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metaplotter: Reuse plotting #119

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3048b57
metaplotter_pl_timescale: Import read_data() from metaplotter instead
rht Dec 5, 2018
9faa8f3
metaplotter_pl_timescale: Deduplicate plotting
rht Dec 9, 2018
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53 changes: 27 additions & 26 deletions metaplotter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -128,32 +128,33 @@ def plotting(output_label, timeseries_dict, riskmodelsetting1, riskmodelsetting2
plt.savefig(outputfilename)
plt.show()

timeseries = read_data()
if __name__ == "__main__":
timeseries = read_data()

# for just two different riskmodel settings
plotting(output_label="fig_contracts_survival_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", \
riskmodelsetting2="two", series1="contracts", series2="operational", plottype1="mean", plottype2="median")
plotting(output_label="fig_reinsurers_contracts_survival_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", \
riskmodelsetting2="two", series1="reincontracts", series2="reinoperational", plottype1="mean", plottype2="median")
plotting(output_label="fig_premium_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", riskmodelsetting2="two", \
series1="premium", series2=None, plottype1="mean", plottype2=None)
# for just two different riskmodel settings
plotting(output_label="fig_contracts_survival_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", \
riskmodelsetting2="two", series1="contracts", series2="operational", plottype1="mean", plottype2="median")
plotting(output_label="fig_reinsurers_contracts_survival_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", \
riskmodelsetting2="two", series1="reincontracts", series2="reinoperational", plottype1="mean", plottype2="median")
plotting(output_label="fig_premium_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", riskmodelsetting2="two", \
series1="premium", series2=None, plottype1="mean", plottype2=None)

raise SystemExit
# for four different riskmodel settings
plotting(output_label="fig_contracts_survival_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", \
riskmodelsetting2="two", series1="contracts", series2="operational", additionalriskmodelsetting3="three", \
additionalriskmodelsetting4="four", plottype1="mean", plottype2="median")
plotting(output_label="fig_contracts_survival_3_4", timeseries_dict=timeseries, riskmodelsetting1="three", \
riskmodelsetting2="four", series1="contracts", series2="operational", additionalriskmodelsetting3="one", \
additionalriskmodelsetting4="two", plottype1="mean", plottype2="median")
plotting(output_label="fig_reinsurers_contracts_survival_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", \
riskmodelsetting2="two", series1="reincontracts", series2="reinoperational", \
additionalriskmodelsetting3="three", additionalriskmodelsetting4="four", plottype1="mean", plottype2="median")
plotting(output_label="fig_reinsurers_contracts_survival_3_4", timeseries_dict=timeseries, riskmodelsetting1="three", \
riskmodelsetting2="four", series1="reincontracts", series2="reinoperational", \
additionalriskmodelsetting3="one", additionalriskmodelsetting4="two", plottype1="mean", plottype2="median")
plotting(output_label="fig_premium_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", riskmodelsetting2="two", \
series1="premium", series2=None, additionalriskmodelsetting3="three", additionalriskmodelsetting4="four", \
plottype1="mean", plottype2=None)
raise SystemExit
# for four different riskmodel settings
plotting(output_label="fig_contracts_survival_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", \
riskmodelsetting2="two", series1="contracts", series2="operational", additionalriskmodelsetting3="three", \
additionalriskmodelsetting4="four", plottype1="mean", plottype2="median")
plotting(output_label="fig_contracts_survival_3_4", timeseries_dict=timeseries, riskmodelsetting1="three", \
riskmodelsetting2="four", series1="contracts", series2="operational", additionalriskmodelsetting3="one", \
additionalriskmodelsetting4="two", plottype1="mean", plottype2="median")
plotting(output_label="fig_reinsurers_contracts_survival_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", \
riskmodelsetting2="two", series1="reincontracts", series2="reinoperational", \
additionalriskmodelsetting3="three", additionalriskmodelsetting4="four", plottype1="mean", plottype2="median")
plotting(output_label="fig_reinsurers_contracts_survival_3_4", timeseries_dict=timeseries, riskmodelsetting1="three", \
riskmodelsetting2="four", series1="reincontracts", series2="reinoperational", \
additionalriskmodelsetting3="one", additionalriskmodelsetting4="two", plottype1="mean", plottype2="median")
plotting(output_label="fig_premium_1_2", timeseries_dict=timeseries, riskmodelsetting1="one", riskmodelsetting2="two", \
series1="premium", series2=None, additionalriskmodelsetting3="three", additionalriskmodelsetting4="four", \
plottype1="mean", plottype2=None)

