corrected code bugs preventing database creation

utils/DSbiobank.py

@@ -24,6 +24,7 @@ from datetime import datetime
 import pandas as pd
 import os
 
+
 def stats_calc(array):
     """ Statistics calculator
 
@@ -76,14 +77,15 @@ def stats_calc(array):
 
 
 def database_generator(data_directory, train_inputs, train_targets, rsfMRI_mean_mask_path, dMRI_mean_mask_path):
-    subDirectoryList, number_of_subjects = directory_reader(folder_location=os.path.join(os.path.expanduser("~"),data_directory), subject_number=None, write_txt=False)
+    subDirectoryList, _ = directory_reader(folder_location=os.path.join(
+        os.path.expanduser("~"), data_directory), subject_number=None, write_txt=False)
 
     dmri_imaging_dictionary = {}
     rsfmri_imaging_dictionary = {}
 
     dictionary_labels = ['w_reg', 'min', 'max', 'mean', 'med', 'std', '1p', '25p', '75p', '99p',
                          '0.1p', '0.2p', '0.3p', '0.4p', '0.5p', '0.6p', '0.7p', '0.8p', '0.9p',
-                         '99.1p', '99.2p', '99.3p', '99.4p', '99.5p', '99.6p', '99.7p', '99.8p', '99.9p'
+                         '99.1p', '99.2p', '99.3p', '99.4p', '99.5p', '99.6p', '99.7p', '99.8p', '99.9p',
                          '2p', '3p', '4p', '5p', '95p', '96p', '97p', '98p']
 
     dmri_mean_volume = Image(dMRI_mean_mask_path).data
@@ -95,40 +97,49 @@ def database_generator(data_directory, train_inputs, train_targets, rsfMRI_mean_
 
         index += 1
 
-        subject_t0 = datetime.now()
-
-        w_dMRI, w_rsfMRI = regression_weight_calculator(data_directory, subject, train_inputs, train_targets, rsfMRI_mean_mask_path, dMRI_mean_mask_path)
+        w_dMRI, w_rsfMRI = regression_weight_calculator(
+            data_directory, subject, train_inputs, train_targets, rsfMRI_mean_mask_path, dMRI_mean_mask_path)
 
-        # ------------------
+        # ------------------ BOanaPelea626273532!C
 
-        dmri_path = os.path.join(os.path.expanduser("~"), data_directory, subject, train_inputs)
+        dmri_path = os.path.join(os.path.expanduser(
+            "~"), data_directory, subject, train_inputs)
         dmri_volume, _ = resampleToPixdims(Image(dmri_path), (2, 2, 2))
-        dmri_volume = np.subtract(dmri_volume, np.multiply(w_dMRI, dmri_mean_volume))
-        
-        min_val, max_val, mean_val, med_val, std_val, perc1, perc25, perc75, perc99, perc0_1, perc0_2, perc0_3, perc0_4, perc0_5, perc0_6, perc0_7, perc0_8, perc0_9, perc99_1, perc99_2, perc99_3, perc99_4, perc99_5, perc99_6, perc99_7, perc99_8, perc99_9, perc2, perc3, perc4, perc5, perc95, perc96, perc97, perc98 = stats_calc(dmri_volume)
+        dmri_volume = np.subtract(
+            dmri_volume, np.multiply(w_dMRI, dmri_mean_volume))
+
+        min_val, max_val, mean_val, med_val, std_val, perc1, perc25, perc75, perc99, perc0_1, perc0_2, perc0_3, perc0_4, perc0_5, perc0_6, perc0_7, perc0_8, perc0_9, perc99_1, perc99_2, perc99_3, perc99_4, perc99_5, perc99_6, perc99_7, perc99_8, perc99_9, perc2, perc3, perc4, perc5, perc95, perc96, perc97, perc98 = stats_calc(
+            dmri_volume)
 
-        dmri_imaging_dictionary[subject] = [w_dMRI, min_val, max_val, mean_val, med_val, std_val, perc1, perc25, perc75, perc99, perc0_1, perc0_2, perc0_3, perc0_4, perc0_5, perc0_6, perc0_7, perc0_8, perc0_9, perc99_1, perc99_2, perc99_3, perc99_4, perc99_5, perc99_6, perc99_7, perc99_8, perc99_9, perc2, perc3, perc4, perc5, perc95, perc96, perc97, perc98]
+        dmri_imaging_dictionary[subject] = [w_dMRI, min_val, max_val, mean_val, med_val, std_val, perc1, perc25, perc75, perc99, perc0_1, perc0_2, perc0_3, perc0_4, perc0_5, perc0_6,
+                                            perc0_7, perc0_8, perc0_9, perc99_1, perc99_2, perc99_3, perc99_4, perc99_5, perc99_6, perc99_7, perc99_8, perc99_9, perc2, perc3, perc4, perc5, perc95, perc96, perc97, perc98]
 
         del dmri_path, dmri_volume, w_dMRI, min_val, max_val, mean_val, med_val, std_val, perc1, perc25, perc75, perc99, perc0_1, perc0_2, perc0_3, perc0_4, perc0_5, perc0_6, perc0_7, perc0_8, perc0_9, perc99_1, perc99_2, perc99_3, perc99_4, perc99_5, perc99_6, perc99_7, perc99_8, perc99_9, perc2, perc3, perc4, perc5, perc95, perc96, perc97, perc98
 
