DEV: function

spatialtopology · Jul 11, 2023 · 1b0dc1a · 1b0dc1a
1 parent 3c7ad85
commit 1b0dc1a
Showing 1 changed file with 34 additions and 31 deletions.
diff --git a/spacetop_prep/physio/p01_grouplevel_01SCL_sandbox.py b/spacetop_prep/physio/p01_grouplevel_01SCL_sandbox.py
@@ -284,37 +284,7 @@ def main():
         if baselinecorrect == True:
             baseline_length = source_samplingrate* np.abs(SCL_epoch_start)
             physio_df['physio_eda_blcorrect'] = physio_df['physio_eda']
-            for i in range(len(dict_onset[event_name]['start'])):
-                if i == 0:
-                    baseline_average = []
-                    baseline_start_ind = dict_onset[event_name]['start'][i] - baseline_length
-                    baseline_stop_ind = dict_onset[event_name]['start'][i]
-                    baseline_average = physio_df.loc[baseline_start_ind:baseline_stop_ind]['physio_eda'].mean()
-                    # print(baseline_average)
-                    start_index = physio_df.index[0]
-                    stop_index = dict_onset[event_name]['start'][i+1] - baseline_length
-                    # print(start_index, stop_index)
-                    physio_df.loc[start_index:stop_index,'physio_eda_blcorrect'] = physio_df.loc[start_index:stop_index]['physio_eda'] - baseline_average
-                elif i < len(dict_onset[event_name]['start'])-1:
-                    baseline_average = []
-                    baseline_start_ind = dict_onset[event_name]['start'][i] - baseline_length
-                    baseline_stop_ind = dict_onset[event_name]['start'][i]
-                    baseline_average = physio_df.loc[baseline_start_ind:baseline_stop_ind]['physio_eda'].mean()
-                    # print(baseline_average)
-                    start_index = dict_onset[event_name]['start'][i] - baseline_length
-                    stop_index = dict_onset[event_name]['start'][i+1] - baseline_length
-                    # print(start_index, stop_index)
-                    physio_df.loc[start_index:stop_index,'physio_eda_blcorrect'] = physio_df.loc[start_index:stop_index]['physio_eda'] - baseline_average
-                elif i == len(dict_onset[event_name]['start'])-1:
-                    baseline_average = []
-                    baseline_start_ind = dict_onset[event_name]['start'][i] - baseline_length
-                    baseline_stop_ind = dict_onset[event_name]['start'][i]
-                    baseline_average = physio_df.loc[baseline_start_ind:baseline_stop_ind]['physio_eda'].mean()
-                    # print(baseline_average)
-                    start_index = dict_onset[event_name]['start'][i]  - baseline_length
-                    stop_index = physio_df.index[-1]
-                    # print(start_index, stop_index)
-                    physio_df.loc[start_index:stop_index,'physio_eda_blcorrect'] = physio_df.loc[start_index:stop_index]['physio_eda'] - baseline_average
+            baseline_correct_per_trial(event_name, physio_df, dict_onset, baseline_length)
             # also baseline correct the timepoints prior to the first event of interest
             # TODO: downsample before saving
             resamp = nk.signal_resample(
@@ -420,6 +390,39 @@ def main():
                     }
         logger.info("__________________ :+: FINISHED :+: __________________\n")
 
+def baseline_correct_per_trial(event_name, physio_df, dict_onset, baseline_length):
+    for i in range(len(dict_onset[event_name]['start'])):
+        if i == 0:
+            baseline_average = []
+            baseline_start_ind = dict_onset[event_name]['start'][i] - baseline_length
+            baseline_stop_ind = dict_onset[event_name]['start'][i]
+            baseline_average = physio_df.loc[baseline_start_ind:baseline_stop_ind]['physio_eda'].mean()
+                    # print(baseline_average)
+            start_index = physio_df.index[0]
+            stop_index = dict_onset[event_name]['start'][i+1] - baseline_length
+                    # print(start_index, stop_index)
+            physio_df.loc[start_index:stop_index,'physio_eda_blcorrect'] = physio_df.loc[start_index:stop_index]['physio_eda'] - baseline_average
+        elif i < len(dict_onset[event_name]['start'])-1:
+            baseline_average = []
+            baseline_start_ind = dict_onset[event_name]['start'][i] - baseline_length
+            baseline_stop_ind = dict_onset[event_name]['start'][i]
+            baseline_average = physio_df.loc[baseline_start_ind:baseline_stop_ind]['physio_eda'].mean()
+                    # print(baseline_average)
+            start_index = dict_onset[event_name]['start'][i] - baseline_length
+            stop_index = dict_onset[event_name]['start'][i+1] - baseline_length
+                    # print(start_index, stop_index)
+            physio_df.loc[start_index:stop_index,'physio_eda_blcorrect'] = physio_df.loc[start_index:stop_index]['physio_eda'] - baseline_average
+        elif i == len(dict_onset[event_name]['start'])-1:
+            baseline_average = []
+            baseline_start_ind = dict_onset[event_name]['start'][i] - baseline_length
+            baseline_stop_ind = dict_onset[event_name]['start'][i]
+            baseline_average = physio_df.loc[baseline_start_ind:baseline_stop_ind]['physio_eda'].mean()
+                    # print(baseline_average)
+            start_index = dict_onset[event_name]['start'][i]  - baseline_length
+            stop_index = physio_df.index[-1]
+                    # print(start_index, stop_index)
+            physio_df.loc[start_index:stop_index,'physio_eda_blcorrect'] = physio_df.loc[start_index:stop_index]['physio_eda'] - baseline_average
+
 # %% -------------------------------------------------------------------
 #                        argparse parameters
 # ----------------------------------------------------------------------