improved plotting, xkcd style methods describing the psth estimation
This commit is contained in:
parent
cb7924ffd5
commit
fffd7226e2
@ -1,10 +1,9 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import scipy.io as spio
|
||||
import scipy.stats as spst
|
||||
import scipy as sp
|
||||
import seaborn as sb
|
||||
from IPython import embed
|
||||
sb.set_context("paper")
|
||||
|
||||
|
||||
def set_axis_fontsize(axis, label_size, tick_label_size=None, legend_size=None):
|
||||
@ -140,7 +139,7 @@ def plot_bin_rate(spike_times, bin_width, max_t=30, dt=1e-4):
|
||||
|
||||
def get_convolved_rate(times, sigma, max_t=30., dt=1.e-4):
|
||||
time = np.arange(0., max_t, dt)
|
||||
kernel = sp.stats.norm.pdf(np.arange(-8*sigma, 8*sigma, dt),loc=0,scale=sigma)
|
||||
kernel = spst.norm.pdf(np.arange(-8*sigma, 8*sigma, dt),loc=0,scale=sigma)
|
||||
indices = np.asarray(times/dt, dtype=int)
|
||||
rate = np.zeros(time.shape)
|
||||
rate[indices] = 1.;
|
||||
@ -202,6 +201,24 @@ def plot_comparison(spike_times, bin_width, sigma, max_t=30., dt=1e-4):
|
||||
ax2 = fig.add_subplot(412)
|
||||
ax3 = fig.add_subplot(413)
|
||||
ax4 = fig.add_subplot(414)
|
||||
ax1.spines["right"].set_visible(False)
|
||||
ax1.spines["top"].set_visible(False)
|
||||
ax1.yaxis.set_ticks_position('left')
|
||||
ax1.xaxis.set_ticks_position('bottom')
|
||||
ax2.spines["right"].set_visible(False)
|
||||
ax2.spines["top"].set_visible(False)
|
||||
ax2.yaxis.set_ticks_position('left')
|
||||
ax2.xaxis.set_ticks_position('bottom')
|
||||
ax3.spines["right"].set_visible(False)
|
||||
ax3.spines["top"].set_visible(False)
|
||||
ax3.yaxis.set_ticks_position('left')
|
||||
ax3.xaxis.set_ticks_position('bottom')
|
||||
ax4.spines["right"].set_visible(False)
|
||||
ax4.spines["top"].set_visible(False)
|
||||
ax4.yaxis.set_ticks_position('left')
|
||||
ax4.xaxis.set_ticks_position('bottom')
|
||||
|
||||
|
||||
|
||||
ax1.vlines(times[times < (100000*dt)], ymin=0, ymax=1, color="dodgerblue", lw=1.5)
|
||||
ax1.set_ylabel("spikes", fontsize=10)
|
||||
@ -211,7 +228,7 @@ def plot_comparison(spike_times, bin_width, sigma, max_t=30., dt=1e-4):
|
||||
set_axis_fontsize(ax1, 10)
|
||||
ax1.set_xticklabels([])
|
||||
|
||||
ax2.plot(time, inst_rate, label="instantaneous rate")
|
||||
ax2.plot(time, inst_rate, lw=1.5, label="instantaneous rate")
|
||||
ax2.set_ylabel("firing rate [Hz]", fontsize=10)
|
||||
ax2.legend(fontsize=10)
|
||||
ax2.set_xlim([1.5, 3.5])
|
||||
@ -219,7 +236,7 @@ def plot_comparison(spike_times, bin_width, sigma, max_t=30., dt=1e-4):
|
||||
set_axis_fontsize(ax2, 10)
|
||||
ax2.set_xticklabels([])
|
||||
|
||||
ax3.plot(time, binn_rate, label="binned rate")
|
||||
ax3.plot(time, binn_rate, lw=1.5, label="binned rate")
|
||||
ax3.set_ylabel("firing rate [Hz]", fontsize=10)
|
||||
ax3.legend(fontsize=10)
|
||||
ax3.set_xlim([1.5, 3.5])
|
||||
@ -227,7 +244,7 @@ def plot_comparison(spike_times, bin_width, sigma, max_t=30., dt=1e-4):
|
||||
set_axis_fontsize(ax3, 10)
|
||||
ax3.set_xticklabels([])
|
||||
|
||||
ax4.plot(time, conv_rate, label="convolved rate")
|
||||
ax4.plot(time, conv_rate, lw=1.5, label="convolved rate")
|
||||
ax4.set_xlabel("time [s]", fontsize=10)
|
||||
ax4.set_ylabel("firing rate [Hz]", fontsize=10)
|
||||
ax4.legend(fontsize=10)
|
||||
|
||||
@ -55,31 +55,36 @@ def plot_bin_method():
|
||||
dt = 1e-5
|
||||
duration = 0.5
|
||||
|
||||
spike_times = create_spikes(0.025, duration)
|
||||
spike_times = create_spikes(0.018, duration)
|
||||
t = np.arange(0., duration, dt)
|
||||
|
||||
bins = np.arange(0, 0.5, 0.05)
|
||||
bins = np.arange(0, 0.55, 0.05)
|
||||
count, _ = np.histogram(spike_times, bins)
|
||||
|
||||
plt.xkcd()
|
||||
set_rc()
|
||||
fig = plt.figure()
|
||||
fig.set_size_inches(5., 2.5)
|
||||
fig.set_facecolor('white')
|
||||
spikes = plt.subplot2grid((3,1), (0,0), rowspan=1, colspan=1)
|
||||
rate_ax = plt.subplot2grid((3,1), (1,0), rowspan=2, colspan=1)
|
||||
spikes = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
|
||||
rate_ax = plt.subplot2grid((7,1), (3,0), rowspan=4, colspan=1)
|
||||
setup_axis(spikes, rate_ax)
|
||||
spikes.set_ylim([0., 1.25])
|
||||
|
||||
spikes.vlines(spike_times, 0., 1., color="darkblue", lw=1)
|
||||
spikes.vlines(bins, 0., 1.05, color="red", lw = 2)
|
||||
for c in count:
|
||||
spikes.text(bins-(bins[1]-bins[0])/2, 0.75, str(c), fontdict={"color":"red"})
|
||||
spikes.vlines(spike_times, 0., 1., color="darkblue", lw=1.25)
|
||||
spikes.vlines(np.hstack((0,bins)), 0., 1.25, color="red", lw=1.5, linestyles='--')
|
||||
for i,c in enumerate(count):
|
||||
spikes.text(bins[i] + bins[1]/2, 1.05, str(c), fontdict={'color':'red'})
|
||||
spikes.set_xlim([0, duration])
|
||||
|
||||
#rate_ax.step(bins[1:], count/0.05)
|
||||
|
||||
rate = count / 0.05
|
||||
rate_ax.step(bins, np.hstack((rate, rate[-1])), where='post')
|
||||
rate_ax.set_xlim([0., duration])
|
||||
rate_ax.set_ylim([0., 100.])
|
||||
rate_ax.set_yticks(np.arange(0,105,25))
|
||||
fig.tight_layout()
|
||||
plt.show()
|
||||
#fig.savefig("bin_method.pdf")
|
||||
fig.savefig("../lecture/images/bin_method.pdf")
|
||||
plt.close()
|
||||
|
||||
|
||||
def plot_conv_method():
|
||||
@ -94,11 +99,13 @@ def plot_conv_method():
|
||||
rate = np.convolve(rate, kernel, mode='same')
|
||||
rate = np.roll(rate, -1)
|
||||
|
||||
plt.xkcd()
|
||||
set_rc()
|
||||
fig = plt.figure()
|
||||
fig.set_size_inches(5., 2.5)
|
||||
fig.set_facecolor('white')
|
||||
spikes = plt.subplot2grid((3,1), (0,0), rowspan=1, colspan=1)
|
||||
rate_ax = plt.subplot2grid((3,1), (1,0), rowspan=2, colspan=1)
|
||||
spikes = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
|
||||
rate_ax = plt.subplot2grid((7,1), (3,0), rowspan=4, colspan=1)
|
||||
setup_axis(spikes, rate_ax)
|
||||
|
||||
spikes.vlines(spike_times, 0., 1., color="darkblue", lw=1.5, zorder=2)
|
||||
@ -112,11 +119,11 @@ def plot_conv_method():
|
||||
rate_ax.set_ylim([0, 50])
|
||||
rate_ax.set_yticks(np.arange(0,75,25))
|
||||
fig.tight_layout()
|
||||
fig.savefig("conv_method.pdf")
|
||||
fig.savefig("../lecture/images/conv_method.pdf")
|
||||
|
||||
|
||||
def plot_isi_method():
|
||||
spike_times = create_spikes(0.08, 0.5)
|
||||
spike_times = create_spikes(0.09, 0.5)
|
||||
|
||||
plt.xkcd()
|
||||
set_rc()
|
||||
@ -124,8 +131,8 @@ def plot_isi_method():
|
||||
fig.set_size_inches(5., 2.5)
|
||||
fig.set_facecolor('white')
|
||||
|
||||
spikes = plt.subplot2grid((3,1), (0,0), rowspan=1, colspan=1)
|
||||
rate = plt.subplot2grid((3,1), (1,0), rowspan=2, colspan=1)
