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pybind11/example/eigen.py

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transparent conversion of dense and sparse Eigen types
2016-05-05 18:33:54 +00:00
#!/usr/bin/env python
from __future__ import print_function
import sys
sys.path.append('.')
from example import fixed_r, fixed_c
from example import fixed_passthrough_r, fixed_passthrough_c
from example import dense_r, dense_c
from example import dense_passthrough_r, dense_passthrough_c
from example import sparse_r, sparse_c
from example import sparse_passthrough_r, sparse_passthrough_c
Add tests for doubling row- and col-vectors Passing a non-contiguous one-dimensional numpy array gives incorrect results, so three of these tests fail. The only one passing is the simple case where the numpy array is contiguous and we are building a column-major vector. Subsequent commit will fix the three failing cases.
2016-07-05 19:03:02 +00:00
from example import double_row, double_col
Test eigen converts slices of 3d arrays correctly
2016-07-05 20:03:19 +00:00
from example import double_mat_cm, double_mat_rm
Add support for Eigen::Ref<...> function arguments Eigen::Ref is a common way to pass eigen dense types without needing a template, e.g. the single definition `void func(Eigen::Ref<Eigen::MatrixXd> x)` can be called with any double matrix-like object. The current pybind11 eigen support fails with internal errors if attempting to bind a function with an Eigen::Ref<...> argument because Eigen::Ref<...> satisfies the "is_eigen_dense" requirement, but can't compile if actually used: Eigen::Ref<...> itself is not default constructible, and so the argument std::tuple containing an Eigen::Ref<...> isn't constructible, which results in compilation failure. This commit adds support for Eigen::Ref<...> by giving it its own type_caster implementation which consists of an internal type_caster of the referenced type, load/cast methods that dispatch to the internal type_caster, and a unique_ptr to an Eigen::Ref<> instance that gets set during load(). There is, of course, no performance advantage for pybind11-using code of using Eigen::Ref<...>--we are allocating a matrix of the derived type when loading it--but this has the advantage of allowing pybind11 to bind transparently to C++ methods taking Eigen::Refs.
2016-08-03 20:50:22 +00:00
from example import cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6
Tests can skip by exiting with 99; fix eigen test failure This allows (and changes the current examples) to exit with status 99 to skip a test instead of outputting a special string ("NumPy missing"). This also fixes the eigen test, which currently fails when eigen headers are available but NumPy is not, to skip instead of failing when NumPy isn't available.
2016-07-08 21:44:12 +00:00
try:
import numpy as np
Add missing scipy run-time dep to eigen test scipy is imported in pybind11/eigen.h when it encounters a sparse matrix, which gets tested in the eigen test.
2016-07-12 18:16:46 +00:00
import scipy
Tests can skip by exiting with 99; fix eigen test failure This allows (and changes the current examples) to exit with status 99 to skip a test instead of outputting a special string ("NumPy missing"). This also fixes the eigen test, which currently fails when eigen headers are available but NumPy is not, to skip instead of failing when NumPy isn't available.
2016-07-08 21:44:12 +00:00
except ImportError:
# NumPy missing: skip test
exit(99)
transparent conversion of dense and sparse Eigen types
2016-05-05 18:33:54 +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]])
def check(mat):
check(): Stricter check in tests Previous version would give false 'OK' if, for example, we were supposed to get [1, 2, 3] but instead got [2, 1, 3].
2016-07-05 18:59:28 +00:00
return 'OK' if np.sum(abs(mat - ref)) == 0 else 'NOT OK'
transparent conversion of dense and sparse Eigen types
2016-05-05 18:33:54 +00:00
Test that check() catches wrong order of elements Fails --- next commit will tighten test.
