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plot_figure6.py
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plot_figure6.py
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import os
import sys
import numpy as np
import numpy.random as npr
import matplotlib.pyplot as plt
import gamma_renewal as gr
from cell_params import params as PARAMS
import cell_models as cmz
from scipy.signal import welch
import analyze_simulated as a_s
import matplotlib.gridspec as gs
import matplotlib.cm as cm
def get_stuff(location1, location2, location3):
spikes1 = np.load(location1 + '/SPIKES.npy')
isi1 = np.load(location1 + '/ISIs.npy')
spikes2 = np.load(location2 + '/SPIKES.npy')
isi2 = np.load(location2 + '/ISIs.npy')
spikes3 = np.load(location3 + '/SPIKES.npy')
isi3 = np.load(location3 + '/ISIs.npy')
return spikes1, spikes2, spikes3, isi1, isi2, isi3
def gamma_stuff():
# Gamma renewal stuff
N_trials = 10
mean_isis = 1 / np.linspace(0.001, 0.012, N_trials)
shapes = 0.5
rates = shapes / mean_isis
N = 200000
max_spike_length = 480000 * 10
S_list = []
for ix, val in enumerate(rates):
S_list.append(gr.gamma_renewal(val, N, shapes, abs_ref=2, binary=False))
s3, end_time = gr.binary_from_ts(S_list, use_min=True)
s3 = s3.T
if end_time > max_spike_length:
s3 = s3[:max_spike_length, :]
return s3, ts_from_binary(s3)
def ts_from_binary(array, data_type=np.float64):
"""
converts binary array of spiketrains to nested list of time-stamps. Assumes that spiketrains are the last dimension
and that dt=1
:param array: 2d array with the last dimension being single, binary spiketrains
:param data_type: type of data to use in output array
:return: nested list of arrays of time-stamps
"""
n1 = array.shape[0]
out_list = [[] for _ in range(n1)]
for i in range(n1):
out_list[i] = (np.where(array[i, :])[0].astype(data_type))
return out_list
def poisson(rate, N, binary=False, abs=0):
"""
simulates homogeneous Poisson process
:param rate: rate
:param N: number of points to simulate
:param abs: absolute refractory period in units of time
:return:
"""
X = npr.exponential(1 / rate, N) + abs
ts = np.cumsum(X)
if binary:
ts = ts.astype(int)
S = np.zeros(np.max(ts + 1))
S[ts] = 1
return ts, S
else:
return ts
def get_poisson_spiketrain(T1=int(5e6)):
# Get Poisson spike-train for comparison
pois_abs = 2
pois_rate = 0.012
N = 100000
ts_poisson, S_poisson = poisson(pois_rate, N, binary=True, abs=pois_abs)
if len(S_poisson) < T1:
print('error, S_poisson is not long enough')
sys.exit()
return S_poisson[:T1], ts_poisson
def get_srm02(T1):
rate = 0.01
tau_ref1 = 6
seed = 42
if os.path.isfile(os.getcwd() + '/srm02_fR4.npy') is False:
print('generating S_srm02...')
S_srm02 = np.any(cmz.srm02(PARAMS, 1, rate, tau_ref1, T1, seed=seed, ISI=False), axis=0)[0]
np.save(os.getcwd() + '/srm02_fR4.npy', S_srm02)
else:
S_srm02 = np.load(os.getcwd() + '/srm02_fR4.npy')
return S_srm02
def isi_histograms(i_srm02, i_pois, srm_c, pois_c, font, fig1, gs0):
x_axis = np.arange(0, 1000)
y_srm02 = np.histogram(i_srm02, np.linspace(1, 1000, 1001), density=True)[0]
