experimentsimulator.py

This module is used for performing Monte-Carlo virtual experiments over a series of input simulation cases and sensor arrays.

class ExperimentStats(mean=None, std=None, max=None, min=None, med=None, q25=None, q75=None, mad=None)

Bases: object

Dataclass holding summary statistics for a series of simulated experiments produced using the experiment simulator. All summary statistics are calculated over the ‘experiments’ dimension of the measurements array so the arrays of statistics have the shape=(n_sims,n_sensors,n_field_comps, n_time_steps). Note that the n_sims dimension refers to the number of input multi-physics simulations (i.e. SimData objects) that the virtual experiments were performed over.

mean

Mean of each sensors measurement for the given field component and time step as an array with shape=(n_sims,n_sensors,n_field_comps,n_time_steps).

std

Standard deviation of the sensor measurements for the given field component and time step as an array with shape=(n_sims,n_sensors, n_field_comps, n_time_steps)

max

Maximum of the sensor measurements for the given field component and time step as an array with shape=(n_sims,n_sensors,n_field_comps,n_time_steps)

min

Minmum of the sensor measurements for the given field component and time step as an array with shape=(n_sims,n_sensors,n_field_comps,n_time_steps)

med

Median of the sensor measurements for the given field component and time step as an array with shape=(n_sims,n_sensors,n_field_comps,n_time_steps)

q25

Lower 25% quantile of the sensor measurements for the given field component and time step as an array with shape=(n_sims,n_sensors, n_field_comps, n_time_steps)

q75

Upper 75% quantile of the sensor measurements for the given field component and time step as an array with shape=(n_sims,n_sensors, _field_comps, n_time_steps)

mad

Median absolute deviation of the sensor measurements for the given field component and time step as an array with shape=(n_sims,n_sensors, n_field_comps, n_time_steps)

__init__(mean=None, std=None, max=None, min=None, med=None, q25=None, q75=None, mad=None)

class ExperimentSimulator(sim_list, sensor_arrays, num_exp_per_sim)

Bases: object

An experiment simulator for running monte-carlo analysis by applying a list of sensor arrays to a list of simulations over a given number of user defined experiments. Calculates summary statistics for each sensor array applied to each simulation.

__init__(sim_list, sensor_arrays, num_exp_per_sim)

Parameters:

• sim_list (list[mh.SimData]) – List of simulation data objects over which the virtual experiments will be performed.

• sensor_arrays (list[ISensorArray]) – The sensor arrays that will be applied to each simulation to generate the virtual experiment data.

• num_exp_per_sim (int) – Number of virtual experiments to perform for each simulation and sensor array.

get_sim_list()

Gets the list of simulations to run simulated experiments for.

Returns:

list[mh.SimData] – List of simulation data objects.

get_sensor_arrays()

Gets the sensor array list for this experiment.

Returns:

list[ISensorArray] – List of sensor arrays for the simulated experiment.

run_experiments()

Runs the specified number of virtual experiments over the number of input simulation cases and virtual sensor arrays.

Returns:

list[np.ndarray] – List of virtual experimental data arrays where the list index corresponds to the virtual sensor array and the data is an array with shape=(n_sims,n_exps,n_sens,n_comps,n_time_steps).

calc_stats()

Calculates summary statistics over the number of virtual experiments specified. If run_experiments() has not been called then it is called to generate the virtual experimental data to perform the statistical calculations.

Returns:

list[ExperimentStats] – List of summary statistics data classes for the virtual experiments. The list index correponds to the virtual sensor array.