simpeg.meta.MultiprocessingRepeatedSimulation

class simpeg.meta.MultiprocessingRepeatedSimulation(simulation, mappings, n_processes=None)

Bases: MultiprocessingMetaSimulation, RepeatedSimulation

A multiprocessing version of the RepeatedSimulation.

This class makes use of a single simulation that is copied to each internal process, but only once per process.

This simulation shares internals with the MultiprocessingMetaSimulation. class, as such please see that documentation for details regarding how to properly use multiprocessing on your operating system.

Parameters:

simulationsimpeg.simulation.BaseSimulation

The simulation to use repeatedly with different mappings.

mappings(n_sim) list of simpeg.maps.IdentityMap

The list of different mappings to use.

n_processesoptional

The number of processes to spawn internally. This will default to multiprocessing.cpu_count(). The number of processes spawned will be the minimum of this number and the number of simulations.

Attributes

clean_on_model_update	A list of solver objects to clean when the model is updated
counter	SimPEG Counter object to store iterations and run-times.
deleteTheseOnModelUpdate	HasModel.deleteTheseOnModelUpdate has been deprecated.
mappings	The mappings paired to each simulation.
needs_model	True if a model is necessary
sensitivity_path	Path to directory where sensitivity file is stored.
simulation	The internal simulation.
simulations	The list of simulations.
survey	The survey for the simulation.
verbose	Verbose progress printout.

model

Methods

Jtvec(m, v[, f])	Compute the Jacobian transpose times a vector for the model provided.
Jtvec_approx(m, v[, f])	Approximation of the Jacobian transpose times a vector for the model provided.
Jvec(m, v[, f])	Compute the Jacobian times a vector for the model provided.
Jvec_approx(m, v[, f])	Approximation of the Jacobian times a vector for the model provided.
dpred([m, f])	Predicted data for the model provided.
fields(m)	Create fields for every simulation.
getJtJdiag(m[, W, f])	Return the squared sum of columns of the Jacobian.
make_synthetic_data(m[, relative_error, ...])	Make synthetic data for the model and Gaussian noise provided.
residual(m, dobs[, f])	The data residual.

join