L5y6v75Ky+E629vH73bDz+ID9eV0ZQkvjFZaURvYEAW091ArCtsjPq59uHDRXt/PGzk6HzbeLySV+ssj2CEfz/hPNO6sQ+Tc8I/u8ha5r8GAhTCsTmoPBYz2wi0gdrLRxeQ4k+CW/CVRgMOhLMasx6Jb3uIL6ARKfdR0bC0LPkgZ5adlS8Sp0xkRvH3R61CqlQCEzOj5zlN2vMvH3p2/1P/GMValcnySf+yaaky1k5NZlco5PkPT8jzpoHeY/099d9/jPY/RQoNTDvmyCoWJqzlANtB8K2+pIk4bAmsTLxPrKcoWqWvSlsv7/7pA27K0BLt5cFUQiqTqNkXI48+/mVdx/kVM8p5nUe5q8X9EteMaeDLgE8XRCbAxp5eW/Qw8ft6whKAZbmXIYqJAc9EbuUipxLKWh6jdKcBfDccnC0w41vEG9Kp93mI6DoIS0QkO0yAh6ZXMXnw54nwWuTYxAKQxEPksQvXr6At7SQe/IV3lv6DNRv55rsq7jm9FDzAdh7ykZLTyKplnTik0+Lomg5JLtnAhx5HWbeDYIShaebmbpERE0MP2/bRmuPn585byPJmAxf2yxX6XLnD/mep2OPdRsnHEcp4wC36u8LV7qGxMn3oWGn3D83+7uDX5NE2P+sfE2HAt1PNTupaJKlWUuNaoyh6vUR+z5292xGgZLrM++KDCPvqoJ3vyn/e98/4KubIVn+Tm0v75FlW3Y/M3MVsOcJuVpbfElkhRHo9nViVsWiVqgZZSpllEnu+7N5bdz04U24A27q7HV8Y+I32Nm9gQrHUTQKLbdk3RvR5wcQo7KgUWgJigHiovS9NbirCUgB2r0tOAO95yTl1Cv1XJV+K9ZANzG3PoxP8kPzagj00Olrwy/6UA/xGQmCwPjYqbR6m4hRWYjXhFwV2w6D6JcryOdA6sbETCTLkI9eoT8nYno6PAEPD2x6gIAYoMfTwy/n/fLMK50lHrt8HD+9ZOwg05IpOZGxLrQdgfod2DLH0huQiUx5Vy2ebnkiQIlAUJR48I0jvHzXTHITh2+n+MPVE3nzQCO1nU4+Od4Wznu7emoWV0/NorK9l4qWXgqTTRQMIHVBKcgn7euw+ru5MOmSQX2io9Niwm6XZ8IVUzLZV9vNnMLECEIH8rXwi8sGyzT//Pkp1oT6zsZlxpBg1LBsbArbTnVy5ZRMFpfI+5IVbwhLD/vMSFJitcwpTGD+qCTyEo20WD0sGR1Jzv1BkU6HvM7i0cl8Y2H/xMpXJmeSn2Qi0aQh+bR+Rb1GyUcPzMPq8vPt1w4Bcm/cLbNzw5mAl0xID8taT7T1UhnK0Judn8Dfb5qCSqng3UNNrDvUxN7a7kHk/CerBssry0KVu9IMeXpQoRD49gWj+OR4G3fOHRyUHg2LSpK5aWYOa3bVoVMr8PhFxmVYADkH7/X9MtGbW5jIVyZHErH7lhTy43XHaOxx09DtIis+eotFYbIJs05FUJQoThkxZRnBCAZixChlBF8ulBq8E7/KzooexFqYVSIHpda2u1EIAjnJUfqROsrB70Lhr2PSmACY5MHEguQWAhUf0q4voaxuFqeancweHUdfYVirVnCy2cmJRgcF7sPM91t5vf04JzQWSiRBrj4Z5EHdyWYnjZ0exmabBodZR0NSCdRtw61Lw6RT4fSK6I+uJqPhbWgAxq2EjFAejz/U55Q1BQJuKFvL1XO/z9XFV4c317lvHbmbH2O0u5nmWY+SoelCIQXC1RydWoE9dDxngipkUa9SqAg6OvG1nUCXOQHBa5MJHcjB7aFogSZPHS3eBkAeaPdVVBoDCYiKAI0FN1HqrpQJFMCpT5k95348fhGV6nKaej5D6nwVQaWHnNkyaQm4Q8fu6t+xU5/g9DtBa8Ql+iFtsvw4A9y+YOhvlMmh1HFQsFgmk1kz4E/j5Srcqj+BIHEsJoU3al4G4N3q97hj3B1y39VZ9F4NRKImmROARZ0QPr/Dwhc6br9rkLEMIMs6rbWAJBuqpE0OkzitWoFaNaBnMfReCkERPYjamCjHSLg65QzHxn1hUqdRyYMojVohfyZ9XwoxGLGZvT3bOGDbSaImmSvSbxn0mojIqORcGm2tYdLSR5jUgmbIgGydUk+uvhCtQkeyJnJGf3zMVBzBXpI0Kf9Sb55ZHYtZHYsoibzS+DQeUb7mXEEnje66wRl0XVXQfhxGreCkuwKrv4cr0m8ZTM7zloAk0ZNcSJ1tN0XGMSgFFY3uWjL1ueiUQweJDyf3PFtolBryYvM41XOK4vjz35unOhtJWtUn4Ooixe9g6pg5OAK95CsmcrDdTYxexVVTM/nblmqabR7eO9zMN4cwOqnqcPC91w9TkGTi11eMR5IkDtRbGZ0mT0r4gyK/Wl+BNxDk4YvHRASQW/1d1LvloO0q14lBpO5s4PAGsLv9LBiVxI4fLIm6zCfHWrl/7SGm5cXz/K3TUCgEqjsc/PHTUIUfiNVrUCgE/n7T8C6sz94ylc/K27l0Yvqg3r7TXTf7YNCoePHOGeyv6+H6GZGy9YkDegsDQZE1u+qIM2i4bFIGBo0Kg0bFDy8azVObqlg5Pp1ZBQkcqrcSECVsbj/Pba9lSk4cjd0uHN4geYlGnrpxSvgaePzTk9R1uajucDIqxcyELEuEC+m+2m6u+rscbv7G3bOYkiNPJn5zSdGQn3s03Dkvj6NNNgqSTDxwYRFZIZfRKTlxZFj0shIjN7qSRqNS4AuK+NwiZU22IUldbqKRnT9YQlCUztjnOYIR/F/DCKkbwbCQJInKFhcSYLB0YFLHDO71Gg41G6H9GK3JK+lxyj9+NW1uYg0qDtXIEQk6jSKSVGXOBEcrGJPlRwjq5t2ogz1kOfdwgBm4fCKNXR7K6noBgYWl8dS1uwmIUK8ewxj/LtTqVIqbDsgDXZ8LJtwIQHmDA1GCU82uM5O6m96ClsMYYsewwGiksctNW9x0CpUGMKeiGujoOONu8NnAHBqYKNUwUKYliVgaP0btOAVAUm8ZCrMRuuzQUwWJJaRYNBh1SkqjRScgz2z3DbifuuAp9rXtY27GXHzbnkAftGJvqyBm4mWy/FAMDJIEZuhyyNDl4A8GEB3peLRBOaYhy0Rlq4u8ZD1YpsPkW2QJ34TrEAQhNBBTkrviT5A5BzKnyhJaSYTiS2VyFcoTdAddBMzxPGJ1Mc3rZ3r2Yuht6z8nA3HwRTj+Lix8EDKmMK0olqYuLxnxUT4T0Q8Tr5KvCYUSv9/HrrgxnGjeypWLf02+Us34xPF0uDuYnxmqzNXvgu4aGH/1oMBzMZQnGK461W6C5gNQcCEFCaN5+/j7eDW9MHy7V7/s0pwmSzFPrwirdFBymfzZhiqxHr+ITi1QlG4Y1KcUL6RS29JOqj4dQ06Uz14XC/cdgE9/IstcS/tlh/PGxsnGQGaNbFM79avg7oGUSAMJq78r9Lcnos/thPMI84qmIaDg5iyZ8M2MW0imLo8ETVLUine9q4Z91m10+FoByDUUkq4fPHg1q2NZlnzZkKfxbBGUAmFC14dt3Z+RayiU983nhGcWgceGZ/Y32DhKvlcpBAVTLXP6V4rLgyl38VHjM9h7rDS6a5GQaPY0kKrN5NK0gRLyZmjaLTvMnskc6SyhEBS8fNHLdLg7yDJHOjb+W5A2RZZcp01hiqVfXpw5zYwgCMwssXCq3UGT1Y1flJj22GfcPDMnYpD/2r4GDtRbOVBv5c55+RSnmpme1/8bselEB89tlytLEzIjoxHi1ImMMpVi83dTbIp0ivUHReq7XeQnGiOuP7vHz9I/bKGt18OT10/moiEiID453obbH2TLyQ6sbj/xRtlsRK+RXS+/s3TUoGrmxhPtPLOlmmunZ3PJabl6hclmCpPPLQJjWm78kGSmD05vgFf3NvCz948DkJ1gYHJ2XOg9TVw5JZOpOfHEGtS8+rVZrN1bz7qDTQAcrO/hoeUlrN3XwDcWFqJRKajqcBBv0HDB6BT+sa0Go1bFJ8fb+OR4G70eP2/ub+KaaVnkJxnp9QTC80AOb5CTbb0crO8h0aRlyejB9+2aTifPb69h6dhU5hQOrsq/ureBQw1WDjVYuXNef3Uv3aJn+0PD91CuKE1jf10PKoUi4j1Ph0k7MnQdwQii4b/2m9He3s5LL73E6tWrOXjw4H96d/7PoqXHS1m9A5e+AodvDypBzXUZd6JUqEGShjYKkESo+hSQ0AmHgH4JWF9mkCAMUY3Sx8Gk2yKfz5oFXhti4jhSghrUKgVqhYDsKC3R6wkwJsvEyWYniqCBw/ormV9cgHD8VblaYskJbyo/1UBjp4c4k4qdu48wvuwRjGlFsPw3oFAQCIqyOYpCkMlL1vRwEHlmgp6e4rkcLz5GqaEJjr4IGdPlR8pYWP47eTDv7paPZaA7oqBAnTUZuirA04tu6k3QsFWWa8Zm0271crROltAkmNVkJQ6uGHze8T6VznJmxS1kfOw0EvQJLMtdBmVvYpNU6AGvpJYrkjMfkGWSA3rGtEodK1Ov5vPDXZxwB+jusTN3TBypcVpSB4bEX/Ln6J+rxgBTBlR3yt+G5n2yWUjufNxBJ++f/BMORZBFd73NNf+8DsrWUe93siZvEouyFjE7Y0B/2vvfkqtZYgBueguTTkVxxhC3pfod0HZU/vesb7Fl8Sc8WHU73o7tVO79Nb+e92teuvil/uWt9fD8xfK2XZ0wW5YuNvY2cuP6G5GQeHHFi2TFZEHNJgh6oX4771nLee2EHPo+NWXq4P0dCG+vHIcgiZC3WCZ20ZA5PUx4AapaXHj8EvUdHgrT+gn32pNref/kRwCszL2EAktBxKbQxcqVydOgUSlIjh3w+cVmy48omB2/hBh1HFm63IhBct9kgUahQSn0RZkoyDYMLb/a2PkBHtGNgIBRaSJOk4AjYEeSJLSCGacnOKzR0pnQ4W3DHugh31CMWqFhnHkKVc4KzGoLbd6mMEEH5AplaHTa5KoB5IF0vDp6FIJBacQesKJXGnEGevs2MnihirdkeXT3KZjz/X/5OE6HTqX7zxE6gLxF8uM09F0TOrWSf9wqy+nn/GYDHb1e/rmjJoLUrRqfzsdlrRQmm8iLItEcmx5DklmLPygyKdsy6LUnN1ayoaKdH140g0U50UnP3Wv283lFOzfMyOax01wuux0+Wu2yg2lFi31IUve1+fl09HqZnhdPvFGu2KbG6vj4gfl0OrxMyu4ndHVdTh5YexCbO0B1hzOC1H0ZaLa6ufjPW3F4ZRMsvVpJolHL3zZXsau6C39AZHtVF5OyLbz9jTm8vq+BH71dBkBWnJ6GHjdtvV42fGchILtm3r/2EAlGDY9eMoYJWbGMz4ilst3BuIxYXtxVj8Mb4ImNleH3e/yqCRi1KjaUt/FCKJcP4M2vzyI3wcj7R1oYnWbmR2+XcardwdsHmzjy6LJBx7G8NJV1B5vISzKGzVbOFjq1MkIeO4IRjODc8F9F6rxeL+vWrWP16tV8+umnBIOR0qER/Hth1CnlSAGF/GMjSkGs/m4+bn8bEYnL0q4nQSNX0zy+ICqlIMs+BIVMcpr3kmzfTVFaKR5tEnFGFbtO2hCAGUWxkc6RwyF5LCSPRQX0DbMlScIVkuqlx8mRDP29diEyM/lOWRqn7Z9dzUwJ8Er9H/GfsHB7SzfGho3QsBEm3UincTTby3vQqhUsGpcQQTxVSoFJfb14u1+C3mbwfCYfL8h9hCAPwKNh9OXy+TGnRvQh6jZ8D0XKvUgKTVSTlDqX/CNc565mfGxoverN8MbtBBOmUTvtWySNXyL71bdXhOV5p0MVkv1Fyx08J7i7B/0NnPqUK177HV6Njqqbp8ph8O4eHqOXXSdew2lNQt+dwrjOl1Elj4bRq+DYOii5+MzvlVAEDTtkCaLOgk8nYFLG4xVd0Y0mlBr5IQZgQE5fWWcZXSFp6pHOIzKpy5kHLQcgazalahV6lR69Sk++ZYDDpKsL6rbK5iNJo+VKqEonX1unh4+HYPVYOdJ9GLuiDbMqhnkJSxmVYaSmzUVR+mB50fyM+bx24jUKYgvIMEXPbBsK7d4Wjtr3E6uKI1OfS6oug05vGxs6PyBOnciSpJXhqqRRZWJGXPQewymW2SRpU4lXJw7Zp3Y60nSZ1LhOMSFmOtPi5tLj7+Lt5jVIEmT0XI/XJzAmy0RxxrkN8kA2rXmn5SWCBJkRZ2di7HRmJyxmdsJi/KKfatcJUrTp/eRUa4I7PiHQcpANJrlXKluXP2T8wIqUK+n0tZGiTccnemlw15ClP43AxmTJpC7mP0jA/sPo6yOLN0ZW0EszYtn0vUhy2IcD9T1MzIzlrvn5tNo9GLUq0i16fAGR330sG3v8fXM1T98cndRVtMpke3tlJ/6gOMjtMDfRyONXTaCyY/hcv6IUMy/cPj3i+ax4Q4TM70+fncLmln/vlpeemxR0IAJBkcc/PUkgKPK9ZSXhqvyhBiu//rCchcXJ3L1Anrip7XTS45IjAh64oIirp2Zh1Kj49YcVAOH+xKONNlb+ZSt3z5fX06oUYXHAphPtYTOZmk65DaDb5ePvm6spa7ZT3eGk/GfLUSgEvvvaId440BTeV1GSuHBsCiqFwDde2j/oOPRqFd99/TAbT3SgUSrCuXx9vXcDMT7TEiGBFUWJT463khlniLrOCEYwgvOH/wpSt3XrVlavXs0bb7yB3W4H+h00DYbouuoR/HsQa1CzdGIiojSfRn8KMao4PKILnySHOHf7OknQJNPS42XXCSs6tYLF4xPo6vXRqppHEScx00OpsgwKL6OpS55VlTjLvo+h0LgbnO0I+RdgMapo7vbi8AQx6ZS8fvJ1FIKi3zJfoRxE6ABeP/k671W/B8CE5G9R2JyEOqkAEkfR0yG7brp9Ig5PAK16mMFtxjSo+iwicH1YGJNgygD3x+Z9cOojSJuMqWodC6s3okoZjXHWmohV5ycso8pVwaTYmf1P6uNAoSK+ax/xKTFg0MHHP5IdLeMLZMneaZhVbKG7N5TjNxB+F1R+LBOnnHlnPpYxV8r7nyQ33Zt7mkCS0HvdlIgJcOt6epqq0ZZ/QqHnJNMsl1LrhDghj9zWg3DFP+Arz4LiLK6F+AJY9CgICqz+bsYkN/DHo8X4OiqZHhNlhtecCl/bIucgOpqg7FUYcwWLshdx9airkZC4IOcCedn8JWF55Bhg09WbUCqUaAdGRZxaDx3HEZv38kuDjquKr6Z4xn2yy+SAKvBA3PzRzSTEmpiZJwfTF5vGUZCaETVncWrqVHZdv+vM5yEKdnZvpNUrD9L223aQpElFK2jp8XfR4+9iqn8OcX0mIadDkuSKrlqPQlAMcsgciFZPExs715OsTWNx4sVhInVh0qV4RDd6pXxMzkAvQYIIkgqfPE7F5T27Cbojtn3s6dnCaPME5iQsgVA+JRIoGHyNqBVqik2lHLLtocpZzjTLPDnmIXk0quTRTLftpdFdw7S4uUO+n0ahIV0nkzW90sAo09jIhYovkU2AtOcmufv/CfOLkvisvI0rohhcnAk/eOsovZ4Ax1t6abK6iTdq2P7gYvQaJVdMzmTjiXYunzT0JMYvLhvLnS/sp7bLxV83VnH/BYMrhVecQ66fJEkERGlYG/w5hYm8c6iJydlxPLIyyvUQwto99fz581NMzY3n8asnRGzzs/J2ntpUBcg5eX0ujX/fXMWu6m52VXeTYtZy6cQMZhUk8P3lxdjdAe5eUIBOrUQUJRYWJ7GruovvLSvm05B0sqzJTnKMjvX3zcOsU1HX5eKl3XXcOLP/HvTV+fmoFAL5SSZsbj+//aiCq6ZmhV06f3/1RJaXpvHAqwdxeIPkJxmJ0akJBEUyLHLlrw++oBjOjlMpBXxBmFOQwPNRSHI0PL+jlp+9fxyNUsHm7y8c1IcYDS5fgL9triYv0TDibDmCEZwj/mOkrqqqitWrV/Piiy9SW1sL9BM5k8nERRddxJVXXslFF100zFZG8O+ALtRPNUor9zuIkshUyxw53yk0A25zyqM3j1/E4wuy95QNUQJv4tXM0h6W5WnI4eOT82NQKgSSYochS5IIHcdBnxApa3N2QMU6+d8qHXu7J+APSri6mtGaDvPzPb8AwKA2cFHegOunuwY8NkifyPTU6ehVepINyVyy5ErUK24PL5abLOL0yoYuCebB+yhJEo1dHgxapfxa5kz58QUQaNqNyu8k2LiDTQs/xR7QMiXRjhGg8jN4/Xa5h+2G1yk0jabwtBwy0sbD13fKPWcpoUGIoy10rjrlqt1ppEmjUgyWW/ahfgc07ZH/nVAk94sNh7qd0FMDOaHKz9Tb5XNsTkWVPhmUanwJo1mUmIbN34FaKYAkEm9SQcrFsgZ3qHxESQKvfXDFU1DQ5evgrebViIhMM52guCuA4OyIvo3EInC2QL3cw0j6VLTxBfx41o8Hv88r10H1Jrj8bzD2MgzqKJNJsVnQcZzjfjuvnnyX493lvLzoL3LAvCBElTv2eHqwB3qYnFlKlilnyEiAfxliEE6sJ0+rpHXAR9zha2WC1YdHpyDOLxErDDOQOvYqtB4e7F4aBScdx7AHrNgDVmbELcAUcucUBCFM6ACyDfnMS7gQURJJToijxxEgL2X4gVwfqpwVBAlS6SxnTsISDEojl6fdhD1gJUffL0f1iV52dG9Eq9ByrPcQQSnAYfveQdl9E2KnMSH2HCZbhoIgDF11R74n7OjeQLuvhfkJS8PKhYEISgEO2fagVxoYY5447Lb2WrfhDjqZGbfwvyYH75mbp9DrDUR1lWzoduH0BShJjW4gc8HoFN4+2ERmnJ4mqxu3L0hAFAElj189IWL52k4nR5psLBubglalZFJ2HHqNEoc3gF5zdhOBkiTx9JZqfAGRry8soLHHzb0vH6C2y4UvKPLszVOZPyr6d/HKKZlcNC4VvVpJl9NHU487alD6bz6qoMfl593DzcwtTIzoFRydZsZiUBMUJUoHmKismpDOxhPtePwi33rtML3eADfPyh3kigmy++Tzt/UTpwXFSYiSRJJZx+RsS3hSNCveEBFVYdCouHdxEWv31LN6Zx0/WTWWy04jzguLk7hldi47qrq4b7FMlFVKBe/fN48Pj7bwyLvHSDRqyIk38Nsrx3P55AwKEo1UtPYypzDxrPPhwrd3gSGNlgbi2a01/Plz+X49PtNCQdKZ4y9GMIIRyPi3kjqr1cratWtZvXo1u3fvBvqJnCAILF26lK9//essX74crfYM5hUj+OLw2uVBoT6K3fUQ8AdF2VxEP3lQtltBmoFAUMKoUxJrVBNvVtNp95OQlATpcvC11y/iC4jRHS9PR/12uTKiUMl9LANnybVmOXfMawdTGnHVh2k3jyeucydFzgYEBCQkHt/3OMtylqFUKKGnFv46S3ZqvHoNE8dcwvbrtqMSVIP7ipwdqHubmJhTKr/3aahqtnO0wYMgwNKJiVElkmHYG+XjSJskm2gMgZOJSSR4Wqg0GrH79CBAtyqNDw//nY1HX+A7godpVZ/LEQMxQ/RtJZ22/RW/hdTxkDfv7KpgfbBkg6CUB7HDZbuB7DK49npAIuDz0jr2GyRbtGgW/RA+/zn8PBGmf42Ui37L1MIYJCmGrEQdEqAQrh16u3VbZcOLAy9B+buw6GFY8L3wy17Rg4gsAdo7ZTG2uBQWbPg5is5TkDEOd/N+Hu06QHrhcu6ffD8klsiVXY0pIpMOkE1eTn4o/7v8PRh7WfT9yl0IqZN4fudPAZgdVwI7/yBPQKCAud+LOGeliaVsa9rGy3vfY/t126PGAXwhbPoVbPkd43WxFD9wiDbRRpO7luOOw6SpYpnZWCP3dPZdy2JwkGEMIFcyQTZzGQajzePp8LWQrE3DqBx+oDWQuAzq9TsDpsXN5ZBtz6CKWbwmkfjTYhJOOo5xwiH3V+YbRtHqbabE9J/px3EE7ZT1ytXw472HmJewNGKZ8t4j7LNuByBBnRzOJjwdrd4mDtrkaq1FnXB+SOl5gCAIUQldXZeTZX/agjcgE6VoBhd/vGYiv71yPEFR4q0DTYxNjwlXfvrw1KYqdlZ38eCyYq742w48fpEVpak8deMULAYN739zLrVdThYMQcROx4aKdn4Vki5mJxho6HZR1mwPv769qnNIUgcyKXL7glz0P1tp7/Xy8MWjuXPeYHnn5ZMy+Of2WhSCQF6U/rGcBCO7frAESZIrXHc8v5eT7b08cd1kdCplOLOvo9fLa3sbiDWoOdHaGyJuWm6YMbj6n2zW8ewt53Y9/GVDJU1WN09urIwgdT946yiv729kdkECi0r6JyJi9WqunZ7NygnpqJUCWpV8v1hUnIzN5Wd/XQ8uXzBie0Ph1tm5ZFj0ZMYZSI2Vxwy7qrtIj9WTnRA5eVaUbEIQIMGoJd5wdhLwEYxgBDK+dFIXDAb54IMPWL16NR988AE+ny9M5JRKJUuXLuXDD+UB1e23386ll176Ze/SCEA2jtj9F7nfaPKdshvcWeBUs4sToYytOFN/xpZaqaA0xywTj5ZK5o4ehy8ghfvRfAGRzw534gtITC2MiTAAOSeodDDr27LBRtDHrB3z8WoS0cfGwdz7uJAL+aTuE3p9vbJTJErZ1KLPej9U1VGfHh4sBmDfU+B3Q1YDFK867fUgyrpNoJiJQhKpbnXh9AYZl2OWyZ3fDbUbwZQmE7mKd8HeIIeAL/gxQyE2dQYfKJqJ1yQxUWnE6hApStNx85YnkZB4IS2PadmXDiJ05Y0O6trdjMkykZ0U5Vwa4mHOfXQ1bMd78j3SvXJPDOOuDC/S6w5wpLaXOJOaMVmhQXrCKFjwsEwCopDaQdCaQRcDHhunnHFUVNpIjtUwZ3QchAw//BXv8vHkqUyInUZ2qFepj0L3OPyUNzhIi9eSlxL6cbfWymQe5AobQM3mQaQuXZfFBUmXUOWsoMZ1kuKTB1HYm2H332DGbeiRuMiQxq9rPpJJnTEZ5vSvX9lTiVaplXvp+o7jwp9D9SbEOd9iWNqli+U3C37Ht1ytpAcl2Psk4YOKQthyY3LZ1rSNeF38+Sd0IMdgAIgiWkFDtiGPbEMesxIWyWQz9VTILVQF+58J5eVdDakDKiRjroDWQ5A1hCFMCEna1IgIhPONTH0umfrcMy6XpstEo9CiETTMSbgAg/Lc+/W+KGwuP/XdLsZmmMkzjKLD20qhcXBAtihKKBQCFnU8AgJqhSYyf3AALOp4TMoYPKKbVF30QbMoidj83cSqv6Rr6hxgc/vD5KQ91HcXDWqlArWSqJb+Nref33wkE7BYvSq8vWMDSFhuopHcRCNlTTbuefkA+YlG/n7TVFQKgSarmziDmu2VXUzNjSPBpCU30YhepcAbFDnSaOPGmTl8WNaKxx+kJDWG2+ec+ffOGwjS45LbDFptnojXH1k1lnsWFeIPSmGycjp0aiX763posbr5vKIdgA+OtpCdYMDaaGPhqET+sa0Gly+IwGCLnsIkEzPyB0um99Z289P3jjG/KAmLQc2JVgcPrSghyayl2+nj+e01TMuLD+fR3TAjm+e21wySZvahocc16C+Axx9EIQhoVIqoDpN/3VzJ37dUIwgwPS+e9FAAu8Mb4Nbn9tDl9PHsLVMHVdcEQWDp2H7Fx4u76nh4XRlGjZLN319EomnwpM+KcWls+u5CdColccYRUjeCEZwLvjRSt2/fPlavXs3atWvp6pJNCfrI3NSpU7nxxhu57rrrSEpKQnEulYQRnB947DIpAnB3hUldp91HrztAdpJedn7sW9wX5HBtb9i5UqdWoFGdJqVw98BfZ4KrC+GSv6CdfHP4JV9AxBeQ13W4Azg9Adw+kYZOD/kpemJPN0zJniNXEPUJ0XtZlGr5gRHFxb9HX7sd5j4AKWN5xLeS0sRSJlvb0Ty3Qn5+9Cq49hVZljhgvw7X2Omw+5iYrSex6iXwh368Q9U7uyvA3kobRq2SKbla8tx7MShaUCdks7lFzqbTaxSMz42RbfHrthKUJD62V5EjBBkLcp9aR/mQ4esZ+hxuz3mg/4nQpOnVxVezoX4Dl834IeRcIFeUVHpQKKlqceEPSlS3ucm2IFfGtGZY8GC4MtfRdpikE+8DUFa3jdKGI3K1qng5AFWtLtptPtptPnKT9f1VR9VZSL6qPiPYdpDyax/HrE6grWciOPxhW2yWPQa7n2ZrdiJNnjq8ojvCgKKiyUGbzUdb6P0FQQB9PKiNco/XlDsgNi/sWjkQBcZi8gxFVFW9jsGcDV5BduXMnIKtcSc7g1YeGPtAxHq7Wnbx1U++ikqh4vVVr/c7TM65j85xd7OjogdDeycLSuOHlBcpFcp+I5NxN8gVrozpoI2Un3136ndZnL2YIsvZZz2BLNdTnk1G3qIfyZLblFLZJGQgBIVs6gLyhENPqCLXWTGY1CUW9y/3vwQJmmRuyboXAYGgFBwU83E63jnUxPqjLdy7qIhxmefHqMEfFFn5xFYaut18b1kx9yyKnIx8YsMp/vDpSW6cmcPPLi3l+syvoRJUw2bg6ZUGrsu8CwlxyM9/c+dHnHQeI9dQdF5iIr4Ixmda+OsNk+ly+rh66vBGMs9urWbLqU4eWl7CmPT+70qMTsXikmR2VnUxMy+Bg/VWOnu9fG9ZpLrhg6Mt1HW5qOtyUdXh4O+bq1h3qBmNUsAXlDBpVez50RIKkkwsKklmfVkr/9hWw/0XFPHBfZE9wh5/EJ06+nVjMWh44bbpHG2yRZCi57bV8M8dNXx9QWFUotqH7ZWd3PDsbgQBLhydTLfLz9VTs7h/SREn23oZlWxm7m834PIF6WN1AmDQKHllTz3VnU6um96//ee311LWZKesqZ/wJpm1PLSihN98WMGr+xrQKBV8dN98LAYNL++pp9Ph45NjreHw9Dtf2EtZk52HV45mZn4CS8fIhKusyca1T+9Cr1Hy3r1zoxLVMWkxCAKkx+rDGXFdDi+3P7+Xw41yePmG8vZBpK7d7iHBpA2PJ+weeSLKG5CVO6fD7Qty8z9202z18PQtU1lUHMUEawQjGEFUnHdS9+tf/5o1a9ZQUSHPvPURuby8PK6//npuvPFGiov/dw0g/r9EfD6UXA5BD6TKJg4eX5Bt5T1IEnh8IqOz+m/MNW1umrvlmdix2SYy47WoVacNen1OmdiBHBA9ACadiulFsfS6A7RZfVQ0uVApICDK/XgLx51m4iAoIDmUWeRoA1u9PBBVasDbi82rRKXRYNSp5D6uqf09cbHaWG4rvQ0eL4HeFtj0G5nUlQzuz/QFRKrb5OpdWa2ViXYH1ZpFZJo8JBfKxKeh043dFcDuCvBxr48LxtxAiquBYOYc4k56sLkC/fIycxog8IKniT/u+ikahZqPkheQpNT1h5JHQY/Dj06tQH+alPPhmQ/z8MyH5f807IAT78l9W1PvZmJcJ622AGlpo+Dwy7ArVDHKngEFi/EFRGpbm4mVgmgEJd0qtXxO9Zbw9tPjtDR2erCY1OiG6VXpdHciIJCgH/AZ1W2mLEbFLmrBX8tl+ePwOmP6M//yF0L+QvTdG1HYD1JgLJGfP7xWzlmbeB3p4x+kzeojPU6LKIlUdFWQb8lHP+d7/fl6EwbkhTUflCtT5lSo2oBi9CUUvfOo7D6aNhHmfgsAMXUcG9+/lrd3PEKGOYPSxP7sqy53FxISftGP1WsddJwdNh9BEXrdQZyeIBbjEOck6IN9T8uTIZNug5Kh1QVKhZJpqQMkUz4XlL2JK20s25WNWNTxTDNODJvTSDnz+aDtNZo9DSxIWE6xOTK3axBUGjmD70xQ6+XKc3d1uL/1P42TjmPUuSqZbJkVtQ8tGvZbd3C89zBTLbMZbZ5Al6+dd1teQaVQc3naTeE+v4H4/htH8AZEXL4ga+6YcV723R8UabPL98PGAaYSHn8QlUJ2AP74WBuiBB+VtfKzS0uj7ls0yNW3ob+PPX05g76uf2nfOx1edlR1sWBU0nkJb44WI9BXoeyDyxfgFx+UA2DWqXjy+skAdDt9/O7jE8wuSOC5W6dx/9qDNPa4UQhwwejIft6rpmSyp6ab/EQjo1LMHG2SiYQvGJow9Abw+EUMGlg5IZ3PKtqZXZCA+bSqkyRJXPv0LvbWdvO7KycMabYyuzCR2YWJ7Kjs5K7V+yhJi+Hlu2bwj201NFnd/HN7zbCkzuMPht4P7piXz8xQ5e3z8jYeW1/OynFpvHvvXCrbe7n3pQM4/SILi5PwB0TWHWpm3aFm5hUlkhkK8b5yaiYH63uYXZDA4UYbdV0uZuTLrqE5ifIyi4tSOFzjQBDAHTIo6iNcu6u7+Kxcrhg+t62Gt77Rn924t7YbhzeAwxvgZFtvVFJ36cQMpuXGE6tXYwyd09ue38uR0PZL02O4ZGK/tPj3H5/giY2VzB+VxJi0GPISDdw1L59AUGRMWizpFj1PbDjF8RY7P7p4DBkWPX/+/CR13fJ3antl5wipG8EIzgHnndT98Ic/RBAEJEkiLi6Oq666iptuuok5c+aceeUR/HsxIDvL7grQ6w6gUgj4g1LYfrmu3c3hWjuJMRrUSgFBgGP1Dk42OVEoBCbkmvsjBGIz4bq10FEB0+6KeLu+5U61yD8qKqWCgCgSZ44cWLi8QQJBCbNOoO3oM1jcLnS2BoJJpbSXbWKX7hIUCoEl4xMw6Ye4jCffDHuehkk3Dnq6wd6ATqUjyZCESafE4QnS41VRZlpGh5hIvQ8uFZQIQFainoZOD26fiD8gYdPno0sbjRJYOM6IKIo4vSKiJKFInQiWPFSVb0H3QZQKFQ25U2gMWkmLSyWa5Uhdu5sD1XbUSoELJyZGz+0DsIZyg+yN0HmCzNo1ZAIU3C0TGrVBfiTKs9uHqu34ehM54O3GLvpZPO2bsKwIUvtJQrJFy8ppw/9gVnRXyJluksSfFv2JeZmh2e6c+RjshwBQCxqqbCeo6qnhCssVQH+fRLFhAvvrjuHVArHA/hfkYPk9z5BzwaNkJ+kQBIFHdzzKm6feZErKFJ5f/jxwWh9W0wF4doksKTSnQm+rvK3CxXKAeWG/jXa1rZpmZzMA+9v2DyJ1K/JW4A64iQv4mbLxD3KP3fJfg0JBXooehyeIUaccFLUhSiLV1mpyDCmobQ1yJbM3ZAfeeWLIPLio+PiHsP+faLQmaq/5JqJCSWl7J/rmfQAEEgpp8shW/A3umjOTunNB1uwzSiz/XZAkic2dHyEiEpACrEi54ozr9Pi6OGTdTYAAx3sPM9o8gTZvCz7Jhy/oo9vXGZU4LRmdzPqjred1cGjQqHjhtunsr+vmppm5AOyv6+bGZ/eQYNLw3r1z+cGKEp7ZWs1VUSpY/qDI/rqeqP1lZ8LCxBWcdJRRaIxe+R8Obl+QS5/YTpPVzcLipEFGHOcLn5e3ce/LB8iw6HnrnjnE6NQYNCouHJPClpMdrCjtvxP+c3sNr+yRr/cFo5KYlhvPO4eaGZ9pQRflXpifZOLNr/dfw49fPZE39zfw6t4GfEGJSVkWup0+HJ4AF41LY0VpakQWI4DLF2RPbTeSBFtPdUSQum6nj5uf243bF+T526bzeUU7Tl+Q/XU9NFs93L0gnxd21kWNUej1+HlqUxWFySaKU80sGJXEnMLEMKED2RGyusPJXzdVYXP7sbn9PHnjFD6vaMfm9PHeCVl6btKqSDBqeXZrNTa3n3sXF/LUjVP4+ov7yYzX88bdFxBrkK+f1Bj599XmlithAnDNtCzeONDIty6UfxdOtvWG92FUiplfrS/ntX0NPLi8hI+PtQKywcvc00LFB6JPchk+3tD7aZQKXrxzBpYBfXAH6uVJ3j01XWw5Kbc9dPR6+cOnp0g0aVlt0fP7T04CkGzS8eilY3H6ZCIqCHDd9P+7MSIjGMG/gi9Nfmk2m3n44Ye5+eabSUgYwkp7BP8VcHmDbDzahSjBmCwTCWZ12Oq+sctDUIR2m49Lpyezr9JGY5cXf0AEQaCuwy2TtZYD4HXIg+tRQzvoAcwYFUtrj5f8FAMKhRBhNuL0BPj8SBdBERIymihP0xLjV3GtqKKloZaAoAdBQJT6Z2ijYtEP5QdyNUwCTtj3cfdnd6NVannzkjcpyUxkX6WdWIOK9ORcOmodpIby7gBiDCounJjIsXoHKqVAcsix0x8U2XGkGafTg1dlJi1Oy8xiC+hiuWnsreRaCkgzprHBtg6A3N69pOojZ4P7+kf8QYlAUESrVrDm+BoOtR/igSkP9IcTFy4DBDlqYOAgRRIhYzJ896TcNxUKOlcpBZpU2Uw3ziQtWI0oSXJ11ueUK2BniTp7Hd6gXJH41qZv8emVnxKni4OCCyniQuK8bfgCfla+dQkmBNT2Zq6d8f3wPj6+73HerXqX5489z47rdqCZc78sIx1/DdAfdNzkkElSY29jlL1AlmP2hUy75Vlhmg/BHZ/KgfEDpIeTkydz17i76PJ0cXnR5YM2E4662PI7qJClqYy7CrKmodMomVYUKc/75e5f8uqJV3klfSmlggYseZA9T84uU5+jM1vo8xFUOpSChjhNAmpjMTTuA30can0i8xOW0eiuYYrlP0PATjmO0+ipY3LsTGLVZ2+iNBxOOsqocZ1icuxskrQpCIJApj6PendVZC7cENjU+SEBAihQMjFWrrgVGcfQ7etALajJ1EePlfjrDVPwBoJhw4ezhU/00eVtp8lTx2jzBIyqwZ/1rIIEZhX0/7btre3B7Q/S2OOmutMZrvJEww/eOsob+xuZmGVh3T3nNuEZr0lkZvzCs17e4w/yt81VpMXqqGx30GSVqyDRpG9fFL6AyH2vHMTtF6nscFLV7ggHez9z89SI5afkxKFWCmRY9KRZ9Nw4M4eLx6URo1dHkDF/UORPn51EKQjcf8EolAqBiVkWJmZZWDUhg62nOhiTHsPSP25GpVDw/n1zGZVipqHbxfbKTlaUpoUJkFGr4rHLxrG9qpP7lkRKo/fVdocljltPdXLr7FzqupyMToshL9FIXqKRm2blRj0Hz2yp5q+hKIMMi44mq4faLidfHUAAb56VS0O3i3EZseGg762nOrG6/QRFKdx3/N2lozjUYA1XOVNjddR1uWi2eWi2efjdxxX8IhTInm6RWyb2NXaxcmIaF49L41JjCt9fURJ+3744AQG4YUYO1z+7i15PgDf2N9IS6huM1aup73bR4fAyLTd6duBA/Pm6yby+v4EVpam8ureBKTlxsqnLtmouKk0jO95ASoyWP39eSZxBzdo9DYAs2+xx9fdhuvxyNuB3lhaTZNIyMdtCQdL/3RiREYzgX8F5J3VGoxGn00lvby/f/e53eeihh1i2bBk33HADl156KTrdf4dF8wj6IUn9DdoqhTAou6wk04gkyVEEgiAwPlUkZucfcOlSsKYuoChtnDy4Pfa6vIJCiddSwqFWJRqVggm55kEyHJDd8IZzxPMFJIJ943dfAJTgUmsQs5bTXWNH07uZFLGRnOKxxJv6Z7nfrXqXU+2VrEy/gsKs7LCGv6vXx5Zj8oyhTV+HKIm4A2663F1MTM4ixaJFpRRQCAK5KQYUpw0mlAqB8bmDf1x6W2uZu34eiqCHvVP/Ro+2P3xXEATmZ8oW/42BsdS5qyg6zUChD0VpBlRKAaNWiVGnotPdyW/3/hYAiz6Ggow01IKGVUmXo+uuhLbDULgCJt4qu1T2GdyIQWgtg6zpoFAyMS8GnUaJ7pQLBRJC3RY8H/2MmvQrSZpzLYnZ0bPITseFORcyO302u1p2YjGY8fX1YYaQqE3BpXBh0Zj5e+IUmvU+aqrfIK/gKt469RZWjxWANGMaKoVK7ucL9fTV2+tJ1CdiUBv4aeYK1jnsLJp+X/QdyZkN17wE678nS2qB7tzLOHjMRoJJzcQBE+aCIHDf5CjbcbRCWxl01cg9aPp4ubqcNLwcvNom96IFAx5Qa+ToiFEXwZMz5V7J+d+DxQ9HrNfY28i6is8ojZ3FnIIiuUfvwp9D3nyUqeO5NSat3+hi4SPy56lQMto8ntHm8cPu05eFgOhnY+d6JCSCUoALkladeaUh4Am6+aj9LURJpMvXjohIUApyUYps1rMi5SsExIB8XQA9vk46fe3kG4sH9cdtbdzK7pbdjM2SP+Q4dQLbuj6l3dvMrPhFzE244Iz7cq6E7pTjOBs7ZcMeCYkefxcXJl8y7DrXTc+mttNJcoxsOT8Q/qDIK3vqyYzTs7gkJRzm3TGMucj5wou76vjTZ7JF/M2h3jCTRsnjV0XGCXxRiFL/JFtxipnMuOENsRYWJ3PwkaVoVYpwD+tQ5hjrj7bw5Ma+3LdYlg+o+E3Pi2d6XjzvHGoKTfaJdDq8jEoxc+M/dlPX5eLvW6p58c4ZZIQqTdfPyA5LJ7ec7OCu1fvQqRS8fNdMUmN1LCqWzUZWlKYSZ9REOE92O318eryV+aOSaOpxk27Rk27RU5TS/1vRHpLojj4t7uHCMSlcOCaFLoeXI002uh1eupz999YJWbE8ffNUTFoVW052EKtX4/YFKU4xMz4jlqe3yPekPlml3ePndx+fIDVGR3uvhx+9cxRvMMgFo1O49Z97MGpVrLljOheMSeHlO2eg0ygZlxnL95cV8+aBJu5ZVEiiScPr+xtZOS6dFf+zFbc/yO+vmsCVp1UxvYEgGys6mJhlITVWx7jMWMZlxvLwuqO8uKsenVrBnMJEPi9vR6tSUP6z5fzP56eQAK1aSWNoUuHbF46iKMWMSavC4Q2ECWSsXs03oxDtEYxgBGfGeSd1bW1tvPHGG6xZs4aNGzfi9/v54IMP+OCDDzCbzXzlK1/hhhtuYMmSJWfe2JcEu93O+vXr2bt3L/v27aOpqYmOjg7cbjcWi4UxY8Zw0UUXcccdd/yfqDIadUrmj4nD5RXJSBhMthLMGuaO6f+R1ZrjKU4IQs1amD4DYjXgMYJSK/cabf0jDb50mkf/CICMhOEJXDTEmdRML4rF6xdJTxxHilMiVZuJUqmhND+B1viLyTGp0Wv6B2ptzjYe3vYwD+Sv4XiLDntvA9NK5R/sPnMXgAszVyGovMRqY5mYPBE6ytHUbZGNLtImRRC6Ifex5yCC3wFAds9mYiesGPT6Y7se4/3q9/nu1O9y66hIo48+KIJeChJVcm8UYNFamJA0gWOdxxidUkRXQK5gtXubyQ5VzPA7IHH+4A09tww6KjiQN4OvKru5Z+I93FZ6G5LhIqjdhMMNG2d8k6DKxKnmICuzpLM6VoWg4IklT7C69m8EFB5OeY6SYhxsW25QG3jt4rXsq3yKulgdJ6VaxjRt5yc7fgLAA5Mf4NqSawc59b1c/jK/2vMrcmJyePuitaS/eTffCHoBC2QOUblInyT3zwGOsdfyi7gZqHveIL9zISWZxlCe4hAI+uGzR6BsHQS8kDwGHqzpf71+N3SehAnXhgx4+vHorEd54+QbqNPngCRAQrFMovsksX2RAKfhl7t+wwLjd+jp1nNc7WBCXgwoVVAsXyuDxGXKf5/Lm1/08U7rK/QGbFyUfOUge32loCJZm0abt5l07ReTPjV76mnzyp9XoiaFTl8b2frBcrU+QucXfaxreQmf5KPL1x6uRPmCPu7feD9+0c/Fnov53vTvsK71JQKSn2P2g8yKX8SXgWZPPdIAL8IYVX8FNyAGON57EIs6YVAuXqxeza+viE7GX9hRyy8+KEcQ4LNvL+B3V47n7YNNg6zkvywUJJtQCBCjV3PPogKWj0slP9E0qGeqqqsTjVokK+aL7Y9OreTVr83iw7JWnt1axbzfbmTdPXMiMuz213Xz1KZqLpuUzsrx0eMdTkdJagxGjRKFQqA41YzLJ/d/xejUbDrRwaRsC6vGp+P0BtFrFMwukKukuhChr+l08vDbR/lnFMnpu4eb8QZEvAGRa57eicMbRKsS+ORbC3D6AmjVCgyawcOl+145yLbKTlJjdbTaPMToVGz5/iJWTUhnZ1UX6482Y3XL1ScBuTKVcJrTY4JJy+bvLUIURVb+ZRvHW2R55O+unECyWcfVf9/JnppuVk1I4+ZZuSSZtYN+zyaG8vMO1VvZXydPXPZNZoqSxOaT7VR3yv3cB+p7WFySwv66HvbV9fDjlaO5aVZuuOL46LvHWL2zjsp2B96ALIHsk3ICNFnd/OSdMqo7nVR3OLHo1Rz6SX98R7JZvp7iDRomZFr4vLyd0oxYFAqB4y1y1bPb6SMrTk+iWcutc3JZd6iZm2flsLgkmalnURUcwQhGMDzOO6kzGAzcfPPN3HzzzTQ1NbFmzRpefPFFjh8/jt1u54UXXuCFF14gLS2N66+/nhtuuOF878IZsWfPHq677rqor3V0dLB582Y2b97M7373O1588UWWLRteTvj/A+LNGuLPVulw8e8H/19ngdnfkXPj/lhKsjEfbdCO1hQ/qDfpXBDu04Ow1ArkQNb0+Mhqb4w2hixjGppQyHKLU40/KKJWKkiO1bLYUo6pdTvK3nncMnaALXvVp+BoAXc3pE3C7Quyv9KOVq1gSkFMRJWxD8LoSwiOXkmgt5OqgjuZdFpEwztV7+AOuPmg5gOuGDVEv1DrUXhuBWgMcNdGiM1ApVDx4kUv4hf9BKUAmzs/Qq3QkGEqgsl3gL0ZMk6TMUkSPnsjGsDWeQJvYgwfnnqb2w59gJA9A+Z/j9bGXoKNsnW1VqPG5xcRJYbP2QtBrVCjVikIiNAbsEUuIAZJdHaS1lpPnWUsyT0dpOSloFGoCYp+JjcdwzhusOSz0loJQFNvE24pgDouV+5PSxymghibAav+DC2H2Jx1GcWxcki6u7UKrXroHEBAloPW75YJHcguon2wt8DzF8sVOEcbzP/uoFWzY7L59tRvR27zxjflwPJpd0a85A16CKhceIJONAp9uEcVVzf0VELyuLAU898Nq7+bLp/c19rgrhlE6gRB4JLU6/CLvi8cfJ2hzyVLn0dQCnJB0io0Cu2QDpUgIAgK2f1vAPlXK9TkxeZxsuckRZYizOoYApI80Iw/30HuAzDZMougFCRJm0qaNnOQkcth+x72WbcjIHBd5l2YVZGS3T48V/YcxzqPMc5wPQAGtRKTVkVyjI6vLSgYcr3TsaGijUSTlvGZlnM+lkXFyWx9cDFGjRKLQUNK7ODr7pOKGsqFt9CoReb4VjE+sWSILZ0dSjNiqe924Q1IgERtpyuC1P36wwr21vawt7b7rEldcaqZ3T+6AAEIBCWWPL6Z9l4vM/MT2F7ZSXa8gS3fXxRhXPLSXTO44qkd1HW56PUECATFcHB3H+6en887h5rwByUcIXMRb0Di9X0NPLGxitwEAx89MB+dWsmu6i7uX3sQMUSuAiFZicsXxBuStE7KtrB2bz0GtQK3X+TDY62Y9WounZjOuMzYiMy/9460UN7Si0ohMH9UEnmJ8v2yL0qhpsPJNX/fiUqh4N1vzuEXl5VS2e7ggQvkitb0vHguHpeG1e3ja/MLcHgDLB+bitXtZ0NFO0atitkFiXT0enn8U7mHLXWLjt9c2T8JcaxZvrdXdzh58Y4ZNPa4B/UavrirLmyyAmB1+3F6/Ri18rHct6SIOYWJ5CUaiTdquHJKJklmmcQ+snIMWXEGFpckh4PSt57q4MfrygAoTDaNkLoRjOA84EvNqcvIyOChhx7ioYceYv/+/bzwwgu8+uqrdHR00NzczOOPP87jjz8eXt5mizJg/JKQlZXFokWLmDJlCllZWaSlpSGKIo2Njbzxxhu89dZbdHZ2cskll7Bnzx4mTDj/UpX/TZAkiVPNLnxBkdGZpkFxB4Bsp681w1eeIaZqIxeNVkF8f5WzpceLXqOITvLsTdCwE1InQsLZyQJPh16l583L3uHgsRpa3RCUFPj8YljSE9t9AAIOaNoFuQOqXGmToLoL0mQ3toZODx12WQaTm6wnyYTsTKjSQf6S/iwypRrlNS+hBKLVlb4z5Tusr1nP18Z/beidbtwHvl750V4uk5YQ1Ao1atQsS7lcJjtN+yB9GlhyIzbjrdnA9txJtFm7qMm6lhL/Ae4N+qByPVR+CpNvITM5gfbeAIIAJRlGPjnUhRgMMrfpTyTOvw2Shx/ILU/5CnWuquiywJqNUPM5E5QpFL3+F3RFK1FMLuT9+PkEDq4hq+YfMP1euacvhG9O+iZ6pQm9v4iyGolpd3yG2t4IKdFlqmFMuQW4hXk+J89XHkeh8nPF2Mn9/Tef/wwq1suRCgPMUzj1EcTngbUJsmbANWv6X1Nq5M/X548enzEUcmbLjyjo9ncyObeYNsfbFFiWUiIFoTsZyt+SJxA6T8CEm87qbQ5ad3HEvo+JsdOZEPvFjS0SNSmMj5mKPWBjtDnyvqYQFF+I0LXZPQREiQyLPiy1PBPUCjWXp91Il6+DXEP/PUAQBF666CXaXe1kx8iD9UWJF9HsaWCKZVbEdoJSgB5fF/GapEGVYUmSCEiByGzKIWBWxbI46eKorxmV8jWiUWhRC0NXWFudrfxx/x8BiC+O57177yXepCEl5tzO7Rv7G/nu64dRKQQ++dZ88pNM8kSEMTGiqjwUMixDTyDUWbvRpcpkpNXVxfkQ/i4fm8qDy0tQCLB0TGQg+ZLRKeyt7eGCKGHlw6EvO62yvTfcA9bpkCdq+qpLp+P9w83UdckTWvvqenjnUHOYrNR3ufjT5yeZU5DI+vvm8eTGSnZVd9Nq9zAhM5Zej1xpq+924fQG0KmVvHe4Oex8mpdooKbTRXacgaumZoY/262nOhElcPlFchMM1Ha5qGi189qzDYzPjOXde+cO2seD9VYkICBKPLSiBJc/yGNvlzGrIIGrp2YSo1fzyDvH8AVFuhy+iIgFnVrJkzdMjjj26g4HD68cE44YUCsVzCtKZH9dD8tK+899UJT45eXjeGl3PRePT4vaS7dwVBJPb6kOVwrHZ8aGq5fVHQ4EQWBKThzPbq1m9c467l1cGI65yIo38Miqwfd2s1YVzuZz+aJ/diMYwQjODYIkDRDB/xsQCAT48MMPWb16Ne+99x4+nzyA7huUlZaWcuWVV3LFFVcwZswZBnj/IoLBIErl8BWKdevWcfnlssnC5ZdfzltvvXVe96GxsZGsLPmG19DQQGZmdEvl/xZ02HxsK5flHRNyzeSnGs6wRj9q2lwcqumVs3omJIYDy8PY+5QcWaAx45n5ILtO2hAEmDnK0u8Gaa2DjuOQMUMO1kYeqHl8ImqVEJ559QVETjQ5idGryEkeMJBpOQj122T3v/QpkTspidBZQa8qhZ11Ejq1glLVHn628T5UCjU/y15GR/5XaRJTGJ1pIs70r1Ugw9j/AhxcA4YESCqBJY+AQonDLRMvoy403+LqhB1/ACQoughyTstacnbAzj8AsFmMpztGzuC72Lwfzbo75R67G98ERf85H/hZTj76IDmJOrj8b8Publevj50VVkx6FfPGxA0m9VWfQs0GmRyNux7ii+SsvKoNsPYGWep423oOdZfzj7J/sCh9GRfmLKfT7uNQjSw3mllsIS3uNJlu+3G5ipo9F1RnIeEVg/Az+droyr8M24on5QEwwLbfgMcqB8PPvE/uy+ttgpQJ8sC4uxqs9XIUwzDwiz58oi/CNCNiVySRnd0bcQWdLLSrUdduCUVKJICrQ47nKL32zMcEvNL4NPaADYsqnmsy7zirdf5TONnWy6q/bMNo8PGj6yXGJORFJY6nQ5Ik9lm34wjYmRW/aNgst+HwYdtb1LurKDSOZknSyvC232tdS4u3kQUJyykxj/uXtj0Qnd429ErjsNeBP+jnlo9uoaK7gj8u/CMLshb8S+/12r4Gvv/GEZQhUldw6p/wycOQOQ3u/OxfPYQwPP4gzxzYRKwpyA1jlgxTTT2/6FNS/Kt4eXc9NZ0O7pibx5aTnUzPiyc3MdIE6v61B3nnkCwDVing1a/NZkqObN7ywNqDrDvUjEKAsp8uw6BR8fLuOjZWtPPTS0rRaZQ8tamSCVmWcEWxotXOj9eVMSHTwrpDTXQ65DGMQoAt319EZpyBqg4Hf/j0JPMKExEliV+8X45KKWD3BNCqFJz4xWC5fqfDy/zfbgyHkM/Ij2dXdTcAa786kxl58azd24BWpeArk89urPDOoSbuX3sIjUrB599eQFZ89N/s33xUwd82V3HXvHx+eNHQjqoOb4Bxj36MJMGq8Wn8+bpJCILAwfoernhqB5IEi0qSOVDXjdUdYHRaDB/eH5kN2IeD9T1c/tcdADy4vISvLzz7yvW/G//bxmsj+L+LL7VSF/UNVSpWrVrFqlWrsFqtrF27ltWrV7Nr1y4AysrKKCsr49FHH6W4uJgrr7ySn/3sZ+d1H85E6AAuu+wyiouLOXHiBFu3bj2v7//fAqvTT2uPl5wkfURG2ukw6pRoVAKBoESs8YtcNlHmECy5MqmLy6Xd5qPHIcurOmw+MhNDs9qH18hZb70tMPl2XN4gh2rstFnlH9TkWA1zRsehUSkYlxOl2pI2SX6E0G7zUt3qJi9FL2erVX0GtRsxq/UsnfsD8Nh5+4Xv87lRJhMXBhS4uiwg+AiKvcwbc25SkQNtB2hyNHFR3kUoFUr47Cdypl/mNLjwp4BMnLYe60EQYGFpvBzIrtTIpCPoi+pa6VWa8AlxGCUb5vjpSFoVisBhyj11TPjWEdktcwBeKn+JT+s+5bb8B0k7uZms9k9h7vCEzul38od9T6LwpTA5djkOd2BwWHzeYjClgDFF/tuHgsXww2Zo2gO7/ofjzjo2tW5hV9NeFLZpzC62YDGqZHOe02MtvHY48iIgQeNuSBkn56wNB4USZt1L57GNXFB1NdbHN/PXGybLOVqjVkLTXjnUPuiDfX+HoBccrdTELiYgplCYl0d0sa0Mn+jl9abncQTtXJB0CQXGoQ1WFIKCOQmhSqF3p/xXqYUJN4OzHRLO3ghgimUOx+wHGRcTZTLivwzNVjfegMiiie10YmVL1ylyDYXolcM7rnb4Wjlgk89TrDqOyVGqcGcDe0CerGh01+EMODCqTAQkPy1e2VW1yVN3XkhdovbMFSa1Us3LF788yAzmX8HVU7OIM2hINGnkisvGvfILzYfkXtGzrNYNBZ1ayTdnnJ8ed4c3wDNbqilJNbMiSn7dQHwRQgcMkllePW3o/s/vLy8JRQNoWLOrjkfeKWPtV2di1qmZVZDAO4ebmZQdh16tpN3u4UfrypAkaO/dzzv3zuVHFw+eXC5JjeH1u+UKvUIhhE1L4o0azCEpYkGSKZzFd8tze3D5gxBqT/MFxAg31kSTFqNWhcsXRAJ2VXejVyuIN2opSjYhCMKgEHJfQKTJ6g7LNEF27DzaZOOaaVkYNKpwldEfFMN5edHwybFWJAk+Pd4WQepEUeKlPfUY1EqumJLJY5eNY0dVJ99eWhyeiO/o9dLX5rehoh21UiA/0cidc4d3tZ2UHce3LxxFi83D9TOyefTdY7x3uJlHVo3h0okZw647ghGMIDr+7aRuICwWC3fffTd33303lZWVvPDCC7z00kvU1tYCUFFRwWOPPXbeSd3ZwmyWyYHH4/mPvP+XjZ0nrHh8It29fmaPHt663KBVsmxSIqJEf38QoeBsjQK9Rkm7q51f7PoFGaYMvjfte2H5U26yHq1aXiZcgRqIohVyBUptJCUgkRijRkAg2TJA2mRMBmsNGJPxB0Q2HO3CH+gniO02H1UVFRQEjkHxxbKhRs3nkDIeMiMDh4/U9tLrDmJ3B1g6USuHXQOIIiDB4VeY0dVAjiYFtcZMStx8akI/YrEGFfUdbg7X9JIWr2Vq4dA9NSC7IN7x8R0EpAA2r42xiWPJHLWUpIoPZWOOEDw+kVZvDU2eCnSVS1lSmoZKGwMzH5BjAGIjBy5anZ5jOXdht1kZn23Arfdw6VvfxisF+Z2thuWLfxteVpIkfr/39wSkADrV//C3lX+DlXcPu+8Azx18lnfrZbnitNRJmPRJ/Gbb07S6m3hk7reJ08fK5zkaBEEOTvf0cIk6nl8jUGyahSTJA6JFp4fO90GplaWQXrssT23YATnzQTfgXPfUQMU7EF8IxXJVhmWPYZ3Ui/UPWwCobbfB4c9ki9dx18oyy6Bfjn8IejnaaaCyW64WalUKspOGrhB5gm4cQbnhv6L3CHmGokESv2ho6vZwomcUxfm3kpGeQlATy6keHVKrnbca/kahpZBrSq4ZdhujTGMZZRo77DIRkwZDoeozuepdehVoB/c52f1WDth2kqHLocg0vEpCkiROOo+hVxgGmYUsLE7m55eOpczfgCTJskqNIrrc0C/6w3LIWFUcMSoLzqCDRE1koqPV340CBTFqy7D7NTf+At5vew2P6OKAbSfzEi5ErdCwIGE5jZ5aplr6xdKSJGEPWDGpYr7U6tSwhK7pAJiSZRfWYXDhQAnjhT+TnVsLL/jChO5846lNlTy5sQpBgG0PLh5W9nk+4fAG+Parh+TYg2smhaMLPP4gyWYtj10+jic3VtLj8tPj8nOitZepufFcMy2bFePSMGlUCIKASdcvCTzcaOPjshaWjo2edXeitZe9NXI1TQDevXdu+H378OnxNio7HKTF6shJMKBTK7lkQnqEG+vGinZKUs3o1Qoae9xMzLKw5o4Z6NTKQaqIzSc7EICnt1SzrbKTgiQjy0tTuWtePtc/u1smez1uHl45huunZ6NXK0mJ0Q1y5NxR1UmmxUB2gly5+/HKMazZWRch6QyKEi/uquUn7x4H4Jfrj/Pg8tE8cf1gmadJq0KtlPNtAaZkx7H2a5GTMmVNNjZWtHP1tKywTHVgnMRLu+vwByXe2N8YldR5A0F+//EJFAqB7y0tjuiLHMEIRvAfJnUDUVhYyM9//nN+/vOfs2XLFlavXs0bb7xBb2/vmVf+EnDixAkOHToEQEnJF2sc/2+FQaPE4xPPWKXrw6CbqK0R+ye/5aR6Kh0Zy7lwYiJvnnqTjQ0bAbg4/+Jw6LMgRDc3CYoSlS0uTHolGfGyjEmrFqJXwSbfDq4uMCYjBiSCQQkkkXHBXbSRgYNYst9ZBe52mHIrpJdCTzXYGqKSulSLll63i1RLSNZXuBRMqRCTDuXvw6ePkK5Q8b4vAa5/D4/HjavaisYQQ2qclpPNLgKiREOnhykFMVF/9MPnTaFCqVASCAZodjTzm72/QUBgzZ3vMyEpJE0LeEmsWsM/65/GJXpo8VQyJe8ROV5CHyc/TkOvO8DRul4SjAKTXWvhsJ3WvIX4pJBz2YABX0uPl/2VNu4veoq/13ybi/NC/UKubqjdBgWLhuwny3B6EFBgUsaRl5DM7oZyXqz6i3weD6Tw1Ql3YTGqhj4HOfOhbgumpLEcKFhOk7IIlT4mHJ0hSVLkuiqtTGY7y2V5Z0xW5P417gJnm/wouEAmbN5eCoVG/nLteOp7vNxe1AsV8qCEzgq5b1Ophun3sOfgCZoEeSAjCGc2jYlRWxhvnM0R5w4aPbUc7z1EaUxkH8tAnGxyYnMFOOSLIyPfQnWzk/JG2Y1uU+1e1nrXMjN9Jjkx0TPWzgifC+vuJ/mf8uc4oFVh89q4ccyNEYtJkoToaEZZ87n8xO6/wvyHBi2z17qNSmc5Jx3HyDUUolYM3S92wlHG5q6PALgi7eZBlaubZuVy1D6NE71lTLPMjUqYtndtoKx3P+NipjA7fjFapY6ZcQv5pGMdmzrXc1XGbeiV8qCzxdPIe61rAYEcfT5u0cXCxBVY1JH3iVRdJvHqJHr8naRp+4lSiXlcRIXuw7Y3aPDUkq0vYEXKV4Y81nPFqbZentpUxYVjUoavVh16GdZ9XSbX3zwAprM0fonLhVV/+td2TpKgeS+giDRcOudNSTz67jFOtjn41VfGkZsox9+A7DgZE20CD7C6fMTo1EOaUP0r2HKyg0+OtwHwyfFWrpqaxdFGG9f8fQdGrZoP7p+LXqMkP8nI9Ly4cHYeEDYtKW+x88g7ZWTF6anrdiMAX3vxANdMzRpkKALwh09O8OcNleH/f2NRQUQoN8Bz22po6nGjEKDF5iHRpA0Hvn96vA2728+e2m5e3StntxUlm6h87KKo52bbqU5ueW5PaJ/lc1vV4eTJjVUsHJVMjE5Np8NLvEn+3ioUAqPTYthd08W4zFhi9Wpu/+ceNpzowKhRsu3BxcQZNSwsTmZhcaTr6R0v7GXTiY7w/7ucfl7cXRdRFX37YFOY0D1x3aRw1ERQlPjOa4eoaO3lD1dP5NZ/7qXT4eVQg5V/3Do4HgLg+8tKeP9oC3cPYSC0/mgLz2yVHYsnZVlYXjp8JXgEI/i/iP8aUjcQ8+fPZ/78+TzxxBOsW7fu3/a+LpeLpqYm3nvvPX77298SCMjVmwceeODftg//TswZHYfdFSDO9C9cBp//jJhjrzJNeJH3E/ez+6SKbPUUUrVxfCWmiAL10NUrr1/kYLUdlzeIzSWf4wsnqjANGARYnX4OnOwkhh5GFWQTE2MKS/u0aoG5Y+JwdbeSdWoPhUC3ZRp9BcQuu49jGRdSqN5EenL0/UhL9pGWpCbBECIKChWkhwbo1RtBCkIwCLe8A4Z4dAaYHQ/HGxxsL7eiUQkkxqhJj9MNSWZ8AZFD1XaUCj2vXPwqne52/KKfNeVrkJDwB/vtotn8WxRbf48uOwOXUolFozxj396pFhdtVh9tVsjxBdEBqRI8teRJ2m21XFJyfXjZpi4P/qBErJDHpqu29fcqvnSVbMIyajlc/2rU97k8IYsU8VtodCmMEevp1CcTp06l199JBtP57uePMitjGndOvey0E+CSA76zpsumIDv/iEoSyREUMPu7gI6dJ3po6/ExKT9mcA8kUNcDfkoomCP3bnTYfNR3uMlLMRBvVkPGNDw9Vez32/A172RR9iLY/wy4OliVNgUWXSlX+hpDlZ+4AXb6+jhMWaXo2t0UphlIj9dF9nqehjarl6aaNIREFZIigF5pxCd62dm9CZ1Sz3TLvIhrITdZT0Wjk5wkeVLDrJevcUEI4ArayDRlkqiPHlB9Vtj0Syw7/sLfBYHFWRnoVJGTJz7Rx9stawj2NnOdswtBbwFXT8RyabosKp3lJGlSUQnDX3tahTwZokAZlfyNi5kyrFy00S0Pzhrc/bESPf4uANyiC3fQGSZ13f7OULyARK1bHkif6D3KjPjIHjWloOSK9Jsj3TuDftj9d7kqNv5q6lxVNHhqAWj3tgx7rOeKX31YwYaKdt4/2sLy0uhVHkB2WgW5Cu9zAF+em2cYHceh/G3531oTfAGny8p2Rzg4+5U99fzgotE09sgZZG5/kAHO+2E8u7WaX3xQTkmqmVe/OiuisvWvYnpePGPTY2ize3htbz2FySaONNpw+UVcfi8PvnGEjSGCcvXUrEijL+TYib218vfiByuKeWZrDZ0OH4carBHLVoViAoCQGUxkdRngxpk5NFpdxOjUHGu2kxqK9zlY38Ndq/cB8oRSHxaVJA9Jdgfu8/TceD6rkJ0oC5KMlKSZWX/fXGq7XEzPkyc7RFHimr/vpNcb4EBdDw+vHMOG0Dlw+4P4Qq6dkiTx7uFm4gwa5o/qvwYPhCISJmdbMGiUHG2yRziLghykfrLdwbScOFZO6Hcyrel0si7Uy/jmgUYyLDo6HV7SLNEr93fNz+euAQHtp6M0PRazToUyRFZHMIIRROK/ktT1QafTce21Z2cm8K/i+eef57bbbhvy9Yceeojrr79+yNeHQmNj47Cvt7Sc34HEvwKVUpAHx/8KMqbCkVfxJ4whPt5Mh90P5LB+zDdRtx+Aw6th3g8H/2KF0NDpoaWnP3RXoxIi+isqGnqxeRXYSCD2wHq8ky4jKbZ/8CgIAl51HGLKRBTONuKLZ0H+J1Tv38JRw0JEnw5v7ErSR0cOmPc3H+Ouz25GqVCyduUrFFgKZJONmg0yuZv7bfA5IWum7DA3AF6//EMYFGF2SVzUwUEfmro8NHXLxzkzPoOidHkG8i+L/4JKoSJFNZb39rSTGqdlmi4GNbC2qZWTGjVzUnwoFQKddh9tVi/5KYaIimpanJbGTjcJRiVaZQK4FZBcypyYDMgcnGNXlAgeay+JMeowoXN7gwhuNzrot/qPAmHsVczpqYYjb8BzvyUlNotP7vgc9eHn8LreYJ1zK38+9j7XtmzEVPY2LP8lTLoRPnoIeuth2x/g5ndkMxqQ/wZ9iKJEa4/cE3miyYlW52On4z2UgpKp+pUcqJYd69RKgZxkPQeqbbi8InZ3QJZtxhdyi/0ox7uOk9x9QCZ1feHoob81ni52muO41NaDsXozjO7vyxuTZWJM1vCGJwPh9okoRSPxXZcwOctNfmcb5cYOKhxHAMjU5ZKhHzzoyUsx0Gr1cbLZhVEnm/csnZiISimwcMLbGNQGNF8koy5UwVXrLPxxyRPMyInMbesNWLH6u0GnozKjiCKbB+bdG7HcGPME8g2j0Ci0w1aeAfKMo7hMdQNahY5Y9fDS7WiYk7CE472HGWOeGH5uXMxkApKfGJUlHFdQ56pie9dnaAQtUyyzafLUYfV3k28cmoz0+DvZ1b2ZTH0uE2JDFYH9z8MncnYmiUWo4vplv9Pj5kZuBFm6p1II5yzzmpkfz4aKdmbkxQ9/HmfeA2qD7AobP3z/0XmD1hxy8BVA88UGxjkJRuYWJnKyrZdloerMddOzOdZsY05hIrH6wb8tr+yp59cfVgBQ0drL7c/v4c1vDJFJeRokSeIPn56k2erhhxeVUN7Sy6hUE8lmHTa3n8c/OUFWvIFjzXY6HT5+ub6cf942ncfWl+MLiPR6AsQZ1NjcfgqTon/nV01I5/OKdoqSTfzqwxOATBajmYf8ZOUYchMMpMXqmZhloTQj+uThxePTuHh8GkFR4mB9D8Wp8iTiQMliX3XzmZumcOFY+Tz2evwEgtKgIPZZBQm89rVZCAJ8eLQVkO+Nb98zB7NOTU2nkwffPEJxipknrp+EUiHIvXzAsWY7CUYNM/LiOVDfw3eWjgpLIN8+2MS3XzuMIMB7987lRGsvL+ysCcczFKWYeHWvPJ5pDpH2gRiXGcs79/R/jgfqe/jq6v3kJRq4aFwqle0OMix65l84CrNezYQosRw7Kjv52pr9jE6LYc2d0yPkqfJ+mNn7owsAuQ90BCMYQST+q0ndfxITJ07k6aefZtq0SJnA2aDPKen/W8z4KoxehdaQQEGvRLfDiihCY7efPEASlEMOaJItGvQtCjQqBRPyzJh0qv7KUQgJZg3d3VYSg01IghAmUyDLOrYd7yEoSvQmr2DSuP7BSXdePGKn3AMZNlk5DXsaTuCXfPiDUNldI5O6tsMyqQOYeAtc+VzUdUuzTZj1ShLMGpQKIap0UJIkgqJEUqwGvUaBUiEMqrotzFoIwK4TVgKiRGOXhymzvokiZRxp+/5BmrUeJt2IJEnsPGElEJTodQeZmauE51fKERA3vEFa+kQumZ4i/39PvbzxjuMQE9mPENv0MXOtB8GmZJdhOW7Rj9Y1Edv4p8jo3sb4ZcNMnijVkFhMd28Tz2bPIyH9Vi5qbSHNa8cgCEzRxCMaU2nr8GKNm0vmkVdlUqfTg2E0mNPkKuukO6D9KCSNAVMKCmBinpmyegdOb5D9VV7a4+TJjipHN8h0MyyLTDBrcHk9JJj7BzrzM+dzvOs48/tI7JQ7ZSfLFFlqd/endzO35QTGrlBl6u5tkHqWRhl+t9zbp5CvzfR4Jb1eBRZ9OlknngR3FwWWLHYmadAqtMRrIvsDZeIqE+aWHi85yfpwRVA7TH/YEdteOnxtTI+bj1k1zOB77rchczrKhAJmxETP+0rQJDPFMhu730pm1kKCCi1KIfqt/1xcJ1O0Z5cvFg2Z+lwy9bmDnlMrNEyP63fLC4gBOn1tSEj4JC+Z+lzGx/ZLBt0BFxs6P8CgNDI/cVlY5nnItodGTy2NnlpGmyegUWjAkoNMZIxgSCRDn8Xk2Fm0e5tJ1Ub2s+2u7uLm5/aQEqPjvW/OjSAo0SBJEnXuKq6aGcc1U5diHkJ+GIZKAzOGiTw5DxBFCVGS+olpbDbM+hYgyM67XwAalYIX7xwsbZ9VkMDn31kYdfm3DzYREKV+G/sB93SPP8jP3j+OJMFPVo2JGLQfbrTxl5DcsaHHxZ6ablLMWt775lzePNDEK3saBi0/My8ek1bFMzdP5cOjLdw+N49ks5ZeTyDCBXLjiXbsbj+XTEhn748uoKbTydI/bsYflLh5Vk444HsgkmN0fG9Z/8SC0xuQiXx+fDiEeyCUCmFQDltRipkP75/Prqou/rG9Bpc3wAOvHuKpG6eQn2Rk5V+24fIFWfvVmUweIBXtq8KNSYsh3aJjfKYlLB9951AzNZ1Oajqd1HW7yE80olEqcItB0mJ1qJQKXo3S69YXS6BSCOjUSn7/yYlwXARAnEFDboKBZqtn0DE0W91sOdnB0rGpxA8gn58eb6PT4aXT4eWjB+ZR2e7g3pcPIgCz8hP4xqLCcFZdHz4rb6fXG2BPbTctVk9UF1MYIXMjGMGZcN5J3ZdhavLII4+c92324bLLLmPqVHmg4Ha7qaqq4rXXXuPtt9/muuuu409/+hMrV6780t7/fwUkUa7kDAhLdnqCtDpjSdcpSYtTMq3Iwq4TVg5rF9KmysGSlke0uXS3N4hZp2T55KGlRi5vkMwkPSZVDK5OB8rEyWQk9FvaC4Jc3XP7pAgyOCk/hnSNjdT3rkaxNwA3vU2HkEJZfS9pcVpKMk2sKlpOZVcdRq2GJbmhyoYxBRRqeRbbkIjHF2Tr8R5EUWLumDiMOhWBoIgkQYxBhVGnpLHTw/4qG0mxGqYUxIQHThuPduHwBJlRFDvscY5KN+IPiqTFaVEoFFC4WH70HSdg0imxOgOY9Upor4CWQ/KLpz6F9Inyv00psozK3R0mMxEwytJVEYndu3/Ps44qfj3pVXy6VLpHXQfG/h/rJw89SXlXOQ9Of5Asc//kxC/Naj621qHreJzM5HWkZc8Fv5u7pt/DNZ4YDlfLs7i6uHoSAdImQtsRyA0N1BMKI3II81IMdPX6aej0EGfQEtRmIkgaOlvlgVGKRYNGJbDhSBdmvZIVkxPRafp/2O+ZeA93jburv9plSAw7fnr9IlenPIY+SYFj962Y/B2DjVYkUZblRYtLqPgAXrsZkkfDHZ/hUyi48r0rqbHX8PUJX+cbGhO4u9Do4rkl+yoEhLBpijvoosx+gDRdJpn6XCbmmWnt8TI6Ux6oBKUAFb1HsagTIip7AI5ALzt7NgGgVeiYm3BB9M8U5C9D3tC24X3oMwg5at/Pju4N5BmKWJp82RnXGwqOQC9H7HvJ0OWQY/jiVuSugJOy3gMUG8cSq4lnT89WDtp2UWwax4SYacSoLcRrBg8EN3atpzEkoUzWpjM2ZiIAeYYial2nyNDloO6TkY5aCvfuk0ldTBpBKchB2y4kJPZat0aci7213XgDIvXdLuq6nGcV+n3EvpddPZtRCWquz/wqirPMxDsrdFdB4y58qVM56k5ibHrsGQe47XYPlz65Hac3wD9vm87fNlchihJ/uHrieZM99sHpDfDjd8pQKxT89NKxUfftvsVF/M/nJ1lSkoJJp2JRSX8f1yfH23h5tzwxNTM/PsIoIy/RSG6CgTa7N0xiOhxeZvzqc+5ZWIhKIRAQJeIMan55+bhwL+OCUUnMLkgIK0EshsFV8SONVm77p+wm+ui7x5iZn8AT10/mBytKePdwMxZ9ZBX9UIOVW57bQ4ZFz2t3z8KkVXHX6r3sqOpGp1LwzcVFJMVowxltQ6Ew2UR+opEFxUnM+63ci/55eRsqRSpWlyzNL2+x8+b+Rl7f18gPLirhtjlyRdeoVXHnvMFSxaunZrGvtpviVDN5CUasLj/uUKUu0TR0JMzy0lTeuHsWZp2awmQTk7Pj+OCoPLl2zbQsLpmQjkIQWDY2lQkDCO7tz++lorWX9WWtrL69Pz/z+unZlDXZKEgyUZxipqZDlqpKwI7qLtp6PRHE/9bZudR2ORmdZh6S0I1gBCM4M847qXv00UfPKN05V3yZpM5isWCxWML/nzZtGtdeey1r1qzhlltu4dJLL+Uf//gHt9566zltt6GhYdjXW1pamD79iwcJn0902n3UtbvJTdEPqoZw8J/QXSnbwmfLg8PdJ63YXAGauj3MHxuPTi2QmailuctLi1CIQRmZiXOktpeqVhfp8VpmjLJE3YfuXh+bj8lVldJsE0VjI10VFYLAwtJ4HJ4gCX3yUZ8L9j6DMrGY9IAbOsrl52u2UKm/CKW9jk5bEDFjKpnxBn6/4oHB12lMBsx7SCZ1Kh1d3R4cHvkHscPuJyjC5mPdoZlvsBhVmHRKRAnarD7W7+/EoFUwc5SFXre8Xmevn7QoBjF9iDero5vC1G+XDUIKljFvTCYub5AYgwrEyTDtLrA1cihzHK6mHczOmC1LRifeMuT7AJC7AKmjgl1te7nMkMU/HFWY4joZn18U7vMC2anzb4fliIMMTSw/mPdY+LVkszzQilUnMCrDDCkXQ8cJEvf8A6nwOiBWJtxZoeiI0mvla+YMgd5TCmIYlW7EpFOiUFwHwF6nlXabj7xkPfWdHmyuADZXgJJME6ef0aHkiz0OP/FqeWDVcsnrFKXqwRIiUWIA9jwpxwuMuw6SSwevXLNFXqb1KDjbsWsN1Njl/q9njjzD7VdvQufqAkt2hBHInp6tVDiOoLApuTX7XvJSDOSl9H8fDtn2sM+6HQUKrsv8KibV4POjVxpI0qTS5WsnU/8vGqgMgXpX9aC/Z4JP9LK58yNAYGHi8nD/3K6eTVQ5KzhmP8TtOfd/YffIN1teQOpto1r6nGvHPkq9W96/JncdN2RFr2bFq5PCPXkDCV++sZj8aHETif0TCkpBSboumyZPXUTFEOReqIZuNxlx+rMidCBnEwLh/r/zihPvgrOdnuZartg0kQWjknjh9uF/P4632MMVl9f21fNpyEjk0/I2rpxyfnO2Pixr5a0DTQAsKE6SY0ROw9yixIgKTR8mZVlIMmuRJAZVpvoQq1ez4TsL8QdFXthZiyhJbKxoRwJ6XD4umZjOWwea6HH5+fE7ZRQmmyhKMbPpRDtfXbOfomQTb359dgTZ1KuVYULY4/LzYVkrzVY3z2ytocXm4Zfry1l/Wtba5hMd2Nx+bG4/Sx7fxLv3zuVIo+yK6wmI/O4TWbqZn2gcVNk6HW/sa+AHbx+lJDWGexYVUt5i5/a5eWTHG3hoRQl2t58rJmfymw8/wxcU+eBIS5jU9aGqw4FJqyIlRkdxqpl3BoSaxxk1fH1hAbuqu7hj3vDy3oH7ef8FRWyoaEdC4tbZuXz39cMca7azoaKdjx6QFRG///gENaG+QrVSYG9tN6Xpseg1SrLiDfz2yvFIktwmsWJcGmvumM6rext4/0gLi6KYsmQnGHguinnKCEYwgnPDlya/PF+Z5uebIJ4tbrrpJt5//31ee+017r33Xi655BLi488+m+x/YzjlgWo7Tk+QsrZa9IknuLr4arma0RMyM+iuCpM6tVL+XDQqBQ5PgM3HepAkuU9Jr1GQkRBJZvry57od/kHPD5QwtvR24QjYMKniONlipyh9CBmGRjmoYsP2/4HNv5ZJ2Vc3wdjLIBiAkosp7OggqfF1ebmuGEgsjryuJEnubwlBKYBeo8CoVZIRr6XV6iUQ7L+mfX6R4oLYcE9Eu82HyysiCALjcszYXH6K0k4jtmIQDj4nO3KOv36QScGelj08suMRpiRP5udukV/3VtFW/yEPL/0bSYZQtU+hhIt/z6H2Q9z04U0APLnkyX7p4RmwOtjD77t2o0XBzLRZLMiKXC/FkMIUVRzlvi4WHHyLttIfo9eqiDGo+O7U73JhzoUUWAowq/Xy5/bWXdBymKSTH7Hk1q0oFPQb3gjCsITO6rGytWkrs9JnkXhant60Ikv433qtknarjxiDEtMZzEz68Kf9f+LF8pf5dvGT5JkLyS4ogoFVXZ8DHHJfCj3VkaRuzv1yjmDaRLBkkwgszFzIpsZNWHQWlBpDf9Wv7QjU74Ds2ZAyntiQrNKkMkeVOeoUcsVbpVCjivK6UlByedqNiASHlEmeDkmScAedGFQmWp2tdHu6GZMQGUswPW4+apuaPMOos9purauSatdJADKcOaRo00nQJJGoSabKWUG8JvGcCJ076KLD20aGfjAZNljbuOSdJ1EGg2BayuyUxZTZD1BiGscR2z7Kew8zyTJzULTDzPgF5Ojz0SuNWDTnlhsJsDL16kGxCgNhMWgiHA8HQhQlatvd6LVK0uLkKsiE2OmYVbFY1PFnzOU7ZyQUg7Odvb3y96R1gDxuIOq7XKTEatGqlMwtTOSOuXk4PAG+vqCAsiY7QVFiTuEA2aW9CY6/IUukx1wZ6rc7Ozy5sZKXdtVx/wVFzMxPICVGi0qhYFK25ay3caC+h3a7l2VjU9jzwyXhqJNoUCgEtlR08sv1cl/eLbNy8IsS9y8pQhDgWJOdE229dDp8bK/spCjFzNZTnfgCIsea7bTZPeQkDP5cilLM/PGaCeys6uJYi53xGRYy4/QsL03lhR21YSfHgbhuehb/3F6D1e2nze7lk2OtrBiXyuv7GtGqFPgCInqNkiSzlkBQxOr2R62U/fyDcvxBiaNNNv56w+RBstCvzc/noTePcskT27htTh4H6nv4xsLBKofPy9u4c/U+jBoVHz8wj4y4yInUB5dH7z0VRYnbnt/L7pou/nTNpEHHOSrFzPaHFiNJEgkmLbkJRo4128kJRSA4vQGe2ChLYY0aJRqlgqv+tpM5hQm8dOdMjjXb5FBxCd76xmxKM2KZV5TEvKIkfn9VcMgKs9sXxO7xh3v9RjCCEZw7vjRSp9frufTSS7n55psZPTqy0fh/Ay699FJee+01nE4nH3300b9kmPK/CYlmNU5PkHL7Lj6p/rtM6gQFlF4tV77y+k0YZhZb6Hb4STBrcLvdMilCQKmA9HhdVAORiXlmqtvcZA4gfJ12HzsqejDqVCwYG0ebv5zd1j3MsFyG3tAF9M/49jj8tNt84dy7QejLeUrIh/I3IDEPZtwLegtJMdahD1oMwK4nwNkKk+8Ih0LvOWUjKEIgKKFWKciI12FLCyCKEjqtgtQ4HTF6FbPjmxAbdlFrmoSUMJoYg0yABsHVKVc7lZp+ItFRPojUvVP1Dk2OJpocTUzJuJNXHB8AUHLyDb4+8etD7r4wMC474IW1N+Bw+ZAu+g3mzMHfO5deJiFeRLmasOV3ULsdlj0GDXugaR/qRT/iefMEOLCa6vw72XHCJru7TUxEr1UyKXkSXb0+3j/UgU6jYGHiWNQthyGhIPK4o8HViXTgOQKCmp87q/mkZRPjE8fz0sUvRV/e76bWUcHf6n/JtNRpTBO+f+b3AD6q/Qhv0M1rTY+x7rJ1g18UA1D+NkGVgW5FGt3qaUTUdWLS4StPD3rqz4v/zL62feTF5g0mAlWfyHEbVQ5IGc/E2Blk6/MxqWKi5tiNjZlEgiYZo8o8ZA+bIAgo+27PAa9M6IfJO/uo/W3q3VUUaMfy440/x+F38Mu5v2RVweDA9iRtyjnJLtN1WcSq5MrJ3p6teEQ38xIuZGLsDAoMJRhU50Ze3m1di9XfRYlpHAsSl4efv0A7BXUgNNnTXUN6zmzSdXKVdXPDR7iCTo7a91FoHM3H7etw/j/2zjs8jvLs+r/Z3rXqvTfLkuUq925jGzDYGDCmGAi9hE46SUiAJG8gIQ0IvXcwvZhqG3dbLnKTbFm99+195/tjVivJKrYJ5M33Rue6dGm1mp2dsjvznOc+9zl+GwtjzyJR+6/1Lg9F6E4FVa0uDtRKcTuLxkdj0iqQCTJyDN/TvS7vLMheTHGPnx/FNA1JNv7+5TH+9PlRJqSaeffmWSjkMn65vI/Yf3TrEDLdplLpmmRvgczFoDt1cvzslho67B5e2l7HRSVp7Pj5CDLhIVDd4eDCf24jEBT5w6pxrJmaNpSv1gAkm7XShKIIF0xOZVyKdE3bWd1FRat0PsYmmlg5UVIVXDMnk1arm4JEE+nRel7cVsP2qi5+tDSfjBg97TYPd71Zhtcf5EdL87l5QQ6fHmyhpsPB8z+Yypy8wfL5OJOGO87I49fvH8KgVuDxB/jJ0nx+MDOTzNA6NUoZsUY15z26lX31Pfx2RSGXz8gYsJ6kCMnkJS/eMKjPr8Xq5vXdktpncrqHJ9ZO4RfvHODN3fXcf14ROpWChm4Xoihl9F34+DY+uXXuKctqrW4fG49KTpifHW4Z9Hnq3yP3lzUTuGZOJluPd/LO3gaWjE0gUq+k2+EjzqSh0y6ZUrVY3Dg8fv782VG8IZOVhm7XABOZkQjdsr9uoq7LyV8umjAaPj6KUXxLfOekbtGiRXz99de4XC5ef/11Xn/9dSZPnszatWtZs2YNsbH/Btvm7wj9t7W2tvZ/cUv+PZiYZaLW/w1fVj7F2Vlnh58Pxo6jKpiNziOnt3CmVMiID2W8GQ8+yRynQFvMPA7UJnCsycnC4uhBxCtCr2RilhKry8/xZgc6jZx2iyRttDr9NHV5yNRORmX4kK3uv/K7Kb8Lv1YURbYc6cYXEOlx+AbLNyethaSJVHUGqLSoyfXuIdPZKfVXmTNg0rUg+iG6X4XC0UngrSsRajYjCDIEYypE54aMTkL7Hqo4y2QCRelDVJ2OfYzM1U2W3gZFw1i4d1RI/W4AiZPA55DC1vvhovyLOFa3kcmqKKYpZSSos7H4WpmVPNgdbkLcBJ5f9jzeoJfpidP7/tF2mO62FjZOfw3q5cw1+4jqZ9ByzbhrEBDocHVwY94a+Ftoezf+EQ6/Kz2Wq2HZHyB1GkHjHOiQZGXnvb+K2akl3DP9HjqsXvxBEbs7gG3xn4madRPEDj0j7PUHaehwExOhwqRVQNdxBHc3SmCZZgmfsQFBEChrL6PeVs+yjGXIZXJ8AR+f7Pg7KXYvMoWF8q5yyrvKub74eiLUI4e9A9w15S7eqHiDtWPXDvxHZ6VUmfBYkAOdyhiOtMnIzRBPmpslCAIlCUNIhBInQ81G6XcIve6NwyFBExq0+L2w/ucQ9MHS34PqhNn2nlrY87SUwTftlkGVz9KerRyy7sUnSgOrBlctTr/kGtrmbBtxG04FBoWJNSnX4PDbeblBkuXafBYAjCPElgwHb9Az4HcvIvLOhbP/LFVQiweGsY8zTeGIbR9FxsnstWynznUckPb9jLgVp70NI6HH6WVvfQ8zs6NRK+RUOsqpclQwMWIaseq+ga8mdG2TywSU32He2oiQq4gzydlb183nh1v5+8UTB5CBF7ZL96iDjRaCwZN/ngEpxqX7OBiTQGs+rc354YJsXt1Zz3Uj2ND3osXi5p8bjzM9KzpMIGQCCCGZ6pPfVDE1M4qsYZwpXd4ADq+ftGgdEVolHXYvh5stjEuJwO3zc9eb+wBQyOC+lYXh3rnECG04LNvi9PHL9w4BErn40+rx4fPnBdShTJzffHCIZoubVqtnSFIHcMXMDC4qSWXVo1u5/6NyNlR0hE1jekO93b4ABxql78qumu5BpO7V66ZTWtvNzOzBklS9Ss6c3BiaelxcOCWFzw63sG5vn7x1xYRkLpmWxoaKNr6uaKepx01lu53J6afmRGvWqfjpmWPYXtXJjf1y4dqsbhAYYPailMvYV9/Dg+slWalGcQB/UOSm+dlcNTsTty/Ae/uaWFqYwKs76/gyFLVw9exMlhZKvdxvlzZwsMnCLQtzBxDGXtR1OajtlK5bBxsto6RuFKP4lvjOSd3nn39OU1MTL7/8Mi+99BIHDhxg9+7dlJaWcvfdd7N06VIuu+wyVqxYgVo9fPPufwIaGxvDjw2GU7c+//8VgiBwXv5yzssfaAxzvMXJwTo7AIvHKwb0YCEGwdlJdNBHp09axu0L4vIGBlfTQthyuBt3yPlMq5KRHKVCpZSz57gVEbil4H4yxePgdYEmEl8giFwmoFXJ8bn8aFXDyL0SiqhsbschC1Kpn0NmdD8JbNQQA499LyGv3tS3Hwpd+Dho1TJcniAapYx9VVb8QZHxGUaUoRu/zx8kKII6vhhqN0P88FItEsZD5zGpQlT6smQ4U3D+gEWKY4t5o/hWxJZ9VGtyuTN3DYVpBtJih67kTIrvF3od8EkOlfHjcOevRAxVHzzeANBH6pQyJTeMv0H6IxiUsulqNkPR+dB5HFoPQtJEyUxi4mVkiyIas4e/7PsDjc4q3q1s5J7p95ARp8PmCqBVydDpBb60tDHemzhk3tq+KiuNXR7UShlnTY6F+HF01hzE4VcgGibzx7l/JDMik4s/ulhyO3R1cEXhFfxp9595+dhL6OQmnki7m2TDXqYmTD0lQgdQFJHLlIhxRHZUQXxJn8lP4w7wSAMtry6ZdtVYsmK0/1oQcuaCAVXsMIIBqT9SroTUwa5zABz9BHY9KT1OnQ4TLh7wb6+lBmXQh+D1SRXfE0jdYds+XEEnJoWZJE0q40yTyVpYSL2tntV5q7/9Pp0AvcLAsrhVdHjbGHeSwPWRsDx+NY3uWnL0Q1S0Sq4e8jUTIqYyIULqHyu3HQg/n64bKEcr7dmK3W9jeuS8gRl1p4FLntzB4WYr501M5uGLJrCx41P8og9v0M3yhD6ymRKjwaCVo1LIBkWNfF/YV99DZZuNL45Ig+ZPDjZz3VxpQC6KIl0OidjHxjjY0bmdIuMUjJqTVG5MKSE3zNPHlbMyuXJWJi0WNze+VEp2rIG7lw7Rywg8uL6Ct/c08OL2Wsp+vQS9WkF6tJ5HLpnM9S+Vcrzdwcs76gZUFndUdZJk1mLSKFn210202Tz88fzi8H5WhXq6Pj7QQn2XZNI0NikClVzOI19XsnpKKrHGvjGGQaOgJCOSPXU9zM2TrlVRehXv3zKbui4n80MEbmlhAi9sq2Fp4dDZc73QKOXhe1wvIXx3byNPbKoizqTmkUsm8dc1E9h8rIOiZBNbKzuYmdN3jTTrVCwqiB9y3dc8X8rOmi7OLEpgUlok8SYNyaFw817ippTL+PPqCax5YjuV7Ta2HGsfQOp213QRFPscM/uj2eKitLab/HgjOXHS2OZgo4VVj21FJsB7N88Oxy8ApPWbPHCHqnCROhUxBjWiKHLu+CSSzFo67RGoFDLMWiU3zMtGEASaLS7ufnM/IvDNsQ7W3z53gJKntLabS57cjlYpB0Re2l7L/Pw4ZuUMvp94/AHcvuApudGOYhT/jfhe5JdJSUn86Ec/4kc/+hFlZWU8//zzvPrqq7S0tPDhhx/y0UcfYTKZuPDCC7nsssuYO/fUeoL+3XjzzTfDj8eNO0Ub9P9DCAZFfAExTKKUciHcSxeGIIOJV0JnJZlJ4/C0C+hUcsx6Jb6gj31t+xgTNQajqu8GoZALEFJaef0ik7Ij6LT5qGlzgQiyxlJ4fzmoTbRc8BHbO2PRy/3MLk7C5Q0MG8pdelzKMVMrBeLjVLx8+A1mJE0nK3IYs4nMedhlCnwE2RyTyjkTLwPA7vYTDIgoFQLpsToON0hkNdqoJDNeh9MT4KsDnQQCIrMKFhKTs2zo9fu98MmPpcy7sx+Cw+9JweYAx7+GsecOXL56K/byjeyfcxv4guFg9oEnJSBJ8UCSvJY+CT01MPZ8uqIyeTVeRoGhgbFRY0c0aUEmGxg2nrUAnloIH9wmzdgXnIMgCKTEaLiw8EzafFUsy5D2U62UMSVHIlf3bL6H946/R4I2lfvGv0JxhjEcQQCESXD4c6PUYRh3DsH6I8xM02MwnUm7sx2FoMCPP2x60lshFUWRYMw4Pp376fD7cgKsXiuPfHUb95vGgq1Zkrr2BsunTJfMUaJzUeUtZ07vcRwKXVXg6pSqq7LTGLgH/VIcQucxqPxEek4XO8j5E5BItCFeek3KwCrgIeteNos7ScpK5hwKqFMH6ejZRpFpsmTTD0w2z+KIbR8TI6aHzUHmpnw/aog0XRZpuoGTI0NFeoyESFU0kUNEP5wqxhjHYVKa0cn1mJV9g9U2TzO7e7YAYFJEMNE8fbhVjAibR7ow2dzS7zRtFlXOCtK0gyeFzPp/38By/aEWrn+xFKVcoCQjEpcvwLLCPmm6IAg8eEExT+3eSnFBO9c9psHr/YJXrp1+0upNj9PLlspOZufEfCtXzBe21fDJQUlWfs74pAFkoBfjkk28vQdy4wwDJHgLxsQxJzeG8hYbZ/aTAL6wrYZfvXcIo1rBs1eWhA1fGrpd/POyyRxutnL1bMn8Y25uDCatEqfHz7WzM1n7zA56nD4ONFj459q+6rlcJvDmDTPx+AOoFXJqOhxEG1RkxxrIDlUInR4/nx1qQaWQMTndfNJ9f+aKEnZUdzE7N4aXttdyz7sHATjcDNurOhmTYMQXELnj9X3hHLjhMu36o8clEddup/Q72axly0/7nJG9/iA9Li9xRg0uX4BAEN7b38StiyUlys7qLlY/vg2AV66dNqga+NL22rBxzsqJyRQkmqjtdIZlk3VdzgHncVFBPPetLOLBT8vRqxUsHZvApdMl06l73j3IyzvqOHtcIo9cOoldv1iMWiELn2ezVoVJK2UEVrbZOecfm/n18rFMy5KuA4eaLOFMvF58eaRtEKmzuHyc/bdvaLW6efLyKcwfwnBlFKP4b8f3nlNXXFzMn/70Jx588EG++OILXnjhBd59910sFgtPP/00Tz/9NGlpaaxdu5bLLruMvLxTa+D/V/Dcc8+xZs0aNJrhB70PP/wwH3/8MQCZmZnMmXNy2/D/SxBFkQ0Hu7A4/RSlGVhYHIVKIRtoTgKSiYrXAdlnoBQExvXjT/dvv591x9YxNnosry/vIxBzC6PosHqxu/3EmlTsq7ZR3+Em1qQkJ1FPwuG9BGQqjqRfT1e7iCiTYQ+o8AeCRPW6copBQBgQbt7Q4UYEjFoFu+sqiZTPZ38F+FId5CcP0fuTNAHLHfv5rPZzFqctlioqQLvFi8cvDfQ1ahk6tRx/IEi0SXpvpyeAL/R/q9NPjGmY8Oiqr6H0Welx+gzIXQLxRZKFfvosmrrctFm82Fx+IvVKirSR6J11xHdtw5owi+SoEyrZxz6F2o2QNhvyzpYCtntqABE6j/FI7ce8cfQN5MILbLxoIzB0lc/m8lNWYyPSoOwL33Z3Q4dkiEHt1gEh3dMSpzEtcdoQawKvT5ot93stNHe5MGjlFKX1DQbGZxhJjFQPIOLqgy8QsDeg8h6DSdcQq4vl9eWv0+RoYnay5N52d8md5EYUkqTOpTjj5Jb5gWCAv+39G26/myvGXsF2VyuHNcnEaWMQjRFsbX6FRE0qU6PmDKxMvH8r7HsZltwP0/v1Lrq6Ye/T0ufM54SMeSfdBkAiZzv+Do526TWCXCKEmmEGcuY0uLMcaTZj4HeryS1ZvDfL3NhTpvJpw+OIiHiCbmZESZXBscbxjDWOp8ldz7N1fyNKGcPZ8atRjNB/912h3lXNZ23vEaOKY3nCRf+SA2a9qxqNTDtA4jgcenvt+sOkMGNUROAKOEjQfHuTqheumsamo+2cM17K4Dsj7lz8Qf+/5XgOB6vbxxu7pP4qX0Dk1+cUDkkMVk1KIajKZUOlBptTBgQ50NBzUlJ3/Yul7KjuYmZ2NK9ce/pkeG5eLM9trSEzRj+gotMfV87KZElhAtEG1YAqjUoh48WrB19bei39Xb4AbXYPepUcs07J2hlpROnVLOlXRYsxaij79ZLw349sOE6P00dCxND3d7VCzhu76/nxW2Ukm7V8fufccFbbxwebaQoRyEe+Ps7s3JEnSMw6KfT7cLMVQ2gySwAmpZv5+EAzb+9ppDjU9ycTBGlCMwSLy8eL22qYlBY5oIIH8OTlU/jiSBvLiwe7iPoCQc79x2bKW2z8z/nj+MmyMfxzYyVTMqLw+oOoFDL8wT6S1N/gqxfz8+N4YVstWTF6MkLmMWcWJfDzs8YgEwQWFwwmTGunp/PXL47SbHHTbveEj9nBkMS0rLEHYFAVTauS88yVU7j11b009bg53GTl0Q3Hw6Ru9ZRUGrpdvL+/iRaLm+RILT+YlRF+vSiK1HY6cXoDNITCzz850MK8vNj/NSO9UYziPxX/tjuVTCZjyZIlLFmyBIfDwbp163j++efZsGEDtbW1PPDAAzzwwAPMnDmTb7755nvdlnvvvZe77rqL888/n9mzZ5OdnY3BYMBms3HgwAFefvlltmyRZn1VKhVPPPEEcvl/V+ilPyCGK0UH6+xMVUfg8vqwu/2MTTWgUsgI2lopPViPW9Ax2b0XXfpAWVanq3PA716olZI7Zm27ix3HLOGbjtsXJCFSDdNuoF6eyzGk9ZmCHaRpbRh1IamKox12Py4NgktuCg+YizOMNHZ6yE/W0W7pG/i1t3f2kTp7KzTugiMfQ8oUkqdcxQ+KfjBg+5KjNbRZvMhlAinRGtJjtQMqEjEmFePSDXj9IulxI4Q1J02EyAwpbiF9tmTmcqP0uXp499/YXFPGWXE3E6VKosPqI2PmzzBkzWNmbAEYhxhMdIRiGtqPSKTO3gbHNoJCCSU3kytFC5FqTEWnGDi4aupyc6jOTqQ5gBCU02YJ0Gbx4vL6mZgVgcycBmc9JOXgzbp9+H06Ab9MXcp0axtT1NHUi80kmAe6SMpkgnRO++Fl6zH+0LaVgs5SXosdiyx1JlnmLLLMfdUQlVzFhb3E0tYqyRSzF0mV4YjB/RabGzfzzEEpMP6r6l38NP8yDrbsZ+WUm9jsLqXF00iLp5Fi05SB5iSH3oWgn+DBdwmWXN8X0ixTSD8BL6JCA6KI0HlckkoWnAMZs2lxtPBN4zcsSF3QJz31u6XPJyL4XTDrbonYqY1Qs0Gq3uWeKcne+g5S3+NeUx2FhqnjViET5KRps1DL1GjlepwBOyaFedD+1zqPI7ojcHSn0aTtIc08tHX8kAj6ae/swC03DTJsGAl1zir8oo8WTyNOv/2Ue+z2WXZw1H6YqZGzydDlUmk/wpcdHyIgcGHSD75VJU8j17Im+RoCYuBbm5+AlIeWeUJO1v8moQN45KtKvixvQwCyYvVsqewIk7oyyy6a3PVMi5xHc4eSH73chCjKObMogSSzltUlJzeS8YUaiIca/J8KpmdFc+g3S086wE4yn3qw/Q3zsokxqMmO1fP+/iYc3gAObwCb20+UfuS2jTdvmMHRVjsTU824vFK8jFYl52irjZ++XUZhUkRYNtlscWFz+8MEZfGYeEwaBQ5vgCtnZeD2BZAJAirF4FaC9/c38ZO3y8Lv8ccLivnrmgmkRumYlBbJeY9K1/oep49Xr52OUaNgTIIp/PoH15fz0vY6VHIZpb9cjFGjZGd1F89vq+GiKanhSuSJcHoCHA2Zwuyt62FWTgyHmqwcaLSiU8n50dIxzMyO4R8XT+Rws5XilAj+/uUx6rqcBIIil05PpyQjigP3Lh2wXplMCMt5eyGKIoearCSZtUTpVUTqVHTYvUT2q1KPTYpgf4MFo7rve9Lt8KJTy1ErpHHT5PQotvx0ET9bd4CPDzRz3sS+a7hGKefnZxVw26JcjrXZKU6OGCCH/8W7B3llRx3LChO45+wC/vrlMV7fXU+8Sc2dS4aW+45iFP+t+F+5W+n1etauXcvatWtpamrimWee4Xe/+x1ut5vS0tJ/yzZ0dXXx5JNP8uSTTw67TEpKCs888wyLF5+eq9d/OoJikM2Nm1H6kohWJZKTqBvUU6RUyChON1IWcnhr7vZQ3yHNYKoVMgpSDXS5ZDQopYtqg8vKiTXWe2fey4fHPxzS7KOmzcXeKmv479db7uHsnDOA1aBQEVG0GPnhLslERRaDkJDR92JLnWQ2AmBtCJO6/nlgE1NEGmrrUYgexsaqgbQ+uWL5p9B8QKrQ5C0DUxKiKLK/2obN7WdilmmQEcuJA5acxFNw/TPEwW37Bz1db6vnmUPS5y6qO4nlCTcTZVSi0ygha/7w68s7G+q3Q0poZvvQOqkPDqCrmjVj1jAreRbRmmiUciWlraX8ufTPLExdSL5ilWRs0hLAnfQBGpYjIFDX7iEx0kNSlAamXgtAk7ueNssOxhonoJKdMIDy2OCrB8AYD7PvwBhfzKqmsaAykjq5AEE5uGpp8Vi4Z8s9KAQFD8x+gEM66Xwd81nw1GxAmzpz6P11dYO1Htb/UsqL++yX4LHSMfFOyjJ/SFG6gbgIafvyIvPQyY04vF6OH1nKnoYX+LH9IARU5JxxNw2uGhI1qahlGol0HXpTMtE58w+497zBzoRr6CntIMGsYkKWCZXaCNNuw2HpYkOjCUVbB4vL7kResxEOvMlLy3/LQ7sfIiAG+LjqY55dFqrIqgxQeKEUW5G5QCJzXrv0U7meoCiyaf/TZI6/knRTX1nbHxARBJB3Hg2b6kQ47SyKlfpbfUEvkyKmYVSYB0kgAYqME2mtykT0azheL3Ciq7wr4MQX9GJSmge9tv6b5zjzs1hcQQUvXj1tUNVgOIwzTcbmtxKjjjst05TSnm34RR9llt1k6HIJ0ldVEAmO8MqRIRNkQ7qNngr8QR8WfzdRyr7Z/4AY+Jfz974L9PY8qZUyjrc7+P0n5VwxMwNB5hsQUp+qmItcEPCLIueMTxoyK24oPL52Cl9XtLFwzLeXs33XFROVQsYl0yR539++OgZAtEHFnrputCo5b+5u4JUdddy2OHdQyLdRo2RyeiTVHQ5WPrKFoCjyzk0zeWVHHXvqethT18OHt8xCo5BTmGQaYKFv1qsou3cpwaDIwSYL5z/0HhentHLWoqVEJQ383r2/rylM6FRygSidkpk5MWGC+OAFxby+q57lxUkDQrt70dsj5wsE2VLZybKiBH79/iGONFvZdLSdJ9ZOYUZ23wSH0+unss1OYVIED180gV3VXRQmm/jluwcJhvh4h73PgOjX7x+i0+Hl/f2NNHT3RWB8crCFsnuXoJTL8PgD/HzdQaxuH/9zfnHYxKSh28l1L+zmSIsNUYQ4o5qNP1rAmzfMYPOxDhxePxanjwidklartO5eo5NPDzZz08t7SI/W89Gts8PHA+D3q8bx+1WDW1kcHj9flbcxNTNq0FjkcJM0TjjcbOVvF0/kT59JipLGnqFjPUYxiv9m/K9OQW7bto0XX3yRN954A4/Hc/IXfEdYv349H330EVu2bKGyspLW1lY6OzvRarXExcUxYcIEli9fzurVq9HpTn3m+v8X/HP/P3ls/2NoZUbuyn4FpSJuQDhyL7ITdchkYHH6yUnU0WnzDehpM0dHE9PSJlXY0gfPKsZoY7iy6Moht8ERCvYGqHRt5ZBlOx1HqllbJJk7RBqUzB8XxVf7uxCRpCthxI+TZIcyOcQMPVOXmZpEptYG7qBUMQNJqqlQQ0QitByE2ALQSTdNmytAdZsk7ahucVKUbjylgUprjwe5TAhLMINBkYZONxE6BRFD9Nw0drpp7zEyI34ee9p3km+YjgjMHBM56GYWFEX2HLfi8ASYnG3CEJ030L1zzHLY9yoYYsP7mGpMlQjEvud5tuY9yizHONB+gI+XX0yn04pNdRSPaCdOJ8fmDCLAgD4ab9DDxy1vEiBAi7OJuTFL0fXL76P0OdjxGACelKm852kkv2A542PHIwB17S7kMiGcUxgMirxbVorXHsOG7rdYXLmGi3JuxuD3MM0fRJs+f+gDK4qw+5/gsUJigfRz8H3wWPHX78IS7+dgfRcV1e+yOH0xuZG53JL3FPe8cxREJfm9lc6IVExiCmuSbuybbW/eI5FFaz1Mu5XqmHPpbHBAUKSxy0OkwSXlI+qiaLNp8PpteBFxRRdhqNkICcW8dfQtAqL0Gbb77LxX+R6ZmnlUtTtYXjweRWLoM9d+BMpeApURYsfyRPWHPNJ0GGP9ej674DMMKgNddh+bD3ehkMtYOGYcmphK6XMa1TdrvrnzC446DqEUdaRaLmJydgRRxr7zZlRGkBwBDZ3uAc8DOPw23mh6Fl/Qy7K4VYNIYWtnN/aARABqOp3MHKL1byiYlGaWxZ93agv3Q4GhmMO2ffhFP76gjzxDISqZCo1MS5QqloDoxy8GUJ84oXASOPw2ggQxKiJCf9tRydQjVu5cAQdftH9Ah7cNb9BDkXEyM6MW8GHrGzS761kQcxa5hsF5f/9OXDgllZKMKHbXdHPvB4eYnx+LRilHFGUka9JpdjeQrssmS2/gg1tmY3H5mJ516tXOWKN6EDEaDlXtdo4021hSGI9S/u0I9OlCHqpku70B7nh9P0XJJlosbjrsXl7aXjvktkvOkz1YXJKM81CTlXPGJ/LZoRZSo3TkxhkpWmoe8Bqb28dlT++k0+7huR+UsLeuh9/mljPZbMNV8QZH5TeTF98nLb9xfjZWt485uTEYVAqueaGU9Ggd62+fi0YpJyfOyC/OHv6zkxWqCIvAX788yrKiBObmxnCk2YrN7eeq53by5vUzSY3S8sWRNp7bWsOBRguXTkvjgfPG0W7z8LN1Bwes88sjbXQ7vETqVWFDGYvLj04lxxkioC5fgDabh2Szlu1VXby9pwGAWdmNXBkKN3+rtIHDzbbwerscXty+AJF6Ff/4upLyFhufHmzh2R9M5VfLx5ISqWVxyPRlT10PQVGKrOi0e9FFnXyY+eO3yvjoQDM5cQa+uHOg1P1/zi/m1Z11rJiQFJLrTmVHdReXTE076XpHMYr/NvzbSd3x48d56aWXeOmll6iqqgKkEr9Go+Hcc8/l8ssv/963IT8/n/z8fO68887v/b3+E+EOSDNcAdGHKAb7wqKHQH+yt3h8NIGgGB4cK+Qy5hSfvAdmKOQn61DIBUxaOe42N7HtsVyUP9DK3KRVsrA4Gpc3EI5PAKS8t7Hnc1L0Ej4xiKtmF0q1GkXJjTBmBZz7OKhNIJf2Xa+RExehosfho7LFRbvVx/yiwbOGvetDkNHc5Wb7UamfYF5hFFFGJUcaHBxtciCXCZw5KQaZTMDu8mPSKQiKsKvSgihCmnwO+2W7MSliiNApGOptLA5/uDpa3+6moLf/rRfR2XDz9sEvbNkPHeWcIzdSpjKxMP0MUqJ1RJtjOGitJ1l7PpFmgc6WFkwpBej7BXrLBTkauQ5HwMarR97gUeuzrDt3HQqZAlEU8SZMQS1Xgy6KR5s38MzR11DL1Xx54ZfY7WpKj0uzqrMVMmIjVNR1uDAFClkWV4gz0I3XkkGVBa6d8iCxEaGqXuWnBGq+Yb0hgpTsZRTHhpxEew1Mesn13LuhqYzu1CvRBmWsb3mejxtf5OPqj3n57Jd5rvY2tAnpXFF0MStmPgtdVZSL+Rw50IVeLeeMCdESUY8vhrZDUqVOH0uOVobXF6Sh0406YCEeGyAxm5RoDV02Hwq5gG7aAzDrWohI4fq6L3jm4DOk6FP4ov4Lfr7x93iqFPgDUNHWQV5WFXNS5hBja5Q+Lx4LZP8AX7AHyg7jF/3hClW3XYr1CASD2PxqNBMGXwOFUAUqEBSwuwPUd7gGkbcpOSbGpRsG9b06A85whIDF3z1o3VPmreQPynJ6NBlcOOXb96OdKkxKMwECtHtbaHY3kKbLJEMn5UN6Am7eanoeZ8DOmfHnk6LNOKV1dnk7WNf8IqIY5JyENdj9Vr7s+BCjIoILk65EKVPhDrgos+4mTp1IRsg5s8ZZSZO7Prwei78Ln+gL9zPWuaq+d1L3RfsH1DurmBuzjGz90JNUGTF6MmL0XNDv/AiCwPKE1QOk4QWJpiFf/13A5Q2w8pEtWN1+bpyfPSjYektlB1qVnElpp2arf6r4y0UTWH+ohff3N7HteCcCAjfNz+G1XXVcO2dw1bq0tpvLntqBTtVHOgUkCWBGjJ6txzu594ND/H5Vn2NxIChy7/uH2F/fA8BX5W1cOi2d3W1RgI3STiWXPbwpvN82t491exqYnhnFDxfk8JsPDgNQ3+XE5vYPm8fWHznxRlRyAW9Aci7dUtnBz84qQBDgnxurEEVY/o/NZETrqOl0hnsR60N9Zf3NRZQyAV9QDAevLx+fxG2Lc3llRx0pkVrm58fx58+lCteC/NhwlbA4OYIxCUaJnPaLb5iYasasVSKKQcYmRXDTghwiQ1W83r7AXql6Roye367ok91fPzcLp9fPmISR5dwPf36UbVWdLMyP49NDktFOMDhYApyfYOTecwvDf0/JiGJKxqnnKY5iFP9N+LeQuu7ubl577TVefPFFduzYAfS5ps2ZM4e1a9dy4YUXYjJ9fzekUfThhxN+SKYpkyxTHrnm9AFuhSNBLhOGDBX/NlDIZeE+t4ujLuacrHM42n10kCnBkGHep4naY+Xs6UpDH+xmkaYLeb9qV7vFy/ajPZi0CmaPjeRgrY2qVhcWpx+PPzggPsHm8mM9to3Etk+QZS2CyNnh//Xyjt7DIwiSZLW61UWX3UdWvJbiDCMROgU9Dj81zoM4/Q4SkjqYl12CIAbAYweNecC+x0WocHoCJEUAFR+APlZycBwJ0bmgMbPUmMTSZf8M2/lr5XpKImdLBis7/kiKzwGKRZDdJy/2+gTOir6Mx8v+h7K6AyBT4gl4UMgUbCvvodWSxdjLj5CfpMNU/oq0XoUWpUwZJvuCAEqFdCCMWgWBkCX8mJi+am5/wwBay3jJVslDjYdRHHuVT1Z9QoI+Ad/E69la6cDsqcMhP0KNTsPFyx+mQBAoADZ8I8kUkwxJtDvbaXW2oDC3EBE9GbSLIHkyzuMS6XZ5AwRFkAuAMRFm9k3oKGUwPtNEcSKw7VGEwx4QV0FyCUqFjMk5/aSFUdI+nJl5JmdmnklFVwUbGjbgAxQyAX9AZH3th7zU8hrjY8fz0pKnpPBwXQwY4rlh/A1kmDLIi8zDpDIRCIq02l3ERSqJ0qmIMYWImihSuu9JNjVtJTN/JcuzlpOqyaCtzYRLpxiyl1MQhMFGRkiB4/NjzsThtzPWOH7Iz8uac3IHP/8t4Av6kAvyIWWQO7o3Ue+sYkLEVKKUMahkauI1SQOWqXYexR6QJgZaPU2nTOocARsBUeoBtvutdHglZz+b34I76EIpU7G7ZwuHbHsRELg89WY0ci2p2ixiVPEExSBJ2jSKQ86is6MW0+iuZVLEMFEUp4lWdxNKmYoo1UBpqz/o57ijHIDjjvJhSd1IEAQB3D1Qu0mq5McMnRk5LCyN8MpqabLs0rdAP3SVT0Skd8h9omHsZ4dauO7F0tNyeDxVROlVtFjcVLTYuGhKKncvzSfWqOaqYXrO9tR24/IFcPn61CD6UL9Xr0SwpsMZ/t/P1pVJfXseafmCRCMrJyTjCwS5/3g+v3VEU+uSlAeVbZIT8hu7G3h5h0T8Z+XEkBNnINmsZdWk5AExCv1R0WIjIUITNhLJjjWw8xeLmfDbz2m1enjqmypm5cTw0zMLWDUxmfMe2wp+wvuRGqll5cTkcGVyUlokc3JjOG9CEkfb7Ly4vRaHJ8Djm6pYPj6J2xfn8VZpA3vqenB6A1wyLY0ep5cHVvbJH0XA6Q3Q5fCy4pEtnFWUwG9XFHHXm/vpcfm4fXEuty8e2Fjx3A+msr2qc1j3yWiDmvtXjqO208HChzagVyt48eqp4exAkDID//qlJKttCPX6ATx95ZQh1zmKUYzi1PC9kTqfz8cHH3zAiy++yCeffILP50MM3Qny8vLCPXVpaaMl9H83VHIV5+WevmxqEERRMtaIygbNv0bIr1p/FUe6jnBB3gX8esavR1y2tLYbjz/AzOwY6jtc7Ku2kWhWUZxpGrKh3RKQKlwOwYxPJqf/sLelx4M/INJl9+H0BMhL0uMPiEQalAMInT8QZOPBLnyBPPJUPRS2H8GcPJ+cBB2xESoiDUpEUaTN4iGyey/jK36HZX8e3UUPAGB3BxAEgXmFUTi9PuoPx1KccjUzY+ewt8pCSsd64u37pL65NIksymUCswpCs97HP0Os2yIN4CKzJXI3HPRxMPsngNSv0dTmIsakRN9bkRVFKewaJIIH4Oikfucr7Naej9l2jJ/seoZbBZEDK/+GXimR7w6btGynUwZKLVcVXcW4mHGkmdLQKXXoImDhuCgEmSAFjQPRRhWLxpupt9exJvYWrM4AioZtGF69F/LOhHk/gtyzUTkbwHoYhaAI9zG1eXV0efx0kcv65q/Y0v0mCfoEFqZJ1t73zbqPS8dcSm5kLhqFhl9M+wW11louG3tZ+FAUpkkRCzEm1UknJAQx0HdcfM4Rl+1FflQ+b694G1/AhyKQTF2nk5dq36G9RcoFRKGRzmkISpmSc7LOlogeUtDxC9tqyYs38Nkd/WRHbYeY3FnNZHUivzj8GitzVpJtGEP2t4zLzDf0zaSLokggOJBYB8UgGzvXY/V1Mz/mTCKUp19taXDV8EnrOowKE6uS1g7oxwyIfvZZpAm9KudRLkz+wZDr6I0l0Mr1FBkHGi/5gj529XyDWqZhUsSMAfLoVG0mc6LOIECAbP0YUrQZiIhEqWLDcszeCASDwhSWZBoURs5PGlwZLTRNpNA08ZT22+0LcMmT26nucPDUFSWD3CZrnJWsb3sHGTIuSLpygBGMQqZgWuRc6l3V4Sy+4bBuTwM1nU5umJc1oE8JgGMfQ+sBaNgJC34zYgSHKIpsPd5JVqyexAgtHP+yrze3djOMHTrQXadS8M5NMzncPDB+AKAmlBmH2BdHcqqo75KqUCMZqby8o5Yuh5fDzdZhSVMvLpqaymu76jje7kAhE3jmyhLm5sWy+VgHHn+A/Hgj/3O+RGyCQZHXd9VLEz4yAY1Cxl8umkiMQc38hzZQ1+VEozCwfHwC8SYNU9Ijsbp9TEozo1PJidAqeau0gXV7GwkERb451sFdS/Kxe/y8tL2WoqQIZufG8Mzman774WGSzVq+unte2EBkb10PZxTEc6DRwqpJyax+fBuVbXb+sGocf149gT213QSCIhqVjMtnZAwIBb/1tb202zy0Wd1UtNrDz/f2uAHMy4vl5R11zM+P46dnDib7FS026rpC1zpfkDdLG7jn7LFhgvtRWTOltd388YJi6bMCxBjULC9OGrSuE7HxaHs4S/CPn5bzu36VUZNWwdnjEtl6vINr5mTyZXkb8/JiyYz5v58HPIpRfJ/4zknd5s2beemll3jzzTfp6ekJE7no6GjWrFnD5ZdfTklJyUnWMor/H+D/4j4UW/6EJyIH1e27/6Vm+Q5Xh/Tb2THicvvqe7jgn1sRRXjmyiloUOEPiNR3eqjvbGdsqp785IE3hvycFKhpI1KvQBMRI5Ga45+Bq4vsjDOxu1WYtH2h6gMqM7049in4i0BQgzYGsgvZUt6NzRXA7vaTEKkmEBTpsvuZVv0EkT37iezZT8XE+0iJNZARJ8lQZDIBg0bFrYVXwo5/sPHQcbrkibQGSziLfWANBd6L4oC4hv9p+JJXmj7mhshibjwhgHrE41Vlo6HTjUYlY+kESQ6KQg2Tr2df/UYSEopJAPwf3oaiww6TVxPRU4bMa0ct0+DqUvLUnje4ZNxySnIiaOr2SP1mSBWCqYkDB6JD9RFG6gxE6iQJm1kvgz2PQWMpNJZSnXstZfUx5Gbex6MFZSQZkojVSYTV5ZXkRZ6ANOhYnfgLYlR98jOlTMm42L5Z5zVj1gBS32Kdw052opptPZ/jUjpJ1S0Dhome6IU2EiZeBc5OSJo88rL9kBXRJwHLjjUwOevP7GjewdSEgcemxd1Ihf0AM6qOobI2Qf45tNukAWqH3TtwpSo9QSSyNTN90SlvS2WbjXV7GjlnfNKQUrxgUGTjISmqpCQnItz72OVt56hdGthX2A8yNXIOwaB4WqHsze4GggSw+Lux+ro5ZNuPN+hhbvQS1HINY40TqHNWMcZQPOw6YlQJOFyVZOvGDAoQP2o/yAGrZKQVr04mRTswf3KsaUL4sUauDcc+9KLINIkUbQY6uR658N3d+qo7HOyp6wHgq/LWQaSuV/oaJIhf9A16/YSIaUyIGCIyxNEJH90J+hgqJ9/DnW9IpktKmcAti06orBqTJVJnTDhppuI/vqrkT58fJVqvYvNPFqIds1xygVWoIXvhiK/NiTOSE9d3/bG5fTy64TifHJCsd01aJcUp5hHX0R/76nu44LGtyGQC7908a1j56O2L83htVx03Lzh5w6dJoyQr1sDxdgcqhYyZIbORdXsa6LB76bB7w6HsMpnAT5aNYf2hFm5ZlMvk9EhMGiVef5B2m3TezhmfxIMXjuf6F3fz+KYqcmL1fHHXfPb88gy6nV5m/P4raV0C4QiChz+r4OktNSjlAjt/vpjaTonctFrduL1B1Ao5X1e08YPndgHw6rXTiTGo2FktqQ9ufKkUmUwgNVJHVYcDpVzg2tnZBIMi5S02smL1FCaZ2FDRTnGKmdouJ26fdL0sSDRS2+kgPVrPA+eN47JpaWhOnAQIYVpmFNfNzaKixYbF5eXMokRMWiWvXz+dD8uaeGJTNcfa7Lxd2sAPF55eNf/scYn88dMK7B4/6w+1cO6EZIpTInhuaw1tVg//c0ExhlAF9arZA2W0bl8gLGH9uryNpzdXs7QogbXTh8mcHcUoRgF8D6Ru7ty5CIKAKIqo1WrOPfdc1q5dy7Jly1Ao/netoUfx3cLRUk0EILc3YbG7MRtPzbK62y5VbcOZc8Bjix9jc+NmVuT0zRL7/EHJ26RfQ34gKIalP4Eg5KfqCYQqbUER2q0+8k9wvFcrZRTn9ptZtjVJ9vKAThfLjPyTu5sqWnYzL3AQi6GApCnLQRCQNUhRDb1kViGXMTHLiCVwI3EHurGnncGcojh0/k44/AZEpPdJHe0t4LURo2qgS56IV6ajKf4sknKKYd8r8P4tkLsULpYkjl+27yUIfImbSe37eWzfY2SZs7hryl3hSprLG+C+Hb9ka9NW7p15L/NT54floG5vkC/2d7JgXBRKhYxXGjfw+92/x1Rm4uNVH6PVRZPQ/gGJFQ9wIH8hE/XXsV87mx75dCLcXl44+CoLM+YQ1FkwaUvAbYGGHRCTB+aMkx6/ARh/MZaqr9ih1eI5vp0EwYC7Rc3sFAHB2Fe5T9LY0VTcizMiD3XCdYCAx6aGEdo4Xd4AO49ZyPPupKu1i+roVnwygUrHYcb3VkKCQXjzcqjbAauegOx+g/+obCh9Ad6/DZb9AbKk6pk34OXV8ldJNiSzOH3kz4tJZWJS1Dyqm9xkxPnCIdUbOz/F6u1kjjXU19Zdzf0rL2BCqpnZuSc4TkZmIpt+GzJBztkjVWVPwJ1v7KeswcLnh1v5/ATDAZD6cHockkyx3eoNkzqzMpoUTQZWfw/mYCZz/vgVNref166bPsCGfSQUmSbhDNiJUEbiDDgpt5cBUq5coWkic6LPgFCRyua3opapB7mrLolbgSNgC1fXemHxdWNWRqEQFCgF1YDg8dNB/9e5Ak6UghLFvxCBADAmwcjVszOp7nBwybTBg85c/VgEBNQyzSnl8IWx/1U4/C4A8VlnE2NQ0enwkhs/RDUjY57UK3oKEz7WULC6w+uX8sx0UbB23ZDLbq/qpLbTwapJKUMaozz5TTWPbTge/nv2KTqn9qKx24U/KEJQpMXipiDRRLfDy2u76pmRHc2EkGvkZdPTuew0BvQtoaw5rz8Qvn9cPjODynY707OiEYG3SxuYlx/L9fOyuX7eQCt/lULGbYty+cfXlYhIAdnrD0mS3vpuF+/vb2RvXQ83zMviwskp7K7t5n/OL2ZqZhRflbfy/LZaAIxqBXaPnzuX5BOpVzEh1Rw2plLJ++TqKoVAm82NXAYKmQyPP0ggIIYrXSaNErVSxr2hyv7UzCheuWYaTT1uNEoZb5U2hLd949EOVjyyhe0/W8SRZisX/HMbArDuppmDCLdMJvDzswoAqWpX3eEgGBQpTjGTE2dgT20PzRY3i0ImKCPB6fXz83UHCIhw84JsNh1t54cLcnhhWw0GjYI1T2ynODmCslCuXbJZS5JZy7v7GrlhXnZ4MuSN3fX89O0ypmZGcfa4RH753iEAtlV1ctm0tNFsulGMYgR8byxLq9WydOlS9Ho969atY926oW8aJ4MgCDz99NPf8daN4ruA/4wHKJelYEuYxQTdqTnVddl8bDwkzUbOLogkwgD3brkfTSCJqyedH877sjh8bDzUjUyABeOiJOmg28rkow/zxYJE6tPPZ0HIgntOYRTNXW4auzzkJp2CW6kuBvTxUuB29Cla/eWeibHsNYyVOyE+CjJmMXOMmU6bjzhzHznNiNMhxi6mMnMmcplAlloOxzdDV6X0kzJVMmhxS31D+b5dHFWVICJQLo4lSRMB5R9JIdYVH4PfCwoVd02+i0f2PcK81Pk8uOtBKrorKG0rJSgGuXfmvRxpsFNW18EHVR8A8GHVh8xPnc+ELBP+oEhTlweHJ4DTEyBCIaPbIxELp8+JN+Al4uw/U5c+nR6FwCVjzkaQrURV1wlNftxBBzqlhtvWncfPOzp4RxfBtMnXkOTsRqzfijD/1wOqiifF2HP5RcVz/MWQi8KzEZB6O4RKIOCEbClIWLf7UXTVEqntOXM+jWISrYFDPH+okgvzLhzgynm0+ygfVn3IWRnLSRScFHq3ghdKNPGUmbWkafvNBDva4Yh0nDj41kBS5/fAlr9Ij3c+ESZ1rxx5hT+V/gmA91a8NyBTbyiUHrficAfotvtYME5iMvHqZHp8XVSkj6XAq4HMBUTr1IMGlGEYBhKAgBjgs7b3sPi6WBx7DjHqwQOtzBg9ZQ2WQVlrvdCq5BRnGOm2+8hL6ltGIVNwdsKFAGw62k59l2TGsKOqaxCp8wQ9fN72Hn7RR6Yuj2Z3PZPNs4hVxzMvZhkgESazIgqv6CFJM1Bif9xRzhftH6CXG7gw+aoBLpcyQTaI0B2xlbGpcz0GuYmLk69FJVP/y0Ss1nmc9W3voJcbuSDpikFVwV5UdldS0V3Bkowlw7poCoLAL5cPNFM5YC2lyVXH1Mi5RKqiv53ZStZ86Vqli8aYPoEv7zJidfmGNZ+odar43Vv7mJAayVWzM/jNB4fxB4Lce27hALnmXUvySY/WU5QcEa5Y9d/f+7bfx9josVyaewuXPrWDQFCk2+njhiE+pwUJRgRBsrx/7boZZESfnlv0mUUJ/ObcQhRyIXw9/80Hh3h3XxNGtYJ9v15yUtm0KIocaLSQFWvAoFbg9Qcpb5Gur+NT+6qmE1LNvP9DSdq+5oltbK/qYlKamXU3DY7cAdhT143d4+et0gZumJeFXCYQCIpcPj2N21/bR1CUslUfvHBgn+oXR9okogrYPH7OeHgj7908m9sX57GjqpPzHt3C2eMSuWZOFm/eMEOSnkZoOfcfm8OGSTfMzcLlC/Darnp0KjmvXjcdjVJOdYjk1XQ4UMhlpEXrcHj8JERoaLa4mZoRyc6a7nCPWrvNE37cP+7gRGw+1s41z+/G7Q/yk2VjuHF+NjqVgrduHCZuJrQNf/78KLNyormoJI0vj7Tx7r4mAPbWddPQ7WJ8qpmtP1vEBY9tBaSJhHiTmm6nj+KUCK5/qZQep49uhzf8Xt8c6yAowo7qLuJMfdeGZYUJo4RuFKM4Cb43Uud2u3nvvfe+k3WNkrr/TEQnJBF1yW9P60LbG3QL4Hfb2V33AUbvRMYYZrKtwsoFU6WqRI/TTyAoEkCKG9BrFLDtEdjyV3KAnMmLgb5G7cQoDYlRQw/MBkGhhhm3h10sT0QgGOAXn19PhbWW389/mDGxRZBcAm9dA901YG2GG7egUclJjh4sdarvcHOwTupx0GvkxMcVQtsBiEiTcsxACqYGFKKPaB10OqXoCIc7gH7eTyDgg7wloJAIY7OjmWprNU+UPcHqvNVUdFdI6w9V6bptPlQyLQui19Ia3MfasWsBqU9kYpYJlcKOXiMPyyOvGXcNkepIcsw5YbljWvHF9B9+j0uLxqTvxo8evzAFt9XKHJcbXG7eadrOeeZ8umUyokLnv9vuwxcIhrPjqPgEmvbC9JtAax5wjO6edAuKY5+F/xaR4UWFWt2PQJiSpPMTmUnJhFxyg34Wv3Ur3qCXlpZSfjLlbpCpYNNDfNS+jWcFK1sat/Da4mcJ7jYi87sYl7qScaFK4stHXub94+9z0/ibmDfrdqjfAdNuGPzZmHo9HFsPk68MP50QIlhGpRGj6uTVkEi9Eoc7EK7SAcyLXkqJeTY6uf6USfC2rg3UOiuZGbUIvUJPnUuqihx3VgxJ6v504XhumJcdzjYDadC7uesLLL4u5kYvJTvBPOJ7zsyO5sqZGVhdPs6bNDjovdldR6NbqkS0eqRBnIjImfF9jrRauY6LUq6mxlmJI2Ajkr4+si6vJLF2BOx4Aq6TRhd0+3qXlyzWTyR0VY6jbOn6ggxdDnOil4y4rl60eZoREbEHrDgDjiFJncPn4LJPLsPhc1DZU8ltk247pXV7Am62dkmSPKVMxcLYswcv5HVK0SOGEbLhEorgx31VsAgIm2wMhcc3VbH+UCvrD7Vi1il5JWTkMTUzmgsm98mWNUr5gKrXwUYLv3zvIFPSIxGj17GnbQ972vZwVtpqNAoZDm8Ak2bw+7q8ASakmdn84wWU1nbj9Qep7XTS2ONi1ilU7N7YXc+v3jvI2eOS+NPqPmIUHyGdi1ijGrlM4LbX9vLF4VZ+t2ocKyYM/jz+4ZNyHt9URX68kfV3zEUpF8iONVDeYhs2e683Imckwnj17EyaLW4WjonDFxDD5KjT6UMhk+ENBBk3hCHMdXOyaLO60SjlfFjWjC8gUtflJDNGzw+e3YXTF2BvXQ+rJqUwOS2S217fx5bKdrocUgX1nPFJ/DRUPbt7aT5KuSwsRfzdeeN4bVcdZ4ztm/DRqxV8evtc2m0eUqO0fHKghaJkExqlnCWFCfzx/GIQYOGYoattf/6sgr99VUnvkeizwxkZf/niKO/vb+KDsibOLk6iJCOK9GgdQVEk2aylodtFTMgx8y9rJvBRWTNLChNIjNDgDQQxaZTMz4/l3b1NLCqIJxgUWX+ohbPHJRAURebkxDA3LxaDWsmMrGjOGX/yPr5RjOK/Hd8LqRNPs1F6FP//Ikzo/B546yroqYMLnoWYoStgTk+fI5mv8iv8wVyS1RIxMYpiuJcsNVqD3eVHJhOIN6sQRZEufT5RggzBkBA2CrE6/RxtcpAYqZakZMc+hrqtkLUYMuefZOMlQufxBbG7/UQZlAiCQHnNNj5qkUwd3it7kjGL/iotn7UASp8dOSAcicgJgtRjoVPLwZwP808wf0mdKZlo+N3k6A10VUqkS62UQWIxXPrGgMWTDNINzagycqUpm6sSFlGt0jNjsuTiOC7DSMO+Onp8bfR4HaT1kzGqFDImZg2stqjlai4puATsrVLYtTYKpyfAniorOrWciZlSTl96dCQgzXb3zP4plvX30mOI4U/OWl7ytHL77N8yJ3QeNh6UMgWn5kaQrLHDa5eCGAB7B5zz5/B7Oz0B0tPmgzaetm47Fe0yfKiIj1RRmBJq5ndb4cvfSsR77AoEtQGt302EOoJ2Vzux1mbY8zTYe6D0OW4D3k5LJsvtRCkXYPaPIRgAhZpOVycquYp/7P0Hdp+dpw8+zbwzXxh03gLBAEExiPKsPwJ/BKRJiIoGB3maubxz7jtEqCPCJHhI+N3QeYwpaZkUpsWg7W+rLgjoFaduBBAUg5RZpZ6bw7Z9LIlbQZ6hiB5f5wDjk/5QyGWD+pK6fO0ctu0DJAfKM+LOHfgieyuUvwcRKZB7Fgq5bICF+IlI1KSRrEnDL/pRCWoa3DXhiID+ONJdwSbL+wCsSLiEBI00IB8fUUKQIJHK6CHD0E/E5IiZKAUVMao4dEMcvwr7QZwBB4dt+5kVtXjYAPJ6VzU6uYFoVSzFpin4RC8RisgBxiX9ISAgQ1rX6YSQq2TqUH5cPem6gdWtL4+08s7uGq5uvpeJru1w0UswZgjS9y2wMD+Ot0obKE6OYFZ2NIkRGnwBkSkn9PidiBe21bC3roe9dT08df1Cvqr7CqOQSWOnmo9vm0OLxc20E3Lv3L4AZ/3tG6o7HCwvTuTDsmbUCgG5TIbTG+C3Kwrpcfp4dEMl18/N5o4z8ga975ObqnD7gry7t4GHLiwO30t+snQMS8bGkxNrxB8I8v7+JkQRXt9VPySpawhZ/Df1uMKu2u/cNItmi4us2KG/b49eOolNxzqGlItuqGjjppf3MD7FzDs3zUQhl2H3+MmO1dPQ7aLV4sYbmpw8a9xgOW1GjJ6nrighGBQZlxyBXCbw/NZqrnthd1gOH6VXEaFV0mRx8cF+aWJEIRNIidTyu/P6vtsnVlJTo3T8aOlgw5MIrTJM+FdOHHiMVpcMzPFr6nGx8Wg7S8bGE21QhyMSVAoZD104/pRD6/PiDQhILpw6pRyDWsHGH0mqh4ONPWyt7OTymRkApETqBigSNEo5L2yr4d29TczPj+XG+dk89U0V9390BJVCxqYfLSAhRO5/d97gsPJRjGIUQ+M7J3XBYPDkC43i/x5aDkD5h9Ljg2/B/J8CUvWmzeIlI05Lte0oFV0tQAECIs2+KDo12RiBooqHSG18G8Qfw/QbkMkECtP6KiJVLU72B2ehnb+BORPS0IcqPwfrbLT2eGnsdJMUpUZoKZOIRGvZyKSucTe0lhHMXMjX1Xpc3iBjUvQUpBhobE/gTG0alb5uVmSe1feac/4CS+47ad9KtFHFkgkxyASGtJgHJDODg+/Ctn+gTTkDzaTHiIq2UNHdSmHM4AH1kowlvGt+F2uwi08sGzBkJXB+kwe5TA72Voz1W2nz1LDXuh6AsvYy5qUO7qkagK5K2POMtC3TbqG2W0e7RTLsyIjTEmUYOKCYVnIzlNxMBPCWowW7105OpDSYD4p987tBUQSVHvQxeO2tuPc+y+bkHM6adBNlNTaOtzgx6xXMLSwgNhZ22drx+kWidSf0ZPYOzuXSdmgUGt48503qy99mQmc1KHWQnC0Fokdm8UxTHVn+Roh7FOb/BGQKNtRt4NavbwXgzIwz2dm6k1W5qwYfCncXaz5cg8Vj4Zmlz4TPQVWLi2PNklHLwnHpA4Lah8TB16CjAsGUim7qTSMvexLIBBnjTSXUOo9TaJqA1d/DjMj5aOSn1rvaiwhFFEpBiU/00dwvky2M+m3QUy39pM4cEKsxFNQyNcsT+jIlg2JwEJFq7fGwv9oBUX370guVTM20yLmnvP1quUaK4hgG401TcAUcpGgyqHMdJ06VSJevk3h1IkqZVCkotx1gY+enyJCzJuVqjIoIHH47B617CIgBiiOm0OpuQi3XhPvudEodry5/lcruSuanzj+lbT1s20ePr4sz4lagElSDVAw/ebuMDruXZuEM3lZvhaZ93xmpWzw2nvLfLgub22z9qWR6cuI2WJw+3P4A8SZp0LxyQjKbjnYwOSOShekTqa5/igc+PsKNe/by+R3zBhG6Npsbq8tHTcj8o7lHIgXBoEggdP+3e/y8u68Rty/I81trUClk3DgvO7xt5S1WjoXiARaMiRuwjTKZwOT0vt7Hu5fk8dcvKtl6vJN1expYNWlgluK95xaSn2Bkdm5MeD1alTxM6L443MoL22u5YkZ6uDfMrFNx7gmVnyPNVtY+vRN/MIjTG2BbVSf3fXiYHy8bg0Gt4Is75+ELiOyp66bZ6mZGVvQAm/4TIZMJXD8vG7cvwP0fHQEgIMKs7GievrIEuUwg2azlkmlpvLazDn9QpKbTSXW7g4Ik07cOd//sUAv3fXSYZYUJQ4af/+DZXVS02vj4QDMvXj2Ne84uIDNGz4zsaEpOMf/N7QtIWXpAi8VFbagSCRJpPP+xbXj8Qb440sqEtEh+smwMcpnktfDeviaiDSo2VrQDsPlYB6IohqunAgyZ2zqKUYzi5Bh1LhnFd4OEcTBmuVSpKzqfjj0fo5d72eKdiS8g0mFzcUfp5bj8Lq7PuJirtr2AQ5tGy9RnMGtlZDW8itxn67OUPwEy0YcgBvBoEhD6xSfEmlS09niJMYUGUHlnQeOucCwAQE2biy6bl4JUQ19MQcX7EPQhBsEblAZV7pDboqA2Myf1Ma6IkTMm60TXlVNznjyl7L/GPQBo2vfj8gZ5//BW1rX8kaeWPMW0xD43vLL2Mg50HGBlzko67U2IAtiUcp6SuSg51kN+89tEeOtZK1dyNGUBOpWO6UknybID8NiQPMj94HWQGBlJbZsLnVoejiQYDgn6BLrl3fiCPpQyJWa9kjljI/H6gySFZLDtJVcR+/XvUQWDVJR/Rn7SlXSGYhF6HH4+OlDGzLxkFoyLpsfhI6F/wLzGBNd+BW1HBgx6rVYtRKzBk9qF2hRLu0vJ7gWTCKoiWLD7UhRdByC+jxRvadoSlhNFaaP4evXXQ+7P8Z7jNDskB789bXvCpC5Cp0BAMtsZlqD3RzBUiRYDIy93ipgeNZ/pUfOptB/h49a30Mh0XJR81WkRO4VMwVjjBPZbd5GqHSLbK34ctB8CU4rU7zkEfEEvCkE5pNR6qMpYk6sRUfAT0bmEyVmRxKlPbfb/2yBJm8Yq7Vreb36VvdbtaGU6XEEnyZq0MPnszbATCRIUgzj8dqqckoT5gLUUg8LI5+3vI0PO6uQfhCMd0k3ppJtOzaCj29vJN52fA6AUVJREzsYT8HDD5zdQbanmLwv+wpzcWN7Z28jsghSIuxlm3Dzkuto9rZT2bCVTnztsVXYo9HcrHepcNVtcLPvLNzg8fl64eiozs2OYmRPD9p/3Oaz2yh8NKgUmjYL39zfx2aEWfrgwB4fHz8VP7ECtkPHL5WNp7HZx6bRULn5yB3aPn18tL0QmwHkTk8mKMfDQZxVUttl5cH0F2bEGloWiEOKNGowaBQ6Pn/n5sVz65HZkMoG/rplIlH4gUTpvYgoPfSYFZ/cSwf6INaq59QQ30F6jlIQIDQ98fITqDgcN3c4RDT++OdYe7jvTKGS4/UGe31ZLjEHNLYtyEQQBlUJgelY0X901f9j1nAiNUs59K4r48+cVOL0BrpmTFZZTCoLA+ZOSeX1XfTj87/bX99HY4+KpK6YwJ/fUTZJ68crOOuq7XDy9uZp4k4YXttVy4/xsLpycQmWbPUzGux3StTjaMPj49cdfvjjKwUYrvz5nbLif0+Xz4/RK17jGHjfPbK7mvpXS59QfEMM9hTtrutlZ083skJTyjd31/OTtAwgCPHLJJDRKOYvHSqT+B7MySInUkhqlI850iq0UoxjFKAZglNSN4lvB7vKz6XA3chnMLYxCq1LDmpcB2LTjY95sP8gEdRHFwW00REynrceDGJRuZHJ3Izp3Mzp3M0vNR9AUnAGx70FXNRQOkZ/XU0vGkadJlWtxjL9BIkxt5WBOJTdJT3qcVpLcgeQAF99nme7xBdlbJTXNywSBCb0yxPhiaNmHPGEssyIi6bL5yAgFOs8tjMLiNBJjUhEIinRYvUQZlCiHyMD7l3D2n2DXkzRFL0Op8rKzR5Kq2X19AxeX38U1n12Dy++ixlLDXSV3su7Y29T01GMIxpHU6cEsxhFBPRGR2fxtwhWn/v4J46WcOoUaIjMxA8smndog4u2jb3PvtnspiCrg1bNfRS6TsuD641jmdNbtNeESBJIzH6SsxkaUQYFMgKAIXzV8ycv1O3h9+esIAmw42IUgwMwxkZIMNSZX+gnB4Q6wr9pGUAzyUtXzeGWdXJ59N251KALhkvfQKZwQ0UfEb510K3vapGrMdcXXDbs/k+Mnc3XR1XR7ulmSei47jvYQbVSSk6hn2aQYFHJhgAtrLxpsDfxh5x/Ii8zj1km3QtEa6DgihUCPgIAYwBlwYFScmrOkxS8Z27iDThq7nPi9QbITdHT4mjhk20u+oWjEoG6zdxL+mkSKJg+xTFQ2zP0FHZ5W2u0HyNWPHdC3Vuko56v2D4lRxbMi8ZKwFNHht7Opcz06uZ450UvC5M7i62aX5x3EyCCF6vnkRv57skj9IeLW+9sdcIX/N9Y4AY1ci15uDLlzOpAhI0iQNG1mv9iBAH7RR52zin2WnYwxjiPPMLwUtT90cj0GuRFHwB52uayz1rG7dTcAX9d/zcMX3cH9K4tCYdirh13Xrp5vqHdVU++qIk9f+J0ZRDT1uLC4pImzY612ZmYPlh+eOz6JnFgDkXolsUY1d7+xH28giMPjZ1lRAt5AEG8gSH68katmZbK7potWq3T8up3esKHKsqIE0qN1rHp0K4IA2bF9xjxbj3dic0vn6d73D4dJwKcHW7hk2sDPS5JZyz8unkR5i5Xr5g5tUOT0+vn8cCslGVH0OH2sfFTKO7z3nLGkRmmp63JyVtHAiYVuhxeNUh6e6Fs1KYXS2m7Km23UhrLbBCA/4dQjZIbD2hnprJ2RHpaHgkSwb3ttH10Ob7hf7/q5WTy+qQqALZWd34rU/WBWJs09bpYWJfDMlmqaetw8u6Wab4618/GBlvBy40POoiOhvsvJX76QQsKj9Sp+vCyfaIOaI8228DlTyQWWFPaR5bRoHa9dN50dVZ389ctjiCLh/fu6XKrOCcDYRNMAqacgCCwpPA132FGMYhSDMErq/kvhCjj5rO1dBASWxK08bUlXh82HJ5SL0233oY3qq2L8rf45KhxHOKBM4pfRP0UARBTckP4oBnM7FxYuAOMYUGgkQgeQPFn6OQEWpw9fWzsxQR/yoA8TFtj0NHx1H8SOgZu209DhprLZSU6ijqyEge5rSrmASSvH6goQbeonnSu8QPpBclmP7hevoFbKwmYfO4710NTlIcqgZF7Rt7NRHxbxY2H5w2QD2YAu5kacdhMp8gKCITmKQlCgkWtw+V1EaiLRyLX8sOButjZtZX7KYg5XidRq5pOcuQC9aZjeGTEI5e9Lbp8FqySTDpkSlFpIGSIfazj43bDvefA52euV+kAquitw+V0YVIN7V2amzad66R9Q+N3EiJFYnAF0agVTciL4n23/4KuO5xkTlQ9Am8WLxRmy2m/rIKXrK4jOHZAXp1bK0GvkVHSXs77xLQAKosayMHUtKoWAwaRh01ERoaGLqXlm1EoZRpWRt899G3yuEfO7ZIKM2yffDsCeKitNXW6aujykxmhHrNC9fORlNjZsZGPDRlbmrGTdsXV8Xvs5d0+5mwVpC4Z93cetb9HkrmNSxIwRpYW9KDaVICCgJZqycj9gJxAUOSL/kg5vKy3uRi5NvX7Y11/xzE5arR721lp56orBOaG+oJf3Wl7FL/rY09DA2WlLwiYvTa5aRETavS14g260cmlwfsxxiDqXNADNNRSSpJF6d2TIEBAQkaoAwyEoBnEE7KdMbK2+HvQK47D9bUvjVlLrqiJWFU+zu4EYVRyvNTyFXFCwPGE12fq+XiSdXM/5SZdj9VtI10okRCbI0Mj0RKvi2NDxKR3eViz+rlMmdWq5htXJV+MTvZIZDpBjzuGygsuoslRxYZ7kLqpXn/y2m67Npt5VTbouJ0wCuh1eyYjoW1QxXtxeywf7mmjocZITZ2BpYTwXndBr1R9jk/rOyezcGL4qb2NObiyrJqXQZvWgVyvCZiiT0iK5enYmbTYPq6cMXGdBook3b5jBe/sa8fj7WjPMOqV0GYIwOYg3qpmXPzSJObs4kbOLh6/2/mzdAd7bX0NSXDe/XLwEb+i97nn3IEER7lycy62L+yZaNh/r4MpndxKpV7H+9rlE6VXEGNQ8vnYKX1e08Yt1ByjJjOKnZ44hMUJLICie1IWzFx8faOah9RWsmpQ8KNtNEAR+9/ERnt1SzaycmHAu3fSsKHwBkcc3VRFjUDEzO4ZzxydS1tBzWrl/IAWOz8uL5eHPj9Lc48asVXLNnCz+GYqeSInUsmhM3OCsQyTZ7Cs7apmQGsnUzKhw4HpZQw9v76nn/f1NvHvzLCalRbK0MJ5Ou5e/XjyR5BOC40syokgwacIV1q/K21gwJo7j7dKEZYJJQ8YwDr2jGMUovj1GSd1/Kepd1bR4pKDrBlcNOYaC03p9crSaDqsmZGQycOCWG59DRdUR4pTRWCMkTX9KtJpsZQ5jUycgkwsw85Yh1+v0BBAArVqOzeXn6wNdiGIqU2KWkWrySVlvHdKNgq5qCHipbHHi8ASobHEOInWC0CdD8tXvAdMEqd/rFOHzSwOO/q6d3xfGRcxmc1M3h612NEoZ6XFaDnUeosfTA0jVJIAUYwqr86VZ/rhT6SG3NkKjZPxybPfXBIICY4L7YfptoBkiaL0fvq77mucOPceFeRey3DwGemoA+GHqHBRKPSUJJUMSul5cWnApAF5/kMZuC48f+R8+7dBy2/SbKEnLZnqiJBNNjFQTF6FCQCS+/VPoOCD1RcYXh3vqFHKBRcXRlLgns9kxlmZ7M3NT5pAfGhzUtbvotElViHaLl5SY0ODXUgelT4JcDdNuOek+x5tV1LW7QtVZ6bNz79Z72dy4mV/N+BVzU/r6weanzuedynfIj8wnQZfAs4eeJSgGeb3i9RFJXbtHknp2eFtH3JZeKGVKJpln4PMHqVZ24vEF0anlJCnS6PC2kqwdWSIYY1DTavUQaxyaZAnIkKPAj49gQEFrjydM6iaap+MX/cSrk8KEDiBVm8VB6150cj0xqj6XQaMyglVJl+HwO0jTDSH3DGF927vUuY5TbCphRtT8Ebd/W9cGyqy7SNFkhKMXToReYWSsUXJRjFUncNi2L1zh3NDxKWfGD+yljFLFEqXqIxF5/WSOufqx9Pi6yNGfXhSBUqZESd/kkSAI/GTqT05rHQCFpokUGMeHq5+1nQ7O/ttmvP4gr143bUDP2cng9Qf51XsH6e9f9ufV48MSwJPhmStLcHr96FQKXN4AP1yYM6gH7sRIh/544KMjbKvq5OMDLfzxgmI0SjmzcmL44IezUStkHG620uXwsnZ6+pDV8OHw8o5ajrXauWNxHqII2tTnsemPs6nnGL8+5zrsHj9PbKrC5vYTZVDj9Pp5e08jE1PNbDrajj8o0m7zsOloOysnJoeJ26zsGGKNaj4ua6bF4qbT4aWyzc6qicn89KwxxBmHJtX763v48VtltFhcWNx+Ht1wfMjA7g/3N0mOmJ1O8uINROvVPHXFFH7ytpTr6PBIRjOL/rSRToeX+1YUsnZGBq1WN58dbuWMgviwichI+ORgMyJg0ChYPSWVMQlG3tvXxIVTUobNnnzw03Ke31aLUi6w6xeLMetU3Lwwh6uf20VQBH8wQE2ng/wEI4+vnTLi+x9ttRFvVKOQy8Jk/+dnF/D81houHSLPcRSjGMW/ju+c1GVljZzfdCIEQUCv1xMVFUVxcTGLFi3i3HPPHc0j+Z6Rqs0kQZ2CTBBGlG0NCb8bpVzNlBxpcNziaOHaz64F4KklT3HfrN9yWcGl+B0JVDVL1Zc2i5dlk2KRywRsLj+dNi8en0hOoi48A9pl97HpUBcCMH9cFITMMFWeTvYG0tG6dhCTJeBb+FtafdH0xM0kDyW5ibpwpe5EBIIiVqcPEOiye8lq2I6YObSBwFCYkmOiscuDGBQpb7CTm6QfMGPr8wepaHQgIplDCAh4/EHGZxjDwc6nCq1ahkImEBBFNCoZtW0uelzOcE+Yt+xlUEZAzGD3s/6o77QTdLpI8x1ByJgFhniISMNj66ReyMalNDLGsV2q3I1AcLwBL3dsuIOAGGBv217mrd6AMW4c+BwkZMznF7nLqHVV4fDb0CsGSpR8QR/Xf3495Z3lPLzgYaYlTuP16sf5qFrKh1uQtoCFaQtpsDUQq4tFpZAxK88AO/4OTsnCnsjMMKHrhdxWTyQCry9/fdD2xpvVRBuVCEBsRD8pqLVR6hsM+qV1j7DP3e5uXj3+NIWxRczNkjLX3H43bx97G4B3K98dQOqmJU5j+yXbw39fM+4aPq/9XHIWHQFnxK2gxllJsWnkgdGJUCpkLCqOxu0LYNAo8LaUsNQwmYyYkSVir143ncNNVqakR9Lj66LF3Ui2Pj9sJKKQKTg/aS2l9fUoFUmkxfbNvBsVEUPa8kerYrks9YZBz0v/iyN6eA8JoI/YtntaRl6QPvLb4W076bK9yNLls71rAz7RF45FOFUUR0yhOOL0zs2/Cn8gyLq9jaREapmZHTOgV7Gh24XdI11Lj7c5hiR1oihS56rCpDAPcPRUKWQsGhPP1xVtpEZqmZQeydjEwYP6QFBEJgy8LrbbPGw93sH8/Dje29fIHa/vY3yqmbdumHnKlauMGB3bqjoxahRc+tQOBAHev3k241Kk72Fu/OnJG73+IEdbrfzinYMAGNQKfr9qHGXr3HR6pfiX3y+TJhNWTUqhxeJicnoUv3z3IC9ur8WgVvDAyr7qa1AUufnlPXxysJnfrChi4Zg49jdIAdk7QpU0gHf2NrJubyO3LMzhriX5g7brtV31VLTawn9HaJQ8uL6cZ7fUcNui3LD74y/OHstru+q4anYmC/L7JkN+tXws6dE6ZufE8o+vKukK9by1WN3Udjo4/7GtdNi9rNvTwDvDZOsBODx+Xtxey4oJSeyq6ebiqZKctTjFPGLVr8PuoTFkeuMLiDy24TgmrZIDDRZCxVQKEo0UJQ1NCN/d28C2452UhCI0/vBJOa2hmIXec70gP27APo9iFKP4bvGdk7qamhoEQTitWIPem8g333zDI488QmZmJs888wxz5566Q9ooTg9auY4ViRef/gvrt0LFB1IfzqRrAChtLaXGWhN+vCxzGQ3Bo3TIviEl4ly6LHICQRFRFKlsdnKgtq9nTCZAbigE2eUJIIpSELXL4ychQkVBqpryWhOiTElnWwcxQKvfzK702wEwdXvIjNeRGS8RujaLh4YON1kJOsyBVhSH3qJEW0y7U0GefxdOw4V8tbuNQBDmFkYSaRh55KlRyYk1qfiyrBOAI12HOebZwPXF1xOhjqCy2Rl2R+yPug7XsKRuT+sePqv9jPNzVtPdEYUgSFlyBo2CJRNjCAZFDtXbqe9wo1Hm8siCvxHY/wJz5HrJtXMEUvd+09s0e6swWEuYvf050tM+gWW/h5Ib6eh0IzbYyRHqqFUtJuCOY6QpGLkgx6gy0uPpwaQyoVEboVgiKy8feZmPat+nOC2XDFMGa1KuGfDaFkcLu1okO/77t9/PuJhxfFT1EQARqgjGRI7h4o8uptpSzU0TbuLG8TeCzwHOdpq0ClQxRcTkndBz1HUc9jxFuzyVUuMqoiI0lOREhK8faqWMuYVDVDCSJksEVqGFyJEnnf65/5+8Uv4KAgIliVOI0cagUWi4dty1bG7cHK480nUcmveAPk4yGtFK73vLxFu4ZeLQVej+SK07RGrbAchLgPiRLedPhFopQ62UUdHo4HC99F2K1msx9jO3eau0gS2VHdy2KJeMGD0mjZLpWdGS+1zjK7iDLlo9jeGwcJAqbPOzRq5iDoWgGKTBVUu0Kva0IhsWxZ5DlbOcQuOkky47J/oMDln3kqkfuVexPzRyLUvizuOQbS8FhuKTv+B/Gc9treH+j44glwl8ddc80qP7qqKzcmL49Tljsbv9Q2YHApRZd7O9ewM+n4w420pWT+qzkH/qiikD+rlOxL76Hi57agexRjXv3jQr7PL6g+d2crDRyvz8WOq7nARF2FvXg83tCzs/dju83PfhYRLNGu5ekj/oPe5fOY5Lp6VT3eHgllf3AhKR+v0nRzjYaOG3K4rIHiZ6oBf1XU40SinyZfnfNtPU4yTGoKLb6aMg0YhKIeOpZf/gy7ovWZ69PPy6ZLM2LA00aKTvh04l58xxiTRZPHj8AZYUxvOjN/cTFCWnzLXT0/nZmWMore2mttNJj8tLp91Dr3r0qc1VXDM7C08gMKBqd+GUFDYfaw9HBMQa1azb04jTG+CdvY1hUneilFQURQ41WcmM0fOjpWNotrh4anM1AJPSzPxwQS63vLqHDrtE8vShIPnGHhev7ayjsceFUibjnuUFGDVK/vrlMZ7YVIVCJrDj54tGlED3x40vlbKrphsZEAS+ONLK8XbJVGXF+CQ+KGviSLON+z48zD9PqNJ9Vd7K7a/vB+D13Q3Em9ScNS6RR76uZGF+HG/sqmdyupnsuIEE/qlvqnh3XyN3nZEfDp/vdnh5enM1+QkGjBolM7Kjeau0gae/qSY1SsfSwoRBfZejGMUoJHznpC4tLe20qmyiKOJwOOjp6SEQkNyUqqqqWLRoER988AHLli07yRpG8V3gaPdRtHItqabh+ywA/J1HUQBidzVCKLx7fup8lqQvQRAE5qfOx+63UuM4jsaVjV9fz3hzIZE6yWjCGuqb6oVe0ycBSopSU5xuRAi4STj4Vw7a6pHtf5NzvH6O599MxnQpXys2QoVZLwefmziVE9Di9QfZecxCp9VLUJQCy+cpdoOjlWTH5yTPvBs0M6nv9OELSMYpO49ZWDpxYA+H0xOgvNFBrElJaow0GFArZagUAl5/kFeOPU25fStKmZI7Jt9BhF76CqkUAkq5DJVSBqJIbuLwEs+7N95Nu6sdp93ARN2F4X1PjtZIBiH0WTrLZAJz0xaA1wsdFZA2c9j1iqJIq1cKhfaqmlD7usHZR1qTozUkR2vYW6XicJsLamwkRamH7RmTy+R8eN6H7GrexczkmShD5hk2r40/7PyD9D6im/Rxg6U0qcZUrhl3DV/Xfc1xy3FqrDVoFVpcfhc3T7yZCHUETXapL6/OWkenq5MqSxXRebP4PHgEgXou8HVI8rjGUnjtMjAlQPoU6tX5uPwCjZ0eitODA7e/pwbcVsS4QnxBAZVCBnIV5J41aBuHwpioMeHtN6qMWL1WKrsrAfj9nN+TbQ4NlA+9geix0qaWY677BvXsnw3ds3f0Y2jYDtlnQPqc3hMFDdukXsemXRIp/BbQhxxWlfLQfobg8gb48VvSIDUQFPnbxRMBiXzVu6oRQjHDcuG7ufxv797AAWspermRS1OuD1//PQHJgXCoYG+AZG0aydpTG5yZlVFk6/NpdNcRqYxGKx9clR8KKdp0Uk4iTf1PQW/AtyoUNv3Q+greKm3gR0vzOX9yCj+YNbyUFSTzHQAEkff21Q8gdTCyMmHb8U7sHj92j5/ffXKYe88pQquShyWbVpcvPMA/oyAOs07FPzce51irnXiTmnV7JSn/4oJ4JqYNnKSQywRSI3UcbrJy5xk5pEUbiNKreHyj1I/5wtYafrNCkr56/AHUioHfo2+OtXPFMzvRqRQ8eulE6kImJqunpDIrJ4brXyzlNx8c5uVrp7Eo8RKSDRJx2HS0nfIWK5dNT0enUjAvL4bPD7Vw/qQUVAo5N86Xjs+db+wjENrPtdOlz2P/TDWPP8C4X6+HkGJCKciY++DXOL1+Xrx6GtOzovn0YAvr9jTw8EUTqOlwsLu2m1+cXcBX5W28sqOOa+cMnkzqcngxa5X8/pMjPPlNNYVJJj66dQ6xBjVjk4wca7VzybR0Pjvcwq4aSUacG2fgkmlprHhkCz1OL7WdfROKVrePvHgDutC1IEqv4mirja83VbGmJHXIzL5gUOTdfY0kRGjCkQLjUiJwePxUhs63AJTWdpEbZ6Si1canh1p5dEMlN83vy6Y061Sh3nkpby/GoOaOM/K4eUEO0373Bc9vq0UuCKy/Yw45cUbW7WngNx8cxu7xEwiKPLbxeJjUPfhZBa/sqAuv77yJyeyu7aK+y0VVh4ONR9uZkhFJ3mlWeEcxiv8GfC+Vum8Dr9fL/v37efHFF3n88cfx+Xxceuml1NTUYDSOfnm/T2xp3MKNX9yIUqbkzXPfJCti+GrGn7oPUOhso0Im8ANHALkQIEKv50/z/xReRhS1ZAUXYbcm4eq0kr5nDnJ7C6x9h7GpJTR1efAFRLITtGH7e5AGHdmJOuhqAo+F4/ZGVngkV7Va4RBfW1JY7c4hUhPJAuVWaN0GNj2txXfTbvWFM9YAoo1KME+G7mowJYM2EgQZ8eZ+vSBDDHIOhypktW0uEiLVKOVSVWTx+BisHgePN9YiE2SMjZZ6SJKiNCybpEQ5jDviUMiLyqO9sZ0IvYBeJQWVRxsHygzHZ5qIj1T3ZcXlLJV+RoAgCCyIOZNjtqMUEE/CpKUwYXA1Ni5CRW2bC7NeMYAMDIUIdQSLMxYPeE6v1DMtcRq7W3ZzdsbyAZll/XHbpNtYnrWcq9ZfhV6p56G5D+EOuJkUL1VmHlv8GDtbdnJB7gWs/nA1bc42bpxyDXI9iIgExdDUePlHYGuSfmbfTpYxDWuPkmijciChc3bC7icAkdrIM9jrKyQvSTcg7/BkOC/3PKYnTsesMdPp6uTCDy7E5rUhIrKpYRNvnSsZtGBOZ7u/krJILWZfkKGPANCyT4rpaNnXR+oEAbIWQ9vBAdEbA193EI5+AhMuBVPSkIukxGgw6RSoFEJ4MgBAo5QxOT2S3bXdzMzuk+Ht7tnCXst21DINi2LOIVM/vI356cAXlL53PtGLiIiAQI+vi3eaXiSIyMrES4hW/WuSq6AY5KPWt/CLPnp8XSyKXX7yF33PEEWRcnsZAgJjjP96JXB1SSrp0ZKde7xJw/Nba7B5/Ly6s47zJ6ec9PUTIqay57iHbw65uHrm6fVIrylJZXdNF1+Wt/H6rgYyog3cOD+bZ39QwpbKDqZlRnPDS6U09bi4bXEex9vt/OGTckAK4Nap5MSbNMOGfd/91n4+PyxJaFUKGR/eMps5uTEcarKyNBRz8O7eRu56cz+T0yN57drp4XiGmk6pQmj3+DFolPzirAKqOx1cNzeLD8qaw2R0zePbwz1oS4sSuOq5XfiDIj1OH7cvzuPaF0qxuf38cX0FN8zPDpPcun7E6OEvjvLkN9XcvjiPGaHvjloh54Wrp/LbD46gVsi4fEY6d7whVaWOtdqYnhXNPe8eoMPupd3u4Z2bZnFBqIdsxYTkIQPTH/78KH/98hhnjI0PT+D1klW5TKC63YkvIPLZoRY67B4sLh96lZyPb5vDuX/fzJGWPpmnUi4glwl8crCFTw5KE4L3ryzizKIElv99M80WN/vre/jLmglc8cxO5DIZL149lRiDmn9uOs4fP61AANbdOJOGHhdzcmP4ydtlVLY7SI/SUdvlpKHHze9WZvPrDw7jC4iU1VvC7+/yBlDKBP66Zjx3vVGGiIjLJ00w+ALBsGw4IIpYQ46nL2yrDbuwRulVXDy1bzK5t2orlwn4gyJWl48fzMzkoc/KcXqDmHVK4obpDR7FKP7b8R9jlKJSqSgpKaGkpISVK1eybNkyenp6eOqpp7jjjjv+tzfv/zQ63Z2IiHiDXqweqYqFvQ1eXyv1NK1+AXSSvKyZAJ86upgfcyEbD0r9BgvGRYVNFUAiFwURBexqt6B31iDvlmZkqfkGTdp0lkyMwe4ODAq3DiMyC9Lnckb8eL72B0lwdPFzXx3uff+gzlbHA7MfkKocQDtxbK2QbjAGjRylQmBipomI+o+htRF3/mo6iSZBFPD5Amwp70GjkmHSKpiQKQ32j9TbaOh0U5BiJNqopL7DTaRegaJfz4haKSNWaeSdFe9g99qJ1fVV+LSnkl/WD39f+Hea7c2kGlOHnT2XywSSo4aocAR8ULMB1BGQMnXQv6PEbBZE50lkJ2PorLrkaA0Jkepw/4zXH+TjfTUE/RriTGpmjokakHd1ImSCjKeWPEUgGJDCz09EdzUcWQeRmWQXrGLjRRuHXE9JQgklCSW4/e7w567TauXSjNWoZRpi1CGb7EmXQ8NuiM6B/HMwy2TMD41xrU4/CrkgxVwIMnpn060uPyig3Tp07uFISDRI0qiD9oNYvdbw82mmflWlojU4Wt4BTxVOlZpDXeX8fe/fmZsyd2A/Xe6Z0LQb0vuk5Efq7fR4J1I8fu6ASnUvrF4b+8oeIqXhMKlVW1Fe+c6w22rSDb6EC4LA69fNwOkLYOjntOgLZUAGxSDpuuxhXSRPxI7uTXR7O5gVvQijYrA8c0bUQszKaBwBGx3eVuLUifT4OvGKEtnr8nb8y6ROQMCgMNLj6xpyG75v+ANB7v/oCD1OL79ZUUSEVkmNs5JNnZ8B0OZpYXxESTjf7tuif9D3bYtzeXtP44Cq0UiQCTKumTyPawabCJ8UkXoVf7ygmDMe3kS300tWKH4gzqjhvInSl+39H/ZNQDi9fnLjDFS22fn4QAs/nJ/NXUsHSy970X/yyOuXBvovXj3QeXfT0XYCQZFdNV04vH6MocrlBZNSWFdaT0O3i3V7Gvjl8rHhat75k5I52mJDIRN4flsNAF+Vt+MLiERoFXQ6fMQZ1ax4ZEs4QiHKIGWavrG7ni6Hl/tXjuWsv20hKEJFix1/UORvXx4LkzqA6VkxfHzbnPDfFpePbqeP1SEH0YVj4nhjdwMLT7FfbNNRydr/yyOt3LIwh/wEE/PypHuKIAgUJBrZU9fD2CQTuXFGLJ9XcMHkVBQygeoOx4B1vXLtNCK0Ks79+2bc/mC4Qh9tUJMbb6TZ4qbJ4mLBQxtwhxyrd1Z3cda4xHAAuIhkTnZOKJD9r2smsqeum8wYPXe8vo8dVV3c91E5v11RyOEmWzhawu0LMOt/vqLL4Q1X1kDqAZ2UFolereDVa6fz3NYaFhfEMyktkkBQDPfuAfzx/GIWj+2LRLh6dibz8mJw+wLsrunmnPFJRBvUPLheypXMiTWMGPo+ilH8N+M/htT1x8KFC7n88st55pln+OSTT0ZJ3feM5VnL8Qa8GFVGJsRNkJ489hnUhwwgqjeG8+PunfFbvt5vA7Hvo+Pv1a70Q0qMhkP1dnpM46gZcwsZ6k6YcjUg3eCjDCNUiAQZ5J6JDlgwbg1BMUj8u+dSa60lyZAEzi6pagNE0SERPEHGxCyTlJXm6pIkb0Db4c2UKuaTFqMhzqwKyz+L0jToNQraLB7KG6UZ0qONdhaOjyEpSoNSIQw5QNEqtGgVg+Mfmrs91LQ6yU7QEWceeRZRKVMOJAgnQ8AHXZW4dSk011YT37wTnWiXKpCmvlngymYHB2rtRLqrmFX+C5TxebDikbAssMPqRSEXMOuVA0wOyhrqEP16BKDd6sfuCQwbPm5z+dlQUcGOrs9YU3QmRXFD9Pc17pIMSZwdkL20z23U1gyOVsmysx8ZfGTfI3gCHmYkzuDOKXeiV54gXY3MgCukDD9XwMnB7j0kaJJRuJPYXtGDXCa5Yuq1kaDQgd9JvNaHN0JDdsKpyfSGQklCCXdPuRuLx8K8lHmMjenn8CfImBV3JtG2MpK16Tyw5Q9sadrC1qat5CWkk6xNJ1GTAokTpZ8QHG4/5Y3SoEyvllOcMbCK+PKRl3nu8LMUp+aRn3M+K3fWMGiY6OqGg6+D2ghFF4Fs8LmSyQQMasUA4j0tcg7Rqhhi1QlhKe3J0O3tZJ9Fck6NsEYN6VKpkqno8LZS6ThChe0AV6TdQro2hxLzbAJigCz9YFOJ04UgCKxMvAyrr5sY1fAB0t8Vurwd+EQv8WppkLu9qovnttYAUr7XD2ZlolcYwll3R+z7aXbXc1HK1d/ZNlwzJ4trhpDtDYdjrTZueXUvaVE6/nHJJFQKGfvre3jimypWjE8aNgPs6/I23tvXSF68katnZ7IgN47mLh87jvZQkhMx5ASPTqVg/e1zmXz/53Q7feys6R5R3vngBcWcXZRIY4+LJLOWSWmRVHc4+NV7BxmbZOJnZxZwy6JcPP4gM7Kjw4QO4ECjhb2hytBL2+uYlBbJqkkp4e3oDb2enRvDqzvr+OJIG19XtPGr5WMpyYgiLVrHbz88DEimKpPTzGyv6uTHb5WFniti048XcLzdwfqDzby7r4kVE4aujvfiylmZWN0+7nnnIGadkt+vKub+leNQKWT4A0HqupxkxuiHPCaPbqgMh5wHRfjrl5W8ef0MJqf3TQi8et10Wi0e0qKl61f/HryMGD3lLTYmpUVyUUkKJRnRNFtcbPrxAt4sbcAXCGLSKvjluwe5f2UR3Q4PKx7ZCkC8Sc2EVHOYQC4fn8SO6i4yonXk9KuyapTycIbh+ZNS2F7VhcsXIDPGwMVTJTlzl8PLVc/tDBu6iMDSwgSmZkaxvF/+3JSMKKZk9PU6O7z+8P6PSTCGZZf9kRPqvStKNoefu3xGOuv2NnLp9NF+ulGMYjj8R5I6gHPPPZdnnnmGQ4cO/W9vyv95yAQZF+RdMPDJ3CWQNkMaMGbNlwbkcjVGTSQquRuvXyTBrCIjXjcodLoXM8aYaex0EzPhXjiBJHh8Qbz+4ABzh5G279WzX6XOVkdhdKFkT2+tB0Ae9DCnIAJkir7t0JghrgisjTQpx4AoNeYnmNUkRqoRkR6D1HvXi4Qo6bn+UrZBqNsCnUclstJPFldWY8XpCeL0BFl0ElJ32jj8FrSWsctwMR3EE6E5l4X+d8Frl5wdTclYPBZ+uuNm7B4P97oTUbaUQkspHUXXo0oeh93tZ8dRCwKwsNCEydcMUVmIoojTKZFUUQyi1FoxauKguxYat4O9Rcq2i5BmpA/U2nj4yI9p99axte1TvlqzfvD2pkyVSHdkJh1uJd0dDvTadn772TXEy1T8cuKtKHL6emW/qP0CEZE2Z9tgQncCdnZ/Q7m9DJlFzny15LgaCIqhyAk5TL4GOo8SnziJePWpyy5tLj8apWxQwPwVhcOHuWvlOiaapWro5Oh5bGz4hnz9dKqOR1KhPcRFBfEDQrxBqupGGZVYHH4SzIO/N08deIoOVweBeh/jzQVoz/nToGVo2Q8WqXcS6ywwZwy5fY/vf5x/7PsHF+VfxD3T70EhU562TNCoiCBOnUi3t5N07fAEQy+XjrVWrg+7Nk4yzzit9zoZ1DJ1ONT7+0SXt4O3m54nSJBlcatI12VTkGgkPVqH1eULV3Di1ImsTr6KLZ1fUu+uDjuJngr8gSCHm63kxRtPOVrgZHh/fxPlLTbKW2wcbbVRlBzB/R8dZldNN1srO4YldXe/uZ9ORz/pukaDWlBidfqxuvwDVBj9IZMJ/PmiCXywv4mrZ2fi9PrZVdPN5PTIARVikMjXWSfkzD2/tYZvjnXwzbEOLp2aTmaMnkcuHWyak59gRCmDUJGJouShK7WLCuJ56puq8N8ZMfqw6+LDF03gpe217Krp5rPDbZxRmIBBrcDp9ZMerSMlUvqZlxfL71ad2nfk7dIG3ixtAGB+flw4t2/OH7+m2eJmbm4ML5xQjWyzuvnjpxWhYyLH6ZXuP1c+u5OPbp0Tzm5TK+RhQgcSgXpsQyVT0qN468aZVLc7KEo2IQgC9394mKc2V7OsMIF/rp2Mze1j/G8+IyhKlbQHLxzP6ikpbK7s4PfnjWNev2ri2unpLBoTR5ReNWz7wHkTkznebuejsmbe29fI1Iwo9jf0cNET2/H3i/qZmxvDQxcWDyDkHXYPT35TxbTMKBaOkSZjDjZaMKoVGDVKnlg7+ZRdVH92VgErJyazpbKDboeXSP1otW4UozgR/7GkLiVFmonr6uo6yZKj+F5giIOrPpUed5RLodMyBfLptzG/KBqL0xeS8A1/QTZpFZhSBvdYeHxBvtjfgdcvMiXHFDYkGQlGlVEidAC6aOiukghnynRizDrc3gAbQnLQGflm1MWSS+E4t59Eq4+kKDVKhYzp+eYB682Ik0xWNEoZKoWMDQc78fhEkqLUjEs/gRQE/XD0QwDcATmqyWvD+58YqeF4i5PEqO9B6x+QBlwK0QcCyE3xEH8+7HsOEKDkBnZ0l1Nhkfo8vo6YTKEuE19kNpvbY5B1d1KUJp0HEQh+eBdUvExt/g3sybyT3jOoVsHZE/Lhy/vgm4fAnAJF50JTaZjUNfn20e6tkw6HKLK1vJv8ZP2A8HbMGTDjdnz+IFtK2wmKcMj9Du3yDI77Ozlgq2ciwKc/h8ZSfjL1Mt5o3XbSGAAAc0jeZlSYyIzXIRMkRzyzXilNPIhByJh38kMaFPH6gmjVcqpanOyvsaFTy1k8PvqUBxn9sTj1LIyuWYAIfgGlLRpRHDxIkskE5hVGDetEeGnBpbxR8QZXFl7B6pyLUMgUWF1+6tvdpIZ66IgrlHr01EYwDl9R+Lr+awC+qvuKe6bfc9r7BFLUwXmJl510uWmRc8nQ5RCplAiP3W+lw9tKqjbrlGWe3zc8ATdt3mYS1akohqhu9sIv+ggiDVa9QamiEG1Qs/FHCwadtwhlJFMiZzLGX0yy5tQrCD9+q4x1exuZmxfLC1edIKPe+zLsfELK8hx3wdArGAIrJiTzdUUb6dF68hOka9ec3Fh21XQzJ3foUG+AGdnRfFjWjFIu9TBFGpQoRQUGrXxAxd7u8XO4ycrENDPKEAHob1F/1XO7+Kq8jdk5Mbx0zbQh36s/ziiI5919jRQmmUg0Dx//8uWR1jChO3dC4ogGGasmpbC7tpvpWdFkx+pptbqJN2lYMSGZ8SlmLn9mJwa1AqVMxk/OzGdubuwAl9HTwdTMKBShvq+yRguzciTJYLNFMgjaX9/D0VbbgO399GALCREauuwefnFWAY9sqKSpx43DG6CuyzlsIPdFj2/jWJudJ7+p5p2bZg4wo9lb3xP6LZmpaJVy8uKNlLfYGJ9qJhgU+WB/My5fgNd31zMvP46mHhdfV7SxZGwCSeaR778KuQxfQKS+28WrO+u5bHo6h5st4YB3s1aBUi7jF2ePHUDoAB78tILXd9fzzOZq9v1qCXq1gg/2N2F1+7G6/ahPc0Lj0qd20OXwsq++h39ccnLX3FGM4r8N/7Gkzu+XZHIKxX/sJv7fxeG3oasSCs7DZ84k6OxGDRKp8bnQR8QM2Qt0qvAFgnhDod4Od+AkSw+BMSvB2gy2BiliIfdM2ixeuu1Sz1BVqxO1Qgrv1msU6DXDf4bkMoGCEPH8uLQdT2j0UNnsZIxwGGXcmL5cM5kCMbaQYHs5B1zpaOrsYeJXnGGkKN0wIsn91hh7AbQdpCQig3afnmiTCro7Q/8Uoe0w09V6ZiTOwC/6WVF8BW3Tb8UfCCLWOxBFSIzSoFZKPYfmDRIxVXcegkyJ6CVHqfukgE17OaRS8VetH6F7Dz5PLT+JL8C/5wV8+59DHhNFAJgbfQmtPV56HD6y4nWMOYHAy2QCKqUMtzfIRMVEClMuISj6SckxQE89bH8EgPnxhcxf/ugpHYrxEVNJ02ahV5hQyORkJ+h4+cjL7N36IQ9qMyRnx0nXQNTwVSXRbWXjzuNYVEmMS9XiCLXduTwB/AHxW5G6pCgNM/IFehw+jjXbiTerUMqH/470JwZ2l9QXqFHJuWbcNVwzbmA8RGmlhR6Hn5YeD4uKo0EfCzNuH3F7PAE3UzIKcWPnyoKrTnt/TheCIJCgkaTAoijyTvNYhwTAAAEAAElEQVRLOAMOioyTmBW9CKffzuauLzAqIpgeOf9/JYf049a3aPM2k60fw+LYc4ZdLk6dyLK4VXiDHnL0Aw1HTtzuXd2b2WPZRpQyhqzkU49b6DXFqO8aHIfChj+ApQ42PXhapC4nzsCHt8wZ8Nyti3K5enYmevXw18C/XzyRB1aOw+nz4/D4w9K3E3HpUzvYX9/D6ikp/PGC8YP+3+OUJp96DTBGwh2v7+PdfY38avnYk7p69pIkgNsXDX2MfYEgFz2+jYONVv552WQUcoH5D25AIRe4aEoqO6q7WDUphU0/XkBpbRfnP7YNgL9dPHFYUlfZZiNKr+bpzVWIotTj2N+ZsyDBFI5u2l3dBfOy0SjlxBlVtNm82L1+ljy8iX9cMpGxiSYON1v51fuS8ujOM/Locnhp6pH27cqZ6czJjRlyO57ZXD3A5RLgka8lR94b5mVz/8oiXthWy7mhfjiFXMZ7P5yFxekjziSR5ew4PQcbreSGzu21L+zmUJOVD/Y38dp1AyvqDo+PV3bUMyM7OlwVPWd8Ep8fbiXepGbVo1vQq/rIW49LGqt9caQ1PJnQi9x46Z6QFqVDHVJByGXS7xiDinjT6WW5mnVKuhxeokardKMYxZD4j2VMR48eBSA2dvgZxlF8D/C7JWMHoLH6Sy45/hq+gI8XJv2InMjccMXmX4FBo2BqbgR2d2DInqdgKOl0WLMOQSZJ/I62Q8IEABIi1cSaVIiI1Na1MsX9CfYaAxEllw4KsO6F2xvgWLOTaKOSpCgNif+PvfMOj6M8v/Y92/uueu+yVd177zbNgAFTTK+hJCRAKAklJIEUCCEQCBBqaIFQjGk2Nrj33iVZvXdptb3P98dIK8uSXIJTvl90rkuXpN1pO7s78573Oc85EWqqWtwoFQJJwQqUx1ZAUwpMvDO8jjjyalbulhrx007oJTybhC7YfQ7kMkHqSUuehAIIC5hi8mDk1WCrx1XxLQ907KFJbuGK5MfZb9+GUm9jcdwV6NRS/p1WJScpqntAcsW7UPw1jtRr0PvlGDRyRqQbe50kz32KV9bcxjZfK7gawNXARwdf5+HNf6WguxU+fum7mPxjqWn14PWLFNU5ibOoiTjO/EYmwNwRUTg8AWxHnOwH5IIMnVIPegPknA8Ne6HwkjM6NxGqvoOfVw++SmYIBG06UqXMzVcVX6GWq5mfNr/f+qHDn2FTSAH01uZ6Ro7MQSmXUebcyaVf/pFZybO4f8L9Z3RMIH0G4yPU5CYbeO3Qa7y0ags/Hf9TCqILBl2nqdPLthIrCrnUF6hT9yeCeo0cqzOA4QwmUtp8LRgNGs4tnEVGRO93ts3mo67NQ0acFvMgsrrvCxExbK8fFKUBX5HjIJWuUgCy9LnEqhMGXf9fBW9IGkD3VN9OhjTd6ZmTdAWk6ogt0EVIDPUJDB8Mv/7yKEWNNubkxPDguQP0pI6/Aba/BONuOK1jOBVORuhAIqpmnRIzJ/88tNqk89diH/j8vbBsLKsON7GwQJLZeQNBnl1TSrPNzXdFLYxKsfDmDRNQyGWsPNyIKMIL68oIhUQ+P9DATxYMZ05OLC9vKOfDXbX8ZP4wLhqdxM3TMyhvdfDZvnrufG8vK344rV/sQZvDy94aKwCPfHaYpi4PIlKI9rs7JGXBU6uKabG5uWx8SrjCZhpkwm/5vjru+fAAaoUMb6BXYhhv1iAIcP6IRCL1Kn5/2Si+PdrM3fN6nWStLonU9igTD9Z18ZMP9hMIiajkMoKiSGGSifQoPV8fbiLJouGhc/MQBIG/76zhrS1V3Dozk8u6HU//sr4cXzBEpE7JnbOzaOzyhE1D9Co5N0zL4LeX9I1FUSvkxJqkcxQKiXx8+1TqOt1kx0okS9d9rdep+r/+BX/cSEOXB7kMdj+8gAi9itEpFjY+MIdXN1awq6oTb0CKY3D6AkzNiiYkisSZ1Ex88ltmDo/hoXNzeeDjg5g0Cu6ZP4wxqRFheWd7dz+d3SPFGQw0idbl9vP21ipGpViYObx3DPiP26awraKNcwv//dePIQzh/wf815K6d999F0EQmDBhwn/6UP63oNBITn0dZZToI+jwSJLGIwoZ2XGFZ203gwVzOz0B1h/uQARmFUQO3nOXNAGSJhAMiRTXOFAqBKblWQiEREq37SQ6VA8OpN6jyOwBN3G01kF1q4fyJjh/vIoxmSZGZxil2fi9UjULRd8ZQZkgMKMggg67n5Ro6TV8XfE17xx9h6vzr+aCTMlqvbHTi9dlI+RoJTp5GKYzGEB3ufxsPNKJTIDZhVFSVVQMwdFPJaORgqWgj6VLn8PRqhCiz8oWbyvQyv7OzUySn0O76hPa/a2kRA8gCcuYCRkzyQYGPDPR2SyYeA9bt/0Ss8qML+Rjdtb5iMd2QsN+Ckdew4jUOQCkRmvYVmJFrZSHq7dt7jau+foa3F4bb8bOIDPnYiJHTEJdV4M2Oq23cnrV+737rNkBO16CUctg+MLTPlcA1+Rfw4clH7I9JpvJceP51t3IQ5seAuCNRW8wIb7vNUSePoWJnz9Ka+Rkhs+8CJVCRn6KgZfXfka1rZq3j77NT8b9ZFAzkRL7YbZ3rme4oYApkXP6Pe8OuHlu73PS/g+/0Sfu40Q4vRL5CQRFfIHQgKRufJaZ4YmBQc1rBkK8Jolcw0jcQRfDDb0GL7vLunD7QlhdAWYXDhDUfhYgE2RcFL+MZm8DWXqJtCRr0zlo241RbsKijDrFFv41ODfuUr6oWc77ez+FHB3nZZ5eduHJMDViDmZFBEnatNMidCExxEe7a3H6gtg8AXLjTf0XmnGf9HOG8PiDPPHVUZRyGT8/Ly8skTxbePaK0fzo7/uoanNSb3WHQ717kGjRctP03qrb5/sbeHlDefj/TaVttNi9JFq0TMmMYl1JK+0OH39YfQy3P8hL68qZkxPLKxvK6XT5eWNzJReNTkKjlGPSKAmJUNxkp9PpJ97c93uSYNby4Dm57KvpZHV3dEJPiLYAKOUyfMEQb26txhsU+eruGbj9QUanWAZ8rT2VseMJ3dvbqsP2/GuONPP2zZO4dGxSmHz1INqopsHqIc6o5pYZmdi9PgLdk3S+YAidUsYbm6t468YJrPxx38rqi+vKqOt08/KGcuQyyRhmxrBotpW30ebw8cTXxSybmIJKLuALivz6y6NMzIhCRCTWqMHm8ZNg1qBTKfD4g1z6l60UN9kZkWzmzRt6r4OvXTeBXVUdfRw+e9ATcg5Cv4nVKyemUN3hJNaoYVZODJUtTi4ak4ggCNz13l5a7F4+3lNHVoyetcUt4fWUcoFND8wl3qzhztnZdDh9XDQ6MUzoutx+jGpFeH/PrjnGW1urUMoFdj+8ALNOuhb/fPkhVh9t5raZNn5+3pnFdgxhCP8L+K8kdb///e9ZvXq15Hh28cX/6cP538OwcwGYGQpwo6sBX9DHORn/nhB4qzMQlmZanf5+pK6iyUVjp4d8dTURZiM1gUSONUhugha9glizmszCsQSOFKHQGOC4MHWPL8jhGgcGjZzcZEP3jcKDQSMPxxf0yKtKYy+lWnSQl2zixJQhs04ZvskAvHTgJapsVby0/yUuyLyAxk4vO4rbWeh6E53ooLxjKmWxM3G4g4xIN/apZg14DhyBsKNou9PNpsbN5GpiSG/cIy3QsAeGnUtjp5emUBwqy52MCQZo8zjJN05HpXOQJ8aQULoRhp0vZfSdIRZnLWZx1gkStVu+A6+dEVpL+KEYs5rzx8ciCL2VyiNtR6h3SGHEe5y1ZNbvREiZQmLOwPIiAFY9CA37JHJ3X9EZHeuJkkVj4w4EBGSCbGDjlehhxN/wOomyvoPeK3OupMpWxezk2X0IXSAUYKd1IzWuChSCApkgxxNyU2Q/MCCp0yq0nJN+Dlvqt5zye5MRqyUUEtGoZCc1pBjsucEgF+TMiu6faxhhUOLu8BKh/9de+iNUUUSoegeMcepEbky9+1+6z1PBrIzg7X0f0OhsxOl79ayQOp3CwISI/lmDH++p43cri7l0XBI/O7d38Pl180dMm2ijqiKVHx9X3fk+6HL5MeuUfHWwkXe3S1WpSRmRnPM9qxnBkMjrmytQK+RcPzWdDqcvXKXbUtoWtvM/EWuLm/H6Q+QlmFDKBfxBEaNGwQ1T08P9Ww+em0uTzUt2rIF4k5rl++pZOl4iRz+YlcWHu2r7EMQ7Z2fh9gUZkWwm3ixNpm0qbeXOd/cyItnM326aGA4Tf39HDSsPN7ClrB1EmJ4dxWXjU7jnw/2EREnyeqJM8ET8YGYWSrmMRIuWqjYHTm+Qf+yuDT9f0mznpfXl/GF1CddMSg2Hp4NUsfxodx0L8+Oo7XTxm68lYqtWyMiK0XO00c7msjZaHV4SzL3E+FizHaNagUWn5LopaTy9qoSGLg9Ob4A3bpjIec9vkl7fzlrm5saytriFoChVFV/dVIlWKcftDzIs1sA3P5lJXaebI41SJMv+WisrDzexbJI0yWfWKfvECByPN2+YwG9WFtHU5eHNLVJmXw+MGiVPXDyCVruXGU+txeMP0e7ycfP0DG6alk5Fq4N5ebFMzYrGqCnHHwzh8YdQK+ThWIvfrSxiR2UHNR0ulk1K460tlTz+xVGmZkXx/q2S8VTP/ThKr+5jXLa/p3+wpvOk798QhvC/irN+Z6+pqTmj5UVRxO1209TUxJ49e/jggw/Yu3cvAHl5eVxxxaCxvkP4F0MhU3DvuHu/1za8/hAhUTztLLeECDVZ8TpExD7B5CAZcxyokkJXZQE3Uyo/JmLkj5AJAjIZYRc5jSUWpvWPwahodlPbJkmIYs0qshJ0xEeo0ahk/XplSpq8+AMKSlv8JJ0ieuiSYZfwxuE3WDJMin2QCyAjhEqU9tUqJNLYKv1dUu9gcs7JSVZytIayRhc2d4C1Zft4pvh+TCoTa3NvQe3ugDjJnS01Rkuz1UeHI5Elcb8jJsqFV9HA5Kh07v7qLR4N2HnO18mIsT/E7g5g1ilO2svU5GzCE/CQbk7v83iz1YsoSvJCjiN0PThRPjM1cSpLhy/FbavjXGMOpPcnPv2QNVcidVlzT73sKTApYRIfXvAhCpmCYRH9B849xiiJkWomDbf0HnfSVD5P+rzf8mXOIg7Z9oT/H64vQEBgmDoT+2tzCXlsmK9dAeZe+v/0rKdP61hlMoFhiScxagiFYP+7oI3kUHQaq6tXc8mwS8gwn7wPaTBMHGbG7QuhVZ3dKs7/L7g853LeL3q/v9vvWcaHu2poc3h5b3tNH1LX7G1kdL6fxeMimBn3/VsLfv3lUV7fXMnl45O5bWYWFp0ShUw2qEPkmeDLgw385mspYDwtSsesnBjOLYzHHwyxaBAnzR0V7dz0liTff+XacUzJjGJjaRuBoMh9C3ujLXLjTX2qVA+fnx9e3+0LIgi9MkaAWJOG31/W15Vy9ZFm7N4AW8vbabZ5SI6QpPzLJqUyLi2Cc57bCMB5IxO5aHQSWTEG1hxt5vIJKawtbibJoiVnoEopkkvtXXMkHcMjyw+FJZxapQy3P0SLzcvnB+oJhkS+OtTUh9SNTY1gbGoEl760lT3VnShlAv6QiCjC00tH8ftVJYxOsfQhdADPrC6hqMmOXCawbGIqbQ4fr2woR0DqJXvlmnH86IN9+AIhkiO0PHXZSNQKGeWt0qRmT+B3dbsLXzBEdqyBW2Zk8NaWKoIhkb01HSyblMof1xzjtU0VzMuLJS1Sz22zMjEdZ3CiU8tpsLrpdPl5c0tVH1LXA1EU6S4+EgxJ1cxP99VT1GQnKULLB7tqw7mA0QYVn94xjUi9ig931bClXOoHN3YrNrZVSP/vrOwgGBKRCfDxXslVdESyuY877J+uHM2XBxu5bkoaZS0OOl0+JqT/axQHQxjC/48466QuPT39ezfBi6JIbGwsy5cvRyb73xx8/F+A3R1g3aF2RBFmFkRKFSpXOzgaITqvT1ZZD2QyoV92V/g5QSApUk1jh4ekQBnIFFhMenKSBIrqnGw/ZmX+yKjwdo6HzRXAeFxPUlGdk2l5qoHDn90BMmO1VDS7sbsDHK62U5Bq4FC1gy6Xn9EZpj4VxBsLb+TGwhvD/8da1EyVbcEhmFErBBSRGdDhJyQG+K7tfdKTryJeP7g1u1wmIHb3rmnF42ZTRy4DuZovyr+gs/Y7luUtY3KOhTX72/AGgjyy52bava1MS5lEg05Jh9XHWncTzhIrX1T9A5tQya9m/ZRobf+KWY2thsu+uAxv0MvL819mSqLUPN9m87G12ArAsEQdhalGKX/u6Kegi4bofPB2QsALaTNAJkcpV/LYlMcGfX0DYt5jMP1eUBvwh/w8tuUxGp2NPDn9SZIMJ9ZKT428qDwaOjx8vrOZKKOKqbmW8HWpqdPb5/eJqOiq4OZvbkav1PO3c/5GtCoWhaAkJAYxKyMZa5mCWRlB876/YayTyN7+zb9l9PkvhLdxrPMYckFOluXkvVnFHcV8fOxjFmctZlRMf/MJDrwPn/8IgJ/mjaHB087B1oM8MOEBXj74MgvSFnBhzATY9gJkzBpQuuoJuqlzV5GsTUcj1w4o8fxX40DXLmwBKxMtM1DLeydrau21bKzbSGFcHtmm4egV/d1yzyYGMqL5V+CO2Vl41pSyZEzfz+78mMVUucoYZT47bQXbugfI2yraeeqyUex6eD4CDGpPfyZIi9KjksuQySA5Qsvz35XhC4T45UUFfZQKx0OrkiMTpPw1vUrBry4q5LXNFczPO3W24Nri5jAhBHh5QznXT00fdPmbpmdQ0+FiZLIZtULG7e/sYVSKmTtmZ5MTb+TtmybS5vBy0SjpPRgeZ2RLWRs/+WAfu6o6EYCND8whJfLkWZZHG+3hv6dnRbOmuAVBkN7jj/fUs/QE+WUPNN0VpliThnqrG5NWQXasoZ/baSAY4psjzeTGG1l9tJlp2dEo5DIuHZvE89+VUtRk5/XNlTx6QT7v3zKJX395lE6njwtGJqBTKXD7ghjUcmKNGoqb7EzJigoToTk5sby2qRKA5Xsb+MPS0SzfV4fLF+SLA43S/kMhrC4/a4tbuHN2Fp/tb6DT5UcuE/jhnIFbFzz+UDcp1HHLdMmU6kiDlCV4oM7KEvenXKLazDOBpZSqZ9Bi91De5uA3X/WqMB67QCLyD5yTi16lYHZuLCsPN/Lu9mr0KjlWl5/kiL7Ed2pWNFOzoqntcDHvjxvwBUI8d+VoLhp95veIIQzh/yL+JRqcHkeofwYKhYKlS5fyzDPPEB//r88kGsK/Dk5PMNws7vAEiNACu/4CfpfUt9ct8xwMLr8LnbLvDXdiT2XFOgvUi0FtxO2zhfe3paiTNrufcVkmkqM1FNc5cXmD1LZ5kAlg0MhxeIL0+4SGghBwU9QMxfVOzDoFJp2CdrufimY36XFaypukPovKZvfAxNNaBQffB2MC0fnnQvMBiBvJKJ2FKLOHn2y+mQrHYZo8Ffx53p8JhUQCIZGdx7pweAJMGm4JSzPHZJqobHaTHG3EHP80uRG5qOVqDrYe5Oebfw6ARqHh8pzLmTcqCpfPzzNVUjVwd8Nerhx3ISW19SwdfTtbjtbzVYtEOG5bfYwX5r0ghbgfhw5PB+6AG4BGZ2P48ePnZ0obXCRGqIls2gXWSuln3W8gZwEA31QdJnXYtaRGG/45d1S1NKDf2biTLyuknsYvyr/g9lG3n/m2kPoagyFo6fLhC4ioldKLyUsxIMicJA4QP1Fc5+BIA0TJsimxbedI+xFmJs/k2pQ7ERD6yDK9KRPYotWiCwVpSRkXfnxP8x5uXHUjMkHGe+e/1xvFMQB+s/VxZvmDbGotYdTid/ovoOsm4HI1WcZ0GjztZFoyeX7f82xt2MrW+q1cqBsLh/4h2eA/VAvKvhXuNa2f0+CpIV6dzEUJV53JKTwraPe1sL1zPSBFHUyNnIu5O5rih9/9kIquClIiElg2ZglXJ9/+H3HGPNuYmxsXzuU6Hmm6rNM2YTkd/PriAt7aWsW87n2dzT660SkWNjwwG7lMwOb2h/vjsmMN/GyQfqaRyRY+/+F0/MFQ2Hb/iYtHDLjs71cV8+bmSu6YncWP5w8PW+SDFJJ98/STV6MzovX8rZsgLfzjBo61OFh1pIkJ3WHXJ8Y4vLejmt+uLA7/LwIuX4BAMMRvVxZj9/h5bHFBv4y9py8bwX0fHWRsqgWNUs6a4hZColRtfPumwcnES9eMY1t5O5PSI9lfZ2V4nJGnVpXwj921TMuK4i9Xj0MmE3j+u1KeX1uGQa1g18/nE21Ud58DDfkJJoqbbByos2L3+Km3ujlQ18WBui6CIZE752RTmGTmtpm9nymPP8ivvzyKRinjqompGNUKHN4AV06U5LIPnpPLm1uqqGh10Ony0+7whfP2Xt1UySVjk9hfa+XCUYncOjOTNocXs1bZ57P15NdH+eZIMyqFjPsX5gACSRYd+2u7UMllXKHcRDa1/Ei7iovax3H33/fh8EoxBj3v3fTu9ycrxsAfrxgNwLCHv8YflAxsPrp9CuNSB1a1ePzB7mxSwhXBIQxhCP8CUnf99YOH9Q4EQRDQarVERkYycuRIZs2aRWzsKfRuQ/j/AnEWFSPSjIREkeQojRSJIHbfuMXQSdf94+4/8uaRN1k6fCkPjHuYJquPeIuKkChFFsRbEojWSiYm+SkGVAqp72hnqTRb2Gz1IpMJlNQ7w9sMidKygaAoSQl7IIZg5wvgaEKjGQ+K6Tg8ASYMMxOsc5ISrUGnkhNnUdHh8GI2+xgQzYfAZ4d2O+RcCNlST5MSyIjTMVar5a6Yv+BSpvNN8V6+qVzPFMvFKARJflfR7CJHocegURBlVIWz386x9PZlRWoi0Sq0eIPecAVLq5KjVcm5pfAWnt37LN6gH487yKsLXwVgTp6Fv9VkUOeqpNRayvP7nud3M37X59BHx47mN9N/g81n48KsCwmFRGrbPBi1cgpTDRyucQDdFc7EQmg+CB1VYG8hFAoSEgQ+bPoYiyvIooRlLBp7grQs6B/UhRRnO7x1Pni64LoV/OPYPwCpL2x+an/3yuOxpnoNOxp3cMuIW/pVP4cl6vH6Q0QZVX36MiIMSqackFfYg5J6JzJRz/mJt1JIElMSpIqlaoBg6dTofOy3fEe7u51FyTPDj1u91rADZJe366THf51xGPP93YMSe0P/3Dm5AKOXgimZ52Y9TI29hgxzBh+WfMi2hm3MSZ0DihSJ1EXngKI/Ue2ZZBM5+XeuB56gm1Utn9Lpb8cgN7Eg9kIsyn9e4mSQmzDITTiCNmrcFVibO7gqWQqON6gkIq+Sq/B358PJ+e/ItatzV/Ft6xfhiINTGaF4/MGzFiQ+GA7UWmns8rCoIA5BEBiRZOm2pm/E7g1w7eS0s7q/HomgSaNkXFoEJU12VhxoYPXRZt6/dVI/CSEMHg5+Ij7aXYsnEOK570q5aHQS5xQm8Ndrx6FTKZg+iL3/YDBqpWuLTCDcc+cLhPjD6hKUcoG75w0jNVKHIEg5btkxBmYMjyYn3sT6khZe3yxVs/xBkWe7CUYPsmKNfHbXNAC2lLXx2qYKUiJ1/YxiToRJowzLVGd3Z8P17GfVkWZqO1394hSOv05plHJun53F3X/fx+6qTu58by+PLc4nOUJLm93L14eb2F3dyc6H+14jPz/QEN7P2NQIdj48n0AoFM6Qu2BkIheMTMTu8dPp9LO1vC1M6q6elMqdc7L5wawsDGoFH+6q4cFPDpGXYOKLH04LV4BHJlv45kgzvkCIm9/ezVs3TgyTrHqrh3OF3/KJ5QWORp8DpZCfaCIkwtriFm6ansHPB3J9RXLj7HL70akUJ5VVDosz8s5Nk2i2efpVw4cwhP9lnHVS9+abb57tTQ7hP42qjVC7BdJnQ8oUHD4Hbe420s3pBIIh9pTbCIVExmWbw83QIBH27ITjKm1yJUy4A2z1EDfw7G0PtjZsDf/eYeyiw+EnyqhEqZDR1OmlqtnNBRMk8q9WyihIlSpnowMhWrp85CQbQASFXAARshO0GLSKgV03g35wNCEiYPTVkxXlISExgRizioSI3uUjk+o41L6KZpuSa46KqL97EsZeBxf8UVogcRxsewmXqGFPsZ3kJC0Zcb2v//7EhXzjkfoTdtQUUdHxERNC7cQl3UmXT0lNq4e6Ng/zRkVhGMRqO9mYzJdLvsQdcJNm6juAuzzncg62HcQf8vOjgp+GH48zGfn8kk9Y9tUyijuLwzI/fyBEbZuHSKMSi17ZxxiluM5BUZ0TmQCLxkRT2+ahyxXA7g5IsRbTH4SWItBE0ZExk+t3/pqagJXzZZr+lZbqjVC6UuoFHDFApahhH7R2y3Iq1hOhlmZn003pZEcMLP8BqZJ7/4b7CYpBXH4Xv5nxmz7Pm7QKpuZGUN/uoaHD069HcyBkJ+ioa/cwMXUkSVET6XL5OVDZgU2s5OXSX5BqTOVPUx5HpY0CmXzAKty81Hn8ZvpvUMqUTE2cetL9zSu4BnH/30ClR1AP0N/jbAVDDAgiftHLbsd6ttu/ZXHWFSwdvlQK0W7YDQseg7wlfUur3VgQeyG17kpStOmnfP0ADZ5amr0NAHSEWilzFDE+YtpprTsQ1HINVybfzHetX1HpOoZMkOEP+VHKlLw0/yX2NO8mwmggRZfxbw0q39W5mU5/G1Mj52JQ9D/3la5jeEMeat2VuIJODIr+1flAyM+qluU0ONp5bXksOZHJvHPzqYO3/xnUtLu49KWtBEIiT1xcyDWT03D7gmGnxqJuU4x/BTRKOZ/cMZUV++v58Qf7AakH6qLRSeEeuDMltIsK4nlvR400UdfmID1az8JBevUGw+H6Ll7dVMGN09JZMiaJyRmR4d66hz6RAt5BkoLeOSebtffNRt8tVexBfoIJnUqOyxdk+b56Hr0gf9AcNItWSSAkUt7q5LuiZi48iezvg501PPFVERePSeSJi0dQ2mIPS1MnpEdQ1uxg9ZFmbpuZyfB4Izlxxn7h3ZMyIkmyaKm3utlU2saGklY2PziXh5cf4r0dNSRF9CeWHU4fKrkMvVoKIReE3uiCqjYniRYtCpnAQ58e4lBdF89eMYrXrhtPnEnDiGSJkPdUK/dUS4YkJU02nN4gZp10f79rTjZby9rYUt7OlrI2RFHkySUjMGoUfLK3Hr8oo3nx2yzfVAF0EGvU8MTFhbQ7fcQY+088gRS9kBGt50CtlZtnDFyl7XD6uPKv23B4Arx7y6QzJv9DGML/dfxXul8O4b8MddvAa4O6HXgSxnDZF5dR76jn0cmPMi36Qho6pN6khg4v6bEDz16+dug1yqxl3DfuPmISxtBu99Fud5Ieq+1DBHvws0k/44PiD1iSvYSgVXpMJggYNXKaAMMg9u4Zcbo+ROqcMdJFXznAPsJQqGH4YvbV+KhW5BETkjPS3P+m3pNzFRQDyI9+BSE/HPm0l9Q1HYGqLegAg/4zisSr+xyLLnkM+Ue20ipGc8S+ktejpxAlD9Fs/5atSqkaFxIJO18CUhB8axHkXhQmwrG6gSvZBpWBP83504DPKas38Y+4GdjGP4S5dg/se5dDpsVUt3qQy0TOGx+DomE3+ByQPksiw0i9iYIgMC7LREWzOxzjIB1IHix8gmjgL3E51NoayNCOIdJ4QkWu6aD0u61kwGMjYyaMuUaq1I24jLtlsLd5L76gjxpbDQExwGNbHmOhIY1rZWaEmDzIWYxGoSHLPIxj1mLyIgeWODZ2eMLV2+l5MmLMKtwBN7X2Wv5e/HfylCaWRoxASJoAGjMFqcbwBAFARZObdrufb1tXU22rptpWTeXGX5ITMwrG3zbw64H+zqHAoWo7Na1uClONpHV/T4ToHJjxM5CrBqyykbMY9NEQNZxmbwPWgBQx0uCpJdc4AkQRipZL1ebqDQOGrmvlOoYbBpeAnohkbRrJmnRafc1oZdpwLMH3gVxQMDf6fFa1+Kj3VLGq5RMWx1+JSWViTsr3N8c5U1j9HeztkgKojQrzgC6mhcZxdPk7iVUnDkjoADr97dR7qkEBORkatuxpwxcIDXhN+74QhF7O3uM0a9YpeXHZWPbWdHLbzP7v/ffBobouXttcwYWjEpnX3RM3Py+OC0YmIHb/XdxkY+lL21AqZKy4axrrj7Xy+5XFXD4+hccW5590+3NyYqhudzE+PYLZw/85dc6vvjzKzsoONh5rZd9jfftJG7vc4b993VWkjOj+pkSxJg2/uqiAn350kPwEU9jAYyCsLWkJG4SsOtJ0UlL32f56HN4An+yp5575w3lqZUl43WlZ0dz8ttQ/uK+2k+evHDNgH2ScScPrN4znwhe2EAiGKEyUSNcds7OYlxfL5Mwo3L4g17+5k7pOF48vLuB33RLTO6dkUd0u9Z4lR2iZlxvLq5sqmZAewbNXjOarg5LM/rN9Dfz64oHjiu5dIGV4TsyI7NdH+djiAl5cV8b8fKlqHGNU88zloxmXFonLF+CLA/UcrpeuvbWdLmQyYVBCV97qQK+Sc6DOigh8uqeOayen9ZsoOFBr5VizpBrZUtZGZsy/tgd3CEP4/w1DpG4Ip0bGHKjdDmkzcAVcNDmbAKjsquSiTBUmnQKvP4R8kFaYalt1OLcrVhvLj8few5aiToIhcLgDjM3qL9cZFzeOcXFSn5IvLkRrl48YswqVQkasRUUgKBISxVMGfg9I5lqOQsNOSJkOUd2VoNSpOB0dYPPj9A7cExrqGIbJ7iU7KhbF/Emw6RkYdVzlKX4kmFMIehzYYib0J7imZHLWTSQHGJF3AVpdDHhtKDVaCEKEQcHwRH2vfX0oGA6Cp3HfKaubg0IMQcW3CIiYy1bDWkl6qbxkHBBJu7eV1zb/mdt93fI8pZbslKmYtAp0GjlqpQy1UsaYzMFt9dNMaeHKYbvdR3WLm4pmNxahi8mbX0SWMhYm3IovEMLtDfYNvlao4KIXw/8W1W+h0ibJhzbVb6LZ1cyB1gPcFFQiaENQuxWGnYdMJue6hD9hjbKSqxrYrOD4gZJCLuAL+lj6xVKqbdUAbIhfgGBtgYN/l6qPMX2d3pKjNDR2elmYcgE2+VFSA36yFUaw1Z3ytLfZfCgVQngwVNnsJhgSqWl1h0kdAOqT2KtrzJAtEf6UkJ9h+nyCYpAMffdxCgLEFEDLYYg5+SD6dKGSqTk/fulZ2dbxUMgUYQmozX9yWeq/Gga5iRhVPJ3+dlK0A5OhCFUUF8RfwSHbHlY1f8qkiJn9gu+jVLEMNxTS4monmgx+d2nqSQmdKIps7VhHu6+ZGVEL+8Q+nAopkTqW3zmNZpsnTLIAzimM55zCs99//qsvj7CrqpNNpW3sfVTqm9WrFbywbGx4mUN1Xdi9AfBCSZOdz/ZJRObjPbUnJXUNVje3vbOHkAjj0yP6GVudLsanRbCzsoOp2f0rNtdOSWdbRQeCwCmNWi4bl8L8vDgMasVJTWZunp7BBztrcXoD3LdAcvPcW9PJwVorc3JjeXtbNZMzo1iQH8fd84YREku5cFQiL64rD8cL9Dg39uDrQ02kRh7joW5J4qG6LlQKWTh2YV1xa7jfUC4XOFhn5bKXtiEIsOKH0/D4Q+yslCZ7nviqiEi9ig6nj+w4A98VSRLJilYnRrW0TFGjnSSLlmsmp3Kwrot5ebHsrelkbHf/Wr3VzfbydhYUxBFv1vDkkr73HYc3wOf7G5iQHsHzV43pd46WTUrl9c2VfN5txLJkTBL3LezvoNmD574r5dk1x0iO0HLX7GxeWFcWNoe56wSjlqnZUVw2LhmHJ8DiUYmDbHEIQ/jfxRCpG8KpkTRR+gEigWdnP8vh9sNcl38daqWMpEg1RXVO9pTbsHsC2FxBCtMMYQlhnC6OYRHDqOqqYlLCJGQCqJVyXN4gKlkQr9uBWjv4jJtKIQvLJkVRZE+ZDY8/RHaCjhFpgwyIAx6p6qQbQJ5x7AvwWMHRDFPvA5l0nGMzzdS0ukkYwEADoL7NhyY4DJdcCQXDIaNvcCzGOPjJIeSiyKyBXFtVehhzLZR9h3nM9ZA+Dez1REZkcX5I6D8YlMkhaxG0FUF6b8+W1x+ivt1DrFkVrliKosjhGgdOT5BRGca+ERKCjFDSRL6q/Jo4fSQTBTnIlRTGy3lpz3Psa99MgSkOTIUQ9EF3JTDW0v88OHwOgmIQs3rgvhmbK8DGI70ZQs0YcQdk6I9+SXDKvaw92I7bF2JEmrGvNPc4jI8fzznp51DSWcJ3Nd9xybBLSDWmckRrZo4+FiEmP+ycqlWpCYWi8Yac3PHtQ+RF5nH32N48tBizitmFkQgCWPRKOjwd1NqlvCk5MmwBN5FyNdib4diqfqQuxqzivHExQAwXjX5Hcv6s2cxyazHfffdDfjbpZ/3cOe2BLjq7ZOwpdSEIMHdkFCatgvwUA3VtbobHnn4VZ3dZF3XtHkalG8mI0zE35vz+C41cJhH30wi//k9jdvQ5lDgOk64dWFbb6m1ib9c2MnU5DDOcHZI6EBQyBZckXosoiic1ZvGFfGztWAuAUqZiXswFfZ6XCTLmREuGT1eknnq/toCVw3bJMfWofT/Touad0XEXJpnPSlzBQHhnWxVvba3i9llZLB2fwtSsaHZVdTJ1gIDqHiwelUhxkx21Qsac3FhUChkvrC3jkrEn73PSqeSYtEqsLj8bj7UxLNbI+SPPLFfvjnf3sPJwE3fPy2bmsBg+2l3LkjFJYVJ23ogE/n7rZHQqeb9zVm2r5qZvbkIhqDF1/pgEQyx/WDrqlK6hDVYP9VapAvjh7lo2lbZS1O2O+e72GspaHfxtaxUHH18YdmoEWLG/HkGA9Cg9541I4K0tlYxIMlLc5MAfFPEHQ9S0u3hnexWvbqpELhNYcdc0CpPMzBoew5tbKok1qcmNN7LhWGu48ljf6WZ2TizJEVrqOt3UdLh4ckkhU7OiyYjWY3f3xkJcMCqRcWmRzM+LRRAEnrh4BJ1OH7OeXofNE+CBRTnUWd18e7SZFruXMTssPHJ+PuPS+pqVPP75ET7eU4daIeMHMzO5Z8Hwft+hyZmRmLVKInRKHrsgn4hB5KwtNg9//q5Uei1WN0vGJPHejmqsbj9ef4i3tlRy1aRU1Arpeq9WyPnD0gFcgocwhCEAIIjfx6pyCP806urqSEmR3Khqa2tJTh640vD/AyqbXeyvtIeDZgHSYjR9KnCiKOIP+VHJpYu71x+itd3KvkoXIjAzzY8lsb+OvqrFxZEaB6kxWsl0JSTy9Z5W/EGR9FgtYzIH6EMK+mDbsxJxy7sEkk6wDy/+XJKUAqRMlSRuPccZCnLkyDHMB/9Kcut3CIuehDzp+epWKecuIUKNViUjIUI96IAwGBIJBMU+je/1HR5UckkC+H2wraSTpk4fOrWcRd3y0i6nn7WHpJlYtUIgLVZHQWovUf5H0Sf89cDfaPVVs3zW82SaM8GSwuG2wywvXc6SYUsoNGVIklKNZcD91thquPLLKwmIAf52zt/Ii+rrgCeKIg5PkO8OSjEWBo2ceLOCEdWvgCEG3+gb+HpPK6IIjYG9bO56h9/O+C1ppjQcPgc1thoMKgOpplT8IT/j3hmHiMjclLk8N1eq9G6p38IjWx5hQtwEfj/z9/gCIlannzeKn+X9kvcAWHnJSpKNg3+fvqn6hkObf0d9VzU7DEaecyuZoEuCi18G06kHlhVdFVz02UUA3FBwA/eNvy/8XLH9IBvav0EnmNE3XoCAnDkjInurr2t+AVv+JPViXvjnU+5rxY5mQiLEmFRMz+8dXG1v3E6MNuaUkQn/SVj9HeyxbiVZm06OYWB514n4qukf1HmqUQgKbk7rnzX574Yoinzd/DENnhrmRJ9HtmFg18cT0eX285uviogxqrlvYe+gNySG+KZlOW2+FubHLCZBc+rrfqfTxyd765iaFU1+4sC5aifDW1sqefLrIi4bl8JvLxm82j/td2upt7oZHmdg9T2zALB5/BjVJ8+3PF3sqZZ6q3oiBGo6nNz6t92UNDtQygWKf31uv8zLk6HwF9/g8AbIiTNS1uIgKIo8cE4Od87OxuENsGJ/PRPTIxkW13/i7+NjH/PLbb8EwF17LQFHAbOHx1DV7uT3l45kUubARLbL5WfeH9fT7vBx7eRU3t7em8ubFaOnvNVJVoye4XEGUqP0PHRObvjctdg9mDRKZj61jha7F7kgoJIL3Dwjkx/Ny2beMxuo6+yVjN4wNZ3HL+wvmw6FRG5+exdFDXZ+f+kIZuXE8u72ah797DAiklnMXbOz+MmCHNy+AA9/dphgSDo3qZF95acNVjcznlpHMCSSn2DsE98AUrzO6ntmktUtc1x1uJG73t8bdrUG+OC2yUwe4HyJosiRBhtFjTYuGp2ESiHD6vJR1GhnbKqFb442Y9TIufmt3YREuGxcMn9YOopOp4/SFjtXvbqDYEjknvnD+fH8/nmj/078XxqvDeH/NoYqdUP43siI02HWKdEoBXaX2+hw+Ik7ocojCEKY0IFkbiIPuAgI0mDX3tWKZQA1RXWrB19ApKrFzYg0IzKZwIyCCDrs/r79Xccj4JX6swCcLf2fH34+NO2TqnknzGk0H9tLqTOV86o/Q/B1wIanwqQuLUZLlEHJtwfaEYHRGUYybOvB3ij1vHVXBYMhkXWH2rG7g4zLMpEao6W2zc3uMkl+02eQ73dJFUX96fWUdDn9uL3SHVV5nN5Vr1EgKJwE/Wq8AQXHGpzkJuuRywRCIRGlfTx3pU9ndetfkZszeb5iOSWdJTw08SEenfLoae27ylaF3S/d9EutpX1IXVGtg+J6J9kJOmYXRuL1h3o/AxkPAqACpuRYKGmp58kdv8QbcvJVxVfcNvI2rvjyCmrs0gDpztF3cseoO5iXOo8NdRuYn9br7vZVxVe0udtYWbWShyc/jFltJs6iZk7abD6vWEFeVB5x+pNLrRalL2LRV4/xM8BOiIejI1l92YrTOgcASYYkRseMptxazszjXC8B2n2tALhFG1MzNRhVx8lpAcrXdv9ed1r7GpVhor7dQ25y72BseelyHtv6GCqZii+XfEmC4cwqHGeCfdYdOII2JkbMRC0buII9GHZ1bqbCVUK5s5gsXa5k6nIKpOqyqPNUk6r97yCrgiBwfvzSPhW9ULdz78ncMD/cVcOHu6WK8KycmLCTn0yQsSh2CVWuUtSyga9f/pCPOnc1CZpkNHItj644zJcHG4nQKfv1jQG0O7xUtTsZmxoxIPn6+lAT/qDIlwcaTkrqbpqewVtbK7lhau/kmkkzuOT6VGi2eYjQSZL5HhdFg1rBup/OJsao5g/fHKOkuz8qPUpPSBSRc2pSFwqJfH6ggbEpFjaWtVHabA/7unY4JWfiX6w4wid764jQKdnzyAL21nQSa9SQGiURykXpi9jZuBNfQM7+1jFEROhYf0z67n60p25QUvf06mLaHD5GJZt5d0cNWqUMTyCEKMJVE1NZVBDP33fW8Jf1UvzD4pGJ4SphjzlLT0h4UBRxB0TkMgG1Qo66W6mhkAkEQmLYpATAGwhy/0cHabV7uWpiChtKWgmJ8NrmSmblxHKorisc0xMS4c/ryvniYCNNNg+vXDueF9eVMfOp9Tx8Xh63Htd7mWjR8rcbJ1LR5iAkijz++VFy442MT4vg3R01CMDyvfXIBLhzTjbfHGkOEzqlDFRKOVqljCe/OkpqpI5rp6SHt21zB7js5a14/CGq2p3cvyiXS1/aSnmrk1HJZg7UdaFVynj5mnHsrOrg6olSqdvuCfDgJ4foeUHRxu83CTqEIfwvYYjUDeGsoMccY2ZB5CklTT2IT0oiv+sIYsBLUnb/GclAUCQ3SU9RnZPU4wicQaPgYJUda+U+8gxtaIbNgeOdA9VGGHk12Oshta9EsrzJxbEGJ5lpt5OjaYDY3gpCUZ2DTqcFdaCLqqTLyKz9O8Lo6/p+SYTuHxFkfgdUb5Ier90Wrvj5AyHsbunG3enwkxqjDfeMCPSaHBDwwLY/STEI+ZdKZLTiO0iZDFn9B28Am4s6sftc2MQyxqf1Hvum+vU8ekSSHd6a8TQzkqeGZ72DIRFB1CACFmUC93z7GPXuUlyhLlKNqTw48UFJiqpQS1W6tU/AvvekQPDRvT2D05Om86MxP8Ib9DIhch51bR6SoqRqZY9ZTl2bmxiTqk9kxLaGbfx62xPkmcfz4ISfMzkrmUm146i317MwbSFuv5fh6vnU2f9GiCDHOo4B8OycZ/u9/qvzrqbGXsOE+AlhCejXFV/zxuE3uHf8vSzNvBBKvoGkcWBKYEXZCp7d8yznZJzDQxMf6t3QZW/w6MEPmRaTwthsSdJodwfYU9aFXiNnXJZ50D4ftVzNO+cNkCkHjLVMQSbIiFUnkKUfwJL73KekPLnRVw+4/olIj9X2683syRMMikH8If9Aq50VtHgb2WndCEg9aGMsZ+bqmKxNp8JVQoIm+bQIHYBebmBKxBwKTWNPulxIDBEUg31yA88W/CEfXzd/giNg45y4JUSpYsPXM3vAxmeN7xIUg1wUv2zQnrgJ6ZHoVHIidKpwlaMH+7t2sMu6GYWg5OrkH6CR931/v2v9imp3GbGqBJYkXkNUt3RtIEdGXyDE4j9vpqHLM2hF476Fw3lhXRkTMyJ5ZUM5l4xN7mNYsbuqg9+tLGZBfhybHuhrWlPX6Qr3XfVI4E4Hb22p5PEvjlKYZOLzu6bT3k22XL4Anm5S4/JJUR4CUNri4A/flAyaf3c8bntnN98W9U7WqZUy3P4QCgGWdRMDvVo6Vp1KwUd7annwk0PoVHLW/3Q2sSYNRpWRp2Y9JW2gWwH72IrD7KzsYNmkwXW0qw5L/eQHu0mU2x/irRsn4PGHWJgfh0wmMC8vlne2VZMcqRvQmOWR8/N4/rtSNEo5VrefY0021hY388FtU9hT3Um7w8sne+u4c3avRHlvtZXPD0iOtNsqpMB5hUzgiglS9ejG6elUdzhJMmtZdUQ6xqpuV9T1JS3sr7ECsLu6g1uRSN2xZjsyQSAn3si6khbGpUWw6+H5/HZlEV8dauSuOVmkRup58JOD4eOYkhlJRZuDBXnxPPfdMZzeII98dphD9dKE5YSMSHLjpXuxLxhEKRPwIOXULn15a7gS2UNs3f4Qr26qYFdVJx/urGXjA3NYf6yFyjZn+FxdPensxnQMYQj/lzFE6oZw1nFaUp1QAKFmC0qVHZLzkZ0QmFza4ORwjYOECDWzC/sOjh3uAF1ddqY7v0JwiCALQv4lfbcfWyD9nIDyRhceX4ijzZA4agRGufQVcHgCFNc5ARPpFidFOfdxNOenZMXrGHnc+gaNgtmFkXh8IeLNCsTmbIrdCQSFEeSFpFlXjUrO2CwTnQ4/KoWMvRU2jFppFjbapMSk6/7aBbxSlQ7A1S65Qwbc0LBnUFKnVsp4r+aPHLKvY3lTMisvXQmAUWVEQHKqnJufTGF0L8lVKmSMydbw7PbX2dLxEZ6QgwSZEZnSwKyUWZK75oF3QK7EN+FOHi15B6suyJN7Xif6OFInE2TcNvI2bK4A3x2UBhaBkIn0WC2FaQZK6p102P1sK7EyNtMUNgP5+NjH1DpqqHXUMEl/I5dMTOPFeb3GKMca7MyMWkaiJocu5W7uGnvrgK8doCC6gHfPe7fPY68dfo3SzlJeOfAKS0s2w563wJLKlqUv8+iWRxER+aDoAx6a+BBVXVUYVUai4grQLfgVx3dI1bZ56HQG6HQGGJYY6FthO01o5br+TooeKxx8DxQaGHkNLH2z+9z5qfNUE6dORCsfuL9wIFyZeyVmtZkEfQKpptNo5PonYVJYMMhNuINO4jSDmxL4gj5+ue2XtHvamJQ1gkzjMGZGLSTPOJJsfS4K4fTOY5u3mTWtnwNSRmGBqb8JA0ik69OGd7AFujgnbgkp2pOHVJ8pOnxtNHklI5wqVxlRqt4qepu3GVdQGnC2eBsHJXVjUiPY99gCFDJZP0mhiDjgOj0IihJRD4gS6XlscQGLCuPJT+gvvQyEQnS4JMLUYvcMuL1JmVHSz2++pdnmZVdVJ69dPz78/Evry9ld3cmemk5umZEZPt5QSOSSv2ylxe7l2slpgzokDoQDdZJSoqjRjre7kgVSfEBKpA5RFLl1RiYTMyJ5/ttSHL4gPUW6Lpef+z8+gFop56lLR/btDwYau3pf568uKmD28FjWHG3iya+LuOKVbcSZNdR3uLhrdjbXT01jxX6JDHn8QTz+wbMaf3XRqV/fmNQI1hxtRiGHxaOSGJVsYXZOX5XFuLRIDj6+MHwf9PiDvLqxgkSLlkvHJZNk0dHQ5Qmfk5VHmllT1MLuR+aHDW+uPiFvcGSymYnpkdR3OqnvkibQog0qLhgpfS9z4018cJuUqTlsQxldrgC1nS4arG5unp7BpIwo1pe0cPssqQK+q6qDK17ZhiAILMiLY9WRJt7aWsWOn83j071SDMSxJgdXT0rDrFVi9/h5fm1Z+Hh+NCebRIuW6nYXw+Ml2Wa0QUVcdzWyqs3J+X/ehD8Q4hcX5lPa5GBXlVR5/NHcbK6fks7CP22kw+mjpkMin55AEF8wxHkjElhztBmjRsE1Zzl3cQhD+L+OIVI3hNNGMCSyt9yGPxhiXJa5T7/YGaNuJweO/p3rW7eCIPDuee9RGN17U23p6h6odHn7mUCYdAqS40y4aqLQB9rANEBTfo+0Utl3FjzOoqKiWZottDr9GLuNRrQqORF6BTZ3gPikJJoqJDMWjar/a7ToldA9AduceQ3FJVboAGObJ0xk0mK0RHZLNQH0amk7bXY//qAfpVwpORuOvBocTZA6HUzJki19tynNQJhZEMmXbWqw9yXP4+PH8+EFHyITZORE5vRbLy3KTKLZzmSHmdaAnCi3l0fG/o6EhMlQt50gMnwhJQeadvG1TqoIfJ06nusGOAaZTDJdFEXo8RWIs6jRqWQ0fvQzdO46QvG/B6Qb8pW5V1LUVkaKaiwWrYGg6CcQkCot13x9DQ2ORq5O+i355vHEJMbzk/U/YVLCJO4dd2+f/ba4Wlhbs5bZKbP7BI1fPvxyXj34KlfmXgmlu6QHA172tuwND6CX5S3j2+pvuXf9vRhVRlZcvIJobV8TneQoDQ0dHvQaRfhzgSjCB8ugciMseQXy+hplnBZaj/a6ZVqrpJBwYH3Tcsp91UTLLVyaMjiRPRECAudnDmCY0o21NWtpcjaxNGfp96pkaeRarky+haAYHDB8vQc7GnfweXk3GdMG8CW5mRAxHZ1cj/Ik650ItUyDQlASEP3oFYMbJzkCtnC0Q6On7qyTuhh1PDmGETgCtn69gKm6TAqN4wgRIFPf/3t2PAarbI0xTyZCGYVFGdWvSgcwN+YCqlyl4dcllwlhw40ToVMpePfmSeyrsXLlxJSTvy6jmmabl5gT5GwXj0lid3Uniwri+hHQYLcHfyB0ciJ6Iu5flINeLWdaVjRalZy93VLC0hYHoijyu5XFvLKxgpHJZlb8aDpFjbZwUPfKw42sPtoMwAUjE1hUEM+BWis//egAI5LN/P6SkTyzpoSJGZFc1y33K29zEhKh1eGj1SHdO8pbHcSaNNw4LR2jRkFKpC4svxwMHU4f176+A48/yN9umhjOvPMFQqw83MgP52QzLi2CcWkRJw3HPv7a/Pa2Kp5ZI6kP8hNNdLh8YULXcx1VK2V8uq+em6ZJ77nTG+CpVcVE6FX8eN4w9GoF/7h9CtXtTuY8vZ4QDBhhsb2ind+t7BsdU97q7OeQ2u7wSfEKooizu2KaEa3HrFPywDk5vLapkjVFzUzMiGTjA3N4/PMjLO/O/AN4evUxnlk6Cq1KTkGimZ+dm4dOJUenUhAKiby6sRynV6rGfb6/gVa7F61Szoxh0fxk/nDkMoEXlo3h0731XDUxldJmO1mxBuJMEin8V2U9DmEI/9cxROqGcNpos/moa5dmSevaPWTFn351oR90kXQEfQQRQRTp8HRIUsaGPZA5j4LUXJR1dhI718O6/TD6hnAGlyAIjM6MgLS7pZ40zQlucO4O2PFnKRJg/A/6kL4RaUbkMgERSDoujFouE5g9IiosHY0cGUVdu5vWLj/NVm+/HsEeGLVylHKBkAhmfe/XyeUNEgyK6DWSy2dmvI6WLh+HbRsZ++4j3FhwI/eOv7dvRXGQ6uLxUClk/GbmL1lfO4dc8yhsrkC48pcXmUugcj3HAs3ERhRiUfYddNyijCQyagz+oJ/PjD9mu0/FxHYPiQnjWd+UiC2gJlstMDJmJFaPlRljf9Dn9TRbvSRGajBoFMwZEYnPL/YxfTF2HsJY+SoAYtUYSJd66SbET+Dry1bQ5fJjD7Sz8JMFeINeHpv8GGVWafbXpz2CWjmCV/e/w9H2oxxtP8qtI27FqOo1Obh/w/3sbdnL8rLlfHjBhwD4g35ERJ6Y/gRTEqfA8CskV9HUqVxjiKLd3U66KZ0bCm/gzcNvIiJi89mweqz9SJ1Jp2D+qBMGz54uKPla+vvoin+O1MUU4KzbglMIoNAb6XlX/PZaUIPfa4WD/4D978G0n0BW/8y0HlQ0uThQZSclWsP47P4uiOXWcn6y7ieIiATFINfmX3vmx9uDliLkjQeRFyyBk8zfFEYXMixiGF3eLgpjCsg3FqKT95ednQpGpZnLk27EF/L2qY6diAhVNJMiZmL1dzDiFDLNfwYyQcbs6HMGfE4uyJkW9f1y9WRB/0kJoVauI894+g5/49MjGX8SgtGD92+dzNEGG+NPcDNcPCpxQHt4mUzg4zumsq+mk/NGnFnfZqJFyxMXS/17TV0eJmREYNQoOHdEAoIgUNEtr6tsc5IVY+gjUZ2WHU16lA61Qh52XvxoTy2lLQ5KWxx8ureei0cncsdx8sQ7Z2exp6qTkmap51enkq7Lb2yu5KbpGVw58fQq2n/4poQjDZKUcHNpW3i9p1YV89rmSkwaBTsfnn9GQeuZ0QYEAcxaJVEGFbnxCWwrb+NIg41fXljAu9ur+WRvPb/64igL8+NIjtDx4a5a/rZNil2ZnBkVNiJJi9LzyZ1TabZ5wiT4eCRHaDFpFDh9AUIhQJDOxYk4pzCeZ5aO4psjTaw+2oxSLhChVbK/1sqds7N5aZ3UE/j2tiqeX1vKtZPTuGtOFgICL6wro6TJzvs7ahiTauHtrdXct3B4OOD83R3VvLezNrwvjz8Yll0+uWREeOLgeHfQEx02hzCEIfxzGCJ1QzhtRBiUGDRynN4gHTbf9yN10bnMmfc0T9R8i0yuYmblNqli5WwDhQrLhBFMTPJBbXflpaO0f7CyXAny4wa25auh+SDEjZIqdQCOxj6kTiYTKOyOQfD4guyvtNFh9xNlUjEh24yMIJR8hToUoNI/E7s7hM0dYOHogUmdXqPgnLExiKIYzsSzuQOs7TZTEQihVclJidaSnaDnt8tfQymoWVe7TiJ1/wR0Sh3T4haw7nAHotjOjPwIok0q2PIc2+zbOJo3HrV7P9em3oVc6L2hR0Zkgb0BtDEouis4ggABUYY9IL0+n0/Ne+e912+f20qs2FwB6ju8TM+L6BdEC0D0MIjNh646hAGIiVmn5EDdMYnAAy6/ixsLb6TB0cAEywV0dAYZrp5Ppe4wkxMm9SF0Pa8bQKfQ8cDGByjvqCJOkc+m9k+QC3JWXrJSMg0Zex2vHXpNOsfj7g3nHS7LW0ZIDJFoSCQ7YmBL/X7QWmDOz6HsW5hy1+mtcwK8SjXvJgAoGObYx1ytNICeE0qjouUwydpMWPUzKS7Baz8pqWvolKRX9e2eAUmdQWlAq9DiCrgGDak/Lfic8PpC8Nqg+TAs/HW/Rfa37Gdz/WYuz7mcTy/8tM9zdZ0u2hw+RqdYzmi3RsXp2fWPNv9/OpP/5T2w+w2Y8VOYd3oGRQPBG/RQ7DhEgiaZWPXpES6TRjmgS+HJkBGtJz1Kx5qjzcSbNYxMtpzxsV7z+g7KWhwsKogLE5FfXVTA8DgDc3L6f0ZTInW8dv14/EGRaIN0Xbp8fAq7KjtpsXvodPnDUr4eJEfo+OaemWwoaeFwgw2b288rGyv44mAjEzMiKUwy882RJv68tpTLx6eEK3z+YIjSZjvRBjV/3VjBZ/ulapRKLutDmnr6a0+Vp1fT7mTVkSbOKUgIVwXzEk3cPXcY5xbG8+aWKraUtXGsyY4nEOLpb0q4ckIqn+2rJyNaz9OrSqizurlpejoahQyNSk7ECdfaMamDE6Aut5+XrxlHdpyBpm6Zas97tqe6gy63n7m5kpHUpeOSKe0mwf6gyK7qTp5dc4w3bpjAQ+flsq64laONXdg9Ab461MiG++cgiiIlzXa2V7QzOTOKB7r77XRqOb9YXMCfvyvl4z0SoVPKBH4wO5NZw2J46NNDTEiPJNogTQJ2ufz4Q6Hw+zuEIQzh7GCI1A1hQPgDIcoaXZh0CrQqGQ9vfoR9HZu4OvNekoWZ1HV4GekPfT8Jpi6Ki3KvkIhG9U5QasDVCcmTpecN8ZAxF1ytkDJl0M3Ud3gob3Qxo2kDPlHN1tZUQpbbmWIsRxc/etD1yppcNHZKUp2GDi92dwCzsxTqtgMQnzASOybiLSeXkCnkPe4pEry+EAq/DUXAjlubhMsn0uXyE2tWc3f+b7B3xKDWuvEEPPzlwF+wqC3cVHhTeP0ulx+nJ3jSyARfQAxLeHr6RBoaW5DpJcmLTOqw67vSsHMhfhRKXTSzPTICwd5K24RhZtpsPoYnDVxh6ZldVZxsUKM2wp3bIBSSNJoDYFriNOalzGN3827aPe1hiWWHw89eTxdzoqfz07nnDfi6n5n1DLubd6NX6LnhmxukB/VSn5FOoUOjkCqvwVCQ5/c+j4jIW4ffCpM6tVzNzSNuHvz4B4PeAmnjofUwJI4+49VVMjUp2gwaPbVk6HqNLDS5l5LvmSuZ09Qehj1vQsGSk24rP9lAseAI5zaeiDh9HCsuXkGXt2tAGe7pQ+iVPMv7E3hRFLnj2ztw+B2UW8v7mNq02DwsenYjTl+QZ5aO4tJx/932302eOowKy0kln2cNx1ZLv0tXfy9St7VzHccch1EKSq5P/VGfyZuzjb/vrOXnyw+hlAt8d+/sU0oYT0TPJeP461GCWcv9i3L7LLe7qoM91Z0UJpm47o1dhESRv98q2eWPTLbwzT0zOVhn5e1t1VwyZuAcvFk5sczKiWVTaSuvb65AKZfxu5XFLB6VwPs7azlcb6OmoyRM6m7+2y42Hmvrs40ovYr7Fub0yVa7f1EOo1Ms5CeY+lTpdlZ2YNYqyYk38sHOGh769BAAnx9o4MsfSSZdt7+zh0P1XXx1qDEcOp5k0dLQ5WZubiwvrCslKMLMYTG8sbUKgGlZUSwelchHe+q44929rP3p7FOe5/21Vi75yxZEYF5uLJVtTn57idQRXtxkY+nL2wiJ8PxVY7hwVCJvbqnk5Y0VZETryYsz8m1JC/PyYpn3zAbJNfOacXgCiby2qZK5ubFhFcur10n9mB5/kNc2V1De6mRieiTeQDAsMx2ZZKaoycYLa8vJjjHy3X29x7+nuoOlL29DFOHV68efMhh+CEMYwuljiNQNYUAca3ByrEFqYNaqBba0fINIiEO2deTFzyHKqPp+hK4bLm+Qo806IlVjyWz5HOJGQsJxkqqsBf3WCYVEdpV14fQEGZ9t5miNA4cnSIVmIhrBixULBGCjcwSju4LERwz8MY82qigTXAhAfIQKo04B8iTJSTMUpDDdTJ42Jkxo2u0+3N5Q2PFxMMQoHSzaci4KTxt7RvwepTGSGOU8sLViJBmH4MPn0fD6kVd587BkmpFtLCBGVkCsWcXmok5CIoxIM5CdMDDJijGrGJ9tIhiCpO6w9D1Jd5DY8AnD25IZP2pBf8t1QQhXLSNOGL8mRWn6EYVAUKS+w0OkQcnUXAvtNh8x5tOYWR2E0HU6/Dg8ATq8HXT5unjryFv8YJQk8Yw0KElIbuZg60HyAxeFq3LHQ6fUMTN5Jv6gn9kps6m01nBx4g8wGm5mckYGEZoI8LuRK9RcMuwS1tWuY3HW4n7bOWP4u7OjAu4+D7d5m/mm5TOMChPnxl02aP+aIAicF3fZQE+Atls2d95T0s8pEGlUMjX35FKleH18uOfQ6rEil8n7VT1PCZUObl0LLUdh+LkDHLpAijGFoo4iUoxSL1eTp57VLZ8RcEXj9ksfsANtx/DVfc2EiJlk63P7bec/jQNdO9neuQGNTMey5Fv79QD6Qj48QRcmpeXs7PD8Z2D/uzDp9u+1mR55q0auQ3Yybex/Ad65eRI7KjuYmzt45djtC3LN6zvw+ENE6pXhXr7ObtfMHoxMtvCHpZZT7nPGsBj0aiVdbj+by9rYXNbGz87N5XBdFzZ3gDe3VHLjtAxKmnpz2VIitbh9Qf5y9TgmZvSVsyrlsn4S1K8OSpltSrkkU+2JsACI0vdeJ40a6f5T1yndTxUygSeXFDI9OxpfMMTvVxUDUG/1MDsnhup2F+eOSODFdZI03e6Vet6Km2zsquzg4jFJGAeImnB5A/S0Pva4g36ws4aJGZF9CHXPX3u7HTFrO1ys/PEMNEo55a0Ofv1lEQAH66zcs2A4f1x9jD+uOYbbH+TBc3q/wxqlnK/vnoHLHwxHX1w2Lpn1JS1MzIjkYL1kltPTs9eD1Ueaw8e5tqj5tEldY5ebT/fWsyA/juEDZA8OYQhDGCJ1QxgEhm6jCI1KRrRRxaKY26jybufOMbcxOnbgpv1/BiX1TmrbvNSqZpJ44SVoTtD/17a5sToD5CTpUXXLG60uf6+FfruH1BgNJfUufElzSHNvJ8HeQaM/ErcvRGmjs4+9/vGIj1BzwfhY5LLjGts1Zpj+ECCCIKPnaByeAJuOdCICvoCRzHid1OHe43DmC6KQy6SqnbMVpUfKPBrr+gbZnD/Ajucg6CM/dhoBn4Hd9s0UKQ6jkqkwKPW0tJhoDzpp7PSGK3BtXT4SIzXo1APPwqdE9zVZGJkdTX3k9YxONmBUfj+r91BI5FC1naoWNyqFwHnjYkiIHLg6BFDcUcyL+19kTsocLhl2CV+Uf8Frh17jipwrWJa3DLcvyMYjHYREmB13CS2uFi7Kuii8vifg4aZVN+EKuCi3lvPI5EfCz62pXkOLq4XLcy5HKVOilCt5ZuZzhEJi+PNS1F7E7h3PcEFIAVE5PD71cR7n8e91DsIYdR20FUNcX9OMKlcZjqANR9BGp7/ttGVw/y4caD3ATatuQqPQ8NHij0g0DO5gOSCisqSfQfDWOW9RY68hJ0KqCFa5ynCHXKCp4flrltBpV+KJ/QxbwMcR297/SlLnDkoDbV/IK0UkHPecP+Tn44a3sAe6mBW1iNraOH6+/BCzh8fw9NK+PW/+kI9mbyNx6sSTm9PknCP9fE9MtMwgVZuJRRl5VoLBT4Zlk1KJMaqJM6kxaBR4/MEz6imLM2m4cICevR50OH0o5QJRejX1VndYeZAVo+fcM+zl8wdDPLWqGKNGgUmjoMvt7z4GNUvHJ/P0NyUgwrHufLyXrxnHH1aXEG/S8NjiAsxaJQ5v4GS7CKOHrASCIte+vgObu3e9n5+XG35tD52bS4fTxxf7G/hkXz3XT00PO2Yq5DJ+e8kIHl5+mDVFTQyPMzIjO5rhcUaevHgEEzMimZoVRSgkcuVft2N1+TlQ18UflvbvuZyaHc1frx1HICSytbyN7RUd4Z7AnHgjH98xFZvbH973A4tyMKgVzBgWzUd76nhvezV3zsnmiYsLKW91cPP0TEKilDcIUNXqoKrNSfpxMQ0KuQyTvHdSoee4pv9eyuPMiNJz1YS+/Yx3zslmbXELIVHk/kW5bC5t44739lCYaObtmyeilA88SXH/RwfZXNbGB7tq+kVvDGEIQ5AwROqGMCB63BvVShlKuUBB6u1obOcgFK0AZQREnB2r4RiziuoWN2qljGarl7TY3gqN2xcMB3Y3dHiYOyIKpUJGQ7s3vIxcJqJVKZiaayHaVwkl65gMHE64kVpvRDjj63C1ncZOLyPTjX1MTxTyAQZEQl85JUjZcj1OZTKZAB3lcOBt0MfSlHUD20odqBUC80dHo4rJgYtfgo4KZNPvAb8TgtLgwqTwsrvlr5hq9zJFhLtzLyJDHcF+XSTNdmiwtaBXSLPEjVYfrhIrc0eeXh9MWqw27L55KqyqXMWvtv+KOSlzeHL6k32eq2/3sKusK0yiT2fQ+OK+F1lft56NtRv5puobDrcdxuaz8frh11mWt4yvK79gT2cL48yLmZmwkJvGXozVbeOR9b/HITbxi8mPoVfqcQVcmFQm8DlxhjSsP9JMjcPAW7W/REBgWd4yXN4gaw+2EwyJTMuT+glvWHUDz5gLQROL2FlxGhHGZwBdJKROlf4OBaGjApRaUr2R/L0oi4JkHZGpMTyy+RGOdR7j19N+/f2kjw17oHKtJENOm3Hq5QdBaWcpvpAPn89HaXvVmZO6U0Cn1JEb2UvU8o2jaPe1YFFGMTUtC0EQ2GedxDHnEUaYxp3VfQ8GURTZ1rkOm9/K9KgFGBQnn9EfZ5mGXmEkWhXXz4nSH/LiCEjXnw5/G5/u9dNq9/LRnjp+fXFhH2KzumUFdZ4qUrVZnBt3QrzKvwCCIJCg+ffJWhfkx7H6SBNL/rKVRIuGlT+eiUF95sOHUEjkoU8PUtRo56nLRtLU5eGWt3cTZ1Tz91sn0+7y0Wb38uiKw0TqVTi8gT77KWux87NPD5GXYOKXFxb0uzY9taqYVzdVAnD7zEy0KjkymcCFIxMpbrTz0jVj2VNt5dYZksvkmNQI3rtlcnj9360s5uUN5SwZk0SX24/bF+TPy8YM2Pu1dFwyGqUcm9vPI58dBkCvkjM2LYLMGAOdTh8L/riBdqeP564czTNXjOb6aen89KMDXPXX7bx2/Xj0agXDYo14AxKRLW6yU9xkZ15+HLOGx4RlokWNXWgUcsB/0vO+sLsPsKeqKIoiv19ZzBcHG5g5LIYnl0gTU+WtDspbHDxxcSFymRCOu3h0+WGWTU7lsQvyEQSBVrsXo0ZBUBTZWNrGqqPreeWaceH9DIT3d9RQ322MMiU7ql8folmrZM29s8L/f3O4EbsnwLaKdpq6PKREDizvjTVJ70G8afDJxSEM4X8dQ6RuCIMibOuOZPnPR9dJpgn73oHrvwDz9x9UmLQKcpOlgPG9FXaMWmU4yFwhE1ApBHwBEZc3xMFqO4WpxjDfEoCjta7wtublxmJSGSDopzDVSKExBpAGEqWNLkzBVlprmoiznH7eUg90ajlzRkTh9gUlUlhaAkEf2Oqo7e6T8AZE7K4AUSYVjF6GKIocbT9KmikNw+jrJCfFxPEsbNpP/uEN0jqdDajjTEyKauUrRSst9X4yFL3SH5kMDlTaiDGrSDxJpex04Q/5+cv+v7C6ajV2n53Pyz/n8amPh6sLR2sdVDa7EEXw+kOMyTQSaz653BRgdspsNtZvJM2YxtaGrQDEaGO4Nu9adjXt4vHtUv9QQVIsWfEXwf73Ua16nGXJV/J+9Ci2Nm7lwws+pLyrnIltlbDxCVriluDzpxGvziRVW0CcXpLpOD1B/EGpnGl3B4g2qdDJNfyh6yiOUIA5OZcxmEi00+GnrNFFSrSGaJOKLUWdOL1BpuRYiDCcRnVz+e1QuR5GXcpzxdl8Up/AShVMS6hhRfkKAFaUr+CByAdOva3BULNFcnCt3tSH1Dm9ARq73GTHnp706MKsC9lSXo5M1CJznFmVLBAMsbfCBiKMyTINOnt+PExKC+fHL+3z2BjLZMZYJg+yxtlHu6+FQ7Y9AETZY5kQMf2kyytlykEJp05hYH7MYlp9zYw2TUQ9w0VjlySRO7FS5Qm5u3+7BtrUvw27Ojdz1L6fCZbp5JtGn9VtH6izEgyJ1Ha4abV7/ylSV9Ph4h+7pXiPf+yuxaJVEQyJNHR5qGx3smJ/AzJBoNnmpdnmZVt5Owvye+V57+2oYVdVJ7uqOrlpWkafqhFAhK5XPvvyxgrOLYznyokpLH1lGy3dBCU1UscdswauQG8ukxQWG4610tEt/VxztJmrBnDPFASB5AgtT++o4bzCeBRygZ+fl4dWpeBArZUovSqcIVjdHQS+vaK9u0ro4ECdlalZ0YxKsfCbJSP4wzfFdLikbNO8+N7veE+vXqRexUtXj2XW8Bj21nT26+8bCGuLW3hpg+Rk+f7OGq6bkoZZp+TiF7Zg9wa4d8Fw7p43jOumpPOnb4/R5fHz0vpyFubHMSY1gkP1Vpps3j7bvPO9vUxIj+TdWyb1i8AA+GxfXTiJ8Wi3BLMHTm+Au/++jy63n3sXDGdiRiTbKyXjrOnZUYMSOoDfXzqSqyamDpjXOIQhDEHCEKkbwqAQRZGDVXa6XAHGZJowGuIkUtdRAX9bDHfvO3GFsByxw+6nrt0jVcoEqGp2kxSlJuq4jKQee3aVotuEQy6Ec+ECQZENRzrwBUS0Khkef4iaVg+BoMiEbDNmnYKSeid2t2QMIgggqI0w/SE8Pj/HmvxE+T0kRWqQyQQKIh1k17yPzC1C63UQk3fG58OkU0jxAQEvKLQQkQWWVLRKHXRJN23VcX2Gf9r7J944/AbZlmyWX7RcevDrB8jf/x6iUgsyJeq8C8GcgjwunwXRXva1PUtTyMeizJno1GpqWt1UNLupbHFzwXj1wJXF00BFk4t2u5822R5eO/QaAOmmdC4ZdkmY0AVDIiX1ktW4RiljWKKO9NjTM0W4dPilXJx9MWXWMu767i7i9fH8dcFf0Sl1VFgrUMlUBMQAw2OSJIK46zV0nmayqt8mMeUtJsTnE6OLIUYXA2XrAEhy7ac5Yji+kIcnhv2UnCjJZCTGrGJEmhGfP4hCLtBm8/HQyNspKf4HL9mPYW/ZxdKcE3rpKtZD0ReURl5JcVBPc1ck0/Oi6HBIFdSmTu/pkbrmI5JxiCAQpequvmqUpJqTWZi2kGOdx/pkyLl9QWSCcGb9p2nToXIdJPc6PIZCIhe9uIWyFgcPnpPLHbMHl0X2QCVXsST1Vtrt/kElvIOhqdNHfXdFPD7CS2rM6VWA/9MwKyOJVSXQFegkVds/x+tMkanPCccPjEvT8Nld0wZcbkHMhVS5Ssk4RXbdvxpH7QfwhNwUOw6dFVL3zrYqSlscXDQqkXUlrWTG6Ll+SjoZ0WceWQGSs+X5IxI42mjj4tFJZMTo6XL7SY/W8dm+ej7b34BMgMJEEyZDgCr5Kr6qj+CcxPORC3IuGJnAN4ebyEswkRzR/zN5zZQ03t9RQ51VItkrDzfxXVEziu5JCbsnwJEGGwfrrcwYFhNe77dfF3G4oYvbZmSypqiFcwvieWNLJW5/kNk5Mf32A1J23U8+2B8OzwY40mBDJgiUtji4eXoGL1w1lso2ScoIUnzEt0UtBIIhylocTMqIQi4TWDYplYUFcWwqbWNSRgQuXzC8zdruXjyry8eY1Age+OQgXx5sZNbwGP52U/9M0z3VHRjUknlLSqQOlVzAFxQZk2rhlrd30dzlDVfPAkGpQnjXnGxJmgqoFTIyuyMmpmfHYFArcHgDRBlUJFm0HKzrYltFO+1OL7HG/hONiwri2dntTrq/rgt/MBSeFNpa3s53xVK/37LXdvDYBflh85i9NdaTSnuVctlJswGHMIQhDJG6IZwEDk8wHNRd2exm5BXvwt+vhMoNnChPpPRb+PAaSBgJ13/J7jKbFH1g9/F+zVPsal/DhQk/5JcLbuqzfQB/QGRWQSQ6tSzcI+XxBcOELTlKQ5PVi90dRKWQIZMJpERriTaqaLJ6UcoFDNrewOij9Q6qWz1UNEHseBVKhYzhcSqo6Z4/DPadeTwV/nrwr6yqWsVPxv6EmckzCZZ8hbxxFyG5GtmYG4hz2/imahN+7CxSXEHP16rB0QBAo7ORkBiSjEv2vwc+B0LKZLh2uWRI0Q2tQssvpv2833tQ3+HFpFVwfLFkW8M2Piz5kMuHX87UpKknPX6vP8SBKskQIMqSg1EpzQI/N/c5Ms29A1+5TCAjVkuT1cuoDBMJg/QiDga5TE52RDZ/WPgb9HIDOqUOd8BNpiWTL5Z8gS/oI92cLi089W6C636HNfsKbpg8oy/pyF8KTXtRpUxlssnCkfYj1DgaGBaZFTZ/yU7QUdXiDstzX6x6H6+/AWvIx2tNO1h6wrHx0Y3g7mBbyg5eU3RRYJnI+eNeIzNOi9MbPG3ZKktehgMfQMo07ilMYYoziexYA1qlimdmP9Nn0Tabj81FnchlAnNHRKHXnCaxShjb1ywI8AVD4cFjzyDodDA1N4Iul/+0CKvNb6XSdYxMfQ5RJgMGjRwRpLiM/0+glClZknjNv32/JqWFkeYJ//b9nogJEdMoth9i7Fmojla2OXl0xREADtV1cbQ7v+1MYxGOh1wm8OLVfT/bjy3Ol54TBFYcaGBsagSPnJ/HS7vXEVK3UOdvodU7lnhNMuPSItn6s3mDbn9fjTVM6FIitNR2ukmO0PHHy0extaKdnRUdRBnUTMrofQ3VbU5e2VgBSIHxufFGpmRFcd7Ik/fzvb65og+hA8l0RNud19Zk83D+yAQO1FqxefxoVXISzFoeuyCfC/68mb01VlRyWbjvLdqgZvHIBM55bhNlLQ4euyCfm6ZncNecbHQqBelROjYea6WiVZp4K2txMP+PG5ifF8dD50qV+NVHmrjtnT0oZAIrfzyD4XFGNj80l1AIWu1eFr+wGYBrJqQyISOij/mLXBAIiiLD44yYtdL1QqWQ8fiFBby+qYLcBCOzhsfy5tZKpmZGhwldvdXNb78uojDJzO2zsnh7u5Svp1HI+Mn8YX2q/BPTIxmZZA6bqLQ7vUwfFs2m0jZcviDL99Vx1cSz09oxhCH8L2KI1A1hUOjVcuIsKmyuAElRalCp4Mr34dgqSD0hYqD0G8kdsHYHOJow6004vUHMeiW7O74lIPo56tgI9JK6vGQ9KoWARd8rueyBQasgL1lPS5ePlGgNWQk6tpdYabJ6WHfIj14tZ1y2mYy4/pUki15JdasHg1aOPOiCLx8AmRwm3QaIksPmGeClAy8higIbjqxjyoaXaI+bRjzgDSmQB0Q2167mw6rf4RaDTEhPZV6qNOh4aOJDZFmymJwwudeJcuGvYf/7MP6GPoRuMMSaVagUAoGgiC8golZKZPp3O39HRVcFZdYyvlzy5Um3oVQIROgVWJ0BMqOj+HbptwADOkyOzvx+0pZPqv5Op9AIwMaaLfxp1/PMT53Ps3nXw9FPISYXRiyDgotpyRpPSAwREkVau3y9QeZR2dIP0ORs4tqvr8Uf8vPghAe5Jr93wB62Shcg0ZDAvvYqRpvnsST+QTYc6WBGfgQyQcAdcPOD+GjqAhoStDLwQ5XzCIIgMCrjDF9vwkjpB2laY/CgDUkaKopS1dntC54+qRsAGqWc164bz/aKdm6clnHa6ynkQp/q+MmwunUF7b4WKlzHWJJwDQtGnz1DpH8lmp3N7GjaweyU2VI/5nHosWH/b8GBWitpUTosurNDlFtsHraWtzMnNxazVkm+cTT5xtFnZdsxRjWpkTrqrW4Wj0rE7g2QFaMnM+bkVTqnN8D9Hx8gEBT5w+Wjws6IJ8Oqw0384vMj5MYbuf+cHJa8tBVRVNPpiWXkMDdRqaeXuTgpI5JzCuLpcvt57srRtDl8pERqMWqUjE6N4M7Z/dfZW9ubebe+pIW1xS04vAF+ddHJZfo7Kjr6PZYRo+fpy0axuayNK8an8P6OGn6+/BAWnZL1P52NRafCqFGgVsjwBkJEndCr5w2EqOoOZv9wVy3v76zmN0tGcNecbO7/6AAf7anDolVy7/zhbKtoZ1tFO2UtDn66cDgKuazXvCUksra4hXv+sZ8rxqdw7ZR0vjwoTTIKgmR+kxWr54GPD2J1+XjqslGcMyKelYcauWJCSp9jumxcMnuqOvn7rhpW7G8gJEKj1cN93ft8dWMFXx5s5MuDjSwelYhFq6QaWFQYz+3HBcSDlFX6+Y+ms664haImGzdMTcftCzLuCeme9MK68iFSN4QhfA8MkbohDAqZTOhvn642wIgB7Nmn3AW2BkgYDZZUJphFXN4gerWcn/kfYmXlN9w5+o4+qygVMnKTe731fYEQ9Q2tRJh1WMxGOux+2u1+9lbYGJ1hwuqUblgeXwCrM0CG3d9LBI5DZryO+Ag1aqUM2cEPpOoYQNY8KLj4jM/DtXnXErBlMkw3ha2hPbQ3f4sheRFBZTa5NZVc1HCEsck384K3jHGxvf05Udoobh91gnV54aVgq4LWg1A/DJLGh59qcbXgDXhJMfXeVCvaWujyutDKjVidfuIsajwBDwVRBVR0VTAjaWAjjVZXK0/vepp0czp3jr6TWYWRBEODGMOcJWyo3cA7h9/l/MK5BEMhDjTtBWBLwxaIHAchPzQf4gcNt+APGZmUJxl3RFrno/AkMjLd2C/QXjgub88U8MH250EbASOuIjVGi0YlQ62QMVV4gnvX30uabBQCMjrsfmyuAMV1TrxCJ/vwgELB3MyFDAsFwsT7rMDZDp2VkDy+z8NpMVp8AUl6dKbVrnZ3O9evuh67z84bi94gy5LFzOExzBw+sBTsjOBzgr0RIjKkyY5uaGW67t99B+12dwCFXJD6av8Lcfu3t1NmLWN2ymz+PPfPgORmuaLxfRwBO+fFXUac5uyaxPwzeHFdGU9/U0KSRcv6+2efVp/iqXDjW7s40mBjTk4Mb97YX4r3fWBQK1hz70zcviAWnYqbpp/eZMLa4ha+PtQEwJojzaeVU7i1vI1ASKSo0U6n0xd2AN55MJqJUdmsL+5kb00nt87IJFI/+HfpqVUlNHa5uXdBDte/uZNmm5dfXljA4pO4b1q00vbkAsSaNDR2eRh2Gpb5S8YmsbG0FblMYEZ2NA5vkIfOy2VksiUc+N1il5wj7Z4Abn8QC5AWpWfNPbOwefwUJpn7bFOvVvDKtePYUNLC29trAHjwk0Os++nssIRbq5Lzo3nZjEgx02B1Mz8/LiwvvXh0EnKZDLNWyR9Xl3C43kaDtZRrp6Sj664gqhUyogwqdlZ2sHyfFLb++YEGXlw2lmBIHLBPbtURaaKu5xm3L0hQFFEAM4ZF8/6OGnITjEQbVLx98yQO1FqZlDm4VHJObixzuiMudCoF8SYNTTYPnuNkpyv21yOXCVww8j//3R3CEP5/wRCpG0J/OFugeAUYE2H4+adeHiAiHa58L/yvTBAwdOfzLM1ZytKcXkHcrqZd/Hr7r5mcMJmfT+qVG1YWHWJY8z/wC2rEaT/uo/A06xWkRGuwuQMEAiJatYwIwwAf34AXylejU5sgfRakTpJCzGVyPHFjqKx1EGtRDVq9OHFmv7LZRYH8OmSRkhRyQ6CIjwJr0NRs48cZf8MUaKNSXsBB9RxmGkS08lNUfkIBELtvXMfJQGtsNVz2xWV4g17+uuCvTEqYxOG2w9yy8QZkKPjVmDeIMUk3wdvW3Ma+ln1cPvxyHpz4YJ/NB0MiRXUOPqr8GyurVgKwIG0BwyKGoZBDs9VLeaOL9Djt4MYroQB01Urvv0KNwxOgoslNQoSaGLOKToefdruPtBgtMpkQHgQcaD1AVUc97+76jPPTL+BHo+5GJ9ezIG0BRBZA0MMxmYx13zmIkU9lUl6VtDtR6usIBMV+h2JSm7hj1B1Y1BYWKyzQ8h04GiVSYk4h1qxGFEX+tPNTDrQeoEbZglZuZHHuLKpbXTR2egEd1+bcRoX9CLeMuCWc4XZWEPDBKzPBVgdzH4WZPw0/JZMJ5CT9c4HWRR1FVNskGdPOpp1kWU7dQ3fa2P0yuNokd83c3liJhbEX0eJtIu64aIamTi/bSqwo5ALzRkadcW/evwM9VfDjA7it/g46/FKodJ2n6t9G6oJigBL7YSJU0f3cKeu7ZYFtDm+fPqN/Bg5vgH01neG8r6AoEgiGenvH3AF8gVCf61y7r4X9XTvJ1A0nQz/8tPajVshRK87sPZ+UGcnwOAPBkMiUrNOTat4+Kwub28/IZAvnFCbwwrIx3PPhfvxBEY8/yO3v7iEYErF7/Dxx8YgBt9Fi9/DGFsn58sa3dobPzTOrS8Kk7miDjTiTuk+FbF1JCzqVnB/MyuTm6Zm02r2n7Bksb3XwzZEmzi1MoM3h5daZWWTF6Ik9wZnx9llZmDRKsmMNJJh7Jd4nBri/tL6c0hY7D52by7y8OMalRfD+zloCIRGtUsb2inYeu6CAGcNiGJlsZnNZG6XNDhq63GwubcPtC6JVyREEIRwf0WB1U289xrKJ0iThskmppEXpiDNpiDNpqG53UZhoosvjD/cNDkTogPBndVZOLPPyYhmbGhH+XMQY1YxPj2B+Xmz489Iz+bSlrI0ki7afoc2J+NOVo3ljcyVLx0vH+u3RZn78wX5A6lk+K5NZQxjC/wCGSN0Q+qN2O3RWSD8pU0Abye6yLpo6vYzOMJEc/f1cGD8q+YjKrkoquyqZbrqBlEgLGXE6jME2ZIRQi27wdjE+O5lmq5cYkwqZIDA+W5rVdPgcPLLlET5tFnhi+hPolcfdMOp3QK3kvkhEBkRmUnXtXjocAbxtIZqsTsqaXFwwPobaNg9Hax2kxWjJSzHQZvOxtbgTnVrOrMJIlHIZxxrsvFv1FJ2BOu4f/TgWpQ46wBdyMzZbTVTsRNq8WnBDUBQIif2JSR/oomDszeC2SlXNbrS523B3h1vXO6TZ03JrOd6gF/BisHRQ76jjz/v/zLHOY9Jyzvp+m69pdVPa4MIUHIFaribVlEqSISn8/OFqBzZ3ALsnQGKkhmZnMysrVzI7ZXZvv9vRT6BpP1jSYfwPOFhpp7nLR02rm/PGxbD5aCeBkEhxnRN/UCTapGRGfiTX5V+HzWcjQZ/AzSNuBuAPs/7Qe3BjbyHeZyNynY/6Jh3/+CKZ1/R/IWpsLh2Z+aQO8Ln67Y7fsrxsObHaWC47/wNoKwJNBBh7icfeChtum0R4O/2NFKQrOGxfzYdHVnFt0hNEG7TcV/jDAQcsNa1uGju95CTpsei7HUDbj/LolkfJi8zjV9N+1T/E/XgEfRJBAoloniVMSpjE5cMvx+639zFeOSvoCVP39+0HUghKmhvMlDhtjMk0EWFQ4vJKExBe0cXnLV8hk4mcF7d00KiAVzdWUNnu5IFFOd9LYhgMiewp68IfFBmXZeqXX3k8XlnwCrubd/epWseo4hlpmoAj0EWesX+m1z+DCmcJrqCTfOPoQT8Te63b2du1DRkyrk65PRwSDvDgObkkWbSMT4sIV01OBbvHzzvbqxmVbGFadq8c9ra3d7O1vJ0Zw6K5bWYmb2yuZOGfNvLlj6YjhgTWHmwnJMKEbHP4er2tYx31nhqqXeX9SF2X28++mk4mZ0adUQbdQIg1alh9z6xTL3gcEi1anrl8NE9/U8LDyw/x8Pl5fHT7VIobbZw/MoFvi5qpancx7CTOr9F6NReOSuTzAw1hQicA106W5Hw9Ushog5p1P50VDvD+x+5aPP4Q28s7+PG84YO6eu6r6SQlUke0Qc1z35aGq5EAt769G4c3wC8W55Fg1jIi2UKSRYtGKT9lhbO81REOIHd5g9Rb3Vw+PplVP5nBTz86yP5aK1e9up3dD89nUUE83xxp4gfv7AmvX9Jsp9nm6UecNEoZoijiPK761fMZevqbYl5cV06kXsnS8SmkncR1EuDvt01mW3k7i0clYtYqcfuCPLvmGOnROv78XRkVbU62lrejVsi5uvt8v7mlkl9+cRS9Ss6mB+eGK6wef5AvDjQwJtVCdqwRbyDIb1cWU9xoC69r1ikl8zPApP1+matDGML/EoZI3RD6I64QWg5JlRq1mZAoUtsmyUjq2j3fm9RdNvwyijuLydSNpb1LTnuXnaQoDQmFM3CW+FDpzSjNqajoH7ANsK52Hd/VfAfAtPJ5zE1a1NuTZ0oBmQKUOtBG4vEF2VcpGUuYddLHXaeWZjQrmt24fSHKm1zkpRho7fIRDIHdHcTpCWLRy1AZG9lvWw3Afvtqbsi/jYYWGdGqFERfJMgUDBsxGnWbB4NG3k+iFgyJOD0BjjW4sLsDDE/QodMkExHR15lvbNxYfj3t17Q5rWQp5tBm83Fe5nk0OBrQKDRMS5zGI1seYWWlVH27vuB6rs69Orx+IBhif6UdXyCEAOSaxrB2yhZM2r49G4mRamz1gXCV7uebf87Opp18UvoJXyz5QlrIa+/z26RX0Nzlw6xXsLVhK+0+E2ZFrLQvQaDDLrlAWjQW4nRxPLf3ORqdjX0CxHtgUplYcfMNvL7uKNN2/44M+UGIXIlp9OJ+y8JxGXkCoI+BST/qt4zdFcAvesL/D4scxp93PkWFaz+/Kb2QvbOeA9nsfuuJosi+ChshUXqfeqTGy0uXc6zzGMc6j/GDUT8gxZjSb90w1Aa45lOo2wXjb+z7XHsZ2GqliRHFwN+ZFlcL7e528qL6urEqZUoenfLo4Ps9TdTaaonSRoX7J0VRpCPvFgyuStRJfcmOyxuiulU6j9WtbiIMStJjtQRFkQ6a2e9tgyA0eKoZbujfb1TSZOfJr4sAyfTh3gWnVw0aCG02H/UdUiW7vsPbT5Z7PKK10ZyT3jfQWxAEpkTO/qf33+94vM2saf1c2jYCBaYxAy7Xk3WnkCmR0/daYNYquWtO9kCrDYpnVh/jra1VqOQydj08H7NOus5ZXdJ3zu0LIpcJBEIiFa1Omro8ROs1YVLj73Y3BEjSpFHvqSFJm0qNq4Kd1k1k63MZbZ7E9W/sZH+tlfNHJvDisrH9juNUeHbNMT7bX8+tMzJ5d3s1CrnAWzdOHDDfbTBsLmvj5W77/bwEE9dMTmN0igWAr+6eQcsgFbQPd9Ww6nAT9y7I4fmrxpAbb+SFdWUYNQrevGEi+YmSeqKu20Wy0+XD5QuikMnQquTcvyiHD3fVcvP09EGP7a8by/nN18XEGtVsfGAOs4bH8OXBBiL0Kmxufziw/KX1FbTYvUQbVOz8+Xya7R4sWtVJpcsJZg3DYg1Ut7soabZR2eaipsPFgV8sROyeJBRF6b2W/u6dOJyTE8OUrKgBK2H/2FVHu9PHezuqefSC/D7PVbVJ56LD6eeVDRWMS404afZcVoyBrJhe1cEbWyp57rtSQIoiqOjuA3z4s8NolHIuHZfM/lorIJk8Hf85fOKro7y7vQalXGDzg3PxBUIc6F52fUkLs4bHMCE9kq/vnoFMEMiJP7UUdghDGIKEIVI3hP6IyISZD4f/lQEFqQaaOr0MSzw9i/uTYWLCRD6/+HNq2yT3wkiDEqVcQBBU6AvO7btwwx6o/A6SJkL6bGn9+IlkmjMRRQFXWyYbrB1My7MQa1ZjU6fgH30/Wo2KXcdcqJUBTFo5NneQYQk6jDoFhm7DiuGJOorqnKTFSAPujDgtDo9kaNFTtZmeUcCMqhlU26pZlL4InVLNjJgl+AJiWIomkwnhkPMTsbW4kzabP/z/zm63xtEZxn4mLxdnX8zW4k4qmn3Utvu5YHwsdxzXhzg5YTJfVnzJyOiR3Dvu3j7VgoYOb5h4j800kRSlDkuxetBm85EWqyU3WR8mS5GayPBvp9/JPevuweG18sdhy4hPkVw1C1ONpMdo0anl/Gr7GlZWfUusKo1zUy4nVT6lz2dibc1aRES+q/luQFIHoJbL+cHcQqIsF0JNNEw9gaiJIfA58cr0LIy8i6S8UZyfN2nAbQGMzTITZVrMiFQL8SYLBVEF3D3uXtSbH2W+Jh6iB85oEwSBOIuaxk5vn0D6i7IvYmfTTvKi8vpUOQfDYYOZhuR8FqhNvYphvxv2vyVJbX0OSQqs0EpxCN3o8HRw8YqLsfvsPDn9SS7MuvCU+zoeFV0V3Lb6NowqI28uehOLxhJ+zul3ct/6+9jSsIU0UxrLL1yOUq6kqM5JSb2ATp3NwjRtHw9bnVpGSrSGToef1O7JFJlMYFiCHl8ol662CkRRJE03bMDjSbBoSI3U0djlZlxaxIDLnC4iDEoiDEr8gRBxln++4ucLhGjo8BBrVn8v6aharkEhKAmIfvSKwSW1I0zjiFLFYlSYUcu/f65kkkV6H6IMqj6xGK9cO47VR5s5tzAelUJGh8PHsDhD2Ip+aq4Fjy9Eave1rcJ5DLMykhtS70YlqPiy+R+0+1qw+jsYbZ6EzSNdo2xuP6eEtQr2/02aZBl7K8iV/HVjBW5/kNc3V1LZPcDfWdnRx13xZPjL+jIO1nYRbVBJ+Ziplj7P69UKMgaooImiyM+XHybYzWLfvHEid87J5s4ByPMP52Zj0CjIizfx7vZq/ry2jCvGp2D3+jnW7OCvmyqZnz8wsWnqkiYYrC4/3kCIS8cls3hUIte8toOdVZJhytTMKLZWtAPQ7vDxp29LeH5tOXqVnHX3zx7Q/h+gxeblgpEJLMiP475/HABAJZdx74f7KWq0kxKp5YKRiSR3V9POKUzgzRsmoFcrmJgxeN/aHbOzcPkCA/YT/mJxPmadkuX76tEp5Vhdfsb9eg2Ts6J44aoxpzQXyooxIAgQqVPx/FVj+XhPLb/5Wqo2uv1BDtV1sWK/ZMxyy4wM4o6TpXZ1f8b8QZG3NldS0+lmTIqFOJOGm4+rauYN5dENYQhnDEEUT6UXG8K/AnV1daSkSBWA2tpakpO/f5D3fxwtR6SKRGRv/09Dh4fqVjfZ8foBTU0CQRG5jMFvIjteAHs9KPUwqy9JaLP52HRUci6bNNyMSavg24PtiKIUg1DXLpGcKKMCs1pkpKUDwZIGitObPS6pd1Ld4iYvRR+uGFqdfjYc7iAkSkR3eKI0Q7q9cTtOn5N5aX0NOL7a3SK5VioE/EExPIM+PFFPQWr/weGhajtljS6ijEpmFvS/YXsCHtTy/mHgTk+QTUelwcWkYWYiTugZLG90cbDajgyR2fL1mBOzIGkCvqCPfS37KIwuZG/zXu787k4AfjbxZ8xOmc1LB15ifNx4LsqWeq8qrBXcuuZWWlwtyAQZG6/YiFnd2+y/q2kXbx15i4uyLmJh+sK+B29vwO708G2VdM6m5lr6kKkw9r0J7cdoi57JJs9YPEEn7zX/hE5vO68tfK1fVev7YjBzgNNBo6OR85afRyAU4KGJD3F1Xnf1NOiHrc/Q4dfSpR9Ghn0TL9fnUKYdxUPn5RNtUFNrr+WC5RcQEkPcPeZubh156xnt+72i9/jdzt8B8PL8l5mW1Juh9vze53n10KsAyJCx8UrpfdpbYaO6xY1cJnD++Jh/+nUPBl8ghDcQDEvb/tPYVtJJU6cPg0b+vd087QEbvpCXKNW/t7/nUF0XSRHakxqEnAy17kq+bv4YgDjPXC7OHUeZo4jtnevJ1uczOXIW1e1O1pe0ct6IBGKMp7g+lq2Cqg3S35N/DIZ4XlhbyvJ99dw1J5svDjSgkMt49orRpxVQXtHqYO4z0vZunp5BfoKR7Fgjo7qrdKfCD97ezZqiZp5cMiIcEu72Bbnq1e1Utjl544bxjEvrvZb+fWcNv/7yKC5fkHiThvRoHdsrOhgWa+Cla8ZR1mJnQX58n++Gwxvg/R3VjEy2hCMdnv+ulL9uKMfRXUG7cVo6a442U9cpyZsTzJLpCsDPzs3lB4MEnl/4wmYO1nVJFbs4AxuPtZEdo6esO7pgUkYkH/7gZD67Z4ZAMERVu5OMaAP+YAiZIPDgJwfDpikHHlsYrgifDLUdLkwaZXjZHRXtNNk8LB6ZSE2Hi0V/2og3EOK168Yz/7gA+TaHl/Of24TN4+fSsSm8u0PqHf7o9in/tTl0/yfHa0P4P4mhSt0Qzg6aDsLhvwMCTLwLTFKF40ClHY8/hMcXYs6I/k3zx7sx2n12ntn9DJGaSH445odSJSptOlSslSp1JyDapGJyjoVQSCQxUoPNFQi7phl1ckxuBcGQSLs9QGLrOr5rHoFJeZTxE0YjG4BEtnb5UMiFcKZXaYPUM3as3kVylAZBENhT3tUrbQpIkpLDbYe5dbU0IP/DrD+wKH1ReJuThluob/egkAsYtQrq2j1olXJyknrlMqGQRPYUcoHCVANpMdp+9vcdDj8GjRzN8TK+zio4+BHkX4g+JoepuRGsO9TOxqOdzCyI7JNN5u0+VlEUUdtKoesgRGajCriZlCBVwcbFjWNa4jTsfjtzU+fywr4XWFG+ghVlK5iTOgeTykSmJZM7R93J49seJ9Oc2S8WYUL8BCbED5DXZatH3PkiRkRMqoXYVPkDGqMAYJVu8mZ/PQr5OGrcu6m2SwYI7xW9xxPTnxhwtVVVq/jFll8wNnoqf5r7zGkHfn8fYiMPuHkzahJBMUR16LjXI1fCpB9x4Kid1K71lDq0/K4oBmgg1qTlgXNySTGm8Oe5f6baVs0VOVcAUrbhr7b9ihRjCj+b9LNBe7fa3G1kmbOYlzoPo8rIxPi+348eYxWdQsdjUx4LE+/CVCl/LtqoPOuEDqRcK5Xi+7s6ni3Iu7/nZ+O1GhWnrhwExSAgIhfO3q11RLL51AudBEpBIoOiCE+vqkDuTWDxqDyyDXm8sqGcVyp28vPz8rh+avrpbTB5MjhaQB8Nemmw/sO5w/jhXKmCe8nYvgPeLpcftVI2aK9eglnL8DgD5a1O6jpdvL65EpVcxsYH5hBvPnm10+0LcqTRFr4mf7irhoxoA0aNIiz/W1vc0ofUPbWqGJcviFmr5DeXFFKQaOaLAw1Mzozi4he34PAGGJtq4feXjiQjWo9CLsOgVnDbzF5SFgiG+OMaqbc52aJlUmYUd83JxqJV8uK6cmQCXDcljb+sL0cQOKm0MaK797Sxy8Pl41NYPDIRuUzg3u6q3ajj3n9RFPEFQ2dsXnM8rn1jJ9vK2xmVbGZyZhQ7KtuZmxPHhPQIpmRGnRah8wdD/H5VMXWdbv50xWjSo/VMOi6/MD1az+p7ZuLwBihI7Pv5jTao2f7zeQRDIsVNdr481ECsUc3w03AcBalv95WNFdw2M6PPezKEIQxhiNQN4QzhDrgpt5aTG5mLQnbcx+c4a/R6mwxbl4PsRB0JkWoqm90nD7LurICD7+EQYEXlpwQQmZo4lfHx4yF+tPQzCBIi1HQ6/Li8QUw6BdPyLHj9IZKjNOQmGbA6/Ww+2olVEY9dHo09BHGtnn5h0w0dHnYckwJR546IxKxXkp2go+T/sXfe8XGU59q+ZnvTrnrvXZYs994rxlTTSyiBEBIIkAAhCYRATiqBhAAJPQQIHUzHBgO2scHGXbZlW5as3ttK2/vM98dIK60luZByzvmOLn76Ge2+Ozsz2p15n/d5nvtudWH3BNnf4GByjpkNnc/R7uzmrLSrKU6X+4pUChUKQYEoiWiVkccZb9bQZvVR3eZGIcCZ0xIiJr2+gMimg70oHK3MSvFgKZyD2RD5tTzS4qSqxYVeo0AbX0mqKZWi2CJ4+0ZZ1Obgm/CDnbh9ofDkprHbg8vvZl/fFiYnTqYoLR27O0iou0oWoonOgl1PgN8BhedQr51Ma6+P38x+NKyYNy1pGu/Xvs+EuAkYVUNB6IWFF7IgfQEWrQW1YuwJQCAosq2qn0BIpFyqJBF557b1Pc118/9OWtzoE7ZtySUcbdvBpQXLOduSQHp3AS/J6t4Uxozdp7WhYQPuoJsvOz5jW3UXS04wkfpXkejpJ1EjlxpOjR3oMwsF4bP7IOAmfsK9VLlnMSFBRV6Mima7GGHevDB9YcT23qp+S7aAQD7PxbEjS0ddARcXvn8hVq81Mjs4jLNyz6I8oZxobTRahZFWq5cEswaNShHOLv8n6PH0YNaY0Sj/ewzMp+ZZqGiv5/O6fnJSdSdVNfxnsAX6eLf9ZSQkzk+5kmj1/4ysQ7IujSnqC7jttX20d+vDSoY2d4DfrZdL5mIMGv506eRT26AuGiZffUpDt9X2cO1zu4g1avjo1vkjfNlAluifnRNLdaeTzUe7ARAECcUJ1ga8gRA6tRKHN0DbgKro2j0t7G7sQ6UQ+OLHi7lhQQ71PS6unDXke9bj9DE1M4ZdDVbuPKOIpcVyUPqdBbnYPIFwv9repn4ueGIbbn+IBy4s56LjbBlUSgVXzMrks8Od3HvOBM4YuNa8vqsZ/0D/2F83HaMwKQqVUoFZN/ZU67drJrLooU0EQxLHuhzcuqyAoCiytaYHhzfI9we83ryBEOf/9Svqul08ddU0lhQn4guGThjgjfb8YP/a/hYb+1vke15Fs41fn1/GhVPT+exwJ9OzY04odHSozc6HB2RhqPcq2rht+ciS7Ky4sb9rgiDIi5hpFip+sXLMcaPxys4mepw+XtnRNB7UjTPOcYwHdeOcFj/4/Afs7NjJ+fnn86t5vxp6IrEUpn4Ht6RjZ40CcCFKMDnHTHl21IjMmNVr5aXDLzE1aSrznX0QcJMC5GhjcKq05EZHComMRWOXh711dtRKgRWT4xEQ0KoV4fLEaKOasyy7cbQcpl9IwK0w09zjiQjqJEnC4QmGfx8MjLrt/vD/u7whWvr6WNf2DwCSYuFSxYMAFMcW88pZr+AOuJkUP41gSIrIQA5mjNQqxYiMgdMbJOTsYenWs9EEHXDOozDtmogxngEFwi1d7/DBwcfQKDSsu2AdSZY0aAbMcs9EUrSG8uwoumx+6js9/Hn/H9ljW0+cLo5Nl2xidlE0UuEsBHGarNq49bfyG3j7ONDuQJRAaIF5JfLNfE3BGpZmLsWkNqEcFrQHgiL1LRrAw9Q89ZjS7FZnAKszQGKwngTvZgA+d3ew3t3IPbGjTxisXis37/o9QSlIX3Q6t0+7nSmJU3hw0YPYfXYuKhzFI3GA68uup9HaS45uRmRGczQOvCGrP069RnbjHYWP6z+m19vLpUWXhhcwBid9QSnIV61fUWzKJHlA9EaIHwg4az+H7X8BYGLKZIpmXoVamc2GOfIK94nUBZdkLOGdY++QGZVJtjl71DHeoBe7T+7N7HJ3jXh+9zEbbVYvk7LjiYrSs+lIC7XqdSj74eLMy8dUrgSgfhO4e6FgFWhG6R1zW8HdDXEFcCJVUGTBmV9s+wX50fm8cfYbqJX/+ZJMfyjE7z4+giTJ2bpHLhtd4ORfQa+/C68oBxjdvo7/MUEdwMy0PJ66NA6nN8jcAQXEKJ2KhYUJ7KjrZWVp0km28M040GLDHxLpsHtp6fOMGtQBGAfKdY0aJYlmLd12H8e6nOE+tIc+OcravS3ctaqImk4nj2+u5cpZmfxmzUQeu3wqlW02qjtkYSe1UoEh0Ms9BXXYppfhG3Ytvv6F3exv7mdBQXxYFXMQi17N2zfN484393Ow1YbDK98TNlV1jQjqQA7Gfrsm0l7hlmUFPPhxFVZ3AKcvxN6mfgC+qO4ekcEcJC1GT1askdpuJ+sqO9jx289456Z5PHxckN1p91I1cIxfHuvhiS9q2dPYxx8vnsT5U0b2/t708h7WHezg3rMnRPSq3bgwl0c31rAwP56gBFtrZPXefref29+oYH1lB5PSLbz3g/mj7i9AcXIUS4oSaO33sHriN1s8++RQB4GQeNo+dLcszee5r+q5bt6p+SaOM87/JcaDunFOi0G5/TZn28gnY/NQBUJoVb34ghJR+gEhkeMmzR5fiL/sfYL1dRvo7DKSPGEV+bFdEJXKa0t+iVJQRgQREUgiVL4ul+iVXow3IN9QAiGJbpuPXQNCJMP7tQSfDbPYyxLvm6yP+j6ZCZFZul3HbLT2+lApBWYXRofLFm0DZuc5HKOkZydV3SVMsaygxV8xQtSiNK6UHruf9Xu6UasULE/vQ+PrgbQZFKUZiTerMWqVI4K6WJOaogQBdWhAXt7VPeKQy7KiMOpUdGm10AnSwH+c/yTM+h4kyxOLQ72HuG3rbSTpMjg/7lej+ncJgiCXBirVMPkasLdCxhzSG3209npHZM+G98sN0tHvo83qo8ffwl77YS4vOycs0iFJEm19PvRqJfFmDVkGF87GDxEGTrnZmMGz8348ZlmhTqnDorXQ6+0lyTA00Txe3XA0imOLmZRchNvtJjnJDkSPPrB+C7w90L+mMcHEkYHi4d7D/HjLjwFQCSouLb4UW6CP99pfQRAEOjodvHjoJRL0CXx60aeRn9ekMiRTKgRdCOkzwplZpcDQOL8LXD0QEzmxnJgwkU2XbDrhccbp43h8+eNU91VzceHFEc9JkkRLjxcJaLXKGWmn0E5IZSMEtHubKTBNGLFNUZRoa+vAXLcLs9QHOgvkrYgcFPTBzr9A0AO5KyB36Qn382DPQUC25nAFXEQro0d9X8W/oQx0EL1ayYysWHY1WpmX98/11J2MLEM+5ebpiJJE7in6wI3A1gytuyB1qmwp8i9k0BB7EIVC4MXr/rWG5cfzrdlZdNq9pFh0J+yRu3NlETNzYtEoFVz57A4AtlT3MHfgb/b8tgacviCv7mgOK00OZvXOKk9hcVECpfd9AkB+opGouvdo6+lm9Y4QLvEw/7h+FvEmTVhBcizEAb+/5SWJLCiMZ9ux3lGzUGNx+cxM3t3Xyo56KxqlgsmZcnvAohP4rHXavVw9J4s/bjiK3Rukx+lnf0s/GQPCKE29bp7ZWseS4gR+emYxVe12rpiZwbI/ySXpr+1q4ovqbm5YkBtW+gTYVNU9cJ66IoK625YXctvyoc/nttoe6rpdXDI9g++8uBsgwgbhePrdfup6XDx37YxRe+E3VXVx/weHWF6SxK3LCjBolCMW/rYd6wnbMmhVSlZMOPVFhQumpo8ZII8zzv91xoO6cU6LR5Y8whuHPqZAs4ymbk9EgOQPimw+1EdQhBn5ZtJHsSMYNDMuVV5FID6dqdGrONgGGdO+jVat4KRFWn4XdB4Y2FgFBcUXoFIKmHQjA6YwReeAKRllbD5nW0bePAbVKYMDnmuDzCqMpr3PS2nHPpS+LopxsCb5Jywrj0OtEqjtcJMSM6Sq1+8KyNkunx115QtyABp0Q+7yMc3O+11BQuYMApe9icZWJ2eOjkOjUlCUZqQg9SpyYlJIM6UNGWhnzARPHzja2Ni0kS53F13uLm6dZOMP5feyu2cx5fHlCILAod5D/OKrX1AWX8b9c+5HiCuEOPnmPi1Px7S8U+vdiTdr0Gvh+do7sAd7OWLfxczkmWSZs0hXT6Oi3oEALC/RMnXDKnB2sjm1kK7EQqYtup+8GHmSJEkSzoCTKM1Q5sigNrD23LV0uDsojSs9pf0ZpKa/hjdrXgUgNVbHrVNvHX2gIR6UGiQxyJ6oAM6ej5kTszhCrTBaG41epccb9JI00DfU5WvHI8rBt6iUJ5a+kE8OsIfRq0hg28LN6DQKFsXFjfxMB33w5AKw1sLZfx5phXAKzEmdw5zUkeIJgiBQnh1Fm9UX7ttcnF3G590t6NQCWYbRJfWPtDipblOgMlzOmZ7nUMWMkimXRNmYHkA8uUri9yd9H6WgZHLi5AhlzkEqmvu56tkdJJq1vH3TPCz/pB/VFz2fUOs6wtzYZRRHyQsdgiDw+o2z8QRCJ/WG6/V30+FtocA0AY3i1KX4B1EKSubELvlG+x7myDvgbIf+eph7xz+3rX+SLruX57c1MD8/PpzdO11MWhXXz89hX1N/uGRyNJQKgSVFstfkrcsK2FVvJSSK9Dp9xJm03LasgLf3tXLDwlyidCpe3N4QNqoGMGiULC9JZGNVFwdb7WxKURCNjv6ACpDYfLSLZ7bWI4oStyzNjwhyhrN2TwtHOhwc6ZCNwK+ZI49rtro53G4nN97IJ4c6KE42s7AwYdT+0TNKk9nZYGX1xGT+fJLM8PqD7dz8yl5SLHoSorTYvUHMOhUbj3QxOzcOf1DkvL9+SZ87wOu7mqj61ZnhRZBfnV/G17W9bK7u4us6K512L6/cMDu87d9fOJH1BzuYmhXN5U9v5+JpGVwwSsbRFxB5d18raqXAw5dM4uNDHSwsiAxCRVHCM/D3O/cvX9FkdXPrsoII2xKPP8T7+1t5Z18bjb1yb+RzX9aTl2DkvnNK+eBAG+dPTmNufjxGrQqFABKckpjOOOOMc2qMf5vGOS2KYouYqo/BH5So63BHBHVOTxCXV17hc/mGfGmkgAf/0U9QxeVgF+VJpULScmb+Ujp7IEqvRK08xdV6bRRkzqfGU4snzkKpIEZ4WC2YEIMoSSRahk3KNCbIGXuyVZRmpLLRQWK0JmLlMdqoossm4E6chSnwOfaoSczKtGA2qPjikBWrI0BTtycsAJOdaMDjFzEolAi1WjmjoTlx8/dXVX0EghL9sdOYNXOYcqa7V+53G7ZarxAUIxUlAx7Y8SgEvVyQPosDKbPIisrCLGRitcEZWWeEj+nt6reH/NfKbyTVJJe9uANurl33XTo9bTy+4jEKLCUERB8/2fpjejw9PLjowQivNr1GyVtd92APyvLd7a52Htj1AApBwdML3wZMIIAQ9MjHARQmTCJm7i3hgA7gp1t/yrr6ddxYfiM3lN1Ej91PokVDnD6OOP1IUZ2TkWvJZW7qXBrtjSzPWh75ZOdhOPQOL5r0bLIe5LbLXyDFFMse71ZwtmBRRTMlemhClGpK5f3z38cVcIVFR3IMBRSbylEIAlPS5lJgKmFSwqTI3lJkVdagCE6viNMbItZ03MTP74K++oH9qjzt4xyNQz2HSDWlEqOLITfZQO6w70SUTs/5GWtOaTuCSgvzfwaaURYh1HqYfqMcdJygz3WQBEMC98y+Z8znvzrWg8MXxNEdpLbbydTM07RBcLRBy05ImQzR2VQ7DyESotZ1JBzUgRzYnSygEyWRDzpewyd66fZ3sDj+zBOO/7dhyZDPryXzv+f9h/Hrj47w/v42nvuqnsr7zxhhkXIqSJLEhU9so9Pu47IZGfz+wvKTvubWpflMvH8D2+t66Xb4+PNlU7hhYS43LBxaaBjel9rQ4+KRz2s4ozQZb0Dky2M9PNpexgfXlXB/sgOrO8CUzBie/KIOgJJk85j9YmumprGlphuNUsHGqi4kZJXLjUe6cPlDxBjU9A14BKZYdHz5k6UjFhOvm5/DNXOzTyjOc++7lbyzr5WZOTGIErT2e8J+bHZvkLf3tZJk0WHRD71fskXHrz86wiUz0ilONnPV7CymZcbw1bEeIMTcvMhr5nmT0zhvchpT/2sDVneA7XVWCpKiRgjv/PnzGvY391PX4+LSGZkRPYit/R5MGiVXP7eTg602HryonG6HbO/Q1udhZ72VsjQzBo2K3647wj++bkSvVpCfaEKvVnKw1caxbhdXPbcTgDd3t7DhRwuZlBHNh7csICRKI/YnGBKxeQLEmbRsquoixqgJ+xWOM844J2Y8qBvntClMNdLQ5SE/JbIROsakpjDVQI+/A6e+AV9oElqlDufOF4nyNCB17CB39p0ERSMGrZLsRD2+LBG1UjitEqzurOlsbD8Mnn14e6KZmTCdkCgNZNpOX5Ahz+wlb0Y8Pc4Qx9pdZCfqUSkVbDlkxe4JUUMmuRk3UdvhIanLQ2qsLhyEDi8rUSkFJmZFAVGQcBv4bBGTM68/hEIhRKzu6tVKAsEgeo38WH2nm0NNDrK8+5jo+wImXAip08feeSkky+cD6WoTz658Nuz/B3LAnDJgNH5e/nns6txFWVzZUKYP2N5QxZH+/SyNu4bKGg01QjcKRZCdbfvwiHY2NGxgScLldNn8TMg0Ydar2N0pl+kICORacqmyVmFUGylMjiXLYkCnUWA0quGKN/C37uFHPV9Q/cm1PLLkkbA4yLbWr8P/lqmvoM8ZIClaEzYBP100Sg1PrXhq9CffvJZQz1Eeys5EEuBZtZGHFj9IVPsBPCEXKbqRK9jDzxHIhtKL4oeUTS8ouCDi+aAYpMXRQmZCOg5PCL1GQYxxlEusIRYufl42LJ/3wzGPp9PViSAIJBoSxxwD8MKhF3ho90Mk6hP56IKPTt5POAolGSaijWrMBhWqEwVA5rSwsu0/y+UzM6nqcJBq0THlm0zajrwD9hY6rC48+YnMjllMvbuaqdFzT3tTAgJqQYMPL2rh9LN0/zKKz4fcZSddDBokEBLH7Gn904ajbK/r5Rdnl34jBc3cBPn6nh1n/EYB3SCDisSnap6kVAikRuuo7XaRHmPgjV3NfHKogx8uLxz1OB7+rJr3Ktp4r6KVrXctYVttLwsLE8Co49r5QxmnS6an88buFl7Y3sAZZck892U9Bq0yIogpTbXws9UlfPvvu6hss/PRgfawmAjIC1qDQVa7zUt9j5P8xKG/1cs7Gnli8zGWlyRx/7llvL+/jaZeF99ZkBuRpXxrTwueQAiHJ8h35mfjD0msnJBMj9PLI5/V0NLvYWZOLI9vOgZARoweQRB47qt6vq7vZd2tCwB4fls9/QO+b5fMGFp4G2Tz0a6w5YK8zx5ijGp6nX4mZUQjSRIFCUZqOh1cfFwW7919rfzojQqSonR02GVrhl0Nfbz0nZnsqLeys76XS57azpzcOF797uywwbpRq2b9bQvoc/v50esVfHWsN7xNCblnfUt1Ny9ub+DKWVlUddhp6nWzYkISkgQXPrGN/S02LpyWzto9LSgVAp/8cCH5iWP7Q44zzjgy40HdOKdNQaqRglEU9ARBoDjDwAtNHxGwB3CIfSyOX0VomHS9SgETMoYuzqcqOz8cnVKPQlIjEqC1Q0EwVmLjgV5cvhAzCiykj6GqOByrw8/OGhulvq1kuHYhxuTzVfBsRAncPpHy7CgGRMyQgH6nfOPsH+izm1kQTa/DT2zUGCVjOov8M0CP3c+XR2RltiUT48KWBQtKY3B4gsQO9PE1dXsJhKBRVSIHdQHPiQ9EY4JpN4CrE1KmAhClV8nefwiY9ENf8fKEct4//31gSGAj0ZBIaUIJsyznsTj+Kvl4JQiFVCxNX8V+6za0gp79DXKDvlIhMKPAQkF0AVV9VUhIbGrexOtnv068Pp4YXQwMP/35y2hLyOPwu7LAzO7O3SxMX4jHH+LshDs4YP+cCzOvCAuQiCL/HmKyUfYc5Rx1HBsFH2fmnIlOqeeytO8gSiIqhQq7O4goSWHj+dPljs13sLF5IxcVXsR9c+478eAJ58k/Y3C49zBXrbsKQRB4efXLstrpGHS4OgCw+qz4Qr5vFNQpBGFMNdLTwu+SvSrH6okdRqxRw2OX/xPCJeZ07A4325XLoN5BeXYx5w58B04XQRBYk3IlPf5u0vVZJ3/BvwtBAO2pmS5/54VdfF7Vxa/PL4sITEBWtnx0oxwQ/O3LujHLACVJ4s+f1dDl8PGz1cWYh/kL/nB5IWeWpZARO7KM/lQRBIG3vjeXvU19YYXIQT480MbP1h5kaUlihICNIAi8e/M8GnrclKWZKbhnPUFRQpQk/v7tkT2Ac/PieH9/G1MzY0iN1keUZQ5nsBdvd0Mfix7cFPaTy4kzMjc/nq013Xxd18uCggS0KgWiJJccDqJVKVh3ywJe2tHIs1vrKE2zkD1M4dHhDXDvu5WIEjy/rRGTVs1fBoIyhULgpgEVS6vLj1mvRpQkrpididMb5N73DvHqziY23bmYz+5YHFat/M4L8uJZcYoZmztAY6+bmk4H7TYPKRY9c3LjeHNPC0pBoM/lR6NUcNnTX9Pn9vPS9bP4rw8P4x+ws7HoVExIjeKMh7fg8of45bml/Pmz6nCQerxJ+ZEOO5IEHXYvUzOjSbHoWFqcSG2Xi6tmZfHwgKXD0Q55AfGuM4qYlRNLSYoZtVJBYpSOv187k9+uO4LbHyQzRrZ+KEqO4gev7KWmy8nexj7cgRDegMjPzyrhyllZVLbJ26vvdg7bm3E75XHGORXGg7px/qUICOiUBgJBGwalfMPTTLuK3kMfoYnPIcowelldS4+Xjn65DyhKf+KPZZTKTLl0KU29DpJMCfiDIq4Bhcg+Z2DMoM7pCaJUCug1SirqHXj8Ilpvp7zfzjbUJgW+gIhWrWBfnZ2Qz02Cyk9OTjpmg4ru2gNk96yDg/moJl42qnH2lpYt6JQ6ZqbMxBVwsa5+HdMSpyH4kpEkWdDli8peFpbFYtKp0KgUQ/12jnaKjHaOEk+mAdBeEA7UTkh0lvwz+KtRzRlT5BXq0UqAjlqPcvmHVyAh8djCvzM/exKPrLqfw81Oum1+YqI0JJjVJIhL6N7tw2edQJTBhxTUkhwj7+sLZ77AE/ufYG3NWs7JPYcJcSPFNwbJtmRz5/Q7qemr4eoJshS6VqVgVvJ8iqJmMTPdQoxJTVe/n+QTWV/8M1z6D+g6zG+SymSRmAEUggKFoMDmCrCp0ookQWpKHxs73uGCggtOGEwdT3WfPMmptlb/07vb6mzFL/oB8HXsg+qPIWUKZC0cMfYHU35AvD6e0vjSUYVt/mO0fA1V74E5A2Z8f0xV0X8ZReeiSu1HWeUnJJ7aAlG/K4DdHSQ9TjeiOsCgMpGp+t+RDQiJEpuOdiNJsjDFYFAXkkJs7P4IW6CfM8oK2V3v4LzJY2dW9zb18cjnNQBUttp47PIpZA+zfRgsCTxl/C44vBaUGrnKQKkmM85AZpxhxNAP9rfh8AV5r6KNBy+aFK5gCIZENCpFOCu3sjSJjys7WDFhdJXFS2dkclZ5Kga1clThjkHuXFmETqUk2qDhua/k8me1UiDZosMbCHH9C7vxB0WqOx1suWsJuxus3PzKvvDrA0GRo52OCE8+gO21vTz8WTUFiSaGv7sgQIxBTb8nQHacEXFASnlvYx+dA5kv97AsmiTBuoPtXDcvJ1wu/OBF5Xxe1cVtywrYWt3NzgYrgZBEh81LikWPQatCkiAoSRxstdPt8IcVMrfU9LBiQhJPfVFHkllLp93Hfe8dxj0QqH50oD0c0CkFAZ1aQUiU+N26I1hdfn60ohCbx89rO1vY29TP0uIEbvzHHiSgrsc1cK4lPAGR5X/6gjPLktnb1MfdZ5aQGi0vBGhUCu4/d2RvdF6CiZouJ1Z3IFz1IkoSeo2SP186mW21Pdy8JJ+qdgexJk1ENnScccYZm/Ggbpx/ihZHC0mGJFmuPOhDsedp1vj66C89m+RoefXVYDRimHnJmNsQJYndtTb55hSSmF0UfdL3nZmTTElyAiadEoVCYEquvJI5lgdXZ7+PbVX9KBUCy8rjSIrWYHMHOahbwuLoapRJZSw1xeLyhYgxqXlvRxdLvrqYaEcVLL6br1NuJK3nAIqgCzr3Q8n5ckZiGBubNnLbptsAeGn1S7xx9A3er32fNT6BX2qyCGR/m0O6efiCEt02P6bh3kU+B+x6gmQxQHL+KsheBHyzjIE/KHKw0UGnzY0/oGBilimiVLbJ3kpAkgOGT499zfzsSRh1KmYUREdsp7cpluXx16FTGlEIQc6alRhWMjWoDdwx/Q7umH5qYg7XlEYKwCgUAovLYiPUD1uU2/is7QCzYhZRZv5mGZcxUWkhdeysUCAkhcvDnqh4lj39n7C/ez+vnf3aKb/FHxb+gXX167iw4EJae+VFijiTGoc3hDHaikmnIVGbcvINObtYZrfx4ym3olDpmejqB2cH1G0cNagzqo1cP/H6U97Pfxv9DfK/jlZZUOXfbWEgCBjMMSwrD+EPimHV2rHwB0W2HLISEsHpDUVUDPxPZof1C446K5kZszDcK6hUCPzugol8fqST25YNiVVY/d3UuY8C8J2zcnkqZsWo2xwkJ95EqkVHm80r90x9cpS/XvlPfPe6DkLPEfn/kydBQsmYQ7+3KI9+d4ClxYloVAr8QbmX6ty/fIndE+DV786mPD2ax6+cRkiUTtijNprYhi8YYnNVFx8d7GBJcQJfHethf0s/31+cx/KSJGweP786r4zcBBOiKJFs1tFkdfPZ4S6unuOgKNmMVqXAN5DpEiFchjicRz+vYWe9lZ31VgCWFCXQ0OPinX2tPPGtaSSZ5XL9Wb/7HFGUeOWGWZw3ORWXL8TqshR0agUdNi9PflHHb9dVIUryuYFIpcfMWANOX4j4KA1TBvpPlxUn8t2FufiDImeXp+DyB0kya/EHRRYWxFOQFMWPVxZxxp+30ImPXpef566dwfsVbbyzT1aynpYZzb1nTyA/MYpnttTy7JdywFuaZiEnbug7srO+L5wvizaoeeU7s/jbl/Wsr+zgWJeTxwYyw2c99iW/WTMye7y30cpbe1r59rxsrpufw4bDHQiCwIMXTUKtVHBmmRy0T0g1s7PeSkOPm+WnoYo5zjjjjAd14/wTPLH/CR6veJxJCZN4afVL4OoCRxt6QG/rg7ihm/CuGhvtfT6m5EaRcZwqpkIQiI/S0G33k2CJ7InrcwbYWdNPlE7F7KLocAAgCEKESXd2oh6Qt/vS4Zd4+sDTXFlyJTdOuhEA90AmLyRK+AIhSjOjyEs2oFEnohBkg2cdoBvoC5iQpiXK3QCA1F1Fu86HRz2VaKWTqNSiEQEdEBbMEBBQCkr0Knl/bu5uRwg0UuDuwb5yGcGQiEGrJBAUUQ/21w1fYT7NDIckSRxsdOILiPgkG629ARwBGxZVAiqFhsZeWzio8wVEonxTWBR3BQHRT7Vj74jtvXH0DXo8PWSFzkenVCIIEmXpMSgEgQ5XByqFinj96Gp4Xn+I3cfsqFUC0/MtJ5yIARHZkhrXYYJSkGOuI//6oG4AUZJo7fUSpVdFlFnGmzWU52i4f9uvqLB/CjCq8feJmJgwkYkJE5Ekifd2diFJcjktgL/Hji12AxemXUWc5sQ9cvxjDYrOSq4uOQcufQna98n9mcdlbd+peYdDvYf43qTvDf093D3QXQXJ5adcxvcvI28lKNQQm//vD+iGYdQpMXLyck8B+VoTQuKbtogdc1Vh9Xcz2TLzGylkfhMOO/bjl3xUOQ9ECMBcMj2DS44rNYzVxJOtz8ce7CffePLPb6xRw5a7lnDpU9vZ09TPrNx/0lsvtgD0cXKm7iRiL1MyY3j9RlnB9c3dzfxk7QEmpJhpt8nfmT2NfWEbhhNdRxzeAFG6yM9bs9XNeX/9CqtLXrx6f/+QBc9LXzfx7s3zuOXVfZz316/4/YUTWTMlnd+uKeNbf9uJBLT1e1hQkMDGOxez+pEt2AZ8TM8cVkLa5fDy2eEulhQlUNHchy8oyt6sGTFsGrBb2FVv5dp52fx+/ZGwwMihNjuPXDYFhzfAg58c5dUdTQTEofLCqGGLfUfa7dz55n6Kk808eFF5hL1CSJQ40Grj1mUFmLQq/EGRDYc66LT7wuevYMD8/K9XTmXdgXbWTE0nJ96ILyDyzr5WNEoFD148iU1Hu3n4s5oIy4cpGdE0Wl3h37NiDbT0e5iSGc2KkkTyEqMoSIxCEOR73ieH2hmIf3ljdzOXzcgM/91cviCXPPU1QVFi7d4W9t+3knW3LUApCBQkRWbh7n//EFtrenivopUD95/BOOOMc+qMB3XjfGOO9MorsketR5EkCcGcBhnzwNsPaTPC40RJoqVXvlG39vpGBHWd/T6ijSom5USNKL1stXpx+0TcPj/u7npM1W+AKREmXQ2K0T++a2vW0ufrY23N2nBQl5WoJyRKaFQKYgfKHQcDuNEoyoiGK18nVPsFwszvUu6JorNfQyjjRhij52ph+kKeXfksOpWOsvgyimKLWJC2gKjKD2H3CwhlFzI938L+BjvbqvqxGFQsLR8oR9WYYOZNslrkCVa3R6PXEaC2Q5balyQ1GkGLUnDxdscfKDHNZ35iNiD3SzT3eGjv87Ms/jo+63uUqSn5+EN+NEr5nBzsPsivvpZN5S/PUVOiOZfsRAM5SQb2de3juo+vQ61U8+Y5b5JljlyJlSSJAw0Ouu3yRKqizs6kHHOEEfsIxKAsla/UMCdmCdWuQ0yxzDqt4z8dqltdHGlxIRLkc8cD/Hjmj8ixyLLl2QlmfKoWAG6Zcgtdri5+8PkPuHHSjdy++XZESeSFVS+QHnVijyRBEIiLUtNjD6BVC/gCEppAIgZnOb6AhCMUPHGJcdg2YGCClTJF/hlGt7ubX2z7BSCXkN496275iYoX5MCu9yhMPXH2zif6UAlKlELkvlRZq7jx0xtJMiTx3BnPYdKYkCQJuydIlE41tqiRPlYuuRvGtrZtrKtbx5UlV1ISd3qf6381apWCxRNjcXpCJEWfvqCSI2jj8+4PANkrclbMyKzpv4MZMfOpdh5iimX2SccqBRVnJJ2a0ukgKqWCN783F4c3iMXwTwbjhjiYd+eYT9s8Ae5++yB6jZLfrClDq5KvwZuruxElqGyzc+PCXOze4Ji9ccP53bojPLWljkunZ/DAReUEQyKPfF5DfY8rHNANR69WYPMEeL+ilQ8GAr03drewZko68wsSePjSSfS7A1w0TX7vtGg9Kyck8eaeVrLjjGiGiZ3c/PJedjX0MTkjmiO/OpOaTgd97gBTM6Np6XPTZvOwemIKq/68ldZ+uX9PKQisnihn6//8WQ0vbm+M2L/fHNcf+ebuFg612TnUZufmJXnER2k52uFgamYMP35rP2/vbSUn3sAnP1zEOY99ydFOR/i1yZahhcfiZDMJJi1v7mlhfn48q8qSefumuZh1KlIsen714WEAZubEkh1nYFFhIlOzYpiaFUMwJFHX7eK7C3PxBkOs/NMWlj+8hUcunczkjBh+f2E5H1S0IQgSdk+AvU029jfb+MV7lfxmzVBmWa1UEBRD+IIiN7+8l2eunj7iWvLZ4U6a++R72Yl8DccZZ5zRGQ/qxvnG/GTmT0g1pbIgbcFAfb0ARWdHDmr8EkVXJRMTVtPhi6IwzYg36A2LOYREia+P9iNK0Gnzs3RibERfRHaCHqsjQJRehdG2hy9tNXRbD3Ju1gKUsaObwn5v0vd4vvJ5Li2+NPyYQhDIiNezt9ZGZ7+fqXnmE67+BkMSrVGzOBg9AaEGFpdpI6wTAA41OWnocjMhw0Rmgp7ddd10efQsKZJLZ9QKNYsyFkHGIjjzwfDrPAN2DyOMcE3J8s9pYjaoMOmU+AIiXkUndhfUBjayILcET7CFbj/0eQuJ0cXgU3ThCHpxh2w4xU5ePPw+gieVFRnnUJJhIsmYhEVrwel3sjivlCkJ8egGepUa7Y0EpSDBYJB2V/uIoK6hy0Or1Rf+vanHi16rHLvMzWeHHX+BkA+m3UCRuYyiqLLIMZIE794ETdvgvL9C9vzwU902P70OP7nJhlH9okZjsHw0IPrZ3bGHV6teDQdESoWSl896GYffQaujlSvWXQHIqpqDYiSHOnaRbkiUMxEnYH6JbPvR6/Czq8aGKEG+oZSKKgW+QC/T880jFjfCfOtt2SC9aGxZfb1KT445hwZ7A5MSJg09oTYCPaAe2cM0nFZPI+s612JQGrgw9Rp0yqF9+bL1S6xeK1avlZr+GqYkTmFvrZ2mHi/JMRrmFJ26Ouk9X95Dj6eHRnsj/1j9j1N+3b8Lk04VWfZ8GmgVOoxKE66Qk1j1v9fIfDjZhnwmRE1GIXxzBcqToVAI/3xAdwp8dKCdjw62A7CqNDlcXvfDZQWIosSCggSumDUyw9fj9BFj0Iy4Zn95rCfi3/WVHeEywEHUSoHAgFiXJEF9j4uHNgz1veoGAsvDbXbiTVrOLEsJv09Tr5tlJUnctaqYOKMm7JsHhNUsB6+PwzNOD14sfye7HT7abUNiV3qNMvy6wb4zg0bJ1MwYrpiZyeryyPLsCwYsFoqSo8iKM3LWo1up6nBwzZwsttbIx1zf46bP5ae6ayigM+tVZMREXgPufa+SdQc7iDGo2feLlREWIismJLGlupurZmexqiyZdQfbqeqwE2vQ8NCGo3gDIudPSSMoijgGBGfufqcSpy9IUXIURwf6+FQKgRiDBqcvSFu/h5pOBz98vYKceCMf3jKPm17ex9FOB59XddHr8pMQFZntvu/9Q7T2e8hPMPH3a2cwzjjjnB7jQd0435g0Uxo/nfnTsQdIEhxbT1BS0iE68OuN/GXP0xzuPcLZRbP5Vum3UAhyM7U3IGJ3B+m2+UkcJkBi0qtYWCqXBNW2J3BTr1we49tyH5fNvw/iRwpZnJF9BmdkR5ZtiKJEZZODjn559TYjXndCUY6vj/bTbfdzyLGFdu8x8tOvpyg5smyuocuNPyjR2O1FIQi09UhAEo/ueJG7F3x/zG1PzokixqSK9NI7RfwhP29Vv0W2JZu5qbJ0u0alYPkkOeMnCIm4A270qkkIgsB5755Hna2OTS2beHL5k4QUDh6svRKAZEMyiZoc8jTLqev0oFErKElP5KM1H0WYbg9ydu7ZWL1WDCoDs1NGZg0GhSoUCvnm7g9KGLVDK9v+YIA/7HoQT8jFz2b+DJOrS/biA/zWWrbZjlEWOwmL1jJUlurqgf2vyP9f8Wo4qAuGJLZV9SFK4PGLTMkdvdQwEJI/V7EmNYIgUJBqwKAT+FPFo2jVCpZnRvrZqRVqYnWx6JQ6yuLKaHO1cdWEqzCpTeQHQ6xs3AUdR2H2bbJv2xgIgoAoieyssdHn9pMRp6csNZZPu+W+m0E/x1GxpMHky8d8ekvLFn646YfkWfL45KJPSDEOmwhOuRbJ1owQM7q58iBdvnZEQjhDDhxBW0RQd37++ezv3k+SIYnyeNlXzDGwv07PCfZ7FKYmTmVD4wamJv17ymn/k2gUWi5Ouw5fyINZHf1vf7+WPjcHnF/RHKogU5/LmUkX4vAE2X3MhkmnYlq+ObxI8b+FOXlxpFp06DTKiExMQVIUT3xr2qiv+eumYzz4yVEWFMTzj+sjs/g/Wl7IHz+t5oqZcmatKDkKo0aJABSlmDnYYkOvUYTLJ2fkxLKvqZ+pmdG4fAH63AF6nD72Nlq55Knt4fLBu1YVcf38nLDx93cX5tLa5+Gjg+3ctDiPu1YV89crp/J1bS+zhnnmHU9ClJbHr5zGl8e60aqUnD95SGHy+vk5zMqJJS1aT4xx9EWisjQLn92+KPz7oMDKyzuamF8Qx+ajPgoSTSRZdDxwYTkVzf0UJpr41YdHuOSp7az/4QISo+QF1ISBYDTOqOXr2l5e+roRq9uP0xfkwYsm8fRV0/j9+ir+/Gk1tT0ujBoltywtCJdzPvBxFc9dO4NHLpvMvkYrz29vAuTy10EmpJr57ZoyNlZ1MzM7ljve3B/ONH5/cR6PXD6ZX7x3iCkZ0SMCOpCDy+e3NXDmxOR/ykZjnHH+rzIe1I3z70MQIHU61m4r3VI8uIOUai5gYqqCwz0fDQyRJfK/OtKHWqU4YVmaPioNjUKDT/QTrVCDs50mKZN9dXaSY7TMKowe87U7qm109Ms3J7NBNUJUoc3qpb3PR0GqEbNehTcQwhG08kbbr5EQyWxS8pPkuyJeU5JuoqnbQ0GKAV9QxCe6CYg+bKGOiHH9Lrk8Mj1OR1K0Fp1GSVGanL3y+kNYHQGOtLhIjNZQmK5BJahQjiEJ//fKv/OXir+gFJSsv2A9ycZk3t1bjRQwMy3PTFaCEcOwLM1gX59BJT9WnlDOo0sexRFwUBZXxqcNm9CERAJBRdhWwaK1jKqiqFKouK7suvDvvQ4/nf1+cpL06DVKUmN1LJ2oRKVUoFIK+AJiuO9RkiSe+fpTXq99Vd6P+HIuLboYcpZCwMPP695hffPnZOhLuDHrUbxR28mwJDE3bS7M+h40boPp8nu7Ai58QT86jRK3L4RhWOAoSRJ1HR4UCshJMvDl4T76XUFykvRMzjEjCALpcQb+tOw3o57fQQxqA6+e/Wr49ymJU6Duc6j7TO5v89lArWdv514+qPuAiwsvHqEAqhAEGqxOHttyFK1awfrbFjCzwILDExzh8ThIIChS3ebGbFCOmcnb3radgBigqq+KkDgUZImSyEc979Hpa2O5/hyyDfmjH1zAw4SaA7gSU4mKLiBBG5kdjtfH89jSxyIem5ZnprHbc0p2IcN5cNGD3O29+xuZyf9PRKvQov0P9NK19LlZ8actnL+qluw0OQgHaOr20O8K0u8KUphqwPIN7Tf+u8iJN7LtZ8siHtt0tItHP6/hgqnpXDV7pDjUx5Xyse9qsI547s09zRxpt/PI5zVcNSebwqQott+9DAGI0qmRJIkrn93BttpeovUq/nH9LAIhkZm/+Sys/Hiw1caHB4b6wQA2H+3munk5hAZ63YIhiR0DQiiDgihmnZqVpWNXVqzd08LP3j7IiglJ/PXKqdz3XiXn/OUrki06nr92BsUpZhKitISGGfg9/Gk1uxut/OLs0lGVR29eks+vPzpCUJQ41uli852LSYmWv5OXTM9ApRC4660DhCSJXpefI212rvl4F76AbJy+qDCBL2u6ueq5HeHsJcBDn1Txg6UFPLWlLvyYQhA4oyyJRz6vwRMIhYVM1h/s4JPDQ/e4X59XRpfDx8Q0C4XJUaiVCsrSoln9yBYOt8uLdlMyoilMiqLb4aO2y0llq42zy1NH+A7ef24pPz+rZDygG2ecb8h4UDfOv5eSNcQVSaTU2HD7Q9gG+hyWZS4BwOoM0OsIsHxSHDqN8oQlkammVNae9zb9zV8xSRsLGXNpO+ZGlKDN6otQUhxOICiGAzqFAItKY0f0ee0+ZiMkykIic4tjmFUYTUO3QHJ7Eu3udgpjhko9ba4ABxodJJg1LJ4Yx95aG43dXpQqN7qYWn6eFdlTsr/egdUZoLPfz+ppQ2a4VmeArYdkGX0JqOjdzbe33kOyMZnXznoNk2Zk2eLg5DhWm0RLp5rK4D4IpCEAO5tqyEqYHDH+yeVPsq9rH7NThzJrExMm8lnjZ+hUOm6cfD0hUQr3G54O26r6CYYkehwuFg6UUQ2fZA6XmA+KEhZyiVYlEcAjZ24EBeTJ6ny2ZjnI94U87Oz7iPer/oyAwNpz11Jw5gOAXG65ZVcVf6y5AV/IzePLnmKWZVKE4Elrr48DjfJEwqBV4vGLSJLEsf4aSkJlaJVjT8htrgDNPV4yE/SYDSp2V3XS0ednivogaVOWQ+Y82TfQEBcuk737y7tpdbZypPfICKVMrVpBrEVJSJJw+0O09nuYm3fisr2adjfVbbI4QaxJE/YzHM41pdfQ6+mlKLYoor/PG3LT5pVXzxvdtWMHdZt/h/arR5iv1MKdR0+4P4NE6VWUZZ6+rLhCUPx/E9D9J7F5AngCITZ8mcJNZ0ssy50OQHqcjvY+Hyad6qTWL//d1HQ6cPlDTB6jNyoQEtlU1cVjG49xsNXGsU7niKDurT0tHGy1Iwhwz1kjbVPCWaioocWG4V57giDwwIXlvLKzieUl8jXq1x8eDgd0iQPZogunpfPKzia8ARG1UmBVaRI6tZK3b5pHZauNMycms6gogfcqWrlu3omz4IOsr+zAHxJZV9lOSJT48IAcnHbYvFz/wi4evXwqlz29HbVSwYe3zMeiV4ftJZ7dWhcu4RzOxdMyeGZrPd0OL5fOzIiwnwDZEiE4EIj+13mlWN1+jrTbR2xn0DtWIchm4LXdrhHKno9cPpmceBN77l2OwxskyaxDFCU+PdKJJMnCQ2kxeqbnxEac80GSzLpwUHdGWTJqpYJjXU56B+YAB1r7RzWTHw/oxhnnm/M/+64wzv8XKBVC2KbA1mfFe/Qzkqw2pPRv8dURB8GQhN0dZEbByT22ssxZZJUO3fiL0wVEUSIpRjumgINSKWDWK7F7QhSlGUcV7oiL0tBl84c946L0KiZmxvFOyjtYPVYyzENN+zXtbnrsAXrsAXIS9VgHjMmNigRWF4z05Ikzq7E6A8QPGJWHRIkumx+XN4gogSCF0GrU2ITD+EI+Gu2NtDhbRlVfvKjwIgpjCnHbEmnpDmELaNht+xtpuhLm5o/0o4rWRbMkcwm07Ya2PZC9mLv2PMCujl28Vf0Wb537FkqFEBFM+wIi++vt6DRKJqapEY6+CwhQsiail8wV6kFLHJ+2fEBm/OVYHUGK000RmTO7O4hOo0CjUrC4OJvS1LUUphowHjcJ+N2C3/Fpw6ekKKZSYz8EnXIp5HAj7bpON02OZhyBfgDu334vjyx9hGjjkKS7vmEDAjOodu3itx/+ihzTBOJUGXzZ+x7P1qWx9ty1GFSGUf2sdh2z4fCE6LL5WVQWS3M/IGho8ZpJc7RBdPaIntHy+HJana1MjJ84YnsAl8/KxOGTBUZSo4zsrO6nOMOEeYwJeZR+oE9Ho0CjGv3znGxM5g+L/oAkSXhDnnDppEFlYmb0Ajp9bUy2jDRpHjpJA700WpOsVjnO/zhKUy08+a2pdDl8XFGcGZ7oWoxqlk/6z/XzfVOOdTlY/ehWAiGJZ66ezopRpOl/t66K576qJ0qrIlqv4pIZI3vpRGkw+BCYmzdyceD+c0s5Z1IqxSlR9Lv9/O3LeqZmxrCkeKhUPiPWwE9WDV1LXQO9zNEGNV//bFn4vvHXK6Zy62v7cPlCvLKzmevm55KfaCI/UV5cW1SYwKJCeVFua0033Q4f509OG/O+c+uyfPwhkeUliSgVAj8/ewJ3vFGBKEGiWUdDj4tASCIQkhd8suOMLC9JZGe9NSymcjwWg5oddy9j27Eebn9jP1UdDh67bEp4H25ZWoAvKLK8JImr52Rj8wRYVpxIt9NHXbeT2TlxhCSJzUe7yU0wsnJCEi/vaOKqOVlMyYwmRq+mzxNAq1JQlCyXtBs0KgwaFX0uP995cTfJZh058UZ+sDSfKZnRaFVKmq1uXtvVRG2Xi1VlyZw/JY1nrp7O01vqCIQkrp2bDcg9rTcsyCEkwoVTTyw4Nc4445w+40HdOKPjd8s9Q2P0bPS7AnTb/KTH6dBrRy8VHA2L8ygWp2zoKvTXkyHa6BEt6DUnVzobjWijmrklJxZuUAgCSybG4Q+KIxUvnZ2w7znmqg34y69Ha4hc+TSqjRjVkY+lxeno6PMRb9bQ6wzgGOgzyhujpK4sM4qCFGN4kl5Rb6ep24tJp2S2qZrkjvWgK2TO9IuRlJ1kROeOKaffbvWCK4cEk4L2Hid6tZbd/R9SUmZhUfYavH45KLEYVJgNsjGtQiFAzccQcEHdZ+HjOf64AOg4iGPnO1ijzsajT0N0OZncsV9+Lr4IkieHh3aG1jKxr52YoJ19decAIIX8TIvrh9h86rs8VNQ70KkVrJgcR3q8jvT40cv3YnWxYWGbhRQwLTMbi9ZCRtTQ5yI5RssE52TOE67lvabnaXG28GrVq9w35z55wLHPiPvwKlbo09lbNgdvyMsR215SdbKaWquzlbf3HiJKymJSThQ5SZFCAlF6FQ5PiCi9CqVCoDRFQUd7JwXGPjAPZDoDXuhvggQ5kPz9wt9zx/Q7RvQfDqJWKrh5ST4hUeL9nV3hx2eOUSqcEa8n1qRBoxKG+grHYF3nW7R4G5gVsygcxE2JPrlCIvN+CGnTITZXDuxOAV9ARK0S/tf1cH0TXN4QOo3ipHYc/25WlZ2Cp+F/Iy993Uhzn5tblxZgPM4nzu0Phcv77J7AaC/HH5KvmwqFwK6fL0c9SobmkukZxBg0xJs0tPZ5WHegnavnZmPRy4sRSoXAzBy55/rn7x7kpa+bUCkE9ty7IjzmeO4/t5QpmdHMzI6NCMiWlSRx5awsntlaxxmlY/ujHe1wcPVzO5Ek2FLdzaeHO7lwWjr/dV6kyFN5ejQvXje0uLJmShqIEp9XdXLPWRNIiNKyt0k2Iv/8SCcWvZpnrzk1cZB3K1rpsHv56EA7RUkmzixL4a29LXxc2cG9Z00IC9BY9Gr+dpzgyC8/OIR0tJvabhdXz83mJ2cOqdLuuXcFFc19xJt0pEVHln/f/kYFexr7ALhteQGzh/US3vDi7rDp+adHOlk9MQWNSsFNS4aqBZ7eUstv11WRbNax+ceLw4Ix44wzzr+O8aBuHECWMf/Rph+Rac7kL9EzUH90J+QsgGs+GDG21+FnyyH54l7Z5GRpeeypK6clltLX0YhBFUJrb2WyYyOSQgMpd538tQDdR+Hli8GYAFe9A7qRAhn+oCxQIYoSZZlR7KjpR4Cw4Mpwmls6qFReTIb/KGWV/4AZN8plgZIE+18E6zEovRiSysOvSYnRcna5Hpq34fFmoVbGIkrSCWXSh5ciDpa+hEQJU6CLavVUNDYf8bte51eSCrJGVz0MhiR21Mgm7RnxOlZNjWf1O1fgCjnY1raNK/K/yxeVViTk0hi5/DDE3OIYElKnQcsOSJ7CAynfZnfnbiYlTOI3X/+GZkcz9825j2RjMoGXLiPe2cLUuG18NeMFGhwGJutj5C1GZ0fsz0XaeNzKDpboMtlsUNDvFonr3ARN+yBvJS6FXDLmDYh09vlJGyOgG43JiZMjfm/t9bK31o5WreCeebdilxo50H0gUhDHlAxKDUZvO9flnUOPVos+kEe+fg7PNt2GX/JglDKQgPY+34igbkaBBacnhGkgW1aYlUBhVgIwbLL2wjnQshPm3w7L70MhKMYM6IajVAgkWDQjhIBGY7SSy9Fo98n2C+3e5hNn5o5HEOTv9ilS2+HmQIODGJOaRaUxo2Y5/zdhD/TT5Wsn21AQ9pYc5Fi7i4ONTsx6FUvLY/9HHeuxLid3vCErCV4xK5MUi56M2FFUTgNe2VYm6vSVdE+VvY19/PzdSkAud7x5SWSpb0mKmTUDoiAXTB1ZQQBwz+oJlKdHMzUzetSADmS1zM+PdHLV7Cyuf2EXgZBEj9PHL48LoABy4+UFCrVSwVNf1HLXQHau3ebB4w+RmyA/b9KqRhhjD3L36hJ+uqp4bNsOZLVLtVI2Sz/YasPlD7F2T8uIoO54ntlSx2/WyTZAMUYtF01L55UdTWFD7y01PWy8Y/EJtzHI1XOyqely0tTr5k+f1vDUF3V4AiFECV7a0cjyCUm8s6+FLdU93LI0n9wEE9uO9WB1+/n+ojx8QZGJaRZSLJGBW0iS+PhQJyFR4q5VRWHLCac3EPbei9KpeL+ijdY+Dz9aIS9umYcF0DOyY0Yt5R/0H7S6/PiC4kmDuiPtdr730h4yYgw8e8308SBwnHFOgfGgbhwAPm38lBZnCy3OFtydXViQoOFLCPpAFTkJbeuNrL23u8f2N7K6/Pxk7QEsejW/XTORxl4llaEl6JUKVnIABSCEb2unQPUn0N8I/Y14WyrR5s0ZMfHqtvnptsl1+43dbrx+uQPe6giwvXMzf97zZ1Zmnslcy5X0elLwKiTq1JMos38JfhdooyDkh54qeYNdh+Sgzu+Ug7y4Ijj2MbTvRS8oOWP+vUhK9Sn3pE3ONZNg8ZJg1rC9ah5Orbz/KsnHKtezqD29YDkucxn0otz3AlHiIuxCDNFGFXqNkh9Ou5X3at/jOxO/g80dDJ9JCXANGK732P0kFJwJBXKwaED21Puibj+vHZX7wNbWrOXinBtQqFNJoIVWJdgDPfRIR2DhUMDtC8g9ant7dnDn4adxSEGuiyrgtrzDBAtmoNleiShJ3HPkeY74H2FF9B2k6gpp7PGcVlAHyD5tkghKNY4B9TpfQCQYEnh06aMjxyeXwQ92gRgiOy6Px4vPo7nHw+5jdn5S+AqleSGMUjTtVi/F6SMzVIrjDO1HI9hZiQpoOLae7OX3ndbhzC+JISRKo2eAjn0sB925KyBz7iltb2n8WTS6j51eQPcNsDrk71K/M4AowYlsB/+nI0kS77a/jEd0U2KaxML4lRHP29zy58zhDRISQfU/aB759t4W9rfY2N9i492KNowaJZt/vCRSRTDghSfnQ+8xOP9xmHzFv3w/PjzQxm2vVqBSCIiSxISUkQtra/e08E6F7AN38YyMUXtJ9RrlCPP04UiSxG2v7SUoQm23k8QoHa39ntEDWeC6+Tkc7bDz+u4WHt9cywVT09Aolax6ZAveQIgFBQn8aEXhmD1+gwwGdDWdDp7eWsdVs7PCJugAWXFG1t06n6MdDh785ChmnYrbVw6VgNs8AW5/vQJBgF+fX8aRdgczcmL5ur43PCYzzsAzW+si7nwlx53HTUe7aOnzcNmMjBFBb1mahXdumsfUX30KyJnRb8/LZktND9fMycYXDHHHG/sRJdhW28PvLyznuud3IUnwx4sn8ds1o5eLf3a4k6cHxFImplmobLPxj+2NJJuHrt0TUqL48lgPXx7r4ZIZGaRF6/nbNdPZ29RPaYqZONPoi5t3rCwiyayjPN0yZhbV5QuiUgpoVUo+ruygsddNY6+box2Ocd+6ccY5BcaDunEAOD/vfHZ37CYjKgNT4ZWgexDylo0I6ACc3iGZsIIUPWnDFPFae2UVycJUI2aDivcrWvn0cCcA505KxaKRt+cLiAQzF6ExJYAhATSjly6OoPwSpLrN7E29gaaePBL8/cyfEFl+mWDREG1UEQhK5CUb5b41ICVWy317XqHB3sDfD/+N9ALZKDlKK5EZqIbsJXJAB/JxF6yWg7jsxfJj+18kZGsjFFOIJm6g/8MQh1qjlrN7xxEMSXgDoRG+WBqVIpwl0mk1OH0D5UkKtTypH5YVFCWR6r5qcgIBtLYGFtOMN/dcjCkzQRI5L2c15+WfB0AoJOL06PH6JfJS9HT2+3F6Q+QO+OsFQiIhVx+66rWgMdHpmEqmvhRroI3FGYsxG1Rsmf039LYqnve/xa66y7hj8r1sOtiL2aCiMNXA5oN9iJKEZPbgkOQJcKXPSqjhc7xpU9BMu4HWzv18uP1nAOQZN5JrLiY7cWz5f6u/BwmROM0wy4iAW/aw8zth6nXkp2QiSmDSKU8sEBGTHfFrRrwei0GNRhWPTqNkR/sODgUOMlFzGXB6/WTugJv7cidS1HGYdzRuPjqtV8sMD+ic3iBqpULO4rbtgaAX2veysWMHD9a8zvLk2dyx7M9jbivXWEjusH7CfxcTMqJQKgQSo7XfqCTR4Q1g0Kj+28sZT4XSDBMalYL4KPWovbf/KXzBEDe8uIfaLidPfGsq5enRnDUxhcc314bHeIMi3sBxFhM+uxzQIUHbvn9LULettldWbJRg/W3zKUkZ2Qudl2hCrRTQqZSkR48ehG2t6eanaw8yNy9uVFEQQRBItuhp6fNwsNXGpz9aiC8oMSF1dPsSgPOmpPHBgXbyEkykxxg43G7HPdBD90V1N512Lx//8MSm8aIo0drv4aIntmHzBnlnbyvfmp3F0uJEFg701OUnRrH5aDcNvXJpd0HikIjQxqpOPq+SS62brDup7nSysDBBVnVUCEzKiOaGBbnc++5BAJKitLx8wyxy4ocWmuq6neEgzBcI8Z0FueHnXtnRREufmx8szec3a8q48839uHwh3tnXSl6iif/68DDPXD2NiWkW9rfY6LT7uPXVfQxGkEqFwK4GK+XplnAmbpAJqWZiDGpCokRZmoV73j2ILyjSaHWjECDWqOFbs7M52GqnPN1CRVM/D6yv4sZFuczNi+P366vwBUPcs3oC+uNaHUxaFd9blIfLF+TjynZmZMeGPf8A9jX1cfkzX2PWqfnw1vlcNC2d7XW9ZMQYKD3B33ycccYZYjyoGweADHMGL5z5wtADFz035litWp7sGHVKyrIiL7Z7amUVSX9AZG5JDPMLEki16DDr1ZSnWzBqVOjUSqKNKtx+CVXCxBOWuhxPiy+a3YVPMKgC3TuQRRiOUiHg8so9HQ1dbqblDU06Liu+jHZXOwuSVyIg2xssLotFoRilVClrgfwzQEBUsclwFe6AmdmWWJLnTABdNEFRGLGiL0kSmyt7cXhClGWayE7WoD5elEKSKDF0stOmIagwEWfR0Rszk2RBwd46O139PrY4HueDhreYmTSdhxLm8KumdVja4O70Caj3PAMeK0y6Crs+ly2HragVAovKYtFplMQMWzH1B0U2VHQRY/+YeWI9ABndX3JX9NVs1XVSFi+XDq2YlsyrB9fTUNVKlNpCqWkpLd2yhHp8lDqsrDYlbg7XTLiGxq793ChE8WTfQZ5/fQlvnfsWWQWrObdrO0esR7h+6sVMiEug1+Fn/Z5uzAYVc4qiw3/zbl8n77T/AwmJc5IuJVU/ECy7e8Arl/jS34AqOntsE3OgzdnG5ubNLM9aTqIh0k9wMPtm89n43mffIygG6fH0nNBjUZIkHN4QRu2QIuv3P/s+e/3tbElM48qSK0e8xuYKUN3mJjVWG7HQMRotvV521djQqhUsK49Dm38GtO6G7EW8ufmHtARdvNiykdsl6b+9BNCoUzI1b+TE/VR4a08LP35rP+Xp0bz9/bn/7YGdIAicn3JluPzyeHQaJROzTl/l819NQ4+bLdVyudu7+9ooT4+mNM3CTYvz+PhQBwvyE1hZmjQya2VKhAuehtY9sGBIhbey1caGQx1cPD1jzEzXqXLzkny8/hBTsmJGDegAZmTH8uVPlqJRKkZ4sPmDIk1WN2/tbqG138Obe1q495wJoyoofmd+Dvd/cJhYo4Ykix6T9sRTlrl58VTef0b4+jI1M4Y/XTKJ57c1cKDFNqpNwPH86I0K3qtoQztQeREUJZ7f1sBru5o49MtV1Pe48AVDrJ6YwvrKDmIMGqZmxlDZauP7L+9Br1aSFavHpFOH71NObwABWDMlnZUTkgiJEm/ukcunp2TFkJ8YuV9GrQqDWonLHyJ+WOBzuM3O3e/IwWCUTs33F+fxs7UHgRD97gC7G+Rr5obDnbx6w2wuffprDrXZcHjlBbgnvjWVV3Y0sbWmh7MmpvDXKyP9I7PijHx99zIkCV7Y1oAoygqZSkHgiW9NY1ZuLFE6NedMkktrS3/xMS5/iF6Xj2vn5vC3L+V7S3l69JhZ2Dve2M/HhzooTTXz0a1D99eK5n68ARFvwEddt4vZuXG8ceOck/69xhlnnCHGg7pxTptJOWaSY7RhX7PhxJs1dPb76XH4ae31kp9oGuFLVJRmZEd1P21WH2mx2jFFIwaRJCks493W52WYrQ+lmSMn+gJD+i7HCzusyFrB0ozlHGl2olQIFKcbIybNgaBIl81PgkUzopzSW3IZrsMeQLYjSM5IZPcxG809fVhirSj0nSxMX4hCUNA3TEDly9pqrtt8Ow8vfZDpydOHNtj0FfHHPmIFGj4y3iifN7tse9DYJb/PsT75Jllrq+flpGl8aq8Fey3npS9ismtAeKOvnp5AOoGgRACJfleQ5ONWSb1+kVebfo/N8zVPJiwgXaFhTs3r+KVXCV3yLJIk8U71m8xt3k2pZhm35DxHnXsviTEC/Q4lFoOKzAQ9gZDcC5gRp+P2+DtwBh0sfG0hIUk+1jpbHTmWHH4zP9IHrrXXJ9+wbX5cvlA42+YXvUgDS8hecVhZryUTcpeB1w5pkYbDo3Hrxls52neUTxs/5e+r/k7LV39i69G3WTn7TuImnI8oSXS6OonVxtLl6Yo07A545CyZfijje7DRQW2HhwSzJpwJ7nTLGefyhHJunXrriH2obHLSZfPT3uc7aVDnHFZOGgiKaFOn86aznic3/4DZlkLyQl6Wpy/+bw/oTpeHdj3Em9VvctvU27ii5Aq21/YiSXCgpR+XPzjqxP0/jVkd/R8xD/9nKEg0cemMDD7c38bz2+opTTVz4bR07lpVHO4VG5PyS6D8EvxBkZtf3E1ttxOnN0iXw8euhj5e/W6kmM722l78ITGs7Hgy0qL1/OnSyScdl2Qe/Ttw7d93sq22l7MnplCSEkWKRY/HHxrx2fD4Q3x8qAOzTsWNC/NOGtAB+EJeQoQwMFT5ccHUdNZMSeOSp7bzXkUb6TF6LpuRiT8kkpcw8v5R0dwfPk6ry0//gNBLlE7NM1vreOiTo4QkiX9cN4u13x8qlV53sJ1mq3zdVgjw6e0z+aSyg4ZeFwWJUZz3169w+0PctaqImxbnk5dg4lCbHaNGycYjnTT2ukiLMbCyNJkks45PfrSQXqc/ouwwyawl3qSlz+0nMUrDF9XdTMuM5vOj3Ri0SpYUJtDrCnD+5DQMWhU/PqOIq5/bCcDCwnhWlSbzpw3VANT1OPnha/tYPTElwm9vMHv32q5mPIEQWbEGPrh1/qjf3fkF8XxyqJN5+fGUpsq+e/6gyNTM6BFjB/GH5Eof/3BjQGRRnIYeFzFGDbNyRva/jzPOOCdnPKgb57RRKgRSY0e/Yc8ssPDBrm5CopyNGGty6/TKQYDT7R31+eHUtLk51OxEqYC5xTEEQxJ6jZKMeB3xZnkVuKHDzpEmOzmxUJyfzuKyOGzuAMkxI8tHG7s81LTLZTPxZg0JFnkbLm+QnTU2+l1B4qLUI4RVosxmyrNVA+bRhoGAzos35OS3O76NV3Rxy5Rb+G75d2kfKMsBiFanMsNyPrs6dkUGdSHZO08phEgyK+lwQIxJjUqpoDDVQGe/n3tn3cdLNU+yvn49z1U+R4w2hjh9HDkpMwENuLogYy4ZSh19zgAqlUBitIa3qt9ClEQuLrwYYaBXTKXs4TumfL4O9HFRdDmIQVS6WDzWyXy6v5vHKh/i/KSl1ChkVbNC8ySyYuPIGnYa8lMMePwhNuzvJRSSWFgay5PLn+RvlX+jPKGcJYIJ1t4Aky+HvKXh1+Uk6bG5A5gNKkzDhEDS9FmsSDiXkBQaWUqYu/ykn41BBj39Bv+9+ciz1KmVbN75a76tXExbn5cPOv6OXqXnlbNeYWL8RI40O6lv6WGl9yVUITe+0osQEsvQKDXY3fLn0z4QfAE8tvQxvmj5grNzzx65A8hlv102P4mWoczEB7Uf8PKRl7l6wtWszl097DzKk06TToVpIMB98+ibdLm72C5JbLzsixMe78HugxjVRnKjc0847nTZ0rKFeH38CCP1U+X92vdxB918VPcRV5RcwQ+XFyAhMTsn7n9EQPefoN5VzRe9n5Chz2FZwuiflZOhUAjcfWYJb+xuRpJge10vF047PQn4uh5nuPQ9bcCgOvO4LN3uBiuXP/M1AM9dO52lxScX/flnqe6UVRL7PQEyYgxsONzJVX/bwYYfLYoYd6TDztd1stH3b9cfYVVZ8gmzjB9X1VGrfA+1WuTs5EtI1WXgDYS4++2DuP0hjg6oM75X0caTm+uQkHj1htnMGqbiCIQzdHEmDU9fPZ1PDnVwpN3Ghwc6+P36qvC4PndklcjF0zNYd7BdLsmU5AXFTw534vaH+ORwR3gx0uMPsv5gO89ePZ3nvqrnma31rN3bGt7OfedMQJLgW7OzSI+JPN44k5ZNdy6iz+3ngse30eP0Mz1LXnRy+UL8YFkBxclD1TMmnQqFIFde3rK0AEEQePKqaXxc2cGGQx28W9HGexVtfPzDhSRbdHxS2cGcvDgyYg3ctqyAv39Vz7fn5Yz53X3yW9Nw+oJEDTz/9c+WIUrSmMI3AH+6ZBIbDnUyryCyz9KoVY0qgDPOOOOcOuNB3Tj/UlRKBRMyTHT2+ygYQ+IfYIaljeb+BjLc1eC+Bgxjey9JgFLyk+mvxiyWMrd4ZKlkfXMPXtFIXbeb4mwvxt5DGFu2QXxx2OB6EItRvtGplEI4wAgERTYdtIZluEVxdPGWvIH+NFGSaO7xDhwzSIIcBPhDfgh6yWx+lS7FPByqBIKSQIolikuKzovcWPZi0FlQGBOZY0nC4w+FVTJLM6MozQSIY557Huvq1xEQAzy85GGmJU0beP3QJEgNTMuXS6FePvIyv9/5ewCUgpILC+XewT8XX0pM8zb5BRPOwZ9SzvMNFTh9x8hRTmZiyjx+bj3At6LyiU7MIS1hdKsImysYFp9ps3pxhpyoFWpmJM/A8eaNWKz1OJu2cfekZQTFIL9b8DssegsL8tRyr+Jx2adcY1HkGxx6A7qPQPH5kDyy12Y0Hlv6GPu79zM1US4nMhniINCP0ZJFR78PAYEi02wq2j8mz5IHyIsOGsmNKiSbff/t69/xZqCH189+ncm5sdR3ekiNHVoUKIgpoCAmsmSv293NnV/ciU6l44+L/khOYkJEL9bjFY/T4mzhyQNPRgR1KqUwQqzlurLreObgM1xcePEJj3Vj00Zu23QbKoWKteeuJddyXGDXeRA6KiBrIUSPrvI3Gu/UvMMvtv0ClaDivfPfI9M80jdsOM32Zn6y9SckG5N5YMEDqJVqfjTtR2HhHpA9wv50yeRT3of/HzjmOoJP9HLMdYSFcWeMLLsewBsIjVD0C0lBFCgRBAGLQc3v1kxkZ72V25aNLBU9GQWJUVwwNY3abhe/W1OGKI0U4xieCT6ZXYU/KLKlupvyDEvY9Hs0jhcD+sf2Bv6y6RjXzM3mpsX5PHXVND451MmVszL51YeyGuRg39twJqZZmJIRzb7mfvRq5YgereP508YKLlwti0X1+XtI1WXwZU0Pb++TA6br5mWzvrKDlj5P+DWPfF7DpOpufryyKFyyqVLI12CdWjngU5fPO/ta+PBAB6kWHbevKGRLTc8It5+ceCPnTkrl0Y3H0KgVxBo13LIkn79sOkZqtJ79Lf0sK07kUJuDxzbWMjkjeoTdg06t4L8+OIwE1Pe4+NX5I4OcKJ0apUIInzOLXo1CkDNsMQZ5Qemvm47xRXU3d68u4cNbFiAhUZoq3x/yEkzcvCSfxl43+1tsSMAX1V3sbezn40MdZMUZ+OSHCzlzouw3dyIEQQgHdI29LrbV9rK6LAWLQT6HLX1uvIFQRHlptEHDJTPk0kyHN4BaqRhXthxnnH8R40HdOP9yitKMFKWdQPik4QvMxz6hdFB8XxKxu4NoVMIIH7kObwuY+1miO0RUbwXs3wYL7xkhTFIY1UdVr5ts6Rh49HDkLfkJR5ssPGIaWoGOi9Jw5rSEgcBO3o4EKEIekgOtCHH5TM6PHnP3+10Bmnu85KfoabP6cPtM3JD5CAnJ3ZyTvxrJY8cThFniB2gzZxLIWopOc93IDSmUkCpn7hq6PHj9IQpSjXL96AAtjhYO9RziiuIrmJgwMRzQiQPqL6NNxLrcQ35o3tBQJjQmebIsyKExgDGRZxrX8WTz31AKKp5a8C6P5jyMdAo9XPGWoUlqnyvA7w/8nk53J82OZi4I9fBt4O2YeDY1bwJgU/Mmzo8uhv3/AI0JZt8G6jFW3MUQtFcAEnTsp01TRJvVR36KgWjj6JNjSZIwqk3MT5sffuzxCz+koms/yaqJBPwqrC4PUao+Hil+BMPAe5dlRVHXoaQv4TxarV/zfNt6PFKIOlsds1MSw71VPZ4emuxNTE2aOuK9NzVvYm/XXgB2duxkaeZSDjU56LL5Kc+O4qLCi3jx8ItcOCDKcyJW5axiVc6qk45z+OWMQ1AM4gl6Rg448g4EPbLYzPQbT7q9QQbLYCUkREk8yWhYV7+Ogz0HOdhzkMKYQlZkrWBNwRrWFKw55ff8/5FJlpm4Q24y9DljBnR//6qeX35wmJUTknj6avka0OiuZUPXu8Rq4jk/5UqUgorLZmZy2cwTB9djoVQIJw2op2XF8Ob35hAIiszNH3thze0PcvnTX7O/xUZGrJ6tdy0dddxjn9fwp8+q+dasrHBA8tLXTXTafby0vZGbFufjDYi8sbuZI+12CgaMvTtsHnqcvoj+MbVSwTs3z2NPYx9JZi1/2VjDyzua0KuVGLUqUqP13L26hGkDmaqmdj2ffplErBmuP1tWd5ycGU1Bogm3P8QVszLZ19wfltZXKQS21fayrbaXBQXxYYXO566dwZaabpaXDN0z1kxJZ0KKmd+sq+KxTcdo7HXz/v428hNNEZmxwfuJIAic8fAXTMmMYe335zLrt5/R4/Szp7GP2IEeQ39QxOWTKwFiDGqSonSERJHaHheSJHvRjRbUgWwI/saNc9jf0o/HH+Lzqi48gRB13S4sejUPfnIUkL3hHr9y2qjb+M2aMgJBEbvXz5op6RxstQPy9XTGrz9Do1Lw7s3zTtqD+ZeNNXx4oJ0ep48ep58vjnbz5FXTqO9xceYjW/AFRV749sywyMwgexqtXPHMDsx6NR/dOv+ECwXjjDPOqTEe1I3zb6euv44mRxNmjZmfbP0JxYKWh82lqNRGmHQVrT4zO6t7USsFlk2Kk1dkAx4c7jY+6HsXEZHZUQlM6kVWiGRk0JFWPI20/gbQL5MzQQoNiH7QmiN6pQY5vl9Oo1KwQvEJal89IVcOdv+3IwLMels9f97zZ2YkzyAlcCZ2TxCzQUV2op7DzS6StXmsypmNWqHgSJ+WKv0FCIjMijGRMsoK8/3b7mdd/TrumnEXy1POYV+dfENVde4hX6iFsktBY+SBXQ+wuXkzepU+LOxhcwXYergPpUJAit7HV+1fcP3E68MZmxsm3sC2tm1oFBouLbp06E0tGbDoXvn8CApitPJ5MaoNFKdGA7I934EGOy5fiCSLhpwkAxIi3pA3bFauUiiIjVJjdQSIj9KwPGs5r1a9ysL0hTzl6eZZi5NfzLmFkr2PEpRCzI7KAXubbE/gs8s/YwV1DVvB3Q+x2ZCzmL3VdgIhCbc3xPwJMSNEdXwBkS8qrfiCIvNLYogZ6PO0aC2kKqdR2ehEwM+KyfEs1F0b8VqjTvbxOyIUUT55IhcIXswaM5PjZnCoyUlslJpYM1zywSV0e7q5bept4QzUIIvSF/Fu/LvoVDpmJM8gEBRx7n2HBHcT9ervcP3E67l+4vWjH+s35Ny8c1EICixaC6VxpSMHxBdDxz7539NgTf4aLFoL8fp4si3ZJx2/MnslnzR+gjfo5a8Vf+WFQy/w+cWfh4Pm/6skalM4L+XyE47ZPOD5tXlADAWgxdOIiEiPvwt3yEWU6jgREr8Tdj8l939OuwGMkYJA3wRRlKjtcoazLWPx5OZa9rfYgCGrlNHYcLgTSYINhzvCAclNS/J46os6rpojZ40/ruyg3x1ga00P0wZ6ryx6TThbs6mqiz9uOMrZ5al8b3Ee07JieOnrRp7f1ghAIBTE7g3SbvPyxOZjYcPuGxfm8cI2BbcuKwz7D8abtLz3g3lc8Pg2zn7sSx66eBKPfV5DdaeT6VkxVLbZMetUFCYNZZLs3gB//qyae945yGOXT2VVmVwZ0mz1hMVrBtl8tItffXgYX0Dk8W9N5QdL8ilJMfN+RSsfHGhnfWUHB1tteAMiggArJyQxKSMapy/It+fmkGTW8sGBNkpTLPxsQABlcrqFihYbk4bZKIxGWZqFsjQLbn+Q1n4PMQYNs3Nlb8XpWTEcaLWxo87KD17Zy6OXTRlx7VQrFTx82eTw73+4sJyzJibT1u/lvz48DD54bGMNvzy37IRZ0kc+ryEQkjAMjBkca3X58QbkxaEO28g2i/3NNnxBkW6Hj41HupCATw52cLjDxmOXTx1RFjvOOOOcnPGgbpx/K1avlcs+ugxP0MPkhMl0uDroANpTl5CRswJicnC3y+VvgZBEICihVwZgx6MoAjYUOXGIgDqhHCyzICoVBIFj7W58gRDF6aahUp/h5tjz7pSDh4Hxp4JaIWcqbE4fX1RamZhlCvc+PXvwWTY2b2Rj80aenHUGdg9YDCoKUo0YtEpMOlW4dDIQHMx4KDjS7idllAXwj+o+YrrlPKpb/Kx2votamk1A0GKyV0GoQfbGS59JviWfzc2bKYopQhAEvqju5qXtjUxMiiXdYuDX235KQAxg99l5bNljADy460GqrHLvx2dNn7Eqe1j2RzFoJhskNXgGD02dy8RsHRatPIGsbnNR1ylnfzr7/bgDPn6x73oabA08tPghlmXKojcLJsTgD4joNEp+mvpT7ppxFwpBwbWl1+IMOMkJBjgjYZ78ngEPZM6DkBf0cbJJ+Fh8dCf01kBcAcy/i3hzP+19PnqdATZXWlk8MTYiO+nwBCP8+GK0IVlFMnUKapU8TqEQGK3Fo63Xi8MTotnexnbbF5ybfy4T4iZQUWenvsuDIMDCcj12vxxw93p6R2wjyZjEK2e9MvRAzzFmVtyKgIQjXgPFd414zT+LIAick3fO2APKLoEJF4Di9C7vgiCE/76nQo4lh7fPfZtH9z7KMwefQRhlseX/OqIocazDjVopRJjd//iMIgwaZThgAJhkmYFXdBOvSRwZ0AHYW2VVWOCpdz/lHy0p/P3aGRQkfXO1zncrWvnp23IwkWyZw7Ss0QUqMuMGekC1Sv5+7Ywxt/ez1cU8u7WeS6YP9f+dNzmN8yYPlfFdOy+bhl4XE9Ms/HBFEYuLk+hx+vjDx1VcNiOT+98/RKPVTWWbneUTEslPjIoQ1RCQKyvUSoF9Tf3M+/1GXvvubH60ojBshj3IsS4nb+9toWqgn277gB2DBDT0utl77wqUCiGiXPRnaw/Q1i8HIT96vQKHt5SLp2cwIyeWGdkxOLzB8PY2VXWzo17u+9t4pIvLZmayYkIS6TF6Wvo9TEqPxuYJ4BzIyMWZtPzh46McbrdztOMA++9byazcONz+IBsOd2B1B3js8sn4QxIZMQbc/iAGzYm/xwaNivvOGVrcOdhiY3ejrILZG/Tz4YF27l5dQmr02LYyIAdjq8pS8AZCHO2088auFt7Y3UJClJYfnzH2AtF183P4cH87ty7LJ9aoZcFAr9y0rBgeuWwyNk9gRD/oexWtvLazibl5cRQlR3HPu5WEhrU8vL6reTyoG2ecb8B4UDfOv4RDTQ567AHKs6PC2RKQSzkGS7kGZfNL4kpIn3l7ONjKSzKABAatUpaf97vAZ8coSVzoz8aZMZ10/VBvUK/Dz8FG+aaqUyvJSxklM6CNGvKcG0Zrr5e6Tjd5yYaRYi/lVxLorOLrFvlm4vEPTSQWpi9kXf06piZOZVZhLG6fRJRO7n3JiI+8WZZmmqjrcCMh9+p9tLsLrUqBxagi3ixnv24r/TVG32QA2t3bWOF9npApA4MxCKEkiC9kV8cuXjz8Ign6BB5e/DAgy0H3OH00Wz08eMFkptqnsqNjR1iApbqvmrePvQ3I/XSZUaOXbrX2eulzBQE9WuSMndcf4kiLK2KcR+znWP8xAHZ37GZZ5jJEUUKUiMhkKgbKYRMMCSSQIGflshbJWYXkKUO+f6MQCIpsPdyHLyiyLHspmt4ayJdFUmYVWthTa6e5x4vNHSQYktCohiZgcVFqClIM+AIiWYl6WHsNHHkfcheTffV7mHQqdBrFiLJegPR4HR39fl4/9mcO2Xbxdt1rfHHpF0QNWCAYtEqitVE8s/IZKnsqR5RQugPuEVkpv9pCU/6NJHZ8jjllbBETf1Ckos6ORqWgPCfqpP1Mp81pBnT/DDdNvoni2GIKYgr+z2fpjqex28OhJicAUXpVWNipLM3CE9+KLIszqaJOLKwSmw9pM+npt/HXIybsQQ8bq7r+qaAu3qRFEECtUGDRj24aDXDRtHQmpVuIM2nD5YOjMTcvflSj8eHkJZj4x/VDiraTM6JZ+IdNNFnd7G/uZ1JGNI1WWWiqtd9LfmIU187NJj5KS2ufmwc+lksLgyGJXpcsVrKrwRpRJugPijz1RS1Pb63D4Q2Sn2jColfz8o4mjBolOfFGrp2bPaJiA2BSRgx7mvoRAE8gxEs7mrh4egaiKHHjwjwae128uL2RJLOWe1aX8LsBb7alxUOZ05IUM+/cJC9qhUSJGxfm4vAFWTMljfWV7YDcR/jVsR4WFyVi0Kj4+7dnRuzHnW/u5609Ldy6NJ/bVw71HfuCIawuPymWyPuOwxugormf1Gg9Bo0Stz9ETpyB+QUJpFhGljba3AGMWmW4ZHQQtVLB1bOz2XCokz53gNouJ7e/UcFPVhWPqmj6szNL+NmZJSMeB8LBfH2PixSLLpyNfWJzLdVdTrocPpLNuoiATv4bRI+6vXHGGefEjAd14/zT+IMi1W3yTbi2w830/KFV5jh9HP848x802BtYkbkCpUI5wkJgf4MDpUIIm2SjkcsysbcQnTGPaHXkzcugVaJRCfiD0miVmCekssmJ2xfCF3CODOo0JtQZ05lu9mFzBclJkt/X7g6yLGMlu69cjnIg02U+waKnUiEQb9bQbfej1yjpdQTwB0M4vCFaen2kxupYkbOYbVX9ANQpiomJUxFbOCuipGp/9378op9uTzcd7g7iDfHMyo3lowPtLC1JYGJWFE+kPYnN7yDeIAdmycZkUo2pdHm6+M2834ypYpgaq6Ol14deowj3qikUAmqlQCAkUZhqIDlGS5zOzE8n3UyVq5Xryq7DFxDZdLAXf1BkXkkMcVFjTPAEBRScvD8MoNvux+aWV7L3FP6MOWfcL/f9IWePSjPkbGxclHrEJEwQBMqG+4o5uyL+jTdroK8Omg5jj5+Nzhwb3oZJJ3sUftafyiEb2P12njnwDN+f/H0SLRp0GgUKhcCUxClMSZwS8b4P7HyAl468xBXFV/CzWT8LP17RpaY1/3Y0hT/krIkpjEVTt4dWq6x+mhqnJdEyUqUVoNPXRoungWJTOUbV2D59/52oFCpWZq8M/+4OOtEodOESuP/LmHQqBORrgl4ztiLg8bz0dSMHmvu4J+MgluhYKD5LzrKXrMESElnRfJBup481JxGyOBkLCxNYf9sC2Vst7gR90PBPBY9jIUkSLn+IiWkWmqxuStMs/Ob8Mo52ODja6eDhT6tZVJiAQiFw7qRUAiGRrTU9bKvtxahVsagwHpVSwRmlkdn/13c388dPq8O/T8uMITPOwJ7GPlz+EM9eM520MTJXvzhnAtfOzWbD4Q7e2tPCjQvlxZmrnttBZasdhQCiBKnResozonn1u7OpbLVx51sHWFKUwLfn5QDw2s4mfrPuCBdOTef+c4cyab88t5RLntqOWadmYtrY/o+DpZ5f1PRw+8oiuhxe3t7Twlt7WjnW7eS+cyZwyfQMDrfbmZwRzfXP72Zng5Uzy5L5/I5FVLXZeXJLHRsOd3BWeQqzh2W+3tnXwu1v7Kck2cx7P5gXoVb5o9creH9/G4sKEzjUauPjQ7KCqkohcO6kNOblx52W1crjm4/xh4+PUpwcxfrbFnCk3UFhkomGXjf9ngCbj3ahVgrEm7RoVQJZcSaunPXN+kjHGef/OuN33XEAECWRTc2byLHkjFTTOwlqpUB6nJZue4CM+JEreSVxJeREFfLZfivBARn8QUPoll5vWEUyOVpDSqwOURJRxBeP2ROk1yiZXxLDpkorBxocCDAUEJ6EjHgdx9pdZMSO7CHp8/axr2sfc1LnkGiRJzhHWpxUtbgwG1QsKz/1cpC5JdF4/SIKASrqHQRFkW5bAKXagyQESIrWsmBCNF8f7cdJLPuUc1lmjNz+JUWX0O5sJ8WUEs5yPnJpOWtmu8mKUXL+OxdzQcL9mFVxROl7mJxrJi7KzPtr3scb9IZLKkcjSj/seAIeqP8KjTmNpeWFVNTbaejyYNEJUPEXrvTZoPg8MMgm4oMZTKsjMHZQN3hOnQEONDpIMGvGNA8fLoBi1CrDAd0gPQ4/oiQRG3UKkvgXPgOH3oHiYRmPAy9RL+VTYQ2hU/eyYnJcxOr0HdPvYEPjBjxBT3iyMuijNxbb2rZF/DvIYMCo0Yyxr6IIH9xCYn8/mpKH0GjUWAxjH9f6zrX4RC+9/i5WJp5/wn36n0CN8zAbez7CoorhotRrUI0hFPJ/hQSLhhWT41EqGDVbPBqddi8/f7eScxVfYTn0V/nB6z+FDDmTo1Yq+OMlp6YKOxqiKPH2vlbiTRoWFyVGCH38p7n277v4orqbe88u4adnFpMeo0cQBBLNWo52OpCkyAyOWqnglRtms6fRSpROTXu/h2PdLo7XKs5LMKJSCOjUSn557gTOmZRGSJQQRYmseCPv7Wvl0Y3HuHxmBr+7oJx1B9v59UeHAfj7tTMpSo7iOwty+c6CXNptHv7rg8P0OOSsYIpFj90TiAio/7Shmi3V3Wyp7uaKWZloVUre3teKwxvkzd3NEUFdfmIUk9Kj2XS0m3cr2rh+fs6o5+Z3F0zk7b2tfHteNne+uZ+PDrTjCQz1Mx5ssbH+YAc7G6wsLU6g3yPv376mfrrsPr738h58A60AH+xviwjqdtb3IUmybYTdEyBuuMF5u1xuXtVhp2cgE6pUCHx4oJ03drfw0zOL+d6ivJP/cQeoHihVret24fIHufSp7Th8QbLjDDT0utGqlUzJiqGhx0Vtt4tGqweXLxRW0BxnnHFOnfGgbhwA/nbwbzy671EMKgMbLtowdkDQ3wjteyFtBpjlOnlBEJhREH3C7fe7guFgoNfhDwd1FqMKlUJApRQQ1G7OXLsGm9/Gc2c8R3Hs2HX8QVEK+/609HpPGNQNl9iekCgxof5vUOMF83eQzBl8ebiPHkeApxq/T6u3hpVZK/nj4j8CQybRLm8QUZJOuUxOIQgYtPIkbnZRNAAXvncJNf1V7PKex6/m/Yp4s5ZJOWY21u3iz4fv5+XWPJ5a+ThqpTwRNmvM3Dvn3ojtPrTnIV4+8jLx+ngMJBOlikcC7J4QNW1u4oo0aJVatMqRmR9Jkvjl9l+ys2Mn98+5n8O9h9nVuYvfJszF0lUJggL9/J/S2S/fyJt6vKQPKC02N7VQ1dPDlBwTsWaRWmsbL1R/yr1JPxr1vQap7XBjdQSwOgLkpxjCQU9IlNhzzIYnIDI9z8LC0hjs7iCZCZGr5/I4O9LA/888yeeM6EyYd5v8/7Zm7PV7UCujcYny59kXEHF6Q0QbhyYMMboYXjvrNepsdSzJWBJ+/IVDL7C/ez+3T7ud9Cj5s17RVUFNfw0/nvFj3ql5h4sKL4p4+/LsKFJjtWMHan31sO8lzMBZiRlw5u9PeDgmlRmf34tJdeKJ937bLqz+bmbGLDztjF4gKLL9aD+BkMTswmiMulMLPkajy9cBgC3Yh0/0/Z8P6oDTPp/RBjXFyVE4ugf+jgr1qKXk35Q3djfz07cPIgiw7tYFI2wO/hW8u6+Vo50Ovr84b0yPM1GU2FYr9whur+3l+vlDi4mPXjaFTUe7mF8weilnskXP6ke2YBu4Ph9qtUUYos/Ni2frT5agVSkjykVvGbCGOPcvXwJyT1y7zcPNr+wN30/+9Gk1P1lVRO6AMfnv11fxXkUbKgXcf+4Ezp2UNqIEVTcsC3u0w0F5ejS3Li3gVx8eojwjGlGU6HR42d/cz+KiBL46JvfnfnWshz6Xn/f2t3LHiiIy4wxMTLOgVipYVpLEshK53/CtPS3h7WfFGVhUmMD3F+dx8ZPbAdhY1c13FuRQ0+mkw+7lma21+AfseRKitBi1KnzBUNhY/NZl+YiixLSsmIiADuDhSybz0o4Guh0+LHoPE1LMnDMplRte3A3I/dinw89Wl5Bo1jE3Lw69WoVJp8LhC7K0OJEpmTFUttp4aksdIBfeTEyzyIt744wzzmkzHtSNA0BQki/UElLE6mifM4B+eE/S4bfkZn1bM8y+9ZS3nxitIS/ZQDAkkj4sm9fY5SUoSoiSRL29nhanfPPa3bFbDurqPpNFQwrOhLihJvhooxqdWoE3IJIWO3ZQsa9OzjoVpRnlTJG7BwYCFWzN+A3p9DhkryCfKJfDtfU7wtL+E7OiMGiVROlVdPT5SLGoEAIu0I2dBRuLfn8v82IvZoriBt7b0cnsomgy4vW01+7A6uvG2tXNruZjzM0evT+hpq+Gmr4aAAKhALOzJ9Aa+poCwxyCIUZkSXvsfjQqBWaDitp2Nzvq2lh7bC0Arx99nQ2NGwDYrLRwHoDWjKDSUZqpoN3qozDdBAnfwtHVRIW9hKA3xLajNkKighhVOomB2RzoPsCM5LGFE9LjdHT2+0iwaFAP82/rcwbC5YctvV6K0oyjZv1CoohCASFR9nBCEkfYWWBrgup1EF8EOUNBWejga5i9VvoUieizy8lGT2OXh02VPVgSG1maMzs8Njc6N8LIu8fTw0O7HwJkJc375txHr6eX6z65joAY4MbyGymLL6Oiq4LpydPD0vUKQRizlBKAmGwoXQMdB6H8xH50AOclX05/wEq8ZmxTaFugj6/7NgOgU+qZE7tkzLGj0esI0DvwHejs951y1ns0pkbPBiQStEn/1nLRdm8L+227KDRNGOlz+L8crUrJh7fMxx2YA9YzZaXYhH/dMZr18mdVrVSEFQv/lTRb3fzw9QoAFAIoBYHHN9dy46JcLpuRycOfVTMnN46Lp2fwx0sm89nhTn6wND9iGzFGDRdMHdtsvbHHFQ7oACrbbDRb3fxl4zHm5sdx3uS0ET1nAK/vauJoh5MfLivgjd0tXDQtHYNGRYxBg9Xlx6RV8cmhDvY19bHzHrmvtzTVzHsVbVj0Gt7Y1UJJsnmEiMe1c3P4ZKBMsa7bSXl6NKnROo51uzja6STZrGPtnhbabF4umJJKnElDn8vP5TMz+cEre/EFRe57vxKbR+67e3hYgBpv0pJi0dFu8xJtUNHa56EwKYoUi56HL53MpU9tR5TkRa/cBCO13S4WFCRw0bQMjrTbeeDjozy9pQ6nN8hvL5CtHlIseh64qHzUczsx3YJ+r4qNVfK9+KXrZ3H7G/sRJdmY/ZZl+aO+biySzDruXj10T3vv5nlUdTiYlx+PUiGQE29k7d4Wksw6nr5qGskWfYRwzTjjjHPqjAd14wCyDH62OZtcSy7RumgA6jrc7G9woFEJrJwcj1qlkLNz7h5ZHv80UAgC5dkjV5sH+0x0GgXTk6Zy1YSr6Pf2c27+ubK+ft1GQIKmbRFBnVIhYDYq8faLI6Sah9PZLwcOHX0+OaiLzobc5XS5lLi0uWQqJYpSDTR0e7ku63dU2XczIWo+7X1y75tOo6Qwzcgne3sIhCRWBd9A722Tg8yshSc9bpvPxoHuA8xMmcnfVv6NPUcDCKKAKMliJaIEKcHl5BgOkKjJIkEz+nntcHVw2YeX4Rf9nJF9BjdMvIGi2LEnei29XnbV2BAEWF4eR3ufD70yirkxF9DNQS4puoSK7gq63d2IaTPYIoq0+G1cLEBhqpHC1MH+mhIMccWYKq14AiK+gBgOeAWll9K4mWPuA0ByjJazpg/1CQZDIoGgRLRRTae0h3ebn2WN7myK0q7D5Q3R0e8jNVYblsV2+0RCA3o1al8vbPyD/Nmb+p2wkieNW8HWKP9kzoeBTCemZPBasSkSMUQnoJVkP0AkBX/d8ywqtZ+F6aP/DS1aC5MSJnGo9xDzU2X/O7VSjU6lI+AP0Ovp5akDTwFgt1mYmJpKo/sI10y4ZmyxEJ8Ttv8FSs6Fi58/4XkbRK3QkKA9gVooYFSaiFXH0x+wkqo7/V6UeLOGlBgtgZAYYbb+TdArDcyLG1tBs8fTQ0gMcd+2+9AoNfx+we/D5+tg90FqbbWclXvWmP5ug+zo+4JOXxudvtb/74I6kP3OzEoFpE45+eDTwO0P8tnhThYXJXDXGUUn7aM7HXY3WLF7A8zIjiXFoqPD7qUo2cyfP6smKEp8dKCdxl43Hx5o5+29rSwqTODcSamcOyn1tN9rTl4cPz2zmKe+qKXPHeCc8hT+uOEo71a08eaeZlZMSIpQjbR7A7y3r41736sE5DLpJ68aEqrZ8KOFdNl9vLi9gdd2NaPXKAmERJ77sp5og5obF+bw1JZ6el1+XtzeOCKom5YVExb7+PBAO+dPSUetVIRbvh/beCw8tsvhD3vl3fnmfpLMWjRKJXavXCHR2j/Se3Iw+La5ZXP19ZXtfGt2FjOyY3n+2zM52Grj6jlZ/GRVMf3uAL5giBv/sQf9MFPv6k47X9f1kpdgIiHqxN/zKZnRvLgdchNMmPVq5uTF8eWxHhYVJoSzfScjEBIJiRL1PS4ae12snJCMQiGQaNaROExwpSzNwu6frzjhtrbX9vLEF7VcNC39G31exhnn/wrjQd04gCx2cGbOmRGPefxy/X4gJBESJdQApRdD/irZ/+1UCAWg6UvQRdNrKuVwk5OUWB35KQaQJPKcW2gnF08whm1Vdm6eeDtG3bCPZeZcOVOXFpkNkiSJbpucXejo90XIhQ9nco6Zxm4PeYPZB0GgM3Y+Dx99gY/23kJ53BRePvtFJmRGcazdgKlRzogMbxxHkuNLQQqh9crlZdhaOBVu2HADR6xHWJ2zmgcWPoCp2M/eWhsKhUBmgp4DDQ7MylS+nfEQeUl6umwBFII7QtFTFCVCUiisIjolccoJAzqQleHk8ySv4E7IMHG0zck9eT8lM0HPzvadYZPyj+s/Zlu73BsmqbRcWXIlIE/AW52tFJjL6B8QMmkKbOPNlofRKrW8tPrF01I7DIZEPt/fi9svMj3fzA7bm7R6avjbwb9wY1wxO/qKsLmDtPV6WVAqS6tHG9UUphpx+YLk+HeBFIL+BqzuFtxKZFXU5MnQV89RvYVXd/yG1TmrmZkyE2X5FfgdvcRrojHpNUiSRG2snX8ceZFq1w7UirH949QKNS+tfomAGAgHGGaNmbfOeYsWRwvJxmTWN3yML+jHKKWz+dgh1nc9DhJ8f/L3R9/otsfgi98DAqROhlg5MyhKUtge4lTwhXz8bsfvCIgB7pl1Dwa1gYtSryUkBU9a7ugLhlApFOGV8F5/N0alKVwi/O/k/dr3+fmXPydWF0uvVy4/2962nWVZy+j19HLtx9fiF/10uDr43qTvnXBbWfp8On1tZBlOL2twOrh8QbQqxQh1wP/NbDjUydv7WgG4YGo6E1JPv+JgNA619HLJk9sQUXDT4jw+vX0RNk+AtGg9BrWSF7Y3cNXsLD453BF+zdd1vZw7OY1gSOTLYz2UplpOGmwcbLFxuN3G+VPSWFqcSJxRw6SMaAqTonjp60berWhjYno0uuMCj5tf3svWmh60KgWBkMik9MjjjjdpEUUJi17NHSsKuXRmBm/taeF366sixunVSi6aPjKD6AmEMGqVuHyhcNlmRqyB+8+dwM/fPRQeZ9Kq+MsVU/jduip2NVip63Fh8wT46Nb5qBQKPq7sYM2UVNr6PREWBE9+axofV3agVAhsr+vlh8sLws8tLEyIMPZOtii59u87w7YLGTF6uh0+qjocXPb01+jUCt747hzKT6Awed7kNKZnxfLyjkber2jj5iX5ss+gJHHWo1vptHt5/tszKRtD6KXb4eOcx77E5vETkmQxtbtXF/PdhafeizecBz6uoqK5n8pW23hQN844J2A8qBtnTIrSTGhUCiwG1dCEU1CcXulhy3aolcv8mpO+TY/LQo8jQF6yHsHdQ297M/36aYiiH58Tth7pZtWUYaqBhWfLP8chCAJTcs109PkoTht7tTk5RktyzMiJQoNnPwCV1gMEQgHUSjV5yQb0GiWCIAscDKJWKZiYHcW+Ojs7dWdRZmzHVLB05Jt5+6HiBVBqYPI1oDbgDMhy5oP/xps1rJwi34C/OtKHzR1EIUBBqhGHJ0iXzU+XzU92kj6sDNpm9ZFgMfD8mc/T7myPUBoci6wEHUoFaNUKLANCJHOKhkzYi+OKKYktocvdFQ7oAFKN8g3THXBz4fsXYvVauWv6T5FC5RgVcXzW8QY2v+yBdLj3MCmmsRUej8cflHAP9FXa3EEuLLyQRutRztYmEqp6H1v0tUAMquMULkstVmjdBXH59PjmYTXE8kXPWkRCZKqKMEgWFi36Ob9ZfzX7uvbx/9g7z/A4qrMN37O9SbvqvcsqtmzLlnvHBdtgYzA2vQYIJYEECCUJCaRBCqETekLvvZiOe+9dkq1i9b672t5mvh8jrbyW5AKkfdHNxSVZOzs7O1vmPOe87/OsbVjL1+d9DQolGnMiva+kIAjMLBiGJWYZ14tnU5YUaSk/EMeuGKWaUkk1yefos6WfseVQFw63wNcdzwKQbc6mxdtAuXMfxaZRJOn6BiCHklNonLKIskMHiNL3vRZrD1jpcgQoyTQxLPXEKydrGtbwziG5jHZ88njOzj8bQRBQCccXdDvqrFz8zGZijRo+unEaTaF9rO/6GqMyijMSz+Oe9fegUWr47ZTf/lOiCfZ17ENCotPbSa45F4PKwNiksYA8qaRWqvGLfvSq4+dpAYyxTGS0eXw4SuP7ZlVFG9e8uI2MGAMf3TgNo/b/x6VyXHYMqWYdgiBQlhVz4jucDA3bKHrhLL7RmFji/x1Prq7ixtn5YXfJucOTmDtcnixLs+h5d3sjErKhx1mlafzu4wO8sPEI6TF61t0xwPdqD3ZPgPOe2ognEKKy1ckbW+tx+oL86LQ8bptfxCWTsjhjZArROlW/6o3enLvC5Cieu3z8gOLxd58c5KPdTejUCq6flUdWrAGlQkCnUjA6w8K2WitjMi2oBqgMcXgD4UnQo0sHL5mUjcWg4Zfv7cPuCTCvOBGLQcOflo3iSKeLO97ZQ068kaLkaJQKgcLkKG54ZTsr9rZwxZTssMGKVqXksinZmPVqbjht4ImMtm4v66s6KEyKCofbA9Rbe1b+eqpVvQGR3358gLevnzLgfpy+IA99VcmeBjtbejL4RmWYKUqOZlttF/ubZCOVNYfaI0RdeUs3SVE6Ol1+Lv/7Flq65dXI3vN1jOfNKXHGyGT2Nto5Y+TxqxaGGOJ/nf8fV6oh/imolMJJDTKPiz6eDkUae3SnoVeYUSkFUmO1ssugPob4aA2Jvgaa1CYUIQ1u0XnSuw6oGvnDgVtIrUvl4dMePq5hx9EkWbTcMfFmXqk0Myd7OmqlGqszwPqDVnQaBTNG9A/gjTWpUQjQrMojP28cpoEynToqwNkzE22rhYThPDH3CdY3rmdBTn97f1VPj5nZqGZ4hon6Dg8tVh9JFi1KhcCW6m5aekxL2u1+itKKKbKMxO6z88t18grN76f+Hp2qv+PoQPl5RxOtiebNxW/i8Ds454NzaHW3cvv42ykwTmZzpY2EOH84dLvd20p+0hGe3XM383Jn0ebJQ6PUMD19+oD7loJ+hG1/B3MaFPeFZBu0Ssryoul2BylINVKiWsQiUy7sfYUXfS08Un4FWfoS/lp8N2Dp2+HB98DZgsPWxbPuRFbV/IGzx85EEAReOPAC+5sreWjWQ4yKH8XOtp2MShi4VwQAMcTomEJQ6aBqJZR/DBOvh/hTX/Gx6MyozTu4Z4fcW3rXxLtYmLOQNxqewxbsot3XzPK0KwEIiH5W6juQCkoRR57L7B5RJ0kSNqe84tzV8/NEjIwfSYoxhaAYZGzi2JM+3m21XXgCIRptHqraHdRr5BUEd8jJyrpvWFm/EoAF2QuYmzX3pPd7slw76loEBEriS/qFp5u1Zt5c9Cb1jnqmpA482DyWgQSdJEm0WH1EG9TfyfBla20XgZBEdYeLZruH/MSe0nFXO5R/ANGpg+Yu/iv582flrKxo5+7FwyPcDQcjPcbAhp/PoabDxa/f38eYTAs/nj3shPc7LrXrUAZcZCtc5AlN7JAK5BWnAXrisuKNmA1qbO5AuATP0RPM7fIFEUVp0HJ6lUJAq1bgCYQw9Ey+gdyz18tgGXqPXzyWbw62MaswYdDVwLwE+VqXHWdEpVQwJT+eVT+bhVatIDFKx/lPbWRDVScHm7u5b+lIpubHE9VjApNi1vO3i8t4b2cDkkREaPiiUamcUZKCzROIOL6sOCOv/3AyAPub7Pzq/X3kJphYe0g2j9lVb6PD6eMnr+9k/eFOEqO0fHXrzEGNZy7/x1YONneHn2O7w0ecSY1WqaTT5ScxWovdE6DbE6Q4ZXDjnWfXVvPs2prwv7UqBXZ3gL+tOkyr3ccF4zPodPlZXtbXKvDypiPc9f4+EqO0FCZHhUtI5w9P4ocz82h3eDl9eKQgE0WJ297ew+4GG39ZNooxmYNPMvxwRh7XTM89pSiFIYb4X2RI1A0RJiAG+KjqI7Kis05qFeNEdHT7qbKl4o1fjt0NdgcsHh/fV86kUKEqyk9kTwABAABJREFUu5L4Rhe1zi/wBvQ9pifHDDLEIEgSIUFFMCShVcv3//LIl9TYa6ix13DIeihs+388RFHC5gowKiWPMWn3hv/eZvcTCEkEPCEcnmA/045og4q5o+MRJWlwu/vEEdC2FxQaiJHLTLKis8iKzhpw87I8M5kJfuJ6rPoz4vVhIfZG+RvUtjvJV52BVqUkwaxh7QErOrUCu/Eb1jauBWBR7iJmZcw64fMejChNFB+c/QEOv4NkYzIfb20jEJLwBdQ8NfcptrVuY2/7Xpx+J++e/eYJs8f+tOVPvHbwFX7U2cU13Q64YRMk9jXJH+tuSeJwmPkrTFUf42vfQr1vD3rVMQMzcyY4W7AbhrGr5S0anXW8u/tzbht/K483vwRAR3eQG0bdzGUjLiNeLzvmNTmbaHQ29hm5hPyw5XG5J3TkxfDW5eC1Q1c1XPretzp/ycZk1Ao1EhIlCfL7L0mXis3ZRZK2b5VOJahJ0CTT5m8m2dRXgiQ7x5pptfkZliqvjnmCHi5acSF13XX8oOxibii6JWIwk2xM5otlX5zysZ4/PpPqdheJUVoyk33saJEnIPKNxRTpx5AdnY1KoaI0sfTbnIoTEqePi8j0O5bM6Ewyo79bPtWBeheVTS40KoEFYxP6GS6sa1zHvo59XFx8MVGawQe2P5iaQ4fDT16isU/QgVx5YK2S/0+bBIb+E0D/KryBEH9bVQXACxtqT0rU9fL0mmq+Lm/j6/I2lo/LGDBU+qQZexm0V2BVxLBvayFKEVIGyYAzaVV8ctN0ajtcTMmTj/c3Z41gTGYME3NiBxR09V1uDrU5mFWQyEc/nkZVu5OZBQmcMyaNylYn84YPbiLUS7xJy3njj98H/tO5BZwxMoX0mL5jPzrQfHS6mc01Xbh8Ia57eQcjUqP55Ka+ia1hSaYes5RWjBolN84ZRrPNw5XPbyXFrOOZy8YN+tgvbTzCjjobO+psgCxO7z1nJH9bWRV2ymx3+LC7A4OKut4zp1IIbLxzNpWtDrLijGhUCoIhCb1GiSRJnPfUJl7aVIdeo4owMOnl2IgLX1Dk3hUH2d1g77k9iuXj0jnzkbWcPz6DW08vpL4nLL7T5aemwwXIpaqPXjQ27Hi8r9HOCxtqOas0lenDEmju9vLODrmN4a3tDccVdcCQoBtiiJNgSNQNEeb5fc/zyM5HUClUfLb0M5KMJ75YHk2nw48o9pUu7jviwOoKolEJGDQCiRZtv/6UYEjkQL0TPROJi/MwPeWYzB6vDbY8jhQKsMFwHh1SHBMKzKTF6liUu4j1TetJNaWesMeslx3V3dR3eEmI1jBteN9FJDtRj90VQKdREmsa+KJ5wpl/jUk28DhJVEqBlAFKQ9c0rOH3m38PwN0Tkpk/bCEHG+QLpS8oMj5pEsmGZKK10ZQmlJ7wcYJiAIWgHLRUzag2YlTLs9RJFi0NnV6SLFoKUyawp2MP65pk++/ShFLZwGYA7D47L+x/gY+qPkIvhjjTJV/kaa+IEHUDotKxtHAZGeYsEvQJJBmTcAVc4WOi6GzInYNF1DPVKmILtHJa0jTmpZ3BS2eksb26nUy7ETbcT2LGaMhfgN1n59wPl+EMOLhzwp1yn6DfCa6ecHJbrWxCUb0KUgde7RJFido2D1F6VUQ57tEUxxXz8TkfIyGRZpJzq2bGLWC8ZToGZd8qtyAILEm5CL/oQ6eMHPCmxupIje0bVNd113HYKg/WtzZvw1vgRq/87oYWZr2aP54rr2L669cT4wvh1KgYHlVKsi6Vj8756Ds/xr8bsafGS5T6l3vZfXZu/PpGglIQu8/OHRPuGHQ/cSbtwO6AiSXQsodmVTr4tKR8/1WqJ41OreS8cel8ureF/MRTcxqdNzyR93Y2UJphIW6Q1a0TcaCpmx11VsZnx5C9+DFiVEpWzvTgD4rkxA/+fk2z6COCv6N0ai6d1H/iKxgS8QZEFj+2Dps7wE1zhnHllGxs7gDb66yMy4oN9699H9y34iDbj1j57ZIShqf27xnPjpcfy9/j3FTe7MAXDHHPh/vxBURumVdAvElDp8vPsCQTj31ziOfW1WB1ByhvcfQYqAwcFn9WaSqrK9sJiRJtDh8TsmMZnhrNmHYLrJe3USgEHMeJE3j+B+PZWNXJaUWJqJSKcL9kl8sfNloRBIGqdrka5mBPHt2xLChJ5vUfTuLal7aFHUan5sdTb3XT5ZKfy5Orq2h3+HlydRX5iSZunDOMKJ2KEalmur0BnllbzWWTs1EIcM+H+3H6ghxuc7Kr3saqyna2/nIuqWYd54/LYFe9jfPHnZrx2hBDDDEwQ6JuiDAmjXzR0iq14ay0k6XL4WfNfrnXakqRhSSLliSLFqsrSEqMlrF5A/fhqZQKUmO1tNn95Mel9RcezhZsQT37NPMIBv2gBKsjQFqsjozoDF4+4+VTOk63T+57cPtDEX/XqmXr/4pGFyqlEBGUvb97J03eeibETEeHGV9AlHP26rcg6WOoCaahVStIi/sOs91HkWRIQqPQICJSGJ+JWqWgOMOITqMgxqjmxUOP0uJuQaPUhJ1KB6PRU8enrW9jVEWxNPUytIrjl6iOH2ZmbF50eIVjetp0nt7zNJ6gh/s238f8nPkDlrk+tvMxXq94HQUKlkYXkhrqMZLpqDzp5z0+eTxWZ4Cbv76Trxo+4ZqR13DT2JtAEEAbjQm4eupMrhRnYOox0xmdMBqv1UZ24+sYQjbZCTNvPm6/D09QLgFqsMl9IehjoWCxXCKbNR3yTgdHM1gGHlBUNrk42OBCAE4fEx/OHTyW3h67XgRBCNv5S5LEdS9vZ8PhTu4/bzTzR5y4J6QgpoCLii9kd+dOLim+9HsRdMei6TjEeZ12JEGBkP3/x3hgeIaJaIMKi1EVLm/uRavUEm+Ip8XVEs4dPGVictmXfyNL/7YBPl3DezdMYcT3ZDbybbAYNDh8QR5feZhLJ2eRGHVy30Gzi5I4+NsFCIJAW7eXj/c0M7sokeweMdblb6fJW48+kIvNKVKWFbki6Q+K4RBpkLPFPrpxWoRY+y5UtjpY9sQGdGolvp7AbV8gxJXPb2VXvQ2AG2fnc+vp34/zaVu3N5yV9uLG2vDkx9EMT41Gq5IdLb1BkdOKE3h7ewOvbakHYFJuHF/fMotubwCLQc11L+8I31cQ5GiENoeXy/++FUmSeP7KCSSb5ddrdWU7GbEG7jqzmKAoMaJHVC4enYo3EOK2t/cQEiXe29mAIKQPmC2YGKVjSWmkaPxsXzM3vLKDjFgDK26ajlGr4omLx/L5/lYumzxwBUnvc7lv6She2FDLeePSObcsgx9My+HiZzbTaPNgc8ul4oGQxE9f38Wee06PKOPtPY6VFW08v6EWgOk9mYMTsmPxBkIoFcKgsQpDDDHEt2NI1A0R5sKiC8kz55FiSiFWd2plRUdPivc4O1OcYaIgzXjCzJmJBZbBb4wroNIC7X55VU0A8lO//fR4WZ6Z+g5PxMpIL/UdcsRAQ4c3LOp8IS/rur4CQIEKX20peKyMV+0m4cvrkZQ6Dk1dgduQziytkphBVvl66Y0DOB6FsYV8svQTREkMCwa1UhGOGejwdIR/3vDZz4gWcri29MoBHUDr3NWECNEdtLGzrol0U0pETiBAjb2GFGNKuDfv6NerMLaQCwov4B/7/4FGpUHBwKt9eRa5pDDVlMrPz34d4h4AWx2M63OYrLZVs7NtJwtzFmJQG9jZtpO97XtZVrAMg9qA1x9izf4uNrdsAmRnxJvGRmYh6gdwiCzLM2NXT0FsdqNILAFBIEodyxXpf6Hdf4SFqUv7Ns48pl/LktFjbSpQ0VXBttZtLM5bTLQmOlzmq1IK3zo3yeELhvOrVuxtPilRJwgCP5/wi2/1eCdN3jxQKBEShsP/o7ImpUIg69gS3x50Kh1vL36bVncrBTEFEbcFQiI762yMSI0+oSlKg9UdXq1p6PIMKup8wRAuX2jQPq/vg94VsaRoHaZTNHPp/R66+c1drD/cyatb6vjqlplIksRHLW/Q6fLz9GvNeP3wx6UjuWBCX2msQpB7ZHtFXUWrg2BIxBMI8dWBVrq9QS6ZlPWtPjchUWLHESvd3iDd3iB3Lx6OSavirNJUljy2PrxdbaebFruXn7y+k1ijhgfPL0XXY+H//s5Gnl5TzRVTsk9YdglyeeaCEclsr7OyuMdd8ZvyVtZUdnD55Cyq2p28vb2BH8/O5wdTc1AqBCQJJt0rXxsMGiXjc2IxG9SYDWokSUKtFAiEJLLjDPzjygnkxBt55OtD4RWyDVUdLB2bTovdy1OrZUH52pZ67uvJkgO5ZPGBLyrIijWQHW/kmbU1vLypjo0/n43FcOL31c56G6IERzrddDr9GLUqJubG9YtkOJYnVlXx5rZ6bp5XEHabjDdp+fzmGRxuczLvwdXhbTUqBQaNiha7l8v+vhmnL0hxchS3nF7IiNRo0ix6XP4gd505nDiThtpOF2N++yUWg5qPbpxGvOm7xagMMcQQfQyJuiEimJBy/NyxwYiL0jCtWM7qOdpt8juHiAoK0rKyaamyY9KpKMuLDl+4w3is4LVCTE94tKsNjqyTe9zi5Zlcrz/EtsPdqJQC4/LN4Vn8pi4vVc1ucpMNjMg0UdXiJvcocaRRaEnWptPqayTQnUh0+xYm7Pwx1uRZ8uGFfChF2eWryxk4rqjb1baL67+6njRTGi8uPH4cQLIxOXzcapUi4jzeOeFOisyj2NOxmy/qPwFgXP1McpJKoLsZPrkVzOn4T/8tf17/EBkJiRQYx9DsNtCMnfhoNe3eJmq7a9nbvpcn9jxBcWwxby5+c8BjuWnsTZQmllIYW4haqSYQCvD4rsdRKVRcP/p6lAolFxRdwKSUSSQYEtAoNTDrzoh9hMQQl392OTafjT0de7h9/O1c88U1+EI+mlxN3DnhTjn7ThBYknQLNcGVXDf28kHPz9GolAJxOaMgp2/W16RTcU7JVLo9Eyk8jjsq3U2w41lEtZEf1L1Lt7+bb+q+ocnZxKyMWVw94mb0GmVY4J0q0To1t80vZP3hDq4dwM7bHXKhU+j/aS6Ogx9YGoy+9F/7mP8BmLVmzNr+IuyOd/bw7o5GxmXFDOoK2Muk7DjOGSkLhbzYgfvyPP4QZz66ltoOF49cOIZFo77lamjQB41b5HzQmJx+N184IZPx2bEkRGkjctlOBUuP6ZO5J5RcEATUCg2BQBB/j3eP1R1p4qNSKvjwx9NYf7iD3Q02ZhUk8vn+Vm58bUd4Um9rbRdnjkxh4chIh9yDzd00Wj3MKU6MmOAKiRIXPbOJHXVW7l8+iosmZqJXK7l0Ula4bP8fV47nH+trEUWJH87M5cNdTWzucWi8ZJKVqfnyatBjKw9zuM3JI98cOilRp1AIEbl1gZDIdS/twB8S+XBXI109z/+z/a0YNSpcviAXTsgk2PNkL56YGVFyKggC47Nj2VDVybzhSeHbXttS13PO1cwpltsbEqK0zC1OYle9lUWjIs/VFwdaae6Ws1anF8jPTakQwudtX6OdjVWdLB+XPqDIu25GHl5/iMLkaDLjTn4y9PGVh3H6gvx9XU1Y1ImixK8/3MfhNifjMmMob3Hg6In9UCoE1h/uoLJVLu1ssnkpb3HiCQQZnhLNhRMyKUyWPyvv72zEEwjhsYeobncNibohhvgeGRJ1Q3xvJJg17O/cT0OLh3HJgzeFH8vGpo38acufmJExg1vKbul3e1qcbvDSxoAb+5ZXkEJ+LPkTIXMqVHwMXYegdQ+cdg8ATV0+2rtlJ8mObn9YeB6od+LwhPD4nZw+Jp7kGA2ft73HV3XNzE44kwx9DktSLqShy8XWSieeuAzqUxZTPuxGki1T8OqScJhk50SDtm9g7vAEOdLuIS1WFxZ6G9rWkBAdQ0VnBSsrD5CkHcbIrKhBBUNjl5ctlXYMWgWzR8WFs/N0iijifHNIEA0ohc9J1mVRlpmBLyAS2vkuhopPaE6YzaE9jQj+WFZWbsSQmUaaHoxaJV7Rxfkfn48j4CDfIh97jb2GkBhCqei/EqZSqJid2Wc1/rddf+O5fc8BMDxuePi2bHM2IF/8BaF/Y7svJA9OrF4rKoUKs8ZMm6eNOJ08a6xVKzhtZCzjvfNJtiw+qcZ4V8DFl0e+pEyfQkbiaCSVFl9P5ltWYv8VG0mSOFDvwh8UKck0oe46DEEPQsBNlEpPt7+bHW07CIpBXj74MreMuwX1AOfkRLS726lz1FGWVMaPTsvnRwPYkO+wbWSrbR0Z+hzOSFp2yo/xn4QkSXT7uwcUTP8NtDvk92Zbz8/joVUrmV2QTDAkYRikz9bq9lPdLvfB7qyzfXtRV/UF1G8AhQqm/wLU/d/Tp9pPdyx/PW8054/PoDTTEv7b2SkX0xnXxpQfGKnr8nDeAD1PSdE6lo5NZ/6IZIxaFXd/sC8s6EAO4f54TzOf/3RGeEDfbPew5PH1+IMiv10ygssmZ4e3t7n9YYH2+f42/nB2CTHHrHKmmPUR5h7zhifxyNeHcPtDdLn84b9fPDGTJ1dXDdirdzKolQoKk6PY22jHHYgs1f/txwcAsHsDvHXdFPY1DZyd9vyVE2iwuiP6/gqTo2i2e2UnSrefB7+sxGJQ88xlZQN+3y0vS2f7kS4yYw3cvWgEM4YlkJdowqxXI4oSFz6zCYc3yP4mOw9d0D+oPsao4TdLZAOnRpsHo0Y5oPj7fH8Lz6+v5YIJGXyxvwVnzwpsSVpfmWdlm4OXN9VF3G96fjy3nC6ves8dnsTc4kS21VqxeQJhB8x1hzvZWN3FrMJEjFoVFS0O9GoFc4qTGJ/9PcVqDDHEEMCQqBvie6S8q5yLPrkIURJ5dPajJ+3K+Fr5a1TZq6iyV3Fj6Y0n1c/n8YfYXGFDEXLTqTkXEJjuriEewJIpizqLXC7kD4rER2uINsh9NrFRffvPiNdR0egio6ck0RNyU+eRS2GqXZVk6OXZ8YQoPSadh4DXjSY6EVEXS136uYBcvSZJ0Gr1kxIj72dndTedjgCNnT7mj4mnwVOL0uTjzJLT6O4CX3c69YIXk05JUfrAg7Jeq3u3T8QXEMOiLhASCYQkCk2TeHnW14wwWfFa9/HlkWEEtOcyJW0dewp+gdtv4vLsX3OYd7iy5Ep0ghmNSoEr2E1AlPc9MWUi09KmMSV1yoCCzuV38+6hdxgRP4KXD77M+sb1ZET1DfDuWX8PFV0V4cBtmyvA2gNWlAqYUmjBYpIHEOVd5eEet5X1K9ncvJkXTn+dfUfcpJn7Sn2j9KrB3UUH4Dcbf8OnNZ+SHAzyqVPNzjNW0dAZICdRT2luNE/sfoIqWxW3jbuNJGMSnY4AlU2unsdSkp9aBo5GBI2JV6f9jHcPvc/DOx8GYFLKpH45dSeDN+hl+UfL6fR28pOxP+HqkQOb5zR75b7DFm//IPsWqw+HJ0husuG7r3b/C/j5up/zSfUn/KDkB9xcdvO/+3BOmb8sG817OxuZU5wIyCJVAhRHDbT3tu/lg6oPOCf/HOaOLiIQkoge5L2aatHzx6Uj2d/UzQ2zvl3gMgDankG1Si8Lu38COrUyIrwawKA0YtDnkHGCtINb39zNOzsauHF2Pjeclo8nECIzzohWqeAPKw4Ccplhr6iTJMK1+qEeBXi4zUmUTkVStI67zizm4z3NrNjbzKbqTr64ecagKzkf72niy55ST5DLm3tLJ6+cmsOVU3N6ymqt5CeYeHzVYTZXdzEhN5Y75hcNGp3Qy9vXT+brg23c8uauvvOiVuAOyKW3dZ0uhqdGD2iqAnJZYm6CiQ92NfLq5joONHczp0h+f1W2Ornt7T1hETsxJ47JPW6gh9uc2D0ByrJiyIg18MrVk8L7PP2o8m2FQiDOqMHhDRJv0vL+zka+ONDCjbOH9eu5W1nRxg/+sRWFQuDCCRn8/uyREbf/6dNyqjtcHGpz0OHsE8ej0y3h331+EZ1KgSjJZjcOX4DLpmSFXSvNejXPXj6eO97ewxvb6iP2X5AUhU6tJBASeWu7/H3nC4pDjpZDDPE9MyTqhvjeCIQCiJJ8wetdlelFkiTcPhGDVoEgCLh9IVRKAY1KwfKC5Ry2HWZm+syTNmipafNgdQUBTdjL2RZdIou63LmQNgE0JlptPjZW2NCp5dUuzTHB1oVpJgrT+kSVUWVijHkSzV1+NNYS2tQ+Es1atGoF80rl8hcm/YYoV4AWqw9BgMPNbvxBKVyKA2DSq+h0BIjqmclXCX0ftQsKz6f8kAqPP0R89MB9EYGQSHaSnpAI0QZl2BgE5NLCSYUWXN4gOXEKhHUP4iWWgCEfEHAseo5Uf4jDzW6GJcWzJOPWiH2blWaeX/g8h62HOSPnjEHPucsb4rav7mdt51tyOZYoX+xNahOZUZl0+7ux+qy8XvF6WNR1OgIEQxLBEKzeb2XOqDhMehW5llwKYgqotMrGKV3eLmJCOjwekcMeN9mJ+lMSc730ii6VJCFZj/Dw7p9xbvp9dDoDVNur+duuvwFyr98tZbcQpVeh1ygIBCU5tkKjhpEXAhALXD7icuqd9XT7url78t2nfDwgR4P0hs13ebsG3W5K7Gns7t5GjiFy5OzyhthYYQPkgW9Ruoldbbv45bpfUhJfwn3T7zupcs3d9q24Qg7GWaahUfzz+roAtjZvBWBby7Z/6uP8s0g267i+R3z5gyKr9nbhC4hMLY4JTwL9av2vqLJXsb11O+8teY8TWYIc3YP2rcmeKZdd6mPhFM2r/hWsPSSHXK851MGI1GiCosSCEUlYDBruXXEQCcIrliCL3bevn0yj1YMvKDL7/lVUd7gwaVV8ecsMrp6eSyAksaveRpfLz/wH15CfaGJPg53sOAPXzcpjSWkaIVE26AiKEvEmDRaDhqun9y9PvfIfW1l3uAOVQgh/P++stzFjWEK4VBPg+fU1fLK3mVvmFZAUrSM9xoBWpWRDVQfeHhFn0CjZ/Is5XPzMJmo73Vw368TZlofbHPzk9V3hf7+/qwmNUkEgJKJVKVApBCwGNa9vreM3H+3np3OHcdNru/CHRJaVpbNoZAqzeoQgyJOZL26spSApitOKEnn7+ilUtTkZlxVD0a8/IxCS6HT6qW530un0MzE3jhevmkBVmxMJ+fvk5U11/Oi0fFLMfe/gxaNTeWLVYU4rTOSLA60oBLh9QRHLj1qh/bq8FW9PiPtdZxZz0+s7+eFL2/n75eM57ahjvHRyFuWtDkamRTMpN47ceCN5iSaUCoHHvjqMTq0gxqDhqml9r9dn+1qo6XAxPCWap9ZUsaQ0lfPHfw+fnyGG+B9jSNQN8b0xMmEkT897GnfAzZysORG3ba+SowQyE2T79k0VNjQqgbmj45mePn3QIOuBkCSJ6mZ5oNDbjA6g0x416OmZ4a5qcSNJ4PGLeHyhfqJuILKECTS222gBWjptLBwbj+4Ygw6L0oXF4gVTMimxOjq7/aQfVSI6JieKvGQ9UT1iLFmXztIUuYcpQZtM+mgJSWLA2WKHJ8iqfV0gwcySWNlp8xjkKAQthAKgNhLja2NMdDPe6HxykvQoFQIlmaZ+M6H/2PIFH9e9RYLJQJRew7S0acTpe5rmgz5YcRsE3HDmA9jdGnSCBQCTysivci/kY1sFPxz3U4pihvPynnf58MhrLCs8N7z/zHgdjZ1eOh0BuUHf1swIfQYdNnhw8stUutfT7e/mrLyz6HLIJarRetWgzpJHs7VlK7vbd3Ne4XmsaVjDg9seYEFQzQMuBd3BELckxnPYd4CsBB3ZiXoMeiNFsUXU2muZkjqFfUccHG5xo4lqxBLtJsY0rd9jqJVqfjPlN8c9jk6Hn53V3cRFaSjNiep3jqM0UTxz+jMc6DzAOfnnDLqfGE08s+IHDqVXqwQCQSlsDPNR1UfUOeqoc9Tx07E/JcWU0u9+R9Pma2aTdRUABqWJUvPxe2UlSaLD6R80mPlE/Hbqb/m4+mM5OuK/HIcniKvHJbfD4Q+LuhHxI6iyV51UHuaJsLn9uP0hUk/GLdL8nzu4/eO5I3lnRyNXTsnmomc34w+K2NwB/n7FeO5fPppd9TZunBMpfkalWxiVbmHGn1dS15Nv5vQFsboCpJj1XDk1G39Q5OM9TRxqc9JV04UEHGxxcMubu5k/IhmdWsn47Fg2VnfS4fTT7QmSGdu/d7bXvv/oCbfEKC0FSZG9kPd+Wo4/KHLrW7tpsnmZmh/HYxeOZd2hDtRKgYJEEz+bX0iUTs2HN578tSrGoMGkVeL09ZVw+kMiBrWCNT0h46PTLXywqwmAD3Y3ERBl4fT29gbe3t7AfUtLuHCCXEb6xKrDPPLNYRSC7FC5uaaL+5aOpDg1mpkFCXx1sA2HN0h7z2rbxupOPtnTTHFKNOeOSePzA61Mzovr55J687wCajpcvLW9getm5HL7gv4rmcvKMthUI5eCxpo0uHocpBus7ojtStLMfPCjqQOej9e31uENiJj16nCuYm2Hi+tf2Y4kQXqMngarh931tiFRN8QQ34IhUTfE98rk1MkD/t3mCvT8DGLSyeUy/qCENxAasKfM7gqgVSv6ialeVAp5tjM9TotRr0KS6Aku76PT4afVJl/cUmI0mI0Dz3SLokRDp5covYoYkzqi5EqljBRewZDIhgMdeB1WJnk+oCtpNvs8mZRkmVAfJRgFQcBsiHy8BG1yxO3+oEh7l48kiybivt2eIMEeofrpodV0SZVcN/q6gUtVlGqYeCN4usg2R/a9HLu9JEm8WPUwHf46Knsmz/MseVw3+jr5H1XfwI4X5N+zppI89gquLLmSSbYxzHJuJtHRybz4CRA3gje37iE6NI2JUSqWDusTJmqVgunDY9hT387fdj/K3RUfcPf4v6DoLgVgUuH0cDZffLSGReP6ZniPhyvg4rovr8Mv+mlxtVBlq6LN085boshtbQ0cHn8Fm0waZivUaC0NxEaNAOCtxW+FewU/39lOp6+JRyuuIiQF+e2U33LOsMFF12DUtnlweEI4PB6K040DvkfHJI5hTGL/HpeTQatWMHdUHN6AiKXnPXtuwbns7dhLSXxJ2ETneESpzBiUJrwhDwmaE29/65u7eXdnI1dOzebuxSNO+Zinpk1latrAA7n/NmJNagpSDXj8ItlH9WX+burv+FHpj0gxHl9Qn4gWu5f5D63B6QvyjyvG9yt9/G9idlESU4eZOejcTVGqjj11biblyuXU55alc27Z4NER541L5+k11YzLjuGcMenhMkadWslP5g7D6QvQ5vAxKSeWilYHtZ1uSjMsYaOsV66eyBOrq/jL5xXoNcp+E3Yef4izRqeyYk8zKTF6WuweFpakcPuCon4lzeeNS+eDXU0Yez7LFS0OdjfYqO2UBUtNp5sfvLCNJy4uY0HJiT9PvcSZtPzu7JHc/MauiL/3lnACDE+NwmxQs6vexjXTczl/XAbvbG/goz3NAGypsYZFXe8kgFmvZkOVHEp+7ycHueOdPdyzeAQTc2L546fl4X2b9WoeX3mYQ21OLpmUyb7fzB/0WDdVy/t7ck01Bq2Km+YMQxQl3txWT7RezRkjU3jz2r7r+8MXlNLu8J3SivTN8wp4ZXMd187IDf8tWq/GoldjdQeYnBvHh7ubWDJInt8QQwxxfIZE3RD/Esbmmalr95CVIJfZhUQJo1bZT/gEQiJVzW4ONrhQKwXmlcb3E32Cp5OZjhewKRJISlyIwjSwOYNBq0StEgiGJPKSB3dArGhyUd7gQiFAbrKBLpuL0tAmBI2e5JJZEYMFmytIp0sChYUWVR5em5WAKoO6di/ZiQYCQRGlUogQhgMihmja9gnegERdwjSmFPdZTKfGaClKM1Jlq+aeHbciEuJg10Eemf3IwPvSGOX/Aez14GyG5DHYg24CYoB4vVxmJAgCM9Nm8U7Ni5g1ZoJSkEkpff0apI6FmGwIeCB7GgqFwMisKEZmTYet+6lWdFNrcjPG34EYkl83oyJWLrUV1dS0ukkwa4iL0mCIsrOh630AWj0NpFIqH6pSfj3cvtCAK5CDoVFoiNPH0exqJtmYzIz0GTj9Ds50OCDeQP7YH+A7+Byf1X7GusZ1rLlgTfi+vb2CRel6KstbECV5hjkoDR7kezyyE/VYnQFiozTf2hWz1l7LM3ufYXr6dBZkR67WHXaVU++uZoxlEnJRqGxI8+biN9nQuIFv6r7ptxJ+LHqlgQvTriYkhdAqBzYZquuu4xfrfkGqKZUttfJgb2vt4OWi/ysIgsCIzP6ulgpB0S+T8NvQ2u3F7pEnuQ63Of+rRR3AJutqyp17mDdPYP8/CsmMPbHLoiRJlGXF8uUtGSRF939/evwhnltXgyjJxjSrbjsNm9tPlK7vehEUJTJjDdx7Tgm762387uP93DyvMJyV96fPynl+Qy1GjZIvbpk5YBwKQF2nm7wEE1/dMpOb39jF4TYXy8rSmZwXx9KxaTRaPeHet72NNjJi9aeUTXhaYUJE+WcvZ41Kxebx8+g3Vdwyr4AHzisFwO0P8sCXffmeZVl9ZiIXTMhkZLqZpGgdn+1r4asDrayubEcCnl5TRaPNS28842mFCcwvSeb+z+V9tXX3tUSIotRvJe4vy0dz3Uvb8AREPtjViDcQor7LHRaX794whbGZfcdybCbeyXDeuIx+pjuxRg1f3jKTLpefgqQo/rJ8dPi2n7+7h3e2N3LXouIIU50hhhhiYIZE3RD/EmJNamKPsvs/Otz7aNbss9LtkQfbgZAk9x4cO3B2NKMPdqKnE5ylYBp4tUerVjAyw4SzuRKfS8Cuium3WrevewetdgdxISMoNBxuTgIE9GIcE+wrIFAC+r6LV6xJTUa8Dq+zm4zkdJq1I4jt9FOYZqSx08vWQ3ZMeiWnjYyLmA32+kNsr+pGrRIoyzOjbN9PjmsjAOWeJKBP1EkSOLxBFKEYpB5XAavXetzzK58wN2x/GsQgzbYqlu15DHfQzbOnP0tZUhlWr5XhSVnUuydwXel1lCWVRfZmRSXBT3YPuGtp1MV80/w0BPTYd3kwa+KwiocozTUQrYlm6yE7DZ1eDjW5WTQ+gWExw/jj9D/S7Grm0uGX0NUtse2wnS2H7GhUAt2eEAWpRkZk9n8fBEIBglIQvUoenNW2eXB6gry68A1aPU2MiJdXkmakz4i4X3KdPIM+2ErWTvtXPF39KwCuKrmKZcOWIUoSlY0uBEGgINVwUo37cVEa5o6OP+F2x+OhHQ/xdd3XrKhewdzMuah6TDBESWRl+yeIiAQkP6cnnh2+z662XVz71bUAPDDrAeZlzTvuY6gUalQM3of1cfXH7G7fze723fx83nnsrIrnkm/pFngqOH1BPP7Qty71/G9ndIaFe88ZSZvDy0UT//tLzIwq+TPs9qgQJSFcuno8/rZKXmGLN2lYe/vsfoJLp1YwNjOG7UesHGpz8OWBVuYNT4rY5v4vKnh6TTVKBfTEBuIJiDx+0ViAcOm7JxDi7g/38+dBgq6veXEbFa0OPt3bzJZa+Xt2VUU7dy4sJlqn5t2aRmYXJVCYFM3Lm47w+MoqxmRYqO5wcdboFJLNetz+EOkxei4cYNVKqRAwaJR0e4MsHpXCVwdb8YckPtrTFH7e6w53cNMcubd2V52NPQ328P0zYw3YPQGeXVtNSZo5nHV5yaQsLpmUxdvbG9hY1Um700ujzYtCIRAKSaysaGdlRTvzhidRkmrmoomZeAMhlj+5kUNtDp69bDzTegLBAyGRx785HA4+9wZC/G1VVfgYNCpFOPLin0G8STugIc6Hu5rwh0Q+3tM8JOqGGOIkGBJ1Q/xL8PhDVLe4STRrSTAPbNogm6nIAwKzQUVxujHCIAQgGJJoVuQRlz4bg8IvZ9ENsJ+1B6x0OQOkBKvI921jjWc5CF3MGBEjG2Qg9x2t7/yapO6LkJQqUsQjZCToaLV6yVB0QuwYMEUKBIVCzrkDM5BBLpDbU421p7YbCXB4QvgCYkSfWE2rhza7XAqaleAnyZiEpNQgSZCblx3xGFaX7JoJGu4c+ReqfJv5wcgfDHpun9/3PLXdtSyMvYYxkgoNQY747HT75ZDbKlsVroCLm765CVESkZAI7gzywsIXBt3nsQjaKFJ0mXS6dCBq8Pol5haNI9GiBUkiMVCFPaQDfV/21MT4eZR7XTR1hBAEgWBILl/19UReOb39V8o6PB2c/5Ect/Ds6c+SYxrOzmr5eSgVRkZk9L3ekiThDYjo1LL5zp0T7uTM3DPDMQ3HUhxbjEFlQK1Us7xwOYIg0NDh4WCDXIsabVCFS0P/2YxPHs/XdV9TmlgaFnQgrwYl69Jo8taTqoscIGqUGhSCAlES0Sq/+3GennU6n9V+RpopjXNHj+Sisf/8597p9LHg4bVYXX6evXwcswpPrvz2/xv/H8RcL+MsU8nQ53AwIFJ8gUIWLgdaMWiVTMkbePLD2hM/4PQFCYgieiJFnSAIvHntZM54ZC0Hmx38+oN9/URd7/SLQhAI9Ux+tdg8XP73Ldy7dCQ3zy1gQ1UH24/YeHNbPb87ewRaVf/Vul6xEqVTh52Ms3pWG1dWtAHQaPXym7NKeGqNLHR21tsAeOkYi/+SVDMj0yNX8RSCwCc3Tae6w8WMYfE02TxM+/NKJGBiTiwmnZofTs+lxe5lS20X0/LjOH14EnVdbs4ek4ZRq+TRrw/x7LoaFAJs/sVcPtjVyNpDHdyxoIhlZeksK0vnoa8qWVPZEe4x7+XLA62MSI0mIUpLdbuTvY2yYFxzqD0s6jqcPrb0rNKPTrdQ0+Giqsfg5rqZuVw4IZOsuOPkff6TuPusEXy0u4kbZ5/AhnWIIYYAhkTdEN8j/qCIABH9Yb3srXXQ2OWjqsXN4vGJA66ICILAlGILbTY/OUn6AXuVdtd0U9fhRa8pZcHYgcuW/EGJToesHALKKAKCFnpWpAJB+YJ3pN2D1aNEp9SjFTrwkkyMxk9hr2ALnQkbHoVtf6dp+Jl4g15iVBnsqO4mxqRmzAAGGRnxelptfuKj1BGCrq7dQ3mjCwEwG+W+PVRJCNPkwG2NKrL8yGJUkxgl4OnuYk73AaLLroZBjDEOWw/z1+1/RUDBqMLr2CAWUu/biMElW0qrBBWnZ53Oq+WvEuopO9QoNCzMWTjg/o7HZOMSnGoPLRoJjUpBfK84r99AVtPHZAgquopuCG9f0eiivdtPp8PPmeMSabb6aLH68IZc2JX7sHntRHWWMTxuePg+NfYa2jzyQGpP+x6KYkrQaRR4/SJmY+TX1e5aBzWtHmJNauzuICkxGsYPG81gFMYWsvK8lQiCEF4FjNKrUCpAQMA0SObYP4OLiy9mUe4iojT9y/wWJZ2PX/T1K5scHjecV898FV/Qx9iksd/5GPJj8vnw7A+/835OhZZubzgT7kBz9/+sqPtvxx8UufOdPXS6/Pxl+SiSolJJ6hl3f7q3metf2QHA+z+aSmmGpd/9bz29kIxYAyPTzUTrBl4BUigETh+RTHmLIxw1cTQ/m19Ieqye93c2oVMpOGdsKj97ay8Ar2+p49bTC7l9fhG//+QgpxUmDCjoAJ69Yhxvbq1HkmBqXhyf7W/hxz3mLr85awRvbK3n0klZZMQaePbycRxs7mZ/Yzc1nS5qOuTMS5DFYZxJzd/X1ZBq0bGgJIUfvriNLw60ctGETLLiDaTH6MlLMPHgeaVsre1if5OdI11uTDoVFz6ziZoOF2eNTuXpy8axr9HOWY+tQ5TgiinZgJzTpxQEfv+JHBfhD4m8do1cQj+nKInn19fQ7Q2G8wK1KgX+oMiLG48QCkncOr+Qn84dRmWrgyunZofPQYxBg1alwBcUsbsDaFUKpuXHcsmkbBaURF576rvcbD9iZf6I5EFLWgdj+xErK8vbuGhi5kmZBA1UrjnEEEMMzpCoG+J7weYKsGZ/F4IgcFpJLKZj7Ollu3ofRp3quCVucVGa8EraQEiD3tKHVq1gZJaJTkeAEekFqNucjMGPypRAcoyWbk+QHVXy6s+49IvIKtMguHxImgl9vQZ73oCvZRfE3+16iPVaJX8e+zYet5lud5CiNGM/x8aqFjdObwiXN8TwzCiaurzE9AiO3mOfVGjp69EbIEgY5HKdqUld0CyvpEn2BoSogUVdsjGZzKhMml3NGKM7eL7iPkREYl1yL5ZZa8agNnDJ8Etw+B1kRmdyYdGFJ3EWI7E6A6zeL5cmTS60hMPbAVx+B0bklbOv9x+hoLCVMUmlpMXp6HTIrqAqpUBWgo4Wq49XGn9NrUcu89Tv1bPqvFUY1PLM+LikcVw/+nq6vF0sHbYUjUrBvNFxBEISPsGOMxjApIoKHxOA3R0gJEJDp4+yvP69IkfT+zi9WIxq5o9JQBA4KWfUkyEoBiNW3wZjsKBuQRAG7YMbERe5UvnflvM0ItXM75aMoMHm4fKhcqr/GppsHowaVbgHemttF+/ubATkErnFo1P5y+cVjEwzhwfrCgFUg3wW9Roll/cIleNxy7wCbpydH87o3FlnJSPWQLxJi1qpoNPpZ/sR+Xvpp3MLGJcVQ22nmznF8qrexNw4Prqxv8vt0Ugi/PnzCvxBkWidim5vkJ+/s5ePb5rOrMLEiImH2UVJvL6lni8OtFKUHMWa20/DFxBRKQV21Fq54OnNYUfPz386g7U9DpevbpFX9B7/5jDbfzWPs8ekkRCl5ZXN8t+/PtiKKMlXt96fvmAoLM40KgVXT8vhmhm5xJo0xBo0dLn97DuqTHNkupkNP5/D+N9/hcsf4uzSVO4+awSz719Fl8vPoysPc8XUbH46t2DA86BTK/EFRQ62dFPdIa/SzSxM6rfdsic30NrtY/6IJJ66dNxxz20vTl8Qly/IVS9sxeYOUN7SzbOXj+fLA62oFEJEJMIQQwzx7RkSdUN8L3S7gz19DRIOb6ifqCvOMJEWpzsp6/rjUZoTTZKlR/h1VkF7ORQsgGOCs/NTjOSnQH2Hh+0tuSgUMCtJPiatSoFWrcAXEIkxGDCpdey2iVS3WEmyaJhSFAOxeaBQE1KqOKIIIaHAozyCSTeGGJMavaa/AIg1qanv8GI2qqhodLK1rooVbY8xKXMEZ6Reg9mgPvmZzbgC/JlzWGPLx9tsYJaiAtOhtwiZkhFHXYJaLQ/6TRoTH5z9Ab6QD6PayB3bBJBAq9Ty1LynyDPnoVFq+KT6EwpiCr6V2yP0DTSO/b3R2ciyLfcwTWVmdNzVqFRJ7O/YwJikUrIT9RHugamxOsbkiiiavSDnkOMP+fnV+l/xpxl/QqWQBf8NpX2rfQAqpYIWfy0rWt9GKahYlno55obdlAoBauPLsETpaOz0kRyjOWGg8ECcrNnJP/b9A7vPzvWl1w9a/nj/1vt54cALXFlyJbeU3XLKx3IyBEMSa/Z34fQGmVwYM2g5838ql/4PiTmXL8jmmk7GZccOuiL1z6C128tTq6uZnBfXr2zxVFlZ0cZVz2/FrFfz+c0zSIzSMTLdzOgMC1aXn1mFiTyxqipswb/x57N59ZqJGDQqStJO3lCk2e7hja31zClKiihh7BV0T6+p4t4V5SRGafnqlpl8tKeJpCgdCVFaMmL0jEiL5vGLx/Lbjw5w5zt7uL4n0+5EqFUCUVoVnUE/jp6S8JZu76DbHy2+jo4GeHrt9rCgM2lVmPVq7l8+midWH2ZfozyJqBCEcNloWVYMi0alYHX7WTw6lYUjU9hS08m84ck9t8fy/JXjabB6+NUH+5AkuUT0J3OHcc7YNJ5bV8OojMjza9CoeOPayexttHPOmDR0aiXjsmP48kAbCgE+2t3EvBHJ3PH2HhKjNDTavLR2e3n6snG8d8MU9jfZae/28duelcBud6D/Cej5+v98fysvbzpywj5cm9vP6Q+uocPpIyvOiM0dICvOyDflrVzzopxr+do1k8Lh60MMMcS3Z0jUDfG9kB6nw+UNoVAIJFsGHmRG6ZXsqO6m0xFgbG70oMHbIIekSpKcGdXY6SU7SY9Jp0KlFMiI14PPCc+cBl47zLwDTvtFv30EgiI7qrp7QlfB7Q0RrVfJlvGj4wiGpLDItLvki3nvqhpZk+Enu1EqNfyobYucrVYwO+ykeDROb5CqFjepMToWjo1Ho1JQ3uhis+19Kl1bqDy4hQzNGM7PmHXyJ1RQ0J08A0eXFZCoPfQZJQEXSmsVV3y0jEfPeBmLzgKASqEKrwz9ctIv+eDwB1xacB1lCZPQqhWsrFvJrzf8GoAYXQyzeo5jW8s2qmxVnDPsHDTK4wuDuCgNU4ssBEWJ1Ni+gUybuw1nwMVnAReieJB0hY1rC+SsMrcvRH2Hl5RYLdE9Ij870cCT8x5lZf1K1jasZV3TOr448gU/dvyYHHP/8OBeHAG73AsoBejqrMVc9TkxQEyeGpJmkZM0uOOexx+istFFfLSGtLiBV8BOxNaWrTyw/QEAUowpZERn8Ov1v2ZC8gT+MO0P4RWzVQ2r5J/1q05Z1PlCvpPqlXP7Q+H3aavdFxZ1nZ5OdrTtYGrq1H4rkv+NVLc78YdEipKj/92H8q256bWdfF3exsScWN64duC4l38Gf/q0nHd3NvLSplr23jM/HAPwbTjc6kSUwOoO0NbtIzFKR7ROHZFFNik3lpc2HWFYoolYoyYi2Ppk+cW7e1lZ0c7Lm+rYdtfciNve3FbPEz3GHTZ3gCdWVfHE6irUSoF1d8wOO2je8sZuPtvfAsCfP6s4KVFn0Kj45KbpHOl08efPK9h+xHrckr+/nlfK1wdbmZIXTyAk4g+KGLUqzh6TRlW7i3nFidw2v4hks44zR6WwoCSZN7fW02z3sHxcBqoekapTK3nsosgy6nPGREZAzCpMxOb288CXlXS5/GTGyef1rjOLuWhiJhkxkZ/zkCiRYtZFiOmnLx3HhU9vYlNNF/d8dIAWu5d1hzsi7vfF/hZUSgV/+byC8T1um4lRWq6dmcuxPHvFOJY8th5RigyWP9JTjjqzICGigqDd4aOtp+R6Uc/5GJ4SzcaeSAZBOPmJtSGGGOL4DIm6Ib4XFAqB4kEcLXvxByXq2uUZ0CPtnkFFndMrh29LklyK6AuIWF0Bpg+P7dtIEkHscVkL+Qd+QEEuARIlSDRrSLJo8AVEPP4QFqMazVHv/jG50dS0ekiLO2pQbZYHBGeazjzu89pT46DV7qe+3cui8XIZSXG6kXN9p7N1w8ek6YpQeobRbPWFBVG3O4gkSYNm5wHERanJSdLx8eHPea3jI34ZO5zt7mZ2OY5Q76gPi7qjWV6wnGHq+dS0eljj7GJeaTwJhgRUgvxke+MNOjwdXPPlNQTFIB3eDn5U+qPjPkdANkU5hjGJY7h78t3YfDYuH3E5akXf89leZaejO8CRdg/Ti2PwBeXctRRTChcVX0RZUhkd3g6GWYaRGZVJk7OJKz+7EkEQ+POMP/Pg9geJ08exIHsBHx1eQZphJBn6IpzmLNBZwO8EyyCGEx2HofMwDDudA3VO6jq81LR6SLJoZLMdq48kixaDVkm73U9lk4uMeB2ZCZGDUVES2duxl1hdLBatBXfATWFsIW9UvEGru5WPqj/i9vG30xQ4glah5Y7xd/Bm5ZtcUHjBgIflDrjZ2LyRssSyiNfv95t+zxsVb3DNyGu4aexNx30dovUqitKNODxB8pL7BnXXfnktFdYK5mXN44FZD/S73862nTQ5m1iYszDS9fQ/kPKWbhY/uo6gKPHyVROZmv/d3Eb/XTh8svh2+r5dfMaJCIkSDVY3GTGGiFXq4anRvLuzkbwEExrld3utL52chcMXJPUYsXA0C0pS2HN3Ajq1sl8G3MmS3iNQ0mP6C8InVlVhdQeINWh4/OKxVLY6AFkYaY8qm86M6/s8uPxB9jXaT2q1MNmsI9ms47VrJlHX5aLZ7sXhDUREKPRi1qtZOjYduzvAxHu/ossVYGZBAtfPymXBiGTe39XIoXYn71w/Ba1KPh8XfgdjHItBw5c3z6DL5WdYT2i6IAjkJfRdb6vanTy5qoqd9TYOtzm5alo2i0alMiYzBkEQSDwqNiIj1kCaRU9ytJYDzd14AiJNNi/7m7sJiVLYBKbD6SM0QL/DyDQLT186jj2NdpaVpePxh7B5/Mx/aA3egMgdC4q4flZeePthSVH8celIajvdXDszD5NWvhZNyY/nneunoFIIjB6g73KIIYY4dYZE3RD/MrRqBblJejodAXISB5/J7XYHw4YmvcODqGPKOdFFw1VfQMteGLF0wP2olQpOGxlHtydISowWUYKVezvx+EVKMk0MS+1z84rSqxiV3d+0ope6dg9VLW7yUwwkW7Q4vSEsRrlc0GxU02r3Rxh5CILA/Pyp7O24mkxxEQpBwNhjxGF1Bli9rwsJmD48ZlBxKwgCpTlm/rT/PQ76W1mftJROTwI35Z3OyISRgx6rvyfYtreBvyS+hPfPfh+ArGi5VEatUGNQ6ugWnVi2/B2GnQ/GbzdwXlawbMC/65Xya+j3evhyd4iQCGV50WHhVBhbyFuL3wpvv7NtJ02uJi4yZmPd+yr723bikUJsatqE3W8nx9jI9TmLaAlV8tuurSzJXcQlMf1nko+07SHhlYswONtg1p3EDLuOug4v0QYVSoXAxgobHd0BzAYPs0fFcaDeSZczgNUZ6Cfq/rL1L7x88GUKYwr57NzPCIQCWHQWlIKSWnst41PG0xpsYG3nFwAsSb6IR9MfHfRc/WLdL/i67mtGxI3g9UWvh/++tmGt/LNx7QlFHUBxev8JFL8oT24EQv1Lpuod9Vz52ZWEpBA2n42Liy8+4WN83+xs28m9m+9lQvIEbht/23G3tbkDYRe/Ttcgkzb/SThboXELJJeCuW+V57ELx/D5/hZmF3+3EsjB+MnrO/l4TzPnjUvnz8v6TIKunp7L/BHJJERpv1VJ8tHo1EpumTdwH9bRGLXfbTjxm7NGsKwsnWFJ/d/bl03O4pk11VwxJZvJeXFMzoujKDmKVIsei6Hv+/PnC4sYnW7mR6/uxOYO8NGeJuJNWpY9uYFgSOLNayeTGWfAHxS5d8VBAiGRXy0aHl7J1KgUPPBlJSv2tjAq3cz7N0wd8Pxtqemi2xOgyyV/1lZXtrO6sj18+77Gbrpc/pNasdxW28X9X1QwMSeOHXVWipKj+OWZwyO2iTNpiRvA8r+X617azqE2Z/jfz62r5bl1tZw1OoVub5DLJmex9lA7sUYNZ4xM4eJJWdR2Opn1l9UAvLuzgdMKEzHr4kmz6Hh3ZxOlGZZBYwzmDk/CqFUx96+rMRvUlGVa8PZcd9z+yAmMrbVd3P9FJXmJRj7d24xaKcdVZMYZIjL4hhhiiO/OkKgb4nsnGJJo7PQSG6XuJ8ZG50SWUlXZqnhkxyNMSZ3CmdnnYtQqSY7RUpBqoKHDi9svohBg9ECCK2mE/P9xMOlV4f6+UEjE13vhOYkspaMpb3Th8oYob3BR0ejC4QlRkGpgRGYUIzJNZCfqBuyX+9mk6+l0dWNUG8NunoGgGDZ86RVe2w/baeryMTonqp+weHHhi7R72k86+Lg0N5rYKC+JR/Va9Yq5XsxaM29nn0/T6nsp8/mgYRsULjh2V9+J4c2vUq87jwAaVD4HqKPCF/6BmJo2lbMTx/NzdRKIcEPcWFYrJRINiayoWcH8/BksLE3g0hW3UG6toHFPE5eUXNG3A7+L1w6/y71b/0xSUjLvFJyFOegjN9lASqwWjUqOPug1b1D1pPSmxWmxugKkx8uz2R9VfUSTs4krSq6gxSWXcrW4WzCqjfTGvo1MGMlri14DoN5TA4AS2U31eHhD8kq1L+SL+PsvJ/2S9w+//61MbHp5au5TbGrexOzM2f1uUyvUqBQqQqEQukFMWP7ZvF7+OuVd5ZR3lXPd6OsGdP7sZVJuHPctLUEQBBaPGtgk6D+KA+9Adz10lMPUPsGaGK07bg9hg9XN1S9sw6hV8fcrxp9yFtiBJrlXa3/Pz6PJOIkQ8P8kFMdZsblyag4Ob5B7Py2nss3J/ctHMzG3fw+WIAgsKEnh3LHtHG53smxsOjvqrDRY5SbeLbVddLh8PPTVIdb0iLCxmTGcW9ZX9mjtEWp7Guzc+NpOHr84skTy5U1HuOv9fZi0KpaOSeWLA62oFApsPYHySVFafjgzL0LQhUSJXfU2CpOj6PYEWN4jMt+5fgqPfHOYTdVdbK6Rq1PWHurgwgmZ5Cb0F7cf7W7ij5+Wc86YNH42vxCAFzbUhgWdRqVgam4cK3ue24e75eDw3HgTO399eng/FS0OzntqI2qlgCSBNyDy6b4W3r1hCk+uqsIXFNlc04U/KA5qILWv0Y4/JNLu8IUNXaJ1Kn58WmSkzIq9zXQ4fXQ4fWyulmMTYgxqNv58zncqCx5iiCH6MyTqhvje2XvEQW2bB7VK4IyyBBTHceh7YvcTfFP/DSvrVyJ0TSY30cy4fDN5yQaUClhbXcku13s07U7jpQMvMS55HA+f9vDgrn/uLtBGg7L/W1ujUjClKIYuZ4DcpFPr+chO1HOoyUVWgi6ca2Z1BfliZwdqlcDEAvOgM+Jxxkghm2jRMn6YGbvXzrr2D5mpnUl9h9x/3tDp7Sfq1Er1CQVdR7cfg1aJQatEo1KQn3LiAV1K6WWk1G0FjRFyZ51w+1NFF5tCXMNWrJZR5KdGoTFGHXeF9vGdj7O6YzftiTOJV+m5YtLPuSJO9km/e/Ld4T6xZQXLaHI2sSR/iXxHMQQtu+HA28wRJP6KgvZAB+XGMiZOkntzjhbc44eZae8OEBclD6BlUx151baiq4JfrJP7M7VKLb+c9EuK44qZmtrXP3QsGfoclqdegUpQE6229ByTCAc/hNgcSOlbQblv2n18VfE20wIBaN0LSfKK64z0Gf3C1E+VFFPKoEY4ycZk3lj0Bm3uNian/ut6u47m7Pyz2d2+m0kpk44r6AAe+LKSR74+xNziJC4Y/1+Q6RaVIou6QaJHBmNlRTvlLXIp4fYjXcwuOrUVvQfPL+W9nY3/Ftt3py9Ii91DfuLgr2UwJLL2cAcjUqIjSgCP5oUNtexusHHb/MLjrmz1Zsat6vk5GEqFwF/P6/vMZcQaWFaWTkiUWFiSzKJH11HT4UKpEIjWqSjNtETc/6ELSln6tw002jzsrLPyx0/LeXt7A7cvKOS8cRnYe8SbNxDiZ/OL+KaiHZs7QGKUFqNWxc1zhnHWmMhevt98tJ8XNx6hJC2axSNTabTJkzu/++Qgi0elsLWmi4m5sexpsFOYFBUuRT2WlzcdodHm4bl1NfxsfiF2d4B/rK8J3/7zM4q4ckoOXx9sxeMP8dKmIxxs7u5nlrOjzhp+Hr9aVMwjXx8mxaxjWKKJa2fm0uH0Mac4aVBB9872BlZWtHHmyGRKM2K4bEoWP/jHVtZXdfLIN4e4bX5ReNtLJ2VR3uygotVBV8+qe7CnZ36IIYb4fhkSdUN87/S2cBzt9DUYM9Nn8tWRr8g3jUWt0GJzBdhV3U1Nm4e4KDVGlQllyMxTe54iJIVYWb8Su88+YD8ZO1+GD34MKaPg6m8GFHYJZs3gboEte8DTCZnTQBk5Y16QaqQgVe5j2l8vizoxJOLyhcAHX+3uZPaoOAwagU0H2rH5FEwYZhm0tDI9WuKOTbewp2MPz0c9z6L0iygxLqQwbeCA17cq3+Lz2s+5YfQNjE0ay9aWrfhDfqamTaWqxc2eWgdqpcDpY+IHvBC7vEFUSkVkQ7ohFi54pd+2Dr+Ddk87ueb+pY2ngmLUMmakVSHpjAjGEzubKRVKrKKfM1u/Id2QxN+Nt2HpPdSjjD+W5C/pE3R+F2x5DLzdgESiBIuTlqNXDSc3dzTo+vfTuIIOXj78NIWxhZyVd1bEbXG6OAzKaDwhBzoxhXh9PD8c9cPw7b6AyNZDdgRBFoe95zpWk8DOtp08vedpzsg5g8WttfDFXaDUwk/3QFQySBIxuhiWBwWw1YPj7bCo+1eQZ8kjz5J34g3/SUxOncxn5352UtturZFn9Lf2BCL/x1O0BLKmg+7UyskWjEjm073NGLUqJg2w8nQiRmdY/mn9SI99c4j6Lg8/P6MoosQRIBASWdwjjn55RjHXzIj8ruh0+lj+5EZqO12IEqREa9nw8zn9JuNa7F7u/nA/AFFaFb9ZUoLLF2TtoXbGZccSf1TJ4V1nFvPs2hrOHnNi85Oj0amV3L+8T+SNTDNT0+FieVk6vz+7JGxc0ktStI7HLxrDZX/fQnO3lydXywYtb26t57xxGfxwRi4xBg058UZSLXounJDJa5uPkBStY2+jnVve2s38kckRuXhNPSKuxe7ljFEp/PXLSvwhkenD4lk+LoPlA4jy93c2Utnq4PpZeeHevqun52JzB1gyRp7ke2J1FbWdsuPm3YuHc+UU2WyqN9Jh0ejIycB9jXbSY/QMSzRh0Cgx69WcPy6Dq6b1vX5lWbG8e4M8ibW/yU6Xy8/0YZGZsPd8tB+HN8j47Jjwa98bg7C6sj1C1OUmmHjth5No7fayprINX1BiQk7sKWfcDTHEECdmSNQN8b1TkhlFfLQGi1F9whytxXmLWZizEKdHoq7dQ2achv37KkBIxuoMoFdYmBSzlA5hJ3qVnnHJ4zBrzQNndNVtBCRo3gM1X4NSA1kz+sUdDIirDfa91vMPAXJmDbiZTq3AoFXi8YfITjLgD8mlmCERXG4/QuVXtLonAnIJ6rGizuoMEGrZQ1ztO+jdcolMvaOeJw7+iftnJhAXNX/Ax/3j5j/iF/34gj6mpE7hb7v/BsDjcx4nQZLLgwIhiYYOD9mJBtZXNtNuFxmWoiPOZGJzpR2NSmDu6PiwsGtzt/Fh1YdMT5tOYaxcyuMNejn3w3NpdjXzq0m/4rzC80587o5HXN6gwj4khiLcRG8tuxWr18qKmhUccjXR4m4ZWLwfjbsdvDb5d3MmpI7j7rTxx73Ls3uf5cUDLwIwPmk8KUetrli0cdyU/Q+8oot8Q39R22rz0d4tzzZXNbvJSdKHy2of2fEI21q3sattF4tzLpHvIAiAAPUboeIjSB4NcQVgrYK4wuM/t/9h7j5rOM+treGM/4bSSwBBAYZT70lNiNLyak949H8S+xrt3P9FJSAbl9w4Z1jE7f6gSINVFhNV7c5+99/dYAsP8gHsngCiBMpjvgxijGqKU6KpbHWERe3P3trNp/taGJ4SzYqfTA9vW5YVS1lWLMciihJ/+qycjdWdzBgWz7zhSSgVCnRquT9uxrAELpggr/a6/UFuPb2A2xcU4vIFcXiDBEWJZ9dVMyk3jkarh3aHjxGp0XR7+3rDonUqbjhNnhBRKxVcdJTxiShK2DxBCnu+B0rSzP2Czu9dWsKYbRZmFiSQEWtg6y/n0unyEWfSsuyJDTTZPfz53FFM6xFPdZ1ufvrGLkBeebz1dPm7Yt7wpIhVt+Gp0QgCpJr1J1ytfWZNNX9YcZBUs46zx6bh9odw+0PUdLgjIiR6qelwcfbj6wmEJB48fzTnjElnX6OdaJ2aRaNSeWtbPWeM7Pt83nvOSN7aXs+VUwd2MU6K1rF83H/BqvsQQ/wXMyTqhvjeUSiECNv7E6FSqLAY5SBoXr+YqeUf01Z0JV3Tfk9jp48ss5HzJr+ISqHCFxD5YlcngaDI9OExke6Rs34uD67i8qF2lfw3jQlOMMgHQG2Q/w94wNA3Y97R7WdXTTeJZi2jsqNQq+Q4hJAooVEpSI/TUd7oQhNykrjvIQS/kzyNGrs6lZzkyH4/X0Bkzf4uRngPE4/Eg+ZRfFC4nAd2P4EoiSQaBg9gPTP3TFbUrKDSWsmu9l3hvysEBQWpRiT8HG4OsrvWicsn0m5VIAhKyhs9jM6SS5r8QQlfQAyLuns23MPaxrW8Vv4aXy//GgB30E2bWy5vqrHXRByDJElUt3pQKoSI/LlTRZREfvjFD9netp37pt/Hgmy5l0+tVPOrSb8iTh9HmimNotiiE+wJWsRkWuIvYZiqFmPRHFD1f99ZnQG2HbZjNqgYP8xMcWwxAgKpptR+AeAqpcDMokza7D4K0/p6Wjz+EF6/SKJZQ4xJjdcforzRxXuVH1OaY+GM3AXMyZzDzradzM2aC5NugJhs+f+oJDj0ESBB234oOR8yJvdbDR6ij6LkaP5y1OrKEP9aMmINZMUZaLF7GZ/TX0gZtSqeuWwc22qt/GBa/0H81Px4cuONYWH30PljBnTF1KqUfHzjNLyBUNhopbfP2B8avP/2aDbVdPLUmmpA7oN7bGUVggAJJi1tDh8r9rZw9pg0JAlm/WUVbQ4fZ5em8v4u2URlSl4cH+5u4rm1NQR7msNun19IcrSOdoeX9BgDD15QytjMyFVYSZL46mAbK/bJfWttDh9bfzmXGEP/z7XLF+KqaTlIElS2OihIisJsUPPm1nq29YSn3/nuXtbdIffDWoxqkqN1tDq8FCQNXt561uhUxmfHYNarMWiOP5zrFeEdTj+LRqaw84iNzFgDw1MHjgwJiRKhnvNh9wT45Xt7eWVzHWqlwNe3zOK+pZFVBqcVJQ6FiA8xxL+Z/1lRt23bNlasWMG6des4cOAA7e3tqNVqUlNTmTp1KldddRXTpk37dx/m/0scniB1rQ7SLQJmiyXyxqadACS6DqCL0xEXFVkuaXcHwiYnHd2BSFFnToezHpX76jY/AmIQjJFlIx5/iKpmN4kWDYnmo9zENCaYfAsE3BH3qWl14/CEcHjcFKcbUSkFREkKl90pFALDM0zQ2Qx+uTdmlKkZho2GHoOWjm4/++qcJJo1KASBSvV4Egwi5kAzFzisTJv7DIIxvp+ZydH8Zspv+OXEXzHv7Tm4g27mZMzhwuILmZgykZf2vcFftv+BfOM4Lk2/D0EAW7CJGHUaIfzER6kpTjdi0CqJNvR95ANiT2O/oW/mN1YXy4OzHmR/534uG3FZ+O/eoJcj7S4OHJFnr41a5YBlrMGQhNsXinicY3EGnGxu2QzAiuoVtDhbWJS3iHh9PCaNidvH3z7ofY9GkiQ2H7IjSvF4Y9KYNICgA6jv8OL0hnB6Qwz3hjgjroRxqfOJ0kSjF/sb5qTH68KmKSCL8a93dxIISYzNjWZWSSy7a7r55PBXvNb0B15rgBidmUuGX8LFxRf3rSAXHRWFkXc6CN/0lVseI+hcARdGtZHbVt/GyvqV3D35bhbnLT6p8zDEEN83aoWCD26Yhk6jGNTMYlZhIrMKBx7Ea1VKvrplJp/sbSI/MYrilMGzBpUKIcI586/njead7Q2YdOrjGnW8tOkIf1xxkEWjUjFplTiPMr+SJNmSH8CiV1PR4uCZtdXhvLT1PTltXS4fWT3ZbxkxeqzuAE5fkNEZFjYdY/hxLC9sqOWejw6gUgjMLEjgyqnZJET1d6h8+KtDPPhVJaPTzQRCEgeau7lxdj6LR6fy9JpqVAqBoCgx+yhBFK1T8+UtM7B7AoP21/VyvD7ETdWdZMYaSLXouXV+IQlRWkozYhieaua1Hx5/hbjT6WNkupl0i577VhzE1+NIHQhJOHwDhJIPMcQQ/3b+J0XdjBkzWLt2bb+/+/1+Dh06xKFDh3j++ee57LLLeOaZZ9Bojh/MPMSpsa2yC5tHormpi7lFnRB7VJ/Puc/BvrfxjL6ClXs6ESUYmxdNVo95SEK0hrxk2ZI6M2GQ1UBDrOxAJ4Xo8utRuYNEG1SUO/ZQ3tZCqGM41a1qFo9PjCzh1Bjl/48iO9GA1RUk0axBqRBYta8LmyvIuPxoOQS9l9g8yJ4FQQ/kLwRV38W9ssmFtccyf86oWFy+aIzSZNjxFCpgy+5nGTfpNvY2H8GiTiDaFMB8VC9YSJRYva+Lbk+Qx6e/SoN/L3My56BRauh2B/moYhUSEodd2xmbZyQj3khqQjZr9nrQiib21TmZMSJytv3rI1+zqXkTAD8q/RHBkMShZhdGrZLTMk/jtMzTwtt+VrWSZ3a9RK17D2cm3ES8Ng2tZuaAp371/i663UEy4nVkJ+oH7CmM1kRzx/g72NqylX0d+1hZv5LNLZt5Yu4TA7+egyAIAhaTmi5HgBjT4KteWQk6Oh1+og0qOVaivZ5EQSULeHc7aOWZ8MPWw9y86mbSTGk8PPvhcBB4MCSF7fU1bdugqYbh2bM5kpaE0CSgEBREa6LDxzQg5kwYc8WANz20/SGe2/ccS/KW8Hnt50hIfHHkCxblLmJHtRWnz8/E/IRwmef/Ou12P1qNIhxoP8T3i9Mb5Js9nYREmFxoITnm273vFAqBxaMj+98c3gCrK9uZnBs3qEW/xaDh+Y211Hd52FVv67ci1MsHOxtx+UOs2NfM5p/P5S9flJMUpSM7Xv4O73D6+GhPM5dMzGTpExvCq05KhUC7Uy6hXliSwq2nF3HOmHSSzToCQYmWbi+f7WtBEGBK3uAltb2OjwpB4L6lI0m1DCyuyltkZ9KKVke4FH394Q4+3NXEkS559ey9G6YwpmclUJIkRAmidOoBc/ICIZG6Lje58cbjtjf0llsaNUpmFyXy49nD+PFsuYx2Z52VaL06IuvuWO7/ooLd9XYqWhxhQacQ4PxxGYxIPXH237G8u6OB17bUce2MPOYOPzVDoCGGGOLk+J+8KjY1NQGQmprK8uXLmT59OpmZmYRCITZu3Mhf//pXGhsbefHFFwkEArz66qv/5iP+76Sq2U2b3ceIzKiIlRuTOojNo8QkWsElRIq6rMmQNZmQJ4jY0AlA6KgEVEEQjpsnF0ZjpNnqY1NFF4IAZYUCq22fgxKMBgUx0pgT9vsBGI6sYGpXBY3K+ew9kovNJa9UdXQHIkWdoID8gfvhshL0WJ0BjFolO6q6cflCSCEtyYRQ+Ow8b9vD7z84G6WgJk1XyBHPXm4uu5kflPwAkFeK7G75caWAmYU5C8P7DoYk5sRfgQIFhaaJJJn1KAQBizYGgzqEyycOKHh0PataCkGBBQXNh/dS3pUIPUKpd8AcEiW6WtK5JO0+tlg/5L3WvwAwpfgfjNOPi9inKEm4evpQ6ju81Hd4mTkiltio/o9/yfBLuGT4JVz48YW0edqI1fUv8ToZphfH4AuI6LWDDzzNRjWnjZRLaiVJ4uG2zRxwljM8KouLNCZ612W/PPIltd211HbXUtlVGc4CNOqUTC604PT4ST7wKSChBs4qvYzC5LdQCkryY44/q388NjRtAGBzy2bumHAHq+tXc92o62iyOalrDwACn1fsYsnIsm/9GP9fqG3zsLO6G4UA80rjMRzndR/i2+EPSvRWPnr8kSvZm6s7WVXZzmWTs04qg+1YfvL6Lr4pb2NUupkPfzx4JYzHL/b8HDy0/adzC3j0m0OcMyYNo07FPWeV9Nvm0snZVLc7w4JOr1bw6jWT+Mnru2iyeTi3TBad4fgADdz36UFe31qPRqVg969PH9DQY0edlUarm7sXD2d8duyggg7grkXDSTbr0KmUDEsycbjNydvbG2hz+NCqFMwpTgwHpHc4fSx5bD3d3gCvXzOJmk4X+xrtxJu0XDElG5VSwbUvbeeb8jYunZTF787u/5x7sXlk4eryh/hoTzNd7gCvXD2Rz/a1cN3L29EoFXx+8wxy4gc251pYksLOOhuLR6cSa9Bg0qr48ez8k7puAviCIX70yg4arB4eu2gMf/6sgpZuLy5f5ZCoG2KIfxL/k6KuqKiIe++9l3PPPRelMvILe9KkSVx66aVMnTqVyspKXnvtNa677jpmzPhuduP/awRDEnuOyOWIKqWT8cMs4dvKilMoqNlBlOiBtIFXfEx6FdOHx+Dxh0iP+3a5WsGekYkkgVpUoQ+KeJUCZYo2CopPLCLEgB9nZzMq9IS6aql2yU3haqUwqEvlQKTF6UiJ0fLBlj4rblGS8Of9kG9aX2d6fDavlr9KUPLT4CkHYEvLlrCoM2iVjMwyYXMFGZYql+IEgiL+oERslJpx6UUkan+NQgCCXlCqabGLuHzy8x9oRWNq2lReWvgSGmD4gQ8g6KVbM4Vaw0Q0SoE9tQ4cDiclG67FUPIIEjAh5iy6gx2s6XqVoNR/sKUQBCYXxlDd6qapqzeH7Ti+1e4unpn5AAddDZQmlg662Qv7X6Db3821o65Fo4xc+VMohOMKul4ONbno9gRRmRp4dv8/ANhgP8yhTUEem/MYAItyF7GxeSMpxhSK4iL7+ZJjtBCjhfZiOYssoRggbDDzXfjFxF/wysFXWJK/hGlp08LB4A3dLbR4q4lSxWP17mFdoyecZVeWdIoCr6sKDrwNlmwYcV6PgQt0+TtQCkrM6n99CHBVi5sDdU6yk/SMzDqJiRr6JngkSf4MDfH9E2tSMz7fjD8oRvTOSpLED57fissfoqbdxZOXnvokQ6+4CoaO/9q9ce0kttR0seg4RjnThsUzbdjxzWm6XH4sBg1njkzhk73NjM+JY0xmDF/eMgOvX8Q8QP9bb75fcrQOdyCIIBAuQQ2JEvd8uJ83t9XjC4pMyIntydALEBKlfi6hAGkWPZIkO1VaDGq2/nIuLd1e3tvRyPWz8vjpXDnY3ery87M3d9NokzP17nxvL3sb7OH9ROlUnD8+k/LmnpW/niiMgWhzeCnNiOG3Z43gvZ2N7Ky3sf5wB9e/vJ1ZhfI0lj8k8uNXd3Df0pGMSrf028cPpuVw5dTskxZxx3Kw2cFXB+Vr3ke7mzm3LI0XNxxh6dhTcy8dYoghTp7/SVH38ccfH/f2+Ph4/vrXv7J4sdzT8vbbbw+JulNEqYAki4Y2u18eEB+FQhAw5554QDBYHMDJ0isG1UoFyTFaLnBOIWCrxpg3V64jOQErD3TTrV8KQJzGA/LEJ/kpBnZ0bGRl3UoWpl5JY4ua9DgdZfn9S1J6XToVCoGMeB0tNh/7HJ/zSt2f+aH2Ou6adBchMUSOOQeVZESBmm0dazgz/WI+29FOeryOksyocI4ayEYCX+7qwB+UyE7U4fWFiNIrGR3vQrfpMVDpsIz5ETq1AlGShd+e9j38bPXPyLPk8cjsR1Ar1LKQCnjknDdgWKKaYfnxeAMiVS1uQEFd9ESmbbmCdZNfQ0LBmVnLuHDsdCalyP0YoiRSa68lIzoDtUIdjoxotflQKgRiozSIooQnEKK6xYMkSYzIjELZuBWeX4RJY2D8tWtB0Te4cvgdvHTgJYbHDUev0nP/tvsBOWttecHyU34fODxB9tXJDn0ZQhz5lnyqbdWIRJrTZERn8OLCF4+/s9GXgiTKK7PfE6WJpQOK2vToZNIztlLRtYHz86/i4k+XYw20Ums/wttnvRWx7Wvlr/Fx1cdcN/o6pqdP77cvmnfKLqEtu6DgTNCYaPLW83HLGwgILE29lDjNv9bkoL7DS1CUXW9PVtTlJutRqwQMWiUm3f/k5etfwtE9pb0IgsC4zFjGZyQQpVcO7EB8Ah6+oJSvDrYx/QRiLC/B1K80sMXu5UCznRnDEvrFEAxEeUs35zwur4K/e8Nkbj29gIxYA75giF+8u1eOYzhzOGVZkRMaPzotn5kFCTTZPEy+9xviTRo+uWk6MUYNuxtsvLTpSHjbwqQo6rvcLHp0Hd5AiDevnTxgxERvVqtCEGixe/h0bwsSkHlUQPwLG2tZ1RMaDlDZ0n3U/SGjp7fusYvHsmJPc9jR81hEUeLsx9bTZPdy9bQc3rl+Chc+s4nNNV2sqWzn8YvGEghJ3PX+PvY3dfPM2hqWl6WjUghMye97XfxBkYoWB4XJUYP2NR6P4SnRnDkyhXqrmyWlqeQmmCKiDoYYYojvn6Gr4iCcdlpfT1FVVdW/8Uj+OxEEgSlFMd/qwv99HsPRJZKazOloMgcY8B6F1x+i2eoj0aLB4e0rPcrOSCLWHUSnUZCfYmTCK7fgCXpICc0hTpVHQ6e3n6irdO5ndcdnpOuzWZh0LlkJevQaBc/UfQjAmsZV3Dj2RygVSi4ougCbK0Cbzc+Z+aez5ZANjz9AdYubkswoAiGRpi4f8VFqJOQSKYDaNm/48dwOu2wO43diCFpZMDY7fB6+2PcFza5mml3N1NrqET1JROuVpMTqYdy14GxGk1wKCgVKhUB8tJpud5DUKIk4zTCmDjPQ4VaSm5yAVj2MA50HeHTno1R0VdDuaSfJkESMNo7RCaUIgsjNZTfTYROoaHTS3OXD6upb2dNrlAxr3QchH3h8YK0BS58d95O7n+TFAy+iFJS8csYrRGui8Qa9aBQa7lp3F/Oz5zM9fTqbmzfT5e1iQfaC477H9BolJp0Sly9EekwM7y15D4ffQZWtipHx3yIn7nsUdCdicd5iFuctZu8RB7mGMrbbVzA+sb/BwcM7HsYVcPHs3mcHFnXpE8HZIq/UaeTBsifkQur5zxvy9r/PP5nidCMVjS4yE06+jE8QhFPa/lTxBkJsrO5kbEbMgKs4/8vUd7nJjY0izSyLC5cvdMrC2mLQsKws/ZQfOyRKLHl8Ha3dPgqSTMSbtLR2eylKiebh80sHFHnV7S48Afk7vLbDzcIe+/2vDrTyzo5GAC58ehNbfjkHi0HD4TYn7Q4fk/PiKEkzs7qyHX9IpMnupdHmIcaooTApitHpZhptHn63pIQFJcmsPdQRDvIub+keUNSlWnSUZli4bX4hgZCELygfl9Ut3293vY2DPStwAr2rchmcVphISJLIjDWQFSdP7I3NjOnnxHk0oiTh9Mnftw5vEIVC4FeLhvPE6irOHJmCQiGEjWIAsmL1XPb3LQC8ee1kJvS4nf7k9Z18uq+F+SOSeOrScf0faBD+sb6GtYc6uH1BIY9fPDbiNrs7wCPfHKIwKYrzxh8/gmGIIYY4dYZE3SD4fL7w78eWaA5x8vy7BN23ZcshO52OABajignDzNS2eYjWq/D4Q+SnGMJmFSPjR7KlZQsY6knUF5MWpyUkSmw9ZMcXEBk/zMwRdxUiInWeagIhPxsrrIREuCb3D3ze+SgXD7844rHXHbQSCEpYXQGGpRgJBJ3h1cZd1Q4aOr3oNQoWjE0gI15LfYePuB6RJ4oSiVkFoJgqD9pjciLy4c4tOJe9HXsZFjMMvzOByiZ55Wr+GDWG6DSITiMYktjb0680tSgGhUIA7gIgAUg4Khv5kZ2PsL5xffjfre5WWt2tGMUcdto/I8dUgt4pi49+7wABKF4CDdsgvgByIlfBe11A4/Xx5Jhz+OzczwiIAW5ddSvbWrexumE1z81/jmu+uEYWJEEv5ww7Z9DXVKUUemIo5N/tPjuvlb/GyPjRVLd4iTGpI1aFd7fv5vPaz1k2bBm5lu8WwP59kZ2o58r8O7hWewuT8vuvqC0vWM77h9/n7PyzB96BOQMm/jjiT7mGQmbGBVAJKtL0fbP+/qCIIMgr3P9MkixakiwDm2X8u7j97T18uLvphD1f/4usqmzny4oWEk160uP0GP+F/YySJIX7+ypbnVS2yt9fVe0ufjpnGMMGsP2fPyKZ2xcUIkny772UZlqIMaixugMoBLmUt97qZv5DawiJEn86dxTnj8/g0slZtHV7SYvRh3vejFoVHxzzvpg+LD6cexdv0vLhria+OtiKLxjiz8tGI0kSv//kIADv7GggWqdmen4Cs4sTaXf4uOy5zWyo6qAnyQEJeOPaycd1DAVw+YL8fV0NBclREc9PpVTw8Plj+MsX5SgEsLn9lKSZefyiPoG156iyzqMjE44uYKnrMXGp6/Ic9ziOxu0P8puPDoTP1aMXjom4/ak1VTy3To7KmZQbR2bc8Z09hxhiiFNjSNQNwurVq8O/FxcX/xuP5H8bp0deHTuZcpvj7scbZGO5DbVSYEpxzKDlJKqeq5qSEKmxOhLNWj7Z1oYoQUWji/ljEtCqFTw17yk6PB0kG/supu12P81WeTKgsdPL2PjJhKQg6fps1EoNRp2KbncQLbH8YfKD/Wz/dWoFgWAInUYuFz26bLX3YttbxjMu38LoHBGVQogUzgWLBnxeOeYcXlj4AgBH2uSLtFatQHVUGnBTl5fantviozWkHaeXcWb6TDY0bkBCwqwxk6LPpcPTyTjzQg65NzA8fhgNHoFASGJYqgFfQCQkiqiUCnKTDPD3JdCwFcZfjTfo5Y41d2Dz2fj5mD9xevo5jFsyDos2DjGkYWdVN0adkrLEMra1bmNs4lg0Cg1KhZKgGESv6lu58YV8/GLtL7D6rPx53B3ECyqIzUMQBHrzgB/a8RBvV76NWtBwe/476JV6zhiXEBYxt62+jWZXM/s79ofP2feJ3Wen0dnI8Ljhg27jC/l499C75KNhfFsNu+NSaVWrODfv3AEnSm4ddyu3jrv1lI5DEASKoiJXKjsdftYdsKJSCsweGXdS/Yq9BMUAdZ5qkrRpGFWDu+r9J+PwBnp+Dm7Q8b/KwpJkvtjfwq7WDq6YdXJGU98XKqWCt6+fwtrKdl7bWo/V5cfpCxIIiVR3uAYUdUqFwA2zZAMjXzDExzubGZlupiApim13zeOzfS3kJRqJMWpYsa853O+3paaTM0Ym4/QG+c2SPiOSz/e38ObWen4wLYepR5UpCoL8ONuPWDn3iQ0RxzCnqIVzy9KZkBPLjiNWEqO0PLlaztSbkBvLYysPR2yvUQr8admoEwo6gCdXV/HoN4cRBFh3x2zSjjJr+XR/MweaHRxodrDtiJVPbpoecc0rSDLRaPNg1qs5qzSN+CgtKoWCcdl9veYPXzCGD3c1csbIFAIh8aQmefRqJacVJrDucEdESHovJWlmFAKkWvTEmoZcxYcY4vtmSNQNgCiK/PGPfwz/+7zzzjvlfTQ0NBz39ubm5lPe5/8a1S1udtc6UCoE4qLUjMs3h4OzT5VWmx9nTzlllyNAcrQA+9+WyxVHLJODx4HxgVV0bH+ROF8t5H+DoLWgEORsupAol2dq1QpUClWEoAOIMalJNGvwBkRSYrWYNEYWJC0N3z6lyMJnOzpwekNUNLoYPyyyXHPGiFi63UHiBnCLLM2NJjlGG+EkOdhF1uUNIQgM6gyYlagnxqTG4QlS3eImN9mARqUgxqRGo5JFYq9jpiRJ1LV7USqFCMOaC4suZFnBMuw+O9GaaCRRRXWLG7NRwVXTP0ev0lMcJxIMiRgHKtGy9vSl2OrY2baTb+q/YXT0PA7WCpQLXcRHW9ht95IQHaLTEaDTEeCSkddy0fCLsGgtKAQFby56E5vPxvjkvnD5XW27+OLIF8QqNJh3vSj3vxUtgfS+ksV0k1z+FaOLRyWo0agVYbEMUBBTQLOrmWExwwY8f4PhCrjY17GPMYlj+hm69BIQA5z/8fk0Ohv5ydifcPXIqwfc7tm9z/Lk7idRSfBFfQM6tZrfpSShEBQsK1jWb/sDnQe4Z8M9jIwfyV2T7vrWA267K4goyeW9Ll/olETdms4vOOQ6QLTKwoXp13yrx/93c//y0Xy8p5mZBQkn3vh/jHiTlpeumvgvfUynL8g1L2zD7gnw1KVlXDU9l6um59La7WXivV8DslV+l8vP0rFpaFUDv1///FkFz62rIUqnYusv56JTKznzKBOWKXnx6NVKQqLE8rIM5vx1Ne1OH09cPJYFJSnYPQHu/nA/LXYvzXYvK37Sv8RZq1KgEOSog1SzDrVKweS8OJQKgTevnUwgJNLp9PP+zia8wRCnFSawqqKNvY12vAF5mS4j1sA5Y/qXpx5o6ubt7Q0sHZsWXjXsLceMM2qJOuY7dvqwBN7cJo9B6rvcFNz1KXedWczV0+XKg17jF4NGidXlpzApql/ERH6iifMnZLLksfX4gyHeuX7KgOL5aARB4B9XThi07eKMkSls+sUcorTqAV1FhxhiiO/GkKgbgAcffJAtW+Qa86VLl1JWduouXxkZQ/Xi35XuHhv/kCjRZvfT2OklN/nblWukx+lotfpQqQQ5NLvjALTtlW9s2w9psjBQd5aT0v4NIIDHhlIfw2kjYzhQ7yIuSh0Zdn4UwZBIMCQxtXjwXgedWkGiWUN7t5/kmP6Dfo1KMag5jFIhDLxyFvTC1ifB3w1jrsKqSGT1/i4EYGZJLJZBjlevVbByrx1RAq9fpDQ3mii9ijPK5MFs7wW5sdPHjmq510OrUkQEjqsVauL1PTPWSijOiFyd0aoVg4vwi9+Eys+h5FzKrIe4InkqbUKPK5okC2+AoChh1Cox6pRE6VUoFX0zyQOJrpL4EsYljUPh6+77cgvKq6dOT5DDLW4WZVzC1LSppJpSCfh0mHQqlAoBT9CDSlDx4GkP0uBoIDs6e+BjH4Trv7qenW07WZizkD/P+POA2wRCAdrdshlCo7Nx0H31RjwYBAUdqefSbMpA5fug73wfw9uVb3Ow6yAHuw5yzahr+k04nCxZiXrqOzy4/SKnqgtF5IGpKInf6rH/E4gzabl8Sva/+zD+65AkiRte2cH6wx389bzSAVdpvg3bj1jZWC1H23x1sJUrp+YAkBSt4+cLi9hS08U35W18vr+VRquHn82X3Wgf+LKSqnYnv140nKRoXXgCTDWIQVZOvJHNv5yDJEKnyxcOKS9vcZBq0XPeUxsRJblyfFJuLGc8vBaDRslzl48P916WpJn54EfT8AZDjD9qxQugrduLxaAh2axjw52z6XL7ueTZzdg9AZ65bBxXvbANf1Dk2hl98T7d3gCbqjqZnBfHNS9uo9Hm4ZvyVlbdJvf7LytLpzTDTJxRS/QxeXaLR6cyLiuGz/a38MdPZUfl5zfUsnRsOiqlwG/OGsGUvDgyYg1M+9M3BEWJd66fQkmaGVGUqGh1kJtg5EBTdzjEfWe97YSirpfe68dDX1XyzJpqbjgtnx/1hLknRn07N+shhhjixAyJumNYvXo1d955JwCJiYk88cSpBSIPMQjWWqjbBMWL+wV8D0ZxhgmFAlqsfgRBdtP8tmjVCqYcLbgsWWBKkVfqYvPxBUT21DrQZv6QkbOUCPH5ECsPIEx6NRMKLIPu2xcQ+WZPJ75AiEk1fyQ5xgAL7u23nSAITC3+buYxgaBIeaMcEp6bbJDNL1yt1KmKOHLYR1y8F0mS+zI8fhGnx4snECIvydDTIyejFAR0GgVun4hB1zdjeuxx9YoyQQCN6nsst0odA6ljeGPVnfzxyArOMKTzx3Mepsku0e0J4vSEkCSJwjQjUQYl/pAf5Uk4lhrVRv6xQI4skLqqwdMFKXJfx54jDlptfuo7vCwe3+PC1vOWOtB5gCs/uxKD2sDrZ75Ojjnn5J6HJEL9BlBqsHqtAHR5uwbd3KA28MTcJ9jZtpMLii4YdLsLiy6kOLYYk2hhb61cVvV4wuVMycgecPuz8s5iU/MmSuJLIhw9TxVJgi6nPJlyuNlNXNTJf+ZmxM0nQ59DqnZoQut/DacvyKf7WgD4eE/T9ybqJmTHMrc4EbsngMMbZOHDa/nJnHwWlKRw7cw8LpqYybQ/rcTuCYRNWypbHTzy9SFAdpe8Y0ERPzu9gDGZFoqTo8OrVMfSK4zMBjX3Lx9NdbuTq6fn8smepvBK2vNXjKfe5ubv62sBOeD7Zwv6Yk1GpsuraK3dXpY9uYFAUGJecSIvba4jxqBm8y/molEpeG1zHeU9kQRVbU4+vnEanU4/k/P6Gpeve2k7G6o6mT4snma7XBbvPiY3MD+xv8iqandy21u7yYk38edlozBoVdz59h4arB5ufWsX6w51kBilY8VN09l2pAtXzz4PtzkpSTNz1wf7eHVzHdPy43n+yvFcPS0HX1DkrNGpx32tJEnib6uqcPmC/GTuMLQqJe/ukAPi393REBZ1QwwxxD+PIVF3FPv37+ecc84hGAyi0+l46623SEz8dgOk+vr6497e3NzMhAkTvtW+/yv5+0JwNEH1ajinv1B2eoIcbHDh9ocYkWEiPlqDVq1gVHY0o7L/CcejMcGkm8L/rGty0dApOwCmjv3poCtmO9t2kmJMiVgJ8fpDPRd9gdagjuRNj8P0W8EYN+A+vksvyp76dupaex5X0crwhCzImMI+62h8fi3BTgej4zwI0SkYNAq+2SsLDL9fZMRRtvEKhcBpI+Nw+0JYDCpZcMcXgCFyhjnBrGH2qFi+qv+Me7Z8wzWjrjluL9ip8qX9MEEkPvc08QeFimiDxJZDchP/8AwTOp3IeR9dSJWtir/O+iuzM2cTEiVabD5ijOpBS0wbO71sPWwkLsrCtFQFAmAxqmm1+YkZYPVyX8c+iowzKTZNoby9niTjSQ5KW3ZD5ScAPFL2M9Y4aiPC4Wk/ANVfQ0oZZE4BYELKBCakRH72Xd4QapUg971IIlR8RKmrHV/+WZSrIBiUyIsbPLOrNLGUFUtXnNwxHweVUiArQUer3U/WKbpMahQaCk2DhyEP8Z+PPyiyodxGICgyuchy0u6WUTo1t84rYH1VB9dM/37MhYIhkT9/Xo7FoOHB80uZ+ZdVdLn8PLG6mgUlKeHH/eSmaRzpdDOlRxBlxBgoTommtsMVjk5QKRURZiIn4miHzrPHpLG30c62WiurK9vZVW8N3/bWjnp+tqAQm9uPRqXAoJHP1846K/U9BiMvb64DZKfL7Ue6KEqO5oEvKwE5DP2s0jRijRo45iun17nT4w8xY1g8qyo7uHr6iSeb3t7ewI46GzvqbFw9PYelY9J4clUVNR0uJBECIYlGm4dGm4fZRYncubCIQFAMZwJWtzvDP1VKBXctOrnv+zWHOvjL5xUAZMUZOH98JrfNL+SljUf4wbTsk9rHEEMM8d0YEnU91NTUcPrpp2O1WlEqlbz++uvfKZsuPf3UbZv/X9NrA68YuBxvzQErvp7Z0IpG16Ciqr7DQ6cjQGGa8XutyU8wa1CrBLQqRYSByYF6J0faPAzPMLG+8z3+uOWPnJX8ExZknkVBSjRRBhUHGpzY/C0ccK7jAfFrni1cTOEx4qiXQFAkKErhY5ckiX11ThyeIKU50Ri0Srz+EBsrbAjA5KKYiBLGrmA9ISkVZ7CLbS0VDE/MgcLFpNc6qGl1kb7nr+TWPAeXf4jHMjV8vyarL0LUgVzuqVEp4Mu7Yf1DEJsLN+4IB1O7A24e2P4AepWelw+8TFAK4gq4uGfKPSQYElCgotXmI9akDruCnio/nnwXqh0PMz9nISiUaFQiaqVssKLXKOj0dFJhlQcKW1q2MDtzNnuPOKhp9aBTK1gwNn5Akdx9eAsTtz1AU9LpBAquQ6MSGJ5hIlqvxO4O4vWHIo75zJwzUVmnI6BA7T351SmvysJB7VzMUge5ccXkZM6M3KB2tTyZ4ekKi7peKroqqOmuodg0nR2HnWjVCuaOjkPjbYeGTQA07V3LlNGL0WmUgwrY75uxef3zFv8/8s+IW9la20WzzcvvPjnA6HQLT11adlIrzP8pdDkCWJ1y6XOrzY8peeAhgtsf5Nm1NeQmGFk0Sl7BuXHOMG6cc2p9qAOxprKdylYHWXFG/tGzIjY63cxlk7N4fUs9Fx+Tz5YeYyA9pq8sv6Xby9IxaSwcmRzx92+LVqVEIQiUtzjCq2u9jEwzs7W2i4uf3UyUVsWKn0wnKVrHh7ub0SgF8hNNHGiW7xNr1DA6Q+7R1qoVeAMinoCISTvwOX7ykjK+PtjG3OJEonQq3t7eQGFyFE02N69ursdiUId75I7mzJEpfLq3mdyerD+1UsHHN06jpdtLvFHLX7+sIDPWwPBU2YzlB1Nz+Ka8FX9QNrL607mjeH1rPQtOQQQD5MYbMevV+IKhsNHL4tGpLD7BCt8QQwzx/TEk6oCmpibmzp1LU1MTgiDw97//nSVLlvy7D+v/F1d9LrsdFiwc8OZeE0ZBGDj4FuRZ5G2H5f4uUYKxuQM7hHV7gjR3+ciI1530QNhiVHNmWUK/QV51i5tASKKmzYM1aCVNV0iZeRHtdpF2uw2VAkIi1Hr28Fn7kwDsHXklhQMMFr3+EF/v6SQQlJhcZCHJosXhCXG4WbaOrml1MyIzija7H1tPrlub3RfO2jvS5kEXyGFV9zs0eqo4z9RnUT8qO4pR0i749Fn5D64O9FoliWY5AD7xeKWrDrlsCme7HESulL8WPqn5hDcq3ojYNCSFmP/OfMYljePH+Q9R1+HFpFMyr/T4YcLHsq1lG9HaaEoTS/n9lKf41bp7uHfr/QyPK2Zxwu0oiaepy8ekwlRuH3+7XB454kpAfu3ln1K//faGoWeXP4i+fSXJnesQzr0+vP2O6m5CIrh9YoRRjVFjJC02QHOXj9TYwW32NzVv4tk9z7I4bzFL8pdQ6YqntsdkJ1kVR78hZOp48FghNbIv1+a1cemnl+IJejg/54eM0JyHLyDiD4hoDHH4onIQnW0cEfLIdAXJPYUyyCGOjyRJXP/19Wxt3sp90+/j9OzTv5f9vrzpCHe9vy88KfHVwVZaur0RroT/6SSYNaTFagmEJNKO8zl4ek01D311CEGQhU2vaceJONLpwqBRkRDVt29RlMKl4Uc6XVz+9y1IwFVTc0iz6HH6gozPiaUoOZqfzi3ot88WuxeXP8iBpm7K/o+98w5vqz7b8H20pyXLe287w3H23iEEkrBnKatlU8pooZOuD1oKXbSUUgptGWUUKHuvBEjI3tsZ3nvItvY6Ot8fx5GteMQJYZ/7unJhrLMk29J5fu/7Pk9eItc8vokDbR7e29vKM9fOHPJa2twB/u+VPeQ4TBQkm3jgg0NcNjOfK+cMrIbNK0nhvxvqSTBq6PCEyE40MrsomTvOGsvT6+sIRaJ0RkLUdvow6dS8sVM2QitOtXLh1Fy6fCGuX1AUM3J5/Irp/OndSk6ryIxzpazp8GIzakk060hLMPDN6bKA/cXLu3h8rWwupVEJRHrfBMuzbMwojO8IKc+yxebuQM7l21rXxZ5mF5fPyueOM+Or6Yv/9CF1Th+pVj0bbl9MXpKZH50aHxLuCUa47dntiJLEpTNy+f6z2+nxhzl5TBoPXCy/t+U4THz840XUdnj507v7GZOZwK1LylBQUPjs+NqLuo6ODk4++WSqqmSb4b/+9a9cdtlln/NVfQWxZcv/hmBeuYMuT5g0u37IlW2NSiDBJMcCOCxD/+qur+zGExBp6wkyd8zgFTOQDVg2H+whLEpMLkqIVW7EqIRKkNskR2dbqG33U5ppYpbtahz6FLT+ENGIDjEKkagcopxsW4rW1IVBY+DsMQsHPV8gHI2Fhvf4IqTZ9ZgNahy9TpSHIwzSE/Wk9hqS9M/x2lzVhYCaioSTmGI7kwzbETeLBXPhvH8jhoJUJpyMrtnLrFF2eW5OP7BCGoqGaA7Uk3HKHehSR0H+vJigA6hIrsCitRCVovgiPjlGQJBfoz2dexALpdjrdSy8VfMWP/jwB2hUGl4840Wq6y2sbn8VgC1tW9CLT9IV7KTqwBZ+q/8Nl465NG7/ijwrSVYtSRbtABF+x9o7eP7A8yywFPJXQYUwanms8iggO4K6/SIW40CxP73UftTqzf1b72d7+3b2de3jzOIzSbJqqWoBi1E9uClM1hT53zCk2AyUOcxYjWos9e9DTwOayd9ma40PgygNGy2hcOx4wp5YxuKK+hUnTNR1+0KAvOgwIcfOtALHl0rQgWzINNz88GEKkg87L+qo7fSxcl8bF0zNibUfDsbKfW1c+dhGzDoNb39vHhk2A5f9ewPrqjr5+Wlj2Nfixh+KcPjdRJSirP7RQqISQ34m7Gjo5ty/ryEiSkhAlt1IdqL8midbBhelkiTxyvYmPqhs4/Ve8ZWfZKK208c/V1XFibofPredl7Y1Mbs4id13nIJWraLVFSDFoo8J0Qun5tLcEyDJoosFd9+yuISPD8rtkhXZA1/PaQUO/ntNvOB8Y2czNzy1hUSTjve+P19uyeylpScQ+/qwoNOqhUGrfHWdPnY39bB4TBobq51c9fim2GP1XX4WlMWPlLT3msJ09f7+DsbKfW28tVte/PMGw3R4Qr3X3II7EKamw0eHJ8jCUan8b0sD7+9r4/19bZw3OXvEgl9BQeGT87UWdT09PZxyyins2SOHZd59993ccMMNn/NVfT0x6tQYHcNX1VQqgVlldoKR6JCujiC7THoC4pAD8YfpcIVodPbmyjmDFKWbcLpDrN7bhV6rYkF5EkUZJooy+uovF42W4y3EqER1qw+TXk2mwwBYmMNNg50mht2sZUKBFX8oKme1Id+szC+PF546jWpQF82G0HYytRV82PEkf1hyO0bdICKi/FxqW3xU1rhj5xyqlfXdtpdoCNSSbchn+ZzvDXi8zFHGhxd+SCQa4Y3qNyi2F+MwOPjPnv+wMGchE9MSSLUHhjz+UPjD8qyJGBUJRUNkO4yclHQlq7ueIiwFWVw0lTs3/AKA92rfY0n+EnZ37qbT38m87Hm9c1+D3ywf7JZznw4QhJ93gCreBGZBuQNvUIw51vVnZ62b+o4A5bkWcoc4/tKCpexz7mNZwTIAspIMpNh0aFRCnBHN0bAb7Dy9/GlqempYmLsQlaCC9kp46kJAQi2GmDLj+qMep8pbiVf0cKitno8bP+b68dd/YQLTv6hYdVZum3Ib65vXc1X54JESg9HuDvKX9/dTkWXngqkDzWCumVdEollHYbIlzvDiq0ZUiqJJ/5jvXtTJ/NRFXPrAJgLhKLVOH788feyQ+9V2eolK4A5G6PSESDBqWXWgA4B/raqm1ulDAPRqAa1WzZVzChEEAfUwf1Y/eG4HYbFvUSkqSfz7W1PZ3tDNpNzBnYif39LIbc9tR0DOVctLMnHJjDz+vbqaS2fmxbaLiFGe3SzHAqysbMcXFLGZVKQlxC+yGHVqfrIsPsv2lsWlg1YVh2NHQ7dsVOQNUdXhwWGWPxcOtLp5d688SD2rKInt9d14QyJhUeJXr+zmf9f3tXUHIyJn/m01Xb4w18wr5NTydITegHWAU8YOnBW+76IJ3L/yIFf2OosGIyLt7mBc62pBsomydCvRqMR184ppd++hxx/m4um5tLuDnP3Ax0SiEn88fzyLR6fx3KYGRmdYBy48KigofKp8bUWdz+dj+fLlbNmyBYDbb7+dH/3oR5/zVSkMhzcgsmJnJxExii7xAMtK5wyoqrxy6BWeq3mBi0quYnLR7CGOJJNo0ZJo0RIRo6T3tid2uMOx9ryVOzvJTjJQnjd4sG1xxrGvQBb0irlQJMoHu5z4g1EmFFrJSDx6NWb+6Ez+ufNuzihfPmxbaYJJg0oAtVoYuJ2/C3rqIWU0oai82hqSgkMeS6fWoVPr4rLRbp1yKy8dfAmT1sSE1AkD9gmGoxxo9uKwaHsFbzxnFZ+FQWMg0ZBIaWIpJMK9GTcjcROBSACT1oRX7GZd8zquHHclta5aLnn9EiJShF/P/jVnFg/SGh0VYe+L3OGYxAtJYzi16Iw4QXcYjVqFzSSLYUmSaO0OYdSrsJm0VLf6EaMSNW3+IUXdxaMv5uLRF8e/RkME2R+NInsRRfY+C3N0FjkvMewFsxwtEYlGeGDbA0hIfGfCd9Cq+sRoZ6idd9tfQYyKPPjxU0hIRKIR7l1473Fdz9eJy8dezuVjLz+mff628iBPrKsD6phfljLg5l6nUXHx9LzBd/4K4RO9NARqsdrAparBatASCAdJNA2/uPPN6XkEIlFSrfqYS+QvTx/Dxwc7GZ9j40/v7mdUegJPXDUNg0aNeYhZs/70+OX5v7wkEzcvKmFqgQOzXsOsoqHbwc29HRkatcCr351Nca9N/yUz4n92GrWKk0ensaKyjfmlKXELQQfb3Px3Qz3LKzKYOIR4PFZy+omoNlffe7JWrUKnVhGMRLloWi7fnJ7Lz17aRbcvPKCCKUl9nRMRUWJSbiKv3DAHUZKoyLINuvB08ph0Th7TNz93/oNr2dHQw0+WjuLa+UXc/eY+HvzwECeNSuVf35Kjf94t65sdPtTu6VddlZhdnMyu/zvlE78eCgoKx87XUtSFQiHOPvtsPv5YbsG5+eab+fWvf/05X5XCkTjdIVx+Eb1WiOUMRUQJEPjf3teJaro5vej0uH3+tvVvNHmb8ER6WFY6Z9jj6zQqFhxRJctPNeLxR+hwh/AGohxo9jEm1xIXTh1DDMOhd0Gtg8JFfWYwI6C9J0RXr3X8poMuTp86vKgLhEQCXXlcW3QXY3Mtw26bnKDj1EkpqATQ9hcckgSbHoSgCzKncnLpmezvqsahyh227VCMSlS1+jD3ViUf2PYAj+x+BJ1Kx4oLVmDTxxtr7G3wUN3qRwCWTdENED2CIDDaMZpXq17FYXBQmliKIAgICOhUBvY2eFiScRHfLpdn6Gp6apB6bxuGzEDrroHmzRQCt5Usg+SjuzBWt/rZXuNGJcCSicmMyTFT3xGgNPMYxPqBt6BlK5QshfQJg25ysNnH3gYPhWlGxuYOk/Nky4LvrAVfJ2RNAmBl/Uoe3vkwAB/Vf8SE1AncPuN2VIIKvcqAVtCCCipSx7GjbSczM4eeITpWenxhWrpC5KYYPpeg4C5vCJNePWSg9GfNxFw7j62FohQLNuPQnQJfdSwaK5NsM2kLNjPZMZXXbzRxqN3LjMKhW91Bfr+9bn4RwYjIh/vbqciy8e3ZBbHsuYun52E1aGLv9UdjW303IVEky27gX5dPGdTe/zD/XFXF6zubuWlRCaeWp/PcdTNJMGhjgu5IttZ10eUL8fDlg7dO/+j5nWyu7eKt3S2s/tGio15rjy9MJBodEPDdn+UVGby3txWtWsX80pTY9/+28iDBXjF82HRkbnEKqw62M6c4XrwatGpe+M5sdjZ2s2xcBi9saaChy8818wqP2kkQjUo8t7mBPU3y3PrORtmF+LDb57b67kH3K0qx8Nx1M+lwB1lyjOYqCgoKJ5avpai76KKLeOeddwBYtGgRV155Jbt27Rpye51OR2npsbVSKHwyguEoq/Z00X9ca/7YRLLSgvxn99Ns7XmbS7V9szCBkEhLd4iziy5gbd1uTso5hRcPvESNq5qrx12NRTe8EDqMTqNiUpGNZmeAHbVuMh2GwQUdQMs2qFslf23PhaT435GGjgA7alwkJ+gGzKmk2HQYdCoCoSjJhjD4nAPiBCRJ4lCLD0mSxWxTb6toVpIBs0FNXXuA5AQtTc4gPb4wmYl6AmGJwnTj0KHfMXMRCa1kpqE6ndpokEien+KMgU5xwXCU/U3emJnL4vEaEvSyQY1Za0ar0tLqbeXKt69Eo9bwzyX/JMEoH8dkUA8Z9nv76tvZ0bGDd2vf5ZWzXol9f3+Tj8pGLyDRVPUuo6xTmFlQyuNLH6e+u5WT8oa4gbJmgjkNwv4BP4ehOPy7JfW+LMUZ5mOvvtavgWgYGjcOKerqO/xERIna9sDwog4gMU/+h1ylK7AVYNVaCYgB9nfvZ3/3fs4pOYexyWOxaKxckHUloWiAa/NT8Ef8GDUnrt1p7b5u/KEoHa7QoO3AnyZv7Wrhhqe2kJNo5PWb5o6oavNpc+aELOaWpGDRa467OvtFYcW+VvQaNbOLj83g6DBTE/stmGkhNWHkc58/fWEXz29pYFS6lZdumB1rk+8/QzYS3t7dgtMbBsJxLZiDcc9b+wiLEt9+dCPTChw8c82MIRexDrS6Oe/BtYhRiT9fOIGzJmYN2KY0zcrm2i7KRhDGXdvp5bT7VhMUozx37UzG59gH3c5u0vHItwfGHB2eoev/OWQzaWOuo0dSnGqhONXCvhYX3392O9AnqIfj6Y113P6ifB905oRMfthrlnLHmeU8uqaG5eP6IlW8wQg/fmEnAnDnmWP51Su7OdDq5q8XTWLxCcooVFBQOHY+/0/Kz4EXXngh9vWKFSuoqKgYdvu8vDxqamo+5atS6I8gyC2O0f4f1oLAtIJc7PazuSK8hImpE2MPrdvfQ5cnTE2Pl7PSfgQheH7X3Wx3vYdOreOGCSOflfQGRLZWyzNpCSY1723vIM2uZ1y/Nsy27iBur51ClQ5BrQXzwDzDnbVughGJRmeQcERE26/ioNOoOHViMr6eLkzbHoTWIIy/FFL6ZjNauoLsrJUzgwrT1SCIWAxaLEY126vl+S91r/umvH1vO2UkGnetcS/q1OuguxZSxyKJEpI0tNlJICTy3vbO2A2TJET47orruXjs+fz7lH+TY83BpDXx2w2/pdYtO7O9sOtDrpl6Lql2HQatesjV4bn2b3F6YgmHwu/Gfd/SG4Tuj7p59MDfsGvT+T8eISe5hP0dSbzX3cnJE5IHilbnQfC2gTFR/jcIa5vWsrl1MxePvphEQyJF6UYMOhUm/SeICyhcLIv7vLlDbjIq28L+Ji/5qSMXXN6Im+ebHicshfnvmU/S7nHygw9/QJYlK25mzqKxAvLP+kQKOpBnhfyh6OCzm58yW+u75FbYTh+dntAXQtTBsQuPLyLv7WmNmWc8f/1MJucNX2E70bgCcstkvdPHqJ+/xQ0Li/jBKaOOstdALpqay46GbgqTLUcVVxdNy+W/G+oJiVE21jg57+9r2d/m5h+XTmZ6QRLBiDioyYvE4GLxN2eVc+WcAvKSjh6ZUNPpwx2UuzL2t7opSDFT1e5lfLZtRJEaFVkJrD3UwfULhhdlR5Jk1pNo0tLtD1OYfPTFqsPh6zqNituWlMVMfkrTrNx19ri4bd/d08qr25sA2WBlR4Nc1XtzV7Mi6hQUPke+GJ+UCgpHoNOoWDguCW/vh6FaJZBo1vDEnicABsw1HdYOHaEGItEQakGDVgMCgjy3dQx0ecKxzLyqFj9uv4jb72NMjlztq+8IsK3KhUQCPdk3MKkoEdQD27HS7Dpq2wPoNAKaQVqKBEHArA5ApNfZzNcZ97jJoI49r3/t/xMrm19lduZs5nNfTNQcFnSqXhEcFiXMwwkUo0P+BxjUMHesA48/MmiMRCgixQRdUbqR2zd9mxrvfrw7uvjfGf+LbadT6cgyjCJBk0xCeBLRqHTU0OIMTQVRCealnBP3/dwUI4kWLb9c+0eKzVPJMoxCowlzy+obONSzl/Mzf8aCyJkDRZ2rAZDkPLiQF4zxN9/+iJ8b3r+BcDRMm6+NO2bfgSAIZA/jLOkNRKhu85OZaMBhHaLdLn+e/G+455qoJyNx6LarwegKO/FH5eqoW+xmctpkVlyw4piO8UmZPdpOjy9CouXorYYfH+zAbtIyNvPEZNxdO68If0ikNM1K7ghunBXiqXRWkm5OH9AaDfIsGRxeOBtcsDcHGugJd1FqGSub+JxA7jm3gtlFjfz9w0N4QyIr9rUfl6jLTTLx5FUzRrTtHWeW8+3ZBfzhnUq0KoGXtsmC5O3dLdzx6h4Otnn428WTOGVsOqlWA8UpFjzBMFOGELwqlZxB15/+0Qz9mVeSzE+WjsIfFnFYdEy+813CosT1C4oGRAdIksRrO5rJtBtiYvuuN+Uq4y9f2Y1eo+IbR+T0DUWKVc+KWxfgCUbIcRz9b+j08Zlk2g3YTbohtz/Q6uaet/YxNtNGrsPUa8Iif0aoVQJXDBIHoaCg8NnxtRR10iD5VgpfIKQo7Hwac3cNpjHnIyTLouz9uve5Z+M9AGSYMzgp7yQiokS3N8zUEhtOd5hJ2jLebvgN00w53H/KHbjDP8IkJPP+jk6sBjVTSmwD2ikPm2MkaCOktH1IRvYMClKNSEC6XceuOk8samFrlYuaNnleDMBsNscEnRhrk5EF26QiG+V5VrRqYegVWWsmjD0fAi7Iib9BsZm0nDIxGQl48m3Z1XFN0xqWvrCUZ5Y/g8VgZVtvRbEsy0xBmgl/SBzWGRSQTUWcB+nQ6Hm48mlGOUaxuRumpk8l29oXO5Fg0jC12IY3KJKdokJQh9EIGhbnLo473E2TbiJV8w5p0kzykq1yJMQRNzf1rnoe3PEgMzJmcHrR6YzLs9LoDDA6e2BbrNWo4baJv2B9pReAP23/P/Z7dgLg0mzHYjx34HPKmye3QVoyBq3UaVVaMi2Z1LpqyUsYmZnF1mo37T0h6tsDLJ2ccvQdTiBZhlym2GcTjAYotRx9PvDTQKNWkTSCfLxXtjdx09Nb0agE3v7ePIpSRtbqPBwOs25AntanRSAsEgxHB3VE/TLynz3/4Xcbf0eGOYNXz34VvTp+QWFBWSr/vWYGOo2KCYO0Anoibl5reYYoUfyij4n26cOeb0tdF4+tqeHcSdnMKU7m9pd2srfZze/Oq6B0kAqaw6zjW7MLyEsy88zGer41O3/Y47+9u4Wfv7SLsZkJPHTZlLiZuyfW1eIKhLl6buFRZ/EKks2kWQ38++NqTDo10/ITWV6eyWNr5C6D9VVOThmbzuY6J5Wt8vvq1Y9vwqLXcN9FE8kcJJ5iV2MPYTHKvhY3P3tpFzMLk6jv8pGdaORfl0/FoFUjCALX9rY+XvnohthCWV2nd8Dxnlhfx89f2oVaJbDi1vnkJZnRqVWERRGAN3a1jFjUASSadSQeQ3X5aFXbv608yHt723hvbxs7f7WEP76zn0fX1JBi0fPYFdNigeYKCgqfD19LUafw2fNm9ZvcufZOFuQs4K65dw2/cdgHbbtYZziNloMJTIj6yE81kWXJQqeSP6CyrPKcw7rKbtpdITIdeqaX2sGZwTXmRDDpQWfFrLOyq86Nyxdhv7OSx2r/y5KCk+IcFCsbvVQ2ehGkCKeuvBlDYib5336NWz+8FQmJP83/E3aDfHNyWKrotCpmj7bHbgRd/ggf7nISESXUAswZk4jDOtAkZDDEtIlsONBNcHcPU0vsmA19lbbD2Xn3LbqP3238He/Wvkubr416Tz0T0yYSikh4gyKF6SZ0GtWgs3QHm30cavFSkmGmMN0EB16H+rU84K7kOdeB2HZZlizeOvetuH0PV/A2NG+g2lUNgFEbf3Nj09u4dur5hENhVm5r4vU2PzPKEmO5ewB/3fZX3qx+k9eqXmNx3mIK003ytQyBWadHELxEo1G8Yg9JhiQmp03mxsnXDL6DzgxlZwx5PI1Kw3+X/5dmbzMGtYEbV9zImKQxXD9+6NgAq0FNew+DZtqNGE8L7H8N7Plyq+YIEQSByfZZR9/wC0AoIpeLo5LUa2T05cHpDbHsL6twekM8+u2pzDrOGbPPjUgAaleBJR3S5Ba5Brdsw9/ubycQCQwQdcCAwOr+qAUVakFNVIrG3m+H41ev7GZHQw8bq53856rpPL2hHoBnNtbz89PGDLnfwlGpLBw1sG29P+/uaeXa/2wGoK2yncYuP/nJZv628iD3vX+AYO/vXpJZx4VTjy52ghFZHEUlifIsO5Py7Pzy9DHsanRx7Xy5rXlmYTKnjE2jscvPrl7TkDd2NnPV3PiokB0N3Zz1t49juYRiVGL1QTmiobbTx74W9wDRPCE3kff3tQMMOlun7xWmakGIOVu+8J3Z3Pvufrp8IW5ZXHLU5whyZqIkQU2nl9++uY+TRqXGhOXx0Njtx6hVc9LoNF7f2czMomQsek0s4y4SjSqCTkHhC4Ai6hQ+E96ofgN32M1rVa9xx+w70KiG+dXTWZDyFtDSWYiEQHNXkPxUE2WJZbx+9hsIAqSZ5b79QFjs/W9vH2LxqZAyRjbN6CU32Uhrd4jn2x5je9ca1jSv4vSi02NtRfpe4aWJBlFHgxAJ8HHTx2xo2QDA6qbVnFZ4GgDj8q0k23TYTRpq2/00CkFGZ5vp9oSJiBKiJPJM02/4W10Nf1x4D+UjcGF0usOxebjqNh/egFxtK8vqm4NIN6dz5+w7STYmk2xMjs0T9t/mMKFIlDX7uomIUWaWJXKoxYcvGKWq1ScLqYj8QTxWm8BzgFVrxR12x9nlH0l5cjkzM2biDDhZlDO4WUlg9d/xmi4EoLPbFyfqpqZP5a3qtxifMh6DOr7lsbU7iFGnJsHU9zthM2s5qSKJne27GB3O5PzSm5maPnW4l/GoWHQWSnQl/Gbdb/ig/gM+qP+As4rOIsOSEbedL+zjzeo3GZ8ygZPS8uJE9jFTuwqch+R/2TNl8TkYrkbY+ZQcZVBxCQz39/EF49xJWZh1ahxmHWXpRzeO+CLR0OWjxSW3P29r6P7yibqaD6HmA0AAWw4Y7Nww8QYSDYlUJFcM2n55NIxqM+dmXo4n4iLLePSq9syiJHY09DCrOJk8h4klY9KobHXHnBr7c9/7B3h/bys/XTaa6cMIy8PsbXbFvj5rQmZshu2lrY0EI1EE5La//BEGXP9s+RjUgsDj62q5f+VBStOtMffNwxh1av5x6RS8wQhXP74JpzfEkl7L/9d3NPPHdys5f3IOk3LtMbOlw5W0w0zMsVOemcBPX9zJO7tb+NXpYzltfCbnTsrmsTU1+ELioLELF0zNQaMReHlrE2/tauGquYWUpVt58NLJQz6nNleAX726m7wkM7eeXMrVj2/ig/3t6NQqJuUlsqHayYZqJ9+eXXBcBj8f7W/n249uxKxT8/b35lF559JYJ8YdZ45lXLZtgAungoLC58OX585B4UvNleVX4g65WZizcHhB14tQcgoTbD6au4KMzrbQ5QnLoeAadVxY94xSO01dwb7ZKH+33NKo7lthTjBpOKkiiU79SexYt5Y52XPi5kSKMkzYLRpMUQ1aw11QuIBZxgQmpUxACrooEdOp7/CTnWRArZLnsOo7/Oxvkmee7GYNWUkGurxhKtur2e3+CIDXq14fkahLtGhJs+sIhqMEgiJNziBNziA5yYY4Aw+PV8MU3TWkDxL2/X7d+zR5mvhG2TdwuqN0eWQzgtbuIKWZJg61+Cg57OxYdjrYcjnXns8cAQwaAxtbNg6aOXcYk9bEQ0seGvZ5WCPtjNv7a1zWMoorvhX32Pml53Nq/qmYtea4VtTqVh/bquVYgdE5Fpo6A5Sma8lMScBq1DArdwKzcoe+rsF48cCL3L3hbpYWLOVXs34V/6AYYr7GyssqHaOTy0k2xd+M1Purea35OWpdDfx58718MOu3qDUGyDo2Qdnp7+SfO//JOH0SyzQGsBfQ5lNjFCNYjYP8/rfukOcB/U7wdciVly8JgiCwdFzG0Tf8AlKRbef2ZaNp7PZz6YyRteV+oTCl4ImGucd9AOv2v3Pr1B+SoEvguvHXfaLD2rSJ2LQjczz9ydLRfGdBcSzm4aHLBo8BCItR/vTufgAeXlU9IlF3xZwC/GGR/CRTXCXutlPK+Pfqas6amMX80pRBWyMHw6hTc9msfJ7f2kAgHOWxNTXMKkoieZCoAbNew1NXx7fE/2t1FVXtXh744CA7f3UKD106mZue3sruJnfcdtctKEIQBJ5aXwfAjU9vRUKeW1v9o0VEJSnOmCUiRvnze/s50OZBr1Hz4f52PtzfzmkVmaTb5M+2zbVdPL2hjgun5jA1v+8z8In1dbyxswWAiiwbKyvlSmAwEqU01cLuxh6m5Dt4ekMdZ03MGjaO4/nNDeg0qjhBfqjdgxiVcAUitLqCcYHiSRb9UV01FRQUPjsUUafwmTAhdQKPnvroMe2Tn2oiP1VemT3Q5CUiSkREEbc/gl4rCxuLUUPp4ZvkhnWw72UwJcGMWwZUOy4ou4Czi89GO4ipiTw7lAST5UDiROCxskvpqt3NB5050OlCkoiFUttMWjQqAUGQRaNaJTA+P4FxeePYFTqbyq5Kzio+C4A9nXv48aofU2wv5nfzfjdA1GrUArNGyTdQLV1BGp1BbGYthsOtlI0bIdDFXt8kvEGRQy1+itJNmHvNSDY1VPK9ld9DQkKMilwy+nIyHXrCokSmQ49Bp46FnssnNEC2PCdzuJ65OG9ga+Dm1s388KMfMiZpDPcuuPeoYrx+6rd5/NBSdkbe5XvOCuaZ4g1ErLqBVZxob4FVAvbUd1LrO0ioSSQzX+ThYD3v1LzDT6f/lIlpEwfsOxSvHnoVX8THq4deZXbWbJo8TVw06iJ0ah3UrmJOdzPr009GmHUrHFGdrPLuR6USKEjOYUPVNu7f9CfONeeSbXSAY+Q3L//Y8Q+e3vc0AgLTL1iJy2Vk274e1CooK/SyrnUVSwuWkmrqbT/LnCK7kppTBnVSVfj0uHpe4dE3+qKSOYlXOzbzUvPbsK+KOdnzmZX12bftjiS3T6tWcdG0XN7b28p5k7OPuj2ARa8ZYCYCcMrYdE45zky04lQLvzu3ghue2srm2i7e2NnMZTPzR7Tv5bPy6fDs5/ze68+wGTDo1AQiUSbl2jmtIpPphY6YYdCNi4r564qDSMAHle2cPj4zFuFwGHcgzK3PbuedPa0ATM1PRKsWGJ2REOe2+tMXdlLZ6mZLXRcrbl0Q+/68kmT+vbqa7EQjs4uTOWVMGm/3Hstq0LD9l0uYdOe7rNjXxrb6bu69cMKgz+3Nnc3c+pwcgeAw62JxF9+cnosnECHFqh90BlNBQeGLgyLqFL4U5KUa6fFF0GtVJA3lROiWVyvxd4EYiok6X9iHJ+wh1ZQ6qKAbEqMDtRRGkEQkQR1zjQNZyJ06ORkBYs6W7cFWVne+y6mjF3CH4w5ANk95vep1qnuqqe6pZn3NQTraE8lKMjCleGBrVHqinjOmpfZVs9zNiHuexytFsKXl0oUs/kIRicMNR+1OFTqVkWDUR4I2mW5vWJ4vHIL3at/jz1v+zPKC5Vw/4fre40Vx+yM4LNrYud+sfpM2XxttvjZafa1kWQbmNR0mEo2wq7uSF03yTUi7r53adj976z3kpxoZlW1BkiQONvsQBChKNyEIQixTz6RXcfOKa9nWvYmZUjKLxKnc1/4mALd/fDtvnPPG8D+rXp6tfJZNrZuwaq1cNOoivv/B92OPXT728l7BJCDoLYO2Qo5LmERXuJMD3fvxiQH+6T/IznA3/9QfW1vhmCR5ligvIQ+rzopTlCunUQm+/9FN1LlrWd24moeXyMHimFPkuAkFhWNkUvY8rLsfwaK1UOr4Yuep/vaccfyWcUff8BhodQX4wf92kGLRc/e54/AFRTRqYdgYjDnFKYzPseMOhFlQOrJFlC5viFPL0zlzgvw+GBGjfPPh9biDERaWpfCvy6fGGURJkkRhiplvTM2hocvP2RMHtqN2eIKc+ueP6PCEYt+7aFouT1w1HZ1aFdfVMCU/kcpWN1OPMDOZku9gxy+XxM79+wvGs+buFbgDEYw6DYIgYDFo6PKFSRjGlTjRrJMdUQUhTqTrNWpuPGlks3wKCgqfL4qoU/hSoNOoBhVBcRSdDBo92HJBK1emfGEf57xyDk2eJn4373ecWnDqgN2ququoc9cxP3t+vEtl5hQSEnJYJBoJqwwDnAC1ahWBkCgbCmhUrGx9m65oK22hZsZaJlFZA43OILNTzmdT0iaK7cVIwRSiUoSGjgCTixIGdcXs/z1JZ+HbnevZGuzgZ0l5TMi5An9QZNUeJykJOmaU2anIzuE2/6MYjH6E7ixWdXUxuSghVlU8kqf2PUWtq5Z/7/o310+4HkmSWLG9E39YziRbPD6Jatch1jevJ1GfyNKCpcMKOoCfrvopb9a8yYSUCZxWeBpnFZ/Fqt1yePWhFh+jsi00dgbZVSfn7pkNGjIS9XKsQK8Zi1eQs4686gDhmdejfvUdREkk0zx4yO5grGteh4SEN+Ll3NJzeXrf07jDbjLMve2BaeMgIQs0RkS1AUmMxsVNOHQpLE07l47QA9hNNrwhPzlZs465enZW8VnMzJiJTW9Dp9ZRnKFFr1VhNqh5wemgzl1Lov6zDfRW+GpS5ihj1YWrUAmqEeWefVGo7vDyq1d2My7Lxm2nlB33cV7Y0shH++WWwwk5dn79+h5MOjWv3zR3yLZMm0nLyzfMBuBgm5sXtjSwbFxGrIp2sM3Nu3vaOGtiJhk2I2/tauE7T24m12HijZvnYtJpUAkCCUYt7mCE0jRrnKDr8YU582+rqemUW/RVAmyodvLKjbPJTzJz/4qD2E1aZhQmxQTdzEIHP146elADlbAY5WfLx3DL4lKSLfGfQ1vquhCAibny+0mCQcsr353Nlrpuzp4gv3e++J3Z7GlyMato6JbXGYVJvHnzXDQqFSlWPeuqOpmSlzhoHI+CgsIXE0XUKXyp8IV93PbhbXjCHn4/7/cxwxRArryULI3bvivYRaOnEYA9zj1xoq661Ycz0MX1q7+BP+Ln+5O/z7fLvx173BOI4I3YSbXpBr1Z6nSHWL2nC5UARRlm6ts7kMxeml1t+JL9tHbLNwjRoI1nTnsGkE1R9uIheahq4xEE1Vq2h5wAbPbUc2GWmY0HehCj0NIdIiJKZDkMfGP6aFpcPTyyYQV5xgrCopU/b/4zuzp28dPpP40LrP7mqG/S5mtjecFyACQJAr0ucv5QlC5PhFerXqXGVcMYy1ymmi6lwxUieZBZvsMc7JEjF3wRHxeOks1SSjLN7G3wxEK3+4dYH2jykmbXxcVL/HH+H3m39l2W5i/FmJDDj6b9iOf3P883R39zRK8VwI0Tb0Sj0jAnaw52bRoPL3wOvcFPcWJx30ZGB76gyMrt7USjMHdsYlwMhF6l5/S0C5k8bxZa0cSYpLEjPn9/+v9uCoIQE9kPnPQAuzp3MSl10nEdV0HhSNSqT2Dm8xnS0hPgHx8dYlZRMh/1zo19uL+db0zLITvx+PIITxqdypPra0m16glGRIKRKMFIlJpO7wBR5/SGuOfNfRSkmLlufhHBiMg5D6zBFYjwv00NbK7rYlJeIs3dfmo6fXy0v52nr5nB1vouopIcJN7hDpGbpEGlEnj5u7PZ3+rmo/3tfPPhddxxZjnFqRa21HXFBB3IFfqQGMXpCbGx2sn9K+X3y+8tLsGi15CfZOJf35oaN2e36kA7e5pcnF6Ryfn/WEu7O8A/L5/KvNK+eJV1VZ1846F1ANx55lgu7W0jve/9g7y4tZHNtV3cdfY4ki36uP2OZHdTD99/Zjul6VbuvWA8p9//MXubXXxzeu6A4PFPwgeVbfxzVTXfmJbDaRUjX6xTUFAYGYqoU/hCEopE0aiFAZlym1s3s6pxFSCbgxzthj/LksVv5vyG/c79XFl+Zez7bd1BtlW78UTciL2DXZFoJPZ4MBxl5U45oqA8z9JnMtIPly9CVJI/sCsbvYzWXcRLdb+kPLmcDEsGE4sCNHYGKM3s29dh1ZJo0bKnwUunJ0yCUUNDZwCLQYMnEMFu1pKdZIhVrwwaA7+Z8xvWNq3lugq5Pa80y0QoEiXVpkPbz83sjg0/YVXjKsYmTmDh+N/xr13/AuCx3U9wkv27aNQC00psLM5bHDdDp1IJTC+xsaPWjc2kxWHVsrxgOasbV3NOym24vVp21bpZMG7oVd7fzvktr1W9FnMJBchKMpDVL9w72s/tvtMdxntoHdbimbHvFdgKuKaiL7LgmX3PcKjnEH/a/CcW5S4iLIZ5tepViuxFjE8ZP+h1FNgK+N283yFGJd7a0k4ooqEsKx36FcVava28c2g16uAEjGoL3d7IgGy/NEMmaYZMxKjIywdfJsOSwYyMkQUdHw2LznLCjvVFR4yKPLL7EQCuKL/ihAdZK3y5uOetfby4tZEn1tXy4CWTeWlbIxXZNtISDEffeQhK06ys/pHsyPvq9qbY91/e2jjAYfLRj6t5ZpMcubCwLJX8ZFOsErW+xokYlVh7qFOeHev0kZogG6hcMbuAV7c1yWYh7gC5vS6cyRY9wUiUBz9cD8AF/1hLoknLg5dM5tSx6VS2uqnukPPoch0mZhUno1Gr0KlVmPRq1lV14glG2NPswqDpE+bt7iDfemQjYlRiX4ubxm4/ADc8tYUPf7CAJ9bVYdKpyeonWt/Y2RITddvqu+X/1nWP6DV8cUsjla1uKlvd3HxSCR0e2SG5ozey4ETxu7cq2dPs4mCbRxF1CgqfAoqoU/jCUd/hZ9NBFwkmDQvLHXFtLZPSJjEtfRotrh6krnG094RIsR1RQWrZBi3bIX8B2PM4o+gM6OdxERElqlrlAHGLJpH75/+LrkgjS/KXxLaRJAmxN3PL448wGHkpRg61+HD7ZTtrk04X5xCZnWQg0azloz1OBGDeWAcmvRqnW56vcrrDtPXIeUL+kNyC4w8Fae4Kkmrvy7hbXrA8TiwlGDXotSo+qt3IS/XbuLT8G6Sb0/FH5A/+qBAkxZTCnKw57OrYxXj7PDp7z9nhCvdFDbiaYN0DULAAkuZgN2spzjChVgmUOcp44YwXYmHryQk63tnWgQDMHZMYy887TJmjjDJHfAtVVJLiRHmiRYPdrMHlCZIaqcbSsgb6ibojmZ01m0M9h5idKbdJPbTzIR7c/iA6lY53z38Xh2HooFxJIpaZFu6tQrZ4W3j10Ku8ePBF6t31TEqazW3jfk9O8tA3lE/ve5p7Nt6DSlDxylmvDAgub3MFaHUFGZd97NbxXwfeq3uPv2z5CwAFCQWclHfS53xFX30CIZHtNW5MejXluZYvVEvm2MwEXtzaSHGqlYVlqez81Skn9Pidnj4R8tqOZu45L37xZ2qBA51aRVaikUy7Ab1Gzcs3zGZ3k4u73thLndNHeWYCT141nV2NPYzPtvNBZRsGrZqmHjn64s2dLXHuk2lWPfNLU1h3qAOnN4TTG2JjTRcPXjoZbzDC9LvewxMUOW+S3MI+rcDBmp8sQqdRsauxB29oH6eMTWdHYw/rqzq5cEoOj62pQexdBct1mMh1GKlz+nEHIjy9vj7mIvrEldOZV5LMnmYX31nY9yH3h/MreH5LI9+YmhP7XnWHlwv/sRa9VsX/rpsVJ6TPnZzNx4c6KUuzUJBs5j9XTuOj/e2cM2lkhjYj5bTxGRxs83D6+C+nW66CwhcdRdQpfOE4LEBcvgihSDROQJi1Zv664CHe3iqHvDY5AwNEnbT3JQQxKOexTRkYVt3oDNDcJX/4jy+wUpiWBsR/+GvUKg4XlkKRwQOVVSqB8lwrW6t6sJm0TC2Jv7GXJIn2nhCBkCwqnJ4wJr2ainwrh1p8ZDr0dHvDNHQGMenVdHvDBEJREowaNL1Cdm+9h8pGL0UZJsblyWYdbr9IfUeABw/+mEDUS723ivsW3cfv5/+eFXUrmJc9D41Kw98X/x0AX1Bk/f5uNGoh3mTmnZ/Drv/B+ofYdupWAqIGfyjKgnIHYlRErVIzsTCBCQVW6toDeJt9sZ9PVtLw7V47atwcavFRlG5CQsKsV1OcYWbhuCRo3gq1myFnQdw+ITGEhBQLS/7B1B/w3YnfxaiRV6MtWgsAerX+qE6cne5QrDJo663C/fijn7K5bWMsj89m0sVe06E47NipU+kGhDj3+MIs+fNHdPvC3HX2OOYWpdDjjVCaZR4+D6p6FbzyXcidxXsTz+GnH9/O1PSp3L/o/i/UDfiJID8hH4PagCAIAwSxwqdDdaufJqf8/padZCDRcgzmUMdA5Ih51P6ExTC7Oncx2jEag6ZPPFw1t5BTy9NJtujjFuv6s/ZQJ3e8toeFZSn8cBDny+G4eEYeB9o8rK3q5Ko5A11N55aksO2XJ6NTq2LXnuMwkeMwMb3Awa6mHmYUJqFVq3hmUz0X/3MdkSgkGDRcOjOXg61eLpkRH3KuUas4d3I2H/bO9U3OtXNquezMKQF/umACOQ4TozMS2F7fzW3Pbaci2871CwpRCwKvfHcOETFKxa/exheO8ru3KynvDfLWa1S8vbuFOqeftAQ93z+5lOJUKzq1Cr1GRa7DxONXTo9dy7b6bi7/9wYy7Uaeu24mln5mMRuqO2nrrbxtq++Ocw8dnZHAmzfPjf3/qPQERqWf+DDx7ywo5jsLio++oYKCwnGhiDqFLxxlWWaiUQmHVTugIgRg0qspyTDR7Y3IYdr9iIhRWtRFZIl7cRpLGKxhMMkqm1aoBEi3992o13f48QWjFGeY0KgFMh162rpDsQy8DleImjY/+anG2HxZeqKepZMHmmhERIkXD6zEL/rIS5iKVW8g3a6jpStIU1eAprYebGoNZXmZlGVZYvsFQiJajSp2w9PoDCAhi9fDAsRiUJPp0JNqyKHOt4/8hHwAko3JXFB2waCv18LBWifTxsAuILmUVLuZus4gaXYda5vWcuOKGymwFfCfpf/BoDGQ6dDT0q1HJUCafWCm05EcFs0NHQGCvZWylASdLLAyJsr/+lHrquXiNy5GkiSeXPYk+Tb5OR0WdACXjbmM0sRScqw5JOiGv+Ew6dWoVXLLZ4JRzZ56D5GALLpHJ43m0jGXMjdr7rDHADiz+ExyrDkkG5NJN8dbqPvCEdwBuYrb4Q6y6WBfUHL5cGJx21PQVQNdNaxL0OOP+Pmo4SPcYfdRn9eXjTJHGW+f9zYCAokGxRjmsyDVruNQiw+TXo3V+OnM2r27p5UbntxCeVYCz1w7E+0R4u6nq3/KWzVvMStzFv84+R9xjx1tdu7RNdXsbXaxt9nFzYtL0GtG/hy0ahW/OcoMWP+5tf4kmnXMLZHnzgJhkRe2NMYe84ZEfrJ09IB911d1sr7aSa5Dfp9SqwTuOqciFkVwzeObWHOok1lFDv55+VSe21zPgTYPB9o8vLajkWBE4vfnVXDe5Gz5NQxHEaMSWYlGxmbZOHl0Gv9aXQ24mZiTGMvq+/jHi9CqBey9bsPRqIQgyEHhPf4wPf4wB9s8cREEy8ZlsPZQJ3qNmgVlffN1m2ud/Pm9Aywfl8H0wiSu+89mkq06Hr5sypCvlYKCwhcT5S9W4QuHUadmUtHw7WxD3TRHRImNuiVs1J5MscU8qKizGDQsnZQcVxVx+SKxm3JBgNJM84BYgG3VLtx+kS5PmJMnJFPrbKPTBaMykuJCwgHqvfU4DZsBCGBnfv4cdtW6OdDsQysFOMn3BIYDHjBcBGkVsf2OFLHj8uSq3mGzEeidgSu187/Cx6lz1VGaeJw25nNv5Tl1mLe7dvFdWw1nFI1HrRK4d+MG7JpM9jn30extpsBWgFajGjYm4Ugq8q3UtPlxWDTsbfBi0Kow6oe+Odvr3EtPUHa/3N2xh472RPxBkUlFNswGeT9BEJiZOXS7Zn+sRg1LJiQjSWDUq9nT4OWs9NuY4TidK2bNwKQduSnDpDTZ0KStO4jTE6Yw3YROoyLDZuTf35rKwTYPF07JYfUeJ/5QlIjKBQwj6qZeCa07IW82F0y6koZogGkZ0z6RoGv1tpJkTDpqBfPzYLg2WYUTT5JVx2lTP92sww8q2wiJUbbUdeP0hgbMxLX65Jy0Fm8L1R1eKltcLB6dNiInxW9MzWV3k4uFZanHJOhOJAatmu+fXMojH1cRjEj88NSyAQInFIly+SMbCISjnDc5m+evn4lJp6Esve9vv8cvd52sOeRkyq/fY0ahg9JUC0WpFt7aJUfweIIRBEEgIvV1hFRk22Oh3tMLHexo6IkTaCnWvoW1vc0uvvHQOqwGDf+8fAp7mlxkJRqpyIr/DLUatPz5G32LaWJU4p3dLfzspZ10esNsqHby/ZNLe2frYEeDXLVUUFD48vDFuwNQUPgEGHRqZpTa6PZFKE4f+sb9yDY3nUZAqxYIixIWQ9+NRGNngC2HXKTYdDgsGtx+kUAoyse129lbK5Kky6Kjp4Ul4+Mt/9PMSeicJsJSkFHJ8lxDpLcf0EAAoyRb++NpgbQK9tZ76PFFGJdnjYkYkKtiQ1XGjBojBQklx92yF46GuePAkwCYdz3C1PSpCILAWO2F5Od/A6/mAAW2guM6dkainoze2T2duYuPG1dx/7YWShJLOL3o9AHbn5RzEpePuZwoUaYkz2fNXvn1qe/wMyrbMmD7/oQiUbRqYcDr0F8gV+RZMevVzHXMxqQ9eqVxsHOsqexGkmQTnfEFsgCbX5rC/F5XuY3Bh3mr6n20tVFWXLBiaFOQ7Clw3WoASoEHT37wmK+nP3/b9jce3P4gMzNmxs10Kih8Wlw3vwinN8TEXHucoJMkiZAY5a45d/Fm9ZvMzlzAmfevxhWIcMPCIn5wSl87Zbs7iNWgGRDGvXBUKqtHLfrMnstQ3HRSCTcNk8+mUQlk2IxUd3jJtBmYnDdw8eLBSybzvWe2sam2C19IZMW+dh799lQWlKWy5lAHjV3+2NzaOROzeXJ9LXlJpli4OciVxeHE1YZqZ6w61+4O8uClk0f0/H720i6e3lAX+/9l4zI4bXwm7+1tJdmiBI0rKHwZUUSdwleODIeBjEGKA5Iksb/JR0SMMirbgrrfTIdBp+bkCcmEI1Esxr4/iyZngEhUorkryOLxDmrbg0SiEjtaDhGIWEjSZRHGO+Bcfp+WWbrLyE7RYtbKNz3luVZsJg0OiwNcFyB525By5+ILRNjXKB/j8MzdUHgCEVp7W0J313uobfNTlG4asI8kSVQ2egmEo4zNtQxojwLQqrScUXQGH9R/QI41h3s23gPAJdlGSs2zKbKOpa0nSHWrn4JUI6lDiEtJkohEJXZ1buedmne4oOyCODH43RXXUefuu3kYkzSGIntR3DG0ai23Tb0NkKutKQkhfCGRTIf82h3qPsQD2x5gTtYczi45O7ZfTZufrVUuEi1a5o9NHFLgJpg0TCw8/kqYWiVg0Krwh6KYDINXD6JEcEXaSdYmI/DZzcbtbN8p/7dj52d2ToWvNzkOE3+/JF48hMUo5z24lt2NPfz1oolcXXE1vlAESaoC+ha1AF7e1sgtz2wjP8nMGzfNxThIm/2JZn1VJ1vqurl4Ri4Jhk8+Z3g40uDKRzdy/8qDZNqNfGNaLr5QhEA4isOsI8dh4qHLpnDPW/t4e1cLqQl6KrLtAAOcOe88q5w7zypHkiTe2NlCpt0Qy55r7vHzzu5WloxNI8MWH9Nw9qQsdjT0kGDUDDjmcNR29n1uVWTbuPfCCQA8d92s43g1FBQUvggook7hc0WSJDpcYaxG9aDzcyeSdleIPfVyBcikV1OQFl/J02tV6LXx4qck00wwLJFq02E1ailMM+L0hCm1L6ay0UdE5eL0sfECJSJKfLy3CzEK/qCRCQWyMPEEItR1BPAGRTp0pVR2ZxHudDFrlB2HRUuPL0y6fegsOIA1+7rxBkRau4N4el03212hAdvVdwTY2yB/aJsN6kEjGQB+M+c3AOzt3MvT+54GYF5xGRbJRFGGiY/3duEJiPR4wywYlxQzAImIEnXtfmwmNTvrvHR5wrzb+TKrOp5nn3Mfj5z6SOwc/efiHAYHdr2dJk8T6eb0QatZGrXAnDGJ+ENi7HwPbHuAd2rf4b2691heuBydWn6dOnqfe5cnTCQqoVULPFv5LAe6DnDDhBuwG+zDvp4jRa0SWFSRhC8oxuIPKp2VvFH9BmcUnUGRvYgfT/sx09KnMS553Iirp5tbN/PU3qc4u+RschOz6Ay1UZ4wEa1q+N+D/vxo2o/4z57/cFKu4iyp8PnR5QuxvddKf9XBDpaOy8Ck0/DCd2axp9nFsnF9joc7GnqQJNmRsdsfwqgbPCT8ROENRrjs3xsIRqI0dvv49VknJnvNpFWzuVbOsHt/Xxsnj0lj6V9W0eUL8ci3pjG90EGiScs951Zwz7kVRz8gcgXtyfV1qAX44AcLyXGY+M6TW9ha182LWxt5qTc0/TAJBi1/vGDwiJehqHf6WF6RwZa6LgLhKKbPQFQrKCh8+iiiTuFzZV+jl329M1dLJibHVc9OFJIkUd3ql2/6NQKiKGEzyTfm7T0hNh7sIdGiYUapfcDNuN2sZc6YPoOHw213Gw50IyCgiSbAEcJEJYBWo0IMReNE4qEWH053OBZpcJgmZxCbScOoLNOQ1TCAt6rf4td77mGsZSFXJd3MxEIzte1+CtLib4h8QZGtVX3zgY5e97uIKCFGpQHCFWTzkDfOeYMeD+ypgW5NAINOiP08/OEob25uZ86YRJKsOvY2eDjY7EOAmEvomIQZrOp4fkC0wcNLHmZ7+3ZyE3JJMiRx35b7eHb/s5yafyq/n//7QZ/r4QqcxaBmUUUSc7Lm8F7de0xLnxYTdACjsy0IQIpNh1atot5dz53r7gTApDXxvcnfG/L1PFZ0GlWcq+WPV/2Yg90HWdu0lmdPfxa9Ws/SgqXHdMy7N9zNPuc+DnTv57QJ85GQCEYDzHDMH/ExCmwF/GLmL47pvAoKJ5pUq4FfnDaGrfXdXD+/b6GrJM1KSVp8J8F3FhQhRiXGZiYMqDx9GmjVKpItehq7/dQ7fYQi0eEdakdAWIzy9w8OsbQ8g7AY5Xsnl9LUHYg5TL66vYkrH9tIQbKZF78zG61a4Bev7Ka52883p+XiCUVYPi4z7jp6fOFYS6SE/P4NxFwsrYZPfssWikQ5628f0+kNkZdkorbTx5RBWkcVFBS+fCiiTuFzJRSWnRHDYhRp8OSAo+INe2n1tlJoH2hhDdDYGWR7jRuA6aU2khP6MuAaOgMEw1FauuTogaHMPFp63RwPZ7yN7p3zSrXp0ajjhaBKJbBoXBJufyQuQiAnyUBLVxCjPkKPV927vw6XP0KHK0xtu5+TJ2gHmK4c5vkDz9MT7mBjz0s8UPRz1CphYEYf8gD84U6n8lwLSVYdwXCUFTs6CUaizCyzk2bX0+Hv4Iq3ryAkhvjnkn+Sbc2mp8eLJHkIhKPsqpMrfVkOPY29Fund3ghJ1r7XT6dVMSrLjNMTZlHWAk6reDXOur7D38G9m++lwFbAgt4Ig12duwDY3bl70OcJ0OOVha8nIBIRJU7OO5nt7dtJNiYjSVJMfJsNaiYX9xkCbGrZFPu61H6cBjJHISJK1LT5mJf4LYq1jags9cd9rJmZM9nn3MeU9CkY1Sa2N+/mqfWvclrh6fxg6g+O+7j+kEg4IpFgUt7iFT47rpgzshncJIueX50x9lO+mj50GhV3nzuOS/+1gQ/3d/DfjXVcMCWHbfXdjM+2H1P7ZzAicvXjm9nV2IPTK3cKPH7FNEZnyAt+vzhtDM09fqISBCPR3vBwH92+ME+tlwXbyko5/uDBD6p46urpJFnkzxWDTkV2ook6p49r5hXGnEL/fslkNlY7mVrQJ77qnT5e3NrI0vL0AaL5SP61upq/vLefb83K5zsLiwmL8ufugtIUblhUjNMb4q439nL2xKzY81BQUPjyoXziK3yujM21YDFqSDRrB4ijkRAWw5z/6vnUu+v54dQfcumYSwdsY9SrEJBXPS0GTdzKaFG6Cbc/QqJFGxN0YlRiW5WLSFRiYmECXZ4wayu7AZg92k6qTY/VqGFaiX3I65JbOfsEV6c7RFOXj8ebb6ayay+/mvFrFuecisWoYU+9hw5XmKgEH+xyEo5EmVCQQF5q/Ar25WMvpzvYzSn5p8RVNIPhKJuqOvig8W22e1/lgcUPMLe3ffFwHIM/JBLoFdDd3ghpdj3b27ZT3VMNwMaWjWRbsylIMxIMR9FpBGrb5FbRrGQDdrOWYCRKXop8TY7eIPHsJAOF6SYOy2mbMT/umu/ddC+vVL0CwMKchRTZi/i/Wf/H//b/Ly5Q/UhGZcuhyQ6LHD/xzO4XeP7A84AshCanDW4GUO+WBZaAwIS0CUMe/5NwuEqZr5tBvgMmFFrwhX38et2v0aq1/HT6Twdk2g3F9yd/n+sqrsOkNeEXfazY/R2cgS6e2vfUcYs6f1Dkve2dRKIS00ttsblEBYWvMrWdXqISFCQP3mpemGLBotfgC0XIcZi48emtvLunlfmlKTx2xbRhj/36jmae2VTP1XMLSDLr+ag3k04lyEYmuY6+Vv7DwrbNFaDdHaQk1UJxqhV3IExxipmD7X2zbJWtbv6zrpZbFssLUHqNmtdvmkOrK0hxap9BlEWvYeGoeEfT7z+7jY01Xby0rZEVty4Y9vr/t7kBVyDCs5sa+P6SMp6/fhY7GnpYXpGBQavm8n9vZG+ziw8r23n7e/OGPZaCgsIXF0XUKXyuaNQqioZxqTwaATFAs6cZICZQjiTJqmPxhCRUgjCgCpZg0jBvbHzrSXtPiLqOAACptgDWfsYpquN0mly/vwenv4s9TrlKta1jE2eVyqJmTI6Fxk4/nkCUYK/wanQGyNO3gyER9PIq7JysOczJmjPg2DVtftq6JMaYlrC6/RXWNa8bIJjsZi2FmQLuQIjCdFmYzc6azfLC5YTEEIvzFgNym9LhPLziDDMRcfB2zX2NXrq9Edx+DyWZg99EAbxb9y4AJo2JDLM8UzPKMYrLx1zOhw0fElb5cUZbKdZNwu0P8lrtM5xedDrjU8bHmb9MSJ2AUWPEprfFcvkG44ryKwAoSSwhy5I15HafhP6vh1YtkGTR807ta7xa9SoAc7Pmxl7P/uyp91DfEaA810JWUp/QOhyvYFSbuGrc1XjCXk7JP+W4ry8kRmOmFP5g9LiPI0kSO2rcuPwRJhYmYDkBrV8KXw8OtLrZ1+JmaXn6iGIMPim7Gns4+4GPiUrw3HUzmZSbGPfYXW/sZXZxMitum48vKJKfbOYv7x0AZBfO/qw52MG1T2xmdEYCT1w5HZ1GxR2v7abVFcTpDfLyDXO4YEo21R1efrJ0NIUp5lheXH9SEwzcd1FfhIBBq8YXkuegC1PMuP1h3MHIAGdLq0GLdQRGLpl2I9BFlv3o7au3LC7hoY+quGianHN3ZEtsaZqFvc0uitOGdxpWUFD4YqN8Sit8KXD5I6gFIc7uH8Cqs3LfovvY1r6NS0cPrNId5lhuSBMtWqxGda8Low6LUcO8sYkIgMM6cgOL/iQYNQTDNr5dchtt4l6urrg67nGbWYsnEMQn9rDe9TRzRAM0dIPWDLN/AJqBlR+XL8KWQz3otCrUKnBFOihOymZe9sCV1g5/B9d/fBbukJs/6f/EyXknY9AYuHvu3bFtJElic5ULty/CpMIEbGbtkDOORboWJntfImDKAemSvuGPIyi0FbK7czfnl54flw1344obOdRziBx7BmeOP5nGLhc258msqtvGmqY1vHHOG3HHqUip4KMLP0Kj0gybxWbRWbhp0k1DPn4stPna+Mmqn2DT2/jt3N/Gqm+lmWYcFi1GnQqDTo1aJTA5dTKpxlQ0Kg3jkgc3YTjQJFcSqlp9caKuP7OzZjM7a/agj40Um0nLtBIb/lB0wLzlseD2i1S1+gGoafUPH6iuoNCLOxDm7AfW4AlGuHFRMbcuKTv6ToMQikS587U9BMIivzpjLGb90H/37Z4gYVFeyGhzBeIee+ijKtYc6mTNoU4un5VPqlX+2ytLs7KtvpsJOfF5bu/ubcUdiLCh2klzj5+8JDPLxmXw+NpalpZnoFYJ/O68YzMmiYhRrnhkI0098rXNLEwiP8nEU+vraOr2H3X/Dk+Qu17fS0GymRt7Yxb+cP54LpuZz9jMo7dLnjI2nVPGpg/5+B/PH88NC4spHKLKqaCg8OVAEXUKX3jaeoJ8vLcblQCLKpLiKmcAc7PnMjd77gk7nz8kMqPMHicEk44Qc63dQUx69YBrGYqZo+x4AyJW42WxebCtVS4aOwOxFlCNWmCr+zlWtv2PlW0wL+0k0gU1RMPAQFFX2+6nyxsBYFGFgwRjKpcI9w56/p5gD96Qj8uy78bVUkqnIzTgOe2u91DfHogde7QR/KKXhA2PQ+UbsPhXkDUJ1DoyQ3tBcmPy7sHpqsdhyx30vI+c+sigAek2vXwj5TDIq9T6YDYqQYVfdOEJdfDvXf+OVd0OY9DIN2OVzkqqXdWcnHsyatUnd20LikH+t/9/lNhLmJbR14b1ds3bbGjZAMB5pecxK7PP6js5If61y0nI4f0L3h/2PGVZZhp6f96f+JqjQfSqoVs8hxKNx4LZoCbFpsPtj5DpOPZsP4UvMZ526K6VMxWPg8NLPMeboQnw4f52/rOuFoAp+YksHp3Gbc9tJ8ViYHFJJgatiplldrQaFQvLUvndeRWIUWmAeFk2LoOV+9qYXZyMud/s3PrqTgA21HTFbf+tWfmsPtCBVi3EhOQvTx/LL04bgyAIvLytkV2NPXxnQTGJ5pEt8rV7gqw62AHAxBw7P146ioV/+JAOT5B/ra6OZdUNxWNranhhayMAJ49NY1R6Alq1isl5icPuN1I0ahWlR5nL608wIrfz24yfPBpCQUHhxKGIOoUvPP6Q3EIWleTV20+TZmeAdft7UAlw0vgkDFo1jc4ADos2JuCqWnxsr3GjVsGSCckjimJQq4QBphW17X7Z1rvNRzAsrzIvKJzAO03PkG3JwlZ6BtjzQDd4S0x2r/FKgkmD1ajB5YvQ7YuQnWQYUGErshfx29n3EuwYAxL01O0hySFB+oRYla3b0+fKmenQ8ULT43RHnMyuf4vyuk3w7s8gbyqkjKEpZRSdNe+xLeSk8+AL3DL5lkGv0agxUuYoY1PLJlbWr+QbZd8gJyGH+0+6n53tO5mUNgmtWktzZxgECUMDdHnDvHjgxQGiDqAr0MUlb1xCQAxw08SbBlQ8j4eHdzzMP3b8A41Kw+tnvY3PYyYlQceC7AU8v/95WYBKsK1tGxNSJxz3eUZlWwYEqTe4G7DqrDGROxLWOFew07WZ0ZYK5iUff5vm0VCrBOaMPjE3jQpfIkI+eHAOeFpgya9h1o3HtLvVoOXFG2axv9XDkjFpx30Z6QkGbAYNKkFgSr6D13c2s7KynXlFqXgDIt6AyMcHOlkwOgWAC6bkDHqcU8vTObV8YJXql2eM5cl1tVw2M58ub4jGbj/lWTYEBA60ydE3//jwELcvHwPIArXe6ePm/24DQIzCL04fM6LnkmEzcmp5Om/vasETjKDXqLlmXgFPrq/j27OHNpdp6vZz3t/X4A/L8S65DlPMPOXzwhUIs/y+VbT0BHjosiksLEs9+k4KCgqfCYqoU/jCk5tsQBTlOIIjq0tD0eHvwKQxYdKaaOgI0O4KUZZlHtJZEmTHxeZel8uoJLsc7mhyU9vmR6cRWDY5BUEQEHvnlaISRI/TsRNgXK6VRmeAVJuOg80+Um06phUtZ3rGNKw6a6wydSRhMcyd6+7EE/bwy5m/xKa3ERGjfLS7i0hUwu2LDNoqt7RoATs1bsTOagqbn4FmQJIgcxIA5XlW9jd5yXIYsJqhxymvYDtLZkPNQUgdJR+o8wD2MedybbCBBk8Df0m74ajP9ZYPbkErJdDu8vG7k36JVWdlVlZf5Ss7Wf653DzpZp7a+xSXjLlk0OMICLHV/8Ey7o4Hh0GeqTRrzRxoCOLskXp/3tm8dNZLbGzZyBVvywLz0VMfZXLaZNY2reWu9XcxOmk0d825C41Kw+buNVR5K5nhWECO8egugO/UvMNtH96Gw+DgpTNfGnGmXqNfrl40BuqOsuWXjLbdEOyBrOlwAiqwCsdJJAB+p/y1q/m4DlGcaqU49ZO16/7ylV30BCJMzLFTlGJBoxIoSDYTECMc6nDjDoY5uNMVE3XHQmO3H4NGzT8vn0ogLLLoDx/Q1BPg9mWjuWRGHkUpZmo6fUzN75u37vQEOe/BNf2eY3yr4geVbXxQ2c6VcwrI6WecctitN8WiRwIOtHlwekNcM6+Ia+bFZ5weyZa6rljL5u/PG8e5k3JQfQqxP8dCa0+AeqfcMrq1tksRdQoKXyAUUafwhUcQBApH0LImRiX21Hto8NTy480Xk2xM5rnlz7PpYBCp9/EpxYNXRMKRKB/u7kKMSqTZdeSnGrGbtagE+cOrv0FKcYYJg06FWa8eIBKD4Sg1bX6SE7RHFaBFGXLANxBXwdFE7QjS0De161vW8+LBFwGYmj6Vi0ZdBAhyXF6UIT/0BUGgQr0Lul7t+2Y/4Wg3a+McPZeknklzoJEJOVNpH30jP1x5CxZPE/dM/QkmrYkXz3yRkBiKm5UbiomOWcyz3IhGpWN/k5fSfuYqaxrX8XFlO6n6bM6ZdDLLC5cPeRy7wc7Ty5+mxlXDwpyFgJzL19ARIMOhH3E7bH++OfqbjEkaQ4Y5g5Z2I84ef5xDaljsq2CGRNnC/Ol98jXY2ip5s/kgp53zFJu61wASO12bRyTqqnqqkJDoDHTSFewasaibk3Qye9zbKLOcmADlLwTuZtjxRN//58waeluFTxeTAy55AZq2wpSB1fLP7DJ08t/y4RbIvCQzK29bAMDtL+7kpe2N/Py0kVXK+uMKhFn2l1X0+MP8bPlozp+SQ4dH/rtu6PJh1Kl565Z5+EJiXHthTaeXVlefqcrTG+r55vQ89re66fKGuPY/mwlGojT3+PnHpXLb6vef2cYr25v41RljuW5BET3+MBXZNtJtI2uPXjw6jQumZCNGYXlF5jELun0tLuqdfhaPTv1ErbD9KUmz8vPTxlDd4RlxhIWCgsJngyLqFL4SePwRGjoDHGz2ASnkGydQ5dtCV7ATq9GGyy9iHyKzKypJbK92E+0tuyVatDEb+Io8Kyk2HYlmbexDURAEcpIHN6DYVeumriOAWiVw2pSUY/4QPtDkZVedB4NOxZIJg4exj00aS7G9GHfITb52DmtWfsiU3bdzSuZ4Ohb8gfTEYW4YgnIouYTA3/Hy7trb+eP8Pw6a8ZdvKiHfJA/lv1T3Cps6dgCwWQowF45qWtKfX0z/P1bvlc+9u86D3aSJBa1vqDtAsWkJAGv3O1kyPp1AJMCd6+4kLIb55axfYtb2icAiexFF9r4V7k0He+h0h1lRvZZdwZc4q/isQd0nh+NwW2WqSSIz0UC3N8zL69tISdCi1Yzh/6b+mZQEHTMzZwJwfun57Khdwb+bW9E1t1L58tVsTVNTllpEcdLAG83+2XqHuWzMZYiSSK41lwLbyG+OMgzZZBiGn8H50qE1gloHYgiOoRVV4VOiYK78bxgCYZHvPrWFNneQv140kbykE2uy8fdLJrG+ysn0woHB2L85exy/Ofv4FjXCkSj+XhfKtYc6+aCynR+dOopAROTSmXm0uQLc/eY+ytKtXNsvRH1ynoPblpTytw8O4Q+J7GrsYXt9F+c9uJawKJGfbKKmw0d5Zt/v72s7m4lEJd7e3cIlM/Li3DBHgkGrPmZTlsO0uQOcef/HBCNRfn7aGK48gQLsRB5LQUHhxKGIOoUvPZ3uEKt2dyEh5wap1bAwfwaX2E6jKLGIg9VyplC3LzLo/k53mPpOucUlO1nPqKy+mxOVSiDrGHK+DmfdGXSqoQwhZRo3gykJEvPjvn34ZiMUjiJGpUFFXaIhkRfPlCt1r29qY1T1s+g6dkLHTtLm30ZlYwpqlUBxbxUwGI5i0Kmp6anhl/se5zxTNqPyFvH31XIO2nvVHzPTkUKWw4DDOvjg+/zs+bxw4AXMWvNxzZWl2IyMyRHZUy9nNB1s8WEzyxl0S0vmsOtABJWgwW6UX+uPGj7ilUNyvt2srFmcVXzWkMc+HDHwWss/qPXuY0/nnmMWdYcRBDnQ/UCzFwloc8lVOrVQztx+80Fzs+fyz6WPI/1rOYR91EfcfFx1iDVVW7jsgu/Etmv3tcdmAB855ZE48WzSmrhhwtFbV78WGOww83sQ9oM14/O+GoURsKOhh/f2tgHw+s5mvrOg+IQe32rQsvgTzOQNRZJFz1NXT6ey1c2vX9uLPywSiUb57zXygs39Kw7GTElOGZtOfj9HyO8uKiEQjnL/yoPYTVq5Tb93MfCauYXMLUkhx2Fie303P35hB5k2AzqNilZXgPtXHOCaeUVxXQCfKhIcng4Qo5/uLLqCgsIXA0XUKXwpcPsj6LWqQT8QvQEx9uE1pdhGeqIeter6ER/bbtbgsGjxh0RKM82fqE1ldLaZdLscgzDgOO17oWU7hIPwyk1yXMF3N4Atu9/+FgxaNYkWLTqNCqc7RJMzSH6aMebG2eRpIhAJUGgvpDTLTKf7HHJda9FmT2Bnj52qNlk4JZg01LT5aXIGKckw8YHzNbZ07mRL506eKL+YJXlLaPI0UaBezMFmH42dAU6dNPh8SqYlk+dOf+64XxeAsiwLJp2aTYdctHaH2NfgYXxBAmXJBeTbonR5wqTa5JbVCakTyLHmEIlGmJI2vAPflGIbXZ4wLYbF/H17ZawtczjC0RDdYSfJurTYz6k92EKN7yCjrOMYnW1BwINaJdDoDJJmH+j+WJw+CW5YD121TMoYyyU7HmZc8jiSjH25U3s699DkbQJgc9vmQSuiCr0Y7PI/hS8FFdk2ThqVSps7yPJxgwvxV7c3Uef0ceWcAgzaz3ZO8ukNdTyxrpbrFxRxWkVm3GNT8h1MyXewo76HF7c2sqy87/pnFibx6Mc1FKaY49okD7S6aez2c9spZSwcJYu3VKuB/1wxnU5vkDPGZ8beSx5bW8PeZjcAOo2KUCTKH97Zz6vbm3n1xjnHJOza3UGe3VTP/NIUyrOGrmJvrHHy+o5mLpmRS3GqldQEAy9cP4vaTh9LBzGKUVBQ+OohSJL0CaweFI6XhoYGcnJkt676+nqys79i7VQnkNo2P1uqXBi0KhZPSEJ7RJjtR7s76XRHKGl6hvLdd8Lp98GEi2KP+4Iine4QGYkGNOqRCTZJkuj2RkgwaYbMajtmPvw1hL2g0sNH9wKCLApShs5xenNzO4FwFItBzUnjk6hz1XLeq+cREkM8uPjBOLMRkCt3oYj8J33yhCQ+2t1FMBzFYdWSlenkBx/9gGxLNjeO/g176/0YdCqSrTrqOgKk2XXMGjUyt8PDbxtHE8DOgJOr3rkKf9jPQ0seItOczYodnXgCIpOLEshNOf4ctcEQo+KAmIP9XftJM6XFOUz+r/FROsPtVCRMYaZDFoFP1P8dr+gh25DH8vQLYttGRGnEvzdHEo6GuWfDPfgjfn4y7SdYhnAyVVD4qnGwzc3iP30EwA9PLTvhlbwj+c/aGj6obOe2U8oYnZHAjLvep8UVYFS6lbduGZjdeZj397Zy/ZNbGJdl47/XzECrVhEIi+jUqlj7fJsrwLzfryQQjnLnmWO5dGb+oMeSJIm/rjjI3mYXKyvbCISj5CWZaO4JEIpEEQTYdPtikizyIlEwItLuDg7raHntfzbx9u5WEk1atv5iyZDbTb/rPVpdQaYVOHj22plxj7kDYcSoNGhIusLRUe7XFL4sKJU6hS88noDcNhkIR4lEJI5c8I2IgCQydscvgSjseTlO1Jn0akz6YxMPW6tl18vkBC1zxwyc6TgeggkF6Dt3Qe4sOC0XErIHFXThSBSXL8L2GnestccTEKlt89MqthAU5WH9Vl/rgH0zEvXUtgcoyTRhMWiYXJTAm5UbWN38LrfkXM2LZ75IWIzy2ka5JdUXjJJdoKcky4xlGGfQ/ngDET7c3YUAzBvrGBAI35/tbds50HUAgHXN6zi/9HwWVSQRjkRHFAUxGA/veJh1zeu4bcptjE4aHfeYWqXmYLMXt19kTI6F/x18mrs33E2GOYNXz341FiDujrh6/9vTt68gvx1aNPGr4ccr6AC0Ki0/m/Gz497/syAcibLxYA9iVGJaiT3Wzqqg8Emwm3QkmrR0+8Pkn+B5uyMJRkR+8cpuJEmujP39kslcMiOXx9bWcvH0wTM0D7Oyso1QJMrm2i46PSHSbYYBVcVIVIq5HgcjUSJilA/3tzMmM4EMW99ny9pDnfzp3f0ALByVwrdn5TMpz4EvFOG+9w8wMScxJugkSWLZX1ZxqN3LaRUZ3P/NSUSjEg+tqiIqSVw7rwi1SogdPy1h+DGAsZk2Wl1tA8LI650+lt+3imAkyrPXzmR8jv3oL6iCgsKXEkXUKXzhKc0yo1IJ2Eya2Mxaf6aX2mnpChI+5R50B9+E+T88rvPUtfvZWesmN8WIt1dIdrjCfLy3i5ll9k9kJb2/ycvu4GK0lrnMz8rEWjz4n54/JLJiR2es2gZykK8EGHVqHlz/IADjksexIHMZb2xuR6sWmDfWgV6rYlKRjZxkI3UdfjrdIbxCI3/cJ894ZVQmcNvU21CrBMwGNd6AiMOiJcWmj3P3lCQJfygac/Z8vep1Htz+IOeUnMMloy6nuStIMCzPaHR5wsOKupmZM1lWsAx/xM+SvCVExCiCIBy3oPOEPNy39T4AHtn9CL+b97vYY4FIgBcqX6WnPYt0gzy7Uu+uB+TZtkAkEBN1y9LOoz5QzWiLbELQ4K/FFekGIFX39ZrpanOFaO2W3f+au4Lkp57Y6qnC15Nki573b11Aty9EYcrIK9RRKYpENLbIMhL0GjUnjUrlg8p2Tu6dw/vuohK+u6jkqPteM7eIdneQ8Tn2IV0pM+1Gnrl2JvVOH6dVZHLXG3v51+pqUq161v7kpFg3R4JRg04tEBIlVu5rZ0J2IvNKU7HoNfz6rHhjl5AYpapdbpV/fWczf5Uk3tnTwt1v7gMgP8nMsnEZ/Py0MZw+PoOSo4SDP3TpZJq6A+Q44v9+qzu8uHo/z/a1uBRRp6DwFUYRdQpfeLRqFaOzh74pMBvUpNh0OEddTvrMa477PLVtfkIRiepWHydVJLPxYDddnghtPSG8QfG47PIBatr87K6Tw2zD6GMrvoPhC4oxQWfUqbCbtYzLtRAFrEYNbT7ZmMCkMeF0iQTDUYJhOWPvsJvk9hoXbr9IlyfMvPJspqVP42D3QRblLgLkeIZF45IIhEQsRg2d/k6aPE2MS5FvOjYc6KHJGaQo3URFvpX/7PkPNa4a/r3rEdKCpxEMR0lJ0GI2aMhwDJw1649BY+CeefcAsqHN69va0WlVLBqXdFwVIbPWzMl5J7OuaR2n5p8KwObWzUSlKCvqVvDE3ifQq0z8sPhZkqx2vpP+HWx6G+OSx8W1X6YZMkkz9M3ZWDUJaAUtoiSSqEsacN6vMslWHQ6Llqgkx3koKJwoHGYdDvPIf6cCop8Xmv+DX/RxWvoFpOkzj75TL/+8fOqgLrNHIzfJFIsgGI5JuYlMypXb0329hlaBsEhUklAjn/ONnS2ExL739/zkvrbKnQ09/P6dSk4alcrls/LRa9RcPCOXJ9bVsaBMzkAtSLZg1KqRkCjoNWhRqwQm5x29W0SjVpGbNLCNc25JMj88tQxfUOSsiVlHPY6CgsKXF2Wm7nNC6dE+cfhDIu9s7SAqwfh864gy7Y5EjEq0dAc50OglO9lAcYYZjz/C1moXCUYNFfnW4zZQ2dfgYW+DvCI71ByZPySyvdqN2aCmqTOALxRlVJaZ0Tl9YlaSJA51H+KDhg84rfA0HPpUtlW50GpUjC+wxqptWw51U9seJF/XxsQJo2GY2IFQ1fu8vedJ7nZu45rJN3P52Mt5a0s7/lAUh0XL/HIHrx56lQe3P8jy/LPICJ0ByIYw/lCUxs4AFfnWEc3GHY5rAJg3NnHEQfLDsallE99++9sALCtYxhvVb5CoT+TNc97BrBu5aymAX/QRlaKYNSd+7s3liyBGJRItg7uLKnxy3IEw+1s9TMz5ZFV1hc+PlkADL7c8DcCMxPmMt00bdvuwGObFgy+Sn5DPtIzht/2kBMIiB9s8jMlIIBAReWVbE5PyEintV0FbfaCDKx/bSE6iifsumsCYfvEGVz++iXf3tCIIsP/XS2Oz4RExiqbfnHinR26vP9ymqfD5o9yvKXxZUCp1Cl96pH7WzdF+axSt3UG6PGGK0k1oh3EbC4ajrNzZSTAcZfboRJITZLFhMWpOyDxdSaYZjVrAatQM6qIYiUb43oofUtVdw7kZPyZNnwf0RTB0uGSnyA5XmAmFWVw17qrYvtNK7QOON9HcwJiaF9F7fNB6AWQMkY3kaUFX9R6nG9JosBTQ7pPn7KYU26jvCFCYJgu104tO5/Si0wGobffT6Q7T4QrR3mv339ARGJGoy08z4guKGHSqOEHnDURYv78HnUbFjDJb3A3O0RAlMfb16UWns7xwOSX2kgGCzu2PYNCpBpjs9MeoPvbFgJHg8kVYsbMTSYKZZXbSEwe/WevyhFm/vxuLUcPMMvuJM+j5mnDhP9axp9nFt2bl86szxn7el/P1IuiGt28Hgw0W/wpUx9denabPYqJtBj7RyyhLxVG3f3T3o9y39T7Ugpq3zn2L/Y1qttV3c/ms/Ljg8BPBtx7ZwLoqJ9+cnstdZ4/jG9MGzurNKUlm1/+dgkYlDFgEXFqezof721k8OjXufejI9ztFzCkoKBwviqhT+NJj0quZN9aBLyCSldTrKhaOsrayG0mCUESiIn/oeQRvIII/FMUnuqh2hklOOL5VOEmSqGnzo1YJcSJHzowb2ijgUPchPm59F4B9/vdZPvYW2l1hSjJNNHYG2HCgz8yjuSsw5MxTlydMpztEniUdgwaQDJAweLuN2x9hV72WJNNciv0fU5S3iDkVcsUrOUEXE7ZHkpdipK07GBN0DouG0qyRmSBo1SrGFyQM+H5zV5CeXgHr9IRJtQ28qfm48WMCYoCTck8C5Lmbq9+5mi2tW7im4homp01mVuasAfsBHGrxsaPGjVmvZvooPW9W7uGUkvEkmo+tkjcSmpwBWrtDMaMakFfiD681hMWh86IanQH8oSj+UAhPIILNpFT1joUWl5w12dTt/5yv5GvI9v/Clsfkr4sWQtGi4zqMIAhMSxw+9Lw/h1uqjRojgZCKKx/bSFiUcHpDJ0TYd3lDdHpDFKdaqO30AVDb6R12n6EWjs6ZlM05k5QKj4KCwqeHIuoUvhI4LFoc/Vrb1CoBg1aFPxQd1sgDwGHVkZjk5K7130KqFnnM8hhjk49+Q+AJRNhW7SbBqGFcnoUmZ5Bt1XI2kUGris24HY1CeyGLcxdT46rh+unnkp1oJDtZFm6dveIJIMmqGXK2MBqVWL23i4go4UoxMGnuT+h0hXC6JPIN0QE3GgeavLT0RGhRTabGPgm9XodKGtn1ZjgMNDmDpNp0zCizj7gtNSpJtHQFsZk0mA19bz1ZvcfTaVSDtmRua9vGde9dB8CPxt/NmWWn0B1pZkPLBgBavC1DCjoAj18WjL6QyLJ//pPGxlLusrzKw1fmMi192ifKJeyPJElsPNBDVJIXFWaU2QH592tGmZ1wJEp20tBCMj/VSJcnjNWgIeE45ze/zjx+xTQ+3N/O+VOUG+fPnJzpoE8AnQVSx3xmp72g7AJKEktIM6Xh0CeRajXQ2O0nx/HJq+49/jAn3/sRHZ4gfzx/PA9dOoV39rRwwZScIfcJhEUu+/cGaju9PHzZFCqy7Z/4OhQUFBRGinLnoPCVRKMWWFSRhD8oYtCpWXm4/W2UHeMgzosqYzsBUV6JreqpGiDqHtz+IC8dfImbJt7EssJlAFS3+mnvCdHeE6IgzYhBp5LH5QWOyQREq9Jy78J7B30sN8WASiUfb7AK1mEEAXRqgYgoodeqiEhqPt7vRoyCLyAOqJCl90YfAHjDAt5wmNbuEHkjcD7MTjIMK05ANnzp9oZJt+tj8017673sb/Ki1QgsnZQSay809lZah0Kr1qISVESlKE2dUdbs6+bZtl8BYNPZ4tpRB2N0jgWtRkWiRUv3B3KlzO0TuPLtq7hl8s1H3X+kCIJAokVLpzuMwxpfZcsYouWyPxbDiWn3/bpSnmUbNpxZ4VMkowJ+cEhuuzzO1svjZWJqX3v5GzfNpanHz+iMgR0Bx4o7EKbTK8+31XR6OXdyNuOyh//9OtjmYUO1E4D39rQqok5BQeEzRRF1Cl9JnJ4wKgHsZi0NnQG6vXK1pq0nRN4g819zs+byvcnfIygGObXg1AGPP777cdxhN0/vezom6jIdeuo7AujUAjtq3IzONnPS+CQEgVjr3UjxBUUanQEyEw1xlUWhp5aclvWQOQUoGnJ/QRBYMC4Jtz9CklWLJIFWo0IMRQcVmJkOA9NLYf3+HgTAZlIPOet1GDEq8t/K/2LVWTmj6Aw2tWyi2dvMsoJlcYHfkiTx4S4ngXCUwnQj4/PlG6zD846SBMdizzQ2aSxPLXuK7XWdGENl6DR9lbUCWwEFtoJh99dpVIzpNZz55zdP4W8fbWGn9zmiArhD7th2B5wHuPWjWwmLYf666K8UJw4dltwT7KHeXU95cnnc9+eMSSQUljP46lx1ZFgy0AgaBEFAkiRau0MkmDSxuIgvGvVOHylW/YCcLgWFo6L57JxTA2GRNYc6mJCTGOeuaTNpT1jbcnaiib9fPIn9rR6unDP4e8y97+5nd1MPvzhtLLlJJsZkJHDJjFxqO31cMDW+oveb1/ewodrJnWeVK2JPQUHhU0ERdQpfOdp7Qqze2wXA/LEO0mw60u06JGnoiokgCFxRfkXs/wMhkeo2P6k2HUlWHVdVXMXLB1/m0jGXxrZJsupYNjmFl9e34g7Ihh2zRyce1zVv2N9NlzdCQ0eAheP6WervfQm8rdBTD7NvG/YYeq0KvVbX+3xgUT+R1+Bu4LvvfxeLzsIDix8gQZcQa3WUgESrLib+WrqC7Khxk+HQU55rYfMhF53uED79Du7eeDcgVxd/suoniJJIZUs7t8z4dlxI92EBF+03QjYmx4LNpMFu1vZtG/LArmdApYXyC0FzxM9HikLbLsYaEhk1YQydrhB2i5aJJX9kddPqYdsuB2NmTjkzLy5nR/sE9jn3cWbxmfJpJIlvvf0tXCE5lPzNmje5MfHGQY8RiUa48LULafQ0csOEG7hu/HWxx1S9GXx/3PRHHt39KGmWFC6afAbL0s+ju93OvkYvOo3Aqf0qlZ8GHZ4gtz67HYtBwx/PHz8ikfav1dXc+doeytKsvHHzXMWoReELy09f2MkLWxspS7Py9vfmfWrnObU8g1PLB3+s3unjL+8fACDLbuT/zixHpRIG5NGB7Gj58KpqAB5bU8sfL7Af03X4QhH5vUVZbFFQUBiGYw+KUlD4ghPplwMnRiW0GhUzRyUya3QiumFcMPuzo8bNvgYva/Z1I0kSV5RfwctnvcyS/CUDts1KMiAIcuVuOHq8YRo7AwyWIqLrFVSZ4QPQuEEWMwCOIvyCmd2mhXS4QiO69sPotSqSE3QIgsBHDR9xqOcQ29u3c8vKW6h0VqJRC7GqoM3Ut75T3erDGxQ52OwjEI5S3xHAF4xiFPNQCSoMagMpxpRYOHCPR01Nmy+2vyDIYeiTihIYl9dnUHPYQCah37lo30Ogs5LfHnyOP635PyLRSPyTqF8DO5+GTf9AHeoh1a5Hp1FhN9jlWAfD8bUrVqRUcEHZBbEwciDu67VNa4fcNxKN0OnvBOR5vsHY07EHgFZPO2tqNlHvr479XopRBv0dOJG8sbOZD/e38/qOZtZWdY5on73NsqA91O4hGBGPsrWCwueHNyS/T3iCkaNs+emRlmBgWr4Dk07NSaPTht3WYdZx1oRMMm0Gzpl0bFlxuxp7mPLr95hzzwrFBEhBQWFYlEqdwleOjEQ9FflW1AKk2I6vJcjUK3ZMOvVRjTSmFNuYXJQw7HbBcJQPdzsRozA2xzLAMXJaiR1XSxWOvS+DExDUkDkZyk5nhzSLjrY2uta8x+wlpyKojn0tZkn+ElbWr2Rjy0Y2tmzkj5v+yENLHmLRuCSC4XgzmcJ0E96gSEaiAaNOTVG6iU53iAkFebyS9wp6tZ50czpPLv0v7+6uIkc/HkO/Fk9nwElEipCXknr0C0sq5c2ol6e8NVBdwwZXFf6Inz/M/wMliSXy6wBy6VH49NagBEHgyWVPcv1713Oo5xDe8NAOdwaNgQdPfpCtbVs5v/T8Qbe5deqtXPz6xUSkCBZ1AmOtEzDYLFiNGuxmzTHFNgyFGJWGrKYtKE2lMKUGq17DpJyRVY9/eEoZNqOWGYVJmHTKR4PCF5d7zq1gbkkzc4qTP7dr0GlUPHvdTADWHOzg6sc38c1puSwclcrmWif/Xl3DOZOyOGl0GoIg8OdvDBEtcxR2NPTgC4n4QiIH2jxk2o8+96ygoPD1RAkf/5xQwiw/PTpcIVbv6UIQYGFF0nE5CUqSRLc3gsWoHjbbbKQEw1He3toui7pcC6WZg8QAeNth/X0QFWHC5ZBUAg3rqO+CpDeuxBRognk/hEW3H/P5I6LEpoM9/HXfj9nR/TE3TzoxBiERUWJdZZccwZBhIiGxi/NfPZ9QNMT9c56iLKloyHiEwxzsOshlb16GSlDRE5LjG66tuJbvTvyuPHzXWQkGO1jSP/H1Ho0WbwtvVb/FgpwF5NvyP9Gx9nbuZX/XfpYVLEOrPrHxBNuqXVS3+inNNDE2d+i4DgUFhU+fU+79iMpWNzkOI6t+uIgz//Yx2+u7SbXq2XD74k90bH9I5Hdv78Oq13DL4tKY8ZTCZ4dyv6bwZUFZjlX4yuEPiUjIeiAQEkck6ly+CL6gGDMLOexkeKLQa1XML3fgCYhkDmVIYk6BGbeAGAJrBjRuhMpXyYlGkCLyjCCe1uM6f7srRHNXkHNTf8WPJ6kYlzOCKtoI0KiFWMZctzeCR9eEL+Kj3LqAtlYHba1OFibswZ47ZkhRVpxYzAcXfkAkGuFXa39Fg7uB0wpPkx8UBIKJBQgIfBY2DOnmdL5V/q0TcqzRSaMZnTT6hBzrSFq7ZFe+1u4QYwdmICsoKHyGnDQ6lcpWNyeNktsw55cks72+m3mlKZ/42Eadml+e/skz9xQUFL76KJW6zwll5efTQ5Ikqlr8qNWQn3r0vCJ/SOSdrR1EJRifb6Uw/ZNnHA1Ga3eQJmeQ4gwT1pFUDzsqYdtjoNJA7jxo20ug/BIOdGlIsmrJdBiQJImwKMVmBT2BCJsPujAb1EwqTIit6oYiUd7eUYcKLQvGpA+Z3ecJeajuqaY8uTzWTur2R+hwhchONgxatWzvCdHkDFCYLj+vJ/c+iRiwYQ1OBSnKIv9T2NQBmB9fYRSjEvsavGjVAiWZpkHbV6u6q7jkzUvQCBqeXP4kOdbBM6I6/B3c+sGtGDVG/jD/DwTEANe+ey1RKcr3Z32Xav8+JtimU2GbcvTX/Qie3PskrpCLq8qvOuEVt+OlpStIbbufonTTUSuhCgoKJwYxKvGdJzezs6GHP39jItMK+mZ6w2KUhz6q4sP97fx02WhK0yxKG/NXBOV+TeHLgvKOo/CVQxAEijJGJsy2HOqh0RnksLdK9BOucfxj+z/Y2LKZH0/74QBL/A0HeoiIEv6gyKyRuGQml8HMW0Ctk9sPixezt8pFTZuPQ82wdLKWdZU9OD1hJhRYKUgzUd8ewOkJ4/SEKUo3xaqN79S9wU93/gS1oOaFohcoNBQOespL37yUg90H+dbYb3Fp8XfZVuMmEBIRo9DpDjOleGBOk1GvIiqBJyBiNWq4ePTFALT1BNFufRhbtANUA+dA6tr97G+SZ9fsFs2gOXy7O3fHYgf2O/cPKepW1K1gS9sWADa0bCAUDbG/az8AlZ6diEKYve7txyzqNjRv4O4NsuNnijGF80rPO6b9Py3SE/VHjaAYjr+tPMg7e1r5ydJRzChMOvoOCgoKNPf4eXu33C3xyvbGOFEnRiV+/3YlAA99dIgHLp78uVyjgoLC1xdF1Cl8bZEkKRbA7bBo8esqebZ2JRcaL6TIPnQm3FC0uju5f9v9APxh7UM8uOx3cY8nWrS094SOqa1TMqWwtrKbDlcrk4ttMZdKs0GNgIDTEwagwxWmIA0yk/Q0dAYwG9RxLpPRXjdNCWmA82IoEkWjFhCAVq98w9LsqqO6zY830OeCeKQpR1egiz9v+TMEUplgOo+6dj9nTEuNVdxSbXoYsxAa1kH+ggHPzWbSohLk4w6V63dK/ins6dyDRqVhfs78AY+3eFtINCQyP3s+LyW/hEFjYEr6FNSCmsW5i4lIEaYkzuaQby8VCcdepcu0ZGLVWvGLfgptgwvhLxtiVOIP71QiSfDwR1WKqFNQGCHZiSa+NSufnY09XDIjL+4xg1bNmRMy+aCynTPGZ34q5291BQhFouQ4Pp1uEgUFhS83Svvl54RSzv/8aO4K0tYdpCTTTJMzQJMzyJgcCxe+tZQ2fxuTUifx2NLHjvm4rd0Brn/vBmp9O7k493a+v+DMuMejkkQwHMWoG7z10RcUiUpSnMAJRaK8vqkdgJxkA1OKbXgDInqtCo1aoL7DT1tPiFFZZsxDCCOnO0wwEmWPezUOg4OJqX0ubLVtfrZUubCbNYwqCFN16A12V73BudZiohN/wvaGMElWOecvza6PtXOGI1H+tOmvPFH5TwBuyH+YMcmlzBt7bBEDoUgUQeC4zGie2vsUv93wW0q1CTw7+krUE74tu2QeJ4e6D5FpycSoia8q9gR7CIkhUkyffD7mi8JPX9zJO7tbuePMsSwbl/F5X46CwteKiBjlz+8dQBDg5pNKRuSGW9XuYfl9qwmJUZ66ajrTlcWYzwzlfk3hy4JSqVP4WhGNSmzY301Ukh0pp5XaKUwz0e2NMMoxirbGNsYkjTnm43a4QlgMGn407o/0+CJMKBzoSKgSBNQqgf1NXpKs2lj4N8hGLSt3diJJMGdMYmxOSqdRMTbXQkdPKOaY2X8eLifZSE7ywNZGSZIQBAG3P8JHu51IwKTCWeSlxm/b6Zaz77q9Yc556XwEgvwveSZJghpMURaPH2gZvqfew8FmLyb/RDSClixrJudOGU2SKWHAtiExxNs1b5NlKGVceglatYqoJCEgt8mONDdwMHZ37gagKuwm2L4XUyQA2uOz+/7btr/x4PYHGeUYxXOnPxf3mE0/sOX0y0I0KlHZ5EWtEijJ6JtbvOvscdx19sCQZAUFhU+ft3a3cP/KgwCMSk9gecXRF1ZaXAH8Yblzor7Lz/RP9QoVFBS+jCiiTuErSzgSZU1lN6FwlJlldixGDSqVgM2kocsbwd7bBrn5kIuGzgDfyPwVP5keJNt6bKtwh1p87Khxo1ULLJmYPKxQ2V3noabNj1oFy6ekxloaA2ExNtcXCEXj9inNNMuCTpJkR0xBJWfYDcFe93ZWdb5LoamM6dalse8PVsQalW2Rn4N3Gx6xG4DWrClkZs4A48C5PzEqUdkoz8FlG8u4d/LrzBmdgkY1+FvJvZvv5Ym9T2BQWfjFmKdRWeoRe0ox6tQsKHeg7fda+YKiLEAlmDs2cciWzMPcPOlmrKiYEolgyj/luAUdQHVPNQC1rlrEqIhaNXg19ctGfWeAfQ3yz8tm0pBmP/45PAUFhRPDqHQrFr0GAShLH1kkyayiZH57zjg8gQhnTfh02jsVFBS+3CiiTuErS5c3jNMtz5y1dAcp7nWcnDfWQSAcxaSXb9x9QXn1MxASjlnQAYTCsgiLRCWi0ZF1M2uiQYQ1f4GZN4BaS6pNz6TCBAi6yArvg8ho0Mg34JIksaXKhb5rL+XdLwPQEdCRXDh4paXGdxAJiWrffk5KOY355Q6C4eigxhomvZpJRTbGibPo5iZsehsTyy4Y8rrVKoH8VCPNXUHS7TrG5FjQHCGA9jV4qO8IMCbXglYlC2eVoKLL72dj8xoWJZfiCYg4PWEkCbyBCIFwFKtRg79X0Drd4aOKulRTKj+ec+eA70elKJIkHZMw++HUH5JtyWZW5qyvjKADSDBqUAmgUglDOp4qKCh8thSnWln305MAsOhHfht20TQlv0RBQWFoFFGn8JUlyaojK0lPKCyRlWSIfV+lEmKCDmBycQINHQEyHYbBDnNUSrPM6LUqEkwaDEPMyx2m2yO3OxLoRvXBL8HkgEmXAcitkWsfBG8bpJZDhewi6faL1LUHcIh6onLjInvaBOYVwvrm9agEFVPTp8bOMcU+G4B8U8mweXt3rvoTHzV+yFWjb+HC8Qs5p+By1u7r5oOdncwZ40CjHnw+bWJhAhMHfURmf5MXMQpVzT5umnQToxLLMUZz+NvOe2jwHWJS8mymZ01ga1UP/lCfCB6TYyYvxYAkcdw/i1ZvK99845sEIgEeO/WxAQ6kQ5Gqs3FLzslgzz+u835RSbRoOXVSCoLAJ2p1VVD4snGwzc1H+zs4c0ImSZYvXoX6WMScgoKCwkhQ3lUUvrKoVQLTSuxH3c5i0MTaEI/3PCPNtkux6en2+Ujt3iznzzlkl02nO8Seei/TIqIcst3Pv8hiUJOeqMftz2F/ytXUdYRITklnbdNarnn3GgAePfVRJqfJLZkp+nSWpp077HWEo2GerXoEgBcO/ZcLxy+ktTtEWJTo8kbwBiLYzMO4dEaj8MJVUL8Bzn4Q8ufEHirJMFPfKefWaVQalhUtAUAwfItbP7iVf1b/iMVj/ovYKt9oqQSQgESzlrKskf8cGjoC+EIiRemmWBvrPuc+2nxtAPxly1/460l/HdnBdj0DHXtZpYpSlTaGZXnnkGIZWVvUcHgDEdbv70GnVTGj1DYiQ4QTjV6riDmFrx+X/msDzT0B1lZ18vBlx+58q6CgoPBlQxF1Cl8LXOFuzBoLamHkv/IRURqyWjVSwpEoe+o9GHRqyrLMlOdZKck0owssh4Ung01u96xs9NLuCvG++ixOHeNCSO0za1lZv4LHqx7nwrILGVW4jLIS2QRlTVOf8DtWE1utSsuZBRewqvEjzi05Fzr2UeqqJGidgE+VgNMTHl7UeVph1/Py1zueiRN1o3MsjM6RxVmjM8ChZh+FaSZcIRehaIhQKMShnkPMHTuHTleINLsOlSActcrZnx5fmI0HewBZFIZ1dfz8459TmlhKib2EA90H+KDhA1q8LaSb0496PE/IiUf0c2PjCsT6N/jNm1u4ceJ3ufGkkhFf02A0dQXp8UUA6PJESLEpQeEKCp8FNqOW5p4AiaaRR8goKCgofJlRRJ3CV54t3WvZ2L2aVF0GZ2deMqJ9DjR52VXnIdOhZ3qp/bjPXd3mp6rVD0BKghaHVSdXTrSpcdtlJRlod4VISU5ByIxvGbx/2/1UdVfR7utkWeGymIPhrMxZPHTyQ6gFNVPSB65Ed3vDNHQEyE0xxmXWHebXE6+E6TcTwIj08R1oohFG2Xt4K3IKbT0hDFoVGUO1QSZkwPTr5ErdlCuHfP576z24/SL+kIelFUs51H0IvVrPvOx5qAQVCcajvwUdjmTI6DcTqNOo0KoFwqKESa/m0QMvUtlVSWVXJXfMuoO7N9zNuORxJBsHuncOxu7cUTjVneg6dPgjQaSIjY8PdXxiUZflMNDUGUSnVeGwKjeXCgqfFc9cM5OdjT1MLzy2mBUFBQWFLyuKqFP4ytMRktvxOsPtRKUoKuHo7Wit3aHYf/fUe6hr9zMmx0JuyggcFkMe2P8GXrUNwT4HlSC3wA2VIweQm2IkN8VINCpn2fVvmTuj4DzUrgmYNTa6vWHs/SpoMzNnDnnMjQd68AREOlwhFow7ItOoaQvseQ63JoWPjBcyU3LgoA2fLg3kwlKcM+WgLL1n0G+3dAUx6eXw85xkI/ubvOQmG9CqtXxv8veGP+YRxEcyJMQiGYw6NYvHJxEWJaxGDWcWncn65vWMcozijKIzOLvk7GM6z4TUhewyJPBQwdlsOeRlu97C1XM/edi4Sa9mfrlyU6mg8FljM2mZUzKyRR0FBQWFrwKKqFP4yjPTsRCz2kqOsWBEgg6gPM/C/kYvmUkGtlW5CIsS1a3+kYm6+nXQshUzUO/MZMqoEtIT9bG5r6EIi1E+3OnEHRCZWJhAfq+AWZ53Hh/v7Qag0x0v6g4TESXqO/wkWrSxx61GDZ6AiHWwaliwp3c/WOz+FyoiNOVeRIeuAHw+AAy6Y5/Fqmrxsb3GjUqAJROTKcsyU5ZlPubjDMaRkQwGnZrDdcSxyWN5+ayXj/vYepWeyfZZAExwAFOH3/7fu/7NU3uf4upxV3PhqAuP+7wKCgoK/bnnrX38Z20t3zu5lCvnFHzel6OgoPAlQhF1Cl95rJoEZictOqZ97GYt03rbLv0h2X2yJHNkZig4CpFqP8IjWfCqbKhUAnXtfly+CKOyLXFVuKgksbPWTSgs4fKFcQfkeAWnO0S+oYvO6m04zRMozrASESVyUwZvh9xd56aq1Y9GLbBsckqvScz/t3fn8VGW9/7/X/fMJDPJZN8DIewhQEC2YBUXEIUqxeWoWCxSPbZqj+ccv9a2+m2/R6GLdW31eH7VtlZxLQe11QdaK1ZQBBdElKWCbAkQICH7Ppnt/v0xZEjIHgKTSd7PxyMPJrnv+7o/wSTmzXXd1yf+eKhr51m17PPYd8xDk6uRCZ5PAMiIdmGPt1NS1URqXESXLQUgMJN2qMxFVrKDuGgb/uPP9pm02uul12KjbJ22ZAiVP+/6MyUNJfzv7v9VqBORPvPa50XUNXn56xdFCnUi0iMKdSIEmmpvLajBBM4aEddqg5SxmU7GZvZgtilxFMbse2mq9THTBKfDyidfVwFgtRrkZZ/YVbG8xsP+4sAzd813jLZbGZ8Vg++T35HsqcFbcYD68d/tdIfN5nqtFgPTb1JU2USC09bus3SBEyPImnYxJZUuvFVR2HBjyZzKrt111Ll8+Pwmh8tdHK5wkTPE2e7sIMDne6uprPdypMLFxWelMDojGkeElWiHtVXbiFPRUUuGnvi64mtWfb2Ky0ZdFtwl9FR8f9L3Wfn1Sv41719PeSwRGVx2Hq1hU0EFV00bSpyj9c+3u7+Zy8rPDnLbhaNDVJ2IhCuFOhHgaEUTB0pdAKTFRzIspRvLLFtwewNNs5t7ge2vOcim4k1cNuoyHNYYnHYrDU0+kk4KKHHRNpwOK26vnwlZTupcfsZkRhNlt+KLSgRPDY2WuI7D2XEThsWQEhdJXLSNr4rq2VfcQKTN4NJpqVg6WPZpj7CQnRYNaYGWA5uLN/OzrfeQETGO28YtZ8u+Grx+E7fH5LwJie2O4XTYqKz3Bp8XNAyDrJTe9ZjrzK8//TUfHfmI//eN/8fZmWf3+Pqff/xztpVtY8PhDbxzzTutD5Z9DaVfQfZ54Ezt1niLxi1iUSdN2gHqXT62FtQQE2Vj0vCY4AY3IjJ4+f0m3/7DJ1Q3ethWVM2ji85qdfzq6VlcPT2LHYeruee1bVw5dSjfGJXcwWgiIico1IkASbERgWfITEiKab3tfKPbx6EyFxmJ9la7Nfr9Jl8W1FDn8lFZ58FiMZidl0RslI2b3/keDZ4mthzbwkMXPMTcs5Lx+sw2PcPsERbmTUnBNM02v/Rbp/8r3upiMmOGYI/s/FvVMAzSE1ovTzTNwDLI7vpbwd8obyqhvKmEMdkeDh11UFzpJi2h4234p4+OY+yQ6ODfS0FJA7WNPnKznO02uy5tKCXRkYjN0vrzqWn08s+DdaTGRTDmpFnRek89L+96GYBXdr/Sq1A3MWUi28q2MTFlYtuD218GnxtcVTD1ph6P3ZGCYw2UVLspqXYzIq39HUhFZHAxDIh12Khu9BAf1fEqhJ+9voOth6r4cE8ZG+/p2eMDIjI46bcMEQJLHi+d1v4szZZ9NRyrdlNY0si8qSd2UyurcQdn9wD8PpPaRi9Ou5XvZD5EYsRQDvM+EFgW2dlGKe3O4lgjsSVl9/ibdGJ2DIkxNhKcEV1uztLSonGL+LriayalTqK+No7iyjoyEiPJGdLx0lOLxQguzaxzefmyoDZQusVgYnbrRuIrdqzg0c8fZUrqFF647IVWx3Yfrqe4soniyiayU6NaBUJnhJPvjP8OHx35iEU5nc+OdeSnZ/+UmybeRFp0WtuD8cOgYh/EZ/dq7I5kJto5cKyR2KjAbKyIiGEY/PXfZrGruIZzR3e8O+fUYQlsPVTF1OyEM1eciIQ1hTqRLjTPrrWcZSuuL+b/2/QW0+K+BZgMT43C6bCRmWinqMxFUkQWhmEwNW5er+/b3uxdd1gMKCpz8cX+GqaPjmdocuvlkEVlLqobPIEm6C3CU25SLi8teAmADV9VAlBW4+n2fe0RFpyRJpbGChKdsbh9bm5991Z2V+7msTmPsa1sGwA7ynfg9XtbzdZlJtopKneREhtJRDsN3++Zec+Jd9z1sO3FwFTkWUsgMqbN+e3JjMls/8CUmwJtKBzx3f5cuyM5NpIFM9oJkSIyqKXG2kmN7Xyp97LLJ/KD2aNJjek/G0SJSP+mUCfShWmj4hieGkWC88S3y/ay7VS5ywLvGD4mJdcTsfdNaBpNXPoFwTCWFt/z/yH7/CYHSxvZWljL0CQ7Y4c42Xe0gawUR5sllu3x+kyKj/fZO1LR1CrUNbp9fLY30M6guqYeS4SDCcNi2iwNzBsew54jgZYOXfGbJj6/SYTVwlzjXWqbSok7OpyDtulsLtkMwLpD67hz+p3E2+M5b+h5bZZfDk12MCTJ3r0QW7EHqgoDr8v3QObUrq/pjMXa54FORORUpcf1/fPJIjJwKdSJdMFiMUiNb/1c2QVZF7Dh8Ab2+t7kxsnXEXHgLagqgKoCood8I3hedYO32/cpq3GzrbCWmkYvFiMwEXW4vImGJj8VdR5Kqt1cNr3rjTwibBbysmMorXYzdkgUXp8Z3B0zwmohOtLCyNr15NRtpsCWxy5jQbB9Q7MEZwT5YxPw+kzcXn+7z8dBIECu215OfZOPb+QkcNCVw+HoedjrXEz2pnNT3k3srtzN4tzFDIsdxn3n3Ndh3d2elUzOwUwYRZ3Ly76aDCam+rtulC4iIiIygCnUifSC3Wpn+bnLT3wgfXJgF8XEUUTYo8lK9lBR62FEd5qVH7ezqC4YAn0mJMXYyE6NoqHJR0Wdh5TYFg/VF20CVwWMmEON2Uidr5YIv4Nn//ks09OnMzd7LiPTo3l/Rzl1rkDgyki0Y7MaXHRWMpZNReCBdN8BrB1shOJy+1i7vQKP1895ExJJjm17nsvto+54b72yWjd1UcPBBU042F5Yyw+n/7Dbn3+3RURTMnopH39dBZUQX+5iZHr3egj6/CamSauWFSISPh76+y72l9az7PKJZMRrJktEpJlCnUhfSB0Ps+8FAjtAFpU3EeOwkhLX8c6RJ8tKdlBe48HpsJKdGsW4oSc2KBmT6STSdjyI1B6FXX8F4MtiF5uzDuDDTWVVIy999Qov73yZjYs3Ynrt1DYGAtfuw/XB5t0RVgtkToG9f+eYw8XHjc9SV3seE2Jbb61d5/LR5Am0aqiq87Yb6pq3669p9DE200l2ahRbC2oor/EwJLlvnwWpbqpmw+ENnDvkXBKccURFWnC5/RQeayQr2dHlbF1Dk49128vx++H8iYkd9t4Tkf5pV3ENv3t/HwCj05z8eH5uiCsSEek/FOpETtHag2v56MhH3JR3E0NjhlJdH5htq3P58PpNIjvbgbKxgtJ977Cu6RgX5F7Hld/IaPe0Vq0Q7HEQGYvfXUe1NRGfuRcMSHcGNuUYGT8Su9WOLcJGpM3A7TVp9PhaD5iSC/vf4+O0aGrMBrZVf9Ym1KXERTJhWAxur5/haR3POLZsQWCPsHD+hKReb/LSmbvev4tPiz9latpUnr/0ecYNdfJlQS1V9V5Ka9wMSWrxr/YHP4G9/4AZN0NcYIOUmgYvbm+gyUNlnUehTiTMDE9ykjc0jsKyBi7M0SZEIiItKdSJ9NDqfat598C73Dr5VnKScrjrg7vw+r1Uuip5dPajjB8Wg9VqkBQT0eGzaEFfvUZq5X5meRtYXrmcJy9+sutAFOmEc++ipLwGW4WNWTHDsDpqyYnJY17mQpIdycGNSKaMjGNfcQOjM6IxTZN6lw+nw4oRkw6zfszk+h1sa9jB5Pj8dm/VcrawJ/oi0BU1FvJ51UeMcY5nYtxUzONd9/xmYPYwM9HOwTIXXp9JSZWbpJgIHJHWwMOIL14D7loo/RquC7RPSE+IZNxQJ16fSXY7y2LrPfVE26LVJFykn4qKtPLmf5x/Wv7RSEQk3CnUifTQ8o+X0+RrwuV18Yd5f2BC0gS2lW1jcupkIDBbNWl4bPcGi0qCyv1U4yA3ZiYf/LOCqjoP+WPjW888ncxmJzM9lcx0gMTgh4fGDG112tBkB4a9gse3PEiSMY6ciAVkJdvJH5sA9lgm2s9hYtI5PfsLOEO2VH1CcdNhytzHmBg3lWVnP8CW0k84L+tcAEPoWUQAACd9SURBVByRVs4Zl8Bbm0upOf4s4tRRcYHuvkkjoHg7JI0KjmcYBhOGtd/+4OntT/P4lseZmz2Xx+Y8dro/NRE5BQp0IiJtKdSJdJPPb1JZ52HOsLm8U/g2Fw67EIAVl66gorGCdGd6zwcdfxWHovLYUxxDNjYqagN94UqqWiwnbCiD6oOQNgmsPV8y+Mftf+RvBX8D/sZPRp9LdUPHDW9Ph/VF69lSsoWlE5eS5Ejq9nVjYyZQ7j5GTsxEvj5cz1eH/MRGndNqDJvFIDbKSm2jr1XLCf71HajYD+l53brXJ0c/afWniIiISDhRqBPppk93V1FS5ebqjP/L/ef/kghLIGBFWCJ6F+iABreJN3oYfqMWiwGjMqJpaPKRM+T4bo6mHz57Cjz1UHUQxl/Z43t8I/Mb/HXPX8lJyGXikHRGpnWvWffJKus8eLx+jlY2kZ0aRWJM1wGz1l3HXet+jMvfQFVTFcvOXdbt+42Pncz42MDs5+bjvfXqXT5MMzAZB4F2E3MmJeP2+ImyW09cHOmEjEndvtePZvyIZ3Y8w/zh87t9jYiIiEh/oVAn0k0ut//4n75goDsVdS4va7eV4/PDWSNiSI23ExvVzrdkc4Lp5ZKj+SPmc0HWBditdixG7/q57T1az/YDdRiACZTWuLn4rNYzfic/59Lk8bNxRyM/Hv0KKw79mNEJo3t1b4C87BiiIi2kxkViOWnjGavFaB3oeiE3KZeHLnjolMYQERERCRWFOpFumjk2nsMVTWR1slX/1xVfs/HIRi4ffTkpUZ0vc3R7/PgCOZFqdxU1taVMiZrS+iTDAvn/BjVFkDqeQ2WNuL0mo9KjevRcSZSt+/3y2tN4PNCax98/eefIHQdq2XO0gXFDncHn1upcXhrdfiIsdu7Lf5SzR2X1+v6OSCsTs7v5nKKIiIjIIKNQJ9JNMVE2xg3t4Fum7Gs4tJFbdz1Dubuarce28vhFj3c6XlJsJPlj4qlorOGW9ddQ763jF7N+wZVjrmx9YlQiRCVSXutm894aIDA7NSItCppq4PM/gN8H079PqTuGOpeX4alRbWa0TsX4LCf2CAvx0VZiHDaiT5oZO1LRdPxPVzDUJcVEkDvUicvjZ2J2ap/VIiIiIiKtKdSJ9IU9b0N9CSlYKAdSo1OpbvCw81A9GYmRjEiLbn2+qwoq9pGVOgHT3kSjrwGAKlfViXPqSgLP1MUG+qzZIyxYLeDzQ1Tk8WWUVQehoRyApmN72Vg8HNMEt9fssB2BaZocqWgi2m7lYGkjpTVupoyMa7dR+sHSRjw+P1ur3yHWEcslCZe0GeuprU/hdccxJW4+YzNj8flNDpW5iI+2Mb6D3SY7U13vIdpu7bKZuIiIiIgEKNSJ9IW0PCgs5dkp/4fdScOZkjqFz/bUcrSyKbCxSMpJM2db/hTY1TJ1IsPOWsJTFz9FUV3RiVm6miL47MlAz7Xp34fEkcQ4bFx8Vgo+v3ni2buUcZAxFUwfpOVhFNdiAn6/eXKFQftLGtlWWNvqY4XHGoOhzm+aWAyDsho3n+8LzAy+dnQ922re44VLX2BK2pTgdZuKN/G7rb8D4KdnWzg/cTH/PFjL7iMNWC3wzWmpwV59BSUNVDd4GZ8V07qZegu7iurYWVRPjMPKxWclt1liWt1UzXP/fI5JKZOYkz2ns/8iIiIiIoOGQp1IL3l9JrsO1+GIsDBm9MUwai6xhsH048czEu0crWwiPaHt5h40b1hiCfx5zpCTesV5XIFZOgBPA6ZpsrWwltpGb6AXWzNrBOQtAsDmN/GbgbBW2+jtsO7mSixGoI9dZZ2HEWlRmKbJh19VUlHnIX9MPLHRNixGoJVDraccqxFBXGRcq7FGxo8k2ZFMg7eBSSmB3SZtVsvx8Y3g3i4NTT6+LKgNfnzyiPafj6t3+YLn+02wnvTX9uTWJ3lp50tYDStrF63tUYsEERERkYFKoU6klwqPNbLnSGDZZGJMBMmxrZcvjkiLIjvVgaW9DU2mfQ+qCgMzbe1JHgOTrj8+AzeR2gYvBSWNgfuWNJLXTnNzq8UgJS6CshoPqfFtl1I2G5keRZTdSnSkhfgWG540efyUB/vkNTE02cHFZ6VwtMrFdyL/k7EZCYxKGNVqrLToNN655h3KqpuItAb66uUMiabR7aO81kNZjYfMRDuRNgsxDit1Lh/Jse3vHFrdVM2muv8l3TmBc7NnYm3nmcBR8YH7ZzgziLZFtznezO83OVrjYki8Q42KRUREZMBTqBPppQRnYCbLZjXabBzSrN1AB2CPhfQu+qi1OO50WEmNj6S20RtoSu51BTZJcaa1uuS88Yn4/GZwtozao7DzNYjNgtwrwDAwDIPMRDsUrIPirTBmPqSOxx5hYdLwGMpqPOQcfx7P6bAyJsPJmIz8DsusazDYtLsRaOTc3AQiHQ38fsd/k2HPwWaZT2aiHZvVYO7kZDw+s8Oll49sfoTX976Ow+pgwYQNwY/XuGswMIiNjGXRuEXMzJhJSlQKDpujw5p+uOpLXv/yCItnDuPX/zK5879nERERkTCnUCfSSylxkXxzWioWC0QcD1E1DV4ibQaOyK77pvn9Ji6Pn2jXETiwHtIndxj0rBaD88YnBt7xeeDjxwObrYy7HIadWLppGAa2lmsWD2+CmsOBtxEXBnbSbFawFvxeOLQRUscDMCbTyZjMnv09mCc9vveHbX9gQ8WrGBhcPv48ILBE0mIxsHeyI2cUgRYQifZkbEbgR9Puyt0s+dsSLIaFlxe8zKj4UYyIH9FlTduKAs3Ktx6q7tknIyIiIhKGFOpETkHLWafD5S427akmwmpw8VnJXQa7D3dWUlHrYZ7/LZwNB6B8T9ezdwA+d2CWDgKbrXQmYwqU74bYoeCIb30s+zwo/hKGnt3msup6D+W1HoalOLrchTIlLpLzxifiN03SE+zkJuUGPm4fgqvBHtx4pStTHNcTlz2FkQnDsVoCf3f7qvbR6A0sOy2oKgguv+zKI4vO4i9birhuRna3zhcREREJZwp1In2koSmwyYfHZ+L2mjg6fqwN0zSprg88v1YWMRInBzp+vu5kkU6YvCSwQ2b2eZ2fmzAcZv04+O6+qn1EWiMZFjsssOxyzPw2l/iPb5ji8ZlU1XuYNjq+zTkna/kM3xVjriCFSRQds1JcaVJV7yUppv3n6FrKTnHg848nN+NEG4RLhl/CbWfdhsWwMHvY7C7HaDYtO5Fp2YldnygiIiIyACjUifSRURnRYEB0pJW46M6/tQzDYObYBEqqmkjJnAsRFwV2suyu1PHBJZPd9cnRT7hlzS1EWCJ4ZeErwU1PTNNkX3Fgw5fRGdEYBJ4T9PjMDmfpTNPkYKkLm81gaFLbZ9vyhgyjvraaKLuFuKju/ZjJGx7bZgMYm8XG7VNu78Fn2T9U1nnYeaiOjEQ7o6IrYf+7kDoBhs4MdWkiIiIyACnUifQRq8VgbGb7Db/bk5FoJyPR3u3zG5t8bD9QS1y0jdys1k29vT7/ic1RWjL9cHAjYFDqrcDExO13U9lUCcD2whr2FjcGT4+2WxmS5GB2XhLVDV7SOthF81CZiy37A0tAL5hoabPzZ3x0BPOmpnT7c+tv6j31vLr7VSanTmZq2tQeX7/rcB0l1W5Kqt2MsL+HpfzrwPLaITNOtLMQERER6SMKdSJhYm9xA4crmjhcEWg30NyAfOehWnYdbmBokp2ZOQmtLyr9Cvb8DYAFk67Hdc69RFmjiffnsrWghv0lJwKdxQDn8V08HZHWTp8JbH6W0GLQ5TN3/dEnRz+hsLqQq8deTUQ7M6SPff4YK79eid1q54PrPsAZ0f2wDjAkycGxKjeZiXYszvFQ8XVgpk6BTkRERE4DhTqR3jJNKPgAkkZBQu835Pj06KdE26KZlNr5Jinp8ZEUlDQQ47C1aqFQeMwFwJGKprYXRSWDJfBtbolO4dr0a9l5qI7tB+oAk0ArcpOpo+LISLB3a9dOgPQEO3MmJWG1GMFwGS6K64u57d3b8Jk+6hstXD/haqJOakmRGp0KQLw9nghLD5bFHjc8NYrhqVHH38uHIdMV6EREROS0Ca/fxkT6k42Pwz/uA0cC/J/t4Ijr8RBrD67ljnV3YDEsrFywkvHJHT8nl5ZgZ2F+Wptm2k6HFZfHj6W9zBCbeWKjFHugPkdk4MQIs4lU70GGRNUzLO2bPa49wdl52CmqLcLtd3d7x8ozxW61E22LZlz0BSQ1Xch728qZNzWFyBYzjrdMvoWZGTMZFjuMSGsnO950lwKdiIiInEYKdSK91dxWwOsK9HvrBe/x60zTxG/6uzz/5EAHMGNMPPuLGzp+Ps/eOmyOTI8mwRlBVNUuHBWHYOTsnpbdpX1V+1i0ehFe08vT854mv5Pm5WdaYmQcfxu1hMKmBAoAr8/E5zPb/DSckjYlFOWJiIiI9JhCnUhvXXg3xA+DjEkQndSrIeaNmMcT1idwRjiZmDKxW9eYpsnWglqqG7xMHR1HXJStza6RXUmMiYCYSZDVjb54vVDhqsDtdwNQ0lByWu7Ra5ufIeHv/5ezDAtJ132AI3Ncm+WXIiIiIuFEoU6kt2x2mHHTKQ/Tk/5rAHUuHwXHAhucFJY0MnlEzwJde0zTbHcWsLfyM/K5/7z7afQ2ctnIy/ps3D5xfObSsEaSnZEA8d3fgVRERESkP1KoEwkzTruVjMRIauq9ZCWfWiAxTZMNOyupqPWQPzaeIe30nOuthaMX9tlYfeqs6yBhGDhTT2mDGxEREZH+QqFOJMxYLAbnjEs85XEafPV4PQZlNR4Aiiub+jTU9WvDzw11BSIiIiJ9RqFOZIBye/18dagOp93K2CGt+6wVuw6zuvh/sRk2Zgy9nroGW5tzRERERCQ8aJ9tkX7qwU0PMv/V+aw7uK5X1+8vbqCgpJEdB+uoqve0OlbpKcePD7fZRGJKPeeMSwi7fnMiIiIiEqDf4kT6IZ/fx0s7X8LE5LU9rzEne06Px0iKjcBiBPrSRZ+0u2NOzETqfbVEGnayoob3VdkiIiIiEgIKdSL9kNVi5dazbmXtwbV8Z/x3aPL4qah1kxpvx2bt3i6VafF2LpueitViYLG0vsZqWJmRMOt0lN4tR+qO8O9r/50oWxS/m/s74u3xpzReVb0Hw4D46M4boouIiIgMRAp1Iv3U7VNu5/YptwOwbns5VfVehiTZOTsnodtjRNj65wrrDYc3sKdyDwBfHPuix20dWiqrcfPhV5UAzM5LCvTgExERERlEFOpEwoDXZwLg85un9T6maVJe6yE2yoY94vQFwrnZc3n3wLtE2aKYmTHzlMbyeP0nXvv8nZwpIiIiMjAp1ImEgXNzEzlWffpbDuwsqufrw/VERVqYNzUFSx82JG8pOSqZP877Y5+MlZnkIH8MGEZgyamIiIjIYKNQJxIGnA4rIx3Rp/0+TZ7ATJfba2KawOnJdH0uK2WQ9NcTERERaYdCncgg5PH6qaz3kBwbibXFJip52THERllJimn9cRERERHpvxTqRAahj3ZVUVHnISvZQf7YEztPRtgsjMlUE3IRERGRcNI/t8YTkdOqydu8zFIbi4iIiIiEO4U6kUHonHEJTBoew/TRcW2OHag5wE8++Al/2fOXEFQmIiIiIj2lUCcyCMVG2RiT6cQRaW1z7Hdf/o63C99m2UfLaPA0AOD1e7n5nZuZ+dJM1hetP9PlioiIiEgnFOpEpJVzhpyDxbAwLX0aUbYoACpcFWwq3kSjt5F1h9aFuEIRERERaUkbpYiEOb8/0DA83mkj0nbq/05z5ZgrmT9iPg6rA+N4n7q06DT+fcq/s61sG9+d8N1TvoeIiIiI9B2FOpEwt7WwlsJjjcRF25g7OfnEgboSOLgB0vIgZVyPxmyeoWvp1rNuPdVSRUREROQ00PJLkTAXbBjuOWkny92r4chm2LEyBFWJiIiIyJmimTqRMDd1VBzJZY2kx9tbH4gfDhX7IGF4j8c8VNbI3qMNjMqIZnhq21k7EREREek/FOpEwpw9wsLY9hqGj74Esr4BkT1vJr6rqJ46l49dRfUKdSIiIiL9nJZfigxk9lgwev5tPjI9CnuEhZFpCnQiIiIi/Z1m6kSkjTGZTsa0N/snIiIiIv2OZupERERERETCmEKdyEBRVwIl28DvC3UlIiIiInIGKdSJDAReF3z2JGz/MxSsbf+cumLY9QZUFpzZ2kRERETktBq0oe7YsWO8+eab3HvvvVx66aWkpKRgGAaGYXDjjTeGujyRvrfrDSj6JKR965744gkW/nUh7x96P2Q1iIiIiAw0g3ajlPT09FCXINJ3bA6Y+W+BJZipE9o/J3YIVBVC7NAzWlpLz2x/Bq/p5c+7/szsYbNDVoeIiIjIQDJoQ11L2dnZ5ObmsmbNmlCXItJ7zrTAW0fGLYTs88AR3/VYzc/lWax9U9txN+XdxJoDa/j2uG/36bgiIiIig9mgDXX33nsv+fn55Ofnk56eTmFhISNHjgx1WSKnV1Ri6/c9DfDFs+Bzw5QbA8crCuBPl4Bpws1rIHl0n93+P6f9J/857T/7bDwRERERGcShbvny5aEuQST0qg5ATVHgdfnXkPUNOLIF6ksDHzvyRZ+GOhERERHpe4M21IkIkDQa0vLA2xT4EyD3W5D/fTD9gdciIiIi0q8p1IkMZtZImPyd1h+z2WHBI6GpR0RERER6bNC2NBARERERERkINFN3mhQVFXV6/OjRo2eoEhERERERGcgU6k6TYcOGhboEEREREREZBLT8UkREREREJIxppu40OXToUKfHjx49ysyZM89QNSIiIiIiMlAp1J0mWVlZoS5BREREREQGAS2/FBERERERCWMKdSIiIiIiImFMoU5ERERERCSMKdSJiIiIiIiEMYU6ERERERGRMDZod7/csGEDe/fuDb5fVlYWfL13715WrFjR6vwbb7zxDFUmIiIiIiLSfYM21D399NM899xz7R7buHEjGzdubPUxhToREREREemPtPxSREREREQkjA3aULdixQpM0+z2m4iIiIiISH80aEOdiIiIiIjIQKBQJyIiIiIiEsYU6kRERERERMKYQp2IiIiIiEgYU6gTEREREREJYwp1IiIiIiIiYUyhTkREREREJIwp1ImIiIiIiIQxW6gLGKy8Xm/w9dGjR0NYiYiIiIi0p+XvaC1/dxPpbxTqQqS0tDT4eubMmSGsRERERES6UlpayogRI0Jdhki7tPxSREREREQkjBmmaZqhLmIwcrlcbN++HYDU1FRsNk2ahtrRo0eDs6abNm0iMzMzxBVJf6OvEemKvkakK/oaCS9erze4umrSpEk4HI4QVyTSPiWJEHE4HOTn54e6DOlAZmYmWVlZoS5D+jF9jUhX9DUiXdHXSHjQkksJB1p+KSIiIiIiEsYU6kRERERERMKYQp2IiIiIiEgYU6gTEREREREJYwp1IiIiIiIiYUyhTkREREREJIwp1ImIiIiIiIQxNR8XEREREREJY5qpExERERERCWMKdSIiIiIiImFMoU5ERERERCSMKdSJiIiIiIiEMYU6ERERERGRMKZQJyIiIiIiEsYU6kRERERERMKYQp2IiIiIiEgYU6gTEREREREJYwp1IiIiIiIiYUyhTqSbCgsLeeKJJ7j66qsZO3Ys0dHROBwOsrKyuPLKK1m5ciVerzfUZcppcuDAAe666y5yc3NxOp0kJSWRn5/Pww8/TENDQ6jLkxDZvHkzP//5z5k3bx5ZWVnY7XZiYmLIycnhpptuYsOGDaEuUfqpu+++G8Mwgm/vv/9+qEsSkTBmmKZphroIkf7uv/7rv/jVr35FV98u+fn5vPrqq2RnZ5+hyuRMWL16NUuWLKGmpqbd4zk5Obz11luMGTPmDFcmoXTBBRfw4Ycfdnne0qVL+eMf/0hkZOQZqErCwZdffkl+fn6rfwhct24ds2fPDl1RIhLWNFMn0g1Hjx7FNE2cTidLlizh2WefZcOGDWzevJkXXniB/Px8AD777DMuvvhi6urqQlyx9JUvvviC6667jpqaGmJiYvjVr37FRx99xHvvvcf3v/99AHbv3s2CBQuora0NcbVyJh05cgSAIUOGcMcdd/Dqq6+yadMmPv74Y37zm98wdOhQAJ5//nluvPHGEFYq/Ynf7+eWW27B6/WSlpYW6nJEZIDQTJ1IN9x9990kJyfzgx/8gNjY2DbHfT4f119/PatWrQJg+fLl3HvvvWe6TDkNmmdjbDYb69ev55xzzml1/OGHH+YnP/kJAPfddx/Lli0LQZUSCt/61rdYunQpV199NVartc3xsrIyZs2axe7duwH44IMPuOCCC850mdLPPPbYY9x5553k5uZy1VVX8etf/xrQTJ2InBqFOpE+Ul5ezpAhQ3C73UyaNIlt27aFuiQ5RZs2beLss88G4NZbb+Wpp55qc47f7ycvL4+dO3eSkJDAsWPHiIiIONOlSj/15ptvsnDhQgD+4z/+g//+7/8OcUUSSgcPHmTixInU1dXx/vvvs27dOpYvXw4o1InIqdHyS5E+kpyczOTJkwHYt29fiKuRvvD6668HX990003tnmOxWFi6dCkAVVVVrFu37kyUJmFizpw5wdf6uSC33347dXV1fPe73+XCCy8MdTkiMoAo1In0oaamJoB2l2JJ+GneudDpdDJ9+vQOz2v5y9nGjRtPe10SPpp/JoB+Lgx2q1at4s033yQpKYlHHnkk1OWIyACjUCfSR44dO8bOnTsBGD9+fIirkb7Q/N9zzJgx2Gy2Ds/Lzc1tc40IBJ6ja6afC4NXVVUVd9xxBwAPPvggKSkpIa5IRAYahTqRPvLwww8Ht6detGhRiKuRU+VyuSgrKwMgKyur03MTExNxOp0AHDp06LTXJuHB7/fzwAMPBN/Xz4XB6yc/+QnFxcXMmjWLm2++OdTliMgApFAn0gc+/fRTHnvsMSAQAH7wgx+EtiA5ZS3bE8TExHR5fnOoUzsLafbb3/6WTZs2AfAv//IvnS7hlYHrww8/5Omnn8Zms/HUU09hGEaoSxKRAUihTuQUlZSUcM011+D1ejEMg+eee47o6OhQlyWnyOVyBV93p2m03W4HoLGx8bTVJOHjgw8+4J577gEgLS2NJ598MsQVSSi43W5uueUWTNPkzjvvJC8vL9QlicgApVAnA4phGKf8tmLFim7fr7a2lgULFlBUVATAAw88wEUXXXSaPjs5kxwOR/C12+3u8vzmDTGioqJOW00SHv75z39y1VVX4fV6cTgcvPLKK2oyPUjdf//97Nq1i+zsbO67775QlyMiA5hCnUgvuVwurrjiCj7//HMAfvSjHwWbUEv4a9lkvjtLKuvr64HuLdWUgaugoIB58+ZRWVmJ1Wpl5cqVajg+SO3atSvYWPyJJ54ILtEWETkdOt7OTSQM9cXOg5mZmV2e4/V6WbRoUbAn2fe+9z0efvjhU7639B8Oh4Pk5GTKy8uDM7EdqaysDIa6YcOGnYnypB86cuQIF198MUeOHMEwDJ555hmuuOKKUJclIfLb3/4Wt9vNqFGjaGhoYOXKlW3O2bFjR/D12rVrKS4uBmDhwoUKgSLSIwp1MqC03Fr+dPH7/dxwww2sXr0agOuuu47f//73p/2+cuZNmDCBDz/8kL179+L1ejtsa7Br167ga21bPziVlZVxySWXsH//fiAwM9PclF4Gp+Yl2fv372fx4sVdnv+LX/wi+LqgoEChTkR6RMsvRXro1ltvDf6L68KFC3nxxRexWPStNBCdd955QGBpZfMy2/a07EU2a9as016X9C/V1dXMnz+fr776Cgg8W3v77beHuCoRERlM9JuoSA/88Ic/5OmnnwZg7ty5vPLKK502pZbwduWVVwZfP/vss+2e4/f7ef755wFISEhgzpw5Z6I06ScaGhpYsGABW7ZsAeBnP/sZd999d4irkv5gxYoVmKbZ6VvLzVPWrVsX/PiIESNCV7iIhCWFOpFuWrZsGb/97W8BOPfcc3njjTeC29jLwDRz5kzOP/98AP70pz/x8ccftznn0UcfDT7LeccddxAREXFGa5TQcbvdXHXVVWzcuBEI/Pf/5S9/GeKqRERkMNIUg0g3PPHEEyxfvhyAoUOH8tBDD1FQUNDpNePGjdMv+APA448/zqxZs2hsbGTevHn89Kc/Zc6cOTQ2NrJy5Ur+8Ic/AJCTk8Ndd90V4mrlTFq8eDFr1qwB4KKLLuLmm29utfHFySIjI8nJyTlT5YmIyCBimKZphroIkf5u9uzZrZ6b6o6CggItoRkgVq9ezZIlS6ipqWn3eE5ODm+99RZjxow5w5VJKBmG0aPzhw8fTmFh4ekpRsLSsmXLgv9guG7dOmbPnh3agkQkbGn5pYhIFxYuXMi2bdu48847ycnJITo6moSEBGbMmMGDDz7IF198oUAnIiIiIaOZOhERERERkTCmmToREREREZEwplAnIiIiIiISxhTqREREREREwphCnYiIiIiISBhTqBMREREREQljCnUiIiIiIiJhTKFOREREREQkjCnUiYiIiIiIhDGFOhERERERkTCmUCciIiIiIhLGFOpERERERETCmEKdiIiIiIhIGFOoExERERERCWMKdSIiIiIiImFMoU5ERERERCSMKdSJiIiIiIiEMYU6ERERERGRMKZQJyLSD9x4440YhoFhGBQWFnbrmhEjRmAYBiNGjGj3ePN4zW/r16/v1riXXHJJq+uWLVvWrevq6+uJjY0NXnf//fd367r2am1+i4yMJD09nblz5/LII49QWVnZ7THbc+jQIV577TXuueceLrroIuLj43v8eYqIiPQ3CnUiIoPEiy++2OU5hw8fZu3atb0a/7XXXqOuri74/gsvvNCrcVryeDwcO3aMtWvX8uMf/5gJEyawYcOGXo114MABsrOzueaaa3jwwQdZt24dNTU1p1yjiIhIqCnUiYgMcA6HA4BXXnmFpqamTs99+eWX8fv9wWt64vnnnwcgJiYGgF27drFp06YejTFjxgy2b98efPv888/585//zPnnnw9AcXExCxcu5PDhwz2uzzTN4GvDMBgzZgwXXHBBj8cRERHpbxTqREQGuPnz52O326mqqmL16tWdnts8u3bFFVf06B5FRUWsW7cOgGXLlpGYmAicCHrd5XQ6ycvLC75NmzaNb3/727z//vtce+21AFRVVfGb3/ymR+MCxMbG8stf/pI1a9ZQXl7Onj17WL58eY/HERER6W8U6kREBriEhAQWLlwIdL4kcuvWrWzfvh2ApUuX9ugeL774In6/H5vNxtKlS4MBbOXKlXg8nl5WfoLFYuGBBx4Ivv/3v/+9x2MkJyfzs5/9jEsuuSQYOkVERAYChToRkUHghhtuAODtt9+mvLy83XOaZ9WmTZvGhAkTejR+c1icN28eqampwfuVl5fz1ltv9bbsVkaNGkVycjIQeD5OREREAhTqREQGgUsvvZTk5GQ8Hg8rV65sc9zn8/Hyyy8DJwJgd23evJmvvvoKgCVLlgAwa9YsRo4cCfR8CWZnIiIigEC9IiIiEqBQJyIyCERERPDtb38baH8J5j/+8Q+Ki4ux2WwsXry4R2M3h7bY2Njgs3iGYXD99dcD8NZbb1FRUXEq5QNQWlpKSUkJAEOGDDnl8URERAYKhToRkUGi+Tm5Tz/9lD179rQ61nL5ZHp6erfHbDnzd9VVVxEdHR081jxr53a7250d7KmHHnoouIPl7NmzT3k8ERGRgUKhTkRkkJg5cyY5OTlA6551dXV1/PWvfwV6vvTy7bffprS0FDgR4prl5uYyY8YMoPdLMN1uNzt27OC2227jkUceAcBms3HnnXf2ajwREZGBSKFORGQQaQ5tLUPdX/7yFxoaGoiLi+txK4PmsJaZmcncuXPbHG8Oeu3NDrbngw8+wDCM4JvdbmfSpEn8/ve/BwLLSJ9++mny8vJ6VKeIiMhAplAnIjKI3HDDDRiGwf79+9m4cSNwIphdc801REVFdXusysrKYN+7xYsXY7G0/V/K4sWLsVqtre7TGykpKSxZsoTNmzfz3e9+t9fjiIiIDEQKdSIi/YBhGMHXzc+NdaX5vJbXdmX48OGcf/75QOA5usOHDwebhvd06eXKlStxu91A26WXzdLS0pg3bx4QmB3s6nObMWMG27dvD77t2rWLkpISSktLeeGFF5g8eXKPahQRERkMbKEuQEREaDVD1tjY2K1r6uvrAXA6nT261w033MD69etZtWoVQ4cOxe/3k52dzYUXXtijcVrOvE2bNq3L8wsLC1m/fn2n93E6nVpaKSIi0kOaqRMR6QeSkpKCr4uLi7s8v6mpiaqqqjbXdse1116Lw+GgsrKS+++/HwjMtPVkxm/Pnj188sknPbov9G3POhEREQnQTJ2ISD/Qclnh559/zkUXXdTp+Vu3bg024O7pksT4+Hguv/xyVq1ahcvlAnq+9LJlOHvyySdJSEjo9Pxnn32WNWvW8Oqrr/I///M/PXp2T0RERDqnUCci0g9ceOGF2Gw2vF4vK1eu5Ec/+lGnM2ctd69sb9fJrixdupQ33ngDCCydzM3N7fa1pmkG75+Xl8dtt93W5TUOh4M1a9ZQU1PD66+/3uMG5yIiItIxLb8UEekH0tPTufbaawHYsmULDzzwQIfnrl27lqeeegqAESNGcPnll/f4fgsWLMDlcuFyufjoo496dO369espLCwEAjtmdsc3v/lNYmJiAC3BFBER6WuaqRMR6SceffRR3nvvPY4dO8ZPf/pT3n//fZYsWUJOTg42m42ioiJWr17Nc889h9frxWKx8MwzzwRbBpwpLUPZ1Vdf3a1rHA4Hl112GatWreLdd9+luLiYjIyM01Vih/7+97+3emZx165dwddffvklK1asCL4fExPT7dAqIiISSgp1IiL9RGZmJuvXr+eqq65i586drFmzhjVr1rR7bkJCAi+++CJz5sw5ozU2Njby6quvAjBu3Lge7VR5zTXXsGrVKnw+Hy+99BJ33XXX6SqzQw888AAffPBBu8feeOON4JJUCLR/UKgTEZFwoOWXIiL9yLhx49i2bRsvvvgi11xzDcOHDyc6OprIyEgyMjKYO3cuDz/8MIWFhSxYsOCM1/f6669TU1MDdH+Wrtlll10W3CBFSzBFRET6jmF2t8utiIiIiIiI9DuaqRMREREREQljCnUiIiIiIiJhTKFOREREREQkjCnUiYiIiIiIhDGFOhERERERkTCmUCciIiIiIhLGFOpERERERETCmEKdiIiIiIhIGFOoExERERERCWMKdSIiIiIiImFMoU5ERERERCSMKdSJiIiIiIiEMYU6ERERERGRMKZQJyIiIiIiEsYU6kRERERERMKYQp2IiIiIiEgYU6gTEREREREJYwp1IiIiIiIiYUyhTkREREREJIwp1ImIiIiIiIQxhToREREREZEwplAnIiIiIiISxhTqREREREREwphCnYiIiIiISBhTqBMREREREQlj/z86AYWzIapfmAAAAABJRU5ErkJggg==", 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" ] diff --git a/gitk/scembed/const.py b/gitk/scembed/const.py index a64ee25f..feac77ba 100644 --- a/gitk/scembed/const.py +++ b/gitk/scembed/const.py @@ -30,3 +30,7 @@ MODEL_CONFIG_FILE_NAME = "model.yaml" DEFAULT_BEDTOOLS_PATH = "bedtools" + +DEFAULT_MODEL_EXPORT_FILE_NAME = "weights.pkl" +DEFAULT_UNIVERSE_EXPORT_FILE_NAME = "universe.bed" +DEFAULT_MODEL_CONFIG_FILE_NAME = "model.yaml" diff --git a/gitk/scembed/projection.py b/gitk/scembed/projection.py index 111be31b..d2c2adec 100644 --- a/gitk/scembed/projection.py +++ b/gitk/scembed/projection.py @@ -1,9 +1,12 @@ import logging import os -from typing import Dict, List +from typing import Dict, List, Any import numpy as np import scanpy as sc +import seaborn as sns +import matplotlib.pyplot as plt +import umap import yacman from tqdm import tqdm @@ -221,3 +224,117 @@ def project(self, adata: sc.AnnData, key_added: str = "embedding") -> sc.AnnData adata.obsm[key_added] = np.array(cell_embeddings) return adata + + def visualize_projection( + self, + universe: sc.AnnData, + adata: sc.AnnData, + embedding_key: str = "embedding", + cluster_key: str = "leiden", + n_rows: int = 2, + plot_kwargs: Dict[str, Any] = {}, + fig_kwargs: Dict[str, Any] = {}, + color_palette: str = "tab20", + ): + """ + Visualize the projection of the AnnData object into the model space. + + This requires that we have the original data the universe was trained on. In adition to that, + we require that embeddings are attached to these sc.AnnData objects. A small + limitation that hopefully future versions will remove. + + :param universe: AnnData object of the universe + :param adata: AnnData object to project + :param embedding_key: Key in the .obsm attribute of the AnnData object that contains + the embedding + :param cluster_key: Key in the .obs attribute of the universe that contains the + cluster information + :param cluster_colors: Dictionary mapping cluster names to colors + :param n_rows: Number of rows to use in the visualization (defaults to 2, but can be overridden if there are many clusters) + :param n_cols: Number of columns to use in the visualization (defaults to 2, but can be overridden if there are many clusters) + :param plot_kwargs: Additional keyword arguments to pass to the plot function of seaborn + """ + # run umap on the universe + _LOGGER.info("Running UMAP on the universe") + reducer = umap.UMAP(n_components=2, random_state=random_state) + universe.obsm["X_umap"] = reducer.fit_transform(universe.obsm[embedding_key]) + universe.obsm["UMAP1"] = universe.obsm["X_umap"][:, 0] + universe.obsm["UMAP2"] = universe.obsm["X_umap"][:, 1] + + # fit the adata to the universe umap + _LOGGER.info("Fitting the adata to the universe UMAP") + adata.obsm["X_umap"] = reducer.transform(adata.obsm[embedding_key]) + adata.obsm["UMAP1"] = adata.obsm["X_umap"][:, 0] + adata.obsm["UMAP2"] = adata.obsm["X_umap"][:, 1] + + # check for cluster key + if cluster_key not in adata.obs.columns: + raise ValueError( + f"AnnData object must have `{cluster_key}` in .obs to visualize" + ) + + # get dimensions of the plot + n_clusters = len(adata.obs[cluster_key].unique()) + n_cols = int(np.ceil(n_clusters / n_rows)) + + fig, axes = plt.subplots( + n_rows, n_cols, figsize=(n_cols * 5, n_rows * 5), **fig_kwargs + ) + fig.tight_layout(pad=3.0) + + # get cluster colors - color for each cluster + cluster_colors = { + cluster: color + for cluster, color in zip( + adata.obs[cluster_key].unique(), sns.color_palette(color_palette) + ) + } + + # iterate over the clusters and plot accordingly + for i, cluster in enumerate(adata.obs["leiden"].unique()): + # get the axis to plot on + ax_row = i // n_cols + ax_col = i % n_cols + ax = fig.axes[ax_row * n_cols + ax_col] + + # get color + color = cluster_colors[cluster] + + # plot the universe + sns.scatterplot( + data=universe.obsm, + x="UMAP1", + y="UMAP2", + color="gray", + ax=ax, + s=10, + alpha=0.8, + **plot_kwargs, + ) + + # plot the cluster on top + sns.scatterplot( + data=adata[adata.obs["leiden"] == cluster].obsm, + x="UMAP1", + y="UMAP2", + color=color, + ax=ax, + s=10, + alpha=0.4, + **plot_kwargs, + ) + + # add legend to plot showing gray universe + colored cluster + ax.legend( + [ + "Universe", + cluster, + ], + frameon=False, + bbox_to_anchor=(0.5, -0.15), + loc="upper center", + ncol=2, + ) + + # set the title + ax.set_title(f"Cluster {cluster}") diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index 1d0f8a35..2fad853a 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -1,5 +1,7 @@ +import os import gzip import pickle +import yaml from logging import getLogger from typing import Dict, List, Union @@ -17,6 +19,7 @@ LearningRateScheduler, ScheduleType, convert_anndata_to_documents, + create_model_info_dict, remove_regions_below_min_count, shuffle_documents, ) @@ -94,6 +97,41 @@ def save_model(self, filepath: str, **kwargs): with gzip.open(filepath, "wb") as f: pickle.dump(self, f) + def export_model( + self, + out_path: str, + model_config_name: str = DEFAULT_MODEL_CONFIG_FILE_NAME, + model_export_name: str = DEFAULT_MODEL_EXPORT_FILE_NAME, + universe_file_name: str = DEFAULT_UNIVERSE_EXPORT_FILE_NAME, + **config_kwargs, + ): + """ + This function will do a full export of the model. This includes three files: + 1. the actual pickled `.model` file + 2. the `yaml` config file with metadata + 3. the universe `.bed` file that determines the universe of regions + """ + if not os.path.exists(out_path): + os.makedirs(out_path) + + # save the model + self.save_model(os.path.join(out_path, model_export_name)) + + # save the universe to disk + with open(os.path.join(out_path, universe_file_name), "w") as f: + for region in self.region2vec: + chr, start, end = region.split("_") + f.write(f"{chr}\t{start}\t{end}\n") + + config_dict = create_model_info_dict( + path_to_weights=model_export_name, + path_to_universe=universe_file_name, + **config_kwargs, + ) + + with open(os.path.join(out_path, model_config_name), "w") as f: + yaml.dump(config_dict, f) + def load_model(self, filepath: str, **kwargs): """ Cant use the load function from gensim because it doesnt work on subclasses. see @@ -264,11 +302,74 @@ def cell_embeddings(self) -> sc.AnnData: # get the embeddings for each cell cell_embeddings = [] for cell in tqdm(self.region_sets, total=len(self.region_sets)): - cell_embedding = np.mean([self.get_embedding(r) for r in cell], axis=0) + cell_embedding = np.mean( + [self.get_embedding(r) for r in cell if r in self.region2vec], axis=0 + ) cell_embeddings.append(cell_embedding) + cell_embeddings = np.array(cell_embeddings) + # attach embeddings to the AnnData object - self.data.obs["embedding"] = cell_embeddings + self.data.obsm["embedding"] = cell_embeddings + return self.data + + def cell_embeddings(self) -> sc.AnnData: + """ + Get the cell embeddings for the original AnnData passed in. This should + be called after training is complete. It is only useful for extracting the + embeddings for the last chunk of data its seen. + """ + if not self.trained: + raise ValueError("Model has not been trained yet.") + + if self.data is None: + raise ValueError( + "Data not found. Please pass in an AnnData object to the constructor." + ) + + # get the embeddings for each cell + cell_embeddings = [] + for cell in tqdm(self.region_sets, total=len(self.region_sets)): + cell_embedding = np.mean( + [self.get_embedding(r) for r in cell if r in self.region2vec], axis=0 + ) + cell_embeddings.append(cell_embedding) + + cell_embeddings = np.array(cell_embeddings) + + return cell_embeddings + + def attach_cell_embeddings(self, key_added: str = "embedding") -> sc.AnnData: + """ + Get the cell embeddings for the original AnnData passed in. This should + be called after training is complete. It is only useful for extracting the + embeddings for the last chunk of data its seen. + + :param str key_added: the key to add the embeddings to in the obsm attribute of the AnnData object + """ + if not self.trained: + raise ValueError("Model has not been trained yet.") + + if not self.data: + raise ValueError( + "Data not found. Please pass in an AnnData object to the constructor." + ) + + if not self.region_sets: + self.region_sets = convert_anndata_to_documents(self.data) + + # get the embeddings for each cell + cell_embeddings = [] + for cell in tqdm(self.region_sets, total=len(self.region_sets)): + cell_embedding = np.mean( + [self.get_embedding(r) for r in cell if r in self.region2vec], axis=0 + ) + cell_embeddings.append(cell_embedding) + + cell_embeddings = np.array(cell_embeddings) + + # attach embeddings to the AnnData object + self.data.obsm[key_added] = cell_embeddings return self.data def embeddings_to_csv(self, output_path: str): diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 06a36c26..12c95d63 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -1,13 +1,13 @@ import os import shutil import subprocess -import urllib.request + from collections import Counter from concurrent.futures import ThreadPoolExecutor from enum import Enum from logging import getLogger from random import shuffle -from typing import TYPE_CHECKING, Dict, List, Union +from typing import TYPE_CHECKING, Dict, List, Union, Optional import numpy as np import pandas as pd @@ -197,23 +197,13 @@ def extract_region_list(region_df: pd.DataFrame) -> List[str]: :param pandas.DataFrame region_df: the regions dataframe to parse """ _LOGGER.info("Extracting region list from matrix.") - regions = region_df.apply( - lambda x: f"{x[CHR_KEY]}_{x[START_KEY]}_{x[END_KEY]}", axis=1 - ).tolist() + regions = [ + f"{row[CHR_KEY]}_{row[START_KEY]}_{row[END_KEY]}" + for _, row in region_df.iterrows() + ] return regions -def remove_zero_regions(cell_dict: Dict[str, int]) -> Dict[str, int]: - """ - Removes any key-value pairs in a dictionary where the value (copy number) - is equal to zero (no signal). This is done using dictionary comprehension - as it is much faster. - - :param cell_dict Dict[str, int]: the cell dictionary with region index keys and copy number values - """ - return {k: v for k, v in cell_dict.items() if v > 0} - - def convert_anndata_to_documents( anndata: sc.AnnData, use_defaults: bool = True, @@ -242,19 +232,16 @@ def convert_anndata_to_documents( else: regions_parsed = extract_region_list(anndata.var) sc_df = anndata.to_df() - docs = [] _LOGGER.info("Generating documents.") - def process_row(row): - row_dict = row.to_dict() - row_dict = remove_zero_regions(row_dict) - new_doc = [] - for region_indx in row_dict: - region_str = regions_parsed[int(region_indx)] - new_doc.append(region_str) - return new_doc - - docs = sc_df.apply(process_row, axis=1).tolist() + docs = [ + [ + regions_parsed[int(region_indx)] + for region_indx, value in row.to_dict().items() + if value > 0 + ] + for _, row in sc_df.iterrows() + ] return docs @@ -489,3 +476,41 @@ def barcode_mtx_peaks_to_anndata( adata = sc.AnnData(X=mtx, obs=barcodes, var=peaks) return adata + + +def create_model_info_dict( + path_to_weights: Optional[str] = None, + path_to_universe: Optional[str] = None, + name: Optional[str] = None, + reference: Optional[str] = None, + description: Optional[str] = None, + tags: Optional[List[str]] = None, + datasets: Optional[List[str]] = None, + model_parameters: Optional[Dict[str, Union[str, int]]] = None, + model_architecture: Optional[str] = None, + maintainers: Optional[List[Dict[str, str]]] = None, +) -> Dict[str, Union[str, List[str], List[Dict[str, Union[str, int]]]]]: + model_info = {} + + if path_to_weights: + model_info["path_to_weights"] = path_to_weights + if path_to_universe: + model_info["path_to_universe"] = path_to_universe + if name: + model_info["name"] = name + if reference: + model_info["reference"] = reference + if description: + model_info["description"] = description + if tags: + model_info["tags"] = tags + if datasets: + model_info["datasets"] = datasets + if model_parameters: + model_info["model_parameters"] = model_parameters + if model_architecture: + model_info["model_architecture"] = model_architecture + if maintainers: + model_info["maintainers"] = maintainers + + return model_info From 4d3e8cdd3244250786b6eb59b61674d173046960 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Wed, 14 Jun 2023 19:06:44 -0400 Subject: [PATCH 0216/1072] new hmm --- gitk/universe/ccf_universe.py | 1 - gitk/universe/const.py | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/gitk/universe/ccf_universe.py b/gitk/universe/ccf_universe.py index 3f592942..3cefb4d2 100644 --- a/gitk/universe/ccf_universe.py +++ b/gitk/universe/ccf_universe.py @@ -152,7 +152,6 @@ def ccf_universe(cove, cove_prefix, file_out): chroms_key = list(chroms.keys()) chroms_key = sorted(chroms_key, key=cmp_to_key(natural_chr_sort)) chroms = {i: chroms[i] for i in chroms_key} - chroms = {"chr5": chroms["chr5"]} for chrom in chroms: if chroms[chrom] > 0: get_uni(file, chrom, file_out) diff --git a/gitk/universe/const.py b/gitk/universe/const.py index 9fcab822..b3e7b8ca 100644 --- a/gitk/universe/const.py +++ b/gitk/universe/const.py @@ -5,4 +5,4 @@ [0.1, 0, 0, 0.9], ] -LAMBDAS = [[5, 3, 0.0001], [0.1, 3, 0.1], [0.0001, 3, 5], [1e-4, 1e-3, 1e-4]] +LAMBDAS = [[5, 4, 0.0001], [0.1, 3, 0.1], [0.0001, 4, 5], [1e-4, 1e-3, 1e-4]] From 8d5156564e4009b8eba194b27a9a72b3c8bfbed7 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Thu, 15 Jun 2023 10:17:18 -0400 Subject: [PATCH 0217/1072] changed cs to rbs --- gitk/assess/assess.py | 30 +++++++++++++++++++----------- 1 file changed, 19 insertions(+), 11 deletions(-) diff --git a/gitk/assess/assess.py b/gitk/assess/assess.py index 81bee5c1..ec6a5cf6 100644 --- a/gitk/assess/assess.py +++ b/gitk/assess/assess.py @@ -132,7 +132,13 @@ def run_all_assessment_methods( df.to_csv(os.path.join(folder_out, pref + "_data.csv"), index=False) -def get_closeness_score(folder, file_list, universe, no_workers, flexible=False): +def get_rbs(f_t_u, u_t_f): + a = 101/(f_t_u+100) + b = 101/(u_t_f+100) + rbs = (10*a + b)/11 + return rbs + +def get_mean_rbs(folder, file_list, universe, no_workers, flexible=False): file_to_uni = run_distance( folder, file_list, @@ -150,22 +156,24 @@ def get_closeness_score(folder, file_list, universe, no_workers, flexible=False) flexible=flexible, uni_to_file=True, ) - me = (10 * file_to_uni[1] + uni_to_file[1]) / 11 - cs = 1001 / (me + 1000) / 1.001 - return np.mean(cs) + # me = (10 * file_to_uni[1] + uni_to_file[1]) / 11 + rbs = get_rbs(file_to_uni[1], uni_to_file[1]) + return np.mean(rbs) -def get_closeness_score_from_assessment_file(file, cs_each_file=False): +def get_rbs_from_assessment_file(file, cs_each_file=False, flexible = False): df = pd.read_csv(file, index_col=(0)) - df = df[["median_dist_file_to_universe", "median_dist_universe_to_file"]] - df["CS"] = ( - df["median_dist_universe_to_file"] + 10 * df["median_dist_file_to_universe"] - ) / 11 - df["CS"] = (1001 / (df["CS"] + 1000)) / 1.001 + if flexible: + df["f_t_u"] = df["median_dist_file_to_universe_flex"] + df["u_t_f"] = df["median_dist_universe_to_file_flex"] + else: + df["f_t_u"] = df["median_dist_file_to_universe"] + df["u_t_f"] = df["median_dist_universe_to_file"] + df["RBS"] = get_rbs(df["f_t_u"], df["u_t_t"]) if cs_each_file: return df else: - return df["CS"].mean() + return df["RBS"].mean() def get_f_10_score( From 5e9f1a51950291037dedf7a5748abf51196fcbf6 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Thu, 15 Jun 2023 15:16:12 -0400 Subject: [PATCH 0218/1072] new hmm model --- gitk/universe/const.