2016-07-05 20:46:09 +00:00
print("should_give_NOT_OK = %s" % check(ref[:, ::-1]))
transparent conversion of dense and sparse Eigen types
2016-05-05 18:33:54 +00:00
print("fixed_r = %s" % check(fixed_r()))
print("fixed_c = %s" % check(fixed_c()))
print("pt_r(fixed_r) = %s" % check(fixed_passthrough_r(fixed_r())))
print("pt_c(fixed_c) = %s" % check(fixed_passthrough_c(fixed_c())))
print("pt_r(fixed_c) = %s" % check(fixed_passthrough_r(fixed_c())))
print("pt_c(fixed_r) = %s" % check(fixed_passthrough_c(fixed_r())))
print("dense_r = %s" % check(dense_r()))
print("dense_c = %s" % check(dense_c()))
print("pt_r(dense_r) = %s" % check(dense_passthrough_r(dense_r())))
print("pt_c(dense_c) = %s" % check(dense_passthrough_c(dense_c())))
print("pt_r(dense_c) = %s" % check(dense_passthrough_r(dense_c())))
print("pt_c(dense_r) = %s" % check(dense_passthrough_c(dense_r())))
print("sparse_r = %s" % check(sparse_r()))
print("sparse_c = %s" % check(sparse_c()))
print("pt_r(sparse_r) = %s" % check(sparse_passthrough_r(sparse_r())))
print("pt_c(sparse_c) = %s" % check(sparse_passthrough_c(sparse_c())))
print("pt_r(sparse_c) = %s" % check(sparse_passthrough_r(sparse_c())))
print("pt_c(sparse_r) = %s" % check(sparse_passthrough_c(sparse_r())))
Add tests for doubling row- and col-vectors Passing a non-contiguous one-dimensional numpy array gives incorrect results, so three of these tests fail. The only one passing is the simple case where the numpy array is contiguous and we are building a column-major vector. Subsequent commit will fix the three failing cases.
2016-07-05 19:03:02 +00:00
def check_got_vs_ref(got_x, ref_x):
return 'OK' if np.array_equal(got_x, ref_x) else 'NOT OK'
counting_mat = np.arange(9.0, dtype=np.float32).reshape((3, 3))
first_row = counting_mat[0, :]
first_col = counting_mat[:, 0]
print("double_row(first_row) = %s" % check_got_vs_ref(double_row(first_row), 2.0 * first_row))
print("double_col(first_row) = %s" % check_got_vs_ref(double_col(first_row), 2.0 * first_row))
print("double_row(first_col) = %s" % check_got_vs_ref(double_row(first_col), 2.0 * first_col))
print("double_col(first_col) = %s" % check_got_vs_ref(double_col(first_col), 2.0 * first_col))
Test eigen converts slices of 3d arrays correctly
2016-07-05 20:03:19 +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):
print("double_mat_cm(%d) = %s" % (slice_idx, check_got_vs_ref(double_mat_cm(ref_mat), 2.0 * ref_mat)))
print("double_mat_rm(%d) = %s" % (slice_idx, check_got_vs_ref(double_mat_rm(ref_mat), 2.0 * ref_mat)))
Add support for Eigen::Ref<...> function arguments Eigen::Ref is a common way to pass eigen dense types without needing a template, e.g. the single definition `void func(Eigen::Ref<Eigen::MatrixXd> x)` can be called with any double matrix-like object. The current pybind11 eigen support fails with internal errors if attempting to bind a function with an Eigen::Ref<...> argument because Eigen::Ref<...> satisfies the "is_eigen_dense" requirement, but can't compile if actually used: Eigen::Ref<...> itself is not default constructible, and so the argument std::tuple containing an Eigen::Ref<...> isn't constructible, which results in compilation failure. This commit adds support for Eigen::Ref<...> by giving it its own type_caster implementation which consists of an internal type_caster of the referenced type, load/cast methods that dispatch to the internal type_caster, and a unique_ptr to an Eigen::Ref<> instance that gets set during load(). There is, of course, no performance advantage for pybind11-using code of using Eigen::Ref<...>--we are allocating a matrix of the derived type when loading it--but this has the advantage of allowing pybind11 to bind transparently to C++ methods taking Eigen::Refs.
2016-08-03 20:50:22 +00:00
i = 1
for chol in [cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6]:
mymat = chol(np.array([[1,2,4], [2,13,23], [4,23,77]]))
print("cholesky" + str(i) + " " + ("OK" if (mymat == np.array([[1,0,0], [2,3,0], [4,5,6]])).all() else "NOT OKAY"))
i += 1
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