def __init__(self,
source=None,
target=None,
nsyn=None,
delays=0,
weights=None,
delay_queue=None,
metadata={},
**kwargs):
self.source_gid_or_population = source
self.target_gid_or_population = target
# Used for jumping off point for making a matched nest simulation:
self.original_weights = weights
self.original_delays = delays
self.nsyn = nsyn
self.synaptic_weight_distribution = util.discretize_if_needed(weights)
self.weights, self.probs = self.synaptic_weight_distribution.xk, self.synaptic_weight_distribution.pk
self.delay_distribution = util.discretize_if_needed(delays)
self.delay_vals, self.delay_probs = self.delay_distribution.xk, self.delay_distribution.pk
self.delay_queue_initial_condition = delay_queue
util.check_metadata(metadata)
self.metadata = metadata
# Defined at runtime:
self.delay_queue = None
self.delay_ind = None
self.simulation = None
for key in kwargs.keys():
assert key in ['class']
def __init__(self,
source=None,
target=None,
nsyn=None,
delays=0,
weights=None,
delay_queue=None,
metadata={},
**kwargs):
self.source_gid_or_population = source
self.target_gid_or_population = target
# Used for jumping off point for making a matched nest simulation:
self.original_weights = weights
self.original_delays = delays
self.nsyn_input = nsyn
self.synaptic_weight_distribution = util.discretize_if_needed(weights)
self.weights, self.probs = self.synaptic_weight_distribution.xk, self.synaptic_weight_distribution.pk
self.delay_distribution = util.discretize_if_needed(delays)
self.delay_vals, self.delay_probs = self.delay_distribution.xk, self.delay_distribution.pk
self.delay_queue_initial_condition = delay_queue
util.check_metadata(metadata)
self.metadata = metadata
# Defined at runtime:
self.delay_queue = None
self.delay_ind = None
self.simulation = None
for key in kwargs.keys():
assert key in ['class']
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 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 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 initialize_probability(self):
'''Initialize self.pv to delta-distribution at v=0.'''
self.p0 = util.discretize_if_needed(self.p0)
self.pv = util.get_pv_from_p0(self.p0, self.edges)
util.assert_probability_mass_conserved(self.pv, 1e-15)
def test_continuous_no_N():
discretize_if_needed(sps.norm(loc=2, scale=1.5))
def test_dist_json_delta():
discretize_if_needed(json.dumps({'distribution':'delta', 'loc':.5}))
def test_discrete_rv():
test_dist = discretize_if_needed(sps.rv_discrete(values=(.02, 1)))
def test_dist_xk_pk_off_by_one():
discretize_if_needed(((0,1,2,3),(.5,.25,.25)))
def test_dist_specified():
discretize_if_needed(((0,1,2,3),(.25,.25,.25,.25)))
def test_discrete_rv():
# sps.rv_discrete(values=([.02],[1]), )
discretize_if_needed(sps.rv_discrete(values=([.02],[1])))
def test_scalar():
discretize_if_needed(.02)
def test_continuous_N():
test_dist = discretize_if_needed((sps.norm(loc=2, scale=1.5), 25))
def test_dist_dict_delta():
discretize_if_needed({'distribution':'delta', 'loc':.5})
def initialize_probability(self):
'''Initialize self.pv to delta-distribution at v=0.'''
self.p0 = util.discretize_if_needed(self.p0)
self.pv = util.get_pv_from_p0(self.p0, self.edges)
util.assert_probability_mass_conserved(self.pv, 1e-15)
def test_scalar():
test_dist = discretize_if_needed(.02)