|
||||
spikes = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)
|
||||
rate = plt.subplot2grid((7,1), (3,0), rowspan=4, colspan=1)
|
||||
setup_axis(spikes, rate)
|
||||
|
||||
spikes.vlines(spike_times, 0., 1., color="darkblue", lw=1.25)
|
||||
@ -133,18 +140,19 @@ def plot_isi_method():
|
||||
for i in range(1, len(spike_times)):
|
||||
t_start = spike_times[i-1]
|
||||
t = spike_times[i]
|
||||
spikes.annotate(s='', xy=(t_start, 0.5), xytext=(t,0.5), arrowprops=dict(arrowstyle='<->'))
|
||||
spikes.annotate(s='', xy=(t_start, 0.5), xytext=(t,0.5), arrowprops=dict(arrowstyle='<->'), color='red')
|
||||
|
||||
i_rate = 1./np.diff(spike_times)
|
||||
|
||||
rate.step(spike_times[1:], i_rate,color="darkblue", lw=1.25, where="pre")
|
||||
rate.step(spike_times, np.hstack((i_rate, i_rate[-1])),color="darkblue", lw=1.25, where="post")
|
||||
rate.set_ylim([0, 75])
|
||||
rate.set_yticks(np.arange(0,100,25))
|
||||
|
||||
fig.tight_layout()
|
||||
fig.savefig("isi_method.pdf")
|
||||
fig.savefig("../lecture/images/isi_method.pdf")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
#plot_isi_method()
|
||||
#plot_conv_method()
|
||||
plot_isi_method()
|
||||
plot_conv_method()
|
||||
plot_bin_method()
|
||||
|
||||
@ -1,9 +1,7 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import scipy.io as scio
|
||||
import seaborn as sb
|
||||
from IPython import embed
|
||||
sb.set_context("paper")
|
||||
|
||||
|
||||
def plot_sta(times, stim, dt, t_min=-0.1, t_max=.1):
|
||||
@ -31,10 +29,15 @@ def plot_sta(times, stim, dt, t_min=-0.1, t_max=.1):
|
||||
fig.set_facecolor("white")
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(time, sta, color="darkblue")
|
||||
ax.plot(time, sta, color="darkblue", lw=1)
|
||||
ax.set_xlabel("time [s]")
|
||||
ax.set_ylabel("stimulus")
|
||||
ax.xaxis.grid('off')
|
||||
ax.spines["right"].set_visible(False)
|
||||
ax.spines["top"].set_visible(False)
|
||||
ax.yaxis.set_ticks_position('left')
|
||||
ax.xaxis.set_ticks_position('bottom')
|
||||
|
||||
ylim = ax.get_ylim()
|
||||
xlim = ax.get_xlim()
|
||||
ax.plot(list(xlim), [0., 0.], zorder=1, color='darkgray', ls='--')
|
||||
@ -56,13 +59,17 @@ def reconstruct_stimulus(spike_times, sta, stimulus, t_max=30., dt=1e-4):
|
||||
s_est[i, :] = np.convolve(y, sta, mode='same')
|
||||
|
||||
plt.plot(np.arange(0, t_max, dt), stimulus[:,1], label='stimulus', color='darkblue', lw=2.)
|
||||
plt.plot(np.arange(0, t_max, dt), np.mean(s_est, axis=0), label='reconstruction', color='silver', lw=1.5)
|
||||
plt.plot(np.arange(0, t_max, dt), np.mean(s_est, axis=0), label='reconstruction', color='gray', lw=1.5)
|
||||
plt.xlabel('time[s]')
|
||||
plt.ylabel('stimulus')
|
||||
plt.xlim([0.0, 0.25])
|
||||
plt.ylim([-1., 1.])
|
||||
plt.legend()
|
||||
plt.plot([0.0, 0.25], [0., 0.], color="darkgray", lw=1, ls='--', zorder=1)
|
||||
plt.plot([0.0, 0.25], [0., 0.], color="darkgray", lw=1.5, ls='--', zorder=1)
|
||||
plt.gca().spines["right"].set_visible(False)
|
||||
plt.gca().spines["top"].set_visible(False)
|
||||
plt.gca().yaxis.set_ticks_position('left')
|
||||
plt.gca().xaxis.set_ticks_position('bottom')
|
||||
|
||||
fig = plt.gcf()
|
||||
fig.set_size_inches(7.5, 5)
|
||||
|
||||
Reference in New Issue
Block a user