lasdi.param

Attributes

getParam1DSpace

Classes

ParameterSpace

Functions

`get_1dspace_from_list`(config)
`create_uniform_1dspace`(config)

Module Contents

lasdi.param.get_1dspace_from_list(config)

lasdi.param.create_uniform_1dspace(config)

lasdi.param.getParam1DSpace

class lasdi.param.ParameterSpace(config)

param_list = []

param_name = []

n_param = 0

train_space = None

test_space = None

n_init = 0

test_grid_sizes = []

test_meshgrid = None

parser

test_space_type

n_train()

n_test()

createInitialTrainSpace(param_list)

createTestGridSpace(param_list)

getParameter(param_vector): convert numpy array parameter vector to a dict. Physics class takes the dict for solve/initial_condition.

createHyperMeshGrid(param_ranges)

param_ranges: list of numpy 1d arrays, each corresponding to 1d parameter grid space.

The list size is equal to the number of parameters.

Output: paramSpaces

tuple of numpy nd arrays, corresponding to each parameter. Dimension of the array equals to the number of parameters

createHyperGridSpace(mesh_grids)

mesh_grids: tuple of numpy nd arrays, corresponding to each parameter.

Dimension of the array equals to the number of parameters

Output: param_grid

numpy 2d array of size (grid size x number of parameters).

grid size is the size of a numpy nd array.

appendTrainSpace(param)

export()

load(dict_)