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[deprecations] replace scipy.hist with np function ...

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and deprecation of normed keyword in nphistogram
This commit is contained in:
Jan Grewe 2020-10-27 00:01:18 +01:00
parent 5be1c25187
commit 8ab483d3a8
5 changed files with 20 additions and 17 deletions
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16
pointprocesses/lecture/firingrates.py
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@ -11,7 +11,7 @@ def get_instantaneous_rate(times, max_t=30., dt=1e-4):
indices = np.asarray(times / dt, dtype=int) indices = np.asarray(times / dt, dtype=int)
intervals = np.diff(np.hstack(([0], times))) intervals = np.diff(np.hstack(([0], times)))
inst_rate = np.zeros(time.shape) inst_rate = np.zeros(time.shape)
for i, index in enumerate(indices[1:]): for i, index in enumerate(indices[1:]):
inst_rate[indices[i-1]:indices[i]] = 1/intervals[i] inst_rate[indices[i-1]:indices[i]] = 1/intervals[i]
return time, inst_rate return time, inst_rate
@ -20,16 +20,16 @@ def get_instantaneous_rate(times, max_t=30., dt=1e-4):
def plot_isi_rate(spike_times, max_t=30, dt=1e-4): def plot_isi_rate(spike_times, max_t=30, dt=1e-4):
times = np.squeeze(spike_times[0][0])[:50000] times = np.squeeze(spike_times[0][0])[:50000]
time, rate = get_instantaneous_rate(times, max_t=50000*dt) time, rate = get_instantaneous_rate(times, max_t=50000*dt)
rates = np.zeros((len(rate), len(spike_times))) rates = np.zeros((len(rate), len(spike_times)))
for i in range(len(spike_times)): for i in range(len(spike_times)):
_, rates[:, i] = get_instantaneous_rate(np.squeeze(spike_times[i][0])[:50000], _, rates[:, i] = get_instantaneous_rate(np.squeeze(spike_times[i][0])[:50000],
max_t=50000*dt) max_t=50000*dt)
avg_rate = np.mean(rates, axis=1) avg_rate = np.mean(rates, axis=1)
rate_std = np.std(rates, axis=1) rate_std = np.std(rates, axis=1)
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=cm_size(figure_width, 1.2*figure_height)) fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=cm_size(figure_width, 1.2*figure_height))
ax1.vlines(times[times < (50000*dt)], ymin=0, ymax=1, color="dodgerblue", lw=1.5) ax1.vlines(times[times < (50000*dt)], ymin=0, ymax=1, color="dodgerblue", lw=1.5)
ax1.set_ylabel('Spikes') ax1.set_ylabel('Spikes')
ax1.set_xlim(0, 5) ax1.set_xlim(0, 5)
@ -43,7 +43,7 @@ def plot_isi_rate(spike_times, max_t=30, dt=1e-4):
ax3.set_ylabel('Firing rate', 'Hz') ax3.set_ylabel('Firing rate', 'Hz')
ax3.legend() ax3.legend()
ax3.set_ylim(0, 450) ax3.set_ylim(0, 450)
fig.savefig("isimethod.pdf") fig.savefig("isimethod.pdf")
plt.close() plt.close()
@ -52,9 +52,9 @@ def get_binned_rate(times, bin_width=0.05, max_t=30., dt=1e-4):
time = np.arange(0., max_t, dt) time = np.arange(0., max_t, dt)
bins = np.arange(0., max_t, bin_width) bins = np.arange(0., max_t, bin_width)
bin_indices = np.asarray(bins / dt, np.int) bin_indices = np.asarray(bins / dt, np.int)
hist, _ = sp.histogram(times, bins) hist, _ = np.histogram(times, bins)
rate = np.zeros(time.shape) rate = np.zeros(time.shape)
for i, b in enumerate(bin_indices[1:]): for i, b in enumerate(bin_indices[1:]):
rate[bin_indices[i-1]:b] = hist[i-1]/bin_width rate[bin_indices[i-1]:b] = hist[i-1]/bin_width
return time, rate return time, rate
@ -68,7 +68,7 @@ def plot_bin_rate(spike_times, bin_width, max_t=30, dt=1e-4):
_, rates[:, i] = get_binned_rate(np.squeeze(spike_times[i][0])) _, rates[:, i] = get_binned_rate(np.squeeze(spike_times[i][0]))
avg_rate = np.mean(rates, axis=1) avg_rate = np.mean(rates, axis=1)
rate_std = np.std(rates, axis=1) rate_std = np.std(rates, axis=1)
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=cm_size(figure_width, 1.2*figure_height)) fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=cm_size(figure_width, 1.2*figure_height))
ax1.vlines(times[times < (100000*dt)], ymin=0, ymax=1, color="dodgerblue", lw=1.5) ax1.vlines(times[times < (100000*dt)], ymin=0, ymax=1, color="dodgerblue", lw=1.5)

