SimPEG.meta.DaskSumMetaSimulation

class SimPEG.meta.DaskSumMetaSimulation(simulations, mappings, client)

Bases: DaskMetaSimulation, SumMetaSimulation

A dask distributed version of SumMetaSimulation.

A meta simulation that sums the results of the many individual simulations.

Parameters:

simulations(n_sim) list of SimPEG.simulation.BaseSimulation or list of dask.distributed.Future

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

mappings(n_sim) list of SimPEG.maps.IdentityMap or list of dask.distributed.Future The map for every simulation. Every map should accept the

same length model, and output a model appropriate for its paired simulation.

clientdask.distributed.Client, optional

The dask client to use for communication.

Attributes

clean_on_model_update	A list of solver objects to clean when the model is updated
client	The distributed client that handles the internal tasks.
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 future mappings paired to each simulation.
mesh	Mesh for the simulation.
model	The inversion model.
needs_model	True if a model is necessary
sensitivity_path	Path to directory where sensitivity file is stored.
simulations	The future 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.

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.