jgrewe/gp_neurobio
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

csi stuff

Browse Source
This commit is contained in:
Ramona 2018-11-23 16:34:02 +01:00
parent db78cae061
commit 9942fd37e1
1 changed files with 22 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

37
code/spikes_analysis.py
View File

@ -62,14 +62,22 @@ for deltaf in df_map.keys():
df_phase_binary[deltaf][idx] = binary_spikes
# make dictionaries for csi
csi_trains = {}
csi_rates = {}
# for plotting and calculating iterate over delta f and phases
for df in df_phase_time.keys():
csi_trains[df] = []
csi_rates[df] = []
beat_duration = int(abs(1/df*1000)) #ms
beat_window = 0
while beat_window+beat_duration <= cut_window:
beat_window = beat_window+beat_duration
for phase in df_phase_time[df].keys():
# csi calculation
# trains for synchronity and rate
trials_binary = df_phase_binary[df][phase]
train_chirp = []
@ -99,18 +107,23 @@ for df in df_phase_time.keys():
r_train_beat = np.mean(rbs)
csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat)
print(csi_train)
csi_rate = (np.std(spikerate_chirp) - np.std(spikerate_beat)) / (np.std(spikerate_chirp) + np.std(spikerate_beat))
print(csi_rate)
# add the csi to the dictionaries with the correct df and phase
#csi_trains[df][phase] = csi_train
#csi_rates[df][phase] = csi_rate
csi_trains[df].append(csi_train)
csi_rates[df].append(csi_rate)
'''
# plot
#plot_trials = df_phase_time[df][phase]
#plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
plot_trials = df_phase_time[df][phase]
plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
# calculation
#overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000
'''
smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
fig, ax = plt.subplots(2, 1, sharex=True)
@ -125,15 +138,9 @@ for df in df_phase_time.keys():
ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
plt.show()
'''
'''
for trial in range(len(trials_binary)):
spike_rate = np.zeros(len(spike_bins)-1)
for idx in range(len(spike_bins)-1):
bin_start = spike_bins[idx]*sampling_rate
bin_end = spike_bins[idx+1]*sampling_rate
spike_rate[idx] = np.sum(trials_binary[trial][bin_start:bin_end])
embed()
exit()
'''
for i, k in enumerate(sorted(csi_rates.keys())):
print(csi_rates[k])