SimPEG.meta.MultiprocessingSumMetaSimulation

class SimPEG.meta.MultiprocessingSumMetaSimulation(simulations, mappings, n_processes=None)

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