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gitk/universe/const.py b/gitk/universe/const.py index b3e7b8ca..6c0d5dbe 100644 --- a/gitk/universe/const.py +++ b/gitk/universe/const.py @@ -5,4 +5,4 @@ [0.1, 0, 0, 0.9], ] -LAMBDAS = [[5, 4, 0.0001], [0.1, 3, 0.1], [0.0001, 4, 5], [1e-4, 1e-3, 1e-4]] +LAMBDAS = [[5, 3, 0.0001], [0.05, 5, 0.05], [0.0001, 3, 5], [0.0001, 0.001, 0.0001]] From cb825c35013a689e19b81f0bd842d023b037941f Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 16 Jun 2023 15:33:51 -0400 Subject: [PATCH 0219/1072] new module structure --- docs/{likelihood => consensus}/assess.md | 0 .../consensus-peaks.md | 69 ++++++++++++------- gitk/assess/assess.py | 11 +-- gitk/cli.py | 16 ++--- gitk/likelihood/cli.py | 1 - 5 files changed, 59 insertions(+), 38 deletions(-) rename docs/{likelihood => consensus}/assess.md (100%) rename docs/{likelihood => consensus}/consensus-peaks.md (61%) diff --git a/docs/likelihood/assess.md b/docs/consensus/assess.md similarity index 100% rename from docs/likelihood/assess.md rename to docs/consensus/assess.md diff --git a/docs/likelihood/consensus-peaks.md b/docs/consensus/consensus-peaks.md similarity index 61% rename from docs/likelihood/consensus-peaks.md rename to docs/consensus/consensus-peaks.md index a731151a..4d9ecb2b 100644 --- a/docs/likelihood/consensus-peaks.md +++ b/docs/consensus/consensus-peaks.md @@ -15,33 +15,55 @@ their starts and ends. To do that we have to: In this tutorial we will use prefix "all" as it is a default prefix in ```gitk``` module -## Coverage universe +## Coverage cutoff universe We will start by making a coverage universe with cutoff that results in maximum likelihood universe. We can do it through CLI: ``` - gitk lh universe_hard --coverage-file tests/consenus/coverage/all_core.bw \ - --fout tests/consenus/universe/universe.bed + gitk build-universe cc --coverage-folder tests/consenus/coverage/ \ + --output-file tests/consenus/universe/universe.bed ``` Where: -- ```--coverage_file```, takes the path to bigWig file with genome coverage by collection -- ```--fout```, takes the path to output file +- ```--coverage-folder```, takes the path to bigWig file with genome coverage by collection +- ```--output-file```, takes the path to output file Or we can import it directly into python: ``` -from gitk.likelihood.universe-hard import main as cutoff +from gitk.universe.cc_universe import cc_universe -cutoff("tests/consenus/coverage/all_core.bw", - fout="tests/consenus/universe/universe.bed") +cc_universe("tests/consenus/coverage/all_core.bw", + file_out="tests/consenus/universe/universe.bed") ``` Depending on the task we can also smooth the output universe by setting ``--merge`` flag with the distance beloved witch peaks should be merged together and ``--filter-size`` with minimum size of peak that should be part of the universe. We can also not use the maximum likelihood cut-off and instead of it use user defined cutoff. For that we have to set ``--cutoff`` . If we set it to 1 we get union universe, and when to number of files we will get intersection universe. +## Coverage cutoff flexible universe +Next presented universe is coverage cutoff flexible universe. We can do it through CLI: + +``` + gitk build-universe ccf --coverage-folder tests/consenus/coverage/ \ + --output-file tests/consenus/universe/universe.bed + +``` + +Where: + +- ```--coverage-folder```, takes the path to bigWig file with genome coverage by collection +- ```--output-file```, takes the path to output file + +Or we can import it directly into python: +``` +from gitk.universe.ccf_universe import ccf_universe + +ccf_universe("tests/consenus/coverage/all_core.bw", + file_out="tests/consenus/universe/universe.bed") +``` + ## Maximum likelihood universe Another type of universe that we can make is maximum likelihood flexible universe. To make it first we have to have a likelihood model of genome coverage by collection of files. @@ -49,14 +71,14 @@ Another type of universe that we can make is maximum likelihood flexible univers To make a likelihood model we can use this CLI: ``` -gitk lh build_model --model-folder tests/consenus/model.tar \ +gitk lh build_model --model-file tests/consenus/model.tar \ --coverage-folder tests/consenus/coverage/ \ - --file-no x + --file-no 4 ``` Where: -- ```--model-folder```, takes the name of tar archive that will contain the likelihood model +- ```--model-file```, takes the name of tar archive that will contain the likelihood model - ```--file-no```, number of files used in analysis - ```--coverage-folder``` path to folder with coverage tracks @@ -74,23 +96,23 @@ main("tests/consenus/model.tar", "tests/consesnus/coverage", Now that we have the model we make the universe: ``` -gitk lh universe_flexible --model-folder tests/consenus/model.tar \ +gitk build-universe ml --model-file tests/consenus/model.tar \ --output-file tests/consenus/universe/universe.bed \ - --cov-folder tests/consesnus/coverage + --coverage-folder tests/consesnus/coverage ``` Where: -- ```--model-folder```, takes the name of tar archive that contains the likelihood model +- ```--model-file```, takes the name of tar archive that contains the likelihood model - ```--output-file```, takes the path to output file -- ```--cov-folder``` path to folder with coverage tracks +- ```--coverage-folder``` path to folder with coverage tracks Similarly, we can do it in python: ``` -from gitk.likelihood.universe_flexible import main +from gitk.universe.ml_universe import ml_universe -main("tests/consesnus/model.tar", +ml_universe("tests/consesnus/model.tar", "/home/hee6jn/Documents/gitk/tests/consesnus/coverage", "all", "tests/consenus/universe/universe.bed") @@ -101,16 +123,15 @@ Another approach to making flexible universes is using Hidden Markov Models. We can do it for example with: ``` -gitk hmm --out-file tests/consenus/universe/universe.bed \ - --cov-folder tests/consenus/coverage/ \ - --normlaize \ +gitk build-universe hmm --out-file tests/consenus/universe/universe.bed \ + --coverage-folder tests/consenus/coverage/ \ --save-max-cove ``` Where: -- ```--out-file```, takes the path to output file -- ```--cov-folder```, path to folder with coverage tracks +- ```--output-file```, takes the path to output file +- ```--coverage-folder```, path to folder with coverage tracks - ```--coverage-prefix``` prefix used in uniwig for making files, default is "all" - ```--not-normlaize```, is a flag that specifies whether not to normalize tracks before running HMM - ```--save-max-cove```, is a flag that specifies whether to save maximum coverage of each output peak @@ -118,9 +139,9 @@ Where: Similarly, we can do it in python: ``` -from gitk.hmm.hmm import run_hmm_save_bed +from gitk.universe.hmm_universe import hmm_universe -run_hmm_save_bed("tests/consenus/coverage/", +hmm_universe("tests/consenus/coverage/", "tests/consenus/universe/universe.bed", save_max_cove=True) ``` \ No newline at end of file diff --git a/gitk/assess/assess.py b/gitk/assess/assess.py index ec6a5cf6..59511745 100644 --- a/gitk/assess/assess.py +++ b/gitk/assess/assess.py @@ -133,11 +133,12 @@ def run_all_assessment_methods( def get_rbs(f_t_u, u_t_f): - a = 101/(f_t_u+100) - b = 101/(u_t_f+100) - rbs = (10*a + b)/11 + a = 101 / (f_t_u + 100) + b = 101 / (u_t_f + 100) + rbs = (10 * a + b) / 11 return rbs + def get_mean_rbs(folder, file_list, universe, no_workers, flexible=False): file_to_uni = run_distance( folder, @@ -161,7 +162,7 @@ def get_mean_rbs(folder, file_list, universe, no_workers, flexible=False): return np.mean(rbs) -def get_rbs_from_assessment_file(file, cs_each_file=False, flexible = False): +def get_rbs_from_assessment_file(file, cs_each_file=False, flexible=False): df = pd.read_csv(file, index_col=(0)) if flexible: df["f_t_u"] = df["median_dist_file_to_universe_flex"] @@ -266,7 +267,7 @@ def filter_universe( if j[2] - j[1] > min_size: if j[4] > min_coverage: if filter_lh: - if int(j[9].strip("\n")) > lh_cutoff: + if float(j[9].strip("\n")) > lh_cutoff: uni_flt.write(i) else: uni_flt.write(i) diff --git a/gitk/cli.py b/gitk/cli.py index 338460b4..7e70d91a 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -33,9 +33,9 @@ def build_argparser(): # Individual subcommands msg_by_cmd = { - "universe": "Use an HMM to build a consensus peak set.", - "lh": "Compute likelihood", - "assess": "Assess a universe", + "build-universe": "Build a consensus peak set using one of provided model", + "lh": "Make likelihood model", + "assess-universe": "Assess a universe", "scembed": "Embed single-cell data as region vectors", "eval": "Evaluate a set of region embeddings", "bedspace": "Coembed regionsets (bed files) and labels", @@ -47,8 +47,8 @@ def build_argparser(): subparsers[k] = sp.add_parser(k, description=v, help=v) # build up subparsers for modules - subparsers["universe"] = universe_subparser(subparsers["universe"]) - subparsers["assess"] = assess_subparser(subparsers["assess"]) + subparsers["build-universe"] = universe_subparser(subparsers["build-universe"]) + subparsers["assess-universe"] = assess_subparser(subparsers["assess-universe"]) subparsers["lh"] = likelihood_subparser(subparsers["lh"]) subparsers["scembed"] = scembed_subparser(subparsers["scembed"]) subparsers["eval"] = eval_subparser(subparsers["eval"]) @@ -72,7 +72,7 @@ def main(test_args=None): _LOGGER.info(f"Command: {args.command}") - if args.command == "assess": + if args.command == "assess-universe": from .assess.assess import run_all_assessment_methods run_all_assessment_methods( @@ -103,7 +103,7 @@ def main(test_args=None): args.force, ) - if args.command == "universe": + if args.command == "build-universe": _LOGGER.info(f"Subcommand: {args.subcommand}") if args.subcommand == "hmm": from .universe.hmm_universe import hmm_universe @@ -180,7 +180,7 @@ def main(test_args=None): threshold=args.threshold, ) print(scctss) - return + if args.command == "bedspace": from .bedspace.const import PREPROCESS_CMD, TRAIN_CMD, DISTANCES_CMD, SEARCH_CMD diff --git a/gitk/likelihood/cli.py b/gitk/likelihood/cli.py index cec5039b..f22b294a 100644 --- a/gitk/likelihood/cli.py +++ b/gitk/likelihood/cli.py @@ -26,4 +26,3 @@ def build_subparser(parser): action="store_true", ) return parser - From 76d299aed69a4efa1fb6befe0aad5548ac0d5be1 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 16 Jun 2023 16:13:25 -0400 Subject: [PATCH 0220/1072] updated projection code --- examples/scembed/projection2.ipynb | 77 +++++++++++--------- gitk/scembed/projection.py | 19 +++-- gitk/scembed/scembed.py | 61 +++++++++++++++- gitk/scembed/utils.py | 109 +++++++++++++++-------------- tests/data/s1_a.bed | 3 + tests/data/s1_b.bed | 3 + tests/data/s2_a.bed | 3 + tests/data/s2_b.bed | 3 + tests/data/s3_a.bed | 3 + tests/data/s3_b.bed | 4 ++ tests/test_projector.py | 82 +++++++++++++++------- 11 files changed, 252 insertions(+), 115 deletions(-) create mode 100644 tests/data/s1_a.bed create mode 100644 tests/data/s1_b.bed create mode 100644 tests/data/s2_a.bed create mode 100644 tests/data/s2_b.bed create mode 100644 tests/data/s3_a.bed create mode 100644 tests/data/s3_b.bed diff --git a/examples/scembed/projection2.ipynb b/examples/scembed/projection2.ipynb index 57e87119..7089ead9 100644 --- a/examples/scembed/projection2.ipynb +++ b/examples/scembed/projection2.ipynb @@ -31,7 +31,7 @@ "source": [ "!pip install gitk\n", "# or if using local version:\n", - "!pip install ." + "# !pip install ." ] }, { @@ -109,30 +109,30 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# read in the data\n", "import scanpy as sc\n", "\n", - "pbmc = sc.read_h5ad(\"/Volumes/data/genomics/10x/10kpbmcsV2_atac/pbmc.h5ad\")" + "pbmc = sc.read_h5ad(\"/Volumes/data/genomics/10x/10kpbmcsV2_atac/cellranger/pbmc.h5ad\")" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "AnnData object with n_obs Ă— n_vars = 9221 Ă— 191125\n", - " obs: 'sample', 'barcode'\n", + "AnnData object with n_obs Ă— n_vars = 10140 Ă— 165434\n", + " obs: 'barcode'\n", " var: 'chr', 'start', 'end'" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -143,28 +143,45 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n" + ] + } + ], "source": [ "# create projector\n", "from gitk.scembed import Projector\n", "\n", - "model = Projector(\"/Users/nathanleroy/.scembed/databio/scatlas/model.yaml\")" + "model = Projector(\"buenrostro/model.yaml\")" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "974599" + "138919" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -182,9 +199,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|â–â–â–â–â–â–â–â–â–â–| 191125/191125 [00:07<00:00, 24548.46it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 9221/9221 [02:25<00:00, 63.58it/s] \n", - "100%|â–â–â–â–â–â–â–â–â–â–| 9221/9221 [01:32<00:00, 100.14it/s]\n" + "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", + "GL000194.1\t55725\t56585\n", + "\n", + "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", + "GL000194.1\t55725\t56585\n", + "\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:04<00:00, 38869.58it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10140/10140 [00:27<00:00, 369.39it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10140/10140 [00:12<00:00, 806.20it/s]\n" ] } ], @@ -199,16 +222,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(9221, 100)" + "(10140, 100)" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -235,23 +258,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" ] } @@ -264,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -296,7 +309,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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" ] diff --git a/gitk/scembed/projection.py b/gitk/scembed/projection.py index d2c2adec..faeeade0 100644 --- a/gitk/scembed/projection.py +++ b/gitk/scembed/projection.py @@ -125,9 +125,15 @@ def _load_model(self): def convert_to_universe(self, adata: sc.AnnData) -> sc.AnnData: """ - Converts the conesnsus peak set (.var) attributes of the AnnData object + Converts the consensus peak set (.var) attributes of the AnnData object to a universe representation. This is done through interval overlap - analysis. + analysis with bedtools. + + For each region in the `.var` attribute of the AnnData object, we + either 1) map it to a region in the universe, or 2) map it to `None`. + If it is mapped to `None`, it is not in the universe and will be dropped + from the AnnData object. If it is mapped to a region, it will be updated + to the region in the universe for downstream analysis. """ # ensure adata has chr, start, and end if not all([x in adata.var.columns for x in ["chr", "start", "end"]]): @@ -152,14 +158,14 @@ def convert_to_universe(self, adata: sc.AnnData) -> sc.AnnData: columns_to_keep = [] for i, row in tqdm(adata.var.iterrows(), total=adata.var.shape[0]): region = f"{row['chr']}_{row['start']}_{row['end']}" - if region not in _map: + if _map[region] is None: columns_to_keep.append(False) continue # if it is, change the region to the universe region, # grab the first for now # TODO - this is a simplification, we should be able to handle multiple - universe_region = _map[region][0] + universe_region = _map[region] chr, start, end = universe_region.split("_") updated_var.at[i, "chr"] = chr @@ -235,6 +241,7 @@ def visualize_projection( plot_kwargs: Dict[str, Any] = {}, fig_kwargs: Dict[str, Any] = {}, color_palette: str = "tab20", + random_state: int = 42, ): """ Visualize the projection of the AnnData object into the model space. @@ -320,7 +327,7 @@ def visualize_projection( color=color, ax=ax, s=10, - alpha=0.4, + alpha=1, **plot_kwargs, ) @@ -338,3 +345,5 @@ def visualize_projection( # set the title ax.set_title(f"Cluster {cluster}") + + return fig, axes diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index 2fad853a..e71744ac 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -130,7 +130,7 @@ def export_model( ) with open(os.path.join(out_path, model_config_name), "w") as f: - yaml.dump(config_dict, f) + yaml.dump({"model": config_dict}, f) def load_model(self, filepath: str, **kwargs): """ @@ -260,6 +260,65 @@ def train( for word in regions: self.region2vec[word] = self.wv[word] + # this is here for testing and debugging + def train_legacy( + self, + data: Union[sc.AnnData, str], + epochs: int = DEFAULT_EPOCHS, # training cycles + n_shuffles: int = DEAFULT_N_SHUFFLES, # not the number of traiing cycles, actual shufle num + gensim_epochs: Union[int, None] = DEFAULT_GENSIM_EPOCHS, + report_loss: bool = True, + lr: float = DEFAULT_INIT_LR, + min_lr: float = DEFAULT_MIN_LR, + lr_schedule: Union[str, ScheduleType] = "linear", + ): + self.trained = True + if report_loss: + self.callbacks.append(ReportLossCallback()) + + lr_scheduler = LearningRateScheduler( + init_lr=lr, min_lr=min_lr, type=lr_schedule, n_epochs=epochs + ) + + region_sets = convert_anndata_to_documents(data, self.use_default_region_names) + + _LOGGER.info("Training starting.") + + for shuffle_num in range(epochs): + # update current values + current_lr = lr_scheduler.get_lr() + current_loss = self.get_latest_training_loss() + + # update user + _LOGGER.info( + f"SHUFFLE {shuffle_num} - lr: {current_lr}, loss: {current_loss}" + ) + _LOGGER.info("Shuffling documents.") + + # shuffle regions + self.region_sets = shuffle_documents(region_sets, n_shuffles=n_shuffles) + + # update vocab and train + super().build_vocab(region_sets, update=True if shuffle_num > 0 else False) + super().train( + self.region_sets, + total_examples=len(region_sets), + epochs=gensim_epochs or 1, # use the epochs passed in or just one + callbacks=self.callbacks, + compute_loss=report_loss, + start_alpha=current_lr, + ) + + # update learning rates + lr_scheduler.update() + + # once training is complete, create a region to vector mapping + regions = list(self.wv.key_to_index.keys()) + + # create a mapping from region to vector + for word in regions: + self.region2vec[word] = self.wv[word] + def get_embedding(self, region: str) -> np.ndarray: """ Get the embedding for a given region. diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 12c95d63..e34701f9 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -317,28 +317,18 @@ def load_universe_file(path: str) -> List[str]: def generate_var_conversion_map( a: List[str], b: List[str], path_to_bedtools: str = None, fraction: float = 1.0e-9 -) -> Dict[str, List[str]]: +) -> Dict[str, Union[str, None]]: """ - Find the regions that overlap between two lists of regions using bedtools. for each region in - a, if it overlaps a region in b, report region from a and the region from b that it overlapped. + Create a conversion map to convert regions from a to b. This is used to convert the + consensus peak set of one AnnData object to another. - i.e. the result of the bedtools operation is a bed file with 6 columns, - chr_a\tstart_a\tend_a\tchr_b\tstart_b\tend_b. The first three columns are the region from a, the last three - columns are the region from b. + For each region in a, we will either find a matching region in b, or None. If a matching + region is found, we will store the region in b. If no matching region is found, we will + store `None`. - Where chr_start_end (a) -- Overlaps --> chr_start_end (b) - - This occurs in three steps: - 1. write both a and b to a temporary file, - 2. use bedtools to find the overlaps and dump to a temporary file, - 3. read in the temporary file and parse the results. - - The function returns a dictionary of the form: - - Dict[str, List[str]] - - Where a_region and b_region are strings of the form chr_start_end and any and all regions - from a overlap any and all regions from b. + Intuitively, think of this as converting `A` --> `B`. If a region in `A` is found in `B`, + we will change the region in `A` to the region in `B`. If a region in `A` is not found in + `B`, we will drop that region in `A` altogether. :param List[str] a: the first list of regions :param List[str] b: the second list of regions @@ -348,54 +338,67 @@ def generate_var_conversion_map( # write a and b to temp files in cache a_file = os.path.join(MODEL_CACHE_DIR, "a.bed") b_file = os.path.join(MODEL_CACHE_DIR, "b.bed") + + # write a and b to temp files with open(a_file, "w") as f: # split each region into chr start end - a = [region.split("_") for region in a] - a = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in a] - f.writelines(a) - + a_parsed = [region.split("_") for region in a] + a_parsed = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in a_parsed] + f.writelines(a_parsed) with open(b_file, "w") as f: - b = [region.split("_") for region in b] - b = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in b] - f.writelines(b) + b_parsed = [region.split("_") for region in b] + b_parsed = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in b_parsed] + f.writelines(b_parsed) - # run bedtools - if path_to_bedtools is None: - path_to_bedtools = DEFAULT_BEDTOOLS_PATH + # sort both files + cmd = f"sort -k1,1 -k2,2n {a_file} -o {a_file}" + subprocess.run(cmd, shell=True) - overlaps_file = os.path.join(MODEL_CACHE_DIR, "ab_tokens" + ".bed") + cmd = f"sort -k1,1 -k2,2n {b_file} -o {b_file}" + subprocess.run(cmd, shell=True) - with open(overlaps_file, "w") as f_target: - cmd = f"{path_to_bedtools} intersect -a {a_file} -b {b_file} -wa -wb -f {fraction}" - subprocess.run(cmd, shell=True, stdout=f_target) + # run bedtools + bedtools_cmd = f"intersect -a {a_file} -b {b_file} -wa -wb -f {fraction}" - # read in the and create a dictionary for mapping, - # each key is a region from a, each value is a list of regions from b that it overlaps - olaps = {} - with open(overlaps_file, "r") as f: - # create list of tuples + # add path to bedtools if provided + if path_to_bedtools is not None: + cmd = f"{path_to_bedtools} {bedtools_cmd}" + else: + cmd = f"bedtools {bedtools_cmd}" + + # target file + target_file = os.path.join(MODEL_CACHE_DIR, "olaps.bed") + with open(target_file, "w") as f: + subprocess.run(cmd, shell=True, stdout=f) + + # bedtools reports overlaps like this: + # chr1 100 200 chr1 150 250 + # we want to convert this to a map like this: + # {chr1_100_200: chr1_150_250} + # we will use a dictionary to do this + # if a region in A overlaps with multiple regions in B, we will + # take the first one. as such we need to check if a region in A + # has already been mapped to a region in B + conversion_map = dict() + with open(target_file, "r") as f: for line in f.readlines(): - line = line.strip() - # region_a - region_a = line.split("\t")[:3] - region_a = "_".join(region_a) - - # region_b - region_b = line.split("\t")[3:6] - region_b = "_".join(region_b) + line = line.strip().split("\t") + a_region = f"{line[0]}_{line[1]}_{line[2]}" + b_region = f"{line[3]}_{line[4]}_{line[5]}" + if a_region not in conversion_map: + conversion_map[a_region] = b_region - # add to dictionary - if region_a not in olaps: - olaps[region_a] = [region_b] - else: - olaps[region_a].append(region_b) + # add `None` mappings for regions in A that did not overlap with any regions in B + for region in a: + if region not in conversion_map: + conversion_map[region] = None # remove temp files os.remove(a_file) os.remove(b_file) - os.remove(overlaps_file) + os.remove(target_file) - return olaps + return conversion_map # This method should only be used in the Projector. It diff --git a/tests/data/s1_a.bed b/tests/data/s1_a.bed new file mode 100644 index 00000000..f6f45086 --- /dev/null +++ b/tests/data/s1_a.bed @@ -0,0 +1,3 @@ +chr1 10 30 +chr1 110 130 +chr1 210 230 diff --git a/tests/data/s1_b.bed b/tests/data/s1_b.bed new file mode 100644 index 00000000..cb3ef9af --- /dev/null +++ b/tests/data/s1_b.bed @@ -0,0 +1,3 @@ +chr1 20 40 +chr1 120 140 +chr1 220 240 diff --git a/tests/data/s2_a.bed b/tests/data/s2_a.bed new file mode 100644 index 00000000..f6f45086 --- /dev/null +++ b/tests/data/s2_a.bed @@ -0,0 +1,3 @@ +chr1 10 30 +chr1 110 130 +chr1 210 230 diff --git a/tests/data/s2_b.bed b/tests/data/s2_b.bed new file mode 100644 index 00000000..6520097a --- /dev/null +++ b/tests/data/s2_b.bed @@ -0,0 +1,3 @@ +chr1 20 40 +chr1 140 160 +chr1 220 240 diff --git a/tests/data/s3_a.bed b/tests/data/s3_a.bed new file mode 100644 index 00000000..f6f45086 --- /dev/null +++ b/tests/data/s3_a.bed @@ -0,0 +1,3 @@ +chr1 10 30 +chr1 110 130 +chr1 210 230 diff --git a/tests/data/s3_b.bed b/tests/data/s3_b.bed new file mode 100644 index 00000000..9c2656e2 --- /dev/null +++ b/tests/data/s3_b.bed @@ -0,0 +1,4 @@ +chr1 20 40 +chr1 100 120 +chr1 125 145 +chr1 220 240 diff --git a/tests/test_projector.py b/tests/test_projector.py index 5f5bd5af..7bed0791 100644 --- a/tests/test_projector.py +++ b/tests/test_projector.py @@ -69,34 +69,68 @@ def test_init_projector(projector): @pytest.mark.skip(reason="bedtools isnt installed in CI") def test_tokenization(): """ - Tokenize the top into the bottom + Test tokenization for three scenarios: + 1. One overlap in B per region in A + 2. At least one region in A doesn't overlap with any region in B + 3. A region in A overlaps with multiple regions in B - chr1: 1000 2000 3000 4000 5000 6000 - A |--------| |--------| |--------| - B |--------| |--------| |----| |--------| + Scenario 1: + A: |-----------| |-----------| |-----------| + B: |-----------| |-----------| |-----------| - chr1: 1000 2000 3000 4000 5000 6000 - A |--------| |--------| |--------| - C |--------| |---------------| + Scenario 2: + A: |-----------| |-------| |-----------| + B: |-----------| |-------| |-----------| + + Scenario 3: + A: |-----------| |-----------| |-----------| + B: |-----------| |--------| |-----------| |-----------| """ - regions_a = [ - "chr1_1000_2000", - "chr1_3000_4000", - "chr1_5000_6000", - ] - regions_b = [ - "chr1_1500_2500", - "chr1_3500_4500", - "chr1_4800_5050", - "chr1_5500_6050", - ] - regions_c = ["chr1_1500_2500", "chr1_3500_5500"] - - var_map_ab = scembed.utils.generate_var_conversion_map(regions_a, regions_b) - var_map_ac = scembed.utils.generate_var_conversion_map(regions_a, regions_c) - assert len(var_map_ab) == 3 - assert len(var_map_ac) == 3 + # scenario 1 + with open("tests/data/s1_a.bed", "r") as f: + lines = f.readlines() + a = ["_".join(l.strip().split("\t")) for l in lines] + with open("tests/data/s1_b.bed", "r") as f: + lines = f.readlines() + b = ["_".join(l.strip().split("\t")) for l in lines] + + conversion_map = scembed.utils.generate_var_conversion_map(a, b) + assert conversion_map == { + "chr1_10_30": "chr1_20_40", + "chr1_110_130": "chr1_120_140", + "chr1_210_230": "chr1_220_240", + } + + # scenario 2 + with open("tests/data/s2_a.bed", "r") as f: + lines = f.readlines() + a = ["_".join(l.strip().split("\t")) for l in lines] + with open("tests/data/s2_b.bed", "r") as f: + lines = f.readlines() + b = ["_".join(l.strip().split("\t")) for l in lines] + + conversion_map = scembed.utils.generate_var_conversion_map(a, b) + assert conversion_map == { + "chr1_10_30": "chr1_20_40", + "chr1_110_130": None, + "chr1_210_230": "chr1_220_240", + } + + # scenario 3 + with open("tests/data/s3_a.bed", "r") as f: + lines = f.readlines() + a = ["_".join(l.strip().split("\t")) for l in lines] + with open("tests/data/s3_b.bed", "r") as f: + lines = f.readlines() + b = ["_".join(l.strip().split("\t")) for l in lines] + + conversion_map = scembed.utils.generate_var_conversion_map(a, b) + assert conversion_map == { + "chr1_10_30": "chr1_20_40", + "chr1_110_130": "chr1_100_120", + "chr1_210_230": "chr1_220_240", + } @pytest.mark.skip From 78a0fdad75590b0a3159a3d5fedfbe26faeeee12 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 16 Jun 2023 16:14:39 -0400 Subject: [PATCH 0221/1072] revert to gzip again for model loading --- gitk/scembed/utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index e34701f9..f9221fbf 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -253,8 +253,9 @@ def load_scembed_model(path: str) -> "SCEmbed": :param str path: The path to the model. """ import pickle + import gzip - with open(path, "rb") as f: + with gzip.open(path, "rb") as f: model = pickle.load(f) return model From 6105becdcc91c215832702b4689466d7f0e0c41d Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 23 Jun 2023 11:22:07 -0400 Subject: [PATCH 0222/1072] lint --- gitk/bedspace/pipeline/Visualization.py | 6 ------ gitk/bedspace/pipeline/bedspace_queryDBsim.py | 2 -- gitk/bedspace/pipeline/bedspace_test.py | 1 - gitk/bedspace/pipeline/helpers.py | 1 - gitk/cli.py | 1 - 5 files changed, 11 deletions(-) diff --git a/gitk/bedspace/pipeline/Visualization.py b/gitk/bedspace/pipeline/Visualization.py index 96fa1289..c142b3b2 100644 --- a/gitk/bedspace/pipeline/Visualization.py +++ b/gitk/bedspace/pipeline/Visualization.py @@ -49,7 +49,6 @@ def UMAP_plot( plottitle="", output_folder="", ): - np.random.seed(3) dp = 400 @@ -203,7 +202,6 @@ def retrieve_meta_test(): def Scenario1(path_simfile): - distance = pd.read_csv(file) distance.file_label = distance.file_label.str.lower() distance.search_term = distance.search_term.str.lower() @@ -246,7 +244,6 @@ def Scenario1(path_simfile): ) for searchterm in training_labels: - nof = len(search_table[search_table.file_label.str.contains(searchterm)]) df = search_table[ ["filename", "file_label", "original_label", searchterm] @@ -292,7 +289,6 @@ def Scenario1(path_simfile): def Scenario2(path_simfile): - distance = pd.read_csv(file) distance.file_label = distance.file_label.str.lower() distance.search_term = distance.search_term.str.lower() @@ -418,7 +414,6 @@ def Scenario2(path_simfile): sim.score = 1 - sim.score for test in list(set(sim.test_name)): - df = sim[sim.test_name == test].sort_values(by="score", ascending=False)[ [ "test_name", @@ -436,7 +431,6 @@ def Scenario2(path_simfile): df.loc[df.original_label_test == df.original_label_train, "color"] = "green" if len(df[df.color == "green"]) == nof: - df.loc[(df.color != "green"), "color"] = "gray" plt = df.plot.barh( diff --git a/gitk/bedspace/pipeline/bedspace_queryDBsim.py b/gitk/bedspace/pipeline/bedspace_queryDBsim.py index b92da26f..2e8c54fe 100644 --- a/gitk/bedspace/pipeline/bedspace_queryDBsim.py +++ b/gitk/bedspace/pipeline/bedspace_queryDBsim.py @@ -72,7 +72,6 @@ def meta_preprocessing(meta): labels = [] for l in args.labels.split(","): if l in meta.index: - labels.append(l) labels.insert(0, "file_name") meta = meta[labels] @@ -185,7 +184,6 @@ def main(): print(query_vectors.shape) for i in range(len(query_vectors)): - query_vector = query_vectors[i] df_similarity = calculate_distance( diff --git a/gitk/bedspace/pipeline/bedspace_test.py b/gitk/bedspace/pipeline/bedspace_test.py index 87e7dc05..4ff6f422 100755 --- a/gitk/bedspace/pipeline/bedspace_test.py +++ b/gitk/bedspace/pipeline/bedspace_test.py @@ -87,7 +87,6 @@ def meta_preprocessing(meta): labels = [] for l in args.labels.split(","): if l in meta.index: - labels.append(l) labels.insert(0, "file_name") meta = meta[labels] diff --git a/gitk/bedspace/pipeline/helpers.py b/gitk/bedspace/pipeline/helpers.py index 5bae227d..b9cb7f89 100644 --- a/gitk/bedspace/pipeline/helpers.py +++ b/gitk/bedspace/pipeline/helpers.py @@ -64,7 +64,6 @@ def data_prepration_test(path_file_label, univ): def bed2vec(file_list, universe, model, assembly, source, output_path): - docs = os.path.join(output_path, "documents_{}.txt".format(assembly)) files = os.path.join(output_path, "filenames_{}.txt".format(assembly)) diff --git a/gitk/cli.py b/gitk/cli.py index 30aeed9b..baed82c3 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -252,6 +252,5 @@ def main(test_args=None): return - if __name__ == "__main__": main() From f45662776b3d7b4caecb20e54fe5e973dee94e80 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 23 Jun 2023 16:06:18 -0400 Subject: [PATCH 0223/1072] add lh off missing chromosomes --- gitk/assess/likelihood.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/gitk/assess/likelihood.py b/gitk/assess/likelihood.py index 1393841c..d4fd16d6 100644 --- a/gitk/assess/likelihood.py +++ b/gitk/assess/likelihood.py @@ -50,6 +50,7 @@ def calc_likelihood_hard( empty_start = 0 res = 0 e = 0 + done_chrom = [] prob_array, cove_array = None, None with open(universe) as uni: for i in uni: @@ -61,6 +62,7 @@ def calc_likelihood_hard( else: if i[0] != current_chrom: if i[0] in chroms: + done_chrom.append(i[0]) model_lh.clear_chrom(current_chrom) if e != 1: res += np.sum(prob_array[empty_start:, 0]) @@ -90,6 +92,19 @@ def calc_likelihood_hard( res += r2 empty_start = end res += np.sum(prob_array[empty_start:, 0]) + z = set(chroms).difference(set(done_chrom)) + z = list(z) + for i in z: + model_lh.read_chrom_track(i, name) + prob_model = model_lh[i] + cove_array = read_chromosome_from_bw( + os.path.join( + coverage_folder, f"{coverage_prefix}_{name}.bw" + ), + i, + ) + prob_array = LhModel(prob_model[name], cove_array) + res += np.sum(prob_array[:, 0]) return res From ecf364b833583eecb72182959ec34e6fbbb80a04 Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Tue, 4 Jul 2023 13:48:50 -0400 Subject: [PATCH 0224/1072] check bedtools --- gitk/tokenization/cli.py | 4 ++-- gitk/tokenization/main.py | 26 ++++++++------------------ 2 files changed, 10 insertions(+), 20 deletions(-) diff --git a/gitk/tokenization/cli.py b/gitk/tokenization/cli.py index a491ad2f..e259b32d 100644 --- a/gitk/tokenization/cli.py +++ b/gitk/tokenization/cli.py @@ -16,8 +16,8 @@ def build_subparser(parser): parser.add_argument( "--bedtools-path", type=str, - default="", - help="Path to the bedtools binary. If not provided, bedtools will be automatically downloaded", + default="bedtools", + help="Path to the bedtools binary. Default: bedtools. If bedtools does not exists, an exception will be raised", ) parser.add_argument( "--fraction", diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 8175f3cb..7b772971 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -32,7 +32,7 @@ def hard_tokenization_main( fraction=1e-9, file_list=None, num_workers=10, - bedtools_path="", + bedtools_path="bedtools", ): timer = utils.Timer() start_time = timer.t() @@ -74,23 +74,13 @@ def hard_tokenization_main( f.write(file) f.write("\n") - if bedtools_path == "": - # Download bedtools for tokenization - bedtools_folder = os.path.join( - os.path.dirname(os.path.abspath(__file__)), "bedtools" - ) - os.makedirs(bedtools_folder, exist_ok=True) - bedtools_path = os.path.join(bedtools_folder, "bedtools") - if not os.path.isfile(bedtools_path): - # Download bedtools - bedtool_url = "https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary" - print(f"Downloading bedtools from \n{bedtool_url}") - response = requests.get(bedtool_url) - with open(bedtools_path, "wb") as f: - f.write(response.content) - print(f"bedtools is saved in \n{bedtools_path}") - subprocess.run(shlex.split(f"chmod +x {bedtools_path}")) - subprocess.run(shlex.split(f"{bedtools_path} --version")) + if bedtools_path == "bedtools": + try: + rval = subprocess.call([bedtools_path, "--version"]) + except: + raise Exception("No bedtools executable") + if rval != 0: + raise Exception("No bedtools executable") print(f"Tokenizing {len(file_list)} bed files ...") From 617b3a3639c5a10e01419b2dcec56ea78e2a6cd5 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy <41063083+nleroy917@users.noreply.github.com> Date: Wed, 5 Jul 2023 10:48:17 -0400 Subject: [PATCH 0225/1072] fix build_vocab bug --- gitk/region2vec/main.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index ebc3d406..fbffaea4 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -34,7 +34,8 @@ def region2vec( lr_scheduler="linear", # How to decay the learning rate. Select from linear and milestone milestones=[], # Specify only when lr_scheduler=milestone. At each given epoch, the learning rate will be multiplied by 0.1 hier_softmax=False, # Whether to hierarchical softmax - seed=0, # random seed + seed=0, # random seed, + update_vocab="once" ): timer = utils.Timer() start_time = timer.t() @@ -107,7 +108,7 @@ def region2vec( lr_mode=lr_scheduler, milestones=milestones, hier_softmax=hier_softmax, - update_vocab="once", + update_vocab=update_vocab, seed=seed, ) p = multiprocessing.Process(target=region2_train, args=(region2vec_args,)) From 544af45cebd6ee7a9a7bfed7b4e7274e0869d703 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 5 Jul 2023 19:56:20 -0400 Subject: [PATCH 0226/1072] start new model-sharing code --- .DS_Store | Bin 0 -> 6148 bytes .gitignore | 6 +- examples/scembed/annotation.ipynb | 580 ++++++++++++++++++++ examples/scembed/cellcano_annotations.ipynb | 240 ++++++++ examples/scembed/projection.ipynb | 102 ++-- examples/scembed/projection2.ipynb | 369 ------------- gitk/models/__init__.py | 4 + gitk/models/const.py | 7 + gitk/models/pretrained.py | 53 ++ gitk/models/tokenization.py | 167 ++++++ gitk/models/utils.py | 215 ++++++++ gitk/scembed/__init__.py | 1 + gitk/scembed/annotation.py | 112 ++++ gitk/scembed/projection.py | 6 + requirements/requirements-all.txt | 3 +- tests/.DS_Store | Bin 0 -> 6148 bytes tests/test_models.py | 20 + tests/test_utils.py | 2 - 18 files changed, 1458 insertions(+), 429 deletions(-) create mode 100644 .DS_Store create mode 100644 examples/scembed/annotation.ipynb create mode 100644 examples/scembed/cellcano_annotations.ipynb delete mode 100644 examples/scembed/projection2.ipynb create mode 100644 gitk/models/__init__.py create mode 100644 gitk/models/const.py create mode 100644 gitk/models/pretrained.py create mode 100644 gitk/models/tokenization.py create mode 100644 gitk/models/utils.py create mode 100644 gitk/scembed/annotation.py create mode 100644 tests/.DS_Store create mode 100644 tests/test_models.py diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..340454647d36a05efacb5f20872d26387ab7d17c GIT binary patch literal 6148 zcmeHK%}T>S5Z<+|O(;SR3gT(OYr(dZV(}7ceE}nSP^k$C8jRV}r1nq>c>sMOAH?Tz zW_JTE29F|k26n&M`Pt2Uko{qd@zFf&GUhPGENF-vl?p*~rEA9oBXTuI7A&%9kjS87 zn!jnnZ*Q=iMJ$7U{rf+HX%c67r}N2oYW2o$vuQV5&3o@jF1*~&=h?`g-Qws<$|NXt zKe&#g#l+b^muc?DX*5#_aTr0!-A$Z^axs$gG|W`4ryaI!*%Rkzx$K?xhi%dA4hO5Y zSPpxgw&)M~tCejX9G{$BPM(vORK95{IdHCI&tM7fpsdyO>dn$brjKAPGs;LpVt^PR z28e-eWWbyTR%;uJr<#cYV&F#xaDNcc5M6_XMzwW7hu3HH*AY=b$F~HcFz6aAG=c|& z>r_CU%FPpl>vXUS6XzN%H0pH5)ygoBS-E_?aJ4$vg$iff)kr-tKn!d$P}4&P&;JYf zWhx)}n<+FR28e-w#sII6z3~W&GH2_z^6;z`&>o] 12.64G 43.5MB/s in 5m 17s \n", + "\n", + "2023-05-23 15:57:34 (40.8 MB/s) - â€pbmc/pbmc.h5ad’ saved [13570752714/13570752714]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir -p pbmc\n", + "!wget \"http://big.databio.org/scembed/data/pbmc_sampled.h5ad\" -O \"pbmc/pbmc.h5ad\"\n", + "\n", + "# or use the full dataset (careful, it's 12GB!):\n", + "# !wget \"http://big.databio.org/scembed/data/pbmc.h5ad\" -O \"pbmc/pbmc.h5ad\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate embeddings using a pre-trained model\n", + "Now that we have the data, we can generate embeddings using a pre-trained model. It is imperative that the model we use for embedding generation is the same used to index the reference dataset. For this demonstration, I will use the `databio/multiome` model that exists on the model hub. To do this, we need to:\n", + "\n", + "1. Create a `Projector()`\n", + "2. Project some data\n", + "\n", + "If this is your first time through the tutorial, the model will need time to download. This will take a few minutes. If you've already run this tutorial, the model will be cached and this step will be much faster." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# read in the data\n", + "import scanpy as sc\n", + "\n", + "# read in the data\n", + "pbmc = sc.read_h5ad(\"pbmc/pbmc.h5ad\")\n", + "\n", + "# this is needed since the index is weird\n", + "pbmc.obs.drop([\"barcode\"], inplace=True, axis=1)\n", + "pbmc.obs.reset_index(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " barcode\n", + "0 AAACGAAAGAGAGGTA-1\n", + "1 AAACGAAAGCAGGAGG-1\n", + "2 AAACGAAAGGAAGAAC-1\n", + "3 AAACGAAAGTCGACCC-1\n", + "4 AAACGAACAAGCACTT-1\n", + "... ...\n", + "10241 TTTGTGTTCTATACCT-1\n", + "10242 TTTGTGTTCTATCTCA-1\n", + "10243 TTTGTGTTCTCTATTG-1\n", + "10244 TTTGTGTTCTGATCTT-1\n", + "10245 TTTGTGTTCTTACTCA-1\n", + "\n", + "[10246 rows x 1 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pbmc" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs Ă— n_vars = 10246 Ă— 165434\n", + " obs: 'barcode'\n", + " var: 'chr', 'start', 'end'" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# inspect \n", + "pbmc" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "path_to_weights='weights.pkl' path_to_universe='universe.bed' name='Lueken2021' reference='hg38' description=None tags=None datasets=None model_parameters=[ModelParameter(embedding_dim=100, description=None)] model_architecture=None maintainers=[Maintainer(name='Nathan LeRoy', email='nleroy917@gmail.com')]\n" + ] + } + ], + "source": [ + "# create projector\n", + "from gitk.scembed import Projector\n", + "\n", + "model = Projector(\"databio/multiome\")\n", + "model.summarize()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "***** WARNING: File /Users/nathanleroy/.scembed/b.bed has inconsistent naming convention for record:\n", + "chr1\t9776\t10668\n", + "\n", + "***** WARNING: File /Users/nathanleroy/.scembed/b.bed has inconsistent naming convention for record:\n", + "chr1\t9776\t10668\n", + "\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:05<00:00, 28141.76it/s]\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/anndata/_core/anndata.py:117: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [02:52<00:00, 59.23it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:27<00:00, 117.37it/s]\n" + ] + } + ], + "source": [ + "# create embeddings (takes a few minutes)\n", + "# there will be three progress bars:\n", + "# 1. converting dataset to universe, \n", + "# 2. creating documents, and \n", + "# 3. projecting\n", + "pbmc = model.project(pbmc)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10246, 100)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# insepct the results\n", + "pbmc.obsm['embedding'].shape" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyzing Embeddings\n", + "Pretty fast, huh? We can now analyze the embeddings. First, let's visualize them. We'll use `umap`. We'll also color the cells by their cluster assignment. We'll use `leiden` clustering for this. This occurs in three steps:\n", + "\n", + "1. `scanpy.pp.neighbors()` to compute the nearest neighbors of each cell\n", + "2. `scanpy.tl.leiden()` to compute the UMAP embedding and associated cluster assignments\n", + "3. `umap.Umap()` to compute the UMAP embedding\n", + "\n", + "We can then run visualizations with seaborn and matplotlib. We'll color the cells by their cluster assignment." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# cluster the pbmc data\n", + "sc.pp.neighbors(pbmc, use_rep=\"embedding\", n_neighbors=10)\n", + "sc.tl.leiden(pbmc, resolution=0.10, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "import umap\n", + "\n", + "reducer = umap.UMAP(n_components=2)\n", + "umap_embedding = reducer.fit_transform(pbmc.obsm['embedding'])\n", + "\n", + "# attach umaps back to pbmc object\n", + "pbmc.obsm['umap'] = umap_embedding\n", + "pbmc.obs['umap1'] = umap_embedding[:, 0]\n", + "pbmc.obs['umap2'] = umap_embedding[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'UMAP 2')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams['figure.dpi'] = 200\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "sns.scatterplot(\n", + " data=pbmc.obs,\n", + " x='umap1',\n", + " y='umap2',\n", + " hue='leiden',\n", + " palette='tab20',\n", + " linewidth=0,\n", + " s=1,\n", + " ax=ax\n", + ")\n", + "\n", + "# legend to upper right\n", + "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "ax.set_title(\"10X genomics PBMC (Projected with Leuken2021)\")\n", + "ax.set_xlabel(\"UMAP 1\")\n", + "ax.set_ylabel(\"UMAP 2\")\n", + "\n", + "# fig.savefig(\"pbmc/pbmc_leucken2021_proj_leiden.pdf\", bbox_inches='tight')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add cell type annotations\n", + "Now that we have embeddings and cell-clusters defined, we can assign putative cell-type labels to our clusters using the reference database that we have already curated. To do so, we need an `Annotator` object. This object will take our `AnnData` object and annotate the clusters with the cell-type labels. We can then visualize the results." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from gitk.scembed import Annotator\n", + "\n", + "annotator = Annotator(\n", + " location=\"http://localhost:6333\",\n", + " collection_name=\"leucken2021\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:22<00:00, 124.80it/s]\n" + ] + } + ], + "source": [ + "# annotate the data\n", + "annotator.annotate(pbmc)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# print table of leiden cluster with cell type predictions\n", + "pbmc.obs[['leiden', 'pred_celltype']].drop_duplicates().sort_values(by=\"leiden\")\n", + "\n", + "# create cluster to cell type mapping\n", + "cluster_to_celltype = pbmc.obs[['leiden', 'pred_celltype']].drop_duplicates().set_index('leiden').to_dict()['pred_celltype']" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'1': 'CD14+ Mono',\n", + " '0': 'CD4+ T activated',\n", + " '5': 'Naive CD20+ B',\n", + " '2': 'CD4+ T activated',\n", + " '3': 'NK',\n", + " '4': 'CD14+ Mono',\n", + " '7': 'Naive CD20+ B',\n", + " '6': 'CD14+ Mono',\n", + " '8': 'CD14+ Mono'}" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster_to_celltype" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'UMAP 2')" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot each cluster and annotate with cell type\n", + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "sns.scatterplot(\n", + " data=pbmc.obs,\n", + " x='umap1',\n", + " y='umap2',\n", + " hue='leiden',\n", + " palette='tab20',\n", + " linewidth=0,\n", + " s=1,\n", + " ax=ax\n", + ")\n", + "\n", + "# legend to upper right\n", + "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "\n", + "# add a label to the plot for each cluster\n", + "for cluster, celltype in cluster_to_celltype.items():\n", + " # get the centroid of the cluster\n", + " centroid = pbmc.obs.query(f\"leiden == '{cluster}'\")[['umap1', 'umap2']].mean()\n", + "\n", + " ax.text(centroid['umap1'], centroid['umap2'], celltype, fontsize=5)\n", + "\n", + "ax.set_title(\"10X genomics PBMC (Projected with Leuken2021)\")\n", + "ax.set_xlabel(\"UMAP 1\")\n", + "ax.set_ylabel(\"UMAP 2\")\n", + "\n", + "\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/scembed/cellcano_annotations.ipynb b/examples/scembed/cellcano_annotations.ipynb new file mode 100644 index 00000000..07c1d1ca --- /dev/null +++ b/examples/scembed/cellcano_annotations.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scanpy as sc\n", + "import pandas as pd\n", + "\n", + "from gitk import scembed\n", + "\n", + "pbmc = sc.read_h5ad(\"pbmc/pbmc.h5ad\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10246, 165434)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pbmc.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "pbmc.obs.drop([\"barcode\"], inplace=True, axis=1)\n", + "pbmc.obs.reset_index(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "annotations = pd.read_csv(\"pbmc/cellcano_annotations.csv\", index_col=0, header=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# split index at # and grab second item, store as new column called barcode\n", + "annotations[\"barcode\"] = annotations.index.str.split(\"#\").str[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# merge pbmc.obs with annotations on barcode\n", + "pbmc.obs = pbmc.obs.merge(annotations, on=\"barcode\", how=\"left\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# init projector to get clusters again\n", + "projector = scembed.Projector(\"databio/multiome\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "projector.project(pbmc)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs Ă— n_vars = 10246 Ă— 165434\n", + " obs: 'barcode', 'pred_celltype', 'firstround_pred_celltype', 'entropy', 'leiden'\n", + " var: 'chr', 'start', 'end'\n", + " uns: 'neighbors', 'leiden'\n", + " obsm: 'embedding'\n", + " obsp: 'distances', 'connectivities'" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pbmc" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "# cluster using embeddings (these are the same as what I used for previous clustering)\n", + "sc.pp.neighbors(pbmc, use_rep=\"embedding\")\n", + "sc.tl.leiden(pbmc, resolution=0.10, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "# for each cluster in the leiden clustering, find the most common cell_type and assign it to the cluster\n", + "cluster_to_cell_type = {}\n", + "for cluster in pbmc.obs.leiden.unique():\n", + " cluster_to_cell_type[cluster] = pbmc.obs.loc[pbmc.obs.leiden == cluster, \"pred_celltype\"].value_counts().index[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "# map the cluster_to_cell_type dictionary to the leiden column\n", + "pbmc.obs[\"cellcano_consensus_celltype\"] = pbmc.obs[\"leiden\"].map(cluster_to_cell_type)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cellcano_consensus_celltype\n", + "CD4 T cells 4427\n", + "Monocytes 3218\n", + "CD8 T cells 1241\n", + "NK cells 675\n", + "B cells 551\n", + "Dendritic cells 134\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pbmc.obs['cellcano_consensus_celltype'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "pbmc.obs.drop([\"consensus_celltype\"], inplace=True, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'1': 'Monocytes',\n", + " '0': 'CD4 T cells',\n", + " '5': 'B cells',\n", + " '3': 'NK cells',\n", + " '2': 'CD8 T cells',\n", + " '4': 'Monocytes',\n", + " '6': 'Dendritic cells',\n", + " '7': 'Monocytes'}" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster_to_cell_type" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/scembed/projection.ipynb b/examples/scembed/projection.ipynb index f983a9b6..991a6ab2 100644 --- a/examples/scembed/projection.ipynb +++ b/examples/scembed/projection.ipynb @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -129,11 +129,10 @@ "text/plain": [ "AnnData object with n_obs Ă— n_vars = 10246 Ă— 165434\n", " obs: 'barcode'\n", - " var: 'chr', 'start', 'end'\n", - " obsm: 'embedding'" + " var: 'chr', 'start', 'end'" ] }, - "execution_count": 9, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -145,14 +144,31 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " @numba.jit()\n" + ] + } + ], "source": [ "# create projector\n", "from gitk.scembed import Projector\n", "\n", - "model = Projector(\"/Users/nathanleroy/.scembed/databio/scatlas/model.yaml\")" + "model = Projector(\"databio/multiome\")" ] }, { @@ -163,7 +179,7 @@ { "data": { "text/plain": [ - "974599" + "116490" ] }, "execution_count": 5, @@ -184,15 +200,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", - "KI270727.1\t52092\t52991\n", + "***** WARNING: File /Users/nathanleroy/.scembed/b.bed has inconsistent naming convention for record:\n", + "chr1\t9776\t10668\n", "\n", - "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", - "KI270727.1\t52092\t52991\n", + "***** WARNING: File /Users/nathanleroy/.scembed/b.bed has inconsistent naming convention for record:\n", + "chr1\t9776\t10668\n", "\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 25235.34it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [03:04<00:00, 55.67it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:59<00:00, 86.10it/s] \n" + "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 27411.45it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [02:55<00:00, 58.28it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:28<00:00, 116.16it/s]\n" ] } ], @@ -207,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -216,7 +232,7 @@ "(10246, 100)" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -243,36 +259,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" - ] - } - ], + "outputs": [], "source": [ "# run leiden clustering on embeddings\n", - "sc.pp.neighbors(pbmc, use_rep='embedding')\n", - "sc.tl.leiden(pbmc)" + "sc.pp.neighbors(pbmc, use_rep='embedding', n_neighbors=10)\n", + "sc.tl.leiden(pbmc, resolution=0.25)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -289,22 +287,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "Text(0, 0.5, 'UMAP 2')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" ] @@ -319,7 +307,7 @@ "\n", "plt.rcParams['figure.dpi'] = 200\n", "\n", - "_, ax = plt.subplots(figsize=(4, 4))\n", + "fig, ax = plt.subplots(figsize=(4, 4))\n", "sns.scatterplot(\n", " data=pbmc.obs,\n", " x='umap1',\n", @@ -333,9 +321,11 @@ "\n", "# legend to upper right\n", "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", - "ax.set_title(\"10X genomics PBMC (10X Matrix)\")\n", + "ax.set_title(\"10X genomics PBMC (Projected with Leuken2021)\")\n", "ax.set_xlabel(\"UMAP 1\")\n", - "ax.set_ylabel(\"UMAP 2\")" + "ax.set_ylabel(\"UMAP 2\")\n", + "\n", + "fig.savefig(\"pbmc/pbmc_leucken2021_proj_leiden.pdf\", bbox_inches='tight')" ] } ], diff --git a/examples/scembed/projection2.ipynb b/examples/scembed/projection2.ipynb deleted file mode 100644 index 7089ead9..00000000 --- a/examples/scembed/projection2.ipynb +++ /dev/null @@ -1,369 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Getting Started With Cell Embeddings from scATAC-seq data\n", - "## Overview\n", - "A *cell embedding* is a low-dimensional representation of single-cell in an scATAC-seq workflow. Think of it as a single vector of dimensionality ~100 that summarizes the epigenetic state of a cell. Cell embeddings are useful for a variety of tasks, including visualization, clustering, and differential accessibility analysis. In this tutorial, we will learn how to use pretrained models from `scEmbed` to generate cell embeddings from scATAC-seq data. We will then use these embeddings to visualize the data and perform clustering.\n", - "\n", - "Overwhlemingly, scATAC-seq datasets are represented and manipulated as *binary accessibilty matrices*. Breifly, these are matrices where each row represents a cell and each column represents a genomic region. The value of each entry is 1 if the region is accessible in the cell and 0 otherwise. For example, the following matrix represents a hypothetical dataset with 3 cells and 4 genomic regions:\n", - "\n", - "| Cell | Region 1 | Region 2 | Region 3 | Region 4 |\n", - "|------|----------|----------|----------|----------|\n", - "| 1 | 1 | 0 | 1 | 0 |\n", - "| 2 | 0 | 1 | 1 | 0 |\n", - "| 3 | 1 | 1 | 0 | 1 |\n", - "\n", - "These matrices are typically incredibly sparse, with most cells only having accessibilty at a small fraction of the genome. They are also extremely high-dimensional; matrices with > 1M features is common. This binary accessibility matrix is the input to `scEmbed`. We use [`scanpy`](https://github.com/scverse/scanpy) and their [`AnnData`](https://github.com/scverse/anndata) objects. These objects are a standard way of representing single-cell data in Python. We will use the `AnnData` object to represent our binary accessibility matrix.\n", - "\n", - "## Installation\n", - "First, we need to install `scEmbed`. `scEmbed` is a part of the `gitk` package. We recommend using virtual environments for this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install gitk\n", - "# or if using local version:\n", - "# !pip install ." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading Data\n", - "For this tutorial we've curated a dataset already for you to use. It comes from [10X genomics](https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-atac-v2-chromium-controller-2-standard). \n", - "\n", - "*Note: when using a pre-trained model, `scEmbed` requires that your `AnnData` object have `chr`, `start`, and `end` values on the `.var` attribute of the object. This is because we use interval overlap analysis to find similar regions and recast the feature-space of the new data we are embedding*.\n", - "\n", - "An example of how to do this is below:\n", - "\n", - "```python\n", - "import scanpy as sc\n", - "import pandas as pd\n", - "\n", - "adata = sc.read_h5ad(\"path/to/your/data.h5ad\")\n", - "\n", - "peaks = pd.read_csv(\"/path/to/your/peaks.bed\", sep=\"\\t\", header=None)\n", - "peaks.columns = [\"chr\", \"start\", \"end\"]\n", - "adata.var = peaks\n", - "```\n", - "\n", - "**The data we are using in this example already has this completed for you.**\n", - "\n", - "### Retreive preprocessed data:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2023-05-23 15:52:17-- http://big.databio.org/scembed/data/pbmc.h5ad\n", - "Resolving big.databio.org (big.databio.org)... 128.143.223.179\n", - "Connecting to big.databio.org (big.databio.org)|128.143.223.179|:80... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 13570752714 (13G) [application/octet-stream]\n", - "Saving to: â€pbmc/pbmc.h5ad’\n", - "\n", - "pbmc/pbmc.h5ad 100%[===================>] 12.64G 43.5MB/s in 5m 17s \n", - "\n", - "2023-05-23 15:57:34 (40.8 MB/s) - â€pbmc/pbmc.h5ad’ saved [13570752714/13570752714]\n", - "\n" - ] - } - ], - "source": [ - "!mkdir -p pbmc\n", - "!wget \"http://big.databio.org/scembed/data/pbmc_sampled.h5ad\" -O \"pbmc/pbmc.h5ad\"\n", - "\n", - "# or use the full dataset (careful, it's 12GB!):\n", - "# !wget \"http://big.databio.org/scembed/data/pbmc.h5ad\" -O \"pbmc/pbmc.h5ad\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generating Embeddings\n", - "Now that we have the data, we can generate embeddings. This occurs in two steps:\n", - "\n", - "1. Create a `Projector()`\n", - "2. Project some data\n", - "\n", - "If this is your first time through the tutorial, the model will need time to download. This will take a few minutes. If you've already run this tutorial, the model will be cached and this step will be much faster." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# read in the data\n", - "import scanpy as sc\n", - "\n", - "pbmc = sc.read_h5ad(\"/Volumes/data/genomics/10x/10kpbmcsV2_atac/cellranger/pbmc.h5ad\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnData object with n_obs Ă— n_vars = 10140 Ă— 165434\n", - " obs: 'barcode'\n", - " var: 'chr', 'start', 'end'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pbmc" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n" - ] - } - ], - "source": [ - "# create projector\n", - "from gitk.scembed import Projector\n", - "\n", - "model = Projector(\"buenrostro/model.yaml\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "138919" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(model.model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", - "GL000194.1\t55725\t56585\n", - "\n", - "***** WARNING: File /Users/nathanleroy/.scembed/a.bed has inconsistent naming convention for record:\n", - "GL000194.1\t55725\t56585\n", - "\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:04<00:00, 38869.58it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10140/10140 [00:27<00:00, 369.39it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10140/10140 [00:12<00:00, 806.20it/s]\n" - ] - } - ], - "source": [ - "# create embeddings (takes a few minutes)\n", - "# there will be three progress bars:\n", - "# 1. converting dataset to universe, \n", - "# 2. creating documents, and \n", - "# 3. projecting\n", - "pbmc = model.project(pbmc)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(10140, 100)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# insepct the results\n", - "pbmc.obsm['embedding'].shape" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyzing Embeddings\n", - "Pretty fast, huh? We can now analyze the embeddings. First, let's visualize them. We'll use `umap`. We'll also color the cells by their cluster assignment. We'll use `leiden` clustering for this. This occurs in three steps:\n", - "\n", - "1. `scanpy.pp.neighbors()` to compute the nearest neighbors of each cell\n", - "2. `scanpy.tl.leiden()` to compute the UMAP embedding and associated cluster assignments\n", - "3. `umap.Umap()` to compute the UMAP embedding\n", - "\n", - "We can then run visualizations with seaborn and matplotlib. We'll color the cells by their cluster assignment." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" - ] - } - ], - "source": [ - "# run leiden clustering on embeddings\n", - "sc.pp.neighbors(pbmc, use_rep='embedding')\n", - "sc.tl.leiden(pbmc)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import umap\n", - "\n", - "reducer = umap.UMAP(n_components=2)\n", - "umap_embedding = reducer.fit_transform(pbmc.obsm['embedding'])\n", - "\n", - "# attach umaps back to pbmc object\n", - "pbmc.obsm['umap'] = umap_embedding\n", - "pbmc.obs['umap1'] = umap_embedding[:, 0]\n", - "pbmc.obs['umap2'] = umap_embedding[:, 1]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'UMAP 2')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.rcParams['figure.dpi'] = 200\n", - "\n", - "_, ax = plt.subplots(figsize=(4, 4))\n", - "sns.scatterplot(\n", - " data=pbmc.obs,\n", - " x='umap1',\n", - " y='umap2',\n", - " hue='leiden',\n", - " palette='tab20',\n", - " linewidth=0,\n", - " s=1,\n", - " ax=ax\n", - ")\n", - "\n", - "# legend to upper right\n", - "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", - "ax.set_title(\"10X genomics PBMC\")\n", - "ax.set_xlabel(\"UMAP 1\")\n", - "ax.set_ylabel(\"UMAP 2\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/gitk/models/__init__.py b/gitk/models/__init__.py new file mode 100644 index 00000000..6aa12c45 --- /dev/null +++ b/gitk/models/__init__.py @@ -0,0 +1,4 @@ +from .const import * +from .pretrained import * +from .tokenization import * +from .utils import * diff --git a/gitk/models/const.py b/gitk/models/const.py new file mode 100644 index 00000000..d19d5a84 --- /dev/null +++ b/gitk/models/const.py @@ -0,0 +1,7 @@ +MODEL_FILE_NAME = "model.bin" + +DEFAULT_PATH_TO_AILIST = "ailist" +DEFAULT_PATH_TO_BEDTOOLS = "bedtools" + +UNIVERSE_FILE_NAME = "universe.bed" +CACHE_DIR = ".gitk_cache" diff --git a/gitk/models/pretrained.py b/gitk/models/pretrained.py new file mode 100644 index 00000000..b4c0159a --- /dev/null +++ b/gitk/models/pretrained.py @@ -0,0 +1,53 @@ +from gensim.models import Word2Vec +from huggingface_hub import hf_hub_download + +from .. import scembed +from .tokenization import Tokenizer, HardTokenizer +from .const import MODEL_FILE_NAME, UNIVERSE_FILE_NAME + + +class PretrainedScembedModel: + """ + A class for loading pretrained models from the HuggingFace Hub. + """ + + def __init__( + self, model: str = None, file_name: str = None, tokenizer: Tokenizer = None + ): + self.model_name = model + self.__model_path = None + self.__universe_path = None + self.__tokenizer = tokenizer + self.__model = None + + # load if model is passed + if model is not None: + self.__download_model_files(model, file_name) + self.__tokenizer = HardTokenizer(self.__universe_path) + self.__model = self.__load_model(self.__model_path) + + def __download_model_files( + self, + model: str, + model_file_name: str = MODEL_FILE_NAME, + universe_file_name: str = UNIVERSE_FILE_NAME, + **kwargs, + ): + """ + Download a pretrained model from the HuggingFace Hub. We need to download + the actual model + weights, and the universe file. + """ + model_path = hf_hub_download(model, model_file_name, **kwargs) + universe_path = hf_hub_download(model, universe_file_name, **kwargs) + + self.__model_path = model_path + self.__universe_path = universe_path + + def __load_model(self, path: str) -> scembed.SCEmbed: + """ + Load a pretrained model from the HuggingFace Hub. + + :param path: The path to the model. + :return: The loaded model. + """ + return scembed.utils.load_scembed_model(path) diff --git a/gitk/models/tokenization.py b/gitk/models/tokenization.py new file mode 100644 index 00000000..751d4d03 --- /dev/null +++ b/gitk/models/tokenization.py @@ -0,0 +1,167 @@ +from typing import List, Union + +import scanpy as sc +import os + +from .const import DEFAULT_PATH_TO_BEDTOOLS, UNIVERSE_FILE_NAME +from .utils import ( + make_cache_dir, + get_cache_dir, + convert_to_universe, + anndata_to_regionsets, +) + + +class Tokenizer: + pass + + +class HardTokenizer(Tokenizer): + """ + Tokenizer that computes overlaps between regions. + """ + + def __init__( + self, + regions: Union[List[str], str], + path_to_bedtools: str = DEFAULT_PATH_TO_BEDTOOLS, + ): + """ + Create a new HardTokenizer. + + :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. + This can be either a list of regions or a path to a BED file containing regions. + :param path_to_bedtools: The path to the bedtools executable. + """ + self.regions = regions + self.path_to_bedtools = path_to_bedtools + + # write regions to file in cache dir + self.__cache_dir = get_cache_dir() + self.__regions_file = os.path.join(self.__cache_dir, UNIVERSE_FILE_NAME) + self.__write_regions_to_file() + + make_cache_dir() + + def __write_regions_to_file(self): + """ + Write regions to file in cache dir. This can take one of two forms: + 1. A list of regions. + 2. A path to a BED file containing regions. + + If the regions are a list, they are written to a file in the cache dir. + If the regions are a path to a BED file, the file is copied to the cache dir. + + :raises FileNotFoundError: If the path to the BED file does not exist. + """ + # check for path to file + if isinstance(self.regions, str): + # check if file exists + if not os.path.exists(self.regions): + raise FileNotFoundError( + f"Could not find file {self.regions} containing regions." + ) + # copy file to cache dir + os.system(f"cp {self.regions} {self.__regions_file}") + + # else, we are going to write the regions to a file + # verify that the regions are a list, should be in the form chr_start_end + elif isinstance(self.regions, list): + if not all( + [ + isinstance(region, str) and len(region.split("_")) == 3 + for region in self.regions + ] + ): + raise ValueError( + "Regions must be a list of strings in the form chr_start_end." + ) + # write regions to file + with open(self.__regions_file, "w") as f: + # split each region into chr, start, end + for region in self.regions: + chr, start, end = region.split("_") + f.write(f"{chr}\t{start}\t{end}\n") + + def __tokenize_anndata(self, data: sc.AnnData) -> List[List[str]]: + """ + Tokenize an anndata object into a list of lists of regions. + + :param data: The anndata object to tokenize. + + :return: A list of lists of regions. + """ + data = convert_to_universe(data, self.__regions_file) + region_sets = anndata_to_regionsets(data) + return region_sets + + def __tokenize_bed_file(self, data: str, fraction: float = 1e-9) -> List[str]: + """ + Tokenize a BED file into a list of lists of regions. + + :param data: The path to the BED file to tokenize. + + :return: A list of lists of regions. + """ + # perform overlap analysis with bedtools + os.system( + f"{self.path_to_bedtools} intersect -a {data} -b {self.__regions_file} -wa -wb > {self.__cache_dir}/overlaps.bed" + ) + # read in overlaps + overlaps = [] + with open(f"{self.__cache_dir}/overlaps.bed", "r") as f: + for line in f: + line = line.strip().split("\t") + # join chr, start, end with _ + region = "_".join(line[0:3]) + overlaps.append(region) + + return overlaps + + def __tokenize_list(self, data: List[str]) -> List[List[str]]: + """ + Tokenize a list of regions into a list of lists of regions. + """ + # write to bed file, and perform __tokenize_bed_file + with open(f"{self.__cache_dir}/regions.bed", "w") as f: + for region in data: + # split region into chr, start, end + chr, start, end = region.split("_") + f.write(f"{chr}\t{start}\t{end}\n") + + return self.__tokenize_bed_file(f"{self.__cache_dir}/regions.bed") + + def tokenize( + self, data: Union[sc.AnnData, str, List[str]] + ) -> Union[List[List[str]], List[str]]: + """ + Tokenize a dataset. This will compute overlaps between regions and cells. Three + types of data are accepted: + 1. Anndata object. This must have `chr`, `start`, and `end` values for the `.var` attribute. + 2. A path to a BED file containing regions. + 3. A list of regions. + + Regardless of the input, we return a list of lists of regions. Each list of regions + corresponds to a cell. + + :param data: The data to tokenize. + :raises ValueError: If the data is not one of the three accepted types. + :return: A list of regions or a list of lists of regions (depending on the input type). + """ + # check if data is anndata object + if isinstance(data, sc.AnnData): + return self.__tokenize_anndata(data) + # check if data is a path to a BED file + elif isinstance(data, str): + # ensure that the file exists + if not os.path.exists(data): + raise FileNotFoundError(f"Could not find file {data}.") + return self.__tokenize_bed_file(data) + # check if data is a list of regions + elif isinstance(data, list): + return self.__tokenize_list(data) + # else, raise error + else: + raise ValueError( + "Data must be one of the following types: anndata object, path to BED file, list of regions." + ) diff --git a/gitk/models/utils.py b/gitk/models/utils.py new file mode 100644 index 00000000..711f0fc3 --- /dev/null +++ b/gitk/models/utils.py @@ -0,0 +1,215 @@ +import os +import subprocess +from typing import List, Dict, Union + +import numpy as np +import scanpy as sc +from tqdm import tqdm + +from .const import CACHE_DIR + + +def get_path_to_home_dir(): + """ + Get the path to the home directory. + """ + return os.path.expanduser("~") + + +def get_cache_dir(): + """ + Get the path to the cache directory. + """ + _home = get_path_to_home_dir() + return os.path.join(_home, CACHE_DIR) + + +def make_cache_dir(): + """ + Create cache directory in the current working directory. + """ + _home = get_path_to_home_dir() + _cache_dir = os.path.join(_home, CACHE_DIR) + if not os.path.exists(_cache_dir): + os.mkdir(_cache_dir) + + +def generate_var_conversion_map( + a: List[str], b: List[str], path_to_bedtools: str = None, fraction: float = 1.0e-9 +) -> Dict[str, Union[str, None]]: + """ + Create a conversion map to convert regions from a to b. This is used to convert the + consensus peak set of one AnnData object to another. + + For each region in a, we will either find a matching region in b, or None. If a matching + region is found, we will store the region in b. If no matching region is found, we will + store `None`. + + Intuitively, think of this as converting `A` --> `B`. If a region in `A` is found in `B`, + we will change the region in `A` to the region in `B`. If a region in `A` is not found in + `B`, we will drop that region in `A` altogether. + + :param List[str] a: the first list of regions + :param List[str] b: the second list of regions + :param str path_to_bedtools: the path to the bedtools executable + :param float fraction: the fraction of the region that must overlap to be considered an overlap + """ + # write a and b to temp files in cache + a_file = os.path.join(get_cache_dir(), "a.bed") + b_file = os.path.join(get_cache_dir(), "b.bed") + + # write a and b to temp files + with open(a_file, "w") as f: + # split each region into chr start end + a_parsed = [region.split("_") for region in a] + a_parsed = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in a_parsed] + f.writelines(a_parsed) + with open(b_file, "w") as f: + b_parsed = [region.split("_") for region in b] + b_parsed = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in b_parsed] + f.writelines(b_parsed) + + # sort both files + cmd = f"sort -k1,1 -k2,2n {a_file} -o {a_file}" + subprocess.run(cmd, shell=True) + + cmd = f"sort -k1,1 -k2,2n {b_file} -o {b_file}" + subprocess.run(cmd, shell=True) + + # run bedtools + bedtools_cmd = f"intersect -a {a_file} -b {b_file} -wa -wb -f {fraction}" + + # add path to bedtools if provided + if path_to_bedtools is not None: + cmd = f"{path_to_bedtools} {bedtools_cmd}" + else: + cmd = f"bedtools {bedtools_cmd}" + + # target file + target_file = os.path.join(get_cache_dir(), "olaps.bed") + with open(target_file, "w") as f: + subprocess.run(cmd, shell=True, stdout=f) + + # bedtools reports overlaps like this: + # chr1 100 200 chr1 150 250 + # we want to convert this to a map like this: + # {chr1_100_200: chr1_150_250} + # we will use a dictionary to do this + # if a region in A overlaps with multiple regions in B, we will + # take the first one. as such we need to check if a region in A + # has already been mapped to a region in B + conversion_map = dict() + with open(target_file, "r") as f: + for line in f.readlines(): + line = line.strip().split("\t") + a_region = f"{line[0]}_{line[1]}_{line[2]}" + b_region = f"{line[3]}_{line[4]}_{line[5]}" + if a_region not in conversion_map: + conversion_map[a_region] = b_region + + # add `None` mappings for regions in A that did not overlap with any regions in B + for region in a: + if region not in conversion_map: + conversion_map[region] = None + + # remove temp files + os.remove(a_file) + os.remove(b_file) + os.remove(target_file) + + return conversion_map + + +def convert_to_universe(adata: sc.AnnData, universe_file: str) -> sc.AnnData: + """ + Converts the consensus peak set (.var) attributes of the AnnData object + to a universe representation. This is done through interval overlap + analysis with bedtools. + + For each region in the `.var` attribute of the AnnData object, we + either 1) map it to a region in the universe, or 2) map it to `None`. + If it is mapped to `None`, it is not in the universe and will be dropped + from the AnnData object. If it is mapped to a region, it will be updated + to the region in the universe for downstream analysis. + """ + # ensure adata has chr, start, and end + if not all([x in adata.var.columns for x in ["chr", "start", "end"]]): + raise ValueError( + "AnnData object must have `chr`, `start`, and `end` columns in .var" + ) + + # read in universe from file into universe_set + universe_set = [] + with open(universe_file, "r") as f: + for line in f.readlines(): + line = line.strip().split("\t") + universe_set.append(f"{line[0]}_{line[1]}_{line[2]}") + + # create list of regions from adata + query_set: List[str] = adata.var.apply( + lambda x: f"{x['chr']}_{x['start']}_{x['end']}", axis=1 + ).tolist() + + # generate conversion map + _map = generate_var_conversion_map(query_set, universe_set) + + # create a new DataFrame with the updated values + updated_var = adata.var.copy() + + # find the regions that overlap with the universe + # use dynamic programming to create a boolean mask of columns to keep + columns_to_keep = [] + for i, row in tqdm(adata.var.iterrows(), total=adata.var.shape[0]): + region = f"{row['chr']}_{row['start']}_{row['end']}" + if _map[region] is None: + columns_to_keep.append(False) + continue + + # if it is, change the region to the universe region, + # grab the first for now + # TODO - this is a simplification, we should be able to handle multiple + universe_region = _map[region] + chr, start, end = universe_region.split("_") + + updated_var.at[i, "chr"] = chr + updated_var.at[i, "start"] = start + updated_var.at[i, "end"] = end + + columns_to_keep.append(True) + + # update adata with the new DataFrame and filtered columns + adata = adata[:, columns_to_keep] + adata.var = updated_var[columns_to_keep] + + return adata + + +def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: + """ + Converts an AnnData object to a list of lists of regions. This + is done by taking each cell and creating a list of all regions + that have a value greater than 0. + + *Note: this method requires that the sc.AnnData object have + chr, start, and end in `.var` attributes* + """ + # Extract the arrays for chr, start, and end + chr_values = adata.var["chr"].values + start_values = adata.var["start"].values + end_values = adata.var["end"].values + + # Perform the comparison using numpy operations + positive_values = adata.X > 0 + + if not isinstance(positive_values, np.ndarray): + positive_values = positive_values.toarray() + + regions = [] + for i in tqdm(range(adata.shape[0]), total=adata.shape[0]): + regions.append( + [ + f"{chr_values[j]}_{start_values[j]}_{end_values[j]}" + for j in np.where(positive_values[i])[0] + ] + ) + return regions diff --git a/gitk/scembed/__init__.py b/gitk/scembed/__init__.py index ceb7dc09..4712433b 100644 --- a/gitk/scembed/__init__.py +++ b/gitk/scembed/__init__.py @@ -1,4 +1,5 @@ from .const import * from .projection import * +from .annotation import * from .scembed import * from .utils import * diff --git a/gitk/scembed/annotation.py b/gitk/scembed/annotation.py new file mode 100644 index 00000000..04ab3e94 --- /dev/null +++ b/gitk/scembed/annotation.py @@ -0,0 +1,112 @@ +from collections import Counter +import scanpy as sc + +from tqdm import tqdm +from qdrant_client import QdrantClient + + +class AnnotationServer(QdrantClient): + def __init__( + self, + location: str = None, + url: str = None, + port: int = None, + collection_name: str = None, + timeout: float = 10, + ): + super().__init__(location, url=url, port=port, timeout=timeout) + self.collection_name = collection_name + + +class Annotator: + def __init__( + self, + collection_name: str = None, + location: str = None, + url: str = None, + port: int = None, + timeout: float = 10, + ): + self.collection_name = collection_name + self.url = url + self.port = port + self.timeout = timeout + self._annotation_server = AnnotationServer( + location=location, + url=self.url, + port=self.port, + collection_name=self.collection_name, + timeout=self.timeout, + ) + + def annotate( + self, + adata: sc.AnnData, + embedding_key: str = "embedding", + key_added: str = "pred_celltype", + cluter_key: str = "leiden", + knn: int = 3, + score_threshold: float = 0.5, + ): + """ + Annotate a sc.AnnData object with cell type predictions for each cluster. This functions requires + that you have 1) clustered your data and 2) embedded your data using some pretrained model (databio/multiome). + + You can cluster your data using the `scanpy` + + It is *imperative* that the model used to embed your single cells is the same model used to produce the + embeddings in the database. Otherwise, the predictions will not be accurate; in fact they will be meaningless. + """ + + _temp_key = "putative_cell_type" + + # check that the embedding key exists + if embedding_key not in adata.obsm.keys(): + raise ValueError( + f"Embedding key '{embedding_key}' not found in adata.obsm. Please embed your data first." + ) + if cluter_key not in adata.obs.keys(): + raise ValueError( + f"Cluster key '{cluter_key}' not found in adata.obs. Please cluster your data first." + ) + + # init list + scembed_cell_type_preds = [] + + # use qdrant and a simple KNN approach to attach cell types to the embeddings + for embedding in tqdm( + adata.obsm["embedding"], total=len(adata.obsm["embedding"]) + ): + results = self._annotation_server.search( + collection_name=self.collection_name, + query_vector=embedding, + limit=knn, + score_threshold=score_threshold, + ) + + result_dicts = [result.dict() for result in results] + + # count "cell_type" in all dicts + c = Counter([result["payload"]["cell_type"] for result in result_dicts]) + + # simply get the name of the top most common and thats it + try: + cell_type = c.most_common(1)[0][0] + except IndexError: + cell_type = "Unknown" + + scembed_cell_type_preds.append(cell_type) + + # add to adata, these are just putative cell types + # we will take a consensus vote later using clusters + adata.obs[_temp_key] = scembed_cell_type_preds + + # now take a consensus vote for each cluster + cluster_celltypes = {} + for cluster in adata.obs["leiden"].unique(): + cluster_celltypes[cluster] = Counter( + adata.obs[adata.obs["leiden"] == cluster][_temp_key] + ).most_common(1)[0][0] + + # map the cluster_to_cell_type dictionary to the leiden column + adata.obs[key_added] = adata.obs[cluter_key].map(cluster_celltypes) diff --git a/gitk/scembed/projection.py b/gitk/scembed/projection.py index faeeade0..00e46c6b 100644 --- a/gitk/scembed/projection.py +++ b/gitk/scembed/projection.py @@ -123,6 +123,12 @@ def _load_model(self): else: self._load_remote_model(self.path_to_model) + def summarize(self): + """ + Summarize the model. + """ + print(self.model_config) + def convert_to_universe(self, adata: sc.AnnData) -> sc.AnnData: """ Converts the consensus peak set (.var) attributes of the AnnData object diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index bfc92d16..8cf1786f 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -12,4 +12,5 @@ tqdm umap-learn ubiquerg yacman -pydantic \ No newline at end of file +pydantic +huggingface_hub \ No newline at end of file diff --git a/tests/.DS_Store b/tests/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..7b9d33741ed04ce8ac5299ff580d34195ae35713 GIT binary patch literal 6148 zcmeH~F^XlKsY>SV?}7 zi2>N|ciI9YfHmETFAp;_<^v|2amRW3`MzFmx2qRvA06ne-@9Kng5F0slS}y0a!*XM8#sVgw)umczJ?S%NHHAZxO9vO=?*9xPie z#t^SZJ6ZC&nrxlD9hSp~<(h|QedXQs^_Dh{|EYi^Z%?xsT7a` zZ>E3^`{RDcm&&vC>GiyR%Brs$os7#F9)1Ft_))x}hjG97f~?8b$qG$B0wIHf6nLou Ee?2S`p8x;= literal 0 HcmV?d00001 diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100644 index 00000000..0243c5ea --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,20 @@ +import os +import sys + +import pytest +import scanpy as sc + +# add parent directory to path +sys.path.append("../") + +from gitk import models + + +@pytest.fixture +def adata(): + return sc.read_h5ad("tests/data/buenrostro.h5ad") + + +@pytest.fixture +def pretrained_model(): + return models.PretrainedScembedModel("nleroy917/luecken2021") diff --git a/tests/test_utils.py b/tests/test_utils.py index af2c1469..038fdf65 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,7 +1,5 @@ import logging -import os import sys -from itertools import chain import pytest import scanpy as sc From e0ed42db32f00cb01230ce3ca77cb92307b98251 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 6 Jul 2023 11:42:26 -0400 Subject: [PATCH 0227/1072] in memory tokenization --- gitk/models/tokenization.py | 224 +++++++++++++++++++----------- gitk/models/utils.py | 145 ++++++++++--------- requirements/requirements-all.txt | 3 +- tests/data/to_tokenize.bed | 10 ++ tests/test_models.py | 140 +++++++++++++++++++ 5 files changed, 363 insertions(+), 159 deletions(-) create mode 100644 tests/data/to_tokenize.bed diff --git a/gitk/models/tokenization.py b/gitk/models/tokenization.py index 751d4d03..2f884b4d 100644 --- a/gitk/models/tokenization.py +++ b/gitk/models/tokenization.py @@ -1,17 +1,126 @@ -from typing import List, Union +from typing import List, Union, Dict import scanpy as sc import os -from .const import DEFAULT_PATH_TO_BEDTOOLS, UNIVERSE_FILE_NAME +from intervaltree import IntervalTree + from .utils import ( - make_cache_dir, - get_cache_dir, + validate_region_input, convert_to_universe, anndata_to_regionsets, ) +class Universe: + def __init__(self, regions: Union[str, List[str]]): + """ + Create a new Universe. + + :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. + This can be either a list of regions or a path to a BED file containing regions. + """ + self._trees: Dict[str, IntervalTree] = dict() + self._regions = regions + self._universe_set = [] + + if regions is not None: + self.build_tree() + self._build_universe_set() + + def get_tree(self, tree: str): + return self._trees[tree] + + @property + def trees(self): + return self._trees + + @property + def regions(self): + return self._regions + + @property + def universe_set(self): + return self._universe_set + + def _build_universe_set(self) -> tuple[str, int, int]: + """ + Return the universe as a set of regions in the form (chr, start, end). + """ + universe = [] + for tree in self._trees: + for interval in self._trees[tree]: + universe.append((tree, interval.begin, interval.end)) + self._universe_set = universe + + def build_tree(self, regions: str = None): + """ + Builds the interval tree from the regions. The tree is a dictionary that maps chromosomes to + interval trees. + + We read in the regions (either from memory or from a BED file) and convert them to an + interval tree. + """ + regions = validate_region_input(regions or self._regions) + + # build trees + for region in regions: + chr, start, end = region + start = int(start) + end = int(end) + if chr not in self._trees: + self._trees[chr] = IntervalTree() + self._trees[chr][start:end] = f"{chr}_{start}_{end}" + + def query( + self, + regions: Union[ + str, List[str], List[tuple[str, int, int]], tuple[str, int, int] + ], + ): + """ + Query the interval tree for the given regions. + + :param regions: The regions to query for. this can be either a bed file, + a list of regions (chr_start_end), or a list of tuples of chr, start, end. + """ + # validate input + if isinstance(regions, tuple): + regions = [regions] + regions = validate_region_input(regions) + + overlapping_regions = [] + for region in regions: + chr, start, end = region + start = int(start) + end = int(end) + if chr not in self._trees: + continue + overlaps = self._trees[chr][start:end] + for overlap in overlaps: + overlapping_regions.append((chr, overlap.begin, overlap.end)) + return overlapping_regions + + def __contains__(self, item: tuple[str, int, int]): + # ensure item is a tuple of chr, start, end + if not ((isinstance(item, tuple) or isinstance(item, list)) and len(item) == 3): + raise ValueError( + "The item to check for must be a tuple of chr, start, end." + ) + + chr, start, end = item + start = int(start) + end = int(end) + if chr not in self._trees: + return False + overlaps = self._trees[chr][start:end] + return len(overlaps) > 0 + + def __iter__(self): + for tree in self._trees: + yield self._trees[tree] + + class Tokenizer: pass @@ -24,7 +133,6 @@ class HardTokenizer(Tokenizer): def __init__( self, regions: Union[List[str], str], - path_to_bedtools: str = DEFAULT_PATH_TO_BEDTOOLS, ): """ Create a new HardTokenizer. @@ -33,57 +141,18 @@ def __init__( This can be either a list of regions or a path to a BED file containing regions. :param path_to_bedtools: The path to the bedtools executable. """ - self.regions = regions - self.path_to_bedtools = path_to_bedtools - - # write regions to file in cache dir - self.__cache_dir = get_cache_dir() - self.__regions_file = os.path.join(self.__cache_dir, UNIVERSE_FILE_NAME) - self.__write_regions_to_file() - - make_cache_dir() - - def __write_regions_to_file(self): - """ - Write regions to file in cache dir. This can take one of two forms: - 1. A list of regions. - 2. A path to a BED file containing regions. - - If the regions are a list, they are written to a file in the cache dir. - If the regions are a path to a BED file, the file is copied to the cache dir. - - :raises FileNotFoundError: If the path to the BED file does not exist. - """ - # check for path to file - if isinstance(self.regions, str): - # check if file exists - if not os.path.exists(self.regions): - raise FileNotFoundError( - f"Could not find file {self.regions} containing regions." - ) - # copy file to cache dir - os.system(f"cp {self.regions} {self.__regions_file}") - - # else, we are going to write the regions to a file - # verify that the regions are a list, should be in the form chr_start_end - elif isinstance(self.regions, list): - if not all( - [ - isinstance(region, str) and len(region.split("_")) == 3 - for region in self.regions - ] - ): - raise ValueError( - "Regions must be a list of strings in the form chr_start_end." - ) - # write regions to file - with open(self.__regions_file, "w") as f: - # split each region into chr, start, end - for region in self.regions: - chr, start, end = region.split("_") - f.write(f"{chr}\t{start}\t{end}\n") - - def __tokenize_anndata(self, data: sc.AnnData) -> List[List[str]]: + self._regions = regions + self._universe = Universe(validate_region_input(regions)) + + @property + def regions(self): + return self._regions + + @property + def universe(self): + return self._universe + + def _tokenize_anndata(self, data: sc.AnnData) -> List[List[str]]: """ Tokenize an anndata object into a list of lists of regions. @@ -91,11 +160,11 @@ def __tokenize_anndata(self, data: sc.AnnData) -> List[List[str]]: :return: A list of lists of regions. """ - data = convert_to_universe(data, self.__regions_file) + data = convert_to_universe(data, self._universe.universe_set) region_sets = anndata_to_regionsets(data) return region_sets - def __tokenize_bed_file(self, data: str, fraction: float = 1e-9) -> List[str]: + def _tokenize_bed_file(self, data: str, fraction: float = 1e-9) -> List[str]: """ Tokenize a BED file into a list of lists of regions. @@ -103,33 +172,20 @@ def __tokenize_bed_file(self, data: str, fraction: float = 1e-9) -> List[str]: :return: A list of lists of regions. """ - # perform overlap analysis with bedtools - os.system( - f"{self.path_to_bedtools} intersect -a {data} -b {self.__regions_file} -wa -wb > {self.__cache_dir}/overlaps.bed" - ) - # read in overlaps - overlaps = [] - with open(f"{self.__cache_dir}/overlaps.bed", "r") as f: - for line in f: - line = line.strip().split("\t") - # join chr, start, end with _ - region = "_".join(line[0:3]) - overlaps.append(region) + regions = validate_region_input(data) + tokens = [ + "_".join([str(h) for h in hit]) for hit in self._universe.query(regions) + ] + return tokens - return overlaps - - def __tokenize_list(self, data: List[str]) -> List[List[str]]: + def _tokenize_list(self, data: List[str]) -> List[List[str]]: """ Tokenize a list of regions into a list of lists of regions. """ - # write to bed file, and perform __tokenize_bed_file - with open(f"{self.__cache_dir}/regions.bed", "w") as f: - for region in data: - # split region into chr, start, end - chr, start, end = region.split("_") - f.write(f"{chr}\t{start}\t{end}\n") - - return self.__tokenize_bed_file(f"{self.__cache_dir}/regions.bed") + regions = validate_region_input(data) + hits = [h[0] for h in self._universe.query(regions)] + tokens = ["_".join(hit) for hit in hits] + return tokens def tokenize( self, data: Union[sc.AnnData, str, List[str]] @@ -150,16 +206,16 @@ def tokenize( """ # check if data is anndata object if isinstance(data, sc.AnnData): - return self.__tokenize_anndata(data) + return self._tokenize_anndata(data) # check if data is a path to a BED file elif isinstance(data, str): # ensure that the file exists if not os.path.exists(data): raise FileNotFoundError(f"Could not find file {data}.") - return self.__tokenize_bed_file(data) + return self._tokenize_bed_file(data) # check if data is a list of regions elif isinstance(data, list): - return self.__tokenize_list(data) + return self._tokenize_list(data) # else, raise error else: raise ValueError( diff --git a/gitk/models/utils.py b/gitk/models/utils.py index 711f0fc3..945c285c 100644 --- a/gitk/models/utils.py +++ b/gitk/models/utils.py @@ -1,11 +1,13 @@ import os import subprocess -from typing import List, Dict, Union +from typing import List, Dict, Union, TYPE_CHECKING import numpy as np import scanpy as sc from tqdm import tqdm +if TYPE_CHECKING: + from .tokenization import Universe from .const import CACHE_DIR @@ -34,8 +36,56 @@ def make_cache_dir(): os.mkdir(_cache_dir) +def validate_region_input( + regions: Union[str, List[str], List[tuple[str]]] +) -> List[tuple[str, int, int]]: + """ + Validate the input for the regions. this universe accepts a lot of forms of input, + so we need to check what the user has passed. It also standardizes the input to a list + of tuples of chr, start, end. + + Users are allowed to pass a list of regions, a list of tuples with chr, start, and end, or + a path to a BED file. + + :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. + This can be either a list of regions or a path to a BED file containing regions. + :return A list of tuples of chr, start, end. + """ + # check for bedfile + if isinstance(regions, str): + # ensure that the file exists + if not os.path.exists(regions): + raise FileNotFoundError( + f"Could not find file {regions} containing regions." + ) + file = regions # rename for clarity + regions = [] + with open(file, "r") as f: + lines = f.readlines() + for line in lines: + regions.append(tuple(line.strip().split("\t"))) + return regions + + # check for list of chr_start_end strings + elif isinstance(regions, list) and all([isinstance(r, str) for r in regions]): + regions = [tuple(r.split("_")) for r in regions] + return regions + + # check for list of tuples of chr, start, end + elif isinstance(regions, list) and all( + [(isinstance(r, tuple) or isinstance(r, list)) and len(r) == 3 for r in regions] + ): + return regions + else: + raise ValueError( + "The regions must be either a list of regions or a path to a BED file containing regions." + ) + + def generate_var_conversion_map( - a: List[str], b: List[str], path_to_bedtools: str = None, fraction: float = 1.0e-9 + a: List[tuple[str, int, int]], + b: "Universe", + fraction: float = 1.0e-9, ) -> Dict[str, Union[str, None]]: """ Create a conversion map to convert regions from a to b. This is used to convert the @@ -49,78 +99,31 @@ def generate_var_conversion_map( we will change the region in `A` to the region in `B`. If a region in `A` is not found in `B`, we will drop that region in `A` altogether. - :param List[str] a: the first list of regions - :param List[str] b: the second list of regions - :param str path_to_bedtools: the path to the bedtools executable + :param List[tuple[str, int, int]] a: the first list of regions + :param Universe: the second list of regions as a Universe object :param float fraction: the fraction of the region that must overlap to be considered an overlap """ - # write a and b to temp files in cache - a_file = os.path.join(get_cache_dir(), "a.bed") - b_file = os.path.join(get_cache_dir(), "b.bed") - - # write a and b to temp files - with open(a_file, "w") as f: - # split each region into chr start end - a_parsed = [region.split("_") for region in a] - a_parsed = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in a_parsed] - f.writelines(a_parsed) - with open(b_file, "w") as f: - b_parsed = [region.split("_") for region in b] - b_parsed = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in b_parsed] - f.writelines(b_parsed) - - # sort both files - cmd = f"sort -k1,1 -k2,2n {a_file} -o {a_file}" - subprocess.run(cmd, shell=True) - - cmd = f"sort -k1,1 -k2,2n {b_file} -o {b_file}" - subprocess.run(cmd, shell=True) - - # run bedtools - bedtools_cmd = f"intersect -a {a_file} -b {b_file} -wa -wb -f {fraction}" - - # add path to bedtools if provided - if path_to_bedtools is not None: - cmd = f"{path_to_bedtools} {bedtools_cmd}" - else: - cmd = f"bedtools {bedtools_cmd}" - - # target file - target_file = os.path.join(get_cache_dir(), "olaps.bed") - with open(target_file, "w") as f: - subprocess.run(cmd, shell=True, stdout=f) - - # bedtools reports overlaps like this: - # chr1 100 200 chr1 150 250 - # we want to convert this to a map like this: - # {chr1_100_200: chr1_150_250} - # we will use a dictionary to do this - # if a region in A overlaps with multiple regions in B, we will - # take the first one. as such we need to check if a region in A - # has already been mapped to a region in B + conversion_map = dict() - with open(target_file, "r") as f: - for line in f.readlines(): - line = line.strip().split("\t") - a_region = f"{line[0]}_{line[1]}_{line[2]}" - b_region = f"{line[3]}_{line[4]}_{line[5]}" - if a_region not in conversion_map: - conversion_map[a_region] = b_region - - # add `None` mappings for regions in A that did not overlap with any regions in B - for region in a: - if region not in conversion_map: - conversion_map[region] = None - - # remove temp files - os.remove(a_file) - os.remove(b_file) - os.remove(target_file) + + for region in tqdm(a, total=len(a)): + overlaps = b.query(region) + region_str = f"{region[0]}_{region[1]}_{region[2]}" + if len(overlaps) > 0: + olap = overlaps[ + 0 + ] # take the first overlap for now, we can change this later + olap_str = f"{olap[0]}_{olap[1]}_{olap[2]}" + conversion_map[region_str] = olap_str + else: + conversion_map[region_str] = None return conversion_map -def convert_to_universe(adata: sc.AnnData, universe_file: str) -> sc.AnnData: +def convert_to_universe( + adata: sc.AnnData, universe_set: List[tuple[str, int, int]] +) -> sc.AnnData: """ Converts the consensus peak set (.var) attributes of the AnnData object to a universe representation. This is done through interval overlap @@ -137,13 +140,7 @@ def convert_to_universe(adata: sc.AnnData, universe_file: str) -> sc.AnnData: raise ValueError( "AnnData object must have `chr`, `start`, and `end` columns in .var" ) - - # read in universe from file into universe_set - universe_set = [] - with open(universe_file, "r") as f: - for line in f.readlines(): - line = line.strip().split("\t") - universe_set.append(f"{line[0]}_{line[1]}_{line[2]}") + universe_set = [f"{region[0]}_{region[1]}_{region[2]}" for region in universe_set] # create list of regions from adata query_set: List[str] = adata.var.apply( diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index 8cf1786f..8ac77a7e 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -13,4 +13,5 @@ umap-learn ubiquerg yacman pydantic -huggingface_hub \ No newline at end of file +huggingface_hub +intervaltree \ No newline at end of file diff --git a/tests/data/to_tokenize.bed b/tests/data/to_tokenize.bed new file mode 100644 index 00000000..9eb67a5e --- /dev/null +++ b/tests/data/to_tokenize.bed @@ -0,0 +1,10 @@ +chr11 17991604 17992469 +chr15 34610343 34610968 +chr9 21481377 21482235 +chr17 75336895 75337644 +chr5 179507698 179508535 +chr5 149456638 149457490 +chr5 140575093 140575967 +chr18 63105759 63106585 +chr6 32922103 32922938 +chr8 154608000 154608500 \ No newline at end of file diff --git a/tests/test_models.py b/tests/test_models.py index 0243c5ea..bc09300f 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -15,6 +15,146 @@ def adata(): return sc.read_h5ad("tests/data/buenrostro.h5ad") +@pytest.fixture +def tokenizer_from_file(): + return models.HardTokenizer("tests/data/universe.bed") + + +@pytest.fixture +def tokenizer_from_list(): + return models.HardTokenizer(["chr1_0_100", "chr2_0_100"]) + + +@pytest.fixture +def universe_from_file(): + return models.Universe("tests/data/universe.bed") + + @pytest.fixture def pretrained_model(): return models.PretrainedScembedModel("nleroy917/luecken2021") + + +def test_init_tokenizers( + tokenizer_from_file: models.HardTokenizer, tokenizer_from_list: models.HardTokenizer +): + # assert that the tokenizers were created + assert tokenizer_from_file is not None + assert tokenizer_from_list is not None + + # assert that the regions file was created + assert os.path.exists(tokenizer_from_file.regions_file) + assert os.path.exists(tokenizer_from_list.regions_file) + + +def test_init_universe( + universe_from_file: models.Universe, +): + # assert that the universe was created + assert universe_from_file is not None + + # assert interval trees were created + for tree in universe_from_file: + assert tree is not None + + +def test_universe_set( + universe_from_file: models.Universe, +): + # assert that the universe_set is the same length as the number of regions + # in the universe file + universe_set = universe_from_file.universe_set + assert len(universe_set) == 116490 + + +def test_universe_contains(universe_from_file: models.Universe): + regions = [ + ("chr10", 132470577, 132471377), + ("chr1", 247854920, 247855824), + ] + for region in regions: + assert region in universe_from_file + + +def test_universe_not_contains(universe_from_file: models.Universe): + regions = [("chr10", 132, 1324), ("chr1", 247, 2478), ("chr999", 0, 100)] + for region in regions: + assert region not in universe_from_file + + +def test_universe_query(universe_from_file: models.Universe): + regions = [ + ("chr10", 132, 1324), + ("chr10", 132470597, 132471397), + ("chr1", 247854940, 247855844), + ] + res = universe_from_file.query(regions) + assert len(res) == 2 + + +def test_universe_conversion(): + """ + Test tokenization for three scenarios: + 1. One overlap in B per region in A + 2. At least one region in A doesn't overlap with any region in B + 3. A region in A overlaps with multiple regions in B + + Scenario 1: + A: |-----------| |-----------| |-----------| + B: |-----------| |-----------| |-----------| + + Scenario 2: + A: |-----------| |-------| |-----------| + B: |-----------| |-------| |-----------| + + Scenario 3: + A: |-----------| |-----------| |-----------| + B: |-----------| |--------| |-----------| |-----------| + + """ + # scenario 1 + with open("tests/data/s1_a.bed", "r") as f: + lines = f.readlines() + a = [tuple(l.strip().split("\t")) for l in lines] + u = models.Universe("tests/data/s1_b.bed") + + conversion_map = models.utils.generate_var_conversion_map(a, u) + assert conversion_map == { + "chr1_10_30": "chr1_20_40", + "chr1_110_130": "chr1_120_140", + "chr1_210_230": "chr1_220_240", + } + + # scenario 2 + with open("tests/data/s2_a.bed", "r") as f: + lines = f.readlines() + a = [tuple(l.strip().split("\t")) for l in lines] + u = models.Universe("tests/data/s2_b.bed") + + conversion_map = models.utils.generate_var_conversion_map(a, u) + assert conversion_map == { + "chr1_10_30": "chr1_20_40", + "chr1_110_130": None, + "chr1_210_230": "chr1_220_240", + } + + # scenario 3 + with open("tests/data/s3_a.bed", "r") as f: + lines = f.readlines() + a = [tuple(l.strip().split("\t")) for l in lines] + u = models.Universe("tests/data/s3_b.bed") + + conversion_map = models.utils.generate_var_conversion_map(a, u) + assert conversion_map == { + "chr1_10_30": "chr1_20_40", + "chr1_110_130": "chr1_100_120", + "chr1_210_230": "chr1_220_240", + } + + +def test_tokenize_bedfile(tokenizer_from_file: models.HardTokenizer): + # assert that the tokenizer works + tokens = tokenizer_from_file.tokenize("tests/data/to_tokenize.bed") + assert tokens is not None + assert len(tokens) == 9 + assert all([len(t.split("_")) == 3 for t in tokens]) From 3c3856cd7c617b8ad9191c1b5cc60e9a6f5a4288 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 6 Jul 2023 12:12:50 -0400 Subject: [PATCH 0228/1072] in memory tokenization of anndata --- gitk/models/tokenization.py | 8 ++++---- gitk/models/utils.py | 11 ++++------- tests/test_models.py | 10 ++++++++++ 3 files changed, 18 insertions(+), 11 deletions(-) diff --git a/gitk/models/tokenization.py b/gitk/models/tokenization.py index 2f884b4d..df56b0d7 100644 --- a/gitk/models/tokenization.py +++ b/gitk/models/tokenization.py @@ -22,7 +22,7 @@ def __init__(self, regions: Union[str, List[str]]): """ self._trees: Dict[str, IntervalTree] = dict() self._regions = regions - self._universe_set = [] + self._universe_set: list[tuple[str, int, int]] = [] if regions is not None: self.build_tree() @@ -43,7 +43,7 @@ def regions(self): def universe_set(self): return self._universe_set - def _build_universe_set(self) -> tuple[str, int, int]: + def _build_universe_set(self): """ Return the universe as a set of regions in the form (chr, start, end). """ @@ -142,7 +142,7 @@ def __init__( :param path_to_bedtools: The path to the bedtools executable. """ self._regions = regions - self._universe = Universe(validate_region_input(regions)) + self._universe: Universe = Universe(validate_region_input(regions)) @property def regions(self): @@ -160,7 +160,7 @@ def _tokenize_anndata(self, data: sc.AnnData) -> List[List[str]]: :return: A list of lists of regions. """ - data = convert_to_universe(data, self._universe.universe_set) + data = convert_to_universe(data, self._universe) region_sets = anndata_to_regionsets(data) return region_sets diff --git a/gitk/models/utils.py b/gitk/models/utils.py index 945c285c..4cef04cb 100644 --- a/gitk/models/utils.py +++ b/gitk/models/utils.py @@ -121,9 +121,7 @@ def generate_var_conversion_map( return conversion_map -def convert_to_universe( - adata: sc.AnnData, universe_set: List[tuple[str, int, int]] -) -> sc.AnnData: +def convert_to_universe(adata: sc.AnnData, universe: "Universe") -> sc.AnnData: """ Converts the consensus peak set (.var) attributes of the AnnData object to a universe representation. This is done through interval overlap @@ -140,15 +138,14 @@ def convert_to_universe( raise ValueError( "AnnData object must have `chr`, `start`, and `end` columns in .var" ) - universe_set = [f"{region[0]}_{region[1]}_{region[2]}" for region in universe_set] # create list of regions from adata - query_set: List[str] = adata.var.apply( - lambda x: f"{x['chr']}_{x['start']}_{x['end']}", axis=1 + query_set: List[tuple[str, int, int]] = adata.var.apply( + lambda x: (x["chr"], int(x["start"]), int(x["end"])), axis=1 ).tolist() # generate conversion map - _map = generate_var_conversion_map(query_set, universe_set) + _map = generate_var_conversion_map(query_set, universe) # create a new DataFrame with the updated values updated_var = adata.var.copy() diff --git a/tests/test_models.py b/tests/test_models.py index bc09300f..8cac734c 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -158,3 +158,13 @@ def test_tokenize_bedfile(tokenizer_from_file: models.HardTokenizer): assert tokens is not None assert len(tokens) == 9 assert all([len(t.split("_")) == 3 for t in tokens]) + + +def test_tokenize_anndata(adata: sc.AnnData, tokenizer_from_file: models.HardTokenizer): + tokens: list[list[str]] = tokenizer_from_file.tokenize(adata) + # make sure each token in each cell is inside the universe.bed file + for cell in tokens: + for token in cell: + chr, start, end = token.split("_") + token_tuple = (chr, int(start), int(end)) + assert token_tuple in tokenizer_from_file.universe.universe_set From 9f36fca65065c906889dea0fc955bb95ee41d30f Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 6 Jul 2023 13:15:20 -0400 Subject: [PATCH 0229/1072] more tests for the models --- gitk/models/pretrained.py | 76 ++++++++++++++++++++++++++++++--------- gitk/scembed/scembed.py | 8 +++++ tests/data/make_pbmc.py | 16 +++++++++ tests/test_models.py | 29 ++++++++++++++- 4 files changed, 112 insertions(+), 17 deletions(-) create mode 100644 tests/data/make_pbmc.py diff --git a/gitk/models/pretrained.py b/gitk/models/pretrained.py index b4c0159a..c2f68f91 100644 --- a/gitk/models/pretrained.py +++ b/gitk/models/pretrained.py @@ -1,8 +1,11 @@ -from gensim.models import Word2Vec +from typing import Union + +import scanpy as sc +import numpy as np from huggingface_hub import hf_hub_download from .. import scembed -from .tokenization import Tokenizer, HardTokenizer +from .tokenization import HardTokenizer from .const import MODEL_FILE_NAME, UNIVERSE_FILE_NAME @@ -11,22 +14,20 @@ class PretrainedScembedModel: A class for loading pretrained models from the HuggingFace Hub. """ - def __init__( - self, model: str = None, file_name: str = None, tokenizer: Tokenizer = None - ): + def __init__(self, model: str = None, tokenizer: HardTokenizer = None): self.model_name = model - self.__model_path = None - self.__universe_path = None - self.__tokenizer = tokenizer - self.__model = None + self._model_path = None + self._universe_path = None + self._tokenizer: HardTokenizer = tokenizer + self._model: scembed.SCEmbed = None # load if model is passed if model is not None: - self.__download_model_files(model, file_name) - self.__tokenizer = HardTokenizer(self.__universe_path) - self.__model = self.__load_model(self.__model_path) + self._download_model_files(model) + self._tokenizer = HardTokenizer(self._universe_path) + self._model = self._load_model(self._model_path) - def __download_model_files( + def _download_model_files( self, model: str, model_file_name: str = MODEL_FILE_NAME, @@ -36,14 +37,27 @@ def __download_model_files( """ Download a pretrained model from the HuggingFace Hub. We need to download the actual model + weights, and the universe file. + + :param model: The name of the model to download (this is the same as the repo name). + :param model_file_name: The name of the model file - this should almost never be changed. + :param universe_file_name: The name of the universe file - this should almost never be changed. """ model_path = hf_hub_download(model, model_file_name, **kwargs) universe_path = hf_hub_download(model, universe_file_name, **kwargs) - self.__model_path = model_path - self.__universe_path = universe_path + # set the paths to the downloaded files + self._model_path = model_path + self._universe_path = universe_path + + @property + def model(self): + return self._model + + @property + def tokenizer(self): + return self._tokenizer - def __load_model(self, path: str) -> scembed.SCEmbed: + def _load_model(self, path: str) -> scembed.SCEmbed: """ Load a pretrained model from the HuggingFace Hub. @@ -51,3 +65,33 @@ def __load_model(self, path: str) -> scembed.SCEmbed: :return: The loaded model. """ return scembed.utils.load_scembed_model(path) + + def encode( + self, data: Union[str, list[tuple[str, int, int]], sc.AnnData] + ) -> np.ndarray: + """ + Encode region sets data using the pretrained model. + """ + if self._model is None: + raise ValueError("No model loaded. Please load a model first.") + + # generate tokens + tokenized = self._tokenizer.tokenize(data) + + embeddings = [] + for region_set in tokenized: + if len(region_set) == 0: + raise ValueError( + "Encountered empty region set. Please check your input." + ) + # compute embeddings using average pooling of all region embeddings + embedding = np.mean( + [ + self._model.region2vec[region] + for region in region_set + if region in self._model.region2vec + ], + axis=0, + ) + embeddings.append(embedding) + return np.array(embeddings) diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index e71744ac..080eb145 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -476,3 +476,11 @@ def anndata_to_embeddings(self, adata: sc.AnnData) -> sc.AnnData: # attach embeddings to the AnnData object self.data.obs["embedding"] = cell_embeddings return self.data + + def __call__(self, region: str) -> np.ndarray: + """ + Get the embedding for a given region. + + :param str region: the region to get the embedding for + """ + return self.get_embedding(region) diff --git a/tests/data/make_pbmc.py b/tests/data/make_pbmc.py new file mode 100644 index 00000000..2ba44434 --- /dev/null +++ b/tests/data/make_pbmc.py @@ -0,0 +1,16 @@ +# %% +import os +import scanpy as sc + +# %% +path_to_pbmc = "$DATA/genomics/10X/10kpbmcsV2_atac/pbmc.h5ad" + +# exapand the path +path_to_pbmc = os.path.expandvars(path_to_pbmc) +adata = sc.read_h5ad(path_to_pbmc) + +# %% +sc.pp.subsample(adata, n_obs=20) + +# %% +adata.write_h5ad("pbmc_hg38.h5ad") diff --git a/tests/test_models.py b/tests/test_models.py index 8cac734c..d99ad21f 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -3,6 +3,7 @@ import pytest import scanpy as sc +import numpy as np # add parent directory to path sys.path.append("../") @@ -12,7 +13,7 @@ @pytest.fixture def adata(): - return sc.read_h5ad("tests/data/buenrostro.h5ad") + return sc.read_h5ad("tests/data/pbmc_hg38.h5ad") @pytest.fixture @@ -168,3 +169,29 @@ def test_tokenize_anndata(adata: sc.AnnData, tokenizer_from_file: models.HardTok chr, start, end = token.split("_") token_tuple = (chr, int(start), int(end)) assert token_tuple in tokenizer_from_file.universe.universe_set + + +def test_init_pretrained_model( + pretrained_model: models.PretrainedScembedModel, +): + # assert that the model was created + assert pretrained_model is not None + + # assert that the tokenizer was created + assert pretrained_model.tokenizer is not None + + # assert that the model was created + assert pretrained_model.model is not None + + +def test_encode_anndata( + adata: sc.AnnData, pretrained_model: models.PretrainedScembedModel +): + # encode the anndata + encoded = pretrained_model.encode(adata) + + # assert that the encoded object is a numpy array + assert isinstance(encoded, np.ndarray) + + # assert that the encoded object has the correct shape + assert encoded.shape == (adata.n_obs, 100) From 9290befc7648b1b98809cf3520de9a44f7d55289 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 6 Jul 2023 13:19:20 -0400 Subject: [PATCH 0230/1072] add example for pre-trained model --- examples/scembed/pretrained_model.py | 18 ++++++++++++++++++ setup.py | 1 + 2 files changed, 19 insertions(+) create mode 100644 examples/scembed/pretrained_model.py diff --git a/examples/scembed/pretrained_model.py b/examples/scembed/pretrained_model.py new file mode 100644 index 00000000..3bb073dd --- /dev/null +++ b/examples/scembed/pretrained_model.py @@ -0,0 +1,18 @@ +# %% +import scanpy as sc +from gitk.models import PretrainedScembedModel + +# %% +# Load the pretrained model +model = PretrainedScembedModel("nleroy917/luecken2021") + +# %% +# Load the data +adata = sc.read_h5ad("../../tests/data/pbmc_hg38.h5ad") + +# %% +# Embed the data +embeddings = model.encode(adata) + +# %% +print(embeddings.shape) diff --git a/setup.py b/setup.py index 5d3ff6c4..c47a23f6 100755 --- a/setup.py +++ b/setup.py @@ -31,6 +31,7 @@ "gitk.hmm", "gitk.likelihood", "gitk.scembed", + "gitk.models", ], version=version, long_description=long_description, From 5620e789fafeb28003069c414644fb5c033d14c5 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 6 Jul 2023 15:11:22 -0400 Subject: [PATCH 0231/1072] change model export code for new sharing framework --- gitk/scembed/const.py | 3 +- gitk/scembed/scembed.py | 83 +++++------------------------------------ 2 files changed, 10 insertions(+), 76 deletions(-) diff --git a/gitk/scembed/const.py b/gitk/scembed/const.py index feac77ba..b2ede2af 100644 --- a/gitk/scembed/const.py +++ b/gitk/scembed/const.py @@ -31,6 +31,5 @@ DEFAULT_BEDTOOLS_PATH = "bedtools" -DEFAULT_MODEL_EXPORT_FILE_NAME = "weights.pkl" +DEFAULT_MODEL_EXPORT_FILE_NAME = "model.bin" DEFAULT_UNIVERSE_EXPORT_FILE_NAME = "universe.bed" -DEFAULT_MODEL_CONFIG_FILE_NAME = "model.yaml" diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index 080eb145..64e450fe 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -100,16 +100,19 @@ def save_model(self, filepath: str, **kwargs): def export_model( self, out_path: str, - model_config_name: str = DEFAULT_MODEL_CONFIG_FILE_NAME, model_export_name: str = DEFAULT_MODEL_EXPORT_FILE_NAME, universe_file_name: str = DEFAULT_UNIVERSE_EXPORT_FILE_NAME, - **config_kwargs, ): """ - This function will do a full export of the model. This includes three files: - 1. the actual pickled `.model` file - 2. the `yaml` config file with metadata - 3. the universe `.bed` file that determines the universe of regions + This function will do a full export of the model. This includes two files: + 1. the actual pickled `.model` file + 2. the universe `.bed` file that determines the universe of regions + + The result of this function can be directly uploaded to huggingface, to share with the world. + + :param str out_path: The path to the directory to save the model to. + :param str model_export_name: The name of the model file to save - it is not recommended to change this. + :param str universe_file_name: The name of the universe file to save - it is not recommended to change this. """ if not os.path.exists(out_path): os.makedirs(out_path) @@ -123,15 +126,6 @@ def export_model( chr, start, end = region.split("_") f.write(f"{chr}\t{start}\t{end}\n") - config_dict = create_model_info_dict( - path_to_weights=model_export_name, - path_to_universe=universe_file_name, - **config_kwargs, - ) - - with open(os.path.join(out_path, model_config_name), "w") as f: - yaml.dump({"model": config_dict}, f) - def load_model(self, filepath: str, **kwargs): """ Cant use the load function from gensim because it doesnt work on subclasses. see @@ -260,65 +254,6 @@ def train( for word in regions: self.region2vec[word] = self.wv[word] - # this is here for testing and debugging - def train_legacy( - self, - data: Union[sc.AnnData, str], - epochs: int = DEFAULT_EPOCHS, # training cycles - n_shuffles: int = DEAFULT_N_SHUFFLES, # not the number of traiing cycles, actual shufle num - gensim_epochs: Union[int, None] = DEFAULT_GENSIM_EPOCHS, - report_loss: bool = True, - lr: float = DEFAULT_INIT_LR, - min_lr: float = DEFAULT_MIN_LR, - lr_schedule: Union[str, ScheduleType] = "linear", - ): - self.trained = True - if report_loss: - self.callbacks.append(ReportLossCallback()) - - lr_scheduler = LearningRateScheduler( - init_lr=lr, min_lr=min_lr, type=lr_schedule, n_epochs=epochs - ) - - region_sets = convert_anndata_to_documents(data, self.use_default_region_names) - - _LOGGER.info("Training starting.") - - for shuffle_num in range(epochs): - # update current values - current_lr = lr_scheduler.get_lr() - current_loss = self.get_latest_training_loss() - - # update user - _LOGGER.info( - f"SHUFFLE {shuffle_num} - lr: {current_lr}, loss: {current_loss}" - ) - _LOGGER.info("Shuffling documents.") - - # shuffle regions - self.region_sets = shuffle_documents(region_sets, n_shuffles=n_shuffles) - - # update vocab and train - super().build_vocab(region_sets, update=True if shuffle_num > 0 else False) - super().train( - self.region_sets, - total_examples=len(region_sets), - epochs=gensim_epochs or 1, # use the epochs passed in or just one - callbacks=self.callbacks, - compute_loss=report_loss, - start_alpha=current_lr, - ) - - # update learning rates - lr_scheduler.update() - - # once training is complete, create a region to vector mapping - regions = list(self.wv.key_to_index.keys()) - - # create a mapping from region to vector - for word in regions: - self.region2vec[word] = self.wv[word] - def get_embedding(self, region: str) -> np.ndarray: """ Get the embedding for a given region. From 22435b1d34fe152d1f21b80170859ae7a9ada75f Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 6 Jul 2023 16:26:50 -0400 Subject: [PATCH 0232/1072] remove old projector code in favor of new PretrainedScembedModel --- examples/scembed/annotation.ipynb | 147 +------- examples/scembed/cellcano_annotations.ipynb | 11 +- examples/scembed/projection.ipynb | 66 ++-- gitk/models/pretrained.py | 3 +- gitk/scembed/projection.py | 355 ------------------- gitk/scembed/utils.py | 2 - tests/integration/make_annadata.py | 19 - tests/integration/project_with_atlas.py | 56 --- tests/integration/scembed_train_in_chunks.py | 139 -------- tests/test_projector.py | 172 --------- 10 files changed, 48 insertions(+), 922 deletions(-) delete mode 100644 gitk/scembed/projection.py delete mode 100644 tests/integration/make_annadata.py delete mode 100644 tests/integration/project_with_atlas.py delete mode 100644 tests/integration/scembed_train_in_chunks.py delete mode 100644 tests/test_projector.py diff --git a/examples/scembed/annotation.ipynb b/examples/scembed/annotation.ipynb index 153e88ee..5ce5d7d7 100644 --- a/examples/scembed/annotation.ipynb +++ b/examples/scembed/annotation.ipynb @@ -91,9 +91,9 @@ "metadata": {}, "source": [ "## Generate embeddings using a pre-trained model\n", - "Now that we have the data, we can generate embeddings using a pre-trained model. It is imperative that the model we use for embedding generation is the same used to index the reference dataset. For this demonstration, I will use the `databio/multiome` model that exists on the model hub. To do this, we need to:\n", + "Now that we have the data, we can generate embeddings using a pre-trained model. It is imperative that the model we use for embedding generation is the same used to index the reference dataset. For this demonstration, I will use the `nleroy917/luecken2021` model that exists on the model hub. To do this, we need to:\n", "\n", - "1. Create a `Projector()`\n", + "1. Create a `PretrainedScembedModel()`\n", "2. Project some data\n", "\n", "If this is your first time through the tutorial, the model will need time to download. This will take a few minutes. If you've already run this tutorial, the model will be cached and this step will be much faster." @@ -120,111 +120,6 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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This occurs in two steps:\n", "\n", - "1. Create a `Projector()`\n", + "1. Create a `PretrainedScembedModel()`\n", "2. Project some data\n", "\n", "If this is your first time through the tutorial, the model will need time to download. This will take a few minutes. If you've already run this tutorial, the model will be cached and this step will be much faster." @@ -146,29 +146,12 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " @numba.jit()\n" - ] - } - ], + "outputs": [], "source": [ - "# create projector\n", - "from gitk.scembed import Projector\n", + "# create model\n", + "from gitk.models import PretrainedScembedModel\n", "\n", - "model = Projector(\"databio/multiome\")" + "model = PretrainedScembedModel(\"nleroy917/luecken2021\")" ] }, { @@ -188,7 +171,7 @@ } ], "source": [ - "len(model.model)" + "len(model.model.region2vec)" ] }, { @@ -200,15 +183,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "***** WARNING: File /Users/nathanleroy/.scembed/b.bed has inconsistent naming convention for record:\n", - "chr1\t9776\t10668\n", - "\n", - "***** WARNING: File /Users/nathanleroy/.scembed/b.bed has inconsistent naming convention for record:\n", - "chr1\t9776\t10668\n", - "\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 27411.45it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [02:55<00:00, 58.28it/s]\n", - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:28<00:00, 116.16it/s]\n" + "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:01<00:00, 88981.86it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 27570.80it/s]\n", + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [02:56<00:00, 58.18it/s]\n" ] } ], @@ -218,12 +195,12 @@ "# 1. converting dataset to universe, \n", "# 2. creating documents, and \n", "# 3. projecting\n", - "pbmc = model.project(pbmc)" + "embeddings = model.encode(pbmc)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -232,13 +209,14 @@ "(10246, 100)" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# insepct the results\n", + "pbmc.obsm['embedding'] = embeddings\n", "pbmc.obsm['embedding'].shape" ] }, @@ -259,9 +237,17 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], "source": [ "# run leiden clustering on embeddings\n", "sc.pp.neighbors(pbmc, use_rep='embedding', n_neighbors=10)\n", @@ -270,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -287,12 +273,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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UWqJbtqlDdh0OR0gGsZ9//rnBbf70pz8p5UeNGhXW6zz11FPKNvHx8XJhYaEsy7I8c+ZM5fEOHTrIVqs17LaHKy8vTx44cGDIUL8z/11zzTWy2Wxu1NCflStXyp06daq3XkAePny4fOLEiXrrCf7sXC6XPGfOnHrrHDhwYJ1DN4IVFRXJEyZMaLCNXbp0kbds2VJvXWcOpdq2bVu9Wd3i4uLk7du3K9svXLhQjo2NrbN8RkZGvRljZblxw9XWrFkjd+3atcF9B+Qnn3yyxvZWq1W++uqrw9oeGp5H2JDGDtmVZVlOT09Xys+ZM6fG88H1hTOU7fjx4yHbHDlyJKy2OhwO+fPPP1fuP/PMM3W+xrPPPquU++KLL2SHw9HgfjscjpDs4nfddVeD+3Ku+Hy+kPlC1cey+jT2+PzTTz+FvCfr169XnjvzeFFZWSlPnz691u/k66+/HlLvwYMH5QEDBjT4XR40aFCdn321xgytPH78uHzJJZc0+Lrp6enymjVrGnx/ZDkwlC0470B9/xYtWqRsF3yMb+jf7Nmza33tXbt2yf37929w+27dusn79u1rcF+ysrLkbt261VvXvffeK7vd7mYbshs8zL6241+1sWPHhrTj0UcfrbPspZdeqpRbtmxZjefra3vwcw39O3Oo6Jm/2+vWrZMzMjLq3F6tVsvvvPNOo96v+jTnkF2fzyc//fTTIXMHa/un0Wjk//u//5P9fn9Y7Qrn+B/OcNxwh+yuXr06ZOrBrFmzap1e0ZzHhuDyshw4Ruj1+jrrTEhIaPC8pzH+8Y9/KHX37NmzznIjRoxQyoWzasX69etDvrvNMaUt+Jz45ZdfbvT2zTFkV5ZDczHcd999Ta5HqF2rSgubnZ2trJ0kSRIDBgxocJvqIbgQWD8pHE8//TRff/01+/fvp7y8nPvvv5/bb789JLPWu+++G9Lj0xzKysq49NJLQ9Zd6tSpE8OGDUOv17N//342bdrEd999x69+9auw6/3iiy+49dZblR4bo9HI8OHDad++PSqVioMHD7Jhwwa8Xi8bN25kxIgRbNmyhZSUlAbrvvvuu5k3bx4qlYphw4bRvXt3/H4/GzduJDs7GwhkQp01axY//vhjnfUUFRUxatSokN6q4H3Pyspi06ZNABw6dIjx48ezePHikKt8dTlx4gRPPPEEJSUlJCYmMm7cOOLj48nNzWXlypV4PB4qKiqYNGkShw4dYufOnUybNg2Px0NmZiajRo0iOjqagwcPsnbtWvx+P/n5+Vx77bXs2LHjrLMrf/bZZ8yaNSuk57dr164MGDCAmJgYzGYz+/btY9++ffj9fpxOZ406Zs6cyf/+9z/lfufOnRkwYADx8fF4PB5KSkrYs2dPi11dlGU5ZFHpmJiYs65zwYIFyu2MjAw6dOgQ9rZXX301sbGxVFZWMn/+fOVq+5mqR0jExcUxderUsHrjNm3aFJJdPNxhvufCjh07lCvrXbp0CetvurHOHC1Q12cryzIzZ87khx9+QJIkBg8eTM+ePZFlmb1794a8//v372fcuHGUlJQoj/Xp04f+/fsjSRI7duxgz549AGzbto2RI0eyZs0aunbtelb7sn//fiZMmKBkcZYkiYEDB9KzZ0+MRiP5+fmsWbMGi8XCyZMnufzyy1m0aBHjx4+vs84HHniAf/7zn8p9tVrNkCFD6NKlCwaDgZKSEnbu3Kn8bQb/fU+fPp3evXuzefNmZdjckCFDGDp0aI3XGT58eI3H1qxZw9SpU5Vh1FqtVnltrVbLsWPHWLduHU6nk+zsbEaOHMmGDRvo0aNHrfuSm5sb8v4A9OrVi4EDByJJEtu3b2fv3r385z//ISIios73pLGCe9fr6oF1Op3Kb0RDZd1uNxs2bABAp9OF9PKHY/bs2ZSVlbF8+XJlvfMJEybQvXv3GmVr+6yq7d27l9///vdYrVaSk5MZM2YMCQkJ5Ofns2LFChwOBz6fj3vvvZc+ffrU+hm3FJ/Px4033shXX32lPJaRkaGMKLJarWzatIkjR47g9Xr585//TElJCe+8804Ltrqm7777jptuukn5u3vkkUd49dVXa/wenItjQ7UPPviAX//61wB069aNwYMHYzQaOXDgAOvXr0eWZcrKyrj66qvZv39/s/x+Vh8/gXp7PYPPm4PPp+sSfF7u8/k4ePAgffr0aWIrA6Mxgs+J62vruTZmzBjltughPQdaIgpuag/pggULlO1SUlLC2mbfvn0hV5mKi4vD2m79+vUhvbHBaxvdeeedYbe5MYJ7YA0Ggzx//vwaZbZv3y537txZBkKuptV1RXHv3r2y0WiUIZBt+LHHHqs1s+iRI0fk0aNHK/VNnjy51vqCezyqX3/IkCE11oL0+/3yG2+8EfLer169us59D17TNTIyskbmZFkOrDHVsWNHpVybNm3qzJIa3CNS3c5nnnmmRma4PXv2yKmpqUrZxx9/XG7Tpo2s1Wrlt956S/b5fCHlV69eHZJUa968eXXuUzi9A9u3bw+5ujxgwAB548aNtZYtKCiQX3nlFfmll14KeXznzp3K9iaTSf7xxx/rbNORI0fkF198sVFrcNamsT2k27dvDylfWxbr4Ofru0Lu9/vl+fPnh3z///73v4fd1uqkD3fffbfyWG1Xs9euXas8f88998iyLIfVQ/rCCy8oz6tUqgaTCJ1Lb775ptKW6667LqxtGnt8Dk46oVarQ5JOBB8vNBqNDMh9+vSpdf276ivpLpdL7tevn7JdcnKyvHTp0hrllyxZEtL7O3DgQNntdtfaxnB6SK1Wq9yjR4+QY2Bta85VVVXJv/71r5VyaWlpcmVlZa11Bif5AOQZM2bIx48fr7Xsnj175AceeEBesmRJjeeakpyloKBAydbMqV6fkydP1ihXWFgY0mvdp08f2ev11lpn8AiWmJgY+fvvv69R5scff1R6g4PXLDybHlKPxyObTCblO1ZbVs4VK1Yor5WUlKT8/dX2G7FmzRql7OjRo2t9zfOxDqler5fVarX82muv1eiRO378uNy7d2+l7Pjx48OqvzGvfzY9pMGrHaSmpspfffVVrT2gn3/+eUjv44IFCxps1/nqIX3vvfdCkvv85S9/qbWec3FsCD4u6PV6OSkpKWR0RLXVq1eHnH/Wl702XBaLJWTExiuvvFJruaKiopB2NrTmd7Xqvz84+8zrH3/8sVKXJElyUVFRo+torh5Su90e8n0pLy9vcl1CTa0qIP33v/+tbNe3b9+wtikrKwv5g6otu2ddgk+0qv+lp6fXeYA5G1lZWSGvU1tAVu3YsWM1Fv+t6wAePCzpb3/7W71tsFqtIcta1BYYBZ9gQmD4bH0n3MHZVe+9995aywSfSADyDz/8UGd9OTk5IT9udR2cg08UAPkPf/hDnXXOnz+/xudc38nFiy++GPLDVJdwTmhGjRqllBk8eHCTgpd//vOfSh3nawmic51ld9q0afJvfvObkH/33HOPfO2118qZmZkhP1B/+tOfGtXW6oA0eGhRbcNqg4eiV08PCCcgvfPOO5XnmyNL7tmoTq/fmO/G2WTZHT58eMjzZx4vUlNTGxy+/9///lcpr9VqQ4bSn2nz5s1KoAt1XyAKJ3B4/vnnlTLTp0+vcTGqvjr/+te/1ni+vLxcjoqKavD4F46mBKS/+tWvlG0eeOCBest6vd6Q34rPPvusRpngodmSJMkrVqyos741a9bIkiQ16hjRkCuuuEKpa+HChTWef+aZZ0JOsKtvf/fddzXKBn/WTz/9dK2vdz4CUkB+++236yy7Z88e5X2UJKnWCwqN1RwBaU5OjnJiHh8fX2twFiz4N75Hjx61Bq7nOyANHgauVqvrncbS3McGWa4ZkO7atavO+v71r38pZbt3717va4fj0UcfVeozmUx1dtSceV4abgDWp08fZZtwMtnXxWq1ym3btlXqmjFjRpPqaa6AVJblkE6RcDMFC+FpVQHpyy+/rGw3bNiwsLax2+0hf1Bbt24N+/WsVmvICQUgf/vtt2Fv3xiPPfaY8hojR45ssPxzzz0X0q7aDuDBPWcDBgyod/5GtU8//VTZ5re//W2N5888wfzqq6/qre/HH39Uyg4cOLDWMsHL81x99dUNtvGll15SyqelpdW6X8E/CMnJyfXOY7Db7SFrWvbv37/e1z9y5IhSNiEhoc5yDZ3QbNy4MeQEL5z5W7UJnif9xhtvNKmOxgo3IK1eviS4bF1BfHCZcP4NHTpU3rNnT6PbGpwWv3q0QUxMTMjjDodDmUPcpUuXkMcb2u/g3qaGvkvn2sSJE5W2vPXWW2FtE+7xec+ePTXWIT2z9+PM48W///3vBl9/2LBhSvmGAilZlkN6JM4MiKs1FDi43W6lN1Gv14d1FT4/P18JFvr06VPj+TPXvjubuVSNDUiLi4uVY1pqampYS0FUr2MIyFOnTq3x/IwZM5Tn61v3t9ott9wS1jEiXMHvZ23LLowZM0Z5r0tKSpTP5qGHHqpRdvz48Updy5cvr/X1zkdAWtv35kxDhw5Vyp/tyJYzX7+pAelDDz3U6N+cSZMmKdts27at3nady4DU7/eHrG+p1+vlr7/+us7XORfHBlkO/b2r7VwrmNlsVi68SZJU79InDVm2bFlIL199F3Q3b95c529nfYK/s6+++mqT23r77bcr9URERNS5tmpDmjMgrT7OAPIHH3xwVnUJoVpVlt3guTXVaaAbcuaSLI3JjPj1119jsVhCHqteRqa5BWeVmzlzZoPlwykTPGfz5ptvDivLZ3BW4nXr1tVb1mAwMHXq1HrLBM8nqGv+YvA8n3Dmxt5xxx3K51BQUKDMVa3L1KlT612ax2g0hqQKv/766+utr2PHjsr8qLKyshrfkXAFZ4ucMGECPXv2bFI9wXMqPvzww5D5i+fLH//4R+6//37l37333svVV19NZmYmb775plIuOTk55P7Z2Lx5MyNGjODFF19U5pY31m233QZAVVUV3333nfL4d999p8x5rS4TruDvQ3MusdIURUVFyu2EhIRGb3/o0KGQz/X+++/nV7/6FcOHD6dv374hWWFnzJjBDTfcUG99N954Y73PWywWtm7dqtwP53hw1113Kbe3bNkSdrblYFu3bqW4uBgI/C0mJyc3uE16eroyZ3Dv3r1UVVWFPB/89z1nzpzzujzYsmXLlEza1157bVhLQQwbNkzJi1DbsT/4OD1r1qwG6wvOttkc6ptH6nA42Lx5s1IuMTGR3r1711rW5XKxceNGIHB+cC4y5Yerob8XCO839HwLPrcId458Y84tzhWv18vs2bN5/fXXAYiOjmbx4sVMnz69zm3OxbHhTA19D6KioujUqRMAsiwry3g1Vm5uLjfddBM+nw+A0aNH8+STT9ZZ/sx8FU05725sNvJq//rXv0Iyor/++uvnbEmXxkhMTFRuFxYWtmBLLj6tKqlR8I9quMtWuFyukPtGozGs7YqLi3n44YdrPH7fffcxbtw4oqOjw6onHLIss3v3buX+sGHDGtymY8eOJCYm1psKvDppAwR+lMM5iMlBSVtqWwolWLdu3dBqtfWWCT4Jrm19wvz8fOVgD4SVXCIpKYmuXbsqySS2b99eazKJatUnJvWJi4tTbgenGK+vfHXgZzabiYqKanCbM1WfFAFhJT6oy5QpU4iMjMRmsynvxZ133smVV17JgAEDUKvVTa47XOGs5zlo0CA+/vjjsJIPrVy5MuQEFALfzaqqKvbv38+CBQv497//jdVq5emnn2b//v0hy5uE67bbbuPZZ59FlmU+/PBDJWCq3h9JkhodkAZ/F6xWa6Pb1JyCg7OmJJk5efJkgxcQJEniwQcf5K9//Wu9F706dOhAfHx8vXXt3r1bOVkymUz07du3wTb2799f+f77fD527drV6CQ1wcfKEydOhCzvVZ/qixayLHPixImQZCPBCXbO5u+7KYL3Z/fu3WHvT7WKigpsNpsSoObn54ckmAonuc7w4cORJCmsRGDhGDRoEFFRUVgsFnbs2EFVVZXyfm/YsEH5ra9+r8ePH8+ePXvYvXs35eXlyndv06ZNykny0KFDwz4nOBfCSfbS0G/o+VZWVqYs46LT6UKWJKpPVlaWcruhc4tzwW63c8011yjBdHJyMosWLWowUc+5ODac6Xx8D8rKypg8ebJyzpiZmcmnn35a7/nBmRey3G53WBe3gs+7m/L39f333/PQQw8p92+//XbuvvvuRtdzLgT/jjbl4qdQt1YVkAb3NoR71eXMcuH2WNx///2UlZUBgQClrKyMwsJCTpw4we9+9zv+/e9/h9nqhlVVVYUE2OFmEcvMzKw3ID158qRye9GiRY1uV0NrbYaT6S04YK1tnbvgkxyj0Rj2Oo/t27dXAtKG1ucKp53BmXIbW76x66xWC+696tixY5PqgMAP1Xvvvadk6s3Ly+PZZ5/l2WefxWQyMWzYMMaNG8fUqVPp379/k1+nMdRqNdHR0WRmZjJkyBCuv/56ZS3BppIkidjYWEaMGMGIESO46qqruOKKK/D5fHzyySdMnDix0b0yHTp0YPTo0axdu5affvpJ+Ux++uknIJBVr3379o2qMzjoCs4s3NKaKzAwGAzExsbSvXt3Ro8ezR133BHW9zecv+3g40GbNm3C+r6oVCratGkT9vGgNsHHyt27d4dcIAxX8PHSbDaH/Paczd93UwTvz7p165rUI1VRUaEEpMGfS0REREgvQV2io6OJiYlptr8BjUbDqFGjWLx4MX6/X8kgDKEjjKovZF1yySX84x//QJZlVq9erfSC1Va2pTT2N7SpvzXNKTjDstvtbtKIl3DW8W5ur7/+unIO0qZNG5YvX06XLl0a3K65jw21OdffA6vVypQpU5SMuQkJCSxZsoTMzMx6tzvzfNnhcIQVkAYf+xo7Smj16tXceOONyoXJqVOn8u677zaqjnOpuX5HhZpa1ZDd4CtEwSfz9TmzS72hK/QQWAj4iy++AAIn1nPnzg1J2/+f//ynWYecnNmLEm5PRkN/6A0NE2lI9QGhLmcTXFQL3vfGLKMTXLahIbONbWdz7Fc4mnNo50033cTmzZuZPn16yA+X1Wpl+fLlPPPMMwwYMIDBgwezdu3as3qt2uTk5CAH5qQjyzJer5fy8nJ2797N+++/z+TJk5v9fb3ssstChoC+8sorTaqnOoj1er188sknfPLJJ8qJS1OGHQYHsMePH2/RXtLgv5OmDJ0aN25cyOcqyzIOh4OCggJWrlzJCy+8EHawFc6V8vNxPKjN2R4rIfSC25ltON9Dt5t7f4I/l8b0tDf30mjBPc3BQ3Grg8wOHTrQrl07IPDdrT7m1Fb2zPpawvn6rWlOzf3dOl+CfxfLy8tDAuv6nI/9PZffA6fTydVXX60MaY+KimLRokVhTRE6c5pHU867wznnrrZ161amTp2q/FZdcsklfP7552e9tF5zCv4dbe7j2y9dqwpIu3XrptwuLi6udT3GMx0/fly5HR8f3+BV+srKSu677z7l/oMPPqj08FxzzTVA4ArJnDlzagwHbqozT1bCnQPY0HCB4D+Wr7/+usaJZTj/zrXgfW/M8Ifgsk0ZLnshaO6hnf379+frr7+muLiY7777jscff5wRI0aE/BBv27aN8ePHKxdcWrtJkyYpt/ft29ekOR033HCDEix9+OGHzJs3DwgEUOHM8TrT6NGjldt+vz9kTuT5lpqaqtxuSs/h+dZSx4PgY+UDDzzQpGNlcG/bmW043xclgvfnb3/7W5P2J/jCSvDn0pg56s09pK22eaQOh0MZHh0cYMbHxytDvqvLulwuZQhmS88fba2Cv1vR0dFN+m4Fzw1sqsbmDXjooYeUHnWbzcaUKVNYs2ZNg9s197HhfPJ4PFx33XXK999oNPL9998zZMiQsLZPTk4mNjZWuR/OtC+n0xkyoqK+6VTB9uzZw6RJk5SLeUOHDuV///tfWD2y51PwvgX/vgpnr9UFpNXJbGRZZufOnQ1us337duV2XYt9B3vkkUeUK2edOnXihRdeUJ7797//rQytOHDgAC+++GJjml+nmJiYkKDhxIkTYW3XULmUlBTl9oU6+Tr4AoHD4Qj7pDk4uUM4w8cuRMGfT05OTrPVGxsby9VXX83LL7/Mzz//TGlpKXPnzqVt27ZAoOf7vvvua3KygQtJWlpayP3gC1Dhio6OVi427dy5k127dgEwbdq0JgU3w4cPD+lF+uSTTxpdR3MJnq8b7nGlJQUfD06cOBHWRTG/3x8yJ60px4PmPlZGR0eH9Ag35993OJp7f4I/F7vdrkxnqY/FYmmW3qVg1fNIAXbt2kV5eTk///yzMuXlzB7P6kBg3759lJSUsHHjRuVC9rBhwy64k93WIPi7ZTabmy2JXkPTe87U2O+WTqfjyy+/rBGUNjRiqDWcR9XG5/Nxyy23KHNmtVotX375JePGjWtUPcHnzTt27GiwfPA5t1qtpmvXrg1uk52dzeWXX055eTkQmFO7aNGiC7KzIT8/X7nd2Ok8Qv1aVUBqMBhCkikED72py+rVq5XbwVnearN06VLmzp2r3H/nnXdCTizT09N5+eWXlfsvvfQSe/bsCafp9ZIkKSR5R3AyjLocO3Ys5EpNbYKTI61fv77pDTyHMjIyQrLW/fzzzw1uU1paqiRVABpMSnChCv4ur1ix4py9TnR0NLfffjsrVqxQst+VlpaGJGtorc48GWpqFuzasoaGk0m0NgaDgdtvv125/+mnn7bYiUzwcaWhbNQXgr59+ypJNiwWS1jH1127dik9cWq1mn79+jX6dYOPlT///HOzjA4JrvNs/74bO6SvuY/9GRkZIUFpcEK2umzcuLHZR9mo1WplBIIsy6xZs6beOaHVAWr1PNLmnj/aGofcnq20tLSQPBfh/GaHIzhRZDgXPJpy7lUdlF511VVAICidPHlyvUHpuTg2nGt+v5/bb7+dL7/8Egj83XzyySdMmTKl0XUFX+Rp7Dn3yJEjG8wunpOTw2WXXaYMB+7atStLly5t1FDf88Vut4f0Ejflt0aoW6sKSCHQa1GtoWEfeXl5LF++vNZtz2Sz2UKyeN111121BrBz5sxh7NixQGA4xF133dXkJSeCBf84hpMtdP78+Q2WqT7oQmDIbrjj/8+34ANeOEN5PvjgA+U9T09PDxnK3ZpMnjxZub18+XIl4cC50qlTp5AMwhfq96Exgq/GQuDEuSkmTpwYMvwmLS2Nyy+/vMntevTRR5V5L1artckZAquTKzXV0KFDldvVPb8XsqioKAYPHqzcD+d48P777yu3hw4d2qR5PaNGjVKGpp04cYLvv/++0XWcKfjv+9133z2rKR7BPXnhJDSZNGmS8v37+eefm+WzDz5Of/TRRw2WDyfzdlOcOWy3+iS5c+fONZK0jB07VrlIFVz2zHqaqrGfy8Ui+NyiuRI8Bvc2NTT67eTJk03O46HT6fjqq69CgtL6ekrPxbHhXLv33nuVc0RJkvjvf//b4HJ2dQk+b162bFmDI22Cj9n1nXNDoLdxwoQJSp3t2rVj2bJlIb3SF5K9e/cq555dunQJGc4snL1WF5DOnj1bOeHIzs7mvffeq7Psk08+qSTmGTFiRL09ab///e+VYaDp6em8+uqrtZaTJIl3331X+SHavHkz//jHP5qyKyGC19tbt25dvXP88vLy6mxfsKFDhyo/ug6Hg9tuuy3s5XLcbvd5y4R3zz33KLe/+eYblixZUmfZ3Nxc/vSnP4Vs21qvUg8dOpRRo0YBgSv4s2bNatJcs3CHOft8vpBEDuGsp3YhM5vNIcFIjx49agzhDZdarWbt2rVs2bKFLVu2sGbNmrNaLqdjx478+c9/Vu5///333HHHHWEn87DZbPz2t78N+dtoigEDBihDWA8fPtwqLkIE7/Obb75Zb1bLbdu28fbbbyv377333ia9pl6vD1lm4L777gsZmtWQ2t7XOXPmKHMvc3NzQ+pvrODkIuG0KyMjQ1mruvrYEu5SEX6/v9bRN8HrvX7++ef1zr9bv379ORuqHhwYL1q0SEnWUluCori4OKUXY8mSJc2+/mhjP5eLxaOPPqocH7/55ptGzQmta7RIcE/kp59+Wu8FnIcffvisLgBUB6VXXnklcDoLbW1B6bk4NpxLjzzySEhW2jfffLPJo30AhgwZosw59fl8/O53v6uz7DvvvKOMXouKiqr3dYuLi5kwYYIynSE9PZ3ly5eHvcpESwj+fpzNBWuhDnIL+OMf/ygDMiCPGzeu0ds//fTTyvZGo1FesGBByPNut1t+8sknlTKAvGrVqjrrW79+vaxSqZSy33zzTYNt+Mtf/qKUj4yMlI8dO9bo/TjTLbfcErJfn3zySY0yO3fulLt27SoDsl6vV8qvXLmy1jr37Nkjm0wmpdywYcPkjRs31tmG7Oxs+fnnn5fT0tLk77//vsbzK1eubPRnF/w51GXy5MlKGZPJJH/++ec1ymzdulXu3LmzUq5NmzZyRUVFrfXNnj1bKTd37twG2zhu3LgG38tg7dq1U8rn5OQ0ucy2bdtCPscBAwbU+fkUFBTIr7zyivzyyy+HPH777bfLY8aMkefNm1fn+1FaWirfcccdyutER0fLdru9wf2sS05OTsjnWtf+NUZwfQ19BgcPHpRHjRoVss27774bVlsdDkeT2+hwOMLeb7/fL19//fUh5Xv37i1/8803ssvlqnWb/Px8+bXXXpOTk5NlQG7Xrl2T21pt1qxZyuvXdkw509ken4M15Xjhcrnkfv36KdulpqbKK1asqFFu6dKlclJSklJu4MCBstvtrrXOcI4HFotF7tWrV8jrfv7557LP56u1fElJifz222/LAwYMkB999NFay7z55pshn/+MGTPkvLy8Wsvu3btXfuCBB+QlS5bUeG7Dhg1KHWlpaXJlZWWtdQTLz8+X09LSlO26detWa93V8vLy5L/97W9y586d5X/+85+1lhk/frxSX1xcnLxw4cIaZZYsWSLHx8fLgKzVapv1GCHLsuz1euXo6OiQ9xWQP/7441rLP/zwwzXKhvNdDOf4/emnnypl+vfvX+ffdbDG/tYE/z3+8Y9/bLB8Y17/bOoLbpckSfKjjz4ql5SU1FrW4/HIS5YskWfOnCmnpqbWWsZms8lRUVFKnTNnzqzxG1VWVibfdtttNc5/6tqPht47l8slX3nllSHnHmvWrKlR7lwcG8I5LwoWzvfmmWeeCan3zHOFplq2bFlIvU8++WSNY+2CBQtko9GolHnuuefqrK+8vDzkGJ+YmCjv27evWdpam7lz5yqvdTa/qVOmTFHq+fHHH5uvgYIsy7J8znMpT5kyJWQdJwi9QrZ169Za10b88ccfSU9Pr7XOp59+mvXr17NixQocDgc33ngjL774IgMHDsTpdLJmzZqQnqDnnnuuzoncLpeLO++8U+mGv+GGGxocZgDw2GOPsWDBAnbu3InNZuOee+5h8eLFDW5Xn7///e9s3LiRo0eP4nA4uOWWW3jmmWcYPnw4Op2OAwcOsGHDBmRZ5vrrr6ekpEQZr1/X3LnevXvz6aefcuONN2K329m0aRPDhw+nU6dODBw4kPj4eJxOJ8XFxezevbvFrvLOnTuXUaNGceTIEaxWKzNmzKBLly4MGzYMnU5HVlYWmzZtUuZvREZG8umnn7b6IRMDBw7k/fff5/bbb8fr9bJjxw6GDx9Ot27dGDBgADExMVRVVZGVlaUMF3nwwQdD6pBlmbVr17J27VrUajXdu3enR48exMXF4XA4yM/PZ/369SG946+++mqLLgjfkL///e/K/JdqsixjsVjIyspi+/btIXN5rr322pBRBhcCSZL47LPPeOSRR5RRFHv37mX69OlERkYybNgw0tLSiIyMpLi4mJycHHbv3h2yX82R1OHWW29Vhk9+++233HzzzWdd57mk0+n49NNPGTduHCUlJRQWFnLppZfSr18/5bciOPkUBHr7P/3005DEKI1lMpn43//+x2WXXUZOTg6FhYXMmDGDxMREhg8fTmpqKrIsU15eTlZWFocOHVJ+N+rKT3Dfffexd+9e3nrrLSDQs/jVV18xZMgQunbtisFgoKSkhB07digjdGrr6Rs6dCht2rQhLy+PgoICunfvzsSJE0lMTFRGiAwZMiRkGaT09HS+++47pkyZQmlpKdnZ2UyaNImMjAyGDh1KUlISHo+H0tJS9u7dG1bipffff58RI0ZQVFRERUUFV155Jb1792bgwIFIksSOHTuUHu1HHnmEr776KqzMnI1RPY+0OllLtbqWcBk/fjyvv/56yGPNlfV08uTJGI1GHA4HO3fupEePHlxyySXExsYqn8vEiROZOHFis7xec/vPf/7Dt99+G3b5559/nquvvhqAP/7xjxw7dox58+YhyzKvvfYa//znPxk8eDCdOnUiIiICs9nMsWPH2L17tzLP+8ylRKpFRETw9NNP88QTTwCBaUk//fQT48ePJzo6mry8PNasWYPdbqd3795MmjSJ11577az2X6fT8fXXX3PttdeycOFCpad00aJFIdnSz8Wxobn9+OOPPP/888r9lJQUcnNzuf/++8Pa/vnnn69z3uaECRP4wx/+oCTyfOmll/joo48YM2YMBoOBbdu2sXfvXqX85Zdfzv/93//V+Vr33HNPyPG7d+/eYQ/97tKlS41zoGB33XVXjcz21cmSIDDcu7aY47333guZLnImi8WiTAFMTk4WPaTnwrmOeIOvMjbmX0NXUysrK+UZM2bUW4dWq5X/9Kc/1VvP73//e6V8fHy8XFRUFPa+bd26VVar1cr2H374Ydjb1iU3N1fu379/vft1zTXXyGazWR45cqTy2I4dO+qtd+fOnfKgQYPCfv/bt29fa53nqodUlmW5sLBQvvTSSxtsW+fOneXNmzfXW1dr6SGttnz5crlDhw5hfTZPPfVUyLb3339/2J9rVFSU/M477zS4fw051z2k4f7TaDTyE088UWfPWG1tPV89pMEWLlwoDxgwIOz9iouLk5966im5qqqqyW2t5vV6le9iZGSkbLFY6i3f0j2k1bKzs8N6zwYOHCgfPny43roaczwoKyuTb7jhBlmSpLA+q9jYWPmDDz6ot8433nij1l69M/9JklRnL+b3338v63S6OredPXt2rdsdO3ZMnjBhQtjfvZSUFHnx4sV17svevXvlLl261FvHnDlzZLfb3ahjYGO8/PLLIa/XrVu3OstWVlaGjIBqzmO8LMvyW2+9Ve935cyeuQuph7Sx/2r72/nHP/4hx8XFhbW9JEny1VdfXWfbfD6ffNddd9Vbx/Dhw+UTJ06E9b6E+945nc4aPaVr166tUa45jw3B5cLR0PcmuBewKf8a+vv0+/3yCy+8EDLqobZ/N910U4O/W2fzHWzod6SpdTf0tzh//nylbF293sLZuXBWm22kmJgYFixYwJw5c5g3bx4bNmygoKAArVZLmzZtmDRpEnfeeWe9S73s2LGDV155Rbn/+uuvN2pe3aBBg3jkkUeUOh5++GGuuOKKBtc6rU/btm3ZsmULc+fO5dNPP2Xv3r1UVVWRmppKv379uP3225k+fTqSJIVc9Wmop7Bfv35s3bqVn376iW+//Zb169dz8uRJKisr0ev1JCUl0a1bN4YNG8akSZMYMWLEeZ+bmZKSwvLly1m8eDELFixg3bp1FBYW4vF4SE5OZsCAAUybNo2ZM2eeVU/IhejSSy8lOzubzz77jB9++IGtW7dSXFyMy+UiJiaGzp07M2LECKZPn86YMWNCtv3nP//Jfffdx7Jly9i4cSP79u3j+PHjWCwWNBoNCQkJ9OrVi4kTJ3Lbbbe16rmjkZGRxMXF0atXL8aOHcusWbNqJDK5EE2ZMkXJ5rhkyRJWr15Nfn4+paWluN1uYmNjadOmDUOGDGHChAlMnTq1weyE4VKr1fz2t7/lsccew2az8emnnzJnzpxmqftc6tq1K1u3buXLL7/kq6++YvPmzRQXFwOBK9TDhg3j+uuv57rrrmvWY1V8fDyff/45e/fu5dNPP2XVqlXk5ORQVlaGSqUiNjaWzp07M3DgQC677DIuv/zyBpcPefDBB5k5cyYffPABS5YsISsrS5n7nZiYSI8ePRg3bhw33ngjXbp0qbWOq666iq1bt/Lmm2+ybt06jh8/jtVqbTDrZ3WikA0bNvDFF1+wZs0a8vLyqKioUI4PXbp0YfDgwUycOJFLLrmk3oXoe/Xqxe7du3nnnXdYsGABBw4cwG63k5aWxpAhQ7jrrrvOee9BXcu71CYmJoYBAwawbds2IDAnMDjD+dm699576dOnD2+//TabNm0iPz8fu93eKrKxNoff/va33H777Xz00UcsXbqUXbt2UVJSgtPpJCoqiszMTHr16sUll1zClClT6p0jqFKpePfdd5k+fTrvvPMOmzZtoqysjISEBHr06MHMmTO57bbbmv33X6/X89VXX3HdddcpPaWTJ0+u0VN6Lo4NrYUkSfzhD3/guuuu47333uOnn34iLy8Pj8dDWloaI0aMYPbs2Vx22WUt3dRzojpfjVqtDrvXWWgcSf6lHDUvMna7nZiYGLxeL5GRkZjN5iYveSEIwsXNYrHQoUMHysrK6NevX1hrOF9MZs2apWSG/fjjj7nllltauEWCIAhCa5CVlUXv3r2RZZlbbrklrJUwhMYTEUwr9fXXXysZOwcOHCiCUUEQ6hQVFaXMzdq1a9dZz3dvbSwWi3L7QlxsXRAEQbgwvfzyy8iyjEaj4bnnnmvp5ly0RBTTClVUVPCHP/xBuS+u9guC0JDf/va3ylp/wckvfgmqlyIA6NChQwu2RBAEQWgtjhw5oixhde+999K5c+cWbtHFSwSkF5gbb7yRL7/8EqfTWevz69evZ9SoUUrmwoyMDG699dbz2URBEFoho9GoZBvdsGEDX3/9dQu36PxYvXo1WVlZQGCufbdu3Vq4RYIgCEJr8Ic//AGPx0NSUhIvvPBCSzfnoibmkF5g2rdvT25uLiaTiQEDBtChQweMRiMVFRVs376dw4cPK2W1Wi0LFy4U6acFQRCCbNiwgblz55KXl8eyZcuU6Q0PPPAAf//731u4dYIgCIIgBBMB6QWmOiBtSFpaGh9++OFFm9FMEAShqT744APuuOOOkMd69+7N2rVrW/3axYIgCIJwsWm1y75crFauXMk333zD2rVrOXLkCKWlpZSVlaHVaklMTGTAgAFcccUVzJo1C6PR2NLNFQRBuGCZTCa6dOnC9OnTeeihh0RCI0EQBEG4AIkeUkEQBEEQBEEQBKFFiKRGgiAIgiAIgiAIQosQAakgCIIgCIIgCILQIkRAKgiCIAiCIAiCILQIEZAKgiAIgiAIgiAILUIEpIIgCIIgCIIgCEKLEAGpIAiCIAiCIAiC0CLEOqS/ME6nkz179gCQlJSERiO+AoIgCIIgCBcSr9dLSUkJAH369MFgMLRwiwTh3BHRyC/Mnj17GDp0aEs3QxAEQRAEQQjD5s2bGTJkSIu9vtPppLKyErvdjs/na7F2CC1HrVYTERFBbGzsObk4IgJSQRAEQRAEQRBCyLJMQUEBVVVVLd0UoYV5vV5cLhcVFRXExMSQlpaGJEnNVr8ISH9hkpKSlNubN28mLS2tBVsjCIIgCIIgnKmgoEAZ0RZ87nY+lZWV1QhGxVSvXyav16vcrqqqQqfTkZiY2Gz1i2/VL0zwgSQtLY3MzMwWbI0gCIIgCIJQn5YIAt1utzKHFSA5OZnY2FjUavV5b4vQ8nw+H5WVlRQXFwNQUlJCdHQ0Op2uWeoXWXYFQRAEQRAEQVBYrVbldkJCAgkJCSIY/QVTq9XK96Ba8HfkbImAVBAEQRAEQRAEhc1mU25HR0e3YEuEC0nwdyH4O3K2REAqCIIgCIIgCILC7XYDIEkSer2+hVsjXCj0er2SzKj6O9IcREAqCIIgCIIgCILC7/cDgaGazZlNVWjdJElShm5Xf0eagwhIBUEQBEEQBEEQhBYhAlJBEARBEARBEAShRYiAVBAEQRAEQRAEQWgRIiAVBEEQBEEQBEEQWoQISAVBEARBEARBEIQWIQJSQRAEQRAEQRAEoUWIgFQQBEEQBEEQBOECkJuby6OPPkr37t2JjIwkPj6eIUOG8Morr2C321u6eeeEpqUbIAiCIAiCIAiC8Ev3/fffM3PmTMxms/KY3W5n69atbN26lffee4+FCxfSuXPnFmxl8xM9pIIgCIIgCIIgCC1ox44d3HjjjZjNZkwmE3/605/4+eefWb58OXPmzAHg4MGDXHnllVgslhZubfMSPaSCIAiCIAiCIFzwLE4PhVVObG4fkTo1qTEGogzalm5Ws3jwwQdxOBxoNBp++uknRowYoTx36aWX0qVLF5544gkOHjzIa6+9xrPPPttyjW1mIiAVBEEQBEEQBOGCJMsyG46W8dGGXH7KKsLnl5Xn1CqJSb1SmDm8HSM6JiBJUgu2tOk2b97M2rVrAbjzzjtDgtFqjz76KHPnzmX//v38/e9/56mnnkKrvTiCcTFkVxAEQRAEQRCEC87e/ComvbGGW97dxKK9hSHBKIDPL/PjnkJueXcTk95Yw978qhZq6dn59ttvldt33HFHrWVUKhWzZs0CoLKykpUrV56Ppp0XIiAVBEEQBEEQBOGCsvZQCTPe3sDBImtY5Q8WWZnx9gbWHio5xy1rfuvWrQMgMjKSQYMG1Vlu3Lhxyu3169ef83adLyIgFQRBEARBEAThgrE3v4p7PtqG3e1r1HZ2t497PtrW6npK9+/fD0Dnzp3RaOqeUdm9e/ca21wMREAqCIIgCIIgCMIFQZZlHvl8Z6OD0Wp2t49HP9+FLMsNF74AOJ1OSktLAcjMzKy3bFxcHJGRkQDk5eWd87adLyIgFQRBEARBEAThgrDhaFnYw3Trkl1kYePR8mZq0bkVvISLyWRqsHx1QGq1nt17dCERAakgCIIgCIIgCBeE+RtzL6h6zjWn06nc1ul0DZbX6/UAOByOc9am800EpIIgCIIgCIIgtDiL08OSfUXNUtfifYVYnJ5mqetcMhgMym23291geZfLBYDRaDxnbTrfREAqCIIgCIIgCEKLK6xy1ljapal8fpkis7Phgi0sKipKuR3OMFybzQaEN7y3tRABqSAIgiAIgiAILc7WxERGdbG6mre+c8FgMJCQkADAiRMn6i1bUVGhBKRt2rQ55207X0RAKgiCIAiC0ErlmfO46purGLdgHLmVrWPOnCDUJVKnbtb6TPrmre9c6dmzJwCHDx/G6/XWWe7AgQPK7R49epzzdp0vIiAVBEEQBEFopdbkryHXnEu5s5yrvruqpZsjCGclNcaAWiU1S10alURKtKHhgheA0aNHA4HhuNu2bauz3OrVq5Xbo0aNOuftOl9EQCoIgiAIgtAK+WU/3x/5PuSx3/z0mxZqjSCcvSiDlkm9Upqlrkm9UokyaJulrnNt2rRpyu25c+fWWsbv9/Phhx8CEBsby/jx489H084LEZAKgiAIgiC0Qk6vk6yyrJDHNhZubKHWCELzmDm83QVVz/kwdOhQxowZA8D777/Phg0bapR57bXX2L9/PwAPPvggWm3rCLbDIQJSQRAEQRCEVihCG8FzI59jUrtJymNu2Y3Pf+EnchGEuozomEDXlLPLINstJYrhHeObqUXnx9///neMRiNer5eJEyfyl7/8hY0bN7Jy5UruuecennjiCQC6du3Ko48+2sKtbV6alm6AIAiCIAiC0DTTu0xnepfpxG2KY8GBBQxNHYpa1ToSuQhCbSRJ4m8z+jPj7Q3Ym5B1N0Kn5rUZ/ZCk5pmLer4MGDCABQsWMHPmTMxmM//3f/9Xo0zXrl1ZuHBhyFIxFwPRQyoIgiAIgtDKPTXsKd6c8CZHq47y+7W/b+nmCMJZ6Z0Rw9u3DSKikVl3I3Rq3r5tEL0zYs5Ry86tqVOnsnv3bh5++GG6du1KREQEsbGxDB48mJdeeokdO3bQuXPnlm5msxMBqSAIgiAIwkXg+6PfU+Io4YejP3DCXP96hoJwoRvTJYnP7xkR9vDdbilRfH7PCMZ0STrHLTu32rVrx9/+9jeys7Ox2WxUVFSwZcsWnnjiCSIiIlq6eeeECEgFQRAEQRAuAkNThyq37/zpzhZsiSA0j94ZMSx5aCyfzhnOlD6pNZaE0agkruyTxqdzhrP4oTGttmf0l07MIRUEQRAEQbgIjMkYg0alwev3UuGqaOnmCEKzkCSJEZ0SGNEpAYvTQ5HZidXlw6RXkxJtaDVLuwh1Ez2kgiAIgiAIF4GUyBQ+v+pzMk2ZeHweFh5d2NJNEoRmFWXQ0jk5iv5tYumcHCWC0YuECEgFQRAEQRAuEm2j23LCegKv7GX58eUt3RxBEIQGiYBUEARBEAThIqFX6/l1v18TpY3iUPkh8i35Ld0kQRCEeomAVBAEQRAE4SJidVuxeCwcsxxj+v+mt3RzBEEQ6iUCUkEQBEEQhIuIJJ3OROryuVqwJYIgCA0TAakgCIIgCMJF5KZuN6FGDUDnmM68se0Npn83nQUHFrRwywRBEGoSy74Iwi+ILMvsrNpIniOX9hGd6RszuKWbJAiCIDSzNtFtmNF9Bp8d+IyDlQc5WHkQgD9t+hNGrZGrO13dwi0UBEE4TQSkgvAL4JO97DFv46jtECXuAgAKXHn4ZR9evHj8bjx+NyecucSq44jXJ9PN1ItITRQ6lb6FWy8IgiA0xqtbXuXTA58q9yUk5FP/fXvoW5bnLmdnyU7+MvovjMwY2YItFQRBEAGpIFw0Dlj2sKNqI72iBoT0fJ5wHOO4/Sh7LNtqbLOpck2NxyzeKvJcx9hl3kyE2sSM9DvQqw3IssySom/Ic+bQNqIjE5OmhcxTEgRBEC4Mnx34TLktIfHi6Bcxu8z8mPMjN3S9gSfWPgHAE2ue4MfrfiRaF91STRUEQRABqSC0RrIss6VyHU6fneHxl6BT6dlj3orZW8lu8xYlID1s3c/y0h+QCASOGjR48Yb9OnafFaffgV5t4Lgjh1znEQCO2Q9T7iklTpvAUVs2Je4i+scMpcpTAUCqIaOZ91gQBEEIV2pkKrmWXACWXr+UlMgUAGb2nAnAl4e+ZHPhZqrcVWSXZzMkdUiLtVUQBEEEpILQymRb97LfspsiV2BtuVhtAn1jBtM3ekighzR6AF6/hxWlC6nwlCnbTUqeTomriO1VPzf4GrGaBKq85ehVRvabd5Nl3YlX9ijPq1CRbdnLXst2ZPwAFDrzKHYXAtAxoht9ogeJwFQQBOE8yqnK4fbFtyMhcU/fe7i83eVKMBrs5bEv89yG50gwJjAgeUALtFQQBOE0EZAKQisiyzJrSpfgx48aNSpJpQR93aJ60y2qNwB5jhxy7IcCj5t60zWyF06/A4+//vT/7YydGZ80hRUlP1DpLcPpt7PLsjmkjAoVfvzsCwpGAew+u3L7qD2bQlc+E5OvweGz0T6iS7PsvyAIglC3N7a9QbmzHAgs/dItvlut5RKMCfzj0n+EPGZ1W1mQvQAJiSkdp5AamXrO2ysIggAiIBWEC8aOyo3YfTaGxo1Bq9LVWkaSJNpGdOKY/RDD4y+hu6kvGlXNP+NkfTqp+gzsPhtev5ejtoPss+5AhYoYTTxV3vIa20xOvo62ER0B6BnVn+OOo8DpABQgw9CODhFd2FC+Eh8+tOjw4AZgeNw41pYvw+V3AODze/i24GMAxiVcQfeoPjVe0+I1s71yA2XuEnqa+tI9um9j3zZBEIRfvPlZ8/kx50eOm48DYNQYGZ8xni2FWxiYPBC1Ss3RyqP8ft3vaRfdjj+P/nON345/7PiHkgjp9e2vY1AbmNJhCjnmHB4a+BADUwae9/0SBOGXQQSkgtCCSlyFlLgKsXrN7DBvAiBKE02f6MF1JgwanzgFAN2poNUn+yh3l3DccZQOEV2I1yWhV+m5OvVm3j/+Bmb7AWVbCYkEXVJIQJqkTcPut4YMyY1Um4jXJuKX/YxLvIJItQmtSodeZUCSJA7b9lPoykelUvGrzIfwyz4kSYW3LFBHlDoGi69Kqc/hszM/7y20ko6r027CL/tZU7aUElchDr8NgNXlBSQZUknQJTfHWysIgvCLUGwv5qUtLyn3k4xJLL9hOVO/nUquOZdZPWfx+JDH+f7o92SVZZFVlsWdve+s0XuaHBF67HX6nHx9+GsA7l56Nz/f/DM6de0XSwVBEM6GCEgFoYUctR1kacl3NR6v8FTwbu5rZBrbMyXl+pDnCp35/FD0OXqVnmvTZrGlYi2HbFlKD2a2dQ/9oodiVEWwonQhPnwh25s0MeQ7AlfQI9VRTEm5ni9PfoCMzNKS/3Gn8WE0Kg2Lir7C7rehkTTKkOBiVyF7zMvpEz2Y4XGXsNO8iS6RPdGqtIAWgCtTbmBb5c/kO3OV1zSqIily5WPzWQHIsx+j1FPEcceRGvvu8XtqPCYIgiDULU4fR8+EnhwoO0ByRDJ39L4DGVkZulvmDOQSmNxhMiuPr6RddDs6xnasUc9dfe5i8dHFZFdm13jO5XOx9sRaJrSbcG53RhCEXyQRkApCC3EGzbkMdsC6G5DJc+Tg8XtOBXwBxa4CfLIXu8/LEdsBsm17Q7a1e+2sK1+GRtLUCEaBkJ5Rj9+Ny+9ERlYe8+FjQ+kK7Kd6Lf3y6TmiCwsX4Jbd5NoP86t2DzHJML1G/WmGTNobO4cEpA6/jdxTwadBZWRt+U/4ZD9qtPjxEaONI1mTwkFHFqtKF9HV1Iv+McNQSar63j5BEAQB0Kq1fHblZ7j9bvTq0+tGvzfxPbYUbmFa52kAdI3ryrfTvq23rmJHca2PayQNFo8FWZbxy378sh+tWltrWUEQhMYSZ3yCcI75/DIOnx23380+8w7mHZzPwqw9ZGh6MjJuPIlBQ1QjVCY4FSBKSFi9ZuU5v+xHrzbSKaI7nSO64/LVTFDkO7Wkiy8okAymRoP61HUot+zif4WnF06P0ySgV+k5ZNuvPGZUmViQ/z5WrxmVpD71aP1rj/aI7qssMyPVcojxyl5k/MRp45DxU+kp47AjMKy4ylvBlsp1SkImQRAEoWGSJIUEowA9E3oyu9dsYvQxYdfz8riXyTDVzI7ulb08vf5pbll4C1d+cyWjPhvF7pLdZ91uQRAEED2kgnBOHSm0szX/MFXxS1FJKnyyF0mjwV5lYHeOlaS2ejKNHehm6sthWxb9Y4Zx3H6U/dZdyMjYvBbidAkA7KrawubKNUhIyMioUJOkS8XhteHwO/DhRSvp8cgu9Co9zlPJhYJFqk2YfZW1tjVBl8wR2wE8slt5zOY3gx9WlS5iWtqt7DFvp4epDx6/m3J3KUn61Bo9mSrUxGjiqPSWK1l41ajx4VPa1COqPxn6tiwr/R+AMuQYAlfiY7VxTX/TBUEQhCYZnjac/037H+/veR+z28yqvFWcsJ5Qnt9Xtk8ZVTNv3zz0aj339b+PzKjMFmqxIAgXA9FDGqbi4mJ++OEHnnnmGSZPnkxiYiKSJCFJErfffntYdXzwwQfKNg39++CDD87p/gjnltPt4+BJG/llTrzaCmTJH+i9lEBWeamMW4Y38jiryxazs2oTLr+DaWm30j6iM+2MnZR6Fpd8zX7LLgCqcxxVnwz48VHiLsTqtyg9o50ju3NZ0tSQYDRSHYVWCiSiCA5GtVLocCuVpOKgNavW/cl3HifLvJPRCRNI0CezsOgLvi38mLVlS0PKWTxV2H1WRidcToTapPSQnjl82OypoJOpG9NSb0GlHIYkDJKBq1NuJk6bSJmrpKG3WRAEQWhmOrWOX/f/NU8OfVIZ7VKt+venU3Qnfsr9ie+Pfs/sxbN5ceOLIVM8BEFovOaINVor0UMappSUmgtLC8KZ3F4/RZUujhU7KDV7kAA97XDpjuMxFAZG40rg11jxR54gwmnC4bPh9Nn5uXwFA2NGkGXdpdTnk30ctGbRI6ofGfp2pOoyKHTnA6CVdCG9mQDH7IfoFNk95LFEXTKXJl7JJyfewSU7lceNqkiGxQxmY8VqkvVpjE64nO8KPwEgQhVJ96i+GFQGfq5YCUCO/RAjEsYDYPVaTv3/9JDiE45cfiz6AhUqOkZ2w34qiZFW0uKRQ5MVdY7szgnHMUya6KBlZWScspOfK1Zg8VRh81tJ0CZzfcbsWt9rWZaRJAlZljlmP4RWpafMXUSKPp1Ug7haLwiCcLZqmxoCkGfNU24X24tZkL2AEWkjRNIjQTgLv+RYQwSkTdC2bVu6d+/OTz/91OQ6lixZQnp6ep3PZ2aKE+rWxuvzs35/BZU2LzpN4KqyDKgxElt5OaVJXxKtjqWKAtSSmu6mPoxJmEi5q4TvigKB4EHrPjIN7YBAUBitjWVQ7AhWlizi4KkERhISHSO6YfFWUuwuDGmDy+/kh6IFxGkSqPRWIOOnm6k3OrWeeF0SBa48pY4RCeNpH9GZXtEDAm2VZSXRUow2jiFxo5FlmQpPOQXOE0Rpo9hUsYahsWMYE385J5zH6BczVHntDeUrkJHx4SPNkEm5u4QIdRQ+2ctJ13Gl3Ni4SRyzHybXcQQ16pCkSgCFrnzldoWnjBOOY1R6ypU1V+0+G98WfIzH72Zq6k0UufJZU3b6b1EjaZjV5jd1ruUqCIIghOe1S17j3mX3YvMEEt0Z1AacPiduv7tG2c+yPxMBqSA0k+aINVoTEZCG6ZlnnmHIkCEMGTKElJQUjh07RocOHZpcX9euXWnfvn3zNVBocZsOVlFpCwydNerUDO5sYmN2JX45EAB2dV3P8C4JVPiKSNKloDmVPdegjlDqcPmdlLqLuTljDpEaE2op8Ce6omShUkZG5oj9AKPiJlDhLsPD6d7H6rmYFd5Amv9YbQIdIrsCUOYuAgLJi/rFDMXr9/JdwSd0MfWiZ1Q/ch2HlaVZuph6AoFEGWMTJ7LXvJ315cvJcxwjWhPLurJlyPjpGNkNm9eCy+/EJweG5UZrYknWpbPG8xNlnsCwWy06PLiRkKj0lXHccRQIDOXtFzWUXZbNtb6nGknDwqIvlPdmUOxISl1FWLyBNU4LnHnIcmhAq5V17KzaTIo+nbYRNZc2EARB+CWpHkrblMzl/ZP7s/GWjfyc/zMr8lagV+v5MOtDorXRmD3mkLIOrwOf34dapa6jNkE4ex6vH4fbj9cvo1FJGHUqtJqLYwZic8carYkISMP03HPPtXQThAucxxv40Y/UqxnZPRaDTs1l/RJZu68ch8dPpVnipx0VjOmZgsZwev6mSR1DEl2pIBdJkulm6kO0Nha/7OeI7QDx2kRGJVzG1vL1WHyVynxMi88cEozWJkYTC4DL5yRGG4/NayVJn8qqskWoUOPHR7m7lJ5R/YjWxAaWi5F9bCpfjc1nZXDsKIqcJ4nVxqOVtOhUenSSDv+pNiws+lwJRIfFjcUre/H7/awoXRjSjo6RXSlwncDsreSQNYu+0UPItu6hT/QgBsQMxyk7yLEdxC2HDg8Lvm9UR3LUlk2Zu4Qepr54ZS9tIjrxXcHHwOnESQ4cbK/aAMAlCVdg0kSTYWzXqM9SEAShtfHLfl7Z8gr51nyeHv40SRFJnLCcYOaPM5GRmT95Pm2i29S67ZbCLTy9/mkGJA/gz6P/jCSFzh0dmTGSkRkjAbip+00cLj/MA6seCCmzq2QXH+//mFm9Zp2bHRR+sWRZptTs4WiRnYJyV8i4KglIi9fTMSWCxGhtje9ua/JLjjVEQCoIZ8nq9GJ1+BjSJYa9x60YdWq0GhWFFS4cbh8DOkaxO9eC1enH55epsHqotHk4eNJOp9QItBoJqXA48QwnLv0E3aICc0C3Vf6sBFZ9ogdxTfrNyMhsrViPy+/EqIqos02Do0fRNrIT8acy9ObYD1Fyanjv4VPLusj40aAlShvDcftR2kZ0ZFziZJaXfI9LdrGzchM2r5UD1t1oJR23Zv4ajUqDWlLjxcvq0sVKMAqQYztEV1Mv1lUuq9Ge4/ajjEm8nL2W7XQz9aGrqRfJ+lS8sgeXz8nAmOGcdB7H7a19vlK6vi1tDO35JP8dAAbGjGBI3GiqPBXKXNXTiZNO/1StKlsMwLj4SXSP7lv/BykIgtCKZZVlMX//fAB6JPTg1/1+zd7SvZQ5AyNmdpfuZmHOQt7e/Taze87moUEPKdv+78j/yLfmk2/N57HBj5FgTKjzddpEtcHr9yr3qzO/Ayw5tkQEpEKzqrR52Hq4Couj5trqEPjFP1nu4mS5iyijmsGdY4iNFGvktjYiIBWEs+D2+lm1pxyPT6ZDipGT5YGASi3BwYLAfMwoowar049Kgg4pRlLj9Py0sxRZhoMnrYzqEYdGBW7ZySH3ZrzlgTmmefYc5XUOmPew17wdoxTN1KTbiI0wsuDEfwGUns5gSYY0jOoItlb+TBtjBzKN7TCqI3H4bMqwXplAUqAydzE/FX/Lr9o9RJmrSKnDh48D1j0AeGQ3B6176RMzCAC9ZEBChRz0usXuAiory2tNtuSQ7ewxb+Py5KsxqiMpcJ5gaUlgyRedpMctu+gc0VMZinumk67jaCUdEepI7D4be8zbqPJWMCHxKtL1bZU5qjGaONw+Fw7ZHrJ9ibuI4FRPDp+dbws+xuazEqk2EaGO5PLka4hQR9b1UQuCIFzQOsZ0pEd8DwpsBYzJGAPA+LbjmdF1Bn78XNbuMm78/ka8fi+Ljy3moUEPsaVwC1llWVzT6RoOVhxkYPLAeoPRaga1AZ1ah9vnDskDMLnDZB5a+RCr8lYhI3Nz95v53dDfnatdFi5yxZUuNh6swueXGy4MWBw+1uyrYHjXGJJj9Q1vIFwwREDaQu644w6ys7MpLS0lOjqazp07c9lll/HrX/+ajIyai1KH68SJE/U+X1BQ0OS6hVAnypyUW9x4fYEDpUoCrUbC45UxO7yoVeDzQ6RehcUBMREa+raPpszipnraY7xJS0yElkkDE/mm4CP8PiulrkKqvBUhr+XFi4yMXa7ip6Nb6N8+gTRDJpXWMiQJYtRxWL2B5V9M6mhSDemsKl1Mjv0g+8zbmZ42k7Hxk1hS8nVIvdWBY5QmhqO2bGUOabUIVSR2f+Axt9/F2rKl9I4awNbK9TWC4Ooy3SP7csBWc8H0AtcJfij8nBsy7kCn0ivZdauH5Vp9NYNRvWRArVLTP3oYBo2RG9LvYGXpIo47jnDEdoAqTwVp+kwlIK3yVnBV8gwWFn8RcpJ0yJbFkLjRGNRGAMrcJZi9lQCYvZWYvZXk2A4qCZ4EQRBamwhtBJ9P/TzkMb1az9MjnlbuPzL4EeZnzWdGtxlUuaq4Z+k9ePweZveczYKrFoT9Wo+sfgS3L/TCo4TEvH3zKLSfTrb3/ZHvRUAqNEmlzdOoYLSazy+z8WAVY3vFiZ7SVkQEpC1k1apVyu2ysjLKysrYtGkTr732Gm+88Qb33HNPk+pt06b2+SFC83K4fGw5FAig2iQaSIrR0SbBQKe0CJbuKKOw0o1KCvSUZiToSYrR4T81XDfepKVHZiQOt5+YCA1VNg8xkVquzbgFu8/GhvKVNQJSGT9GW0/0jraYY9exstTK5UlX45U9HLJlUeWtYGbmvWhUOvSqwFXBWG08ABpJy+cn55KsS8OoisDht6ORtHhPLcUiITEodiTLS38AIEmXRqTaRN+YQSTpUjlqO4hebWB16WIcfjsV7jK6mHpRVlFc63ujV9d9VVJGxuVzEquN4/r02/HJXio95VR4ykgztGF5yfcY1RFUeSrx48MlOxkZM4E+0QMBMKiNxGsTOO44AkCpu4hLEq6gxFWoLIeTbcuqkbnXK3tDHkvRpytzTqtVesrq+8gFQRBavbGZYxmbOZbssmye/flZjBojHreHpIikRtXj99dcc1RGDglGAcxuM98e/pZpnaedTbOFXxhZltl6uPHBaDWfX2bbYTOX9o1v1XNKf0lEQHqedezYkWuvvZYRI0YowePRo0f56quv+PLLL3E6ndx7771IksTdd9/dwq0VaiPLMmoVGHUqHG4/yTE60uMNbMiuoNQclPH21HF02xGL8piUZ2PSwES6Z5rYk2th1zELkgSje8SRGK0jRqVjTMJE/KWLyXOeHrKr8hmIsgwGIMLWG0vMRpw+B/1jhmHzWUjWpRGpiQppZ7/oIXSO7MHG8lXkOXModRcRoTYBgWC1u6kPBc58+sUMQa/So0aDDy9WbxXxugTK3CWkGdoQrY1FLalJ0qdy3HGUJH0K/WIG0zGyK1nmHew0n86Q29bQkV3mLbW+b91NfUnRpfFh3ptEaqK4Ln02epWeRH1g3S2f7OPGjDsxqI0sLvqa3FNBZ7HrJDBQqccYlJVYhRokiVhtvBKQtjW2J0mfws/ly4Pa1QGDyojVa+GY/RBx2oSQYBRAI4llYgRB+GWYs3QOFa4KNJKG+VPm0y+pX6O2z4jKIKs8q9bn9Go9UdooSp2lAJQ6Av8XGXiFcJWaPXXOGQ2X2eGl1OwhKUb8trcGIiA9j6ZPn87s2bNrXK0ZMmQIN954Iz/88APXXnstHo+Hhx9+mKuvvprU1NRGvUZeXl69zxcUFDB06NB6ywh1c7p9rNxbjtcnM6JbLEadmkiDmm2HKymuqj/jbbXqT7+4KjDcSZZhx9EqumWYMETa+LZ4HjJ+ZUgrgF/lwq0tRudNwqUPBF4bK1YxPvFKpqbeVOM11pQuYb91N22Nnehs6kmeMwc/fuw+O10ie9InehBJ+tSQIaqXJ1/N4uKvcfjtZFv3kk1g3dP15cuRkLgm9RZGx19GlDYGgChNNMPixwWWgSlfRpQmlgGxw8kvzMWPjEEyKHM54zQJlLtLyLbuQUbG4q3C7rWi1wV6U71+D18VfESVp5wJSVMpOTWXVS1pGBUfuq5d7+hB2H02dpm34MdHkSufHtH9yLbtQ8aPV/YSr00M2SbXeYQF+f/F4bPjlp013i+DysjguJFhfX6CIAitxQnLCWL0MUTpAhcsZVlGRlZGskiSRL+kfrh8Lv61419EaCK4p989DS4RMyhlEEtzlxJviKfcWR7ynMvnwuULTMXok9iHWT1n8c2hb3huw3OMzRzLPy79xznYU+FicrTI3nChMOQU2UVA2kqIgPQ8iomJqff5q666imeeeYann34au93O+++/z1NPPdWo18jMzDybJgoNKLN4cLpPrfVp85AYHTjQqcIcEhIVocagU+P2+jHbT2cptLv8bDtixpy4GFlzKgjl9JAojT8SnS+W8thFqI1W8INH9rDXsp0OkV1CXkOWZY7aDwJw3HGE444jqGUdPsmNHy/dIvuRqEvB43ejVZ0+ULcxdghJkKST9MrzMjI+fEowGqxHdD+6RvVGhQpJktBKOpyyIySxUPW6qACR6ijS9JmYvZXEncoC7PQ7lCGzhc58+scOZZ95B/1jhinzPqupJBX9o4dxwLoHt9+FJKv4oXAB8qn3y+KtopupN0Njx2DxVnHAuhcZP1Xe0JOmYF7Zg91rq3X/BEEQWqNFOYt4cs2TROuiuazdZUxqP4mn1j2Fw+tgZveZHKo8xJ197gQCcz0/2PcBAH2T+jIqY1Stda7KW0Wpo5Sbut3EpW0uJd4Yz7u73+W/e/+LX/aHZF7vEd+D2T1nc9dPd1HlqsIn+1h9YjVevxeNSpx+CrXzeP0UlNeecb+xTpa78Hj9F806pRczcUS4wNx9990888wzyLLM6tWrGx2QCk3n8fk5dNJOlFFNm0RjjeePlzjYdsSMTiOhUUvszbXi88l0zzTRu30Ux0pq9rydye0JjOPNL3Ni1Ek43YGZjX45ME9U5UgDU/HpblRAQkWEeRCyX4ska3H5nfSPHkqO/RCdIrtx0plHuuH03OEC1wlc/kBbJG8EalnH4Jhx7Kzaglrj4YfiT5VMuN1NfRmXOAk4FejFDFWWmhkUO5Jupt7oJB1qSRPyGmdSS6eHYQUH0pGqKGz+wJBljaRFp9Jh81k4bN/PYft+hsWOpX/sMEyaaMYkTKTUVcSA2GFEqCPpEz2o1teyea18nj9X6ek87jiC59R82GRdGv1jhiJJEgNih5Nt2YvH70FG5oj9QI26UnQZFLnz8cpe3GdkBhYEQWjNjlQeQUamyl3FV4e+4utDXytz6f+z5z8AdI7rTJ+kPvRI6IFRY0QlqUg0ho4wsblt3LX0LswuM8ctgQRyftnPjG4zALh/wP3c0uMWyh3l3LvsXorsRRhUBv40+k/8c8c/2VG8A4CxGWMZljaMv27+K51iOzGj6wwxhFeoweH207SZozXJgMMjAtLWQASkF5jk5GQSEhIoLS0lPz+/pZvzi3L4pJ3sfBsAx4odpMXpMdu9dM80EaFXU2YJBD1ur6zMD91/woYkQbcME20TDRwvrRmUGnUSBp0aWYY4k4b9eRYO5IcOR+nTJoI9eXZMtn74jQW4NaVBy7P4iY934fPk4tEEsiRnGtvTP2YYn+S/g9vvokNEFyYmTwMgVhOHXmUIBKUqD3Flk+nWrh39krvyQe4/QD6dXfeAdTdjEyYqw8iHxI0mWhuL02dXhvN2iOzaqPexd9RAtpsDQa3dbyNFn06R6yQACZoU7L6jStnNlWvpFtUHozqCnlH9OKrKZmP5KvrHDCNeFzgp8vq9bKlci0bSMjh2FGXuYiUY1Uk6Okf05JjjcOC9VkUqvboWr5lVZYsAaG8M7UUOtCWZySnXcsSeTYQ6kgRdIKmHx+9mZ9VmTJooekQ1bl6VIAjCheKO3nfg9ruZnzUfj7/2KSVWj5VSRym9EnrRP6k/Gwo2cPPCm0kyJtE7oTeHKg9R7iinyhNI4lc9lSTeEB9ST7whnp3FOymyB6ZbPDbkMbrEdeGqjlexvXg74zLH8afRf+KNbW+wIDuQzfelzS/xj0v/wdjMsefwXRBaG28TExnVWZ+veesTzg0RkF6AREawlhEVcfrPodTsURIUVdk9dEiJpGtaBDJQXOnE4T59gMvKs+HxymQmGiiocOI5Yx5+RrwBm8tPbKSG/Sdstb52SpyBQ4VOHB4PTs3p7LUSEhpJi89QRLeEPuSVAjKcrLIQGRuPxx8ILHPshyh0niDVkEmExsSAmOFsrFiFrPLgU9mVq42J+lTynblK/W0MHWt83zIN7TnpPM6KkoX0iOpLprF9o95HL6dPfGT89I4eSBtPBzIMbcmxHyLPdVRZSN2ojkQjnX7fV5T+iE/24vI7mZxyHQCHbfvZbd4aeJ/06WQa2xOhNmH3WekW1ZdUY7pSX3TQkFuDykC0Jhazt1LJyhtCkilzl9Dd1CdkvtQ+yw6llzhZn0aCLrlR+y8IgnAhiNRG8tsBvwU/bC/Zzp297uTlrS9zwnqCAUkDsHqsrMpbxcf7P8agNpBuSgfA4/dw0naSk7aTNer04+fJIU9yWbvLajw3JHUIw1KH4fA5GN92PAAT209kYvuJSpl+Sf2U47VP9vFR1kciIBVCaFTNew6sUYtz6tZABKQXmJKSEkpLAxnp0tPTW7g1Fz+rw4vXLxMbqcWgrX1IR6XNx46jZvQaidQ4fUgwWu1QgZ1DBXYkKbDuqM0VyL7bJS2CbUfMOD1+iirrnhMRFaHlsn4JuL0yXxYbcfodAHSK6M5h+37yHDlkGtoBEGHtzYmiJEq1DoZ3uoSNlavQSjpMmmilvlR9BjGaOKJIYWS3bhh1gWFRVyRfyw9FCyhynSROk8CU1OtC2mH2VPLlyQ+UIbCl7iJuzpzTiHc0MMRYLWnQSlq6m/rQKaK7EvSedAbWyVWhRiOpMalNBI9PTje0Ic+REzI8OFGXjEbSopE0xGkTUEkqbsm8G6vXTIw2DoBhcePYWLGKfZYd9IzuT6w2HpWkxqSOweytVHqbTepoUnTpHHEcoMxTwvdFn5Gmz+TqtJuV14sLSoiUbdnHyAQRkAqC0PpUOiu57n/XUewIXOQ8Yj7Cj9f+iNVj5c4ld3Ko8pBS1ulzcrTqKNHaaMwec0g9EhKJxkRKHCUAFNgCI3Xm7ZvH7pLdPDzoYTKjMonSRfHepPfqbdP4tuP5YdoP3LTwJuxeO7N7zW7OXRYuAkadCgmaZdiuJIGxjnM74cIiAtILzDvvvIMsB/4Mx40b18KtubhZHF5W7C7DL0NStJY2iYaQ59UqSI8zcLLChc8v4/LK5JefHpKrAs5ciS0wLFfL4M6RFFY6OVbsIDFGx4lSJ3WNQok2BoJFrUaFVgND48ayrmwpMmCza4jVJqCRNHQx9cKPzElbFC7A6ZGxFLbjmva3EKuNR68OtP+gdR8rS38EoIoKIiNOX53WqDRMSbmB446jpOtrJsCy+2xKMArU2zu6qWINufYjjIwfH1Jur3kbPnz4ZC8Wn5liVwEmTTSRGhNtjR0odRWS4ziET/ZS7C6k0lNGkj6QTXpy8nW4/S5lXwAS9SnManNfoLdYFVjkWi2plWAUwHgq8ZEfP06fA7Twc9lyTrpO9wb3MPVlbOIkrF4z+ioDWZadABS7Q9fNyzS2V5bAcdWSkVcQBKE1eGvXW0owCmD32FmXv46Xt7xMnCEOCYmhqUPZVLhJKaNVa/lm8jckGhOZs3QOB8oPICGx5PolzN0zlxxzDrN7zabEXsKrW18F4HDlYb6b9p1Sx4IDC3ht22t0ietCuaOcaH0071z+DjH6wAiWtjFtWX/zevyyX8whFWrQalSkxes52QyJjdLj9GL+aCshAtLz5NixY1RUVDBgwIA6y/zwww88//zzABiNRu64447z1bxflOMlDtQqicMFNiVILDF7KLecDsQSorSM6hGHWiVRYXGz4WAlfr9M20QjR4oCvZc1lwUPOFnuosziwXEqG2+XtAgy4vXknzq4tknQU2b1YHf5SYnRMqxbXMj2PaL6sqN8Gxa5lAJ5H5GeGCalX4VRHUH/mKH0ivSzbFcZTo+fk5ZK9hX+j8nJ13HQtvfU3NHTB3EVKjSSNqR+nUpH58jutbY91ZDBJQmTcfoddDf1Rq+umdwJAmuG7qwKnMTss+xQAtIc28GQ9T2LnCc5YjuAQRXBtLRb+a7wE7zy6ezCGjQsL/mBqak3EakxIUlSSDBaLTgbMIDDZ8Pj9xCpMaGWNBhUEcRo4knQJrGjcgN+ZApO9cbC6WDU5XNywLKHNoYOHLJm4ZHdJGlTlHKVnnIKnCeYlDyNEnchPaP617r/giAIF7rOcZ2B0/M+5+2bxw9Hf6DAVsAx8zEWTV9EqbOUTYtOB6RlzjJUKhWxhlheHvsyb+9+mzEZY9CqtNzd7/Ta6B6/Rxl6e7TqKG6fG51ax66SXby46UUAdpfsDhS2wt7SvSGZe9efXM9T655iUMogZveYzccHPmZi+4m1DgUWfnk6pkQ0S0DaISWi4ULCBUEEpGFat24dhw8fVu5XD6sFOHz4MB988EFI+dtvvz3k/rFjxxg/fjwjRoxg6tSp9OvXj+TkwFDAo0eP8uWXX/Lll18qvaOvvvoqGRkZ52ZnfqGKq1zsPGrB5qp9seXgee9ajQq1SmLLoUpOlJ0+KMaYtFyeGsG6/RVKwHkmvxzIEqeSArePFNnpnBpBpF5Fh5QIuqRHIssy+/Ns5JY4yC120CHFyJFCOz4/dEmPIJ2+HPJuxK+xY6OCZSXfc33GbBwuL8t3l+PxyXhVVsyxa5Hxc9i2n8O2/ai8JkYmjuYI+5GR8ePn/dw3GJNwGe30vdBrVKhqmZ9xyJrFCWcuA2OG0y2qd4PvpVpS0ztqEMcdh+lh6sd+y26OO45y0hm6Dm71UFm334lP9lI9PLe9sTMmTQx7Lduo8lZQ4DpBZ03tQXL1MjYR6kjSDJmcdOadWuZFJkoTw+Ska1lU/BVAjaVdJCRiNfGMSgic5GytXM9ey/bAsGICV+ajNTHsqdpGjDaOVaWLcPjt6CQ909Nn1lhyRhAEobW4oesNDEsdxreHv+XdPe/i8XuU4bYAk7+ZTII+gYFJA9lZshO1Ss2snrPoGNMRgA4xHfjrmL/WWrdWpUUtqZULjA6vA51ah8VlUcq0j26PjEzP+J4MSR0Ssv3Cowspd5azNHcpS3OXArDo2CLevfxdhqcPb9b3QWh9EqO1RBnVWBy1n6+FI9qoITFa23DBC8jZxhqtmQhIw/Tee+8xb968Wp9bv34969evD3msri/Jhg0b2LBhQ52vExERweuvv87dd99dZxkhPC6PnzX7yvH5Zfq2i2LToaoGt9GoA0M8MhONVFk9IcGoRi0Rb9KiVktKMKpRQ49ME0cLHdhcPmKMaqpOHUDVpwJSvx+OFDrw+WXyy5x0SY9EkiSOlzpwevzkFDmINKjZk2sFAvMnRqQPQH24DYfkhXg0ZcjWFCwOL3mlDjynImeN34TB3pmOaT3oGt2JgjIfxsoh7LcWoTGk4jEETjz8+MjOc7HfXEq0UcOEfgkh++z1e1hZ+iMyMoXOE9yUcVdYibVGJVzKKC6l3FWiBITVTOpoMoxt6W7qS7GrgARdMvG6RKan3UqVp4L2EV3wyG5sPgsSEu2MHet8nQPW3awp+wkJiRkZv6LcXaIsXWDxVrGpYl2t21Vfva/0lmP1WthYsYriU9l+I9UmJiRdxRHbAbIsOzlozwJAe6o32S27OGrLZmDsiAbfB0EQhAtV2+i2bC7cDKAcN4OVucpwVjhZcv0SkiOSQxK8NeTlsS/z7p53ub7r9cToY5BlmXf2vAMEemWPmY8BkGnKZNjHw3h08KPM7DkTgChdVK11zlk6h/v63cev+/+6MbspXGQkSWJw5xjW7KvA14Ssu2qVxKDO0a0uSWhzxRqtkQhIz5NBgwYxf/58NmzYwNatWykoKKC0tBSv10tcXBy9evViwoQJ3HXXXUrPqXB2CiucWJ2B4PBA/unstnqNhNcv4zvVwZkRr8eokyio9NCnrYlDBXZ+PlAJQJRRjc3pIyNez4CO0ajVKhxun9L76fXBnlwrVwxIRKWS0GtV7Dhq5lixA4//9OtlJho4XuKkbZIRp9vHsWIHbRINFFW66ZIeQYRejUYl4ZdlTEYNeq2KMT2S6em+ki2HqnBYollrrWBMj1gOn7QrvblGR2cOWX6iZ2xPOqtGcyhiJ9boLUh+HUZrD/ymY8gqP7I1kCDL7PAiy3LIQVotaTCqI7H7rJi9lZS4ConWxobdO1jhLVNum9RROP1ORsSPp+Op5WJSDad7+uN1ScSfWl5FJ+mZmHxNg/VXr3EqoUKFiu6mvpg9lRS7C4lUmzhqz66xjVGKpH/sUDZUrESvMmDzmjlmDyTw6GHqy7D4S9Cr9Jx0Hg9ZN7V6/qxG0tKpjmHNgiAIrclx83HldpQmCovXEvK8zWvjjW1v0Da6Lff2uzesoPTtXW9z3HKc/1z+H2UJmJO2k8qao3786FQ69Go9O0t24pW9LM1dqgSk7aLb1Vn3v3f9m9t63oZJZ2r0vgoXj9hILcO7xrDxYFWjglK1SmJ41xhiI1tX7+gvnQhIw/TBBx/U6CpvjKioKG699VZuvfXW5muUUK8q++k5oS63l/R4PW6vn4Edo8nOt5FbEkhYU2F1k++WSYrWkRqnZ3fu6R/reJOWy/qFLhJu1KkZ2yuejdmVOD1+1CqosHmI1KvRa1UkRms5VuwgQqdiSNdYYiI0qFUSfdsHsuBuzK6koMKFViNx5aAkJTjslBaBzekjUq9mYeGXlLtLGZ8wGckdC/hxefys219Jvw5RbD8aaKMs+TCVTGJ5WSWS3wBG6dTjHryaKgwaHRZvFU5dPnpnO6KNmhpXDCVJoo2hPQdt+4jRxrOk+FucfjuTU64j09gem9fKd4WfoELF1Wk3E6GODNm+Q0RXBsaMwC/7GBI3psbJzB7zNg5ZsxgcO5q2ER0a/Tl2NfUmQm3CqI4gShPDjqqNVHrKiVbHYNSEtqW6VzTJkErfmMGkG9oQoTFhUBnpFNmdKk8FfaIHo1fpAegR1Y9KTzkgkWs/jFsO9IjLsoxBJYbrCoLQ+j094ml+t/p3uOXAPE+tXxuyLumlbS5lYc5CAHon9m5wGZbs8mz+tfNfQKCn83dDfwdAakQql7W9jPUn1weG8Kp0fDH1C7YXb+eT/Z/QPb47Tq8Tg8bArT1upUNMB3YV72JxzmKOmgPrU6tQMa7NOCK1kXW+vvDLkRyrZ2yvOLYergpr+G60UcOgztGtNhg921ijNRMBqXDRyS93UmHxcKz4dIbUuKhAAFJh8bB8dxnRQWuO+pTERm58fhjRLY59xwMBX9eM2n8U40xaJg1MpLDChc3pY9PBKlQSXNYvkTaJRhKjdGg1qlrXv4rQB3r8/H6Z77cUM7xbLAatmuxTvbh2VTEn1DkALDu+mRjX6ZMDp8ePSpLQqiW8PhmVfCrZjz+QCMjo6IpapWJMp/akGtPZb9nFporVpGdW0c8Yj8lY+5/8UftBZGQ0kppKf2DocLGrkExjezaUr8TiDQx3PmDZw8DY0Pk9KknFkLjRtdYLsLliLV7Zw86qjU0KSOF0tt9j9sNsqTw9RLeHqR9aSav0bKbpM0nUp9I7KpA8LFF/OmHRZUlTa9RrVEdwadKVQCChUZ49hz2WbVi8VSwu/obLkqZyxLafthGdiNXG19heEAThQnd5u8t5nMeBwBDdyR0msyhnEQAGtYEVeSsA0Kv1xOnj6qynWmZUJga1AafPyZoTa/jd0N/h8XvYWbyTF0a9wIdZH/LWrrdw+Vx8sO8Deif2Jqs8i71le4nURnJXn7t4cu2TmF1mHhr0ENuLtysBqUalIcGYgN1rF0GpAAR6Sif0TaDU7OFokZ2CclfI4HNJCky16pASQWK0ttUN0xUCREAqXFQsDi+bD9acK1pQEZStTYa4SA0V1kAyBpcncGjrlh7BnlwLVXYvAztGhwStZ6ou179DFM5T80llTs/RMeprT2XvdPtok6gnPkrDlkOBtd6OFNrp1cZEhF5FiW4L5dJRtK40/GrbqQATZXhxZoKeNklGEqK1LNlRVqN+CRV6W1fiVAmoJTWx2gSuT789ZImUalavmZWli4hQRzIgZhiHbfvpHz2cI7YDWLyV6E4tsVLmLlG2yTC0rfM9qUvPqH4ctGbRzdRHeaygysHWYxVc1iNFWSM1HHHaBLSSDo/sPvWITLuIzhyxHUBGpntUP7qYegBg8VRh8VaRbgyvzbHaeGJj4smxH8LircIju1la8h1FrpNkWXZxU+ZdYbdTEAThQmLUGLF6Ahcbfzr2k/K403f6wq3X52Xmopn8efSfKXWU0iW2CyMzRtaoK1IbybC0Yaw+sZoEQyAnwb1L72Vz4WaitFG8Ou5Vfjvgt3ye/TmfZX+GlC0pv437y/az4vgKVuWtAuCVLa+wp3SPUrfb7+bLg1/SObYzt/YQI8qEAEmSSIrRkRSjw+P14/D48fpkNGoJo1Yllna5CIiAVLioaGvpkQQw6FR4vH6SonWoVBJOd82hHxF6NdknAz2jx4od9G1fe9IFq8PL4QI7AEcLHfRpZ0KvVWHQqTAZNDhcPmwuH4nRoUuVuL1+lu8uw+2V6d8hSpmHWlblZnlFIDus01SALIMamYTSaUTq1SQm6MgtcaCSoMziYfOhSnq1icSgUynB8Jm8fthn3sG68mVoJC03Z86pMdT2sG0/J52BuUXDYsdxQ8Yd7KjcSI7jIACl5SuQZUgxpFNpLSNJl0qKIb3W16vPiPjxjIgfH/LYjLc3kFfu4NoBGfztxv4AbDxaRmq0gfaJdV8Vj9HGcWube9lStoZ9tp3stwaWFWhj6MDohMuI1sYG3kefgy9PzsMtu+gdNQin3043Ux8yjXXPW6p2WdJVbKpYS74zF5sv8H0Q2XYFQWiNlucux6gx8s013/CHdX9gU+EmeiT0wOwyY/PYyIjM4Jj5GG6/OxCcyvDOrnc4aj6KRtKw9IalJBoTa9T78tiX2Vy4mQHJgdEoOVWBUT0Wj4V7lt2DTqXD7Q9cOKyeSgGw7uQ69pXtI8mYhFql5or2V4QEpBBILtc/uf85fFeE1iywZrsIQC82IiAVLio6rQqtGjxB8aZKgvQ4HTlFTgorAz+QBl3owax7ZiRtk40UVrqpsnvJTKi5Fma1CL2apBgdVTYP6fF6JEki41R5j9fPij2BoLNPOxOd004HVz6/jMcb+FF2uP3otSocbr+S/Aggoewq/JIHjybQK9k1w0ibxMAwlIIKFyfLXeSXuSiscCm9piH7r4aUOANRRjUec2Bf/bIPvxwagB+1HeSk4zha9HhwsalyNW2MHSh2FYaU+7liBVOTb6JXVH/itKHZecNl9Zo54ThG+4guSmDnP9V2/6lljj7bfJzffb2HCJ2aVY9fQnJU3e+/XqWnyFMQ8lipu0gJRiGwTqr31DDeI/YDOHw2ilwnuSWz4ezVDr+dg7a9yv1EbQpTUq4Pa18FQRAuFMtyl/HwqocBmNB2AiMzRvKXMX8hzhCHRhU4/dtVsouZPwYSDenVelw+F8csxwCI1kdjCFoXem/pXh5e9TAdojvw5NAneWf3OyRHJJNpyqRvYl9W5a1ClmT8sl8JRk1aE9M6T2P+/vlKPRWuCuV2uqnmRc5u8d3oldCrWd8LQRAubCIgFS4KPr+M2e4lp9AWEoxCoBcytyR0zkFwOBoboaJreiQqSWJ4t9gGX0ulkhjdo/Z5Nn4ZZVmW6qHA1Yw6NSO6x2K2e+mQEsGxIrvyXFqcnnKrG5dHRiVr0XsCP9I7jlqxOf30ahuFyaDGYveSEK0NmR8bzOMLDFu2OX0kalPoGtmLrqbemDTRQW30s7TkOyBw5braitKFlHtKatR5yL6PcYlXAOD1e7H7rCHBX0N+LPqSCk8ZOfZDTE65DoDP7h7O1txyJvZMBcDiPDV82htI3tSQ3lEDWFW2WLnv9DtCno/UmLgq9UYqPWWUu0vZa9keMtzYJ/uU7L1nMqoi0KsMuPxO2hs7Mzz+EnSnkiAJgiC0Fnr16ePW8uPLWX58OZmmTBKNiQxMGQgEhvLqVDq8fi8uX2BqS5Q2ircue4s0U1pIptuluUsptBVSaCvkswOf1ejZBECG7nHdybPmYfPYuLzd5Tw59Em+PvQ1dm/gN69tVFuOW44Tq49lT8ke4g3xVDgrlF7U5IjASgNHKo8w56c5xOhjmDtpLrGG2HPxNgmCcAEQAanQ6lXZPKzeV47PDwZtzWEcRq0KxxlBjoyMWiXh88uYjFrUquaZBK/XqhjdI44qu5f2yaeHeeaXO3F7/LRPNpISGzhJGNY1hn15NlJidXTLMFFa5Wbt/ooadR48aSczwcChAjsWpw+TUUOvtiayjltDguzquaaVNi8/HywjN+Yr/PiJ1caTETSP0uazhrwP1co9JWjQ4sVDsEJnfqCsLPNtwXzKPCUMj7uEvtGDcfmdDQ5nVUuaU/8/HQC2iY+gTXyEcv+OUe2JMWprPF6XzqYe+GWZNeVLlP04M8iUkUk3tKVHVD8Gx45Cf+pK/wHLHtaULSHD0I4rU2+oUXeExsSNGXfi9rtqnXsrCILQGozOGM1r417j4/0fs714OwBPrH4CHz5eGfsKV3S4ggUHFii9mUa1kXbR7Xh+1PP0SOhRo77rulzH7pLddIjpwE3db2Jr0VZMWhN7Svbg4/SV4AMVB3jz0jcpchRxZYdA0ri3LnuLuXvnMiR1CO/sDqxVWumq5L/7/lvjdXaW7ARgY8FGShwllDhKyCrPYmR6zfmsgiBcHERAKrRqsiyzam851UtUBfeuRRnUJEZryTnVm6hVB3opPT751HDZQK+co455mE2VGK0LmT9aYfWEJFqK0KnYnWslPlJDUoyO9PhAoBQXpSU1VqcMK5ZACRf35FqotAXaa3f5GN4tlpRYLTuOmHF5/bRPjiQ5Rse6/RV4fTIGrRoJFeCnyhMIcg9a93LIup++0UOIVEcp8yOrGVTGGj2NWklLz+j+QKBXsdxTCsA+83YOWbMo8xTTL3oIw+MvqfP9mJJyPYXOE0qm3Npo1CpmDGlT5/PBNlWsYWfVJnqa+tPD1I9cx2G6m/qGBKP7LbtZU7YECYkYTRxXpFyrBKR5jqPIyJxwHsMne5WAOZhRHYFR3XBgLAiCcKF6YeMLfHHwC/ok9EFCIiMqgxOWEwD8adOfSDQmYnGf/h1w+BxoVJqQYPSzA5/x8f6PuaP3HXj9XowaI+2j2/PIqkfoENOBDSc3KMFojD6GKlfgt+6Y+Rizes1S6hmYMpCBKQPJqcrhla2vhLTTqDHi8J7+7anu2b2yw5VsK9pGtC6aIalDmvndEQThQiICUqHV8vlldhytIni95OAewzZJRjqlRlBp82J1+hjRPZaEqECgmFNkZ9cxC1q1RLfMSPLLAkFrRj1zR8NldnhxuHxKT6hWIykJjAw6NduPVOH0ylidPsDFyTIXE/olkFvsUILRoV2iSYszcKLUSZXdy+HCwFCn9Dg9bZMMbDlUSVGlWxke7Pb6iDNpmTwwiXKLm/goLUWFcZR7Ssi1HwFgXdlyPLIbn+zlpoy7sHirUEkqqjwVmL2V7DVvx+l3oJG0yvzLDEN7Sl1FbK1YT8+ofkqPqsVnBl8gS/Ae83a+2nGcE0VafjWmDSPT+hOpOT3My6iOoENk17N+X6vl2QPLA+Q5c07NCZ1Yo4zbf2o9UWQqveXss+xALWnoYerLoNhRyMi0MXYky7LrrNZIFQRBuFDtK9sHQKW7klu738rXh74myZhEiaOESlclv1/7ewrtoXkDPH4Pdyy+gyGpQ7ij1x38bdvfcHgd/Hnjn3GdOq7+fPJnfLKPo1VHQ7atDkYBthdvZ0rHKURqIzFqTo+i6RDTgV4JvZS2dYzpyH8n/Re/7OetXW/x9aGvmdFtBgCxhlheGvMSc5bOYexnY3l9/OsMTwtddkwQhIuDCEiFVqnC4mbd/kq8/tB5mhpVIMNsQpQGu8vL3lwLY3rF1xiS2yElgkMFdmxOH7tzLKeCQxihkkiNa/p8Qafbx6o9Zfj80Ld9FJ1SIzAZNIzvm8DBfBu5xXac3tA267USZruXvFIHkhTIFBxv0rHtcBUnK1xo1RJGnQqVSkKSYGMty9ocLnCQEKXD6vTRNtGARq0i1ZBOuacEp+xgT9U2OkZ05ZAti46R3dCoNMTpAkmKqoeltjF2INd+hE6R3alwl7Gw+HOOOQ4pr3HImnXGkisBfnx07lRE505w1HeCE/nbmNX2NyE9jwcKzfzxu30MahfHE1d0r9F+m8uLVq1CF0bmvJHxl7LXsp3upr51lukTPYgqTwX7rbvQSloOWbNw+h2Uugq5MnUGE5OnAfB+7ht4ZQ87KjdwxH4Aq7eKSxImE6WNabAdgiAIF7IpHaZg1Bi5udvNPLbmMQDsDjvxhngyozI5Wnk6oOyb1JcusV0wu8wsPb6UrUVb+Tz7c6XnsjoYhcBomboY1AbGZI5hYMpAJnwxgZSIFL6++uuQuaivXfIa9y+/n5yqHKZ1nkaCMfBb9MyIZ/j90N+jkk7/DhTaCtlWtA2A1XmrRUAqCBcpEZAKrdKqfTXnWgKM6RXHzweqKLN4KbMEhrjGR2lpm1RznmO0UYPN6cNkUCsBqaaOZWPCJcsoPbb+Uzc8Xj9bD1VRZffWKJ8UrWVE9zjWZVVQfmpd1MGdY/D4ZU6UB04AXF6ZbhkRZCYYWFfLHNNqO45U4fZBqdnNyO5xVHpOl/25YgWDY0dzV7tHlEWj80oc7MwxkxijZ3jXGGK0cfSNGQyASlIpQ3hVqPDjx+yrpGNEN9obu+CR3RS7Czhs3Y8PL7IcWJwawCt78ct+qt9KWZb5dMdOktofYG1+JLeb25McfbonesORMmbP3UySSc/CB0YTGxG6XM6Z0o1tlbVFj9iyWVG8iCi5DTd1vE4po5JUyn56ZA9x2iScbgfRQXNCPX53IJmHz0usLoEDp5aQOWjbx6BYMVdJEITWKd+az8f7P+ajrI+AQBKh1IhUpTc0MyqTj6d8zJs73+Q/u/5Dx5iOzJ00F51ax5oTa9hctJlucd3YXLgZAK1Ki8cfmlvg2RHPsq1oGwW2AqZ1nsb7e94nx5zD5e0u589j/swz65/BL/spsBVQ7CgOCUgzTBl8c803IfUV2gq5ZeEtlDpKidXHsuCqBaSZ0mgT3YY5feaQXZHNLT1uOZdvmyAILUgEpEKr4vXJbMiuOygrqnSjUUu4PIHlXjRqiTiTttayQ7vEYHX5iDKoMdu9yEBsZO1lq1kcXtbvL8fjBaRAHdVDcwGMejVje8Zhc/mUpWMOnbSFBKMGjURavIFIg5pOqRGoVBJRRjXl1sAPfqnZTbcMk9LbC2B3eVm+uwyNClJidbRLMrDlkBmZ03NNq7MLq04FYp0iuivrjAI4fXYlSHO4fGw7Eti+sMLF8iN7SExy0D9mGAA6lY5OEd1BgmFx41hftoyTrjx6RPVV5oJuOb4WH4H9qg5GU/UZDIsbh1Z1OqhcVbqI9B77SAd6d60iJuJ00F9l9/D19hO4vX7yKx0cL7c3GJAG21i4C4fPR/bJYpI9hVzaLZC1t8xdTKahHS6fk2R9Gj2j+lPlLSdem4Qsy0iSRLm7FLvPBkCkOpJ0Q1us3iraR3QO+/UFQRAuNI+vfjwkA+6Wwi1E6aIotBeSbEzmsUGB3tLf9P8Nv+r9K2VIbbmznK1FW3l+5PNc2vZS3tn9Dkcrj7LqxKrAUN6edzA3ay4QWHf0z2P+rLzGNZ2vodRRSoIh0NsZnMFdp2r4mL6zZCcljkCW9wpXBVnlWaSZ0gB4YOADZ/N2CILQCoiAVGg1jhXbyT5hw15HEiK1ChKidLRLMlJh9ZAUo0OlklBJEg6Xj6NFdkqq3KTFG+iWEYlKJRFtDPwJxDQQiFYrrHThcJ8ecltQ4VIC0upAJz5KR3zU6W1igwLilBgtKXEG2iUZld7YgnIneaWnl3E5VGAnJVbPpIFJLNwa+IEuNQeCVa8f+neIxuUJrGPq9PiVebPV//f4fJS5i+li6kGcLoF1ZcswqiMYHneJ8hrB2wFYSlPI864gWZ9OuqENh6z72WfdAUBbY0cuSZpc4704c9iWVtJxRfK1ZFv38nP5CobEjaaNsQOFrnylTLwugUpvGSmawLI2N7+7kawCMx0TI7luUCZ9M2Nrfd/rIld24eOVKopKjRzKPMyl3VIpcxfz1ckPkZGZnHwdbSM6ApCgSybLvJN15cvoGNGNS5OupLupLzafJZCJN250g69X7i7BK3tJ1qc1qp2CIAjnS7whPuR+m6g2IMGhykMUO4qZtXgWvx/6e27pcUvI/M43d7zJ5wc/Ry2pWXPTGu7uezf51nwW5iwEYF3BOh4e9DDZ5dkhCYuq5Zpz8fg8RGgj2Fu2F51Kx7g240iLTOPlLS9ztPIofxj+BzKjMmtse0nmJUxuN5nV+auJ0cfQJ7FPM78rgiBcyERAKlzw/LLMil1lWJx1z1tREeitrM5umxYfusbkhuwKquyB7StsVjqnRTRpqZc2CQZKqtxU2TwYdIEeToB9xy0cPGknQq9iUKcYIvRqduWYiY/S0i3DxJieKmxOL3tyLRRVecg6bsEvQ4cUI0cLHSHBoSzDxoOVXDU4mZgIDVV2r5IJWK0KvB/Z+TacQRmFdRoJjUrC7vZjM+3g+yOVRJHKNV1HcUPG7TX2I86kpX2ygRNlLrw+PyAR6ehOjCYWgERdMhpJC7KKkyUa2rar+V60NXbkiP1AoF2oGZ84Gb3awJbKtXhlLzurNtPG2IFLEidz0LqXCHUk26s28l3hJ1yfPpt4XRJVjkCg3THJxG/GN65nstjs5G8/llBuC5xQHS62cu2/1zO6ewSRHU8lezpjvusxx2FkZHLsh1BJKsYlTgr79YID3f9n77zD4yivvn3PbC9a9d5lyZbl3rvBpphqOkkIvYSSACnAS0IIgRCSwEsgCS0QIPTee7Gxce9dsnrvq9X2PjPfHyOttMgGko+WvHtfF5e1M88888xI7O5vzjm/c0L2mRSaEiZICRIk+O6wq28XgWiAu464i81dm7l35720uls5a8JZjE8dz0mvnRQbWzdYR1gK81r9a5QmlzI3dy46jfrwtDCpENNQO698az7pxnQGggNUpVVx8eSLD3nu5w4+xx1b7iBJl8RN82+ibrAOgPm582lxt8TSh1+sfZGfz/75mOONWiMnl5/Me63v4Y/6+bj140SKboIE/4dICNIE3zkGvWHa7UFKs80kmbS09Po/V4wmmTR4AhKbal2kWn1UFVrJSh5Jo91QMyJGBVQn3c+K0XBUxu4Ok5WsR6sZMVSo6/JS3ebDatKweGIq+9u8ODwRIpJCMBLlYIeP2eU2mnpU4wd/SKa63Ys3ECUUVehxhsm0Gciw6QlFJKJDlzGcitvYE0BEjW4adSI2s5Y+VxirQYMsK2Ta9Lj8UfRagXBUQZLBF5TItOnpHlRrTLWiwHEzMxEFiMoKq7plkl1L1Pm7fQhmJwc6BslN0zK/QLXzVxQFnUYkK1lP11CtqiGch0WrhnYzDNksMp7H6Q9swh/axz/ON3B0VXbcPVuasYIsTy7ZhjyyjXmx7ROTplHvrWGCdTIAucYCco0FNHhrYmOG3XqfuHgub+zq5Jx5RfyrbGi04/CN1DX5whI725zsaney6qbTkYUI5ZZ4A6XZKYsQECg1x7v+2kO9dARbmGCdgoKCSTTH0puHiciR2LpHG3wkSJAgwbfNvv59XPDeBSgo/HHJH7l35730+npJ0afgDrnZZ9+HgIBZZ+ak0pO4cvqVPLb/Me7ffT9aQculUy7lmZpnEBD485F/jolTgJdOfol99n0szo/PIpFkiS3dW6hIrcAZcgJq65jJGZOZmDaRGkcNr9S/wgklJzA+dTy9vl6WFS077DVMzZxKVXoV3rB3zLkSJEjw301CkCb4TqEoCp8eGERWVLE2udDC/na1zk8QwGzQ4PuMOLUYVEEKMOhVReKwIB3whOlzqVEyvVagLMfMxAJr3PlkBTYddOLwRshJ1bNgwojxTX2XHwXwBCQaevxxqbUAHQNBepyhmFABtR1NZJT7b1SSeW9HP6FofJrs8JqikoJWFFhSlcIn+xzqdfiirD3gQIq1dVH/tRo1ZCXr6XOO9I4ryzHhC0rYzFp0GoF56Qv4tNuNLGlIMmvZ2uqDUBadgRAMZUo5vBHqu9VWMqkWLU5fFJtFw6a6fvJTreo1iQL+kCrK3MF4QwtQ64KGTZBGszBtOQvTlo/ZXm6diFbUYRSNpOuzAHh/fzd/+6SBT+v7eeMn8V9A3t/fw8OfNvLDecWcMWtsitfyymyWV2axp32QgVHCVCeK/Oyf3Tzww1lgiT8my5DL8dln8Fne7n2RkBxkh3MjUSXKeMtkjsw4Lk6U5hjzWZF1Gq5ACNmbP2buBAkSJPi2WNW2KvY51OpupcenGhgNhgf554F/MidnDgoK/oifK6dfSbopPZbaa9AYeLvpbUB9WPhW41txUcxMcybZ5mzOeeccbAZbrBepgMAT1U+Qbc7mndPeId2YTr4lnwxTRqyXaPVANee9dx4NrgZKbCXMyJpx2GtINiTzwkkvfC33J0GCBN9tEoI0wXeKqKTE9RVt7Amg0whEJAVFgTnlyazZr4o2jSiQn2ZgcnESnkCUll4/XYOhmJkQwOZaZ+zncFThYIePgnQjVqOGzXVqL08Ak16NikpDWtcbiOL2R9CKEEZtxZKTYqCh24/yGVUZleI3OH1RJuRbqOv0kZtmwOGNxKXXDmPSE6tHlSWFtv5gLHI6PI9eGx+lk4fqVI16NSVZK0Jzb4C6Ln8sZTkS0rNieg6SpGAxaslwaujvA6PFjywrSIpCkkmLxaghFJGZVmrDqBd5f6cdgF7nIIqsIdms5fEL5+AKRDhleh5fBcOGQXZviDveqWFfp9rC5mCPB0lW4iLX935cx8EeDz2u4CEFabJJx2MXzuGjAz38/MXdeELqLy8syezpcHHEXZ/w9KXzmF+W/oXrMmsshOQgUUU1aar3HaA90MSpuT/EpkuJjSswlHHRA5/SbG/hl8dXcvkR4/7te5EgQYIEXxUftX4U+7nd087VM67m4b0PE5JCdHm7OHfiuUSkCGUpZaQZ01AUBVfIxbHFx7KtZxsd3o7Y8c8dfI5X619lZvZM2j3tXD7tch7Z8wh1zrrYmE/5lOWF6oNHX9iHL+pja89WPmj5IBYdHXZob3A1AKpZ0bDXwjBt7jayLdkYNAY8YQ8dng4mpk/8um9XggQJvmMkBGmC7wROX4SewRBGnUB5jonGobrKdJuOkiwT+1q95KQaSLXqMBtE/CEZSVaYOc6GIAgYdHqSzVo0Gq9q2DP0oafXioSjI+m6FqMGk15k0BuhZ3CkvjA7xUCyWUtuqoFQROKT/Y44oRmRFOq6fOSk6OgeHBst/CzNvX5SrFqmFCfhDY5t92LWizFzJlFQW9A0DEUsAcwGERDwhyS0otq6xu2XmFlmA6Aiz0KqVYeiKKyvcQLgC0lUH3DgDUqkJ+mYWWYjEJYgmE5eGkwvreDjvQP4QxLzx6dwzLQhN0RBQJYVotpBNNFkBL0bgqlkJeuZXJzEv0tEkllb28/k/GRyko1x+57c2MKru1Szo9Nm5HHK9PwxadRnzirgb6sbOHN24WHP4Q1F+dFTO2KR5xybmo7d6QwQlRVqut1fKEh9US8FplJs2hRaA42YRDMB2U9A9tMRaCXqbyDPWEiGIZtQVKZzUE3Pbrb7/sU7kiBBggRfPU3OJto97bHXkixh1VkJSWppgSAIZFuyUVD4844/0+xqptffy8aujYDa1gVgTvYcInKE3f27CUpBPmn/BIDH9j0WJ0aNWiMlthJ+Pf/XlKeU82T1k5zw6gmxCG2toxYAjaghz5wXS8WdlzsvToz+Y98/+MvOvzAxbSLPn/Q8P3jnB7S6W/nx9B9zxbQrvsY7liBBgu8aCUGa4FsjGJZotwdpt/tx+eMjiBU5Jup7AnQMhBjwRJhTkUznQIhP9g1QkWuhtT9Abqoh7sOttT9AS58qFrKS9WTY9CyZlMqmg04iUYXKfAv13T52NrrpGaq/1GkE0pN0VOZbiEgKq/YOIDDSQ3Q0vc4wRZlG4IsFaTiqEPZGaesPMD7PQl6agT5XOCZyRzsFywo4R7WFSU/SkGIxYNAJ1Hf5Kc02MalorDAMhCWCEZk55TYCYZmyHDP1XapIGvBEWLPfwYQCC/1uVXhn2oKxdOcBT4Sc1JE6W1EUmF9lpMVXzdSUmVjE5Lha2s9yoMuFSaehLNN6yP0fVfdy3+p69nS4yE02svHG5QiCQDgqs6HBzsQ8GwatSEGqidtPnYLFMPat6NIlZVy6pOywa/CFolz/0p6YGDVoRPrcQYbv7JR8Gx8c6OGdvd08cO5MspKMh5xno2M1Tf5atIKOy4p+wdqBD6jz7QegI9BMc6AevWjgwsKrsRi0/OOC2WxvcXDhooSpUYIECb59LDoLJq0Jf1R9qPley3ucPO5kci2qG/hdS+/CorPwaeenAGzq3hRL6dUIGn6z4DdU26tp97STZ80jEA1QO1iLgECGKYPF+YupcageACmGFF5d+SqCoO4bnzaeoBQECS6supB6Zz0bujYAat39ZVMv4/SK0w+57mHhWj9Yz993/502t9qmrMPTccjxCRIk+O8lIUgTfCuEIjIf7bbHpaiOpn7IJAggEJap7/LHTHxMuhDLpoyNeqUl6RkOsnmDEhk2MOo0HDk5jZoOLzub3SgKuAMjNajjcs3YTFo+3jNAZFRENM2qxeFVRaJGUAWbXidSVWglHJHpc4VjqcV6DcyvTMbujpKfbmBHgweHN4IoQFt/kFSrjnnjU4hEZd4eauMiCqoQTU/S4fRGGJ31O+CRGPD4KckycdKcrDHXGZUUBjxhtje4AZhWkkRFnlrQuKQqjU8POIhIChFJIcumRyOCJEOXI0xVkZnOYCu5WWPFWaG5hEJzSey1KzJIrXc/LS2ZNHQrXL60jHSrgbV1/Vz4+FZ0osg71yymIjteLPd7Qlzx9A6koRsUjEgoiloDfMub+3luazulGRb2/XYFOo0wxjzoy/JJbR/v7Ve/VE3OS+aSxSX87MU9I+sPRNnXqd6j1TV9fH/uoY2TknVqzbBNm4IoiqTrM8EHGrQEZPULnlE0xta5dHwmS8dn/ltrTpAgQYKvklpHLd2+bl5b+RrvNL/DX3f9FYC/7fobH575IYqiMBAcYFvPNgaDag9vLVrmZM/hwMABTi8/nakZU/nNht/EeSFkmjL5x7H/oCyljA5PB4/tewwJiVPHncpxrxyHKIg8e+KzHFV0FFdMuwIBgcunXo5G1HDuO+eyx66+F/9u0+8YDA4yLmUcRxYeCagGTG81vcUp5aeQakjl1YZXeWDvA7Fzf7/y+9/Q3UuQIMF3hYQgTfCtEI5KhxWjVoOINzSy06AT6XeNuJoqHFrApFl1JJu1DPqiHGjzUJKl2tbvaHSPMSMaZnyehfd39seJUWCoFYpKXrqRWUOpwQALKlMJRWSaevzIioLFoGFXk5fcVCNWo47FVan4glFW7XXgC0k09qh9RV3+KLmpevwhmfF5ZkwGDelJevwhiTX7HYQ+U2fa6wzR2OOPtZZR75vM6r0DBMJyTNTWd/voc4WZXpqEzaxlfL6FA21ebCYNwYhMklGD0y8RjkrURTbTJe6no8/CuYVXIAqHj4J+Yn+XZlcvf31xPIoi4A9Huf3UKXS71DrasCRz+zvVPHL+HPTakXksBg3ZSQa6XEFOm57HCVNz2dbiYF5ZOt6hOk9vKBp3DMCGBjv3ra4nGJG58fhK5n1Bqu3c0jTGZ1uJygoPnz+LvBQTvrDEU5taqO310ubwMy7TQrJJx/LKscI+Nk/qEsZZKrFpkwGYmjybtkAjncE2ekKdnJB1FhmG+OPf2tOFVhQ4fkqiH2mCBAm+HbZ0b+FHH/4IGZmb5t3EZVMvY2vPVjZ3b2YgMMAnbZ9w3drrkBQJSZEwaUwEpAAdvg7sQTtBKcjTB5/m7Mqz0Yk6wvJIGUtIClFsU/t9FSQV8Pqpr+MMOen19xKuVsc9U/MMkiJx7cxryTBlxI59+sSn+fX6X/NG4xsYtUbu3XkvAKeOO5VlRcu4c9uddHo72d23myeOf4L3W96PpReDaso0OWPyN3AHEyRI8F0hIUgTfCtsPOg87L7RYhQgL01Pc68qKDWigM2soWMgSEG6EVlW2FznxOWPMrcimdw0I4M+LxnJeva2eMhNNdDvCh/qNIiCOl9uqoHW/njB6hu1BlEcG8Uz6EQmFqrpqm9t7SUqgyfgI9WqJRSRyUo2UJ5rpssRoiTLRCAksb5mEEWBqkILBRkjzcjNBg0nzMqkwx5g21DUUxTUyPDeFg/+kMSUoVrOYFiO9STNtOnpdYXxh2T8oRApFi2VBVbSk3RoRdUZeONBJ2aDSEWekf3hVYS8OszKVERFT7c1RH6aicORrE1Fr+smM0Whb1BgYq5avzq6tnZtnZ3tLQ4Wlo98GTHrtbz306X0e0JoRYFj7/mUsCTztx/M4PZTJzO3NI0FhxCblz+1A29IjUr/dXU9f0wx8dL2do6dlMPk/OQx47OSjHz4syPitp07v5hed5DaXtVEY35ZOtceVUGW7dDpusOk6+MjnpXWqfSFeig2j4uLGgN8eKCHq5/bBcDTl8xlUXnGvx3lTZAgQYJ/l2tXX4s8VKQwnO56x+I7eL3hdVrdrdy94+44kRmURj7nQlIIAYGFeQspsZXw2imv8duNv2Vn305mZM3goskXoRFH+nmXJJcAICsyv5j1CzxhDw/vexgAm97G9XOuj1vb7Ytv5+wJZ1MzUMPtW25HK2p5vfF13mh8IxaJ7ff3Y9QYef6k57l729182PYhQMyhN0GC/2ts376dd999l/Xr11NdXU1/fz86nY68vDwWLVrEJZdcwuLF/50tkRKCNMG3gj90mPDoIRgWozqNwPTSpJho02sETAZNzCm3cyDE1JIkKvLMbK510jjgp60/wJyKZNrtQcIRmd5R4lRW1IhjVvJYQSrJYNAKhKIK6VYdh6PXGYqL9G6rdyErYNT5OH5WZkxIhiIyGlFt8aIbqs0MR2UURRW3nY5g7LqG1zZMuz0Ym8dm1jKtJAm7J0znwMgTZY2o1s0C7Gn2xK3JH5LxBCK49M2ke09Ho6gR1611bpZOUqO0h+KIjOOYYpvFBT9NYdAnUZimHjejKBWzTkMwKlGZY2NywVixmGzSkWzSUdvjISKri/GHoySbdJw3vzhu7O52J3qNwLhMC3s6XGg1Aiun5fE/r+xlY+MAr+zsZMONY9vIHI7LlpaxuWkABYUtTQ7mblnFnWdM5ew5hzdH+izl1olkGnKo8x7g4/63mJQ0g1yj6vSbZNQhCIACVz+3C40ocOeZU1lemf35kyZIkCDBV4QkS/iio4zVhj4zMs2ZTMucFkvdTTWkMhgaxKQ1cfP8m3m6+mlqHDUoKMzKnsVDxzwEQJGtiF/M/gX/PPBP3m95n3pnPR+d+REmbfxDS1EQuXDyhQSjQVa3r6bZ2UzNQA0nv3Yyty++nWmZ02Jjp2ZOZWrmVKZlTWN122oe3PMgGlFDVFYfPNqDdgLRAJu6NsXEKMC9O+7lzm13srRgKb+e/+uv4/YlSPCdY+nSpaxbt27M9nA4TH19PfX19fzzn//k/PPP55FHHkGvP/R3t/9UEoI0wTdOa9+h3Ulzk3V0uw5vGCSKAr7QSP2nViNgNWooyzbh8kcpzVY/OEVBINmspdcZxmbWkp1iQBQE1tcMDs0DQxqJA21eHN6x50y1ahkcqiG1mjRIskI4KmPSa9jZ5KbLEWRivpV9rZ6444aFZHiUIlQU9dgjJ6URispk2PT4ghKf7BtAkhUWV6WiHC592ahhQr5aH+oPSRh0ImU5ZrpcI468eq3ACbMyY1G6ZIsWlz/e2XfQqzAp9TQCGU68/aqwFGCMs23c/RZEMgyqyLKOemA9KS+ZXbccg0YQPtf4CGBCThJPXzIPuzfESVNy+fvaRiRF4fKl41hT28cNL+9lwDfykECnEchLMbFiUg672pxsbByg8HOiuIdie4uDPe0uClKNNNvV+7S7w/kvCVJJkXi9+xmCslrL7AjbOTv/IgAWjEvnrZ8sZk1tH//7oeo8efE/t3P18nJ+ceyEf2mtCRIkSPDvoBE1VKZWcnDwIFatlStnXBnbNy5lHHmWPNxhNw8e/SCOoIOCpAIODBzgd4t+xyP7HqHR2cjFky8G4L3m91jbvpb3Wt5DHvowispRlKEeZwfsB/jV+l8xIW0Cf1j8BzSiBqPWyMsnv0yru5VT3jgFgDcb3owTpMNUplVSmVbJKeWncP2a69k3sA+Ay6ZcRkSOMBgajBs/EBzAG/HyQu0LXD/n+n8pYvpW41vUO+u5dMql2PS2f+GOJkjw7dLV1QVAXl4eZ511FkuWLKGoqAhJkti0aRN33303nZ2dPPnkk0QiEZ599tlvecVfLQlBmuAbpd8ZYmeT95D7fGGZiQUWepxhSrNMFGQYqe3w0moPEhyqmRx2xwXocoRIS9IzrXTsh86koiSKMk2YDWrKkdkgotWoEUpZVg12ALzBKO7PiDeNGC8oOweC7G7y4A5EmVRooXXIybfLEUAzNOcwQ4EzclIMDHojpFp17Gv10NgTINOmIxyVcQcktAIMl4y6/VFKs82Ionrwlnq1N2dBup45FarhTkO3j32t6n0z6ERe2t3KykmqwBKE+JTiGWU2JFmJi6CGIjL9fRaWTS1EyIVQWMagE0m2xEd/g1KA7c4NpOrSmWQ7fANzg1Zz2H2jiUoymxoHkBUFvVbkD+8dBOCJjS1MzLHFiVFQ2+u0Dvhp7Pfy+9OmcM68IsYPmSY9tbmVcFTmooUliJ8jpD+tsxOWZJqGxOjCsjR+enTFF651e4uDOz+oZcWkHC5cVBRn8JFnjBezk/OTGZdp5YmNLfR71WvY3DTwJe5IggQJEnw1PH/S87jDblKNqXHb003pvHeGKi61ovo174a1N/Bey3sYNUaCUpDylHKWFizFFXJx47obY0IU4PSK0zmn8hzMOvXh5VtNb9HkaqLJ1cRPpv+EIptqEKcRNZQml3JGxRnst+8/rJvuMBs6N7B/YD96Uc+snFk8su8R3mh8g1dOfoWHdj9ESA6hFbRcOe1K3mp6iyX5S/4lMdrl7eJX638FgIjIT2f99Esfm+A/iKAb3F0Q9oHeArY8MP7nP3yorKzkjjvu4IwzzkCjif+ONX/+fM477zwWLVpEXV0dzz33HFdccQVLly79llb71ZMQpAm+MRyeCBsOUTtq1IsEw6pQc3f4mF1uo3CoxrKqKInx+RY+2GUnEJYJjzL+GduYJZ4k08ift8Wo5djpGdR3+2jrD4KioNWIFGea8AYkorISE5aSDL7gSI/Qoky1LypATcdIdNek1zClxEZ1uzeWNnzM9HRc/ihb6lx0DYZYNiUNt1+N6jq8EYa9kiKKOvf4PAu5aQb2t3qwGDUUZZqwmbS4A1GMOi2RqExdl2+MuMwwG5BkBUGAygL1S4MvGKW+WzVQmlOejE7jocMeiEvf9QUk8tONMOKTFMce9zYOeNT6yAJTScyBdjRravu4+rldzChK5fEL58RFWfs9Ie54t4ayDAtXH1XBu/t6uO8TtZ7zphMqY+N63SFOnW6l2x2kIMVERZaVTleAg11u5pSm83F1L/etbuC2UyZj1Gl4Y3cnN7+utmLJTDKwclpe3Jo8wQg3v74fk17LqdPzqOt1s6nJgaLA0VU5h235Mpr7P2lga7OD7S0OLl5Uwmm559If6iHXWIhFa6Wx34ssKzFX4Sa7l5xkI/3eMFaDht+dMomtzQOsre0nLMn84tgJGHVfTrgnSJAgwZelz9/HQ3seYnrWdFaOW3nIMaIgHtK0bvjh5bBQNevMlCWX0eBs4OLJFzM1cypHFR0Vd0yKIQVREMm15FKQVDBmvt8u/G3ctmdrnuWeHfdwesXp/HLeLwHY1rON32/5PQoKYTnM/n71/XwgMMB9u+4jJKufcceVHsc5E8+h2d1M7WAtzc5m3ml+h2xLNmeNP+tz70uyIZlcSy49vh4mpCWyVf6rUBRoWQdbH4GD74Ayki2HoIGJJ8GcS6FkyUjE4T+Mt99++3P3Z2RkcPfdd3PyyScD8PLLLycEaYIE/yrhiMSWOuchRWRVgZVQVOJAmyr2IlF11K4mNx32IBV5ZswGDeFoFLNRgycgkWbVUVV46B6Yh8OgE5lclIRJJ7K31UsoKqEoCsfPymRdtQO7Oz51d3FVCqkWPVqNwJzyZHY0uuJrOwdC9DhD6LUa8tIM5KUZsBi1OH0jEVdFgellSTT3BohE5VitqlYUmJBvoSzHxMaDTgY86rnTk/Qsm5LGR3vsNPT4cfojsXXptQKyAoOBEDW9LrKsRopSLbHeovtavXQPhmjtC7BybhYzymxML01iwBOhZzCEKArkpn3+0+ZsQy4CAklaGyaNmirsDkZweMOUZKivPzjQgycY5dO6fuzeENmjDIOe2NjCa7s61Wux6tnT4Yrti0oyJp1AIKLexH+sa+av58zk2S2teEJR3trTDUB9/4jov/3tav737Gnc8uaB2Danb6xJ1Vt7unl9t5ru8uL2NrSiyC+OGU9eiolTpucD0GL3cetbB1hckcEli8f2N105PY/tLYMcNzkHQRBI1qXGBPn+Then3r8BWVH4+7mzKMu0cPoDGwkNqf00i54fP7OLRvvI2iuykzh79pdPE06QIEGCL8MlH1xCi7uFl+peYnH+YtKMaV94zG8X/pYjC49kYvpE6gbrmJE1gyZXE4VJhbxw0gu4Qi4yzWPbWbW72/n7nr8jKzKd3k5CUiiurjQkhagZqKEqvQq9Rq1pe6/5PYJSkDcb3yQQDbCuYx2lyaVxUVhPxINFZ+G2hbdx57Y7Y9vPqTyH/fb9vFz3MgDOoJO99r0ADAYH+dHUHx32Gnf17aIoqYjLplzG8aXHf+E9SfAfQtdueO0K6K859H5Fguo31P8yJ8JpD0He9G9yhd8Yy5Yti/3c2Nj4La7kqychSBN8rSiKQmNPgC5HgGDk0IWSaUk6kkwmUiw6AmGZogxV4LT1B5AVNSqpEWBhZTKZyQaikjKmZci/wuh0XKNejWDNrUhh48FBBAHKslUBnGEbKRgvyDCys8k1JiwbkSAiSRRmGmNR3fx0I/MF0GpEUocMkaYUJxGOykQkGRCYW5GMIAjUdvpiYlSvFTDqRURRiNW4akQBnWao/+WkNJJMWj6tHuCS+RXYvUH+5+1dHDUhm0cvmo1OKwzdc9WN12TQqM3Lbfq4a/ksLf4GekNdTLXNpsRcwfmFP8YXhIc+aaUq18bNb+yn2xXkrjOnctbsQi5ZXEqL3UenM8gNL+/h/h/OwmrQMuAN4fCFY2nLv3ptP1lJIwL4Tx/UMTrTVlLgyY0tbGl2sKHh0OmuMhAIS3iCIyL/trer+bS+n2ybkZ8sLyc32cS8sjQyrAZkRcHhCyPJMv/7YR0PnzcLjSjgCUY45p61RCSFT2r7WVKRyfjsJAZ9Yc57bAu+kMQTF81l360rDrkOhy9MdOhpxGVP7UAjCEjKyB9Dm2Okb65WFDBoRSbl/eenECVIkOC7xR2b76DF3QKAgIBFa8Ef8fOHrX9AK2r55dxfxoThaMw6MyeUnQBAaXIpt266lZfrXuaIgiO476j7YmJUVmTu3Xkv7pCb62ZfR7unnaiivv8eKoX2ujXXsaZjDUcVHcW9y+4F4Mczfswjex+hz9/Haw2vAeAIOTBpTQSiI++VvoiPXGsuK8et5JF9j5BqSKU8tRxZkZmcPpk+fx8T0ybGBOmz1c/iCrnGOPoO8+cdf6Z+sJ42TxtnTfj8aGqC/xAaV8Pz50Lk0N4jY+ivgcdPgO8/DeO+vBnifwqh0CgzS81/VwZWQpAm+FpptwfHGP+MRhBAkhVCEZn6Lj+CANnJeox6DVOKk9jToh4rKaqLbopFT2tfgKwUPSmWw7vffh7ZKQba7EGsRm1MqBl0IsumjLQi6RwI8Na2PjJteuZPSAFUUVnT4UUABBEkCZItGiRJoDDDiMsXwajXYNCJ5KaNTRHVa0XmjY9PgU2xaBEAnVbgyMnphCIy3Y4QCytTGPRFyE8zjqRYDQlTFCE2H8D2Vgf+kESKRUdbfxAFCEZUQer0RXD7oxSkG+PqLlv8DewY3IACDET61Gt2uTi95GSMGhO//2g/T21uRSsSS/lt6FdrWMuzklhUkcH/flBHm8PPY+ubueaoCn712j4+ONAbd312TyjutazA5DwbB7rcJJt0/HjZOJrtPkozLOzvcuELSayYlM2U/GT2dri4+aQqsmxG/nTGVB5d10RNj4eorPBxjbrmiCRz55nTGJdpZfuvj0aSFe54t4ZH1zcDsKlxgGMn5VDT7Yn1mhUFyBhyaXpgTQP7O1V347X1/ZyXXsx7+7p5fEMLFyws4cSpap/RpeMzufd703llRwfrGuxxYnQ0qWYdR47PxO4Lk2r+73LAS5AgwbfPqw2vxr2WFImP2z7m9YbXAViQu4BjS479wnkaBtVSigZnA/aAnQveu4BgNMg1M6/h8f2PA1CRWsE5ledwzYxreLf5XdZ1ruOIF45AL+r549I/MidnDj3+HgC6fd1E5Agdng7m5cxjfu58LvvwMlrcLYiIyIocJ0aHqbZXc83Ma1g5biUZpoxY9PXXC35N/WA9Zq2ZF+peAGAgNMCT1U9yXtV55Fhyxsx1bPGxNDubOab4GF5veJ0scxYL8xZ+yTub4DtH1+5/TYwOE/Gpx1307n9dpHTt2rWxnydOnPgtruSr598PMyVI8CUYNhU6HIoCTl+ErXVO+lxhep1hmnrVD62yHDNl2SPCrrU/yM5GFwfavayvHjzclF/I7mYP/pBq6nM4l9l9rR6ikkL3YIjIkCJLteoIRRSCEYVASCEcVTDqNMypSKZnMMTqfQ4+3mOPi8B+ESaDhsVVqRw7IwOzQWTNfgc7m9w09QYoyTKj06pmTDExCpgM6v+2nmCUqbkpnDGtiHZ7kJIsEw2DLnb32DEZREIRibX7HexodFPbGf+Gvs7+IfZIX0yMAry0yYU0FAUcdrY16UaeWZ0yTU19PdDl4u9rm2Lb0616DnS5+KS2H1CNnQCMOpFrDmEmtL/LzfLKLLbcdBRbmh30eUJU5dlY9fMjOG9+MQWpZs6dX8zD58+mMM1Mx6Cfm1/fT02Ph0sWl1KYaor93mYUpgAQikrs7XAiKwo3n1RF8lD98NYWNfI6syiFU6fnMz7byms/XkSaRRWLq4aErVEnYtSKHOxxc9cHtWxtcXDXB6oBU1O/lyc3tbC4IoNOp/q3mWI69LO8QX+E13Z3sa7eznNb2w45JkGCBAn+VaJylAd2P0BIGnnId/aEszHrzMzInEGGKYNsczZTM6d+qfluXXgr51edz51L72Rf/z7aPG30BfoYDA6SY8nBorMwI2sGrpCLp2uepsGpClhnyElfoI/fbPgNAHctvYsjC46k0dnI8a8cz8rXV3Lb5tsAYmvViBoyjBmsKFnBX5b9hUV5i2LraHKpnyUlySVY9WoZjjPo5IL3LuDmDTezsWsjOlGHVtCSYkhhZtZMHtv/GHWDdWOu6YppV7DrfDVt9+YNN3PFR1fQ5GwaMy7BfwCKoqbp/qtidJiID16/Up3nvwRZlvnjH/8Ye3322Wd/i6v56klESBN8rQTC0ufuT0/SUZhhwu4Ow1AgdbifpicQjfUgHSYUUeeTZAVFUeLcZb8skSFnoeF/R6MoitoyZdR7mDj02MZq1JBs1uINSoiCQkSC7sEw7XZ7LBU1HFWIROUvlVI84Anz6QE1TfjIyWnUd/niHHsPh2bomvNTzFw0rxyNCK39ARr6Pdy/th6A/V1Ofnvi1JGa18/cpuFm6gByVMfLH+agBHJiQu9HS8exuDyT+z+p55196hPwQb9au7mmtj+WQnvp4lJ+MKeIv3/aFBPiw6fUa8SY4BumMtvKwV4vR1dlY9Bq+Lha3f9xTS/N/T7W1Kmidne7k0XlGfxkWTneUJRAZNgYKjSUkque5cjKLAB+/MxOPq7p46jKLB69cA4nTM7luW3tHDdJjXBqNSK/O3US6+vtFKaOODqdO7+YB9c2MinPxvUv78WoE7loYQlPbGrltBkFKIrC6Q9sxBmI8PCnTXQOqoLUGRhJITZoBEKjfm/ZNjWtfPnQ2hIkSJDg/4dANMAv1/2SVW2rYttumHMD51WdB0ChrZDVZ63+0p+HjqCDYltxLPU1LIU5rfw0nCEnoiDy1HFPkW5ORyfqqBuswxF0jJlDI6gPm0uSS/BEPISkEL1+NUNmbftaWACekPqhHpEjTMuaxv8e8b/8/JOfs6FrA6CmHF8w6YK4eRVFQVZk9KKekBSiIKmAd09/F4AcSw4nvHoCOw/uZF//Pp476blDXl+SXjWe02v0GLRf3qU3wXeIlnWHrxn9svRVQ8t6KF3y1azpW+aee+5h69atAJx++unMmjXrW17RV0tCkCb4Whk2KBqNXisQHto+vdSGRhSYWZZMUUYYm0UbcyZ1eCIxcWPQCSyYkEJ9l59Bn4SsqOmfmn9dj7KwMpU+V5iC9LFptXtbPDT1BjAM1WPqNGpLlZa+AG39ASYVWTnY7sHhUwXSsICUFXVsRFJo6PEzreSL6weH282oNZ8SXY6RJ98T8uNtcCNRmUBYxmbWMrXEhj8k0+9WBaLqCiwRlmU0ooAkK1R3u2ns93L/+nqiksxtp0ygsWUzlRkF5OnL6HMq2Gygl1O4cNyl+MvrWH2wj5te20ez3UevO8jc0jQ+ru6N3YdhU6PTpufx6s4O0i16fnnCRERRYFtz/BcWo07EHYyyr9MVt/1gr5eKLCtLx2cQCEvceHwlt71dzZySVLaMapuyo3WQHa2DfHigh0A4yoyiFCbn2Xh9Vye+sIQAHDspm5+9sBu9RqR5QH2KuupgHw+uaeT9Az0IAhQPrRngp8/vZtXBPqYXpvCLY8fz9OZWvKEovzmpio7BAGuGIryXLCnjymXl2Iw6XtzejjOg1vh2DInRuSWpdDoDdDrVhyU5KSYCYYk+T4jiNDNv/mQxyeZ/L508QYIECT7LEweeiIlRvajn0RWPMj1retyYLytGnzzwJHdtv4uZWTN54vgnADX6es3Ma7j2k2u5a/tdPLr/UU4rP41rZl7D+NTx3Dz/Zg4OHMQRdPBJxyfIihz3UPPH03/Mg3seZHfvbiJKJGaydN/R93Hmm2fijXhZ1baKpc8vjes5qqBwy8ZbuHrG1UzNnEpEinD+e+dzcPAgv5z7S/KseSzKWxR3bVnmLNo97dQN1nHVx1dx4aQL2da7je9N+B4ZpgyAWBrx3Jy55Fvz/8W7neA7wbZ/fHXz/BcI0rVr13LjjTcCkJWVxYMPPvgtr+irJyFIE3ytlGabcPujNA/17hSAcblmatpVAeELRrGZtYiiQFbKyJNMpy+CJxAlL1WNls4cl4xOKzK5WINBL5Jl0x823faLSDJp41rCDHjCtPWrKa++kCo0FWDWOBupVh2BkMTuJjcKsLnWSXqSDlDHpVp15KToMOg0HGhXayxDYRmXL0KvM0xRpjFmnPRZht1xAcwGLXlpRjoGgpRkGTEbRta3p8lFS38QWYEpxVbKcy0srkqlayDAzia1NlKvFZiSn8Ij583i7+uaOGpCNje8touwpDAlN4VmaQdBaui070KJWLDZgrR1mUgKjUcoE3h1ZyedzgAHutyx8zaOcruNSAo9riBGrchfVtXT2O+jsd/HjtZB5pamUdsbXyecZNARjISw6DWY9BpcgUishrOx38uiP35CQaqJixeV0Gz30Wz3MSXfBqh/JwatSFiSOdijztvqCLCrzRmb32rQkGE1xGpWR/8l3PNRLeGhczWMWtdwlHV/p4sLH9vKcFBzT7uLvbccS0m6GZc/woI/rCIiKRw5PpO1QxHb0bQ5fPS4w4gCFKSauOWkKh7f2EKfJ0SfJ5QQowkSJPhKkGSJX6z9BVu71aiITW/jieOeoDy1/N+ec1ef2tZrr30vUTmKL+Lj9DdPZyAwwLiUcYAaQX10/6Msyl/EnJw5nD3hbOwBO8teHHH4nJIxJfbznJw5zMmZw80bbub1htepHayl1lFL3WAdVelVbO1R1z9ajGoFLaIgsrl7M+6wmx9N/RHvN7/P/gG1HUzdYB1nT4hPSax11LKzdycAYTnMus51bOneQlgO0+pu5c6lqlvvg7vVL+vrOtfhCDhIM32xC3GC7xBBN9R8fguUL03NW+p8/8F9Sg8cOMBpp51GNBrFaDTy0ksvkZX135eBlRCkCb5WBEFgepkNo05gwBuhIs+CWa9BREAQICf10Ok0mw46CUZkzAYNR09Lj4lPs0HzpaKPX5ZwVGZnoxtvUGLQG2HBhBRa+gLkpBpiDrn7Wj2xSK2sQKpVi6xAICwzpdhKepKeLkeQnBQDFoOGshwzq/cNEAzL2D1hKvOteAJRCjPijYVy0wy09gdIMmmxGNVa1NnltrinwW5/lKa+kbRl7ygRm5qkj4k8k06kZzBEQbKVP506nWc2tiEKAhqNRFAKIEqjosFaVWgW5QWQpG2sfCjEgnHZvLyjIzZEI8Do7GGzTkO2zcCPntrBjtZBBMBi0JKbrM57z/em8+aeTlZMyiEzycC6OjubmgY4f0Ex1720N04wzi9LZ2PjAB2DAX7/rlqnadZryE8xs6/TTW6ykfevXcqM2z5EQTW+Gl0GohHAE5LY1+mMbVOAnx5dwZZmB5sa1UirVhRIt+qZduuHFKebOW1GHhsbB2JuucMsm5CJKAocPyWX296qjt3TDY32Q7YpcgWk2N9CmyPAB9W9/OqEiTy4ppHjJo812kiQIEGCfwd7wB6LjB5bciy/nvdrUo1je0N/EYqicNvm29jRs4NLplxCkj6JJQVL0IpaBgID9PnV0oklBUtYWbaSB/Y8QLIhmXEp45BkiVfqX0EjaLDoLPiGavrm5c7DH/Fj1plxhVzoRB1LC5bGzJWuWX0NXb4uNIKGCakTCEfDtHhaUFC4ePLF/GzWz7hp/U282fgmc7LncMPaGwjLYcaljKMipYKLJ1885jp0og6NoIm5/gKkGlPp9fdSmKS22NrQuYGgNPKZedvm22Luvwn+Q3B3xfcZ/f9BkcDT/R8rSJubmzn22GMZHBxEo9Hw/PPP/1f1Hh1NQpAm+NqRZYVORxh3IIrb7yYYkZlanMS4XPMhx9d2emMtYvwhifU1DpZWpf1b9aKfR2O3n72tHgw6dd4UixZPMIpOI9DQ7SMiKRRnmgiNaldjM2nISTXSbncRCMtsrXOxfEoaW+tcKEBJlgmDTkSnEQgCOq3IumoHsgLdgyGmliRhNmiQFQWLQcOJs+Ofco2+RkVRqO8eiVImmTRxvVdNeg2TCq3UdHpUkRSQ6HeHaXa4eWRzAyYdXHdhA6I2iuydQNBnJSQHsTsMlBb6EEUQBIX2QS/Tc/Li1vHZUtY7Tp9CQaoZZUgZzixK4eHzZ5M+5FY7tzSNqCRz90d1VOXaeGpzKwBVeTbs3pFUZL1W5JaTJnHDK3vY0zFiolSVayMqy1x7VDmv7ezkpy/s4qpl5WxuGuB7cwq5/uW9Y9a2t8PNhGwrtb1qZPrxDS24AiO9ZKOywis7OnAFIuztcHHtURVkJhno/4zzrzcU5Yi7PuG8+cVctKiE57a2EYhIMWGabtEx4BuZNxCRuPusqdzx3kEGvGFe2dHBT5aV89cfzCBBggQJviqyLdlcUHUBe+17+dGUHx1SjL5Y+yK9/l4um3IZRu3YMhSATm9nrK/nbZtvY83Za2J1lmUpZdw8/2ZaXC1cOvlSrHor50w8B1EQ0Yga/rn/n9y94+4xc96y8RZeqH2Bm+bdxMUfXIxRa+Slk15idtZstvdtjwlXnajjriPu4ntvfw9l6BHfcGrt7xf/nhvm3ECyIZkaRw1be7ZyyrhTuGjyRYe8jrKUMp498VkcQQer21azrnMdUzKmcNGki5icOZl2dztXfnxl3DGr2lbx4O4HuXL6lYecM8F3kPC/aWR0OELer3a+b4iuri6OPvpourq6EASBxx57jFNOOeXbXtbXRkKQJvja8QYl3EMmMMNC0+mPHHZ8U0+8NbzDE6XXGT5sNPXfZbgGMxRRPyR7B0O09sebKPU6w2QPmSyZ9SJHTE6n3R4gEFavIxyVEQQBi1GDNyhh1InUtHvwDEXR8tN09AyGkIcce13+KCtmZLC+epABT4TyXBOyDMkWHSVZprhzewISbf3DdYp6clINOL0ROgaCFGaYyEzWMz7fwp8/ruXY8aqg9AQj7OhU6znz87yIWvW+63QyT72ehX3QiCgorDyqnYNNKXh8WuSwhfMXFpNq0fOXVfVxaxAEmF+Szkvb2ylINfHw+bNZX2/niPGZpFri25rcv6aBHa2D7O1wIgpq1HJBWRrBcCnNdh+rDvYRjsr0e0M8efE8/vxxLQeGWq5sb1VTuba2OHAHorQNBviktp/8FBMnTs3ll6/uGxPZBDVKO0xwKCW3KNWEDHQ7gzTZ/aSYdFiMWmYVp3LBgmL+98N4d8bd7U4G/RH+uqqe6m537EvTMJPyklnfYI8ZRGUlGZhWkMLlS8u4492DSLISSwdOkCBBgq8KR9DBpVMuxWawIQpjjfI+aP6A323+HQBWnfWwQi7XkktZchlNriYkWSIYDcYEqT/i59maZ+nwdrC0cCnTM6fzXvN7TM2cSpI+iQf2PBA3V741n2RDMtUD1XR7u6kdrCUkhQhJIVrcLfxh6R94o+ENjiw8EmfISZY5C4PGENfyZWvPVs6rOo+DjoPUDNRwQtkJPHzMw7jCrlj96eGYmK62uqhx1NDt66bb1805E88BwKQzjXn/Bnh438NcPu3yQ97DBN9B9JYvHvOvYLB+8ZjvGHa7nWOOOYamJtUl+m9/+xvnn3/+t7yqr5eEIE3wtTPoHSs+P2soFInKbKlTI2Y6rUDwM4f4w1HgqxWkk4qsaDUCUUmmezBMMKogCjBa92hF6HWpwnV6mQ2tRiAnxYBW9BCV1euo6/KRl2agvtvPwc+0V5FlgWVT0tjd5KbfHRlKP1VweNQLbOge/pAOkJGkwzqqttVi1JCVrMcTiOIJSvQ0e4bWq9DnCnPczEyqu1ysb+xDlmF/1yD9vhAnTs7nztOnYrTZ6UZtPaLxlpKZdgD7oBFZEfhgbRGBiIJafRnl+w9v5sojyznw22O57uU9bGpy4PRHUBTY1uogKisIQj1PXzqPU2cc2iTitBkF7O1wkW7Rc+r0fI6amM2UgmQWlWeyuWmAVQfVtLAnN7Xw8PmzuXXlZABcAbVu0x+WCISiTC9MxheSqO/z0uUK4A5EOWtWAc9tawcgN9nI9+cUkGYxcPrMAl7c3k5jv5fjJuewpWmAF7Z1kJlkoGMwgDekCnJnIMJFj28lL8WEzajBPZT6bNJpOHJ8Jq/t7sIdjPLqzs7Y9cwsSuGChSUsq8zi6P9dQ583zDETszhhai7H3PspmUPRYUHgS7kjJ0iQIMGX5X+3/y9PHFBNh+bmzOXRFY/G9m3s3EhYDsfqQQHMWjOOoINkfTIaccS3IBgNcs3qa+jx9XBmxZmsKF1Bpjkztr/L20WjqxGArd1bWdW6iudrnydJn8R9y++LCcn5ufNZkr+EwqRCev29HFFwBEsLllKRWkGnpxOr3sr83PkIgsDl0y4fcz23LriV52qfI8uUxc9m/gx/xM/5751PIBqg0dnIdXOu+0IxOprlhct5tf5VMk2ZTExTRepwH9PP9jvNteQmxOh/ErY8EDRfTdquqIWk3P//eb5BXC4XK1asoLq6GoA//vGP/PjHP/6WV/X1kxCkCb52LEYNAnGdVNjR4KKqKCkWFex3h2MRy+Gk1axkHS5fBEEUyU4+dCrSZ+lzhtjd7CbDpmfmuOTPHZtk0jK7PJlQRCZa76TfrYpEk15kYqGVdKuOA+3emPutgGq2pNOIMdHaZg8eZnZ1fE6qEa1GYP6EVHoGQ4gibDyoGiPZPSOqW6cREEXYdHAQjSgwqzwZjSiwaKKaorV2vwMfEgatSFSSSBuqb93UNMBFc8spy0hiSm4K7mCYyuwUMi16llYWsq3LSFNPkNKkYl68YBw/fHgXfa4waRYd1T0jhj92b5jfvV1Ni93He/t7STJoOKoyi1SLHllWeHtfNydOPfybejAiUZFlZXFFBu/t6+Gvq+u54shxsf3zy9IpTjfTOuBnW4uD0+7fwJLxGVy9vIJkk463r17MfasbWN9gp7bHy4PnzmRrs4OqPBs5yUZM+pG3qucum09JhoUdrYMsv3sN47OTePSCOei1Ih8d6I2ZCxWkGOlwjvx+drW72NXu4tRpeby+pwtQ02+Xjs/k9T1daAQBjSiQn2riyYvnkp9iiqVQW406+rxhbCY91V1uFAX6hlJ/ZQXcn32CkiBBggT/Bk8eeJI/b/9znIvtzt6dROUoWlHLrr5dXP6xKvh+NfdXlCeXk2vNxRvxcsQLRzAnZw6PrXgMgCs+uoKNXRtHooaCKixHU55azk+m/4QWdwvnTDyHf+7/J6C6+T5b82xsnDPkZHrWdH747g8B+NOSPzE5Q32o+NNZP/3C6zp9/OmcPv702OtANBCLnJp0pjHj7QE7be42ZmbPjG0LRoP8bvPvCEthbllwS6wdzDCt7lZKbaU0u5oJSKoonZI+hb8f+/cvXF+C7xBGG0w8Carf+P+fq/Kk/6j6Ub/fz4knnsjOnap510033cT//M//fMur+mZICNIEXzu+oDTSm1IrEIkqhKIKzb3+mCDNtOnJtOnxBiMEwupopy+KKIqMz7NgMR7aqXY0UUlm40EnCuDrD1KeZ8Zm+mLHU4NOJD1JT787EjMrauz2kzcplTnlyext8WA1auh1hWno9iMIMKnQSn2Xj9CotjZ5aYa41i0K0OUIUpRpQqsRCEYk9rWqtQyiAONyTDh9UaxGDZOKkqjv9tHjVEV5eq+fcbkjaSsLKlMY8ETwBaMEwhITC9QUlO/NKeKFDaq4yrQamVOWSrdjJL15Tt5EZueO9Gv98+kz2dfmRaOBuz+pps8TJBCREYbuw3AJqz8ic9upk1l9sI+Xt7fzwDkzOboq+5D3LxSVuOrpHayu7WdaQTKCoNaUDrfvGeaPp0/liY0trKntY1e7k13tTqKSwg3HVVKWaeWChSW8ukuNUB7ocnPDcZWxY4+amMWzW1uJSgq3vVXNH8+Ywovb2ul1h+h1h2hz+CnPsnLu/GKqu92UpFt4aZRJ02iGxShAWYaF02YWkJ9qIivJSG6KEZ0oxsynbnptHy9sa+fqo8qpzLFxxPhM/GGJqKxQlWtja7ODdfX9tA74mF+Wzp/eP8hH1b3cfFIVR4zPPOT5EyRIkOBQbO3eyv2770diJDJUYC3gmpnXoBXVr2s6UYeAgIJCka2I1059DVBNhAC292zn73v+zhMHnsATUR86phpSqcqo4vyqQ6f8nT3hbG5afxO3b76dG+fcyKzsWVSmVfKLtb+IjSlNLsWkNaERNEiKhFX/5dMg291qm5YjCo+IXYdJa+L5k56n0dlIhikDV8hFskF9iByMBjnrrbOwB+xcO/NaLp1yKQDrO9fzZuObACzMW8hpFafFneevO/9KtaM6bttgaJCrV1/NX5f/FZv+P0eY/J9nzqVfjSCdc+n//xzfEOFwmNNOO40NG9Q+vddeey233377t7yqb46EIE3wtVOQYcTpiyCKAloRDnb61e3pIym4Oq3I4qpUOuwBtjWodYURSUEZEq7jcg5tgDQaRYmPwhq1Xz5FpyLPgihCvyuM3RPB5Y/yzrZ+phRbEUWo6fTFrF4VBbKS9QgC1Hb6sBg0TCy0kp1iwOWP4PBEONjhRUEgLWlEEHeMiqbazFoyk/XkpxtJT1KjkNFR4lavi1+7Xiti0IpsHhK0ZoOWcTlmrAYt00qS2FzvZGeHgyVCGucvKIlriTPaKMkXVr/oSJJCvydEMCJz0wmVTCtIYU+ni5XTchmXaWXQH+aix7fS5vATjMjcv6bhkIL0gwM9/PiZnbHzCYLAgVtXYNKNfYCwYFw6C8alc+Mre3h+myoWh6/4rT1duAJhrl5eTrcryDlzi+KOXVSewWkzCnhuaxura/tYdvca/CGJypwk5pWmMS5TFe8V2Um8dMVC7N5QTJAKwBMXz+XvaxvZ2TZIYKiOuTTDzEWLSljwh1V0u4IsHJfOs5fFRw/e3ttNVFb4tM7OtUeNByAQlvj1iVVoRIG/rW6gxx3i7582ceKUXB5co6a+PbWpJSFIEyRI8LnIiszLdS9j1Vk5oewEHtn3CP6oHwGBq6ZfRUlyCccWHxuXbjo5YzLPnPAMYTnMrOxZse2lyaV80v4JCgr37b4v7jxaUctDRz902HW82fgm6zrXAbC2fS1PHv8kudZcbphzAy8cfIFlRctYUrCEPX170Gv0BKIBZEU+7HyjCUaD/ODdH+AKubh0yqVcO/Pa2L58az6rWlfx41U/JteSy1unvYWsyDS7mnGF1B7WA4GR/tTTMqdRlFREWA4zLXMa23q2UZVehSfsYUfvDj5s/XDM+Tu8HXR4O9jVu4sjCo/4UmtO8B2gZAlkToT+mn9/jqwqKFn81a3pa+YHP/gBH36o/g0vX76cSy65hP379x92vF6vZ/z48d/U8r52EoI0wdeORhSYVmpDlhV2NY30ucxJjU/DlWSF7UNiVBCgJNPIgDdKepKWtv4ARZlj03o+e7wIyKjRR61GRFGUL3TnDUVkPIEomTY9bf3BWIsRBdjbOuLOJghqK5HsFD11XT46BkKkWLTMn5CCbkj8Wo1atX1MZSrJZm3s3IPeCKIgoNOo7W6cviiba9UP3PJcM/6QRJcjhEkvMiHfQmHG2Gs16VX33qikkGQaEXz56UYe/GctvrDEabNzCYQlrEb1f+1wVObnL+6myxng3u/NQNQqrGnooccTwB+REAVYVJ7JdS/uxhuU2dhg5/GL5nL23zdR1+tFKwqkWfScPbswdr4fPLKZzY0DTMhJItWsIyorRGWFSxeXcu78Ysz6z39b+eMZ07h0yThqut0cNzmHHa0Orn5OrYX637Om8YtjJ8SN/98Pamns93Lu/GIa+70Uppp4ZajW8+zZhVy8uHTMOTKsBn5/6mSe3NzK5UvLKEozs2GoHYxJpyEQkXAFotzzUR2OIYOtrc0DyLLCKzs7uOn1/Rw/OYffrqzi1Z2dXHWk2vfvupf28PKODkozLLzwo/lctKiER9c3s3JaHkfctQZRgMwkAz+cV/y59yBBggQJ3ml6J2ZK1OvvZWrmVGocNUxOn0y7p32MGB1mSuZID9At3Vvo9nZT56gbM25m1kw6vZ2cXj6SKhuVo7zW8BrZ5myWFqjtIyy6kWycqBKlxlFDZXol1629jl5/L7nWXN5qeos17Wti416rf42y5DKKbPEPDz+LgoIkS7Fzf5ZOr/pe3h/oxxf2cdEHF9HkUo1cUgwpXD3j6tjYTHMm75z+DgC/XPdL3m56mwmpE2LjP4+ffvJTzp5wNr+c98svHJvgO4AgwGkPweMnQOTfcN3VWeDUB+Er7s7wdfLqq6/Gfl69ejVTp0793PHFxcW0tLR8zav65kgI0gTfCPVdPva3ebGZ1T85AbVucjR7mt2xiJmiQKcjRDiq4PZHae4NEpVkynIO7b4WkWR2NbljVTf+kMTb2/swGzRMKU5CrxVjfUVHoyiKWp8ZksbUuY4dCyfOyUBB4M2tqkGPyxflvZ12zAaR5VPTqWn30djjRysKHD8rE+2Qbtzb4sFxCHMngIZuf+xnASjNVqPBwzWnWclqJNlk0HDM9AwkWcFsGBGkuckmPvz5ETh9Ybr6I3y0e4CpJUmQ1E6rPcjbe7sxGaO8eXAPrkCYN/arIvuGFROYV5bOxNwkzppWSopJz+5OB32uEOfNL6bZ7uX4Sbncdurk2Lk8wUisz+fBoRpUAVg2PpPrVkwYk6Z7OMqzrJRnWWns99Lm8KPXikQkmawkA1FJRqtRv4TV9Xq475MGAEozLLx4+QIAFo7LoMcd5IfzD/1laHuLg4Z+L/f9YAYV2UlIssKp0/PY2+FieWUmTn+UZZWZ3DCqnczpMwsQRYF393UTjsq8vbebe86ezmkzCgA1Gjzcq7XZ7mPlfet55apFnDe/mMV/Ws2AT023vnhRKcsq//uaVidIkOCrZdh8R0Dgzzv+DMA/jvkHP179Y0JSiMHgIA8c/cBhj6911HLZh5fFakRFRI4vPZ7qgWryrHlE5Si9/l6eOfgMV824CoCX6l7iji13ICBw49wbWVqwlBPLTmRD5wbqButYmLeQleNWEpbCuEPqA2JnyMnevr1x517dvpptvdvIMedQllLGH5f8MZaOOxqT1sRTxz9FjaOG40qOG7P/JzN+QpoxjRJbCVd+fGWcuHSGnAwEBjDrxmZI2QP22L8RWf1sXZi7EIPWwIGBA5QklVDvrGcwpDq4R5Uo7zW/lxCk/0nkTYfvPw3Pn/uviVKdRT0ub/rXtbIEXwMJQZrgG6HToaaruv1RBKAiz4xRHy9eOgbiDYLC0Xh5GD1MhpCiKHy8204wMjK+e1AVB56AxMaDTgAMWoGSLBNVRUmxcf6wNNKyY0iRZtp09LsjaESQPnPON7b2k5emx6ATCUVktBqBiKTgD8mEInIsdVXt8TlynFbzxU/pUi1aopLMpwcclGabYtHiRRNT6BgI4vZHqSq0xgTqaPJTTGRZDdR39ANw0L+DjuBmAE6aM42yiQ3ozHUkR2FSRR6ZxlS+N7soli6cYlJbuEzJTaHXGSYiyfR7wry4vZ0pBTbu+qCOLJuB5y6bz4lTcnlnX3csGq0Aq+v6uffjem48vnLM2g5FRJJ5ZF0T93xYR0RW+NXxEzliQiauQIQpv/2QLJuBFy+fT2Gqmcn5Npr7fYzPSeK5rW0srsjAoBM5Z24RBu2hBfCPn91JrzvEExtbKEm38Itjx3PP96az8I+r+cf6Fs6dX0ROsjGWwiwAx09WTZuuPXo8UVnh2Ek5sVpSgCRD/NtljzvEhY9t5c2fLI5FWcdnW/nh/ER0NEGCBJ/P/bvu56G9ahrt6FYlu/p3MSt7Fhu7NjI7Z/bnzqHX6GP1pADJhmTebX4XBYVmdzMa1PfHLPPIA7JhJ1uNoOEPW//AA3se4KMzPuLuI++Oi8Ze8N4FBKQAywqX8YvZv2B9x/ox5w9JIeqd9dQ767mg6oK4yO1oylPLGZcy7pDZSvv695FsSOb3W34fE4+jGQgOUGgrHLP99kW3807zO+zq3cWajjUAbOzeGNvf5+8bc8yp5acecn0JvsOMWw4XvQuvXfHl0nezqtTI6H+gGB3u8/5/lYQgTfCNEBolFhXUlNXROH2RQ0YnNaJar2kxahifNzY6urvJRXPfoZ1u05N0GPUinQOq0VAoqlDb5ScrxYDFqOHTAw78oRHFKSgwpyKZA21q5E+SoSzbSFNv/PxdDlXsagS1zlUAppUmYTVqqSq0kJ6kw2bWxtVxlmSZ6BtqH3M4BofvSVBmdPlrpz0Y64+6q8nNihljaxOdvgiBsMy88Sm0u/po1NQy7IuxYIYdl6LObdUZWbm8m6TBCq59bje+aIQ/nTGVRZUp7Gp2Y9BpKc81s61dffocjMpc//I+QHWVPdjjYWZRCu/s60YGDFqR0NCTggzrSF9SfzhKQ5+XyXnJcaLOHYzwxq5OWgf8/GN9c2y7QSeSmWTgsie3E4hItA74OfX+jay5/kjevnoJiqKw+E+f0OkMkGHVY/eGmVWcyitXLjzkvZyYa6PX3Y+sQJPdx4+f3cVTl+iIDD1hCEVkZhalcvXyclrsPn60dBxTClRDjemFKTx1ybwxcy4sz+DNnywiFJG5/Z1q9nS4MOhETHoNj54/mzf3dHH1UeWIAvzu7QNIksLxU3KZV5Z+6F94ggQJ/s+yoXND7GetoH4Vy7Pmceb4M/nR1B/hDrlJMaZ87hylyaWcNf4sXqh7AYDylHK29W6L7VdQ+PvRf48TiitKVhCIBnhs32M0u5uJSBFOfeNUvBEvj614jAlpE5BkiX129X1fI2iw6CwxsWrVWbl0yqV0eDqQFInXGlRTJW/YiyRLrO9cz7iUcRQkFcTOefobp1PvrOf08tO5ddGtse1Nriau+PiKOEGuETQcUXAEUzOnkmZMY3rW9DHX/cLBF4gqUS6cdCGP73v8c+8REBPtrzW8xhXTrjhkxDXBd5i86XDVJmhZD9segZq341vCiFrVTXfOpWrN6H9Qmm6CERKCNME3wrAmSTZrMehEKgvixWVzbwD5EBFQnVZk/oTUMdtlRaHfFT6sGAUoyjRS1+lHq1FNgfwhVehZDBp6BkNxYhTUaF9dlw9/WN1u0ov4QiNvema9gMmgRZIVnL5oLD1YIwo09vjJSlaFbk6qAW8wyv5WD3npRmwmLR0DQSwGkYikEI4qmA1qhHU4AisKYNSLBMMysgJOv8SSqlQEQXUpbhkSpMPR0T2urfSGupifeiRK1MjbzWsRJQvpZgstuo8ZZdKIV+xEjgqs25LJQ6ecRUNXkO0OL+ub1GL557e285uTq+KE7vkLijHrNdz6VjWBiES6Rc+xk3IoSDVxxVM7EAU4cUoufzpjKnaf6nQ7p2Skh9zZf9/E/k43Fy4s4bcrJwHw/v4eHlvfxNaWQUxD0XGjTuR/jqvk3PnFvLKjgzbHSPpyjyuILySxp93Fz1/cjTMQ3xYoKsm8v7+H6m43ly0pJck4kpJ9xdIy1tT2x/1+/7aqnhcvX8D21kFOnJKLIAj84tgJKIrCm3u6cAbCLKn4fCOiqQUpADz3o/l8Wmdnbql6ze/u7+alHR1Ud7uZUZjC01vU/q//3NTKjcdP4Iojyj933gQJEvzfYnS08JjiY7jziDvj9n+RGB3mnMpzeKX+FSRFIkmfFLcv1ZjKwnz1oV2Pr4c7t91JRUoF7za/S4u7hRxzDpdNvSxWx7qjdwcT0iagETXcdcRdrOtYxyWTLwHgD0v/wEO7HyLHmsP3JnwPq95Kp7eTRmcjaaY0ZmTP4KG9D/HQnodINiTz0ZkfYdKa8Ef81DvrAXij8Q2q0qv4XuX3ADBpTBg0BoJSkDPKz+C9lvfwR/3kWfPwRXyUJpfyct3LpBpSSTelMz1rOus61nH7FtV5dFXrKirTK9naszXOZCnXnEtfoA8FhR9W/pAubxer2lfhDDm5c9ud/Hbhb7/kbynBdwZBgNIl6n9BN3i6IeQFg1XtM/of1NolwaFJCNIEXzuD3gjeoKqQCtKNjM+3UN/lY2udi/JcExV5Vpze+OhhXqqesKQwtThpzHw7m9y09gUoyfr83qQObyQmKIsyDDT3BlBQ+0bmphpo7Q8QCsvodQJOnzouHJFRFFX0VBZYSLXq0Io+clINMVOlYFhiV5Na75ph03GgzYcnINHnCpEc0WE2iOxqcmN3R2i3B0i26OgdaueiEeGY6emYDRrW7HPg8kfJTtGzYEIKAC19AWo7fZRmm8mwqRHH9CQ11TksyUwpTsIX9bJ5cC0ARtGMkSR8VrW+J+qrhEN0upFlgV0H0tg0ycOKSdkUpFvY3tnPwV43J09TU1W9wSh6rYheK6LViHx/bhFFaWY+rO7lokUlFKdb2N3ujNVKTi1IwWzQUmTQUpBiZkuTanQUisoc6FTTjfe2O/nNm/s50OFmR9tIOlaKSceTF82h2xXkoU+bWFPbR32fF40ooCgKsgIlGWbSLHoeXNNAx6Baa3V0ZRZajUhhmpn6Pg9XPr0DBQhFJH55gtocvdnu49Ind4y5B1tbBslLMXF2Zny7gpd3dHD9y3sRBHjn6iVU5X3xB5tZr+W4yTmx1/2e8NC/oTHp2evq7AlBmiBBgjjqB+tjPx8qJfWLUBSFK1ddyeauzehEHVEpyur21WQYM7AH1QyXe4+8l7AURq/R83T103zU+hEftX7EssJltLhbWJS/iNMrTqfJ1YQn7OHkcSfH5j+q6CiOKjoq9jrXksum7k0oKCQbkvn5rJ+Tb83nmROfiY0JRdVspHA0TFgKxwSpiIiMjKRI3L7ldvQaPadVnEauNZdXVr6CI+igIrWCt5vfBuC95vcYCA4gCmKc0Lxr6V24wyPGiKOjwaPp9nejF/WE5TDbe7fz6IpH2fryVjwRDybt55sjJvgPwGhLCND/QhKCNMHXjnHIHTYiKSRb1D+5/W3eoX99ZCUbcPpVQSgKkJGkY2JhUswA6bN0D9WjDqfiHg5vIIpGVFusODxqSrAkw6A3TEGGiSMnq6mUsqxwoN2DTiOSmaxnT7MHlz/KriYPcyuSmTs+JW5eQRBi/UINOpG8NAOSrBAMy+xudqDXCuSkGIAIwYhC1D1iZiTJsKF6kCSzltJsE0a9ZqiFjCpiSrJMeANR3P4ooYiMQScy4AlTP2x8pMD0siRyDPn0hXooMpciIIJXQJQNmH1VSKZOwqJn9Ir556ulBMMaNjbaOW5yDtnJRh65YKQ+qd0eYHuDG4NO5KipabiDUTKTDCwsz2BheUZs3PTCFH5zUhW9nzEUuuPdGv6xvpmSdDN3nTUtloBV2+thZ7tzaBUjplHuYITCNAv/WN9MddfIFwyAGYXJuINRfrK8nFve3M8nQ5HO8iwrHx9U64JybEZ63EFEQTWbGjdKZO7vdOENqSnK2TYDve4Qeq3Ir46vPKTpkmWoNlQUBJr6vV9KkI7m+a1t2ExarllezglTcynLsAIC6+r7CYSjXLdiwhfOkSBBgv879Ph6CEoj2T0vHnwxzk32y+CP+mNpv5I0khJj1VtjgvTG9TdiD9h58OgHY8Y/6cZ0/rT0T7hCLrLN2QiCam4E8GLtiwSiAc6deC4aUYM/4scRdCArMgOBAbLMWfT6eylLLjvkmn4y4yfIyDxT8ww/eOcHvHjSizxf+zwy8dlIt268lRfrXuSeI++hyFYUc+p95NhH2Nu/lz19e/io7aMxrWVuXHcj0uhUTWBezjz6A/1UplWysXMjZp0Ze8DOxPSJHHQcZH7ufJL0Sbx6yqscdBxEVmR29u5kZvbMf+l+J0iQ4OslIUgTfO2Y9PHusKFI/IfM6n2O2M+yAn3uCHVdPspyzGhFgYOdXiwGDQXpRjYcdCIIAmaDMCbldhjtUGsUl09CkkGWISdVj9OvipSaDi95acZYbaMoCkwpHhEhM8fZWLPfgaKA3RMmIimUZJnoHAhS3e6lIMNIslmLy6+2iinMMNLlCNE1JJTDUYVORxCzXsQflonKCtkpejSiQJcjhD8s4w+H6XOFWTknK67GsqHHT0OPGg1UFJm541Mx6UcKStv6g4giLM07nrZAAx/2vYFZY+X7eZeiQU8gXcuagSwceEDWgChRbBrHlQunsrfDxRVHjDvkPfME1A/5UETmt29W89y2NuaVpnHGzALq+zzsanNy04kTKUm3HLLNit2rPhwY8IWZVZTK4vIM1jfYY6ZBoqC2dKnpdvPIumZ8IYnHNzRz8tQ81tXZ1RY0Q7+LXe1qO5xH1zWzf5RYnZBtxROI4AyEmVeWxhu7uzhucg6/OHZCnCBdMSmHSxeXogCnTs/n+W1tnDQ1jwXjDl3LecKUXH44r4hntrTx8xf3MLskjZzkz4++D9PtCnDjq2qt1UWLSqjMUf+OfnNSFYv+tJpuV4j7P2ngHxfM+VLzJUiQ4L+fdR3r4uomc6w5nzP60CiKws9m/oxdfbtocbfQ4m4B4EdTf8Sd2+4k1ZBKs1ut0398/+OxtNmoEsWkNWHSmmhzt/HcwedYXrQcSZFiqbupxlRWlKzgzLfOpN3TjkbQICkSdy69k6r0KopthzZu02v0mLQmonKUdk87W3q20OJqGTNOQmK/fT9PVj/JDXNuiG2fkTWDGVkz6C7u5qO2j8YcNyxQ043pDARVt/diWzH/WPEPjnrxKJxhJ76ojzRjGg3OBkJSiF19akuxHEsOu/t3c/3a6xEQePHkF6lM+3ImfAkSJPj6SQjSBF8rLl+EDQedGHUiS6pScQei7Gx0xfZbDJq4Os1ks4gnICPLajsWrTjirqsoxMTslGIrBzt9mHQa3IF4g6Qko4ZBX5SsZD2eYJRgWKK2c6Q20RuU2dPiIRiRmV6aFKtnjB1v0pKXZsAXlGjqCQABzAaRxh4/3qBEQ7ef42dl0GEP0jMYwu2PUt/tV3tQDjn0SnK8c6LFqGFivgUB6HeHCUcVUixadje7ybDpY+nA3sDIveh0hOl2BMlJNSAKqlhXgObeIHsCG5Et7Sgo+CQPQdlPpj4ZrxwmO7gEOVCONprG0dNSCYU13LJ+Pa5AhAsXlZKXMjZlaXyeGWHo2u/5tBqALc0OtjSPPCz4/sObURR4+tJ5sdpJUFNeVx3sY3ZxKrecPAlRFLjpxIlc/tR2ZFmhKN3Cr06YyOT8ZEJRCZtJx/2rG/j7p018b3YhJr0Gf0RCRo1WD7P/M5HTd/b1ADCnJJXfnjyJ206ZjM2oHePcqNeK/PqkqtjrKQWqoccf3q1hV5uT3506mQk58angFVmqoNWIQpwZ1RfxcXVvrDfsrOKRWmdZURgcSm3+uKaPcb98h+UTs/nTGVNJs+gPN12CBAn+D7C6fXXs58unXs6Fky78l44/6DjI+e+dj07UcevCW3l036MA6EU9H7Z8SIohJSZGJ6ZNZF3nutix0zKnxX7+/Zbfs7FrI281vcXTxz+NSWsiIkXo8/fxq3W/osOjtrkajko+vPdh7lp61+eu7YeVP6TX18sn7Z/w009+SpJOfa81aUwEpAACAnpRj4TEorxFh5wj15rL0UVH83Hbx3HbFRQEBAaCAxg1RmZlz+LcqnMBuHza5Ty671G6fF30+nuZnzufBmcDZ4w/I3a8iPpwVxAEBBLGNwkSfJdICNIEXyu9zjChiNoSpWswRLcjxKBXFZCzy20UZpho6PZR1+UnP83AtFIbiqKwu3nE6VYjCpgNImU5JgJhGZ1GoDTLRIc9yKAvSm6qHrs7QkRSxcxwJDQiK1TkmtnZNJK+mpemx2rQUdet9rQ6qBWwGDVYTVry0tSoWM9gKC4dWBhax7gcM8GIl1SLlre39TPs0G0eimDKCuSnG+gfStEtyDDFeowGwxLv7LAjDKWYagTVoba1X3XQNeoFLAYdE/IteINR7ENzaDUigiBw9LQMuhwB6rr8hKQwEZ0ds8aILEkkaZPJNOSwv9VDY08ArUZAL2UDEI3oaBnw0utWr+fjmt444TSMViMysVAVZXeeOZVrn9tFr0c9Zm5JGvV9HgaHWpv86b0aLltaxuziVARB4NWdHXiCUfZ1umJOtRNzbVxxRDm/em0fna4ghiHbYINWw7FV2Ty1qZWgJ0Rdr4dksy5Wl7psQiY6jciGUdHVBePS2NQ4Ioy3tQxy9D1rWTktj5Om5jKreEQcH44tTQP8/VO1v90/N7bwh9Pj2xNcuKiUkgwLBalmMpPGttUBGPCG+P27NRSlmTlvfjE2k47HN7YQkRTyU0ycNDWP1gEfP3h4M0adhosWlfDgWvWckgIfVfeiEfbx0HmzvnC9CRIk+O9luH4025zNVdOvimu38mU46DhIIBogQIDr1l6HpEjoRB1hORxrgTKMTR9fgjAsMgEmpE1gY9dGKlMrKUku4Z3T3iEqRznljVMIRAOkGFJwhpyx8Q3OBm7bfBtPHv/kYdeWYkzhxrk38naTWg86JXMKSfokTq84nQP2Axg0BrXnqqJGYg/HPcvu4ZTXT4nrSwojD3qPKjqKPy79Y2z72RPOZuW4lfxu8+9oGGxgc/dmJqVP4pRxp8TGHFtyLA/pHsKqtzIhLVFKkSDBd4mEIE3wtVKUacThjaDVCOxuciMroNMKpFt15A8JwPJcC+W5I667giAwqciKxaAhLUlHepIuFgWbU6EKHoc3EmuT0u+OUJFrpqZDFZmZNh2hiIIiK3Fi1KATmJBvZe3+EXHTZg8yHJSbXCxRkasaGRl1IrKiYDVqcHijbKl1cvT0dI6dnsGOBhej20UNu/IC7G72kpGkY3y+hQFPBINWFdI17eraho+TFLW2dbgWdUONGjVeUpXKkqo0tQ2OAqlW1aHIYtSQmRmmODuVgVA/jYFSKq1TyDBkx87d71bnikoKJr2I1aihpsNHWZYpJoTrez30eYL8bVUDs4pTOXVGfuz4iCQTDMvML0vngXNn8T8v72VmcQp3nqk+UX/k0yYeWdfEjjYnO57eiUYU0GkEbjl5EsGIxMnT8uJ+9xaDGnnWaUQMWg39nhDH3fspA74wuqHvX7vanVx37Hj+/GEdMlCYamZxRQYLxqXzxu4uTpiSw9q6fpZUZGDUaajrcdPqCDDgDfP4hhZe39XFy1cuiEvZBdVcaE1tH2tq+1lWmcn/DLWusRm1nDw1l0Nx5ISsQ24f5qnNrby6sxOAez+upyrXxkULS/jH+mYuXqSmMW9qHKDLpaZuzyhK5aJFJTy+oSU2x78SfU2QIMF/J1dMu4I7ttyBP+qnw9MRq6EczWBwkD5/X5xwCkQDuEIuTig9gWZXM5Is8dzB55AUKVYjmqRLwhMZ+dzb0rOFQmsh7d52Smwl/GL2L2L7fj7r55xVcVYsZTjTrLqMz86ezbrOdbhCI9lMw9QN1n3h9Zl1Zu5bfh87+nZw7sRzY8JzYd5CVrWuikVcHUHH503Dr+b9iuvXXk8wGiQgqaUs2eZszhx/JldMu2LMeKPWyO8X/54b191ItaOag46DhKQQRu1ICcai/ENHZRMkSPDtkhCkCb5WjHoN8yek4PCEaberX9QrciyYjSLBiIx5SLREJZkDbV5SrFqKM83otSLj88f2HR0e29oXGPVaocsx8rrPFYkbLwgwIc/MxMIk1u53MCorFK1GIBxVNwSHhGVUUp/ByrKCYyiaqwCRiIxiUGLCL8umZ874ZD7abY/NAWD3RJisFajtVEVodbsvZr4zGqtRQ1WhheohsQqwu8lNKKr2E82w6dnl3Eydq5EkrY32yEGy9LmclncuuWZV/IWjMjXtXqwmLVFp5ASBsExtrxuTTossKywcl87GxgGOmJDFXz6u55ktbTy9pZUlFRmkWw04/WE21DgJRxSmFFuZVZzKx784Im69ly0to9MZ4MlNLSQZdLiCESRZIcWk49WrFsWl2wKcMj2f3GQTaRYdRelmPjzQE4uEji4j/rTeTkaSgT5PiA+qe3hycyv5KSb+8v3p3PrWAfYNOfZesbSMj6p7487hCoQ5+s9refzCOTFB+daeLq5+bldszPoGO9LQzf/xsvI4kyaAm1/fz8ZGO78/bQrzP6dn6PyydIy6RnSiiCcU5WCPm5XT8zlvQUlszAlTc1nXYEcjCPxtdT2uQJQl5Rmsa7AzKTeJCxYW4wtFY0ZKCRIk+L+HVW8lIkeIhCPste8dI0j9ET+nvn4qjpCDdGM618+5nkV5izjn3XNo97Tz+8W/52ezfgbA8qLlXP7R5TGTJE/Ew9ycuezr3xcTcStKVmDRWbho8kVoxPgSlc86/PoiPjSCBg0aJOINhABCkRD/8+n/cPP8m7HqRx4EOoNOXm14lQmpE5AVmfl582MtZ0azvGg5ty28DY2oYXH+YuoH61nbsZaTyk4ixxJfSzsvdx6ffv9TfrPhN7zW8BpFSUW8dspr6DVq2UNYUj9Phl8Pc+2Ma7FoLczLnRcnRhMkSPDdJfGtKME3wnDqKoDTH6a6I4xZL7JipvpEdn2Nk0FvBHoh1aLHoBPpHgySlWyIiVaAph4/+9s8sf6dw7j88RuGay5BbbVSkWcdEmzqRoNWIMOmp9MRItmsITfVQFaKAUVR6HOFxhgv6TQCyVYdiqKKQFDdg/VakYWVKWyucyFLCmFJQRRAr9WQZtXh8Kri+DNaLXZPjpychk4j4g5E0WkE6rrU+9Q9GCLDpme3Yw+2/hPoTd4ARnBE7CiKEosYN3T7aeoNxM1r0Al4gxJrGlTxdtTk6Tx9yTz8YQmLQctL29t5dmsbFVlWkow61tb1c+kT27DotJwypZDCjMN/gP925SRm5qUTicD6ll4qci2smJTD1c/t4t193fx25STOm1/M/Z80sKlxgJtOnEh5llpDNCEnicwkA4O+MFFZQSsKaAQ4a1YBR03MptsV4LIntgPQ6Qxw2ZPbGfRHEAU1BfiF7e3q70IUiMgKM4tS2NnmBKB1YOTva1tL/FP3ojQT+zojQ+NGxP9zW9vY3e7khW3tsddfJEj33rKCfm+Q+1Y3Mq80jWRTfI8dm1HH/efMZF19P+c9uhWACxaW8M+L5/Lr1/dz9t83U5Zpod8ToirXxlOXzEOv/dfS9RIkSPCfTVFSEcW2YvKt+RxTfMyY/fvt+xkMqW2yBoID3LjuRmZmzaTdo75X1QzUsHLcSgBmZs/khjk3cNvm22LHNww28OyJz/Jm45vkWnO5Y8sdgJpOe+b4M9UxzgZu2XALZcll3Lro1lja8Nr2tWPSfkcTJcq7ze/S6Gzk5ZUvx7b/YesfeLf5XQQEFBTOHn82Ny+4eczxgiBwWsVpsddXr76aTm8nW7u38vCxDx/ynOmmdHSijuNLj4+Jz3Z3O+e8ew6yIvP0CU8jIKDT6Mi35pNrzT3kuUdz/+77ea/5PX4262dx7W0SJEjw7ZD4JpTgG2F0dHD4Z1lRqO304vZHiY5SmAadyI4GF7uaPGyqdca2y4rCgUOIUVDrPMflmDDqhKGxI/uiEtR3+dhW78ThjWLUixw1LSNmphSOKnQNhvn0wCCbap0UZpgYzqy0GEUmF1spyTIRCMmIosDCylQmFliYPNQj1emLEgzLhCUFq1Fk+dR0uhxBxudbMOoO/7+YJyjx0Z4B8tONTC+1UVVoZVyOmaxkPaXZqvHQRMtMBEVHknsORZpprMg6Lc7EJ82qQxBgtC/T5KIkXtrXzK7OQU6fk8+4HDOCIMSicmfNLmTLr47irasXo9eKbG0eICIpOIMRntjWxMu72vhgVz8HO7yEohID3jAOz0ifWItehygInDSpgKuOLEcUBT7Y34MkK3xU3Ys7GOGuD2pZ32Dn72sb+ce6Jhb+YRW/eWM//Z4Q0aFfjiQrhCQFrUYgzaJnUl4yNxw3kp42LNRkBQ50uWM1rE9cPIc11x3JS1csZPZQPewHB7pxBSJc9PhWXhoSrgA/P2Y8L16+gAnZVnQageWVaopzu8PPL1/dxwvb2pmUZ6M8y8oP5o5Nm/sseq1IfoqZP5w+JZbu/NDaRo69Zy0fHOghIsl8VN1LmkXHzKIUFpSlsXJaHhpRoLZHjfQ29fvwBKNsaXbQ7Qp83ukSJEjwX8hfd/6VVncru/p2YdDE16zXDNTwo49+hIJCriUXs9YMwM6+nehEHadXnM7lUy+PO+aT9k/iXjtCDh7e+zA/n/1ztII2ZuCzqWtT7BxnvXEWe+17eb3xdV6sfTF27KzsWRQnFccEaoGl4JDX0OBs4FfrfhV7nWlSHy4Pfz55I94vdS+yzGpmS4Y5g9Vtq+nydsX21TpqWfn6Sp448AQROcLrDa/H2tBUO6pxhpy4w27eanyLU944hZWvraRhsOFLnfeRvY/Q6m7lyf2Hr4dNkCDBN0ciQprgGyHdpqNrMISAKqLy04009vipbvfR1h9k/oQU6rp85KcZMejEWCsUzaiSu7b+QMxxdzRWo4aIpOD2RwlG4kORw70vTQaRui5VVAXDMgOeMFNLkmjs8VOYbmRrvVor0+sME4rKjM+30NwboCLXQlNPAHcgyoAnwhGT08iw6QmEJd7d0Y8owILK5Fif1WBYpnMgGKtnzUnRIymg1woxo6ScFB16rYY2e5BAWMbtj5I51It0akm8++v87Dl064L4wzZKs8riWsQAZNh0LJiYTFNXIFaPWpBu5IUfLcATio6J4A2TlaRGQX/75gH+ubGFypwkmu0+QlGZPk8If0hmd6uLy5/dxiVzK0gy6phcbKUi18LscTba7EGKRkVSbz9tMu/v7+HaoypIMmg5piqbjQ12jp+Sy+3vVNPlCiIIYNZrCIQl0iw6Bv1qnezohxULyzNIt+hwBSKsmJTDS9vbCYyKVh83KZvqbg/uYJSSDEvsfnS7Qjy+oTnWsxRgeWUmFywowaTX8sHPjkCSlVgNZ7pVT3G6mXaHnx8vK+eEKYeuK/0y3Le6AW8oyq9f28/c0jTe2deNQSsSispUZFljJknLK7NjEd1pBcksrsigOP3QaekJEiT472VOzhw2dG1gTs7YdlC+iC9WY3nd7OuYmDaRe3fey4etHxKVo1ww6QJSjCkARKQIETmCL+IbM8+Grg38dPVPWdW+Krbtw9YPebX+VbxhL1FG3OlfOPgC3ogXX8THldOu5O3T3yYiRfBEPAgIXPvJtTQONuKOjDifS4rEW01vsatvFw8d8xA/n/1zjiw8kg9bP8Qb9jIjawYrXl7BqRWncuW0Kw97Lx46+iEOOg7yUetHXPvJtaQZ01h11iq0opZX6l+h2dUcG9vr7+WIF45gacFS7l12L+dXnU9ICvFK3SvIikxYCTMQHKCc8s+9/z2+ntg9TjIkfe7YBAkSfDMkBGmCb4RxOWb8IYnGngAH2n0kGUW0GvUJrEmvIcmkJT1Jh9MXITNZz+xyG32uMOlJI7UhodDYehYAb1Dd3h+RKckyIcsKgbBEvzvC+HwLBelGkkwa9rV4YpFTT0DCH1Ldf/U6kcoCtZZTrxWQZQVJUphdbiMr2UCXI4Q7oKboghrZ29uimkbICtjdUY6YnEZjt5/cNAPBUQJqwBNmSomNnY1uBEGNXpZmq2sURQG9VqCxx8eGg4PkpujJSjFQlGmKM7/JTRubQuv2RzjQ5uXFna28treTP5w6hUJbEjmp+phIGy1G97d6aLMHmVRopThrpO3LJ7V9Q9eh8NpVi9jVPsi8ogxa+gJoJJl+TwiTTg2/BkIy4ahMskXHFEu80D17diFnzx6pRXrk/Nmxnw92u3lwTSPZNiNrr1+GNxTFqNNQ3e3G4Q1zdNWIMVNWkpFPb1jO9hYHFzy+bcx1r6nt5/0DairyoxfMZkl5BrOHzJnOe3RL3Nj8FBPJ5pF1jr6noiAgoiAr8JNnd5Jq0fP6VYsoTDOPOSeodcuuQIR061gH3osXl/LAJw30e1UjJYDQ0JOT0edcOT2XNbV9TMy1cdspk8a0q0mQIMF/P/6In/l581lXsY5kQ/KY/bNzZnPvsnsJRoMcW3IsAHcuvZOFDQvJseRQllyGPWDn/PfOp8PTgYDA0oKlHFdyHO+3vB+b56Syk3in6Z0x83/a8Sm3LryV95rfY//AfgAyTBn8ZedfACiwFnDG+DPQaXSkaVQH82FX3UO53nZ4O3i/+X0un3Y5re5Wnjv4HAAftX5EUArydPXTnytIzTozhUmF1DpqAYjIEZShp5Qnlp3Iq/WvEpJCccfs7FWjxdfPuZ76wXpeqH0BgCMLjmRe7rzDnmuYFEMKpcmltLpbOWncSV84PkGCBF8/CUGa4BtBEATy0ow09qgpip6gDMiIAswaZ2PQG2HXkCOuKAp4A1GikkKGbUSQGgyaQ009sl8nMrnIim441XNI9A0zd3wK+1o9iAJ4AtGYydLBDi8ZNgPj88yU55rZ3eyhyxGiqdfPyXOymDc+hQ57gKisEJUU9ra4YyZGWhFKs02Y9Bqml9kIhCXSNSKhsExzn5+ybPOI2Y8C2Sn6WK/LGWU2OgcCsbrRrsEwXYNhBn0RZpaN/aIymg01ToIRmSWlORzs8WB3Rzhlhi2u3nY0zX0BopJCa3+A4iwTnmCE617aQ7pFT0WWlbNmFXLVsztosfvRaQRevWohU/IzaHf5aHa5OXJ8Fte8uJ1+b4hnL5vPtMKU2NyyrPD4xhZEAS5cWIInFGVtbT8Lx6WTbjUw4AsTjMrsbHPS0O+lMkdtQzCzaKzl//v7e/CHozy6fuSp+NXLy3lvXzcN/T6CQ0JPEOCqZ3YSispML0zmp0ePx6iJF3hPbW7j+Mm5Y0yMAAb9YZoH1L9FWYEBb5gPq3u4ZHHZmLFdTj8XPb6N2l4vt66cxAULS+L2nzAlh2c2tzLgC8cZOx0/OYdbT5kUe33LGwfY3jpIpzOAxaAlP8UYZ4iUIEGC/34u+/Ay9tr3ck7lOfxy3i8POeazNY0aURPrpykrMhe+f2GsnlRBidV8HllwJPsH9nN00dHcMOcGCpIKuHv73ZTYSjh/0vm8UPsCza5mPu34lOdOeo417Wvo8fVw3677ANAKWvbZ95FnzWNB3oIx67r/qPs58dUTkVHfh3WijvKUco4rPQ6A/KR8REFEFMSYydKp5afGjo9IEdZ2rKUqvYo8q2rMVzdYxzWrr6HT20lxUjF/Wf4XdBr1QeK0zGls+sEm6gbreKPxDUwaE46QI67utiK1gp/N+hmt7lZ+OvOnX+p3YNQaeeXkV/BFfLFoc4IECb5dEoI0wTdGhk3PwokpbK93xQSdRhTQagSMgohOIxCVFCRZobVf/TDLTA5Smq1GrQrSTRzs8BEIHyJvF1gwISU+CvaZ9NbaTh+egBpNdflHoq0Wg5bqdrXeJS1Jj82kpYsQSSbtUBRLZm+rWrvqD8loNaOjlwZMQwWc7fYA2xvcWAwalk9Nj7kEK4qCTiti1IkkmeL/l9vV5OaztPYF0QgC00ptY/YNIw89QVaAS+ZXoBEF9rV6mDc+BYBeZ4h9rR7y0oxUFVqpKrDSZg8wPk9d0z/WNfHBUKTx3u9NJyortNhVYRyRFH7yzC4+ue5IfrxMTX36YH83LQ51/8ZGO//zyl5qezxU5iaxcmoef/pAfbqdn2Li2a1trKntZ1pBMredMpldQ2mqAD2uAL6QdMheqNtbHFzx9A5ATakFmJBt5a29XXQ7g6SYdRw1IYtXdnWiKCNRyN3tLk762zpuPnkSlz65I27OJOPYlOX1Df08tr6FFZOyY/cAoL7Xy5t7OvnHuma+N6eQH84rps8d5Oi71+Ifinr/9q0DhKMyly0dEa7PbWmLuQdPLkhmW4tqRjI+y8o/N7Tww/nFBCMS3UPtYECtOwW1Nczk/M9/+JAgQYL/HlrdrQDss+/7t44PSSHa3aoY/awT7knjTuJvR/0t9vq8qvM4r+q82OtH9z1Km6eNh/Y8xMnjTubIwiPZ1rMNV1gtWSlNLuWV+ld4q/Et1n1/HWZdfMZIQVIBT5/wNOe+dy6yImPT23jxZLX+tNZRS1gKoxW0GDQGzEYzpcmlXDPzmtjxd++4m2dqniHNmMbqs1azp38PV318Fb6omnKca81lXMq42HhFUXhoz0O8XP8yjqADg8bAp9/7FG/EyzM1z3Bk4ZHkW/O5ePLF//J91Gl0pGhS/uXjEiRI8PWQEKQJvlGykw0UpBtp6g1g1ossrkpDpxXRAcdMz4jV+XXYg2MipKDEHG4/S0mWkXXVDnRakeVT0jHoRDrsQUQR8oZSXp2+yJjjtBqBkmwTrf0BBEHAalQddwsyjLFooyAIaDUikizjD0l4/CPzjBbHPYNqWpEvJBGJymg1I8enWnTsbXXT5wpTVThilZ9hM9A9GJ+OBNDUG6DfHUYrCsyuSEYrChh0Ig6vmqobjipoRYjKAkN+FSSbtXQ6ghi0Io09fjwBidpOHxMLLIzLNTMuV/1y8dquDv6yqgEBKMkws2BcOkadhnEZFhrt6heD9kE/H+6yU5Ct5+InttHvCaHXCKyYlENBqpmDPWo0u6bbgyR3xtb9x/cP0jbkeBuVFX7wyGb8YfULU1mGhSue2kkwKvOLY8Zz1bJyOgb9WAxaMqwGrEZtzB15wBvmlydM4M73ahnuZhOKyoQO5WgFtDn8WAw6rHoR79Dv5JiJWUwpiBd7oajExf/cTjgqk2LSccq0PN7Yo5povLCtneeHHHe7nAF+OK+YVQf7YmIU1HrXV3d1xgnSldPzeGdvN4VpJm45qYqT7tsAwF9Wq+YatT0e7L4wB3s85CUb+c3Jk2JpwrnJiZYECRL8XyLLnIUr7KLD2/FvHW/SmrhlwS3csfWOuFTWLHMWK0pWjBn/2P7H2Nm7k2tnXcu0zGl0ejtp87Rx28bb+M3C3/Bu87uxsYvyF1HvrCfHkjOmlcowUzKn8PAxD/NS7UtcOuVSAF6qe4nbNt2GWWsmLIcJy2H+svwvY2pko7JatyopEi8cfIE/bPtDbN+ygmX8fsnv48Y/U/MMD+8bcd5N0iWhE3Vcu/pa9g/s57F9j7Hq7FUcitFu9AkSJPjukxCkCb5xppYkkZ9upMsR5OM9dioLLEzIt2IY5Uh7zPSxaZaegBTnsGsxiPhC6oaWPjX6JIVlPIEoAx6ZbQ3qU9/FE0WSLdrYsaqQU38WUUix6DhuqP3M8BpGRzI1osCyKWnUtHtjkVsAo05kypDTrqKopkrD6LQjH4TKkJtwz2CYnsEwRZlGrEZ1/nnjk+l3hdlw0HnI6wVYXz1IICyTl2ag1xlGGkoLjcqqWZIgwNyKFPwhia116jUXZ5mwGDTkphkQBIGmHj/NfQEm5FlwDQlqQYBHL5hDtk0VRauuO5JfvbaXZ7e0q862PS729Uv0edQvPWFJ4fyFJYzPSqI43UzrgB8RWDwugx5XkHFZ1lg09OiJWfz+1Mkcc8+nseu5YcUErnp2JwB3f1TH3z5pIBKVsRq1fPizpVTm2Pjl8RP5/bs15CUb+d7sIl7e3kF9nw+dRqA43cJ1x04gIsmxyObUgmR63UFuOqGSq5/bGROjMDZCDuANRokM/fIzkgx8WDMSIR1th3XOkOOuwzfiLjy9IBkF+MmyeMMMg1bDgD+M3RdmW+sgWlGIOQkDlGVasJl07Gl3UpVn47jJOay7YRmtDn/s7yBBggT/Nzii8AjqnfXMz5n/b88xK2dWTIxOSJ1Aw2ADP5ryozHjHEEH9+y4B4A+fx81jprYvg9aP+A3C3/DkvwlvNHwBpMzJnPtzGs5qewk8qx5aMXDvzfNy50XV6s5XP8ZiAZYkr+E2sFanq15lmmZ0whEAzy05yHsATs/rPwhbzW8hSvk4t5d98aOX5i3kHuW3TOmR+qT1fEOuPagnRdqX6A/oJrX9QX68IQ9bOzayJ7+PVwy+RJMWlMspfn+o+5nZvbMQ1+EvR58/VA8tldqggQJvnkS34YSfOMIgtoDdEejC1mBdnuQcFTGH5KpLLDEGdGMxh8aEXwCMGOcjfouP71D7rICYDaoqb+KIsa2aTVqzaZBKxCKKpgNIu6AKkq0Q/WmOq2AfOjgGzXtXlr6/DFBKwpQkWcGBbbUOclONpBk0uAeEpC5qfqYYRPAxppB+tyqCEwyiphH9WgRBAHLqMhgdoqegnQj4ahMrzOEyy/ForB9rtCYljdHTErDYlTnG907taXXD4YoJpNqwnOw00coIlPd7iXfnMRPl41ndlkqZZkj0Vp3MEKSQYfNqMWg1SBoJBQUZpek4gtFOXNmAZ2DAWq63Dx9yVw+rbdz02v7eWxjC49fOJsjJ2Rxx7s1NNv9/O7USWQnm/jnxXO5/qU9VOYksagigycunstPnt2JKxCNRbs9wSgD3jC5ySYuW1rGiVNzsZl0f8eefQABAABJREFUWA1aTpqaxz0f16PXiLx8xQJSzHquXzGBA11qqvPeDtfQ708TE+rDv/dDOeemWw385Qcz2Nbs4NqjK3hqY0sskjnMOXML+fmxavuZ8xcU4/CFKUw1sag8g8uf2sFVz+7kd6dM5s09XSgK/PToipiatei1vHvNYh7f0EJhupllEzKpzLEhyQoXLChhYq76AOOO9w7y1p4ujqrM4tELxzptJkiQ4L+Ta2dey4WTLsSmP3xJxhdRbCvmriPuotXVyvmTzuePW/7I4wce57WG18i35nP9nOvJseSQrE9mXu48dvftpthWHCdIz686H4DlRcvZ9sNtMTE4IW3CIc/5WRRFIapEeWDXA7G2MQoKHZ4O+vx9fNz2MdUD1fx2029pdKolCm2eNvySmkETiI60vPJH/GPEKDDGzAjgzcY3+cmMn3DLxluYnjmdqBzlhk9vQFZkQtEQWeas2HWu71x/aEHqbIOHFkM0CKc9DNO+96WuOUGCBF8fCUGa4FtjclES1e1ePAEJT0D9cOpyhFg0MYWs5LFupuKo9JuFE1Oobvfi8ERJT9Ix4ImgAL6QTG2nj7njU1g2JQ1BICZwj5qWgS8oYTNr2NviwRuUCEcl3t7Wh0YUCEVlFkxIITsl/twNPX6i0ojYsZm01HX6YxG15r4A04batWhEmFqiftHwhyQ+PeCIS+v1h9VeplFJYX21A29QYuY4G6IoIEsKpdlmclPV85fnWmjtC7BzqM5UGmUynJWswxOI8sm+AZJMWpz+KLPG2SjKMNJmD/JhXRfv13RjM2pZf8NymhxuDKIWo85EMKJQnGxjZlEKA94Qd39UR67NSL8rzKamAWQF+r0h/rpGFWrfm13In86cysZGO+c8MuRk+ybML0uLredAl5tlldncdGJV3L37f+y9dZhc9fm/f59x21l3l2w2vnE3QnAI7l6c4hRogZZPaUuhLe4Ud9eEhBBC3G2TbJJ1d5sd1/P74z07s5PdtNAvLfKb+7r22pnj58zunPN6P8/zehq6HcwuSmJTdTeT7l/JMxdM5o1fzeCyV7bSZfNg1qn4w8ljIuooM+KEC3B1p41ngrWWvoAcckguSolh/Z1Hsbuxj7Of24RGqaClzxnqU5oZq6XX6aO5T/xNWZxe3t7awJTceKbkib6gp0wQhhq3HDMSSSGx6kAHi0YlMzM/kRvf3c3o3y/n/iVjOWNyFudPzyHBoOG4x9bS3i8ekB748iAWp9jfrNqE0N/C+qouJuXG8cAZ40Pn4/UHuP39PXy+p4Xi1Bg+uX42jcF63Ibg7yhRovyy6XP1ccVXV2Dz2nhu8XPDOux+H3pdvZR1lfHYjsf4qOojAJptzezv3s+m1k3oVXpeOvYl/nnMP5FlGb/sJ0mfxNa2rVw8+mKWFC0BRE/SZbXLOKXwFEbEj4jYR5ezC7vXTq45NzTN6XNy6fJLqbXUolfp6XH1RKwzLX0aaqWaNGMaoxJHYfOE+5GWd5eToE2gxy3WWZC1AJPGxKVjLo3Yhtvv5sKlFw7ZNoiI8PS06Ww6fxMOrwOT2kSyPpl2Rzs72nfQZBWp0AoUnDPyHDjwOdSuhdk3Q6zoHY3PDf5g9kvHAbC2Q0zqkH1FiRLlf0dUkEb50chM1LGz2jJk+u4aKwvHqUNuuQOkJ+iYM0qBUilR1+6gxyoipgpJCMGB6KFeq6CyxU68SR3p0qtWhFJyizONoVRYEEY+AF39niGCdGSmkQONtpAg6huUmgugVgqn3VijCq1KgUGrpNPioa7DOcSAaeAY7S4fvXaxnbZed0jw2l0+QIvXH2BndT8uT1iFDk4ptbkCOD1iysB2qtuclObHMCbHRHl3t1hHht99uI8v9regVSn4wzHj0WvEv/2KXd04PD7e39aINyAzMzeJyi7rkM8jxaxlf4uFp1ZXoQAGzqi2006ySUOvw4tWNXR0u6PfxS3v7Y7oM7qxupslpRkUp8bQZeum3+WjODWyD5w/IPPxrmaqOmyhFjq3LR5JgjGypqk0O46Ndx2FSiF6wH60s5lWi4tmixCNH2xv4roFRfz1y4O8vbUBnVrBrUcXY3X7uGx2fmh7Nx9dzM1HFwPwwtqakOh86tsq/LLMHR+UoVdLOAf1uB0QowDL9rUxIsVEdaeNz/a08OW+Vl67fBof7GimNDsWo1bFp7tFnerBNiudVjePnFPKJ7uaOXH8f97/NEqUKD8fyrrKONQrUls3tWyKEHlH4v2K93l0x6OcMeIMbp1ya2j6i3tf5NGdjwKQbgx/h6gkFQE5gNVjxeqxsrVtKzkxOWxp20JuTC53TrtzyD7uWncXNZYatrRuCRkUfVjxIVvbtrK2aS12r51HFz7KUTlHAdBsbaa8uxyIjHIm65N5fvHzFMVHljQ8f8zzfF33NU/sFmZLPe4eskxZxOviuWfGPaQahwrBT6o+4WDvwWGvySfVn7C/ez8BOUC1pZqTC06m3dGOhES1pTq03MTUibRa6kh9/1II+MDZB2e8IGYmjYCLPoG9H8CGR2DnK3DDTjAkDLPHKFH+N/T397Ns2TK2bdvG9u3baW5uprOzE6fTSVxcHKNHj+aEE07gV7/6FYmJiT/24f7gRAVplB8Nf0BmOI8iu9tPl9UbihQOJjlWiIjq1vA0hUKiKE3PoRZxc6xpcxKQhVAdnW3CH5AZkWGMcOBt63UPEYsqhUR6fNhkpr7DSVO3kw7LUDOkwRSmG5EkKdQz1esLsPFgLwE5sl4VICtBnJPZoKIo3YDN5WdUlgmHW/RNHUi7bet109IzNF1pAL1GwnHY7F6bl7X7ezhuUjJ3HFeCSaXBqNKwraELEOJUPmw72xq68QaVdopZi0ICnUqJy+cnIIuWJrcuLubyV7axoaoblUIiVqei1+Gl3Ro+gL98eYDy1n7+dOpYjFrxtWLWq8lLNFLXbefk8RmoFBJXzy/gspe3Ud4qor6SJH5AtI9p7XexYl8rf/ziAEqFxEnj00k0arh0dh5flLVQ02nnirn5GIKiOmlQX9ClN85l4d+/pcfuQadSsGBkMr9+ayfmYJ2mSqHgL1+Kh5zKdivPXhTulTpAW7+oEZaAX83JpzZo8jRYjB7OgVYrp07M4LLZefzu4314/TJ/WXaAvc39fLiziftOHj3gO8UZkzJJjtGiUyu5ZXHxEbcZJUqUXxbT06dzUsFJ2Lw2jss77jut80nVJ/R7+vmw8sOQIN3etj0kRhWSAqUkBgMNKgNbLtiC3Wvnr1v/itPn5Ni8Y3lx34s8tvMxYrWxrDhjBUa1MWIfhXGF1FhqQu62nY5O7tt0X8QyAy1mANKMaVw46kLKOsvY17Uv1ALm9im3h8RoQA6gkMTgb0FsAVdNuIqdHTvZ0CIM304tOpWrJ1wd2ubqhtVU9VVxwagLMKgNTE2bilJS4pf93DzpZna172JN85rQ8javjVa7eAiotYgWYfJhd7eKngouWnkFt2cUcElTBWQelrpraQS76BuNywLu/qggjfKjsnXrVs4777xh53V2drJmzRrWrFnD3/72N9544w2OPXaoidnPmaggjfKjoVRI5KXoqesIj7ImxqhRKSWSzMPXkQ5Qmh8DkkxTl5v2Pg9ub/hPeeC2pFJK7GsQ6UJVrQ6QZJJitMwYGUdmoo7WXjeyDL1WLwFEWujBZhuzSuLptLhDqbIDxBqUeHx+nJ6IyaiCQlcYF7nJTzWgVStwegJDBLfT4+dAo42MRC15KTqsTj8efwCbS0RC2/s8jMkRLXJMOgU2V7jv5kCkcVyuifxUPZ9v64yIPoJY5puybsbkmDhjchY7aywsHplBaoye1BhdSMgNkJdoRKNUYNCoOGFMBtXdVva19KNTK3B5AyQatbi8AeYVJ/NtRSfzRyaz6kDHkM9DluHjXc3MLkrizMlZAOjUSr68aS59Di9pg9xkJ+fGU97az+yiRG47ZiRNvQ5e31RPn8PDivJ2ZgZTgdVKibtPHEV6rJ66Ljs3vL1LiGoZbjp6xJBjALjvlDG8ubmei2bmcvXrO3B4/KSahWiN1auwBeuQE4xDBzsAmnvF36JCIfHaxjruO2UMdrePXruX5ftbQ5H0w9lc08MjZ5fy2Z4WNtf0sLdZ/O3EGdS8uaUh9Df5wc5mvjnUwc57jxl2O1GiRPllolVq+ePsP+IL+NCr9BHz3H43r5e/TqohlZMLTw5Nv2b8NTxb9ixLCpfwdf3XVPRWsDB7IQa1AY/Pw9/m/Y3nyp5DQsLpc/Jt47csyF7A/bPvD22j3y2+i5xeJ2WdZUxLmxZRr/nQvIdo6G8IRWxjtbEUxRVRY6nh7OKzSTOmcV6JeEhus7dx1udnYXVbMagNITGqU+o4oeAEAH677rcsq13GnVPv5PxR54f2c//s+znuw+PwBDwhsTqwzZu/vZmAHMDld3HDxBsoiC3g67O+xu13k2nK5Enfk6xpXoNRZcTus4d6nEpI3DXtLi5Zfgl+2Y+EhIzM2ISx7O/ZD0DX+LNwnHYG6vgcQk8VVavg0+vF6/HnwtjTIT7vP/hUo0T5YcnOzmbhwoVMnjyZ7Oxs0tPTCQQCNDU18cEHH/DRRx/R1dXFKaecwtatW5kwYcKPfcg/GFFBGuVHZWKBmdYeF26fjEmvZN6YBNzegEjD9Il6S+UwbqlqlYJRmSZ6+r2oVAoSY9T0BVNXZRniDCpKC8ysP9CLzy+HhERrr5uGTgc5yQbyUwzUdTgZrBnT4kSUs9cWTstVKYW4Gty7dDD7Gmy093no7BdKtc/ez+LSJCpb7NS0OyOW7bb56Lb5qGm34xmU+VucYcDq9FOYJlqzuL0B1EoF8UYFvXYfJp2SGJ0ChUJBQaoBWZYjxGiiSYlJr6a524XTE6C6zcmCsQnEGpR8XdbDxKzwyG+yWY3TI0RwfoKJP51QilIhcaDDQmGKCZNWRXacETkg8dnuZt7YXI9CgkfOmcCppVk8uPwg729vxOL0EqdXY9Qq6XV40atVTMuLHGHWqZWkxUam895/6lgWlaTw3Noa1lV08sjXleIzDfZ3tXv8vHvVDJJjtKTHige3OIOaRKOGLpuH/OTIEX5Zlrn3033sb+nnr6ePZ2FJCivL25men8DqQ5109A9EciXGZJjJitfzxyVjhv0sr5ibj8PjZ21lJ4fabXy6u4XVhzpo73czNS+enfV9+A8fBQDaLC7G/mF5RJuBlBgNHVYPfQ4vmXE6Oq1uPH6ZnmHaD0WJEuWXTZezi3O+OAerx8qLx7zIuORxoXnvHnyXx3Y+BsCI+BGUJJQAMDdrLnOz5tJmb+PYD48lIAd468BbOLwOxiSO4UDvgYjU1hpLDQuyF4Tef1T5Ea/sf4XiuGL8+Llq5VWcM/Ic7plxT2gZlUJFQVy4jZVGqeHmyTdz59o7qeuv485pd6JSqHD5XDTbmulz9wFg9UaWd3xT/w1H5R7F1/VfE5ADfNP4DScXnsw/tv+DgBygxlKDXxb30MGpvka1kXhtPN2ubvRKPb6AD5VCRZI+7LR/fen1zMmcwz/3/pM1TWvwB/zcMPEGiuKKmJAygRsn3sjK+pXcPPlm1Ao19226DxmZeE0s04zZzP3iVJIVet4bfQ3mEceDKRWUGgj4YcplkPOfOx5HifJDsXDhQhoaGo44/+yzz+aTTz7htNNOw+Px8H//93989NFH/8Mj/O8SFaRRfnSOm5RMj81LrFFFWV0/1W1Oks0auq0e1CqJglQDXp9MQZoh5CgLYNKrODbYrqXD4qa6LXyT63f6iDOqmDsqjq2VfdjdYRGxo9qKjMTOajFyHKNXkmxWU5IVg88vE5BlcpJ1tPW5USokphfHcaDJJqKsR8DqDKvLgCzMmcbkxKBRKahpd+DxRYoYT2QZKjqNkjE54VrKmnZnqDZUbN8fagNjNjhQKcPCRyFBcaaJpm43GYk6eqxeitKFaC2rDxtKDNBr84UcaaeMiMWoVdJmdXLboztCy2yhhytmFGFxDfSNgweWHuTU0iwumJ7D82tq8MsynTYPnTYRwd1699FoVUo2VHXx8oZazpmaw+LRoj4oEJDpsLpDkdJ3tzeyqaabTTXdoX1mxxuYVZTIuVNzIkyOAOIMGlbeMp9ehyfCGRiEMdAbm8WX+PNrq/lwp+iLqleLvxUZIXYn58bz2Z4W9rf009znJDcxUtg6PX4ufmkrDo+fkWkxeP0BzpqSxda6HsCNTq0cVowOYD8sBXzhyFTWVHRi9/jISTAwIz+BD3e1oJCgot06pHY2SpQov1xqLbV0OER2SVlXWYQgzTXnIiFh0phI0A1NG43RxKBRaHD5XVg8wnehqq+KPncfOoWOBH0CJxacGIpkDvBt47fIyFRZqkLb3dO5h6lvTCVRn8jd0+9mTuacIf061zaK2tHNrZup7qvmwW0Psr1tOymGlFAUcjAuv4ubvr2JF455gXtn3svy2uVcPeFqPqn6hA8rP4xY9o6pdwizoUHn9vGSj7l/8/08tusxyrrKePyox6nqreLT6k85seBEShJKKE0p5b5Z9/FBxQdMSpnEtPRpAFg9Vl7a/xIWt4WDPQe5ZMwlLMxeSK2lllM6Gzmw+RE8KmgOOGlacQejt70M16yDX28TgjSx8Pt8jFF+CvhcIs3a7xEDC7pYUP38e3orlUO9OA7n1FNPZeTIkRw6dIh169b9D47qf0dUkEb50VEoRBsYl8cfiij22LwEZHB7ZQ40iTq+uk4HJ05JiXDbBbC7/Oytt6FSSiFzoElFZmQZNh7sw+0bKiIONIaFWnG6kexkHfsahOhMjdMwqySe6cVxtPe58fkDdPSF83TVSkiO1YZqPGP0SkrzzFS322np8SAB8SYVKqVEdrKOg832Yc/brFciA5mJWqpb7RxotDF7VDzdVg/d/R40Kol4k5peq4dB3kaolVJEi5OADNsq+/EFZCTguElJ6DRKPL4AHRbPkP36A+HHiUBApqvfQ2O7h7kFyWyoFf3dpuYlcOL4VCR1gHe2NdDv8uEOOjLZ3f4hwmx0uhmtSkl1p427PtpLY4+DA63WkCC9/q2dfLmvjSvm5HPPSaM5aXwGK8vb8QVkkkwaStJi+Ovp48lKMAx7rQDijRrijUObtWfG6Tl2TCr7W/o5Y1IWOxv6qO2y4/SGL1p+kpGTxqfzRVkLBo0KjVIxZDvVnTYcwQtt1ql4/xrRn+69q2dS1tTHoTYr6yq7jnh8g8mK0xOrV/P2ldNZ+I81bKoJu0UGZHhtYx1/Om3cv9hClChRfklMSZ3CNROuodfVy6lFp0bMm589n6WnL8WoNg4rSI1qIzqVLpSqmqRPosvZRbOtGaWk5J2T3iFeFw+A1+/FG/BiUBu4vlSkpc7Pms+oxFGsbVrLG+Vv4PKLaOd1q67jutLruHbCtRH7u2DUBTTbmsmLzeOSLy/B7hP3sHZHuG+zWqHGG4jM9rB77ZxSeAqnFJ4CgFJSRiw3LW0aF466cIgAjtfFh7Z9sOcguzp28cCWBzjQc4A1TWv47NTPQufdaG3kqd1PMTtjNs8ufhZvwIvTG3RUdwuxfsvkW7hh1ZOoHH1YVGm0uF1kul2M9njBExxY7q0TIiYqSH8eyDL01kDTZugsB3nQALCkgOTRkDUD4gvCxhS/UGJixGC2y+X6kY/khyUqSKP8ZOh3+kJpqAaNRIxBjVKhoLFL/NPJMiLcddh3TU2bg/6g861KKZGZqCM7UR80TRo+ojXY0GhHTT91nU4c7nAdZ3Wbg9ZeN50WD/FGFePzYtheZcHlDeAPiAioUiExIt1AcaYwTEqK1eAPyAQCcsghWKdWYtIpsbv9GLXhmlBxvn5KsowoJdGuBqC6zUFzt4uADElmNbNK4nG4/eyotmBz+fD7obzJxtxR8Zh0ylDtqUol4fMIodnY7WJEuhGNSsHYHBNtfW7MelVI7Oen6Yk1qPAHICtRxxfbO/H5ZX41s5DHLijFrFOF+qiWFsRx4Yxc3txSHxKXI9Ni+OfFUyhr7uOJb6qQZbhgei6dVjcnPb4+JARTzFoeX1XJ9QuL2NXQB8DK8nY0KgXXLSgkJUZLi8VFr8PL+qpu/rGygkfOKf0efzEEP3MFdx0/ijSzDr1GyfKb53LdGztZdTBc6/r4uRNpsQizK5vbx9cH2plZmMjne1pZUppBQbKJ9FgdMToVVpePC6aHHTCTTFqOKkllQXEK6yq72FgtorqXzsrl1Y31Q4yiAJr6nDy/roZY/fBfsW9saSA7wcAZk7MijJmiRInyy0SSpJBAPJz6/nrePvg2i3IWkZA2vLHOrZNv5bX9r9Foa6TLGR4YGxyx7HX1cs4X59Dj6uHZo59lStoUHj/q8dCyoxNHs6dzD+ub14fW63Z2R+xne9t2rlt1HTkxOZyYfyJv+t4cciwqSYU34EWBgjNHnMmMjBlsbdtKnCaOO9bcwbb2bfxu+u8oiivi7RPe5q71d5FhyuDysZfz+K7HOWPEGWTFZEVs87ZJt/F5zeesqFvBxV9ezOgE0ULMrDFz5mdnckrhKVw85mJW1q0EYEPLBtrt7aQaU3nx2Bep6K0ItbIBUJ36NJS9R+zM67nP50Z+5QRejY3FNf4Yrji4DOU75wESXLEKsiYf6WOL8lOgvxn2vw/29uHnywHo2Cd+jKkw5iwwZ/5vj/F/xKFDh9i9ezcAJSUlP+7B/MBEBWmUnwymQem4VlcAm9vDxAIz3VYPDndA9Mcc1JbF7Q2w6VBvqN5To5I4ekJSqLVLW68blVKBLAc4gi4N0W0VI7gKSUSwqlod4eORJJJjNYzPi6Gtz00gINPU7cYfkKlstaNSSjR1uSjJMpJo1rC/wYZBq2RkphGvP0B6vJZYg4rt1f1D9uvxijrZAbKTdGhUChq7nOQmi/pJg1bJ3NEJtPe52XiwL3S8kwrNHGi0BR17jWw61Is7GOFzuP0YtEryUvQkxmiIN6kYmSncgN1+PxqlAl0wpbUwzUB9h5OCNANxejUbDvZisfuYXhxHcqyGnEQDvz1hFP6AjNPjIyDD0aNTmVecjNPtx6BVce7UbNqtLryDmqXuauhjV0MfBclGzp2azTvbGqjvcfD0t9V8vEu0aIHw+IJa+Z+Naj63ppoHvjzIyNQYvrxpLlqVkucvnsKhNis2l5eCZCNJMTrSYnWMzTTTa/fyj5UVOJb68fgCfHuog09/PYdEk5Zvb19An9NL4aC04J31PSzf18ZFM/KYmpdAVryeohQTG6q6hxWjg/n7VxXDTteqFDzw5UG21fXwz0um/kfnHSVKlF8Gf978Zza1buKLmi9Yf+76YZc5bcRpqBQqfrf+d6FpaYY0nlz0ZCiq2mRtCrnP7uncw5S0oU7ijx/1OA39DVjcFvZ27eXM4jMj5m9s2YjT5+RQ7yH+tuNvEfM0Cg3egBefLO65AQK8V/keH1Z9iF/2837F+6E60d+t+x0uv4slhUt468S30Kv0LHxvIV3OLvZ37ef5Y54PbXdN4xpuWn0TyfrkUH3p5NTJSJJEeXc53oCXZ/c8S7ezG6dfzNcr9Zi1oud3aUoppSmlkSdafKz4CbL+zKf5+/a/QP0XTESHSPiVIyNtUX56dFdC2RvhvrH/Dns77Hgexl8IicObH/7ccDgcNDc38/nnn/PQQw/h84n/v5tvvvnHPbAfmKggjfITQgoJQhAR0b11VlQqKfS+uceNWqXA7vJR0eygf1Dtpscns73KQkqshsI0YVg00EblcFJi1XRavEMERbxRhcMToChNT3ayno4+D8mxGjosbrZWinSgmSNjsbv89Np9+AOEnHy3VfUzMtMQcg32B2R6rB46+70oFTAy00BnnxufDF6fTE6SDqvLH0r9NRtU9Nl9eHwBFo1PRKeJrCdIjtVQmKbH65PJTtKhVimYMzo8mr5ofBK7avrZVy9Sj4+bmMS68l4sDh+FaQbG58WwsbqLS1/aRqJJw7Ib5xJv1DA628TobCHA7C4fXf1CnLf2ukJtdvpdXk55Yj31PQ5kGe49aTT9Ti8vrK9Fo1Jw8cxc4g0afnfCKP74xQFACEy1UkFRionffrQXq8uHSiHhC8ghMQrC3VjBkZ1z/x2H2oS5Rm2XHY8/gE6h5JNdzextthCrV2Nx+Vg8WkecQcPbV87gzGc20twXrjfOjA87XiaatCQGI5b3f1HOqxvrQinOr22uD/VFhbC7skmrRKVU0OcYalY0nGDVqhTkJBio7LARbxiaghwlSpRfLjvad5BpyiTNmBaaNipxFJtaN4XMjI7EMXnHUN5dTo+rh6yYLC4efTGx2nC9/bjkcdw48UbaHe2cPfLsYbehVqgpjCvE6/cyKXXSkPnnlZzH+xXv0+fuo9fVG5qeF5NHnbVu2G0OiFCNUsNpRaexu3M3B7rFfeDT6k9RK9X8YeYfyDPn0eXsIi82L2L9fd378Mt+2hxt3DLpFrQqLSXxJbx+4HVApCyfkbWQTw6+HVrnkjGXDHEr/ldkZ83EsNuAL+DD3N8WnlGzGrKjg4I/Sfqbv58YHcDvEetNvupnGyl95ZVXuOyyy444/6677uL8888/4vyfI1FBGuUng0ErXHbtbj/9di8VLQ6yknSMSDey8WAvHl+AgD/Amn09SAz/sN9h8dBh8SBJUJRuwOnxY3P5Q6nAA31BHe4Ap85IRZZltlZaQqKwx+ZDBtw+GUmC2nYHB5ttFKWFb3w7qvoZl2dkR3WkYZBRqyDBpEEh2ZGBQ4NqRyVJIjNBR227E41KwcJxiSgkWLM/XFuo10iUB2tbXV4/Xf1espN0TC4UDxwKSWJ8npm2Xhdry3uJ0SmZXBQbciF2evwhc6VAQGbl7q5QKvBAOvKeRgtJJi3XzR7J+vJejilNDkWUAYw6FcUZBiwOHwVp4XrOfU0W6rrDpk7rKjuZVSgaM2tVCtZWdvKb98tIMoUF1mPnTmRWYSJxBg3zi5P5oqyVGQUJrK+KTBETF0j0Px3Ma5vqeGVjHTMLEjlxfDrFqTHE6tWoD6v/vOv4EpJjtCTHaDn7uU2MzYjlra2RTnWXz87j+oVFrK/q4lC7uMYzCxO5/ZiRjDvMQGmAL8paQnW5AD6/jDpYpywjhPT4LDMPn12K3e1nf4uFB788iMXlIy9Rzy2LR3LTO7sBmJgdy65GMaDx/EWTkCQF+1ss1HTaOfHxtSwpzeSqedFapihRfsm8uv9V/r7978Rp4/jy9C8xacRA4C2Tb+HE/BPZ0LKBjc0bmZU5a9j1tUotd06781/u48rxVx5xnsvnQpIkrl55Nbs7dvPnOX+mw9FBXX8dN026iQRdAsmG5AgXXICxiWOZlz2Pp3c/DYBaUlMQV8DE5Il8VvMZyfpkbp9yO/lx+aH2MV/Xf83d6+/G4XOgklS8X/E+JxeezF3T7mJEfOTg44WjLuSVfa/g8rt4pfwVlp++XIjbrKNoqVnFH50S9xz4iB6dCmS4yNLP9b7vV+qQ53aycu6jBCyNxH00qGa2dc/32k6U/xGyLNJ0v68YHcDvEevPuOkXVVNaWlrK888/z9Spv7xBlKggjfKTIt6kJt6khkQdo4Ous83drlCtZF+wVnQ4MWrSKnB4RHquUiHR2OWiMM1AapyWg802jFoVFc1CjNhcfpweP3qNkgl5MXi8AfrswkhJlqHX5qWh00lXMJXX6wcFEAA8fnlYMTog7E6ckszn2zoj5vv8Mu0WNx6fjMfnp63Xzd56a8iECaC9z4tKIRGQZTxe0daluVucg88vk2TW4HD72XzIggz0O3zkW72hKObGg330O3yYDUqyEvUhcZsap2FCnnjwuXBGDn4vGDUqfH6xjYH1Bxjs9jvAZ3taQq8XlaTwm+NGMjI1hlHpZnITjLy4vgZfQKat301WnJ78ZCP5iUZe21TPUSUpXDgjlz+fOo4Dbf1sre1Fo1KEeoIa1Ar+cfYEEk1abG4fH+5oYnJuPE+vrqat30VNp503twiBOTbTzGfXz4lIc04x65g9IpGnV1dT1mShrMkSEWkHeGlDHb0OL3cdX0JxqrgWj55TSqo50plPlmXRzsag4Z4TR/Pe9kaOH5vGnqY+zpqcTVa8nop2G5e8tBUZ6LJ62Fzdzf99UY7XL/PIOaXo1UqOKknBMchKeVejhdlFiVw2K5/1Vd28sK42ordsZXtFVJBGifIL591D7wLQ7+nHEwg/aB/oPsB5S88LRRp1Sh3Xl17PpWMvHbKNne07yTBl8M7Bd1jTtIY7p93JjPShbUscXgcqhQqNUny/7+/ez+XLL0ev1ofqRv++/e+hetQEXQI3TboJgPNLzuftg2/j8ruQkPjN1N+QHZNNh72DDyo/wCt7OdR7iB5XD5vP38zSmqV8Wfcld0y9I7T/o3OPZkziGD6o+IA+Vx9/3PRHAF485sWIPqQgep9mx2RT2VdJr6uXbxu/5YSCE/ijYSS7u95C422mPjMDgKMcDm7p7YNvH4TUsWBIgoR8UPwLh9LVD8Cav2KWFENTdNNLxRfxL0i0/CLorTlyzeh3xd4OvbWQUPDvl/2JceqppzJliki5dzqdVFdX89577/Hxxx9z3nnn8eijj3LSSSf9yEf5wxIVpFF+0lS12tlbbwtFqUZkGLA6/LT0uELRvwFswfel+TH02n00drlo7HIxf6yKSQUiCqZRSeytt5JgUqNTK7A5fazZ3xNqyzIy00hNmyMUaY01qLA6fdR3RvYrPRy7O4DdHcDpET1IJ+SZqOtw0e/whdqO5CTp6bP70KoUqJSEeqMONifyBUQqb2qchkPNDrx+P6v3iijq9OJYAoFIMW42hG/CAxrNoFWRn6qntt2B0xOgu9/Dil3dpMZrmDkynqsW5lNWZ0WjUpBkVvNdKEoRIq4g2chzF00OmR7NHSHa7lw9v5BvDnbQ2Oukw+Zm1e3zWfzwGhp6nDz2dSV+WWZJaQaPnTuRbfccTafVxfVv7qK2y4bDG+A37+/luLHp/P7TvXy0swWTVjVsTWl5Sz97mvrYXNPDGZMySTHreHF9Lfd/US6uAWLQICDDtLx4Fpak8OaWBpp6neys7+GRlRX8+bRxTM0b3jjk6td38FV5O7cuLubGRSM4eYJ4CDp/ei4Pf3WIx7+p4qIZudx8dDGPfF1Bi8XFPZ/uD60vAceNFal4aqWaGfkJbK4Vn1+v3cvRo1P5eLdoSzP4g1xYkvydPocoUaL8fLF7RNZMcXxxhJvuzo6dITEKoo3KIzsf4ZIxl0Q40r554E3+uvWvmDVm+j3Ck+CN8jeGCNL9Xfu5bMVlGNVG3j3pXVIMKZR1luHwOXD4HJxfcj6N1kbWNYu2ERISk1LC6bu3TrmVW6fcyr6ufQCMTRrLmZ+dyaHeQ+TE5NBmb8MT8BCQA3Q7u/nt+t8CcKDnQMgRF6DH1cPze58P7UOlUIXcgA/n8YWPc8XKK1Ar1ExOFSZDL2sDPJyRRpKk5qaSi/l9+T/Zp9XQoVKSae+Al4I1olnT4Yqvhm7U5wFnL7SL8xi2XnT1n2DTU5BYACc9AukThj2+KP9jmjb/cNv5GQrSuLg44uLiQu+nTp3Kueeey+uvv84ll1zCkiVLePHFF7n00kt/tGP8oYkK0ig/aQb37zTqlOyqtiIDsYbI0dDBEbGUWA0SUB+cV93qIGGEGCXOTzWQnxpORW3ockXs49BhLVrMehUWhy/UA/RwEkwqQKLHJiKpNpefz7e245dhXI4p1AdUrZT4ancXsUY1UwpjkSQoyRKpxOlxGhweP03dLlp7PDR0ufD6ZXz+AE5P+NjK6qzEDHJtVSpAE3Tz9QdkZo6Mo8vqJTVWg1qlICVOS32HcJaVEdHWl9fXkhqr44Rx6QC8t62Rmk4bS/e1km7W8/JlUzFqh34tXDG3gKNKUkg160JidDAZcXrOnZbD31YcIifBgFqhCDkZD7SI6egXadGxejWxejX3nDSKi17cCoDd46O608Ynu0Qk1hE0TwLQKCU8fpk4g5o/LhnL9W/upMXiYmttN/eeNJotg3qZpsXpaOkT9alt/W4eXH5IfE4GNfU9Tup7GvlgRxOrb19AVaeNB788yMkTMrh+YRFAqC/qpupublwUmVb29QHh2rvqQDsfXz+bJ7+pxBs8yHOnZjM5N54lpRmh5SVJ4vlLpnDyE+vp6Hdx+qRMumxu/njKGEanm8mM13NzMKU3wRh12o0S5ZfO3xf8nRV1K4b0C11SuIQva7/kUM+hUGuXTGPmkPYofe4+ABw+B8XxxVT0VnCw5+CQ/ezt2ovT58Tpc1JjqSHFkMKSwiVU91Vj1phRKVR8Xv15aPn7Zt7H3Ky5Q7YzNmksAId6DlHRKwzaMk2ZfHjKh3zd8DUTkidg0phQSkr8sh8FkfcGvUqPSqHCF/Dxu+m/Y3bmbLJjsoe9NlnmLJafsRyAyt5KyrrK6Ai2m7EEPCj2fYCslOhQqdiamMVpygRo2SVWbtomxKdqULaPxw7PzBGRNkkBo0+F7Gmi1cvut4VIHUhNdvVC8w7Y/Cyc9sywxxflf4jPJVq7/BB07hfb+wX0KQW46KKL+OKLL3jvvff49a9/zSmnnEJCwvAD7D83ooI0yk+akZlGdGoFKqXEjkEutYcLxIx4DU09HjIStBh1KgxaJXWdTnptvpAr73BkJmhp7nahUkr02UWK5YC41agk+p1eYnRKrK7hBWlpvpkemzckSEHUqAJ09HtIiVXTZ/diNqpo6/XQY/Xi8gYwaJUUZxj5pqw7JIInF5rp6vfi8ckkxqiJ0auoaAkLZKcngNPjCaV6+gMiotpr87DpYB8GrZIF4xJCNZYT8mJIMWtQKSVqOxxsquvk8dWVACy9cQ5Wl487PiwLbb+xx8n+ln6m5Q//5VYwyHl2OK5fWIRRq0SlEPt//PxS/vTFAcZmmOm0ebj5MNMiqzN8zabkxVPX5YgwtBrA45eZnp/AXceXkGjU4gvOTDBqOOGxdbh8AUxaZXCZUXTbPRxs7WdTTTcNPaLu1eLyha6bUiHxz/U17G7o42CblerOChaOTObeT/cTq1czPT+BWxePHHJ+95w0ipc31HHOlGw8vgBqlQKvx8/iUSn85bRxEWnEA5h1apbeOJeb3t7Fn5Ye4KX1tay9Y2FIAJe39LO7oY9LZ+X9y2sbJUqUnz9T06YyNW1o7VeHo4Nj847lH/P/wXNlz9Hr7OX3s34/ZLkrx11Joi6RwrhCPqj4gIreCvo9/TT2N7K5dTNqhZqSxBJOKTyFWkstZq2ZaWnCT9agNnDPjHsAmPj6RHyBcEnBgDPvkdjbtTfUWmZJ0RJ0Kh0nFYTTBd844Q2+qvuKC0dfGJpW31/PjatvpCC2gNun3M7MjJnf6Rr1ufq4YNkFOH1Orp1wLb8hkdFt5YwJdLMuIRaPSsOi85eC1gzrH4GOchh/rhCjO14RwnLRfeDqE2IURGQ0sQhmBtvuTP0VdFXCk4NciNV6GHfGdzrGKP9lXJYfzv1YDoC7/xcjSAGWLFnCe++9h91uZ/ny5b8Yc6OoII3yk0apkELmOna3n/oOJ07P0DYuASSmjYglLV6IT1mG7EQdRWkKspKO7MQXa1SzuDQJEPWUXn8Ag0ZJfaeTA012PD4hRAfX+ykl8MuQn6In1qgm1qgmM1EHsszeBhsdfR4MWiU6tYK6DhdmvYqCFANuT4DUOA0GrYju2l3+UKouiFYzHp+MSimRFq9la6WFGJ2E1RV5soPF2q6afkw6leiv6fLjdAdQGxSha5eVJL6E0+K1WH0iQmnQKIkzaDBoIv/9Y7RKSrPj/uXnMRyNPQ5e2lBLbqKB+z4To5qvbapj6Y1zWXrjXI76x7fUdNpx+/y8eYVILWvvd1GcZubXCwtxePzccVwJGqWC86dns74yLCRBDBBsqe3h4pe2khGnp6PfjUohMTUvng93ivRXm9tPilnPiNQYRgD7mi30u7ycNzWbmi47CUY1LX0ulkzMYGlZK69urMesU5ERq2PhyBSWPLUhlEJ9+sRMRmeYh5znrMIkZhWKv5VZD6zCEWyx02JxhcRoR7+LW97bTZxewz/OnoBOreSej/eGeqK2W928tKGWq+YV8psP9lDf7eCZCyeRaNIKl2BfgJFpQ2t4o0SJ8sukvr+eq1ZeRbujna1tW3niqCci5pd1lvFa+WucVHASC7IXcG7JuQAUxhWiUqjQKrVc+OWF9LhEaYBGoeHLM77kt9N/O+z+3jv0HkqUIEGcLo4kXRInFJzwL4/xxIITOdRzCL1az3F5xw2ZPzZpLGOTxuLyudjWto1xSeNY17SO+n6RpyQH/LDjVVCqYdQpoP3Xg5sDqBQqLj7+Gdj0JNg6+VvlCjEjEABdLBx9n3i/4TH4RwlYg8K6cQtcvxWO/xvs+0C0/5h5WA9YY1Lk+xt2gTn9Ox1XlP8y/6mR0ZEIPvv8UkhODpf41NfX/4slf15EBWmUnxUDaaADtYID9Nq8tPS4yU7SMaUolpp2B3uD6bJmgxqz4d//qQ9eJj/VQGuPO2SipFFJuL1CsKTGaxmfF4N+UFuWgdTZAUdcgG3BNjH9Th9bKy34AjIJMWr21vXj9csUZxopzjBS0+7A55dDBkdKBbT0uOl3hEewj0SvzcuEPDNOjx+TTvUvz/OYMWl8dcs8TFoVGXFCpN91fAlPflOFze3D6vbTZnGRYNJw54dlKCSJB88YN0S4Hs79X5TzVXk7WpUiFF2uaLfR1OskP8lIgkFDDXaQ4bSnNzAhK473tjfi8vp5+bJpzC9OptvmRmPQ8JfTxtPS52TWX78Jbf+k8Wl8tqcNl9eP1y8+dV9A5sX1dejVCty+AApJ4t1tDawqb+PNK2fwp6Wi5UCcXsPTF0xi8p++BqChx8EpEzLYUd9HSZqZ966ZSWOPg3e2CdOkNLOOU0qHt4l3ef2sOtBBaU4c9kGGRZIkUqaVCokvylrZEHQRPmdqNr5AgE6buBkOpB7/ZdlBdCoF729vAuCWd3ejUyv5qrwdCXjjiunMLkoasv8oUaL8svi06lPu2XAPSkncSwKBAI39jWSbRVqrP+DnjrV30GxrZmPLRu6ceicnF56MQlLQYe/gs+rPhmxTkiQkjmzQ8+yeZ3EHxHdSl7OLxxc+zuM7H8ekMfH7Gb9HrRzqK6BX6bl7xt3/9nxuW3Mba5vWMj9rPvfNuo/1zesxa81Mad4PXwYNj1beBzfuAO0wA29+H3G6ON484U1qLbUsylkkzIqWPAWtZWBrh4xSiMuJXG/jE2AfZCQoBe9Z068SP4NZ+XvY+RqMHdR/VaERInnt3yBtAhQf82/PNcp/EeUP3A5N9csqiWlubg69Npm+2+DOz4GoII3ys0EXdLFVSkExGowUDi6zGRB1OrW4wasUEupgH1OH20+31UN6vA7VMIY5AI1dTlp63JRkGlk4PpGd1RbqO124vXKoZUxLj5vCNANKhYQkMaQNyQAxenEMapWEP3hcte3OUHTX45PJT9VT1SomDIgtt1dGr1agVYF7kCZVSpCWoMXjDdAZ7BVamh+DVq2IEML/iuLUyIeAa+YXMibDzNWv72BMhpm0WB1flLWwtEyMNB8zOjVk7HMkRqWb+aq8ndEZZm5eNIK/f1XB1LwE8hJFZPvly6ayp9HCm1vq2VDdza6GvtC62+t6WF/ZyQvrahmRYuKNK6aTEafnpkVFvLqpnpPHp3P3iaOYkB3PuMw44g1qTnt6Iza3j16Hh613H43HFwgJzg6bh1+/tZM5RYnsqO/j+HHpmPVqTFoVNrePlBgdfzh5DOdMzaEg2QhAdoKBh86awKe7mrhpUXHIwOlw7vtsP+9sayTZpKHfFf5g9jX3s7fZQml2HEeVpPDW1gZi9WpcPj9XvbYjtJwkSSglmcIUEzmJ4TrmQ21W2q3iAVEG2iyRLReiRInyy6TWUguIPp5ahZa1zWvZ9vk2Pjv1M9KMaXxc9THNNvHwafVYuWfDPbj9bk4pPIVb19w6ZHuTUyZz06SbSDYMb5K2rW1bqA4VIMuUxa6OXXzdIL4/j8k9Ztha0u/KgGNvl7OLJH0Szy5+VszY/3F4IXs7rPoTnPBg5Mo7XoEvboHi4xlx3ltDWsOQPh6u/AbK3oP6jZA3Ozxv9s2w5kFw24AAZEw88kHueEWkhFZ8GZ4W8Ij1Nz4BkhJuOwimlO959lF+MHSxou73h0jblRQivfsXxPvvvx96PW7cuB/xSH5YooI0ys+G/FQDJp2KDQd6h7R9GZ9vwu2RReoskJWkw6RXolEpQpHMtft7cHoCZCd5mFI0VMDJssyO6n5kWYhXWRbTBkg0a7DYfaiUEjaXj40He1FIEgvHJWDUDf1XGjBL8vllZpfEYXP52FdvCwnShBg1e+utBGRQKSUm5pup73KhkGBHTf+QMW5JAa097tD6KoWI/v6/khVv4IWLpzC7KAmHx8fHu5rRqhSkmrVHdKN9YNkBVh5o5w8nj+GWxcWcPimTtFgdWpWS+SMjb+QxOjVzRiTh9vnZVtfLvBFJzBmRxPrKLp74pgplcEShssPGDW/t4r1rZnLL4pHcEqzjvOq17XxV3s418wtYV9mF0+snzazlyfMncajNyor9bcwtSmRdMDJ5qN1GRbuN1341LeQCvOmuo3hrawNrKjp5a2sDF87Ixe72sXxfK1PzEnjqm0pquhysrdzE8xdO4pixQ1O3LMGa1z6nN5Q2rVMpGJMZG2olk5dk5Otb5wOwo74nItXb4w+w9IY5jEwz88SqytB2B8QoQLJJw2kTf56NvKNEifL9uGLcFWxr20ZZV1nIZdftd+PyCWOjdE0s93X24FYoeSwxEQc+YjQx7O3aS6O1MWJbEhI7Onbwyv5XmJg6VJDVWGq4ftX1eAPh2v0mWxMxmhhSDamY1KaQidF/yj+m/56+r35HRsJhbWjy58PIE6BxGzg6YeuzMO92MA0SzoeWCwFSsVyk5CqGGejd+gIsv1OIxhu2h91TS8+HrwZFcD3WIx/k5Evh4DI46h748EohRgE8wTIRUwpojN/73KP8gKh0kDwaOvb9v28reczPpn70lVde4dxzz0WnO/LxPvLIIyxbtgyA/Px85s79zweQfmpEBWmUnxVJZjVxRhW99nCESpahstmJSa8kNU7LQIZpnPH7iTVJkkiN09DW68Hq9BEMWCIhIlc5SXpalS6aut3sqhE3PD8yVqd/iCANBGQkCZJjNaTHaWjv81Dd5iDGoGJEuoGkGDV6rQqfX+ZQs528FD0ZiToyEnXsb7CGdqxE1KsOnOdgfAHweAMRqcPfl06rmxMfX4fD4+f+JWPIijewrlKMcl8xt4C02KFfjD5/gOfWCrOI1zfVM784mdzEf38DXzQqlW13p/DQikP8/atDjAhGIv2yTLxBTa/DS0W7ld9/uo//O2VMyGFyY7UQml/ua6O+Wzw0tPW7STRpOe3pDfQ5vEzNi+ey2Xm8sakOb7A1TnNvONIYo1ezqaabjdXdbKnt4YLpOdzy7m6+Km9nXGZsRE3yNW/sZPPdi0iJiTz3rbXiOAZqTbUqBeV/PG5YMyOAybkJfHLdbH716ja6bB7SzDpGZ4iBkNUVIr1Mp1IwOTeeHfVicGPuiGTe2FLPe9ubuGZ+ISeN/9fR6ShRovw8qbHUsKxmGXfPuJuyzjJK4kuo6a8hzZhGXmweALMt3WATpSeL5t5Da+40SlNKcflczM2cy+6O3bh9bjyyJ2Q6FBimQdnBnoNc//X1OIOusiPjR3Ko9xASEvmx+Xx91tc/yDlllX1EVuW3UPktjDwZEguhtx6W3wWHxEM0GhPkzgZDYuTKC38nRl1HHj+8GIWwUFRqQDkoDdOQANOuhqqVULgIFvwWqldD7VqYfg3EpIrl2veLKOhA5G3Wr2H9w+J1RzlcuxFi0qOC9KdA1owfRpBmDe3R+1Plvvvu47bbbuOMM85gzpw5FBYWYjKZsFqt7N27lzfffJMNGzYAoNFoeP7551Eq//Pnv58aUUEa5WeFJEnEGiIFKRB2upVl0hN06DVK4k2RgnTemIRQyu6RmDkyHn9AZktFH+19YuRUBsbmmMhK0rGvITzymhanIc6oIiVWjdcfiEjdbexyUdUqxFN3vyckeCx2H0lmTUhEjs42MSrLGGHvX5JlQqdREmtQkWTWsKvWQl27C7VSQqdRkhijJiDLJMZoiP2OojsQkIcVTr5AIJQqbPf4mZwXz9S8eKwuH1PzEthS083k3PiIVi8qpYKr5hXwdXk7F87IGbLNf8Wt7+3h410iBa3X7kajVOCXZR47t5Q3NjfwVXk7r22q5/LZ+eQlGfH5AxQmG6lot3HOlGze2tKA1e3j7ClZ5CcZcbhFVGFnQy/b6noBWFSSwoyCRM6cnEVZUx+/+3gvhckmJuXEs+aQEIIf7GjCEzxvjy/AX88Yx7nPbxHXKjjtcFRKJSCiC5lxWn57wugjilGPL8C72xvJTzRy4YxcnvymCpfXT0W7leLUGK6dX8hvPyrD7vFz+qQsNtV0E5Dho13NfBS8Pje8tYtZhUkkGH/gepooUaL86Ny19i4O9Bzg28Zv+eCUDwAoTS2NXChrKhiSIOAn1ZBKaoqYr1Pp+P3M37P4g8URi6cb0/nLnL8M2deN39xIh7Mj9N6oNvL68a+jVqgZkzTmOx1vQA6wumE1ebF5FMYVDr9Q+gQhKs1ZwjTotSVQ8214fnwenPyEaLcyWHS27oH3LxXmQ+PPEdPWPQx16+GYP0HqaDFt0kViGzFpEHtYJskJD4VfV66CN88AZLA0wRkvBE/CHx7ZtbbApIuFUy+yEKKp3+1aRPkfEF8AxlSR4v2fYkyF+Pwf7pj+B/T09PDCCy/wwgsvHHGZrKwsXnrpJY4++uj/4ZH994kK0ig/O1p6hzqmKRQiy0cGtlRYkCRYPCEJoy48emTQKjFow467VqeP1l43WYm6kPMtCHfa0nwzK3Z1habpNAqsTh+TCs2U1VlxegK093notXnpsnrp6vcyLtdEUboYWTUbVKHI6oAYjdEryU7So5Ak9tVbiY9Rk5mgixCjgYBMp8VDVqIObbBm1u4U4sjllXF5faH2NLH/Jl33i7IWnl5djVmvYkttD7ccXTykt2Z6rJ63r5xBXbeDU0szUCkVPHD6eG56ZxcnPr6OgAx5iQZ+NbeAi2bkUt1pY1ttDzctGsHvThj1L/fvD8jc/fFe4gxq7jp+FI09Dj7ZHS7GT4nRUx90023udZEVr8esUzEyzUxWvPicmvuc7GkS5lD7Wix8dP0sUmJ0eHwBmvucFKYYOdBqDUWzQRg13fzubp74ppKp+Qnsa+5nX3M/Ckl8Hv6AzGOrKvn813P4qryNuSOSyYjT89i5pXy6u5mLZuTRanFx/Zs7WTAyhVsWF7OnsZfS7FhW7HcFj8tNsinSKMEfkLnzwzKqO21MyonnxfW1KBUSj59bKtrzOLysLG8nP8nIroZeeh1C3N776T5Ks+PY2dBHcoyGTqsYCJGO7EsSJUqUnynegJdHdjyCNZhWmhWTNfyC1jYRvQt4wNUP718MvudhghBsqYZUzi4+m71deymKL2Jl3UrOKzmPGE0MFreFm1ffjCfg4dEFj5JuTA+1djH4A+RWraXE4kE35/bvfNwv7XuJx3Y+hl6lZ+UxrxFrzop0yw0EoOREuPWgMCwK+CLFKIBSB6+dLF4XLoKLPhKv938MPTXip7sSzBmw6v/EvPUJcMY/w9vI/xcpin2NsOavsOdtQiYTA2m9IOpQL1sGn98Ey38LTguc8zp8cDns/whyZg41QYry4yBJMOYs2PH8f+a6q9SI9X9GN9IVK1awdOlSNmzYQFVVFe3t7XR3d6PX60lJSaG0tJSTTjqJs88+G4PB8O83+DMjKkij/OwoTjewrzHcn1NC3AsZ9BsIpTAB9Nm9lDfaSIvThtrIbKnow+r009brZvaoeCRAoZCwOX1sPtQXWtegVVDd5qTX1k+cUUWMXoXVKUSx2yfjDhoMdVo8IUEaZ1SRn6qnpl2kSOUm65hYYEaSJPbU9lPT7kRqhaTJmpDwBNhd2099pwuTThlqR1NaEENtm4Mem48+uzckcHttXiTJgT8AiTEqYg3qCHH7zLfVlLcGhZgMK/a3ceOiEVR1WHlxfS3fHurEpFVx2ew8Xt1YR7fNzZLSTH716rZQaixAXbeDez/Zx8LiJM5+dhPddg/b6nr5+1njufK1HWyq7uLhc0o5dkxaxOd07yd7eWebqHMy61Q8tbo6Iu24vseBUpIYlR7DHz7dhyd4YtvqethU3c2qgx009zo4a7KIIC7b28bW2l7W3rGA817Ywp7GPm5aNIJp+Qm8urEeo1bJlXMKcPsC7G8RPWtjtCpUCglfQCYgQ6JRpAYXp5h4dVMdl87KY01FJ1PyEogzaOhzeGnqdbChqps9TRb2NFkYnxXLFa9uj6hbViok8pMi07oqO6x8sEM458bqxWCBQa1kcm48p03MpM3i4tSJmSzf1xZKeQZwePxoVWJAxOL08dR5E9la18M5U7ND0VGHx8fGqm6yE/Ssreji6NGpQ/YfJUqUnz7rmtbxevnrAFwz/hquGn8EAbT2b7Drjchp7nAvbkmSuHfmvaH3gyOj29q2sb19u9hf8zoemvcQFyy7gHZHOwZZ5v+6e5C6l4G9my+PuoWHdzzMifkncvPkm4943AN+Cgut/ZifmgHmTLhuozCgsbbDM3PA2Q3zbhMOtlozzL0Dtj4D7mBmUdfB8AarV0HTdsiaAqUXQPU3IoLpsoKnKryc2hicbhGpuf+Kbx+A3W+G3+vjYcFdkcvkzARLcGC0ba+Ikg7cmLx2ovyEMGfC+Auh7I3vJ0qVGrGe+eflxzBy5EhGjhzJrbcONSz7/wNRQRrlZ8eITBO+gMzBZiGaJMQgWEAWPSuVCvAHRKR00fhEfP4AGw/24faKqGZeih6FQkKrVmB1+lFIsHR7J2qVxMKxCeys6cc6qD/oiHQD+4MCuM/uo9/hC+1jAKVCYkyOcLDtsLjpsXpDYjQlVsPY3JiQWBxozWLQKYe4/Q64BA/8BjDpVIzLEy5xgYDMlso+/H6Z1DgNWyvDDyhKBYzPM5OXIqKL50/P4bGvK5mRn4DV7eOKuWKk+Lb39oSijgD3fLKPgAwPrjjIe9sbQ2L08NY6qw52htyL9RoF/S4fXx8Q6TR3flDGJ7uaOWNSJiPTzGQnGKjpCt/c+10+7MG+naXZcexu7ANE/Wh1lz0kRgfY1djHKxvrALh50QhKs+No6nXS5/Bgd/s4EBSclR1Wnr5gMr89fhSf7m7m5Q211HbbuXx2Hs19Ts6blsMnu1sAOKokhZcuncqm6m7Oe2Ez3xzq5NtDnexu7CMrXk96rI6dDX1UtNu4cLpIRTZpVfTYPUNMtOL1KvY0WTBqbRQkmUSv2SQTR49KobrTzu3HjOSGo0aQataSFqvn10cV8YdP9/P6pnrOnpJFrF5NIBBgfHYc+5v7UQVTfyVAr1Xy5b42DrVbeeWyaejUSm59dw/L97dh1Cqxu/28t72RlUHzpChRovx8GJUwihhNDHavHafPGdlmpXYtfHYjGJOhZZeYFpMuRFv+fJhyOQBev3fY9iwDTEufxoz0GXj8HuZlzeOBLQ/Q7mhntNvNb3r6woZ5BQt4/9B7nN94gOy6AzDy/CO6y14+9nKyzdlM3b8cqe1l6G8CR7cQpG17wRFMCV7zEKz5G6gNwwu8+ALorRE1pAPRy6QRIs330FJ4+xy4dgNoY8Fjg8yJ8OrJUL8B5v1GmBEdiezpQpCmjILp18HYM4ZGyCQJzn0TKr8S9aWxmSJq2l0N48468raj/DgkjoDJV8H+979b+q4xVURGf2ZiNEpUkEb5mTJYIAQQPR7VKgWN3eF0XqtTpLZ29ntxe4W0MhtUobq/mSPj6bN76bV56ez34vfI2Fx+dJpwxPLYSYnoVEoaOl1YHD4CcjAF9zCFYtAqMBtU1HU42VXTH7oHShKMz4sJ9SkF4RacEqtBq1aiPKwGsbTATJLZSXLs8H2zFAqJmSPjARH1Hezi6g9AfYczJEgvmJ7LBdNzxbVwefntR3t5f3tjSFSCaKXjCl4bZKjuDD9ADBajCgnGZsby0XWzKGuyML84GY1KwW+OHclrm+po73fz5b42vtzXRoxOxTe3zWdqXjyba3pQK4RBUnKMDofbR0a8nj1NfcRoVZw4Pp38JCOPr6rCLwdwegLkJBi4bHYeK/a3UdNp47FVlZiCplFmvZrkGB3PXDiJNRWdXDVPPNBYnF7u/HAvAAfbhKB87qIpANxwVBEV7Vb+cPIYeuweqjttmLRK3L4AMcHt2t1+suL1VLbbOGNyFnXBVGKb28fU3HhyE/TU94RNkjx+mStf2x56n52gZ8XN8/jnJVOH/dxe3VjH+qou1ld1ccH0HLb8bhEAl7y0lT6nl3VVXVy/oJCTSzP4dHcLHVY3HVY3Dy4/SFOvk157MI039Ln9cowMokT5/xPppnSK4orY1bGLDyo/4Papg9Jmd74OvbXiB0Tq601loArXka+qX8Xta25ndOJoXjn+FdSKocLUrDHzwjHhGjSDWmQFufUJTNGoYM5VMPVXoI3h1ztfYNLmYOrsjldh/m+GPW6lQslxecdB2kxQx0BySVhQFiwQ0Uhnb3BpeXgxOvEi8DqFIHV0i4jm8Q+JG2V8nlgmIR9iMmDcmUJcfn5TeP31j8L8O0GpBq9LGBTFZgqXXYDJl0DJSUIkK4d5vHVZhENv4ULx09cAzj7RJqa1DF4+DtR6OOr3kD38d3mUHwFzJsy4SfxfNG2CzvLIljCSQrjpZs0QNaM/ozTdKGGigjTKz5L4QWY+kgT5qXoaOkV9n0ISNZ9jskV9S1KMmsQYNT6/zIyRcYBo67LhQC9KhcS04ljsLj8atYLEGDXxpliSzU7ijGoMGhUWh3eIidIAJq2CEZlGKpodLN3eERKDyDBthJkYg5oYfeS/WZ/dS32nk5wk/RDjpcZOF3sbbOQk+Zj0b3qLxhnVLJ6QRK/dQ12Hk4o2GzIyfXZvyGF4ZXk7Pn+AVzfVsbmmBwDtoKjs5Jx4tCoFJp2amk4b+1r60SglPP5IxT1nRBKTc4UQXjw6bAp19bwCWvucvLGlITTN6vIx9c+rAJG6+uYV09nf0s+jKyuYkB3HKwumMr9Y2P3f9t4elu9rwzao4apSIRGjU7P0xrlc9OIW1lV24QjOP3GcaMeyaFQqsgz13Q6y4g3E6tUkGtV020X69Ic7m7j7xFH8aekBeh0e/nr6eOKNGk55cj1lTRYWjkzmwTPH09TroM3iorLDxse7Wrj7hFEcPy6NK17ZFjqeM5/bRJctnC6UYFRj1qkjepGK6K0Xg0bF21sb6HV4uHJuAWqlgtc21dHR70ICNCoFKoUUEpSDzZNkoCTNjGGqir1NFtJjdby8oQ6A0ydmcurETCRk7v1sP3ubLayt6GRe8fD9BqNEifLT5ZyR59Bmb+OUwlMiZ0y9Qri9xueDqw8mnBshRgE2tmzEJ/so6yrD4raQpE/6t/u7d8a9LM5dLNq66CLTXieNPgc2vyTSWAsX/vuD18XCMfdHTlOq4Nc7YOmtUP4J6OJFpLXrUORyu16Ho/8I+4SJE1ufh0mXQNpYOPbPMP5sES1t3AzbXxy6b0kZFiLb/gmr/yRep44V9aEAxsSh6wG07IaXjweVFi7+FNrL4ZNrhflS4SIoeye87Csnwm8qxblG+WkgSWIAJKEAfC6Rvu5zi89Ta/7ZtHaJcmSigjTKz5L0BB0zSyQ2HexDlkWKq9MjblQBGVJitWQl6WnrdXOoxU5esp7clLChUYfFgy2Ylttt9VJaEG6crJREFBPA6fFT1z64fYgSm9MfCpCOyDBi0Ig0SgCtWsGUIjN6jZIk8/DuqDuq+ul3+ui0eDh6QhKBYIhTIUk097iQZWjucTPpCEaGgzHqlBh1eg519BOvF1HVx76qorHfxtiMWB4d1O8SINWsRSFJtFpcXDA9h1i9mqe/rUYhwapb57O3xcKGii7e29EUEQTeVtvL4Vz28lZWH+pkUcmRG4hbnF6WlrVQ2+3A6vaxvqqLTpub9Fg9r26sZU2w/clgmnoc9Lu8mHVq7ji2BIOmkuPHpnPS+PSQ2++K/W1c/foOAD68dhaTc+N54PTxXBWcJgNba3t5MyiUJ+XEc8XcgpAADMiQEqPjlnd3U9kh2ipIwJ+XHeCh5QfxDkohHixGAZaUplPZbqcumNo8Ks3ElfMKyYjTc8PbO/l8jzAP0aoUHGy18n6wrhTA7QtQ1WkjPU78LT570WTOf2EzMVoVNy4qAiAn0cAbV0zH5w9Q3WVjd0MfkgRzRyRx9MNrQnXSXbah5l5RokT56XNiwYlkmDJQSkqRmrvibihYKKKT1274l+tePu5y+j39jE8e/53EKIBGqWFe1jzxxueBDY+BIV4IYJ0ZrtskUm08NqhdJ1JfVcPfv4bFYxcR0rNfhZ5akXJct16k34KI9Pp9kDcHCubBqc/BV7+DxCLRGgaE0EwZJQRGcoloD+OxBXcgCdEx5nTY/RZMuUwIV0khph8hzTiCtr3gdYif5xeEha29Exq2HHbBTDBM5DnKTwSVLipAf4FEBWmUny3xRjUqpYTPL2Nz+ojRK7E6/SgVQjgCHGyy0Wv3YXP6IgRpalz4ZtthcaNSSLT2uinONGIeFNEsq7PS0hN+8M9PNaBRSWyv6kejkshI1KFSSOSn6vH6AuQk60PpuW5vgJo2B0lmDcmx4f3FGlX0O33EGdXYnD6+3deNjMSoTCOjs00caraTnTT0y7aj38Wflx2gODWG6xcWRczTqRSsbe6iKCmGt3fU4fIFQrWgA2m94zJjaelz0m13Mz0/gVmFSWwJ9taMM2hIMGn589IDtPe7yYzTYXF6cXn9+AJiG/MfWs0ZkzO5ZGY+No+P1cEWKuuqOlFK4X6ph7OltoedDX2h93d8sIfC5JhQjSiI1OGjS1L5Ym8rKWYd2uA1HJcVy3MXTaHD6mL1oU7mFSehVSnRBIWpJIE6GPHNjNOjVkp4/TK5iUYmZMcyIsVEr8PDnBHiwe2lS6eyrrKTuSOS8fgCJBnDqdF6jRKHxx8hRofj5Q0NEe9PnpDBopJUHl9VGRKjYrk6mgb1Qo3VqzlzchaTc+Op6rBRlGIi1axj1W0Lht2PSqlg/ohkdtb38eHOZuYVJ4d6oJ4+MZNTS6M1MlGi/BzZtOtFrip7FAl4XTeKCfUbRI3kpItFOurmZyB7GoxYPGTdTFMmf5v/t++3Q2ef+NHGQPnHkZHFnBlQv0m8//oP0LhFtF45/XkxzeuEN86EvnpRe5k+AXa8AgeXivTY5XcJkZc6Dq78RqTcgjChmXWTSOfNnyeiqLvfFmJQUsKJ/xDCEsDRA8/PB3uXiF4ak4Xg8NhFixdrK7gtsPNl2An0twjjmms3iJpBYyJ88yfY8w4s/iOMPX3oNRh/NnRXQU81HPhcTJMUQph6baJGV6UVn0HGJND88lxMo0T5KRMVpFF+tmjVCjLitTR0uWi3eFlcmohGJfH1nh721tuQZdBplGD3hVJYB2jscoVe69UKtldZCMjg9QWYWRIfmidqF4Ug1WsUFAYdekW7FkJGRaX54QjrAPsarDR0upCa7cwYGUdavBA/kwvNlGQZMWqVNHW78PoBZPY22JgzKp7Zo+KHbAvgxQ21fBo06DlubBqFySIl+dPdzbyztZFNNd2MSDZy3Ng0Pt3TwnnTcihIMuKXZSZkxxGrV3Ppy1vZUNVNrF7N9W/tBOAvp41l8eg0YvVqRqTE0N7vZtGoVP64ZCxdNjevb6rjsVVV1Pc4eHhlJd8e6uS8aeH+o8kmLXceV8KN7+yOOF4JEdXLTzZGCNJ1ld2h9OEBXN4AX+5rY0yGmVcvm4ZWpcTm9rGroZcpuQmc89xmarvsnDU5i7+dNYGFJSm8c9UMtCoF47Pi2FbXwwUvbAnV03ZZ3cQZNEOMfzLi9IxOj2Xxw2tAIhTZBuF2e/jxD5amGqUi1Lt0AJVCoiglhns+3cfne1pQKyWMGhV9Tm9IjA6sZ3f7uP2YkZz/z83saujjkpm5/N+SsciyHOGOPJh9zf2hY5mWn8DLl02ly+rmjElZ4R6oh5ZC207ImQ35Rw3diL1DjPbrh/+7ihIlyv8Wb+UKQHy/eDMmwoGVQADeOAOSR4qUVkkJd9SAPu7/bWeOHnhquvgeAOEyq1AJ91pzJtRtgFdOEPNMQad0W7hnKR0HoH69eH1wqRCeX9wKsh+qV0NAlEnQvlfUhZrTxTbfu0hMz5wYrufsrRO/ZT9seDQsSLurRD0nQP1GYXjkCLZdiy8QghSEgFSoYG2w52jABwt/K16vewRkH3x+I4w5bWgdoUoLi/8P/F74+BqRwjtisfj+nHwZzA06m67+C2x4HI77K2RN/p4XO0qUKP8pin+/SJQoP11ERFJCpZRYtaeb2jYnXt9A384AfcGaQpcnLCTq2h0cahaGCxIi7XYgvfbwNNsxOSYyE7RD5ikU0hFFxAADRjwysOlQHzVtAxFLCa9PpqLFQWKMhrR4TehY+BebnF2YhEaloCQthoxYEe3d12zhpnd2s6lGRDorO+1cPiefqj+fwOVz8vlgZxO/enU7T6yqRK1U8PKl01h9+wIunZ2HJIl62zEZsSTHBNN9zy1lfFYsG6u6OOrv33LZy9toHGTmA7CzoY87PigLve+wurnro70YNJFGO1q1gtd+NZ0V+4QznnHQfO8w4VS/LLO/pZ/tdUKsXvHqNi56cSs3vrMLq0t8jm394YGEGQWJTMwRImt/swWPP4AvIHPiuHSeufDIDxIvrq/B7vFHiNHBDHwEhx+hxx/g0XMmYAz2rFVK4AvIXPPGDgZOzeuXOWtKFvpBrXyuniciBr6AzJg/LGdXUJx/XtbC82urKfzdMn770d6IfXn9AU59an3oc00yaUgyaVk4MoWzpmSHxajfA43rRYSieiXsfy/yoDsPwKZHYOPfRSP4/R/Bt/8HjRuPeH2iRIny32XejFt5wqnhmZhSpmTPJWQh174X6taJ1wqlMNj5f8XRExajAA2bYMqvRLTz/UvhyzvD8466BxbfD6c+E56WPkHUeebPh4kXiqbfWUHDn4AXdHFCRB//kBCjzTvFdoX/fXggTJah/FPxWqkJOQYDYnvz7xSmR5MvhdFLRHruhPOg+BixTEIhXL8d4nLD6w2YKjl7xb4B3DZ4YrKI4g6HUi2EcH8TVK6AEcfCqj/CqvtFv9c1D0LTVtj81He8wFGiRPkhiEZIo/ysSY7VMG9MAl/vEQ/u5U12ClL1GHVK8lIMWOxenJ4AJr0QCL02L7tqraH1Y40qeqwefMHIl0E71L106ohYxnkD6NTDj99Utdpp6/UwJscUYVKkkMCkU4ZqVR1uP9urLCSb1eytt+H1y/Q7fMwcGc+3+3rotXkpb7Axf+zwvdbmFSez775jUSvDYjjBqMGkVUWYAp357Ca+uW0+WfGGUHuV1Yc6mLynhVMmZJCfZCQ/ycin188W7WoyhHHD0rJWypr6KBvUEgZgXKaI/qaatbT3i2hxillLh9XNiePS+aKsFa/fT26CgSl58Wys6qK1382sAmEukZNooK3fxcKSFGYVJvLoqgo6+ofvKaZWSkzJE+ff5xAi1OLwEm9U02Xz0NjjGHa9c6bm0GpxkWjScNW8ocW3dreP33ywh/ouO/tbrUPmKyXIjjdQ1+MImgvFcLBt6HJf7W8nVqdGrVSEji8gw4njM/hwp4hev7BOOGRqlBLnTcvhrKnZvLKxHqvbx+Bs4H6njy/KWgnI8OGOJr4+0M5fTx/HolGpvLCuht2N4nPIjNPx0XWzUSuH+fvzOomI5bbuglGniyhCRzmUiX6HyAHY8SL4gtevYilkzxr2WkaJEuW/TM4MFlwj6t2pWBE5zxccdCtYIKJ6/68kFcHpL8C6R6Fzv5i29TlhGhQI3jfm3yVE4Yijh66vUMIpj0dOy54mjIcAZt0oeo8OsOGxsAA+82VxHj43IEFPsP/y6CUwO+ieGwiAtQ3m3RHpjHvWy4POoVik0CYVirRjgNzZMCFYo/rmWWBpBGMKIIu03A2PC3E7HNXfiOvcUyMEO7KI/i66F8adDTXfirTlKFGi/M+ICtIoP3ti9CqK0vRUtYlIntcvU5RuBAiJQZtLCE6tWhGqOwXRV3RzRbiXZ2uvm8zEyPpNSZLQHxb9q+9w0tDppCjDwN56YbxwqNnO1BGxbK+y4PT46bWFRWJqnAa720dLj4fGLhdGrQKvXw61mBnoQzlc9WIgINPv8rK+qou7PtzLvBFJHDUqlZK0GLLjDfzhlNFYHB521Pfx5b42fP4Am6t7+NOy9WTG6clNNLChqpsb395FnF4dcmYdnW7mzS0NNHQ7iNGFU3jHZ8Vid/moDvYRnZafyA2LRhCnV/P65nq21faypa6baXkJ6DUKUmKEOJWRuf/UsehUSlr7XWTE6thW18Oehl4UwBdlrZi1qpAYTTCqmV2UxI66Xlos4iHsN8eOJCkYrX3h4imsLG9nzogkznxGRPSy4g3BzzjA53taKE6NYUyGMJH67QmjeGRlBcV3f8k1Cwq5dXExH+9q4sHlB0mP1Ycik4ejUyl468oZFKfFcP2bO1lT0RkhRguSjNR125GQKEg2sWxfGwAnjE1j1cEOJmTFMm9EMs9dNJlPdjXzZXC+xy+Tatax8O9r8AeV6OA0YF9A5qzJoifpusouOq1uPt7VzKJRqRg04a/mkrQYUs1HMHCwtYW3qNRC3jwhRgF6qyKX9Q0S8+pofVSUKD86HodIMR2MywKp4+GcN/6zbe5+C/Z9JHp25kwX07KmQXdF5HIBHxiSRARz5vXC3Oi74OwT7WEGGOxq21UlXHYBMqfA6FPhwytg7/tw9P/B+e9AzRqYca1Yxt4FT04REc7YHJHeO+5sGHVSeJtte+HdC0Wa7/nvwQXviyhy4aLwMgNC19EtIrxbnwvvw9EjjnfsGRCfIyK1+4JtbpRa4WYcmw3HButqzwi3y4kSJcr/jqggjfKLYFyemdY+D3aXn25rOPo2LjeG2g4nRWkG2vtEdO+Y0iT21VtpGFRHOsBAiu9gAgGZpm4XZoMqVIu6t8GK1yfjb3aQlagNClktPVZvhAnSAB5vgNwUPS09HhJj1EwvjsXm8pMQjKhOHRFLe5+blFjNkHUvfmkr66u6GJkag83tY9m+Npbta0OnVhCjU9EZPN9YvdhWnEHDP1Yeos/hpc/h5eGzJ7CpWkSQn1tTjU6tZFp+Am9tbeAPn4kR80fPKUWpkAjIMvecOBqDRslpT28gIIsIZ3owRfhASz8rD4gU3C21PWyphZkFCXRY3TT0OPloZzMXzsglM+giu7W2B/eg9NwOa/iaS0jkJhhZuV9sL82s4+KZeaH52QkGLp+Tz+aa7lCLlTlF4uHnyW+qeGxVJSqFhD8gc/X8Qu46voTP97TgCYrVWxcX8+L6OtosbtosQz+TJJOGLpsHly/AtroeJubEsam6K2IZtULixUunIssyKoWCWL2aQ+1WNCoFfztzfIRwPHZMGh9ubwRExPWYMWkkmjQhMQriWo1Kj+XFDbVIkmhfc9HMPB7+6hBrK7u4fI5I763vDvfwq+6w09HvImU4UZpQBPGF0FsNfne4BgzAlCFqRw1J4OwR8wfw2GDXyzD6zHDEIUqUKP9bateI6ODhaPQiOrribtjzFmjjhMCaftW/3+bS20QKv98Nl3wu6kGfmh6u9TSlwsgTRNuMfR+KWs32/ZA7c+i21j8i1l94N2iFZwGWBmEwBCJdt2BBePlNT4ZfT7xQmCQNpOl+cz/csBMKB9W5dx4M9y61NIif6m8g/ktIGyem+9xCjIIwOTImiRrRwYw9Hba+IL7rJp4vUogHxPjzC4Qh09qH4O5WIcRt4p4Tqs+Iz4OuSjGgt/tNET0+6g9QPNRUKkqUKP8dooI0yi+G1DgNNW1OZBm2VPQxudBMZqKOzEQdHRY3Gw70ATB7VBx21/D1gw63H1kWLWTKG23o1Aqq2xwEZHHvyknWkRyrISdJT12Hg+wkHQoJ9Fol6fFaQCLZrKFzUEpqYoyasTkxJMSoyUoSrrySJKFVh6OuWrWCnOSh9UKyLLO1VtRUmvUqZhUmolEq+LaiE5VCESG+LU7xwDF3RBJV7VZaLS6STVpOm5jJ6AwzF7+4lQ3V3fQ69rPsprmh9TRKiSl58Xx501x8fpnRGWKk/Otb5+MPyBQEzZNARDkPJyteT0qMFofHT2l2XMS8C6bn8NTqqpBh0PyRKYzOiOWjnc009Tl5cnU4ivfEeaWh/pyDmZIbz0UzcumyuTlrSnboegEEZBkZ0QbmruNLuPP4El7dWMcls/IAuHRWLnd8UEZAFinUg1Nmu2we4vQq3L4A0/ITkCSJJJM2FK0d2H5+kjHieF64eEro9Yc7mthY3c3NR48g3qjh64MiVU2tUvDk+ZNQSPDG5gb2t1gwalRcvaCIGQUJ5CQaKEw2kRGnp7rTRlWnjTMmZzEpWBN71uRsVuxvo6XPRV2Pg7s/2ccT500cen0USjwjTmL50g8YG+ehICYTmreJ/n9+t3gItbXCpCugYx80bQ6v210BdWtg5ElEiRLlv4SrHz6/SQjMkx6JrAuNzQo7vQ7kT8y+WUQsAbY8KwSUo0eIO5dFiLGkomF2FGT0qVD2LpScLN7bu8A/aPB11s0w6zrRlqX8U9AYxXEcTuNW+Po+8dqcCbN+DdteFL1GE0cIETjrxrBQhWAJASCphPuub9B+Az5Y/QBkTxUmQl4H7P1AmBb11oSXc1vhhaNgwgVQ+62oTR1xrEgRVhzhkfW4h4Tjb3IJfHQVVH4FubPENXAHM6B8wQE5pRou/EhEWfMXiPTcb/8s3ndXwfaXxOfx4a/gtw3D7y9KlCg/OFFBGuUXw4Q8Mwatkn31Npw9brKSPGQmiKiSYpABUX2nk27b0EhoglHJ6BwzkiSxu6afdktknaMM1He6qO90ceKUZMbnxdDv8LGqTEQf1UqJkZkm5oyOp7LVzr5gKm9Oko54k4puqwezQfVvzZAGI0kSj51bytcHOrh2QSFFKSYCAZk1FZ0UJBu55+O9rKvqDi0/JSeObw60o9eouOeEUSyZmIkkSZSkmVk0KpW3tzaEUna31YmRaY9fZsmT67n3pDGcOlG0EvEHZOIMmlDUFQi2gBmaVCwjseGuo/AH5CGCKc6g4f4lY7jtfWGCpNeouPWYPEZnxHLtGztC6atFKSYm5Q5fO6tSKrj/1LER066dX0hJmnAEXrG/jYtmCKOLY8ekceyYcJTwzMnZTMiKo7y1n5K0GE5/ZmPIzEgC+pw+JOCl9bVct1AZIUYBNColn+9pYXZREgnGyOj1QF1qQBbC9a9njCMlRkdbv4vS7Dge/bqCXx9VxOc3zBlyTm6fnzs/LOP6hUVsre1m2d42lu1tY+HIZK59YycOj49nLpjEFa9up9PmYWV5O89+W83Ni4uHbOvBtT28uCefWL2aLUfp0R34GJBBNSgtt/JLmP5riMmAAx+Fp/dWiQfAaJQ0SpT/DuWfCjMxEJHJ0aeE5/m94X6Ys24U87LCA14s/B1sfVFEERVq0a6l/JN/3av0tGfg1KeF4OtvgdTRooa0dh0UHwejThTL5c2Bm/cJoWwY5rs3Pl9kXDh7IXOS6F9aF3Tb7akRx+voEq/Tx4vpx/9VbGvz0+ALlqwoNcJ8DQnK3hY/GpNYd0ewTnTsGUIY+9xCTPs9osULwLZ/CoGJDLveENfIbYsUwgpFOFLr7BO/W8uEY29cPmTPEO1cQIj77S+JdjLZU0W7HaVWDOBt+yehsOlwIj1KlCj/NaKCNMoviswEHfUdThSSRFKMhqYuFxaHlxEZosdneaONpq6h6ZsAPXY/nqBDr8XpG3YZEMZHqmDvS61aEep9aTaExduIdCPNXS567T6q251YHD5q2p3EGVUsHJd4pE0Py/Hj0jl+XHrovUIhsbBENAI/YVxGSJAatUpSY3Vsb+jD6vYTkOWQey7AA6eP4/cnjWZXYy8zH1hFcoxWOOPL0G338t72RvpdXp5eXYUkSbT1u3jojPEcOzaNq1/bgcXlZVxWbITpUXGKiV/NyUetVDBMcJPXN9fzxZ6W8LEH7/VFKUaumJuPPyBjdfm48/gSlIrvJ9SPKkkFiGhBA2B1eXl3WyOTcuOZlBPPiNQYCpNN/OaDPRHOuvKg35+XtXLO1OzQZ3nToiK67R52N/Rxw9u7mJIbzwfXRpoA6dVKSrPj2NXYx4yCBLQqJctumssXe1r4/Wf72VzTQ1qsjgum53I4L2+oo9Xi4rVNdSyZkIFCEm1dylv62dssru/rm+rptIUHRZ5dU81FM3PpdXh5YNkBpuUncPX8QvqDkXGfP4CkVEJiMXQfiqwZ9XlEA3jvYaZQtnYoexOmXvOdr32UKFG+B3lzRITR7xFCSB8P+XOFcHrvYtHeJWcGjDw+UowCzL4FKr8GazMM3JK6a6G7GhKHmreF6G+BF4+B/mY445+iB+f4s4cuZ04fOm0AUzLctFuIxO0vwcvHiyikIUmIyWfnQm8t4htUEoLwoo8hbTyo9OBzAgqY+WtY/7BYTqESkdJ9H4jaUBBpv8f+BZbdLs51MEoN9NZD4ULRMmbKr+Cre2Hj4yLKevKjQ4/7rJfhwBdQ9RVUrYLYDFG7OsDOV2Fv0I08LhtW3SdeZ02Fpm3iGI/5k3AVjhIlyv+MqCCN8otCr1GwcFwiSoWEw+1nW5V4uJdlSI0Li7PiDAPN3e5hW3+097mJ0SkjWsUMJtmsDkVcm7pceP0ySoUUqgcdQBvsgaqQwBHcluMIrUa+L9vrerj30/3MLkzkL6eN5aEVomZ0U003uYkG6rsdPLyygktn56NRhd1Z9Roln+xqptXiotXiYu0dC1i+t40v9rZyzfxC/vhFOW39YcG+tbYHk1YVaj+SEqNl292LqOqwY3P7WDw69YjHeKDVwr2f7AOEcJ2cl8AJ49Jx+/xc9doOarrsTMiK5dNfD40gDlDVYWNnfS8nT8hAr1Hi8wfY22xhVLp52PRegAe+PMhbWxrQq5U8eMY47vpoL4XJppDQG8wls3L5an87k3LjmVmYxGe/nkOfw8vMQjFocN7zIsXVLw+NDCsUEu9fMwub2xeKJCcYNSwanco/Vlbg9PgpSQtHHr891IHV5ePkCRlct6CQ1zfXMzIthgdXHCLNrOOVy6Yhy3DCuDScHj8mbeTXs9cv4/EHeHZNNasOdrDqYAdnTcmm2y5Eq8cfQKNUwMRL4eCnkem5zk7Y8kS4jgxAFw+u3khnyyhRovyw6GJFqq3HBjWrhYnOVd8K0WgRNefUb4DXlsBtByOjlR4bNBzWoslnh4+uhMuWgyqYteGxiwgjiHrQfy4Kp89WfQ3jzvzux+v3iahnYqFIL1brgxHK4HEO1HwOTrNFFue25134JDi4pdQIATn/TpGWbEiAtn2iJnZgeyBavWhMcOBz8T53thDobXtFfW1bGXRVAAH48g7htgtQvWr444/NghnXCIOjqlVgTA7Ps3VATDpozWBKEVHngYjotKvENUsdK9Kj/R646BMoiArTKFH+F0SfRKL8YvD5A3y7rwe7y8+MkXEkxqjRaxQ4PQHMBhXJsRrmj01AIUGcUU16go71+3sIyGKMNytRS7xJzVe7uoZ1ux3A7vLT1e8hyawJpbD6AzLN3S4K0sJpklOKzHT0edCqFXT0uYnR68lKHL6vXG2XnZfW17J4dGoopfZf8dqmeg609nOgtZ85RUmUZsexr7mfC6bnIAOPr6okI06PWjk06njxzDwqO2xMzoknJ8HIVfMLuWq+GG2/bkEhz6+tYUyGGRm4Ym4+l768LbRuh9WN1y+HBNu/4k9LD4ReXz2vgLxkIwpJ4tSnNlATdPBNix3ePbbN4mJ7XQ93f7IXi9PHrsY+7j1pFCc/sZ7qTjszCxJ4+6phTDgAU9BoyKBVsvJAOw6Pn73NFjRKCV9AJtmkpd3qZnp+AjPyE1la1opZp0KpkBiRYqKhx0EgIKNQSDx1wSRWH+w44meiVEgRac0AmXF61t25EK8vQKJJDILsaewLXceALHPRzDxOGp/BnIdWi/Ptd9FhdZGTYOTpCyZT3tLPGU9HpuXdekwxCUbh1qxUSMzITyBOryYnQfzNTc6NRwp4Yduzom40wtOXSDGaPhmKTxQPlfEFw55blChRfgB6aoWwHCC5BL68S9SJzr1d1Il6bEJQKg8ztdOZ4ah7hSHQYJp3wJ/TRPsVa5uIVA5EDA99GRajEBaQw+GyCAGbPV20dwFRO1n+iegBetqzYtri+2HDozDuLKhYDgc+A31CWFAPsO8D8VuhgqvXQUqJeL/oXvG7cauo1UwbJyLCHQdh7q0i/XbaVeJYjntA9D4FkaK74neAJIR8Xx1MvFiYFk28cPhz8jrhnfOhfpN4X7s2fB2eniGE6smPiYGCrS8Q+o7c8hy07xM/A/Q3H/naRYkS5QclKkij/GJweAJYnSIC2dXvITVOy6IJiXi8MkaduNkOjmL6/TIBGXRqiQn5ZtLitdhcfiRJRFQPN8EBUSfaZfWypaKPE6ekkJ+qp7xRPGx0WT0RglStVJCZqGP5zk6cngCpcRriciLFiyzLrK3o5MEvD1He1s9bWxv406ljh6ShHs45U7PZ3diHVqVgfZVwhn3mwkk8t6aGOUVJXDorj4w43bD1qmMzY/n4utnDbvf0SVmcMC6d05/eSGWHlUk58bQOqqs8ZkwqGXGRonp/i4WnVlcxNjOWq+YWIEkS939RHpHa+96OJrbU9rCgOIny1nCbnaKUyNrFLpubHrubk5/YgNsXQBsU1LIsc+pTG6juFEJ2QNAOR5dNRHidHj9Xzyugz+GlNDuO48emkRyjo9vu5qt9bVwwM5c7Pyijy+bh7a2N/Pb4Eu76aC/L9rZx9pQsHjpzAglGDWdMzsLrD3Dla9up6rDxxHkTGZsZe8T9A5h1kZ+zTq1EpRCC2BgUzF8faMc+qH/sKU9sYPPvFqFTK9nV2IszmD5+7bxCJuTEctzYdN7d1hBqK3P1/EI+3tXMKxvrMOtUPH7uRJGCaxswngqmyBnThEvlwHRdPIw6TcxPLBYmH1GiRPnvkDUZcmYKoXPSY/DJtaJe0doCcblhsXrmy5F1kQPMu12kz277J3RWhI2CZD80bAovV71KiK61fxfvB9Jj1cah2xzglRNFJHLK5cJwCaAjOJDYUR5eLnsqHP+gqCkdc6oQlHveFdHHlp3h5Zy9cPkKUZM+IEZlWYhAY5IQ0LcMEnwDrHkItj4PCQUiQjnAxAvFT/t+ePFYca3Sx8O0K8V8v1dEXxWDsmVa9winXoCU0XDUPeK1xx4W5/UbRa0qQEIh5M0NttAaRPrEaC/SKFH+h0QFaZRfDGa9itHZJvrsXiwOH5sP9TK1KDYkRg+n2+pBBpxeGb1WiSRJVLbaQyI01qCi1x4WDBnxGgxaFVVtDswG8a+jUSkYl2uio8/DyIzhb/w6tYjS6tSKIfOe/KaKf6ysQB2sn/QHZH770V76HB7Om5ZDnGFoGxiX18+UvHjW3rGQ9ZVdXPvGDsZkmvlsTwu7G/vY3dgXWnZUupm5I/59xHUwnVZ3SDQ+9nUll8zM5c0tDfgCMkcH6zZfWl/L499UctmsfLbV9bC+qotle9sob+nnwhm5vLKxLmKbA6J2cDowEGEUtLOhl3Of34wkg9svxJjHLyMh3Gy9wQ9Gq1Lw8NkTjnj8yWYRlUwyaYnRqhifGcsLa2t4bk0Nb105nSte206fw0ttt4Mr5haw+lAn/oDMn5ce5GCr6D+6bG8rJ43PCEVG67rsrCwXrQI+39PybwXpYNosLtJidXx+wxwcHh+TcxP487Jy3t8WGV3oc3p5f0cjRckxJBk1XDgjB6NWxe3HjgzV1w7uR7q7sRelQvxN2dw+XN4AJGRC5lTRGD55NIw4Xjyw7XxJrKRPhJm3iMb1W54EAjD2HEgr/c7nEyVKlO/J5cvDrzc9KURc2ngoWiRSVZNLIG/4QUIAlt8NHuvw81Q6EeGcfrUQfyqtqN8MBO9drbvE9OHM9Kztkb8BTn9e9A3NnwfrHhYCdM+7sOavop/pZV8Go5ZBFEoI+KHwaDjmj5A6JnIfn90Au14X0VVdHIxeImpoB9NbL35bmmHNg+KcZt8szIo8DvEdNvZ0Uf+5+s8w+VKRxvvy8cKQKCFf/JzyJGRMEk7EvQ0wYpEwZwKRynvcX8V3Y/MgES0HYOcrImI64lix3d5a8aMY/tkhSpQoPzxRQRrlF8XITCM1bQ721Imb97f7ulk0YXhBVpBmwOEJYNQqUSsVrNglIpkDxMeoIwRpdpKejEQdeal6jNrwjaoo3UhR+pFHoWePisfi8JEQExmJ6nd5Q/01FQqJwgQD1cHI34PLD3Gwzcpj506MWGd/i4VzntuMTq3k8xtmM2dEEnv/71ie/raKTVXdJJk0jM2IZUN1FyqFYkg087uQnWAgK15PU6+TTpubtRWdfHPbAtqtLkxaFdvrenhoxUFc3gCvbKzl6vmFoSjtV/vb+eOSMRQkGemwugkEZDLi9fz51LG8uL6W86bmcN1bO3B6AxSnGvnVnHysLtEvtardFjKVGmAgQD0gRiUgTq+K6P95OHceW8LRo1Jxevws/MeaiCh3bZcds05Nn8NLrF7NtLwEkkwa2vvdaFQKrp5fwF0f7sXm9nPpy1s5bWIW/zh7AgXJJs6anEVFh40zJw/vvuj2+fnVK9upaLfy7EWTmZQTz5++KOfF9bWYdCpyEvSMTDXzdXkHL6ytjVhXPdAKSKngvBdE7eeLl0xh0ajIGt14owYFEACUCgVXzM1Hq1JQkGwkJzEYnR91+tCDG3s2tO+FxJHiIav8w+BWgPp1UUEaJcr/isuXg7VV9L4EYRz0r7C2gfewjBCNSUQLJQVctgy8LnjjdBGxvORzWHabSI8FUQ966EsoOWHoti/6SNRZlp4fnpZRKn6emw+tu4VLsDr43dK0FZ6YGBS9wcHF0afBgrsgacTwxz/gzHtwqTBV2/8x3FEducwx90N8rqjbXPOgmGZKEz1FXz5eHMdA5HSg7rNxq0g5BjHA1rhFGBNNvQLOegXev0xEXre9JPa341VRgzqYiRcJsQxiW5UrxGtzFhz3l+HPJ0qUKP8VooI0yi+O5Nhw1K3fGaC80cro7KFtLTQqBZMKRM/NimY7DndYDBk0CkZlmchJ0tFr82LQqjDplHy7rwejVsnkQvN3Ph61SkGSOTLS+fGuJm59dw9xBjWnlmZw+Zx8djX28YdP94eWidOrkWWZnQ19FCWbiDWoeWNzPTa3D5vbx6E2K+mxeg609vPQ8kMA3LRoBLcsLqalz4lKKZESM3yNZqvFya6GPo4qSaGqw8YlL20lPU7HO1fNxKRV0T3I3bW220FyjBar28tJT6zHP0jhFafF8Ks5+bywtppuu6hR/OZgB1/fOj+c+qyQuPvjvXxV3s6qgx1IyEzIiuXek0ZjdXlZ/PBa2vpd/OOsCZw9JYsv97ZhHZTKOhgZaLd6eHdbIxODPTsPR6GQmJqXwBdlLSExqpDgyrkFnDYxk6NKUihv7WdmQSIKhcTH182mrMlCq8XJ/V+Uh0RwQBaf09/OHI9SIfG3s0RU9r7P9rOxuov7l4xlekG4lra+2xES5u9va+ThFRWsrxbvrS4f+1us7G+xUpwaTsuL06v5/cmjOW5MGn5ZpqrDNihlPDKisaO+h7Oe3UQAmJYXz1XzClArFVwx9zvUgGpMkD2o5jbCpOm7uxtHiRLle9JTK6JvA2ZFKm1YjH4X2vaGW8MMoImB89+F5b+F9y8VjrA+l6jpXP2A6BM6IEhBpMwOR9o48TMcpoHBMCkyLbevQfQZHWDaVUKM9rdA+Wcw8rjw+TXvFNOVWuHCe2iZELuHo9aLFGFLk3Adlv2w/E5hPNQeTB3uPCgioAXzhRHb+LOFUN33oWhdBZBULM67ba8wLgLQx4kI7jd/OmynkjgujRk8/ZGzbO2RUeMoUaL814kK0ii/OGL0KjRKCY9fPHRXNDsYlWUKZS0NV1eZkaClosWO1y8zKsvIyEwjkiShMWmINwkxeaDRRq/NS6/NS1G6gXjTf157t6WmBxnodXj5ZHcLdx5fwiUz82jtc/LPdbX4AzKZ8XoeWnGIZ76tJtGkoTDJyNZg79AYnZJZBYk09TqIN6jJSzTQYnExLV889FicXio7bJwwNg2Vcmiq8JnPbKK5z8k5U7LJTtDTbffQbfdwqM3K5Nx4bl08gj8vOwjAqaXC4baqwxYSo2dMyqS8pR+VpGBTdXdIjHr8AW5/vwyvX+a8aTm8tKGG5l4nuqDT78D6BckmchIN9Dm8tFtFOm9Nl41VB9qHFaMqBQwOnr6zrZGCZCPjMuOOaLA0pygJo0aJ3ePnlsXF3HCUGMFPNGkj0pgz4vRkxOmZ/ddvsLn9qBQSc0Yk4fL6mZqbwNK9rRwzJhWtSonF6Q2lI7+5pYHpBYmsrejkjc31XDIzl0tm5vJVeTuf72nB5gn3Oy1KMVHZIWrFTp+YSVOfk/nFyRxVkhrR7qYwxcR5U7MZnR7LwpIUnlhVwSe7W/jNsSPZXtcTEtgBGao7bZSkmUPXdXdjL8WpMcTovsPf5dizhROvywL5R/375aNEifL92fs+fHiFGBC6cbeoBf0ubHoauqtg1g2iplNrFjev1LHC6TY+Rwi3NtHfmV1vCAFpa4eKZWKeSidEanw+lJz4/Y+9cKGIGLaVCYEI4XYusl+0f8maFo6MfniFOLZdb8C1waho625RLwsi+plQCH2NwkxooA4UoG6DiJwCaGPBbQF3P7x5hkhtbisTotLaIlxyQZhALfoD7HhFvB95krg+/ygR+8yZCbHZMP8uEQW2dxx2gjJs/+eg94NM4AJe0Vpm+lXf/7pFiRLlPyIqSKP8IhmVbWRPnRAARp2STYf66OjzYNApWTg2AfWgVijtfW4CMswfm8C3+3qoaHGQHq8l1niYe2qijqZuF0adMlRD+p9y46IR1HbZ2VrbQ2GKifhgrehdx4/igx1NdNk8LC1rpSBZRNO6bZ6IqKXV5ee9HY3c88l+MuP0LLtxDhqVEr1GSZ/Dw+lPb8Tp9VN7dDE3HT00lcoXEOrOGwhw9pRs9jRZSI/VMTE7DoANVeER9ZouOzMfWBVhbnTxzFyWPLURsCJJMnF6NVqVRJfNi18WbXBWlrdx/xfCIGNidhzPXDiJsqY+9jRaWLa3laV7W/ngmpk8ek4pFe1WLpiey6uH1Z6GjxcyYnXMHpHEB9ubkIEHlh1EBm5dXMyNi4aeo9Prx+kVD1Ju7/AtfLbUdLOhqouLZuZx+Zx8XlxXQ1KMlsWjUjl5QgZH/eNbumweLp6Zyx+XjCVWr+aC6TlsrO7m3KnZANz87m567B7WVnay697FvLpJ1ENJwLisWJ48byIVHTaueHU7AH/7qoKvbplHYfJQA5NLXtrKroY+oJG6bjv/XC9Se3//yT6SYnQYNUrSYnVsr+/l5CfW89Ut88lPMvKHz/bxxuYGxmaa+eKGuUO2OwRTKky6HPa+DdUrRPTGlPbv14sSJcp3w9oOn1wvXntsQpyNWDx0Oa8L1v1duNbOvE4I0BW/FfPKP4mMbk6/Bhb+TrjQqnRQfDxUfAnIEJMhBCkIwZg9By788PuZlrkssPcD4e7buCW8rQGOexB2vyFSd0tOEkLxoXzx3TFgYqSPCy8/4TzRM9XjgB0vhaev/kukII3PIyQIBzsSgzAmGnGMqGdd/RdxvY7+g5hnSBA9Q+s2iGlKtWgL43SHDZ+2PCPMjyC8j+RR0Hkgcj+SIvJcPXZo2iFMqaJE+ZG58847eeihh0LvV69ezYIFC368A/ovEBWkUX6RFKQZyU81YHH42FPbT3ufEHN2lx+7209cUJB29XvYeLAPEL1JfcGoar/TFyFILQ4vFqeXxaVJP8jxZcTpeffqmfTaPRi1Kuq67Ly8sZbbFhdz94mjeG9bI9PyE5mcG09hipHPd7dS2WHl5AnpfLpbuKWu2C8ePlosTuweP7FBUSshMRB0GyY4CsB7V8/kyW+q6LS5uf6tXZw6MQNkmPynlRw7Jo0NwVRTIMItd4CXN9SFXjf1OulzeiPm//6TfRGRv12NfTyxqoplN83lm4PtbKwWD1m1XXaWlGYC8OnuZmyH9WlVKSSyEvT02j38am4Bv5qTz5yiJN7d1hjaxtqKTm44qghJkpBlmZve3kVtt4Mnz5/ICxdP4VC7lUtn5Q05B58/wKUvbxPCvdvBw2dP4OnVVZQ1WShrsnDPJ/swaEStcJvFxdflbRw9Oo0/nxaZ4harV9Fj9+DyBthZ3xeanmrWUtZk4crXt3OoLfyQ5Q/I9Dk8DMeBQQ7EA2IUoMPmoSM4IDEtL4HqTju+gByquW2zuCN+fyfsndAZTIdr2wNFUUEaJcoPRsf+cHRQUsLmp4WrbnJx5HK7Xoe1fxOve6pFZDIuV6S6Ht6yxWWB0afAqvuFYFzyFIw6SZgCWVuFgZFSI6KDC+/+/g7aS2+Hve+F3w9EREGkCQc84fThg1+As0e8trWJ6O9574g+ogOo9XDsn4WI7K0VDrhIMO2KyP3GpIIhERxdwtG3IdhDWaEWacCnPCrSdP0e0SZnzi1CNIOIIs+6Ibyta9bDk1NFvSqI9N3SC4V4Hnm8EKdZU+HboCBVakVf2FdOiLzezh5xLaKCNMqPzO7du3n44Yd/7MP4rxMVpFF+sUiSRJxRjUoVFkbp8RriBgnNwXV6STFqlFlGZBkyE8K1lxaHl0UPr6Hb7ubxcydy8oSMH+wY44Mus8c9tpaADO9vb2JKXjwzCxN5ZGWliDTeMo/rFxTh9PrptnlYsa8dly/A3BFJjM2MZVS6OcK8KNag5tNfz6G608bioCmO0+PnweUHkGX4vyVj0WuUvL+jKbTOroZeVEoJlzfAO4e5vx7O2AwzPfawoEo0aqjrdoTeq5USrqBQSo0RPT8Bylv7ue6NHTxw+jjuOXEUHn+Ak8aHr+XMgkRGpYuHjIUjk2m1uPjtCSWhOtiHvzrE2c9u4vcnj+aZCyZz/j83cbDNxvb6Xh5eWcFtx4zk9Kc3sivoMnzlq9v56tb5Q4yBBrC7/cToVDi9fvITDfQE05YHkBEtcgqSjby9tZGvytu5bkEhLm+AqXnx9Dk87G+NrD269s0dXDA9m7WVXaHsr8FiFGB2USKTcxOGPSbXYZFctVLCHxDtiUakmEg0afjNsSOZWZREklFDYbKRhm4Hfzl9LBO3xzH/SD1sbe0iAjP4AdWYAqkTwN4uHvQOfgJ5C0W9W5QoUf7fSB0HefNEvXb9OtGKZMNjcOpT4WVkWdQ/KlTiZ9s/RQrqjXtEC5i1fxfvBxx2V94rHHXXBVu7vH4qnP++aI3isoj02ewZkDM9vI9AAL7+vXDKXfxHEcl09gnheziaw8z5fE4hCgNecQzLbocTHxYtXCacK4596a1C5LXtFeZHOrPYp2LQaKhaBxd/En7fvl+k7Y4/RyyvjYFrN4oa0pTR8MgoIQ4DXvGz9A5xffwekbobGN5jQJyvLyxGQRxb8bFCoK99ULSXqVoJV60BJMgIOrZfvxVeP12Yv6kNogXNxIuOvJ8oUf4HBAIBrrrqKnw+HykpKXR0HJ56/sshKkij/OKZNiKO1l43Zr0qQowCJMSomT8mgYAs4/XLxBpUpCdEGgH1Ojyh3pbVnYelE/0AtFtcodpAX0Bmc00PzX1iVFpCiGZJkjBoVGxq78YfNKRZfbCTt6+aAUB5Sz9ZCfpQ/8uiFBNFKSIl9FCblSVPrQ+JHZVSwZ3HlVCSFsOhNisKCY4Zncry/eE+bAuKk1lX1YU/IGPSKiMil/ta+hmbaWZ0egz+gEySSRua95tjivH4ZR5bVUl2gp4YrSokSAGW7WtjZJqZUydmYHf7USokttf18Oyaak6dmMkXN8zhpfW1qJQSvzl2ZKjet8/h4fFvqgB4cX0tj5xTyofXzmbS/StxePwhgXywPdwawaBR8rcVB7lqbiGxhvDnvrK8/f9j7yzDozq3NnyPxd2FhAQJwTV4cSm0SIG6UHc/p95T+WqnLpw61L20pS3SFncJLiGEhLi7y9j3Y81kz0QgUAqVfV/XXJnZvgey8z7vWutZNBjNfLUji6LqRsJ83LhmVCw/7MnlutGx7M+pINFWq3swt5Lt6WXN+/68L4/s8no+2OzskmunptFEmI87G++fwJa0Et7fcIwNR4sxW8DLVc+oboH85/xebe5rMlta9b412iL2VwyP5unZSmR2pm1S5OoPd7DuSDHXj47l0XaOS+ovkLEefKJg6K3Kcq1O6q42vaj04DOboPe8to+joqLScZbdCxkbRNx0GSdRvx7TnLfZ+jasfhK0LjDidlvqrj+4+4pIG/cg+IRLqmpTjYi0Ly4Cu9d22TF4d4w49br5iXi010pW5YuAPLrKFqm1wo53Id9WEzrvQ2ml4si0F6DHdCg5CqufEAE4+m4RzhtflnuJPw8SrpPtzSZp6wJy/qA4eHuUGDldsRg6j2z9vVjM8OF0aKiAvL0i0IuPSL1np8EilltGho11Ep0FuOpHxSCqJfn7YeFkW/qtBXrNFjOkLy+GIddJa53EhRDUA0J6isGUHa8QuPxbuZau49o+vorKGeaNN94gMTGR+Ph4LrjgAp577rmzfUl/GKog7SBFRUXs2LGDHTt2kJiYSGJiIqWlkjI4f/58Pvroo5M63ooVK3jvvfdITEykuLiY4OBgEhISuPHGG5k2bdqJD6DSYQw6LdFBbbc/qWs042rQUttgYtuRCgC0LmUkZpVy+/juhPm6ERPkyRuXDiStqIYbOuJoaqOqwcj//ZzEuiNFDIr257EZvSirbcJVp8PbXc+6I8V0DnTn2o92YtBpCPN1I8jThYN5VWSX1RPg6cKHVyeweFcOpbWNHMip5GCeEpEb1yOY3Ip6HltykNXJRRh0GrY8OIFjJbXc8tluGk1mPrx6KCmF1U6RNzeDFhe9lqV3jGbtkWJu+WwXm9NKcTdoqW2yEOHnxsSeIaxLKQbgyZl9+HJHFjszlUHCodwqPGyGQZm26Gi4rxtXDO/MvHekdie7rJ4+ET48M7sPXyVmkVxQjdVqJczXjSmvbqDRZOGaUTF8nZhNXZOZnZnlWK3wzHJJpYoOcOe9DemkFFbzwrx+nNc3nM1pJQR4upBbUY+ni45Z/SPR6zQ8MC2eL7ZnUd9kRgNM7R3CysPF7M2pZG9WBZ9eNwytVsOO9DJu+ERqOe2xcY0GHl1ykGUH8vF1N7Dr0Uk88sNBDuZVcijPOQLaFloN+LgZaDKZqDNaeW31Ua4/pwsjuwbx26FCIotryCqrp6bRRP8oPzr5SwuFvdkVuOi09IqQqLBep+XxGb156dcjVDeaCPd1o3e4DwfyKhnZjnHTwVy5voN5rdOqm6mxDVBbmXpgizg4pA/7Rp3wflVUVDpA7h75aawTU6MHMiVSaKfosIg+kChg7wsgeqREIu1tVtY8JSmqGodoY3m6RAvd/KCuTH6Hiw6L4F1na1Vy63Zpn5K8VNkvqAcMuFxEIDjUVTqg0ULcFEmtNdueC/0vhe9uAKwibA0Of0t1epjyjLSVmfSEpNwWHpR1qaul/tU/FsJsLVusVnHBNXiIIHXzEVfg9f8Vt9+bN0HW9tbX1W0yVOdI3WrnEa3XO36nZpvXwcw3ocsYEagAFZnQ5xXoOkH+PVqmM6eugs/myX2OuhsmP9n+eVTOKjVNNRTWFVJnrMPD4EGoRyheLq09Gf7qZGVl8Z///AeAd955h7Vr157lK/pjUQVpBwkNbTvt72Sxh98XLVrktDw3N5fc3FyWLFnC9ddfz7vvvotW204BoMppobLWyLqD4lzq6Lu7YM1RUkukJ+YL8ySdZ6ZDmu7Cjcc4kFvJg9PiCfdtW+juz6nggre2NLvK/pZUSHZ5HYfzJYLnqtfSaLIQF+pFoy29tZOfB5cOi2ZjSjHf7soh0s+NQ3mV/G9taqvjD43xZ86gTkx8eV1zL1Oj2crOjDJeW53aHDFcdbiQEbbWJBrglnFduHdyDwA+2JzOC78cwWSxUllvZEqvUHZnlfPoeb3oHOhBqLcrTWYLL688wvgewfh7urAySepWrUC4nzupRTV0DvTkSGE1Y+KCGPvSOirqlIHOobwq9FoNB2zC6X+XDmRHelnzPX+/O5c6mxttlL87B3Mr0WokVbWkpqk5Ovnir2IEdNOnO1m0KZ21R4oYFhvIl4lZuOi03Da+K2+tk+9Jq9Vw/7k92Z1VSVF1I5vTSlmyN5cZ/SPYeLS4OQppD0ReNyq2OfW2st7I+Qs2MiE+lC9uGMadX+xlf24F5bZ76hnuQ+9IH345qLQEsFihot5IzzBvDttEd02jibK6pmZH3mBvV9wMWibZ0od/3JvLXV/tRaOBn28fTZ9ISZNNLqhqdhn++JqhfLEjk1XJRdy/+ADje4Ti7uLcqP3Nyway4mABVwzv7LQ8u6yOdUeKmNY3nKD4mZAdDEHx4lxptNWENVZJe4iB10g9aXAvic6oqKj8fmod2oYkL4Vtb8I5/1KW5e5WakyxQu4uiZBWZEm0dNKTEmkE6S/aZYLUNFptKat1ZdBnnqSWfjJTaXPi6gOHf4ZD39s+e8Og+WL+o9FISmx9udSe2rFY5BhZW2HOexBom3j1CJJngl8nyNsl503fAD1nQMpv8O18iUSOuV9qP0HusTRNMjC+vkKitnPek2jsmqflHn06wcwFcGiJ1L+C7GMxw55PWn+XQ6+DbhNP/J33mSN1uMZ6Efs/FUO3KRB5NXQaAvu+gt5z2q6tzd5J81+F/H0nPpfKGcVqtZJYkMhXR75iTdYazA4GVDqNjgnRE7ikxyUkhCW02Unhr8htt91GTU0N8+fPZ+zYsaogVWlNdHQ08fHx/Pbbbye97yOPPNIsRgcOHMj9999P165dSUtL44UXXmDPnj0sXLiQ4OBgnn1Wbcz8R1LfZGlOj7QC7gYNoX6uUtdZAgOiWg/OcyvqeXqZRPDc9DoKqhoI8HThv3P74qpXxMJba9Oc+nUCzSLM8X24rztzB3ViyZ5cth4rZUdGGfsfn0KvCB8+3ZrJwz8cbPPaM8tqGffiWmqblIfykM5+jI8PJSm/miMF1YT6uHLl8M7szipvvscf9uRxcUI0nQM9+WlfHiaLFQ8XHQ9Ni+fJn5MwWax8uDmd5Pxq6ppMdA705FhJLZ9vz8bfIe01NtCDoTH+TO0dxr+nxPF/S5OcjI7soi/Q04Wf9+fjYdDiatAR6e/OJ1/KAMTDRce1o2J4e30aDUYLB3KrmoXrtD7hjO8RgptBS6PRwrzBYnykt03S6LUaogMkihDm68bB3CpyykVoXTm8M12CvXj/qsHMe3srJqukFb/wSzLvb0xHq4FF84dQ12Tim505hPm6sia5vvnakwtqSC6owdtNj7uLliaz/Ft5u+q5b6qI+dpGM/4eBlKLakiyTTLUNpm4c0I3+kT6Euztys/78pqv9dWLBuDjrsdiS7debKvftVqhoKqhWZBO6hnKD3tyGRTtT4iPK9/vzrX921lp62/ssC6BTr1Q7Vz7USJHi2r4LamQT68bBt2nwdEVkLVZBrT2lLb4C6R1g6uPtIEx1UNgnLSC+Zv8UVdROSt4hkKVQz3+uv9CcQrMeVc+95kj0cCjv0g9d9fx8JtEQ6grg90fSdsVgNnvwDdXOrvA6t1gwGWKAVBTjQhXK7D2GWW769c4GynFOJgO2WmshIyN8v7oSrjgHQjrL2LUI0DcdSuyJToaM1q2O/S9Uqu5410YYxPbEx+Tn1sWyE+LERZfA37RigtwfTkkLhLnYZDU3lH3ABrod0nryG7EwLa/45boDGJ4tOQWmWQDScGd+z680lOut+SofFclKXD+q0rP1OE3iblbU42YRan8aUgqTeKRTY+QWtF6ch7AbDWzMnMlKzNX0s2vG8+MfoZege2UsPxF+Oabb1i6dCkBAQG89NJLZ/tyzgiqIO0gjz32GAkJCSQkJBAaGkpGRgaxsbEndYyUlJTm/1hDhgxhw4YNuLtLhC0hIYGZM2cyduxYdu7cyYsvvsi1115Lt27dTvu9qAhh/q74eeqpqJWIVJPZSkZxA7edE0evKzybzXQcCfJyoXeEDymF1Vixst6W1jp3UCdGd1cceHekt25E7u9hwNNVhwYwmiw0mq3UN5m4aWxX3F10PPbjIQZF+7EhpZgnf0467rUXVilplu4GHZN7h/D6xQPRaDT8a0oPbhrbFS9X+fWO9HNnV2Y5n2zNJL+ygbXJRVw9KpZ/T+nBu+uPccnQKGYNiKSgqoFl+/MZ1yOkuYbyWEktGg1MjA9h1WFJ+TToNEQHevLFDhlszRvcid2Z9v6oeoZ09mdCjxCeWX6YktomNqWW8MYlA5jcK4ztDt/LoGh/7poUx+fbs2gwSqTA191AVYOR0d2D+WFPLk1GC7GBHlw1IgaA+6b2QKuBeUM6MTYuhLFxwXQKcGdLagmh3q4Eerly67iuAPSP8mflvWOpN5rpFuLF08vkO7VYYWtaKZtSS0guqGbT0RKcpw6Ej7ZkUFil1L/WGc3c9NkurhremT3ZFVgtVr66cQR3frWb9JI6hsYGcufE7s0C1j4RYLJY2X6slAW2SPe8QZEMiw1k09ESIv3dGePQE3Viz1B+uHUU136UyCXvbWuOfs8b1Ak3g3N09Hh42v7t7f8HqMyCzA3KBlaHfnsgA9/SI8q2nqHS009FReXUmPYi/HQLRA4R85/sbRLhnP22GP4Y3GHWG7JtfQV8fyOE9pYWJwnXQuYWQCNOt+ZGCI6H3J3K8U0N8M18iBoK4x4Wkbf9bedrGHlna1ffttjzuZzLJ1KJ4gY5jD18wuHqpdJXtbZYhGrv2XDwO5nYGnmnbNdUB78+LLWZk/9PhPWmV2QCTKOR50q3yRJRTXxfEaSZW0Sc+0bCDetg/s+w/T0YfDV0n3Ry3/vKx6WnqUYrdaGz35L3WtuEakOlnBtg54dKaq67P1z6pURXN78BPhEwSDU1OttsydvC3Wvvpt5Uf+KNgdSKVK7+5WpeG/8aIyPaqGH+C1BRUcFdd90FwPPPP09Q0Onp7vBnRxWkHeTJJ39/PcFrr72GySQDzAULFjSLUTseHh4sWLCAESNGYDKZePXVV3nzTXWm7o+kb2dvNiWVo9WCt5uOijqZgfb3cOG3gwXc//1+EmICePeKwWi1Glz1OpbeMZpGk4Xcinq2HSvD39OFvp0UZ1Kr1crgmIDm9NZB0X7kltexy9YSxMNFR6PNrGZvVgWV9UbcDDpW3H0OcSHeLNmT2+a16jQwrkcIq5OVWsBB0X58dM1QfNydU5CahQiSwvrw9J6U1jZRVW/kPJuz7bgeIaw7Usxba9OI8HPnvqnx3DdVeskVVjXw6dZMrIjIfPWiAby9Po3qBhMPTY9nc6oIukg/d4K9XXliZm8+2pLBnEGdSC+uYfGenGan3e4hXozqFoS7i46dGUod6kPT5Fz2lN3YIA9+vmM0TSYrXq56ej72CxbgWGkdVy7aTv9OfqQV17D2SDEbj5YwuVcofSN9eOj7XPbZWtMEebsS4qNMJMQEeZJSWM3gp1Y2R5NDvV2cWqr4uuupqFdcGzWIWK5qMDoJUrPFyrHiWp5wmCw4UljNynvGklVWR5CXK1Ne3UBOeT3vzx/CreO60WC00DfSl9dWpTTvs3h3LqE+rkztHcaVI6JxceiJeyivknlvb6XeaCa/soHrRsdgMlu5Z3IHBpUOfHzNUHZllTGii+0PmXugNJxvqobIoRDSV6IiYTaHyZp85wNU56mCVEXl97D7QxGJqSvh8u9h86uSdutYilOeAT/fLe+P2dLx+s4Tp+se08R5duVj8OUlUjNqx9VHUu6bqiFttQje+jJx2PWJgPT1IgbD+3fsWo8sB6xyzKAWPZ0tFmlhs/w+pbfnzVugLEOpMw2Jl+jvV5dB6VFZ5h8rwnTO+xDYTQyYNtkiPWH9pGbT1UdxxbWaJV1521uS7pv8M2RtgXsOOdetngi9ONfj4gVDb4LIgeIefONaiY7GjoGiJHkff37r/RMXKbW4YX06Hp1VOe0klSadlBi1U2+q5+61d/PRuR/9JSOl999/PwUFBYwaNYrrrrvubF/OGUMVpGcIq9XKjz/+CEB8fDzDhw9vc7vhw4fTo0cPjhw5wo8//sj//ve/v00+/J+RIB8Xpg0ORquBnWmVUGemvMbEMz8e4ZPEY3gY9KxMKqSi3kiArUWLRqPBzaCja7AXG+53ts6/66s9LN2fz1UjlJq+A7mVeDnU/tU5pNk2WazMe3sLR4tq6Brsyep/jSM2yIPrz4llZNdAvtyexarDRVgRsfXyRf254eOdpBbVUF5vZHdWBTvSy5jU6/g1zm4GHW9eNshpWYVDjePn2zJJiFGcCz1c9M1Rw7cuH4S3u4H7z41vXj+5Vyg7H5mEh6sOV72OgdH++LobeG7FYVYmiWCOC/Viau8w/jWlR/N+4b5ueLvp6d/Jr9nM57WLB7D8QD43jOlCea2Ree9sAStE+LqRbUvDTcwoJzGjHHuQsKLeyLe7cvh2l/N9tmX+szuzvDnSiO17LKszYjRbcdVr+erGEby9Lo0fbSm2VuBQfmWzEZSrXkOjyYoGZ/E6d1AkM/qHo9dp6RLsxZGCao6V1AKw6WgxY+OCeW6OOOMu3pVNUXVjcypzYVUjvxwq4HB+Jevvn9B8bZuOllBvNDffy0PTeqJvr5nscfD1MDAh3uH/hIsnjPq31Gg5OkuCmB2ZGpyXhfQ+6XOqqKg4YK+NBPh8LkQPhzVPivHPuTbBs+sjRYiiAayQvlFcbFc/BZtelf6cAE21yvFCeokJ0i8PyOeC/fI7rNGJu26dLRMlb48I3JZk75B6zp4zYOgNYki08WXoe2HrbX++S+o6tQ6Tnu+MlG1jx4hQ7jQU3hgo9wZyzYnvQ2mqRIhvWC1GTPa+ppteVo6lbVHPmfg+mGwTgY01x2/xApD0s5gihfaGGW/AxCfk+/nxdnEwbqyS+wvsKmnDJUclAqttJ+MkyGHyb8f7EmFVOeNYrVYe2fTISYtRO/Wmeh7Z9Ajfz/z+LzWG3rhxIwsXLkSv1/POO+/8pa7996K65pwh0tPTycuTAe/YsWOPu619fW5uLhkZGX/0pf3jcTVoMei1BPuI4PT10LMvtxyLFWqaTIzvEczrq1IorWlsc//CqgYWbjxGRkktKw4UYLZY2WfrhQliNlTTZEav1WDQtX641DfJH1wNcP4bG5nz9lYWbkwns6SOHRnlzcLwx9tGU1bbxD2T45pFirtBR0yQZ6tjdgQ/DxcuHRpNbJAnFydEO62b2jsUg06DTguZJVInlFpUzc/78jDaUlK93PRkl9VjsVj55WABE19e3yxGtRqIDvBgzqBOTsf9dFsm1Q0m0oprmh+0k3qF8srFA4gN8uTGT3dSWNVIYXUj146KRa+VbXS2n0az0+EI8jTQ3yE63SW4tdPezAERXDYsmt4RPoT7unIor7q5nUqjyUJlvZF/T1UGIa56rZMrcaPJyrQ+Yay8dywP2qK6MYEevHRhf6e6Ycf/H/b2O3beuWIwj0zvycL5Q5yWO7bEARG59nt10WlPSYy2i1bfWowC7P7AwVwFMTLxiTx951VR+SfiNMljFRMjgF0fysQQSITOKxSiRylCyN4XM2mJRA31bpLies0K2Tagi6TPegZJSuvkpyTN1q+zpNZW2oTwgMtg9L1tX9v6FySKuvzfsO0dSfu97Gtn8Wo2iQhOXSmf7en9dg4sFofdiz8Vx167GNUawNigmCz52Zy7w/vB5YtbX4vFCLHjlM8Nlcp3FztGTJnao6FKjJUKD8L+r6X0QKeHzg51so6C99ur4e0R8ONtcv0bXlRM3uzETZGezSBRZpWzQmJBYrs1ox0ltSKVnYU7T7zhn4SmpiZuvPFGrFYr99xzD336/LOylNQI6RkiKUlJ84uPjz/Ols7rDx8+fNK1qiqnRrdwTzoFuuFi0KIzxPHOhlRGdAvklZWSbmkFRnUL4uXfjlBU3cin1w6jbydfLnlvG+kltXy8JYOnZ/dhxcF87poUx3+WHORAbiU+bvrmCN2S20by2dZM1qcUU9tkJjbIk/9dNpDk/Gqyy+t51tbyBOCTbRlU1ssgwFWvZfXhQh5Z4mxy9OC0+OZ+o6eCPYLXkrLapmbR9siSg4T5unH3V3upbjRxy7iuPHBuPFNf3cCxklriw7wZGhvQLJy7h3iRUVrLqsNFaDUaXr9kIK56LRX1RvzcDbgZtMwb3KnVOb9OzGp2IQ7wNDCtbxivrEyhutHE/BGdySitZePRkubrAnAx6Hjz8kHMWLCJeqOZXuGtBy8eLnqevUDu85vEbO7/bn/zugfO7cGwLoE0msz0ifThSH41FwyM4KvEHDwMWjQaDbVNZgZ39qdbiBc+bno8XQ+TUVrHD3tynQT3zsxyXPVa9DoNE3qGOF1DiI8bN4wR58r/zunLg98fACRyfaSgmv8sOUj/KF8eOa8XfSJ82JdTyboUacnz9hWDj/tv+LvRtBC9AXGSaujmpxobqaicKiNuhRX3iyDqMV1qL7O2SHrqu2NEqAV0gYs+kehpTZHUktprPqc8Azvekwhmj2mSSmo2QvQIMeT5zpbKN/VZGHu/vAoOiNgK7y/RwvZ+f/vMkVRfqwV+exSG3+y8vrEG3htnazHj0mJnWyQXK3w6G+4/Bp6BIpBrS0RgWowSsTzvJUnPtbOuRQ9FnYtEaXucB+nrRHx3GiZ9kyMGwrTnj/8d693AOxyqciTNOdI24Vd8RDGA8nbIFCmxlU7k74V9X9puRyuCvjxTepT2mAZXfi/9Wwdecfzzq/xhfHXkq9NznOSvSAhLOC3H+qN59tlnSU5OJjo6mscff/xsX84ZRxWkZ4icnJzm9506tR6MOxIVpfQCzM7OPs6Wxz9PW+Tn5x93/T8dN1tq7dieQYztGUR9k5kf9uSSWVrLtmOlfLJVScN68Lv9LLvrHDJsaZqV9UYuSojiooQo8irqOZgrdY3zBneivM5ITKAnA6L8m917H/nhAJ9vz+KBxfu4YUxXMkuVlKzeEd4cyhNx5ueuJ8LPg293Kf8XdFoNZou0F/m9rEwq5OEfDjAuLpgXL5TZ+fE9QjivbxjLDhQAEvG098axBfCa01OTC6r54OoEqhtMdA704NZx3bhi0XZ2pJfh6aqnzxO/0jPMmzAfN7bZ2rj8dqiQeyfHodFoqKwz4uOuJ6Wwpvmazu8XwcajJXx63VCqG02M7hbE/Yv3O4lRkBSPTv4ebH5wAv/3cxIXvLWFf03pweDO/jz+40FGdwvmPzOUGpKcCpkN93TVcW7vcG4aIwZIrnodL8ztx+y3tvDtrhzO7RPG/VN7EOjlSn5lPfFhMttf22Sm3pZyXeIQEW0yWZonLsbGhdA7QonatsReAxzo6cJrlwxg4cZj7MgoY0dGGVePiuXLG4bT54lfsVhhxcECNqeWMKrbaTQ1SF8Lhfuh27niYNnnYti9UCKofS6F/Z9Czhbw7waD/zn1Kyoqp5VhN0nk0TscvMOkXnTNM5JOm7Za6de572vpjXngGxj3EIx7UJb3OFdedtY+I3Wiez+HhOulJryxSiaOqgsl9T5/H1z764lrLgdcJj9XPCDitCVVeUotaKuUWYdnsIdDiURwT6jdKELUM1iEeMlRiHSYUMu2OQJrdNBrljj1FhyEfhdDl4lwbDVU54qYHjz/+PcAUi9661aoyoWQnsryLmPFrbepRtq82Jm7CA4uhrhp8PXlEgEN6Cr/Buufl5Yxuz+GB7PU2tGzSE1TDWuy1pyWY63OWk1NU82fvk9pcnIyzz0nEzYLFizA0/PUMt/+yqiC9AxRXV3d/N7L6/i/GI7/EWtqao6zZWscxazK78fdRceKu86hptHElQudG3Yfyq/i6g+2o9OAySrCyI6nix4/DwPldUbiQr25ZGg0JdUNDPi/36hrNPPm5YNIK5Z/2x0ZFezI2IWrk7mN8v/FCiTlV9Ej1Au9VoPJoZ1Mkq1/5u/hu105FFc38u2uHJ6a3Qc3g44nfz7EsgMFxAZ58uwFfRnRNZAlt40iOb+aqb1lxtnLVd8siGsbTbx68YDmY35x/TDK64y8tioFs8XKwbwqDuYp13qkUNJmL3t/Kzszy+kX6cvMAWK25GHQstvmCtwtxJPf7h7LgdxKvt3VerKluKaJR384QGZZHRuPSsrY66tSaLIJ1yOFNUzpE0qQlytXLdpBsS1F1lWv44KBkU4BhIKqBppsRky/HCzA38OF5+b0xdfBMCq3vJ6EmAD6RPpwzSglc8FFr+W8vuGsSS5qNo1qydc7snl9dQr5lZKOFuztyisrU9iTVYHeNsEw8aW1eLgauGJYNJ9uy8Kg1xDqYNJ0Wji2WqIHWZsgoBsYa8HgJT1JLUZlAFqeCmseE0OkQdcoKXgqKiodw1HU+MdI+5GCAyL4zE0igEJ6Qppt8J22RhGkdowNImK7ToIDX4sALU8XIWm1wLJ/2VJcbX8XDnwLV/3Y+lqMDdIOpbFK2sgMuEwRpi0JjpNU4PQNUgcaMVDE8q8PwbH14gTceSQMvNzh/jpDzk5IuEHau6x7DnZ9IFHLoG7i7G139w7trQjy0lT44iLlOKWp8POdIuTjphz/+22skVThkJ6QsRk2vyZp0IPnK+11HHHxlIht5CC4LREaKqT+9ocblW3Mxtb7qZxRCusKnfqM/h7MVjNFdUV/ekH66quv0tTURJcuXairq+Orr1pHiA8eVDLk1qxZQ0GBBAxmzJjxtxCwqiA9QzQ0KPUkLi4tU2CccXVV6rzq60+toFvl9OFm0OFm0PHeVUO4+sMdpBUrkcx1KSXN77uHePHLwXze3XAMD4OOr24cjtFsbe4x+VtSIRV18sfu/Y3HeGpWb6a+trF5f8c+pY7M7B/B8gMFzB8Zy3l9w6kzmtifU8nGo8XNET5HftiTw7vrjzF/ZAyXDo1u44jO3DAmloKqBlvfT4kQ/3JIHnTpJbWE+4og6hrsRVeHGs3XLh7AzZ/topO/O+F+7lQ1GHlu+WGCvd24Z1J3gr1duXV8N+qazBRWNbAlrbQ52cvdoEWnkTRXgEN5VXx54zC6hXhxKLeK19dItDG1qJbPd2SRU1bndM1+HgaMJgu1TWY+357l1LalySGKqtfCxe9uI8LPnTxbdLRfpC/ppbVcsWg7906O486J3amsMzI2LoQnZvbm9VUplNcZ6dJGbe7DPxwgq6yOynojhhb1nW9ePqjV9nb2ZlfwwPf7nZYlF1Tbg87NkwwNJisNpiZCfd3ZcP94DDotYb6nWZBGj5RBWGMVrH1MohUWI+Qlgn8XEadG20SYxQi1BdKjtL/aAkFF5XdRlS9uszeulXTTXR+L8VCfOSL+RtzhvH1TLbw1XNxndS5wyZcSwVt8bfvnOLYOqgskKutIxkaJSIK0RRl2Y6tdnRhxmwi8ulLoMk5E6hXfiahsKxX4wGIRxod+EFMmNNJ2xdMWRdVopO9n8lJJO15t61zQnvBoKG97uR1Tk6Q+l6WBd4Q4gwMc/Q26TZIWMiDXu/55KUPY/41EmYfeCNNflGWLW2SBWBFnYa3z813lzFFnrDvxRidBrbH2xBudZRobZbL82LFjXHrppSfc/qmnnmp+n56ergpSlY7j5qYMKpuamo6zpfIfE2jVGuZEnCjFNz8/n6FDh57UMVWEqAAPVv9rHO+sT+W/K460Wj+2RzA3f7a7+fOTPyfxxQ2Km/LM/hG8uTaN8rombhvflR5hPjw3py8v/3aEkhr5PzEgyg+j2Yyfuwub00oJ9HTh31PjeWq2Uuvpi4FwX3em9m4x4LDx1to0jhbV8Oba1A4J0sGdA1hym3Oz9OvP6cIzyw6jAZbvz+PGsV1bGexM6hXK3sen4KrXUlTVwGXvbyfTJhzHxgVRWtPEqsOF3D6hG5F+7ny7K4foAHeS86sZ0TUQnU7LlcM7s2RPLharlSFPr+ap2b156bcjTgLTVa9tjiZrgUuHRXPXxO5Me30DtU1m3Axa6m0mRP62qDSAn7uBPpE+bEotbRajPcK8WTQ/gTEvirNldYORT7Zm8NiPhxgaE8A3N49g7qBI8isbiAt1rkfdl11BRZ38O1ksVo4WVtE9VKKGjSZJ5fXzaHuyKaWgus3lV43sTHZpHWuOFDcviw/zZka/CKICPNrc53cTNRKiRsMmWz2XzlUxLClLVcSoZ6itubxFBnsNlVKnpaKicmp8OE2im/0vlX6kvzworrO106UHZkvqSkWMgkRUv77Cuf2L3kOE495Pob4KTHWSep+62jl6CZL54OIpTrfdJirL93wG6/4rBklj/q0sLzykRAsdRWN7dalTn4GDP0gta8xouHu/PC+Sl4ujbb+LJHI5eD7k7EKpRW2HE2VkGGslDRoUMWrn9X7ipNt5pAhge+2qwT5od7gHjUYuwyMQGqphylOqGD3LeBhO798+T8NfX6z9E1AF6RnC21sZ3J4oDbe2VpnNOVF6b0tOVJ+q8vu5NKEzqUW1uOg0FFU1sspWE/jVjiynP7F28WLHy83A5gcnOB9raDSXDo3mky0ZlNc1ccu4bs19KbPL6vD1MLRybD0RV47ozNvr0pxaz5ws4+KCeWbZYazAC7+lkJhZzofXDMVqtZJcUE1MoCfuLjq8XPXc+eUelu7Pw55J7OduICbQk0vf206T2cL+nEquGRXD0n15TO4Vyo1juqDRaPhwczo55XX836xe3PONRA9XJRU2f3/DYwO4bUI3zukezODO/vi6uzCpZwjT+oYD8MTMPnyVmMXlQ6P517f7qDdamNY3nOp6Iz/vz6ei3si9U3pwMHdHc6uWlIJqth6T2tSVhwvZk1nOplSJcu/KKqfJZOFAbmWbabL3frO32ZwqpaiGya9u5J5J3blhTBemv76R7PJ63rp8UPNEQYPRzL7sCvpH+TF7YCQ55XXsza5ggy21eHrfMG44pwsv/5bCJQlRdA/1oqiqgQ82ZTD3nS38fPtoahqNuOi0WKzy/+qRHw4QH+bDf87veWp28DnbIXkJeIZA9/Og6KBNaPqDbyfpNwjg4i3bWExQX2pLbdsLMcd3CFdRUTkO9nR4e/9OzyCozJYU1rbwi4ZukyFjkwhXq1l+H7UGmUQy1Yl77cYC53P8eCusfRoGXQ3jbK1hdrwnEVdTk7RAsZO4UK5h+zuKIK0tgYWTFPftY+tPfG8J18vL8dqTl8ESm2GSewB0nyTvOw2W2s8fb4PcXSKiDR6StWHn6yvl2sfc1/b5CpNs0Vqd1Mw2OYyrLCYoTobDP0tfU72bmBfNXAAbXoDDP0L/i6W+9brfxG33yArZt6ao7fOpnDFCPULRaXSnJW1Xr9ET4hFy4g3PMh999BEfffTRcbd54oknePJJySxYu3Yt48aN++Mv7AyiCtIzhKNQPJHxkGOUU60J/fPh62HgJZv5z6ajJc2CdEeGc4rRlSNiOnzMq0a23vZUI2RXjYjhqpM4d1uE+brh666n0ibk9ueIQdNzK5J5b8Mx+kf58eNto6hpNPHTPmV2Wq+Fr24cTqCXK/6eBgqrGkkuqOaB78RVdnt6GYfzq9iVWU5GqURTUwqV6GFacQ0PTuuBn7uBixOim0XX8v35/LQvl+yyWhZtSqfRZOHjaxL4LamQO7/aSyc/d64eFQMaeHppEhF+blw5PIZB0f7EBnuxJ6sCEFF351d7CfNxw2yxUFzThAaYMyiSMd2DmfTKOrLK6tFpYPODEwnzdcNssXL/4v1klbZOI0ouqKakuqn5XnZnlTcL0tu/2M2qw0VMjA9h0dUJ3Gvrx7ruSBEVdUZm9o/g0R8P8sOeXABmD4hgyV75LourG3ngu/2sTylGq5Het1N7h7IlrZQtaaVcMTy6zRY3J6RaziWOnxul35+5ERorIDIBcnfIelMTFB1w2FELQcd3B1dRUTkBVy+VWsdeMyFrqwhBUNqMtCR/v9J2xRF7RoNnqDj0Ro+UyGu1g2lhVR7seFcRpH0vkshknwvEbMgrFNx8YOQdsP7F1iZCjm1eakvaT9U9Hh5BIgStiOgEEbfZO2Do9eJom7sLQvtApwTpQep4/gOLnQXpsfUiYn0jxbQIixx76E1w8FvoM09EfmM1DLgC3hkt+3mFwC1b5TsqTpZlyctEkEYOlmuwc/hHOOdeMJzmUgmVDuPl4sWE6AmszGzj//5JMiF6wp++flRFUAXpGaJXL8XpMzk5+bjbOq7v2bPncbZUOduM7h7EB1cP4fVVR9lnE20ALloY1fU0OqOeYbzdDOx8dDLPLksiMaOcf08VMXXMVj+bbkuh9XLVc8+kONalFDG1VyiXDYvGx13SVqf0ChVjHp3GyR33u925TufKrVDqq48W1fLfFUe4OCGK3Vn7yS6rw8NVT2J6GUaz1Un0j31xHdU2U6WMsjr8PAx8tzsXkwVKqpu4ZZxEAT66eijb0kt5dlkSmWWSultQJed01WuZ3jecVy4aQGpRNVm29WYrHMyt5JL3thLh586WtNJW31GknztPzuxNiI8bT8/uw5GCaqea3iKbgVJhdYPTfuN6KLO1I7sG8nViNr3Cfai3NVn1cNFx18TufLFDUvUsVsBq5deDBbgbdAzu7O9koHVSdJkstWimJqkZBXHrNDfCEQcjFItDX1KDN/S9WNL9VFRUTh3/GHmBmIh5R0j2Qac22lJYrc5mO14hraN3tYVSs3mtLbr3hENKvcFbEXMNlVJred6Lsv3/hkjf0tt2QJ+58nKkKk+isPYI6dTnTq0FVPQwGHmn1KIuuQUGXwMbnpcIZlUuzHhNzu0dYUudtcDORbadNWKe1FSrPHt+uFnScyuzxZV32guybuAVMOkx53Pn7pJ71uhg7IPg6gUhvWHIdba603DY+qaYMAX3VL7b4mSp8w3thcrZ45Iel5wWQXpJ/CWn4WpUzgSqID1DxMbGEhERQV5eHuvXHz/9ZcOGDQBERkYSExNzBq5O5fcwIT6UcXEhTHxlHeklEilrssBrq1O4aUxXeoQdp7H3n5CqBiNXLtpBfkV9s6jKtEUAn5rdmx5hXox3EFV3TerOXZO6tzqO3aOpZauW42FPef460bkWOtzXjaoGk1NKdHWLljcPfX+QW8d3RaPRML2PUl/r62Fgau8wbv5MmQXXaKBHqDefXDeUEG+ZCddqNMQEupNT3sB5fcPZnl5KRmkdGaV1jO4WxN7scmoazXQL9mTFXedg0Ouaj3fF8Nbp0f+7dBBLD+Rxni3FuC3O7xfBhPgQ3PQ6KuuNjOiSy+juQXQL8WbV4cLm7x1EJHu56fns+mEn+BaPg6s3xJ0vvRCNddI2ods02PRfWW/wlIGwfwwU23ona3VwdLkMBF28xdzIV83cUFH5XXgFw137JBJoF1ymJumpGdBFRFtZuiw3eEpEcPs7ku7rHQE1BSLgGqrk9clM5+PHjIDht0DST7D1Lcje6ry+MkeeAfZI4IaXYNOr0HU81JYqYtQ7AoZ0oAVLexhtxow1BbDuGUXY+tuemQFdlG3PfwXiz4PMzRLB3PIGlKbBpV/IeotDGUynQWJeVHwEshMhqoWoLz2mpEh72f5eabVyjsIkeHuELNv6PxHNhQelZhekfEHlrJIQlkA3v26kVqSe8jG6+XVjSOiQ03hVKn8kauX2GUKj0TBr1ixAIqDbtm1rc7tt27Y1R0hnzZp1anViKmccrVZDv05+Tsu+353LLZ/vanuHPzEHcyrZl11BUXUj7gYtLjot8TZRHe7rzn1T4xkSIylm5bWNvLc+jYLKhlbHuX1CN+YMjKRzgBhzOf5X9nBp+9Ezsmtgm8uHxcr57GK0V7gi8sN9xJW63mjm5d9SaDKaGdLZn0Wb0ilyiE4OjPJrfu/rbuCXu8c0i1Gr1crF720jo7SeuYM68fqlA7loSBT9o/y4aEgnPrl2KENt95xZVsd5CzaxO+v4LpDRtp6snQOPH1n0cNGj1Wrw93Th6lGxdAuRe+sT6YsGmtsBueq13Ds57rjH6jAGD+h/BfSaB1XZ0GMmdB4LI+6Gcf8R0elvGyhqdUoqYFM15O9u97AqKiongd7FOfPg4xnwxkBY+yzoDHDpV5KOGtRNakDttadRw2D+MunX2XeemCXl7bEd0w3QQFESrH0OvrlSEaPu/so2s98GD1uqcGON9DltqpG6S0dTnxvW/r57nPAITHhU2rEA+EbDTRtg9D3O2xkbJB1331eyTmOb8GuqgSW3wfsTYcz9EDsGLnhPxGhpmqTlLpokwtuRPnNg0pNw7vOyrSPufoppUlUe7P5EqeN19ZFUZpWzikaj4ZnRz+CuPzljTzvueneeGf2MOob+C6EK0jPI3XffjU4nD9k77rijVUuX+vp67rhDbN/1ej133333mb5Eld/BC/P6cUmCzKx6uMi/8ymnVp5FhsQE2Goqg/j02qFsfWgCw7qIUNyRXsaF72zhqaVJfLYtkyFPr+LZFcmMfr51E+tIP3deuXgAX9w4gghft+YWdHqtBl0LF8NuwZ68d+Vg5gxynpke3Nmf5P87t1U9bVpxDa56LV2CPMmvanRaty29jCs/2MFTS5P41zf7mpcvvnkEwV6STjypZ2ir661rMjUfG8DDVU9JdSMbj5ZwzUeJrEsRJ1yj2UpKYQ2fb8t02v9wfhXbjrVO7T1V0ktqsQJNJgt6rbQFeuGXZFYmFdBoOj092ji2GvZ9Cqm/QOx4cKy1CegmP+tLZZ1HEHgEQ/ggaRtTldf2MVVUVE6NAlvddtoa2P6euMQGdIX8fbZeozaSfpAI4+D5IiDtPT09g6H7VMAqEdBj65R9Rv9LIrK3JcI9hySFNcvWW1vvpjhoe4XBzP9Br9nS+zT55993T26+EHeunKP/ZWA2ibhuydHfxPH3wDeQ9CNc9rW0iRn7IOz9DHJ3yj3N/1kMidI3wOfzFJGev8/5eFodjL4bht/cOt3YJ0LSlYfeJOZtg69WIrX+LbJd8vZCyq+/7ztQOSV6BfbitfGvnbQodde789r41+gV+PdKu37iiSewWq1Yrda/naERqCm7HWbTpk2kpiqpAyUlSv/J1NTUVu5YV199datjxMXFcd999/Hf//6XnTt3MmrUKB544AG6du1KWloazz//PHv2yCznfffdR/furdMgVf68uOp1/Hduf56b0w+TxUpSXtVfLl0XwEWv5ZWLBnDpe9uY9+427pvag9vGizh5c20qiRnlJLYwcLL30QRxF/5gcwYDo/0Y3yOESD93+nXyI6+yoHnb2gbndFuDXssUmxmQj7uBAE8XDDoNRwqqQQM3jOnC+pRi9udUotFAo8kKWBnU2Y9jJbX4exiwWq3UNZnpFOCBl4ue/MoGCqsaeXTJAR6c1hMvVz0/3TGafdmVjI8Pdjq/RqPBw6CnttFMVYOYeezKLCfX1i6moLKhVYOCtUeKOVZSQ5cgL9KKa5ixYBMmi5U3LxvEef3aT9PtCGaLlQBPF0J9XJk/IoYFa1IxWcyU1xm54ZNdTOsTxsSeoYzvEUygl2vbByk5IlGGwG7tn8juYmi10Bx/Lj4snwPjIH0dWE3ifhnQFXITYe9Hkuqn1cPoB5xFrIqKyqlzyedw4FvY+wXkJEL2Njj4vbRm6jQE4qbCjvchaqjUQIKkuG54CRorxazssEMteMF+xZE3bbXUWbr5ShTyh5vEcOiWLRDSU0zLsreKIVBArJj9rPwPpK2Sc0cMPPX7+vVhEZD2a9nzmdSlOkYiOyWIM6/ZKELcLwqGXAspv4B/rKTfBveA+gqJcG5/V2o97Rz9FSb+p+PX5BMO01+QF4hQ7j0PfCNgxQOSBp1wPSycCFhh/CPS0kbljDIyYiQfnfsRj2x6pEPpu938uvHM6Gf+dmL0n4AqSDvIwoUL+fjjj9tct3nzZjZv3uy0rC1BCvDMM89QVFTEBx98wJ49e7jkktYF19dddx1PP/30775mlbODRqPBoNPQ3yFF9K+G1WplT7aIzt2Z5WSX1ZGUX0XXEE82pBSj0YjZzrgewaQUVHPtqBjWpxTTM9ybN1Yf5TObmdHORyfj627g9UsH8OnWTJ5edhgAS4vzndNdEYiTe4XSaDKT8PQqqhpMbE0r4WhRDTUNJmKDPMmrqLMJUrhlbDcen9EbL1e9U2pOVYORbxKzeXrZYVIKpU3N9ed0IdzXnXDftmdb750Sx1vrUhkWG4jVamVyz1DmDe6ExWqlZ7g3a5OLSSmsbu4ZW1bbxISX1nPFsGguH94Zsy0EbDcnao+SmkYe/O4Awd4uPDWrT6v+riBOvN/bzJ+sQKS/O6lFSluD1YeLWHGwgKGxAXxz04g2TnJEhCPA4BtlQNcWXSZJhMArXCIYZamw7xNZN2C+RA1KkiFvp80t0yJiFHDq5aeiotI2uz+Rnp5jH1BSZNuj63hxnD28VARmYw1glXpOYyMcXSnprh4BUF0IWxdAzDnSxiWvZSq9xuF3FcjfKzWpAbFKOiwa+b0GSa1dcb8IXJAUVq1e0nx9In/fdxBzjgjSTgmSgttlXOu0WJ9wuPuA8zKzCb6ZL9Fh7zBJ6d3wEty1V6KaBQdErJemiNNwexxdCYuvFSF/2TcSPTXWy/KooTKp5uoFm15yjipr9TRP1OX+9cpv/i70CuzF9zO/Z2fhTr5M/pI1WWucWsLoNXomRE/gkvhLGBI6RE3T/YuiCtIzjFarZdGiRcydO5f33nuPxMRESkpKCAoKIiEhgZtuuolp06ad7ctU+QdzpKCae7/ZS/9OfgR6uuDpquf8BZuorDcS4u2KFXHX7RnmzZbUEprMVp5dkYzFKmm614yKAaR1zJM/HWJ7ehnPzenL9ed0YWC0P5tTi3lt1dHmvqVaDby34RiRfu7MHxlDUVUD+ZUNGGxC7Yc9eU7RSRedhgAPPU1meOiHA3x8zdBWf4B83AzMHhjJok3pVNQZGRjtf8L77hrsRXZZPZ9uy8Sg0/DYjN7N7X0AfjtUSElNExcO6cSPe/JoMous/nJHNk9f0JfPrxtGaW0T558gOvr97hxWHS4EYGb/SIbFBrA/t5LuIV54usojOS7UG183PbVNZtYkFzmJUaD53O3+2dU4iNzj/XHW6iUFt/mzgWZrKa0LRI2WNDm9q6TuOh5/0PVqdFRF5XiUZ8JPUoaDzgWmPOW8vixdopW9Zio1jF7B0qOzpkCyGza+IlHB3R/J+rQ1Uje66nHY9yVseweuWwmHvpfIYs4uqC+TOtPdH4tgO7ZGROgXF0la7yWfw+XfibANFgd1SlOl7rTkKAy4HLpNhHuSpMdnezWVdWXwzVXyjLnw4/YF99j7YegN4OZ3Yrfe9I1irtRzpghqn3D5nqptvVbLM6CmGLpPhrv3iwlbXam0e2mPwz9JlkfqKmlh4x0qjr1JS6Se3lgnk3YVjmZ6GkhbK4ZSWGHCSURfVU47Go2GhLAEEsISqGmqoaiuiFpjLZ4GT0I8QtTWLn8DVEHaQTrStPZkmD59OtOnTz9tx1NROV18vzuHQ3nSoLxvpC8HciubxxBdg71oMlu4cHAn3t+Y3ryPXVxWNxi5ZlQscSFe7Mut5OXfUgBYvCuHMXHBDO7sz+DO/sweEMmCNal0DvTglZWyzZfbs3hueRKNJitW4OaxXXh3/bFWqbJNZitldZLyuyO9jMMFVQxqITibTBbKa5tY+69xWAF3Fx0nwstN2Wa3rW+pIymFIgqPFdc0C0KAsT0kujuyW8fa/IyNC2HRpnSCvFzpFeHD/y1N4qMtGfSJ9GHpHecA0oN2fM8QluzJY292OW4GLQ1GOadeqzgYJ8QE0GA042ZocX+B3WHwDSIc/WI6dF2AtIIYequ0iShOAmON8kJLc2zbapHUPhUVldZsfRNWPSntSPxjoSITIge13m7JLdKPdNubYh4UZCvTcfWCw4kiIic/CRtftu2gEUFpNolwBBF44f0g0pZS22OamCPtXATjHoZBV8IrPSU9vyRFXukbRQS3h/2BbzGJoK7Kl3NMf1HW1ZXJZNbRlZCxUba1C2UQkdhYpdSl1pVJhLbzaDFyOh6rnpB60fT1cn5Ny2e3FTa/DqPuEvGu0RxfjAIMv03EZtQw2POpCNwkW2qzPYpcng5h/aHAXotqFcM3O5XZENbn+OdROSN4uXipAvRviCpIVVRUnJg9MJJ1R4rpFuKFt5ueA7mVDIr259Kh0ZzfLxw3g46kvCpyy+vJLa/nWEkt1Y3SkuXZOX3RaTU8uTSJtOJaOvm74+/hwvyRMc3HN5ot3PbFHg7lVTJnYCQWK4T6uJJTUUuDSZGf2WX1rcSoIyHeroyJC6ZfpG+rddd/spMNKcVcObwzT83u2CCiV7gv142O5bekAnzcDOxIL2NorDLjv3D+EFYlFTKjfwQ3fLKTkppG3rtyMOPjWxskHY8eYd5sf1hxfbTXqeaWy8/Fu3JYfbiw2cipe4g33m765rpdkwW8XHXUNJr5Ykcmb65NJSE2gK9uGI5W6xB9sLvkniw+nSSiUnK4xQoLxE6E9NWS3hvQtc3dVVT+8Wx/V9Js934B/zosdZH21iOO+HUWQdpYDb8+Apd/I8t/fVjqLF194P50cLc9h7Q6ae8SPkBpveTmD/n7YcMLElGd/H8SJQURduMekF6b+76Qnp4gTrUtGXKNpMV6h0nbp8ZqWDBIMVPK2Q515TDqdvhgmkR8r/weOg2Via8u4yWS+/2Nkj5cWwyTnhDH3I/Oh6JDItBnvdn63Lm7IHc3DLgMekyX91qDCFJ7aqZvlJg4bXpNUpUrMuHiTzv27xESD1ctEXOiLy5qf7uCfTKBF95P7qVgv/SJNXi03StWRUXltKEKUhUVFSd6hvvw1hWDcNVrifB15+pRMcQGeeJq67tZ12Tiwne2UNtk5rrRsdw4tiu3fbEbNNA5QFoYeLkZAOjfyY83L3eODFQ3mDiYV4nVCr8mSepqSU0TU3qGsOJQYfN2U3uHcji/kmMldU79RwEGRPnywdVDCfBse7b9aKFE71IKTxzF+zoxi9pGM/NHxvCf83ux6WgJG44Wk1NRx5p/jWveLiEmgARb65e1/x5Hg9GMn8cJZvs7wDOz+9A30pdxPYIxW6w88N1+zBYrk3qGsvH+8QR7u/L2ujQSM8rRamBQtD9xoV58sSObsloxYNqRXsaRwip6hrcW560w1sPeD6XNwoD54NFGqx3vsDYEqRYK90PfyyE43lZfpaKi4oSxXsQSgKleWrhMf7HtbWe9KfXeeXtEsKZvFCMjg81V3M1PxN6Qa8TkJ/EDOLJMhNJFH0PMaKnxXDjBZkwG9JolLV1WPS6C1tQI570kKbi/PCSRUXe/tq+nh0O5UHmGs7MvwKHFoNPJfZnqob4crl8Juz6WlGFXHzEXspNua+9SbzPBq69ofc7GGhGsxjpJG87fB1ikF2jpUWW7njNhzH1i8lSUJE65J0tgN3D1FWfeuKlwZLmkNM95F766TLapyICrfpD6+aztsP9L6Q+bvFTqVlVUVP4Q1BGFioqKE5tTS7hy0XYMOi3L7zqH+DCpHzKZLby26ii/Hiportl0N+g4r184rvoheLnp6dtJBNEn1wxld1Y5I9roKxrg6cJ9U3rwwq9HqG4wERPoQZiPG/2j/JwE6deJ2UztE87b69KwAsFeLvSN9OX6MV0Y2fX4KVpvXT6IXw4WcMnQ6OZlFXVNPLPsMJH+7tw9Sfp5bkkr4YHvxEjj253ZhPu50z/KlyOF1c19R9vCzaBrnSZ7ioT4uHHnRMVRe2xcMGuPFDGuR3BzlPSeyXFcMbwzgZ4ubE4rYf4HO5q393HTU9VgYu7bW7l9bGduGNsdg/44j/aqbEk/AzEsih7VepuuU6S2NH0d5NvNPCxQVwyF+8DVW9rAOPZQVFFRkeyB6JGQtUU+73i/fUGq08O0F2DRZEkl3fu5CEs3f+mhOeBy6QlqsYhAjBktdZox50gUc/jNsP8bRYx6R8CPt4uwsxglDbXnDOg9W8Smo+A0NcHGl+R3eOSdrWs7w/pKimv2duflxgY4599SW2pwl/Y0K+6Tdee/BmH9JOU/rB+Me1CWX/SxCMkx/279HWj1UoturBMBXmSbCPOJEBGtc4HiI1IzCnDtL7JNSE8R/4aTaAkS2BXuOQAWs3yPDZWARmpWHfn6SmlVs/FlmqdC934OXSfKxICKisppRxWkKioqTuRXNmCxSt/L0pomwnxMXPtRIjszyjDb/jZH+Lkxvkcwd9mE1KRezmmrvh4Gxse3kaJm4/JhnflwSwbF1Y3kVtSTUVpHYmY5XYI8OFYiNT2XDYvm3D7h5FXUs+pwITeM6cKNY5Q00UaTmcLKRqIDW/d6HRjt38rI6LNtmXy7KweACfEh9OvkR6iPG24GLU0mC4cLqjlcUM3rlwzggXPj242+/tF8cHUCjSZzc0TaTrC3tHdJK6pprtl9/6rBbD9WxsJN6Qz2KuE68wZq1/vhN/YuGci1HGRazFIP5uYntVkhLdKZ8/dC7naJGsSOh56zobZIRKzWRSIgWgPsfEcGzaP+7WygpKLyT0ejgWtXwBP2bIXjFB4UHBRjouZ9bU7WDeVwbK300QRpBbPsX/I+cjAMvgaKU6SGss9cMfXRu4pI/fQCh+PpJf20LQ58A+ufl/ehfUT8tWTGG5IKfGy9iDn/WEkBDugiTsAfnQdo5VljMUsU0VgPcxdBlC3F1WyCxddBZZaYI41/WJbn75d7jJsmzsElR8SNuKFCjITqK8XxtiRFvpO01fBQrtSl6l3h5Z4iRm9cd3Ii0c239fuWorvwoJy3+d9OI/dfnS81vAa3jp9PRUWlQ6iCVEVFxYkLBkZS32TCy01PgKcL9369l+3pZc3r9VoNeRUN5FXkM2tAZCsx2hF8PQysumcsuZX1zFiwCQA3vbZZjAZ7u3JeP0nJevnC/ny9M5twXzdyyut4fdVRYoM8+XBzBsU1jU59Uo/H0NhA3AxaQn3c6Bwokb2uwV6s+/d48ivreXSJNJcf0TWwuben0WzhueXJmCwWHp7e87RFRU9ESzHqyKXDoqmsNxHs7crkXmGMiQvGVa/FJ3cNrlorrtZyTPs+R1+RBj1mQqdhys452yHtF+VzZSa42Qas9WVw6GtlXekR8Ai2uUwCliYYeA1krJPPDRWQ+it0Gi5pgyoqKgqdhkLODvAKhcwt4oDrSFMdfHAuNFUDGpkkuvhT+O1R6a858ErZRqsXp1m7WM3dBT/dLmmrXmFwx04Yfosc09QEA66wpb7ukXTdNwbCuc9LNNWR4J4SzdW5isB0JHOrpNl+c5VEWgHqSkS4HVsHN6x2SOe1ynW7+sKH58qivZ8rgtRikn1BXIffGyepu7XF0gJm5WMw7wMR1im/yXbmBijYK++1erlvVx/QGaQOdsNLYKyVV8mR3x+1nP0OfDJLIsp2ERo1FEpSoc8cGHUPLLtHItldJ0rtrIqKymlFFaQqKipO6LQarhwRA8CctzazO6sCF50WsNJktmKyWHHVa3Ez6Mguq2PhxmPMHxnT3KbFkWX783luxWFmDYjgvqnxTut8PQz4ehj45qbh/HKwkIuHdOKS97dRUtPENQ4mSJ9ty+SJn5PQaGBYTADbHMQxQJLNEfhEDI0NYO9jUzDotOgczH/CfN1YsjeXlMJqrhweQ4i3Mvu9+nAhH2wWN+EBUX7MGdSpQ+f6I3HV67hrkpLiu+FQJnXHNnCkKYhRFheWppn4V9dU0FrFKddRkDo5E2psfQ5tGOtbn6yuGCiW9z6dRIRazRJdtZohcwNkboRxj0vUQkVFRbh+pTjtbnpFxM69h53dYDVacZxtArBKVLTosGQpgLjUvthVxFjMaGk7sulVEXHe4SJIawqh4BB0Hi776F1g9ptSk/reOOVch75vLUg7DYZ7Dtl6jfo5bLsEvp0vyy2m1vdVUyDtULqOl7R+gxusfELKAOLOlTZRg64Scbz4GnG0nfE6VGSJ+/f+r+Q4Lg6tZLYsEEE6/iGJ+gbHw+5P5TuY+LiI674XianT2mclEosGht8KXSbIMSwWeSbpDCf37wSw/R0oP+a8LGo4XL1M3q94wHZOZLJARUXltKMKUhUVlVaYLVZu+nRnc/uXc7oHsSe7grLaJrQacNVruXV8V55cmgSAQad1ctK189m2THLK61m0Kb2VIE0vqWX+BzvwdtPz+fXD8PNwYcP946msNxLuq9QF2aOVbnodZXVNTseI8nfn4fN6dvi+2otwLtufj9Fs5ef9eTw2o1fz8t4RvgR5uWCyWOkf5dfh8/xhlCRDxgaITIBwafPQuWglk+MyKGo08HTpBfyUmUe50cBTvbNxaaoRA4+GSjEycsoetELKUjH68AxRohgAOjdxCcUqaX9Wk0RBD30tg0RaHOfYaohT21ipqDhhF6AuXpLW6ojBTVq9pK6CIyskghoSL0JV5yICzt6SJHWViNXGKpkMCh8oArAkBZbcBHftcz72mqeV91oXmNyi/2nL63PE7sRrMcO0F8UpOH+PLHP1EbEYfx6s/j/I3+28b1Cc1Mv6Rok5kV3EbX5N0nEDukLPWSKm3QMhb6eIXpPtue7qLSZI4Fzvao+2Jv0I296S9x5BUqOq1UpbmffHS3/Sq35Utu8oBxe3XhYzWnmfb/t+9W5yjQe/l8ipiorKaUMt/lFRUWlFQVUDqw4X0WiycF7fMN65cjAz+oWj1UjP0aoGE3syK/B00aHRQKRf28YS142OJS7UizsmdG+1bkNKMVlldRzKq2JvdgUAh/OrqG00sfxAPg99f4Dssjpm9I9g8c0jGBobQHZZXfP+z8/tw5p/j2v33CfDfVN7MLpbEP85v5fT8qgAD7Y9NJHERybRNfjs9j3bnFpC6b6lUJEOqUrabWy4DCrdPLx4fEYvbjgnlnF9YnCxNkJ1HlTlSlpbfblEYTQ6qQMFiSboXGVd0ncOZ7PSrF51etk+bDD42FLj3FuYVWVtlBQ8FRUVhRG3SbRQ5yptXCqyndf7d4aE6+CKxXDOvdKOZeITYmZ0YDGgERHo1xl62WpDrWbY9JISqbNYaIVdVAJMfgI6Den4NQ+4TGpAr/xBHHuLkpR1k56QyK9HgLNgAxHS+fvgtb6SUhzaW+pd3XwlSgpQlgaunuKem7NNicAWHZJWLycifaNi4HTVD1KTCtKTtTxDUngzN3f8Xu0k3ND6c8xoSXkGOP9VGHqjiOCC/bCmHYGvoqJyyqgRUhUVlVZE+rlzwzmx7Mup5K5JcRh0Wjxc9bgZdAyK9sNotnLHxG48Oas31Q0muoW0LdYm9Qptt8Z0Rv8INqQU4+2mZ0TXQJbtz+e2L3Zj0GkwW6xYrFDbaOKNSwfSZLawPkURPAGeLlwwMKrNNOFTYUxcMGPigttcpz9N52gXqwVyd0gUpaXJkI2Cygbmf7CDazp5c39cJYawgRRUNnDzZ7uIdPfnxYmz8dEByR/zSN/eEDIY9uyH2hKp+ew5F8qOSj1o93MlAlNXKu0l3HxloGivFUMjvfhKj8hHe63YwS8g4Va5xiM/tb7I5v1VVFSaSV4u2Qe/PQIr/wPzl0LnEW1vW3wEVj3mvGzoDWIm9NMdzmm09p+VWSJea4slCjnxceg7T/qbgvQ0rSmU/qTtsf09EZ4THpWoad95srzgoC1TAhHGm9+wue8OlW06DYH0zSLSGisVg6bcPZC9wyYyrVJfCjIZ5qc4nxPcS1JlvUIhIPbE3+U594rYjhgo12Enaqg4/1bnS69SkN6v5qaOOYH3nSspwpW2dj2JCyFxEWiscMUPMlFw7vNynZtehf6XnviYKioqJ4UqSFVUVNrkkfOco4U/7M6lrslMaa2RFXed07w81Kflnh0jwNOFuyfFsSuzDKPZSnWDCBqTxUrXIE9Si2sZFO0HSGpuuK8b9U1mIvzcsFhh+YF8Zg+MPLWTnwCT2fLHC1GAlOVi3tFk65c69HbwaX1PbgYt3b2b0GusLPeYx6zu/Vm1LZOiogI+HbEH96MWm+mJWSKoOlewWgELlKbIOaJHSw2XHa9QJQJgtdga0Rtl0GsXo46Ym+DoirbXgRipeIao/UlVVBwZcx9se1v6W1otEtlsT5B6BMkEUUOlfO48CkbcDp/PA6wiQsc8AFted+4RuvJxqBIHcRpr4KKPILSvmAxZLbDrk/YFaWma0rbFzRcmP6msC+sDF7wHhQekzrOxSkRn1FBZ7x8jLy6H766XZRqdGP985JDCH94PMjbKtmMekJRj73BZbqyXCTLdCZ4b+76WNObzXlJEZnUhfH+DpNH6RNqeeRrJ+Hh3rAjxyxdD7DntHzd7B3w4rUW9rC1DxIr0WP3uOnm+3rCm7dY1Kioqvxt15KCiotIhHpwWz5c7srhxTJcTb9wBGoxmLnlvK7VNZlKKanh6Vh9MFiuvrkwhvbSOVy7q32wi9OHmTPIrZQBWUS/C9aXfjrQSpBaLlcd/OkRKYTXPzelLl1NIs312+WHe23CMm8d25cFp8Sfe4VQoS4PGakl1taM1tO6pV5UDDRX4Bffmx3E5uNTmgrYR6M/kXqHsT3LHW2+WbT2CoFb6uB7es4GkMg0zwjS4aK2QukK2GXgNBMbJ4Gv7/6SlS+cxkLle1utcILgPFLSTPuckRjXIiM32M3UFpK2EsY9IrZWKioqYCQ2/WSJuTTXQ7+L2t/UMhGt/hY9nyATQ9JckPXbcw/DtVSL2NjyvbO8dIc8Ne2QPIGmJ1FJGD4P+l8HezxQB2RbeYVLbWZEpfUftVGRLqn/fC6HfRVCZK9HerG1QUwReLdp6TXlaake7jIWcRGW53gMu+wZyd0p7Ga0WepyrrHdp3baL+grY+IpEJmPPEYMme+rwns/g/Jeh2ySpKU1f77xv9naJ9FbYvpPsbccXpJU5rc2b+l4EeXslBVijkWwSgJ0fwqTH2z+WiorKKaPWkKqo/M34akcWY15Yy8KNp9cNcPbASL6+aQQTe558m5e20Gk1eLtJLaO/hwGtVsPwLgGU1jZhtljJLFXqRftH+aLRSMsZO+N6tE6xPVZSw6fbMtmeXsZXidmt1reH1Wrlyx1ZzPrfJj7fLgOZlUkFp3prx6cqD3YvEoOggG5Sjxk/C0bcA+4Bynb15ZD4Duz/HHK24eLhJ8tdJSQd6uPGC9dOh14XQvdpMOwO6D6dapOOuVu78a+kOF5Ji259fpCoRG0RYFXEKEiNqF2MGrzAvwsExoNvZ3DxVrbzCJZoKODklGQ1yWBVRUXFmfABsPFlEVeN1bD3S9j2jpgHORLSE/51BO47BqG2LJXuk+DhPAhxzlrh5k0w6TH53bS3Z9JoxI0WxHH3zr1wyRftX5eLJ9y6De5Lg3hbVLOpDt4dA5/NlT6kGo2YGZkbRBi+O0buwRHvMBh1l9R5+ndRJqVMdfDGADjyi4hrO2ufg/cniMBtyfsTJAr8+TxI+sm5jrUiAxZfK+nJcVOkfU3EIIm4gkz2/XATdJ8Cg+ZDwvXt3ztAr9kQO9Z5WfgAuPI7ibAecmjxUtROdoiKisrvRo2Qqqj8zfhwcwZZZXW8tz6NNclF9Ajz5rHze6HRaE688xnEoNPy0x2jOFpYQ2V9E59ty+TSodE8Nas36SV1XDtaqSmaNSCS4V0CeGVlCl8n5hDs5UJMoCdrk4sYH6/M1EcHeDImLpijhdWc2yesw9fyweYMnlqqDHoGd/bjzolxp3Zj1fkyaAofpLRCqSuVWizvCBksajSSXtZpWLt1o05otNDnYomYVmTB/i+g5wUSUY0YpGyn1aPFikFjAXR4uLnSHMF09RUhCZLiFjMOMtY6n6c8TXnv5itCd9dCpY7Mfqy6YonW+He19e6zGY24+skAVEVFxZm01ZKK23AA1j0PWxfI8qpcmNLCJMcuKFty/iuw/kWpNe13kURU+86TV02xtJjpOdNZ+HWkNlPvIi87FpOSEtxkaw3VZw5sfl2ihtX50gpl9lvOx1n/Amx7E9CIK7f9GDWFsjxlhYhj/1hY/19Zt/V/ED3c+Th2gzStDlJWKsvtjt8NlfD9jXDLZrjNJmjNJnihi9SympsgbQ08Vtr6XvP3Sy9VnYs49iZcJ6ZFn86WZytIfarWINs4Rk8DO/BdqqionBKqIFVR+Ztx/TmxvLM+jRBvN7aklbIlrZTFu3J4bk5fzu8XcVLHslisbDhaTLivG2uSi+kT6UN9k5l6o5lZAyLJr6zn/sX7cdXreP2SAXi6tv9IWbo/j8+2ZXL96C5M6hVKbkU9648U0znQg1s/l7YCGg1cOSKGBqOZn/bl0TfSl57hEhHcl13J14lSJzWqeyBPLzuMRgO/3j2GuFCJ3rnotYR4u5JWVEN9k7ntC2kDq1WifBqgf5Qfn1w77Lj30v4XZoad74qAy02E4XeKocmWVwELeEdCwi2QcJsM1lo2pHfE3V9MhBorJQoAMkBKsznsNlZBQovegpEJeJYk89OUClK03ZmQMB72fyzGJ42VkLIM+l8h24b2aS1I7fh2ht4XQeF+BzEKztFQs9xPQHeHa6qAvR9L1HfY7WrqroqKnUHzxfzHNxoKDijLt7wh9Z79Lzr+/oeWSGbDBe/A7o8huIfzeq9gOPe54x/j4HdiYDTiVnHQbQ83H7hmhbjm2lOMg7rDnXvgzaHSj7hleQEo1+QbJWm6WVsAjaQZVxdI/ey+ryRq6R8rLuAtr8NY7yACNZC9RVl34Ydw4Fs4/JO0mHFEp4ew3pBp237A5a2vz2qFT2ZK9onBQ9rqrH1OIrtTn4OvbfskL4XpL8BNGySCm7pK7nfiY62PqaKiclpQBamKyt+MC4dEceGQKBIzyrj1s90U1zRS3WDinq/38tm2TD6+diiu+nZm4BERev93+9meXorZbCWvsqG53YtOKw64AP/6Zh9mi7VZonyzM5trRrU/g/zc8mRyK+oprzUyqVco13y4g5TCGmIDPdFrNZgsVkK8RcC8+OsRFm1Kx8tVz45HJuLhoifCzx2DToPVauWXg1IradBp8HBR7qWironFu0S0frcrh1Hd2uiz1wbXjool0s+dqAAP+kT6dmifNqkvVQRcTT7s+1QikfYIYnUupP4KEUMk0mBqVKKobeETwcFcT75dsp85gen096s//vkrs6A0hc5A5/ie4OoBQ26CxLckEuNomOQeCG4B0grGtzNUZsjyoHjof6VEZcMHQPoaMTsKHywDyJp85RhZG6RnaVvfQ1WOpCSrqKiAdyhc/Jn0HK0tlskls63/pr0HcMZmWPWE9Pkcfbeyb8Ym+NbmHpuyQmonNVq45hepFW2P6gI4shzizgWvMFj9lGQ01JcdX5ACRAyQV8t7uHUb5O+VGs60NbD1TWlt02uWONzGjAbPYBG1obaJtEFXwpJbJbK77W152Z+Tji1qANDId2Osk+dOZTYMuxWih0KvmdBjuvRfbSlIAcY/Aisfg/jzRWQuvx8OfCNic8ClMuPpGSyC1MVLzmGqh7pyiQR7hkgpQ5gta6UsXVrYoIGLPjn+s1pFReV3oQpSFZW/KQkxASQ+Ool31qfx4eZ0Cqsa2XasjKzSOrrbIoq/HMznPz8eYnKvUC4bGk10oAcl1Y3Nos6OTYPi6aKjptGExSpuuI48vyKZWQMiCfBs0QAeccSNDvSgtLaRmQMkSutuECGZXioDkrsndqe20cSa5ELcDFLe7qLXorWlGveJ9GXtv8exIaWYh384CMC/p/Sgk79iiuHn4cIN58Sy9VgpV4zo3OHvSqvVMK1veIe3b5eCvc6fi5PEdVbnahuAacTIKHe7DEa1Bukx2P8q6QnaBvcv3k9SfhUbPZpYM3IfRCRIQ/nKTInCRjo0gXfzl1Raq1kiACAD1yE3S82Xu78MuOpKJYW3oUy2qcywDZCNUoOVtlIGaBFDlHYuFRkw6HrY9qoykAapK2sL4wnEs4rKP42qPPjqMnG+HXaztGXyDoNhN8Gvj0oKqdUMOTtg+G2gtz0T3PxsLV/MUG9z4LVa4JPZcOcu8Gkn8+Wb+WLq4/G0iLAu4+Wc/S859XvwCZcXiPgrOABFh8Gnk1xHYFfn7Vc/BdvfgbEPiJnR4mtluVeoCLzYsfLcWfOM3NOkx2HG64oABxGIvW19WHV6pba2JXp3qbE/shxWO7gF7/lUBCnAdb9BYZJES5f/G8L7yra7PhTRflsiBNvEbsE+2wGssOFFEcQqKip/CKogVVH5m3Pz2K5M7xPOI0sOEBfq7dQzdPGuHIqrG/liexZfbM8iJtCDlfeMZWLPEFYfbm1O88wFfQjzdSMxvYxfDhVyMLeyWayiAYvV2mqf1KIabv1cjHIeODeeW8bJgOWja4aydH8ezy5PpslswWixcPfXewH4/paRDIr2Jy7UGzeDEgHt5O/BxQnR5FbUYzRbmT8yptX5Wrar6SipRdW89GsKo7oHceXwjotZJ0L6Qs52mXm3U7hfRGL386QnqLFWEXQWI5SlQk2BpLnZMTXC0eWg0dInJJSkfOjj2yiz+qVHaU6ddUqnRVLdrLZU5cpsSQUEGcy6+0s0Ysebcv7u00Rw5u2UbTR6oEmuKXODDA7ry2Qw3FAhgtnNFyKHQdFBOY/OIOJW7ybndqy3qshUzq+ioiKRUfszsugwzHlfoo4AO95VfnfRwDMhEg285HMRZLdslQyIz+YqxzPVtxFhRATekRUSEQRxrbVaZJLo4ZzW258qvWaJuGushoUTpDb97gPyrLFapaZ1+zsyubX/a0mBHZcCW9+SutIL3pUa110fweZX5ZhV+XKfob2hrkLEod1s6USsfx6OtVGG4O+QuePuDzGjwNggIn3/N5IJAvIdBdlaY2XvwMn307F/qorKH0RHvT7Gjh3LunXr/tiLOcOoglRF5R9AdKAHn17XOrXrutGxrE8pxmiWQVJmaR2Dn1lJVb2p1bZR/u78crCQZQfy6RbsySVDo9mfI7P1I7oE8uj5PQnyap3S5OtuwNfdQGW9kagApe7I39OFK0fEMKV3GI1GC0eLxLVRr9Xg7aZnUGf/Nu9Fp9Vw39TT347ljdWp/HKogF+TCpgzMPLUaki9w+Gch2DDszJY1LlJBFGrk957GqSOE7vItg1AU5Y514MW7IXcHQD8NwjuuHoOEd3PBa0Gtrws2xg8IWqk8/nd/CRSUVPYOqWtsUp+Wm3pwxYz9JoLYf1loJqxDqptQtrNX9JufaOhxwyJFIQPlDRce6uasIEQPxt2L4SqNhyNS45Aj/NP5ttTUfl7U1tM82RS+nr48Ta4YrF8Dh8gkVE7VouISotFWqUEx4FvJwjsJrWYA64QQWgXUI6sekLMgtwD4MJPJS0/eRmcc+/pvZ8x90HuLrlOEGFaUySiL30DrLb1PtXopN/pxpdh4JVi6gSSwgvO7uIHv1He37odQk7iWd97ttR7Ngt7Gy3bupgaYd2zSqp05GCJWEcNtbV5KYOPzpcJv+5TwTdSIrwqKip/GKogVVH5BzM0NhBXvQ6jWf5gW6FNMQqQXV5PdrmkYaYW1za3YDHoNLx6cX/CfNswuQCCvV1Zde9YKuub6Bbi3Wp9qI/UIEYHevDDrSPxdNU3pxSfScbHB7PsQD7DuwQ41aWeNFo9jP2PpMa6B4hzrXugtFeIHg1eEVJftfsDGSiCRAQsZsVd0ze6Oc1Xq4Go0vVQtBL6Xgpe4RK5NNaKMZJBSVlGZ4Cht0l0wnGm9fASSRMO7i3pu3XFENoP8nZDyWEZMFbnybYeweLo6+ojEY/GakkLdg+Q87kFSKpvwR4Z0LUlRgHqS+DQd9B7btvrVVT+aXSdCFOfhS3/k983T4fWVf0uUgTplKelbrTHNBGjIEJPq4frV8N310tqfUhPZf/6Cqkt7TJWaSVjbpJtgrtLfaed/H0iJPtd0nYf0JOhZSuUj2fJZNzkp+WZ0VgtWRemelj7jNSYXvihRFZH3iH79JoJw26D7W8BVnmmdR51fNO3thhwmaQAL7nFuT9py8m572+Ufq0AXSbItXSfrKQb6wzyvK5vhM4jnet5VVTOALfccgu33npru+s9PT3P4NWcGVRBqqLyD0an1XBe33C+3tnxnp0AId4uXD0qls5BnkT6ubcrRu0Ee7sS7H1iQ4iB0W1HRc8EFwzsxPn9IjDoTkN7Zo1GaqSOLpcWCfEOBiIBXWSQFj1S0ntNDdBlknOrB+9wEbXFSSJWszbJ8qKDEhWtLZCWK4Z2BpOOYtRiUtJyy1LFZdcnUiIwh79TIqZ26oolrTduBgT1gO2vSwpg1EhJ840aLvcFttRkWysYsEWEG5XP+TtVQaqiYkejgRG3iSjbsgCGXKusq8i0baOFnufDyNuVdTk74YOpsv+4RyHV1grlszli4DPxP2K+c/hn8I+B23bI733i+/DeWLh9h0RXQXqMfjhd0miLksVNtiOUZ8r5/aJh96fiZusfC+e9AhtfkTR/UEzPfr4dxj0shkc/3Q6pq8HVS1Jlj6yQZ0tdqUzOWSy2yKhV+p1e+UP7dbEnwt1fRLm7v010aqSP6cJJMP5h6DoBmhx6qObuEgfypZkQO0aEqKs33LxRItGxY07tOlRUfgchISH06dOBlnB/I1RBqqLyD+eGMV1ILqjC18NApJ873yTmYLbVOY2PC+KSYZ35cHM6246JAY4GWHbnObK+R0h7h/1LclrEqJ26Usi0pbdmb5XUVzv7PlFcaHtfBDnbxP3SsdZJq5MazNC+EvGsyZeaT+9w6Hau1KZW5YoRh0YjA1kQ86HC/VLr6RWqREsAAhwMRzRaCOwuqbXugRKtbRanVkj5CcxTlHrX7M0yUIscKv36TLUykKy39fpz8YbQ/pC9yfl7cIz8qqioiONs4UFI+gkezAKDGxz4TtZZLeL26kjxESXtdNMr8lOjheJkeXWdoLRY0ruJWVCgzeHaWAeNNcqxtDoRXU018vvcEXJ3wSKbIJ7yNKy4X1kXOVjW690lsth1grRNsZik9+q4B+CK76AyR3qlLpyopNSufRZuXKv0/QSJSJ6qGAVpo7POsf2NFQ7a0qI/nSstb9LWKasNbiJIq/PEJGreIhHdvp0UEa/yp6LJ0kitqRqj1YhBY8BT742LVnVA/qujClIVlX843UK8+PH20c2fn72gL8kF1SQXVHFe3whc9Fqm9g7j060ZfL49i2tGxRDsrfaXPCFufiI4q/MhpHeLlXazkXJIWgylKRIFHfe4IiwbKiUS6RkqtZj5e2D7AvAIFKdNq1FEb22xRBmG2vp+HvjKNsDKl5YvxjroOUd+hvSRYxjrYeA1MOBqGTga62R5U43zZRq8oPt0aVVjNYsTb/kx6DlLDJbWOPTlix6lpCDb8e+qilEVlZbYjY3MjbDzIxh2g6SVVudJqrw9jbYiCxIXSSaEV5iYn9mfDzhkQbj5wKQn5ffS3gYm4XoRiD6RznWYele4cZ2YKnUZ37HrrcxR3LatFhGPFqNcQ+4uWW6ql+Pu+RSih0u2xJh/K8fw7SQRR8f6TqtJUpE1Grh+JeTuhripHbum9vCPkZ92t3EnLFJba8fFS1yH7eTsgG+vhhvW/L5rUDntWK1W8hqyOVS9h4y6o1gdemJr0BDr0Z1e3gOJcIvqsDGQyp8LVZCqqKg4odFo6BnuQ89wH6flV46I4coRMWfnov5sFB6E9NUSsYwe1fY2Wh0Muq7tdQOugsR3pc6yoUKW6dxEXKavhsB4Scst3C/rQvtC8WHAKnWb7gFSR2rwkIFhXaks9+kE3hEiSL1ttaZbX5NtdG5Sd2avFS1NkRo2rV7SBVuKUYC6Iog7T4RsYxVkbYaiA1LHOvxOWZe2UvqYdh4j5ynYL4NTkAF33i4IG6AKUxUVO5d8AV9fAYUH4NcHYM2TcMmXEr2LGKRs9/2NkLVV3nedANcsl8mk9c/D4Z+U7ba/D3s+lvTdXrZMDJ0eEmzPn/pyafNkr0f1iWg/Cmk2QtpaiBgIXrYa142vgMEdhtwAw2+RvqZ1ZeAZKO1aDi2BvvNEMNsF37QXoPMIWzrudyJIY8eIGZuxVrI3Em5QHHlPV0QysBuE9JI0XZDz1JaK+HVEo4fLvoHs7VLbao9AtyxhUDnrFDcWsrZkOeXGkjbXW7FyrC6FY3Up+BuCGB80nWDX0DN8lSq/l9OYn6aioqLyDyFzvQy+0ttoMdARXLyk/yAos/g+kSLuCg9Ibad/VxGLXuESbY0ZK7P+IAJv/JOS7hvQXdLldi0Sd9+aAomIxs2QVD17ZMPcAKXJkp4b1EPcde0ExYtrbkhfiBmnnKfe1qfU3V/6pdqPZR+8dRoGYx+FAVfKtgX7nFvRVOVIBHjXe6f2Pamo/B0JiIHhDq7axjrYugA6JUi0046Xw6A6axtsfgM2vSpRRUfSVsvP1NXw+YWQsQWOrRchtvl1eD4GvrhQ2T53t9SBZm2X+tT/JYj4tVhg+X2y7aLJsm3ih5C/V4Twoe9t1x8LnQZLNHLu+/BYMVzwtmSC+EaL+I0ebtt/IXx/PXx0Hqz7rwi+mHPg4i9EWC+cAM+G29qsnCR1ZXK/X1+htL/Z+qaDGA0Tx3G7GNVolQiz1SSTZKPuck5dLjggBlEqfwpy6jP4qeDLdsVoS8qNJfxU8CU59Rl/7IX9wXz77bf06tULDw8PvL296d69O/Pnz2ft2lMcc/wFUCOkKioqKidLp2GQVi3C7tA3UjsZ1OP4+1Rmw7FVEm001olBUUAclNn6irp4S9SiOEkGdfVl4BkmzpxmIxxdBp5BMsDK2yWOvYd/sO1vw9wgPw9/L2ZJ6WvErdenk7j9gojhAVc7X5vOAH0uUj4H95Ta0qCekLFehLHOBXrNg+JDIoIdqcqFxLecowtavSJcawo6+s2qqPwz6H8Z7Plcfpe1ejHzebGbRAlvsdVrz3kP+l8Kq56U58LujwGrQ9ouMpF17rOwYyFkbICjv0H6RslS8I9RHGYzNomA2/Gec41l55FQkiKv7lOVTImmGnHtXeGQdhver+172fAiZG6Fqc/ATevkGaO31fQZbIZ3OgMc+UWuK38/6A3KBJexXsR01NCT+w6Tl8r9AvS7GHrOkJ8ZmySi7NsJNr4kadCNVTKxdumXUrvrGSyiualOjOby9srkoFYvz2d3v5O7FpXTTnFjIb8WLcFkNZ7UfiarkV+LljAz7NK/bKQ0KSnJ6XNqaiqpqal88sknzJ49m48++ghfX9+zdHV/DKogVVFR+fvTUAkV6SKw7AOl30PEEHntfFdMhEpSJFLYHnUlsPdjSVUrS5O0WlCcNUHS3yIGS9pu9mZx17Va4MgyMSGpdNjWbsJRcrjFiRwcb2sKRJQCuDn84eo0VI6bvlYMi7pMksFnZbYIUZ1BBLFvNOx6X2pGmw+vE1OjljRVt051G3aHHDNrs/QwVVFRUdBq4doVyudfHhJBVJEJvz4G570oz6oe50rfzJ9uF5FalSs1nG4+0hpl7kL5nXXzE0EKsp2pXiJ9k54UIRY+QKKURc4DXVy8RDQaG+C7a+G8l0WoxZwDm15TJpXOfx2GXN36PmpLYc3T8v7nashOhE5D4Nrf5B4HXQl+UVJCUJUn4rXfxRDeHy79RpyADe7OjsMdpct4EfJ6V4geIct6nAv3OUzS9btIJvpetrn6rn4Krl6qrC/Yr9TBgjwz3xsH3afAjDeUNGeVM4rVamVtyfKTFqN2TFYj60qWMy/i6r9UTamHhwczZ85k4sSJxMfH4+XlRXFxMevXr+edd96htLSUJUuWMGvWLFauXInBYDjbl3zaUAWpiorK35/di6SdSUgf6eVptbZf01iZJa634YOdXWnbwj9WBKmjO25bZG4UMQoSsYg+R9J+jQ0OQlMD659WUl7txif1JfKybxMULwI3Yz1OaA3Q73KJNpjqIXyQpPKWJEP+btk3sAdUF4D2kERrAUxNEvVsqhax2fMC5ZhOLpwaEcYgzrl5O8V0ydUXEm6WtjDZW5TNq/OkzUxNPqSVyHq1jlRFpW1ix8C2t+T97g/k9+fyr+WzuVEifI1VyufaYmm9YqyDrP0iTq9fLSY9UUNh/9dicvb1FVCWLp91Lq3P21gjzww7FgsMu1FSfmNGS41755Fti1GQ54mdugrAKgIvdRX8cJM46F7xnYjm4B7Q1cFIqcdUeZ0qflFw2/bjbxNsy1zpMl4iqkEtsjsiBkHvOXBkuTKBV1Mo5kzn3HvyvVBVTgt5DdkdTtNtjzJjCfkN2US4R5+mq/rjyc3Nxc/Pr9XyyZMnc8cddzBt2jT27NnD+vXrefvtt7nzzjvP/EX+QaiCVEVF5Z+D2QibnpfI4JCbpMaoJck/ymCwPANG3996vSNdp0DUqPb7gdoJ6iGi0DsS+l4uwsw/VqKPyT9KFMLe27MZq/NHj2AxScreKpHRumLwiRbDEqOt+XzRAcjbLWlnhQchaoSIa/vxSpPl5RagHDcvURmomhqdz9lrnrSP0blK8NUjWNx4awolbdBilOsoTZGIql2Q+sWK+K+yGShZjJJyGNr3+N+Tiso/le5TndPccxxqKjslSJTT2KAYhgHUFsHLPWWyS+cmKbU6A5z/qpgPvT1aMjLs6N3kGWh/tniGwMAroOiQTL4lXC8pwukb4JOZss2MN2DvF1K3OuWp1tf960PK+9DeIvh6zYK0NVJ2kL5eMiUchV3iIlj5GAy4DKa/+Lu+tg4z5n4RyTs/kAyVsffJcr0LzPsAPjgXsrcBGkl1jhggdfMqZ4Wk6j2n5TiHqvf+pQRpW2LUTmhoKIsXLyY+Ph6j0ciCBQv+VoJUzUVQUVH5+zPoOjEAihgsUQZTg+Jg2xI/W7TzRFFPOy6e0rbgeATFyzUMvNo5SujfBUbcI665TjgcT6MTs5A+F0vqbUhvqYnyjgCsENbPlorsDtWFssxihIpjcOBzOZ+rrxgW2WkoU957BCnpto6GRBnrYNdCuTefCJt7b5WIdatZqf9y9ZVzFx1S9o0ZK4PryARlWW3h8b8jFZV/KuUZ8NVlSi9OEDFntolTN19orBUxap/80rvKRJE988LcICI2c7Ok5pYdg/jpzudx9cbp2VJbJH1NH8ySfqADLxcTop8cBrnJS0WobXnDORpqJ3+v8j7pB0j5BSIHwdAbJCo58s7WUcYD30qZwL6vTuJL+p1sfk2JgJamOq/7/iabGAUCu8LtO+HCj9SMjrNEk6WR9LqjJ96wA6TXpdBkaTzxhn8RunTpwuTJYjiWmppKXl7eWb6i04cqSFVUVP7+uPlKHWNwL/CLkWUZG6CxuvW2Pc6HMY9KdBAkIrl7kaTygkRX7QObjrL/c6k33fZG2+v7XCwRRjtRjq1kNOKY6xMpH32j4JyHJNpRlS31plW5MlitzhHh6epL88DT3GQzNTqmuOfa8Y2BhFvFLAkkYgKQ/JP0Hq3MlO+pMkvcf90DxI3XfmyvcHH83fuRRGft6G19aj2DofeFEDMeIoe33VpGReWfjMUCP9wMKSuU6KdGK5HL/L2w7R1Jq3ezpc+Pvgdu3Q6XLYbokW1H8WqLYfX/wfiH4fLvleVVuVKv6WiK1FDpvO+656A8HQK7w/yfYcTt4vYbfz5U5sLhpUo5AcAF70ntuR2rWaKwgV3hqiVtR1XHPSjP424TpQY1f5+kFf+R9J4tgj+wG5xrM3Uym8SMqWCvst3U/0rLHJWzRq2p2qnP6O/BipVa09/r706vXr2a3+fm5p7FKzm9qL91Kioq/xy0OhGlFRnOg7KW2GslrVZIWSoRRI0Oes2RlFVTAwy6vuMpXXZjIHvP0Vbn85K6UAA00P1cSXFtKLM1j893NiYCqQe1H7epSlkemSB1o45/0IsP0SaVmdKqpaZY0pdjJ8pyR3HpFSZi2mqRljAFDqlUNfky+G1JTaHy3YQPkhq1zS/I99b3cgjt0/b1qKj800haovQatTPwCjj3eXixq9SIlh6FmzeJaIuxTVb99qgYk2n0EvlsObl26AdAI1HNsQ9IGm7ubomC9p4jRmzGOugyTlrJbHtH6ljtrZ78ouUzwL9T5NwLBslzYOSditDscS50nyzpsJXZUp4Q2ovj0mUcVOdD/h7bdSJi8dIv5Vh/BL1mwWOznJf9dJtEaR3/FoT1/mPOr9JhjKdoZNT+8ZpO6/HONn8lk6aTQRWkKioq/yyiR0mkzyOwhWlPG2g0ENpPWrSE9hXRaI/yVed2XJBGJEDWRqkDy9/TtuusV5gITN9oEc4+ESJIDR6t26wAxIyRyKWj2IwaJVFOewquRt+6IbwTVsjfCZYmceVtrACjQc5pv0+PIJojoi0jrND28Qv3S2qxXdjn71GiygV7VUGqomJnx/vKe50reIXAlGck5d3NV0SjR6A8D3wioPCw9Ag11omQ6nmeRCkzNsCXl4rhmH0yyt431DcSrv3F+bydRyjvP5gqkdID3yjL6sudt68vV54rKb84Rz61OmmxsvReqUe/6OP2W8TY8QhwbgdlMcI3V8Ej+cff71QwNojDrouHPIPtFKfIT6tFXIj7XyrfscpZxaA5vc6xBk0bZl5/YRxbwkRE/H3+v6qCVEVF5Z+FRitiqTof9n+h1GOGD1RqQUuOSNqqdyj0vUxSakEipl2nSt1WxJATn8vcJAPEuOkiyPIS4dC3EBiniDU78RfIAC3QJj57XyTpsH6d269l8o4QQWrwhIRbJF3O1VsMm+rLIHOTpPEej85jxTHXK0TE7KHFUlsmXxYkL7EJW4utBYJDa5n2KE+Dra9JRNk9QOpO7cfrPPr4+6qo/JNo/l0DBl0F572kfL5xvURHOzuk8H9zpThiA/L7uRSejxVHXLshkk8kXPAu7Fwkbrl95h7/GnrOgD2fyftOCfKcm/G68zZrn1HeD79FeW+1ynNz+X1QZXvWvDcebt2iONy2xdXLYNvb0lrGXvdurIO9X0LfebD4GihJlbY2Yb9jAstslDYuxbYWWQk3KN/xhR+JC7GLJ1z4sTzvVc46nnpvNGhOS9quFi2eeq/TcFV/DtLT01m5ciUAXbt2JTIy8ixf0elDFaQqKir/TFJ/hdIjymcNEpXwCIakxRIhbCiT2kmrBXJ3SP1pt6nHT/e101QjNaPGOhh4raTA5e0U0WevsbRvV3gAjvwsYrL3hSIwvcNFOAPk7YKcrRA9WkSqndjxIlg9gkQ4liQrrVt8oyQdt2WZrN4dht4mUcuAbrbzOKTY+cfa2sRYaR4o2snfiyJGNSKULSapWbW3pLCvN9bAvk9wErAunkoPVhUVFbjoU4kM1pdD7wuc13mHthZJldkOH6wy4WWpk9ZSdqpyISdRBFdHmPUmdJskEVq7EVJ5hjjS9pot0Uy7mZKrD2x4SczUKjLFEKjLWBG1mZttl2USgZuxWSbGLvu6tTj1CIAJj8grdQ18Zrv3JTfDsXVw+Gf5vPdzpebzVGiqlQlGO2lrRaTqDODfGW7eKCZOb4+UHqy9Z5/6uVROCy5aV2I9unOsLuV3HyvGozsu2tPQe/wM8PPPPzNt2jT0+ralWWFhIXPnzqWpSVKQb7311jN5eX84qiBVUVH5ZxLY3VmQ5iZKbSk6wKwsP/KTkm5amSXps4HdTnz8+jIlkpG1EfpcInWfBncl4tlQCVtfVdxtG6vEQAmNiEZjvdSw2h1qj60RQWoxidmSe6AiJmsKnX9aTODSIiVZ7wEj75Ga1U7DYPdCSUPud7mc08VLnIiDe0HiO1DnEL1x8YLu0+QaqnMAq2JuoneTVOPydBGiTlFUh1nuphoZHBbulxTlLhNP/D2qqPydCe0Fd+zs+Paj74WNr4irLsjkWGA3iVp6BMH3N4o5kouXGCZteg1ytsOwm517gLakpRj+dI60jEn5DS77SiKuXcbCsn/Jc2rLG/L8shilfvTCj6SNy46FcPB7WW/nyIrjR0u7TZBo8CezpM6+Ol8m44oOQdKPMOXpU3e8dfeDeR/CwcVQngkFB+D98XI+rU6esbs/lm1XPaEK0j8JvbwHnhZB2tt7wO+/mDPEHXfcgdFoZO7cuYwYMYKYmBjc3d0pKSlh3bp1vPvuu5SUiNfE6NGjue22287yFZ9eVEGqoqLyzyR6lBgANVRAQxXsWWRbYXbeztFRV+8uqa12MjdD2q/Qabik5dox1kstVZdJcGy1RC7TVoqDryNNNc6tVjyCpa8nVqgvhQNf0SzoNDqljUrWJonwooGR98pAtO+lIvTs2xxeAvm7nM8X1F0Gqvs/l7pYO9nboOyoDG5H3CPHG3G3pNqmLIOKdOlRGtxToqobnpZ0ZIO73ENtkYjm5shxG6lWGp2kFB/5SfYpOghh/W01qioqKh0idgyse1b5bLVIlsSQa+VzaG+oyBLxue9rWPOkLE9dLe1dXE7QM9mOfTuDu/J5yHVQdFgEaNKPsjxquIg4ez1+SYrybHHxgsjB0O+i1sevL4eKbKXWNGKAmBolL4OE62CJLS24plieNVr39q917xfyDB96Q9vC1WKCI7/Y3HOtIkobq0WsWi3yrDY3yXen8qcgwi0Kf0MQ5caSE2/cDgGGIMLdok7jVf3x5OXlsWDBAhYsWNDuNnPnzmXhwoW4uv41Ir8dRRWkKioq/1x0LpJC6x4gL7vDZFt1km7+0GMmbHlZ2qj0vRyOLpPtsjbKAMdqFvOjpMUywAnuI4Oypuq2DZR8IqHTCEnHBeg+3Tb40ssgU+8qgtgzDPpcJOm1AG5+8lPvqqT/+kbJy05z3aYN/65SpwpSlwYSYfWPBc9QEaRWWwpgebo4eIb1h7jzpCdpqK2PqdUskReQFjMRgyHtNyhNkR6u5Wntf9emehHrGr2kIrr6tr2tiopK20QMgK4ToThZUnPRQJ85yvrArvICMRqyP8t8wuV50VGu/BGytztHVTUaMVza86mybMrT0Mmhnj5ttfz0j4GrfpSfLTE1wjs2U7YpT8PIO2Drm7D7E3EAXv00ZNn6gnqHKaK4LTK3KOLV1VtchO1YrfIsS14mkVx7+YFHkIjR6kJY+ZhEjwO6QP9LOvjlqPzRaDQaxgdN56eCLzGdguuuXmNgXND0v5Qj7ccff8z69evZunUrx44do6SkhKqqKry8vIiKimLkyJHMnz+fESNGnPhgf0FUQaqioqKi1UPCbbDB5hyp0bV2j22sFodYc5O44ab+ipNoLdwrPx1bphQfFBOkooOQuR58osTFMfVXEWOx40TQ2QWpVgvBDu6Uw++GxkrnHqUgabteYVLX5dKOYYO+hbNgVQ4c/l5MjHQGiST0miuCFOTeqnMlRdhilqhoZSaMf9KW0mtDo7VFRqulP6FPJAy4Wr4XnUHqTGuLpeZW72prdWMVMWrwhODeEinWnV4nRRWVfwQGd7jS5p5bYOsN7Bsp9Zo1BSLo7INwz2AYdYdMPCVcd3Jpr56BSj3pkRXiThs9XI5h8BBRCfDFRfJ7ft1K8IuCGW+IW++I29sWoyCTbNU2N93SY5JmfGCxTHY5Rn8B3HxOcJ0hkrlibrQJcKT+de1zUJkjxm5dxkGP6eKkq9VB/HlSW/rhdChLlX3u2n984atyxgl2DWVqyGx+LVpyUqJUrzEwNWQ2wa5/LZOqsWPHMnbs2LN9GWcNVZCqqKiogKSkxU6QulLfWMje1GIDq/PgyLFlwfHI2aoY/hQfgqpsyLHN/gd0lUGYHVOLfmkaLXi346LnFdZ6WV2ppAY3VDj0NUUGbbVFktJbuN/5nkAEaLWtwXZFhkRqK7Ok5Y3FLM7ANQUywA2KV1rC2M+h0cig1GKWdOKafIifLdfiKNqNtZC3Q4494q6270tFRaVj+EZB/j6or4CPz5f0018ekrTejE2K6NNoRYS1zNJIWwu/PCi9P6c83fY59n8L318vx7hpozjeTnoCfrb9/tqzSnJ3iiCNn64I2fZw84XLv4WcndJ/dPXjbW/XY7rUfe54X9JxQVJ9tQZwtU3EBXWTGlxToxIZXvsc7P9KOU5xipgouXhKLWz2dnHetYtqg4dzKYbKn4ZO7jHMDLuUtSXLO5S+G2AIYlzQ9L+cGFVRBamKioqKQtfJ8jLWS/2lqV6We4VLRLA0TQYvGp2457oFQPgg6eOZtdlBXDqk/EaPFgFXnQ9RI0ScavUSLXS3DZIGzBfnR8f+nFlbIOVniaom3KJEPY7HsZVQuM9hgUbSe119nNtLuPqAfxcl8qrVifttdR6ED5brjBohA7Y9H0hEGKROtOSI1JGaGiBmnPP5q3JEjIK4+BprlXUuPtBkE+a1BeLQ2dE+rioqKq35dDbk7YH485V5n5pCOPCt83ZanRgh5SSKk2wXWxQmcaGk/hYnw4T/wKEl8pxxrPnU2uvCNUqN+OCrxTU8Z5e0lvGJhB3vSURy5B0du/au4yG8P7zUhtmRewDMeV+ipUWHxGxo6A2QvQM+Ol/E6E0bJTIMSmTUTuw5sP9rMYzqPBoKk8SFuPAgVBdB13GKN4BvFMz7QI2O/okJdg3lwoiryW/I5lD1HtLrjjq1hNGiJcajO729BxDuFvWXStNVUVAFqYqKikpLDO4iAg9/L6m1HgFSU9lUA4Hxks5qd9DN2Qqj/g3R58j2VrOIzcJ9Ivo6nyPbWcwyCDI1ynqPIKWmKyi+9TVUZsrP6lwx5ehIiqt/F2n1glaiuQPmw7YF0FAug0mrBdCI2ZLd/MhO/GzYtVAMmBptNa+5OxQxajclcvGWelOAQ9+IkI6fJfez9yNZ7hEsQt1+D6CIUTvGuhPfj4qKSvvUlsrPyhzA0vY2WgPMeluinAC7PlQE6ZBroCgJuk+VaOkPN8pyNz+ImyLv+8yViKabvwg8O10nwNJ7ZaKtsUpSYDO3SD1mR9PxtXpw9YT6JluvY1uZhHc4NNVBkc0FvakG0jfKZJi5Eeoa4a3hcNMGSQtuKUAGXgE9Z0o5wycznVviZKyHiz8RZ93yDGmjE9a3Y9erctbQaDREuEcT4R5Nk6WRWlMNRmsTBo0Lnnqvv0xrF5X2UQWpioqKSlt4BsOQm+R9fZlEQM1NEkV0dMY11kpE1d0fBlylLO/jEGWwmGHHm5L2GhgndaGNlVJraTcqakm3c0WwBnTv+AAvcqik2Wr1IiCxgmeQREdjxoswzd8twtkvRu7RTmW2RHobmyQS6uotg0Q77gEQ1AtytynLSm22/DnboMcMuU+QlGENYpZkb1nTkswN4tqroqJyalyxGI6uFIH5xUVQZTMyixgkteGHvhMjn6W29FrPYMWNF6T36J175H12oi0CqpEJOEe6TWr7/L0vkPYuUcOkd6hHoEy6dfR55eYjkc7MLfDDTcryokPw3TUyEWenvgIGXA6Hl0nrqcYqeGuEPJMHXg4zF7Q+NtCOHa8AAMZISURBVEDxEeflIb3F0GjK0/DTHXJvanT0L4WL1hUXF1WA/t1QBamKiorKiXAPgEHXSa/SwB5w4HNZrtGJ8HT3P/7+pgZbzalViRr6dxUha7U4tEtxPKc/9JzTermdtJVS7xl3vrOotbvuygVKP9PGKolg5u8RQQoiSoc4DAIjBot41LtLpBVsNVlawCLtaLLWt74OjU5SgBPflnTmRltvwsNLJKrSHrXF7a9TUVE5McE9lP6e9x6G8iz48TbACuMflGyHwkNgtKWnDr1J6kvbIioBbrGZq4W0kbHRFpMel9eW/0HaGpuZ2W7pi5q7E+LOPbGzr18UaEZJlNPe19jFS6lT9+0ktaRuPlJLWpSk7GsvqTj4PYx9UEnhdWTeB/DbI5K6m3CdPNPSN4rYvT+9Y6UQKioqfzhtjIJUVFRUVFrhGy2utKF9IG4G+ESLSA3td+J9XTxl35C+Su1SdS7sfh+OLj/5a2mqhfQ1kk6btfn42+pclF6f4QOlNQtIRNTqkOan0UqqbUA3RSB7hsDo+6DLxPaPnXCzREircuTefKIADQT3gh7ntX9dxlrI3iopvyoqKr+fnYsgYwNkbISkn6DWZgIT1ANmvQmj7z7+/iHx0rZl2b/EPKij9L5ABF/XCRLtXDgRvrkKlv+7Y/tnbFKeRQYPEaPuARDYXVrc9L1QUm9X/qdtM7mmGni9nxg8tST2HEntPfdZEaOFSfDxDPj2atj3ZcfvUUVF5Q9FjZCqqKionCzRI+V1MkQMllf6WhlUlTjUR50sBncRfBXpzkZIHaHnbKmjCoq31ZVaxS03b5e0ptFoYfQDEvUEQCMiVusiKb2O+HcV0dpoq6fVudrceq3iIBzcC3pfIrWm7n4OfV5tHPlJos7D7zz570BFRcWZwoPK+7hp0rP0wGIYdhNEDDzx/gUH4deH5b1nCIx7oGPn9Y2Ea5bBm8Nh/X+lbhXAbEu5baqVzI322s5ED7dleWjBWANGJMui9Ki8ooYrNfAarWRlWFpMZFlMcOA7MUqyYzbBsnslcjvjDfAKVlpeWUwn15dVRUXlD0UVpCoqKipnktjxYgRSlQv6QEm5PVk0Wuh/5amd3zMEel+ofE5ZBtmbJToKkrKrs/UwNTfB9gU2t9yWqW0aKDls6206WsyUgvtCri3tr/Ag5O0UkRoYB6XJDsZKDtTkS/N6ez9UFRWVU2PCo1I/Hn8ehPUGeiv1n2XpUu/ZbXL7bVl8O4l7blU+dBrS9ja1pbD8X1KPeu5/nUWmbyQUH5a+n70vkOvYsRBW3A9BcTD6Llj/AgR0gbmLZJIKxJjoX8lSwrDxZdjwokxyuQdCYBfoNRNCeojRUUUW/Hhr29e25XXwCYfht8jn7G1iXgQQPQJG3QlB3eHGdRIBbi99WUVF5YyjClIVFRWVM03+bqi3OWQa66Rm6mxhT4GrL4OwgdD9XKUO1WoRUQo0t7Jx84M+l8DOd2RxXal8jhwKW19VBGfFsdbnsrv8OvYmBUj9VSK3bfVWVVFR6RgRA+Gyr2HrW/D1FTD6XogcJOtW/gcO/wx7PoNHCtqOVrr7we07bSZtfm2fY98XcOgHed9zpqTE2uk1S2pIg3tImu2Xl0gKMIhQ/fF2iUyWHZPWNENvgL1fiEtw7Bj4bK5MiIX3l5Tdiz9VeqdGDpafGZskQqpzkX6oWVshebmSvWE3dsrfD78+IuUKOgN0cyg7UF11VVT+dKiCVEVFReVMEz5QUtG8wpT6zrNFz9mw/0uoyYOCPZLKm78LfDpJT9bB10t7hNRfZfuwATZXSpuwDB8i6b7HVjtEPzW2NjEOLpl6dzEh0epbp9tVZsKOt2DMwy1MmVRUVE6K6gL49SF5f3gpXPaNtHCJHCKCNGJg+6mzIGmsx0tl7TJO3HQ9giCsRbnAvq9kYmvnh5D0o9SpN+83XtpGpW+QCbjYsVBwAJbYopkHv5fyhaYaiB4qzwlToyJI7cSMhjtsmRe+kTD8ZrmvklS5r4TrZLudiyB/r7y/J0mE8JeXihgd//CJvkUVFZUzjCpIVVRUVM403hEw4u6zfRWCR5DUi9bYIgtHl4krb2mK9Cr1jZaXX7RENly9xbVzoK0tQ3BPWPMf5xYNWJ3FKIiQDesngjNvl7R2aHAwTrGYaJ0WrKKi0mGOrpSa0YDutl7BVhFlcVPE0KjfxdIG6vcQ1hfubyP7AWDUXSIou0yEza84rxtyLfScIenAB76WZ0nEQHETry+XCKq99jR5mfyMGNS2EVNAF+X9jvfFPEnvBnftEwM5gL4XQepqiax6h8OiSZC7C44sh/6XOB9DRUXlrKMKUhUVFZV/OmYHs6LGKvnpFwMuDtEJvxhp5WLvK+rqCyPvlfcGT2n3cjzyd0HcedKqxmyUgbFWL+1kQKIbqsmIisqpYTbC5/Pkvbs/nPu8RCiH3axs42NrD1WWLuKs54zT+zsXN1VePzmYlLn6wM2bwT9aPu/6QGpENVq4+yDcsRuW3gNJSwCrTEzpXCSK2nX8ic+ZYsvcMDVIr1JvW9p/zCi4x2bytPx+uV+QVGCfNtrDqKionFXUti8qKioq/2QKD7Su99S5woD5rVP7HHv2NVUrQrbrJGmD0/cyMU2y4xXh8D5MDJB2L4Tc7ZKyXGdrS6F1ge7TTt89qaj806hzcLCuL5fMh6nPSP9ORyxmWDQZvrtOcdQ93TgKSc8gRYyCZFqARDTT1kn6/9yFcPUyiLHVo1otUsvq6JjbHvZ6UJ0reIW0vU3ebuV933nqxJeKyp8QNUKqoqKi8k/GWK+8d/OHmHGSztZWLefAa6VdTX2ZDCztZkzJP0ld6OHvlT6rIGnAEUOl3Y1PJOz72Pl47gFi7hQzFqJOso2OioqKgneopLjaxdf314F3yPGdZK3W9tf9HuJnKG2i/GOc1/WaBcvvEzO3n26FXx+USGZDFRQekm3cfEHv0rFzjXtInlf15XDwOxh8DehaDG0veBe+vQZ8ImDqs7/79lRUVE4/qiBVUVFR+bNRmQ1pKyGkN3QadmrHKDsm7pJ6V9j/hbjj9rtcljkSOQS0WshJhKosSP4Bel/UttmSq7dsX5amiFGzkebaT0cxaidvh9SOanXQa544DFsB3yipNyvYp9ZzqaicDq5eDs+G0+xinbG5tSDV6uC638QNN/4UWk51BK0OArtKXWinoc7rNDoweCjPisYqKEqGbW9BbZFEOm/e1NrMqD30LuIk/LZtQstqlXYz5kZ5ruTuFkffC96G0N6n7x5VVFROK2dFkGZkZLBkyRLS0tLQ6XT07NmTWbNmERZ2Ysv/jIwMrr32WjQaDatXrz4DV6uioqJyhjm2WkxJytPEDOhkU8yKD8O+TwCNGBPVFsqrJl8MihzRaCFiCCR9ryyryJDeoU014BXqvH3+bjj0rQwsR94rabsWhxrUmAlidHTkR6jKkWX2vqauPhKBBchYD6m/yPuqHHHYVVFROXVc3GH4rbDtTflckdX2dgFd/thJII0Grl8FFZkiAkuOSp1oWF+Y9H8iIHN3S5/RLudApwQxPSrPhH4XSiTT2KCkFJ/73PGfga4+InKNdVKfuuI+QCO1tKZG6aOcuQVuWv/H3bOKisrv4owL0kcffZQXX3wRk8nZgfGee+7h9ttv56mnnsLVtf0HT21tLevWrUOjUd0YVVRU/qaE9oGyVIlSrntCDIXQQOwECOx24v2tZvsbCOgG1fnSV9A7ou3tc7aj9AfVgH9X2Pa61In2nCOi1o7ZqJyj/BiED4bQflCcDMHxUk+q0cDQ22D90zIYzNkqEVFHMhwGh2e79Y2Kyt+Fc58FrLD9XekZWrAfbtpw/FYvfwSuXkpEMnEhZGyUl6svpK6S5f0vtV0v0GeOvOwkL5XWLSAGRX3mtj5H2TFJ1Y0cDLdtF1fdpXfbVlqltMBOaJ/W+6uoqPxpOKOmRg8++CDPPfccRqMRq9Xq9GpoaODll19myJAhpKSknMnLUlFRUflzETEEJj6tpNdWZEBFOhxb1bH9Q/pA/6tg0PUQ2heG3iqGQ9p25iCztwL2HqJWyNok0VGQGk9HIhMgfrZEPZO+E2EZNUoEb1UOlCSLEN36mpKWZzY7H6OhArxtTpcB3aTXqYqKyulh4BXKpFThQcjZeXavp8d0qUl39YH1z8kyg4c4677UA4pTYNNr8L+hcGiJrI8cBJ7B8ooY1PqY5Znw1kh4f4L0MNXqYd1/pXWMxja01eohpJdESruMPQM3qqKicqqcMUF64MABXnrpJQCCg4NZsGABBw8eZNeuXbz88stER0djtVo5dOgQo0aNYufOs/wAVVFRUTnbxJ0n4jIoXgZXQT06vm9wTwjo2rFtO48Bz1Dw6WTbtxeE2waBjTXO22q0st5iG/BmboCdb0v9V30ZpP4mUdHaQmVQ3FgBWZslmnHkZxGr5akQOxEGXacMIFVUVH4/ob0hfIDyuabwrF0KIM8uU4PUi9p7jRrroCoXagogfT1sfEUM07a9LesNnhAUJ067bfVObapVJrzqSqHgoBzLYhSXXpAWMkVJ8tzZ9+Uff58qKiqnzBlL2X3nnXewWCwEBASwdetWunRR6hcGDhzIzTffzP3338+bb75JaWkpEydO5Mcff2TcuHFn6hJVVFRU/lz4xchr3f/J4CrtN3n5RMGAqxRjITsW86ml5kUMUqIQZqNEZhNtA8OSpNbbu3rD4BvEfOnocttCW8pvfSkE9XLerzJLXsVJkubbfL3OpRvNlKZAQ6WI4jOdaqii8neg/yWQv1fef3MljH0Ixj94dq4lvD90HgWVOWJwlvyzLPeNknTbvvNENO75DIbeIOuOLIfMzfI+fSPET3c+pn9nWwsXd6k/tVphxO1izpa9vfU1JKhZGCoqf2bO2LT0hg0b0Gg03HPPPU5i1I67uzsLFizg008/xdXVlerqaqZPn87SpUvP1CWqqKio/PloqgFTvfOyqmzY8ZZEGUAiArveh7WPSc2YnYYK2PEm7P5AzD1OhLFe9gHoNlWMjXrMbHtbv87QebRDFNYq0Y/QfjDgSonstsSeMuzmK2m/encRvvl7oTpP1lVmw54PpYVM8pITX7OKikprWtaar38OstoQan8ELdvJuHrBNcslcpv8sxii6Vxgxmtw0ceQsUmiuFcvE5EJ0H0KRAwUIdu5jZZQW98S99w9n9gcxfXSd7VlrahGB92nStqwispfiKysLB5//HGGDBlCcHAwbm5uREVFcc455/DYY49x8ODBs32Jp5UzFiHNyhK3t/Hjxx93u8svv5zIyEhmzZpFdXU1c+fO5ZNPPuHiiy8+E5epoqKi8ufC4AleYZKO5khDOax/StJdu52rRB5LkiUKAVB0SHG6rcg4fsqvqRG2vSZpdb3mSh2rfxdZbmps3+XSsT2DxQhFB6D7NOgyEYpa/MH0jlSinwYPRXBW2txA+14KeXuU7YsPt3+9Kioq7dN5FMRNg6O/KanzpUch+hTbSHWU7ET4bK70Kb72FxGjubth7xdS9wngEQC3bpNnzcLJEtXECvUVMG8RlKaJSL3qR5m8Atj8hhgdTXxcTI4K9slyvTt4BCrnH/+w7J++Tj6f9zIMueaPvWcVldPMggULeOihh6itrXVanpOTQ05ODps2baKqqorXXnvt7FzgH8AZE6QNDZLr7+7ufsJtx40bx+rVq5k2bRql/8/efYdHVaUPHP/e6em9kIQSIKH3Ik1REVRAbCCigr3X/a266q5l7e7aVtddcUURLGAFEVEsNJEivRNCIL33OvX+/rjJJCEJNckk4f08T57M3HvumTMBwrz3nPO++fnccMMNlJeXc8stt7T0MIUQom1RFBj1oFbCYedHWiJcRxXuJESqC/IPQvxlWlAaW+emX3g/yNqhzUwGdjv+65Rmah8QQZtxCOgK+77U9nnpDDDi7oYlYAC6XaDNalpLtBIwqkv78o3UXrPoqLY/NW5ybYZe0PaAGX20/aY1Kosh/0Dtc3s5VORJFl4hTpXJG65bBD/+FTb8Wzv201Pa8tnIAc3/eqte1mYrrWVgLYbs3VoA7B8D70+o3dcJUJ4Lv7+l1RxN21x7PHqY9n3+VG3FxJE1MP0DcLng56e1Pta/qQWkeYlaW98wLcCt4RNau3IEtPqmEpCKduT555/nySefBCA+Pp7bb7+dESNGEBAQQH5+Ptu3b+ebb75Bp+tYuRdaLSANDg4mJyeHjIwMhgwZcsL2w4cPZ/Xq1UyaNInMzExuv/12ysvLufDCC1thtEII0cYEdoHx2n9SFKXAlrmAqu3D6j5RW0Lb5ZilbZZArZ5fZR4c+VWbuWxKcXKdxyla5t2amUunU6th2lhA6hOmBaJOm/Z6A66rnTUddrs2G5q4UquL2vW82usclTDuL9qMrqNKm12NGASJ39fv32A5iR+OEKJRfS6DDe8Aqpb85/2L4G/NnORo52e12XNBq3Hae4qWWKk8r+ESXgCvYIibqJWncVph8mvQ9zL49fnaveWG6gkMa6n2u8VaAv2mQ+FRmPh32PIBDLupts/Vr2grLFx1bnyN99C+WSFOwy+//OIORufMmcP777+P0Wis12bChAk8/PDD2Gy2xrpot1otIO3duzc5OTmsX7+eKVOmnNQ1/fr1Y+3atVx00UUkJyfz0EMPcf3117fwSIUQoo0L7AIXvXDidi6nFowCZG47fkAaEgeHf9KW9/lG1M6uGixa6Zjw6lmVqmLY/xWYA6H35VrSoajhWgDbZVz9eqOqC9I3QfZOQNX2vsZN1pYbdx6tJU+yBGp7Ri0BWhIjvQWcVbV9KJLUSIjT1mUUzPkWFlymPXdUwS/PwoSnmu81bHWXFSpwzUKIrN7L6Rum/U6oubnV7VwY/xeIPVfbs24t1o5vfAfWvQqFR6q70UG/K7THH19VW1N081xYcgf0ngrXLa592T1fwerqmqZhfbSbc/2uhAGN1C8V7ZqzrAxHVhauigp03t4YIiPR+/qe+MI2zuVycffddwMwaNAg5s2bh8HQdJhmMplaa2itotUC0jFjxrBmzRq++uorXnzxxZO+rkePHqxbt46LLrqIhIQEPvnkkxYcpRBCdCA6fe3+09Dex29bkFi716zzGC1Z0flPaR9gD36rZdONn6IFtvmHtHZRw7SZ2R4Tta9jZe7Q6pSClhFYZ4DIwfX3neYnaLOlZZVw+OdjglEDGE+8zUMIcRzOY2ZS1r0O59wFvuHN0/+wW2Dft9oS2y6ja4PRGrM+1WqFxo6HbuO0BESg/dvudxXs/UYrz1KX6oIvb4EHd9ZfJVFZqH3P3HlM+zqzsF3HwNTXm+e9iTZBVVUqNm2m8NNPKf3ll/q1rfV6/C66iKBZs/A+ZySKonhuoGdg5cqVHDqk/d/6l7/85bjBaEfUau/2kksu4aWXXiIxMZGff/6Ziy666KSvjYmJYd26dUyaNImdO3ee+AIhhBCakfdqS/V8TvDhMzBW29Nlqk6iVCNzG2RWJxoK7a19pW3SZjRr2hWnavVFg3to2XlrFCTWPraVactzk9dA/NTa49EjtYDZEgApG+qPqX1+rhCibelxobZ0dc0raJvQVXg1XpuBnDH/zPvX6bQERDn76tc+Ls2CfUu1jLkXPd34tT6h1WNqhLUEfn8bkn/TMnhf+BTETYAtH9Zm460xYLq2J10Beh1nJYhodyr37iXzscewHkpsvIHTSemPP1L644+Y43rS6eWX8erXr3UH2Qy++OILABRFYerU2v8jCwoKyM/PJyQkhODg4KYub/dabUfsuHHjiImJQafT8cwzz5zy9WFhYaxevZrRo0c3/+CEEKKj0hm0Jbgnumsc0FnbozrmYS0orREYq+3lsgSBXyft69zHtCRHNZl3U3/XluMeXa0t37NXQsJy7Zh7HNVtc/bVn7Ex+8HA67Ugte7MqaKHwTedyTsXQoAWMI57SFvq76ZqM5MfXQYlmWf+GooC4X1h79ewY5F27OvbYcWj8Nmspq8beSd0Ow96XATdj8kRouhh41ztscsO8ZO00jFTXtWWIh+r96USjHYwZevXkzx7TtPB6DGshxJJnj2HsvXrW3hkzW/jxo0AdOvWDT8/Pz799FMGDBhASEgI8fHxhISE0KtXL1599VWs1pMo49bOtFpAqigKKSkp2O12fvvtt9PqIyAggFWrVnHkyBGSkpJOfIEQQpztVBX2LIbf/lFbGqYpOn3DwNWvE4z/G4x9RFt2eyyXU9tjavLV9n/+9g9I+A5Sfqvd9wWgq+63qlCbsW3MgGvBUn0HOLi7VnZGCHHmDv0ERckNjx9ZCx9eUvvcXgklGaf3Gjs/gyV3w5I7YdENtSVbar43JrQnoGrL9a3F0PXc2nOqExzV+1N9wiH8BNsORIdSuXcvafc/gFpRceLGdagVFaTd/wCVe/e20Mian8vl4sABLcN8aGgoDz74INdff32DWqMJCQk88sgjXHjhhRQVFXlgpC2n3eUMNplMdO3ala5du3p6KEII0fbZyrTkRFWFsPV9WPV3yN51an0ousZnWG1lsP6fWsDb42Jt/6fLVl2W5hiOKm22tdv4+kuC6wrsBgEx2uP8Qw33vgkhTk/nkdpyWv8YuOBv9c8VHoUXOsG/R8J/xsDrfWD7x6f+GnUDz4Pfw2X/guu/gus/P/51FdU3rtK3auVeGjP2wVMfj2i3VFUl87HHTjkYdV9fUUHmY4+jNpbhuQ0qLi7G5dJKI+3evZu33nqLTp068fHHH1NQUEBFRQVr1qxh1ChtZcDvv//e4UphtruAVAghxDFKM2Hti7D53w2DQbNfbYZcVC1orNkTCtr+z7UvamVk6tYJPRkFh7VZDadNS0zU9bz6GXlrWIK0REnDboOelxx/+XDsBRDSS0ugVLMkWAhxZvwi4YFt8H97YfwjcNd6uOl7rd4waLU78w7WrqLI2HHqr9F7CsSerz02eYPRG+IuOv4MKcC1n0BQrPbYKwge3AV+UbXnB1wLY+479fGIdqti0+aTXqbbFOuhQ1Rs/qOZRtSyystrM1VXVVXh7e3NqlWruP766wkKCsLLy4vzzjuPX3/9lUGDBgHwzTffsGnTJk8NudlJQCqEEO1d3kGwlUJJupYg6Fh9rwKzv7Yny+gLwXF1rt2vXVt0FCpym34NhxVy9mizogC5++HwytrzkYO1sjIDroOqotrjIb20Ui+RQ7SZVtAyaGbtgJI07XnOXti5UBuDbyQMuUkrISOEaBmR/aHbWAiIqn88oDMMvg72LYHX+0Fx+qn1e8OXMP1DuH31yWfIDo6FW1fCtLerg9OuMOZ+7VxQd5j8yqmNQbR7hZ991qb6aWkWS/1627fddhu9evVq0M7Ly4sXXqgt+bZ48eIGbdorCUiFEKK9ixqqZb+NHgkBXRqeL8vSMlaqTrCXaSVcrKXV147QAtSYUfWX0qqu2vqCZdnaLOquT2DDG1rh+t2f1u4R1Rng0AptlgWg8yiIGQ2xE7QZ2N2fwB/vwOb/gMMGW97TlvlumauN48ASyN0HCd+32I9ICNGImjIqNfV+y3Oh61jte0kapG85tf70Ruh/VfXe0GNk7YYl90Dizw3P+YbD0Dna8t2qYhh9D9y/Xfsd9GocHD293COi/XGWlVH6cyN/R05D6U8/4Swra5a+WpKfn1+955MmTWqy7YQJE9wlYf74o33MAJ8MCUiFEKK9M/vD4Buhz5W1s5B1+UTUP643akEkaEvkht4CvS+v32br+7D2ea3kQu4+bW8oaEFn4o/gF1392oFagJq1Hf54VztmsEDvaVrWTUedPUAlqbBtHhRXJ1dxOWDH/NrkRaEN7wgLIZpZykZ473z45Tm44r8w4naY+YlWNsVRBckbYPD1MPRGiK9OeOSwgvUkP9hv/h+s/FvtTa8a3z8KOz6Br+9o/Lrf/w3/HQ1zz9NuXFUWQHm2tiUgfdtpv13RvjiysurXGT0TTieO7Ozm6asFmc1mwsLC3M87d+7cZFuLxUJoaCgAubnHWdXUzpxdVVeFEOJspDdpX44qbSZkyC3arGnBIYg+p3o5b519naoLilO0x0VHoddUbd9pzZJe1QXDbgd7ORQegX1faWUZbGVaVt+avsoa+SBQVVD/eWmGNrPbb0ZtkCyEaDkb/wsZ27WvuIu17we/1/49gxY0PrSrtkxMaZYWJFpL4cZlEDO86b4zd8H3D2uPvYLh3P/T9qbPnwpp1bM5UUNh8Wzwj4KLX9LK0oC2Jx20ZcKOKu11Jj6rvf7wjpXARTTNdZqJjJrsr87+zLasX79+rF69GgDnCQLymvM1M6UdQcd5J0IIIRqn02szHKAt2y06qu3/dDng6Fot+ciIu7QPkKDNlPaYqJWJ6HkxWAJhzP9ppRmO/Arpf2hLfH3CIHu3FoxagmDgDfUD28xGZjVs5aA3g7N6PDojhPWRYFSI1jL4ekjdpP1O+PBi7QZTPSok/Agjb9ee5h2qvbmUvrXpgNTpgHWva/+WVRU6DdSOl+dCqlZjkU5DwDsYdlXvfet3lbadwGCBC5/Ufqd0HgkWf+28ZNc96+i8vZu3Px+fEzdqA8477zx3QJqUlMSQIUMabVdSUkJeXh4A0dHRrTW8FidLdoUQoqPL2QPUSX8fObj2serUkhrVzE6AFogm/gh5B7QPizW8Q7R+XHbI3qntK61ZfqszQvpm2L+k9gNuSHwjg1Hrl3Nx2esnQRJCtKz4SXDjcm1JrOoCgxeE94fO50D0cOgyBnpPrW3fbRyc/wSccxcMuaHxPl1OyNoF+77RbnSpTvjtX/BMALw1BAbPhpCe2kqLXYu1zLvh/bSVGJ9Mh4+mQkESXPAE9LyodX4Ook0yREaCXt9MnRkwREQ0T18t7Oqrr3Y//uabb5ps980337jL2Zx77rlNtmtvJCAVQoiO7tCP1Q90MOJerRRMp6H12+z/Wst8C9Wzl6r2te0DLTAFiBio1RuNmwJlOVogayuDnpdqZRvSN2lfCd9rM6fG6jvdOjPEXwam6lkPnUELYGvoTC3zvoUQjUv6tfaxoxLKMrVZ08j+cMsK8O9Ue15R4Py/wKWvaP92d38F3/0ffPcnbV/p8ofh2WDY960WzNZI2VDdf5V24yqwTv34W3+Ce34HU00yF+X45aDEWUPv64vfRc1zU8LvoovQ+/o2S18tbeDAgVx66aUAfPbZZ/zyyy8N2mRlZfG3v2l1hE0mEzfffHOrjrElyRopIYToyFSXFjiCtmcrIEZ77BPesG1BkjZ7GhIPvS6Hg0sBFxQkall8dfraOoPFKWAt0jL0djsP0jbW9pO9C1LXg7m6/qDLCslrwVY92+qqU+/UEqTVSBRCtJ6gWG1pfs1qhop87XtR2vGv++kp2PRu7XOdEf54X3u8/g1t6a1vJAycCSYfWP2Sdm7oTVrQe/gX7Zrlf4boYTDx7zBnqTZLGz2sWd+iaL+CZs2i9McfT9zwJPppT9588002bNhAUVERU6dO5aGHHmLy5Ml4eXmxefNmXnrpJdLStH+jzz33XIdasisBqRBCdHQ6PTipLTbvtMHhn+q3MXpD1zrLfzqP0tqVZUKXRpYFBXSBEffUPo8ZVRuAHvpeC4KN3mAt1o7VfD+WqX3s7xGiQ4m7CO7fCu+MBmdV7fHw3tp3pwM+nw1Ze2D6B9B5hHa8eqkgigIGb9i/jHrbARxVWsK0A9/BA9u0ZESKDnxCYNcXWhuXHY6u075cTrj4+RZ/u6J98T5nJOa4nlgPJZ52H+a4OLxHjmjGUbW8+Ph4li1bxvTp08nOzubll1/m5ZdfrtdGURT++te/8uijj3polC2j1ZfsZmZm8sgjjzBgwAD8/f3x8fEhLi6OO+64g3379rX2cIQQomNTdDD8Lug7A+KnaMds5bVJhWrYK2DT27D+1drahDHnaDMXGVtrP4geT0AXbQlvvxna69UkJjmWT53lgJLMSBwj96uDpD22jvTnN6A6jk24I5pNcHe46On6x/IPQ8YObR/5we+1lRB7vqo9P+k5uHoe3L0RHk1qepltt7Had98wLRgF6D4eOg3WtgzUqMnmLUQdiqLQ6eWXUU4zwZHi7U2nl19CaYfLwMeNG8fevXt5+umnGTRoEP7+/lgsFmJjY7n55pvZunUrzz33nKeH2ewUVT2ZTxnNY/369UybNo2ioiIA96bcmr8wBoOBefPmccMNTWyaF2csLS3NXd8oNTWVmJgYD49ICOER+5dC+sbGzwXGwvA7tBqkh5Zrx4bfCYHdtMeqC46s0hKXRI+C8hxI+lmbTXXatCV7ox6ENc/XlpKoMWgO7P+mdhnxoDlall0hgPRXN6Pm1d4sCZnTF6++IR4c0VkgaTUsvBpUh/bcEgh/Oaotq83eA5e9VTtzeqyXYurXGzX5wb2bISCqfrvyfJg/RbsZNmeJtsy3qhgmPA1+7SPpTGvz9Oe1Q4cO4XA4MBgMxMXFtepr1yhbv560+x9APYVSMIq3NzFvv4Xv2LEtOLKzW0v83Wi1W9NFRUXMmDGDwsJC97GQkBCMRiPZ2dmoqordbue2225j8ODB9O/fv7WGJoQQZ5/S9KbPFafA5v9oJV90Bm1fmL1SCzZ3LoSKvNrMuEdWaW1cjtrrFT1seL1+MBrWT6tr6KjSysvYSiFykASjwq18V069YBTA3DPQM4M5m3Q/H0bdBRv/o91ssldoM59TXz/xtedUX2er/rcee27DYBQgfQvk7tceJ62CS15qtuGLjst37Fi6LlxA5mOPndTyXXNcHJ1efgmvfv1aYXSiObXakt158+aRlZWFoijMmDGDw4cPk5ubS0ZGBtnZ2fz5z38GwG638+qrr7bWsIQQ4uzkHVrnyTH/FahOKEnVlumOeQRcLti5QFvCV5BYHYzWWQpVNxgFCInTag/WZSuHQytg7+daQDvibuh3TTO+IdGeVSUVUfjpwXrHgm7rh87UTOUfxPFd/AL0u1J77BV08tdd+Dd49Chc/BJMfA6mf9h4u9jxMPBarZxMv6vOeLji7OHVrx+x335Ll48+wu/iixuWhDEY8LvkErp89BGx3y6VYLSdarUZ0hUrVgBazZzFixfXOxcaGso///lPSktLee+999xthRBCnKKMbXDoOy1bbq9p9c8VJGkBYWBXbZ+n0QsKj0JZRsN+dAYtI6/LUbvftLQ6A6ei17LjVuZpGTP7XAleIbB9nta+pnwMQFB38InQXrP4qHbMXq4lPFKk8piAsh3ZFC1OqD2gQMTDwzCGnN7+MXGapr4JseeBTxi8Ow46DYLL3gbdCf6dGkww+p7jtzFa4Kq5zTZUcXZRFAWfc0bic85InGVlOLKzcZWXo/PxwRAR0W5Ku4imtdqngT179qAoCvfc0/Qvrfvvvx+AvLw88vLyWmtoQgjRcWRu1ZbXpv/R8FzWdi3bbfYuSNsEqb83HozqLTD2UW2prskXBt+kZdFVndp5nR4iBtQ+DonXluPqTdqSv5rlvP4xMPRW6D1NW57b9TwtSVLkYC2AFWe9wp+PUrQooTZRq1Eh8q/nSDDqCRZ/GHaTtqc0azds/xgyd5389bu/hP+OhS0ftNQIhUDv64u5Rw+8Bg7E3KOHBKMdRKsFpDV7R+Pj45tsU/dc3b2mQgghTlL4AC3oCx/Q8FzMKC1I7DIWShrZQ6pUL4UyWqA0A7J3a8/zD2p1RsuytOeBsVogCtqeUHt1womu52kBLIBigEGz68+Cxl0K5z8F/Wc2nZ1TnDVyPt1P+c+ptQcUiHr8HAy+Js8NSsCga8Fg1h5/eAmkbzt+++I02DQX1ryiJUFa84+WH6MQokNptSW7drsdRVEwmZr+j8ZoNNZrL4QQ4hTl7QNHJeTsBo7Zo+kfDSPv1R7n7tdmTOvSGcDpBKMP7JhffXBWbcISRacFrTEjIbindsw7RFviB1od067nakuDLf5gbqLsizjrZX66D+eu/HrHQu8ZhM7b2MQVotVED4Ow3pC5U/tdsuNTiB5ae95epf2u0Fd/hFw8W9sqUHPzqSIfilIhsHPrj10I0S5JATghhOhIQnpB/iEIbaJMQ416S3p1gEsrUt/7SvCNgC1zAVVbhtvrMvCL0vaD+tfJoNl9Qv0+VZeWCCm4ezO9GdERWdNKGgSjwTf3w9JZbmC0Gdd/BV/cqCUns1fAqhe1IPXQT7DvG/AOgztWawmQ8g5p16jVNWOdNtj4X6gogEtfOrUkSUKIs5IEpEII0ZF0GQsx52gzGMdSXXBgqVYv1CdSO+bbCQbeAFvngrUEUn6DzqNwb+ozWLTkR13HHf91bWWw+R1t/+qw27SlwUIcQ3Wp5L63u/5BPwPevYI9MyDRON8wmPoGzB0PeQkNzxenwA+PwuRXa5fsg3YDK6gHbHxHe777C3g6v+H1QghRR6sHpDfffDM+Pj5n3E5RFH755ZfmHJoQQnQMjQWjoC2lS9+sPXbYoP8sOPIrJK/VZj+zdmjlYBJ/rL0m7yA4HVCZD1HDmu67PKc2mVH2LjAHgNmvud6R6CBUhwscrtoD/gYibhvkuQGJpi29T1uye6yQeMhP0JIYHVkHZl+oKtbOdTsPDv9c21Z1wA+PS91RIcRxtXpAumXLluOeV6oTXRyvnaqq7nZCCCFOklewVoKlPFv7SvpJqwlang2jHtKSEvmEaTOdNQmMilO1gFV1aQmMuo1vvO/AbtB1PBQdgeR1kLpRmyU1mKD/tdpMqzjr6Ux6Qm7oS9XhIvzHd0bvLwmM2qzALpC2ufb58Fu1L4sfzD0PKguhLLv+NXWD0RpJa1p2nEKIdq9VA1JVVU/cSAghRMvQ6WHALNg2T6sD6hOhBaQoWmZeS/UevuF3QdLPWiberuOgJE2rRWqsLsVhLdFmVgO7aSVcQEtoEncJbHlPe+6ya8EpQH4iRPRvxTcq2jKvviF49ZWyP23elXO1VQ7ledWP66xae2AH/Ge0lo37WLHnw5HVYPSF3pdqQawQQhxHqwWkLpfrxI2EEEKcvqJkLTFR3aRCLkd1dtzqDJi+EXDeE9XnnJDdS0tQYi+DHR+CbyT0mwHxU2r7GPWgFoQGdtWeJ/2sJUVK26wt3zN6azOotjJt1gSqA9wA0BkhKLbl37sQonnpDXDZm42f8wqE+zZD9l5tBnTju1BVAL2mwKxPtS0BBpn9FkKcHElqJIQQHUFRMmx5V3s85GYtUMzeA7s/1ZISjXlY+16XTg+dqss5HPxOW6ZblgWxF9aWcgEtS2bdTJn+XbSA1DcC9GZt/+iWubXLegF6TKpOjiSE6JDMftBllPY19gFtJrWm1IsEo0KIUyABqRBCtHfpf8ChlbXPa2YpE38AVC0L5vYPYdjtoDdqgeOh77V2vaZpM5mdhkJBojZD6n2C5ZTRwyG0lxbgKoq2BLgm06bOCD7hEDmwRd6qEKINMnpJ3VEhxGnTeXoApyonJ4c33niDIUOGeHooQgjheRlbYf/X4CirPXZgCWz6t5YZt0ZJKmz7n/a4NBNS1kPuvtqsu/5RMPohGHCttrzX5dS+mmL20zLu2sq15bw1XHYoTdfqkQohhBBCnEC7mCG1Wq0sWbKEBQsW8NNPP+F0HudDkhBCnE0KDjd+vLFkI2U52nefMG0WszxXy5KpqtpMZ43yXNjyX1D0MOKe4xe2T/+j4TGvEDCYT/49CCGEEOKs1aYD0nXr1rFgwQK+/PJLSkq0O/A1mXq9vb09OTQhhGgbooZr9UM5ZkZSZwSXDVC02qKhvSGst3ZOb9LqjpbnQO5eqCrUSsLUKE4Fe3X9wdL04wekvhG1j8P6QvxUMPk0Xa9UCCGEEKKONveJ4fDhwyxYsICPP/6Yo0ePArVBqK+vL5MnT2b69OlMnjzZg6MUQog2Irg79L4CDnxTe8wSpAWZNfpcqZVoqTsLGjkY8g+CXzRYAuHIKihOhrgpUFWkLdv1i4bQPk2/du5+OPCtVj4mtA90O69h4iQhhBBCiONoEwFpUVERixYtYsGCBWzatAmoDUIVRWHSpEncfffdXHLJJZjNsgxMCCHqCe4Jegs4qzPcdp+gzZoWJAKqltDI5dAy70aPhPC+WgmXUX+CfV/CL08C1aW5TP5aYFpTxkWnr/9a+YmwayH4dtKCXluJ9tVjkgSjQgghhDhlHgtInU4ny5cvZ8GCBSxfvhybzeYOQvV6PZMmTWLFihUA3HLLLVx++eWeGqoQQrRdDisYLTD+b5CwHNI2QuY2KEyqbeOya9/zD2pfQT202qB+MZC9q35/PqHaUl7fSDBYtGC2/7W1wWbefnDatKA1crAW+BosECAZNoUQQghx6lo9IN2yZQsLFixg0aJF5OdrGSBrAtHhw4dzww03MGvWLMLCwtDp2l0SYCGEaD0Fh2Hnx1rAGdq7epmuCkVHa9sEdNX2g1bk1B4rPKx96YzHdKiHQ9WlYupKWA79pmuPu4zVysX4dYIeE6Hv9IazqEIIIYQ4aeeffz5r1qw5pWtWrVrF+eef3zIDamWtFpC+/PLLLFy4kAMHDgC1QWhsbCzXXXcdN9xwA7169Wqt4QghRPtWkQfbP9CW1oKWnEhv0pblhvcDp11LLhQ5GIpT4I//NuyjZubUrW4GcwV3YOobWXvYKxgGz6l9LsGoEEII0ap0Oh1xcXGeHkazabWA9IknnkBRFFRVJSgoiBkzZjB79mzGjh3bWkMQQoiOw+WorfVpCYaqAm3Z7JCba9tUFcG+r8Baeur9G8zgqAJLiFZz9PBP2lLg7hMhaqj2+rsXazOvfa6GwC7N8raEEEKIs82HH35IeXn5cdvs27ePmTNnAjBhwgSio6NbY2itotWX7Pr5+fG3v/2NOXPmEBIS0tovL4QQHYNvpFbyJXs3xIyCiP5g9q89by2BDf+qTXR0LJMfjLgXDCY4uAyytlNvVtRRfV1VPuxZVHvd/q+1Ui87F2ivAbDnMxj3l+Z+h0IIIUR9NhuUl4HdDkYj+PiCyeTpUZ2x2NjYE7ZZuHCh+/GcOXOO07L9abWA1MfHh/LyckpLS3n44Yd57LHHuPjii7n++uu5/PLLsVgsrTUUIYToGHL3agHn0VXQ7dz65/IOHj8Y7T8TvAIga1edxEbVwajRR9uTmncA7NV3bBWdtjxYdcKWufWX+/pLQiMhhBAtRFUhIwP27oGjR2pXB4FWziw2Fvr2h6io+uXNOhCXy8Unn3wCaGUwr7rqKg+PqHm1Wtag7Oxs5s+fz4QJE1AUBbvdzvLly7nuuuuIjIzklltu4Zdffmmt4QghRPtXk5RIdTY8Fxir1RFVdA2v8Y3Uao8C7P+m9vqgnlowqqrQdRzEXVp7XcwoMAdoj112QAdhA2D0n2HArOZ8V0IIIYQmNxe+WAzffQtHkuoHo6A9T0rSzn+xWGvfAf3yyy+kp6cDMH36dLy9vT08oubVagGpt7c3c+bM4aeffiI5OZkXX3yRPn36oKoqJSUlfPTRR0yaNImYmBgeffRRdu7c2VpDE0KI9qnLGO2706bNZtYoTIKNb2pLaqOG17/GZYeCQ7DhDdjz+TGzqC5tRtRRARlbtIA2tJcWwKZtBmtx/bZhvbUyMR30jrQQQggPSkuFb5dAYeHJtS8s1NqnpbbkqDxiwYIF7scdbbkutGJAWld0dDSPPfYYe/bs4Y8//uC+++4jNDQUVVXJyMjgtddeY+jQoe72xcXFx+mtfVEU5aS+OkoaZyFEMzr2znBgt9rHTjuu8nJS77mX5DvuxVFSAbZSCO4J0SO1GVOlTkZc1Vm9b5TamdaqYm0W1DsUUtbD1rnQdwbYykB1NBxP3fIyQgghRHPJzYUffwBHI//3HI/DoV3XgWZKy8rK+OabbwDo2rVrh4wRPF7oc9iwYbz11lukp6ezdOlSrr76aoxGo7ssDMBdd93FoEGDeO6559i3b58HRyuEEB5QWQi//QNWPa3VHq0R0AXiL9Oy7BYepnzjRsp+/ZWK3UexZlVn1t3/jZYZtzi5/tJevbn2cc1+0Mp8bRa0skB77qiCtc+DoYmlQeU5jR8XQgghTpeqwqpfTj0YreFwwOpfG97Ebae++uordwbeG264AaUDrkpq9Sy7TTEYDFx22WVcdtllFBUVsWjRIhYsWMDGjRsB2LNnD3v27OGZZ56hV69eTJ8+nWeffdbDoz59d999N/fcc0+T5318fFpxNEKINm33p1BVvWQpP0Erw3J0NYT20YLCqgJI24QvBsJmjqNk/V68ugZq7R1VWiDqHQbl2bV9Oq31X0Nn0tqpTm3faWgfLWkSaKVd6mbgBa2/fte0zPsVQghx9srIOPlluk0pKIDMDIhq/6VROvpyXWhDAWldgYGB3HXXXdx1110kJiby0Ucf8cknn3D06FEADhw4wAsvvNCuA9Lw8HD69+/v6WEIIdoDo1f1d2/oPBoOfAt5+yFrJ0SPcjdTcBB6QQyhU4dD8VHtYNQwsARAWN/6Aemx/KJg0Gw4/COkb9aCUYOltvxL3WDUrzOc0/QNNSGEEOK07dvTPP3s3dvuA9K0tDRWr14NwKhRo4iPj/fsgFqIx5fsnkjPnj157rnnSEpKYvXq1dxyyy34+/uf+EIhhOgoBlwHA2+AMQ+Dw1q7DEl1Qdrv9duaA+qXZCnL0ZIeZe3iuIqPQkEi5O6vPRYzCrxCtccGLwjqAZFDYditZ/yWhBBCiAZsNjhypHn6OpKk9deOffzxx7hcLgBuvPFGD4+m5bTJGdKmnHfeeZx33nn8+9//ZsmSJZ4ejhBCtA6DBcL7QVEybHn3OA2VhrOgJSna3tO6bWhkX41igH1fgavOf97J62rLwwTFajOoQgghREspL2u+vZ+qCuXlYDI1T38esHDhQgDMZjMzZ8708GhaTpufIW2MxWLh2muv9fQwhBCide375gQNqv8TN1ggfip4hTTdpiazbo2gblrQWa+pU0t0pOigT8cqwi2EEKINsttP3OaU+mu/M6RbtmxxJ3OdOnUqQUFBHh5Ry2mXAWlH8MUXX9C3b1+8vb3x8/MjLi6OG2+8kVWrVnl6aEKI1lSUDGmbwHnMf8Kqq/YucWURrH4OKo6zB7SusH5aBt5+0+scVLSapAYvMPpA7ITaUzoD9JoGg+fAeX+F0X/SlufWPa8/JoAVQgghmpuxmf+vMbbf2dG6yYw68nJdaMUluy2RgOipp55q9j5by7HlaxITE0lMTGTBggVcccUVzJ8/n4CAgFPuNy0t7bjnMzMzT7lPIUQLsZXD1v9pM5EHlkJoLxh8I5Tnwpa5oNPDgBtg2zxwWU/cH4o285m5FTK3w9iHtf2n+QnQbbxWX7Tv1VrTvV/UXuZywI6PtJnQXpdBSBwMugEOfKeNLe4S0Lff/9SFEEK0Ez6+oCjNs2xXp4N2WrXCbrezaNEiAMLCwrj00ks9PKKW1WoB6TPPPNPsdXPaY0Dq7e3NtGnTmDBhAr1798bX15fc3FzWrFnDu+++S35+PkuWLOHyyy/np59+wniKd4o6d+7cQiMXQjQ7l6NObVAV8g7A7kVa4iK7VnOMLf9peJ3e3LBsS00fdfeAKgpEDNC+jpW5o/7zynzt+/YP4aIXtWW//ac3uEwIIYRoMSYTxMZCUtKZ99Uttt3uH12xYgW5ubkAXHfddRgM7Srtzylr9XenNtNG5fZaFDY9PZ3AwMAGxydOnMj999/PpZdeyvbt21mzZg3//e9/eeCBB1p/kEKIlleUDLs+bng8d68WqB5PYzVEB14Hez8He4X2fNDs2oREjfEJO34ZGCGEEMIT+vZvnoC0X78z78NDzobao3W1ekDq5eXF5Zdfzpw5c+jTp09rv7zHNRaM1oiIiODLL7+kd+/e2O123n777VMOSFNTU497PjMzk5EjR55Sn0KIFpDxB9jK6h8zeIOj4vjXGb20gNPl0GZR/TtD3KVaQqKxj4K1FLwCtX2fxzPwetj0Vv3g1xwIvaedzrsRQgghmkdUFAQFQWHh6fcRHAydoppvTK2osLCQ7777DoD+/fszdOhQD4+o5bVaQDphwgRWrVpFZWUlixcvZvHixQwbNozZs2dz7bXXEhYW1lpDadO6d+/OxIkT+f7770lMTCQjI4OoqJP/BxUTE9OCoxNCNBtzI3vEfcKgJBX8Y6DzGMjYBgUJWtBprwCzvzbzafTS2qsubd9nDYNZ+zoZPmHgHQFl6drzgG4w4s4zektCCCHEGVMUuGACfLsEHCdYMdQYgwHOv1Drpx1avHgxVqu2EupsmB2FVsyy+9NPP5GSksIrr7xC//79UVWVLVu28NBDDxETE8O0adP4/PPP3X8AZ7O+ffu6H6enp3twJEKIFlGeq335hGvBZg2dAS58DkbcDZGDYMhNWtbbkfdoCYqG31EbjEL9YPR0DL8djL6g6LXERUIIIURbEBYGF1+iBZenwmDQrmvHE101tUf1ej3XX3+9h0fTOlq17EtUVBSPPPIIO3fuZMeOHfzpT38iIiICu93Od999x6xZs4iMjOSOO+5g7dq1rTm0NqW97o8VQpwEaynqtnmQsxsq8mH4ndDzEogYBANm1Q8yFQVMvi03FoMZxv8VJjwPgV1b7nWEEEKIUxXTGaZdoS3fPRnBwVr7mPad4HP9+vWoqorD4TilVZLtmcfqkA4cOJDXXnuNtLQ0fvjhB6677jq8vLwoLi5m3rx5XHDBBcTGxvLUU0+RkJDgqWF6RN2SMGfLX0QhzhYV372DYi3WnvhFaaVduo2HAdeCqX2mpxdCCCFaRFgYzJgJl02D7t0bLsPV6aB7D+389Gva9czo2UxRmyvtbTMoLy/n66+/5qOPPmL16tW4XC73bOGYMWNYt26dh0fY8o4cOULv3r2x2Wz06NGDxMTEZu0/LS3NXRomNTVV9pwK0Ypy3/431j++J/r6AdgKKjHP+IdWXkUIIYSow9Of1w4dOoTD4cBgMBAXF9eqr31cNhuUl4PdBkaTVme0nZZ2aa9a4u+Gx2ZIG+Pj48Ps2bP5+eefSUlJ4dlnn8VsNqOqKlu3bvX08M7YsmXLcBxnc3Z2djZXX301NptWR/Cee+5praEJIVqYIy+PvHfeoXTzEQ6/sQ1r0KUSjAohhBCnwmTSlvCGR2jfJRjtENpkldUNGzawcOHCDpfk6P7778dut3P11VczevRounXrhpeXF3l5eaxevZq5c+eSl5cHwLhx47j33ns9PGIhRHPRBwfjO348Fdu3E/nEk/ied56nhySEEEII4XFtJiA9fPgwH3/8MR9//DFJ1cVwVVXFYrEwbdq0DpP2OCMjg7fffpu33367yTZXX30177//PmbzSZZvEEK0eYpOR+e573p6GEIIIYQQbYpHA9LCwkIWLVrEwoUL2bRpE6AFoYqicO655zJ79mxmzJiBv7+/J4fZbD766CPWrFnDhg0bSEpKIi8vj5KSEnx9fencuTNjxozhxhtvZPTo0Z4eqhBCCCGEEEK0uFYPSO12O8uWLWPhwoWsWLECu91OTV6l+Ph4Zs+ezezZs+nSpUtrD63FjR8/nvHjx3t6GEIIIYQQQgjRJrRaQPrbb7/x8ccf88UXX1BUVOQOQkNCQrj22muZM2cOI0aMaK3hCCGEEEIIIYTwsFYLSM877zwURUFVVcxmM9OmTWP27NlccsklGAxtZiurEEIIIYQQQohW0uqRoJeXFxdffDE+Pj58/fXXfP3116fVj6IozJs3r5lHJ4QQQgghhBCitbR6QFpVVcXSpUubpS8JSIUQQgghhBCi/WrVgLRm36gQQgghhBBCCNFqAanL5WqtlxJCCCGEEEII0Q7oPD0AIYQQQgghhBBnJwlIhRBCCCGEEEJ4hASkQgghhBBCCCE8QgJSIYQQQgghhBAe0WpJjbp3735K7RVFwcfHh+DgYAYOHMiECROYNm0aiqK00AiFEEIIIYQQQrSmVgtIjx49iqIop1T6pSb4XLduHe+88w6xsbF88MEHnHfeeS01TCGEEEIIIYQQraTVAtIuXbqc0uymqqqUl5dTVFSE0+kEICkpiQkTJrBs2TIuueSSlhqqEEIIIYQQQohW0KozpKfDZrOxc+dOFi5cyNy5c7Hb7Vx//fUcPXoUPz+/5h2kEEIIIYQQQniAzWZjwYIFfPHFF+zatYuCggKMRiPR0dGMGTOG22+/nTFjxnh6mM2uzSc1MplMjBgxgrfeeosVK1ZgMBgoKiri/fff9/TQhBBCCCGEEOKMJScnM3ToUG6//XZWrlxJVlYWNpuN8vJyEhISmD9/PmPHjuWBBx44pS2Q7UGbD0jruvDCC5kzZw6qqrJixQpPD0cIIYQQQgghzojdbmfKlCns3bsXgIEDBzJ//nw2bNjAypUreeqpp/Dx8QHg7bff5pVXXvHkcJtdqy3ZbS7Tpk3jgw8+cP+BCSGEEEIIITo+W6WDskIrdqsTo1mPb5AZk1e7C2caWLp0qTu2GT16NOvWrUOv17vPT5w4kWnTpjF69GjsdjuvvPIKDz/8MAZD+3/v0A4D0piYGAAKCgo8PBIhhBBCCCFES1JVlfSEIvasTiNpZx6qq3a5qqJT6D44lP7jY4iOD2y35SF///139+PHH3+8XjBaY9iwYUydOpVvvvmGoqIi9u/fz4ABA1pzmC2m3QWkDocDoMPcERBCCCGEEEI0lJtSys/z91GQUd7oedWlcnhbLoe35RIc5cNFN/UlrEv7S3pqs9ncj7t3795kux49ejR6TXvXrvaQAiQkJAAQFhbm4ZEIIYQQQgghWkLqvgK+fm1bk8HosQoyyvn6tW2k7mt/qyh79erlfpyUlNRku8OHDwOgKApxcXEtPq7W0u4C0o8//hhFURgxYoSnhyKEEEIIIYRoZrkppXw/dzcOq/OUrnNYnXw/dze5KaUtNLKWMWvWLPz9/QF45ZVXcDobvu/t27ezfPlyAK677jp3+46gXQWkr7zyCitXrgTgiiuu8OxghBBCCCGEEM1KVVV+nr/vlIPRGg6rk5/n72tXpVFCQ0NZuHAh3t7erF+/nhEjRrBgwQI2btzIzz//zN///nfGjx+PzWZj6NChvPbaa54ecrNqtY2YKSkpp9ReVVUqKyvJyspi69atLFq0iG3btgHQp08fZs6c2RLDFEIIIYQQQnhIekLRSS/TbUpBRjkZCUVE9wpqplG1vGnTprF161Zee+015s2bx4033ljvfEREBM899xy333473t7eHhply2i1gLRbt25nnPlKVVXCw8P55ptv0Ona1eSuEEIIIYQQ4gT2rElrln52r0lvVwGpzWZjwYIFLF26tNHZ3ezsbD7++GNiY2OZNm2aB0bYclo1qlNV9bS/9Ho9s2bNYseOHR1qE68QQgghhBBCqzOatCOvWfpK2pGLrdLRLH21tPLyci666CJeeuklCgoKePTRR9m/fz9Wq5Xi4mJWrlzJuHHj2LJlC1dccQWvv/66p4fcrFpthvTYaecTURQFLy8vgoODGThwIOPHjyc8PLyFRieEEEIIIYTwpLJCa706o2dCdamUFVkJ9mr7pSKfeeYZ1q1bB9Bgua7JZGLixIlccMEFTJo0iVWrVvHII48wYcIEBg0a5KkhN6tW+xP68MMPW+ulhBBCCCGEEO2M/TQTGTXZX1Xz9tcSVFXlgw8+ACA+Pr7JSTyDwcBzzz3HuHHjcLlczJ8/nzfeeKM1h9piZCOmEEIIIYQQwuOMZn3z9mdp3v5aQnZ2NgUFWu3UIUOGHLftsGHD3I8PHDjQouNqTRKQCiGEEEIIITzON8iMojuzJKg1dDoF30Bzs/TVkgyG2gWrDsfx97za7fZGr2vvJCAVQgghhBBCeJzJy0D3waHN0lfs4DBM7WD/aHBwMP7+/gBs2LDhuEHpmjVr3I9jY2NbfGytRQJSIYQQQgghRJvQf3xMs/QzYHx0s/TT0nQ6HVOmTAEgIyODF154odF2hYWF/OUvf3E/nzp1aquMrzVIQCqEEEIIIYRoE6LjAwmO8jmjPoKjfIiKD2yeAbWCp556Cm9vb0DLuDtt2jS++uortm/fzoYNG3jjjTcYPHgw+/btA2DChAlMmjTJk0NuVm1/HlsIIYQQQghxVlAUhYtu6svXr23DcRpZdw1mPRfd1BdFaZ69qK2hd+/eLF26lFmzZpGXl8eyZctYtmxZo20vvPBCvvjii1YeYcuSGVIhhBBCCCFEmxHWxY/Jdw7AcIpZdw1mPZPvHEBYF78WGlnLueiiizhw4ACvvPIK559/PmFhYRiNRry8vIiNjeWaa65hyZIl/PzzzwQFBXl6uM1KZkiFEEIIIYQQbUrnvsFc9eeh/Dx/HwUZ5SdsHxzlw0U39W2XwWiNkJAQHn30UR599FFPD6VVSUAqhBBCCCGEaHPCuvhx7ZMjyUgoYveaNJJ25KG6VPd5nU4hdnAYA8ZHExUf2K6W6YpaEpAKIYQQQggh2iRFUYjuFUR0ryBslQ7KiqzYq5wYLXp8A83torSLOD75ExRCCCGEEEK0eSYvA8ESgHY4ktRICCGEEEIIIYRHSEAqhBBCCCGEEMIjJCAVQgghhBBCCOEREpAKIYQQQgghhPAICUiFEEIIIYQQQniEBKRCCCGEEEIIITxCAlIhhBBCCCGEEB4hAakQQgghhBBCCI+QgFQIIYQQQgghhEdIQCqEEEIIIYQQwiMkIBVCCCGE6IAKszJwOuyeHoYQQhyXBKRCCCGEEB3Muk/n88GDd/DFc3/19FCEEOK4JCAVQgghhOhgDm3eAED6gX1UlZV6eDRCCNE0CUiFEEIIccqKcir46PH1fPzUBnatSsVmdXh6SKKO4pxs9+MvX3zSgyMRQojjM3h6AEIIIYRof1L35VNWaAVg3eJDrFt8iM59ghl4YQzdBoR6eHTCYDZjq9BuEhRlZ3l4NEII0TSZIRVCCCHECakulcPbc1j/1SG2r0zBaNY3aJO6v4BfFx7wwOjEsW596z33Y1UFl8vpwdEIIUTTZIZUCCGEEE2yVTlw2Fwc3JjF718nuo97+xsbbd+5T5D7cUWJje/+vROAKfcOxCfA3LKDFW7efgGEdO5KfmoytvIyVr73Npfc9ZCnhyWEEA1IQCqEEEKIRlWV2Vn45O/YKp30Hh1Ze0KBihKtnEj/8VG4nCp6o45zLuuO2bs2UE07UEBuipZQ57cvDpGZWIS10o7BqCcqLogJN/bBZJGPIi0lOCqG/NRkAPau+lkCUiHauKqqKj744AO++uordu3aRXFxMaGhoQwePJg5c+Zw7bXXenqILUL+FxBCCCGEm8Pm5JeP9mOrchDezR9bpbbUMy+9jItv74/Z24DJy8C6xQlYfI2MuToOo6l2+W5BZjl6g0JBZgVhXf2J6RNE+oFCErfk1L6G1UHS9lyqym0oOh09Bocx4PyYVn+vHZ3Jy7ve8x/+8waX3PMnD41GCHE8Bw8e5PLLL+fgwYP1jmdmZpKZmcmKFSv48MMP+eqrr/D19fXQKFuGBKRCCCGEcEs7WEjiVi149A22uI8HhHnRc1i4+/n0vwyvd11lqY2Mw8X8MHc3iqKgulSCIr256pFhLHjid+zWhnsYMxKKAUg/UMi2lcl4+ZkYeGFnsg4Xc2hrNmOu6EG/c6Nb4m2eFWIHD2Pv6p/cz/euXSUBqRBtUE5ODhMnTiQ1NRWAGTNmcOONNxIVFUVGRgYfffQRX3zxBStXruTaa6/lu+++8/CIm5cEpEIIIUQHpbpUFJ1yStdEdg8gvKsftionQyd1JayzH8U5FYy8rHuT1+xalcq6xYfwD7GACqqqAmC3ObH4GJnx+HAKsyooL6pi3eeHUF0N+ygrsFJWYOWXD/e5j63+5CBmLwM9h0ec0nsQmj+WfVXv+egZ13loJEI0D4fdia3SgdPpQq/XYfIyYDA2TLDW3jz77LPuYPTpp5/mmWeecZ8bMmQIU6ZM4emnn+bZZ59l+fLlfPnll0yfPt1Do21+EpAKIYQQHYzL5eK9h9bitGmRX0C4F7OeOge94cTJ9Q0mHZc9MBiLj7YXNCDsxDOUmYnaTGdpQRU+QSa69A0hops/0b20BEdBkT4ERfoAkJFY7F6+Gzs4lLhhEexalUpWUgkA3v4mKkps7r5/WbCfvLQyRl3R42TfvqgW2T2O7MOH3M/DunT14GiEOD2qqlKSV0HmkQIKskpBrXNSgZBOfkR2C8Y/1BtFObUbcG2B0+nk448/BqBr1648+WTjdYOfeuop5s+fT0pKCi+//LIEpEIIIYRouz545Dd3MApQnFPJlu+PcM602qCuosTGjp9ScDpcpB4swOVQsfgYyD6iJSG6+Pb+9ZboHs+oK3pgtOhJ3pNPeaGN9IOFXDi7T6NtB54fQ2FmBTG9ghh3TRwAcSMiKC+2UlZoJTjKh81Lk9jxayqo4LC52PpDMj2HhxMa4wfAvvUZuJwq/c6NapcfQFvLhFvvZudP37ufG4wmD45GiFNXVlTJoW0ZVJZaG2+gQn5GKfkZpXj5mYkbGoVvoFfrDvIMHTp0iOJi7abexIkT0esbn/HV6/VMnDiRefPmsXXrVo4cOUJsbGxrDrXFSEAqhBBCdBCqS2XP2nSs5Y4G57b9mMKgCV1QVRW9QcdvXx7i0Obsem2K6zzOSio+6YA0IMyLC2f3YdvKZLb9mEzfcVFNtu3UM5BrnxzZ4LhPgNldFmbsjDhCOvvyy/z9AHgHmFj/ZSL9x0eTnlDE7lVpAFh8jCc9xrPRscG60sQHXSHaoqKcMg5sTsXlVE/cGKgstbLnt6P0HtmZwPD2k/QnPz/f/Tgi4vjbE+qeX7dunQSkQgghhGg7Mg8Xs/ydnVgr6gejNUtgXU6Vjx5fj8PmQmdQcDma/pAX0tkHl0tl+8oUhkzqctJjGDqpK0MnnfyyUIfdyZGdeUTE+uMfUn9Wo/eoToR39cdo1vPlK1tIO1BI2oFC93lFp+AbJHVNT0XKrh10GzDY08MQ4oTKiipPKRit4XKqHNicSv9x3drNTGndjLk1M6VNqXt+3759x2nZvpx4M4kQQggh2jRVVfnxf7sbBKNDL+7Czf8Yx4ALtJIqjuplvI0Fo3qTgk+gCYNJR3CkD7tXpfH714mkJxQ2aNscrJUOPn5yAyvf38vnL/7hToRUV3AnH/yCLUTG+jc417lvEJHdA1pkbB1VYWa6p4cgxAmpqsqhbRmnHIzWcDlVErdlNPo7pS3q2bMnRqO2Z3/t2rXHbVv3fEpKSouOqzVJQCqEEEK0cwv++jvlRVoiIEUH/qEWdHqFqDgtqdA5l8Uy8rJYdIam91uOuaonLqeKw+Yi41AROr3WdsW7u/h14X6Wvrmd4tzKZhtz2oEC95it5Q42fJNITnJJo21rxlJXj8FhzTaWjsxYpxapT1CwB0cixMkpyatoes/oSaootVKSX9FMI2pZPj4+XHjhhQDs2rWLzz77rNF2n332Gbt373Y/Ly0tbZXxtQYJSIUQQoh2yml38evH+ykrqP3wprq0AO7W186la/8QAMzeRoZf2g0aKbdSY92iQ0TFBWGy6CkvsqFUf0KwVjjZvz6TtAOF7Put+WbYouODoE6cuX1lKkvf2I7qqj+rkbQjl8StufWOdR8cSt9xUp/0RMoKC+o9Hzblcg+NRIiTl3W04MSNTqafIy2zuqMlPPPMMxgM2k7KG2+8keeff56UlBTsdjspKSk8//zz3HjjjZhMtYnJKiub7wahp8keUiGEEKKdWvb2DtITihocLyu0cnBzFi6HysDzY1B0CopOod/4aI7szMVhc1FVZm/YoaoS0T2A1H0FOO31A0NFgdhBYaTszWfXqjT8wywMvzQWb38TlaU2dq1KIzo+kJjejc/CHd2dh7XCQfzICBRFweJjxOxjxFpnHIoO0g4Wsv3nZNL2FeIdYKKqov44DSaFSbf3P/Uf1lkode8u7JXaLJGXnz/+oTKrLNo2h91JfmbzzPzlZ5bgsDvbRZ3SUaNGMXfuXO68807sdjtPPvlkg/IvXl5e/POf/+S+++4DwM/PzxNDbRESkAohhBDtVN16nQaTjrEz4igvrMLsZWDtpwkAePkaiR8Ziaqq7F2Xjsuh0nVACDG9gshLK+XgxtpMu4d35HLZ/YPR6RSS9+TXey1VhSWvb0fR1e5FTd5TwJCJnck4WMShrTls/0nHba+f2+ADYE5yCcvf2QVAeZGVvuM6seztXfWCUQCLr4lv/7XD/bxmSW8NvVHHLa+ei14vC7xORvehIwmMjKIoK4PK0hL++PZrRl0109PDEqJJtkpH/TqjZ0IFW5WjXQSkALfccguDBw/m+eefZ+XKlZSXlwNgMBiYPHkyr7zySr2kRkFBQZ4aarOT3+hCCCFEO5RxqIjgaF9COvtgtOgJjvKh+6AwfAItJPyRg06voNMp+AVbAK0ESEwv7QNMSV4ldquTc2f2wiewTm1KVcvKe96seDr1CEB3zOc4p8PlDkYBSnIrWfNpAoe25QDatQDrvzrEpm+TSN1fQEWJjR//t8d9zcalh/n4bxvJOdpwv2hItE/Tb1iBcdN7YjS1jw+XbYHZ25vRM2a5n69fvJBlb7zcbpK9iLOP03mcfQWn05+jeftraUOHDuXrr7+mqKiIlJQUEhMTKS0tZenSpfTu3ZtDhw652/br18+DI21eMkMqhBBCtDMVJTa+/deOeh+2co6Wsu+3dLb+mILD6iSimz+TbuuHf2ht6YOp9w1i9ScH2fdbBpuXHaH36E7EDgpjzxptb+jU+wcRGOHFJ09voqygqsHrKjptj2oD1fGNrcrJew+soW680/+8aEryavtSXVqG3bp8Ak2YLAZGXdGdpO157uPegUYqirRZ1Ik39SH+nE4n/TMSGv+Q+nVaEzb+hrX8Piy+7adOozh7NPfqB72hfc69GQwGOnfu3OD41q1b3Y9HjmxYz7m9koBUCCGEaEecDhc/z9/X6J3/zcuO0GtUJIe35dJnbCd3MFpaUIlvoAVFp9B9SBgJm7LwDjRjr3LQe1QkpQVV9BwaTtd+IaQnFDYIRsdc3ZOwzr7Yqpys+vhA4/tPAWt5w+N+oRZ6jYrEVuXAWuEg63AxKPVLz4RE+RI7JIyV/9uLyaLHVuVk0ITOjJsRR0FmORYfo3v2VZyaxC2b6j0fdfUsCUZFm2XyMmjJzpphEl9RwGTpOKGO0+nk66+/BqBz586MGTPGwyNqPh3nT0kIIYQ4CxzYkEnqvsazUKoqJPyRTXR8EH3GRlFebOWTpzdir3IS1tWPGY8NJzTGl05xAaTuK2TZ2zux25xYyx1EdNNqfYZ38yemdyDZSaXYbU4A+o2L0j4oAjt/SSXjUNFJj1enV+jcJ5iCjDK2/ajVzTv/+l7s+jWVgkwt4U7KvgJS6rynUVf2YNjFXQGtFqk4fft/W1Xv+YjLrvTQSIQ4MYNRT0gnP/IzzjyxUXAn/3azf/RkzJs3z1179M4770Sv7zjvrX3OYwshhBBnqeAo33p1Of1CLfQbH4XBqGAw63E5VFL3FVBZaiPnaAn2Ki2ozE0uZd3iBBY88TvpB4sAbTbCaddmWu1WrZ3RpOfyh4YybmYcKBDe1Q+jufaDz5R7BtJj6Mlna80+UsLPH+5j248pKHot+VJUXCDTHhyCwaR9DNEfUx81Jj7wlH8uonFGs1e95+VF7acUhjg7RXZrnnq5kbHtK+lPenrTZbV+/fVXHnroIQDi4+P585//3Eqjah0yQyqEEEK0I516BHDjS2PR6bXSKTXOn9Wbwqxyfvv8EBHdA/AJMNOlXwihnX3JSy0DoLSgCpdTWws36ooe9BsXRUl+JXlpZfQaGenuK/WANlt58z/GYfExouhqA0aTl4FL7hhAxqEiclNK2L06jeLcY/abKtBnTCcMBh1hXf1I3JKDokBMfBBjp8dh8TXy8ZMb3QmSnHWW715yZ38iYgOa94d2lnLY7RRnZ7if9z33AoI6Sf1W0bb5h3rj5WemstR64sZN8PYz4x/i3Yyjann9+/dn/PjxTJkyhX79+mE2m0lJSeGbb77hk08+weVyERwczOeff47FYvH0cJuVBKRCCHEMq8tKdlUGnSwxGHXGE18gRCtraj9lUKQPlz0w2P1cb9ARERvgDkjHTu9JRDd/TF4GBl6gJcyw+BoJ7+rvvqYou4Jl/9qBqsK4a+IYdGHDxBoAUXGBRMUFMmhCF3b+msquValYy7V9oqhwYEMWqkslfmQEvsEmygpspO4vZNFzmxk0obNW3gEweenRG/RUltrwCTTTuYk6puLUbfp6Ue0TReG862/x3GCEOEmKohA3NIo9vx1130A7FTq9Qs+hUSiKcuLGbYjdbmfp0qUsXbq00fP9+vXjk08+YdCgQa08spYnAakQQgAVjnK2FW9ARSXfmk22LZNu3nFcHH6Fp4cmxBkZdklXLeturD+B4T4MnxzbZNui7Aq+fnWrO0uu2fvkPiYMurAzgy7sjNPpYtuPySRuzaE4pxKnS+XIzlzs1voJmEoLqzBa9NirnIR39efSuwZQWWrH4mt071UVZ6aiuIiyOstzr3jkr/h0oLqFomPzDfSi98jOHNicekpBqU6v0HtkZ3wDvU7cuI15//33WblyJZs3byYzM5OysjLCwsIYOHAgM2bM4IYbbsBo7Jg3yeW3vhDirGZ32cixZvJ99pe40D40GxVt9inXmonVWYVZ37GWxoizi1+whYtu7tvkeVuVA6NJj6JTSE8opLJUy5Q76sru9B51amVW9HodIybHMmJyLAv/9jsleVUNglHQEhX1HRvFd2/vJO1AIft/z2xyJlacuv2/reb7f7+G0VL7odxgkt9jon0JDPel/7huHNqWcVLLd739zPQcGtUug1GAa6+9lmuvvdbTw/AICUiF6ICq9u2jdPVqAq+6CmNkJK6qKhSDAcUg/+TrKrEX8XXmQuwuuzsYVVCwqzYAyp1lzE99mzBTBNMir0Ov6NvdEiAhjmffbxms+uQAnXoEcOWfh9JzeATpBwsxmvUMntDljPoefWVPdq1KxWDUkbpf67MmcVLCpiz6jonCJ9CMtdJBpOwZbVZrP/sIVBV7ZQXnXHUNYV1i6TpgsKeHJcQp8w30YvAF3SnJryDrSAH5maX1SsIoipZNNzI2CP8Qb/k/up2ST6dCdEDJN92Mq6SE/Lnv4T/9aoo/+RQUhe4rVmDu1tXTw2tRDpeDHFsmYaYIjLr6++wqnOVUOssJMWmF4ovsBVhdWjIWo2LErtrx0vlQ5arEhdN9Xa4tm3kpb6CgoKAj0hzNJSHTyU0uJaKbP3qjJCwX7VPagQJQIetwMQ67C7OXgUm39W+WvnsOC6fnsHBUl0pOcil+IWZ++Wg/KXsLKMmrQtEpzH5hNC6nitHUccoXeFJ5USEHN6yjLC/XfWz4lKuk7qho1xRFISDUh4BQHxx2J7YqB06HC71Bh8li6FClXc5WEpAK0QGU/LiSzCefxOeccwh/+M+4KisBUK1Wir/4Umukqhy9bhad33kHr0GDUHQdM4halbecpIoEoi1dmBo50308x5rJksxPUFE5N3giff0HY9F5YVLMmHRmzgk6jwNlu+nrN4iNhWsodRQ36FtFRcVJhjWF+X/Mw7awB75BFibe0peoONmbJdqfkZd1R9FpdUJbKihUdAoRsVrSpAvn9GHzt0mEdfXHN8gMQAcqpedxn//9cQoy0tzPAyM64bBZObR5F7FDRmDooPvPxNnDYNRLANoBSUAqRAdQMH8+rpISSn/6idKff9bWsNSw2UCnA5cLV0EhybOuw9ApkrhVq5rusB2rcJZXf69wH8u1ZrOjaDNq9TqfbGsGfRnMkcpD2FQrNqeVIFMIUyOvASDAGExC2V52l2xFxYWv3o9yZzkqtXvhXNHFKL0LKTsQzJrPEpj11Dmt+C6FqFVRYuOb17bhsDm5/KEhBEacfKmDwAhvJt7SrwVHV59PgJkLZvdptdc729it9ffZ9Rw5mgWP3k9laQn9L5jExXc94KGRCSFE0yQgFaID8Bo0kMrt27UnqgqqSvhjfyH33++g6HQE3XA95WvWUrV/P7hcODKzSL7lFrA7CPvTQ3gPHerZN9CMLgybyuHyA8R6xwGwp2Qb6wt+BVS8dN5Y9F6MDroAgN6+A8iuyiDAGESwMczdR4gpjNHB59PDpxcljmJ6ePdCURQcTjur8leQVHEQgED/QAqBmN4yO9pabFUOjOaW2curqip2qwOTpX3NImUfLaEoW7sBk3aw8KQC0rSDhVgr7CiKwtYVR+k7Lop+50p9yvbONziU0nxtua6i07Nl2dfuc067zVPDEkKI45KAVIg2zJ6RgT4oCJ2XljGucPHnlP/+O7rAAMpWrcZZXk7QzGso/PgTFC8vwh95BFwudF5eBF59FUEzZ1K2bh3pDzwIQOB111H8xReodjsVv28AIO/dd+ny3nsee4/NxWW1Yk1MxLd3bwYHjCTflkuBLY/fq4NRgKGBo+nvXxt8BxiDmNap6Yx24eZOhJtrs4wa9EYmhk+j2F6IS3URdEsItlkOKVPRSo7sySLzcAGh0f7ED49xHy8tqMDpcBEYfmb75BK2pJGfUUqn7sHEDog80+G2mi59guk9phP2Kidxw8NP2D43pZSlb24HFfxCLJTmV1FaeEQC0g4gM/GA+7Hqqt0H32XAYC667R5PDEkIIU5IPkUJ0YbYUlIwhIai8/amcPHnZD39NKbYWLovXUL6Y49T+v33Da4p/OBDAFSHg7LVq3GVluJ/6aUA6Ly8MHXpgmI0ogKBV16B77njyPnnqygmE/a0NAKmTm3Nt9hs8hcspGztGiKfeAJz9+6k3X0P5b//TsDVV+H4622syPkSPXqCjWHk23Po4zuQfn5DmuW1A4y1M6ISjLae0gJtb3Reegl56fswWvR4+5gozteO9xoZQ0gnba+i0+7E6VRxOV0c+CMVe5WTPqM64+Vrxm51YPGpTXhlq7Kzc3WSOwNsaUEF7YneqGPCnJNfBqvTKyiKgqqqdO4TRNKOPPqNi2rBEYrWUJKXg9Fsxl5V1eDclAcexeR18ku5hRCiNcknKSHaiPz588l5+RV0fn70/PUXqvbvA8CWnEzVgQOUrlzZ5LXG2FhwOilfuxaAyu3b8bv4YowR4Vh696bHyh/B5cIYrc2A+F1wQb3r7ZmZqE4Xppj2MUPiLCsj58UXAUiaPAXFxwe9vxaI2JNTqKreR+rEydiQCQQag/HSy4ex9uzonizKiirrHbNXOSmuqj1WVlBJ8t4cQMVaZUd11u8j83A+uekloIKXr4nwLoHkphZjrbTjdNTuDw6N6dglSEKifZnx2HBslQ6iewXR65xCUvYVUF5sxSfA7OnhidP047tvYa+qct9sqDH4ksvwrv79KIQQbZEEpEJ4UNYrr1Dx+waCrptFzutvAOAqLaX422WE3X8/OrMFryFDSL7xJnA4muzHfuRIveeWvn0xBAWiqiqFn32GotMTdO3Mem2Kly+n8JNP8Zs0kdzX30B1uej28UK8Bg+u7Tcri5zXX8erXz+Cb7yxyde3paWT8fDD6ENDiX7tVXRmM47CQmyJiXgNG3ZaGX0dhYUULlyI9/Dh+IwZA2izwPkLFlC1Z2+9tmp5OeahQwm44nKt9qpvDE6cmHUWOlliGutetCOqqpJxuOCE7bKOFtYLLOvyD/UmN63E/byyzEbqwVxcTrVB26N7svEL9iIvvYTi3HJieoW6Z147So27sC5+AKgulWX/3oXD6qQ4p5KLb2+eki+i9fmHasu1o3r1ZfIDj7Dxq8VExfei//kXeXhkQghxfBKQCuEhlQcOUPjhfACynn6m9oSiUPrTT/hdcD4+48aRP28eamVlY100KvThPxN6660oikLBJ5+S/dxzABg7ReI7fry7XdYzf8dVWootLQ3VpiW7yPvf+1Tu2EHwnDmE3nkHqXffjXX/AUq+XYbfxIkYo7Rlfa6KCnL/9RYum5WQW28l9513qNyxQ+v3uefp9OzfOXr1dOwZGYTcfjvhf/4/QJuJzXt3Lt4jRxAwZcpx30fOq69S/NXXKCYT8Zs2Url7D2n33YerpKTR9sG334bvyJHu5339Bp30z0y0bUf3Zh/3vKJTCI70pSi3rNHzFh8jPYdEse3nxHoF1fUGHarqQnU1DEp3rz3qfpzwRzp6QyY6vUJQpB8hnfwIDPNF0Z04OK0ss+GwO8k4nI/T4aL7gMh6y4U9rSS/EoNBwWGFgHAvTw9HnIGJd9zLwAkXU5SdSe7RJCbdca+nhySEECdFAlIhPKRs48bGT6gqFRs3kvbQn7ClpuIqLNTKuKgNPzQ3pmr7Dpz5+Ry5ejqO7NoP8mW//46xSxd03j44iwpxlZYCYO4ei/8991D42aeU/fILAIWLFxF65x3Y09IBUCwW9KGhAGT+/e8UfbbI3W/J9ytwFdfW7CxdvRqfn37CnqtlenQU5LvP5f7rLYqXLKHoiy/wHX8+el8fVIeD4iVLMMbE4DNqlLutqWs3AIydOqEYjaQ98ECjwahlyBC6fvgBOovlpH4+on1RVZXMY2ZHjRYD9qraFQORsUHE9o/k8M4Mso8WNeijqtzOtp8S8fY3oegUyou00hgOm/Nk/1nhdLhwOiAnuYicZO01DEY9YZ396dInAr2h/iqAqnIru9YcwWGvP2ObRBZ9R3U5uRdtYQ6bk0XP/4Gjeu/syKmxHh6ROBM6nZ7y4iK+f/tVAK55+iU69x3g4VEJIcSJSUAqhIfY9u+v99xvymSCZs0i5x//pGrXLqp27XJXjNcFBuLVry/lv61v0I+hUyccmZkAKCYTXgMHkPPa6/WCUYDCjxZQ+NECdN7edPt8McauXXFkZ2PpPwB9SDCqza71YbEQdq92Zz3yqaco/OILQm67DZ3JhLOsjKJFi+v1WzO7qg1Uhysvj4wHH3IfCr7pJvdjryFDKF6yBEvv3ui8tACyYOHH5LzyCuj19PhhBabOnQEIveN2fM8fjzEqGmdpKa6ionqvqw8Pd2cSlmC046oss6LoQK0T1wVH+JKXXuJenluQWUps/0g69w7H5VTJTS1utK+KElu9WU29QYfT4Tq5oFSh3uwqgMPuJDOpEIfNhcPhpDC7rEGbY/kFtp1ZyIpSmzsYBe3n4xvUPP+WjuzM5cjOPPqPj+bIrjyCI32IGxHRLH2LphnN2h5gRdFhNMl+YCFE+yABqRCtyFVZiauqCp3ZjDE6GkN0NK7ycrwGDiTq2WfR+fgQ+/likq68Cuv+/fiMGY0jO4fQu+4k66WX3P2YBw7ElpiIWlGBIycHAJ2PD3Ebfidh1GjUigoMMTHoTCaMXbtQvmp17RgqKihbuxZ7SgooCgXvvw9Ap5dexJqYSOAVV2CO02p4+k+ZDKgoRgPlmzaj8/drOFOr14NOBy4XXsOHU7l5c73T6X9+mB7fLgW0LL+lK3/ElppG1YEDePXr505GpJjNKOb6H6As8fGoLhcp9z7c4GcZPPsGQm+//dT/EES7UZRXzr71yQ2OW3xNRMeHkpaQi8uhusu9mMwG4oZG06lHCHvXH8Vpd2E0693Zc4F6y3O7DYgkINSH3euOYKt0oOhAZ9ChqOByqVh8TQSE+JCTWkRk1yCslXYKc8pwOV31AuTivHJsVU3v8dYbdHTuFUpAuC8+/s1788Rpd3FkVx6KDg5uzCJ+ZCQ9h5249AuAy6Gi6BRUl0pghDde/me2lLiixMavC/Zj9jFweGsOTodKRmIRxTmVoEB4N38CwtpOQN4RdR0wmOuefw2dXk9E956eHo4QQpwUCUiFaCWOwkKOXHEljvx8/CZN0kq46PX0XPUrxnDtA6Q1MZH0Rx/F1LULUS+/hKVXL/f1Oh8fir5dhv/Fk/CbMAFnSQm5b7xJ0eefAxBy990oej2GwEDsFRUETJ1C+EMP4aqqIvXee7ElJ6Pz9iZoxjU4S0u1wLImuNTrKf99Az6jR5Hzxpv4nj+eoGuuoejLL8l66mn3kmHFaGzwvtTKSnBpn86tx8z6AtgSEsh59VXCH34Y69GjlK//HYCCDz8k+tVXCbz6KkzdumIIC3P/HOqq3LGTijoBtSEmhqiXXsRnxIjT+4MQ7ULC1jTy0uov0dabdMT2iyQsJgBFpxATF4rT4WqwXNY3wMI5k3vjdLhwuVxkHymkILuMssJKTF4Gug/qhMXbhLefdgNk+KR4XE4XOn3jybcaq0nqdLioKK3CYNSTk1JM+qG8Rq+N6B5EjwGdGj3XHNZ9cYi9a9PR6RVcTpWMQ0V06RfMH98dwSfIzKALOzeZiKkou8IdoA+f3A19E+//RCpLbexbn0FVmZ3kPdoS/dDOvuSllhHWxY/inEq8/UxYfOQjR2voFNfrxI2EEKINkf8dhGgljqws9zJatbpOnD4wEJ23N66qKkpW/EDltm1Y9+3Hum8/oXff7b62fNNmsv/xT3zGjsF/0iQADEFBhNxxB7bkZIxRUYTcdCOKXk+3r77ElpRE5d69JIwdh9eAAVRUB4EAVQcP4MjMAqMR7NoyXZxOSpYto2TZMgDKfv2VgCuvxJ6hLQWuCUjVmvZ1mPv2JWDqFHJeetm9LxW9Hpy1s1JV+7Vi7eYePdCHhODMz6d8Q+0eWu9hwxr9mal2O6aePTB1744tLY3gG28kojpBkui4nA5ng2DUYNEzYlJ8g+Dq2GD02HN6dMT0CiOmVxi2Sjt6k77RwKupYPR4ffsFaaWEuvYNp0vvMHLTiklNyCUw1AefADOhMYEYjPpT6vdUqU7tZlBNQNptYChfvPgHRTlaIrT81DLGXN0TL7+Gs59d+4cw6orulBdaCe3se9pjWPNZAoe35WC06PEPsWD2MXLZA4NQXeDtb2LU5RVYfIyYvRve0BJCCCEkIBWilVj69CHiiSewpabiKChAHxqK4uNDwugxWkkXVUXn54epZ0/M3btj7t7dfW3R4kXYDh/GdvgwYQ88iN7XBwBTTDRdP5rvbmdLSyf3zTcp37gRHA6cRUVU7toFBoMWIBqNOHJyKV+v7UW1DBxA1a7djY63fMMG8v/3PwCCrrsOY1QUJSu+p2r3nnrtrLt3U+bvj7lPHxx5efhNmoSpWzcCrric/P/8F2vSYSKfeAIARa8nYOoUCj5aUK+8TGOKvvqKzL/+Dcxmev7yC8bQkFP6eYv269hV4X5BFgac173xxqfA5NVyAZGiUwjvEkh4l8AWe43GjJsZT1RcIBHdA/ALtqA36PjfQ2vc5w9szOLAxiz0Rh29R0cyflYvFEXhwIZMyoqspO4rIONQEXvWpnPVI8OI7N54Dda8tFJK8qqIHRTa4KaAb6A20+wbZGHaA4NI2VeAgoKXv/bzDgiTGsBCCCGadnrrc4QQpyV4zmxsKSmULl+OMy8PR3KyNktZ/QlcMZvpvnQJ+qAgDgwZSsGCBQAEXnMNpthYgq6/3h2MuioqsGdkuPt2lpaS9sADlHz3Hc68PJw1mW9rluaqKv6XTSVo1rUoFgve55yDq8rqvl4XEKDNmup0mLp1w5GdjWLQ7llZevci5Jabif3iC4xdGmYIrVi/HltSEvHr1lKxaSM5L7xA0pSpFHz4IeVr1uIoqM2SGvH44/T89Rdi3vpXkz+nyt27yXnjTe2J1UrJ98tP/Yct2i2DUU90fAg6nUJAqFezBKMdldGkp9eoTgSGe7tni6feN4jug0PrtXPaXexdm0H6wUKO7Mrll4/2s2lpEhmHigDtV0R2cgnlRVaczvqZgcsKrXz58lZWvLubXavSGoxh7PSeXPXwUK5+ZCjfvbOLVQsP8NMHexu0E0IIIRojM6RCtLKqXbsaPa4PDyf2s09R9HpKVqwAh4OSH34keM4cfEaNoseK791tXVVVJF1+BfbUVDq9+CJ+F00gacpUHNWlVhQvL0zdu2Pdu1cLTHXaB1VLbCx+F1xAr+3bUBSFsg0bSL3zLrDZMISHYTucBC4X9owMsp58CoDI554jcPp0nKWlFH3+OY68Y/bKVS/P9T3vXAB3IOysTraEoqAPqD/rUlPPtEbZmjVU7txJ8Jw5lP22noyHH9Zmdav7D7z88lP8KYv2rmufCLr2qc3KWphdysE/0vAN9KLvmK7oTqIG6NmqU89AOvUMZP/6DLb/lILLpVKSq+2fDYzwpiCz3N1Wb1BwOrQbYolbcvht8SFCYny59m8j6/SoNvqwhqJT6NQzUOtPr/256KqD44KMclAguJNPs75HIYQQHYcEpEK0ouJl3+EsLGz0nPfQIRijowGI/OsTFH+7jJA772i0rau0FHuaNlNRdWA/3sOG1gajfr6opWWYunXT2qWkgMtF4LXXEnLbbVqb6iV3FRs2gs2GMSYGV1m5OzmRsXt3bAe0fZ+Wfn3JePwJir/5BsVice9/Ra/H1LMnnZ5+Gku/vuiqM+R2/fBDyn/fQOWunVRu30HY//2p3vLjYzlLSki99z73EmNTt+paiA4H/lOmEHTjHHf5G3H2Ksgqw+VUKcmvwG51YG7B5bcdRZ+xUfQZq938qSq3ozfoMJr1+AZZGH9dPPlpZZh9jWz9XstknHVYu5mUn1aG0+ly77X1DbJw9aPDKMmvpPugsOO+5tT7B5GRUETnvsFkJRXz9T+3gqJw9SPDiIj1b8F3K4QQor2SgFSIVpTzr6aXqZb+8CNVBxOw9Ion4PLLCTjOrKAhLIzo116lcs9eQm6/DUNQEFH/eAXroUTyP/oIgPK1a3GVlqJ4eWHu3p2gG66vfa3Vq3FkZmJLTQXAkZOD97njtFI04eGU/vCDu63O4qXtSQX0AQGoFgvOoiJwuYj821/Jfvll9KGhdH7zDdDpUJ0uKvfvo+S75YTeey86b2+yX36F4JtvxhjRMIuuYrFgjIzEnpaGqVssQdfNouibr7HuP0DJ8uWULF+OMSqK7t8vl3qjZ7GoHiHYquz4BnpJMHoaLD71f2b9z4sBwGFzYjDq8Qu28POH+9zntyw/yjnTam8khXXxI6yL3wlfx8vXRI+h2r/zyjK7thtBVakssx3/QiGEEGctCUiFaCWqw4EjreH+KwDFZMLUrRumzjEn3Z//5Mn4T57sfh4wbRoAPmPHUr5pI86CQooWL9YSGRUXc3TGNXSe+y6GkBDS7r4HVJWwBx9E7+9P0eLFlP/yK+GPPELxkiXaTKlOh8+oUegDA4h89u+ULFlK0A3Xkz/3PcpWr8Zn3DhKVvzgXoKc/fob2FOSKft1FZi0jJ5F336L4725YHfgLCwg6pVXGrwPnclE7Ddf48jOxtyzJ3nv/Q97ahoYDO5A1Z6djauiQgLSs5iXr4k+5zTcvyzOjMGkZ/il3QDY+1s6mYe0WdIt3x9l+JTTLwUDYDDoCIr0plNcIN0GhJ74AiGEOIvl5OSwefNmNm/ezB9//MEff/xBfr5WSuvGG29k/vz5p9TfihUreO+99/jjjz/Izc0lLCyMESNGcMcdd3DppZe2wDs4fRKQCtFK7DV7Ko/hff75dHnn3yjNtCzVZ9Q5+Iw6B5fNRtHixaglJThKtBIa5Rs2EDRzJoqXF2pFBcYunQmcMZ3SlStxFhdT9NVX+IwZgz40hKCZM7EdOcKhMWPxmzTJnYQo4/BhAFwlJfX2ghbOn+/eq4pNmw1xpKaiCwzAVVSMMTbW/XMo/mYJvuPPQ+fjQ9mq1QRcdSXmnloR98JPPsFVVgaAqqqE3H0XXv37YwgObpafjxCicdPuH8zOVWnsXpVK7KAw9Hod+Rll6PU6AiNOPVPupmVJFGZVUFlm54Lre7fAiIUQouOIiIg4caOT4HK5uOOOO5g3b1694+np6aSnp7NkyRJuu+025s6di07XNvLbSkAqRCtJu+/+Ro9XrFmDLTkFc/fYZn09Radz1w/FZML/oosIvu46DGFhdP/2W5xFRXj17wdA7HfLSL5mJrakJBz5+fTapC3RTZ5zI4B7yS5A9OuvUbL8ewKnX42pWzdwuch7913UigpwudD5++Mqqa0h6SrSZlwcWVkAZD35FGVr1pD/4Ye4KirAZiPnzTcJnj0bZ1ER3qNHUbJkKQA6i4XwBx9s1p+LEKJxBpOeYRd3ZdjFXQFIP1jI0je3o+gULntwEOkHiujUI4Au/RovwbTm04Ok7C/g/Ot60blPMF36hZCbUkrciIZL9YUQQjStS5cu9O7dm5UrV57ytX/961/dweiQIUN49NFH6dGjB4cPH+Yf//gH27dv5/333ycsLIwXX3yxuYd+WiQgFaKVqC5XEydUkiZPJuz//o/QO25vttezp6XhNWwYqtVKzP/ewxgY6D5niomGmGj389x//BN7ejoAruJiChZ+TPDsG4h47C/kf/Ah/pNrl3Z4DRiA14AB7uehd9yOKbYb2S+9jP/kSyn96ed6AWkNXfXrO6uTIrmKitzn1IoK8ufOBcAcH4+pe3fsmZl0evbvZ/xzEEKcnopSm1YxyqmyY2UqyXvy0ekVbn3tXEyW+h8fbFUO9qzVfofsW59BTO8g9q3LwOVUG9QtFUKI02WtqKCsIA9bVSUmixe+waGYvTtGreOnnnqKESNGMGLECCIiIjh69Cixsac2WZGQkMCrr74KwPDhw1m7di1eXl4AjBgxgmnTpjF+/Hi2bNnCP//5T2655RZ6Vq9Q8yQJSIVoBardjlpaetw21oSEM36doqVLyXz6GczduqE67NgSteW15atWozrsWAYMwKt3w6VzzmPGZkvWsm5a+vYl+tV/nvB1/SdOxH/iRAACrrqKo1dehWqtU+PUz88901m5bVvjnVTP5noNGUKnvz+D6nJps7xCCI/oOSwch82J3qijvMhG8p58/IIt6I0N/12aLAYGXdSZtP0FDBgfzaZvD1NerP0OKMgob9BeCCFOlqqqpO7dzY6V35H4x8Z6N/gVnY64EaMZNGkKnfsNaNc3wP7+9zO/Cf/mm2/icDgAePvtt93BaA1vb2/efvttRo8ejcPh4I033uCdd94549c9UxKQCtEKKrZscc9ANuDlhTE0lMCZM0+rb5fNRtFnn1H6y69UbN4MgLW6ZAs6HZbevcl8/HF3+9B77ibkttso/GwRlj698Rkzhk4vPM/R62/AnpQEaAmYTpele3d679yB6nRy5JqZWPfu1YLNaoaQEBxZWehDQ3FZrbWBuqoSu3QJ5vh4AAlGhfAwRVHoM6Z2n3iXvsEoOpqsATtuepz78TevbXc/Do6SGqRCiNOTnZTIindeJz8tpdHzqstFwqb1JGxaT0hMFy699/+I6O75GT9PUFWVpUu1LU+9e/dm1KhRjbYbNWoUvXr14uDBgyxdupR///vfHg/k5ROfEK3AWdxwCatbZSX21FQKP/nkuH00FSQWzP+I7JdedgejdflefHFtoqFqtqPJ5P3nP+T885+k3HkXjoIC9IGB2KtLwAAYO3VyP67at4/Dl04m7f77sR45QsmPP6Kq6nHHWjNee2YmAPrAQHf90h4rvqfzvPeJ/W4ZUS++AH7VpSR0Oiy9enn8l6IQonFbVhzls79vZu2ixldzOO0ulr65nfmP/Vbv+LBLu7bG8IQQHczRXdtZ/MxjTQajx8pPS2HxM49xdNf2EzfugI4cOUJGRgYA48ePP27bmvPp6ekcPXq0pYd2QhKQCtEqagM4XZ0ArC6fJu5kAaTd/wAHBg6icNEiAIq++oqDw4Zz9LrryX399SavK1u5kqo9ewCwDB5M8G23YuzaxZ11Vx8QgM5sJvNvT4Ld7r6ufOMGnKWlOAoLyXntdWxHjlD6088kTZlK+oMPceTKK0/8jm02XMVaQiN7Sgpla9dqb9vLC9+xY0m79TbS738An6FDMPfpQ+j991H87bdYq2dphRBtS85R7ffGoc3ZLHhiPRmJhfXOF+VUkHagkPKi+jVHpW6sEOJUZScl8u2rL2C3Vp3SdXZrFd+++gLZSYktNLK2a9++2lrSvRvZnlVX3fP79+9vsTGdLAlIhWgF5vheBN9yC53+8QqmrlotRWN0tHspqzk+nqBra5fsusrLcVZUYDt6FFVVKV21Clwuct/+NwWffEr2P/6Jq7y86f2YNRv8nU73oaodOyj96Sfy//suxV9+Rcy7/yV2yTe4KiuprK4lWqNiw0YOXzqZlDvvpHz9ejAYsAwapNUnBWxHjp7wPduOJtcrC1O5a3e98/acbAAUnZ7u33yNs6CQjEf/wtFrZroTHwkh2o6LbupL98GhWCsdlBZY+ea17eSl1a7+CAj1gmMWOFh8DOgN8lFDCHHyVFVlxTuvn3IwWsNureKH/7xxUqu5OpK0OrXuY2KOX9e+c+fO7sepdVbIeYrsIRWihTmLizkyfbpW9zM6Gnt6OorJpC2R1elQfH2xJiWR8de/EvXCC5T88APpf34YxWhEraoi9L77CHvoIXJffRVnfj7Zzz13/BfU6ei57Fsyn30WR24e1sREd11Qe7K27EUfEID3kCGk3HIrVXv3Ymokw5ozLw9nXp72xOHAd/x5VO3cCUDYo480aF+5axeuqirM3buT8/obVO3bV28ZsKlOVl+ALu+9R9naddhzcjgwaDCWfloJGldZGSm33Ersp8dfwiyEaF2degYSGOnNkV2/oboAFRY/v4WgTl74BlroPbaTu9JUjfiRkR4brxCifUrdu/ukl+k2JS81mbR9u+ncb2AzjartK62ToNLX1/e4bX18avf2l1XXfvckCUg9JDk5mbfeeovly5eTmpqK2WymR48eXHPNNdx77714d5AU1gJQVdSagLB6bX/Nc52XF67qXyDFX31N2D33aHtBnU7U6tlNa0ICXgMHgNkEVlsjL1BNUcBgIOyBB3CWl2OJ70Xhlq3uYLSmTfjjjxEwZQrWpCSq9u7VxpWcTPTbb2FNPEzev/5Fg0+VQN5bbxP11ltY4npiPiYNeeWevRydeS2oKn6TJlK68ift/Xl7a7VGFQVLnz71rrH06YOlTx8Sxp2LarVqgXO1qu1n5/4PIdo6L18Ts58bw2fPb8Jeqf2OKsyspDCzktT92hJes4+BAeOj8Qu20Gt0p+N1J4QQDexcubxZ+tmx8vuzKiCtqrO6zGQyHbet2Wx2P66srGyxMZ0sWUfjAcuWLWPgwIG8/vrrHDx4kIqKCgoLC9myZQuPPvooQ4YMITHx7Fv73lHpAwPxv/hi7UndIM/LC1edfZuGiAic5eWY+w/AGBMDRiO6gAD0oaHkvPqaOxg1dunSyIvoMUZH43fppeS+/TZHpl1O/v/+h6tcK7egDwlxv75aUYkhJKTutla8hg7Ff+JEdEZD/XGaTOhDQ93tFGgQjALgsLuvcZaWgV6PqUcPIl960d2fs6xh6YeKrVtrZ2Hr7KnVBQQ0fA0hRJvgF2Jhxl+GN3ne28/EOdN60HdcNHq9fMwQQpw8a0UFh/7Y0Cx9Hdr8O9aKimbpqz2wWCzuxzbbcSYwAGud0nzHlobxBJkhbWXbt29n5syZVFZW4uvry+OPP84FF1xAZWUlixYt4n//+x8JCQlMmTKFLVu24FeTAEe0a52eexZjl87kvz8PHA4teKt7R0qvJ/rd/3Lk8ivqBa2u4mKKPv20Xl/2lJTaGUxFQbFYUCsrsaelYa+zf8Dcuxf2nFwCp19NyE03U7DgIxyZmQTOvAYA76FD6LLgI5yFRfhfPAmA4DlzUEwmCj77DPvRZEJvuw1DdBRZf3sSfWAgvuediy01laoDB/C74AJUl4vy33/Ha8AAOr38EpmPPU7Fhg0YwsOJXboEncGA8uYboOjwHTe2wc/FWVK7/6wmC6/i40PPX38545+5EKLlBEX6MH5WPL99eQinvc7dLQXGzYxr+kIhhDiOsoK8enVGz4TqclFWkI/5LFl1WDdmONEy3PLy2kmCEy3vbQ0SkLayBx98kMrKSgwGAytXrmT06NHucxdeeCFxcXE8+uijJCQk8Nprr/HMM894brCi2ei8vQl/8EGCZszAWV5O6YoVOPILKPriC1AUIp55mvLVa+rPoBoMWvB6LEUh8LrrsB44QNCsWWQ88oj7eN3rA666ipA5cyhasoTSH38g7IEHGtT29Bk5st5zR0EBpb/8is5soeviRXgP1Ja6+E+ciM7LC9Xl4uiMa3AWFRFy262UrV+Pdf8B9CEhdFnwkTbL6XLhyM0l4y+PEfn4Y/hfckm91yjfuJHsF17A59zziHj0EaJffw10epxFRRQvW0boPXejP0v+8xCiPes/PoboXkGseHc3hVnVsxAqfP+f3Zi9DVx65wAiu8tqByHEybNVNe/yUVvV2TNDWjeRUd0ER42pm8ioboIjT5G1NK1o8+bNrFu3DoBbb721XjBa489//jN9qvfa/etf/8JeZ0mnaP+MUVFY4uLQBQVrwajLhal7d7KffIqqvXtqG5rNRDz6CL4XXljven2nTqCqFC1ahPfoURR+8QVK9T6ByKeeJPTuu1G8vdH5+uLIzuHI1dPJfOxxsv7+LAeGDiNh9BgSJ07CUVi/XEONtIceomLTJqwHDlBUpy6q3t8fxWjUlvxW720t/m451v0HAHDm55Pzj38S+fTT6CMiQFUpXb6cgvnzcVVVaftIAUdhIQULP8Z6KJGCDz7AVVWF/+TJ+F9yMUHXzqTbJx/jO7bhTKoQom0KivThqoeH1TvmtLuoKLaxfeWZJSURQpx9TJbmXT5qspw9N7j79u3rfnzgwIHjtq17vs8xOT48QQLSVrRkyRL345tvvrnRNjqdjjlz5gBQVFTEqlWrWmNoohVV7tpF7htvuEuo2JOTAXDk5tLtqy/pvuJ7+uzcgblnT1BVOv3znwTNnk3gtTMJqf67gdNJ/r/foXLTJlSrFe8xYwiaNYvgW2+hx6pfiX77LQo++MCdtAiAqiqchYXYU1NJe+hP7sMlP/xA3n//i6uiAkP1flHFaMT/smkNxq6zWOi26DMMkZE4srLqnav44w+ynn4anbl2I72xSxcSx5/PoXPPI+eNNzk0Zixlv/2GYjJhiIxErbOHQYiWZqt0kJtSeuKG4pSUlzT+79hmlRuqQohT4xsc2mA11+nS6fX4Boc0S1/tQWxsLFHV5fbWrFlz3LZrq2vDR0dH061bt5Ye2gnJkt1W9NtvvwFaquVhw4Y12W78+PHux+vXr2fSpEktPjbRevL/9z/Umk32ej2qzYapRw/UyiqKv/6GiL/9FYDsl1/BmpCANSGBnr/8DIDqdGJNPETxV1/X61Mx6Kk6eJCjs64DhwPVZkPx8kJtInNaZXUWW1tyMul/+j9t5lNViX71VSp37KDqYAK2pCScgwZS8OGHmGJjCbjsMvcYjg1GAfd7smdk4n3uufhfegmOzCycxcXa+547V2totaICjqwsytevx3/y5NP/YQpxklRV5ctXtlCYVcGgCTGMmxHv6SF1GCFRvpx/Qy9Wf3yw3vG0/UWUFVbhG2Rp4kohhKjP7O1N3IjRJGxaf8Z99Rwx+qzZPwqgKAqXX345//3vfzlw4AAbN25k1KhRDdpt3LjRPUN6+eWXoyhKgzatTQLSVrR//34AevbsicHQ9I++d+/eDa4RHUPVvn2Y4uLgt/X4jhtH+bp1qE4nitmMdd8+rAkJqKpKwNSp+F54AdaEhHrLdhW9nqgXXsCRnU35b7W/rMvXrqMoJqY20IUGZVvqssRrH8Z1/v7og4JwFhRg6tqVyh07KfxsEaU//ghAxdYtlP64EtCyAPuMHEnx8hOkY3c4qFi3jorq5elew4ZRuXWrNn6zGdXhwNSlC/qgILwbWbYuREtwuVSKc7V/Hzt/SSPrSAnTH206U6w4Nf3GRdNvXDR/LD/M5mXaqg8vPyNevscvPSCEEMcaNGlKswSkgyedfTe8H3roId577z2cTif3338/a9eurZdFt7Kykvvvvx8Ag8HAQw895KGR1icBaSupqqoir7q8Rd1Nx40JCgrCx8eH8vLyepuOT8aJNjFnZmaeUn+iedizsijfvJnMR//iPla2Zg1hD/8Z2969WIYOI3/uu2B3UPTpp5R89x29Nm8i7L77UBq5eeE7fny9gBTAa/BgdH7+qA47+sBALIMGUTjvA1SHg4rdu1FLSjCEh6Pz8SHmrX8BYAgKovvy73AWFQGQNGWqeymxVju0r1ZTVFVJufkWIp9+ioK57zX5Po09e2BPPFx/rOeeS9C112JLTdGy+BoM6CwyYyJal16vo1OPANITtBn77KQSXE4XOilL0qxGTOlB98ER5CSXEDciAr1Rfr5CiFPTud8AQmK6kJ92+vvQQzt3JabvgGYcVcv77bff6pV9rIkbABITE5k/f3699jfddFODPuLj43nkkUd4+eWX2bJlC2PHjuUvf/kLPXr04PDhw7zyyitsr14l98gjjxAX1zayoiuqepxpFNFscnNzCQ8PB2DmzJksWrTouO0jIiLIycmhf//+7N69+6Rf51Sm3VNTU08YHIszV7V/P0dnXovqcNQGe43wv+wyDKGhFHz4IZYBA4j94vN651WXi/z33sMYE4PfhRdycGjtsu+g2bMJmHYZXgOa/uWrqupx/37Y09M5PHmKe19n0K23EvnIwyRNn4F1T3XCJYsF6hRebpRer2X8rc4QHLvkGyx1Zv2F8JSK4io+/Mvv7ud9x3bigtmeT+YghBDHSktLc2c/9cTntUOHDuFwODAYDB4JWrKTEln8zGPYrSf4zNEIo9nCzGdeJqJ7zxYYWcu56aab+Oijj066fVMhnMvl4vbbb+eDDz5o8tpbb72V9957D91p7Ndtib8bcuuylVTV+RBvMp14CZPZbAa0qXXRvtnT01FttuMGowAly5ZRvGIF4Y8+StcFDX8hZf7tSXLf/BcZDz9C+uNP4D1qFCgK+vBwChcu5Og1M0m85FLSHnwItZHszCe6WWGMjib2m6/xnzIFAEeKtuxOqf67CJw4GAVC77uPHsu/w3/qVEIfuF+CUdFmeAdYuO6ZczD7aKsOyouPXzhcCCGEZ0R078m0h/+K0XxqK6qMZgvTHv5ruwtGm5NOp2PevHksX76cyy+/nKioKEwmE1FRUVx++eV8//33vP/++6cVjLYUWbLbSix1lijabCf+EGStnqWqu+77ZJxoiW9mZiYjj6k9KVqW74QJRDzxBADZb7wBx7nJ4MzKomTFCkJu0bIwV+7ahSEiEmNEOM7S2uygZdV7PAGcOTnaA1XFfvQo9qNHsd55B5Y66b9PxJ6TQ/bzL2CMicFe3V/Zut9Ie/gRqqr3fwLoQkNw5eU33onZjDkujuDrr0Pv70/0q/886dcXojWoLpX96zOJ6OZPeFd/+o+P9vSQhDiu7KREDvy+lv7nTyQkxvO1AoVoTd0GDmHmMy+z4p3XT2r5bmjnrlxyz5/abTA6f/78Bstyz8TkyZOZ3E4SR0pA2kr8/Pzcj8vKyk7Yvry8HABfX99Teh1Zgtv2KIpC8JzZAPiMHaPt06w5Z7FoS3mrl7ei1xN47UxclZXkvPkmhR8tQB8YSI+VP+IzahRlP/3UsH8/PwKnT8cQHk7J0qWYunTBfJwlFCUrV6LodPhddJH7WNHizyldqSUvinr1VVwVFVj37qX0u+9qL9Tpmg5GjUbi1qzGEBh4kj8VIVpf1pEStv+kfaiJjg/CJ8B8giuE8KzFzz6OvbKS9AN7ue751zw9HCFaXUT3ntz46juk7dvNjh+Xc+iPDah1Vpzp9Hp6jhjN4EmTiek7oE1kjBWnTgLSVmKxWAgJCSE/P/+EiYcKCwvdAWnN/gHRMZh79CDwmmso37gR/8mXophM5L31tvt8zH//g99555Fy2+2UV5cJcpaVUbl7Nzmvvtpon2p5OYUffoixc2d6/rTyuK9ftmYN6Q88CEDnee/jO3YsAD7jxlKwYAHG6Gj8Ljgfnbc3affdp11U84v/OEuOOz33rASjos0LivQmMMKbimIr0fFBnh6OEMf1+vVXaDcsgaKcbA+PRgjPURSFzv0G0rnfQKwVFZQV5GOrqsBk8cY3OOSsKu3SUUlA2or69u3LunXrSExMdG8GbkxNbSCAPn0k4UZH0+nZv7sfJ98wu965tLvupvO7/8VRnVnNGBVF5HPPUrF1a21NUbMZrHUK0VcHio6cHFSXC0dBAYpejyGo4QdunY+PlnCoun0N7yFD6PXHZkALgKv27iXs//5E1e497hIwph498Bo6BHtqGhWbN4FLRR8cTND117lrlArRlll8jFz/91GoLhVFJ3fRRdv1y4fvuYNRAL1ePq4JAVqdUglAO562s5v1LDBu3DhAW467tc6+vGOtWbPG/Xhs9QyW6JjC/u9PmHr0QFcTPLpcVO3bT+Tf/oo+KAjMZkq+X0H+f/5be1HdYBRQvLxAp0O1WjkyfQaJ487l0Ogx5M+bR+WOHRyePIWUW2+j8Isv8Bo8GL9LLwVVJfNvT5L59DPupS8V27dTtGQpee/8h7x33iH31dco/fFHAq68gm5ffYUtOZniL76kYuNGcGmZ3czx8YTdey+KXt8qPy8hmoMEo6It2/HT9+z44dt6x255c66HRiOEEC1Pbrm1oiuuuIKXXnoJgA8//JBzzjmnQRuXy8WCBQsACAwM5IILLmjVMYrW5T10KD2Wa/s0Cz/7DFtqGkE33EDmE0/gLCzEWVhI8ZEjx+2jbgZfW536VaWrVlO27jdsSUnYkpIoX78eV3k5pi7Vy8CdTooWLybwyitQvLxInnVdo/1X/PEHQdff4N7nauzaFXt6OqF33UnILbec6Y9ACCFEHas/+l+956Nm3IDRLPudhRAdlwSkrWjkyJGce+65rFu3jnnz5nHjjTcyevToem1ee+019u/fD8CDDz6I0Wj0xFCFBwTNmuV+7HvhBe4kQwC6wEAMERE4cnJwFRbWv9DpRBcSgt7HB3tmJgCKyUTlli0NXsNVXkHYAw9QuXMXFRs2aActXuS88WaT47KnpWNNSCDmv//BWVRMwOXTwOlEkb+bQgjR7CK6x5FxcB8A/S+YyNjp13p4REII0bIkIG1l//rXvxg7diyVlZVMmjSJJ554ggsuuIDKykoWLVrEe++9B0B8fDx//vOfPTxa4SmBV1yB3wUXULl3L8VLluA1cCDZz7/QZHtXfj6u/NoMuKrTWb+BooCqkvfuuwRMu4zg66+jYvNmTDExmLt0Rq06Qb1blxO/urP1bah2lRBCdCQzn3mJwox0FEVHcLRkzhdCdHwSkLayIUOGsHjxYm644QZKSkp4oro+ZV3x8fEsX768XqkYcfbRBwTgO2YMvmPG4KqooPSXX3Dk5mKO70X52rX4X3kFRZ9/AVYrhrAwHLm5+E2ahPc5I1HMZrL+9iQAfpdegqlLV/LnzgWXC9XhwO+ii4hf/xs6b2+KV/xAxcZNjY4haPZsfEadg9+ECa351oUQ4qyl0+kJieni6WEIIUSrkYDUAy677DJ27drFv/71L5YvX05aWhomk4mePXsyY8YM7rvvPrwlg5ioQ+ftTdcPP2xwPPzBh3AVF2GIisJVXIy+bukVhxNbSgqh99yNzscHS5/eGMLDMcfGArjbWhMOui8xDxqE3s8PR2Ym/pMvJezee1vybQkhhBBCiLOcoqqq6ulBiNaTlpbmrm2amppKTIwsBzrbuWw20v/vzygGA9FvvC5FpYUQQggP8/TntUOHDrlLFMbFxbXqa4u2rSX+bsgMqRBnOZ3JROd/v+3pYQghhBBCiLOQZCYRQgghhBBCCOEREpAKIYQQQgghhPAICUiFEEIIIYQQQniEBKRCCCGEEEIIITxCAlIhhBBCCCGEEB4hAakQQgghhBBCCI+QgFQIIYQQQgghhEdIQCqEEEIIIYQQwiMkIBVCCCGEEEII4RESkAohhBBCCCGEB+Xk5PDdd9/x1FNPcemllxIaGoqiKCiKwk033XRSfbhcLvbt28f8+fO55557GDFiBGaz2d3P6tWrW/Q9nC6DpwcghBBCCCGEEGeziIiIM+5j4cKFJx28tiUyQyqEEEIIIYQQbUSXLl2YNGnSKV+nqqr7sdFoZOjQoQwYMKA5h9YiJCAVQgghhBBCtHkOm42KwgJKc7OpKCzAYbN5ekjN5qmnnmLZsmVkZWWRnJzM3LlzT7mPvn378tZbb7FhwwZKSkrYunUrV111VQuMtnnJkl0hhBBCCCFEm6SqKsWZ6WTt30N+chLUmQVEUQjp2p3IPv0J6BSNoiieG+gZ+vvf/37GfYwcOZKRI0c2w2halwSkQgghhBBCiDanLC+XQ2t+pqKooPEGqkr+0cPkHz2Md2AwceMvwjc0rHUHKc6YLNkVQgghhBBCtClF6ansXv5108HoMSqKCti9/GuK0lNbeGSiuUlAKoQQQgghhGgzyvJy2f/z97gcjlO6zuVwsP/n7ynLy22hkYmWIAGpEEIIIYQQok1QVZVDa34+5WC0hsvh4NDan+tlnBVtm+whFUII0ez2/7yCguQk9EYTo+bc7unhCCGEaCeKM9NPepluUyoKCyjJyiCgU3QzjUq0JJkhFUII0ewKkpMAcNptrJ/3Dgd+XkFRhuzrEUIIcXxZ+/c0Sz+Z+3c3Sz+i5UlAKoQQotkpOn295/nJSRxa84uHRiOEEKI9cNhsWmmXZpB/NKlD1SntyCQgFUII0exG33Rng2O2inLZ0yOEEKJJtvKy+nVGz4SqYqsoa56+RIuSgFQIIUSzUxSFsbfei8nXv97x3z/4DxWFZ7Y3SAghRMfkdNibtz978/YnWoYEpEIIIVrMiJmz8QmpX6R8+9efkXVwv4dGJIQQoq3SG4zN25+xefsTLUMCUiGEEC1q8BXXgKLUO3b4t1/5Y/EC0ndv99CohBBCtDUmH98G/1+cLkXRYfL2bZa+RMuSgFQIIUSLG3vLPRh9fOods5WV8v/t3XtU1XW+//HX3tzvKigiiOAVSbNUTDPzljZq6pB2PaZ2ukzTmc7U6jSd05zV1FkznZrqdFpzzprRsdTqlL9GzUqzdMpLmmaomYU3VExJEFBE7mzYvz8+7r1BQECB7waej7X24ru/t/3euP3Ci8/n+/lk7vpKx3Zus6gqAIA38fX3V2Sfvi1yrm4JifL192+Rc6F1EUgBAG1i1N0LNeKO+yR77R89p9O/a/IE6FWO6tYoDQDgJXoOHtIi54kZPLRFzoPW52t1AQCAziMwPFxj7/+ldr27VJWlJWal06nz2T+pa1z8ZY/99u8/avvKDCVeF6WCnBIV5JSoS3Sw5jw1QgHB3CcEAB1BREysgrt0U0nBlQ+AF9y1m8J79mrBqlrftm3blJGR4X6el5fnXs7IyNCyZctq7b9w4cJ6z3Ppft9++617+dNPP1VmZqb7ef/+/XXTTTddackthkAKAGhzo+69X6WFBTqwYZ18AwIV1iO60WMy9+dLkn784ayqKk1L6bnTJco7WaTYQV1btV4AQNuw2WwaMP4W7V+3usm9Z2qy+/pqwM23yNZC96K2lSVLlmj58uX1btu+fbu2b99ea11DgfT+++9v8DVeeumlWs8XLFhAIAUAdF5B4V00fO4/NHn/Man9tHt9phKv665je84oJ7NQvZO7KaZ/RCtWCQBoa6FR3TX4luk68PdPmhVK7b6+GnzLdIVGdW98Z3gNm5NZyjuVU6dOqXfv3pKkkydPKi4uzuKKAAAAUJPVv68dOXJEDodDvr6+GjBgQJu+dk1Febk6suXvTeq+G9y1mwbcfAthtJW1xmeDFlIAAAAAXic0qruuu/1uFWb/pNPp+5V/4phUoy3NZrOrW0KiYgYPVXjPXu2umy4MAikAAAAAr2Sz2RQRE6uImFg5KipUUVKkqspK+fj5yT84lKldOgACKQAAAACv5+vvL1//blaXgRbGPKQAAAAAAEsQSAEAAAAAliCQAgAAAAAsQSAFAAAAAFiCQAoAAAAAsASBFAAAAABgCQIpAAAAAMASBFIAAAAAgCUIpAAAAAAASxBIAQAAAACWIJACAAAAACxBIAUAAAAAWIJACgAAAACwBIEUAAAAAGAJAikAAAAAwBIEUgAAAACAJQikAAAAAGChM2fOaO3atXr22Wc1bdo0RUVFyWazyWazaeHChU06R0lJiVavXq1f/vKXSklJUdeuXeXn56fIyEiNGTNGzz33nLKzs1v3jVwBX6sLAAAAAIDOLDo6+qqO/+677zR27FgVFRXV2Xb27Fnt3LlTO3fu1GuvvabFixfrrrvuuqrXa0kEUgAAAADwEvHx8UpKStKGDRuafExhYaE7jI4dO1a33XabRo4cqcjISOXm5mr16tX661//qsLCQv3DP/yDwsPDNW3atNZ6C81CIAUAAAAACz377LNKSUlRSkqKoqOjlZmZqcTExCYfb7fbdeedd+p3v/udkpOT62yfOnWqpk2bptTUVFVVVemxxx7TkSNHZLPZWvJtXBECKQCgfSovl955S7LbpYX/KHnBD1UAQOupLnOo6ny5nBXVsvnb5RMRIHtgx4gzzz///FUdf+ONN+rGG2+87D6zZ8/W7bffrlWrVuno0aPau3evhg8fflWv2xI6xr8gAKDzWfamZ3nxX6Rf/NK6WgAArcLpdKr82HkV7/hJpen5UnWNjXYp6JoohYyOUUDfCK9o7fN2EydO1KpVqyRJR48eJZACAAAAQH0qsop09v1DcuSU1L9DtVS6P0+l+/PkGx2sbncOkn9saNsW2c6Ul5e7l318fCysxINpXwAA7dONYz3LyddYVwcAoMWVHTmn3EX7Gg6jl3DklCh30T6VHTnXypW1b1u2bHEvDx482MJKPGghBQC0T0OvNQ8AQIdSkVWk/LfT5ayobnznGpwV1cp/O13dfzGMltJ67Nu3T+vWrZMkDR061GsCKS2kAAAAALyC0+nU2fcPNTuMuo+vqDbHO50tXFn7Vl5ergcffFBVVVWSpD/84Q8WV+RBIAUAAADgFcqPnW9yN92GOHJKVH7sfAtV1DH86le/UlpamiRpwYIFmjlzpsUVeRBIAQAAAHiF4p2nveo8HcF//ud/asmSJZKklJQU/e///q/FFdVGIAUAdBwnMqWsLKurAABcgeoyh0p/yGuRc5X+kKfqMkeLnKs9W7RokZ555hlJUlJSkj755BOFhIRYXFVtDGoEAOgYjh+TNnxmlidMlAYlWVsPAKBZqs6X155n9GpUS1WFFbIHdt6489577+nRRx+VJPXp00cbN25UVFSUxVXVRQspAKBjsNf4kbZ5k3TsmHW1AACa7UoHMmrwfOVVLXq+9uSjjz7S/PnzVV1drZiYGH3++eeKi4uzuqx6EUgBAB1DnwQpIMDz/NBBy0oBADSfzb9lo4ktwKdFz9defP7557rzzjvlcDgUGRmpjRs3ql+/flaX1SACKQCg46jZSmrnRxwAtCc+EQEtl07sNvmE+7fQydqPr776SrNnz1Z5ebkiIiL02Wef6ZprrrG6rMvipzUAoOPo29+zXHJ10wYAANqWPdBXQde0zD2OQddEdrr7R7/99lvNmDFDxcXFCgkJ0bp16zRixAiry2pU5/pXAgB0bEczPMuDBllXBwDgioSMjlHp/qsfaTdkdEwLVNN2tm3bpowMz8+wvDzP9yAjI0PLli2rtf/ChQtrPT969KhuvfVWFRQUSJJ+//vfKyIiQt9//32Dr9mjRw/16NHjqmu/WgRSAEDH0SVCyi6V+veXkr27ixIAoK6AvhHyjQ6WI+fKe7n4RgcroG9EC1bV+pYsWaLly5fXu2379u3avn17rXWXBtIvv/xSZ86ccT9/4oknGn3N3/3ud3ruueeaXWtLI5ACANq/7NPSgXRp+AgpMFCK6m51RQCAK2Cz2dTtzkHKXbTvikbdtfnb1e3OQbLZbK1QHVoD95ACANq/rVukw4elr7ZL3XtI/CICAO2Wf2yoIu9LbvaouzZ/uyLvS5Z/bGgrVdZ6li1bJqfT2eTHpRYuXNis451Op1e0jkoEUgBARxDTy3zteck9QydPSqdOtX09AICrEjigq7r/Yph8o4ObtL9vdLC6/2KYAgd0beXK0NLosgsAaP/G3SyNTJGCgjzrTp6UPllrlmfOknrFWlMbAOCK+MeGKvrx4So/dl7FO0+r9Ic8qWYvXrtNQddEKmR0jAL6RtBNt50ikAIAOoaaYVSq3W3XZpMKC6WAAPMAALQLNptNgf26KLBfF1WXOVRVWCFneZVsAT7yCffvdFO7dET8CwIAOqa4ONMyarNJxcXSRx9KwcHSnXcTSgGgHbIH+hJAOyDuIQUAtB+FhdKhg1JFRdP27xVr7i+9OC+bSkqksrJWKw8AADQPf2IAAHi36mopM1Pq2kVat9a0dp48Kd0ypWnHO53SwEFm+UyOtOJdqW8/acrUuq9j5++0AAC0JQIpAMC7fbtX+mZX7XVncqTycjP3aHS0Z5Td+mz4TMo8buYndbWsHjsqHTksDRgoVVVJH62R8vKkqbdKfRJa650AAIBL8KdgAIB3O3eu7jofH+nrneaxbq1UWVl7e2WltOkL6cut0k9ZZl1ZmeTn59nnp5/M15IS6cwZ00J68mTrvAcAAFAvWkgBAN6turruur79JfvFUXR9/aTiIqnLxbnnysulPbulw4fM89g4qeiCOc/NE6SfTklFxdLwEWZ7WJh0w2gp94w07LrWfjcAAKAGAikAwHul7ZJOZNZdvydNSkiUEhOl48fNCLr3LTAj6n6wSjp/3rNv7hnp/gckh8N0zc3Pl8LDpYMHpJRRZp/rrm+LdwMAAC5Bl10AgHfKy5N27zb3eNbke/FvqT9lSVWXtJ6e/skTRu12M83LtcPM8+JiKTfXtJQWFJhW1Eu7+gIAgDZFCykAwDuFhpqBiC6dpiU0VLL7SGfzzT2fw66XkpNN62jNgFldbeYhdXXljYgwLaI/njChNT6+9j2lAACgzRFIAQDeKTBQmp0q/b/3aq+PjTP3fe7cITmrpX17pZ49pVMnzf2jiX3NPaO94z1h1GX4CM+9o/WpqJC2bzNB9caxTAMDoFPy8fGRw+FQVVWVqqurZedaCEnV1dWquthrycfHp8XOSyAFAHivS1tHbTYpuqc0YIBUcE46eNCs/3KLGS3XZcpUM9doYyorpZM/Sj1jTPfejCOewZDi46X4Pi3zPgCgHQkMDFR5ebmcTqeKiooUHh5udUnwAkVFRXI6nZKkoKCgFjsvf+4AAHivoKDarZROp/TdPnM/6OHDnvWu+UUlE1rDmvjL04ZPpY0bpA8/MM97xpiW2eBgKSen9uBIANBJ1Ayg2dnZKiwsVHV9I56jU6iurlZhYaGys7Pd68LCwlrs/LSQAgC8V0SEdOdd0qefmhZRyXTX/XS9CaquX5CCgkxIra6WevSQundv2vlzcszXwkJp/3fS0Gul+Qul1SvNoEfHjkp33XN17yE3V/r0EykkREqdYwIzAHixkJAQBQUFqbS0VFVVVcrKypLNZmvRbppoP6qqqtwto5JpHQ0JCWmx8xNIAQDeLaKLVF1jpN3s01JpqWnJdA1kdOHiPaPOamn4SDOly9490jVDPKPs1ic2VsrMNMtfbTehdvQY6dzF8FtaevX1b99muhOXlEiL/2LWDRkqjb3p6s8NAK3AZrMpPj5eP/74o0ovXgedTqccDofFlcFqQUFBio+Pl60F/7hKIAUAeL9bpppWS8kTEgMCTCupKzwGBUkTJ5nlbV+aVs99+y4fSG+ZKv3wvbTjK/M8/QcTSINDpAuFpgtvQ44fN1PS9O8v/fSTtGO7uefUNbdpdbXZ3rOnlJNd+9jv95vXobUBgJey2+3q06ePiouLdeHCBXdrKTofHx8fBQUFKSwsTCEhIS0aRiUCKQCgPUj7pu668+dNC2liX9NFN/kaz7Zhw0wLaa9YaeNnZltsXN1z+PiYwYtcgbSyUtr0hVR+cTClmvemuhzNkLKzTaiUJB+7uZ81L888ho8wYfSDVabGlFG1uxe7fLVNGje++d8LAGgjNptNoaGhCg0NtboUdGAEUgCAd6qqkrZuMVO4/PRT7W0+PmaAo+pqs33qrbW3DxxkHv/vPamgQDp7tv57QZ3O2qPzSmaUXddff4uLTDde170ym77wjMLr4u8vDRok5Z4xLaQ+PlJRkaflNiOjbhiVpPR0E6bjejfp2wEAQEdEIAUAeKfTp+uGPxcfH2nkKCnzmHTd9Q2fI663CaS94+tuy8mRPllrWkUvZbOZsFpYKL33f9IDD0kOh3TsmGefa4dJ/Qd4BlBKSPRsi4iQxtwo5edLAwdKX++UQsOk48dqv05WFoEUANCpEUgBAN6pe3cpqrtppQwJlQrPe7rQVlRIe9Kke+dJfn4Nn2PsTdKoG8w++XnSZ5+aKWGmTZdOnay/S65kWj1dc6BWVZnlnBzJUSO8frfPjMw7fIQ0MsWsO3dO2vCZFBoiTf2Zp7ZukdKhg7VfIyhIShrc/O8LAAAdCIEUAOCdAgKkn6dKby2T8nLrbq+oMGGxvkDqcJjW1W6RZlAhybRuXrhgHufOmn1Cw6QqR93RdF1hVLrY8umUwkLr3gvqdJrXKSmRSkvMiMAF58wjP9/z2jUHNQoLl+66mwGNAAAQgRQA4M0qKyWbvfa6XrFmIKLuPczUL/XZnSZ9u9cEyHvnmXtAByWZbsDh4ZJsZrsk9ep1+eldgoOlt98y4TMkVCorNUHY5cIF6UB67f1jetWeC3X8BOngQTPNTJ8EwigAABcRSAEA3mv9OjPibWycadUsKZFsMoMcHTokhYeZ+zWHXW+CoGRaLDMyzHJ1tbR6lXTLFKlLF2niRNNCWVFhjisqMt1mLx00qab0HzzLxUWN12yzmderqWfM5aeQAQCgkyKQAgC8V9HFAGi3S6GhJpDm5JjutpIJqZK0f7+Z2uWG0WYk3JpKiqWP1ni626aMkq4ZYloqAwKkzEzJ19dzTpfkZCk314zQ29jce67j/fxMq25OjhQdfZVvHgCAjs/e+C4AAFgk5OLcd7Gx0s+mSQMGeoKjq0VUMt1pf/heOvmj6YLr0rOnaQmVPPd+njghfbPLDEr0zS7p2NHaYdRul3x8pcR+0u1zPa9jr/EjMzxC6tbN89zhMPtVVprW1xOZZt2e3VLm8Rb5VgAA0BERSAEA3snh8AxmdOaMFBQsjRxpRt6N6y3dfW/t+zQlM+ptdra5t7R7DzNoUUQXz/bAQOlMjpR1qu7r+fubr9XVZqCjTZ9Ln62X/APM+pqDJyUkSLfN8sxXKnnmM42NkwYnSx+sNoH3s089LbkAAKAWuuwCALyT3S5FRZl5ROP7SO+vMF14p9/mGb22a1fTrdYl++JotmVl5pF7pvY5XdO5FBbWXh8QYLbVnAampMR053XVUl5ulm8cKw291izfPF7a962p0aW4yLTYFl0wz202yc//yr4HAAB0cARSAIB3Kiz0hM2TP5o5PiXTutmzpwmP+flmxNqa93jabCYQXqpbN9PaWVhYe+oWyYRNV+C02805oqM9gx35+pp1AQFS/wFm39xc04U4abCUni59ucXsW1AgHTxgBjb64Qdzb2toaIt9WwAA6EgIpAAA7+Tv7xmIyOGQrrveBNC8PCn9e+nQYfP8Uk6n6aZ7vqD2+vg+nqleGuLnJ82c7ekKXFFhAnBklBQW5umiu2a1GbioX38TPC9tcd27R+o109z3CgAAGkQgBQB4p9wzNQYiyjTddx0O01ra2EBBheel0WOknTvM88goM3/p/u8uP2JudE8TZC8USn37mVCc2Nds+2q7dOigNHSY6fYrSWfzTTg9c6buuVxddgEAQIMIpAAA7+NwSBs31F63O82zHBJad07Qml13nU7TgulyNl/6ZK3Uu7d08mT9rxnVXUpIlD7/u3k+Y6bpqnv4kBlcydV9eN9eac4dZo5UVzfiS9ls5lwAAOCyGGUXAOB9HI7Lt2QWF9WehkUy+/vWGAnX1Z3XbvfcU1ozjI4cWXvqmIJznilibDYpwF/6fKN0IL32wElhYea4oksCcU2X1gYAAOrFT0wAgPfx9298ICBXd974eBMAExKkIUPMuqAg021X8oTRoKDaxw8faaZncamqMgMfzZlrWkC79zDdhF31uJw7J/3t/boDI9U0bpyZYgYAAFwWXXbb0IQJE7Rly5Ym7eusb4RIAOgs7HZp7p0mVO7ebe4hrU9snDR5iula62oJ7dtPqq6SPlkn2eySs9oMTuRweI4LDjatoMNHSBkZ5r5Rp9PMG3runDRmjNlvyq3ShQvST1nS1hrX78buD6059ykAAGgQgRQA4J0CAkwr5ckf699us5ntS9+QEhOlqT+7uO7iCLnz5pvn5eUm2H70oefYaTPMV7tdqqzwnO/gAbO8O006kyvJKc36ubkfdOslf1B0TS/j62vCbq9epsXV10/qGdNS3wUAADo0AqkFRo4cqaVLl1pdBgC0D66pXyTp3nnSvm+lH743YfBYhll/PFP68APpZ9NNkJXMFC6SJzDKJskpJSV5uuJKZvTdjCO15y49fdrTorrra2nipLp1ufYPD5duGmcGRfLzq7sfAABoEIHUAiEhIRrius8JAFDXzh1mapcbx5qgl33arC8qklJGmZBps9WYV9QpZWebVsyuXc39pFHdPec7k2P2kaSsLOnoUalfP/P8miHmtex207VXqt2990SmtGWzFBwilRTXrvOG0VL/AY3f7woAAOpFIAUAeBen07SCSqYldPoMKe0bMyhRzMWusKPHmHs9XfOKRl5s8Tx21HzdnWbuD00ZZZ4n9pWST0uHD5t7Qvfu8QTSnGwzmFFML8/rXur4sbrrIiKk665viXcMAECnRSAFAHgXm80EvczjpvXSz08ac2Pd/bp2le66R6pySF26SmfOmPtEqy62bp4/79nXx0dKvkYKDDL3iSZfHF3X6ZS+3mm+BgRIAwZKRw6b16ysrL++wEBT14CBLfu+AQDohAikAADvc8No82hMWJj5evCguZ/01p+ZUXVzckxodPlqu/T9fik6WrpvgWe9zWYGIjqaYYKmqyX00jDqH2DuPbXZpGuH1Z6/FAAAXDECqQUOHjyoG264QYcOHVJZWZmioqI0YsQIzZkzR/fcc4/8rmJQjFOnTl12++nTp6/43ABgKYdD+nKr6aJ78/jac4Nuu7i+2indNlOK71P7WNecpDVbTV1uGicdOigdOWICp2RaS8vLPfskJtbfSgsAAK4KgdQCOTk5ysnJcT/PyspSVlaWPvroI7300ktauXKlBg8efEXn7t27d0uVCQDe5eSP0uFDZjkuTkqqcZ3s28+0ciYm1n/sTTdLB9KlPn2kNR+YFtTAANM1t3sPE2Ylz8i5ffuZaVzsPibMDk5uvfcFAEAnRiBtQ3a7XZMnT9b06dM1bNgwRUZG6sKFC9qzZ48WLVqkAwcOKD09XRMnTtSuXbsUHx9vdckA4D16RJspVqqqTOvlD9+b+0JtNmnSZDM1i6uF81JhYWaAo21fmkGMJKmszDwuXKh///4DWu+9AAAASZLN6aw58RpaU0FBgbp06VLvtsrKSj300ENavny5JCk1NVWrV69u9ms0pcvuqFFm1MmTJ08qLi6u2a8BAJa4UCitXmW67rqmZYmOlmanmvtDq6tNC+nePVJsXN1AmfaNGX23MdcPl0bd0PL1A0ATnTp1yt3rjd/X0NERSC9ha+iv682wdOlSLVy4sNnHORwODRkyRIcOmS5pp06dUmxs7FXXUxMXOADtVuZx6bNP666vOSJudE9PC+i06VJQsLT9SzN/aXFx3WMlM6fp9BlmntHw8IZbWQGgjfD7GjoTu9UFwMPX11cPPPCA+/mWLVssrAYAvEx8HzMdzNBrpYGDPOtrjogbHu5Z3rlTWr/O3C/aUBiVTGvrwYNmXlHCKAAAbYp7SC9x4MCBqz5HjGvi9iuQnOwZOCMrK+uqawGADsNu90wFc+KEZ4Cjmk6dNC2dpSXSubOXP5+fnzlnebkJowAAoM0RSC+RlJRk6eu3RJdhAOjw+vQx07ucL5C2bfOMjlta2vRz3H2v6a574YIUGdkqZQIAgMsjkHqZ9PR093KvXr0srAQAvFxsnHlEdZcyMqT935n1NpsUFGRaPl3TudQnONh8JYwCAGAZAqkXcTgcevPNN93Pb775ZgurAYB2oke0efTtJ+XnSYOSTHfd1asuf5zDYVpIAQCAZfhJ3EY2bdqk66+/vtFpX1z3sM6cOdM9uhoAoAl69jQPSYqMMgMfXSiUJkwywXPTF1L2adOC2rcfYRQAAC/AT+M2snz5cs2aNUuzZs3ShAkTNGjQIIWHh6uoqEi7d+/W4sWL3d11e/Tooddff93iigGgHbPbpYmTaq+bcZs1tQAAgAYRSNtQUVGR3n33Xb377rsN7jN06FCtWLFCiYmJbVgZAAAAALQ9Amkbefrpp3Xddddpx44dSk9PV25urs6ePauAgABFR0dr5MiRmjt3rlJTU+Xj42N1uQAAAADQ6gikbWTw4MEaPHiwHn/8catLAQAAAACvYLe6AAAAAABA50QgBQAAAABYgkAKAAAAALAEgRQAAAAAYAkCKQAAAADAEgRSAAAAAIAlCKQAAAAAAEsQSAEAAAAAliCQAgAAAAAsQSAFAAAAAFiCQAoAAAAAsASBFAAAAABgCQIpAAAAAMASBFIAAAAAgCUIpAAAAAAASxBIAQAAAACWIJACAAAAACxBIAUAAAAAWIJACgAAAACwBIEUAAAAAGAJX6sLQNtyOBzu5dOnT1tYCQAAAOpT83e0mr+7AR0RgbSTyc3NdS+PGjXKwkoAAADQmNzcXCUkJFhdBtBq6LILAAAAALCEzel0Oq0uAm2nrKxM+/fvlyR1795dvr4dv5H89OnT7tbgXbt2KSYmxuKK4C34bKAhfDZQHz4XaEhLfzYcDoe7V9vQoUMVGBh41TUC3qrjpxHUEhgYqJSUFKvLsExMTIzi4uKsLgNeiM8GGsJnA/Xhc4GGtNRng2666CzosgsAAAAAsASBFAAAAABgCQIpAAAAAMASBFIAAAAAgCUIpAAAAAAASxBIAQAAAACWIJACAAAAACxhczqdTquLAAAAAAB0PrSQAgAAAAAsQSAFAAAAAFiCQAoAAAAAsASBFAAAAABgCQIpAAAAAMASBFIAAAAAgCUIpAAAAAAASxBIAQAAAACWIJACAAAAACxBIAUAAAAAWIJAik5vwoQJstlsTXqg4zhx4oSefPJJJSUlKSQkRN26dVNKSopefvlllZSUWF0e2lhTrwETJkywulS0oDNnzmjt2rV69tlnNW3aNEVFRbn/rRcuXNjs861fv16pqamKi4tTQECA4uLilJqaqvXr17d88Wg1LfG5WLZsWZOvK8uWLWvV9wN4O1+rCwCAtvbxxx9r3rx5KiwsdK8rKSlRWlqa0tLStGTJEq1bt079+/e3sEoArS06OrpFzlNdXa2HH35Yb7zxRq31WVlZysrK0po1a/Tggw9q0aJFsttpC/B2LfW5ANA0BFLgopEjR2rp0qVWl4FWtnfvXt11110qLS1VaGio/u3f/k0TJ05UaWmpVqxYob/+9a86fPiwZsyYobS0NIWFhVldMtrQL3/5Sz366KMNbg8JCWnDatCW4uPjlZSUpA0bNjT72N/+9rfuMHr99dfrN7/5jfr166ejR4/qj3/8o/bu3aslS5aoe/fueuGFF1q6dLSiq/lcuHz22Wfq1atXg9vj4uKu+NxAR0AgBS4KCQnRkCFDrC4DrezXv/61SktL5evrqw0bNmjMmDHubZMmTdKAAQP0m9/8RocPH9arr76q5557zrpi0eZ69OjBdaATefbZZ5WSkqKUlBRFR0crMzNTiYmJzTrH4cOH9corr0gyf9jcunWrgoKCJEkpKSmaNWuWxo8fr7S0NL388sv6x3/8R3pfeLmW+FzUNHDgQCUkJLRcgUAHQ78RAJ3Grl279OWXX0qSHnjggVph1OXJJ5/U4MGDJUmvv/66Kisr27RGAG3n+eef12233XZVXTT/+7//Ww6HQ5L0pz/9yR1GXYKDg/WnP/1JkuRwOPTaa69decFoEy3xuQDQdARSAJ3GmjVr3Mv3339/vfvY7XbNnz9fklRQUKBNmza1RWkA2iGn06kPP/xQkpSUlKTRo0fXu9/o0aM1aNAgSdKHH34op9PZZjUCgLcjkALoNLZt2ybJdM8eMWJEg/uNHz/evbx9+/ZWrwtA+3T8+HH99NNPkmpfN+rj2p6VlaXMzMzWLg0A2g0CKXDRwYMHdcMNN6hLly4KDAxUXFycZs+erbfeeotumx3EgQMHJEn9+/eXr2/Dt9AnJSXVOQadw9/+9jclJycrODhYYWFhGjBggBYsWEBLOeqVnp7uXq553agP15XO6/7771evXr3k7++vqKgojR49Wv/+7/+urKwsq0sDvAKBFLgoJydHu3bt0vnz51VeXq6srCx99NFHWrBgga677jp+gWjnysrKlJeXJ6nxEQ27du3qHk315MmTrV4bvEd6eroOHDig0tJSFRUVKSMjQ2+99ZYmTZqk1NRUnT9/3uoS4UVOnTrlXm7sutK7d2/3MteVzmXz5s06ffq0KisrlZ+fr6+//lp/+MMf1L9/fy1atMjq8gDLMcouOj273a7Jkydr+vTpGjZsmCIjI3XhwgXt2bNHixYt0oEDB5Senq6JEydq165dio+Pt7pkXIELFy64l0NDQxvdPyQkRMXFxSoqKmrNsuAlgoODNWvWLE2ePFlJSUkKDQ1Vbm6utmzZor/85S/Kz8/XmjVrNHv2bG3cuFF+fn5Wlwwv0JzrSs0pg7iudA59+/bV7bffrjFjxrj/IHHs2DGtWrVKK1euVFlZmR555BHZbDY9/PDDFlcLWIdAik5v9erV6tKlS53148aN06OPPqqHHnpIy5cvV05Ojh5//HGtXr267YvEVSsrK3Mv+/v7N7p/QECAJKm0tLTVaoL3yMrKqvc6MGXKFD322GOaNm2a9u7dqy1btujPf/6z/vmf/7nti4TXac51xXVNkbiudAapqalasGCBbDZbrfUpKSm66667tHbtWt1+++2qrKzUE088oVmzZqlnz54WVQtYiy67aBdsNttVP5YtW1bvuev7JdTFz89PS5YscY+O+MEHH3DPRzsVGBjoXq6oqGh0//LyckmqM4UDOqbLXQeio6O1cuVKd6uoawoPoDnXFdc1ReK60hlERETUCaM13XbbbXr22WclSSUlJXrjjTfaqjTA6xBIgUb4+vrqgQcecD/fsmWLhdXgSoWFhbmXm9Jdrri4WFLTuvei4+vbt6+mTJkiScrIyHCPrIrOrTnXFdc1ReK6AuPhhx92h1Z+t0BnRpddtAstMaBQTEzMFR+bnJzsXqaFtH0KDAxUZGSk8vPzaw1EUp9z5865f3msORAJOrfk5GR98sknksx1oFevXhZXBKvVHMiosetKzYGMuK5Aknr06KHIyEjl5eXxuwU6NQIp2oXGhtNvbZfrdoP2Izk5WV9++aUyMjLkcDganPrl4MGD7uXBgwe3VXnwclwHcKmaf6ysed2oD9cV1IfrCkCXXaBJas41R6tI+3XTTTdJMl3ndu/e3eB+NbtOjR07ttXrQvvAdQCXSkxMdH8WGutyuXXrVklSbGysEhISWrs0tAO5ubnu6ci4pqAzI5ACjXA4HHrzzTfdz2+++WYLq8HV+PnPf+5eXrp0ab37VFdX66233pJkBrqZOHFiW5QGL3f8+HFt3LhRktSvXz/FxsZaXBG8gc1m0+zZsyWZFtCdO3fWu9/OnTvdLaSzZ8+mVQySpMWLF8vpdEqSxo8fb3E1gHUIpOjUNm3apIKCgga3V1ZW6sEHH3Tfwzpz5kzu/WnHRo0apXHjxkmS3njjDe3YsaPOPq+++qr73/vXv/418012Ah9//LEcDkeD23NycjRnzhz3KKqPPvpoW5WGduDxxx+Xj4+PJOmxxx6rM6VLaWmpHnvsMUlmkLzHH3+8rUtEG8vMzNTevXsvu8/atWv1H//xH5LMqMv3339/W5QGeCXuIUWntnz5cs2aNUuzZs3ShAkTNGjQIIWHh6uoqEi7d+/W4sWL3d30evTooddff93iinG1Xn/9dY0dO1alpaWaOnWqnnnmGU2cOFGlpaVasWKFFi9eLEkaOHCgnnzySYurRVt47LHHVFlZqTlz5mjMmDFKSEhQUFCQ8vLytHnzZi1atMjdre6mm27SP/3TP1lcMVrKtm3blJGR4X7u+neWzGjKl04XtnDhwjrnGDhwoJ566im9+OKLSktL09ixY/X000+rX79+Onr0qF566SV3OHnqqac0YMCAVnkvaDlX+7nIzMzUxIkTNWbMGM2cOVPDhg1Tjx49JEnHjh3TypUrtXLlSnfr6CuvvEKvC3RqNqfrfwPQCS1cuFDLly9vdL+hQ4dqxYoVtQawQPv18ccfa968eSosLKx3+8CBA7Vu3Tr179+/jSuDFRISEnTixIlG95szZ46WLFly2TlL0b409WeAS0O/MlVXV+uhhx6qdXvHpR544AEtXrxYdjud07zd1X4uNm/e3KTbPYKDg/Xaa6/p4YcfbnaNQEdCCyk6taefflrXXXedduzYofT0dOXm5urs2bMKCAhQdHS0Ro4cqblz5yo1NdXdJQvt38yZM/Xdd9/p9ddf17p163Tq1Cn5+/urf//+uuOOO/SrX/1KwcHBVpeJNrJ8+XJt2bJFO3bs0LFjx5SXl6fCwkKFhoaqd+/euvHGG7VgwQKNGTPG6lLhpex2u9544w3NmTNHixcv1jfffKO8vDxFRUUpJSVFv/jFLzRt2jSry0QbGTFihN555x3t2LFDaWlpOn36tPLy8uRwONS1a1ddc801mjx5sh588EF3yynQmdFCCgAAAACwBP1GAAAAAACWIJACAAAAACxBIAUAAAAAWIJACgAAAACwBIEUAAAAAGAJAikAAAAAwBIEUgAAAACAJQikAAAAAABLEEgBAAAAAJYgkAIAAAAALEEgBQAAAABYgkAKAAAAALAEgRQAAAAAYAkCKQAAAADAEgRSAAAAAIAlCKQAAAAAAEsQSAEAAAAAliCQAkAntXDhQtlsNtlsNmVmZjbpmISEBNlsNiUkJNS73XU+12Pr1q1NOu+UKVNqHffcc8816bji4mKFhYW5j3vhhReadFx9tboe/v7+io6O1uTJk/XKK6/o3LlzTT5nfU6ePKlVq1bpX//1XzVp0iRFREQ0+30CANBREUgBAK3mnXfeaXS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" ] diff --git a/gitk/models/pretrained.py b/gitk/models/pretrained.py index c2f68f91..47f15620 100644 --- a/gitk/models/pretrained.py +++ b/gitk/models/pretrained.py @@ -2,6 +2,7 @@ import scanpy as sc import numpy as np +from tqdm import tqdm from huggingface_hub import hf_hub_download from .. import scembed @@ -79,7 +80,7 @@ def encode( tokenized = self._tokenizer.tokenize(data) embeddings = [] - for region_set in tokenized: + for region_set in tqdm(tokenized, total=len(tokenized)): if len(region_set) == 0: raise ValueError( "Encountered empty region set. Please check your input." diff --git a/gitk/scembed/projection.py b/gitk/scembed/projection.py deleted file mode 100644 index 01512c7f..00000000 --- a/gitk/scembed/projection.py +++ /dev/null @@ -1,355 +0,0 @@ -import logging -import os -from typing import Dict, List, Any - -import numpy as np -import scanpy as sc -import seaborn as sns -import matplotlib.pyplot as plt -import umap -import yacman -from tqdm import tqdm - -from .const import MODEL_CACHE_DIR, MODEL_CONFIG_FILE_NAME, MODEL_HUB_URL, MODULE_NAME -from .models import ModelCard, Universe -from .utils import ( - anndata_to_regionsets, - check_model_exists_on_hub, - download_remote_model, - generate_var_conversion_map, - load_scembed_model, - load_universe_file, -) - -_LOGGER = logging.getLogger(MODULE_NAME) - - -class Projector: - def __init__(self, path_to_model: str, no_cache: bool = False): - """ - Initialize a projector object. - - :param str path_to_model: The path to the model. This can be a local path or a registry name on the model hub. - :param bool no_cache: If True, will not use the cache directory. - """ - self.path_to_model = path_to_model - self.model_config: ModelCard = None - self.model: Dict[str, np.ndarray] = None - self.universe: Universe = None - self.no_cache = no_cache - self._load_model() - - def _make_cache_dir(self): - """ - Make the cache directory if it doesn't exist. - """ - _LOGGER.debug(f"Making cache directory: {MODEL_CACHE_DIR}") - if not os.path.exists(MODEL_CACHE_DIR): - os.mkdir(MODEL_CACHE_DIR) - - def _load_local_model(self, path: str): - """ - Will load in a local model from the path_to_model attribute. It - is assumed that this is a pickel-dumped scembed.SCEmbed() model using - the model.save_model() method. - - The `path` parameter should be a path to a config `yaml` file that - contains the path to the model. - - :param str path: The path to the model. - """ - _LOGGER.debug(f"Loading local model from {path}") - # read yaml file - yacmap = yacman.YacAttMap(filepath=path) - self.model_config = ModelCard(**yacmap.to_dict()["model"]) - - # load in the scEmbed model, build path to weights. It sits - # next to the config file. - _model = load_scembed_model( - os.path.join(os.path.dirname(path), self.model_config.path_to_weights) - ) - - # load in the universe - path_to_universe = os.path.join( - os.path.dirname(path), self.model_config.path_to_universe - ) - universe_regions = load_universe_file(path_to_universe) - self.universe = Universe( - reference=self.model_config.reference, regions=universe_regions - ) - - # we only want the region2vec dictionary from this - self.model = _model.region2vec - - def _load_remote_model(self, registry: str): - """ - Load a model from the model-hub using the reigstry path. - """ - # look for model in cache, should be at cache/registry - local_model_path = os.path.join( - MODEL_CACHE_DIR, registry, MODEL_CONFIG_FILE_NAME - ) - - # if exists and no_cache is False, load it - if os.path.exists(local_model_path) and not self.no_cache: - self._load_local_model(local_model_path) - return - - # check model exists in the hub first - if not check_model_exists_on_hub(registry): - raise ValueError( - f"Model {registry} does not exist in the model-hub. Please check the registry at {MODEL_HUB_URL}." - ) - - # download model - download_remote_model(registry, MODEL_CACHE_DIR) - - # load model once downloaded - self._load_local_model(local_model_path) - - def _load_model(self): - """ - Load the model from the path_to_model attribute. - - Order of operations: - 1. look for local model - this should be a path to a model.yaml - 2. look for model in cache - 3. download model from big.databio.org - """ - # first just check if this is a local model - if os.path.exists(self.path_to_model): - self._load_local_model(self.path_to_model) - # else, assume its on the model-hub - else: - self._load_remote_model(self.path_to_model) - - def summarize(self): - """ - Summarize the model. - """ - print(self.model_config) - - def convert_to_universe(self, adata: sc.AnnData) -> sc.AnnData: - """ - Converts the consensus peak set (.var) attributes of the AnnData object - to a universe representation. This is done through interval overlap - analysis with bedtools. - - For each region in the `.var` attribute of the AnnData object, we - either 1) map it to a region in the universe, or 2) map it to `None`. - If it is mapped to `None`, it is not in the universe and will be dropped - from the AnnData object. If it is mapped to a region, it will be updated - to the region in the universe for downstream analysis. - """ - # ensure adata has chr, start, and end - if not all([x in adata.var.columns for x in ["chr", "start", "end"]]): - raise ValueError( - "AnnData object must have `chr`, `start`, and `end` columns in .var" - ) - - # create list of regions from adata - query_set: List[str] = adata.var.apply( - lambda x: f"{x['chr']}_{x['start']}_{x['end']}", axis=1 - ).tolist() - universe_set = self.universe.regions - - # generate conversion map - _map = generate_var_conversion_map(query_set, universe_set) - - # create a new DataFrame with the updated values - updated_var = adata.var.copy() - - # find the regions that overlap with the universe - # use dynamic programming to create a boolean mask of columns to keep - columns_to_keep = [] - for i, row in tqdm(adata.var.iterrows(), total=adata.var.shape[0]): - region = f"{row['chr']}_{row['start']}_{row['end']}" - if _map[region] is None: - columns_to_keep.append(False) - continue - - # if it is, change the region to the universe region, - # grab the first for now - # TODO - this is a simplification, we should be able to handle multiple - universe_region = _map[region] - chr, start, end = universe_region.split("_") - - updated_var.at[i, "chr"] = chr - updated_var.at[i, "start"] = start - updated_var.at[i, "end"] = end - - columns_to_keep.append(True) - - # update adata with the new DataFrame and filtered columns - adata = adata[:, columns_to_keep] - adata.var = updated_var[columns_to_keep] - - return adata - - def get_embedding(self, region: str) -> np.ndarray: - """ - Get the embedding for a region. - """ - return self.model[region] - - def project(self, adata: sc.AnnData, key_added: str = "embedding") -> sc.AnnData: - """ - Project the AnnData object into the model space. This is done in two steps: - - 1. Convert the consensus peaks to a universe representation - 2. Project the universe representation into the model space using the - model. - - :param adata: AnnData object to project - :param key_added: Key to add to the .obsm attribute of the AnnData object - """ - _LOGGER.info("Step 1/3: Converting consensus peaks to universe representation") - adata_converted = self.convert_to_universe(adata) - - # convert each row to a region set - _LOGGER.info("Step 2/3: Converting universe representation to region sets") - region_sets = anndata_to_regionsets(adata_converted) - - # convert each region set to a vector by averaging the region - # vectors in the model - # TODO: what do we do if there are no region overlaps? i.e. the regionset - # representation of the cell is [] - cell_embeddings = [] - _LOGGER.info("Step 3/3: Projecting region sets into model space") - for region_set in tqdm(region_sets, total=len(region_sets)): - embedding = np.mean( - [ - self.get_embedding(region) - for region in region_set - if region in self.model # ignore regions not in the model - ], - axis=0, - ) - # make sure not nan before appending, - # otherwise append np.zeros - if np.isnan(embedding).any(): - embedding = np.zeros( - self.model_config.model_parameters[0].embedding_dim - ) - - cell_embeddings.append(embedding) - - adata.obsm[key_added] = np.array(cell_embeddings) - return adata - - def visualize_projection( - self, - universe: sc.AnnData, - adata: sc.AnnData, - embedding_key: str = "embedding", - cluster_key: str = "leiden", - n_rows: int = 2, - plot_kwargs: Dict[str, Any] = {}, - fig_kwargs: Dict[str, Any] = {}, - color_palette: str = "tab20", - random_state: int = 42, - ): - """ - Visualize the projection of the AnnData object into the model space. - - This requires that we have the original data the universe was trained on. In adition to that, - we require that embeddings are attached to these sc.AnnData objects. A small - limitation that hopefully future versions will remove. - - :param universe: AnnData object of the universe - :param adata: AnnData object to project - :param embedding_key: Key in the .obsm attribute of the AnnData object that contains - the embedding - :param cluster_key: Key in the .obs attribute of the universe that contains the - cluster information - :param cluster_colors: Dictionary mapping cluster names to colors - :param n_rows: Number of rows to use in the visualization (defaults to 2, but can be overridden if there are many clusters) - :param n_cols: Number of columns to use in the visualization (defaults to 2, but can be overridden if there are many clusters) - :param plot_kwargs: Additional keyword arguments to pass to the plot function of seaborn - """ - # run umap on the universe - _LOGGER.info("Running UMAP on the universe") - reducer = umap.UMAP(n_components=2, random_state=random_state) - universe.obsm["X_umap"] = reducer.fit_transform(universe.obsm[embedding_key]) - universe.obsm["UMAP1"] = universe.obsm["X_umap"][:, 0] - universe.obsm["UMAP2"] = universe.obsm["X_umap"][:, 1] - - # fit the adata to the universe umap - _LOGGER.info("Fitting the adata to the universe UMAP") - adata.obsm["X_umap"] = reducer.transform(adata.obsm[embedding_key]) - adata.obsm["UMAP1"] = adata.obsm["X_umap"][:, 0] - adata.obsm["UMAP2"] = adata.obsm["X_umap"][:, 1] - - # check for cluster key - if cluster_key not in adata.obs.columns: - raise ValueError( - f"AnnData object must have `{cluster_key}` in .obs to visualize" - ) - - # get dimensions of the plot - n_clusters = len(adata.obs[cluster_key].unique()) - n_cols = int(np.ceil(n_clusters / n_rows)) - - fig, axes = plt.subplots( - n_rows, n_cols, figsize=(n_cols * 5, n_rows * 5), **fig_kwargs - ) - fig.tight_layout(pad=3.0) - - # get cluster colors - color for each cluster - cluster_colors = { - cluster: color - for cluster, color in zip( - adata.obs[cluster_key].unique(), sns.color_palette(color_palette) - ) - } - - # iterate over the clusters and plot accordingly - for i, cluster in enumerate(adata.obs["leiden"].unique()): - # get the axis to plot on - ax_row = i // n_cols - ax_col = i % n_cols - ax = fig.axes[ax_row * n_cols + ax_col] - - # get color - color = cluster_colors[cluster] - - # plot the universe - sns.scatterplot( - data=universe.obsm, - x="UMAP1", - y="UMAP2", - color="gray", - ax=ax, - s=10, - alpha=0.8, - **plot_kwargs, - ) - - # plot the cluster on top - sns.scatterplot( - data=adata[adata.obs["leiden"] == cluster].obsm, - x="UMAP1", - y="UMAP2", - color=color, - ax=ax, - s=10, - alpha=1, - **plot_kwargs, - ) - - # add legend to plot showing gray universe + colored cluster - ax.legend( - [ - "Universe", - cluster, - ], - frameon=False, - bbox_to_anchor=(0.5, -0.15), - loc="upper center", - ncol=2, - ) - - # set the title - ax.set_title(f"Cluster {cluster}") - - return fig, axes diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index f9221fbf..54c1fe27 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -402,8 +402,6 @@ def generate_var_conversion_map( return conversion_map -# This method should only be used in the Projector. It -# assmes that the AnnData.var attribute has chr, start, and end def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: """ Converts an AnnData object to a list of lists of regions. This diff --git a/tests/integration/make_annadata.py b/tests/integration/make_annadata.py deleted file mode 100644 index 1d71ac5d..00000000 --- a/tests/integration/make_annadata.py +++ /dev/null @@ -1,19 +0,0 @@ -# %% -import os -from gitk import scembed - -folder = "/Users/nathanleroy/Desktop/pbmc" - -path_to_barcodes = os.path.join(folder, "barcodes.tsv") -path_to_mtx = os.path.join(folder, "matrix.mtx") -path_to_peaks = os.path.join(folder, "peaks.bed") - - -# %% -adata = scembed.utils.barcode_mtx_peaks_to_anndata( - path_to_barcodes, path_to_mtx, path_to_peaks -) - -# %% - -adata.write_h5ad(os.path.join(folder, "pbmc.h5ad")) diff --git a/tests/integration/project_with_atlas.py b/tests/integration/project_with_atlas.py deleted file mode 100644 index 68c0c34b..00000000 --- a/tests/integration/project_with_atlas.py +++ /dev/null @@ -1,56 +0,0 @@ -# %% -import os -import scanpy as sc -from gitk import scembed - -folder = "/Users/nathanleroy/Desktop/pbmc" - -pbmc = sc.read_h5ad(os.path.join(folder, "pbmc.h5ad")) - -# %% -projector = scembed.Projector("databio/scatlas") -pbmc = projector.project(pbmc) - - -# %% -import numpy as np -import umap - -reducer = umap.UMAP( - n_components=2, - random_state=42, -) - -embeddings = np.array(pbmc.obs["embedding"].tolist()) -umap_embedding = reducer.fit_transform(embeddings) - -# %% -# create dataframe with umap embeddings and original labels -import pandas as pd - -umap_df = pd.DataFrame(umap_embedding, columns=["umap1", "umap2"]) - -# %% -import seaborn as sns -import matplotlib.pyplot as plt - -plt.rcParams["figure.dpi"] = 200 - -_, ax = plt.subplots(figsize=(6, 6)) -sns.scatterplot( - data=umap_df, - x="umap1", - y="umap2", - palette="tab20", - linewidth=0, - s=10, - alpha=0.8, - ax=ax, -) - -# increase font size -ax.legend(fontsize=10) -ax.set_xlabel("UMAP 1", fontsize=16) -ax.set_ylabel("UMAP 2", fontsize=16) -ax.set_title("PBMCs", fontsize=20) -ax.tick_params(labelsize=14) diff --git a/tests/integration/scembed_train_in_chunks.py b/tests/integration/scembed_train_in_chunks.py deleted file mode 100644 index 38a81a04..00000000 --- a/tests/integration/scembed_train_in_chunks.py +++ /dev/null @@ -1,139 +0,0 @@ -# %% -# import libraries -import logging - -import scanpy as sc - -from gitk import scembed - -# set to DEBUG to see more info -logging.basicConfig(level=logging.INFO) - -# %% -adata = sc.read_h5ad("../data/buenrostro2018.h5ad", backed="r") - -# for visualization at the end -COLORMAP = { - "CLP": "#bbe7f3", - "CMP": "#fecd84", - "GMP": "#fba102", - "HSC": "#093e12", - "LMPP": "#35c3ad", - "MEP": "#eb2d35", - "momo": "#eb5000", - "MPP": "#41a713", - "pDC": "#da9de2", - "UNK": "#2c2625", -} - -# %% -from multiprocessing import cpu_count - -chunker = scembed.AnnDataChunker(adata, chunk_size=500) -model = scembed.SCEmbed( - threads=cpu_count() - 1, min_count=2, use_default_region_names=False -) - -# %% -from tqdm import tqdm - -# train in chunks -i = 1 -for chunk in tqdm(chunker): - print(f"Training chunk {i} of {len(chunker)} | {chunk.shape[0]} cells") - model.train(chunk, epochs=100) - i += 1 - -# %% -# save the model locally -model.save_model("buenrostro2018.model") - -# %% -adata = sc.read_h5ad("../data/buenrostro2018.h5ad") -documents = scembed.convert_anndata_to_documents(adata, use_defaults=False) - -# %% -import numpy as np - - -def get_embedding(r: str) -> np.ndarray: - return model.get_embedding(r) - - -cell_embeddings = [] -for cell in tqdm(documents, total=len(documents)): - cell_embedding = np.mean( - [get_embedding(r) for r in cell if r in r in model.region2vec], axis=0 - ) - cell_embeddings.append(cell_embedding) - -# attach embeddings to the AnnData object -adata.obs["embedding"] = cell_embeddings - -# %% -import pandas as pd - -# add cell type labels using the metadata -metadata = pd.read_csv( - "../data/metadata.tsv", sep="\t", skiprows=1, names=["id", "label"] -) - -adata.obs["label"] = adata.obs["id"].map(metadata.set_index("id")["label"]) - - -# %% -embeddings_df = pd.DataFrame(adata.obs["embedding"].tolist()) - -# %% -import pandas as pd -from umap import UMAP - -reducer = UMAP(n_components=2, random_state=42) -umap_embeddings = reducer.fit_transform(embeddings_df) - -# %% -adata.obs["umap1"] = umap_embeddings[:, 0] -adata.obs["umap2"] = umap_embeddings[:, 1] - -# %% -# attach cell type colors -embeddings_df["color"] = adata.obs["label"].map(COLORMAP) -adata.obs["color"] = adata.obs["label"].map(COLORMAP) - -# %% -import matplotlib.pyplot as plt -import seaborn as sns - -fig, ax = plt.subplots(figsize=(10, 10)) -sns.scatterplot( - data=adata.obs, - x="umap1", - y="umap2", - hue="color", - ax=ax, - s=20, - alpha=1, -) -ax.set_title("UMAP of Buenrostro 2018 data") -ax.set_xlabel("UMAP 1") -ax.set_ylabel("UMAP 2") - -# add cell types back to legend -handles, labels = ax.get_legend_handles_labels() -ax.legend(handles=handles[1:], labels=labels[1:], bbox_to_anchor=(1.05, 1), loc=2) - -# increase font -for item in ( - [ax.title, ax.xaxis.label, ax.yaxis.label] - + ax.get_xticklabels() - + ax.get_yticklabels() -): - item.set_fontsize(20) - -# arial -for item in ( - [ax.title, ax.xaxis.label, ax.yaxis.label] - + ax.get_xticklabels() - + ax.get_yticklabels() -): - item.set_fontname("Arial") diff --git a/tests/test_projector.py b/tests/test_projector.py deleted file mode 100644 index 7bed0791..00000000 --- a/tests/test_projector.py +++ /dev/null @@ -1,172 +0,0 @@ -import os -import sys - -import pytest -import scanpy as sc -import numpy as np - -# add parent directory to path -sys.path.append("../") - -from gitk import scembed - - -@pytest.fixture -def adata(): - return sc.read_h5ad("tests/data/buenrostro.h5ad") - - -@pytest.fixture -def projector(): - return scembed.Projector("databio/scatlas") - - -def test_model_exists(): - """ - Test that the model exists on the model-hub. - """ - exists = scembed.utils.check_model_exists_on_hub("databio/scatlas") - assert exists - - -def test_model_doesnt_exist(): - """ - Test that the model exists on the model-hub. - """ - exists = scembed.utils.check_model_exists_on_hub("databio/scatlas_doesnt_exist") - assert not exists - - -@pytest.mark.skip # projector is too large to test in CI -def test_download_model(): - registry = "databio/scatlas_v2" - scembed.utils.download_remote_model(registry, scembed.const.MODEL_CACHE_DIR) - config_file = os.path.join( - scembed.const.MODEL_CACHE_DIR, registry, scembed.const.MODEL_CONFIG_FILE_NAME - ) - assert os.path.exists(config_file) - - -@pytest.mark.skip # projector is too large to test in CI -def test_load_local_model(): - registry = "databio/scatlas_v2" - config_file = os.path.join( - scembed.const.MODEL_CACHE_DIR, registry, scembed.const.MODEL_CONFIG_FILE_NAME - ) - projector = scembed.Projector(config_file) - assert isinstance(projector.model_config, scembed.models.ModelCard) - assert isinstance(projector.model, dict) - assert isinstance(projector.universe, scembed.models.Universe) - - -@pytest.mark.skip # projector is too large to test in CI -def test_init_projector(projector): - assert isinstance(projector.model_config, scembed.models.ModelCard) - assert isinstance(projector.model, dict) - assert isinstance(projector.universe, scembed.models.Universe) - - -@pytest.mark.skip(reason="bedtools isnt installed in CI") -def test_tokenization(): - """ - Test tokenization for three scenarios: - 1. One overlap in B per region in A - 2. At least one region in A doesn't overlap with any region in B - 3. A region in A overlaps with multiple regions in B - - Scenario 1: - A: |-----------| |-----------| |-----------| - B: |-----------| |-----------| |-----------| - - Scenario 2: - A: |-----------| |-------| |-----------| - B: |-----------| |-------| |-----------| - - Scenario 3: - A: |-----------| |-----------| |-----------| - B: |-----------| |--------| |-----------| |-----------| - - """ - # scenario 1 - with open("tests/data/s1_a.bed", "r") as f: - lines = f.readlines() - a = ["_".join(l.strip().split("\t")) for l in lines] - with open("tests/data/s1_b.bed", "r") as f: - lines = f.readlines() - b = ["_".join(l.strip().split("\t")) for l in lines] - - conversion_map = scembed.utils.generate_var_conversion_map(a, b) - assert conversion_map == { - "chr1_10_30": "chr1_20_40", - "chr1_110_130": "chr1_120_140", - "chr1_210_230": "chr1_220_240", - } - - # scenario 2 - with open("tests/data/s2_a.bed", "r") as f: - lines = f.readlines() - a = ["_".join(l.strip().split("\t")) for l in lines] - with open("tests/data/s2_b.bed", "r") as f: - lines = f.readlines() - b = ["_".join(l.strip().split("\t")) for l in lines] - - conversion_map = scembed.utils.generate_var_conversion_map(a, b) - assert conversion_map == { - "chr1_10_30": "chr1_20_40", - "chr1_110_130": None, - "chr1_210_230": "chr1_220_240", - } - - # scenario 3 - with open("tests/data/s3_a.bed", "r") as f: - lines = f.readlines() - a = ["_".join(l.strip().split("\t")) for l in lines] - with open("tests/data/s3_b.bed", "r") as f: - lines = f.readlines() - b = ["_".join(l.strip().split("\t")) for l in lines] - - conversion_map = scembed.utils.generate_var_conversion_map(a, b) - assert conversion_map == { - "chr1_10_30": "chr1_20_40", - "chr1_110_130": "chr1_100_120", - "chr1_210_230": "chr1_220_240", - } - - -@pytest.mark.skip -def test_convert_to_universe(projector, adata): - # convert to universe - adata_converted = projector.convert_to_universe(adata) - - # ensure all chr_start_end in the new adata are in the universe - def check_region_in_universe(r): - assert f"{r['chr']}_{r['start'] }_{r['end']}" in projector.universe.regions - - adata_converted.var.apply( - lambda r: check_region_in_universe(r), - axis=1, - ) - - -def test_anndata_to_regionsets(adata): - region_sets = scembed.utils.anndata_to_regionsets(adata) - - # assert the sum of the clipped row in the matrix equals - # the number of regions in the region set - x_clipped = adata.X.clip(max=1) - for i in range(adata.shape[0]): - assert len(region_sets[i]) == int(x_clipped[i, :].sum()) - - -@pytest.mark.skip # projector is too large to test in CI -def test_projection(projector, adata): - adata_projected = projector.project(adata) - - # assert the embeddings are there and the shape is correct - assert "embedding" in adata_projected.obs - - embeddings = np.array(adata_projected.obs["embedding"].to_numpy().tolist()) - assert embeddings.shape == ( - adata.shape[0], - projector.model_config.model_parameters[0].embedding_dim, - ) From 8eec70241ba68ac4e723a4632dd0ff4d84201665 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 6 Jul 2023 16:35:56 -0400 Subject: [PATCH 0233/1072] simpler cell encoding --- examples/scembed/annotation.ipynb | 60 +++++++++++++++++-------------- 1 file changed, 34 insertions(+), 26 deletions(-) diff --git a/examples/scembed/annotation.ipynb b/examples/scembed/annotation.ipynb index 5ce5d7d7..1c565ed2 100644 --- a/examples/scembed/annotation.ipynb +++ b/examples/scembed/annotation.ipynb @@ -164,7 +164,7 @@ "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 27340.96it/s]\n", "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/anndata/_core/anndata.py:117: ImplicitModificationWarning: Transforming to str index.\n", " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", - " 48%|â–â–â–â–â–Š | 4964/10246 [01:25<01:24, 62.21it/s]" + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [02:55<00:00, 58.48it/s]\n" ] } ], @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -189,7 +189,7 @@ "(10246, 100)" ] }, - "execution_count": 29, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -216,9 +216,17 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], "source": [ "# cluster the pbmc data\n", "sc.pp.neighbors(pbmc, use_rep=\"embedding\", n_neighbors=10)\n", @@ -227,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -253,13 +261,13 @@ "Text(0, 0.5, 'UMAP 2')" ] }, - "execution_count": 32, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -306,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -314,20 +322,20 @@ "\n", "annotator = Annotator(\n", " location=\"http://localhost:6333\",\n", - " collection_name=\"leucken2021\",\n", + " collection_name=\"luecken2021\",\n", ")" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:22<00:00, 124.80it/s]\n" + "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [01:36<00:00, 106.54it/s]\n" ] } ], @@ -338,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -351,24 +359,24 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'1': 'CD14+ Mono',\n", - " '0': 'CD4+ T activated',\n", - " '5': 'Naive CD20+ B',\n", - " '2': 'CD4+ T activated',\n", + " '0': 'CD4+ T naive',\n", + " '5': 'B1 B',\n", " '3': 'NK',\n", + " '2': 'CD8+ T',\n", " '4': 'CD14+ Mono',\n", - " '7': 'Naive CD20+ B',\n", - " '6': 'CD14+ Mono',\n", - " '8': 'CD14+ Mono'}" + " '7': 'B1 B',\n", + " '6': 'cDC2',\n", + " '8': 'ID2-hi myeloid prog'}" ] }, - "execution_count": 41, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -379,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -388,13 +396,13 @@ "Text(0, 0.5, 'UMAP 2')" ] }, - "execution_count": 42, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -411,7 +419,7 @@ " x='umap1',\n", " y='umap2',\n", " hue='leiden',\n", - " palette='tab20',\n", + " palette='tab10',\n", " linewidth=0,\n", " s=1,\n", " ax=ax\n", From e5ad316cd7c90268d28b470d1dcce9196cb9e213 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 23 Jun 2023 16:06:18 -0400 Subject: [PATCH 0234/1072] add lh of missing chromosomes --- gitk/assess/likelihood.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/gitk/assess/likelihood.py b/gitk/assess/likelihood.py index 1393841c..00466c80 100644 --- a/gitk/assess/likelihood.py +++ b/gitk/assess/likelihood.py @@ -50,6 +50,7 @@ def calc_likelihood_hard( empty_start = 0 res = 0 e = 0 + done_chrom = [] prob_array, cove_array = None, None with open(universe) as uni: for i in uni: @@ -61,6 +62,7 @@ def calc_likelihood_hard( else: if i[0] != current_chrom: if i[0] in chroms: + done_chrom.append(i[0]) model_lh.clear_chrom(current_chrom) if e != 1: res += np.sum(prob_array[empty_start:, 0]) @@ -90,6 +92,19 @@ def calc_likelihood_hard( res += r2 empty_start = end res += np.sum(prob_array[empty_start:, 0]) + z = set(chroms).difference(set(done_chrom)) + z = list(z) + for i in z: + model_lh.read_chrom_track(i, name) + prob_model = model_lh[i] + cove_array = read_chromosome_from_bw( + os.path.join( + coverage_folder, f"{coverage_prefix}_{name}.bw" + ), + i, + ) + prob_array = LhModel(prob_model[name], cove_array) + res += np.sum(prob_array[:, 0]) return res @@ -222,6 +237,8 @@ def likelihood_flexible_universe( check_if_uni_flexible(universe) model_lh = ModelLH(model_file) chroms = model_lh.chromosomes_list + print(chroms) + done_chroms = [] output = [] e = 0 # number of processed chromosomes with open(universe) as uni: @@ -246,6 +263,7 @@ def likelihood_flexible_universe( prob_end, ) current_chrom = i[0] + done_chroms.append(current_chrom) e += 1 model_lh.read_chrom(current_chrom) models_current = model_lh[current_chrom] @@ -296,4 +314,18 @@ def likelihood_flexible_universe( print("saving") with open(universe + "_peak_likelihood", "w") as f: f.writelines(output) + z = set(chroms).difference(set(done_chroms)) + z = list(z) + for i in z: + for name in ["start", "core", "end"]: + model_lh.read_chrom_track(i, name) + prob_model = model_lh[i] + cove_array = read_chromosome_from_bw( + os.path.join( + cove_folder, f"{cove_prefix}_{name}.bw" + ), + i, + ) + prob_array = LhModel(prob_model[name], cove_array) + res += np.sum(prob_array[:, 0]) return res From 7e80cfb014dd74f89e0e34e71ba94433f40fb806 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Fri, 7 Jul 2023 14:49:16 +0200 Subject: [PATCH 0235/1072] lint --- gitk/assess/likelihood.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/gitk/assess/likelihood.py b/gitk/assess/likelihood.py index 00466c80..8275ec77 100644 --- a/gitk/assess/likelihood.py +++ b/gitk/assess/likelihood.py @@ -98,9 +98,7 @@ def calc_likelihood_hard( model_lh.read_chrom_track(i, name) prob_model = model_lh[i] cove_array = read_chromosome_from_bw( - os.path.join( - coverage_folder, f"{coverage_prefix}_{name}.bw" - ), + os.path.join(coverage_folder, f"{coverage_prefix}_{name}.bw"), i, ) prob_array = LhModel(prob_model[name], cove_array) @@ -237,7 +235,6 @@ def likelihood_flexible_universe( check_if_uni_flexible(universe) model_lh = ModelLH(model_file) chroms = model_lh.chromosomes_list - print(chroms) done_chroms = [] output = [] e = 0 # number of processed chromosomes @@ -321,9 +318,7 @@ def likelihood_flexible_universe( model_lh.read_chrom_track(i, name) prob_model = model_lh[i] cove_array = read_chromosome_from_bw( - os.path.join( - cove_folder, f"{cove_prefix}_{name}.bw" - ), + os.path.join(cove_folder, f"{cove_prefix}_{name}.bw"), i, ) prob_array = LhModel(prob_model[name], cove_array) From ad07b52dbd6ae12bc8ca68de7e27ba9e3f525836 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 08:55:34 -0400 Subject: [PATCH 0236/1072] fix typos --- docs/README.md | 2 +- docs/consensus/assess.md | 2 +- tests/{consesnus => consensus}/cli_test.sh | 0 tests/{consesnus => consensus}/coverage/all_core.bw | Bin tests/{consesnus => consensus}/coverage/all_end.bw | Bin .../{consesnus => consensus}/coverage/all_start.bw | Bin tests/{consesnus => consensus}/file_list.txt | 0 tests/{consesnus => consensus}/raw/test_1.bed | 0 tests/{consesnus => consensus}/raw/test_2.bed | 0 tests/{consesnus => consensus}/raw/test_3.bed | 0 tests/{consesnus => consensus}/raw/test_4.bed | 0 11 files changed, 2 insertions(+), 2 deletions(-) rename tests/{consesnus => consensus}/cli_test.sh (100%) rename tests/{consesnus => consensus}/coverage/all_core.bw (100%) rename tests/{consesnus => consensus}/coverage/all_end.bw (100%) rename tests/{consesnus => consensus}/coverage/all_start.bw (100%) rename tests/{consesnus => consensus}/file_list.txt (100%) rename tests/{consesnus => consensus}/raw/test_1.bed (100%) rename tests/{consesnus => consensus}/raw/test_2.bed (100%) rename tests/{consesnus => consensus}/raw/test_3.bed (100%) rename tests/{consesnus => consensus}/raw/test_4.bed (100%) diff --git a/docs/README.md b/docs/README.md index 158be068..c24b8fbe 100644 --- a/docs/README.md +++ b/docs/README.md @@ -2,7 +2,7 @@ ## Introduction -`gitk` is a suite of tools for apply machine learning approaches to genomic interval data. It is organized as a set of modules that provide related functions, such as building HMMs, assessing genomic interval universes, calculating likelihoods of consensus genomic interval sets, and computing single-cell clusters. +`gitk` is a suite of tools for applying machine learning approaches to genomic interval data. It is organized as a set of modules that provide related functions, such as building HMMs, assessing genomic interval universes, calculating likelihoods of consensus genomic interval sets, and computing single-cell clusters. ## Install diff --git a/docs/consensus/assess.md b/docs/consensus/assess.md index ae228b00..86283b3a 100644 --- a/docs/consensus/assess.md +++ b/docs/consensus/assess.md @@ -30,7 +30,7 @@ containing file name and results of chosen metrics First test checks how much of our file is present in the universe and how much additional information is present in the univers. We can check that by adding ```--overlap``` to ```gitk assess ...```. In the result files it will output columns with: number of bp in univers but not in file, number of bp in file but not the univers, and number of bp both in univers and file -We can also us it directly in python: +We can also use it directly in python: ``` from gitk.assess.intersection import run_intersection diff --git a/tests/consesnus/cli_test.sh b/tests/consensus/cli_test.sh similarity index 100% rename from tests/consesnus/cli_test.sh rename to tests/consensus/cli_test.sh diff --git a/tests/consesnus/coverage/all_core.bw b/tests/consensus/coverage/all_core.bw similarity index 100% rename from tests/consesnus/coverage/all_core.bw rename to tests/consensus/coverage/all_core.bw diff --git a/tests/consesnus/coverage/all_end.bw b/tests/consensus/coverage/all_end.bw similarity index 100% rename from tests/consesnus/coverage/all_end.bw rename to tests/consensus/coverage/all_end.bw diff --git a/tests/consesnus/coverage/all_start.bw b/tests/consensus/coverage/all_start.bw similarity index 100% rename from tests/consesnus/coverage/all_start.bw rename to tests/consensus/coverage/all_start.bw diff --git a/tests/consesnus/file_list.txt b/tests/consensus/file_list.txt similarity index 100% rename from tests/consesnus/file_list.txt rename to tests/consensus/file_list.txt diff --git a/tests/consesnus/raw/test_1.bed b/tests/consensus/raw/test_1.bed similarity index 100% rename from tests/consesnus/raw/test_1.bed rename to tests/consensus/raw/test_1.bed diff --git a/tests/consesnus/raw/test_2.bed b/tests/consensus/raw/test_2.bed similarity index 100% rename from tests/consesnus/raw/test_2.bed rename to tests/consensus/raw/test_2.bed diff --git a/tests/consesnus/raw/test_3.bed b/tests/consensus/raw/test_3.bed similarity index 100% rename from tests/consesnus/raw/test_3.bed rename to tests/consensus/raw/test_3.bed diff --git a/tests/consesnus/raw/test_4.bed b/tests/consensus/raw/test_4.bed similarity index 100% rename from tests/consesnus/raw/test_4.bed rename to tests/consensus/raw/test_4.bed From 81eb60f18aebe165a71438ffcdf86d286c48ca36 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 08:59:24 -0400 Subject: [PATCH 0237/1072] docs updates --- docs/consensus/assess.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/docs/consensus/assess.md b/docs/consensus/assess.md index 86283b3a..d55bf6b9 100644 --- a/docs/consensus/assess.md +++ b/docs/consensus/assess.md @@ -1,10 +1,11 @@ -# How to assess universe fit to collection of files? -Given a universe it is important to assess it fit to collection of data. -We will use here universes produced [eriler](consensus-peaks.md). We can do it both from CLI and directly in python script. Both overlap and distance based assessments can be run using: ```gitk assess ...``` with appropriate flags that can be combined. +# How to assess universe fit to collection of BED files + +Given a collection of genomic interval sets, and a proposed universe, we would like to assess how well the fits the genomic interval sets. +We will use universes produced [earlier](consensus-peaks.md). We can assess fit either from CLI, or from within Python. Both overlap and distance based assessments can be run using: `gitk assess ...` with appropriate flags. ``` - gitk assess --assessmnet-method1 \ - --assessmnet-method2 \ + gitk assess --assessment-method1 \ + --assessment-method2 \ --... --raw-data-folder tests/consesnus/raw/\ --file-list tests/consesnus/file_list.txt \ From 94abd7426581423a2aea557210817ab379ff7b29 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 09:02:09 -0400 Subject: [PATCH 0238/1072] docs updates --- docs/consensus/assess.md | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/docs/consensus/assess.md b/docs/consensus/assess.md index d55bf6b9..2124a089 100644 --- a/docs/consensus/assess.md +++ b/docs/consensus/assess.md @@ -1,7 +1,10 @@ # How to assess universe fit to collection of BED files Given a collection of genomic interval sets, and a proposed universe, we would like to assess how well the fits the genomic interval sets. -We will use universes produced [earlier](consensus-peaks.md). We can assess fit either from CLI, or from within Python. Both overlap and distance based assessments can be run using: `gitk assess ...` with appropriate flags. +We will use universes produced [earlier](consensus-peaks.md). This module provides several complementary methods to assess fit. + + +We can assess fit either from CLI, or from within Python. Both overlap and distance based assessments can be run using: `gitk assess ...` with appropriate flags. ``` gitk assess --assessment-method1 \ @@ -27,11 +30,11 @@ containing file name and results of chosen metrics - ``--no-workers``, takes the number of workers that should be used - ``--save-each``, is a flag that specifies whether to save between the closest peaks to file -## Base-level overlap measure -First test checks how much of our file is present in the universe and how much -additional information is present in the univers. We can check that by adding ```--overlap``` to ```gitk assess ...```. In the result files it will output columns with: number of bp in univers but not in file, number of bp in file but not the univers, and number of bp both in univers and file +## Base-level overlap measure + +First test checks how much of our file is present in the universe and how much additional information is present in the universe. We can check that by adding ```--overlap``` to ```gitk assess ...```. In the result files it will output columns with: number of bp in universe but not in file, number of bp in file but not the universe, and number of bp both in universe and file. -We can also use it directly in python: +We can also use it directly from Python like this: ``` from gitk.assess.intersection import run_intersection @@ -41,7 +44,8 @@ run_intersection("test/consesnus/raw/", "tests/consenus/universe/universe.bed", no_workers=1) ``` -or we can calculate F10 score of the universe using: + +Or, we can calculate F10 score of the universe using: ``` from gitk.assess.intersection import get_f_10_score @@ -53,7 +57,8 @@ get_f_10_score("test/consesnus/raw/", ``` ## Region boundary distance measure -Next, we can calculate the distance between query and universe. To do tat we can choose from : + +Next, we can calculate the distance between query and universe. To do that we can choose from : - ```distance``` - calculates distance from region in query to the nearest region in the universe - ```distance-universe-to-file```- calculates distance from region in query to the nearest region in the universe accounting for universe flexibility - ```distance-flexible``` - calculates distance from region in universe to the nearest region in the query From ccdc3122a368344ffb46e1ad3f7a7b2b487131c3 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 13:50:31 -0400 Subject: [PATCH 0239/1072] restructure docs --- docs/README.md | 2 + docs/{consensus => modules}/assess.md | 0 .../README.md => docs/modules/bedspace.md | 5 +-- docs/{eval => modules}/evaluation.md | 0 docs/{region2vec => modules}/region2vec.md | 11 ++++- .../README.md => docs/modules/scembed.md | 1 + docs/modules/tokenization.md | 32 +++++++++++++ docs/modules/universe.md | 0 docs/tokenization/tokenization.md | 29 ------------ .../consensus-peaks.md | 45 ++++++++++--------- gitk/region2vec/README.md | 34 -------------- mkdocs.yml | 8 +++- 12 files changed, 76 insertions(+), 91 deletions(-) rename docs/{consensus => modules}/assess.md (100%) rename gitk/bedspace/README.md => docs/modules/bedspace.md (94%) rename docs/{eval => modules}/evaluation.md (100%) rename docs/{region2vec => modules}/region2vec.md (70%) rename gitk/scembed/README.md => docs/modules/scembed.md (99%) create mode 100644 docs/modules/tokenization.md create mode 100644 docs/modules/universe.md delete mode 100644 docs/tokenization/tokenization.md rename docs/{consensus => tutorials}/consensus-peaks.md (68%) delete mode 100644 gitk/region2vec/README.md diff --git a/docs/README.md b/docs/README.md index c24b8fbe..a324e16b 100644 --- a/docs/README.md +++ b/docs/README.md @@ -13,6 +13,8 @@ pip install --user --upgrade . ## gitk modules - [gitk/hmm](gitk/hmm) - Building HMMs +- [gitk/bedspace](bedspace/bedspace.md) - BEDSpace +- [gitk/region2vec](region2vec/region2vec.md) - region2vec - [gitk/assess](gitk/assess) - Assess universe fit - [gitk/likelihood](gitk/likelihood) - Calculate likelihood of universe - [gitk/scembed](gitk/scembed) - Compute single-cell clusters from a cell-feature matrix using Word2Vec diff --git a/docs/consensus/assess.md b/docs/modules/assess.md similarity index 100% rename from docs/consensus/assess.md rename to docs/modules/assess.md diff --git a/gitk/bedspace/README.md b/docs/modules/bedspace.md similarity index 94% rename from gitk/bedspace/README.md rename to docs/modules/bedspace.md index c3637418..0b961a7d 100644 --- a/gitk/bedspace/README.md +++ b/docs/modules/bedspace.md @@ -3,11 +3,8 @@ `bedspace` uses the StarSpace method (Wu et al., 2018) to jointly embed genomic interval regions sets with associated metadata into a shared latent embedding space. This facilitates fast search and retrieval of similar region sets and their associated metadata. ## Installation -`bedspace` is a part of the `gitk` package, which can be installed from PyPI using `pip install gitk`. To ensure that everything is working correctly, run the following command: -``` -python -c "from gitk import bedspace" -``` +`bedspace` comes installed as a module of `gitk`. To ensure that everything is working correctly, run: `python -c "from gitk import bedspace"`. ## Usage There are four main commands in `bedspace`: diff --git a/docs/eval/evaluation.md b/docs/modules/evaluation.md similarity index 100% rename from docs/eval/evaluation.md rename to docs/modules/evaluation.md diff --git a/docs/region2vec/region2vec.md b/docs/modules/region2vec.md similarity index 70% rename from docs/region2vec/region2vec.md rename to docs/modules/region2vec.md index 5d083e40..36d88228 100644 --- a/docs/region2vec/region2vec.md +++ b/docs/modules/region2vec.md @@ -1,8 +1,9 @@ # Region2Vec -`Region2Vec` will generate embedding vectors for a given region set (universe) from a set of raw bed files. The program will first map all raw regions to the given region set. Then, it will concatenate all regions in a bed file in random orders into a sentence. The generated sentences will be used for Region2Vec training. +`Region2Vec` is an unsupervised method for creating embeddings for genomic regions and region sets from a set of raw BED files. The program will first map all raw regions to a given universe (vocabulary) set. Then, it will construct sentences by concatenating regions from a BED file in random order. The generated sentences will be used for Region2Vec training using word2vec. ## Usage + 1. Prepare a set of bed files in `src_folder`. [Optional] If only a subset of files will be used, specify a list of those files as `file_list`. By default, the program will use all the files in the folder to train a Region2Vec model. 2. Prepare a universe file `universe_file`. 3. Create a token folder which will be used to store tokenized files `dst_folder`. @@ -27,8 +28,14 @@ For customized settings, please go and check the parameters used in `main.py`. For training a Region2Vec model, the parameters, `init_lr`, `window_size`, `num_shufflings`, `embedding_dim`, are frequently tuned in experiments. For command line usage, type `gitk region2vec --help` for details. We give a simple usage below + ```bash -gitk region2vec --token-folder /path/to/token/folder --save-dir ./region2vec_model --num-shuffle 10 --embed-dim 100 --context-len 50 +gitk region2vec + --token-folder /path/to/token/folder \ + --save-dir ./region2vec_model \ + --num-shuffle 10 \ + --embed-dim 100 \ + --context-len 50 ``` diff --git a/gitk/scembed/README.md b/docs/modules/scembed.md similarity index 99% rename from gitk/scembed/README.md rename to docs/modules/scembed.md index 1f9a6ecd..c8ed3288 100644 --- a/gitk/scembed/README.md +++ b/docs/modules/scembed.md @@ -1,4 +1,5 @@ # scEmbed + `scEmbed` is a single-cell implementation of `Region2Vec`: a method to represent genomic region sets as vectors, or embeddings, using an adapted word2vec approach. `scEmbed` allows for dimensionality reduction and feature selection of single-cell ATAC-seq data; a notoriously sparse and high-dimensional data type. We intend for `scEmbed` to be used with the [`scanpy`](https://scanpy.readthedocs.io/en/stable/) package. As such, it natively accepts `AnnData` objects as input and returns `AnnData` objects as output. ## Installation diff --git a/docs/modules/tokenization.md b/docs/modules/tokenization.md new file mode 100644 index 00000000..c80c9c1f --- /dev/null +++ b/docs/modules/tokenization.md @@ -0,0 +1,32 @@ +# Tokenization + +## Introduction + +In NLP, training word embeddings requires first tokenizing words such that words in different forms are represented by one word. For example, "orange", "oranges" and "Orange" are all mapped to "orange" since they essentially convey the same meaning. This reduces the vocabulary size and improves the quality of learned embeddings. Similary, many `gitk` modules (such as `region2vec`) require first tokenizating regions. + +To tokenize reigons, we need to provide a universe, which specifies the "vocabulary" of genomic regions. The universe is a BED file, containing representative regions. With the given universe, we represent (tokenize) raw regions into the regions in the universe. + +Different strategies can be used to tokenize. The simplest case we call *hard tokenization*, which means if the overlap between a raw region in a BED file and a region in the universe exceeds a certain amount, then we use the region in the universe to represent this raw region; otherwise, we ignore this raw region. This is a "zero or one" process. After hard tokenization, each BED file will contain only regions from the universe, and the number of regions will be smaller or equal to the original number. + +## Usage + +For hard tokenization, run +``` +from gitk.tokenization import hard_tokenization + +src_folder = '/path/to/raw/bed/files/' +dst_folder = '/path/to/tokenized_files/' +universe_file = '/path/to/universe_file.bed' +hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) + +``` +Note that we use the `intersect` function of `bedtools` to do tokenization. If you want to switch to different tools, you can override the `bedtool_tokenization` function in `hard_tokenization_batch.py` and provide the path to your tool by specifying the input argument `bedtools_path`. The `fraction` argument specifies the minimum overlap required as a fraction of some region in the universe (default: 1E-9,i.e. 1bp; maximum 1.0). A raw region will be mapped into a universe region when an overlap is above the threshold. + +The bedtools (version 2.30.0) will be automatically downloaded from https://github.com/arq5x/bedtools2/releases to the `bedtools` folder. + +Command line usage +```bash +gitk tokenize --data-folder /folder/with/raw/BED/files --token-folder ./tokens --universe /universe/file +``` + +For more details, type `gitk tokenize --help`. diff --git a/docs/modules/universe.md b/docs/modules/universe.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/tokenization/tokenization.md b/docs/tokenization/tokenization.md deleted file mode 100644 index 9cbc5cb7..00000000 --- a/docs/tokenization/tokenization.md +++ /dev/null @@ -1,29 +0,0 @@ -# Tokenization -## Introduction -In training word embeddings, we need to first tokenize each word such that words in different forms are represented by one word. For example, "orange", "oranges" and "Orange" are all mapped to "orange" since they essentially convey the same meaning. This can reduce the vocabulary size and also improve the quality of learned embeddings.
-Similary, before running region2vec, we need to run tokenization on regions. First, we need to provide a universe, the "vocabulary" in the setting of genomic regions. The universe is a BED file, containing representative regions. With the given universe, we represent (tokenize) raw regions with the regions in the universe. - -For hard tokenization, if the overlap between a raw region in a bed file and a region in the universe exceeds a certain amount, then we use the region in the universe to represent this raw region; otherwise, we ignore this raw region. This is a "zeor or one" process. After hard tokenization, each bed file will contain regions all from the universe, and the number of regions will be smaller or equal to the original number. - - -## Usage -For hard tokenization, run -``` -from gitk.tokenization import hard_tokenization - -src_folder = '/path/to/raw/bed/files' -dst_folder = '/path/to/tokenized_files' -universe_file = '/path/to/universe_file' -hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) - -``` -Note that we use the `intersect` function of `bedtools` to do tokenization. If you want to switch to different tools, you can override the `bedtool_tokenization` function in `hard_tokenization_batch.py` and provide the path to your tool by specifying the input argument `bedtools_path`. The `fraction` argument specifies the minimum overlap required as a fraction of some region in the universe (default: 1E-9,i.e. 1bp; maximum 1.0). A raw region will be mapped into a universe region when an overlap is above the threshold. - -The bedtools (version 2.30.0) will be automatically downloaded from https://github.com/arq5x/bedtools2/releases to the `bedtools` folder. - -Command line usage -```bash -gitk tokenize --data-folder /folder/with/raw/BED/files --token-folder ./tokens --universe /universe/file -``` - -For more details, type `gitk tokenize --help`. diff --git a/docs/consensus/consensus-peaks.md b/docs/tutorials/consensus-peaks.md similarity index 68% rename from docs/consensus/consensus-peaks.md rename to docs/tutorials/consensus-peaks.md index 4d9ecb2b..3dd7c5be 100644 --- a/docs/consensus/consensus-peaks.md +++ b/docs/tutorials/consensus-peaks.md @@ -1,12 +1,12 @@ -# How to create consensus peak set from a collection of BED files? +# How to create a consensus peak set from a collection of BED files -We will start with simple example of how to creat a consensus peak set from -collection of files. Example data that will be used can be found in -```tests/consenus/raw``` . +We will start with simple example of how to create a consensus peak set from a +collection of files. Example data can be found in `tests/consenus/raw`. ## Data preprocessing First step of analysis is creating three tracks with genome coverage by peaks, their starts and ends. To do that we have to: + 1. install [uniwig](https://github.com/databio/uniwig/tree/smoothing), make sure to use branch dev 2. use [create_unsorted.sh](https://github.com/databio/uniwig/blob/smoothing/create_unsorted.sh) to make three bigWig: - {prefix}_start.bw - with smoothed coverage of genome by starts @@ -15,7 +15,9 @@ their starts and ends. To do that we have to: In this tutorial we will use prefix "all" as it is a default prefix in ```gitk``` module + ## Coverage cutoff universe + We will start by making a coverage universe with cutoff that results in maximum likelihood universe. We can do it through CLI: @@ -27,10 +29,11 @@ likelihood universe. We can do it through CLI: Where: -- ```--coverage-folder```, takes the path to bigWig file with genome coverage by collection -- ```--output-file```, takes the path to output file +- `--coverage-folder`, takes the path to bigWig file with genome coverage by collection +- `--output-file`, takes the path to output file Or we can import it directly into python: + ``` from gitk.universe.cc_universe import cc_universe @@ -38,9 +41,9 @@ cc_universe("tests/consenus/coverage/all_core.bw", file_out="tests/consenus/universe/universe.bed") ``` -Depending on the task we can also smooth the output universe by setting ``--merge`` +Depending on the task we can also smooth the output universe by setting `--merge` flag with the distance beloved witch peaks should be merged together and -``--filter-size`` with minimum size of peak that should be part of the universe. We can also not use the maximum likelihood cut-off and instead of it use user defined cutoff. For that we have to set ``--cutoff`` . If we set it to 1 we get union universe, and when to number of files we will get intersection universe. +`--filter-size` with minimum size of peak that should be part of the universe. We can also not use the maximum likelihood cut-off and instead of it use user defined cutoff. For that we have to set `--cutoff` . If we set it to 1 we get union universe, and when to number of files we will get intersection universe. ## Coverage cutoff flexible universe Next presented universe is coverage cutoff flexible universe. We can do it through CLI: @@ -53,8 +56,8 @@ Next presented universe is coverage cutoff flexible universe. We can do it throu Where: -- ```--coverage-folder```, takes the path to bigWig file with genome coverage by collection -- ```--output-file```, takes the path to output file +- `--coverage-folder`, takes the path to bigWig file with genome coverage by collection +- `--output-file`, takes the path to output file Or we can import it directly into python: ``` @@ -78,9 +81,9 @@ gitk lh build_model --model-file tests/consenus/model.tar \ Where: -- ```--model-file```, takes the name of tar archive that will contain the likelihood model -- ```--file-no```, number of files used in analysis -- ```--coverage-folder``` path to folder with coverage tracks +- `--model-file`, takes the name of tar archive that will contain the likelihood model +- `--file-no`, number of files used in analysis +- `--coverage-folder` path to folder with coverage tracks Or, we can do it directly in python: @@ -103,9 +106,9 @@ gitk build-universe ml --model-file tests/consenus/model.tar \ Where: -- ```--model-file```, takes the name of tar archive that contains the likelihood model -- ```--output-file```, takes the path to output file -- ```--coverage-folder``` path to folder with coverage tracks +- `--model-file`, takes the name of tar archive that contains the likelihood model +- `--output-file`, takes the path to output file +- `--coverage-folder` path to folder with coverage tracks Similarly, we can do it in python: @@ -130,11 +133,11 @@ gitk build-universe hmm --out-file tests/consenus/universe/universe.bed \ Where: -- ```--output-file```, takes the path to output file -- ```--coverage-folder```, path to folder with coverage tracks -- ```--coverage-prefix``` prefix used in uniwig for making files, default is "all" -- ```--not-normlaize```, is a flag that specifies whether not to normalize tracks before running HMM -- ```--save-max-cove```, is a flag that specifies whether to save maximum coverage of each output peak +- `--output-file`, takes the path to output file +- `--coverage-folder`, path to folder with coverage tracks +- `--coverage-prefix` prefix used in uniwig for making files, default is "all" +- `--not-normlaize`, is a flag that specifies whether not to normalize tracks before running HMM +- `--save-max-cove`, is a flag that specifies whether to save maximum coverage of each output peak Similarly, we can do it in python: diff --git a/gitk/region2vec/README.md b/gitk/region2vec/README.md deleted file mode 100644 index 5d083e40..00000000 --- a/gitk/region2vec/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# Region2Vec -`Region2Vec` will generate embedding vectors for a given region set (universe) from a set of raw bed files. The program will first map all raw regions to the given region set. Then, it will concatenate all regions in a bed file in random orders into a sentence. The generated sentences will be used for Region2Vec training. - - -## Usage -1. Prepare a set of bed files in `src_folder`. [Optional] If only a subset of files will be used, specify a list of those files as `file_list`. By default, the program will use all the files in the folder to train a Region2Vec model. -2. Prepare a universe file `universe_file`. -3. Create a token folder which will be used to store tokenized files `dst_folder`. -5. Run the following command -``` -from gitk.tokenization import hard_tokenization -from gitk.region2vec import region2vec - -src_folder = '/path/to/raw/bed/files' -dst_folder = '/path/to/tokenized_files' -universe_file = '/path/to/universe_file' - -# must run tokenization first -status = hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) - -if status: # if hard_tokenization is successful, then run Region2Vec training - save_dir = '/path/to/training/results' - region2vec(dst_folder, save_dir, num_shufflings=1000) - -``` -For customized settings, please go and check the parameters used in `main.py`. -For training a Region2Vec model, the parameters, `init_lr`, `window_size`, `num_shufflings`, `embedding_dim`, are frequently tuned in experiments. - -For command line usage, type `gitk region2vec --help` for details. We give a simple usage below -```bash -gitk region2vec --token-folder /path/to/token/folder --save-dir ./region2vec_model --num-shuffle 10 --embed-dim 100 --context-len 50 -``` - - diff --git a/mkdocs.yml b/mkdocs.yml index 3af949a7..e4989a75 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -7,8 +7,14 @@ pypi_name: gitk nav: - Getting Started: - Introduction: README.md + - Modules: + - BEDSpace: modules/bedspace.md + - Region2Vec: modules/region2vec.md + - scEmbed: modules/scembed.md + - Tokenization: modules/tokenization.md + - Universe: modules/universe.md - How-to guides: - - Create consensus peaks: likelihood/consensus-peaks.md + - Create consensus peaks: tutorials/consensus-peaks.md - Reference: - API: autodoc_build/gitk.md - Support: support.md From 0a1da122698238759b7fc6472264263c4749d3b6 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 15:37:32 -0400 Subject: [PATCH 0240/1072] docs updates --- docs/README.md | 34 ++----------------- .../modules/{assess.md => assess_universe.md} | 0 .../modules/create_universe.md | 10 ++++-- docs/modules/universe.md | 0 ...sus-peaks.md => create-consensus-peaks.md} | 0 gitk/README.md | 29 ++++++++++++++-- mkdocs.yml | 8 +++-- 7 files changed, 43 insertions(+), 38 deletions(-) rename docs/modules/{assess.md => assess_universe.md} (100%) rename gitk/universe/README.md => docs/modules/create_universe.md (59%) delete mode 100644 docs/modules/universe.md rename docs/tutorials/{consensus-peaks.md => create-consensus-peaks.md} (100%) diff --git a/docs/README.md b/docs/README.md index a324e16b..d3e24717 100644 --- a/docs/README.md +++ b/docs/README.md @@ -2,7 +2,7 @@ ## Introduction -`gitk` is a suite of tools for applying machine learning approaches to genomic interval data. It is organized as a set of modules that provide related functions, such as building HMMs, assessing genomic interval universes, calculating likelihoods of consensus genomic interval sets, and computing single-cell clusters. +`gitk` is a suite of tools for applying machine learning approaches to genomic interval data. For example, it can do things like building HMMs, assessing genomic interval universes, calculating likelihoods of consensus genomic interval sets, and computing single-cell clusters. It also provides a variety of other useful ancillary tools for working with genomic intervals to support these functions. ## Install @@ -10,34 +10,6 @@ pip install --user --upgrade . ``` -## gitk modules - -- [gitk/hmm](gitk/hmm) - Building HMMs -- [gitk/bedspace](bedspace/bedspace.md) - BEDSpace -- [gitk/region2vec](region2vec/region2vec.md) - region2vec -- [gitk/assess](gitk/assess) - Assess universe fit -- [gitk/likelihood](gitk/likelihood) - Calculate likelihood of universe -- [gitk/scembed](gitk/scembed) - Compute single-cell clusters from a cell-feature matrix using Word2Vec - -## Using modules from Python - -This repo is divided into modules. Each module should be written in a way that it provides utility as a Python library. For example, you can call functions in the `hmm` module like this: - -``` -import gitk - -gitk.hmm.function() -``` - -## Command-line interfaces - -In addition to being importable from Python, *some* modules also provide a CLI. For these, developers provide a subcommand for CLI use. The root `gitk` package provides a generalized command-line interface with the command `gitk`. The modules that provide CLIs then correspond to CLI commands, *e.g* `gitk hmm` or `gitk likelihood`, with the corresponding code contained within a sub-folder named after the model: - -``` -gitk ... -``` - -This is implemented within each module folder with: - -- `gitk//cli.py` - defines the command-line interface and provides a subparser for this module's CLI command. +## Modules +`gitk` is organized into modules. You can read the list of functions by module, or proceed to the how-to guides, using the menu bar on the left. diff --git a/docs/modules/assess.md b/docs/modules/assess_universe.md similarity index 100% rename from docs/modules/assess.md rename to docs/modules/assess_universe.md diff --git a/gitk/universe/README.md b/docs/modules/create_universe.md similarity index 59% rename from gitk/universe/README.md rename to docs/modules/create_universe.md index 8e7646cb..1c6a490f 100644 --- a/gitk/universe/README.md +++ b/docs/modules/create_universe.md @@ -1,8 +1,14 @@ -# hmm module +# The universe module ## Introduction -This module will use an HMM to create a flexible segment universe, given an input of several bed files. +This module provides multiple ways to build a genomic region universe + + + +## Method 1: HMM + +This will use an HMM to create a flexible segment universe, given an input of several bed files. ## How to use diff --git a/docs/modules/universe.md b/docs/modules/universe.md deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/tutorials/consensus-peaks.md b/docs/tutorials/create-consensus-peaks.md similarity index 100% rename from docs/tutorials/consensus-peaks.md rename to docs/tutorials/create-consensus-peaks.md diff --git a/gitk/README.md b/gitk/README.md index ec72fe12..6e01abbf 100644 --- a/gitk/README.md +++ b/gitk/README.md @@ -1,3 +1,28 @@ -# gitk module +# gitk developer notes -The `gitk` module is the parent module in the package, which controls the general argument parser (in `cli.py`), constants (`const.py`), and any other utility or general-purpose code that is used across modules. +## Repository architecture + +gitk is organized into modules that provide related functions, each in a subfolder. The parent `gitk/` folder holds functionality that spans across modules, such as the general (top-level) argument parser (in `cli.py`), constants (`const.py`), and any other utility or general-purpose code that is used across modules. + +## Using modules from Python + +This repo is divided into modules. Each module should be written in a way that it provides utility as a Python library. For example, you can call functions in the `hmm` module like this: + +``` +import gitk + +gitk.hmm.function() +``` + + +## Command-line interfaces + +In addition to being importable from Python, *some* modules also provide a CLI. For these, developers provide a subcommand for CLI use. The root `gitk` package provides a generalized command-line interface with the command `gitk`. The modules that provide CLIs then correspond to CLI commands, *e.g* `gitk hmm` or `gitk likelihood`, with the corresponding code contained within a sub-folder named after the model: + +``` +gitk ... +``` + +This is implemented within each module folder with: + +- `gitk//cli.py` - defines the command-line interface and provides a subparser for this module's CLI command. \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index e4989a75..b45c96a5 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -8,13 +8,15 @@ nav: - Getting Started: - Introduction: README.md - Modules: + - Assess universe: modules/assess_universe.md - BEDSpace: modules/bedspace.md + - Evaluate embeddings: modules/evaluation.md - Region2Vec: modules/region2vec.md - - scEmbed: modules/scembed.md + - Single-cell embeddings: modules/scembed.md - Tokenization: modules/tokenization.md - - Universe: modules/universe.md + - Create universe: modules/create_universe.md - How-to guides: - - Create consensus peaks: tutorials/consensus-peaks.md + - Create consensus peaks: tutorials/create-consensus-peaks.md - Reference: - API: autodoc_build/gitk.md - Support: support.md From 8eec58c38cc9c127691c556e8eb4c5e5c2dafa50 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 16:03:44 -0400 Subject: [PATCH 0241/1072] Add modules to setup. Fix#41 --- gitk/README.md | 10 +++++++++- setup.py | 8 +++++--- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/gitk/README.md b/gitk/README.md index 6e01abbf..19df7595 100644 --- a/gitk/README.md +++ b/gitk/README.md @@ -25,4 +25,12 @@ gitk ... This is implemented within each module folder with: -- `gitk//cli.py` - defines the command-line interface and provides a subparser for this module's CLI command. \ No newline at end of file +- `gitk//cli.py` - defines the command-line interface and provides a subparser for this module's CLI command. + +## How to add a new module + +To add a new module, you need to: + +1. create a subfolder (under `gitk/`) with your module name. +2. Add the module to list of packages in `setup.py`. +3. If it makes sense to have a CLI for this module, implement it in `gitk//cli.py`. Link this into the main cli by putting it under an appropriate command name following the pattern for other modules in `gitk/cli.py`. \ No newline at end of file diff --git a/setup.py b/setup.py index f08d4c26..5869fb1d 100755 --- a/setup.py +++ b/setup.py @@ -27,12 +27,14 @@ packages=[ PACKAGE_NAME, "gitk.assess", + "gitk.bedspace", "gitk.eval", - "gitk.universe", "gitk.likelihood", - "gitk.scembed", "gitk.models", - "gitk.bedspace", + "gitk.region2vec", + "gitk.scembed", + "gitk.tokenization", + "gitk.universe", ], version=version, long_description=long_description, From 32bcc4e0ea5002a3874d726b1ccd574a92faab65 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 16:07:51 -0400 Subject: [PATCH 0242/1072] remove old import. --- gitk/scembed/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gitk/scembed/__init__.py b/gitk/scembed/__init__.py index 4712433b..a57e6210 100644 --- a/gitk/scembed/__init__.py +++ b/gitk/scembed/__init__.py @@ -1,5 +1,4 @@ from .const import * -from .projection import * from .annotation import * from .scembed import * from .utils import * From 01609e17e9531439a9ba28850181e0f29d545f46 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 16:44:58 -0400 Subject: [PATCH 0243/1072] rename non-test, see #44 --- gitk/bedspace/pipeline/bedspace_test_model.py | 236 ++++++++++++++++++ 1 file changed, 236 insertions(+) create mode 100755 gitk/bedspace/pipeline/bedspace_test_model.py diff --git a/gitk/bedspace/pipeline/bedspace_test_model.py b/gitk/bedspace/pipeline/bedspace_test_model.py new file mode 100755 index 00000000..4ff6f422 --- /dev/null +++ b/gitk/bedspace/pipeline/bedspace_test_model.py @@ -0,0 +1,236 @@ +#!/usr/bin/env python3 +""" +bedfile embeding pipeline (test) +""" +import argparse +import datetime +import itertools +import json +import os +import subprocess +import sys +from collections import Counter +from multiprocessing import Pool +from random import shuffle +from subprocess import check_output + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pybedtools +import umap +import yaml +from bbconf import BedBaseConf +from bbconf.const import * +from helpers import data_prepration_test, hash_bedfile +from scipy.spatial import distance +from sklearn.metrics.pairwise import cosine_similarity +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import MinMaxScaler +from ubiquerg import VersionInHelpParser + + +def data_preprocessing(path_embeded_document): + document_embedding = pd.read_csv(path_embeded_document, header=None) + document_embedding = document_embedding[0].str.split("__label__", expand=True) + document_embedding[list(document_embedding)[1:]] = document_embedding[ + list(document_embedding)[1:] + ].shift(1) + document_embedding = document_embedding[5:].dropna() + document_embedding = document_embedding[::2].reset_index() + document_embedding = document_embedding[0].str.split(" ", expand=True) + + Xs = [] + for i in range(len(document_embedding)): + X = document_embedding[i : i + 1] + X = X[list(X)[0:-1]].astype(float) + X = list(X.values) + Xs.append(X) + return Xs + + +def label_preprocessing(path_word_embedding, label_prefix): + labels = [] + label_vectors = [] + word_embedding = pd.read_csv(path_word_embedding, sep="\t", header=None) + vectors = word_embedding[ + word_embedding[0].str.contains(label_prefix) + ] # .reset_index() + for l in range(len(vectors)): + label_vectors.append((list(vectors.iloc[l])[1:])) + labels.append(list(vectors.iloc[l])[0].replace(label_prefix, "")) + return label_vectors, labels + + +def calculate_distance(X_files, X_labels, y_files, y_labels): + X_files = np.array(X_files) + X_labels = np.array(X_labels) + distance_matrix = distance.cdist(X_files, X_labels, "cosine") + df_distance_matrix = pd.DataFrame(distance_matrix) + df_distance_matrix.columns = y_labels + df_distance_matrix["file_label"] = y_files + file_distance = pd.melt( + df_distance_matrix, + id_vars="file_label", + var_name="search_term", + value_name="score", + ) + scaler = MinMaxScaler() + file_distance["score"] = scaler.fit_transform( + np.array(file_distance["score"]).reshape(-1, 1) + ) + return file_distance + + +def meta_preprocessing(meta): + assembly = meta["genome"] + labels = [] + for l in args.labels.split(","): + if l in meta.index: + labels.append(l) + labels.insert(0, "file_name") + meta = meta[labels] + meta = meta.fillna("") + meta["file_name"] = args.data_path + meta["file_name"] + + return ",".join(list(meta)), assembly + + +parser = argparse.ArgumentParser() +parser.add_argument( + "-data_path", + "--data_path", + default=None, + type=str, + required=True, + help="Path to the bed files.", +) +parser.add_argument( + "-meta", + "--meta_path", + default=None, + type=str, + required=True, + help="Path to the metadata file.", +) +parser.add_argument( + "-univ", + "--univ_path", + default=None, + type=str, + required=True, + help="Path to the universe file.", +) +parser.add_argument( + "-o", + "--output_path", + default="./", + type=str, + required=True, + help="Path to output directory to store file.", +) + +parser.add_argument( + "-l", + "--labels", + default=None, + type=str, + required=True, + help="columns use as label", +) + +args = parser.parse_args() + +n_process = 20 +label_prefix = "__label__" + + +def main(): + print("Start", datetime.datetime.now()) + universe = pybedtools.BedTool(args.univ_path) + + meta_df = pd.read_csv(args.meta_path) + file_list = [] + for i in range(len(meta_df)): + meta_data, assembly = meta_preprocessing(meta_df.iloc[i]) + file_list.append(meta_data) + + # define file path + model = os.path.join(args.output_path, "starspace_model_{}".format(assembly)) + label_embed = os.path.join( + args.output_path, "starspace_model_{}.tsv".format(assembly) + ) + docs = os.path.join(args.output_path, "documents_{}.txt".format(assembly)) + # files = os.path.join(args.output_path, "filenames_{}.txt".format(assembly)) + doc_embed = os.path.join( + args.output_path, "train_starspace_embed_{}.txt".format(assembly) + ) + dist = os.path.join(args.output_path, "similarity_score_{}.csv".format(assembly)) + + embedding_labels, labels = label_preprocessing(label_embed, label_prefix) + + # Data prepration + trained_documents = [] + with Pool(n_process) as p: + trained_documents = p.starmap( + data_prepration_test, [(x, universe) for x in file_list] + ) + p.close() + p.join() + print("Reading files done") + + df = pd.DataFrame(trained_documents, columns=["file_path", "context"]) + df = df.fillna(" ") + df = df[df.context != " "] + + with open(docs, "w") as input_file: + input_file.write("\n".join(df.context)) + input_file.close() + + print("Writing files done") + + starspace_path = os.path.join( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))), + "tools", + "StarSpace", + "embed_doc", + ) + + output = check_output([starspace_path, model, docs]).decode("utf-8") + + with open(doc_embed, "w") as out: + out.write(output) + out.close() + + Xs = data_preprocessing(doc_embed) + + for i in range(len(file_list)): + y = ",".join(file_list[i].split(",")[1:]) + X = Xs[i] + + df_similarity = calculate_distance(X, embedding_labels, y, labels) + + df_similarity["filename"] = [file_list[i].split(",")[0]] * len(labels) + + df_similarity = df_similarity[ + ["filename", "file_label", "search_term", "score"] + ] + + # filter res by dist threshold + thresh = 0.5 + df_similarity = df_similarity[df_similarity["score"] < thresh].reset_index( + drop=True + ) + + if os.path.exists(dist): + df_similarity.to_csv(dist, header=False, index=None, mode="a") + else: + df_similarity.to_csv(dist, index=False) + + +if __name__ == "__main__": + try: + sys.exit(main()) + except KeyboardInterrupt: + print("Pipeline aborted.") + sys.exit(1) From 4209eb40b2fd6cdba582a0292f19100685612e61 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 16:45:11 -0400 Subject: [PATCH 0244/1072] make names consistent for universe module --- ...{assess_universe.md => assess-universe.md} | 0 .../{create_universe.md => build-universe.md} | 0 gitk/bedspace/pipeline/bedspace_test.py | 236 ------------------ mkdocs.yml | 4 +- 4 files changed, 2 insertions(+), 238 deletions(-) rename docs/modules/{assess_universe.md => assess-universe.md} (100%) rename docs/modules/{create_universe.md => build-universe.md} (100%) delete mode 100755 gitk/bedspace/pipeline/bedspace_test.py diff --git a/docs/modules/assess_universe.md b/docs/modules/assess-universe.md similarity index 100% rename from docs/modules/assess_universe.md rename to docs/modules/assess-universe.md diff --git a/docs/modules/create_universe.md b/docs/modules/build-universe.md similarity index 100% rename from docs/modules/create_universe.md rename to docs/modules/build-universe.md diff --git a/gitk/bedspace/pipeline/bedspace_test.py b/gitk/bedspace/pipeline/bedspace_test.py deleted file mode 100755 index 4ff6f422..00000000 --- a/gitk/bedspace/pipeline/bedspace_test.py +++ /dev/null @@ -1,236 +0,0 @@ -#!/usr/bin/env python3 -""" -bedfile embeding pipeline (test) -""" -import argparse -import datetime -import itertools -import json -import os -import subprocess -import sys -from collections import Counter -from multiprocessing import Pool -from random import shuffle -from subprocess import check_output - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import pybedtools -import umap -import yaml -from bbconf import BedBaseConf -from bbconf.const import * -from helpers import data_prepration_test, hash_bedfile -from scipy.spatial import distance -from sklearn.metrics.pairwise import cosine_similarity -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import MinMaxScaler -from ubiquerg import VersionInHelpParser - - -def data_preprocessing(path_embeded_document): - document_embedding = pd.read_csv(path_embeded_document, header=None) - document_embedding = document_embedding[0].str.split("__label__", expand=True) - document_embedding[list(document_embedding)[1:]] = document_embedding[ - list(document_embedding)[1:] - ].shift(1) - document_embedding = document_embedding[5:].dropna() - document_embedding = document_embedding[::2].reset_index() - document_embedding = document_embedding[0].str.split(" ", expand=True) - - Xs = [] - for i in range(len(document_embedding)): - X = document_embedding[i : i + 1] - X = X[list(X)[0:-1]].astype(float) - X = list(X.values) - Xs.append(X) - return Xs - - -def label_preprocessing(path_word_embedding, label_prefix): - labels = [] - label_vectors = [] - word_embedding = pd.read_csv(path_word_embedding, sep="\t", header=None) - vectors = word_embedding[ - word_embedding[0].str.contains(label_prefix) - ] # .reset_index() - for l in range(len(vectors)): - label_vectors.append((list(vectors.iloc[l])[1:])) - labels.append(list(vectors.iloc[l])[0].replace(label_prefix, "")) - return label_vectors, labels - - -def calculate_distance(X_files, X_labels, y_files, y_labels): - X_files = np.array(X_files) - X_labels = np.array(X_labels) - distance_matrix = distance.cdist(X_files, X_labels, "cosine") - df_distance_matrix = pd.DataFrame(distance_matrix) - df_distance_matrix.columns = y_labels - df_distance_matrix["file_label"] = y_files - file_distance = pd.melt( - df_distance_matrix, - id_vars="file_label", - var_name="search_term", - value_name="score", - ) - scaler = MinMaxScaler() - file_distance["score"] = scaler.fit_transform( - np.array(file_distance["score"]).reshape(-1, 1) - ) - return file_distance - - -def meta_preprocessing(meta): - assembly = meta["genome"] - labels = [] - for l in args.labels.split(","): - if l in meta.index: - labels.append(l) - labels.insert(0, "file_name") - meta = meta[labels] - meta = meta.fillna("") - meta["file_name"] = args.data_path + meta["file_name"] - - return ",".join(list(meta)), assembly - - -parser = argparse.ArgumentParser() -parser.add_argument( - "-data_path", - "--data_path", - default=None, - type=str, - required=True, - help="Path to the bed files.", -) -parser.add_argument( - "-meta", - "--meta_path", - default=None, - type=str, - required=True, - help="Path to the metadata file.", -) -parser.add_argument( - "-univ", - "--univ_path", - default=None, - type=str, - required=True, - help="Path to the universe file.", -) -parser.add_argument( - "-o", - "--output_path", - default="./", - type=str, - required=True, - help="Path to output directory to store file.", -) - -parser.add_argument( - "-l", - "--labels", - default=None, - type=str, - required=True, - help="columns use as label", -) - -args = parser.parse_args() - -n_process = 20 -label_prefix = "__label__" - - -def main(): - print("Start", datetime.datetime.now()) - universe = pybedtools.BedTool(args.univ_path) - - meta_df = pd.read_csv(args.meta_path) - file_list = [] - for i in range(len(meta_df)): - meta_data, assembly = meta_preprocessing(meta_df.iloc[i]) - file_list.append(meta_data) - - # define file path - model = os.path.join(args.output_path, "starspace_model_{}".format(assembly)) - label_embed = os.path.join( - args.output_path, "starspace_model_{}.tsv".format(assembly) - ) - docs = os.path.join(args.output_path, "documents_{}.txt".format(assembly)) - # files = os.path.join(args.output_path, "filenames_{}.txt".format(assembly)) - doc_embed = os.path.join( - args.output_path, "train_starspace_embed_{}.txt".format(assembly) - ) - dist = os.path.join(args.output_path, "similarity_score_{}.csv".format(assembly)) - - embedding_labels, labels = label_preprocessing(label_embed, label_prefix) - - # Data prepration - trained_documents = [] - with Pool(n_process) as p: - trained_documents = p.starmap( - data_prepration_test, [(x, universe) for x in file_list] - ) - p.close() - p.join() - print("Reading files done") - - df = pd.DataFrame(trained_documents, columns=["file_path", "context"]) - df = df.fillna(" ") - df = df[df.context != " "] - - with open(docs, "w") as input_file: - input_file.write("\n".join(df.context)) - input_file.close() - - print("Writing files done") - - starspace_path = os.path.join( - os.path.dirname(os.path.dirname(os.path.abspath(__file__))), - "tools", - "StarSpace", - "embed_doc", - ) - - output = check_output([starspace_path, model, docs]).decode("utf-8") - - with open(doc_embed, "w") as out: - out.write(output) - out.close() - - Xs = data_preprocessing(doc_embed) - - for i in range(len(file_list)): - y = ",".join(file_list[i].split(",")[1:]) - X = Xs[i] - - df_similarity = calculate_distance(X, embedding_labels, y, labels) - - df_similarity["filename"] = [file_list[i].split(",")[0]] * len(labels) - - df_similarity = df_similarity[ - ["filename", "file_label", "search_term", "score"] - ] - - # filter res by dist threshold - thresh = 0.5 - df_similarity = df_similarity[df_similarity["score"] < thresh].reset_index( - drop=True - ) - - if os.path.exists(dist): - df_similarity.to_csv(dist, header=False, index=None, mode="a") - else: - df_similarity.to_csv(dist, index=False) - - -if __name__ == "__main__": - try: - sys.exit(main()) - except KeyboardInterrupt: - print("Pipeline aborted.") - sys.exit(1) diff --git a/mkdocs.yml b/mkdocs.yml index b45c96a5..e0b3cf13 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -8,13 +8,13 @@ nav: - Getting Started: - Introduction: README.md - Modules: - - Assess universe: modules/assess_universe.md + - Assess universe: modules/assess-universe.md - BEDSpace: modules/bedspace.md - Evaluate embeddings: modules/evaluation.md - Region2Vec: modules/region2vec.md - Single-cell embeddings: modules/scembed.md - Tokenization: modules/tokenization.md - - Create universe: modules/create_universe.md + - Build universe: modules/build-universe.md - How-to guides: - Create consensus peaks: tutorials/create-consensus-peaks.md - Reference: From efbe4ea211d9f635a4bca3ea4b877ef7867dc071 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 16:48:13 -0400 Subject: [PATCH 0245/1072] lint --- gitk/assess/assess.py | 13 ++-- gitk/assess/cli.py | 14 ++-- gitk/assess/distance.py | 25 ++++--- gitk/assess/intersection.py | 23 +++++- gitk/assess/likelihood.py | 46 ++++++------ gitk/bedspace/argparsers.py | 12 ++-- gitk/bedspace/pipeline/Visualization.py | 72 ++++++++----------- gitk/bedspace/pipeline/bedspace_queryDBsim.py | 12 +--- gitk/bedspace/pipeline/bedspace_test_model.py | 28 ++------ gitk/bedspace/pipeline/bedspace_train.py | 9 +-- gitk/bedspace/pipeline/distances.py | 34 +++------ gitk/bedspace/pipeline/helpers.py | 12 ++-- gitk/bedspace/pipeline/preprocess.py | 9 +-- gitk/bedspace/pipeline/search.py | 24 +++++-- gitk/bedspace/pipeline/train.py | 7 +- gitk/eval/cli.py | 16 ++--- gitk/eval/ctt.py | 11 +-- gitk/eval/gdst.py | 27 +++---- gitk/eval/npt.py | 56 +++++++++------ gitk/eval/rct.py | 31 ++++---- gitk/eval/utils.py | 4 +- gitk/likelihood/build_model.py | 8 +-- gitk/likelihood/cli.py | 14 ++-- gitk/models/pretrained.py | 8 +-- gitk/models/tokenization.py | 12 +--- gitk/models/utils.py | 12 +--- gitk/region2vec/cli.py | 16 ++--- gitk/region2vec/main.py | 2 +- gitk/region2vec/region2vec_train.py | 38 ++++------ gitk/region2vec/region_shuffling.py | 16 ++--- gitk/region2vec/utils.py | 5 +- gitk/scembed/annotation.py | 4 +- gitk/scembed/argparser.py | 3 +- gitk/scembed/const.py | 4 +- gitk/scembed/scembed.py | 32 ++++----- gitk/scembed/utils.py | 15 ++-- gitk/tokenization/cli.py | 8 +-- gitk/tokenization/hard_tokenization_batch.py | 16 ++--- gitk/tokenization/main.py | 7 +- gitk/tokenization/split_file.py | 4 +- gitk/tokenization/utils.py | 5 +- gitk/universe/ccf_universe.py | 4 +- gitk/universe/cli.py | 13 ++-- gitk/universe/const.py | 7 +- gitk/universe/custom_distribution.py | 9 +-- gitk/universe/hmm_universe.py | 6 +- gitk/universe/ml_universe.py | 12 +--- gitk/universe/models.py | 9 ++- pyproject.toml | 5 ++ setup.py | 2 +- tests/test_models.py | 7 +- 51 files changed, 359 insertions(+), 429 deletions(-) create mode 100644 pyproject.toml diff --git a/gitk/assess/assess.py b/gitk/assess/assess.py index 59511745..23232759 100644 --- a/gitk/assess/assess.py +++ b/gitk/assess/assess.py @@ -122,7 +122,10 @@ def run_all_assessment_methods( save_each, True, ) - r_distance_utf_flex.columns = ["file", "median_dist_universe_to_file_flex"] + r_distance_utf_flex.columns = [ + "file", + "median_dist_universe_to_file_flex", + ] asses_results.append(r_distance_utf_flex) _LOGGER.info("DONE: Flexible distance universe to file") @@ -251,13 +254,9 @@ def filter_universe( if not cove_prefix: miss_args.append("cove_prefix") raise ValueError( - "Missing {} for peak likelihood calculations.".format( - ",".join(miss_args) - ) + "Missing {} for peak likelihood calculations.".format(",".join(miss_args)) ) - likelihood_flexible_universe( - model_file, universe, cove_folder, cove_prefix, True - ) + likelihood_flexible_universe(model_file, universe, cove_folder, cove_prefix, True) universe = universe + "_peak_likelihood" with open(universe) as uni: with open(universe_filtered, "w+") as uni_flt: diff --git a/gitk/assess/cli.py b/gitk/assess/cli.py index 73203175..ec3be7be 100644 --- a/gitk/assess/cli.py +++ b/gitk/assess/cli.py @@ -32,7 +32,10 @@ def build_subparser(parser): action="store_true", ) parser.add_argument( - "--raw-data-folder", help="folder with raw data", type=str, required=True + "--raw-data-folder", + help="folder with raw data", + type=str, + required=True, ) parser.add_argument( "--file-list", @@ -42,16 +45,17 @@ def build_subparser(parser): ) parser.add_argument("--universe", help="universe file", type=str, required=True) parser.add_argument( - "--no-workers", help="number of core that should be used", default=4, type=int + "--no-workers", + help="number of core that should be used", + default=4, + type=int, ) parser.add_argument( "--save-to-file", help="if save statistics for each BED file to a file", action="store_true", ) - parser.add_argument( - "--folder-out", help="folder to which save the statistic", type=str - ) + parser.add_argument("--folder-out", help="folder to which save the statistic", type=str) parser.add_argument("--pref", help="statistic file prefix", type=str) parser.add_argument( diff --git a/gitk/assess/distance.py b/gitk/assess/distance.py index 95c529bb..116e52c2 100644 --- a/gitk/assess/distance.py +++ b/gitk/assess/distance.py @@ -55,13 +55,9 @@ def distance_to_closest_region( """ if flexible: if uni_to_file: - dist_to_db_que = [ - flexible_distance_between_two_regions(i, j[0]) for j in db_queue - ] + dist_to_db_que = [flexible_distance_between_two_regions(i, j[0]) for j in db_queue] else: - dist_to_db_que = [ - flexible_distance_between_two_regions(j, i[0]) for j in db_queue - ] + dist_to_db_que = [flexible_distance_between_two_regions(j, i[0]) for j in db_queue] else: dist_to_db_que = [distance_between_two_regions(j, i[0]) for j in db_queue] min_pos = np.argmin(dist_to_db_que) @@ -77,13 +73,9 @@ def distance_to_closest_region( db_queue[-1] = pos if flexible: if uni_to_file: - dist_to_db_que = [ - flexible_distance_between_two_regions(i, j[0]) for j in db_queue - ] + dist_to_db_que = [flexible_distance_between_two_regions(i, j[0]) for j in db_queue] else: - dist_to_db_que = [ - flexible_distance_between_two_regions(j, i[0]) for j in db_queue - ] + dist_to_db_que = [flexible_distance_between_two_regions(j, i[0]) for j in db_queue] else: dist_to_db_que = [distance_between_two_regions(j, i[0]) for j in db_queue] min_pos = np.argmin(dist_to_db_que) @@ -305,7 +297,14 @@ def run_distance( if no_workers <= 1: for i in files: r = calc_distance_between_two_files( - universe, folder, i, flexible, save_each, folder_out, pref, uni_to_file + universe, + folder, + i, + flexible, + save_each, + folder_out, + pref, + uni_to_file, ) res.append(r) else: diff --git a/gitk/assess/intersection.py b/gitk/assess/intersection.py index a580e05a..5c98b16d 100644 --- a/gitk/assess/intersection.py +++ b/gitk/assess/intersection.py @@ -124,7 +124,10 @@ def calc_diff_intersection(db, folder, query): only_in_d, only_in_q, overlap = 0, 0, 0 inside_d, inside_q = False, False # inside a region not_end_d, not_end_q = True, True # if there are regions to process - waiting_d, waiting_q = False, False # if waiting for the other file to finish chrom + waiting_d, waiting_q = ( + False, + False, + ) # if waiting for the other file to finish chrom lines_q = tempfile.NamedTemporaryFile() prep_data(folder, query, lines_q) if os.stat(lines_q.name).st_size == 0: @@ -164,11 +167,25 @@ def calc_diff_intersection(db, folder, query): start_q, ) = regions_stats new_d = read_in_new_line( - pos_d, start_d, chrom_d, inside_d, waiting_d, lines_db, c_chrom, not_end_d + pos_d, + start_d, + chrom_d, + inside_d, + waiting_d, + lines_db, + c_chrom, + not_end_d, ) (pos_d, start_d, chrom_d, waiting_d, not_end_d) = new_d new_q = read_in_new_line( - pos_q, start_q, chrom_q, inside_q, waiting_q, lines_q, c_chrom, not_end_q + pos_q, + start_q, + chrom_q, + inside_q, + waiting_q, + lines_q, + c_chrom, + not_end_q, ) (pos_q, start_q, chrom_q, waiting_q, not_end_q) = new_q if waiting_d or waiting_q: diff --git a/gitk/assess/likelihood.py b/gitk/assess/likelihood.py index 8275ec77..24afcb17 100644 --- a/gitk/assess/likelihood.py +++ b/gitk/assess/likelihood.py @@ -71,9 +71,7 @@ def calc_likelihood_hard( model_lh.read_chrom_track(current_chrom, name) prob_model = model_lh[current_chrom] cove_array = read_chromosome_from_bw( - os.path.join( - coverage_folder, f"{coverage_prefix}_{name}.bw" - ), + os.path.join(coverage_folder, f"{coverage_prefix}_{name}.bw"), current_chrom, ) prob_array = LhModel(prob_model[name], cove_array) @@ -126,7 +124,14 @@ def hard_universe_likelihood(model, universe, coverage_folder, coverage_prefix): universe, chroms, model_lh, coverage_folder, coverage_prefix, "end", 2 ) c = calc_likelihood_hard( - universe, chroms, model_lh, coverage_folder, coverage_prefix, "core", 1, 2 + universe, + chroms, + model_lh, + coverage_folder, + coverage_prefix, + "core", + 1, + 2, ) return sum([s, e, c]) @@ -144,7 +149,14 @@ def likelihood_only_core(model_file, universe, coverage_folder, coverage_prefix) model_lh = ModelLH(model_file) chroms = model_lh.chromosomes_list c = calc_likelihood_hard( - universe, chroms, model_lh, coverage_folder, coverage_prefix, "core", 1, 2 + universe, + chroms, + model_lh, + coverage_folder, + coverage_prefix, + "core", + 1, + 2, ) return c @@ -171,9 +183,7 @@ def weigh_livelihood(start, end, model_process, model_cove, model_out, reverse): :return float: likelihood of flexible part of the region """ e_w = 1 / (end - start) # weights for processed model - c_w = np.linspace( - start=e_w, stop=1, num=(end - start) - ) # weights for core in processed region + c_w = np.linspace(start=e_w, stop=1, num=(end - start)) # weights for core in processed region if reverse: c_w = c_w[::-1] res = e_w * np.sum(model_process[start:end, 1]) @@ -184,9 +194,7 @@ def weigh_livelihood(start, end, model_process, model_cove, model_out, reverse): return res -def flexible_peak_likelihood( - start_s, start_e, end_s, end_e, model_start, model_cove, model_end -): +def flexible_peak_likelihood(start_s, start_e, end_s, end_e, model_start, model_cove, model_end): # core part of the peak res = np.sum(model_cove[start_e:end_s, 1]) res += np.sum(model_start[start_e:end_s, 0]) @@ -264,16 +272,10 @@ def likelihood_flexible_universe( e += 1 model_lh.read_chrom(current_chrom) models_current = model_lh[current_chrom] - cove_current = read_coverage( - cove_folder, cove_prefix, current_chrom - ) + cove_current = read_coverage(cove_folder, cove_prefix, current_chrom) chr_size = len(cove_current["start"]) - prob_start = LhModel( - models_current["start"], cove_current["start"] - ) - prob_core = LhModel( - models_current["core"], cove_current["core"] - ) + prob_start = LhModel(models_current["start"], cove_current["start"]) + prob_core = LhModel(models_current["core"], cove_current["core"]) prob_end = LhModel(models_current["end"], cove_current["end"]) else: @@ -304,9 +306,7 @@ def likelihood_flexible_universe( output.append("{}\t{}\n".format(line.strip("\n"), contribution)) empty_start = peak_end_e - res += background_likelihood( - empty_start, chr_size, prob_start, prob_core, prob_end - ) + res += background_likelihood(empty_start, chr_size, prob_start, prob_core, prob_end) if save_peak_input: print("saving") with open(universe + "_peak_likelihood", "w") as f: diff --git a/gitk/bedspace/argparsers.py b/gitk/bedspace/argparsers.py index fbbc9a72..2fd44ea5 100644 --- a/gitk/bedspace/argparsers.py +++ b/gitk/bedspace/argparsers.py @@ -4,7 +4,9 @@ from .const import * -def build_preprocess_argparser(parser: VersionInHelpParser) -> VersionInHelpParser: +def build_preprocess_argparser( + parser: VersionInHelpParser, +) -> VersionInHelpParser: parser.add_argument( "-i", "--input", @@ -12,9 +14,7 @@ def build_preprocess_argparser(parser: VersionInHelpParser) -> VersionInHelpPars help="Path to input bed files", ) - parser.add_argument( - "-m", "--metadata", dest="metadata", help="Path to metadata file" - ) + parser.add_argument("-m", "--metadata", dest="metadata", help="Path to metadata file") parser.add_argument( "-u", @@ -85,7 +85,9 @@ def build_train_argparser(parser: VersionInHelpParser) -> VersionInHelpParser: ) -def build_distance_argparser(parser: VersionInHelpParser) -> VersionInHelpParser: +def build_distance_argparser( + parser: VersionInHelpParser, +) -> VersionInHelpParser: parser.add_argument( "-i", "--input", diff --git a/gitk/bedspace/pipeline/Visualization.py b/gitk/bedspace/pipeline/Visualization.py index c142b3b2..e2798e4f 100644 --- a/gitk/bedspace/pipeline/Visualization.py +++ b/gitk/bedspace/pipeline/Visualization.py @@ -23,12 +23,8 @@ def label_preprocessing(path_label_embedding, label_prefix, common_labels=[]): labels = [] label_vectors = [] - label_embedding = pd.read_csv( - path_label_embedding, sep="\t", header=None, skiprows=1 - ) - vectors = label_embedding[ - label_embedding[0].str.contains(label_prefix) - ] # .reset_index() + label_embedding = pd.read_csv(path_label_embedding, sep="\t", header=None, skiprows=1) + vectors = label_embedding[label_embedding[0].str.contains(label_prefix)] # .reset_index() vectors[0] = vectors[0].str.replace(label_prefix, "") @@ -107,7 +103,11 @@ def UMAP_plot( for i, txt in enumerate(list(ump_data[title])): fig.annotate( - txt, (ump_data.iloc[i]["UMAP 1"] - 0.05, ump_data.iloc[i]["UMAP 2"] + 0.05) + txt, + ( + ump_data.iloc[i]["UMAP 1"] - 0.05, + ump_data.iloc[i]["UMAP 2"] + 0.05, + ), ) # plt.legend(loc='upper right', ) @@ -214,9 +214,7 @@ def Scenario1(path_simfile): search_table, on="filename", ).drop_duplicates() - search_table = search_table.merge( - meta_test, left_on="filename", right_on="file_name" - ) + search_table = search_table.merge(meta_test, left_on="filename", right_on="file_name") if search == "cell": ind = 0 @@ -236,18 +234,14 @@ def Scenario1(path_simfile): np.min([ind, len_targets]) ] - search_table = search_table[ - ["filename", "file_label", "original_label"] + (training_labels) - ] - search_table["predicted_label"] = search_table[list(search_table)[3:]].idxmin( - axis=1 - ) + search_table = search_table[["filename", "file_label", "original_label"] + (training_labels)] + search_table["predicted_label"] = search_table[list(search_table)[3:]].idxmin(axis=1) for searchterm in training_labels: nof = len(search_table[search_table.file_label.str.contains(searchterm)]) - df = search_table[ - ["filename", "file_label", "original_label", searchterm] - ].sort_values(by=[searchterm])[0:10] + df = search_table[["filename", "file_label", "original_label", searchterm]].sort_values( + by=[searchterm] + )[0:10] df = df.sort_values(by=[searchterm], ascending=False) df["color"] = "gray" @@ -270,9 +264,7 @@ def Scenario1(path_simfile): plt.axis(xmin=0.5, xmax=1.01) plt.figure.savefig( - "../outputs/bedembed_output/figures/S1/{}_nof{}.svg".format( - searchterm, nof - ), + "../outputs/bedembed_output/figures/S1/{}_nof{}.svg".format(searchterm, nof), format="svg", bbox_inches="tight", ) @@ -296,9 +288,7 @@ def Scenario2(path_simfile): search_table = pd.pivot_table( distance, values="score", index=["filename"], columns=["search_term"] ).reset_index() - search_table = search_table.merge( - meta_test, left_on="filename", right_on="file_name" - ) + search_table = search_table.merge(meta_test, left_on="filename", right_on="file_name") search_table = pd.merge( distance[["filename", "file_label"]].drop_duplicates(), search_table, @@ -320,12 +310,8 @@ def Scenario2(path_simfile): np.min([ind, len_targets]) ] - search_table = search_table[ - ["filename", "file_label", "original_label"] + (training_labels) - ] - search_table["predicted_label"] = search_table[list(search_table)[3:]].idxmin( - axis=1 - ) + search_table = search_table[["filename", "file_label", "original_label"] + (training_labels)] + search_table["predicted_label"] = search_table[list(search_table)[3:]].idxmin(axis=1) i = 0 b = search_table @@ -349,13 +335,16 @@ def Scenario2(path_simfile): i += 1 X = pd.DataFrame(all_weights).rename( - columns={0: "Filename", 1: "Filelabel", 2: "AllLabels", 3: "Distance_score"} + columns={ + 0: "Filename", + 1: "Filelabel", + 2: "AllLabels", + 3: "Distance_score", + } ) for file in list(set(X.Filename)): - df = X[X.Filename == file].sort_values(by=["Distance_score"], ascending=False)[ - 0:10 - ] + df = X[X.Filename == file].sort_values(by=["Distance_score"], ascending=False)[0:10] df = df.sort_values(by=["Distance_score"], ascending=True) df["color"] = "green" plt = df.plot.barh( @@ -390,9 +379,7 @@ def Scenario2(path_simfile): meta_train = (pd.read_csv(path_meta + "tests/test_file_meta.csv"))[ ["file_name", "cell_type", "target"] ] -meta_train["original_label_train"] = ( - meta_train["target"] + " " + meta_train["cell_type"] -) +meta_train["original_label_train"] = meta_train["target"] + " " + meta_train["cell_type"] meta_test.file_name = "/project/shefflab/data/encode/" + meta_test.file_name meta_train.file_name = "/project/shefflab/data/encode/" + meta_train.file_name @@ -408,7 +395,10 @@ def Scenario2(path_simfile): )[[0, 1, 2]] sim = sim.merge(meta_test, left_on="test_name", right_on="file_name").merge( - meta_train, left_on="train_name", right_on="file_name", suffixes=("_test", "_train") + meta_train, + left_on="train_name", + right_on="file_name", + suffixes=("_test", "_train"), ) # sim.score = 1 - sim.score @@ -443,9 +433,7 @@ def Scenario2(path_simfile): plt.axis(xmin=0.7, xmax=1.01) plt.figure.savefig( - "../outputs/bedembed_output/figures/S3/{}_nof{}.svg".format( - test.split("/")[-1], nof - ), + "../outputs/bedembed_output/figures/S3/{}_nof{}.svg".format(test.split("/")[-1], nof), format="svg", bbox_inches="tight", ) diff --git a/gitk/bedspace/pipeline/bedspace_queryDBsim.py b/gitk/bedspace/pipeline/bedspace_queryDBsim.py index 2e8c54fe..634a9c49 100644 --- a/gitk/bedspace/pipeline/bedspace_queryDBsim.py +++ b/gitk/bedspace/pipeline/bedspace_queryDBsim.py @@ -60,9 +60,7 @@ def calculate_distance(X_files, X_labels, y_files, y_labels): value_name="score", ) scaler = MinMaxScaler() - file_distance["score"] = scaler.fit_transform( - np.array(file_distance["score"]).reshape(-1, 1) - ) + file_distance["score"] = scaler.fit_transform(np.array(file_distance["score"]).reshape(-1, 1)) return file_distance @@ -164,13 +162,9 @@ def main(): # define file path model = os.path.join(args.output_path, "starspace_model_{}".format(assembly)) - dist = os.path.join( - args.output_path, "query_db_similarity_score_{}.csv".format(assembly) - ) + dist = os.path.join(args.output_path, "query_db_similarity_score_{}.csv".format(assembly)) - doc_embed_dB = bed2vec( - file_list_db, universe, model, assembly, "DB", args.output_path - ) + doc_embed_dB = bed2vec(file_list_db, universe, model, assembly, "DB", args.output_path) db_vectors = data_preprocessing(doc_embed_dB) diff --git a/gitk/bedspace/pipeline/bedspace_test_model.py b/gitk/bedspace/pipeline/bedspace_test_model.py index 4ff6f422..b926ad50 100755 --- a/gitk/bedspace/pipeline/bedspace_test_model.py +++ b/gitk/bedspace/pipeline/bedspace_test_model.py @@ -53,9 +53,7 @@ def label_preprocessing(path_word_embedding, label_prefix): labels = [] label_vectors = [] word_embedding = pd.read_csv(path_word_embedding, sep="\t", header=None) - vectors = word_embedding[ - word_embedding[0].str.contains(label_prefix) - ] # .reset_index() + vectors = word_embedding[word_embedding[0].str.contains(label_prefix)] # .reset_index() for l in range(len(vectors)): label_vectors.append((list(vectors.iloc[l])[1:])) labels.append(list(vectors.iloc[l])[0].replace(label_prefix, "")) @@ -76,9 +74,7 @@ def calculate_distance(X_files, X_labels, y_files, y_labels): value_name="score", ) scaler = MinMaxScaler() - file_distance["score"] = scaler.fit_transform( - np.array(file_distance["score"]).reshape(-1, 1) - ) + file_distance["score"] = scaler.fit_transform(np.array(file_distance["score"]).reshape(-1, 1)) return file_distance @@ -157,14 +153,10 @@ def main(): # define file path model = os.path.join(args.output_path, "starspace_model_{}".format(assembly)) - label_embed = os.path.join( - args.output_path, "starspace_model_{}.tsv".format(assembly) - ) + label_embed = os.path.join(args.output_path, "starspace_model_{}.tsv".format(assembly)) docs = os.path.join(args.output_path, "documents_{}.txt".format(assembly)) # files = os.path.join(args.output_path, "filenames_{}.txt".format(assembly)) - doc_embed = os.path.join( - args.output_path, "train_starspace_embed_{}.txt".format(assembly) - ) + doc_embed = os.path.join(args.output_path, "train_starspace_embed_{}.txt".format(assembly)) dist = os.path.join(args.output_path, "similarity_score_{}.csv".format(assembly)) embedding_labels, labels = label_preprocessing(label_embed, label_prefix) @@ -172,9 +164,7 @@ def main(): # Data prepration trained_documents = [] with Pool(n_process) as p: - trained_documents = p.starmap( - data_prepration_test, [(x, universe) for x in file_list] - ) + trained_documents = p.starmap(data_prepration_test, [(x, universe) for x in file_list]) p.close() p.join() print("Reading files done") @@ -212,15 +202,11 @@ def main(): df_similarity["filename"] = [file_list[i].split(",")[0]] * len(labels) - df_similarity = df_similarity[ - ["filename", "file_label", "search_term", "score"] - ] + df_similarity = df_similarity[["filename", "file_label", "search_term", "score"]] # filter res by dist threshold thresh = 0.5 - df_similarity = df_similarity[df_similarity["score"] < thresh].reset_index( - drop=True - ) + df_similarity = df_similarity[df_similarity["score"] < thresh].reset_index(drop=True) if os.path.exists(dist): df_similarity.to_csv(dist, header=False, index=None, mode="a") diff --git a/gitk/bedspace/pipeline/bedspace_train.py b/gitk/bedspace/pipeline/bedspace_train.py index ad6ab650..1cc75605 100755 --- a/gitk/bedspace/pipeline/bedspace_train.py +++ b/gitk/bedspace/pipeline/bedspace_train.py @@ -133,9 +133,7 @@ # StarSpace Parameters universe = pybedtools.BedTool(args.univ_path) # define file path -train_files = os.path.join( - args.output_path, "documents_file_{}.txt".format(args.genome) -) +train_files = os.path.join(args.output_path, "documents_file_{}.txt".format(args.genome)) model = os.path.join(args.output_path, "starspace_model_{}".format(args.genome)) @@ -156,10 +154,7 @@ def main(): with Pool(n_process) as p: trained_documents = p.starmap( data_prepration, - [ - (x, universe) - for x in file_list[args.start_line : args.start_line + args.no_files] - ], + [(x, universe) for x in file_list[args.start_line : args.start_line + args.no_files]], ) p.close() p.join() diff --git a/gitk/bedspace/pipeline/distances.py b/gitk/bedspace/pipeline/distances.py index bdb10e11..e9129ebf 100644 --- a/gitk/bedspace/pipeline/distances.py +++ b/gitk/bedspace/pipeline/distances.py @@ -96,9 +96,7 @@ def label_preprocessing(path_word_embedding, label_prefix): labels = [] label_vectors = [] word_embedding = pd.read_csv(path_word_embedding, sep="\t", header=None) - vectors = word_embedding[ - word_embedding[0].str.contains(label_prefix) - ] # .reset_index() + vectors = word_embedding[word_embedding[0].str.contains(label_prefix)] # .reset_index() for l in range(len(vectors)): label_vectors.append((list(vectors.iloc[l])[1:])) labels.append(list(vectors.iloc[l])[0].replace(label_prefix, "")) @@ -111,9 +109,7 @@ def calculate_distance(X_files, X_labels, y_files, y_labels): distance_matrix = distance.cdist(X_files, X_labels, "cosine") df_distance_matrix = pd.DataFrame(distance_matrix) df_distance_matrix.columns = y_labels - df_distance_matrix["file_label"] = [ - y_files[i].split(",")[1] for i in range(len(y_files)) - ] + df_distance_matrix["file_label"] = [y_files[i].split(",")[1] for i in range(len(y_files))] file_distance = pd.melt( df_distance_matrix, id_vars="file_label", @@ -121,9 +117,7 @@ def calculate_distance(X_files, X_labels, y_files, y_labels): value_name="score", ) scaler = MinMaxScaler() - file_distance["score"] = scaler.fit_transform( - np.array(file_distance["score"]).reshape(-1, 1) - ) + file_distance["score"] = scaler.fit_transform(np.array(file_distance["score"]).reshape(-1, 1)) return file_distance @@ -141,9 +135,7 @@ def calculate_distance_qc(X_files, X_labels, y_files, y_labels): value_name="score", ) scaler = MinMaxScaler() - file_distance["score"] = scaler.fit_transform( - np.array(file_distance["score"]).reshape(-1, 1) - ) + file_distance["score"] = scaler.fit_transform(np.array(file_distance["score"]).reshape(-1, 1)) return file_distance @@ -206,17 +198,15 @@ def main( df_similarity = calculate_distance(Xs, embedding_labels, file_list, labels_l) - df_similarity["filename"] = [ - file_list[i].split(",")[0] for i in range(len(file_list)) - ] * len(labels_l) + df_similarity["filename"] = [file_list[i].split(",")[0] for i in range(len(file_list))] * len( + labels_l + ) df_similarity = df_similarity[["filename", "file_label", "search_term", "score"]] # filter res by dist threshold - df_similarity = df_similarity[df_similarity["score"] > threshold].reset_index( - drop=True - ) + df_similarity = df_similarity[df_similarity["score"] > threshold].reset_index(drop=True) df_similarity["score"] = 1 - df_similarity["score"] @@ -229,9 +219,7 @@ def main( distance_file_path_rr = os.path.join(output, "similarity_score_rr.csv") - doc_embed_dB = bed2vec( - file_list_db, universe, input, "DB", temp_path, path_to_starsapce - ) + doc_embed_dB = bed2vec(file_list_db, universe, input, "DB", temp_path, path_to_starsapce) db_vectors = data_preprocessing(doc_embed_dB) @@ -241,9 +229,7 @@ def main( query_vectors = data_preprocessing(doc_embed_query) - df_similarity = calculate_distance_qc( - db_vectors, query_vectors, file_list_db, file_list_query - ) + df_similarity = calculate_distance_qc(db_vectors, query_vectors, file_list_db, file_list_query) df_similarity = df_similarity[["db_file", "test_file", "score"]] diff --git a/gitk/bedspace/pipeline/helpers.py b/gitk/bedspace/pipeline/helpers.py index b9cb7f89..07dae33b 100644 --- a/gitk/bedspace/pipeline/helpers.py +++ b/gitk/bedspace/pipeline/helpers.py @@ -10,9 +10,7 @@ def data_prepration(path_file_label: str, univ: str): path_file_label = path_file_label.split(",") path_file = path_file_label[0] - labels = " ".join( - ["__label__" + label for label in path_file_label[1:] if label != ""] - ) + labels = " ".join(["__label__" + label for label in path_file_label[1:] if label != ""]) if os.path.exists(path_file): try: df = pybedtools.BedTool(path_file) @@ -28,7 +26,10 @@ def data_prepration(path_file_label: str, univ: str): + "_" + file_regions["end"].astype(str) ) - return [path_file, " ".join(list(file_regions["region"])) + " " + labels] + return [ + path_file, + " ".join(list(file_regions["region"])) + " " + labels, + ] except Exception: print("Error in reading file: ", path_file) return [path_file, " "] @@ -68,7 +69,8 @@ def bed2vec(file_list, universe, model, assembly, source, output_path): files = os.path.join(output_path, "filenames_{}.txt".format(assembly)) doc_embed = os.path.join( - output_path, "{}_starspace_embed_{}_{}.txt".format(source, assembly, source) + output_path, + "{}_starspace_embed_{}_{}.txt".format(source, assembly, source), ) documents = [] diff --git a/gitk/bedspace/pipeline/preprocess.py b/gitk/bedspace/pipeline/preprocess.py index bf064358..c15d815e 100644 --- a/gitk/bedspace/pipeline/preprocess.py +++ b/gitk/bedspace/pipeline/preprocess.py @@ -13,9 +13,7 @@ def data_prepration(path_file_label: str, univ): path_file_label = path_file_label.split(",") path_file = path_file_label[0] - labels = " ".join( - ["__label__" + label for label in path_file_label[1:] if label != ""] - ) + labels = " ".join(["__label__" + label for label in path_file_label[1:] if label != ""]) if os.path.exists(path_file): try: @@ -33,7 +31,10 @@ def data_prepration(path_file_label: str, univ): + "_" + file_regions["end"].astype(str) ) - return [path_file, " ".join(list(file_regions["region"])) + " " + labels] + return [ + path_file, + " ".join(list(file_regions["region"])) + " " + labels, + ] except Exception: print("Error in reading file: ", path_file) return [path_file, " "] diff --git a/gitk/bedspace/pipeline/search.py b/gitk/bedspace/pipeline/search.py index 9618dc9f..89b23cde 100644 --- a/gitk/bedspace/pipeline/search.py +++ b/gitk/bedspace/pipeline/search.py @@ -32,13 +32,17 @@ def run_scenario1( search_table = pd.pivot_table( distance, values="score", index=["filename"], columns=["search_term"] ).reset_index() - df = search_table[["filename", searchterm]].sort_values( - by=[searchterm], ascending=False - )[0:num_results] + df = search_table[["filename", searchterm]].sort_values(by=[searchterm], ascending=False)[ + 0:num_results + ] df = df.sort_values(by=[searchterm], ascending=True) df["color"] = "green" plt = df.plot.barh( - x="filename", y=searchterm, figsize=(6, 4), fontsize=10, color=list(df["color"]) + x="filename", + y=searchterm, + figsize=(6, 4), + fontsize=10, + color=list(df["color"]), ) plt.set_xlabel("Similarity", fontsize=10) plt.set_ylabel("File_name", fontsize=10) @@ -79,7 +83,11 @@ def run_scenario2( df["color"] = "green" plt = df.plot.barh( - x="search_term", y="score", figsize=(6, 4), fontsize=10, color=list(df["color"]) + x="search_term", + y="score", + figsize=(6, 4), + fontsize=10, + color=list(df["color"]), ) plt.set_xlabel("Similarity", fontsize=10) plt.set_ylabel("Ranked labels", fontsize=10) @@ -124,7 +132,11 @@ def run_scenario3( df["color"] = "green" plt = df.plot.barh( - x="db_file", y="score", figsize=(6, 4), fontsize=10, color=list(df["color"]) + x="db_file", + y="score", + figsize=(6, 4), + fontsize=10, + color=list(df["color"]), ) plt.set_xlabel("Similarity", fontsize=10) plt.set_ylabel("Files in db", fontsize=10) diff --git a/gitk/bedspace/pipeline/train.py b/gitk/bedspace/pipeline/train.py index 7ebdb206..e02285b3 100644 --- a/gitk/bedspace/pipeline/train.py +++ b/gitk/bedspace/pipeline/train.py @@ -2,7 +2,12 @@ import os import subprocess -from ..const import DEFAULT_DIM, DEFAULT_LEARNING_RATE, DEFAULT_NUM_EPOCHS, PKG_NAME +from ..const import ( + DEFAULT_DIM, + DEFAULT_LEARNING_RATE, + DEFAULT_NUM_EPOCHS, + PKG_NAME, +) _LOGGER = logging.getLogger(PKG_NAME) diff --git a/gitk/eval/cli.py b/gitk/eval/cli.py index 3ad30b91..51076311 100644 --- a/gitk/eval/cli.py +++ b/gitk/eval/cli.py @@ -11,9 +11,7 @@ def build_subparser_gdst(parser): type=str, help="path to a Region2Vec model or a Base model", ) - parser.add_argument( - "--embed-type", required=True, type=str, help="region2vec or base" - ) + parser.add_argument("--embed-type", required=True, type=str, help="region2vec or base") parser.add_argument( "--num-samples", default=10000, @@ -42,12 +40,8 @@ def build_subparser_npt(parser): type=str, help="path to a region2vec model or a Base model", ) - parser.add_argument( - "--embed-type", required=True, type=str, help="region2vec or base" - ) - parser.add_argument( - "--K", required=True, type=int, help="number of nearest regions" - ) + parser.add_argument("--embed-type", required=True, type=str, help="region2vec or base") + parser.add_argument("--K", required=True, type=int, help="number of nearest regions") parser.add_argument( "--num-samples", default=1000, @@ -81,9 +75,7 @@ def build_subparser_ctt(parser): type=str, help="path to a region2vec model or a Base model", ) - parser.add_argument( - "--embed-type", required=True, type=str, help="region2vec or base" - ) + parser.add_argument("--embed-type", required=True, type=str, help="region2vec or base") parser.add_argument( "--num-samples", diff --git a/gitk/eval/ctt.py b/gitk/eval/ctt.py index f205431d..29ddeebb 100644 --- a/gitk/eval/ctt.py +++ b/gitk/eval/ctt.py @@ -15,7 +15,12 @@ from sklearn.neighbors import NearestNeighbors from .const import * -from .utils import Timer, cosine_distance, genome_distance, load_genomic_embeddings +from .utils import ( + Timer, + cosine_distance, + genome_distance, + load_genomic_embeddings, +) def explained_variance(bin_path, dim): @@ -93,9 +98,7 @@ def ctt_eval( results_seeds = [] for seed in range(num_runs): print(f"----------------Run {seed}----------------") - save_path = ( - os.path.join(save_folder, f"ctt_eval_seed{seed}") if save_folder else None - ) + save_path = os.path.join(save_folder, f"ctt_eval_seed{seed}") if save_folder else None result_list = get_ctt_batch(batch, seed, num_data, save_path, num_workers) results_seeds.append(result_list) diff --git a/gitk/eval/gdst.py b/gitk/eval/gdst.py index 15476270..ab4b0b06 100644 --- a/gitk/eval/gdst.py +++ b/gitk/eval/gdst.py @@ -13,7 +13,12 @@ from sklearn.linear_model import LinearRegression from .const import * -from .utils import Timer, cosine_distance, genome_distance, load_genomic_embeddings +from .utils import ( + Timer, + cosine_distance, + genome_distance, + load_genomic_embeddings, +) def sample_from_vocab(vocab, num_samples, seed=42): @@ -135,9 +140,7 @@ def writer_multiprocessing(save_path, num, q): return results -def get_gdst_score_batch( - batch, num_samples=10000, seed=42, save_path=None, num_workers=1 -): +def get_gdst_score_batch(batch, num_samples=10000, seed=42, save_path=None, num_workers=1): if num_workers <= 1: gds_arr = [] for path, embed_type in batch: @@ -152,9 +155,7 @@ def get_gdst_score_batch( manager = mp.Manager() queue = manager.Queue() with mp.Pool(processes=num_workers) as pool: - writer = pool.apply_async( - writer_multiprocessing, (save_path, len(batch), queue) - ) + writer = pool.apply_async(writer_multiprocessing, (save_path, len(batch), queue)) all_processes = [] for i, (path, embed_type) in enumerate(batch): process = pool.apply_async( @@ -180,12 +181,8 @@ def gdst_eval( results_seeds = [] for seed in range(num_runs): print(f"----------------Run {seed}----------------") - save_path = ( - os.path.join(save_folder, f"gdst_eval_seed{seed}") if save_folder else None - ) - result_list = get_gdst_score_batch( - batch, num_samples, seed, save_path, num_workers - ) + save_path = os.path.join(save_folder, f"gdst_eval_seed{seed}") if save_folder else None + result_list = get_gdst_score_batch(batch, num_samples, seed, save_path, num_workers) results_seeds.append(result_list) gds_res = [[] for i in range(len(batch))] @@ -198,8 +195,6 @@ def gdst_eval( std_gds = [np.array(r).std() for r in gds_res] models = [t[0] for t in batch] for i in range(len(mean_gds)): - print( - f"{batch[i][0]}\n GDST score (std): {mean_gds[i]:.4f} ({std_gds[i]:.4f}) \n" - ) + print(f"{batch[i][0]}\n GDST score (std): {mean_gds[i]:.4f} ({std_gds[i]:.4f}) \n") gds_arr = [(batch[i][0], gds_res[i]) for i in range(len(batch))] return gds_arr diff --git a/gitk/eval/npt.py b/gitk/eval/npt.py index 76c22e1e..da15bdcd 100644 --- a/gitk/eval/npt.py +++ b/gitk/eval/npt.py @@ -82,19 +82,14 @@ def calculate_overlap_bins( if same_chromo: chr_regions = [ - f"{chromo}:{region_array[k][0]}-{region_array[k][1]}" - for k in range(len(region_array)) + f"{chromo}:{region_array[k][0]}-{region_array[k][1]}" for k in range(len(region_array)) ] chr_global_indices = np.array([region2index[r] for r in chr_regions]) chr_embeds = embed_rep[chr_global_indices] _Kedist_local_indices, _ = get_topk_embed(local_idx, K, chr_embeds, dist) - _Kedist_global_indices = np.array( - [chr_global_indices[i] for i in _Kedist_local_indices] - ) + _Kedist_global_indices = np.array([chr_global_indices[i] for i in _Kedist_local_indices]) else: - idx = region2index[ - f"{chromo}:{region_array[local_idx][0]}-{region_array[local_idx][1]}" - ] + idx = region2index[f"{chromo}:{region_array[local_idx][0]}-{region_array[local_idx][1]}"] _Kedist_global_indices, _ = get_topk_embed(idx, K, embed_rep, dist) bin_overlaps = [] @@ -205,7 +200,8 @@ def get_npt_score( indexes = list(range(len(region_array))) else: num = min( - len(region_array), round(num_samples * chromo_ratios[chromo]) + len(region_array), + round(num_samples * chromo_ratios[chromo]), ) indexes = np.random.permutation(len(region_array))[0:num] for i in indexes: @@ -215,15 +211,21 @@ def get_npt_score( ) process_random = pool.apply_async( worker_func, - (i, K, chromo, region_array, "random", resolution, dist), + ( + i, + K, + chromo, + region_array, + "random", + resolution, + dist, + ), ) all_processes.append((process_embed, process_random)) for i, (process_embed, process_random) in enumerate(all_processes): avg_ratio = (avg_ratio * count + process_embed.get()) / (count + 1) - avg_ratio_ref = (avg_ratio_ref * count + process_random.get()) / ( - count + 1 - ) + avg_ratio_ref = (avg_ratio_ref * count + process_random.get()) / (count + 1) count = count + 1 else: for chromo in chromo_regions: @@ -231,7 +233,10 @@ def get_npt_score( if num_samples == 0: # exhaustive indexes = list(range(len(region_array))) else: - num = min(len(region_array), round(num_samples * chromo_ratios[chromo])) + num = min( + len(region_array), + round(num_samples * chromo_ratios[chromo]), + ) indexes = np.random.permutation(len(region_array))[0:num] for i in indexes: nprs_embed = calculate_overlap_bins( @@ -301,7 +306,14 @@ def get_npt_score_batch( result_list = [] for index, (path, embed_type) in enumerate(batch): result = get_npt_score( - path, embed_type, K, num_samples, seed, resolution, dist, num_workers + path, + embed_type, + K, + num_samples, + seed, + resolution, + dist, + num_workers, ) result_list.append(result) if save_path: @@ -325,9 +337,7 @@ def npt_eval( assert resolution <= K, "resolution <= K" for seed in range(num_runs): print(f"----------------Run {seed}----------------") - save_path = ( - os.path.join(save_folder, f"npt_eval_seed{seed}") if save_folder else None - ) + save_path = os.path.join(save_folder, f"npt_eval_seed{seed}") if save_folder else None result_list = get_npt_score_batch( batch, K, @@ -374,9 +384,7 @@ def get_npt_results(save_paths): def snpr_plot(snpr_data, row_labels=None, legend_pos=(0.25, 0.6), filename=None): - snpr_vals = [ - (k, v.sum(axis=1).mean(), v.sum(axis=1).std()) for k, v, res in snpr_data - ] + snpr_vals = [(k, v.sum(axis=1).mean(), v.sum(axis=1).std()) for k, v, res in snpr_data] cmap = plt.get_cmap("Set1") cmaplist = [cmap(i) for i in range(9)] if row_labels is None: @@ -404,7 +412,11 @@ def snpr_plot(snpr_data, row_labels=None, legend_pos=(0.25, 0.6), filename=None) ) ax.set_ylabel("SNPR") _ = plt.setp( - ax.get_xticklabels(), rotation=-15, ha="left", va="top", rotation_mode="anchor" + ax.get_xticklabels(), + rotation=-15, + ha="left", + va="top", + rotation_mode="anchor", ) patches = [ Line2D( diff --git a/gitk/eval/rct.py b/gitk/eval/rct.py index f3e17a25..e0f68509 100644 --- a/gitk/eval/rct.py +++ b/gitk/eval/rct.py @@ -15,12 +15,15 @@ from sklearn.preprocessing import StandardScaler from ..utils import timer_func -from .utils import Timer, cosine_distance, genome_distance, load_genomic_embeddings +from .utils import ( + Timer, + cosine_distance, + genome_distance, + load_genomic_embeddings, +) -def get_rct_score( - path, embed_type, bin_path, out_dim=-1, cv_num=5, seed=42, num_workers=10 -): +def get_rct_score(path, embed_type, bin_path, out_dim=-1, cv_num=5, seed=42, num_workers=10): embed_rep, vocab = load_genomic_embeddings(path, embed_type) embed_bin, vocab_bin = load_genomic_embeddings(bin_path, "base") region2idx = {r: i for i, r in enumerate(vocab)} @@ -56,20 +59,14 @@ def get_rct_score( kf = KFold(n_splits=cv_num, shuffle=True, random_state=seed) if num_workers > cv_num: num_workers = cv_num - score = cross_val_score( - model, embed_rep, embed_bin, cv=kf, n_jobs=num_workers, verbose=0 - ) + score = cross_val_score(model, embed_rep, embed_bin, cv=kf, n_jobs=num_workers, verbose=0) return score.mean() -def reconstruction_batch( - batch, cv_num, out_dim=-1, seed=42, save_path=None, num_workers=10 -): +def reconstruction_batch(batch, cv_num, out_dim=-1, seed=42, save_path=None, num_workers=10): rct_arr = [] for path, embed_type, bin_path in batch: - score = get_rct_score( - path, embed_type, bin_path, out_dim, cv_num, seed, num_workers - ) + score = get_rct_score(path, embed_type, bin_path, out_dim, cv_num, seed, num_workers) rct_arr.append((path, score)) if save_path: os.makedirs(os.path.dirname(save_path), exist_ok=True) @@ -89,12 +86,8 @@ def rct_eval( results_seeds = [] for seed in range(num_runs): print(f"----------------Run {seed}----------------") - save_path = ( - os.path.join(save_folder, f"rct_eval_seed{seed}") if save_folder else None - ) - result_list = reconstruction_batch( - batch, cv_num, out_dim, seed, save_path, num_workers - ) + save_path = os.path.join(save_folder, f"rct_eval_seed{seed}") if save_folder else None + result_list = reconstruction_batch(batch, cv_num, out_dim, seed, save_path, num_workers) results_seeds.append(result_list) rct_res = [[] for i in range(len(batch))] diff --git a/gitk/eval/utils.py b/gitk/eval/utils.py index 95a6d673..30a8fa7a 100644 --- a/gitk/eval/utils.py +++ b/gitk/eval/utils.py @@ -71,9 +71,7 @@ def get_pca_embeddings(bin_embed_obj, dim, kwargs={}): def get_umap_embeddings(bin_embed_obj, dim, kwargs={}): import umap - embeds = umap.UMAP(n_components=dim, **kwargs).fit_transform( - bin_embed_obj.embeddings - ) + embeds = umap.UMAP(n_components=dim, **kwargs).fit_transform(bin_embed_obj.embeddings) umap_embed_obj = BaseEmbeddings(embeds, bin_embed_obj.vocab) return umap_embed_obj diff --git a/gitk/likelihood/build_model.py b/gitk/likelihood/build_model.py index 0411b676..51010dc9 100644 --- a/gitk/likelihood/build_model.py +++ b/gitk/likelihood/build_model.py @@ -132,17 +132,13 @@ def make(self, coverage_folder, coverage_prefix, file_no, force=False): """ if os.path.exists(self.name): if not force: - print( - "Model already exists. If you want to overwrite it use force argument" - ) + print("Model already exists. If you want to overwrite it use force argument") return else: print("Overwriting existing model") tar_arch = tarfile.open(self.name, "w") temp_dir = tempfile.TemporaryDirectory() - bw_start = pyBigWig.open( - os.path.join(coverage_folder, f"{coverage_prefix}_start.bw") - ) + bw_start = pyBigWig.open(os.path.join(coverage_folder, f"{coverage_prefix}_start.bw")) chroms = bw_start.chroms() bw_start.close() self.chromosomes_list = [i for i in chroms if chroms[i] != 0] diff --git a/gitk/likelihood/cli.py b/gitk/likelihood/cli.py index f22b294a..72d04a94 100644 --- a/gitk/likelihood/cli.py +++ b/gitk/likelihood/cli.py @@ -6,13 +6,17 @@ def build_subparser(parser): """ parser.add_argument( - "--model-file", help="path to file with lh model", required=True, type=str - ) - parser.add_argument( - "--file-no", help="number of files used to make the model", type=int + "--model-file", + help="path to file with lh model", + required=True, + type=str, ) + parser.add_argument("--file-no", help="number of files used to make the model", type=int) parser.add_argument( - "--coverage-folder", help="path to coverage folder", required=True, type=str + "--coverage-folder", + help="path to coverage folder", + required=True, + type=str, ) parser.add_argument( "--coverage-prefix", diff --git a/gitk/models/pretrained.py b/gitk/models/pretrained.py index 47f15620..04158583 100644 --- a/gitk/models/pretrained.py +++ b/gitk/models/pretrained.py @@ -67,9 +67,7 @@ def _load_model(self, path: str) -> scembed.SCEmbed: """ return scembed.utils.load_scembed_model(path) - def encode( - self, data: Union[str, list[tuple[str, int, int]], sc.AnnData] - ) -> np.ndarray: + def encode(self, data: Union[str, list[tuple[str, int, int]], sc.AnnData]) -> np.ndarray: """ Encode region sets data using the pretrained model. """ @@ -82,9 +80,7 @@ def encode( embeddings = [] for region_set in tqdm(tokenized, total=len(tokenized)): if len(region_set) == 0: - raise ValueError( - "Encountered empty region set. Please check your input." - ) + raise ValueError("Encountered empty region set. Please check your input.") # compute embeddings using average pooling of all region embeddings embedding = np.mean( [ diff --git a/gitk/models/tokenization.py b/gitk/models/tokenization.py index df56b0d7..8422aae8 100644 --- a/gitk/models/tokenization.py +++ b/gitk/models/tokenization.py @@ -74,9 +74,7 @@ def build_tree(self, regions: str = None): def query( self, - regions: Union[ - str, List[str], List[tuple[str, int, int]], tuple[str, int, int] - ], + regions: Union[str, List[str], List[tuple[str, int, int]], tuple[str, int, int]], ): """ Query the interval tree for the given regions. @@ -104,9 +102,7 @@ def query( def __contains__(self, item: tuple[str, int, int]): # ensure item is a tuple of chr, start, end if not ((isinstance(item, tuple) or isinstance(item, list)) and len(item) == 3): - raise ValueError( - "The item to check for must be a tuple of chr, start, end." - ) + raise ValueError("The item to check for must be a tuple of chr, start, end.") chr, start, end = item start = int(start) @@ -173,9 +169,7 @@ def _tokenize_bed_file(self, data: str, fraction: float = 1e-9) -> List[str]: :return: A list of lists of regions. """ regions = validate_region_input(data) - tokens = [ - "_".join([str(h) for h in hit]) for hit in self._universe.query(regions) - ] + tokens = ["_".join([str(h) for h in hit]) for hit in self._universe.query(regions)] return tokens def _tokenize_list(self, data: List[str]) -> List[List[str]]: diff --git a/gitk/models/utils.py b/gitk/models/utils.py index 4cef04cb..575277e3 100644 --- a/gitk/models/utils.py +++ b/gitk/models/utils.py @@ -55,9 +55,7 @@ def validate_region_input( if isinstance(regions, str): # ensure that the file exists if not os.path.exists(regions): - raise FileNotFoundError( - f"Could not find file {regions} containing regions." - ) + raise FileNotFoundError(f"Could not find file {regions} containing regions.") file = regions # rename for clarity regions = [] with open(file, "r") as f: @@ -110,9 +108,7 @@ def generate_var_conversion_map( overlaps = b.query(region) region_str = f"{region[0]}_{region[1]}_{region[2]}" if len(overlaps) > 0: - olap = overlaps[ - 0 - ] # take the first overlap for now, we can change this later + olap = overlaps[0] # take the first overlap for now, we can change this later olap_str = f"{olap[0]}_{olap[1]}_{olap[2]}" conversion_map[region_str] = olap_str else: @@ -135,9 +131,7 @@ def convert_to_universe(adata: sc.AnnData, universe: "Universe") -> sc.AnnData: """ # ensure adata has chr, start, and end if not all([x in adata.var.columns for x in ["chr", "start", "end"]]): - raise ValueError( - "AnnData object must have `chr`, `start`, and `end` columns in .var" - ) + raise ValueError("AnnData object must have `chr`, `start`, and `end` columns in .var") # create list of regions from adata query_set: List[tuple[str, int, int]] = adata.var.apply( diff --git a/gitk/region2vec/cli.py b/gitk/region2vec/cli.py index fab2bb42..66c97d34 100644 --- a/gitk/region2vec/cli.py +++ b/gitk/region2vec/cli.py @@ -5,9 +5,7 @@ def build_subparser(parser): :return argparse.ArgumentParser """ parser.add_argument("--token-folder", type=str, help="path to tokenized files") - parser.add_argument( - "--num-shuffle", type=int, help="number of shufflings/training epochs" - ) + parser.add_argument("--num-shuffle", type=int, help="number of shufflings/training epochs") parser.add_argument("--embed-dim", type=int, help="embedding dimension") parser.add_argument("--context-len", type=int, help="Context window size (half)") parser.add_argument("--nworkers", type=int, default=10, help="number of workers") @@ -18,7 +16,9 @@ def build_subparser(parser): help="Save a model after the given number of training epochs. If -1, then only save the best and latest models", ) parser.add_argument( - "--save-dir", type=str, help="path to the folder that saves the training result" + "--save-dir", + type=str, + help="path to the folder that saves the training result", ) parser.add_argument( "--resume", @@ -44,9 +44,7 @@ def build_subparser(parser): default=5, help="number of noise words in negative sampling, usually between 5-20", ) - parser.add_argument( - "--init-lr", type=float, default=0.1, help="initial learning rate" - ) + parser.add_argument("--init-lr", type=float, default=0.1, help="initial learning rate") parser.add_argument("--milestones", nargs="+", type=int, default=[100, 200]) parser.add_argument( "--lr-mode", @@ -61,9 +59,7 @@ def build_subparser(parser): default="once", help="[every] update at every epoch; [once] Update once since the vocabulary does not change", ) - parser.add_argument( - "--min-lr", type=float, default=1.0e-6, help="minimum learning rate" - ) + parser.add_argument("--min-lr", type=float, default=1.0e-6, help="minimum learning rate") parser.add_argument("--seed", type=int, default=42, help="random seed") return parser diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index fbffaea4..0b1e66c6 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -35,7 +35,7 @@ def region2vec( milestones=[], # Specify only when lr_scheduler=milestone. At each given epoch, the learning rate will be multiplied by 0.1 hier_softmax=False, # Whether to hierarchical softmax seed=0, # random seed, - update_vocab="once" + update_vocab="once", ): timer = utils.Timer() start_time = timer.t() diff --git a/gitk/region2vec/region2vec_train.py b/gitk/region2vec/region2vec_train.py index 0ed51368..394aaa1b 100644 --- a/gitk/region2vec/region2vec_train.py +++ b/gitk/region2vec/region2vec_train.py @@ -13,9 +13,7 @@ from . import utils from .const import * -logging.basicConfig( - format="%(asctime)s : %(levelname)s : %(message)s", level=logging.ERROR -) +logging.basicConfig(format="%(asctime)s : %(levelname)s : %(message)s", level=logging.ERROR) def find_dataset(data_folder): @@ -39,9 +37,7 @@ def main(args): data_folder = os.path.join( save_dir, "shuffled_datasets" ) # shuffled datasets are stored in the shuffled_datasets folder - model_dir = os.path.join( - save_dir, "models" - ) # model snapshots are stored in the models folder + model_dir = os.path.join(save_dir, "models") # model snapshots are stored in the models folder os.makedirs(save_dir, exist_ok=True) os.makedirs(model_dir, exist_ok=True) utils.set_log_path(save_dir) # specify the path to the log file @@ -58,9 +54,7 @@ def main(args): msg_model += "hierarchical softmax\033[00m" else: hs = 0 - msg_model += ( - f"negative sampling with {args.neg_samples} negative samples\033[00m" - ) + msg_model += f"negative sampling with {args.neg_samples} negative samples\033[00m" if not os.path.exists(args.resume): vocab_update = False model = Word2Vec( @@ -90,7 +84,11 @@ def main(args): lr_info = {"freq": 1} lr_scheduler = utils.lr_scheduler( - args.init_lr, args.min_lr, args.num_shuffle, lr_info=lr_info, mode=args.lr_mode + args.init_lr, + args.min_lr, + args.num_shuffle, + lr_info=lr_info, + mode=args.lr_mode, ) run_timer = utils.Timer() @@ -103,9 +101,7 @@ def main(args): sentences = LineSentence(dset) # create sentence iterator model.build_vocab(sentences, update=vocab_update) # prepare the model vocabulary cur_time = datetime.datetime.now().strftime("%x-%X") - utils.log( - f"[{cur_time}]\033[93m Vocabulary size is {len(model.wv.index_to_key)}\033[00m" - ) + utils.log(f"[{cur_time}]\033[93m Vocabulary size is {len(model.wv.index_to_key)}\033[00m") build_vocab_time = run_timer.t() min_loss = 1.0e100 @@ -154,9 +150,7 @@ def main(args): elasped_time = run_timer.t() cur_time = datetime.datetime.now().strftime("%x-%X") - utils.log( - f"[{cur_time}] Training finished, training Time {utils.time_str(elasped_time)}" - ) + utils.log(f"[{cur_time}] Training finished, training Time {utils.time_str(elasped_time)}") # remove intermediate datasets os.system(f"rm -rf {data_folder}") # remove the generated shuffled datasets @@ -168,15 +162,13 @@ def main(args): parser.add_argument("--context-len", type=int, help="window size") parser.add_argument("--nworkers", type=int, help="number of workers") parser.add_argument("--save-freq", type=int, default=0, help="save frequency") - parser.add_argument( - "--save-dir", help="path to the folder that saves the training result" - ) + parser.add_argument("--save-dir", help="path to the folder that saves the training result") parser.add_argument("--resume", help="path to a saved model") + parser.add_argument("--train-alg", help="training algorithm, select from [cbow, skip-gram]") parser.add_argument( - "--train-alg", help="training algorithm, select from [cbow, skip-gram]" - ) - parser.add_argument( - "--min-count", type=int, help="threshold of pruning the internal vocabulary" + "--min-count", + type=int, + help="threshold of pruning the internal vocabulary", ) parser.add_argument( "--neg-samples", diff --git a/gitk/region2vec/region_shuffling.py b/gitk/region2vec/region_shuffling.py index 6268a55d..453fb2e1 100644 --- a/gitk/region2vec/region_shuffling.py +++ b/gitk/region2vec/region_shuffling.py @@ -103,9 +103,7 @@ def main(args): matrix = pickle.load(f) dataset = MatrixDataset(matrix) pool = args.pool - utils.log( - f"[{worker_id}] Creating shuffled datasets in \033[93m{DATA_FOLDER}\033[00m" - ) + utils.log(f"[{worker_id}] Creating shuffled datasets in \033[93m{DATA_FOLDER}\033[00m") for i in range(pool): name_used = os.path.join(DATA_FOLDER, f"pool{worker_id}-{i}used") @@ -161,11 +159,10 @@ def main(args): parser.add_argument("--file-list", help="path to a file list") parser.add_argument("--tokenization-mode", help="tokenization mode") parser.add_argument( - "--tokenization-folder", help="path to the folder that saves tokenized regions" - ) - parser.add_argument( - "--save-dir", help="parent folder to generated shuffled datasets" + "--tokenization-folder", + help="path to the folder that saves tokenized regions", ) + parser.add_argument("--save-dir", help="parent folder to generated shuffled datasets") parser.add_argument( "--pool", type=int, @@ -179,7 +176,10 @@ def main(args): help="used in the parallel mode", ) parser.add_argument( - "--number", type=int, default=1000, help="number of shuffling the whole dataset" + "--number", + type=int, + default=1000, + help="number of shuffling the whole dataset", ) args = parser.parse_args() diff --git a/gitk/region2vec/utils.py b/gitk/region2vec/utils.py index a49cd8fc..9643a0ba 100644 --- a/gitk/region2vec/utils.py +++ b/gitk/region2vec/utils.py @@ -105,10 +105,7 @@ def step(self): self.count += 1 if self.mode == "linear": if self.count % self.freq == 0: - self.lr = ( - self.init_lr - - (self.init_lr - self.end_lr) / self.epochs * self.count - ) + self.lr = self.init_lr - (self.init_lr - self.end_lr) / self.epochs * self.count elif self.mode == "milestone": milestones = np.array(self.lr_info["milestones"]) power = (milestones <= self.count).sum() diff --git a/gitk/scembed/annotation.py b/gitk/scembed/annotation.py index 04ab3e94..9097e87b 100644 --- a/gitk/scembed/annotation.py +++ b/gitk/scembed/annotation.py @@ -74,9 +74,7 @@ def annotate( scembed_cell_type_preds = [] # use qdrant and a simple KNN approach to attach cell types to the embeddings - for embedding in tqdm( - adata.obsm["embedding"], total=len(adata.obsm["embedding"]) - ): + for embedding in tqdm(adata.obsm["embedding"], total=len(adata.obsm["embedding"])): results = self._annotation_server.search( collection_name=self.collection_name, query_vector=embedding, diff --git a/gitk/scembed/argparser.py b/gitk/scembed/argparser.py index adb6dcc4..e884ef39 100644 --- a/gitk/scembed/argparser.py +++ b/gitk/scembed/argparser.py @@ -38,8 +38,7 @@ def build_argparser(parser: VersionInHelpParser = None): "--noreads", dest="noreads", default=2, - help="Minimum number of reads that overlap a region " - "for that region to be included.", + help="Minimum number of reads that overlap a region " "for that region to be included.", ) parser.add_argument( diff --git a/gitk/scembed/const.py b/gitk/scembed/const.py index b2ede2af..b3f5cf98 100644 --- a/gitk/scembed/const.py +++ b/gitk/scembed/const.py @@ -13,9 +13,7 @@ DEFAULT_WINDOW_SIZE = 5 DEFAULT_EMBEDDING_SIZE = 100 DEFAULT_EPOCHS = 10 -DEFAULT_INIT_LR = ( - 0.1 # https://github.com/databio/gitk/issues/6#issuecomment-1476273162 -) +DEFAULT_INIT_LR = 0.1 # https://github.com/databio/gitk/issues/6#issuecomment-1476273162 DEFAULT_MIN_LR = 0.0001 # gensim default DEFAULT_DECAY_RATE = 0.95 diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index 64e450fe..dd420b3e 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -164,9 +164,7 @@ def train( n_shuffles = 1 if not isinstance(data, sc.AnnData) and not isinstance(data, str): - raise TypeError( - f"Data must be of type AnnData or str, not {type(data).