def getMutualInfoFromPair(pair, exp_frame):
first_response = exp_frame[exp_frame.cell_id == pair[0]]['num_spikes']
second_response = exp_frame[exp_frame.cell_id == pair[1]]['num_spikes']
first_response_dims = [1, 1 + first_response.max()]
second_response_dims = [1, 1 + second_response.max()]
discrete_system = DiscreteSystem(first_response, first_response_dims,
second_response, second_response_dims)
return calcInfo(discrete_system)
def do_1d_check(method, qe_method):
xc = x.copy()
yc = y.copy()
s = DiscreteSystem(xc, (1, 10), yc, (1, 10))
# calculate all entropies
s.calculate_entropies(method=method, calc=allcalc, qe_method=qe_method)
# check output assinged
assert_(s.H == getattr(s, 'H_%s' % method.replace('-', '')))
v = np.array([s.H[k] for k in allcalc])
assert_array_almost_equal(v, alltrue, decimal=2)
# check didn't do something nasty to inputs
assert_array_equal(x, xc)
assert_array_equal(y, yc)
def mask_mutualinfo_matrix(nifti_file,
masks,
outfile,
mask_thresh=0,
nbins=10):
"""
Calculates mutual information matrix for a set of mask mean timeseries'
"""
from pyentropy import DiscreteSystem
mutualinfo_mat = np.zeros((len(masks), len(masks)))
input = nib.load(nifti_file)
input_d = input.get_data()
if len(input.shape) > 3:
ts_length = input.shape[3]
else:
ts_length = 1
mean_bin_ts_array = np.zeros((len(masks), ts_length), dtype='int')
for count, mask in enumerate(masks):
mask_coords = core.get_nonzero_coords(mask, mask_thresh)
mask_array = [
input_d[mask_coord[0], mask_coord[1], mask_coord[2], :]
for mask_coord in mask_coords
]
mean_ts = np.mean(mask_array, axis=0)
l = np.linspace(min(mean_ts), max(mean_ts), nbins)
mean_bin_ts_array[count, :] = np.digitize(
mean_ts, l) - 1 # set range to start at 0
if count > 0:
for prev in range(count):
sys = DiscreteSystem(mean_bin_ts_array[count], (1, nbins),
mean_bin_ts_array[prev], (1, nbins))
sys.calculate_entropies(method='qe',
calc=['HX', 'HXY', 'HiXY', 'HshXY'])
mutualinfo_mat[count, prev] = sys.I()
mutualinfo_mat_sym = core.symmetrize_mat(mutualinfo_mat, 'bottom')
np.savetxt('%s.txt' % outfile, mutualinfo_mat_sym)
return mutualinfo_mat_sym
def test_toy1():
s = DiscreteSystem(x, (3, 3), y, (1, 2))
s.calculate_entropies(method='plugin', calc=toycalc)
v = np.array([s.H[t] for t in toycalc])
assert_array_almost_equal(v, toytrue)
#!/usr/bin/env python
import numpy as np
import pyentropy
from pyentropy import DiscreteSystem
import matplotlib.pyplot as plt
N = 10
Nq = 50
Nx = 1
noise = 1
ntrials = 10
for trial in xrange(ntrials):
x = N * np.random.rand(Nx, 10000)
y = np.zeros(10000)
xq = np.empty((Nx, 10000), dtype=int)
for n in range(Nx):
xq[n, :] , _, _ = pyentropy.quantise(x[n, :], Nq)
y += x[n, :]
y += noise*np.random.randn(10000)
yq, _, _ = pyentropy.quantise(y, Nq)
s = DiscreteSystem(xq, (Nx, Nq), yq, (1, Nq))
s.calculate_entropies(method='plugin', calc=['HX', 'HXY'])
print(s.I())
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(xq, yq, s=2)
ax.set_title('MI: %s' % s.I())
fig.savefig('mi_%d.png' % trial)
plt.close()