#pdb.set_trace()
#pdb.set_trace()
62 changes: 4 additions & 58 deletions metaplotter_pl_timescale.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,68 +5,14 @@
import time
import glob

def read_data():
# do not overwrite old pdfs
#if os.path.exists("data/fig_one_and_two_rm_comp.pdf"):
# os.rename("data/fig_one_and_two_rm_comp.pdf", "data/fig_one_and_two_rm_comp_old_" + time.strftime('%Y_%b_%d_%H_%M') + ".pdf")
#if os.path.exists("data/fig_three_and_four_rm_comp.pdf"):
# os.rename("data/fig_three_and_four_rm_comp.pdf", "data/fig_three_and_four_rm_comp_old_" + time.strftime('%Y_%b_%d_%H_%M') + ".pdf")
from metaplotter import read_data

upper_bound = 75
lower_bound = 25

timeseries_dict = {}
timeseries_dict["mean"] = {}
timeseries_dict["median"] = {}
timeseries_dict["quantile25"] = {}
timeseries_dict["quantile75"] = {}

filenames_ones = glob.glob("data/one*.dat")
filenames_twos = glob.glob("data/two*.dat")
filenames_threes = glob.glob("data/three*.dat")
filenames_fours = glob.glob("data/four*.dat")
filenames_ones.sort()
filenames_twos.sort()
filenames_threes.sort()
filenames_fours.sort()

#assert len(filenames_ones) == len(filenames_twos) == len(filenames_threes) == len(filenames_fours)
all_filenames = filenames_ones + filenames_twos + filenames_threes + filenames_fours

for filename in all_filenames:
# read files
rfile = open(filename, "r")
data = [eval(k) for k in rfile]
rfile.close()

# compute data series
data_means = []
data_medians = []
data_q25 = []
data_q75 = []
for i in range(len(data[0])):
data_means.append(np.mean([item[i] for item in data]))
data_q25.append(np.percentile([item[i] for item in data], lower_bound))
data_q75.append(np.percentile([item[i] for item in data], upper_bound))
data_medians.append(np.median([item[i] for item in data]))
data_means = np.array(data_means)
data_medians = np.array(data_medians)
data_q25 = np.array(data_q25)
data_q75 = np.array(data_q75)

# record data series
timeseries_dict["mean"][filename] = data_means
timeseries_dict["median"][filename] = data_medians
timeseries_dict["quantile25"][filename] = data_q25
timeseries_dict["quantile75"][filename] = data_q75
return timeseries_dict



def plotting(output_label, timeseries_dict, riskmodelsetting1, riskmodelsetting2, series1, series2=None, additionalriskmodelsetting3=None, additionalriskmodelsetting4=None, plottype1="mean", plottype2="mean"):
def plotting(output_label, timeseries_dict, riskmodelsetting1, riskmodelsetting2, series1, series2=None, additionalriskmodelsetting3=None, additionalriskmodelsetting4=None, plottype1="mean", plottype2="mean", labels=None):
# dictionaries
colors = {"one": "red", "two": "blue", "three": "green", "four": "yellow"}
labels = {"profitslosses": "Profits and Losses (Insurer)", "contracts": "Contracts (Insurers)", "cash": "Liquidity (Insurers)", "operational": "Active Insurers", "premium": "Premium", "reinprofitslosses": "Profits and Losses (Reinsurer)", "reincash": "Liquidity (Reinsurers)", "reincontracts": "Contracts (Reinsurers)", "reinoperational": "Active Reinsurers"}
if labels is None:
labels = {"profitslosses": "Profits and Losses (Insurer)", "contracts": "Contracts (Insurers)", "cash": "Liquidity (Insurers)", "operational": "Active Insurers", "premium": "Premium", "reinprofitslosses": "Profits and Losses (Reinsurer)", "reincash": "Liquidity (Reinsurers)", "reincontracts": "Contracts (Reinsurers)", "reinoperational": "Active Reinsurers"}

# prepare labels, timeseries, etc.
color1 = colors[riskmodelsetting1]
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138 changes: 3 additions & 135 deletions metaplotter_pl_timescale_additional_measures.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,145 +5,13 @@
import time
import glob

def read_data():
# do not overwrite old pdfs
#if os.path.exists("data/fig_one_and_two_rm_comp.pdf"):
# os.rename("data/fig_one_and_two_rm_comp.pdf", "data/fig_one_and_two_rm_comp_old_" + time.strftime('%Y_%b_%d_%H_%M') + ".pdf")
#if os.path.exists("data/fig_three_and_four_rm_comp.pdf"):
# os.rename("data/fig_three_and_four_rm_comp.pdf", "data/fig_three_and_four_rm_comp_old_" + time.strftime('%Y_%b_%d_%H_%M') + ".pdf")
from metaplotter import read_data
from metaplotter_pl_timescale import plotting as _plotting

upper_bound = 75
lower_bound = 25

timeseries_dict = {}
timeseries_dict["mean"] = {}
timeseries_dict["median"] = {}
timeseries_dict["quantile25"] = {}
timeseries_dict["quantile75"] = {}

filenames_ones = glob.glob("data/one*.dat")
filenames_twos = glob.glob("data/two*.dat")
filenames_threes = glob.glob("data/three*.dat")
filenames_fours = glob.glob("data/four*.dat")
filenames_ones.sort()
filenames_twos.sort()
filenames_threes.sort()
filenames_fours.sort()