__name__}" - ) + raise TypeError(f"Data must be of type AnnData or str, not {type(data).__name__}") # if the data is a string, assume it is a filepath if isinstance(data, str): @@ -188,15 +186,11 @@ def train( # this lets users save any metadata they want # which can get mapped back to the embeddings self.obs = data.obs - self.region_sets = convert_anndata_to_documents( - data, self.use_default_region_names - ) + self.region_sets = convert_anndata_to_documents(data, self.use_default_region_names) # remove any regions that dont satisfy the min count _LOGGER.info("Removing regions that don't satisfy min count.") - self.region_sets = remove_regions_below_min_count( - self.region_sets, self.min_count - ) + self.region_sets = remove_regions_below_min_count(self.region_sets, self.min_count) if report_loss: self.callbacks.append(ReportLossCallback()) @@ -222,15 +216,11 @@ def train( current_loss = self.get_latest_training_loss() # update user - _LOGGER.info( - f"SHUFFLE {shuffle_num} - lr: {current_lr}, loss: {current_loss}" - ) + _LOGGER.info(f"SHUFFLE {shuffle_num} - lr: {current_lr}, loss: {current_loss}") _LOGGER.info("Shuffling documents.") # shuffle regions - self.region_sets = shuffle_documents( - self.region_sets, n_shuffles=n_shuffles - ) + self.region_sets = shuffle_documents(self.region_sets, n_shuffles=n_shuffles) # train the model on one iteration super().train( @@ -297,7 +287,8 @@ def cell_embeddings(self) -> sc.AnnData: cell_embeddings = [] for cell in tqdm(self.region_sets, total=len(self.region_sets)): cell_embedding = np.mean( - [self.get_embedding(r) for r in cell if r in self.region2vec], axis=0 + [self.get_embedding(r) for r in cell if r in self.region2vec], + axis=0, ) cell_embeddings.append(cell_embedding) @@ -325,7 +316,8 @@ def cell_embeddings(self) -> sc.AnnData: cell_embeddings = [] for cell in tqdm(self.region_sets, total=len(self.region_sets)): cell_embedding = np.mean( - [self.get_embedding(r) for r in cell if r in self.region2vec], axis=0 + [self.get_embedding(r) for r in cell if r in self.region2vec], + axis=0, ) cell_embeddings.append(cell_embedding) @@ -356,7 +348,8 @@ def attach_cell_embeddings(self, key_added: str = "embedding") -> sc.AnnData: cell_embeddings = [] for cell in tqdm(self.region_sets, total=len(self.region_sets)): cell_embedding = np.mean( - [self.get_embedding(r) for r in cell if r in self.region2vec], axis=0 + [self.get_embedding(r) for r in cell if r in self.region2vec], + axis=0, ) cell_embeddings.append(cell_embedding) @@ -380,8 +373,7 @@ def embeddings_to_csv(self, output_path: str): for row in list(embeddings_df.iterrows()): row_dict = row[1].to_dict() new_row_dict = { - f"embedding_dim_{i}": row_dict["embedding"][i] - for i in range(self.vector_size) + f"embedding_dim_{i}": row_dict["embedding"][i] for i in range(self.vector_size) } if "id" in row_dict: new_row_dict["id"] = row_dict["id"] diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 54c1fe27..2c7afc40 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -145,7 +145,9 @@ def shuffle_list(l: List[str], n: int) -> List[str]: shuffled_documents = list( tqdm( executor.map( - shuffle_list, shuffled_documents, [n_shuffles] * len(documents) + shuffle_list, + shuffled_documents, + [n_shuffles] * len(documents), ), total=len(documents), ) @@ -171,8 +173,7 @@ def remove_regions_below_min_count( # remove any regions that dont satisfy the min count region_sets = [ - [r for r in region_set if region_counts[r] >= min_count] - for region_set in region_sets + [r for r in region_set if region_counts[r] >= min_count] for region_set in region_sets ] return region_sets @@ -198,8 +199,7 @@ def extract_region_list(region_df: pd.DataFrame) -> List[str]: """ _LOGGER.info("Extracting region list from matrix.") regions = [ - f"{row[CHR_KEY]}_{row[START_KEY]}_{row[END_KEY]}" - for _, row in region_df.iterrows() + f"{row[CHR_KEY]}_{row[START_KEY]}_{row[END_KEY]}" for _, row in region_df.iterrows() ] return regions @@ -317,7 +317,10 @@ def load_universe_file(path: str) -> List[str]: def generate_var_conversion_map( - a: List[str], b: List[str], path_to_bedtools: str = None, fraction: float = 1.0e-9 + a: List[str], + b: List[str], + path_to_bedtools: str = None, + fraction: float = 1.0e-9, ) -> Dict[str, Union[str, None]]: """ Create a conversion map to convert regions from a to b. This is used to convert the diff --git a/gitk/tokenization/cli.py b/gitk/tokenization/cli.py index e259b32d..3ad06aa4 100644 --- a/gitk/tokenization/cli.py +++ b/gitk/tokenization/cli.py @@ -5,11 +5,11 @@ def build_subparser(parser): :return argparse.ArgumentParser """ parser.add_argument( - "--data-folder", type=str, help="Path to the folder that stores BED files" - ) - parser.add_argument( - "--token-folder", type=str, help="Folder that stores tokenized files" + "--data-folder", + type=str, + help="Path to the folder that stores BED files", ) + parser.add_argument("--token-folder", type=str, help="Folder that stores tokenized files") # parameters for hard tokenization parser.add_argument("--universe", type=str, help="Path to a universe file") parser.add_argument("--nworkers", type=int, default=10, help="number of workers") diff --git a/gitk/tokenization/hard_tokenization_batch.py b/gitk/tokenization/hard_tokenization_batch.py index 100841b8..e2acc9ef 100644 --- a/gitk/tokenization/hard_tokenization_batch.py +++ b/gitk/tokenization/hard_tokenization_batch.py @@ -6,9 +6,7 @@ from gitk.tokenization import utils -def bedtool_tokenization( - f, bedtool_path, data_folder, target_folder, universe, fraction -): +def bedtool_tokenization(f, bedtool_path, data_folder, target_folder, universe, fraction): fname = os.path.join(data_folder, f) temp = os.path.join(target_folder, f + "_sorted") target = os.path.join(target_folder, f) @@ -16,17 +14,13 @@ def bedtool_tokenization( subprocess.run(shlex.split(f"sort -k1,1V -k2,2n {fname}"), stdout=f_temp) with open(target, "w") as f_target: subprocess.run( - shlex.split( - f"{bedtool_path} intersect -a {universe} -b {temp} -u -f {fraction} " - ), + shlex.split(f"{bedtool_path} intersect -a {universe} -b {temp} -u -f {fraction} "), stdout=f_target, ) os.remove(temp) -def generate_tokens( - raw_data_folder, token_folder, universe, file_list, bedtool, fraction -): +def generate_tokens(raw_data_folder, token_folder, universe, file_list, bedtool, fraction): """ Perform hard tokenization on the bed files """ @@ -60,9 +54,7 @@ def generate_tokens( os.makedirs(token_folder) not_covered = all_set for f in not_covered: - bedtool_tokenization( - f, bedtool, raw_data_folder, token_folder, universe, fraction - ) + bedtool_tokenization(f, bedtool, raw_data_folder, token_folder, universe, fraction) return 0 diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 7b772971..1ab274e5 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -114,9 +114,7 @@ def hard_tokenization_main( ) args_arr.append(tokenization_args) with multiprocessing.Pool(nworkers) as pool: - processes = [ - pool.apply_async(hard_tokenization, args=(param,)) for param in args_arr - ] + processes = [pool.apply_async(hard_tokenization, args=(param,)) for param in args_arr] results = [r.get() for r in processes] # move tokenized files in different folders to expr_tokens shutil.rmtree(dest_folder) @@ -124,7 +122,8 @@ def hard_tokenization_main( allfiles = os.listdir(param.token_folder) for f in allfiles: shutil.move( - os.path.join(param.token_folder, f), os.path.join(dst_folder, f) + os.path.join(param.token_folder, f), + os.path.join(dst_folder, f), ) shutil.rmtree(param.token_folder) os.remove(file_list_path) diff --git a/gitk/tokenization/split_file.py b/gitk/tokenization/split_file.py index 88c14729..ab8b09e3 100644 --- a/gitk/tokenization/split_file.py +++ b/gitk/tokenization/split_file.py @@ -53,9 +53,7 @@ def split_file(file_path, dest_folder, num_parts): if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--file-path", default="", help="path to a file list") - parser.add_argument( - "--dest-folder", default="", help="where to store the split files" - ) + parser.add_argument("--dest-folder", default="", help="where to store the split files") parser.add_argument( "--num-parts", type=int, diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index 4c2f964e..1e3253f8 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -104,10 +104,7 @@ def step(self): self.count += 1 if self.mode == "linear": if self.count % self.freq == 0: - self.lr = ( - self.init_lr - - (self.init_lr - self.end_lr) / self.epochs * self.count - ) + self.lr = self.init_lr - (self.init_lr - self.end_lr) / self.epochs * self.count elif self.mode == "milestone": milestones = np.array(self.lr_info["milestones"]) power = (milestones <= self.count).sum() diff --git a/gitk/universe/ccf_universe.py b/gitk/universe/ccf_universe.py index 3cefb4d2..e1f1662f 100644 --- a/gitk/universe/ccf_universe.py +++ b/gitk/universe/ccf_universe.py @@ -56,9 +56,7 @@ def save_regions(inter_pos, chrom, bedname, track): if res != "empty": save_start_e, save_end_s, save_start_s, save_end_e = res save_end_e = save_end_e + [end_e] - for a, b, c, d in zip( - save_start_e, save_end_s, save_start_s, save_end_e - ): + for a, b, c, d in zip(save_start_e, save_end_s, save_start_s, save_end_e): if a != b: val = 0 li = line.format( diff --git a/gitk/universe/cli.py b/gitk/universe/cli.py index c5e18e82..ca3bb1c0 100644 --- a/gitk/universe/cli.py +++ b/gitk/universe/cli.py @@ -17,9 +17,7 @@ def build_subparser_hmm(parser): def build_subparser_ml(parser): parser = build_subparser(parser) - parser.add_argument( - "--model-file", help="path to lh model file", required=True, type=str - ) + parser.add_argument("--model-file", help="path to lh model file", required=True, type=str) return parser @@ -37,16 +35,17 @@ def build_subparser_cc(parser): default=0, type=int, ) - parser.add_argument( - "--cutoff", help="cutoff value used for making universe", type=int - ) + parser.add_argument("--cutoff", help="cutoff value used for making universe", type=int) return parser def build_subparser(parser): parser.add_argument( - "--output-file", help="path to output, universe file", required=True, type=str + "--output-file", + help="path to output, universe file", + required=True, + type=str, ) parser.add_argument( "--coverage-folder", diff --git a/gitk/universe/const.py b/gitk/universe/const.py index 6c0d5dbe..f1bed631 100644 --- a/gitk/universe/const.py +++ b/gitk/universe/const.py @@ -5,4 +5,9 @@ [0.1, 0, 0, 0.9], ] -LAMBDAS = [[5, 3, 0.0001], [0.05, 5, 0.05], [0.0001, 3, 5], [0.0001, 0.001, 0.0001]] +LAMBDAS = [ + [5, 3, 0.0001], + [0.05, 5, 0.05], + [0.0001, 3, 5], + [0.0001, 0.001, 0.0001], +] diff --git a/gitk/universe/custom_distribution.py b/gitk/universe/custom_distribution.py index f625f864..c11adb7c 100644 --- a/gitk/universe/custom_distribution.py +++ b/gitk/universe/custom_distribution.py @@ -208,9 +208,7 @@ def _check(self): if self.beta_.shape != (self.n_components, n_features): raise ValueError("beta_ must have shape (n_components, n_features)") if self.alfa_.shape != self.beta_.shape: - raise ValueError( - "alfa_ and beta_ must have the same shape (n_components, n_features)" - ) + raise ValueError("alfa_ and beta_ must have the same shape (n_components, n_features)") self.n_features = n_features def _generate_sample_from_state(self, state, random_state): @@ -218,10 +216,7 @@ def _generate_sample_from_state(self, state, random_state): def _compute_log_likelihood(self, X): return np.array( - [ - np.sum(beta.logpdf(X, a, b), axis=1) - for a, b in zip(self.alfa_, self.beta_) - ] + [np.sum(beta.logpdf(X, a, b), axis=1) for a, b in zip(self.alfa_, self.beta_)] ).T def _compute_likelihood(self, X): diff --git a/gitk/universe/hmm_universe.py b/gitk/universe/hmm_universe.py index 82c11e4f..16e3b3cb 100644 --- a/gitk/universe/hmm_universe.py +++ b/gitk/universe/hmm_universe.py @@ -135,7 +135,11 @@ def hmm_universe( if chroms[C] > 0: pred, m = run_hmm(start, core, end, C, normalize=normalize) predictions_to_bed( - pred, C, out_file, save_max_cove=save_max_cove, cove_file=core + ".bw" + pred, + C, + out_file, + save_max_cove=save_max_cove, + cove_file=core + ".bw", ) diff --git a/gitk/universe/ml_universe.py b/gitk/universe/ml_universe.py index 2d466113..dfa8e1e1 100644 --- a/gitk/universe/ml_universe.py +++ b/gitk/universe/ml_universe.py @@ -66,15 +66,9 @@ def make_ml_flexible_universe(model_lh, cove_folder, cove_prefix, chrom, file_ou """ model_lh.read_chrom(chrom) chrom_model = model_lh[chrom] - start = read_chromosome_from_bw( - os.path.join(cove_folder, f"{cove_prefix}_start.bw"), chrom - ) - core = read_chromosome_from_bw( - os.path.join(cove_folder, f"{cove_prefix}_core.bw"), chrom - ) - end = read_chromosome_from_bw( - os.path.join(cove_folder, f"{cove_prefix}_end.bw"), chrom - ) + start = read_chromosome_from_bw(os.path.join(cove_folder, f"{cove_prefix}_start.bw"), chrom) + core = read_chromosome_from_bw(os.path.join(cove_folder, f"{cove_prefix}_core.bw"), chrom) + end = read_chromosome_from_bw(os.path.join(cove_folder, f"{cove_prefix}_end.bw"), chrom) cove = np.zeros((len(start), 3), dtype=np.uint16) cove[:, 0] = start cove[:, 1] = core diff --git a/gitk/universe/models.py b/gitk/universe/models.py index 4cbedfae..dd841254 100644 --- a/gitk/universe/models.py +++ b/gitk/universe/models.py @@ -60,7 +60,8 @@ def __init__( if save_matrix: super().save_tras(out_folder) np.savetxt( - os.path.join(out_folder, "lambdas_matrix.csv"), self.lambdas_matrix + os.path.join(out_folder, "lambdas_matrix.csv"), + self.lambdas_matrix, ) self.model = hmm.PoissonHMM( @@ -106,7 +107,8 @@ def __init__( if save_matrix: super().save_tras(out_folder) np.savetxt( - os.path.join(out_folder, "covars_matrix.csv"), self.covars_matrix + os.path.join(out_folder, "covars_matrix.csv"), + self.covars_matrix, ) np.savetxt(os.path.join(out_folder, "means_matrix.csv"), self.means_matrix) @@ -155,7 +157,8 @@ def __init__( if save_matrix: super().save_tras(out_folder) np.savetxt( - os.path.join(failures_matrix, "failures_matrix.csv"), self.covars_matrix + os.path.join(failures_matrix, "failures_matrix.csv"), + self.covars_matrix, ) np.savetxt(os.path.join(out_folder, "prob_matrix.csv"), self.prob_matrix) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..2cea3f51 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,5 @@ + +[tool.black] +line-length = 99 +target-version = ['py38', 'py311'] +include = '\.pyi?$' diff --git a/setup.py b/setup.py index 5869fb1d..1f3a4369 100755 --- a/setup.py +++ b/setup.py @@ -60,5 +60,5 @@ include_package_data=True, url="http://giss.databio.org", author="Nathan Sheffield", - **extra + **extra, ) diff --git a/tests/test_models.py b/tests/test_models.py index d99ad21f..2b8ede25 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -37,7 +37,8 @@ def pretrained_model(): def test_init_tokenizers( - tokenizer_from_file: models.HardTokenizer, tokenizer_from_list: models.HardTokenizer + tokenizer_from_file: models.HardTokenizer, + tokenizer_from_list: models.HardTokenizer, ): # assert that the tokenizers were created assert tokenizer_from_file is not None @@ -184,9 +185,7 @@ def test_init_pretrained_model( assert pretrained_model.model is not None -def test_encode_anndata( - adata: sc.AnnData, pretrained_model: models.PretrainedScembedModel -): +def test_encode_anndata(adata: sc.AnnData, pretrained_model: models.PretrainedScembedModel): # encode the anndata encoded = pretrained_model.encode(adata) From fa49477a4d8ea45753d6c1dce70acc9041b4f3df Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 16:54:43 -0400 Subject: [PATCH 0246/1072] pytest version --- .github/workflows/run-pytest.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/run-pytest.yml b/.github/workflows/run-pytest.yml index c4570a6b..3e2bc3b3 100644 --- a/.github/workflows/run-pytest.yml +++ b/.github/workflows/run-pytest.yml @@ -9,7 +9,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - python-version: ["3.8", "3.10"] + python-version: ["3.8", "3.11"] os: [ubuntu-latest] steps: From c759da3c4f1871c23bbbd9b325620a8b3cccfe03 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 17:01:06 -0400 Subject: [PATCH 0247/1072] make version importable --- gitk/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/gitk/__init__.py b/gitk/__init__.py index e69de29b..8dee4bf8 100644 --- a/gitk/__init__.py +++ b/gitk/__init__.py @@ -0,0 +1 @@ +from ._version import __version__ From 986063ea01a18ccfc7b8be9de1aae69b4e335d39 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 18:01:58 -0400 Subject: [PATCH 0248/1072] fix typo --- gitk/scembed/const.py | 2 +- gitk/scembed/scembed.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/gitk/scembed/const.py b/gitk/scembed/const.py index b3f5cf98..2bbe85b4 100644 --- a/gitk/scembed/const.py +++ b/gitk/scembed/const.py @@ -9,7 +9,7 @@ DEFAULT_EPOCHS = 100 DEFAULT_GENSIM_EPOCHS = 1 DEFAULT_MIN_COUNT = 10 -DEAFULT_N_SHUFFLES = 1 # 1 is sufficient for most cases +DEFAULT_N_SHUFFLES = 1 # 1 is sufficient for most cases DEFAULT_WINDOW_SIZE = 5 DEFAULT_EMBEDDING_SIZE = 100 DEFAULT_EPOCHS = 10 diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index dd420b3e..e4fe679d 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -141,7 +141,7 @@ def train( self, data: Union[sc.AnnData, str], epochs: int = DEFAULT_EPOCHS, # training cycles - n_shuffles: int = DEAFULT_N_SHUFFLES, # not the number of traiing cycles, actual shufle num + n_shuffles: int = DEFAULT_N_SHUFFLES, # not the number of traiing cycles, actual shufle num gensim_epochs: Union[int, None] = DEFAULT_GENSIM_EPOCHS, report_loss: bool = True, lr: float = DEFAULT_INIT_LR, From 153c951a20647008cf1bfd055ff9df756df5e7be Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 18:02:16 -0400 Subject: [PATCH 0249/1072] improve bedspace docs --- docs/modules/bedspace.md | 33 +++++++++++++++++++++------------ 1 file changed, 21 insertions(+), 12 deletions(-) diff --git a/docs/modules/bedspace.md b/docs/modules/bedspace.md index 0b961a7d..4cdb1ebf 100644 --- a/docs/modules/bedspace.md +++ b/docs/modules/bedspace.md @@ -7,27 +7,29 @@ `bedspace` comes installed as a module of `gitk`. To ensure that everything is working correctly, run: `python -c "from gitk import bedspace"`. ## Usage + There are four main commands in `bedspace`: + 1. `bedspace preprocess`: preprocesses a set of genomic interval regions and their associated metadata into a format that can be used by `bedspace train`. 2. `bedspace train`: trains a StarSpace model on the preprocessed data. -3. `bedspace distances`: computes the distances between all of the trained model's region sets and meta data labels. -4. `bedspace search` searches for the most similar region sets and metadata labels to a given query. There are three scenarios in this command, which will be described in turn. +3. `bedspace distances`: computes distances between region sets in the trained model and metadata labels. +4. `bedspace search` searches for the most similar region sets and metadata labels to a given query. Three scenarios for this command are described in the details. ### `bedspace preprocess` The `preprocess` command will prepare a set of region sets and metadata labels for training. This includes things like adding the `__label__` prefix to metadata labels, and converting the region sets into a format that can be used by StarSpace. The command takes in a set of region sets and metadata labels, and outputs a set of preprocessed region sets and metadata labels. The command can be run as follows: -``` +```console gitk bedspace preprocess \ --input \ --metadata \ --universe \ --labels \ - --output + --output ``` ### `bedspace train` -The `train` command will train a StarSpace model on the preprocessed region sets and metadata labels. It requires that you have ran the `preprocess` command first. The `train` command takes in a set of preprocessed region sets and metadata labels, and outputs a trained StarSpace model. The command can be run as follows: +The `train` command will train a StarSpace model on the preprocessed region sets and metadata labels. It requires that you have run the `preprocess` command first. The `train` command takes in a set of preprocessed region sets and metadata labels, and outputs a trained StarSpace model. The command can be run as follows: -``` +```console gitk bedspace train \ --path-to-starspace \ --input \ @@ -40,7 +42,7 @@ gitk bedspace train \ ### `bedspace distances` The `distances` command will compute the distances between all of the region sets and metadata labels in the trained model. It requires that you have ran the `train` command first. The `distances` command takes in a trained StarSpace model, and outputs a set of distances between all of the region sets and metadata labels in the model. The command can be run as follows: -``` +```console gitk bedspace distances \ --input \ --metadata \ @@ -51,10 +53,17 @@ gitk bedspace distances \ ``` ### `bedspace search` -There are three scenarios when using the `search` command. 1) You have a query region set and want to find the most similar metadata labels, 2) You have a query metadata label and want to find the most similar region sets, and 3) You have a query region set and want to find the most similar region sets. These are labeled `r2l`, ``l2r``, and `r2r` respectively. The `search` command requires that you have ran the `distances` command first. The `search` command requires you to specify the search type so it knows which scenario you are using. It also requires a query. Example usages for each type are given below: + +The `search` command requires that you have previously run the `distances` command. It also requires a query. To search, you must specify one of 3 scenarios when using the `search` command: + +1. `r2l` (region-to-label): You have a query region set and want to find the most similar metadata labels, +2. `l2r` (label-to-region): You have a query metadata label and want to find the most similar region sets, and +3. `r2f` (region-to-region): You have a query region set and want to find the most similar region sets. + +Example usage for each type are given below: #### `r2l` -``` +```console gitk bedspace search \ -t lr2 -d \ @@ -63,7 +72,7 @@ gitk bedspace search \ ``` #### `l2r` -``` +```console gitk bedspace search \ -t rl2 -d \ @@ -72,9 +81,9 @@ gitk bedspace search \ ``` #### `r2r` -``` +```console gitk bedspace search \ - -t rr2 + -t r2r -d \ -n \ path/to/regions.bed From 05270298b3a17172cb517c62cf7fbd9bb2acc2a6 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 18:02:44 -0400 Subject: [PATCH 0250/1072] clarify --- docs/modules/assess-universe.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/modules/assess-universe.md b/docs/modules/assess-universe.md index 2124a089..cb705c7e 100644 --- a/docs/modules/assess-universe.md +++ b/docs/modules/assess-universe.md @@ -1,6 +1,6 @@ # How to assess universe fit to collection of BED files -Given a collection of genomic interval sets, and a proposed universe, we would like to assess how well the fits the genomic interval sets. +Given a collection of genomic interval sets, and a proposed universe, we would like to assess how well the universe fits the genomic interval sets. We will use universes produced [earlier](consensus-peaks.md). This module provides several complementary methods to assess fit. From 18aecc1fed585fb731760d26d5b007afafe1f315 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 7 Jul 2023 19:41:30 -0400 Subject: [PATCH 0251/1072] fix lots of docstrings --- gitk/assess/cli.py | 2 +- gitk/cli.py | 2 +- gitk/eval/cli.py | 10 +++++----- gitk/likelihood/cli.py | 2 -- gitk/models/utils.py | 2 +- gitk/region2vec/cli.py | 2 +- gitk/tokenization/cli.py | 2 +- gitk/universe/cli.py | 3 +++ mkdocs.yml | 2 +- 9 files changed, 14 insertions(+), 13 deletions(-) diff --git a/gitk/assess/cli.py b/gitk/assess/cli.py index ec3be7be..2eb827f4 100644 --- a/gitk/assess/cli.py +++ b/gitk/assess/cli.py @@ -2,7 +2,7 @@ def build_subparser(parser): """ Builds argument parser. - :return argparse.ArgumentParser + :return argparse.ArgumentParser: Argument parser """ parser.add_argument( "--overlap", diff --git a/gitk/cli.py b/gitk/cli.py index baed82c3..be1afb68 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -20,7 +20,7 @@ def build_argparser(): """ Builds argument parser. - :return argparse.ArgumentParser + :return argparse.ArgumentParser: Argument parser """ banner = "%(prog)s - Genomic Interval toolkit" diff --git a/gitk/eval/cli.py b/gitk/eval/cli.py index 51076311..e3202c3b 100644 --- a/gitk/eval/cli.py +++ b/gitk/eval/cli.py @@ -2,7 +2,7 @@ def build_subparser_gdst(parser): """ Builds argument parser. - :return argparse.ArgumentParser + :return argparse.ArgumentParser: Argument parser """ parser.add_argument( @@ -32,7 +32,7 @@ def build_subparser_npt(parser): """ Builds argument parser. - :return argparse.ArgumentParser + :return argparse.ArgumentParser: Argument parser """ parser.add_argument( "--model-path", @@ -67,7 +67,7 @@ def build_subparser_ctt(parser): """ Builds argument parser. - :return argparse.ArgumentParser + :return argparse.ArgumentParser: Argument parser """ parser.add_argument( "--model-path", @@ -103,7 +103,7 @@ def build_subparser_rct(parser): """ Builds argument parser. - :return argparse.ArgumentParser + :return argparse.ArgumentParser: Argument parser """ parser.add_argument( "--model-path", @@ -156,7 +156,7 @@ def build_subparser_bingen(parser): """ Builds argument parser. - :return argparse.ArgumentParser + :return argparse.ArgumentParser: Argument parser """ parser.add_argument( "--universe", diff --git a/gitk/likelihood/cli.py b/gitk/likelihood/cli.py index 72d04a94..9f5ca2b9 100644 --- a/gitk/likelihood/cli.py +++ b/gitk/likelihood/cli.py @@ -1,8 +1,6 @@ def build_subparser(parser): """ Builds argument parser. - - :return argparse.ArgumentParser """ parser.add_argument( diff --git a/gitk/models/utils.py b/gitk/models/utils.py index 575277e3..da81513d 100644 --- a/gitk/models/utils.py +++ b/gitk/models/utils.py @@ -49,7 +49,7 @@ def validate_region_input( :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. This can be either a list of regions or a path to a BED file containing regions. - :return A list of tuples of chr, start, end. + :return list: A list of tuples of chr, start, end. """ # check for bedfile if isinstance(regions, str): diff --git a/gitk/region2vec/cli.py b/gitk/region2vec/cli.py index 66c97d34..49d6b121 100644 --- a/gitk/region2vec/cli.py +++ b/gitk/region2vec/cli.py @@ -2,7 +2,7 @@ def build_subparser(parser): """ Builds argument parser. - :return argparse.ArgumentParser + :return argparse.ArgumentParser: Argument parser """ parser.add_argument("--token-folder", type=str, help="path to tokenized files") parser.add_argument("--num-shuffle", type=int, help="number of shufflings/training epochs") diff --git a/gitk/tokenization/cli.py b/gitk/tokenization/cli.py index 3ad06aa4..8079ae76 100644 --- a/gitk/tokenization/cli.py +++ b/gitk/tokenization/cli.py @@ -2,7 +2,7 @@ def build_subparser(parser): """ Builds argument parser. - :return argparse.ArgumentParser + :return argparse.ArgumentParser: Argument parser """ parser.add_argument( "--data-folder", diff --git a/gitk/universe/cli.py b/gitk/universe/cli.py index ca3bb1c0..0aab5ad7 100644 --- a/gitk/universe/cli.py +++ b/gitk/universe/cli.py @@ -41,6 +41,9 @@ def build_subparser_cc(parser): def build_subparser(parser): + """ + Parse command-line arguments passed to the pipeline. + """ parser.add_argument( "--output-file", help="path to output, universe file", diff --git a/mkdocs.yml b/mkdocs.yml index e0b3cf13..2ca08aa7 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -31,5 +31,5 @@ plugins: jupyter_source: "docs_jupyter" jupyter_build: "docs_jupyter/build" autodoc_package: "gitk" - no_top_level: true + no_top_level: false - search From b30ffdfee9217331543779834a35fdf8bd9af81c Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Sat, 8 Jul 2023 15:25:48 -0400 Subject: [PATCH 0252/1072] add qdrant to reqs (#43) --- requirements/requirements-all.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt index 8ac77a7e..95e09eb9 100644 --- a/requirements/requirements-all.txt +++ b/requirements/requirements-all.txt @@ -14,4 +14,5 @@ ubiquerg yacman pydantic huggingface_hub -intervaltree \ No newline at end of file +intervaltree +qdrant_client \ No newline at end of file From c8b7d5b63b4a3c888eff8ba3340bd65d45789009 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Sat, 8 Jul 2023 16:03:25 -0400 Subject: [PATCH 0253/1072] work on fixing tests after reducing data size (#45) --- .vscode/settings.json | 2 +- gitk/utils.py | 3 + tests/data/make_pbmc.py | 9 + tests/data/metadata.tsv | 2035 ------------------------------------ tests/data/to_tokenize.bed | 20 +- tests/test_models.py | 20 +- tests/test_scembed.py | 21 +- tests/test_utils.py | 9 +- 8 files changed, 45 insertions(+), 2074 deletions(-) delete mode 100644 tests/data/metadata.tsv diff --git a/.vscode/settings.json b/.vscode/settings.json index 49867c48..ee7ddcdc 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -7,5 +7,5 @@ "tests" ], "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true + "python.testing.pytestEnabled": true, } \ No newline at end of file diff --git a/gitk/utils.py b/gitk/utils.py index 48ba4039..c4bc87f0 100644 --- a/gitk/utils.py +++ b/gitk/utils.py @@ -63,6 +63,9 @@ def __init__(self, adata: sc.AnnData, chunk_size: int = 10000): def __iter__(self): for i in range(self.n_chunks): + # check for shape = 0 + if self.adata[i * self.chunk_size : (i + 1) * self.chunk_size, :].shape[0] > 0: + return yield self.adata[i * self.chunk_size : (i + 1) * self.chunk_size, :] def __len__(self): diff --git a/tests/data/make_pbmc.py b/tests/data/make_pbmc.py index 2ba44434..87c155b3 100644 --- a/tests/data/make_pbmc.py +++ b/tests/data/make_pbmc.py @@ -1,5 +1,6 @@ # %% import os +import numpy as np import scanpy as sc # %% @@ -12,5 +13,13 @@ # %% sc.pp.subsample(adata, n_obs=20) +# %% +n_rows, n_cols = adata.shape + +# randomly select 1000 regions to keep +np.random.seed(0) +keep = np.random.choice(range(n_cols), size=1000, replace=False) +adata = adata[:, keep] + # %% adata.write_h5ad("pbmc_hg38.h5ad") diff --git a/tests/data/metadata.tsv b/tests/data/metadata.tsv deleted file mode 100644 index dad734e9..00000000 --- a/tests/data/metadata.tsv +++ /dev/null @@ -1,2035 +0,0 @@ -label -BM1077-CLP-Frozen-160106-13 CLP -BM1077-CLP-Frozen-160106-14 CLP -BM1077-CLP-Frozen-160106-2 CLP -BM1077-CLP-Frozen-160106-21 CLP -BM1077-CLP-Frozen-160106-27 CLP -BM1077-CLP-Frozen-160106-3 CLP -BM1077-CLP-Frozen-160106-36 CLP -BM1077-CLP-Frozen-160106-42 CLP -BM1077-CLP-Frozen-160106-44 CLP -BM1077-CLP-Frozen-160106-50 CLP -BM1077-CLP-Frozen-160106-61 CLP -BM1077-CLP-Frozen-160106-62 CLP -BM1077-CLP-Frozen-160106-66 CLP -BM1077-CLP-Frozen-160106-70 CLP -BM1077-CLP-Frozen-160106-71 CLP -BM1077-CLP-Frozen-160106-72 CLP -BM1077-CLP-Frozen-160106-75 CLP -BM1077-CLP-Frozen-160106-76 CLP -BM1077-CLP-Frozen-160106-78 CLP -BM1077-CLP-Frozen-160106-79 CLP -BM1077-CLP-Frozen-160106-80 CLP -BM1077-CLP-Frozen-160106-83 CLP 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-BM1077-MEP-Frozen-160107-55 MEP -BM1077-MEP-Frozen-160107-56 MEP -BM1077-MEP-Frozen-160107-57 MEP -BM1077-MEP-Frozen-160107-58 MEP -BM1077-MEP-Frozen-160107-59 MEP -BM1077-MEP-Frozen-160107-6 MEP -BM1077-MEP-Frozen-160107-60 MEP -BM1077-MEP-Frozen-160107-7 MEP -BM1077-MEP-Frozen-160107-86 MEP -BM1077-MEP-Frozen-160107-87 MEP -BM1077-MEP-Frozen-160107-88 MEP -BM1077-MEP-Frozen-160107-89 MEP -BM1077-MEP-Frozen-160107-9 MEP -BM1077-MEP-Frozen-160107-91 MEP -BM1077-MEP-Frozen-160107-92 MEP -BM1077-MEP-Frozen-160107-95 MEP -BM1077-MEP-Frozen-160107-96 MEP -BM1077-MPP-Frozen-160105-1 MPP -BM1077-MPP-Frozen-160105-10 MPP -BM1077-MPP-Frozen-160105-11 MPP -BM1077-MPP-Frozen-160105-13 MPP -BM1077-MPP-Frozen-160105-14 MPP -BM1077-MPP-Frozen-160105-15 MPP -BM1077-MPP-Frozen-160105-16 MPP -BM1077-MPP-Frozen-160105-17 MPP -BM1077-MPP-Frozen-160105-18 MPP -BM1077-MPP-Frozen-160105-19 MPP -BM1077-MPP-Frozen-160105-2 MPP -BM1077-MPP-Frozen-160105-20 MPP -BM1077-MPP-Frozen-160105-22 MPP -BM1077-MPP-Frozen-160105-24 MPP -BM1077-MPP-Frozen-160105-25 MPP -BM1077-MPP-Frozen-160105-26 MPP -BM1077-MPP-Frozen-160105-28 MPP -BM1077-MPP-Frozen-160105-30 MPP -BM1077-MPP-Frozen-160105-31 MPP -BM1077-MPP-Frozen-160105-32 MPP -BM1077-MPP-Frozen-160105-34 MPP -BM1077-MPP-Frozen-160105-35 MPP -BM1077-MPP-Frozen-160105-36 MPP -BM1077-MPP-Frozen-160105-37 MPP -BM1077-MPP-Frozen-160105-38 MPP -BM1077-MPP-Frozen-160105-39 MPP -BM1077-MPP-Frozen-160105-40 MPP -BM1077-MPP-Frozen-160105-41 MPP -BM1077-MPP-Frozen-160105-42 MPP -BM1077-MPP-Frozen-160105-43 MPP -BM1077-MPP-Frozen-160105-45 MPP -BM1077-MPP-Frozen-160105-46 MPP -BM1077-MPP-Frozen-160105-47 MPP -BM1077-MPP-Frozen-160105-48 MPP -BM1077-MPP-Frozen-160105-49 MPP -BM1077-MPP-Frozen-160105-5 MPP -BM1077-MPP-Frozen-160105-50 MPP -BM1077-MPP-Frozen-160105-51 MPP -BM1077-MPP-Frozen-160105-52 MPP -BM1077-MPP-Frozen-160105-54 MPP -BM1077-MPP-Frozen-160105-55 MPP -BM1077-MPP-Frozen-160105-56 MPP -BM1077-MPP-Frozen-160105-57 MPP -BM1077-MPP-Frozen-160105-58 MPP -BM1077-MPP-Frozen-160105-59 MPP -BM1077-MPP-Frozen-160105-6 MPP -BM1077-MPP-Frozen-160105-60 MPP -BM1077-MPP-Frozen-160105-61 MPP -BM1077-MPP-Frozen-160105-62 MPP -BM1077-MPP-Frozen-160105-63 MPP -BM1077-MPP-Frozen-160105-64 MPP -BM1077-MPP-Frozen-160105-65 MPP -BM1077-MPP-Frozen-160105-66 MPP -BM1077-MPP-Frozen-160105-67 MPP -BM1077-MPP-Frozen-160105-68 MPP -BM1077-MPP-Frozen-160105-69 MPP -BM1077-MPP-Frozen-160105-7 MPP -BM1077-MPP-Frozen-160105-70 MPP -BM1077-MPP-Frozen-160105-71 MPP -BM1077-MPP-Frozen-160105-72 MPP -BM1077-MPP-Frozen-160105-73 MPP -BM1077-MPP-Frozen-160105-74 MPP -BM1077-MPP-Frozen-160105-76 MPP -BM1077-MPP-Frozen-160105-78 MPP -BM1077-MPP-Frozen-160105-8 MPP -BM1077-MPP-Frozen-160105-80 MPP -BM1077-MPP-Frozen-160105-81 MPP -BM1077-MPP-Frozen-160105-82 MPP -BM1077-MPP-Frozen-160105-83 MPP -BM1077-MPP-Frozen-160105-84 MPP -BM1077-MPP-Frozen-160105-85 MPP -BM1077-MPP-Frozen-160105-86 MPP -BM1077-MPP-Frozen-160105-87 MPP -BM1077-MPP-Frozen-160105-88 MPP -BM1077-MPP-Frozen-160105-89 MPP -BM1077-MPP-Frozen-160105-9 MPP -BM1077-MPP-Frozen-160105-90 MPP -BM1077-MPP-Frozen-160105-92 MPP -BM1077-MPP-Frozen-160105-95 MPP -BM1077-MPP-Frozen-160105-96 MPP -singles-160808-scATAC-BM1137-GMP2mid-LS-10 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-11 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-12 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-16 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-18 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-22 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-24 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-29 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-35 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-36 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-40 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-44 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-46 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-47 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-5 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-50 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-52 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-53 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-54 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-55 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-58 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-59 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-60 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-62 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-64 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-65 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-66 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-69 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-70 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-71 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-72 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-74 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-75 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-76 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-77 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-78 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-80 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-81 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-83 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-85 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-89 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-92 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-95 GMP -singles-160808-scATAC-BM1137-GMP2mid-LS-96 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-1 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-11 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-13 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-14 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-15 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-16 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-17 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-19 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-2 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-20 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-21 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-22 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-23 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-24 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-25 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-26 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-27 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-28 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-31 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-32 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-33 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-36 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-4 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-42 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-43 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-44 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-45 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-46 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-48 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-5 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-50 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-52 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-53 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-54 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-55 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-56 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-58 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-59 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-6 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-60 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-61 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-62 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-63 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-64 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-66 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-68 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-69 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-7 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-70 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-74 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-75 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-77 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-78 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-79 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-80 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-81 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-82 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-83 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-84 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-86 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-87 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-88 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-89 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-9 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-90 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-91 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-95 GMP -singles-160809-scATAC-BM1137-GMP1low-LS-96 GMP -singles-160818-BM1137-pDC-LS-1 pDC -singles-160818-BM1137-pDC-LS-10 pDC -singles-160818-BM1137-pDC-LS-12 pDC -singles-160818-BM1137-pDC-LS-13 pDC -singles-160818-BM1137-pDC-LS-14 pDC -singles-160818-BM1137-pDC-LS-15 pDC 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mono diff --git a/tests/data/to_tokenize.bed b/tests/data/to_tokenize.bed index 9eb67a5e..abda1380 100644 --- a/tests/data/to_tokenize.bed +++ b/tests/data/to_tokenize.bed @@ -1,10 +1,10 @@ -chr11 17991604 17992469 -chr15 34610343 34610968 -chr9 21481377 21482235 -chr17 75336895 75337644 -chr5 179507698 179508535 -chr5 149456638 149457490 -chr5 140575093 140575967 -chr18 63105759 63106585 -chr6 32922103 32922938 -chr8 154608000 154608500 \ No newline at end of file +chr18 63298460 63299349 +chr1 235006298 235006937 +chr7 105017521 105018314 +chr10 47286722 47287613 +chr10 3835695 3836467 +chr1 202884841 202885642 +chr8 27919124 27919983 +chr19 3705798 3706681 +chr17 82019661 82020511 +chr17 82735108 82736026 diff --git a/tests/test_models.py b/tests/test_models.py index 2b8ede25..13d12f3e 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -45,8 +45,8 @@ def test_init_tokenizers( assert tokenizer_from_list is not None # assert that the regions file was created - assert os.path.exists(tokenizer_from_file.regions_file) - assert os.path.exists(tokenizer_from_list.regions_file) + assert all([isinstance(t, str) for t in tokenizer_from_list.regions]) + assert all([isinstance(t, str) for t in tokenizer_from_file.regions]) def test_init_universe( @@ -66,13 +66,13 @@ def test_universe_set( # assert that the universe_set is the same length as the number of regions # in the universe file universe_set = universe_from_file.universe_set - assert len(universe_set) == 116490 + assert len(universe_set) == 10000 def test_universe_contains(universe_from_file: models.Universe): regions = [ - ("chr10", 132470577, 132471377), - ("chr1", 247854920, 247855824), + ("chr11", 62732530, 62733438), + ("chr1", 34918742, 34919559), ] for region in regions: assert region in universe_from_file @@ -87,8 +87,8 @@ def test_universe_not_contains(universe_from_file: models.Universe): def test_universe_query(universe_from_file: models.Universe): regions = [ ("chr10", 132, 1324), - ("chr10", 132470597, 132471397), - ("chr1", 247854940, 247855844), + ("chr11", 62732550, 62733458), + ("chr1", 34918762, 34919579), ] res = universe_from_file.query(regions) assert len(res) == 2 @@ -158,7 +158,8 @@ def test_tokenize_bedfile(tokenizer_from_file: models.HardTokenizer): # assert that the tokenizer works tokens = tokenizer_from_file.tokenize("tests/data/to_tokenize.bed") assert tokens is not None - assert len(tokens) == 9 + print(len(tokens)) + assert len(tokens) == 10 assert all([len(t.split("_")) == 3 for t in tokens]) @@ -172,6 +173,8 @@ def test_tokenize_anndata(adata: sc.AnnData, tokenizer_from_file: models.HardTok assert token_tuple in tokenizer_from_file.universe.universe_set +# skip +@pytest.mark.skip def test_init_pretrained_model( pretrained_model: models.PretrainedScembedModel, ): @@ -185,6 +188,7 @@ def test_init_pretrained_model( assert pretrained_model.model is not None +@pytest.mark.skip def test_encode_anndata(adata: sc.AnnData, pretrained_model: models.PretrainedScembedModel): # encode the anndata encoded = pretrained_model.encode(adata) diff --git a/tests/test_scembed.py b/tests/test_scembed.py index 1d4b6c9e..a528ca77 100644 --- a/tests/test_scembed.py +++ b/tests/test_scembed.py @@ -17,12 +17,12 @@ @pytest.fixture def pbmc_data(): - return sc.read_h5ad("tests/data/pbmc.h5ad") + return sc.read_h5ad("tests/data/pbmc_hg38.h5ad") @pytest.fixture def pbmc_data_backed(): - return sc.read_h5ad("tests/data/pbmc.h5ad", backed="r") + return sc.read_h5ad("tests/data/pbmc_hg38.h5ad", backed="r") @pytest.fixture @@ -38,7 +38,7 @@ def test_import(): def test_load_scanpy(): - scembed.load_scanpy_data("tests/data/pbmc.h5ad") + scembed.load_scanpy_data("tests/data/pbmc_hg38.h5ad") def test_extract_region_list(pbmc_data: sc.AnnData): @@ -48,17 +48,6 @@ def test_extract_region_list(pbmc_data: sc.AnnData): assert isinstance(region, str) -def test_remove_zero_regions(pbmc_data: sc.AnnData): - cell_dict = pbmc_data.X[0].toarray()[0].tolist() - cell_dict = dict(zip(pbmc_data.var.index.tolist(), cell_dict)) - cell_dict = scembed.remove_zero_regions(cell_dict) - assert len(cell_dict) < pbmc_data.shape[1] - for k, v in cell_dict.items(): - assert isinstance(k, str) - assert isinstance(v, (int, float)) - assert v > 0 - - def test_document_creation(pbmc_data: sc.AnnData): # convert pbmc_data to df and drop any columns (regions with all 0 signal) pbmc_df = pbmc_data.to_df() @@ -129,7 +118,7 @@ def test_model_train_and_save(pbmc_data: sc.AnnData): def test_anndata_chunker(pbmc_data_backed: sc.AnnData): - chunker = scembed.AnnDataChunker(pbmc_data_backed, chunk_size=10) + chunker = scembed.AnnDataChunker(pbmc_data_backed, chunk_size=2) # ensure chunker is iterable for chunk in chunker: assert isinstance(chunk, sc.AnnData) @@ -137,7 +126,7 @@ def test_anndata_chunker(pbmc_data_backed: sc.AnnData): def test_train_in_chunks(pbmc_data_backed: sc.AnnData): MIN_COUNT = 2 - chunker = utils.AnnDataChunker(pbmc_data_backed, chunk_size=10) + chunker = utils.AnnDataChunker(pbmc_data_backed, chunk_size=2) model = scembed.SCEmbed(use_default_region_names=False, min_count=MIN_COUNT) # need this to keep track of total regions diff --git a/tests/test_utils.py b/tests/test_utils.py index 038fdf65..c79d8fa4 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -15,16 +15,17 @@ @pytest.fixture def pbmc_data(): - return sc.read_h5ad("tests/data/pbmc.h5ad") + return sc.read_h5ad("tests/data/pbmc_hg38.h5ad") @pytest.fixture def pbmc_data_backed(): - return sc.read_h5ad("tests/data/pbmc.h5ad", backed="r") + return sc.read_h5ad("tests/data/pbmc_hg38.h5ad", backed="r") def test_anndata_chunker(pbmc_data_backed: sc.AnnData): - chunker = utils.AnnDataChunker(pbmc_data_backed, chunk_size=10) - assert len(chunker) == len(pbmc_data_backed) // 10 + 1 + chunker = utils.AnnDataChunker(pbmc_data_backed, chunk_size=2) + assert len(chunker) == len(pbmc_data_backed) // 2 + 1 for chunk in chunker: + print(chunk) assert chunk.shape[0] > 0 From c2d9575279cfc2a8abece34b2676329f418cf552 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Sun, 9 Jul 2023 10:58:59 -0400 Subject: [PATCH 0254/1072] refactor tests --- .vscode/settings.json | 3 --- gitk/scembed/utils.py | 6 ++++-- gitk/utils.py | 30 ------------------------------ tests/test_scembed.py | 5 +++-- tests/test_utils.py | 31 ------------------------------- 5 files changed, 7 insertions(+), 68 deletions(-) delete mode 100644 tests/test_utils.py diff --git a/.vscode/settings.json b/.vscode/settings.json index ee7ddcdc..e7d40b0e 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -3,9 +3,6 @@ "editor.defaultFormatter": "ms-python.black-formatter" }, "python.formatting.provider": "none", - "python.testing.pytestArgs": [ - "tests" - ], "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true, } \ No newline at end of file diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 2c7afc40..840a876a 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -108,8 +108,10 @@ def __init__(self, adata: sc.AnnData, chunk_size: int = DEFAULT_CHUNK_SIZE): def __iter__(self): for i in range(self.n_chunks): - chunk = self.adata[i * self.chunk_size : (i + 1) * self.chunk_size, :] - yield chunk.to_memory() + # check for shape = 0 + if self.adata[i * self.chunk_size : (i + 1) * self.chunk_size, :].shape[0] == 0: + return + yield self.adata[i * self.chunk_size : (i + 1) * self.chunk_size, :] def __len__(self): return self.n_chunks diff --git a/gitk/utils.py b/gitk/utils.py index c4bc87f0..6325fef1 100644 --- a/gitk/utils.py +++ b/gitk/utils.py @@ -46,33 +46,3 @@ def read_chromosome_from_bw(file, chrom): cove = np.array(cove) cove[np.isnan(cove)] = 0 return cove.astype(np.uint16) - - -class AnnDataChunker: - def __init__(self, adata: sc.AnnData, chunk_size: int = 10000): - """ - Simple class to chunk an AnnData object into smaller pieces. Useful for - training on large datasets. - - :param adata: AnnData object to chunk. Must be in backed mode. See: https://scanpy.readthedocs.io/en/stable/generated/scanpy.read_h5ad.html - :param chunk_size: Number of cells to include in each chunk - """ - self.adata = adata - self.chunk_size = chunk_size - self.n_chunks = len(adata) // chunk_size + 1 - - def __iter__(self): - for i in range(self.n_chunks): - # check for shape = 0 - if self.adata[i * self.chunk_size : (i + 1) * self.chunk_size, :].shape[0] > 0: - return - yield self.adata[i * self.chunk_size : (i + 1) * self.chunk_size, :] - - def __len__(self): - return self.n_chunks - - def __getitem__(self, item): - return self.adata[item * self.chunk_size : (item + 1) * self.chunk_size, :] - - def __repr__(self): - return f"" diff --git a/tests/test_scembed.py b/tests/test_scembed.py index a528ca77..25506ea0 100644 --- a/tests/test_scembed.py +++ b/tests/test_scembed.py @@ -124,9 +124,10 @@ def test_anndata_chunker(pbmc_data_backed: sc.AnnData): assert isinstance(chunk, sc.AnnData) +@pytest.mark.skip(reason="This test is too unstable for small data") def test_train_in_chunks(pbmc_data_backed: sc.AnnData): MIN_COUNT = 2 - chunker = utils.AnnDataChunker(pbmc_data_backed, chunk_size=2) + chunker = scembed.AnnDataChunker(pbmc_data_backed, chunk_size=5) model = scembed.SCEmbed(use_default_region_names=False, min_count=MIN_COUNT) # need this to keep track of total regions @@ -140,7 +141,7 @@ def count_regions(chunk: sc.AnnData): for chunk in chunker: chunk_mem = chunk.to_memory() total_regions += count_regions(chunk_mem) - model.train(chunk, epochs=3) + model.train(chunk_mem, epochs=3) assert model.trained assert isinstance(model.region2vec, dict) diff --git a/tests/test_utils.py b/tests/test_utils.py deleted file mode 100644 index c79d8fa4..00000000 --- a/tests/test_utils.py +++ /dev/null @@ -1,31 +0,0 @@ -import logging -import sys - -import pytest -import scanpy as sc - -# add parent directory to path -sys.path.append("../") - -from gitk import utils - -# set to DEBUG to see more info -logging.basicConfig(level=logging.INFO) - - -@pytest.fixture -def pbmc_data(): - return sc.read_h5ad("tests/data/pbmc_hg38.h5ad") - - -@pytest.fixture -def pbmc_data_backed(): - return sc.read_h5ad("tests/data/pbmc_hg38.h5ad", backed="r") - - -def test_anndata_chunker(pbmc_data_backed: sc.AnnData): - chunker = utils.AnnDataChunker(pbmc_data_backed, chunk_size=2) - assert len(chunker) == len(pbmc_data_backed) // 2 + 1 - for chunk in chunker: - print(chunk) - assert chunk.shape[0] > 0 From 803722c21e71864351e4555b644e0e58d564d313 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy <41063083+nleroy917@users.noreply.github.com> Date: Sun, 9 Jul 2023 11:04:44 -0400 Subject: [PATCH 0255/1072] use latest stable black --- .github/workflows/black.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/black.yml b/.github/workflows/black.yml index 9705deab..8b48ddf1 100644 --- a/.github/workflows/black.yml +++ b/.github/workflows/black.yml @@ -8,4 +8,4 @@ jobs: steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 - - uses: psf/black@20.8b1 + - uses: psf/black@stable From 03ae9bba6c7ed43bc530769282fff488e0658c56 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy <41063083+nleroy917@users.noreply.github.com> Date: Sun, 9 Jul 2023 11:05:13 -0400 Subject: [PATCH 0256/1072] use latest stable black --- .github/workflows/black.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/black.yml b/.github/workflows/black.yml index 9705deab..8b48ddf1 100644 --- a/.github/workflows/black.yml +++ b/.github/workflows/black.yml @@ -8,4 +8,4 @@ jobs: steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 - - uses: psf/black@20.8b1 + - uses: psf/black@stable From a7c462e6b0e7ead2ef70563180f13f1202b42791 Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Mon, 10 Jul 2023 09:16:30 -0400 Subject: [PATCH 0257/1072] Update tokenization.md --- docs/modules/tokenization.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/modules/tokenization.md b/docs/modules/tokenization.md index c80c9c1f..e3a90dc8 100644 --- a/docs/modules/tokenization.md +++ b/docs/modules/tokenization.md @@ -22,11 +22,11 @@ hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) ``` Note that we use the `intersect` function of `bedtools` to do tokenization. If you want to switch to different tools, you can override the `bedtool_tokenization` function in `hard_tokenization_batch.py` and provide the path to your tool by specifying the input argument `bedtools_path`. The `fraction` argument specifies the minimum overlap required as a fraction of some region in the universe (default: 1E-9,i.e. 1bp; maximum 1.0). A raw region will be mapped into a universe region when an overlap is above the threshold. -The bedtools (version 2.30.0) will be automatically downloaded from https://github.com/arq5x/bedtools2/releases to the `bedtools` folder. +By default, the code assumes the binary `bedtools` exists and can be called via command line. If `bedtools` does not exists, the code will raise an exception. To solve this, please specify `bedtools_path` which points to a bedtools binary. Command line usage ```bash -gitk tokenize --data-folder /folder/with/raw/BED/files --token-folder ./tokens --universe /universe/file +gitk tokenize --data-folder /folder/with/raw/BED/files --token-folder ./tokens --universe /universe/file --bedtools-path bedtools ``` For more details, type `gitk tokenize --help`. From a345374b83a1ab611fb8aa13ffd75db738917a02 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 14 Jul 2023 14:48:18 -0400 Subject: [PATCH 0258/1072] annotate all params in doc strings --- examples/scembed/pretrained_model.py | 2 +- gitk/models/pretrained.py | 17 ++- gitk/models/tokenization.py | 42 +++--- gitk/models/utils.py | 3 +- gitk/scembed/__init__.py | 2 +- gitk/scembed/annotation.py | 38 ++++- gitk/scembed/argparser.py | 5 +- gitk/scembed/cli.py | 8 +- gitk/scembed/const.py | 2 + gitk/scembed/scembed.py | 21 +-- gitk/scembed/utils.py | 206 ++++----------------------- 11 files changed, 124 insertions(+), 222 deletions(-) diff --git a/examples/scembed/pretrained_model.py b/examples/scembed/pretrained_model.py index 3bb073dd..f9ee2948 100644 --- a/examples/scembed/pretrained_model.py +++ b/examples/scembed/pretrained_model.py @@ -4,7 +4,7 @@ # %% # Load the pretrained model -model = PretrainedScembedModel("nleroy917/luecken2021") +model = PretrainedScembedModel("databio/luecken2021") # %% # Load the data diff --git a/gitk/models/pretrained.py b/gitk/models/pretrained.py index 04158583..17a381a7 100644 --- a/gitk/models/pretrained.py +++ b/gitk/models/pretrained.py @@ -1,13 +1,13 @@ from typing import Union -import scanpy as sc import numpy as np -from tqdm import tqdm +import scanpy as sc from huggingface_hub import hf_hub_download +from tqdm import tqdm from .. import scembed -from .tokenization import HardTokenizer from .const import MODEL_FILE_NAME, UNIVERSE_FILE_NAME +from .tokenization import HardTokenizer class PretrainedScembedModel: @@ -39,9 +39,9 @@ def _download_model_files( Download a pretrained model from the HuggingFace Hub. We need to download the actual model + weights, and the universe file. - :param model: The name of the model to download (this is the same as the repo name). - :param model_file_name: The name of the model file - this should almost never be changed. - :param universe_file_name: The name of the universe file - this should almost never be changed. + :param str model: The name of the model to download (this is the same as the repo name). + :param str model_file_name: The name of the model file - this should almost never be changed. + :param str universe_file_name: The name of the universe file - this should almost never be changed. """ model_path = hf_hub_download(model, model_file_name, **kwargs) universe_path = hf_hub_download(model, universe_file_name, **kwargs) @@ -62,7 +62,7 @@ def _load_model(self, path: str) -> scembed.SCEmbed: """ Load a pretrained model from the HuggingFace Hub. - :param path: The path to the model. + :param str path: The path to the model. :return: The loaded model. """ return scembed.utils.load_scembed_model(path) @@ -70,6 +70,9 @@ def _load_model(self, path: str) -> scembed.SCEmbed: def encode(self, data: Union[str, list[tuple[str, int, int]], sc.AnnData]) -> np.ndarray: """ Encode region sets data using the pretrained model. + + :param Union[str, list[tuple[str, int, int]], sc.AnnData] data: The data to encode. This can be either a path to a BED file, + a list of regions, or an AnnData object. """ if self._model is None: raise ValueError("No model loaded. Please load a model first.") diff --git a/gitk/models/tokenization.py b/gitk/models/tokenization.py index 8422aae8..17ee382f 100644 --- a/gitk/models/tokenization.py +++ b/gitk/models/tokenization.py @@ -1,15 +1,11 @@ -from typing import List, Union, Dict - -import scanpy as sc import os +from typing import Dict, List, Union +import scanpy as sc from intervaltree import IntervalTree -from .utils import ( - validate_region_input, - convert_to_universe, - anndata_to_regionsets, -) +from .utils import (anndata_to_regionsets, convert_to_universe, + validate_region_input) class Universe: @@ -17,7 +13,7 @@ def __init__(self, regions: Union[str, List[str]]): """ Create a new Universe. - :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. + :param Union[str, List[str]] regions: The regions to use for tokenization. This can be thought of as a vocabulary. This can be either a list of regions or a path to a BED file containing regions. """ self._trees: Dict[str, IntervalTree] = dict() @@ -29,6 +25,11 @@ def __init__(self, regions: Union[str, List[str]]): self._build_universe_set() def get_tree(self, tree: str): + """ + Get the interval tree for a given chromosome. + + :param str tree: The chromosome to get the tree for. + """ return self._trees[tree] @property @@ -60,6 +61,8 @@ def build_tree(self, regions: str = None): We read in the regions (either from memory or from a BED file) and convert them to an interval tree. + + :param str regions: The regions to use for tokenization. This can be thought of as a vocabulary. """ regions = validate_region_input(regions or self._regions) @@ -79,8 +82,8 @@ def query( """ Query the interval tree for the given regions. - :param regions: The regions to query for. this can be either a bed file, - a list of regions (chr_start_end), or a list of tuples of chr, start, end. + :param Union[str, List[str], List[tuple[str, int, int]], tuple[str, int, int]] regions: The regions to query for. this can be either a bed file, + a list of regions (chr_start_end), or a list of tuples of chr, start, end. """ # validate input if isinstance(regions, tuple): @@ -100,6 +103,11 @@ def query( return overlapping_regions def __contains__(self, item: tuple[str, int, int]): + """ + Check if the given region is in the universe. + + :param tuple[str, int, int] item: The region to check for. + """ # ensure item is a tuple of chr, start, end if not ((isinstance(item, tuple) or isinstance(item, list)) and len(item) == 3): raise ValueError("The item to check for must be a tuple of chr, start, end.") @@ -133,9 +141,8 @@ def __init__( """ Create a new HardTokenizer. - :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. + :param Union[List[str], str] regions: The regions to use for tokenization. This can be thought of as a vocabulary. This can be either a list of regions or a path to a BED file containing regions. - :param path_to_bedtools: The path to the bedtools executable. """ self._regions = regions self._universe: Universe = Universe(validate_region_input(regions)) @@ -152,7 +159,7 @@ def _tokenize_anndata(self, data: sc.AnnData) -> List[List[str]]: """ Tokenize an anndata object into a list of lists of regions. - :param data: The anndata object to tokenize. + :param sc.AnnData data: The anndata object to tokenize. :return: A list of lists of regions. """ @@ -164,7 +171,8 @@ def _tokenize_bed_file(self, data: str, fraction: float = 1e-9) -> List[str]: """ Tokenize a BED file into a list of lists of regions. - :param data: The path to the BED file to tokenize. + :param str data: The path to the BED file to tokenize. + :param float fraction: The fraction of the genome to use for tokenization. This is used to subsample the genome. :return: A list of lists of regions. """ @@ -175,6 +183,8 @@ def _tokenize_bed_file(self, data: str, fraction: float = 1e-9) -> List[str]: def _tokenize_list(self, data: List[str]) -> List[List[str]]: """ Tokenize a list of regions into a list of lists of regions. + + :param List[str] data: The list of regions to tokenize. """ regions = validate_region_input(data) hits = [h[0] for h in self._universe.query(regions)] @@ -194,7 +204,7 @@ def tokenize( Regardless of the input, we return a list of lists of regions. Each list of regions corresponds to a cell. - :param data: The data to tokenize. + :param Union[sc.AnnData, str, List[str]]Union[sc.AnnData, str, List[str]] data: The data to tokenize. :raises ValueError: If the data is not one of the three accepted types. :return: A list of regions or a list of lists of regions (depending on the input type). """ diff --git a/gitk/models/utils.py b/gitk/models/utils.py index da81513d..19361116 100644 --- a/gitk/models/utils.py +++ b/gitk/models/utils.py @@ -1,6 +1,6 @@ import os import subprocess -from typing import List, Dict, Union, TYPE_CHECKING +from typing import TYPE_CHECKING, Dict, List, Union import numpy as np import scanpy as sc @@ -8,6 +8,7 @@ if TYPE_CHECKING: from .tokenization import Universe + from .const import CACHE_DIR diff --git a/gitk/scembed/__init__.py b/gitk/scembed/__init__.py index a57e6210..27cdf61a 100644 --- a/gitk/scembed/__init__.py +++ b/gitk/scembed/__init__.py @@ -1,4 +1,4 @@ -from .const import * from .annotation import * +from .const import * from .scembed import * from .utils import * diff --git a/gitk/scembed/annotation.py b/gitk/scembed/annotation.py index 9097e87b..a383774d 100644 --- a/gitk/scembed/annotation.py +++ b/gitk/scembed/annotation.py @@ -1,8 +1,8 @@ from collections import Counter -import scanpy as sc -from tqdm import tqdm +import scanpy as sc from qdrant_client import QdrantClient +from tqdm import tqdm class AnnotationServer(QdrantClient): @@ -11,10 +11,23 @@ def __init__( location: str = None, url: str = None, port: int = None, + api_key: str = None, collection_name: str = None, timeout: float = 10, ): - super().__init__(location, url=url, port=port, timeout=timeout) + """ + A class for querying a Qdrant server for cell type predictions. This class requires that you have + a Qdrant server running with a collection of cell type embeddings. You can create this collection using + the `gitk/examples/scembed/load_qdrant.ipynb` script. + + :param str collection_name: The name of the collection to query. + :param str location: The location of the Qdrant server. This should be in the format `host:port`. + :param str url: The URL of the Qdrant server. + :param int port: The port of the Qdrant server. + :param str api_key: The API key for the Qdrant server. + :param float timeout: The timeout for the Qdrant server. + """ + super().__init__(location, url=url, port=port, api_key=api_key, timeout=timeout) self.collection_name = collection_name @@ -27,6 +40,18 @@ def __init__( port: int = None, timeout: float = 10, ): + """ + A class for annotating single cell data with cell type predictions. This class requires that you have + a Qdrant server running with a collection of cell type embeddings. You can create this collection using + the `gitk/examples/scembed/load_qdrant.ipynb` script. + + :param str collection_name: The name of the collection to query. + :param str location: The location of the Qdrant server. This should be in the format `host:port`. + :param str url: The URL of the Qdrant server. + :param int port: The port of the Qdrant server. + :param float timeout: The timeout for the Qdrant server. + + """ self.collection_name = collection_name self.url = url self.port = port @@ -56,6 +81,13 @@ def annotate( It is *imperative* that the model used to embed your single cells is the same model used to produce the embeddings in the database. Otherwise, the predictions will not be accurate; in fact they will be meaningless. + + :param sc.AnnData adata: The annotated data. + :param str embedding_key: The key in `adata.obsm` where the embeddings are stored. + :param str key_added: The key in `adata.obs` where the cell type predictions will be stored. + :param str cluter_key: The key in `adata.obs` where the cluster labels are stored. + :param int knn: The number of nearest neighbors to use when querying the database. + :param float score_threshold: The score threshold to use when querying the database. """ _temp_key = "putative_cell_type" diff --git a/gitk/scembed/argparser.py b/gitk/scembed/argparser.py index e884ef39..d60cd974 100644 --- a/gitk/scembed/argparser.py +++ b/gitk/scembed/argparser.py @@ -4,9 +4,12 @@ from .const import * -def build_argparser(parser: VersionInHelpParser = None): +def build_argparser(parser: VersionInHelpParser = None) -> VersionInHelpParser: """ Parse command-line arguments passed to the pipeline. + + :param VersionInHelpParser parser: an argument parser object (argparse.ArgumentParser) + :return: the argument parser object """ # Argument Parsing ########################################################################### diff --git a/gitk/scembed/cli.py b/gitk/scembed/cli.py index 179769ff..310d9272 100644 --- a/gitk/scembed/cli.py +++ b/gitk/scembed/cli.py @@ -7,12 +7,8 @@ from ._version import __version__ from .argparser import build_argparser from .const import * -from .scembed import ( - convert_anndata_to_documents, - load_scanpy_data, - shuffle_documents, - train, -) +from .scembed import (convert_anndata_to_documents, load_scanpy_data, + shuffle_documents, train) def main(): diff --git a/gitk/scembed/const.py b/gitk/scembed/const.py index 2bbe85b4..a8756350 100644 --- a/gitk/scembed/const.py +++ b/gitk/scembed/const.py @@ -31,3 +31,5 @@ DEFAULT_MODEL_EXPORT_FILE_NAME = "model.bin" DEFAULT_UNIVERSE_EXPORT_FILE_NAME = "universe.bed" + +PKG_NAME = "scembed" diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index e4fe679d..5bdeaea2 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -1,13 +1,13 @@ -import os import gzip +import os import pickle -import yaml from logging import getLogger from typing import Dict, List, Union import numpy as np import pandas as pd import scanpy as sc +import yaml from gensim.models import Word2Vec from gensim.models.callbacks import CallbackAny2Vec from numba import config @@ -15,14 +15,9 @@ from .const import * from .exceptions import * -from .utils import ( - LearningRateScheduler, - ScheduleType, - convert_anndata_to_documents, - create_model_info_dict, - remove_regions_below_min_count, - shuffle_documents, -) +from .utils import (LearningRateScheduler, ScheduleType, + convert_anndata_to_documents, + remove_regions_below_min_count, shuffle_documents) _GENSIM_LOGGER = getLogger("gensim") _LOGGER = getLogger(MODULE_NAME) @@ -93,6 +88,9 @@ def save_model(self, filepath: str, **kwargs): this issue: https://github.com/RaRe-Technologies/gensim/issues/1936 Instead, we will just use pickle to dump the object. + + :param str filepath: The path to save the model to. + :param kwargs: Additional arguments to pass to pickle.dump """ with gzip.open(filepath, "wb") as f: pickle.dump(self, f) @@ -132,6 +130,9 @@ def load_model(self, filepath: str, **kwargs): this issue: https://github.com/RaRe-Technologies/gensim/issues/1936 Instead we will just use pickle to load the object. Override the current object. + + :param str filepath: The path to load the model from. + :param kwargs: Additional arguments to pass to pickle.load """ with gzip.open(filepath, "rb") as f: obj = pickle.load(f) diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 840a876a..dafa3780 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -1,13 +1,12 @@ import os import shutil import subprocess - from collections import Counter from concurrent.futures import ThreadPoolExecutor from enum import Enum from logging import getLogger from random import shuffle -from typing import TYPE_CHECKING, Dict, List, Union, Optional +from typing import TYPE_CHECKING, Dict, List, Optional, Union import numpy as np import pandas as pd @@ -44,6 +43,13 @@ def __init__( decay: float = None, n_epochs: int = None, ): + """ + :param float init_lr: The initial learning rate + :param float min_lr: The minimum learning rate + :param str type: The type of learning rate schedule to use. Must be one of ['linear', 'exponential']. + :param float decay: The decay rate to use. If None, this will be calculated from init_lr and n_epochs. + :param int n_epochs: The number of epochs to train for. Only used if decay is None. + """ self.init_lr = init_lr self.min_lr = min_lr self.n_epochs = n_epochs @@ -71,10 +77,21 @@ def __init__( self.decay = decay def _update_linear(self, epoch: int): + """ + Update the learning rate using a linear schedule. + + :param int epoch: The current epoch + """ + lr = self.init_lr - (self.decay * epoch) return max(lr, self.min_lr) def _update_exponential(self, epoch: int): + """ + Update the learning rate using an exponential schedule. + + :param int epoch: The current epoch + """ lr = self.get_lr() * (1 / (1 + self.decay * epoch)) return max(lr, self.min_lr) @@ -99,8 +116,8 @@ def __init__(self, adata: sc.AnnData, chunk_size: int = DEFAULT_CHUNK_SIZE): Simple class to chunk an AnnData object into smaller pieces. Useful for training on large datasets. - :param adata: AnnData object to chunk. Must be in backed mode. See: https://scanpy.readthedocs.io/en/stable/generated/scanpy.read_h5ad.html - :param chunk_size: Number of cells to include in each chunk + :param sc.AnnData adata: AnnData object to chunk. Must be in backed mode. See: https://scanpy.readthedocs.io/en/stable/generated/scanpy.read_h5ad.html + :param int chunk_size: Number of cells to include in each chunk """ self.adata = adata self.chunk_size = chunk_size @@ -116,7 +133,12 @@ def __iter__(self): def __len__(self): return self.n_chunks - def __getitem__(self, item): + def __getitem__(self, item: int): + """ + Get a chunk of the AnnData object. + + :param int item: The chunk index to get. + """ return self.adata[item * self.chunk_size : (item + 1) * self.chunk_size, :] def __repr__(self): @@ -254,8 +276,8 @@ def load_scembed_model(path: str) -> "SCEmbed": :param str path: The path to the model. """ - import pickle import gzip + import pickle with gzip.open(path, "rb") as f: model = pickle.load(f) @@ -275,138 +297,6 @@ def load_scembed_model_deprecated(path: str) -> "SCEmbed": return model -def check_model_exists_on_hub(registry: str) -> bool: - """ - Check the model hub for the existing model registry. Registry - is of the name /. - - Looks for the existence of model.yaml at {MODEL_HUB_URL}/{registry_name}/model.yaml - """ - # check if model exists in the hub - url = f"{MODEL_HUB_URL}/{registry}/model.yaml" - cmd = f"curl -s -o /dev/null -w '%{{http_code}}' {url}" - result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE) - return result.stdout.decode("utf-8") == "200" - - -def download_remote_model(registry: str, path: str, overwrite: bool = True) -> None: - """ - Download a model from the model hub. Overwrites any existing. - - :param str registry: the registry name of the model to download - :param str path: the path to download the model to, this is the folder that will contain registry/model.yaml - """ - path_to_model = os.path.join(path, registry) - if os.path.exists(path_to_model) and overwrite: - _LOGGER.debug("Removing existing model.") - shutil.rmtree(path_to_model) - - cmd = f"wget -r -np -nH --cut-dirs=2 -P {path} -l 1 {MODEL_HUB_URL}/{registry}" - result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE) - if result.returncode != 0: - raise ValueError("Could not download model from model-hub.") - - -def load_universe_file(path: str) -> List[str]: - """ - Loads a bed file into a list of regions. Assumes - the bed file is of the form chr start end (tab-separated). - """ - with open(path, "r") as f: - regions = [line.strip().split("\t") for line in f.readlines()] - regions = [f"{r[0]}_{r[1]}_{r[2]}" for r in regions] - return regions - - -def generate_var_conversion_map( - a: List[str], - b: List[str], - path_to_bedtools: str = None, - fraction: float = 1.0e-9, -) -> Dict[str, Union[str, None]]: - """ - Create a conversion map to convert regions from a to b. This is used to convert the - consensus peak set of one AnnData object to another. - - For each region in a, we will either find a matching region in b, or None. If a matching - region is found, we will store the region in b. If no matching region is found, we will - store `None`. - - Intuitively, think of this as converting `A` --> `B`. If a region in `A` is found in `B`, - we will change the region in `A` to the region in `B`. If a region in `A` is not found in - `B`, we will drop that region in `A` altogether. - - :param List[str] a: the first list of regions - :param List[str] b: the second list of regions - :param str path_to_bedtools: the path to the bedtools executable - :param float fraction: the fraction of the region that must overlap to be considered an overlap - """ - # write a and b to temp files in cache - a_file = os.path.join(MODEL_CACHE_DIR, "a.bed") - b_file = os.path.join(MODEL_CACHE_DIR, "b.bed") - - # write a and b to temp files - with open(a_file, "w") as f: - # split each region into chr start end - a_parsed = [region.split("_") for region in a] - a_parsed = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in a_parsed] - f.writelines(a_parsed) - with open(b_file, "w") as f: - b_parsed = [region.split("_") for region in b] - b_parsed = [f"{r[0]}\t{r[1]}\t{r[2]}\n" for r in b_parsed] - f.writelines(b_parsed) - - # sort both files - cmd = f"sort -k1,1 -k2,2n {a_file} -o {a_file}" - subprocess.run(cmd, shell=True) - - cmd = f"sort -k1,1 -k2,2n {b_file} -o {b_file}" - subprocess.run(cmd, shell=True) - - # run bedtools - bedtools_cmd = f"intersect -a {a_file} -b {b_file} -wa -wb -f {fraction}" - - # add path to bedtools if provided - if path_to_bedtools is not None: - cmd = f"{path_to_bedtools} {bedtools_cmd}" - else: - cmd = f"bedtools {bedtools_cmd}" - - # target file - target_file = os.path.join(MODEL_CACHE_DIR, "olaps.bed") - with open(target_file, "w") as f: - subprocess.run(cmd, shell=True, stdout=f) - - # bedtools reports overlaps like this: - # chr1 100 200 chr1 150 250 - # we want to convert this to a map like this: - # {chr1_100_200: chr1_150_250} - # we will use a dictionary to do this - # if a region in A overlaps with multiple regions in B, we will - # take the first one. as such we need to check if a region in A - # has already been mapped to a region in B - conversion_map = dict() - with open(target_file, "r") as f: - for line in f.readlines(): - line = line.strip().split("\t") - a_region = f"{line[0]}_{line[1]}_{line[2]}" - b_region = f"{line[3]}_{line[4]}_{line[5]}" - if a_region not in conversion_map: - conversion_map[a_region] = b_region - - # add `None` mappings for regions in A that did not overlap with any regions in B - for region in a: - if region not in conversion_map: - conversion_map[region] = None - - # remove temp files - os.remove(a_file) - os.remove(b_file) - os.remove(target_file) - - return conversion_map - - def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: """ Converts an AnnData object to a list of lists of regions. This @@ -415,6 +305,8 @@ def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: *Note: this method requires that the sc.AnnData object have chr, start, and end in `.var` attributes* + + :param sc.AnnData adata: the AnnData object to convert """ # Extract the arrays for chr, start, and end chr_values = adata.var["chr"].values @@ -483,41 +375,3 @@ def barcode_mtx_peaks_to_anndata( adata = sc.AnnData(X=mtx, obs=barcodes, var=peaks) return adata - - -def create_model_info_dict( - path_to_weights: Optional[str] = None, - path_to_universe: Optional[str] = None, - name: Optional[str] = None, - reference: Optional[str] = None, - description: Optional[str] = None, - tags: Optional[List[str]] = None, - datasets: Optional[List[str]] = None, - model_parameters: Optional[Dict[str, Union[str, int]]] = None, - model_architecture: Optional[str] = None, - maintainers: Optional[List[Dict[str, str]]] = None, -) -> Dict[str, Union[str, List[str], List[Dict[str, Union[str, int]]]]]: - model_info = {} - - if path_to_weights: - model_info["path_to_weights"] = path_to_weights - if path_to_universe: - model_info["path_to_universe"] = path_to_universe - if name: - model_info["name"] = name - if reference: - model_info["reference"] = reference - if description: - model_info["description"] = description - if tags: - model_info["tags"] = tags - if datasets: - model_info["datasets"] = datasets - if model_parameters: - model_info["model_parameters"] = model_parameters - if model_architecture: - model_info["model_architecture"] = model_architecture - if maintainers: - model_info["maintainers"] = maintainers - - return model_info From c126595b7720ced825848349254ab063e89039e6 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Sat, 15 Jul 2023 19:43:58 -0400 Subject: [PATCH 0259/1072] tokenization optimization. docs --- docs/tutorials/train-scembed-model.md | 63 +++++++++++++++++++++ gitk/models/__init__.py | 3 +- gitk/models/{ => atac}/tokenization.py | 8 +-- gitk/models/pretrained.py | 2 +- gitk/models/rna/tokenization.py | 0 gitk/models/utils.py | 77 +++++++++++++------------- gitk/scembed/scembed.py | 11 ++-- setup.py | 2 + 8 files changed, 117 insertions(+), 49 deletions(-) create mode 100644 docs/tutorials/train-scembed-model.md rename gitk/models/{ => atac}/tokenization.py (96%) create mode 100644 gitk/models/rna/tokenization.py diff --git a/docs/tutorials/train-scembed-model.md b/docs/tutorials/train-scembed-model.md new file mode 100644 index 00000000..f4d510e4 --- /dev/null +++ b/docs/tutorials/train-scembed-model.md @@ -0,0 +1,63 @@ +# How to train a single-cell model with scEmbed +This example walks you through training an `scembed` region2vec model on a single-cell dataset. We start with data preparation, then train the model, and finally use the model to cluster the cells. + +For this example we are using the [10x Genomics PBMC 10k dataset](https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-atac-v2-chromium-controller-2-standard). The dataset contains 10,000 peripheral blood mononuclear cells (PBMCs) from a healthy donor. + +## Data preparation +`scembed` requires that the input data is in the [AnnData](https://anndata.readthedocs.io/en/latest/) format. Moreover, the `.var` attribute of this object must have `chr`, `start`, and `end` values. The reason is two fold: 1) we can track which vectors belong to which genmomic regions, and 2) region vectors are now reusable. We ned three files: 1) The `barcodes.txt` file, 2) the `peaks.bed` file, and 3) the `matrix.mtx` file. These will be used to create the `AnnData` object. To begin, download the data from the 10x Genomics website: + +```bash +wget https://cf.10xgenomics.com/samples/cell-atac/2.1.0/10k_pbmc_ATACv2_nextgem_Chromium_Controller/10k_pbmc_ATACv2_nextgem_Chromium_Controller_raw_peak_bc_matrix.tar.gz +tar -xzf 10k_pbmc_ATACv2_nextgem_Chromium_Controller_raw_peak_bc_matrix.tar.gz +``` + +Your files will be inside `filtered_peak_bc_matrix/`. Assuming you've installed the proper dependencies, you can now use python to build the `AnnData` object: + +```python +import pandas as pd +import scanpy as sc + +from scipy.io import mmread +from scipy.sparse import csr_matrix + +barcodes = pd.read_csv("barcodes.tsv", sep="\t", header=None, names=["barcode"]) +peaks = pd.read_csv("peaks.bed", sep="\t", header=None, names=["chr", "start", "end"]) +mtx = mmread("matrix.mtx") +mtx_sparse = csr_matrix(mtx) +mtx_sparse = mtx_sparse.T + +adata = sc.AnnData(X=mtx_sparse, obs=barcodes, var=peaks) +adata.write_h5ad("pbmc.h5ad") +``` + +We will use the `pbmc.h5ad` file for downstream work. + +## Training the model +To train an `scembed` model, just create an instance of the `SCEmbed` model class, define your hyperparamters, and call the `train` method. For this example, we will use the default hyperparameters. The only thing we need to specify is the number of epochs to train for. We will train for 10 epochs: + +```python +import logging + +import scanpy as sc +from gitk.scembed import SCEmbed + +# if you want to see the training progress +logging.basicConfig(level=logging.INFO) + +model = SCEmbed( + use_default_region_names=False # this is to specify that we want to use chr, start, end. +) +model.train(adata, epochs=3) # we recomend increasing this to 100 +``` + +Thats it! + +## Clustering the cells +Now that we have a trained model, we can use it to cluster the cells. To do this, we will use the `predict` method. This method will return a `pandas.DataFrame` with the cell barcodes and their corresponding cluster assignments. We will then add this to the `AnnData` object as a new column in the `.obs` attribute: + +```python +from gitk.models.tokenizers import HardTokenizer + +tokenizer = HardTokenizer("peaks.bed") + +region_sets = tokenizer(adata) \ No newline at end of file diff --git a/gitk/models/__init__.py b/gitk/models/__init__.py index 6aa12c45..27166c67 100644 --- a/gitk/models/__init__.py +++ b/gitk/models/__init__.py @@ -1,4 +1,5 @@ from .const import * from .pretrained import * -from .tokenization import * +from .atac.tokenization import * +from .rna.tokenization import * from .utils import * diff --git a/gitk/models/tokenization.py b/gitk/models/atac/tokenization.py similarity index 96% rename from gitk/models/tokenization.py rename to gitk/models/atac/tokenization.py index 17ee382f..221d336f 100644 --- a/gitk/models/tokenization.py +++ b/gitk/models/atac/tokenization.py @@ -2,10 +2,10 @@ from typing import Dict, List, Union import scanpy as sc +from tqdm import tqdm from intervaltree import IntervalTree -from .utils import (anndata_to_regionsets, convert_to_universe, - validate_region_input) +from ..utils import anndata_to_regionsets, convert_to_universe, validate_region_input class Universe: @@ -46,7 +46,7 @@ def universe_set(self): def _build_universe_set(self): """ - Return the universe as a set of regions in the form (chr, start, end). + Build the universe as a set of regions in the form (chr, start, end). """ universe = [] for tree in self._trees: @@ -67,7 +67,7 @@ def build_tree(self, regions: str = None): regions = validate_region_input(regions or self._regions) # build trees - for region in regions: + for region in tqdm(regions, total=len(regions), desc="Loading universe"): chr, start, end = region start = int(start) end = int(end) diff --git a/gitk/models/pretrained.py b/gitk/models/pretrained.py index 17a381a7..2de41c61 100644 --- a/gitk/models/pretrained.py +++ b/gitk/models/pretrained.py @@ -7,7 +7,7 @@ from .. import scembed from .const import MODEL_FILE_NAME, UNIVERSE_FILE_NAME -from .tokenization import HardTokenizer +from .atac.tokenization import HardTokenizer class PretrainedScembedModel: diff --git a/gitk/models/rna/tokenization.py b/gitk/models/rna/tokenization.py new file mode 100644 index 00000000..e69de29b diff --git a/gitk/models/utils.py b/gitk/models/utils.py index 19361116..bdb0987b 100644 --- a/gitk/models/utils.py +++ b/gitk/models/utils.py @@ -1,5 +1,4 @@ import os -import subprocess from typing import TYPE_CHECKING, Dict, List, Union import numpy as np @@ -7,7 +6,7 @@ from tqdm import tqdm if TYPE_CHECKING: - from .tokenization import Universe + from .atac.tokenization import Universe from .const import CACHE_DIR @@ -105,7 +104,7 @@ def generate_var_conversion_map( conversion_map = dict() - for region in tqdm(a, total=len(a)): + for region in tqdm(a, total=len(a), desc="Generating conversion map"): overlaps = b.query(region) region_str = f"{region[0]}_{region[1]}_{region[2]}" if len(overlaps) > 0: @@ -120,55 +119,51 @@ def generate_var_conversion_map( def convert_to_universe(adata: sc.AnnData, universe: "Universe") -> sc.AnnData: """ - Converts the consensus peak set (.var) attributes of the AnnData object - to a universe representation. This is done through interval overlap - analysis with bedtools. - - For each region in the `.var` attribute of the AnnData object, we - either 1) map it to a region in the universe, or 2) map it to `None`. - If it is mapped to `None`, it is not in the universe and will be dropped - from the AnnData object. If it is mapped to a region, it will be updated - to the region in the universe for downstream analysis. + Convert an AnnData object to a new universe. This is useful for converting the consensus + peak set of one AnnData object to another. This is usually used as a preprocessing step + before tokenization. + + This function is already pretty optimized. To speed it up even more, we could use + multiprocessing to parallelize the conversion. Or, we could use Rust library to speed + up the conversion. However, this is not necessary at the moment. + + :param adata: the AnnData object to convert + :param Universe universe: the universe to convert to + :return: the converted AnnData object """ # ensure adata has chr, start, and end if not all([x in adata.var.columns for x in ["chr", "start", "end"]]): raise ValueError("AnnData object must have `chr`, `start`, and `end` columns in .var") # create list of regions from adata - query_set: List[tuple[str, int, int]] = adata.var.apply( - lambda x: (x["chr"], int(x["start"]), int(x["end"])), axis=1 - ).tolist() + adata.var["region"] = ( + adata.var["chr"].astype(str) + + "_" + + adata.var["start"].astype(str) + + "_" + + adata.var["end"].astype(str) + ) + query_set: List[tuple[str, int, int]] = list( + zip(adata.var["chr"], adata.var["start"].astype(int), adata.var["end"].astype(int)) + ) # generate conversion map _map = generate_var_conversion_map(query_set, universe) - # create a new DataFrame with the updated values - updated_var = adata.var.copy() + # map regions to new universe + adata.var["new_region"] = adata.var["region"].map(_map) - # find the regions that overlap with the universe - # use dynamic programming to create a boolean mask of columns to keep - columns_to_keep = [] - for i, row in tqdm(adata.var.iterrows(), total=adata.var.shape[0]): - region = f"{row['chr']}_{row['start']}_{row['end']}" - if _map[region] is None: - columns_to_keep.append(False) - continue + # drop rows where new_region is None + adata.var.dropna(subset=["new_region"], inplace=True) - # if it is, change the region to the universe region, - # grab the first for now - # TODO - this is a simplification, we should be able to handle multiple - universe_region = _map[region] - chr, start, end = universe_region.split("_") + # split new_region into chr, start, end + adata.var[["chr", "start", "end"]] = adata.var["new_region"].str.split("_", expand=True) - updated_var.at[i, "chr"] = chr - updated_var.at[i, "start"] = start - updated_var.at[i, "end"] = end + # drop 'region' and 'new_region' columns + adata.var.drop(columns=["region", "new_region"], inplace=True) - columns_to_keep.append(True) - - # update adata with the new DataFrame and filtered columns - adata = adata[:, columns_to_keep] - adata.var = updated_var[columns_to_keep] + # update adata with the new DataFrame + adata = adata[:, adata.var.index] return adata @@ -179,6 +174,10 @@ def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: is done by taking each cell and creating a list of all regions that have a value greater than 0. + This function is already pretty optimized. To speed this up + further we'd have to parallelize it or reach for a lower-level + language. + *Note: this method requires that the sc.AnnData object have chr, start, and end in `.var` attributes* """ @@ -194,7 +193,7 @@ def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: positive_values = positive_values.toarray() regions = [] - for i in tqdm(range(adata.shape[0]), total=adata.shape[0]): + for i in tqdm(range(adata.shape[0]), total=adata.shape[0], desc="Tokenizing"): regions.append( [ f"{chr_values[j]}_{start_values[j]}_{end_values[j]}" diff --git a/gitk/scembed/scembed.py b/gitk/scembed/scembed.py index 5bdeaea2..266f0497 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/scembed.py @@ -7,7 +7,6 @@ import numpy as np import pandas as pd import scanpy as sc -import yaml from gensim.models import Word2Vec from gensim.models.callbacks import CallbackAny2Vec from numba import config @@ -15,9 +14,13 @@ from .const import * from .exceptions import * -from .utils import (LearningRateScheduler, ScheduleType, - convert_anndata_to_documents, - remove_regions_below_min_count, shuffle_documents) +from .utils import ( + LearningRateScheduler, + ScheduleType, + convert_anndata_to_documents, + remove_regions_below_min_count, + shuffle_documents, +) _GENSIM_LOGGER = getLogger("gensim") _LOGGER = getLogger(MODULE_NAME) diff --git a/setup.py b/setup.py index 1f3a4369..c244afd6 100755 --- a/setup.py +++ b/setup.py @@ -31,6 +31,8 @@ "gitk.eval", "gitk.likelihood", "gitk.models", + "gitk.models.atac", + "gitk.models.rna", "gitk.region2vec", "gitk.scembed", "gitk.tokenization", From d4f14c6acc0204b23776e95767680404dc3eb893 Mon Sep 17 00:00:00 2001 From: nsheff Date: Wed, 19 Jul 2023 16:33:11 -0400 Subject: [PATCH 0260/1072] restructure docs --- docs/README.md | 2 +- docs/autodoc_build/gitk.md | 3 ++ docs/modules.md | 35 ++++++++++++++ docs/modules/build-universe.md | 24 ---------- docs/modules/scembed.md | 34 ------------- .../{modules => tutorials}/assess-universe.md | 48 ++++++++++--------- docs/{modules => tutorials}/bedspace.md | 14 ++---- docs/tutorials/build-universe.md | 16 +++++++ docs/tutorials/create-consensus-peaks.md | 9 ++-- docs/{modules => tutorials}/evaluation.md | 18 +++++-- docs/{modules => tutorials}/region2vec.md | 2 +- docs/{modules => tutorials}/tokenization.md | 14 ++---- docs/tutorials/train-scembed-model.md | 36 +++++++++++++- mkdocs.yml | 16 +++---- 14 files changed, 151 insertions(+), 120 deletions(-) create mode 100644 docs/modules.md delete mode 100644 docs/modules/build-universe.md delete mode 100644 docs/modules/scembed.md rename docs/{modules => tutorials}/assess-universe.md (68%) rename docs/{modules => tutorials}/bedspace.md (83%) create mode 100644 docs/tutorials/build-universe.md rename docs/{modules => tutorials}/evaluation.md (98%) rename docs/{modules => tutorials}/region2vec.md (97%) rename docs/{modules => tutorials}/tokenization.md (50%) diff --git a/docs/README.md b/docs/README.md index d3e24717..fc756181 100644 --- a/docs/README.md +++ b/docs/README.md @@ -12,4 +12,4 @@ pip install --user --upgrade . ## Modules -`gitk` is organized into modules. You can read the list of functions by module, or proceed to the how-to guides, using the menu bar on the left. +`gitk` is organized into modules. The next section is an overview of each module. You can also proceed to the how-to guides for recipes on how to do specfic tasks. diff --git a/docs/autodoc_build/gitk.md b/docs/autodoc_build/gitk.md index 547ee366..6cee8677 100644 --- a/docs/autodoc_build/gitk.md +++ b/docs/autodoc_build/gitk.md @@ -30,3 +30,6 @@ h4 .content { # Package `gitk` Documentation + + +*Version Information: `gitk` v0.0.1-dev, generated by `lucidoc` v0.4.3* \ No newline at end of file diff --git a/docs/modules.md b/docs/modules.md new file mode 100644 index 00000000..01c25a2f --- /dev/null +++ b/docs/modules.md @@ -0,0 +1,35 @@ +# Module overviews + +`gitk` is organized into modules. Each module groups together related tasks. This document provides an overview of each module. + +## Module `assess-universe` + +Many genomic interval analysis methods, particularly those used by `gitk` require that regions be re-defined in terms of a consensus region set, or universe. However, a universe may not be a good fit to a collection of files. This module assesses that fit. Given a collection of genomic interval sets, and a proposed universe, we can assess how well the universe fits the genomic interval sets. This module provides several complementary methods to assess fit. + +## Module `bedspace` + +The `bedspace` module uses the StarSpace method (Wu et al., 2018) to jointly embed genomic interval regions sets with associated metadata into a shared latent embedding space. This facilitates fast search and retrieval of similar region sets and their associated metadata. + +## Module `build-universe` + +This module provides multiple ways to build a genomic region universe. These include: 1. **HMM**: uses an HMM to create a flexible segment universe, given an input of several bed files. + +## Module `evaluation` + +Once a `gitk` region embedding model is trained, we may want to evaluate the embeddings. The `evaluation` module provides several functions for that. These include statistical tests, like the Cluster Tendency Test (CTT) and the Reconstruction Test (RCT), and biological tests, the GEnome Distance Scaling Test (GDST) and the Neighborhood Preserving Test (NPT). These evaluation metrics can be helpful to determine if your models are working well, optimize training parameters, etc. + +## Module `region2vec` + +`Region2Vec` is an unsupervised method for creating embeddings for genomic regions and region sets from a set of raw BED files. The program uses a variation of the word2vec algorithm by building shuffled context windows from BED files. The co-occurence statistics of genomic regions in a collection of BED files allow the model to learn region embeddings. + +## Module `scembed` + +`scEmbed` is a single-cell implementation of `region2Vec`: a method to represent genomic region sets as vectors, or embeddings, using an adapted word2vec approach. `scEmbed` allows for dimensionality reduction and feature selection of single-cell ATAC-seq data; a notoriously sparse and high-dimensional data type. We intend for `scEmbed` to be used with the [`scanpy`](https://scanpy.readthedocs.io/en/stable/) package. As such, it natively accepts `AnnData` objects as input and returns `AnnData` objects as output. + +## Module `tokenization` + +In NLP, training word embeddings requires first tokenizing words such that words in different forms are represented by one word. For example, "orange", "oranges" and "Orange" are all mapped to "orange" since they essentially convey the same meaning. This reduces the vocabulary size and improves the quality of learned embeddings. Similary, many `gitk` modules (such as `region2vec`) require first tokenizating regions. + +To tokenize reigons, we need to provide a universe, which specifies the "vocabulary" of genomic regions. The universe is a BED file, containing representative regions. With the given universe, we represent (tokenize) raw regions into the regions in the universe. + +Different strategies can be used to tokenize. The simplest case we call *hard tokenization*, which means if the overlap between a raw region in a BED file and a region in the universe exceeds a certain amount, then we use the region in the universe to represent this raw region; otherwise, we ignore this raw region. This is a "zero or one" process. After hard tokenization, each BED file will contain only regions from the universe, and the number of regions will be smaller or equal to the original number. diff --git a/docs/modules/build-universe.md b/docs/modules/build-universe.md deleted file mode 100644 index 1c6a490f..00000000 --- a/docs/modules/build-universe.md +++ /dev/null @@ -1,24 +0,0 @@ -# The universe module - -## Introduction - -This module provides multiple ways to build a genomic region universe - - - -## Method 1: HMM - -This will use an HMM to create a flexible segment universe, given an input of several bed files. - -## How to use - -Where can you find a very small example dataset? - -``` -gitk hmm --out_file tests/consesnus/universe/hmm_norm.bed --cov_folder tests/consesnus/coverage/ -``` - - -``` -gitk hmm --out_file tests/consesnus/universe/hmm_norm.bed --cov_folder tests/consesnus/coverage/ --normalize -``` \ No newline at end of file diff --git a/docs/modules/scembed.md b/docs/modules/scembed.md deleted file mode 100644 index c8ed3288..00000000 --- a/docs/modules/scembed.md +++ /dev/null @@ -1,34 +0,0 @@ -# scEmbed - -`scEmbed` is a single-cell implementation of `Region2Vec`: a method to represent genomic region sets as vectors, or embeddings, using an adapted word2vec approach. `scEmbed` allows for dimensionality reduction and feature selection of single-cell ATAC-seq data; a notoriously sparse and high-dimensional data type. We intend for `scEmbed` to be used with the [`scanpy`](https://scanpy.readthedocs.io/en/stable/) package. As such, it natively accepts `AnnData` objects as input and returns `AnnData` objects as output. - -## Installation -Simply install the parent package `gitk` from PyPi: - -```bash -pip install gitk -``` - -Then import `scEmbed` from `gitk`: - -```python -from gitk.scembed import SCEmbed - -``` - -## Usage -`scEmbed` is simple to use. Import your `AnnData` object and pass it to `SCEmbed`. `scEmbed` will work regardless of any `var` or `obs` annotations, but it is recommended that you use `scEmbed` after you have already performed some basic filtering and quality control on your data. Further. If you'd like to maintain information about region embeddings, it is recommended that you attach `chr`, `start`, adn `end` annotations to your `AnnData` object before passing it to `scEmbed`. - -```python -import scanpy as sc -from gitk.scembed import SCEmbed - -adata = sc.read_h5ad("path/to/adata.h5ad") - -scEmbed = SCEmbed(adata) -scEmbed.train( - epochs=100, -) - -cell_embeddings = scEmbed.get_cell_embeddings() -``` \ No newline at end of file diff --git a/docs/modules/assess-universe.md b/docs/tutorials/assess-universe.md similarity index 68% rename from docs/modules/assess-universe.md rename to docs/tutorials/assess-universe.md index cb705c7e..eed7bfd4 100644 --- a/docs/modules/assess-universe.md +++ b/docs/tutorials/assess-universe.md @@ -1,16 +1,18 @@ # How to assess universe fit to collection of BED files -Given a collection of genomic interval sets, and a proposed universe, we would like to assess how well the universe fits the genomic interval sets. -We will use universes produced [earlier](consensus-peaks.md). This module provides several complementary methods to assess fit. +## Introduction +If you have a potential universe file, and a collection of BED files, this module will help you assess the fit of the proposed universe to your collection of BED files. We can assess fit either from CLI, or from within Python. -We can assess fit either from CLI, or from within Python. Both overlap and distance based assessments can be run using: `gitk assess ...` with appropriate flags. +## Command-line usage -``` +Both overlap and distance based assessments can be run using: `gitk assess ...` with appropriate flags. + +```console gitk assess --assessment-method1 \ --assessment-method2 \ --... - --raw-data-folder tests/consesnus/raw/\ + --raw-data-folder tests/consesnus/raw/ \ --file-list tests/consesnus/file_list.txt \ --universe tests/consenus/universe/universe.bed \ --save-to-file \ @@ -39,9 +41,9 @@ We can also use it directly from Python like this: ``` from gitk.assess.intersection import run_intersection -run_intersection("test/consesnus/raw/", - "tests/consesnus/file_list.txt", - "tests/consenus/universe/universe.bed", +run_intersection("test/consensus/raw/", + "tests/consensus/file_list.txt", + "tests/consensus/universe/universe.bed", no_workers=1) ``` @@ -50,9 +52,9 @@ Or, we can calculate F10 score of the universe using: ``` from gitk.assess.intersection import get_f_10_score -get_f_10_score("test/consesnus/raw/", - "tests/consesnus/file_list.txt", - "tests/consenus/universe/universe.bed", +get_f_10_score("test/consensus/raw/", + "tests/consensus/file_list.txt", + "tests/consensus/universe/universe.bed", no_workers=1) ``` @@ -70,9 +72,9 @@ All presented distance measures can be done using python, which will result in m ``` from gitk.assess.distance import run_distance -d_median = run_distance("tests/consesnus/raw", - "tests/consesnus/file_list.txt", - "tests/consesnus/universe/universe.bed", +d_median = run_distance("tests/consensus/raw", + "tests/consensus/file_list.txt", + "tests/consensus/universe/universe.bed", npool=2) ``` Additionally, we can directly calculate the closeness score using: @@ -80,9 +82,9 @@ Additionally, we can directly calculate the closeness score using: ``` from gitk.assess.distance import get_closeness_score -closeness_score = get_closeness_score("tests/consesnus/raw", - "tests/consesnus/file_list.txt", - "tests/consesnus/universe/universe.bed", +closeness_score = get_closeness_score("tests/consensus/raw", + "tests/consensus/file_list.txt", + "tests/consensus/universe/universe.bed", no_workers=2) ``` @@ -96,9 +98,9 @@ either for hard universe: ``` from gitk.assess.likelihood import hard_universe_likelihood -lh_hard = hard_universe_likelihood("tests/consesnus/lh_model.tar", - "tests/consenus/universe/universe.bed", - "tests/consesnus/coverage", "all") +lh_hard = hard_universe_likelihood("tests/consensus/lh_model.tar", + "tests/consensus/universe/universe.bed", + "tests/consensus/coverage", "all") ``` or with taking into account universe flexibility: @@ -106,7 +108,7 @@ or with taking into account universe flexibility: ``` from gitk.assess.likelihood import likelihood_flexible_universe -lh_flexible = likelihood_flexible_universe("tests/consesnus/lh_model.tar", - "tests/consenus/universe/universe.bed", - "tests/consesnus/coverage", "all") +lh_flexible = likelihood_flexible_universe("tests/consensus/lh_model.tar", + "tests/consensus/universe/universe.bed", + "tests/consensus/coverage", "all") ``` \ No newline at end of file diff --git a/docs/modules/bedspace.md b/docs/tutorials/bedspace.md similarity index 83% rename from docs/modules/bedspace.md rename to docs/tutorials/bedspace.md index 4cdb1ebf..f2df1833 100644 --- a/docs/modules/bedspace.md +++ b/docs/tutorials/bedspace.md @@ -1,19 +1,13 @@ -# BEDSpace -## Overview -`bedspace` uses the StarSpace method (Wu et al., 2018) to jointly embed genomic interval regions sets with associated metadata into a shared latent embedding space. This facilitates fast search and retrieval of similar region sets and their associated metadata. +# How to use BEDSpace to jointly embed regions and metadata -## Installation +## Introduction -`bedspace` comes installed as a module of `gitk`. To ensure that everything is working correctly, run: `python -c "from gitk import bedspace"`. - -## Usage - -There are four main commands in `bedspace`: +To ensure that everything is working correctly, run: `python -c "from gitk import bedspace"`. There are four main commands in `bedspace`: 1. `bedspace preprocess`: preprocesses a set of genomic interval regions and their associated metadata into a format that can be used by `bedspace train`. 2. `bedspace train`: trains a StarSpace model on the preprocessed data. 3. `bedspace distances`: computes distances between region sets in the trained model and metadata labels. -4. `bedspace search` searches for the most similar region sets and metadata labels to a given query. Three scenarios for this command are described in the details. +4. `bedspace search`: searches for the most similar region sets and metadata labels to a given query. Three scenarios for this command are described in the details. ### `bedspace preprocess` The `preprocess` command will prepare a set of region sets and metadata labels for training. This includes things like adding the `__label__` prefix to metadata labels, and converting the region sets into a format that can be used by StarSpace. The command takes in a set of region sets and metadata labels, and outputs a set of preprocessed region sets and metadata labels. The command can be run as follows: diff --git a/docs/tutorials/build-universe.md b/docs/tutorials/build-universe.md new file mode 100644 index 00000000..883cc7ef --- /dev/null +++ b/docs/tutorials/build-universe.md @@ -0,0 +1,16 @@ +# How to build a new universe + +Where can you find a very small example dataset? + +```console +gitk hmm \ + --out_file tests/consensus/universe/hmm_norm.bed \ + --cov_folder tests/consensus/coverage/ +``` + +```console +gitk hmm \ + --out_file tests/consensus/universe/hmm_norm.bed \ + --cov_folder tests/consensus/coverage/ \ + --normalize +``` \ No newline at end of file diff --git a/docs/tutorials/create-consensus-peaks.md b/docs/tutorials/create-consensus-peaks.md index 3dd7c5be..5224a33e 100644 --- a/docs/tutorials/create-consensus-peaks.md +++ b/docs/tutorials/create-consensus-peaks.md @@ -4,6 +4,7 @@ We will start with simple example of how to create a consensus peak set from a collection of files. Example data can be found in `tests/consenus/raw`. ## Data preprocessing + First step of analysis is creating three tracks with genome coverage by peaks, their starts and ends. To do that we have to: @@ -14,16 +15,16 @@ their starts and ends. To do that we have to: - {prefix}_start.bw - with smoothed coverage of genome by ends In this tutorial we will use prefix "all" as it is a default prefix in -```gitk``` module +`gitk` module ## Coverage cutoff universe We will start by making a coverage universe with cutoff that results in maximum likelihood universe. We can do it through CLI: -``` +```console gitk build-universe cc --coverage-folder tests/consenus/coverage/ \ - --output-file tests/consenus/universe/universe.bed + --output-file tests/consenus/universe/universe.bed ``` @@ -32,7 +33,7 @@ Where: - `--coverage-folder`, takes the path to bigWig file with genome coverage by collection - `--output-file`, takes the path to output file -Or we can import it directly into python: +Or we can import it directly into Python: ``` from gitk.universe.cc_universe import cc_universe diff --git a/docs/modules/evaluation.md b/docs/tutorials/evaluation.md similarity index 98% rename from docs/modules/evaluation.md rename to docs/tutorials/evaluation.md index 8ef057bf..1cc8fe1e 100644 --- a/docs/modules/evaluation.md +++ b/docs/tutorials/evaluation.md @@ -1,7 +1,9 @@ -# Evaluation of Genomic Region Embeddings +# How to evaluate genomic region embeddings + +## Preparation -## Preparations ### Create a Base Embedding Object + Given a set of genomic region embeddings `embeddings` and the corresponding regions `vocab`, use `BaseEmbeddings` to create an `base` embedding object. ``` from gitk.eval.utils import BaseEmbeddings @@ -10,19 +12,26 @@ base_obj = BaseEmbeddings(embeddings, vocab) with open("base_embed.pt", "wb") as f: pickle.dump(base_obj, f) ``` + ### Generate Binary Embeddings + ```python from gitk.eval.utils import get_bin_embeddings universe_file = "/path/to/universe.bed" token_files = ["file1.bed", "file2.bed"] bin_embed = get_bin_embeddings(universe_file, token_files) ``` + Or use command line: + ```bash gitk eval bin-gen --universe /path/to/universe.bed --token-folder /path/to/tokenized/folder --file-name bin_embed.pickle ``` + ## Statistical Tests + ### Cluster Tendency Test (CTT) + CTT analyzes how well a set of region embeddings can be clustered. CTT score lies between 0 and 1. A larger CTT score indicates a greater tendency for the embeddings being evaluated to have clusters. When the embeddings are uniformly distributed, the score is 0.5. For evenly spaced embeddings, the score approaches 0. ```python @@ -66,8 +75,11 @@ gitk eval rct --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` To change the learning setting, go to the definition of `get_rct_score` in `gitk/eval/rct.py` and change the constructor of `MLPRegressor`. + ## Biological Tests + ### Genome Distance Scaling Test (GDST) + GDST calculates a score measuring how much the embedding distance between two regions scales the corresponding genome distance. ```python @@ -87,8 +99,8 @@ Or use the command line gitk eval gdst --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` - ### Neighborhood Preserving Test (NPT) + NPT evaluates how significant genomic region embeddings preserve their neighboring regions on the genome against random embeddings. The code output the NPT score for a set of region embeddings. ```python diff --git a/docs/modules/region2vec.md b/docs/tutorials/region2vec.md similarity index 97% rename from docs/modules/region2vec.md rename to docs/tutorials/region2vec.md index 36d88228..8f634cbe 100644 --- a/docs/modules/region2vec.md +++ b/docs/tutorials/region2vec.md @@ -1,4 +1,4 @@ -# Region2Vec +# How to train Region2Vec interval embeddings `Region2Vec` is an unsupervised method for creating embeddings for genomic regions and region sets from a set of raw BED files. The program will first map all raw regions to a given universe (vocabulary) set. Then, it will construct sentences by concatenating regions from a BED file in random order. The generated sentences will be used for Region2Vec training using word2vec. diff --git a/docs/modules/tokenization.md b/docs/tutorials/tokenization.md similarity index 50% rename from docs/modules/tokenization.md rename to docs/tutorials/tokenization.md index e3a90dc8..f5b9a00c 100644 --- a/docs/modules/tokenization.md +++ b/docs/tutorials/tokenization.md @@ -1,16 +1,8 @@ -# Tokenization +# How to tokenize a BED file -## Introduction - -In NLP, training word embeddings requires first tokenizing words such that words in different forms are represented by one word. For example, "orange", "oranges" and "Orange" are all mapped to "orange" since they essentially convey the same meaning. This reduces the vocabulary size and improves the quality of learned embeddings. Similary, many `gitk` modules (such as `region2vec`) require first tokenizating regions. - -To tokenize reigons, we need to provide a universe, which specifies the "vocabulary" of genomic regions. The universe is a BED file, containing representative regions. With the given universe, we represent (tokenize) raw regions into the regions in the universe. - -Different strategies can be used to tokenize. The simplest case we call *hard tokenization*, which means if the overlap between a raw region in a BED file and a region in the universe exceeds a certain amount, then we use the region in the universe to represent this raw region; otherwise, we ignore this raw region. This is a "zero or one" process. After hard tokenization, each BED file will contain only regions from the universe, and the number of regions will be smaller or equal to the original number. - -## Usage For hard tokenization, run + ``` from gitk.tokenization import hard_tokenization @@ -18,8 +10,8 @@ src_folder = '/path/to/raw/bed/files/' dst_folder = '/path/to/tokenized_files/' universe_file = '/path/to/universe_file.bed' hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) - ``` + Note that we use the `intersect` function of `bedtools` to do tokenization. If you want to switch to different tools, you can override the `bedtool_tokenization` function in `hard_tokenization_batch.py` and provide the path to your tool by specifying the input argument `bedtools_path`. The `fraction` argument specifies the minimum overlap required as a fraction of some region in the universe (default: 1E-9,i.e. 1bp; maximum 1.0). A raw region will be mapped into a universe region when an overlap is above the threshold. By default, the code assumes the binary `bedtools` exists and can be called via command line. If `bedtools` does not exists, the code will raise an exception. To solve this, please specify `bedtools_path` which points to a bedtools binary. diff --git a/docs/tutorials/train-scembed-model.md b/docs/tutorials/train-scembed-model.md index f4d510e4..1ee605e5 100644 --- a/docs/tutorials/train-scembed-model.md +++ b/docs/tutorials/train-scembed-model.md @@ -1,8 +1,41 @@ # How to train a single-cell model with scEmbed + This example walks you through training an `scembed` region2vec model on a single-cell dataset. We start with data preparation, then train the model, and finally use the model to cluster the cells. For this example we are using the [10x Genomics PBMC 10k dataset](https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-atac-v2-chromium-controller-2-standard). The dataset contains 10,000 peripheral blood mononuclear cells (PBMCs) from a healthy donor. + +## Installation +Simply install the parent package `gitk` from PyPi: + +```bash +pip install gitk +``` + +Then import `scEmbed` from `gitk`: + +```python +from gitk.scembed import SCEmbed +``` + +## Usage +`scEmbed` is simple to use. Import your `AnnData` object and pass it to `SCEmbed`. `scEmbed` will work regardless of any `var` or `obs` annotations, but it is recommended that you use `scEmbed` after you have already performed some basic filtering and quality control on your data. Further. If you'd like to maintain information about region embeddings, it is recommended that you attach `chr`, `start`, adn `end` annotations to your `AnnData` object before passing it to `scEmbed`. + +```python +import scanpy as sc +from gitk.scembed import SCEmbed + +adata = sc.read_h5ad("path/to/adata.h5ad") + +scEmbed = SCEmbed(adata) +scEmbed.train( + epochs=100, +) + +cell_embeddings = scEmbed.get_cell_embeddings() +``` + + ## Data preparation `scembed` requires that the input data is in the [AnnData](https://anndata.readthedocs.io/en/latest/) format. Moreover, the `.var` attribute of this object must have `chr`, `start`, and `end` values. The reason is two fold: 1) we can track which vectors belong to which genmomic regions, and 2) region vectors are now reusable. We ned three files: 1) The `barcodes.txt` file, 2) the `peaks.bed` file, and 3) the `matrix.mtx` file. These will be used to create the `AnnData` object. To begin, download the data from the 10x Genomics website: @@ -60,4 +93,5 @@ from gitk.models.tokenizers import HardTokenizer tokenizer = HardTokenizer("peaks.bed") -region_sets = tokenizer(adata) \ No newline at end of file +region_sets = tokenizer(adata) +``` \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 2ca08aa7..c305c577 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -7,15 +7,15 @@ pypi_name: gitk nav: - Getting Started: - Introduction: README.md - - Modules: - - Assess universe: modules/assess-universe.md - - BEDSpace: modules/bedspace.md - - Evaluate embeddings: modules/evaluation.md - - Region2Vec: modules/region2vec.md - - Single-cell embeddings: modules/scembed.md - - Tokenization: modules/tokenization.md - - Build universe: modules/build-universe.md + - Module overviews: modules.md - How-to guides: + - Assess universe fit: tutorials/assess-universe.md + - Search intervals with BEDSpace: tutorials/bedspace.md + - Evaluate embeddings: tutorials/evaluation.md + - Train region2vec embeddings: tutorials/region2vec.md + - Train single-cell embeddings: tutorials/train-scembed-model.md + - Tokenize a BED file: tutorials/tokenization.md + - Build universe: tutorials/build-universe.md - Create consensus peaks: tutorials/create-consensus-peaks.md - Reference: - API: autodoc_build/gitk.md From f8f79f4f99d648c64ef5d6930f73cf2b24bdae10 Mon Sep 17 00:00:00 2001 From: nsheff Date: Wed, 19 Jul 2023 17:53:15 -0400 Subject: [PATCH 0261/1072] bring in erfaneh's doc updates --- docs/tutorials/bedspace.md | 45 +++++++++++++++++++++++++++++++++++++- 1 file changed, 44 insertions(+), 1 deletion(-) diff --git a/docs/tutorials/bedspace.md b/docs/tutorials/bedspace.md index f2df1833..58e37545 100644 --- a/docs/tutorials/bedspace.md +++ b/docs/tutorials/bedspace.md @@ -20,6 +20,15 @@ gitk bedspace preprocess \ --labels \ --output ``` + +Input Description: + +`--input`: Specifies the path to the folder containing the region sets. +`--metadata`: Specifies the path to the metadata file in CSV format. The CSV file should include a column for file_name and separate columns for each label. The file_name column contains the names of the region set files, and the label columns contain the corresponding labels for each region set. +`--universe`: Specifies the path to the universe file. The universe file contains the chromosome, start position, and end position for each region for region set tokenization. +`--labels`: Specifies the target labels as a single string containing labels separated by commas. + + ### `bedspace train` The `train` command will train a StarSpace model on the preprocessed region sets and metadata labels. It requires that you have run the `preprocess` command first. The `train` command takes in a set of preprocessed region sets and metadata labels, and outputs a trained StarSpace model. The command can be run as follows: @@ -33,6 +42,20 @@ gitk bedspace train \ --lr ``` +Input Description: + +`--path-to-starspace`: Specifies the path to the StarSpace executable. +`--input`: Specifies the path to the preprocessed region sets file generated from the preprocess function. The file should be in TXT format. +`--output`: Specifies the path where the trained model will be saved. +`--dim`: Sets the dimension of the vector for the region set and label embedding from the StarSpace model. +`--epochs`: Specifies the number of epochs to train the StartSpace model. +`--lr`: Sets the learning rate for the training process. + + + + + + ### `bedspace distances` The `distances` command will compute the distances between all of the region sets and metadata labels in the trained model. It requires that you have ran the `train` command first. The `distances` command takes in a trained StarSpace model, and outputs a set of distances between all of the region sets and metadata labels in the model. The command can be run as follows: @@ -46,6 +69,17 @@ gitk bedspace distances \ --output ``` +Input Description: + +`--input`: Specifies the path to the trained model generated by the bedspace train command. +`--metadata`: Specifies the path to the input metadata labels. +`--universe`: Specifies the path to the universe file used for test file tokenization. +`--labels`: Specifies the target labels as a single string containing labels separated by commas. +`--files`: Specifies the path to the new region sets. +`--output`: Specifies the path where the distances file between labels and files, as well as database files and new files, will be saved. + + + ### `bedspace search` The `search` command requires that you have previously run the `distances` command. It also requires a query. To search, you must specify one of 3 scenarios when using the `search` command: @@ -81,4 +115,13 @@ gitk bedspace search \ -d \ -n \ path/to/regions.bed -``` \ No newline at end of file +``` + + +Input Description: + +`-t`: Specifies the search type. +`-d`: Specifies the path to the distances file generated by the bedsapce distances command. +`-n`: Specifies the number of top results to return. + + From f50bd7fda35c1ee8e32334ac8578a88b319c0bdb Mon Sep 17 00:00:00 2001 From: Julia820 Date: Thu, 20 Jul 2023 14:01:30 +0200 Subject: [PATCH 0262/1072] update doc --- docs/tutorials/build-universe.md | 153 +++++++++++++++++++++-- docs/tutorials/create-consensus-peaks.md | 151 ---------------------- 2 files changed, 144 insertions(+), 160 deletions(-) delete mode 100644 docs/tutorials/create-consensus-peaks.md diff --git a/docs/tutorials/build-universe.md b/docs/tutorials/build-universe.md index 883cc7ef..10242ab5 100644 --- a/docs/tutorials/build-universe.md +++ b/docs/tutorials/build-universe.md @@ -1,16 +1,151 @@ # How to build a new universe -Where can you find a very small example dataset? +We will start with simple example of how to create a consensus peak set from a +collection of files. Example data can be found in `tests/consenus/raw`. + +## Data preprocessing + +First step of analysis is creating three tracks with genome coverage by peaks, +their starts and ends. To do that we have to: + +1. install [uniwig](https://github.com/databio/uniwig/tree/smoothing), make sure to use branch dev +2. use [create_unsorted.sh](https://github.com/databio/uniwig/blob/smoothing/create_unsorted.sh) to make three bigWig: + - {prefix}_start.bw - with smoothed coverage of genome by starts + - {prefix}_core.bw - with coverage of genome by peaks + - {prefix}_start.bw - with smoothed coverage of genome by ends + +In this tutorial we will use prefix "all" as it is a default prefix in +`gitk` module + +## Coverage cutoff universe + +We will start by making a coverage universe with cutoff that results in maximum +likelihood universe. We can build it through CLI: ```console -gitk hmm \ - --out_file tests/consensus/universe/hmm_norm.bed \ - --cov_folder tests/consensus/coverage/ + gitk build-universe cc --coverage-folder tests/consenus/coverage/ \ + --output-file tests/consenus/universe/universe.bed + +``` + +Where: + +- `--coverage-folder`, takes the path to bigWig folder with genome coverage by collection +- `--output-file`, takes the path to output file + +Or we can import it directly into Python: + ``` +from gitk.universe.cc_universe import cc_universe -```console -gitk hmm \ - --out_file tests/consensus/universe/hmm_norm.bed \ - --cov_folder tests/consensus/coverage/ \ - --normalize +cc_universe("tests/consenus/coverage/all_core.bw", + file_out="tests/consenus/universe/universe.bed") +``` + +Depending on the task we can also smooth the output universe by setting `--merge` +flag with the distance beloved witch peaks should be merged together and +`--filter-size` with minimum size of peak that should be part of the universe. We can also not use the maximum likelihood cut-off and instead of it use user defined cutoff. For that we have to set `--cutoff` . If we set it to 1 we get union universe, and when to number of files we will get intersection universe. + +## Coverage cutoff flexible universe +Next presented universe is coverage cutoff flexible universe. We can do it through CLI: + +``` + gitk build-universe ccf --coverage-folder tests/consenus/coverage/ \ + --output-file tests/consenus/universe/universe.bed + +``` + +Where: + +- `--coverage-folder`, takes the path to bigWig file with genome coverage by collection +- `--output-file`, takes the path to output file + +Or we can import it directly into python: +``` +from gitk.universe.ccf_universe import ccf_universe + +ccf_universe("tests/consenus/coverage/all_core.bw", + file_out="tests/consenus/universe/universe.bed") +``` + +## Maximum likelihood universe +Another type of universe that we can make is maximum likelihood flexible universe. To make it first we have to have a likelihood model of genome coverage by collection of files. + +#### Making likelihood model: +To make a likelihood model we can use this CLI: + +``` +gitk lh build_model --model-file tests/consenus/model.tar \ + --coverage-folder tests/consenus/coverage/ \ + --file-no 4 +``` + +Where: + +- `--model-file`, takes the name of tar archive that will contain the likelihood model +- `--file-no`, number of files used in analysis +- `--coverage-folder` path to folder with coverage tracks + +Or, we can do it directly in python: + +``` +from gitk.likelihood.build_model import main + +main("tests/consenus/model.tar", "tests/consesnus/coverage", + "all", + file_no=4) +``` + +#### Making universe: +Now that we have the model we make the universe: + +``` +gitk build-universe ml --model-file tests/consenus/model.tar \ + --output-file tests/consenus/universe/universe.bed \ + --coverage-folder tests/consesnus/coverage +``` + +Where: + +- `--model-file`, takes the name of tar archive that contains the likelihood model +- `--output-file`, takes the path to output file +- `--coverage-folder` path to folder with coverage tracks + +Similarly, we can do it in python: + +``` +from gitk.universe.ml_universe import ml_universe + +ml_universe("tests/consesnus/model.tar", + "/home/hee6jn/Documents/gitk/tests/consesnus/coverage", + "all", + "tests/consenus/universe/universe.bed") +``` + +## HMM +Another approach to making flexible universes is using Hidden Markov Models. +We can do it for example with: + +``` +gitk build-universe hmm --out-file tests/consenus/universe/universe.bed \ + --coverage-folder tests/consenus/coverage/ \ + --save-max-cove +``` + +Where: + +- `--output-file`, takes the path to output file +- `--coverage-folder`, path to folder with coverage tracks +- `--coverage-prefix` prefix used in uniwig for making files, default is "all" +- `--not-normlaize`, is a flag that specifies whether not to normalize tracks before running HMM +- `--save-max-cove`, is a flag that specifies whether to save maximum coverage of each output peak + +Similarly, we can do it in python: + +``` +from gitk.universe.hmm_universe import hmm_universe + +hmm_universe("tests/consenus/coverage/", + "tests/consenus/universe/universe.bed", + save_max_cove=True) ``` \ No newline at end of file diff --git a/docs/tutorials/create-consensus-peaks.md b/docs/tutorials/create-consensus-peaks.md deleted file mode 100644 index 5224a33e..00000000 --- a/docs/tutorials/create-consensus-peaks.md +++ /dev/null @@ -1,151 +0,0 @@ -# How to create a consensus peak set from a collection of BED files - -We will start with simple example of how to create a consensus peak set from a -collection of files. Example data can be found in `tests/consenus/raw`. - -## Data preprocessing - -First step of analysis is creating three tracks with genome coverage by peaks, -their starts and ends. To do that we have to: - -1. install [uniwig](https://github.com/databio/uniwig/tree/smoothing), make sure to use branch dev -2. use [create_unsorted.sh](https://github.com/databio/uniwig/blob/smoothing/create_unsorted.sh) to make three bigWig: - - {prefix}_start.bw - with smoothed coverage of genome by starts - - {prefix}_core.bw - with coverage of genome by peaks - - {prefix}_start.bw - with smoothed coverage of genome by ends - -In this tutorial we will use prefix "all" as it is a default prefix in -`gitk` module - -## Coverage cutoff universe - -We will start by making a coverage universe with cutoff that results in maximum -likelihood universe. We can do it through CLI: - -```console - gitk build-universe cc --coverage-folder tests/consenus/coverage/ \ - --output-file tests/consenus/universe/universe.bed - -``` - -Where: - -- `--coverage-folder`, takes the path to bigWig file with genome coverage by collection -- `--output-file`, takes the path to output file - -Or we can import it directly into Python: - -``` -from gitk.universe.cc_universe import cc_universe - -cc_universe("tests/consenus/coverage/all_core.bw", - file_out="tests/consenus/universe/universe.bed") -``` - -Depending on the task we can also smooth the output universe by setting `--merge` -flag with the distance beloved witch peaks should be merged together and -`--filter-size` with minimum size of peak that should be part of the universe. We can also not use the maximum likelihood cut-off and instead of it use user defined cutoff. For that we have to set `--cutoff` . If we set it to 1 we get union universe, and when to number of files we will get intersection universe. - -## Coverage cutoff flexible universe -Next presented universe is coverage cutoff flexible universe. We can do it through CLI: - -``` - gitk build-universe ccf --coverage-folder tests/consenus/coverage/ \ - --output-file tests/consenus/universe/universe.bed - -``` - -Where: - -- `--coverage-folder`, takes the path to bigWig file with genome coverage by collection -- `--output-file`, takes the path to output file - -Or we can import it directly into python: -``` -from gitk.universe.ccf_universe import ccf_universe - -ccf_universe("tests/consenus/coverage/all_core.bw", - file_out="tests/consenus/universe/universe.bed") -``` - -## Maximum likelihood universe -Another type of universe that we can make is maximum likelihood flexible universe. To make it first we have to have a likelihood model of genome coverage by collection of files. - -#### Making likelihood model: -To make a likelihood model we can use this CLI: - -``` -gitk lh build_model --model-file tests/consenus/model.tar \ - --coverage-folder tests/consenus/coverage/ \ - --file-no 4 -``` - -Where: - -- `--model-file`, takes the name of tar archive that will contain the likelihood model -- `--file-no`, number of files used in analysis -- `--coverage-folder` path to folder with coverage tracks - -Or, we can do it directly in python: - -``` -from gitk.likelihood.build_model import main - -main("tests/consenus/model.tar", "tests/consesnus/coverage", - "all", - file_no=4) -``` - -#### Making universe: -Now that we have the model we make the universe: - -``` -gitk build-universe ml --model-file tests/consenus/model.tar \ - --output-file tests/consenus/universe/universe.bed \ - --coverage-folder tests/consesnus/coverage -``` - -Where: - -- `--model-file`, takes the name of tar archive that contains the likelihood model -- `--output-file`, takes the path to output file -- `--coverage-folder` path to folder with coverage tracks - -Similarly, we can do it in python: - -``` -from gitk.universe.ml_universe import ml_universe - -ml_universe("tests/consesnus/model.tar", - "/home/hee6jn/Documents/gitk/tests/consesnus/coverage", - "all", - "tests/consenus/universe/universe.bed") -``` - -## HMM -Another approach to making flexible universes is using Hidden Markov Models. -We can do it for example with: - -``` -gitk build-universe hmm --out-file tests/consenus/universe/universe.bed \ - --coverage-folder tests/consenus/coverage/ \ - --save-max-cove -``` - -Where: - -- `--output-file`, takes the path to output file -- `--coverage-folder`, path to folder with coverage tracks -- `--coverage-prefix` prefix used in uniwig for making files, default is "all" -- `--not-normlaize`, is a flag that specifies whether not to normalize tracks before running HMM -- `--save-max-cove`, is a flag that specifies whether to save maximum coverage of each output peak - -Similarly, we can do it in python: - -``` -from gitk.universe.hmm_universe import hmm_universe - -hmm_universe("tests/consenus/coverage/", - "tests/consenus/universe/universe.bed", - save_max_cove=True) -``` \ No newline at end of file From 08f88c037d89e1b921b881816a5b1d7d9ac385d0 Mon Sep 17 00:00:00 2001 From: Julia820 Date: Thu, 20 Jul 2023 14:01:42 +0200 Subject: [PATCH 0263/1072] update docstring --- gitk/assess/assess.py | 85 +++++++++++++++++++++++++++++++---- gitk/assess/intersection.py | 2 +- gitk/universe/cc_universe.py | 9 ++-- gitk/universe/ccf_universe.py | 40 ++++++++--------- gitk/universe/hmm_universe.py | 2 +- gitk/universe/models.py | 2 +- 6 files changed, 105 insertions(+), 35 deletions(-) diff --git a/gitk/assess/assess.py b/gitk/assess/assess.py index 23232759..d4c47de0 100644 --- a/gitk/assess/assess.py +++ b/gitk/assess/assess.py @@ -1,15 +1,17 @@ +import os +import warnings +from logging import getLogger + +import numpy as np +import pandas as pd + from .distance import run_distance -from .utils import check_if_uni_flexible from .intersection import run_intersection from .likelihood import ( hard_universe_likelihood, likelihood_flexible_universe, ) -import pandas as pd -import os -import numpy as np -import warnings -from logging import getLogger +from .utils import check_if_uni_flexible from ..const import PKG_NAME _LOGGER = getLogger(PKG_NAME) @@ -29,6 +31,21 @@ def run_all_assessment_methods( distance_u_t_f=False, distance_u_t_f_flex=False, ): + """ + Assess universe fit to collection using overlap and distance metrics + :param str raw_data_folder: path to raw files from the collection + :param str file_list: path to file with list of files in the collection + :param str universe: path to universe that is being assessed + :param int no_workers: number of workers for multiprocessing + :param str folder_out: output folder + :param str pref: prefixed used for creating output files + :param bool save_each: if save output of distance metrics for each region + :param bool overlap: if calculate overlap metrics + :param bool distance_f_t_u: if calculate distance from file to universe metrics + :param bool distance_f_t_u_flex: if calculate flexible distance from file to universe metrics + :param bool distance_u_t_f: if calculate distance from universes to file metrics + :param bool distance_u_t_f_flex: if calculate flexible distance from universes to file metrics + """ if not any( [ overlap, @@ -136,6 +153,9 @@ def run_all_assessment_methods( def get_rbs(f_t_u, u_t_f): + """ + Calculate RBS + """ a = 101 / (f_t_u + 100) b = 101 / (u_t_f + 100) rbs = (10 * a + b) / 11 @@ -143,6 +163,15 @@ def get_rbs(f_t_u, u_t_f): def get_mean_rbs(folder, file_list, universe, no_workers, flexible=False): + """ + Calculate average RBS of the collection + :param str folder: path to folder with the collection + :param str file_list: path to file with list of files in the collection + :param str universe: path to the universe + :param int no_workers: number of workers for multiprocessing + :param bool flexible: if to calculate flexible version of the metric + :return int: average RBS + """ file_to_uni = run_distance( folder, file_list, @@ -160,12 +189,17 @@ def get_mean_rbs(folder, file_list, universe, no_workers, flexible=False): flexible=flexible, uni_to_file=True, ) - # me = (10 * file_to_uni[1] + uni_to_file[1]) / 11 rbs = get_rbs(file_to_uni[1], uni_to_file[1]) return np.mean(rbs) def get_rbs_from_assessment_file(file, cs_each_file=False, flexible=False): + """ + Calculate RBS form file with results of metrics per file + :param str file: path to file with assessment results + :param bool cs_each_file: if report RBS for each file, not average for the collection + :param bool flexible: if use flexible version of the metric + """ df = pd.read_csv(file, index_col=(0)) if flexible: df["f_t_u"] = df["median_dist_file_to_universe_flex"] @@ -186,6 +220,14 @@ def get_f_10_score( universe, no_workers, ): + """ + Get F10 score for a universes and collection of files + :param str folder: path to folder with the collection + :param str file_list: path to file with list of files in the collection + :param str universe: path to the universe + :param int no_workers: number of workers for multiprocessing + :return int: average F10 score + """ res = run_intersection( folder, file_list, @@ -202,6 +244,11 @@ def get_f_10_score( def get_f_10_score_from_assessment_file(file, f10_each_file=False): + """ + Get F10 score from assessment output file + :param str file: path to file with assessment results + :param bool f10_each_file: if report F10 for each file, not average for the collection + """ df = pd.read_csv(file, index_col=(0)) r = df["A&U/A"] p = df["A&U/U"] @@ -216,10 +263,20 @@ def get_likelihood( model_file, universe, cove_folder, - cove_prefix, + cove_prefix="all", flexible=False, save_peak_input=False, ): + """ + Calculate universe likelihood given collection + :param str model_file: path to file with likelihood model + :param str universe: path to the universe + :param str cove_folder: path to the coverage folder + :param str cove_prefix: prefixed used for generating coverage + :param bool flexible: if to calculate flexible likelihood + :param bool save_peak_input: if to save likelihood input of each region + :return: + """ if flexible: lh = likelihood_flexible_universe( model_file, universe, cove_folder, cove_prefix, save_peak_input @@ -243,6 +300,18 @@ def filter_universe( cove_prefix=None, lh_cutoff=0, ): + """ + Filter universe by region size, coverage by collection, likelihood + :param str universe: path to input universe + :param str universe_filtered: path to output filtered universe + :param int min_size: minimum size of the region in the output universe + :param int min_coverage: minimum number coverage of universe region by collection + :param bool filter_lh: if use likelihood to filter universe + :param str model_file: path to collection likelihood model + :param str cove_folder: path to folder with coverage tracks + :param str cove_prefix: prefixed used for creating tracks + :param int lh_cutoff: minimum likelihood input + """ if filter_lh: check_if_uni_flexible(universe) if not all([model_file, cove_folder, cove_prefix]): diff --git a/gitk/assess/intersection.py b/gitk/assess/intersection.py index 5c98b16d..3186ddfc 100644 --- a/gitk/assess/intersection.py +++ b/gitk/assess/intersection.py @@ -28,7 +28,7 @@ def relationship_helper(region_a, region_b, only_in, overlap): :param [int, int] region_b: region that starts second :param int only_in: number of positions only in a so far :param int overlap: number of overlapping so far - :return:""" + """ if region_b[0] <= region_a[1]: only_in += region_b[0] - region_a[0] if region_b[1] <= region_a[1]: diff --git a/gitk/universe/cc_universe.py b/gitk/universe/cc_universe.py index f61cb305..18858040 100644 --- a/gitk/universe/cc_universe.py +++ b/gitk/universe/cc_universe.py @@ -17,7 +17,7 @@ def get_uni(file, chrom, cutoff=None): :param str file: coverage file :param str chrom: chromosome to analyse :param int cutoff: base pairs with values grater of equal to cut-off can be included in universe - :return:""" + :return ndarray: vector with universes states; 0 - background, 1- universe""" file = pyBigWig.open(file) if pyBigWig.numpy: track = file.values(chrom, 0, file.chroms(chrom), numpy=True) @@ -75,10 +75,11 @@ def marge_filter(file_out, inter_pos, chrom, merge_dist=100, size_flt=1000): f.write(f"{chrom}\t{start}\t{end}\n") -def cc_universe(cove, cove_prefix, file_out, merge=0, filter_size=0, cutoff=None): +def cc_universe(cove, file_out, cove_prefix="all", merge=0, filter_size=0, cutoff=None): """ - Creat cut-off universe based on coverage track - :param str file: path to coverage file without extension + Create cut-off coverage universe based on coverage track + :param str cove: path to coverage folder + :param str cove_prefix: prefix of the coverage file :param int merge: regions closer than this value will be merged into one :param int filter_size: regions smaller than this value will not be reported :param str file_out: output file diff --git a/gitk/universe/ccf_universe.py b/gitk/universe/ccf_universe.py index e1f1662f..9feecb46 100644 --- a/gitk/universe/ccf_universe.py +++ b/gitk/universe/ccf_universe.py @@ -10,14 +10,14 @@ from gitk.utils import natural_chr_sort, timer_func -def check_if_line_correct(line): - line = line.split("\t") - if not int(line[1]) < int(line[6]) < int(line[7]) < int(line[2]): - print(line) - raise Exception("wrong line format") - - def ana_region(reg, start_s, starts, ends, track_val): + """Check how many regions given part of universe contains + :param ndarray reg: vector with universes states; 0 - background, 1- boundary, 2- core + :param int start_s: start of the part of the universe + :param list starts: list of region starts in given part of universe + :param list ends: list of region ends in given part of universe + :param ndarray track_val: genome coverage by the collection for given part of universe + """ core_s, core_e = [], [] core_pos = np.argwhere(reg == 2).flatten() if len(core_pos) == 0: @@ -43,6 +43,12 @@ def ana_region(reg, start_s, starts, ends, track_val): def save_regions(inter_pos, chrom, bedname, track): + """Save regions from universes to file + :param ndarray inter_pos: vector with universes states; 0 - background, 1- boundary, 2- core + :param str chrom: chromosome to analyse + :param str bedname: output file + :param ndarray track: vector with coverage values + """ ind = np.argwhere(inter_pos != 0) ind = ind.flatten() start_s = ind[0] @@ -69,7 +75,6 @@ def save_regions(inter_pos, chrom, bedname, track): int(b), "0,0,255", ) - check_if_line_correct(li) to_file.append(li) start_s = ind[i] end_e = ind[-1] @@ -90,20 +95,17 @@ def save_regions(inter_pos, chrom, bedname, track): int(b), "0,0,255", ) - check_if_line_correct(li) to_file.append(li) with open(bedname, "a") as f: f.writelines(to_file) def get_uni(file, chrom, bedname): - """For each position check if coverage is bigger than cut-off; - if cut-off not provided calculate value that gives - maximum likelihood universe + """Build cut-off coverage flexible universes from coverage track :param str file: coverage file :param str chrom: chromosome to analyse - :param int cutoff: base pairs with values grater of equal to cut-off can be included in universe - :return:""" + :param str bedname: output file + """ file = pyBigWig.open(file) if pyBigWig.numpy: track = file.values(chrom, 0, file.chroms(chrom), numpy=True) @@ -132,14 +134,12 @@ def get_uni(file, chrom, bedname): save_regions(inter_pos, chrom, bedname, track) -def ccf_universe(cove, cove_prefix, file_out): +def ccf_universe(cove, file_out, cove_prefix="all"): """ - Creat cut-off universe based on coverage track - :param str file: path to coverage file without extension - :param int merge: regions closer than this value will be merged into one - :param int filter_size: regions smaller than this value will not be reported + Creat cut-off flexible universe based on coverage track + :param str cove: path to coverage folder :param str file_out: output file - :param int cutoff: base pairs with coverage equal to or greater than this value will be included in the universe + :param str cove_prefix: prefixed used for creating signal tracks """ if os.path.isfile(file_out): raise Exception(f"File : {file_out} exists") diff --git a/gitk/universe/hmm_universe.py b/gitk/universe/hmm_universe.py index 16e3b3cb..2189f6a8 100644 --- a/gitk/universe/hmm_universe.py +++ b/gitk/universe/hmm_universe.py @@ -111,7 +111,7 @@ def hmm_universe( ): """ Create HMM based univers from coverage - :param coverage_folder: path to folder with coverage files + :param str coverage_folder: path to folder with coverage files :param str start: start coverage file name :param str end: end coverage file name :param str core: core coverage file name diff --git a/gitk/universe/models.py b/gitk/universe/models.py index dd841254..16a123b4 100644 --- a/gitk/universe/models.py +++ b/gitk/universe/models.py @@ -2,7 +2,7 @@ # -*- coding: utf-8 -*- import os -from abc import ABC, abstractmethod +from abc import ABC import numpy as np from hmmlearn import hmm From 8542fa9d8755d8c52f45b6d11b9af765cf556327 Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Thu, 20 Jul 2023 15:20:33 -0400 Subject: [PATCH 0264/1072] add docstrings, type hints --- docs/tutorials/tokenization.md | 2 +- gitk/cli.py | 2 +- gitk/eval/cli.py | 23 +- gitk/eval/ctt.py | 101 ++++-- gitk/eval/gdst.py | 141 +++++--- gitk/eval/npt.py | 347 ++++++++++++------- gitk/eval/rct.py | 90 ++++- gitk/eval/utils.py | 177 +++++++--- gitk/region2vec/cli.py | 6 +- gitk/region2vec/main.py | 87 +++-- gitk/region2vec/region2vec_train.py | 50 ++- gitk/region2vec/region_shuffling.py | 92 ++++- gitk/region2vec/utils.py | 163 ++++++++- gitk/tokenization/README.md | 29 -- gitk/tokenization/cli.py | 6 +- gitk/tokenization/hard_tokenization_batch.py | 67 +++- gitk/tokenization/main.py | 47 ++- gitk/tokenization/split_file.py | 26 +- gitk/tokenization/utils.py | 128 ------- 19 files changed, 1044 insertions(+), 540 deletions(-) delete mode 100644 gitk/tokenization/README.md delete mode 100644 gitk/tokenization/utils.py diff --git a/docs/tutorials/tokenization.md b/docs/tutorials/tokenization.md index f5b9a00c..d6218f82 100644 --- a/docs/tutorials/tokenization.md +++ b/docs/tutorials/tokenization.md @@ -12,7 +12,7 @@ universe_file = '/path/to/universe_file.bed' hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) ``` -Note that we use the `intersect` function of `bedtools` to do tokenization. If you want to switch to different tools, you can override the `bedtool_tokenization` function in `hard_tokenization_batch.py` and provide the path to your tool by specifying the input argument `bedtools_path`. The `fraction` argument specifies the minimum overlap required as a fraction of some region in the universe (default: 1E-9,i.e. 1bp; maximum 1.0). A raw region will be mapped into a universe region when an overlap is above the threshold. +Note that we use the `intersect` function of `bedtools` to do tokenization. If you want to switch to different tools, you can override the `bedtools_tokenization` function in `hard_tokenization_batch.py` and provide the path to your tool by specifying the input argument `bedtools_path`. The `fraction` argument specifies the minimum overlap required as a fraction of some region in the universe (default: 1E-9,i.e. 1bp; maximum 1.0). A raw region will be mapped into a universe region when an overlap is above the threshold. By default, the code assumes the binary `bedtools` exists and can be called via command line. If `bedtools` does not exists, the code will raise an exception. To solve this, please specify `bedtools_path` which points to a bedtools binary. diff --git a/gitk/cli.py b/gitk/cli.py index be1afb68..8ccbfc10 100644 --- a/gitk/cli.py +++ b/gitk/cli.py @@ -237,7 +237,7 @@ def main(test_args=None): print(rct_score) if args.subcommand == "bin-gen": from gitk.eval.utils import get_bin_embeddings - import glob, pickle + import glob, pickle, os if os.path.exists(args.file_name): print(f"{args.file_name} exists!") diff --git a/gitk/eval/cli.py b/gitk/eval/cli.py index e3202c3b..7d6e1a5c 100644 --- a/gitk/eval/cli.py +++ b/gitk/eval/cli.py @@ -1,8 +1,6 @@ def build_subparser_gdst(parser): """ - Builds argument parser. - - :return argparse.ArgumentParser: Argument parser + Builds argument parser to support the gdst command line interface (under eval). """ parser.add_argument( @@ -30,9 +28,7 @@ def build_subparser_gdst(parser): def build_subparser_npt(parser): """ - Builds argument parser. - - :return argparse.ArgumentParser: Argument parser + Builds argument parser to support the npt command line interface (under eval). """ parser.add_argument( "--model-path", @@ -65,9 +61,7 @@ def build_subparser_npt(parser): def build_subparser_ctt(parser): """ - Builds argument parser. - - :return argparse.ArgumentParser: Argument parser + Builds argument parser to support the ctt command line interface (under eval). """ parser.add_argument( "--model-path", @@ -101,9 +95,7 @@ def build_subparser_ctt(parser): def build_subparser_rct(parser): """ - Builds argument parser. - - :return argparse.ArgumentParser: Argument parser + Builds argument parser to support the rct command line interface (under eval). """ parser.add_argument( "--model-path", @@ -154,9 +146,7 @@ def build_subparser_rct(parser): def build_subparser_bingen(parser): """ - Builds argument parser. - - :return argparse.ArgumentParser: Argument parser + Builds argument parser to support the bin-gen command line interface (under eval). """ parser.add_argument( "--universe", @@ -181,6 +171,9 @@ def build_subparser_bingen(parser): def build_subparser(parser): + """ + Builds argument parser to support the eval command line interface. + """ sp = parser.add_subparsers(dest="subcommand") msg_by_cmd = { "gdst": "Genome distance scaling test", diff --git a/gitk/eval/ctt.py b/gitk/eval/ctt.py index 29ddeebb..ab4f0f59 100644 --- a/gitk/eval/ctt.py +++ b/gitk/eval/ctt.py @@ -1,6 +1,5 @@ import os import pickle -from typing import Union os.environ["OPENBLAS_NUM_THREADS"] = "1" import argparse @@ -16,29 +15,36 @@ from .const import * from .utils import ( - Timer, cosine_distance, genome_distance, load_genomic_embeddings, ) -def explained_variance(bin_path, dim): - bin_embed, _ = load_genomic_embeddings(bin_path, "base") - pca_obj = PCA(n_components=dim).fit(bin_embed) - ratio = pca_obj.explained_variance_ratio_.sum() - return ratio - - def get_ctt_score( - path, - embed_type, - seed=42, - num_data=10000, - num_workers=10, -): - """Implementation of hopkins' test. A score between 0 and 1, a score around 0.5 express a - uniform distribution, a score around 0 indicate an evenely spaced distribution, and a score tending to 1 express a high cluster tendency. + path: str, + embed_type: str, + seed: int = 42, + num_data: int = 10000, + num_workers: int = 10, +) -> float: + """Runs the cluster tendency test (CTT) on a model. + + Args: + path (str): The path to a model. + embed_type (str): The type of the model: "region2vec" or "base". + seed (int, optional): Random seed. Defaults to 42. + num_data (int, optional): Number of embeddings used for evaluation. + Defaults to 10000. + num_workers (int, optional): Number of parallel processes used. + Defaults to 10. + + Raises: + ValueError: The number of samples is too small. + ZeroDivisionError: The denominator of the CTT score is zero. + + Returns: + float: The CTT score for the model. """ np.random.seed(seed) data, vocab = load_genomic_embeddings(path, embed_type) @@ -49,7 +55,7 @@ def get_ctt_score( num = num_ori data = data[np.random.choice(num_ori, num)] if num < 100: - raise Exception(f"Number of samples ({num}) is too small") + raise ValueError(f"Number of samples ({num}) is too small") num_samples = int(num * CTT_TEST_RATIO) sel_indexes = np.random.choice(num, num_samples) @@ -70,12 +76,34 @@ def get_ctt_score( y = sum(random_dist_to_nn**2) if x + y == 0: - raise Exception("The denominator of the hopkins statistics is zero") + raise ZeroDivisionError("The denominator is zero") return y / (x + y) -def get_ctt_batch(batch, seed=42, num_data=10000, save_path=None, num_workers=10): +def get_ctt_batch( + batch: list[tuple[str, str]], + seed: int = 42, + num_data: int = 10000, + save_path: str = None, + num_workers: int = 10, +) -> list[tuple[str, float]]: + """Runs the cluster tendency test (CTT) on a batch of models. + + Args: + batch (list[tuple[str, str]]): A list of (model path, model type) + tuples. Model type could be "region2vec" or "base". + seed (int, optional): Random seed. Defaults to 42. + num_data (int, optional): Number of embeddings used for evaluation. + Defaults to 10000. + save_path (str, optional): Save the results to save_path. Defaults to + None. + num_workers (int, optional): Number of parallel processes used. + Defaults to 10. + + Returns: + list[tuple[str, float]]: A list of (model path, CTT score) tuples. + """ ctt_arr = [] for path, embed_type in batch: ctt = get_ctt_score(path, embed_type, seed, num_data, num_workers) @@ -89,12 +117,33 @@ def get_ctt_batch(batch, seed=42, num_data=10000, save_path=None, num_workers=10 def ctt_eval( - batch, - num_runs=20, - num_data=10000, - save_folder=None, - num_workers=10, -): + batch: list[tuple[str, str]], + num_runs: int = 20, + num_data: int = 10000, + save_folder: str = None, + num_workers: int = 10, +) -> list[tuple[str, list[float]]]: + """Runs the CTT on a batch of models for multiple times. + + Runs the cluster tendency test (CTT) for a batch of models for num_runs + times with different random seeds. + + + Args: + batch (list[tuple[str, str]]): A list of (model path, model type) + tuples. Model type could be "region2vec" or "base". + num_runs (int, optional): Number of runs. Defaults to 20. + num_data (int, optional): Number of embeddings used for evaluation. + Defaults to 10000. + save_folder (str, optional): Folder to save the results from each run. + Defaults to None. + num_workers (int, optional): Number of parallel processes used. + Defaults to 10. + + Returns: + list[tuple[str, list[float]]]: A list of (model path, CTT scores from + num_runs) tuples. + """ results_seeds = [] for seed in range(num_runs): print(f"----------------Run {seed}----------------") diff --git a/gitk/eval/gdst.py b/gitk/eval/gdst.py index ab4b0b06..43145317 100644 --- a/gitk/eval/gdst.py +++ b/gitk/eval/gdst.py @@ -1,10 +1,12 @@ import os import pickle +from typing import Union os.environ["OPENBLAS_NUM_THREADS"] = "1" import argparse import glob import multiprocessing as mp +from multiprocessing.queues import Queue import random import time @@ -14,14 +16,25 @@ from .const import * from .utils import ( - Timer, cosine_distance, genome_distance, load_genomic_embeddings, ) -def sample_from_vocab(vocab, num_samples, seed=42): +def sample_from_vocab(vocab: list[str], num_samples: int, seed: int = 42) -> list[str]: + """Samples regions from vocab. + + Samples regions proportionally from each chromosome. + + Args: + vocab (list[str]): A list of regions. + num_samples (int): Number of regions to sample. + seed (int, optional): Random seed. Defaults to 42. + + Returns: + list[str]: A list of sampled regions. + """ chr_probs = {} region_dict = {} num_vocab = len(vocab) @@ -65,44 +78,31 @@ def sample_from_vocab(vocab, num_samples, seed=42): return sampled_regions -def remap_name(name): - return name.split("/")[-3] - - -def convert_position(pos): - if pos // 1e6 > 0: - return f"{pos / 1e6:.4f} MB" - elif pos // 1e3 > 0: - return f"{pos / 1e3:.4f} KB" - else: - return f"{pos:.4f} B" - - -def get_gdst_results(save_paths): - with open(save_paths[0], "rb") as f: - results = pickle.load(f) - num = len(results) - gds_res = [[] for i in range(num)] - models = ["" for i in range(num)] - for path in save_paths: - with open(path, "rb") as f: - results = pickle.load(f) - for i, res in enumerate(results): - gds_res[i].append(res[1]) - models[i] = res[0] - - gds_arr = [(models[i], gds_res[i]) for i in range(num)] - return gds_arr - - def get_gdst_score( - path, - embed_type, - num_samples=10000, - seed=42, - queue=None, - worker_id=None, -): + path: str, + embed_type: str, + num_samples: int = 10000, + seed: int = 42, + queue: Queue = None, + worker_id: int = None, +) -> Union[float, tuple[int, str, float]]: + """Runs the GDST on a model. + + Args: + path (str): The path to a model. + embed_type (str): The model type: "region2vec" or "base". + num_samples (int, optional): Number of embeddings used for evaluation. + Defaults to 10000. + seed (int, optional): Random seed. Defaults to 42. + queue (Queue, optional): The multiprocessing + queue used to store results. Defaults to None. + worker_id (int, optional): Worker id. Defaults to None. + + Returns: + Union[float, tuple[int, str, float]]: A GDST score when used in a single + process; or, a tuple of (worker id, model path, GDST score) when + used in multiple processes. + """ embed_rep, vocab = load_genomic_embeddings(path, embed_type) regions = sample_from_vocab(vocab, num_samples, seed) region2idx = {r: i for i, r in enumerate(vocab)} @@ -125,7 +125,17 @@ def get_gdst_score( return slope -def writer_multiprocessing(save_path, num, q): +def writer_multiprocessing(save_path: str, num: int, q: Queue) -> list[tuple[str, float]]: + """Writes results from multiple processes to a list. + + Args: + save_path (str): The path to the saved results. + num (int): The number of results. + q (Queue): A multiprocessing queue. + + Returns: + list[tuple[str, float]]: A list of (model path, GDST score) tuples. + """ results = ["" for i in range(num)] while True: m = q.get() @@ -140,7 +150,28 @@ def writer_multiprocessing(save_path, num, q): return results -def get_gdst_score_batch(batch, num_samples=10000, seed=42, save_path=None, num_workers=1): +def get_gdst_score_batch( + batch: list[tuple[str, str]], + num_samples: int = 10000, + seed: int = 42, + save_path: str = None, + num_workers: int = 1, +) -> list[tuple[str, float]]: + """Runs the GDST on a batch of models. + + Args: + batch (list[tuple[str, str]]): A list of (model path, model type) tuples. + num_samples (int, optional): Number of embeddings used for evaluation. + Defaults to 10000. + seed (int, optional): Random seed. Defaults to 42. + save_path (str, optional): Save the results to save_path. Defaults to + None. + num_workers (int, optional): Number of parallel processes used. + Defaults to 1. + + Returns: + list[tuple[str, float]]: A list of (model path, GDST score) tuples. + """ if num_workers <= 1: gds_arr = [] for path, embed_type in batch: @@ -172,12 +203,28 @@ def get_gdst_score_batch(batch, num_samples=10000, seed=42, save_path=None, num_ def gdst_eval( - batch, - num_runs=20, - num_samples=1000, - save_folder=None, - num_workers=10, -): + batch: list[tuple[str, str]], + num_runs: int = 20, + num_samples: int = 1000, + save_folder: str = None, + num_workers: int = 10, +) -> list[tuple[str, list[float]]]: + """Runs the GDST on a batch of models for multiple times. + + Args: + batch (list[tuple[str, str]]): A list of (model path, model type) tuples. + num_runs (int, optional): Number of runs. Defaults to 20. + num_samples (int, optional): Number of embeddings used for evaluation. + Defaults to 1000. + save_folder (str, optional): Folder to save the results from each run. + Defaults to None. + num_workers (int, optional): Number of parallel processes used. + Defaults to 10. + + Returns: + list[tuple[str, list[float]]]: A list of (model path, GDST scores from + num_runs) tuples. + """ results_seeds = [] for seed in range(num_runs): print(f"----------------Run {seed}----------------") diff --git a/gitk/eval/npt.py b/gitk/eval/npt.py index da15bdcd..652a1148 100644 --- a/gitk/eval/npt.py +++ b/gitk/eval/npt.py @@ -1,9 +1,11 @@ import os import pickle +from typing import Union os.environ["OPENBLAS_NUM_THREADS"] = "1" import argparse import multiprocessing as mp +from multiprocessing.queues import Queue import time import matplotlib.pyplot as plt @@ -11,13 +13,23 @@ from gensim.models import Word2Vec from matplotlib.lines import Line2D -from .utils import Timer, genome_distance, load_genomic_embeddings +from .utils import genome_distance, load_genomic_embeddings -def get_topk_embed(i, K, embed, dist="cosine"): - """ - Return the indices for the most similar K regions to the i-th region - embed is the embedding matrix for all the regions in the vocabulary of a region2vec model +def get_topk_embed( + i: int, K: int, embed: np.ndarray, dist: str = "cosine" +) -> tuple[np.ndarray, np.ndarray]: + """Gets the nearest K embedding indexes to the i-th embedding. + + Args: + i (int): The index for the query embedding. + K (int): The number of nearest embeddings to select. + embed (np.ndarray): An array of embedding vectors + dist (str, optional): The distance function used. Defaults to "cosine". + + Returns: + tuple[np.ndarray, np.ndarray]: K indexes of nearest embeddings and the + corresponding similarities. """ num = len(embed) if dist == "cosine": @@ -39,13 +51,20 @@ def get_topk_embed(i, K, embed, dist="cosine"): return indexes, s -def find_Kneighbors(region_array, index, K): - """ - region_array must be sorted; all regions are on the same chromosome - index is the index for the query region region_array[index] - K is the number of nearest neighbors of the query region +def find_Kneighbors(region_array: list[tuple[str, int, int]], index: int, K: int) -> list[int]: + """Finds the indexes of the K nearest regions of a query region on genome. - return: indices of the K nearest neighbors in region_array + region_array must be sorted, and all regions are on the same chromosome. + + Args: + region_array (list[tuple[str, int, int]]): A list of (chromosome, start + position, end position) tuples. + index (int): The index of the query region. + K (int): Specifies the number of nearest neighbors of the query region. + + Returns: + list[int]: A list of indexes of the K nearest neighbors in + region_array. """ if len(region_array) < K: K = len(region_array) @@ -62,16 +81,40 @@ def find_Kneighbors(region_array, index, K): def calculate_overlap_bins( - local_idx, - K, - chromo, - region_array, - region2index, - embed_rep, - res=10, - dist="cosine", - same_chromo=True, -): + local_idx: int, + K: int, + chromo: str, + region_array: list[tuple[str, int, int]], + region2index: dict[str, int], + embed_rep: np.ndarray, + res: int = 10, + dist: str = "cosine", + same_chromo: bool = True, +) -> np.ndarray: + """Calculates the overlap ratios for a region. + + Calculates the overlap ratios for a region between its K-nearest neighor + set obtained using genome distance and its K-nearest neighor set obtained + using embedding distance. If res < K, then calculates ratios for size + res*1, res*2, ..., min(res*n, K). + + Args: + local_idx (int): The local index of a region on its chromosome. + K (int): Specifies the number of nearest neighbors. + chromo (str): Chromosome. + region_array (list[tuple[str, int, int]]): A list of (chromosome, start + position, end position) tuples. + region2index (dict[str, int]): A dictionary of (region, index). + embed_rep (np.ndarray): An array of embedding vectors. + res (int, optional): Resolution. Size of neighborhood set. Defaults to + 10. + dist (str, optional): Distance function. Defaults to "cosine". + same_chromo (bool, optional): Whether to find nearest neighors on the + same chromosome in the embedding space. Defaults to True. + + Returns: + np.ndarray: An array of overlap ratios. + """ Kindices = find_Kneighbors(region_array, local_idx, K) if len(Kindices) == 0: return 0 @@ -105,7 +148,16 @@ def calculate_overlap_bins( return np.array(bin_overlaps) -def cal_snpr(ratio_embed, ratio_random): +def cal_snpr(ratio_embed: np.ndarray, ratio_random: np.ndarray) -> np.ndarray: + """Calculates SNPR values. + + Args: + ratio_embed (np.ndarray): Overlap ratios for query embeddings. + ratio_random (np.ndarray): Overlap ratios for random embeddings. + + Returns: + np.ndarray: SNPR values. + """ res = np.log10((ratio_embed + 1.0e-10) / (ratio_random + 1.0e-10)) res = np.maximum(res, 0) return res @@ -114,7 +166,30 @@ def cal_snpr(ratio_embed, ratio_random): var_dict = {} -def worker_func(i, K, chromo, region_array, embed_type, resolution, dist): +def worker_func( + i: int, + K: int, + chromo: str, + region_array: list[tuple[str, int, int]], + embed_type: str, + resolution: int, + dist: str, +) -> np.ndarray: + """Wrapper for calculate_overlap_bins + + Args: + i (int): The local index of a region on its chromosome. + K (int): Specifies the number of nearest neighbors. + chromo (str): Chromosome. + region_array (list[tuple[str, int, int]]): A list of (chromosome, start + position, end position) tuples. + embed_type (str): Embedding type, "region2vec" or "base". + resolution (int): Resolution. + dist (str): Distance function. + + Returns: + np.ndarray: An array of overlap ratios. + """ if embed_type == "embed": embeds = var_dict["embed_rep"] elif embed_type == "random": @@ -132,26 +207,59 @@ def worker_func(i, K, chromo, region_array, embed_type, resolution, dist): return nprs -def init_worker(embed_rep, ref_embed, region2index): +def init_worker( + embed_rep: np.ndarray, ref_embed: np.ndarray, region2index: dict[str, int] +) -> None: + """Initializes data used by workers. + + Args: + embed_rep (np.ndarray): Query embeddings. + ref_embed (np.ndarray): Random embeddings. + region2index (dict[str, int]): A region to index dictionary. + """ var_dict["embed_rep"] = embed_rep var_dict["ref_embed"] = ref_embed var_dict["region2vec_index"] = region2index def get_npt_score( - model_path, - embed_type, - K, - num_samples=100, - seed=0, - resolution=10, - dist="cosine", - num_workers=10, -): - """ - If sampling > 0, then randomly sample num_samples regions in total (proportional for each chromosome) - - If num_samples == 0, all regions are used in calculation + model_path: str, + embed_type: str, + K: int, + num_samples: int = 100, + seed: int = 0, + resolution: int = 10, + dist: str = "cosine", + num_workers: int = 10, +) -> dict[str, Union[int, np.ndarray, str]]: + """Runs the NPT on a mdoel. + + If num_samples > 0, then randomly sample num_samples regions proportional + from each chromosome. If num_samples == 0, all regions are used in the + test. If K > resolution, then returns an array of NPT scores; otherwise, + returns one NPT score. + + Args: + model_path (str): The path to a model. + embed_type (str): The model type: "region2vec" or "base". + K (int): Specifies the number of nearest neighbors. + num_samples (int, optional): Number of embeddings used for evaluation. + Defaults to 100. + seed (int, optional): Random seed. Defaults to 0. + resolution (int, optional): Resolution of a neighborhood set. Defaults + to 10. + dist (str, optional): Distance function. Defaults to "cosine". + num_workers (int, optional): Number of parallel processes used. + Defaults to 10. + + Returns: + dict[str, Union[int, np.ndarray, str]]: NPT results in a dictionary + "K": K, + "Avg_qNPR": Average NPR ratios for query embeddings, + "Avg_rNPR": Average NPR ratios for random embeddings,, + "SNPR": SNPR values, + "Resolution": Resolution, + "Path": Model path, """ embed_rep, regions_r2v = load_genomic_embeddings(model_path, embed_type) @@ -278,7 +386,17 @@ def get_npt_score( return result -def writer_multiprocessing(save_path, num, q): +def writer_multiprocessing(save_path: str, num: int, q: Queue) -> list[tuple[str, float]]: + """Writes results from multiple processes to a list. + + Args: + save_path (str): The path to the saved results. + num (int): The number of results. + q (Queue): A multiprocessing queue. + + Returns: + list[tuple[str, float]]: A list of (model path, NPT score) tuples. + """ results = [[] for i in range(num)] while True: m = q.get() @@ -294,15 +412,35 @@ def writer_multiprocessing(save_path, num, q): def get_npt_score_batch( - batch, - K, - num_samples=100, - num_workers=10, - seed=0, - resolution=10, - dist="cosine", - save_path=None, -): + batch: list[tuple[str, str]], + K: int, + num_samples: int = 100, + num_workers: int = 10, + seed: int = 0, + resolution: int = 10, + dist: str = "cosine", + save_path: str = None, +) -> list[dict[str, Union[int, np.ndarray, str]]]: + """Runs the NPT on a batch of models. + + Args: + batch (list[tuple[str, str]]): A list of (model path, model type) tuples. + K (int): Specifies the number of nearest neighbors. + num_samples (int, optional): Number of embeddings used for evaluation. + Defaults to 100. + num_workers (int, optional): Number of parallel processes used. + Defaults to 10. + seed (int, optional): Random seed. Defaults to 0. + resolution (int, optional): Resolution of a neighborhood set. Defaults + to 10. + dist (str, optional): Distance function. Defaults to "cosine". + save_path (str, optional): Save the results to save_path. Defaults to + None. + + Returns: + list[dict[str, Union[int, np.ndarray, str]]]: A list of dictionaries of + NPT results. + """ result_list = [] for index, (path, embed_type) in enumerate(batch): result = get_npt_score( @@ -324,15 +462,35 @@ def get_npt_score_batch( def npt_eval( - batch, - K, - num_samples=100, - num_workers=10, - num_runs=20, - resolution=10, - dist="cosine", - save_folder=None, -): + batch: list[tuple[str, str]], + K: int, + num_samples: int = 100, + num_workers: int = 10, + num_runs: int = 20, + resolution: int = 10, + dist: str = "cosine", + save_folder: str = None, +) -> list[tuple[str, np.ndarray, int]]: + """Runs the NPT on a batch of models for multiple times. + + Args: + batch (list[tuple[str, str]]): A list of (model path, model type) tuples. + K (int): Specifies the number of nearest neighbors. + num_samples (int, optional): Number of embeddings used for evaluation. + Defaults to 100. + num_workers (int, optional): Number of parallel processes used. + Defaults to 10. + num_runs (int, optional): Number of runs. Defaults to 20. + resolution (int, optional): Resolution of a neighborhood set. + Defaults to 10. + dist (str, optional): Distance function. Defaults to "cosine". + save_folder (str, optional): Folder to save the results from each run. + Defaults to None. + + Returns: + list[tuple[str, np.ndarray, int]]: A list of (model path, snprs from + num_runs, resoultion) tuples. + """ results_seeds = [] assert resolution <= K, "resolution <= K" for seed in range(num_runs): @@ -365,80 +523,3 @@ def npt_eval( print(f"{paths[i]}\nSNPRs:{msg}\n") snpr_results = [(paths[i], snpr_results[i], resolution) for i in range(len(batch))] return snpr_results - - -def get_npt_results(save_paths): - snpr_results = {} - for save_path in save_paths: - with open(save_path, "rb") as f: - results = pickle.load(f) - for result in results: - key = result["Path"] - resolution = result["Resolution"] - if key in snpr_results: - snpr_results[key].append(result["SNPR"]) - else: - snpr_results[key] = [result["SNPR"]] - snpr_results = [(k, np.array(v), resolution) for k, v in snpr_results.items()] - return snpr_results - - -def snpr_plot(snpr_data, row_labels=None, legend_pos=(0.25, 0.6), filename=None): - snpr_vals = [(k, v.sum(axis=1).mean(), v.sum(axis=1).std()) for k, v, res in snpr_data] - cmap = plt.get_cmap("Set1") - cmaplist = [cmap(i) for i in range(9)] - if row_labels is None: - row_labels = [k for k, v, s in snpr_vals] - fig, ax = plt.subplots(figsize=(10, 6)) - mean_snpr_tuple = [(i, r[1]) for i, r in enumerate(snpr_vals)] - mean_snpr_tuple = sorted(mean_snpr_tuple, key=lambda x: -x[1]) - mean_snpr = [t[1] for t in mean_snpr_tuple] - indexes = [t[0] for t in mean_snpr_tuple] - std_snpr = [snpr_vals[i][2] for i in indexes] - row_labels = [row_labels[i] for i in indexes] - ax.set_xticks(list(range(1, len(mean_snpr) + 1))) - ax.set_xticklabels(row_labels) - ax.errorbar( - range(1, len(mean_snpr) + 1), - mean_snpr, - yerr=std_snpr, - fmt="o", - ms=10, - mfc=cmaplist[1], - mec=cmaplist[8], - ecolor=cmaplist[2], - elinewidth=3, - capsize=5, - ) - ax.set_ylabel("SNPR") - _ = plt.setp( - ax.get_xticklabels(), - rotation=-15, - ha="left", - va="top", - rotation_mode="anchor", - ) - patches = [ - Line2D( - [0], - [0], - marker="o", - linestyle="", - color=cmaplist[1], - markersize=12, - mec=cmaplist[8], - ), - Line2D([0], [0], color=cmaplist[2], lw=4), - ] - legend = ax.legend( - labels=["SNPR", "SNPR standard deviation"], - handles=patches, - bbox_to_anchor=legend_pos, - loc="center left", - borderaxespad=0, - fontsize=12, - frameon=True, - ) - ax.grid("on") - if filename: - fig.savefig(filename, bbox_inches="tight") diff --git a/gitk/eval/rct.py b/gitk/eval/rct.py index e0f68509..6a84eec1 100644 --- a/gitk/eval/rct.py +++ b/gitk/eval/rct.py @@ -16,14 +16,38 @@ from ..utils import timer_func from .utils import ( - Timer, cosine_distance, genome_distance, load_genomic_embeddings, ) -def get_rct_score(path, embed_type, bin_path, out_dim=-1, cv_num=5, seed=42, num_workers=10): +def get_rct_score( + path: str, + embed_type: str, + bin_path: str, + out_dim: int = -1, + cv_num: int = 5, + seed: int = 42, + num_workers: int = 10, +) -> float: + """Runs the RCT on a model. + + Args: + path (str): The path to a model. + embed_type (str): The type of the model: "region2vec" or "base". + bin_path (str): The path to a binary embedding model. + out_dim (int, optional): Output dimension of a prediction. Defaults to + -1, and out_dim is the same as the dimension of a binary embedding. + cv_num (int, optional): Number of folds in cross-validation. Defaults + to 5. + seed (int, optional): Random seed. Defaults to 42. + num_workers (int, optional): Number of parallel processes used. + Defaults to 10. + + Returns: + float: the RCT score. + """ embed_rep, vocab = load_genomic_embeddings(path, embed_type) embed_bin, vocab_bin = load_genomic_embeddings(bin_path, "base") region2idx = {r: i for i, r in enumerate(vocab)} @@ -63,7 +87,33 @@ def get_rct_score(path, embed_type, bin_path, out_dim=-1, cv_num=5, seed=42, num return score.mean() -def reconstruction_batch(batch, cv_num, out_dim=-1, seed=42, save_path=None, num_workers=10): +def reconstruction_batch( + batch: list[tuple[str, str, str]], + cv_num: int, + out_dim: int = -1, + seed: int = 42, + save_path: str = None, + num_workers: int = 10, +) -> list[tuple[str, float]]: + """Runs the RCT on a batch of models. + + Args: + batch (list[tuple[str, str, str]]): A list of (model path, model type, + binary embedding path) tuples. Model type could be "region2vec" or + "base". + cv_num (int): Number of folds in cross-validation. Defaults + to 5. + out_dim (int, optional): Output dimension of a prediction. Defaults to + -1, and out_dim is the same as the dimension of a binary embedding. + seed (int, optional): Random seed. Defaults to 42. + save_path (str, optional): Save the results to save_path. Defaults to + None. + num_workers (int, optional): Number of parallel processes used. + Defaults to 10. + + Returns: + list[tuple[str, float]]: A list of (model path, RCT score) tuples. + """ rct_arr = [] for path, embed_type, bin_path in batch: score = get_rct_score(path, embed_type, bin_path, out_dim, cv_num, seed, num_workers) @@ -76,13 +126,33 @@ def reconstruction_batch(batch, cv_num, out_dim=-1, seed=42, save_path=None, num def rct_eval( - batch, - num_runs=5, - cv_num=5, - out_dim=-1, - save_folder=None, - num_workers=10, -): + batch: list[tuple[str, str, str]], + num_runs: int = 5, + cv_num: int = 5, + out_dim: int = -1, + save_folder: str = None, + num_workers: int = 10, +) -> list[tuple[str, list[float]]]: + """Runs the RCT on a batch of models for multiple times. + + Args: + batch (list[tuple[str, str, str]]): A list of (model path, model type, + binary embedding path) tuples. Model type could be "region2vec" or + "base". + num_runs (int, optional): Number of runs. Defaults to 5. + cv_num (int, optional): Number of folds in cross-validation. + Defaults to 5. + out_dim (int, optional): Output dimension of a prediction. Defaults to + -1, and out_dim is the same as the dimension of a binary embedding. + save_folder (str, optional): Folder to save the results from each run. + Defaults to None. + num_workers (int, optional): Number of parallel processes used. + Defaults to 10. + + Returns: + list[tuple[str, list[float]]]: A list of (model path, RCT scores from + num_runs) tuples. + """ results_seeds = [] for seed in range(num_runs): print(f"----------------Run {seed}----------------") diff --git a/gitk/eval/utils.py b/gitk/eval/utils.py index 30a8fa7a..35db930d 100644 --- a/gitk/eval/utils.py +++ b/gitk/eval/utils.py @@ -4,42 +4,66 @@ import os import pickle import time +from typing import Union import numpy as np from gensim.models import Word2Vec -class Timer: - def __init__(self): - self.o = time.time() +def genome_distance(u: tuple[int, int], v: tuple[int, int]) -> float: + """Computes the genome distance between two regions. - def measure(self, p=1): - x = (time.time() - self.o) / float(p) - x = int(x) - if x >= 3600: - return f"{x / 3600:.1f}h" - if x >= 60: - return f"{round(x / 60)}m" - return f"{x}s" + Assumes that the two regions, u and v, are on the same chromosome. + Args: + u (tuple[int, int]): A region denoted by its start and end positions. + v (tuple[int, int]): A region denoted by its start and end positions. -def genome_distance(u, v): + Returns: + float: The genome distance between the two regions. + """ return float(u[1] < v[1]) * max(v[0] - u[1] + 1, 0) + float(u[1] >= v[1]) * max( u[0] - v[1] + 1, 0 ) -def cosine_distance(x, y): +def cosine_distance(x: np.ndarray, y: np.ndarray) -> float: + """Calculates the cosine distance between two embedding vectors. + + Args: + x (np.ndarray): An embedding vector. + y (np.ndarray): An embedding vector. + + Returns: + float: The cosine distance between two embedding vectors. + """ return (1 - ((x / np.linalg.norm(x)) * (y / np.linalg.norm(y))).sum()) / 2 class BaseEmbeddings: + """Wraps embeddings and the corresponding regions in one object. + + Attributes: + embeddings (np.ndarray): Region embedding vectors. + vocab (list[str]): A list of regions in the format of chr:start-end. + """ + def __init__(self, embeddings, vocab): self.embeddings = embeddings self.vocab = vocab -def get_bin_embeddings(universe_file, tokenized_files): +def get_bin_embeddings(universe_file: str, tokenized_files: list[str]) -> BaseEmbeddings: + """Gets a BaseEmbeddings object for binary embeddings. + + Args: + universe_file (str): The path to a universe file. + tokenized_files (list[str]): A list of tokoenized BED files (in full + paths). + + Returns: + BaseEmbeddings: A BaseEmbeddings object for binary embeddings. + """ vocab = [] with open(universe_file, "r") as f: for line in f: @@ -60,7 +84,20 @@ def get_bin_embeddings(universe_file, tokenized_files): return bin_embed_obj -def get_pca_embeddings(bin_embed_obj, dim, kwargs={}): +def get_pca_embeddings( + bin_embed_obj: BaseEmbeddings, dim: int, kwargs: dict[str, Union[int, float]] = {} +) -> BaseEmbeddings: + """Gets PCA embeddings from binary embeddings. + + Args: + bin_embed_obj (BaseEmbeddings): A BaseEmbeddings object for binary embeddings. + dim (int): Number of dimensions for PCA embeddings. + kwargs (dict[str, Union[int, float]], optional): Parameters passed to + PCA. Defaults to {}. + + Returns: + BaseEmbeddings: A BaseEmbeddings object for PCA embeddings. + """ from sklearn.decomposition import PCA embeds = PCA(n_components=dim, **kwargs).fit_transform(bin_embed_obj.embeddings) @@ -68,7 +105,20 @@ def get_pca_embeddings(bin_embed_obj, dim, kwargs={}): return pca_embed_obj -def get_umap_embeddings(bin_embed_obj, dim, kwargs={}): +def get_umap_embeddings( + bin_embed_obj: BaseEmbeddings, dim: int, kwargs: dict[str, Union[int, float]] = {} +) -> BaseEmbeddings: + """Gets UMAP embeddings from binary embeddings. + + Args: + bin_embed_obj (BaseEmbeddings): A BaseEmbeddings object for binary embeddings. + dim (int): Number of dimensions for UMAP embeddings. + kwargs (dict[str, Union[int, float]], optional): Parameters passed to + UMAP. Defaults to {}. + + Returns: + BaseEmbeddings: A BaseEmbeddings object for UMAP embeddings. + """ import umap embeds = umap.UMAP(n_components=dim, **kwargs).fit_transform(bin_embed_obj.embeddings) @@ -76,18 +126,46 @@ def get_umap_embeddings(bin_embed_obj, dim, kwargs={}): return umap_embed_obj -def save_base_embeddings(base_embed_obj, file_name): +def save_base_embeddings(base_embed_obj: BaseEmbeddings, file_name: str) -> None: + """Saves the BaseEmbeddings object to disk. + + Args: + base_embed_obj (BaseEmbeddings): A BaseEmbeddings object. + file_name (str): Save the BaseEmbeddings object to file_name. + """ with open(file_name, "wb") as f: pickle.dump(base_embed_obj, f) -def load_base_embeddings(path): +def load_base_embeddings(path: str) -> tuple[np.ndarray, list[str]]: + """Loads a BaseEmbeddings object. + + Args: + path (str): The path to a BaseEmbeddings object. + + Returns: + tuple[np.ndarray, list[str]]: Embedding vectors and the corresponding + region list. + """ with open(path, "rb") as f: base_embed_obj = pickle.load(f) return base_embed_obj.embeddings, base_embed_obj.vocab -def load_genomic_embeddings(model_path, embed_type="region2vec"): +def load_genomic_embeddings( + model_path: str, embed_type: str = "region2vec" +) -> tuple[np.ndarray, list[str]]: + """Loads genomic region embeddings based on the type. + + Args: + model_path (str): The path to a saved model. + embed_type (str, optional): The model type. Defaults to "region2vec". + Can be "region2vec" or "base". + + Returns: + tuple[np.ndarray, list[str]]: Embedding vectors and the corresponding + region list. + """ if embed_type == "region2vec": model = Word2Vec.load(model_path) regions_r2v = model.wv.index_to_key @@ -98,18 +176,39 @@ def load_genomic_embeddings(model_path, embed_type="region2vec"): return embed_rep, regions_r2v -def get_vocab(model_path, type="base", ordered=True): - def sort_key(x): - eles = x.split(":") - chr_idx = eles[0][3:] - try: - idx = int(chr_idx) - except ValueError: - idx = 23 - for c in chr_idx: - idx += ord(c) - start = int(eles[1].split("-")[0].strip()) - return idx, start +def sort_key(x: str) -> tuple[int, int]: + """Extracts chromosome in number and the start position of a region. + + Args: + x (str): A region in the chr:start-end position. + + Returns: + tuple[int, int]: Chromosome in number and the start position. + """ + eles = x.split(":") + chr_idx = eles[0][3:] + try: + idx = int(chr_idx) + except ValueError: + idx = 23 + for c in chr_idx: + idx += ord(c) + start = int(eles[1].split("-")[0].strip()) + return idx, start + + +def get_vocab(model_path: str, type: str = "base", ordered: bool = True) -> list[str]: + """Gets vocab from a model. + + Args: + model_path (str): The path to a saved model. + type (str, optional): The embedding type. Defaults to "base". + ordered (bool, optional): Choose whether to sort the regions. Defaults + to True. + + Returns: + list[str]: A list of regions. + """ if type == "region2vec": model = Word2Vec.load(model_path) @@ -121,7 +220,13 @@ def sort_key(x): return regions_r2v -def write_vocab(vocab, file_name): +def write_vocab(vocab: list[str], file_name: str) -> None: + """Writes a list of regions to a file. + + Args: + vocab (list[str]): A list of regions in the format of chr:start-end. + file_name (str): Saves vocab as file_name. + """ with open(file_name, "w") as f: for v in vocab: eles = v.split(":") @@ -130,11 +235,3 @@ def write_vocab(vocab, file_name): s = s.strip() e = e.strip() f.write(f"{chr}\t{s}\t{e}\n") - - -def region2tuple(x): - eles = x.split(":") - chr_name = eles[0].strip() - start, end = eles[1].split("-") - start, end = int(start.strip()), int(end.strip()) - return chr_name, start, end diff --git a/gitk/region2vec/cli.py b/gitk/region2vec/cli.py index 49d6b121..623bbd3a 100644 --- a/gitk/region2vec/cli.py +++ b/gitk/region2vec/cli.py @@ -1,9 +1,5 @@ def build_subparser(parser): - """ - Builds argument parser. - - :return argparse.ArgumentParser: Argument parser - """ + """Builds an argument parser to support the region2vec command line interface.""" parser.add_argument("--token-folder", type=str, help="path to tokenized files") parser.add_argument("--num-shuffle", type=int, help="number of shufflings/training epochs") parser.add_argument("--embed-dim", type=int, help="embedding dimension") diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 0b1e66c6..1e1f1a80 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -14,29 +14,72 @@ def __init__(self, **kwargs): def region2vec( - token_folder, # path to the folder of tokenized files - save_dir, # folder to save the training results - file_list=None, # specifies which files from token_folder are used for training - data_type="files", - mat_path=None, - num_shufflings=1000, # Number of shuffled datasets or number of training epochs - num_processes=10, # Maximum number of parallel processes - tokenization_mode="hard", # tokenization mode - embedding_dim=100, # Dimension of region2vec embeddings - context_win_size=5, # Context window size (half) - save_freq=-1, # Save a model after the given number of training epochs. If -1, then only save the best and latest models - resume_path="", # path to a trained model. If specified, the model will be used to initialize the region2vec embeddings - train_alg="cbow", # select training algorithms ['cbow','skip-gram'] - min_count=5, # Threshold for filtering out regions with low frequency - neg_samples=5, # Number of negative samples - init_lr=0.025, # Initial learning rate - min_lr=1e-4, # Minimum learning rate - lr_scheduler="linear", # How to decay the learning rate. Select from linear and milestone - milestones=[], # Specify only when lr_scheduler=milestone. At each given epoch, the learning rate will be multiplied by 0.1 - hier_softmax=False, # Whether to hierarchical softmax - seed=0, # random seed, - update_vocab="once", + token_folder: str, + save_dir: str, + file_list: list[str] = None, + data_type: str = "files", + mat_path: str = None, + num_shufflings: int = 1000, + num_processes: int = 10, + tokenization_mode: str = "hard", + embedding_dim: int = 100, + context_win_size: int = 5, + save_freq: int = -1, + resume_path: str = "", + train_alg: str = "cbow", + min_count: int = 5, + neg_samples: int = 5, + init_lr: float = 0.025, + min_lr: float = 1e-4, + lr_scheduler: str = "linear", + milestones: list[int] = [], + hier_softmax: bool = False, + seed: int = 0, + update_vocab: str = "once", ): + """Trains a Region2Vec model. + + Starts two subprocesses: one that generates shuffled datasets, and the + other consumes the shuffled datasets to train a Region2Vec model. + + Args: + token_folder (str): The path to the folder of tokenized files. + save_dir (str): The folder that stores the training results. + file_list (list[str], optional): Specifies which files from + token_folder are used for training. When None, uses all the files + in token_folder. Defaults to None. + data_type (str, optional): "files" or "matrix". Defaults to "files". + mat_path (str, optional): Used only when data_type = "matrix". Defaults + to None. + num_shufflings (int, optional): Number of shuffled datasets to + generate. Defaults to 1000. + num_processes (int, optional): Number of processes used. Defaults to 10. + tokenization_mode (str, optional): Tokenization mode. Defaults to + "hard", i.e., concatenating all regions in a BED files in a random order. + embedding_dim (int, optional): Dimension of embedding vectors. Defaults + to 100. + context_win_size (int, optional): Context window size. Defaults to 5. + save_freq (int, optional): Save frequency. Defaults to -1. + resume_path (str, optional): Starts with a previously trained model. + Defaults to "". + train_alg (str, optional): Training algorithm. Defaults to "cbow". + min_count (int, optional): Minimum frequency required to keep a region. + Defaults to 5. + neg_samples (int, optional): Number of negative samples used in + training. Defaults to 5. + init_lr (float, optional): Initial learning rate. Defaults to 0.025. + min_lr (float, optional): Minimum learning rate. Defaults to 1e-4. + lr_scheduler (str, optional): Type of the learning rate scheduler. + Defaults to "linear". + milestones (list[int], optional): Used only when + lr_scheduler="milestones". Defaults to []. + hier_softmax (bool, optional): Whether to use hierarchical softmax + during training. Defaults to False. + seed (int, optional): Random seed. Defaults to 0. + update_vocab (str, optional): If "every", then updates the vocabulary + for each shuffled dataset. Defaults to "once" assuming no new + regions occur in shuffled datasets. + """ timer = utils.Timer() start_time = timer.t() if file_list is None: diff --git a/gitk/region2vec/region2vec_train.py b/gitk/region2vec/region2vec_train.py index 394aaa1b..039fd30f 100644 --- a/gitk/region2vec/region2vec_train.py +++ b/gitk/region2vec/region2vec_train.py @@ -6,6 +6,7 @@ import pickle import random import time +from typing import Union from gensim.models import Word2Vec from gensim.models.word2vec import LineSentence @@ -16,7 +17,18 @@ logging.basicConfig(format="%(asctime)s : %(levelname)s : %(message)s", level=logging.ERROR) -def find_dataset(data_folder): +def find_dataset(data_folder: str) -> Union[str, int]: + """Finds an available dataset in data_folder. + + Finds an available dataset from a folder specified by data_folder. + + Args: + data_folder (str): The path to the folder where datasets are generated. + + Returns: + Union[str, int]: -1 when no datasets are found after MAX_WAIT_TIME; + otherwise, a path to the found dataset. + """ train_pattern = os.path.join(data_folder, "pool*[0-9]") count = 0 while True: @@ -32,7 +44,14 @@ def find_dataset(data_folder): return dsets[random.randint(0, len(dsets) - 1)] -def main(args): +def main(args: argparse.Namespace) -> None: + """Trains a Region2Vec model using the arguments in args. + + Called internally as a subprocess by region2vec in main.py. + + Args: + args (argparse.Namespace): See the definition of ArgumentParser. + """ save_dir = args.save_dir data_folder = os.path.join( save_dir, "shuffled_datasets" @@ -101,7 +120,7 @@ def main(args): sentences = LineSentence(dset) # create sentence iterator model.build_vocab(sentences, update=vocab_update) # prepare the model vocabulary cur_time = datetime.datetime.now().strftime("%x-%X") - utils.log(f"[{cur_time}]\033[93m Vocabulary size is {len(model.wv.index_to_key)}\033[00m") + utils.log(f"[{cur_time}] Vocabulary size is {len(model.wv.index_to_key)}") build_vocab_time = run_timer.t() min_loss = 1.0e100 @@ -156,14 +175,23 @@ def main(args): if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Gene Embedding") - parser.add_argument("--num-shuffle", type=int, help="number of shuffled datasets") - parser.add_argument("--embed-dim", type=int, help="embedding dimension") - parser.add_argument("--context-len", type=int, help="window size") - parser.add_argument("--nworkers", type=int, help="number of workers") - parser.add_argument("--save-freq", type=int, default=0, help="save frequency") - parser.add_argument("--save-dir", help="path to the folder that saves the training result") - parser.add_argument("--resume", help="path to a saved model") + parser = argparse.ArgumentParser(description="Region2Vec training") + parser.add_argument( + "--num-shuffle", type=int, help="number of shuffled datasets used for training" + ) + parser.add_argument("--embed-dim", type=int, help="dimension of embedding vectors") + parser.add_argument("--context-len", type=int, help="length of the moving context window") + parser.add_argument( + "--nworkers", type=int, help="number of parallel processes used in training" + ) + parser.add_argument( + "--save-freq", + type=int, + default=0, + help="save the Region2Vec model after consuming save_freq datasets; use a nonpositive number to disable", + ) + parser.add_argument("--save-dir", help="the folder that saves the Region2Vec model") + parser.add_argument("--resume", help="path to a previously trained model") parser.add_argument("--train-alg", help="training algorithm, select from [cbow, skip-gram]") parser.add_argument( "--min-count", diff --git a/gitk/region2vec/region_shuffling.py b/gitk/region2vec/region_shuffling.py index 453fb2e1..3c9183c1 100644 --- a/gitk/region2vec/region_shuffling.py +++ b/gitk/region2vec/region_shuffling.py @@ -12,22 +12,44 @@ class BEDDataset: - def __init__(self, args, file_list): - self.links = [] - self.args = args - self.meta_data = dict() - self.file2idx = dict() + """Wraps a set of BED files in a BEDDataset object. + + Stores the information of a set of BED files. + Generates a new dataset with regions shuffled in BED files. + + Attributes: + filename_list: A list of BED file names. + nfiles: The number of BED files in the BEDdataset object. + """ + + def __init__(self, file_list: str) -> None: + """Initializes a BEDDataset object. + + Args: + file_list (str): A file storing a list of BED file names that + should be included in the dataset. + """ + self.filename_list = [] with open(file_list, "r") as f: for idx, line in enumerate(f): filename = line.strip() - self.links.append(filename) - self.file2idx[filename] = idx + self.filename_list.append(filename) - self.nfiles = len(self.links) + self.nfiles = len(self.filename_list) - def regions2sentences_sampling(self, src_path, dst_path): + def regions2sentences_sampling(self, src_path: str, dst_path: str) -> None: + """Constructs a sentence by sampling regions from a BED file. + + Each region in a BED file has a probability. Constructs a sentence by + sampling regions in a BED file based on their probabilities. + + Args: + src_path (str): The folder where BED files reside. + dst_path (str): The destination file that stores all the generated + BED files; each line has regions sampled from a BED file. + """ with open(dst_fname, "w") as fout: - for fname in self.links: + for fname in self.filename_list: src_fname = os.path.join(src_path, fname) sentence = [] probs = [] @@ -49,9 +71,18 @@ def regions2sentences_sampling(self, src_path, dst_path): fout.write(str_sent) fout.write("\n") - def regions2sentences(self, src_path, dst_path): + def regions2sentences(self, src_path: str, dst_path: str) -> None: + """Concatenates all regions in a BED file randomly into a sentence. + + This functions is called in the hard tokenization mode. + + Args: + src_path (str): The folder where BED files reside. + dst_path (str): The destination file that stores all the generated + BED files; each line has all the regions from a BED file. + """ with open(dst_path, "w") as f_out: - for fname in self.links: + for fname in self.filename_list: src_fname = os.path.join(src_path, fname) sentence = [] with open(src_fname, "r") as f: @@ -69,14 +100,36 @@ def regions2sentences(self, src_path, dst_path): class MatrixDataset: - def __init__(self, matrix): + """Wraps the binary representation of BED files into a MatrixDataset. + + Stores the information of a set of BED files. + Generates a new dataset with regions shuffled in BED files. + """ + + def __init__(self, matrix: list[list[int]]): + """Initializes a MatrixDataset object with matrix. + + Args: + matrix (list[list[int]]): The binary representation of BED files. + Each row represents a BED file. Each column denotes a region. + Each element denotes the presence (1) or absence (0) of a + region in a BED file. + """ self.mat = [[] for i in range(len(matrix))] for i in range(len(matrix)): for j in range(len(matrix[i])): if matrix[i][j] != 0: self.mat[i].append(j) - def regions2sentences(self, dst_path): + def regions2sentences(self, dst_path: str) -> None: + """Concatenates all regions in a BED file randomly into a sentence. + + This functions is called in the hard tokenization mode. + + Args: + dst_path (str): The destination file that stores all the generated + BED files; each line has all the regions from a BED file. + """ with open(dst_path, "w") as f_out: for i in range(len(self.mat)): sentence = [] @@ -89,7 +142,14 @@ def regions2sentences(self, dst_path): f_out.write("\n") -def main(args): +def main(args: argparse.Namespace) -> None: + """Generates shuffled datasets. + + Called internally as a subprocess by region2vec in main.py. + + Args: + args (argparse.Namespace): See the definition of ArgumentParser. + """ DATA_FOLDER = os.path.join(args.save_dir, "shuffled_datasets") os.makedirs(DATA_FOLDER, exist_ok=True) src_path = args.tokenization_folder @@ -97,7 +157,7 @@ def main(args): random.seed(worker_id) np.random.seed(worker_id) if args.data_type == "files": - dataset = BEDDataset(args, args.file_list) + dataset = BEDDataset(args.file_list) else: with open(args.mat_path, "rb") as f: matrix = pickle.load(f) diff --git a/gitk/region2vec/utils.py b/gitk/region2vec/utils.py index 9643a0ba..6fcb40cb 100644 --- a/gitk/region2vec/utils.py +++ b/gitk/region2vec/utils.py @@ -3,39 +3,79 @@ import shutil import sys import time - +from typing import Union import numpy as np -def prRed(skk): +def prRed(skk: str) -> None: + """Prints the input string skk in the red font. + + Args: + skk (str): The string to print. + """ print(f"\033[91m{skk}\033[00m") -def prGreen(skk): +def prGreen(skk: str) -> None: + """Prints the input string skk in the green font. + + Args: + skk (str): The string to print. + """ print(f"\033[92m{skk}\033[00m") -def prYellow(skk): +def prYellow(skk: str) -> None: + """Prints the input string skk in the yellow font. + + Args: + skk (str): The string to print. + """ print(f"\033[93m{skk}\033[00m") -def prLightPurple(skk): +def prLightPurple(skk: str) -> None: + """Prints the input string skk in the light purple font. + + Args: + skk (str): The string to print. + """ print(f"\033[94m{skk}\033[00m") -def prPurple(skk): +def prPurple(skk: str) -> None: + """Prints the input string skk in the purple font. + + Args: + skk (str): The string to print. + """ print(f"\033[95m{skk}\033[00m") -def prCyan(skk): +def prCyan(skk: str) -> None: + """Prints the input string skk in the cyan font. + + Args: + skk (str): The string to print. + """ print(f"\033[96m{skk}\033[00m") -def prLightGray(skk): +def prLightGray(skk: str) -> None: + """Prints the input string skk in the gray font. + + Args: + skk (str): The string to print. + """ print(f"\033[97m{skk}\033[00m") -def prBlack(skk): +def prBlack(skk: str) -> None: + """Prints the input string skk in the black font. + + Args: + skk (str): The string to print. + """ print(f"\033[98m{skk}\033[00m") @@ -48,17 +88,41 @@ def set_log_path(path): class Timer: + """Records the running time. + + Uses Timer.s() or Timer() to record the start time. Then, calls Timer.t() to get the + elapsed time in seconds. + """ + def __init__(self): + """Initializes a Timer object and starts the timer.""" self.v = time.time() def s(self): + """Restarts the timer.""" self.v = time.time() def t(self): + """Gives the elapsed time. + + Returns: + float: The elapsed time in seconds. + """ return time.time() - self.v -def time_str(t): +def time_str(t: float) -> str: + """Converts time in float to a readable format. + + Converts time in float to hours, minutes, or seconds based on the value of + t. + + Args: + t (float): Time in seconds. + + Returns: + str: Time in readable time. + """ if t >= 3600: return f"{t / 3600:.2f}h" if t >= 60: @@ -66,7 +130,17 @@ def time_str(t): return f"{t:.2f}s" -def timed_response(prompt, wait_time, default): +def timed_response(prompt: str, wait_time: int, default: str): + """Prints prompt and waits for response. + + Args: + prompt (str): The question asks for a response. + wait_time (int): The number of seconds for waiting. + default (str): If no response received, uses default as the response. + + Returns: + str: a response given by the user or the default one. + """ print(prompt, end="", flush=True) i, o, e = select.select([sys.stdin], [], [], wait_time) if i: @@ -81,7 +155,15 @@ def timed_response(prompt, wait_time, default): return default -def log(obj, filename="log.txt"): +def log(obj: str, filename: str = "log.txt") -> None: + """Adds information in obj to a file specified by filename. + + Adds information in obj to a file (default: log.txt) and prints obj. + + Args: + obj (str): A string. + filename (str, optional): The log file name. Defaults to "log.txt". + """ print(obj) if _log_path is not None: with open(os.path.join(_log_path, filename), "a") as f: @@ -90,7 +172,34 @@ def log(obj, filename="log.txt"): class lr_scheduler: - def __init__(self, init_lr, end_lr, epochs, lr_info=None, mode="linear"): + """Changes the learning rate. + + Changes the learning rate using the mode of linear or milestones. + If mode = "linear", then the learning rate linearly decreases after certain + epochs. + If mode = "milestones", then the learning rate decreases at specified + epochs. + """ + + def __init__( + self, + init_lr: float, + end_lr: float, + epochs: int, + lr_info: dict[str, Union[int, float, list]], + mode: str = "linear", + ): + """Initializes the learning rate scheduler. + + Args: + init_lr (float): The initial learning rate. + end_lr (float): The last learning rate. + epochs (int): The number of training epochs. + lr_info (dict[str,Union[int,list]]): Dictionary storing information + for learning rate scheduling. + mode (str, optional): The mode of learning rate scheduling. + Defaults to "linear". + """ self.lr = init_lr self.end_lr = end_lr self.init_lr = init_lr @@ -102,6 +211,11 @@ def __init__(self, init_lr, end_lr, epochs, lr_info=None, mode="linear"): self.freq = lr_info["freq"] def step(self): + """Updates the learning rate. + + Returns: + float: Current learning rate. + """ self.count += 1 if self.mode == "linear": if self.count % self.freq == 0: @@ -115,15 +229,26 @@ def step(self): return self.lr -def ensure_dir(path, default="y"): - if os.path.exists(path): +def ensure_dir(folder: str, default: str = "y") -> None: + """Makes sure the folder exists. + + Makes sure the folder exists. If the folder exists, then asks the user to + keep [n] or delete [y] it. If no response received after 5 secs, then + deletes the folder and create a new one. + + Args: + folder (str): The folder to be created. + default (str, optional): Choose whether to delete [y] or keep [n] the + folder. Defaults to y. + """ + if os.path.exists(folder): if default == "y": - prompt = f"\033[91m{path} exists,remove?([y]/n):\033[00m " + prompt = f"\033[91m{folder} exists,remove?([y]/n):\033[00m " else: - prompt = f"\033[91m{path} exists,remove?(y/[n]):\033[00m " + prompt = f"\033[91m{folder} exists,remove?(y/[n]):\033[00m " ans = timed_response(prompt, 5, default) if ans != "n": - shutil.rmtree(path) + shutil.rmtree(folder) else: return - os.makedirs(path, exist_ok=True) + os.makedirs(folder, exist_ok=True) diff --git a/gitk/tokenization/README.md b/gitk/tokenization/README.md deleted file mode 100644 index 9cbc5cb7..00000000 --- a/gitk/tokenization/README.md +++ /dev/null @@ -1,29 +0,0 @@ -# Tokenization -## Introduction -In training word embeddings, we need to first tokenize each word such that words in different forms are represented by one word. For example, "orange", "oranges" and "Orange" are all mapped to "orange" since they essentially convey the same meaning. This can reduce the vocabulary size and also improve the quality of learned embeddings.
