[WIP] Clinician report demo

.gitignore

@@ -1,3 +1,4 @@
+ramutils/test/test_data/*.png
 out.html
 .cache/
 ramutils/test/plotting/sample_plots/
@@ -9,7 +10,6 @@ scratch/
 *.pdf
 *.synctex.gz
 *.swp
-*.png
 *.zip
 *.ipynb_checkpoints/
 

demos/report_generation.py

@@ -1,21 +1,16 @@
-from __future__ import print_function
-
-import os.path
-from ramutils.parameters import FilePaths, FRParameters, PS5Parameters
+from ramutils.parameters import FilePaths, FRParameters
 from ramutils.pipelines.report import make_report
 
 from ramutils.tasks import memory
 
-memory.cachedir = "~"
-
-
+memory.cachedir = "scratch/ramutils_test"
 
 paths = FilePaths(
-    root='/',
-    dest='/scratch/zduey/',
-    data_db='/scratch/zduey/'
+    root="~/mnt/rhino",
+    dest="scratch/ramutils_test/",
+    data_db="/scratch/report_database",
 )
 
 params = FRParameters()
-make_report("R1001P", "FR1", paths, exp_params=params, stim_params=None,
-            joint_report=False, rerun=False, sessions=None)
+make_report("R1384J", "FR5", paths, exp_params=params, stim_params=None,
+            joint_report=False, rerun=False, sessions=[0], clinical=True)

ramutils/pipelines/report.py

@@ -19,7 +19,7 @@ def make_report(subject, experiment, paths, joint_report=False,
                 retrain=False, stim_params=None, exp_params=None,
                 sessions=None, vispath=None, rerun=False,
                 trigger_electrode=None, use_classifier_excluded_leads=False,
-                pipeline_name="report"):
+                pipeline_name="report", clinical=False):
     """ Constructs a report and saves out all the necessary data to re-construct the report
 
     This pipeline should be used for generating single session reports for both record-only and
@@ -59,6 +59,9 @@ def make_report(subject, experiment, paths, joint_report=False,
         classifier training
     pipeline_name : str
         Name to use for status updates.
+    clinical: bool
+        If True, builds a demo clinical report instead of the internal RAM
+        report
 
     Returns
     -------
@@ -117,7 +120,8 @@ def make_report(subject, experiment, paths, joint_report=False,
                                          pre_built_results['target_selection_table'],
                                          pre_built_results['classifier_evaluation_results'],
                                          paths.dest,
-                                         hmm_results=pre_built_results['hmm_results'])
+                                         hmm_results=pre_built_results['hmm_results'],
+                                         clinical=clinical)
             return report.compute()
 
     final_pairs = generate_pairs_for_classifier(ec_pairs, excluded_pairs)
@@ -182,7 +186,8 @@ def make_report(subject, experiment, paths, joint_report=False,
     report = build_static_report(subject, experiment, data.session_summaries,
                                  data.math_summaries, data.target_selection_table,
                                  data.classifier_evaluation_results,
-                                 hmm_results=output, dest=paths.dest)
+                                 hmm_results=output, dest=paths.dest,
+                                 clinical=clinical)
 
     if vispath is not None:
         report.visualize(filename=vispath)

ramutils/reports/generate.py

@@ -4,16 +4,15 @@
 import json
 import os.path as osp
 import random
-import itertools
 
 from itertools import compress
 from jinja2 import Environment, PackageLoader
 import numpy as np
-from pkg_resources import resource_listdir, resource_string
+from pkg_resources import resource_listdir, resource_filename, resource_string
 
 from ramutils import __version__
 from ramutils.reports.summary import FRSessionSummary, MathSummary, FRStimSessionSummary
-from ramutils.events import extract_experiment_from_events, extract_subject
+from ramutils.events import extract_experiment_from_events
 from ramutils.utils import extract_experiment_series
 
 
@@ -68,9 +67,12 @@ class ReportGenerator(object):
     """
 
     def __init__(self, subject, experiment, session_summaries, math_summaries,
-                 target_selection_table, classifier_summaries, hmm_results=None, dest='.'):
+                 target_selection_table, classifier_summaries, hmm_results=None,
+                 dest='.', clinical=False):
         self.subject = subject
         self.experiment = experiment
+        self.clinical = clinical
+
         self.session_summaries = session_summaries
         self.math_summaries = math_summaries
         self.target_selection_table = target_selection_table
@@ -301,7 +303,7 @@ def generate(self):
             raise NotImplementedError("Unsupported report type")
 
     def _render(self, experiment, **kwargs):
-        """Convenience method to wrap common keyword arguments passed to the
+        """ Convenience method to wrap common keyword arguments passed to the
         template renderer.
 
         Parameters
@@ -311,7 +313,20 @@ def _render(self, experiment, **kwargs):
             Additional keyword arguments that are passed to the render method.
 
         """
-        template = self._env.get_template(experiment.lower() + '.html')
+        if self.clinical:
+            from base64 import b64encode
+
+            template = self._env.get_template('clinical_target_selection.html')
+
+            for hemi in ["left", "right"]:
+                with open(resource_filename("ramutils.reports.static",
+                                            "r1384j_{}.png".format(hemi)), "rb") as infile:
+                    encoded = b64encode(infile.read())
+                img_string = "data:image/png;base64,{}".format(encoded.decode())
+                kwargs["{}_hemisphere_image".format(hemi)] = img_string
+        else:
+            template = self._env.get_template(experiment.lower() + '.html')
+
         return template.render(
             version=self.version,
             subject=self.subject,
@@ -364,20 +379,26 @@ def generate_open_loop_fr_report(self):
         )
 
     def generate_closed_loop_fr_report(self, experiment):
-        """ Generate an FR5 report
+        """ Generate a closed loop stimulation report
 
         Returns
         -------
-        Rendered FR5 report as a string.
+        Rendered stimulation report as a string
 
         """
+        stim_params = FRStimSessionSummary.stim_parameters(self.session_summaries)
+        multistim = False
+        if len(stim_params) > 1:
+            multistim = True
+
         return self._render(
             experiment,
             stim=True,
+            multistim=multistim,
+            date=self.session_summaries[0].session_datetime,
             combined_summary=self._make_combined_summary(),
             classifiers=self._make_classifier_data(),
-            stim_params=FRStimSessionSummary.stim_parameters(
-                self.session_summaries),
+            stim_params=stim_params,
             recall_tests=FRStimSessionSummary.recall_test_results(
                 self.session_summaries, experiment),
             feature_data=self._make_feature_plots(),

ramutils/reports/static/plots.js

@@ -9,12 +9,18 @@ var ramutils = (function (mod, Plotly) {
      * @param {Array} serialPos - Serial positions (x-axis)
      * @param {Object} overallProbs - Overall probabilities per serial position
      * @param {Object} firstProbs - Probability of first recall per serial position
+     * @param {Object} plot_first -- If true, plot probability of first recall
      */
-    plotSerialpos: function (serialPos, overallProbs, firstProbs) {
+    plotSerialpos: function (serialPos, overallProbs, firstProbs, plot_first = true) {
       const mode = "lines+markers";
       let data = [];
+      max_prob = 0
 
       for (let name in overallProbs) {
+        current_max = Math.max(...overallProbs[name])
+        if (current_max > max_prob) {
+          max_prob =  current_max
+        };
         data.push({
           x: serialPos,
           y: overallProbs[name],
@@ -23,14 +29,20 @@ var ramutils = (function (mod, Plotly) {
         });
       }
 
-      for (let name in firstProbs) {
-        data.push({
-          x: serialPos,
-          y: firstProbs[name],
-          mode: mode,
-          name: name
-        });
-      }
+      if (plot_first) {
+        for (let name in firstProbs) {
+          current_max = Math.max(...firstProbs[name])
+          if (current_max > max_prob) {
+            max_prob =  current_max
+          };
+          data.push({
+            x: serialPos,
+            y: firstProbs[name],
+            mode: mode,
+            name: name
+          });
+        }
+      };
 
       const layout = {
         title: "Probability of Recall as a Function of Serial Position",
@@ -40,7 +52,7 @@ var ramutils = (function (mod, Plotly) {
         },
         yaxis: {
           title: 'Probability',
-          range: [0, 1]
+          range: [0, max_prob + 0.01]
         }
       };
 
@@ -103,9 +115,10 @@ var ramutils = (function (mod, Plotly) {
      * @param {Object} nonStimRecalls
      * @param {Object} stimRecalls
      * @param {Object} stimEvents
+     * @param {bool} plot_stim_events
      */
-    plotRecallSummary: function (nonStimRecalls, stimRecalls, stimEvents) {
-      const data = [
+    plotRecallSummary: function (nonStimRecalls, stimRecalls, stimEvents, plot_stim_events = true) {
+      let data = [
         {
           x: nonStimRecalls.listno,
           y: nonStimRecalls.recalled,
@@ -122,13 +135,15 @@ var ramutils = (function (mod, Plotly) {
           marker: {size: 12},
           name: 'Stim recalls'
         },
-        {
+      ]
+      if (plot_stim_events == true) {
+        data.push({
           x: stimEvents.listno,
           y: stimEvents.count,
           type: 'bar',
           name: 'Stim events'
-        }
-      ];
+        })
+      };
 
       const layout = {
         title: 'Number of Items Stimulated and Number of Items Recalled',

ramutils/reports/summary.py

@@ -1029,9 +1029,16 @@ def recall_test_results(summaries, experiment):
             df = df[df.list > -1]
 
         results = []
+        n_correct_recalls = df.recalled.sum()
+        n_items = len(df)
         for name, group in df.groupby(['stimAnodeTag', 'stimCathodeTag',
                                        'amplitude', 'stim_duration',
                                        'pulse_freq']):
+            if name[0].find(",") != -1:
+                target_name = "Multi-Site"
+            else:
+                target_name = "-".join([name[0], name[1]])
+            single_target_results = {"target": target_name}
             parameters = "/".join([str(n) for n in name])
 
             # Stim lists vs. non-stim lists
@@ -1047,13 +1054,16 @@ def recall_test_results(summaries, experiment):
                 n_correct_stim_list_recalls, n_correct_nonstim_list_recalls],
                 [n_stim_list_words, n_nonstim_list_words])
 
-            results.append({"parameters": parameters,
-                            "comparison": "Stim Lists vs. Non-stim Lists",
-                            "stim": (n_correct_stim_list_recalls,
-                                     n_stim_list_words),
-                            "non-stim": (n_correct_nonstim_list_recalls, n_nonstim_list_words),
-                            "t-stat": tstat_list,
-                            "p-value": pval_list})
+            single_target_results['list'] = {
+                "parameters": parameters,
+                "comparison": "Stim Lists vs. Non-stim Lists",
+                "stim": (n_correct_stim_list_recalls,
+                         n_stim_list_words),
+                "non-stim": (n_correct_nonstim_list_recalls, n_nonstim_list_words),
+                "delta_recall": 100 * ((n_correct_stim_list_recalls/n_stim_list_words) -
+                                 (n_correct_nonstim_list_recalls/n_nonstim_list_words)) / (n_correct_recalls / n_items),
+                "t-stat": tstat_list,
+                "p-value": pval_list}
 
             # stim items vs. non-stim low biomarker items
             n_correct_stim_item_recalls = group[group.is_stim_item == True].recalled.sum(
@@ -1071,13 +1081,15 @@ def recall_test_results(summaries, experiment):
                 [n_correct_stim_item_recalls, n_correct_nonstim_item_recalls],
                 [n_stim_items, n_nonstim_items])
 
-            results.append({
+            single_target_results['item'] = {
                 "parameters": parameters,
                 "comparison": "Stim Items vs. Low Biomarker Non-stim Items",
                 "stim": (n_correct_stim_item_recalls, n_stim_items),
                 "non-stim": (n_correct_nonstim_item_recalls, n_nonstim_items),
+                "delta_recall": 100 * ((n_correct_stim_item_recalls/n_stim_items) -
+                                 (n_correct_nonstim_item_recalls/n_nonstim_items)) / (n_correct_recalls / n_items),
                 "t-stat": tstat_list,
-                "p-value": pval_list})
+                "p-value": pval_list}
 
             # post stim items vs. non-stim low biomarker items
             n_correct_post_stim_item_recalls = group[group.is_post_stim_item == True].recalled.sum(
@@ -1089,14 +1101,17 @@ def recall_test_results(summaries, experiment):
                 [n_correct_post_stim_item_recalls, n_correct_nonstim_item_recalls],
                 [n_post_stim_items, n_nonstim_items])
 
-            results.append({
+            single_target_results['post_stim_item'] = {
                 "parameters": parameters,
                 "comparison": "Post-stim Items vs. Low Biomarker Non-stim Items",
                 "stim": (n_correct_post_stim_item_recalls, n_post_stim_items),
                 "non-stim": (n_correct_nonstim_item_recalls, n_nonstim_items),
+                "delta_recall": 100 * ((n_correct_post_stim_item_recalls/n_post_stim_items) -
+                                 (n_correct_nonstim_item_recalls/n_nonstim_items)) / (n_correct_recalls / n_items),
                 "t-stat": tstat_list,
-                "p-value": pval_list})
-
+                "p-value": pval_list
+            }
+            results.append(single_target_results)
         return results
 
     @staticmethod

ramutils/reports/templates/clinical_base.html

@@ -0,0 +1,33 @@
+{% set sections = [] %}
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8">
+    <title>{% block title %}{% endblock %}</title>
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-beta.2/css/bootstrap.min.css" integrity="sha384-PsH8R72JQ3SOdhVi3uxftmaW6Vc51MKb0q5P2rRUpPvrszuE4W1povHYgTpBfshb" crossorigin="anonymous">
+    <link rel="stylesheet" type="text/css" href="https://cdn.datatables.net/1.10.16/css/jquery.dataTables.css">
+    {% for stylesheet in css.values() %}
+      <style>{{ stylesheet }}</style>
+    {% endfor %}
+    <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
+    {% for script in js.values() %}
+      <script>{{ script }}</script>
+    {% endfor %}
+  </head>
+  <body>
+    <div class="container">
+      <div class="jumbotron" style="text-align: center;">
+        <h1 class="display-4">Stimulation Report</h1>
+        <p class="lead">Courtesy of Nia Theraputics: {{ datetime.now().strftime('%Y-%m-%d %H:%M') }}</p>
+      </div>
+      <div>
+        {% block report %}{% endblock %}
+      </div>
+    </div>
+    <div id="footer-spacer"></div>
+    <script src="https://code.jquery.com/jquery-3.2.1.slim.min.js" integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN" crossorigin="anonymous"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.3/umd/popper.min.js" integrity="sha384-vFJXuSJphROIrBnz7yo7oB41mKfc8JzQZiCq4NCceLEaO4IHwicKwpJf9c9IpFgh" crossorigin="anonymous"></script>
+    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-beta.2/js/bootstrap.min.js" integrity="sha384-alpBpkh1PFOepccYVYDB4do5UnbKysX5WZXm3XxPqe5iKTfUKjNkCk9SaVuEZflJ" crossorigin="anonymous"></script>
+    <script type="text/javascript" charset="utf8" src="https://cdn.datatables.net/1.10.16/js/jquery.dataTables.js"></script>
+  </body>
+</html>