<commit_before># Copyright 2014-2016 The ODL development group
#
# This file is part of ODL.
#
# ODL is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ODL is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ODL.  If not, see <http://www.gnu.org/licenses/>.

"""Test reconstruction with ASTRA."""

# Imports for common Python 2/3 codebase
from __future__ import print_function, division, absolute_import
from future import standard_library
standard_library.install_aliases()

# External
import pytest
import numpy as np

# Internal
import odl
import odl.tomo as tomo
from odl.util.testutils import skip_if_no_largescale
from odl.tomo.util.testutils import (skip_if_no_astra, skip_if_no_astra_cuda,
                                     skip_if_no_scikit)


@pytest.fixture(scope="module", params=['float32', 'float64'],
                ids=[' dtype=float32 ', ' dtype=float64 '])
def dtype(request):
    return request.param


# Find the valid projectors
projectors = [skip_if_no_astra('par2d astra_cpu uniform'),
              skip_if_no_astra('par2d astra_cpu nonuniform'),
              skip_if_no_astra('par2d astra_cpu random'),
              skip_if_no_astra('cone2d astra_cpu uniform'),
              skip_if_no_astra('cone2d astra_cpu nonuniform'),
              skip_if_no_astra('cone2d astra_cpu random'),
              skip_if_no_astra_cuda('par2d astra_cuda uniform'),
              skip_if_no_astra_cuda('par2d astra_cuda nonuniform'),
              skip_if_no_astra_cuda('par2d astra_cuda random'),
              skip_if_no_astra_cuda('cone2d astra_cuda uniform'),
              skip_if_no_astra_cuda('cone2d astra_cuda nonuniform'),
              skip_if_no_astra_cuda('cone2d astra_cuda random'),
              skip_if_no_astra_cuda('par3d astra_cuda uniform'),
              skip_if_no_astra_cuda('par3d astra_cuda nonuniform'),
              skip_if_no_astra_cuda('par3d astra_cuda random'),
              skip_if_no_astra_cuda('cone3d astra_cuda uniform'),
              skip_if_no_astra_cuda('cone3d astra_cuda nonuniform'),
              skip_if_no_astra_cuda('cone3d astra_cuda random'),
              skip_if_no_astra_cuda('helical astra_cuda uniform'),
              skip_if_no_scikit('par2d scikit uniform')]

projector_ids = ['geom={}, impl={}, angles={}'
                 ''.format(*p.args[1].split()) for p in projectors]


# bug in pytest (ignores pytestmark) forces us to do this this
largescale = " or not pytest.config.getoption('--largescale')"
projectors = [pytest.mark.skipif(p.args[0] + largescale, p.args[1])
              for p in projectors]


@pytest.fixture(scope="module", params=projectors, ids=projector_ids)
def projector(request, dtype):

    n_angles = 200

    geom, impl, angle = request.param.split()

    if angle == 'uniform':
        apart = odl.uniform_partition(0, 2 * np.pi, n_angles)
    elif angle == 'random':
        # Linearly spaced with random noise
        min_pt = 2 * (2.0 * np.pi) / n_angles
        max_pt = (2.0 * np.pi) - 2 * (2.0 * np.pi) / n_angles
        points = np.linspace(min_pt, max_pt, n_angles)
        points += np.random.rand(n_angles) * (max_pt - min_pt) / (5 * n_angles)
        apart = odl.nonuniform_partition(points)
    elif angle == 'nonuniform':
        # Angles spaced quadratically
        min_pt = 2 * (2.0 * np.pi) / n_angles
        max_pt = (2.0 * np.pi) - 2 * (2.0 * np.pi) / n_angles
        points = np.linspace(min_pt ** 0.5, max_pt ** 0.5, n_angles) ** 2
        apart = odl.nonuniform_partition(points)
    else:
        raise ValueError('angle not valid')

    if geom == 'par2d':
        # Discrete reconstruction space
        discr_reco_space = odl.uniform_discr([-20, -20], [20, 20],
                                             [100, 100], dtype=dtype)

        # Geometry
        dpart = odl.uniform_partition(-30, 30, 200)
        geom = tomo.Parallel2dGeometry(apart, dpart)

        # Ray transform
        return tomo.RayTransform(discr_reco_space, geom,
                                 impl=impl)

    elif geom == 'par3d':
        # Discrete reconstruction space
        discr_reco_space = odl.uniform_discr([-20, -20, -20], [20, 20, 20],
                                             [100, 100, 100], dtype=dtype)

        # Geometry
        dpart = odl.uniform_partition([-30, -30], [30, 30], [200, 200])
        geom = tomo.Parallel3dAxisGeometry(apart, dpart, axis=[1, 0, 0])

        # Ray transform
        return tomo.RayTransform(discr_reco_space, geom,
                                 impl=impl)

    elif geom == 'cone2d':
        # Discrete reconstruction space
        discr_reco_space = odl.uniform_discr([-20, -20], [20, 20],
                                             [100, 100], dtype=dtype)