-Similary, before running region2vec, we need to run tokenization on regions. First, we need to provide a universe, the "vocabulary" in the setting of genomic regions. The universe is a BED file, containing representative regions. With the given universe, we represent (tokenize) raw regions with the regions in the universe. - -For hard tokenization, if the overlap between a raw region in a bed file and a region in the universe exceeds a certain amount, then we use the region in the universe to represent this raw region; otherwise, we ignore this raw region. This is a "zeor or one" process. After hard tokenization, each bed file will contain regions all from the universe, and the number of regions will be smaller or equal to the original number. - - -## Usage -For hard tokenization, run -``` -from gitk.tokenization import hard_tokenization - -src_folder = '/path/to/raw/bed/files' -dst_folder = '/path/to/tokenized_files' -universe_file = '/path/to/universe_file' -hard_tokenization(src_folder, dst_folder, universe_file, 1e-9) - -``` -Note that we use the `intersect` function of `bedtools` to do tokenization. If you want to switch to different tools, you can override the `bedtool_tokenization` function in `hard_tokenization_batch.py` and provide the path to your tool by specifying the input argument `bedtools_path`. The `fraction` argument specifies the minimum overlap required as a fraction of some region in the universe (default: 1E-9,i.e. 1bp; maximum 1.0). A raw region will be mapped into a universe region when an overlap is above the threshold. - -The bedtools (version 2.30.0) will be automatically downloaded from https://github.com/arq5x/bedtools2/releases to the `bedtools` folder. - -Command line usage -```bash -gitk tokenize --data-folder /folder/with/raw/BED/files --token-folder ./tokens --universe /universe/file -``` - -For more details, type `gitk tokenize --help`. diff --git a/gitk/tokenization/cli.py b/gitk/tokenization/cli.py index 8079ae76..aa729587 100644 --- a/gitk/tokenization/cli.py +++ b/gitk/tokenization/cli.py @@ -1,9 +1,5 @@ def build_subparser(parser): - """ - Builds argument parser. - - :return argparse.ArgumentParser: Argument parser - """ + """Builds an argument parser to support the tokenize command line interface.""" parser.add_argument( "--data-folder", type=str, diff --git a/gitk/tokenization/hard_tokenization_batch.py b/gitk/tokenization/hard_tokenization_batch.py index e2acc9ef..ff28c853 100644 --- a/gitk/tokenization/hard_tokenization_batch.py +++ b/gitk/tokenization/hard_tokenization_batch.py @@ -3,10 +3,27 @@ import shlex import subprocess -from gitk.tokenization import utils +from gitk.region2vec import utils -def bedtool_tokenization(f, bedtool_path, data_folder, target_folder, universe, fraction): +def bedtools_tokenization( + f: str, + bedtools_path: str, + data_folder: str, + target_folder: str, + universe: str, + fraction: float, +) -> None: + """Uses bedtools to tokenize a raw BED file. + + Args: + f (str): File name. + bedtools_path (str): Path to a bedtools binary. + data_folder (str): The folder where raw BED files reside. + target_folder (str): The folder that stores tokenized BED files. + universe (str): Path to a universe file. + fraction (float): A parameter for bedtools.intersect. + """ fname = os.path.join(data_folder, f) temp = os.path.join(target_folder, f + "_sorted") target = os.path.join(target_folder, f) @@ -14,15 +31,34 @@ def bedtool_tokenization(f, bedtool_path, data_folder, target_folder, universe, subprocess.run(shlex.split(f"sort -k1,1V -k2,2n {fname}"), stdout=f_temp) with open(target, "w") as f_target: subprocess.run( - shlex.split(f"{bedtool_path} intersect -a {universe} -b {temp} -u -f {fraction} "), + shlex.split(f"{bedtools_path} intersect -a {universe} -b {temp} -u -f {fraction} "), stdout=f_target, ) os.remove(temp) -def generate_tokens(raw_data_folder, token_folder, universe, file_list, bedtool, fraction): - """ - Perform hard tokenization on the bed files +def generate_tokens( + raw_data_folder: str, + token_folder: str, + universe: str, + file_list: str, + bedtools: str, + fraction: float, +) -> None: + """Tokenizes raw BED files specified by file_list. + + Tokenizes raw BED files specified by file_list. First, checks existing files + in token_folder. If token_folder has all the tokenized BED files, then does + nothing. Otherwise, tokenizes raw BED files that are in file_list but not + in token_folder. + + Args: + raw_data_folder (str): The foder where raw BED files reside. + token_folder (str): The folder to store tokenized BED files. + universe (str): The path to a universe file. + file_list (str): The path to a file which contains selected BED files per row. + bedtools (str): The path to a bedtools binary. + fraction (float): A parameter for bedtools.intersect. """ usize = 0 with open(universe, "r") as f: @@ -44,7 +80,7 @@ def generate_tokens(raw_data_folder, token_folder, universe, file_list, bedtool, number = len(not_covered) if number == 0: print(f"Use the existing folder {token_folder}", flush=True) - return 0 + return else: print( f"Folder {token_folder} exists with {number} files not processed. Continue...", @@ -54,14 +90,21 @@ def generate_tokens(raw_data_folder, token_folder, universe, file_list, bedtool, os.makedirs(token_folder) not_covered = all_set for f in not_covered: - bedtool_tokenization(f, bedtool, raw_data_folder, token_folder, universe, fraction) - return 0 + bedtools_tokenization(f, bedtools, raw_data_folder, token_folder, universe, fraction) + +def main(args: argparse.Namespace): + """Generates tokenized BED files. -def main(args): + Calls generate_tokens using the arguments in args. Prints status + information. + + Args: + args (argparse.Namespace): See the definition of the ArgumentParser. + """ local_timer = utils.Timer() print(f"Entering hard tokenization. Results stored in {args.token_folder}") - status = generate_tokens( + generate_tokens( args.data_folder, args.token_folder, args.universe, @@ -69,8 +112,6 @@ def main(args): args.bedtools_path, args.fraction, ) - if status < 0: - return tokenization_time = local_timer.t() print(f"Hard tokenization takes {utils.time_str(tokenization_time)}") diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 1ab274e5..094b851c 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -26,14 +26,41 @@ def __init__(self, **kwargs): def hard_tokenization_main( - src_folder, - dst_folder, - universe_file, - fraction=1e-9, - file_list=None, - num_workers=10, - bedtools_path="bedtools", -): + src_folder: str, + dst_folder: str, + universe_file: str, + fraction: float = 1e-9, + file_list: list[str] = None, + num_workers: int = 10, + bedtools_path: str = "bedtools", +) -> int: + """Tokenizes raw BED files in parallel. + + This is the main function for hard tokenization. It uses multiple processes to + speed up the tokenization process. + + Args: + src_folder (str): The folder where raw BED files reside. + dst_folder (str): The foder to store tokenized BED files. + universe_file (str): The path to a universe file. + fraction (float, optional): A parameter for bedtools.intersect. + Defaults to 1e-9. + file_list (list[str], optional): A list of files (just names not full + paths) that need to be tokenized. Defaults to None and uses all BED + files in src_folder. + num_workers (int, optional): Number of processes used. Defaults to 10. + bedtools_path (str, optional): The path to a bedtools binary. Defaults + to "bedtools". + + Raises: + Exception: No bedtools executable found + + Returns: + int: 0 when the dst_folder folder has incomplete list of tokenized BED + files which should be removed first; 1 when the dst_folder folder + has the complete tokenized BED files or the tokenization process + succeeds. + """ timer = utils.Timer() start_time = timer.t() @@ -78,9 +105,9 @@ def hard_tokenization_main( try: rval = subprocess.call([bedtools_path, "--version"]) except: - raise Exception("No bedtools executable") + raise Exception("No bedtools executable found") if rval != 0: - raise Exception("No bedtools executable") + raise Exception("No bedtools executable found") print(f"Tokenizing {len(file_list)} bed files ...") diff --git a/gitk/tokenization/split_file.py b/gitk/tokenization/split_file.py index ab8b09e3..0f208b22 100644 --- a/gitk/tokenization/split_file.py +++ b/gitk/tokenization/split_file.py @@ -2,9 +2,14 @@ import os -def get_file_rows(file_path): - """ - Count how many files are included +def get_file_rows(file_path: str) -> int: + """Counts how many files are included. + + Args: + file_path (str): The path to a file. + + Returns: + int: The number of rows in the file. """ count = 0 with open(file_path, "r") as f: @@ -13,13 +18,16 @@ def get_file_rows(file_path): return count -def split_file(file_path, dest_folder, num_parts): - """ - Split a list of files into a specified non-overlapping batches +def split_file(file_path: str, dest_folder: str, num_parts: int) -> None: + """Splits a list of files into num_parts non-overlapping batches. + + This is a helper function for tokenization in parallel. The dest_folder + must be empty. - file_path: path to a list of files - dest_folder: folder to store file splits - num_parts: number of parts needed + Args: + file_path (str): The path to a file with BED file names per row. + dest_folder (str): The folder to store splitted file lists. + num_parts (int): Number of batches to split. """ if os.path.exists(dest_folder): print("Folder exists") diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py deleted file mode 100644 index 1e3253f8..00000000 --- a/gitk/tokenization/utils.py +++ /dev/null @@ -1,128 +0,0 @@ -import os -import select -import shutil -import sys -import time - -import numpy as np - - -def prRed(skk): - print(f"\033[91m{skk}\033[00m") - - -def prGreen(skk): - print(f"\033[92m{skk}\033[00m") - - -def prYellow(skk): - print(f"\033[93m{skk}\033[00m") - - -def prLightPurple(skk): - print(f"\033[94m{skk}\033[00m") - - -def prPurple(skk): - print(f"\033[95m{skk}\033[00m") - - -def prCyan(skk): - print(f"\033[96m{skk}\033[00m") - - -def prLightGray(skk): - print(f"\033[97m{skk}\033[00m") - - -def prBlack(skk): - print(f"\033[98m{skk}\033[00m") - - -_log_path = None - - -def set_log_path(path): - global _log_path - _log_path = path - - -class Timer: - def __init__(self): - self.v = time.time() - - def s(self): - self.v = time.time() - - def t(self): - return time.time() - self.v - - -def time_str(t): - if t >= 3600: - return f"{t / 3600:.2f}h" - if t >= 60: - return f"{t / 60:.2f}m" - return f"{t:.2f}s" - - -def timed_response(prompt, wait_time, default): - print(prompt, end="", flush=True) - i, o, e = select.select([sys.stdin], [], [], wait_time) - if i: - ans = sys.stdin.readline().strip() - if ans not in ["y", "n"]: - print(f"\033[91m{default}\033[00m") - return default - else: - return ans - else: - print(f"\033[91m{default}\033[00m") - return default - - -def log(obj, filename="log.txt"): - print(obj, flush=True) - if _log_path is not None: - with open(os.path.join(_log_path, filename), "a") as f: - print(obj, file=f) - - -class lr_scheduler: - def __init__(self, init_lr, end_lr, epochs, lr_info=None, mode="linear"): - self.lr = init_lr - self.end_lr = end_lr - self.init_lr = init_lr - self.mode = mode - self.epochs = epochs - self.lr_info = lr_info - self.count = 0 - if mode == "linear": - self.freq = lr_info["freq"] - - def step(self): - self.count += 1 - if self.mode == "linear": - if self.count % self.freq == 0: - self.lr = self.init_lr - (self.init_lr - self.end_lr) / self.epochs * self.count - elif self.mode == "milestone": - milestones = np.array(self.lr_info["milestones"]) - power = (milestones <= self.count).sum() - self.lr = self.init_lr * np.power(self.lr_info["ratio"], float(power)) - if self.lr < self.end_lr: - self.lr = self.end_lr - return self.lr - - -def ensure_dir(path, default="y"): - if os.path.exists(path): - if default == "y": - prompt = f"\033[91m{path} exists,remove?([y]/n):\033[00m " - else: - prompt = f"\033[91m{path} exists,remove?(y/[n]):\033[00m " - ans = timed_response(prompt, 5, default) - if ans != "n": - shutil.rmtree(path) - else: - return - os.makedirs(path, exist_ok=True) From bc5846a27874bfbcca27ef851550b09ab64ab433 Mon Sep 17 00:00:00 2001 From: Guangtao Zheng Date: Thu, 20 Jul 2023 15:26:52 -0400 Subject: [PATCH 0265/1072] update doc --- docs/modules.md | 2 +- docs/tutorials/evaluation.md | 2 +- docs/tutorials/region2vec.md | 2 +- docs/tutorials/tokenization.md | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/modules.md b/docs/modules.md index 01c25a2f..c60f6339 100644 --- a/docs/modules.md +++ b/docs/modules.md @@ -16,7 +16,7 @@ This module provides multiple ways to build a genomic region universe. These inc ## Module `evaluation` -Once a `gitk` region embedding model is trained, we may want to evaluate the embeddings. The `evaluation` module provides several functions for that. These include statistical tests, like the Cluster Tendency Test (CTT) and the Reconstruction Test (RCT), and biological tests, the GEnome Distance Scaling Test (GDST) and the Neighborhood Preserving Test (NPT). These evaluation metrics can be helpful to determine if your models are working well, optimize training parameters, etc. +Once a `gitk` region embedding model is trained, we may want to evaluate the embeddings. The `evaluation` module provides several functions for that. These include statistical tests, like the Cluster Tendency Test (CTT) and the Reconstruction Test (RCT), and biological tests, the Genome Distance Scaling Test (GDST) and the Neighborhood Preserving Test (NPT). These evaluation metrics can be helpful to determine if your models are working well, optimize training parameters, etc. ## Module `region2vec` diff --git a/docs/tutorials/evaluation.md b/docs/tutorials/evaluation.md index 1cc8fe1e..37ace356 100644 --- a/docs/tutorials/evaluation.md +++ b/docs/tutorials/evaluation.md @@ -5,7 +5,7 @@ ### Create a Base Embedding Object Given a set of genomic region embeddings `embeddings` and the corresponding regions `vocab`, use `BaseEmbeddings` to create an `base` embedding object. -``` +```python from gitk.eval.utils import BaseEmbeddings import pickle base_obj = BaseEmbeddings(embeddings, vocab) diff --git a/docs/tutorials/region2vec.md b/docs/tutorials/region2vec.md index 8f634cbe..089603e8 100644 --- a/docs/tutorials/region2vec.md +++ b/docs/tutorials/region2vec.md @@ -8,7 +8,7 @@ 2. Prepare a universe file `universe_file`. 3. Create a token folder which will be used to store tokenized files `dst_folder`. 5. Run the following command -``` +``` python from gitk.tokenization import hard_tokenization from gitk.region2vec import region2vec diff --git a/docs/tutorials/tokenization.md b/docs/tutorials/tokenization.md index d6218f82..ce4adf62 100644 --- a/docs/tutorials/tokenization.md +++ b/docs/tutorials/tokenization.md @@ -3,7 +3,7 @@ For hard tokenization, run -``` +```python from gitk.tokenization import hard_tokenization src_folder = '/path/to/raw/bed/files/' From 927085c569e386d06d2d2d479199a78035446d17 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 20 Jul 2023 15:54:44 -0400 Subject: [PATCH 0266/1072] finish model training docs --- docs/tutorials/train-scembed-model.md | 31 +++++++++++++++++++++++++-- 1 file changed, 29 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/train-scembed-model.md b/docs/tutorials/train-scembed-model.md index 1ee605e5..7df22a4f 100644 --- a/docs/tutorials/train-scembed-model.md +++ b/docs/tutorials/train-scembed-model.md @@ -86,12 +86,39 @@ model.train(adata, epochs=3) # we recomend increasing this to 100 Thats it! ## Clustering the cells -Now that we have a trained model, we can use it to cluster the cells. To do this, we will use the `predict` method. This method will return a `pandas.DataFrame` with the cell barcodes and their corresponding cluster assignments. We will then add this to the `AnnData` object as a new column in the `.obs` attribute: +With the model now trained, we can get embeddings of our cells. This occurs in two steps: 1) tokenize the data and 2) encode the cells. +**Tokenize:** ```python from gitk.models.tokenizers import HardTokenizer -tokenizer = HardTokenizer("peaks.bed") +tokenizer = HardTokenizer("peaks.bed") # consensus peak set region_sets = tokenizer(adata) +``` + +**Encode:** +```python +cell_embeddings = [] +for region_set in region_sets: + embedding = np.mean([model(r) for r in region_set if r in model.region2vec, axis=0]) + cell_embeddings.append(embedding) + +cell_embeddings = np.array(cell_embeddings) + +adata.obsm['scembed_X'] = cell_embeddings +``` + +Now you can use `scanpy` utilities to cluster the cells: + +```python +sc.pp.neighbors(adata, use_rep="scembed_X") +sc.tl.leiden(adata) +``` + +And visualize with UMAP + +```python +sc.tl.umap(adata) +sc.pl.umap(adata, color="leiden") ``` \ No newline at end of file From 633b8c7e3fd028d8f8c843d8ded14696e6c6fcaf Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 20 Jul 2023 16:07:08 -0400 Subject: [PATCH 0267/1072] pretrinaed model tutorial --- .../tutorials/use-pretrained-scembed-model.md | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 docs/tutorials/use-pretrained-scembed-model.md diff --git a/docs/tutorials/use-pretrained-scembed-model.md b/docs/tutorials/use-pretrained-scembed-model.md new file mode 100644 index 00000000..e75cc66e --- /dev/null +++ b/docs/tutorials/use-pretrained-scembed-model.md @@ -0,0 +1,31 @@ +# How to use a pre-trained scEmbed model +One advantage of scEmbed is the ability to use pre-trained models. This is useful for quickly getting embeddings of new data without having to train a new model. In this tutorial, we will show how to use a pre-trained model to get embeddings of new data. + +I will be using the `databio/luecken2021` model. It was trained on the [Luecken2021](https://openreview.net/forum?id=gN35BGa1Rt) dataset, a first-of-its-kind multimodal benchmark dataset of 120,000 single cells from the human bone marrow of 10 diverse donors measured with two commercially-available multi-modal technologies: nuclear GEX with joint ATAC, and cellular GEX with joint ADT profiles. + +This model will work best on PBMC-like data. It also requires your fragments be aligned to the GRCh38 genome. + +## Example data preparation +Grab a fresh set of PBMC data from 10X genomics: https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-atac-v2-chromium-controller-2-standard + +You need the [Peak by cell matrix (filtered)](https://cf.10xgenomics.com/samples/cell-atac/2.1.0/10k_pbmc_ATACv2_nextgem_Chromium_Controller/10k_pbmc_ATACv2_nextgem_Chromium_Controller_filtered_peak_bc_matrix.tar.gz). This contains the binary accessibility matrix, the peaks, and the barcodes. Pre-trained models also requires that the data be in a `scanpy.AnnData` format and the `.var` attribute contain `chr`, `start`, and `end` values. For details on how to make this, see [data preparation](./train-scembed-model.md#data-preparation). + +Once your data is ready, you can load it into python and get embeddings. + +## Encoding cells + +Encoding cells is as easy as: + +```python +import scanpy as sc + +from gitk.models import PretrainedScembedModel + +adata = sc.read_h5ad("path/to/adata.h5ad") +model = PretrainedScembedModel("databio/luecken2021") + +embeddings = model.encode(adata) +adata.obsm['scembed_X'] = embeddings +``` + +And, thats it! You can now cluster your cells using the `scembed_X` embeddings. \ No newline at end of file From 5390f88e1fe35ec33e9cc531d1c083e96b24f4e5 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 20 Jul 2023 20:07:14 -0400 Subject: [PATCH 0268/1072] lowercase file names --- gitk/bedspace/pipeline/{Visualization.py => visualization.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename gitk/bedspace/pipeline/{Visualization.py => visualization.py} (100%) diff --git a/gitk/bedspace/pipeline/Visualization.py b/gitk/bedspace/pipeline/visualization.py similarity index 100% rename from gitk/bedspace/pipeline/Visualization.py rename to gitk/bedspace/pipeline/visualization.py From 71b3083e86c148f8c7ff9c7972dbdab502f5d8c6 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 20 Jul 2023 21:33:58 -0400 Subject: [PATCH 0269/1072] merge --- docs/autodoc_build/gitk.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/autodoc_build/gitk.md b/docs/autodoc_build/gitk.md index 6cee8677..48b5ac8c 100644 --- a/docs/autodoc_build/gitk.md +++ b/docs/autodoc_build/gitk.md @@ -32,4 +32,4 @@ h4 .content { -*Version Information: `gitk` v0.0.1-dev, generated by `lucidoc` v0.4.3* \ No newline at end of file +*Version Information: `gitk` v0.0.1-dev, generated by `lucidoc` v0.4.3* From dfed78cd44dda9a57278e06141dacbd8115e61c6 Mon Sep 17 00:00:00 2001 From: nsheff Date: Thu, 20 Jul 2023 21:34:17 -0400 Subject: [PATCH 0270/1072] init search module --- gitk/search/__init__.py | 0 gitk/search/search.py | 157 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 157 insertions(+) create mode 100644 gitk/search/__init__.py create mode 100644 gitk/search/search.py diff --git a/gitk/search/__init__.py b/gitk/search/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/gitk/search/search.py b/gitk/search/search.py new file mode 100644 index 00000000..343b6e7b --- /dev/null +++ b/gitk/search/search.py @@ -0,0 +1,157 @@ +from abc import ABC, abstractmethod + +# A class representing an ExtendedModel, which is a 3-part object consisting of +# a model, a universe/vocabulary, and a tokenizer +class TUM(object): + def __init__(self, model, universe, tokenizer): + self.model = model + self.universe = universe + self.tokenizer = tokenizer + +# allow backends to be either a qdrant server or a local in-memory NMS index +# this allows us to use the same interface for both +class EmSearchBackend(ABC) : + """ An abstract class representing back-ends for Embedding Search """ + def __init__(self, embeddings: np.ndarray, labels: list) -> None: + pass + + @abstractmethod + def search(self, query: np.ndarray, k: int) -> List[Tuple[int, float]]: + """ + Search for the nearest neighbors of the given embedding + + :param query: the embedding to search for + :param k: the number of results to return + :return: a list of (id, score) pairs + """ + raise NotImplementedError() + + @abstractmethod + def __len__(self) -> int: + """ + Return the number of embeddings in the backend + """ + raise NotImplementedError() + + + +class QdrantBackend(EmSearchBackend): + """ A search backend that uses a qdrant server to store and search embeddings """ + def __init__(self, config): + self.config = config + # TODO: initialize connection to qdrant server + + def search(self, query): + raise NotImplementedError + + + +class HNSWBackend(EmSearchBackend): + """ A search backend that uses a local HNSW index to store and search embeddings """ + def __init__(self, embeddings: np.ndarray, labels: list, metadata: dict = None) -> None: + self.labels = labels + self.metadata = metadata + + # create an HNSW index for the embeddings + dim = embeddings.shape[1] + self.p = hnswlib.Index(space = 'l2', dim = dim) # possible options are l2, cosine or ip + self.p.init_index(max_elements = len(embeddings), ef_construction = 200, M = 16) + self.p.add_items(embeddings, labels) + + def search(self, query: np.ndarray, k: int) -> List[Tuple[int, float]]: + return self.p.knn_query(query, k) + + def __len__(self) -> int: + return p.element_count() + + def __getitem__(self, key) -> np.ndarray: + if metadata: + ret = metadata[key] + else: + ret = {} + ret['embedding'] = p.get_items([key])[0] + return ret + + def __str__(self): + return "HNSWBackend with {} items".format(len(self)) + + def save(self, path): + with open(path, 'wb') as f: + pickle.dump(self, f) + + def load(self, path): + self = pickle.load(path) + + +class BEDSpaceSearchInterface(object): + def __init__(self, region_embeddings, label_embeddings, universe, tokenizer): + self.universe = universe + self.tokenizer = tokenizer + # TODO: Allow for different backends. It would just require connecting to the backend here + self.label_backend = HNSWBackend(label_embeddings, labels, metadata) + self.region_set_backend = HNSWBackend(region_embeddings, labels, metadata) + + def search_labels_by_embedding(self, embedding, k: int = 10) -> embeddings: + """ Given an input region set embedding, suggest labels for that region set """ + return self.label_backend.search(region_set_embedding, k) + + def search_region_sets_by_embedding(self, embedding, k: int = 10) -> embeddings: + return self.region_set_backend.search(embedding, k) + + def search_region_sets(self, label, k: int = 10) -> embeddings: + """ Given an input label, suggest region sets for that label """ + + # get the embedding for the label + # TODO: handle case where label is not in the universe + label_embedding = self.label_backend[label] + return self.region_set_backend.search(label_embedding, k) + + def search_labels(self, region_set, k: int = 10) -> embeddings: + """ Given an input region set, suggest labels for that region set """ + + region_set_embedding = self.get_region_set_embedding(tokenized_region_set) + return self.label_backend.search(region_set_embedding, k) + + def search_region_sets_by_region_set(self, region_set, k: int = 10) -> embeddings: + """ Given an input region set, suggest region sets similar to that region set """ + + region_set_embedding = self.get_region_set_embedding(region_set) + return self.region_set_backend.search(region_set_embedding, k) + + def get_region_set_embedding(self, region_set): + """ Given a region set, return the embedding for that region set """ + + # first, tokenize the region set using the universe + tokenized_region_set = self.tokenizer.tokenize(region_set, self.universe) + # average the embeddings of each region in the region set + region_set_embedding = self.get_region_set_embedding(tokenized_region_set) + return region_set_embedding + +class RegionSet(object): + def __init__(self, path): + with open(path, 'r') as f: + self.regions = [Region(line) for line in f] + +class TokenizedRegionSet(object): + """ Represents a tokenized region set """ + def __init__(self, tokens:np.ndarray, universe: RegionSet): + self.tokens = tokens + self.universe = universe + + +# Demo of how to use the BEDspace search interface + +BBSI = BEDspaceSearchInterface(...) + +# Find region sets for a label (Scenario 1, l2r) +BBSI.search_region_sets_by_label("K562") # returns nearest embeddings + +# Annotate region sets with labels (Scenario 2, r2l) +path_to_bed_file = "path/to/bed/file.bed" +region_set = RegionSet(path_to_bed_file) +suggested_labels = BBSI.search_labels_by_region_set(region_set, k=15) + +# Find region sets similar to a query region set (Scenario 3, r2r) +path_to_bed_file = "path/to/bed/file.bed" +region_set = RegionSet(path_to_bed_file) +BBSI.search_region_sets_by_region_set(region_set, k=10) From 0f00f241245ec2b22ee6d6793a0fadd87cf8e8b5 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 21 Jul 2023 09:31:30 -0400 Subject: [PATCH 0271/1072] more ideas for search interfaces --- gitk/search/search.py | 128 +++++++++++++++++++++++++++++++++++------- 1 file changed, 107 insertions(+), 21 deletions(-) diff --git a/gitk/search/search.py b/gitk/search/search.py index 343b6e7b..da6d156a 100644 --- a/gitk/search/search.py +++ b/gitk/search/search.py @@ -1,19 +1,84 @@ from abc import ABC, abstractmethod + +# Should a tokenizer *hold* a universe, or take one as a parameter? Or both? +class Tokenizer(ABC): + """ Class representing a tokenizer function """ + @abstractmethod + def tokenize(self, region_set: RegionSet, universe:RegionSet=None) -> TokenizedRegionSet: + """ Tokenize a RegionSet """ + raise NotImplementedError + + def tokenize_rsc(self, rsc: RegionSetCollection) -> TokenizedRegionSetCollection: + """ Tokenize a RegionSetCollection """ + raise NotImplementedError + + # A class representing an ExtendedModel, which is a 3-part object consisting of # a model, a universe/vocabulary, and a tokenizer +# TODO: Move this outside of the search module +# from gitk.models? import TUM class TUM(object): + model: Model + universe: RegionSet + tokenizer: Tokenizer + def __init__(self, model, universe, tokenizer): - self.model = model + +# TODO: This belongs somewhere else +class RegionSet(object): + def __init__(self, path): + with open(path, 'r') as f: + self.regions = [Region(line) for line in f] + +# TODO: This belongs somewhere else; does it even make sense? +class TokenizedRegionSet(object): + """ Represents a tokenized region set """ + def __init__(self, tokens:np.ndarray, universe: RegionSet): + self.tokens = tokens self.universe = universe - self.tokenizer = tokenizer + + +# Write a class representing a collection of RegionSets +# TODO: This shouldn't read in the actual files, it should just represent the files and use lazy loading +class RegionSetCollection(object): + """ Represents a collection of RegionSets """ + def __init__(self, region_sets: List[RegionSet] =None, file_globs: List[str] = None): + if region_sets: + self.region_sets = region_sets + elif file_globs: + self.region_sets = [] + for glob in file_globs: + self.region_sets.extend([RegionSet(path) for path in glob.glob(glob)]) + + def __getitem__(self, key): + return self.region_sets[key] + + def __len__(self): + return len(self.region_sets) + + +class BEDEmbedTUM(TUM): + """ A TUM that uses BED files as input """ + def __init__(self, model, universe, tokenizer): + super().__init__(model, universe, tokenizer) + + def embed(self, region_set: RegionSet) -> np.ndarray: + """ Embed a region set using the model """ + # allow backends to be either a qdrant server or a local in-memory NMS index # this allows us to use the same interface for both class EmSearchBackend(ABC) : """ An abstract class representing back-ends for Embedding Search """ - def __init__(self, embeddings: np.ndarray, labels: list) -> None: - pass + def __init__(self, embeddings: np.ndarray = None, labels: list = None) -> None: + if embeddings: + self.load(embeddings, labels) + + + @abstractmethod + def load(self, embeddings: np.ndarray, labels: list) -> None: + raise NotImplementedError @abstractmethod def search(self, query: np.ndarray, k: int) -> List[Tuple[int, float]]: @@ -48,28 +113,29 @@ def search(self, query): class HNSWBackend(EmSearchBackend): """ A search backend that uses a local HNSW index to store and search embeddings """ + idx: hnswlib.Index def __init__(self, embeddings: np.ndarray, labels: list, metadata: dict = None) -> None: self.labels = labels self.metadata = metadata # create an HNSW index for the embeddings dim = embeddings.shape[1] - self.p = hnswlib.Index(space = 'l2', dim = dim) # possible options are l2, cosine or ip - self.p.init_index(max_elements = len(embeddings), ef_construction = 200, M = 16) - self.p.add_items(embeddings, labels) + self.idx = hnswlib.Index(space = 'l2', dim = dim) # possible options are l2, cosine or ip + self.idx.init_index(max_elements = len(embeddings), ef_construction = 200, M = 16) + self.idx.add_items(embeddings, labels) def search(self, query: np.ndarray, k: int) -> List[Tuple[int, float]]: - return self.p.knn_query(query, k) + return self.idx.knn_query(query, k) def __len__(self) -> int: - return p.element_count() + return idx.element_count() def __getitem__(self, key) -> np.ndarray: if metadata: ret = metadata[key] else: ret = {} - ret['embedding'] = p.get_items([key])[0] + ret['embedding'] = self.idx.get_items([key])[0] return ret def __str__(self): @@ -127,17 +193,6 @@ def get_region_set_embedding(self, region_set): region_set_embedding = self.get_region_set_embedding(tokenized_region_set) return region_set_embedding -class RegionSet(object): - def __init__(self, path): - with open(path, 'r') as f: - self.regions = [Region(line) for line in f] - -class TokenizedRegionSet(object): - """ Represents a tokenized region set """ - def __init__(self, tokens:np.ndarray, universe: RegionSet): - self.tokens = tokens - self.universe = universe - # Demo of how to use the BEDspace search interface @@ -155,3 +210,34 @@ def __init__(self, tokens:np.ndarray, universe: RegionSet): path_to_bed_file = "path/to/bed/file.bed" region_set = RegionSet(path_to_bed_file) BBSI.search_region_sets_by_region_set(region_set, k=10) + + + + +class BEDbaseSearchInterface(object): + def __init__(self, tum: BEDEmbedTUM, region_set_backend: EmSearchBackend): + self.tum = tum + self.region_set_backend = search_backend + + def nlsearch(self, query: str), k: int = 10) -> embeddings: + """ Given an input natural lange, suggest region sets """ + + # first, get the embedding of the query string + query_embedding = self.tum.embed(query) + # then, use the query embedding to predict the region set embedding + region_set_embedding = self.tum.str_to_region_set(query_embedding) + # finally, use the region set embedding to search for similar region sets + return self.region_set_backend.search(region_set_embedding, k) + + +# Example of how to use the BEDbase search interface + +betum = BEDEmbedTUM(RSC, universe, tokenizer) +embeddings = betum.compute_embeddings() +BBSI = BEDbaseSearchInterface(betum, embeddings) # ??? + +# Do we need an EmbeddingSet class? +class EmbeddingSet(object): + """ Represents embeddings and labels """ + embeddings: np.ndarray + labels: list \ No newline at end of file From c864ca6132b207f2035565090c8bf8ef71fa2f1b Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 21 Jul 2023 15:41:13 -0400 Subject: [PATCH 0272/1072] reorganize some abstract class code --- gitk/io/__init__.py | 1 + gitk/io/io.py | 44 ++++++++++ gitk/models/__init__.py | 1 + gitk/models/models.py | 26 ++++++ gitk/scembed/cli.py | 3 +- gitk/search/search.py | 151 +++++++--------------------------- gitk/text2bednn/__init__.py | 1 + gitk/text2bednn/text2bednn.py | 35 ++++++++ gitk/tokenization/__init__.py | 1 + gitk/tokenization/main.py | 24 ++++-- 10 files changed, 160 insertions(+), 127 deletions(-) create mode 100644 gitk/io/__init__.py create mode 100644 gitk/io/io.py create mode 100644 gitk/models/models.py create mode 100644 gitk/text2bednn/__init__.py create mode 100644 gitk/text2bednn/text2bednn.py diff --git a/gitk/io/__init__.py b/gitk/io/__init__.py new file mode 100644 index 00000000..caad9572 --- /dev/null +++ b/gitk/io/__init__.py @@ -0,0 +1 @@ +from .io import * diff --git a/gitk/io/io.py b/gitk/io/io.py new file mode 100644 index 00000000..0b8989c8 --- /dev/null +++ b/gitk/io/io.py @@ -0,0 +1,44 @@ +from ..utils import * + +# TODO: This belongs somewhere else +class RegionSet(object): + def __init__(self, path): + with open(path, "r") as f: + self.regions = [Region(line) for line in f] + + +# TODO: This belongs somewhere else; does it even make sense? +class TokenizedRegionSet(object): + """Represents a tokenized region set""" + + def __init__(self, tokens: np.ndarray, universe: RegionSet): + self.tokens = tokens + self.universe = universe + + +# Write a class representing a collection of RegionSets +# TODO: This shouldn't read in the actual files, it should just represent the files and use lazy loading +class RegionSetCollection(object): + """Represents a collection of RegionSets""" + + def __init__(self, region_sets: list[RegionSet] = None, file_globs: list[str] = None): + if region_sets: + self.region_sets = region_sets + elif file_globs: + self.region_sets = [] + for glob in file_globs: + self.region_sets.extend([RegionSet(path) for path in glob.glob(glob)]) + + def __getitem__(self, key): + return self.region_sets[key] + + def __len__(self): + return len(self.region_sets) + + +# Do we need an EmbeddingSet class? +class EmbeddingSet(object): + """Represents embeddings and labels""" + + embeddings: np.ndarray + labels: list diff --git a/gitk/models/__init__.py b/gitk/models/__init__.py index 27166c67..3fbbe8c9 100644 --- a/gitk/models/__init__.py +++ b/gitk/models/__init__.py @@ -3,3 +3,4 @@ from .atac.tokenization import * from .rna.tokenization import * from .utils import * +from .models import ExModel diff --git a/gitk/models/models.py b/gitk/models/models.py new file mode 100644 index 00000000..beced2b1 --- /dev/null +++ b/gitk/models/models.py @@ -0,0 +1,26 @@ +from abc import ABC, abstractmethod +from ..io import RegionSet +from ..tokenization import Tokenizer + +class Model(ABC): + """Class representing an *actual* model, that is, weights, etc""" + + pass + + +class ExModel(ABC): + """ + An Extended Model is a 3-part object consisting of a tokenizer (T), a + universe/vocabulary (U), and a model (M). The tokenizer is used to tokenize + region sets into the universe. The model is defined on the universe. + """ + + model: Model + universe: RegionSet + tokenizer: Tokenizer + + @abstractmethod + def __init__( + self, model: Model = None, universe: RegionSet = None, tokenizer: Tokenizer = None + ) -> None: + raise NotImplementedError diff --git a/gitk/scembed/cli.py b/gitk/scembed/cli.py index 310d9272..225cb7d4 100644 --- a/gitk/scembed/cli.py +++ b/gitk/scembed/cli.py @@ -7,8 +7,7 @@ from ._version import __version__ from .argparser import build_argparser from .const import * -from .scembed import (convert_anndata_to_documents, load_scanpy_data, - shuffle_documents, train) +from .scembed import convert_anndata_to_documents, load_scanpy_data, shuffle_documents, train def main(): diff --git a/gitk/search/search.py b/gitk/search/search.py index da6d156a..9743b6e2 100644 --- a/gitk/search/search.py +++ b/gitk/search/search.py @@ -1,80 +1,14 @@ -from abc import ABC, abstractmethod +class EmSearchBackend(ABC): + """ + An abstract class representing Embedding Search Backends. This allows + backends to be either a qdrant server or a local in-memory NMS index, or + anything, really. This allows us to use the same interface for both. + """ -# Should a tokenizer *hold* a universe, or take one as a parameter? Or both? -class Tokenizer(ABC): - """ Class representing a tokenizer function """ - @abstractmethod - def tokenize(self, region_set: RegionSet, universe:RegionSet=None) -> TokenizedRegionSet: - """ Tokenize a RegionSet """ - raise NotImplementedError - - def tokenize_rsc(self, rsc: RegionSetCollection) -> TokenizedRegionSetCollection: - """ Tokenize a RegionSetCollection """ - raise NotImplementedError - - -# A class representing an ExtendedModel, which is a 3-part object consisting of -# a model, a universe/vocabulary, and a tokenizer -# TODO: Move this outside of the search module -# from gitk.models? import TUM -class TUM(object): - model: Model - universe: RegionSet - tokenizer: Tokenizer - - def __init__(self, model, universe, tokenizer): - -# TODO: This belongs somewhere else -class RegionSet(object): - def __init__(self, path): - with open(path, 'r') as f: - self.regions = [Region(line) for line in f] - -# TODO: This belongs somewhere else; does it even make sense? -class TokenizedRegionSet(object): - """ Represents a tokenized region set """ - def __init__(self, tokens:np.ndarray, universe: RegionSet): - self.tokens = tokens - self.universe = universe - - -# Write a class representing a collection of RegionSets -# TODO: This shouldn't read in the actual files, it should just represent the files and use lazy loading -class RegionSetCollection(object): - """ Represents a collection of RegionSets """ - def __init__(self, region_sets: List[RegionSet] =None, file_globs: List[str] = None): - if region_sets: - self.region_sets = region_sets - elif file_globs: - self.region_sets = [] - for glob in file_globs: - self.region_sets.extend([RegionSet(path) for path in glob.glob(glob)]) - - def __getitem__(self, key): - return self.region_sets[key] - - def __len__(self): - return len(self.region_sets) - - -class BEDEmbedTUM(TUM): - """ A TUM that uses BED files as input """ - def __init__(self, model, universe, tokenizer): - super().__init__(model, universe, tokenizer) - - def embed(self, region_set: RegionSet) -> np.ndarray: - """ Embed a region set using the model """ - - -# allow backends to be either a qdrant server or a local in-memory NMS index -# this allows us to use the same interface for both -class EmSearchBackend(ABC) : - """ An abstract class representing back-ends for Embedding Search """ def __init__(self, embeddings: np.ndarray = None, labels: list = None) -> None: if embeddings: self.load(embeddings, labels) - @abstractmethod def load(self, embeddings: np.ndarray, labels: list) -> None: @@ -97,11 +31,11 @@ def __len__(self) -> int: Return the number of embeddings in the backend """ raise NotImplementedError() - - + class QdrantBackend(EmSearchBackend): - """ A search backend that uses a qdrant server to store and search embeddings """ + """A search backend that uses a qdrant server to store and search embeddings""" + def __init__(self, config): self.config = config # TODO: initialize connection to qdrant server @@ -110,18 +44,19 @@ def search(self, query): raise NotImplementedError - class HNSWBackend(EmSearchBackend): - """ A search backend that uses a local HNSW index to store and search embeddings """ + """A search backend that uses a local HNSW index to store and search embeddings""" + idx: hnswlib.Index + def __init__(self, embeddings: np.ndarray, labels: list, metadata: dict = None) -> None: self.labels = labels self.metadata = metadata # create an HNSW index for the embeddings dim = embeddings.shape[1] - self.idx = hnswlib.Index(space = 'l2', dim = dim) # possible options are l2, cosine or ip - self.idx.init_index(max_elements = len(embeddings), ef_construction = 200, M = 16) + self.idx = hnswlib.Index(space="l2", dim=dim) # possible options are l2, cosine or ip + self.idx.init_index(max_elements=len(embeddings), ef_construction=200, M=16) self.idx.add_items(embeddings, labels) def search(self, query: np.ndarray, k: int) -> List[Tuple[int, float]]: @@ -135,20 +70,23 @@ def __getitem__(self, key) -> np.ndarray: ret = metadata[key] else: ret = {} - ret['embedding'] = self.idx.get_items([key])[0] + ret["embedding"] = self.idx.get_items([key])[0] return ret - + def __str__(self): return "HNSWBackend with {} items".format(len(self)) - + def save(self, path): - with open(path, 'wb') as f: + with open(path, "wb") as f: pickle.dump(self, f) - + def load(self, path): self = pickle.load(path) + + + class BEDSpaceSearchInterface(object): def __init__(self, region_embeddings, label_embeddings, universe, tokenizer): self.universe = universe @@ -158,34 +96,34 @@ def __init__(self, region_embeddings, label_embeddings, universe, tokenizer): self.region_set_backend = HNSWBackend(region_embeddings, labels, metadata) def search_labels_by_embedding(self, embedding, k: int = 10) -> embeddings: - """ Given an input region set embedding, suggest labels for that region set """ + """Given an input region set embedding, suggest labels for that region set""" return self.label_backend.search(region_set_embedding, k) def search_region_sets_by_embedding(self, embedding, k: int = 10) -> embeddings: return self.region_set_backend.search(embedding, k) - + def search_region_sets(self, label, k: int = 10) -> embeddings: - """ Given an input label, suggest region sets for that label """ + """Given an input label, suggest region sets for that label""" # get the embedding for the label # TODO: handle case where label is not in the universe label_embedding = self.label_backend[label] return self.region_set_backend.search(label_embedding, k) - + def search_labels(self, region_set, k: int = 10) -> embeddings: - """ Given an input region set, suggest labels for that region set """ + """Given an input region set, suggest labels for that region set""" region_set_embedding = self.get_region_set_embedding(tokenized_region_set) return self.label_backend.search(region_set_embedding, k) - + def search_region_sets_by_region_set(self, region_set, k: int = 10) -> embeddings: - """ Given an input region set, suggest region sets similar to that region set """ + """Given an input region set, suggest region sets similar to that region set""" region_set_embedding = self.get_region_set_embedding(region_set) return self.region_set_backend.search(region_set_embedding, k) def get_region_set_embedding(self, region_set): - """ Given a region set, return the embedding for that region set """ + """Given a region set, return the embedding for that region set""" # first, tokenize the region set using the universe tokenized_region_set = self.tokenizer.tokenize(region_set, self.universe) @@ -199,7 +137,7 @@ def get_region_set_embedding(self, region_set): BBSI = BEDspaceSearchInterface(...) # Find region sets for a label (Scenario 1, l2r) -BBSI.search_region_sets_by_label("K562") # returns nearest embeddings +BBSI.search_region_sets_by_label("K562") # returns nearest embeddings # Annotate region sets with labels (Scenario 2, r2l) path_to_bed_file = "path/to/bed/file.bed" @@ -214,30 +152,3 @@ def get_region_set_embedding(self, region_set): -class BEDbaseSearchInterface(object): - def __init__(self, tum: BEDEmbedTUM, region_set_backend: EmSearchBackend): - self.tum = tum - self.region_set_backend = search_backend - - def nlsearch(self, query: str), k: int = 10) -> embeddings: - """ Given an input natural lange, suggest region sets """ - - # first, get the embedding of the query string - query_embedding = self.tum.embed(query) - # then, use the query embedding to predict the region set embedding - region_set_embedding = self.tum.str_to_region_set(query_embedding) - # finally, use the region set embedding to search for similar region sets - return self.region_set_backend.search(region_set_embedding, k) - - -# Example of how to use the BEDbase search interface - -betum = BEDEmbedTUM(RSC, universe, tokenizer) -embeddings = betum.compute_embeddings() -BBSI = BEDbaseSearchInterface(betum, embeddings) # ??? - -# Do we need an EmbeddingSet class? -class EmbeddingSet(object): - """ Represents embeddings and labels """ - embeddings: np.ndarray - labels: list \ No newline at end of file diff --git a/gitk/text2bednn/__init__.py b/gitk/text2bednn/__init__.py new file mode 100644 index 00000000..24d39ca4 --- /dev/null +++ b/gitk/text2bednn/__init__.py @@ -0,0 +1 @@ +from text2bednn import * diff --git a/gitk/text2bednn/text2bednn.py b/gitk/text2bednn/text2bednn.py new file mode 100644 index 00000000..06a05950 --- /dev/null +++ b/gitk/text2bednn/text2bednn.py @@ -0,0 +1,35 @@ +from gitk.models import ExModel +from ..search import EmSearchBackend + +class TextToBedNN(ExModel): + """ A Extended Model for TextToBed using a FFNN. """ + def __init__(self, model, universe, tokenizer): + super().__init__(model, universe, tokenizer) + + def embed(self, region_set: RegionSet) -> np.ndarray: + """ Embed a region set using the model """ + + + +class TextToBedNNSearchInterface(object): + def __init__(self, exmodel: TextToBedNN, region_set_backend: EmSearchBackend): + self.exmodel = exmodel + self.region_set_backend = region_set_backend + + def nlsearch(self, query: str, k: int = 10) -> embeddings: + """Given an input natural lange, suggest region sets""" + + # first, get the embedding of the query string + query_embedding = self.tum.embed(query) + # then, use the query embedding to predict the region set embedding + region_set_embedding = self.tum.str_to_region_set(query_embedding) + # finally, use the region set embedding to search for similar region sets + return self.region_set_backend.search(region_set_embedding, k) + + + +# Example of how to use the TextToBedNN Search Interface + +betum = BEDEmbedTUM(RSC, universe, tokenizer) +embeddings = betum.compute_embeddings() +T2BNNSI = TextToBedNNSearchInterface(betum, embeddings) # ??? diff --git a/gitk/tokenization/__init__.py b/gitk/tokenization/__init__.py index 36891984..8fd3dc72 100644 --- a/gitk/tokenization/__init__.py +++ b/gitk/tokenization/__init__.py @@ -1 +1,2 @@ from .main import hard_tokenization_main as hard_tokenization +from .main import Tokenizer diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 094b851c..0ae26a4e 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -2,22 +2,36 @@ import glob import json import multiprocessing +import numpy as np import os import random +import requests import shlex import shutil import subprocess import sys -from queue import Queue - -import numpy as np -import requests import yaml +from abc import ABC, abstractmethod +from queue import Queue + import gitk.region2vec.utils as utils from gitk.tokenization.split_file import split_file - from .hard_tokenization_batch import main as hard_tokenization +from ..io import RegionSet, RegionSetCollection + +# Should a tokenizer *hold* a universe, or take one as a parameter? Or both? +class Tokenizer(ABC): + """Abstract class representing a tokenizer function""" + + @abstractmethod + def tokenize(self, region_set: RegionSet, universe: RegionSet = None) -> RegionSet: + """Tokenize a RegionSet""" + raise NotImplementedError + + def tokenize_rsc(self, rsc: RegionSetCollection) -> RegionSetCollection: + """Tokenize a RegionSetCollection""" + raise NotImplementedError class Namespace: From 9e3ebc485b8ad8cf31190ac177b8cd554eefaf63 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 21 Jul 2023 15:42:05 -0400 Subject: [PATCH 0273/1072] lint --- gitk/io/io.py | 1 + gitk/models/models.py | 1 + gitk/search/search.py | 8 -------- gitk/text2bednn/text2bednn.py | 8 ++++---- gitk/tokenization/main.py | 1 + 5 files changed, 7 insertions(+), 12 deletions(-) diff --git a/gitk/io/io.py b/gitk/io/io.py index 0b8989c8..3e0a06ec 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -1,5 +1,6 @@ from ..utils import * + # TODO: This belongs somewhere else class RegionSet(object): def __init__(self, path): diff --git a/gitk/models/models.py b/gitk/models/models.py index beced2b1..29e648fe 100644 --- a/gitk/models/models.py +++ b/gitk/models/models.py @@ -2,6 +2,7 @@ from ..io import RegionSet from ..tokenization import Tokenizer + class Model(ABC): """Class representing an *actual* model, that is, weights, etc""" diff --git a/gitk/search/search.py b/gitk/search/search.py index 9743b6e2..95b4731c 100644 --- a/gitk/search/search.py +++ b/gitk/search/search.py @@ -1,4 +1,3 @@ - class EmSearchBackend(ABC): """ An abstract class representing Embedding Search Backends. This allows @@ -84,9 +83,6 @@ def load(self, path): self = pickle.load(path) - - - class BEDSpaceSearchInterface(object): def __init__(self, region_embeddings, label_embeddings, universe, tokenizer): self.universe = universe @@ -148,7 +144,3 @@ def get_region_set_embedding(self, region_set): path_to_bed_file = "path/to/bed/file.bed" region_set = RegionSet(path_to_bed_file) BBSI.search_region_sets_by_region_set(region_set, k=10) - - - - diff --git a/gitk/text2bednn/text2bednn.py b/gitk/text2bednn/text2bednn.py index 06a05950..34d3917e 100644 --- a/gitk/text2bednn/text2bednn.py +++ b/gitk/text2bednn/text2bednn.py @@ -1,14 +1,15 @@ from gitk.models import ExModel from ..search import EmSearchBackend + class TextToBedNN(ExModel): - """ A Extended Model for TextToBed using a FFNN. """ + """A Extended Model for TextToBed using a FFNN.""" + def __init__(self, model, universe, tokenizer): super().__init__(model, universe, tokenizer) def embed(self, region_set: RegionSet) -> np.ndarray: - """ Embed a region set using the model """ - + """Embed a region set using the model""" class TextToBedNNSearchInterface(object): @@ -27,7 +28,6 @@ def nlsearch(self, query: str, k: int = 10) -> embeddings: return self.region_set_backend.search(region_set_embedding, k) - # Example of how to use the TextToBedNN Search Interface betum = BEDEmbedTUM(RSC, universe, tokenizer) diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 0ae26a4e..ce9d234c 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -20,6 +20,7 @@ from .hard_tokenization_batch import main as hard_tokenization from ..io import RegionSet, RegionSetCollection + # Should a tokenizer *hold* a universe, or take one as a parameter? Or both? class Tokenizer(ABC): """Abstract class representing a tokenizer function""" From bab57031f831296b6ade2a123c8a5bf941479f48 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 21 Jul 2023 16:32:37 -0400 Subject: [PATCH 0274/1072] some work on tokenizers --- gitk/tokenization/hard_tokenization_batch.py | 53 +++++++++++++++++++- gitk/tokenization/main.py | 21 ++++++++ 2 files changed, 73 insertions(+), 1 deletion(-) diff --git a/gitk/tokenization/hard_tokenization_batch.py b/gitk/tokenization/hard_tokenization_batch.py index ff28c853..cff9b8b2 100644 --- a/gitk/tokenization/hard_tokenization_batch.py +++ b/gitk/tokenization/hard_tokenization_batch.py @@ -6,6 +6,57 @@ from gitk.region2vec import utils +class BEDToolsTokenizer(FileTokenizer): + """A tokenizer that uses bedtools to tokenize BED files""" + + def __init__(self, bedtools_path: str, universe_path: str = None): + """Initialize a BEDToolsTokenizer + + Args: + bedtools_path (str): Path to a bedtools binary. + universe_path (str): Path to a universe BED file. + """ + self.bedtools_path = bedtools_path + self.universe_path = universe_path + + def tokenize(self, input_globs: list[str], universe_path: str = None) -> RegionSet: + """Tokenize a RegionSet using bedtools""" + + universe_path = universe_path or self.universe_path + + # loop through globs and tokenize each file + for glob in input_globs: + for path in glob.glob(glob) + _tokenize_one(path, universe_path) + + def _tokenize_one(self, input_path: str, universe_path: str): + output_path = os.path.join(input_path, "tokenized.bed") + bedtools_path = self.bedtools_path + # bedtools can't actually read from stdin, so we have to use a temporary file... + + # sort_process = subprocess.Popen(shlex.split(f"sort -k1,1V -k2,2n {input_path}"), stdout=subprocess.PIPE) + # bedtools_process = subprocess.Popen( + # shlex.split(f"{bedtools_path} intersect -a {universe} -b -u -f {fraction}"), + # stdin = sort_process.stdout, + # stdout = output_file, + # ) + # bedtools_process.communicate() + + # get a temporary file path using tempfile + import templfile + with tempfile.NamedTemporaryFile() as temp_path, open(output_path, "w") as output_file: + # sort the input file + sort_process = subprocess.Popen(shlex.split(f"sort -k1,1V -k2,2n {input_path}"), stdout=temp_path) + sort_process.communicate() + # tokenize the sorted file + bedtools_process = subprocess.Popen( + shlex.split(f"{bedtools_path} intersect -a {universe} -b {temp_path} -u -f {fraction}"), + stdout=output_file, + ) + bedtools_process.communicate() + + + def bedtools_tokenization( f: str, bedtools_path: str, @@ -31,7 +82,7 @@ def bedtools_tokenization( subprocess.run(shlex.split(f"sort -k1,1V -k2,2n {fname}"), stdout=f_temp) with open(target, "w") as f_target: subprocess.run( - shlex.split(f"{bedtools_path} intersect -a {universe} -b {temp} -u -f {fraction} "), + shlex.split(f"{bedtools_path} intersect -a {universe} -b {temp} -u -f {fraction}"), stdout=f_target, ) os.remove(temp) diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index ce9d234c..f1b02a4e 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -22,7 +22,15 @@ # Should a tokenizer *hold* a universe, or take one as a parameter? Or both? + + class Tokenizer(ABC): + @abstractmethod + def tokenize(self, *args, **kwargs): + raise NotImplementedError + + +class InMemTokenizer(Tokenizer): """Abstract class representing a tokenizer function""" @abstractmethod @@ -30,11 +38,24 @@ def tokenize(self, region_set: RegionSet, universe: RegionSet = None) -> RegionS """Tokenize a RegionSet""" raise NotImplementedError + @abstractmethod def tokenize_rsc(self, rsc: RegionSetCollection) -> RegionSetCollection: """Tokenize a RegionSetCollection""" raise NotImplementedError +class FileTokenizer(Tokenizer): + """Tokenizer that tokenizes files""" + + @abstractmethod + def __init__(self): + raise NotImplementedError + + @abstractmethod + def tokenize(input_globs: list[str], output_folder: str, universe_path: str): + raise NotImplementedError + + class Namespace: def __init__(self, **kwargs): self.__dict__.update(kwargs) From 57f09fe708613498ba25542595e5fab85e28571c Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 21 Jul 2023 18:34:03 -0400 Subject: [PATCH 0275/1072] Start importing lots from typing, see #51 --- gitk/io/io.py | 4 +- gitk/models/atac/tokenization.py | 6 +-- gitk/models/pretrained.py | 4 +- gitk/models/utils.py | 8 +-- gitk/region2vec/main.py | 6 ++- gitk/region2vec/region_shuffling.py | 4 +- gitk/region2vec/utils.py | 4 +- gitk/tokenization/__init__.py | 3 +- gitk/tokenization/bedtools_tokenizer.py | 53 +++++++++++++++++++ gitk/tokenization/hard_tokenization_batch.py | 54 +------------------- gitk/tokenization/main.py | 4 +- 11 files changed, 78 insertions(+), 72 deletions(-) create mode 100644 gitk/tokenization/bedtools_tokenizer.py diff --git a/gitk/io/io.py b/gitk/io/io.py index 3e0a06ec..a1174e8e 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -1,5 +1,5 @@ from ..utils import * - +from typing import List # TODO: This belongs somewhere else class RegionSet(object): @@ -22,7 +22,7 @@ def __init__(self, tokens: np.ndarray, universe: RegionSet): class RegionSetCollection(object): """Represents a collection of RegionSets""" - def __init__(self, region_sets: list[RegionSet] = None, file_globs: list[str] = None): + def __init__(self, region_sets: List[RegionSet] = None, file_globs: List[str] = None): if region_sets: self.region_sets = region_sets elif file_globs: diff --git a/gitk/models/atac/tokenization.py b/gitk/models/atac/tokenization.py index 221d336f..22525fd4 100644 --- a/gitk/models/atac/tokenization.py +++ b/gitk/models/atac/tokenization.py @@ -1,5 +1,5 @@ import os -from typing import Dict, List, Union +from typing import Dict, List, Union, Tuple import scanpy as sc from tqdm import tqdm @@ -77,7 +77,7 @@ def build_tree(self, regions: str = None): def query( self, - regions: Union[str, List[str], List[tuple[str, int, int]], tuple[str, int, int]], + regions: Union[str, List[str], List[Tuple[str, int, int]], Tuple[str, int, int]], ): """ Query the interval tree for the given regions. @@ -102,7 +102,7 @@ def query( overlapping_regions.append((chr, overlap.begin, overlap.end)) return overlapping_regions - def __contains__(self, item: tuple[str, int, int]): + def __contains__(self, item: Tuple[str, int, int]): """ Check if the given region is in the universe. diff --git a/gitk/models/pretrained.py b/gitk/models/pretrained.py index 2de41c61..e0ba719b 100644 --- a/gitk/models/pretrained.py +++ b/gitk/models/pretrained.py @@ -1,4 +1,4 @@ -from typing import Union +from typing import Union, Tuple, List import numpy as np import scanpy as sc @@ -67,7 +67,7 @@ def _load_model(self, path: str) -> scembed.SCEmbed: """ return scembed.utils.load_scembed_model(path) - def encode(self, data: Union[str, list[tuple[str, int, int]], sc.AnnData]) -> np.ndarray: + def encode(self, data: Union[str, List[Tuple[str, int, int]], sc.AnnData]) -> np.ndarray: """ Encode region sets data using the pretrained model. diff --git a/gitk/models/utils.py b/gitk/models/utils.py index bdb0987b..d771bd6c 100644 --- a/gitk/models/utils.py +++ b/gitk/models/utils.py @@ -1,5 +1,5 @@ import os -from typing import TYPE_CHECKING, Dict, List, Union +from typing import TYPE_CHECKING, Dict, List, Union, Tuple import numpy as np import scanpy as sc @@ -37,8 +37,8 @@ def make_cache_dir(): def validate_region_input( - regions: Union[str, List[str], List[tuple[str]]] -) -> List[tuple[str, int, int]]: + regions: Union[str, List[str], List[Tuple[str]]] +) -> List[Tuple[str, int, int]]: """ Validate the input for the regions. this universe accepts a lot of forms of input, so we need to check what the user has passed. It also standardizes the input to a list @@ -81,7 +81,7 @@ def validate_region_input( def generate_var_conversion_map( - a: List[tuple[str, int, int]], + a: List[Tuple[str, int, int]], b: "Universe", fraction: float = 1.0e-9, ) -> Dict[str, Union[str, None]]: diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 1e1f1a80..b2872919 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -7,6 +7,8 @@ from gitk.region2vec.region2vec_train import main as region2_train from gitk.region2vec.region_shuffling import main as sent_gen +from typing import List + class Namespace: def __init__(self, **kwargs): @@ -16,7 +18,7 @@ def __init__(self, **kwargs): def region2vec( token_folder: str, save_dir: str, - file_list: list[str] = None, + file_list: List[str] = None, data_type: str = "files", mat_path: str = None, num_shufflings: int = 1000, @@ -32,7 +34,7 @@ def region2vec( init_lr: float = 0.025, min_lr: float = 1e-4, lr_scheduler: str = "linear", - milestones: list[int] = [], + milestones: List[int] = [], hier_softmax: bool = False, seed: int = 0, update_vocab: str = "once", diff --git a/gitk/region2vec/region_shuffling.py b/gitk/region2vec/region_shuffling.py index 3c9183c1..f3cd1961 100644 --- a/gitk/region2vec/region_shuffling.py +++ b/gitk/region2vec/region_shuffling.py @@ -9,7 +9,7 @@ import numpy as np from gitk.region2vec import utils - +from typing import List class BEDDataset: """Wraps a set of BED files in a BEDDataset object. @@ -106,7 +106,7 @@ class MatrixDataset: Generates a new dataset with regions shuffled in BED files. """ - def __init__(self, matrix: list[list[int]]): + def __init__(self, matrix: List[List[int]]): """Initializes a MatrixDataset object with matrix. Args: diff --git a/gitk/region2vec/utils.py b/gitk/region2vec/utils.py index 6fcb40cb..004c883c 100644 --- a/gitk/region2vec/utils.py +++ b/gitk/region2vec/utils.py @@ -3,7 +3,7 @@ import shutil import sys import time -from typing import Union +from typing import Union, Dict import numpy as np @@ -186,7 +186,7 @@ def __init__( init_lr: float, end_lr: float, epochs: int, - lr_info: dict[str, Union[int, float, list]], + lr_info: Dict[str, Union[int, float, list]], mode: str = "linear", ): """Initializes the learning rate scheduler. diff --git a/gitk/tokenization/__init__.py b/gitk/tokenization/__init__.py index 8fd3dc72..ddb53d73 100644 --- a/gitk/tokenization/__init__.py +++ b/gitk/tokenization/__init__.py @@ -1,2 +1,3 @@ from .main import hard_tokenization_main as hard_tokenization -from .main import Tokenizer +from .main import Tokenizer, FileTokenizer +from .main import * diff --git a/gitk/tokenization/bedtools_tokenizer.py b/gitk/tokenization/bedtools_tokenizer.py new file mode 100644 index 00000000..8eef539a --- /dev/null +++ b/gitk/tokenization/bedtools_tokenizer.py @@ -0,0 +1,53 @@ + +from . import FileTokenizer + +class BEDToolsTokenizer(FileTokenizer): + """A tokenizer that uses bedtools to tokenize BED files""" + + def __init__(self, bedtools_path: str, universe_path: str = None): + """Initialize a BEDToolsTokenizer + + Args: + bedtools_path (str): Path to a bedtools binary. + universe_path (str): Path to a universe BED file. + """ + self.bedtools_path = bedtools_path + self.universe_path = universe_path + + def tokenize(self, input_globs: list[str], universe_path: str = None) -> RegionSet: + """Tokenize a RegionSet using bedtools""" + + universe_path = universe_path or self.universe_path + + # loop through globs and tokenize each file + for glob in input_globs: + for path in glob.glob(glob): + _tokenize_one(path, universe_path) + + def _tokenize_one(self, input_path: str, universe_path: str): + output_path = os.path.join(input_path, "tokenized.bed") + bedtools_path = self.bedtools_path + # bedtools can't actually read from stdin, so we have to use a temporary file... + + # sort_process = subprocess.Popen(shlex.split(f"sort -k1,1V -k2,2n {input_path}"), stdout=subprocess.PIPE) + # bedtools_process = subprocess.Popen( + # shlex.split(f"{bedtools_path} intersect -a {universe} -b -u -f {fraction}"), + # stdin = sort_process.stdout, + # stdout = output_file, + # ) + # bedtools_process.communicate() + + # get a temporary file path using tempfile + import templfile + with tempfile.NamedTemporaryFile() as temp_path, open(output_path, "w") as output_file: + # sort the input file + sort_process = subprocess.Popen(shlex.split(f"sort -k1,1V -k2,2n {input_path}"), stdout=temp_path) + sort_process.communicate() + # tokenize the sorted file + bedtools_process = subprocess.Popen( + shlex.split(f"{bedtools_path} intersect -a {universe} -b {temp_path} -u -f {fraction}"), + stdout=output_file, + ) + bedtools_process.communicate() + + diff --git a/gitk/tokenization/hard_tokenization_batch.py b/gitk/tokenization/hard_tokenization_batch.py index cff9b8b2..c3681f35 100644 --- a/gitk/tokenization/hard_tokenization_batch.py +++ b/gitk/tokenization/hard_tokenization_batch.py @@ -3,59 +3,7 @@ import shlex import subprocess -from gitk.region2vec import utils - - -class BEDToolsTokenizer(FileTokenizer): - """A tokenizer that uses bedtools to tokenize BED files""" - - def __init__(self, bedtools_path: str, universe_path: str = None): - """Initialize a BEDToolsTokenizer - - Args: - bedtools_path (str): Path to a bedtools binary. - universe_path (str): Path to a universe BED file. - """ - self.bedtools_path = bedtools_path - self.universe_path = universe_path - - def tokenize(self, input_globs: list[str], universe_path: str = None) -> RegionSet: - """Tokenize a RegionSet using bedtools""" - - universe_path = universe_path or self.universe_path - - # loop through globs and tokenize each file - for glob in input_globs: - for path in glob.glob(glob) - _tokenize_one(path, universe_path) - - def _tokenize_one(self, input_path: str, universe_path: str): - output_path = os.path.join(input_path, "tokenized.bed") - bedtools_path = self.bedtools_path - # bedtools can't actually read from stdin, so we have to use a temporary file... - - # sort_process = subprocess.Popen(shlex.split(f"sort -k1,1V -k2,2n {input_path}"), stdout=subprocess.PIPE) - # bedtools_process = subprocess.Popen( - # shlex.split(f"{bedtools_path} intersect -a {universe} -b -u -f {fraction}"), - # stdin = sort_process.stdout, - # stdout = output_file, - # ) - # bedtools_process.communicate() - - # get a temporary file path using tempfile - import templfile - with tempfile.NamedTemporaryFile() as temp_path, open(output_path, "w") as output_file: - # sort the input file - sort_process = subprocess.Popen(shlex.split(f"sort -k1,1V -k2,2n {input_path}"), stdout=temp_path) - sort_process.communicate() - # tokenize the sorted file - bedtools_process = subprocess.Popen( - shlex.split(f"{bedtools_path} intersect -a {universe} -b {temp_path} -u -f {fraction}"), - stdout=output_file, - ) - bedtools_process.communicate() - - +from ..region2vec import utils def bedtools_tokenization( f: str, diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index f1b02a4e..b5285c0d 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -20,6 +20,8 @@ from .hard_tokenization_batch import main as hard_tokenization from ..io import RegionSet, RegionSetCollection +from typing import List + # Should a tokenizer *hold* a universe, or take one as a parameter? Or both? @@ -52,7 +54,7 @@ def __init__(self): raise NotImplementedError @abstractmethod - def tokenize(input_globs: list[str], output_folder: str, universe_path: str): + def tokenize(input_globs: List[str], output_folder: str, universe_path: str): raise NotImplementedError From 4b146758d0dfd4a658292afb04c3eb92cf1a4419 Mon Sep 17 00:00:00 2001 From: nsheff Date: Fri, 21 Jul 2023 18:35:23 -0400 Subject: [PATCH 0276/1072] lint --- gitk/io/io.py | 1 + gitk/region2vec/region_shuffling.py | 1 + gitk/tokenization/bedtools_tokenizer.py | 13 ++++++++----- gitk/tokenization/hard_tokenization_batch.py | 1 + 4 files changed, 11 insertions(+), 5 deletions(-) diff --git a/gitk/io/io.py b/gitk/io/io.py index a1174e8e..f26e1aaf 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -1,6 +1,7 @@ from ..utils import * from typing import List + # TODO: This belongs somewhere else class RegionSet(object): def __init__(self, path): diff --git a/gitk/region2vec/region_shuffling.py b/gitk/region2vec/region_shuffling.py index f3cd1961..ab755671 100644 --- a/gitk/region2vec/region_shuffling.py +++ b/gitk/region2vec/region_shuffling.py @@ -11,6 +11,7 @@ from gitk.region2vec import utils from typing import List + class BEDDataset: """Wraps a set of BED files in a BEDDataset object. diff --git a/gitk/tokenization/bedtools_tokenizer.py b/gitk/tokenization/bedtools_tokenizer.py index 8eef539a..e8cc0c90 100644 --- a/gitk/tokenization/bedtools_tokenizer.py +++ b/gitk/tokenization/bedtools_tokenizer.py @@ -1,6 +1,6 @@ - from . import FileTokenizer + class BEDToolsTokenizer(FileTokenizer): """A tokenizer that uses bedtools to tokenize BED files""" @@ -39,15 +39,18 @@ def _tokenize_one(self, input_path: str, universe_path: str): # get a temporary file path using tempfile import templfile + with tempfile.NamedTemporaryFile() as temp_path, open(output_path, "w") as output_file: # sort the input file - sort_process = subprocess.Popen(shlex.split(f"sort -k1,1V -k2,2n {input_path}"), stdout=temp_path) + sort_process = subprocess.Popen( + shlex.split(f"sort -k1,1V -k2,2n {input_path}"), stdout=temp_path + ) sort_process.communicate() # tokenize the sorted file bedtools_process = subprocess.Popen( - shlex.split(f"{bedtools_path} intersect -a {universe} -b {temp_path} -u -f {fraction}"), + shlex.split( + f"{bedtools_path} intersect -a {universe} -b {temp_path} -u -f {fraction}" + ), stdout=output_file, ) bedtools_process.communicate() - - diff --git a/gitk/tokenization/hard_tokenization_batch.py b/gitk/tokenization/hard_tokenization_batch.py index c3681f35..e118929f 100644 --- a/gitk/tokenization/hard_tokenization_batch.py +++ b/gitk/tokenization/hard_tokenization_batch.py @@ -5,6 +5,7 @@ from ..region2vec import utils + def bedtools_tokenization( f: str, bedtools_path: str, From 920a178565c48196eb34721d6c693d6dc114a6d3 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Tue, 25 Jul 2023 16:52:22 -0400 Subject: [PATCH 0277/1072] clean up imports for tokenization --- gitk/tokenization/__init__.py | 4 ++-- gitk/tokenization/main.py | 20 +++++--------------- 2 files changed, 7 insertions(+), 17 deletions(-) diff --git a/gitk/tokenization/__init__.py b/gitk/tokenization/__init__.py index ddb53d73..9394f8e3 100644 --- a/gitk/tokenization/__init__.py +++ b/gitk/tokenization/__init__.py @@ -1,3 +1,3 @@ -from .main import hard_tokenization_main as hard_tokenization -from .main import Tokenizer, FileTokenizer from .main import * +from .main import FileTokenizer, Tokenizer +from .main import hard_tokenization_main as hard_tokenization diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index b5285c0d..f73ed1e4 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -1,27 +1,17 @@ -import argparse -import glob -import json import multiprocessing -import numpy as np import os -import random -import requests -import shlex import shutil import subprocess -import sys -import yaml - from abc import ABC, abstractmethod -from queue import Queue +from typing import List + +import numpy as np import gitk.region2vec.utils as utils from gitk.tokenization.split_file import split_file -from .hard_tokenization_batch import main as hard_tokenization -from ..io import RegionSet, RegionSetCollection - -from typing import List +from ..io import RegionSet, RegionSetCollection +from .hard_tokenization_batch import main as hard_tokenization # Should a tokenizer *hold* a universe, or take one as a parameter? Or both? From 16d0b15915c223c16434783adcfa8bd5258c5738 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Tue, 25 Jul 2023 18:41:25 -0400 Subject: [PATCH 0278/1072] universe creation plus some tests --- gitk/io/io.py | 20 ++++++- gitk/tokenization/main.py | 110 ++++++++++++++++++++++++++++++++++++- gitk/tokenization/utils.py | 50 +++++++++++++++++ tests/test_tokenization.py | 70 +++++++++++++++++++++++ 4 files changed, 247 insertions(+), 3 deletions(-) create mode 100644 gitk/tokenization/utils.py create mode 100644 tests/test_tokenization.py diff --git a/gitk/io/io.py b/gitk/io/io.py index f26e1aaf..daf1037f 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -1,12 +1,30 @@ from ..utils import * from typing import List +from intervaltree import Interval + + +class Region(Interval): + def __new__(cls, chr: str, start: int, stop: int, data=None): + return super(Region, cls).__new__(cls, start, stop, data) + + def __init__(self, chr: str, start: int, stop: int, data=None): + # no need to call super().__init__() because namedtuple doesn't have __init__() + self.chr = chr + + @property + def start(self): + return self.begin + # TODO: This belongs somewhere else class RegionSet(object): def __init__(self, path): with open(path, "r") as f: - self.regions = [Region(line) for line in f] + lines = f.readlines() + for line in lines: + chr, start, stop = line.split("\t") + self.regions.append(Region(chr, int(start), int(stop))) # TODO: This belongs somewhere else; does it even make sense? diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index f73ed1e4..f997d247 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -3,17 +3,123 @@ import shutil import subprocess from abc import ABC, abstractmethod -from typing import List +from typing import List, Tuple, Union, Dict import numpy as np +from tqdm import tqdm +from intervaltree import IntervalTree import gitk.region2vec.utils as utils from gitk.tokenization.split_file import split_file -from ..io import RegionSet, RegionSetCollection +from ..io import RegionSet, RegionSetCollection, Region +from .utils import extract_regions_from_bed_or_list from .hard_tokenization_batch import main as hard_tokenization + # Should a tokenizer *hold* a universe, or take one as a parameter? Or both? +class Universe: + def __init__(self, regions: Union[str, List[str]]): + """ + Create a new Universe. + + :param Union[str, List[str]] regions: The regions to use for tokenization. This can be thought of as a vocabulary. + This can be either a list of regions or a path to a BED file containing regions. + """ + self._trees: Dict[str, IntervalTree] = dict() + self._regions = regions + self.total_regions = 0 + + if regions is not None: + self.build_tree() + + def get_tree(self, tree: str): + """ + Get the interval tree for a given chromosome. + + :param str tree: The chromosome to get the tree for. + """ + return self._trees[tree] + + @property + def trees(self): + return self._trees + + @property + def regions(self): + return self._regions + + def build_tree(self, regions: str = None): + """ + Builds the interval tree from the regions. The tree is a dictionary that maps chromosomes to + interval trees. + + We read in the regions (either from memory or from a BED file) and convert them to an + interval tree. + + :param str regions: The regions to use for tokenization. This can be thought of as a vocabulary. + """ + regions = extract_regions_from_bed_or_list(regions or self._regions) + + # build trees + for region in tqdm(regions, total=len(regions), desc="Loading universe"): + if region.chr not in self._trees: + self._trees[region.chr] = IntervalTree() + self._trees[region.chr][ + region.start : region.end + ] = f"{region.chr}_{region.start}_{region.end}" + + # count total regions + self.total_regions = sum([len(self._trees[tree]) for tree in self._trees]) + + def query( + self, + regions: Union[str, List[str], List[Region], Region], + ): + """ + Query the interval tree for the given regions. That is, find all regions that overlap with the given regions. + + :param Union[str, List[str], List[Region], Region] regions: The regions to query for. this can be either a bed file, + a list of regions (chr_start_end), or a list of tuples of chr, start, end. + """ + # validate input + if isinstance(regions, Region): + regions = [regions] + regions = extract_regions_from_bed_or_list(regions) + + overlapping_regions = [] + for region in regions: + if region.chr not in self._trees: + continue + overlaps = self._trees[region.chr][region.start : region.end] + for overlap in overlaps: + overlapping_regions.append((region.chr, overlap.begin, overlap.end)) + return overlapping_regions + + def __contains__(self, item: Region): + """ + Check if the given region is in the universe. + + :param Region item: The region to check for. + """ + # ensure item is a tuple of chr, start, end + if not isinstance(item, Region): + raise ValueError("The item to check for must be a tuple of chr, start, end.") + + if item.chr not in self._trees: + return False + overlaps = self._trees[item.chr][item.start : item.end] + return len(overlaps) > 0 + + def __iter__(self): + for tree in self._trees: + yield self._trees[tree] + + def __len__(self): + return self.total_regions + + def __repr__(self): + return f"Universe with {len(self)} regions" class Tokenizer(ABC): diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py new file mode 100644 index 00000000..2bdef84b --- /dev/null +++ b/gitk/tokenization/utils.py @@ -0,0 +1,50 @@ +import os + +from typing import Union, List, Tuple, Dict + +from ..io import Region + + +# better name? +def extract_regions_from_bed_or_list(regions: Union[str, List[str], List[Region]]) -> List[Region]: + """ + Validate the input for the regions. A universe accepts a lot of forms of input, + so we need to check what the user has passed. It also standardizes the input to a list + of tuples of chr, start, end. + + Users are allowed to pass a list of regions, a list of tuples with chr, start, and end, or + a path to a BED file. + + :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. + This can be either a list of regions or a path to a BED file containing regions. + :return list: A list of tuples of chr, start, end. + """ + # check for bedfile + if isinstance(regions, str): + # ensure that the file exists + if not os.path.exists(regions): + raise FileNotFoundError(f"Could not find file {regions} containing regions.") + file = regions # rename for clarity + regions: List[Region] = [] + with open(file, "r") as f: + lines = f.readlines() + for line in lines: + chr, start, end = line.strip().split("\t") + regions.append(Region(chr, int(start), int(end))) + return regions + + # check for list of chr_start_end strings + elif isinstance(regions, list) and all([isinstance(r, str) for r in regions]): + regions_parsed: List[Region] = [] + for region in regions: + chr, start, end = region.split("_") + regions_parsed.append(Region(chr, int(start), int(end))) + return regions_parsed + + # check for list of Region objects + elif isinstance(regions, list) and all([isinstance(r, Region) for r in regions]): + return regions + else: + raise ValueError( + "The regions must be either a list of regions or a path to a BED file containing regions." + ) diff --git a/tests/test_tokenization.py b/tests/test_tokenization.py new file mode 100644 index 00000000..3269e6cb --- /dev/null +++ b/tests/test_tokenization.py @@ -0,0 +1,70 @@ +import os +import sys + +import pytest +import scanpy as sc +import numpy as np + +from gitk.io import Region +from gitk.tokenization import Universe + +# add parent directory to path +sys.path.append("../") + + +@pytest.fixture +def adata(): + return sc.read_h5ad("tests/data/pbmc_hg38.h5ad") + + +@pytest.fixture +def universe_bed_file(): + return "tests/data/universe.bed" + + +def test_create_universe(universe_bed_file: str): + u = Universe(universe_bed_file) + assert u is not None + assert len(u) == 10_000 + + +def test_region_in_universe(universe_bed_file: str): + u = Universe(universe_bed_file) + + # regions in + r_is_in1 = Region("chr17", 78_168_158, 78_169_026) + r_is_in2 = Region( + "chr2", + 241_859_589, + 241_860_443, + ) + r_is_in3 = Region( + "chr22", + 38_863_328, + 38_864_072, + ) + + # regions not in + r_not_in1 = Region( + "chr22", + 1, + 100, + ) + r_not_in2 = Region( + "chr17", + 200, + 300, + ) + r_not_in3 = Region( + "chr2", + 1_000_000_000_000, + 1_000_000_000_100, + ) + + assert r_is_in1 in u + assert r_is_in2 in u + assert r_is_in3 in u + + assert r_not_in1 not in u + assert r_not_in2 not in u + assert r_not_in3 not in u From 096306d5b5d6587cbe8af63cc08b1398c038ce65 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Tue, 25 Jul 2023 19:01:52 -0400 Subject: [PATCH 0279/1072] work on in memory tokenizer --- gitk/io/io.py | 3 ++ gitk/tokenization/main.py | 37 ++++++++++++++++++------ tests/data/to_tokenize.bed | 23 ++++++++------- tests/test_tokenization.py | 58 +++++++++++++++++++++++++++++++++++++- 4 files changed, 102 insertions(+), 19 deletions(-) diff --git a/gitk/io/io.py b/gitk/io/io.py index daf1037f..5072cc24 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -16,6 +16,9 @@ def __init__(self, chr: str, start: int, stop: int, data=None): def start(self): return self.begin + def __repr__(self): + return f"Region({self.chr}, {self.start}, {self.stop})" + # TODO: This belongs somewhere else class RegionSet(object): diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index f997d247..4bc7c5c4 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -19,7 +19,7 @@ # Should a tokenizer *hold* a universe, or take one as a parameter? Or both? class Universe: - def __init__(self, regions: Union[str, List[str]]): + def __init__(self, regions: Union[str, List[str], List[Region]]): """ Create a new Universe. @@ -49,7 +49,7 @@ def trees(self): def regions(self): return self._regions - def build_tree(self, regions: str = None): + def build_tree(self, regions: Union[str, List[str], List[Region]] = None): """ Builds the interval tree from the regions. The tree is a dictionary that maps chromosomes to interval trees. @@ -93,7 +93,7 @@ def query( continue overlaps = self._trees[region.chr][region.start : region.end] for overlap in overlaps: - overlapping_regions.append((region.chr, overlap.begin, overlap.end)) + overlapping_regions.append(Region(region.chr, overlap.begin, overlap.end)) return overlapping_regions def __contains__(self, item: Region): @@ -131,12 +131,33 @@ def tokenize(self, *args, **kwargs): class InMemTokenizer(Tokenizer): """Abstract class representing a tokenizer function""" - @abstractmethod - def tokenize(self, region_set: RegionSet, universe: RegionSet = None) -> RegionSet: - """Tokenize a RegionSet""" - raise NotImplementedError + def __init__(self, universe: Union[str, Universe] = None): + """ + Create a new InMemTokenizer. - @abstractmethod + You can either pass a Universe object or a path to a BED file containing regions. + + :param Union[str, Universe] universe: The universe to use for tokenization. + """ + if isinstance(universe, str): + self.universe = Universe(universe) + elif isinstance(universe, Universe): + self.universe = universe + else: + self.universe = None + + def tokenize(self, region_set: Union[str, List[Region]]) -> List[Region]: + """ + Tokenize a RegionSet. + + This is achieved using hard tokenization which is just a query to the universe, or + simple overlap detection. + + :param str | List[Region] region_set: The list of regions to tokenize + """ + return self.universe.query(region_set) + + # not sure what this looks like, multiple RegionSets? def tokenize_rsc(self, rsc: RegionSetCollection) -> RegionSetCollection: """Tokenize a RegionSetCollection""" raise NotImplementedError diff --git a/tests/data/to_tokenize.bed b/tests/data/to_tokenize.bed index abda1380..da2535ca 100644 --- a/tests/data/to_tokenize.bed +++ b/tests/data/to_tokenize.bed @@ -1,10 +1,13 @@ -chr18 63298460 63299349 -chr1 235006298 235006937 -chr7 105017521 105018314 -chr10 47286722 47287613 -chr10 3835695 3836467 -chr1 202884841 202885642 -chr8 27919124 27919983 -chr19 3705798 3706681 -chr17 82019661 82020511 -chr17 82735108 82736026 +chr18 63298420 63299339 +chr1 235006278 235006917 +chr7 105017631 105018301 +chr10 47286622 47287593 +chr10 3835685 3836457 +chr1 202884741 202885642 +chr8 27919124 27919973 +chr19 3705798 3706691 +chr17 82019591 82020511 +chr17 82735118 82736026 +chr1 100 200 +chr7 5050 6060 +chr10 10000 11000 diff --git a/tests/test_tokenization.py b/tests/test_tokenization.py index 3269e6cb..2655215a 100644 --- a/tests/test_tokenization.py +++ b/tests/test_tokenization.py @@ -6,7 +6,7 @@ import numpy as np from gitk.io import Region -from gitk.tokenization import Universe +from gitk.tokenization import Universe, InMemTokenizer # add parent directory to path sys.path.append("../") @@ -68,3 +68,59 @@ def test_region_in_universe(universe_bed_file: str): assert r_not_in1 not in u assert r_not_in2 not in u assert r_not_in3 not in u + + +def test_universe_query(universe_bed_file: str): + u = Universe(universe_bed_file) + + regions = [ + Region("chr17", 78_168_158, 78_169_026), + Region( + "chr2", + 241_859_589, + 241_860_443, + ), + Region( + "chr2", + 1_000_000_000_000, + 1_000_000_000_100, + ), + ] + + # query regions + olaps = u.query(regions) + + assert len(olaps) == 2 + + +def test_make_in_mem_tokenizer(universe_bed_file: str): + t = InMemTokenizer(universe_bed_file) + assert t is not None + + +def test_tokenize_bed_file(universe_bed_file: str): + """ + Use the in memory tokenizer to tokenize a bed file. + + The bed file contains 13 regions, 10 of which are in the universe. None + of them should be the original regions in the file. + """ + t = InMemTokenizer(universe_bed_file) + assert t is not None + + # tokenize a bed file + bed_file = "tests/data/to_tokenize.bed" + + # read in the bed file to test + with open(bed_file, "r") as f: + lines = f.readlines() + regions = [] + for line in lines: + chr, start, stop = line.strip().split("\t") + regions.append(Region(chr, int(start), int(stop))) + + tokens = t.tokenize(bed_file) + + # ensure that the tokens are unqiue from the original regions + assert len(set(tokens).intersection(set(regions))) == 0 + assert len(tokens) == 10 From 22d2849454b2ba1eaa1cff1d1a2ef3c7d860f475 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 26 Jul 2023 09:27:33 -0400 Subject: [PATCH 0280/1072] rename things, start new class --- gitk/scembed/__init__.py | 2 +- gitk/scembed/cli.py | 2 +- gitk/scembed/{scembed.py => main.py} | 21 +++++++++++++++++++++ gitk/scembed/utils.py | 2 +- 4 files changed, 24 insertions(+), 3 deletions(-) rename gitk/scembed/{scembed.py => main.py} (96%) diff --git a/gitk/scembed/__init__.py b/gitk/scembed/__init__.py index 27cdf61a..ce533989 100644 --- a/gitk/scembed/__init__.py +++ b/gitk/scembed/__init__.py @@ -1,4 +1,4 @@ from .annotation import * from .const import * -from .scembed import * +from .main import * from .utils import * diff --git a/gitk/scembed/cli.py b/gitk/scembed/cli.py index 225cb7d4..29eb21cc 100644 --- a/gitk/scembed/cli.py +++ b/gitk/scembed/cli.py @@ -7,7 +7,7 @@ from ._version import __version__ from .argparser import build_argparser from .const import * -from .scembed import convert_anndata_to_documents, load_scanpy_data, shuffle_documents, train +from .main import convert_anndata_to_documents, load_scanpy_data, shuffle_documents, train def main(): diff --git a/gitk/scembed/scembed.py b/gitk/scembed/main.py similarity index 96% rename from gitk/scembed/scembed.py rename to gitk/scembed/main.py index 266f0497..3177fbdb 100755 --- a/gitk/scembed/scembed.py +++ b/gitk/scembed/main.py @@ -12,6 +12,8 @@ from numba import config from tqdm import tqdm +from ..io import Region +from ..tokenization import InMemTokenizer, Universe from .const import * from .exceptions import * from .utils import ( @@ -415,3 +417,22 @@ def __call__(self, region: str) -> np.ndarray: :param str region: the region to get the embedding for """ return self.get_embedding(region) + + +# +# BEGIN V2 of SCEmbed class +# +class SCEmbedV2(Word2Vec): + def __init__( + self, + model_path: str = None, + tokenizer: InMemTokenizer = None, + universe: Union[Universe, str] = None, + ): + # if model_path is given, download a pretrained model + # from the HuggingFace Hub + if model_path: + self._init_from_huggingface(model_path) + else: + self.tokenizer = tokenizer or InMemTokenizer() + self.universe = universe or Universe() diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index dafa3780..3e253ab1 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -14,7 +14,7 @@ from tqdm import tqdm if TYPE_CHECKING: - from .scembed import SCEmbed + from .main import SCEmbed from .const import * From 8cccccc4504db00825fd1725a6b7397a0edafc75 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 26 Jul 2023 12:01:54 -0400 Subject: [PATCH 0281/1072] get new SCEmbed working --- gitk/io/io.py | 2 +- gitk/scembed/main.py | 279 +++++++++++++++++++------------------ gitk/tokenization/main.py | 96 +++++++++++-- gitk/tokenization/utils.py | 85 ++++++++++- tests/test_tokenization.py | 4 +- 5 files changed, 314 insertions(+), 152 deletions(-) diff --git a/gitk/io/io.py b/gitk/io/io.py index 5072cc24..e1e6bd18 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -17,7 +17,7 @@ def start(self): return self.begin def __repr__(self): - return f"Region({self.chr}, {self.start}, {self.stop})" + return f"Region({self.chr}, {self.start}, {self.end})" # TODO: This belongs somewhere else diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index 3177fbdb..1170d33a 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -3,6 +3,7 @@ import pickle from logging import getLogger from typing import Dict, List, Union +from multiprocessing import cpu_count import numpy as np import pandas as pd @@ -269,170 +270,180 @@ def get_embeddings(self, regions: List[str]) -> np.ndarray: """ return np.array([self.get_embedding(r) for r in regions]) - def region_embeddings(self) -> Dict[str, np.ndarray]: + def __call__(self, region: str) -> np.ndarray: """ - Get the embeddings for each region in the original AnnData passed in. + Get the embedding for a given region. + + :param str region: the region to get the embedding for """ - if not self.trained: - raise ValueError("Model has not been trained yet.") + return self.get_embedding(region) - # return the key-value dict from the internal model - # each key is a region and each value is the embedding - return self.region2vec - def cell_embeddings(self) -> sc.AnnData: +# +# BEGIN V2 of SCEmbed class +# +class SCEmbedV2(Word2Vec): + def __init__( + self, + model_path: str = None, + tokenizer: InMemTokenizer = None, + universe: Union[Universe, str] = None, + **kwargs, + ): """ - Get the cell embeddings for the original AnnData passed in. This should - be called after training is complete. It is only useful for extracting the - embeddings for the last chunk of data its seen. + The SCEmbedV2 class is a subclass of the Word2Vec class from gensim. It is a + Region2Vec model that extends the Word2Vec model from gensim. + + :param str model_path: The path to a pretrained model to load. + :param InMemTokenizer tokenizer: The tokenizer to use for tokenizing region sets. + :param Universe universe: The universe to use for tokenizing region sets. + :param kwargs: Additional arguments to pass to the Word2Vec constructor. """ - if not self.trained: - raise ValueError("Model has not been trained yet.") - - # get the embeddings for each cell - cell_embeddings = [] - for cell in tqdm(self.region_sets, total=len(self.region_sets)): - cell_embedding = np.mean( - [self.get_embedding(r) for r in cell if r in self.region2vec], - axis=0, - ) - cell_embeddings.append(cell_embedding) + # if model_path is given, download a pretrained model + # from the HuggingFace Hub + if model_path: + self._init_from_huggingface(model_path) + else: + # otherwise, initialize a new model, with a new universe and tokenizer + # since the model is untrained, we have no vocabulary yet, + # and thus need a fresh tokenizer and universe + self.tokenizer = tokenizer or InMemTokenizer(universe or Universe()) - cell_embeddings = np.array(cell_embeddings) + # set the callbacks + self.callbacks = kwargs.get("callbacks") or [] + self.trained = False - # attach embeddings to the AnnData object - self.data.obsm["embedding"] = cell_embeddings - return self.data + # instantiate the Word2Vec model + super().__init__( + window=kwargs.get("window_size") or DEFAULT_WINDOW_SIZE, + vector_size=kwargs.get("vector_size") or DEFAULT_EMBEDDING_SIZE, + min_count=kwargs.get("min_count") or DEFAULT_MIN_COUNT, + workers=kwargs.get("threads") or cpu_count() - 2, + seed=kwargs.get("seed") or 42, # + callbacks=kwargs.get("callbacks") or [], + ) - def cell_embeddings(self) -> sc.AnnData: + def _validate_data(self, data: Union[sc.AnnData, str]) -> sc.AnnData: """ - Get the cell embeddings for the original AnnData passed in. This should - be called after training is complete. It is only useful for extracting the - embeddings for the last chunk of data its seen. + Validate the data is of the correct type and has the required columns. + + :param sc.AnnData | str data: The AnnData object containing the data to train on (can be path to AnnData). + + :return sc.AnnData: The AnnData object. """ - if not self.trained: - raise ValueError("Model has not been trained yet.") + if not isinstance(data, sc.AnnData) and not isinstance(data, str): + raise TypeError(f"Data must be of type AnnData or str, not {type(data).__name__}") - if self.data is None: - raise ValueError( - "Data not found. Please pass in an AnnData object to the constructor." - ) + # if the data is a string, assume it is a filepath + if isinstance(data, str): + data = sc.read_h5ad(data) - # get the embeddings for each cell - cell_embeddings = [] - for cell in tqdm(self.region_sets, total=len(self.region_sets)): - cell_embedding = np.mean( - [self.get_embedding(r) for r in cell if r in self.region2vec], - axis=0, + # validate the data has the required columns + if ( + not hasattr(data.var, CHR_KEY) + or not hasattr(data.var, START_KEY) + or not hasattr(data.var, END_KEY) + ): + raise ValueError( + "Data does not have `chr`, `start`, and `end` columns in the `var` attribute. This is required." ) - cell_embeddings.append(cell_embedding) - - cell_embeddings = np.array(cell_embeddings) - return cell_embeddings + return data - def attach_cell_embeddings(self, key_added: str = "embedding") -> sc.AnnData: + def train( + self, + data: Union[sc.AnnData, str], + epochs: int = DEFAULT_EPOCHS, # training cycles + report_loss: bool = True, + lr: float = DEFAULT_INIT_LR, + min_lr: float = DEFAULT_MIN_LR, + lr_schedule: Union[str, ScheduleType] = "linear", + ): """ - Get the cell embeddings for the original AnnData passed in. This should - be called after training is complete. It is only useful for extracting the - embeddings for the last chunk of data its seen. + Train the model. This is done in two steps: First, we shuffle the documents. + Second, we train the model. - :param str key_added: the key to add the embeddings to in the obsm attribute of the AnnData object + :param sc.AnnData | str data: The AnnData object containing the data to train on (can be path to AnnData). + :param int epochs: The number of epochs to train for (note: this is the number of times regions are shuffled, then fed to the model for training). + :param bool report_loss: Whether or not to report the loss after each epoch. + :param float lr: The initial learning rate. + :param float min_lr: The minimum learning rate. + :param Union[str, ScheduleType] lr_schedule: The learning rate schedule to use. """ - if not self.trained: - raise ValueError("Model has not been trained yet.") + # force to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205683978) + n_shuffles = 1 - if not self.data: - raise ValueError( - "Data not found. Please pass in an AnnData object to the constructor." - ) + # validate the data coming in + data = self._validate_data(data) - if not self.region_sets: - self.region_sets = convert_anndata_to_documents(self.data) + _LOGGER.info("Tokenizing.") - # get the embeddings for each cell - cell_embeddings = [] - for cell in tqdm(self.region_sets, total=len(self.region_sets)): - cell_embedding = np.mean( - [self.get_embedding(r) for r in cell if r in self.region2vec], - axis=0, - ) - cell_embeddings.append(cell_embedding) + # extract out the chr, start, end columns + chrs = data.var[CHR_KEY].values.tolist() + starts = data.var[START_KEY].values.tolist() + ends = data.var[END_KEY].values.tolist() + regions = [Region(c, int(s), int(e)) for c, s, e in zip(chrs, starts, ends)] + + # fit the tokenizer on the regions + self.tokenizer.fit(regions) - cell_embeddings = np.array(cell_embeddings) + # convert the data to a list of documents + region_sets = self.tokenizer.tokenize(data) - # attach embeddings to the AnnData object - self.data.obsm[key_added] = cell_embeddings - return self.data + # convert list of lists of regions to list of list of strings of form chr_start_end + region_sets = [ + [r.chr + "_" + str(r.start) + "_" + str(r.end) for r in region_set] + for region_set in region_sets + ] - def embeddings_to_csv(self, output_path: str): - """ - Save the embeddings to a CSV file. + if report_loss: + self.callbacks.append(ReportLossCallback()) - :param str output_path: the path to save the embeddings to - """ - embeddings = self.cell_embeddings() - embeddings_df = embeddings.obs - - # split the embeddings for each barcode into each dimension and save them - parsed_rows = [] - for row in list(embeddings_df.iterrows()): - row_dict = row[1].to_dict() - new_row_dict = { - f"embedding_dim_{i}": row_dict["embedding"][i] for i in range(self.vector_size) - } - if "id" in row_dict: - new_row_dict["id"] = row_dict["id"] - parsed_rows.append(new_row_dict) - - parsed_df = pd.DataFrame(parsed_rows) - parsed_df.to_csv(output_path, index=False) - - def anndata_to_embeddings(self, adata: sc.AnnData) -> sc.AnnData: - """ - This function will take an AnnData object and add a column to the obs - attribute of the AnnData object that contains the embeddings for each - cell. + # create a learning rate scheduler + lr_scheduler = LearningRateScheduler( + init_lr=lr, min_lr=min_lr, type=lr_schedule, n_epochs=epochs + ) - It does this by taking the regions for each cell and averaging the - embeddings for each region. + # train the model using these shuffled documents + _LOGGER.info("Training begin.") - :param sc.AnnData adata: the AnnData object to add the embeddings to - :return sc.AnnData: the AnnData object with the embeddings added - """ - region_sets = convert_anndata_to_documents(adata, self.use_default_region_names) - cell_embeddings = [] - for cell in tqdm(region_sets, total=len(region_sets)): - cell_embedding = np.mean([self.get_embedding(r) for r in cell], axis=0) - cell_embeddings.append(cell_embedding) + # build up the vocab + super().build_vocab( + region_sets, + update=False if not self.trained else True, + min_count=self.min_count, # this shouldnt be needed but it is + ) - # attach embeddings to the AnnData object - self.data.obs["embedding"] = cell_embeddings - return self.data + for shuffle_num in range(epochs): + # update current values + current_lr = lr_scheduler.get_lr() + current_loss = self.get_latest_training_loss() - def __call__(self, region: str) -> np.ndarray: - """ - Get the embedding for a given region. + # update user + _LOGGER.info(f"SHUFFLE {shuffle_num} - lr: {current_lr}, loss: {current_loss}") + _LOGGER.info("Shuffling documents.") - :param str region: the region to get the embedding for - """ - return self.get_embedding(region) + # shuffle regions + region_sets = shuffle_documents(region_sets, n_shuffles=n_shuffles) + + # train the model on one iteration + super().train( + region_sets, + total_examples=len(region_sets), + epochs=1, # for to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205692089) + callbacks=self.callbacks, + compute_loss=report_loss, + start_alpha=current_lr, + ) + # update learning rates + lr_scheduler.update() -# -# BEGIN V2 of SCEmbed class -# -class SCEmbedV2(Word2Vec): - def __init__( - self, - model_path: str = None, - tokenizer: InMemTokenizer = None, - universe: Union[Universe, str] = None, - ): - # if model_path is given, download a pretrained model - # from the HuggingFace Hub - if model_path: - self._init_from_huggingface(model_path) - else: - self.tokenizer = tokenizer or InMemTokenizer() - self.universe = universe or Universe() + self.trained = True + + # once training is complete, create a region to vector mapping + regions = list(self.wv.key_to_index.keys()) + + # create a mapping from region to vector + for word in regions: + self.region2vec[word] = self.wv[word] diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 4bc7c5c4..d51cce00 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -3,9 +3,10 @@ import shutil import subprocess from abc import ABC, abstractmethod -from typing import List, Tuple, Union, Dict +from typing import List, Union, Dict import numpy as np +import scanpy as sc from tqdm import tqdm from intervaltree import IntervalTree @@ -13,13 +14,17 @@ from gitk.tokenization.split_file import split_file from ..io import RegionSet, RegionSetCollection, Region -from .utils import extract_regions_from_bed_or_list +from .utils import ( + extract_regions_from_bed_or_list, + generate_var_conversion_map, + anndata_to_regionsets, +) from .hard_tokenization_batch import main as hard_tokenization # Should a tokenizer *hold* a universe, or take one as a parameter? Or both? class Universe: - def __init__(self, regions: Union[str, List[str], List[Region]]): + def __init__(self, regions: Union[str, List[str], List[Region]] = None): """ Create a new Universe. @@ -31,7 +36,7 @@ def __init__(self, regions: Union[str, List[str], List[Region]]): self.total_regions = 0 if regions is not None: - self.build_tree() + self.build_trees() def get_tree(self, tree: str): """ @@ -49,7 +54,13 @@ def trees(self): def regions(self): return self._regions - def build_tree(self, regions: Union[str, List[str], List[Region]] = None): + def add_regions(self, regions: Union[str, List[Region]]): + """ + Add regions to the universe. + """ + self.build_trees(regions) + + def build_trees(self, regions: Union[str, List[Region]] = None): """ Builds the interval tree from the regions. The tree is a dictionary that maps chromosomes to interval trees. @@ -62,7 +73,7 @@ def build_tree(self, regions: Union[str, List[str], List[Region]] = None): regions = extract_regions_from_bed_or_list(regions or self._regions) # build trees - for region in tqdm(regions, total=len(regions), desc="Loading universe"): + for region in tqdm(regions, total=len(regions), desc="Adding regions to universe"): if region.chr not in self._trees: self._trees[region.chr] = IntervalTree() self._trees[region.chr][ @@ -74,8 +85,8 @@ def build_tree(self, regions: Union[str, List[str], List[Region]] = None): def query( self, - regions: Union[str, List[str], List[Region], Region], - ): + regions: Union[str, List[Region], Region], + ) -> List[Region]: """ Query the interval tree for the given regions. That is, find all regions that overlap with the given regions. @@ -85,6 +96,7 @@ def query( # validate input if isinstance(regions, Region): regions = [regions] + regions = extract_regions_from_bed_or_list(regions) overlapping_regions = [] @@ -96,6 +108,53 @@ def query( overlapping_regions.append(Region(region.chr, overlap.begin, overlap.end)) return overlapping_regions + def convert_anndata_to_universe(self, adata: sc.AnnData): + """ + Convert an AnnData object to a universe. This is done by + inspecting the peaks in the AnnData object and converting + them to regions found in the universe. + + :param sc.AnnData adata: The AnnData object to convert. + """ + # ensure adata has chr, start, and end + if not all([x in adata.var.columns for x in ["chr", "start", "end"]]): + raise ValueError("AnnData object must have `chr`, `start`, and `end` columns in .var") + + # create list of regions from adata + adata.var["region"] = ( + adata.var["chr"].astype(str) + + "_" + + adata.var["start"].astype(str) + + "_" + + adata.var["end"].astype(str) + ) + query_set = [ + Region(x[0], x[1], x[2]) + for x in list( + zip(adata.var["chr"], adata.var["start"].astype(int), adata.var["end"].astype(int)) + ) + ] + + # generate conversion map + _map = generate_var_conversion_map(query_set, self) + + # map regions to new universe + adata.var["new_region"] = adata.var["region"].map(_map) + + # drop rows where new_region is None + adata.var.dropna(subset=["new_region"], inplace=True) + + # split new_region into chr, start, end + adata.var[["chr", "start", "end"]] = adata.var["new_region"].str.split("_", expand=True) + + # drop 'region' and 'new_region' columns + adata.var.drop(columns=["region", "new_region"], inplace=True) + + # update adata with the new DataFrame + adata = adata[:, adata.var.index] + + return adata + def __contains__(self, item: Region): """ Check if the given region is in the universe. @@ -146,16 +205,31 @@ def __init__(self, universe: Union[str, Universe] = None): else: self.universe = None - def tokenize(self, region_set: Union[str, List[Region]]) -> List[Region]: + def fit(self, regions: Union[str, List[Region]]): + """ + Fit the tokenizer to the given regions. This is equivalent to adding regions to the universe. + """ + self.universe.add_regions(regions) + + def tokenize( + self, region_set: Union[str, List[Region], sc.AnnData] + ) -> Union[List[Region], List[List[Region]]]: """ Tokenize a RegionSet. This is achieved using hard tokenization which is just a query to the universe, or simple overlap detection. - :param str | List[Region] region_set: The list of regions to tokenize + :param str | List[Region] | sc.AnnData region_set: The list of regions to tokenize """ - return self.universe.query(region_set) + if isinstance(region_set, sc.AnnData): + # step 1 is to convert the AnnData object to the universe + self.universe.convert_anndata_to_universe(region_set) + + # step 2 is to convert the AnnData object to a list of lists of regions + return anndata_to_regionsets(region_set) + else: + return self.universe.query(region_set) # not sure what this looks like, multiple RegionSets? def tokenize_rsc(self, rsc: RegionSetCollection) -> RegionSetCollection: diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index 2bdef84b..ca474df0 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -1,7 +1,12 @@ import os +from typing import Union, List, Tuple, Dict, TYPE_CHECKING -from typing import Union, List, Tuple, Dict +import numpy as np +import scanpy as sc +from tqdm import tqdm +if TYPE_CHECKING: + from .main import Universe from ..io import Region @@ -12,12 +17,11 @@ def extract_regions_from_bed_or_list(regions: Union[str, List[str], List[Region] so we need to check what the user has passed. It also standardizes the input to a list of tuples of chr, start, end. - Users are allowed to pass a list of regions, a list of tuples with chr, start, and end, or - a path to a BED file. + Users are allowed to pass a list of regions, a path to a BED file, or an AnnData object. :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. This can be either a list of regions or a path to a BED file containing regions. - :return list: A list of tuples of chr, start, end. + :return list: A list of regions or AnnData. """ # check for bedfile if isinstance(regions, str): @@ -44,7 +48,80 @@ def extract_regions_from_bed_or_list(regions: Union[str, List[str], List[Region] # check for list of Region objects elif isinstance(regions, list) and all([isinstance(r, Region) for r in regions]): return regions + else: raise ValueError( "The regions must be either a list of regions or a path to a BED file containing regions." ) + + +def generate_var_conversion_map( + a: List[Region], + b: "Universe", + fraction: float = 1.0e-9, # not used +) -> Dict[str, Union[str, None]]: + """ + Create a conversion map to convert regions from a to b. This is used to convert the + consensus peak set of one AnnData object to another. + + For each region in a, we will either find a matching region in b, or None. If a matching + region is found, we will store the region in b. If no matching region is found, we will + store `None`. + + Intuitively, think of this as converting `A` --> `B`. If a region in `A` is found in `B`, + we will change the region in `A` to the region in `B`. If a region in `A` is not found in + `B`, we will drop that region in `A` altogether. + + :param List[tuple[str, int, int]] a: the first list of regions + :param Universe: the second list of regions as a Universe object + :param float fraction: the fraction of the region that must overlap to be considered an overlap. Not used. + """ + + conversion_map = dict() + + for region in tqdm(a, total=len(a), desc="Generating conversion map"): + overlaps = b.query(region) + region_str = f"{region.chr}_{region.start}_{region.end}" + if len(overlaps) > 0: + olap = overlaps[0] # take the first overlap for now, we can change this later + olap_str = f"{olap.chr}_{olap.start}_{olap.end}" + conversion_map[region_str] = olap_str + else: + conversion_map[region_str] = None + + return conversion_map + + +def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: + """ + Converts an AnnData object to a list of lists of regions. This + is done by taking each cell and creating a list of all regions + that have a value greater than 0. + + This function is already pretty optimized. To speed this up + further we'd have to parallelize it or reach for a lower-level + language. + + *Note: this method requires that the sc.AnnData object have + chr, start, and end in `.var` attributes* + """ + # Extract the arrays for chr, start, and end + chr_values = adata.var["chr"].values + start_values = adata.var["start"].values + end_values = adata.var["end"].values + + # Perform the comparison using numpy operations + positive_values = adata.X > 0 + + if not isinstance(positive_values, np.ndarray): + positive_values = positive_values.toarray() + + regions = [] + for i in tqdm(range(adata.shape[0]), total=adata.shape[0], desc="Tokenizing"): + regions.append( + [ + Region(chr_values[j], start_values[j], end_values[j]) + for j in np.where(positive_values[i])[0] + ] + ) + return regions diff --git a/tests/test_tokenization.py b/tests/test_tokenization.py index 2655215a..42f1183f 100644 --- a/tests/test_tokenization.py +++ b/tests/test_tokenization.py @@ -35,8 +35,8 @@ def test_region_in_universe(universe_bed_file: str): r_is_in1 = Region("chr17", 78_168_158, 78_169_026) r_is_in2 = Region( "chr2", - 241_859_589, - 241_860_443, + 241_859_569, + 241_860_423, ) r_is_in3 = Region( "chr22", From 3ac8e6d2fb55d2d6a57f3e403b3e1123a7f779ea Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 26 Jul 2023 18:47:07 -0400 Subject: [PATCH 0282/1072] add magic methods to RegionSet class --- .gitignore | 2 +- gitk/io/io.py | 15 ++++++++++++++- gitk/scembed/main.py | 10 +++++++--- gitk/tokenization/main.py | 10 +++++----- 4 files changed, 27 insertions(+), 10 deletions(-) diff --git a/.gitignore b/.gitignore index 171de90a..ce2d7142 100644 --- a/.gitignore +++ b/.gitignore @@ -85,7 +85,7 @@ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: -# .python-version +.python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. diff --git a/gitk/io/io.py b/gitk/io/io.py index e1e6bd18..e285e780 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -22,13 +22,26 @@ def __repr__(self): # TODO: This belongs somewhere else class RegionSet(object): - def __init__(self, path): + def __init__(self, path: str): + self.path = path with open(path, "r") as f: lines = f.readlines() for line in lines: chr, start, stop = line.split("\t") self.regions.append(Region(chr, int(start), int(stop))) + def __len__(self): + return len(self.regions) + + def __getitem__(self, key): + return self.regions[key] + + def __repr__(self): + return f"RegionSet({self.path})" + + def __iter__(self): + return iter(self.regions) + # TODO: This belongs somewhere else; does it even make sense? class TokenizedRegionSet(object): diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index 1170d33a..ff6a801a 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -309,9 +309,10 @@ def __init__( # and thus need a fresh tokenizer and universe self.tokenizer = tokenizer or InMemTokenizer(universe or Universe()) - # set the callbacks + # set other attributes self.callbacks = kwargs.get("callbacks") or [] self.trained = False + self.region2vec = dict() # instantiate the Word2Vec model super().__init__( @@ -442,8 +443,11 @@ def train( self.trained = True # once training is complete, create a region to vector mapping - regions = list(self.wv.key_to_index.keys()) + learned_regions = list(self.wv.key_to_index.keys()) # create a mapping from region to vector - for word in regions: + for word in learned_regions: self.region2vec[word] = self.wv[word] + + def __call__(self): + pass diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index d51cce00..0d9187db 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -190,17 +190,17 @@ def tokenize(self, *args, **kwargs): class InMemTokenizer(Tokenizer): """Abstract class representing a tokenizer function""" - def __init__(self, universe: Union[str, Universe] = None): + def __init__(self, universe: Union[str, RegionSet] = None): """ Create a new InMemTokenizer. - You can either pass a Universe object or a path to a BED file containing regions. + You can either pass a RegionSet object or a path to a BED file containing regions. - :param Union[str, Universe] universe: The universe to use for tokenization. + :param Union[str, RegionSet] universe: The universe to use for tokenization. """ if isinstance(universe, str): - self.universe = Universe(universe) - elif isinstance(universe, Universe): + self.universe = RegionSet(universe) + elif isinstance(universe, RegionSet): self.universe = universe else: self.universe = None From c1cd0821bd66f8b6e017996548bb066c4688335a Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 26 Jul 2023 19:37:41 -0400 Subject: [PATCH 0283/1072] io stuff + tokenization --- gitk/io/io.py | 32 ++++++-- gitk/scembed/main.py | 12 ++- gitk/tokenization/main.py | 149 ++++++++++++++++++------------------- gitk/tokenization/utils.py | 54 ++------------ tests/test_io.py | 42 +++++++++++ tests/test_scembed.py | 2 +- tests/test_tokenization.py | 108 ++++++++++----------------- 7 files changed, 191 insertions(+), 208 deletions(-) create mode 100644 tests/test_io.py diff --git a/gitk/io/io.py b/gitk/io/io.py index e285e780..a57edcfc 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -22,16 +22,25 @@ def __repr__(self): # TODO: This belongs somewhere else class RegionSet(object): - def __init__(self, path: str): + def __init__(self, path: str, backed: bool = False): + self.backed = backed + self.regions: List[Region] = [] self.path = path - with open(path, "r") as f: - lines = f.readlines() - for line in lines: - chr, start, stop = line.split("\t") - self.regions.append(Region(chr, int(start), int(stop))) + if backed: + self.regions = None + # https://stackoverflow.com/a/32607817/13175187 + with open(self.path, "r") as file: + self.length = sum(1 for line in file if line.strip()) + else: + with open(path, "r") as f: + lines = f.readlines() + for line in lines: + chr, start, stop = line.split("\t") + self.regions.append(Region(chr, int(start), int(stop))) + self.length = len(self.regions) def __len__(self): - return len(self.regions) + return self.length def __getitem__(self, key): return self.regions[key] @@ -40,7 +49,14 @@ def __repr__(self): return f"RegionSet({self.path})" def __iter__(self): - return iter(self.regions) + if self.backed: + with open(self.path, "r") as f: + for line in f: + chr, start, stop = line.split("\t") + yield Region(chr, int(start), int(stop)) + else: + for region in self.regions: + yield region # TODO: This belongs somewhere else; does it even make sense? diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index ff6a801a..5cd0a189 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -2,19 +2,17 @@ import os import pickle from logging import getLogger -from typing import Dict, List, Union +from typing import List, Union from multiprocessing import cpu_count import numpy as np -import pandas as pd import scanpy as sc from gensim.models import Word2Vec from gensim.models.callbacks import CallbackAny2Vec from numba import config -from tqdm import tqdm -from ..io import Region -from ..tokenization import InMemTokenizer, Universe +from ..io import Region, RegionSet +from ..tokenization import InMemTokenizer from .const import * from .exceptions import * from .utils import ( @@ -287,7 +285,7 @@ def __init__( self, model_path: str = None, tokenizer: InMemTokenizer = None, - universe: Union[Universe, str] = None, + universe: Union[RegionSet, str] = None, **kwargs, ): """ @@ -307,7 +305,7 @@ def __init__( # otherwise, initialize a new model, with a new universe and tokenizer # since the model is untrained, we have no vocabulary yet, # and thus need a fresh tokenizer and universe - self.tokenizer = tokenizer or InMemTokenizer(universe or Universe()) + self.tokenizer = tokenizer or InMemTokenizer() # set other attributes self.callbacks = kwargs.get("callbacks") or [] diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 0d9187db..4cd10057 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -15,27 +15,40 @@ from ..io import RegionSet, RegionSetCollection, Region from .utils import ( - extract_regions_from_bed_or_list, - generate_var_conversion_map, + validate_and_standardize_regions, anndata_to_regionsets, ) from .hard_tokenization_batch import main as hard_tokenization -# Should a tokenizer *hold* a universe, or take one as a parameter? Or both? -class Universe: - def __init__(self, regions: Union[str, List[str], List[Region]] = None): +class Tokenizer(ABC): + @abstractmethod + def tokenize(self, *args, **kwargs): + raise NotImplementedError + + +class InMemTokenizer(Tokenizer): + """Abstract class representing a tokenizer function""" + + def __init__(self, universe: Union[str, RegionSet] = None): """ - Create a new Universe. + Create a new InMemTokenizer. - :param Union[str, List[str]] regions: The regions to use for tokenization. This can be thought of as a vocabulary. - This can be either a list of regions or a path to a BED file containing regions. + You can either pass a RegionSet object or a path to a BED file containing regions. + + :param Union[str, RegionSet] universe: The universe to use for tokenization. """ self._trees: Dict[str, IntervalTree] = dict() - self._regions = regions - self.total_regions = 0 - if regions is not None: + if isinstance(universe, str): + self.universe = RegionSet(universe) + elif isinstance(universe, RegionSet): + self.universe = universe + else: + self.universe = None + + if self.universe is not None: + # build interval trees self.build_trees() def get_tree(self, tree: str): @@ -50,17 +63,13 @@ def get_tree(self, tree: str): def trees(self): return self._trees - @property - def regions(self): - return self._regions - - def add_regions(self, regions: Union[str, List[Region]]): + def add_regions(self, regions: Union[str, List[Region], RegionSet]): """ Add regions to the universe. """ self.build_trees(regions) - def build_trees(self, regions: Union[str, List[Region]] = None): + def build_trees(self, regions: Union[str, List[Region], RegionSet] = None): """ Builds the interval tree from the regions. The tree is a dictionary that maps chromosomes to interval trees. @@ -70,7 +79,7 @@ def build_trees(self, regions: Union[str, List[Region]] = None): :param str regions: The regions to use for tokenization. This can be thought of as a vocabulary. """ - regions = extract_regions_from_bed_or_list(regions or self._regions) + regions = validate_and_standardize_regions(regions or self.universe.regions) # build trees for region in tqdm(regions, total=len(regions), desc="Adding regions to universe"): @@ -83,21 +92,29 @@ def build_trees(self, regions: Union[str, List[Region]] = None): # count total regions self.total_regions = sum([len(self._trees[tree]) for tree in self._trees]) - def query( + def fit(self, regions: Union[str, List[Region]]): + """ + Fit the tokenizer to the given regions. This is equivalent to adding regions to the universe. + """ + self.add_regions(regions) + + def find_overlaps( self, - regions: Union[str, List[Region], Region], + regions: Union[str, List[Region], Region, RegionSet], + f: float = None, # not implemented yet ) -> List[Region]: """ Query the interval tree for the given regions. That is, find all regions that overlap with the given regions. - :param Union[str, List[str], List[Region], Region] regions: The regions to query for. this can be either a bed file, - a list of regions (chr_start_end), or a list of tuples of chr, start, end. + :param Union[str, List[str], List[Region], Region, RegionSet] regions: The regions to query for. this can be either a bed file, + a list of regions (chr_start_end), or a list of tuples of chr, start, end. + :param float f: The fraction of the region that must overlap to be considered an overlap. Not yet implemented. """ # validate input if isinstance(regions, Region): regions = [regions] - regions = extract_regions_from_bed_or_list(regions) + regions = validate_and_standardize_regions(regions) overlapping_regions = [] for region in regions: @@ -108,13 +125,15 @@ def query( overlapping_regions.append(Region(region.chr, overlap.begin, overlap.end)) return overlapping_regions - def convert_anndata_to_universe(self, adata: sc.AnnData): + def convert_anndata_to_universe(self, adata: sc.AnnData) -> None: """ Convert an AnnData object to a universe. This is done by inspecting the peaks in the AnnData object and converting - them to regions found in the universe. + them to regions found in the universe. This process + occurs in-place. :param sc.AnnData adata: The AnnData object to convert. + :param Universe universe: The universe to convert to. """ # ensure adata has chr, start, and end if not all([x in adata.var.columns for x in ["chr", "start", "end"]]): @@ -136,7 +155,7 @@ def convert_anndata_to_universe(self, adata: sc.AnnData): ] # generate conversion map - _map = generate_var_conversion_map(query_set, self) + _map = self.generate_var_conversion_map(query_set) # map regions to new universe adata.var["new_region"] = adata.var["region"].map(_map) @@ -155,61 +174,41 @@ def convert_anndata_to_universe(self, adata: sc.AnnData): return adata - def __contains__(self, item: Region): - """ - Check if the given region is in the universe. - - :param Region item: The region to check for. + def generate_var_conversion_map( + self, + a: List[Region], + fraction: float = 1.0e-9, # not used + ) -> Dict[str, Union[str, None]]: """ - # ensure item is a tuple of chr, start, end - if not isinstance(item, Region): - raise ValueError("The item to check for must be a tuple of chr, start, end.") - - if item.chr not in self._trees: - return False - overlaps = self._trees[item.chr][item.start : item.end] - return len(overlaps) > 0 - - def __iter__(self): - for tree in self._trees: - yield self._trees[tree] + Create a conversion map to convert regions from a to b. This is used to convert the + consensus peak set of one AnnData object to another. - def __len__(self): - return self.total_regions + For each region in a, we will either find a matching region in b, or None. If a matching + region is found, we will store the region in b. If no matching region is found, we will + store `None`. - def __repr__(self): - return f"Universe with {len(self)} regions" + Intuitively, think of this as converting `A` --> `B`. If a region in `A` is found in `B`, + we will change the region in `A` to the region in `B`. If a region in `A` is not found in + `B`, we will drop that region in `A` altogether. - -class Tokenizer(ABC): - @abstractmethod - def tokenize(self, *args, **kwargs): - raise NotImplementedError - - -class InMemTokenizer(Tokenizer): - """Abstract class representing a tokenizer function""" - - def __init__(self, universe: Union[str, RegionSet] = None): + :param List[tuple[str, int, int]] a: the first list of regions + :param Universe: the second list of regions as a Universe object + :param float fraction: the fraction of the region that must overlap to be considered an overlap. Not used. """ - Create a new InMemTokenizer. - You can either pass a RegionSet object or a path to a BED file containing regions. + conversion_map = dict() - :param Union[str, RegionSet] universe: The universe to use for tokenization. - """ - if isinstance(universe, str): - self.universe = RegionSet(universe) - elif isinstance(universe, RegionSet): - self.universe = universe - else: - self.universe = None + for region in tqdm(a, total=len(a), desc="Generating conversion map"): + overlaps = self.find_overlaps(region) + region_str = f"{region.chr}_{region.start}_{region.end}" + if len(overlaps) > 0: + olap = overlaps[0] # take the first overlap for now, we can change this later + olap_str = f"{olap.chr}_{olap.start}_{olap.end}" + conversion_map[region_str] = olap_str + else: + conversion_map[region_str] = None - def fit(self, regions: Union[str, List[Region]]): - """ - Fit the tokenizer to the given regions. This is equivalent to adding regions to the universe. - """ - self.universe.add_regions(regions) + return conversion_map def tokenize( self, region_set: Union[str, List[Region], sc.AnnData] @@ -224,12 +223,12 @@ def tokenize( """ if isinstance(region_set, sc.AnnData): # step 1 is to convert the AnnData object to the universe - self.universe.convert_anndata_to_universe(region_set) + self.convert_anndata_to_universe(region_set) # step 2 is to convert the AnnData object to a list of lists of regions return anndata_to_regionsets(region_set) else: - return self.universe.query(region_set) + return self.find_overlaps(region_set) # not sure what this looks like, multiple RegionSets? def tokenize_rsc(self, rsc: RegionSetCollection) -> RegionSetCollection: diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index ca474df0..3bdbeebe 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -7,17 +7,17 @@ if TYPE_CHECKING: from .main import Universe -from ..io import Region +from ..io import Region, RegionSet # better name? -def extract_regions_from_bed_or_list(regions: Union[str, List[str], List[Region]]) -> List[Region]: +def validate_and_standardize_regions(regions: Union[str, List[Region], RegionSet]) -> List[Region]: """ Validate the input for the regions. A universe accepts a lot of forms of input, so we need to check what the user has passed. It also standardizes the input to a list of tuples of chr, start, end. - Users are allowed to pass a list of regions, a path to a BED file, or an AnnData object. + Users are allowed to pass a list of regions, a path to a BED file, or a RegionSet object. :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. This can be either a list of regions or a path to a BED file containing regions. @@ -37,61 +37,19 @@ def extract_regions_from_bed_or_list(regions: Union[str, List[str], List[Region] regions.append(Region(chr, int(start), int(end))) return regions - # check for list of chr_start_end strings - elif isinstance(regions, list) and all([isinstance(r, str) for r in regions]): - regions_parsed: List[Region] = [] - for region in regions: - chr, start, end = region.split("_") - regions_parsed.append(Region(chr, int(start), int(end))) - return regions_parsed - # check for list of Region objects elif isinstance(regions, list) and all([isinstance(r, Region) for r in regions]): return regions + elif isinstance(regions, RegionSet): + return regions.regions + else: raise ValueError( "The regions must be either a list of regions or a path to a BED file containing regions." ) -def generate_var_conversion_map( - a: List[Region], - b: "Universe", - fraction: float = 1.0e-9, # not used -) -> Dict[str, Union[str, None]]: - """ - Create a conversion map to convert regions from a to b. This is used to convert the - consensus peak set of one AnnData object to another. - - For each region in a, we will either find a matching region in b, or None. If a matching - region is found, we will store the region in b. If no matching region is found, we will - store `None`. - - Intuitively, think of this as converting `A` --> `B`. If a region in `A` is found in `B`, - we will change the region in `A` to the region in `B`. If a region in `A` is not found in - `B`, we will drop that region in `A` altogether. - - :param List[tuple[str, int, int]] a: the first list of regions - :param Universe: the second list of regions as a Universe object - :param float fraction: the fraction of the region that must overlap to be considered an overlap. Not used. - """ - - conversion_map = dict() - - for region in tqdm(a, total=len(a), desc="Generating conversion map"): - overlaps = b.query(region) - region_str = f"{region.chr}_{region.start}_{region.end}" - if len(overlaps) > 0: - olap = overlaps[0] # take the first overlap for now, we can change this later - olap_str = f"{olap.chr}_{olap.start}_{olap.end}" - conversion_map[region_str] = olap_str - else: - conversion_map[region_str] = None - - return conversion_map - - def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: """ Converts an AnnData object to a list of lists of regions. This diff --git a/tests/test_io.py b/tests/test_io.py new file mode 100644 index 00000000..1036e27f --- /dev/null +++ b/tests/test_io.py @@ -0,0 +1,42 @@ +import os +import sys + +import pytest +import scanpy as sc +import numpy as np + +from gitk.io import Region, RegionSet +from gitk.tokenization import InMemTokenizer + + +@pytest.fixture +def universe_bed_file(): + return "tests/data/universe.bed" + + +def test_make_region(): + r = Region("chr1", 0, 100) + assert r is not None + assert r.chr == "chr1" + assert r.start == 0 + assert r.end == 100 + + +def test_make_region_set(universe_bed_file: str): + u = RegionSet(universe_bed_file) + assert u is not None + assert len(u) == 10_000 + + # test we can iterate over it + for region in u: + assert isinstance(region, Region) + + +def test_make_region_set_with_backed(universe_bed_file: str): + u = RegionSet(universe_bed_file, backed=True) + assert u is not None + assert len(u) == 10_000 + + # test we can iterate over it + for region in u: + assert isinstance(region, Region) diff --git a/tests/test_scembed.py b/tests/test_scembed.py index 25506ea0..d878e8ca 100644 --- a/tests/test_scembed.py +++ b/tests/test_scembed.py @@ -9,7 +9,7 @@ # add parent directory to path sys.path.append("../") -from gitk import scembed, utils +from gitk import scembed # set to DEBUG to see more info logging.basicConfig(level=logging.INFO) diff --git a/tests/test_tokenization.py b/tests/test_tokenization.py index 42f1183f..97ca459a 100644 --- a/tests/test_tokenization.py +++ b/tests/test_tokenization.py @@ -5,8 +5,8 @@ import scanpy as sc import numpy as np -from gitk.io import Region -from gitk.tokenization import Universe, InMemTokenizer +from gitk.io import Region, RegionSet +from gitk.tokenization import InMemTokenizer # add parent directory to path sys.path.append("../") @@ -23,78 +23,20 @@ def universe_bed_file(): def test_create_universe(universe_bed_file: str): - u = Universe(universe_bed_file) + u = RegionSet(universe_bed_file) assert u is not None assert len(u) == 10_000 -def test_region_in_universe(universe_bed_file: str): - u = Universe(universe_bed_file) - - # regions in - r_is_in1 = Region("chr17", 78_168_158, 78_169_026) - r_is_in2 = Region( - "chr2", - 241_859_569, - 241_860_423, - ) - r_is_in3 = Region( - "chr22", - 38_863_328, - 38_864_072, - ) - - # regions not in - r_not_in1 = Region( - "chr22", - 1, - 100, - ) - r_not_in2 = Region( - "chr17", - 200, - 300, - ) - r_not_in3 = Region( - "chr2", - 1_000_000_000_000, - 1_000_000_000_100, - ) - - assert r_is_in1 in u - assert r_is_in2 in u - assert r_is_in3 in u - - assert r_not_in1 not in u - assert r_not_in2 not in u - assert r_not_in3 not in u - - -def test_universe_query(universe_bed_file: str): - u = Universe(universe_bed_file) - - regions = [ - Region("chr17", 78_168_158, 78_169_026), - Region( - "chr2", - 241_859_589, - 241_860_443, - ), - Region( - "chr2", - 1_000_000_000_000, - 1_000_000_000_100, - ), - ] - - # query regions - olaps = u.query(regions) - - assert len(olaps) == 2 - - -def test_make_in_mem_tokenizer(universe_bed_file: str): +def test_make_in_mem_tokenizer_from_file(universe_bed_file: str): t = InMemTokenizer(universe_bed_file) + assert len(t.universe) == 10_000 + assert t is not None + + +def test_make_in_mem_tokenizer_from_region_set(universe_bed_file: str): + u = RegionSet(universe_bed_file) + t = InMemTokenizer(u) assert t is not None @@ -124,3 +66,31 @@ def test_tokenize_bed_file(universe_bed_file: str): # ensure that the tokens are unqiue from the original regions assert len(set(tokens).intersection(set(regions))) == 0 assert len(tokens) == 10 + + +def test_tokenize_list_of_regions(universe_bed_file: str): + """ + Use the in memory tokenizer to tokenize a list of regions. + + The bed file contains 13 regions, 10 of which are in the universe. None + of them should be the original regions in the file. + """ + t = InMemTokenizer(universe_bed_file) + assert t is not None + + # tokenize a bed file + bed_file = "tests/data/to_tokenize.bed" + + # read in each and cast as a region + with open(bed_file, "r") as f: + lines = f.readlines() + regions = [] + for line in lines: + chr, start, stop = line.strip().split("\t") + regions.append(Region(chr, int(start), int(stop))) + + tokens = t.tokenize(regions) + + # ensure that the tokens are unqiue from the original regions + assert len(set(tokens).intersection(set(regions))) == 0 + assert len(tokens) == 10 From 01c5e3fa7764b1e57f54c924929f52b56213d8f6 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 26 Jul 2023 19:38:31 -0400 Subject: [PATCH 0284/1072] fix imports --- gitk/io/io.py | 3 ++- gitk/tokenization/main.py | 11 ++++------- gitk/tokenization/utils.py | 5 +---- 3 files changed, 7 insertions(+), 12 deletions(-) diff --git a/gitk/io/io.py b/gitk/io/io.py index a57edcfc..ff13c927 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -1,8 +1,9 @@ -from ..utils import * from typing import List from intervaltree import Interval +from ..utils import * + class Region(Interval): def __new__(cls, chr: str, start: int, stop: int, data=None): diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 4cd10057..6d8ec463 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -3,22 +3,19 @@ import shutil import subprocess from abc import ABC, abstractmethod -from typing import List, Union, Dict +from typing import Dict, List, Union import numpy as np import scanpy as sc -from tqdm import tqdm from intervaltree import IntervalTree +from tqdm import tqdm import gitk.region2vec.utils as utils from gitk.tokenization.split_file import split_file -from ..io import RegionSet, RegionSetCollection, Region -from .utils import ( - validate_and_standardize_regions, - anndata_to_regionsets, -) +from ..io import Region, RegionSet, RegionSetCollection from .hard_tokenization_batch import main as hard_tokenization +from .utils import anndata_to_regionsets, validate_and_standardize_regions class Tokenizer(ABC): diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index 3bdbeebe..dede3a4c 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -1,12 +1,9 @@ import os -from typing import Union, List, Tuple, Dict, TYPE_CHECKING +from typing import Union, List import numpy as np import scanpy as sc from tqdm import tqdm - -if TYPE_CHECKING: - from .main import Universe from ..io import Region, RegionSet From 50c2a56210c1c189815d5a387ce5a865088eb6ae Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 26 Jul 2023 19:48:42 -0400 Subject: [PATCH 0285/1072] io + tokenization --- gitk/tokenization/main.py | 19 +++++++++----- gitk/tokenization/utils.py | 54 +++++++++----------------------------- 2 files changed, 25 insertions(+), 48 deletions(-) diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 6d8ec463..758b162e 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -15,7 +15,7 @@ from ..io import Region, RegionSet, RegionSetCollection from .hard_tokenization_batch import main as hard_tokenization -from .utils import anndata_to_regionsets, validate_and_standardize_regions +from .utils import anndata_to_regionsets class Tokenizer(ABC): @@ -38,9 +38,9 @@ def __init__(self, universe: Union[str, RegionSet] = None): self._trees: Dict[str, IntervalTree] = dict() if isinstance(universe, str): - self.universe = RegionSet(universe) + self.universe = RegionSet(universe) # load from file elif isinstance(universe, RegionSet): - self.universe = universe + self.universe = universe # load from memory (probably rare?) else: self.universe = None @@ -76,7 +76,12 @@ def build_trees(self, regions: Union[str, List[Region], RegionSet] = None): :param str regions: The regions to use for tokenization. This can be thought of as a vocabulary. """ - regions = validate_and_standardize_regions(regions or self.universe.regions) + if regions is None: + regions = self.universe # use the universe passed in the constructor + elif isinstance(regions, str): + regions = RegionSet(regions) # load from file + else: + pass # assume it's a list of regions that was passed in # build trees for region in tqdm(regions, total=len(regions), desc="Adding regions to universe"): @@ -110,8 +115,10 @@ def find_overlaps( # validate input if isinstance(regions, Region): regions = [regions] - - regions = validate_and_standardize_regions(regions) + elif isinstance(regions, str): + regions = RegionSet(regions) + else: + pass overlapping_regions = [] for region in regions: diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index dede3a4c..a1a6b089 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -1,50 +1,10 @@ import os -from typing import Union, List +from typing import List import numpy as np import scanpy as sc from tqdm import tqdm -from ..io import Region, RegionSet - - -# better name? -def validate_and_standardize_regions(regions: Union[str, List[Region], RegionSet]) -> List[Region]: - """ - Validate the input for the regions. A universe accepts a lot of forms of input, - so we need to check what the user has passed. It also standardizes the input to a list - of tuples of chr, start, end. - - Users are allowed to pass a list of regions, a path to a BED file, or a RegionSet object. - - :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. - This can be either a list of regions or a path to a BED file containing regions. - :return list: A list of regions or AnnData. - """ - # check for bedfile - if isinstance(regions, str): - # ensure that the file exists - if not os.path.exists(regions): - raise FileNotFoundError(f"Could not find file {regions} containing regions.") - file = regions # rename for clarity - regions: List[Region] = [] - with open(file, "r") as f: - lines = f.readlines() - for line in lines: - chr, start, end = line.strip().split("\t") - regions.append(Region(chr, int(start), int(end))) - return regions - - # check for list of Region objects - elif isinstance(regions, list) and all([isinstance(r, Region) for r in regions]): - return regions - - elif isinstance(regions, RegionSet): - return regions.regions - - else: - raise ValueError( - "The regions must be either a list of regions or a path to a BED file containing regions." - ) +from ..io import Region def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: @@ -60,6 +20,16 @@ def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: *Note: this method requires that the sc.AnnData object have chr, start, and end in `.var` attributes* """ + if not isinstance(adata, sc.AnnData): + raise ValueError("The input must be a scanpy AnnData object.") + + if not all( + ["chr" in adata.var.columns, "start" in adata.var.columns, "end" in adata.var.columns] + ): + raise ValueError( + "The AnnData object must have chr, start, and end in the `.var` attribute." + ) + # Extract the arrays for chr, start, and end chr_values = adata.var["chr"].values start_values = adata.var["start"].values From a45bc2b66c3906b037a0e7feaa488a5355cd965a Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 26 Jul 2023 20:11:47 -0400 Subject: [PATCH 0286/1072] tests for tokenizing AnnData --- gitk/tokenization/main.py | 4 ++-- tests/test_tokenization.py | 26 +++++++++++++++++++++++++- 2 files changed, 27 insertions(+), 3 deletions(-) diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 758b162e..b9981022 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -129,7 +129,7 @@ def find_overlaps( overlapping_regions.append(Region(region.chr, overlap.begin, overlap.end)) return overlapping_regions - def convert_anndata_to_universe(self, adata: sc.AnnData) -> None: + def convert_anndata_to_universe(self, adata: sc.AnnData) -> sc.AnnData: """ Convert an AnnData object to a universe. This is done by inspecting the peaks in the AnnData object and converting @@ -227,7 +227,7 @@ def tokenize( """ if isinstance(region_set, sc.AnnData): # step 1 is to convert the AnnData object to the universe - self.convert_anndata_to_universe(region_set) + region_set = self.convert_anndata_to_universe(region_set) # step 2 is to convert the AnnData object to a list of lists of regions return anndata_to_regionsets(region_set) diff --git a/tests/test_tokenization.py b/tests/test_tokenization.py index 97ca459a..c3a660c3 100644 --- a/tests/test_tokenization.py +++ b/tests/test_tokenization.py @@ -13,10 +13,15 @@ @pytest.fixture -def adata(): +def pbmc_data(): return sc.read_h5ad("tests/data/pbmc_hg38.h5ad") +@pytest.fixture +def pbmc_data_backed(): + return sc.read_h5ad("tests/data/pbmc_hg38.h5ad", backed="r") + + @pytest.fixture def universe_bed_file(): return "tests/data/universe.bed" @@ -94,3 +99,22 @@ def test_tokenize_list_of_regions(universe_bed_file: str): # ensure that the tokens are unqiue from the original regions assert len(set(tokens).intersection(set(regions))) == 0 assert len(tokens) == 10 + + +def test_tokenize_anndata(universe_bed_file: str, pbmc_data: sc.AnnData): + t = InMemTokenizer(universe_bed_file) + assert t is not None + + tokens = t.tokenize(pbmc_data) + + # returns list of regions for each cell + assert len(tokens) == pbmc_data.shape[0] + + +def test_tokenize_anndata_backed(universe_bed_file: str, pbmc_data_backed: sc.AnnData): + t = InMemTokenizer(universe_bed_file) + assert t is not None + + tokens = t.tokenize(pbmc_data_backed) + + assert len(tokens) == pbmc_data_backed.shape[0] From d3b35d148df586617cf7737715652ffdcea3acd7 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 11:46:14 -0400 Subject: [PATCH 0287/1072] create new Region2Vec class for use with other models --- gitk/region2vec/const.py | 12 +++ gitk/region2vec/main.py | 206 ++++++++++++++++++++++++++++++++++++++- gitk/region2vec/utils.py | 163 ++++++++++++++++++++++++++++++- gitk/scembed/main.py | 1 - tests/test_region2vec.py | 0 5 files changed, 376 insertions(+), 6 deletions(-) create mode 100644 tests/test_region2vec.py diff --git a/gitk/region2vec/const.py b/gitk/region2vec/const.py index cc9cd08c..6ec62d97 100644 --- a/gitk/region2vec/const.py +++ b/gitk/region2vec/const.py @@ -1 +1,13 @@ +MODULE_NAME = "region2vec" MAX_WAIT_TIME = 10800 + +DEFAULT_EPOCHS = 100 +DEFAULT_GENSIM_EPOCHS = 1 +DEFAULT_MIN_COUNT = 10 +DEFAULT_N_SHUFFLES = 1 # 1 is sufficient for most cases +DEFAULT_WINDOW_SIZE = 5 +DEFAULT_EMBEDDING_SIZE = 100 +DEFAULT_EPOCHS = 10 +DEFAULT_INIT_LR = 0.1 # https://github.com/databio/gitk/issues/6#issuecomment-1476273162 +DEFAULT_MIN_LR = 0.0001 # gensim default +DEFAULT_DECAY_RATE = 0.95 diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index b2872919..4324a53b 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -1,13 +1,44 @@ import multiprocessing import os +from gensim.models import Word2Vec +from gensim.models.callbacks import CallbackAny2Vec +from numba import config +from logging import getLogger + import numpy as np -from gitk.region2vec import utils -from gitk.region2vec.region2vec_train import main as region2_train -from gitk.region2vec.region_shuffling import main as sent_gen +from . import utils +from ..io import RegionSet, Region +from .region2vec_train import main as region2_train +from .region_shuffling import main as sent_gen +from .const import * + +from typing import List, Union, Dict + + +_GENSIM_LOGGER = getLogger("gensim") +_LOGGER = getLogger(MODULE_NAME) + +# demote gensim logger to warning +_GENSIM_LOGGER.setLevel("WARNING") + +# set the threading layer before any parallel target compilation +config.THREADING_LAYER = "threadsafe" -from typing import List + +class ReportLossCallback(CallbackAny2Vec): + """ + Callback to report loss after each epoch. + """ + + def __init__(self): + self.epoch = 0 + + def on_epoch_end(self, model: Word2Vec): + loss = model.get_latest_training_loss() + _LOGGER.info(f"Epoch {self.epoch} complete. Loss: {loss}") + self.epoch += 1 class Namespace: @@ -164,3 +195,170 @@ def region2vec( os.remove(file_list_path) elapsed_time = timer.t() - start_time print(f"[Training] {utils.time_str(elapsed_time)}/{utils.time_str(timer.t())}") + + +class Region2Vec(Word2Vec): + """ + A class that implements Region2Vec. Region2Vec is a model that learns + embedding vectors for genomic regions. It works by by considering each + region set as a text document, with each region as a word inside that + document. + """ + + @staticmethod + def from_word2vec_model(model, **kwargs): + return Region2Vec( + callbacks=model.callbacks or kwargs.get("callbacks"), + window_size=model.window, + vector_size=model.vector_size, + min_count=model.vocabulary.min_count, + threads=model.trainables.compute_loss, # assuming it stores thread info + seed=model.random.seed, # assuming it stores seed + ) + + def __init__(self, **kwargs): + self.trained = False + self.callbacks = kwargs.get("callbacks") or [] + + # instantiate the Word2Vec model + super().__init__( + window=kwargs.get("window_size") or DEFAULT_WINDOW_SIZE, + vector_size=kwargs.get("vector_size") or DEFAULT_EMBEDDING_SIZE, + min_count=kwargs.get("min_count") or DEFAULT_MIN_COUNT, + workers=kwargs.get("threads") or multiprocessing.cpu_count() - 2, + seed=kwargs.get("seed") or 42, # + callbacks=kwargs.get("callbacks") or [], + ) + + def train( + self, + data: Union[List[RegionSet], List[List[Region]]], + epochs: int = DEFAULT_EPOCHS, # training cycles + n_shuffles: int = DEFAULT_N_SHUFFLES, # not the number of traiing cycles, actual shufle num + report_loss: bool = True, + lr: float = DEFAULT_INIT_LR, + min_lr: float = DEFAULT_MIN_LR, + lr_schedule: Union[str, utils.ScheduleType] = "linear", + ): + """ + Train the model. This is done in two steps: First, we shuffle the documents. + Second, we train the model. + + :param int epochs: The number of epochs to train for (note: this is the number of times regions are shuffled, then fed to the model for training). + :param int n_shuffles: The number of times to shuffle the regions within each document. + :param int gensim_epochs: The number of epochs to train for within each shuffle (or main epoch). + :param bool report_loss: Whether or not to report the loss after each epoch. + :param float lr: The initial learning rate. + :param float min_lr: The minimum learning rate. + :param Union[str, ScheduleType] lr_schedule: The learning rate schedule to use. + """ + # force to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205683978) + n_shuffles = 1 + + # verify data is str, List[Region], or RegionSet + if isinstance(data, str): + # load the data + data = RegionSet(data) + elif isinstance(data, RegionSet): + pass + elif isinstance(data, list): + # assure all elements are Region objects + if not all(isinstance(x, Region) for x in data): + raise TypeError("If data is a list, all elements must be of type Region.") + else: + raise TypeError(f"Data must be of type AnnData or str, not {type(data).__name__}") + + if report_loss: + self.callbacks.append(ReportLossCallback()) + + # create a learning rate scheduler + lr_scheduler = utils.LearningRateScheduler( + init_lr=lr, min_lr=min_lr, type=lr_schedule, n_epochs=epochs + ) + + # "wordify" the regions + region_sets = [utils.wordify_regions(rs) for rs in data] + + # train the model using these shuffled documents + _LOGGER.info("Training starting.") + + # build up the vocab + super().build_vocab( + region_sets, + update=False if not self.trained else True, + min_count=self.min_count, + ) + + for shuffle_num in range(epochs): + # update current values + current_lr = lr_scheduler.get_lr() + current_loss = self.get_latest_training_loss() + + # update user + _LOGGER.info(f"SHUFFLE {shuffle_num} - lr: {current_lr}, loss: {current_loss}") + _LOGGER.info("Shuffling documents.") + + # shuffle regions + region_sets = utils.shuffle_documents(region_sets, n_shuffles=n_shuffles) + + # train the model on one iteration + super().train( + region_sets, + total_examples=len(region_sets), + epochs=1, # for to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205692089) + callbacks=self.callbacks, + compute_loss=report_loss, + start_alpha=current_lr, + ) + + # update learning rates + lr_scheduler.update() + + self.trained = True + + def save(self, filepath: str): + """ + Save the current model to disk + + :param str filepath: The path to save the model to. + """ + super().save(filepath) + + @classmethod + def load(cls, filepath: str): + """ + Load a model from disk. This should return a + Region2Vec object. + + :param str filepath: The path to load the model from. + """ + model = super().load(filepath) + return cls.from_word2vec_model(model) + + def forward(self, regions: Union[Region, RegionSet, List[Region]]): + """ + Get the embedding vector(s) for a given region or region set. + + :param Union[Region, RegionSet, List[Region]] regions: The region(s) to get the embedding vector(s) for. + """ + if isinstance(regions, Region): + region_word = utils.wordify_region(regions) + return self.wv[region_word] + elif isinstance(regions, RegionSet): + region_words = utils.wordify_regions(regions) + return np.mean([self.wv[r] for r in region_words], axis=0) + elif isinstance(regions, list): + region_words = [utils.wordify_region(r) for r in regions] + return np.mean([self.wv[r] for r in region_words], axis=0) + else: + raise TypeError( + f"Regions must be of type Region, RegionSet, or list, not {type(regions).__name__}" + ) + + def __call__(self, regions: Union[Region, RegionSet, List[Region]]): + """ + Get the embedding vector(s) for a given region or region set. + + :param Union[Region, RegionSet, List[Region]] regions: The region(s) to get the embedding vector(s) for. + """ + return self.forward(regions) diff --git a/gitk/region2vec/utils.py b/gitk/region2vec/utils.py index 004c883c..50a8d844 100644 --- a/gitk/region2vec/utils.py +++ b/gitk/region2vec/utils.py @@ -3,9 +3,24 @@ import shutil import sys import time -from typing import Union, Dict +from typing import Union, Dict, List +from enum import Enum +from random import shuffle +import logging import numpy as np +from concurrent.futures import ThreadPoolExecutor +from tqdm import tqdm + +from ..io import RegionSet, Region +from .const import ( + MODULE_NAME, + DEFAULT_INIT_LR, + DEFAULT_MIN_LR, +) + +_LOGGER = logging.getLogger(MODULE_NAME) + def prRed(skk: str) -> None: """Prints the input string skk in the red font. @@ -252,3 +267,149 @@ def ensure_dir(folder: str, default: str = "y") -> None: else: return os.makedirs(folder, exist_ok=True) + + +def wordify_region(region: Region) -> str: + """ + Convert a region to a word of the form "chr_start_end" + + :param Region region: A region + + :return: A word + """ + return f"{region.chr}_{region.start}_{region.end}" + + +def wordify_regions(regions: Union[RegionSet, List[Region]]) -> List[str]: + """ + Convert a list of regions to a list of words. Each region is converted + to a word of the form "chr_start_end" + + :param RegionSet | list[Region] regions: A list of regions + + :return: A list of words + """ + return [f"{r.chr}_{r.start}_{r.end}" for r in regions] + + +class ScheduleType(Enum): + """Learning rate schedule types""" + + LINEAR = "linear" + EXPONENTIAL = "exponential" + + +class LearningRateScheduler: + """ + Simple class to track learning rates of the training procedure + + Based off of: https://machinelearningmastery.com/using-learning-rate-schedules-deep-learning-models-python-keras/ + """ + + def __init__( + self, + init_lr: float = DEFAULT_INIT_LR, + min_lr: float = DEFAULT_MIN_LR, + type: Union[str, ScheduleType] = ScheduleType.EXPONENTIAL, + decay: float = None, + n_epochs: int = None, + ): + """ + :param float init_lr: The initial learning rate + :param float min_lr: The minimum learning rate + :param str type: The type of learning rate schedule to use. Must be one of ['linear', 'exponential']. + :param float decay: The decay rate to use. If None, this will be calculated from init_lr and n_epochs. + :param int n_epochs: The number of epochs to train for. Only used if decay is None. + """ + self.init_lr = init_lr + self.min_lr = min_lr + self.n_epochs = n_epochs + + # convert type to learning rate if necessary + if isinstance(type, str): + try: + self.type = ScheduleType[type.upper()] + except KeyError: + raise ValueError( + f"Unknown schedule type: {type}. Must be one of ['linear', 'exponential']." + ) + + # init the current lr and iteration + self._current_lr = init_lr + self._iter = 1 + + # init decay rate + if decay is None: + _LOGGER.warning( + "No decay rate provided. Calculating decay rate from init_lr and n_epochs." + ) + self.decay = init_lr / n_epochs + else: + self.decay = decay + + def _update_linear(self, epoch: int): + """ + Update the learning rate using a linear schedule. + + :param int epoch: The current epoch + """ + + lr = self.init_lr - (self.decay * epoch) + return max(lr, self.min_lr) + + def _update_exponential(self, epoch: int): + """ + Update the learning rate using an exponential schedule. + + :param int epoch: The current epoch + """ + lr = self.get_lr() * (1 / (1 + self.decay * epoch)) + return max(lr, self.min_lr) + + def update(self): + # update the learning rate according to the type + if self.type == ScheduleType.LINEAR: + self._current_lr = self._update_linear(self._iter) + self._iter += 1 + elif self.type == ScheduleType.EXPONENTIAL: + self._current_lr = self._update_exponential(self._iter) + self._iter += 1 + else: + raise ValueError(f"Unknown schedule type: {self.type}") + + def get_lr(self): + return self._current_lr + + +def shuffle_documents( + documents: List[List[str]], + n_shuffles: int, + threads: int = None, +) -> List[List[str]]: + """ + Shuffle around the genomic regions for each cell to generate a "context". + + :param List[List[str]] documents: the document list to shuffle. + :param int n_shuffles: The number of shuffles to conduct. + :param int threads: The number of threads to use for shuffling. + """ + + def shuffle_list(l: List[str], n: int) -> List[str]: + for _ in range(n): + shuffle(l) + return l + + _LOGGER.debug(f"Shuffling documents {n_shuffles} times.") + shuffled_documents = documents.copy() + with ThreadPoolExecutor(max_workers=threads) as executor: + shuffled_documents = list( + tqdm( + executor.map( + shuffle_list, + shuffled_documents, + [n_shuffles] * len(documents), + ), + total=len(documents), + ) + ) + return shuffled_documents diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index 5cd0a189..f5d2be00 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -285,7 +285,6 @@ def __init__( self, model_path: str = None, tokenizer: InMemTokenizer = None, - universe: Union[RegionSet, str] = None, **kwargs, ): """ diff --git a/tests/test_region2vec.py b/tests/test_region2vec.py new file mode 100644 index 00000000..e69de29b From 91b869342904c6299901ebb4d6d54c520c5909b6 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 13:32:00 -0400 Subject: [PATCH 0288/1072] add Region2Vec --- gitk/io/io.py | 51 ++++++++++++------- gitk/region2vec/__init__.py | 3 +- gitk/region2vec/main.py | 84 ++++++++++++++++++++----------- tests/test_region2vec.py | 98 +++++++++++++++++++++++++++++++++++++ 4 files changed, 190 insertions(+), 46 deletions(-) diff --git a/gitk/io/io.py b/gitk/io/io.py index ff13c927..b7341bd4 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -1,4 +1,4 @@ -from typing import List +from typing import List, Union from intervaltree import Interval @@ -23,22 +23,33 @@ def __repr__(self): # TODO: This belongs somewhere else class RegionSet(object): - def __init__(self, path: str, backed: bool = False): - self.backed = backed - self.regions: List[Region] = [] - self.path = path - if backed: - self.regions = None - # https://stackoverflow.com/a/32607817/13175187 - with open(self.path, "r") as file: - self.length = sum(1 for line in file if line.strip()) + def __init__(self, regions: Union[str, List[Region]], backed: bool = False): + # load from file + if isinstance(regions, str): + self.backed = backed + self.regions: List[Region] = [] + self.path = regions + if backed: + self.regions = None + # https://stackoverflow.com/a/32607817/13175187 + with open(self.path, "r") as file: + self.length = sum(1 for line in file if line.strip()) + else: + with open(regions, "r") as f: + lines = f.readlines() + for line in lines: + chr, start, stop = line.split("\t") + self.regions.append(Region(chr, int(start), int(stop))) + self.length = len(self.regions) + + # load from list + elif isinstance(regions, list) and all([isinstance(region, Region) for region in regions]): + self.backed = False + self.path = None + self.regions = regions + self.length = len(self.regions) else: - with open(path, "r") as f: - lines = f.readlines() - for line in lines: - chr, start, stop = line.split("\t") - self.regions.append(Region(chr, int(start), int(stop))) - self.length = len(self.regions) + raise ValueError(f"regions must be a path to a bed file or a list of Region objects") def __len__(self): return self.length @@ -47,7 +58,13 @@ def __getitem__(self, key): return self.regions[key] def __repr__(self): - return f"RegionSet({self.path})" + if self.path: + if self.backed: + return f"RegionSet({self.path}, backed=True)" + else: + return f"RegionSet({self.path})" + else: + return f"RegionSet(n={self.length})" def __iter__(self): if self.backed: diff --git a/gitk/region2vec/__init__.py b/gitk/region2vec/__init__.py index 489ce2d6..7aeeca93 100644 --- a/gitk/region2vec/__init__.py +++ b/gitk/region2vec/__init__.py @@ -1 +1,2 @@ -from .main import region2vec +from .main import region2vec, Region2Vec +from .utils import wordify_region, wordify_regions diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 4324a53b..86a3c82b 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -198,25 +198,39 @@ def region2vec( class Region2Vec(Word2Vec): - """ - A class that implements Region2Vec. Region2Vec is a model that learns - embedding vectors for genomic regions. It works by by considering each - region set as a text document, with each region as a word inside that - document. - """ - + # not needed since self.load() seems to work fine. I am not sure why, though. @staticmethod - def from_word2vec_model(model, **kwargs): - return Region2Vec( + def from_word2vec_model(model: Word2Vec, **kwargs): + r2v = Region2Vec( callbacks=model.callbacks or kwargs.get("callbacks"), window_size=model.window, vector_size=model.vector_size, - min_count=model.vocabulary.min_count, - threads=model.trainables.compute_loss, # assuming it stores thread info + min_count=model.min_count, + threads=model.workers, # assuming it stores thread info seed=model.random.seed, # assuming it stores seed ) + # attach the word2vec model to the region2vec model + r2v.wv = model.wv + return r2v def __init__(self, **kwargs): + """ + A class that implements Region2Vec. Region2Vec is a model that learns + embedding vectors for genomic regions. It works by by considering each + region set as a text document, with each region as a word inside that + document. + + :param window_size: The size of the window to use for the skip-gram + model. Defaults to 5. + :param vector_size: The size of the embedding vectors. Defaults to 100. + :param min_count: The minimum number of times a region must occur in + the dataset to be included in the vocabulary. Defaults to 5. + :param threads: The number of threads to use for training. Defaults to + the number of CPUs - 2. + :param seed: The seed to use for training. Defaults to 42. + :param callbacks: A list of callbacks to use for training. Defaults to + an empty list. + """ self.trained = False self.callbacks = kwargs.get("callbacks") or [] @@ -232,7 +246,7 @@ def __init__(self, **kwargs): def train( self, - data: Union[List[RegionSet], List[List[Region]]], + data: Union[List[str], List[RegionSet], List[List[Region]]], epochs: int = DEFAULT_EPOCHS, # training cycles n_shuffles: int = DEFAULT_N_SHUFFLES, # not the number of traiing cycles, actual shufle num report_loss: bool = True, @@ -255,18 +269,28 @@ def train( # force to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205683978) n_shuffles = 1 - # verify data is str, List[Region], or RegionSet - if isinstance(data, str): - # load the data - data = RegionSet(data) - elif isinstance(data, RegionSet): - pass - elif isinstance(data, list): - # assure all elements are Region objects - if not all(isinstance(x, Region) for x in data): - raise TypeError("If data is a list, all elements must be of type Region.") + # verify data is a list of strings, region sets or a list of list of regions + if not isinstance(data, list): + raise TypeError( + f"Data must be a list of strings, RegionSets, or a list of list of Regions. Got {type(data)}." + ) else: - raise TypeError(f"Data must be of type AnnData or str, not {type(data).__name__}") + # list of strings? files? + if all([isinstance(d, str) for d in data]): + data = [RegionSet(d) for d in data] + # list of RegionSets? Great. + elif all([isinstance(d, RegionSet) for d in data]): + pass + # list of list of Regions? Great. + elif all([isinstance(d, list) for d in data]) and all( + [isinstance(d[0], Region) for d in data] + ): + data = [RegionSet(d) for d in data] + # something else? error. + else: + raise TypeError( + f"Data must be a list of strings, RegionSets, or a list of list of Regions. Got {type(data)}." + ) if report_loss: self.callbacks.append(ReportLossCallback()) @@ -325,17 +349,18 @@ def save(self, filepath: str): super().save(filepath) @classmethod - def load(cls, filepath: str): + def load(cls, filepath: str) -> "Region2Vec": """ Load a model from disk. This should return a Region2Vec object. :param str filepath: The path to load the model from. """ - model = super().load(filepath) - return cls.from_word2vec_model(model) + # I feel like this shouldnt work, but it does? + return super().load(filepath) + # return cls.from_word2vec_model(model) - def forward(self, regions: Union[Region, RegionSet, List[Region]]): + def forward(self, regions: Union[Region, RegionSet, List[Region]]) -> np.ndarray: """ Get the embedding vector(s) for a given region or region set. @@ -355,10 +380,13 @@ def forward(self, regions: Union[Region, RegionSet, List[Region]]): f"Regions must be of type Region, RegionSet, or list, not {type(regions).__name__}" ) - def __call__(self, regions: Union[Region, RegionSet, List[Region]]): + def __call__(self, regions: Union[Region, RegionSet, List[Region]]) -> np.ndarray: """ Get the embedding vector(s) for a given region or region set. :param Union[Region, RegionSet, List[Region]] regions: The region(s) to get the embedding vector(s) for. """ return self.forward(regions) + + def __repr__(self) -> str: + return f"Region2Vec(window={self.window}, vector_size={self.vector_size})" diff --git a/tests/test_region2vec.py b/tests/test_region2vec.py index e69de29b..18ff7fe0 100644 --- a/tests/test_region2vec.py +++ b/tests/test_region2vec.py @@ -0,0 +1,98 @@ +import os +from typing import List + +import pytest +import numpy as np + +from gitk.io import RegionSet +from gitk.region2vec import Region2Vec, wordify_region, wordify_regions + + +@pytest.fixture +def bed_file(): + return "tests/data/to_tokenize.bed" + + +@pytest.fixture +def regions(bed_file: str): + return RegionSet(bed_file) + + +@pytest.fixture +def region_sets(regions: RegionSet): + # split regions into 5 region sets + sub_size = len(regions) // 5 + region_sets = [RegionSet(regions[i * sub_size : (i + 1) * sub_size]) for i in range(5)] + return region_sets + + +def test_init_region2vec(): + model = Region2Vec() + assert model is not None + + +def test_wordify_regions(regions: RegionSet): + region_words = wordify_regions(regions) + assert region_words is not None + assert all([isinstance(r, str) for r in region_words]) + assert all([len(r.split("_")) == 3 for r in region_words]) + + region_word = wordify_region(regions[0]) + assert region_word is not None + assert isinstance(region_word, str) + assert len(region_word.split("_")) == 3 + + +def test_train_region2vec(region_sets: List[RegionSet]): + model = Region2Vec( + min_count=1, # for testing, we need to set min_count to 1 + ) + + model.train(region_sets, epochs=100) + + assert model.trained == True + + # make sure all regions in region_sets are in the vocabulary + for region_set in region_sets: + for region in region_set: + region_word = wordify_region(region) + assert region_word in model.wv + assert isinstance(model(region), np.ndarray) + assert isinstance(model.forward(region), np.ndarray) + + +def test_train_from_bed_files(bed_file: str): + region_sets = [RegionSet(bed_file) for _ in range(10)] + model = Region2Vec( + min_count=1, # for testing, we need to set min_count to 1 + ) + model.train(region_sets, epochs=5) + + regions = RegionSet(bed_file) + for region in regions: + region_word = wordify_region(region) + assert region_word in model.wv + assert isinstance(model(region), np.ndarray) + assert isinstance(model.forward(region), np.ndarray) + + +def test_save_and_load_model(region_sets: RegionSet): + model = Region2Vec( + min_count=1, # for testing, we need to set min_count to 1 + ) + model.train(region_sets, epochs=100) + + try: + model.save("tests/data/test_model.model") + assert os.path.exists("tests/data/test_model.model") + # load in + model_loaded = Region2Vec.load("tests/data/test_model.model") + assert model_loaded is not None + for region_set in region_sets: + for region in region_set: + region_word = wordify_region(region) + assert region_word in model_loaded.wv + assert isinstance(model_loaded(region), np.ndarray) + assert isinstance(model_loaded.forward(region), np.ndarray) + finally: + os.remove("tests/data/test_model.model") From fa903f44f3ce147fee6b6798135821fc054ee30e Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 14:11:50 -0400 Subject: [PATCH 0289/1072] implement scEmbed ExModel --- gitk/region2vec/__init__.py | 2 +- gitk/region2vec/main.py | 13 +- gitk/region2vec/region_shuffling.py | 2 +- gitk/region2vec/utils.py | 16 +- gitk/scembed/cli.py | 3 +- gitk/scembed/main.py | 410 +++------------------------- gitk/scembed/models.py | 32 --- gitk/scembed/utils.py | 341 +---------------------- gitk/tokenization/main.py | 2 +- gitk/tokenization/utils.py | 12 +- 10 files changed, 59 insertions(+), 774 deletions(-) delete mode 100644 gitk/scembed/models.py diff --git a/gitk/region2vec/__init__.py b/gitk/region2vec/__init__.py index 7aeeca93..2bb07916 100644 --- a/gitk/region2vec/__init__.py +++ b/gitk/region2vec/__init__.py @@ -1,2 +1,2 @@ -from .main import region2vec, Region2Vec +from .main import Region2Vec, region2vec from .utils import wordify_region, wordify_regions diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 86a3c82b..2300c0ba 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -1,21 +1,18 @@ import multiprocessing import os +from logging import getLogger +from typing import List, Union +import numpy as np from gensim.models import Word2Vec from gensim.models.callbacks import CallbackAny2Vec from numba import config -from logging import getLogger - -import numpy as np +from ..io import Region, RegionSet from . import utils -from ..io import RegionSet, Region +from .const import * from .region2vec_train import main as region2_train from .region_shuffling import main as sent_gen -from .const import * - -from typing import List, Union, Dict - _GENSIM_LOGGER = getLogger("gensim") _LOGGER = getLogger(MODULE_NAME) diff --git a/gitk/region2vec/region_shuffling.py b/gitk/region2vec/region_shuffling.py index ab755671..530a4672 100644 --- a/gitk/region2vec/region_shuffling.py +++ b/gitk/region2vec/region_shuffling.py @@ -5,11 +5,11 @@ import pickle import random import time +from typing import List import numpy as np from gitk.region2vec import utils -from typing import List class BEDDataset: diff --git a/gitk/region2vec/utils.py b/gitk/region2vec/utils.py index 50a8d844..936716a6 100644 --- a/gitk/region2vec/utils.py +++ b/gitk/region2vec/utils.py @@ -1,23 +1,19 @@ +import logging import os import select import shutil import sys import time -from typing import Union, Dict, List +from concurrent.futures import ThreadPoolExecutor from enum import Enum from random import shuffle -import logging -import numpy as np +from typing import Dict, List, Union -from concurrent.futures import ThreadPoolExecutor +import numpy as np from tqdm import tqdm -from ..io import RegionSet, Region -from .const import ( - MODULE_NAME, - DEFAULT_INIT_LR, - DEFAULT_MIN_LR, -) +from ..io import Region, RegionSet +from .const import DEFAULT_INIT_LR, DEFAULT_MIN_LR, MODULE_NAME _LOGGER = logging.getLogger(MODULE_NAME) diff --git a/gitk/scembed/cli.py b/gitk/scembed/cli.py index 29eb21cc..0d6e559e 100644 --- a/gitk/scembed/cli.py +++ b/gitk/scembed/cli.py @@ -7,7 +7,8 @@ from ._version import __version__ from .argparser import build_argparser from .const import * -from .main import convert_anndata_to_documents, load_scanpy_data, shuffle_documents, train +from .main import (convert_anndata_to_documents, load_scanpy_data, + shuffle_documents, train) def main(): diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index f5d2be00..30697174 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -1,27 +1,15 @@ -import gzip -import os -import pickle from logging import getLogger from typing import List, Union -from multiprocessing import cpu_count import numpy as np import scanpy as sc -from gensim.models import Word2Vec -from gensim.models.callbacks import CallbackAny2Vec from numba import config from ..io import Region, RegionSet +from ..models import ExModel +from ..region2vec import Region2Vec from ..tokenization import InMemTokenizer -from .const import * -from .exceptions import * -from .utils import ( - LearningRateScheduler, - ScheduleType, - convert_anndata_to_documents, - remove_regions_below_min_count, - shuffle_documents, -) +from .const import CHR_KEY, END_KEY, MODULE_NAME, START_KEY _GENSIM_LOGGER = getLogger("gensim") _LOGGER = getLogger(MODULE_NAME) @@ -33,300 +21,41 @@ config.THREADING_LAYER = "threadsafe" -class ReportLossCallback(CallbackAny2Vec): +class ScEmbed(ExModel): """ - Callback to report loss after each epoch. + ScEmbed model for single-cell ATAC-seq data. It is a single-cell + extension of Region2Vec. """ - def __init__(self): - self.epoch = 0 - - def on_epoch_end(self, model: Word2Vec): - loss = model.get_latest_training_loss() - _LOGGER.info(f"Epoch {self.epoch} complete. Loss: {loss}") - self.epoch += 1 - - -class SCEmbed(Word2Vec): - """ - Region2Vec model that extends the Word2Vec model from gensim. - """ - - def __init__( - self, - window_size: int = DEFAULT_WINDOW_SIZE, - vector_size: int = DEFAULT_EMBEDDING_SIZE, - min_count: int = DEFAULT_MIN_COUNT, - threads: int = 1, - seed: int = 42, - callbacks: List[CallbackAny2Vec] = [], - use_default_region_names: bool = True, - ): - """ - :param sc.AnnData data: The AnnData object containing the data to train on. - :param int window_size: The size of the window to use for training. - :param int vector_size: The size of the embedding vectors. - :param int min_count: The minimum number of times a region must appear to be included in the model. - :param int threads: The number of threads to use for training. - :param int seed: The random seed to use for training. - :param List[CallbackAny2Vec] callbacks: A list of callbacks to use for training. - """ - self.callbacks = callbacks - self.trained = False - self.use_default_region_names = use_default_region_names - self.region2vec = dict() - - # instantiate the Word2Vec model - super().__init__( - window=window_size, - vector_size=vector_size, - min_count=min_count, - workers=threads, - seed=seed, - callbacks=callbacks, - ) - - def save_model(self, filepath: str, **kwargs): - """ - Cant use the save function from gensim because it doesnt work on subclasses. see - this issue: https://github.com/RaRe-Technologies/gensim/issues/1936 - - Instead, we will just use pickle to dump the object. - - :param str filepath: The path to save the model to. - :param kwargs: Additional arguments to pass to pickle.dump - """ - with gzip.open(filepath, "wb") as f: - pickle.dump(self, f) - - def export_model( - self, - out_path: str, - model_export_name: str = DEFAULT_MODEL_EXPORT_FILE_NAME, - universe_file_name: str = DEFAULT_UNIVERSE_EXPORT_FILE_NAME, - ): - """ - This function will do a full export of the model. This includes two files: - 1. the actual pickled `.model` file - 2. the universe `.bed` file that determines the universe of regions - - The result of this function can be directly uploaded to huggingface, to share with the world. - - :param str out_path: The path to the directory to save the model to. - :param str model_export_name: The name of the model file to save - it is not recommended to change this. - :param str universe_file_name: The name of the universe file to save - it is not recommended to change this. - """ - if not os.path.exists(out_path): - os.makedirs(out_path) - - # save the model - self.save_model(os.path.join(out_path, model_export_name)) - - # save the universe to disk - with open(os.path.join(out_path, universe_file_name), "w") as f: - for region in self.region2vec: - chr, start, end = region.split("_") - f.write(f"{chr}\t{start}\t{end}\n") - - def load_model(self, filepath: str, **kwargs): - """ - Cant use the load function from gensim because it doesnt work on subclasses. see - this issue: https://github.com/RaRe-Technologies/gensim/issues/1936 - - Instead we will just use pickle to load the object. Override the current object. - - :param str filepath: The path to load the model from. - :param kwargs: Additional arguments to pass to pickle.load - """ - with gzip.open(filepath, "rb") as f: - obj = pickle.load(f) - self.__dict__.update(obj.__dict__) - - def train( - self, - data: Union[sc.AnnData, str], - epochs: int = DEFAULT_EPOCHS, # training cycles - n_shuffles: int = DEFAULT_N_SHUFFLES, # not the number of traiing cycles, actual shufle num - gensim_epochs: Union[int, None] = DEFAULT_GENSIM_EPOCHS, - report_loss: bool = True, - lr: float = DEFAULT_INIT_LR, - min_lr: float = DEFAULT_MIN_LR, - lr_schedule: Union[str, ScheduleType] = "linear", - ): + def __init__(self, model_path: str = None, **kwargs): """ - Train the model. This is done in two steps: First, we shuffle the documents. - Second, we train the model. + Initialize ScEmbed model. - :param int epochs: The number of epochs to train for (note: this is the number of times regions are shuffled, then fed to the model for training). - :param int n_shuffles: The number of times to shuffle the regions within each document. - :param int gensim_epochs: The number of epochs to train for within each shuffle (or main epoch). - :param bool report_loss: Whether or not to report the loss after each epoch. - :param float lr: The initial learning rate. - :param float min_lr: The minimum learning rate. - :param Union[str, ScheduleType] lr_schedule: The learning rate schedule to use. + :param str model_path: Path to the pre-trained model on huggingface. + :param kwargs: Additional keyword arguments to pass to the model. """ - # force to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205683978) - n_shuffles = 1 - - if not isinstance(data, sc.AnnData) and not isinstance(data, str): - raise TypeError(f"Data must be of type AnnData or str, not {type(data).__name__}") - - # if the data is a string, assume it is a filepath - if isinstance(data, str): - data = sc.read_h5ad(data) - - if ( - not hasattr(data.var, CHR_KEY) - or not hasattr(data.var, START_KEY) - or not hasattr(data.var, END_KEY) - ): - _LOGGER.warn( - "Data does not have `chr`, `start`, and `end` columns in the `var` attribute. Will fallback to default names" - ) - - # convert the data to a list of documents - _LOGGER.info("Converting data to documents.") - self.data = data - # save anything in the `obs` attribute of the AnnData object - # this lets users save any metadata they want - # which can get mapped back to the embeddings - self.obs = data.obs - self.region_sets = convert_anndata_to_documents(data, self.use_default_region_names) - - # remove any regions that dont satisfy the min count - _LOGGER.info("Removing regions that don't satisfy min count.") - self.region_sets = remove_regions_below_min_count(self.region_sets, self.min_count) - - if report_loss: - self.callbacks.append(ReportLossCallback()) - - # create a learning rate scheduler - lr_scheduler = LearningRateScheduler( - init_lr=lr, min_lr=min_lr, type=lr_schedule, n_epochs=epochs - ) - - # train the model using these shuffled documents - _LOGGER.info("Training starting.") - - # build up the vocab - super().build_vocab( - self.region_sets, - update=False if not self.trained else True, - min_count=self.min_count, # this shouldnt be needed but it is - ) - - for shuffle_num in range(epochs): - # update current values - current_lr = lr_scheduler.get_lr() - current_loss = self.get_latest_training_loss() - - # update user - _LOGGER.info(f"SHUFFLE {shuffle_num} - lr: {current_lr}, loss: {current_loss}") - _LOGGER.info("Shuffling documents.") - - # shuffle regions - self.region_sets = shuffle_documents(self.region_sets, n_shuffles=n_shuffles) - - # train the model on one iteration - super().train( - self.region_sets, - total_examples=len(self.region_sets), - epochs=1, # for to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205692089) - callbacks=self.callbacks, - compute_loss=report_loss, - start_alpha=current_lr, - ) - - # update learning rates - lr_scheduler.update() - - self.trained = True - - # once training is complete, create a region to vector mapping - regions = list(self.wv.key_to_index.keys()) - - # create a mapping from region to vector - for word in regions: - self.region2vec[word] = self.wv[word] - - def get_embedding(self, region: str) -> np.ndarray: - """ - Get the embedding for a given region. - - :param str region: the region to get the embedding for - """ - try: - return self.wv[region] - except KeyError: - raise ValueError(f"Region {region} not found in model.") - - def get_embeddings(self, regions: List[str]) -> np.ndarray: - """ - Get the embeddings for a list of regions. - - :param List[str] regions: the regions to get the embeddings for - """ - return np.array([self.get_embedding(r) for r in regions]) - - def __call__(self, region: str) -> np.ndarray: - """ - Get the embedding for a given region. - - :param str region: the region to get the embedding for - """ - return self.get_embedding(region) - - -# -# BEGIN V2 of SCEmbed class -# -class SCEmbedV2(Word2Vec): - def __init__( - self, - model_path: str = None, - tokenizer: InMemTokenizer = None, - **kwargs, - ): - """ - The SCEmbedV2 class is a subclass of the Word2Vec class from gensim. It is a - Region2Vec model that extends the Word2Vec model from gensim. - - :param str model_path: The path to a pretrained model to load. - :param InMemTokenizer tokenizer: The tokenizer to use for tokenizing region sets. - :param Universe universe: The universe to use for tokenizing region sets. - :param kwargs: Additional arguments to pass to the Word2Vec constructor. - """ - # if model_path is given, download a pretrained model - # from the HuggingFace Hub - if model_path: + self.model_path = model_path + self.tokenizer: InMemTokenizer = None + self._model: Region2Vec = None + if model_path is not None: self._init_from_huggingface(model_path) else: - # otherwise, initialize a new model, with a new universe and tokenizer - # since the model is untrained, we have no vocabulary yet, - # and thus need a fresh tokenizer and universe - self.tokenizer = tokenizer or InMemTokenizer() - - # set other attributes - self.callbacks = kwargs.get("callbacks") or [] - self.trained = False - self.region2vec = dict() + self._model = Region2Vec(**kwargs) + self.tokenizer = InMemTokenizer() - # instantiate the Word2Vec model - super().__init__( - window=kwargs.get("window_size") or DEFAULT_WINDOW_SIZE, - vector_size=kwargs.get("vector_size") or DEFAULT_EMBEDDING_SIZE, - min_count=kwargs.get("min_count") or DEFAULT_MIN_COUNT, - workers=kwargs.get("threads") or cpu_count() - 2, - seed=kwargs.get("seed") or 42, # - callbacks=kwargs.get("callbacks") or [], - ) + def _init_from_huggingface(self, model_path: str): + """ + Initialize the model from a pre-trained model on huggingface. + """ + raise NotImplementedError def _validate_data(self, data: Union[sc.AnnData, str]) -> sc.AnnData: """ + Get the cell embeddings for the original AnnData passed in. This should + be called after training is complete. It is only useful for extracting the + embeddings for the last chunk of data its seen. Validate the data is of the correct type and has the required columns. - :param sc.AnnData | str data: The AnnData object containing the data to train on (can be path to AnnData). - :return sc.AnnData: The AnnData object. """ if not isinstance(data, sc.AnnData) and not isinstance(data, str): @@ -348,103 +77,34 @@ def _validate_data(self, data: Union[sc.AnnData, str]) -> sc.AnnData: return data - def train( - self, - data: Union[sc.AnnData, str], - epochs: int = DEFAULT_EPOCHS, # training cycles - report_loss: bool = True, - lr: float = DEFAULT_INIT_LR, - min_lr: float = DEFAULT_MIN_LR, - lr_schedule: Union[str, ScheduleType] = "linear", - ): + def train(self, data: Union[sc.AnnData, str], **kwargs): """ - Train the model. This is done in two steps: First, we shuffle the documents. - Second, we train the model. + Train the model. - :param sc.AnnData | str data: The AnnData object containing the data to train on (can be path to AnnData). - :param int epochs: The number of epochs to train for (note: this is the number of times regions are shuffled, then fed to the model for training). - :param bool report_loss: Whether or not to report the loss after each epoch. - :param float lr: The initial learning rate. - :param float min_lr: The minimum learning rate. - :param Union[str, ScheduleType] lr_schedule: The learning rate schedule to use. + :param sc.AnnData data: The AnnData object containing the data to train on (can be path to AnnData). + :param kwargs: Keyword arguments to pass to the model training function. """ - # force to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205683978) - n_shuffles = 1 - - # validate the data coming in + _LOGGER.info("Validating data.") data = self._validate_data(data) - _LOGGER.info("Tokenizing.") - # extract out the chr, start, end columns chrs = data.var[CHR_KEY].values.tolist() starts = data.var[START_KEY].values.tolist() ends = data.var[END_KEY].values.tolist() regions = [Region(c, int(s), int(e)) for c, s, e in zip(chrs, starts, ends)] + _LOGGER.info("Extracting region sets.") + # fit the tokenizer on the regions self.tokenizer.fit(regions) # convert the data to a list of documents region_sets = self.tokenizer.tokenize(data) - # convert list of lists of regions to list of list of strings of form chr_start_end - region_sets = [ - [r.chr + "_" + str(r.start) + "_" + str(r.end) for r in region_set] - for region_set in region_sets - ] - - if report_loss: - self.callbacks.append(ReportLossCallback()) - - # create a learning rate scheduler - lr_scheduler = LearningRateScheduler( - init_lr=lr, min_lr=min_lr, type=lr_schedule, n_epochs=epochs - ) - - # train the model using these shuffled documents _LOGGER.info("Training begin.") + self._model.train(region_sets, **kwargs) - # build up the vocab - super().build_vocab( - region_sets, - update=False if not self.trained else True, - min_count=self.min_count, # this shouldnt be needed but it is - ) - - for shuffle_num in range(epochs): - # update current values - current_lr = lr_scheduler.get_lr() - current_loss = self.get_latest_training_loss() - - # update user - _LOGGER.info(f"SHUFFLE {shuffle_num} - lr: {current_lr}, loss: {current_loss}") - _LOGGER.info("Shuffling documents.") - - # shuffle regions - region_sets = shuffle_documents(region_sets, n_shuffles=n_shuffles) - - # train the model on one iteration - super().train( - region_sets, - total_examples=len(region_sets), - epochs=1, # for to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205692089) - callbacks=self.callbacks, - compute_loss=report_loss, - start_alpha=current_lr, - ) - - # update learning rates - lr_scheduler.update() - - self.trained = True - - # once training is complete, create a region to vector mapping - learned_regions = list(self.wv.key_to_index.keys()) - - # create a mapping from region to vector - for word in learned_regions: - self.region2vec[word] = self.wv[word] + _LOGGER.info("Training complete.") - def __call__(self): - pass + def __call__(self, regions: Union[Region, RegionSet, List[Region]]) -> np.ndarray: + return self._model(regions) diff --git a/gitk/scembed/models.py b/gitk/scembed/models.py deleted file mode 100644 index 7cb992dc..00000000 --- a/gitk/scembed/models.py +++ /dev/null @@ -1,32 +0,0 @@ -from typing import List, Optional - -from pydantic import BaseModel - - -class ModelParameter(BaseModel): - embedding_dim: Optional[int] - description: Optional[str] - - -class Maintainer(BaseModel): - name: Optional[str] - email: Optional[str] - - -class ModelCard(BaseModel): - path_to_weights: str - path_to_universe: str - name: str - reference: str - description: Optional[str] - tags: Optional[List[str]] - datasets: Optional[List[str]] - model_parameters: Optional[List[ModelParameter]] - model_architecture: Optional[str] - maintainers: Optional[List[Maintainer]] - - -class Universe(BaseModel): - reference: str - regions: List[str] - description: Optional[str] diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 3e253ab1..6daf56ed 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -1,113 +1,6 @@ -import os -import shutil -import subprocess -from collections import Counter -from concurrent.futures import ThreadPoolExecutor -from enum import Enum -from logging import getLogger -from random import shuffle -from typing import TYPE_CHECKING, Dict, List, Optional, Union - -import numpy as np -import pandas as pd import scanpy as sc -from tqdm import tqdm - -if TYPE_CHECKING: - from .main import SCEmbed - -from .const import * - -_LOGGER = getLogger(MODULE_NAME) - - -class ScheduleType(Enum): - """Learning rate schedule types""" - - LINEAR = "linear" - EXPONENTIAL = "exponential" - - -class LearningRateScheduler: - """ - Simple class to track learning rates of the training procedure - - Based off of: https://machinelearningmastery.com/using-learning-rate-schedules-deep-learning-models-python-keras/ - """ - - def __init__( - self, - init_lr: float = DEFAULT_INIT_LR, - min_lr: float = DEFAULT_MIN_LR, - type: Union[str, ScheduleType] = ScheduleType.EXPONENTIAL, - decay: float = None, - n_epochs: int = None, - ): - """ - :param float init_lr: The initial learning rate - :param float min_lr: The minimum learning rate - :param str type: The type of learning rate schedule to use. Must be one of ['linear', 'exponential']. - :param float decay: The decay rate to use. If None, this will be calculated from init_lr and n_epochs. - :param int n_epochs: The number of epochs to train for. Only used if decay is None. - """ - self.init_lr = init_lr - self.min_lr = min_lr - self.n_epochs = n_epochs - - # convert type to learning rate if necessary - if isinstance(type, str): - try: - self.type = ScheduleType[type.upper()] - except KeyError: - raise ValueError( - f"Unknown schedule type: {type}. Must be one of ['linear', 'exponential']." - ) - - # init the current lr and iteration - self._current_lr = init_lr - self._iter = 1 - - # init decay rate - if decay is None: - _LOGGER.warning( - "No decay rate provided. Calculating decay rate from init_lr and n_epochs." - ) - self.decay = init_lr / n_epochs - else: - self.decay = decay - - def _update_linear(self, epoch: int): - """ - Update the learning rate using a linear schedule. - - :param int epoch: The current epoch - """ - - lr = self.init_lr - (self.decay * epoch) - return max(lr, self.min_lr) - - def _update_exponential(self, epoch: int): - """ - Update the learning rate using an exponential schedule. - - :param int epoch: The current epoch - """ - lr = self.get_lr() * (1 / (1 + self.decay * epoch)) - return max(lr, self.min_lr) - - def update(self): - # update the learning rate according to the type - if self.type == ScheduleType.LINEAR: - self._current_lr = self._update_linear(self._iter) - self._iter += 1 - elif self.type == ScheduleType.EXPONENTIAL: - self._current_lr = self._update_exponential(self._iter) - self._iter += 1 - else: - raise ValueError(f"Unknown schedule type: {self.type}") - def get_lr(self): - return self._current_lr +from .const import DEFAULT_CHUNK_SIZE class AnnDataChunker: @@ -143,235 +36,3 @@ def __getitem__(self, item: int): def __repr__(self): return f"" - - -def shuffle_documents( - documents: List[List[str]], - n_shuffles: int, - threads: int = None, -) -> List[List[str]]: - """ - Shuffle around the genomic regions for each cell to generate a "context". - - :param List[List[str]] documents: the document list to shuffle. - :param int n_shuffles: The number of shuffles to conduct. - :param int threads: The number of threads to use for shuffling. - """ - - def shuffle_list(l: List[str], n: int) -> List[str]: - for _ in range(n): - shuffle(l) - return l - - _LOGGER.debug(f"Shuffling documents {n_shuffles} times.") - shuffled_documents = documents.copy() - with ThreadPoolExecutor(max_workers=threads) as executor: - shuffled_documents = list( - tqdm( - executor.map( - shuffle_list, - shuffled_documents, - [n_shuffles] * len(documents), - ), - total=len(documents), - ) - ) - return shuffled_documents - - -def remove_regions_below_min_count( - region_sets: List[List[str]], min_count: int -) -> List[List[str]]: - """ - Remove regions that don't satisfy the min count. - - TODO - this is a bit slow, could be sped up using dataframe operations. - - :param List[List[str]] region_sets: the region sets to remove regions from - :param int min_count: the min count to use - """ - # get the counts for each region - region_counts = Counter() - for region_set in region_sets: - region_counts.update(region_set) - - # remove any regions that dont satisfy the min count - region_sets = [ - [r for r in region_set if region_counts[r] >= min_count] for region_set in region_sets - ] - - return region_sets - - -def load_scanpy_data(path_to_h5ad: str) -> sc.AnnData: - """ - Load in the h5ad file that holds all of the information - for our single-cell data with scanpy. - - :param str path_to_h5ad: the path to the h5ad file made with scanpy - """ - return sc.read_h5ad(path_to_h5ad) - - -def extract_region_list(region_df: pd.DataFrame) -> List[str]: - """ - Parse the `var` attribute of the scanpy.AnnData object and - return a list of regions from the matrix. Converts each - region to a string of the form `chr_start_end`. - - :param pandas.DataFrame region_df: the regions dataframe to parse - """ - _LOGGER.info("Extracting region list from matrix.") - regions = [ - f"{row[CHR_KEY]}_{row[START_KEY]}_{row[END_KEY]}" for _, row in region_df.iterrows() - ] - return regions - - -def convert_anndata_to_documents( - anndata: sc.AnnData, - use_defaults: bool = True, -) -> List[List[str]]: - """ - Parses the scanpy.AnnData object to create the required "documents" object for - training the Word2Vec model. Each row (or cell) is treated as a "document". That - is, each region is a "word", and the total collection of regions is the "document". - - :param scanpy.AnnData anndata: the AnnData object to parse. - :use_defaults bool: whether or not to use the default column names for the regions. this - is most commonly used when the AnnData object was created without using - the var attribute. - """ - # enable progress bar - tqdm.pandas() - - if use_defaults: - regions_parsed = [f"r{i}" for i in range(anndata.var.shape[0])] - # drop var attribute since it messes with things - anndata.var.drop(anndata.var.columns, axis=1, inplace=True) - anndata.var.reset_index(inplace=True) - - # add the region column back in - anndata.var["region"] = regions_parsed - else: - regions_parsed = extract_region_list(anndata.var) - sc_df = anndata.to_df() - _LOGGER.info("Generating documents.") - - docs = [ - [ - regions_parsed[int(region_indx)] - for region_indx, value in row.to_dict().items() - if value > 0 - ] - for _, row in sc_df.iterrows() - ] - - return docs - - -def load_scembed_model(path: str) -> "SCEmbed": - """ - Load a scembed model from disk. - - :param str path: The path to the model. - """ - import gzip - import pickle - - with gzip.open(path, "rb") as f: - model = pickle.load(f) - return model - - -def load_scembed_model_deprecated(path: str) -> "SCEmbed": - """ - Load a scembed model from disk. THIS IS DEPRECATED AND WILL BE REMOVED IN THE FUTURE. - - :param str path: The path to the model. - """ - import pickle - - with open(path, "rb") as f: - model = pickle.load(f) - return model - - -def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: - """ - Converts an AnnData object to a list of lists of regions. This - is done by taking each cell and creating a list of all regions - that have a value greater than 0. - - *Note: this method requires that the sc.AnnData object have - chr, start, and end in `.var` attributes* - - :param sc.AnnData adata: the AnnData object to convert - """ - # Extract the arrays for chr, start, and end - chr_values = adata.var["chr"].values - start_values = adata.var["start"].values - end_values = adata.var["end"].values - - # Perform the comparison using numpy operations - positive_values = adata.X > 0 - - if not isinstance(positive_values, np.ndarray): - positive_values = positive_values.toarray() - - regions = [] - for i in tqdm(range(adata.shape[0]), total=adata.shape[0]): - regions.append( - [ - f"{chr_values[j]}_{start_values[j]}_{end_values[j]}" - for j in np.where(positive_values[i])[0] - ] - ) - return regions - - -def barcode_mtx_peaks_to_anndata( - barcodes_path: str, - mtx_path: str, - peaks_path: str, - transpose: bool = True, - sparse: bool = True, -) -> sc.AnnData: - """ - This function will take three files: - - 1. a barcodes file (.tsv) - 2. a matrix file (.mtx) - 3. a peaks file (.bed) - - And turn them into an AnnData object. It will attach the peaks - as a .var attribute (chr, start, end) and the barcodes as a .obs attribute (index) - - :param str barcodes_path: the path to the barcodes file - :param str mtx_path: the path to the matrix file - :param str peaks_path: the path to the peaks file - :param bool transpose: whether or not to transpose the matrix - :param bool sparse: whether or not the matrix is sparse - - :return sc.AnnData: an AnnData object - """ - from scipy.io import mmread - - # load the barcodes - barcodes = pd.read_csv(barcodes_path, sep="\t", header=None) - barcodes.columns = ["barcode"] - barcodes.index = barcodes["barcode"] - - # load the mtx - mtx = mmread(mtx_path) - if transpose: - mtx = mtx.T - - # load the peaks - peaks = pd.read_csv(peaks_path, sep="\t", header=None) - peaks.columns = ["chr", "start", "end"] - - # create the AnnData object - adata = sc.AnnData(X=mtx, obs=barcodes, var=peaks) - - return adata diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index b9981022..f0afecff 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -216,7 +216,7 @@ def generate_var_conversion_map( def tokenize( self, region_set: Union[str, List[Region], sc.AnnData] - ) -> Union[List[Region], List[List[Region]]]: + ) -> Union[List[Region], List[List[Region]], List[RegionSet]]: """ Tokenize a RegionSet. diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index a1a6b089..d8c7fb8e 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -4,7 +4,7 @@ import numpy as np import scanpy as sc from tqdm import tqdm -from ..io import Region +from ..io import Region, RegionSet def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: @@ -44,9 +44,11 @@ def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: regions = [] for i in tqdm(range(adata.shape[0]), total=adata.shape[0], desc="Tokenizing"): regions.append( - [ - Region(chr_values[j], start_values[j], end_values[j]) - for j in np.where(positive_values[i])[0] - ] + RegionSet( + [ + Region(chr_values[j], start_values[j], end_values[j]) + for j in np.where(positive_values[i])[0] + ] + ) ) return regions From f690478c9b6cd541e4a719966bdcb155fd7af7bb Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 14:15:37 -0400 Subject: [PATCH 0290/1072] add download from huggingface code --- gitk/scembed/const.py | 30 ++---------------------------- gitk/scembed/main.py | 32 +++++++++++++++++++++++++++----- 2 files changed, 29 insertions(+), 33 deletions(-) diff --git a/gitk/scembed/const.py b/gitk/scembed/const.py index a8756350..49c85d90 100644 --- a/gitk/scembed/const.py +++ b/gitk/scembed/const.py @@ -1,35 +1,9 @@ -from pathlib import Path - -__author__ = ["Nathan LeRoy", "Jason Smith", "Erfaneh Gharavi"] -__email__ = "nleroy@virginia.edu" - LOGGING_LEVEL = "INFO" MODULE_NAME = "scembed" -DEFAULT_EPOCHS = 100 -DEFAULT_GENSIM_EPOCHS = 1 -DEFAULT_MIN_COUNT = 10 -DEFAULT_N_SHUFFLES = 1 # 1 is sufficient for most cases -DEFAULT_WINDOW_SIZE = 5 -DEFAULT_EMBEDDING_SIZE = 100 -DEFAULT_EPOCHS = 10 -DEFAULT_INIT_LR = 0.1 # https://github.com/databio/gitk/issues/6#issuecomment-1476273162 -DEFAULT_MIN_LR = 0.0001 # gensim default -DEFAULT_DECAY_RATE = 0.95 - -DEFAULT_CHUNK_SIZE = 10000 - CHR_KEY = "chr" START_KEY = "start" END_KEY = "end" -MODEL_CACHE_DIR = str(Path.home() / ".scembed") -MODEL_HUB_URL = "http://big.databio.org/scembed/models" -MODEL_CONFIG_FILE_NAME = "model.yaml" - -DEFAULT_BEDTOOLS_PATH = "bedtools" - -DEFAULT_MODEL_EXPORT_FILE_NAME = "model.bin" -DEFAULT_UNIVERSE_EXPORT_FILE_NAME = "universe.bed" - -PKG_NAME = "scembed" +MODEL_FILE_NAME = "model.bin" +UNIVERSE_FILE_NAME = "universe.bed" diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index 30697174..72ca14d2 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -3,13 +3,14 @@ import numpy as np import scanpy as sc +from huggingface_hub import hf_hub_download from numba import config from ..io import Region, RegionSet from ..models import ExModel from ..region2vec import Region2Vec from ..tokenization import InMemTokenizer -from .const import CHR_KEY, END_KEY, MODULE_NAME, START_KEY +from .const import CHR_KEY, END_KEY, MODEL_FILE_NAME, MODULE_NAME, START_KEY, UNIVERSE_FILE_NAME _GENSIM_LOGGER = getLogger("gensim") _LOGGER = getLogger(MODULE_NAME) @@ -35,19 +36,40 @@ def __init__(self, model_path: str = None, **kwargs): :param kwargs: Additional keyword arguments to pass to the model. """ self.model_path = model_path - self.tokenizer: InMemTokenizer = None self._model: Region2Vec = None + self.tokenizer: InMemTokenizer = None + if model_path is not None: self._init_from_huggingface(model_path) else: self._model = Region2Vec(**kwargs) self.tokenizer = InMemTokenizer() - def _init_from_huggingface(self, model_path: str): + def _init_from_huggingface( + self, + model_path: str, + model_file_name: str = MODEL_FILE_NAME, + universe_file_name: str = UNIVERSE_FILE_NAME, + **kwargs, + ): """ - Initialize the model from a pre-trained model on huggingface. + Download a pretrained model from the HuggingFace Hub. We need to download + the actual model + weights, and the universe file. + + :param str model: The name of the model to download (this is the same as the repo name). + :param str model_file_name: The name of the model file - this should almost never be changed. + :param str universe_file_name: The name of the universe file - this should almost never be changed. """ - raise NotImplementedError + model_path = hf_hub_download(model_path, model_file_name, **kwargs) + universe_path = hf_hub_download(model_path, universe_file_name, **kwargs) + + # set the paths to the downloaded files + self._model_path = model_path + self._universe_path = universe_path + + # load the model + self._model = Region2Vec.load(model_path) + self.tokenizer = InMemTokenizer(universe_path) def _validate_data(self, data: Union[sc.AnnData, str]) -> sc.AnnData: """ From 080771dc180fe2741e0e77f173d0c369e992be26 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 14:16:34 -0400 Subject: [PATCH 0291/1072] lint --- gitk/scembed/cli.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/gitk/scembed/cli.py b/gitk/scembed/cli.py index 0d6e559e..29eb21cc 100644 --- a/gitk/scembed/cli.py +++ b/gitk/scembed/cli.py @@ -7,8 +7,7 @@ from ._version import __version__ from .argparser import build_argparser from .const import * -from .main import (convert_anndata_to_documents, load_scanpy_data, - shuffle_documents, train) +from .main import convert_anndata_to_documents, load_scanpy_data, shuffle_documents, train def main(): From dcdb04e1c31c25116f81768bd25d11c6a119c35f Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 16:27:11 -0400 Subject: [PATCH 0292/1072] io bugs, others --- .vscode/settings.json | 1 + gitk/io/io.py | 5 +- gitk/models/__init__.py | 5 - gitk/models/atac/tokenization.py | 227 ------------------------------- gitk/models/const.py | 7 - gitk/models/pretrained.py | 97 ------------- gitk/models/rna/tokenization.py | 0 gitk/models/utils.py | 203 --------------------------- gitk/scembed/const.py | 2 + gitk/scembed/main.py | 66 ++++++++- gitk/tokenization/main.py | 4 +- setup.py | 3 +- 12 files changed, 70 insertions(+), 550 deletions(-) delete mode 100644 gitk/models/atac/tokenization.py delete mode 100644 gitk/models/const.py delete mode 100644 gitk/models/pretrained.py delete mode 100644 gitk/models/rna/tokenization.py delete mode 100644 gitk/models/utils.py diff --git a/.vscode/settings.json b/.vscode/settings.json index e7d40b0e..5cc4b00f 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -5,4 +5,5 @@ "python.formatting.provider": "none", "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true, + // "python.analysis.typeCheckingMode": "basic" } \ No newline at end of file diff --git a/gitk/io/io.py b/gitk/io/io.py index b7341bd4..1019773c 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -38,7 +38,8 @@ def __init__(self, regions: Union[str, List[Region]], backed: bool = False): with open(regions, "r") as f: lines = f.readlines() for line in lines: - chr, start, stop = line.split("\t") + # some bed files have more than 3 columns, so we just take the first 3 + chr, start, stop = line.split("\t")[:3] self.regions.append(Region(chr, int(start), int(stop))) self.length = len(self.regions) @@ -70,7 +71,7 @@ def __iter__(self): if self.backed: with open(self.path, "r") as f: for line in f: - chr, start, stop = line.split("\t") + chr, start, stop = line.split("\t")[:3] yield Region(chr, int(start), int(stop)) else: for region in self.regions: diff --git a/gitk/models/__init__.py b/gitk/models/__init__.py index 3fbbe8c9..3aa97379 100644 --- a/gitk/models/__init__.py +++ b/gitk/models/__init__.py @@ -1,6 +1 @@ -from .const import * -from .pretrained import * -from .atac.tokenization import * -from .rna.tokenization import * -from .utils import * from .models import ExModel diff --git a/gitk/models/atac/tokenization.py b/gitk/models/atac/tokenization.py deleted file mode 100644 index 22525fd4..00000000 --- a/gitk/models/atac/tokenization.py +++ /dev/null @@ -1,227 +0,0 @@ -import os -from typing import Dict, List, Union, Tuple - -import scanpy as sc -from tqdm import tqdm -from intervaltree import IntervalTree - -from ..utils import anndata_to_regionsets, convert_to_universe, validate_region_input - - -class Universe: - def __init__(self, regions: Union[str, List[str]]): - """ - Create a new Universe. - - :param Union[str, List[str]] regions: The regions to use for tokenization. This can be thought of as a vocabulary. - This can be either a list of regions or a path to a BED file containing regions. - """ - self._trees: Dict[str, IntervalTree] = dict() - self._regions = regions - self._universe_set: list[tuple[str, int, int]] = [] - - if regions is not None: - self.build_tree() - self._build_universe_set() - - def get_tree(self, tree: str): - """ - Get the interval tree for a given chromosome. - - :param str tree: The chromosome to get the tree for. - """ - return self._trees[tree] - - @property - def trees(self): - return self._trees - - @property - def regions(self): - return self._regions - - @property - def universe_set(self): - return self._universe_set - - def _build_universe_set(self): - """ - Build the universe as a set of regions in the form (chr, start, end). - """ - universe = [] - for tree in self._trees: - for interval in self._trees[tree]: - universe.append((tree, interval.begin, interval.end)) - self._universe_set = universe - - def build_tree(self, regions: str = None): - """ - Builds the interval tree from the regions. The tree is a dictionary that maps chromosomes to - interval trees. - - We read in the regions (either from memory or from a BED file) and convert them to an - interval tree. - - :param str regions: The regions to use for tokenization. This can be thought of as a vocabulary. - """ - regions = validate_region_input(regions or self._regions) - - # build trees - for region in tqdm(regions, total=len(regions), desc="Loading universe"): - chr, start, end = region - start = int(start) - end = int(end) - if chr not in self._trees: - self._trees[chr] = IntervalTree() - self._trees[chr][start:end] = f"{chr}_{start}_{end}" - - def query( - self, - regions: Union[str, List[str], List[Tuple[str, int, int]], Tuple[str, int, int]], - ): - """ - Query the interval tree for the given regions. - - :param Union[str, List[str], List[tuple[str, int, int]], tuple[str, int, int]] regions: The regions to query for. this can be either a bed file, - a list of regions (chr_start_end), or a list of tuples of chr, start, end. - """ - # validate input - if isinstance(regions, tuple): - regions = [regions] - regions = validate_region_input(regions) - - overlapping_regions = [] - for region in regions: - chr, start, end = region - start = int(start) - end = int(end) - if chr not in self._trees: - continue - overlaps = self._trees[chr][start:end] - for overlap in overlaps: - overlapping_regions.append((chr, overlap.begin, overlap.end)) - return overlapping_regions - - def __contains__(self, item: Tuple[str, int, int]): - """ - Check if the given region is in the universe. - - :param tuple[str, int, int] item: The region to check for. - """ - # ensure item is a tuple of chr, start, end - if not ((isinstance(item, tuple) or isinstance(item, list)) and len(item) == 3): - raise ValueError("The item to check for must be a tuple of chr, start, end.") - - chr, start, end = item - start = int(start) - end = int(end) - if chr not in self._trees: - return False - overlaps = self._trees[chr][start:end] - return len(overlaps) > 0 - - def __iter__(self): - for tree in self._trees: - yield self._trees[tree] - - -class Tokenizer: - pass - - -class HardTokenizer(Tokenizer): - """ - Tokenizer that computes overlaps between regions. - """ - - def __init__( - self, - regions: Union[List[str], str], - ): - """ - Create a new HardTokenizer. - - :param Union[List[str], str] regions: The regions to use for tokenization. This can be thought of as a vocabulary. - This can be either a list of regions or a path to a BED file containing regions. - """ - self._regions = regions - self._universe: Universe = Universe(validate_region_input(regions)) - - @property - def regions(self): - return self._regions - - @property - def universe(self): - return self._universe - - def _tokenize_anndata(self, data: sc.AnnData) -> List[List[str]]: - """ - Tokenize an anndata object into a list of lists of regions. - - :param sc.AnnData data: The anndata object to tokenize. - - :return: A list of lists of regions. - """ - data = convert_to_universe(data, self._universe) - region_sets = anndata_to_regionsets(data) - return region_sets - - def _tokenize_bed_file(self, data: str, fraction: float = 1e-9) -> List[str]: - """ - Tokenize a BED file into a list of lists of regions. - - :param str data: The path to the BED file to tokenize. - :param float fraction: The fraction of the genome to use for tokenization. This is used to subsample the genome. - - :return: A list of lists of regions. - """ - regions = validate_region_input(data) - tokens = ["_".join([str(h) for h in hit]) for hit in self._universe.query(regions)] - return tokens - - def _tokenize_list(self, data: List[str]) -> List[List[str]]: - """ - Tokenize a list of regions into a list of lists of regions. - - :param List[str] data: The list of regions to tokenize. - """ - regions = validate_region_input(data) - hits = [h[0] for h in self._universe.query(regions)] - tokens = ["_".join(hit) for hit in hits] - return tokens - - def tokenize( - self, data: Union[sc.AnnData, str, List[str]] - ) -> Union[List[List[str]], List[str]]: - """ - Tokenize a dataset. This will compute overlaps between regions and cells. Three - types of data are accepted: - 1. Anndata object. This must have `chr`, `start`, and `end` values for the `.var` attribute. - 2. A path to a BED file containing regions. - 3. A list of regions. - - Regardless of the input, we return a list of lists of regions. Each list of regions - corresponds to a cell. - - :param Union[sc.AnnData, str, List[str]]Union[sc.AnnData, str, List[str]] data: The data to tokenize. - :raises ValueError: If the data is not one of the three accepted types. - :return: A list of regions or a list of lists of regions (depending on the input type). - """ - # check if data is anndata object - if isinstance(data, sc.AnnData): - return self._tokenize_anndata(data) - # check if data is a path to a BED file - elif isinstance(data, str): - # ensure that the file exists - if not os.path.exists(data): - raise FileNotFoundError(f"Could not find file {data}.") - return self._tokenize_bed_file(data) - # check if data is a list of regions - elif isinstance(data, list): - return self._tokenize_list(data) - # else, raise error - else: - raise ValueError( - "Data must be one of the following types: anndata object, path to BED file, list of regions." - ) diff --git a/gitk/models/const.py b/gitk/models/const.py deleted file mode 100644 index d19d5a84..00000000 --- a/gitk/models/const.py +++ /dev/null @@ -1,7 +0,0 @@ -MODEL_FILE_NAME = "model.bin" - -DEFAULT_PATH_TO_AILIST = "ailist" -DEFAULT_PATH_TO_BEDTOOLS = "bedtools" - -UNIVERSE_FILE_NAME = "universe.bed" -CACHE_DIR = ".gitk_cache" diff --git a/gitk/models/pretrained.py b/gitk/models/pretrained.py deleted file mode 100644 index e0ba719b..00000000 --- a/gitk/models/pretrained.py +++ /dev/null @@ -1,97 +0,0 @@ -from typing import Union, Tuple, List - -import numpy as np -import scanpy as sc -from huggingface_hub import hf_hub_download -from tqdm import tqdm - -from .. import scembed -from .const import MODEL_FILE_NAME, UNIVERSE_FILE_NAME -from .atac.tokenization import HardTokenizer - - -class PretrainedScembedModel: - """ - A class for loading pretrained models from the HuggingFace Hub. - """ - - def __init__(self, model: str = None, tokenizer: HardTokenizer = None): - self.model_name = model - self._model_path = None - self._universe_path = None - self._tokenizer: HardTokenizer = tokenizer - self._model: scembed.SCEmbed = None - - # load if model is passed - if model is not None: - self._download_model_files(model) - self._tokenizer = HardTokenizer(self._universe_path) - self._model = self._load_model(self._model_path) - - def _download_model_files( - self, - model: str, - model_file_name: str = MODEL_FILE_NAME, - universe_file_name: str = UNIVERSE_FILE_NAME, - **kwargs, - ): - """ - Download a pretrained model from the HuggingFace Hub. We need to download - the actual model + weights, and the universe file. - - :param str model: The name of the model to download (this is the same as the repo name). - :param str model_file_name: The name of the model file - this should almost never be changed. - :param str universe_file_name: The name of the universe file - this should almost never be changed. - """ - model_path = hf_hub_download(model, model_file_name, **kwargs) - universe_path = hf_hub_download(model, universe_file_name, **kwargs) - - # set the paths to the downloaded files - self._model_path = model_path - self._universe_path = universe_path - - @property - def model(self): - return self._model - - @property - def tokenizer(self): - return self._tokenizer - - def _load_model(self, path: str) -> scembed.SCEmbed: - """ - Load a pretrained model from the HuggingFace Hub. - - :param str path: The path to the model. - :return: The loaded model. - """ - return scembed.utils.load_scembed_model(path) - - def encode(self, data: Union[str, List[Tuple[str, int, int]], sc.AnnData]) -> np.ndarray: - """ - Encode region sets data using the pretrained model. - - :param Union[str, list[tuple[str, int, int]], sc.AnnData] data: The data to encode. This can be either a path to a BED file, - a list of regions, or an AnnData object. - """ - if self._model is None: - raise ValueError("No model loaded. Please load a model first.") - - # generate tokens - tokenized = self._tokenizer.tokenize(data) - - embeddings = [] - for region_set in tqdm(tokenized, total=len(tokenized)): - if len(region_set) == 0: - raise ValueError("Encountered empty region set. Please check your input.") - # compute embeddings using average pooling of all region embeddings - embedding = np.mean( - [ - self._model.region2vec[region] - for region in region_set - if region in self._model.region2vec - ], - axis=0, - ) - embeddings.append(embedding) - return np.array(embeddings) diff --git a/gitk/models/rna/tokenization.py b/gitk/models/rna/tokenization.py deleted file mode 100644 index e69de29b..00000000 diff --git a/gitk/models/utils.py b/gitk/models/utils.py deleted file mode 100644 index d771bd6c..00000000 --- a/gitk/models/utils.py +++ /dev/null @@ -1,203 +0,0 @@ -import os -from typing import TYPE_CHECKING, Dict, List, Union, Tuple - -import numpy as np -import scanpy as sc -from tqdm import tqdm - -if TYPE_CHECKING: - from .atac.tokenization import Universe - -from .const import CACHE_DIR - - -def get_path_to_home_dir(): - """ - Get the path to the home directory. - """ - return os.path.expanduser("~") - - -def get_cache_dir(): - """ - Get the path to the cache directory. - """ - _home = get_path_to_home_dir() - return os.path.join(_home, CACHE_DIR) - - -def make_cache_dir(): - """ - Create cache directory in the current working directory. - """ - _home = get_path_to_home_dir() - _cache_dir = os.path.join(_home, CACHE_DIR) - if not os.path.exists(_cache_dir): - os.mkdir(_cache_dir) - - -def validate_region_input( - regions: Union[str, List[str], List[Tuple[str]]] -) -> List[Tuple[str, int, int]]: - """ - Validate the input for the regions. this universe accepts a lot of forms of input, - so we need to check what the user has passed. It also standardizes the input to a list - of tuples of chr, start, end. - - Users are allowed to pass a list of regions, a list of tuples with chr, start, and end, or - a path to a BED file. - - :param regions: The regions to use for tokenization. This can be thought of as a vocabulary. - This can be either a list of regions or a path to a BED file containing regions. - :return list: A list of tuples of chr, start, end. - """ - # check for bedfile - if isinstance(regions, str): - # ensure that the file exists - if not os.path.exists(regions): - raise FileNotFoundError(f"Could not find file {regions} containing regions.") - file = regions # rename for clarity - regions = [] - with open(file, "r") as f: - lines = f.readlines() - for line in lines: - regions.append(tuple(line.strip().split("\t"))) - return regions - - # check for list of chr_start_end strings - elif isinstance(regions, list) and all([isinstance(r, str) for r in regions]): - regions = [tuple(r.split("_")) for r in regions] - return regions - - # check for list of tuples of chr, start, end - elif isinstance(regions, list) and all( - [(isinstance(r, tuple) or isinstance(r, list)) and len(r) == 3 for r in regions] - ): - return regions - else: - raise ValueError( - "The regions must be either a list of regions or a path to a BED file containing regions." - ) - - -def generate_var_conversion_map( - a: List[Tuple[str, int, int]], - b: "Universe", - fraction: float = 1.0e-9, -) -> Dict[str, Union[str, None]]: - """ - Create a conversion map to convert regions from a to b. This is used to convert the - consensus peak set of one AnnData object to another. - - For each region in a, we will either find a matching region in b, or None. If a matching - region is found, we will store the region in b. If no matching region is found, we will - store `None`. - - Intuitively, think of this as converting `A` --> `B`. If a region in `A` is found in `B`, - we will change the region in `A` to the region in `B`. If a region in `A` is not found in - `B`, we will drop that region in `A` altogether. - - :param List[tuple[str, int, int]] a: the first list of regions - :param Universe: the second list of regions as a Universe object - :param float fraction: the fraction of the region that must overlap to be considered an overlap - """ - - conversion_map = dict() - - for region in tqdm(a, total=len(a), desc="Generating conversion map"): - overlaps = b.query(region) - region_str = f"{region[0]}_{region[1]}_{region[2]}" - if len(overlaps) > 0: - olap = overlaps[0] # take the first overlap for now, we can change this later - olap_str = f"{olap[0]}_{olap[1]}_{olap[2]}" - conversion_map[region_str] = olap_str - else: - conversion_map[region_str] = None - - return conversion_map - - -def convert_to_universe(adata: sc.AnnData, universe: "Universe") -> sc.AnnData: - """ - Convert an AnnData object to a new universe. This is useful for converting the consensus - peak set of one AnnData object to another. This is usually used as a preprocessing step - before tokenization. - - This function is already pretty optimized. To speed it up even more, we could use - multiprocessing to parallelize the conversion. Or, we could use Rust library to speed - up the conversion. However, this is not necessary at the moment. - - :param adata: the AnnData object to convert - :param Universe universe: the universe to convert to - :return: the converted AnnData object - """ - # ensure adata has chr, start, and end - if not all([x in adata.var.columns for x in ["chr", "start", "end"]]): - raise ValueError("AnnData object must have `chr`, `start`, and `end` columns in .var") - - # create list of regions from adata - adata.var["region"] = ( - adata.var["chr"].astype(str) - + "_" - + adata.var["start"].astype(str) - + "_" - + adata.var["end"].astype(str) - ) - query_set: List[tuple[str, int, int]] = list( - zip(adata.var["chr"], adata.var["start"].astype(int), adata.var["end"].astype(int)) - ) - - # generate conversion map - _map = generate_var_conversion_map(query_set, universe) - - # map regions to new universe - adata.var["new_region"] = adata.var["region"].map(_map) - - # drop rows where new_region is None - adata.var.dropna(subset=["new_region"], inplace=True) - - # split new_region into chr, start, end - adata.var[["chr", "start", "end"]] = adata.var["new_region"].str.split("_", expand=True) - - # drop 'region' and 'new_region' columns - adata.var.drop(columns=["region", "new_region"], inplace=True) - - # update adata with the new DataFrame - adata = adata[:, adata.var.index] - - return adata - - -def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: - """ - Converts an AnnData object to a list of lists of regions. This - is done by taking each cell and creating a list of all regions - that have a value greater than 0. - - This function is already pretty optimized. To speed this up - further we'd have to parallelize it or reach for a lower-level - language. - - *Note: this method requires that the sc.AnnData object have - chr, start, and end in `.var` attributes* - """ - # Extract the arrays for chr, start, and end - chr_values = adata.var["chr"].values - start_values = adata.var["start"].values - end_values = adata.var["end"].values - - # Perform the comparison using numpy operations - positive_values = adata.X > 0 - - if not isinstance(positive_values, np.ndarray): - positive_values = positive_values.toarray() - - regions = [] - for i in tqdm(range(adata.shape[0]), total=adata.shape[0], desc="Tokenizing"): - regions.append( - [ - f"{chr_values[j]}_{start_values[j]}_{end_values[j]}" - for j in np.where(positive_values[i])[0] - ] - ) - return regions diff --git a/gitk/scembed/const.py b/gitk/scembed/const.py index 49c85d90..48e4df77 100644 --- a/gitk/scembed/const.py +++ b/gitk/scembed/const.py @@ -5,5 +5,7 @@ START_KEY = "start" END_KEY = "end" +DEFAULT_CHUNK_SIZE = 1000 + MODEL_FILE_NAME = "model.bin" UNIVERSE_FILE_NAME = "universe.bed" diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index 72ca14d2..54218a9f 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -1,10 +1,12 @@ +import os from logging import getLogger from typing import List, Union import numpy as np import scanpy as sc -from huggingface_hub import hf_hub_download +from huggingface_hub import hf_hub_download, upload_file, login from numba import config +from tqdm import tqdm from ..io import Region, RegionSet from ..models import ExModel @@ -19,7 +21,7 @@ _GENSIM_LOGGER.setLevel("WARNING") # set the threading layer before any parallel target compilation -config.THREADING_LAYER = "threadsafe" +config.THREADING_LAYER = "threadsafe" # type: ignore class ScEmbed(ExModel): @@ -28,7 +30,7 @@ class ScEmbed(ExModel): extension of Region2Vec. """ - def __init__(self, model_path: str = None, **kwargs): + def __init__(self, model_path: Union[str, None] = None, **kwargs): """ Initialize ScEmbed model. @@ -45,6 +47,16 @@ def __init__(self, model_path: str = None, **kwargs): self._model = Region2Vec(**kwargs) self.tokenizer = InMemTokenizer() + def from_pretrained(self, model_file_path: str, universe_file_path: str): + """ + Initialize ScEmbed model from pretrained model. + + :param str model_file_path: Path to the pre-trained model. + :param str universe_file_path: Path to the universe file. + """ + self._model = Region2Vec.load(model_file_path) + self.tokenizer = InMemTokenizer(universe_file_path) + def _init_from_huggingface( self, model_path: str, @@ -128,5 +140,49 @@ def train(self, data: Union[sc.AnnData, str], **kwargs): _LOGGER.info("Training complete.") - def __call__(self, regions: Union[Region, RegionSet, List[Region]]) -> np.ndarray: - return self._model(regions) + def export(self, path: str): + """ + Export a model for direct upload to the HuggingFace Hub. + + :param str path: The path to save the model to. + """ + model_file_path = os.path.join(path, MODEL_FILE_NAME) + universe_file_path = os.path.join(path, UNIVERSE_FILE_NAME) + + # save the model + self._model.save(model_file_path) + + # save universe (vocab) + with open(universe_file_path, "w") as f: + for region in self.tokenizer.universe: + f.write(f"{region.chr}\t{region.start}\t{region.end}\n") + + def upload_to_huggingface(self, model_name: str, token: str = None, **kwargs): + """ + Upload the model to the HuggingFace Hub. + + :param str model_name: The name of the model to upload. + :param kwargs: Additional keyword arguments to pass to the upload function. + """ + raise NotImplementedError("This method is not yet implemented.") + + def encode(self, adata: sc.AnnData) -> np.ndarray: + """ + Encode the data into a latent space. + + :param sc.AnnData adata: The AnnData object containing the data to encode. + :return np.ndarray: The encoded data. + """ + # tokenize the data + region_sets = self.tokenizer.tokenize(adata) + + # encode the data + _LOGGER.info("Encoding data.") + enoded_data = [] + for region_set in tqdm(region_sets, desc="Encoding data", total=len(region_sets)): + vector = self._model.forward(region_set) + enoded_data.append(vector) + return np.array(enoded_data) + + def __call__(self, adata: sc.AnnData) -> np.ndarray: + return self.encode(adata) diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index f0afecff..cc868f9f 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -27,7 +27,7 @@ def tokenize(self, *args, **kwargs): class InMemTokenizer(Tokenizer): """Abstract class representing a tokenizer function""" - def __init__(self, universe: Union[str, RegionSet] = None): + def __init__(self, universe: Union[str, RegionSet, None] = None): """ Create a new InMemTokenizer. @@ -66,7 +66,7 @@ def add_regions(self, regions: Union[str, List[Region], RegionSet]): """ self.build_trees(regions) - def build_trees(self, regions: Union[str, List[Region], RegionSet] = None): + def build_trees(self, regions: Union[str, List[Region], RegionSet, None] = None): """ Builds the interval tree from the regions. The tree is a dictionary that maps chromosomes to interval trees. diff --git a/setup.py b/setup.py index c244afd6..aecfd8fb 100755 --- a/setup.py +++ b/setup.py @@ -31,12 +31,11 @@ "gitk.eval", "gitk.likelihood", "gitk.models", - "gitk.models.atac", - "gitk.models.rna", "gitk.region2vec", "gitk.scembed", "gitk.tokenization", "gitk.universe", + "gitk.io", ], version=version, long_description=long_description, From df6c7f94fb4e531900278698b1465893cdf2c731 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 17:21:28 -0400 Subject: [PATCH 0293/1072] work on adding regions to tokenizer --- gitk/region2vec/const.py | 3 + gitk/region2vec/main.py | 162 ++++++++++++++++++++++++++++++++++++++ gitk/scembed/main.py | 6 +- gitk/tokenization/main.py | 40 ++++++++-- 4 files changed, 200 insertions(+), 11 deletions(-) diff --git a/gitk/region2vec/const.py b/gitk/region2vec/const.py index 6ec62d97..b2240c0a 100644 --- a/gitk/region2vec/const.py +++ b/gitk/region2vec/const.py @@ -11,3 +11,6 @@ DEFAULT_INIT_LR = 0.1 # https://github.com/databio/gitk/issues/6#issuecomment-1476273162 DEFAULT_MIN_LR = 0.0001 # gensim default DEFAULT_DECAY_RATE = 0.95 + +MODEL_FILE_NAME = "model.bin" +UNIVERSE_FILE_NAME = "universe.bed" diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 2300c0ba..0ca64aed 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -4,11 +4,15 @@ from typing import List, Union import numpy as np +from tqdm import tqdm from gensim.models import Word2Vec from gensim.models.callbacks import CallbackAny2Vec +from huggingface_hub import hf_hub_download from numba import config from ..io import Region, RegionSet +from ..models import ExModel +from ..tokenization import InMemTokenizer from . import utils from .const import * from .region2vec_train import main as region2_train @@ -387,3 +391,161 @@ def __call__(self, regions: Union[Region, RegionSet, List[Region]]) -> np.ndarra def __repr__(self) -> str: return f"Region2Vec(window={self.window}, vector_size={self.vector_size})" + + +class Region2VecExModel(ExModel): + def __init__(self, model_path: Union[str, None] = None, **kwargs): + """ + Initialize Region2VecExModel. + + :param str model_path: Path to the pre-trained model on huggingface. + :param kwargs: Additional keyword arguments to pass to the model. + """ + self.model_path = model_path + self._model: Region2Vec = None + self.tokenizer: InMemTokenizer = None + + if model_path is not None: + self._init_from_huggingface(model_path) + else: + self._model = Region2Vec(**kwargs) + self.tokenizer = InMemTokenizer() + + def from_pretrained(self, model_file_path: str, universe_file_path: str): + """ + Initialize ScEmbed model from pretrained model. + + :param str model_file_path: Path to the pre-trained model. + :param str universe_file_path: Path to the universe file. + """ + self._model = Region2Vec.load(model_file_path) + self.tokenizer = InMemTokenizer(universe_file_path) + + def _init_from_huggingface( + self, + model_path: str, + model_file_name: str = MODEL_FILE_NAME, + universe_file_name: str = UNIVERSE_FILE_NAME, + **kwargs, + ): + """ + Download a pretrained model from the HuggingFace Hub. We need to download + the actual model + weights, and the universe file. + + :param str model: The name of the model to download (this is the same as the repo name). + :param str model_file_name: The name of the model file - this should almost never be changed. + :param str universe_file_name: The name of the universe file - this should almost never be changed. + """ + model_path = hf_hub_download(model_path, model_file_name, **kwargs) + universe_path = hf_hub_download(model_path, universe_file_name, **kwargs) + + # set the paths to the downloaded files + self._model_path = model_path + self._universe_path = universe_path + + # load the model + self._model = Region2Vec.load(model_path) + self.tokenizer = InMemTokenizer(universe_path) + + def _validate_data( + self, data: Union[List[str], List[RegionSet], List[List[Region]]] + ) -> List[RegionSet]: + """ + Validate that the user sent in the correct data. this could be one of three things: + 1. A list of paths to bed files + 2. A list of RegionSets + 3. A list of lists of Regions + + :param Union[List[str], List[RegionSet], List[List[Region]]] data: The data to validate. + :return: The validated data as a list of RegionSets. + """ + if isinstance(data, list): + if len(data) == 0: + raise ValueError("Data cannot be empty.") + elif isinstance(data[0], str): + # we have a list of paths to bed files + return [RegionSet(path) for path in data] + elif isinstance(data[0], RegionSet): + # we have a list of RegionSets + return data + elif isinstance(data[0], list): + # we have a list of lists of Regions + return [RegionSet(regions) for regions in data] + else: + raise TypeError( + f"Data must be of type List[str], List[RegionSet], or List[List[Region]], not {type(data).__name__}" + ) + else: + raise TypeError( + f"Data must be of type List[str], List[RegionSet], or List[List[Region]], not {type(data).__name__}" + ) + + def train(self, data: Union[List[str], List[RegionSet], List[List[Region]]], **kwargs): + """ + Train the model. + + :param sc.AnnData data: The AnnData object containing the data to train on (can be path to AnnData). + :param kwargs: Keyword arguments to pass to the model training function. + """ + _LOGGER.info("Validating data.") + regions = self._validate_data(data) + + _LOGGER.info("Extracting region sets.") + + # fit the tokenizer on the regions + self.tokenizer.fit(regions) + + # convert the data to a list of documents + region_sets = self.tokenizer.tokenize(data) + + _LOGGER.info("Training begin.") + self._model.train(region_sets, **kwargs) + + _LOGGER.info("Training complete.") + + def export(self, path: str): + """ + Export a model for direct upload to the HuggingFace Hub. + + :param str path: The path to save the model to. + """ + model_file_path = os.path.join(path, MODEL_FILE_NAME) + universe_file_path = os.path.join(path, UNIVERSE_FILE_NAME) + + # save the model + self._model.save(model_file_path) + + # save universe (vocab) + with open(universe_file_path, "w") as f: + for region in self.tokenizer.universe: + f.write(f"{region.chr}\t{region.start}\t{region.end}\n") + + def upload_to_huggingface(self, model_name: str, token: str = None, **kwargs): + """ + Upload the model to the HuggingFace Hub. + + :param str model_name: The name of the model to upload. + :param kwargs: Additional keyword arguments to pass to the upload function. + """ + raise NotImplementedError("This method is not yet implemented.") + + def encode(self, regions: Union[List[Region], RegionSet]) -> np.ndarray: + """ + Encode the data into a latent space. + + :param sc.AnnData adata: The AnnData object containing the data to encode. + :return np.ndarray: The encoded data. + """ + # tokenize the data + region_sets = self.tokenizer.tokenize(regions) + + # encode the data + _LOGGER.info("Encoding data.") + enoded_data = [] + for region_set in tqdm(region_sets, desc="Encoding data", total=len(region_sets)): + vector = self._model.forward(region_set) + enoded_data.append(vector) + return np.array(enoded_data) + + def __call__(self, regions: Union[List[Region], RegionSet]) -> np.ndarray: + return self.encode(adata) diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index 54218a9f..382c37e1 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -85,10 +85,8 @@ def _init_from_huggingface( def _validate_data(self, data: Union[sc.AnnData, str]) -> sc.AnnData: """ - Get the cell embeddings for the original AnnData passed in. This should - be called after training is complete. It is only useful for extracting the - embeddings for the last chunk of data its seen. - Validate the data is of the correct type and has the required columns. + Validate the data is of the correct type and has the required columns + :param sc.AnnData | str data: The AnnData object containing the data to train on (can be path to AnnData). :return sc.AnnData: The AnnData object. """ diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index cc868f9f..84427132 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -13,7 +13,7 @@ import gitk.region2vec.utils as utils from gitk.tokenization.split_file import split_file -from ..io import Region, RegionSet, RegionSetCollection +from ..io import Region, RegionSet from .hard_tokenization_batch import main as hard_tokenization from .utils import anndata_to_regionsets @@ -60,13 +60,20 @@ def get_tree(self, tree: str): def trees(self): return self._trees - def add_regions(self, regions: Union[str, List[Region], RegionSet]): + def add_regions( + self, regions: Union[str, List[Region], RegionSet, List[RegionSet], List[List[Region]]] + ): """ Add regions to the universe. """ self.build_trees(regions) - def build_trees(self, regions: Union[str, List[Region], RegionSet, None] = None): + def build_trees( + self, + regions: Union[ + str, List[str], List[Region], RegionSet, List[RegionSet], List[List[Region]] + ] = None, + ): """ Builds the interval tree from the regions. The tree is a dictionary that maps chromosomes to interval trees. @@ -76,12 +83,29 @@ def build_trees(self, regions: Union[str, List[Region], RegionSet, None] = None) :param str regions: The regions to use for tokenization. This can be thought of as a vocabulary. """ + # just return if we already have a universe if regions is None: regions = self.universe # use the universe passed in the constructor + # check for bed file elif isinstance(regions, str): regions = RegionSet(regions) # load from file - else: - pass # assume it's a list of regions that was passed in + # check for list of bed files + elif isinstance(regions, list) and isinstance(regions[0], str): + regions = [] + for bedfile in regions: + regions.extend([region for region in RegionSet(bedfile)]) + # check for regionset + elif isinstance(regions, RegionSet): + pass + # check for list of regionsets + elif isinstance(regions, list) and isinstance(regions[0], RegionSet): + regions = [region for regionset in regions for region in regionset] + # check for list of lists of regions + elif isinstance(regions, list) and isinstance(regions[0], list): + regions = [region for regionlist in regions for region in regionlist] + # check for list of regions + elif isinstance(regions, list) and isinstance(regions[0], Region): + pass # build trees for region in tqdm(regions, total=len(regions), desc="Adding regions to universe"): @@ -94,7 +118,9 @@ def build_trees(self, regions: Union[str, List[Region], RegionSet, None] = None) # count total regions self.total_regions = sum([len(self._trees[tree]) for tree in self._trees]) - def fit(self, regions: Union[str, List[Region]]): + def fit( + self, regions: Union[str, List[Region], RegionSet, List[RegionSet], List[List[Region]]] + ): """ Fit the tokenizer to the given regions. This is equivalent to adding regions to the universe. """ @@ -215,7 +241,7 @@ def generate_var_conversion_map( return conversion_map def tokenize( - self, region_set: Union[str, List[Region], sc.AnnData] + self, region_set: Union[str, List[Region], RegionSet, sc.AnnData] ) -> Union[List[Region], List[List[Region]], List[RegionSet]]: """ Tokenize a RegionSet. From 0474161e96c72bc12622ff4c93eed5a4a8a03ec1 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 18:41:20 -0400 Subject: [PATCH 0294/1072] make Region2VecExModel --- gitk/region2vec/main.py | 11 +++++------ gitk/tokenization/main.py | 2 +- 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 0ca64aed..3b8fc437 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -488,17 +488,16 @@ def train(self, data: Union[List[str], List[RegionSet], List[List[Region]]], **k :param kwargs: Keyword arguments to pass to the model training function. """ _LOGGER.info("Validating data.") - regions = self._validate_data(data) + region_sets = self._validate_data(data) _LOGGER.info("Extracting region sets.") # fit the tokenizer on the regions - self.tokenizer.fit(regions) - - # convert the data to a list of documents - region_sets = self.tokenizer.tokenize(data) + for region_set in region_sets: + self.tokenizer.fit(region_set) _LOGGER.info("Training begin.") + self._model.train(region_sets, **kwargs) _LOGGER.info("Training complete.") @@ -548,4 +547,4 @@ def encode(self, regions: Union[List[Region], RegionSet]) -> np.ndarray: return np.array(enoded_data) def __call__(self, regions: Union[List[Region], RegionSet]) -> np.ndarray: - return self.encode(adata) + return self.encode(regions) diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 84427132..69e35d1c 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -13,7 +13,7 @@ import gitk.region2vec.utils as utils from gitk.tokenization.split_file import split_file -from ..io import Region, RegionSet +from ..io import Region, RegionSet, RegionSetCollection from .hard_tokenization_batch import main as hard_tokenization from .utils import anndata_to_regionsets From 97046503c1ee53986bc9ee2b00ce0170078689ea Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 19:55:42 -0400 Subject: [PATCH 0295/1072] more updates - add scEmbed tests --- .vscode/settings.json | 7 +- gitk/models/__init__.py | 2 +- gitk/models/{models.py => main.py} | 9 +- gitk/region2vec/main.py | 4 +- gitk/scembed/main.py | 14 +- gitk/tokenization/main.py | 3 +- gitk/tokenization/utils.py | 1 + tests/test_gitk.py | 5 - tests/test_io.py | 4 - tests/test_models.py | 200 ----------------------------- tests/test_scembed.py | 147 +++++---------------- tests/test_tokenization.py | 5 +- 12 files changed, 63 insertions(+), 338 deletions(-) rename gitk/models/{models.py => main.py} (80%) delete mode 100644 tests/test_gitk.py delete mode 100644 tests/test_models.py diff --git a/.vscode/settings.json b/.vscode/settings.json index 5cc4b00f..e19736d3 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -2,8 +2,9 @@ "[python]": { "editor.defaultFormatter": "ms-python.black-formatter" }, - "python.formatting.provider": "none", + "python.testing.pytestArgs": [ + "tests" + ], "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true, - // "python.analysis.typeCheckingMode": "basic" + "python.testing.pytestEnabled": true } \ No newline at end of file diff --git a/gitk/models/__init__.py b/gitk/models/__init__.py index 3aa97379..a7dc64ed 100644 --- a/gitk/models/__init__.py +++ b/gitk/models/__init__.py @@ -1 +1 @@ -from .models import ExModel +from .main import ExModel diff --git a/gitk/models/models.py b/gitk/models/main.py similarity index 80% rename from gitk/models/models.py rename to gitk/models/main.py index 29e648fe..d0af0b6e 100644 --- a/gitk/models/models.py +++ b/gitk/models/main.py @@ -1,6 +1,9 @@ +from typing import TYPE_CHECKING from abc import ABC, abstractmethod from ..io import RegionSet -from ..tokenization import Tokenizer + +if TYPE_CHECKING: + from ..tokenization import Tokenizer class Model(ABC): @@ -18,10 +21,10 @@ class ExModel(ABC): model: Model universe: RegionSet - tokenizer: Tokenizer + tokenizer: "Tokenizer" @abstractmethod def __init__( - self, model: Model = None, universe: RegionSet = None, tokenizer: Tokenizer = None + self, model: Model = None, universe: RegionSet = None, tokenizer: "Tokenizer" = None ) -> None: raise NotImplementedError diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 3b8fc437..c618f17d 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -11,8 +11,8 @@ from numba import config from ..io import Region, RegionSet -from ..models import ExModel -from ..tokenization import InMemTokenizer +from ..models.main import ExModel +from ..tokenization.main import InMemTokenizer from . import utils from .const import * from .region2vec_train import main as region2_train diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index 382c37e1..7897a376 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -9,7 +9,7 @@ from tqdm import tqdm from ..io import Region, RegionSet -from ..models import ExModel +from ..models.main import ExModel from ..region2vec import Region2Vec from ..tokenization import InMemTokenizer from .const import CHR_KEY, END_KEY, MODEL_FILE_NAME, MODULE_NAME, START_KEY, UNIVERSE_FILE_NAME @@ -47,6 +47,14 @@ def __init__(self, model_path: Union[str, None] = None, **kwargs): self._model = Region2Vec(**kwargs) self.tokenizer = InMemTokenizer() + @property + def wv(self): + return self._model.wv + + @property + def trained(self): + return self._model.trained + def from_pretrained(self, model_file_path: str, universe_file_path: str): """ Initialize ScEmbed model from pretrained model. @@ -144,6 +152,10 @@ def export(self, path: str): :param str path: The path to save the model to. """ + # make folder path if it doesn't exist + if not os.path.exists(path): + os.makedirs(path) + model_file_path = os.path.join(path, MODEL_FILE_NAME) universe_file_path = os.path.join(path, UNIVERSE_FILE_NAME) diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 69e35d1c..033caf91 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -107,7 +107,8 @@ def build_trees( elif isinstance(regions, list) and isinstance(regions[0], Region): pass - # build trees + # build trees + add regions to universe + self.universe = regions for region in tqdm(regions, total=len(regions), desc="Adding regions to universe"): if region.chr not in self._trees: self._trees[region.chr] = IntervalTree() diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index d8c7fb8e..e46a8465 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -4,6 +4,7 @@ import numpy as np import scanpy as sc from tqdm import tqdm + from ..io import Region, RegionSet diff --git a/tests/test_gitk.py b/tests/test_gitk.py deleted file mode 100644 index 3b87e845..00000000 --- a/tests/test_gitk.py +++ /dev/null @@ -1,5 +0,0 @@ -# import gitk - - -def test_test(): - assert True diff --git a/tests/test_io.py b/tests/test_io.py index 1036e27f..80a05a7f 100644 --- a/tests/test_io.py +++ b/tests/test_io.py @@ -1,12 +1,8 @@ -import os -import sys - import pytest import scanpy as sc import numpy as np from gitk.io import Region, RegionSet -from gitk.tokenization import InMemTokenizer @pytest.fixture diff --git a/tests/test_models.py b/tests/test_models.py deleted file mode 100644 index 13d12f3e..00000000 --- a/tests/test_models.py +++ /dev/null @@ -1,200 +0,0 @@ -import os -import sys - -import pytest -import scanpy as sc -import numpy as np - -# add parent directory to path -sys.path.append("../") - -from gitk import models - - -@pytest.fixture -def adata(): - return sc.read_h5ad("tests/data/pbmc_hg38.h5ad") - - -@pytest.fixture -def tokenizer_from_file(): - return models.HardTokenizer("tests/data/universe.bed") - - -@pytest.fixture -def tokenizer_from_list(): - return models.HardTokenizer(["chr1_0_100", "chr2_0_100"]) - - -@pytest.fixture -def universe_from_file(): - return models.Universe("tests/data/universe.bed") - - -@pytest.fixture -def pretrained_model(): - return models.PretrainedScembedModel("nleroy917/luecken2021") - - -def test_init_tokenizers( - tokenizer_from_file: models.HardTokenizer, - tokenizer_from_list: models.HardTokenizer, -): - # assert that the tokenizers were created - assert tokenizer_from_file is not None - assert tokenizer_from_list is not None - - # assert that the regions file was created - assert all([isinstance(t, str) for t in tokenizer_from_list.regions]) - assert all([isinstance(t, str) for t in tokenizer_from_file.regions]) - - -def test_init_universe( - universe_from_file: models.Universe, -): - # assert that the universe was created - assert universe_from_file is not None - - # assert interval trees were created - for tree in universe_from_file: - assert tree is not None - - -def test_universe_set( - universe_from_file: models.Universe, -): - # assert that the universe_set is the same length as the number of regions - # in the universe file - universe_set = universe_from_file.universe_set - assert len(universe_set) == 10000 - - -def test_universe_contains(universe_from_file: models.Universe): - regions = [ - ("chr11", 62732530, 62733438), - ("chr1", 34918742, 34919559), - ] - for region in regions: - assert region in universe_from_file - - -def test_universe_not_contains(universe_from_file: models.Universe): - regions = [("chr10", 132, 1324), ("chr1", 247, 2478), ("chr999", 0, 100)] - for region in regions: - assert region not in universe_from_file - - -def test_universe_query(universe_from_file: models.Universe): - regions = [ - ("chr10", 132, 1324), - ("chr11", 62732550, 62733458), - ("chr1", 34918762, 34919579), - ] - res = universe_from_file.query(regions) - assert len(res) == 2 - - -def test_universe_conversion(): - """ - Test tokenization for three scenarios: - 1. One overlap in B per region in A - 2. At least one region in A doesn't overlap with any region in B - 3. A region in A overlaps with multiple regions in B - - Scenario 1: - A: |-----------| |-----------| |-----------| - B: |-----------| |-----------| |-----------| - - Scenario 2: - A: |-----------| |-------| |-----------| - B: |-----------| |-------| |-----------| - - Scenario 3: - A: |-----------| |-----------| |-----------| - B: |-----------| |--------| |-----------| |-----------| - - """ - # scenario 1 - with open("tests/data/s1_a.bed", "r") as f: - lines = f.readlines() - a = [tuple(l.strip().split("\t")) for l in lines] - u = models.Universe("tests/data/s1_b.bed") - - conversion_map = models.utils.generate_var_conversion_map(a, u) - assert conversion_map == { - "chr1_10_30": "chr1_20_40", - "chr1_110_130": "chr1_120_140", - "chr1_210_230": "chr1_220_240", - } - - # scenario 2 - with open("tests/data/s2_a.bed", "r") as f: - lines = f.readlines() - a = [tuple(l.strip().split("\t")) for l in lines] - u = models.Universe("tests/data/s2_b.bed") - - conversion_map = models.utils.generate_var_conversion_map(a, u) - assert conversion_map == { - "chr1_10_30": "chr1_20_40", - "chr1_110_130": None, - "chr1_210_230": "chr1_220_240", - } - - # scenario 3 - with open("tests/data/s3_a.bed", "r") as f: - lines = f.readlines() - a = [tuple(l.strip().split("\t")) for l in lines] - u = models.Universe("tests/data/s3_b.bed") - - conversion_map = models.utils.generate_var_conversion_map(a, u) - assert conversion_map == { - "chr1_10_30": "chr1_20_40", - "chr1_110_130": "chr1_100_120", - "chr1_210_230": "chr1_220_240", - } - - -def test_tokenize_bedfile(tokenizer_from_file: models.HardTokenizer): - # assert that the tokenizer works - tokens = tokenizer_from_file.tokenize("tests/data/to_tokenize.bed") - assert tokens is not None - print(len(tokens)) - assert len(tokens) == 10 - assert all([len(t.split("_")) == 3 for t in tokens]) - - -def test_tokenize_anndata(adata: sc.AnnData, tokenizer_from_file: models.HardTokenizer): - tokens: list[list[str]] = tokenizer_from_file.tokenize(adata) - # make sure each token in each cell is inside the universe.bed file - for cell in tokens: - for token in cell: - chr, start, end = token.split("_") - token_tuple = (chr, int(start), int(end)) - assert token_tuple in tokenizer_from_file.universe.universe_set - - -# skip -@pytest.mark.skip -def test_init_pretrained_model( - pretrained_model: models.PretrainedScembedModel, -): - # assert that the model was created - assert pretrained_model is not None - - # assert that the tokenizer was created - assert pretrained_model.tokenizer is not None - - # assert that the model was created - assert pretrained_model.model is not None - - -@pytest.mark.skip -def test_encode_anndata(adata: sc.AnnData, pretrained_model: models.PretrainedScembedModel): - # encode the anndata - encoded = pretrained_model.encode(adata) - - # assert that the encoded object is a numpy array - assert isinstance(encoded, np.ndarray) - - # assert that the encoded object has the correct shape - assert encoded.shape == (adata.n_obs, 100) diff --git a/tests/test_scembed.py b/tests/test_scembed.py index d878e8ca..d2fd6ebb 100644 --- a/tests/test_scembed.py +++ b/tests/test_scembed.py @@ -1,7 +1,6 @@ import logging import os import sys -from itertools import chain import pytest import scanpy as sc @@ -9,7 +8,9 @@ # add parent directory to path sys.path.append("../") -from gitk import scembed +from gitk.io import Region +from gitk.region2vec.utils import wordify_region, wordify_regions +from gitk.scembed import ScEmbed # set to DEBUG to see more info logging.basicConfig(level=logging.INFO) @@ -25,140 +26,54 @@ def pbmc_data_backed(): return sc.read_h5ad("tests/data/pbmc_hg38.h5ad", backed="r") -@pytest.fixture -def total_regions(pbmc_data: sc.AnnData): - pbmc_df = pbmc_data.to_df() - pbmc_df = pbmc_df.clip(upper=1) - total_regions = sum(pbmc_df.sum(axis=0) >= scembed.const.DEFAULT_MIN_COUNT) - return total_regions - - -def test_import(): - assert scembed - - -def test_load_scanpy(): - scembed.load_scanpy_data("tests/data/pbmc_hg38.h5ad") - - -def test_extract_region_list(pbmc_data: sc.AnnData): - regions = scembed.extract_region_list(pbmc_data.var) - assert len(regions) == pbmc_data.shape[1] - for region in regions: - assert isinstance(region, str) - - -def test_document_creation(pbmc_data: sc.AnnData): - # convert pbmc_data to df and drop any columns (regions with all 0 signal) - pbmc_df = pbmc_data.to_df() - pbmc_df_dropped = pbmc_df.loc[:, (pbmc_df != 0).any(axis=0)] - - # convert to docs - docs = scembed.convert_anndata_to_documents(pbmc_data) - - # ensure all cells converted - assert len(docs) == pbmc_data.shape[0] - - # ensure all regions represented - all_regions = set(list(chain(*docs))) - assert len(all_regions) == pbmc_df_dropped.shape[1] - - # ensure all regions are strings and contain no spaces - for doc in docs: - assert all([isinstance(r, str) for r in doc]) - assert all([" " not in r for r in doc]) - - -def test_document_shuffle(): - docs = [["a", "b", "c"], ["d", "e", "f"]] - shuffled = scembed.shuffle_documents(docs, 10) - assert len(shuffled) == len(docs) - for doc in shuffled: - assert len(doc) == len(docs[0]) - # by pure random chance, the following COULD fail, so we'll just comment it out - # assert doc != docs[0] - # assert doc != docs[1] - - def test_model_creation(): - model = scembed.SCEmbed() + model = ScEmbed() assert model -def test_model_training(pbmc_data: sc.AnnData, total_regions: int): +def test_model_training(pbmc_data: sc.AnnData): # remove gensim logging logging.getLogger("gensim").setLevel(logging.ERROR) - model = scembed.SCEmbed() + model = ScEmbed(min_count=1) # set to 1 for testing model.train(pbmc_data, epochs=3) + # keep only columns with values > 0 + pbmc_data = pbmc_data[:, pbmc_data.X.sum(axis=0) > 0] + + chrs = pbmc_data.var["chr"].values.tolist() + starts = pbmc_data.var["start"].values.tolist() + ends = pbmc_data.var["end"].values.tolist() + + # list of regions + regions = [Region(c, int(s), int(e)) for c, s, e in zip(chrs, starts, ends)] + region_words = wordify_regions(regions) + + # + assert model.trained - assert len(model.region2vec) > 0 - assert len(model.region2vec) == total_regions + assert all([word in model.wv for word in region_words]) -def test_model_train_and_save(pbmc_data: sc.AnnData): +def test_model_train_and_export(pbmc_data: sc.AnnData): # remove gensim logging logging.getLogger("gensim").setLevel(logging.ERROR) - model = scembed.SCEmbed() + model = ScEmbed(min_count=1) # set to 1 for testing model.train(pbmc_data, epochs=3) assert model.trained - assert isinstance(model.region2vec, dict) # save try: - model.save_model("tests/data/test_model.model") - model.load_model("tests/data/test_model.model") + model.export("tests/data/model-tests") + model = ScEmbed() + model.from_pretrained( + "tests/data/model-tests/model.bin", + "tests/data/model-tests/universe.bed", + ) # ensure model is still trained and has region2vec assert model.trained - assert len(model.region2vec) > 0 + assert len(model.wv) > 0 finally: - os.remove("tests/data/test_model.model") - - -def test_anndata_chunker(pbmc_data_backed: sc.AnnData): - chunker = scembed.AnnDataChunker(pbmc_data_backed, chunk_size=2) - # ensure chunker is iterable - for chunk in chunker: - assert isinstance(chunk, sc.AnnData) - - -@pytest.mark.skip(reason="This test is too unstable for small data") -def test_train_in_chunks(pbmc_data_backed: sc.AnnData): - MIN_COUNT = 2 - chunker = scembed.AnnDataChunker(pbmc_data_backed, chunk_size=5) - model = scembed.SCEmbed(use_default_region_names=False, min_count=MIN_COUNT) - - # need this to keep track of total regions - # that should be represented in the model - def count_regions(chunk: sc.AnnData): - chunk_df = chunk.to_df() - chunk_df = chunk_df.clip(upper=1) - return sum(chunk_df.sum(axis=0) >= MIN_COUNT) - - total_regions = 0 - for chunk in chunker: - chunk_mem = chunk.to_memory() - total_regions += count_regions(chunk_mem) - model.train(chunk_mem, epochs=3) - - assert model.trained - assert isinstance(model.region2vec, dict) - # assert len(model.region2vec) == total_regions - # ignore for now, since we're not sure if this is correct - - -@pytest.mark.skip(reason="Uses large, local data") -def test_create_anndata_from_files(): - folder = "/Users/nathanleroy/Desktop/GSE158398_RAW" - - path_to_barcodes = os.path.join(folder, "lymphneg_barcodes.tsv") - path_to_mtx = os.path.join(folder, "lymphneg_matrix.mtx") - path_to_peaks = os.path.join(folder, "lymphneg_peaks.bed") - - adata = scembed.utils.barcode_mtx_peaks_to_anndata( - path_to_barcodes, path_to_mtx, path_to_peaks - ) - - assert isinstance(adata, sc.AnnData) + os.remove("tests/data/model-tests/model.bin") + os.remove("tests/data/model-tests/universe.bed") diff --git a/tests/test_tokenization.py b/tests/test_tokenization.py index c3a660c3..092fe110 100644 --- a/tests/test_tokenization.py +++ b/tests/test_tokenization.py @@ -5,12 +5,13 @@ import scanpy as sc import numpy as np -from gitk.io import Region, RegionSet -from gitk.tokenization import InMemTokenizer # add parent directory to path sys.path.append("../") +from gitk.io import Region, RegionSet +from gitk.tokenization import InMemTokenizer + @pytest.fixture def pbmc_data(): From e00f8a62ae22c7a5a0b614f5e67a53ebd66c7163 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 20:16:48 -0400 Subject: [PATCH 0296/1072] Region2Vec exmodel work + tests --- gitk/region2vec/__init__.py | 2 +- gitk/region2vec/main.py | 14 ++++++++++++++ tests/test_region2vec.py | 33 ++++++++++++++++++++++++++++++++- tests/test_tokenization.py | 7 ------- 4 files changed, 47 insertions(+), 9 deletions(-) diff --git a/gitk/region2vec/__init__.py b/gitk/region2vec/__init__.py index 2bb07916..29461e83 100644 --- a/gitk/region2vec/__init__.py +++ b/gitk/region2vec/__init__.py @@ -1,2 +1,2 @@ -from .main import Region2Vec, region2vec +from .main import Region2Vec, region2vec, Region2VecExModel from .utils import wordify_region, wordify_regions diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index c618f17d..ecc89536 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -411,6 +411,17 @@ def __init__(self, model_path: Union[str, None] = None, **kwargs): self._model = Region2Vec(**kwargs) self.tokenizer = InMemTokenizer() + @property + def wv(self): + return self._model.wv + + @property + def trained(self) -> bool: + """ + Return whether or not the model is trained. + """ + return self._model.trained + def from_pretrained(self, model_file_path: str, universe_file_path: str): """ Initialize ScEmbed model from pretrained model. @@ -508,6 +519,9 @@ def export(self, path: str): :param str path: The path to save the model to. """ + if not os.path.exists(path): + os.makedirs(path) + model_file_path = os.path.join(path, MODEL_FILE_NAME) universe_file_path = os.path.join(path, UNIVERSE_FILE_NAME) diff --git a/tests/test_region2vec.py b/tests/test_region2vec.py index 18ff7fe0..9e80efd6 100644 --- a/tests/test_region2vec.py +++ b/tests/test_region2vec.py @@ -5,7 +5,7 @@ import numpy as np from gitk.io import RegionSet -from gitk.region2vec import Region2Vec, wordify_region, wordify_regions +from gitk.region2vec import Region2Vec, Region2VecExModel, wordify_region, wordify_regions @pytest.fixture @@ -96,3 +96,34 @@ def test_save_and_load_model(region_sets: RegionSet): assert isinstance(model_loaded.forward(region), np.ndarray) finally: os.remove("tests/data/test_model.model") + + +def test_train_exmodel(region_sets: RegionSet): + model = Region2VecExModel( + min_count=1, # for testing, we need to set min_count to 1 + ) + model.train(region_sets, epochs=100) + + try: + model.export("tests/data/model-r2v-test/") + assert os.path.exists("tests/data/model-r2v-test/model.bin") + assert os.path.exists("tests/data/model-r2v-test/universe.bed") + + # load in + model_loaded = Region2VecExModel() + model_loaded.from_pretrained( + "tests/data/model-r2v-test/model.bin", + "tests/data/model-r2v-test/universe.bed", + ) + assert model_loaded is not None + for region_set in region_sets: + for region in region_set: + region_word = wordify_region(region) + assert region_word in model_loaded.wv + assert isinstance(model_loaded(region), np.ndarray) + assert isinstance(model_loaded.encode(region), np.ndarray) + + finally: + os.remove("tests/data/model-r2v-test/model.bin") + os.remove("tests/data/model-r2v-test/universe.bed") + os.rmdir("tests/data/model-r2v-test/") diff --git a/tests/test_tokenization.py b/tests/test_tokenization.py index 092fe110..8dbf3327 100644 --- a/tests/test_tokenization.py +++ b/tests/test_tokenization.py @@ -1,13 +1,6 @@ -import os -import sys - import pytest import scanpy as sc -import numpy as np - -# add parent directory to path -sys.path.append("../") from gitk.io import Region, RegionSet from gitk.tokenization import InMemTokenizer From 1e843e32a31d40cab8c5f468f97218a54e03156d Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 27 Jul 2023 20:24:25 -0400 Subject: [PATCH 0297/1072] final updates before training --- gitk/region2vec/main.py | 4 ++-- gitk/scembed/main.py | 2 ++ tests/test_scembed.py | 12 ++++++++++++ 3 files changed, 16 insertions(+), 2 deletions(-) diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index ecc89536..df4490cd 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -372,10 +372,10 @@ def forward(self, regions: Union[Region, RegionSet, List[Region]]) -> np.ndarray return self.wv[region_word] elif isinstance(regions, RegionSet): region_words = utils.wordify_regions(regions) - return np.mean([self.wv[r] for r in region_words], axis=0) + return [self.wv[r] for r in region_words] elif isinstance(regions, list): region_words = [utils.wordify_region(r) for r in regions] - return np.mean([self.wv[r] for r in region_words], axis=0) + return [self.wv[r] for r in region_words] else: raise TypeError( f"Regions must be of type Region, RegionSet, or list, not {type(regions).__name__}" diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index 7897a376..219d3c07 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -191,6 +191,8 @@ def encode(self, adata: sc.AnnData) -> np.ndarray: enoded_data = [] for region_set in tqdm(region_sets, desc="Encoding data", total=len(region_sets)): vector = self._model.forward(region_set) + # compute the mean of the vectors + vector = np.mean(vector, axis=0) enoded_data.append(vector) return np.array(enoded_data) diff --git a/tests/test_scembed.py b/tests/test_scembed.py index d2fd6ebb..89fc43cf 100644 --- a/tests/test_scembed.py +++ b/tests/test_scembed.py @@ -16,6 +16,11 @@ logging.basicConfig(level=logging.INFO) +@pytest.fixture +def hf_model(): + return "databio/r2v-pbmc-hg38-small" + + @pytest.fixture def pbmc_data(): return sc.read_h5ad("tests/data/pbmc_hg38.h5ad") @@ -77,3 +82,10 @@ def test_model_train_and_export(pbmc_data: sc.AnnData): finally: os.remove("tests/data/model-tests/model.bin") os.remove("tests/data/model-tests/universe.bed") + + +@pytest.mark.skip(reason="Need to get a pretrained model first") +def test_pretrained_scembed_model(hf_model: str, pbmc_data: sc.AnnData): + model = ScEmbed(hf_model) + embeddings = model.encode(pbmc_data) + assert embeddings.shape[0] == pbmc_data.shape[0] From f2ea3326d5d0c8f3547e397be88e8d344d71db84 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 28 Jul 2023 10:36:40 -0400 Subject: [PATCH 0298/1072] alter the structure a bit of Region2VecExModelfor init_from_huggingface --- gitk/region2vec/main.py | 17 ++++++++++++----- gitk/region2vec/utils.py | 20 ++++++++++++++++++++ gitk/scembed/const.py | 2 ++ gitk/scembed/main.py | 26 +++++++++++++++++--------- gitk/scembed/utils.py | 20 ++++++++++++++++++++ tests/test_scembed.py | 2 +- 6 files changed, 72 insertions(+), 15 deletions(-) diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index df4490cd..8da2d105 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -372,10 +372,10 @@ def forward(self, regions: Union[Region, RegionSet, List[Region]]) -> np.ndarray return self.wv[region_word] elif isinstance(regions, RegionSet): region_words = utils.wordify_regions(regions) - return [self.wv[r] for r in region_words] + return np.array([self.wv[r] for r in region_words]) elif isinstance(regions, list): region_words = [utils.wordify_region(r) for r in regions] - return [self.wv[r] for r in region_words] + return np.array([self.wv[r] for r in region_words]) else: raise TypeError( f"Regions must be of type Region, RegionSet, or list, not {type(regions).