         # ------------------
 
-        rsfmri_path = os.path.join(os.path.expanduser("~"), data_directory, subject, train_targets)
+        rsfmri_path = os.path.join(os.path.expanduser(
+            "~"), data_directory, subject, train_targets)
         rsfmri_volume = Image(rsfmri_path).data[:, :, :, 0]
-        rsfmri_volume = np.subtract(rsfmri_volume, np.multiply(w_rsfMRI, rsfmri_mean_volume))
+        rsfmri_volume = np.subtract(
+            rsfmri_volume, np.multiply(w_rsfMRI, rsfmri_mean_volume))
 
-        min_val, max_val, mean_val, med_val, std_val, perc1, perc25, perc75, perc99, perc0_1, perc0_2, perc0_3, perc0_4, perc0_5, perc0_6, perc0_7, perc0_8, perc0_9, perc99_1, perc99_2, perc99_3, perc99_4, perc99_5, perc99_6, perc99_7, perc99_8, perc99_9, perc2, perc3, perc4, perc5, perc95, perc96, perc97, perc98 = stats_calc(rsfmri_volume)
+        min_val, max_val, mean_val, med_val, std_val, perc1, perc25, perc75, perc99, perc0_1, perc0_2, perc0_3, perc0_4, perc0_5, perc0_6, perc0_7, perc0_8, perc0_9, perc99_1, perc99_2, perc99_3, perc99_4, perc99_5, perc99_6, perc99_7, perc99_8, perc99_9, perc2, perc3, perc4, perc5, perc95, perc96, perc97, perc98 = stats_calc(
+            rsfmri_volume)
 
-        rsfmri_imaging_dictionary[subject] = [w_rsfMRI, min_val, max_val, mean_val, med_val, std_val, perc1, perc25, perc75, perc99, perc0_1, perc0_2, perc0_3, perc0_4, perc0_5, perc0_6, perc0_7, perc0_8, perc0_9, perc99_1, perc99_2, perc99_3, perc99_4, perc99_5, perc99_6, perc99_7, perc99_8, perc99_9, perc2, perc3, perc4, perc5, perc95, perc96, perc97, perc98]
+        rsfmri_imaging_dictionary[subject] = [w_rsfMRI, min_val, max_val, mean_val, med_val, std_val, perc1, perc25, perc75, perc99, perc0_1, perc0_2, perc0_3, perc0_4, perc0_5, perc0_6,
+                                              perc0_7, perc0_8, perc0_9, perc99_1, perc99_2, perc99_3, perc99_4, perc99_5, perc99_6, perc99_7, perc99_8, perc99_9, perc2, perc3, perc4, perc5, perc95, perc96, perc97, perc98]
 
         del rsfmri_path, rsfmri_volume, w_rsfMRI, min_val, max_val, mean_val, med_val, std_val, perc1, perc25, perc75, perc99, perc0_1, perc0_2, perc0_3, perc0_4, perc0_5, perc0_6, perc0_7, perc0_8, perc0_9, perc99_1, perc99_2, perc99_3, perc99_4, perc99_5, perc99_6, perc99_7, perc99_8, perc99_9, perc2, perc3, perc4, perc5, perc95, perc96, perc97, perc98
 
         # ------------------
 
-    dmri_imaging_df = pd.DataFrame.from_dict(dmri_imaging_dictionary, orient="index", columns = dictionary_labels)
+    dmri_imaging_df = pd.DataFrame.from_dict(
+        dmri_imaging_dictionary, orient="index", columns=dictionary_labels)
     dmri_imaging_df.to_pickle('utils/dmri_stats.pkl')
 
-    rsfmri_imaging_df = pd.DataFrame.from_dict(rsfmri_imaging_dictionary, orient="index", columns = dictionary_labels)
+    rsfmri_imaging_df = pd.DataFrame.from_dict(
+        rsfmri_imaging_dictionary, orient="index", columns=dictionary_labels)
     rsfmri_imaging_df.to_pickle('utils/rsfmri_stats.pkl')
 
 
@@ -143,6 +154,7 @@ if __name__ == '__main__':
     train_inputs = "dMRI/autoptx_preproc/tractsNormSummed.nii.gz"
     train_targets = "fMRI/rfMRI_25.dr/dr_stage2.nii.gz"
 
-    database_generator(data_directory, train_inputs, train_targets, rsfmri_mean_mask_path, dmri_mean_mask_path)
+    database_generator(data_directory, train_inputs, train_targets,
+                       rsfmri_mean_mask_path, dmri_mean_mask_path)
 
-    print('---> Finished!')
\ No newline at end of file
+    print('---> Finished!')