cbadc.simulator.numerical_simulator.FullSimulator

class cbadc.simulator.numerical_simulator.FullSimulator(analog_system: Union[AnalogSystem, ChainOfIntegrators, LeapFrog], digital_control: Union[DigitalControl, MultiLevelDigitalControl], input_signal: List[_AnalogSignal], initial_state_vector: array = None, state_noise_covariance_matrix: ndarray = None, atol: float = 1e-15, rtol: float = 1e-10, seed: int = 4212312513432239834528672)[source]

Bases: _BaseSimulator

Simulate the analog system and digital control interactions in the presence on analog signals.

Parameters

analog_system (cbadc.analog_system.AnalogSystem) – the analog system
digital_control (cbadc.digital_control.DigitalControl) – the digital control
input_signals ([cbadc.analog_signal.AnalogSignal]) – a python list of analog signals (or a derived class)
initial_state_vector (array_like, shape=(N), optional) – initial state vector.
state_noise_covariance_matrix (array_like, shape=(N, N), optional) – the covariance matrix of white noise contributions at the state vector during each control step.
atol (float, optional) – Relative and absolute tolerances. The solver keeps the local error estimates less.
rtol (float, optional) – Relative and absolute tolerances. The solver keeps the local error estimates less.

analog_system

the analog system being simulated.

Type: cbadc.analog_system.AnalogSystem

digital_control

the digital control being simulated.

Type: cbadc.digital_control.DigitalControl

t

current time of simulator.

Type: float

rtol, atol

Relative and absolute tolerances. The solver keeps the local error estimates less than atol + rtol * abs(y). Effects the underlying solver as described in scipy.integrate.solve_ivp(). Default to 1e-3 for rtol and 1e-6 for atol.

Type: float, optional

Yields: array_like, shape=(M,)

Methods

`__init__`(analog_system, digital_control, ...)
`observations`()	return the current analog system observations.
`reset`([t])	reset initial time of simulator and digital control
`state_vector`()	return current analog system state vector \(\mathbf{x}(t)\) evaluated at time \(t\).

Attributes

res

observations() → ndarray

return the current analog system observations.

in other words we return

\(\mathbf{y}(t) = \mathbf{C}^{\mathsf{T}} \mathbf{x}(t)\)

Returns: returns the analog system observation :math:`mathbf{y}(t)
Return type: array_like, shape=(N_tilde,)

reset(t: float = 0.0): reset initial time of simulator and digital control

state_vector() → ndarray

return current analog system state vector \(\mathbf{x}(t)\) evaluated at time \(t\).

Returns: returns the state vector \(\mathbf{x}(t)\)
Return type: array_like, shape=(N,)