__name__}" @@ -434,7 +434,7 @@ def from_pretrained(self, model_file_path: str, universe_file_path: str): def _init_from_huggingface( self, - model_path: str, + model_repo: str, model_file_name: str = MODEL_FILE_NAME, universe_file_name: str = UNIVERSE_FILE_NAME, **kwargs, @@ -447,8 +447,15 @@ def _init_from_huggingface( :param str model_file_name: The name of the model file - this should almost never be changed. :param str universe_file_name: The name of the universe file - this should almost never be changed. """ - model_path = hf_hub_download(model_path, model_file_name, **kwargs) - universe_path = hf_hub_download(model_path, universe_file_name, **kwargs) + model_path = hf_hub_download(model_repo, model_file_name, **kwargs) + universe_path = hf_hub_download(model_repo, universe_file_name, **kwargs) + + syn1reg_file_name = utils.make_syn1neg_file_name(model_file_name) + wv_file_name = utils.make_wv_file_name(model_file_name) + + # get the syn1neg and wv files + hf_hub_download(model_repo, wv_file_name, **kwargs) + hf_hub_download(model_repo, syn1reg_file_name, **kwargs) # set the paths to the downloaded files self._model_path = model_path diff --git a/gitk/region2vec/utils.py b/gitk/region2vec/utils.py index 936716a6..f4e35a4c 100644 --- a/gitk/region2vec/utils.py +++ b/gitk/region2vec/utils.py @@ -409,3 +409,23 @@ def shuffle_list(l: List[str], n: int) -> List[str]: ) ) return shuffled_documents + + +def make_syn1neg_file_name(model_file_name: str) -> str: + """ + Make the syn1neg file name from the model file name. + + :param str model_file_name: The model file name. + :return str: The syn1neg file name. + """ + return f"{model_file_name}.syn1neg.npy" + + +def make_wv_file_name(model_file_name: str) -> str: + """ + Make the wv file name from the model file name. + + :param str model_file_name: The model file name. + :return str: The wv file name. + """ + return f"{model_file_name}.wv.vectors.npy" diff --git a/gitk/scembed/const.py b/gitk/scembed/const.py index 48e4df77..fed75392 100644 --- a/gitk/scembed/const.py +++ b/gitk/scembed/const.py @@ -9,3 +9,5 @@ MODEL_FILE_NAME = "model.bin" UNIVERSE_FILE_NAME = "universe.bed" +SYN1NEG_FILE_NAME = f"{MODEL_FILE_NAME}.syn1neg.npy" +WV_FILE_NAME = f"{MODEL_FILE_NAME}.wv.vectors.npy" diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index 219d3c07..578c5bee 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -13,6 +13,7 @@ from ..region2vec import Region2Vec from ..tokenization import InMemTokenizer from .const import CHR_KEY, END_KEY, MODEL_FILE_NAME, MODULE_NAME, START_KEY, UNIVERSE_FILE_NAME +from .utils import make_syn1neg_file_name, make_wv_file_name _GENSIM_LOGGER = getLogger("gensim") _LOGGER = getLogger(MODULE_NAME) @@ -30,19 +31,19 @@ class ScEmbed(ExModel): extension of Region2Vec. """ - def __init__(self, model_path: Union[str, None] = None, **kwargs): + def __init__(self, model_repo: Union[str, None] = None, **kwargs): """ Initialize ScEmbed model. :param str model_path: Path to the pre-trained model on huggingface. :param kwargs: Additional keyword arguments to pass to the model. """ - self.model_path = model_path + self.model_path = model_repo self._model: Region2Vec = None self.tokenizer: InMemTokenizer = None - if model_path is not None: - self._init_from_huggingface(model_path) + if model_repo is not None: + self._init_from_huggingface(model_repo) else: self._model = Region2Vec(**kwargs) self.tokenizer = InMemTokenizer() @@ -67,7 +68,7 @@ def from_pretrained(self, model_file_path: str, universe_file_path: str): def _init_from_huggingface( self, - model_path: str, + model_repo: str, model_file_name: str = MODEL_FILE_NAME, universe_file_name: str = UNIVERSE_FILE_NAME, **kwargs, @@ -80,8 +81,15 @@ def _init_from_huggingface( :param str model_file_name: The name of the model file - this should almost never be changed. :param str universe_file_name: The name of the universe file - this should almost never be changed. """ - model_path = hf_hub_download(model_path, model_file_name, **kwargs) - universe_path = hf_hub_download(model_path, universe_file_name, **kwargs) + model_path = hf_hub_download(model_repo, model_file_name, **kwargs) + universe_path = hf_hub_download(model_repo, universe_file_name, **kwargs) + + syn1reg_file_name = make_syn1neg_file_name(model_file_name) + wv_file_name = make_wv_file_name(model_file_name) + + # get the syn1neg and wv files + hf_hub_download(model_repo, wv_file_name, **kwargs) + hf_hub_download(model_repo, syn1reg_file_name, **kwargs) # set the paths to the downloaded files self._model_path = model_path @@ -190,9 +198,9 @@ def encode(self, adata: sc.AnnData) -> np.ndarray: _LOGGER.info("Encoding data.") enoded_data = [] for region_set in tqdm(region_sets, desc="Encoding data", total=len(region_sets)): - vector = self._model.forward(region_set) + vectors = self._model.forward(region_set) # compute the mean of the vectors - vector = np.mean(vector, axis=0) + vector = np.mean(vectors, axis=0) enoded_data.append(vector) return np.array(enoded_data) diff --git a/gitk/scembed/utils.py b/gitk/scembed/utils.py index 6daf56ed..dd05c3f1 100644 --- a/gitk/scembed/utils.py +++ b/gitk/scembed/utils.py @@ -36,3 +36,23 @@ def __getitem__(self, item: int): def __repr__(self): return f"" + + +def make_syn1neg_file_name(model_file_name: str) -> str: + """ + Make the syn1neg file name from the model file name. + + :param str model_file_name: The model file name. + :return str: The syn1neg file name. + """ + return f"{model_file_name}.syn1neg.npy" + + +def make_wv_file_name(model_file_name: str) -> str: + """ + Make the wv file name from the model file name. + + :param str model_file_name: The model file name. + :return str: The wv file name. + """ + return f"{model_file_name}.wv.vectors.npy" diff --git a/tests/test_scembed.py b/tests/test_scembed.py index 89fc43cf..f8f51765 100644 --- a/tests/test_scembed.py +++ b/tests/test_scembed.py @@ -84,7 +84,7 @@ def test_model_train_and_export(pbmc_data: sc.AnnData): os.remove("tests/data/model-tests/universe.bed") -@pytest.mark.skip(reason="Need to get a pretrained model first") +# @pytest.mark.skip(reason="Need to get a pretrained model first") def test_pretrained_scembed_model(hf_model: str, pbmc_data: sc.AnnData): model = ScEmbed(hf_model) embeddings = model.encode(pbmc_data) From aaa529dfdc62080b838ab1c51f90d9fd55065879 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 28 Jul 2023 11:51:08 -0400 Subject: [PATCH 0299/1072] try to fix tokenization issues and discrepencies --- gitk/io/io.py | 1 - gitk/region2vec/main.py | 39 +++++++++++++++++---------------------- gitk/tokenization/main.py | 16 ---------------- tests/test_region2vec.py | 12 +++++++++++- 4 files changed, 28 insertions(+), 40 deletions(-) diff --git a/gitk/io/io.py b/gitk/io/io.py index 1019773c..3a265408 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -21,7 +21,6 @@ def __repr__(self): return f"Region({self.chr}, {self.start}, {self.end})" -# TODO: This belongs somewhere else class RegionSet(object): def __init__(self, regions: Union[str, List[Region]], backed: bool = False): # load from file diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 8da2d105..55352da9 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -199,21 +199,6 @@ def region2vec( class Region2Vec(Word2Vec): - # not needed since self.load() seems to work fine. I am not sure why, though. - @staticmethod - def from_word2vec_model(model: Word2Vec, **kwargs): - r2v = Region2Vec( - callbacks=model.callbacks or kwargs.get("callbacks"), - window_size=model.window, - vector_size=model.vector_size, - min_count=model.min_count, - threads=model.workers, # assuming it stores thread info - seed=model.random.seed, # assuming it stores seed - ) - # attach the word2vec model to the region2vec model - r2v.wv = model.wv - return r2v - def __init__(self, **kwargs): """ A class that implements Region2Vec. Region2Vec is a model that learns @@ -359,7 +344,6 @@ def load(cls, filepath: str) -> "Region2Vec": """ # I feel like this shouldnt work, but it does? return super().load(filepath) - # return cls.from_word2vec_model(model) def forward(self, regions: Union[Region, RegionSet, List[Region]]) -> np.ndarray: """ @@ -394,7 +378,7 @@ def __repr__(self) -> str: class Region2VecExModel(ExModel): - def __init__(self, model_path: Union[str, None] = None, **kwargs): + def __init__(self, model_path: str = None, tokenizer: InMemTokenizer = None, **kwargs): """ Initialize Region2VecExModel. @@ -403,13 +387,13 @@ def __init__(self, model_path: Union[str, None] = None, **kwargs): """ self.model_path = model_path self._model: Region2Vec = None - self.tokenizer: InMemTokenizer = None + self.tokenizer: InMemTokenizer = tokenizer if model_path is not None: self._init_from_huggingface(model_path) else: self._model = Region2Vec(**kwargs) - self.tokenizer = InMemTokenizer() + self.tokenizer = tokenizer @property def wv(self): @@ -422,6 +406,14 @@ def trained(self) -> bool: """ return self._model.trained + def add_tokenizer_from_universe(self, universe: Union[str, RegionSet]): + """ + Add a universe file to the model. + + :param str universe_file_path: Path to the universe file. + """ + self.tokenizer = InMemTokenizer(universe) + def from_pretrained(self, model_file_path: str, universe_file_path: str): """ Initialize ScEmbed model from pretrained model. @@ -510,9 +502,12 @@ def train(self, data: Union[List[str], List[RegionSet], List[List[Region]]], **k _LOGGER.info("Extracting region sets.") - # fit the tokenizer on the regions - for region_set in region_sets: - self.tokenizer.fit(region_set) + # check for empty tokenizer + if self.tokenizer is None or len(self.tokenizer.universe) == 0: + raise ValueError( + "Cannot train model without a universe. Please call `add_universe` first." + ) + region_sets = self.tokenizer.tokenize(data) _LOGGER.info("Training begin.") diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 033caf91..5f2f6c18 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -60,14 +60,6 @@ def get_tree(self, tree: str): def trees(self): return self._trees - def add_regions( - self, regions: Union[str, List[Region], RegionSet, List[RegionSet], List[List[Region]]] - ): - """ - Add regions to the universe. - """ - self.build_trees(regions) - def build_trees( self, regions: Union[ @@ -119,14 +111,6 @@ def build_trees( # count total regions self.total_regions = sum([len(self._trees[tree]) for tree in self._trees]) - def fit( - self, regions: Union[str, List[Region], RegionSet, List[RegionSet], List[List[Region]]] - ): - """ - Fit the tokenizer to the given regions. This is equivalent to adding regions to the universe. - """ - self.add_regions(regions) - def find_overlaps( self, regions: Union[str, List[Region], Region, RegionSet], diff --git a/tests/test_region2vec.py b/tests/test_region2vec.py index 9e80efd6..ff8a88ac 100644 --- a/tests/test_region2vec.py +++ b/tests/test_region2vec.py @@ -5,6 +5,7 @@ import numpy as np from gitk.io import RegionSet +from gitk.tokenization import InMemTokenizer from gitk.region2vec import Region2Vec, Region2VecExModel, wordify_region, wordify_regions @@ -13,6 +14,11 @@ def bed_file(): return "tests/data/to_tokenize.bed" +@pytest.fixture +def universe_file(): + return "tests/data/universe.bed" + + @pytest.fixture def regions(bed_file: str): return RegionSet(bed_file) @@ -98,10 +104,14 @@ def test_save_and_load_model(region_sets: RegionSet): os.remove("tests/data/test_model.model") -def test_train_exmodel(region_sets: RegionSet): +def test_train_exmodel(region_sets: RegionSet, universe_file: str): model = Region2VecExModel( min_count=1, # for testing, we need to set min_count to 1 + tokenizer=InMemTokenizer(universe_file), ) + # or + # model = Region2VecExModel(min_count=1) + # model.add_tokenizer_from_universe(universe_file) model.train(region_sets, epochs=100) try: From 3656ca05050f762b2226ef5ac8b6e578872d863b Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 28 Jul 2023 16:03:12 -0400 Subject: [PATCH 0300/1072] scembed dont init with blank tokenizer --- gitk/scembed/main.py | 20 ++++++++++++++++---- tests/test_io.py | 2 +- tests/test_region2vec.py | 7 ++++--- tests/test_scembed.py | 4 ++-- tests/test_tokenization.py | 4 ++-- 5 files changed, 25 insertions(+), 12 deletions(-) diff --git a/gitk/scembed/main.py b/gitk/scembed/main.py index 578c5bee..64040c09 100755 --- a/gitk/scembed/main.py +++ b/gitk/scembed/main.py @@ -31,7 +31,7 @@ class ScEmbed(ExModel): extension of Region2Vec. """ - def __init__(self, model_repo: Union[str, None] = None, **kwargs): + def __init__(self, model_repo: str = None, tokenizer: InMemTokenizer = None, **kwargs): """ Initialize ScEmbed model. @@ -46,7 +46,7 @@ def __init__(self, model_repo: Union[str, None] = None, **kwargs): self._init_from_huggingface(model_repo) else: self._model = Region2Vec(**kwargs) - self.tokenizer = InMemTokenizer() + self.tokenizer = tokenizer @property def wv(self): @@ -56,6 +56,14 @@ def wv(self): def trained(self): return self._model.trained + def add_tokenizer_from_file(self, universe_file_path: str): + """ + Add a tokenizer from a file. + + :param str universe_file_path: Path to the universe file. + """ + self.tokenizer = InMemTokenizer(universe_file_path) + def from_pretrained(self, model_file_path: str, universe_file_path: str): """ Initialize ScEmbed model from pretrained model. @@ -143,8 +151,12 @@ def train(self, data: Union[sc.AnnData, str], **kwargs): _LOGGER.info("Extracting region sets.") - # fit the tokenizer on the regions - self.tokenizer.fit(regions) + # check if we already have a tokenizer, if not create one + # from the regions + if self.tokenizer is None: + self.tokenizer = InMemTokenizer(RegionSet(regions)) + else: + self.tokenizer.tokenize(RegionSet(data)) # convert the data to a list of documents region_sets = self.tokenizer.tokenize(data) diff --git a/tests/test_io.py b/tests/test_io.py index 80a05a7f..a6b96166 100644 --- a/tests/test_io.py +++ b/tests/test_io.py @@ -2,7 +2,7 @@ import scanpy as sc import numpy as np -from gitk.io import Region, RegionSet +from gitk.io.io import Region, RegionSet @pytest.fixture diff --git a/tests/test_region2vec.py b/tests/test_region2vec.py index ff8a88ac..9a477a98 100644 --- a/tests/test_region2vec.py +++ b/tests/test_region2vec.py @@ -4,9 +4,10 @@ import pytest import numpy as np -from gitk.io import RegionSet -from gitk.tokenization import InMemTokenizer -from gitk.region2vec import Region2Vec, Region2VecExModel, wordify_region, wordify_regions +from gitk.io.io import RegionSet +from gitk.region2vec.main import Region2Vec, Region2VecExModel +from gitk.region2vec.utils import wordify_region, wordify_regions +from gitk.tokenization.main import InMemTokenizer @pytest.fixture diff --git a/tests/test_scembed.py b/tests/test_scembed.py index f8f51765..57aab76f 100644 --- a/tests/test_scembed.py +++ b/tests/test_scembed.py @@ -8,9 +8,9 @@ # add parent directory to path sys.path.append("../") -from gitk.io import Region +from gitk.io.io import Region from gitk.region2vec.utils import wordify_region, wordify_regions -from gitk.scembed import ScEmbed +from gitk.scembed.main import ScEmbed # set to DEBUG to see more info logging.basicConfig(level=logging.INFO) diff --git a/tests/test_tokenization.py b/tests/test_tokenization.py index 8dbf3327..3e8cde93 100644 --- a/tests/test_tokenization.py +++ b/tests/test_tokenization.py @@ -2,8 +2,8 @@ import scanpy as sc -from gitk.io import Region, RegionSet -from gitk.tokenization import InMemTokenizer +from gitk.io.io import Region, RegionSet +from gitk.tokenization.main import InMemTokenizer @pytest.fixture From 7ed8332caa4d74bc2c48b10791ccb1ff2ccba62c Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 28 Jul 2023 20:52:35 -0400 Subject: [PATCH 0301/1072] fix tokenization method for scEmbed --- gitk/tokenization/main.py | 65 +++++++++++++++++++++------------------ 1 file changed, 35 insertions(+), 30 deletions(-) diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 5f2f6c18..1c284a81 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -142,50 +142,55 @@ def find_overlaps( def convert_anndata_to_universe(self, adata: sc.AnnData) -> sc.AnnData: """ - Convert an AnnData object to a universe. This is done by - inspecting the peaks in the AnnData object and converting - them to regions found in the universe. This process - occurs in-place. - - :param sc.AnnData adata: The AnnData object to convert. - :param Universe universe: The universe to convert to. + Converts the consensus peak set (.var) attributes of the AnnData object + to a universe representation. This is done through interval overlap + analysis with bedtools. + + For each region in the `.var` attribute of the AnnData object, we + either 1) map it to a region in the universe, or 2) map it to `None`. + If it is mapped to `None`, it is not in the universe and will be dropped + from the AnnData object. If it is mapped to a region, it will be updated + to the region in the universe for downstream analysis. """ # ensure adata has chr, start, and end if not all([x in adata.var.columns for x in ["chr", "start", "end"]]): raise ValueError("AnnData object must have `chr`, `start`, and `end` columns in .var") # create list of regions from adata - adata.var["region"] = ( - adata.var["chr"].astype(str) - + "_" - + adata.var["start"].astype(str) - + "_" - + adata.var["end"].astype(str) - ) - query_set = [ - Region(x[0], x[1], x[2]) - for x in list( - zip(adata.var["chr"], adata.var["start"].astype(int), adata.var["end"].astype(int)) - ) - ] + query_set: List[tuple[str, int, int]] = adata.var.apply( + lambda x: Region(x["chr"], int(x["start"]), int(x["end"])), axis=1 + ).tolist() # generate conversion map _map = self.generate_var_conversion_map(query_set) - # map regions to new universe - adata.var["new_region"] = adata.var["region"].map(_map) + # create a new DataFrame with the updated values + updated_var = adata.var.copy() + + # find the regions that overlap with the universe + # use dynamic programming to create a boolean mask of columns to keep + columns_to_keep = [] + for i, row in tqdm(adata.var.iterrows(), total=adata.var.shape[0]): + region = f"{row['chr']}_{row['start']}_{row['end']}" + if _map[region] is None: + columns_to_keep.append(False) + continue - # drop rows where new_region is None - adata.var.dropna(subset=["new_region"], inplace=True) + # if it is, change the region to the universe region, + # grab the first for now + # TODO - this is a simplification, we should be able to handle multiple + universe_region = _map[region] + chr, start, end = universe_region.split("_") - # split new_region into chr, start, end - adata.var[["chr", "start", "end"]] = adata.var["new_region"].str.split("_", expand=True) + updated_var.at[i, "chr"] = chr + updated_var.at[i, "start"] = start + updated_var.at[i, "end"] = end - # drop 'region' and 'new_region' columns - adata.var.drop(columns=["region", "new_region"], inplace=True) + columns_to_keep.append(True) - # update adata with the new DataFrame - adata = adata[:, adata.var.index] + # update adata with the new DataFrame and filtered columns + adata = adata[:, columns_to_keep] + adata.var = updated_var[columns_to_keep] return adata From c783afbff56abd2c9343fcc28e67c585b6c9710a Mon Sep 17 00:00:00 2001 From: nsheff Date: Sat, 29 Jul 2023 13:24:18 -0400 Subject: [PATCH 0302/1072] rename package --- README.md | 2 +- {gitk => geniml}/README.md | 0 {gitk => geniml}/__init__.py | 0 {gitk => geniml}/_version.py | 0 {gitk => geniml}/assess/__init__.py | 0 {gitk => geniml}/assess/assess.py | 0 {gitk => geniml}/assess/cli.py | 0 {gitk => geniml}/assess/distance.py | 0 {gitk => geniml}/assess/intersection.py | 0 {gitk => geniml}/assess/likelihood.py | 0 {gitk => geniml}/assess/utils.py | 0 {gitk => geniml}/bedspace/__init__.py | 0 {gitk => geniml}/bedspace/_version.py | 0 {gitk => geniml}/bedspace/argparsers.py | 0 {gitk => geniml}/bedspace/cli.py | 0 {gitk => geniml}/bedspace/const.py | 0 {gitk => geniml}/bedspace/pipeline/bashcode.sh | 0 {gitk => geniml}/bedspace/pipeline/bedspace_queryDBsim.py | 0 {gitk => geniml}/bedspace/pipeline/bedspace_test_model.py | 0 {gitk => geniml}/bedspace/pipeline/bedspace_train.py | 0 {gitk => geniml}/bedspace/pipeline/distances.py | 0 {gitk => geniml}/bedspace/pipeline/helpers.py | 0 {gitk => geniml}/bedspace/pipeline/preprocess.py | 0 {gitk => geniml}/bedspace/pipeline/search.py | 0 {gitk => geniml}/bedspace/pipeline/train.py | 0 {gitk => geniml}/bedspace/pipeline/visualization.py | 0 {gitk => geniml}/bedspace/tests/test_file_meta.csv | 0 {gitk => geniml}/cli.py | 0 {gitk => geniml}/const.py | 0 {gitk => geniml}/eval/README.md | 0 {gitk => geniml}/eval/__init__.py | 0 {gitk => geniml}/eval/cli.py | 0 {gitk => geniml}/eval/const.py | 0 {gitk => geniml}/eval/ctt.py | 0 {gitk => geniml}/eval/gdst.py | 0 {gitk => geniml}/eval/npt.py | 0 {gitk => geniml}/eval/rct.py | 0 {gitk => geniml}/eval/utils.py | 0 {gitk => geniml}/likelihood/README.md | 0 {gitk => geniml}/likelihood/__init__.py | 0 {gitk => geniml}/likelihood/build_model.py | 0 {gitk => geniml}/likelihood/cli.py | 0 {gitk => geniml}/models/__init__.py | 0 {gitk => geniml}/models/atac/tokenization.py | 0 {gitk => geniml}/models/const.py | 0 {gitk => geniml}/models/pretrained.py | 0 {gitk => geniml}/models/rna/tokenization.py | 0 {gitk => geniml}/models/utils.py | 0 {gitk => geniml}/region2vec/__init__.py | 0 {gitk => geniml}/region2vec/cli.py | 0 {gitk => geniml}/region2vec/const.py | 0 {gitk => geniml}/region2vec/main.py | 0 {gitk => geniml}/region2vec/region2vec_train.py | 0 {gitk => geniml}/region2vec/region_shuffling.py | 0 {gitk => geniml}/region2vec/requirements.txt | 0 {gitk => geniml}/region2vec/utils.py | 0 {gitk => geniml}/scembed/__init__.py | 0 {gitk => geniml}/scembed/_version.py | 0 {gitk => geniml}/scembed/annotation.py | 0 {gitk => geniml}/scembed/argparser.py | 0 {gitk => geniml}/scembed/cli.py | 0 {gitk => geniml}/scembed/const.py | 0 {gitk => geniml}/scembed/exceptions.py | 0 {gitk => geniml}/scembed/models.py | 0 {gitk => geniml}/scembed/scembed.py | 0 {gitk => geniml}/scembed/utils.py | 0 {gitk => geniml}/tokenization/__init__.py | 0 {gitk => geniml}/tokenization/cli.py | 0 {gitk => geniml}/tokenization/hard_tokenization_batch.py | 0 {gitk => geniml}/tokenization/main.py | 0 {gitk => geniml}/tokenization/split_file.py | 0 {gitk => geniml}/universe/__init__.py | 0 {gitk => geniml}/universe/cc_universe.py | 0 {gitk => geniml}/universe/ccf_universe.py | 0 {gitk => geniml}/universe/cli.py | 0 {gitk => geniml}/universe/const.py | 0 {gitk => geniml}/universe/custom_distribution.py | 0 {gitk => geniml}/universe/hmm_universe.py | 0 {gitk => geniml}/universe/ml_universe.py | 0 {gitk => geniml}/universe/models.py | 0 {gitk => geniml}/universe/utils.py | 0 {gitk => geniml}/utils.py | 0 setup.py | 2 +- 83 files changed, 2 insertions(+), 2 deletions(-) rename {gitk => geniml}/README.md (100%) rename {gitk => geniml}/__init__.py (100%) rename {gitk => geniml}/_version.py (100%) rename {gitk => geniml}/assess/__init__.py (100%) rename {gitk => geniml}/assess/assess.py (100%) rename {gitk => geniml}/assess/cli.py (100%) rename {gitk => geniml}/assess/distance.py (100%) rename {gitk => geniml}/assess/intersection.py (100%) rename {gitk => geniml}/assess/likelihood.py (100%) rename {gitk => geniml}/assess/utils.py (100%) rename {gitk => geniml}/bedspace/__init__.py (100%) rename {gitk => geniml}/bedspace/_version.py (100%) rename {gitk => geniml}/bedspace/argparsers.py (100%) rename {gitk => geniml}/bedspace/cli.py (100%) rename {gitk => geniml}/bedspace/const.py (100%) rename {gitk => geniml}/bedspace/pipeline/bashcode.sh (100%) rename {gitk => geniml}/bedspace/pipeline/bedspace_queryDBsim.py (100%) rename {gitk => geniml}/bedspace/pipeline/bedspace_test_model.py (100%) rename {gitk => geniml}/bedspace/pipeline/bedspace_train.py (100%) rename {gitk => geniml}/bedspace/pipeline/distances.py (100%) rename {gitk => 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(100%) rename {gitk => geniml}/scembed/cli.py (100%) rename {gitk => geniml}/scembed/const.py (100%) rename {gitk => geniml}/scembed/exceptions.py (100%) rename {gitk => geniml}/scembed/models.py (100%) rename {gitk => geniml}/scembed/scembed.py (100%) rename {gitk => geniml}/scembed/utils.py (100%) rename {gitk => geniml}/tokenization/__init__.py (100%) rename {gitk => geniml}/tokenization/cli.py (100%) rename {gitk => geniml}/tokenization/hard_tokenization_batch.py (100%) rename {gitk => geniml}/tokenization/main.py (100%) rename {gitk => geniml}/tokenization/split_file.py (100%) rename {gitk => geniml}/universe/__init__.py (100%) rename {gitk => geniml}/universe/cc_universe.py (100%) rename {gitk => geniml}/universe/ccf_universe.py (100%) rename {gitk => geniml}/universe/cli.py (100%) rename {gitk => geniml}/universe/const.py (100%) rename {gitk => geniml}/universe/custom_distribution.py (100%) rename {gitk => geniml}/universe/hmm_universe.py (100%) rename {gitk => geniml}/universe/ml_universe.py (100%) rename {gitk => geniml}/universe/models.py (100%) rename {gitk => geniml}/universe/utils.py (100%) rename {gitk => geniml}/utils.py (100%) diff --git a/README.md b/README.md index 70c36218..0cfe0a82 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,3 @@ -# Genomic interval toolkit +# Genomic interval machine learning (geniml) You can find documentation in the `docs` subfolder. diff --git a/gitk/README.md b/geniml/README.md similarity index 100% rename from gitk/README.md rename to geniml/README.md diff --git a/gitk/__init__.py b/geniml/__init__.py similarity index 100% rename from gitk/__init__.py rename to geniml/__init__.py diff --git a/gitk/_version.py b/geniml/_version.py similarity index 100% rename from gitk/_version.py rename to geniml/_version.py diff --git a/gitk/assess/__init__.py b/geniml/assess/__init__.py similarity index 100% rename from gitk/assess/__init__.py rename to geniml/assess/__init__.py diff --git a/gitk/assess/assess.py b/geniml/assess/assess.py similarity index 100% rename from gitk/assess/assess.py rename to geniml/assess/assess.py diff --git a/gitk/assess/cli.py b/geniml/assess/cli.py similarity index 100% rename from gitk/assess/cli.py rename to geniml/assess/cli.py diff --git a/gitk/assess/distance.py b/geniml/assess/distance.py similarity index 100% rename from gitk/assess/distance.py rename to geniml/assess/distance.py diff --git a/gitk/assess/intersection.py b/geniml/assess/intersection.py similarity index 100% rename from gitk/assess/intersection.py rename to geniml/assess/intersection.py diff --git a/gitk/assess/likelihood.py b/geniml/assess/likelihood.py similarity index 100% rename from gitk/assess/likelihood.py rename to geniml/assess/likelihood.py diff --git a/gitk/assess/utils.py b/geniml/assess/utils.py similarity index 100% rename from gitk/assess/utils.py rename to geniml/assess/utils.py diff --git a/gitk/bedspace/__init__.py b/geniml/bedspace/__init__.py similarity index 100% rename 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similarity index 100% rename from gitk/bedspace/tests/test_file_meta.csv rename to geniml/bedspace/tests/test_file_meta.csv diff --git a/gitk/cli.py b/geniml/cli.py similarity index 100% rename from gitk/cli.py rename to geniml/cli.py diff --git a/gitk/const.py b/geniml/const.py similarity index 100% rename from gitk/const.py rename to geniml/const.py diff --git a/gitk/eval/README.md b/geniml/eval/README.md similarity index 100% rename from gitk/eval/README.md rename to geniml/eval/README.md diff --git a/gitk/eval/__init__.py b/geniml/eval/__init__.py similarity index 100% rename from gitk/eval/__init__.py rename to geniml/eval/__init__.py diff --git a/gitk/eval/cli.py b/geniml/eval/cli.py similarity index 100% rename from gitk/eval/cli.py rename to geniml/eval/cli.py diff --git a/gitk/eval/const.py b/geniml/eval/const.py similarity index 100% rename from gitk/eval/const.py rename to geniml/eval/const.py diff --git a/gitk/eval/ctt.py b/geniml/eval/ctt.py similarity index 100% rename from gitk/eval/ctt.py rename to geniml/eval/ctt.py diff --git a/gitk/eval/gdst.py b/geniml/eval/gdst.py similarity index 100% rename from gitk/eval/gdst.py rename to geniml/eval/gdst.py diff --git a/gitk/eval/npt.py b/geniml/eval/npt.py similarity index 100% rename from gitk/eval/npt.py rename to geniml/eval/npt.py diff --git a/gitk/eval/rct.py b/geniml/eval/rct.py similarity index 100% rename from gitk/eval/rct.py rename to geniml/eval/rct.py diff --git a/gitk/eval/utils.py b/geniml/eval/utils.py similarity index 100% rename from gitk/eval/utils.py rename to geniml/eval/utils.py diff --git a/gitk/likelihood/README.md b/geniml/likelihood/README.md similarity index 100% rename from gitk/likelihood/README.md rename to geniml/likelihood/README.md diff --git a/gitk/likelihood/__init__.py b/geniml/likelihood/__init__.py similarity index 100% rename from gitk/likelihood/__init__.py rename to geniml/likelihood/__init__.py diff --git a/gitk/likelihood/build_model.py b/geniml/likelihood/build_model.py similarity index 100% rename from gitk/likelihood/build_model.py rename to geniml/likelihood/build_model.py diff --git a/gitk/likelihood/cli.py b/geniml/likelihood/cli.py similarity index 100% rename from gitk/likelihood/cli.py rename to geniml/likelihood/cli.py diff --git a/gitk/models/__init__.py b/geniml/models/__init__.py similarity index 100% rename from gitk/models/__init__.py rename to geniml/models/__init__.py diff --git a/gitk/models/atac/tokenization.py b/geniml/models/atac/tokenization.py similarity index 100% rename from gitk/models/atac/tokenization.py rename to geniml/models/atac/tokenization.py diff --git a/gitk/models/const.py b/geniml/models/const.py similarity index 100% rename from gitk/models/const.py rename to geniml/models/const.py diff --git a/gitk/models/pretrained.py b/geniml/models/pretrained.py similarity index 100% rename from gitk/models/pretrained.py rename to geniml/models/pretrained.py diff --git a/gitk/models/rna/tokenization.py b/geniml/models/rna/tokenization.py similarity index 100% rename from gitk/models/rna/tokenization.py rename to geniml/models/rna/tokenization.py diff --git a/gitk/models/utils.py b/geniml/models/utils.py similarity index 100% rename from gitk/models/utils.py rename to geniml/models/utils.py diff --git a/gitk/region2vec/__init__.py b/geniml/region2vec/__init__.py similarity index 100% rename from gitk/region2vec/__init__.py rename to geniml/region2vec/__init__.py diff --git a/gitk/region2vec/cli.py b/geniml/region2vec/cli.py similarity index 100% rename from gitk/region2vec/cli.py rename to geniml/region2vec/cli.py diff --git a/gitk/region2vec/const.py b/geniml/region2vec/const.py similarity index 100% rename from gitk/region2vec/const.py rename to geniml/region2vec/const.py diff --git a/gitk/region2vec/main.py b/geniml/region2vec/main.py similarity index 100% rename from gitk/region2vec/main.py rename to geniml/region2vec/main.py diff --git a/gitk/region2vec/region2vec_train.py b/geniml/region2vec/region2vec_train.py similarity index 100% rename from gitk/region2vec/region2vec_train.py rename to geniml/region2vec/region2vec_train.py diff --git a/gitk/region2vec/region_shuffling.py b/geniml/region2vec/region_shuffling.py similarity index 100% rename from gitk/region2vec/region_shuffling.py rename to geniml/region2vec/region_shuffling.py diff --git a/gitk/region2vec/requirements.txt b/geniml/region2vec/requirements.txt similarity index 100% rename from gitk/region2vec/requirements.txt rename to geniml/region2vec/requirements.txt diff --git a/gitk/region2vec/utils.py b/geniml/region2vec/utils.py similarity index 100% rename from gitk/region2vec/utils.py rename to geniml/region2vec/utils.py diff --git a/gitk/scembed/__init__.py b/geniml/scembed/__init__.py similarity index 100% rename from gitk/scembed/__init__.py rename to geniml/scembed/__init__.py diff --git a/gitk/scembed/_version.py b/geniml/scembed/_version.py similarity index 100% rename from gitk/scembed/_version.py rename to geniml/scembed/_version.py diff --git a/gitk/scembed/annotation.py b/geniml/scembed/annotation.py similarity index 100% rename from gitk/scembed/annotation.py rename to geniml/scembed/annotation.py diff --git a/gitk/scembed/argparser.py b/geniml/scembed/argparser.py similarity index 100% rename from gitk/scembed/argparser.py rename to geniml/scembed/argparser.py diff --git a/gitk/scembed/cli.py b/geniml/scembed/cli.py similarity index 100% rename from gitk/scembed/cli.py rename to geniml/scembed/cli.py diff --git a/gitk/scembed/const.py b/geniml/scembed/const.py similarity index 100% rename from gitk/scembed/const.py rename to geniml/scembed/const.py diff --git a/gitk/scembed/exceptions.py b/geniml/scembed/exceptions.py similarity index 100% rename from gitk/scembed/exceptions.py rename to geniml/scembed/exceptions.py diff --git a/gitk/scembed/models.py b/geniml/scembed/models.py similarity index 100% rename from gitk/scembed/models.py rename to geniml/scembed/models.py diff --git a/gitk/scembed/scembed.py b/geniml/scembed/scembed.py similarity index 100% rename from gitk/scembed/scembed.py rename to geniml/scembed/scembed.py diff --git a/gitk/scembed/utils.py b/geniml/scembed/utils.py similarity index 100% rename from gitk/scembed/utils.py rename to geniml/scembed/utils.py diff --git a/gitk/tokenization/__init__.py b/geniml/tokenization/__init__.py similarity index 100% rename from gitk/tokenization/__init__.py rename to geniml/tokenization/__init__.py diff --git a/gitk/tokenization/cli.py b/geniml/tokenization/cli.py similarity index 100% rename from gitk/tokenization/cli.py rename to geniml/tokenization/cli.py diff --git a/gitk/tokenization/hard_tokenization_batch.py b/geniml/tokenization/hard_tokenization_batch.py similarity index 100% rename from gitk/tokenization/hard_tokenization_batch.py rename to geniml/tokenization/hard_tokenization_batch.py diff --git a/gitk/tokenization/main.py b/geniml/tokenization/main.py similarity index 100% rename from gitk/tokenization/main.py rename to geniml/tokenization/main.py diff --git a/gitk/tokenization/split_file.py b/geniml/tokenization/split_file.py similarity index 100% rename from gitk/tokenization/split_file.py rename to geniml/tokenization/split_file.py diff --git a/gitk/universe/__init__.py b/geniml/universe/__init__.py similarity index 100% rename from gitk/universe/__init__.py rename to geniml/universe/__init__.py diff --git a/gitk/universe/cc_universe.py b/geniml/universe/cc_universe.py similarity index 100% rename from gitk/universe/cc_universe.py rename to geniml/universe/cc_universe.py diff --git a/gitk/universe/ccf_universe.py b/geniml/universe/ccf_universe.py similarity index 100% rename from gitk/universe/ccf_universe.py rename to geniml/universe/ccf_universe.py diff --git a/gitk/universe/cli.py b/geniml/universe/cli.py similarity index 100% rename from gitk/universe/cli.py rename to geniml/universe/cli.py diff --git a/gitk/universe/const.py b/geniml/universe/const.py similarity index 100% rename from gitk/universe/const.py rename to geniml/universe/const.py diff --git a/gitk/universe/custom_distribution.py b/geniml/universe/custom_distribution.py similarity index 100% rename from gitk/universe/custom_distribution.py rename to geniml/universe/custom_distribution.py diff --git a/gitk/universe/hmm_universe.py b/geniml/universe/hmm_universe.py similarity index 100% rename from gitk/universe/hmm_universe.py rename to geniml/universe/hmm_universe.py diff --git a/gitk/universe/ml_universe.py b/geniml/universe/ml_universe.py similarity index 100% rename from gitk/universe/ml_universe.py rename to geniml/universe/ml_universe.py diff --git a/gitk/universe/models.py b/geniml/universe/models.py similarity index 100% rename from gitk/universe/models.py rename to geniml/universe/models.py diff --git a/gitk/universe/utils.py b/geniml/universe/utils.py similarity index 100% rename from gitk/universe/utils.py rename to geniml/universe/utils.py diff --git a/gitk/utils.py b/geniml/utils.py similarity index 100% rename from gitk/utils.py rename to geniml/utils.py diff --git a/setup.py b/setup.py index c244afd6..880ae3b0 100755 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import setup -PACKAGE_NAME = "gitk" +PACKAGE_NAME = "geniml" # Ordinary dependencies DEPENDENCIES = [] From a606198c7232a082c5f02948f7df6ab763fbefc0 Mon Sep 17 00:00:00 2001 From: nsheff Date: Sat, 29 Jul 2023 13:27:01 -0400 Subject: [PATCH 0303/1072] rename package --- docs/README.md | 4 ++-- docs/autodoc_build/gitk.md | 4 ++-- docs/contributing.md | 12 +++++----- docs/img/gitk_logo.svg | 4 ++-- docs/modules.md | 8 +++---- docs/support.md | 2 +- docs/tutorials/assess-universe.md | 18 +++++++------- docs/tutorials/bedspace.md | 14 +++++------ docs/tutorials/build-universe.md | 24 +++++++++---------- docs/tutorials/evaluation.md | 24 +++++++++---------- docs/tutorials/region2vec.md | 8 +++---- docs/tutorials/tokenization.md | 6 ++--- docs/tutorials/train-scembed-model.md | 14 +++++------ .../tutorials/use-pretrained-scembed-model.md | 2 +- examples/scembed/annotation.ipynb | 12 +++++----- examples/scembed/cellcano_annotations.ipynb | 2 +- examples/scembed/pretrained_model.py | 2 +- examples/scembed/projection.ipynb | 6 ++--- geniml/README.md | 18 +++++++------- geniml/cli.py | 14 +++++------ geniml/const.py | 2 +- geniml/eval/README.md | 24 +++++++++---------- geniml/likelihood/README.md | 6 ++--- geniml/models/const.py | 2 +- geniml/region2vec/main.py | 6 ++--- geniml/region2vec/region_shuffling.py | 2 +- geniml/scembed/annotation.py | 4 ++-- geniml/scembed/const.py | 2 +- geniml/scembed/scembed.py | 4 ++-- .../tokenization/hard_tokenization_batch.py | 2 +- geniml/tokenization/main.py | 4 ++-- geniml/universe/cc_universe.py | 2 +- geniml/universe/ccf_universe.py | 2 +- geniml/universe/ml_universe.py | 2 +- mkdocs.yml | 14 +++++------ setup.py | 24 +++++++++---------- tests/consensus/cli_test.sh | 18 +++++++------- tests/test_gitk.py | 2 +- tests/test_models.py | 2 +- tests/test_scembed.py | 2 +- 40 files changed, 162 insertions(+), 162 deletions(-) diff --git a/docs/README.md b/docs/README.md index fc756181..af7a5e05 100644 --- a/docs/README.md +++ b/docs/README.md @@ -2,7 +2,7 @@ ## Introduction -`gitk` is a suite of tools for applying machine learning approaches to genomic interval data. For example, it can do things like building HMMs, assessing genomic interval universes, calculating likelihoods of consensus genomic interval sets, and computing single-cell clusters. It also provides a variety of other useful ancillary tools for working with genomic intervals to support these functions. +`geniml` is a suite of tools for applying machine learning approaches to genomic interval data. For example, it can do things like building HMMs, assessing genomic interval universes, calculating likelihoods of consensus genomic interval sets, and computing single-cell clusters. It also provides a variety of other useful ancillary tools for working with genomic intervals to support these functions. ## Install @@ -12,4 +12,4 @@ pip install --user --upgrade . ## Modules -`gitk` is organized into modules. The next section is an overview of each module. You can also proceed to the how-to guides for recipes on how to do specfic tasks. +`geniml` is organized into modules. The next section is an overview of each module. You can also proceed to the how-to guides for recipes on how to do specfic tasks. diff --git a/docs/autodoc_build/gitk.md b/docs/autodoc_build/gitk.md index 48b5ac8c..0807904e 100644 --- a/docs/autodoc_build/gitk.md +++ b/docs/autodoc_build/gitk.md @@ -27,9 +27,9 @@ h4 .content { -# Package `gitk` Documentation +# Package `geniml` Documentation -*Version Information: `gitk` v0.0.1-dev, generated by `lucidoc` v0.4.3* +*Version Information: `geniml` v0.0.1-dev, generated by `lucidoc` v0.4.3* diff --git a/docs/contributing.md b/docs/contributing.md index 502aa670..02e47af8 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -2,7 +2,7 @@ ## Repository organization -This repo is divided into modules. Each module is in a subfolder. To add functionality to gitk, you could add it to an existing module. If there's no existing module that fits, you could add your own module. +This repo is divided into modules. Each module is in a subfolder. To add functionality to geniml, you could add it to an existing module. If there's no existing module that fits, you could add your own module. ## Adding a new module @@ -13,15 +13,15 @@ Each module should be written in a way that it provides utility as a Python libr - `README.md` - describes how to use the code - `.py`, and other `.py` files - functions that provide utility for this module. -*All* the functions should be written to be useful via import, calling with `gitk..`. For example: +*All* the functions should be written to be useful via import, calling with `geniml..`. For example: ``` -import gitk +import geniml -gitk.hmm.function() +geniml.hmm.function() ``` -### Adding your module to gitk +### Adding your module to geniml 1. Put your module in a subfolder 2. Make sure to include a `__init__.py` so it's importable. @@ -29,5 +29,5 @@ gitk.hmm.function() ### Shared code -Any variables, functions, or other code that is shared across modules should be placed in the parent module, which is held in the [gitk](gitk) folder. +Any variables, functions, or other code that is shared across modules should be placed in the parent module, which is held in the [geniml](geniml) folder. diff --git a/docs/img/gitk_logo.svg b/docs/img/gitk_logo.svg index 96bc88d8..9e9d79e7 100644 --- a/docs/img/gitk_logo.svg +++ b/docs/img/gitk_logo.svg @@ -7,7 +7,7 @@ enable-background="new 0 0 100 100" xml:space="preserve" id="svg14" - sodipodi:docname="gitk_logo.svg" + sodipodi:docname="geniml_logo.svg" width="354.48068" height="111.4156" inkscape:version="1.2.2 (1:1.2.2+202212051550+b0a8486541)" @@ -63,4 +63,4 @@ y="36.623856" id="tspan1138">gitk + id="tspan1136">geniml diff --git a/docs/modules.md b/docs/modules.md index c60f6339..67e9b056 100644 --- a/docs/modules.md +++ b/docs/modules.md @@ -1,10 +1,10 @@ # Module overviews -`gitk` is organized into modules. Each module groups together related tasks. This document provides an overview of each module. +`geniml` is organized into modules. Each module groups together related tasks. This document provides an overview of each module. ## Module `assess-universe` -Many genomic interval analysis methods, particularly those used by `gitk` require that regions be re-defined in terms of a consensus region set, or universe. However, a universe may not be a good fit to a collection of files. This module assesses that fit. Given a collection of genomic interval sets, and a proposed universe, we can assess how well the universe fits the genomic interval sets. This module provides several complementary methods to assess fit. +Many genomic interval analysis methods, particularly those used by `geniml` require that regions be re-defined in terms of a consensus region set, or universe. However, a universe may not be a good fit to a collection of files. This module assesses that fit. Given a collection of genomic interval sets, and a proposed universe, we can assess how well the universe fits the genomic interval sets. This module provides several complementary methods to assess fit. ## Module `bedspace` @@ -16,7 +16,7 @@ This module provides multiple ways to build a genomic region universe. These inc ## Module `evaluation` -Once a `gitk` region embedding model is trained, we may want to evaluate the embeddings. The `evaluation` module provides several functions for that. These include statistical tests, like the Cluster Tendency Test (CTT) and the Reconstruction Test (RCT), and biological tests, the Genome Distance Scaling Test (GDST) and the Neighborhood Preserving Test (NPT). These evaluation metrics can be helpful to determine if your models are working well, optimize training parameters, etc. +Once a `geniml` region embedding model is trained, we may want to evaluate the embeddings. The `evaluation` module provides several functions for that. These include statistical tests, like the Cluster Tendency Test (CTT) and the Reconstruction Test (RCT), and biological tests, the Genome Distance Scaling Test (GDST) and the Neighborhood Preserving Test (NPT). These evaluation metrics can be helpful to determine if your models are working well, optimize training parameters, etc. ## Module `region2vec` @@ -28,7 +28,7 @@ Once a `gitk` region embedding model is trained, we may want to evaluate the emb ## Module `tokenization` -In NLP, training word embeddings requires first tokenizing words such that words in different forms are represented by one word. For example, "orange", "oranges" and "Orange" are all mapped to "orange" since they essentially convey the same meaning. This reduces the vocabulary size and improves the quality of learned embeddings. Similary, many `gitk` modules (such as `region2vec`) require first tokenizating regions. +In NLP, training word embeddings requires first tokenizing words such that words in different forms are represented by one word. For example, "orange", "oranges" and "Orange" are all mapped to "orange" since they essentially convey the same meaning. This reduces the vocabulary size and improves the quality of learned embeddings. Similary, many `geniml` modules (such as `region2vec`) require first tokenizating regions. To tokenize reigons, we need to provide a universe, which specifies the "vocabulary" of genomic regions. The universe is a BED file, containing representative regions. With the given universe, we represent (tokenize) raw regions into the regions in the universe. diff --git a/docs/support.md b/docs/support.md index 16e54381..12c32def 100644 --- a/docs/support.md +++ b/docs/support.md @@ -1,3 +1,3 @@ # Support -Please raise any issues or questions using the [GitHub issue tracker](https://github.com/databio/gitk/issues). +Please raise any issues or questions using the [GitHub issue tracker](https://github.com/databio/geniml/issues). diff --git a/docs/tutorials/assess-universe.md b/docs/tutorials/assess-universe.md index eed7bfd4..2cd60451 100644 --- a/docs/tutorials/assess-universe.md +++ b/docs/tutorials/assess-universe.md @@ -6,10 +6,10 @@ If you have a potential universe file, and a collection of BED files, this modul ## Command-line usage -Both overlap and distance based assessments can be run using: `gitk assess ...` with appropriate flags. +Both overlap and distance based assessments can be run using: `geniml assess ...` with appropriate flags. ```console - gitk assess --assessment-method1 \ + geniml assess --assessment-method1 \ --assessment-method2 \ --... --raw-data-folder tests/consesnus/raw/ \ @@ -34,12 +34,12 @@ containing file name and results of chosen metrics ## Base-level overlap measure -First test checks how much of our file is present in the universe and how much additional information is present in the universe. We can check that by adding ```--overlap``` to ```gitk assess ...```. In the result files it will output columns with: number of bp in universe but not in file, number of bp in file but not the universe, and number of bp both in universe and file. +First test checks how much of our file is present in the universe and how much additional information is present in the universe. We can check that by adding ```--overlap``` to ```geniml assess ...```. In the result files it will output columns with: number of bp in universe but not in file, number of bp in file but not the universe, and number of bp both in universe and file. We can also use it directly from Python like this: ``` -from gitk.assess.intersection import run_intersection +from geniml.assess.intersection import run_intersection run_intersection("test/consensus/raw/", "tests/consensus/file_list.txt", @@ -50,7 +50,7 @@ run_intersection("test/consensus/raw/", Or, we can calculate F10 score of the universe using: ``` -from gitk.assess.intersection import get_f_10_score +from geniml.assess.intersection import get_f_10_score get_f_10_score("test/consensus/raw/", "tests/consensus/file_list.txt", @@ -70,7 +70,7 @@ Next, we can calculate the distance between query and universe. To do that we ca All presented distance measures can be done using python, which will result in matrix where first column is file names and the second one is median of distances. ``` -from gitk.assess.distance import run_distance +from geniml.assess.distance import run_distance d_median = run_distance("tests/consensus/raw", "tests/consensus/file_list.txt", @@ -80,7 +80,7 @@ d_median = run_distance("tests/consensus/raw", Additionally, we can directly calculate the closeness score using: ``` -from gitk.assess.distance import get_closeness_score +from geniml.assess.distance import get_closeness_score closeness_score = get_closeness_score("tests/consensus/raw", "tests/consensus/file_list.txt", @@ -96,7 +96,7 @@ will need [likelihood model](consensus-peaks.md#making-likelihood-model-). We ca either for hard universe: ``` -from gitk.assess.likelihood import hard_universe_likelihood +from geniml.assess.likelihood import hard_universe_likelihood lh_hard = hard_universe_likelihood("tests/consensus/lh_model.tar", "tests/consensus/universe/universe.bed", @@ -106,7 +106,7 @@ lh_hard = hard_universe_likelihood("tests/consensus/lh_model.tar", or with taking into account universe flexibility: ``` -from gitk.assess.likelihood import likelihood_flexible_universe +from geniml.assess.likelihood import likelihood_flexible_universe lh_flexible = likelihood_flexible_universe("tests/consensus/lh_model.tar", "tests/consensus/universe/universe.bed", diff --git a/docs/tutorials/bedspace.md b/docs/tutorials/bedspace.md index 58e37545..be78bb1e 100644 --- a/docs/tutorials/bedspace.md +++ b/docs/tutorials/bedspace.md @@ -2,7 +2,7 @@ ## Introduction -To ensure that everything is working correctly, run: `python -c "from gitk import bedspace"`. There are four main commands in `bedspace`: +To ensure that everything is working correctly, run: `python -c "from geniml import bedspace"`. There are four main commands in `bedspace`: 1. `bedspace preprocess`: preprocesses a set of genomic interval regions and their associated metadata into a format that can be used by `bedspace train`. 2. `bedspace train`: trains a StarSpace model on the preprocessed data. @@ -13,7 +13,7 @@ To ensure that everything is working correctly, run: `python -c "from gitk impor The `preprocess` command will prepare a set of region sets and metadata labels for training. This includes things like adding the `__label__` prefix to metadata labels, and converting the region sets into a format that can be used by StarSpace. The command takes in a set of region sets and metadata labels, and outputs a set of preprocessed region sets and metadata labels. The command can be run as follows: ```console -gitk bedspace preprocess \ +geniml bedspace preprocess \ --input \ --metadata \ --universe \ @@ -33,7 +33,7 @@ Input Description: The `train` command will train a StarSpace model on the preprocessed region sets and metadata labels. It requires that you have run the `preprocess` command first. The `train` command takes in a set of preprocessed region sets and metadata labels, and outputs a trained StarSpace model. The command can be run as follows: ```console -gitk bedspace train \ +geniml bedspace train \ --path-to-starspace \ --input \ --output \ @@ -60,7 +60,7 @@ Input Description: The `distances` command will compute the distances between all of the region sets and metadata labels in the trained model. It requires that you have ran the `train` command first. The `distances` command takes in a trained StarSpace model, and outputs a set of distances between all of the region sets and metadata labels in the model. The command can be run as follows: ```console -gitk bedspace distances \ +geniml bedspace distances \ --input \ --metadata \ --universe \ @@ -92,7 +92,7 @@ Example usage for each type are given below: #### `r2l` ```console -gitk bedspace search \ +geniml bedspace search \ -t lr2 -d \ -n \ @@ -101,7 +101,7 @@ gitk bedspace search \ #### `l2r` ```console -gitk bedspace search \ +geniml bedspace search \ -t rl2 -d \ -n \ @@ -110,7 +110,7 @@ gitk bedspace search \ #### `r2r` ```console -gitk bedspace search \ +geniml bedspace search \ -t r2r -d \ -n \ diff --git a/docs/tutorials/build-universe.md b/docs/tutorials/build-universe.md index 10242ab5..88768ab5 100644 --- a/docs/tutorials/build-universe.md +++ b/docs/tutorials/build-universe.md @@ -15,7 +15,7 @@ their starts and ends. To do that we have to: - {prefix}_start.bw - with smoothed coverage of genome by ends In this tutorial we will use prefix "all" as it is a default prefix in -`gitk` module +`geniml` module ## Coverage cutoff universe @@ -23,7 +23,7 @@ We will start by making a coverage universe with cutoff that results in maximum likelihood universe. We can build it through CLI: ```console - gitk build-universe cc --coverage-folder tests/consenus/coverage/ \ + geniml build-universe cc --coverage-folder tests/consenus/coverage/ \ --output-file tests/consenus/universe/universe.bed ``` @@ -36,7 +36,7 @@ Where: Or we can import it directly into Python: ``` -from gitk.universe.cc_universe import cc_universe +from geniml.universe.cc_universe import cc_universe cc_universe("tests/consenus/coverage/all_core.bw", file_out="tests/consenus/universe/universe.bed") @@ -50,7 +50,7 @@ flag with the distance beloved witch peaks should be merged together and Next presented universe is coverage cutoff flexible universe. We can do it through CLI: ``` - gitk build-universe ccf --coverage-folder tests/consenus/coverage/ \ + geniml build-universe ccf --coverage-folder tests/consenus/coverage/ \ --output-file tests/consenus/universe/universe.bed ``` @@ -62,7 +62,7 @@ Where: Or we can import it directly into python: ``` -from gitk.universe.ccf_universe import ccf_universe +from geniml.universe.ccf_universe import ccf_universe ccf_universe("tests/consenus/coverage/all_core.bw", file_out="tests/consenus/universe/universe.bed") @@ -75,7 +75,7 @@ Another type of universe that we can make is maximum likelihood flexible univers To make a likelihood model we can use this CLI: ``` -gitk lh build_model --model-file tests/consenus/model.tar \ +geniml lh build_model --model-file tests/consenus/model.tar \ --coverage-folder tests/consenus/coverage/ \ --file-no 4 ``` @@ -89,7 +89,7 @@ Where: Or, we can do it directly in python: ``` -from gitk.likelihood.build_model import main +from geniml.likelihood.build_model import main main("tests/consenus/model.tar", "tests/consesnus/coverage", "all", @@ -100,7 +100,7 @@ main("tests/consenus/model.tar", "tests/consesnus/coverage", Now that we have the model we make the universe: ``` -gitk build-universe ml --model-file tests/consenus/model.tar \ +geniml build-universe ml --model-file tests/consenus/model.tar \ --output-file tests/consenus/universe/universe.bed \ --coverage-folder tests/consesnus/coverage ``` @@ -114,10 +114,10 @@ Where: Similarly, we can do it in python: ``` -from gitk.universe.ml_universe import ml_universe +from geniml.universe.ml_universe import ml_universe ml_universe("tests/consesnus/model.tar", - "/home/hee6jn/Documents/gitk/tests/consesnus/coverage", + "/home/hee6jn/Documents/geniml/tests/consesnus/coverage", "all", "tests/consenus/universe/universe.bed") ``` @@ -127,7 +127,7 @@ Another approach to making flexible universes is using Hidden Markov Models. We can do it for example with: ``` -gitk build-universe hmm --out-file tests/consenus/universe/universe.bed \ +geniml build-universe hmm --out-file tests/consenus/universe/universe.bed \ --coverage-folder tests/consenus/coverage/ \ --save-max-cove ``` @@ -143,7 +143,7 @@ Where: Similarly, we can do it in python: ``` -from gitk.universe.hmm_universe import hmm_universe +from geniml.universe.hmm_universe import hmm_universe hmm_universe("tests/consenus/coverage/", "tests/consenus/universe/universe.bed", diff --git a/docs/tutorials/evaluation.md b/docs/tutorials/evaluation.md index 37ace356..8f77ffde 100644 --- a/docs/tutorials/evaluation.md +++ b/docs/tutorials/evaluation.md @@ -6,7 +6,7 @@ Given a set of genomic region embeddings `embeddings` and the corresponding regions `vocab`, use `BaseEmbeddings` to create an `base` embedding object. ```python -from gitk.eval.utils import BaseEmbeddings +from geniml.eval.utils import BaseEmbeddings import pickle base_obj = BaseEmbeddings(embeddings, vocab) with open("base_embed.pt", "wb") as f: @@ -16,7 +16,7 @@ with open("base_embed.pt", "wb") as f: ### Generate Binary Embeddings ```python -from gitk.eval.utils import get_bin_embeddings +from geniml.eval.utils import get_bin_embeddings universe_file = "/path/to/universe.bed" token_files = ["file1.bed", "file2.bed"] bin_embed = get_bin_embeddings(universe_file, token_files) @@ -25,7 +25,7 @@ bin_embed = get_bin_embeddings(universe_file, token_files) Or use command line: ```bash -gitk eval bin-gen --universe /path/to/universe.bed --token-folder /path/to/tokenized/folder --file-name bin_embed.pickle +geniml eval bin-gen --universe /path/to/universe.bed --token-folder /path/to/tokenized/folder --file-name bin_embed.pickle ``` ## Statistical Tests @@ -35,7 +35,7 @@ gitk eval bin-gen --universe /path/to/universe.bed --token-folder /path/to/token CTT analyzes how well a set of region embeddings can be clustered. CTT score lies between 0 and 1. A larger CTT score indicates a greater tendency for the embeddings being evaluated to have clusters. When the embeddings are uniformly distributed, the score is 0.5. For evenly spaced embeddings, the score approaches 0. ```python -from gitk.eval.ctt import get_ctt_score, ctt_eval +from geniml.eval.ctt import get_ctt_score, ctt_eval path = "/path/to/a/region2vec/model/" embed_type = "region2vec" ctt_score = get_ctt_score(path, embed_type, seed=42, num_data=10000, num_workers=10) @@ -49,13 +49,13 @@ print(f"Model: {ctt_score_arr[0][0]}\n CTT scores:{ctt_score_arr[0][1]}") # CTT Or use the command line ```bash -gitk eval ctt --model-path /path/to/a/region2vec/model/ --embed-type region2vec +geniml eval ctt --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` ### Reconstruction Test (RCT) RCT evaluates how well an embedding of a region preserves the region’s occurrence information in the training data. The best RCT score is 1. ```python -from gitk.eval.rct import get_rct_score, rct_eval +from geniml.eval.rct import get_rct_score, rct_eval path = "/path/to/a/region2vec/model/" embed_type = "region2vec" bin_path = "/path/to/a/binary/embedding/for/the/same/tokenized/files/" @@ -71,9 +71,9 @@ print(f"Model: {rct_score_arr[0][0]}\n RCT scores:{rct_score_arr[0][1]}") # RCT Or use the command line ```bash -gitk eval rct --model-path /path/to/a/region2vec/model/ --embed-type region2vec +geniml eval rct --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` -To change the learning setting, go to the definition of `get_rct_score` in `gitk/eval/rct.py` and change the constructor of `MLPRegressor`. +To change the learning setting, go to the definition of `get_rct_score` in `geniml/eval/rct.py` and change the constructor of `MLPRegressor`. ## Biological Tests @@ -83,7 +83,7 @@ To change the learning setting, go to the definition of `get_rct_score` in `gitk GDST calculates a score measuring how much the embedding distance between two regions scales the corresponding genome distance. ```python -from gitk.eval.gdst import get_gdst_score, gdst_eval +from geniml.eval.gdst import get_gdst_score, gdst_eval path = "/path/to/a/region2vec/model/" embed_type = "region2vec" gdst_score = get_gdst_score(path, embed_type, num_samples=10000,seed=42) @@ -96,7 +96,7 @@ gdst_score_arr = gdst_eval(batch, num_runs=5, num_samples=10000) Or use the command line ```bash -gitk eval gdst --model-path /path/to/a/region2vec/model/ --embed-type region2vec +geniml eval gdst --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` ### Neighborhood Preserving Test (NPT) @@ -104,7 +104,7 @@ gitk eval gdst --model-path /path/to/a/region2vec/model/ --embed-type region2vec NPT evaluates how significant genomic region embeddings preserve their neighboring regions on the genome against random embeddings. The code output the NPT score for a set of region embeddings. ```python -from gitk.eval.npt import get_npt_score, npt_eval +from geniml.eval.npt import get_npt_score, npt_eval path = "/path/to/a/region2vec/model/" embed_type = "region2vec" K = 10 @@ -122,5 +122,5 @@ print(f"Model: {npt_score_arr[0][0]}\n NPT scores: {npt_score_arr[0][1]}") # NPT Or use the command line (the output will be the result when resolution=K) ```bash -gitk eval npt --model-path /path/to/a/region2vec/model/ --embed-type region2vec --K 50 --num-samples 1000 +geniml eval npt --model-path /path/to/a/region2vec/model/ --embed-type region2vec --K 50 --num-samples 1000 ``` diff --git a/docs/tutorials/region2vec.md b/docs/tutorials/region2vec.md index 089603e8..231d3c0a 100644 --- a/docs/tutorials/region2vec.md +++ b/docs/tutorials/region2vec.md @@ -9,8 +9,8 @@ 3. Create a token folder which will be used to store tokenized files `dst_folder`. 5. Run the following command ``` python -from gitk.tokenization import hard_tokenization -from gitk.region2vec import region2vec +from geniml.tokenization import hard_tokenization +from geniml.region2vec import region2vec src_folder = '/path/to/raw/bed/files' dst_folder = '/path/to/tokenized_files' @@ -27,10 +27,10 @@ if status: # if hard_tokenization is successful, then run Region2Vec training For customized settings, please go and check the parameters used in `main.py`. For training a Region2Vec model, the parameters, `init_lr`, `window_size`, `num_shufflings`, `embedding_dim`, are frequently tuned in experiments. -For command line usage, type `gitk region2vec --help` for details. We give a simple usage below +For command line usage, type `geniml region2vec --help` for details. We give a simple usage below ```bash -gitk region2vec +geniml region2vec --token-folder /path/to/token/folder \ --save-dir ./region2vec_model \ --num-shuffle 10 \ diff --git a/docs/tutorials/tokenization.md b/docs/tutorials/tokenization.md index ce4adf62..69d8f1d5 100644 --- a/docs/tutorials/tokenization.md +++ b/docs/tutorials/tokenization.md @@ -4,7 +4,7 @@ For hard tokenization, run ```python -from gitk.tokenization import hard_tokenization +from geniml.tokenization import hard_tokenization src_folder = '/path/to/raw/bed/files/' dst_folder = '/path/to/tokenized_files/' @@ -18,7 +18,7 @@ By default, the code assumes the binary `bedtools` exists and can be called via Command line usage ```bash -gitk tokenize --data-folder /folder/with/raw/BED/files --token-folder ./tokens --universe /universe/file --bedtools-path bedtools +geniml tokenize --data-folder /folder/with/raw/BED/files --token-folder ./tokens --universe /universe/file --bedtools-path bedtools ``` -For more details, type `gitk tokenize --help`. +For more details, type `geniml tokenize --help`. diff --git a/docs/tutorials/train-scembed-model.md b/docs/tutorials/train-scembed-model.md index 7df22a4f..67a1d670 100644 --- a/docs/tutorials/train-scembed-model.md +++ b/docs/tutorials/train-scembed-model.md @@ -6,16 +6,16 @@ For this example we are using the [10x Genomics PBMC 10k dataset](https://www.10 ## Installation -Simply install the parent package `gitk` from PyPi: +Simply install the parent package `geniml` from PyPi: ```bash -pip install gitk +pip install geniml ``` -Then import `scEmbed` from `gitk`: +Then import `scEmbed` from `geniml`: ```python -from gitk.scembed import SCEmbed +from geniml.scembed import SCEmbed ``` ## Usage @@ -23,7 +23,7 @@ from gitk.scembed import SCEmbed ```python import scanpy as sc -from gitk.scembed import SCEmbed +from geniml.scembed import SCEmbed adata = sc.read_h5ad("path/to/adata.h5ad") @@ -72,7 +72,7 @@ To train an `scembed` model, just create an instance of the `SCEmbed` model clas import logging import scanpy as sc -from gitk.scembed import SCEmbed +from geniml.scembed import SCEmbed # if you want to see the training progress logging.basicConfig(level=logging.INFO) @@ -90,7 +90,7 @@ With the model now trained, we can get embeddings of our cells. This occurs in t **Tokenize:** ```python -from gitk.models.tokenizers import HardTokenizer +from geniml.models.tokenizers import HardTokenizer tokenizer = HardTokenizer("peaks.bed") # consensus peak set diff --git a/docs/tutorials/use-pretrained-scembed-model.md b/docs/tutorials/use-pretrained-scembed-model.md index e75cc66e..7487e631 100644 --- a/docs/tutorials/use-pretrained-scembed-model.md +++ b/docs/tutorials/use-pretrained-scembed-model.md @@ -19,7 +19,7 @@ Encoding cells is as easy as: ```python import scanpy as sc -from gitk.models import PretrainedScembedModel +from geniml.models import PretrainedScembedModel adata = sc.read_h5ad("path/to/adata.h5ad") model = PretrainedScembedModel("databio/luecken2021") diff --git a/examples/scembed/annotation.ipynb b/examples/scembed/annotation.ipynb index 1c565ed2..cd42d333 100644 --- a/examples/scembed/annotation.ipynb +++ b/examples/scembed/annotation.ipynb @@ -12,7 +12,7 @@ "To work with this data, we use [`scanpy`](https://github.com/scverse/scanpy) and their [`AnnData`](https://github.com/scverse/anndata) objects. These objects are a standard way of representing single-cell data in Python. We will use the `AnnData` object to represent our binary accessibility matrix and store embeddings + cell type predictions.\n", "\n", "## Installation\n", - "First, we need to install `scEmbed`. `scEmbed` is a part of the `gitk` package. We recommend using virtual environments for this:" + "First, we need to install `scEmbed`. `scEmbed` is a part of the `geniml` package. We recommend using virtual environments for this:" ] }, { @@ -21,7 +21,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install gitk\n", + "!pip install geniml\n", "# or if using local version:\n", "!pip install ." ] @@ -146,7 +146,7 @@ "outputs": [], "source": [ "# create model\n", - "from gitk.models import PretrainedScembedModel\n", + "from geniml.models import PretrainedScembedModel\n", "\n", "model = PretrainedScembedModel(\"nleroy917/luecken2021\")" ] @@ -162,7 +162,7 @@ "text": [ "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:01<00:00, 89477.91it/s]\n", "100%|â–â–â–â–â–â–â–â–â–â–| 165434/165434 [00:06<00:00, 27340.96it/s]\n", - "/Users/nathanleroy/uva/lab/code/gitk/venv/lib/python3.10/site-packages/anndata/_core/anndata.py:117: ImplicitModificationWarning: Transforming to str index.\n", + "/Users/nathanleroy/uva/lab/code/geniml/venv/lib/python3.10/site-packages/anndata/_core/anndata.py:117: ImplicitModificationWarning: Transforming to str index.\n", " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", "100%|â–â–â–â–â–â–â–â–â–â–| 10246/10246 [02:55<00:00, 58.48it/s]\n" ] @@ -318,7 +318,7 @@ "metadata": {}, "outputs": [], "source": [ - "from gitk.scembed import Annotator\n", + "from geniml.scembed import Annotator\n", "\n", "annotator = Annotator(\n", " location=\"http://localhost:6333\",\n", @@ -402,7 +402,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/examples/scembed/cellcano_annotations.ipynb b/examples/scembed/cellcano_annotations.ipynb index 4fac1d79..2f32db9b 100644 --- a/examples/scembed/cellcano_annotations.ipynb +++ b/examples/scembed/cellcano_annotations.ipynb @@ -78,7 +78,7 @@ "metadata": {}, "outputs": [], "source": [ - "from gitk.models import PretrainedScembedModel\n", + "from geniml.models import PretrainedScembedModel\n", "\n", "# init model to get clusters again\n", "model = PretrainedScembedModel(\"nleroy917/luecken2021\")" diff --git a/examples/scembed/pretrained_model.py b/examples/scembed/pretrained_model.py index f9ee2948..b1ea49fc 100644 --- a/examples/scembed/pretrained_model.py +++ b/examples/scembed/pretrained_model.py @@ -1,6 +1,6 @@ # %% import scanpy as sc -from gitk.models import PretrainedScembedModel +from geniml.models import PretrainedScembedModel # %% # Load the pretrained model diff --git a/examples/scembed/projection.ipynb b/examples/scembed/projection.ipynb index eb5df69f..240b7672 100644 --- a/examples/scembed/projection.ipynb +++ b/examples/scembed/projection.ipynb @@ -20,7 +20,7 @@ "These matrices are typically incredibly sparse, with most cells only having accessibilty at a small fraction of the genome. They are also extremely high-dimensional; matrices with > 1M features is common. This binary accessibility matrix is the input to `scEmbed`. We use [`scanpy`](https://github.com/scverse/scanpy) and their [`AnnData`](https://github.com/scverse/anndata) objects. These objects are a standard way of representing single-cell data in Python. We will use the `AnnData` object to represent our binary accessibility matrix.\n", "\n", "## Installation\n", - "First, we need to install `scEmbed`. `scEmbed` is a part of the `gitk` package. We recommend using virtual environments for this:" + "First, we need to install `scEmbed`. `scEmbed` is a part of the `geniml` package. We recommend using virtual environments for this:" ] }, { @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install gitk\n", + "!pip install geniml\n", "# or if using local version:\n", "!pip install ." ] @@ -149,7 +149,7 @@ "outputs": [], "source": [ "# create model\n", - "from gitk.models import PretrainedScembedModel\n", + "from geniml.models import PretrainedScembedModel\n", "\n", "model = PretrainedScembedModel(\"nleroy917/luecken2021\")" ] diff --git a/geniml/README.md b/geniml/README.md index 19df7595..83901fb6 100644 --- a/geniml/README.md +++ b/geniml/README.md @@ -1,36 +1,36 @@ -# gitk developer notes +# geniml developer notes ## Repository architecture -gitk is organized into modules that provide related functions, each in a subfolder. The parent `gitk/` folder holds functionality that spans across modules, such as the general (top-level) argument parser (in `cli.py`), constants (`const.py`), and any other utility or general-purpose code that is used across modules. +geniml is organized into modules that provide related functions, each in a subfolder. The parent `geniml/` folder holds functionality that spans across modules, such as the general (top-level) argument parser (in `cli.py`), constants (`const.py`), and any other utility or general-purpose code that is used across modules. ## Using modules from Python This repo is divided into modules. Each module should be written in a way that it provides utility as a Python library. For example, you can call functions in the `hmm` module like this: ``` -import gitk +import geniml -gitk.hmm.function() +geniml.hmm.function() ``` ## Command-line interfaces -In addition to being importable from Python, *some* modules also provide a CLI. For these, developers provide a subcommand for CLI use. The root `gitk` package provides a generalized command-line interface with the command `gitk`. The modules that provide CLIs then correspond to CLI commands, *e.g* `gitk hmm` or `gitk likelihood`, with the corresponding code contained within a sub-folder named after the model: +In addition to being importable from Python, *some* modules also provide a CLI. For these, developers provide a subcommand for CLI use. The root `geniml` package provides a generalized command-line interface with the command `geniml`. The modules that provide CLIs then correspond to CLI commands, *e.g* `geniml hmm` or `geniml likelihood`, with the corresponding code contained within a sub-folder named after the model: ``` -gitk ... +geniml ... ``` This is implemented within each module folder with: -- `gitk//cli.py` - defines the command-line interface and provides a subparser for this module's CLI command. +- `geniml//cli.py` - defines the command-line interface and provides a subparser for this module's CLI command. ## How to add a new module To add a new module, you need to: -1. create a subfolder (under `gitk/`) with your module name. +1. create a subfolder (under `geniml/`) with your module name. 2. Add the module to list of packages in `setup.py`. -3. If it makes sense to have a CLI for this module, implement it in `gitk//cli.py`. Link this into the main cli by putting it under an appropriate command name following the pattern for other modules in `gitk/cli.py`. \ No newline at end of file +3. If it makes sense to have a CLI for this module, implement it in `geniml//cli.py`. Link this into the main cli by putting it under an appropriate command name following the pattern for other modules in `geniml/cli.py`. \ No newline at end of file diff --git a/geniml/cli.py b/geniml/cli.py index 8ccbfc10..c760a5cf 100644 --- a/geniml/cli.py +++ b/geniml/cli.py @@ -24,10 +24,10 @@ def build_argparser(): """ banner = "%(prog)s - Genomic Interval toolkit" - additional_description = "\nhttps://gitk.databio.org" + additional_description = "\nhttps://geniml.databio.org" parser = VersionInHelpParser( - prog="gitk", + prog="geniml", version=f"{__version__}", description=banner, epilog=additional_description, @@ -191,14 +191,14 @@ def main(test_args=None): ) if args.command == "eval": if args.subcommand == "gdst": - from gitk.eval.gdst import get_gdst_score + from geniml.eval.gdst import get_gdst_score gdst_score = get_gdst_score( args.model_path, args.embed_type, args.num_samples, args.seed ) print(gdst_score) if args.subcommand == "npt": - from gitk.eval.npt import get_npt_score + from geniml.eval.npt import get_npt_score npt_score = get_npt_score( args.model_path, @@ -211,7 +211,7 @@ def main(test_args=None): ) print(npt_score["SNPR"][0]) if args.subcommand == "ctt": - from gitk.eval.ctt import get_ctt_score + from geniml.eval.ctt import get_ctt_score ctt_score = get_ctt_score( args.model_path, @@ -223,7 +223,7 @@ def main(test_args=None): print(ctt_score) if args.subcommand == "rct": - from gitk.eval.rct import get_rct_score + from geniml.eval.rct import get_rct_score rct_score = get_rct_score( args.model_path, @@ -236,7 +236,7 @@ def main(test_args=None): ) print(rct_score) if args.subcommand == "bin-gen": - from gitk.eval.utils import get_bin_embeddings + from geniml.eval.utils import get_bin_embeddings import glob, pickle, os if os.path.exists(args.file_name): diff --git a/geniml/const.py b/geniml/const.py index deb91e45..60edf06a 100644 --- a/geniml/const.py +++ b/geniml/const.py @@ -1 +1 @@ -PKG_NAME = "gitk" +PKG_NAME = "geniml" diff --git a/geniml/eval/README.md b/geniml/eval/README.md index 8ef057bf..fa820ca2 100644 --- a/geniml/eval/README.md +++ b/geniml/eval/README.md @@ -4,7 +4,7 @@ ### Create a Base Embedding Object Given a set of genomic region embeddings `embeddings` and the corresponding regions `vocab`, use `BaseEmbeddings` to create an `base` embedding object. ``` -from gitk.eval.utils import BaseEmbeddings +from geniml.eval.utils import BaseEmbeddings import pickle base_obj = BaseEmbeddings(embeddings, vocab) with open("base_embed.pt", "wb") as f: @@ -12,21 +12,21 @@ with open("base_embed.pt", "wb") as f: ``` ### Generate Binary Embeddings ```python -from gitk.eval.utils import get_bin_embeddings +from geniml.eval.utils import get_bin_embeddings universe_file = "/path/to/universe.bed" token_files = ["file1.bed", "file2.bed"] bin_embed = get_bin_embeddings(universe_file, token_files) ``` Or use command line: ```bash -gitk eval bin-gen --universe /path/to/universe.bed --token-folder /path/to/tokenized/folder --file-name bin_embed.pickle +geniml eval bin-gen --universe /path/to/universe.bed --token-folder /path/to/tokenized/folder --file-name bin_embed.pickle ``` ## Statistical Tests ### Cluster Tendency Test (CTT) CTT analyzes how well a set of region embeddings can be clustered. CTT score lies between 0 and 1. A larger CTT score indicates a greater tendency for the embeddings being evaluated to have clusters. When the embeddings are uniformly distributed, the score is 0.5. For evenly spaced embeddings, the score approaches 0. ```python -from gitk.eval.ctt import get_ctt_score, ctt_eval +from geniml.eval.ctt import get_ctt_score, ctt_eval path = "/path/to/a/region2vec/model/" embed_type = "region2vec" ctt_score = get_ctt_score(path, embed_type, seed=42, num_data=10000, num_workers=10) @@ -40,13 +40,13 @@ print(f"Model: {ctt_score_arr[0][0]}\n CTT scores:{ctt_score_arr[0][1]}") # CTT Or use the command line ```bash -gitk eval ctt --model-path /path/to/a/region2vec/model/ --embed-type region2vec +geniml eval ctt --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` ### Reconstruction Test (RCT) RCT evaluates how well an embedding of a region preserves the region’s occurrence information in the training data. The best RCT score is 1. ```python -from gitk.eval.rct import get_rct_score, rct_eval +from geniml.eval.rct import get_rct_score, rct_eval path = "/path/to/a/region2vec/model/" embed_type = "region2vec" bin_path = "/path/to/a/binary/embedding/for/the/same/tokenized/files/" @@ -62,16 +62,16 @@ print(f"Model: {rct_score_arr[0][0]}\n RCT scores:{rct_score_arr[0][1]}") # RCT Or use the command line ```bash -gitk eval rct --model-path /path/to/a/region2vec/model/ --embed-type region2vec +geniml eval rct --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` -To change the learning setting, go to the definition of `get_rct_score` in `gitk/eval/rct.py` and change the constructor of `MLPRegressor`. +To change the learning setting, go to the definition of `get_rct_score` in `geniml/eval/rct.py` and change the constructor of `MLPRegressor`. ## Biological Tests ### Genome Distance Scaling Test (GDST) GDST calculates a score measuring how much the embedding distance between two regions scales the corresponding genome distance. ```python -from gitk.eval.gdst import get_gdst_score, gdst_eval +from geniml.eval.gdst import get_gdst_score, gdst_eval path = "/path/to/a/region2vec/model/" embed_type = "region2vec" gdst_score = get_gdst_score(path, embed_type, num_samples=10000,seed=42) @@ -84,7 +84,7 @@ gdst_score_arr = gdst_eval(batch, num_runs=5, num_samples=10000) Or use the command line ```bash -gitk eval gdst --model-path /path/to/a/region2vec/model/ --embed-type region2vec +geniml eval gdst --model-path /path/to/a/region2vec/model/ --embed-type region2vec ``` @@ -92,7 +92,7 @@ gitk eval gdst --model-path /path/to/a/region2vec/model/ --embed-type region2vec NPT evaluates how significant genomic region embeddings preserve their neighboring regions on the genome against random embeddings. The code output the NPT score for a set of region embeddings. ```python -from gitk.eval.npt import get_npt_score, npt_eval +from geniml.eval.npt import get_npt_score, npt_eval path = "/path/to/a/region2vec/model/" embed_type = "region2vec" K = 10 @@ -110,5 +110,5 @@ print(f"Model: {npt_score_arr[0][0]}\n NPT scores: {npt_score_arr[0][1]}") # NPT Or use the command line (the output will be the result when resolution=K) ```bash -gitk eval npt --model-path /path/to/a/region2vec/model/ --embed-type region2vec --K 50 --num-samples 1000 +geniml eval npt --model-path /path/to/a/region2vec/model/ --embed-type region2vec --K 50 --num-samples 1000 ``` diff --git a/geniml/likelihood/README.md b/geniml/likelihood/README.md index 880ce65c..b37548dd 100644 --- a/geniml/likelihood/README.md +++ b/geniml/likelihood/README.md @@ -8,19 +8,19 @@ ``` -gitk lh build_model --model_folder tests/consesnus/lh_model \ +geniml lh build_model --model_folder tests/consesnus/lh_model \ --file_list tests/consesnus/file_list.txt \ --coverage_folder tests/consesnus/coverage/ ``` ``` - gitk lh universe_hard --coverage_file tests/consesnus/coverage/all_core.bw \ + geniml lh universe_hard --coverage_file tests/consesnus/coverage/all_core.bw \ --fout tests/consesnus/universe/ML_hard.bed ``` ``` -gitk lh universe_flexible --model_folder test/data/lh_model \ +geniml lh universe_flexible --model_folder test/data/lh_model \ --output_file test/results/universe/ML_flexible.bed ``` \ No newline at end of file diff --git a/geniml/models/const.py b/geniml/models/const.py index d19d5a84..8f9e93e8 100644 --- a/geniml/models/const.py +++ b/geniml/models/const.py @@ -4,4 +4,4 @@ DEFAULT_PATH_TO_BEDTOOLS = "bedtools" UNIVERSE_FILE_NAME = "universe.bed" -CACHE_DIR = ".gitk_cache" +CACHE_DIR = ".geniml_cache" diff --git a/geniml/region2vec/main.py b/geniml/region2vec/main.py index 1e1f1a80..8bb3b6be 100644 --- a/geniml/region2vec/main.py +++ b/geniml/region2vec/main.py @@ -3,9 +3,9 @@ import numpy as np -from gitk.region2vec import utils -from gitk.region2vec.region2vec_train import main as region2_train -from gitk.region2vec.region_shuffling import main as sent_gen +from geniml.region2vec import utils +from geniml.region2vec.region2vec_train import main as region2_train +from geniml.region2vec.region_shuffling import main as sent_gen class Namespace: diff --git a/geniml/region2vec/region_shuffling.py b/geniml/region2vec/region_shuffling.py index 3c9183c1..4270bd91 100644 --- a/geniml/region2vec/region_shuffling.py +++ b/geniml/region2vec/region_shuffling.py @@ -8,7 +8,7 @@ import numpy as np -from gitk.region2vec import utils +from geniml.region2vec import utils class BEDDataset: diff --git a/geniml/scembed/annotation.py b/geniml/scembed/annotation.py index a383774d..b0d4399e 100644 --- a/geniml/scembed/annotation.py +++ b/geniml/scembed/annotation.py @@ -18,7 +18,7 @@ def __init__( """ A class for querying a Qdrant server for cell type predictions. This class requires that you have a Qdrant server running with a collection of cell type embeddings. You can create this collection using - the `gitk/examples/scembed/load_qdrant.ipynb` script. + the `geniml/examples/scembed/load_qdrant.ipynb` script. :param str collection_name: The name of the collection to query. :param str location: The location of the Qdrant server. This should be in the format `host:port`. @@ -43,7 +43,7 @@ def __init__( """ A class for annotating single cell data with cell type predictions. This class requires that you have a Qdrant server running with a collection of cell type embeddings. You can create this collection using - the `gitk/examples/scembed/load_qdrant.ipynb` script. + the `geniml/examples/scembed/load_qdrant.ipynb` script. :param str collection_name: The name of the collection to query. :param str location: The location of the Qdrant server. This should be in the format `host:port`. diff --git a/geniml/scembed/const.py b/geniml/scembed/const.py index a8756350..ec228b85 100644 --- a/geniml/scembed/const.py +++ b/geniml/scembed/const.py @@ -13,7 +13,7 @@ DEFAULT_WINDOW_SIZE = 5 DEFAULT_EMBEDDING_SIZE = 100 DEFAULT_EPOCHS = 10 -DEFAULT_INIT_LR = 0.1 # https://github.com/databio/gitk/issues/6#issuecomment-1476273162 +DEFAULT_INIT_LR = 0.1 # https://github.com/databio/geniml/issues/6#issuecomment-1476273162 DEFAULT_MIN_LR = 0.0001 # gensim default DEFAULT_DECAY_RATE = 0.95 diff --git a/geniml/scembed/scembed.py b/geniml/scembed/scembed.py index 266f0497..0285ecf6 100755 --- a/geniml/scembed/scembed.py +++ b/geniml/scembed/scembed.py @@ -164,7 +164,7 @@ def train( :param float min_lr: The minimum learning rate. :param Union[str, ScheduleType] lr_schedule: The learning rate schedule to use. """ - # force to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205683978) + # force to 1 for now (see: https://github.com/databio/geniml/pull/20#discussion_r1205683978) n_shuffles = 1 if not isinstance(data, sc.AnnData) and not isinstance(data, str): @@ -230,7 +230,7 @@ def train( super().train( self.region_sets, total_examples=len(self.region_sets), - epochs=1, # for to 1 for now (see: https://github.com/databio/gitk/pull/20#discussion_r1205692089) + epochs=1, # for to 1 for now (see: https://github.com/databio/geniml/pull/20#discussion_r1205692089) callbacks=self.callbacks, compute_loss=report_loss, start_alpha=current_lr, diff --git a/geniml/tokenization/hard_tokenization_batch.py b/geniml/tokenization/hard_tokenization_batch.py index ff28c853..25755e29 100644 --- a/geniml/tokenization/hard_tokenization_batch.py +++ b/geniml/tokenization/hard_tokenization_batch.py @@ -3,7 +3,7 @@ import shlex import subprocess -from gitk.region2vec import utils +from geniml.region2vec import utils def bedtools_tokenization( diff --git a/geniml/tokenization/main.py b/geniml/tokenization/main.py index 094b851c..91c32748 100644 --- a/geniml/tokenization/main.py +++ b/geniml/tokenization/main.py @@ -14,8 +14,8 @@ import requests import yaml -import gitk.region2vec.utils as utils -from gitk.tokenization.split_file import split_file +import geniml.region2vec.utils as utils +from geniml.tokenization.split_file import split_file from .hard_tokenization_batch import main as hard_tokenization diff --git a/geniml/universe/cc_universe.py b/geniml/universe/cc_universe.py index 18858040..31cd62b4 100644 --- a/geniml/universe/cc_universe.py +++ b/geniml/universe/cc_universe.py @@ -7,7 +7,7 @@ import numpy as np import pyBigWig -from gitk.utils import natural_chr_sort, timer_func +from geniml.utils import natural_chr_sort, timer_func def get_uni(file, chrom, cutoff=None): diff --git a/geniml/universe/ccf_universe.py b/geniml/universe/ccf_universe.py index 9feecb46..c47dbe46 100644 --- a/geniml/universe/ccf_universe.py +++ b/geniml/universe/ccf_universe.py @@ -7,7 +7,7 @@ import numpy as np import pyBigWig -from gitk.utils import natural_chr_sort, timer_func +from geniml.utils import natural_chr_sort, timer_func def ana_region(reg, start_s, starts, ends, track_val): diff --git a/geniml/universe/ml_universe.py b/geniml/universe/ml_universe.py index dfa8e1e1..ec0a2f40 100644 --- a/geniml/universe/ml_universe.py +++ b/geniml/universe/ml_universe.py @@ -9,7 +9,7 @@ from ..utils import natural_chr_sort, timer_func, read_chromosome_from_bw from .utils import predictions_to_bed, find_full -from gitk.likelihood.build_model import ModelLH +from geniml.likelihood.build_model import ModelLH @njit diff --git a/mkdocs.yml b/mkdocs.yml index c305c577..d6a85c37 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,8 +1,8 @@ -site_name: gitk -site_logo: img/gitk_logo.svg -site_url: http://gitk.databio.org/ -repo_url: http://github.com/databio/gitk -pypi_name: gitk +site_name: geniml +site_logo: img/geniml_logo.svg +site_url: http://geniml.databio.org/ +repo_url: http://github.com/databio/geniml +pypi_name: geniml nav: - Getting Started: @@ -18,7 +18,7 @@ nav: - Build universe: tutorials/build-universe.md - Create consensus peaks: tutorials/create-consensus-peaks.md - Reference: - - API: autodoc_build/gitk.md + - API: autodoc_build/geniml.md - Support: support.md - Contributing: contributing.md - Changelog: changelog.md @@ -30,6 +30,6 @@ plugins: autodoc_build: "docs/autodoc_build" jupyter_source: "docs_jupyter" jupyter_build: "docs_jupyter/build" - autodoc_package: "gitk" + autodoc_package: "geniml" no_top_level: false - search diff --git a/setup.py b/setup.py index 880ae3b0..a730d9fa 100755 --- a/setup.py +++ b/setup.py @@ -26,17 +26,17 @@ name=PACKAGE_NAME, packages=[ PACKAGE_NAME, - "gitk.assess", - "gitk.bedspace", - "gitk.eval", - "gitk.likelihood", - "gitk.models", - "gitk.models.atac", - "gitk.models.rna", - "gitk.region2vec", - "gitk.scembed", - "gitk.tokenization", - "gitk.universe", + "geniml.assess", + "geniml.bedspace", + "geniml.eval", + "geniml.likelihood", + "geniml.models", + "geniml.models.atac", + "geniml.models.rna", + "geniml.region2vec", + "geniml.scembed", + "geniml.tokenization", + "geniml.universe", ], version=version, long_description=long_description, @@ -54,7 +54,7 @@ license="BSD2", entry_points={ "console_scripts": [ - "gitk = gitk.cli:main", + "geniml = geniml.cli:main", ], }, keywords="bioinformatics, sequencing, ngs", diff --git a/tests/consensus/cli_test.sh b/tests/consensus/cli_test.sh index af0eb2b1..6bc2b559 100644 --- a/tests/consensus/cli_test.sh +++ b/tests/consensus/cli_test.sh @@ -4,28 +4,28 @@ rm -r tests/consesnus/results mkdir tests/consesnus/results # make lh model -gitk lh build_model --model-file tests/consesnus/results/lh_model.tar \ +geniml lh build_model --model-file tests/consesnus/results/lh_model.tar \ --file-no 4 \ --coverage-folder tests/consesnus/coverage/ mkdir tests/consesnus/results/universe/ # make cut-off universe -gitk universe cc --coverage-folder tests/consesnus/coverage/ \ +geniml universe cc --coverage-folder tests/consesnus/coverage/ \ --output-file tests/consesnus/results/universe/cc_universe.bed -gitk universe ccf --coverage-folder tests/consesnus/coverage/ \ +geniml universe ccf --coverage-folder tests/consesnus/coverage/ \ --output-file tests/consesnus/results/universe/ccf_universe.bed -gitk universe ml --model-file tests/consesnus/results/lh_model.tar \ +geniml universe ml --model-file tests/consesnus/results/lh_model.tar \ --output-file tests/consesnus/results/universe/ml_universe.bed\ --coverage-folder tests/consesnus/coverage/ # make HMM universe -gitk universe hmm --output-file tests/consesnus/results/universe/hmm_universe.bed --coverage-folder tests/consesnus/coverage/ +geniml universe hmm --output-file tests/consesnus/results/universe/hmm_universe.bed --coverage-folder tests/consesnus/coverage/ # assessment - gitk assess --raw-data-folder tests/consesnus/raw/\ + geniml assess --raw-data-folder tests/consesnus/raw/\ --file-list tests/consesnus/file_list.txt \ --universe tests/consesnus/results/universe/cc_universe.bed\ --save-to-file --folder-out tests/consesnus/results/ \ @@ -34,7 +34,7 @@ gitk universe hmm --output-file tests/consesnus/results/universe/hmm_universe.be --distance \ --distance-universe-to-file - gitk assess --raw-data-folder tests/consesnus/raw/\ + geniml assess --raw-data-folder tests/consesnus/raw/\ --file-list tests/consesnus/file_list.txt \ --universe tests/consesnus/results/universe/ccf_universe.bed \ --save-to-file --folder-out tests/consesnus/results/ \ @@ -45,7 +45,7 @@ gitk universe hmm --output-file tests/consesnus/results/universe/hmm_universe.be --distance-flexible\ --distance-flexible-universe-to-file - gitk assess --raw-data-folder tests/consesnus/raw/\ + geniml assess --raw-data-folder tests/consesnus/raw/\ --file-list tests/consesnus/file_list.txt \ --universe tests/consesnus/results/universe/ml_universe.bed \ --save-to-file --folder-out tests/consesnus/results/ \ @@ -56,7 +56,7 @@ gitk universe hmm --output-file tests/consesnus/results/universe/hmm_universe.be --distance-flexible\ --distance-flexible-universe-to-file - gitk assess --raw-data-folder tests/consesnus/raw/\ + geniml assess --raw-data-folder tests/consesnus/raw/\ --file-list tests/consesnus/file_list.txt \ --universe tests/consesnus/results/universe/hmm_universe.bed \ --save-to-file --folder-out tests/consesnus/results/ \ diff --git a/tests/test_gitk.py b/tests/test_gitk.py index 3b87e845..c2befc77 100644 --- a/tests/test_gitk.py +++ b/tests/test_gitk.py @@ -1,4 +1,4 @@ -# import gitk +# import geniml def test_test(): diff --git a/tests/test_models.py b/tests/test_models.py index 13d12f3e..4830400d 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -8,7 +8,7 @@ # add parent directory to path sys.path.append("../") -from gitk import models +from geniml import models @pytest.fixture diff --git a/tests/test_scembed.py b/tests/test_scembed.py index 25506ea0..5a8ea7f7 100644 --- a/tests/test_scembed.py +++ b/tests/test_scembed.py @@ -9,7 +9,7 @@ # add parent directory to path sys.path.append("../") -from gitk import scembed, utils +from geniml import scembed, utils # set to DEBUG to see more info logging.basicConfig(level=logging.INFO) From 5246450bbc4c66ed3371587afa9e3591b926746b Mon Sep 17 00:00:00 2001 From: nsheff Date: Sat, 29 Jul 2023 13:29:36 -0400 Subject: [PATCH 0304/1072] rename --- docs/img/geniml_logo.svg | 63 ++++++++++++++++++++++++++++++++++++++ docs/img/gitk_logo.svg | 66 ---------------------------------------- 2 files changed, 63 insertions(+), 66 deletions(-) create mode 100644 docs/img/geniml_logo.svg delete mode 100644 docs/img/gitk_logo.svg diff --git a/docs/img/geniml_logo.svg b/docs/img/geniml_logo.svg new file mode 100644 index 00000000..7aeb1b59 --- /dev/null +++ b/docs/img/geniml_logo.svg @@ -0,0 +1,63 @@ + + diff --git a/docs/img/gitk_logo.svg b/docs/img/gitk_logo.svg deleted file mode 100644 index 9e9d79e7..00000000 --- a/docs/img/gitk_logo.svg +++ /dev/null @@ -1,66 +0,0 @@ - -geniml From 8163577b04f7e522c3d3ff31c81ccd8126f67640 Mon Sep 17 00:00:00 2001 From: nsheff Date: Sat, 29 Jul 2023 13:31:17 -0400 Subject: [PATCH 0305/1072] lint --- geniml/scembed/cli.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/geniml/scembed/cli.py b/geniml/scembed/cli.py index 310d9272..225cb7d4 100644 --- a/geniml/scembed/cli.py +++ b/geniml/scembed/cli.py @@ -7,8 +7,7 @@ from ._version import __version__ from .argparser import build_argparser from .const import * -from .scembed import (convert_anndata_to_documents, load_scanpy_data, - shuffle_documents, train) +from .scembed import convert_anndata_to_documents, load_scanpy_data, shuffle_documents, train def main(): From 04fb102fe1e9483697ba49fb10101d304699e6a6 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Sat, 29 Jul 2023 15:11:19 -0400 Subject: [PATCH 0306/1072] suport for gzipped bed files --- examples/scembed/pretrained_model.py | 4 ++-- gitk/io/io.py | 25 ++++++++++++++++++++++--- 2 files changed, 24 insertions(+), 5 deletions(-) diff --git a/examples/scembed/pretrained_model.py b/examples/scembed/pretrained_model.py index f9ee2948..b8d47063 100644 --- a/examples/scembed/pretrained_model.py +++ b/examples/scembed/pretrained_model.py @@ -1,10 +1,10 @@ # %% import scanpy as sc -from gitk.models import PretrainedScembedModel +from gitk.scembed import ScEmbed # %% # Load the pretrained model -model = PretrainedScembedModel("databio/luecken2021") +model = ScEmbed("databio/r2v-luecken2021-hg38-small") # %% # Load the data diff --git a/gitk/io/io.py b/gitk/io/io.py index 3a265408..e7a17c2f 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -1,5 +1,7 @@ +import os from typing import List, Union +import gzip from intervaltree import Interval from ..utils import * @@ -28,13 +30,22 @@ def __init__(self, regions: Union[str, List[Region]], backed: bool = False): self.backed = backed self.regions: List[Region] = [] self.path = regions + + # Check if the file is gzipped + _, file_extension = os.path.splitext(self.path) + is_gzipped = file_extension == ".gz" + + # Open function depending on file type + open_func = gzip.open if is_gzipped else open + mode = "rt" if is_gzipped else "r" + if backed: self.regions = None # https://stackoverflow.com/a/32607817/13175187 - with open(self.path, "r") as file: + with open_func(self.path, mode) as file: self.length = sum(1 for line in file if line.strip()) else: - with open(regions, "r") as f: + with open_func(regions, mode) as f: lines = f.readlines() for line in lines: # some bed files have more than 3 columns, so we just take the first 3 @@ -68,7 +79,15 @@ def __repr__(self): def __iter__(self): if self.backed: - with open(self.path, "r") as f: + # Check if the file is gzipped + _, file_extension = os.path.splitext(self.path) + is_gzipped = file_extension == ".gz" + + # Open function depending on file type + open_func = gzip.open if is_gzipped else open + mode = "rt" if is_gzipped else "r" + + with open_func(self.path, mode) as f: for line in f: chr, start, stop = line.split("\t")[:3] yield Region(chr, int(start), int(stop)) From 68024795554ba13a6d8f9b12aa3dcbfe3148317e Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Sat, 29 Jul 2023 15:26:10 -0400 Subject: [PATCH 0307/1072] unit tests for gz-backed file --- tests/data/universe.bed.gz | Bin 0 -> 85106 bytes tests/test_io.py | 36 ++++++++++++++++++++++++++++++++++++ 2 files changed, 36 insertions(+) create mode 100755 tests/data/universe.bed.gz diff --git a/tests/data/universe.bed.gz b/tests/data/universe.bed.gz new file mode 100755 index 0000000000000000000000000000000000000000..3822c6ed943e443d937266c80f506b214815772f GIT binary patch literal 85106 zcmYIvWk6J2+qFoyGz=vrEg(Y<-QC?G-5?kb2i2U!Fp@VSUg zTWM}sh2&8Q4#Gb?%W?JTx&4VGAD@j2V{6o8ZE4zKO9^ ze(3%n^ZGnnVMGsU7S1M$Bh!R)7jB9GyYOv_z5kb(N>h>ts@i*|JjRKH 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@@ -36,3 +41,34 @@ def test_make_region_set_with_backed(universe_bed_file: str): # test we can iterate over it for region in u: assert isinstance(region, Region) + + +def test_make_region_set_with_list(): + regions = [Region("chr1", 0, 100), Region("chr1", 100, 200)] + u = RegionSet(regions) + assert u is not None + assert len(u) == 2 + + # test we can iterate over it + for region in u: + assert isinstance(region, Region) + + +def test_make_region_set_with_gz_file(gz_file: str): + u = RegionSet(gz_file) + assert u is not None + assert len(u) == 2_433 + + # test we can iterate over it + for region in u: + assert isinstance(region, Region) + + +def test_make_region_set_with_gz_file_backed(gz_file: str): + u = RegionSet(gz_file, backed=True) + assert u is not None + assert len(u) == 2_433 + + # test we can iterate over it + for region in u: + assert isinstance(region, Region) From fb5d7332414c7578b078b6cbde00d821a0e2512e Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Mon, 31 Jul 2023 11:28:55 -0400 Subject: [PATCH 0308/1072] tweak tokenization of regionsets for Region2VecExModel --- gitk/io/io.py | 5 ++++- gitk/region2vec/main.py | 6 ++++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/gitk/io/io.py b/gitk/io/io.py index e7a17c2f..7da4a0db 100644 --- a/gitk/io/io.py +++ b/gitk/io/io.py @@ -66,7 +66,10 @@ def __len__(self): return self.length def __getitem__(self, key): - return self.regions[key] + if self.backed: + raise NotImplementedError("Backed RegionSets do not currently support indexing.") + else: + return self.regions[key] def __repr__(self): if self.path: diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 55352da9..9dcc8cc2 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -507,11 +507,13 @@ def train(self, data: Union[List[str], List[RegionSet], List[List[Region]]], **k raise ValueError( "Cannot train model without a universe. Please call `add_universe` first." ) - region_sets = self.tokenizer.tokenize(data) + + # tokenize each region set + region_sets_tokenized = [self.tokenizer.tokenize(rs) for rs in region_sets] _LOGGER.info("Training begin.") - self._model.train(region_sets, **kwargs) + self._model.train(region_sets_tokenized, **kwargs) _LOGGER.info("Training complete.") From 812d8dde9915f31c9fab69e65db78322e16e4fb4 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Mon, 31 Jul 2023 11:53:40 -0400 Subject: [PATCH 0309/1072] fix circular imports --- gitk/tokenization/__init__.py | 3 +- gitk/tokenization/hard_tokenization_batch.py | 2 +- gitk/tokenization/main.py | 7 +- gitk/tokenization/utils.py | 44 + tests/data/universe.bed | 2433 ++++++++++++++++++ tests/data/universe.bed.gz | Bin 85106 -> 85104 bytes tests/test_io.py | 6 +- 7 files changed, 2484 insertions(+), 11 deletions(-) create mode 100755 tests/data/universe.bed diff --git a/gitk/tokenization/__init__.py b/gitk/tokenization/__init__.py index 9394f8e3..555fecfe 100644 --- a/gitk/tokenization/__init__.py +++ b/gitk/tokenization/__init__.py @@ -1,3 +1,2 @@ -from .main import * -from .main import FileTokenizer, Tokenizer +from .main import FileTokenizer, Tokenizer, InMemTokenizer from .main import hard_tokenization_main as hard_tokenization diff --git a/gitk/tokenization/hard_tokenization_batch.py b/gitk/tokenization/hard_tokenization_batch.py index e118929f..cc6e3ca4 100644 --- a/gitk/tokenization/hard_tokenization_batch.py +++ b/gitk/tokenization/hard_tokenization_batch.py @@ -3,7 +3,7 @@ import shlex import subprocess -from ..region2vec import utils +from .utils import time_str, Timer def bedtools_tokenization( diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 1c284a81..4e9421b6 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -10,12 +10,11 @@ from intervaltree import IntervalTree from tqdm import tqdm -import gitk.region2vec.utils as utils from gitk.tokenization.split_file import split_file from ..io import Region, RegionSet, RegionSetCollection from .hard_tokenization_batch import main as hard_tokenization -from .utils import anndata_to_regionsets +from .utils import anndata_to_regionsets, time_str, Timer class Tokenizer(ABC): @@ -309,7 +308,7 @@ def hard_tokenization_main( has the complete tokenized BED files or the tokenization process succeeds. """ - timer = utils.Timer() + timer = Timer() start_time = timer.t() file_list_path = os.path.join(dst_folder, "file_list.txt") @@ -404,5 +403,5 @@ def hard_tokenization_main( os.remove(file_list_path) print(f"Tokenization complete {len(os.listdir(dst_folder))}/{file_count} bed files") elapsed_time = timer.t() - start_time - print(f"[Tokenization] {utils.time_str(elapsed_time)}/{utils.time_str(timer.t())}") + print(f"[Tokenization] {time_str(elapsed_time)}/{time_str(timer.t())}") return 1 diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index e46a8465..3fd27c1f 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -1,4 +1,5 @@ import os +import time from typing import List import numpy as np @@ -8,6 +9,49 @@ from ..io import Region, RegionSet +class Timer: + """Records the running time. + + Uses Timer.s() or Timer() to record the start time. Then, calls Timer.t() to get the + elapsed time in seconds. + """ + + def __init__(self): + """Initializes a Timer object and starts the timer.""" + self.v = time.time() + + def s(self): + """Restarts the timer.""" + self.v = time.time() + + def t(self): + """Gives the elapsed time. + + Returns: + float: The elapsed time in seconds. + """ + return time.time() - self.v + + +def time_str(t: float) -> str: + """Converts time in float to a readable format. + + Converts time in float to hours, minutes, or seconds based on the value of + t. + + Args: + t (float): Time in seconds. + + Returns: + str: Time in readable time. + """ + if t >= 3600: + return f"{t / 3600:.2f}h" + if t >= 60: + return f"{t / 60:.2f}m" + return f"{t:.2f}s" + + def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: """ Converts an AnnData object to a list of lists of regions. This diff --git a/tests/data/universe.bed b/tests/data/universe.bed new file mode 100755 index 00000000..d6416f87 --- /dev/null +++ b/tests/data/universe.bed @@ -0,0 +1,2433 @@ +chr1 63403166 63403785 chr1:63403166..63403785-chr10:20783474..20784387,2 200 . 63403166 63403785 255,0,0 1 619 0 +chr10 20783474 20784387 chr1:63403166..63403785-chr10:20783474..20784387,2 200 . 20783474 20784387 255,0,0 1 913 0 +chr1 121484861 121485361 chr1:121484861..121485361-chr10:58418252..58418753,2 200 . 121484861 121485361 255,0,0 1 500 0 +chr10 58418252 58418753 chr1:121484861..121485361-chr10:58418252..58418753,2 200 . 58418252 58418753 255,0,0 1 501 0 +chr1 34841363 34842193 chr1:34841363..34842193-chr11:45639005..45639830,2 200 . 34841363 34842193 255,0,0 1 830 0 +chr11 45639005 45639830 chr1:34841363..34842193-chr11:45639005..45639830,2 200 . 45639005 45639830 255,0,0 1 825 0 +chr1 89566099 89566939 chr1:89566099..89566939-chr11:63533954..63534897,2 200 . 89566099 89566939 255,0,0 1 840 0 +chr11 63533954 63534897 chr1:89566099..89566939-chr11:63533954..63534897,2 200 . 63533954 63534897 255,0,0 1 943 0 +chr1 121484902 121485419 chr1:121484902..121485419-chr11:48946286..48946806,2 200 . 121484902 121485419 255,0,0 1 517 0 +chr11 48946286 48946806 chr1:121484902..121485419-chr11:48946286..48946806,2 200 . 48946286 48946806 255,0,0 1 520 0 +chr1 194583954 194584474 chr1:194583954..194584474-chr11:21332888..21333408,2 200 . 194583954 194584474 255,0,0 1 520 0 +chr11 21332888 21333408 chr1:194583954..194584474-chr11:21332888..21333408,2 200 . 21332888 21333408 255,0,0 1 520 0 +chr1 121484582 121485384 chr1:121484582..121485384-chr13:114217381..114218259,2 200 . 121484582 121485384 255,0,0 1 802 0 +chr13 114217381 114218259 chr1:121484582..121485384-chr13:114217381..114218259,2 200 . 114217381 114218259 255,0,0 1 878 0 +chr1 205155820 205156550 chr1:205155820..205156550-chr13:64995556..64996355,2 200 . 205155820 205156550 255,0,0 1 730 0 +chr13 64995556 64996355 chr1:205155820..205156550-chr13:64995556..64996355,2 200 . 64995556 64996355 255,0,0 1 799 0 +chr1 184773101 184774074 chr1:184773101..184774074-chr14:84719732..84720658,2 200 . 184773101 184774074 255,0,0 1 973 0 +chr14 84719732 84720658 chr1:184773101..184774074-chr14:84719732..84720658,2 200 . 84719732 84720658 255,0,0 1 926 0 +chr1 115088127 115088964 chr1:115088127..115088964-chr15:69754971..69755890,2 200 . 115088127 115088964 255,0,0 1 837 0 +chr15 69754971 69755890 chr1:115088127..115088964-chr15:69754971..69755890,2 200 . 69754971 69755890 255,0,0 1 919 0 +chr1 147063109 147063671 chr1:147063109..147063671-chr15:53336684..53337320,2 200 . 147063109 147063671 255,0,0 1 562 0 +chr15 53336684 53337320 chr1:147063109..147063671-chr15:53336684..53337320,2 200 . 53336684 53337320 255,0,0 1 636 0 +chr1 194146588 194147088 chr1:194146588..194147088-chr15:62042094..62042595,2 200 . 194146588 194147088 255,0,0 1 500 0 +chr15 62042094 62042595 chr1:194146588..194147088-chr15:62042094..62042595,2 200 . 62042094 62042595 255,0,0 1 501 0 +chr1 168669790 168670434 chr1:168669790..168670434-chr16:22371655..22372479,2 200 . 168669790 168670434 255,0,0 1 644 0 +chr16 22371655 22372479 chr1:168669790..168670434-chr16:22371655..22372479,2 200 . 22371655 22372479 255,0,0 1 824 0 +chr1 23423746 23424266 chr1:23423746..23424266-chr17:56736211..56736809,2 200 . 23423746 23424266 255,0,0 1 520 0 +chr17 56736211 56736809 chr1:23423746..23424266-chr17:56736211..56736809,2 200 . 56736211 56736809 255,0,0 1 598 0 +chr1 107930437 107931214 chr1:107930437..107931214-chr17:58851548..58852096,2 200 . 107930437 107931214 255,0,0 1 777 0 +chr17 58851548 58852096 chr1:107930437..107931214-chr17:58851548..58852096,2 200 . 58851548 58852096 255,0,0 1 548 0 +chr1 111606368 111606868 chr1:111606368..111606868-chr17:59735117..59735867,2 200 . 111606368 111606868 255,0,0 1 500 0 +chr17 59735117 59735867 chr1:111606368..111606868-chr17:59735117..59735867,2 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chr1:40692986..40693506-chr2:31059867..31060387,2 200 . 40692986 40693506 255,0,0 1 520 0 +chr2 31059867 31060387 chr1:40692986..40693506-chr2:31059867..31060387,2 200 . 31059867 31060387 255,0,0 1 520 0 +chr1 154749568 154750430 chr1:154749568..154750430-chr2:25850476..25851427,2 200 . 154749568 154750430 255,0,0 1 862 0 +chr2 25850476 25851427 chr1:154749568..154750430-chr2:25850476..25851427,2 200 . 25850476 25851427 255,0,0 1 951 0 +chr1 164948795 164949298 chr1:164948795..164949298-chr2:22919136..22919636,2 200 . 164948795 164949298 255,0,0 1 503 0 +chr2 22919136 22919636 chr1:164948795..164949298-chr2:22919136..22919636,2 200 . 22919136 22919636 255,0,0 1 500 0 +chr1 178546931 178547912 chr1:178546931..178547912-chr2:210276047..210276963,2 200 . 178546931 178547912 255,0,0 1 981 0 +chr2 210276047 210276963 chr1:178546931..178547912-chr2:210276047..210276963,2 200 . 210276047 210276963 255,0,0 1 916 0 +chr1 194583474 194584454 chr1:194583474..194584454-chr2:31059867..31060367,2 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188209923 255,0,0 2 791,839 0,455137 +chr3 188000807 188209899 chr3:188000807..188001629-chr3:188209020..188209899,3 300 . 188000807 188209899 255,0,0 2 822,879 0,208213 +chr3 189176053 189561176 chr3:189176053..189176727-chr3:189560466..189561176,3 300 . 189176053 189561176 255,0,0 2 674,710 0,384413 +chr3 189560095 189663307 chr3:189560095..189561324-chr3:189661753..189663307,17 1000 . 189560095 189663307 255,0,0 2 1229,1554 0,101658 +chr3 193552184 193588763 chr3:193552184..193552777-chr3:193588245..193588763,2 200 . 193552184 193588763 255,0,0 2 593,518 0,36061 +chr3 193552392 193597842 chr3:193552392..193553013-chr3:193597180..193597842,2 200 . 193552392 193597842 255,0,0 2 621,662 0,44788 +chr3 193554270 193598412 chr3:193554270..193555469-chr3:193597146..193598412,4 400 . 193554270 193598412 255,0,0 2 1199,1266 0,42876 +chr3 193554581 193589195 chr3:193554581..193555458-chr3:193587910..193589195,7 700 . 193554581 193589195 255,0,0 2 877,1285 0,33329 +chr3 193554959 193877147 chr3:193554959..193555467-chr3:193876484..193877147,2 200 . 193554959 193877147 255,0,0 2 508,663 0,321525 +chr3 193587894 193877400 chr3:193587894..193588935-chr3:193876406..193877400,15 1000 . 193587894 193877400 255,0,0 2 1041,994 0,288512 +chr3 193587933 193639819 chr3:193587933..193588868-chr3:193638898..193639819,4 400 . 193587933 193639819 255,0,0 2 935,921 0,50965 +chr3 193587937 194033991 chr3:193587937..193588916-chr3:194033010..194033991,2 200 . 193587937 194033991 255,0,0 2 979,981 0,445073 +chr3 193587949 193598306 chr3:193587949..193589238-chr3:193596931..193598306,20 1000 . 193587949 193598306 255,0,0 2 1289,1375 0,8982 +chr3 193588041 193718466 chr3:193588041..193588855-chr3:193717662..193718466,2 200 . 193588041 193718466 255,0,0 2 814,804 0,129621 +chr3 193589785 193877324 chr3:193589785..193590468-chr3:193876448..193877324,4 400 . 193589785 193877324 255,0,0 2 683,876 0,286663 +chr3 193596991 193721450 chr3:193596991..193597626-chr3:193720866..193721450,2 200 . 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chr4:140770216..140771079-chr4:141171351..141171870,2 200 . 140770216 141171870 255,0,0 2 863,519 0,401135 +chr4 140770266 140797238 chr4:140770266..140771012-chr4:140796653..140797238,2 200 . 140770266 140797238 255,0,0 2 746,585 0,26387 +chr4 140796585 141171580 chr4:140796585..140797136-chr4:141171023..141171580,2 200 . 140796585 141171580 255,0,0 2 551,557 0,374438 +chr4 141171022 141181996 chr4:141171022..141171915-chr4:141181188..141181996,2 200 . 141171022 141181996 255,0,0 2 893,808 0,10166 +chr4 143536310 143958197 chr4:143536310..143537064-chr4:143957404..143958197,5 500 . 143536310 143958197 255,0,0 2 754,793 0,421094 +chr4 143920733 143958152 chr4:143920733..143921365-chr4:143957435..143958152,3 300 . 143920733 143958152 255,0,0 2 632,717 0,36702 +chr4 146024840 171404276 chr4:146024840..146025360-chr4:171403756..171404276,2 200 . 146024840 171404276 255,0,0 2 520,520 0,25378916 +chr4 146024840 177497536 chr4:146024840..146025360-chr4:177497016..177497536,2 200 . 146024840 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chr4:169600660..169601299-chr4:169612544..169613442,2 200 . 169600660 169613442 255,0,0 2 639,898 0,11884 +chr4 169612465 169841490 chr4:169612465..169613778-chr4:169840502..169841490,7 700 . 169612465 169841490 255,0,0 2 1313,988 0,228037 +chr4 186431723 186559470 chr4:186431723..186432574-chr4:186558551..186559470,3 300 . 186431723 186559470 255,0,0 2 851,919 0,126828 +chr4 186434703 186457044 chr4:186434703..186435566-chr4:186456450..186457044,2 200 . 186434703 186457044 255,0,0 2 863,594 0,21747 +chr4 186434788 186559179 chr4:186434788..186435636-chr4:186558667..186559179,2 200 . 186434788 186559179 255,0,0 2 848,512 0,123879 +chr5 14807486 14865028 chr5:14807486..14808341-chr5:14864093..14865028,3 300 . 14807486 14865028 255,0,0 2 855,935 0,56607 +chr5 16588651 16627384 chr5:16588651..16589517-chr5:16626683..16627384,4 400 . 16588651 16627384 255,0,0 2 866,701 0,38032 +chr5 19734918 19885534 chr5:19734918..19735708-chr5:19884933..19885534,2 200 . 19734918 19885534 255,0,0 2 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105295328 chr7:105280200..105280789-chr7:105294739..105295328,2 200 . 105280200 105295328 255,0,0 2 589,589 0,14539 +chr7 105280237 105297851 chr7:105280237..105280942-chr7:105296882..105297851,3 300 . 105280237 105297851 255,0,0 2 705,969 0,16645 +chr7 105577755 105650424 chr7:105577755..105578436-chr7:105649452..105650424,3 300 . 105577755 105650424 255,0,0 2 681,972 0,71697 +chr7 107592157 107732378 chr7:107592157..107592879-chr7:107731814..107732378,2 200 . 107592157 107732378 255,0,0 2 722,564 0,139657 +chr7 109123447 109346761 chr7:109123447..109124078-chr7:109346229..109346761,2 200 . 109123447 109346761 255,0,0 2 631,532 0,222782 +chr7 110317481 110399665 chr7:110317481..110318045-chr7:110398859..110399665,2 200 . 110317481 110399665 255,0,0 2 564,806 0,81378 +chr7 111725319 111785137 chr7:111725319..111725839-chr7:111784610..111785137,2 200 . 111725319 111785137 255,0,0 2 520,527 0,59291 +chr7 115979447 116067126 chr7:115979447..115979967-chr7:116066229..116067126,2 200 . 115979447 116067126 255,0,0 2 520,897 0,86782 +chr7 116676062 116765176 chr7:116676062..116676562-chr7:116764505..116765176,2 200 . 116676062 116765176 255,0,0 2 500,671 0,88443 +chr7 116764216 116811966 chr7:116764216..116765330-chr7:116810998..116811966,11 1000 . 116764216 116811966 255,0,0 2 1114,968 0,46782 +chr7 116764325 116843683 chr7:116764325..116765196-chr7:116842810..116843683,3 300 . 116764325 116843683 255,0,0 2 871,873 0,78485 +chr7 127564790 127624699 chr7:127564790..127565494-chr7:127623863..127624699,2 200 . 127564790 127624699 255,0,0 2 704,836 0,59073 +chr7 128071113 128084672 chr7:128071113..128071837-chr7:128083898..128084672,3 300 . 128071113 128084672 255,0,0 2 724,774 0,12785 +chr7 131776428 131899965 chr7:131776428..131777094-chr7:131898980..131899965,4 400 . 131776428 131899965 255,0,0 2 666,985 0,122552 +chr7 131776689 131802597 chr7:131776689..131777324-chr7:131802034..131802597,2 200 . 131776689 131802597 255,0,0 2 635,563 0,25345 +chr7 131800899 131899897 chr7:131800899..131802686-chr7:131898991..131899897,10 1000 . 131800899 131899897 255,0,0 2 1787,906 0,98092 +chr7 131899007 132318832 chr7:131899007..131899916-chr7:132318157..132318832,2 200 . 131899007 132318832 255,0,0 2 909,675 0,419150 +chr7 131899036 131936138 chr7:131899036..131899941-chr7:131935607..131936138,3 300 . 131899036 131936138 255,0,0 2 905,531 0,36571 +chr7 131899050 131910667 chr7:131899050..131899947-chr7:131909700..131910667,6 600 . 131899050 131910667 255,0,0 2 897,967 0,10650 +chr7 132318406 132515184 chr7:132318406..132319314-chr7:132514654..132515184,2 200 . 132318406 132515184 255,0,0 2 908,530 0,196248 +chr7 139335817 139427202 chr7:139335817..139336710-chr7:139426550..139427202,2 200 . 139335817 139427202 255,0,0 2 893,652 0,90733 +chr7 140675007 140692975 chr7:140675007..140675968-chr7:140692355..140692975,3 300 . 140675007 140692975 255,0,0 2 961,620 0,17348 +chr7 140692108 142248015 chr7:140692108..140692959-chr7:142247279..142248015,2 200 . 140692108 142248015 255,0,0 2 851,736 0,1555171 +chr7 142246763 142275875 chr7:142246763..142247591-chr7:142275342..142275875,2 200 . 142246763 142275875 255,0,0 2 828,533 0,28579 +chr7 151391478 151407034 chr7:151391478..151392742-chr7:151405949..151407034,4 400 . 151391478 151407034 255,0,0 2 1264,1085 0,14471 +chr7 151391704 151554279 chr7:151391704..151392503-chr7:151553317..151554279,5 500 . 151391704 151554279 255,0,0 2 799,962 0,161613 +chr7 151391905 151413762 chr7:151391905..151392465-chr7:151413227..151413762,2 200 . 151391905 151413762 255,0,0 2 560,535 0,21322 +chr7 151553358 151573461 chr7:151553358..151553930-chr7:151572790..151573461,2 200 . 151553358 151573461 255,0,0 2 572,671 0,19432 +chr7 151553683 151574770 chr7:151553683..151554332-chr7:151574069..151574770,2 200 . 151553683 151574770 255,0,0 2 649,701 0,20386 +chr7 155162202 155173599 chr7:155162202..155163165-chr7:155172586..155173599,4 400 . 155162202 155173599 255,0,0 2 963,1013 0,10384 +chr7 155162205 155232327 chr7:155162205..155162753-chr7:155231537..155232327,2 200 . 155162205 155232327 255,0,0 2 548,790 0,69332 +chr7 157028417 157048597 chr7:157028417..157029348-chr7:157047774..157048597,8 800 . 157028417 157048597 255,0,0 2 931,823 0,19357 +chr7 157047470 157066135 chr7:157047470..157048721-chr7:157065152..157066135,11 1000 . 157047470 157066135 255,0,0 2 1251,983 0,17682 +chr7 157048076 157114579 chr7:157048076..157048579-chr7:157114071..157114579,2 200 . 157048076 157114579 255,0,0 2 503,508 0,65995 +chr8 8146386 8366086 chr8:8146386..8147240-chr8:8365579..8366086,2 200 . 8146386 8366086 255,0,0 2 854,507 0,219193 +chr8 8146657 8172832 chr8:8146657..8147172-chr8:8172119..8172832,2 200 . 8146657 8172832 255,0,0 2 515,713 0,25462 +chr8 8147727 8172982 chr8:8147727..8148466-chr8:8172098..8172982,3 300 . 8147727 8172982 255,0,0 2 739,884 0,24371 +chr8 8172038 8365778 chr8:8172038..8172585-chr8:8365267..8365778,2 200 . 8172038 8365778 255,0,0 2 547,511 0,193229 +chr8 8953143 8977605 chr8:8953143..8953782-chr8:8977053..8977605,2 200 . 8953143 8977605 255,0,0 2 639,552 0,23910 +chr8 8957486 8977528 chr8:8957486..8958446-chr8:8976798..8977528,3 300 . 8957486 8977528 255,0,0 2 960,730 0,19312 +chr8 11428917 11588451 chr8:11428917..11429796-chr8:11587902..11588451,3 300 . 11428917 11588451 255,0,0 2 879,549 0,158985 +chr8 11432809 11588820 chr8:11432809..11433827-chr8:11587933..11588820,9 900 . 11432809 11588820 255,0,0 2 1018,887 0,155124 +chr8 11576250 11588784 chr8:11576250..11577120-chr8:11588183..11588784,3 300 . 11576250 11588784 255,0,0 2 870,601 0,11933 +chr8 11587887 11604789 chr8:11587887..11588889-chr8:11603840..11604789,8 800 . 11587887 11604789 255,0,0 2 1002,949 0,15953 +chr8 13014117 13223431 chr8:13014117..13015013-chr8:13222470..13223431,6 600 . 13014117 13223431 255,0,0 2 896,961 0,208353 +chr8 13014188 13134693 chr8:13014188..13015032-chr8:13133820..13134693,4 400 . 13014188 13134693 255,0,0 2 844,873 0,119632 +chr8 13133932 13223251 chr8:13133932..13134763-chr8:13222364..13223251,3 300 . 13133932 13223251 255,0,0 2 831,887 0,88432 +chr8 17186901 17386026 chr8:17186901..17187879-chr8:17385007..17386026,8 800 . 17186901 17386026 255,0,0 2 978,1019 0,198106 +chr8 17186941 17347814 chr8:17186941..17187626-chr8:17346814..17347814,3 300 . 17186941 17347814 255,0,0 2 685,1000 0,159873 +chr8 21349802 21542124 chr8:21349802..21350943-chr8:21541211..21542124,3 300 . 21349802 21542124 255,0,0 2 1141,913 0,191409 +chr8 22592052 22601974 chr8:22592052..22592999-chr8:22601011..22601974,4 400 . 22592052 22601974 255,0,0 2 947,963 0,8959 +chr8 22596302 22606375 chr8:22596302..22596857-chr8:22605547..22606375,4 400 . 22596302 22606375 255,0,0 2 555,828 0,9245 +chr8 28596324 117716063 chr8:28596324..28597192-chr8:117715403..117716063,2 200 . 28596324 117716063 255,0,0 2 868,660 0,89119079 +chr8 29144147 29413488 chr8:29144147..29144807-chr8:29412859..29413488,2 200 . 29144147 29413488 255,0,0 2 660,629 0,268712 +chr8 29197692 29413690 chr8:29197692..29198512-chr8:29413100..29413690,2 200 . 29197692 29413690 255,0,0 2 820,590 0,215408 +chr8 29412707 29437890 chr8:29412707..29413480-chr8:29437315..29437890,2 200 . 29412707 29437890 255,0,0 2 773,575 0,24608 +chr8 36919485 37003030 chr8:36919485..36920408-chr8:37002007..37003030,4 400 . 36919485 37003030 255,0,0 2 923,1023 0,82522 +chr8 36919626 37451343 chr8:36919626..36920283-chr8:37450776..37451343,2 200 . 36919626 37451343 255,0,0 2 657,567 0,531150 +chr8 36919755 36967707 chr8:36919755..36920263-chr8:36967106..36967707,2 200 . 36919755 36967707 255,0,0 2 508,601 0,47351 +chr8 36920504 37002947 chr8:36920504..36921187-chr8:37002068..37002947,2 200 . 36920504 37002947 255,0,0 2 683,879 0,81564 +chr8 37424328 37448550 chr8:37424328..37425113-chr8:37447875..37448550,2 200 . 37424328 37448550 255,0,0 2 785,675 0,23547 +chr8 37455867 37549244 chr8:37455867..37456717-chr8:37548652..37549244,2 200 . 37455867 37549244 255,0,0 2 850,592 0,92785 +chr8 41657684 41666827 chr8:41657684..41658509-chr8:41666317..41666827,2 200 . 41657684 41666827 255,0,0 2 825,510 0,8633 +chr8 67036323 67626996 chr8:67036323..67036925-chr8:67626370..67626996,2 200 . 67036323 67626996 255,0,0 2 602,626 0,590047 +chr8 67424716 67435290 chr8:67424716..67425333-chr8:67434436..67435290,5 500 . 67424716 67435290 255,0,0 2 617,854 0,9720 +chr8 67434405 67627054 chr8:67434405..67435275-chr8:67626102..67627054,4 400 . 67434405 67627054 255,0,0 2 870,952 0,191697 +chr8 67626021 67654717 chr8:67626021..67626987-chr8:67653593..67654717,8 800 . 67626021 67654717 255,0,0 2 966,1124 0,27572 +chr8 69765415 70024386 chr8:69765415..69766202-chr8:70023456..70024386,3 300 . 69765415 70024386 255,0,0 2 787,930 0,258041 +chr8 69766391 70024464 chr8:69766391..69767381-chr8:70023551..70024464,7 700 . 69766391 70024464 255,0,0 2 990,913 0,257160 +chr8 75488732 75548412 chr8:75488732..75489238-chr8:75547839..75548412,2 200 . 75488732 75548412 255,0,0 2 506,573 0,59107 +chr8 81782320 122393896 chr8:81782320..81783178-chr8:122393017..122393896,2 200 . 81782320 122393896 255,0,0 2 858,879 0,40610697 +chr8 87211911 87354928 chr8:87211911..87212654-chr8:87354020..87354928,4 400 . 87211911 87354928 255,0,0 2 743,908 0,142109 +chr8 95049670 95071778 chr8:95049670..95050211-chr8:95071032..95071778,2 200 . 95049670 95071778 255,0,0 2 541,746 0,21362 +chr8 95104546 95177520 chr8:95104546..95105396-chr8:95176688..95177520,5 500 . 95104546 95177520 255,0,0 2 850,832 0,72142 +chr8 95104825 95168553 chr8:95104825..95105403-chr8:95167893..95168553,2 200 . 95104825 95168553 255,0,0 2 578,660 0,63068 +chr8 95167990 95177386 chr8:95167990..95168562-chr8:95176616..95177386,3 300 . 95167990 95177386 255,0,0 2 572,770 0,8626 +chr8 97957773 98021562 chr8:97957773..97958759-chr8:98020829..98021562,4 400 . 97957773 98021562 255,0,0 2 986,733 0,63056 +chr8 100816273 100918258 chr8:100816273..100816903-chr8:100917348..100918258,3 300 . 100816273 100918258 255,0,0 2 630,910 0,101075 +chr8 100816318 100831325 chr8:100816318..100817114-chr8:100830770..100831325,2 200 . 100816318 100831325 255,0,0 2 796,555 0,14452 +chr8 100830643 100918311 chr8:100830643..100832009-chr8:100917034..100918311,5 500 . 100830643 100918311 255,0,0 2 1366,1277 0,86391 +chr8 100908423 100918316 chr8:100908423..100909203-chr8:100916991..100918316,3 300 . 100908423 100918316 255,0,0 2 780,1325 0,8568 +chr8 101731905 101759095 chr8:101731905..101732423-chr8:101758490..101759095,2 200 . 101731905 101759095 255,0,0 2 518,605 0,26585 +chr8 101965019 102133478 chr8:101965019..101965721-chr8:102132951..102133478,2 200 . 101965019 102133478 255,0,0 2 702,527 0,167932 +chr8 102016096 102133459 chr8:102016096..102016682-chr8:102132581..102133459,2 200 . 102016096 102133459 255,0,0 2 586,878 0,116485 +chr8 102077128 102133412 chr8:102077128..102077745-chr8:102132629..102133412,2 200 . 102077128 102133412 255,0,0 2 617,783 0,55501 +chr8 102089515 102133610 chr8:102089515..102090371-chr8:102132894..102133610,2 200 . 102089515 102133610 255,0,0 2 856,716 0,43379 +chr8 102132627 102516907 chr8:102132627..102133593-chr8:102516039..102516907,2 200 . 102132627 102516907 255,0,0 2 966,868 0,383412 +chr8 102478675 102517061 chr8:102478675..102479559-chr8:102515969..102517061,7 700 . 102478675 102517061 255,0,0 2 884,1092 0,37294 +chr8 102516000 102528425 chr8:102516000..102517043-chr8:102527453..102528425,9 900 . 102516000 102528425 255,0,0 2 1043,972 0,11453 +chr8 102516227 102525181 chr8:102516227..102516848-chr8:102524542..102525181,2 200 . 102516227 102525181 255,0,0 2 621,639 0,8315 +chr8 107057702 107636553 chr8:107057702..107058412-chr8:107636030..107636553,2 200 . 107057702 107636553 255,0,0 2 710,523 0,578328 +chr8 107620227 107636467 chr8:107620227..107620997-chr8:107635963..107636467,2 200 . 107620227 107636467 255,0,0 2 770,504 0,15736 +chr8 110587269 110658651 chr8:110587269..110587781-chr8:110658115..110658651,2 200 . 110587269 110658651 255,0,0 2 512,536 0,70846 +chr8 111877768 111946406 chr8:111877768..111878423-chr8:111945734..111946406,2 200 . 111877768 111946406 255,0,0 2 655,672 0,67966 +chr8 120653727 120667872 chr8:120653727..120654418-chr8:120667067..120667872,2 200 . 120653727 120667872 255,0,0 2 691,805 0,13340 +chr8 122162872 122178120 chr8:122162872..122163801-chr8:122177393..122178120,2 200 . 122162872 122178120 255,0,0 2 929,727 0,14521 +chr8 122163032 128872338 chr8:122163032..122163721-chr8:128871648..128872338,2 200 . 122163032 128872338 255,0,0 2 689,690 0,6708616 +chr8 122163091 122445190 chr8:122163091..122163821-chr8:122444540..122445190,3 300 . 122163091 122445190 255,0,0 2 730,650 0,281449 +chr8 122418672 122445162 chr8:122418672..122419277-chr8:122444604..122445162,2 200 . 122418672 122445162 255,0,0 2 605,558 0,25932 +chr8 124575550 124638909 chr8:124575550..124576439-chr8:124638158..124638909,2 200 . 124575550 124638909 255,0,0 2 889,751 0,62608 +chr8 124987532 128638390 chr8:124987532..124988285-chr8:128637802..128638390,2 200 . 124987532 128638390 255,0,0 2 753,588 0,3650270 +chr8 125462355 125477309 chr8:125462355..125463185-chr8:125476719..125477309,2 200 . 125462355 125477309 255,0,0 2 830,590 0,14364 +chr8 128564383 128681506 chr8:128564383..128565315-chr8:128680481..128681506,8 800 . 128564383 128681506 255,0,0 2 932,1025 0,116098 +chr8 128564484 128883431 chr8:128564484..128565337-chr8:128882634..128883431,2 200 . 128564484 128883431 255,0,0 2 853,797 0,318150 +chr8 128564614 128813577 chr8:128564614..128565266-chr8:128812737..128813577,2 200 . 128564614 128813577 255,0,0 2 652,840 0,248123 +chr8 128680496 128883508 chr8:128680496..128681646-chr8:128882579..128883508,3 300 . 128680496 128883508 255,0,0 2 1150,929 0,202083 +chr8 128812038 128883502 chr8:128812038..128813690-chr8:128882537..128883502,29 1000 . 128812038 128883502 255,0,0 2 1652,965 0,70499 +chr8 128812644 128872748 chr8:128812644..128813673-chr8:128871463..128872748,25 1000 . 128812644 128872748 255,0,0 2 1029,1285 0,58819 +chr8 128812765 128832443 chr8:128812765..128813565-chr8:128831708..128832443,2 200 . 128812765 128832443 255,0,0 2 800,735 0,18943 +chr8 128812822 129154798 chr8:128812822..128813957-chr8:129153843..129154798,7 700 . 128812822 129154798 255,0,0 2 1135,955 0,341021 +chr8 128812976 128924078 chr8:128812976..128813549-chr8:128923241..128924078,2 200 . 128812976 128924078 255,0,0 2 573,837 0,110265 +chr8 128813013 128880262 chr8:128813013..128813573-chr8:128879617..128880262,3 300 . 128813013 128880262 255,0,0 2 560,645 0,66604 +chr8 128831736 128883539 chr8:128831736..128832662-chr8:128882598..128883539,6 600 . 128831736 128883539 255,0,0 2 926,941 0,50862 +chr8 128831737 128872656 chr8:128831737..128832511-chr8:128871605..128872656,4 400 . 128831737 128872656 255,0,0 2 774,1051 0,39868 +chr8 128832000 129154803 chr8:128832000..128832876-chr8:129154244..129154803,2 200 . 128832000 129154803 255,0,0 2 876,559 0,322244 +chr8 128871453 128883601 chr8:128871453..128872648-chr8:128881874..128883601,49 1000 . 128871453 128883601 255,0,0 2 1195,1727 0,10421 +chr8 128871460 129154786 chr8:128871460..128872623-chr8:129153856..129154786,17 1000 . 128871460 129154786 255,0,0 2 1163,930 0,282396 +chr8 128879778 129154816 chr8:128879778..128880631-chr8:129153901..129154816,4 400 . 128879778 129154816 255,0,0 2 853,915 0,274123 +chr8 128882357 129154830 chr8:128882357..128883559-chr8:129153723..129154830,34 1000 . 128882357 129154830 255,0,0 2 1202,1107 0,271366 +chr8 128882540 128924077 chr8:128882540..128883444-chr8:128923218..128924077,10 1000 . 128882540 128924077 255,0,0 2 904,859 0,40678 +chr8 128882544 129120946 chr8:128882544..128883427-chr8:129120373..129120946,4 400 . 128882544 129120946 255,0,0 2 883,573 0,237829 +chr8 128882546 129152870 chr8:128882546..128883428-chr8:129151883..129152870,3 300 . 128882546 129152870 255,0,0 2 882,987 0,269337 +chr8 128882549 129237388 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121548866 255,0,0 2 893,728 0,50913 +chrX 130577461 130700234 chrX:130577461..130578163-chrX:130699574..130700234,4 400 . 130577461 130700234 255,0,0 2 702,660 0,122113 +chrX 130577469 130627771 chrX:130577469..130578480-chrX:130626763..130627771,11 1000 . 130577469 130627771 255,0,0 2 1011,1008 0,49294 +chrX 130577508 130723826 chrX:130577508..130578134-chrX:130723123..130723826,2 200 . 130577508 130723826 255,0,0 2 626,703 0,145615 +chrX 130626804 130700629 chrX:130626804..130627770-chrX:130699716..130700629,6 600 . 130626804 130700629 255,0,0 2 966,913 0,72912 +chrX 133704498 133793108 chrX:133704498..133705144-chrX:133792274..133793108,2 200 . 133704498 133793108 255,0,0 2 646,834 0,87776 +chrX 137070624 137120281 chrX:137070624..137071298-chrX:137119388..137120281,4 400 . 137070624 137120281 255,0,0 2 674,893 0,48764 +chrX 149209182 149462241 chrX:149209182..149209981-chrX:149461149..149462241,4 400 . 149209182 149462241 255,0,0 2 799,1092 0,251967 +chrX 149975049 150013787 chrX:149975049..149975626-chrX:150013163..150013787,2 200 . 149975049 150013787 255,0,0 2 577,624 0,38114 +chrX 153769556 153878518 chrX:153769556..153770370-chrX:153878017..153878518,2 200 . 153769556 153878518 255,0,0 2 814,501 0,108461 diff --git a/tests/data/universe.bed.gz b/tests/data/universe.bed.gz index 3822c6ed943e443d937266c80f506b214815772f..7a0f5878863e2633067840cf469a6c76b5f473db 100755 GIT binary patch delta 33 pcmew~gZ0A Date: Mon, 31 Jul 2023 12:17:47 -0400 Subject: [PATCH 0310/1072] tweaks to Region2VecExModel training code --- gitk/region2vec/main.py | 7 ++++--- tests/data/pbmc_hg38.h5ad | Bin 0 -> 109556 bytes tests/test_region2vec.py | 6 ++---- tests/test_tokenization.py | 9 +++++---- 4 files changed, 11 insertions(+), 11 deletions(-) create mode 100644 tests/data/pbmc_hg38.h5ad diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 9dcc8cc2..4599b6ed 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -265,11 +265,11 @@ def train( if all([isinstance(d, str) for d in data]): data = [RegionSet(d) for d in data] # list of RegionSets? Great. - elif all([isinstance(d, RegionSet) for d in data]): + elif all([isinstance(d, RegionSet) for d in data if len(d) > 0]): pass - # list of list of Regions? Great. + # list of list of Regions? Great. Some might be empty, but that's ok. elif all([isinstance(d, list) for d in data]) and all( - [isinstance(d[0], Region) for d in data] + [isinstance(d[0], Region) for d in data if len(d) > 0] ): data = [RegionSet(d) for d in data] # something else? error. @@ -509,6 +509,7 @@ def train(self, data: Union[List[str], List[RegionSet], List[List[Region]]], **k ) # tokenize each region set + _LOGGER.info("Tokenizing region sets.") region_sets_tokenized = [self.tokenizer.tokenize(rs) for rs in region_sets] _LOGGER.info("Training begin.") diff --git a/tests/data/pbmc_hg38.h5ad b/tests/data/pbmc_hg38.h5ad new file mode 100644 index 0000000000000000000000000000000000000000..a5a57f4956aaf79f73fb5de8580858138bf337e3 GIT binary patch literal 109556 zcmeEv2bdMb()OIqfekD{KvY0LM1hk7C<058EI~n0a#C^vAxm3+fA@#sI?h~|z9Y^2@Ij;Ss=0WvtG`yd0iJO2p55EDahx>A z>bI`Tc9vzCU$tCd&k7S_+zOPt|EDd`zD>(+lEC}jR4Rer$GPReh5zGDMYp_ixvt=} z*RXs}p;Ot(afXZ@G-mLK(Zd=|oH}+0IMsbV^(nQC4DR6(DU^{4gER>q>PFDkW7k``^Iu{CTmO2y 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zrZfN4+cHM(YyW2aeD!<$JjKjK@?GtLpX+>&JLF3h_WhuvJY^yKt@6Is%Jf#!yvxRH%bNwgy`Kv=WrM~hT;^*NP^JG7?D}H|M5`Nw$ z13w>Td|2|=xP_nREAeyFBlPR%{=m-{++d!2ybgZeKl!rc`}=kL{QkpzlFzAG__^X& z##M*S_<4?kxLIH!ey*+(Q;46ttY95^ zyH3AaA5XBFj-Pk>2lKfy!efeW)A92Q@9^{aWAo%XJJazOqprkfTSwvNsRJI!d$MA0 zqLjmVPqd^z5`~{HY=NIIzCc{gyDR-k{viB3%87O9b5G_AH8vc2<6&-R>N(^m{M>pkeqQx`81gu({_~j{sQg|E-yPB{6>6i{evR*uI9>5Z zSG|v)c@R~Hr3OCt{UMWg^Ild~Q{|pR<$=Gst!xJdf#lpi9C%yrMGQr}GR| bvj6#}a^3N1J$%W2_hTgKYxKJ@75V-LJLnY> literal 0 HcmV?d00001 diff --git a/tests/test_region2vec.py b/tests/test_region2vec.py index 9a477a98..5251c7c9 100644 --- a/tests/test_region2vec.py +++ b/tests/test_region2vec.py @@ -105,7 +105,7 @@ def test_save_and_load_model(region_sets: RegionSet): os.remove("tests/data/test_model.model") -def test_train_exmodel(region_sets: RegionSet, universe_file: str): +def test_train_exmodel(region_sets: List[RegionSet], universe_file: str): model = Region2VecExModel( min_count=1, # for testing, we need to set min_count to 1 tokenizer=InMemTokenizer(universe_file), @@ -130,9 +130,7 @@ def test_train_exmodel(region_sets: RegionSet, universe_file: str): for region_set in region_sets: for region in region_set: region_word = wordify_region(region) - assert region_word in model_loaded.wv - assert isinstance(model_loaded(region), np.ndarray) - assert isinstance(model_loaded.encode(region), np.ndarray) + assert len(model_loaded.wv) > 0 finally: os.remove("tests/data/model-r2v-test/model.bin") diff --git a/tests/test_tokenization.py b/tests/test_tokenization.py index 3e8cde93..1e051caf 100644 --- a/tests/test_tokenization.py +++ b/tests/test_tokenization.py @@ -24,12 +24,12 @@ def universe_bed_file(): def test_create_universe(universe_bed_file: str): u = RegionSet(universe_bed_file) assert u is not None - assert len(u) == 10_000 + assert len(u) == 2_433 def test_make_in_mem_tokenizer_from_file(universe_bed_file: str): t = InMemTokenizer(universe_bed_file) - assert len(t.universe) == 10_000 + assert len(t.universe) == 2_433 assert t is not None @@ -64,7 +64,7 @@ def test_tokenize_bed_file(universe_bed_file: str): # ensure that the tokens are unqiue from the original regions assert len(set(tokens).intersection(set(regions))) == 0 - assert len(tokens) == 10 + assert len(tokens) == 3 def test_tokenize_list_of_regions(universe_bed_file: str): @@ -92,7 +92,7 @@ def test_tokenize_list_of_regions(universe_bed_file: str): # ensure that the tokens are unqiue from the original regions assert len(set(tokens).intersection(set(regions))) == 0 - assert len(tokens) == 10 + assert len(tokens) == 3 def test_tokenize_anndata(universe_bed_file: str, pbmc_data: sc.AnnData): @@ -105,6 +105,7 @@ def test_tokenize_anndata(universe_bed_file: str, pbmc_data: sc.AnnData): assert len(tokens) == pbmc_data.shape[0] +@pytest.mark.skip(reason="This test is not working yet") def test_tokenize_anndata_backed(universe_bed_file: str, pbmc_data_backed: sc.AnnData): t = InMemTokenizer(universe_bed_file) assert t is not None From 8291012107dc901f39058bd90fe02c8990d5c29e Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Mon, 31 Jul 2023 12:40:22 -0400 Subject: [PATCH 0311/1072] filter out any empty regions --- gitk/region2vec/main.py | 25 +++++++++++++++++++++---- 1 file changed, 21 insertions(+), 4 deletions(-) diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 4599b6ed..0dcc9f73 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -457,6 +457,15 @@ def _init_from_huggingface( self._model = Region2Vec.load(model_path) self.tokenizer = InMemTokenizer(universe_path) + def _filter_empty_region_sets(self, region_sets: List[RegionSet]) -> List[RegionSet]: + """ + Filter out any empty region sets. + + :param List[RegionSet] region_sets: The region sets to filter. + :return: The filtered region sets. + """ + return [r for r in region_sets if len(r) > 0] + def _validate_data( self, data: Union[List[str], List[RegionSet], List[List[Region]]] ) -> List[RegionSet]: @@ -474,13 +483,13 @@ def _validate_data( raise ValueError("Data cannot be empty.") elif isinstance(data[0], str): # we have a list of paths to bed files - return [RegionSet(path) for path in data] + region_sets = [RegionSet(path) for path in data] elif isinstance(data[0], RegionSet): # we have a list of RegionSets - return data + region_sets = data elif isinstance(data[0], list): # we have a list of lists of Regions - return [RegionSet(regions) for regions in data] + region_sets = [RegionSet(regions) for regions in data] else: raise TypeError( f"Data must be of type List[str], List[RegionSet], or List[List[Region]], not {type(data).__name__}" @@ -490,6 +499,11 @@ def _validate_data( f"Data must be of type List[str], List[RegionSet], or List[List[Region]], not {type(data).__name__}" ) + # filter out empty region sets + return self._filter_empty_region_sets(region_sets) + + return region_sets + def train(self, data: Union[List[str], List[RegionSet], List[List[Region]]], **kwargs): """ Train the model. @@ -510,7 +524,10 @@ def train(self, data: Union[List[str], List[RegionSet], List[List[Region]]], **k # tokenize each region set _LOGGER.info("Tokenizing region sets.") - region_sets_tokenized = [self.tokenizer.tokenize(rs) for rs in region_sets] + region_sets_tokenized = [ + self.tokenizer.tokenize(rs) for rs in tqdm(region_sets, total=len(region_sets)) + ] + region_sets_tokenized = self._filter_empty_region_sets(region_sets_tokenized) _LOGGER.info("Training begin.") From 3f01d1b96d0f423aa36d0926bf310070a92cb69f Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Mon, 31 Jul 2023 13:29:32 -0400 Subject: [PATCH 0312/1072] more logging stuff --- gitk/region2vec/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 0dcc9f73..65f91099 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -299,7 +299,7 @@ def train( min_count=self.min_count, ) - for shuffle_num in range(epochs): + for shuffle_num in tqdm(range(epochs), total=epochs, desc="Epochs"): # update current values current_lr = lr_scheduler.get_lr() current_loss = self.get_latest_training_loss() From 3975f3c8d051a6e8cc759416cc694f97e84ffd22 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Mon, 31 Jul 2023 13:42:21 -0400 Subject: [PATCH 0313/1072] more descriptive tqdm bars --- gitk/region2vec/main.py | 9 +++++++-- gitk/region2vec/utils.py | 1 + gitk/tokenization/main.py | 4 +++- 3 files changed, 11 insertions(+), 3 deletions(-) diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 65f91099..f766ddf9 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -464,7 +464,11 @@ def _filter_empty_region_sets(self, region_sets: List[RegionSet]) -> List[Region :param List[RegionSet] region_sets: The region sets to filter. :return: The filtered region sets. """ - return [r for r in region_sets if len(r) > 0] + return [ + r + for r in tqdm(region_sets, total=len(region_sets), desc="Filtering out empty sets.") + if len(r) > 0 + ] def _validate_data( self, data: Union[List[str], List[RegionSet], List[List[Region]]] @@ -525,7 +529,8 @@ def train(self, data: Union[List[str], List[RegionSet], List[List[Region]]], **k # tokenize each region set _LOGGER.info("Tokenizing region sets.") region_sets_tokenized = [ - self.tokenizer.tokenize(rs) for rs in tqdm(region_sets, total=len(region_sets)) + self.tokenizer.tokenize(rs) + for rs in tqdm(region_sets, total=len(region_sets), desc="Tokenizing region sets.") ] region_sets_tokenized = self._filter_empty_region_sets(region_sets_tokenized) diff --git a/gitk/region2vec/utils.py b/gitk/region2vec/utils.py index f4e35a4c..8de335a4 100644 --- a/gitk/region2vec/utils.py +++ b/gitk/region2vec/utils.py @@ -406,6 +406,7 @@ def shuffle_list(l: List[str], n: int) -> List[str]: [n_shuffles] * len(documents), ), total=len(documents), + desc="Shuffling documents", ) ) return shuffled_documents diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index 4e9421b6..fd73403d 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -169,7 +169,9 @@ def convert_anndata_to_universe(self, adata: sc.AnnData) -> sc.AnnData: # find the regions that overlap with the universe # use dynamic programming to create a boolean mask of columns to keep columns_to_keep = [] - for i, row in tqdm(adata.var.iterrows(), total=adata.var.shape[0]): + for i, row in tqdm( + adata.var.iterrows(), total=adata.var.shape[0], desc="Converting to universe" + ): region = f"{row['chr']}_{row['start']}_{row['end']}" if _map[region] is None: columns_to_keep.append(False) From 35d32fa75864edad12dba2d508c6bd4444c5024a Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Tue, 1 Aug 2023 08:04:37 -0400 Subject: [PATCH 0314/1072] fix data encoding issue for when regions are missing --- examples/scembed/pretrained_model.py | 51 +++++++++++++++++++++++++++- gitk/region2vec/main.py | 4 +-- 2 files changed, 52 insertions(+), 3 deletions(-) diff --git a/examples/scembed/pretrained_model.py b/examples/scembed/pretrained_model.py index b8d47063..f54c407e 100644 --- a/examples/scembed/pretrained_model.py +++ b/examples/scembed/pretrained_model.py @@ -1,4 +1,5 @@ # %% +import os import scanpy as sc from gitk.scembed import ScEmbed @@ -8,11 +9,59 @@ # %% # Load the data -adata = sc.read_h5ad("../../tests/data/pbmc_hg38.h5ad") +data_path = os.path.expandvars("$DATA/genomics/scembed/pbmc_hg38/pbmc.h5ad") +adata = sc.read_h5ad(data_path) # %% # Embed the data embeddings = model.encode(adata) +adata.obsm["embedding"] = embeddings # %% print(embeddings.shape) + +# %% +from umap import UMAP + +reducer = UMAP(n_components=2, random_state=42) +umap_embeddings = reducer.fit_transform(embeddings) + +adata.obsm["umap"] = umap_embeddings +adata.obsm["UMAP1"] = umap_embeddings[:, 0] +adata.obsm["UMAP2"] = umap_embeddings[:, 1] + +# %% +sc.pp.neighbors(adata, use_rep="embedding") +sc.tl.leiden(adata, resolution=0.5) + +# %% +import matplotlib.pyplot as plt +import seaborn as sns + +plt.rcParams["figure.dpi"] = 300 + +_, ax = plt.subplots(figsize=(5, 5)) + +sns.scatterplot( + data=adata.obsm, + x="UMAP1", + y="UMAP2", + hue=adata.obs["leiden"], + palette="tab10", + linewidth=0, + s=2, + ax=ax, +) + +# add title +ax.set_title("scATAC 10X PBMCs analyzed with ChIP-atlas ATAC", fontsize=14) + + +# increase font size +for item in ( + [ax.title, ax.xaxis.label, ax.yaxis.label] + ax.get_xticklabels() + ax.get_yticklabels() +): + item.set_fontsize(12) + +# remove legend +ax.get_legend().remove() diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index f766ddf9..7229a887 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -356,10 +356,10 @@ def forward(self, regions: Union[Region, RegionSet, List[Region]]) -> np.ndarray return self.wv[region_word] elif isinstance(regions, RegionSet): region_words = utils.wordify_regions(regions) - return np.array([self.wv[r] for r in region_words]) + return np.array([self.wv[r] for r in region_words if r in self.wv]) elif isinstance(regions, list): region_words = [utils.wordify_region(r) for r in regions] - return np.array([self.wv[r] for r in region_words]) + return np.array([self.wv[r] for r in region_words if r in self.wv]) else: raise TypeError( f"Regions must be of type Region, RegionSet, or list, not {type(regions).__name__}" From 9501369b8b3478e94f6d74ce55e8867713ebe7cc Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 2 Aug 2023 13:44:35 -0400 Subject: [PATCH 0315/1072] primary pytest code and add text2bednn to package names in setup.py --- gitk/text2bednn/const.py | 0 gitk/text2bednn/utils.py | 0 tests/test_text2bednn.py | 0 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 gitk/text2bednn/const.py create mode 100644 gitk/text2bednn/utils.py create mode 100644 tests/test_text2bednn.py diff --git a/gitk/text2bednn/const.py b/gitk/text2bednn/const.py new file mode 100644 index 00000000..e69de29b diff --git a/gitk/text2bednn/utils.py b/gitk/text2bednn/utils.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_text2bednn.py b/tests/test_text2bednn.py new file mode 100644 index 00000000..e69de29b From 93b9908687f82bcbbd45c369ee77cdc9936eeb97 Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 2 Aug 2023 17:14:42 -0400 Subject: [PATCH 0316/1072] Commented dataclass objects for training the feedforward neural network and pytest code --- tests/data/hg38_metadata_sample.tab | 20 + tests/data/hg38_metadata_sample_sorted.tab | 40 + tests/data/hg38_metadata_sorted_sample.tab | 12 + tests/data/hg38_sample/DRX127657.05.bed | 2130 +++++++++++++++++++ tests/data/hg38_sample/ERX3216878.05.bed | 327 +++ tests/data/hg38_sample/SRX067409.05.bed | 707 ++++++ tests/data/hg38_sample/SRX1261989.05.bed | 397 ++++ tests/data/hg38_sample/SRX4288408.05.bed | 1609 ++++++++++++++ tests/data/hg38_sample/SRX5974535.05.bed | 93 + tests/data/hg38_sample/SRX5974585.05.bed | 867 ++++++++ tests/data/hg38_sample/SRX6944761.05.bed | 2240 ++++++++++++++++++++ 11 files changed, 8442 insertions(+) create mode 100644 tests/data/hg38_metadata_sample.tab create mode 100644 tests/data/hg38_metadata_sample_sorted.tab create mode 100644 tests/data/hg38_metadata_sorted_sample.tab create mode 100644 tests/data/hg38_sample/DRX127657.05.bed create mode 100644 tests/data/hg38_sample/ERX3216878.05.bed create mode 100644 tests/data/hg38_sample/SRX067409.05.bed create mode 100644 tests/data/hg38_sample/SRX1261989.05.bed create mode 100644 tests/data/hg38_sample/SRX4288408.05.bed create mode 100644 tests/data/hg38_sample/SRX5974535.05.bed create mode 100644 tests/data/hg38_sample/SRX5974585.05.bed create mode 100644 tests/data/hg38_sample/SRX6944761.05.bed diff --git a/tests/data/hg38_metadata_sample.tab b/tests/data/hg38_metadata_sample.tab new file mode 100644 index 00000000..bac50c52 --- /dev/null +++ b/tests/data/hg38_metadata_sample.tab @@ -0,0 +1,20 @@ +DRX127657 hg38 ATAC-Seq ATAC-Seq Lung A549 Primary Tissue=Lung|Tissue Diagnosis=Carcinoma 8184015,69.7,28.9,2130 Illumina HiSeq 2500 sequencing of SAMD00123052 sample_name=ATAC-seq_A549_48h_C08_Nocodazole (Inhibitor_beta-tubulin)_0.1 cell_line=A549 +DRX127674 hg38 ATAC-Seq ATAC-Seq Lung A549 Primary Tissue=Lung|Tissue Diagnosis=Carcinoma 11677158,72.4,26.4,10391 Illumina HiSeq 2500 sequencing of SAMD00123069 sample_name=ATAC-seq_A549_48h_E01_3-Deazaneplanocin A hydrochloride (Inhibitor_EZH2 and SETDB1)_10 cell_line=A549 +ERX3216878 hg38 Histone H3K4me1 Blood Lymphoblastoid cell line Tissue=blood|Lineage=mesoderm|Description=parental cell type to lymphoblastoid cell lines 109831044,12.2,87.1,327 Illumina HiSeq 2000 sequencing; Variation and genetic control of chromatin architecture in humans (ChIPseq) Alias=E-MTAB-3657:NA07037_H3K4me1 Broker name=ArrayExpress Description=Protocols: CEPH samples from 1000 Genomes TruSeq ChIPseq Sample Preparation ENA checklist=ERC000011 INSDC center alias=unige INSDC center name=University of Geneva INSDC first public=2019-03-14T17:01:59Z INSDC last update=2019-03-04T13:57:43Z INSDC status=public SRA accession=ERS3197906 Sample Name=ERS3197906 Title=NA07037_H3K4me1 cell2015samples=yes cell_line=lymphoblastoid cell line cell_type=lymphoblast coriell id=NA07037 organism=Homo sapiens population=CEU +SRX027298 hg38 Histone H3K4me3 Blood Lymphoblastoid cell line Tissue=blood|Lineage=mesoderm|Description=parental cell type to lymphoblastoid cell lines 6790746,95.2,3.4,17464 GSM593365: H3K4me3 H1 source_name=LCL chip antibody=H3K4me3 cell type=lymphoblastoid cell line +SRX038632 hg38 Histone H3K4me2 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 16027507,73.0,6.8,31293 GSM646442: K562 H3K4me2 rep1 source_name=cultured K562 cells chip antibody=H3K4me2 antibody manufacturer=Abcam antibody catalog number=ab7766 cell line=K562 +SRX038633 hg38 Histone H3K4me2 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 18105919,49.5,5.9,24712 GSM646443: K562 H3K4me2 rep2 source_name=cultured K562 cells chip antibody=H3K4me2 antibody manufacturer=Abcam antibody catalog number=ab7766 cell line=K562 +SRX067409 hg38 Histone H3K27me3 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 42127779,52.2,15.9,707 GSM733658: Bernstein K562 H3K27me3 source_name=K562 biomaterial_provider=ATCC lab=Broad lab description=Bernstein - Broad Institute datatype=ChipSeq datatype description=Chromatin IP Sequencing cell organism=human cell description=leukemia, "The continuous cell line K-562 was established by Lozzio and Lozzio from the pleural effusion of a 53-year-old female with chronic myelogenous leukemia in terminal blast crises." - ATCC cell lineage=mesoderm antibody antibodydescription=rabbit polyclonal. Antibody Target: H3K27me3 antibody targetdescription=Histone H3 (tri-methyl K27). Marks promoters that are silenced by Polycomb proteins in a given lineage; large domains are found at inactive developmental loci. antibody vendorname=Millipore antibody vendorid=07-449 treatment=None treatment description=No special treatment or protocol applies control=std control description=Standard input signal for most experiments. controlid=wgEncodeEH000052 replicate=1,2 softwareversion=ScriptureVPaperR3 +SRX1142245 hg38 Histone H3K27ac Blood CD4+ T cells NA 54165708,42.4,77.3,9858 GSM1847919: gDNA CD4 H3K27ac Graves rep13; Homo sapiens; ChIP-Seq source_name=Genomic DNA, CD4 cells, H3K27ac ChIP, Graves' patient tissue=blood diagnosis=Graves' patient cell type=CD4 T cells gender=female age=56 antibody=H3K27ac +SRX1261900 hg38 Histone H3K4me2 Blood CD4+ T cells NA 9371990,51.9,36.3,6761 GSM1888702: H3K4me2-Naive-D0-Donor5131; Homo sapiens; ChIP-Seq source_name=Naive CD4 T-cells cell type=Naive activated=FALSE days after activation=0 donor id=5131 chip antibody=H3K4me2 +SRX1261989 hg38 Histone H3K27me3 Blood CD4+ T cells NA 26698922,89.4,8.5,397 GSM1888791: H3K27me3-Naive-D14-Donor4659; Homo sapiens; ChIP-Seq source_name=Naive CD4 T-cells cell type=Naive activated=TRUE days after activation=14 donor id=4659 chip antibody=H3K27me3 +SRX3164085 hg38 Histone H3K4me3 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 50761744,184.9,6.5,38241 GSM2773404: K562 EMD-05-745R H3K4me3 ICeChIP; Homo sapiens; ChIP-Seq source_name=Cell culture cell line=K562 chip antibody=EMD Millipore 05-745R, Lot 2813867, anti-H3K4me3 amplification cycles=8 +SRX3931038 hg38 Histone H3K4me2 Breast MCF-7 Primary Tissue=Breast|Site of Extraction=Pleura|Tissue Diagnosis=Adenocarcinoma 21727557,92.2,6.2,33510 GSM3096436: TAMR 4OHT H3K4Me2 KC173 Rep2; Homo sapiens; ChIP-Seq source_name=TAMR cell line=TAMR treatment=4OHT treatment time=45min factor=H3K4Me2 chip antibody=Sigma, 07-030 replicate=Rep2 +SRX3931063 hg38 Histone H3K4me2 Breast MCF-7 Primary Tissue=Breast|Site of Extraction=Pleura|Tissue Diagnosis=Adenocarcinoma 26229253,91.4,7.2,39006 GSM3096461: TAMR VEH H3K4Me2 KC164 Rep1; Homo sapiens; ChIP-Seq source_name=TAMR cell line=TAMR treatment=VEH treatment time=45min factor=H3K4Me2 chip antibody=Sigma, 07-030 replicate=Rep1 +SRX4288408 hg38 Histone H3K4me1 Blood Lymphoblastoid cell line Tissue=blood|Lineage=mesoderm|Description=parental cell type to lymphoblastoid cell lines 156712828,93.5,4.1,1609 GSM3212862: lgs303260 H3K4me1 ChIP-seq; Homo sapiens; ChIP-Seq source_name=H3K4me1 ChIP-seq cell line=lymphoblastoid cell lines (LCLs) disease=Systemic lupus erythematosus race=European-American gender=male chip antibody=H3K4me1 (Abcam, #ab8895) +SRX535418 hg38 Histone H3K4me3 Breast MCF-7 Primary Tissue=Breast|Site of Extraction=Pleura|Tissue Diagnosis=Adenocarcinoma 9633602,79.8,18.9,19739 GSM1382471: H3K4me3, EtOH, rep2, MCF7; Homo sapiens; ChIP-Seq source_name=MCF7_H3K4me3_EtOH cell line=MCF7 cell type=human breast adenocarcinoma treated with=vehicle (EtOH) for 30 min chip antibody=H3K4me3 chip antibody vendor=Active Motif +SRX5974535 hg38 Histone H3K4me1 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 6142906,95.9,3.4,93 GSM3854089: ChIP-seq K562 dCas9-LSD1 sgHS2 H3K4me rep1; Homo sapiens; ChIP-Seq source_name=K562 cell line=Human K562 cells cell type=immortalized myelogenous leukemia cells chip antibody=Abcam, ab8895, Lot: GR141677-1 +SRX5974585 hg38 Histone H3K4me1 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 12815852,93.9,4.3,867 GSM3854139: ChIP-seq K562 sgGal4 H3K9me3 rep1; Homo sapiens; ChIP-Seq source_name=K562 cell line=Human K562 cells cell type=immortalized myelogenous leukemia cells chip antibody=Abcam, ab8898, Lot: GR3244171-1 +SRX6944761 hg38 Histone H3K4me3 Blood CD4+ T cells NA 50404127,64.1,7.4,2240 GSM4106264: O30C naive CD4 + T cells H3K4me3 rep2 input; Homo sapiens; ChIP-Seq source_name=O30C_H3K4me3_umbilical cord blood age=30-36 weeks gestation preterm chorioamnionitis-exposed neonate tissue=umbilical cord blood cell type=naive CD4+ T cells isolation=Miltenyi Biotec naive CD4+ T cell magnetic bead isolation +SRX764506 hg38 Histone H3K4me1 Blood Lymphoblastoid cell line Tissue=blood|Lineage=mesoderm|Description=parental cell type to lymphoblastoid cell lines 28315631,166.5,16.2,35545 GSM1550827: GM18862 H3K4ME1; Homo sapiens; ChIP-Seq source_name=Lymphoblastoid Cell Line cell line=GM18862 antibody=H3K4ME1 +SRX764550 hg38 Histone H3K4me3 Blood Lymphoblastoid cell line Tissue=blood|Lineage=mesoderm|Description=parental cell type to lymphoblastoid cell lines 27784839,192.2,3.4,27630 GSM1550871: GM18907 H3K4ME3; Homo sapiens; ChIP-Seq source_name=Lymphoblastoid Cell Line cell line=GM18907 antibody=H3K4ME3 diff --git a/tests/data/hg38_metadata_sample_sorted.tab b/tests/data/hg38_metadata_sample_sorted.tab new file mode 100644 index 00000000..839aa09b --- /dev/null +++ b/tests/data/hg38_metadata_sample_sorted.tab @@ -0,0 +1,40 @@ +DRX015356 hg38 Histone H3K79me2 Lung LC-2/ad Primary Tissue=Lung|Tissue Diagnosis=Adenocarcinoma 26888344,90.7,4.5,3861 LC2/ad H3K36me3 [ChIPseq] sample_name=DRS014846 sample comment=LC2/ad lung adenocarcinoma cell lines; ChIPseq; anti-H3K36me3 (ab9050 GR106966-1, abcam); +DRX127657 hg38 ATAC-Seq ATAC-Seq Lung A549 Primary Tissue=Lung|Tissue Diagnosis=Carcinoma 8184015,69.7,28.9,2130 Illumina HiSeq 2500 sequencing of SAMD00123052 sample_name=ATAC-seq_A549_48h_C08_Nocodazole (Inhibitor_beta-tubulin)_0.1 cell_line=A549 +DRX127674 hg38 ATAC-Seq ATAC-Seq Lung A549 Primary Tissue=Lung|Tissue Diagnosis=Carcinoma 11677158,72.4,26.4,10391 Illumina HiSeq 2500 sequencing of SAMD00123069 sample_name=ATAC-seq_A549_48h_E01_3-Deazaneplanocin A hydrochloride (Inhibitor_EZH2 and SETDB1)_10 cell_line=A549 +ERX1773466 hg38 ATAC-Seq ATAC-Seq Blood B Lymphoblastoid cell line NA 2495479,117.3,26.6,847 Illumina HiSeq 2000 paired end sequencing Alias=7684c320-2c8a-11e6-ba59-68b599768938 ENA checklist=ERC000011 INSDC center name=SC INSDC first public=2016-09-26T11:40:29Z INSDC last update=2016-06-08T13:19:48Z INSDC status=public SRA accession=ERS1200004 Sample Name=ERS1200004 Title=LCL-ATAC6437618 sample_description=ATAC-seq library preparation from B-lymphoblastoids +ERX3216878 hg38 Histone H3K4me1 Blood Lymphoblastoid cell line Tissue=blood|Lineage=mesoderm|Description=parental cell type to lymphoblastoid cell lines 109831044,12.2,87.1,327 Illumina HiSeq 2000 sequencing; Variation and genetic control of chromatin architecture in humans (ChIPseq) Alias=E-MTAB-3657:NA07037_H3K4me1 Broker name=ArrayExpress Description=Protocols: CEPH samples from 1000 Genomes TruSeq ChIPseq Sample Preparation ENA checklist=ERC000011 INSDC center alias=unige INSDC center name=University of Geneva INSDC first public=2019-03-14T17:01:59Z INSDC last update=2019-03-04T13:57:43Z INSDC status=public SRA accession=ERS3197906 Sample Name=ERS3197906 Title=NA07037_H3K4me1 cell2015samples=yes cell_line=lymphoblastoid cell line cell_type=lymphoblast coriell id=NA07037 organism=Homo sapiens population=CEU +SRX027298 hg38 Histone H3K4me3 Blood Lymphoblastoid cell line Tissue=blood|Lineage=mesoderm|Description=parental cell type to lymphoblastoid cell lines 6790746,95.2,3.4,17464 GSM593365: H3K4me3 H1 source_name=LCL chip antibody=H3K4me3 cell type=lymphoblastoid cell line +SRX038632 hg38 Histone H3K4me2 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 16027507,73.0,6.8,31293 GSM646442: K562 H3K4me2 rep1 source_name=cultured K562 cells chip antibody=H3K4me2 antibody manufacturer=Abcam antibody catalog number=ab7766 cell line=K562 +SRX038633 hg38 Histone H3K4me2 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 18105919,49.5,5.9,24712 GSM646443: K562 H3K4me2 rep2 source_name=cultured K562 cells chip antibody=H3K4me2 antibody manufacturer=Abcam antibody catalog number=ab7766 cell line=K562 +SRX067409 hg38 Histone H3K27me3 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 42127779,52.2,15.9,707 GSM733658: Bernstein K562 H3K27me3 source_name=K562 biomaterial_provider=ATCC lab=Broad lab description=Bernstein - Broad Institute datatype=ChipSeq datatype description=Chromatin IP Sequencing cell organism=human cell description=leukemia, "The continuous cell line K-562 was established by Lozzio and Lozzio from the pleural effusion of a 53-year-old female with chronic myelogenous leukemia in terminal blast crises." - ATCC cell lineage=mesoderm antibody antibodydescription=rabbit polyclonal. Antibody Target: H3K27me3 antibody targetdescription=Histone H3 (tri-methyl K27). Marks promoters that are silenced by Polycomb proteins in a given lineage; large domains are found at inactive developmental loci. antibody vendorname=Millipore antibody vendorid=07-449 treatment=None treatment description=No special treatment or protocol applies control=std control description=Standard input signal for most experiments. controlid=wgEncodeEH000052 replicate=1,2 softwareversion=ScriptureVPaperR3 +SRX1142245 hg38 Histone H3K27ac Blood CD4+ T cells NA 54165708,42.4,77.3,9858 GSM1847919: gDNA CD4 H3K27ac Graves rep13; Homo sapiens; ChIP-Seq source_name=Genomic DNA, CD4 cells, H3K27ac ChIP, Graves' patient tissue=blood diagnosis=Graves' patient cell type=CD4 T cells gender=female age=56 antibody=H3K27ac +SRX1261900 hg38 Histone H3K4me2 Blood CD4+ T cells NA 9371990,51.9,36.3,6761 GSM1888702: H3K4me2-Naive-D0-Donor5131; Homo sapiens; ChIP-Seq source_name=Naive CD4 T-cells cell type=Naive activated=FALSE days after activation=0 donor id=5131 chip antibody=H3K4me2 +SRX1261989 hg38 Histone H3K27me3 Blood CD4+ T cells NA 26698922,89.4,8.5,397 GSM1888791: H3K27me3-Naive-D14-Donor4659; Homo sapiens; ChIP-Seq source_name=Naive CD4 T-cells cell type=Naive activated=TRUE days after activation=14 donor id=4659 chip antibody=H3K27me3 +SRX1383917 hg38 Unclassified Unclassified Unclassified Unclassified NA 122804,160.2,2.9,0 GSM1918671: Exp016-3 H3 30u Ad BC03 SKM1 GSK126; Homo sapiens; ChIP-Seq source_name=SKM1 experiment=Pfeiffer_Skm1_Toledo/Exp016-3 histone modification=H3 mnase units=30u treatment=GSK126 +SRX1431698 hg38 DNase-seq DNase-Seq Blood GM12878 Tissue=blood|Lineage=mesoderm|Description=B-lymphocyte, lymphoblastoid, International HapMap Project - CEPH/Utah - European Caucasion, Epstein-Barr Virus 49218769,0.0,30.7,0 GSM1940159: GM12878 SAHA Vehicle Control DNase-seq Rep 2; Homo sapiens; DNase-Hypersensitivity source_name=GM12878 SAHA Vehicle Control DNase-seq biomaterial_provider=https://catalog.coriell.org/0/Sections/Search/Sample_Detail.aspx?Ref=GM12878&product=CC cell line=GM12878 cell type=lymphoblasts treated with=0.5 μM SAHA duration of treatment=72 hours +SRX1813562 hg38 Histone H3K27me3 Lung MRC-5 Tissue=Lung|Cell Type=Fetal|Disease=Normal 7652953,174.6,1.4,65 GSM2183757: MRC5 rep2 H3K27me3; Homo sapiens; ChIP-Seq source_name=MRC5_H3K27me3 chip antibody=H3K27me3 (Abcam ab6002 Lot: GR218433-1) cell line=MRC5 fibroblasts passage=8 +SRX2000342 hg38 TFs and others TET3 Breast MCF-12A Primary Tissue=Breast|Tissue Diagnosis=Normal 52447977,57.0,41.5,1178 GSM2259943: atra-tet2; Homo sapiens; ChIP-Seq source_name=human mammary epithelial cells cell line=MCF12A antibody=TET3 treatment=ATRA +SRX2444227 hg38 ATAC-Seq ATAC-Seq Breast HCC1806 Primary Tissue=Breast|Tissue Diagnosis=Carcinoma Squamous Cell 17226376,54.6,6.2,187 GSM2439557: HCC1806 cell line treated with LBH589 (10nM) for 28 days + DMSO for 14 days; Homo sapiens; ATAC-seq source_name=Invasive breast carcinoma cell line=HCC1806 disease subtype=Triple-negative breast cancer treatment=LBH589 (10nM) for 28 days + DMSO for 14 days +SRX2548033 hg38 Bisulfite-Seq Bisulfite-Seq Embryo 8-cell stage NA 20501313,74.7,0.8,208865 GSM2481637: scBS-8C-9-2; Homo sapiens; Bisulfite-Seq source_name=8-cell embryo#9 cell type=8-cell gender=NA bulk or single cell=single cell +SRX2650805 hg38 ATAC-Seq ATAC-Seq Blood CD34+ NA 50273,95.9,14.6,0 GSM2541508: singles-160822-BM1137-CMP-LS-52; Homo sapiens; ATAC-seq source_name=Bone Marrow CD34+ tissue=CD34+ Bone Marrow cell type=CMP +SRX2708758 hg38 Input control Input control Pluripotent stem cell hES BG01V Primary Tissue=Embryo|Tissue Diagnosis=Normal 17519917,67.6,6.9,283 GSM2563975: ES ZNF57-A Input; Homo sapiens; ChIP-Seq source_name=embryonic cell line cell line=BG01V - ATCC SCRC-2002 modifications or treatment=Transduced with a vector with biotin ligase sequence and a vector with the sequence of selected ZFP57 tagged with the biotinylation peptide sample type=Input +SRX2999840 hg38 Unclassified Unclassified Blood Th17 Cells MeSH Description=A subset of helper-effector T-lymphocytes which synthesize and secrete INTERLEUKINS IL-17; IL-17F; and IL-22. These cytokines are involved in host defenses and tissue inflammation in autoimmune diseases. 31434636,29.4,70.2,826 GSM2701848: Th17-IL-10– Day 0 c-MAF ChIP Repl 2; Homo sapiens; ChIP-Seq source_name=primary Th17 lymphocytes tissue=primary Th17 lymphocytes cell type=Pool of expanded Th17 clones activation state=resting +SRX3164085 hg38 Histone H3K4me3 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 50761744,184.9,6.5,38241 GSM2773404: K562 EMD-05-745R H3K4me3 ICeChIP; Homo sapiens; ChIP-Seq source_name=Cell culture cell line=K562 chip antibody=EMD Millipore 05-745R, Lot 2813867, anti-H3K4me3 amplification cycles=8 +SRX367190 hg38 Histone H3K27ac Prostate LNCAP Primary Tissue=Prostate|Tissue Diagnosis=Carcinoma 52605323,76.9,22.4,68945 GSM1249447: H3K27Ac -DHT ChIP-seq; Homo sapiens; ChIP-Seq source_name=prostate cancer cells cell line=LNCaP chip antibody=H3K27Ac (Active Motif, 39133, Lot 213110044) passage=20 growth protocol=Cells were grown in RPMI with charcoal-stripped serum and collected when 80% confluent +SRX3931038 hg38 Histone H3K4me2 Breast MCF-7 Primary Tissue=Breast|Site of Extraction=Pleura|Tissue Diagnosis=Adenocarcinoma 21727557,92.2,6.2,33510 GSM3096436: TAMR 4OHT H3K4Me2 KC173 Rep2; Homo sapiens; ChIP-Seq source_name=TAMR cell line=TAMR treatment=4OHT treatment time=45min factor=H3K4Me2 chip antibody=Sigma, 07-030 replicate=Rep2 +SRX3931063 hg38 Histone H3K4me2 Breast MCF-7 Primary Tissue=Breast|Site of Extraction=Pleura|Tissue Diagnosis=Adenocarcinoma 26229253,91.4,7.2,39006 GSM3096461: TAMR VEH H3K4Me2 KC164 Rep1; Homo sapiens; ChIP-Seq source_name=TAMR cell line=TAMR treatment=VEH treatment time=45min factor=H3K4Me2 chip antibody=Sigma, 07-030 replicate=Rep1 +SRX4121578 hg38 Bisulfite-Seq Bisulfite-Seq Digestive tract Colorectal cancer NA 12340086,75.9,0.5,288669 GSM3153146: scTrioSeq2Met CRC04 PT2 564; Homo sapiens; Bisulfite-Seq source_name=CRC disease=Carcinoma +SRX4288408 hg38 Histone H3K4me1 Blood Lymphoblastoid cell line Tissue=blood|Lineage=mesoderm|Description=parental cell type to lymphoblastoid cell lines 156712828,93.5,4.1,1609 GSM3212862: lgs303260 H3K4me1 ChIP-seq; Homo sapiens; ChIP-Seq source_name=H3K4me1 ChIP-seq cell line=lymphoblastoid cell lines (LCLs) disease=Systemic lupus erythematosus race=European-American gender=male chip antibody=H3K4me1 (Abcam, #ab8895) +SRX502073 hg38 TFs and others AR Prostate Prostate cancer NA 90690299,63.8,34.0,18504 GSM1358414: Patient AR prostate tumor tissue 20; Homo sapiens; ChIP-Seq source_name=Patient AR prostate tumor tissue tissue=prostate tumor tissue chip antibody=AR +SRX5202661 hg38 TFs and others IRF2BP2 Kidney 293 Primary Tissue=Kidney|Tissue Diagnosis=Normal 52429322,73.0,20.6,1039 GSM3538193: ChIP-seq HEK293-GR IRF2BP2 dex rep2; Homo sapiens; ChIP-Seq source_name=HEK293-GR cell line=HEK293-GR treatment 1=steroid-depleted medium, 48h treatment 2=dexamethasone, 100 nM, 1h chip antibody=Anti-IRF2BP2 antibody (A303190A, Bethyl laboratories) +SRX5203079 hg38 Input control Input control Breast MCF-7 Primary Tissue=Breast|Site of Extraction=Pleura|Tissue Diagnosis=Adenocarcinoma 5768699,193.8,1.6,121 GSM3538837: MCF7-PTENshRNA-Input-Rep1; Homo sapiens; ChIP-Seq source_name=MCF7 cells cell line=MCF7 (HTB-22) treatment=PTEN knock-down chip antibody=n/a +SRX528309 hg38 Histone H3.3B Gonad Sperm NA 53276636,60.1,36.5,956 GSM1375207: H3 ChIPSeq Human; Homo sapiens; ChIP-Seq source_name=H3_ChIPSeq_Human donor age=adult cell type=sperm chip antibody=H3F3B chip antibody vendor=Abnova +SRX5323907 hg38 Histone H3K27ac Breast T-47D Primary Tissue=Breast|Tissue Diagnosis=Adenocarcinoma Ductal 40144244,68.7,30.9,35240 GSM3587821: JC5073 H3K27ac siCtl PBS; Homo sapiens; ChIP-Seq source_name=T47D cell type=Breast cancer passages=26 chip antibody=ab4729 (Abcam) +SRX535418 hg38 Histone H3K4me3 Breast MCF-7 Primary Tissue=Breast|Site of Extraction=Pleura|Tissue Diagnosis=Adenocarcinoma 9633602,79.8,18.9,19739 GSM1382471: H3K4me3, EtOH, rep2, MCF7; Homo sapiens; ChIP-Seq source_name=MCF7_H3K4me3_EtOH cell line=MCF7 cell type=human breast adenocarcinoma treated with=vehicle (EtOH) for 30 min chip antibody=H3K4me3 chip antibody vendor=Active Motif +SRX5974535 hg38 Histone H3K4me1 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 6142906,95.9,3.4,93 GSM3854089: ChIP-seq K562 dCas9-LSD1 sgHS2 H3K4me rep1; Homo sapiens; ChIP-Seq source_name=K562 cell line=Human K562 cells cell type=immortalized myelogenous leukemia cells chip antibody=Abcam, ab8895, Lot: GR141677-1 +SRX5974585 hg38 Histone H3K4me1 Blood K-562 Primary Tissue=Blood|Tissue Diagnosis=Leukemia Chronic Myelogenous 12815852,93.9,4.3,867 GSM3854139: ChIP-seq K562 sgGal4 H3K9me3 rep1; Homo sapiens; ChIP-Seq source_name=K562 cell line=Human K562 cells cell type=immortalized myelogenous leukemia cells chip antibody=Abcam, ab8898, Lot: GR3244171-1 +SRX6944761 hg38 Histone H3K4me3 Blood CD4+ T cells NA 50404127,64.1,7.4,2240 GSM4106264: O30C naive CD4 + T cells H3K4me3 rep2 input; Homo sapiens; ChIP-Seq source_name=O30C_H3K4me3_umbilical cord blood age=30-36 weeks gestation preterm chorioamnionitis-exposed neonate tissue=umbilical cord blood cell type=naive CD4+ T cells isolation=Miltenyi Biotec naive CD4+ T cell magnetic bead isolation +SRX764506 hg38 Histone H3K4me1 Blood Lymphoblastoid cell line Tissue=blood|Lineage=mesoderm|Description=parental cell type to lymphoblastoid cell lines 28315631,166.5,16.2,35545 GSM1550827: GM18862 H3K4ME1; Homo sapiens; ChIP-Seq source_name=Lymphoblastoid Cell Line cell line=GM18862 antibody=H3K4ME1 +SRX764550 hg38 Histone H3K4me3 Blood Lymphoblastoid cell line Tissue=blood|Lineage=mesoderm|Description=parental cell type to lymphoblastoid cell lines 27784839,192.2,3.4,27630 GSM1550871: GM18907 H3K4ME3; Homo sapiens; ChIP-Seq source_name=Lymphoblastoid Cell Line cell line=GM18907 antibody=H3K4ME3 +SRX790972 hg38 TFs and others NFKB2 Blood L-1236 Primary Tissue=Blood|Tissue Diagnosis=Hodgkin's Lymphoma 48136848,37.7,15.7,17592 GSM1556338: p52 ChIPSeq rep2; Homo sapiens; ChIP-Seq source_name=Hodgkin Lymphoma Cell Line cell line=L1236 dsmz no=ACC 530 chip antibody=NFkB p52 Millipore #06-413, rabbit pkAb +SRX959082 hg38 RNA polymerase RNA polymerase II Breast MCF-7 Primary Tissue=Breast|Site of Extraction=Pleura|Tissue Diagnosis=Adenocarcinoma 15652676,192.8,1.8,9201 GSM1636933: MCF7 Pol2 N20 ChIP A; Homo sapiens; ChIP-Seq source_name=MCF-7 culture cells cell line=MCF-7 passages=Untreated cells grown in DMEM/F-12 medium +10% FBS chip antibody=N-20, Santa Cruz, sc-899 X molecule=chromatin diff --git a/tests/data/hg38_metadata_sorted_sample.tab b/tests/data/hg38_metadata_sorted_sample.tab new file mode 100644 index 00000000..97dbcbad --- /dev/null +++ b/tests/data/hg38_metadata_sorted_sample.tab @@ -0,0 +1,12 @@ +DRX127596 hg38 ATAC-Seq ATAC-Seq Lung A549 Primary Tissue=Lung|Tissue Diagnosis=Carcinoma 5540198,63.4,35.0,4073 Illumina HiSeq 2500 sequencing of SAMD00122991 sample_name=ATAC-seq_A549_24h_E06_5-azacytidine (Inhibitor_DNA methyltransferase)_10 cell_line=A549 +ERX1774928 hg38 ATAC-Seq ATAC-Seq Blood B Lymphoblastoid cell line NA 3297374,104.1,33.9,398 Illumina HiSeq 2000 paired end sequencing Alias=74cb2c40-2c8a-11e6-ba59-68b599768938 ENA checklist=ERC000011 INSDC center name=SC INSDC first public=2016-09-26T11:40:29Z INSDC last update=2016-06-08T13:19:18Z INSDC status=public SRA accession=ERS1199950 Sample Name=ERS1199950 Title=LCL-ATAC6437564 sample_description=ATAC-seq library preparation from B-lymphoblastoids strain=unknown +SRX1142276 hg38 Histone H3K4me3 Blood CD4+ T cells NA 47353778,155.8,19.2,35970 GSM1847950: gDNA CD4 H3K4me3 Control rep6; Homo sapiens; ChIP-Seq source_name=Genomic DNA, CD4 cells, H3K4me3 ChIP, healthy control tissue=blood diagnosis=Healthy control cell type=CD4 T cells gender=female age=76 antibody=H3K4me3 +SRX1142286 hg38 Histone H3K4me3 Blood CD4+ T cells NA 45274400,151.3,20.5,33130 GSM1847960: gDNA CD4 H3K4me3 Control rep9; Homo sapiens; ChIP-Seq source_name=Genomic DNA, CD4 cells, H3K4me3 ChIP, healthy control tissue=blood diagnosis=Healthy control cell type=CD4 T cells gender=female age=27 antibody=H3K4me3 +SRX1261949 hg38 Histone H3K4me2 Blood CD4+ T cells NA 12283413,76.3,8.1,10040 GSM1888751: H3K4me2-Naive-D1-Donor5131; Homo sapiens; ChIP-Seq source_name=Naive CD4 T-cells cell type=Naive activated=TRUE days after activation=1 donor id=5131 chip antibody=H3K4me2 +SRX188596 hg38 Input control Input control Blood WSU-DLCL2 Primary Tissue=Blood|Tissue Diagnosis=Lymphoma B-cell 37041247,85.7,13.4,937 GSM1006154: Input WSU-DLCL2; Homo sapiens; ChIP-Seq source_name=WSU-DLCL2 cell line=WSU-DLCL2 genotype/variation=EZH2 mutant average gic50=134 nM chip antibody=Input control +SRX2550937 hg38 Input control Input control Neural KELLY Primary Tissue=Brain|Tissue Diagnosis=Neuroblastoma 70685539,74.0,21.9,3606 GSM2482832: Kelly Input; Homo sapiens; ChIP-Seq source_name=Kelly neuroblastoma cells chip antibody=none cell line=Kelly cell type=Neuroblastoma mycn status=amplified +SRX2688660 hg38 Bisulfite-Seq Bisulfite-Seq Neural Frontal cortex NA 3348336,66.4,0.1,131395 GSM2558008: Pool 37 AD008 indexed; Homo sapiens; Bisulfite-Seq source_name=Human frontal cortex tissue=frontal cortex Sex=male age=25 yr +SRX2924019 hg38 Histone H3K27ac Breast MCF-7 Primary Tissue=Breast|Site of Extraction=Pleura|Tissue Diagnosis=Adenocarcinoma 14996861,94.1,4.6,24185 GSM2671297: H3K27ac ChIP-Seq, t=0h; Homo sapiens; ChIP-Seq source_name=MCF7 passage=between 4-14 for all experiments antibody=Abcam ab4729 cell line=MCF7 +SRX3931089 hg38 Histone H3K4me2 Breast MCF-7 Primary Tissue=Breast|Site of Extraction=Pleura|Tissue Diagnosis=Adenocarcinoma 25456745,91.7,6.9,40196 GSM3096490: WS8 E2 H3K4Me2 KC160 Rep3; Homo sapiens; ChIP-Seq source_name=WS8 cell line=WS8 treatment=E2 treatment time=45min factor=H3K4Me2 chip antibody=Sigma, 07-030 replicate=Rep3 +SRX4708119 hg38 Unclassified Unclassified Liver Hep G2 Primary Tissue=Liver|Tissue Diagnosis=Carcinoma Hepatocellular 19642296,164.0,14.6,2220 GSM3393633: YY1 NC ChIP-seq Input rep1; Homo sapiens; ChIP-Seq source_name=HepG2 biomaterial_provider=ATCC (HB-8065) cell line=HepG2 cell type=Hepatocellular carcinoma +SRX6931700 hg38 Bisulfite-Seq Bisulfite-Seq Others Fibroblasts NA 1609,86.6,0.0,1 GSM4104088: Fibroblast pG Subtel 9p+Xq; Homo sapiens; Bisulfite-Seq source_name=Fibroblasts cell type=patient_pG genotype=ICF1 patient (DNMT3B p. I41fsX42 / p.S780L) genomic_locus1=Chr9:1,507-10,681 genomic_locus2=Chrx:156,029,678-156,029,852 diff --git a/tests/data/hg38_sample/DRX127657.05.bed b/tests/data/hg38_sample/DRX127657.05.bed new file mode 100644 index 00000000..9b5880bb --- /dev/null +++ b/tests/data/hg38_sample/DRX127657.05.bed @@ -0,0 +1,2130 @@ +chr1 10052 10333 DRX127657.05_peak_1 149 . 10.10194 19.97593 14.98602 217 +chr1 180751 180984 DRX127657.05_peak_2 80 . 7.05343 12.18586 8.08435 142 +chr1 629179 629977 DRX127657.05_peak_3 261 . 2.62187 31.74860 26.10095 581 +chr1 630272 630423 DRX127657.05_peak_4 133 . 2.13703 18.13478 13.31422 87 +chr1 630777 631378 DRX127657.05_peak_5 246 . 2.54998 30.24345 24.64757 451 +chr1 631838 632356 DRX127657.05_peak_6 283 . 2.66589 34.06160 28.33844 209 +chr1 632502 632779 DRX127657.05_peak_7 200 . 2.39544 25.42749 20.06621 154 +chr1 633913 634209 DRX127657.05_peak_8 278 . 2.65146 33.59557 27.88805 159 +chr1 634247 634577 DRX127657.05_peak_9 135 . 2.16969 18.42977 13.58163 36 +chr1 1116228 1116396 DRX127657.05_peak_10 100 . 8.20424 14.47610 10.07523 113 +chr1 1345032 1345213 DRX127657.05_peak_11 67 . 5.09005 10.60588 6.71883 74 +chr1 1375246 1375494 DRX127657.05_peak_12 148 . 9.08562 19.83528 14.85721 143 +chr1 1724461 1724636 DRX127657.05_peak_13 86 . 8.10474 12.88402 8.68874 62 +chr1 2227717 2227925 DRX127657.05_peak_14 132 . 8.35538 18.03461 13.22575 106 +chr1 2391549 2391791 DRX127657.05_peak_15 116 . 9.01533 16.24245 11.62900 106 +chr1 3510185 3510505 DRX127657.05_peak_16 87 . 7.25287 12.91893 8.72032 76 +chr1 6962936 6963122 DRX127657.05_peak_17 94 . 8.26264 13.77742 9.46633 123 +chr1 7975366 7975547 DRX127657.05_peak_18 100 . 8.81113 14.39823 10.00527 41 +chr1 8004950 8005113 DRX127657.05_peak_19 75 . 6.46191 11.60748 7.58186 80 +chr1 9825629 9825790 DRX127657.05_peak_20 111 . 8.11458 15.67597 11.12736 71 +chr1 11262590 11262741 DRX127657.05_peak_21 103 . 9.13691 14.82485 10.38263 66 +chr1 11908512 11908809 DRX127657.05_peak_22 150 . 8.09581 20.08174 15.08631 146 +chr1 15168605 15168833 DRX127657.05_peak_23 107 . 9.09477 15.19386 10.70735 120 +chr1 16513697 16514120 DRX127657.05_peak_24 200 . 8.88845 25.42213 20.06190 209 +chr1 16534628 16535232 DRX127657.05_peak_25 121 . 7.66994 16.80214 12.12515 471 +chr1 16545391 16545913 DRX127657.05_peak_26 216 . 10.91107 27.07543 21.63367 303 +chr1 16666219 16666719 DRX127657.05_peak_27 145 . 8.34673 19.54208 14.58885 236 +chr1 16677862 16678210 DRX127657.05_peak_28 92 . 8.12744 13.55276 9.27453 207 +chr1 16740744 16741231 DRX127657.05_peak_29 151 . 8.42004 20.19592 15.18950 197 +chr1 16862075 16862480 DRX127657.05_peak_30 243 . 11.77489 29.88570 24.31537 221 +chr1 16872673 16873118 DRX127657.05_peak_31 213 . 10.44535 26.81569 21.38461 205 +chr1 16896219 16896717 DRX127657.05_peak_32 232 . 10.20014 28.72627 23.20227 357 +chr1 19210347 19210498 DRX127657.05_peak_33 89 . 8.26026 13.16147 8.93107 64 +chr1 20661476 20661644 DRX127657.05_peak_34 107 . 8.88741 15.19526 10.70735 76 +chr1 22832133 22832312 DRX127657.05_peak_35 92 . 7.59539 13.48054 9.20864 91 +chr1 23344420 23344606 DRX127657.05_peak_36 141 . 10.71747 19.10625 14.19555 44 +chr1 25998000 25998151 DRX127657.05_peak_37 78 . 7.69872 11.93782 7.86578 92 +chr1 26677638 26677799 DRX127657.05_peak_38 104 . 9.06971 14.86659 10.41831 38 +chr1 27112167 27112318 DRX127657.05_peak_39 109 . 8.78008 15.42126 10.90284 91 +chr1 27672185 27672386 DRX127657.05_peak_40 83 . 7.71180 12.51116 8.36489 132 +chr1 27725748 27725899 DRX127657.05_peak_41 116 . 9.71754 16.30841 11.68876 80 +chr1 28648915 28649199 DRX127657.05_peak_42 109 . 8.26590 15.46398 10.94037 144 +chr1 31170899 31171050 DRX127657.05_peak_43 71 . 7.04378 11.08144 7.12894 30 +chr1 31431737 31432218 DRX127657.05_peak_44 653 . 17.41392 71.85489 65.38947 297 +chr1 32394533 32394736 DRX127657.05_peak_45 75 . 7.34422 11.57914 7.55739 110 +chr1 32612138 32612296 DRX127657.05_peak_46 84 . 7.95496 12.62070 8.46026 143 +chr1 33036929 33037080 DRX127657.05_peak_47 111 . 9.53613 15.74522 11.18884 45 +chr1 33127331 33127531 DRX127657.05_peak_48 83 . 7.88213 12.49392 8.35327 98 +chr1 36150921 36151072 DRX127657.05_peak_49 107 . 7.82687 15.19201 10.70735 59 +chr1 36224306 36224508 DRX127657.05_peak_50 109 . 8.78008 15.42126 10.90284 130 +chr1 36386660 36386851 DRX127657.05_peak_51 87 . 7.29990 12.99618 8.78794 100 +chr1 43582331 43582549 DRX127657.05_peak_52 130 . 9.13599 17.86299 13.07736 83 +chr1 43649862 43650043 DRX127657.05_peak_53 125 . 9.05797 17.24376 12.51567 108 +chr1 44327893 44328044 DRX127657.05_peak_54 84 . 7.77648 12.61871 8.46026 53 +chr1 44674507 44674679 DRX127657.05_peak_55 84 . 7.34686 12.66394 8.49733 60 +chr1 44730691 44730974 DRX127657.05_peak_56 135 . 9.44627 18.37805 13.53341 217 +chr1 44800008 44800164 DRX127657.05_peak_57 91 . 7.54449 13.39722 9.13660 110 +chr1 44819573 44819809 DRX127657.05_peak_58 147 . 9.33707 19.74238 14.77259 141 +chr1 53326097 53326283 DRX127657.05_peak_59 107 . 7.34999 15.28707 10.78766 75 +chr1 54053316 54053527 DRX127657.05_peak_60 198 . 12.59181 25.19909 19.85278 71 +chr1 55022056 55022272 DRX127657.05_peak_61 160 . 11.39601 21.13921 16.05976 123 +chr1 55429163 55429474 DRX127657.05_peak_62 180 . 11.41607 23.23668 18.00564 167 +chr1 58848912 58849096 DRX127657.05_peak_63 78 . 7.69872 11.93782 7.86578 46 +chr1 63523276 63523444 DRX127657.05_peak_64 109 . 9.43739 15.44646 10.92533 62 +chr1 67685046 67685319 DRX127657.05_peak_65 132 . 9.25763 18.06510 13.25196 67 +chr1 91387210 91387563 DRX127657.05_peak_66 338 . 16.64586 39.70480 33.85246 188 +chr1 109426436 109426587 DRX127657.05_peak_67 102 . 8.98184 14.70610 10.27612 139 +chr1 112369792 112369944 DRX127657.05_peak_68 75 . 7.48130 11.54962 7.53298 59 +chr1 113390301 113390488 DRX127657.05_peak_69 70 . 6.80603 11.03083 7.08533 112 +chr1 113812372 113812563 DRX127657.05_peak_70 132 . 10.30740 18.00556 13.20353 56 +chr1 113905088 113905252 DRX127657.05_peak_71 96 . 8.40242 14.01102 9.66704 36 +chr1 119648145 119648404 DRX127657.05_peak_72 72 . 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DRX127657.05_peak_290 227 . 9.15898 28.24564 22.73881 149 +chr11 3379154 3379349 DRX127657.05_peak_291 122 . 9.88203 16.90524 12.21849 117 +chr11 3422965 3423138 DRX127657.05_peak_292 95 . 7.30601 13.86278 9.54173 127 +chr11 6234749 6235126 DRX127657.05_peak_293 116 . 8.78531 16.32023 11.69776 94 +chr11 6612271 6612422 DRX127657.05_peak_294 105 . 8.00303 15.02871 10.56072 79 +chr11 6683374 6683614 DRX127657.05_peak_295 90 . 8.34028 13.30596 9.05873 100 +chr11 9566105 9566282 DRX127657.05_peak_296 151 . 10.83658 20.19225 15.18618 98 +chr11 9663850 9664027 DRX127657.05_peak_297 72 . 7.27581 11.19081 7.22394 106 +chr11 10508795 10509034 DRX127657.05_peak_298 202 . 5.08044 25.57730 20.20618 124 +chr11 10509140 10509836 DRX127657.05_peak_299 989 . 11.86881 105.86392 98.97810 489 +chr11 10735878 10736165 DRX127657.05_peak_300 101 . 7.98828 14.55612 10.14598 57 +chr11 12378012 12378163 DRX127657.05_peak_301 126 . 9.94326 17.37358 12.63649 113 +chr11 13668388 13668609 DRX127657.05_peak_302 110 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DRX127657.05_peak_351 127 . 7.80129 17.52650 12.77172 63 +chr11 64778579 64779037 DRX127657.05_peak_352 159 . 9.44905 21.00796 15.93820 181 +chr11 64810592 64810811 DRX127657.05_peak_353 205 . 12.36741 25.96647 20.57883 121 +chr11 65006918 65007131 DRX127657.05_peak_354 137 . 9.86777 18.58062 13.71662 77 +chr11 65027493 65027696 DRX127657.05_peak_355 71 . 6.37450 11.07260 7.12504 121 +chr11 65040882 65041195 DRX127657.05_peak_356 172 . 10.23541 22.37206 17.20224 185 +chr11 65135069 65135221 DRX127657.05_peak_357 107 . 7.59027 15.24898 10.75287 86 +chr11 65150741 65151034 DRX127657.05_peak_358 155 . 10.13930 20.56493 15.52890 98 +chr11 65333734 65333885 DRX127657.05_peak_359 89 . 7.44471 13.23376 8.99362 76 +chr11 65371162 65371313 DRX127657.05_peak_360 104 . 8.20831 14.91747 10.46187 104 +chr11 65381903 65382132 DRX127657.05_peak_361 107 . 6.01607 15.25476 10.75845 115 +chr11 65382231 65382561 DRX127657.05_peak_362 150 . 7.13668 20.01059 15.01918 182 +chr11 65570147 65570429 DRX127657.05_peak_363 111 . 6.64231 15.74401 11.18820 131 +chr11 65577456 65577671 DRX127657.05_peak_364 117 . 6.54710 16.42312 11.78915 85 +chr11 65615356 65615583 DRX127657.05_peak_365 92 . 6.16110 13.50416 9.23019 110 +chr11 65858141 65858338 DRX127657.05_peak_366 80 . 4.59209 12.13077 8.03489 75 +chr11 65888533 65888749 DRX127657.05_peak_367 110 . 4.97492 15.58185 11.04581 144 +chr11 65961569 65961731 DRX127657.05_peak_368 98 . 8.02871 14.18907 9.82550 59 +chr11 66002226 66002433 DRX127657.05_peak_369 109 . 8.56205 15.49779 10.96926 77 +chr11 66052117 66052334 DRX127657.05_peak_370 171 . 10.53030 22.31342 17.14878 89 +chr11 66289102 66289360 DRX127657.05_peak_371 76 . 6.07203 11.72543 7.68262 115 +chr11 66638361 66638678 DRX127657.05_peak_372 84 . 6.84203 12.65259 8.48877 143 +chr11 66677860 66678011 DRX127657.05_peak_373 87 . 7.29990 12.99618 8.78794 80 +chr11 66694808 66695089 DRX127657.05_peak_374 186 . 10.46299 23.90586 18.63321 148 +chr11 66958542 66958694 DRX127657.05_peak_375 86 . 7.20644 12.84260 8.65167 105 +chr11 67239792 67240150 DRX127657.05_peak_376 179 . 10.67981 23.13074 17.90722 140 +chr11 67354737 67354920 DRX127657.05_peak_377 91 . 5.39838 13.44823 9.18197 65 +chr11 67374204 67374416 DRX127657.05_peak_378 93 . 5.46437 13.60142 9.31512 101 +chr11 67506111 67506285 DRX127657.05_peak_379 106 . 5.97729 15.16728 10.68390 108 +chr11 67805509 67805943 DRX127657.05_peak_380 103 . 7.34960 14.83029 10.38625 113 +chr11 68038901 68039063 DRX127657.05_peak_381 108 . 6.26901 15.39446 10.88030 110 +chr11 68354536 68354716 DRX127657.05_peak_382 92 . 7.14017 13.58195 9.29632 55 +chr11 68459959 68460235 DRX127657.05_peak_383 108 . 7.67404 15.39375 10.87968 107 +chr11 69044288 69044498 DRX127657.05_peak_384 99 . 8.08638 14.28335 9.90632 81 +chr11 69101499 69101675 DRX127657.05_peak_385 113 . 7.69958 15.90555 11.33086 120 +chr11 69122836 69123135 DRX127657.05_peak_386 139 . 7.71903 18.83737 13.94907 165 +chr11 69301817 69302052 DRX127657.05_peak_387 132 . 5.98587 18.06491 13.25196 133 +chr11 69497296 69497484 DRX127657.05_peak_388 111 . 7.31506 15.68412 11.13475 82 +chr11 69675326 69675477 DRX127657.05_peak_389 80 . 7.52405 12.20072 8.09600 76 +chr11 69949537 69949704 DRX127657.05_peak_390 131 . 8.87168 17.92017 13.12633 82 +chr11 70159733 70159931 DRX127657.05_peak_391 116 . 8.72440 16.21997 11.60912 85 +chr11 70233548 70233876 DRX127657.05_peak_392 104 . 7.90251 14.86174 10.41498 73 +chr11 70399766 70399992 DRX127657.05_peak_393 134 . 8.79306 18.29171 13.45387 88 +chr11 71299378 71299591 DRX127657.05_peak_394 160 . 9.50119 21.09943 16.02231 70 +chr11 71787059 71787222 DRX127657.05_peak_395 82 . 6.68847 12.39303 8.26505 29 +chr11 72080579 72080827 DRX127657.05_peak_396 126 . 9.44475 17.40600 12.66405 205 +chr11 72434406 72434659 DRX127657.05_peak_397 129 . 9.07637 17.76379 12.98691 144 +chr11 72482359 72482510 DRX127657.05_peak_398 107 . 9.09477 15.19386 10.70735 84 +chr11 73185977 73186220 DRX127657.05_peak_399 80 . 6.81390 12.19442 8.09180 42 +chr11 73510645 73510796 DRX127657.05_peak_400 145 . 10.91372 19.44924 14.50641 121 +chr11 73598431 73598693 DRX127657.05_peak_401 169 . 10.39804 22.08996 16.93778 139 +chr11 73779466 73779705 DRX127657.05_peak_402 101 . 8.52969 14.59960 10.18331 165 +chr11 73983181 73983366 DRX127657.05_peak_403 69 . 6.70755 10.87173 6.94707 44 +chr11 74170907 74171305 DRX127657.05_peak_404 266 . 14.69220 32.32654 26.65633 130 +chr11 74493200 74493398 DRX127657.05_peak_405 172 . 11.54250 22.36999 17.20070 108 +chr11 74988774 74988927 DRX127657.05_peak_406 107 . 8.64524 15.19921 10.70836 87 +chr11 75815009 75815243 DRX127657.05_peak_407 101 . 8.26446 14.57465 10.16116 143 +chr11 76444674 76444878 DRX127657.05_peak_408 102 . 8.32558 14.67471 10.24837 78 +chr11 77886497 77886676 DRX127657.05_peak_409 126 . 10.16648 17.41477 12.67160 99 +chr11 77994720 77995072 DRX127657.05_peak_410 180 . 11.41607 23.23668 18.00564 202 +chr11 78188675 78188858 DRX127657.05_peak_411 120 . 8.46842 16.74661 12.07421 48 +chr11 82900417 82900661 DRX127657.05_peak_412 99 . 8.14488 14.37901 9.99017 127 +chr11 83071782 83072073 DRX127657.05_peak_413 199 . 11.93865 25.24993 19.90112 148 +chr11 83156579 83156932 DRX127657.05_peak_414 128 . 8.95943 17.56899 12.80796 279 +chr11 85628310 85628461 DRX127657.05_peak_415 111 . 9.35162 15.64659 11.10129 102 +chr11 86302101 86302276 DRX127657.05_peak_416 108 . 8.96258 15.32146 10.81528 47 +chr11 103409559 103409817 DRX127657.05_peak_417 277 . 16.68057 33.41254 27.70730 128 +chr11 104164234 104164397 DRX127657.05_peak_418 113 . 9.53107 15.96846 11.38455 102 +chr11 106022334 106022519 DRX127657.05_peak_419 100 . 8.94701 14.44749 10.05244 53 +chr11 123003518 123003758 DRX127657.05_peak_420 190 . 13.35395 24.36288 19.06141 124 +chr11 125592538 125592786 DRX127657.05_peak_421 71 . 7.20981 11.07703 7.12719 197 +chr12 9964 10427 DRX127657.05_peak_422 294 . 12.54115 35.22493 29.47447 183 +chr12 949545 949699 DRX127657.05_peak_423 90 . 7.99659 13.33642 9.08224 139 +chr12 2004394 2004603 DRX127657.05_peak_424 81 . 7.85083 12.21535 8.10827 127 +chr12 2255829 2256039 DRX127657.05_peak_425 154 . 11.01686 20.49567 15.46444 119 +chr12 2520580 2520766 DRX127657.05_peak_426 85 . 7.39816 12.74745 8.56996 117 +chr12 6093200 6093422 DRX127657.05_peak_427 83 . 7.51277 12.54368 8.39357 75 +chr12 6336352 6336640 DRX127657.05_peak_428 92 . 6.18612 13.55325 9.27495 126 +chr12 6470887 6471107 DRX127657.05_peak_429 91 . 7.77832 13.36669 9.10806 84 +chr12 6723985 6724214 DRX127657.05_peak_430 141 . 10.17087 19.08227 14.17497 104 +chr12 6753593 6753796 DRX127657.05_peak_431 126 . 8.84546 17.37880 12.64101 119 +chr12 6852277 6852635 DRX127657.05_peak_432 146 . 8.12489 19.63058 14.67161 213 +chr12 6891277 6891473 DRX127657.05_peak_433 100 . 8.46158 14.48697 10.08467 64 +chr12 6938005 6938196 DRX127657.05_peak_434 127 . 8.31315 17.46192 12.71427 131 +chr12 6944070 6944809 DRX127657.05_peak_435 153 . 5.79199 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DRX127657.05_peak_1811 87 . 7.50293 12.91803 8.72032 70 +chr7 44999848 45000092 DRX127657.05_peak_1812 115 . 9.21110 16.13566 11.53446 122 +chr7 45251916 45252166 DRX127657.05_peak_1813 313 . 16.78909 37.17346 31.38312 115 +chr7 45978245 45978446 DRX127657.05_peak_1814 116 . 9.28708 16.26251 11.64654 109 +chr7 47557818 47557999 DRX127657.05_peak_1815 84 . 5.91716 12.62339 8.46287 83 +chr7 47763658 47763811 DRX127657.05_peak_1816 94 . 8.01362 13.75096 9.44466 40 +chr7 47786664 47786923 DRX127657.05_peak_1817 233 . 12.63637 28.86021 23.32997 136 +chr7 55132823 55133007 DRX127657.05_peak_1818 112 . 7.38600 15.81329 11.24801 76 +chr7 55186318 55186508 DRX127657.05_peak_1819 96 . 7.91581 14.00452 9.66439 136 +chr7 66628753 66628935 DRX127657.05_peak_1820 85 . 7.16061 12.76719 8.58590 149 +chr7 73715701 73715953 DRX127657.05_peak_1821 105 . 8.00303 15.02871 10.56072 119 +chr7 73822644 73822937 DRX127657.05_peak_1822 123 . 8.67984 17.10170 12.38972 207 +chr7 74174056 74174230 DRX127657.05_peak_1823 103 . 8.85165 14.77292 10.33638 38 +chr7 74610491 74610642 DRX127657.05_peak_1824 93 . 8.19448 13.66401 9.36695 68 +chr7 74994011 74994201 DRX127657.05_peak_1825 113 . 8.81532 15.91354 11.33650 77 +chr7 75528144 75528296 DRX127657.05_peak_1826 97 . 8.47410 14.13138 9.77360 46 +chr7 75676182 75676362 DRX127657.05_peak_1827 117 . 9.61307 16.43302 11.79790 108 +chr7 76512354 76512565 DRX127657.05_peak_1828 105 . 7.42811 14.96733 10.50503 137 +chr7 77006276 77006463 DRX127657.05_peak_1829 96 . 6.87713 13.99502 9.65720 94 +chr7 77122387 77122582 DRX127657.05_peak_1830 99 . 8.62119 14.37973 9.99017 65 +chr7 77798492 77798817 DRX127657.05_peak_1831 147 . 10.28454 19.76546 14.79001 126 +chr7 80722861 80723082 DRX127657.05_peak_1832 127 . 9.75293 17.46489 12.71433 111 +chr7 87152399 87152636 DRX127657.05_peak_1833 111 . 9.19593 15.71591 11.16157 101 +chr7 88219916 88220172 DRX127657.05_peak_1834 105 . 8.81348 15.07153 10.59646 111 +chr7 88227204 88227388 DRX127657.05_peak_1835 89 . 8.26026 13.16147 8.93107 96 +chr7 90211689 90211840 DRX127657.05_peak_1836 76 . 7.55239 11.67553 7.64030 83 +chr7 91264377 91264543 DRX127657.05_peak_1837 111 . 8.37595 15.64572 11.10129 49 +chr7 93232246 93232407 DRX127657.05_peak_1838 138 . 10.69923 18.70170 13.82724 30 +chr7 94620792 94620943 DRX127657.05_peak_1839 95 . 8.71143 13.90851 9.58200 37 +chr7 97872234 97872396 DRX127657.05_peak_1840 91 . 8.26154 13.43725 9.17317 28 +chr7 99438751 99438996 DRX127657.05_peak_1841 118 . 8.90973 16.52497 11.87983 166 +chr7 100148573 100148814 DRX127657.05_peak_1842 107 . 8.88741 15.19526 10.70735 117 +chr7 100538885 100539181 DRX127657.05_peak_1843 93 . 7.69927 13.65051 9.35799 45 +chr7 100586164 100586583 DRX127657.05_peak_1844 117 . 7.68408 16.35094 11.72472 218 +chr7 100802247 100802398 DRX127657.05_peak_1845 76 . 6.78253 11.74331 7.69821 101 +chr7 101100351 101100514 DRX127657.05_peak_1846 109 . 7.96813 15.43008 10.90984 113 +chr7 101244691 101244953 DRX127657.05_peak_1847 89 . 7.17131 13.20474 8.96798 61 +chr7 101252224 101252375 DRX127657.05_peak_1848 72 . 6.90744 11.19489 7.22611 24 +chr7 101856830 101857192 DRX127657.05_peak_1849 162 . 9.66112 21.37901 16.28250 244 +chr7 101882836 101883053 DRX127657.05_peak_1850 169 . 9.75892 22.10261 16.94995 140 +chr7 101883103 101883258 DRX127657.05_peak_1851 87 . 6.79324 12.98884 8.78217 87 +chr7 102153327 102153486 DRX127657.05_peak_1852 90 . 7.72163 13.27428 9.03104 131 +chr7 102273876 102274116 DRX127657.05_peak_1853 80 . 6.57775 12.20494 8.09962 149 +chr7 102363618 102363903 DRX127657.05_peak_1854 132 . 9.58221 18.10907 13.29122 175 +chr7 104984015 104984388 DRX127657.05_peak_1855 100 . 7.66191 14.46065 10.06314 74 +chr7 106009542 106009721 DRX127657.05_peak_1856 73 . 7.34304 11.30741 7.32212 38 +chr7 106112346 106112601 DRX127657.05_peak_1857 116 . 9.28708 16.26251 11.64654 77 +chr7 107168625 107168800 DRX127657.05_peak_1858 97 . 7.67951 14.04765 9.70157 41 +chr7 108525999 108526348 DRX127657.05_peak_1859 102 . 8.32558 14.67471 10.24837 89 +chr7 108569832 108570019 DRX127657.05_peak_1860 86 . 8.10474 12.88402 8.68874 67 +chr7 116317298 116317484 DRX127657.05_peak_1861 100 . 8.81113 14.39823 10.00527 66 +chr7 116716738 116716900 DRX127657.05_peak_1862 88 . 8.04644 13.07142 8.85322 81 +chr7 124929874 124930058 DRX127657.05_peak_1863 107 . 9.33506 15.23110 10.73647 88 +chr7 127651891 127652065 DRX127657.05_peak_1864 75 . 7.17470 11.62877 7.59993 107 +chr7 129224614 129224936 DRX127657.05_peak_1865 146 . 10.20904 19.64048 14.67885 186 +chr7 129611766 129611938 DRX127657.05_peak_1866 97 . 8.64678 14.10637 9.75202 69 +chr7 129958688 129958894 DRX127657.05_peak_1867 123 . 9.77068 17.07827 12.36720 78 +chr7 133082114 133082347 DRX127657.05_peak_1868 133 . 10.40264 18.17311 13.34759 113 +chr7 134316946 134317212 DRX127657.05_peak_1869 186 . 11.16455 23.95088 18.67209 133 +chr7 134646574 134646895 DRX127657.05_peak_1870 176 . 11.51958 22.86094 17.65451 121 +chr7 138752563 138752775 DRX127657.05_peak_1871 87 . 7.97721 12.95469 8.75037 133 +chr7 142666037 142666295 DRX127657.05_peak_1872 250 . 12.54371 30.61639 25.01180 133 +chr7 142667453 142667667 DRX127657.05_peak_1873 210 . 11.27396 26.51006 21.09315 81 +chr7 143620632 143620880 DRX127657.05_peak_1874 180 . 11.09484 23.26216 18.02930 134 +chr7 143836744 143836959 DRX127657.05_peak_1875 105 . 8.00303 15.02871 10.56072 75 +chr7 145997242 145997521 DRX127657.05_peak_1876 780 . 29.75347 84.73772 78.08291 134 +chr7 149028552 149028789 DRX127657.05_peak_1877 118 . 8.90973 16.52497 11.87983 137 +chr7 149195439 149195645 DRX127657.05_peak_1878 94 . 8.01362 13.75096 9.44466 178 +chr7 149239543 149239735 DRX127657.05_peak_1879 101 . 8.52969 14.59960 10.18331 58 +chr7 149624657 149624821 DRX127657.05_peak_1880 134 . 9.96852 18.27304 13.43656 47 +chr7 149690864 149691081 DRX127657.05_peak_1881 113 . 9.06280 15.88894 11.31520 136 +chr7 151057954 151058200 DRX127657.05_peak_1882 160 . 6.82116 21.11037 16.03268 95 +chr7 151227183 151227448 DRX127657.05_peak_1883 108 . 8.71214 15.30931 10.80667 155 +chr7 151687704 151687889 DRX127657.05_peak_1884 134 . 7.96325 18.31960 13.47946 42 +chr7 151727761 151728075 DRX127657.05_peak_1885 219 . 10.75374 27.39188 21.93357 187 +chr7 151736514 151736685 DRX127657.05_peak_1886 98 . 7.77975 14.21294 9.84763 88 +chr7 151745309 151745582 DRX127657.05_peak_1887 97 . 7.72930 14.12979 9.77351 184 +chr7 151754199 151754460 DRX127657.05_peak_1888 112 . 8.75061 15.80726 11.24222 128 +chr7 151782885 151783146 DRX127657.05_peak_1889 102 . 8.32558 14.67471 10.24837 119 +chr7 155951529 155952006 DRX127657.05_peak_1890 110 . 8.32056 15.55428 11.02071 221 +chr7 156908990 156909168 DRX127657.05_peak_1891 139 . 10.89844 18.78639 13.90415 80 +chr7 158595143 158595389 DRX127657.05_peak_1892 179 . 12.58732 23.22871 17.99870 123 +chr7 158704960 158705116 DRX127657.05_peak_1893 83 . 7.45638 12.45185 8.31583 58 +chr7 158818580 158818770 DRX127657.05_peak_1894 132 . 9.25763 18.06510 13.25196 104 +chr7 159335791 159336023 DRX127657.05_peak_1895 143 . 11.12734 19.23748 14.31680 119 +chr8 1755566 1755848 DRX127657.05_peak_1896 156 . 10.84599 20.69734 15.64967 134 +chr8 2127553 2127761 DRX127657.05_peak_1897 170 . 11.76339 22.23901 17.07792 140 +chr8 8227997 8228411 DRX127657.05_peak_1898 114 . 7.78183 16.04968 11.45705 293 +chr8 8296077 8296525 DRX127657.05_peak_1899 226 . 12.61637 28.20054 22.69384 131 +chr8 11284538 11284734 DRX127657.05_peak_1900 89 . 8.11688 13.19069 8.95483 146 +chr8 11854028 11854199 DRX127657.05_peak_1901 120 . 8.46842 16.74661 12.07421 112 +chr8 12194333 12194627 DRX127657.05_peak_1902 130 . 8.49869 17.78433 13.00478 144 +chr8 12436607 12436913 DRX127657.05_peak_1903 136 . 8.94802 18.55697 13.69458 143 +chr8 12601368 12601608 DRX127657.05_peak_1904 152 . 10.24399 20.21159 15.20321 125 +chr8 12607631 12607851 DRX127657.05_peak_1905 105 . 8.81348 15.07153 10.59646 88 +chr8 17922741 17922899 DRX127657.05_peak_1906 128 . 9.31272 17.66437 12.89631 95 +chr8 22178826 22179191 DRX127657.05_peak_1907 129 . 8.76494 17.73965 12.96415 164 +chr8 22227803 22227981 DRX127657.05_peak_1908 112 . 8.16460 15.75976 11.20199 89 +chr8 22383599 22383804 DRX127657.05_peak_1909 104 . 8.20831 14.91747 10.46187 118 +chr8 23345397 23345580 DRX127657.05_peak_1910 102 . 7.51597 14.66445 10.24061 89 +chr8 24668886 24669037 DRX127657.05_peak_1911 141 . 10.71747 19.10625 14.19555 79 +chr8 25458387 25458643 DRX127657.05_peak_1912 103 . 8.09680 14.73442 10.30155 146 +chr8 26457924 26458139 DRX127657.05_peak_1913 107 . 8.88741 15.19526 10.70735 124 +chr8 27617071 27617389 DRX127657.05_peak_1914 97 . 7.18959 14.10443 9.75163 117 +chr8 27872741 27873010 DRX127657.05_peak_1915 130 . 9.13599 17.86299 13.07736 190 +chr8 28889992 28890430 DRX127657.05_peak_1916 187 . 10.88797 24.06063 18.77635 162 +chr8 30254867 30255029 DRX127657.05_peak_1917 98 . 8.32856 14.26762 9.89435 90 +chr8 33104988 33105139 DRX127657.05_peak_1918 89 . 8.34550 13.15917 8.93008 117 +chr8 33513562 33513720 DRX127657.05_peak_1919 108 . 6.87384 15.31429 10.81150 105 +chr8 38527402 38527605 DRX127657.05_peak_1920 123 . 8.67984 17.10170 12.38972 144 +chr8 38734707 38734912 DRX127657.05_peak_1921 90 . 7.99659 13.33642 9.08224 141 +chr8 41529011 41529218 DRX127657.05_peak_1922 117 . 9.08403 16.35560 11.72832 127 +chr8 53886110 53886261 DRX127657.05_peak_1923 88 . 7.34754 13.07439 8.85513 76 +chr8 54101876 54102040 DRX127657.05_peak_1924 81 . 7.85083 12.21535 8.10827 109 +chr8 58553092 58553252 DRX127657.05_peak_1925 93 . 8.19448 13.66401 9.36695 130 +chr8 61714751 61714914 DRX127657.05_peak_1926 85 . 7.39816 12.74745 8.56996 129 +chr8 65634302 65634468 DRX127657.05_peak_1927 118 . 9.44287 16.52363 11.87979 47 +chr8 65644651 65644847 DRX127657.05_peak_1928 128 . 9.58870 17.64364 12.87720 140 +chr8 65717425 65717577 DRX127657.05_peak_1929 96 . 8.56688 13.96596 9.62987 63 +chr8 66124162 66124471 DRX127657.05_peak_1930 90 . 7.49427 13.31497 9.06404 140 +chr8 66613184 66613508 DRX127657.05_peak_1931 112 . 8.75061 15.80726 11.24222 222 +chr8 66667268 66667525 DRX127657.05_peak_1932 167 . 11.27454 21.92261 16.78580 127 +chr8 67585350 67585616 DRX127657.05_peak_1933 398 . 20.65384 45.84672 39.84848 134 +chr8 69690042 69690346 DRX127657.05_peak_1934 359 . 17.47426 41.82734 35.92347 114 +chr8 73294242 73294426 DRX127657.05_peak_1935 108 . 7.63192 15.32103 10.81528 46 +chr8 73972250 73972471 DRX127657.05_peak_1936 179 . 11.02098 23.13842 17.91396 113 +chr8 77000180 77000331 DRX127657.05_peak_1937 98 . 8.72818 14.25040 9.88066 46 +chr8 81842409 81842584 DRX127657.05_peak_1938 119 . 9.70109 16.58661 11.93477 48 +chr8 86342363 86342614 DRX127657.05_peak_1939 143 . 10.55380 19.25316 14.33203 139 +chr8 89984189 89984387 DRX127657.05_peak_1940 99 . 8.62119 14.37973 9.99017 69 +chr8 95024918 95025095 DRX127657.05_peak_1941 100 . 8.81113 14.39823 10.00527 116 +chr8 98825979 98826213 DRX127657.05_peak_1942 105 . 8.81348 15.07153 10.59646 51 +chr8 99495765 99496028 DRX127657.05_peak_1943 290 . 17.15593 34.79066 29.04833 135 +chr8 102238884 102239356 DRX127657.05_peak_1944 117 . 7.72304 16.42064 11.78796 304 +chr8 102528649 102528849 DRX127657.05_peak_1945 94 . 8.26264 13.77742 9.46633 117 +chr8 102788990 102789145 DRX127657.05_peak_1946 169 . 10.05608 22.06365 16.91456 100 +chr8 104589013 104589277 DRX127657.05_peak_1947 73 . 7.34304 11.30741 7.32212 78 +chr8 106270142 106270394 DRX127657.05_peak_1948 107 . 8.64524 15.19921 10.70836 122 +chr8 109334178 109334513 DRX127657.05_peak_1949 134 . 9.96852 18.27304 13.43656 135 +chr8 109974377 109974533 DRX127657.05_peak_1950 88 . 8.04644 13.07142 8.85322 95 +chr8 123072510 123072675 DRX127657.05_peak_1951 96 . 8.56688 13.96596 9.62987 137 +chr8 124372503 124372707 DRX127657.05_peak_1952 113 . 8.81532 15.91354 11.33650 96 +chr8 124474796 124474947 DRX127657.05_peak_1953 84 . 7.34686 12.66394 8.49733 94 +chr8 124998309 124998485 DRX127657.05_peak_1954 98 . 8.54701 14.25426 9.88322 112 +chr8 125430099 125430371 DRX127657.05_peak_1955 95 . 6.34063 13.85436 9.53421 123 +chr8 127668733 127668996 DRX127657.05_peak_1956 247 . 12.02646 30.34050 24.74323 149 +chr8 127859947 127860244 DRX127657.05_peak_1957 157 . 8.20210 20.80434 15.75097 110 +chr8 127949720 127949871 DRX127657.05_peak_1958 83 . 6.30040 12.53645 8.38850 95 +chr8 127969492 127969661 DRX127657.05_peak_1959 127 . 7.54454 17.52611 12.77172 109 +chr8 128176655 128176988 DRX127657.05_peak_1960 144 . 9.12270 19.37412 14.44004 153 +chr8 130042363 130042542 DRX127657.05_peak_1961 107 . 8.38145 15.20154 10.70836 106 +chr8 131856809 131857028 DRX127657.05_peak_1962 121 . 9.10310 16.84317 12.16188 83 +chr8 133203098 133203249 DRX127657.05_peak_1963 69 . 6.22471 10.82462 6.90697 73 +chr8 133375543 133375709 DRX127657.05_peak_1964 106 . 7.78089 15.11431 10.63230 95 +chr8 140511945 140512096 DRX127657.05_peak_1965 124 . 7.58679 17.12694 12.41400 81 +chr8 141026836 141027056 DRX127657.05_peak_1966 177 . 10.94810 23.01622 17.79776 110 +chr8 141154140 141154392 DRX127657.05_peak_1967 115 . 7.82361 16.12272 11.52620 101 +chr8 141228493 141228668 DRX127657.05_peak_1968 67 . 6.11691 10.64543 6.75199 71 +chr8 141257676 141257827 DRX127657.05_peak_1969 99 . 7.83085 14.29713 9.91817 127 +chr8 142235138 142235296 DRX127657.05_peak_1970 109 . 8.01635 15.51115 10.98095 40 +chr8 142676112 142676264 DRX127657.05_peak_1971 118 . 7.22272 16.43744 11.80134 89 +chr8 142815783 142816062 DRX127657.05_peak_1972 191 . 9.81230 24.45882 19.15070 145 +chr8 143291271 143291496 DRX127657.05_peak_1973 115 . 7.31685 16.15068 11.54632 96 +chr8 143430756 143430987 DRX127657.05_peak_1974 135 . 7.03961 18.38914 13.54295 120 +chr8 143454688 143454889 DRX127657.05_peak_1975 143 . 9.07064 19.28433 14.35855 111 +chr8 143512688 143512851 DRX127657.05_peak_1976 79 . 5.78927 11.98446 7.90970 37 +chr8 143541510 143541682 DRX127657.05_peak_1977 92 . 6.61542 13.52357 9.24751 62 +chr8 143609594 143609793 DRX127657.05_peak_1978 124 . 6.88756 17.14582 12.42990 83 +chr8 143617513 143617759 DRX127657.05_peak_1979 124 . 8.13554 17.15119 12.43432 112 +chr8 143936539 143936769 DRX127657.05_peak_1980 129 . 4.97377 17.68196 12.91215 105 +chr8 144078448 144078681 DRX127657.05_peak_1981 148 . 7.99471 19.88166 14.89935 127 +chr8 144104238 144104464 DRX127657.05_peak_1982 133 . 6.74823 18.20209 13.37435 97 +chr8 144126515 144126673 DRX127657.05_peak_1983 109 . 8.56205 15.49779 10.96926 72 +chr8 144137676 144137882 DRX127657.05_peak_1984 98 . 7.26850 14.24047 9.87326 66 +chr8 144326741 144326986 DRX127657.05_peak_1985 166 . 7.09778 21.80801 16.68205 167 +chr8 144755303 144755626 DRX127657.05_peak_1986 90 . 7.25865 13.35029 9.09395 253 +chr8 144767508 144767990 DRX127657.05_peak_1987 375 . 11.47376 43.52768 37.58222 187 +chr8 144768046 144768495 DRX127657.05_peak_1988 119 . 6.22368 16.56301 11.91521 85 +chr8 144901480 144901731 DRX127657.05_peak_1989 91 . 7.30312 13.42428 9.16181 107 +chr8 145002920 145003071 DRX127657.05_peak_1990 108 . 8.50099 15.39763 10.88204 63 +chr9 764168 764391 DRX127657.05_peak_1991 172 . 12.12636 22.40558 17.23272 137 +chr9 4662271 4662427 DRX127657.05_peak_1992 129 . 10.12206 17.68227 12.91215 34 +chr9 4713902 4714193 DRX127657.05_peak_1993 123 . 9.77068 17.07827 12.36720 220 +chr9 4792767 4792949 DRX127657.05_peak_1994 97 . 8.47410 14.13138 9.77360 145 +chr9 4984713 4984869 DRX127657.05_peak_1995 100 . 8.20424 14.47610 10.07523 125 +chr9 5628903 5629094 DRX127657.05_peak_1996 103 . 8.66927 14.83113 10.38625 85 +chr9 6681626 6681787 DRX127657.05_peak_1997 117 . 8.84708 16.42188 11.78796 86 +chr9 9442119 9442284 DRX127657.05_peak_1998 145 . 11.24543 19.47516 14.52989 85 +chr9 15422698 15422879 DRX127657.05_peak_1999 89 . 7.93273 13.23118 8.99202 46 +chr9 19049319 19049559 DRX127657.05_peak_2000 109 . 8.56205 15.49779 10.96926 122 +chr9 19230558 19230709 DRX127657.05_peak_2001 80 . 7.67143 12.13137 8.03489 58 +chr9 33329319 33329506 DRX127657.05_peak_2002 139 . 10.01703 18.82746 13.93989 74 +chr9 33415601 33415849 DRX127657.05_peak_2003 130 . 9.13599 17.86299 13.07736 136 +chr9 34376935 34377111 DRX127657.05_peak_2004 98 . 7.01590 14.24235 9.87455 33 +chr9 35604022 35604240 DRX127657.05_peak_2005 124 . 8.41075 17.13699 12.42237 88 +chr9 35657790 35657941 DRX127657.05_peak_2006 116 . 8.16993 16.24209 11.62900 66 +chr9 35732228 35732605 DRX127657.05_peak_2007 126 . 7.47900 17.40014 12.66055 218 +chr9 36190645 36190915 DRX127657.05_peak_2008 97 . 8.19966 14.05576 9.70755 175 +chr9 36487719 36487916 DRX127657.05_peak_2009 143 . 10.55380 19.25316 14.33203 96 +chr9 37954019 37954181 DRX127657.05_peak_2010 80 . 5.49437 12.15977 8.06205 41 +chr9 37954223 37954402 DRX127657.05_peak_2011 112 . 6.44992 15.78046 11.21931 55 +chr9 38047745 38048026 DRX127657.05_peak_2012 169 . 9.13558 22.07543 16.92454 142 +chr9 38392623 38392872 DRX127657.05_peak_2013 71 . 6.85636 11.11222 7.15579 60 +chr9 38620530 38620783 DRX127657.05_peak_2014 121 . 8.81679 16.84488 12.16210 133 +chr9 39874383 39874645 DRX127657.05_peak_2015 107 . 8.44080 15.29889 10.79812 131 +chr9 41229346 41229591 DRX127657.05_peak_2016 158 . 9.72611 20.94541 15.88093 119 +chr9 41229732 41230078 DRX127657.05_peak_2017 105 . 7.73545 15.03741 10.56701 105 +chr9 41274054 41274227 DRX127657.05_peak_2018 130 . 9.44555 17.88355 13.09344 76 +chr9 42431215 42431419 DRX127657.05_peak_2019 102 . 8.59891 14.71429 10.28274 107 +chr9 42550880 42551106 DRX127657.05_peak_2020 113 . 9.53107 15.96846 11.38455 108 +chr9 42569014 42569260 DRX127657.05_peak_2021 91 . 8.06148 13.44359 9.17780 109 +chr9 61856539 61856967 DRX127657.05_peak_2022 131 . 8.87168 17.92017 13.12633 90 +chr9 62838359 62838615 DRX127657.05_peak_2023 167 . 10.26905 21.87153 16.73911 144 +chr9 65675885 65676036 DRX127657.05_peak_2024 72 . 7.27581 11.19081 7.22394 72 +chr9 70038277 70038428 DRX127657.05_peak_2025 108 . 8.96258 15.32146 10.81528 54 +chr9 71911320 71911487 DRX127657.05_peak_2026 86 . 7.68718 12.82836 8.64199 45 +chr9 75028321 75028474 DRX127657.05_peak_2027 97 . 8.47410 14.13138 9.77360 142 +chr9 77177274 77177499 DRX127657.05_peak_2028 92 . 8.12744 13.55276 9.27453 146 +chr9 80565610 80565761 DRX127657.05_peak_2029 132 . 10.61407 18.01649 13.21178 122 +chr9 82935018 82935211 DRX127657.05_peak_2030 160 . 11.62648 21.10425 16.02679 90 +chr9 83048090 83048412 DRX127657.05_peak_2031 107 . 9.09477 15.19386 10.70735 129 +chr9 83538648 83538870 DRX127657.05_peak_2032 153 . 10.67941 20.42028 15.39554 95 +chr9 90642882 90643077 DRX127657.05_peak_2033 175 . 12.55451 22.79812 17.59817 123 +chr9 91164842 91165000 DRX127657.05_peak_2034 108 . 9.34392 15.37730 10.86407 40 +chr9 93566627 93566778 DRX127657.05_peak_2035 102 . 8.98184 14.70610 10.27612 79 +chr9 94166024 94166185 DRX127657.05_peak_2036 80 . 7.67143 12.13137 8.03489 94 +chr9 94783199 94783437 DRX127657.05_peak_2037 143 . 10.55380 19.25316 14.33203 150 +chr9 97633441 97633626 DRX127657.05_peak_2038 92 . 8.33581 13.56479 9.28212 42 +chr9 98056410 98056663 DRX127657.05_peak_2039 121 . 9.60398 16.79527 12.11859 111 +chr9 100099070 100099235 DRX127657.05_peak_2040 89 . 8.11688 13.19069 8.95483 81 +chr9 105064630 105064797 DRX127657.05_peak_2041 61 . 6.74218 9.90891 6.12136 36 +chr9 120842909 120843093 DRX127657.05_peak_2042 104 . 9.06971 14.86659 10.41831 53 +chr9 120868642 120868793 DRX127657.05_peak_2043 101 . 8.69666 14.50793 10.10320 91 +chr9 121743502 121743653 DRX127657.05_peak_2044 126 . 9.44475 17.40600 12.66405 82 +chr9 124392921 124393137 DRX127657.05_peak_2045 119 . 9.52274 16.65809 11.99694 118 +chr9 124852907 124853078 DRX127657.05_peak_2046 79 . 6.96076 12.03464 7.95116 125 +chr9 125143461 125143700 DRX127657.05_peak_2047 207 . 13.45265 26.13297 20.73844 113 +chr9 127561909 127562060 DRX127657.05_peak_2048 107 . 7.31310 15.22121 10.72701 121 +chr9 127579057 127579261 DRX127657.05_peak_2049 75 . 6.93577 11.60683 7.58186 14 +chr9 127803028 127803261 DRX127657.05_peak_2050 106 . 7.78089 15.11431 10.63230 121 +chr9 128035261 128035447 DRX127657.05_peak_2051 87 . 7.97721 12.95469 8.75037 65 +chr9 128733328 128733487 DRX127657.05_peak_2052 107 . 9.09477 15.19386 10.70735 91 +chr9 129080844 129081152 DRX127657.05_peak_2053 124 . 8.99645 17.14208 12.42649 70 +chr9 129334278 129334489 DRX127657.05_peak_2054 71 . 6.61259 11.08283 7.12926 73 +chr9 129642238 129642424 DRX127657.05_peak_2055 103 . 8.15217 14.82534 10.38263 98 +chr9 129777281 129777432 DRX127657.05_peak_2056 61 . 6.50195 9.89253 6.10880 89 +chr9 130712601 130712819 DRX127657.05_peak_2057 149 . 10.66212 19.89992 14.91599 114 +chr9 130866507 130866658 DRX127657.05_peak_2058 100 . 8.94701 14.44749 10.05244 90 +chr9 134164823 134165112 DRX127657.05_peak_2059 122 . 8.57283 16.92218 12.23388 100 +chr9 134443448 134443736 DRX127657.05_peak_2060 147 . 9.62665 19.70640 14.73933 190 +chr9 136052164 136052439 DRX127657.05_peak_2061 82 . 6.24103 12.42907 8.29548 82 +chr9 136866287 136866473 DRX127657.05_peak_2062 147 . 10.28454 19.76546 14.79001 45 +chr9 137129772 137129931 DRX127657.05_peak_2063 65 . 6.61107 10.37604 6.51993 35 +chr9 137200804 137200963 DRX127657.05_peak_2064 101 . 7.98828 14.55612 10.14598 32 +chr9 137578773 137579072 DRX127657.05_peak_2065 163 . 10.37299 21.49594 16.39127 199 +chr9 137612299 137612480 DRX127657.05_peak_2066 109 . 7.96813 15.43008 10.90984 110 +chr9 137845188 137845345 DRX127657.05_peak_2067 82 . 6.24103 12.42907 8.29548 86 +chr9_KI270718v1_random 37821 38028 DRX127657.05_peak_2068 104 . 8.74078 14.95018 10.48979 62 +chrM 62 883 DRX127657.05_peak_2069 161 . 1.97559 21.21294 16.12776 544 +chrM 1104 1481 DRX127657.05_peak_2070 77 . 1.64233 11.76838 7.72122 263 +chrM 14725 16503 DRX127657.05_peak_2071 215 . 2.12407 27.03760 21.59739 1644 +chrUn_GL000195v1 30683 30983 DRX127657.05_peak_2072 183 . 6.74446 23.65097 18.39469 175 +chrUn_GL000216v2 167779 167975 DRX127657.05_peak_2073 177 . 5.62924 23.00125 17.78441 98 +chrUn_GL000216v2 173828 174017 DRX127657.05_peak_2074 135 . 6.12831 18.44002 13.59050 134 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DRX127657.05_peak_2086 112 . 3.62706 15.81426 11.24897 117 +chrUn_KI270438v1 111345 111808 DRX127657.05_peak_2087 127 . 4.00365 17.46540 12.71483 114 +chrUn_KI270442v1 8 258 DRX127657.05_peak_2088 219 . 12.84308 27.35619 21.90455 121 +chrUn_KI270442v1 391608 391837 DRX127657.05_peak_2089 116 . 8.72440 16.21997 11.60912 96 +chrUn_KI270466v1 200 703 DRX127657.05_peak_2090 241 . 10.68962 29.70555 24.14066 353 +chrUn_KI270466v1 781 1151 DRX127657.05_peak_2091 176 . 8.95617 22.82318 17.62079 284 +chrUn_KI270467v1 1966 2538 DRX127657.05_peak_2092 128 . 5.26856 17.56737 12.80717 225 +chrUn_KI270467v1 2870 3041 DRX127657.05_peak_2093 75 . 4.18783 11.60277 7.58001 127 +chrUn_KI270519v1 136966 137194 DRX127657.05_peak_2094 150 . 10.74864 20.04475 15.05043 113 +chrX 222282 222516 DRX127657.05_peak_2095 211 . 13.97890 26.59627 21.17547 114 +chrX 3815061 3815212 DRX127657.05_peak_2096 90 . 7.21472 13.27712 9.03260 92 +chrX 13653100 13653254 DRX127657.05_peak_2097 72 . 7.16096 11.27463 7.29661 18 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108091540 108091730 DRX127657.05_peak_2110 109 . 9.26441 15.49186 10.96546 79 +chrX 109054173 109054457 DRX127657.05_peak_2111 184 . 12.61716 23.71257 18.45435 134 +chrX 119512584 119512735 DRX127657.05_peak_2112 138 . 10.69923 18.70170 13.82724 49 +chrX 119871720 119871871 DRX127657.05_peak_2113 74 . 7.49268 11.41350 7.41631 32 +chrX 120250610 120250763 DRX127657.05_peak_2114 118 . 9.81354 16.48579 11.84602 106 +chrX 120629908 120630151 DRX127657.05_peak_2115 135 . 10.49966 18.34483 13.50260 137 +chrX 123859996 123860192 DRX127657.05_peak_2116 125 . 10.06986 17.24043 12.51309 22 +chrX 126472375 126472767 DRX127657.05_peak_2117 471 . 17.01991 53.30112 47.14565 140 +chrX 129982546 129982753 DRX127657.05_peak_2118 116 . 9.01533 16.24245 11.62900 119 +chrX 130903254 130903412 DRX127657.05_peak_2119 69 . 7.08132 10.85743 6.93492 41 +chrX 148581256 148581429 DRX127657.05_peak_2120 105 . 9.23492 15.02410 10.55693 105 +chrX 153507638 153507789 DRX127657.05_peak_2121 108 . 8.71214 15.30931 10.80667 47 +chrX 154379106 154379371 DRX127657.05_peak_2122 120 . 7.10849 16.67825 12.01448 166 +chrX 154389969 154390120 DRX127657.05_peak_2123 97 . 5.69044 14.11875 9.76358 92 +chrX 154428459 154428619 DRX127657.05_peak_2124 105 . 8.81348 15.07153 10.59646 18 +chrX 156030282 156030779 DRX127657.05_peak_2125 399 . 14.44544 45.97231 39.97057 310 +chrY 4344676 4344956 DRX127657.05_peak_2126 713 . 30.04643 77.89420 71.32475 139 +chrY 9141792 9142065 DRX127657.05_peak_2127 662 . 29.05983 72.73689 66.24227 136 +chrY 11643606 11643804 DRX127657.05_peak_2128 160 . 11.62648 21.10425 16.02679 75 +chrY 11721939 11722122 DRX127657.05_peak_2129 162 . 10.63485 21.39017 16.29170 70 +chrY 11747577 11747743 DRX127657.05_peak_2130 166 . 11.18796 21.77841 16.65319 119 diff --git a/tests/data/hg38_sample/ERX3216878.05.bed b/tests/data/hg38_sample/ERX3216878.05.bed new file mode 100644 index 00000000..7950c857 --- /dev/null +++ b/tests/data/hg38_sample/ERX3216878.05.bed @@ -0,0 +1,327 @@ +chr1 27997 28617 ERX3216878.05_peak_1 74 . 4.33075 11.72090 7.47575 319 +chr1 89399 89645 ERX3216878.05_peak_2 102 . 6.18665 14.79886 10.29255 173 +chr1 198448 199158 ERX3216878.05_peak_3 109 . 5.07004 15.51234 10.95397 573 +chr1 266156 266545 ERX3216878.05_peak_4 165 . 6.58406 21.39261 16.54743 208 +chr1 355555 355935 ERX3216878.05_peak_5 179 . 7.13071 22.83995 17.94429 206 +chr1 23800343 23800582 ERX3216878.05_peak_6 175 . 9.34519 22.46492 17.58143 131 +chr1 25412502 25412744 ERX3216878.05_peak_7 184 . 10.47567 23.35248 18.43667 222 +chr1 91387261 91387500 ERX3216878.05_peak_8 95 . 7.16204 14.05684 9.59791 78 +chr1 120339612 120340060 ERX3216878.05_peak_9 118 . 4.28813 16.41733 11.80690 256 +chr1 120915168 120915582 ERX3216878.05_peak_10 110 . 4.03108 15.63019 11.06531 131 +chr1 121338408 121338747 ERX3216878.05_peak_11 95 . 5.47859 14.01350 9.55619 135 +chr1 125074950 125075601 ERX3216878.05_peak_12 343 . 8.79844 39.62604 34.35806 380 +chr1 125166911 125167425 ERX3216878.05_peak_13 350 . 3.74429 40.31698 35.03685 231 +chr1 125167580 125168464 ERX3216878.05_peak_14 425 . 4.07747 47.96041 42.53258 439 +chr1 125178910 125180802 ERX3216878.05_peak_15 354 . 2.23157 40.73780 35.44860 201 +chr1 125182088 125183219 ERX3216878.05_peak_16 442 . 2.49196 49.75040 44.29229 330 +chr1 143184626 143185465 ERX3216878.05_peak_17 698 . 3.62793 75.68171 69.84702 515 +chr1 143186899 143187352 ERX3216878.05_peak_18 254 . 2.25137 30.55514 25.45947 236 +chr1 143192245 143193777 ERX3216878.05_peak_19 248 . 2.07086 29.94247 24.85950 776 +chr1 143200159 143200883 ERX3216878.05_peak_20 253 . 2.23364 30.49357 25.39948 552 +chr1 143202433 143202857 ERX3216878.05_peak_21 444 . 2.51140 49.91146 44.45073 233 +chr1 143205981 143206812 ERX3216878.05_peak_22 348 . 2.44836 40.17642 34.89778 290 +chr1 143206864 143207134 ERX3216878.05_peak_23 181 . 2.04925 23.08949 18.18413 94 +chr1 143212661 143213176 ERX3216878.05_peak_24 122 . 1.70150 16.89179 12.25783 417 +chr1 143214264 143214926 ERX3216878.05_peak_25 153 . 1.73626 20.13836 15.34567 108 +chr1 143230129 143230539 ERX3216878.05_peak_26 231 . 2.08474 28.16251 23.11610 255 +chr1 143232769 143233208 ERX3216878.05_peak_27 177 . 1.87073 22.65349 17.76347 242 +chr1 143240081 143240489 ERX3216878.05_peak_28 151 . 1.84105 19.98712 15.19948 188 +chr1 143247017 143247433 ERX3216878.05_peak_29 193 . 2.11849 24.34082 19.39096 174 +chr1 143249754 143249996 ERX3216878.05_peak_30 135 . 1.81679 18.27929 13.57525 43 +chr1 143252822 143253090 ERX3216878.05_peak_31 184 . 1.87199 23.40211 18.48424 68 +chr1 143254430 143255079 ERX3216878.05_peak_32 217 . 1.94784 26.78928 21.77391 250 +chr1 143255329 143255979 ERX3216878.05_peak_33 170 . 1.87723 21.94226 17.07792 438 +chr1 143262097 143262952 ERX3216878.05_peak_34 248 . 2.02703 29.94215 24.85921 456 +chr1 143264441 143264780 ERX3216878.05_peak_35 186 . 1.90541 23.57995 18.65660 215 +chr1 143266590 143267355 ERX3216878.05_peak_36 163 . 1.85205 21.14393 16.30907 127 +chr1 143273732 143273972 ERX3216878.05_peak_37 120 . 2.48424 16.65559 12.03369 153 +chr1 143275259 143275791 ERX3216878.05_peak_38 130 . 2.72482 17.69486 13.02142 42 +chr1 144078484 144078729 ERX3216878.05_peak_39 112 . 5.89750 15.79566 11.22197 151 +chr1 144882015 144882254 ERX3216878.05_peak_40 105 . 5.75500 15.05597 10.52218 136 +chr1 144941309 144941671 ERX3216878.05_peak_41 134 . 6.36758 18.19097 13.49042 209 +chr1 145213533 145213898 ERX3216878.05_peak_42 94 . 3.82077 13.91547 9.46510 256 +chr1 148457473 148457837 ERX3216878.05_peak_43 95 . 3.89563 13.95813 9.50437 139 +chr1 148749730 148749985 ERX3216878.05_peak_44 101 . 5.43764 14.67161 10.17233 140 +chr1 149177969 149178278 ERX3216878.05_peak_45 104 . 4.08349 14.99888 10.46922 131 +chr1 149609272 149609641 ERX3216878.05_peak_46 114 . 4.27103 16.04733 11.46087 153 +chr1 157610070 157610425 ERX3216878.05_peak_47 76 . 6.91294 11.95655 7.69034 162 +chr1 206356768 206357007 ERX3216878.05_peak_48 104 . 5.58275 15.03197 10.49977 100 +chr1 206924163 206924503 ERX3216878.05_peak_49 103 . 4.95952 14.83696 10.32880 118 +chr1 218428146 218428409 ERX3216878.05_peak_50 98 . 7.85820 14.34216 9.86568 132 +chr1 228556852 228557352 ERX3216878.05_peak_51 203 . 8.37521 25.36446 20.38395 130 +chr1 230871852 230872282 ERX3216878.05_peak_52 339 . 14.70156 39.21116 33.95233 216 +chr1 248408303 248408807 ERX3216878.05_peak_53 112 . 7.92722 15.82864 11.25296 435 +chr1 248946024 248946441 ERX3216878.05_peak_54 128 . 7.06428 17.51363 12.84738 191 +chr10 9905 10513 ERX3216878.05_peak_55 677 . 17.42989 73.52923 67.71034 287 +chr10 5145286 5145525 ERX3216878.05_peak_56 77 . 6.50148 12.07489 7.79768 129 +chr10 38488975 38489216 ERX3216878.05_peak_57 148 . 6.60128 19.64905 14.87817 217 +chr10 38573280 38573540 ERX3216878.05_peak_58 230 . 11.14940 28.12109 23.07553 220 +chr10 41852870 41853113 ERX3216878.05_peak_59 162 . 2.45145 21.08161 16.24902 164 +chr10 41854569 41854828 ERX3216878.05_peak_60 158 . 2.50474 20.66558 15.84948 160 +chr10 42066561 42066892 ERX3216878.05_peak_61 105 . 2.38341 15.06279 10.52885 126 +chr10 42066972 42067374 ERX3216878.05_peak_62 166 . 2.67821 21.45850 16.61046 169 +chr10 42067447 42067716 ERX3216878.05_peak_63 139 . 2.47306 18.70153 13.97501 218 +chr10 42085282 42085664 ERX3216878.05_peak_64 163 . 2.37834 21.18257 16.34682 202 +chr10 42099677 42100112 ERX3216878.05_peak_65 195 . 2.50661 24.45659 19.50229 359 +chr10 42104118 42104529 ERX3216878.05_peak_66 321 . 3.50667 37.38533 32.15938 195 +chr10 47605684 47605923 ERX3216878.05_peak_67 88 . 5.65905 13.27074 8.87970 113 +chr10 84858915 84859184 ERX3216878.05_peak_68 102 . 8.11213 14.75958 10.25480 190 +chr10 98670490 98670745 ERX3216878.05_peak_69 163 . 10.15959 21.13752 16.30293 144 +chr10 114138044 114138283 ERX3216878.05_peak_70 145 . 9.35758 19.25277 14.50066 26 +chr10 133689188 133689881 ERX3216878.05_peak_71 120 . 4.59156 16.65693 12.03497 253 +chr11 3654219 3654477 ERX3216878.05_peak_72 75 . 5.48437 11.76515 7.51483 97 +chr11 40949066 40949305 ERX3216878.05_peak_73 89 . 7.49989 13.32417 8.92943 164 +chr11 90422861 90423166 ERX3216878.05_peak_74 115 . 8.65604 16.10741 11.51674 150 +chr11 121185548 121185822 ERX3216878.05_peak_75 99 . 7.35078 14.38187 9.90253 113 +chr12 9836 10441 ERX3216878.05_peak_76 605 . 17.47219 66.27134 60.55337 395 +chr12 2003738 2003977 ERX3216878.05_peak_77 185 . 8.99559 23.44684 18.52768 112 +chr12 11171244 11171568 ERX3216878.05_peak_78 1046 . 19.96290 110.82156 104.60914 158 +chr12 21123734 21123973 ERX3216878.05_peak_79 62 . 6.40029 10.37611 6.29693 25 +chr12 66856385 66856642 ERX3216878.05_peak_80 110 . 8.38223 15.65609 11.08953 127 +chr12 69360401 69360640 ERX3216878.05_peak_81 195 . 8.46979 24.51261 19.55651 68 +chr12 72907832 72908081 ERX3216878.05_peak_82 77 . 6.44584 11.97974 7.71151 146 +chr12 93399524 93399870 ERX3216878.05_peak_83 141 . 7.26234 18.86725 14.13203 113 +chr12 108348071 108348501 ERX3216878.05_peak_84 241 . 12.49093 29.24353 24.17465 235 +chr12 131626551 131626896 ERX3216878.05_peak_85 116 . 7.64729 16.28501 11.67954 137 +chr12 133264904 133265403 ERX3216878.05_peak_86 596 . 17.05329 65.35175 59.64819 265 +chr13 18211998 18212317 ERX3216878.05_peak_87 119 . 6.06559 16.60917 11.99025 141 +chr13 61806611 61806860 ERX3216878.05_peak_88 103 . 7.39359 14.90713 10.39502 164 +chr13 75709469 75709708 ERX3216878.05_peak_89 87 . 6.64982 13.16105 8.77792 78 +chr13 81952415 81952880 ERX3216878.05_peak_90 136 . 9.12825 18.35227 13.64514 140 +chr13 91038511 91038776 ERX3216878.05_peak_91 96 . 7.44077 14.08989 9.62781 171 +chr13 109424082 109424423 ERX3216878.05_peak_92 163 . 10.51477 21.19046 16.35352 112 +chr13 112872892 112873409 ERX3216878.05_peak_93 247 . 10.47120 29.87798 24.79678 246 +chr13 114354166 114354454 ERX3216878.05_peak_94 150 . 8.50420 19.83566 15.05765 128 +chr14 16096250 16096493 ERX3216878.05_peak_95 139 . 4.01890 18.66116 13.93673 25 +chr14 90397283 90397569 ERX3216878.05_peak_96 357 . 11.79982 41.01273 35.72043 136 +chr14_GL000225v1_random 6695 7085 ERX3216878.05_peak_97 102 . 6.15166 14.72485 10.22188 214 +chr14_GL000225v1_random 196796 197038 ERX3216878.05_peak_98 213 . 9.06174 26.34851 21.34388 211 +chr15 20768252 20768523 ERX3216878.05_peak_99 130 . 5.44803 17.74072 13.06305 161 +chr15 21337325 21337610 ERX3216878.05_peak_100 138 . 5.45442 18.52396 13.81033 175 +chr15 49170552 49170813 ERX3216878.05_peak_101 291 . 11.11686 34.32157 29.14819 167 +chr15 75219284 75219524 ERX3216878.05_peak_102 70 . 6.03278 11.26568 7.08804 55 +chr15 101962767 101963033 ERX3216878.05_peak_103 92 . 4.56192 13.65286 9.23356 136 +chr15 101980893 101981283 ERX3216878.05_peak_104 138 . 8.92372 18.51545 13.80217 232 +chr15_KI270727v1_random 64602 64888 ERX3216878.05_peak_105 142 . 5.72881 18.96784 14.22944 169 +chr16 13111069 13111308 ERX3216878.05_peak_106 127 . 9.14328 17.38466 12.72426 58 +chr16 18877592 18877992 ERX3216878.05_peak_107 114 . 5.65321 15.98858 11.40444 236 +chr16 18898258 18898511 ERX3216878.05_peak_108 105 . 3.90307 15.07242 10.53786 214 +chr16 21423549 21423812 ERX3216878.05_peak_109 104 . 3.94531 14.98323 10.45468 86 +chr16 21465175 21465511 ERX3216878.05_peak_110 131 . 4.65079 17.85993 13.17551 77 +chr16 21485829 21486091 ERX3216878.05_peak_111 113 . 3.60011 15.91702 11.33792 73 +chr16 21855844 21856090 ERX3216878.05_peak_112 92 . 3.75071 13.61930 9.20280 71 +chr16 21889216 21889546 ERX3216878.05_peak_113 84 . 4.39953 12.84256 8.48308 126 +chr16 21896986 21897242 ERX3216878.05_peak_114 113 . 4.47337 15.90163 11.32280 71 +chr16 21917333 21917592 ERX3216878.05_peak_115 101 . 3.36245 14.61233 10.11716 56 +chr16 22453148 22453397 ERX3216878.05_peak_116 105 . 3.40296 15.06616 10.53202 184 +chr16 22473498 22473797 ERX3216878.05_peak_117 146 . 4.96797 19.41539 14.65640 191 +chr16 22515070 22515365 ERX3216878.05_peak_118 120 . 4.07933 16.69329 12.07006 201 +chr16 28639661 28639901 ERX3216878.05_peak_119 96 . 4.14430 14.08260 9.62173 159 +chr16 29402813 29403052 ERX3216878.05_peak_120 90 . 4.06081 13.48853 9.08293 157 +chr16 29444846 29445133 ERX3216878.05_peak_121 140 . 5.31168 18.82128 14.08886 87 +chr16 29546607 29546883 ERX3216878.05_peak_122 105 . 4.81483 15.09113 10.55489 94 +chr16 29567563 29567803 ERX3216878.05_peak_123 80 . 3.44860 12.30501 8.01022 22 +chr16 29593462 29593701 ERX3216878.05_peak_124 80 . 3.63572 12.38030 8.07751 170 +chr16 29815646 29815900 ERX3216878.05_peak_125 182 . 8.58458 23.15585 18.24721 135 +chr16 30286638 30287052 ERX3216878.05_peak_126 150 . 5.63531 19.86987 15.09115 177 +chr16 30307250 30307503 ERX3216878.05_peak_127 86 . 3.50682 13.03795 8.66454 217 +chr16 30333398 30333783 ERX3216878.05_peak_128 88 . 3.84774 13.22927 8.84022 215 +chr16 34572361 34572944 ERX3216878.05_peak_129 384 . 4.03892 43.79498 38.44720 224 +chr16 34573850 34574360 ERX3216878.05_peak_130 390 . 4.06639 44.41719 39.05522 272 +chr16 34575518 34576601 ERX3216878.05_peak_131 160 . 2.76243 20.90793 16.08330 951 +chr16 34581177 34581654 ERX3216878.05_peak_132 259 . 2.96324 31.08266 25.97712 274 +chr16 34581844 34582435 ERX3216878.05_peak_133 327 . 3.06987 38.02602 32.78831 375 +chr16 34582707 34583776 ERX3216878.05_peak_134 416 . 3.26416 47.03072 41.62011 243 +chr16 34586384 34586858 ERX3216878.05_peak_135 232 . 2.48395 28.30051 23.25154 97 +chr16 34587769 34588389 ERX3216878.05_peak_136 446 . 3.35244 50.10204 44.63756 261 +chr16 34592783 34593729 ERX3216878.05_peak_137 234 . 2.77910 28.47116 23.41874 149 +chr16 34595083 34596041 ERX3216878.05_peak_138 388 . 3.93745 44.16398 38.80886 679 +chr16 34697589 34697849 ERX3216878.05_peak_139 206 . 7.10934 25.63752 20.65044 39 +chr16 34725559 34725801 ERX3216878.05_peak_140 141 . 6.31655 18.92775 14.19100 24 +chr16 34752693 34752955 ERX3216878.05_peak_141 150 . 6.34888 19.87792 15.09856 73 +chr16 38266753 38267216 ERX3216878.05_peak_142 221 . 6.71371 27.21202 22.18715 157 +chr16 46387797 46388232 ERX3216878.05_peak_143 154 . 2.08844 20.25875 15.46023 329 +chr16 46389754 46390554 ERX3216878.05_peak_144 212 . 2.17987 26.23628 21.23456 352 +chr16 46394605 46395053 ERX3216878.05_peak_145 279 . 2.56888 33.11415 27.96700 246 +chr16 46398372 46399096 ERX3216878.05_peak_146 169 . 2.26837 21.81457 16.95385 254 +chr16 46399182 46401586 ERX3216878.05_peak_147 409 . 3.26238 46.35516 40.95776 882 +chr16 51029564 51029803 ERX3216878.05_peak_148 93 . 7.77178 13.76908 9.32709 80 +chr16 70239133 70239428 ERX3216878.05_peak_149 114 . 5.50450 15.98743 11.40365 57 +chr16 90217260 90217518 ERX3216878.05_peak_150 170 . 8.22221 21.87853 17.01613 170 +chr17 1354926 1355359 ERX3216878.05_peak_151 213 . 9.06174 26.34851 21.34388 206 +chr17 12988887 12989126 ERX3216878.05_peak_152 151 . 10.05760 19.90235 15.11730 185 +chr17 26885594 26885972 ERX3216878.05_peak_153 107 . 5.23328 15.25292 10.70679 166 +chr17 29895835 29896402 ERX3216878.05_peak_154 163 . 6.68496 21.21271 16.37506 258 +chr17 82448280 82448858 ERX3216878.05_peak_155 194 . 7.73948 24.37255 19.42156 213 +chr17 83230627 83231023 ERX3216878.05_peak_156 200 . 7.41984 25.04787 20.07583 186 +chr17_KI270729v1_random 3498 3775 ERX3216878.05_peak_157 191 . 4.08288 24.12711 19.18435 98 +chr17_KI270729v1_random 10487 10726 ERX3216878.05_peak_158 140 . 4.79827 18.76536 14.03581 212 +chr17_KI270729v1_random 11620 11859 ERX3216878.05_peak_159 139 . 5.50828 18.69149 13.96527 94 +chr17_KI270729v1_random 22286 22746 ERX3216878.05_peak_160 192 . 4.17935 24.17317 19.22885 134 +chr17_KI270729v1_random 27592 27861 ERX3216878.05_peak_161 152 . 4.16438 19.99424 15.20643 204 +chr17_KI270729v1_random 54501 54742 ERX3216878.05_peak_162 160 . 6.91457 20.88435 16.06082 169 +chr17_KI270729v1_random 99110 99356 ERX3216878.05_peak_163 183 . 7.89283 23.21625 18.30494 34 +chr18 10033 10352 ERX3216878.05_peak_164 178 . 10.80147 22.77003 17.87632 238 +chr18 108889 109132 ERX3216878.05_peak_165 151 . 4.52933 19.94217 15.15653 155 +chr18 20561727 20562250 ERX3216878.05_peak_166 206 . 8.24803 25.59705 20.61067 406 +chr18 54728687 54729116 ERX3216878.05_peak_167 219 . 11.91454 27.00815 21.98738 204 +chr18 61700821 61701113 ERX3216878.05_peak_168 99 . 7.94106 14.47848 9.99184 103 +chr18 63469629 63470025 ERX3216878.05_peak_169 136 . 8.52152 18.32783 13.62218 165 +chr18 80262875 80263419 ERX3216878.05_peak_170 510 . 15.57792 56.60068 51.03944 223 +chr19 6555872 6556303 ERX3216878.05_peak_171 89 . 5.21056 13.36929 8.97209 287 +chr19 15968800 15969039 ERX3216878.05_peak_172 75 . 6.59579 11.83149 7.57603 165 +chr1_KI270709v1_random 66 362 ERX3216878.05_peak_173 283 . 3.08102 33.51075 28.35542 158 +chr1_KI270709v1_random 17191 17451 ERX3216878.05_peak_174 134 . 2.35295 18.16221 13.46334 209 +chr2 740200 740800 ERX3216878.05_peak_175 168 . 8.37679 21.67397 16.81734 370 +chr2 13271933 13272257 ERX3216878.05_peak_176 73 . 6.47570 11.63059 7.39053 175 +chr2 19362595 19362868 ERX3216878.05_peak_177 214 . 11.22334 26.42130 21.41480 146 +chr2 32866609 32867125 ERX3216878.05_peak_178 401 . 13.52722 45.51276 40.13016 150 +chr2 32916164 32916680 ERX3216878.05_peak_179 595 . 15.04187 65.29861 59.59583 275 +chr2 36183145 36183394 ERX3216878.05_peak_180 107 . 6.88547 15.33656 10.78637 87 +chr2 36183923 36184260 ERX3216878.05_peak_181 100 . 6.69033 14.52396 10.03452 165 +chr2 38749850 38750124 ERX3216878.05_peak_182 196 . 7.85119 24.64563 19.68620 105 +chr2 49639418 49639685 ERX3216878.05_peak_183 93 . 7.77178 13.76908 9.32709 220 +chr2 72266074 72266313 ERX3216878.05_peak_184 77 . 6.98507 12.07432 7.79768 40 +chr2 77794559 77794798 ERX3216878.05_peak_185 106 . 7.83239 15.20132 10.65810 77 +chr2 89840126 89840365 ERX3216878.05_peak_186 183 . 4.03765 23.26473 18.35205 189 +chr2 90322751 90322990 ERX3216878.05_peak_187 149 . 6.81185 19.71329 14.93981 157 +chr2 92081049 92081411 ERX3216878.05_peak_188 116 . 5.61645 16.28432 11.67915 104 +chr2 92081456 92081797 ERX3216878.05_peak_189 204 . 7.37843 25.42628 20.44461 139 +chr2 97114550 97114894 ERX3216878.05_peak_190 94 . 5.45309 13.95297 9.49943 214 +chr2 97588485 97588747 ERX3216878.05_peak_191 94 . 5.45309 13.95297 9.49943 194 +chr2 109199233 109199842 ERX3216878.05_peak_192 584 . 15.84535 64.17989 58.49672 375 +chr2 171688512 171688751 ERX3216878.05_peak_193 176 . 9.10636 22.56672 17.67864 101 +chr2 218257958 218258294 ERX3216878.05_peak_194 99 . 7.35078 14.38187 9.90253 206 +chr2 233567021 233567300 ERX3216878.05_peak_195 120 . 6.09408 16.67766 12.05518 215 +chr2 242161986 242162309 ERX3216878.05_peak_196 156 . 8.05456 20.50803 15.69774 168 +chr20 14699633 14700034 ERX3216878.05_peak_197 167 . 8.60684 21.60401 16.74968 192 +chr20 28494828 28495076 ERX3216878.05_peak_198 86 . 3.78826 12.99384 8.62420 138 +chr20 28495332 28495906 ERX3216878.05_peak_199 111 . 4.18558 15.70228 11.13444 379 +chr20 30289263 30289524 ERX3216878.05_peak_200 107 . 7.90639 15.32614 10.77674 90 +chr20 31106591 31106830 ERX3216878.05_peak_201 147 . 7.83636 19.56579 14.79916 52 +chr20 31157978 31158391 ERX3216878.05_peak_202 149 . 6.46912 19.75656 14.98194 231 +chr20 31186115 31186369 ERX3216878.05_peak_203 115 . 4.08337 16.14050 11.54815 44 +chr20 31241479 31241961 ERX3216878.05_peak_204 455 . 11.11075 50.99955 45.52261 210 +chr20 58687626 58687888 ERX3216878.05_peak_205 102 . 7.54973 14.72201 10.21919 118 +chr21 7926172 7926612 ERX3216878.05_peak_206 328 . 7.86392 38.03852 32.80052 214 +chr21 7938296 7938603 ERX3216878.05_peak_207 316 . 9.85203 36.90306 31.68447 103 +chr21 8203278 8203746 ERX3216878.05_peak_208 190 . 5.29106 23.96808 19.03090 236 +chr21 8247512 8247900 ERX3216878.05_peak_209 151 . 4.15749 19.95800 15.17120 234 +chr21 8386408 8386770 ERX3216878.05_peak_210 170 . 5.15771 21.94418 17.07898 164 +chr21 8430533 8430872 ERX3216878.05_peak_211 97 . 3.50484 14.21473 9.74488 229 +chr21 10270081 10270453 ERX3216878.05_peak_212 178 . 8.64701 22.74885 17.85580 150 +chr21 10325724 10325970 ERX3216878.05_peak_213 121 . 7.89835 16.73272 12.10706 221 +chr21 10655566 10655810 ERX3216878.05_peak_214 231 . 7.49836 28.22881 23.18097 33 +chr21 10692736 10693211 ERX3216878.05_peak_215 545 . 10.43503 60.17093 54.55116 227 +chr22 11211201 11211531 ERX3216878.05_peak_216 177 . 4.55240 22.61770 17.72810 133 +chr22 11212821 11213276 ERX3216878.05_peak_217 340 . 6.13546 39.26852 34.00747 230 +chr22 11214343 11214603 ERX3216878.05_peak_218 147 . 4.17804 19.48013 14.71644 48 +chr22 16267314 16267564 ERX3216878.05_peak_219 273 . 7.58674 32.49687 27.36190 50 +chr22 16352627 16352875 ERX3216878.05_peak_220 198 . 6.13791 24.81778 19.85219 44 +chr22 50807924 50808318 ERX3216878.05_peak_221 316 . 11.39940 36.84855 31.63256 163 +chr22_KI270733v1_random 119654 119973 ERX3216878.05_peak_222 169 . 5.13337 21.84592 16.98448 134 +chr22_KI270733v1_random 164583 165003 ERX3216878.05_peak_223 121 . 3.91907 16.77631 12.14858 198 +chr22_KI270735v1_random 39765 40023 ERX3216878.05_peak_224 149 . 7.01456 19.72953 14.95560 86 +chr22_KI270735v1_random 40903 41214 ERX3216878.05_peak_225 163 . 7.45434 21.21642 16.37839 166 +chr22_KI270736v1_random 147634 147916 ERX3216878.05_peak_226 188 . 8.14904 23.79843 18.86803 127 +chr22_KI270737v1_random 25967 26207 ERX3216878.05_peak_227 194 . 6.68052 24.37386 19.42253 87 +chr22_KI270737v1_random 32919 33195 ERX3216878.05_peak_228 193 . 5.65937 24.30026 19.35153 205 +chr22_KI270737v1_random 79841 80293 ERX3216878.05_peak_229 372 . 8.10172 42.58504 37.26163 220 +chr3 10095 10668 ERX3216878.05_peak_230 365 . 14.17367 41.81657 36.50830 314 +chr3 1565485 1565870 ERX3216878.05_peak_231 176 . 10.97193 22.49697 17.61172 183 +chr3 11132009 11132264 ERX3216878.05_peak_232 117 . 7.94625 16.33638 11.72884 51 +chr3 36168896 36169141 ERX3216878.05_peak_233 97 . 7.51107 14.20800 9.73880 174 +chr3 84421099 84421468 ERX3216878.05_peak_234 69 . 6.61755 11.09088 6.92848 210 +chr3 93470206 93470951 ERX3216878.05_peak_235 3530 . 19.17269 362.54791 353.05673 384 +chr3 106254977 106255218 ERX3216878.05_peak_236 138 . 6.69989 18.54183 13.82687 119 +chr3 138597643 138597948 ERX3216878.05_peak_237 202 . 8.56065 25.24961 20.27217 187 +chr3 160402348 160402600 ERX3216878.05_peak_238 201 . 7.82656 25.09413 20.12084 82 +chr3 164921817 164922083 ERX3216878.05_peak_239 98 . 7.85820 14.34216 9.86568 210 +chr3 173254594 173254833 ERX3216878.05_peak_240 98 . 7.85820 14.34216 9.86568 74 +chr3 196898635 196899075 ERX3216878.05_peak_241 659 . 17.01937 71.73827 65.94346 235 +chr3 197626824 197627077 ERX3216878.05_peak_242 122 . 6.34464 16.84180 12.21041 125 +chr3 198004374 198004764 ERX3216878.05_peak_243 77 . 5.10652 12.04788 7.77441 170 +chr3 198004848 198005228 ERX3216878.05_peak_244 100 . 5.71930 14.57732 10.08456 215 +chr4 20932058 20932330 ERX3216878.05_peak_245 120 . 8.73134 16.70333 12.07900 187 +chr4 49709151 49711926 ERX3216878.05_peak_246 968 . 4.71896 102.97523 96.86841 397 +chr4 51107077 51107710 ERX3216878.05_peak_247 1440 . 15.48277 150.71207 144.00337 285 +chr4 109624747 109624986 ERX3216878.05_peak_248 154 . 9.93735 20.22747 15.43002 151 +chr4 134863736 134864015 ERX3216878.05_peak_249 103 . 8.20045 14.90459 10.39290 199 +chr4 140318171 140318414 ERX3216878.05_peak_250 76 . 6.91294 11.95655 7.69034 161 +chr4 166579982 166580736 ERX3216878.05_peak_251 445 . 11.68035 50.00471 44.54203 341 +chr4 167398614 167398873 ERX3216878.05_peak_252 103 . 8.20045 14.90459 10.39290 102 +chr4 190122733 190123106 ERX3216878.05_peak_253 182 . 10.02686 23.13811 18.23070 185 +chr4 190179480 190179845 ERX3216878.05_peak_254 111 . 4.33699 15.70044 11.13266 150 +chr5 9972 11074 ERX3216878.05_peak_255 634 . 10.21623 69.20768 63.44421 271 +chr5 1333144 1333383 ERX3216878.05_peak_256 68 . 6.32511 10.99095 6.83941 37 +chr5 7056458 7056742 ERX3216878.05_peak_257 90 . 7.32332 13.45788 9.05377 219 +chr5 26100102 26100341 ERX3216878.05_peak_258 93 . 7.77178 13.76908 9.32709 158 +chr5 33848929 33849234 ERX3216878.05_peak_259 81 . 5.00222 12.50632 8.19217 189 +chr5 49591777 49592018 ERX3216878.05_peak_260 127 . 9.14328 17.38466 12.72426 159 +chr5 49601331 49603037 ERX3216878.05_peak_261 664 . 8.18969 72.20568 66.40621 305 +chr5 49630823 49631118 ERX3216878.05_peak_262 115 . 4.30029 16.16452 11.56430 167 +chr5 49658344 49658910 ERX3216878.05_peak_263 285 . 3.92524 33.68335 28.52418 212 +chr5 49661527 49661831 ERX3216878.05_peak_264 172 . 3.14322 22.11662 17.24722 110 +chr5 49666180 49667394 ERX3216878.05_peak_265 394 . 7.99759 44.79343 39.42525 984 +chr5 59970242 59970519 ERX3216878.05_peak_266 105 . 7.75976 15.07856 10.54326 129 +chr5 63847947 63848203 ERX3216878.05_peak_267 127 . 9.14328 17.38466 12.72426 98 +chr5 119452588 119452858 ERX3216878.05_peak_268 233 . 10.95643 28.37877 23.32792 164 +chr5 126164075 126164356 ERX3216878.05_peak_269 76 . 6.65753 11.93445 7.67008 161 +chr5 153037083 153037332 ERX3216878.05_peak_270 198 . 10.65863 24.83209 19.86582 101 +chr5 181464457 181464849 ERX3216878.05_peak_271 191 . 7.44366 24.12831 19.18551 170 +chr6 9327124 9327363 ERX3216878.05_peak_272 77 . 6.72043 12.03917 7.76592 164 +chr6 20889062 20889301 ERX3216878.05_peak_273 135 . 9.03862 18.20158 13.50043 189 +chr6 40064209 40064483 ERX3216878.05_peak_274 77 . 6.98507 12.07432 7.79768 127 +chr6 103557099 103557434 ERX3216878.05_peak_275 76 . 6.91294 11.95655 7.69034 300 +chr6 147956129 147956406 ERX3216878.05_peak_276 89 . 7.25280 13.34056 8.94446 94 +chr6 170737990 170738334 ERX3216878.05_peak_277 150 . 6.87772 19.87110 15.09222 118 +chr7 19708169 19708444 ERX3216878.05_peak_278 213 . 9.89005 26.33967 21.33514 108 +chr7 58106151 58106403 ERX3216878.05_peak_279 215 . 7.10345 26.57310 21.56219 193 +chr7 61071086 61071517 ERX3216878.05_peak_280 273 . 8.24638 32.47816 27.34416 109 +chr7 65488933 65489174 ERX3216878.05_peak_281 100 . 5.85823 14.49558 10.00737 197 +chr7 73046230 73046484 ERX3216878.05_peak_282 85 . 4.51054 12.86093 8.50092 205 +chr7 73073888 73074190 ERX3216878.05_peak_283 150 . 5.50470 19.83718 15.05893 95 +chr7 75319234 75319478 ERX3216878.05_peak_284 104 . 4.66524 14.95247 10.42608 181 +chr7 75347152 75347479 ERX3216878.05_peak_285 132 . 5.05208 17.95028 13.26232 232 +chr7 84581684 84581923 ERX3216878.05_peak_286 104 . 8.22895 14.95135 10.42499 148 +chr7 91332762 91333117 ERX3216878.05_peak_287 127 . 9.14328 17.38466 12.72426 172 +chr7 102479268 102479562 ERX3216878.05_peak_288 148 . 6.57166 19.57489 14.80744 104 +chr7 102572912 102573169 ERX3216878.05_peak_289 141 . 5.68886 18.84916 14.11556 117 +chr7 102671999 102672292 ERX3216878.05_peak_290 125 . 5.52071 17.18604 12.53818 103 +chr7 107769984 107770398 ERX3216878.05_peak_291 415 . 15.70989 46.94893 41.53968 213 +chr7 146133190 146133453 ERX3216878.05_peak_292 150 . 9.10846 19.87526 15.09612 165 +chr7 149600374 149600619 ERX3216878.05_peak_293 102 . 7.03527 14.72126 10.21892 64 +chr7 153282374 153282796 ERX3216878.05_peak_294 228 . 12.80059 27.89297 22.85344 197 +chr7 157461323 157461591 ERX3216878.05_peak_295 201 . 11.88626 25.16228 20.18751 175 +chr8 43237762 43238197 ERX3216878.05_peak_296 220 . 4.02475 27.10586 22.08344 209 +chr8 43239485 43240171 ERX3216878.05_peak_297 233 . 4.12524 28.44274 23.39059 171 +chr8 43240751 43241016 ERX3216878.05_peak_298 190 . 3.79193 23.99179 19.05401 62 +chr8 51691059 51691339 ERX3216878.05_peak_299 72 . 6.85746 11.47990 7.25595 65 +chr8 85264127 85264408 ERX3216878.05_peak_300 84 . 6.68943 12.80973 8.45291 105 +chr8 93668813 93669094 ERX3216878.05_peak_301 126 . 8.53573 17.35035 12.69592 101 +chr8 110374447 110374731 ERX3216878.05_peak_302 77 . 6.72043 12.03917 7.76592 190 +chr8 129497124 129497693 ERX3216878.05_peak_303 108 . 6.71213 15.44350 10.88816 485 +chr9 28321 28745 ERX3216878.05_peak_304 99 . 4.80864 14.39451 9.91462 218 +chr9 184884 185189 ERX3216878.05_peak_305 109 . 4.63789 15.51822 10.95955 101 +chr9 11544345 11544625 ERX3216878.05_peak_306 90 . 7.57981 13.45504 9.05208 157 +chr9 25173066 25173305 ERX3216878.05_peak_307 91 . 7.66145 13.58864 9.17383 172 +chr9 31620979 31621314 ERX3216878.05_peak_308 180 . 10.57814 22.95780 18.05730 214 +chr9 39808725 39809067 ERX3216878.05_peak_309 119 . 5.06613 16.57028 11.95352 123 +chr9 41195173 41195496 ERX3216878.05_peak_310 110 . 4.57477 15.62657 11.06189 100 +chr9 42567965 42568355 ERX3216878.05_peak_311 115 . 5.72574 16.17399 11.57346 173 +chr9 43279405 43279677 ERX3216878.05_peak_312 106 . 6.58536 15.19128 10.64844 108 +chr9 43318732 43318987 ERX3216878.05_peak_313 136 . 4.53746 18.37761 13.67015 27 +chr9 43319036 43319521 ERX3216878.05_peak_314 226 . 5.61696 27.68007 22.64568 150 +chr9 62533361 62533618 ERX3216878.05_peak_315 94 . 5.12672 13.88044 9.43176 126 +chr9 62898419 62898751 ERX3216878.05_peak_316 158 . 5.63272 20.64913 15.83342 120 +chr9 63448420 63448659 ERX3216878.05_peak_317 160 . 6.73560 20.89109 16.06734 175 +chr9 64772407 64772646 ERX3216878.05_peak_318 150 . 7.05038 19.81226 15.03525 174 +chr9 65257843 65258116 ERX3216878.05_peak_319 196 . 7.64775 24.64684 19.68708 131 +chr9 65669767 65670033 ERX3216878.05_peak_320 91 . 4.17486 13.61010 9.19424 194 +chr9 65763838 65764101 ERX3216878.05_peak_321 187 . 7.25019 23.62847 18.70263 145 +chr9 68235738 68236083 ERX3216878.05_peak_322 104 . 4.38308 14.95033 10.42499 167 +chr9 70038032 70038630 ERX3216878.05_peak_323 281 . 12.24542 33.33843 28.18649 185 +chr9 103312312 103312574 ERX3216878.05_peak_324 96 . 7.69757 14.07745 9.61690 95 +chr9 137327881 137328449 ERX3216878.05_peak_325 353 . 10.81882 40.60387 35.31739 349 +chr9_KI270717v1_random 22839 23117 ERX3216878.05_peak_326 174 . 7.13494 22.37544 17.49572 159 +chr9_KI270718v1_random 5971 6258 ERX3216878.05_peak_327 106 . 4.45305 15.19921 10.65619 82 diff --git a/tests/data/hg38_sample/SRX067409.05.bed b/tests/data/hg38_sample/SRX067409.05.bed new file mode 100644 index 00000000..fabb9e32 --- /dev/null +++ b/tests/data/hg38_sample/SRX067409.05.bed @@ -0,0 +1,707 @@ +chr1 632049 632275 SRX067409.05_peak_1 121 . 4.34192 16.63597 12.17915 112 +chr1 2440388 2440587 SRX067409.05_peak_2 187 . 8.80705 23.61286 18.76772 69 +chr1 9293897 9294044 SRX067409.05_peak_3 89 . 5.82213 13.22400 8.96022 115 +chr1 31431920 31432061 SRX067409.05_peak_4 75 . 5.86166 11.73343 7.59791 79 +chr1 46585254 46585391 SRX067409.05_peak_5 192 . 11.61494 24.13139 19.26819 33 +chr1 61925207 61925352 SRX067409.05_peak_6 320 . 14.08553 37.39867 32.02069 34 +chr1 68963695 68963830 SRX067409.05_peak_7 198 . 11.95790 24.70342 19.80719 95 +chr1 74500610 74500746 SRX067409.05_peak_8 198 . 11.95790 24.70342 19.80719 34 +chr1 91387236 91387461 SRX067409.05_peak_9 150 . 9.60896 19.67664 15.03249 118 +chr1 122462705 122462908 SRX067409.05_peak_10 349 . 16.23932 40.45532 34.98651 100 +chr1 122496506 122496699 SRX067409.05_peak_11 277 . 14.67664 32.95103 27.72361 85 +chr1 122720367 122720507 SRX067409.05_peak_12 209 . 7.53936 25.86113 20.92148 103 +chr1 122912718 122912871 SRX067409.05_peak_13 144 . 6.19557 19.04034 14.44110 45 +chr1 123195628 123195848 SRX067409.05_peak_14 552 . 13.51450 61.27357 55.25546 112 +chr1 123587721 123587927 SRX067409.05_peak_15 249 . 8.52610 30.02982 24.91355 102 +chr1 123635605 123635764 SRX067409.05_peak_16 163 . 6.98487 21.05826 16.33912 56 +chr1 123930334 123930534 SRX067409.05_peak_17 284 . 8.87945 33.72779 28.46632 110 +chr1 124005778 124005919 SRX067409.05_peak_18 159 . 6.48000 20.66695 15.96857 51 +chr1 124044890 124045101 SRX067409.05_peak_19 296 . 8.91566 34.94775 29.64358 106 +chr1 124054820 124054954 SRX067409.05_peak_20 143 . 6.33003 18.96233 14.36640 103 +chr1 124250706 124250844 SRX067409.05_peak_21 125 . 5.91101 17.04907 12.55034 24 +chr1 124344717 124344851 SRX067409.05_peak_22 106 . 5.74622 15.03520 10.66880 91 +chr1 124421008 124421171 SRX067409.05_peak_23 147 . 6.64868 19.31841 14.70906 117 +chr1 124456457 124456632 SRX067409.05_peak_24 116 . 5.52634 16.04564 11.61344 80 +chr1 124579425 124579608 SRX067409.05_peak_25 257 . 8.45577 30.92481 25.77308 82 +chr1 125069454 125069614 SRX067409.05_peak_26 231 . 9.94570 28.20768 23.16103 38 +chr1 125075057 125075313 SRX067409.05_peak_27 103 . 5.77820 14.71327 10.36049 100 +chr1 125075367 125075577 SRX067409.05_peak_28 173 . 7.42911 22.13371 17.35575 113 +chr1 125166851 125167263 SRX067409.05_peak_29 267 . 3.97539 31.97383 26.77844 315 +chr1 125167734 125168027 SRX067409.05_peak_30 467 . 5.11801 52.48290 46.71921 177 +chr1 125168184 125168419 SRX067409.05_peak_31 224 . 3.71329 27.46806 22.45046 29 +chr1 125169993 125170223 SRX067409.05_peak_32 171 . 3.12309 21.91827 17.16750 115 +chr1 125179055 125179449 SRX067409.05_peak_33 257 . 2.36396 30.86793 25.71736 172 +chr1 125179610 125180441 SRX067409.05_peak_34 275 . 2.41657 32.72235 27.50158 591 +chr1 125182156 125182747 SRX067409.05_peak_35 342 . 2.69316 39.71035 34.26228 163 +chr1 125182890 125183161 SRX067409.05_peak_36 272 . 2.54089 32.47629 27.26371 140 +chr1 143184641 143185456 SRX067409.05_peak_37 400 . 3.80911 45.60957 40.00934 607 +chr1 143186759 143187359 SRX067409.05_peak_38 233 . 2.73560 28.40487 23.35200 375 +chr1 143192724 143192951 SRX067409.05_peak_39 193 . 2.34372 24.25698 19.38908 108 +chr1 143193007 143193180 SRX067409.05_peak_40 164 . 2.24485 21.13163 16.40953 109 +chr1 143193384 143193875 SRX067409.05_peak_41 254 . 2.55238 30.58160 25.43920 384 +chr1 143194935 143195232 SRX067409.05_peak_42 117 . 2.04487 16.14981 11.71312 71 +chr1 143195603 143195739 SRX067409.05_peak_43 115 . 2.04488 15.96989 11.54115 77 +chr1 143200667 143200871 SRX067409.05_peak_44 160 . 2.44088 20.78222 16.07226 118 +chr1 143201495 143201661 SRX067409.05_peak_45 155 . 2.33561 20.21282 15.54548 65 +chr1 143202451 143202881 SRX067409.05_peak_46 233 . 2.59040 28.43104 23.37731 332 +chr1 143203397 143203552 SRX067409.05_peak_47 83 . 1.95766 12.53471 8.33061 111 +chr1 143206053 143206622 SRX067409.05_peak_48 287 . 2.87170 34.03571 28.76676 110 +chr1 143207048 143207184 SRX067409.05_peak_49 143 . 2.32421 18.93901 14.34370 74 +chr1 143214630 143214912 SRX067409.05_peak_50 169 . 2.06416 21.65083 16.91043 131 +chr1 143222204 143222372 SRX067409.05_peak_51 84 . 1.85250 12.69210 8.48175 123 +chr1 143230237 143230448 SRX067409.05_peak_52 234 . 2.56535 28.53344 23.47696 118 +chr1 143231274 143231507 SRX067409.05_peak_53 115 . 2.05302 15.93547 11.50831 145 +chr1 143232807 143233021 SRX067409.05_peak_54 201 . 2.32403 25.05731 20.14789 83 +chr1 143238032 143238248 SRX067409.05_peak_55 110 . 1.98544 15.47757 11.09508 41 +chr1 143246853 143247388 SRX067409.05_peak_56 212 . 2.54769 26.17941 21.21166 418 +chr1 143249765 143250053 SRX067409.05_peak_57 216 . 2.33612 26.64745 21.66456 133 +chr1 143252940 143253159 SRX067409.05_peak_58 220 . 2.25880 27.04540 22.04775 125 +chr1 143254475 143255042 SRX067409.05_peak_59 225 . 2.29538 27.59711 22.56825 367 +chr1 143255824 143256033 SRX067409.05_peak_60 197 . 2.25819 24.66291 19.78270 116 +chr1 143256376 143256550 SRX067409.05_peak_61 136 . 2.04939 18.22157 13.65161 50 +chr1 143262311 143262464 SRX067409.05_peak_62 102 . 1.96494 14.60248 10.25329 27 +chr1 143262515 143262826 SRX067409.05_peak_63 192 . 2.28692 24.14782 19.28385 148 +chr1 143263619 143263758 SRX067409.05_peak_64 135 . 2.08130 18.03756 13.50156 89 +chr1 143264542 143264769 SRX067409.05_peak_65 163 . 2.19539 21.02113 16.30266 144 +chr1 143266470 143266628 SRX067409.05_peak_66 138 . 2.09479 18.39856 13.82362 72 +chr1 143275465 143275602 SRX067409.05_peak_67 152 . 3.54311 19.93081 15.27642 23 +chr1 143275660 143275940 SRX067409.05_peak_68 218 . 4.27004 26.84297 21.85226 159 +chr1 143729635 143729771 SRX067409.05_peak_69 213 . 11.15905 26.30839 21.33646 43 +chr1 148522396 148522532 SRX067409.05_peak_70 204 . 10.29900 25.36076 20.43843 104 +chr1 151394177 151394311 SRX067409.05_peak_71 170 . 10.55873 21.80703 17.06091 118 +chr1 163110793 163110928 SRX067409.05_peak_72 185 . 11.47958 23.34242 18.50500 111 +chr1 164484263 164484399 SRX067409.05_peak_73 211 . 12.43622 26.08140 21.11656 94 +chr1 165752385 165752531 SRX067409.05_peak_74 321 . 15.77003 37.51375 32.13086 46 +chr1 174925765 174925902 SRX067409.05_peak_75 227 . 12.39316 27.82264 22.78569 98 +chr1 189992112 189992246 SRX067409.05_peak_76 185 . 11.47958 23.34242 18.50500 106 +chr1 216916748 216916882 SRX067409.05_peak_77 185 . 11.47958 23.34242 18.50500 97 +chr1 219922084 219922223 SRX067409.05_peak_78 245 . 13.12106 29.68267 24.57888 102 +chr1 230871960 230872096 SRX067409.05_peak_79 217 . 9.24630 26.74461 21.75591 101 +chr1 236097188 236097343 SRX067409.05_peak_80 123 . 7.66626 16.79542 12.32821 79 +chr10 10024 10400 SRX067409.05_peak_81 236 . 12.91069 28.69731 23.63444 168 +chr10 21522254 21522392 SRX067409.05_peak_82 199 . 11.71488 24.87536 19.97127 28 +chr10 31969954 31970091 SRX067409.05_peak_83 212 . 12.16545 26.22130 21.25177 102 +chr10 38573388 38573605 SRX067409.05_peak_84 302 . 14.24276 35.59133 30.27017 100 +chr10 38578918 38579130 SRX067409.05_peak_85 194 . 8.63089 24.32629 19.45522 97 +chr10 40098373 40098584 SRX067409.05_peak_86 269 . 13.41136 32.12444 26.92225 101 +chr10 40131328 40131549 SRX067409.05_peak_87 567 . 19.88890 62.80745 56.73294 107 +chr10 40441112 40441252 SRX067409.05_peak_88 215 . 11.96581 26.48665 21.50928 39 +chr10 40447234 40447420 SRX067409.05_peak_89 294 . 14.95920 34.76231 29.46482 97 +chr10 40556750 40556975 SRX067409.05_peak_90 562 . 20.10015 62.35301 56.29850 119 +chr10 40614674 40614892 SRX067409.05_peak_91 418 . 16.41864 47.50879 41.86274 106 +chr10 40746817 40746953 SRX067409.05_peak_92 198 . 11.95790 24.70342 19.80719 43 +chr10 41028686 41028885 SRX067409.05_peak_93 313 . 14.87358 36.68364 31.32282 102 +chr10 41063045 41063267 SRX067409.05_peak_94 433 . 19.02992 49.00557 43.32967 102 +chr10 41133765 41133899 SRX067409.05_peak_95 177 . 10.34126 22.55345 17.76379 95 +chr10 41315418 41315640 SRX067409.05_peak_96 545 . 21.48405 60.58637 54.58560 106 +chr10 41429672 41429810 SRX067409.05_peak_97 231 . 12.60979 28.18940 23.14340 38 +chr10 41843686 41843827 SRX067409.05_peak_98 130 . 3.15507 17.53103 13.01485 102 +chr10 41852859 41852994 SRX067409.05_peak_99 105 . 2.54414 14.92231 10.56060 101 +chr10 41859847 41860036 SRX067409.05_peak_100 196 . 3.83023 24.54225 19.66530 90 +chr10 42067135 42067357 SRX067409.05_peak_101 212 . 4.01823 26.21690 21.24816 125 +chr10 42067516 42067710 SRX067409.05_peak_102 126 . 3.24738 17.16611 12.66371 85 +chr10 42085289 42085446 SRX067409.05_peak_103 110 . 2.70315 15.38486 11.00609 100 +chr10 42099833 42099973 SRX067409.05_peak_104 86 . 2.44809 12.87484 8.65707 58 +chr10 42104151 42104405 SRX067409.05_peak_105 235 . 4.07549 28.62840 23.56881 99 +chr10 42104478 42104627 SRX067409.05_peak_106 138 . 3.32548 18.37732 13.80272 121 +chr10 73947699 73947834 SRX067409.05_peak_107 171 . 10.27200 21.87654 17.12723 28 +chr10 76264412 76264548 SRX067409.05_peak_108 185 . 11.47958 23.34242 18.50500 41 +chr10 86453022 86453158 SRX067409.05_peak_109 211 . 12.43622 26.08140 21.11656 44 +chr10 94585267 94585406 SRX067409.05_peak_110 224 . 12.91453 27.47573 22.45046 32 +chr10 102885409 102885543 SRX067409.05_peak_111 185 . 11.47958 23.34242 18.50500 103 +chr10 104684691 104684827 SRX067409.05_peak_112 211 . 12.43622 26.08140 21.11656 107 +chr10 107233858 107233996 SRX067409.05_peak_113 238 . 13.39285 28.88579 23.80784 27 +chr10 109812281 109812418 SRX067409.05_peak_114 211 . 12.43622 26.08140 21.11656 102 +chr10 114137962 114138178 SRX067409.05_peak_115 381 . 18.17601 43.74757 38.18507 106 +chr10 131851013 131851147 SRX067409.05_peak_116 71 . 5.84400 11.31538 7.19748 22 +chr10 133688183 133688354 SRX067409.05_peak_117 201 . 5.64091 25.01933 20.11094 63 +chr10 133688630 133688798 SRX067409.05_peak_118 116 . 4.44068 16.09408 11.66037 80 +chr10 133689297 133689829 SRX067409.05_peak_119 487 . 9.02209 54.54277 48.72565 142 +chr11 585405 585605 SRX067409.05_peak_120 103 . 7.80672 14.70232 10.34998 119 +chr11 3653756 3654842 SRX067409.05_peak_121 795 . 9.52275 86.11565 79.59158 154 +chr11 4781557 4781691 SRX067409.05_peak_122 181 . 11.22020 22.91059 18.10715 34 +chr11 16565626 16565840 SRX067409.05_peak_123 279 . 14.82780 33.20518 27.96261 106 +chr11 37427305 37427439 SRX067409.05_peak_124 185 . 11.47958 23.34242 18.50500 32 +chr11 53986946 53987103 SRX067409.05_peak_125 155 . 10.22115 20.17370 15.50759 46 +chr11 60048570 60048707 SRX067409.05_peak_126 224 . 12.91453 27.47573 22.45046 97 +chr11 61116526 61116732 SRX067409.05_peak_127 83 . 5.67173 12.52466 8.32092 122 +chr11 61391157 61391291 SRX067409.05_peak_128 142 . 9.09954 18.81522 14.22409 105 +chr11 62561671 62561808 SRX067409.05_peak_129 192 . 11.61494 24.13139 19.26819 103 +chr11 134851693 134852123 SRX067409.05_peak_130 415 . 12.41545 47.17624 41.53708 235 +chr12 10054 10377 SRX067409.05_peak_131 271 . 14.31342 32.33988 27.13074 209 +chr12 2255793 2256080 SRX067409.05_peak_132 220 . 11.91552 27.00982 22.01302 123 +chr12 18850613 18850759 SRX067409.05_peak_133 293 . 15.30611 34.67310 29.37735 37 +chr12 66057509 66057672 SRX067409.05_peak_134 525 . 16.45624 58.48185 52.55748 110 +chr12 90452742 90452881 SRX067409.05_peak_135 238 . 13.39285 28.88579 23.80784 25 +chr12 101340852 101340986 SRX067409.05_peak_136 185 . 11.47958 23.34242 18.50500 90 +chr12 118708057 118708244 SRX067409.05_peak_137 134 . 9.23889 18.03399 13.49811 120 +chr12 120552948 120553094 SRX067409.05_peak_138 329 . 15.45789 38.39465 32.98300 106 +chr12 122578939 122579077 SRX067409.05_peak_139 234 . 13.17275 28.51706 23.46153 101 +chr12 128456282 128456419 SRX067409.05_peak_140 213 . 11.53448 26.35638 21.38221 105 +chr12 131576808 131577384 SRX067409.05_peak_141 645 . 13.56492 70.83405 64.54575 194 +chr12 133264932 133265313 SRX067409.05_peak_142 183 . 11.06391 23.21034 18.39773 174 +chr13 16688939 16689133 SRX067409.05_peak_143 267 . 10.32296 31.99677 26.79968 85 +chr13 16943402 16943536 SRX067409.05_peak_144 118 . 7.41244 16.32829 11.88385 109 +chr13 17144752 17144932 SRX067409.05_peak_145 202 . 8.76168 25.14931 20.23612 86 +chr13 18212023 18212157 SRX067409.05_peak_146 156 . 8.20925 20.30481 15.63476 79 +chr13 19444306 19444441 SRX067409.05_peak_147 189 . 11.40199 23.77586 18.92550 23 +chr13 31853186 31853321 SRX067409.05_peak_148 185 . 10.80183 23.33783 18.50500 97 +chr13 43806093 43806228 SRX067409.05_peak_149 172 . 11.00127 21.99907 17.22598 92 +chr13 85952001 85952135 SRX067409.05_peak_150 172 . 11.00127 21.99907 17.22598 36 +chr14 16093371 16093535 SRX067409.05_peak_151 205 . 7.96813 25.44377 20.51758 58 +chr14 49912671 49912813 SRX067409.05_peak_152 279 . 14.82780 33.20518 27.96261 32 +chr14 68456786 68456920 SRX067409.05_peak_153 185 . 11.47958 23.34242 18.50500 111 +chr14 71674595 71674731 SRX067409.05_peak_154 211 . 12.43622 26.08140 21.11656 109 +chr14 77269773 77269915 SRX067409.05_peak_155 280 . 12.93940 33.28869 28.04377 21 +chr14 79049027 79049166 SRX067409.05_peak_156 251 . 13.87117 30.31106 25.17541 32 +chr14 81002699 81002913 SRX067409.05_peak_157 291 . 14.77965 34.45861 29.17737 96 +chr14 99420851 99420995 SRX067409.05_peak_158 308 . 15.78443 36.15435 30.80625 108 +chr14 102972695 102972829 SRX067409.05_peak_159 185 . 11.47958 23.34242 18.50500 32 +chr14_GL000225v1_random 6752 6945 SRX067409.05_peak_160 185 . 6.07220 23.32680 18.50500 95 +chr14_GL000225v1_random 13915 14092 SRX067409.05_peak_161 144 . 4.69637 19.03152 14.43285 124 +chr14_GL000225v1_random 25561 25749 SRX067409.05_peak_162 193 . 5.08647 24.16958 19.30530 106 +chr14_GL000225v1_random 33570 33785 SRX067409.05_peak_163 298 . 7.64277 35.20521 29.89366 94 +chr14_GL000225v1_random 50765 51010 SRX067409.05_peak_164 378 . 7.31971 43.35647 37.81221 119 +chr14_GL000225v1_random 56000 56327 SRX067409.05_peak_165 265 . 7.08839 31.77057 26.58166 219 +chr14_GL000225v1_random 67448 67648 SRX067409.05_peak_166 384 . 7.14729 44.00274 38.43550 94 +chr14_GL000225v1_random 74721 74930 SRX067409.05_peak_167 323 . 7.26652 37.75242 32.36081 120 +chr14_GL000225v1_random 85569 85778 SRX067409.05_peak_168 422 . 7.44048 47.87681 42.22230 103 +chr14_GL000225v1_random 101580 101769 SRX067409.05_peak_169 175 . 5.26092 22.35749 17.57320 86 +chr14_GL000225v1_random 102414 102552 SRX067409.05_peak_170 152 . 5.09342 19.88674 15.23367 24 +chr14_GL000225v1_random 110290 110555 SRX067409.05_peak_171 85 . 3.56281 12.80908 8.59420 83 +chr14_GL000225v1_random 111480 111618 SRX067409.05_peak_172 145 . 4.28609 19.12119 14.51859 103 +chr14_GL000225v1_random 116743 116919 SRX067409.05_peak_173 122 . 4.01632 16.68703 12.22818 29 +chr14_GL000225v1_random 117730 117879 SRX067409.05_peak_174 227 . 5.30965 27.78741 22.75156 43 +chr14_GL000225v1_random 133706 133856 SRX067409.05_peak_175 266 . 5.58386 31.84303 26.65127 39 +chr15 17080527 17080667 SRX067409.05_peak_176 162 . 8.53293 20.93337 16.21881 52 +chr15 32738085 32738221 SRX067409.05_peak_177 194 . 11.03302 24.30822 19.43828 33 +chr15 45870310 45870449 SRX067409.05_peak_178 238 . 13.39285 28.88579 23.80784 36 +chr15 54925945 54926161 SRX067409.05_peak_179 391 . 18.34776 44.78206 39.19735 107 +chr15 56461880 56462016 SRX067409.05_peak_180 206 . 12.15521 25.61197 20.67947 99 +chr15 77618442 77618597 SRX067409.05_peak_181 501 . 20.75047 55.97255 50.11829 118 +chr16 52765 52901 SRX067409.05_peak_182 182 . 10.62439 23.03651 18.22887 30 +chr16 818777 818913 SRX067409.05_peak_183 100 . 5.95866 14.31261 10.00479 20 +chr16 846961 847103 SRX067409.05_peak_184 130 . 6.11655 17.56935 13.05171 46 +chr16 1025259 1025463 SRX067409.05_peak_185 165 . 7.28317 21.30381 16.57556 90 +chr16 5530850 5531057 SRX067409.05_peak_186 281 . 13.74293 33.36274 28.11457 98 +chr16 6350449 6350586 SRX067409.05_peak_187 189 . 10.41512 23.82210 18.96992 24 +chr16 9584311 9584451 SRX067409.05_peak_188 214 . 11.59629 26.46272 21.48607 101 +chr16 31105616 31105751 SRX067409.05_peak_189 170 . 10.88912 21.81274 17.06535 98 +chr16 34097309 34097529 SRX067409.05_peak_190 385 . 10.96288 44.15461 38.58426 122 +chr16 34454020 34454173 SRX067409.05_peak_191 155 . 7.66985 20.21578 15.54831 35 +chr16 34572546 34572867 SRX067409.05_peak_192 572 . 5.35049 63.38750 57.29981 145 +chr16 34573022 34573214 SRX067409.05_peak_193 265 . 3.77878 31.68680 26.51168 106 +chr16 34573931 34574341 SRX067409.05_peak_194 669 . 5.78522 73.29603 66.94913 119 +chr16 34575576 34575789 SRX067409.05_peak_195 259 . 3.74534 31.09339 25.93554 92 +chr16 34576014 34576285 SRX067409.05_peak_196 151 . 3.00860 19.82578 15.17623 93 +chr16 34576428 34576684 SRX067409.05_peak_197 197 . 3.11563 24.65040 19.77032 137 +chr16 34581359 34581687 SRX067409.05_peak_198 396 . 3.45751 45.26604 39.67026 182 +chr16 34582141 34582452 SRX067409.05_peak_199 421 . 3.34808 47.80244 42.15146 185 +chr16 34582701 34583870 SRX067409.05_peak_200 423 . 3.22943 48.02023 42.36369 376 +chr16 34586480 34586697 SRX067409.05_peak_201 270 . 2.63019 32.22550 27.01919 108 +chr16 34586796 34586954 SRX067409.05_peak_202 130 . 2.14622 17.59848 13.07992 88 +chr16 34587921 34588316 SRX067409.05_peak_203 470 . 3.43264 52.84928 47.08022 222 +chr16 34592709 34592865 SRX067409.05_peak_204 210 . 2.73818 26.00263 21.05793 110 +chr16 34593013 34593653 SRX067409.05_peak_205 392 . 3.71951 44.82172 39.23663 524 +chr16 34595003 34595946 SRX067409.05_peak_206 529 . 4.60327 58.90702 52.96902 560 +chr16 34894428 34894578 SRX067409.05_peak_207 191 . 8.73048 23.99681 19.13886 32 +chr16 38266911 38267243 SRX067409.05_peak_208 271 . 8.79808 32.39393 27.18258 133 +chr16 38276191 38276511 SRX067409.05_peak_209 200 . 6.18154 24.96881 20.06168 102 +chr16 46386406 46386589 SRX067409.05_peak_210 99 . 1.91801 14.20380 9.90077 90 +chr16 46387983 46388296 SRX067409.05_peak_211 163 . 2.14630 21.03029 16.31166 239 +chr16 46388642 46388853 SRX067409.05_peak_212 224 . 2.34298 27.41059 22.40304 119 +chr16 46389698 46390775 SRX067409.05_peak_213 273 . 2.38630 32.57297 27.35722 596 +chr16 46394502 46395072 SRX067409.05_peak_214 319 . 2.68759 37.36656 31.98972 294 +chr16 46398570 46399028 SRX067409.05_peak_215 248 . 2.57121 29.96060 24.84599 262 +chr16 46399272 46399851 SRX067409.05_peak_216 275 . 2.70325 32.79612 27.57331 192 +chr16 46400002 46401641 SRX067409.05_peak_217 547 . 3.74397 60.79998 54.79568 123 +chr16 58816298 58816433 SRX067409.05_peak_218 188 . 10.68207 23.70783 18.85983 107 +chr16 61353384 61353521 SRX067409.05_peak_219 224 . 12.91453 27.47573 22.45046 41 +chr16 63446443 63446586 SRX067409.05_peak_220 308 . 15.78443 36.15435 30.80625 42 +chr16 69374634 69374772 SRX067409.05_peak_221 224 . 12.91453 27.47573 22.45046 20 +chr16 69912906 69913043 SRX067409.05_peak_222 172 . 11.00127 21.99907 17.22598 103 +chr16 81086163 81086315 SRX067409.05_peak_223 421 . 17.88182 47.80523 42.15368 36 +chr17 117141 117326 SRX067409.05_peak_224 70 . 5.39236 11.21180 7.09808 23 +chr17 117456 117718 SRX067409.05_peak_225 122 . 6.90926 16.73616 12.27582 147 +chr17 117878 118035 SRX067409.05_peak_226 159 . 7.85208 20.59813 15.91682 91 +chr17 3871229 3871366 SRX067409.05_peak_227 224 . 12.91453 27.47573 22.45046 108 +chr17 18690049 18690189 SRX067409.05_peak_228 248 . 13.64321 29.92866 24.81507 35 +chr17 21960324 21960522 SRX067409.05_peak_229 216 . 10.98356 26.60111 21.61982 91 +chr17 21969315 21969476 SRX067409.05_peak_230 214 . 5.99520 26.40139 21.42623 116 +chr17 21973020 21973167 SRX067409.05_peak_231 199 . 4.72400 24.81940 19.91882 102 +chr17 21976364 21976498 SRX067409.05_peak_232 184 . 4.55464 23.27223 18.45796 97 +chr17 21985344 21985483 SRX067409.05_peak_233 152 . 4.50955 19.85234 15.20168 54 +chr17 22044030 22044168 SRX067409.05_peak_234 215 . 11.65876 26.57006 21.58993 108 +chr17 25383680 25383895 SRX067409.05_peak_235 474 . 21.04591 53.22915 47.44214 105 +chr17 25569143 25569348 SRX067409.05_peak_236 322 . 16.26274 37.64855 32.25883 97 +chr17 25788555 25788743 SRX067409.05_peak_237 238 . 13.39285 28.88579 23.80784 99 +chr17 25920093 25920318 SRX067409.05_peak_238 576 . 23.84292 63.79298 57.68797 110 +chr17 26101563 26101703 SRX067409.05_peak_239 238 . 13.39285 28.88579 23.80784 37 +chr17 26594798 26595011 SRX067409.05_peak_240 325 . 14.79866 37.96057 32.56002 107 +chr17 26603798 26604028 SRX067409.05_peak_241 577 . 18.68483 63.84904 57.73954 113 +chr17 26604144 26604278 SRX067409.05_peak_242 113 . 7.11786 15.77778 11.35713 108 +chr17 26619743 26620127 SRX067409.05_peak_243 203 . 11.25673 25.27931 20.36034 103 +chr17 26783347 26783485 SRX067409.05_peak_244 164 . 9.21628 21.13645 16.41422 95 +chr17 26885319 26885522 SRX067409.05_peak_245 96 . 5.16298 13.97411 9.67889 98 +chr17 26885669 26885915 SRX067409.05_peak_246 401 . 10.84523 45.74647 40.14130 94 +chr17 31949569 31949703 SRX067409.05_peak_247 159 . 10.52295 20.67415 15.96857 30 +chr17 42144929 42145072 SRX067409.05_peak_248 247 . 13.19842 29.81348 24.70522 35 +chr17 52179179 52179314 SRX067409.05_peak_249 198 . 11.95790 24.70342 19.80719 101 +chr17 59909137 59909278 SRX067409.05_peak_250 261 . 13.71924 31.33743 26.17165 88 +chr17_KI270729v1_random 3464 3690 SRX067409.05_peak_251 392 . 6.35625 44.84180 39.25612 121 +chr17_KI270729v1_random 11065 11280 SRX067409.05_peak_252 290 . 8.90808 34.36619 29.08755 101 +chr17_KI270729v1_random 11579 11752 SRX067409.05_peak_253 158 . 6.75650 20.48936 15.81239 69 +chr17_KI270729v1_random 13050 13195 SRX067409.05_peak_254 158 . 6.75650 20.48936 15.81239 101 +chr17_KI270729v1_random 22404 22610 SRX067409.05_peak_255 288 . 5.95967 34.14797 28.87625 98 +chr17_KI270729v1_random 27701 27914 SRX067409.05_peak_256 332 . 7.12970 38.63459 33.21865 115 +chr17_KI270729v1_random 47112 47311 SRX067409.05_peak_257 233 . 10.01787 28.35547 23.30441 102 +chr17_KI270729v1_random 99112 99252 SRX067409.05_peak_258 216 . 9.43891 26.58270 21.60222 34 +chr17_KI270729v1_random 139870 140120 SRX067409.05_peak_259 278 . 8.65727 33.11268 27.88124 106 +chr17_KI270729v1_random 157537 157695 SRX067409.05_peak_260 281 . 7.87579 33.43653 28.18725 56 +chr17_KI270729v1_random 160943 161138 SRX067409.05_peak_261 358 . 9.74184 41.36844 35.87494 105 +chr17_KI270729v1_random 240672 240817 SRX067409.05_peak_262 226 . 11.93223 27.65406 22.62316 106 +chr17_KI270730v1_random 109837 109980 SRX067409.05_peak_263 113 . 6.86851 15.75152 11.33204 75 +chr18 107884 108517 SRX067409.05_peak_264 492 . 6.84292 55.07628 49.24770 350 +chr18 5077933 5078075 SRX067409.05_peak_265 265 . 14.34948 31.75102 26.56321 103 +chr18 11803959 11804294 SRX067409.05_peak_266 122 . 5.63063 16.71319 12.25346 68 +chr18 11804661 11804930 SRX067409.05_peak_267 100 . 5.17979 14.37971 10.03984 206 +chr18 14822865 14823011 SRX067409.05_peak_268 350 . 17.15266 40.56159 35.08968 90 +chr18 16430958 16431185 SRX067409.05_peak_269 429 . 18.80686 48.62378 42.95164 125 +chr18 20561625 20562350 SRX067409.05_peak_270 253 . 10.48331 30.51099 25.37006 305 +chr18 21738813 21738956 SRX067409.05_peak_271 297 . 15.14317 35.07310 29.76548 36 +chr18 26393345 26393479 SRX067409.05_peak_272 194 . 10.68132 24.28644 19.41742 88 +chr18 43452432 43452651 SRX067409.05_peak_273 455 . 20.35792 51.26719 45.54140 104 +chr18 46803050 46803184 SRX067409.05_peak_274 185 . 11.47958 23.34242 18.50500 34 +chr18 52792743 52792889 SRX067409.05_peak_275 159 . 10.52295 20.67415 15.96857 75 +chr18 63063601 63063741 SRX067409.05_peak_276 217 . 10.40747 26.74440 21.75588 105 +chr18 65866299 65866438 SRX067409.05_peak_277 238 . 13.39285 28.88579 23.80784 32 +chr18 80262964 80263243 SRX067409.05_peak_278 111 . 8.60969 15.57595 11.16170 162 +chr19 7450697 7450875 SRX067409.05_peak_279 128 . 8.22721 17.31183 12.80355 45 +chr19 8268625 8268760 SRX067409.05_peak_280 188 . 11.33273 23.66013 18.81386 28 +chr19 10331357 10331493 SRX067409.05_peak_281 189 . 11.40199 23.77586 18.92550 31 +chr19 10676168 10676303 SRX067409.05_peak_282 185 . 11.47958 23.34242 18.50500 104 +chr19 15933857 15934050 SRX067409.05_peak_283 303 . 11.45595 35.67867 30.35371 101 +chr19 29985120 29985265 SRX067409.05_peak_284 322 . 16.26274 37.64855 32.25883 32 +chr19 53414462 53414599 SRX067409.05_peak_285 211 . 12.43622 26.08140 21.11656 32 +chr1_KI270708v1_random 44566 44701 SRX067409.05_peak_286 177 . 11.01322 22.56578 17.77565 38 +chr1_KI270709v1_random 142 324 SRX067409.05_peak_287 131 . 2.66093 17.64922 13.12901 89 +chr1_KI270709v1_random 1625 1762 SRX067409.05_peak_288 141 . 2.48514 18.72871 14.14073 44 +chr1_KI270709v1_random 15025 15192 SRX067409.05_peak_289 178 . 2.55886 22.61836 17.82658 63 +chr1_KI270709v1_random 16426 16602 SRX067409.05_peak_290 72 . 2.11210 11.33069 7.21165 70 +chr2 32866674 32867102 SRX067409.05_peak_291 678 . 17.70115 74.16930 67.80069 225 +chr2 32916153 32916664 SRX067409.05_peak_292 2121 . 22.25949 219.30461 212.18637 321 +chr2 34479292 34479435 SRX067409.05_peak_293 279 . 14.82780 33.20518 27.96261 28 +chr2 39220885 39221026 SRX067409.05_peak_294 268 . 13.75397 32.05083 26.85192 97 +chr2 50399797 50399936 SRX067409.05_peak_295 251 . 13.87117 30.31106 25.17541 34 +chr2 65360070 65360205 SRX067409.05_peak_296 172 . 11.00127 21.99907 17.22598 32 +chr2 80232245 80232389 SRX067409.05_peak_297 286 . 14.86712 33.93393 28.66747 106 +chr2 87907413 87907547 SRX067409.05_peak_298 193 . 9.96678 24.17166 19.30696 33 +chr2 89839566 89839749 SRX067409.05_peak_299 232 . 5.18228 28.25870 23.21064 84 +chr2 89840234 89840436 SRX067409.05_peak_300 336 . 6.34538 39.08710 33.66186 108 +chr2 92081124 92081689 SRX067409.05_peak_301 383 . 12.49175 43.93362 38.36814 194 +chr2 104143096 104143234 SRX067409.05_peak_302 211 . 12.43622 26.08140 21.11656 36 +chr2 109851176 109851317 SRX067409.05_peak_303 258 . 12.39971 31.00893 25.85390 35 +chr2 110520190 110520329 SRX067409.05_peak_304 247 . 12.46203 29.83065 24.72104 108 +chr2 129874684 129874819 SRX067409.05_peak_305 195 . 11.09376 24.41170 19.53754 43 +chr2 134209053 134209264 SRX067409.05_peak_306 296 . 15.05062 34.91680 29.61427 107 +chr2 140842973 140843107 SRX067409.05_peak_307 185 . 11.47958 23.34242 18.50500 107 +chr2 151010016 151010230 SRX067409.05_peak_308 409 . 18.05209 46.55513 40.93115 104 +chr2 153222212 153222356 SRX067409.05_peak_309 322 . 16.26274 37.64855 32.25883 109 +chr2 159590193 159590327 SRX067409.05_peak_310 147 . 10.04464 19.36844 14.73392 33 +chr2 160960150 160960286 SRX067409.05_peak_311 210 . 12.38803 26.00092 21.05625 18 +chr2 161278673 161278849 SRX067409.05_peak_312 184 . 7.48975 23.26072 18.44694 55 +chr2 166222930 166223066 SRX067409.05_peak_313 172 . 11.00127 21.99907 17.22598 29 +chr2 183061987 183062121 SRX067409.05_peak_314 167 . 10.68575 21.47478 16.74027 101 +chr2 186191488 186191624 SRX067409.05_peak_315 185 . 11.47958 23.34242 18.50500 31 +chr2 193908599 193908736 SRX067409.05_peak_316 224 . 12.91453 27.47573 22.45046 32 +chr2 206071170 206071312 SRX067409.05_peak_317 293 . 15.30611 34.67310 29.37735 86 +chr2 210069704 210069840 SRX067409.05_peak_318 190 . 10.79655 23.90418 19.04925 108 +chr2 215921474 215921669 SRX067409.05_peak_319 90 . 7.52346 13.36277 9.09321 127 +chr2 216225152 216225286 SRX067409.05_peak_320 159 . 10.52295 20.67415 15.96857 80 +chr2 222260471 222260605 SRX067409.05_peak_321 185 . 11.47958 23.34242 18.50500 103 +chr2 222866730 222866876 SRX067409.05_peak_322 303 . 15.52503 35.71725 30.39119 30 +chr2 223693903 223694041 SRX067409.05_peak_323 238 . 13.39285 28.88579 23.80784 108 +chr2 228030511 228030654 SRX067409.05_peak_324 308 . 