y_compare = np.histogram(i_pois, np.linspace(1, 1000, 1001), density=True)[0]
ax = fig1.add_subplot(gs0[0])
ax.plot(x_axis, y_srm02, color=srm_c, lw=2)
ax.plot(x_axis, y_compare, color=pois_c, ls='--', lw=2)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.set_yticks([])
ax.set_xlabel('ISI (ms)', size=font)
ax.set_ylabel('P(ISI)', size=font)
ax.set_xlim(-20, 500)
def spike_autocorrs(ac_srm02, ac_poisson, srm_c, pois_c, font, fig1, gs0):
n_time = 30
YLIMS = (-0.0005, 0.001)
axi = fig1.add_subplot(gs0[1])
axi.plot(np.arange(0, n_time), np.real(ac_srm02[0:n_time]), color=srm_c, lw=2)
axi.plot(np.arange(0, n_time), np.real(ac_poisson[0:n_time]), color=pois_c, ls='--', lw=2)
axi.spines['right'].set_visible(False)
axi.spines['top'].set_visible(False)
axi.yaxis.set_ticks_position('left')
axi.set_xlabel(r'$\tau$ (s)', size=font)
axi.set_ylabel(r'$R_{xx}(\tau)$', size=font)
axi.set_ylim(YLIMS)
#axi.set_yticklabels([-5, 0, 5, 10])
axi.ticklabel_format(axis='y', style='sci', scilimits=(0,0))
if __name__ == '__main__':
file1 = os.getcwd() + '/figure6_1'
file2 = os.getcwd() + '/figure6_2'
file3 = os.getcwd() + '/figure6_3'
s1, s2, s3, i1, i2, i3 = get_stuff(file1, file2, file3)
s4, i4 = gamma_stuff(shapes=0.5)
# SPLIT S2 INTO TWO SETS
# s3 = s2[:, 10:]
# i3 = s2[:, 10:]
s2 = s2[:, :10]
i2 = i2[:, :10]
# Get poisson and srm02 spiketrains for histograms
T1 = int(5e6)
s_pois, i_pois = get_poisson_spiketrain(T1)
s_srm02 = get_srm02(T1)
s_srm02 = s_srm02[500:-650]
i_srm02 = np.nonzero(s_srm02)[0]
i_pois = i_pois[-len(i_srm02):]
i_srm02 = np.diff(i_srm02)
i_pois = np.diff(i_pois)
# Autocorrelations
sample_freq_poisson, pwr_poisson = welch(s_pois, fs=1, nperseg=int(2048 * 2), axis=-1, return_onesided=False)
ac_poisson = np.fft.ihfft(pwr_poisson, axis=-1)
sample_freq_srm02, pwr_srm02 = welch(s_srm02, fs=1, nperseg=int(2048 * 2), axis=-1, return_onesided=False)
ac_srm02 = np.fft.ihfft(pwr_srm02, axis=-1)
# CALC STATS
mu1, sd1, _, _ = a_s.isi_stats(s1.T)
mu2, sd2, _, _ = a_s.isi_stats(s2.T)
mu3, sd3, _, _ = a_s.isi_stats(s3.T)
mu4, sd4, _, _ = a_s.isi_stats(s4.T)
cv1 = sd1 / mu1
cv2 = sd2 / mu2
cv3 = sd3 / mu3
cv4 = sd4 / mu4
CV2_1 = a_s.Cv2_from_spiketrains(s1.T)
LV_1 = a_s.Lv_from_spiketrains(s1.T)
CV2_2 = a_s.Cv2_from_spiketrains(s2.T)
LV_2 = a_s.Lv_from_spiketrains(s2.T)
CV2_3 = a_s.Cv2_from_spiketrains(s3.T)
LV_3 = a_s.Lv_from_spiketrains(s3.T)
CV2_4 = a_s.Cv2_from_spiketrains(s4.T)
LV_4 = a_s.Lv_from_spiketrains(s4.T)
# CALC RATES
x_axis1 = s1.sum(0) / (480000 * 10) * 1000
x_axis2 = s2.sum(0) / (480000 * 10) * 1000
x_axis3 = s3.sum(0) / (480000 * 10) * 1000
x_axis4 = s4.sum(0) / (480000 * 10) * 1000
# Set up plots
colour_samples = np.linspace(0.5, 0.98, 6)
Purples = [cm.Purples(ix) for ix in colour_samples]
Reds = [cm.Reds(ix) for ix in colour_samples]
Blues = [cm.Blues(ix) for ix in colour_samples]
grey = 'grey'
red = 'red'
srm_c = Purples[2]
pois_c = red
gamma_c = Blues[2]
srm_reg_c = Reds[-1]
srm_reg_c2 = Reds[-3]
font = 20
figbackground_c = 'white'
# fig1 = plt.figure(1, [9.5, 7], facecolor=figbackground_c)
fig1 = plt.figure(1, [6, 5], facecolor=figbackground_c)
