Source code for mumott.pipelines.reconstruction.discrete_directions

import logging
import sys

import numpy as np
from numpy.typing import NDArray
import tqdm
from types import SimpleNamespace

from mumott.data_handling import DataContainer
from mumott.methods.basis_sets import NearestNeighbor
from mumott.pipelines.reconstruction.sirt import run_sirt

logger = logging.getLogger(__name__)

[docs]def run_discrete_directions(data_container: DataContainer, directions: NDArray[float], use_gpu: bool = False, maxiter: int = 20, no_tqdm: bool = False, ): """A reconstruction pipeline for the :term:`discrete directions <DD>` algorithm, which is similar the the algorithms first descibed in [Schaff2015]_. Parameters ---------- data_container The :class:`DataContainer <mumott.data_handling.DataContainer>` from loading the data set of interest. directions A N by 3 Numpy array of unit-vectors descibing a grid covering the half unit sphere. use_gpu Whether to use GPU resources in computing the projections. Default is ``False``. If set to ``True``, the method will use :class:`SAXSProjectorCUDA <mumott.methods.projectors.SAXSProjectorCUDA>`. maxiter Maximum number of iterations for the gradient descent solution. no_tqdm: Flag whether ot not to print a progress bar for the reconstruction. """ basis_set = NearestNeighbor(directions) basis_set.integration_mode = 'midpoint' output_coefficients = np.zeros((*data_container.geometry.volume_shape, len(basis_set))) if no_tqdm: iterator = range(len(basis_set)) else: iterator = tqdm.tqdm(range(len(basis_set)), file=sys.stdout) for ii in iterator: sub_geometry, data_tuple = basis_set.get_sub_geometry(ii, data_container.geometry, data_container) if data_tuple is None: continue fake_data_container = SimpleNamespace( projections=SimpleNamespace(weights=data_tuple[1][..., np.newaxis]), geometry=sub_geometry, weights=data_tuple[1][..., np.newaxis]) result = run_sirt(fake_data_container, maxiter=maxiter, enforce_non_negativity=True, use_gpu=use_gpu, use_absorbances=True, absorbances=data_tuple[0][..., np.newaxis], no_tqdm=True) output_coefficients[..., ii] = result['result']['x'][..., 0] return dict(result={'x' : output_coefficients}, basis_set=basis_set)