def test_flux_ex_inh():
# Flux example 2:
v_min = -.02
v_max = .02
tau = .02
w = .005
dv_n = 500
dv = .005 / dv_n
v = np.arange(v_min, v_max + dv, dv)
A = util.leak_matrix(v, tau)
flux_to_zero_vector = np.zeros_like(A.sum(axis=0))
flux_to_zero_vector_tmp, A_tmp = util.flux_matrix(v, w, 100)
A += A_tmp
flux_to_zero_vector += flux_to_zero_vector_tmp
flux_to_zero_vector_tmp, A_tmp = util.flux_matrix(v, -w, 100)
A += A_tmp
flux_to_zero_vector += flux_to_zero_vector_tmp
x = spla.solve(A + np.ones_like(A), np.ones_like(v[:-1]))
FR_ss = np.dot(x, flux_to_zero_vector)
assert np.abs(FR_ss - .899715510935) < 1e-10
def test_flux_ex_inh():
# Flux example 2:
v_min = -.02
v_max = .02
tau=.02
w = .005
dv_n = 500
dv = .005/dv_n
v = np.arange(v_min, v_max+dv, dv)
A = util.leak_matrix(v, tau)
flux_to_zero_vector = np.zeros_like(A.sum(axis=0))
flux_to_zero_vector_tmp, A_tmp = util.flux_matrix(v, w, 100)
A += A_tmp
flux_to_zero_vector += flux_to_zero_vector_tmp
flux_to_zero_vector_tmp, A_tmp = util.flux_matrix(v, -w, 100)
A += A_tmp
flux_to_zero_vector += flux_to_zero_vector_tmp
x = spla.solve(A+np.ones_like(A), np.ones_like(v[:-1]))
FR_ss = np.dot(x, flux_to_zero_vector)
assert np.abs(FR_ss - .899715510935) < 1e-10
def test_flux_ex_inh_border():
# Flux example 2:
v_min = 0
v_max = .02
tau=sps.rv_discrete(values=([.02],[1]))
w = .005
dv_n = 500
dv = .005/dv_n
v = np.arange(v_min, v_max+dv, dv)
A = util.leak_matrix(v, tau)
flux_to_zero_vector = np.zeros_like(A.sum(axis=0))
flux_to_zero_vector_tmp, A_tmp = util.flux_matrix(v, w, 100)
A += A_tmp
flux_to_zero_vector += flux_to_zero_vector_tmp
flux_to_zero_vector_tmp, A_tmp = util.flux_matrix(v, -w, 100)
A += A_tmp
flux_to_zero_vector += flux_to_zero_vector_tmp
x = spla.solve(A+np.ones_like(A), np.ones_like(v[:-1]))
FR_ss = np.dot(x, flux_to_zero_vector)
assert np.abs(FR_ss - 1.47243297098) < 1e-10
def initialize(self):
"""Initialize connection distribution.
Initialization creates the flux_matrix and threshold_flux_vector.
Implemented lazily via a try catch when flux matrix is requested, that
does not exist.
"""
nv = len(self.edges)-1
self.flux_matrix = np.zeros((nv, nv))
self.threshold_flux_vector = np.zeros(nv)
for curr_weight, curr_prob in zip(self.weights, self.probs):
curr_threshold_flux_vector, curr_flux_matrix = util.flux_matrix(self.edges, curr_weight, curr_prob)
self.flux_matrix += curr_flux_matrix
self.threshold_flux_vector += curr_threshold_flux_vector
# Guarantee zero flux:
np.testing.assert_almost_equal(np.abs(np.sum(self.flux_matrix, axis=0)).sum(), 0, 12)
def initialize(self):
"""Initialize connection distribution.
Initialization creates the flux_matrix and threshold_flux_vector.
Implemented lazily via a try catch when flux matrix is requested, that
does not exist.
"""
nv = len(self.edges) - 1
self.flux_matrix = np.zeros((nv, nv))
self.threshold_flux_vector = np.zeros(nv)
for curr_weight, curr_prob in zip(self.weights, self.probs):
curr_threshold_flux_vector, curr_flux_matrix = util.flux_matrix(
self.edges, curr_weight, curr_prob)
self.flux_matrix += curr_flux_matrix
self.threshold_flux_vector += curr_threshold_flux_vector
# Guarantee zero flux:
np.testing.assert_almost_equal(
np.abs(np.sum(self.flux_matrix, axis=0)).sum(), 0, 12)
def test_flux_ex():
# Flux example 1:
v_min = 0
v_max = .02
tau = sps.rv_discrete(values=(.02, 1))
w = .005
dv_n = 500
dv = .005 / dv_n
v = np.arange(v_min, v_max + dv, dv)
A = util.leak_matrix(v, tau)
flux_to_zero_vector = np.zeros_like(A.sum(axis=0))
flux_to_zero_vector_tmp, A_tmp = util.flux_matrix(v, w, 100)
A += A_tmp
flux_to_zero_vector += flux_to_zero_vector_tmp
x = spla.solve(A + np.ones_like(A), np.ones_like(v[:-1]))
FR_ss = np.dot(x, flux_to_zero_vector)
assert np.abs(FR_ss - 5.28031853301) < 1e-10
def initialize(self):
"""Initialize connection distribution.
Initialization creates the flux_matrix and threshold_flux_vector.
Implemented lazily via a try catch when flux matrix is requested, that
does not exist.
"""
nv = len(self.edges)-1
flux_matrix_dense = np.zeros((nv, nv))
self.threshold_flux_vector = np.zeros(nv)
for curr_weight, curr_prob in zip(self.weights, self.probs):
curr_threshold_flux_vector, curr_flux_matrix = util.flux_matrix(self.edges, curr_weight, curr_prob)
flux_matrix_dense += curr_flux_matrix
self.threshold_flux_vector += curr_threshold_flux_vector
M_I, M_J = np.where(flux_matrix_dense != 0)
M_val = flux_matrix_dense[M_I, M_J]
flux_matrix_sparse = (M_I, M_J, M_val)
self.flux_matrix_dict = {'dense':flux_matrix_dense, 'sparse':flux_matrix_sparse}
# Guarantee zero flux:
np.testing.assert_almost_equal(np.abs(np.sum(self.flux_matrix_dict['dense'], axis=0)).sum(), 0, 12)
