MLMC package

The mlmc package provides tools to realize the Multilevel Monte Carlo method.

Subpackages

`plot`	Subpackage provides plot functions to display pdf, violinplot, ...
`quantity`	Subpackage provides methods to represent and handle a quantity of interest.
`random`	Subpackage provides random field generation and GSTools library interface
`sim`	Contains a parent simulation class and a specific synthetic simulation
`tool`	Contains classes that provide an interface to other resources such as HDF5, Gmsh, PBS, ...

Sampler(sample_storage, sampling_pool, ...)

Manages sample scheduling, result collection, and persistent storage.

`SamplingPool`([work_dir, debug])	Abstract base class defining the runtime environment for sample simulations.
`OneProcessPool`([work_dir, debug])	Sampling pool implementation that executes all samples sequentially in a single process.
`ProcessPool`(n_processes[, work_dir, debug])	Sampling pool using multiprocessing for parallel sample execution.

SamplingPoolPBS(work_dir[, debug])

Sampling pool PBS (Portable batch system) runtime environment

`SampleStorage`()	Provides methods to store and retrieve sample data.
`Memory`()	Sample's data are stored in the main memory

Store and manage sample data in an HDF5 file.

Estimate(quantity, sample_storage[, moments_fn])

A wrapper class for moment estimation, PDF approximation, and related MLMC post-processing.

`Moments`(size, domain[, log, safe_eval])	Base class for computing moment functions of a random variable.
`Monomial`(size[, domain, ref_domain, log, ...])	Monomial basis functions for generalized moment evaluation.
`Fourier`(size[, domain, ref_domain, log, ...])	Fourier basis functions for generalized moment evaluation.
`Legendre`(size, domain[, ref_domain, log, ...])	Legendre polynomial basis functions for generalized moments.

LevelSimulation(config_dict[, common_files, ...])

Class for passing simulation configuration and metadata for a given level between a Sampler and a SamplingPool.