        # Geometry
        dpart = odl.uniform_partition(-30, 30, 200)
        geom = tomo.FanFlatGeometry(apart, dpart,
                                    src_radius=200, det_radius=100)

        # Ray transform
        return tomo.RayTransform(discr_reco_space, geom,
                                 impl=impl)

    elif geom == 'cone3d':
        # Discrete reconstruction space
        discr_reco_space = odl.uniform_discr([-20, -20, -20], [20, 20, 20],
                                             [100, 100, 100], dtype=dtype)

        # Geometry
        dpart = odl.uniform_partition([-30, -30], [30, 30], [200, 200])
        geom = tomo.CircularConeFlatGeometry(
            apart, dpart, src_radius=200, det_radius=100, axis=[1, 0, 0])

        # Ray transform
        return tomo.RayTransform(discr_reco_space, geom,
                                 impl=impl)

    elif geom == 'helical':
        # Discrete reconstruction space
        discr_reco_space = odl.uniform_discr([-20, -20, 0], [20, 20, 40],
                                             [100, 100, 100], dtype=dtype)

        # Geometry
        # TODO: angles
        n_angle = 700
        apart = odl.uniform_partition(0, 8 * 2 * np.pi, n_angle)
        dpart = odl.uniform_partition([-30, -3], [30, 3], [200, 20])
        geom = tomo.HelicalConeFlatGeometry(apart, dpart, pitch=5.0,
                                            src_radius=200, det_radius=100)

        # Ray transform
        return tomo.RayTransform(discr_reco_space, geom,
                                 impl=impl)
    else:
        raise ValueError('param not valid')


@skip_if_no_largescale
def test_reconstruction(projector):
    """Test discrete Ray transform using ASTRA for reconstruction."""

    # Create Shepp-Logan phantom
    vol = odl.phantom.shepp_logan(projector.domain, modified=True)

    # Project data
    projections = projector(vol)

    # Calculate operator norm for landweber
    op_norm_est_squared = projector.adjoint(projections).norm() / vol.norm()
    omega = 1.0 / op_norm_est_squared

    # Reconstruct using ODL
    recon = projector.domain.zero()
    odl.solvers.landweber(projector, recon, projections, niter=50,
                          omega=omega)

    # Make sure the result is somewhat close to the actual result.
    assert recon.dist(vol) < vol.norm() / 3.0


if __name__ == '__main__':
    pytest.main([str(__file__.replace('\\', '/')), '-v', '--largescale'])<commit_msg>BUG: Add largescale test for weighting in RayTransform<commit_after># Copyright 2014-2016 The ODL development group
#
# This file is part of ODL.
#
# ODL is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ODL is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ODL.  If not, see <http://www.gnu.org/licenses/>.

"""Test reconstruction with ASTRA."""

# Imports for common Python 2/3 codebase
from __future__ import print_function, division, absolute_import
from future import standard_library
standard_library.install_aliases()

# External
import pytest
import numpy as np

# Internal
import odl
import odl.tomo as tomo
from odl.util.testutils import skip_if_no_largescale, simple_fixture
from odl.tomo.util.testutils import (skip_if_no_astra, skip_if_no_astra_cuda,
                                     skip_if_no_scikit)


@pytest.fixture(scope="module", params=['float32', 'float64'],
                ids=[' dtype=float32 ', ' dtype=float64 '])
def dtype(request):
    return request.param


# Find the valid projectors
projectors = [skip_if_no_astra('par2d astra_cpu uniform'),
              skip_if_no_astra('par2d astra_cpu nonuniform'),
              skip_if_no_astra('par2d astra_cpu random'),
              skip_if_no_astra('cone2d astra_cpu uniform'),
              skip_if_no_astra('cone2d astra_cpu nonuniform'),
              skip_if_no_astra('cone2d astra_cpu random'),
              skip_if_no_astra_cuda('par2d astra_cuda uniform'),
              skip_if_no_astra_cuda('par2d astra_cuda nonuniform'),
              skip_if_no_astra_cuda('par2d astra_cuda random'),
              skip_if_no_astra_cuda('cone2d astra_cuda uniform'),
              skip_if_no_astra_cuda('cone2d astra_cuda nonuniform'),
              skip_if_no_astra_cuda('cone2d astra_cuda random'),
              skip_if_no_astra_cuda('par3d astra_cuda uniform'),
              skip_if_no_astra_cuda('par3d astra_cuda nonuniform'),
              skip_if_no_astra_cuda('par3d astra_cuda random'),
              skip_if_no_astra_cuda('cone3d astra_cuda uniform'),
              skip_if_no_astra_cuda('cone3d astra_cuda nonuniform'),
              skip_if_no_astra_cuda('cone3d astra_cuda random'),
              skip_if_no_astra_cuda('helical astra_cuda uniform'),
              skip_if_no_scikit('par2d scikit uniform')]

projector_ids = ['geom={}, impl={}, angles={}'
                 ''.format(*p.args[1].split()) for p in projectors]


# bug in pytest (ignores pytestmark) forces us to do this this
largescale = " or not pytest.config.getoption('--largescale')"
projectors = [pytest.mark.skipif(p.args[0] + largescale, p.args[1])
              for p in projectors]


weighting = simple_fixture('weighting', ['const', 'none'])


