def test_leak_matrix():
# Leak example 1:
v_min = 0
v_max = .02
tau = .02
dv_n = 1
dv = .005/dv_n
v = np.arange(v_min, v_max+dv, dv)
L = util.leak_matrix(v, tau)
L_true = np.array([[ 0., 100., 0., 0.],
[ 0., -100., 150., 0.],
[ 0., 0., -150., 200.],
[ 0., 0., 0., -200.]])
np.testing.assert_array_almost_equal_nulp(L, L_true)
# Leak example 2:
v = [-.02, -.015, -.01, -.005, 0, .005, .01, .015, .02]
L = util.leak_matrix(v, tau)
L_true = np.array([[-200., 0., 0., 0., 0., 0., 0., 0.],
[ 200., -150., 0., 0., 0., 0., 0., 0.],
[ 0., 150., -100., 0., 0., 0., 0., 0.],
[ 0., 0., 100., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 100., 0., 0.],
[ 0., 0., 0., 0., 0., -100., 150., 0.],
[ 0., 0., 0., 0., 0., 0., -150., 200.],
[ 0., 0., 0., 0., 0., 0., 0., -200.]])
np.testing.assert_array_almost_equal_nulp(L, L_true)
def test_leak_matrix():
# Leak example 1:
v_min = 0
v_max = .02
tau = .02
dv_n = 1
dv = .005 / dv_n
v = np.arange(v_min, v_max + dv, dv)
L = util.leak_matrix(v, tau)
L_true = np.array([[0., 100., 0., 0.], [0., -100., 150., 0.],
[0., 0., -150., 200.], [0., 0., 0., -200.]])
np.testing.assert_array_almost_equal_nulp(L, L_true)
# Leak example 2:
v = [-.02, -.015, -.01, -.005, 0, .005, .01, .015, .02]
L = util.leak_matrix(v, tau)
L_true = np.array([[-200., 0., 0., 0., 0., 0., 0., 0.],
[200., -150., 0., 0., 0., 0., 0., 0.],
[0., 150., -100., 0., 0., 0., 0., 0.],
[0., 0., 100., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 100., 0., 0.],
[0., 0., 0., 0., 0., -100., 150., 0.],
[0., 0., 0., 0., 0., 0., -150., 200.],
[0., 0., 0., 0., 0., 0., 0., -200.]])
np.testing.assert_array_almost_equal_nulp(L, L_true)
def initialize_edges(self):
'''Initialize self.edges and self.leak_flux_matrix attributes.
This method initializes the self.edges attribute based on the v_min,
v_max, and dv settings, and creates a corresponding leak flux matrix
based on this voltage discretization.
'''
# Voltage edges and leak matrix construction
self.tau_m = util.discretize_if_needed(self.tau_m)
if np.sum(self.tau_m.xk <= 0) > 0:
raise Exception('Negative tau_m values detected: %s' %
self.tau_m.xk) # pragma: no cover
# Voltage edges and leak matrix construction
self.edges = util.get_v_edges(self.v_min, self.v_max, self.dv)
# Different leak matrices for different solvers:
self.leak_flux_matrix_dict = {}
self.leak_flux_matrix_dict['dense'] = util.leak_matrix(
self.edges, self.tau_m)
# Backward Euler sparse:
lfm_csrbe = sps.eye(
np.shape(self.leak_flux_matrix_dict['dense'])[0], format='csr'
) - self.simulation.dt * self.leak_flux_matrix_dict['dense']
M_I, M_J = np.where(np.array(lfm_csrbe) != 0)
M_val = lfm_csrbe[M_I, M_J]
self.leak_flux_matrix_dict['sparse'] = (M_I, M_J, M_val)
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 initialize_edges(self):
'''Initialize self.edges and self.leak_flux_matrix attributes.
This method initializes the self.edges attribute based on the v_min,
v_max, and dv settings, and creates a corresponding leak flux matrix
based on this voltage discretization.
'''
# Voltage edges and leak matrix construction
self.tau_m = util.discretize_if_needed(self.tau_m)
if np.sum(self.tau_m.xk <= 0) > 0:
raise Exception('Negative tau_m values detected: %s' % self.tau_m.xk) # pragma: no cover
# Voltage edges and leak matrix construction
self.edges = util.get_v_edges(self.v_min, self.v_max, self.dv)
# Different leak matrices for different solvers:
self.leak_flux_matrix_dict = {}
self.leak_flux_matrix_dict['dense'] = util.leak_matrix(self.edges, self.tau_m)
# Backward Euler sparse:
lfm_csrbe = sps.eye(np.shape(self.leak_flux_matrix_dict['dense'])[0], format='csr') - self.simulation.dt*self.leak_flux_matrix_dict['dense']
M_I, M_J = np.where(np.array(lfm_csrbe) != 0)
M_val = lfm_csrbe[M_I, M_J]
self.leak_flux_matrix_dict['sparse'] = (M_I, M_J, M_val)
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_edges(self):
'''Initialize self.edges and self.leak_flux_matrix attributes.
This method initializes the self.edges attribute based on the v_min,
v_max, and dv settings, and creates a corresponding leak flux matrix
based on this voltage discretization.
'''
# Voltage edges and leak matrix construction
self.edges = util.get_v_edges(self.v_min, self.v_max, self.dv)
self.leak_flux_matrix = util.leak_matrix(self.edges, self.tau_m)
def initialize_edges(self):
'''Initialize self.edges and self.leak_flux_matrix attributes.
This method initializes the self.edges attribute based on the v_min,
v_max, and dv settings, and creates a corresponding leak flux matrix
based on this voltage discretization.
'''
# Voltage edges and leak matrix construction
self.edges = util.get_v_edges(self.v_min, self.v_max, self.dv)
self.leak_flux_matrix = util.leak_matrix(self.edges, self.tau_m)
def initialize_edges(self):
'''Initialize self.edges and self.leak_flux_matrix attributes.
This method initializes the self.edges attribute based on the v_min,
v_max, and dv settings, and creates a corresponding leak flux matrix
based on this voltage discretization.
'''
# Voltage edges and leak matrix construction
self.tau_m = util.discretize_if_needed(self.tau_m)
if np.sum(self.tau_m.xk <= 0) > 0:
raise Exception('Negative tau_m values detected: %s' % self.tau_m.xk) # pragma: no cover
self.edges = util.get_v_edges(self.v_min, self.v_max, self.dv)
self.leak_flux_matrix = util.leak_matrix(self.edges, self.tau_m)
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
