np.savez(base_folder + 'behavioral_traces.npz', **res_bt)
#%%
with np.load(base_folder + 'behavioral_traces.npz') as ld:
res_bt = dict(**ld)
#%%
pl.close()
tm = res_bt['time']
f_rate_bh = old_div(1, np.median(np.diff(tm)))
ISI = res_bt['trial_info'][0][3] - res_bt['trial_info'][0][2]
eye_traces = np.array(res_bt['eyelid'])
idx_CS_US = res_bt['idx_CS_US']
idx_US = res_bt['idx_US']
idx_CS = res_bt['idx_CS']
idx_ALL = np.sort(np.hstack([idx_CS_US, idx_US, idx_CS]))
eye_traces, amplitudes_at_US, trig_CRs = gc.process_eyelid_traces(
eye_traces, tm, idx_CS_US, idx_US, idx_CS, thresh_CR=.15, time_CR_on=-.1, time_US_on=.05)
idxCSUSCR = trig_CRs['idxCSUSCR']
idxCSUSNOCR = trig_CRs['idxCSUSNOCR']
idxCSCR = trig_CRs['idxCSCR']
idxCSNOCR = trig_CRs['idxCSNOCR']
idxNOCR = trig_CRs['idxNOCR']
idxCR = trig_CRs['idxCR']
idxUS = trig_CRs['idxUS']
idxCSCSUS = np.concatenate([idx_CS, idx_CS_US])
pl.plot(tm, np.mean(eye_traces[idxCSUSCR], 0))
pl.plot(tm, np.mean(eye_traces[idxCSUSNOCR], 0))
pl.plot(tm, np.mean(eye_traces[idxCSCR], 0))
pl.plot(tm, np.mean(eye_traces[idxCSNOCR], 0))
pl.plot(tm, np.mean(eye_traces[idx_US], 0))
with np.load(base_folder+'all_triggers.npz') as at:
triggers_img=at['triggers']
trigger_names_img=at['trigger_names']
with np.load(base_folder+'behavioral_traces.npz') as ld:
res_bt = dict(**ld)
tm=res_bt['time']
f_rate_bh=1/np.median(np.diff(tm))
ISI=res_bt['trial_info'][0][3]-res_bt['trial_info'][0][2]
eye_traces=np.array(res_bt['eyelid'])
idx_CS_US=res_bt['idx_CS_US']
idx_US=res_bt['idx_US']
idx_CS=res_bt['idx_CS']
idx_ALL=np.sort(np.hstack([idx_CS_US,idx_US,idx_CS]))
eye_traces,amplitudes_at_US, trig_CRs=gc.process_eyelid_traces(eye_traces,tm,idx_CS_US,idx_US,idx_CS,thresh_CR=.15,time_CR_on=-.1,time_US_on=.05)
idxCSUSCR = trig_CRs['idxCSUSCR']
idxCSUSNOCR = trig_CRs['idxCSUSNOCR']
idxCSCR = trig_CRs['idxCSCR']
idxCSNOCR = trig_CRs['idxCSNOCR']
idxNOCR = trig_CRs['idxNOCR']
idxCR = trig_CRs['idxCR']
idxUS = trig_CRs['idxUS']
idxCSCSUS=np.concatenate([idx_CS,idx_CS_US])
with open(base_folder+'traces.pk','r') as f:
locals().update(pickle.load(f))
triggers_img=np.array(triggers_img)
idx_expected_US= np.repeat( np.nanmedian(triggers_img[:,1]),len(triggers_img[:,1]))
learning_phase=0
print 'early'
else:
if day != session_now:
session_id += 1
session_now=day
chunk=re.search('_00[0-9][0-9][0-9]_',nm.split('/')[9]).group(0)[3:-1]
idx_CS_US=np.where(tr_bh[:,-2]==2)[0]
idx_US=np.where(tr_bh[:,-2]==1)[0]
idx_CS=np.where(tr_bh[:,-2]==0)[0]
idx_ALL=np.sort(np.hstack([idx_CS_US,idx_US,idx_CS]))
eye_traces,amplitudes_at_US, trig_CRs=process_eyelid_traces(eye,tm,idx_CS_US,idx_US,idx_CS,thresh_CR=thresh_CR,time_CR_on=time_CR_on,time_US_on=time_US_on)
idxCSUSCR = trig_CRs['idxCSUSCR']
idxCSUSNOCR = trig_CRs['idxCSUSNOCR']
idxCSCR = trig_CRs['idxCSCR']
idxCSNOCR = trig_CRs['idxCSNOCR']
idxNOCR = trig_CRs['idxNOCR']
idxCR = trig_CRs['idxCR']
idxUS = trig_CRs['idxUS']
idxCSCSUS=np.concatenate([idx_CS,idx_CS_US])
wheel_traces, movement_at_CS, trigs_mov = process_wheel_traces(np.array(whe),tm,thresh_MOV_iqr=thresh_MOV_iqr,time_CS_on=time_CS_on_MOV,time_US_on=time_US_on_MOV)
print 'fraction with movement:' + str(len(trigs_mov['idxMOV'])*1./len(trigs_mov['idxNO_MOV']))
mn_idx_CS_US =np.intersect1d(idx_CS_US,trigs_mov['idxNO_MOV'])
