Update: code cleaned up

Code/csv_generation/Plotting_data_collection.py

@@ -1,3 +1,11 @@
+"""
+Script to analyse and collect data from simulations
+
+__author__ = Nils A. Koch
+__copyright__ = Copyright 2022, Nils A. Koch
+__license__ = MIT
+"""
+
 import pandas as pd
 import h5py
 import json
@@ -6,8 +14,7 @@ import numpy as np
 from ast import literal_eval
 import string
 import matplotlib.cm as cm
-# import Code.csv_generation.SA_collection
-# todo: run some and test
+
 # %% rheo_{}_ex.csv, AUC_{}_ex.csv
 
 ## AUC ###################
@@ -147,7 +154,7 @@ rheo_slope.to_csv('rheo_slope_ex.csv')
 rheo_g.to_csv('rheo_g_ex.csv')
 
 
-# %% todo: Model fI
+# %% Model fI
 # for each model
 # | (index) | mag | alt   | type  | F     | I     |
 # | 0       | -10 | m     | shift | array | array |
@@ -221,19 +228,9 @@ for m in models:
         os.makedirs(folder)
     df.to_csv(os.path.join(folder, '{}_fI.csv'.format(model_save_name[m])))
 
-# #%% todo: rheo/AUC_{}_corr - done in SA_collection.py
-# # | (index) | model | corr   | p_value  | g | color
-#
-# # in rheo_[]_corr.csv and AUC_[]_corr.csv
-# # columns=['model', 'corr', 'p_value', 'local corr', 'local p_value', 'ratio', '$\Delta V_{1/2}$', 'color']
-#
-# rheo_corr = pd.DataFrame(columns=['model', 'corr', 'p_value', 'g', 'color'])
-# rheo_corr.to_csv('rheo_corr.csv')
-#
-# AUC_corr = pd.DataFrame(columns=['model', 'corr', 'p_value', 'g', 'color'])
-# AUC_corr.to_csv('AUC_corr.csv')
 
-# %% firing_values.csv,model_spiking.csv, model_F_inf.csv
+
+# %% firing_values.csv, model_spiking.csv, model_F_inf.csv
 import numpy as np
 import pandas as pd
 

Code/csv_generation/SA_collection.py

@@ -1,3 +1,11 @@
+"""
+Script to analyse and collect data from sensitivity analysis simulations
+
+__author__ = Nils A. Koch
+__copyright__ = Copyright 2022, Nils A. Koch
+__license__ = MIT
+"""
+
 import numpy as np
 import h5py
 import pandas as pd
@@ -13,7 +21,7 @@ top_dir = '../Sensitivity_Analysis/Data'
 for dir_name in next(os.walk(top_dir))[1]: # for each folder in top_file_dir (each model)
     folder = os.path.join(top_dir, dir_name)
     print(folder)
-    # for each alt_type create pd df
+    # for each alt_type create pandas dataframe
     shift_AUC = pd.DataFrame()
     shift_rheo = pd.DataFrame()
     shift_fI = pd.DataFrame(dtype=object)
@@ -277,9 +285,8 @@ shift_df.to_json('./Sensitivity_analysis/rheo_shift_box_kendall_corr.json')
 slope_df.to_json('./Sensitivity_analysis/rheo_slope_box_kendall_corr.json')
 g_df.to_json('./Sensitivity_analysis/rheo_g_kendall_corr.json')
 
-
-#%% todo: CREATE CSV FILES ###################################################################################################
-
+#%% create csv file
+# | (index) | model | corr   | p_value  | g | color
 #% AUC
 AUC_shift_json = pd.read_json('./Sensitivity_analysis/shift_kendall_corr_rel.json', orient='records')
 AUC_slope_json = pd.read_json('./Sensitivity_analysis/slope_kendall_corr_rel.json', orient='records') #, lines=True)