#assert len(filenames_ones) == len(filenames_twos) == len(filenames_threes) == len(filenames_fours)
all_filenames = filenames_ones + filenames_twos + filenames_threes + filenames_fours

for filename in all_filenames:
# read files
rfile = open(filename, "r")
data = [eval(k) for k in rfile]
rfile.close()

# compute data series
data_means = []
data_medians = []
data_q25 = []
data_q75 = []
for i in range(len(data[0])):
data_means.append(np.mean([item[i] for item in data]))
data_q25.append(np.percentile([item[i] for item in data], lower_bound))
data_q75.append(np.percentile([item[i] for item in data], upper_bound))
data_medians.append(np.median([item[i] for item in data]))
data_means = np.array(data_means)
data_medians = np.array(data_medians)
data_q25 = np.array(data_q25)
data_q75 = np.array(data_q75)

# record data series
timeseries_dict["mean"][filename] = data_means
timeseries_dict["median"][filename] = data_medians
timeseries_dict["quantile25"][filename] = data_q25
timeseries_dict["quantile75"][filename] = data_q75
return timeseries_dict



def plotting(output_label, timeseries_dict, riskmodelsetting1, riskmodelsetting2, series1, series2=None, additionalriskmodelsetting3=None, additionalriskmodelsetting4=None, plottype1="mean", plottype2="mean"):
# dictionaries
colors = {"one": "red", "two": "blue", "three": "green", "four": "yellow"}
labels = {"reinexcess_capital": "Excess Capital (Reinsurers)", "excess_capital": "Excess Capital (Insurers)", "cumulative_unrecovered_claims": "Uncovered Claims (cumulative)", "cumulative_bankruptcies": "Bankruptcies (cumulative)", "profitslosses": "Profits and Losses (Insurer)", "contracts": "Contracts (Insurers)", "cash": "Liquidity (Insurers)", "operational": "Active Insurers", "premium": "Premium", "reinprofitslosses": "Profits and Losses (Reinsurer)", "reincash": "Liquidity (Reinsurers)", "reincontracts": "Contracts (Reinsurers)", "reinoperational": "Active Reinsurers"}

# prepare labels, timeseries, etc.
color1 = colors[riskmodelsetting1]
color2 = colors[riskmodelsetting2]
label1 = str.upper(riskmodelsetting1[0]) + riskmodelsetting1[1:] + " riskmodels"
label2 = str.upper(riskmodelsetting2[0]) + riskmodelsetting2[1:] + " riskmodels"
plot_1_1 = "data/" + riskmodelsetting1 + "_" + series1 + ".dat"
plot_1_2 = "data/" + riskmodelsetting2 + "_" + series1 + ".dat"
if series2 is not None:
plot_2_1 = "data/" + riskmodelsetting1 + "_" + series2 + ".dat"
plot_2_2 = "data/" + riskmodelsetting2 + "_" + series2 + ".dat"
if additionalriskmodelsetting3 is not None:
color3 = colors[additionalriskmodelsetting3]
label3 = str.upper(additionalriskmodelsetting3[0]) + additionalriskmodelsetting3[1:] + " riskmodels"
plot_1_3 = "data/" + additionalriskmodelsetting3 + "_" + series1 + ".dat"
if series2 is not None:
plot_2_3 = "data/" + additionalriskmodelsetting3 + "_" + series2 + ".dat"
if additionalriskmodelsetting4 is not None:
color4 = colors[additionalriskmodelsetting4]
label4 = str.upper(additionalriskmodelsetting4[0]) + additionalriskmodelsetting4[1:] + " riskmodels"
plot_1_4 = "data/" + additionalriskmodelsetting4 + "_" + series1 + ".dat"
if series2 is not None:
plot_2_4 = "data/" + additionalriskmodelsetting4 + "_" + series2 + ".dat"

# Backup existing figures (so as not to overwrite them)
outputfilename = "data/" + output_label + ".pdf"
backupfilename = "data/" + output_label + "_old_" + time.strftime('%Y_%b_%d_%H_%M') + ".pdf"
if os.path.exists(outputfilename):
os.rename(outputfilename, backupfilename)