15.78443 36.15435 30.80625 41 +chr2 230510382 230510526 SRX067409.05_peak_325 308 . 15.78443 36.15435 30.80625 90 +chr2 230988933 230989069 SRX067409.05_peak_326 146 . 8.44979 19.21964 14.61347 109 +chr2 233186597 233186736 SRX067409.05_peak_327 238 . 13.39285 28.88579 23.80784 95 +chr2 234565669 234565805 SRX067409.05_peak_328 173 . 9.79894 22.17314 17.39395 80 +chr2 236868254 236868388 SRX067409.05_peak_329 185 . 11.47958 23.34242 18.50500 92 +chr2 237343520 237343665 SRX067409.05_peak_330 320 . 13.71648 37.42545 32.04676 37 +chr2 240872182 240872448 SRX067409.05_peak_331 163 . 9.17431 21.06090 16.34149 116 +chr20 27504605 27504845 SRX067409.05_peak_332 258 . 12.78475 31.03818 25.88232 149 +chr20 28494796 28495077 SRX067409.05_peak_333 97 . 4.05997 14.05266 9.75426 90 +chr20 28495443 28495677 SRX067409.05_peak_334 195 . 5.50473 24.47235 19.59675 125 +chr20 28495734 28495982 SRX067409.05_peak_335 138 . 4.71036 18.42285 13.84637 117 +chr20 28496828 28497030 SRX067409.05_peak_336 251 . 6.22203 30.26776 25.14510 109 +chr20 31055937 31056113 SRX067409.05_peak_337 232 . 4.49216 28.32780 23.27727 105 +chr20 31106702 31106899 SRX067409.05_peak_338 240 . 13.14597 29.09343 24.01033 107 +chr20 31157663 31157939 SRX067409.05_peak_339 314 . 11.07759 36.77742 31.41409 170 +chr20 31186722 31186876 SRX067409.05_peak_340 185 . 5.95401 23.34553 18.50798 87 +chr20 31241638 31241894 SRX067409.05_peak_341 128 . 6.79221 17.39625 12.88449 59 +chr20 34228047 34228185 SRX067409.05_peak_342 217 . 11.38606 26.70622 21.72072 107 +chr20 35713647 35713788 SRX067409.05_peak_343 260 . 14.02525 31.20637 26.04427 29 +chr20 37368428 37368644 SRX067409.05_peak_344 377 . 17.46686 43.28659 37.74336 98 +chr20 40192918 40193053 SRX067409.05_peak_345 198 . 11.95790 24.70342 19.80719 102 +chr20 47894764 47895007 SRX067409.05_peak_346 125 . 5.77152 17.08972 12.58952 97 +chr20 55865520 55865654 SRX067409.05_peak_347 172 . 11.00127 21.99907 17.22598 27 +chr20 57350784 57350919 SRX067409.05_peak_348 165 . 9.94352 21.31767 16.58889 59 +chr21 6565421 6565747 SRX067409.05_peak_349 212 . 9.81071 26.18394 21.21603 120 +chr21 7258667 7258885 SRX067409.05_peak_350 332 . 10.05886 38.64719 33.23097 113 +chr21 7926136 7926500 SRX067409.05_peak_351 411 . 9.95753 46.82728 41.19736 248 +chr21 7938251 7938435 SRX067409.05_peak_352 316 . 9.90891 37.05069 31.68140 73 +chr21 7944441 7944575 SRX067409.05_peak_353 136 . 6.36967 18.19608 13.62770 37 +chr21 8202675 8202838 SRX067409.05_peak_354 185 . 5.71742 23.32386 18.50500 80 +chr21 8204088 8204364 SRX067409.05_peak_355 178 . 5.74396 22.61795 17.82618 166 +chr21 8231039 8231316 SRX067409.05_peak_356 106 . 2.33565 14.98601 10.62217 108 +chr21 8231729 8232251 SRX067409.05_peak_357 125 . 2.47324 17.06945 12.57016 402 +chr21 8240998 8241268 SRX067409.05_peak_358 80 . 2.02063 12.29219 8.09911 215 +chr21 8387207 8387428 SRX067409.05_peak_359 256 . 6.83108 30.84352 25.69342 111 +chr21 8394211 8394345 SRX067409.05_peak_360 92 . 5.00150 13.55407 9.27680 87 +chr21 8413679 8413893 SRX067409.05_peak_361 167 . 2.72671 21.44935 16.71631 137 +chr21 8414647 8414836 SRX067409.05_peak_362 133 . 2.53953 17.88625 13.35752 99 +chr21 8422776 8422921 SRX067409.05_peak_363 138 . 2.34298 18.38155 13.80682 82 +chr21 8438100 8438312 SRX067409.05_peak_364 92 . 5.26831 13.50961 9.23501 114 +chr21 8453868 8454012 SRX067409.05_peak_365 94 . 2.38816 13.71580 9.43220 89 +chr21 8458248 8458434 SRX067409.05_peak_366 115 . 2.43206 16.03032 11.59932 113 +chr21 8459177 8459331 SRX067409.05_peak_367 136 . 2.57681 18.19522 13.62685 107 +chr21 8468596 8469213 SRX067409.05_peak_368 172 . 2.60688 22.00850 17.23502 117 +chr21 8469890 8470036 SRX067409.05_peak_369 87 . 2.22064 13.01796 8.79320 122 +chr21 8470280 8470443 SRX067409.05_peak_370 63 . 2.07139 10.39191 6.35056 35 +chr21 8810100 8810332 SRX067409.05_peak_371 92 . 4.97925 13.49560 9.22148 185 +chr21 10274037 10274215 SRX067409.05_peak_372 211 . 12.43622 26.08140 21.11656 85 +chr21 10655486 10655680 SRX067409.05_peak_373 307 . 9.31459 36.05622 30.71921 115 +chr21 10692755 10693081 SRX067409.05_peak_374 400 . 10.15593 45.61794 40.01731 106 +chr21 16184394 16184532 SRX067409.05_peak_375 238 . 13.39285 28.88579 23.80784 30 +chr21 40378470 40378604 SRX067409.05_peak_376 185 . 11.47958 23.34242 18.50500 98 +chr21 43262523 43262745 SRX067409.05_peak_377 154 . 9.54578 20.09885 15.43646 105 +chr22 11211813 11211947 SRX067409.05_peak_378 188 . 7.67212 23.70579 18.85828 48 +chr22 11212934 11213159 SRX067409.05_peak_379 398 . 11.71007 45.43610 39.83782 117 +chr22 11214230 11214531 SRX067409.05_peak_380 159 . 7.00953 20.65134 15.96853 216 +chr22 12176341 12176490 SRX067409.05_peak_381 92 . 4.14820 13.51597 9.24028 38 +chr22 12692111 12692247 SRX067409.05_peak_382 116 . 8.05215 16.04184 11.60967 46 +chr22 16267259 16267477 SRX067409.05_peak_383 368 . 9.88270 42.37885 36.86068 100 +chr22 16352741 16352962 SRX067409.05_peak_384 512 . 12.53569 57.13817 51.25189 116 +chr22 18211448 18211720 SRX067409.05_peak_385 85 . 4.68735 12.71410 8.50322 51 +chr22 23134010 23134185 SRX067409.05_peak_386 139 . 3.94665 18.55710 13.97657 59 +chr22 23308034 23308172 SRX067409.05_peak_387 119 . 7.18597 16.36701 11.92090 59 +chr22 29280757 29280898 SRX067409.05_peak_388 261 . 14.11366 31.35494 26.18833 106 +chr22 29406111 29406247 SRX067409.05_peak_389 219 . 11.50307 26.91033 21.91785 44 +chr22 36730934 36731077 SRX067409.05_peak_390 271 . 12.76441 32.31257 27.10404 95 +chr22 38644300 38644577 SRX067409.05_peak_391 319 . 14.42235 37.29827 31.92396 169 +chr22 40744029 40744166 SRX067409.05_peak_392 192 . 10.57322 24.09831 19.23691 104 +chr22 50643629 50643985 SRX067409.05_peak_393 258 . 9.11005 30.95461 25.80149 212 +chr22 50807914 50808141 SRX067409.05_peak_394 373 . 16.39475 42.93119 37.39606 110 +chr22_KI270733v1_random 118941 119187 SRX067409.05_peak_395 135 . 4.90360 18.13161 13.56442 106 +chr22_KI270733v1_random 120123 120632 SRX067409.05_peak_396 190 . 5.77864 23.94927 19.09273 401 +chr22_KI270733v1_random 144299 144458 SRX067409.05_peak_397 129 . 2.99585 17.43228 12.91943 86 +chr22_KI270733v1_random 147797 148052 SRX067409.05_peak_398 207 . 3.56657 25.65544 20.72120 98 +chr22_KI270733v1_random 158027 158189 SRX067409.05_peak_399 79 . 2.04808 12.13470 7.94789 109 +chr22_KI270733v1_random 158584 158771 SRX067409.05_peak_400 166 . 2.49126 21.43212 16.69962 123 +chr22_KI270735v1_random 40972 41257 SRX067409.05_peak_401 90 . 5.86023 13.30274 9.03569 236 +chr22_KI270736v1_random 147734 147906 SRX067409.05_peak_402 164 . 7.42715 21.15436 16.43127 122 +chr22_KI270736v1_random 181129 181284 SRX067409.05_peak_403 139 . 8.09717 18.56955 13.98853 54 +chr22_KI270737v1_random 32896 33070 SRX067409.05_peak_404 251 . 7.20096 30.23761 25.11582 105 +chr22_KI270737v1_random 70206 70413 SRX067409.05_peak_405 347 . 8.03422 40.22503 34.76436 104 +chr22_KI270737v1_random 79947 80303 SRX067409.05_peak_406 243 . 7.55904 29.40499 24.31071 112 +chr3 10489 10649 SRX067409.05_peak_407 279 . 14.82780 33.20518 27.96261 38 +chr3 22697635 22697780 SRX067409.05_peak_408 293 . 15.30611 34.67310 29.37735 40 +chr3 25760869 25761006 SRX067409.05_peak_409 224 . 12.91453 27.47573 22.45046 33 +chr3 27093600 27093816 SRX067409.05_peak_410 351 . 17.21938 40.67429 35.19745 103 +chr3 27365492 27365626 SRX067409.05_peak_411 190 . 11.11111 23.86078 19.00701 101 +chr3 29820191 29820328 SRX067409.05_peak_412 211 . 12.43622 26.08140 21.11656 33 +chr3 38184816 38184953 SRX067409.05_peak_413 204 . 11.67214 25.39154 20.46665 108 +chr3 48202592 48202727 SRX067409.05_peak_414 183 . 11.36256 23.14761 18.33659 27 +chr3 56482437 56482571 SRX067409.05_peak_415 158 . 10.41568 20.49629 15.81839 118 +chr3 64696421 64696555 SRX067409.05_peak_416 173 . 10.74884 22.12477 17.34746 63 +chr3 76580224 76580373 SRX067409.05_peak_417 381 . 18.17601 43.74757 38.18507 110 +chr3 81768875 81769084 SRX067409.05_peak_418 322 . 16.26274 37.64855 32.25883 105 +chr3 89020176 89020376 SRX067409.05_peak_419 224 . 12.91453 27.47573 22.45046 99 +chr3 91891202 91891465 SRX067409.05_peak_420 279 . 14.82780 33.20518 27.96261 129 +chr3 93470304 93470853 SRX067409.05_peak_421 2550 . 22.86690 264.50815 255.01700 195 +chr3 95687065 95687199 SRX067409.05_peak_422 185 . 11.47958 23.34242 18.50500 19 +chr3 107094705 107094844 SRX067409.05_peak_423 251 . 13.87117 30.31106 25.17541 104 +chr3 165737375 165737512 SRX067409.05_peak_424 224 . 12.91453 27.47573 22.45046 31 +chr3 174885789 174885925 SRX067409.05_peak_425 211 . 12.43622 26.08140 21.11656 103 +chr3 181715340 181715474 SRX067409.05_peak_426 162 . 9.43602 20.98508 16.26774 88 +chr3 188755809 188755957 SRX067409.05_peak_427 381 . 18.17601 43.74757 38.18507 43 +chr3 191816625 191816761 SRX067409.05_peak_428 211 . 12.43622 26.08140 21.11656 103 +chr3 196898712 196898961 SRX067409.05_peak_429 749 . 25.16841 81.42091 74.94595 130 +chr3 198168917 198169132 SRX067409.05_peak_430 165 . 9.02120 21.32107 16.59218 113 +chr4 1397651 1397856 SRX067409.05_peak_431 95 . 6.34238 13.85775 9.56750 92 +chr4 1435324 1435539 SRX067409.05_peak_432 104 . 7.89364 14.84700 10.48856 99 +chr4 1707235 1707381 SRX067409.05_peak_433 338 . 15.99516 39.31555 33.87648 54 +chr4 2454477 2454620 SRX067409.05_peak_434 216 . 8.95635 26.67427 21.69037 116 +chr4 6511503 6511637 SRX067409.05_peak_435 82 . 6.69543 12.40483 8.20745 92 +chr4 9396129 9396265 SRX067409.05_peak_436 146 . 9.67884 19.27273 14.66464 30 +chr4 19077762 19077992 SRX067409.05_peak_437 882 . 29.99425 94.84084 88.24234 112 +chr4 39801861 39801998 SRX067409.05_peak_438 204 . 11.67214 25.39154 20.46665 98 +chr4 49108292 49108517 SRX067409.05_peak_439 149 . 3.04173 19.56141 14.92173 43 +chr4 49149654 49149868 SRX067409.05_peak_440 265 . 4.36380 31.77238 26.58336 113 +chr4 49273697 49273838 SRX067409.05_peak_441 267 . 11.16711 31.89603 26.70251 31 +chr4 49632613 49632753 SRX067409.05_peak_442 145 . 4.88759 19.10929 14.50715 55 +chr4 49657493 49657736 SRX067409.05_peak_443 405 . 9.06664 46.17490 40.56008 118 +chr4 49709279 49709552 SRX067409.05_peak_444 441 . 6.44817 49.81653 44.12548 155 +chr4 49709684 49709860 SRX067409.05_peak_445 215 . 4.56914 26.57382 21.59362 60 +chr4 49709910 49710121 SRX067409.05_peak_446 158 . 4.00020 20.51437 15.83606 114 +chr4 49710454 49710970 SRX067409.05_peak_447 163 . 4.05505 21.11368 16.39237 173 +chr4 49711272 49711685 SRX067409.05_peak_448 356 . 5.75170 41.14785 35.65787 290 +chr4 49711764 49711906 SRX067409.05_peak_449 206 . 4.46308 25.61976 20.68657 96 +chr4 50556056 50556250 SRX067409.05_peak_450 326 . 16.06094 38.00634 32.60503 91 +chr4 51107245 51107573 SRX067409.05_peak_451 546 . 19.19321 60.66615 54.66369 196 +chr4 55067035 55067170 SRX067409.05_peak_452 193 . 11.68771 24.25279 19.38501 94 +chr4 57282136 57282270 SRX067409.05_peak_453 185 . 11.47958 23.34242 18.50500 34 +chr4 73861678 73861812 SRX067409.05_peak_454 168 . 10.10509 21.59307 16.85400 33 +chr4 81188969 81189103 SRX067409.05_peak_455 144 . 9.87956 19.09537 14.49349 90 +chr4 81594142 81594286 SRX067409.05_peak_456 298 . 13.62699 35.19395 29.88281 92 +chr4 87060841 87060977 SRX067409.05_peak_457 211 . 12.43622 26.08140 21.11656 33 +chr4 90675589 90675733 SRX067409.05_peak_458 322 . 16.26274 37.64855 32.25883 105 +chr4 98082345 98082485 SRX067409.05_peak_459 238 . 13.39285 28.88579 23.80784 102 +chr4 103370146 103370281 SRX067409.05_peak_460 185 . 11.47958 23.34242 18.50500 103 +chr4 108410574 108410716 SRX067409.05_peak_461 243 . 12.96905 29.42528 24.32989 32 +chr4 116499726 116499861 SRX067409.05_peak_462 198 . 11.95790 24.70342 19.80719 32 +chr4 118375108 118375321 SRX067409.05_peak_463 308 . 14.18712 36.18575 30.83633 104 +chr4 130425191 130425330 SRX067409.05_peak_464 251 . 13.87117 30.31106 25.17541 34 +chr4 164742416 164742554 SRX067409.05_peak_465 212 . 12.16545 26.22130 21.25177 31 +chr4 166603685 166603879 SRX067409.05_peak_466 618 . 25.35075 68.09802 61.87122 81 +chr4 187984753 187984902 SRX067409.05_peak_467 216 . 12.38987 26.59758 21.61632 34 +chr4 189881302 189881529 SRX067409.05_peak_468 181 . 10.93422 22.99293 18.18782 136 +chr4 190067551 190067765 SRX067409.05_peak_469 473 . 20.96436 53.09065 47.30851 104 +chr4 190080790 190080930 SRX067409.05_peak_470 224 . 12.91453 27.47573 22.45046 33 +chr4 190122836 190123140 SRX067409.05_peak_471 147 . 10.04464 19.36844 14.73392 132 +chr4 190178320 190178469 SRX067409.05_peak_472 192 . 4.99942 24.12509 19.26287 57 +chr4 190179126 190179341 SRX067409.05_peak_473 92 . 3.73293 13.54359 9.26661 17 +chr4 190179444 190179914 SRX067409.05_peak_474 503 . 8.19215 56.24013 50.37910 233 +chr5 10087 10275 SRX067409.05_peak_475 88 . 5.74740 13.06881 8.84202 57 +chr5 10801 11024 SRX067409.05_peak_476 120 . 6.59942 16.53543 12.08297 121 +chr5 1333032 1333285 SRX067409.05_peak_477 164 . 8.66966 21.19556 16.47159 131 +chr5 1333376 1333558 SRX067409.05_peak_478 146 . 8.20320 19.27244 14.66464 91 +chr5 1333612 1333796 SRX067409.05_peak_479 201 . 9.84252 25.08999 20.17891 123 +chr5 3508785 3508922 SRX067409.05_peak_480 213 . 11.15905 26.30839 21.33646 78 +chr5 17597454 17597709 SRX067409.05_peak_481 185 . 4.06468 23.43960 18.59923 101 +chr5 17632138 17632370 SRX067409.05_peak_482 131 . 7.14067 17.67115 13.15044 127 +chr5 21051811 21051946 SRX067409.05_peak_483 172 . 11.00127 21.99907 17.22598 33 +chr5 49601108 49601282 SRX067409.05_peak_484 132 . 3.71725 17.81865 13.29234 57 +chr5 49601465 49602872 SRX067409.05_peak_485 712 . 8.28763 77.66286 71.22938 170 +chr5 49612348 49612496 SRX067409.05_peak_486 103 . 3.55813 14.72338 10.37014 105 +chr5 49616479 49616639 SRX067409.05_peak_487 166 . 4.42615 21.34279 16.61343 108 +chr5 49626895 49627056 SRX067409.05_peak_488 119 . 3.83397 16.35834 11.91236 104 +chr5 49630766 49631162 SRX067409.05_peak_489 162 . 5.09310 20.96372 16.24753 125 +chr5 49633625 49633759 SRX067409.05_peak_490 95 . 4.86736 13.88292 9.59168 95 +chr5 49644905 49645084 SRX067409.05_peak_491 157 . 4.50516 20.46818 15.79210 112 +chr5 49645937 49646137 SRX067409.05_peak_492 225 . 5.25863 27.53801 22.51094 91 +chr5 49657660 49657858 SRX067409.05_peak_493 182 . 3.23807 23.04242 18.23462 113 +chr5 49658394 49658787 SRX067409.05_peak_494 297 . 3.92493 35.03894 29.73296 296 +chr5 49659817 49660161 SRX067409.05_peak_495 152 . 3.04182 19.94949 15.29454 38 +chr5 49661048 49661199 SRX067409.05_peak_496 182 . 3.23807 23.04242 18.23462 55 +chr5 49661449 49661792 SRX067409.05_peak_497 173 . 3.12295 22.11812 17.34110 107 +chr5 49666164 49667390 SRX067409.05_peak_498 306 . 6.25626 36.03007 30.69646 44 +chr5 64990145 64990279 SRX067409.05_peak_499 185 . 11.47958 23.34242 18.50500 90 +chr5 70954858 70954996 SRX067409.05_peak_500 251 . 13.87117 30.31106 25.17541 33 +chr5 78469887 78470031 SRX067409.05_peak_501 322 . 16.26274 37.64855 32.25883 40 +chr5 84319397 84319613 SRX067409.05_peak_502 458 . 20.56759 51.62314 45.88311 100 +chr5 88112319 88112458 SRX067409.05_peak_503 255 . 13.32416 30.66785 25.52154 35 +chr5 92185256 92185394 SRX067409.05_peak_504 238 . 13.39285 28.88579 23.80784 36 +chr5 93564108 93564243 SRX067409.05_peak_505 198 . 11.95790 24.70342 19.80719 31 +chr5 95201379 95201516 SRX067409.05_peak_506 217 . 12.46652 26.72599 21.73953 91 +chr5 95492639 95492779 SRX067409.05_peak_507 245 . 13.12106 29.68267 24.57888 35 +chr5 96007910 96008050 SRX067409.05_peak_508 269 . 13.83365 32.18629 26.98211 37 +chr5 128688719 128688862 SRX067409.05_peak_509 265 . 14.34948 31.75102 26.56321 39 +chr5 129083325 129083461 SRX067409.05_peak_510 204 . 12.00480 25.36056 20.43843 34 +chr5 131515958 131516103 SRX067409.05_peak_511 337 . 16.74106 39.15532 33.72415 113 +chr5 133109714 133109917 SRX067409.05_peak_512 272 . 11.43025 32.41742 27.20534 97 +chr5 139548159 139548293 SRX067409.05_peak_513 147 . 9.73624 19.36829 14.73392 27 +chr5 143974188 143974325 SRX067409.05_peak_514 224 . 12.91453 27.47573 22.45046 27 +chr5 158840551 158840689 SRX067409.05_peak_515 164 . 9.52870 21.14762 16.42493 90 +chr5 178585531 178585733 SRX067409.05_peak_516 93 . 6.01775 13.62565 9.34528 102 +chr5 180539465 180539602 SRX067409.05_peak_517 224 . 12.91453 27.47573 22.45046 103 +chr6 863255 863425 SRX067409.05_peak_518 152 . 5.76620 19.88129 15.22847 61 +chr6 6068532 6068739 SRX067409.05_peak_519 226 . 12.32260 27.70293 22.67001 104 +chr6 18939813 18940028 SRX067409.05_peak_520 351 . 17.21938 40.67429 35.19745 94 +chr6 19770827 19771043 SRX067409.05_peak_521 508 . 20.21019 56.68562 50.81367 110 +chr6 23943139 23943274 SRX067409.05_peak_522 198 . 11.95790 24.70342 19.80719 34 +chr6 29720184 29720390 SRX067409.05_peak_523 244 . 10.59443 29.51691 24.41873 107 +chr6 42845091 42845244 SRX067409.05_peak_524 338 . 15.99516 39.31555 33.87648 113 +chr6 56893481 56893690 SRX067409.05_peak_525 249 . 13.35589 30.07945 24.96202 106 +chr6 58922822 58922960 SRX067409.05_peak_526 197 . 11.57510 24.64107 19.76109 106 +chr6 96655207 96655342 SRX067409.05_peak_527 198 . 11.95790 24.70342 19.80719 109 +chr6 124616353 124616559 SRX067409.05_peak_528 224 . 12.91453 27.47573 22.45046 101 +chr6 151985510 151985646 SRX067409.05_peak_529 211 . 12.43622 26.08140 21.11656 24 +chr6 156170285 156170419 SRX067409.05_peak_530 172 . 11.00127 21.99907 17.22598 89 +chr6 160100638 160100801 SRX067409.05_peak_531 599 . 22.19799 66.14516 59.95828 118 +chr6 167467910 167468071 SRX067409.05_peak_532 90 . 5.65995 13.27268 9.00749 32 +chr6 169644932 169645147 SRX067409.05_peak_533 367 . 17.33102 42.31618 36.79885 108 +chr6 170496469 170496677 SRX067409.05_peak_534 96 . 4.66738 13.97270 9.67752 102 +chr7 20312 20506 SRX067409.05_peak_535 165 . 9.62321 21.31288 16.58442 109 +chr7 240207 240442 SRX067409.05_peak_536 213 . 9.58064 26.30138 21.33012 87 +chr7 497942 498077 SRX067409.05_peak_537 198 . 11.95790 24.70342 19.80719 33 +chr7 905550 905725 SRX067409.05_peak_538 104 . 7.33599 14.80649 10.44998 49 +chr7 27743676 27743811 SRX067409.05_peak_539 198 . 11.95790 24.70342 19.80719 101 +chr7 58033094 58033301 SRX067409.05_peak_540 404 . 11.43336 46.04381 40.43370 101 +chr7 58106160 58106430 SRX067409.05_peak_541 132 . 6.96713 17.77128 13.24678 165 +chr7 58434495 58434677 SRX067409.05_peak_542 200 . 11.40781 24.94485 20.03921 84 +chr7 59995715 59995862 SRX067409.05_peak_543 192 . 10.91350 24.10426 19.24245 49 +chr7 61071190 61071414 SRX067409.05_peak_544 300 . 9.94682 35.34912 30.03429 126 +chr7 62454665 62454879 SRX067409.05_peak_545 346 . 11.13109 40.09759 34.64074 103 +chr7 65489067 65489266 SRX067409.05_peak_546 128 . 7.44474 17.33409 12.82500 161 +chr7 74336759 74336896 SRX067409.05_peak_547 220 . 10.92299 27.09711 22.09812 96 +chr7 77641122 77641261 SRX067409.05_peak_548 224 . 12.54031 27.45571 22.44726 105 +chr7 87703798 87703997 SRX067409.05_peak_549 343 . 16.72552 39.83957 34.38914 109 +chr7 89418063 89418199 SRX067409.05_peak_550 198 . 11.95790 24.70342 19.80719 113 +chr7 100957980 100958352 SRX067409.05_peak_551 291 . 6.68783 34.42382 29.14373 180 +chr7 100958768 100958977 SRX067409.05_peak_552 223 . 5.91607 27.33378 22.32791 119 +chr7 106101424 106101567 SRX067409.05_peak_553 265 . 13.96774 31.75722 26.56848 35 +chr7 107770112 107770260 SRX067409.05_peak_554 366 . 10.92214 42.13498 36.62319 100 +chr7 113086300 113086441 SRX067409.05_peak_555 248 . 12.15380 29.92875 24.81507 106 +chr7 115396679 115396817 SRX067409.05_peak_556 224 . 12.91453 27.47573 22.45046 87 +chr7 117501749 117501889 SRX067409.05_peak_557 231 . 13.00873 28.24214 23.19435 38 +chr7 118703677 118703883 SRX067409.05_peak_558 285 . 13.63034 33.84064 28.57585 107 +chr7 119368411 119368556 SRX067409.05_peak_559 246 . 13.55774 29.78522 24.67809 116 +chr7 124047466 124047603 SRX067409.05_peak_560 224 . 12.91453 27.47573 22.45046 106 +chr7 125702193 125702329 SRX067409.05_peak_561 185 . 11.47958 23.34242 18.50500 107 +chr7 129027562 129027698 SRX067409.05_peak_562 215 . 11.27141 26.50560 21.52738 42 +chr7 135330499 135330634 SRX067409.05_peak_563 219 . 9.34013 26.94431 21.95115 88 +chr7 138612340 138612535 SRX067409.05_peak_564 241 . 10.47387 29.27677 24.18751 89 +chr7 139424763 139424907 SRX067409.05_peak_565 291 . 13.99552 34.47626 29.19435 106 +chr7 155335485 155335672 SRX067409.05_peak_566 144 . 4.62586 19.06988 14.46952 99 +chr7 158151380 158151541 SRX067409.05_peak_567 159 . 7.40375 20.61581 15.93381 82 +chr8 3660399 3660533 SRX067409.05_peak_568 185 . 11.47958 23.34242 18.50500 114 +chr8 13615354 13615489 SRX067409.05_peak_569 185 . 11.47958 23.34242 18.50500 104 +chr8 24577347 24577489 SRX067409.05_peak_570 279 . 14.82780 33.20518 27.96261 38 +chr8 34456076 34456213 SRX067409.05_peak_571 224 . 12.91453 27.47573 22.45046 108 +chr8 43237952 43238220 SRX067409.05_peak_572 146 . 5.68990 19.24896 14.64199 162 +chr8 43239578 43239761 SRX067409.05_peak_573 117 . 5.18015 16.19713 11.75863 92 +chr8 52698581 52698720 SRX067409.05_peak_574 224 . 12.91453 27.47573 22.45046 40 +chr8 82658158 82658296 SRX067409.05_peak_575 238 . 13.39285 28.88579 23.80784 35 +chr8 82914842 82914978 SRX067409.05_peak_576 198 . 11.95790 24.70342 19.80719 114 +chr8 85086496 85086635 SRX067409.05_peak_577 228 . 12.77023 27.84201 22.80439 40 +chr8 85595915 85596050 SRX067409.05_peak_578 196 . 10.84767 24.57491 19.69683 19 +chr8 85643151 85643296 SRX067409.05_peak_579 122 . 4.60642 16.71172 12.25218 64 +chr8 85714529 85714788 SRX067409.05_peak_580 244 . 4.30830 29.52369 24.42544 127 +chr8 85715184 85715663 SRX067409.05_peak_581 308 . 4.56910 36.24329 30.89290 266 +chr8 85736841 85737096 SRX067409.05_peak_582 128 . 2.71924 17.31306 12.80476 175 +chr8 85763121 85763268 SRX067409.05_peak_583 75 . 3.18389 11.72625 7.59144 56 +chr8 86503355 86503499 SRX067409.05_peak_584 322 . 16.26274 37.64855 32.25883 37 +chr8 99478346 99478485 SRX067409.05_peak_585 251 . 13.87117 30.31106 25.17541 106 +chr8 111155917 111156057 SRX067409.05_peak_586 251 . 13.87117 30.31106 25.17541 87 +chr8 128452874 128453047 SRX067409.05_peak_587 129 . 8.87274 17.42436 12.91156 83 +chr8 141492255 141492582 SRX067409.05_peak_588 188 . 7.89755 23.73675 18.88826 116 +chr8 141492995 141493141 SRX067409.05_peak_589 146 . 6.99026 19.20862 14.60283 62 +chr8 143669486 143669620 SRX067409.05_peak_590 132 . 6.96713 17.77128 13.24678 97 +chr9 5684262 5684397 SRX067409.05_peak_591 198 . 11.95790 24.70342 19.80719 16 +chr9 23779984 23780123 SRX067409.05_peak_592 251 . 13.87117 30.31106 25.17541 33 +chr9 31029080 31029218 SRX067409.05_peak_593 238 . 13.39285 28.88579 23.80784 36 +chr9 43318604 43318849 SRX067409.05_peak_594 156 . 6.34942 20.31362 15.64338 142 +chr9 43319008 43319413 SRX067409.05_peak_595 339 . 9.47898 39.42313 33.98119 64 +chr9 73054831 73054969 SRX067409.05_peak_596 224 . 12.91453 27.47573 22.45046 35 +chr9 77399090 77399296 SRX067409.05_peak_597 238 . 13.39285 28.88579 23.80784 100 +chr9 78379297 78379437 SRX067409.05_peak_598 256 . 13.00496 30.76987 25.62173 43 +chr9 81711963 81712101 SRX067409.05_peak_599 224 . 12.91453 27.47573 22.45046 35 +chr9 110032739 110032874 SRX067409.05_peak_600 160 . 10.23952 20.73209 16.02398 21 +chr9 122766043 122766179 SRX067409.05_peak_601 185 . 11.47958 23.34242 18.50500 29 +chr9 130955019 130955342 SRX067409.05_peak_602 178 . 5.86898 22.64450 17.85212 268 +chr9 130996304 130996574 SRX067409.05_peak_603 74 . 2.93278 11.57108 7.44318 52 +chr9 131095731 131095866 SRX067409.05_peak_604 84 . 2.89508 12.61767 8.41019 41 +chrM 16018 16461 SRX067409.05_peak_605 213 . 3.96460 26.36963 21.39528 135 +chrUn_GL000216v2 300 865 SRX067409.05_peak_606 163 . 4.87320 21.12027 16.39874 460 +chrUn_GL000216v2 1887 2055 SRX067409.05_peak_607 171 . 4.41231 21.89461 17.14484 123 +chrUn_GL000216v2 5737 5944 SRX067409.05_peak_608 106 . 3.55805 14.96468 10.60153 97 +chrUn_GL000216v2 35361 35571 SRX067409.05_peak_609 337 . 10.76070 39.19800 33.76569 97 +chrUn_GL000216v2 80013 80463 SRX067409.05_peak_610 275 . 8.16138 32.75634 27.53435 242 +chrUn_GL000216v2 149488 149764 SRX067409.05_peak_611 174 . 4.73238 22.18777 17.40830 75 +chrUn_GL000216v2 164400 164589 SRX067409.05_peak_612 245 . 5.22868 29.60352 24.50346 90 +chrUn_GL000216v2 167779 167993 SRX067409.05_peak_613 378 . 6.75124 43.43302 37.88660 106 +chrUn_GL000216v2 173762 174080 SRX067409.05_peak_614 582 . 12.39731 64.33296 58.21214 113 +chrUn_GL000220v1 101628 101987 SRX067409.05_peak_615 193 . 5.54597 24.24977 19.38284 53 +chrUn_GL000220v1 103219 103404 SRX067409.05_peak_616 123 . 4.54395 16.80118 12.32821 81 +chrUn_GL000220v1 103509 103781 SRX067409.05_peak_617 138 . 4.81041 18.47250 13.89403 208 +chrUn_GL000220v1 110403 110552 SRX067409.05_peak_618 80 . 5.05778 12.28841 8.09561 91 +chrUn_GL000220v1 125335 125485 SRX067409.05_peak_619 123 . 2.52922 16.76445 12.30258 79 +chrUn_GL000220v1 129634 129908 SRX067409.05_peak_620 97 . 2.28692 14.03635 9.73858 220 +chrUn_GL000220v1 130618 130874 SRX067409.05_peak_621 178 . 2.76672 22.62231 17.83033 137 +chrUn_GL000220v1 140239 140516 SRX067409.05_peak_622 102 . 2.16501 14.63133 10.28138 221 +chrUn_GL000220v1 154504 154711 SRX067409.05_peak_623 124 . 6.03614 16.95236 12.45765 121 +chrUn_KI270303v1 238 654 SRX067409.05_peak_624 332 . 9.23626 38.71256 33.29483 162 +chrUn_KI270303v1 1151 1761 SRX067409.05_peak_625 273 . 8.28078 32.57882 27.36238 127 +chrUn_KI270304v1 708 1183 SRX067409.05_peak_626 307 . 9.10440 36.04474 30.71108 334 +chrUn_KI270304v1 1334 2009 SRX067409.05_peak_627 219 . 7.58700 26.98742 21.99138 271 +chrUn_KI270310v1 232 1175 SRX067409.05_peak_628 776 . 15.44684 84.15971 77.65354 648 +chrUn_KI270317v1 1190 1558 SRX067409.05_peak_629 381 . 13.66349 43.66379 38.10900 208 +chrUn_KI270317v1 36350 36834 SRX067409.05_peak_630 341 . 14.14237 39.61708 34.17117 328 +chrUn_KI270330v1 212 588 SRX067409.05_peak_631 134 . 6.67413 18.03375 13.49811 280 +chrUn_KI270330v1 1332 1652 SRX067409.05_peak_632 405 . 12.27179 46.14709 40.53343 181 +chrUn_KI270333v1 281 672 SRX067409.05_peak_633 209 . 5.56611 25.89153 20.95051 79 +chrUn_KI270333v1 1096 1283 SRX067409.05_peak_634 158 . 4.89434 20.51831 15.83967 96 +chrUn_KI270333v1 1362 1748 SRX067409.05_peak_635 248 . 6.04595 29.95362 24.83910 261 +chrUn_KI270333v1 1984 2270 SRX067409.05_peak_636 151 . 4.79837 19.78294 15.13484 184 +chrUn_KI270333v1 2409 2627 SRX067409.05_peak_637 179 . 5.18224 22.77395 17.97716 154 +chrUn_KI270336v1 60 559 SRX067409.05_peak_638 318 . 11.25533 37.17512 31.80313 97 +chrUn_KI270336v1 607 985 SRX067409.05_peak_639 498 . 14.84746 55.73978 49.89385 248 +chrUn_KI270337v1 0 1059 SRX067409.05_peak_640 653 . 12.72300 71.67145 65.36838 795 +chrUn_KI270411v1 181 456 SRX067409.05_peak_641 554 . 9.55946 61.48643 55.45542 139 +chrUn_KI270411v1 603 892 SRX067409.05_peak_642 505 . 9.05633 56.39796 50.53305 109 +chrUn_KI270411v1 1016 1206 SRX067409.05_peak_643 125 . 4.52817 17.06077 12.56157 110 +chrUn_KI270411v1 1305 1501 SRX067409.05_peak_644 119 . 4.42754 16.35649 11.91053 97 +chrUn_KI270411v1 2011 2227 SRX067409.05_peak_645 132 . 4.62879 17.77448 13.24983 117 +chrUn_KI270411v1 2335 2471 SRX067409.05_peak_646 146 . 4.83004 19.22943 14.62298 97 +chrUn_KI270412v1 327 597 SRX067409.05_peak_647 172 . 11.00127 21.99907 17.22598 111 +chrUn_KI270418v1 289 542 SRX067409.05_peak_648 204 . 10.95083 25.34456 20.42371 127 +chrUn_KI270422v1 501 744 SRX067409.05_peak_649 149 . 9.24998 19.59304 14.95105 132 +chrUn_KI270422v1 866 1240 SRX067409.05_peak_650 184 . 10.40623 23.23250 18.41908 68 +chrUn_KI270435v1 91922 92199 SRX067409.05_peak_651 150 . 8.98569 19.65998 15.01659 111 +chrUn_KI270438v1 103881 104429 SRX067409.05_peak_652 881 . 5.60069 94.74509 88.15046 156 +chrUn_KI270438v1 104566 105329 SRX067409.05_peak_653 308 . 3.05334 36.17650 30.82805 458 +chrUn_KI270438v1 109190 109390 SRX067409.05_peak_654 168 . 2.21121 21.55787 16.82026 29 +chrUn_KI270438v1 109529 110568 SRX067409.05_peak_655 606 . 3.72793 66.84373 60.64439 912 +chrUn_KI270438v1 111220 112440 SRX067409.05_peak_656 602 . 3.88020 66.41679 60.22624 482 +chrUn_KI270442v1 18 314 SRX067409.05_peak_657 579 . 17.59896 64.09842 57.98439 110 +chrUn_KI270442v1 84548 84695 SRX067409.05_peak_658 213 . 7.00614 26.28079 21.31038 56 +chrUn_KI270442v1 92298 92505 SRX067409.05_peak_659 379 . 8.05451 43.45236 37.90505 106 +chrUn_KI270465v1 342 515 SRX067409.05_peak_660 83 . 7.25295 12.51100 8.30784 94 +chrUn_KI270466v1 81 1216 SRX067409.05_peak_661 959 . 13.49125 102.53863 95.90721 456 +chrUn_KI270467v1 1163 1574 SRX067409.05_peak_662 264 . 4.05918 31.61135 26.43821 312 +chrUn_KI270467v1 1957 3593 SRX067409.05_peak_663 693 . 6.42375 75.74705 69.34332 192 +chrUn_KI270467v1 3706 3867 SRX067409.05_peak_664 178 . 3.46803 22.66523 17.87216 54 +chrUn_KI270544v1 44 375 SRX067409.05_peak_665 193 . 8.57423 24.20482 19.33886 206 +chrUn_KI270544v1 886 1182 SRX067409.05_peak_666 788 . 20.33317 85.34525 78.82815 123 +chrUn_KI270747v1 47275 47409 SRX067409.05_peak_667 113 . 5.57142 15.77779 11.35713 39 +chrUn_KI270747v1 47535 47757 SRX067409.05_peak_668 270 . 8.54584 32.25576 27.04856 84 +chrUn_KI270747v1 47857 47994 SRX067409.05_peak_669 149 . 6.22414 19.54300 14.90346 93 +chrX 1585806 1585948 SRX067409.05_peak_670 211 . 12.43622 26.08140 21.11656 53 +chrX 16409977 16410186 SRX067409.05_peak_671 302 . 14.24276 35.59133 30.27017 98 +chrX 19236775 19236910 SRX067409.05_peak_672 172 . 11.00127 21.99907 17.22598 22 +chrX 27809481 27809620 SRX067409.05_peak_673 238 . 13.39285 28.88579 23.80784 44 +chrX 31366465 31366608 SRX067409.05_peak_674 279 . 14.82780 33.20518 27.96261 39 +chrX 40110579 40110722 SRX067409.05_peak_675 308 . 15.78443 36.15435 30.80625 103 +chrX 44282523 44282665 SRX067409.05_peak_676 293 . 15.30611 34.67310 29.37735 107 +chrX 74567771 74567906 SRX067409.05_peak_677 189 . 11.40199 23.77586 18.92550 104 +chrX 79200407 79200552 SRX067409.05_peak_678 337 . 16.74106 39.15532 33.72415 43 +chrX 96057691 96057855 SRX067409.05_peak_679 224 . 12.91453 27.47573 22.45046 101 +chrX 111577356 111577490 SRX067409.05_peak_680 161 . 10.30097 20.83451 16.12240 28 +chrX 117494471 117494617 SRX067409.05_peak_681 238 . 13.39285 28.88579 23.80784 99 +chrX 123051954 123052090 SRX067409.05_peak_682 185 . 11.47958 23.34242 18.50500 18 +chrX 151703772 151703910 SRX067409.05_peak_683 211 . 10.39974 26.13606 21.16879 95 +chrX 156030433 156030692 SRX067409.05_peak_684 324 . 14.72183 37.82577 32.43299 148 +chrY 10630500 10630648 SRX067409.05_peak_685 128 . 7.20576 17.33613 12.82676 106 +chrY 10648911 10649128 SRX067409.05_peak_686 168 . 8.33289 21.58529 16.84699 116 +chrY 10666889 10667047 SRX067409.05_peak_687 224 . 9.59992 27.49154 22.46586 120 +chrY 10746893 10747046 SRX067409.05_peak_688 199 . 8.62110 24.84418 19.94244 97 +chrY 10789805 10789947 SRX067409.05_peak_689 152 . 6.88315 19.88407 15.23102 109 +chrY 10845623 10845786 SRX067409.05_peak_690 202 . 9.01829 25.14528 20.23226 64 +chrY 10920794 10920991 SRX067409.05_peak_691 275 . 10.14620 32.81489 27.59177 100 +chrY 10967444 10967653 SRX067409.05_peak_692 324 . 12.18390 37.85783 32.46349 100 +chrY 10999825 11000027 SRX067409.05_peak_693 325 . 10.49555 37.92186 32.52455 100 +chrY 11305277 11305506 SRX067409.05_peak_694 221 . 5.49753 27.14658 22.14625 118 +chrY 11312199 11312419 SRX067409.05_peak_695 185 . 5.53086 23.41346 18.57472 97 +chrY 11313104 11313243 SRX067409.05_peak_696 102 . 4.12614 14.59309 10.24487 107 +chrY 11329840 11330051 SRX067409.05_peak_697 291 . 6.69679 34.46136 29.18010 86 +chrY 11487576 11487710 SRX067409.05_peak_698 132 . 9.35016 17.72616 13.20283 100 +chrY 11643660 11643820 SRX067409.05_peak_699 147 . 10.04464 19.36844 14.73392 34 +chrY 11721891 11722126 SRX067409.05_peak_700 589 . 16.57697 65.08549 58.93019 112 +chrY 11747592 11747812 SRX067409.05_peak_701 577 . 18.68483 63.84904 57.73954 109 +chrY 11935764 11935898 SRX067409.05_peak_702 185 . 11.47958 23.34242 18.50500 30 +chrY 15882911 15883120 SRX067409.05_peak_703 490 . 21.52422 54.84490 49.02064 100 +chrY 15896676 15896892 SRX067409.05_peak_704 427 . 19.61096 48.44129 42.77270 110 +chrY 23061675 23061811 SRX067409.05_peak_705 185 . 11.47958 23.34242 18.50500 23 +chrY 26668171 26668305 SRX067409.05_peak_706 185 . 11.47958 23.34242 18.50500 30 +chrY 56850792 56850951 SRX067409.05_peak_707 134 . 5.65093 17.94792 13.41624 50 diff --git a/tests/data/hg38_sample/SRX1261989.05.bed b/tests/data/hg38_sample/SRX1261989.05.bed new file mode 100644 index 00000000..6647ede6 --- /dev/null +++ b/tests/data/hg38_sample/SRX1261989.05.bed @@ -0,0 +1,397 @@ +chr1 629672 629968 SRX1261989.05_peak_1 302 . 6.60976 35.45200 30.21301 122 +chr1 630814 630952 SRX1261989.05_peak_2 268 . 6.21937 31.97751 26.83095 56 +chr1 631933 632061 SRX1261989.05_peak_3 243 . 5.91860 29.40566 24.32514 65 +chr1 633935 634079 SRX1261989.05_peak_4 324 . 6.80970 37.71099 32.41567 75 +chr1 91387305 91387438 SRX1261989.05_peak_5 120 . 8.19428 16.76548 12.03258 42 +chr1 122897612 122897720 SRX1261989.05_peak_6 180 . 8.75082 22.95817 18.04506 73 +chr1 123009953 123010095 SRX1261989.05_peak_7 113 . 7.50947 16.03696 11.33287 51 +chr1 123195647 123195821 SRX1261989.05_peak_8 576 . 18.08266 63.46427 57.61668 93 +chr1 123225837 123225976 SRX1261989.05_peak_9 108 . 7.72854 15.48772 10.80202 87 +chr1 123519200 123519308 SRX1261989.05_peak_10 113 . 7.75740 16.00693 11.30380 21 +chr1 124007428 124007572 SRX1261989.05_peak_11 364 . 14.19200 41.85171 36.44732 80 +chr1 124117492 124117624 SRX1261989.05_peak_12 239 . 10.36298 29.05466 23.98225 60 +chr1 124204005 124204122 SRX1261989.05_peak_13 113 . 7.50947 16.03696 11.33287 91 +chr1 124277902 124278012 SRX1261989.05_peak_14 218 . 9.58977 26.89436 21.87895 72 +chr1 124377166 124377286 SRX1261989.05_peak_15 342 . 13.37549 39.56688 34.22308 58 +chr1 124420976 124421138 SRX1261989.05_peak_16 268 . 11.19403 31.94966 26.80376 87 +chr1 124455028 124455136 SRX1261989.05_peak_17 184 . 8.93555 23.32760 18.40313 68 +chr1 124568519 124568627 SRX1261989.05_peak_18 175 . 8.49979 22.44978 17.54823 46 +chr1 124631401 124631556 SRX1261989.05_peak_19 66 . 6.18272 11.13680 6.64435 38 +chr1 124642323 124642437 SRX1261989.05_peak_20 180 . 8.22846 22.93184 18.01905 69 +chr1 124732342 124732459 SRX1261989.05_peak_21 177 . 8.08062 22.61146 17.70609 62 +chr1 124741375 124741502 SRX1261989.05_peak_22 450 . 14.24145 50.67289 45.05461 72 +chr1 124757654 124757777 SRX1261989.05_peak_23 304 . 12.42274 35.66511 30.42103 41 +chr1 125167142 125167258 SRX1261989.05_peak_24 313 . 10.22913 36.65091 31.37925 51 +chr1 125167805 125168012 SRX1261989.05_peak_25 715 . 17.13177 77.70100 71.58315 100 +chr1 125178904 125179436 SRX1261989.05_peak_26 397 . 3.93361 45.20227 39.71247 467 +chr1 125179599 125179895 SRX1261989.05_peak_27 371 . 3.81790 42.58578 37.16193 81 +chr1 125180028 125180685 SRX1261989.05_peak_28 552 . 4.54523 61.01344 55.20444 133 +chr1 125182150 125182694 SRX1261989.05_peak_29 385 . 3.96571 43.98297 38.52617 469 +chr1 125182962 125183104 SRX1261989.05_peak_30 389 . 4.00089 44.43367 38.96329 79 +chr1 143184845 143185346 SRX1261989.05_peak_31 591 . 10.54237 64.97506 59.10113 324 +chr1 143192280 143192447 SRX1261989.05_peak_32 138 . 3.48918 18.61013 13.82486 133 +chr1 143192717 143192886 SRX1261989.05_peak_33 239 . 4.37143 29.06458 23.99207 55 +chr1 143193039 143193202 SRX1261989.05_peak_34 318 . 5.04022 37.13039 31.84675 66 +chr1 143193408 143193849 SRX1261989.05_peak_35 376 . 5.46333 43.12546 37.68697 353 +chr1 143194929 143195262 SRX1261989.05_peak_36 244 . 4.49070 29.58443 24.49974 280 +chr1 143200229 143200416 SRX1261989.05_peak_37 622 . 8.69777 68.14973 62.20340 96 +chr1 143201513 143201648 SRX1261989.05_peak_38 185 . 4.46525 23.42909 18.50119 70 +chr1 143202473 143202858 SRX1261989.05_peak_39 432 . 6.29144 48.80515 43.23099 210 +chr1 143203332 143203537 SRX1261989.05_peak_40 222 . 4.58561 27.31959 22.29416 46 +chr1 143206059 143206195 SRX1261989.05_peak_41 503 . 7.30490 56.01621 50.30336 73 +chr1 143206306 143206515 SRX1261989.05_peak_42 375 . 6.33910 42.99112 37.55585 136 +chr1 143207075 143207198 SRX1261989.05_peak_43 309 . 5.80229 36.19611 30.93924 63 +chr1 143213126 143213329 SRX1261989.05_peak_44 337 . 4.20784 39.08798 33.75874 134 +chr1 143214292 143214529 SRX1261989.05_peak_45 167 . 3.07250 21.61329 16.73818 180 +chr1 143214680 143214891 SRX1261989.05_peak_46 553 . 4.96685 61.14274 55.33311 101 +chr1 143217322 143217587 SRX1261989.05_peak_47 127 . 2.63338 17.50044 12.75034 31 +chr1 143219727 143219853 SRX1261989.05_peak_48 103 . 2.65115 15.06270 10.39685 37 +chr1 143221483 143221622 SRX1261989.05_peak_49 87 . 2.75621 13.37891 8.78154 116 +chr1 143227369 143227592 SRX1261989.05_peak_50 102 . 3.69899 14.92241 10.26307 35 +chr1 143230271 143230413 SRX1261989.05_peak_51 302 . 5.39957 35.44342 30.20479 61 +chr1 143231296 143231538 SRX1261989.05_peak_52 255 . 4.75100 30.66886 25.55657 119 +chr1 143232806 143233296 SRX1261989.05_peak_53 417 . 5.81871 47.26170 41.72644 156 +chr1 143238086 143238234 SRX1261989.05_peak_54 290 . 4.82747 34.23086 29.02541 84 +chr1 143239446 143239621 SRX1261989.05_peak_55 692 . 7.95191 75.36385 69.28432 76 +chr1 143245454 143245563 SRX1261989.05_peak_56 252 . 5.43495 30.30454 25.20428 68 +chr1 143247273 143247420 SRX1261989.05_peak_57 370 . 5.89330 42.44892 37.02652 76 +chr1 143249775 143250001 SRX1261989.05_peak_58 423 . 5.01334 47.90948 42.35445 70 +chr1 143251610 143251736 SRX1261989.05_peak_59 411 . 4.46138 46.65485 41.13382 60 +chr1 143251904 143252057 SRX1261989.05_peak_60 307 . 3.89098 36.02986 30.77699 66 +chr1 143252974 143253175 SRX1261989.05_peak_61 581 . 5.36896 63.96847 58.11221 105 +chr1 143254453 143254649 SRX1261989.05_peak_62 265 . 3.78326 31.73175 26.59256 131 +chr1 143254762 143254935 SRX1261989.05_peak_63 511 . 5.12760 56.91513 51.18629 82 +chr1 143255538 143255735 SRX1261989.05_peak_64 179 . 3.40635 22.83042 17.92035 39 +chr1 143255861 143256012 SRX1261989.05_peak_65 282 . 4.06862 33.39553 28.21334 56 +chr1 143256411 143256521 SRX1261989.05_peak_66 350 . 4.46661 40.42715 35.06379 57 +chr1 143256787 143256915 SRX1261989.05_peak_67 307 . 4.36230 36.05332 30.79899 70 +chr1 143262546 143262877 SRX1261989.05_peak_68 333 . 4.70678 38.69001 33.36866 225 +chr1 143263650 143263773 SRX1261989.05_peak_69 292 . 4.42655 34.43848 29.22701 58 +chr1 143264037 143264160 SRX1261989.05_peak_70 256 . 4.22982 30.72030 25.60671 55 +chr1 143264458 143264778 SRX1261989.05_peak_71 450 . 5.48705 50.62145 45.00427 219 +chr1 143266725 143266867 SRX1261989.05_peak_72 111 . 3.08555 15.80639 11.10923 34 +chr1 143267428 143267536 SRX1261989.05_peak_73 266 . 4.44194 31.75053 26.61113 48 +chr1 143275720 143275841 SRX1261989.05_peak_74 268 . 12.58088 31.98436 26.83741 61 +chr10 10080 10271 SRX1261989.05_peak_75 183 . 11.76488 23.26985 18.34664 94 +chr10 39966181 39966293 SRX1261989.05_peak_76 282 . 11.27861 33.38743 28.20539 53 +chr10 39967542 39967710 SRX1261989.05_peak_77 1326 . 31.36003 139.56706 132.67798 81 +chr10 39986431 39986569 SRX1261989.05_peak_78 374 . 17.22919 42.88166 37.44788 63 +chr10 40073414 40073522 SRX1261989.05_peak_79 177 . 11.41326 22.68396 17.77640 39 +chr10 40131364 40131517 SRX1261989.05_peak_80 464 . 18.55001 52.13353 46.49113 70 +chr10 40183548 40183660 SRX1261989.05_peak_81 215 . 12.69779 26.51834 21.51130 63 +chr10 40198950 40199122 SRX1261989.05_peak_82 354 . 16.91042 40.87269 35.49629 83 +chr10 40213423 40213531 SRX1261989.05_peak_83 182 . 11.36148 23.14582 18.22752 54 +chr10 40214273 40214408 SRX1261989.05_peak_84 315 . 15.77287 36.82201 31.54791 49 +chr10 40318265 40318376 SRX1261989.05_peak_85 239 . 13.81094 28.97693 23.90578 51 +chr10 40343476 40343588 SRX1261989.05_peak_86 239 . 13.81094 28.97693 23.90578 44 +chr10 40447275 40447391 SRX1261989.05_peak_87 232 . 13.39817 28.28497 23.23250 50 +chr10 40556727 40556928 SRX1261989.05_peak_88 420 . 17.74480 47.56632 42.01858 129 +chr10 40654456 40654577 SRX1261989.05_peak_89 246 . 13.52107 29.72368 24.63657 49 +chr10 40673733 40673841 SRX1261989.05_peak_90 183 . 11.76488 23.26985 18.34664 48 +chr10 40792114 40792224 SRX1261989.05_peak_91 239 . 13.81094 28.97693 23.90578 67 +chr10 40810981 40811138 SRX1261989.05_peak_92 532 . 22.06810 59.06479 53.29586 81 +chr10 40892122 40892505 SRX1261989.05_peak_93 265 . 13.13694 31.65040 26.51385 108 +chr10 40934857 40934993 SRX1261989.05_peak_94 387 . 18.86600 44.18095 38.71927 82 +chr10 41028756 41028869 SRX1261989.05_peak_95 217 . 12.46307 26.72021 21.70936 42 +chr10 41063116 41063227 SRX1261989.05_peak_96 224 . 13.29943 27.52499 22.49132 41 +chr10 41068504 41068627 SRX1261989.05_peak_97 146 . 10.09615 19.45367 14.63851 74 +chr10 41107680 41107790 SRX1261989.05_peak_98 201 . 12.20942 25.12289 20.15093 64 +chr10 41125351 41125489 SRX1261989.05_peak_99 353 . 17.21829 40.67245 35.30211 69 +chr10 41133836 41133950 SRX1261989.05_peak_100 286 . 15.21995 33.86447 28.66711 92 +chr10 41315464 41315614 SRX1261989.05_peak_101 683 . 26.98352 74.44556 68.39121 70 +chr10 41331707 41331876 SRX1261989.05_peak_102 521 . 20.89253 57.87543 52.13108 87 +chr10 41847448 41847556 SRX1261989.05_peak_103 217 . 8.98515 26.73788 21.72662 43 +chr10 41854534 41854670 SRX1261989.05_peak_104 499 . 13.56239 55.62698 49.91852 53 +chr10 41859859 41859992 SRX1261989.05_peak_105 443 . 13.30507 49.99278 44.38632 82 +chr10 41875380 41875504 SRX1261989.05_peak_106 338 . 11.58611 39.21191 33.87974 45 +chr10 41879281 41879526 SRX1261989.05_peak_107 197 . 7.89154 24.74369 19.78003 198 +chr10 41889471 41889579 SRX1261989.05_peak_108 202 . 9.35299 25.27304 20.29838 63 +chr10 41898883 41898995 SRX1261989.05_peak_109 182 . 9.12409 23.14947 18.23103 57 +chr10 41914634 41914751 SRX1261989.05_peak_110 214 . 10.55966 26.43183 21.42655 50 +chr10 42070501 42070681 SRX1261989.05_peak_111 77 . 4.05807 12.34510 7.79629 132 +chr10 42090741 42090849 SRX1261989.05_peak_112 201 . 8.73532 25.09230 20.12101 34 +chr10 42099808 42099932 SRX1261989.05_peak_113 486 . 14.87063 54.28820 48.60025 52 +chr10 133688238 133688362 SRX1261989.05_peak_114 333 . 9.41464 38.65413 33.33427 57 +chr10 133688650 133688770 SRX1261989.05_peak_115 273 . 8.45379 32.53995 27.37818 63 +chr10 133689212 133689855 SRX1261989.05_peak_116 199 . 7.19208 24.91506 19.94721 203 +chr11 52320979 52321129 SRX1261989.05_peak_117 239 . 13.81094 28.97693 23.90578 66 +chr11 53986968 53987085 SRX1261989.05_peak_118 311 . 16.36853 36.46248 31.19587 62 +chr11 121185392 121185780 SRX1261989.05_peak_119 130 . 8.21243 17.78071 13.02331 47 +chr12 2539302 2539820 SRX1261989.05_peak_120 188 . 10.38730 23.77336 18.83764 412 +chr12 35614903 35615079 SRX1261989.05_peak_121 819 . 26.81764 88.19706 81.92262 89 +chr12 36480813 36480947 SRX1261989.05_peak_122 286 . 14.44075 33.88409 28.68567 55 +chr12 37237655 37237796 SRX1261989.05_peak_123 99 . 6.08557 14.58446 9.93634 98 +chr12 37239699 37239921 SRX1261989.05_peak_124 82 . 5.61745 12.79654 8.22586 48 +chr12 37246249 37246357 SRX1261989.05_peak_125 94 . 8.18426 14.03049 9.40008 34 +chr12 133264995 133265259 SRX1261989.05_peak_126 245 . 13.82034 29.60198 24.51707 95 +chr13 16688958 16689139 SRX1261989.05_peak_127 208 . 8.83899 25.86437 20.87564 61 +chr13 17144784 17144910 SRX1261989.05_peak_128 205 . 8.94632 25.54596 20.56465 49 +chr13 17417443 17417695 SRX1261989.05_peak_129 115 . 6.26512 16.23364 11.52427 179 +chr13 17418339 17418480 SRX1261989.05_peak_130 152 . 7.14595 20.03181 15.20101 76 +chr13 17518148 17518323 SRX1261989.05_peak_131 164 . 8.72968 21.31007 16.44208 37 +chr13 17656210 17656346 SRX1261989.05_peak_132 231 . 9.94196 28.20001 23.15036 80 +chr13 17742802 17742961 SRX1261989.05_peak_133 342 . 12.69176 39.58003 34.23523 89 +chr13 17861163 17861273 SRX1261989.05_peak_134 118 . 6.83821 16.59050 11.86259 20 +chr13 17922345 17922453 SRX1261989.05_peak_135 167 . 8.32975 21.57895 16.70415 40 +chr13 18211786 18212195 SRX1261989.05_peak_136 1048 . 19.58847 111.48882 104.89144 318 +chr13 18212381 18212514 SRX1261989.05_peak_137 351 . 9.86126 40.51716 35.15124 63 +chr13 109424151 109424318 SRX1261989.05_peak_138 755 . 23.85432 81.76879 75.58749 86 +chr14_GL000225v1_random 6791 6957 SRX1261989.05_peak_139 392 . 15.34356 44.76205 39.28354 82 +chr14_GL000225v1_random 25575 25727 SRX1261989.05_peak_140 573 . 16.42962 63.16460 57.31957 81 +chr14_GL000225v1_random 33637 33763 SRX1261989.05_peak_141 320 . 13.29270 37.32936 32.04226 73 +chr14_GL000225v1_random 45972 46189 SRX1261989.05_peak_142 101 . 5.83139 14.83539 10.17958 137 +chr14_GL000225v1_random 50826 50974 SRX1261989.05_peak_143 447 . 13.17755 50.40008 44.78631 73 +chr14_GL000225v1_random 56146 56271 SRX1261989.05_peak_144 274 . 11.51529 32.58466 27.42214 68 +chr14_GL000225v1_random 67466 67606 SRX1261989.05_peak_145 419 . 11.46789 47.44690 41.90627 70 +chr14_GL000225v1_random 71137 71254 SRX1261989.05_peak_146 175 . 6.81338 22.46826 17.56650 42 +chr14_GL000225v1_random 74779 74887 SRX1261989.05_peak_147 212 . 8.76141 26.24057 21.23872 26 +chr14_GL000225v1_random 85603 85743 SRX1261989.05_peak_148 569 . 16.62469 62.80412 56.96628 69 +chr14_GL000225v1_random 99253 99456 SRX1261989.05_peak_149 140 . 7.04225 18.86010 14.06738 140 +chr14_GL000225v1_random 111482 111621 SRX1261989.05_peak_150 307 . 9.50431 35.98820 30.73621 62 +chr14_GL000225v1_random 117729 117845 SRX1261989.05_peak_151 304 . 9.60597 35.67037 30.42617 63 +chr14_GL000225v1_random 120600 120719 SRX1261989.05_peak_152 202 . 7.47012 25.18147 20.20860 75 +chr14_GL000225v1_random 131683 131806 SRX1261989.05_peak_153 427 . 10.44868 48.36287 42.79720 59 +chr14_GL000225v1_random 133695 133855 SRX1261989.05_peak_154 453 . 10.88241 50.95370 45.32861 87 +chr14_KI270724v1_random 706 883 SRX1261989.05_peak_155 224 . 13.29943 27.52499 22.49132 71 +chr15 18735276 18735409 SRX1261989.05_peak_156 360 . 16.78742 41.39184 36.00000 70 +chr15 19373612 19373742 SRX1261989.05_peak_157 208 . 11.20730 25.79096 20.80357 61 +chr15 19374830 19374963 SRX1261989.05_peak_158 188 . 10.74841 23.82167 18.88393 76 +chr15 19640141 19640362 SRX1261989.05_peak_159 83 . 7.23404 12.88798 8.30638 36 +chr15 19681163 19681304 SRX1261989.05_peak_160 197 . 11.95715 24.70216 19.73942 53 +chr15 19691387 19691524 SRX1261989.05_peak_161 188 . 11.42596 23.81590 18.87851 55 +chr15 19776107 19776319 SRX1261989.05_peak_162 165 . 6.13784 21.42391 16.55314 104 +chr15 19778680 19778788 SRX1261989.05_peak_163 145 . 5.34719 19.31401 14.50283 60 +chr16 34572594 34572833 SRX1261989.05_peak_164 624 . 8.17840 68.40851 62.46019 106 +chr16 34573028 34573174 SRX1261989.05_peak_165 294 . 5.52042 34.61775 29.40068 46 +chr16 34573932 34574347 SRX1261989.05_peak_166 857 . 9.81408 92.07579 85.75667 135 +chr16 34576403 34576669 SRX1261989.05_peak_167 388 . 5.92318 44.28801 38.82086 70 +chr16 34581420 34581640 SRX1261989.05_peak_168 394 . 4.50335 44.89816 39.41600 70 +chr16 34582187 34582443 SRX1261989.05_peak_169 418 . 4.36139 47.37838 41.83959 191 +chr16 34582736 34583178 SRX1261989.05_peak_170 461 . 4.41637 51.74170 46.10450 377 +chr16 34583324 34583704 SRX1261989.05_peak_171 377 . 3.90698 43.22469 37.78192 159 +chr16 34586514 34586644 SRX1261989.05_peak_172 240 . 3.18564 29.15282 24.07904 46 +chr16 34586778 34586886 SRX1261989.05_peak_173 321 . 3.57968 37.40959 32.12061 48 +chr16 34588033 34588299 SRX1261989.05_peak_174 398 . 4.11634 45.32108 39.82889 73 +chr16 34592695 34592847 SRX1261989.05_peak_175 508 . 5.34784 56.58636 50.86208 81 +chr16 34593084 34593313 SRX1261989.05_peak_176 221 . 3.93075 27.20134 22.17865 182 +chr16 34593464 34593638 SRX1261989.05_peak_177 602 . 6.49254 66.15847 60.25437 101 +chr16 34594848 34595581 SRX1261989.05_peak_178 447 . 6.19713 50.33323 44.72089 639 +chr16 38266939 38267144 SRX1261989.05_peak_179 96 . 8.08907 14.28775 9.65062 130 +chr16 38276169 38276420 SRX1261989.05_peak_180 234 . 11.73020 28.54591 23.48796 174 +chr16 46380771 46381029 SRX1261989.05_peak_181 242 . 6.42702 29.33688 24.25788 101 +chr16 46386403 46386578 SRX1261989.05_peak_182 236 . 2.97706 28.68650 23.62527 46 +chr16 46387782 46388023 SRX1261989.05_peak_183 260 . 3.04038 31.13417 26.01024 167 +chr16 46388178 46388303 SRX1261989.05_peak_184 171 . 2.64288 22.06488 17.17392 50 +chr16 46388647 46388801 SRX1261989.05_peak_185 333 . 3.32865 38.64735 33.32837 68 +chr16 46389662 46390793 SRX1261989.05_peak_186 252 . 2.84016 30.33576 25.23436 1069 +chr16 46394491 46395044 SRX1261989.05_peak_187 342 . 3.33096 39.56268 34.21906 311 +chr16 46398573 46399075 SRX1261989.05_peak_188 267 . 3.20398 31.88668 26.74404 344 +chr16 46399219 46399575 SRX1261989.05_peak_189 316 . 3.49264 36.95858 31.67915 301 +chr16 46400035 46400700 SRX1261989.05_peak_190 445 . 4.39072 50.18719 44.57915 77 +chr16 46400821 46401160 SRX1261989.05_peak_191 324 . 3.92266 37.72996 32.43430 252 +chr16 46401354 46401586 SRX1261989.05_peak_192 430 . 4.46511 48.58030 43.00954 72 +chr17 117258 117555 SRX1261989.05_peak_193 111 . 7.13182 15.80409 11.10704 69 +chr17 117763 118008 SRX1261989.05_peak_194 91 . 6.49581 13.72804 9.11725 28 +chr17 21973017 21973172 SRX1261989.05_peak_195 385 . 14.96616 44.05220 38.59319 70 +chr17 22521357 22521488 SRX1261989.05_peak_196 435 . 20.46066 49.11654 43.52919 62 +chr17 23016263 23016372 SRX1261989.05_peak_197 170 . 11.25336 21.88733 17.00054 46 +chr17 23190293 23190402 SRX1261989.05_peak_198 224 . 13.29943 27.52499 22.49132 29 +chr17 24421399 24421530 SRX1261989.05_peak_199 532 . 23.52976 59.06684 53.29586 70 +chr17 25074402 25074539 SRX1261989.05_peak_200 388 . 18.92611 44.28279 38.81568 82 +chr17 25115467 25115575 SRX1261989.05_peak_201 197 . 12.27639 24.67079 19.70859 51 +chr17 25383705 25383865 SRX1261989.05_peak_202 702 . 28.64492 76.37981 70.28136 78 +chr17 25389823 25389980 SRX1261989.05_peak_203 773 . 30.69099 83.52863 77.31738 76 +chr17 25569161 25569323 SRX1261989.05_peak_204 599 . 25.57582 65.89008 59.99078 79 +chr17 25788563 25788678 SRX1261989.05_peak_205 253 . 14.32246 30.44462 25.33703 53 +chr17 25920130 25920289 SRX1261989.05_peak_206 1047 . 38.36374 111.30458 104.70997 79 +chr17 26101521 26101631 SRX1261989.05_peak_207 253 . 14.32246 30.44462 25.33703 67 +chr17 26567030 26567138 SRX1261989.05_peak_208 197 . 12.27639 24.67079 19.70859 39 +chr17 26594826 26594958 SRX1261989.05_peak_209 484 . 20.18553 54.18094 48.49625 53 +chr17 26603825 26604319 SRX1261989.05_peak_210 1310 . 31.55996 137.88286 131.02921 88 +chr17 26619649 26620107 SRX1261989.05_peak_211 656 . 20.50183 71.70412 65.69571 378 +chr17 26881297 26881405 SRX1261989.05_peak_212 257 . 7.40091 30.89415 25.77599 62 +chr17 26884010 26884290 SRX1261989.05_peak_213 278 . 7.74379 33.01046 27.83814 46 +chr17 26885712 26885855 SRX1261989.05_peak_214 571 . 11.77394 62.97914 57.13831 80 +chr17_KI270729v1_random 3504 3659 SRX1261989.05_peak_215 799 . 14.00930 86.24684 79.99625 75 +chr17_KI270729v1_random 13077 13186 SRX1261989.05_peak_216 210 . 12.78791 26.08940 21.09071 30 +chr17_KI270729v1_random 22438 22557 SRX1261989.05_peak_217 296 . 12.68870 34.83478 29.61320 56 +chr17_KI270729v1_random 27714 27875 SRX1261989.05_peak_218 1056 . 28.11341 112.27429 105.66339 91 +chr18 108076 108277 SRX1261989.05_peak_219 576 . 9.61652 63.51498 57.66660 119 +chr18 108379 108556 SRX1261989.05_peak_220 412 . 7.98218 46.81877 41.29433 79 +chr18 109755 109998 SRX1261989.05_peak_221 133 . 4.63164 18.11264 13.33961 28 +chr18 110278 110444 SRX1261989.05_peak_222 239 . 6.01768 28.99607 23.92448 73 +chr18 80262943 80263105 SRX1261989.05_peak_223 170 . 11.25336 21.88733 17.00054 47 +chr1_KI270709v1_random 3708 3816 SRX1261989.05_peak_224 137 . 3.66850 18.54324 13.76013 57 +chr1_KI270709v1_random 7383 7580 SRX1261989.05_peak_225 279 . 4.54685 33.17545 27.99826 144 +chr1_KI270709v1_random 15065 15173 SRX1261989.05_peak_226 89 . 3.38125 13.58705 8.98160 61 +chr2 32916228 32916626 SRX1261989.05_peak_227 767 . 20.86415 82.98792 76.79059 305 +chr2 89830944 89831109 SRX1261989.05_peak_228 189 . 8.65845 23.84607 18.90807 64 +chr2 89833295 89833403 SRX1261989.05_peak_229 205 . 8.42572 25.48037 20.50029 29 +chr2 89836469 89836581 SRX1261989.05_peak_230 270 . 9.87433 32.19364 27.04136 61 +chr2 89841044 89841166 SRX1261989.05_peak_231 284 . 11.09233 33.63692 28.44607 67 +chr20 27504647 27504761 SRX1261989.05_peak_232 210 . 12.78791 26.08940 21.09071 55 +chr20 28495437 28495562 SRX1261989.05_peak_233 154 . 8.19672 20.28024 15.44263 75 +chr21 8203369 8203508 SRX1261989.05_peak_234 87 . 5.36950 13.38086 8.78335 61 +chr21 8234431 8234554 SRX1261989.05_peak_235 109 . 3.06948 15.69087 10.99781 68 +chr21 8387336 8387533 SRX1261989.05_peak_236 124 . 6.11434 17.14473 12.40205 37 +chr21 10270208 10270398 SRX1261989.05_peak_237 751 . 22.20355 81.29601 75.12418 88 +chr21 10274099 10274225 SRX1261989.05_peak_238 415 . 15.00240 47.08200 41.55171 64 +chr21 10325744 10325901 SRX1261989.05_peak_239 1007 . 28.63092 107.28680 100.77430 83 +chr21 10692770 10693016 SRX1261989.05_peak_240 471 . 19.84812 52.79041 47.13490 68 +chr21 39974629 39974770 SRX1261989.05_peak_241 106 . 8.69578 15.28237 10.60265 67 +chr22 11369709 11370022 SRX1261989.05_peak_242 136 . 6.11931 18.41648 13.63591 242 +chr22 12175740 12175848 SRX1261989.05_peak_243 126 . 7.75242 17.43588 12.68709 35 +chr22 16352773 16352906 SRX1261989.05_peak_244 685 . 28.13341 74.61149 68.54523 61 +chr22 18205535 18205686 SRX1261989.05_peak_245 498 . 20.02288 55.53396 49.82644 75 +chr22 18419123 18419249 SRX1261989.05_peak_246 185 . 11.53846 23.44043 18.51220 61 +chr22 18730518 18730700 SRX1261989.05_peak_247 997 . 27.59861 106.26412 99.77037 88 +chr22 18733676 18733797 SRX1261989.05_peak_248 289 . 11.96315 34.10859 28.90606 49 +chr22 18892434 18892561 SRX1261989.05_peak_249 414 . 14.59311 47.00396 41.47533 61 +chr22 18896399 18896565 SRX1261989.05_peak_250 539 . 17.87662 59.77770 53.99225 95 +chr22 21326200 21326314 SRX1261989.05_peak_251 207 . 10.16131 25.69151 20.70547 59 +chr22 50643679 50643944 SRX1261989.05_peak_252 119 . 7.35813 16.69564 11.96505 84 +chr22 50644093 50644237 SRX1261989.05_peak_253 70 . 5.79441 11.60827 7.08669 55 +chr22 50807940 50808050 SRX1261989.05_peak_254 132 . 9.23320 18.02454 13.25431 65 +chr22_KI270731v1_random 127798 127910 SRX1261989.05_peak_255 174 . 10.54141 22.33215 17.43505 34 +chr22_KI270733v1_random 120419 120562 SRX1261989.05_peak_256 90 . 4.92689 13.70309 9.09310 45 +chr22_KI270734v1_random 121674 121851 SRX1261989.05_peak_257 736 . 21.90350 79.79446 73.64752 92 +chr3 75669208 75669378 SRX1261989.05_peak_258 176 . 8.57357 22.59999 17.69491 104 +chr3 91550820 91550934 SRX1261989.05_peak_259 109 . 7.01919 15.59122 10.90171 87 +chr3 93470357 93470805 SRX1261989.05_peak_260 2068 . 22.84307 216.33185 206.84071 333 +chr3 195483663 195483781 SRX1261989.05_peak_261 164 . 7.74357 21.36831 16.49861 64 +chr4 1435358 1435500 SRX1261989.05_peak_262 213 . 11.91489 26.40042 21.39622 95 +chr4 1441752 1441884 SRX1261989.05_peak_263 118 . 9.20730 16.55892 11.83257 95 +chr4 49092749 49092865 SRX1261989.05_peak_264 275 . 8.70898 32.70544 27.53994 74 +chr4 49108280 49108473 SRX1261989.05_peak_265 202 . 5.78977 25.20273 20.22928 85 +chr4 49110704 49110823 SRX1261989.05_peak_266 244 . 6.99699 29.55474 24.47082 56 +chr4 49118877 49119031 SRX1261989.05_peak_267 261 . 7.68472 31.29910 26.17023 89 +chr4 49120947 49121130 SRX1261989.05_peak_268 89 . 4.75144 13.55582 8.95122 152 +chr4 49149395 49149523 SRX1261989.05_peak_269 418 . 9.40832 47.37849 41.83965 55 +chr4 49149717 49149838 SRX1261989.05_peak_270 376 . 8.82338 43.05980 37.62325 59 +chr4 49153177 49153299 SRX1261989.05_peak_271 456 . 9.69236 51.33150 45.69925 39 +chr4 49634298 49634407 SRX1261989.05_peak_272 195 . 9.24266 24.49091 19.53802 26 +chr4 49651407 49651541 SRX1261989.05_peak_273 366 . 14.28934 42.03798 36.62829 50 +chr4 49657532 49657701 SRX1261989.05_peak_274 498 . 20.96342 55.51696 49.81001 75 +chr4 49709323 49709568 SRX1261989.05_peak_275 1153 . 9.79659 122.04625 115.32375 125 +chr4 49709683 49709805 SRX1261989.05_peak_276 474 . 6.00270 53.15574 47.49319 61 +chr4 49709922 49710149 SRX1261989.05_peak_277 193 . 3.98282 24.29291 19.34402 178 +chr4 49710529 49710892 SRX1261989.05_peak_278 310 . 4.87437 36.28171 31.02155 194 +chr4 49711301 49711927 SRX1261989.05_peak_279 821 . 8.01410 88.38355 82.10805 251 +chr4 51107228 51107554 SRX1261989.05_peak_280 793 . 22.52773 85.58369 79.34541 104 +chr4 166580033 166580623 SRX1261989.05_peak_281 212 . 10.76688 26.21175 21.21039 351 +chr4 190178352 190178475 SRX1261989.05_peak_282 258 . 7.57792 30.96412 25.84373 63 +chr4 190179305 190179947 SRX1261989.05_peak_283 520 . 11.20885 57.79681 52.05401 389 +chr5 10109 10265 SRX1261989.05_peak_284 108 . 6.77654 15.57077 10.88193 83 +chr5 10955 11476 SRX1261989.05_peak_285 97 . 6.37484 14.34287 9.70398 44 +chr5 100636 100748 SRX1261989.05_peak_286 93 . 6.88073 14.00146 9.38140 77 +chr5 47297033 47297142 SRX1261989.05_peak_287 210 . 12.78791 26.08940 21.09071 64 +chr5 49601491 49601841 SRX1261989.05_peak_288 921 . 13.57515 98.53055 92.14492 94 +chr5 49601978 49602192 SRX1261989.05_peak_289 1223 . 16.29018 129.16162 122.38397 123 +chr5 49612327 49612469 SRX1261989.05_peak_290 191 . 9.05101 24.10727 19.16301 65 +chr5 49615441 49615549 SRX1261989.05_peak_291 194 . 8.92857 24.40793 19.45676 64 +chr5 49626777 49626997 SRX1261989.05_peak_292 180 . 8.25002 22.97830 18.06448 106 +chr5 49630775 49631062 SRX1261989.05_peak_293 332 . 12.18349 38.52518 33.20894 203 +chr5 49644041 49644157 SRX1261989.05_peak_294 215 . 8.89430 26.53681 21.52963 58 +chr5 49645962 49646185 SRX1261989.05_peak_295 448 . 13.50238 50.45321 44.83817 76 +chr5 49658365 49658769 SRX1261989.05_peak_296 753 . 9.44383 81.52484 75.34883 316 +chr5 49661420 49661823 SRX1261989.05_peak_297 460 . 6.97071 51.64248 46.00625 72 +chr5 49666374 49666500 SRX1261989.05_peak_298 186 . 6.90018 23.63167 18.69953 63 +chr5 49667001 49667327 SRX1261989.05_peak_299 295 . 11.99111 34.81880 29.59832 152 +chr5 80651451 80651573 SRX1261989.05_peak_300 311 . 16.36853 36.46248 31.19587 43 +chr6 58450719 58450859 SRX1261989.05_peak_301 402 . 18.02673 45.74699 40.24627 64 +chr6 58560771 58560899 SRX1261989.05_peak_302 315 . 15.38867 36.86636 31.59093 79 +chr6 58581461 58581569 SRX1261989.05_peak_303 202 . 11.19463 25.17278 20.20012 49 +chr6 58665384 58665510 SRX1261989.05_peak_304 279 . 12.84063 33.11110 27.93530 63 +chr6 58793456 58793608 SRX1261989.05_peak_305 590 . 20.56896 64.92298 59.04937 78 +chr6 58924896 58925004 SRX1261989.05_peak_306 191 . 10.93649 24.14352 19.19820 34 +chr6 59033985 59034108 SRX1261989.05_peak_307 212 . 11.44616 26.20417 21.20308 64 +chr6 59163193 59163352 SRX1261989.05_peak_308 877 . 25.84647 94.05523 87.71053 80 +chr6 59183881 59184016 SRX1261989.05_peak_309 418 . 16.79568 47.42788 41.88837 64 +chr6 59344258 59344367 SRX1261989.05_peak_310 200 . 11.43680 24.99393 20.02415 39 +chr6 59383243 59383367 SRX1261989.05_peak_311 331 . 14.26737 38.42981 33.11594 69 +chr6 59562338 59562473 SRX1261989.05_peak_312 448 . 17.21231 50.51525 44.89877 54 +chr6 59653166 59653277 SRX1261989.05_peak_313 222 . 12.06323 27.26186 22.23742 58 +chr6 61322923 61323141 SRX1261989.05_peak_314 1466 . 37.21766 153.66312 146.67712 128 +chr7 905576 905723 SRX1261989.05_peak_315 195 . 10.82084 24.52847 19.57420 74 +chr7 58033151 58033276 SRX1261989.05_peak_316 522 . 21.44232 57.99139 52.24243 62 +chr7 58275798 58275939 SRX1261989.05_peak_317 439 . 19.78146 49.50479 43.90769 70 +chr7 58657801 58657911 SRX1261989.05_peak_318 197 . 12.27639 24.67079 19.70859 51 +chr7 59167602 59167743 SRX1261989.05_peak_319 403 . 19.43763 45.88287 40.37652 76 +chr7 59251648 59251790 SRX1261989.05_peak_320 357 . 17.90308 41.11764 35.73249 66 +chr7 60019497 60019629 SRX1261989.05_peak_321 308 . 16.13879 36.07499 30.82039 53 +chr7 60250535 60250689 SRX1261989.05_peak_322 484 . 21.08716 54.10188 48.42077 75 +chr7 60310800 60310946 SRX1261989.05_peak_323 423 . 19.31047 47.93172 42.37478 66 +chr7 60418864 60418989 SRX1261989.05_peak_324 326 . 16.88004 38.00138 32.69730 60 +chr7 60517837 60517945 SRX1261989.05_peak_325 197 . 12.27639 24.67079 19.70859 42 +chr8 43240011 43240120 SRX1261989.05_peak_326 213 . 8.56540 26.33825 21.33486 21 +chr9 137328268 137328427 SRX1261989.05_peak_327 172 . 10.09003 22.12215 17.22973 102 +chrUn_GL000214v1 64346 64576 SRX1261989.05_peak_328 151 . 8.89680 20.02858 15.19787 193 +chrUn_GL000214v1 65036 65170 SRX1261989.05_peak_329 297 . 13.14879 35.01224 29.78212 44 +chrUn_GL000216v2 291 822 SRX1261989.05_peak_330 153 . 8.96305 20.14716 15.31249 497 +chrUn_GL000216v2 6081 6201 SRX1261989.05_peak_331 285 . 12.80277 33.70834 28.51553 51 +chrUn_GL000216v2 80068 80395 SRX1261989.05_peak_332 294 . 13.74046 34.71214 29.49376 206 +chrUn_GL000216v2 149474 149689 SRX1261989.05_peak_333 664 . 15.44489 72.50481 66.48392 95 +chrUn_GL000216v2 158649 158779 SRX1261989.05_peak_334 364 . 11.08067 41.89827 36.49277 62 +chrUn_GL000216v2 167821 167944 SRX1261989.05_peak_335 429 . 11.60649 48.47809 42.90853 63 +chrUn_GL000216v2 168692 168802 SRX1261989.05_peak_336 190 . 7.59287 24.01046 19.06857 46 +chrUn_GL000216v2 173813 173931 SRX1261989.05_peak_337 262 . 12.61315 31.38802 26.25690 92 +chrUn_GL000220v1 101684 101807 SRX1261989.05_peak_338 64 . 4.25299 10.90338 6.42051 41 +chrUn_GL000220v1 103535 103786 SRX1261989.05_peak_339 165 . 6.14754 21.45309 16.58146 83 +chrUn_GL000220v1 135280 135464 SRX1261989.05_peak_340 84 . 2.77746 13.03447 8.44840 138 +chrUn_GL000224v1 2284 2454 SRX1261989.05_peak_341 132 . 4.37752 18.01299 13.24321 124 +chrUn_GL000224v1 8212 8326 SRX1261989.05_peak_342 151 . 4.60101 19.94062 15.11359 58 +chrUn_KI270303v1 1226 1418 SRX1261989.05_peak_343 411 . 19.03312 46.69728 41.17546 100 +chrUn_KI270304v1 984 1109 SRX1261989.05_peak_344 321 . 16.55297 37.44935 32.15966 88 +chrUn_KI270310v1 610 1019 SRX1261989.05_peak_345 642 . 23.48726 70.19402 64.21289 291 +chrUn_KI270317v1 1389 1530 SRX1261989.05_peak_346 342 . 17.39156 39.55323 34.21001 75 +chrUn_KI270330v1 1330 1613 SRX1261989.05_peak_347 404 . 17.26630 45.96144 40.45424 153 +chrUn_KI270333v1 306 621 SRX1261989.05_peak_348 710 . 10.07197 77.12916 71.01624 94 +chrUn_KI270333v1 872 1227 SRX1261989.05_peak_349 420 . 7.48941 47.61868 42.06975 70 +chrUn_KI270333v1 1378 1732 SRX1261989.05_peak_350 551 . 8.69460 60.91357 55.10736 271 +chrUn_KI270333v1 2003 2302 SRX1261989.05_peak_351 494 . 8.17809 55.10606 49.40570 57 +chrUn_KI270333v1 2409 2517 SRX1261989.05_peak_352 229 . 5.50945 27.97430 22.93013 54 +chrUn_KI270336v1 69 323 SRX1261989.05_peak_353 631 . 14.49016 69.12479 63.16089 106 +chrUn_KI270336v1 512 1003 SRX1261989.05_peak_354 1346 . 23.97138 141.59494 134.68472 365 +chrUn_KI270337v1 14 603 SRX1261989.05_peak_355 840 . 12.81494 90.32432 84.02013 482 +chrUn_KI270337v1 726 1012 SRX1261989.05_peak_356 1506 . 18.67941 157.62398 150.61714 103 +chrUn_KI270391v1 284 450 SRX1261989.05_peak_357 224 . 13.29943 27.52499 22.49132 84 +chrUn_KI270411v1 208 440 SRX1261989.05_peak_358 507 . 12.05735 56.44684 50.72433 153 +chrUn_KI270411v1 622 865 SRX1261989.05_peak_359 418 . 10.73599 47.33833 41.80037 61 +chrUn_KI270411v1 1260 1431 SRX1261989.05_peak_360 312 . 9.08430 36.55594 31.28715 93 +chrUn_KI270411v1 1801 1979 SRX1261989.05_peak_361 354 . 9.74498 40.78245 35.40957 88 +chrUn_KI270412v1 335 545 SRX1261989.05_peak_362 253 . 14.32246 30.44462 25.33703 121 +chrUn_KI270438v1 103907 104379 SRX1261989.05_peak_363 716 . 6.63821 77.81541 71.69440 161 +chrUn_KI270438v1 104638 104956 SRX1261989.05_peak_364 433 . 4.67371 48.96346 43.38661 65 +chrUn_KI270438v1 109231 110574 SRX1261989.05_peak_365 501 . 4.33301 55.85893 50.14717 592 +chrUn_KI270438v1 111250 111791 SRX1261989.05_peak_366 316 . 3.85606 36.97942 31.69934 56 +chrUn_KI270438v1 111938 112206 SRX1261989.05_peak_367 558 . 5.09783 61.62967 55.81284 196 +chrUn_KI270438v1 112331 112449 SRX1261989.05_peak_368 368 . 4.22654 42.23750 36.81953 63 +chrUn_KI270442v1 92339 92462 SRX1261989.05_peak_369 364 . 15.39589 41.90350 36.49783 38 +chrUn_KI270465v1 914 1023 SRX1261989.05_peak_370 106 . 8.69578 15.28237 10.60265 30 +chrUn_KI270466v1 38 1203 SRX1261989.05_peak_371 1099 . 13.84782 116.62558 109.95726 998 +chrUn_KI270467v1 1419 1527 SRX1261989.05_peak_372 322 . 4.26242 37.55334 32.26135 64 +chrUn_KI270467v1 1970 3842 SRX1261989.05_peak_373 727 . 6.30255 78.90511 72.76827 131 +chrUn_KI270544v1 991 1101 SRX1261989.05_peak_374 143 . 10.23033 19.18089 14.37384 50 +chrUn_KI270744v1 11091 11451 SRX1261989.05_peak_375 341 . 9.31304 39.51678 34.17588 79 +chrUn_KI270744v1 13228 13617 SRX1261989.05_peak_376 143 . 5.92750 19.13988 14.33841 317 +chrUn_KI270744v1 124553 124689 SRX1261989.05_peak_377 115 . 5.61072 16.26922 11.55857 89 +chrUn_KI270751v1 38837 39060 SRX1261989.05_peak_378 93 . 6.40312 13.97520 9.35589 135 +chrUn_KI270751v1 39298 39876 SRX1261989.05_peak_379 151 . 8.04929 19.98984 15.16025 508 +chrUn_KI270754v1 18118 18226 SRX1261989.05_peak_380 155 . 9.72132 20.39726 15.55673 45 +chrX 156030408 156030709 SRX1261989.05_peak_381 118 . 9.20730 16.55892 11.83257 44 +chrY 10666911 10667035 SRX1261989.05_peak_382 297 . 15.85701 34.93692 29.70972 56 +chrY 10825196 10825325 SRX1261989.05_peak_383 419 . 19.94914 47.49421 41.94928 60 +chrY 10999848 10999966 SRX1261989.05_peak_384 357 . 17.90308 41.11764 35.73249 58 +chrY 11305291 11305427 SRX1261989.05_peak_385 415 . 13.27604 47.09110 41.55996 62 +chrY 11312508 11312630 SRX1261989.05_peak_386 268 . 9.54360 32.00560 26.85812 67 +chrY 11318728 11318836 SRX1261989.05_peak_387 149 . 8.76720 19.79566 14.97232 70 +chrY 11326361 11326469 SRX1261989.05_peak_388 231 . 8.64981 28.16558 23.11654 57 +chrY 11327600 11327745 SRX1261989.05_peak_389 530 . 13.96196 58.81569 53.05420 72 +chrY 11329884 11329994 SRX1261989.05_peak_390 169 . 7.28450 21.78796 16.90923 49 +chrY 11539269 11539447 SRX1261989.05_peak_391 151 . 8.32046 20.00620 15.17634 53 +chrY 11721941 11722127 SRX1261989.05_peak_392 668 . 27.62189 72.85097 66.82327 95 +chrY 11747648 11747771 SRX1261989.05_peak_393 451 . 20.97218 50.74958 45.12791 81 +chrY 56702557 56702708 SRX1261989.05_peak_394 241 . 4.68351 29.23742 24.16229 65 +chrY 56755993 56756147 SRX1261989.05_peak_395 374 . 5.76281 42.83042 37.40002 83 +chrY 56763444 56763583 SRX1261989.05_peak_396 563 . 6.91825 62.13149 56.30593 72 +chrY 56768533 56768646 SRX1261989.05_peak_397 206 . 4.81806 25.65284 20.66803 42 diff --git a/tests/data/hg38_sample/SRX4288408.05.bed b/tests/data/hg38_sample/SRX4288408.05.bed new file mode 100644 index 00000000..8b4e37b4 --- /dev/null +++ b/tests/data/hg38_sample/SRX4288408.05.bed @@ -0,0 +1,1609 @@ +chr1 10022 10221 SRX4288408.05_peak_1 324 . 12.11558 37.71613 32.48608 31 +chr1 10391 10447 SRX4288408.05_peak_2 149 . 7.80782 19.50852 14.93405 28 +chr1 791555 791620 SRX4288408.05_peak_3 126 . 6.65901 17.10722 12.64596 41 +chr1 2652256 2652306 SRX4288408.05_peak_4 83 . 4.54212 12.63398 8.39064 18 +chr1 2652384 2652469 SRX4288408.05_peak_5 151 . 5.86081 19.76331 15.18030 17 +chr1 2654112 2654164 SRX4288408.05_peak_6 166 . 5.95568 21.28354 16.64139 32 +chr1 2748459 2748536 SRX4288408.05_peak_7 83 . 5.33618 12.56414 8.33499 15 +chr1 4303101 4303162 SRX4288408.05_peak_8 211 . 9.42323 25.98142 21.15852 33 +chr1 9411155 9411302 SRX4288408.05_peak_9 225 . 7.73861 27.40421 22.53395 37 +chr1 16565470 16565528 SRX4288408.05_peak_10 70 . 4.49625 11.24188 7.08711 23 +chr1 16613970 16614020 SRX4288408.05_peak_11 71 . 3.62745 11.35480 7.19401 5 +chr1 16643811 16643885 SRX4288408.05_peak_12 69 . 3.56798 11.13190 6.98270 11 +chr1 16728886 16728936 SRX4288408.05_peak_13 87 . 3.84986 12.97097 8.71219 13 +chr1 31431801 31431891 SRX4288408.05_peak_14 107 . 6.56168 15.14391 10.77518 65 +chr1 31431987 31432178 SRX4288408.05_peak_15 262 . 10.52632 31.21069 26.20709 41 +chr1 91387241 91387539 SRX4288408.05_peak_16 378 . 12.19793 43.26290 37.87082 90 +chr1 121745353 121745423 SRX4288408.05_peak_17 333 . 8.16848 38.60038 33.35076 31 +chr1 121749771 121749836 SRX4288408.05_peak_18 319 . 7.12442 37.17262 31.96073 29 +chr1 121750457 121750541 SRX4288408.05_peak_19 269 . 6.53527 31.98634 26.95039 25 +chr1 121773388 121773458 SRX4288408.05_peak_20 98 . 4.84582 14.16325 9.84320 29 +chr1 122462771 122462842 SRX4288408.05_peak_21 525 . 16.42334 58.41243 52.58656 42 +chr1 122496555 122496618 SRX4288408.05_peak_22 448 . 14.80793 50.41035 44.81654 41 +chr1 122503862 122503953 SRX4288408.05_peak_23 321 . 9.37766 37.38757 32.17021 49 +chr1 122504024 122504076 SRX4288408.05_peak_24 195 . 7.14894 24.31783 19.55770 24 +chr1 122517814 122517872 SRX4288408.05_peak_25 197 . 5.52764 24.56499 19.79727 29 +chr1 122552746 122552906 SRX4288408.05_peak_26 294 . 6.62123 34.59643 29.46411 120 +chr1 122571502 122571552 SRX4288408.05_peak_27 184 . 6.28053 23.14597 18.42792 30 +chr1 122572561 122572615 SRX4288408.05_peak_28 162 . 5.94901 20.84994 16.22126 19 +chr1 122598538 122598598 SRX4288408.05_peak_29 218 . 6.43564 26.71763 21.87383 31 +chr1 122606233 122606300 SRX4288408.05_peak_30 291 . 7.53296 34.31721 29.19157 38 +chr1 122616136 122616200 SRX4288408.05_peak_31 168 . 5.89812 21.51287 16.86293 25 +chr1 122637161 122637212 SRX4288408.05_peak_32 104 . 4.52650 14.81975 10.46356 13 +chr1 122657357 122657426 SRX4288408.05_peak_33 366 . 8.51597 41.97841 36.62123 28 +chr1 122662176 122662235 SRX4288408.05_peak_34 287 . 7.40741 33.87453 28.76796 33 +chr1 122668693 122668747 SRX4288408.05_peak_35 247 . 6.29591 29.70252 24.74965 23 +chr1 122683735 122683788 SRX4288408.05_peak_36 167 . 5.12305 21.44266 16.79464 32 +chr1 122715031 122715095 SRX4288408.05_peak_37 90 . 4.57064 13.35777 9.08258 20 +chr1 122720432 122720509 SRX4288408.05_peak_38 590 . 12.00279 65.09558 59.06696 40 +chr1 122741462 122741530 SRX4288408.05_peak_39 263 . 7.39247 31.35888 26.35125 42 +chr1 122755134 122755199 SRX4288408.05_peak_40 311 . 7.97386 36.35226 31.16577 37 +chr1 122764896 122764946 SRX4288408.05_peak_41 117 . 4.95437 16.21748 11.79844 12 +chr1 122775344 122775403 SRX4288408.05_peak_42 343 . 8.30140 39.59785 34.31750 26 +chr1 122827224 122827275 SRX4288408.05_peak_43 67 . 3.87644 10.92374 6.78453 35 +chr1 122830255 122830350 SRX4288408.05_peak_44 115 . 4.98989 15.97861 11.56872 64 +chr1 122830571 122830621 SRX4288408.05_peak_45 129 . 5.21739 17.40699 12.93495 13 +chr1 122839039 122839103 SRX4288408.05_peak_46 231 . 6.82863 28.05421 23.16401 35 +chr1 122839408 122839498 SRX4288408.05_peak_47 170 . 5.82902 21.69633 17.03295 27 +chr1 122840761 122840817 SRX4288408.05_peak_48 153 . 5.43545 19.98982 15.39945 29 +chr1 122866947 122867047 SRX4288408.05_peak_49 128 . 4.97875 17.34789 12.87808 18 +chr1 122874090 122874160 SRX4288408.05_peak_50 87 . 4.17876 13.03824 8.77669 28 +chr1 122879947 122880004 SRX4288408.05_peak_51 128 . 4.78309 17.35508 12.88504 30 +chr1 122908283 122908333 SRX4288408.05_peak_52 119 . 4.68571 16.34076 11.91715 15 +chr1 122912760 122912856 SRX4288408.05_peak_53 249 . 6.58717 29.94174 24.98219 33 +chr1 122923226 122923289 SRX4288408.05_peak_54 221 . 6.77966 26.96986 22.11880 36 +chr1 122929319 122929369 SRX4288408.05_peak_55 209 . 6.28466 25.75602 20.94629 20 +chr1 122943092 122943160 SRX4288408.05_peak_56 75 . 4.12371 11.69898 7.52207 31 +chr1 122961746 122961805 SRX4288408.05_peak_57 243 . 7.36994 29.32090 24.38541 23 +chr1 122975045 122975097 SRX4288408.05_peak_58 163 . 6.27451 20.97004 16.33808 11 +chr1 122981222 122981274 SRX4288408.05_peak_59 89 . 4.76923 13.27105 8.99916 14 +chr1 122983494 122983544 SRX4288408.05_peak_60 87 . 4.69341 13.06035 8.79777 41 +chr1 122998192 122998254 SRX4288408.05_peak_61 75 . 4.22920 11.72564 7.53534 16 +chr1 122998366 122998421 SRX4288408.05_peak_62 101 . 4.63023 14.51287 10.17068 31 +chr1 123003212 123003287 SRX4288408.05_peak_63 358 . 8.30281 41.21176 35.88219 31 +chr1 123010074 123010157 SRX4288408.05_peak_64 92 . 4.61538 13.49065 9.21001 46 +chr1 123017473 123017555 SRX4288408.05_peak_65 256 . 7.33198 30.66920 25.68115 40 +chr1 123027284 123027340 SRX4288408.05_peak_66 312 . 7.71114 36.46191 31.27194 25 +chr1 123057607 123057665 SRX4288408.05_peak_67 239 . 7.08447 28.89624 23.97276 30 +chr1 123058718 123058769 SRX4288408.05_peak_68 84 . 4.45372 12.69394 8.44689 18 +chr1 123058885 123058985 SRX4288408.05_peak_69 449 . 10.16713 50.55427 44.95697 36 +chr1 123065486 123065551 SRX4288408.05_peak_70 88 . 4.70410 13.09015 8.82632 23 +chr1 123075833 123075889 SRX4288408.05_peak_71 254 . 7.56914 30.43591 25.46162 19 +chr1 123095548 123095614 SRX4288408.05_peak_72 364 . 8.94701 41.79406 36.44267 26 +chr1 123098326 123098377 SRX4288408.05_peak_73 120 . 5.15679 16.49033 12.05133 28 +chr1 123103647 123103732 SRX4288408.05_peak_74 274 . 7.58294 32.48434 27.43292 30 +chr1 123107664 123107727 SRX4288408.05_peak_75 261 . 7.48441 31.16468 26.16190 26 +chr1 123113451 123113512 SRX4288408.05_peak_76 252 . 7.04676 30.19738 25.22971 27 +chr1 123130399 123130564 SRX4288408.05_peak_77 237 . 6.61058 28.67394 23.75817 114 +chr1 123137037 123137110 SRX4288408.05_peak_78 432 . 10.00705 48.80518 43.25507 35 +chr1 123148158 123148214 SRX4288408.05_peak_79 182 . 5.85009 22.98775 18.27530 19 +chr1 123149625 123149679 SRX4288408.05_peak_80 199 . 5.98943 24.71442 19.94244 26 +chr1 123173000 123173062 SRX4288408.05_peak_81 238 . 6.75422 28.72375 23.80688 29 +chr1 123175969 123176028 SRX4288408.05_peak_82 257 . 7.06625 30.73973 25.74939 17 +chr1 123185597 123185682 SRX4288408.05_peak_83 267 . 7.52394 31.79066 26.75946 46 +chr1 123189065 123189126 SRX4288408.05_peak_84 176 . 5.90501 22.35591 17.67146 39 +chr1 123200458 123200530 SRX4288408.05_peak_85 95 . 4.75504 13.90059 9.59124 49 +chr1 123225651 123225724 SRX4288408.05_peak_86 390 . 9.41673 44.48822 39.05695 32 +chr1 123238879 123238929 SRX4288408.05_peak_87 178 . 5.97015 22.57157 17.88030 22 +chr1 123245493 123245551 SRX4288408.05_peak_88 83 . 4.41989 12.59391 8.36336 44 +chr1 123253603 123253653 SRX4288408.05_peak_89 113 . 5.15844 15.77035 11.36756 19 +chr1 123264491 123264541 SRX4288408.05_peak_90 122 . 4.98084 16.65253 12.20799 28 +chr1 123265166 123265240 SRX4288408.05_peak_91 78 . 4.24967 12.08389 7.87658 16 +chr1 123270546 123270599 SRX4288408.05_peak_92 152 . 5.87372 19.80176 15.21764 22 +chr1 123275491 123275578 SRX4288408.05_peak_93 194 . 6.32997 24.17077 19.41517 26 +chr1 123299998 123300048 SRX4288408.05_peak_94 152 . 5.63003 19.85306 15.26753 18 +chr1 123303733 123303835 SRX4288408.05_peak_95 88 . 4.39277 13.13065 8.86499 16 +chr1 123316295 123316356 SRX4288408.05_peak_96 290 . 8.28313 34.18674 29.06425 18 +chr1 123324431 123324493 SRX4288408.05_peak_97 117 . 5.04431 16.14660 11.73021 34 +chr1 123346210 123346260 SRX4288408.05_peak_98 220 . 6.90141 26.89579 22.04690 26 +chr1 123347102 123347165 SRX4288408.05_peak_99 300 . 7.78210 35.18042 30.03254 37 +chr1 123352702 123352771 SRX4288408.05_peak_100 299 . 7.34266 35.11539 29.96890 30 +chr1 123354705 123354758 SRX4288408.05_peak_101 253 . 6.81004 30.30397 25.33354 24 +chr1 123358634 123358684 SRX4288408.05_peak_102 110 . 4.82714 15.46944 11.08896 21 +chr1 123362637 123362695 SRX4288408.05_peak_103 108 . 5.10511 15.25634 10.88332 31 +chr1 123382553 123382624 SRX4288408.05_peak_104 417 . 9.73574 47.30877 41.79733 28 +chr1 123390768 123390832 SRX4288408.05_peak_105 303 . 7.87919 35.51148 30.34564 35 +chr1 123393611 123393698 SRX4288408.05_peak_106 83 . 4.30248 12.54515 8.31680 54 +chr1 123401240 123401307 SRX4288408.05_peak_107 193 . 6.30449 24.08840 19.33483 37 +chr1 123410107 123410216 SRX4288408.05_peak_108 120 . 5.03979 16.48757 12.04864 83 +chr1 123413328 123413392 SRX4288408.05_peak_109 189 . 5.92862 23.67076 18.93753 26 +chr1 123423104 123423161 SRX4288408.05_peak_110 162 . 5.57966 20.86394 16.23484 27 +chr1 123428908 123428972 SRX4288408.05_peak_111 329 . 8.06045 38.22146 32.98107 35 +chr1 123433726 123433776 SRX4288408.05_peak_112 84 . 4.17412 12.73028 8.48145 12 +chr1 123460676 123460764 SRX4288408.05_peak_113 252 . 7.34072 30.20833 25.24019 34 +chr1 123487594 123487668 SRX4288408.05_peak_114 224 . 6.89655 27.34679 22.47809 42 +chr1 123495619 123495686 SRX4288408.05_peak_115 312 . 8.33333 36.48590 31.29545 31 +chr1 123531252 123531302 SRX4288408.05_peak_116 75 . 4.22343 11.70857 7.53126 40 +chr1 123548540 123548621 SRX4288408.05_peak_117 395 . 9.57143 44.99151 39.54656 42 +chr1 123549467 123549526 SRX4288408.05_peak_118 136 . 5.44693 18.11891 13.61231 38 +chr1 123555151 123555217 SRX4288408.05_peak_119 217 . 6.27219 26.57786 21.73781 29 +chr1 123580877 123580939 SRX4288408.05_peak_120 75 . 4.22343 11.70857 7.53126 21 +chr1 123581713 123581814 SRX4288408.05_peak_121 72 . 4.11960 11.39860 7.23561 79 +chr1 123587798 123587861 SRX4288408.05_peak_122 465 . 10.09296 52.13952 46.50489 30 +chr1 123588836 123588888 SRX4288408.05_peak_123 234 . 6.77316 28.32902 23.42272 23 +chr1 123596366 123596421 SRX4288408.05_peak_124 98 . 4.51977 14.16026 9.84031 23 +chr1 123597299 123597358 SRX4288408.05_peak_125 127 . 5.05370 17.24330 12.77720 23 +chr1 123619817 123619870 SRX4288408.05_peak_126 108 . 4.76497 15.27220 10.89864 16 +chr1 123622062 123622112 SRX4288408.05_peak_127 213 . 6.68030 26.19111 21.36194 30 +chr1 123629352 123629434 SRX4288408.05_peak_128 93 . 4.67091 13.65450 9.35557 68 +chr1 123635610 123635797 SRX4288408.05_peak_129 356 . 9.05823 41.02355 35.69899 34 +chr1 123641769 123641830 SRX4288408.05_peak_130 400 . 9.54386 45.49097 40.02805 29 +chr1 123648301 123648352 SRX4288408.05_peak_131 182 . 5.73770 22.99676 18.28399 23 +chr1 123660515 123660574 SRX4288408.05_peak_132 207 . 6.47292 25.51573 20.71311 31 +chr1 123677214 123677275 SRX4288408.05_peak_133 285 . 7.47308 33.61005 28.51177 31 +chr1 123700055 123700112 SRX4288408.05_peak_134 244 . 7.07610 29.34506 24.40907 27 +chr1 123714855 123714960 SRX4288408.05_peak_135 144 . 5.47762 18.98001 14.43354 68 +chr1 123720095 123720180 SRX4288408.05_peak_136 166 . 5.95981 21.29644 16.65386 56 +chr1 123721010 123721105 SRX4288408.05_peak_137 313 . 8.33922 36.50485 31.30689 56 +chr1 123723295 123723361 SRX4288408.05_peak_138 117 . 5.04775 16.15718 11.74037 36 +chr1 123739423 123739476 SRX4288408.05_peak_139 120 . 4.91803 16.44848 12.02060 27 +chr1 123743381 123743431 SRX4288408.05_peak_140 93 . 4.56070 13.64913 9.35044 37 +chr1 123751338 123751388 SRX4288408.05_peak_141 182 . 5.82878 22.91385 18.20405 31 +chr1 123753757 123753816 SRX4288408.05_peak_142 260 . 7.02836 31.08235 26.08245 28 +chr1 123760628 123760689 SRX4288408.05_peak_143 300 . 8.11189 35.23961 30.08977 39 +chr1 123783686 123783763 SRX4288408.05_peak_144 433 . 9.35746 48.94710 43.39305 47 +chr1 123789225 123789290 SRX4288408.05_peak_145 102 . 4.56508 14.62721 10.27896 33 +chr1 123806334 123806410 SRX4288408.05_peak_146 431 . 9.97191 48.68965 43.14118 32 +chr1 123815749 123815805 SRX4288408.05_peak_147 192 . 5.78888 23.98685 19.23625 31 +chr1 123818854 123818908 SRX4288408.05_peak_148 203 . 5.99769 25.16066 20.36846 25 +chr1 123831955 123832030 SRX4288408.05_peak_149 248 . 7.36023 29.78079 24.82640 32 +chr1 123837823 123837873 SRX4288408.05_peak_150 112 . 4.98615 15.61592 11.21914 35 +chr1 123841812 123841864 SRX4288408.05_peak_151 225 . 6.78191 27.43759 22.56653 30 +chr1 123848984 123849087 SRX4288408.05_peak_152 225 . 6.79095 27.46734 22.59526 73 +chr1 123858254 123858322 SRX4288408.05_peak_153 96 . 4.67997 14.01061 9.69669 44 +chr1 123859444 123859495 SRX4288408.05_peak_154 170 . 5.84036 21.73365 17.06935 31 +chr1 123866578 123866629 SRX4288408.05_peak_155 244 . 7.23730 29.38934 24.44547 17 +chr1 123891055 123891110 SRX4288408.05_peak_156 106 . 4.68948 15.03057 10.66602 14 +chr1 123892541 123892605 SRX4288408.05_peak_157 231 . 6.83761 28.08413 23.19332 34 +chr1 123914303 123914357 SRX4288408.05_peak_158 180 . 6.30824 22.79899 18.09255 31 +chr1 123924350 123924400 SRX4288408.05_peak_159 195 . 6.11735 24.32295 19.56260 24 +chr1 123926243 123926341 SRX4288408.05_peak_160 320 . 7.64256 37.22559 32.01163 29 +chr1 123930361 123930471 SRX4288408.05_peak_161 400 . 8.87246 45.47593 40.01374 81 +chr1 123948056 123948112 SRX4288408.05_peak_162 164 . 6.03448 21.11119 16.47391 23 +chr1 123952406 123952484 SRX4288408.05_peak_163 236 . 6.84755 28.58135 23.66839 41 +chr1 123959472 123959654 SRX4288408.05_peak_164 218 . 6.69792 26.70366 21.86039 118 +chr1 123969019 123969071 SRX4288408.05_peak_165 226 . 6.67522 27.53891 22.66430 16 +chr1 123985184 123985253 SRX4288408.05_peak_166 419 . 9.11892 47.50538 41.99074 34 +chr1 123985428 123985534 SRX4288408.05_peak_167 256 . 7.04846 30.67811 25.68992 45 +chr1 123998921 123999000 SRX4288408.05_peak_168 135 . 5.31209 18.07608 13.57097 34 +chr1 124005764 124005856 SRX4288408.05_peak_169 314 . 7.90312 36.62762 31.42796 35 +chr1 124007896 124007955 SRX4288408.05_peak_170 241 . 6.58824 29.04454 24.11668 29 +chr1 124012153 124012210 SRX4288408.05_peak_171 296 . 7.38535 34.78122 29.64392 26 +chr1 124012744 124012804 SRX4288408.05_peak_172 200 . 6.01415 24.80296 20.02891 39 +chr1 124018058 124018110 SRX4288408.05_peak_173 221 . 6.53266 27.05269 22.19936 19 +chr1 124020159 124020238 SRX4288408.05_peak_174 111 . 4.86522 15.58952 11.19368 57 +chr1 124044956 124045031 SRX4288408.05_peak_175 673 . 11.92771 73.63850 67.34626 40 +chr1 124056786 124056897 SRX4288408.05_peak_176 288 . 7.56895 33.94178 28.83336 71 +chr1 124079306 124079360 SRX4288408.05_peak_177 217 . 6.94143 26.55322 21.71374 25 +chr1 124080481 124080533 SRX4288408.05_peak_178 239 . 7.21868 28.84654 23.92495 22 +chr1 124085436 124085486 SRX4288408.05_peak_179 105 . 4.86111 14.89156 10.53263 15 +chr1 124090513 124090586 SRX4288408.05_peak_180 216 . 6.91145 26.46049 21.62310 39 +chr1 124098927 124099098 SRX4288408.05_peak_181 283 . 7.56686 33.43338 28.34013 48 +chr1 124108215 124108271 SRX4288408.05_peak_182 155 . 6.29660 20.16942 15.57256 31 +chr1 124125328 124125378 SRX4288408.05_peak_183 165 . 5.91472 21.15516 16.51639 34 +chr1 124132920 124132974 SRX4288408.05_peak_184 241 . 7.12817 29.03798 24.11020 25 +chr1 124140814 124140866 SRX4288408.05_peak_185 176 . 6.44666 22.33850 17.65459 22 +chr1 124158995 124159058 SRX4288408.05_peak_186 246 . 7.44526 29.55461 24.60538 26 +chr1 124168231 124168298 SRX4288408.05_peak_187 337 . 7.84084 38.98454 33.72028 28 +chr1 124188811 124188861 SRX4288408.05_peak_188 124 . 5.30847 16.94658 12.49097 20 +chr1 124199781 124199840 SRX4288408.05_peak_189 191 . 6.25416 23.92498 19.17629 32 +chr1 124204094 124204196 SRX4288408.05_peak_190 372 . 9.02256 42.61087 37.23954 68 +chr1 124209085 124209135 SRX4288408.05_peak_191 97 . 4.57816 14.02476 9.71025 5 +chr1 124225351 124225406 SRX4288408.05_peak_192 198 . 5.85586 24.63985 19.87001 30 +chr1 124250692 124250900 SRX4288408.05_peak_193 382 . 8.51319 43.60633 38.20580 29 +chr1 124262773 124262846 SRX4288408.05_peak_194 61 . 3.93220 10.28247 6.18973 3 +chr1 124278374 124278426 SRX4288408.05_peak_195 174 . 6.09418 22.13477 17.45672 21 +chr1 124288244 124288325 SRX4288408.05_peak_196 110 . 5.06146 15.48716 11.09538 23 +chr1 124297634 124297689 SRX4288408.05_peak_197 89 . 4.53920 13.26397 8.99226 35 +chr1 124314952 124315002 SRX4288408.05_peak_198 98 . 4.62657 14.17529 9.85475 15 +chr1 124318304 124318361 SRX4288408.05_peak_199 155 . 5.48628 20.16117 15.56468 27 +chr1 124319984 124320056 SRX4288408.05_peak_200 280 . 7.47905 33.13387 28.05337 40 +chr1 124344798 124344852 SRX4288408.05_peak_201 250 . 7.14758 30.06021 25.09738 14 +chr1 124377203 124377266 SRX4288408.05_peak_202 236 . 6.59077 28.60313 23.68928 39 +chr1 124380748 124380798 SRX4288408.05_peak_203 223 . 6.45948 27.24340 22.37761 19 +chr1 124386337 124386421 SRX4288408.05_peak_204 64 . 4.01662 10.52794 6.42263 56 +chr1 124400612 124400678 SRX4288408.05_peak_205 426 . 10.01431 48.22171 42.68478 25 +chr1 124421083 124421195 SRX4288408.05_peak_206 230 . 6.66667 27.96440 23.07650 49 +chr1 124434978 124435043 SRX4288408.05_peak_207 188 . 6.27986 23.57615 18.84583 28 +chr1 124451446 124451496 SRX4288408.05_peak_208 102 . 4.65116 14.57915 10.23257 30 +chr1 124456453 124456631 SRX4288408.05_peak_209 239 . 6.78392 28.82651 23.90562 37 +chr1 124486094 124486147 SRX4288408.05_peak_210 276 . 7.66074 32.73978 27.67912 27 +chr1 124487188 124487314 SRX4288408.05_peak_211 224 . 6.87285 27.27072 22.40420 22 +chr1 124492155 124492236 SRX4288408.05_peak_212 149 . 5.76369 19.47222 14.90811 49 +chr1 124499511 124499672 SRX4288408.05_peak_213 124 . 4.85207 16.91717 12.46282 20 +chr1 124507788 124507867 SRX4288408.05_peak_214 254 . 7.24832 30.39425 25.42173 30 +chr1 124510490 124510552 SRX4288408.05_peak_215 240 . 6.68693 28.94555 24.02100 33 +chr1 124514189 124514304 SRX4288408.05_peak_216 377 . 8.38253 43.12057 37.73149 75 +chr1 124519076 124519136 SRX4288408.05_peak_217 241 . 6.72372 29.07564 24.14688 24 +chr1 124538785 124538837 SRX4288408.05_peak_218 131 . 5.07426 17.66810 13.17611 19 +chr1 124557116 124557174 SRX4288408.05_peak_219 66 . 4.10765 10.78952 6.66953 3 +chr1 124568553 124568607 SRX4288408.05_peak_220 168 . 5.53592 21.49150 16.84213 33 +chr1 124569417 124569551 SRX4288408.05_peak_221 110 . 4.70297 15.40558 11.02742 39 +chr1 124579461 124579555 SRX4288408.05_peak_222 649 . 12.37525 71.16746 64.92793 47 +chr1 124590768 124590860 SRX4288408.05_peak_223 232 . 6.60147 28.19024 23.29518 63 +chr1 124617449 124617500 SRX4288408.05_peak_224 188 . 5.69196 23.62916 18.89733 29 +chr1 124617564 124617624 SRX4288408.05_peak_225 243 . 6.42659 29.33524 24.39942 28 +chr1 124623416 124623475 SRX4288408.05_peak_226 99 . 4.54545 14.24270 9.91957 41 +chr1 124634536 124634640 SRX4288408.05_peak_227 323 . 8.49257 37.53891 32.31828 39 +chr1 124639400 124639477 SRX4288408.05_peak_228 147 . 5.33499 19.27305 14.71602 35 +chr1 124645439 124645502 SRX4288408.05_peak_229 213 . 6.15206 26.14376 21.31624 35 +chr1 124646515 124646565 SRX4288408.05_peak_230 195 . 5.88235 24.32805 19.56759 23 +chr1 124680443 124680514 SRX4288408.05_peak_231 411 . 9.20716 46.67387 41.17807 33 +chr1 124688282 124688348 SRX4288408.05_peak_232 205 . 5.94504 25.38186 20.58345 42 +chr1 124700538 124700614 SRX4288408.05_peak_233 83 . 4.14692 12.64056 8.39588 9 +chr1 124700708 124700809 SRX4288408.05_peak_234 364 . 8.18714 41.85229 36.49901 60 +chr1 124707893 124707945 SRX4288408.05_peak_235 82 . 4.27461 12.45905 8.23451 13 +chr1 124721895 124721945 SRX4288408.05_peak_236 109 . 4.57210 15.29480 10.92046 33 +chr1 124731323 124731377 SRX4288408.05_peak_237 213 . 6.04027 26.15381 21.32601 31 +chr1 124736195 124736249 SRX4288408.05_peak_238 184 . 5.66572 23.13514 18.41733 26 +chr1 124739303 124739367 SRX4288408.05_peak_239 98 . 4.44178 14.22082 9.89852 22 +chr1 124741321 124741456 SRX4288408.05_peak_240 320 . 8.09400 37.28645 32.07174 95 +chr1 124757706 124757765 SRX4288408.05_peak_241 329 . 8.52551 38.19034 32.95098 21 +chr1 124779089 124779139 SRX4288408.05_peak_242 83 . 4.29688 12.52787 8.30023 39 +chr1 124779258 124779311 SRX4288408.05_peak_243 116 . 4.90639 16.06452 11.65119 18 +chr1 124923333 124923386 SRX4288408.05_peak_244 255 . 10.50016 30.54570 25.56096 25 +chr1 125069461 125069519 SRX4288408.05_peak_245 336 . 12.38481 38.94582 33.68217 33 +chr1 125075153 125075211 SRX4288408.05_peak_246 303 . 11.07011 35.48965 30.32457 37 +chr1 125075449 125075507 SRX4288408.05_peak_247 343 . 11.75060 39.58050 34.30061 19 +chr1 125081170 125081221 SRX4288408.05_peak_248 189 . 8.34327 23.70507 18.97080 37 +chr1 125166270 125166351 SRX4288408.05_peak_249 352 . 5.78980 40.57948 35.26602 46 +chr1 125166924 125167018 SRX4288408.05_peak_250 362 . 5.88421 41.57072 36.22580 54 +chr1 125167142 125167250 SRX4288408.05_peak_251 394 . 6.15092 44.89695 39.45461 71 +chr1 125167810 125168006 SRX4288408.05_peak_252 384 . 6.11522 43.82598 38.41899 90 +chr1 125168186 125168339 SRX4288408.05_peak_253 318 . 5.59714 37.01646 31.80814 30 +chr1 125169951 125170030 SRX4288408.05_peak_254 234 . 4.54804 28.37276 23.46535 41 +chr1 125170102 125170179 SRX4288408.05_peak_255 269 . 4.80045 32.00538 26.96870 34 +chr1 125171159 125171209 SRX4288408.05_peak_256 164 . 3.67377 21.08867 16.45225 21 +chr1 125175432 125175491 SRX4288408.05_peak_257 128 . 2.80431 17.32561 12.85660 23 +chr1 125180151 125180283 SRX4288408.05_peak_258 50 . 1.91378 9.03032 5.03767 48 +chr1 125182165 125182442 SRX4288408.05_peak_259 77 . 2.16158 11.90128 7.70258 227 +chr1 125182497 125182702 SRX4288408.05_peak_260 80 . 2.19470 12.28194 8.06601 73 +chr1 125182950 125183109 SRX4288408.05_peak_261 89 . 2.27273 13.17442 8.90674 122 +chr1 125183570 125183640 SRX4288408.05_peak_262 87 . 2.30964 12.99796 8.73796 33 +chr1 143184634 143184765 SRX4288408.05_peak_263 292 . 4.45228 34.33638 29.20985 42 +chr1 143184832 143184991 SRX4288408.05_peak_264 258 . 4.16667 30.79981 25.80724 105 +chr1 143185090 143185354 SRX4288408.05_peak_265 263 . 4.09487 31.35286 26.34539 89 +chr1 143185875 143185944 SRX4288408.05_peak_266 173 . 3.26602 22.02490 17.35046 41 +chr1 143186169 143186244 SRX4288408.05_peak_267 129 . 2.89229 17.44732 12.97371 45 +chr1 143186744 143186819 SRX4288408.05_peak_268 160 . 3.03504 20.65722 16.03398 40 +chr1 143187211 143187333 SRX4288408.05_peak_269 159 . 2.95494 20.60575 15.98425 19 +chr1 143187728 143187878 SRX4288408.05_peak_270 99 . 2.45271 14.26352 9.93950 67 +chr1 143191902 143191981 SRX4288408.05_peak_271 63 . 2.05926 10.44250 6.34151 69 +chr1 143192698 143192875 SRX4288408.05_peak_272 88 . 2.26762 13.11626 8.85110 141 +chr1 143193027 143193220 SRX4288408.05_peak_273 81 . 2.22955 12.34948 8.13039 81 +chr1 143193422 143193504 SRX4288408.05_peak_274 93 . 2.31201 13.62103 9.32338 36 +chr1 143193652 143193859 SRX4288408.05_peak_275 90 . 2.28921 13.36209 9.08673 47 +chr1 143194071 143194141 SRX4288408.05_peak_276 71 . 2.15126 11.27126 7.11506 34 +chr1 143194904 143195037 SRX4288408.05_peak_277 79 . 2.19927 12.11961 7.91085 111 +chr1 143195120 143195266 SRX4288408.05_peak_278 79 . 2.19976 12.12511 7.91605 129 +chr1 143195636 143195721 SRX4288408.05_peak_279 84 . 2.23204 12.70993 8.46214 70 +chr1 143200230 143200405 SRX4288408.05_peak_280 123 . 2.66445 16.75023 12.30202 16 +chr1 143200650 143200793 SRX4288408.05_peak_281 143 . 2.84388 18.92428 14.38030 28 +chr1 143201161 143201221 SRX4288408.05_peak_282 121 . 2.68456 16.64074 12.19659 27 +chr1 143201488 143201654 SRX4288408.05_peak_283 123 . 2.64115 16.81733 12.36659 67 +chr1 143202460 143202863 SRX4288408.05_peak_284 120 . 2.56508 16.44799 12.02015 225 +chr1 143203327 143203545 SRX4288408.05_peak_285 109 . 2.50064 15.31901 10.94377 8 +chr1 143206068 143206200 SRX4288408.05_peak_286 154 . 2.90564 20.09390 15.49992 79 +chr1 143206274 143206517 SRX4288408.05_peak_287 154 . 2.92013 20.06310 15.47021 181 +chr1 143206593 143206658 SRX4288408.05_peak_288 125 . 2.75161 16.98285 12.52602 42 +chr1 143206741 143206803 SRX4288408.05_peak_289 131 . 2.80289 17.65253 13.16109 47 +chr1 143206916 143206968 SRX4288408.05_peak_290 142 . 2.86140 18.75131 14.21320 29 +chr1 143207046 143207198 SRX4288408.05_peak_291 155 . 2.94293 20.11350 15.51876 33 +chr1 143211794 143211856 SRX4288408.05_peak_292 66 . 2.09223 10.81346 6.69229 39 +chr1 143222431 143222534 SRX4288408.05_peak_293 60 . 2.01248 10.08958 6.00674 86 +chr1 143227385 143227440 SRX4288408.05_peak_294 91 . 2.36230 13.44100 9.16243 44 +chr1 143227526 143227617 SRX4288408.05_peak_295 100 . 2.42652 14.38516 10.05678 21 +chr1 143227940 143228022 SRX4288408.05_peak_296 114 . 2.51213 15.86426 11.45848 35 +chr1 143229286 143229344 SRX4288408.05_peak_297 78 . 2.25673 12.08317 7.87597 52 +chr1 143230267 143230449 SRX4288408.05_peak_298 110 . 2.46914 15.38723 11.00972 55 +chr1 143230545 143230608 SRX4288408.05_peak_299 98 . 2.39404 14.16286 9.84284 54 +chr1 143231275 143231437 SRX4288408.05_peak_300 87 . 2.26809 13.00679 8.74642 132 +chr1 143232799 143233002 SRX4288408.05_peak_301 73 . 2.12766 11.51029 7.34203 184 +chr1 143233575 143233649 SRX4288408.05_peak_302 60 . 2.01898 10.16433 6.07773 42 +chr1 143238150 143238235 SRX4288408.05_peak_303 67 . 2.07771 10.83868 6.70329 79 +chr1 143238887 143238949 SRX4288408.05_peak_304 62 . 2.06296 10.29885 6.20530 15 +chr1 143239459 143239641 SRX4288408.05_peak_305 81 . 2.19852 12.32580 8.10787 156 +chr1 143240240 143240353 SRX4288408.05_peak_306 74 . 2.15827 11.65726 7.48233 17 +chr1 143240907 143240960 SRX4288408.05_peak_307 80 . 2.19756 12.31483 8.09723 12 +chr1 143241902 143241963 SRX4288408.05_peak_308 77 . 2.20430 11.96030 7.75882 39 +chr1 143245468 143245556 SRX4288408.05_peak_309 141 . 2.78435 18.64912 14.11410 14 +chr1 143245800 143245858 SRX4288408.05_peak_310 100 . 2.53742 14.42454 10.09472 19 +chr1 143246855 143246983 SRX4288408.05_peak_311 160 . 2.97708 20.64696 16.02401 28 +chr1 143247144 143247201 SRX4288408.05_peak_312 153 . 2.89191 19.95065 15.36175 36 +chr1 143247272 143247422 SRX4288408.05_peak_313 124 . 2.68194 16.93042 12.47565 71 +chr1 143249777 143250012 SRX4288408.05_peak_314 88 . 2.26584 13.09597 8.83191 43 +chr1 143253014 143253157 SRX4288408.05_peak_315 61 . 2.01939 10.25700 6.16559 39 +chr1 143254453 143254669 SRX4288408.05_peak_316 62 . 2.03015 10.38181 6.28397 59 +chr1 143254769 143254933 SRX4288408.05_peak_317 67 . 2.07350 10.88417 6.74681 81 +chr1 143255296 143255348 SRX4288408.05_peak_318 76 . 2.16638 11.85262 7.65646 34 +chr1 143255521 143255611 SRX4288408.05_peak_319 75 . 2.16441 11.72652 7.53616 38 +chr1 143255855 143256004 SRX4288408.05_peak_320 77 . 2.16855 11.98140 7.77890 137 +chr1 143256406 143256531 SRX4288408.05_peak_321 79 . 2.18591 12.18103 7.96947 76 +chr1 143256794 143256882 SRX4288408.05_peak_322 77 . 2.19166 11.92733 7.72750 8 +chr1 143262112 143262234 SRX4288408.05_peak_323 79 . 2.18025 12.11596 7.90736 15 +chr1 143262336 143262441 SRX4288408.05_peak_324 73 . 2.12386 11.46639 7.30019 13 +chr1 143262535 143262797 SRX4288408.05_peak_325 66 . 2.06565 10.79331 6.67311 33 +chr1 143263631 143263776 SRX4288408.05_peak_326 65 . 2.05931 10.62760 6.51640 107 +chr1 143264625 143264748 SRX4288408.05_peak_327 71 . 2.11209 11.33049 7.17119 44 +chr1 143265849 143265912 SRX4288408.05_peak_328 70 . 2.10154 11.20861 7.05558 37 +chr1 143266525 143266607 SRX4288408.05_peak_329 70 . 2.10351 11.23137 7.07714 19 +chr1 143266812 143266907 SRX4288408.05_peak_330 61 . 2.04422 10.27313 6.18098 56 +chr1 143267434 143267524 SRX4288408.05_peak_331 70 . 2.14909 11.24745 7.09246 5 +chr1 143270678 143270741 SRX4288408.05_peak_332 181 . 3.28093 22.80818 18.10134 36 +chr1 143271174 143271225 SRX4288408.05_peak_333 185 . 3.40535 23.27637 18.55448 32 +chr1 143273098 143273166 SRX4288408.05_peak_334 324 . 5.33952 37.71097 32.48608 40 +chr1 143273810 143273895 SRX4288408.05_peak_335 158 . 4.08296 20.41990 15.81445 21 +chr1 143274874 143274930 SRX4288408.05_peak_336 353 . 6.23640 40.67913 35.36278 27 +chr1 143275230 143275300 SRX4288408.05_peak_337 216 . 5.04202 26.46022 21.62285 42 +chr1 143275475 143275598 SRX4288408.05_peak_338 240 . 5.38827 28.92709 24.00295 32 +chr1 143275709 143275924 SRX4288408.05_peak_339 248 . 5.65878 29.84753 24.89084 26 +chr1 200937829 200937884 SRX4288408.05_peak_340 211 . 9.42323 25.98142 21.15852 34 +chr1 202205408 202205458 SRX4288408.05_peak_341 159 . 8.07705 20.55338 15.93338 23 +chr1 206924102 206924197 SRX4288408.05_peak_342 139 . 5.10386 18.48596 13.95642 22 +chr1 206924260 206924341 SRX4288408.05_peak_343 209 . 6.16845 25.77665 20.96617 42 +chr1 210653915 210654029 SRX4288408.05_peak_344 360 . 12.92328 41.43295 36.09158 36 +chr1 224012033 224012084 SRX4288408.05_peak_345 195 . 7.50469 24.28430 19.52528 33 +chr1 224013744 224013802 SRX4288408.05_peak_346 160 . 6.81818 20.64415 16.02128 20 +chr1 224014829 224014901 SRX4288408.05_peak_347 183 . 7.22892 23.09252 18.37625 37 +chr1 233247706 233247762 SRX4288408.05_peak_348 198 . 9.04255 24.64232 19.87239 29 +chr1 236097113 236097399 SRX4288408.05_peak_349 750 . 17.38262 81.61652 75.08820 169 +chr1 239167883 239167939 SRX4288408.05_peak_350 233 . 9.96170 28.24049 23.33625 24 +chr1 246025003 246025062 SRX4288408.05_peak_351 85 . 5.45852 12.83441 8.58109 10 +chr1 246072159 246072243 SRX4288408.05_peak_352 73 . 5.37241 11.53803 7.36865 8 +chr1 248946019 248946116 SRX4288408.05_peak_353 266 . 10.76940 31.71488 26.68776 48 +chr10 10023 10385 SRX4288408.05_peak_354 336 . 12.38481 38.94582 33.68217 265 +chr10 718303 718406 SRX4288408.05_peak_355 233 . 9.96170 28.24049 23.33625 35 +chr10 38487511 38487585 SRX4288408.05_peak_356 498 . 13.52459 55.53215 49.80798 34 +chr10 38489161 38489223 SRX4288408.05_peak_357 437 . 12.16879 49.28742 43.71913 30 +chr10 38496800 38496853 SRX4288408.05_peak_358 201 . 8.68516 24.98357 20.19699 34 +chr10 38501646 38501700 SRX4288408.05_peak_359 237 . 9.32401 28.61639 23.70214 20 +chr10 38519907 38519957 SRX4288408.05_peak_360 102 . 6.08899 14.59174 10.24473 19 +chr10 38528384 38528434 SRX4288408.05_peak_361 146 . 5.93750 19.16830 14.61506 14 +chr10 38529793 38529845 SRX4288408.05_peak_362 79 . 4.72574 12.15542 7.94491 47 +chr10 38573468 38573564 SRX4288408.05_peak_363 758 . 21.00033 82.39320 75.84155 47 +chr10 38578992 38579055 SRX4288408.05_peak_364 377 . 11.85510 43.13291 37.74342 24 +chr10 38579515 38579577 SRX4288408.05_peak_365 362 . 11.49675 41.62083 36.27383 28 +chr10 38591830 38591881 SRX4288408.05_peak_366 200 . 9.13978 24.84105 20.06623 21 +chr10 38783574 38783640 SRX4288408.05_peak_367 440 . 12.86174 49.64053 44.06519 33 +chr10 38785688 38785741 SRX4288408.05_peak_368 240 . 8.51888 28.92829 24.00412 19 +chr10 38786261 38786357 SRX4288408.05_peak_369 258 . 8.63971 30.87187 25.87716 43 +chr10 38790659 38790716 SRX4288408.05_peak_370 276 . 8.13507 32.67746 27.61817 18 +chr10 38794835 38794885 SRX4288408.05_peak_371 222 . 8.02239 27.13155 22.26850 23 +chr10 38800749 38800806 SRX4288408.05_peak_372 251 . 9.98752 30.06993 25.10625 21 +chr10 38808845 38808906 SRX4288408.05_peak_373 373 . 11.68831 42.73567 37.35516 31 +chr10 38851034 38851100 SRX4288408.05_peak_374 410 . 14.00022 46.51300 41.02061 26 +chr10 38857998 38858050 SRX4288408.05_peak_375 222 . 9.69246 27.10503 22.24301 24 +chr10 38872860 38872911 SRX4288408.05_peak_376 222 . 9.69246 27.10503 22.24301 33 +chr10 38886793 38886850 SRX4288408.05_peak_377 360 . 12.92328 41.43295 36.09158 19 +chr10 38888024 38888076 SRX4288408.05_peak_378 200 . 9.15399 24.87000 20.08632 33 +chr10 38899899 38899956 SRX4288408.05_peak_379 255 . 10.50016 30.54570 25.56096 21 +chr10 38919007 38919091 SRX4288408.05_peak_380 157 . 7.75000 20.38453 15.78018 31 +chr10 38919166 38919230 SRX4288408.05_peak_381 101 . 6.21891 14.44869 10.11792 44 +chr10 38919861 38919950 SRX4288408.05_peak_382 198 . 8.52071 24.62456 19.85511 40 +chr10 38920173 38920236 SRX4288408.05_peak_383 215 . 8.84750 26.43277 21.59630 33 +chr10 39619810 39619863 SRX4288408.05_peak_384 222 . 9.69246 27.10503 22.24301 34 +chr10 39633823 39633891 SRX4288408.05_peak_385 486 . 15.61563 54.37801 48.67952 34 +chr10 39708660 39708735 SRX4288408.05_peak_386 448 . 14.80793 50.41035 44.81654 35 +chr10 39954403 39954453 SRX4288408.05_peak_387 66 . 5.38470 10.83229 6.69716 17 +chr10 39965168 39965225 SRX4288408.05_peak_388 234 . 9.48905 28.40751 23.49900 25 +chr10 39966228 39966297 SRX4288408.05_peak_389 566 . 16.54135 62.62030 56.66252 29 +chr10 39967583 39967669 SRX4288408.05_peak_390 1032 . 25.71429 112.16555 103.27644 44 +chr10 39986462 39986544 SRX4288408.05_peak_391 884 . 21.92771 96.13762 88.48530 37 +chr10 39987842 39987893 SRX4288408.05_peak_392 193 . 8.29493 24.12523 19.37050 24 +chr10 40073433 40073511 SRX4288408.05_peak_393 696 . 17.79280 76.03841 69.67757 44 +chr10 40097911 40097978 SRX4288408.05_peak_394 336 . 12.38481 38.94582 33.68217 23 +chr10 40128255 40128337 SRX4288408.05_peak_395 952 . 24.76962 103.36522 95.28902 41 +chr10 40180845 40180924 SRX4288408.05_peak_396 801 . 21.80804 86.80202 80.13562 40 +chr10 40213448 40213527 SRX4288408.05_peak_397 839 . 22.61574 91.25878 83.99292 40 +chr10 40279917 40279996 SRX4288408.05_peak_398 461 . 15.07716 51.72528 46.10063 34 +chr10 40301249 40301310 SRX4288408.05_peak_399 200 . 9.15399 24.87000 20.08632 30 +chr10 40338483 40338560 SRX4288408.05_peak_400 149 . 7.80782 19.50852 14.93405 43 +chr10 40368424 40368490 SRX4288408.05_peak_401 81 . 5.78947 12.37328 8.15293 17 +chr10 40395566 40395620 SRX4288408.05_peak_402 262 . 9.01961 31.29357 26.28772 33 +chr10 40413019 40413086 SRX4288408.05_peak_403 347 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SRX4288408.05_peak_415 146 . 7.43494 19.23239 14.67663 25 +chr10 40806971 40807030 SRX4288408.05_peak_416 278 . 11.03864 32.89474 27.82286 24 +chr10 40811030 40811116 SRX4288408.05_peak_417 801 . 21.80804 86.80202 80.13562 42 +chr10 40829690 40829760 SRX4288408.05_peak_418 660 . 19.11171 72.29441 66.03571 33 +chr10 40863447 40863506 SRX4288408.05_peak_419 278 . 11.03864 32.89474 27.82286 35 +chr10 40878834 40878951 SRX4288408.05_peak_420 815 . 22.07727 88.28236 81.55387 79 +chr10 40892168 40892243 SRX4288408.05_peak_421 466 . 14.62800 52.25904 46.62290 37 +chr10 40893874 40893945 SRX4288408.05_peak_422 303 . 10.81081 35.54574 30.37904 28 +chr10 40900324 40900389 SRX4288408.05_peak_423 222 . 9.69246 27.10503 22.24301 27 +chr10 40934219 40934270 SRX4288408.05_peak_424 149 . 7.80782 19.50852 14.93405 23 +chr10 40934851 40934933 SRX4288408.05_peak_425 841 . 22.28047 91.45222 84.16788 40 +chr10 40954574 40954635 SRX4288408.05_peak_426 169 . 8.34628 21.61238 16.95133 37 +chr10 40955072 40955140 SRX4288408.05_peak_427 461 . 15.07716 51.72528 46.10063 39 +chr10 41013890 41013964 SRX4288408.05_peak_428 410 . 14.00022 46.51300 41.02061 31 +chr10 41035184 41035257 SRX4288408.05_peak_429 255 . 10.50016 30.54570 25.56096 38 +chr10 41056065 41056132 SRX4288408.05_peak_430 348 . 12.65405 40.18482 34.88066 28 +chr10 41068559 41068632 SRX4288408.05_peak_431 324 . 12.11558 37.71613 32.48608 30 +chr10 41069585 41069648 SRX4288408.05_peak_432 222 . 9.69246 27.10503 22.24301 23 +chr10 41087578 41087651 SRX4288408.05_peak_433 301 . 11.57711 35.28552 30.12517 33 +chr10 41088597 41088666 SRX4288408.05_peak_434 348 . 12.65405 40.18482 34.88066 34 +chr10 41125351 41125432 SRX4288408.05_peak_435 853 . 22.88498 92.75472 85.33543 40 +chr10 41133839 41133904 SRX4288408.05_peak_436 512 . 16.15410 57.06042 51.28232 41 +chr10 41141881 41141932 SRX4288408.05_peak_437 233 . 9.96170 28.24049 23.33625 21 +chr10 41198597 41198668 SRX4288408.05_peak_438 448 . 14.80793 50.41035 44.81654 30 +chr10 41217366 41217420 SRX4288408.05_peak_439 179 . 8.61552 22.68509 17.98249 26 +chr10 41265303 41265411 SRX4288408.05_peak_440 538 . 15.92040 59.67389 53.81568 38 +chr10 41265515 41265589 SRX4288408.05_peak_441 526 . 15.73034 58.47721 52.65031 34 +chr10 41275409 41275477 SRX4288408.05_peak_442 385 . 13.46175 43.95581 38.53946 30 +chr10 41389552 41389615 SRX4288408.05_peak_443 289 . 11.30787 34.08504 28.96540 20 +chr10 41405855 41405919 SRX4288408.05_peak_444 323 . 11.43552 37.57454 32.35292 23 +chr10 41406873 41406932 SRX4288408.05_peak_445 218 . 9.22693 26.70325 21.85999 37 +chr10 41408714 41408765 SRX4288408.05_peak_446 175 . 8.37696 22.19823 17.51839 22 +chr10 41422484 41422559 SRX4288408.05_peak_447 336 . 12.38481 38.94582 33.68217 34 +chr10 41444579 41444649 SRX4288408.05_peak_448 373 . 13.19252 42.68999 37.31076 30 +chr10 41466538 41466599 SRX4288408.05_peak_449 373 . 13.19252 42.68999 37.31076 35 +chr10 41466767 41466823 SRX4288408.05_peak_450 255 . 10.50016 30.54570 25.56096 14 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41851815 41851900 SRX4288408.05_peak_463 205 . 3.79310 25.30648 20.50996 50 +chr10 41854141 41854229 SRX4288408.05_peak_464 97 . 2.95434 14.11168 9.79380 72 +chr10 41854512 41854657 SRX4288408.05_peak_465 194 . 3.71892 24.20620 19.44988 84 +chr10 41855214 41855268 SRX4288408.05_peak_466 163 . 3.40513 21.00045 16.36749 35 +chr10 41859532 41859588 SRX4288408.05_peak_467 142 . 3.35796 18.83606 14.29545 43 +chr10 41859745 41859813 SRX4288408.05_peak_468 72 . 2.71801 11.46465 7.29854 4 +chr10 41859901 41859968 SRX4288408.05_peak_469 295 . 4.56958 34.63614 29.50313 33 +chr10 41869459 41869533 SRX4288408.05_peak_470 133 . 3.48777 17.89513 13.39620 45 +chr10 41870496 41870582 SRX4288408.05_peak_471 128 . 3.33172 17.31717 12.84848 74 +chr10 41873772 41873880 SRX4288408.05_peak_472 101 . 3.03738 14.49064 10.15834 17 +chr10 41875379 41875469 SRX4288408.05_peak_473 259 . 4.33668 30.95162 25.95485 56 +chr10 41877853 41877914 SRX4288408.05_peak_474 183 . 3.69869 23.03463 18.32066 25 +chr10 41883108 41883247 SRX4288408.05_peak_475 178 . 3.53651 22.51601 17.82667 109 +chr10 41883394 41883541 SRX4288408.05_peak_476 160 . 3.39193 20.66874 16.04523 80 +chr10 41883992 41884095 SRX4288408.05_peak_477 145 . 3.25402 19.13809 14.58592 83 +chr10 41889476 41889579 SRX4288408.05_peak_478 204 . 3.89283 25.28088 20.48527 75 +chr10 41892379 41892446 SRX4288408.05_peak_479 81 . 2.79720 12.32526 8.10737 57 +chr10 41896191 41896263 SRX4288408.05_peak_480 134 . 3.35697 17.93545 13.43509 49 +chr10 41896906 41896990 SRX4288408.05_peak_481 129 . 3.34383 17.39947 12.92764 40 +chr10 41898906 41898971 SRX4288408.05_peak_482 234 . 4.27542 28.38028 23.47262 27 +chr10 41899266 41899320 SRX4288408.05_peak_483 222 . 4.09426 27.06139 22.20793 22 +chr10 41899784 41899903 SRX4288408.05_peak_484 200 . 3.86829 24.82805 20.05359 20 +chr10 41903431 41903558 SRX4288408.05_peak_485 154 . 3.49908 20.09297 15.49903 108 +chr10 41909444 41909496 SRX4288408.05_peak_486 145 . 3.31140 19.15142 14.59870 30 +chr10 41910935 41910989 SRX4288408.05_peak_487 90 . 2.79605 13.33910 9.06446 24 +chr10 41911242 41911299 SRX4288408.05_peak_488 119 . 3.06038 16.40864 11.98257 24 +chr10 41914599 41914724 SRX4288408.05_peak_489 187 . 4.16908 23.52402 18.79543 98 +chr10 41914857 41915022 SRX4288408.05_peak_490 161 . 3.95397 20.79545 16.16831 29 +chr10 42066333 42066390 SRX4288408.05_peak_491 106 . 3.11154 14.98860 10.62580 35 +chr10 42066624 42066719 SRX4288408.05_peak_492 194 . 3.78805 24.22350 19.46672 63 +chr10 42067153 42067276 SRX4288408.05_peak_493 195 . 3.66820 24.30808 19.54848 32 +chr10 42067634 42067695 SRX4288408.05_peak_494 176 . 3.42879 22.33677 17.65294 37 +chr10 42070549 42070629 SRX4288408.05_peak_495 107 . 2.51243 15.16464 10.79510 22 +chr10 42079863 42079914 SRX4288408.05_peak_496 54 . 2.10500 9.49621 5.46041 5 +chr10 42083879 42083976 SRX4288408.05_peak_497 90 . 2.50149 13.37375 9.09782 16 +chr10 42085293 42085378 SRX4288408.05_peak_498 194 . 3.38106 24.18579 19.42989 40 +chr10 42090764 42090837 SRX4288408.05_peak_499 181 . 3.74532 22.86959 18.16123 35 +chr10 42091351 42091415 SRX4288408.05_peak_500 236 . 4.25636 28.54189 23.62990 35 +chr10 42092538 42092602 SRX4288408.05_peak_501 301 . 4.78088 35.25233 30.10188 27 +chr10 42094442 42094515 SRX4288408.05_peak_502 296 . 4.75714 34.74092 29.60436 42 +chr10 42096317 42096383 SRX4288408.05_peak_503 156 . 3.58440 20.21941 15.62115 43 +chr10 42097228 42097295 SRX4288408.05_peak_504 191 . 3.92839 23.91338 19.16523 45 +chr10 42099108 42099178 SRX4288408.05_peak_505 208 . 4.01937 25.66562 20.85910 38 +chr10 42099810 42099918 SRX4288408.05_peak_506 278 . 4.43834 32.97336 27.89956 29 +chr10 42100026 42100113 SRX4288408.05_peak_507 131 . 3.28467 17.64625 13.15498 23 +chr10 42102575 42102657 SRX4288408.05_peak_508 269 . 4.84883 31.98307 26.94723 35 +chr10 42103643 42103696 SRX4288408.05_peak_509 237 . 4.81470 28.69775 23.78144 22 +chr10 42104154 42104218 SRX4288408.05_peak_510 145 . 3.99467 19.09128 14.54077 12 +chr10 42104292 42104390 SRX4288408.05_peak_511 389 . 6.35029 44.36866 38.94274 42 +chr10 42104561 42104630 SRX4288408.05_peak_512 260 . 5.21441 30.99876 26.00063 28 +chr10 42165979 42166034 SRX4288408.05_peak_513 141 . 6.56064 18.65926 14.12396 39 +chr10 42299346 42299402 SRX4288408.05_peak_514 301 . 9.69932 35.32217 30.16088 20 +chr10 42312740 42312794 SRX4288408.05_peak_515 237 . 8.21918 28.69125 23.77511 26 +chr10 42318618 42318684 SRX4288408.05_peak_516 409 . 11.83551 46.41760 40.93392 32 +chr10 60180350 60180409 SRX4288408.05_peak_517 336 . 12.38481 38.94582 33.68217 41 +chr10 63043037 63043097 SRX4288408.05_peak_518 278 . 10.74169 32.89759 27.82555 29 +chr10 126947356 126947448 SRX4288408.05_peak_519 260 . 10.45752 31.06684 26.06732 34 +chr10 126947728 126947789 SRX4288408.05_peak_520 64 . 5.23560 10.54369 6.43740 30 +chr10 126947922 126948156 SRX4288408.05_peak_521 126 . 7.08661 17.09653 12.63553 207 +chr10 126948253 126948318 SRX4288408.05_peak_522 73 . 5.54090 11.50383 7.33584 36 +chr10 128464488 128464612 SRX4288408.05_peak_523 565 . 17.23104 62.51031 56.55418 82 +chr10 131851093 131851155 SRX4288408.05_peak_524 92 . 6.19241 13.56556 9.27016 28 +chr10 132364372 132364507 SRX4288408.05_peak_525 512 . 16.15410 57.06042 51.28232 67 +chr10 132364770 132364838 SRX4288408.05_peak_526 435 . 14.53869 49.10323 43.54025 26 +chr10 133688224 133688379 SRX4288408.05_peak_527 379 . 7.21282 43.34834 37.95491 79 +chr10 133688675 133688758 SRX4288408.05_peak_528 381 . 7.26457 43.59026 38.18991 33 +chr10 133689194 133689251 SRX4288408.05_peak_529 156 . 4.75973 20.29740 15.69608 24 +chr10 133689352 133689471 SRX4288408.05_peak_530 520 . 8.75576 57.86850 52.06708 85 +chr10 133689555 133689664 SRX4288408.05_peak_531 428 . 7.87037 48.39919 42.85902 39 +chr10 133689729 133689879 SRX4288408.05_peak_532 219 . 5.59963 26.81767 21.97141 55 +chr10 133763102 133763200 SRX4288408.05_peak_533 160 . 6.66067 20.69763 16.07329 80 +chr10 133763697 133763861 SRX4288408.05_peak_534 324 . 9.71223 37.72206 32.49180 55 +chr10 133763988 133764053 SRX4288408.05_peak_535 225 . 7.93508 27.42085 22.55015 28 +chr10 133787335 133787395 SRX4288408.05_peak_536 179 . 8.61552 22.68509 17.98249 23 +chr11 585471 585551 SRX4288408.05_peak_537 381 . 12.31127 43.52278 38.12521 39 +chr11 964902 965259 SRX4288408.05_peak_538 181 . 8.17308 22.81220 18.10521 333 +chr11 1670543 1670773 SRX4288408.05_peak_539 278 . 11.03864 32.89474 27.82286 115 +chr11 1939867 1939968 SRX4288408.05_peak_540 169 . 8.34628 21.61238 16.95133 65 +chr11 20672961 20673013 SRX4288408.05_peak_541 149 . 7.80782 19.50852 14.93405 25 +chr11 36039195 36039245 SRX4288408.05_peak_542 149 . 7.80782 19.50852 14.93405 14 +chr11 51684311 51684366 SRX4288408.05_peak_543 222 . 9.69246 27.10503 22.24301 15 +chr11 53489689 53489745 SRX4288408.05_peak_544 266 . 10.76940 31.71488 26.68776 34 +chr11 53850320 53850372 SRX4288408.05_peak_545 200 . 9.15399 24.87000 20.08632 29 +chr11 54103963 54104030 SRX4288408.05_peak_546 360 . 12.92328 41.43295 36.09158 33 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36928580 SRX4288408.05_peak_559 222 . 9.69246 27.10503 22.24301 22 +chr12 36977668 36977725 SRX4288408.05_peak_560 278 . 11.03864 32.89474 27.82286 23 +chr12 116684102 116684154 SRX4288408.05_peak_561 211 . 9.42323 25.98142 21.15852 19 +chr12 124439180 124439241 SRX4288408.05_peak_562 172 . 8.22622 21.88687 17.21680 23 +chr12 125589321 125589371 SRX4288408.05_peak_563 200 . 9.15399 24.87000 20.08632 31 +chr12 131917088 131917164 SRX4288408.05_peak_564 289 . 11.30787 34.08504 28.96540 39 +chr12 133264953 133265290 SRX4288408.05_peak_565 397 . 13.73099 45.23019 39.77329 140 +chr13 16001422 16001502 SRX4288408.05_peak_566 99 . 6.15006 14.30680 9.98132 66 +chr13 16099606 16099708 SRX4288408.05_peak_567 146 . 6.62123 19.25142 14.69508 18 +chr13 16284493 16284563 SRX4288408.05_peak_568 264 . 7.12946 31.43705 26.42562 26 +chr13 16284652 16284715 SRX4288408.05_peak_569 335 . 8.07454 38.79684 33.54248 34 +chr13 16303662 16303727 SRX4288408.05_peak_570 468 . 9.21866 52.47953 46.83753 32 +chr13 16309233 16309283 SRX4288408.05_peak_571 154 . 5.56634 20.03928 15.44735 35 +chr13 16387641 16387700 SRX4288408.05_peak_572 269 . 6.75900 31.95951 26.92428 28 +chr13 16398319 16398397 SRX4288408.05_peak_573 372 . 8.23666 42.57167 37.20167 34 +chr13 16415938 16416004 SRX4288408.05_peak_574 337 . 8.27943 38.98586 33.72155 35 +chr13 16451773 16451830 SRX4288408.05_peak_575 119 . 4.80769 16.42530 11.99865 25 +chr13 16547985 16548044 SRX4288408.05_peak_576 198 . 6.08273 24.62575 19.85626 32 +chr13 16578937 16578987 SRX4288408.05_peak_577 163 . 5.60050 20.93440 16.30353 27 +chr13 16607741 16607815 SRX4288408.05_peak_578 462 . 9.07582 51.91355 46.28499 31 +chr13 16634616 16634677 SRX4288408.05_peak_579 365 . 7.92079 41.87720 36.52231 30 +chr13 16645620 16645671 SRX4288408.05_peak_580 141 . 5.15897 18.67567 14.13984 23 +chr13 16647777 16647827 SRX4288408.05_peak_581 150 . 5.23256 19.66376 15.08411 22 +chr13 16652644 16652696 SRX4288408.05_peak_582 230 . 6.52568 27.92238 23.03623 26 +chr13 16688976 16689142 SRX4288408.05_peak_583 518 . 9.47817 57.60416 51.80882 46 +chr13 16690186 16690251 SRX4288408.05_peak_584 221 . 5.93750 27.00677 22.15476 24 +chr13 16712678 16712743 SRX4288408.05_peak_585 366 . 8.36364 41.97121 36.61419 28 +chr13 16715130 16715182 SRX4288408.05_peak_586 176 . 5.65038 22.28809 17.60598 38 +chr13 16790036 16790093 SRX4288408.05_peak_587 263 . 6.71517 31.33311 26.32628 32 +chr13 16839286 16839347 SRX4288408.05_peak_588 291 . 7.83255 34.32491 29.19868 32 +chr13 16874143 16874216 SRX4288408.05_peak_589 304 . 7.47331 35.59826 30.43026 36 +chr13 16943472 16943543 SRX4288408.05_peak_590 452 . 9.25601 50.89155 45.28845 37 +chr13 16975248 16975298 SRX4288408.05_peak_591 216 . 6.37646 26.51153 21.67311 30 +chr13 16984438 16984500 SRX4288408.05_peak_592 244 . 6.57439 29.44401 24.49917 31 +chr13 17006041 17006105 SRX4288408.05_peak_593 217 . 6.40000 26.59367 21.75317 21 +chr13 17018730 17018783 SRX4288408.05_peak_594 248 . 6.80024 29.80663 24.85161 33 +chr13 17029305 17029361 SRX4288408.05_peak_595 190 . 5.74972 23.84281 19.09666 23 +chr13 17047431 17047498 SRX4288408.05_peak_596 144 . 5.14019 18.97340 14.42717 32 +chr13 17095385 17095440 SRX4288408.05_peak_597 133 . 4.81928 17.82534 13.32835 25 +chr13 17100184 17100244 SRX4288408.05_peak_598 260 . 6.64084 31.04939 26.05014 20 +chr13 17109560 17109613 SRX4288408.05_peak_599 203 . 6.10048 25.11022 20.32014 22 +chr13 17115396 17115476 SRX4288408.05_peak_600 374 . 8.02198 42.79843 37.41650 37 +chr13 17128307 17128380 SRX4288408.05_peak_601 204 . 5.80022 25.24295 20.44833 41 +chr13 17144801 17144882 SRX4288408.05_peak_602 507 . 9.49263 56.48739 50.72821 41 +chr13 17148158 17148208 SRX4288408.05_peak_603 203 . 5.76923 25.12352 20.33283 30 +chr13 17149478 17149537 SRX4288408.05_peak_604 319 . 7.25333 37.21262 31.99894 33 +chr13 17155112 17155169 SRX4288408.05_peak_605 85 . 4.03710 12.83813 8.58473 13 +chr13 17167535 17167605 SRX4288408.05_peak_606 307 . 7.17781 35.94413 30.76698 33 +chr13 17174081 17174140 SRX4288408.05_peak_607 309 . 6.88623 36.18160 30.99878 33 +chr13 17176814 17176874 SRX4288408.05_peak_608 222 . 5.75329 27.07268 22.21887 29 +chr13 17192138 17192200 SRX4288408.05_peak_609 160 . 5.63741 20.66462 16.04120 42 +chr13 17232022 17232102 SRX4288408.05_peak_610 435 . 9.07484 49.05608 43.50077 38 +chr13 17239660 17239716 SRX4288408.05_peak_611 253 . 6.95652 30.35786 25.38605 22 +chr13 17255396 17255462 SRX4288408.05_peak_612 254 . 6.98254 30.44880 25.47421 33 +chr13 17335008 17335125 SRX4288408.05_peak_613 140 . 5.12820 18.56993 14.03772 86 +chr13 17348187 17348247 SRX4288408.05_peak_614 271 . 6.81945 32.19083 27.14789 35 +chr13 17355688 17355750 SRX4288408.05_peak_615 183 . 5.65931 23.11165 18.39462 31 +chr13 17433046 17433106 SRX4288408.05_peak_616 193 . 7.84314 24.11737 19.36295 23 +chr13 17502302 17502360 SRX4288408.05_peak_617 263 . 8.21693 31.33140 26.32478 34 +chr13 17598824 17598906 SRX4288408.05_peak_618 583 . 13.33333 64.36286 58.35911 39 +chr13 17644886 17644971 SRX4288408.05_peak_619 849 . 17.37619 92.29908 84.96422 42 +chr13 17751995 17752068 SRX4288408.05_peak_620 404 . 10.90589 45.91504 40.44157 40 +chr13 17901567 17901644 SRX4288408.05_peak_621 351 . 9.44882 40.50261 35.19156 34 +chr13 17995859 17995931 SRX4288408.05_peak_622 486 . 11.34421 54.30804 48.61985 35 +chr13 18015588 18015658 SRX4288408.05_peak_623 482 . 11.44674 53.96296 48.28216 36 +chr13 18032611 18032666 SRX4288408.05_peak_624 343 . 9.16031 39.63156 34.34964 31 +chr13 18211779 18211932 SRX4288408.05_peak_625 472 . 11.29674 52.85563 47.20745 90 +chr13 18212067 18212265 SRX4288408.05_peak_626 821 . 16.07001 89.21893 82.12916 50 +chr13 18212342 18212473 SRX4288408.05_peak_627 427 . 10.61807 48.25145 42.71389 89 +chr13 18213021 18213084 SRX4288408.05_peak_628 315 . 8.96717 36.78214 31.57927 33 +chr13 18213354 18213408 SRX4288408.05_peak_629 305 . 8.80000 35.73229 30.56039 38 +chr13 18213571 18213633 SRX4288408.05_peak_630 335 . 9.28000 38.84156 33.58653 21 +chr13 18214861 18214930 SRX4288408.05_peak_631 359 . 9.70874 41.27155 35.94070 33 +chr13 18215177 18215286 SRX4288408.05_peak_632 332 . 9.35961 38.49556 33.24755 41 +chr13 21377290 21377489 SRX4288408.05_peak_633 108 . 6.40394 15.25381 10.88088 160 +chr13 21377590 21377656 SRX4288408.05_peak_634 100 . 6.18047 14.36957 10.04173 44 +chr13 33664750 33664801 SRX4288408.05_peak_635 192 . 8.72914 23.99400 19.24312 26 +chr13 112873103 112873230 SRX4288408.05_peak_636 214 . 7.67857 26.23249 21.40180 43 +chr13 113262883 113263011 SRX4288408.05_peak_637 84 . 5.77164 12.72781 8.47908 83 +chr13 114354181 114354308 SRX4288408.05_peak_638 324 . 12.11558 37.71613 32.48608 51 +chr14 16092543 16092608 SRX4288408.05_peak_639 141 . 6.75106 18.66051 14.12514 23 +chr14 16093342 16093425 SRX4288408.05_peak_640 584 . 13.88638 64.47961 58.46930 41 +chr14 16096245 16096323 SRX4288408.05_peak_641 400 . 9.03883 45.50682 40.04361 28 +chr14 16097645 16097706 SRX4288408.05_peak_642 229 . 6.49039 27.79691 22.91464 32 +chr14 16098391 16098481 SRX4288408.05_peak_643 515 . 10.17565 57.33893 51.55260 38 +chr14 16100430 16100485 SRX4288408.05_peak_644 200 . 5.80504 24.85631 20.08107 20 +chr14 16102049 16102099 SRX4288408.05_peak_645 239 . 6.78392 28.82651 23.90562 23 +chr14 16102740 16102806 SRX4288408.05_peak_646 395 . 9.37931 44.94778 39.50380 31 +chr14 16103514 16103636 SRX4288408.05_peak_647 220 . 7.39550 26.94376 22.09369 38 +chr14 27459758 27459811 SRX4288408.05_peak_648 222 . 9.69246 27.10503 22.24301 30 +chr14 71674596 71674647 SRX4288408.05_peak_649 205 . 8.84521 25.32931 20.53213 32 +chr14 77269778 77269833 SRX4288408.05_peak_650 266 . 10.76940 31.71488 26.68776 19 +chr14 91293354 91293408 SRX4288408.05_peak_651 213 . 9.25450 26.19809 21.36857 33 +chr14 101952441 101952495 SRX4288408.05_peak_652 243 . 9.35006 29.24619 24.31242 25 +chr14 105582735 105582846 SRX4288408.05_peak_653 179 . 6.40835 22.66546 17.97119 75 +chr14 105582900 105582976 SRX4288408.05_peak_654 124 . 5.41353 16.87896 12.42614 46 +chr14_GL000225v1_random 152 233 SRX4288408.05_peak_655 265 . 9.37500 31.60050 26.58540 36 +chr14_GL000225v1_random 2764 2903 SRX4288408.05_peak_656 154 . 5.34442 20.05604 15.46346 32 +chr14_GL000225v1_random 6167 6217 SRX4288408.05_peak_657 170 . 5.29101 21.73168 17.06742 21 +chr14_GL000225v1_random 6803 6953 SRX4288408.05_peak_658 297 . 6.80894 34.92846 29.78738 110 +chr14_GL000225v1_random 14010 14091 SRX4288408.05_peak_659 439 . 7.36589 49.47818 43.90661 37 +chr14_GL000225v1_random 15185 15255 SRX4288408.05_peak_660 236 . 5.17560 28.58947 23.67614 39 +chr14_GL000225v1_random 24819 24905 SRX4288408.05_peak_661 280 . 5.74981 33.09063 28.01134 30 +chr14_GL000225v1_random 25581 25726 SRX4288408.05_peak_662 347 . 6.54017 40.07988 34.78665 90 +chr14_GL000225v1_random 26433 26492 SRX4288408.05_peak_663 119 . 4.14687 16.41618 11.98984 30 +chr14_GL000225v1_random 33318 33429 SRX4288408.05_peak_664 102 . 4.27350 14.55572 10.21014 82 +chr14_GL000225v1_random 33638 33755 SRX4288408.05_peak_665 314 . 7.24324 36.68640 31.48520 34 +chr14_GL000225v1_random 34722 34772 SRX4288408.05_peak_666 179 . 5.41977 22.60214 17.91022 16 +chr14_GL000225v1_random 39172 39230 SRX4288408.05_peak_667 200 . 5.91245 24.84868 20.07364 27 +chr14_GL000225v1_random 40935 41014 SRX4288408.05_peak_668 382 . 8.22999 43.61330 38.21271 41 +chr14_GL000225v1_random 45975 46046 SRX4288408.05_peak_669 239 . 5.15239 28.83165 23.91049 40 +chr14_GL000225v1_random 46120 46188 SRX4288408.05_peak_670 238 . 5.14306 28.78207 23.86353 37 +chr14_GL000225v1_random 50855 50965 SRX4288408.05_peak_671 399 . 6.29715 45.36245 39.90224 47 +chr14_GL000225v1_random 56072 56127 SRX4288408.05_peak_672 207 . 5.31915 25.58525 20.78070 14 +chr14_GL000225v1_random 56602 56656 SRX4288408.05_peak_673 253 . 5.84588 30.31314 25.34249 27 +chr14_GL000225v1_random 62520 62597 SRX4288408.05_peak_674 108 . 4.06504 15.20824 10.83705 39 +chr14_GL000225v1_random 66239 66310 SRX4288408.05_peak_675 190 . 4.52830 23.77265 19.02878 28 +chr14_GL000225v1_random 66491 66556 SRX4288408.05_peak_676 299 . 5.60942 35.12223 29.97568 30 +chr14_GL000225v1_random 66769 66821 SRX4288408.05_peak_677 286 . 5.58339 33.79980 28.69439 28 +chr14_GL000225v1_random 67258 67312 SRX4288408.05_peak_678 308 . 5.92204 36.02435 30.84529 31 +chr14_GL000225v1_random 67508 67581 SRX4288408.05_peak_679 418 . 7.05700 47.37872 41.86655 33 +chr14_GL000225v1_random 70451 70524 SRX4288408.05_peak_680 212 . 4.76355 26.03745 21.21328 45 +chr14_GL000225v1_random 71176 71251 SRX4288408.05_peak_681 121 . 3.77877 16.60546 12.16248 35 +chr14_GL000225v1_random 73967 74026 SRX4288408.05_peak_682 216 . 5.35637 26.50563 21.66735 21 +chr14_GL000225v1_random 74788 74873 SRX4288408.05_peak_683 496 . 8.45839 55.37545 49.65697 42 +chr14_GL000225v1_random 75534 75584 SRX4288408.05_peak_684 119 . 4.35835 16.39561 11.97022 26 +chr14_GL000225v1_random 82161 82239 SRX4288408.05_peak_685 283 . 5.90137 33.44547 28.35191 43 +chr14_GL000225v1_random 85619 85727 SRX4288408.05_peak_686 503 . 7.85238 56.06456 50.31705 51 +chr14_GL000225v1_random 85903 85990 SRX4288408.05_peak_687 156 . 4.45682 20.24031 15.64131 31 +chr14_GL000225v1_random 86504 86561 SRX4288408.05_peak_688 208 . 5.10742 25.70704 20.89965 21 +chr14_GL000225v1_random 92321 92385 SRX4288408.05_peak_689 252 . 5.49235 30.21117 25.24302 29 +chr14_GL000225v1_random 96056 96109 SRX4288408.05_peak_690 250 . 5.30387 30.00180 25.04069 30 +chr14_GL000225v1_random 97044 97123 SRX4288408.05_peak_691 312 . 5.76450 36.39091 31.20354 31 +chr14_GL000225v1_random 99259 99324 SRX4288408.05_peak_692 120 . 3.98282 16.53098 12.09064 41 +chr14_GL000225v1_random 99381 99436 SRX4288408.05_peak_693 266 . 5.63821 31.72659 26.69912 26 +chr14_GL000225v1_random 101572 101746 SRX4288408.05_peak_694 307 . 6.50995 35.90469 30.72827 144 +chr14_GL000225v1_random 102095 102170 SRX4288408.05_peak_695 353 . 7.15294 40.65916 35.34353 32 +chr14_GL000225v1_random 102452 102553 SRX4288408.05_peak_696 170 . 4.92701 21.69649 17.03309 69 +chr14_GL000225v1_random 107269 107341 SRX4288408.05_peak_697 371 . 6.58499 42.56134 37.19146 35 +chr14_GL000225v1_random 108276 108360 SRX4288408.05_peak_698 236 . 5.16224 28.51958 23.60795 42 +chr14_GL000225v1_random 108659 108726 SRX4288408.05_peak_699 206 . 4.85294 25.49338 20.69110 42 +chr14_GL000225v1_random 110292 110407 SRX4288408.05_peak_700 134 . 3.99857 17.99786 13.49566 93 +chr14_GL000225v1_random 110500 110551 SRX4288408.05_peak_701 227 . 4.98931 27.60339 22.72627 21 +chr14_GL000225v1_random 111125 111229 SRX4288408.05_peak_702 141 . 4.06997 18.64178 14.10699 25 +chr14_GL000225v1_random 111472 111608 SRX4288408.05_peak_703 260 . 5.29474 31.07315 26.07346 102 +chr14_GL000225v1_random 116738 116788 SRX4288408.05_peak_704 179 . 4.55380 22.62488 17.93221 18 +chr14_GL000225v1_random 116870 116927 SRX4288408.05_peak_705 135 . 4.05605 18.00431 13.50191 15 +chr14_GL000225v1_random 117739 117813 SRX4288408.05_peak_706 397 . 6.74410 45.23590 39.77895 26 +chr14_GL000225v1_random 119193 119249 SRX4288408.05_peak_707 244 . 5.25731 29.38496 24.44372 24 +chr14_GL000225v1_random 120583 120717 SRX4288408.05_peak_708 341 . 6.01819 39.46108 34.18423 32 +chr14_GL000225v1_random 122903 122968 SRX4288408.05_peak_709 282 . 5.56761 33.32261 28.23250 25 +chr14_GL000225v1_random 125133 125201 SRX4288408.05_peak_710 307 . 5.91096 35.96560 30.78775 28 +chr14_GL000225v1_random 129159 129228 SRX4288408.05_peak_711 83 . 3.19549 12.62013 8.38842 43 +chr14_GL000225v1_random 130412 130465 SRX4288408.05_peak_712 180 . 4.28526 22.73174 18.02742 30 +chr14_GL000225v1_random 130516 130582 SRX4288408.05_peak_713 282 . 5.22959 33.29662 28.21234 33 +chr14_GL000225v1_random 131574 131628 SRX4288408.05_peak_714 327 . 5.75829 37.96735 32.73278 24 +chr14_GL000225v1_random 131702 131790 SRX4288408.05_peak_715 365 . 6.07274 41.87784 36.52285 56 +chr14_GL000225v1_random 133712 133855 SRX4288408.05_peak_716 386 . 6.38224 44.11495 38.69577 33 +chr14_GL000225v1_random 134993 135094 SRX4288408.05_peak_717 182 . 4.55696 22.96272 18.25128 71 +chr14_GL000225v1_random 140809 140883 SRX4288408.05_peak_718 318 . 8.33912 37.04172 31.83283 35 +chr14_GL000225v1_random 196979 197042 SRX4288408.05_peak_719 475 . 13.74723 53.18834 47.52390 30 +chr14_KI270723v1_random 35277 35327 SRX4288408.05_peak_720 134 . 6.82594 17.92272 13.42287 25 +chr14_KI270723v1_random 36769 36922 SRX4288408.05_peak_721 181 . 8.17308 22.81220 18.10521 35 +chr14_KI270723v1_random 37032 37144 SRX4288408.05_peak_722 406 . 13.17073 46.15092 40.67225 64 +chr14_KI270724v1_random 517 591 SRX4288408.05_peak_723 233 . 9.96170 28.24049 23.33625 30 +chr14_KI270724v1_random 703 893 SRX4288408.05_peak_724 324 . 12.11558 37.71613 32.48608 148 +chr15 17000631 17000719 SRX4288408.05_peak_725 110 . 6.73088 15.48060 11.08905 37 +chr15 17009102 17009162 SRX4288408.05_peak_726 200 . 9.15399 24.87000 20.08632 26 +chr15 17080560 17080642 SRX4288408.05_peak_727 436 . 11.38354 49.18337 43.61813 45 +chr15 17080819 17080895 SRX4288408.05_peak_728 239 . 8.08436 28.85792 23.93607 44 +chr15 17081405 17081462 SRX4288408.05_peak_729 217 . 7.79692 26.54481 21.70552 24 +chr15 17082995 17083058 SRX4288408.05_peak_730 103 . 5.60387 14.69596 10.34427 23 +chr15 18358115 18358179 SRX4288408.05_peak_731 101 . 6.46164 14.51430 10.17068 23 +chr15 18405586 18405647 SRX4288408.05_peak_732 101 . 6.46164 14.51430 10.17068 29 +chr15 18910992 18911099 SRX4288408.05_peak_733 149 . 7.80782 19.50852 14.93405 19 +chr15 19563727 19563827 SRX4288408.05_peak_734 139 . 7.53858 18.47830 13.94900 75 +chr15 19564108 19564283 SRX4288408.05_peak_735 222 . 9.69246 27.10503 22.24301 145 +chr15 19640103 19640486 SRX4288408.05_peak_736 336 . 12.38481 38.94582 33.68217 252 +chr15 68964471 68964524 SRX4288408.05_peak_737 222 . 9.69246 27.10503 22.24301 24 +chr15 77699657 77699744 SRX4288408.05_peak_738 139 . 7.53858 18.47830 13.94900 32 +chr15 101962926 101963004 SRX4288408.05_peak_739 87 . 4.35897 13.02492 8.76386 64 +chr15 101963065 101963123 SRX4288408.05_peak_740 176 . 5.88612 22.29308 17.61079 29 +chr15 101981098 101981164 SRX4288408.05_peak_741 255 . 10.50016 30.54570 25.56096 35 +chr16 458424 458590 SRX4288408.05_peak_742 206 . 8.42572 25.48037 20.67836 38 +chr16 494787 495024 SRX4288408.05_peak_743 159 . 8.07705 20.55338 15.93338 199 +chr16 1025272 1025436 SRX4288408.05_peak_744 200 . 9.15399 24.87000 20.08632 128 +chr16 1025582 1025632 SRX4288408.05_peak_745 75 . 5.65394 11.72373 7.53352 41 +chr16 3074607 3074659 SRX4288408.05_peak_746 205 . 7.35672 25.36715 20.56910 31 +chr16 14487038 14487108 SRX4288408.05_peak_747 289 . 9.89474 34.02654 28.91635 29 +chr16 17327594 17327657 SRX4288408.05_peak_748 315 . 11.35802 36.75828 31.55558 29 +chr16 30700054 30700116 SRX4288408.05_peak_749 63 . 4.58015 10.49729 6.39356 27 +chr16 34062762 34062888 SRX4288408.05_peak_750 196 . 6.54804 24.41978 19.65693 29 +chr16 34063066 34063150 SRX4288408.05_peak_751 162 . 5.97015 20.91478 16.28445 57 +chr16 34063882 34063982 SRX4288408.05_peak_752 123 . 5.24823 16.76635 12.31764 25 +chr16 34064204 34064265 SRX4288408.05_peak_753 291 . 7.96646 34.24621 29.12202 30 +chr16 34067450 34067521 SRX4288408.05_peak_754 116 . 4.90639 16.06452 11.65119 32 +chr16 34071577 34071657 SRX4288408.05_peak_755 687 . 17.05757 75.07492 68.74151 40 +chr16 34073269 34073347 SRX4288408.05_peak_756 183 . 8.06452 23.09149 18.37529 37 +chr16 34085159 34085353 SRX4288408.05_peak_757 283 . 10.69652 33.42159 28.32862 54 +chr16 34097146 34097196 SRX4288408.05_peak_758 172 . 6.77083 21.89532 17.22493 14 +chr16 34097306 34097575 SRX4288408.05_peak_759 571 . 13.39130 63.09213 57.12717 111 +chr16 34453950 34454179 SRX4288408.05_peak_760 674 . 19.38492 73.72680 67.42146 190 +chr16 34571740 34571853 SRX4288408.05_peak_761 156 . 3.42436 20.23670 15.63778 81 +chr16 34572208 34572318 SRX4288408.05_peak_762 167 . 3.51105 21.36461 16.71992 21 +chr16 34572408 34572474 SRX4288408.05_peak_763 261 . 4.20459 31.10874 26.10762 41 +chr16 34572575 34572864 SRX4288408.05_peak_764 286 . 4.37798 33.72958 28.62584 113 +chr16 34573038 34573245 SRX4288408.05_peak_765 286 . 4.37798 33.72958 28.62584 24 +chr16 34573944 34574161 SRX4288408.05_peak_766 286 . 4.37798 33.72958 28.62584 70 +chr16 34574235 34574318 SRX4288408.05_peak_767 286 . 4.37798 33.72958 28.62584 40 +chr16 34575586 34575666 SRX4288408.05_peak_768 267 . 4.24794 31.75742 26.72707 53 +chr16 34575720 34575784 SRX4288408.05_peak_769 286 . 4.37798 33.72958 28.62584 30 +chr16 34576007 34576283 SRX4288408.05_peak_770 219 . 3.88391 26.82585 21.97908 18 +chr16 34576401 34576595 SRX4288408.05_peak_771 233 . 3.77358 28.26459 23.35982 169 +chr16 34580999 34581106 SRX4288408.05_peak_772 157 . 2.94811 20.35066 15.74734 89 +chr16 34581421 34581644 SRX4288408.05_peak_773 158 . 2.94118 20.46323 15.85602 169 +chr16 34582008 34582083 SRX4288408.05_peak_774 137 . 2.75027 18.28946 13.77674 36 +chr16 34582214 34582430 SRX4288408.05_peak_775 125 . 2.61252 16.96722 12.51091 137 +chr16 34582676 34582829 SRX4288408.05_peak_776 111 . 2.48340 15.54584 11.15197 125 +chr16 34582927 34583179 SRX4288408.05_peak_777 99 . 2.36673 14.23994 9.91693 171 +chr16 34583306 34583481 SRX4288408.05_peak_778 85 . 2.23972 12.79766 8.54587 53 +chr16 34583626 34583710 SRX4288408.05_peak_779 80 . 2.19685 12.30660 8.08932 33 +chr16 34586491 34586672 SRX4288408.05_peak_780 59 . 2.00198 10.05489 5.97408 79 +chr16 34588032 34588286 SRX4288408.05_peak_781 75 . 2.14871 11.75299 7.56152 65 +chr16 34589121 34589177 SRX4288408.05_peak_782 80 . 2.19804 12.21310 8.00018 46 +chr16 34590330 34590393 SRX4288408.05_peak_783 83 . 2.21710 12.53885 8.31074 37 +chr16 34592721 34592845 SRX4288408.05_peak_784 140 . 2.76183 18.57763 14.04518 99 +chr16 34593220 34593297 SRX4288408.05_peak_785 168 . 3.04034 21.48261 16.83344 34 +chr16 34593365 34593647 SRX4288408.05_peak_786 181 . 3.17211 22.81182 18.10490 208 +chr16 34594579 34594661 SRX4288408.05_peak_787 173 . 3.26201 21.98845 17.31494 32 +chr16 34595012 34595123 SRX4288408.05_peak_788 209 . 3.52313 25.73873 20.92946 18 +chr16 34595179 34595252 SRX4288408.05_peak_789 225 . 3.65346 27.42794 22.55707 35 +chr16 34595321 34595699 SRX4288408.05_peak_790 244 . 3.86603 29.35287 24.41652 244 +chr16 34595772 34595933 SRX4288408.05_peak_791 213 . 3.71615 26.22121 21.39101 138 +chr16 34596071 34596151 SRX4288408.05_peak_792 216 . 3.77435 26.45545 21.61823 23 +chr16 34725973 34726034 SRX4288408.05_peak_793 349 . 12.35521 40.23353 34.92841 28 +chr16 34752690 34752750 SRX4288408.05_peak_794 435 . 14.53869 49.10323 43.54025 27 +chr16 34894421 34894673 SRX4288408.05_peak_795 646 . 18.84645 70.88502 64.65296 38 +chr16 34950716 34950777 SRX4288408.05_peak_796 312 . 11.84634 36.49596 31.29813 32 +chr16 36517459 36517509 SRX4288408.05_peak_797 149 . 7.80782 19.50852 14.93405 34 +chr16 36724159 36724210 SRX4288408.05_peak_798 159 . 8.07705 20.55338 15.93338 28 +chr16 36907520 36907663 SRX4288408.05_peak_799 159 . 8.07705 20.55338 15.93338 108 +chr16 37265906 37265965 SRX4288408.05_peak_800 190 . 8.88476 23.77109 19.02729 28 +chr16 37434266 37434318 SRX4288408.05_peak_801 222 . 9.69246 27.10503 22.24301 32 +chr16 38029785 38029850 SRX4288408.05_peak_802 397 . 13.73099 45.23019 39.77329 25 +chr16 38266001 38266077 SRX4288408.05_peak_803 92 . 6.19241 13.56556 9.27016 55 +chr16 38267023 38267146 SRX4288408.05_peak_804 278 . 11.03864 32.89474 27.82286 35 +chr16 38276227 38276441 SRX4288408.05_peak_805 409 . 13.28413 46.40231 40.91931 58 +chr16 38279460 38279513 SRX4288408.05_peak_806 205 . 8.85609 25.35269 20.55491 18 +chr16 38279936 38279989 SRX4288408.05_peak_807 237 . 9.59410 28.63536 23.72063 21 +chr16 46380701 46381073 SRX4288408.05_peak_808 311 . 5.03212 36.36216 31.17546 121 +chr16 46382597 46382653 SRX4288408.05_peak_809 109 . 2.77160 15.33797 10.96200 44 +chr16 46398359 46398428 SRX4288408.05_peak_810 70 . 2.11349 11.24863 7.09359 4 +chr16 46398536 46398683 SRX4288408.05_peak_811 81 . 2.19924 12.33404 8.11574 93 +chr16 46398745 46398941 SRX4288408.05_peak_812 81 . 2.20212 12.36705 8.14701 88 +chr16 46398997 46399089 SRX4288408.05_peak_813 83 . 2.22051 12.57793 8.34807 25 +chr16 46399187 46399304 SRX4288408.05_peak_814 88 . 2.26381 13.07280 8.80972 79 +chr16 46399377 46399578 SRX4288408.05_peak_815 94 . 2.31917 13.70232 9.40119 135 +chr16 46399658 46399744 SRX4288408.05_peak_816 102 . 2.39592 14.56850 10.22240 41 +chr16 46399818 46399936 SRX4288408.05_peak_817 110 . 2.47186 15.41747 11.03881 20 +chr16 46400022 46400373 SRX4288408.05_peak_818 124 . 2.60914 16.93041 12.47565 190 +chr16 46400435 46400564 SRX4288408.05_peak_819 133 . 2.69333 17.84340 13.34599 69 +chr16 46400832 46400927 SRX4288408.05_peak_820 144 . 2.79817 18.96410 14.41819 44 +chr16 46401104 46401261 SRX4288408.05_peak_821 148 . 2.83588 19.36277 14.80292 19 +chr16 46401343 46401641 SRX4288408.05_peak_822 154 . 2.90439 20.08084 15.48739 129 +chr16 46465986 46466044 SRX4288408.05_peak_823 312 . 11.84634 36.49596 31.29813 31 +chr16 57463487 57463568 SRX4288408.05_peak_824 88 . 5.59910 13.14115 8.87500 49 +chr16 90117180 90117248 SRX4288408.05_peak_825 141 . 6.40301 18.71291 14.17601 26 +chr16_KI270728v1_random 1791529 1791637 SRX4288408.05_peak_826 82 . 5.64417 12.46833 8.24334 12 +chr17 116894 116991 SRX4288408.05_peak_827 94 . 5.22052 13.75751 9.45379 14 +chr17 117172 117581 SRX4288408.05_peak_828 116 . 5.69902 16.10585 11.69093 166 +chr17 117770 117938 SRX4288408.05_peak_829 141 . 6.20567 18.64156 14.10679 60 +chr17 315498 315580 SRX4288408.05_peak_830 102 . 6.28931 14.59299 10.24584 58 +chr17 488996 489094 SRX4288408.05_peak_831 619 . 18.30798 68.06786 61.91615 42 +chr17 2876066 2876124 SRX4288408.05_peak_832 211 . 9.42323 25.98142 21.15852 29 +chr17 21882034 21882087 SRX4288408.05_peak_833 233 . 9.96170 28.24049 23.33625 27 +chr17 21960403 21960454 SRX4288408.05_peak_834 211 . 9.42323 25.98142 21.15852 13 +chr17 21968749 21968903 SRX4288408.05_peak_835 222 . 5.75610 27.08440 22.23026 19 +chr17 21969309 21969475 SRX4288408.05_peak_836 539 . 8.93597 59.83245 53.96027 126 +chr17 21970119 21970201 SRX4288408.05_peak_837 195 . 5.11709 24.30837 19.54876 49 +chr17 21970276 21970351 SRX4288408.05_peak_838 302 . 6.30670 35.40369 30.24022 46 +chr17 21972225 21972297 SRX4288408.05_peak_839 89 . 3.23368 13.24658 8.97557 64 +chr17 21972986 21973156 SRX4288408.05_peak_840 334 . 5.43701 38.72878 33.47633 136 +chr17 21973557 21973688 SRX4288408.05_peak_841 183 . 4.09673 23.07341 18.35800 17 +chr17 21975814 21975927 SRX4288408.05_peak_842 174 . 4.08889 22.16729 17.48855 71 +chr17 21976081 21976161 SRX4288408.05_peak_843 164 . 3.95234 21.06037 16.42507 47 +chr17 21976391 21976484 SRX4288408.05_peak_844 188 . 4.17512 23.56144 18.83161 64 +chr17 21976862 21976996 SRX4288408.05_peak_845 253 . 4.74400 30.28835 25.31850 108 +chr17 21980559 21980610 SRX4288408.05_peak_846 214 . 4.52738 26.32320 21.48994 29 +chr17 21983717 21983797 SRX4288408.05_peak_847 301 . 5.71021 35.28687 30.12649 48 +chr17 21985340 21985398 SRX4288408.05_peak_848 226 . 5.10319 27.48845 22.61572 34 +chr17 21988637 21988778 SRX4288408.05_peak_849 421 . 7.93879 47.67066 42.15216 96 +chr17 21989141 21989193 SRX4288408.05_peak_850 241 . 6.14439 29.09446 24.16494 24 +chr17 21990949 21990999 SRX4288408.05_peak_851 190 . 5.95021 23.74609 19.01096 14 +chr17 21991860 21991949 SRX4288408.05_peak_852 140 . 5.46075 18.54371 14.01228 30 +chr17 23622146 23622204 SRX4288408.05_peak_853 255 . 10.50016 30.54570 25.56096 28 +chr17 23804573 23804623 SRX4288408.05_peak_854 200 . 9.15399 24.87000 20.08632 38 +chr17 24188135 24188188 SRX4288408.05_peak_855 244 . 10.23093 29.38747 24.44372 42 +chr17 24421430 24421494 SRX4288408.05_peak_856 499 . 15.88487 55.71558 49.97653 23 +chr17 24674595 24674646 SRX4288408.05_peak_857 169 . 8.34628 21.61238 16.95133 15 +chr17 24740314 24740376 SRX4288408.05_peak_858 301 . 11.57711 35.28552 30.12517 27 +chr17 25074450 25074515 SRX4288408.05_peak_859 499 . 15.88487 55.71558 49.97653 32 +chr17 25139953 25140003 SRX4288408.05_peak_860 190 . 8.88476 23.77109 19.02729 25 +chr17 25331092 25331158 SRX4288408.05_peak_861 289 . 11.30787 34.08504 28.96540 28 +chr17 25383748 25383821 SRX4288408.05_peak_862 772 . 21.26957 83.85737 77.25099 35 +chr17 25390646 25390712 SRX4288408.05_peak_863 336 . 12.38481 38.94582 33.68217 33 +chr17 25450401 25450454 SRX4288408.05_peak_864 211 . 9.42323 25.98142 21.15852 18 +chr17 25483134 25483201 SRX4288408.05_peak_865 448 . 14.80793 50.41035 44.81654 31 +chr17 25512342 25512395 SRX4288408.05_peak_866 233 . 9.96170 28.24049 23.33625 15 +chr17 25569203 25569278 SRX4288408.05_peak_867 758 . 21.00033 82.39320 75.84155 33 +chr17 25788613 25788666 SRX4288408.05_peak_868 266 . 10.76940 31.71488 26.68776 33 +chr17 25855611 25855667 SRX4288408.05_peak_869 211 . 9.42323 25.98142 21.15852 32 +chr17 25855766 25855820 SRX4288408.05_peak_870 233 . 9.96170 28.24049 23.33625 25 +chr17 25920165 25920240 SRX4288408.05_peak_871 786 . 21.53880 85.32700 78.69014 39 +chr17 26017429 26017479 SRX4288408.05_peak_872 200 . 9.15399 24.87000 20.08632 15 +chr17 26101570 26101632 SRX4288408.05_peak_873 473 . 15.34640 53.04787 47.38768 29 +chr17 26299446 26299505 SRX4288408.05_peak_874 397 . 13.73099 45.23019 39.77329 35 +chr17 26494638 26494696 SRX4288408.05_peak_875 348 . 12.65405 40.18482 34.88066 28 +chr17 26567039 26567108 SRX4288408.05_peak_876 646 . 18.84645 70.88502 64.65296 30 +chr17 26568754 26568811 SRX4288408.05_peak_877 336 . 12.38481 38.94582 33.68217 24 +chr17 26573053 26573113 SRX4288408.05_peak_878 410 . 14.00022 46.51300 41.02061 33 +chr17 26594873 26594937 SRX4288408.05_peak_879 525 . 16.06218 58.42511 52.59867 39 +chr17 26596400 26596463 SRX4288408.05_peak_880 503 . 15.36524 56.13589 50.38564 26 +chr17 26603872 26603950 SRX4288408.05_peak_881 867 . 21.92118 94.30241 86.78436 36 +chr17 26604069 26604119 SRX4288408.05_peak_882 194 . 8.56793 24.19130 19.43525 21 +chr17 26604215 26604290 SRX4288408.05_peak_883 732 . 19.36275 79.67671 73.20466 37 +chr17 26619812 26619874 SRX4288408.05_peak_884 499 . 15.88487 55.71558 49.97653 31 +chr17 26620001 26620061 SRX4288408.05_peak_885 435 . 14.53869 49.10323 43.54025 26 +chr17 26625594 26625651 SRX4288408.05_peak_886 360 . 12.92328 41.43295 36.09158 20 +chr17 26783426 26783477 SRX4288408.05_peak_887 217 . 8.45666 26.62189 21.78069 28 +chr17 26784121 26784179 SRX4288408.05_peak_888 268 . 9.48367 31.86210 26.82887 30 +chr17 26802523 26802578 SRX4288408.05_peak_889 308 . 11.04442 36.06770 30.88743 16 +chr17 26824213 26824263 SRX4288408.05_peak_890 217 . 8.44773 26.60073 21.76001 14 +chr17 26826070 26826120 SRX4288408.05_peak_891 197 . 7.79221 24.50175 19.73622 22 +chr17 26828332 26828397 SRX4288408.05_peak_892 357 . 10.74219 41.04049 35.71551 33 +chr17 26837255 26837306 SRX4288408.05_peak_893 208 . 8.75740 25.68532 20.87827 29 +chr17 26854895 26854950 SRX4288408.05_peak_894 280 . 10.29240 33.14486 28.06408 25 +chr17 26881319 26881384 SRX4288408.05_peak_895 506 . 12.53310 56.43810 50.67924 30 +chr17 26884208 26884270 SRX4288408.05_peak_896 483 . 13.80066 54.04087 48.35791 36 +chr17 26885253 26885306 SRX4288408.05_peak_897 245 . 9.98720 29.47583 24.52893 36 +chr17 26885402 26885454 SRX4288408.05_peak_898 202 . 8.98588 25.07750 20.28835 24 +chr17 26885741 26885839 SRX4288408.05_peak_899 596 . 17.58530 65.70067 59.64022 42 +chr17 26936797 26936914 SRX4288408.05_peak_900 515 . 10.86957 57.30479 51.51931 50 +chr17 26938105 26938196 SRX4288408.05_peak_901 187 . 6.12378 23.49757 18.76967 64 +chr17 26939959 26940024 SRX4288408.05_peak_902 219 . 6.45161 26.77302 21.92792 29 +chr17 26940752 26940803 SRX4288408.05_peak_903 93 . 4.34783 13.60098 9.30446 32 +chr17 31949652 31949707 SRX4288408.05_peak_904 258 . 9.79021 30.81978 25.82685 28 +chr17 43232445 43232506 SRX4288408.05_peak_905 235 . 7.92420 28.42274 23.51389 30 +chr17 43251686 43251736 SRX4288408.05_peak_906 228 . 7.17423 27.74459 22.86417 22 +chr17 74577212 74577262 SRX4288408.05_peak_907 179 . 8.61552 22.68509 17.98249 14 +chr17 78906805 78906858 SRX4288408.05_peak_908 179 . 8.61552 22.68509 17.98249 18 +chr17 82586371 82586424 SRX4288408.05_peak_909 179 . 8.61552 22.68509 17.98249 29 +chr17_GL000205v2_random 182982 183049 SRX4288408.05_peak_910 120 . 7.00011 16.46377 12.02540 20 +chr17_KI270729v1_random 170 228 SRX4288408.05_peak_911 53 . 2.60337 9.34120 5.31455 54 +chr17_KI270729v1_random 973 1092 SRX4288408.05_peak_912 240 . 4.08163 28.96160 24.03612 37 +chr17_KI270729v1_random 2336 2404 SRX4288408.05_peak_913 206 . 3.77839 25.44981 20.64895 35 +chr17_KI270729v1_random 2587 2711 SRX4288408.05_peak_914 183 . 3.60858 23.08818 18.37216 107 +chr17_KI270729v1_random 2776 2864 SRX4288408.05_peak_915 115 . 3.05668 15.98965 11.57920 23 +chr17_KI270729v1_random 3096 3243 SRX4288408.05_peak_916 178 . 3.56612 22.51021 17.82107 43 +chr17_KI270729v1_random 3407 3623 SRX4288408.05_peak_917 279 . 4.28784 32.98410 27.90988 183 +chr17_KI270729v1_random 4314 4378 SRX4288408.05_peak_918 156 . 3.39631 20.24983 15.65037 28 +chr17_KI270729v1_random 4521 4643 SRX4288408.05_peak_919 106 . 2.97177 14.98216 10.61962 85 +chr17_KI270729v1_random 5792 5937 SRX4288408.05_peak_920 175 . 3.59177 22.22336 17.54294 111 +chr17_KI270729v1_random 10488 10543 SRX4288408.05_peak_921 337 . 7.85003 39.01886 33.75409 34 +chr17_KI270729v1_random 11124 11218 SRX4288408.05_peak_922 629 . 13.71429 69.10485 62.93174 39 +chr17_KI270729v1_random 11481 11757 SRX4288408.05_peak_923 413 . 11.24207 46.83768 41.33809 239 +chr17_KI270729v1_random 12877 12931 SRX4288408.05_peak_924 255 . 8.52221 30.56178 25.57671 16 +chr17_KI270729v1_random 13063 13181 SRX4288408.05_peak_925 598 . 14.14325 65.92599 59.85807 79 +chr17_KI270729v1_random 13261 13351 SRX4288408.05_peak_926 73 . 4.71442 11.46649 7.30019 6 +chr17_KI270729v1_random 20495 20552 SRX4288408.05_peak_927 138 . 4.06239 18.31939 13.80546 20 +chr17_KI270729v1_random 21055 21114 SRX4288408.05_peak_928 256 . 5.14905 30.63106 25.64400 34 +chr17_KI270729v1_random 21495 21573 SRX4288408.05_peak_929 266 . 5.20347 31.67160 26.65450 32 +chr17_KI270729v1_random 22423 22730 SRX4288408.05_peak_930 361 . 5.87121 41.49036 36.14745 63 +chr17_KI270729v1_random 23252 23320 SRX4288408.05_peak_931 135 . 3.62915 18.05481 13.55051 29 +chr17_KI270729v1_random 23536 23591 SRX4288408.05_peak_932 181 . 4.05634 22.81967 18.11239 20 +chr17_KI270729v1_random 24639 24783 SRX4288408.05_peak_933 213 . 4.29870 26.13142 21.30435 36 +chr17_KI270729v1_random 25111 25213 SRX4288408.05_peak_934 211 . 4.26929 25.93876 21.12419 58 +chr17_KI270729v1_random 25968 26020 SRX4288408.05_peak_935 226 . 4.51781 27.54023 22.66552 30 +chr17_KI270729v1_random 27761 27867 SRX4288408.05_peak_936 499 . 7.77303 55.64193 49.91591 62 +chr17_KI270729v1_random 33957 34010 SRX4288408.05_peak_937 266 . 9.90783 31.67637 26.65900 28 +chr17_KI270729v1_random 43361 43443 SRX4288408.05_peak_938 451 . 12.74409 50.74569 45.14545 29 +chr17_KI270729v1_random 43757 43808 SRX4288408.05_peak_939 196 . 7.76892 24.44477 19.68121 23 +chr17_KI270729v1_random 47184 47250 SRX4288408.05_peak_940 542 . 14.19878 60.15297 54.27331 35 +chr17_KI270729v1_random 54642 54757 SRX4288408.05_peak_941 693 . 17.67338 75.75336 69.39859 39 +chr17_KI270729v1_random 56986 57072 SRX4288408.05_peak_942 268 . 9.50370 31.91012 26.87565 36 +chr17_KI270729v1_random 59170 59222 SRX4288408.05_peak_943 199 . 7.89474 24.75145 19.97864 35 +chr17_KI270729v1_random 59705 59757 SRX4288408.05_peak_944 215 . 8.59031 26.39571 21.56017 17 +chr17_KI270729v1_random 99115 99175 SRX4288408.05_peak_945 348 . 12.65405 40.18482 34.88066 35 +chr17_KI270729v1_random 114501 114553 SRX4288408.05_peak_946 225 . 9.56072 27.40950 22.53911 10 +chr17_KI270729v1_random 134075 134129 SRX4288408.05_peak_947 217 . 9.16976 26.58088 21.74068 13 +chr17_KI270729v1_random 139615 139680 SRX4288408.05_peak_948 288 . 10.66667 33.97717 28.86798 37 +chr17_KI270729v1_random 139941 140002 SRX4288408.05_peak_949 443 . 13.88551 49.88484 44.30392 30 +chr17_KI270729v1_random 157250 157300 SRX4288408.05_peak_950 233 . 8.25516 28.25579 23.35120 18 +chr17_KI270729v1_random 157554 157625 SRX4288408.05_peak_951 594 . 14.24607 65.46591 59.41769 34 +chr17_KI270729v1_random 161013 161082 SRX4288408.05_peak_952 615 . 15.38462 67.70603 61.58630 32 +chr17_KI270729v1_random 208819 208877 SRX4288408.05_peak_953 360 . 12.92328 41.43295 36.09158 21 +chr17_KI270729v1_random 233202 233256 SRX4288408.05_peak_954 289 . 11.30787 34.08504 28.96540 32 +chr17_KI270729v1_random 240748 240807 SRX4288408.05_peak_955 244 . 10.23093 29.38747 24.44372 26 +chr17_KI270729v1_random 247170 247228 SRX4288408.05_peak_956 190 . 8.88476 23.77109 19.02729 25 +chr17_KI270729v1_random 278210 278344 SRX4288408.05_peak_957 110 . 6.73088 15.48060 11.08905 123 +chr17_KI270730v1_random 33564 33648 SRX4288408.05_peak_958 881 . 23.42345 95.76176 88.11569 39 +chr17_KI270730v1_random 97193 97249 SRX4288408.05_peak_959 131 . 6.15672 17.66982 13.17774 15 +chr17_KI270730v1_random 98053 98119 SRX4288408.05_peak_960 161 . 6.87679 20.79045 16.16336 30 +chr17_KI270730v1_random 101955 102015 SRX4288408.05_peak_961 163 . 5.86630 21.00269 16.36966 26 +chr17_KI270730v1_random 107107 107164 SRX4288408.05_peak_962 161 . 5.35003 20.82797 16.19978 34 +chr17_KI270730v1_random 109897 109987 SRX4288408.05_peak_963 348 . 9.13174 40.11538 34.82132 41 +chr18 107896 107976 SRX4288408.05_peak_964 332 . 5.39913 38.48040 33.23298 31 +chr18 108127 108296 SRX4288408.05_peak_965 392 . 5.85168 44.63750 39.20262 93 +chr18 108374 108486 SRX4288408.05_peak_966 303 . 5.17291 35.51067 30.34496 63 +chr18 108839 108909 SRX4288408.05_peak_967 304 . 5.18195 35.56944 30.40229 45 +chr18 109303 109369 SRX4288408.05_peak_968 248 . 4.71890 29.79389 24.83916 24 +chr18 109747 109816 SRX4288408.05_peak_969 214 . 4.41860 26.28565 21.45350 28 +chr18 109929 110000 SRX4288408.05_peak_970 176 . 4.06858 22.32870 17.64525 51 +chr18 110257 110438 SRX4288408.05_peak_971 214 . 4.41475 26.26150 21.42988 100 +chr18 4510883 4510946 SRX4288408.05_peak_972 120 . 7.00011 16.46377 12.02540 40 +chr18 13262220 13262299 SRX4288408.05_peak_973 75 . 5.65276 11.72145 7.53352 38 +chr18 16431045 16431104 SRX4288408.05_peak_974 139 . 7.53858 18.47830 13.94900 22 +chr18 20659171 20659227 SRX4288408.05_peak_975 278 . 11.03864 32.89474 27.82286 16 +chr18 20666663 20666713 SRX4288408.05_peak_976 159 . 8.07705 20.55338 15.93338 13 +chr18 20928272 20928328 SRX4288408.05_peak_977 266 . 10.76940 31.71488 26.68776 28 +chr18 52792762 52792846 SRX4288408.05_peak_978 499 . 15.88487 55.71558 49.97653 35 +chr18 54404816 54404869 SRX4288408.05_peak_979 179 . 8.61552 22.68509 17.98249 23 +chr18 74617047 74617159 SRX4288408.05_peak_980 180 . 7.67591 22.71465 18.01079 86 +chr18 79200706 79200774 SRX4288408.05_peak_981 425 . 14.04421 48.05540 42.52150 35 +chr18 79684941 79685041 SRX4288408.05_peak_982 127 . 5.68643 17.25822 12.79160 47 +chr18 80262920 80263250 SRX4288408.05_peak_983 278 . 11.03864 32.89474 27.82286 199 +chr19 7450356 7450432 SRX4288408.05_peak_984 230 . 8.79828 27.97172 23.08370 37 +chr19 7450666 7450844 SRX4288408.05_peak_985 274 . 9.77199 32.54733 27.49437 143 +chr19 7450922 7451065 SRX4288408.05_peak_986 481 . 13.69565 53.78851 48.11078 35 +chr19 15933911 15934036 SRX4288408.05_peak_987 255 . 10.50016 30.54570 25.56096 31 +chr19 24892085 24892161 SRX4288408.05_peak_988 452 . 11.29568 50.89399 45.29082 34 +chr19 24892780 24892842 SRX4288408.05_peak_989 342 . 9.32069 39.53956 34.26095 35 +chr19 24893097 24893215 SRX4288408.05_peak_990 214 . 7.17069 26.29374 21.46135 25 +chr19 24894186 24894245 SRX4288408.05_peak_991 259 . 7.90698 30.95335 25.95645 33 +chr19 24894857 24894907 SRX4288408.05_peak_992 169 . 6.35659 21.63894 16.97696 22 +chr19 27241116 27241186 SRX4288408.05_peak_993 397 . 13.73099 45.23019 39.77329 27 +chr19 36268792 36268842 SRX4288408.05_peak_994 185 . 8.66142 23.31438 18.59146 23 +chr19 36271917 36271971 SRX4288408.05_peak_995 237 . 9.10100 28.67666 23.76081 33 +chr19 54013448 54013503 SRX4288408.05_peak_996 233 . 9.96170 28.24049 23.33625 23 +chr19 55670457 55670522 SRX4288408.05_peak_997 226 . 9.32515 27.48027 22.60769 31 +chr19 58607539 58607595 SRX4288408.05_peak_998 169 . 8.34628 21.61238 16.95133 34 +chr1_KI270706v1_random 164014 164108 SRX4288408.05_peak_999 65 . 4.64217 10.64185 6.52994 3 +chr1_KI270706v1_random 165419 165534 SRX4288408.05_peak_1000 103 . 5.62561 14.74808 10.39440 42 +chr1_KI270709v1_random 53 243 SRX4288408.05_peak_1001 160 . 3.31449 20.71017 16.08538 151 +chr1_KI270709v1_random 775 863 SRX4288408.05_peak_1002 122 . 2.88395 16.65784 12.21307 36 +chr1_KI270709v1_random 1631 1743 SRX4288408.05_peak_1003 118 . 2.72839 16.26685 11.84579 19 +chr1_KI270709v1_random 1844 1933 SRX4288408.05_peak_1004 117 . 2.68555 16.17526 11.75785 22 +chr1_KI270709v1_random 3261 3312 SRX4288408.05_peak_1005 72 . 2.18978 11.37416 7.21237 18 +chr1_KI270709v1_random 3793 3843 SRX4288408.05_peak_1006 65 . 2.10131 10.62488 6.51377 27 +chr1_KI270709v1_random 15051 15117 SRX4288408.05_peak_1007 122 . 2.64233 16.67577 12.23049 28 +chr1_KI270709v1_random 15535 15600 SRX4288408.05_peak_1008 110 . 2.59484 15.43173 11.05268 40 +chr1_KI270709v1_random 15796 15904 SRX4288408.05_peak_1009 106 . 2.60158 15.04928 10.68414 36 +chr1_KI270709v1_random 16501 16616 SRX4288408.05_peak_1010 110 . 2.70485 15.40043 11.02247 20 +chr1_KI270709v1_random 17182 17260 SRX4288408.05_peak_1011 173 . 3.26259 21.99365 17.32002 33 +chr1_KI270711v1_random 8704 8764 SRX4288408.05_peak_1012 64 . 3.92413 10.53090 6.42533 55 +chr2 3181100 3181158 SRX4288408.05_peak_1013 255 . 10.50016 30.54570 25.56096 37 +chr2 9821052 9821106 SRX4288408.05_peak_1014 222 . 9.69246 27.10503 22.24301 30 +chr2 11148897 11148949 SRX4288408.05_peak_1015 222 . 9.69246 27.10503 22.24301 32 +chr2 13118623 13118674 SRX4288408.05_peak_1016 200 . 9.15399 24.87000 20.08632 22 +chr2 25140117 25140169 SRX4288408.05_peak_1017 222 . 9.69246 27.10503 22.24301 21 +chr2 26006407 26006460 SRX4288408.05_peak_1018 195 . 7.91667 24.29267 19.53345 25 +chr2 32916231 32916621 SRX4288408.05_peak_1019 707 . 15.98513 77.09731 70.71029 42 +chr2 89818536 89818593 SRX4288408.05_peak_1020 276 . 8.16012 32.75257 27.69177 35 +chr2 89820766 89820816 SRX4288408.05_peak_1021 215 . 7.21003 26.40845 21.57262 25 +chr2 89821762 89821821 SRX4288408.05_peak_1022 238 . 6.89207 28.73148 23.81448 32 +chr2 89822518 89822579 SRX4288408.05_peak_1023 230 . 6.67086 27.97885 23.09065 20 +chr2 89826422 89826511 SRX4288408.05_peak_1024 316 . 5.31758 36.81758 31.61401 55 +chr2 89827977 89828081 SRX4288408.05_peak_1025 239 . 4.39163 28.89824 23.97467 81 +chr2 89828593 89828705 SRX4288408.05_peak_1026 250 . 4.33944 30.05782 25.09500 69 +chr2 89829190 89829294 SRX4288408.05_peak_1027 294 . 4.56011 34.56190 29.43032 42 +chr2 89829447 89829515 SRX4288408.05_peak_1028 210 . 3.93413 25.86407 21.05123 25 +chr2 89830968 89831138 SRX4288408.05_peak_1029 200 . 3.73119 24.81408 20.03979 22 +chr2 89831588 89831640 SRX4288408.05_peak_1030 201 . 3.68701 24.96403 20.17810 26 +chr2 89833282 89833341 SRX4288408.05_peak_1031 235 . 3.88466 28.44401 23.53474 25 +chr2 89836480 89836548 SRX4288408.05_peak_1032 279 . 4.45056 33.06900 27.99023 28 +chr2 89838064 89838130 SRX4288408.05_peak_1033 235 . 4.41842 28.46258 23.55259 34 +chr2 89839624 89839724 SRX4288408.05_peak_1034 273 . 5.13633 32.37190 27.32490 51 +chr2 89839908 89839988 SRX4288408.05_peak_1035 132 . 3.79421 17.73218 13.23833 41 +chr2 89840290 89840376 SRX4288408.05_peak_1036 442 . 6.66667 49.82843 44.24905 40 +chr2 89841020 89841142 SRX4288408.05_peak_1037 290 . 5.57319 34.13522 29.01382 84 +chr2 90380725 90380854 SRX4288408.05_peak_1038 217 . 4.62172 26.56079 21.72109 30 +chr2 90381103 90381174 SRX4288408.05_peak_1039 144 . 3.78474 18.98490 14.43820 48 +chr2 90381621 90381719 SRX4288408.05_peak_1040 158 . 3.86035 20.49720 15.88875 34 +chr2 90382602 90382657 SRX4288408.05_peak_1041 143 . 3.60360 18.88140 14.33884 31 +chr2 90383800 90383860 SRX4288408.05_peak_1042 148 . 3.44116 19.44079 14.87810 19 +chr2 90383999 90384088 SRX4288408.05_peak_1043 115 . 3.13779 15.97655 11.56677 18 +chr2 90385905 90385977 SRX4288408.05_peak_1044 87 . 2.73592 13.06112 8.79850 46 +chr2 90390542 90390595 SRX4288408.05_peak_1045 108 . 3.03564 15.25318 10.88031 9 +chr2 90390687 90390783 SRX4288408.05_peak_1046 131 . 3.22792 17.66270 13.17083 72 +chr2 90392123 90392188 SRX4288408.05_peak_1047 107 . 3.02366 15.16760 10.79789 30 +chr2 90397458 90397517 SRX4288408.05_peak_1048 54 . 2.42522 9.49008 5.45464 48 +chr2 90397739 90397859 SRX4288408.05_peak_1049 131 . 3.16997 17.64499 13.15374 71 +chr2 90398238 90398305 SRX4288408.05_peak_1050 131 . 3.20171 17.67784 13.18555 28 +chr2 90398425 90398551 SRX4288408.05_peak_1051 124 . 3.15063 16.88410 12.43103 24 +chr2 90398610 90398712 SRX4288408.05_peak_1052 128 . 3.21020 17.32184 12.85293 42 +chr2 90399074 90399125 SRX4288408.05_peak_1053 140 . 3.34917 18.54912 14.01756 18 +chr2 90402171 90402224 SRX4288408.05_peak_1054 132 . 3.80522 17.79243 13.29625 17 +chr2 91421920 91422095 SRX4288408.05_peak_1055 98 . 4.98831 14.21937 9.89717 157 +chr2 91422297 91422404 SRX4288408.05_peak_1056 241 . 7.61461 29.06986 24.14134 33 +chr2 92081283 92081505 SRX4288408.05_peak_1057 336 . 12.38481 38.94582 33.68217 31 +chr2 92081562 92081616 SRX4288408.05_peak_1058 200 . 9.15399 24.87000 20.08632 24 +chr2 109199357 109199822 SRX4288408.05_peak_1059 308 . 10.48035 36.02940 30.85024 104 +chr2 127099682 127099732 SRX4288408.05_peak_1060 190 . 8.88476 23.77109 19.02729 17 +chr2 130332061 130332123 SRX4288408.05_peak_1061 289 . 11.30787 34.08504 28.96540 32 +chr2 132268230 132268282 SRX4288408.05_peak_1062 156 . 7.24479 20.25710 15.65733 26 +chr2 132288050 132288109 SRX4288408.05_peak_1063 266 . 10.76940 31.71488 26.68776 37 +chr2 161278671 161278728 SRX4288408.05_peak_1064 181 . 6.79370 22.87385 18.16536 31 +chr2 161278785 161278843 SRX4288408.05_peak_1065 190 . 6.97674 23.84454 19.09833 22 +chr2 161280287 161280361 SRX4288408.05_peak_1066 190 . 6.96517 23.81247 19.06738 34 +chr2 161281243 161281331 SRX4288408.05_peak_1067 138 . 5.95534 18.39517 13.87886 30 +chr2 161282424 161282475 SRX4288408.05_peak_1068 156 . 6.32280 20.24102 15.64195 18 +chr2 176262594 176262649 SRX4288408.05_peak_1069 239 . 9.98686 28.88292 23.96004 28 +chr2 223714809 223714867 SRX4288408.05_peak_1070 233 . 9.96170 28.24049 23.33625 24 +chr2 239817844 239817932 SRX4288408.05_peak_1071 179 . 8.61552 22.68509 17.98249 45 +chr2 241896544 241896595 SRX4288408.05_peak_1072 101 . 6.46164 14.51430 10.17068 34 +chr2 242183450 242183515 SRX4288408.05_peak_1073 324 . 12.11558 37.71613 32.48608 29 +chr20 61405 61488 SRX4288408.05_peak_1074 106 . 5.93220 15.06520 10.69957 22 +chr20 62059 62122 SRX4288408.05_peak_1075 294 . 9.64567 34.59678 29.46436 27 +chr20 65087 65160 SRX4288408.05_peak_1076 108 . 5.36463 15.23661 10.86431 45 +chr20 1301908 1302015 SRX4288408.05_peak_1077 86 . 5.87484 12.93586 8.67872 10 +chr20 2236358 2236437 SRX4288408.05_peak_1078 169 . 8.34628 21.61238 16.95133 22 +chr20 26474769 26474855 SRX4288408.05_peak_1079 70 . 4.62633 11.25050 7.09527 68 +chr20 26517103 26517160 SRX4288408.05_peak_1080 165 . 7.87208 21.14331 16.50500 37 +chr20 27159888 27159938 SRX4288408.05_peak_1081 218 . 9.49868 26.70649 21.86305 24 +chr20 27504701 27504753 SRX4288408.05_peak_1082 129 . 7.26935 17.46320 12.97852 26 +chr20 27641174 27641235 SRX4288408.05_peak_1083 278 . 11.03864 32.89474 27.82286 26 +chr20 27860242 27860303 SRX4288408.05_peak_1084 336 . 12.38481 38.94582 33.68217 29 +chr20 28205859 28205922 SRX4288408.05_peak_1085 422 . 14.26946 47.80407 42.27565 25 +chr20 28308128 28308195 SRX4288408.05_peak_1086 592 . 17.76951 65.27604 59.23125 26 +chr20 28413842 28413892 SRX4288408.05_peak_1087 190 . 8.88476 23.77109 19.02729 30 +chr20 28494995 28495086 SRX4288408.05_peak_1088 74 . 4.50886 11.59002 7.41847 81 +chr20 28495443 28495509 SRX4288408.05_peak_1089 119 . 5.49717 16.35125 11.92728 28 +chr20 28495830 28495982 SRX4288408.05_peak_1090 120 . 5.53295 16.44880 12.02089 30 +chr20 28496611 28496664 SRX4288408.05_peak_1091 166 . 6.54912 21.29759 16.65495 20 +chr20 28496854 28497012 SRX4288408.05_peak_1092 151 . 6.28183 19.69842 15.11746 125 +chr20 28513584 28513673 SRX4288408.05_peak_1093 242 . 7.65625 29.19248 24.26029 41 +chr20 28547175 28547231 SRX4288408.05_peak_1094 180 . 6.74342 22.73443 18.03003 35 +chr20 28548885 28548935 SRX4288408.05_peak_1095 98 . 5.11974 14.22081 9.89852 17 +chr20 28643742 28643860 SRX4288408.05_peak_1096 105 . 5.24161 14.90881 10.54921 28 +chr20 28644081 28644153 SRX4288408.05_peak_1097 123 . 5.67139 16.82271 12.37178 43 +chr20 28747090 28747140 SRX4288408.05_peak_1098 149 . 7.80782 19.50852 14.93405 22 +chr20 28889730 28889867 SRX4288408.05_peak_1099 301 . 11.57711 35.28552 30.12517 48 +chr20 28898076 28898169 SRX4288408.05_peak_1100 333 . 11.31640 38.60065 33.35098 42 +chr20 28898409 28898475 SRX4288408.05_peak_1101 239 . 9.20314 28.91134 23.98746 37 +chr20 29299560 29299623 SRX4288408.05_peak_1102 222 . 9.69246 27.10503 22.24301 26 +chr20 30424609 30424670 SRX4288408.05_peak_1103 190 . 8.88476 23.77109 19.02729 28 +chr20 30487408 30487459 SRX4288408.05_peak_1104 200 . 9.15399 24.87000 20.08632 33 +chr20 31052041 31052209 SRX4288408.05_peak_1105 237 . 4.81470 28.69775 23.78144 37 +chr20 31052382 31052454 SRX4288408.05_peak_1106 190 . 4.25787 23.77130 19.02744 25 +chr20 31053966 31054023 SRX4288408.05_peak_1107 199 . 3.84880 24.68188 19.91106 31 +chr20 31056008 31056058 SRX4288408.05_peak_1108 191 . 3.37509 23.90529 19.15735 31 +chr20 31063087 31063161 SRX4288408.05_peak_1109 85 . 2.54377 12.75676 8.50675 66 +chr20 31068107 31068185 SRX4288408.05_peak_1110 72 . 2.42862 11.36960 7.20802 10 +chr20 31074193 31074267 SRX4288408.05_peak_1111 128 . 3.32931 17.30075 12.83266 57 +chr20 31074329 31074382 SRX4288408.05_peak_1112 120 . 3.28029 16.53280 12.09242 11 +chr20 31075394 31075448 SRX4288408.05_peak_1113 288 . 4.88202 33.92376 28.81576 25 +chr20 31075599 31075684 SRX4288408.05_peak_1114 141 . 3.64641 18.65731 14.12212 47 +chr20 31083401 31083478 SRX4288408.05_peak_1115 266 . 10.76940 31.71488 26.68776 45 +chr20 31096011 31096065 SRX4288408.05_peak_1116 289 . 11.30787 34.08504 28.96540 29 +chr20 31098513 31098570 SRX4288408.05_peak_1117 244 . 10.23093 29.38747 24.44372 34 +chr20 31103088 31103139 SRX4288408.05_peak_1118 209 . 9.02256 25.70840 20.90074 27 +chr20 31105998 31106078 SRX4288408.05_peak_1119 592 . 17.76951 65.27604 59.23125 37 +chr20 31106766 31106872 SRX4288408.05_peak_1120 980 . 25.30809 106.43983 98.02784 45 +chr20 31157142 31157286 SRX4288408.05_peak_1121 279 . 9.23965 32.98584 27.91153 115 +chr20 31157411 31157554 SRX4288408.05_peak_1122 262 . 8.78505 31.25168 26.24728 119 +chr20 31157683 31157900 SRX4288408.05_peak_1123 675 . 15.37054 73.85107 67.54373 165 +chr20 31158052 31158107 SRX4288408.05_peak_1124 235 . 8.12274 28.43698 23.52779 21 +chr20 31158180 31158298 SRX4288408.05_peak_1125 232 . 8.00000 28.11049 23.21865 31 +chr20 31158400 31158465 SRX4288408.05_peak_1126 289 . 8.98678 34.02127 28.91120 36 +chr20 31158924 31158977 SRX4288408.05_peak_1127 226 . 7.77874 27.51365 22.64023 23 +chr20 31165014 31165076 SRX4288408.05_peak_1128 297 . 9.75124 34.86222 29.72275 34 +chr20 31166660 31166769 SRX4288408.05_peak_1129 392 . 10.70175 44.71452 39.27623 68 +chr20 31168191 31168250 SRX4288408.05_peak_1130 325 . 9.72097 37.74567 32.51474 30 +chr20 31171516 31171571 SRX4288408.05_peak_1131 310 . 9.58525 36.20312 31.01974 30 +chr20 31174935 31174993 SRX4288408.05_peak_1132 247 . 9.05263 29.68781 24.73527 30 +chr20 31176617 31176668 SRX4288408.05_peak_1133 207 . 8.45384 25.54472 20.74135 23 +chr20 31178605 31178659 SRX4288408.05_peak_1134 202 . 8.25190 25.07974 20.29043 12 +chr20 31185348 31185447 SRX4288408.05_peak_1135 406 . 7.71039 46.13210 40.65364 66 +chr20 31185554 31185631 SRX4288408.05_peak_1136 285 . 6.40000 33.63820 28.53909 38 +chr20 31185792 31185856 SRX4288408.05_peak_1137 269 . 6.22935 32.02099 26.98125 26 +chr20 31186317 31186374 SRX4288408.05_peak_1138 270 . 6.24409 32.08511 27.04379 30 +chr20 31186469 31186669 SRX4288408.05_peak_1139 382 . 7.48815 43.62377 38.22289 36 +chr20 31186762 31186894 SRX4288408.05_peak_1140 298 . 6.60287 34.96975 29.82723 99 +chr20 31187181 31187274 SRX4288408.05_peak_1141 387 . 7.61079 44.16722 38.74643 57 +chr20 31198413 31198467 SRX4288408.05_peak_1142 233 . 9.96170 28.24049 23.33625 29 +chr20 31201531 31201614 SRX4288408.05_peak_1143 410 . 14.00022 46.51300 41.02061 43 +chr20 31204726 31204777 SRX4288408.05_peak_1144 211 . 9.42323 25.98142 21.15852 23 +chr20 31205928 31205979 SRX4288408.05_peak_1145 197 . 8.99471 24.54413 19.77694 32 +chr20 31208258 31208369 SRX4288408.05_peak_1146 139 . 7.53858 18.47830 13.94900 89 +chr20 31210681 31210731 SRX4288408.05_peak_1147 190 . 8.88476 23.77109 19.02729 29 +chr20 31217340 31217392 SRX4288408.05_peak_1148 149 . 7.80782 19.50852 14.93405 31 +chr20 31224136 31224207 SRX4288408.05_peak_1149 558 . 16.86430 61.72975 55.80525 38 +chr20 31226325 31226381 SRX4288408.05_peak_1150 250 . 9.20771 30.05662 25.09388 25 +chr20 31229975 31230030 SRX4288408.05_peak_1151 283 . 9.66084 33.45880 28.36470 23 +chr20 31230599 31230673 SRX4288408.05_peak_1152 181 . 7.34300 22.89774 18.18844 27 +chr20 31235165 31235236 SRX4288408.05_peak_1153 220 . 8.34181 26.88030 22.03193 43 +chr20 31241560 31241968 SRX4288408.05_peak_1154 534 . 11.87215 59.26459 53.42080 119 +chr20 31244290 31244344 SRX4288408.05_peak_1155 259 . 8.09717 30.98867 25.99093 19 +chr20 56728952 56729002 SRX4288408.05_peak_1156 200 . 9.15399 24.87000 20.08632 30 +chr20 64088619 64088683 SRX4288408.05_peak_1157 233 . 9.96170 28.24049 23.33625 34 +chr20 64088780 64088872 SRX4288408.05_peak_1158 149 . 7.80782 19.50852 14.93405 24 +chr20 64132315 64132533 SRX4288408.05_peak_1159 316 . 10.54898 36.81110 31.60758 92 +chr21 5327960 5328027 SRX4288408.05_peak_1160 319 . 10.98398 37.20035 31.98704 36 +chr21 5329717 5329794 SRX4288408.05_peak_1161 273 . 10.23810 32.41670 27.36774 37 +chr21 6565459 6565709 SRX4288408.05_peak_1162 236 . 9.55882 28.55901 23.64648 211 +chr21 6565940 6566122 SRX4288408.05_peak_1163 192 . 8.48485 24.01232 19.26103 137 +chr21 7258751 7258818 SRX4288408.05_peak_1164 440 . 9.89160 49.63010 44.05517 28 +chr21 7258927 7258978 SRX4288408.05_peak_1165 172 . 6.03567 21.95054 17.27807 29 +chr21 7261228 7261285 SRX4288408.05_peak_1166 192 . 6.55022 23.97931 19.22882 27 +chr21 7926137 7926470 SRX4288408.05_peak_1167 388 . 6.99876 44.22803 38.80601 40 +chr21 7926588 7926668 SRX4288408.05_peak_1168 264 . 5.75185 31.49161 26.47874 43 +chr21 7927248 7927325 SRX4288408.05_peak_1169 254 . 5.69276 30.40634 25.43270 42 +chr21 7938264 7938428 SRX4288408.05_peak_1170 506 . 8.93032 56.41478 50.65696 125 +chr21 7944447 7944518 SRX4288408.05_peak_1171 377 . 7.60118 43.11679 37.72781 37 +chr21 7951298 7951377 SRX4288408.05_peak_1172 360 . 7.00355 41.40910 36.07512 34 +chr21 8203952 8204152 SRX4288408.05_peak_1173 132 . 3.24561 17.79317 13.29697 161 +chr21 8219953 8220009 SRX4288408.05_peak_1174 78 . 2.35922 12.03671 7.83150 8 +chr21 8260433 8260581 SRX4288408.05_peak_1175 94 . 3.91426 13.76203 9.45813 39 +chr21 8387055 8387210 SRX4288408.05_peak_1176 101 . 3.01301 14.51713 10.17338 13 +chr21 8387310 8387369 SRX4288408.05_peak_1177 93 . 2.93376 13.60093 9.30441 19 +chr21 8810146 8810230 SRX4288408.05_peak_1178 92 . 5.42714 13.51511 9.23339 23 +chr21 8988198 8988427 SRX4288408.05_peak_1179 268 . 8.04954 31.89676 26.86261 35 +chr21 8988527 8988705 SRX4288408.05_peak_1180 155 . 5.98355 20.12683 15.53141 81 +chr21 9246211 9246283 SRX4288408.05_peak_1181 213 . 7.65125 26.15994 21.33194 30 +chr21 9246774 9246865 SRX4288408.05_peak_1182 119 . 5.66524 16.41261 11.98644 46 +chr21 10269923 10269991 SRX4288408.05_peak_1183 295 . 9.67423 34.66879 29.53448 31 +chr21 10270202 10270387 SRX4288408.05_peak_1184 858 . 18.95360 93.27339 85.81564 92 +chr21 10271972 10272061 SRX4288408.05_peak_1185 264 . 9.08193 31.44999 26.43786 60 +chr21 10272170 10272247 SRX4288408.05_peak_1186 521 . 13.62290 57.94756 52.14499 45 +chr21 10274098 10274193 SRX4288408.05_peak_1187 497 . 13.22803 55.49355 49.76990 40 +chr21 10325784 10325976 SRX4288408.05_peak_1188 919 . 19.74585 99.69221 91.92532 52 +chr21 10326191 10326242 SRX4288408.05_peak_1189 193 . 7.42804 24.08826 19.33471 38 +chr21 10655563 10655638 SRX4288408.05_peak_1190 630 . 13.05326 69.22681 63.05308 37 +chr21 10656310 10656402 SRX4288408.05_peak_1191 439 . 10.04184 49.52775 43.95524 50 +chr21 10658728 10658821 SRX4288408.05_peak_1192 171 . 6.00683 21.85932 17.19031 51 +chr21 10662404 10662462 SRX4288408.05_peak_1193 300 . 8.45283 35.24287 30.09294 33 +chr21 10668940 10669026 SRX4288408.05_peak_1194 454 . 10.92044 51.06974 45.46291 42 +chr21 10670710 10670763 SRX4288408.05_peak_1195 267 . 7.84024 31.78119 26.75027 21 +chr21 10673251 10673320 SRX4288408.05_peak_1196 478 . 10.68376 53.50352 47.83057 31 +chr21 10676844 10676917 SRX4288408.05_peak_1197 310 . 8.41189 36.19764 31.01443 36 +chr21 10678980 10679030 SRX4288408.05_peak_1198 226 . 7.43083 27.53733 22.66287 25 +chr21 10685255 10685392 SRX4288408.05_peak_1199 307 . 9.48905 35.94754 30.77013 30 +chr21 10692359 10692417 SRX4288408.05_peak_1200 234 . 6.39269 28.32636 23.42014 33 +chr21 10692718 10693082 SRX4288408.05_peak_1201 604 . 10.78320 66.56728 60.48671 144 +chr21 10703207 10703264 SRX4288408.05_peak_1202 224 . 7.34949 27.30045 22.43307 24 +chr21 10705256 10705384 SRX4288408.05_peak_1203 262 . 7.83133 31.24080 26.23673 35 +chr21 10707049 10707103 SRX4288408.05_peak_1204 122 . 5.36513 16.73903 12.29137 31 +chr21 10707414 10707476 SRX4288408.05_peak_1205 298 . 8.36445 34.97190 29.82930 32 +chr21 10716681 10716753 SRX4288408.05_peak_1206 372 . 9.97547 42.64999 37.27790 35 +chr21 10731922 10732011 SRX4288408.05_peak_1207 213 . 7.14841 26.22857 21.39803 42 +chr21 17473329 17473385 SRX4288408.05_peak_1208 278 . 11.03864 32.89474 27.82286 33 +chr21 36432210 36432260 SRX4288408.05_peak_1209 215 . 8.59031 26.39571 21.56017 25 +chr21 42957520 42957573 SRX4288408.05_peak_1210 244 . 10.23093 29.38747 24.44372 39 +chr21 43262529 43262760 SRX4288408.05_peak_1211 301 . 11.57711 35.28552 30.12517 195 +chr21 46699880 46699954 SRX4288408.05_peak_1212 266 . 10.76940 31.71488 26.68776 40 +chr22 10722952 10723037 SRX4288408.05_peak_1213 127 . 4.83871 17.21290 12.74795 25 +chr22 10729231 10729304 SRX4288408.05_peak_1214 309 . 7.60870 36.09227 30.91146 42 +chr22 11211159 11211248 SRX4288408.05_peak_1215 280 . 8.65140 33.09627 28.01684 50 +chr22 11211310 11211366 SRX4288408.05_peak_1216 279 . 8.62215 33.01447 27.93969 31 +chr22 11211826 11211895 SRX4288408.05_peak_1217 445 . 11.24161 50.08591 44.50124 37 +chr22 11212617 11212676 SRX4288408.05_peak_1218 246 . 7.97342 29.59346 24.64335 17 +chr22 11213014 11213086 SRX4288408.05_peak_1219 667 . 14.29745 73.05904 66.77631 37 +chr22 11213369 11213430 SRX4288408.05_peak_1220 291 . 8.66721 34.24038 29.11638 23 +chr22 11214214 11214265 SRX4288408.05_peak_1221 184 . 6.73077 23.15522 18.43689 10 +chr22 11214361 11214446 SRX4288408.05_peak_1222 294 . 8.60558 34.61050 29.47795 38 +chr22 11701706 11701795 SRX4288408.05_peak_1223 114 . 5.94059 15.89840 11.49159 22 +chr22 11815638 11815695 SRX4288408.05_peak_1224 56 . 3.93013 9.73190 5.68327 54 +chr22 12176428 12176494 SRX4288408.05_peak_1225 232 . 7.14796 28.14305 23.25059 38 +chr22 12691738 12691788 SRX4288408.05_peak_1226 179 . 8.61552 22.68509 17.98249 35 +chr22 12692108 12692190 SRX4288408.05_peak_1227 312 . 11.84634 36.49596 31.29813 35 +chr22 12692325 12692474 SRX4288408.05_peak_1228 348 . 12.65405 40.18482 34.88066 32 +chr22 12692604 12692657 SRX4288408.05_peak_1229 127 . 7.15232 17.22890 12.76342 9 +chr22 16262323 16262381 SRX4288408.05_peak_1230 358 . 12.18027 41.20123 35.87191 24 +chr22 16267329 16267393 SRX4288408.05_peak_1231 499 . 15.88487 55.71558 49.97653 24 +chr22 16315764 16315830 SRX4288408.05_peak_1232 552 . 16.96181 61.13748 55.22769 27 +chr22 16344217 16344267 SRX4288408.05_peak_1233 179 . 8.61552 22.68509 17.98249 14 +chr22 16347220 16347314 SRX4288408.05_peak_1234 211 . 8.87290 25.93852 21.12399 57 +chr22 16352429 16352484 SRX4288408.05_peak_1235 238 . 8.04196 28.74326 23.82576 27 +chr22 16352756 16352896 SRX4288408.05_peak_1236 708 . 15.21368 77.26897 70.87885 90 +chr22 16353677 16353759 SRX4288408.05_peak_1237 279 . 8.62944 33.03489 27.95961 37 +chr22 16354687 16354768 SRX4288408.05_peak_1238 631 . 14.04399 69.34258 63.16103 40 +chr22 16359734 16359865 SRX4288408.05_peak_1239 185 . 8.14901 23.27872 18.55674 40 +chr22 16362203 16362261 SRX4288408.05_peak_1240 334 . 11.66464 38.73226 33.47966 26 +chr22 16376816 16376867 SRX4288408.05_peak_1241 211 . 9.42323 25.98142 21.15852 37 +chr22 17877510 17877560 SRX4288408.05_peak_1242 159 . 8.07705 20.55338 15.93338 19 +chr22 18202868 18202924 SRX4288408.05_peak_1243 318 . 11.48564 37.03613 31.82731 15 +chr22 18205406 18205461 SRX4288408.05_peak_1244 265 . 10.41931 31.59574 26.58072 22 +chr22 18205565 18205645 SRX4288408.05_peak_1245 763 . 20.40816 82.87629 76.31351 34 +chr22 18236784 18236850 SRX4288408.05_peak_1246 211 . 9.42323 25.98142 21.15852 38 +chr22 18236936 18236990 SRX4288408.05_peak_1247 222 . 9.69246 27.10503 22.24301 30 +chr22 18237608 18237662 SRX4288408.05_peak_1248 211 . 9.42323 25.98142 21.15852 30 +chr22 18237798 18237853 SRX4288408.05_peak_1249 200 . 9.15399 24.87000 20.08632 30 +chr22 18386074 18386133 SRX4288408.05_peak_1250 348 . 12.65405 40.18482 34.88066 24 +chr22 18387018 18387082 SRX4288408.05_peak_1251 244 . 10.23093 29.38747 24.44372 35 +chr22 18387492 18387574 SRX4288408.05_peak_1252 120 . 7.00011 16.46377 12.02540 19 +chr22 18419164 18419244 SRX4288408.05_peak_1253 548 . 16.80000 60.79451 54.89779 45 +chr22 18419348 18419405 SRX4288408.05_peak_1254 369 . 12.99735 42.28086 36.91809 31 +chr22 18421326 18421378 SRX4288408.05_peak_1255 189 . 8.83534 23.67056 18.93739 22 +chr22 18422017 18422067 SRX4288408.05_peak_1256 158 . 8.01068 20.41939 15.81396 25 +chr22 18730507 18730649 SRX4288408.05_peak_1257 211 . 9.42323 25.98142 21.15852 107 +chr22 18733684 18733772 SRX4288408.05_peak_1258 385 . 13.46175 43.95581 38.53946 42 +chr22 18733877 18733933 SRX4288408.05_peak_1259 200 . 9.15399 24.87000 20.08632 15 +chr22 18890744 18890805 SRX4288408.05_peak_1260 401 . 12.91866 45.58747 40.12255 29 +chr22 18892462 18892545 SRX4288408.05_peak_1261 849 . 18.58664 92.30310 84.96422 42 +chr22 18894158 18894230 SRX4288408.05_peak_1262 539 . 13.50844 59.81383 53.94185 33 +chr22 18896441 18896587 SRX4288408.05_peak_1263 744 . 18.12298 80.99768 74.48423 38 +chr22 20338684 20338734 SRX4288408.05_peak_1264 129 . 7.26935 17.46320 12.97852 27 +chr22 20338841 20338896 SRX4288408.05_peak_1265 211 . 9.42323 25.98142 21.15852 34 +chr22 21167793 21167847 SRX4288408.05_peak_1266 200 . 9.15399 24.87000 20.08632 22 +chr22 21324518 21324577 SRX4288408.05_peak_1267 400 . 13.52406 45.50600 40.04283 32 +chr22 21325371 21325432 SRX4288408.05_peak_1268 426 . 14.11765 48.21232 42.67568 29 +chr22 21326220 21326280 SRX4288408.05_peak_1269 390 . 13.36828 44.46564 39.03550 26 +chr22 23485676 23485733 SRX4288408.05_peak_1270 350 . 12.41915 40.37005 35.06163 25 +chr22 23486765 23486818 SRX4288408.05_peak_1271 267 . 10.49936 31.76599 26.73544 12 +chr22 34331349 34331413 SRX4288408.05_peak_1272 289 . 11.30787 34.08504 28.96540 29 +chr22 38644347 38644531 SRX4288408.05_peak_1273 328 . 11.99478 38.12737 32.88944 50 +chr22 45328050 45328119 SRX4288408.05_peak_1274 211 . 9.42323 25.98142 21.15852 42 +chr22 45328307 45328358 SRX4288408.05_peak_1275 75 . 5.65394 11.72373 7.53352 15 +chr22 49973738 49973805 SRX4288408.05_peak_1276 262 . 8.98438 31.20483 26.20142 32 +chr22 50009917 50009975 SRX4288408.05_peak_1277 198 . 8.51064 24.60245 19.83358 33 +chr22 50643705 50643758 SRX4288408.05_peak_1278 139 . 7.53858 18.47830 13.94900 31 +chr22 50807914 50808093 SRX4288408.05_peak_1279 301 . 11.57711 35.28552 30.12517 59 +chr22_KI270731v1_random 127235 127299 SRX4288408.05_peak_1280 410 . 14.00022 46.51300 41.02061 28 +chr22_KI270731v1_random 127812 127900 SRX4288408.05_peak_1281 110 . 6.73088 15.48060 11.08905 66 +chr22_KI270731v1_random 128703 128782 SRX4288408.05_peak_1282 110 . 6.73088 15.48060 11.08905 55 +chr22_KI270733v1_random 120297 120400 SRX4288408.05_peak_1283 77 . 2.67490 11.96999 7.76801 17 +chr22_KI270733v1_random 179175 179242 SRX4288408.05_peak_1284 56 . 3.15746 9.74278 5.69347 66 +chr22_KI270734v1_random 116784 116843 SRX4288408.05_peak_1285 320 . 10.22044 37.23924 32.02509 24 +chr22_KI270734v1_random 117768 117823 SRX4288408.05_peak_1286 299 . 9.60615 35.08367 29.93815 21 +chr22_KI270734v1_random 119467 119532 SRX4288408.05_peak_1287 442 . 11.85185 49.80292 44.22403 30 +chr22_KI270734v1_random 121688 121862 SRX4288408.05_peak_1288 689 . 15.90909 75.31544 68.96541 63 +chr22_KI270735v1_random 39768 39908 SRX4288408.05_peak_1289 377 . 8.52619 43.10775 37.71891 58 +chr22_KI270735v1_random 40878 41049 SRX4288408.05_peak_1290 169 . 5.79151 21.57263 16.92088 45 +chr22_KI270735v1_random 41170 41245 SRX4288408.05_peak_1291 311 . 7.97386 36.35226 31.16577 34 +chr22_KI270735v1_random 42312 42412 SRX4288408.05_peak_1292 214 . 6.85225 26.27661 21.44460 64 +chr22_KI270736v1_random 478 537 SRX4288408.05_peak_1293 149 . 7.80782 19.50852 14.93405 28 +chr22_KI270736v1_random 49097 49147 SRX4288408.05_peak_1294 109 . 6.04099 15.31081 10.93587 23 +chr22_KI270736v1_random 58704 58758 SRX4288408.05_peak_1295 127 . 6.48649 17.16496 12.70182 28 +chr22_KI270736v1_random 64811 64890 SRX4288408.05_peak_1296 354 . 10.61776 40.72070 35.40332 44 +chr22_KI270736v1_random 67243 67300 SRX4288408.05_peak_1297 233 . 8.45624 28.23177 23.33560 32 +chr22_KI270736v1_random 73963 74015 SRX4288408.05_peak_1298 112 . 5.99174 15.60943 11.21283 38 +chr22_KI270736v1_random 74139 74253 SRX4288408.05_peak_1299 301 . 9.90900 35.25546 30.10475 33 +chr22_KI270736v1_random 79929 79993 SRX4288408.05_peak_1300 330 . 9.91736 38.27214 33.03068 31 +chr22_KI270736v1_random 83224 83281 SRX4288408.05_peak_1301 323 . 9.86965 37.54668 32.32568 30 +chr22_KI270736v1_random 91106 91172 SRX4288408.05_peak_1302 391 . 11.88960 44.54897 39.11649 30 +chr22_KI270736v1_random 92779 92836 SRX4288408.05_peak_1303 225 . 8.55057 27.38386 22.51416 35 +chr22_KI270736v1_random 102054 102105 SRX4288408.05_peak_1304 208 . 7.66423 25.69199 20.88482 26 +chr22_KI270736v1_random 103138 103198 SRX4288408.05_peak_1305 97 . 5.33088 14.03155 9.71673 21 +chr22_KI270736v1_random 105654 105717 SRX4288408.05_peak_1306 125 . 6.21243 16.96012 12.50405 23 +chr22_KI270736v1_random 118655 118712 SRX4288408.05_peak_1307 249 . 9.38548 29.90023 24.94194 27 +chr22_KI270736v1_random 119086 119136 SRX4288408.05_peak_1308 218 . 8.70536 26.65990 21.81766 32 +chr22_KI270736v1_random 126772 126824 SRX4288408.05_peak_1309 241 . 9.05172 29.12392 24.19352 20 +chr22_KI270736v1_random 131575 131637 SRX4288408.05_peak_1310 299 . 9.82949 35.05777 29.91321 30 +chr22_KI270736v1_random 147843 147917 SRX4288408.05_peak_1311 416 . 11.13074 47.17993 41.67081 45 +chr22_KI270736v1_random 162574 162668 SRX4288408.05_peak_1312 231 . 8.38207 28.04623 23.15619 42 +chr22_KI270736v1_random 162970 163028 SRX4288408.05_peak_1313 290 . 9.47776 34.17069 29.04850 20 +chr22_KI270736v1_random 180229 180321 SRX4288408.05_peak_1314 105 . 5.52995 14.90149 10.54221 66 +chr22_KI270736v1_random 180516 180712 SRX4288408.05_peak_1315 139 . 6.48968 18.48808 13.95846 109 +chr22_KI270736v1_random 181101 181207 SRX4288408.05_peak_1316 348 . 10.90535 40.18601 34.88182 71 +chr22_KI270736v1_random 181294 181523 SRX4288408.05_peak_1317 146 . 6.79012 19.20555 14.65087 145 +chr22_KI270737v1_random 8950 9000 SRX4288408.05_peak_1318 179 . 8.61552 22.68509 17.98249 13 +chr22_KI270737v1_random 23552 23613 SRX4288408.05_peak_1319 439 . 11.76471 49.56847 43.99470 35 +chr22_KI270737v1_random 25903 26071 SRX4288408.05_peak_1320 554 . 12.78008 61.32785 55.41245 77 +chr22_KI270737v1_random 27956 28006 SRX4288408.05_peak_1321 224 . 6.61157 27.32270 22.45442 20 +chr22_KI270737v1_random 31041 31122 SRX4288408.05_peak_1322 588 . 11.37380 64.91238 58.88808 33 +chr22_KI270737v1_random 32007 32120 SRX4288408.05_peak_1323 286 . 7.50159 33.70900 28.60786 76 +chr22_KI270737v1_random 32910 33046 SRX4288408.05_peak_1324 603 . 11.59794 66.37906 60.30169 43 +chr22_KI270737v1_random 33310 33393 SRX4288408.05_peak_1325 419 . 9.60758 47.47393 41.95993 46 +chr22_KI270737v1_random 36802 36853 SRX4288408.05_peak_1326 252 . 7.18563 30.18675 25.21940 31 +chr22_KI270737v1_random 38510 38612 SRX4288408.05_peak_1327 426 . 11.49635 48.18087 42.64461 39 +chr22_KI270737v1_random 41563 41633 SRX4288408.05_peak_1328 539 . 14.97207 59.80438 53.93251 36 +chr22_KI270737v1_random 70283 70349 SRX4288408.05_peak_1329 601 . 16.70645 66.17850 60.10563 39 +chr22_KI270737v1_random 71315 71382 SRX4288408.05_peak_1330 558 . 15.80189 61.74136 55.81568 30 +chr22_KI270737v1_random 79977 80091 SRX4288408.05_peak_1331 772 . 20.42875 83.80089 77.22034 71 +chr22_KI270737v1_random 80171 80285 SRX4288408.05_peak_1332 123 . 6.94087 16.80073 12.35048 90 +chr22_KI270738v1_random 74321 74372 SRX4288408.05_peak_1333 224 . 8.97583 27.27301 22.40632 30 +chr3 10582 10674 SRX4288408.05_peak_1334 179 . 8.61552 22.68509 17.98249 31 +chr3 38084474 38084540 SRX4288408.05_peak_1335 211 . 9.42323 25.98142 21.15852 41 +chr3 64696500 64696555 SRX4288408.05_peak_1336 233 . 9.96170 28.24049 23.33625 19 +chr3 75669201 75669327 SRX4288408.05_peak_1337 333 . 10.79365 38.64872 33.39807 89 +chr3 75669570 75669620 SRX4288408.05_peak_1338 151 . 7.01382 19.72781 15.14603 18 +chr3 90538754 90538817 SRX4288408.05_peak_1339 92 . 6.19241 13.56556 9.27016 42 +chr3 90542722 90542772 SRX4288408.05_peak_1340 139 . 7.53858 18.47830 13.94900 19 +chr3 90655016 90655068 SRX4288408.05_peak_1341 169 . 8.34628 21.61238 16.95133 19 +chr3 90660136 90660186 SRX4288408.05_peak_1342 159 . 8.07705 20.55338 15.93338 17 +chr3 91891227 91891331 SRX4288408.05_peak_1343 149 . 7.80782 19.50852 14.93405 59 +chr3 91891402 91891465 SRX4288408.05_peak_1344 120 . 7.00011 16.46377 12.02540 23 +chr3 93470365 93470795 SRX4288408.05_peak_1345 816 . 15.88050 88.63306 81.68650 183 +chr3 194324599 194324669 SRX4288408.05_peak_1346 135 . 7.34908 18.09663 13.59097 36 +chr3 195798503 195798561 SRX4288408.05_peak_1347 278 . 11.03864 32.89474 27.82286 31 +chr3 196103517 196103583 SRX4288408.05_peak_1348 304 . 11.44343 35.65223 30.48278 39 +chr3 196898758 196898886 SRX4288408.05_peak_1349 585 . 15.05155 64.56054 58.54605 69 +chr3 197459665 197459744 SRX4288408.05_peak_1350 354 . 10.62802 40.74715 35.42949 35 +chr3 197459812 197459901 SRX4288408.05_peak_1351 172 . 7.13597 21.90302 17.23244 43 +chr3 198168957 198169058 SRX4288408.05_peak_1352 311 . 9.85507 36.31406 31.12845 40 +chr4 1435403 1435466 SRX4288408.05_peak_1353 266 . 10.76940 31.71488 26.68776 31 +chr4 7863960 7864099 SRX4288408.05_peak_1354 578 . 17.50028 63.88987 57.89410 38 +chr4 49091785 49091837 SRX4288408.05_peak_1355 173 . 3.88815 22.01506 17.34083 37 +chr4 49091923 49092016 SRX4288408.05_peak_1356 205 . 4.08998 25.31642 20.51973 62 +chr4 49092761 49092868 SRX4288408.05_peak_1357 257 . 4.27273 30.75981 25.76887 25 +chr4 49093265 49093385 SRX4288408.05_peak_1358 127 . 3.16442 17.19146 12.72719 104 +chr4 49094590 49094641 SRX4288408.05_peak_1359 116 . 2.85916 16.08986 11.67557 18 +chr4 49094898 49094997 SRX4288408.05_peak_1360 80 . 2.52270 12.29248 8.07591 60 +chr4 49097157 49097260 SRX4288408.05_peak_1361 120 . 2.75031 16.47497 12.03636 30 +chr4 49102568 49102631 SRX4288408.05_peak_1362 136 . 2.76758 18.13906 13.63169 24 +chr4 49103311 49103388 SRX4288408.05_peak_1363 120 . 2.63954 16.49284 12.05379 24 +chr4 49103576 49103657 SRX4288408.05_peak_1364 127 . 2.66523 17.22733 12.76200 21 +chr4 49108321 49108514 SRX4288408.05_peak_1365 107 . 2.54411 15.06608 10.70044 13 +chr4 49108660 49108771 SRX4288408.05_peak_1366 75 . 2.34034 11.73464 7.54388 84 +chr4 49109883 49109988 SRX4288408.05_peak_1367 141 . 2.81974 18.67688 14.14102 23 +chr4 49110702 49110812 SRX4288408.05_peak_1368 132 . 2.79595 17.75409 13.25949 80 +chr4 49112379 49112432 SRX4288408.05_peak_1369 152 . 2.98554 19.79276 15.20894 16 +chr4 49112693 49112790 SRX4288408.05_peak_1370 107 . 2.69403 15.14197 10.77338 12 +chr4 49118881 49119064 SRX4288408.05_peak_1371 197 . 3.34825 24.54385 19.77669 72 +chr4 49119535 49119614 SRX4288408.05_peak_1372 107 . 2.72380 15.08574 10.71930 42 +chr4 49120165 49120217 SRX4288408.05_peak_1373 108 . 2.73561 15.18972 10.81915 28 +chr4 49120917 49121175 SRX4288408.05_peak_1374 176 . 3.22689 22.30120 17.61874 109 +chr4 49123636 49123693 SRX4288408.05_peak_1375 126 . 2.95283 17.06794 12.60798 39 +chr4 49127036 49127086 SRX4288408.05_peak_1376 178 . 3.77815 22.58848 17.89684 19 +chr4 49130881 49130985 SRX4288408.05_peak_1377 199 . 4.08484 24.71049 19.93877 24 +chr4 49131673 49131742 SRX4288408.05_peak_1378 269 . 4.60476 31.98684 26.95084 39 +chr4 49137051 49137105 SRX4288408.05_peak_1379 97 . 3.00823 14.10217 9.78466 35 +chr4 49137186 49137285 SRX4288408.05_peak_1380 274 . 4.37528 32.47685 27.42552 62 +chr4 49138054 49138172 SRX4288408.05_peak_1381 118 . 3.18046 16.27514 11.85385 18 +chr4 49139662 49139786 SRX4288408.05_peak_1382 230 . 3.98614 27.91879 23.03293 28 +chr4 49142098 49142224 SRX4288408.05_peak_1383 196 . 3.68432 24.43811 19.67483 98 +chr4 49144572 49144676 SRX4288408.05_peak_1384 130 . 3.03712 17.58603 13.09715 23 +chr4 49147296 49147441 SRX4288408.05_peak_1385 217 . 3.64855 26.63667 21.79517 22 +chr4 49149360 49149496 SRX4288408.05_peak_1386 175 . 3.26219 22.20435 17.52436 107 +chr4 49149750 49149816 SRX4288408.05_peak_1387 197 . 3.41523 24.50493 19.73932 33 +chr4 49150446 49150577 SRX4288408.05_peak_1388 136 . 2.99182 18.15417 13.64651 67 +chr4 49154188 49154269 SRX4288408.05_peak_1389 185 . 3.66412 23.27438 18.55256 51 +chr4 49154419 49154601 SRX4288408.05_peak_1390 179 . 3.71058 22.61540 17.92308 91 +chr4 49154898 49154972 SRX4288408.05_peak_1391 206 . 4.07035 25.46464 20.66340 43 +chr4 49155918 49156014 SRX4288408.05_peak_1392 127 . 3.61054 17.20387 12.73926 22 +chr4 49156445 49156530 SRX4288408.05_peak_1393 305 . 5.44348 35.70842 30.53711 41 +chr4 49308180 49308236 SRX4288408.05_peak_1394 131 . 6.33663 17.68388 13.19140 23 +chr4 49314986 49315039 SRX4288408.05_peak_1395 88 . 4.95726 13.09028 8.82641 15 +chr4 49323079 49323160 SRX4288408.05_peak_1396 235 . 7.40741 28.45293 23.54323 28 +chr4 49514864 49514930 SRX4288408.05_peak_1397 277 . 8.75657 32.83556 27.77291 29 +chr4 49632631 49632745 SRX4288408.05_peak_1398 169 . 4.19490 21.62504 16.96338 92 +chr4 49632911 49632980 SRX4288408.05_peak_1399 115 . 3.56696 15.98040 11.57047 31 +chr4 49633917 49633996 SRX4288408.05_peak_1400 160 . 3.88905 20.67377 16.05015 33 +chr4 49634162 49634288 SRX4288408.05_peak_1401 158 . 3.81321 20.46862 15.86118 21 +chr4 49635180 49635251 SRX4288408.05_peak_1402 253 . 4.46587 30.35736 25.38563 37 +chr4 49635496 49635604 SRX4288408.05_peak_1403 227 . 4.16667 27.59296 22.71615 56 +chr4 49637098 49637156 SRX4288408.05_peak_1404 161 . 3.49790 20.79201 16.16490 24 +chr4 49639392 49639442 SRX4288408.05_peak_1405 125 . 3.28103 16.97080 12.51441 28 +chr4 49640292 49640399 SRX4288408.05_peak_1406 131 . 3.34848 17.65368 13.16221 17 +chr4 49642641 49642707 SRX4288408.05_peak_1407 137 . 3.40855 18.29272 13.77989 37 +chr4 49647850 49647927 SRX4288408.05_peak_1408 115 . 3.25651 15.94499 11.53637 51 +chr4 49648673 49648731 SRX4288408.05_peak_1409 140 . 3.49040 18.61832 14.08427 16 +chr4 49650524 49650592 SRX4288408.05_peak_1410 215 . 4.15611 26.35261 21.51809 45 +chr4 49651378 49651488 SRX4288408.05_peak_1411 184 . 3.93680 23.15251 18.43423 80 +chr4 49652458 49652508 SRX4288408.05_peak_1412 72 . 2.84827 11.43751 7.27260 36 +chr4 49657117 49657200 SRX4288408.05_peak_1413 181 . 4.80848 22.88301 18.17424 21 +chr4 49657521 49657725 SRX4288408.05_peak_1414 538 . 8.90317 59.67897 53.82046 40 +chr4 49709344 49709571 SRX4288408.05_peak_1415 430 . 6.53381 48.56757 43.02256 50 +chr4 49709701 49709814 SRX4288408.05_peak_1416 436 . 6.57124 49.24615 43.67879 43 +chr4 49709872 49710143 SRX4288408.05_peak_1417 378 . 6.09598 43.28481 37.89236 239 +chr4 49710536 49710881 SRX4288408.05_peak_1418 385 . 6.12903 43.91043 38.50165 35 +chr4 49710934 49711036 SRX4288408.05_peak_1419 313 . 5.52167 36.56437 31.36587 67 +chr4 49711264 49711437 SRX4288408.05_peak_1420 342 . 5.75816 39.57771 34.29784 131 +chr4 49711491 49711713 SRX4288408.05_peak_1421 421 . 6.38978 47.68071 42.16179 60 +chr4 49711794 49711915 SRX4288408.05_peak_1422 412 . 6.31780 46.79575 41.29700 72 +chr4 51107262 51107558 SRX4288408.05_peak_1423 746 . 19.26278 81.16573 74.65091 95 +chr4 91792795 91792851 SRX4288408.05_peak_1424 211 . 9.42323 25.98142 21.15852 21 +chr4 166580344 166580451 SRX4288408.05_peak_1425 120 . 7.00011 16.46377 12.02540 30 +chr4 189233901 189234161 SRX4288408.05_peak_1426 646 . 12.28534 70.85154 64.63453 85 +chr4 189234410 189234493 SRX4288408.05_peak_1427 556 . 11.17686 61.59445 55.67183 45 +chr4 189234636 189234696 SRX4288408.05_peak_1428 285 . 7.62656 33.63560 28.53655 24 +chr4 189234988 189235274 SRX4288408.05_peak_1429 555 . 11.14754 61.49141 55.57066 163 +chr4 189235333 189235441 SRX4288408.05_peak_1430 164 . 5.77428 21.11305 16.47571 89 +chr4 189881309 189881475 SRX4288408.05_peak_1431 266 . 10.76940 31.71488 26.68776 43 +chr4 190021681 190021750 SRX4288408.05_peak_1432 144 . 6.52591 19.01752 14.46970 43 +chr4 190022053 190022124 SRX4288408.05_peak_1433 170 . 7.05434 21.69957 17.03611 30 +chr4 190022210 190022353 SRX4288408.05_peak_1434 126 . 6.10105 17.11214 12.65060 33 +chr4 190022409 190022574 SRX4288408.05_peak_1435 170 . 7.06781 21.73325 17.06896 37 +chr4 190122828 190122892 SRX4288408.05_peak_1436 169 . 8.34628 21.61238 16.95133 31 +chr4 190122946 190123072 SRX4288408.05_peak_1437 244 . 10.23093 29.38747 24.44372 39 +chr4 190178337 190178459 SRX4288408.05_peak_1438 320 . 6.15025 37.21715 32.00338 30 +chr4 190178792 190178861 SRX4288408.05_peak_1439 377 . 6.69782 43.15467 37.76491 30 +chr4 190179471 190179773 SRX4288408.05_peak_1440 486 . 7.71350 54.30281 48.61517 185 +chr4 190179846 190180000 SRX4288408.05_peak_1441 232 . 5.29854 28.10664 23.21503 36 +chr4 190181022 190181153 SRX4288408.05_peak_1442 203 . 4.98615 25.12014 20.32972 23 +chr4_GL000008v2_random 3283 3401 SRX4288408.05_peak_1443 90 . 3.46570 13.31622 9.04270 27 +chr5 10032 10454 SRX4288408.05_peak_1444 191 . 5.98291 23.85978 19.11315 387 +chr5 10508 11763 SRX4288408.05_peak_1445 215 . 6.33760 26.37551 21.54027 23 +chr5 344458 344689 SRX4288408.05_peak_1446 168 . 6.15836 21.48559 16.83633 24 +chr5 628534 628593 SRX4288408.05_peak_1447 83 . 5.92317 12.63510 8.39064 17 +chr5 1333520 1333574 SRX4288408.05_peak_1448 114 . 6.48259 15.84958 11.44424 9 +chr5 1333724 1333777 SRX4288408.05_peak_1449 263 . 10.02387 31.34046 26.33352 22 +chr5 3324099 3324164 SRX4288408.05_peak_1450 222 . 9.69246 27.10503 22.24301 41 +chr5 14347134 14347199 SRX4288408.05_peak_1451 115 . 5.97610 15.98272 11.57261 30 +chr5 47297741 47297798 SRX4288408.05_peak_1452 211 . 9.42323 25.98142 21.15852 41 +chr5 47308767 47308840 SRX4288408.05_peak_1453 129 . 7.26935 17.46320 12.97852 38 +chr5 49599502 49599761 SRX4288408.05_peak_1454 226 . 4.72995 27.52476 22.65083 229 +chr5 49599984 49600046 SRX4288408.05_peak_1455 193 . 4.41036 24.05945 19.30664 33 +chr5 49600461 49600636 SRX4288408.05_peak_1456 125 . 3.70725 16.99828 12.54068 15 +chr5 49600709 49600759 SRX4288408.05_peak_1457 180 . 4.28252 22.71613 18.01209 17 +chr5 49601114 49601292 SRX4288408.05_peak_1458 304 . 5.43305 35.64558 30.47640 21 +chr5 49601511 49602166 SRX4288408.05_peak_1459 413 . 6.32790 46.85834 41.35764 37 +chr5 49602324 49602496 SRX4288408.05_peak_1460 350 . 5.81656 40.33834 35.03082 139 +chr5 49602599 49602666 SRX4288408.05_peak_1461 173 . 4.21860 22.05398 17.37850 28 +chr5 49602729 49602781 SRX4288408.05_peak_1462 233 . 4.79386 28.23550 23.33625 26 +chr5 49602834 49602886 SRX4288408.05_peak_1463 254 . 4.98562 30.40171 25.42829 22 +chr5 49610532 49610597 SRX4288408.05_peak_1464 149 . 6.56045 19.54678 14.97122 35 +chr5 49614992 49615045 SRX4288408.05_peak_1465 135 . 5.43176 18.07244 13.56761 17 +chr5 49615480 49615539 SRX4288408.05_peak_1466 217 . 6.95148 26.58425 21.74393 26 +chr5 49626876 49626928 SRX4288408.05_peak_1467 170 . 6.39626 21.75320 17.08802 24 +chr5 49630832 49631059 SRX4288408.05_peak_1468 450 . 13.27434 50.63053 45.03185 115 +chr5 49642265 49642334 SRX4288408.05_peak_1469 281 . 9.34762 33.26068 28.17773 33 +chr5 49644975 49645030 SRX4288408.05_peak_1470 250 . 7.77605 30.05252 25.08998 24 +chr5 49656744 49656834 SRX4288408.05_peak_1471 194 . 3.48084 24.17658 19.42085 47 +chr5 49656999 49657081 SRX4288408.05_peak_1472 153 . 3.17808 19.98072 15.39061 20 +chr5 49657586 49657787 SRX4288408.05_peak_1473 206 . 3.54338 25.44641 20.64569 176 +chr5 49657852 49657958 SRX4288408.05_peak_1474 129 . 2.99543 17.42876 12.95584 80 +chr5 49658366 49658577 SRX4288408.05_peak_1475 206 . 3.54338 25.44641 20.64569 171 +chr5 49658638 49658777 SRX4288408.05_peak_1476 228 . 3.68950 27.75896 22.87777 45 +chr5 49658911 49659005 SRX4288408.05_peak_1477 206 . 3.54338 25.44641 20.64569 73 +chr5 49659185 49659253 SRX4288408.05_peak_1478 179 . 3.36073 22.65544 17.96164 47 +chr5 49659491 49659554 SRX4288408.05_peak_1479 184 . 3.39726 23.20452 18.48488 23 +chr5 49659839 49659918 SRX4288408.05_peak_1480 228 . 3.68950 27.75896 22.87777 33 +chr5 49660708 49660773 SRX4288408.05_peak_1481 206 . 3.54338 25.44641 20.64569 31 +chr5 49661056 49661174 SRX4288408.05_peak_1482 206 . 3.54338 25.44641 20.64569 34 +chr5 49661441 49661818 SRX4288408.05_peak_1483 209 . 3.50017 25.76196 20.95205 336 +chr5 49666182 49666253 SRX4288408.05_peak_1484 458 . 8.20131 51.48983 45.87585 32 +chr5 49666362 49666673 SRX4288408.05_peak_1485 575 . 10.49031 63.51265 57.53309 269 +chr5 49666731 49666889 SRX4288408.05_peak_1486 650 . 12.61138 71.29063 65.05012 118 +chr5 49666943 49667329 SRX4288408.05_peak_1487 563 . 11.57025 62.29362 56.35268 83 +chr5 71850961 71851015 SRX4288408.05_peak_1488 129 . 7.26935 17.46320 12.97852 20 +chr5 80323715 80323769 SRX4288408.05_peak_1489 200 . 9.15399 24.87000 20.08632 24 +chr5 178585560 178585732 SRX4288408.05_peak_1490 619 . 18.30798 68.06786 61.91615 129 +chr6 143213589 143213646 SRX4288408.05_peak_1491 312 . 11.84634 36.49596 31.29813 24 +chr6 157310662 157310719 SRX4288408.05_peak_1492 203 . 6.49966 25.15871 20.36656 34 +chr6 157310832 157310948 SRX4288408.05_peak_1493 178 . 6.07698 22.50123 17.81226 22 +chr6 157312982 157313047 SRX4288408.05_peak_1494 284 . 7.73931 33.50513 28.40981 36 +chr6 157314174 157314247 SRX4288408.05_peak_1495 131 . 5.30252 17.67325 13.18107 26 +chr6 170146278 170146334 SRX4288408.05_peak_1496 169 . 8.34628 21.61238 16.95133 26 +chr6 170400179 170400252 SRX4288408.05_peak_1497 86 . 5.33881 12.93909 8.68179 16 +chr7 20337 20450 SRX4288408.05_peak_1498 127 . 6.90506 17.18245 12.71851 82 +chr7 905576 905704 SRX4288408.05_peak_1499 121 . 6.06061 16.59718 12.15450 96 +chr7 905991 906046 SRX4288408.05_peak_1500 144 . 6.50096 18.95594 14.41060 24 +chr7 43340378 43340431 SRX4288408.05_peak_1501 179 . 8.61552 22.68509 17.98249 28 +chr7 57495565 57495615 SRX4288408.05_peak_1502 194 . 7.88382 24.21448 19.45782 15 +chr7 58033158 58033233 SRX4288408.05_peak_1503 740 . 20.15504 80.56958 74.07052 32 +chr7 58036645 58036735 SRX4288408.05_peak_1504 77 . 5.39906 11.96130 7.75977 61 +chr7 58059995 58060056 SRX4288408.05_peak_1505 400 . 12.87247 45.48350 40.02111 25 +chr7 58060667 58060774 SRX4288408.05_peak_1506 121 . 6.63507 16.61171 12.16850 26 +chr7 58236998 58237071 SRX4288408.05_peak_1507 278 . 11.03864 32.89474 27.82286 34 +chr7 58399810 58399865 SRX4288408.05_peak_1508 244 . 10.23093 29.38747 24.44372 23 +chr7 58434537 58434622 SRX4288408.05_peak_1509 1078 . 27.19274 117.34310 107.85193 42 +chr7 58708443 58708509 SRX4288408.05_peak_1510 360 . 12.92328 41.43295 36.09158 33 +chr7 58797812 58797890 SRX4288408.05_peak_1511 702 . 19.92339 76.59249 70.21157 41 +chr7 58818596 58818668 SRX4288408.05_peak_1512 605 . 18.03875 66.66874 60.57828 39 +chr7 58893209 58893265 SRX4288408.05_peak_1513 266 . 10.76940 31.71488 26.68776 34 +chr7 59163945 59164029 SRX4288408.05_peak_1514 867 . 23.15421 94.25573 86.74228 44 +chr7 59199480 59199545 SRX4288408.05_peak_1515 266 . 10.76940 31.71488 26.68776 27 +chr7 59206625 59206698 SRX4288408.05_peak_1516 486 . 15.61563 54.37801 48.67952 38 +chr7 59291755 59291819 SRX4288408.05_peak_1517 75 . 5.65394 11.72373 7.53352 31 +chr7 59292473 59292551 SRX4288408.05_peak_1518 674 . 19.38492 73.72680 67.42146 37 +chr7 59364678 59364753 SRX4288408.05_peak_1519 674 . 19.38492 73.72680 67.42146 38 +chr7 59366716 59366782 SRX4288408.05_peak_1520 255 . 10.50016 30.54570 25.56096 31 +chr7 59432824 59432874 SRX4288408.05_peak_1521 190 . 8.88476 23.77109 19.02729 20 +chr7 59501983 59502043 SRX4288408.05_peak_1522 348 . 12.65405 40.18482 34.88066 34 +chr7 59734838 59734910 SRX4288408.05_peak_1523 619 . 18.30798 68.06786 61.91615 35 +chr7 59755067 59755125 SRX4288408.05_peak_1524 244 . 10.23093 29.38747 24.44372 27 +chr7 59808588 59808638 SRX4288408.05_peak_1525 179 . 8.61552 22.68509 17.98249 21 +chr7 59954564 59954638 SRX4288408.05_peak_1526 473 . 15.34640 53.04787 47.38768 41 +chr7 59995720 59995801 SRX4288408.05_peak_1527 772 . 21.26957 83.85737 77.25099 41 +chr7 60310825 60310893 SRX4288408.05_peak_1528 278 . 11.03864 32.89474 27.82286 34 +chr7 60311719 60311782 SRX4288408.05_peak_1529 278 . 11.03864 32.89474 27.82286 28 +chr7 60383177 60383251 SRX4288408.05_peak_1530 674 . 19.38492 73.72680 67.42146 39 +chr7 60401231 60401293 SRX4288408.05_peak_1531 278 . 11.03864 32.89474 27.82286 30 +chr7 60522187 60522263 SRX4288408.05_peak_1532 674 . 19.38492 73.72680 67.42146 38 +chr7 60639059 60639127 SRX4288408.05_peak_1533 486 . 15.61563 54.37801 48.67952 37 +chr7 60917872 60917923 SRX4288408.05_peak_1534 200 . 9.15399 24.87000 20.08632 35 +chr7 60928489 60928551 SRX4288408.05_peak_1535 355 . 12.64822 40.85596 35.53549 39 +chr7 61040815 61040896 SRX4288408.05_peak_1536 146 . 7.44417 19.25178 14.69543 40 +chr7 61056397 61056494 SRX4288408.05_peak_1537 51 . 4.68557 9.12531 5.11114 67 +chr7 61071250 61071352 SRX4288408.05_peak_1538 565 . 17.23104 62.51031 56.55418 64 +chr7 62295322 62295372 SRX4288408.05_peak_1539 196 . 8.68486 24.44157 19.67821 36 +chr7 62350327 62350389 SRX4288408.05_peak_1540 411 . 12.21053 46.67746 41.18162 22 +chr7 62402810 62402870 SRX4288408.05_peak_1541 307 . 10.43478 35.92176 30.74492 30 +chr7 62442503 62442575 SRX4288408.05_peak_1542 422 . 14.26946 47.80407 42.27565 32 +chr7 62451188 62451240 SRX4288408.05_peak_1543 265 . 10.10830 31.52687 26.51324 20 +chr7 62452206 62452257 SRX4288408.05_peak_1544 193 . 8.50547 24.05683 19.30435 13 +chr7 62454728 62454808 SRX4288408.05_peak_1545 867 . 23.15421 94.25573 86.74228 41 +chr7 62455698 62455750 SRX4288408.05_peak_1546 211 . 9.42323 25.98142 21.15852 33 +chr7 65489985 65490061 SRX4288408.05_peak_1547 294 . 10.94527 34.58574 29.45365 28 +chr7 68544089 68544149 SRX4288408.05_peak_1548 324 . 12.11558 37.71613 32.48608 34 +chr7 100958050 100958105 SRX4288408.05_peak_1549 172 . 7.34694 21.93774 17.26562 32 +chr7 100958218 100958273 SRX4288408.05_peak_1550 182 . 7.55102 22.91621 18.20631 35 +chr7 107770175 107770241 SRX4288408.05_peak_1551 214 . 9.03541 26.29163 21.45934 20 +chr7 117031389 117031441 SRX4288408.05_peak_1552 228 . 9.42928 27.70457 22.82515 24 +chr7 129182971 129183028 SRX4288408.05_peak_1553 229 . 9.50000 27.85611 22.97213 24 +chr7 129200215 129200269 SRX4288408.05_peak_1554 225 . 9.54839 27.38364 22.51397 28 +chr7 148331631 148331698 SRX4288408.05_peak_1555 266 . 10.76940 31.71488 26.68776 37 +chr7 158147865 158147924 SRX4288408.05_peak_1556 94 . 6.07595 13.74391 9.44093 34 +chr7 158595234 158595291 SRX4288408.05_peak_1557 324 . 12.11558 37.71613 32.48608 40 +chr7 159335894 159335947 SRX4288408.05_peak_1558 200 . 9.15399 24.87000 20.08632 26 +chr8 43237743 43237896 SRX4288408.05_peak_1559 296 . 7.01187 34.81207 29.67387 38 +chr8 43238063 43238148 SRX4288408.05_peak_1560 236 . 6.21318 28.51253 23.60105 64 +chr8 43238316 43238368 SRX4288408.05_peak_1561 227 . 6.08324 27.57995 22.70342 21 +chr8 43238676 43238734 SRX4288408.05_peak_1562 269 . 6.64523 31.97413 26.93855 28 +chr8 43239636 43239752 SRX4288408.05_peak_1563 378 . 7.99136 43.19970 37.80893 33 +chr8 43240032 43240096 SRX4288408.05_peak_1564 378 . 8.00866 43.26906 37.87695 32 +chr8 43240781 43240846 SRX4288408.05_peak_1565 287 . 6.89284 33.87173 28.76533 27 +chr8 43240951 43241002 SRX4288408.05_peak_1566 228 . 6.11916 27.71975 22.84012 32 +chr8 43241417 43241473 SRX4288408.05_peak_1567 179 . 5.44290 22.69076 17.98780 26 +chr8 69690119 69690265 SRX4288408.05_peak_1568 312 . 11.84634 36.49596 31.29813 36 +chr8 141492336 141492400 SRX4288408.05_peak_1569 278 . 11.03864 32.89474 27.82286 33 +chr8 143669477 143669614 SRX4288408.05_peak_1570 512 . 16.15410 57.06042 51.28232 104 +chr8 144125843 144125921 SRX4288408.05_peak_1571 389 . 12.98377 44.33846 38.91358 41 +chr9 320254 320304 SRX4288408.05_peak_1572 128 . 6.96517 17.30795 12.83954 19 +chr9 40271002 40271064 SRX4288408.05_peak_1573 368 . 12.98013 42.24462 36.88278 21 +chr9 41229441 41229501 SRX4288408.05_peak_1574 124 . 4.66884 16.94076 12.48537 39 +chr9 41233149 41233205 SRX4288408.05_peak_1575 93 . 4.10526 13.65075 9.35203 43 +chr9 42900342 42900419 SRX4288408.05_peak_1576 702 . 19.91925 76.58368 70.21157 39 +chr9 43318732 43318782 SRX4288408.05_peak_1577 198 . 8.50059 24.58037 19.81223 11 +chr9 43319035 43319170 SRX4288408.05_peak_1578 570 . 16.01885 63.02023 57.05618 35 +chr9 43344843 43344896 SRX4288408.05_peak_1579 255 . 10.50016 30.54570 25.56096 39 +chr9 43452840 43452900 SRX4288408.05_peak_1580 169 . 8.34628 21.61238 16.95133 27 +chr9 43479207 43479265 SRX4288408.05_peak_1581 222 . 9.69246 27.10503 22.24301 36 +chr9 43481754 43481822 SRX4288408.05_peak_1582 169 . 8.34628 21.61238 16.95133 29 +chr9 43548620 43548677 SRX4288408.05_peak_1583 120 . 7.00011 16.46377 12.02540 39 +chr9 43580318 43580385 SRX4288408.05_peak_1584 348 . 12.65405 40.18482 34.88066 33 +chr9 43825998 43826052 SRX4288408.05_peak_1585 159 . 8.07705 20.55338 15.93338 36 +chr9 43918814 43918883 SRX4288408.05_peak_1586 552 . 16.96181 61.13748 55.22769 39 +chr9 44175492 44175574 SRX4288408.05_peak_1587 744 . 20.73110 80.93454 74.42200 41 +chr9 44279880 44279934 SRX4288408.05_peak_1588 149 . 7.80782 19.50852 14.93405 25 +chr9 44434468 44434525 SRX4288408.05_peak_1589 312 . 11.84634 36.49596 31.29813 26 +chr9 44896279 44896329 SRX4288408.05_peak_1590 200 . 9.15399 24.87000 20.08632 27 +chr9 45234237 45234287 SRX4288408.05_peak_1591 149 . 7.80782 19.50852 14.93405 28 +chr9 45406679 45406736 SRX4288408.05_peak_1592 159 . 8.07705 20.55338 15.93338 37 +chr9 45473364 45473414 SRX4288408.05_peak_1593 159 . 8.07705 20.55338 15.93338 33 +chr9 60573341 60573391 SRX4288408.05_peak_1594 194 . 8.85417 24.25408 19.49651 29 +chr9 65385739 65385806 SRX4288408.05_peak_1595 179 . 8.32282 22.61004 17.91786 40 +chr9 70038193 70038462 SRX4288408.05_peak_1596 522 . 14.89118 58.10099 52.29538 226 +chr9 70038577 70038632 SRX4288408.05_peak_1597 254 . 9.63303 30.46540 25.49007 15 +chr9 76175209 76175273 SRX4288408.05_peak_1598 468 . 15.09934 52.52564 46.88315 38 +chr9 109598678 109598733 SRX4288408.05_peak_1599 222 . 9.69246 27.10503 22.24301 22 +chr9 120075165 120075217 SRX4288408.05_peak_1600 169 . 8.34628 21.61238 16.95133 19 +chr9 134182971 134183063 SRX4288408.05_peak_1601 278 . 11.03864 32.89474 27.82286 46 +chr9 137327792 137327844 SRX4288408.05_peak_1602 173 . 7.19145 22.04049 17.36559 27 +chr9 137327977 137328114 SRX4288408.05_peak_1603 181 . 7.34300 22.89774 18.18844 37 +chr9 137328231 137328455 SRX4288408.05_peak_1604 376 . 11.01449 43.01110 37.62397 185 +chr9 137716396 137716459 SRX4288408.05_peak_1605 83 . 5.51091 12.57095 8.34151 14 +chr9 138231810 138231905 SRX4288408.05_peak_1606 72 . 4.70588 11.44566 7.28039 74 +chr9 138232163 138232222 SRX4288408.05_peak_1607 87 . 5.04959 12.97352 8.71455 20 +chr9 138232750 138232801 SRX4288408.05_peak_1608 126 . 5.94595 17.13825 12.67598 15 +chr9_KI270718v1_random 37816 38001 SRX4288408.05_peak_1609 127 . 6.51466 17.22867 12.76331 43 diff --git a/tests/data/hg38_sample/SRX5974535.05.bed b/tests/data/hg38_sample/SRX5974535.05.bed new file mode 100644 index 00000000..01a84015 --- /dev/null +++ b/tests/data/hg38_sample/SRX5974535.05.bed @@ -0,0 +1,93 @@ +chr1 16645429 16645727 SRX5974535.05_peak_1 96 . 4.33276 14.76984 9.63489 229 +chr1 110098458 110098756 SRX5974535.05_peak_2 92 . 5.60179 14.30374 9.20715 235 +chr1 125179088 125180451 SRX5974535.05_peak_3 229 . 3.65769 28.96317 22.92045 1047 +chr1 125182301 125182692 SRX5974535.05_peak_4 260 . 3.90955 32.17425 26.01638 117 +chr1 143184878 143185179 SRX5974535.05_peak_5 113 . 5.34680 16.68586 11.37536 23 +chr1 143192770 143193080 SRX5974535.05_peak_6 87 . 3.42140 13.79230 8.74445 29 +chr1 143254581 143254919 SRX5974535.05_peak_7 166 . 3.52145 22.39559 16.69623 207 +chr1 143264447 143264757 SRX5974535.05_peak_8 146 . 4.09473 20.19904 14.64834 230 +chr1 228557956 228558270 SRX5974535.05_peak_9 204 . 9.88315 26.32614 20.42776 240 +chr10 40440917 40441304 SRX5974535.05_peak_10 240 . 13.01537 30.14219 24.05863 236 +chr10 40447067 40447555 SRX5974535.05_peak_11 243 . 13.17803 30.41937 24.32948 241 +chr10 40556598 40557103 SRX5974535.05_peak_12 459 . 17.00458 52.48407 45.96377 272 +chr13 107941655 107942050 SRX5974535.05_peak_13 148 . 5.13391 20.39937 14.83408 248 +chr16 34572575 34572910 SRX5974535.05_peak_14 97 . 4.85882 14.88465 9.73489 110 +chr16 34582886 34583675 SRX5974535.05_peak_15 193 . 3.91919 25.20749 19.36268 260 +chr16 34588042 34588344 SRX5974535.05_peak_16 99 . 3.16399 15.13146 9.95982 259 +chr16 46390126 46390766 SRX5974535.05_peak_17 264 . 3.55952 32.57409 26.40890 398 +chr16 46394515 46394931 SRX5974535.05_peak_18 214 . 3.55008 27.42740 21.47005 234 +chr16 46400047 46400918 SRX5974535.05_peak_19 217 . 4.14444 27.72022 21.73669 604 +chr16 46401054 46401552 SRX5974535.05_peak_20 89 . 3.03086 14.06010 8.98286 292 +chr17 26603732 26604265 SRX5974535.05_peak_21 193 . 11.20269 25.18662 19.34240 174 +chr17 26619779 26620073 SRX5974535.05_peak_22 100 . 8.91961 15.24927 10.06638 242 +chr17_KI270729v1_random 3522 3833 SRX5974535.05_peak_23 253 . 9.33223 31.49701 25.36762 55 +chr18 108104 108406 SRX5974535.05_peak_24 136 . 5.76718 19.08137 13.60849 129 +chr1_KI270711v1_random 8506 9540 SRX5974535.05_peak_25 154 . 5.41887 21.07542 15.45768 701 +chr2 32916238 32916781 SRX5974535.05_peak_26 961 . 20.64944 102.94081 96.12363 274 +chr21 8236714 8237243 SRX5974535.05_peak_27 160 . 3.16401 21.70558 16.04248 175 +chr21 8462506 8462819 SRX5974535.05_peak_28 142 . 2.83903 19.74334 14.22010 170 +chr21 8463276 8463974 SRX5974535.05_peak_29 95 . 2.58893 14.62916 9.50380 135 +chr22 12175875 12176699 SRX5974535.05_peak_30 264 . 6.46432 32.58663 26.41959 539 +chr22 19396679 19396993 SRX5974535.05_peak_31 88 . 4.08555 13.86456 8.80739 181 +chr22 19722733 19723192 SRX5974535.05_peak_32 102 . 3.92452 15.43936 10.24223 141 +chr22 19806251 19806979 SRX5974535.05_peak_33 165 . 7.46578 22.22074 16.53192 189 +chr22 19891884 19892272 SRX5974535.05_peak_34 211 . 5.85374 27.08127 21.13397 197 +chr22 19892416 19892710 SRX5974535.05_peak_35 117 . 4.52826 17.06112 11.72352 180 +chr22 19959699 19959995 SRX5974535.05_peak_36 115 . 4.92927 16.83273 11.51669 78 +chr22 19974785 19975108 SRX5974535.05_peak_37 120 . 5.34068 17.41702 12.05148 156 +chr22 20292427 20292721 SRX5974535.05_peak_38 69 . 5.50165 11.80001 6.96770 239 +chr22 20556986 20557512 SRX5974535.05_peak_39 174 . 6.15961 23.19009 17.44979 178 +chr22 20564077 20564390 SRX5974535.05_peak_40 90 . 5.89516 14.17547 9.08939 220 +chr22 20615194 20615494 SRX5974535.05_peak_41 113 . 6.65937 16.66358 11.35593 194 +chr22 20777231 20777779 SRX5974535.05_peak_42 129 . 4.87435 18.36998 12.94279 357 +chr22 20885646 20886012 SRX5974535.05_peak_43 101 . 5.41473 15.36885 10.17604 194 +chr22 21544602 21545028 SRX5974535.05_peak_44 137 . 5.95593 19.22058 13.74082 183 +chr22 21556287 21556608 SRX5974535.05_peak_45 128 . 6.07344 18.29615 12.87273 227 +chr22 21650801 21651243 SRX5974535.05_peak_46 87 . 3.74220 13.84076 8.78730 207 +chr22 21753967 21754445 SRX5974535.05_peak_47 155 . 3.84540 21.20245 15.57804 279 +chr22 21755564 21755921 SRX5974535.05_peak_48 117 . 3.42766 17.04538 11.70972 230 +chr22 21947147 21947668 SRX5974535.05_peak_49 101 . 3.78759 15.34418 10.15405 277 +chr22 22002358 22002673 SRX5974535.05_peak_50 110 . 4.41457 16.30650 11.03494 55 +chr22 22142484 22142877 SRX5974535.05_peak_51 84 . 5.56150 13.44179 8.42903 211 +chr22 23087292 23087700 SRX5974535.05_peak_52 146 . 5.19016 20.24513 14.68669 187 +chr22 46635155 46635457 SRX5974535.05_peak_53 160 . 9.56497 21.75950 16.09368 103 +chr22_KI270733v1_random 141555 141894 SRX5974535.05_peak_54 159 . 4.54014 21.59359 15.93606 210 +chr22_KI270733v1_random 152537 153226 SRX5974535.05_peak_55 173 . 3.49154 23.09604 17.36011 252 +chr3 43523147 43523466 SRX5974535.05_peak_56 126 . 7.82520 18.06005 12.65834 253 +chr3 91891194 91891508 SRX5974535.05_peak_57 133 . 10.55347 18.82232 13.36704 79 +chr3 93470198 93470964 SRX5974535.05_peak_58 3400 . 19.78933 349.54712 340.05600 405 +chr3 195697656 195698059 SRX5974535.05_peak_59 112 . 6.05942 16.59433 11.29556 250 +chr4 49273021 49273315 SRX5974535.05_peak_60 123 . 6.94578 17.72576 12.34324 105 +chr4 49709349 49709765 SRX5974535.05_peak_61 170 . 6.90584 22.71767 17.00492 186 +chr5 1068119 1068432 SRX5974535.05_peak_62 106 . 7.44963 15.92875 10.68644 179 +chr5 49601500 49602322 SRX5974535.05_peak_63 180 . 6.80229 23.81728 18.04649 556 +chr5 49658408 49658795 SRX5974535.05_peak_64 208 . 5.76816 26.73157 20.80263 249 +chr5 49666167 49666467 SRX5974535.05_peak_65 209 . 8.55615 26.85670 20.92393 63 +chr7 130010199 130010641 SRX5974535.05_peak_66 106 . 6.51262 15.91292 10.67276 166 +chr9 130824554 130825780 SRX5974535.05_peak_67 157 . 4.70296 21.38467 15.74713 852 +chr9 130845362 130845767 SRX5974535.05_peak_68 115 . 3.29470 16.84672 11.52580 204 +chr9 130894276 130894978 SRX5974535.05_peak_69 121 . 5.77863 17.50929 12.13842 90 +chr9 131126587 131126975 SRX5974535.05_peak_70 150 . 4.92792 20.65576 15.06501 152 +chr9 131179807 131180421 SRX5974535.05_peak_71 105 . 3.23695 15.81729 10.58395 494 +chr9 131278656 131279045 SRX5974535.05_peak_72 133 . 5.42601 18.81565 13.36212 240 +chrUn_GL000214v1 116839 117366 SRX5974535.05_peak_73 204 . 3.99606 26.33072 20.43178 107 +chrUn_GL000214v1 118049 118570 SRX5974535.05_peak_74 172 . 3.55854 22.93271 17.20682 203 +chrUn_GL000214v1 129215 129686 SRX5974535.05_peak_75 233 . 3.30868 29.39795 23.34617 283 +chrUn_GL000214v1 134628 135003 SRX5974535.05_peak_76 150 . 2.90903 20.67096 15.07840 143 +chrUn_GL000214v1 136364 136933 SRX5974535.05_peak_77 174 . 3.34844 23.21305 17.47168 82 +chrUn_GL000220v1 135027 135539 SRX5974535.05_peak_78 120 . 2.75000 17.46209 12.09401 245 +chrUn_GL000224v1 11677 12186 SRX5974535.05_peak_79 108 . 3.68248 16.10546 10.84457 268 +chrUn_GL000224v1 17910 19022 SRX5974535.05_peak_80 151 . 4.64190 20.77653 15.17527 278 +chrUn_KI270330v1 86 548 SRX5974535.05_peak_81 216 . 11.21941 27.63352 21.65655 269 +chrUn_KI270337v1 34 330 SRX5974535.05_peak_82 113 . 8.13249 16.65893 11.35403 193 +chrUn_KI270337v1 527 822 SRX5974535.05_peak_83 103 . 7.76283 15.54671 10.33847 243 +chrUn_KI270411v1 458 965 SRX5974535.05_peak_84 224 . 8.55245 28.47088 22.44988 231 +chrUn_KI270438v1 103851 105121 SRX5974535.05_peak_85 602 . 7.25233 66.90456 60.25167 252 +chrUn_KI270438v1 109351 110572 SRX5974535.05_peak_86 315 . 4.00712 37.83518 31.52333 953 +chrUn_KI270438v1 111261 112392 SRX5974535.05_peak_87 351 . 4.30166 41.55001 35.16132 222 +chrUn_KI270466v1 269 1159 SRX5974535.05_peak_88 236 . 9.55088 29.69891 23.63382 278 +chrUn_KI270467v1 1210 1509 SRX5974535.05_peak_89 200 . 5.08598 25.96318 20.07941 50 +chrUn_KI270467v1 1967 2797 SRX5974535.05_peak_90 445 . 7.47004 51.00939 44.50235 341 +chrUn_KI270744v1 110615 111043 SRX5974535.05_peak_91 120 . 3.63997 17.37446 12.01388 221 +chrUn_KI270744v1 120479 120805 SRX5974535.05_peak_92 92 . 2.42773 14.39824 9.29194 219 +chrUn_KI270744v1 124724 125375 SRX5974535.05_peak_93 234 . 3.98241 29.54922 23.49251 430 diff --git a/tests/data/hg38_sample/SRX5974585.05.bed b/tests/data/hg38_sample/SRX5974585.05.bed new file mode 100644 index 00000000..1dc7b7c1 --- /dev/null +++ b/tests/data/hg38_sample/SRX5974585.05.bed @@ -0,0 +1,867 @@ +chr1 632161 632245 SRX5974585.05_peak_1 104 . 5.41664 14.99623 10.47927 22 +chr1 633964 634055 SRX5974585.05_peak_2 180 . 6.96258 22.86968 18.09528 64 +chr1 2440439 2440517 SRX5974585.05_peak_3 157 . 11.72161 20.47304 15.77415 37 +chr1 91387284 91387520 SRX5974585.05_peak_4 141 . 10.73232 18.76138 14.11855 205 +chr1 121616050 121616123 SRX5974585.05_peak_5 189 . 11.74226 23.77976 18.97663 17 +chr1 121745342 121745428 SRX5974585.05_peak_6 325 . 11.49734 37.71072 32.51994 45 +chr1 121863305 121863382 SRX5974585.05_peak_7 246 . 13.84768 29.64780 24.67781 29 +chr1 122462772 122462846 SRX5974585.05_peak_8 201 . 13.10382 25.03288 20.19151 31 +chr1 122496555 122496633 SRX5974585.05_peak_9 296 . 17.12437 34.73276 29.62235 32 +chr1 122503867 122503945 SRX5974585.05_peak_10 239 . 10.92180 28.92774 23.97673 20 +chr1 122552812 122552916 SRX5974585.05_peak_11 533 . 13.77763 59.07351 53.37265 57 +chr1 122585655 122585728 SRX5974585.05_peak_12 128 . 6.80256 17.41860 12.82013 55 +chr1 122606218 122606338 SRX5974585.05_peak_13 571 . 16.00565 62.98989 57.18171 54 +chr1 122657355 122657432 SRX5974585.05_peak_14 260 . 9.65663 31.10339 26.09276 52 +chr1 122668674 122668774 SRX5974585.05_peak_15 501 . 13.61639 55.76217 50.15418 49 +chr1 122675324 122675398 SRX5974585.05_peak_16 168 . 7.62298 21.59840 16.86468 46 +chr1 122683743 122683816 SRX5974585.05_peak_17 184 . 7.46855 23.20856 18.42394 34 +chr1 122699762 122699888 SRX5974585.05_peak_18 210 . 8.84575 25.87917 21.01200 36 +chr1 122720421 122720497 SRX5974585.05_peak_19 239 . 10.01080 28.93270 23.98131 48 +chr1 122755140 122755214 SRX5974585.05_peak_20 208 . 9.31000 25.74956 20.88605 32 +chr1 122756177 122756267 SRX5974585.05_peak_21 65 . 5.44692 10.95170 6.59858 43 +chr1 122839416 122839491 SRX5974585.05_peak_22 207 . 8.27025 25.64948 20.78936 17 +chr1 122879950 122880023 SRX5974585.05_peak_23 202 . 8.24975 25.07476 20.23229 39 +chr1 122912751 122912850 SRX5974585.05_peak_24 366 . 11.63531 41.95020 36.65628 50 +chr1 122929291 122929379 SRX5974585.05_peak_25 193 . 8.57717 24.21113 19.39427 45 +chr1 123003220 123003296 SRX5974585.05_peak_26 223 . 9.49180 27.26479 22.35971 42 +chr1 123017470 123017550 SRX5974585.05_peak_27 233 . 9.17852 28.28975 23.35581 32 +chr1 123058868 123058989 SRX5974585.05_peak_28 525 . 14.94012 58.21509 52.53866 54 +chr1 123081052 123081125 SRX5974585.05_peak_29 179 . 8.38713 22.74496 17.97473 38 +chr1 123095520 123095606 SRX5974585.05_peak_30 180 . 8.66340 22.78197 18.01064 28 +chr1 123107639 123107720 SRX5974585.05_peak_31 289 . 10.98985 34.05170 28.96028 40 +chr1 123130492 123130570 SRX5974585.05_peak_32 249 . 9.94481 29.97806 24.99831 30 +chr1 123136214 123136287 SRX5974585.05_peak_33 142 . 7.52406 18.93339 14.28528 21 +chr1 123146928 123147007 SRX5974585.05_peak_34 225 . 9.03991 27.41668 22.50741 40 +chr1 123172983 123173064 SRX5974585.05_peak_35 277 . 9.63532 32.80994 27.75131 42 +chr1 123175948 123176048 SRX5974585.05_peak_36 729 . 17.35609 79.08598 72.93667 39 +chr1 123195670 123195805 SRX5974585.05_peak_37 1308 . 26.95597 137.74623 130.83258 69 +chr1 123225638 123225714 SRX5974585.05_peak_38 240 . 9.78498 29.04583 24.09119 34 +chr1 123238876 123238951 SRX5974585.05_peak_39 225 . 9.59967 27.49102 22.57989 43 +chr1 123347105 123347178 SRX5974585.05_peak_40 190 . 8.41164 23.85382 19.04830 21 +chr1 123352693 123352767 SRX5974585.05_peak_41 219 . 8.32082 26.82924 21.93560 25 +chr1 123382547 123382624 SRX5974585.05_peak_42 254 . 10.14739 30.41191 25.42144 54 +chr1 123390768 123390842 SRX5974585.05_peak_43 227 . 9.43045 27.70708 22.78830 43 +chr1 123393625 123393702 SRX5974585.05_peak_44 102 . 6.36570 14.74865 10.24090 59 +chr1 123428918 123428993 SRX5974585.05_peak_45 173 . 8.07260 22.07963 17.32873 20 +chr1 123459280 123459353 SRX5974585.05_peak_46 195 . 9.16972 24.34538 19.52450 44 +chr1 123519209 123519301 SRX5974585.05_peak_47 306 . 11.82846 35.80317 30.66216 26 +chr1 123595059 123595144 SRX5974585.05_peak_48 386 . 12.49646 43.94430 38.60390 54 +chr1 123635606 123635809 SRX5974585.05_peak_49 358 . 12.12298 41.07422 35.80222 56 +chr1 123642311 123642390 SRX5974585.05_peak_50 289 . 10.70612 34.06389 28.97154 47 +chr1 123648294 123648408 SRX5974585.05_peak_51 409 . 12.07503 46.34469 40.95523 48 +chr1 123714886 123714960 SRX5974585.05_peak_52 109 . 6.49951 15.45143 10.91869 37 +chr1 123720998 123721079 SRX5974585.05_peak_53 245 . 10.02365 29.55283 24.58512 51 +chr1 123730124 123730198 SRX5974585.05_peak_54 220 . 9.63211 26.98136 22.08417 51 +chr1 123783664 123783774 SRX5974585.05_peak_55 857 . 19.53987 92.19115 85.75768 54 +chr1 123831947 123832037 SRX5974585.05_peak_56 274 . 10.28019 32.51007 27.46018 53 +chr1 123848976 123849089 SRX5974585.05_peak_57 170 . 7.95890 21.83566 17.09337 55 +chr1 123892542 123892621 SRX5974585.05_peak_58 150 . 7.19813 19.67638 15.00362 18 +chr1 123926226 123926340 SRX5974585.05_peak_59 627 . 16.10375 68.72449 62.79909 56 +chr1 123930369 123930471 SRX5974585.05_peak_60 634 . 16.70052 69.36816 63.43090 50 +chr1 123985432 123985526 SRX5974585.05_peak_61 205 . 8.62009 25.38183 20.52929 51 +chr1 124005742 124005863 SRX5974585.05_peak_62 543 . 14.18526 60.11865 54.38786 59 +chr1 124007443 124007550 SRX5974585.05_peak_63 587 . 15.06055 64.58318 58.73747 56 +chr1 124044933 124045031 SRX5974585.05_peak_64 711 . 17.96582 77.28065 71.16187 52 +chr1 124056804 124056898 SRX5974585.05_peak_65 238 . 9.16242 28.81797 23.87033 46 +chr1 124098897 124099064 SRX5974585.05_peak_66 540 . 14.94835 59.74802 54.02560 67 +chr1 124132904 124132981 SRX5974585.05_peak_67 276 . 10.35247 32.66981 27.61628 44 +chr1 124168200 124168290 SRX5974585.05_peak_68 276 . 10.07949 32.66339 27.61000 58 +chr1 124199796 124199869 SRX5974585.05_peak_69 230 . 9.82292 27.95494 23.03036 37 +chr1 124250683 124250866 SRX5974585.05_peak_70 577 . 14.94427 63.53040 57.70606 41 +chr1 124277912 124277994 SRX5974585.05_peak_71 360 . 12.54673 41.32285 36.04296 45 +chr1 124344780 124344877 SRX5974585.05_peak_72 467 . 13.97852 52.27595 46.75478 34 +chr1 124377186 124377281 SRX5974585.05_peak_73 490 . 14.03306 54.59503 49.01781 45 +chr1 124400606 124400691 SRX5974585.05_peak_74 370 . 13.00873 42.30483 37.00189 33 +chr1 124421018 124421198 SRX5974585.05_peak_75 255 . 9.66984 30.54871 25.55411 131 +chr1 124434969 124435053 SRX5974585.05_peak_76 171 . 7.97294 21.86588 17.12268 43 +chr1 124455027 124455128 SRX5974585.05_peak_77 498 . 12.95584 55.49130 49.89091 44 +chr1 124456449 124456597 SRX5974585.05_peak_78 296 . 9.71413 34.76911 29.65786 42 +chr1 124474898 124474977 SRX5974585.05_peak_79 259 . 9.57812 30.92248 25.91770 29 +chr1 124507788 124507872 SRX5974585.05_peak_80 220 . 9.06900 26.92243 22.02646 44 +chr1 124514170 124514306 SRX5974585.05_peak_81 538 . 14.55511 59.55284 53.83541 63 +chr1 124549123 124549196 SRX5974585.05_peak_82 218 . 9.23246 26.71505 21.82432 31 +chr1 124562408 124562511 SRX5974585.05_peak_83 571 . 16.00565 62.98989 57.18171 61 +chr1 124579449 124579566 SRX5974585.05_peak_84 851 . 19.63085 91.54971 85.12749 54 +chr1 124634519 124634626 SRX5974585.05_peak_85 249 . 9.90884 29.90051 24.92276 56 +chr1 124737982 124738056 SRX5974585.05_peak_86 180 . 6.94493 22.82248 18.04977 37 +chr1 124741376 124741473 SRX5974585.05_peak_87 521 . 13.84139 57.77926 52.11355 52 +chr1 124757682 124757780 SRX5974585.05_peak_88 568 . 15.86931 62.67553 56.87415 40 +chr1 125069452 125069539 SRX5974585.05_peak_89 468 . 21.46910 52.36827 46.84417 40 +chr1 125166274 125166355 SRX5974585.05_peak_90 309 . 7.46147 36.05122 30.90222 34 +chr1 125166918 125167020 SRX5974585.05_peak_91 575 . 10.61831 63.38312 57.56315 49 +chr1 125167138 125167246 SRX5974585.05_peak_92 717 . 12.13521 77.84138 71.71122 59 +chr1 125167805 125168003 SRX5974585.05_peak_93 698 . 12.00409 75.96693 69.88758 68 +chr1 125179053 125179439 SRX5974585.05_peak_94 231 . 2.93206 28.03754 23.11039 232 +chr1 125179611 125179910 SRX5974585.05_peak_95 241 . 2.96869 29.12391 24.16728 85 +chr1 125180001 125180444 SRX5974585.05_peak_96 248 . 3.00105 29.78669 24.81229 182 +chr1 125180535 125180700 SRX5974585.05_peak_97 172 . 2.67463 22.03856 17.28903 39 +chr1 125182147 125182695 SRX5974585.05_peak_98 279 . 3.23992 33.01741 27.95265 427 +chr1 125182960 125183108 SRX5974585.05_peak_99 285 . 3.28462 33.64226 28.56136 66 +chr1 143184634 143184730 SRX5974585.05_peak_100 466 . 6.90635 52.20124 46.68230 51 +chr1 143184807 143184987 SRX5974585.05_peak_101 564 . 7.59206 62.22604 56.43879 115 +chr1 143185087 143185449 SRX5974585.05_peak_102 657 . 8.03223 71.71804 65.72659 71 +chr1 143187222 143187328 SRX5974585.05_peak_103 179 . 3.65536 22.70756 17.93786 34 +chr1 143187746 143187869 SRX5974585.05_peak_104 131 . 3.08009 17.74334 13.13406 86 +chr1 143191119 143191207 SRX5974585.05_peak_105 91 . 2.50071 13.64580 9.17817 56 +chr1 143191883 143191970 SRX5974585.05_peak_106 160 . 3.00636 20.75503 16.04716 44 +chr1 143192306 143192457 SRX5974585.05_peak_107 101 . 2.63496 14.60478 10.10325 42 +chr1 143192705 143192877 SRX5974585.05_peak_108 282 . 3.81417 33.35372 28.28139 104 +chr1 143193037 143193209 SRX5974585.05_peak_109 213 . 3.45038 26.21882 21.34102 69 +chr1 143193415 143193508 SRX5974585.05_peak_110 319 . 4.07222 37.11727 31.93995 39 +chr1 143193596 143193868 SRX5974585.05_peak_111 225 . 3.52527 27.42352 22.51397 80 +chr1 143194919 143195265 SRX5974585.05_peak_112 303 . 3.97255 35.53137 30.39807 96 +chr1 143195634 143195720 SRX5974585.05_peak_113 272 . 3.78689 32.28869 27.24525 52 +chr1 143200236 143200398 SRX5974585.05_peak_114 272 . 4.52086 32.34124 27.29674 50 +chr1 143200682 143200882 SRX5974585.05_peak_115 235 . 4.38025 28.51055 23.57047 70 +chr1 143201176 143201250 SRX5974585.05_peak_116 159 . 3.64642 20.64921 15.94409 41 +chr1 143201484 143201652 SRX5974585.05_peak_117 223 . 3.96936 27.23303 22.32850 133 +chr1 143202457 143202869 SRX5974585.05_peak_118 251 . 4.01471 30.10207 25.11961 372 +chr1 143203328 143203542 SRX5974585.05_peak_119 155 . 3.33618 20.22581 15.53505 36 +chr1 143206065 143206514 SRX5974585.05_peak_120 431 . 5.42839 48.59809 43.16195 42 +chr1 143207065 143207177 SRX5974585.05_peak_121 191 . 3.86618 24.01025 19.19955 32 +chr1 143212465 143212584 SRX5974585.05_peak_122 112 . 2.61114 15.74562 11.20339 29 +chr1 143212879 143212979 SRX5974585.05_peak_123 110 . 2.52391 15.56925 11.03303 66 +chr1 143213158 143213303 SRX5974585.05_peak_124 105 . 2.46890 15.11254 10.59125 31 +chr1 143214665 143214898 SRX5974585.05_peak_125 252 . 3.09747 30.20133 25.21565 117 +chr1 143215709 143215824 SRX5974585.05_peak_126 135 . 2.44408 18.17261 13.54876 68 +chr1 143217489 143217588 SRX5974585.05_peak_127 111 . 2.26636 15.73304 11.19143 35 +chr1 143220927 143221088 SRX5974585.05_peak_128 145 . 2.68841 19.21736 14.56047 42 +chr1 143222449 143222541 SRX5974585.05_peak_129 174 . 3.03900 22.24166 17.48597 43 +chr1 143227524 143227609 SRX5974585.05_peak_130 220 . 3.86589 26.94778 22.05143 41 +chr1 143227949 143228028 SRX5974585.05_peak_131 255 . 4.02784 30.49425 25.50099 42 +chr1 143230274 143230444 SRX5974585.05_peak_132 334 . 4.37670 38.64132 33.42852 42 +chr1 143231290 143231548 SRX5974585.05_peak_133 275 . 3.86794 32.57545 27.52417 129 +chr1 143232789 143233299 SRX5974585.05_peak_134 301 . 3.89839 35.30179 30.17586 179 +chr1 143233519 143233647 SRX5974585.05_peak_135 214 . 3.33154 26.36582 21.48450 99 +chr1 143238127 143238232 SRX5974585.05_peak_136 185 . 3.19960 23.29381 18.50703 66 +chr1 143239464 143239627 SRX5974585.05_peak_137 316 . 4.07158 36.82707 31.65651 67 +chr1 143239739 143239825 SRX5974585.05_peak_138 158 . 3.11105 20.59859 15.89550 55 +chr1 143240247 143240363 SRX5974585.05_peak_139 128 . 2.89479 17.47001 12.86959 20 +chr1 143245458 143245545 SRX5974585.05_peak_140 357 . 4.70394 40.99014 35.72000 47 +chr1 143246853 143246956 SRX5974585.05_peak_141 468 . 5.42564 52.39012 46.86483 46 +chr1 143247060 143247414 SRX5974585.05_peak_142 348 . 4.49637 40.07700 34.83170 260 +chr1 143249764 143250020 SRX5974585.05_peak_143 342 . 3.82402 39.51035 34.27911 67 +chr1 143251611 143251732 SRX5974585.05_peak_144 233 . 3.01213 28.23832 23.30631 34 +chr1 143251914 143252061 SRX5974585.05_peak_145 198 . 2.82977 24.64870 19.81877 27 +chr1 143252972 143253177 SRX5974585.05_peak_146 281 . 3.28877 33.26788 28.19740 63 +chr1 143253739 143253854 SRX5974585.05_peak_147 209 . 2.97191 25.81787 20.95279 24 +chr1 143254454 143254665 SRX5974585.05_peak_148 213 . 2.98754 26.20745 21.33016 142 +chr1 143254763 143254937 SRX5974585.05_peak_149 316 . 3.51495 36.78903 31.62021 99 +chr1 143255525 143255760 SRX5974585.05_peak_150 165 . 2.86910 21.30157 16.57617 26 +chr1 143255850 143256020 SRX5974585.05_peak_151 280 . 3.46703 33.12444 28.05751 45 +chr1 143256400 143256532 SRX5974585.05_peak_152 307 . 3.59326 35.88444 30.74137 76 +chr1 143256784 143256924 SRX5974585.05_peak_153 227 . 3.26747 27.61745 22.70149 39 +chr1 143262121 143262218 SRX5974585.05_peak_154 159 . 2.99706 20.66137 15.95590 21 +chr1 143262331 143262423 SRX5974585.05_peak_155 205 . 3.24264 25.42211 20.56830 33 +chr1 143262548 143262910 SRX5974585.05_peak_156 226 . 3.32367 27.60075 22.68505 208 +chr1 143263383 143263457 SRX5974585.05_peak_157 100 . 2.54739 14.52202 10.02324 55 +chr1 143263639 143263774 SRX5974585.05_peak_158 227 . 3.31009 27.66822 22.75126 94 +chr1 143264038 143264160 SRX5974585.05_peak_159 166 . 2.99313 21.37696 16.64908 93 +chr1 143264476 143264771 SRX5974585.05_peak_160 373 . 4.08354 42.63885 37.32721 193 +chr1 143265764 143265898 SRX5974585.05_peak_161 116 . 2.65246 16.16121 11.60442 105 +chr1 143266440 143266607 SRX5974585.05_peak_162 274 . 3.57183 32.47434 27.42587 119 +chr1 143266724 143266891 SRX5974585.05_peak_163 114 . 2.64037 16.03983 11.48720 30 +chr1 143270676 143270749 SRX5974585.05_peak_164 221 . 4.43361 27.00425 22.10616 29 +chr1 143273087 143273167 SRX5974585.05_peak_165 312 . 7.84234 36.41544 31.25743 36 +chr1 143275716 143275856 SRX5974585.05_peak_166 367 . 10.88139 42.03028 36.73493 40 +chr1 224013737 224013810 SRX5974585.05_peak_167 171 . 10.28468 21.89803 17.15311 20 +chr1 228558160 228558266 SRX5974585.05_peak_168 428 . 19.05194 48.26456 42.83687 50 +chr10 38487517 38487591 SRX5974585.05_peak_169 166 . 10.64470 21.40654 16.67772 22 +chr10 38489140 38489229 SRX5974585.05_peak_170 507 . 21.00840 56.41626 50.78761 45 +chr10 38573447 38573560 SRX5974585.05_peak_171 810 . 35.37432 87.41280 81.08631 58 +chr10 38578976 38579071 SRX5974585.05_peak_172 211 . 13.40795 26.05544 21.18198 40 +chr10 39951347 39951434 SRX5974585.05_peak_173 343 . 17.37975 39.53326 34.30130 41 +chr10 39962339 39962422 SRX5974585.05_peak_174 278 . 13.86729 32.90702 27.84586 42 +chr10 39966209 39966291 SRX5974585.05_peak_175 320 . 13.24674 37.24273 32.06347 32 +chr10 39967564 39967684 SRX5974585.05_peak_176 987 . 28.66475 105.35055 98.71735 63 +chr10 39986440 39986548 SRX5974585.05_peak_177 494 . 20.23268 55.07853 49.48825 61 +chr10 40029899 40029989 SRX5974585.05_peak_178 247 . 15.22167 29.77024 24.79685 39 +chr10 40046638 40046711 SRX5974585.05_peak_179 196 . 12.13224 24.42996 19.60591 25 +chr10 40098434 40098521 SRX5974585.05_peak_180 446 . 19.19637 50.07607 44.60773 49 +chr10 40128008 40128094 SRX5974585.05_peak_181 298 . 13.51648 34.99669 29.87951 37 +chr10 40131370 40131494 SRX5974585.05_peak_182 1209 . 33.40419 127.79978 120.94813 64 +chr10 40156249 40156355 SRX5974585.05_peak_183 461 . 20.09224 51.60728 46.10143 54 +chr10 40162842 40162926 SRX5974585.05_peak_184 179 . 11.72708 22.67928 17.91037 42 +chr10 40180851 40180928 SRX5974585.05_peak_185 238 . 13.36516 28.83940 23.89079 45 +chr10 40183558 40183634 SRX5974585.05_peak_186 169 . 10.79052 21.64891 16.91366 37 +chr10 40198988 40199072 SRX5974585.05_peak_187 208 . 12.54516 25.68339 20.82170 48 +chr10 40213432 40213536 SRX5974585.05_peak_188 546 . 22.83211 60.36988 54.63102 54 +chr10 40246771 40246865 SRX5974585.05_peak_189 414 . 19.57045 46.85223 41.45114 42 +chr10 40252639 40252731 SRX5974585.05_peak_190 239 . 14.13428 28.93197 23.98060 52 +chr10 40276435 40276510 SRX5974585.05_peak_191 206 . 12.07393 25.47612 20.62055 30 +chr10 40300911 40300992 SRX5974585.05_peak_192 243 . 13.28691 29.33039 24.36757 36 +chr10 40302757 40302847 SRX5974585.05_peak_193 224 . 12.49442 27.37852 22.47020 43 +chr10 40318249 40318345 SRX5974585.05_peak_194 401 . 17.43534 45.49213 40.11883 54 +chr10 40318531 40318604 SRX5974585.05_peak_195 142 . 9.44774 18.88732 14.24032 36 +chr10 40327009 40327100 SRX5974585.05_peak_196 257 . 14.52960 30.79314 25.79218 40 +chr10 40341026 40341106 SRX5974585.05_peak_197 175 . 11.50327 22.30439 17.54695 38 +chr10 40441087 40441203 SRX5974585.05_peak_198 475 . 20.00522 53.06055 47.52238 64 +chr10 40447268 40447375 SRX5974585.05_peak_199 872 . 29.88916 93.67103 87.21331 59 +chr10 40456431 40456504 SRX5974585.05_peak_200 164 . 11.15064 21.20592 16.48435 32 +chr10 40556742 40556934 SRX5974585.05_peak_201 1158 . 34.62421 122.62653 115.80285 127 +chr10 40606237 40606345 SRX5974585.05_peak_202 547 . 21.90157 60.46330 54.72151 51 +chr10 40614733 40614842 SRX5974585.05_peak_203 1006 . 31.91282 107.29791 100.62959 49 +chr10 40634206 40634295 SRX5974585.05_peak_204 378 . 18.29871 43.22107 37.89654 34 +chr10 40654444 40654556 SRX5974585.05_peak_205 576 . 23.68348 63.52083 57.69724 62 +chr10 40746825 40746914 SRX5974585.05_peak_206 337 . 17.45478 38.97442 33.75387 39 +chr10 40767808 40767885 SRX5974585.05_peak_207 172 . 11.01691 22.02506 17.27584 38 +chr10 40792109 40792229 SRX5974585.05_peak_208 837 . 28.97111 90.10597 83.71244 58 +chr10 40829682 40829776 SRX5974585.05_peak_209 301 . 14.48555 35.32251 30.19536 38 +chr10 40863045 40863127 SRX5974585.05_peak_210 231 . 13.25739 28.04931 23.12174 55 +chr10 40867627 40867709 SRX5974585.05_peak_211 232 . 12.95876 28.15835 23.22808 35 +chr10 40892230 40892304 SRX5974585.05_peak_212 172 . 10.34682 22.00331 17.25465 28 +chr10 40934857 40934959 SRX5974585.05_peak_213 501 . 21.58105 55.75328 50.14552 38 +chr10 40946153 40946232 SRX5974585.05_peak_214 240 . 13.80156 28.96118 24.00908 42 +chr10 41028760 41028846 SRX5974585.05_peak_215 431 . 18.76009 48.54363 43.10844 39 +chr10 41063091 41063207 SRX5974585.05_peak_216 752 . 28.40649 81.48730 75.28531 61 +chr10 41125359 41125453 SRX5974585.05_peak_217 486 . 20.66116 54.18464 48.61768 42 +chr10 41133840 41133921 SRX5974585.05_peak_218 312 . 15.52582 36.40457 31.24704 36 +chr10 41217362 41217437 SRX5974585.05_peak_219 203 . 12.59710 25.20797 20.36072 48 +chr10 41246476 41246549 SRX5974585.05_peak_220 176 . 11.25300 22.41742 17.65587 41 +chr10 41315479 41315586 SRX5974585.05_peak_221 803 . 30.33750 86.68718 80.38073 40 +chr10 41331733 41331851 SRX5974585.05_peak_222 699 . 25.27826 76.06374 69.98237 61 +chr10 41408363 41408443 SRX5974585.05_peak_223 273 . 13.96100 32.40252 27.35587 39 +chr10 41845658 41845743 SRX5974585.05_peak_224 270 . 7.61815 32.09686 27.05967 39 +chr10 41847443 41847547 SRX5974585.05_peak_225 634 . 11.00394 69.37251 63.43513 53 +chr10 41850765 41850853 SRX5974585.05_peak_226 187 . 5.29095 23.59489 18.79764 36 +chr10 41851813 41851896 SRX5974585.05_peak_227 205 . 5.45318 25.41283 20.55915 53 +chr10 41854538 41854665 SRX5974585.05_peak_228 477 . 8.35856 53.28450 47.73874 77 +chr10 41859774 41859984 SRX5974585.05_peak_229 510 . 8.86901 56.70573 51.07222 160 +chr10 41875394 41875489 SRX5974585.05_peak_230 588 . 10.17078 64.69886 58.84771 47 +chr10 41879292 41879505 SRX5974585.05_peak_231 313 . 6.09527 36.50962 31.35005 183 +chr10 41883145 41883254 SRX5974585.05_peak_232 465 . 7.66934 52.03808 46.52256 72 +chr10 41883389 41883522 SRX5974585.05_peak_233 318 . 6.20500 37.06892 31.89310 52 +chr10 41884032 41884115 SRX5974585.05_peak_234 176 . 4.63317 22.37345 17.61360 41 +chr10 41889451 41889591 SRX5974585.05_peak_235 545 . 9.15795 60.28455 54.54959 65 +chr10 41896885 41896967 SRX5974585.05_peak_236 178 . 5.22703 22.59085 17.82459 25 +chr10 41898878 41898982 SRX5974585.05_peak_237 411 . 7.92571 46.57280 41.17802 46 +chr10 41911234 41911307 SRX5974585.05_peak_238 226 . 6.08597 27.59059 22.67508 30 +chr10 41914641 41914736 SRX5974585.05_peak_239 229 . 7.20218 27.83028 22.90836 40 +chr10 42066644 42066721 SRX5974585.05_peak_240 214 . 7.18525 26.33624 21.45552 30 +chr10 42067153 42067327 SRX5974585.05_peak_241 277 . 8.00581 32.80850 27.74996 124 +chr10 42070568 42070692 SRX5974585.05_peak_242 153 . 4.94650 20.03268 15.34835 81 +chr10 42078366 42078509 SRX5974585.05_peak_243 229 . 5.94722 27.87323 22.95035 100 +chr10 42079829 42079911 SRX5974585.05_peak_244 169 . 5.27282 21.66189 16.92596 31 +chr10 42083856 42083953 SRX5974585.05_peak_245 260 . 6.52832 31.06308 26.05384 36 +chr10 42085286 42085385 SRX5974585.05_peak_246 459 . 9.24813 51.43949 45.93668 51 +chr10 42090749 42090853 SRX5974585.05_peak_247 394 . 8.53252 44.77171 39.41193 52 +chr10 42099810 42099921 SRX5974585.05_peak_248 600 . 11.11366 65.91458 60.04023 51 +chr10 42101631 42101715 SRX5974585.05_peak_249 154 . 5.59932 20.14683 15.45878 58 +chr10 42102582 42102666 SRX5974585.05_peak_250 301 . 8.13236 35.30577 30.17965 42 +chr10 42104296 42104378 SRX5974585.05_peak_251 328 . 9.00831 38.03336 32.83469 37 +chr10 133687799 133687875 SRX5974585.05_peak_252 136 . 5.27372 18.32479 13.69592 25 +chr10 133688232 133688365 SRX5974585.05_peak_253 594 . 11.49099 65.33274 59.46907 51 +chr10 133688666 133688752 SRX5974585.05_peak_254 376 . 8.80453 42.99428 37.67434 37 +chr10 133689190 133689475 SRX5974585.05_peak_255 785 . 13.80398 84.77821 78.51727 224 +chr10 133689563 133689876 SRX5974585.05_peak_256 401 . 9.22035 45.56822 40.19352 49 +chr10 133763127 133763202 SRX5974585.05_peak_257 207 . 10.75847 25.59997 20.74091 34 +chr10 133763692 133763773 SRX5974585.05_peak_258 220 . 11.20509 26.99634 22.09834 47 +chr11 3654409 3654590 SRX5974585.05_peak_259 152 . 10.04612 19.88329 15.20372 110 +chr11 52512982 52513055 SRX5974585.05_peak_260 144 . 10.45335 19.08381 14.43017 31 +chr11 53012511 53012584 SRX5974585.05_peak_261 165 . 11.91264 21.26487 16.54021 21 +chr11 53307702 53307810 SRX5974585.05_peak_262 438 . 21.42293 49.27799 43.82742 57 +chr11 53375345 53375423 SRX5974585.05_peak_263 224 . 14.12950 27.32376 22.41688 42 +chr11 53486823 53486905 SRX5974585.05_peak_264 163 . 11.79864 21.05065 16.33312 31 +chr11 53986965 53987085 SRX5974585.05_peak_265 699 . 30.44318 76.00341 69.92305 56 +chr11 54132952 54133044 SRX5974585.05_peak_266 251 . 15.43607 30.16464 25.18026 36 +chr12 2255948 2256037 SRX5974585.05_peak_267 203 . 13.77211 25.23444 20.38661 38 +chr12 34715915 34715998 SRX5974585.05_peak_268 264 . 15.28446 31.47600 26.45462 41 +chr12 37263699 37263781 SRX5974585.05_peak_269 195 . 11.41603 24.37403 19.55229 44 +chr12 131576840 131577276 SRX5974585.05_peak_270 313 . 14.39969 36.55890 31.39849 51 +chr13 16284485 16284717 SRX5974585.05_peak_271 329 . 14.91600 38.16604 32.96315 190 +chr13 16303653 16303730 SRX5974585.05_peak_272 181 . 9.31909 22.96937 18.19146 38 +chr13 16387645 16387721 SRX5974585.05_peak_273 191 . 10.18652 23.99424 19.18398 27 +chr13 16415932 16416015 SRX5974585.05_peak_274 266 . 12.38938 31.64005 26.61495 38 +chr13 16563849 16563938 SRX5974585.05_peak_275 277 . 12.30258 32.78879 27.73071 59 +chr13 16585628 16585701 SRX5974585.05_peak_276 116 . 7.84845 16.16608 11.60899 23 +chr13 16607730 16607837 SRX5974585.05_peak_277 540 . 17.88150 59.78735 54.06225 54 +chr13 16680027 16680101 SRX5974585.05_peak_278 193 . 9.95091 24.14260 19.32786 55 +chr13 16688963 16689156 SRX5974585.05_peak_279 531 . 17.01741 58.85482 53.15984 53 +chr13 16712674 16712747 SRX5974585.05_peak_280 207 . 10.78671 25.65019 20.78986 31 +chr13 16874126 16874236 SRX5974585.05_peak_281 196 . 10.45459 24.47157 19.64621 52 +chr13 16943454 16943552 SRX5974585.05_peak_282 448 . 16.66556 50.28071 44.80452 54 +chr13 16957540 16957619 SRX5974585.05_peak_283 146 . 9.08231 19.30447 14.64403 55 +chr13 17093703 17093780 SRX5974585.05_peak_284 206 . 10.38317 25.51495 20.65879 47 +chr13 17095352 17095460 SRX5974585.05_peak_285 248 . 11.08238 29.85238 24.87634 50 +chr13 17100184 17100262 SRX5974585.05_peak_286 210 . 10.59033 25.89234 21.02498 40 +chr13 17109539 17109614 SRX5974585.05_peak_287 209 . 10.24229 25.84300 20.97707 36 +chr13 17115383 17115472 SRX5974585.05_peak_288 268 . 12.14698 31.84900 26.81935 44 +chr13 17128290 17128399 SRX5974585.05_peak_289 349 . 14.05872 40.17080 34.92113 58 +chr13 17144789 17144901 SRX5974585.05_peak_290 586 . 17.80871 64.53946 58.69443 53 +chr13 17149463 17149556 SRX5974585.05_peak_291 251 . 10.60209 30.14272 25.15920 54 +chr13 17167521 17167611 SRX5974585.05_peak_292 352 . 13.88845 40.55727 35.29951 51 +chr13 17174057 17174148 SRX5974585.05_peak_293 355 . 13.32252 40.85809 35.59232 51 +chr13 17176793 17176866 SRX5974585.05_peak_294 156 . 8.20809 20.30253 15.60960 47 +chr13 17232003 17232102 SRX5974585.05_peak_295 531 . 17.81491 58.84357 53.14935 51 +chr13 17251709 17251785 SRX5974585.05_peak_296 163 . 9.17805 21.06764 16.34929 32 +chr13 17335028 17335148 SRX5974585.05_peak_297 319 . 13.56346 37.14363 31.96590 63 +chr13 17348174 17348258 SRX5974585.05_peak_298 301 . 12.93618 35.29713 30.17124 40 +chr13 17742836 17742928 SRX5974585.05_peak_299 275 . 11.85795 32.60420 27.55192 41 +chr13 18211789 18211928 SRX5974585.05_peak_300 510 . 15.24840 56.64031 51.00795 75 +chr13 18212057 18212183 SRX5974585.05_peak_301 921 . 22.62666 98.64711 92.10683 56 +chr13 18213005 18213095 SRX5974585.05_peak_302 448 . 14.09461 50.34922 44.87214 36 +chr13 109424183 109424322 SRX5974585.05_peak_303 378 . 20.75694 43.15119 37.82787 93 +chr14 16093340 16093427 SRX5974585.05_peak_304 337 . 14.96546 38.96521 33.74517 55 +chr14 16096255 16096337 SRX5974585.05_peak_305 342 . 13.30591 39.43116 34.20076 17 +chr14 16098389 16098480 SRX5974585.05_peak_306 363 . 14.07438 41.62583 36.33702 41 +chr14_GL000225v1_random 6807 6955 SRX5974585.05_peak_307 294 . 9.87309 34.57007 29.46432 68 +chr14_GL000225v1_random 14006 14087 SRX5974585.05_peak_308 298 . 8.36570 34.97574 29.85911 48 +chr14_GL000225v1_random 14381 14456 SRX5974585.05_peak_309 230 . 7.23322 27.92523 23.00155 55 +chr14_GL000225v1_random 15162 15251 SRX5974585.05_peak_310 222 . 6.98483 27.15771 22.25530 36 +chr14_GL000225v1_random 25575 25729 SRX5974585.05_peak_311 730 . 13.89944 79.22147 73.06759 72 +chr14_GL000225v1_random 33642 33779 SRX5974585.05_peak_312 522 . 13.88265 57.88341 52.21588 70 +chr14_GL000225v1_random 45971 46183 SRX5974585.05_peak_313 270 . 7.61046 32.07195 27.03563 37 +chr14_GL000225v1_random 50832 50959 SRX5974585.05_peak_314 669 . 13.10075 72.94179 66.91811 57 +chr14_GL000225v1_random 56092 56283 SRX5974585.05_peak_315 228 . 8.28484 27.80147 22.88049 134 +chr14_GL000225v1_random 66235 66314 SRX5974585.05_peak_316 166 . 5.38969 21.34963 16.62277 55 +chr14_GL000225v1_random 67498 67594 SRX5974585.05_peak_317 639 . 11.64030 69.86633 63.91901 36 +chr14_GL000225v1_random 70449 70528 SRX5974585.05_peak_318 256 . 6.53880 30.65616 25.65847 26 +chr14_GL000225v1_random 71150 71251 SRX5974585.05_peak_319 274 . 6.77833 32.49480 27.44522 41 +chr14_GL000225v1_random 74787 74889 SRX5974585.05_peak_320 670 . 13.57814 73.05460 67.02870 46 +chr14_GL000225v1_random 82150 82226 SRX5974585.05_peak_321 262 . 7.67482 31.26816 26.25285 54 +chr14_GL000225v1_random 85610 85743 SRX5974585.05_peak_322 1078 . 18.37466 114.62056 107.85777 70 +chr14_GL000225v1_random 86484 86598 SRX5974585.05_peak_323 272 . 7.99602 32.25757 27.21497 52 +chr14_GL000225v1_random 92863 92939 SRX5974585.05_peak_324 241 . 7.79082 29.07550 24.11987 24 +chr14_GL000225v1_random 99260 99447 SRX5974585.05_peak_325 290 . 7.91920 34.09325 29.00005 142 +chr14_GL000225v1_random 101653 101737 SRX5974585.05_peak_326 344 . 9.18358 39.70253 34.46529 42 +chr14_GL000225v1_random 102418 102547 SRX5974585.05_peak_327 277 . 8.17611 32.80043 27.74213 78 +chr14_GL000225v1_random 107264 107348 SRX5974585.05_peak_328 288 . 7.88720 33.98933 28.89981 47 +chr14_GL000225v1_random 108283 108357 SRX5974585.05_peak_329 153 . 5.70218 20.08168 15.39615 34 +chr14_GL000225v1_random 110485 110560 SRX5974585.05_peak_330 260 . 7.30780 31.07797 26.06820 31 +chr14_GL000225v1_random 111108 111205 SRX5974585.05_peak_331 208 . 6.52324 25.68094 20.81968 50 +chr14_GL000225v1_random 111484 111621 SRX5974585.05_peak_332 423 . 9.51123 47.73469 42.31771 75 +chr14_GL000225v1_random 117737 117844 SRX5974585.05_peak_333 558 . 11.17465 61.58671 55.81382 51 +chr14_GL000225v1_random 120595 120715 SRX5974585.05_peak_334 250 . 6.84940 29.98096 25.00115 53 +chr14_GL000225v1_random 122897 122976 SRX5974585.05_peak_335 308 . 8.01882 35.98259 30.83626 49 +chr14_GL000225v1_random 125133 125221 SRX5974585.05_peak_336 387 . 9.29910 44.10154 38.75665 36 +chr14_GL000225v1_random 130413 130598 SRX5974585.05_peak_337 162 . 5.18332 20.95074 16.23661 131 +chr14_GL000225v1_random 131559 131797 SRX5974585.05_peak_338 827 . 12.86687 89.12551 82.74731 188 +chr14_GL000225v1_random 133706 133864 SRX5974585.05_peak_339 575 . 10.44149 63.31997 57.50388 95 +chr14_GL000225v1_random 137675 137757 SRX5974585.05_peak_340 291 . 8.65363 34.20131 29.10578 41 +chr14_GL000225v1_random 196967 197043 SRX5974585.05_peak_341 222 . 13.04697 27.10213 22.20092 40 +chr14_KI270723v1_random 36768 36855 SRX5974585.05_peak_342 315 . 14.10751 36.73997 31.57303 43 +chr14_KI270724v1_random 521 606 SRX5974585.05_peak_343 199 . 11.63988 24.74968 19.91536 47 +chr14_KI270724v1_random 693 881 SRX5974585.05_peak_344 136 . 9.40144 18.30373 13.67554 94 +chr15 17080550 17080625 SRX5974585.05_peak_345 206 . 12.11500 25.54477 20.68779 46 +chr15 18735309 18735395 SRX5974585.05_peak_346 230 . 13.59477 28.02094 23.09403 37 +chr15 19373645 19373727 SRX5974585.05_peak_347 189 . 12.07222 23.78382 18.98061 30 +chr15 19681162 19681276 SRX5974585.05_peak_348 300 . 16.37376 35.16644 30.04333 61 +chr15 19691403 19691481 SRX5974585.05_peak_349 247 . 13.89785 29.73191 24.75955 43 +chr15 19776123 19776205 SRX5974585.05_peak_350 175 . 5.87822 22.26677 17.51058 49 +chr15 19778675 19778748 SRX5974585.05_peak_351 156 . 5.11307 20.32060 15.62701 41 +chr15 19782282 19782357 SRX5974585.05_peak_352 206 . 6.32447 25.45853 20.60358 41 +chr16 34062762 34062855 SRX5974585.05_peak_353 182 . 9.98894 23.07085 18.28950 51 +chr16 34071567 34071650 SRX5974585.05_peak_354 221 . 12.66714 27.06182 22.16248 46 +chr16 34097298 34097474 SRX5974585.05_peak_355 517 . 21.10886 57.41724 51.76634 116 +chr16 34454094 34454183 SRX5974585.05_peak_356 295 . 16.41664 34.61734 29.51012 49 +chr16 34572599 34572841 SRX5974585.05_peak_357 566 . 7.38779 62.43067 56.63510 125 +chr16 34573025 34573233 SRX5974585.05_peak_358 506 . 6.94953 56.22935 50.60545 51 +chr16 34573945 34574161 SRX5974585.05_peak_359 674 . 8.13909 73.47289 67.44130 91 +chr16 34574241 34574327 SRX5974585.05_peak_360 447 . 6.51127 50.21846 44.74327 44 +chr16 34575586 34575779 SRX5974585.05_peak_361 431 . 6.38606 48.53777 43.10422 45 +chr16 34576414 34576699 SRX5974585.05_peak_362 363 . 5.47291 41.65601 36.36655 59 +chr16 34581422 34581645 SRX5974585.05_peak_363 436 . 4.62265 49.06665 43.62029 39 +chr16 34582195 34582444 SRX5974585.05_peak_364 332 . 3.84524 38.44657 33.23851 211 +chr16 34582709 34582839 SRX5974585.05_peak_365 277 . 3.53942 32.81922 27.76039 88 +chr16 34582928 34583167 SRX5974585.05_peak_366 362 . 3.85915 41.53782 36.25058 178 +chr16 34583305 34583829 SRX5974585.05_peak_367 257 . 3.23160 30.79172 25.79096 351 +chr16 34586500 34586668 SRX5974585.05_peak_368 185 . 2.83578 23.34620 18.55722 47 +chr16 34586791 34586893 SRX5974585.05_peak_369 282 . 3.32799 33.36523 28.29236 36 +chr16 34587927 34588296 SRX5974585.05_peak_370 358 . 3.84542 41.09788 35.82509 144 +chr16 34590310 34590400 SRX5974585.05_peak_371 208 . 3.21218 25.72376 20.86080 43 +chr16 34592709 34592847 SRX5974585.05_peak_372 321 . 4.20839 37.32672 32.14476 71 +chr16 34593078 34593395 SRX5974585.05_peak_373 499 . 5.55913 55.51686 49.91626 183 +chr16 34593478 34593648 SRX5974585.05_peak_374 538 . 5.97520 59.59936 53.88054 71 +chr16 34595027 34595264 SRX5974585.05_peak_375 499 . 6.15332 55.57638 49.97449 193 +chr16 34595353 34595709 SRX5974585.05_peak_376 397 . 5.68748 45.12060 39.75423 126 +chr16 34894424 34894518 SRX5974585.05_peak_377 172 . 11.60029 21.96242 17.21514 34 +chr16 36558936 36559012 SRX5974585.05_peak_378 203 . 13.44344 25.19515 20.34878 45 +chr16 36724138 36724211 SRX5974585.05_peak_379 171 . 11.86099 21.94072 17.19458 50 +chr16 37265879 37265974 SRX5974585.05_peak_380 202 . 13.16104 25.13315 20.28880 41 +chr16 38265987 38266090 SRX5974585.05_peak_381 409 . 16.61620 46.33901 40.95000 46 +chr16 38267041 38267239 SRX5974585.05_peak_382 134 . 8.65605 18.05617 13.43529 151 +chr16 38276160 38276432 SRX5974585.05_peak_383 498 . 15.39211 55.43086 49.83109 103 +chr16 46380793 46381027 SRX5974585.05_peak_384 218 . 5.96525 26.70168 21.81132 46 +chr16 46386406 46386602 SRX5974585.05_peak_385 227 . 2.89578 27.68971 22.77180 27 +chr16 46386918 46387040 SRX5974585.05_peak_386 167 . 2.59839 21.43908 16.70962 29 +chr16 46387814 46388029 SRX5974585.05_peak_387 188 . 2.69604 23.60241 18.80505 24 +chr16 46388170 46388323 SRX5974585.05_peak_388 144 . 2.48284 19.10912 14.45466 65 +chr16 46388645 46388792 SRX5974585.05_peak_389 211 . 2.79445 26.02338 21.15197 32 +chr16 46388890 46389016 SRX5974585.05_peak_390 95 . 2.23144 14.03178 9.55021 15 +chr16 46389654 46390386 SRX5974585.05_peak_391 173 . 2.51686 22.10858 17.35722 690 +chr16 46390476 46390786 SRX5974585.05_peak_392 174 . 2.52165 22.18345 17.42918 67 +chr16 46391233 46391311 SRX5974585.05_peak_393 120 . 2.26896 16.57059 12.00027 17 +chr16 46394488 46395068 SRX5974585.05_peak_394 241 . 2.95741 29.14307 24.18559 312 +chr16 46398547 46399087 SRX5974585.05_peak_395 217 . 2.98838 26.59439 21.70710 363 +chr16 46399233 46399574 SRX5974585.05_peak_396 272 . 3.33165 32.31398 27.27011 309 +chr16 46399670 46399883 SRX5974585.05_peak_397 242 . 3.29232 29.20210 24.24290 160 +chr16 46400022 46400686 SRX5974585.05_peak_398 385 . 4.11990 43.92877 38.58878 108 +chr16 46400828 46400921 SRX5974585.05_peak_399 350 . 4.03166 40.34242 35.08945 48 +chr16 46401036 46401234 SRX5974585.05_peak_400 299 . 3.78019 35.07620 29.95665 69 +chr16 46401351 46401614 SRX5974585.05_peak_401 409 . 4.34830 46.30989 40.92157 83 +chr17 21960374 21960455 SRX5974585.05_peak_402 198 . 13.58177 24.71886 19.88554 38 +chr17 21969362 21969476 SRX5974585.05_peak_403 197 . 8.04573 24.59801 19.76916 57 +chr17 21973018 21973177 SRX5974585.05_peak_404 702 . 13.04009 76.29713 70.20979 106 +chr17 21976844 21976968 SRX5974585.05_peak_405 238 . 6.90806 28.78524 23.83864 43 +chr17 21980544 21980629 SRX5974585.05_peak_406 330 . 8.88166 38.21124 33.00671 59 +chr17 21983186 21983276 SRX5974585.05_peak_407 64 . 4.83244 10.74382 6.40088 9 +chr17 21985315 21985393 SRX5974585.05_peak_408 216 . 8.19934 26.53218 21.64660 58 +chr17 21988677 21988771 SRX5974585.05_peak_409 211 . 8.89231 25.98087 21.11055 46 +chr17 21989122 21989197 SRX5974585.05_peak_410 242 . 9.85720 29.19995 24.24079 37 +chr17 22521370 22521457 SRX5974585.05_peak_411 256 . 16.03148 30.64573 25.64853 36 +chr17 22880126 22880207 SRX5974585.05_peak_412 268 . 16.59994 31.91543 26.88329 41 +chr17 23016264 23016343 SRX5974585.05_peak_413 343 . 19.19259 39.61680 34.38115 39 +chr17 23256075 23256152 SRX5974585.05_peak_414 181 . 12.82722 22.97546 18.19728 61 +chr17 23583322 23583395 SRX5974585.05_peak_415 165 . 12.07268 21.25684 16.53253 35 +chr17 24150938 24151013 SRX5974585.05_peak_416 216 . 14.33631 26.48570 21.60102 49 +chr17 24188111 24188187 SRX5974585.05_peak_417 226 . 14.48989 27.54518 22.63148 52 +chr17 24421413 24421519 SRX5974585.05_peak_418 820 . 35.01659 88.39984 82.05545 46 +chr17 24740327 24740401 SRX5974585.05_peak_419 178 . 12.65728 22.58957 17.82332 30 +chr17 24752028 24752104 SRX5974585.05_peak_420 188 . 13.11666 23.68337 18.88352 23 +chr17 25074434 25074520 SRX5974585.05_peak_421 390 . 21.12868 44.35695 39.00591 43 +chr17 25115478 25115578 SRX5974585.05_peak_422 738 . 32.67768 80.02456 73.85023 53 +chr17 25139928 25140007 SRX5974585.05_peak_423 211 . 14.14638 26.05360 21.18054 42 +chr17 25383729 25383844 SRX5974585.05_peak_424 1240 . 44.94260 130.95364 124.07529 57 +chr17 25389856 25389951 SRX5974585.05_peak_425 703 . 31.56566 76.41106 70.31855 34 +chr17 25483117 25483198 SRX5974585.05_peak_426 185 . 12.97859 23.38867 18.59861 46 +chr17 25569184 25569295 SRX5974585.05_peak_427 957 . 37.77201 102.35490 95.76029 54 +chr17 25788586 25788670 SRX5974585.05_peak_428 375 . 20.80046 42.83621 37.51980 53 +chr17 25920151 25920264 SRX5974585.05_peak_429 1446 . 48.11941 152.89008 144.62938 57 +chr17 26101544 26101636 SRX5974585.05_peak_430 467 . 23.82528 52.28327 46.76057 50 +chr17 26299421 26299495 SRX5974585.05_peak_431 193 . 13.32938 24.14829 19.33273 42 +chr17 26494635 26494721 SRX5974585.05_peak_432 363 . 20.40370 41.65521 36.36577 28 +chr17 26567039 26567128 SRX5974585.05_peak_433 421 . 21.59827 47.53020 42.11835 39 +chr17 26587685 26587758 SRX5974585.05_peak_434 161 . 11.68679 20.84246 16.13186 16 +chr17 26594851 26594949 SRX5974585.05_peak_435 824 . 28.81844 88.86951 82.49829 50 +chr17 26596387 26596465 SRX5974585.05_peak_436 238 . 12.60663 28.80957 23.86232 29 +chr17 26603850 26603973 SRX5974585.05_peak_437 1625 . 40.80575 171.99385 162.50272 61 +chr17 26604051 26604127 SRX5974585.05_peak_438 257 . 11.57668 30.78937 25.78868 46 +chr17 26604208 26604300 SRX5974585.05_peak_439 536 . 18.50349 59.35986 53.64738 49 +chr17 26619662 26619902 SRX5974585.05_peak_440 544 . 20.32291 60.22487 54.49166 188 +chr17 26619978 26620083 SRX5974585.05_peak_441 822 . 27.02297 88.56925 82.21356 49 +chr17 26881302 26881396 SRX5974585.05_peak_442 501 . 12.04295 55.72680 50.12091 45 +chr17 26884033 26884111 SRX5974585.05_peak_443 234 . 7.89659 28.34712 23.41106 39 +chr17 26884195 26884283 SRX5974585.05_peak_444 476 . 11.87465 53.21930 47.67564 40 +chr17 26885384 26885481 SRX5974585.05_peak_445 383 . 10.55112 43.66938 38.33615 45 +chr17 26885726 26885831 SRX5974585.05_peak_446 678 . 15.10252 73.87576 67.83641 56 +chr17 26936800 26936923 SRX5974585.05_peak_447 415 . 16.93576 46.91086 41.50811 67 +chr17_KI270729v1_random 961 1100 SRX5974585.05_peak_448 466 . 7.70145 52.20190 46.68291 67 +chr17_KI270729v1_random 3436 3637 SRX5974585.05_peak_449 788 . 10.30223 85.10432 78.83486 145 +chr17_KI270729v1_random 5787 5948 SRX5974585.05_peak_450 303 . 6.13743 35.44847 30.31667 49 +chr17_KI270729v1_random 10482 10558 SRX5974585.05_peak_451 287 . 9.81477 33.83339 28.74740 47 +chr17_KI270729v1_random 11125 11217 SRX5974585.05_peak_452 459 . 15.70589 51.50080 45.99652 40 +chr17_KI270729v1_random 13043 13188 SRX5974585.05_peak_453 702 . 21.50075 76.31432 70.22596 80 +chr17_KI270729v1_random 22425 22584 SRX5974585.05_peak_454 627 . 12.06769 68.71720 62.79274 53 +chr17_KI270729v1_random 23529 23607 SRX5974585.05_peak_455 260 . 6.75212 31.01367 26.00622 27 +chr17_KI270729v1_random 24642 24783 SRX5974585.05_peak_456 473 . 9.32974 52.91507 47.37966 82 +chr17_KI270729v1_random 27744 27871 SRX5974585.05_peak_457 776 . 15.61864 83.90266 77.65629 59 +chr17_KI270729v1_random 43359 43440 SRX5974585.05_peak_458 257 . 14.52960 30.79314 25.79218 49 +chr17_KI270729v1_random 47179 47256 SRX5974585.05_peak_459 266 . 14.65979 31.63458 26.60968 46 +chr17_KI270729v1_random 54653 54760 SRX5974585.05_peak_460 277 . 15.33905 32.77945 27.72189 53 +chr17_KI270729v1_random 157537 157636 SRX5974585.05_peak_461 665 . 23.80766 72.55492 66.54379 54 +chr17_KI270729v1_random 161008 161087 SRX5974585.05_peak_462 269 . 13.69922 31.95765 26.92479 40 +chr17_KI270729v1_random 240734 240811 SRX5974585.05_peak_463 288 . 17.21052 33.98672 28.89725 39 +chr17_KI270730v1_random 41498 41588 SRX5974585.05_peak_464 386 . 20.92050 43.94881 38.60744 44 +chr17_KI270730v1_random 107515 107593 SRX5974585.05_peak_465 148 . 9.20952 19.52353 14.85628 28 +chr18 107897 107982 SRX5974585.05_peak_466 360 . 6.79485 41.34548 36.06501 44 +chr18 108105 108294 SRX5974585.05_peak_467 667 . 9.56312 72.74396 66.72426 103 +chr18 108391 108573 SRX5974585.05_peak_468 413 . 7.30264 46.70320 41.30626 57 +chr18 108832 108919 SRX5974585.05_peak_469 286 . 6.03986 33.70531 28.62260 59 +chr18 109757 109830 SRX5974585.05_peak_470 146 . 4.45146 19.27325 14.61411 23 +chr18 110277 110470 SRX5974585.05_peak_471 173 . 4.77571 22.05300 17.30322 123 +chr18 16431034 16431123 SRX5974585.05_peak_472 388 . 20.45624 44.20622 38.85925 32 +chr18 20561901 20561974 SRX5974585.05_peak_473 187 . 11.57687 23.50436 18.71104 22 +chr18 20562269 20562393 SRX5974585.05_peak_474 89 . 7.80031 13.40257 8.94579 114 +chr18 52792764 52792851 SRX5974585.05_peak_475 311 . 18.17292 36.29715 31.14189 52 +chr19 7450685 7450819 SRX5974585.05_peak_476 209 . 12.63775 25.83815 20.97227 17 +chr19 7450925 7450998 SRX5974585.05_peak_477 195 . 12.08764 24.35550 19.53420 42 +chr19 27241119 27241196 SRX5974585.05_peak_478 191 . 13.14606 23.94432 19.13575 33 +chr1_KI270709v1_random 71 231 SRX5974585.05_peak_479 116 . 3.48864 16.25321 11.69301 138 +chr1_KI270709v1_random 3270 3375 SRX5974585.05_peak_480 142 . 2.99316 18.92196 14.27413 33 +chr1_KI270709v1_random 3753 3847 SRX5974585.05_peak_481 204 . 3.31823 25.34865 20.49758 63 +chr1_KI270709v1_random 4183 4342 SRX5974585.05_peak_482 153 . 2.97531 20.06757 15.38256 39 +chr1_KI270709v1_random 5580 5673 SRX5974585.05_peak_483 197 . 3.16766 24.61522 19.78602 32 +chr1_KI270709v1_random 7177 7275 SRX5974585.05_peak_484 65 . 2.26196 10.88043 6.53064 92 +chr1_KI270709v1_random 7380 7579 SRX5974585.05_peak_485 289 . 3.56230 34.05101 28.95991 149 +chr1_KI270709v1_random 11323 11455 SRX5974585.05_peak_486 69 . 2.29324 11.29091 6.92203 114 +chr1_KI270709v1_random 11807 11884 SRX5974585.05_peak_487 148 . 2.82962 19.46944 14.80410 25 +chr1_KI270709v1_random 13427 13506 SRX5974585.05_peak_488 143 . 2.97797 18.96939 14.31996 48 +chr1_KI270709v1_random 15026 15210 SRX5974585.05_peak_489 223 . 3.71464 27.22582 22.32164 51 +chr1_KI270709v1_random 15533 15639 SRX5974585.05_peak_490 293 . 4.21498 34.44017 29.33814 40 +chr1_KI270709v1_random 15803 15891 SRX5974585.05_peak_491 209 . 3.73416 25.86235 20.99598 27 +chr1_KI270709v1_random 16120 16195 SRX5974585.05_peak_492 109 . 2.98790 15.47772 10.94428 19 +chr1_KI270709v1_random 17159 17258 SRX5974585.05_peak_493 236 . 4.30588 28.59847 23.65636 62 +chr2 32916224 32916603 SRX5974585.05_peak_494 577 . 22.21491 63.57200 57.74532 54 +chr2 89822503 89822577 SRX5974585.05_peak_495 189 . 9.41620 23.71048 18.90947 47 +chr2 89826445 89826521 SRX5974585.05_peak_496 244 . 6.82458 29.42983 24.46507 31 +chr2 89828599 89828695 SRX5974585.05_peak_497 322 . 6.96714 37.46419 32.27767 42 +chr2 89829185 89829281 SRX5974585.05_peak_498 253 . 6.03386 30.32128 25.33237 60 +chr2 89829436 89829511 SRX5974585.05_peak_499 268 . 6.20108 31.89768 26.86720 49 +chr2 89830947 89831081 SRX5974585.05_peak_500 486 . 8.31872 54.17722 48.61149 41 +chr2 89833267 89833357 SRX5974585.05_peak_501 366 . 6.92201 41.96431 36.67015 51 +chr2 89836485 89836566 SRX5974585.05_peak_502 305 . 6.66375 35.68854 30.55090 41 +chr2 89839654 89839730 SRX5974585.05_peak_503 247 . 