[pointprocesses] figures store data
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pointprocesses/lecture/fanoexamples.json
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pointprocesses/lecture/fanoexamples.json
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0.07832850587455076,
|
||||
0.08342021682094802,
|
||||
0.08879488444286426,
|
||||
0.09446030886653425,
|
||||
0.10042238369945232,
|
||||
0.10668553178055554,
|
||||
0.11325283579889839,
|
||||
0.12012383254557651,
|
||||
0.12729551816999657,
|
||||
0.13475692208502688,
|
||||
0.14249741545304714,
|
||||
0.15050036839961184,
|
||||
0.15873556462203425,
|
||||
0.16717441877997627,
|
||||
0.17576514766628382,
|
||||
0.18445498578340594,
|
||||
0.1931730437373695,
|
||||
0.201830863123379,
|
||||
0.21031795748427692,
|
||||
0.21851036438512575,
|
||||
0.2262881573054429,
|
||||
0.23352443175061463,
|
||||
0.24005949730146073,
|
||||
0.24614110184846782,
|
||||
0.25151086147060503,
|
||||
0.256572678671552,
|
||||
0.262613148841502,
|
||||
0.26852652926093534,
|
||||
0.2779880237044444,
|
||||
0.2875992769924568,
|
||||
0.30061413340163257,
|
||||
0.318010385895822,
|
||||
0.3371679856134181,
|
||||
0.3603943142746392,
|
||||
0.3867085789298046,
|
||||
0.41553611675673074,
|
||||
0.4479788001866543,
|
||||
0.4853833326477201,
|
||||
0.5268146420274435,
|
||||
0.5721987876880225,
|
||||
0.6246372867879774,
|
||||
0.6859116021649339,
|
||||
0.7509997263178535,
|
||||
0.8247101378233508,
|
||||
0.9059771701065815,
|
||||
0.9942814443067973,
|
||||
1.0962859697738576,
|
||||
1.2063499982608168,
|
||||
1.3357281088787307,
|
||||
1.4713553100633523,
|
||||
1.6261467711646544,
|
||||
1.8179860605307483,
|
||||
1.9939719155576006,
|
||||
2.1634671489979955,
|
||||
2.4287272552114043,
|
||||
2.6598322688454354,
|
||||
2.9126277571340524,
|
||||
3.249051735827017,
|
||||
3.5261320311198183,
|
||||
3.915493906701363,
|
||||
4.277796877460168,
|
||||
4.5997561832753195,
|
||||
5.175141664189715,
|
||||
5.650563621178341,
|
||||
6.2348488305633705,
|
||||
6.63484420387276,
|
||||
7.266704154720004,
|
||||
7.883368454016868,
|
||||
8.6484684146875,
|
||||
9.469711535607642,
|
||||
10.300120990015513,
|
||||
10.744254704223849,
|
||||
11.63882344236306,
|
||||
13.24353210583636,
|
||||
13.670392418503376,
|
||||
13.898749762818095,
|
||||
16.141602354951313,
|
||||
17.3643456226336,
|
||||
19.51580093051825
|
||||
]
|
||||
}
|
||||
@ -1,3 +1,5 @@
|
||||
import os
|
||||
import json
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.ticker as mpt
|
||||
@ -6,7 +8,7 @@ from plotstyle import *
|
||||
|
||||
rate = 20.0
|
||||
trials = 20
|
||||
duration = 100.0
|
||||
duration = 500.0
|
||||
dt = 0.001
|
||||
drate = 50.0
|
||||
tau = 0.1;
|
||||
@ -65,8 +67,7 @@ def oupifspikes(rate, trials, duration, dt, D, drate, tau):
|
||||
return spikes
|
||||
|
||||
|
||||
def plot_count_fano(ax1, ax2, spikes):
|
||||
wins = np.logspace(-2, 0.0, 200)
|
||||
def count_stats(spikes, wins):
|
||||
mean_counts = np.zeros(len(wins))
|
||||
var_counts = np.zeros(len(wins))
|
||||
for k, win in enumerate(wins):
|
||||
@ -76,6 +77,10 @@ def plot_count_fano(ax1, ax2, spikes):
|
||||
counts.extend(c)
|
||||
mean_counts[k] = np.mean(counts)
|
||||
var_counts[k] = np.var(counts)
|
