def __init__(self, parameters, stimulus, T, dt, name='HHSimulator'):
"""
Initialize derived class to properly connect it to its input models.
It accepts as input an InputConnector object that fully specifies how
to connect all parent models to the current model.
Parameters
----------
parameters : list
A list of input parameters to connect.
stimulus : array of shape (int(T/dt)+1) or callable with signature '(t)'
The input stimulus
T : float
Simulation time
dt : float
Time step
name : str
A human readable name for the model.
"""
self.I = stimulus
self.T = T
self.dt = dt
self.hh = HodgkinHuxley()
input_parameters = InputConnector.from_list(parameters)
super(HHSimulator, self).__init__(input_parameters, name)
def __init__(self, parameters, name='BivariateGaussianMixtureModel'):
input_parameters = InputConnector.from_list(parameters)
super(BivariateGaussianMixtureModel,
self).__init__(input_parameters, name)
self.cov0 = np.array([[0.5, -0.3], [-0.3, 0.5]])
self.cov1 = np.array([[0.25, 0], [0, 0.25]])
def __init__(self,
parameters,
n_timestep=250,
burnin=50,
name="RecruitmentBoomBust"):
input_parameters = InputConnector.from_list(parameters)
self.n_timestep = n_timestep
self.burnin = burnin
# Parameter specifying the dimension of the return values of the distribution.
super(RecruitmentBoomBust, self).__init__(input_parameters, name)
def __init__(self,
parameters,
T=20,
n_integration_steps=1000,
noise=True,
name='LotkaVolterra'):
self.T = T
self.n_integration_steps = n_integration_steps
self.noise = noise
self.X0 = np.array([30, 1]) # capital X is [x,y]
self.sigma_lognormal = 0.1
input_parameters = InputConnector.from_list(parameters)
super(LotkaVolterra, self).__init__(input_parameters, name)
def __init__(self, parameters, num_AR_params=2, num_MA_params=2, size=100, name='ARMA model'):
"""size is the length of the timeseries to be generated by the model.
The AR parameters always need to be passed before the MA parameters."""
if not isinstance(parameters, list):
raise TypeError('Input of ARMAmodel model is of type list')
self.size = size
self.num_AR_params = num_AR_params
self.num_MA_params = num_MA_params
self.total_num_params = num_AR_params + num_MA_params
self._check_num_parameters(parameters)
input_connector = InputConnector.from_list(parameters)
super(ARMAmodel, self).__init__(input_connector, name)
def __init__(self, parameters, n_timestep=100, name="Ricker"):
input_parameters = InputConnector.from_list(parameters)
self.n_timestep = n_timestep
# Parameter specifying the dimension of the return values of the distribution.
super(Ricker, self).__init__(input_parameters, name)
def __init__(self, parameters, size=5, name='Multivariate_g_and_k'):
self.size = size
self.c = 0.8 # fix this
input_parameters = InputConnector.from_list(parameters)
super(Multivariate_g_and_k, self).__init__(input_parameters, name)
def __init__(self, parameters, iid_size=1, name='Iid_Gamma'):
self.iid_size = iid_size
input_parameters = InputConnector.from_list(parameters)
super(IidGamma, self).__init__(input_parameters, name)
def __init__(self, parameters, n_samples=10, name='Bivariate_Normal'):
input_parameters = InputConnector.from_list(parameters)
self.n_samples = n_samples
super(BivariateNormal, self).__init__(input_parameters, name)
def __init__(self, parameters, number_steps=5, name='M/G/1'):
self.number_steps = number_steps
input_parameters = InputConnector.from_list(parameters)
super(MG1Queue, self).__init__(input_parameters, name)