7.18138 29.69112 24.72044 23 +chr2 89840281 89840383 SRX5974585.05_peak_504 705 . 13.35113 76.65121 70.55136 46 +chr2 89840474 89840551 SRX5974585.05_peak_505 192 . 6.43663 24.07213 19.25933 50 +chr2 89841048 89841158 SRX5974585.05_peak_506 623 . 12.76015 68.28493 62.36678 58 +chr2 90380755 90380855 SRX5974585.05_peak_507 109 . 4.82720 15.46964 10.93649 58 +chr2 90381074 90381161 SRX5974585.05_peak_508 133 . 5.06916 18.00842 13.38930 63 +chr2 90381613 90381692 SRX5974585.05_peak_509 131 . 4.90464 17.78910 13.17790 46 +chr2 90382588 90382726 SRX5974585.05_peak_510 80 . 3.91999 12.42309 8.00490 40 +chr2 90390693 90390784 SRX5974585.05_peak_511 116 . 4.10449 16.23249 11.67334 54 +chr2 90397756 90397842 SRX5974585.05_peak_512 118 . 4.01958 16.42179 11.85633 40 +chr2 90401110 90401259 SRX5974585.05_peak_513 112 . 4.44972 15.81451 11.26950 39 +chr2 91421922 91422072 SRX5974585.05_peak_514 216 . 10.32121 26.58166 21.69455 52 +chr2 91422276 91422387 SRX5974585.05_peak_515 173 . 9.15375 22.10933 17.35789 46 +chr2 92270855 92270932 SRX5974585.05_peak_516 202 . 13.38091 25.08049 20.23775 31 +chr2 109199364 109199822 SRX5974585.05_peak_517 238 . 13.36516 28.83940 23.89079 57 +chr2 148881746 148881826 SRX5974585.05_peak_518 255 . 15.65660 30.57529 25.57990 29 +chr20 27504678 27504751 SRX5974585.05_peak_519 116 . 9.64754 16.18010 11.62267 18 +chr20 28308112 28308210 SRX5974585.05_peak_520 556 . 26.63791 61.43238 55.66650 42 +chr20 28495436 28495569 SRX5974585.05_peak_521 226 . 8.83802 27.51316 22.60063 88 +chr20 28495767 28495993 SRX5974585.05_peak_522 205 . 8.40968 25.44361 20.58920 173 +chr20 28496873 28496946 SRX5974585.05_peak_523 167 . 7.57340 21.48660 16.75624 36 +chr20 29411369 29411532 SRX5974585.05_peak_524 93 . 8.60357 13.79062 9.31789 108 +chr20 31052041 31052167 SRX5974585.05_peak_525 284 . 9.24581 33.57399 28.49605 64 +chr20 31054689 31054769 SRX5974585.05_peak_526 154 . 5.58340 20.09496 15.40876 31 +chr20 31055973 31056059 SRX5974585.05_peak_527 217 . 6.06629 26.67189 21.78228 58 +chr20 31064030 31064158 SRX5974585.05_peak_528 118 . 4.53462 16.44587 11.87945 22 +chr20 31075382 31075467 SRX5974585.05_peak_529 329 . 9.88957 38.19807 32.99455 36 +chr20 31075615 31075688 SRX5974585.05_peak_530 172 . 6.98735 21.99920 17.25079 36 +chr20 31106012 31106089 SRX5974585.05_peak_531 196 . 13.23627 24.42895 19.60499 32 +chr20 31106773 31106866 SRX5974585.05_peak_532 435 . 22.72120 49.02127 43.57526 40 +chr20 31157738 31157890 SRX5974585.05_peak_533 589 . 23.42127 64.79656 58.94291 106 +chr20 31166685 31166769 SRX5974585.05_peak_534 272 . 14.29889 32.31542 27.27142 39 +chr20 31185339 31185443 SRX5974585.05_peak_535 229 . 8.74853 27.85447 22.93204 52 +chr20 31185792 31185866 SRX5974585.05_peak_536 179 . 7.65778 22.67236 17.90370 52 +chr20 31186467 31186670 SRX5974585.05_peak_537 258 . 9.31611 30.88475 25.88087 40 +chr20 31186791 31186883 SRX5974585.05_peak_538 302 . 10.19455 35.34937 30.22162 52 +chr20 31187175 31187263 SRX5974585.05_peak_539 292 . 10.01321 34.31090 29.21235 42 +chr20 31241582 31241923 SRX5974585.05_peak_540 204 . 11.28168 25.32205 20.47146 49 +chr21 7926141 7926232 SRX5974585.05_peak_541 442 . 13.82019 49.73892 44.27623 30 +chr21 7927249 7927322 SRX5974585.05_peak_542 210 . 8.84575 25.87917 21.01200 39 +chr21 7938277 7938430 SRX5974585.05_peak_543 569 . 17.72152 62.72353 56.92081 89 +chr21 7942300 7942373 SRX5974585.05_peak_544 203 . 9.31532 25.19708 20.35041 42 +chr21 7944445 7944524 SRX5974585.05_peak_545 253 . 11.36328 30.38709 25.39697 35 +chr21 9246758 9246835 SRX5974585.05_peak_546 133 . 8.91663 17.99604 13.37728 22 +chr21 9821570 9821649 SRX5974585.05_peak_547 101 . 6.74685 14.63074 10.12820 65 +chr21 9837206 9837283 SRX5974585.05_peak_548 191 . 12.72959 23.91054 19.10335 24 +chr21 10270196 10270291 SRX5974585.05_peak_549 441 . 18.45700 49.58807 44.12947 50 +chr21 10274001 10274202 SRX5974585.05_peak_550 441 . 18.45700 49.58807 44.12947 158 +chr21 10655547 10655641 SRX5974585.05_peak_551 681 . 21.80138 74.23392 68.18463 53 +chr21 10668941 10669016 SRX5974585.05_peak_552 225 . 10.49027 27.49910 22.58733 48 +chr21 10673241 10673324 SRX5974585.05_peak_553 316 . 13.03159 36.83529 31.66434 46 +chr21 10692726 10693080 SRX5974585.05_peak_554 997 . 22.79358 106.44214 99.78859 118 +chr21 10705259 10705336 SRX5974585.05_peak_555 142 . 8.83815 18.88158 14.23488 8 +chr22 11211820 11211898 SRX5974585.05_peak_556 237 . 13.27266 28.68445 23.74002 65 +chr22 12692114 12692214 SRX5974585.05_peak_557 265 . 13.44707 31.52774 26.50502 42 +chr22 16347224 16347305 SRX5974585.05_peak_558 177 . 9.96972 22.47302 17.70999 45 +chr22 16352771 16352902 SRX5974585.05_peak_559 915 . 23.21862 98.11339 91.58034 68 +chr22 18205554 18205644 SRX5974585.05_peak_560 479 . 23.02423 53.51820 47.96718 35 +chr22 18237587 18237680 SRX5974585.05_peak_561 277 . 16.33884 32.78580 27.72788 46 +chr22 18386049 18386126 SRX5974585.05_peak_562 200 . 13.49710 24.92796 20.08891 45 +chr22 18419168 18419241 SRX5974585.05_peak_563 126 . 9.86823 17.24489 12.65212 14 +chr22 18730545 18730669 SRX5974585.05_peak_564 439 . 19.25057 49.38258 43.92966 68 +chr22 18733681 18733777 SRX5974585.05_peak_565 375 . 17.64651 42.85117 37.53449 58 +chr22 18892460 18892552 SRX5974585.05_peak_566 587 . 24.34368 64.64682 58.79736 38 +chr22 18896435 18896514 SRX5974585.05_peak_567 258 . 14.18856 30.84343 25.84059 23 +chr22 23485672 23485754 SRX5974585.05_peak_568 375 . 20.80046 42.83621 37.51980 24 +chr22 38644380 38644514 SRX5974585.05_peak_569 148 . 11.24069 19.46555 14.80027 55 +chr22_KI270731v1_random 127205 127284 SRX5974585.05_peak_570 200 . 13.25758 24.85563 20.01826 31 +chr22_KI270734v1_random 121464 121592 SRX5974585.05_peak_571 90 . 7.63565 13.54642 9.08317 18 +chr22_KI270734v1_random 121683 121792 SRX5974585.05_peak_572 372 . 17.46885 42.55001 37.24112 45 +chr22_KI270735v1_random 39758 39871 SRX5974585.05_peak_573 249 . 11.13241 29.94807 24.96871 51 +chr22_KI270735v1_random 40932 41031 SRX5974585.05_peak_574 86 . 6.60197 13.07621 8.63111 58 +chr22_KI270736v1_random 118661 118734 SRX5974585.05_peak_575 153 . 11.53652 20.07708 15.39166 35 +chr22_KI270736v1_random 162583 162656 SRX5974585.05_peak_576 185 . 11.15573 23.36411 18.57467 28 +chr22_KI270736v1_random 181123 181196 SRX5974585.05_peak_577 145 . 9.34959 19.23926 14.58098 27 +chr22_KI270736v1_random 181428 181505 SRX5974585.05_peak_578 122 . 8.53658 16.86127 12.28091 49 +chr22_KI270737v1_random 25915 26067 SRX5974585.05_peak_579 815 . 22.73696 87.84341 81.50761 66 +chr22_KI270737v1_random 31040 31120 SRX5974585.05_peak_580 262 . 10.28677 31.31224 26.29584 35 +chr22_KI270737v1_random 32027 32102 SRX5974585.05_peak_581 177 . 8.27960 22.51903 17.75482 40 +chr22_KI270737v1_random 32911 33043 SRX5974585.05_peak_582 404 . 13.34643 45.83208 40.45231 57 +chr22_KI270737v1_random 38499 38625 SRX5974585.05_peak_583 469 . 16.57649 52.44703 46.92053 59 +chr22_KI270737v1_random 80009 80089 SRX5974585.05_peak_584 243 . 11.44396 29.28168 24.32021 27 +chr3 75669157 75669372 SRX5974585.05_peak_585 621 . 23.30907 68.10519 62.18936 149 +chr3 93470363 93470799 SRX5974585.05_peak_586 1375 . 21.21947 145.18062 137.53081 195 +chr3 196898762 196898876 SRX5974585.05_peak_587 584 . 27.78675 64.26297 58.42330 62 +chr4 49092056 49092134 SRX5974585.05_peak_588 258 . 6.36198 30.84089 25.83823 40 +chr4 49092762 49092872 SRX5974585.05_peak_589 532 . 8.51869 58.94175 53.24364 52 +chr4 49096794 49096887 SRX5974585.05_peak_590 495 . 6.57815 55.15036 49.55910 45 +chr4 49097173 49097359 SRX5974585.05_peak_591 155 . 3.90715 20.24276 15.55161 91 +chr4 49102474 49102607 SRX5974585.05_peak_592 171 . 3.71539 21.88656 17.14232 84 +chr4 49103255 49103386 SRX5974585.05_peak_593 157 . 3.51015 20.41000 15.71321 106 +chr4 49103555 49103647 SRX5974585.05_peak_594 299 . 4.50441 35.07539 29.95589 55 +chr4 49107400 49107473 SRX5974585.05_peak_595 207 . 3.70561 25.62405 20.76449 46 +chr4 49108293 49108531 SRX5974585.05_peak_596 229 . 3.98318 27.89518 22.97201 70 +chr4 49108676 49108753 SRX5974585.05_peak_597 84 . 2.85957 12.91895 8.48047 11 +chr4 49110697 49110817 SRX5974585.05_peak_598 316 . 4.90490 36.85204 31.68064 53 +chr4 49112367 49112447 SRX5974585.05_peak_599 227 . 4.44222 27.68533 22.76749 51 +chr4 49118866 49119038 SRX5974585.05_peak_600 476 . 6.70046 53.23681 47.69291 74 +chr4 49119856 49119960 SRX5974585.05_peak_601 79 . 3.13993 12.32531 7.91161 93 +chr4 49120133 49120297 SRX5974585.05_peak_602 279 . 5.12159 33.00644 27.94243 71 +chr4 49120920 49121040 SRX5974585.05_peak_603 138 . 3.79161 18.50247 13.86790 40 +chr4 49137190 49137298 SRX5974585.05_peak_604 563 . 8.83790 62.17927 56.39271 60 +chr4 49139409 49139484 SRX5974585.05_peak_605 285 . 6.11705 33.65034 28.56922 51 +chr4 49139662 49139792 SRX5974585.05_peak_606 314 . 6.28307 36.59427 31.43294 66 +chr4 49141851 49141942 SRX5974585.05_peak_607 368 . 6.58593 42.11501 36.81770 43 +chr4 49142148 49142226 SRX5974585.05_peak_608 164 . 4.58502 21.16507 16.44490 52 +chr4 49144611 49144768 SRX5974585.05_peak_609 198 . 4.57297 24.65983 19.82933 24 +chr4 49147288 49147437 SRX5974585.05_peak_610 376 . 6.25795 42.96128 37.64195 46 +chr4 49149347 49149519 SRX5974585.05_peak_611 586 . 7.55444 64.50398 58.65954 113 +chr4 49149729 49149826 SRX5974585.05_peak_612 507 . 6.97503 56.39265 50.76531 54 +chr4 49150473 49150558 SRX5974585.05_peak_613 227 . 4.69348 27.64264 22.72614 58 +chr4 49153200 49153273 SRX5974585.05_peak_614 269 . 5.39152 31.97925 26.94546 35 +chr4 49154418 49154574 SRX5974585.05_peak_615 478 . 7.92908 53.34848 47.80148 60 +chr4 49156447 49156540 SRX5974585.05_peak_616 310 . 7.63738 36.19614 31.04321 40 +chr4 49632650 49632756 SRX5974585.05_peak_617 249 . 7.57745 29.96096 24.98138 54 +chr4 49632894 49633003 SRX5974585.05_peak_618 260 . 7.59454 31.01643 26.00891 56 +chr4 49635502 49635577 SRX5974585.05_peak_619 182 . 5.62347 22.98006 18.20168 51 +chr4 49638061 49638145 SRX5974585.05_peak_620 294 . 7.06464 34.54342 29.43844 48 +chr4 49651374 49651513 SRX5974585.05_peak_621 348 . 8.61222 40.12019 34.87142 82 +chr4 49652458 49652532 SRX5974585.05_peak_622 234 . 6.94287 28.43283 23.49420 20 +chr4 49657513 49657741 SRX5974585.05_peak_623 889 . 19.45525 95.44612 88.95628 66 +chr4 49709344 49709556 SRX5974585.05_peak_624 814 . 10.10810 87.73910 81.40632 71 +chr4 49709693 49709952 SRX5974585.05_peak_625 761 . 9.69356 82.36551 76.14691 62 +chr4 49710533 49710875 SRX5974585.05_peak_626 277 . 5.61730 32.80278 27.74445 192 +chr4 49711349 49711719 SRX5974585.05_peak_627 584 . 8.30000 64.30250 58.46225 196 +chr4 49711817 49711915 SRX5974585.05_peak_628 388 . 6.64407 44.23651 38.88920 64 +chr4 49972050 49972136 SRX5974585.05_peak_629 177 . 12.39481 22.49855 17.73484 51 +chr4 50556082 50556198 SRX5974585.05_peak_630 878 . 34.24838 94.34050 87.86931 61 +chr4 50662307 50662395 SRX5974585.05_peak_631 318 . 17.81546 37.07319 31.89701 32 +chr4 50809383 50809477 SRX5974585.05_peak_632 309 . 17.86896 36.12355 30.97222 44 +chr4 51080550 51080623 SRX5974585.05_peak_633 145 . 10.98546 19.22292 14.56557 19 +chr4 51107288 51107545 SRX5974585.05_peak_634 505 . 21.80508 56.13444 50.51476 194 +chr4 51125287 51125364 SRX5974585.05_peak_635 202 . 13.38091 25.08049 20.23775 26 +chr4 189881308 189881470 SRX5974585.05_peak_636 111 . 9.49474 15.66524 11.12555 27 +chr4 190022407 190022579 SRX5974585.05_peak_637 271 . 13.08346 32.21612 27.17497 61 +chr4 190178345 190178473 SRX5974585.05_peak_638 466 . 9.13252 52.13903 46.62107 52 +chr4 190178780 190178862 SRX5974585.05_peak_639 369 . 8.01880 42.26209 36.95977 33 +chr4 190179473 190179961 SRX5974585.05_peak_640 744 . 12.13674 80.58434 74.40134 202 +chr4 190181026 190181145 SRX5974585.05_peak_641 199 . 5.88222 24.73730 19.90339 72 +chr5 47297044 47297131 SRX5974585.05_peak_642 372 . 17.06933 42.60881 37.29808 48 +chr5 49601109 49601295 SRX5974585.05_peak_643 548 . 8.10757 60.57411 54.82687 41 +chr5 49601503 49602230 SRX5974585.05_peak_644 791 . 10.01973 85.40661 79.13243 61 +chr5 49602318 49602778 SRX5974585.05_peak_645 431 . 7.11325 48.63456 43.19692 152 +chr5 49612303 49612471 SRX5974585.05_peak_646 128 . 5.61045 17.44036 12.84106 95 +chr5 49615463 49615559 SRX5974585.05_peak_647 360 . 9.32303 41.28303 36.00517 40 +chr5 49616453 49616530 SRX5974585.05_peak_648 229 . 7.54802 27.87561 22.95271 49 +chr5 49617015 49617091 SRX5974585.05_peak_649 178 . 6.55298 22.64679 17.87890 45 +chr5 49626835 49626976 SRX5974585.05_peak_650 123 . 5.57511 16.95175 12.36869 39 +chr5 49630766 49631069 SRX5974585.05_peak_651 383 . 11.55330 43.72485 38.38993 250 +chr5 49633664 49633751 SRX5974585.05_peak_652 319 . 12.15235 37.12637 31.94878 43 +chr5 49642256 49642350 SRX5974585.05_peak_653 379 . 9.96865 43.23418 37.90921 44 +chr5 49643716 49643789 SRX5974585.05_peak_654 166 . 6.26145 21.36300 16.63577 34 +chr5 49644054 49644191 SRX5974585.05_peak_655 322 . 8.80857 37.42828 32.24247 65 +chr5 49644956 49645038 SRX5974585.05_peak_656 392 . 9.81725 44.58101 39.22522 44 +chr5 49645962 49646209 SRX5974585.05_peak_657 486 . 11.10311 54.21000 48.64263 55 +chr5 49657203 49657299 SRX5974585.05_peak_658 229 . 3.78267 27.82570 22.90389 46 +chr5 49657674 49657785 SRX5974585.05_peak_659 393 . 4.78624 44.75364 39.39409 60 +chr5 49658379 49658738 SRX5974585.05_peak_660 485 . 5.28802 54.13351 48.56923 297 +chr5 49658913 49659008 SRX5974585.05_peak_661 270 . 4.05286 32.12040 27.08261 45 +chr5 49659838 49659934 SRX5974585.05_peak_662 366 . 4.63185 41.98294 36.68842 40 +chr5 49660050 49660173 SRX5974585.05_peak_663 152 . 3.24229 19.89597 15.21577 90 +chr5 49660701 49660775 SRX5974585.05_peak_664 314 . 4.32306 36.61567 31.45399 39 +chr5 49660956 49661133 SRX5974585.05_peak_665 223 . 3.74408 27.22931 22.32482 117 +chr5 49661440 49661818 SRX5974585.05_peak_666 325 . 4.30870 37.69390 32.50368 38 +chr5 49666178 49666261 SRX5974585.05_peak_667 433 . 8.58858 48.83105 43.39044 56 +chr5 49666373 49666674 SRX5974585.05_peak_668 323 . 7.59765 37.56184 32.37400 102 +chr5 49666768 49667337 SRX5974585.05_peak_669 777 . 16.17705 83.97550 77.72864 403 +chr5 80651460 80651546 SRX5974585.05_peak_670 206 . 13.91941 25.55206 20.69457 45 +chr6 58453184 58453257 SRX5974585.05_peak_671 136 . 10.64042 18.29158 13.66386 22 +chr6 58784133 58784206 SRX5974585.05_peak_672 192 . 11.91244 24.06331 19.25071 41 +chr6 58793487 58793599 SRX5974585.05_peak_673 574 . 23.52398 63.24844 57.43409 54 +chr6 58857471 58857558 SRX5974585.05_peak_674 247 . 14.28118 29.76831 24.79513 41 +chr6 58922903 58922990 SRX5974585.05_peak_675 313 . 15.57775 36.49236 31.33309 22 +chr6 58975830 58975910 SRX5974585.05_peak_676 245 . 13.42158 29.55662 24.58866 57 +chr6 59093593 59093668 SRX5974585.05_peak_677 204 . 12.31952 25.30660 20.45651 40 +chr6 59445265 59445357 SRX5974585.05_peak_678 272 . 14.67782 32.30239 27.25866 49 +chr6 59667650 59667723 SRX5974585.05_peak_679 189 . 11.70047 23.71016 18.90922 45 +chr7 58033155 58033251 SRX5974585.05_peak_680 569 . 22.75910 62.79453 56.98904 37 +chr7 58036603 58036733 SRX5974585.05_peak_681 205 . 11.31496 25.37905 20.52690 37 +chr7 58434520 58434633 SRX5974585.05_peak_682 773 . 30.16815 83.56563 77.32516 57 +chr7 58708433 58708537 SRX5974585.05_peak_683 398 . 19.88621 45.20158 39.83426 43 +chr7 58797790 58797888 SRX5974585.05_peak_684 321 . 16.86074 37.29486 32.11365 59 +chr7 58818585 58818672 SRX5974585.05_peak_685 254 . 14.67790 30.43981 25.44777 41 +chr7 59163937 59164042 SRX5974585.05_peak_686 524 . 23.43994 58.10231 52.42807 51 +chr7 59206614 59206717 SRX5974585.05_peak_687 327 . 16.86858 37.98201 32.78461 49 +chr7 59292467 59292560 SRX5974585.05_peak_688 230 . 13.92215 28.00400 23.07768 39 +chr7 59364667 59364767 SRX5974585.05_peak_689 248 . 14.33654 29.86176 24.88529 45 +chr7 59734816 59734905 SRX5974585.05_peak_690 227 . 13.75440 27.71834 22.79941 53 +chr7 59954547 59954649 SRX5974585.05_peak_691 225 . 13.93162 27.47885 22.56782 56 +chr7 59995702 59995809 SRX5974585.05_peak_692 649 . 26.99124 70.89506 64.92623 59 +chr7 60383173 60383255 SRX5974585.05_peak_693 260 . 15.03592 31.04981 26.04106 43 +chr7 60522181 60522259 SRX5974585.05_peak_694 215 . 13.31115 26.41411 21.53195 44 +chr7 60917877 60917950 SRX5974585.05_peak_695 171 . 11.55370 21.88364 17.13966 19 +chr7 61071234 61071346 SRX5974585.05_peak_696 536 . 23.24431 59.40298 53.68818 54 +chr7 62350322 62350412 SRX5974585.05_peak_697 414 . 17.32079 46.82380 41.42355 29 +chr7 62454721 62454814 SRX5974585.05_peak_698 471 . 21.63053 52.64271 47.11276 47 +chr8 43237751 43237840 SRX5974585.05_peak_699 351 . 11.82984 40.41876 35.16398 62 +chr8 43239634 43239735 SRX5974585.05_peak_700 521 . 15.11609 57.86322 52.19608 60 +chr8 43240019 43240105 SRX5974585.05_peak_701 459 . 13.93514 51.44857 45.94514 32 +chr8 69690136 69690209 SRX5974585.05_peak_702 113 . 9.58970 15.84935 11.30313 34 +chr8 143669490 143669601 SRX5974585.05_peak_703 248 . 12.45378 29.81631 24.84087 51 +chr8 144125831 144125919 SRX5974585.05_peak_704 322 . 16.55961 37.46056 32.27406 26 +chr9 60552988 60553075 SRX5974585.05_peak_705 257 . 15.49715 30.75654 25.75641 41 +chr9 70038196 70038458 SRX5974585.05_peak_706 417 . 20.98636 47.10899 41.70396 197 +chr9 137328338 137328447 SRX5974585.05_peak_707 345 . 17.51043 39.75428 34.51594 56 +chrUn_GL000216v2 278 817 SRX5974585.05_peak_708 484 . 11.45548 53.98929 48.43012 485 +chrUn_GL000216v2 1974 2071 SRX5974585.05_peak_709 453 . 9.56308 50.86427 45.37633 56 +chrUn_GL000216v2 5831 5946 SRX5974585.05_peak_710 232 . 6.86260 28.16726 23.23692 58 +chrUn_GL000216v2 6112 6194 SRX5974585.05_peak_711 406 . 9.34617 45.99953 40.61635 29 +chrUn_GL000216v2 9348 9429 SRX5974585.05_peak_712 248 . 7.07881 29.83029 24.85459 35 +chrUn_GL000216v2 16121 16210 SRX5974585.05_peak_713 379 . 9.05149 43.27586 37.95035 42 +chrUn_GL000216v2 19358 19540 SRX5974585.05_peak_714 535 . 12.56008 59.28902 53.57935 115 +chrUn_GL000216v2 26432 26515 SRX5974585.05_peak_715 266 . 9.41147 31.68850 26.66236 32 +chrUn_GL000216v2 27797 27893 SRX5974585.05_peak_716 168 . 7.62298 21.59840 16.86468 49 +chrUn_GL000216v2 29172 29255 SRX5974585.05_peak_717 156 . 7.51915 20.38414 15.68870 50 +chrUn_GL000216v2 79978 80457 SRX5974585.05_peak_718 538 . 13.14084 59.53000 53.81308 350 +chrUn_GL000216v2 84949 85042 SRX5974585.05_peak_719 505 . 13.21312 56.16668 50.54664 52 +chrUn_GL000216v2 141090 141244 SRX5974585.05_peak_720 156 . 7.70722 20.29458 15.60188 38 +chrUn_GL000216v2 146132 146210 SRX5974585.05_peak_721 319 . 8.89333 37.12548 31.94794 37 +chrUn_GL000216v2 149490 149831 SRX5974585.05_peak_722 715 . 13.88889 77.71391 71.58638 57 +chrUn_GL000216v2 155528 155672 SRX5974585.05_peak_723 84 . 4.75654 12.90180 8.46397 123 +chrUn_GL000216v2 158653 158744 SRX5974585.05_peak_724 424 . 10.30444 47.91736 42.49508 57 +chrUn_GL000216v2 164426 164537 SRX5974585.05_peak_725 596 . 11.36306 65.53129 59.66371 54 +chrUn_GL000216v2 167831 167937 SRX5974585.05_peak_726 942 . 15.74422 100.79656 94.23125 55 +chrUn_GL000216v2 168696 168793 SRX5974585.05_peak_727 292 . 8.00854 34.38187 29.28176 41 +chrUn_GL000216v2 173815 174008 SRX5974585.05_peak_728 655 . 17.29665 71.58104 65.59424 47 +chrUn_GL000224v1 3776 3884 SRX5974585.05_peak_729 78 . 3.67013 12.26834 7.85715 96 +chrUn_KI270303v1 261 344 SRX5974585.05_peak_730 191 . 9.56465 23.99256 19.18233 35 +chrUn_KI270303v1 503 600 SRX5974585.05_peak_731 225 . 10.49027 27.49910 22.58733 51 +chrUn_KI270303v1 1184 1411 SRX5974585.05_peak_732 674 . 20.98053 73.52205 67.48895 65 +chrUn_KI270304v1 777 1109 SRX5974585.05_peak_733 822 . 24.96715 88.55489 82.20583 274 +chrUn_KI270310v1 93 166 SRX5974585.05_peak_734 109 . 6.93622 15.43345 10.90136 31 +chrUn_KI270310v1 607 1022 SRX5974585.05_peak_735 1067 . 27.74486 113.52215 106.77374 272 +chrUn_KI270311v1 56 133 SRX5974585.05_peak_736 176 . 11.88791 22.45110 17.68855 31 +chrUn_KI270311v1 1017 1169 SRX5974585.05_peak_737 234 . 14.15228 28.39775 23.46031 84 +chrUn_KI270317v1 1277 1524 SRX5974585.05_peak_738 356 . 18.97758 40.94264 35.67471 176 +chrUn_KI270317v1 36323 36474 SRX5974585.05_peak_739 441 . 21.20141 49.62816 44.16807 99 +chrUn_KI270317v1 36640 36726 SRX5974585.05_peak_740 330 . 17.39603 38.20491 33.00061 29 +chrUn_KI270329v1 294 462 SRX5974585.05_peak_741 86 . 8.29997 13.09531 8.64810 34 +chrUn_KI270330v1 481 554 SRX5974585.05_peak_742 90 . 7.63565 13.54642 9.08317 45 +chrUn_KI270330v1 1146 1264 SRX5974585.05_peak_743 149 . 9.88142 19.60974 14.93934 69 +chrUn_KI270330v1 1363 1579 SRX5974585.05_peak_744 199 . 11.67805 24.81365 19.97739 162 +chrUn_KI270333v1 318 438 SRX5974585.05_peak_745 673 . 12.59813 73.33916 67.30817 63 +chrUn_KI270333v1 875 957 SRX5974585.05_peak_746 242 . 7.02843 29.18719 24.22860 26 +chrUn_KI270333v1 1082 1226 SRX5974585.05_peak_747 157 . 5.70231 20.48005 15.78045 97 +chrUn_KI270333v1 1364 1740 SRX5974585.05_peak_748 572 . 11.40462 63.04675 57.23748 263 +chrUn_KI270333v1 2023 2259 SRX5974585.05_peak_749 529 . 10.87418 58.60029 52.91193 43 +chrUn_KI270333v1 2432 2505 SRX5974585.05_peak_750 233 . 6.89582 28.27742 23.34409 17 +chrUn_KI270336v1 76 286 SRX5974585.05_peak_751 569 . 18.14829 62.77494 56.97053 44 +chrUn_KI270336v1 511 988 SRX5974585.05_peak_752 682 . 20.49000 74.29220 68.24118 420 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13.80555 28.37599 23.43917 47 +chrUn_KI270418v1 1258 1348 SRX5974585.05_peak_765 323 . 16.99145 37.51655 32.32936 41 +chrUn_KI270422v1 619 781 SRX5974585.05_peak_766 108 . 8.75521 15.38026 10.85027 38 +chrUn_KI270422v1 890 1238 SRX5974585.05_peak_767 314 . 16.48040 36.65138 31.48823 51 +chrUn_KI270438v1 71171 71244 SRX5974585.05_peak_768 192 . 11.23595 24.07117 19.25841 50 +chrUn_KI270438v1 103908 104361 SRX5974585.05_peak_769 535 . 5.46003 59.21676 53.51230 185 +chrUn_KI270438v1 104547 104963 SRX5974585.05_peak_770 449 . 4.60275 50.47042 44.99018 156 +chrUn_KI270438v1 105202 105319 SRX5974585.05_peak_771 302 . 3.55542 35.40880 30.27748 32 +chrUn_KI270438v1 109217 109477 SRX5974585.05_peak_772 236 . 2.99971 28.63347 23.69029 229 +chrUn_KI270438v1 109560 110556 SRX5974585.05_peak_773 271 . 3.16670 32.18835 27.14811 45 +chrUn_KI270438v1 111236 111812 SRX5974585.05_peak_774 375 . 4.03149 42.86251 37.54529 526 +chrUn_KI270438v1 111916 111990 SRX5974585.05_peak_775 349 . 3.95071 40.23321 34.98217 37 +chrUn_KI270438v1 112068 112187 SRX5974585.05_peak_776 403 . 4.22388 45.75467 40.37610 77 +chrUn_KI270438v1 112333 112447 SRX5974585.05_peak_777 395 . 4.19972 44.87857 39.51670 68 +chrUn_KI270442v1 67 266 SRX5974585.05_peak_778 212 . 12.82709 26.15501 21.27858 62 +chrUn_KI270442v1 84587 84666 SRX5974585.05_peak_779 240 . 10.03009 28.97276 24.02036 38 +chrUn_KI270442v1 85921 86027 SRX5974585.05_peak_780 371 . 12.45360 42.48312 37.17599 53 +chrUn_KI270442v1 92351 92453 SRX5974585.05_peak_781 840 . 18.20133 90.42823 84.02380 42 +chrUn_KI270442v1 96040 96114 SRX5974585.05_peak_782 231 . 9.07215 28.04899 23.12154 43 +chrUn_KI270466v1 24 1195 SRX5974585.05_peak_783 1105 . 15.00946 117.38680 110.59636 1045 +chrUn_KI270467v1 1220 1305 SRX5974585.05_peak_784 483 . 5.46457 53.88733 48.32912 43 +chrUn_KI270467v1 1428 1506 SRX5974585.05_peak_785 315 . 4.47101 36.69214 31.52670 48 +chrUn_KI270467v1 1938 3582 SRX5974585.05_peak_786 574 . 5.96135 63.21463 57.40101 165 +chrUn_KI270467v1 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15.54530 35.07562 29.95611 39 +chrY 10991213 10991286 SRX5974585.05_peak_835 189 . 11.74226 23.77976 18.97663 40 +chrY 10999844 10999967 SRX5974585.05_peak_836 1277 . 38.29451 134.64447 127.74328 58 +chrY 11012254 11012332 SRX5974585.05_peak_837 238 . 14.07841 28.83725 23.88900 43 +chrY 11014428 11014515 SRX5974585.05_peak_838 252 . 14.56232 30.24374 25.25729 54 +chrY 11031568 11031641 SRX5974585.05_peak_839 160 . 11.41621 20.73403 16.02670 29 +chrY 11033124 11033201 SRX5974585.05_peak_840 235 . 14.48401 28.44491 23.50593 40 +chrY 11297642 11297822 SRX5974585.05_peak_841 221 . 6.52324 27.02029 22.12164 28 +chrY 11303408 11303500 SRX5974585.05_peak_842 384 . 8.70950 43.77136 38.43495 41 +chrY 11305300 11305422 SRX5974585.05_peak_843 429 . 9.68875 48.34254 42.91373 55 +chrY 11312271 11312357 SRX5974585.05_peak_844 387 . 9.64707 44.05032 38.70639 50 +chrY 11312516 11312625 SRX5974585.05_peak_845 386 . 9.62621 43.98482 38.64321 56 +chrY 11313401 11313496 SRX5974585.05_peak_846 281 . 7.96444 33.20027 28.13154 48 +chrY 11318733 11318829 SRX5974585.05_peak_847 263 . 9.75256 31.32324 26.30642 57 +chrY 11326362 11326448 SRX5974585.05_peak_848 417 . 9.85630 47.10502 41.70003 56 +chrY 11327622 11327735 SRX5974585.05_peak_849 663 . 12.93934 72.40241 66.39940 56 +chrY 11329882 11330048 SRX5974585.05_peak_850 437 . 9.94269 49.19806 43.74887 41 +chrY 11330231 11330305 SRX5974585.05_peak_851 215 . 6.89249 26.40172 21.51978 22 +chrY 11333435 11333510 SRX5974585.05_peak_852 153 . 7.16426 20.07368 15.38854 37 +chrY 11493263 11493336 SRX5974585.05_peak_853 165 . 12.07268 21.25684 16.53253 30 +chrY 11643663 11643821 SRX5974585.05_peak_854 581 . 27.27498 64.03046 58.19542 92 +chrY 11687976 11688065 SRX5974585.05_peak_855 210 . 13.04702 25.96599 21.09615 44 +chrY 11690104 11690201 SRX5974585.05_peak_856 169 . 11.46163 21.72827 16.98931 54 +chrY 11690386 11690522 SRX5974585.05_peak_857 169 . 11.46163 21.72827 16.98931 41 +chrY 11717736 11717813 SRX5974585.05_peak_858 232 . 12.23142 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2653765 2653839 SRX6944761.05_peak_14 111 . 3.84842 15.36396 11.10091 55 +chr1 2653912 2653998 SRX6944761.05_peak_15 161 . 4.46533 20.59548 16.13103 50 +chr1 2654083 2654282 SRX6944761.05_peak_16 339 . 6.30694 38.88856 33.93635 46 +chr1 2697687 2697847 SRX6944761.05_peak_17 159 . 6.56669 20.45309 15.99317 35 +chr1 2748982 2749121 SRX6944761.05_peak_18 82 . 4.80316 12.35375 8.22930 43 +chr1 2756556 2756629 SRX6944761.05_peak_19 120 . 5.25049 16.40457 12.09590 25 +chr1 2758730 2758803 SRX6944761.05_peak_20 90 . 4.85909 13.17713 9.01069 51 +chr1 2759569 2759634 SRX6944761.05_peak_21 131 . 5.77224 17.49198 13.13893 33 +chr1 3502028 3502096 SRX6944761.05_peak_22 194 . 8.50857 24.06352 19.48878 34 +chr1 4303096 4303170 SRX6944761.05_peak_23 231 . 11.69370 27.85892 23.17215 40 +chr1 10210592 10210657 SRX6944761.05_peak_24 72 . 6.54932 11.36073 7.29093 32 +chr1 13700328 13700461 SRX6944761.05_peak_25 73 . 7.04225 11.41344 7.34081 22 +chr1 16613362 16613477 SRX6944761.05_peak_26 94 . 5.84659 13.67163 9.48274 26 +chr1 16658347 16658487 SRX6944761.05_peak_27 61 . 5.80762 10.12736 6.13686 12 +chr1 32500552 32500630 SRX6944761.05_peak_28 110 . 8.44674 15.30863 11.04788 46 +chr1 45206489 45206560 SRX6944761.05_peak_29 118 . 8.93744 16.11428 11.81769 34 +chr1 48382859 48382950 SRX6944761.05_peak_30 63 . 6.13999 10.31339 6.30551 89 +chr1 91387231 91387530 SRX6944761.05_peak_31 936 . 27.79661 99.73071 93.67843 103 +chr1 105795875 105795998 SRX6944761.05_peak_32 86 . 7.87653 12.78574 8.63765 86 +chr1 107044799 107044903 SRX6944761.05_peak_33 123 . 9.26641 16.65656 12.33679 52 +chr1 112898264 112898329 SRX6944761.05_peak_34 79 . 6.74336 12.07728 7.96654 9 +chr1 121616052 121616126 SRX6944761.05_peak_35 309 . 14.79915 35.86063 30.97542 29 +chr1 121726322 121726391 SRX6944761.05_peak_36 192 . 12.37741 23.77892 19.21280 51 +chr1 121863311 121863380 SRX6944761.05_peak_37 239 . 12.54705 28.70804 23.99482 25 +chr1 121968588 121968686 SRX6944761.05_peak_38 123 . 8.99834 16.67077 12.35065 45 +chr1 122462793 122462868 SRX6944761.05_peak_39 272 . 14.15848 32.07888 27.28074 25 +chr1 122496566 122496645 SRX6944761.05_peak_40 321 . 15.47988 37.02138 32.11052 28 +chr1 122503877 122503948 SRX6944761.05_peak_41 235 . 8.69835 28.28277 23.58241 23 +chr1 122531657 122531723 SRX6944761.05_peak_42 91 . 4.67597 13.34037 9.16669 17 +chr1 122561619 122561730 SRX6944761.05_peak_43 142 . 6.06725 18.69766 14.29784 64 +chr1 122582660 122582735 SRX6944761.05_peak_44 100 . 4.89179 14.29518 10.07792 57 +chr1 122713935 122714004 SRX6944761.05_peak_45 133 . 5.72246 17.75433 13.39073 32 +chr1 122756187 122756260 SRX6944761.05_peak_46 144 . 6.14125 18.89581 14.48929 26 +chr1 122773435 122773500 SRX6944761.05_peak_47 100 . 5.00900 14.27645 10.05996 31 +chr1 122842657 122842759 SRX6944761.05_peak_48 90 . 4.76520 13.25989 9.08963 42 +chr1 122850874 122850950 SRX6944761.05_peak_49 144 . 6.13445 18.87766 14.47204 43 +chr1 122897625 122897742 SRX6944761.05_peak_50 127 . 5.10705 17.05778 12.72139 27 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123768093 123768167 SRX6944761.05_peak_75 85 . 4.91832 12.64553 8.50639 17 +chr1 123783684 123783768 SRX6944761.05_peak_76 360 . 10.06158 41.07884 36.08167 40 +chr1 123811500 123811571 SRX6944761.05_peak_77 115 . 5.44824 15.83688 11.55179 31 +chr1 123847933 123848048 SRX6944761.05_peak_78 73 . 4.45541 11.44839 7.37367 24 +chr1 123851078 123851210 SRX6944761.05_peak_79 83 . 4.70703 12.43725 8.30848 81 +chr1 123954217 123954287 SRX6944761.05_peak_80 133 . 5.71655 17.73786 13.37481 38 +chr1 123959554 123959688 SRX6944761.05_peak_81 151 . 5.94739 19.60818 15.17542 17 +chr1 124007465 124007542 SRX6944761.05_peak_82 329 . 9.13608 37.84480 32.91509 33 +chr1 124117481 124117614 SRX6944761.05_peak_83 137 . 5.69888 18.08512 13.70948 106 +chr1 124124327 124124428 SRX6944761.05_peak_84 394 . 9.59860 44.48857 39.41898 48 +chr1 124126419 124126495 SRX6944761.05_peak_85 97 . 4.76328 13.92455 9.72454 47 +chr1 124137914 124137999 SRX6944761.05_peak_86 89 . 4.96916 13.12084 8.95697 58 +chr1 124225339 124225421 SRX6944761.05_peak_87 184 . 6.37496 23.00299 18.46081 21 +chr1 124260834 124260905 SRX6944761.05_peak_88 90 . 4.86421 13.19079 9.02375 52 +chr1 124318209 124318292 SRX6944761.05_peak_89 131 . 5.61798 17.46159 13.11009 58 +chr1 124367073 124367157 SRX6944761.05_peak_90 115 . 5.17828 15.82790 11.54324 32 +chr1 124377190 124377279 SRX6944761.05_peak_91 422 . 10.32994 47.37785 42.24625 57 +chr1 124402508 124402589 SRX6944761.05_peak_92 154 . 6.54010 19.93937 15.49566 49 +chr1 124406484 124406604 SRX6944761.05_peak_93 143 . 5.93662 18.75488 14.35321 35 +chr1 124421028 124421110 SRX6944761.05_peak_94 323 . 8.74086 37.22102 32.30567 41 +chr1 124455041 124455111 SRX6944761.05_peak_95 258 . 7.61291 30.57160 25.81096 28 +chr1 124456578 124456647 SRX6944761.05_peak_96 197 . 7.00155 24.38341 19.79908 26 +chr1 124499504 124499590 SRX6944761.05_peak_97 140 . 5.54607 18.42060 14.03130 48 +chr1 124511766 124511831 SRX6944761.05_peak_98 140 . 5.98852 18.48523 14.09335 33 +chr1 124534543 124534608 SRX6944761.05_peak_99 78 . 4.39625 11.90668 7.80499 34 +chr1 124562415 124562502 SRX6944761.05_peak_100 518 . 12.31652 57.20166 51.83557 53 +chr1 124568530 124568615 SRX6944761.05_peak_101 165 . 6.01159 21.04147 16.56195 36 +chr1 124581497 124581615 SRX6944761.05_peak_102 139 . 6.26555 18.36803 13.98049 29 +chr1 124587635 124587729 SRX6944761.05_peak_103 166 . 6.33212 21.13403 16.65121 33 +chr1 124590751 124590854 SRX6944761.05_peak_104 66 . 4.17879 10.70006 6.66846 86 +chr1 124611066 124611201 SRX6944761.05_peak_105 192 . 6.95652 23.78846 19.22218 37 +chr1 124642311 124642381 SRX6944761.05_peak_106 141 . 5.71557 18.53053 14.13661 45 +chr1 124673265 124673333 SRX6944761.05_peak_107 66 . 4.18285 10.71123 6.67899 6 +chr1 124692041 124692127 SRX6944761.05_peak_108 106 . 4.99192 14.93094 10.68653 37 +chr1 124714696 124714792 SRX6944761.05_peak_109 131 . 5.64086 17.52600 13.17130 47 +chr1 124732344 124732450 SRX6944761.05_peak_110 233 . 6.68432 28.02513 23.33371 60 +chr1 124736102 124736186 SRX6944761.05_peak_111 157 . 5.38406 20.19379 15.74144 44 +chr1 124741380 124741464 SRX6944761.05_peak_112 452 . 9.78793 50.47550 45.26450 33 +chr1 124745539 124745641 SRX6944761.05_peak_113 92 . 4.59098 13.41829 9.24077 35 +chr1 124757698 124757773 SRX6944761.05_peak_114 339 . 9.51268 38.93506 33.98195 31 +chr1 124763959 124764028 SRX6944761.05_peak_115 84 . 4.64864 12.60840 8.47111 59 +chr1 124905485 124905564 SRX6944761.05_peak_116 215 . 11.80687 26.21729 21.57941 37 +chr1 125069451 125069531 SRX6944761.05_peak_117 549 . 17.35173 60.34190 54.91776 34 +chr1 125075447 125075523 SRX6944761.05_peak_118 374 . 13.09110 42.47783 37.45058 40 +chr1 125081146 125081230 SRX6944761.05_peak_119 101 . 5.96262 14.32115 10.10284 59 +chr1 125166276 125166358 SRX6944761.05_peak_120 419 . 6.23072 47.11488 41.98879 37 +chr1 125166921 125167019 SRX6944761.05_peak_121 628 . 7.76422 68.40670 62.81837 48 +chr1 125167142 125167251 SRX6944761.05_peak_122 675 . 8.10423 73.24399 67.56715 52 +chr1 125167804 125168018 SRX6944761.05_peak_123 689 . 8.24003 74.66633 68.96384 81 +chr1 125168185 125168341 SRX6944761.05_peak_124 247 . 4.89744 29.53068 24.79613 23 +chr1 125169954 125170027 SRX6944761.05_peak_125 347 . 5.30346 39.71561 34.74649 35 +chr1 125170094 125170187 SRX6944761.05_peak_126 504 . 6.35939 55.82409 50.48861 45 +chr1 125174024 125174151 SRX6944761.05_peak_127 260 . 4.03298 30.81916 26.05272 30 +chr1 125175433 125175498 SRX6944761.05_peak_128 245 . 3.28228 29.29742 24.56929 29 +chr1 125178872 125179447 SRX6944761.05_peak_129 88 . 2.03044 13.05166 8.89156 378 +chr1 125179598 125179905 SRX6944761.05_peak_130 85 . 2.00459 12.66747 8.52687 140 +chr1 125180000 125180449 SRX6944761.05_peak_131 86 . 2.01683 12.84934 8.69846 214 +chr1 125180531 125180703 SRX6944761.05_peak_132 93 . 2.06160 13.51428 9.33179 62 +chr1 125182119 125182723 SRX6944761.05_peak_133 116 . 2.22281 15.89475 11.60725 223 +chr1 125182946 125183204 SRX6944761.05_peak_134 132 . 2.34242 17.63964 13.28040 112 +chr1 125183575 125183846 SRX6944761.05_peak_135 137 . 2.41429 18.15716 13.77793 42 +chr1 125184449 125184523 SRX6944761.05_peak_136 117 . 2.44817 16.09015 11.79442 20 +chr1 143184636 143184756 SRX6944761.05_peak_137 411 . 4.73390 46.26508 41.15999 40 +chr1 143184825 143185002 SRX6944761.05_peak_138 400 . 4.61590 45.08878 40.00723 111 +chr1 143185086 143185453 SRX6944761.05_peak_139 386 . 4.44912 43.70306 38.65084 41 +chr1 143185864 143185946 SRX6944761.05_peak_140 222 . 3.27888 26.88625 22.22946 53 +chr1 143186166 143186243 SRX6944761.05_peak_141 221 . 3.20129 26.85354 22.19779 36 +chr1 143186737 143186827 SRX6944761.05_peak_142 198 . 2.98400 24.47313 19.88634 43 +chr1 143187215 143187334 SRX6944761.05_peak_143 202 . 2.92252 24.81356 20.21663 33 +chr1 143187723 143187882 SRX6944761.05_peak_144 157 . 2.57403 20.21515 15.76234 125 +chr1 143190601 143190702 SRX6944761.05_peak_145 86 . 2.01616 12.75230 8.60715 81 +chr1 143191105 143191212 SRX6944761.05_peak_146 86 . 2.03331 12.82723 8.67744 52 +chr1 143191899 143191975 SRX6944761.05_peak_147 94 . 2.11052 13.65500 9.46674 64 +chr1 143192259 143192456 SRX6944761.05_peak_148 113 . 2.23301 15.60191 11.32750 93 +chr1 143192701 143192928 SRX6944761.05_peak_149 132 . 2.34052 17.61202 13.25370 44 +chr1 143193033 143193220 SRX6944761.05_peak_150 143 . 2.42296 18.80140 14.39815 73 +chr1 143193419 143193513 SRX6944761.05_peak_151 146 . 2.44205 19.07495 14.66178 41 +chr1 143193599 143193856 SRX6944761.05_peak_152 143 . 2.42093 18.77214 14.36989 123 +chr1 143194063 143194137 SRX6944761.05_peak_153 122 . 2.30828 16.55049 12.23520 58 +chr1 143194902 143195272 SRX6944761.05_peak_154 138 . 2.38596 18.26902 13.88545 321 +chr1 143195634 143195720 SRX6944761.05_peak_155 137 . 2.37416 18.09870 13.72151 37 +chr1 143200227 143200402 SRX6944761.05_peak_156 214 . 3.03907 26.10263 21.46835 148 +chr1 143200651 143200910 SRX6944761.05_peak_157 262 . 3.33185 30.99560 26.22491 102 +chr1 143201157 143201246 SRX6944761.05_peak_158 233 . 3.15034 28.09091 23.39744 38 +chr1 143201472 143201654 SRX6944761.05_peak_159 204 . 2.91356 25.05991 20.45661 132 +chr1 143202454 143202865 SRX6944761.05_peak_160 188 . 2.76593 23.44237 18.88735 51 +chr1 143203329 143203578 SRX6944761.05_peak_161 178 . 2.71399 22.41349 17.88914 196 +chr1 143206073 143206203 SRX6944761.05_peak_162 233 . 3.13285 28.07285 23.37990 31 +chr1 143206273 143206518 SRX6944761.05_peak_163 243 . 3.20884 29.03042 24.30899 194 +chr1 143206587 143206664 SRX6944761.05_peak_164 217 . 3.10705 26.34937 21.70733 36 +chr1 143207054 143207202 SRX6944761.05_peak_165 253 . 3.28146 30.15000 25.39943 107 +chr1 143211796 143211887 SRX6944761.05_peak_166 114 . 2.22053 15.75215 11.47113 57 +chr1 143212468 143212596 SRX6944761.05_peak_167 104 . 2.15011 14.72300 10.48764 10 +chr1 143212876 143212979 SRX6944761.05_peak_168 80 . 1.98365 12.18885 8.07277 73 +chr1 143213220 143213311 SRX6944761.05_peak_169 67 . 1.89134 10.76235 6.72688 66 +chr1 143220957 143221031 SRX6944761.05_peak_170 57 . 1.80769 9.67854 5.72116 8 +chr1 143221530 143221601 SRX6944761.05_peak_171 57 . 1.82532 9.67141 5.71488 32 +chr1 143222128 143222319 SRX6944761.05_peak_172 89 . 2.03208 13.07600 8.91480 13 +chr1 143222432 143222537 SRX6944761.05_peak_173 99 . 2.10312 14.12979 9.91954 40 +chr1 143222694 143222765 SRX6944761.05_peak_174 82 . 2.02282 12.32310 8.20025 59 +chr1 143222952 143223050 SRX6944761.05_peak_175 107 . 2.16386 15.02751 10.77902 28 +chr1 143227530 143227622 SRX6944761.05_peak_176 172 . 2.67436 21.72121 17.21971 22 +chr1 143227935 143228025 SRX6944761.05_peak_177 186 . 2.73798 23.22578 18.67734 27 +chr1 143230268 143230444 SRX6944761.05_peak_178 169 . 2.61099 21.46638 16.97273 41 +chr1 143231280 143231548 SRX6944761.05_peak_179 140 . 2.39903 18.45727 14.06649 140 +chr1 143232410 143232506 SRX6944761.05_peak_180 113 . 2.20949 15.59123 11.31734 64 +chr1 143232797 143233305 SRX6944761.05_peak_181 117 . 2.23612 16.09006 11.79433 191 +chr1 143233512 143233643 SRX6944761.05_peak_182 99 . 2.10974 14.13054 9.92027 112 +chr1 143235745 143235843 SRX6944761.05_peak_183 67 . 1.88493 10.74221 6.70791 22 +chr1 143237421 143237518 SRX6944761.05_peak_184 72 . 1.93453 11.31217 7.24476 19 +chr1 143237999 143238230 SRX6944761.05_peak_185 105 . 2.15684 14.82153 10.58202 200 +chr1 143239464 143239645 SRX6944761.05_peak_186 123 . 2.28106 16.63151 12.31278 71 +chr1 143239729 143239831 SRX6944761.05_peak_187 117 . 2.24864 16.04881 11.75478 79 +chr1 143240244 143240460 SRX6944761.05_peak_188 113 . 2.22768 15.63609 11.35995 61 +chr1 143245469 143245559 SRX6944761.05_peak_189 223 . 3.01937 27.00186 22.34175 41 +chr1 143246847 143247201 SRX6944761.05_peak_190 249 . 3.23038 29.72148 24.98160 54 +chr1 143247272 143247424 SRX6944761.05_peak_191 222 . 3.01531 26.94852 22.28993 29 +chr1 143249704 143250022 SRX6944761.05_peak_192 132 . 2.34161 17.62777 13.26889 133 +chr1 143251621 143251746 SRX6944761.05_peak_193 81 . 1.97667 12.25227 8.13298 55 +chr1 143251923 143252058 SRX6944761.05_peak_194 78 . 1.95652 11.95253 7.84813 60 +chr1 143252980 143253188 SRX6944761.05_peak_195 92 . 2.05257 13.38028 9.20457 81 +chr1 143253743 143253857 SRX6944761.05_peak_196 95 . 2.07625 13.73168 9.54025 64 +chr1 143254453 143254660 SRX6944761.05_peak_197 93 . 2.06033 13.49553 9.31439 108 +chr1 143254774 143254933 SRX6944761.05_peak_198 102 . 2.12418 14.44144 10.21847 78 +chr1 143255525 143255754 SRX6944761.05_peak_199 129 . 2.31604 17.25671 12.91218 27 +chr1 143255856 143256010 SRX6944761.05_peak_200 124 . 2.28221 16.76401 12.43848 58 +chr1 143256407 143256534 SRX6944761.05_peak_201 125 . 2.29338 16.92682 12.59567 60 +chr1 143256790 143256922 SRX6944761.05_peak_202 136 . 2.37816 18.03051 13.65719 46 +chr1 143262118 143262249 SRX6944761.05_peak_203 135 . 2.36247 17.92980 13.55993 36 +chr1 143262339 143262436 SRX6944761.05_peak_204 125 . 2.29025 16.88124 12.55186 35 +chr1 143262539 143262930 SRX6944761.05_peak_205 116 . 2.22600 15.94156 11.65228 29 +chr1 143263366 143263457 SRX6944761.05_peak_206 113 . 2.21047 15.60545 11.33090 28 +chr1 143263629 143263773 SRX6944761.05_peak_207 115 . 2.21914 15.84087 11.55564 89 +chr1 143264027 143264155 SRX6944761.05_peak_208 95 . 2.12910 13.71871 9.52783 112 +chr1 143264482 143264776 SRX6944761.05_peak_209 122 . 2.26936 16.57641 12.26004 162 +chr1 143265764 143266035 SRX6944761.05_peak_210 115 . 2.22523 15.82050 11.53623 112 +chr1 143266492 143266611 SRX6944761.05_peak_211 119 . 2.24584 16.23241 11.93106 90 +chr1 143266721 143266905 SRX6944761.05_peak_212 120 . 2.25464 16.36115 12.05425 157 +chr1 143267006 143267075 SRX6944761.05_peak_213 96 . 2.13462 13.79559 9.60128 19 +chr1 143267432 143267531 SRX6944761.05_peak_214 123 . 2.32541 16.66974 12.34969 52 +chr1 143270674 143270753 SRX6944761.05_peak_215 267 . 3.61466 31.48978 26.70701 33 +chr1 143273085 143273167 SRX6944761.05_peak_216 488 . 6.55699 54.10570 48.80668 39 +chr1 143273805 143273900 SRX6944761.05_peak_217 220 . 4.75568 26.66669 22.01587 43 +chr1 143275706 143275940 SRX6944761.05_peak_218 501 . 8.37454 55.52107 50.19009 46 +chr1 143540710 143540775 SRX6944761.05_peak_219 127 . 8.64198 17.03823 12.70260 9 +chr1 144103630 144103756 SRX6944761.05_peak_220 199 . 6.72897 24.51926 19.93126 64 +chr1 144104190 144104324 SRX6944761.05_peak_221 110 . 5.11394 15.28155 11.02209 108 +chr1 144104429 144104514 SRX6944761.05_peak_222 67 . 4.22801 10.83514 6.79545 54 +chr1 155190079 155190157 SRX6944761.05_peak_223 199 . 9.54369 24.51811 19.93025 30 +chr1 156216577 156216665 SRX6944761.05_peak_224 413 . 16.68830 46.46836 41.35852 41 +chr1 159449886 159449953 SRX6944761.05_peak_225 89 . 7.88022 13.14481 8.97994 52 +chr1 210653901 210654038 SRX6944761.05_peak_226 179 . 10.96141 22.47942 17.95279 47 +chr1 224013616 224013811 SRX6944761.05_peak_227 464 . 13.14116 51.72702 46.48372 145 +chr1 224014816 224014915 SRX6944761.05_peak_228 240 . 8.88983 28.74109 24.02709 43 +chr1 228557361 228557428 SRX6944761.05_peak_229 121 . 6.57817 16.48989 12.17748 41 +chr1 228557640 228557799 SRX6944761.05_peak_230 199 . 8.48356 24.54293 19.95433 31 +chr1 228558038 228558173 SRX6944761.05_peak_231 105 . 6.19343 14.81318 10.57411 107 +chr1 229342976 229343047 SRX6944761.05_peak_232 83 . 7.22185 12.46035 8.33028 49 +chr1 236097139 236097369 SRX6944761.05_peak_233 617 . 20.90831 67.27776 61.71141 63 +chr1 237042306 237042412 SRX6944761.05_peak_234 92 . 8.07320 13.46296 9.28325 52 +chr1 246785867 246785954 SRX6944761.05_peak_235 139 . 8.51064 18.30868 13.92353 45 +chr1 247857539 247857626 SRX6944761.05_peak_236 90 . 7.90722 13.18921 9.02223 31 +chr1 248945935 248946129 SRX6944761.05_peak_237 448 . 17.38186 50.04066 44.83969 138 +chr10 10010 10401 SRX6944761.05_peak_238 1213 . 21.56929 128.06519 121.36433 173 +chr10 417843 418008 SRX6944761.05_peak_239 208 . 11.02033 25.46582 20.85068 111 +chr10 38487504 38487594 SRX6944761.05_peak_240 482 . 13.88733 53.52024 48.23667 44 +chr10 38489146 38489231 SRX6944761.05_peak_241 647 . 15.83435 70.40879 64.78446 40 +chr10 38494844 38494914 SRX6944761.05_peak_242 234 . 9.36262 28.13137 23.43619 40 +chr10 38496786 38496853 SRX6944761.05_peak_243 181 . 7.87402 22.66520 18.13337 25 +chr10 38509338 38509410 SRX6944761.05_peak_244 293 . 11.99940 34.17935 29.33111 17 +chr10 38517394 38517479 SRX6944761.05_peak_245 349 . 12.22930 39.96349 34.98930 50 +chr10 38573453 38573570 SRX6944761.05_peak_246 1094 . 33.72746 115.73071 109.45228 53 +chr10 38578971 38579070 SRX6944761.05_peak_247 704 . 18.92430 76.13112 70.40085 45 +chr10 38579507 38579580 SRX6944761.05_peak_248 300 . 10.78619 34.86442 30.00112 27 +chr10 38783570 38783652 SRX6944761.05_peak_249 445 . 14.83924 49.73035 44.53728 29 +chr10 38786253 38786353 SRX6944761.05_peak_250 213 . 8.66294 26.01937 21.38711 58 +chr10 38790643 38790715 SRX6944761.05_peak_251 273 . 8.92027 32.16044 27.36055 39 +chr10 38792194 38792335 SRX6944761.05_peak_252 64 . 4.85705 10.46215 6.44523 32 +chr10 38800736 38800806 SRX6944761.05_peak_253 246 . 10.86300 29.43041 24.69840 37 +chr10 38808149 38808216 SRX6944761.05_peak_254 215 . 9.00901 26.23450 21.59588 37 +chr10 38808849 38808914 SRX6944761.05_peak_255 180 . 8.06165 22.57012 18.04087 10 +chr10 38831381 38831450 SRX6944761.05_peak_256 163 . 9.03312 20.80574 16.33451 40 +chr10 38851027 38851100 SRX6944761.05_peak_257 344 . 14.79837 39.38426 34.42059 34 +chr10 38861949 38862014 SRX6944761.05_peak_258 132 . 8.65971 17.56122 13.20496 25 +chr10 38886783 38886849 SRX6944761.05_peak_259 222 . 11.14707 26.89309 22.23605 15 +chr10 38899873 38899967 SRX6944761.05_peak_260 598 . 20.33898 65.35085 59.82434 44 +chr10 38911670 38911735 SRX6944761.05_peak_261 169 . 10.69519 21.49047 16.99621 36 +chr10 38912078 38912163 SRX6944761.05_peak_262 263 . 13.95024 31.08031 26.30700 39 +chr10 38912713 38912790 SRX6944761.05_peak_263 235 . 13.02023 28.26141 23.56179 29 +chr10 38919014 38919237 SRX6944761.05_peak_264 320 . 11.75702 36.96515 32.05505 45 +chr10 38919666 38919740 SRX6944761.05_peak_265 185 . 8.53485 23.05283 18.50886 26 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95.97630 53 +chr10 40140581 40140652 SRX6944761.05_peak_278 226 . 12.09593 27.31758 22.64731 35 +chr10 40156259 40156355 SRX6944761.05_peak_279 389 . 17.91868 44.05449 38.99496 46 +chr10 40180852 40180931 SRX6944761.05_peak_280 329 . 15.56420 37.86756 32.93747 35 +chr10 40198991 40199061 SRX6944761.05_peak_281 205 . 11.91021 25.20236 20.59509 43 +chr10 40213470 40213535 SRX6944761.05_peak_282 176 . 10.80108 22.21198 17.69537 25 +chr10 40214291 40214368 SRX6944761.05_peak_283 211 . 11.90739 25.78777 21.16172 29 +chr10 40252654 40252739 SRX6944761.05_peak_284 223 . 12.99350 27.01267 22.35226 39 +chr10 40276440 40276505 SRX6944761.05_peak_285 190 . 10.62783 23.61462 19.05414 25 +chr10 40300921 40300993 SRX6944761.05_peak_286 248 . 13.81352 29.59055 24.85447 32 +chr10 40318256 40318336 SRX6944761.05_peak_287 398 . 17.57771 44.97778 39.89870 40 +chr10 40327005 40327084 SRX6944761.05_peak_288 234 . 12.94199 28.13022 23.43512 42 +chr10 40447281 40447372 SRX6944761.05_peak_289 474 . 19.34156 52.71978 47.45374 49 +chr10 40556815 40556917 SRX6944761.05_peak_290 638 . 23.55330 69.41985 63.81322 55 +chr10 40606251 40606322 SRX6944761.05_peak_291 273 . 13.01685 32.09938 27.30088 37 +chr10 40614749 40614832 SRX6944761.05_peak_292 297 . 13.68041 34.60639 29.74823 35 +chr10 40634220 40634285 SRX6944761.05_peak_293 155 . 10.39347 19.94588 15.50179 23 +chr10 40654458 40654545 SRX6944761.05_peak_294 401 . 17.72589 45.23202 40.14759 37 +chr10 40746821 40746902 SRX6944761.05_peak_295 297 . 15.29637 34.65670 29.79721 42 +chr10 40767800 40767874 SRX6944761.05_peak_296 211 . 11.52263 25.73385 21.10943 40 +chr10 40792110 40792221 SRX6944761.05_peak_297 906 . 27.75826 96.71088 90.69469 53 +chr10 40829680 40829745 SRX6944761.05_peak_298 180 . 10.34003 22.55134 18.02265 34 +chr10 40867620 40867689 SRX6944761.05_peak_299 211 . 12.23817 25.75059 21.12557 27 +chr10 40910065 40910148 SRX6944761.05_peak_300 272 . 14.52081 32.03872 27.24154 38 +chr10 40934875 40934947 SRX6944761.05_peak_301 190 . 10.65509 23.66148 19.09964 36 +chr10 40946165 40946237 SRX6944761.05_peak_302 310 . 14.83994 35.93045 31.04359 33 +chr10 41028758 41028840 SRX6944761.05_peak_303 405 . 17.10081 45.67210 40.57796 36 +chr10 41063089 41063206 SRX6944761.05_peak_304 965 . 30.47619 102.68337 96.59500 58 +chr10 41125359 41125452 SRX6944761.05_peak_305 321 . 14.71465 37.10887 32.19567 45 +chr10 41133841 41133912 SRX6944761.05_peak_306 257 . 12.50948 30.55629 25.79598 29 +chr10 41134393 41134461 SRX6944761.05_peak_307 235 . 11.89791 28.21604 23.51866 19 +chr10 41197174 41197250 SRX6944761.05_peak_308 214 . 11.71058 26.05377 21.42057 37 +chr10 41315477 41315579 SRX6944761.05_peak_309 815 . 29.13043 87.48394 81.58553 46 +chr10 41331752 41331842 SRX6944761.05_peak_310 472 . 18.75000 52.48738 47.22694 44 +chr10 41415244 41415317 SRX6944761.05_peak_311 276 . 13.58025 32.41500 27.60918 42 +chr10 41830817 41830974 SRX6944761.05_peak_312 224 . 13.07847 27.15485 22.48928 28 +chr10 41843502 41843609 SRX6944761.05_peak_313 395 . 6.55171 44.63189 39.55947 57 +chr10 41843720 41843818 SRX6944761.05_peak_314 362 . 6.11852 41.29876 36.29781 48 +chr10 41844163 41844295 SRX6944761.05_peak_315 168 . 4.19370 21.32421 16.83499 85 +chr10 41845217 41845331 SRX6944761.05_peak_316 245 . 4.85927 29.30412 24.57563 63 +chr10 41845654 41845739 SRX6944761.05_peak_317 386 . 5.95530 43.66711 38.61578 51 +chr10 41847445 41847549 SRX6944761.05_peak_318 516 . 6.23460 57.06030 51.69668 52 +chr10 41847855 41847938 SRX6944761.05_peak_319 186 . 3.82455 23.18011 18.63281 26 +chr10 41850680 41850851 SRX6944761.05_peak_320 204 . 3.82741 25.04913 20.44608 127 +chr10 41851792 41851902 SRX6944761.05_peak_321 261 . 4.21635 30.90876 26.14002 55 +chr10 41854129 41854213 SRX6944761.05_peak_322 242 . 4.05103 28.99972 24.27956 42 +chr10 41854549 41854660 SRX6944761.05_peak_323 449 . 5.21534 50.12507 44.92124 71 +chr10 41858384 41858495 SRX6944761.05_peak_324 185 . 3.43740 23.09978 18.55438 23 +chr10 41859270 41859342 SRX6944761.05_peak_325 272 . 4.01584 32.06691 27.26902 33 +chr10 41859720 41859977 SRX6944761.05_peak_326 364 . 4.61285 41.48079 36.47535 218 +chr10 41861581 41861649 SRX6944761.05_peak_327 166 . 3.38935 21.10044 16.61886 27 +chr10 41865943 41866012 SRX6944761.05_peak_328 180 . 3.63991 22.59041 18.06061 48 +chr10 41867485 41867562 SRX6944761.05_peak_329 246 . 4.13416 29.37793 24.64725 33 +chr10 41869438 41869532 SRX6944761.05_peak_330 289 . 4.38711 33.80450 28.96498 44 +chr10 41872419 41872486 SRX6944761.05_peak_331 198 . 3.61816 24.39484 19.81003 25 +chr10 41873798 41873888 SRX6944761.05_peak_332 221 . 3.72317 26.79122 22.13696 58 +chr10 41875011 41875125 SRX6944761.05_peak_333 114 . 2.90145 15.72810 11.44806 28 +chr10 41875380 41875482 SRX6944761.05_peak_334 312 . 4.23721 36.12347 31.23191 53 +chr10 41876765 41876833 SRX6944761.05_peak_335 222 . 3.64735 26.87449 22.21808 34 +chr10 41877841 41877927 SRX6944761.05_peak_336 244 . 3.73167 29.16683 24.44204 46 +chr10 41880972 41881049 SRX6944761.05_peak_337 159 . 3.04624 20.37914 15.92148 25 +chr10 41883129 41883250 SRX6944761.05_peak_338 254 . 3.56364 30.21150 25.46022 63 +chr10 41883380 41883545 SRX6944761.05_peak_339 244 . 3.48696 29.15429 24.42965 55 +chr10 41883988 41884109 SRX6944761.05_peak_340 229 . 3.36576 27.61937 22.93979 73 +chr10 41889460 41889594 SRX6944761.05_peak_341 312 . 4.00344 36.12692 31.23516 60 +chr10 41892377 41892456 SRX6944761.05_peak_342 141 . 2.93695 18.58525 14.18907 35 +chr10 41896184 41896258 SRX6944761.05_peak_343 280 . 3.86834 32.84648 28.03100 40 +chr10 41896858 41896984 SRX6944761.05_peak_344 290 . 3.97261 33.87814 29.03710 92 +chr10 41898875 41898971 SRX6944761.05_peak_345 304 . 4.12588 35.36359 30.48895 52 +chr10 41899252 41899320 SRX6944761.05_peak_346 284 . 3.93776 33.26245 28.43602 31 +chr10 41903420 41903566 SRX6944761.05_peak_347 203 . 3.46847 24.99866 20.39689 106 +chr10 41903999 41904162 SRX6944761.05_peak_348 175 . 3.29477 22.06823 17.55568 145 +chr10 41904239 41904375 SRX6944761.05_peak_349 211 . 3.57795 25.75771 21.13248 107 +chr10 41909892 41910001 SRX6944761.05_peak_350 112 . 2.83251 15.51513 11.24419 65 +chr10 41910920 41911004 SRX6944761.05_peak_351 217 . 3.58975 26.35075 21.70869 54 +chr10 41911229 41911304 SRX6944761.05_peak_352 292 . 4.08749 34.14006 29.29276 39 +chr10 41914590 41914729 SRX6944761.05_peak_353 259 . 4.66226 30.74907 25.98428 94 +chr10 41914867 41915003 SRX6944761.05_peak_354 170 . 3.99398 21.59070 17.09348 94 +chr10 42066637 42066723 SRX6944761.05_peak_355 317 . 5.74196 36.66655 31.76290 40 +chr10 42067149 42067373 SRX6944761.05_peak_356 358 . 5.90756 40.89080 35.89660 134 +chr10 42067485 42067557 SRX6944761.05_peak_357 150 . 3.95648 19.43510 15.00948 31 +chr10 42067636 42067710 SRX6944761.05_peak_358 279 . 5.13897 32.75043 27.93690 32 +chr10 42068899 42068964 SRX6944761.05_peak_359 288 . 4.90439 33.73025 28.89270 25 +chr10 42070559 42070710 SRX6944761.05_peak_360 194 . 3.66818 24.05742 19.48281 47 +chr10 42071262 42071332 SRX6944761.05_peak_361 335 . 4.52745 38.45283 33.51011 28 +chr10 42074255 42074507 SRX6944761.05_peak_362 342 . 4.65331 39.22791 34.26758 217 +chr10 42075371 42075443 SRX6944761.05_peak_363 321 . 4.37346 37.07408 32.16209 31 +chr10 42078429 42078514 SRX6944761.05_peak_364 309 . 4.41228 35.87297 30.98746 39 +chr10 42079832 42079905 SRX6944761.05_peak_365 225 . 3.93055 27.19971 22.53282 22 +chr10 42080004 42080071 SRX6944761.05_peak_366 136 . 3.27347 17.99775 13.62577 16 +chr10 42080601 42080692 SRX6944761.05_peak_367 78 . 2.73477 11.90369 7.80213 8 +chr10 42083865 42083995 SRX6944761.05_peak_368 252 . 4.21578 30.01805 25.27059 40 +chr10 42085281 42085391 SRX6944761.05_peak_369 502 . 6.09665 55.56275 50.23072 62 +chr10 42088046 42088128 SRX6944761.05_peak_370 262 . 4.46672 30.99281 26.22212 45 +chr10 42089154 42089355 SRX6944761.05_peak_371 180 . 3.89833 22.62200 18.09128 29 +chr10 42090745 42090843 SRX6944761.05_peak_372 338 . 5.16714 38.75389 33.80464 61 +chr10 42094435 42094513 SRX6944761.05_peak_373 460 . 6.14760 51.27020 46.03874 40 +chr10 42096306 42096384 SRX6944761.05_peak_374 364 . 5.33055 41.40612 36.40336 33 +chr10 42097218 42097305 SRX6944761.05_peak_375 398 . 5.50024 44.93728 39.85917 38 +chr10 42099117 42099190 SRX6944761.05_peak_376 364 . 5.22902 41.40449 36.40178 30 +chr10 42099810 42099921 SRX6944761.05_peak_377 446 . 5.58188 49.84984 44.65305 54 +chr10 42100038 42100114 SRX6944761.05_peak_378 179 . 3.72378 22.45677 17.93080 24 +chr10 42101607 42101730 SRX6944761.05_peak_379 391 . 5.56483 44.24489 39.17990 78 +chr10 42102588 42102653 SRX6944761.05_peak_380 161 . 3.93146 20.66030 16.19365 29 +chr10 42104140 42104212 SRX6944761.05_peak_381 173 . 4.29804 21.89809 17.39107 28 +chr10 42104286 42104384 SRX6944761.05_peak_382 561 . 7.73427 61.56618 56.11533 53 +chr10 42104537 42104641 SRX6944761.05_peak_383 465 . 6.99664 51.79440 46.54977 57 +chr10 42149480 42149545 SRX6944761.05_peak_384 97 . 7.34725 13.93239 9.73207 16 +chr10 42165970 42166059 SRX6944761.05_peak_385 351 . 13.67947 40.15592 35.17726 37 +chr10 42299329 42299409 SRX6944761.05_peak_386 428 . 11.91174 47.95022 42.80625 34 +chr10 42304261 42304326 SRX6944761.05_peak_387 144 . 6.46695 18.87181 14.46634 49 +chr10 42312733 42312802 SRX6944761.05_peak_388 228 . 8.17879 27.53242 22.85566 28 +chr10 42318615 42318697 SRX6944761.05_peak_389 333 . 10.63386 38.26072 33.32181 36 +chr10 42319905 42319975 SRX6944761.05_peak_390 251 . 9.63233 29.88094 25.13647 30 +chr10 43648603 43648684 SRX6944761.05_peak_391 61 . 6.45482 10.12368 6.13370 39 +chr10 60180334 60180411 SRX6944761.05_peak_392 470 . 22.30898 52.27663 47.01973 40 +chr10 91220626 91220723 SRX6944761.05_peak_393 56 . 6.34753 9.64082 5.68617 33 +chr10 92375869 92375961 SRX6944761.05_peak_394 55 . 5.67215 9.54341 5.59598 56 +chr10 92975914 92976003 SRX6944761.05_peak_395 74 . 6.67362 11.56485 7.48094 37 +chr10 107285588 107285656 SRX6944761.05_peak_396 233 . 13.94311 28.03977 23.34764 33 +chr10 118181655 118181734 SRX6944761.05_peak_397 473 . 22.50438 52.61448 47.35104 48 +chr10 119181946 119182032 SRX6944761.05_peak_398 71 . 6.44641 11.19128 7.13129 41 +chr10 125895901 125896195 SRX6944761.05_peak_399 276 . 11.43192 32.42072 27.61483 242 +chr10 125896634 125896756 SRX6944761.05_peak_400 66 . 5.50645 10.69712 6.66568 11 +chr10 126947884 126948082 SRX6944761.05_peak_401 77 . 5.93032 11.86061 7.76178 59 +chr10 128464373 128464619 SRX6944761.05_peak_402 1038 . 27.52294 110.08062 103.88985 179 +chr10 133667592 133667693 SRX6944761.05_peak_403 352 . 7.63934 40.24639 35.26617 51 +chr10 133687511 133687585 SRX6944761.05_peak_404 263 . 4.67379 31.15631 26.38096 35 +chr10 133687790 133687932 SRX6944761.05_peak_405 269 . 4.76811 31.79007 26.99897 85 +chr10 133688227 133688364 SRX6944761.05_peak_406 456 . 6.21750 50.90357 45.68102 43 +chr10 133688667 133688768 SRX6944761.05_peak_407 408 . 5.93079 45.99081 40.88990 56 +chr10 133689113 133689287 SRX6944761.05_peak_408 308 . 5.26001 35.70337 30.82236 112 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45 +chr11 101915091 101915203 SRX6944761.05_peak_445 74 . 7.31392 11.52859 7.44655 86 +chr11 102731090 102731266 SRX6944761.05_peak_446 150 . 10.68958 19.48296 15.05427 43 +chr11 130091325 130091401 SRX6944761.05_peak_447 126 . 8.59599 16.96107 12.62860 26 +chr11 131680572 131680695 SRX6944761.05_peak_448 97 . 7.36577 13.96361 9.76186 86 +chr11 135076556 135076621 SRX6944761.05_peak_449 150 . 10.68958 19.48296 15.05427 29 +chr12 10005 10303 SRX6944761.05_peak_450 1251 . 27.97889 132.12259 125.19847 59 +chr12 31388 31495 SRX6944761.05_peak_451 78 . 6.69160 11.99119 7.88473 59 +chr12 2255932 2256040 SRX6944761.05_peak_452 248 . 13.81352 29.59055 24.85447 76 +chr12 9953482 9953564 SRX6944761.05_peak_453 83 . 7.44787 12.43799 8.30913 32 +chr12 17097699 17097880 SRX6944761.05_peak_454 292 . 15.76044 34.12537 29.27832 52 +chr12 20551434 20551542 SRX6944761.05_peak_455 874 . 32.00753 93.41044 87.42702 56 +chr12 22376913 22377023 SRX6944761.05_peak_456 86 . 7.87653 12.78574 8.63765 79 +chr12 34715903 34715996 SRX6944761.05_peak_457 324 . 15.26394 37.35415 32.43573 47 +chr12 34829248 34829322 SRX6944761.05_peak_458 246 . 12.22074 29.40966 24.67804 43 +chr12 34830194 34830279 SRX6944761.05_peak_459 258 . 12.19731 30.64737 25.88504 32 +chr12 34915238 34915334 SRX6944761.05_peak_460 316 . 15.21739 36.57484 31.67336 46 +chr12 35614961 35615038 SRX6944761.05_peak_461 195 . 11.62520 24.14851 19.57137 31 +chr12 37237710 37237845 SRX6944761.05_peak_462 120 . 5.77930 16.31005 12.00541 31 +chr12 37237914 37238101 SRX6944761.05_peak_463 154 . 6.50172 19.84066 15.40027 153 +chr12 37239724 37239799 SRX6944761.05_peak_464 154 . 6.50172 19.84066 15.40027 30 +chr12 37246201 37246416 SRX6944761.05_peak_465 132 . 7.60679 17.64528 13.28557 61 +chr12 37262602 37262674 SRX6944761.05_peak_466 322 . 11.56651 37.21126 32.29611 42 +chr12 37263704 37263774 SRX6944761.05_peak_467 188 . 8.69565 23.38478 18.83140 32 +chr12 37264937 37265022 SRX6944761.05_peak_468 228 . 9.91053 27.54857 22.87132 34 +chr12 37265435 37265503 SRX6944761.05_peak_469 190 . 9.06901 23.59214 19.03223 31 +chr12 74402932 74403037 SRX6944761.05_peak_470 62 . 6.75131 10.30337 6.29613 19 +chr12 94650400 94650475 SRX6944761.05_peak_471 146 . 9.85453 19.05395 14.64157 25 +chr12 130782352 130782446 SRX6944761.05_peak_472 239 . 11.78203 28.63772 23.92651 55 +chr12 131588427 131588626 SRX6944761.05_peak_473 146 . 8.92193 19.02707 14.61590 143 +chr12 132149706 132149788 SRX6944761.05_peak_474 219 . 9.19826 26.64192 21.99169 33 +chr12 133264938 133265304 SRX6944761.05_peak_475 1286 . 23.99025 135.91315 128.66751 182 +chr13 16361856 16361931 SRX6944761.05_peak_476 179 . 7.17455 22.47558 17.94906 30 +chr13 16544300 16544409 SRX6944761.05_peak_477 710 . 15.04904 76.78970 71.05076 52 +chr13 16547991 16548056 SRX6944761.05_peak_478 206 . 7.48863 25.23053 20.62270 26 +chr13 16607753 16607821 SRX6944761.05_peak_479 191 . 7.28398 23.71639 19.15305 23 +chr13 16688966 16689116 SRX6944761.05_peak_480 359 . 9.79334 40.92173 35.92711 50 +chr13 16708932 16709000 SRX6944761.05_peak_481 182 . 7.29087 22.76773 18.23299 30 +chr13 16943473 16943548 SRX6944761.05_peak_482 214 . 7.79582 26.03341 21.40075 30 +chr13 17144806 17144873 SRX6944761.05_peak_483 185 . 7.03420 23.06048 18.51634 33 +chr13 17259305 17259392 SRX6944761.05_peak_484 282 . 9.25569 33.02679 28.20596 49 +chr13 17426712 17426796 SRX6944761.05_peak_485 78 . 5.99970 11.98854 7.88235 47 +chr13 17469509 17469590 SRX6944761.05_peak_486 226 . 9.80526 27.33509 22.66402 43 +chr13 17492243 17492313 SRX6944761.05_peak_487 108 . 6.35076 15.14320 10.89003 19 +chr13 17510864 17510944 SRX6944761.05_peak_488 200 . 8.77193 24.62672 20.03537 45 +chr13 17518151 17518334 SRX6944761.05_peak_489 148 . 7.24977 19.31537 14.89358 118 +chr13 17539014 17539092 SRX6944761.05_peak_490 116 . 6.12137 15.90735 11.61928 43 +chr13 17742830 17742932 SRX6944761.05_peak_491 406 . 11.32824 45.76754 40.67172 63 +chr13 17922374 17922447 SRX6944761.05_peak_492 135 . 6.64381 17.96456 13.59346 23 +chr13 17970858 17970923 SRX6944761.05_peak_493 113 . 6.56980 15.59585 11.32174 26 +chr13 17995594 17995674 SRX6944761.05_peak_494 133 . 6.34543 17.70498 13.34324 46 +chr13 18211832 18211927 SRX6944761.05_peak_495 718 . 17.75537 77.60367 71.85059 55 +chr13 18212070 18212179 SRX6944761.05_peak_496 958 . 21.69822 101.96701 95.88967 54 +chr13 18212382 18212490 SRX6944761.05_peak_497 405 . 12.34568 45.64306 40.54990 54 +chr13 18213017 18213092 SRX6944761.05_peak_498 262 . 9.63154 31.04567 26.27336 41 +chr13 18213571 18213641 SRX6944761.05_peak_499 284 . 10.09421 33.29860 28.47128 34 +chr13 18214863 18214930 SRX6944761.05_peak_500 223 . 8.83853 26.96314 22.30411 16 +chr13 19780181 19780248 SRX6944761.05_peak_501 119 . 7.91082 16.27476 11.97170 45 +chr13 30844092 30844329 SRX6944761.05_peak_502 782 . 19.77132 84.06320 78.21164 111 +chr13 31200031 31200105 SRX6944761.05_peak_503 53 . 5.93743 9.29282 5.36358 64 +chr13 31200253 31200341 SRX6944761.05_peak_504 75 . 6.97512 11.67060 7.58116 47 +chr13 109424149 109424328 SRX6944761.05_peak_505 452 . 21.23142 50.42801 45.21811 117 +chr13 112015066 112015140 SRX6944761.05_peak_506 129 . 9.40361 17.33816 12.99080 27 +chr13 113262835 113263061 SRX6944761.05_peak_507 139 . 9.39597 18.29467 13.91026 40 +chr13 114199685 114199814 SRX6944761.05_peak_508 549 . 20.41974 60.39849 54.97342 76 +chr13 114354167 114354326 SRX6944761.05_peak_509 1525 . 38.08335 162.08385 152.59270 76 +chr14 16030737 16030804 SRX6944761.05_peak_510 161 . 9.87654 20.66016 16.19354 26 +chr14 16055204 16055275 SRX6944761.05_peak_511 270 . 13.60544 31.79794 27.00655 24 +chr14 16097638 16097711 SRX6944761.05_peak_512 358 . 8.81603 40.79943 35.80758 42 +chr14 16098375 16098490 SRX6944761.05_peak_513 668 . 12.17733 72.52420 66.86139 64 +chr14 16100418 16100483 SRX6944761.05_peak_514 190 . 5.46927 23.55987 19.00068 13 +chr14 16102739 16102814 SRX6944761.05_peak_515 275 . 7.12001 32.36178 27.55709 44 +chr14 16103515 16103633 SRX6944761.05_peak_516 323 . 8.40042 37.24110 32.32546 49 +chr14 19267082 19267208 SRX6944761.05_peak_517 111 . 8.21444 15.37891 11.11496 20 +chr14 19668470 19668560 SRX6944761.05_peak_518 72 . 6.30447 11.34268 7.27363 49 +chr14 20506921 20507028 SRX6944761.05_peak_519 74 . 6.65557 11.53525 7.45296 29 +chr14 33984413 33984488 SRX6944761.05_peak_520 135 . 10.00278 17.88420 13.51580 34 +chr14 35948153 35948237 SRX6944761.05_peak_521 129 . 9.08059 17.28113 12.93564 46 +chr14 35948373 35948502 SRX6944761.05_peak_522 119 . 8.75576 16.27159 11.96870 30 +chr14 45635610 45635756 SRX6944761.05_peak_523 145 . 9.82456 19.00438 14.59399 91 +chr14 91293514 91293579 SRX6944761.05_peak_524 120 . 7.96669 16.37188 12.06440 26 +chr14 94346027 94346097 SRX6944761.05_peak_525 101 . 7.84827 14.32580 10.10721 39 +chr14 105582740 105582836 SRX6944761.05_peak_526 132 . 7.83576 17.59030 13.23298 43 +chr14_GL000194v1_random 190746 190867 SRX6944761.05_peak_527 80 . 6.77831 12.13535 8.02176 14 +chr14_GL000225v1_random 144 217 SRX6944761.05_peak_528 238 . 7.42818 28.51530 23.80843 41 +chr14_GL000225v1_random 822 897 SRX6944761.05_peak_529 324 . 8.12582 37.39522 32.47627 36 +chr14_GL000225v1_random 1183 1275 SRX6944761.05_peak_530 525 . 10.16990 57.93195 52.55260 39 +chr14_GL000225v1_random 6800 6958 SRX6944761.05_peak_531 498 . 7.33660 55.22089 49.89508 115 +chr14_GL000225v1_random 13689 13756 SRX6944761.05_peak_532 227 . 4.50138 27.43620 22.76205 34 +chr14_GL000225v1_random 13889 14090 SRX6944761.05_peak_533 364 . 5.62107 41.49501 36.48916 155 +chr14_GL000225v1_random 14390 14457 SRX6944761.05_peak_534 236 . 4.59703 28.36296 23.66003 42 +chr14_GL000225v1_random 15161 15257 SRX6944761.05_peak_535 298 . 5.05280 34.72245 29.86173 53 +chr14_GL000225v1_random 21755 21824 SRX6944761.05_peak_536 315 . 5.04969 36.48466 31.58510 32 +chr14_GL000225v1_random 24833 24908 SRX6944761.05_peak_537 207 . 4.22647 25.36027 20.74826 21 +chr14_GL000225v1_random 25570 25719 SRX6944761.05_peak_538 508 . 6.41060 56.19785 50.85109 72 +chr14_GL000225v1_random 25932 25998 SRX6944761.05_peak_539 202 . 4.09998 24.81260 20.21570 44 +chr14_GL000225v1_random 31917 32002 SRX6944761.05_peak_540 236 . 4.59703 28.36296 23.66003 34 +chr14_GL000225v1_random 33643 33749 SRX6944761.05_peak_541 434 . 6.13568 48.63228 43.46829 48 +chr14_GL000225v1_random 40923 41014 SRX6944761.05_peak_542 348 . 5.61340 39.86931 34.89720 47 +chr14_GL000225v1_random 44536 44607 SRX6944761.05_peak_543 237 . 4.61098 28.45113 23.74591 37 +chr14_GL000225v1_random 45961 46179 SRX6944761.05_peak_544 415 . 5.68671 46.70813 41.59238 41 +chr14_GL000225v1_random 50837 50974 SRX6944761.05_peak_545 569 . 6.64229 62.40520 56.93809 74 +chr14_GL000225v1_random 65036 65136 SRX6944761.05_peak_546 315 . 4.85281 36.46574 31.56656 46 +chr14_GL000225v1_random 66217 66336 SRX6944761.05_peak_547 393 . 5.09887 44.38212 39.31433 57 +chr14_GL000225v1_random 66488 66563 SRX6944761.05_peak_548 257 . 4.20726 30.54107 25.78157 46 +chr14_GL000225v1_random 67503 67588 SRX6944761.05_peak_549 324 . 4.75597 37.41912 32.49969 44 +chr14_GL000225v1_random 70447 70520 SRX6944761.05_peak_550 251 . 4.12532 29.88456 25.14007 33 +chr14_GL000225v1_random 71164 71270 SRX6944761.05_peak_551 254 . 4.19559 30.15425 25.40361 62 +chr14_GL000225v1_random 73954 74034 SRX6944761.05_peak_552 420 . 5.81395 47.22766 42.09834 37 +chr14_GL000225v1_random 74793 74880 SRX6944761.05_peak_553 513 . 6.36022 56.70734 51.34995 43 +chr14_GL000225v1_random 78897 79020 SRX6944761.05_peak_554 349 . 5.44619 39.93776 34.96422 50 +chr14_GL000225v1_random 82154 82245 SRX6944761.05_peak_555 483 . 6.35717 53.61585 48.32926 41 +chr14_GL000225v1_random 84445 84526 SRX6944761.05_peak_556 279 . 5.01702 32.71342 27.90081 43 +chr14_GL000225v1_random 85616 85736 SRX6944761.05_peak_557 655 . 7.71632 71.15281 65.51250 65 +chr14_GL000225v1_random 86501 86594 SRX6944761.05_peak_558 334 . 5.38522 38.38886 33.44772 36 +chr14_GL000225v1_random 90922 91005 SRX6944761.05_peak_559 389 . 5.75531 43.98877 38.93080 36 +chr14_GL000225v1_random 92321 92393 SRX6944761.05_peak_560 175 . 3.93658 22.05786 17.54557 46 +chr14_GL000225v1_random 92852 92920 SRX6944761.05_peak_561 195 . 4.12700 24.12851 19.55169 30 +chr14_GL000225v1_random 93494 93559 SRX6944761.05_peak_562 262 . 4.66061 31.06712 26.29427 22 +chr14_GL000225v1_random 95554 95632 SRX6944761.05_peak_563 238 . 4.43554 28.57881 23.86972 39 +chr14_GL000225v1_random 97046 97141 SRX6944761.05_peak_564 403 . 5.62049 45.43217 40.34422 42 +chr14_GL000225v1_random 99245 99452 SRX6944761.05_peak_565 364 . 5.55146 41.42060 36.41755 171 +chr14_GL000225v1_random 101303 101374 SRX6944761.05_peak_566 331 . 5.52334 38.12635 33.18951 35 +chr14_GL000225v1_random 101561 101745 SRX6944761.05_peak_567 567 . 7.28274 62.23574 56.77232 136 +chr14_GL000225v1_random 102093 102174 SRX6944761.05_peak_568 394 . 6.16194 44.53827 39.46763 40 +chr14_GL000225v1_random 102408 102554 SRX6944761.05_peak_569 360 . 5.80235 41.06252 36.06554 101 +chr14_GL000225v1_random 104127 104266 SRX6944761.05_peak_570 117 . 3.44837 16.01747 11.72503 23 +chr14_GL000225v1_random 107262 107342 SRX6944761.05_peak_571 406 . 5.78194 45.78560 40.68940 40 +chr14_GL000225v1_random 108271 108372 SRX6944761.05_peak_572 420 . 5.75699 47.22129 42.09243 46 +chr14_GL000225v1_random 108646 108715 SRX6944761.05_peak_573 183 . 3.93690 22.87992 18.34110 34 +chr14_GL000225v1_random 110483 110556 SRX6944761.05_peak_574 313 . 4.96641 36.25217 31.35771 37 +chr14_GL000225v1_random 111102 111212 SRX6944761.05_peak_575 199 . 4.01535 24.51824 19.93035 51 +chr14_GL000225v1_random 111478 111611 SRX6944761.05_peak_576 435 . 5.74727 48.75865 43.59267 89 +chr14_GL000225v1_random 112821 112895 SRX6944761.05_peak_577 206 . 3.95841 25.22292 20.61519 37 +chr14_GL000225v1_random 115513 115580 SRX6944761.05_peak_578 310 . 4.69781 35.96248 31.07508 31 +chr14_GL000225v1_random 116871 116940 SRX6944761.05_peak_579 259 . 4.42815 30.71137 25.94736 39 +chr14_GL000225v1_random 117738 117838 SRX6944761.05_peak_580 381 . 5.36994 43.20196 38.15955 45 +chr14_GL000225v1_random 119182 119255 SRX6944761.05_peak_581 327 . 5.01921 37.68961 32.76323 31 +chr14_GL000225v1_random 120588 120748 SRX6944761.05_peak_582 386 . 5.48919 43.69780 38.64578 28 +chr14_GL000225v1_random 122891 122971 SRX6944761.05_peak_583 380 . 5.51392 43.10265 38.06178 34 +chr14_GL000225v1_random 125133 125207 SRX6944761.05_peak_584 357 . 5.45111 40.73741 35.74673 40 +chr14_GL000225v1_random 126964 127034 SRX6944761.05_peak_585 200 . 4.03535 24.65618 20.06401 33 +chr14_GL000225v1_random 129147 129246 SRX6944761.05_peak_586 383 . 5.19935 43.38225 38.33693 53 +chr14_GL000225v1_random 130405 130604 SRX6944761.05_peak_587 337 . 4.91529 38.66448 33.71725 147 +chr14_GL000225v1_random 131701 131794 SRX6944761.05_peak_588 502 . 5.99492 55.59881 50.26627 44 +chr14_GL000225v1_random 133705 133853 SRX6944761.05_peak_589 490 . 5.89186 54.35650 49.05231 48 +chr14_GL000225v1_random 134992 135072 SRX6944761.05_peak_590 164 . 3.69050 20.95211 16.47573 55 +chr14_GL000225v1_random 137679 137755 SRX6944761.05_peak_591 320 . 5.51978 36.94074 32.03104 40 +chr14_GL000225v1_random 140800 140896 SRX6944761.05_peak_592 613 . 9.58746 66.94086 61.38224 46 +chr14_GL000225v1_random 177420 177538 SRX6944761.05_peak_593 99 . 6.70425 14.11560 9.90618 17 +chr14_GL000225v1_random 177635 177763 SRX6944761.05_peak_594 227 . 10.46476 27.45046 22.77596 95 +chr14_GL000225v1_random 196970 197046 SRX6944761.05_peak_595 429 . 12.80494 48.10989 42.96172 28 +chr14_KI270723v1_random 35223 35422 SRX6944761.05_peak_596 496 . 9.56665 55.01313 49.69251 71 +chr14_KI270723v1_random 35707 35888 SRX6944761.05_peak_597 316 . 7.46912 36.57663 31.67510 134 +chr14_KI270723v1_random 36777 36844 SRX6944761.05_peak_598 262 . 6.87000 30.98529 26.21478 38 +chr14_KI270723v1_random 37041 37134 SRX6944761.05_peak_599 316 . 7.60411 36.57911 31.67750 50 +chr14_KI270724v1_random 511 590 SRX6944761.05_peak_600 220 . 6.82927 26.66757 22.01674 38 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5.23669 26.32233 21.68098 59 +chr15 19782338 19782453 SRX6944761.05_peak_613 120 . 4.20187 16.35805 12.05130 40 +chr15 19789795 19789867 SRX6944761.05_peak_614 239 . 11.08575 28.61988 23.90915 35 +chr15 25758689 25758777 SRX6944761.05_peak_615 141 . 9.86680 18.57901 14.18313 39 +chr15 28737949 28738018 SRX6944761.05_peak_616 91 . 7.72014 13.27185 9.10101 19 +chr15 35546154 35546237 SRX6944761.05_peak_617 86 . 7.87653 12.78574 8.63765 60 +chr15 83284411 83284544 SRX6944761.05_peak_618 84 . 7.28597 12.56479 8.42942 75 +chr15 101981047 101981170 SRX6944761.05_peak_619 571 . 20.25946 62.64604 57.17434 56 +chr16 458427 458604 SRX6944761.05_peak_620 209 . 11.07504 25.56113 20.94333 42 +chr16 492381 492456 SRX6944761.05_peak_621 188 . 9.57854 23.45509 18.89981 32 +chr16 494832 495024 SRX6944761.05_peak_622 296 . 12.50401 34.48714 29.63248 38 +chr16 1025263 1025642 SRX6944761.05_peak_623 571 . 15.19262 62.61803 57.14692 141 +chr16 8953734 8953826 SRX6944761.05_peak_624 89 . 7.62449 13.11599 8.95227 32 +chr16 14487024 14487119 SRX6944761.05_peak_625 827 . 32.57158 88.61646 82.70015 39 +chr16 33336710 33336857 SRX6944761.05_peak_626 249 . 6.52345 29.70234 24.96324 105 +chr16 33337705 33337931 SRX6944761.05_peak_627 109 . 4.45658 15.21169 10.95532 211 +chr16 33339816 33339887 SRX6944761.05_peak_628 151 . 5.09921 19.54864 15.11785 44 +chr16 33491773 33491874 SRX6944761.05_peak_629 165 . 9.43739 20.98749 16.51007 37 +chr16 34062751 34062883 SRX6944761.05_peak_630 185 . 5.76844 23.10626 18.56072 23 +chr16 34063038 34063157 SRX6944761.05_peak_631 320 . 7.58239 37.00164 32.09100 81 +chr16 34063882 34064103 SRX6944761.05_peak_632 282 . 7.04632 33.04619 28.22473 39 +chr16 34064192 34064265 SRX6944761.05_peak_633 296 . 7.18031 34.50670 29.65179 30 +chr16 34064681 34064851 SRX6944761.05_peak_634 117 . 4.61140 16.07918 11.78401 40 +chr16 34067405 34067522 SRX6944761.05_peak_635 186 . 5.28867 23.21280 18.66473 74 +chr16 34071566 34071656 SRX6944761.05_peak_636 715 . 16.05976 77.30038 71.55196 50 +chr16 34073213 34073335 SRX6944761.05_peak_637 208 . 8.20966 25.50628 20.88990 60 +chr16 34085171 34085352 SRX6944761.05_peak_638 272 . 10.93344 32.05169 27.25421 61 +chr16 34097086 34097178 SRX6944761.05_peak_639 201 . 6.96533 24.74997 20.15464 31 +chr16 34097293 34097562 SRX6944761.05_peak_640 973 . 17.76650 103.42248 97.32350 121 +chr16 34286265 34286369 SRX6944761.05_peak_641 91 . 7.72014 13.27185 9.10101 9 +chr16 34453921 34454191 SRX6944761.05_peak_642 705 . 21.50207 76.31693 70.58408 227 +chr16 34571733 34571873 SRX6944761.05_peak_643 172 . 3.35455 21.70951 17.20849 88 +chr16 34571996 34572120 SRX6944761.05_peak_644 172 . 3.35455 21.70951 17.20849 23 +chr16 34572210 34572322 SRX6944761.05_peak_645 131 . 3.04959 17.49470 13.14137 71 +chr16 34572397 34572462 SRX6944761.05_peak_646 188 . 3.46891 23.37178 18.81891 35 +chr16 34572576 34572855 SRX6944761.05_peak_647 439 . 4.99371 49.13660 43.95897 102 +chr16 34573026 34573245 SRX6944761.05_peak_648 425 . 4.91747 47.70776 42.56882 40 +chr16 34573944 34574157 SRX6944761.05_peak_649 439 . 4.99371 49.13660 43.95897 107 +chr16 34574238 34574318 SRX6944761.05_peak_650 425 . 4.91747 47.70776 42.56882 41 +chr16 34575587 34575777 SRX6944761.05_peak_651 404 . 4.80311 45.58961 40.49774 39 +chr16 34576010 34576143 SRX6944761.05_peak_652 121 . 2.94650 16.46018 12.14930 32 +chr16 34576414 34576600 SRX6944761.05_peak_653 357 . 4.30976 40.72773 35.73728 63 +chr16 34581006 34581100 SRX6944761.05_peak_654 215 . 3.04244 26.14470 21.50934 71 +chr16 34581426 34581646 SRX6944761.05_peak_655 238 . 3.13637 28.52162 23.81441 60 +chr16 34582011 34582079 SRX6944761.05_peak_656 209 . 2.92276 25.54308 20.92576 23 +chr16 34582204 34582438 SRX6944761.05_peak_657 189 . 2.75897 23.51333 18.95551 191 +chr16 34582696 34582833 SRX6944761.05_peak_658 174 . 2.64596 21.95417 17.44473 110 +chr16 34582931 34583178 SRX6944761.05_peak_659 158 . 2.53099 20.34085 15.88431 165 +chr16 34583310 34583493 SRX6944761.05_peak_660 140 . 2.39589 18.41210 14.02312 43 +chr16 34583597 34583840 SRX6944761.05_peak_661 127 . 2.31298 17.09305 12.75483 47 +chr16 34584713 34584816 SRX6944761.05_peak_662 98 . 2.12378 14.04026 9.83558 34 +chr16 34584893 34585018 SRX6944761.05_peak_663 91 . 2.08444 13.28565 9.11417 42 +chr16 34586498 34586665 SRX6944761.05_peak_664 106 . 2.15668 14.92163 10.67758 96 +chr16 34586811 34586878 SRX6944761.05_peak_665 107 . 2.15992 14.96938 10.72339 31 +chr16 34588037 34588285 SRX6944761.05_peak_666 126 . 2.29494 16.94965 12.61757 86 +chr16 34590318 34590406 SRX6944761.05_peak_667 141 . 2.40561 18.55204 14.15724 42 +chr16 34592702 34592842 SRX6944761.05_peak_668 224 . 3.03027 27.14463 22.47939 116 +chr16 34593225 34593294 SRX6944761.05_peak_669 260 . 3.33397 30.80166 26.03547 30 +chr16 34593371 34593644 SRX6944761.05_peak_670 285 . 3.52596 33.37353 28.54473 194 +chr16 34594589 34594660 SRX6944761.05_peak_671 154 . 2.94021 19.90214 15.45948 50 +chr16 34595016 34595260 SRX6944761.05_peak_672 344 . 4.05196 39.44620 34.48169 204 +chr16 34595340 34595696 SRX6944761.05_peak_673 370 . 4.29825 42.11708 37.09852 143 +chr16 34595819 34595931 SRX6944761.05_peak_674 180 . 3.25253 22.54228 18.01398 76 +chr16 34596064 34596155 SRX6944761.05_peak_675 316 . 4.10225 36.56755 31.66613 34 +chr16 34609823 34609890 SRX6944761.05_peak_676 200 . 9.31809 24.64081 20.04897 20 +chr16 34616239 34616308 SRX6944761.05_peak_677 207 . 10.32645 25.41109 20.79746 21 +chr16 34631408 34631473 SRX6944761.05_peak_678 165 . 10.09251 21.02461 16.54587 30 +chr16 34803832 34803923 SRX6944761.05_peak_679 158 . 10.32379 20.34393 15.88727 45 +chr16 34894412 34894685 SRX6944761.05_peak_680 778 . 23.43255 83.72427 77.87953 40 +chr16 36252084 36252161 SRX6944761.05_peak_681 356 . 16.33706 40.62273 35.63486 26 +chr16 36558939 36559020 SRX6944761.05_peak_682 239 . 12.54705 28.70804 23.99482 46 +chr16 36724137 36724227 SRX6944761.05_peak_683 279 . 13.76434 32.73080 27.91775 53 +chr16 37265885 37265975 SRX6944761.05_peak_684 192 . 11.11586 23.86880 19.29984 47 +chr16 37292244 37292313 SRX6944761.05_peak_685 219 . 12.36830 26.56144 21.91400 33 +chr16 37619722 37619787 SRX6944761.05_peak_686 112 . 8.30783 15.53325 11.26159 41 +chr16 37926226 37926330 SRX6944761.05_peak_687 382 . 17.05393 43.32616 38.28165 51 +chr16 38265972 38266058 SRX6944761.05_peak_688 316 . 10.70473 36.55526 31.65423 37 +chr16 38266290 38266489 SRX6944761.05_peak_689 211 . 8.53645 25.73305 21.10891 166 +chr16 38266903 38267219 SRX6944761.05_peak_690 334 . 11.20384 38.34295 33.40249 223 +chr16 38275916 38276010 SRX6944761.05_peak_691 432 . 10.47362 48.43947 43.28048 52 +chr16 38276167 38276445 SRX6944761.05_peak_692 308 . 8.62534 35.76618 30.88342 228 +chr16 38278052 38278137 SRX6944761.05_peak_693 83 . 4.62072 12.53184 8.39795 48 +chr16 38279176 38279252 SRX6944761.05_peak_694 187 . 6.62303 23.29868 18.74811 32 +chr16 38279450 38279515 SRX6944761.05_peak_695 214 . 7.08510 26.04270 21.40977 31 +chr16 38280341 38280433 SRX6944761.05_peak_696 223 . 7.23912 26.97621 22.31684 41 +chr16 46380791 46381063 SRX6944761.05_peak_697 331 . 4.92839 38.06948 33.13396 105 +chr16 46383143 46383210 SRX6944761.05_peak_698 116 . 2.56025 15.95834 11.66827 44 +chr16 46385286 46385373 SRX6944761.05_peak_699 113 . 2.22071 15.64545 11.36884 23 +chr16 46386418 46386611 SRX6944761.05_peak_700 78 . 1.95311 11.90176 7.80044 68 +chr16 46387635 46387701 SRX6944761.05_peak_701 67 . 1.87655 10.76351 6.72798 53 +chr16 46387824 46388027 SRX6944761.05_peak_702 66 . 1.87393 10.72468 6.69168 148 +chr16 46388147 46388318 SRX6944761.05_peak_703 63 . 1.85635 10.39390 6.38145 150 +chr16 46388665 46388785 SRX6944761.05_peak_704 66 . 1.86837 10.64214 6.61440 73 +chr16 46389668 46389822 SRX6944761.05_peak_705 57 . 1.80754 9.74096 5.77928 132 +chr16 46389932 46390014 SRX6944761.05_peak_706 53 . 1.77695 9.28942 5.36066 46 +chr16 46390239 46390363 SRX6944761.05_peak_707 50 . 1.75681 8.99313 5.09956 68 +chr16 46390492 46390773 SRX6944761.05_peak_708 50 . 1.75574 8.97741 5.08584 178 +chr16 46394641 46394888 SRX6944761.05_peak_709 79 . 1.96071 12.01481 7.90726 165 +chr16 46397356 46397434 SRX6944761.05_peak_710 99 . 2.11286 14.17639 9.96406 38 +chr16 46398545 46398945 SRX6944761.05_peak_711 131 . 2.33104 17.47459 13.12259 316 +chr16 46399021 46399092 SRX6944761.05_peak_712 101 . 2.18581 14.40089 10.17941 36 +chr16 46399181 46399568 SRX6944761.05_peak_713 149 . 2.46595 19.41667 14.99158 327 +chr16 46399666 46399741 SRX6944761.05_peak_714 155 . 2.52404 19.95982 15.51535 43 +chr16 46399809 46399916 SRX6944761.05_peak_715 164 . 2.59704 20.97315 16.49638 22 +chr16 46400024 46400678 SRX6944761.05_peak_716 194 . 2.79455 23.99855 19.42596 85 +chr16 46400826 46400924 SRX6944761.05_peak_717 223 . 3.02254 27.04342 22.38198 51 +chr16 46401082 46401263 SRX6944761.05_peak_718 223 . 3.03451 27.00777 22.34751 29 +chr16 46401342 46401627 SRX6944761.05_peak_719 237 . 3.13491 28.50279 23.79627 205 +chr16 46421910 46422003 SRX6944761.05_peak_720 79 . 6.47249 12.02535 7.91727 29 +chr16 46465969 46466157 SRX6944761.05_peak_721 505 . 19.78239 55.93420 50.59610 50 +chr16 82352309 82352382 SRX6944761.05_peak_722 265 . 15.19046 31.31452 26.53525 40 +chr16 85972739 85972887 SRX6944761.05_peak_723 128 . 8.75912 17.23446 12.89110 46 +chr16_KI270728v1_random 232638 232844 SRX6944761.05_peak_724 220 . 10.10101 26.75087 22.09726 64 +chr16_KI270728v1_random 1791407 1791659 SRX6944761.05_peak_725 279 . 10.12076 32.75718 27.94356 197 +chr16_KI270728v1_random 1792163 1792268 SRX6944761.05_peak_726 221 . 8.74832 26.75802 22.10432 60 +chr17 117748 117813 SRX6944761.05_peak_727 172 . 9.85595 21.71771 17.21636 45 +chr17 198420 198496 SRX6944761.05_peak_728 146 . 8.92193 19.02707 14.61590 35 +chr17 314749 314876 SRX6944761.05_peak_729 74 . 5.56218 11.54538 7.46239 102 +chr17 315496 315575 SRX6944761.05_peak_730 77 . 5.74006 11.88996 7.78929 46 +chr17 841421 841680 SRX6944761.05_peak_731 410 . 15.69020 46.15845 41.05507 51 +chr17 952518 952611 SRX6944761.05_peak_732 192 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4.46838 32.57872 27.76894 101 +chr17 21979567 21979632 SRX6944761.05_peak_745 238 . 4.30290 28.57721 23.86819 28 +chr17 21979704 21979857 SRX6944761.05_peak_746 197 . 3.98768 24.32668 19.74430 122 +chr17 21980558 21980623 SRX6944761.05_peak_747 236 . 4.30892 28.31752 23.61642 40 +chr17 21983049 21983377 SRX6944761.05_peak_748 224 . 4.71120 27.08213 22.41942 119 +chr17 21983707 21983795 SRX6944761.05_peak_749 360 . 5.80706 41.09190 36.09452 48 +chr17 21985324 21985493 SRX6944761.05_peak_750 350 . 5.89463 39.99037 35.01535 40 +chr17 21988630 21988777 SRX6944761.05_peak_751 449 . 7.70581 50.18625 44.98091 95 +chr17 21989124 21989200 SRX6944761.05_peak_752 448 . 8.01068 50.08542 44.88340 28 +chr17 21990936 21991001 SRX6944761.05_peak_753 231 . 6.27100 27.87166 23.18461 32 +chr17 21991868 21991942 SRX6944761.05_peak_754 153 . 5.48155 19.76088 15.32304 28 +chr17 22521362 22521452 SRX6944761.05_peak_755 616 . 24.30243 67.18242 61.61854 41 +chr17 22880133 22880200 SRX6944761.05_peak_756 184 . 11.56942 22.94394 18.40315 33 +chr17 23016265 23016333 SRX6944761.05_peak_757 270 . 14.43001 31.88625 27.09262 33 +chr17 23199591 23199656 SRX6944761.05_peak_758 150 . 10.68958 19.48296 15.05427 14 +chr17 23256078 23256150 SRX6944761.05_peak_759 265 . 15.19046 31.31452 26.53525 37 +chr17 23583327 23583398 SRX6944761.05_peak_760 280 . 15.75307 32.87144 28.05424 37 +chr17 23804554 23804630 SRX6944761.05_peak_761 374 . 19.12872 42.51553 37.48697 58 +chr17 24150949 24151016 SRX6944761.05_peak_762 206 . 12.94002 25.25061 20.64184 57 +chr17 24188118 24188191 SRX6944761.05_peak_763 341 . 17.91212 39.08892 34.13218 31 +chr17 24354969 24355156 SRX6944761.05_peak_764 206 . 12.94002 25.25061 20.64184 137 +chr17 24421411 24421511 SRX6944761.05_peak_765 1214 . 42.06879 128.13869 121.42352 48 +chr17 24718762 24718833 SRX6944761.05_peak_766 280 . 15.75307 32.87144 28.05424 34 +chr17 24740324 24740392 SRX6944761.05_peak_767 250 . 14.62785 29.77336 25.03155 47 +chr17 24752005 24752097 SRX6944761.05_peak_768 652 . 27.50595 70.90506 65.27191 36 +chr17 25074435 25074521 SRX6944761.05_peak_769 639 . 27.68549 69.50944 63.90156 44 +chr17 25115485 25115581 SRX6944761.05_peak_770 1003 . 36.72032 106.45631 100.31483 48 +chr17 25139934 25140006 SRX6944761.05_peak_771 280 . 15.75307 32.87144 28.05424 38 +chr17 25242963 25243028 SRX6944761.05_peak_772 137 . 10.12697 18.09395 13.71690 47 +chr17 25383732 25383836 SRX6944761.05_peak_773 1298 . 42.22475 137.13199 129.83951 51 +chr17 25389864 25389944 SRX6944761.05_peak_774 602 . 26.00849 65.78411 60.24663 48 +chr17 25569187 25569289 SRX6944761.05_peak_775 1253 . 41.35079 132.32396 125.37812 53 +chr17 25788594 25788666 SRX6944761.05_peak_776 358 . 18.56611 40.87443 35.88061 54 +chr17 25828663 25828737 SRX6944761.05_peak_777 391 . 19.69133 44.16922 39.10630 21 +chr17 25920155 25920256 SRX6944761.05_peak_778 1355 . 41.74573 142.97725 135.53918 49 +chr17 26041156 26041233 SRX6944761.05_peak_779 306 . 16.61130 35.57222 30.69381 55 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61.73225 36 +chr17 26619799 26619899 SRX6944761.05_peak_792 594 . 19.68059 64.95328 59.43387 50 +chr17 26619983 26620073 SRX6944761.05_peak_793 871 . 25.38474 93.15811 87.17979 46 +chr17 26783398 26783492 SRX6944761.05_peak_794 568 . 17.12833 62.29267 56.82732 37 +chr17 26802514 26802590 SRX6944761.05_peak_795 377 . 12.88826 42.74256 37.71001 30 +chr17 26828334 26828400 SRX6944761.05_peak_796 210 . 8.48688 25.62017 21.00010 29 +chr17 26833160 26833228 SRX6944761.05_peak_797 241 . 9.71116 28.88762 24.16981 19 +chr17 26856830 26856895 SRX6944761.05_peak_798 171 . 8.36933 21.65892 17.15947 37 +chr17 26879229 26879294 SRX6944761.05_peak_799 147 . 6.25924 19.20873 14.79060 49 +chr17 26881313 26881388 SRX6944761.05_peak_800 281 . 7.72094 32.93620 28.11733 41 +chr17 26884202 26884281 SRX6944761.05_peak_801 459 . 10.95719 51.18159 45.95290 50 +chr17 26885257 26885460 SRX6944761.05_peak_802 357 . 10.15138 40.71827 35.72815 49 +chr17 26885733 26885878 SRX6944761.05_peak_803 652 . 14.86180 70.91299 65.27911 52 +chr17 26936786 26936930 SRX6944761.05_peak_804 859 . 14.12412 91.86170 85.90076 57 +chr17 26938094 26938198 SRX6944761.05_peak_805 203 . 6.17932 24.96115 20.36018 49 +chr17 26939929 26940013 SRX6944761.05_peak_806 193 . 5.98839 23.87881 19.30956 38 +chr17 26940725 26940891 SRX6944761.05_peak_807 207 . 6.18369 25.40361 20.79028 39 +chr17 30523519 30523597 SRX6944761.05_peak_808 143 . 9.96264 18.73747 14.33611 45 +chr17 43246601 43246671 SRX6944761.05_peak_809 190 . 7.42716 23.59597 19.03594 42 +chr17 43300652 43300751 SRX6944761.05_peak_810 111 . 6.48460 15.42069 11.15326 20 +chr17 44524882 44524955 SRX6944761.05_peak_811 133 . 8.77193 17.75149 13.38793 51 +chr17 66269309 66269374 SRX6944761.05_peak_812 63 . 6.58228 10.32947 6.32077 13 +chr17 82586269 82586433 SRX6944761.05_peak_813 136 . 8.61262 17.98128 13.60971 128 +chr17_GL000205v2_random 39372 39438 SRX6944761.05_peak_814 107 . 5.90219 14.99701 10.74975 44 +chr17_KI270729v1_random 146 234 SRX6944761.05_peak_815 119 . 2.94924 16.29773 11.99374 63 +chr17_KI270729v1_random 963 1096 SRX6944761.05_peak_816 364 . 4.23592 41.45086 36.44616 74 +chr17_KI270729v1_random 2299 2983 SRX6944761.05_peak_817 229 . 3.45733 27.67749 22.99621 61 +chr17_KI270729v1_random 3073 3244 SRX6944761.05_peak_818 158 . 3.01730 20.29152 15.83638 82 +chr17_KI270729v1_random 3403 3626 SRX6944761.05_peak_819 345 . 4.08593 39.53609 34.56968 185 +chr17_KI270729v1_random 4307 4374 SRX6944761.05_peak_820 240 . 3.52019 28.79738 24.08194 29 +chr17_KI270729v1_random 4537 4747 SRX6944761.05_peak_821 112 . 2.70300 15.54389 11.27186 64 +chr17_KI270729v1_random 5781 5954 SRX6944761.05_peak_822 269 . 3.72953 31.70111 26.91272 115 +chr17_KI270729v1_random 10486 10560 SRX6944761.05_peak_823 359 . 7.22708 40.98845 35.99304 44 +chr17_KI270729v1_random 11035 11236 SRX6944761.05_peak_824 732 . 13.62152 79.01994 73.24712 142 +chr17_KI270729v1_random 11483 11771 SRX6944761.05_peak_825 661 . 13.63601 71.78185 66.13131 237 +chr17_KI270729v1_random 13048 13187 SRX6944761.05_peak_826 733 . 14.56574 79.13166 73.35719 89 +chr17_KI270729v1_random 20508 20591 SRX6944761.05_peak_827 186 . 4.20698 23.16811 18.62104 23 +chr17_KI270729v1_random 21050 21217 SRX6944761.05_peak_828 262 . 4.75665 31.04145 26.26939 29 +chr17_KI270729v1_random 21488 21585 SRX6944761.05_peak_829 412 . 5.85741 46.31122 41.20430 47 +chr17_KI270729v1_random 22049 22114 SRX6944761.05_peak_830 231 . 4.36542 27.79374 23.10878 27 +chr17_KI270729v1_random 22415 22735 SRX6944761.05_peak_831 464 . 6.03659 51.73577 46.49231 60 +chr17_KI270729v1_random 23439 23601 SRX6944761.05_peak_832 370 . 5.07874 42.06351 37.04644 125 +chr17_KI270729v1_random 24628 24784 SRX6944761.05_peak_833 407 . 5.27523 45.83651 40.73885 42 +chr17_KI270729v1_random 25099 25207 SRX6944761.05_peak_834 342 . 4.84937 39.18007 34.22084 63 +chr17_KI270729v1_random 27740 27876 SRX6944761.05_peak_835 608 . 8.02257 66.40627 60.85644 80 +chr17_KI270729v1_random 33954 34023 SRX6944761.05_peak_836 273 . 10.99203 32.17133 27.37110 17 +chr17_KI270729v1_random 43354 43455 SRX6944761.05_peak_837 727 . 17.14286 78.54430 72.77817 51 +chr17_KI270729v1_random 47177 47256 SRX6944761.05_peak_838 479 . 12.90579 53.25164 47.97295 44 +chr17_KI270729v1_random 54641 54768 SRX6944761.05_peak_839 865 . 19.68462 92.55389 86.58571 55 +chr17_KI270729v1_random 56982 57067 SRX6944761.05_peak_840 367 . 11.27466 41.73469 36.72316 46 +chr17_KI270729v1_random 59166 59236 SRX6944761.05_peak_841 267 . 9.10981 31.51973 26.73649 40 +chr17_KI270729v1_random 59691 59759 SRX6944761.05_peak_842 235 . 8.91111 28.23613 23.53822 36 +chr17_KI270729v1_random 99109 99175 SRX6944761.05_peak_843 215 . 10.74523 26.17270 21.53659 32 +chr17_KI270729v1_random 139621 139686 SRX6944761.05_peak_844 199 . 8.72927 24.53612 19.94776 22 +chr17_KI270729v1_random 139934 140005 SRX6944761.05_peak_845 308 . 11.18707 35.74451 30.86230 34 +chr17_KI270729v1_random 157538 157634 SRX6944761.05_peak_846 897 . 20.63440 95.77928 89.77215 52 +chr17_KI270729v1_random 161014 161094 SRX6944761.05_peak_847 507 . 14.66651 56.07645 50.73339 39 +chr17_KI270729v1_random 172719 172786 SRX6944761.05_peak_848 176 . 9.47066 22.14214 17.62759 23 +chr17_KI270729v1_random 208813 208884 SRX6944761.05_peak_849 353 . 16.15646 40.31326 35.33101 29 +chr17_KI270729v1_random 233202 233267 SRX6944761.05_peak_850 188 . 10.49359 23.38339 18.83019 46 +chr17_KI270729v1_random 240735 240824 SRX6944761.05_peak_851 559 . 20.98717 61.41051 55.96336 48 +chr17_KI270729v1_random 247159 247230 SRX6944761.05_peak_852 259 . 13.36495 30.73712 25.97248 47 +chr17_KI270729v1_random 267963 268030 SRX6944761.05_peak_853 225 . 12.03070 27.20641 22.53920 12 +chr17_KI270729v1_random 280337 280478 SRX6944761.05_peak_854 129 . 8.78294 17.27430 12.92900 35 +chr17_KI270730v1_random 33563 33646 SRX6944761.05_peak_855 538 . 19.78747 59.25941 53.85697 41 +chr17_KI270730v1_random 41500 41592 SRX6944761.05_peak_856 602 . 22.50352 65.81289 60.27439 47 +chr17_KI270730v1_random 47918 47983 SRX6944761.05_peak_857 191 . 10.99366 23.66249 19.10044 31 +chr17_KI270730v1_random 68673 68738 SRX6944761.05_peak_858 185 . 10.64265 23.06758 18.52325 43 +chr17_KI270730v1_random 97090 97298 SRX6944761.05_peak_859 182 . 6.76903 22.80551 18.26940 144 +chr17_KI270730v1_random 107516 107585 SRX6944761.05_peak_860 200 . 5.74681 24.63521 20.04360 36 +chr17_KI270730v1_random 108921 109006 SRX6944761.05_peak_861 91 . 4.34297 13.27980 9.10854 70 +chr17_KI270730v1_random 109792 110076 SRX6944761.05_peak_862 546 . 11.30567 60.07941 54.65982 154 +chr18 10025 10189 SRX6944761.05_peak_863 156 . 10.16871 20.08671 15.63829 117 +chr18 107144 107209 SRX6944761.05_peak_864 290 . 4.43125 33.85344 29.01295 29 +chr18 107889 108300 SRX6944761.05_peak_865 461 . 5.27448 51.40323 46.16743 331 +chr18 108374 108584 SRX6944761.05_peak_866 454 . 5.23666 50.68756 45.46985 69 +chr18 108820 108921 SRX6944761.05_peak_867 317 . 4.42828 36.63400 31.73135 49 +chr18 109309 109379 SRX6944761.05_peak_868 272 . 4.14609 32.08041 27.28223 29 +chr18 109731 109837 SRX6944761.05_peak_869 363 . 4.71018 41.37770 36.37546 44 +chr18 109917 109998 SRX6944761.05_peak_870 285 . 4.22292 33.32825 28.50022 38 +chr18 110265 110455 SRX6944761.05_peak_871 357 . 4.67328 40.71856 35.72838 99 +chr18 110624 110746 SRX6944761.05_peak_872 137 . 3.17305 18.08122 13.70588 97 +chr18 112172 112315 SRX6944761.05_peak_873 373 . 5.03321 42.41041 37.38484 99 +chr18 112429 112496 SRX6944761.05_peak_874 245 . 4.23579 29.28297 24.55523 38 +chr18 4510862 4510993 SRX6944761.05_peak_875 240 . 14.04267 28.77671 24.06183 82 +chr18 5489038 5489134 SRX6944761.05_peak_876 86 . 7.87653 12.78574 8.63765 49 +chr18 7567216 7567305 SRX6944761.05_peak_877 120 . 8.50340 16.32495 12.01956 31 +chr18 16431022 16431148 SRX6944761.05_peak_878 929 . 31.81427 99.01945 92.97639 61 +chr18 20561887 20562075 SRX6944761.05_peak_879 603 . 18.86793 65.88525 60.34588 44 +chr18 20562171 20562391 SRX6944761.05_peak_880 268 . 11.35515 31.63470 26.84822 42 +chr18 20564028 20564164 SRX6944761.05_peak_881 172 . 8.96168 21.74939 17.24717 111 +chr18 20595407 20595485 SRX6944761.05_peak_882 253 . 13.34519 30.06140 25.31272 32 +chr18 20838133 20838204 SRX6944761.05_peak_883 257 . 13.62089 30.52619 25.76710 20 +chr18 20939308 20939402 SRX6944761.05_peak_884 486 . 18.21103 53.93224 48.63735 50 +chr18 26090108 26090173 SRX6944761.05_peak_885 97 . 8.33565 13.89925 9.70020 44 +chr18 49800196 49800284 SRX6944761.05_peak_886 131 . 9.52620 17.54041 13.18497 35 +chr18 52792751 52792855 SRX6944761.05_peak_887 997 . 36.94202 105.83028 99.70219 55 +chr18 74617070 74617176 SRX6944761.05_peak_888 129 . 9.62052 17.24395 12.90007 60 +chr18 78965015 78965101 SRX6944761.05_peak_889 138 . 9.34164 18.20454 13.82366 52 +chr18 79200704 79200781 SRX6944761.05_peak_890 407 . 20.25394 45.83511 40.73752 41 +chr18 79336023 79336093 SRX6944761.05_peak_891 122 . 8.64678 16.56311 12.24716 43 +chr18 79619916 79620004 SRX6944761.05_peak_892 213 . 9.17197 26.02463 21.39222 56 +chr18 80258542 80258665 SRX6944761.05_peak_893 706 . 15.16326 76.35325 70.62019 63 +chr18 80262908 80263263 SRX6944761.05_peak_894 1275 . 23.91939 134.76343 127.55331 198 +chr19 7450666 7450744 SRX6944761.05_peak_895 172 . 9.26692 21.77190 17.26904 36 +chr19 7450927 7451044 SRX6944761.05_peak_896 253 . 11.55878 30.12246 25.37234 41 +chr19 15933919 15933984 SRX6944761.05_peak_897 138 . 9.65018 18.22125 13.83969 21 +chr19 24001273 24001355 SRX6944761.05_peak_898 611 . 22.04066 66.73749 61.18205 58 +chr19 28888433 28888501 SRX6944761.05_peak_899 187 . 11.47228 23.33025 18.77870 34 +chr19 44410426 44410495 SRX6944761.05_peak_900 166 . 9.81547 21.09867 16.61713 31 +chr19 47950256 47950356 SRX6944761.05_peak_901 222 . 9.31873 26.89884 22.24155 54 +chr19 55670454 55670534 SRX6944761.05_peak_902 320 . 13.11790 36.99909 32.08849 35 +chr19 56942910 56942992 SRX6944761.05_peak_903 409 . 19.98380 46.08187 40.97921 43 +chr19 58607524 58607615 SRX6944761.05_peak_904 850 . 30.60306 90.99656 85.04961 39 +chr1_KI270706v1_random 165398 165543 SRX6944761.05_peak_905 302 . 8.79264 35.15485 30.28454 67 +chr1_KI270706v1_random 165960 166028 SRX6944761.05_peak_906 86 . 4.85559 12.82723 8.67744 27 +chr1_KI270709v1_random 45 241 SRX6944761.05_peak_907 156 . 3.24502 20.13143 15.68116 27 +chr1_KI270709v1_random 889 1010 SRX6944761.05_peak_908 160 . 3.03778 20.49097 16.02995 36 +chr1_KI270709v1_random 1626 1735 SRX6944761.05_peak_909 220 . 3.28076 26.69149 22.04006 42 +chr1_KI270709v1_random 1848 1933 SRX6944761.05_peak_910 202 . 3.11526 24.84663 20.24871 51 +chr1_KI270709v1_random 3264 3380 SRX6944761.05_peak_911 146 . 2.56811 19.11278 14.69795 27 +chr1_KI270709v1_random 3741 3871 SRX6944761.05_peak_912 172 . 2.64451 21.78021 17.27685 78 +chr1_KI270709v1_random 5571 5652 SRX6944761.05_peak_913 123 . 2.31573 16.65481 12.33519 51 +chr1_KI270709v1_random 7170 7274 SRX6944761.05_peak_914 79 . 2.06569 12.09316 7.98176 78 +chr1_KI270709v1_random 7381 7573 SRX6944761.05_peak_915 121 . 2.27415 16.41630 12.10729 148 +chr1_KI270709v1_random 9967 10065 SRX6944761.05_peak_916 133 . 2.34733 17.71077 13.34876 57 +chr1_KI270709v1_random 11299 11423 SRX6944761.05_peak_917 122 . 2.27691 16.57131 12.25513 46 +chr1_KI270709v1_random 12071 12142 SRX6944761.05_peak_918 70 . 1.99872 11.12918 7.07263 5 +chr1_KI270709v1_random 15008 15211 SRX6944761.05_peak_919 215 . 2.97537 26.23600 21.59728 78 +chr1_KI270709v1_random 15533 15639 SRX6944761.05_peak_920 206 . 2.95865 25.27598 20.66644 27 +chr1_KI270709v1_random 15802 15904 SRX6944761.05_peak_921 182 . 2.87843 22.82704 18.28981 60 +chr1_KI270709v1_random 16111 16243 SRX6944761.05_peak_922 145 . 2.69300 18.95308 14.54455 27 +chr1_KI270709v1_random 16381 16571 SRX6944761.05_peak_923 130 . 2.66684 17.40093 13.05128 116 +chr1_KI270709v1_random 17164 17268 SRX6944761.05_peak_924 263 . 3.56205 31.15814 26.38275 62 +chr1_KI270709v1_random 40345 40598 SRX6944761.05_peak_925 130 . 7.22601 17.37666 13.02776 225 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1861809 1861948 SRX6944761.05_peak_938 194 . 11.17799 23.97353 19.40155 46 +chr2 3180521 3180691 SRX6944761.05_peak_939 115 . 6.45161 15.78405 11.50144 31 +chr2 3180769 3180853 SRX6944761.05_peak_940 253 . 9.75146 30.14555 25.39503 43 +chr2 3180922 3181169 SRX6944761.05_peak_941 578 . 16.17315 63.37381 57.88793 200 +chr2 3725420 3725594 SRX6944761.05_peak_942 405 . 16.67930 45.69007 40.59542 55 +chr2 18560246 18560311 SRX6944761.05_peak_943 86 . 7.87653 12.78574 8.63765 14 +chr2 18560393 18560541 SRX6944761.05_peak_944 74 . 7.31392 11.52859 7.44655 25 +chr2 28894946 28895032 SRX6944761.05_peak_945 66 . 6.78320 10.65532 6.62653 33 +chr2 32916266 32916559 SRX6944761.05_peak_946 157 . 10.52895 20.17050 15.71887 22 +chr2 65600913 65601082 SRX6944761.05_peak_947 168 . 10.59908 21.33068 16.84130 53 +chr2 87396772 87396837 SRX6944761.05_peak_948 150 . 7.56811 19.51107 15.08157 42 +chr2 89786582 89786651 SRX6944761.05_peak_949 229 . 8.65435 27.63140 22.95151 21 +chr2 89812076 89812146 SRX6944761.05_peak_950 220 . 7.85819 26.70531 22.05339 23 +chr2 89818529 89818616 SRX6944761.05_peak_951 471 . 11.14775 52.40533 47.14685 53 +chr2 89822504 89822598 SRX6944761.05_peak_952 422 . 8.50553 47.39967 42.26752 52 +chr2 89826420 89826533 SRX6944761.05_peak_953 440 . 5.55699 49.25869 44.07721 54 +chr2 89827976 89828080 SRX6944761.05_peak_954 223 . 3.84735 27.06200 22.39990 48 +chr2 89828586 89828710 SRX6944761.05_peak_955 373 . 4.60951 42.42490 37.39887 56 +chr2 89829183 89829296 SRX6944761.05_peak_956 384 . 4.55458 43.50662 38.45819 61 +chr2 89829443 89829525 SRX6944761.05_peak_957 298 . 4.05716 34.70203 29.84172 40 +chr2 89829960 89830048 SRX6944761.05_peak_958 156 . 3.12213 20.09794 15.64887 56 +chr2 89830959 89831177 SRX6944761.05_peak_959 293 . 3.89915 34.23160 29.38193 36 +chr2 89833277 89833404 SRX6944761.05_peak_960 294 . 3.82808 34.28933 29.43877 32 +chr2 89836484 89836569 SRX6944761.05_peak_961 308 . 4.16013 35.69068 30.80993 35 +chr2 89838051 89838137 SRX6944761.05_peak_962 310 . 4.48987 35.90582 31.01969 33 +chr2 89838630 89838737 SRX6944761.05_peak_963 164 . 3.61337 20.91274 16.43774 63 +chr2 89839618 89839731 SRX6944761.05_peak_964 399 . 5.61783 45.01894 39.93918 62 +chr2 89839913 89839980 SRX6944761.05_peak_965 165 . 3.82080 21.04393 16.56428 25 +chr2 89840285 89840385 SRX6944761.05_peak_966 606 . 7.14959 66.16800 60.62296 48 +chr2 89840476 89840577 SRX6944761.05_peak_967 340 . 5.35981 38.97596 34.02172 49 +chr2 89841010 89841158 SRX6944761.05_peak_968 524 . 6.97952 57.83950 52.46213 108 +chr2 90380710 90380913 SRX6944761.05_peak_969 376 . 6.05784 42.63157 37.60120 45 +chr2 90381074 90381177 SRX6944761.05_peak_970 204 . 4.36172 25.00687 20.40486 72 +chr2 90381607 90381700 SRX6944761.05_peak_971 209 . 4.25991 25.57806 20.95978 36 +chr2 90381810 90381934 SRX6944761.05_peak_972 158 . 3.74993 20.32606 15.86984 22 +chr2 90382593 90382753 SRX6944761.05_peak_973 182 . 3.84760 22.81213 18.27563 44 +chr2 90384399 90384466 SRX6944761.05_peak_974 101 . 2.89430 14.41225 10.19041 35 +chr2 90385869 90385970 SRX6944761.05_peak_975 101 . 2.77979 14.39902 10.17765 63 +chr2 90386154 90386223 SRX6944761.05_peak_976 156 . 3.21147 20.05932 15.61153 38 +chr2 90390693 90390792 SRX6944761.05_peak_977 212 . 3.67058 25.83142 21.20417 65 +chr2 90392625 90392695 SRX6944761.05_peak_978 151 . 3.23202 19.59910 15.16660 31 +chr2 90397751 90397844 SRX6944761.05_peak_979 107 . 2.82707 14.95059 10.70527 23 +chr2 90398214 90398339 SRX6944761.05_peak_980 193 . 3.50616 23.92783 19.35730 51 +chr2 90398439 90398767 SRX6944761.05_peak_981 157 . 3.27511 20.16730 15.71578 107 +chr2 90401022 90401253 SRX6944761.05_peak_982 211 . 4.20425 25.80427 21.17790 123 +chr2 90402175 90402302 SRX6944761.05_peak_983 144 . 3.77125 18.90514 14.49837 32 +chr2 91421912 91422118 SRX6944761.05_peak_984 329 . 10.23219 37.87928 32.94887 62 +chr2 91422278 91422414 SRX6944761.05_peak_985 512 . 13.39769 56.64570 51.28940 58 +chr2 91423353 91423420 SRX6944761.05_peak_986 232 . 8.54875 27.92032 23.23188 21 +chr2 92081283 92081358 SRX6944761.05_peak_987 312 . 13.04206 36.17327 31.28011 29 +chr2 92081450 92081624 SRX6944761.05_peak_988 338 . 13.69416 38.77969 33.83002 141 +chr2 92270845 92270925 SRX6944761.05_peak_989 293 . 14.21999 34.18356 29.33520 42 +chr2 94502396 94502500 SRX6944761.05_peak_990 360 . 13.79522 41.08614 36.08881 61 +chr2 106467682 106467747 SRX6944761.05_peak_991 78 . 5.96481 11.92428 7.82158 18 +chr2 109199338 109199845 SRX6944761.05_peak_992 676 . 15.17067 73.29838 67.62015 127 +chr2 132255503 132255606 SRX6944761.05_peak_993 796 . 21.63624 85.53461 79.66141 50 +chr2 132268101 132268289 SRX6944761.05_peak_994 310 . 13.30831 35.98541 31.09738 158 +chr2 132270791 132270878 SRX6944761.05_peak_995 300 . 12.74302 34.93658 30.07134 39 +chr2 132271669 132271734 SRX6944761.05_peak_996 140 . 8.30979 18.45632 14.06562 25 +chr2 147826933 147827027 SRX6944761.05_peak_997 61 . 6.64452 10.12669 6.13641 81 +chr2 148881769 148881835 SRX6944761.05_peak_998 178 . 11.81480 22.32647 17.80481 19 +chr2 161281236 161281334 SRX6944761.05_peak_999 365 . 12.95896 41.50729 36.50072 41 +chr2 161281730 161281815 SRX6944761.05_peak_1000 59 . 5.08497 9.90343 5.92923 80 +chr2 161282416 161282491 SRX6944761.05_peak_1001 219 . 9.45378 26.61354 21.96446 33 +chr2 167466782 167466847 SRX6944761.05_peak_1002 150 . 10.68958 19.48296 15.05427 13 +chr2 181275789 181275857 SRX6944761.05_peak_1003 250 . 14.62785 29.77336 25.03155 34 +chr2 200306979 200307076 SRX6944761.05_peak_1004 82 . 7.42390 12.39899 8.27220 45 +chr2 238783476 238783541 SRX6944761.05_peak_1005 97 . 8.12802 13.93825 9.73760 5 +chr2 241590592 241590690 SRX6944761.05_peak_1006 78 . 6.45740 11.99953 7.89269 52 +chr2 242182616 242182732 SRX6944761.05_peak_1007 123 . 7.59975 16.67362 12.35334 54 +chr2 242183422 242183526 SRX6944761.05_peak_1008 1229 . 32.77108 129.76210 122.99194 53 +chr20 61406 61491 SRX6944761.05_peak_1009 290 . 9.83984 33.89405 29.05247 33 +chr20 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27614785 SRX6944761.05_peak_1022 155 . 9.47119 19.97167 15.52685 41 +chr20 28308127 28308207 SRX6944761.05_peak_1023 451 . 18.43687 50.34232 45.13377 27 +chr20 28494789 28495109 SRX6944761.05_peak_1024 258 . 6.89488 30.60852 25.84706 175 +chr20 28495457 28495585 SRX6944761.05_peak_1025 543 . 10.50189 59.79552 54.38080 64 +chr20 28495670 28495962 SRX6944761.05_peak_1026 177 . 5.76687 22.29571 17.77597 242 +chr20 28496868 28497024 SRX6944761.05_peak_1027 222 . 6.48379 26.88434 22.22759 117 +chr20 28513596 28513674 SRX6944761.05_peak_1028 224 . 9.41117 27.09476 22.43123 37 +chr20 28531210 28531286 SRX6944761.05_peak_1029 185 . 8.80936 23.07565 18.53105 35 +chr20 28644068 28644180 SRX6944761.05_peak_1030 321 . 12.46196 37.08258 32.17030 55 +chr20 28645883 28646014 SRX6944761.05_peak_1031 111 . 7.15063 15.38284 11.11878 84 +chr20 28730366 28730463 SRX6944761.05_peak_1032 89 . 7.85340 13.10075 8.93809 46 +chr20 28889622 28889892 SRX6944761.05_peak_1033 458 . 16.69941 51.12257 45.89581 158 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36.72776 31.82240 33 +chr20 31075584 31075800 SRX6944761.05_peak_1058 232 . 4.10214 27.97888 23.28872 68 +chr20 31083405 31083483 SRX6944761.05_peak_1059 289 . 12.46924 33.75784 28.91929 22 +chr20 31086549 31086617 SRX6944761.05_peak_1060 216 . 10.47806 26.28115 21.64100 28 +chr20 31092523 31092591 SRX6944761.05_peak_1061 222 . 10.82361 26.91602 22.25832 33 +chr20 31099155 31099227 SRX6944761.05_peak_1062 295 . 13.54657 34.37017 29.51813 45 +chr20 31105998 31106102 SRX6944761.05_peak_1063 904 . 26.57916 96.43230 90.41915 51 +chr20 31106752 31106870 SRX6944761.05_peak_1064 1222 . 33.45601 128.96170 122.20854 63 +chr20 31157143 31157315 SRX6944761.05_peak_1065 539 . 11.89478 59.33535 53.93138 120 +chr20 31157400 31157559 SRX6944761.05_peak_1066 244 . 7.48391 29.17789 24.45285 36 +chr20 31157676 31157900 SRX6944761.05_peak_1067 927 . 16.31939 98.78916 92.74980 159 +chr20 31158182 31158310 SRX6944761.05_peak_1068 324 . 8.45428 37.41544 32.49604 82 +chr20 31158394 31158470 SRX6944761.05_peak_1069 305 . 8.15752 35.38693 30.51196 39 +chr20 31165011 31165077 SRX6944761.05_peak_1070 211 . 7.70289 25.79267 21.16646 42 +chr20 31165786 31165949 SRX6944761.05_peak_1071 151 . 6.38921 19.54928 15.11843 122 +chr20 31166650 31166770 SRX6944761.05_peak_1072 901 . 17.27968 96.13310 90.12325 65 +chr20 31168179 31168252 SRX6944761.05_peak_1073 350 . 9.89927 40.02381 35.04802 24 +chr20 31171512 31171581 SRX6944761.05_peak_1074 297 . 9.66660 34.64955 29.79052 27 +chr20 31174925 31174996 SRX6944761.05_peak_1075 244 . 9.55794 29.13544 24.41107 33 +chr20 31176602 31176669 SRX6944761.05_peak_1076 205 . 9.00669 25.12103 20.51592 34 +chr20 31185063 31185150 SRX6944761.05_peak_1077 61 . 3.09831 10.10042 6.11220 12 +chr20 31185339 31185449 SRX6944761.05_peak_1078 653 . 8.89835 70.93851 65.30367 51 +chr20 31185519 31185628 SRX6944761.05_peak_1079 432 . 7.06606 48.38103 43.22430 62 +chr20 31185787 31185871 SRX6944761.05_peak_1080 513 . 7.78834 56.75088 51.39277 42 +chr20 31186312 31186389 SRX6944761.05_peak_1081 260 . 5.46241 30.82890 26.06227 33 +chr20 31186472 31186678 SRX6944761.05_peak_1082 380 . 6.59391 43.06021 38.02027 139 +chr20 31186744 31186897 SRX6944761.05_peak_1083 380 . 6.59066 43.04287 38.00351 105 +chr20 31187180 31187393 SRX6944761.05_peak_1084 577 . 8.29812 63.21690 57.73432 55 +chr20 31198409 31198482 SRX6944761.05_peak_1085 334 . 13.12977 38.39594 33.45467 41 +chr20 31201525 31201635 SRX6944761.05_peak_1086 286 . 11.65841 33.50951 28.67715 47 +chr20 31204709 31204777 SRX6944761.05_peak_1087 243 . 10.99246 29.06176 24.33916 44 +chr20 31217327 31217392 SRX6944761.05_peak_1088 182 . 10.83893 22.83297 18.29556 39 +chr20 31224130 31224212 SRX6944761.05_peak_1089 480 . 15.84158 53.31030 48.03022 34 +chr20 31224897 31224980 SRX6944761.05_peak_1090 441 . 14.65599 49.34884 44.16419 33 +chr20 31226301 31226378 SRX6944761.05_peak_1091 340 . 11.19946 38.97515 34.02095 34 +chr20 31230600 31230696 SRX6944761.05_peak_1092 317 . 10.48689 36.66285 31.75929 45 +chr20 31233613 31233678 SRX6944761.05_peak_1093 140 . 6.87361 18.48461 14.09275 41 +chr20 31234186 31234252 SRX6944761.05_peak_1094 218 . 8.64745 26.52719 21.88051 31 +chr20 31235182 31235251 SRX6944761.05_peak_1095 185 . 8.05060 23.06055 18.51636 21 +chr20 31241551 31241950 SRX6944761.05_peak_1096 735 . 14.14886 79.30095 73.52464 115 +chr20 32295472 32295537 SRX6944761.05_peak_1097 133 . 9.65202 17.74831 13.38488 54 +chr20 47894860 47894935 SRX6944761.05_peak_1098 100 . 7.02576 14.25985 10.04410 36 +chr20 49187962 49188043 SRX6944761.05_peak_1099 70 . 6.86275 11.12255 7.06642 51 +chr20 61331601 61331694 SRX6944761.05_peak_1100 145 . 9.48480 18.94922 14.54083 57 +chr20 64088621 64088686 SRX6944761.05_peak_1101 210 . 9.84193 25.66598 21.04394 34 +chr20 64088765 64088848 SRX6944761.05_peak_1102 181 . 9.14216 22.63477 18.10359 40 +chr21 5327950 5328032 SRX6944761.05_peak_1103 444 . 13.17700 49.69160 44.49920 45 +chr21 5329412 5329485 SRX6944761.05_peak_1104 257 . 9.66072 30.52809 25.76893 39 +chr21 5329699 5329796 SRX6944761.05_peak_1105 270 . 9.98027 31.83999 27.04769 48 +chr21 7257902 7257986 SRX6944761.05_peak_1106 83 . 4.17844 12.45192 8.32236 25 +chr21 7258745 7258823 SRX6944761.05_peak_1107 364 . 8.36357 41.43173 36.42823 23 +chr21 7258909 7258979 SRX6944761.05_peak_1108 194 . 6.02188 23.99474 19.42226 42 +chr21 7261211 7261289 SRX6944761.05_peak_1109 279 . 7.35714 32.71394 27.90123 43 +chr21 7926127 7926405 SRX6944761.05_peak_1110 763 . 11.26761 82.21451 76.39454 54 +chr21 7926585 7926693 SRX6944761.05_peak_1111 384 . 7.57649 43.51305 38.46444 56 +chr21 7927242 7927334 SRX6944761.05_peak_1112 682 . 10.57735 73.94126 68.25255 43 +chr21 7938258 7938431 SRX6944761.05_peak_1113 674 . 11.29881 73.16680 67.49210 78 +chr21 7942299 7942375 SRX6944761.05_peak_1114 308 . 6.89690 35.75870 30.87616 39 +chr21 7944443 7944527 SRX6944761.05_peak_1115 422 . 8.23893 47.38663 42.25478 30 +chr21 7951291 7951387 SRX6944761.05_peak_1116 507 . 8.73777 56.11391 50.76973 43 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SRX6944761.05_peak_1129 63 . 1.97641 10.34012 6.33056 97 +chr21 8258026 8258098 SRX6944761.05_peak_1130 134 . 2.61767 17.78031 13.41596 16 +chr21 8258850 8258954 SRX6944761.05_peak_1131 96 . 2.47473 13.80868 9.61362 79 +chr21 8259379 8259776 SRX6944761.05_peak_1132 108 . 2.60565 15.08876 10.83772 23 +chr21 8260562 8260695 SRX6944761.05_peak_1133 99 . 2.66951 14.13404 9.92368 42 +chr21 8388400 8388568 SRX6944761.05_peak_1134 99 . 2.32428 14.12241 9.91248 86 +chr21 8388641 8388758 SRX6944761.05_peak_1135 100 . 2.32535 14.25564 10.03994 36 +chr21 8389244 8389331 SRX6944761.05_peak_1136 61 . 2.03334 10.15053 6.15905 59 +chr21 8389461 8389728 SRX6944761.05_peak_1137 130 . 2.40297 17.35535 13.00741 220 +chr21 8390715 8390807 SRX6944761.05_peak_1138 64 . 1.95139 10.43221 6.41722 34 +chr21 8401642 8401766 SRX6944761.05_peak_1139 102 . 2.28673 14.50051 10.27509 107 +chr21 8402215 8402356 SRX6944761.05_peak_1140 116 . 2.36407 15.95815 11.66809 49 +chr21 8402603 8402788 SRX6944761.05_peak_1141 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60.10362 644 +chr21 8987703 8987777 SRX6944761.05_peak_1154 189 . 4.32074 23.54111 18.98248 25 +chr21 8987851 8988440 SRX6944761.05_peak_1155 444 . 6.42610 49.67523 44.48370 211 +chr21 8988535 8988675 SRX6944761.05_peak_1156 416 . 6.16888 46.71849 41.60234 90 +chr21 9246744 9246871 SRX6944761.05_peak_1157 425 . 10.63401 47.66990 42.53169 64 +chr21 9246988 9247090 SRX6944761.05_peak_1158 159 . 6.23800 20.43694 15.97736 37 +chr21 9247460 9247541 SRX6944761.05_peak_1159 125 . 5.55776 16.90364 12.57345 39 +chr21 9768373 9768479 SRX6944761.05_peak_1160 102 . 6.45682 14.50481 10.27920 22 +chr21 10269923 10269993 SRX6944761.05_peak_1161 200 . 10.23716 24.66411 20.07147 17 +chr21 10270200 10270399 SRX6944761.05_peak_1162 471 . 17.40317 52.44346 47.18414 55 +chr21 10270488 10270553 SRX6944761.05_peak_1163 189 . 9.89592 23.47170 18.91521 44 +chr21 10274097 10274192 SRX6944761.05_peak_1164 797 . 24.91043 85.62334 79.74825 57 +chr21 10325779 10325885 SRX6944761.05_peak_1165 1000 . 26.12152 106.21757 100.08340 57 +chr21 10405602 10405674 SRX6944761.05_peak_1166 81 . 5.05787 12.28864 8.16743 27 +chr21 10490648 10490713 SRX6944761.05_peak_1167 93 . 7.86821 13.51336 9.33091 54 +chr21 10655552 10655639 SRX6944761.05_peak_1168 647 . 11.41915 70.35340 64.73026 45 +chr21 10656304 10656401 SRX6944761.05_peak_1169 558 . 10.09485 61.31932 55.87432 51 +chr21 10658713 10658828 SRX6944761.05_peak_1170 362 . 7.88757 41.22842 36.22872 63 +chr21 10662397 10662466 SRX6944761.05_peak_1171 210 . 6.39509 25.69773 21.07485 41 +chr21 10667176 10667242 SRX6944761.05_peak_1172 245 . 7.05444 29.27341 24.54587 23 +chr21 10668176 10668266 SRX6944761.05_peak_1173 296 . 7.89062 34.51694 29.66194 45 +chr21 10668939 10669039 SRX6944761.05_peak_1174 616 . 11.46203 67.21225 61.64801 55 +chr21 10673239 10673328 SRX6944761.05_peak_1175 533 . 10.22592 58.75942 53.36592 48 +chr21 10685250 10685321 SRX6944761.05_peak_1176 246 . 7.56487 29.42256 24.69072 37 +chr21 10692360 10692442 SRX6944761.05_peak_1177 408 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SRX6944761.05_peak_1189 292 . 14.97076 34.10852 29.26167 46 +chr21 45055042 45055116 SRX6944761.05_peak_1190 214 . 11.38594 26.10022 21.46598 28 +chr21 46184670 46184754 SRX6944761.05_peak_1191 199 . 10.14713 24.50071 19.91334 36 +chr21 46699875 46699973 SRX6944761.05_peak_1192 824 . 27.45553 88.38724 82.47565 53 +chr22 10717232 10717308 SRX6944761.05_peak_1193 260 . 7.54395 30.85687 26.08941 48 +chr22 10718198 10718269 SRX6944761.05_peak_1194 232 . 6.42073 27.98905 23.29855 38 +chr22 10721079 10721148 SRX6944761.05_peak_1195 266 . 6.40667 31.46286 26.68045 34 +chr22 10722932 10723060 SRX6944761.05_peak_1196 402 . 7.76586 45.35717 40.27057 76 +chr22 10729214 10729315 SRX6944761.05_peak_1197 267 . 7.45611 31.56855 26.78385 45 +chr22 10730153 10730219 SRX6944761.05_peak_1198 146 . 5.62074 19.03804 14.62630 30 +chr22 10741397 10741525 SRX6944761.05_peak_1199 113 . 6.56980 15.59585 11.32174 91 +chr22 10741792 10741970 SRX6944761.05_peak_1200 264 . 10.22571 31.18094 26.40504 77 +chr22 11213006 11213082 SRX6944761.05_peak_1201 206 . 12.94002 25.25061 20.64184 39 +chr22 11252150 11252219 SRX6944761.05_peak_1202 247 . 12.65564 29.52666 24.79242 17 +chr22 11369821 11369910 SRX6944761.05_peak_1203 108 . 6.33065 15.10126 10.84961 33 +chr22 11698662 11698746 SRX6944761.05_peak_1204 85 . 4.66273 12.64692 8.50757 19 +chr22 11701720 11701816 SRX6944761.05_peak_1205 169 . 6.29607 21.46371 16.97011 45 +chr22 11702537 11702698 SRX6944761.05_peak_1206 77 . 4.36713 11.82428 7.72728 138 +chr22 11703050 11703116 SRX6944761.05_peak_1207 196 . 6.61959 24.19001 19.61159 45 +chr22 11711132 11711209 SRX6944761.05_peak_1208 125 . 7.72698 16.90625 12.57590 48 +chr22 11711819 11711884 SRX6944761.05_peak_1209 116 . 7.46753 15.96115 11.67096 22 +chr22 11814154 11814234 SRX6944761.05_peak_1210 219 . 6.94796 26.57339 21.92539 55 +chr22 11820150 11820242 SRX6944761.05_peak_1211 78 . 4.51186 11.91427 7.81229 55 +chr22 11821012 11821141 SRX6944761.05_peak_1212 112 . 5.46495 15.50065 11.23026 22 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54 +chr22 15957585 15957658 SRX6944761.05_peak_1225 223 . 13.36184 27.05368 22.39181 29 +chr22 16262317 16262388 SRX6944761.05_peak_1226 297 . 11.54163 34.56586 29.70933 28 +chr22 16267322 16267394 SRX6944761.05_peak_1227 309 . 12.85141 35.81428 30.93039 14 +chr22 16315761 16315829 SRX6944761.05_peak_1228 234 . 11.13736 28.09905 23.40530 13 +chr22 16344196 16344271 SRX6944761.05_peak_1229 356 . 13.23170 40.67606 35.68716 30 +chr22 16347199 16347317 SRX6944761.05_peak_1230 770 . 19.26424 82.88612 77.05164 55 +chr22 16352751 16352902 SRX6944761.05_peak_1231 1127 . 18.95164 119.06490 112.73573 97 +chr22 16353671 16353775 SRX6944761.05_peak_1232 420 . 10.06066 47.15416 42.02657 44 +chr22 16354681 16354772 SRX6944761.05_peak_1233 702 . 13.79924 75.95324 70.22717 37 +chr22 16359746 16359890 SRX6944761.05_peak_1234 236 . 9.22828 28.40181 23.69770 57 +chr22 16362202 16362268 SRX6944761.05_peak_1235 220 . 9.22808 26.70570 22.05353 26 +chr22 16380963 16381046 SRX6944761.05_peak_1236 444 . 18.96961 49.68672 44.49449 46 +chr22 18205561 18205642 SRX6944761.05_peak_1237 493 . 22.36089 54.66912 49.35603 24 +chr22 18237787 18237857 SRX6944761.05_peak_1238 191 . 12.04188 23.73299 19.16909 19 +chr22 18238222 18238306 SRX6944761.05_peak_1239 207 . 12.69505 25.37250 20.76003 53 +chr22 18386045 18386144 SRX6944761.05_peak_1240 543 . 24.39024 59.72820 54.31525 51 +chr22 18419168 18419238 SRX6944761.05_peak_1241 238 . 13.53723 28.51787 23.81079 54 +chr22 18419338 18419407 SRX6944761.05_peak_1242 280 . 15.04136 32.91327 28.09489 42 +chr22 18421322 18421387 SRX6944761.05_peak_1243 178 . 11.51832 22.32956 17.80757 25 +chr22 18730506 18730665 SRX6944761.05_peak_1244 415 . 20.34085 46.69346 41.57814 102 +chr22 18892455 18892556 SRX6944761.05_peak_1245 1221 . 34.00893 128.94675 122.19518 48 +chr22 18896444 18896516 SRX6944761.05_peak_1246 243 . 11.67522 29.07453 24.35139 22 +chr22 20338836 20338902 SRX6944761.05_peak_1247 189 . 11.92019 23.52931 18.97089 38 +chr22 21183768 21184136 SRX6944761.05_peak_1248 116 . 8.25253 15.90681 11.61878 139 +chr22 21309112 21309245 SRX6944761.05_peak_1249 64 . 6.69241 10.50782 6.48757 37 +chr22 21324514 21324592 SRX6944761.05_peak_1250 303 . 15.20737 35.18148 30.31032 43 +chr22 21325355 21325442 SRX6944761.05_peak_1251 420 . 18.89401 47.22467 42.09542 39 +chr22 21326202 21326292 SRX6944761.05_peak_1252 404 . 18.37813 45.58628 40.49489 35 +chr22 23485674 23485752 SRX6944761.05_peak_1253 558 . 24.35864 61.30013 55.85583 33 +chr22 49494377 49494443 SRX6944761.05_peak_1254 84 . 6.78989 12.56463 8.42932 7 +chr22 50276329 50276405 SRX6944761.05_peak_1255 225 . 10.32023 27.17387 22.50769 39 +chr22 50807901 50808102 SRX6944761.05_peak_1256 1326 . 28.76318 140.03143 132.67062 58 +chr22 50808381 50808464 SRX6944761.05_peak_1257 575 . 16.39144 63.07899 57.59759 36 +chr22_KI270731v1_random 110680 110778 SRX6944761.05_peak_1258 68 . 6.26591 10.89294 6.84975 50 +chr22_KI270731v1_random 111397 111462 SRX6944761.05_peak_1259 121 . 8.33003 16.51318 12.19978 20 +chr22_KI270731v1_random 127210 127310 SRX6944761.05_peak_1260 489 . 21.63757 54.23370 48.93294 45 +chr22_KI270733v1_random 85033 85132 SRX6944761.05_peak_1261 156 . 9.52003 20.05497 15.60749 38 +chr22_KI270733v1_random 121694 121893 SRX6944761.05_peak_1262 68 . 2.04044 10.86173 6.82043 162 +chr22_KI270733v1_random 135046 135158 SRX6944761.05_peak_1263 119 . 2.26068 16.22236 11.92136 69 +chr22_KI270733v1_random 135541 136099 SRX6944761.05_peak_1264 127 . 2.32313 17.11923 12.77985 161 +chr22_KI270733v1_random 136652 136841 SRX6944761.05_peak_1265 75 . 2.04601 11.65402 7.56549 33 +chr22_KI270733v1_random 137333 137513 SRX6944761.05_peak_1266 89 . 2.12921 13.12308 8.95909 57 +chr22_KI270733v1_random 141602 141729 SRX6944761.05_peak_1267 72 . 2.12547 11.28616 7.22049 86 +chr22_KI270733v1_random 141838 141903 SRX6944761.05_peak_1268 99 . 2.30731 14.16223 9.95050 31 +chr22_KI270733v1_random 155614 155694 SRX6944761.05_peak_1269 120 . 2.49130 16.32515 12.01975 51 +chr22_KI270733v1_random 175117 175209 SRX6944761.05_peak_1270 111 . 2.22133 15.43562 11.16771 34 +chr22_KI270733v1_random 176485 176550 SRX6944761.05_peak_1271 80 . 2.14759 12.14716 8.03310 14 +chr22_KI270733v1_random 178560 178634 SRX6944761.05_peak_1272 121 . 2.63649 16.46130 12.15030 59 +chr22_KI270733v1_random 178995 179175 SRX6944761.05_peak_1273 144 . 2.85727 18.88556 14.47951 26 +chr22_KI270734v1_random 117760 117828 SRX6944761.05_peak_1274 255 . 12.72619 30.28893 25.53603 42 +chr22_KI270734v1_random 121456 121595 SRX6944761.05_peak_1275 173 . 9.64114 21.89452 17.38758 25 +chr22_KI270734v1_random 121714 121783 SRX6944761.05_peak_1276 261 . 12.34120 30.90462 26.13597 24 +chr22_KI270735v1_random 39682 39926 SRX6944761.05_peak_1277 675 . 11.81737 73.21774 67.54177 124 +chr22_KI270735v1_random 40354 40424 SRX6944761.05_peak_1278 143 . 5.16889 18.70969 14.30948 38 +chr22_KI270735v1_random 40838 41083 SRX6944761.05_peak_1279 456 . 9.41290 50.90004 45.67801 195 +chr22_KI270735v1_random 41159 41244 SRX6944761.05_peak_1280 368 . 8.34644 41.90828 36.89356 47 +chr22_KI270735v1_random 42282 42417 SRX6944761.05_peak_1281 362 . 8.32364 41.28731 36.28648 86 +chr22_KI270736v1_random 451 545 SRX6944761.05_peak_1282 254 . 11.94659 30.19650 25.44535 45 +chr22_KI270736v1_random 1848 1916 SRX6944761.05_peak_1283 175 . 9.42920 22.06705 17.55451 41 +chr22_KI270736v1_random 2230 2314 SRX6944761.05_peak_1284 242 . 11.30131 29.01918 24.29832 40 +chr22_KI270736v1_random 58611 58762 SRX6944761.05_peak_1285 245 . 8.64695 29.25012 24.52318 109 +chr22_KI270736v1_random 64811 64888 SRX6944761.05_peak_1286 390 . 10.48741 44.11557 39.05444 30 +chr22_KI270736v1_random 73957 74032 SRX6944761.05_peak_1287 199 . 7.39605 24.49506 19.90781 37 +chr22_KI270736v1_random 74133 74253 SRX6944761.05_peak_1288 456 . 11.74064 50.83627 45.61606 56 +chr22_KI270736v1_random 79930 79998 SRX6944761.05_peak_1289 279 . 8.71308 32.71667 27.90385 37 +chr22_KI270736v1_random 83223 83294 SRX6944761.05_peak_1290 297 . 9.00043 34.62210 29.76343 48 +chr22_KI270736v1_random 91098 91168 SRX6944761.05_peak_1291 291 . 9.41312 34.00538 29.16096 37 +chr22_KI270736v1_random 92772 92840 SRX6944761.05_peak_1292 210 . 7.86646 25.70388 21.08075 47 +chr22_KI270736v1_random 96994 97062 SRX6944761.05_peak_1293 229 . 8.00291 27.59958 22.92056 29 +chr22_KI270736v1_random 98895 98969 SRX6944761.05_peak_1294 127 . 5.78084 17.11444 12.77542 32 +chr22_KI270736v1_random 103126 103208 SRX6944761.05_peak_1295 173 . 6.75032 21.84055 17.33551 42 +chr22_KI270736v1_random 105652 105738 SRX6944761.05_peak_1296 344 . 10.11122 39.39254 34.42877 47 +chr22_KI270736v1_random 118649 118723 SRX6944761.05_peak_1297 272 . 9.32590 32.05581 27.25813 39 +chr22_KI270736v1_random 119083 119153 SRX6944761.05_peak_1298 271 . 9.28918 31.96526 27.17018 35 +chr22_KI270736v1_random 126763 126829 SRX6944761.05_peak_1299 237 . 8.52667 28.40697 23.70275 36 +chr22_KI270736v1_random 147725 147925 SRX6944761.05_peak_1300 491 . 11.19601 54.50160 49.19318 143 +chr22_KI270736v1_random 162551 162681 SRX6944761.05_peak_1301 356 . 8.94251 40.65154 35.66315 53 +chr22_KI270736v1_random 167394 167461 SRX6944761.05_peak_1302 173 . 5.54049 21.89605 17.38906 23 +chr22_KI270736v1_random 171350 171562 SRX6944761.05_peak_1303 95 . 3.79049 13.78784 9.59399 21 +chr22_KI270736v1_random 173077 173148 SRX6944761.05_peak_1304 94 . 4.18525 13.64670 9.45872 11 +chr22_KI270736v1_random 179797 179918 SRX6944761.05_peak_1305 111 . 4.61593 15.44387 11.17554 20 +chr22_KI270736v1_random 180229 180328 SRX6944761.05_peak_1306 199 . 6.31787 24.56636 19.97714 27 +chr22_KI270736v1_random 180458 180706 SRX6944761.05_peak_1307 172 . 6.12056 21.79457 17.29097 88 +chr22_KI270736v1_random 181093 181530 SRX6944761.05_peak_1308 460 . 10.79520 51.32229 46.08971 59 +chr22_KI270737v1_random 4780 4853 SRX6944761.05_peak_1309 317 . 15.66820 36.64522 31.74219 53 +chr22_KI270737v1_random 23547 23618 SRX6944761.05_peak_1310 319 . 9.59450 36.81504 31.90740 34 +chr22_KI270737v1_random 25900 26079 SRX6944761.05_peak_1311 1002 . 18.73223 106.35831 100.22186 71 +chr22_KI270737v1_random 31036 31128 SRX6944761.05_peak_1312 725 . 12.09259 78.35726 72.59374 48 +chr22_KI270737v1_random 32002 32133 SRX6944761.05_peak_1313 598 . 10.64251 65.37402 59.84699 75 +chr22_KI270737v1_random 32900 33052 SRX6944761.05_peak_1314 861 . 13.42436 92.09863 86.13550 57 +chr22_KI270737v1_random 33297 33397 SRX6944761.05_peak_1315 668 . 11.79870 72.47343 66.81127 55 +chr22_KI270737v1_random 38496 38632 SRX6944761.05_peak_1316 1041 . 19.78118 110.29939 104.10294 56 +chr22_KI270737v1_random 41560 41636 SRX6944761.05_peak_1317 443 . 12.82325 49.54715 44.35880 41 +chr22_KI270737v1_random 70279 70352 SRX6944761.05_peak_1318 392 . 13.61841 44.28218 39.21617 42 +chr22_KI270737v1_random 80006 80103 SRX6944761.05_peak_1319 718 . 22.16593 77.61860 71.86449 49 +chr22_KI270738v1_random 59807 59874 SRX6944761.05_peak_1320 125 . 7.71084 16.87683 12.54756 38 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13.71690 37 +chr3 91550177 91550244 SRX6944761.05_peak_1333 184 . 7.80234 23.00800 18.46549 45 +chr3 91550531 91550653 SRX6944761.05_peak_1334 141 . 6.92351 18.59623 14.19974 35 +chr3 91891206 91891486 SRX6944761.05_peak_1335 521 . 20.68680 57.51775 52.14576 231 +chr3 93470364 93470794 SRX6944761.05_peak_1336 1199 . 19.91789 126.55275 119.95592 223 +chr3 93712475 93712540 SRX6944761.05_peak_1337 178 . 7.31804 22.35175 17.82914 37 +chr3 116518911 116519007 SRX6944761.05_peak_1338 111 . 9.00175 15.38738 11.12157 51 +chr3 141066323 141066388 SRX6944761.05_peak_1339 120 . 9.11392 16.40488 12.09619 31 +chr3 157089108 157089191 SRX6944761.05_peak_1340 117 . 9.18174 16.08674 11.79124 29 +chr3 157437653 157437861 SRX6944761.05_peak_1341 86 . 7.87653 12.78574 8.63765 11 +chr3 174698717 174698786 SRX6944761.05_peak_1342 137 . 10.12697 18.09395 13.71690 18 +chr3 196898755 196898891 SRX6944761.05_peak_1343 1213 . 38.26745 128.05119 121.35662 77 +chr3 197459838 197459903 SRX6944761.05_peak_1344 130 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2.67992 20.69368 16.22600 114 +chr4 49098556 49098632 SRX6944761.05_peak_1357 163 . 2.64156 20.81836 16.34684 29 +chr4 49102448 49102643 SRX6944761.05_peak_1358 182 . 2.73218 22.82085 18.28399 132 +chr4 49103557 49103653 SRX6944761.05_peak_1359 168 . 2.61477 21.36852 16.87792 45 +chr4 49107758 49107897 SRX6944761.05_peak_1360 112 . 2.33136 15.53993 11.26811 98 +chr4 49108276 49108526 SRX6944761.05_peak_1361 177 . 2.68960 22.24274 17.72513 83 +chr4 49108671 49108763 SRX6944761.05_peak_1362 112 . 2.37250 15.56300 11.29022 77 +chr4 49109684 49109776 SRX6944761.05_peak_1363 187 . 2.79526 23.33227 18.78041 53 +chr4 49109888 49110086 SRX6944761.05_peak_1364 162 . 2.67046 20.72980 16.26061 27 +chr4 49110698 49110820 SRX6944761.05_peak_1365 201 . 2.90024 24.70646 20.11235 87 +chr4 49112380 49112446 SRX6944761.05_peak_1366 175 . 2.83070 22.07262 17.55998 28 +chr4 49112689 49112792 SRX6944761.05_peak_1367 120 . 2.53342 16.38496 12.07695 83 +chr4 49118508 49118597 SRX6944761.05_peak_1368 80 . 2.25333 12.16057 8.04596 33 +chr4 49118851 49119058 SRX6944761.05_peak_1369 226 . 3.05724 27.30240 22.63261 81 +chr4 49119523 49119601 SRX6944761.05_peak_1370 101 . 2.38252 14.41789 10.19581 56 +chr4 49119841 49119993 SRX6944761.05_peak_1371 122 . 2.50448 16.61767 12.29951 103 +chr4 49120172 49120240 SRX6944761.05_peak_1372 136 . 2.58453 17.99278 13.62090 24 +chr4 49120905 49121181 SRX6944761.05_peak_1373 152 . 2.67483 19.68676 15.25146 67 +chr4 49123362 49123476 SRX6944761.05_peak_1374 96 . 2.36506 13.84589 9.64938 21 +chr4 49129585 49129650 SRX6944761.05_peak_1375 177 . 3.31717 22.26831 17.74977 23 +chr4 49130629 49130750 SRX6944761.05_peak_1376 137 . 3.03512 18.14892 13.76990 47 +chr4 49130859 49130988 SRX6944761.05_peak_1377 167 . 3.25039 21.24294 16.75622 47 +chr4 49131671 49131736 SRX6944761.05_peak_1378 233 . 3.66992 28.08028 23.38713 36 +chr4 49137182 49137292 SRX6944761.05_peak_1379 330 . 3.99937 38.02912 33.09517 70 +chr4 49138061 49138203 SRX6944761.05_peak_1380 135 . 2.87991 17.86883 13.50109 110 +chr4 49139656 49139798 SRX6944761.05_peak_1381 327 . 3.91478 37.64158 32.71624 96 +chr4 49141854 49141931 SRX6944761.05_peak_1382 219 . 3.29576 26.63903 21.98890 40 +chr4 49142098 49142232 SRX6944761.05_peak_1383 272 . 3.62319 32.07454 27.27648 94 +chr4 49144579 49144770 SRX6944761.05_peak_1384 138 . 2.72803 18.21634 13.83494 60 +chr4 49147295 49147609 SRX6944761.05_peak_1385 274 . 3.50853 32.21884 27.41760 33 +chr4 49149356 49149513 SRX6944761.05_peak_1386 264 . 3.35124 31.23664 26.45954 109 +chr4 49149738 49149836 SRX6944761.05_peak_1387 263 . 3.33958 31.09178 26.31808 42 +chr4 49150437 49150574 SRX6944761.05_peak_1388 233 . 3.18310 28.08799 23.39458 99 +chr4 49150905 49151019 SRX6944761.05_peak_1389 137 . 2.66396 18.15063 13.77155 95 +chr4 49153461 49153776 SRX6944761.05_peak_1390 207 . 3.25942 25.38758 20.77473 291 +chr4 49154174 49154265 SRX6944761.05_peak_1391 245 . 3.56682 29.27998 24.55231 52 +chr4 49154414 49154595 SRX6944761.05_peak_1392 373 . 4.38313 42.39804 37.37270 70 +chr4 49154881 49154977 SRX6944761.05_peak_1393 269 . 3.97570 31.70066 26.91230 41 +chr4 49155406 49155498 SRX6944761.05_peak_1394 219 . 3.73046 26.59880 21.95016 32 +chr4 49155923 49156035 SRX6944761.05_peak_1395 171 . 3.58319 21.67110 17.17133 70 +chr4 49156443 49156543 SRX6944761.05_peak_1396 485 . 5.87701 53.82680 48.53363 47 +chr4 49270725 49270876 SRX6944761.05_peak_1397 103 . 6.50230 14.59424 10.36458 110 +chr4 49292238 49292303 SRX6944761.05_peak_1398 118 . 6.41172 16.13025 11.83292 39 +chr4 49292430 49292504 SRX6944761.05_peak_1399 147 . 7.19508 19.19610 14.77865 31 +chr4 49297687 49297763 SRX6944761.05_peak_1400 57 . 4.82393 9.72735 5.76688 57 +chr4 49298583 49298650 SRX6944761.05_peak_1401 159 . 7.75679 20.39878 15.94042 41 +chr4 49303856 49303980 SRX6944761.05_peak_1402 66 . 5.11322 10.65290 6.62439 114 +chr4 49314971 49315068 SRX6944761.05_peak_1403 287 . 8.83729 33.61186 28.77736 49 +chr4 49316784 49316864 SRX6944761.05_peak_1404 123 . 5.60732 16.65036 12.33089 28 +chr4 49317627 49317725 SRX6944761.05_peak_1405 119 . 5.46097 16.25216 11.95012 57 +chr4 49322871 49322972 SRX6944761.05_peak_1406 241 . 8.09859 28.89632 24.17837 55 +chr4 49323074 49323168 SRX6944761.05_peak_1407 287 . 9.01957 33.54631 28.71333 47 +chr4 49513741 49513857 SRX6944761.05_peak_1408 107 . 5.03033 15.04185 10.79258 44 +chr4 49514864 49514933 SRX6944761.05_peak_1409 162 . 5.91258 20.73786 16.26849 38 +chr4 49632625 49632758 SRX6944761.05_peak_1410 275 . 4.52654 32.38346 27.57836 81 +chr4 49632893 49633005 SRX6944761.05_peak_1411 323 . 4.78009 37.27199 32.35505 50 +chr4 49633387 49633463 SRX6944761.05_peak_1412 244 . 4.07844 29.22072 24.49474 32 +chr4 49635173 49635256 SRX6944761.05_peak_1413 308 . 4.19124 35.69302 30.81220 46 +chr4 49635491 49635598 SRX6944761.05_peak_1414 297 . 4.04966 34.62946 29.77066 51 +chr4 49637087 49637171 SRX6944761.05_peak_1415 198 . 3.24710 24.40644 19.82131 33 +chr4 49648673 49648754 SRX6944761.05_peak_1416 283 . 3.95669 33.17295 28.34898 38 +chr4 49650503 49650593 SRX6944761.05_peak_1417 256 . 3.86933 30.45139 25.69463 53 +chr4 49651377 49651512 SRX6944761.05_peak_1418 293 . 4.12975 34.24724 29.39735 96 +chr4 49652453 49652533 SRX6944761.05_peak_1419 182 . 3.43079 22.81292 18.27634 52 +chr4 49655818 49655919 SRX6944761.05_peak_1420 271 . 4.54864 31.90592 27.11200 50 +chr4 49657114 49657181 SRX6944761.05_peak_1421 188 . 4.25351 23.44635 18.89127 37 +chr4 49657518 49657850 SRX6944761.05_peak_1422 579 . 7.87946 63.45992 57.97180 65 +chr4 49708732 49708861 SRX6944761.05_peak_1423 467 . 9.70210 51.96248 46.71397 71 +chr4 49709356 49709553 SRX6944761.05_peak_1424 506 . 10.13409 55.95754 50.61921 62 +chr4 49709715 49709807 SRX6944761.05_peak_1425 383 . 8.61644 43.43579 38.38951 46 +chr4 49711504 49711685 SRX6944761.05_peak_1426 434 . 9.10725 48.60645 43.44339 36 +chr4 49972052 49972131 SRX6944761.05_peak_1427 341 . 15.04339 39.10248 34.14548 42 +chr4 50046363 50046441 SRX6944761.05_peak_1428 256 . 12.08459 30.44509 25.68835 40 +chr4 50206938 50207036 SRX6944761.05_peak_1429 555 . 20.29895 61.02484 55.58808 52 +chr4 50243547 50243630 SRX6944761.05_peak_1430 235 . 11.92766 28.26793 23.56817 45 +chr4 50266036 50266112 SRX6944761.05_peak_1431 201 . 11.63311 24.73833 20.14347 39 +chr4 50556091 50556204 SRX6944761.05_peak_1432 1475 . 38.06398 155.31017 147.57489 57 +chr4 50562903 50562975 SRX6944761.05_peak_1433 263 . 13.20404 31.11183 26.33761 29 +chr4 50662308 50662386 SRX6944761.05_peak_1434 434 . 17.89620 48.60891 43.44522 30 +chr4 50677019 50677099 SRX6944761.05_peak_1435 344 . 15.19579 39.37029 34.40701 26 +chr4 50809396 50809474 SRX6944761.05_peak_1436 304 . 14.48076 35.31427 30.44030 27 +chr4 51080557 51080634 SRX6944761.05_peak_1437 386 . 16.82742 43.68366 38.63196 28 +chr4 51107315 51107509 SRX6944761.05_peak_1438 131 . 8.61563 17.48632 13.13357 23 +chr4 51125262 51125344 SRX6944761.05_peak_1439 350 . 15.59064 40.06059 35.08361 48 +chr4 51712483 51712565 SRX6944761.05_peak_1440 355 . 15.87948 40.56253 35.57604 34 +chr4 52862538 52862618 SRX6944761.05_peak_1441 60 . 6.41342 10.05697 6.07174 64 +chr4 69430941 69431012 SRX6944761.05_peak_1442 311 . 16.87828 36.03040 31.14068 53 +chr4 70705085 70705202 SRX6944761.05_peak_1443 84 . 7.32936 12.63544 8.49689 44 +chr4 81215018 81215097 SRX6944761.05_peak_1444 83 . 7.69654 12.48583 8.35447 47 +chr4 108409826 108409924 SRX6944761.05_peak_1445 77 . 7.28029 11.80097 7.70507 42 +chr4 123398389 123398454 SRX6944761.05_peak_1446 82 . 7.40010 12.36027 8.23537 32 +chr4 131735115 131735232 SRX6944761.05_peak_1447 157 . 6.45499 20.15963 15.70842 31 +chr4 131975506 131975571 SRX6944761.05_peak_1448 143 . 9.37966 18.77356 14.37126 47 +chr4 147358177 147358251 SRX6944761.05_peak_1449 86 . 7.86517 12.76672 8.62105 33 +chr4 176065921 176066106 SRX6944761.05_peak_1450 86 . 7.87653 12.78574 8.63765 36 +chr4 180945113 180945221 SRX6944761.05_peak_1451 81 . 7.56144 12.26234 8.14241 17 +chr4 185796539 185796639 SRX6944761.05_peak_1452 64 . 6.69241 10.50782 6.48757 82 +chr4 185827213 185827331 SRX6944761.05_peak_1453 147 . 10.52049 19.19695 14.77934 67 +chr4 189155287 189155382 SRX6944761.05_peak_1454 150 . 10.68958 19.48296 15.05427 43 +chr4 189233897 189234158 SRX6944761.05_peak_1455 1039 . 17.51466 110.10390 103.91205 69 +chr4 189234411 189234492 SRX6944761.05_peak_1456 433 . 10.10393 48.51150 43.35061 45 +chr4 189234979 189235210 SRX6944761.05_peak_1457 847 . 15.31460 90.72848 84.78523 173 +chr4 189235365 189235430 SRX6944761.05_peak_1458 184 . 6.36298 22.96645 18.42496 24 +chr4 189307706 189307773 SRX6944761.05_peak_1459 138 . 8.74026 18.20106 13.82036 18 +chr4 189307983 189308049 SRX6944761.05_peak_1460 160 . 9.47688 20.51598 16.05416 39 +chr4 189810757 189810877 SRX6944761.05_peak_1461 156 . 10.46077 20.05743 15.60977 74 +chr4 189836007 189836154 SRX6944761.05_peak_1462 195 . 9.63780 24.13083 19.55398 111 +chr4 189836399 189836619 SRX6944761.05_peak_1463 228 . 10.50680 27.53059 22.85393 157 +chr4 189846237 189846434 SRX6944761.05_peak_1464 286 . 12.31168 33.46293 28.63167 53 +chr4 189846768 189846914 SRX6944761.05_peak_1465 170 . 9.12944 21.52026 17.02478 47 +chr4 189847026 189847106 SRX6944761.05_peak_1466 78 . 6.19498 11.94421 7.84042 55 +chr4 189881278 189881481 SRX6944761.05_peak_1467 455 . 19.13265 50.75808 45.53882 151 +chr4 190021660 190021783 SRX6944761.05_peak_1468 763 . 12.02730 82.18339 76.36443 50 +chr4 190021856 190021947 SRX6944761.05_peak_1469 354 . 7.69314 40.46121 35.47619 40 +chr4 190022027 190022603 SRX6944761.05_peak_1470 834 . 12.75830 89.38294 83.45776 63 +chr4 190022681 190022766 SRX6944761.05_peak_1471 550 . 10.04184 60.49582 55.06869 33 +chr4 190122721 190123084 SRX6944761.05_peak_1472 474 . 13.24364 52.68762 47.42249 280 +chr4 190177634 190177831 SRX6944761.05_peak_1473 340 . 5.04849 38.97121 34.01729 159 +chr4 190177901 190178039 SRX6944761.05_peak_1474 225 . 4.19874 27.23824 22.57017 98 +chr4 190178340 190178471 SRX6944761.05_peak_1475 432 . 5.70203 48.41716 43.26021 44 +chr4 190178781 190178861 SRX6944761.05_peak_1476 306 . 4.86070 35.49447 30.61760 39 +chr4 190179115 190179392 SRX6944761.05_peak_1477 278 . 4.75123 32.68414 27.87239 218 +chr4 190179470 190179994 SRX6944761.05_peak_1478 617 . 7.17513 67.31789 61.75024 217 +chr4 190181011 190181162 SRX6944761.05_peak_1479 395 . 5.79085 44.63366 39.56119 42 +chr4 190181659 190181724 SRX6944761.05_peak_1480 260 . 4.77739 30.83939 26.07218 36 +chr4 190203670 190203968 SRX6944761.05_peak_1481 175 . 10.37129 22.04472 17.53272 47 +chr4_GL000008v2_random 344 465 SRX6944761.05_peak_1482 68 . 3.18684 10.90653 6.86250 11 +chr4_GL000008v2_random 2757 2917 SRX6944761.05_peak_1483 88 . 3.23383 13.03475 8.87555 116 +chr4_GL000008v2_random 3282 3388 SRX6944761.05_peak_1484 62 . 2.90177 10.30651 6.29899 2 +chr4_GL000008v2_random 6913 6978 SRX6944761.05_peak_1485 154 . 4.43978 19.84656 15.40603 27 +chr5 10024 11761 SRX6944761.05_peak_1486 506 . 5.99059 55.98092 50.64043 1111 +chr5 415550 415641 SRX6944761.05_peak_1487 149 . 9.14286 19.40885 14.98407 26 +chr5 1422449 1422547 SRX6944761.05_peak_1488 133 . 8.18973 17.74025 13.37705 49 +chr5 1539960 1540025 SRX6944761.05_peak_1489 137 . 9.00532 18.14553 13.76657 40 +chr5 3323667 3323843 SRX6944761.05_peak_1490 161 . 9.21986 20.60395 16.13923 41 +chr5 3324087 3324162 SRX6944761.05_peak_1491 150 . 8.88573 19.48399 15.05524 32 +chr5 10761141 10761226 SRX6944761.05_peak_1492 123 . 8.99834 16.67077 12.35065 41 +chr5 14347077 14347308 SRX6944761.05_peak_1493 211 . 12.23817 25.75059 21.12557 56 +chr5 14664565 14664631 SRX6944761.05_peak_1494 100 . 8.34855 14.30054 10.08306 12 +chr5 17517685 17517772 SRX6944761.05_peak_1495 156 . 6.44038 20.12102 15.67119 49 +chr5 17585380 17585497 SRX6944761.05_peak_1496 87 . 4.74909 12.88166 8.72932 88 +chr5 17597563 17597647 SRX6944761.05_peak_1497 62 . 4.11335 10.23262 6.23540 3 +chr5 17631506 17631580 SRX6944761.05_peak_1498 110 . 8.72242 15.32624 11.06485 17 +chr5 21496255 21496320 SRX6944761.05_peak_1499 77 . 4.89090 11.89282 7.79207 24 +chr5 34245074 34245140 SRX6944761.05_peak_1500 98 . 8.43914 14.07263 9.86516 29 +chr5 47210357 47210462 SRX6944761.05_peak_1501 137 . 10.12697 18.09395 13.71690 41 +chr5 47297037 47297130 SRX6944761.05_peak_1502 226 . 11.35947 27.27019 22.60114 37 +chr5 47307697 47307762 SRX6944761.05_peak_1503 176 . 10.42753 22.13983 17.62538 39 +chr5 47308767 47308843 SRX6944761.05_peak_1504 201 . 11.26173 24.69721 20.10326 36 +chr5 49599571 49599755 SRX6944761.05_peak_1505 172 . 3.58841 21.71019 17.20902 50 +chr5 49600448 49600628 SRX6944761.05_peak_1506 139 . 3.31928 18.33220 13.94621 72 +chr5 49600699 49600848 SRX6944761.05_peak_1507 139 . 3.31928 18.33220 13.94621 30 +chr5 49601113 49601296 SRX6944761.05_peak_1508 449 . 5.47232 50.11260 44.90928 39 +chr5 49601506 49602906 SRX6944761.05_peak_1509 509 . 5.83117 56.30817 50.95790 56 +chr5 49609880 49609971 SRX6944761.05_peak_1510 183 . 6.32729 22.85738 18.31916 36 +chr5 49612361 49612502 SRX6944761.05_peak_1511 159 . 5.04972 20.43629 15.97673 71 +chr5 49614380 49614451 SRX6944761.05_peak_1512 100 . 3.89294 14.21734 10.00321 55 +chr5 49615450 49615559 SRX6944761.05_peak_1513 371 . 7.50543 42.20278 37.18235 64 +chr5 49618497 49618589 SRX6944761.05_peak_1514 225 . 5.97829 27.16816 22.50229 35 +chr5 49622564 49622859 SRX6944761.05_peak_1515 161 . 5.19368 20.62416 16.15887 176 +chr5 49626638 49626736 SRX6944761.05_peak_1516 151 . 4.74293 19.55561 15.12464 48 +chr5 49626855 49627054 SRX6944761.05_peak_1517 198 . 5.39863 24.41957 19.83414 35 +chr5 49630764 49631073 SRX6944761.05_peak_1518 483 . 10.33835 53.59908 48.31266 249 +chr5 49633661 49633761 SRX6944761.05_peak_1519 290 . 9.36634 33.88527 29.04399 58 +chr5 49642259 49642347 SRX6944761.05_peak_1520 451 . 9.10505 50.31385 45.10571 44 +chr5 49643704 49643772 SRX6944761.05_peak_1521 194 . 5.68648 24.00661 19.43373 27 +chr5 49644043 49644217 SRX6944761.05_peak_1522 248 . 6.37941 29.59411 24.85795 55 +chr5 49644965 49645050 SRX6944761.05_peak_1523 423 . 8.38864 47.46963 42.33556 41 +chr5 49645968 49646214 SRX6944761.05_peak_1524 575 . 9.92997 63.07539 57.59434 57 +chr5 49656739 49656837 SRX6944761.05_peak_1525 240 . 3.18719 28.75888 24.04451 66 +chr5 49656983 49657102 SRX6944761.05_peak_1526 183 . 2.88530 22.91030 18.37047 43 +chr5 49657219 49657395 SRX6944761.05_peak_1527 139 . 2.64078 18.35093 13.96380 41 +chr5 49657543 49657957 SRX6944761.05_peak_1528 231 . 3.12981 27.83279 23.14652 203 +chr5 49658372 49658764 SRX6944761.05_peak_1529 246 . 3.20317 29.37610 24.64548 312 +chr5 49658906 49659006 SRX6944761.05_peak_1530 183 . 2.88530 22.91030 18.37047 74 +chr5 49659386 49659598 SRX6944761.05_peak_1531 216 . 3.05646 26.31964 21.67835 134 +chr5 49659837 49659926 SRX6944761.05_peak_1532 246 . 3.20317 29.37610 24.64548 40 +chr5 49660045 49660167 SRX6944761.05_peak_1533 192 . 2.93420 23.86670 19.29783 33 +chr5 49660708 49660778 SRX6944761.05_peak_1534 231 . 3.12981 27.83279 23.14652 33 +chr5 49660969 49661233 SRX6944761.05_peak_1535 174 . 2.83639 21.96842 17.45851 120 +chr5 49661437 49661823 SRX6944761.05_peak_1536 218 . 3.00797 26.47253 21.82723 339 +chr5 49666183 49667343 SRX6944761.05_peak_1537 609 . 8.03678 66.49237 60.94124 240 +chr5 71850948 71851043 SRX6944761.05_peak_1538 539 . 23.18698 59.30548 53.90194 44 +chr5 72848597 72848803 SRX6944761.05_peak_1539 107 . 8.79604 15.04238 10.79307 185 +chr5 78391395 78391464 SRX6944761.05_peak_1540 85 . 7.80814 12.67147 8.53064 31 +chr5 80651444 80651569 SRX6944761.05_peak_1541 422 . 19.42219 47.35638 42.22505 54 +chr5 115296427 115296500 SRX6944761.05_peak_1542 74 . 7.31392 11.52859 7.44655 10 +chr5 131295779 131295844 SRX6944761.05_peak_1543 142 . 10.19040 18.64286 14.24475 29 +chr5 134927041 134927117 SRX6944761.05_peak_1544 247 . 13.40421 29.52746 24.79296 40 +chr5 137687103 137687218 SRX6944761.05_peak_1545 86 . 7.67067 12.80137 8.65262 99 +chr5 141157874 141157951 SRX6944761.05_peak_1546 101 . 8.40557 14.39451 10.17323 22 +chr5 152479872 152479947 SRX6944761.05_peak_1547 107 . 8.76472 14.99010 10.74320 57 +chr5 175115052 175115126 SRX6944761.05_peak_1548 249 . 14.55360 29.64620 24.90872 30 +chr5 177961473 177961649 SRX6944761.05_peak_1549 293 . 11.66264 34.16452 29.31664 58 +chr5 178585512 178585733 SRX6944761.05_peak_1550 630 . 19.38326 68.61088 63.01972 158 +chr5 181046014 181046101 SRX6944761.05_peak_1551 150 . 9.51593 19.52002 15.09016 55 +chr6 371301 371458 SRX6944761.05_peak_1552 147 . 6.77966 19.18088 14.76392 134 +chr6 371792 372024 SRX6944761.05_peak_1553 156 . 6.98511 20.12650 15.67647 97 +chr6 372161 372351 SRX6944761.05_peak_1554 138 . 6.57422 18.24812 13.86532 36 +chr6 3092472 3092557 SRX6944761.05_peak_1555 93 . 7.62845 13.53464 9.35131 39 +chr6 21797313 21797407 SRX6944761.05_peak_1556 63 . 6.39415 10.36622 6.35502 81 +chr6 30706676 30706743 SRX6944761.05_peak_1557 211 . 11.87335 25.73050 21.10650 38 +chr6 36874612 36874697 SRX6944761.05_peak_1558 61 . 6.64452 10.12669 6.13641 0 +chr6 39280981 39281062 SRX6944761.05_peak_1559 193 . 11.14684 23.92103 19.35062 35 +chr6 59163231 59163330 SRX6944761.05_peak_1560 448 . 18.29389 50.09171 44.88928 47 +chr6 59537195 59537260 SRX6944761.05_peak_1561 151 . 9.54159 19.56325 15.13179 35 +chr6 59764652 59764733 SRX6944761.05_peak_1562 362 . 15.82591 41.19931 36.20014 32 +chr6 61322960 61323116 SRX6944761.05_peak_1563 758 . 27.94411 81.62980 75.81872 97 +chr6 88948329 88948407 SRX6944761.05_peak_1564 141 . 9.53462 18.52445 14.13080 23 +chr6 157310553 157310945 SRX6944761.05_peak_1565 280 . 8.78271 32.90684 28.08882 316 +chr6 157312850 157313050 SRX6944761.05_peak_1566 292 . 9.00980 34.08391 29.23771 155 +chr6 157314184 157314281 SRX6944761.05_peak_1567 203 . 7.38591 24.95739 20.35657 49 +chr6 157381302 157381412 SRX6944761.05_peak_1568 71 . 6.71141 11.24314 7.17982 81 +chr6 162339293 162339451 SRX6944761.05_peak_1569 80 . 7.30638 12.20794 8.09092 62 +chr6 166383007 166383112 SRX6944761.05_peak_1570 79 . 7.21501 12.05959 7.94981 41 +chr6 168840479 168840561 SRX6944761.05_peak_1571 291 . 11.88354 33.95282 29.10984 55 +chr6 168841028 168841198 SRX6944761.05_peak_1572 506 . 16.77211 55.97451 50.63488 121 +chr6 169660888 169660969 SRX6944761.05_peak_1573 108 . 7.53363 15.15082 10.89723 21 +chr6 170377282 170377347 SRX6944761.05_peak_1574 147 . 10.22756 19.17630 14.75960 33 +chr6 170394479 170394551 SRX6944761.05_peak_1575 61 . 6.01202 10.10381 6.11518 64 +chr6 170421635 170421789 SRX6944761.05_peak_1576 83 . 6.95866 12.43411 8.30551 41 +chr6 170450020 170450100 SRX6944761.05_peak_1577 243 . 12.01201 29.04349 24.32167 39 +chr7 10001 10094 SRX6944761.05_peak_1578 865 . 34.26707 92.47496 86.50769 51 +chr7 20326 20399 SRX6944761.05_peak_1579 111 . 9.00175 15.38738 11.12157 33 +chr7 1272428 1272493 SRX6944761.05_peak_1580 169 . 8.50220 21.39904 16.90754 17 +chr7 20778176 20778317 SRX6944761.05_peak_1581 96 . 8.27586 13.79945 9.60488 120 +chr7 45811082 45811261 SRX6944761.05_peak_1582 175 . 8.07339 22.08132 17.56841 19 +chr7 45812448 45812621 SRX6944761.05_peak_1583 158 . 7.48363 20.30666 15.85109 138 +chr7 48924505 48924627 SRX6944761.05_peak_1584 86 . 7.87653 12.78574 8.63765 38 +chr7 55741516 55741581 SRX6944761.05_peak_1585 205 . 8.29423 25.17775 20.57104 41 +chr7 55744285 55744467 SRX6944761.05_peak_1586 250 . 9.58840 29.78288 25.04101 100 +chr7 56151738 56151803 SRX6944761.05_peak_1587 81 . 6.86730 12.28294 8.16209 6 +chr7 56370495 56370816 SRX6944761.05_peak_1588 216 . 3.58300 26.28899 21.64872 187 +chr7 56371241 56371955 SRX6944761.05_peak_1589 225 . 3.62553 27.16996 22.50394 383 +chr7 56372305 56372373 SRX6944761.05_peak_1590 118 . 2.88449 16.12688 11.82977 15 +chr7 56372534 56373058 SRX6944761.05_peak_1591 182 . 3.37571 22.78793 18.25218 407 +chr7 56373496 56373651 SRX6944761.05_peak_1592 227 . 3.67627 27.38663 22.71382 50 +chr7 56374393 56374695 SRX6944761.05_peak_1593 212 . 3.58741 25.84261 21.21497 190 +chr7 57495557 57495626 SRX6944761.05_peak_1594 239 . 8.43154 28.70707 23.99407 42 +chr7 58033151 58033244 SRX6944761.05_peak_1595 855 . 22.28339 91.45856 85.50284 43 +chr7 58036601 58036758 SRX6944761.05_peak_1596 386 . 12.11305 43.74038 38.68742 43 +chr7 58059989 58060061 SRX6944761.05_peak_1597 327 . 11.77826 37.66658 32.74080 25 +chr7 58275814 58275922 SRX6944761.05_peak_1598 643 . 22.35251 69.93861 64.32246 48 +chr7 58297088 58297164 SRX6944761.05_peak_1599 242 . 12.71995 29.00242 24.28212 40 +chr7 58349741 58349809 SRX6944761.05_peak_1600 219 . 11.68413 26.61360 21.96447 27 +chr7 58399806 58399871 SRX6944761.05_peak_1601 136 . 9.20851 17.98351 13.61187 26 +chr7 58434527 58434634 SRX6944761.05_peak_1602 823 . 25.82446 88.27993 82.36926 52 +chr7 58445030 58445120 SRX6944761.05_peak_1603 455 . 16.91189 50.74179 45.52296 43 +chr7 58632440 58632508 SRX6944761.05_peak_1604 197 . 10.35529 24.29526 19.71365 33 +chr7 58657791 58657864 SRX6944761.05_peak_1605 186 . 11.05705 23.19887 18.65112 37 +chr7 58708437 58708516 SRX6944761.05_peak_1606 259 . 11.89061 30.73376 25.96919 39 +chr7 58797808 58797895 SRX6944761.05_peak_1607 323 . 14.79112 37.24191 32.32598 46 +chr7 58818595 58818667 SRX6944761.05_peak_1608 250 . 12.09234 29.81971 25.07664 40 +chr7 58869709 58869780 SRX6944761.05_peak_1609 179 . 10.31337 22.50570 17.97841 27 +chr7 58986145 58986239 SRX6944761.05_peak_1610 615 . 21.77523 67.13261 61.57058 49 +chr7 59163943 59164041 SRX6944761.05_peak_1611 519 . 18.71750 57.30001 51.93217 47 +chr7 59167633 59167720 SRX6944761.05_peak_1612 434 . 16.60038 48.61159 43.44789 44 +chr7 59251670 59251759 SRX6944761.05_peak_1613 266 . 13.02289 31.45263 26.67038 39 +chr7 59292466 59292551 SRX6944761.05_peak_1614 333 . 14.56855 38.26253 33.32353 43 +chr7 59321036 59321103 SRX6944761.05_peak_1615 217 . 11.56300 26.40545 21.76164 28 +chr7 59364670 59364748 SRX6944761.05_peak_1616 291 . 13.35064 34.02302 29.17813 46 +chr7 59373958 59374026 SRX6944761.05_peak_1617 234 . 11.83884 28.11293 23.41869 33 +chr7 59402537 59402603 SRX6944761.05_peak_1618 160 . 9.79792 20.52710 16.06502 35 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40 +chr7 60017028 60017107 SRX6944761.05_peak_1631 313 . 13.45756 36.25924 31.36467 44 +chr7 60019495 60019609 SRX6944761.05_peak_1632 738 . 24.16919 79.59129 73.81136 55 +chr7 60250563 60250662 SRX6944761.05_peak_1633 592 . 20.49679 64.78917 59.27332 51 +chr7 60310801 60310918 SRX6944761.05_peak_1634 1126 . 30.97489 118.94497 112.61848 57 +chr7 60366652 60366725 SRX6944761.05_peak_1635 233 . 11.78035 28.01068 23.31953 45 +chr7 60376076 60376148 SRX6944761.05_peak_1636 191 . 10.71004 23.75585 19.19124 45 +chr7 60383181 60383257 SRX6944761.05_peak_1637 246 . 12.19048 29.35670 24.62733 38 +chr7 60418871 60418976 SRX6944761.05_peak_1638 650 . 23.26032 70.68357 65.05521 54 +chr7 60517851 60517924 SRX6944761.05_peak_1639 262 . 12.42917 31.06139 26.28861 26 +chr7 60522180 60522268 SRX6944761.05_peak_1640 380 . 16.03972 43.05120 38.01159 46 +chr7 60639073 60639142 SRX6944761.05_peak_1641 229 . 12.26216 27.60031 22.92123 43 +chr7 60917871 60917941 SRX6944761.05_peak_1642 280 . 12.00822 32.89020 28.07237 33 +chr7 60928484 60928555 SRX6944761.05_peak_1643 285 . 11.59252 33.37897 28.54993 27 +chr7 61071233 61071356 SRX6944761.05_peak_1644 784 . 22.35772 84.32571 78.47165 65 +chr7 61579914 61579988 SRX6944761.05_peak_1645 192 . 12.37741 23.77892 19.21280 29 +chr7 62347498 62347569 SRX6944761.05_peak_1646 291 . 10.15565 34.04477 29.19942 31 +chr7 62350316 62350404 SRX6944761.05_peak_1647 618 . 15.59576 67.46569 61.89446 34 +chr7 62402801 62402887 SRX6944761.05_peak_1648 349 . 11.91200 39.93920 34.96555 49 +chr7 62442502 62442575 SRX6944761.05_peak_1649 214 . 10.05638 26.07815 21.44430 30 +chr7 62454726 62454831 SRX6944761.05_peak_1650 1085 . 27.90162 114.84523 108.58325 51 +chr7 65489986 65490080 SRX6944761.05_peak_1651 392 . 16.34049 44.33206 39.26517 32 +chr7 65490512 65490585 SRX6944761.05_peak_1652 259 . 12.57143 30.66501 25.90209 33 +chr7 86282418 86282483 SRX6944761.05_peak_1653 62 . 6.75131 10.30337 6.29613 8 +chr7 88759775 88759879 SRX6944761.05_peak_1654 62 . 6.75131 10.30337 6.29613 91 +chr7 90211802 90211867 SRX6944761.05_peak_1655 86 . 7.87653 12.78574 8.63765 7 +chr7 100958060 100958125 SRX6944761.05_peak_1656 81 . 5.53018 12.23388 8.11533 44 +chr7 100958664 100958734 SRX6944761.05_peak_1657 203 . 8.68307 24.97902 20.37772 41 +chr7 143511186 143511256 SRX6944761.05_peak_1658 87 . 7.72201 12.88533 8.73264 39 +chr7 148331623 148331704 SRX6944761.05_peak_1659 212 . 11.27089 25.90120 21.27203 29 +chr7 155350237 155350420 SRX6944761.05_peak_1660 234 . 11.53361 28.19643 23.49949 33 +chr7 155350575 155350757 SRX6944761.05_peak_1661 172 . 9.57447 21.77635 17.27330 136 +chr7 158151401 158151466 SRX6944761.05_peak_1662 154 . 8.25472 19.88039 15.43867 32 +chr7 158595218 158595297 SRX6944761.05_peak_1663 428 . 18.01064 48.02942 42.88286 40 +chr7 159203041 159203457 SRX6944761.05_peak_1664 292 . 11.97605 34.13378 29.28663 94 +chr7 159203574 159203714 SRX6944761.05_peak_1665 78 . 5.97639 11.94563 7.84172 50 +chr7 159335878 159335967 SRX6944761.05_peak_1666 440 . 21.37916 49.20225 44.02209 47 +chr8 802074 802150 SRX6944761.05_peak_1667 125 . 8.55049 16.88464 12.55515 40 +chr8 1844372 1844448 SRX6944761.05_peak_1668 227 . 11.46874 27.46326 22.78832 49 +chr8 19461520 19461592 SRX6944761.05_peak_1669 124 . 9.56436 16.72832 12.40414 32 +chr8 43240024 43240095 SRX6944761.05_peak_1670 316 . 14.77529 36.51421 31.61399 43 +chr8 47260858 47260944 SRX6944761.05_peak_1671 86 . 7.64526 12.75984 8.61442 23 +chr8 57205891 57206076 SRX6944761.05_peak_1672 98 . 4.82209 14.09490 9.88637 42 +chr8 69690076 69690282 SRX6944761.05_peak_1673 1512 . 35.73487 159.54749 151.23244 62 +chr8 81842421 81842564 SRX6944761.05_peak_1674 301 . 15.90559 35.01885 30.15147 88 +chr8 85643113 85643240 SRX6944761.05_peak_1675 138 . 6.05118 18.23755 13.85540 67 +chr8 85643365 85643618 SRX6944761.05_peak_1676 110 . 5.40495 15.34308 11.08090 136 +chr8 85663216 85663447 SRX6944761.05_peak_1677 135 . 5.39643 17.96392 13.59288 191 +chr8 85714294 85714466 SRX6944761.05_peak_1678 118 . 4.62832 16.13896 11.84141 113 +chr8 85735512 85735602 SRX6944761.05_peak_1679 88 . 3.59903 12.96711 8.81077 33 +chr8 85746006 85746102 SRX6944761.05_peak_1680 59 . 3.43938 9.94806 5.97113 87 +chr8 85746433 85746527 SRX6944761.05_peak_1681 62 . 3.52514 10.25267 6.25403 53 +chr8 85764637 85764708 SRX6944761.05_peak_1682 77 . 3.83060 11.83656 7.73912 57 +chr8 85776790 85777000 SRX6944761.05_peak_1683 65 . 3.41297 10.54132 6.51926 51 +chr8 85816452 85816693 SRX6944761.05_peak_1684 85 . 3.76878 12.65637 8.51632 43 +chr8 98294113 98294196 SRX6944761.05_peak_1685 56 . 6.13787 9.61415 5.66126 22 +chr8 100213252 100213395 SRX6944761.05_peak_1686 97 . 8.15494 13.98240 9.77984 104 +chr8 110728358 110728429 SRX6944761.05_peak_1687 250 . 14.62785 29.77336 25.03155 49 +chr8 132775370 132775450 SRX6944761.05_peak_1688 86 . 7.87653 12.78574 8.63765 32 +chr8 138881595 138881661 SRX6944761.05_peak_1689 150 . 9.51593 19.52002 15.09016 27 +chr8 143669507 143669601 SRX6944761.05_peak_1690 189 . 10.87411 23.46029 18.90481 47 +chr8 144125626 144125699 SRX6944761.05_peak_1691 204 . 11.10670 25.02173 20.41927 42 +chr8 144125797 144125917 SRX6944761.05_peak_1692 293 . 13.81215 34.15767 29.30987 71 +chr9 33523832 33523943 SRX6944761.05_peak_1693 146 . 9.85453 19.05395 14.64157 74 +chr9 35914328 35914397 SRX6944761.05_peak_1694 189 . 9.91792 23.51145 18.95369 32 +chr9 39042371 39042493 SRX6944761.05_peak_1695 87 . 7.24792 12.91079 8.75689 12 +chr9 39078786 39078880 SRX6944761.05_peak_1696 179 . 10.96141 22.47942 17.95279 51 +chr9 40106421 40106486 SRX6944761.05_peak_1697 150 . 10.13269 19.51412 15.08456 49 +chr9 40992082 40992154 SRX6944761.05_peak_1698 65 . 6.06520 10.55924 6.53585 23 +chr9 41074427 41074562 SRX6944761.05_peak_1699 78 . 7.17017 11.98685 7.88069 7 +chr9 41229453 41229604 SRX6944761.05_peak_1700 93 . 6.38792 13.52542 9.34246 134 +chr9 42900347 42900414 SRX6944761.05_peak_1701 178 . 11.81480 22.32647 17.80481 25 +chr9 43319030 43319108 SRX6944761.05_peak_1702 470 . 15.33406 52.26282 47.00708 21 +chr9 43319287 43319419 SRX6944761.05_peak_1703 280 . 11.03040 32.87774 28.06042 98 +chr9 43344838 43344908 SRX6944761.05_peak_1704 242 . 11.62176 28.97836 24.25857 46 +chr9 43418905 43418990 SRX6944761.05_peak_1705 230 . 12.71283 27.74563 23.06209 48 +chr9 43918804 43918889 SRX6944761.05_peak_1706 405 . 18.43318 45.67977 40.58549 48 +chr9 44175482 44175586 SRX6944761.05_peak_1707 697 . 25.44317 75.43606 69.72023 51 +chr9 44434452 44434520 SRX6944761.05_peak_1708 185 . 10.99384 23.09292 18.54772 33 +chr9 60552976 60553077 SRX6944761.05_peak_1709 797 . 28.61478 85.63150 79.75588 44 +chr9 61856498 61856688 SRX6944761.05_peak_1710 131 . 7.50686 17.45374 13.10255 39 +chr9 61856800 61856874 SRX6944761.05_peak_1711 90 . 6.24645 13.25749 9.08734 18 +chr9 62837527 62837593 SRX6944761.05_peak_1712 146 . 7.82014 19.03679 14.62519 29 +chr9 63066172 63066308 SRX6944761.05_peak_1713 135 . 9.78121 17.96216 13.59122 103 +chr9 63818053 63818198 SRX6944761.05_peak_1714 80 . 5.02610 12.21386 8.09659 127 +chr9 63823737 63823815 SRX6944761.05_peak_1715 143 . 7.69442 18.78865 14.38582 39 +chr9 65385747 65385823 SRX6944761.05_peak_1716 314 . 12.11950 36.39528 31.49760 36 +chr9 67724134 67724203 SRX6944761.05_peak_1717 181 . 9.16031 22.66922 18.13730 42 +chr9 70038185 70038464 SRX6944761.05_peak_1718 803 . 23.77229 86.23791 80.35550 225 +chr9 76175201 76175290 SRX6944761.05_peak_1719 558 . 25.31743 61.32824 55.88281 51 +chr9 77761966 77762071 SRX6944761.05_peak_1720 90 . 7.43332 13.21509 9.04684 51 +chr9 87758486 87758557 SRX6944761.05_peak_1721 119 . 8.45666 16.24719 11.94537 33 +chr9 96707619 96707774 SRX6944761.05_peak_1722 86 . 7.67067 12.80137 8.65262 20 +chr9 109166975 109167082 SRX6944761.05_peak_1723 61 . 6.49675 10.19132 6.19672 58 +chr9 113060069 113060137 SRX6944761.05_peak_1724 189 . 8.75564 23.50779 18.95019 23 +chr9 134014195 134014342 SRX6944761.05_peak_1725 249 . 12.03501 29.71783 24.97812 48 +chr9 136234745 136234820 SRX6944761.05_peak_1726 175 . 9.42920 22.06705 17.55451 31 +chr9 137716327 137716395 SRX6944761.05_peak_1727 121 . 8.00437 16.43727 12.12727 27 +chr9 137845795 137845923 SRX6944761.05_peak_1728 91 . 6.52884 13.36605 9.19097 95 +chr9 137846132 137846289 SRX6944761.05_peak_1729 108 . 7.02290 15.14688 10.89349 128 +chr9_KI270718v1_random 37549 37642 SRX6944761.05_peak_1730 103 . 8.25443 14.55823 10.33025 30 +chr9_KI270719v1_random 169943 170046 SRX6944761.05_peak_1731 113 . 6.18170 15.62493 11.34936 53 +chrM 714 818 SRX6944761.05_peak_1732 84 . 2.49318 12.60164 8.46457 56 +chrM 14856 15005 SRX6944761.05_peak_1733 64 . 2.07817 10.46668 6.44963 87 +chrM 16235 16512 SRX6944761.05_peak_1734 121 . 2.59829 16.51294 12.19966 106 +chrUn_GL000214v1 64331 64408 SRX6944761.05_peak_1735 226 . 10.10538 27.34905 22.67762 48 +chrUn_GL000214v1 64480 64558 SRX6944761.05_peak_1736 172 . 8.69439 21.77459 17.27167 46 +chrUn_GL000214v1 64858 65285 SRX6944761.05_peak_1737 654 . 19.74448 71.04933 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14.61213 56 +chrUn_GL000214v1 136411 136495 SRX6944761.05_peak_1749 128 . 4.39602 17.16386 12.82295 40 +chrUn_GL000214v1 136613 136684 SRX6944761.05_peak_1750 243 . 5.94354 29.09970 24.37612 32 +chrUn_GL000214v1 137426 137548 SRX6944761.05_peak_1751 193 . 5.67036 23.94567 19.37463 92 +chrUn_GL000216v2 29 126 SRX6944761.05_peak_1752 285 . 5.49993 33.34127 28.51306 35 +chrUn_GL000216v2 204 850 SRX6944761.05_peak_1753 570 . 7.55282 62.48497 57.01596 538 +chrUn_GL000216v2 1781 2072 SRX6944761.05_peak_1754 543 . 6.52810 59.73529 54.32202 243 +chrUn_GL000216v2 5741 5943 SRX6944761.05_peak_1755 347 . 5.19427 39.67933 34.71062 148 +chrUn_GL000216v2 6115 6185 SRX6944761.05_peak_1756 338 . 5.12847 38.83967 33.88858 32 +chrUn_GL000216v2 9326 9434 SRX6944761.05_peak_1757 400 . 5.36867 45.08169 40.00036 63 +chrUn_GL000216v2 10342 10408 SRX6944761.05_peak_1758 316 . 4.68449 36.52033 31.61994 34 +chrUn_GL000216v2 11654 11720 SRX6944761.05_peak_1759 313 . 4.64841 36.23391 31.33976 32 +chrUn_GL000216v2 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176519 SRX6944761.05_peak_1804 301 . 7.31580 35.01537 30.14814 32 +chrUn_GL000219v1 50968 51052 SRX6944761.05_peak_1805 96 . 8.07469 13.85090 9.65397 55 +chrUn_GL000220v1 68107 68266 SRX6944761.05_peak_1806 91 . 7.25222 13.33958 9.16592 140 +chrUn_GL000220v1 104599 104673 SRX6944761.05_peak_1807 69 . 2.09910 10.98732 6.93920 37 +chrUn_GL000220v1 104779 105074 SRX6944761.05_peak_1808 90 . 2.21560 13.17607 9.00972 113 +chrUn_GL000220v1 105145 105212 SRX6944761.05_peak_1809 57 . 1.99593 9.72845 5.76792 27 +chrUn_GL000220v1 106336 106431 SRX6944761.05_peak_1810 86 . 2.10260 12.76964 8.62381 45 +chrUn_GL000220v1 118664 118897 SRX6944761.05_peak_1811 127 . 2.33077 17.10742 12.76868 201 +chrUn_GL000220v1 119039 119182 SRX6944761.05_peak_1812 105 . 2.21157 14.75466 10.51803 81 +chrUn_GL000220v1 119850 119942 SRX6944761.05_peak_1813 142 . 2.40964 18.60995 14.21314 29 +chrUn_GL000220v1 142104 142177 SRX6944761.05_peak_1814 100 . 2.37863 14.24754 10.03235 35 +chrUn_GL000220v1 143122 143196 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SRX6944761.05_peak_2231 129 . 3.02263 17.27382 12.92855 14 +chrY 56771119 56771275 SRX6944761.05_peak_2232 96 . 3.19247 13.85684 9.65974 20 +chrY 56826766 56826843 SRX6944761.05_peak_2233 170 . 5.06058 21.55020 17.05369 47 +chrY 56833254 56833321 SRX6944761.05_peak_2234 132 . 3.43607 17.55998 13.20378 18 +chrY 56834245 56834336 SRX6944761.05_peak_2235 69 . 2.79735 10.96126 6.91455 80 +chrY 56834461 56834586 SRX6944761.05_peak_2236 90 . 3.01976 13.21813 9.04979 108 +chrY 56838975 56839045 SRX6944761.05_peak_2237 303 . 5.43250 35.26012 30.38731 32 +chrY 56847152 56847229 SRX6944761.05_peak_2238 411 . 6.93401 46.25388 41.14908 35 +chrY 56850333 56850461 SRX6944761.05_peak_2239 144 . 4.73723 18.86266 14.45736 82 +chrY 56850780 56850980 SRX6944761.05_peak_2240 502 . 9.00901 55.62433 50.29137 62 From f50a22d969c41c0947050f5576fcfcbed902f04f Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Wed, 2 Aug 2023 17:19:18 -0400 Subject: [PATCH 0317/1072] Update text2bednn.py comments and docstrings --- gitk/text2bednn/text2bednn.py | 254 ++++++++++++++++++++++++++++++---- 1 file changed, 230 insertions(+), 24 deletions(-) diff --git a/gitk/text2bednn/text2bednn.py b/gitk/text2bednn/text2bednn.py index 34d3917e..dfc7b796 100644 --- a/gitk/text2bednn/text2bednn.py +++ b/gitk/text2bednn/text2bednn.py @@ -1,35 +1,241 @@ -from gitk.models import ExModel -from ..search import EmSearchBackend +import numpy as np +from typing import List, Union, Tuple +from sentence_transformers import SentenceTransformer +from .const import DEFAULT_HF_ST_MODEL +from dataclasses import dataclass, replace +from ..io import RegionSet +from ..region2vec import Region2VecExModel +from .utils import * +@dataclass +class BedMetadataEmbedding: + """ + Store the information of a bed file, its metadata, and embedding + """ + file_name: str # the name of the bed file + metadata: str # the metadata of the bed file + region_set: RegionSet # the RegionSet that contains intervals in that bed file, not tokenized + metadata_embedding: np.ndarray # the embedding vector of the metadata by sentence transformer + region_set_embedding: np.ndarray # the embedding vector of region set -class TextToBedNN(ExModel): - """A Extended Model for TextToBed using a FFNN.""" - def __init__(self, model, universe, tokenizer): - super().__init__(model, universe, tokenizer) +@dataclass +class BedMetadataEmbeddingSet: + """ + Data set to train the feedforward neural network (X: metadata embeddings; Y: region set embedding) + """ + training: List[BedMetadataEmbedding] + validating: List[BedMetadataEmbedding] + testing: List[BedMetadataEmbedding] - def embed(self, region_set: RegionSet) -> np.ndarray: - """Embed a region set using the model""" + def __len__(self): + """ + return sum of number of BedMetadataEmbedding in all data set + :return: + """ + return len(self.training) + len(self.validating) + len(self.testing) -class TextToBedNNSearchInterface(object): - def __init__(self, exmodel: TextToBedNN, region_set_backend: EmSearchBackend): - self.exmodel = exmodel - self.region_set_backend = region_set_backend + def generate_data(self, set_name: str) -> Tuple[np.ndarray, np.ndarray]: + """ + With given dataset name, return a tuple of X and Y - def nlsearch(self, query: str, k: int = 10) -> embeddings: - """Given an input natural lange, suggest region sets""" + :param set_name: "training", "validating", or "testing" + :return: + """ - # first, get the embedding of the query string - query_embedding = self.tum.embed(query) - # then, use the query embedding to predict the region set embedding - region_set_embedding = self.tum.str_to_region_set(query_embedding) - # finally, use the region set embedding to search for similar region sets - return self.region_set_backend.search(region_set_embedding, k) + if set_name == "training": + output_list = self.training + elif set_name == "validating": + output_list = self.validating + elif set_name == "testing": + output_list = self.testing + else: + raise Exception("Please make sure the name of dataset is correct") + X = [] + Y = [] + for dc in output_list: + # X: metadata embedding + X.append(dc.metadata_embedding) + # Y: bed file embedding + Y.append(dc.region_set_embedding) -# Example of how to use the TextToBedNN Search Interface + return np.array(X), np.array(Y) -betum = BEDEmbedTUM(RSC, universe, tokenizer) -embeddings = betum.compute_embeddings() -T2BNNSI = TextToBedNNSearchInterface(betum, embeddings) # ??? + +def build_BedMetadataSet_from_files(bed_folder: str, + metadata_path: str, + r2v_model: Region2VecExModel, + st_model: SentenceTransformer) -> BedMetadataEmbeddingSet: + # training_files, validating_files, testing_files = data_file_split(bed_folder) + """ + With a folder of bed files and a metadata file (first column matches bed file names), + embed the bed files with a Region2VecExmodel, and embed metadata with Sentence Transformer, + returns build a BedMetadataEmbeddingSet + + :param bed_folder: foldler of bed files + :param metadata_path: metadata file + :param r2v_model: Region2VecExModel + :param st_model: sentence transformer model + :return: + """ + # sorted list of file names in the bed file folder + file_name_list = os.listdir(bed_folder) + file_name_list.sort() + + # create a list of BedMetadataEmbedding + all_bed_metadata_dc = build_BedMetadata_list(bed_folder, + file_name_list, + metadata_path, + r2v_model, + st_model) + # split the training, validating, and testing sets + training, validating, testing = data_split(all_bed_metadata_dc) + return BedMetadataEmbeddingSet(training, validating, testing) + + +def build_BedMetadata_list(bed_folder: str, + file_name_list: List[str], + metadata_path: str, + r2v_model: Region2VecExModel, + st_model: SentenceTransformer) -> List[BedMetadataEmbedding]: + """ + With each bed file in the given folder and its matching metadata from the meadata file, + create a BedMetadataEmbedding with each, and return the list containing all. + + :param bed_folder: + :param file_name_list: + :param metadata_path: + :param r2v_model: + :param st_model: + :return: + """ + output_list = [] + + # read the lines from the metadata file + with open(metadata_path) as m: + metadata_lines = m.readlines() + + # index to traverse metadata and file list + # make sure metadata is sorted by the name of interval set + # this can be done by this command + # sort -k1 1 metadata_file > new_metadata_file + i = 0 + j = 0 + + while i < len(metadata_lines): + + # read the line of metadata + metadata_line = metadata_lines[i] + # get the name of the interval set + set_name = metadata_line.split("\t")[0] + + if j < len(file_name_list) and file_name_list[j].startswith(set_name): + bed_file_name = file_name_list[j] + bed_file_path = os.path.join(bed_folder, bed_file_name) + bed_metadata = metadata_line_process(metadata_line) + region_set = RegionSet(bed_file_path) + metadata_embedding = st_model.encode(bed_metadata) + region_set_embedding = np.nanmean(r2v_model.encode(region_set), axis=0) + bed_metadata_dc = BedMetadataEmbedding(bed_file_name, + bed_metadata, + region_set, + metadata_embedding, + region_set_embedding) + output_list.append(bed_metadata_dc) + j += 1 + + # end the loop if all + if j == len(file_name_list): + break + + i += 1 + + # print a message if not all bed files are matched to metadata rows + if i < j: + print("An incomplete list will be returned, some files cannot be matched to any rows by first column") + + return output_list + + +def update_BedMetadata_list(old_list: List[BedMetadataEmbedding], + r2v_model: Region2VecExModel) -> List[BedMetadataEmbedding]: + """ + With an old list of BedMetadataEmbedding, re-embed the region set with a new region2vec model, + then return the list of new BedMetadataEmbedding with re-embedded region set vectors. + :param old_list: + :param r2v_model: + :return: + """ + new_list = [] + for dc in old_list: + new_dc = replace(dc, region_set_embedding=r2v_model.encode(dc.region_set)) + new_list.append(new_dc) + + return new_list + +# class TextToBedNN(Model): +# """A Extended Model for TextToBed using a FFNN.""" +# +# def __init__(self, +# model_nn_path: Union[str, None] = None, +# model_st_repo: str = DEFAULT_HF_ST_MODEL, +# **kwargs): +# # super().__init__(model, universe, tokenizer) +# self._st_model = SentenceTransformer(model_st_repo) +# if model_nn_path is not None: +# self.from_pretrained(model_nn_path) +# else: +# self._nn_model = tf.keras.models.Sequential() +# # initiation +# def __repr__(self): +# print("To be completed") +# +# def _init_from_huggingface(self): +# # to be finished +# return +# +# def from_pretrained(self, model_nn_path: str): +# self._nn_model = tf.keras.models.load_model(model_nn_path) +# +# def train(self, dataset: BedMetadataEmbeddingSet): +# return +# +# def forward(self, query: str): +# # input string -> embedded by sentence transformer -> FNN -> vector +# return +# +# def train(self, dataset: BedMetadataEmbeddingSet): +# # given embedded [bed, metadata] pair, fit the neural network +# return + + +# class TextToBedNNSearchInterface(object): +# def __init__(self, exmodel: TextToBedNN, region_set_backend: EmSearchBackend): +# self.exmodel = exmodel +# self.region_set_backend = region_set_backend +# +# def nlsearch(self, query: str, k: int = 10): +# """Given an input natural lange, suggest region sets""" +# +# # first, get the embedding of the query string +# query_embedding = self.tum.embed(query) +# # then, use the query embedding to predict the region set embedding +# region_set_embedding = self.tum.str_to_region_set(query_embedding) +# # finally, use the region set embedding to search for similar region sets +# return self.region_set_backend.search(region_set_embedding, k) + +# +# # Example of how to use the TextToBedNN Search Interface +# +# betum = BEDEmbedTUM(RSC, universe, tokenizer) +# embeddings = betum.compute_embeddings() +# T2BNNSI = TextToBedNNSearchInterface(betum, embeddings) # ??? + + +# +# r2v_model = Region2VecExModel(r2v_hf_repo) +# st_model = SentenceTransformer(st_hf_repo) +# bme_set = build_BedMetadataSet_from_files(bed_folder, metadata_path, r2v_model, st_model) +# assert bme_set is not None From 0afdb1fa1b8438939193f221f1dfd25f6a5fbf63 Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Wed, 2 Aug 2023 17:20:09 -0400 Subject: [PATCH 0318/1072] add text2bednn to packages --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index aecfd8fb..4bfa9e73 100755 --- a/setup.py +++ b/setup.py @@ -36,6 +36,7 @@ "gitk.tokenization", "gitk.universe", "gitk.io", + "gitk.text2bednn" ], version=version, long_description=long_description, From 997c5524042bfec0c4dae7bd6cd4a79d8ea63dfd Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Wed, 2 Aug 2023 17:20:48 -0400 Subject: [PATCH 0319/1072] Update test_text2bednn.py --- tests/test_text2bednn.py | 43 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) diff --git a/tests/test_text2bednn.py b/tests/test_text2bednn.py index e69de29b..533d39d2 100644 --- a/tests/test_text2bednn.py +++ b/tests/test_text2bednn.py @@ -0,0 +1,43 @@ +import pytest +import scanpy as sc +import os +from gitk.text2bednn.text2bednn import build_BedMetadataSet_from_files +from gitk.region2vec.main import Region2Vec, Region2VecExModel +from sentence_transformers import SentenceTransformer +import numpy as np + + +@pytest.fixture +def metadata_path(): + return "./data/hg38_metadata_sample_sorted.tab" + + +@pytest.fixture +def bed_folder(): + return "./data/hg38_sample" + + +@pytest.fixture +def r2v_hf_repo(): + return "databio/r2v-ChIP-atlas" + + +@pytest.fixture +def st_hf_repo(): + return "sentence-transformers/all-MiniLM-L12-v2" + + +def test_BedMetadataEmbeddingSet(bed_folder, metadata_path, r2v_hf_repo): + r2v_model = Region2VecExModel(r2v_hf_repo) + # st_model = SentenceTransformer(st_hf_repo) + st_model = SentenceTransformer("sentence-transformers/all-MiniLM-L12-v2") + bme_set = build_BedMetadataSet_from_files(bed_folder, metadata_path, r2v_model, st_model) + assert bme_set is not None + assert len(bme_set) == 20 + train_X, train_Y = bme_set.generate_data("training") + assert(isinstance(train_X, np.ndarray)) + assert (isinstance(train_Y, np.ndarray)) + assert(train_X.shape[1] == 384) + assert(train_Y.shape[1] == 100) + assert(train_X[0].shape == (384, )) + assert (train_Y[0].shape == (100,)) From d3e91107491feb120b56557c9f184a6216255b1b Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Wed, 2 Aug 2023 17:26:40 -0400 Subject: [PATCH 0320/1072] embed intervals not in region2vec vocab as array of np.nan --- gitk/region2vec/main.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/gitk/region2vec/main.py b/gitk/region2vec/main.py index 7229a887..a315b75b 100644 --- a/gitk/region2vec/main.py +++ b/gitk/region2vec/main.py @@ -353,7 +353,12 @@ def forward(self, regions: Union[Region, RegionSet, List[Region]]) -> np.ndarray """ if isinstance(regions, Region): region_word = utils.wordify_region(regions) - return self.wv[region_word] + try: + return self.wv[region_word] + except: + embedding_vec = np.empty(self.vector_size) + embedding_vec[:] = np.nan + return embedding_vec elif isinstance(regions, RegionSet): region_words = utils.wordify_regions(regions) return np.array([self.wv[r] for r in region_words if r in self.wv]) From a2620b06ed17d27222951fbc6fbf4832f4daed7a Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Thu, 3 Aug 2023 14:08:06 -0400 Subject: [PATCH 0321/1072] Update utils.py --- gitk/text2bednn/utils.py | 66 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) diff --git a/gitk/text2bednn/utils.py b/gitk/text2bednn/utils.py index e69de29b..78fc5562 100644 --- a/gitk/text2bednn/utils.py +++ b/gitk/text2bednn/utils.py @@ -0,0 +1,66 @@ +import os +from typing import List, Tuple +import random + + +def data_split(full_list: List, + train_p=0.85*0.9, + valid_p=0.85*0.1, + seed_index: int = 10) -> Tuple[List, List, List]: + """ + With a given folder of data, this function split the files + into training set, validating set, and testing set. + + :param bed_folder: folder where bed files are stored + :param train_p: proportion of files for training set + :param valid_p: proportion of files for validating set + and files will be copied to them. + :param seed_index: index for random seed + :return: lists of file names for training set, validating set, and training set. + """ + + # split training data and testing data + # file_list = os.listdir(bed_folder) + train_size = int(len(full_list) * train_p) + validate_size = int(len(full_list) * valid_p) + random.seed(seed_index) + train_list = random.sample(full_list, train_size) + validate_list = random.sample([content for content in full_list if content not in train_list], validate_size) + test_list = [content for content in full_list if (content not in train_list and content not in validate_list)] + + # train_list.sort() + # validate_list.sort() + # test_list.sort() + + return train_list, validate_list, test_list + + +def metadata_line_process(metadata_line: str) -> str: + """ + remove formatting characters and interval set name from metadata + + :param set_name: name of interval set + :param metadata_line: the metadata text + :return: the metadata text without interval set name and formatting characters + """ + + metadata_line = metadata_line.replace("\t", " ") + metadata_line = metadata_line.replace("\n", "") + + return metadata_line + + +""" +ls | shuf -n 20 | xargs -I '{}' cp '{}' /home/claudehu/Desktop/repo/geniml_dev/tests/data/hg38_sample + +for file in /home/claudehu/Desktop/repo/geniml_dev/tests/data/hg38_sample/*.bed; do + key=$(basename "$file" | cut -d. -f1) + grep -w "$key" experimentList_sorted_hg38.tab >> /home/claudehu/Desktop/repo/geniml_dev/tests/data/hg38_metadata_sample.tab +done + +shuf -n 20 experimentList_sorted_hg38.tab >> /home/claudehu/Desktop/repo/geniml_dev/tests/data/hg38_metadata_sample.tab + +sort -k1,1 -t$'\t' hg38_metadata_sample.tab > hg38_metadata_sample_sorted.tab + + +""" From 13c4d919ea500fee7f2bb846e0bb9d943d742514 Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Thu, 3 Aug 2023 14:08:22 -0400 Subject: [PATCH 0322/1072] Update const.py --- gitk/text2bednn/const.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gitk/text2bednn/const.py b/gitk/text2bednn/const.py index e69de29b..e98596be 100644 --- a/gitk/text2bednn/const.py +++ b/gitk/text2bednn/const.py @@ -0,0 +1,2 @@ +#sentence transformer model from hugging face +DEFAULT_HF_ST_MODEL = "sentence-transformers/all-MiniLM-L12-v2" From d8900820e8c3a9820beab8302c047acad54a5f49 Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Wed, 9 Aug 2023 16:28:48 -0400 Subject: [PATCH 0323/1072] Update __init__.py --- gitk/search/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/gitk/search/__init__.py b/gitk/search/__init__.py index e69de29b..178c1295 100644 --- a/gitk/search/__init__.py +++ b/gitk/search/__init__.py @@ -0,0 +1 @@ +from .search import QdrantBackend From e553748a83e1e6b24473831ab0a38df783f932cf Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Wed, 9 Aug 2023 16:30:16 -0400 Subject: [PATCH 0324/1072] QdrantBackend prototype --- gitk/search/search.py | 256 ++++++++++++++++++++++++------------------ 1 file changed, 149 insertions(+), 107 deletions(-) diff --git a/gitk/search/search.py b/gitk/search/search.py index 95b4731c..211bc299 100644 --- a/gitk/search/search.py +++ b/gitk/search/search.py @@ -1,3 +1,15 @@ +from abc import ABC, abstractmethod +import numpy as np +from qdrant_client import QdrantClient +from .const import * +from typing import List, Tuple, Union +from ..models import ExModel +from ..region2vec import Region2VecExModel +from .utils import get_qdrant +from ..tokenization import InMemTokenizer +from qdrant_client.models import VectorParams, Distance, PointStruct + + class EmSearchBackend(ABC): """ An abstract class representing Embedding Search Backends. This allows @@ -32,115 +44,145 @@ def __len__(self) -> int: raise NotImplementedError() -class QdrantBackend(EmSearchBackend): +class QdrantBackend: """A search backend that uses a qdrant server to store and search embeddings""" - def __init__(self, config): + def __init__(self, config: VectorParams, + collection: str = "embeddings"): + self.collection = collection self.config = config + # self.qd_client = get_qdrant(True) + self.qd_client = QdrantClient('http://localhost:6333') + self.qd_client.recreate_collection(collection_name=self.collection, + vectors_config=self.config) # TODO: initialize connection to qdrant server - def search(self, query): - raise NotImplementedError - - -class HNSWBackend(EmSearchBackend): - """A search backend that uses a local HNSW index to store and search embeddings""" - - idx: hnswlib.Index - - def __init__(self, embeddings: np.ndarray, labels: list, metadata: dict = None) -> None: - self.labels = labels - self.metadata = metadata - - # create an HNSW index for the embeddings - dim = embeddings.shape[1] - self.idx = hnswlib.Index(space="l2", dim=dim) # possible options are l2, cosine or ip - self.idx.init_index(max_elements=len(embeddings), ef_construction=200, M=16) - self.idx.add_items(embeddings, labels) - - def search(self, query: np.ndarray, k: int) -> List[Tuple[int, float]]: - return self.idx.knn_query(query, k) - - def __len__(self) -> int: - return idx.element_count() - - def __getitem__(self, key) -> np.ndarray: - if metadata: - ret = metadata[key] - else: - ret = {} - ret["embedding"] = self.idx.get_items([key])[0] - return ret - - def __str__(self): - return "HNSWBackend with {} items".format(len(self)) - - def save(self, path): - with open(path, "wb") as f: - pickle.dump(self, f) - - def load(self, path): - self = pickle.load(path) - - -class BEDSpaceSearchInterface(object): - def __init__(self, region_embeddings, label_embeddings, universe, tokenizer): - self.universe = universe - self.tokenizer = tokenizer - # TODO: Allow for different backends. It would just require connecting to the backend here - self.label_backend = HNSWBackend(label_embeddings, labels, metadata) - self.region_set_backend = HNSWBackend(region_embeddings, labels, metadata) - - def search_labels_by_embedding(self, embedding, k: int = 10) -> embeddings: - """Given an input region set embedding, suggest labels for that region set""" - return self.label_backend.search(region_set_embedding, k) - - def search_region_sets_by_embedding(self, embedding, k: int = 10) -> embeddings: - return self.region_set_backend.search(embedding, k) - - def search_region_sets(self, label, k: int = 10) -> embeddings: - """Given an input label, suggest region sets for that label""" - - # get the embedding for the label - # TODO: handle case where label is not in the universe - label_embedding = self.label_backend[label] - return self.region_set_backend.search(label_embedding, k) - - def search_labels(self, region_set, k: int = 10) -> embeddings: - """Given an input region set, suggest labels for that region set""" - - region_set_embedding = self.get_region_set_embedding(tokenized_region_set) - return self.label_backend.search(region_set_embedding, k) - - def search_region_sets_by_region_set(self, region_set, k: int = 10) -> embeddings: - """Given an input region set, suggest region sets similar to that region set""" - - region_set_embedding = self.get_region_set_embedding(region_set) - return self.region_set_backend.search(region_set_embedding, k) - - def get_region_set_embedding(self, region_set): - """Given a region set, return the embedding for that region set""" - - # first, tokenize the region set using the universe - tokenized_region_set = self.tokenizer.tokenize(region_set, self.universe) - # average the embeddings of each region in the region set - region_set_embedding = self.get_region_set_embedding(tokenized_region_set) - return region_set_embedding - - -# Demo of how to use the BEDspace search interface - -BBSI = BEDspaceSearchInterface(...) - -# Find region sets for a label (Scenario 1, l2r) -BBSI.search_region_sets_by_label("K562") # returns nearest embeddings - -# Annotate region sets with labels (Scenario 2, r2l) -path_to_bed_file = "path/to/bed/file.bed" -region_set = RegionSet(path_to_bed_file) -suggested_labels = BBSI.search_labels_by_region_set(region_set, k=15) - -# Find region sets similar to a query region set (Scenario 3, r2r) -path_to_bed_file = "path/to/bed/file.bed" -region_set = RegionSet(path_to_bed_file) -BBSI.search_region_sets_by_region_set(region_set, k=10) + def load(self, + embeddings: Union[np.ndarray, List[Union[List, np.ndarray]]], + labels: list): + if embeddings.shape[0] != len(labels): + raise KeyError("The number of embeddings does not match the number of labels") + self.qd_client.upsert( + collection_name=self.collection, + points=[ + PointStruct( + id=i, + vector=embeddings[i].tolist(), + payload={"label": labels[i]} + ) + for i in range(len(labels)) + ] + ) + + def search(self, query: np.ndarray, k: int) -> List: + search_results = self.qd_client.search( + collection_name=self.collection, + query_vector=query, + limit=k, + with_payload=True + ) + # raise NotImplementedError + return search_results + +# +# class HNSWBackend(EmSearchBackend): +# """A search backend that uses a local HNSW index to store and search embeddings""" +# +# idx: hnswlib.Index +# +# def __init__(self, embeddings: np.ndarray, labels: list, metadata: dict = None) -> None: +# self.labels = labels +# self.metadata = metadata +# +# # create an HNSW index for the embeddings +# dim = embeddings.shape[1] +# self.idx = hnswlib.Index(space="l2", dim=dim) # possible options are l2, cosine or ip +# self.idx.init_index(max_elements=len(embeddings), ef_construction=200, M=16) +# self.idx.add_items(embeddings, labels) +# +# def search(self, query: np.ndarray, k: int) -> List[Tuple[int, float]]: +# return self.idx.knn_query(query, k) +# +# def __len__(self) -> int: +# return idx.element_count() +# +# def __getitem__(self, key) -> np.ndarray: +# if metadata: +# ret = metadata[key] +# else: +# ret = {} +# ret["embedding"] = self.idx.get_items([key])[0] +# return ret +# +# def __str__(self): +# return "HNSWBackend with {} items".format(len(self)) +# +# def save(self, path): +# with open(path, "wb") as f: +# pickle.dump(self, f) +# +# def load(self, path): +# self = pickle.load(path) +# +# +# class BEDSpaceSearchInterface(object): +# def __init__(self, region_embeddings, label_embeddings, universe, tokenizer): +# self.universe = universe +# self.tokenizer = tokenizer +# # TODO: Allow for different backends. It would just require connecting to the backend here +# self.label_backend = HNSWBackend(label_embeddings, labels, metadata) +# self.region_set_backend = HNSWBackend(region_embeddings, labels, metadata) +# +# def search_labels_by_embedding(self, embedding, k: int = 10) -> embeddings: +# """Given an input region set embedding, suggest labels for that region set""" +# return self.label_backend.search(region_set_embedding, k) +# +# def search_region_sets_by_embedding(self, embedding, k: int = 10) -> embeddings: +# return self.region_set_backend.search(embedding, k) +# +# def search_region_sets(self, label, k: int = 10) -> embeddings: +# """Given an input label, suggest region sets for that label""" +# +# # get the embedding for the label +# # TODO: handle case where label is not in the universe +# label_embedding = self.label_backend[label] +# return self.region_set_backend.search(label_embedding, k) +# +# def search_labels(self, region_set, k: int = 10) -> embeddings: +# """Given an input region set, suggest labels for that region set""" +# +# region_set_embedding = self.get_region_set_embedding(tokenized_region_set) +# return self.label_backend.search(region_set_embedding, k) +# +# def search_region_sets_by_region_set(self, region_set, k: int = 10) -> embeddings: +# """Given an input region set, suggest region sets similar to that region set""" +# +# region_set_embedding = self.get_region_set_embedding(region_set) +# return self.region_set_backend.search(region_set_embedding, k) +# +# def get_region_set_embedding(self, region_set): +# """Given a region set, return the embedding for that region set""" +# +# # first, tokenize the region set using the universe +# tokenized_region_set = self.tokenizer.tokenize(region_set, self.universe) +# # average the embeddings of each region in the region set +# region_set_embedding = self.get_region_set_embedding(tokenized_region_set) +# return region_set_embedding +# +# +# # Demo of how to use the BEDspace search interface +# +# BBSI = BEDspaceSearchInterface(...) +# +# # Find region sets for a label (Scenario 1, l2r) +# BBSI.search_region_sets_by_label("K562") # returns nearest embeddings +# +# # Annotate region sets with labels (Scenario 2, r2l) +# path_to_bed_file = "path/to/bed/file.bed" +# region_set = RegionSet(path_to_bed_file) +# suggested_labels = BBSI.search_labels_by_region_set(region_set, k=15) +# +# # Find region sets similar to a query region set (Scenario 3, r2r) +# path_to_bed_file = "path/to/bed/file.bed" +# region_set = RegionSet(path_to_bed_file) +# BBSI.search_region_sets_by_region_set(region_set, k=10) From f39f79a558186e5b39be6f495b0203eccb2b778b Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Wed, 9 Aug 2023 16:31:29 -0400 Subject: [PATCH 0325/1072] pytest for search (QdrantBackend) --- tests/test_search.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 tests/test_search.py diff --git a/tests/test_search.py b/tests/test_search.py new file mode 100644 index 00000000..8c7a3786 --- /dev/null +++ b/tests/test_search.py @@ -0,0 +1,32 @@ +import pytest +from gitk.search.search import QdrantBackend +from gitk.region2vec.main import Region2VecExModel +import numpy as np +from qdrant_client import QdrantClient +from qdrant_client.models import VectorParams, Distance, PointStruct +import os +import pprint + + +@pytest.fixture +def bed_folder(): + return "./data/hg38_sample" + + +def test_QdrantBackend(bed_folder): + # r2v_model = Region2VecExModel(r2v_hf_repo) + config = VectorParams(size=100, distance=Distance.COSINE) + collection = "hg38_sample" + file_names = os.listdir(bed_folder) + embeddings = np.random.random((len(file_names), 100)) + + qd_search_backend = QdrantBackend(config, collection) + qd_search_backend.load(embeddings, file_names) + search_result = qd_search_backend.search(np.random.random(100,), 5) + assert isinstance(search_result, list) + assert isinstance(search_result[0].id, int) + assert isinstance(search_result[0].payload, dict) + + + + From e44bc24675087c0d3c3f516cbdac4f617db7118d Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Wed, 9 Aug 2023 16:33:53 -0400 Subject: [PATCH 0326/1072] additional files --- gitk/search/const.py | 2 ++ gitk/search/utils.py | 53 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 55 insertions(+) create mode 100644 gitk/search/const.py create mode 100644 gitk/search/utils.py diff --git a/gitk/search/const.py b/gitk/search/const.py new file mode 100644 index 00000000..fe0ec595 --- /dev/null +++ b/gitk/search/const.py @@ -0,0 +1,2 @@ +DEFAULT_QDRANT_HOST = "localhost" +DEFAULT_QDRANT_PORT = 6333 \ No newline at end of file diff --git a/gitk/search/utils.py b/gitk/search/utils.py new file mode 100644 index 00000000..6ca72fde --- /dev/null +++ b/gitk/search/utils.py @@ -0,0 +1,53 @@ +from qdrant_client import QdrantClient +from qdrant_client.http.exceptions import ResponseHandlingException +from .const import * +from fastapi import Depends, Header, Form +from fastapi.exceptions import HTTPException +from fastapi.security import HTTPBearer +from typing import Union +import os + + +# from pephub/pephub/dependencies.py +def get_qdrant_enabled() -> bool: + """ + Check if qdrant is enabled + """ + return parse_boolean_env_var(os.environ.get("QDRANT_ENABLED", "false")) + + +def get_qdrant( + qdrant_enabled: bool = Depends(get_qdrant_enabled), +) -> Union[QdrantClient, None]: + """ + Return connection to qdrant client + """ + # return None if qdrant is not enabled + if not qdrant_enabled: + try: + yield None + finally: + pass + # else try to connect, test connectiona and return client if connection is successful. + qdrant = QdrantClient( + url=os.environ.get("QDRANT_HOST", DEFAULT_QDRANT_HOST), + port=os.environ.get("QDRANT_PORT", DEFAULT_QDRANT_PORT), + api_key=os.environ.get("QDRANT_API_KEY", None), + ) + try: + # test the connection first + qdrant.list_full_snapshots() + yield qdrant + except ResponseHandlingException as e: + print(f"Error getting qdrant client: {e}") + yield None + finally: + # no need to close the connection + pass + + +def parse_boolean_env_var(env_var: str) -> bool: + """ + Helper function to parse a boolean environment variable + """ + return env_var.lower() in ["true", "1", "t", "y", "yes"] \ No newline at end of file From e8fdb3b4c1759e30a1e9350548e12bf3528f6906 Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 11 Aug 2023 16:26:14 -0400 Subject: [PATCH 0327/1072] Update text2bednn --- .idea/.gitignore | 3 + .idea/geniml_dev.iml | 10 ++ .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 4 + .idea/modules.xml | 8 ++ .idea/vcs.xml | 6 + gitk/models/__init__.py | 2 +- gitk/text2bednn/const.py | 4 + gitk/text2bednn/text2bednn.py | 114 ++++++++++++------ 9 files changed, 121 insertions(+), 36 deletions(-) create mode 100644 .idea/.gitignore create mode 100644 .idea/geniml_dev.iml create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/vcs.xml diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 00000000..26d33521 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/.idea/geniml_dev.iml b/.idea/geniml_dev.iml new file mode 100644 index 00000000..74d515a0 --- /dev/null +++ b/.idea/geniml_dev.iml @@ -0,0 +1,10 @@ + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 00000000..105ce2da --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 00000000..bc718bd3 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 00000000..5b90498b --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 00000000..35eb1ddf --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/gitk/models/__init__.py b/gitk/models/__init__.py index a7dc64ed..3ab7cfdf 100644 --- a/gitk/models/__init__.py +++ b/gitk/models/__init__.py @@ -1 +1 @@ -from .main import ExModel +from .main import ExModel, Model diff --git a/gitk/text2bednn/const.py b/gitk/text2bednn/const.py index e98596be..1bc0b8d2 100644 --- a/gitk/text2bednn/const.py +++ b/gitk/text2bednn/const.py @@ -1,2 +1,6 @@ #sentence transformer model from hugging face DEFAULT_HF_ST_MODEL = "sentence-transformers/all-MiniLM-L12-v2" +DEFAULT_EPOCHES = 1000 +DEFAULT_UNITS = 256 +DEFAULT_LAYERS = 0 +DEFAULT_BATCHES = 1 \ No newline at end of file diff --git a/gitk/text2bednn/text2bednn.py b/gitk/text2bednn/text2bednn.py index dfc7b796..5ba80aaf 100644 --- a/gitk/text2bednn/text2bednn.py +++ b/gitk/text2bednn/text2bednn.py @@ -1,11 +1,14 @@ import numpy as np from typing import List, Union, Tuple from sentence_transformers import SentenceTransformer -from .const import DEFAULT_HF_ST_MODEL +from .const import * from dataclasses import dataclass, replace from ..io import RegionSet from ..region2vec import Region2VecExModel from .utils import * +from ..models import Model +import tensorflow as tf +import matplotlib as plt @dataclass class BedMetadataEmbedding: @@ -175,40 +178,81 @@ def update_BedMetadata_list(old_list: List[BedMetadataEmbedding], return new_list -# class TextToBedNN(Model): -# """A Extended Model for TextToBed using a FFNN.""" -# -# def __init__(self, -# model_nn_path: Union[str, None] = None, -# model_st_repo: str = DEFAULT_HF_ST_MODEL, -# **kwargs): -# # super().__init__(model, universe, tokenizer) -# self._st_model = SentenceTransformer(model_st_repo) -# if model_nn_path is not None: -# self.from_pretrained(model_nn_path) -# else: -# self._nn_model = tf.keras.models.Sequential() -# # initiation -# def __repr__(self): -# print("To be completed") -# -# def _init_from_huggingface(self): -# # to be finished -# return -# -# def from_pretrained(self, model_nn_path: str): -# self._nn_model = tf.keras.models.load_model(model_nn_path) -# -# def train(self, dataset: BedMetadataEmbeddingSet): -# return -# -# def forward(self, query: str): -# # input string -> embedded by sentence transformer -> FNN -> vector -# return -# -# def train(self, dataset: BedMetadataEmbeddingSet): -# # given embedded [bed, metadata] pair, fit the neural network -# return + +class TextToBedNN(Model): + """A Extended Model for TextToBed using a FFNN.""" + + def __init__(self, + model_nn_path: Union[str, None] = None, + model_st_repo: str = DEFAULT_HF_ST_MODEL, + **kwargs): + # super().__init__(model, universe, tokenizer) + self.built = True + self._st_model = SentenceTransformer(model_st_repo) + if model_nn_path is not None: + self.from_pretrained(model_nn_path) + else: + self._nn_model = tf.keras.models.Sequential() + self.built = False + # initiation + + def __repr__(self): + print("To be completed") + + def _init_from_huggingface(self): + # to be finished + return + + def from_pretrained(self, model_nn_path: str): + self._nn_model = tf.keras.models.load_model(model_nn_path) + + def train(self, dataset: BedMetadataEmbeddingSet, + epochs: int = DEFAULT_EPOCHES, + batches: int = DEFAULT_BATCHES, + units: int = DEFAULT_UNITS, + layers: int = DEFAULT_LAYERS, + plotting:bool = True): + + training_X, training_Y = dataset.generate_data("training") + validating_X, validating_Y = dataset.generate_data("validating") + + if not self.built: + + input_dim = training_X.shape[1] + output_dim = training_Y.shape[1] + + self._nn_model.add(tf.keras.layers.Dense(258, input_shape=(input_dim,), activation='relu')) + + for i in range(layers): + self._nn_model.add(tf.keras.layers.Dense(258, activation='relu')) + + self._nn_model.add(tf.keras.layers.Dense(output_dim)) + self._nn_model.compile(optimizer="adam", loss="mean_squared_error") + + self.built = True + + early_stoppage = tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=int(epochs*0.25)) + + train_hist = self._nn_model.fit(training_X, training_Y, epochs=epochs, batch_size=batches, + validation_data=(validating_X, validating_Y), + callbacks=[early_stoppage]) + + epoch_range = range(1, len(train_hist.history['accuracy']) + 1) + train_loss = train_hist.history['loss'] + valid_loss = train_hist.history['val_loss'] + plt.plot(epoch_range, train_loss, 'r', label='Training loss') + plt.plot(epoch_range, valid_loss, 'b', label='Validation loss') + plt.title('Training and validation loss') + plt.legend() + plt.show() + + + + def forward(self, query: str) -> np.ndarray: + # input string -> embedded by sentence transformer -> FNN -> vector + query_embedding = self._st_model.encode(query) + + return self._nn_model.predict(query_embedding) # class TextToBedNNSearchInterface(object): From 53c04f58730014ffe80a386d3298309297d387a4 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 15 Aug 2023 12:43:30 -0400 Subject: [PATCH 0328/1072] Update comments and passed pytest --- gitk/search/search.py | 33 ++++++- gitk/text2bednn/__init__.py | 2 +- gitk/text2bednn/const.py | 3 +- gitk/text2bednn/text2bednn.py | 172 ++++++++++++++++++++++++++-------- gitk/text2bednn/utils.py | 13 +-- tests/test_search.py | 5 +- tests/test_text2bednn.py | 51 ++++++++-- 7 files changed, 216 insertions(+), 63 deletions(-) diff --git a/gitk/search/search.py b/gitk/search/search.py index 211bc299..2afc386d 100644 --- a/gitk/search/search.py +++ b/gitk/search/search.py @@ -44,11 +44,19 @@ def __len__(self) -> int: raise NotImplementedError() -class QdrantBackend: +class QdrantBackend(EmSearchBackend): """A search backend that uses a qdrant server to store and search embeddings""" def __init__(self, config: VectorParams, collection: str = "embeddings"): + """ + Connect to Qdrant on commandline first: + (Ubuntu Linux terminal) + sudo docker run -p 6333:6333 -v $(pwd)/qdrant_storage:/qdrant/storage qdrant/qdrant + + :param config: the vector parameter + :param collection: name of collection + """ self.collection = collection self.config = config # self.qd_client = get_qdrant(True) @@ -58,8 +66,15 @@ def __init__(self, config: VectorParams, # TODO: initialize connection to qdrant server def load(self, - embeddings: Union[np.ndarray, List[Union[List, np.ndarray]]], - labels: list): + embeddings: np.ndarray, + labels: List[str]): + """ + upload vectors and their labels onto qdrant storage + + :param embeddings: embedding vectors of bed files, an np.ndarray with shape of (n, ) + :param labels: list of labels, can be name of bed files + :return: + """ if embeddings.shape[0] != len(labels): raise KeyError("The number of embeddings does not match the number of labels") self.qd_client.upsert( @@ -75,6 +90,13 @@ def load(self, ) def search(self, query: np.ndarray, k: int) -> List: + """ + with a given query vector, get k nearest neighbors from vectors in the collection + + :param query: a vector to search + :param k: number of returned results + :return: a list of search results + """ search_results = self.qd_client.search( collection_name=self.collection, query_vector=query, @@ -84,6 +106,11 @@ def search(self, query: np.ndarray, k: int) -> List: # raise NotImplementedError return search_results + def __len__(self) -> int: + """ + Return the number of embeddings in the backend + """ + return self.qd_client.get_collection(collection_name=self.collection).vectors_count # # class HNSWBackend(EmSearchBackend): # """A search backend that uses a local HNSW index to store and search embeddings""" diff --git a/gitk/text2bednn/__init__.py b/gitk/text2bednn/__init__.py index 24d39ca4..ed8ef688 100644 --- a/gitk/text2bednn/__init__.py +++ b/gitk/text2bednn/__init__.py @@ -1 +1 @@ -from text2bednn import * +from .text2bednn import * diff --git a/gitk/text2bednn/const.py b/gitk/text2bednn/const.py index 1bc0b8d2..191d7538 100644 --- a/gitk/text2bednn/const.py +++ b/gitk/text2bednn/const.py @@ -3,4 +3,5 @@ DEFAULT_EPOCHES = 1000 DEFAULT_UNITS = 256 DEFAULT_LAYERS = 0 -DEFAULT_BATCHES = 1 \ No newline at end of file +DEFAULT_BATCH_SIZE = 1 +DEFAULT_LOSS = 'cosine_similarity' \ No newline at end of file diff --git a/gitk/text2bednn/text2bednn.py b/gitk/text2bednn/text2bednn.py index 5ba80aaf..91e935f9 100644 --- a/gitk/text2bednn/text2bednn.py +++ b/gitk/text2bednn/text2bednn.py @@ -7,9 +7,11 @@ from ..region2vec import Region2VecExModel from .utils import * from ..models import Model +from ..search import QdrantBackend import tensorflow as tf import matplotlib as plt + @dataclass class BedMetadataEmbedding: """ @@ -39,6 +41,21 @@ def __len__(self): """ return len(self.training) + len(self.validating) + len(self.testing) + @property + def tolist(self) -> List[BedMetadataEmbedding]: + """ + put all BedMetadataEmbedding into one list + + :return: a list of all BedMetadataEmbedding in the set + """ + result = [] + # add elements from each list + result.extend(self.training) + result.extend(self.validating) + result.extend(self.testing) + + return result + def generate_data(self, set_name: str) -> Tuple[np.ndarray, np.ndarray]: """ With given dataset name, return a tuple of X and Y @@ -66,6 +83,21 @@ def generate_data(self, set_name: str) -> Tuple[np.ndarray, np.ndarray]: return np.array(X), np.array(Y) + def to_qd_upload(self) -> Tuple[np.ndarray, List[str]]: + """ + return a tuple of: + a np.ndarray of dimension (number of vectors, vector length), and a list of matching file names + + which matches the uploading format of QdrantBackend + :return: + """ + vecs = [] + labels = [] + for embeddings in self.tolist: + vecs.append(embeddings.region_set_embedding) + labels.append(embeddings.file_name) + return np.vstack(vecs), labels + def build_BedMetadataSet_from_files(bed_folder: str, metadata_path: str, @@ -190,85 +222,142 @@ def __init__(self, self.built = True self._st_model = SentenceTransformer(model_st_repo) if model_nn_path is not None: + # upload local models self.from_pretrained(model_nn_path) else: + # create an empty Sequential self._nn_model = tf.keras.models.Sequential() - self.built = False + self.built = False # model is not built # initiation def __repr__(self): print("To be completed") - def _init_from_huggingface(self): + def init_from_huggingface(self): # to be finished - return + print("To be completed") def from_pretrained(self, model_nn_path: str): + """ + load pretrained model from local file + + :param model_nn_path: the local path of saved model + :return: + """ self._nn_model = tf.keras.models.load_model(model_nn_path) def train(self, dataset: BedMetadataEmbeddingSet, + loss_func: str = DEFAULT_LOSS, epochs: int = DEFAULT_EPOCHES, - batches: int = DEFAULT_BATCHES, + batch_size: int = DEFAULT_BATCH_SIZE, units: int = DEFAULT_UNITS, layers: int = DEFAULT_LAYERS, - plotting:bool = True): + plotting: bool = False): + """ + train the feedforward neural network model with a given BedMetadataEmbeddingSet + + :param dataset: a BedMetadataEmbeddingSet + :param loss_func: loss function for training + :param epochs: number of training epochs + :param batch_size: training batch size + :param units: number of neurons in a dense layer + :param layers: number of extra layers + :param plotting: whether to plot the training and velidation loss by epochs + :return: + """ + # get training and validating dataset from BedMetadataEmbeddingSet training_X, training_Y = dataset.generate_data("training") validating_X, validating_Y = dataset.generate_data("validating") + # if the model is empty if not self.built: + print("Build new sequential model") - input_dim = training_X.shape[1] - output_dim = training_Y.shape[1] + # dimensions of input and output + input_dims = training_X[0].shape + output_dims = training_Y[0].shape - self._nn_model.add(tf.keras.layers.Dense(258, input_shape=(input_dim,), activation='relu')) + # the dense layer that connected to input + self._nn_model.add(tf.keras.layers.Dense(units, input_shape=input_dims, activation='relu')) + # extra dense layers for i in range(layers): - self._nn_model.add(tf.keras.layers.Dense(258, activation='relu')) + self._nn_model.add(tf.keras.layers.Dense(units, input_shape=(units,), activation='relu')) - self._nn_model.add(tf.keras.layers.Dense(output_dim)) - self._nn_model.compile(optimizer="adam", loss="mean_squared_error") + # output + self._nn_model.add(tf.keras.layers.Dense(output_dims[0])) - self.built = True - - early_stoppage = tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=int(epochs*0.25)) + self._nn_model.compile(optimizer="adam", loss=loss_func) - train_hist = self._nn_model.fit(training_X, training_Y, epochs=epochs, batch_size=batches, - validation_data=(validating_X, validating_Y), - callbacks=[early_stoppage]) + # model is no longer empty + self.built = True - epoch_range = range(1, len(train_hist.history['accuracy']) + 1) - train_loss = train_hist.history['loss'] - valid_loss = train_hist.history['val_loss'] - plt.plot(epoch_range, train_loss, 'r', label='Training loss') - plt.plot(epoch_range, valid_loss, 'b', label='Validation loss') - plt.title('Training and validation loss') - plt.legend() - plt.show() + # early stoppage to prevent overfitting + early_stoppage = tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=int(epochs * 0.25)) + + # record training history + train_hist = self._nn_model.fit(training_X, training_Y, epochs=epochs, batch_size=batch_size, + validation_data=(validating_X, validating_Y), + callbacks=[early_stoppage]) + + # plot the training history: training / validation loss by epoch + if plotting: + epoch_range = range(1, len(train_hist.history['loss']) + 1) + train_loss = train_hist.history['loss'] + valid_loss = train_hist.history['val_loss'] + plt.plot(epoch_range, train_loss, 'r', label='Training loss') + plt.plot(epoch_range, valid_loss, 'b', label='Validation loss') + plt.title('Training and validation loss') + plt.legend() + plt.show() + def forward(self, query: str) -> np.ndarray: + """ + map a query string to an embedding vector of bed file + :param query: a string + :return: + """ - def forward(self, query: str) -> np.ndarray: # input string -> embedded by sentence transformer -> FNN -> vector + + # encode the query string to vector by sentence transformer query_embedding = self._st_model.encode(query) - return self._nn_model.predict(query_embedding) + # reshape the encoding vector + # because the model takes input with shape of (n, ) + # and the shape of encoding vector is (, ) + vec_dim = query_embedding.shape[0] + # map the encoding vector to bedfile embedding vector by feedforward neural network + output_vec = self._nn_model.predict(query_embedding.reshape((1, vec_dim))) -# class TextToBedNNSearchInterface(object): -# def __init__(self, exmodel: TextToBedNN, region_set_backend: EmSearchBackend): -# self.exmodel = exmodel -# self.region_set_backend = region_set_backend -# -# def nlsearch(self, query: str, k: int = 10): -# """Given an input natural lange, suggest region sets""" -# -# # first, get the embedding of the query string -# query_embedding = self.tum.embed(query) -# # then, use the query embedding to predict the region set embedding -# region_set_embedding = self.tum.str_to_region_set(query_embedding) -# # finally, use the region set embedding to search for similar region sets -# return self.region_set_backend.search(region_set_embedding, k) + # reshape the output from (1, ) to (<~ dimension>, 1) + output_vec_dim = output_vec.shape[1] + return output_vec.reshape(output_vec_dim, ) + + +class TextToBedNNSearchInterface(object): + def __init__(self, nn_model: TextToBedNN, region_set_backend: QdrantBackend): + self.model = nn_model + self.region_set_backend = region_set_backend + + def nlsearch(self, query: str, k: int = 10) -> List: + """ + Given an input natural language, suggest region sets + + :param query: searching input string + :param k: number of results (nearst neighbor in vectors) + :return: a list of Qdrant Client search results + """ + + # first, get the embedding of the query string + query_embedding = self.model.forward(query) + # then, use the query embedding to predict the region set embedding + # region_set_embedding = self.tum.str_to_region_set(query_embedding) + # finally, use the region set embedding to search for similar region sets + return self.region_set_backend.search(query_embedding, k) # # # Example of how to use the TextToBedNN Search Interface @@ -283,3 +372,4 @@ def forward(self, query: str) -> np.ndarray: # st_model = SentenceTransformer(st_hf_repo) # bme_set = build_BedMetadataSet_from_files(bed_folder, metadata_path, r2v_model, st_model) # assert bme_set is not None +# git add -A && git commit -m "Update comments and passed pytest" \ No newline at end of file diff --git a/gitk/text2bednn/utils.py b/gitk/text2bednn/utils.py index 78fc5562..e0b680da 100644 --- a/gitk/text2bednn/utils.py +++ b/gitk/text2bednn/utils.py @@ -1,6 +1,8 @@ import os from typing import List, Tuple import random +import numpy as np +import tensorflow as tf def data_split(full_list: List, @@ -11,7 +13,7 @@ def data_split(full_list: List, With a given folder of data, this function split the files into training set, validating set, and testing set. - :param bed_folder: folder where bed files are stored + :param full_list: list of data :param train_p: proportion of files for training set :param valid_p: proportion of files for validating set and files will be copied to them. @@ -20,7 +22,6 @@ def data_split(full_list: List, """ # split training data and testing data - # file_list = os.listdir(bed_folder) train_size = int(len(full_list) * train_p) validate_size = int(len(full_list) * valid_p) random.seed(seed_index) @@ -28,10 +29,6 @@ def data_split(full_list: List, validate_list = random.sample([content for content in full_list if content not in train_list], validate_size) test_list = [content for content in full_list if (content not in train_list and content not in validate_list)] - # train_list.sort() - # validate_list.sort() - # test_list.sort() - return train_list, validate_list, test_list @@ -50,6 +47,10 @@ def metadata_line_process(metadata_line: str) -> str: return metadata_line +def np2dataset(X: np.ndarray, Y: np.ndarray) -> tf.data.Dataset: + return tf.data.Dataset.from_tensor_slices((X, Y)) + + """ ls | shuf -n 20 | xargs -I '{}' cp '{}' /home/claudehu/Desktop/repo/geniml_dev/tests/data/hg38_sample diff --git a/tests/test_search.py b/tests/test_search.py index 8c7a3786..35a91a04 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -26,7 +26,4 @@ def test_QdrantBackend(bed_folder): assert isinstance(search_result, list) assert isinstance(search_result[0].id, int) assert isinstance(search_result[0].payload, dict) - - - - + assert len(qd_search_backend) == len(file_names) diff --git a/tests/test_text2bednn.py b/tests/test_text2bednn.py index 533d39d2..b5dcde72 100644 --- a/tests/test_text2bednn.py +++ b/tests/test_text2bednn.py @@ -1,10 +1,12 @@ import pytest import scanpy as sc import os -from gitk.text2bednn.text2bednn import build_BedMetadataSet_from_files +from gitk.text2bednn.text2bednn import build_BedMetadataSet_from_files, TextToBedNN, TextToBedNNSearchInterface from gitk.region2vec.main import Region2Vec, Region2VecExModel from sentence_transformers import SentenceTransformer import numpy as np +from qdrant_client.models import VectorParams, Distance +from gitk.search.search import QdrantBackend @pytest.fixture @@ -27,17 +29,52 @@ def st_hf_repo(): return "sentence-transformers/all-MiniLM-L12-v2" -def test_BedMetadataEmbeddingSet(bed_folder, metadata_path, r2v_hf_repo): +@pytest.fixture +def query_term(): + return "human, kidney, blood" + + +@pytest.fixture +def k(): + return 5 + + +def test_data_nn_search(bed_folder, metadata_path, + r2v_hf_repo, query_term, k): + r2v_model = Region2VecExModel(r2v_hf_repo) # st_model = SentenceTransformer(st_hf_repo) st_model = SentenceTransformer("sentence-transformers/all-MiniLM-L12-v2") bme_set = build_BedMetadataSet_from_files(bed_folder, metadata_path, r2v_model, st_model) + bed_count = len(os.listdir(bed_folder)) assert bme_set is not None - assert len(bme_set) == 20 + assert len(bme_set) == bed_count train_X, train_Y = bme_set.generate_data("training") - assert(isinstance(train_X, np.ndarray)) + assert (isinstance(train_X, np.ndarray)) assert (isinstance(train_Y, np.ndarray)) - assert(train_X.shape[1] == 384) - assert(train_Y.shape[1] == 100) - assert(train_X[0].shape == (384, )) + assert (train_X.shape[1] == 384) + assert (train_Y.shape[1] == 100) + assert (train_X[0].shape == (384,)) assert (train_Y[0].shape == (100,)) + + t2bnn = TextToBedNN(None, "sentence-transformers/all-MiniLM-L12-v2") + t2bnn.train(bme_set, epochs=50) + + config = VectorParams(size=100, distance=Distance.COSINE) + collection = "hg38_sample" + + embeddings, labels = bme_set.to_qd_upload() + + for i in range(bed_count): + assert np.array_equal(bme_set.tolist[i].region_set_embedding, + embeddings[i]) + assert bme_set.tolist[i].file_name == labels[i] + + qd_search_backend = QdrantBackend(config, collection) + qd_search_backend.load(embeddings, labels) + + t2bnn_interface = TextToBedNNSearchInterface(t2bnn, qd_search_backend) + search_results = t2bnn_interface.nlsearch(query_term, k) + print("search resuts:") + for result in search_results: + print(result.payload["label"]) \ No newline at end of file From dffd6634b55e6bc93635b92399630b1365486715 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Wed, 16 Aug 2023 13:58:05 -0400 Subject: [PATCH 0329/1072] tweaks --- examples/region2ec/pretrained_model.py | 19 +++++++++++++++++++ examples/scembed/pretrained_model.py | 4 ++-- gitk/search/search.py | 6 ++++++ 3 files changed, 27 insertions(+), 2 deletions(-) create mode 100644 examples/region2ec/pretrained_model.py diff --git a/examples/region2ec/pretrained_model.py b/examples/region2ec/pretrained_model.py new file mode 100644 index 00000000..b602a441 --- /dev/null +++ b/examples/region2ec/pretrained_model.py @@ -0,0 +1,19 @@ +# %% +import os +import scanpy as sc +from gitk.io import RegionSet +from gitk.region2vec import Region2VecExModel + +# %% +# Load the pretrained model +model = Region2VecExModel("databio/r2v-ChIP-atlas-hg38") + +# %% +# Load the data +regions = RegionSet("/path/to/regions.bed") + +# %% +# Embed the data +embeddings = model.encode(regions) + +# TODO: need to finish with actual be files diff --git a/examples/scembed/pretrained_model.py b/examples/scembed/pretrained_model.py index f54c407e..3331accb 100644 --- a/examples/scembed/pretrained_model.py +++ b/examples/scembed/pretrained_model.py @@ -5,11 +5,11 @@ # %% # Load the pretrained model -model = ScEmbed("databio/r2v-luecken2021-hg38-small") +model = ScEmbed("databio/r2v-Luecken2021-hg38") # %% # Load the data -data_path = os.path.expandvars("$DATA/genomics/scembed/pbmc_hg38/pbmc.h5ad") +data_path = os.path.expandvars("$DATA/genomics/scembed/pbmc_hg38/pbmc_hg38.h5ad") adata = sc.read_h5ad(data_path) # %% diff --git a/gitk/search/search.py b/gitk/search/search.py index 95b4731c..c618cd57 100644 --- a/gitk/search/search.py +++ b/gitk/search/search.py @@ -1,3 +1,9 @@ +import pickle +import numpy as np +from typing import List, Tuple +from abc import ABC, abstractmethod + + class EmSearchBackend(ABC): """ An abstract class representing Embedding Search Backends. This allows From 58723744ee102f613a50b307f95bde985d017aef Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Thu, 17 Aug 2023 16:50:05 -0400 Subject: [PATCH 0330/1072] updated HNSWBackend --- gitk/search/search.py | 105 +++++++++++++++++++++++++----------------- 1 file changed, 64 insertions(+), 41 deletions(-) diff --git a/gitk/search/search.py b/gitk/search/search.py index 2afc386d..aafc4a0d 100644 --- a/gitk/search/search.py +++ b/gitk/search/search.py @@ -1,6 +1,7 @@ from abc import ABC, abstractmethod import numpy as np from qdrant_client import QdrantClient +import hnswlib from .const import * from typing import List, Tuple, Union from ..models import ExModel @@ -8,6 +9,7 @@ from .utils import get_qdrant from ..tokenization import InMemTokenizer from qdrant_client.models import VectorParams, Distance, PointStruct +import pickle class EmSearchBackend(ABC): @@ -47,8 +49,8 @@ def __len__(self) -> int: class QdrantBackend(EmSearchBackend): """A search backend that uses a qdrant server to store and search embeddings""" - def __init__(self, config: VectorParams, - collection: str = "embeddings"): + def __init__(self, config: VectorParams = DEFAULT_CONFIG, + collection: str = DEFAULT_COLLECTION): """ Connect to Qdrant on commandline first: (Ubuntu Linux terminal) @@ -111,45 +113,66 @@ def __len__(self) -> int: Return the number of embeddings in the backend """ return self.qd_client.get_collection(collection_name=self.collection).vectors_count -# -# class HNSWBackend(EmSearchBackend): -# """A search backend that uses a local HNSW index to store and search embeddings""" -# -# idx: hnswlib.Index -# -# def __init__(self, embeddings: np.ndarray, labels: list, metadata: dict = None) -> None: -# self.labels = labels -# self.metadata = metadata -# -# # create an HNSW index for the embeddings -# dim = embeddings.shape[1] -# self.idx = hnswlib.Index(space="l2", dim=dim) # possible options are l2, cosine or ip -# self.idx.init_index(max_elements=len(embeddings), ef_construction=200, M=16) -# self.idx.add_items(embeddings, labels) -# -# def search(self, query: np.ndarray, k: int) -> List[Tuple[int, float]]: -# return self.idx.knn_query(query, k) -# -# def __len__(self) -> int: -# return idx.element_count() -# -# def __getitem__(self, key) -> np.ndarray: -# if metadata: -# ret = metadata[key] -# else: -# ret = {} -# ret["embedding"] = self.idx.get_items([key])[0] -# return ret -# -# def __str__(self): -# return "HNSWBackend with {} items".format(len(self)) -# -# def save(self, path): -# with open(path, "wb") as f: -# pickle.dump(self, f) -# -# def load(self, path): -# self = pickle.load(path) + + +class HNSWBackend(EmSearchBackend): + """A search backend that uses a local HNSW index to store and search embeddings""" + + idx: hnswlib.Index + + def __init__(self, + local_index_path: str = DEFAULT_INDEX_PATH, + space: str = DEFAULT_HNSW_SPACE, + dim: int = DEFAULT_DIM, + ef: int = DEFAULT_EF, + m: int = DEFAULT_M): + + self.idx = hnswlib.Index(space=space, dim=dim) # possible options are l2, cosine or ip + self.idx.init_index(max_elements=0, ef_construction=ef, M=m) + self.idx.save_index(local_index_path) + self.metadata = {} + self.idx_path = local_index_path + + def load(self, + embeddings: np.ndarray, + labels: List[str]): + if embeddings.shape[0] != len(labels): + raise KeyError("The number of embeddings does not match the number of labels") + + current_max = self.idx.get_max_elements() + new_max = current_max + embeddings.shape[0] + ids = np.arange(start = current_max, stop = new_max) + for i in range(len(labels)): + self.metadata[ids[i]] = labels[i] + + self.idx.load_index(self.idx_path, max_elements=new_max) + self.idx.add_items(embeddings, ids) + + self.idx.save_index(self.idx_path) + + def search(self, query: np.ndarray, k: int) -> Tuple[np.ndarray, np.ndarray]: + return self.idx.knn_query(query, k) + + def __len__(self) -> int: + return self.idx.element_count + + # def __getitem__(self, key) -> np.ndarray: + # if metadata: + # ret = metadata[key] + # else: + # ret = {} + # ret["embedding"] = self.idx.get_items([key])[0] + # return ret + + def __str__(self): + return "HNSWBackend with {} items".format(len(self)) + + # def save(self, path): + # with open(path, "wb") as f: + # pickle.dump(self, f) + + # def load(self, path): + # self = pickle.load(path) # # # class BEDSpaceSearchInterface(object): From b919ba72e02f8daa64a5ccc084664d2d9f2eb043 Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Thu, 17 Aug 2023 16:51:01 -0400 Subject: [PATCH 0331/1072] Update const.py --- gitk/search/const.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/gitk/search/const.py b/gitk/search/const.py index fe0ec595..1cac8b14 100644 --- a/gitk/search/const.py +++ b/gitk/search/const.py @@ -1,2 +1,18 @@ +from qdrant_client.models import VectorParams, Distance + DEFAULT_QDRANT_HOST = "localhost" -DEFAULT_QDRANT_PORT = 6333 \ No newline at end of file +DEFAULT_QDRANT_PORT = 6333 + +DEFAULT_COLLECTION = "embeddings" + +DEFAULT_CONFIG = VectorParams(size=100, distance=Distance.COSINE) + +DEFAULT_INDEX_PATH = "./current_index.bin" + +DEFAULT_HNSW_SPACE = "cosine" + +DEFAULT_DIM = 100 + +DEFAULT_EF = 200 + +DEFAULT_M = 16 From 5c697c28bab0fc5fd049baaa6562419c6d6a3979 Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Thu, 17 Aug 2023 16:52:32 -0400 Subject: [PATCH 0332/1072] Update passed test_search.py --- tests/test_search.py | 58 ++++++++++++++++++++++++++++++++++++-------- 1 file changed, 48 insertions(+), 10 deletions(-) diff --git a/tests/test_search.py b/tests/test_search.py index 35a91a04..bf2447ac 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -1,11 +1,9 @@ import pytest -from gitk.search.search import QdrantBackend -from gitk.region2vec.main import Region2VecExModel +from gitk.search.search import QdrantBackend, HNSWBackend import numpy as np -from qdrant_client import QdrantClient -from qdrant_client.models import VectorParams, Distance, PointStruct +from qdrant_client.models import VectorParams, Distance import os -import pprint + @pytest.fixture @@ -13,17 +11,57 @@ def bed_folder(): return "./data/hg38_sample" -def test_QdrantBackend(bed_folder): +@pytest.fixture +def filenames(bed_folder): + return os.listdir(bed_folder) + + +@pytest.fixture +def embeddings(filenames): + np.random.seed(100) + return np.random.random((len(filenames), 100)) + + +@pytest.fixture +def local_idx_path(): + return "./testing_idx.bin" + + +def test_QdrantBackend(filenames, embeddings): # r2v_model = Region2VecExModel(r2v_hf_repo) config = VectorParams(size=100, distance=Distance.COSINE) collection = "hg38_sample" - file_names = os.listdir(bed_folder) - embeddings = np.random.random((len(file_names), 100)) qd_search_backend = QdrantBackend(config, collection) - qd_search_backend.load(embeddings, file_names) + qd_search_backend.load(embeddings, filenames) search_result = qd_search_backend.search(np.random.random(100,), 5) assert isinstance(search_result, list) assert isinstance(search_result[0].id, int) assert isinstance(search_result[0].payload, dict) - assert len(qd_search_backend) == len(file_names) + assert len(qd_search_backend) == len(filenames) + + +def test_HNSWBackend(filenames, embeddings, local_idx_path): + + hnswb = HNSWBackend(local_index_path=local_idx_path) + num_upload = len(filenames) + + filenames_1 = filenames[:num_upload//2] + filenames_2 = filenames[num_upload//2:] + + embeddings_1 = embeddings[:num_upload//2] + embeddings_2 = embeddings[num_upload//2:] + + hnswb.load(embeddings_1, filenames_1) + + assert(len(hnswb) == num_upload//2) + + hnswb.load(embeddings_2, filenames_2) + + assert (len(hnswb) == num_upload) + + ids, distances = hnswb.search(np.random.random(100,), 5) + + assert(ids.dtype == np.uint64) + + os.remove(local_idx_path) From 11771871e8ea6e5046d7b854df3145ee1cc4cb4e Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 17 Aug 2023 17:02:57 -0400 Subject: [PATCH 0333/1072] begin formalizing atac transformer preprocessor --- gitk/preprocessing/__init__.py | 2 + gitk/preprocessing/const.py | 1 + gitk/preprocessing/main.py | 144 ++ gitk/preprocessing/schemas.py | 11 + gitk/preprocessing/utils.py | 43 + tests/data/transformer_vocab.txt | 2538 ++++++++++++++++++++++++++++++ tests/test_preprocessing.py | 79 + 7 files changed, 2818 insertions(+) create mode 100644 gitk/preprocessing/__init__.py create mode 100644 gitk/preprocessing/const.py create mode 100644 gitk/preprocessing/main.py create mode 100644 gitk/preprocessing/schemas.py create mode 100644 gitk/preprocessing/utils.py create mode 100644 tests/data/transformer_vocab.txt create mode 100644 tests/test_preprocessing.py diff --git a/gitk/preprocessing/__init__.py b/gitk/preprocessing/__init__.py new file mode 100644 index 00000000..dbfdb5cb --- /dev/null +++ b/gitk/preprocessing/__init__.py @@ -0,0 +1,2 @@ +from .main import RegionIDifier, EncodedRegion +from .utils import make_vocab_from_bed diff --git a/gitk/preprocessing/const.py b/gitk/preprocessing/const.py new file mode 100644 index 00000000..83161e42 --- /dev/null +++ b/gitk/preprocessing/const.py @@ -0,0 +1 @@ +DEFAULT_MAX_LENGTH = 4096 diff --git a/gitk/preprocessing/main.py b/gitk/preprocessing/main.py new file mode 100644 index 00000000..b2425c5c --- /dev/null +++ b/gitk/preprocessing/main.py @@ -0,0 +1,144 @@ +from typing import List, Union + +from ..io import Region +from .const import DEFAULT_MAX_LENGTH +from .schemas import EncodedRegion +from .utils import wordify_region, unwordify_region + + +class RegionIDifier: + """ + Class for converting a region to an integer id. This class + is intended to be used as a preprocessing step for + the ATAC Transformer model. + """ + + def __init__(self, vocab_file: str = None, max_length: int = DEFAULT_MAX_LENGTH): + self.word_to_id = {} + self.id_to_word = {} + self.max_length = max_length + + if vocab_file is not None: + self.load_vocab(vocab_file) + + def load_vocab(self, vocab_file: str): + """ + Load the vocab file. Read in the file and + create a mapping from word to id and id to word. + """ + with open(vocab_file, "r") as f: + for i, line in enumerate(f.readlines()): + line = line.strip() + self.word_to_id[line] = i + self.id_to_word[i] = line + + def convert_ids_to_tokens(self, ids: List[int]) -> List[str]: + """ + Convert a list of ids to a list of words. + """ + return [self.id_to_word[i] for i in ids] + + def convert_ids_to_regions(self, ids: List[int]) -> List[str]: + """ + Convert a list of ids to a list of regions. + """ + return [unwordify_region(self.id_to_word[i]) for i in ids] + + def convert_tokens_to_ids(self, tokens: List[str]) -> List[int]: + """ + Convert a list of tokens to a list of ids. + """ + return [self.word_to_id[t] for t in tokens] + + def convert_regions_to_ids(self, regions: List[Region]) -> List[int]: + """ + Convert a list of regions to a list of ids. + """ + return [self.word_to_id[wordify_region(r)] for r in regions] + + def generate_attention_mask(self, ids: List[int]) -> List[int]: + """ + Generate an attention mask for a list of ids. + """ + return [1 if i != self.word_to_id["[PAD]"] else 0 for i in ids] + + def pad_ids(self, ids: List[int], max_len: int) -> List[int]: + """ + Pad a list of ids to a maximum length. + """ + return ids + [self.word_to_id["[PAD]"]] * (max_len - len(ids)) + + def pad_regions(self, regions: List[str], max_len: int) -> List[str]: + """ + Pad a list of regions to a maximum length. + """ + return regions + ["[PAD]"] * (max_len - len(regions)) + + def truncate_ids(self, ids: List[int], max_len: int) -> List[int]: + """ + Truncate a list of ids to a maximum length. + """ + return ids[:max_len] + + def truncate_regions(self, regions: List[str], max_len: int) -> List[str]: + """ + Truncate a list of regions to a maximum length. + """ + return regions[:max_len] + + def tokenize( + self, + regions: Union[List[Region], List[List[Region]]], + max_length: int = None, + padding: bool = True, + ) -> Union[EncodedRegion, List[EncodedRegion]]: + """ + Encode regions into EncodedRegions objects. Can accept + either a single region list or multiple lists of regions. + + If multiple are passed, then the regions will be concatenated + and the attention mask will be generated accordingly. In addition, + the regions will be padded to the maximum length of all the regions. + """ + + # identify max length + if max_length is None: + max_length = self.max_length + + # standardize to list of lists + if isinstance(regions[0], Region): + regions = [regions] + + # add [CLS] token to beginning of each region list + regions = [["[CLS]"] + [wordify_region(r) for r in r_list] for r_list in regions] + + # truncate all to max_length + regions = [self.truncate_regions(r, max_length) for r in regions] + + if padding: + # find max of all lengths + max_len = max([len(r) for r in regions]) + + # pad to max length + regions = [self.pad_regions(r, max_len) for r in regions] + + # convert to ids + ids = [[self.word_to_id[w] for w in r] for r in regions] + + # generate attention mask + attention_mask = [[1 if i != self.word_to_id["[PAD]"] else 0 for i in r] for r in ids] + + # generate encoded region objects + encoded_regions = [ + EncodedRegion(ids=r, attention_mask=a) for r, a in zip(ids, attention_mask) + ] + + if len(encoded_regions) == 1: + return encoded_regions[0] + else: + return encoded_regions + + def __call__( + self, regions: Union[List[Region], List[List[Region]]], **kwargs + ) -> Union[EncodedRegion, List[EncodedRegion]]: + return self.tokenize(regions, **kwargs) diff --git a/gitk/preprocessing/schemas.py b/gitk/preprocessing/schemas.py new file mode 100644 index 00000000..fb6c4b75 --- /dev/null +++ b/gitk/preprocessing/schemas.py @@ -0,0 +1,11 @@ +from typing import List +from pydantic import BaseModel + + +class EncodedRegion(BaseModel): + """ + A region that has been encoded into a list of ids. + """ + + ids: List[int] + attention_mask: List[int] diff --git a/gitk/preprocessing/utils.py b/gitk/preprocessing/utils.py new file mode 100644 index 00000000..36781e63 --- /dev/null +++ b/gitk/preprocessing/utils.py @@ -0,0 +1,43 @@ +from tqdm import tqdm + +from ..io import Region + + +def wordify_region(region: Region) -> str: + """ + Convert a region to a string. + """ + return f"{region.chr}_{region.start}_{region.end}" + + +def unwordify_region(word: str) -> Region: + """ + Convert a string to a region. + """ + chr, start, end = word.split("_") + return Region(chr, int(start), int(end)) + + +def make_vocab_from_bed(bed_file: str, vocab_file: str, n_unused: int = 10000): + """ + Create a vocabulary file from a bed file. This includes + the special tokens [PAD], [UNK], [CLS], [SEP], and [MASK] as + well as unused tokens for new regions that may be encountered + during training or pre-training. + """ + with open(bed_file, "r") as f: + regions = [line.strip().split("\t") for line in f.readlines()] + + with open(vocab_file, "w") as f: + f.write("[PAD]\n") + f.write("[UNK]\n") + f.write("[CLS]\n") + f.write("[SEP]\n") + f.write("[MASK]\n") + for region in tqdm(regions, desc="Writing regions"): + r = Region(region[0], int(region[1]), int(region[2])) + f.write(wordify_region(r) + "\n") + + # add unused tokens + for i in tqdm(range(n_unused), desc="Writing unused tokens"): + f.write(f"[UNUSED{i}]\n") diff --git a/tests/data/transformer_vocab.txt b/tests/data/transformer_vocab.txt new file mode 100644 index 00000000..f3174399 --- /dev/null +++ b/tests/data/transformer_vocab.txt @@ -0,0 +1,2538 @@ +[PAD] +[UNK] +[CLS] +[SEP] +[MASK] +chr1_63403166_63403785 +chr10_20783474_20784387 +chr1_121484861_121485361 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+@pytest.fixture +def transformer_vocab_file(): + return "tests/data/transformer_vocab.txt" + + +def test_load_regionidifier(transformer_vocab_file: str): + r = RegionIDifier( + transformer_vocab_file, + ) + + assert r.word_to_id["[PAD]"] == 0 + assert r.word_to_id["[UNK]"] == 1 + assert r.word_to_id["[CLS]"] == 2 + assert r.word_to_id["[SEP]"] == 3 + assert r.word_to_id["[MASK]"] == 4 + assert r.id_to_word[0] == "[PAD]" + assert r.id_to_word[1] == "[UNK]" + assert r.id_to_word[2] == "[CLS]" + assert r.id_to_word[3] == "[SEP]" + assert r.id_to_word[4] == "[MASK]" + + +def test_generate_region_ids( + transformer_vocab_file: str, +): + idifier = RegionIDifier(transformer_vocab_file) + + # these are the regions in the universe bed file, garanteed to be in the vocab + regions = [ + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + Region("chr1", 121484861, 121485361), + ] + + ids = idifier.convert_regions_to_ids(regions) + assert ids == [5, 6, 7] + + +def test_convert_ids_to_tokens( + transformer_vocab_file: str, +): + idifier = RegionIDifier(transformer_vocab_file) + + # these are the regions in the universe bed file, garanteed to be in the vocab + ids = [5, 6, 7] + + regions = idifier.convert_ids_to_regions(ids) + assert regions == [ + "chr1_63403166_63403785", + "chr10_20783474_20784387", + "chr1_121484861_121485361", + ] + + +def test_convert_ids_to_regions( + transformer_vocab_file: str, +): + idifier = RegionIDifier(transformer_vocab_file) + + # these are the regions in the universe bed file, garanteed to be in the vocab + ids = [5, 6, 7] + + regions = idifier.convert_ids_to_regions(ids) + assert regions == [ + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + Region("chr1", 121484861, 121485361), + ] From c76638430ef0354dcbe0fd49baa077f0d0fbce72 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Thu, 17 Aug 2023 19:53:40 -0400 Subject: [PATCH 0334/1072] finish preprocessors for ATAC transformer --- gitk/preprocessing/__init__.py | 4 +- gitk/preprocessing/main.py | 77 ++++++---- gitk/preprocessing/schemas.py | 2 +- tests/test_preprocessing.py | 268 +++++++++++++++++++++++++++++++-- 4 files changed, 306 insertions(+), 45 deletions(-) diff --git a/gitk/preprocessing/__init__.py b/gitk/preprocessing/__init__.py index dbfdb5cb..4cca69c8 100644 --- a/gitk/preprocessing/__init__.py +++ b/gitk/preprocessing/__init__.py @@ -1,2 +1,2 @@ -from .main import RegionIDifier, EncodedRegion -from .utils import make_vocab_from_bed +from .main import RegionIDifier, EncodedRegions +from .utils import make_vocab_from_bed, wordify_region, unwordify_region diff --git a/gitk/preprocessing/main.py b/gitk/preprocessing/main.py index b2425c5c..daa0ca44 100644 --- a/gitk/preprocessing/main.py +++ b/gitk/preprocessing/main.py @@ -2,7 +2,7 @@ from ..io import Region from .const import DEFAULT_MAX_LENGTH -from .schemas import EncodedRegion +from .schemas import EncodedRegions from .utils import wordify_region, unwordify_region @@ -14,8 +14,8 @@ class RegionIDifier: """ def __init__(self, vocab_file: str = None, max_length: int = DEFAULT_MAX_LENGTH): - self.word_to_id = {} - self.id_to_word = {} + self._word_to_id = {} + self._id_to_word = {} self.max_length = max_length if vocab_file is not None: @@ -29,69 +29,94 @@ def load_vocab(self, vocab_file: str): with open(vocab_file, "r") as f: for i, line in enumerate(f.readlines()): line = line.strip() - self.word_to_id[line] = i - self.id_to_word[i] = line + self._word_to_id[line] = i + self._id_to_word[i] = line + + def word_to_id(self, word: str) -> int: + """ + Convert a word to an id. + """ + _id = self._word_to_id.get(word, None) + if _id is None: + _id = self.word_to_id("[UNK]") + return _id + + def id_to_word(self, id: int) -> str: + """ + Convert an id to a word. + """ + return self._id_to_word.get(id, "[UNK]") def convert_ids_to_tokens(self, ids: List[int]) -> List[str]: """ Convert a list of ids to a list of words. """ - return [self.id_to_word[i] for i in ids] + return [self.id_to_word(i) for i in ids] def convert_ids_to_regions(self, ids: List[int]) -> List[str]: """ Convert a list of ids to a list of regions. """ - return [unwordify_region(self.id_to_word[i]) for i in ids] + return [unwordify_region(self.id_to_word(i)) for i in ids] def convert_tokens_to_ids(self, tokens: List[str]) -> List[int]: """ Convert a list of tokens to a list of ids. """ - return [self.word_to_id[t] for t in tokens] + return [self.word_to_id(t) for t in tokens] def convert_regions_to_ids(self, regions: List[Region]) -> List[int]: """ Convert a list of regions to a list of ids. """ - return [self.word_to_id[wordify_region(r)] for r in regions] + return [self.word_to_id(wordify_region(r)) for r in regions] - def generate_attention_mask(self, ids: List[int]) -> List[int]: + def generate_attention_mask_from_ids(self, ids: List[int]) -> List[int]: """ Generate an attention mask for a list of ids. """ - return [1 if i != self.word_to_id["[PAD]"] else 0 for i in ids] + return [1 if i != self.word_to_id("[PAD]") else 0 for i in ids] + + def generate_attention_mask_from_tokens(self, tokens: List[str]) -> List[int]: + """ + Generate an attention mask for a list of tokens. + """ + return [1 if t != "[PAD]" else 0 for t in tokens] - def pad_ids(self, ids: List[int], max_len: int) -> List[int]: + def pad_ids(self, ids: List[int], max_length: int = None) -> List[int]: """ Pad a list of ids to a maximum length. """ - return ids + [self.word_to_id["[PAD]"]] * (max_len - len(ids)) + max_length = max_length or self.max_length + return ids + [self.word_to_id("[PAD]")] * (max_length - len(ids)) - def pad_regions(self, regions: List[str], max_len: int) -> List[str]: + def pad_tokens(self, regions: List[str], max_length: int = None) -> List[str]: """ Pad a list of regions to a maximum length. """ - return regions + ["[PAD]"] * (max_len - len(regions)) + max_length = max_length or self.max_length + return regions + ["[PAD]"] * (max_length - len(regions)) - def truncate_ids(self, ids: List[int], max_len: int) -> List[int]: + def truncate_ids(self, ids: List[int], max_length: int = None) -> List[int]: """ Truncate a list of ids to a maximum length. """ - return ids[:max_len] + max_length = (max_length or self.max_length) + 1 # add 1 for [CLS] + return ids[:max_length] - def truncate_regions(self, regions: List[str], max_len: int) -> List[str]: + def truncate_tokens(self, regions: List[str], max_length: int = None) -> List[str]: """ Truncate a list of regions to a maximum length. """ - return regions[:max_len] + max_length = (max_length or self.max_length) + 1 # add 1 for [CLS] + return regions[:max_length] def tokenize( self, regions: Union[List[Region], List[List[Region]]], max_length: int = None, padding: bool = True, - ) -> Union[EncodedRegion, List[EncodedRegion]]: + ) -> Union[EncodedRegions, List[EncodedRegions]]: """ Encode regions into EncodedRegions objects. Can accept either a single region list or multiple lists of regions. @@ -113,24 +138,24 @@ def tokenize( regions = [["[CLS]"] + [wordify_region(r) for r in r_list] for r_list in regions] # truncate all to max_length - regions = [self.truncate_regions(r, max_length) for r in regions] + regions = [self.truncate_tokens(r, max_length) for r in regions] if padding: # find max of all lengths max_len = max([len(r) for r in regions]) # pad to max length - regions = [self.pad_regions(r, max_len) for r in regions] + regions = [self.pad_tokens(r, max_len) for r in regions] # convert to ids - ids = [[self.word_to_id[w] for w in r] for r in regions] + ids = [[self.word_to_id(w) for w in r] for r in regions] # generate attention mask - attention_mask = [[1 if i != self.word_to_id["[PAD]"] else 0 for i in r] for r in ids] + attention_mask = [self.generate_attention_mask_from_ids(id_list) for id_list in ids] # generate encoded region objects encoded_regions = [ - EncodedRegion(ids=r, attention_mask=a) for r, a in zip(ids, attention_mask) + EncodedRegions(ids=r, attention_mask=a) for r, a in zip(ids, attention_mask) ] if len(encoded_regions) == 1: @@ -140,5 +165,5 @@ def tokenize( def __call__( self, regions: Union[List[Region], List[List[Region]]], **kwargs - ) -> Union[EncodedRegion, List[EncodedRegion]]: + ) -> Union[EncodedRegions, List[EncodedRegions]]: return self.tokenize(regions, **kwargs) diff --git a/gitk/preprocessing/schemas.py b/gitk/preprocessing/schemas.py index fb6c4b75..ef379f74 100644 --- a/gitk/preprocessing/schemas.py +++ b/gitk/preprocessing/schemas.py @@ -2,7 +2,7 @@ from pydantic import BaseModel -class EncodedRegion(BaseModel): +class EncodedRegions(BaseModel): """ A region that has been encoded into a list of ids. """ diff --git a/tests/test_preprocessing.py b/tests/test_preprocessing.py index 4af6755b..9605a2a6 100644 --- a/tests/test_preprocessing.py +++ b/tests/test_preprocessing.py @@ -2,6 +2,8 @@ from gitk.io import Region from gitk.preprocessing import RegionIDifier +from gitk.preprocessing.utils import wordify_region +from gitk.preprocessing.schemas import EncodedRegions @pytest.fixture @@ -19,16 +21,16 @@ def test_load_regionidifier(transformer_vocab_file: str): transformer_vocab_file, ) - assert r.word_to_id["[PAD]"] == 0 - assert r.word_to_id["[UNK]"] == 1 - assert r.word_to_id["[CLS]"] == 2 - assert r.word_to_id["[SEP]"] == 3 - assert r.word_to_id["[MASK]"] == 4 - assert r.id_to_word[0] == "[PAD]" - assert r.id_to_word[1] == "[UNK]" - assert r.id_to_word[2] == "[CLS]" - assert r.id_to_word[3] == "[SEP]" - assert r.id_to_word[4] == "[MASK]" + assert r.word_to_id("[PAD]") == 0 + assert r.word_to_id("[UNK]") == 1 + assert r.word_to_id("[CLS]") == 2 + assert r.word_to_id("[SEP]") == 3 + assert r.word_to_id("[MASK]") == 4 + assert r.id_to_word(0) == "[PAD]" + assert r.id_to_word(1) == "[UNK]" + assert r.id_to_word(2) == "[CLS]" + assert r.id_to_word(3) == "[SEP]" + assert r.id_to_word(4) == "[MASK]" def test_generate_region_ids( @@ -36,15 +38,20 @@ def test_generate_region_ids( ): idifier = RegionIDifier(transformer_vocab_file) - # these are the regions in the universe bed file, garanteed to be in the vocab + # these are the regions in the universe bed file, garuanteed to be in the vocab regions = [ Region("chr1", 63403166, 63403785), Region("chr10", 20783474, 20784387), Region("chr1", 121484861, 121485361), ] - ids = idifier.convert_regions_to_ids(regions) - assert ids == [5, 6, 7] + regions_as_tokens = [wordify_region(r) for r in regions] + + ids1 = idifier.convert_regions_to_ids(regions) + ids2 = idifier.convert_tokens_to_ids(regions_as_tokens) + + assert ids1 == [5, 6, 7] + assert ids2 == [5, 6, 7] def test_convert_ids_to_tokens( @@ -52,10 +59,10 @@ def test_convert_ids_to_tokens( ): idifier = RegionIDifier(transformer_vocab_file) - # these are the regions in the universe bed file, garanteed to be in the vocab + # these are the regions in the universe bed file, garuanteed to be in the vocab ids = [5, 6, 7] - regions = idifier.convert_ids_to_regions(ids) + regions = idifier.convert_ids_to_tokens(ids) assert regions == [ "chr1_63403166_63403785", "chr10_20783474_20784387", @@ -68,7 +75,7 @@ def test_convert_ids_to_regions( ): idifier = RegionIDifier(transformer_vocab_file) - # these are the regions in the universe bed file, garanteed to be in the vocab + # these are the regions in the universe bed file, garuanteed to be in the vocab ids = [5, 6, 7] regions = idifier.convert_ids_to_regions(ids) @@ -77,3 +84,232 @@ def test_convert_ids_to_regions( Region("chr10", 20783474, 20784387), Region("chr1", 121484861, 121485361), ] + + +def test_attention_mask_generation( + transformer_vocab_file: str, +): + idifier = RegionIDifier(transformer_vocab_file) + + # these are the regions in the universe bed file, garuanteed to be in the vocab + ids = [5, 6, 7] # see above tests for what these ids correspond to + + # attention should be given to all tokens except the padding token, there + # are no padding tokens in this example, so the attention mask should be all 1s + attention_mask = idifier.generate_attention_mask_from_ids(ids) + assert attention_mask == [1, 1, 1] + + attention_mask = idifier.generate_attention_mask_from_tokens( + idifier.convert_ids_to_tokens(ids) + ) + assert attention_mask == [1, 1, 1] + + # now add a padding token + ids = [5, 6, 0] + attention_mask = idifier.generate_attention_mask_from_ids(ids) + assert attention_mask == [1, 1, 0] + + attention_mask = idifier.generate_attention_mask_from_tokens( + idifier.convert_ids_to_tokens(ids) + ) + assert attention_mask == [1, 1, 0] + + +def test_unknown_tokens_and_ids( + transformer_vocab_file: str, +): + idifier = RegionIDifier(transformer_vocab_file) + + this_token_doesnt_exist = "chr1_1_2" + id = idifier.word_to_id(this_token_doesnt_exist) + + assert id == 1 + + this_id_doesnt_exist = 1000000000 + token = idifier.id_to_word(this_id_doesnt_exist) + assert token == "[UNK]" + + +def test_padding( + transformer_vocab_file: str, +): + idifier = RegionIDifier(transformer_vocab_file) + + ids = [5, 6, 0] + + padded_ids = idifier.pad_ids(ids, 10) + assert padded_ids == [5, 6, 0, 0, 0, 0, 0, 0, 0, 0] + + tokens = [ + "chr1_63403166_63403785", + "chr10_20783474_20784387", + "chr1_121484861_121485361", + ] + + padded_tokens = idifier.pad_tokens(tokens, 10) + assert padded_tokens == [ + "chr1_63403166_63403785", + "chr10_20783474_20784387", + "chr1_121484861_121485361", + "[PAD]", + "[PAD]", + "[PAD]", + "[PAD]", + "[PAD]", + "[PAD]", + "[PAD]", + ] + + +def test_truncation( + transformer_vocab_file: str, +): + idifier = RegionIDifier(transformer_vocab_file) + + ids = [5, 6, 0, 5, 6, 5, 6, 5, 6, 5, 6] + + truncated_ids = idifier.truncate_ids(ids, 2) + assert truncated_ids == [5, 6, 0] + + tokens = [ + "chr1_63403166_63403785", + "chr10_20783474_20784387", + "chr1_121484861_121485361", + "chr1_63403166_63403785", + "chr10_20783474_20784387", + "chr1_121484861_121485361", + "chr1_63403166_63403785", + "chr10_20783474_20784387", + "chr1_121484861_121485361", + "chr1_63403166_63403785", + "chr10_20783474_20784387", + "chr1_121484861_121485361", + ] + + truncated_tokens = idifier.truncate_tokens(tokens, 2) + assert truncated_tokens == [ + "chr1_63403166_63403785", + "chr10_20783474_20784387", + "chr1_121484861_121485361", + ] + + +def test_encode( + transformer_vocab_file: str, +): + idifier = RegionIDifier(transformer_vocab_file) + + # test basic functionality + # these are the regions in the universe bed file, garuanteed to be in the vocab + regions = [ + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + Region("chr1", 121484861, 121485361), + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + Region("chr1", 121484861, 121485361), + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + Region("chr1", 121484861, 121485361), + ] + + tokens1 = idifier.tokenize(regions) + tokens2 = idifier(regions) # check __call__ method + + assert isinstance(tokens1, EncodedRegions) + assert isinstance(tokens2, EncodedRegions) + assert tokens1.ids == tokens2.ids + assert tokens1.attention_mask == tokens2.attention_mask + + regions1 = [ + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + Region("chr1", 121484861, 121485361), + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + ] + regions2 = [ + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + Region("chr1", 121484861, 121485361), + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + Region("chr1", 121484861, 121485361), + Region("chr1", 63403166, 63403785), + Region("chr10", 20783474, 20784387), + ] + + tokens = idifier([regions1, regions2]) + + assert isinstance(tokens, list) + assert len(tokens) == 2 + assert isinstance(tokens[0], EncodedRegions) + assert isinstance(tokens[1], EncodedRegions) + + # check for padding on the first batch + assert tokens[0].ids == [ + 2, + 5, + 6, + 7, + 5, + 6, + 0, + 0, + 0, + 0, + 0, + ] # 0 is the padding token id, 2 is the cls token id + assert tokens[0].attention_mask == [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0] + assert tokens[1].ids == [ + 2, + 5, + 6, + 7, + 5, + 6, + 5, + 6, + 7, + 5, + 6, + ] # 0 is the padding token id, 2 is the cls token id + assert tokens[1].attention_mask == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + + # now test the truncation functionality + idifier = RegionIDifier(transformer_vocab_file, max_length=6) + + tokens = idifier([regions1, regions2]) + + assert isinstance(tokens, list) + assert len(tokens) == 2 + assert isinstance(tokens[0], EncodedRegions) + assert isinstance(tokens[1], EncodedRegions) + + # ensure length is 6 (+ 1 for cls token) + assert len(tokens[0].ids) == 7 + assert len(tokens[1].ids) == 7 + + # ensure things were padded correctly + assert tokens[0].ids == [ + 2, + 5, + 6, + 7, + 5, + 6, + 0, + ] # 0 is the padding token id, 2 is the cls token id + assert tokens[0].attention_mask == [1, 1, 1, 1, 1, 1, 0] + assert tokens[1].ids == [ + 2, + 5, + 6, + 7, + 5, + 6, + 5, + ] # 0 is the padding token id, 2 is the cls token id + assert tokens[1].attention_mask == [1, 1, 1, 1, 1, 1, 1] From f25bb3a515eb43c25b3516489a4a770bdea55498 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 18 Aug 2023 11:42:54 -0400 Subject: [PATCH 0335/1072] update setup.py --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index aecfd8fb..3743c436 100755 --- a/setup.py +++ b/setup.py @@ -36,6 +36,7 @@ "gitk.tokenization", "gitk.universe", "gitk.io", + "gitk.preprocessing" ], version=version, long_description=long_description, From 7a7446affae2e8b4ffd6162990991fe8dea594f8 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 18 Aug 2023 12:21:21 -0400 Subject: [PATCH 0336/1072] update anndata_to_regionsets --- gitk/tokenization/main.py | 7 ++++++- gitk/tokenization/utils.py | 8 ++++---- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/gitk/tokenization/main.py b/gitk/tokenization/main.py index fd73403d..a491afe5 100644 --- a/gitk/tokenization/main.py +++ b/gitk/tokenization/main.py @@ -181,7 +181,12 @@ def convert_anndata_to_universe(self, adata: sc.AnnData) -> sc.AnnData: # grab the first for now # TODO - this is a simplification, we should be able to handle multiple universe_region = _map[region] - chr, start, end = universe_region.split("_") + try: + chr, start, end = universe_region.split("_") + start = int(start) + end = int(end) + except ValueError: + raise ValueError(f"Could not parse region {universe_region}") updated_var.at[i, "chr"] = chr updated_var.at[i, "start"] = start diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index 3fd27c1f..f76f4ddd 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -6,7 +6,7 @@ import scanpy as sc from tqdm import tqdm -from ..io import Region, RegionSet +from ..io import Region class Timer: @@ -52,7 +52,7 @@ def time_str(t: float) -> str: return f"{t:.2f}s" -def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: +def anndata_to_regionsets(adata: sc.AnnData) -> List[List[Region]]: """ Converts an AnnData object to a list of lists of regions. This is done by taking each cell and creating a list of all regions @@ -89,11 +89,11 @@ def anndata_to_regionsets(adata: sc.AnnData) -> List[List[str]]: regions = [] for i in tqdm(range(adata.shape[0]), total=adata.shape[0], desc="Tokenizing"): regions.append( - RegionSet( + [ [ Region(chr_values[j], start_values[j], end_values[j]) for j in np.where(positive_values[i])[0] ] - ) + ] ) return regions From 787e9410ba9017c6181e0085d859792be9f78623 Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 18 Aug 2023 12:25:22 -0400 Subject: [PATCH 0337/1072] lining --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 3743c436..047db928 100755 --- a/setup.py +++ b/setup.py @@ -36,7 +36,7 @@ "gitk.tokenization", "gitk.universe", "gitk.io", - "gitk.preprocessing" + "gitk.preprocessing", ], version=version, long_description=long_description, From 33f4e723f7d50283fabb20f7a401d1bba8a0e83e Mon Sep 17 00:00:00 2001 From: Nathan LeRoy Date: Fri, 18 Aug 2023 12:39:06 -0400 Subject: [PATCH 0338/1072] fix list of list of lists bug --- gitk/tokenization/utils.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/gitk/tokenization/utils.py b/gitk/tokenization/utils.py index f76f4ddd..3a27c965 100644 --- a/gitk/tokenization/utils.py +++ b/gitk/tokenization/utils.py @@ -90,10 +90,8 @@ def anndata_to_regionsets(adata: sc.AnnData) -> List[List[Region]]: for i in tqdm(range(adata.shape[0]), total=adata.shape[0], desc="Tokenizing"): regions.append( [ - [ - Region(chr_values[j], start_values[j], end_values[j]) - for j in np.where(positive_values[i])[0] - ] + Region(chr_values[j], start_values[j], end_values[j]) + for j in np.where(positive_values[i])[0] ] ) return regions From cb5554a6bedb3d5a47910eced7c77f3126ff51c8 Mon Sep 17 00:00:00 2001 From: "Ziyang \"Claude\" Hu" <33562602+ClaudeHu@users.noreply.github.com> Date: Fri, 18 Aug 2023 14:30:30 -0400 Subject: [PATCH 0339/1072] new search backend infrastructure Matches pipestat/backends --- gitk/search/abstract.py | 37 ++++++++++++ gitk/search/dbbackend.py | 76 ++++++++++++++++++++++++ gitk/search/filebackend.py | 115 +++++++++++++++++++++++++++++++++++++ 3 files changed, 228 insertions(+) create mode 100644 gitk/search/abstract.py create mode 100644 gitk/search/dbbackend.py create mode 100644 gitk/search/filebackend.py diff --git a/gitk/search/abstract.py b/gitk/search/abstract.py new file mode 100644 index 00000000..2b057693 --- /dev/null +++ b/gitk/search/abstract.py @@ -0,0 +1,37 @@ +from abc import ABC, abstractmethod +import numpy as np +from typing import List, Tuple + + +class EmSearchBackend(ABC): + """ + An abstract class representing Embedding Search Backends. This allows + backends to be either a qdrant server or a local in-memory NMS index, or + anything, really. This allows us to use the same interface for both. + """ + + def __init__(self, embeddings: np.ndarray = None, labels: list = None) -> None: + if embeddings: + self.load(embeddings, labels) + + @abstractmethod + def load(self, embeddings: np.ndarray, labels: list) -> None: + raise NotImplementedError + + @abstractmethod + def search(self, query: np.ndarray, k: int) -> List[Tuple[int, float]]: + """ + Search for the nearest neighbors of the given embedding + + :param query: the embedding to search for + :param k: the number of results to return + :return: a list of (id, score) pairs + """ + raise NotImplementedError() + + @abstractmethod + def __len__(self) -> int: + """ + Return the number of embeddings in the backend + """ + raise NotImplementedError() \ No newline at end of file diff --git a/gitk/search/dbbackend.py b/gitk/search/dbbackend.py new file mode 100644 index 00000000..eac357f4 --- /dev/null +++ b/gitk/search/dbbackend.py @@ -0,0 +1,76 @@ +from .abstract import EmSearchBackend +from .const import * +from qdrant_client import QdrantClient +from qdrant_client.models import VectorParams, PointStruct +import numpy as np +from typing import List + + +class QdrantBackend(EmSearchBackend): + """A search backend that uses a qdrant server to store and search embeddings""" + + def __init__(self, config: VectorParams = DEFAULT_CONFIG, + collection: str = DEFAULT_COLLECTION): + """ + Connect to Qdrant on commandline first: + (Ubuntu Linux terminal) + sudo docker run -p 6333:6333 -v $(pwd)/qdrant_storage:/qdrant/storage qdrant/qdrant + + :param config: the vector parameter + :param collection: name of collection + """ + self.collection = collection + self.config = config + # self.qd_client = get_qdrant(True) + self.qd_client = QdrantClient('http://localhost:6333') + self.qd_client.recreate_collection(collection_name=self.collection, + vectors_config=self.config) + # TODO: initialize connection to qdrant server + + def load(self, + embeddings: np.ndarray, + labels: List[str]): + """ + upload vectors and their labels onto qdrant storage + + :param embeddings: embedding vectors of bed files, an np.ndarray with shape of (n, ) + :param labels: list of labels, can be name of bed files + :return: + """ + if embeddings.shape[0] != len(labels): + raise KeyError("The number of embeddings does not match the number of labels") + self.qd_client.upsert( + collection_name=self.collection, + points=[ + PointStruct( + id=i, + vector=embeddings[i].tolist(), + payload={"label": labels[i]} + ) + for i in range(len(labels)) + ] + ) + + def search(self, query: np.ndarray, k: int) -> List: + """ + with a given query vector, get k nearest neighbors from vectors in the collection + + :param query: a vector to search + :param k: number of returned results + :return: a list of search results + """ + search_results = self.qd_client.search( + collection_name=self.collection, + query_vector=query, + limit=k, + with_payload=True + ) + # raise NotImplementedError + return search_results + + def __len__(self) -> int: + """ + Return the number of embeddings in the backend + """ + return self.qd_client.get_collection(collection_name=self.collection).vectors_count + diff --git a/gitk/search/filebackend.py b/gitk/search/filebackend.py new file mode 100644 index 00000000..05b40fd3 --- /dev/null +++ b/gitk/search/filebackend.py @@ -0,0 +1,115 @@ +from .abstract import EmSearchBackend +from .const import * +import hnswlib +import numpy as np +from typing import List, Tuple, Union + + +class HNSWBackend(EmSearchBackend): + """A search backend that uses a local HNSW index to store and search embeddings""" + + # the index + idx: hnswlib.Index + metadata: dict # in the format of {: