update dependencies

README.md

@@ -1,6 +1,7 @@
 # vmreact
 These directories represent the vmreact associated scripts that have been generated to parse and clean up the raw data output. They also generate additional measures.
 
+Check out the wiki page for detailed installation instructions:
 
 **`WIKI PAGE: https://github.com/daelsaid/vmreact/wiki`**
 
@@ -10,18 +11,19 @@ Relative paths for each directory within "vmreact" are listed below:
 
 #VMREACT WORKFLOW
 
-MAIN SCRIPT: **vmreact_local_lab_main_workflow_all_sites.sh**
-
-
+MAIN SCRIPT: **``vmreact_local_lab_main_workflow_all_sites.sh``**
 
 Usage: full_inquisit_wkflw_all_sites.sh <subject_number> <timepoint> <site_of_administration> <initial_list>
 
 <subject_number>: numeric ID- ####
+
 <timepoint>: single numeric digit representing timepoint (1,2,3,4..)
+
 <site_of_administration>: location of administration, (either newmex or emoryu).
+
 <initial_list>: If tp2, enter list form as a single numeric digit (1,2,3,or 4). if timepoint 1, leave BLANK
 
-* running this script will:
+**running this script will:
 
 1. Administer the task 
 2. Organize the participant data folder

build/lib/vmreact-data-visualization/vmreact_learning_trajectory_visualization.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar  5 17:09:19 2018
+
+@author: dawlat_local
+"""
+import matplotlib.pyplot as plt
+import pandas
+from matplotlib.backends.backend_pdf import PdfPages
+from prettyplotlib import brewer2mpl
+
+set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
+set1 = brewer2mpl.get_map('Set1', 'qualitative', 9).mpl_colors
+bmap=brewer2mpl.get_map('Dark2', 'Qualitative',4).mpl_colors
+mpl.rcParams['axes.color_cycle'] = bmap
+
+#scored csv 
+patient_scored=pandas.read_csv('')
+test_df=patient_scored.loc[:,'subj_id':'trial7'].dropna()
+
+pt_all_trials=pandas.DataFrame(data=patient_scored.set_index('subj_id').loc[:,'trial1':'trial7']).dropna().astype(int)
+pt_learning_trials=pandas.DataFrame(data=patient_scored.set_index('subj_id').loc[:,'trial1':'trial5']).dropna().astype(int)
+
+
+
+#tp1 tp2 composite scores
+comp=['total_learning',	'corrected_total_learning','learning_rate','proactive_interference','retroactive_interference','forgetting_and_retention']
+comp_2=['total_learning_2',	'corrected_total_learning_2','learning_rate_2','proactive_interference_2','retroactive_interference_2','forgetting_and_retention_2']
+
+#tp1 tp2 scored data 
+y=['trial1', 'trial2', 'trial3', 'trial4', 'trial5', 'listb', 'trial6', 'trial7','total_learning', 'corrected_total_learning','learning_rate','proactive_interference','retroactive_interference','forgetting_and_retention']
+y_2=['trial1_2', 'trial2_2', 'trial3_2', 'trial4_2', 'trial5_2', 'listb_2', 'trial6_2', 'trial7_2']
+
+columns=[c for c in y[0:6]]
+columns=[c for c in comp]
+columns_2=[c for c in comp_2]
+
+plt.cla()
+plt.clf()
+plt.close()
+
+
+
+with PdfPages('/Users/lillyel-said/Desktop/tp1_tp2_patient_learning_trajectories.pdf') as pdf:
+    for index,value in test_df.groupby('subj_id'):
+        val=value.loc[:,'trial1':'trial7']
+        fig, axes = plt.subplots(nrows=1, ncols=1)
+        color = 'blue'
+        fig=pt_all_trials.loc[index][y_2].plot\
+        (ax=axes, y=y_2, subplots=True, fontsize=14, grid=True, yticks=range(0,16), ylim=(0,16),xticks=range(0,8), xlim=(-0.20,7.2),color=color,marker='o', linewidth=3.0, markersize=3.5)
+        fig2=pt_all_trials.loc[index][y].plot\
+        (ax=axes, y=y, subplots=True, fontsize=14, grid=True, yticks=range(0,16), ylim=(0,16),xticks=range(0,8), xlim=(-0.20,7.2), marker='o', linewidth=3.0, markersize=3.5)
+        title= 'Learning Trajectory for Pt: ' + str(index.replace("'", "").replace('.0',""))
+        axes.set_title(title, fontsize=15)
+        axes.legend(['tp1','tp2'])
+        pdf.savefig()
+
+
+
+with PdfPages('/Users/lillyel-said/Desktop/patient_specific_learning_trajectories.pdf') as pdf:
+    for index,value in test_df.groupby('subj_id'):
+        print index,value
+        axes = plt.subplot(111)
+        fig=pt_all_trials.loc[index].astype(float).plot(ax=axes, y=y, subplots=False, fontsize=12, grid=True,yticks=range(0,16), ylim=(0,16), figsize=(14,10), xticks=range(0,6),xlim=(-0.2,4.2), marker='o', linewidth=2, markersize=5,color='purple')
+        fig=pt_all_trials.loc[index].astype(float).plot(ax=axes, y=y, subplots=False, fontsize=16, grid=True,yticks=range(0,16), ylim=(0,16), figsize=(14,10), xticks=range(0,6),xlim=(-0.2,4.2), marker='o', linewidth=2, markersize=5,color='purple')
+        title= 'Patient Learning Trials'
+        axes.set_title(title, fontsize=20)
+    pdf.savefig()
+    plt.cla()
+    plt.clf()
+    plt.close()
+
+
+
+    for ix, value in test_df.groupby('subj_id'):
+        axes2= plt.subplot(121)
+        learning2=pt_all_trials.loc[(ix)][['trial5','trial7']].astype(float).plot(ax=axes2,fontsize=15, subplots=False, grid=True, yticks=range(0,16), figsize=(14,8), ylim=(0,16), xticks=range(0,16), xlim=(-0.2,1.2), marker='o', linewidth=1.75, markersize=5.0, color='#96D38C')
+        axes2.yaxis.set_ylabel=('# of words remembered')
+        pdf.savefig()
+
+    new_df=pt_all_trials[['trial5','trial7']].astype(int)
+    new_df["diff_7_5"] = pt_all_trials["trial7"].sub(patient_testdf["trial5"].astype(int),axis=0)
+    sorted_df=new_df.sort_values(['diff_7_5'])
+    test_group5_7=[]
+
+    for idx,val in sorted_df.groupby(level=0):
+        if (val.loc[:,'diff_7_5'] > 0).bool():
+            print idx, '1'
+            test_group5_7.append([idx,1])
+        if (val.loc[:,'diff_7_5'] < -5).bool():
+            print idx, '3'
+            test_group5_7.append([idx,3])
+        else:
+            print idx , '2'
+            test_group5_7.append([idx,2])
+
+
+    df_with_groupings=pandas.DataFrame(data=test_group5_7,columns=['subj_id','group'])
+    new_df=new_df.reset_index()
+    merged_df=pandas.merge(new_df, df_with_groupings, on='subj_id',copy=True, indicator=False).reset_index()
+
+    plt.cla()
+    plt.clf()
+    plt.close()
+
+
+#trials 5 scores vs trial 7 delayed scores 
+with PdfPages('/Users/lillyel-said/Desktop/patient_specific_learning_trajectories.pdf') as pdf:
+    for index,value in merged_df.groupby('subj_id'):
+        ax2= plt.subplot(121)
+        color = 'coral' if (value['group'] == 1).any() else'skyblue' if (value['group'] == 2).any() else 'lightgreen'
+        learning_to_delay=patient_testdf.loc[(index)][['trial5','trial7']].astype(float).plot(ax=ax2,fontsize=15, grid=True, yticks=range(0,16), figsize=(16,10), ylim=(0,16), xticks=range(0,16), xlim=(-0.2,1.2), marker='o', c=color, subplots=False, linewidth=1.75, markersize=5.0)
+        ax2.set_title='Patient performance grouped based on Trial 5 and 7 scores'
+        handles, labels = axes.get_legend_handles_labels()
+        labels=['group 1: 0+ words remembered', 'group 2: between 0 and -5','group 3: > -5 words remembered' ]
+        display = (0,1,2)
+        ax2.legend([label for i,label in enumerate(labels) if i in display],fontsize=10, bbox_to_anchor=(1.57,1.00), ncol=1)
+        pdf.savefig()
+
+
+
+#trials 5-6 differences 
+
+xtick_labels_5_6=['trial5', 'trial6']
+for index,value in test_df.groupby('subj_id'):
+    val=value.loc[:,'trial1':'trial7']
+    axes1 = plt.subplot(121)
+    fig1=pt_all_trials.loc[(index)][['trial5','trial6']].astype(float).plot(ax=axes1, fontsize=15, grid=True, yticks=range(0,16), figsize=(12,6), ylim=(0,16), xticks=range(0,2), xlim=(-0.2,1.2), marker='.', linewidth=1.5, markersize=5.0, legend=False, color='b')
+    axes1.set_ylabel=('# of words remembered')
+    ax2= plt.subplot(122)
+    learning_to_delay=pt_all_trials.loc[(index)][['trial5','trial7']].astype(float).plot(ax=ax2,fontsize=15, grid=True, yticks=range(0,16), figsize=(12,6), ylim=(0,16), xticks=range(0,16), xlim=(-0.2,1.2), marker='.', linewidth=1.75, markersize=5.0)
+    ax2.legend(fontsize=10,bbox_to_anchor=(1.37,1.10), ncol=1)