@pytest.fixture(scope="module", params=projectors, ids=projector_ids)
def projector(request, dtype, weighting):

    n_angles = 200

    geom, impl, angle = request.param.split()

    if angle == 'uniform':
        apart = odl.uniform_partition(0, 2 * np.pi, n_angles)
    elif angle == 'random':
        # Linearly spaced with random noise
        min_pt = 2 * (2.0 * np.pi) / n_angles
        max_pt = (2.0 * np.pi) - 2 * (2.0 * np.pi) / n_angles
        points = np.linspace(min_pt, max_pt, n_angles)
        points += np.random.rand(n_angles) * (max_pt - min_pt) / (5 * n_angles)
        apart = odl.nonuniform_partition(points)
    elif angle == 'nonuniform':
        # Angles spaced quadratically
        min_pt = 2 * (2.0 * np.pi) / n_angles
        max_pt = (2.0 * np.pi) - 2 * (2.0 * np.pi) / n_angles
        points = np.linspace(min_pt ** 0.5, max_pt ** 0.5, n_angles) ** 2
        apart = odl.nonuniform_partition(points)
    else:
        raise ValueError('angle not valid')

    if geom == 'par2d':
        # Discrete reconstruction space
        discr_reco_space = odl.uniform_discr([-20, -20], [20, 20], [100, 100],
                                             dtype=dtype,
                                             weighting=weighting)

        # Geometry
        dpart = odl.uniform_partition(-30, 30, 200)
        geom = tomo.Parallel2dGeometry(apart, dpart)

        # Ray transform
        return tomo.RayTransform(discr_reco_space, geom,
                                 impl=impl)

    elif geom == 'par3d':
        # Discrete reconstruction space
        discr_reco_space = odl.uniform_discr([-20, -20, -20], [20, 20, 20],
                                             [100, 100, 100],
                                             dtype=dtype,
                                             weighting=weighting)

        # Geometry
        dpart = odl.uniform_partition([-30, -30], [30, 30], [200, 200])
        geom = tomo.Parallel3dAxisGeometry(apart, dpart, axis=[1, 0, 0])

        # Ray transform
        return tomo.RayTransform(discr_reco_space, geom,
                                 impl=impl)

    elif geom == 'cone2d':
        # Discrete reconstruction space
        discr_reco_space = odl.uniform_discr([-20, -20], [20, 20],
                                             [100, 100], dtype=dtype)

        # Geometry
        dpart = odl.uniform_partition(-30, 30, 200)
        geom = tomo.FanFlatGeometry(apart, dpart,
                                    src_radius=200, det_radius=100)

        # Ray transform
        return tomo.RayTransform(discr_reco_space, geom,
                                 impl=impl)

    elif geom == 'cone3d':
        # Discrete reconstruction space
        discr_reco_space = odl.uniform_discr([-20, -20, -20], [20, 20, 20],
                                             [100, 100, 100], dtype=dtype)

        # Geometry
        dpart = odl.uniform_partition([-30, -30], [30, 30], [200, 200])
        geom = tomo.CircularConeFlatGeometry(
            apart, dpart, src_radius=200, det_radius=100, axis=[1, 0, 0])

        # Ray transform
        return tomo.RayTransform(discr_reco_space, geom,
                                 impl=impl)

    elif geom == 'helical':
        # Discrete reconstruction space
        discr_reco_space = odl.uniform_discr([-20, -20, 0], [20, 20, 40],
                                             [100, 100, 100], dtype=dtype)

        # Geometry
        # TODO: angles
        n_angle = 700
        apart = odl.uniform_partition(0, 8 * 2 * np.pi, n_angle)
        dpart = odl.uniform_partition([-30, -3], [30, 3], [200, 20])
        geom = tomo.HelicalConeFlatGeometry(apart, dpart, pitch=5.0,
                                            src_radius=200, det_radius=100)

        # Ray transform
        return tomo.RayTransform(discr_reco_space, geom,
                                 impl=impl)
    else:
        raise ValueError('param not valid')


@skip_if_no_largescale
def test_reconstruction(projector):
    """Test discrete Ray transform using ASTRA for reconstruction."""

    # Create Shepp-Logan phantom
    vol = odl.phantom.shepp_logan(projector.domain, modified=True)

    # Project data
    projections = projector(vol)

    # Reconstruct using ODL
    recon = projector.domain.zero()
    odl.solvers.conjugate_gradient_normal(projector, recon, projections,
                                          niter=20)

    # Make sure the result is somewhat close to the actual result.
    assert recon.dist(vol) < vol.norm() / 3.0


if __name__ == '__main__':
    pytest.main([str(__file__.replace('\\', '/')), '-v', '--largescale'])
{'fraction_non_alphanumeric': 0.10510276859236206, 'fraction_numerical': 0.03293782013875381, 'mean_word_length': 2.280306252658443, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 46, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1883186', 'n_tokens_mistral': 5240, 'n_tokens_neox': 4816, 'n_words': 1192}