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crazy analysis stuff

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Ramona 2018-11-22 16:59:47 +01:00
parent b1fb771d52
commit 9bb8d66478
1 changed files with 41 additions and 21 deletions
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code/spikes_analysis.py
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@ -9,11 +9,14 @@ sampling_rate = 40 #kHz
data_dir = "../data" data_dir = "../data"
dataset = "2018-11-09-ad-invivo-1" dataset = "2018-11-09-ad-invivo-1"
# parameters for binning, smoothing and plotting # parameters for binning, smoothing and plotting
chirp_size = 14 #ms
neuronal_delay = 5 #ms
chirp_start = int((-chirp_size/2+neuronal_delay+50)*sampling_rate)
chirp_end = int((chirp_size/2+neuronal_delay+51)*sampling_rate)
num_bin = 12 num_bin = 12
window = 1 window = 1 #ms
time_axis = np.arange(-50, 50, 1/sampling_rate) time_axis = np.arange(-50, 50, 1/sampling_rate) #steps
bin_size = 1 spike_bins = np.arange(-50, 51) #ms
spike_bins = np.arange(-50, 50+bin_size, bin_size)
# read data from files # read data from files
spikes = read_chirp_spikes(os.path.join(data_dir, dataset)) spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
@ -66,28 +69,33 @@ for deltaf in df_map.keys():
for df in df_phase_time.keys(): for df in df_phase_time.keys():
for phase in df_phase_time[df].keys(): for phase in df_phase_time[df].keys():
# plot trials_binary = df_phase_binary[df][phase]
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
''' sr_chirp = np.zeros(len(trials_binary))
spike_rate = np.zeros(len(spike_bins)-1) sr_beat = np.zeros(len(trials_binary))
for idx in range(len(spike_bins)-1): for i, trial in enumerate(trials_binary):
bin_start = spike_bins[idx]*sampling_rate smoothed_trial = smooth(trial, window, 1/sampling_rate)
bin_end = spike_bins[idx+1]*sampling_rate sr_chirp[i] = np.mean(smoothed_trial[chirp_start:chirp_end])
spike_rate[idx] = np.sum(plot_trials_binary[bin_start:bin_end])/bin_size*sampling_rate sr_beat[i] = np.mean(smoothed_trial[0:chirp_start])
print(np.std(spike_rate)) for rate_chirp in sr_chirp:
plt.plot(spike_rate) for rate_beat in sr_beat:
plt.show() r = np.corrcoef(rate_chirp, rate_beat)
print(r)
embed() embed()
exit() exit()
'''
#csi = (spikerate_chirp-spikerate_befor)/(spikerate_chirp+spikerate_befor)
# plot
#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) smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
fig, ax = plt.subplots(2, 1, sharex=True) fig, ax = plt.subplots(2, 1, sharex=True)
@ -100,5 +108,17 @@ for df in df_phase_time.keys():
ax[1].set_xlabel('time [ms]', fontsize=12) ax[1].set_xlabel('time [ms]', fontsize=12)
ax[1].set_ylabel('firing rate [Hz]', fontsize=12) ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
print(overall_spikerate)
plt.show() 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()
'''