overallsession

[session_id,neuron_id]=region_finder("VISp")
[session_idone,neuron_idone]=region_finder("PL")
[session_idtwo,neuron_idtwo]=region_finder("MOs")
[session_idthree,neuron_idthree]=region_finder("CP")
[session_idfour,neuron_idfour]=region_finder("SNr")
[session_idfive,neuron_idfive]=region_finder("ZI")
[session_idsix,neuron_idsix]=region_finder("SCm")

newone=[]
newone.append(session_idone)
newone.append(session_idtwo)
newone.append(session_idthree)
newone.append(session_idfour)
newone.append(session_idfive)
newone.append(session_idsix)


newone=sum(newone,[])
print(session_id)
print(newone)
apple=list(set(session_id) & set(newone))
print(apple)


#pdb.set_trace()
#dataset=alldat[session_id]
#[new_ids]=what_side(dataset,'Contralateral')
#session_idx ,neuron_idx = region_finder(region)
storer=[]
bins=0.01
test=session_id[1]
word='contrast_right'
other='contrast_left'
#for i in session_id:
    #dat=alldat[test]
    #barea=dat['brain_area']
    #datter=dat['spks'][barea=='VISp']
    #trial_FR = np.sum(datter, axis=0) / (np.shape(dat['spks'])[2] * bins / 1000)
    #norm_FR = trial_FR/ np.mean(trial_FR) #array of normalized FR for each neuron in area on L or R choice trials (n_neurons x n_trials)
    #print(norm_FR)
    #pdb.set_trace()
    #storer.append(norm_FR)
dat=alldat[test]
barea=dat['brain_area']
datter=dat['spks'][barea=='VISp']
#pdb.set_trace()

datter=datter[:,np.logical_and(dat[word]>dat[other],dat['feedback_type']>0)]
#datter=datter[:,dat['feedback_type']>0]

pdb.set_trace()
#if dataset[word][i] >0. and dataset[other][i] ==0.:

trial_FR = np.sum(datter, axis=2) / (np.shape(dat['spks'])[2] * bins / 1000)
norm_FR = trial_FR/ np.mean(trial_FR) #array of normalized FR for each neuron in area on L or R choice trials (n_neurons x n_trials)
#print(norm_FR)
#pdb.set_trace()
#storer.append(norm_FR)


#turn dat['spks'] into [n_neurons, n_trials] array of normalized firing rates on each trial
#bins = 0.01 #10ms bins
#trial_FR = np.sum(dat['spks'], axis=2) / (np.shape(dat['spks'])[2] * bins / 1000)
#norm_FR = trial_FR[:,LorR] / np.mean(trial_FR) #array of normalized FR for each neuron in area on L or R choice trials (n_neurons x n_trials)
    
#print(neuron_id)