simpeg.meta.MultiprocessingSumMetaSimulation#

class simpeg.meta.MultiprocessingSumMetaSimulation(simulations, mappings, n_processes=None)[source]#

Bases: MultiprocessingMetaSimulation, SumMetaSimulation

A multiprocessing version of SumMetaSimulation.

See the documentation of MultiprocessingMetaSimulation for details on how to use multiprocessing for you operating system.

Parameters:
simulations(n_sim) list of simpeg.simulation.BaseSimulation

The list of unique simulations that each handle a piece of the problem.

mappings(n_sim) list of simpeg.maps.IdentityMap

The map for every simulation. Every map should accept the same length model, and output a model appropriate for its paired simulation.

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

A list of properties stored on this object to delete when the model is updated

mappings

The mappings paired to each simulation.

mesh

Mesh for the simulation.

needs_model

True if a model is necessary

sensitivity_path

Path to directory where sensitivity file is stored.

simulations

The list of simulations.

solver

Numerical solver used in the forward simulation.

solver_opts

Solver-specific parameters.

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