# Plot and save
fig = plt.figure()
if series2 is not None:
ax0 = fig.add_subplot(211)
else:
ax0 = fig.add_subplot(111)
maxlen_plots = 0
if additionalriskmodelsetting3 is not None:
ax0.plot(range(len(timeseries_dict[plottype1][plot_1_3]))[200:], timeseries_dict[plottype1][plot_1_3][200:], color=color3, label=label3)
maxlen_plots = max(maxlen_plots, len(timeseries_dict[plottype1][plot_1_3]))
if additionalriskmodelsetting4 is not None:
ax0.plot(range(len(timeseries_dict[plottype1][plot_1_4]))[200:], timeseries_dict[plottype1][plot_1_4][200:], color=color4, label=label4)
maxlen_plots = max(maxlen_plots, len(timeseries_dict[plottype1][plot_1_4]))
ax0.plot(range(len(timeseries_dict[plottype1][plot_1_1]))[200:], timeseries_dict[plottype1][plot_1_1][200:], color=color1, label=label1)
ax0.plot(range(len(timeseries_dict[plottype1][plot_1_2]))[200:], timeseries_dict[plottype1][plot_1_2][200:], color=color2, label=label2)
ax0.fill_between(range(len(timeseries_dict["quantile25"][plot_1_1]))[200:], timeseries_dict["quantile25"][plot_1_1][200:], timeseries_dict["quantile75"][plot_1_1][200:], facecolor=color1, alpha=0.25)
ax0.fill_between(range(len(timeseries_dict["quantile25"][plot_1_1]))[200:], timeseries_dict["quantile25"][plot_1_2][200:], timeseries_dict["quantile75"][plot_1_2][200:], facecolor=color2, alpha=0.25)
ax0.set_ylabel(labels[series1])#"Contracts")
maxlen_plots = max(maxlen_plots, len(timeseries_dict[plottype1][plot_1_1]), len(timeseries_dict[plottype1][plot_1_2]))
xticks = np.arange(200, maxlen_plots, step=120)
ax0.set_xticks(xticks)
ax0.set_xticklabels(["${0:d}$".format(int((xtc-200)/12)) for xtc in xticks]);

ax0.legend(loc='best')
if series2 is not None:
ax1 = fig.add_subplot(212)
maxlen_plots = 0
if additionalriskmodelsetting3 is not None:
ax1.plot(range(len(timeseries_dict[plottype2][plot_2_3]))[200:], timeseries_dict[plottype2][plot_2_3][200:], color=color3, label=label3)
maxlen_plots = max(maxlen_plots, len(timeseries_dict[plottype1][plot_2_3]))
if additionalriskmodelsetting4 is not None:
ax1.plot(range(len(timeseries_dict[plottype2][plot_2_4]))[200:], timeseries_dict[plottype2][plot_2_4][200:], color=color4, label=label4)
maxlen_plots = max(maxlen_plots, len(timeseries_dict[plottype1][plot_2_4]))
ax1.plot(range(len(timeseries_dict[plottype2][plot_2_1]))[200:], timeseries_dict[plottype2][plot_2_1][200:], color=color1, label=label1)
ax1.plot(range(len(timeseries_dict[plottype2][plot_2_2]))[200:], timeseries_dict[plottype2][plot_2_2][200:], color=color2, label=label2)
ax1.fill_between(range(len(timeseries_dict["quantile25"][plot_2_1]))[200:], timeseries_dict["quantile25"][plot_2_1][200:], timeseries_dict["quantile75"][plot_2_1][200:], facecolor=color1, alpha=0.25)
ax1.fill_between(range(len(timeseries_dict["quantile25"][plot_2_1]))[200:], timeseries_dict["quantile25"][plot_2_2][200:], timeseries_dict["quantile75"][plot_2_2][200:], facecolor=color2, alpha=0.25)
maxlen_plots = max(maxlen_plots, len(timeseries_dict[plottype1][plot_2_1]), len(timeseries_dict[plottype1][plot_2_2]))
xticks = np.arange(200, maxlen_plots, step=120)
ax1.set_xticks(xticks)
ax1.set_xticklabels(["${0:d}$".format(int((xtc-200)/12)) for xtc in xticks]);
ax1.set_ylabel(labels[series2])
ax1.set_xlabel("Years")
else:
ax0.set_xlabel("Years")
plt.savefig(outputfilename)
plt.show()
_plotting(output_label, timeseries_dict, riskmodelsetting1, riskmodelsetting2, series1, series2, additionalriskmodelsetting3, additionalriskmodelsetting4, plottype1, plottype2, labels=labels)

timeseries = read_data()

Expand Down