8
statistics/lecture/diehistograms.py
View File

@ -2,10 +2,11 @@ import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from plotstyle import * from plotstyle import *
sketch_style()
# roll the die: # roll the die:
rng = np.random.RandomState(57281) rng = np.random.RandomState(57281)
x1 = rng.random_integers(1, 6, 100) x1 = rng.randint(1, 7, 100)
x2 = rng.random_integers(1, 6, 500) x2 = rng.randint(1, 7, 500)
bins = np.arange(0.5, 7, 1.0) bins = np.arange(0.5, 7, 1.0)
fig, (ax1, ax2) = plt.subplots(1, 2) fig, (ax1, ax2) = plt.subplots(1, 2)
@ -26,5 +27,6 @@ ax2.set_xlabel('x')
ax2.set_ylim(0, 0.23) ax2.set_ylim(0, 0.23)
ax2.set_ylabel('Probability') ax2.set_ylabel('Probability')
ax2.plot([0.2, 6.8], [1.0/6.0, 1.0/6.0], zorder=-10, **lsAm) ax2.plot([0.2, 6.8], [1.0/6.0, 1.0/6.0], zorder=-10, **lsAm)
ax2.hist([x2, x1], bins, normed=True, zorder=-5, **fs) ax2.hist([x2, x1], bins, density=True, zorder=-5, **fs)
fig.subplots_adjust(left=0.125)
fig.savefig('diehistograms.pdf') fig.savefig('diehistograms.pdf')

3
statistics/lecture/displayunivariatedata.py
View File

@ -4,6 +4,7 @@ import matplotlib.gridspec as gridspec
from scipy.stats import gaussian_kde from scipy.stats import gaussian_kde
from plotstyle import * from plotstyle import *
sketch_style()
#rng = np.random.RandomState(981) #rng = np.random.RandomState(981)
#data = rng.randn(40, 1) + 4.0 #data = rng.randn(40, 1) + 4.0
rng = np.random.RandomState(1981) rng = np.random.RandomState(1981)
@ -90,6 +91,6 @@ bw = 0.75
bins = np.arange(0, 8.0+bw, bw) bins = np.arange(0, 8.0+bw, bw)
h, b = np.histogram(data, bins) h, b = np.histogram(data, bins)
ax.barh(b[:-1], h/bw/np.sum(h), bw, **fsB) ax.barh(b[:-1], h/bw/np.sum(h), bw, **fsB)
fig.subplots_adjust(top=0.9, bottom=0.2)
plt.savefig('displayunivariatedata.pdf') plt.savefig('displayunivariatedata.pdf')

6
statistics/lecture/kerneldensity.py
View File

@ -44,7 +44,7 @@ ax.set_ylabel('p(x)')
ax.set_ylim(0.0, 0.49) ax.set_ylim(0.0, 0.49)
ax.set_yticks(np.arange(0.0, 0.41, 0.1)) ax.set_yticks(np.arange(0.0, 0.41, 0.1))
#ax.plot(x, g, '-b', lw=2, zorder=-1) #ax.plot(x, g, '-b', lw=2, zorder=-1)
ax.hist(r, np.arange(-4.1, 4, 0.4), normed=True, zorder=-5, **fsC) ax.hist(r, np.arange(-4.1, 4, 0.4), density=True, zorder=-5, **fsC)
ax = fig.add_subplot(spec[1, 0]) ax = fig.add_subplot(spec[1, 0])
ax.set_xlabel('x') ax.set_xlabel('x')
@ -54,7 +54,7 @@ ax.set_ylabel('p(x)')
ax.set_ylim(0.0, 0.49) ax.set_ylim(0.0, 0.49)
ax.set_yticks(np.arange(0.0, 0.41, 0.1)) ax.set_yticks(np.arange(0.0, 0.41, 0.1))
#ax.plot(x, g, '-b', lw=2, zorder=-1) #ax.plot(x, g, '-b', lw=2, zorder=-1)
ax.hist(r, np.arange(-4.3, 4, 0.4), normed=True, zorder=-5, **fsC) ax.hist(r, np.arange(-4.3, 4, 0.4), density=True, zorder=-5, **fsC)
ax = fig.add_subplot(spec[:, 1]) ax = fig.add_subplot(spec[:, 1])
ax.set_xlabel('x') ax.set_xlabel('x')
@ -66,6 +66,6 @@ ax.set_yticks(np.arange(0.0, 0.41, 0.1))
kd, xx = kerneldensity(r, -3.2, 3.2, 0.2) kd, xx = kerneldensity(r, -3.2, 3.2, 0.2)
ax.fill_between(xx, 0.0, kd, zorder=-5, **fsDs) ax.fill_between(xx, 0.0, kd, zorder=-5, **fsDs)
ax.plot(xx, kd, '-', zorder=-1, **lsB) ax.plot(xx, kd, '-', zorder=-1, **lsB)
fig.subplots_adjust(left=0.15)
fig.savefig('kerneldensity.pdf') fig.savefig('kerneldensity.pdf')

4
statistics/lecture/pdfhistogram.py
View File

@ -24,8 +24,8 @@ ax2.set_ylabel('Probab. density p(x)')
ax2.set_ylim(0.0, 0.44) ax2.set_ylim(0.0, 0.44)
ax2.set_yticks(np.arange(0.0, 0.41, 0.1)) ax2.set_yticks(np.arange(0.0, 0.41, 0.1))
ax2.plot(x, g, zorder=-1, **lsA) ax2.plot(x, g, zorder=-1, **lsA)
ax2.hist(r, 5, normed=True, zorder=-10, **fsB) ax2.hist(r, 5, density=True, zorder=-10, **fsB)
ax2.hist(r, 20, normed=True, zorder=-5, **fsC) ax2.hist(r, 20, density=True, zorder=-5, **fsC)
fig.savefig('pdfhistogram.pdf') fig.savefig('pdfhistogram.pdf')