def test_empty_spikes():
spt1 = np.array([])
spt2 = np.array([2, 4, 5])
assert analysis.calc_corr_coef(spt1, spt2) == 0
assert analysis.calc_corr_coef(spt2, spt1) == 0
def test_correlations_not_exceeding_one():
spt1 = np.array([2, 5, 5.5, 10, 12], dtype=float)
r = analysis.calc_corr_coef(spt1, spt1, 1.)
assert np.abs(r - 1.) < 0.05
def test_correlations_not_exceeding_one():
spt1 = np.array([2, 5, 5.5, 10, 12], dtype=float)
r = analysis.calc_corr_coef(spt1, spt1, 1.)
assert np.abs(r-1.) < 0.05
def test_random_spikes():
spt1 = np.cumsum(np.random.exponential(100, size=1000))
spt2 = np.cumsum(np.random.exponential(100, size=1000))
r = analysis.calc_corr_coef(spt1, spt2, 10.)
assert np.abs(r) < 0.05
def test_empty_spikes():
spt1 = np.array([])
spt2 = np.array([2, 4, 5])
assert analysis.calc_corr_coef(spt1, spt2) == 0
assert analysis.calc_corr_coef(spt2, spt1) == 0
def test_random_spikes():
spt1 = np.cumsum(np.random.exponential(100, size=1000))
spt2 = np.cumsum(np.random.exponential(100, size=1000))
r = analysis.calc_corr_coef(spt1, spt2, 10.)
assert np.abs(r) < 0.05
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('datafile')
parser.add_argument('--save')
parser.add_argument('--bin-size', type=float, default=10.)
args = parser.parse_args()
fname = args.datafile
outfname = args.save
data = io.loadmat(fname)
spt = data['spikeTimes']
n_spt = spt.shape[1]
correlations = np.zeros((n_spt, n_spt))
for i in range(n_spt):
for j in range(n_spt):
correlations[i,j] = analysis.calc_corr_coef(spt[0][i][0,:], spt[0][j][0,:], args.bin_size)
# plot the histogram
if args.save:
np.savez(args.save, correlations=correlations)
else:
plt.hist(correlations.flat, bins=50)
plt.xlabel('Correlation value [a.u.]')
plt.ylabel('# occurrences')
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from scipy import io
from pyNeuro.analysis import calc_corr_coef
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('mat_file')
parser.add_argument('--save')
args = parser.parse_args()
data = io.loadmat(args.mat_file)
spike_times = data['spikeTimes'][0]
corr_coefs = []
for i, spt1 in enumerate(spike_times[:-1]):
for spt2 in spike_times[i+1:]:
r = calc_corr_coef(spt1[0], spt2[0])
corr_coefs.append(r)
if args.save:
np.savez(args.save, corr_coefs = corr_coefs)
else:
plt.hist(corr_coefs, 100)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from scipy import io
from pyNeuro.analysis import calc_corr_coef
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('mat_file')
parser.add_argument('--save')
args = parser.parse_args()
data = io.loadmat(args.mat_file)
spike_times = data['spikeTimes'][0]
corr_coefs = []
for i, spt1 in enumerate(spike_times[:-1]):
for spt2 in spike_times[i + 1:]:
r = calc_corr_coef(spt1[0], spt2[0])
corr_coefs.append(r)
if args.save:
np.savez(args.save, corr_coefs=corr_coefs)
else:
plt.hist(corr_coefs, 100)
plt.show()
