def __init__(self,
source,
target,
nsyn,
**kwargs):
self.source = source
self.target = target
self.nsyn = nsyn
self.weights = kwargs.pop('weights', None)
self.probs = kwargs.pop('probs', None)
self.delay = float(kwargs.pop('delay', 0))
self.metadata = kwargs
if self.weights != None or self.probs != None:
assert len(self.weights) == len(self.probs)
else:
self.weights, self.probs = util.descretize(kwargs.get('distribution', None),
kwargs.get('N', None),
scale=kwargs.get('scale', None))
assert np.abs(self.probs).sum() == 1
# Defined at runtime:
self.delay_queue = None
self.delay_ind = None
self.simulation = None
def example(show=False, save=False):
# Settings:
t0 = 0.
dt = .0001
dv = .0001
tf = .1
update_method = 'gmres'
tol = 1e-14
# Run simulation:
mu = .02
sigma = 0.004
b = np.inf
a = 0
tau_m_dist = sps.truncnorm(float(a - mu) / sigma,
float(b - mu) / sigma,
loc=mu,
scale=sigma)
total = 0
vals, probs = descretize(tau_m_dist, 100)
for ii, (val, prob) in enumerate(zip(vals, probs)):
print ii
network = get_network(dv=dv,
update_method=update_method,
tol=tol,
tau_m=val)
network.run(dt=dt, tf=tf, t0=t0)
i1 = network.population_list[1]
total += i1.firing_rate_record[-1] * prob
print total
# # Visualize:
# i1 = network.population_list[1]
#
# fig, ax = plt.subplots(figsize=(3,3))
#
# i1.plot(ax=ax)
# plt.xlim([0,tf])
# plt.ylim(ymin=0)
# plt.xlabel('Time (s)')
# plt.ylabel('Firing Rate (Hz)')
# fig.tight_layout()
# if save == True: plt.savefig('./singlepop.png')
#
#
#
# if show == True: # pragma: no cover
# fig = plt.gcf() # pragma: no cover
# window = fig.canvas.manager.window # pragma: no cover
# window.raise_() # pragma: no cover
# plotting.show()
return i1.t_record, i1.firing_rate_record
def example(show=False, save=False):
# Settings:
t0 = 0.0
dt = 0.0001
dv = 0.0001
tf = 0.1
update_method = "gmres"
tol = 1e-14
# Run simulation:
mu = 0.02
sigma = 0.004
b = np.inf
a = 0
tau_m_dist = sps.truncnorm(float(a - mu) / sigma, float(b - mu) / sigma, loc=mu, scale=sigma)
total = 0
vals, probs = descretize(tau_m_dist, 100)
for ii, (val, prob) in enumerate(zip(vals, probs)):
print ii
network = get_network(dv=dv, update_method=update_method, tol=tol, tau_m=val)
network.run(dt=dt, tf=tf, t0=t0)
i1 = network.population_list[1]
total += i1.firing_rate_record[-1] * prob
print total
# # Visualize:
# i1 = network.population_list[1]
#
# fig, ax = plt.subplots(figsize=(3,3))
#
# i1.plot(ax=ax)
# plt.xlim([0,tf])
# plt.ylim(ymin=0)
# plt.xlabel('Time (s)')
# plt.ylabel('Firing Rate (Hz)')
# fig.tight_layout()
# if save == True: plt.savefig('./singlepop.png')
#
#
#
# if show == True: # pragma: no cover
# fig = plt.gcf() # pragma: no cover
# window = fig.canvas.manager.window # pragma: no cover
# window.raise_() # pragma: no cover
# plotting.show()
return i1.t_record, i1.firing_rate_record
def __init__(self, source, target, nsyn, **kwargs):
self.source = source
self.target = target
self.nsyn = nsyn
self.weights = kwargs.pop('weights', None)
self.probs = kwargs.pop('probs', None)
self.delay = float(kwargs.pop('delay', 0))
self.metadata = kwargs
if self.weights != None or self.probs != None:
assert len(self.weights) == len(self.probs)
else:
self.weights, self.probs = util.descretize(
kwargs.get('distribution', None),
kwargs.get('N', None),
scale=kwargs.get('scale', None))
assert np.abs(self.probs).sum() == 1
# Defined at runtime:
self.delay_queue = None
self.delay_ind = None
self.simulation = None
