monte_carlo_cmv_global

circuit_analyzer.all.monte_carlo_cmv_global(circuit_model, wavelengths, inst_corners=None, fixed_hierarchy_insts=None, distributions=None, correlation_matrix=None, sample_size=100, parallel=False, parallel_option='number', seed=None, save_path=None)

Run a monte carlo simulation where one or more parameters are modified globally.

This method modifies all properties that are defined in the variability configuration. When a parameter goes beyond the window, it is clipped to the min/max corner value.

This method differs from monte_carlo_global in that it modifies the parameters of the CircuitModelView. monte_carlo_global modifies the CompactModel parameters directly.

Parameters:
circuit_model: CircuitModel

IPKISS CircuitModel used in the simulation.

wavelengths: array-like

An array of wavelengths, for instance: np.linspace(1.54, 1.56, 1001).

inst_corners: dict

Dictionary mapping the name of the instances to the list of fab corner parameters (for instance [‘s’, ‘t’]) that you want to vary. By default, each instance will vary one of their fab corner parameters.

fixed_hierarchy_insts: list

List of names of the instances wherein the variability should remain constant for each instance in the hierarchy.

distributions: dict
correlation_matrix: numpy.ndarray, optional

Correlation matrix of the distributions. If not provided, an identity matrix will be used, indicating no correlation.

sample_size: int, default = 100

Number of Monte Carlo simulations

parallel: bool, optional

Parallelize the calculations to potentially reduce the simulation time.

parallel_option: str

String of what to parallelize, either over ‘wavelengths’ or over the ‘number’ of simulations. Options are ‘wavelengths’ or ‘number’.

seed: int, optional

Seed of the random generator.

save_path: str/path, optional

Path where the save file will be stored, for instance: save_path = “simulation_results.data”.

Returns:
an array of S matrices, one S matrix per sampling.

Examples

>>> import circuit_analyzer.all as ca
>>> cell = MZI()
>>> cm = cell.CircuitModel()
>>>
>>> wavelengths = np.linspace(1.5, 1.6, 501)
>>> s = cm.get_smatrix(wavelengths=wavelengths)  # will give the nominal
>>> with ca.setup('/path/to/configuration'):
>>>     smats = monte_carlo_cmv_global(
>>>         circuit_model=cm,
>>>         wavelengths=wavelengths,
>>>         sample_size=100,
>>>         parallel=False,
>>>         seed=0,
>>>         distributions={
>>>             "neff": partial(NormalDistribution, mu=4.3, sigma=0.001),
>>>         },
>>>     )