105 lines
3.7 KiB
Python
105 lines
3.7 KiB
Python
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
from read_chirp_data import *
|
|
from utility import *
|
|
from IPython import embed
|
|
|
|
# define sampling rate and data path
|
|
sampling_rate = 40 #kHz
|
|
data_dir = "../data"
|
|
dataset = "2018-11-09-ad-invivo-1"
|
|
# parameters for binning, smoothing and plotting
|
|
num_bin = 12
|
|
window = 1
|
|
time_axis = np.arange(-50, 50, 1/sampling_rate)
|
|
bin_size = 1
|
|
spike_bins = np.arange(-50, 50+bin_size, bin_size)
|
|
|
|
# read data from files
|
|
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
|
|
eod = read_chirp_eod(os.path.join(data_dir, dataset))
|
|
chirp_times = read_chirp_times(os.path.join(data_dir, dataset))
|
|
|
|
# make a delta f map for the quite more complicated keys
|
|
df_map = {}
|
|
for k in spikes.keys():
|
|
df = k[1]
|
|
if df in df_map.keys():
|
|
df_map[df].append(k)
|
|
else:
|
|
df_map[df] = [k]
|
|
|
|
# differentiate between phases
|
|
phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
|
|
cut_range = np.arange(-50*sampling_rate, 50*sampling_rate, 1)
|
|
|
|
# make dictionaries for spiketimes
|
|
df_phase_time = {}
|
|
df_phase_binary = {}
|
|
|
|
# iterate over delta f, repetition, phases and a single chirp
|
|
for deltaf in df_map.keys():
|
|
df_phase_time[deltaf] = {}
|
|
df_phase_binary[deltaf] = {}
|
|
for rep in df_map[deltaf]:
|
|
for phase in spikes[rep]:
|
|
for idx in np.arange(num_bin):
|
|
# check the phase
|
|
if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
|
|
|
|
# get spikes between 50 ms befor and after the chirp
|
|
spikes_to_cut = np.asarray(spikes[rep][phase])
|
|
spikes_cut = spikes_to_cut[(spikes_to_cut > -50) & (spikes_to_cut < 50)]
|
|
spikes_idx = np.round(spikes_cut*sampling_rate)
|
|
# also save as binary, 0 no spike, 1 spike
|
|
binary_spikes = np.isin(cut_range, spikes_idx)*1
|
|
|
|
# add the spikes to the dictionaries with the correct df and phase
|
|
if idx in df_phase_time[deltaf].keys():
|
|
df_phase_time[deltaf][idx].append(spikes_cut)
|
|
df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
|
|
else:
|
|
df_phase_time[deltaf][idx] = [spikes_cut]
|
|
df_phase_binary[deltaf][idx] = binary_spikes
|
|
|
|
# for plotting and calculating iterate over delta f and phases
|
|
for df in df_phase_time.keys():
|
|
for phase in df_phase_time[df].keys():
|
|
|
|
# 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
|
|
|
|
'''
|
|
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(plot_trials_binary[bin_start:bin_end])/bin_size*sampling_rate
|
|
|
|
print(np.std(spike_rate))
|
|
plt.plot(spike_rate)
|
|
plt.show()
|
|
|
|
embed()
|
|
exit()
|
|
'''
|
|
|
|
smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
|
|
|
|
fig, ax = plt.subplots(2, 1, sharex=True)
|
|
for i, trial in enumerate(plot_trials):
|
|
ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
|
|
ax[1].plot(time_axis, smoothed_spikes*1000)
|
|
|
|
ax[0].set_title(df)
|
|
ax[0].set_ylabel('repetition', fontsize=12)
|
|
|
|
ax[1].set_xlabel('time [ms]', fontsize=12)
|
|
ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
|
|
print(overall_spikerate)
|
|
plt.show()
|