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pybind11/tests/test_eigen.py

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Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
import pytest
with pytest.suppress(ImportError):
import numpy as np
Fix test suite failure without numpy and improve module init diagnostics Fixes #357.
2016-08-25 15:08:09 +00:00
ref = np.array([[ 0, 3, 0, 0, 0, 11],
[22, 0, 0, 0, 17, 11],
[ 7, 5, 0, 1, 0, 11],
[ 0, 0, 0, 0, 0, 11],
[ 0, 0, 14, 0, 8, 11]])
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
def assert_equal_ref(mat):
np.testing.assert_array_equal(mat, ref)
def assert_sparse_equal_ref(sparse_mat):
assert_equal_ref(sparse_mat.todense())
@pytest.requires_eigen_and_numpy
def test_fixed():
from pybind11_tests import fixed_r, fixed_c, fixed_passthrough_r, fixed_passthrough_c
assert_equal_ref(fixed_c())
assert_equal_ref(fixed_r())
assert_equal_ref(fixed_passthrough_r(fixed_r()))
assert_equal_ref(fixed_passthrough_c(fixed_c()))
assert_equal_ref(fixed_passthrough_r(fixed_c()))
assert_equal_ref(fixed_passthrough_c(fixed_r()))
@pytest.requires_eigen_and_numpy
def test_dense():
from pybind11_tests import dense_r, dense_c, dense_passthrough_r, dense_passthrough_c
assert_equal_ref(dense_r())
assert_equal_ref(dense_c())
assert_equal_ref(dense_passthrough_r(dense_r()))
assert_equal_ref(dense_passthrough_c(dense_c()))
assert_equal_ref(dense_passthrough_r(dense_c()))
assert_equal_ref(dense_passthrough_c(dense_r()))
Eigen: fix partially-fixed matrix conversion Currently when we do a conversion between a numpy array and an Eigen Vector, we allow the conversion only if the Eigen type is a compile-time vector (i.e. at least one dimension is fixed at 1 at compile time), or if the type is dynamic on *both* dimensions. This means we can run into cases where MatrixXd allow things that conforming, compile-time sizes does not: for example, `Matrix<double,4,Dynamic>` is currently not allowed, even when assigning from a 4-element vector, but it *is* allowed for a `Matrix<double,Dynamic,Dynamic>`. This commit also reverts the current behaviour of using the matrix's storage order to determine the structure when the Matrix is fully dynamic (i.e. in both dimensions). Currently we assign to an eigen row if the storage order is row-major, and column otherwise: this seems wrong (the storage order has nothing to do with the shape!). While numpy doesn't distinguish between a row/column vector, Eigen does, but it makes more sense to consistently choose one than to produce something with a different shape based on the intended storage layout.
2017-01-13 00:50:33 +00:00
@pytest.requires_eigen_and_numpy
def test_partially_fixed():
from pybind11_tests import partial_passthrough_four_rm_r, partial_passthrough_four_rm_c, partial_passthrough_four_cm_r, partial_passthrough_four_cm_c
ref2 = np.array([[0,1,2,3], [4,5,6,7], [8,9,10,11], [12,13,14,15]])
np.testing.assert_array_equal(partial_passthrough_four_rm_r(ref2), ref2)
np.testing.assert_array_equal(partial_passthrough_four_rm_c(ref2), ref2)
np.testing.assert_array_equal(partial_passthrough_four_rm_r(ref2[:, 1]), ref2[:, [1]])
np.testing.assert_array_equal(partial_passthrough_four_rm_c(ref2[0, :]), ref2[[0], :])
np.testing.assert_array_equal(partial_passthrough_four_rm_r(ref2[:, (0, 2)]), ref2[:, (0,2)])
np.testing.assert_array_equal(partial_passthrough_four_rm_c(ref2[(3,1,2), :]), ref2[(3,1,2), :])
np.testing.assert_array_equal(partial_passthrough_four_cm_r(ref2), ref2)
np.testing.assert_array_equal(partial_passthrough_four_cm_c(ref2), ref2)
np.testing.assert_array_equal(partial_passthrough_four_cm_r(ref2[:, 1]), ref2[:, [1]])
np.testing.assert_array_equal(partial_passthrough_four_cm_c(ref2[0, :]), ref2[[0], :])
np.testing.assert_array_equal(partial_passthrough_four_cm_r(ref2[:, (0, 2)]), ref2[:, (0,2)])
np.testing.assert_array_equal(partial_passthrough_four_cm_c(ref2[(3,1,2), :]), ref2[(3,1,2), :])
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
@pytest.requires_eigen_and_numpy
def test_nonunit_stride_from_python():
from pybind11_tests import double_row, double_col, double_mat_cm, double_mat_rm
counting_mat = np.arange(9.0, dtype=np.float32).reshape((3, 3))
first_row = counting_mat[0, :]
first_col = counting_mat[:, 0]
Eigen: fix partially-fixed matrix conversion Currently when we do a conversion between a numpy array and an Eigen Vector, we allow the conversion only if the Eigen type is a compile-time vector (i.e. at least one dimension is fixed at 1 at compile time), or if the type is dynamic on *both* dimensions. This means we can run into cases where MatrixXd allow things that conforming, compile-time sizes does not: for example, `Matrix<double,4,Dynamic>` is currently not allowed, even when assigning from a 4-element vector, but it *is* allowed for a `Matrix<double,Dynamic,Dynamic>`. This commit also reverts the current behaviour of using the matrix's storage order to determine the structure when the Matrix is fully dynamic (i.e. in both dimensions). Currently we assign to an eigen row if the storage order is row-major, and column otherwise: this seems wrong (the storage order has nothing to do with the shape!). While numpy doesn't distinguish between a row/column vector, Eigen does, but it makes more sense to consistently choose one than to produce something with a different shape based on the intended storage layout.
2017-01-13 00:50:33 +00:00
np.testing.assert_array_equal(double_row(first_row), 2.0 * first_row)
np.testing.assert_array_equal(double_col(first_row), 2.0 * first_row)
np.testing.assert_array_equal(double_row(first_col), 2.0 * first_col)
np.testing.assert_array_equal(double_col(first_col), 2.0 * first_col)
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
counting_3d = np.arange(27.0, dtype=np.float32).reshape((3, 3, 3))
slices = [counting_3d[0, :, :], counting_3d[:, 0, :], counting_3d[:, :, 0]]
for slice_idx, ref_mat in enumerate(slices):
Eigen: fix partially-fixed matrix conversion Currently when we do a conversion between a numpy array and an Eigen Vector, we allow the conversion only if the Eigen type is a compile-time vector (i.e. at least one dimension is fixed at 1 at compile time), or if the type is dynamic on *both* dimensions. This means we can run into cases where MatrixXd allow things that conforming, compile-time sizes does not: for example, `Matrix<double,4,Dynamic>` is currently not allowed, even when assigning from a 4-element vector, but it *is* allowed for a `Matrix<double,Dynamic,Dynamic>`. This commit also reverts the current behaviour of using the matrix's storage order to determine the structure when the Matrix is fully dynamic (i.e. in both dimensions). Currently we assign to an eigen row if the storage order is row-major, and column otherwise: this seems wrong (the storage order has nothing to do with the shape!). While numpy doesn't distinguish between a row/column vector, Eigen does, but it makes more sense to consistently choose one than to produce something with a different shape based on the intended storage layout.