||||
return mean_counts, var_counts
|
||||
|
||||
|
||||
def plot_count_fano(ax1, ax2, wins, mean_counts, var_counts):
|
||||
ax1.plot(mean_counts, var_counts, zorder=100, **lsA)
|
||||
ax1.set_xlabel('Mean count')
|
||||
ax1.set_xlim(0.0, 20.0)
|
||||
@ -92,37 +97,47 @@ def plot_count_fano(ax1, ax2, spikes):
|
||||
ax2.set_yticks(np.arange(0.0, 1.2, 0.5))
|
||||
|
||||
|
||||
def plot_fano(ax, spikes):
|
||||
wins = np.logspace(-2, 0.0, 200)
|
||||
mean_counts = np.zeros(len(wins))
|
||||
var_counts = np.zeros(len(wins))
|
||||
for k, win in enumerate(wins):
|
||||
counts = []
|
||||
for times in spikes:
|
||||
c, _ = np.histogram(times, np.arange(0.0, duration, win))
|
||||
counts.extend(c)
|
||||
mean_counts[k] = np.mean(counts)
|
||||
var_counts[k] = np.var(counts)
|
||||
def plot_fano(ax, wins, mean_counts, var_counts):
|
||||
ax.plot(1000.0*wins, var_counts/mean_counts, **lsB)
|
||||
ax.set_xlabel('Window', 'ms')
|
||||
ax.set_ylim(0.0, 1.2)
|
||||
ax.set_xscale('log')
|
||||
ax.set_xticks([10, 100, 1000])
|
||||
ax.set_xticklabels(['10', '100', '1000'])
|
||||
ax.set_xticks([1, 10, 100, 1000])
|
||||
ax.set_xticklabels(['1', '10', '100', '1000'])
|
||||
ax.xaxis.set_minor_locator(mpt.NullLocator())
|
||||
ax.set_yticks(np.arange(0.0, 1.2, 0.5))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if not os.path.exists('fanoexamples.json'):
|
||||
homspikes = hompoisson(rate, trials, duration)
|
||||
inhspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
|
||||
wins = np.logspace(-3, 0.0, 100)
|
||||
hom_mean_counts, hom_var_counts = count_stats(homspikes, wins)
|
||||
inh_mean_counts, inh_var_counts = count_stats(inhspikes, wins)
|
||||
with open('fanoexamples.json', 'w') as df:
|
||||
json.dump({'wins': wins.tolist(),
|
||||
'hom_mean_counts': hom_mean_counts.tolist(),
|
||||
'hom_var_counts': hom_var_counts.tolist(),
|
||||
'inh_mean_counts': inh_mean_counts.tolist(),
|
||||
'inh_var_counts': inh_var_counts.tolist()}, df, indent=4)
|
||||
else:
|
||||
with open('fanoexamples.json', 'r') as sf:
|
||||
data = json.load(sf)
|
||||
wins = np.array(data['wins'])
|
||||
hom_mean_counts = np.array(data['hom_mean_counts'])
|
||||
hom_var_counts = np.array(data['hom_var_counts'])
|
||||
inh_mean_counts = np.array(data['inh_mean_counts'])
|
||||
inh_var_counts = np.array(data['inh_var_counts'])
|
||||
fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=2.0))
|
||||
plot_fano(ax1, homspikes)
|
||||
plot_fano(ax1, wins, hom_mean_counts, hom_var_counts)
|
||||
ax1.set_ylabel('Fano factor')
|
||||
ax1.text(0.1, 0.95, 'Poisson', transform=ax1.transAxes)
|
||||
plot_fano(ax2, inhspikes)
|
||||
ax2.axhline(1.0, **lsGrid)
|
||||
plot_fano(ax2, wins, inh_mean_counts, inh_var_counts)
|
||||
ax2.annotate('', xy=(45.0, 0.0), xytext=(45.0, 0.4), arrowprops=dict(arrowstyle="->"))
|
||||
ax2.text(60.0, 0.25, 'most\nreliable', va='center')
|
||||
ax2.text(0.1, 0.95, 'OU noise', transform=ax2.transAxes)
|
||||
plt.savefig('fanoexamples.pdf')
|
||||
plt.close()
|
||||
|
||||
398126
pointprocesses/lecture/serialcorrexamples.json
Normal file
398126
pointprocesses/lecture/serialcorrexamples.json
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,3 +1,5 @@
|
||||
import os
|
||||
import json