gsbase1 = gs.GridSpec(2, 1, hspace=0.7, wspace=0.3)
gs0 = gsbase1[0].subgridspec(1, 2, wspace=0.4)
#gs1 = gsbase1[2].subgridspec(1, 2, wspace=0.4)
gs2 = gsbase1[1].subgridspec(1, 2, wspace=0.4)
# Make histogram and autocorr plots
isi_histograms(i_srm02, i_pois, srm_c, pois_c, font, fig1, gs0)
spike_autocorrs(ac_srm02, ac_poisson, srm_c, pois_c, font, fig1, gs0)
inx = 6
nbins = np.linspace(0, 700, 120)
xlims0 = (-10, 700)
ax2 = fig1.add_subplot(gs2[0])
line_size = 3
# ax2.set_ylim((0.8, 1.4))
# ax2.scatter(x_axis1, CV2_1, label='BSRM', lw=line_size)
# ax2.scatter(x_axis2, CV2_2, label='SRM, low power', lw=line_size)
# ax2.scatter(x_axis3, CV2_3, label='SRM, high power', lw=line_size)
# ax2.scatter(x_axis4, CV2_4, label='Gamma, shape=0.5', lw=line_size)
#ax2.scatter(x_axis5, CV2_5, label='Gamma, shape=0.5', lw=line_size)
#ax2.legend()
ax2.plot(CV2_1, x_axis1, label='BSRM', lw=line_size, color=srm_c)
ax2.scatter(CV2_1, x_axis1, color=srm_c)
ax2.plot(CV2_2, x_axis2, label='SRM, low power', lw=line_size, color=srm_reg_c)
ax2.scatter(CV2_2, x_axis2, color=srm_reg_c)
ax2.plot(CV2_3, x_axis3, label='SRM, high power', lw=line_size, color=srm_reg_c2)
ax2.scatter(CV2_3, x_axis3, color=srm_reg_c2)
ax2.plot(CV2_4, x_axis4, label='Gamma, shape=0.5', lw=line_size, color=gamma_c)
ax2.scatter(CV2_4, x_axis4, color=gamma_c)
ax2.axvspan(np.min(CV2_1), np.max(CV2_3), color=srm_reg_c2, alpha=0.1)
ax2.axvspan(np.min(CV2_4), np.max(CV2_4), color=gamma_c, alpha=0.1)
ax2.set_xlabel('CV2', size=font)
ax2.set_ylabel('Rate (Hz)', size=font)
ax2.spines['right'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.yaxis.set_ticks_position('left')
ax2.set_yticks([1, 6, 11])
ax2.set_xlim(0.97, 1.28)
ax3 = fig1.add_subplot(gs2[1])
line_size = 3
# ax3.set_ylim((0.8, 1.6))
# ax3.plot(x_axis1, LV_1, label='BSRM', lw=line_size)
# ax3.plot(x_axis2, LV_2, label='SRM, low power', lw=line_size)
# ax3.plot(x_axis3, LV_3, label='SRM, high power', lw=line_size)
# ax3.plot(x_axis4, LV_4, label='Gamma, shape=0.5', lw=line_size)
#ax3.plot(x_axis5, LV_5, label='Gamma, shape=0.5', lw=line_size)
#ax3.legend()
ax3.plot(LV_1, x_axis1, label='BSRM', lw=line_size, color=srm_c)
ax3.scatter(LV_1, x_axis1, color=srm_c)
ax3.plot(LV_2, x_axis2, label='SRM, low power', lw=line_size, color=srm_reg_c)
ax3.scatter(LV_2, x_axis2, color=srm_reg_c)
ax3.plot(LV_3, x_axis3, label='SRM, high power', lw=line_size, color=srm_reg_c2)
ax3.scatter(LV_3, x_axis3, color=srm_reg_c2)
ax3.plot(LV_4, x_axis4, label='Gamma, shape=0.5', lw=line_size, color=gamma_c)
ax3.scatter(LV_4, x_axis4, color=gamma_c)
ax3.axvspan(np.min(LV_3), np.max(LV_3), color=srm_reg_c2, alpha=0.1)
ax3.axvspan(np.min(LV_4), np.max(LV_4), color=gamma_c, alpha=0.1)
ax3.set_xlabel('LV', size=font)
#ax3.set_ylabel('Rate (Hz)', size=font)
ax3.spines['right'].set_visible(False)
ax3.spines['top'].set_visible(False)
ax3.yaxis.set_ticks_position('left')
ax3.set_yticks([1, 6, 11])
ax3.set_xlim(0.94, 1.53)
fig1.savefig('/Users/macbookair/downloads/fR4_updated1.pdf', format='pdf')
plt.show()