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@ -306,6 +306,7 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
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fr = len(spikes_pure['base_0'][i])/duration
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frs.append(fr)
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fr = np.mean(frs)
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fr_isi, ax_ps, ax_as = plot_arrays_ROC_psd_single3(
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[arrays[0], arrays[2], arrays[1], arrays[3]],
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[arrays_original[0], arrays_original[2], arrays_original[1],
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@ -345,6 +346,6 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
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if __name__ == '__main__':
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motivation_all_small(dev_desired = '1', c1=10, mult_type='_multsorted2_', devs=['05'], redo=True, save=True, end='all',
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motivation_all_small(dev_desired = '1.5', c1=10, mult_type='_multsorted2_', devs=['05'], redo=True, save=True, end='all',
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cut_matrix='malefemale', chose_score='mean_nrs', restrict='modulation_no_classes', step='50',
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detections=['MeanTrialsIndexPhaseSort'], sorted_on='LocalReconst0.2NormAm')#
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@ -0,0 +1,367 @@
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,coherence_r_exp,MI_r_exp,MI_s,coherence_s,coherence_r,MI_r,f,file_name,amp
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||||
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||||
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|
||||
116,0.0286109118846799,0.0418788146306626,0.0162102610556346,0.011173207575124,0.0132009398916437,0.0191717533653165,285.64453125,gwn300Hz50s0.3,5.0
|
||||
117,0.0283318371877054,0.041464395874922,0.0094082651794942,0.006500094872153,0.0130649862050435,0.0189730034806666,288.0859375,gwn300Hz50s0.3,5.0
|
||||
118,0.0272517138140987,0.0398615614659256,0.0239301438937972,0.0164503030966033,0.0121545454490486,0.0176427407220332,290.52734375,gwn300Hz50s0.3,5.0
|
||||
119,0.0312285548421693,0.0457717531172145,0.0355587095781047,0.0243461518798137,0.0158741437099411,0.023085266482675,292.96875,gwn300Hz50s0.3,5.0
|
||||
120,0.0464460994049091,0.0686136042534672,0.0609214753065437,0.0413483843153773,0.0314006151269033,0.0460280081576275,295.41015625,gwn300Hz50s0.3,5.0
|
||||
121,0.0418370224440966,0.0616570246109462,0.0321297125441734,0.0220244601732638,0.0278275526260747,0.0407158480271503,297.8515625,gwn300Hz50s0.3,5.0
|
|
@ -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)
|
||||
fish_cuts, whole_duration, delay, contrast1, contrast2, repro_position = get_fish_number(b,
|
||||
group_mean,
|
||||
mean_type)
|
||||
#features, mt, name_here, l = get_mt_features3(b, group_mean)
|
||||
#fish_cuts, whole_duration, delay, contrast1, contrast2, repro_position = get_fish_number(b,
|
||||
# group_mean,
|
||||
# mean_type)
|
||||
|
||||
if printing: # todo: also das dauert lange das könnte man optimizieren
|
||||
print('arrays1 ' + str(time.time() - t3))
|
||||
@ -10406,12 +10408,15 @@ 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,
|
||||
dev_desired,
|
||||
delays_length,
|
||||
mean_type)
|
||||
mean_type )
|
||||
except:
|
||||
print('sorting thing')
|
||||
embed()
|
||||
@ -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][
|
||||
np.arange(delays_length['base_0'][f - 1][0], len(frame_dev['base_0'].iloc[f]), 1)]
|
||||
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)]
|
||||
|
||||
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,
|
||||
sorted_on, mean_type, b, mt, mt_nr, delay)
|
||||
array_eod = load_data_arrays(extract, mt_group, sampling_rate, sorted_on, 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))
|
||||
|
Loading…
Reference in New Issue
Block a user