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from plotstyle import *
|
||||
@ -5,7 +7,7 @@ from plotstyle import *
|
||||
|
||||
# parameter:
|
||||
rate = 20.0
|
||||
trials = 10
|
||||
trials = 20
|
||||
duration = 500.0
|
||||
dt = 0.001
|
||||
drate = 50.0
|
||||
@ -91,41 +93,59 @@ def plotserialcorr(ax, isis, maxlag=10) :
|
||||
ax.set_xlim(0.0, maxlag)
|
||||
ax.set_ylim(-1.0, 1.0)
|
||||
ax.plot([0, 10], [0.0, 0.0], **lsGrid)
|
||||
ax.plot(lags, corr, clip_on=False, zorder=100, **lpsAm)
|
||||
ax.plot(lags, corr, clip_on=False, zorder=100, **lpsBm)
|
||||
|
||||
|
||||
def plot_hom_returnmap(ax, spikes):
|
||||
plotreturnmap(ax, isis(spikes)[:200], 1, 0.3)
|
||||
def plot_hom_returnmap(ax, isis):
|
||||
plotreturnmap(ax, isis[:200], 1, 0.3)
|
||||
ax.set_xticks(np.arange(0.0, 301.0, 100.0))
|
||||
ax.set_yticks(np.arange(0.0, 301.0, 100.0))
|
||||
|
||||
|
||||
def plot_inhom_returnmap(ax, spikes):
|
||||
plotreturnmap(ax, isis(spikes)[:200], 1, 0.3)
|
||||
def plot_inhom_returnmap(ax, isis):
|
||||
plotreturnmap(ax, isis[:200], 1, 0.3)
|
||||
ax.set_ylabel('')
|
||||
ax.set_xticks(np.arange(0.0, 301.0, 100.0))
|
||||
ax.set_yticks(np.arange(0.0, 301.0, 100.0))
|
||||
|
||||
|
||||
def plot_hom_serialcorr(ax, spikes):
|
||||
plotserialcorr(ax, isis(spikes))
|
||||
def plot_hom_serialcorr(ax, isis):
|
||||
plotserialcorr(ax, isis)
|
||||
ax.annotate('independent\nintervals', xy=(2.5, 0.05), xytext=(4.0, 0.4),
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.0,0.0),
|
||||
connectionstyle="angle3,angleA=20,angleB=70"))
|
||||
ax.set_ylim(-0.2, 1.0)
|
||||
|
||||
|
||||
def plot_inhom_serialcorr(ax, spikes):
|
||||
plotserialcorr(ax, isis(spikes))
|
||||
def plot_inhom_serialcorr(ax, isis):
|
||||
plotserialcorr(ax, isis)
|
||||
ax.annotate('positive\ncorrelations', xy=(1.5, 0.28), xytext=(3.5, 0.5),
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.0,0.4),
|
||||
connectionstyle="angle3,angleA=20,angleB=60"))
|
||||
ax.set_ylabel('')
|
||||
ax.set_ylim(-0.2, 1.0)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
data_file = 'serialcorrexamples.json'
|
||||
if not os.path.exists(data_file):
|
||||
homspikes = hompoisson(rate, trials, duration)
|
||||
inhomspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
|
||||
homisis = isis(homspikes)
|
||||
inhomisis = isis(inhomspikes)
|
||||
with open(data_file, 'w') as df:
|
||||
json.dump({'homisis': homisis.tolist(),
|
||||
'inhomisis': inhomisis.tolist()}, df, indent=4)
|
||||
else:
|
||||
with open(data_file, 'r') as sf:
|
||||
data = json.load(sf)
|
||||
homisis = np.array(data['homisis'])
|
||||
inhomisis = np.array(data['inhomisis'])
|
||||
fig, axs = plt.subplots(2, 2, figsize=cm_size(figure_width, 1.8*figure_height))
|
||||
fig.subplots_adjust(**adjust_fs(fig, left=6.5, right=1.5))
|
||||
plot_hom_returnmap(axs[0,0], homspikes)
|
||||
plot_inhom_returnmap(axs[0,1], inhomspikes)
|
||||
plot_hom_serialcorr(axs[1,0], homspikes)
|
||||
plot_inhom_serialcorr(axs[1,1], inhomspikes)
|
||||
plot_hom_returnmap(axs[0,0], homisis)
|
||||
plot_inhom_returnmap(axs[0,1], inhomisis)
|
||||
plot_hom_serialcorr(axs[1,0], homisis)
|
||||
plot_inhom_serialcorr(axs[1,1], inhomisis)
|
||||
plt.savefig('serialcorrexamples.pdf')
|
||||
plt.close()
|
||||
|
||||
Reference in New Issue
Block a user