def singlepop(steady_state, tau_m=.02, p0=((0.,),(1.,)), weights={'distribution':'delta', 'loc':.005}, bgfr=100, network_update_callback=lambda s: None, update_method='approx', simulation_configuration=None, tol=None, checkpoint_callback=None, nsyn=1):
# Settings:
t0 = 0.
dt = .001
dv = .001
v_min = -.01
v_max = .02
tf = .1
# Create simulation:
b1 = ExternalPopulation(bgfr)
i1 = InternalPopulation(v_min=v_min, tau_m=tau_m, v_max=v_max, dv=dv, update_method=update_method, p0=p0, tol=tol)
b1_i1 = Connection(b1, i1, nsyn, weights=weights)
network = Network([b1, i1], [b1_i1], update_callback=network_update_callback)
if simulation_configuration is None:
simulation_configuration = SimulationConfiguration(dt, tf, t0=t0)
simulation = Simulation(network=network, simulation_configuration=simulation_configuration, checkpoint_callback=checkpoint_callback)
simulation.run()
b1.plot()
i1.plot_probability_distribution()
i1.plot()
assert i1.n_edges == i1.n_bins+1
# Test steady-state:
np.testing.assert_almost_equal(i1.get_firing_rate(.05), steady_state, 12)
def singlepop(steady_state, tau_m=.02, p0=((0.,),(1.,)), weights={'distribution':'delta', 'loc':.005}, bgfr=100, network_update_callback=lambda s: None, update_method='approx', simulation_configuration=None, tol=None):
# Settings:
t0 = 0.
dt = .001
dv = .001
v_min = -.01
v_max = .02
tf = .1
# Create simulation:
b1 = ExternalPopulation(bgfr)
i1 = InternalPopulation(v_min=v_min, tau_m=tau_m, v_max=v_max, dv=dv, update_method=update_method, p0=p0, tol=tol)
b1_i1 = Connection(b1, i1, 1, weights=weights)
network = Network([b1, i1], [b1_i1], update_callback=network_update_callback)
if simulation_configuration is None:
simulation_configuration = SimulationConfiguration(dt, tf, t0=t0)
simulation = Simulation(network=network, simulation_configuration=simulation_configuration)
simulation.run()
b1.plot()
i1.plot_probability_distribution()
i1.plot()
assert i1.n_edges == i1.n_bins+1
# Test steady-state:
np.testing.assert_almost_equal(i1.get_firing_rate(.05), steady_state, 12)
def test_delay_distribution():
# Settings:
t0 = 0.
dt = .001
tf = .1
# Create populations:
b1 = ExternalPopulation('100*Heaviside(t)')
i1 = InternalPopulation(v_min=0, v_max=.02, dv=.001, update_method='exact')
# Create connections:
b1_i1 = Connection(b1, i1, 1, weights=.005, delays=((0,.05),(.5,.5)))
# Create and run simulation:
simulation = Network([b1, i1], [b1_i1])
simulation.run(dt=dt, tf=tf, t0=t0)
true_ans = np.array([0.38560647739319964, 5.229266329159536])
np.testing.assert_almost_equal(np.array(i1.get_firing_rate([.04, .09])), true_ans, 8)
from dipde.visualization import visualize
dv = .0001
update_method = 'approx'
approx_order = 1
tol = 1e-14
b1 = ExternalPopulation('100', record=True)
i1 = InternalPopulation(v_min=0,
v_max=.02,
dv=dv,
update_method=update_method,
approx_order=approx_order,
tol=tol)
i2 = InternalPopulation(v_min=0,
v_max=.02,
dv=dv,
update_method=update_method,
approx_order=approx_order,
tol=tol)
b1_i1 = Connection(b1, i1, 1, weights=.005, delays=0.0)
b1_i2 = Connection(b1, i2, 2, weights=.005, delays=0.0)
network = Network([b1, i1, i2], [b1_i1, b1_i2])
network.run(dt=.0001, tf=.1, t0=0)
print i1.get_firing_rate(.1)
visualize(network, show=True)
print network.to_dict()
# import matplotlib
# matplotlib.use('Qt4Agg')
# import matplotlib.pyplot as plt
from dipde.internals.internalpopulation import InternalPopulation
from dipde.internals.externalpopulation import ExternalPopulation
from dipde.internals.network import Network
from dipde.internals.connection import Connection as Connection
from dipde.visualization import visualize
dv = .0001
update_method = 'approx'
approx_order = 1
tol = 1e-14
b1 = ExternalPopulation('100', record=True)
i1 = InternalPopulation(v_min=0, v_max=.02, dv=dv, update_method=update_method, approx_order=approx_order, tol=tol)
i2 = InternalPopulation(v_min=0, v_max=.02, dv=dv, update_method=update_method, approx_order=approx_order, tol=tol)
b1_i1 = Connection(b1, i1, 1, weights=.005, delays=0.0)
b1_i2 = Connection(b1, i2, 2, weights=.005, delays=0.0)
network = Network([b1, i1, i2], [b1_i1, b1_i2])
network.run(dt=.0001, tf=.1, t0=0)
print i1.get_firing_rate(.1)
visualize(network, show=True)
print network.to_dict()