build/lib/vmreact-master/scripts/grader/complete_inquisit_output.py

@@ -0,0 +1,54 @@
+import argparse
+import datetime
+import os
+
+from composite_scores import composite_scores
+from inquisit_demo_summary import demo_and_summary
+from inquisit_demo_summary_newageranges import demo_and_summary_new
+from inquisit_grader import grader
+
+format = "%Y_%m_%d"
+current_date = datetime.datetime.today()
+date = current_date.strftime(format)
+
+parser = argparse.ArgumentParser(
+	description='Grades inquisit data, output: frequency counts of responses to demo survey, parsed raw data (all, primacy, recency), scored data (all, primacy, recency), SR responses compiled, subject age ranges and gender, summary ANT scores, word correlations (all, primacy, recency)')
+
+parser.add_argument('-r', dest='raw_data', help='path to raw data', type=str, required=True)
+parser.add_argument('-d', dest='demo_data', help='demo_csv', type=str, required=True)
+parser.add_argument('-s', dest='summary_data', help='summary csv', type=str, required=True)
+parser.add_argument('-o', dest='output_csv_location', help='path to output folder', type=str, default=os.getcwd())
+
+args = parser.parse_args()
+
+if not os.path.isdir(args.output_csv_location):
+	os.mkdir(args.output_csv_location)
+
+all_subj_data_csv = args.raw_data
+demographic_data = args.demo_data
+final_summary_csv = args.summary_data
+
+demo_and_summary(all_subj_data_csv, args.demo_data, args.summary_data,
+				 os.path.join(args.output_csv_location, 'frequency_counts' + '_' + date + '.csv'),
+				 os.path.join(args.output_csv_location, 'subj_age_agerange_gender' + '_' + date + '.csv'),
+				 os.path.join(args.output_csv_location, 'sr_responses' + '_' + date + '.csv'),
+				 os.path.join(args.output_csv_location, 'summary_ant_scores' + '_' + date + '.csv'))
+
+demo_and_summary_new(all_subj_data_csv, args.demo_data, os.path.join(args.output_csv_location,
+																	 'subj_age_agerange_gender_new_age_bins' + '_' + date + '.csv'))
+
+grader(all_subj_data_csv, os.path.join(args.output_csv_location, 'parsed_raw_data' + '_' + date + '.csv'),
+	   os.path.join(args.output_csv_location, 'scored_data' + '_' + date + '.csv'),
+	   os.path.join(args.output_csv_location, 'word_correlations' + '_' + date + '.csv'), 0)
+
+grader(all_subj_data_csv, os.path.join(args.output_csv_location, 'parsed_raw_data_primacy' + '_' + date + '.csv'),
+	   os.path.join(args.output_csv_location, 'scored_data_primacy' + '_' + date + '.csv'),
+	   os.path.join(args.output_csv_location, 'word_correlations_primacy' + '_' + date + '.csv'), 1)
+
+grader(all_subj_data_csv, os.path.join(args.output_csv_location, 'parsed_raw_data_recency' + '_' + date + '.csv'),
+	   os.path.join(args.output_csv_location, 'scored_data_recency' + '_' + date + '.csv'),
+	   os.path.join(args.output_csv_location, 'word_correlations_recency' + '_' + date + '.csv'), 2)
+
+scored_data = os.path.join(args.output_csv_location, 'scored_data' + '_' + date + '.csv')
+
+composite_scores(1, scored_data, os.path.join(args.output_csv_location, 'composite_scores_vakil' + '_' + date + '.csv'))

build/lib/vmreact-master/scripts/grader/composite_scores.py

@@ -0,0 +1,35 @@
+# !/usr/bin/env python2
+
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Feb 27 12:04:33 2018
+
+@author: dawlat_elsaid
+"""
+
+import pandas
+
+def composite_scores(get_comp_scores, input_csv, output_csv):
+	scored_data = pandas.read_csv(input_csv)
+
+	if get_comp_scores == 1:
+		df_trials = scored_data.loc[:, 'subj_id':'trial7']
+		composite_scores = pandas.DataFrame()
+		tmp = pandas.DataFrame()
+
+		composite_scores[['subj_id', 'list_type']] = df_trials[['subj_id', 'list_type']]
+
+		composite_scores['total_learning'] = df_trials[['trial1', 'trial2', 'trial3', 'trial4', 'trial5']].apply(
+			lambda row: pandas.np.sum(row), axis=1)
+
+		tmp['test'] = df_trials[['trial1']] * 5
+
+		composite_scores['corrected_total_learning'] = composite_scores['total_learning'].subtract(tmp['test'])
+
+		composite_scores['learning_rate'] = df_trials['trial5'].subtract(df_trials['trial1'], axis='rows')
+		composite_scores['proactive_interference'] = df_trials['trial1'].subtract(scored_data['listb'], axis='rows')
+		composite_scores['retroactive_interference'] = df_trials['trial5'].subtract(df_trials['trial6'], axis='rows')
+		composite_scores['forgetting_and_retention'] = df_trials['trial5'].subtract(df_trials['trial7'], axis='rows')
+		# composite_scores_transposed=composite_scores.transpose()
+		# composite_scores_transposed.to_csv(output_csv,header=True,index=['measure','score'])
+		composite_scores.to_csv(output_csv, header=True, index=['measure', 'score'])