def test_utility_functions():
'''
Test the (non-public) random_matrix and random_matrix_fixed_column
functions.
'''
# Convenience functions to make the testing a bit easier
def assert_size_and_value(matrix, shape, value):
assert issparse(matrix)
assert matrix.shape == shape
rows, cols = matrix.nonzero()
for row, col in zip(rows, cols):
if callable(value):
if value.func_code.co_argcount == 0:
assert matrix[row, col] == value()
elif value.func_code.co_argcount == 2:
assert matrix[row, col] == value(row, col)
else:
raise AssertionError('Illegal value argument')
else:
assert matrix[row, col] == value
def assert_entries_per_column(matrix, n_entries):
for col_idx in range(matrix.shape[1]):
entries = matrix[:, col_idx].nonzero()[0]
assert len(entries) == n_entries, 'number of entries is %d not %d!' % (len(entries), n_entries)
# with fixed probability
p = 1.
r_m = random_matrix(2, 4, p=p)
assert_size_and_value(r_m, (2, 4), 1.)
# with fixed probability and value
p, value = 1., 2.
r_m = random_matrix(2, 4, p=p, value=value)
assert_size_and_value(r_m, (2, 4), 2.)
# Note: p can also be a function (like value), but as this is not
# documented, this is also not tested
# with a constant function for value
value = lambda : 2.
r_m = random_matrix(2, 4, p=1., value=value)
assert_size_and_value(r_m, (2, 4), value)
# with an index dependent function for value
value = lambda i, j : (i != j) * 2.
r_m = random_matrix(2, 4, p=1., value=value)
assert_size_and_value(r_m, (2, 4), value)
# Test random matrix with fixed number of entries per column
# with fixed probability
p = 0.5
r_m = random_matrix_fixed_column(100, 4, p=p)
assert_size_and_value(r_m, (100, 4), 1.)
assert_entries_per_column(r_m, 50)
# with fixed probability and value
p, value = 0.5, 2.
r_m = random_matrix_fixed_column(100, 4, p=p, value=value)
assert_size_and_value(r_m, (100, 4), 2.)
assert_entries_per_column(r_m, 50)
# Note: p can also be a function (like value), but this is not documented
# with a constant function for value
value = lambda : 2.
r_m = random_matrix_fixed_column(100, 4, p=0.5, value=value)
assert_size_and_value(r_m, (100, 4), value)
assert_entries_per_column(r_m, 50)
# with an index dependent function for value
value = lambda i, j : (i != j) * 2. + 1
r_m = random_matrix_fixed_column(100, 4, p=0.5, value=value)
assert_size_and_value(r_m, (100, 4), value)
assert_entries_per_column(r_m, 50)
if __name__ == '__main__':
import matplotlib.pyplot as plt
from brian.network import clear
from brian.connections.construction import random_matrix
from brian.stdunits import mV,ms
import numpy as np
NE = 8000 # Number of excitatory cells
NI = NE/4 # Number of inhibitory cells
epsilon = 0.15 # Sparseness of synaptic connections
mon_bin = 1*ms
dt = 0.1*ms
C_e = random_matrix(NE, NE+NI,epsilon, value=0.3)
C_ii = random_matrix(NI, NI, epsilon, value=3.0)
C_ie = random_matrix(NI, NE, epsilon, value=2.0)
v_init = -80*np.random.rand(NE+NI)*mV
rme1, rmi1 = run_stdp(NE,NI,v_init,C_e,C_ii,C_ie,mon_bin,dt)
clear()
rme2, rmi2 = run_stdp(NE,NI,v_init,C_e,C_ii,C_ie,mon_bin,dt)
filter_width = 200*ms
width_dt = int(filter_width / mon_bin) # width in number of bins
window = np.exp(-np.arange(-2 * width_dt, 2 * width_dt + 1) ** 2 * 1. / (2 * (width_dt) ** 2))
fig = plt.figure()
ax_inh = fig.add_subplot(2,1,1)
ax_exc = fig.add_subplot(2,1,2)
def test_utility_functions():
'''
Test the (non-public) random_matrix and random_matrix_fixed_column
functions.
'''
# Convenience functions to make the testing a bit easier
def assert_size_and_value(matrix, shape, value):
assert issparse(matrix)
assert matrix.shape == shape
rows, cols = matrix.nonzero()
for row, col in zip(rows, cols):
if callable(value):
if value.func_code.co_argcount == 0:
assert matrix[row, col] == value()
elif value.func_code.co_argcount == 2:
assert matrix[row, col] == value(row, col)
else:
raise AssertionError('Illegal value argument')
else:
assert matrix[row, col] == value
def assert_entries_per_column(matrix, n_entries):
for col_idx in range(matrix.shape[1]):
entries = matrix[:, col_idx].nonzero()[0]
assert len(
entries) == n_entries, 'number of entries is %d not %d!' % (
len(entries), n_entries)
# with fixed probability
p = 1.
r_m = random_matrix(2, 4, p=p)
assert_size_and_value(r_m, (2, 4), 1.)
# with fixed probability and value
p, value = 1., 2.
r_m = random_matrix(2, 4, p=p, value=value)
assert_size_and_value(r_m, (2, 4), 2.)
# Note: p can also be a function (like value), but as this is not
# documented, this is also not tested
# with a constant function for value
value = lambda: 2.
r_m = random_matrix(2, 4, p=1., value=value)
assert_size_and_value(r_m, (2, 4), value)
# with an index dependent function for value
value = lambda i, j: (i != j) * 2.
r_m = random_matrix(2, 4, p=1., value=value)
assert_size_and_value(r_m, (2, 4), value)
# Test random matrix with fixed number of entries per column
# with fixed probability
p = 0.5
r_m = random_matrix_fixed_column(100, 4, p=p)
assert_size_and_value(r_m, (100, 4), 1.)
assert_entries_per_column(r_m, 50)
# with fixed probability and value
p, value = 0.5, 2.
r_m = random_matrix_fixed_column(100, 4, p=p, value=value)
assert_size_and_value(r_m, (100, 4), 2.)
assert_entries_per_column(r_m, 50)
# Note: p can also be a function (like value), but this is not documented
# with a constant function for value
value = lambda: 2.
r_m = random_matrix_fixed_column(100, 4, p=0.5, value=value)
assert_size_and_value(r_m, (100, 4), value)
assert_entries_per_column(r_m, 50)
# with an index dependent function for value
value = lambda i, j: (i != j) * 2. + 1
r_m = random_matrix_fixed_column(100, 4, p=0.5, value=value)
assert_size_and_value(r_m, (100, 4), value)
assert_entries_per_column(r_m, 50)