2017-01-13 00:50:33 +00:00
np.testing.assert_array_equal(double_mat_cm(ref_mat), 2.0 * ref_mat)
np.testing.assert_array_equal(double_mat_rm(ref_mat), 2.0 * ref_mat)
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
@pytest.requires_eigen_and_numpy
def test_nonunit_stride_to_python():
from pybind11_tests import diagonal, diagonal_1, diagonal_n, block
assert np.all(diagonal(ref) == ref.diagonal())
assert np.all(diagonal_1(ref) == ref.diagonal(1))
for i in range(-5, 7):
assert np.all(diagonal_n(ref, i) == ref.diagonal(i)), "diagonal_n({})".format(i)
assert np.all(block(ref, 2, 1, 3, 3) == ref[2:5, 1:4])
assert np.all(block(ref, 1, 4, 4, 2) == ref[1:, 4:])
assert np.all(block(ref, 1, 4, 3, 2) == ref[1:4, 4:])
@pytest.requires_eigen_and_numpy
def test_eigen_ref_to_python():
from pybind11_tests import cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6
chols = [cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6]
for i, chol in enumerate(chols, start=1):
mymat = chol(np.array([[1, 2, 4], [2, 13, 23], [4, 23, 77]]))
assert np.all(mymat == np.array([[1, 0, 0], [2, 3, 0], [4, 5, 6]])), "cholesky{}".format(i)
@pytest.requires_eigen_and_numpy
def test_special_matrix_objects():
from pybind11_tests import incr_diag, symmetric_upper, symmetric_lower
assert np.all(incr_diag(7) == np.diag([1, 2, 3, 4, 5, 6, 7]))
asymm = np.array([[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12],
[13, 14, 15, 16]])
symm_lower = np.array(asymm)
symm_upper = np.array(asymm)
for i in range(4):
for j in range(i + 1, 4):
symm_lower[i, j] = symm_lower[j, i]
symm_upper[j, i] = symm_upper[i, j]
assert np.all(symmetric_lower(asymm) == symm_lower)
assert np.all(symmetric_upper(asymm) == symm_upper)
@pytest.requires_eigen_and_numpy
def test_dense_signature(doc):
from pybind11_tests import double_col, double_row, double_mat_rm
Set maximum line length for Python style checker (#552)
2016-12-12 23:59:28 +00:00
assert doc(double_col) == """
double_col(arg0: numpy.ndarray[float32[m, 1]]) -> numpy.ndarray[float32[m, 1]]
"""
assert doc(double_row) == """
double_row(arg0: numpy.ndarray[float32[1, n]]) -> numpy.ndarray[float32[1, n]]
"""
assert doc(double_mat_rm) == """
double_mat_rm(arg0: numpy.ndarray[float32[m, n]]) -> numpy.ndarray[float32[m, n]]
"""
Port tests to pytest Use simple asserts and pytest's powerful introspection to make testing simpler. This merges the old .py/.ref file pairs into simple .py files where the expected values are right next to the code being tested. This commit does not touch the C++ part of the code and replicates the Python tests exactly like the old .ref-file-based approach.
2016-08-12 11:50:00 +00:00
@pytest.requires_eigen_and_scipy
def test_sparse():
from pybind11_tests import sparse_r, sparse_c, sparse_passthrough_r, sparse_passthrough_c
assert_sparse_equal_ref(sparse_r())
assert_sparse_equal_ref(sparse_c())
assert_sparse_equal_ref(sparse_passthrough_r(sparse_r()))
assert_sparse_equal_ref(sparse_passthrough_c(sparse_c()))
assert_sparse_equal_ref(sparse_passthrough_r(sparse_c()))
assert_sparse_equal_ref(sparse_passthrough_c(sparse_r()))
@pytest.requires_eigen_and_scipy
def test_sparse_signature(doc):
from pybind11_tests import sparse_passthrough_r, sparse_passthrough_c
Set maximum line length for Python style checker (#552)
2016-12-12 23:59:28 +00:00
assert doc(sparse_passthrough_r) == """
sparse_passthrough_r(arg0: scipy.sparse.csr_matrix[float32]) -> scipy.sparse.csr_matrix[float32]
""" # noqa: E501 line too long
assert doc(sparse_passthrough_c) == """
sparse_passthrough_c(arg0: scipy.sparse.csc_matrix[float32]) -> scipy.sparse.csc_matrix[float32]
""" # noqa: E501 line too long
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