hexrd.cli.fit_grains module

class hexrd.cli.fit_grains.GrainData(id, completeness, chisq, expmap, centroid, inv_Vs, ln_Vs)[source]

Bases: _BaseGrainData

Simple class for storing grain output data

To read the grains file, use the load method, like this: > from hexrd.fitgrains import GrainData > gd = GrainData.load(“grains.npz”)

classmethod from_array(a)[source]: Return GrainData instance from numpy datatype array

classmethod from_grains_out(fname)[source]: Read hexrd grains output file

classmethod load(fname)[source]: Return GrainData instance from npz file Parameters ———- fname: path | string

name of the file to load

property num_grains

property quaternions: Return quaternions as array(num_grains, 4).

property rotation_matrices: “Return rotation matrices from exponential map parameters

save(fname)[source]

Save grain data to an np file

Parameters

fname: path | string: name of the file to save to

select(min_completeness=0.0, max_chisq=None)[source]

Return a new GrainData instance with only selected grains

PARAMETERS

min_completeness: float, default=0: minimum value of completeness
max_chisq: float | None, default=None: if not None, maximum value for chi-squared

RETURNS

GrainData instance: new instance for subset of grains meeting selection criteria

property strain

Return symmetric strain tensor as array(num_grains, 6).

The order of components is 11, 22, 33, 23, 13, 23.

write_grains_out(fname)[source]: Write a file in grains.out format

hexrd.cli.fit_grains.configure_parser(sub_parsers)[source]

hexrd.cli.fit_grains.execute(args, parser)[source]

hexrd.cli.fit_grains.write_results(fit_results, cfg, grains_filename='grains.out', grains_npz='grains.npz')[source]