pull merge

motivation.py

@@ -306,6 +306,7 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
                                         fr = len(spikes_pure['base_0'][i])/duration
                                         frs.append(fr)
                                     fr = np.mean(frs)
+
                                     fr_isi, ax_ps, ax_as = plot_arrays_ROC_psd_single3(
                                         [arrays[0], arrays[2], arrays[1], arrays[3]],
                                         [arrays_original[0], arrays_original[2], arrays_original[1],
@@ -345,6 +346,6 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
 
 
 if __name__ == '__main__':
-    motivation_all_small(dev_desired = '1', c1=10, mult_type='_multsorted2_', devs=['05'], redo=True, save=True, end='all',
+    motivation_all_small(dev_desired = '1.5', c1=10, mult_type='_multsorted2_', devs=['05'], redo=True, save=True, end='all',
                    cut_matrix='malefemale', chose_score='mean_nrs', restrict='modulation_no_classes', step='50',
                    detections=['MeanTrialsIndexPhaseSort'], sorted_on='LocalReconst0.2NormAm')#

motivationcalc_coherence-coherence__cell_2021-08-03-ab-invivo-1_dev_05.csv

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utils_paper.py

@@ -10373,14 +10373,16 @@ def save_arrays_susept(data_dir, cell, c, b, chirps, devs, extract, group_mean,
                     t3 = time.time()
 
 
+                    #embed()
+                    mean_type0 = '_MeanTrialsIndex'
                     spikes_pure, fish_number_base, chirp, fish_cuts, time_array, fish_number, smoothened2, smoothed05, eod_mt, eod_interp, effective_duration, cut, devname, frame = cut_spikes_and_eod_three(
                         group_mean, b, extract, chirps=chirps, emb=False,
                         mean_type=mean_type, sorted_on=sorted_on,
                         devname_orig=[dev_desired, '05', 'original'])  # times_sort=times_sort,
-                    features, mt, name_here, l = get_mt_features3(b, group_mean)
+                    #features, mt, name_here, l = get_mt_features3(b, group_mean)
-                    fish_cuts, whole_duration, delay, contrast1, contrast2, repro_position = get_fish_number(b,
+                    #fish_cuts, whole_duration, delay, contrast1, contrast2, repro_position = get_fish_number(b,
-                                                                                                             group_mean,
+                    #                                                                                        group_mean,
-                                                                                                             mean_type)
+                    #                                                                                        mean_type)
 
                     if printing:  # todo: also das dauert lange das könnte man optimizieren
                         print('arrays1 ' + str(time.time() - t3))
@@ -10406,6 +10408,9 @@ def save_arrays_susept(data_dir, cell, c, b, chirps, devs, extract, group_mean,
                     t3 = time.time()
 
                     # embed()
+                    if ('Phase' not in mean_type0) & (mean_type0 != ''):
+                        for i in range(len(delays_length['base_0'])):
+                            delays_length['base_0'][i] = np.arange(0, delays_length['base_0'][i][-1], 1)
                     try:
                         array0, array01, array02, array012, mean_nrs, array012_all, array01_all, array02_all, array0_all = assign_trials(
                             frame,
@@ -10421,6 +10426,9 @@ def save_arrays_susept(data_dir, cell, c, b, chirps, devs, extract, group_mean,
                         'original',
                         delays_length,
                         mean_type)
+                    
+                    #embed()
+                    
                     if printing:
                         print('arrays2 ' + str(time.time() - t3))
 
@@ -10817,8 +10825,7 @@ def phase_sort_arrays(f, delays_length, frame_dev):
                 np.arange(delays_length['control_02'][f - 1][0], len(frame_dev['control_02'].iloc[f]), 1)]
         if 'base_0' in delays_length.keys():
             if delays_length['base_0'][f - 1] != []:
-                frame_dev.iloc[f]['base_0'] = frame_dev['base_0'].iloc[f][
+                frame_dev.iloc[f]['base_0'] = frame_dev['base_0'].iloc[f][ np.arange(delays_length['base_0'][f - 1][0], len(frame_dev['base_0'].iloc[f]), 1)]
-                    np.arange(delays_length['base_0'][f - 1][0], len(frame_dev['base_0'].iloc[f]), 1)]
 
     return frame_dev
 
@@ -11167,7 +11174,7 @@ def check_nix_fish(b, with_fish2='with_fish2'):
 
 
 
-def load_data_arrays(extract, mt_group, sampling_rate, sorted_on, mean_type, b, mt, mt_nr, delay, printing=False):
+def load_data_arrays(extract, mt_group, sampling_rate, sorted_on, b, mt, mt_nr, delay, printing=False):
     array_eod = {}
     ############################################
     t1 = time.time()
@@ -11448,7 +11455,7 @@ def chirps_delete_analysis(eod_local_interp, eod_norm, fish_cuts, time_eod, cut,
 
 
 def cut_spikes_and_eod_three(mt_group, b, extract, cut_nr=0, chirps='', devname_orig=['05'], emb=False, test=False,
-                             mean_type='', sorted_on='LocalReconst', sampling_rate=40000, devname=[], done=False,
+                             mean_type='', mean_type0='', sorted_on='LocalReconst', sampling_rate=40000, devname=[], done=False,
                              counter=0, printing=False, printing_all=False):
     # todo: das könnte man noch vereinfachen dass nur die wichtigen Sachen rauskommen
     t1 = time.time()
@@ -11468,6 +11475,7 @@ def cut_spikes_and_eod_three(mt_group, b, extract, cut_nr=0, chirps='', devname_
             t1 = time.time()
             _, _, _, _, fish_number, fish_cuts, whole_duration, cont = load_durations(mt_nr, mt, mt_group[1], mt_idx,
                                                                                       mean_type=mean_type, emb=False)
+            #embed()
             delay = np.abs(fish_cuts[0])
             if printing:
                 print('first ' + str(time.time() - t1))
@@ -11480,8 +11488,7 @@ def cut_spikes_and_eod_three(mt_group, b, extract, cut_nr=0, chirps='', devname_
                 # load the according EOD arrays
                 # basics und reconstructs
                 t1 = time.time()
-                array_eod = load_data_arrays(extract, mt_group, sampling_rate,
+                array_eod = load_data_arrays(extract, mt_group, sampling_rate, sorted_on, b, mt, mt_nr, delay)
-                                             sorted_on, mean_type, b, mt, mt_nr, delay)
 
                 if printing:  # todo:das dauert ewig!
                     print('second ' + str(time.time() - t1))
@@ -11529,7 +11536,7 @@ def cut_spikes_and_eod_three(mt_group, b, extract, cut_nr=0, chirps='', devname_
                                 mean_type=mean_type)
 
 
-
+                            #embed()
                             lengths = []
                             if printing:
                                 print('Forth ' + str(time.time() - t1))