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Ramona 2018-11-26 17:48:05 +01:00
parent 9942fd37e1
commit bf0fb6025b
1 changed files with 41 additions and 12 deletions
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code/spikes_analysis.py
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@ -8,13 +8,22 @@ from IPython import embed
# define sampling rate and data path # define sampling rate and data path
sampling_rate = 40 #kHz sampling_rate = 40 #kHz
data_dir = "../data" data_dir = "../data"
dataset = "2018-11-09-ad-invivo-1" dataset = "2018-11-14-aa-invivo-1"
#data = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1",\
# "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", \
# "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", \
# "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", \
# "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1",\
# "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1"," 2018-11-20-ae-invivo-1", \
# "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1")
# parameters for binning, smoothing and plotting # parameters for binning, smoothing and plotting
cut_window = 60 #cut_window = 60
cut_window = 20
chirp_size = 14 #ms chirp_size = 14 #ms
neuronal_delay = 5 #ms neuronal_delay = 5 #ms
chirp_start = int((-chirp_size/2+neuronal_delay+cut_window)*sampling_rate) chirp_start = int((-chirp_size/2+neuronal_delay+cut_window)*sampling_rate)
chirp_end = int((chirp_size/2+neuronal_delay+cut_window+1)*sampling_rate) chirp_end = int((chirp_size/2+neuronal_delay+cut_window)*sampling_rate)
num_bin = 12 num_bin = 12
window = 1 #ms window = 1 #ms
time_axis = np.arange(-cut_window, cut_window, 1/sampling_rate) #steps time_axis = np.arange(-cut_window, cut_window, 1/sampling_rate) #steps
@ -35,6 +44,8 @@ cut_range = np.arange(-cut_window*sampling_rate, cut_window*sampling_rate, 1)
# make dictionaries for spiketimes # make dictionaries for spiketimes
df_phase_time = {} df_phase_time = {}
df_phase_binary = {} df_phase_binary = {}
#embed()
#exit()
# iterate over delta f, repetition, phases and a single chirp # iterate over delta f, repetition, phases and a single chirp
for deltaf in df_map.keys(): for deltaf in df_map.keys():
@ -46,9 +57,9 @@ for deltaf in df_map.keys():
# check the phase # check the phase
if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]: if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
# get spikes between 50 ms befor and after the chirp # get spikes between 50 ms before and after the chirp
spikes_to_cut = np.asarray(spikes[rep][phase]) spikes_to_cut = np.asarray(spikes[rep][phase])
spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < cut_window)] spikes_cut = spikes_to_cut[(spikes_to_cut > -50) & (spikes_to_cut < 50)]
spikes_idx = np.round(spikes_cut*sampling_rate) spikes_idx = np.round(spikes_cut*sampling_rate)
# also save as binary, 0 no spike, 1 spike # also save as binary, 0 no spike, 1 spike
binary_spikes = np.isin(cut_range, spikes_idx)*1 binary_spikes = np.isin(cut_range, spikes_idx)*1
@ -95,7 +106,7 @@ for df in df_phase_time.keys():
rbs = [] rbs = []
for i, train in enumerate(train_chirp): for i, train in enumerate(train_chirp):
for j, train2 in enumerate(train_chirp): for j, train2 in enumerate(train_chirp):
if np.array_equal(train, train2): if i >= j:
continue continue
else: else:
rc, _ = ss.pearsonr(train, train2) rc, _ = ss.pearsonr(train, train2)
@ -105,7 +116,8 @@ for df in df_phase_time.keys():
r_train_chirp = np.mean(rcs) r_train_chirp = np.mean(rcs)
r_train_beat = np.mean(rbs) r_train_beat = np.mean(rbs)
embed()
exit()
csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat) csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat)
csi_rate = (np.std(spikerate_chirp) - np.std(spikerate_beat)) / (np.std(spikerate_chirp) + np.std(spikerate_beat)) csi_rate = (np.std(spikerate_chirp) - np.std(spikerate_beat)) / (np.std(spikerate_chirp) + np.std(spikerate_beat))
@ -116,6 +128,9 @@ for df in df_phase_time.keys():
csi_trains[df].append(csi_train) csi_trains[df].append(csi_train)
csi_rates[df].append(csi_rate) csi_rates[df].append(csi_rate)
#csi_trains[df].append(abs(csi_train))
#csi_rates[df].append(abs(csi_rate))
''' '''
# plot # plot
plot_trials = df_phase_time[df][phase] plot_trials = df_phase_time[df][phase]
@ -138,9 +153,23 @@ for df in df_phase_time.keys():
ax[1].set_ylabel('firing rate [Hz]', fontsize=12) ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
plt.show() plt.show()
''' '''
embed()
exit()
for i, k in enumerate(sorted(csi_rates.keys())):
print(csi_rates[k])
fig, ax = plt.subplots()
for i, k in enumerate(sorted(csi_rates.keys())):
ax.scatter(np.ones(len(csi_rates[k]))*i, csi_rates[k], s=20)
#ax.plot(i, np.mean(csi_rates[k]), 'o', markersize=15)
ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
ax.plot(np.arange(-1, len(csi_rates.keys())+1), np.zeros(len(csi_rates.keys())+2), 'silver', linewidth=2, linestyle='--')
#ax.set_xticklabels(sorted(csi_rates.keys()))
fig.tight_layout()
plt.show()
fig, ax = plt.subplots()
for i, k in enumerate(sorted(csi_trains.keys())):
ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
#ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
#ax.set_xticklabels(sorted(csi_trains.keys()))
fig.tight_layout()
plt.show()