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pybind11/example/eigen.ref

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Test that check() catches wrong order of elements Fails --- next commit will tighten test.
2016-07-05 20:46:09 +00:00
should_give_NOT_OK = NOT OK
transparent conversion of dense and sparse Eigen types
2016-05-05 18:33:54 +00:00
fixed_r = OK
fixed_c = OK
pt_r(fixed_r) = OK
pt_c(fixed_c) = OK
pt_r(fixed_c) = OK
pt_c(fixed_r) = OK
dense_r = OK
dense_c = OK
pt_r(dense_r) = OK
pt_c(dense_c) = OK
pt_r(dense_c) = OK
pt_c(dense_r) = OK
sparse_r = OK
sparse_c = OK
pt_r(sparse_r) = OK
pt_c(sparse_c) = OK
pt_r(sparse_c) = OK
pt_c(sparse_r) = OK
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
double_row(first_row) = OK
double_col(first_row) = OK
double_row(first_col) = OK
double_col(first_col) = OK
Test eigen converts slices of 3d arrays correctly
2016-07-05 20:03:19 +00:00
double_mat_cm(0) = OK
double_mat_rm(0) = OK
double_mat_cm(1) = OK
double_mat_rm(1) = OK
double_mat_cm(2) = OK
double_mat_rm(2) = OK
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
cholesky1 OK
cholesky2 OK
cholesky3 OK
cholesky4 OK
cholesky5 OK
cholesky6 OK
Fix eigen copying of non-standard stride values Some Eigen objects, such as those returned by matrix.diagonal() and matrix.block() have non-standard stride values because they are basically just maps onto the underlying matrix without copying it (for example, the primary diagonal of a 3x3 matrix is a vector-like object with .src equal to the full matrix data, but with stride 4). Returning such an object from a pybind11 method breaks, however, because pybind11 assumes vectors have stride 1, and that matrices have strides equal to the number of rows/columns or 1 (depending on whether the matrix is stored column-major or row-major). This commit fixes the issue by making pybind11 use Eigen's stride methods when copying the data.
2016-08-04 17:21:39 +00:00
diagonal() OK
diagonal_1() OK
diagonal_n(-5) OK
diagonal_n(-4) OK
diagonal_n(-3) OK
diagonal_n(-2) OK
diagonal_n(-1) OK
diagonal_n(0) OK
diagonal_n(1) OK
diagonal_n(2) OK
diagonal_n(3) OK
diagonal_n(4) OK
diagonal_n(5) OK
diagonal_n(6) OK
block(2,1,3,3) OK
block(1,4,4,2) OK
block(1,4,3,2) OK
Eigen support for special matrix objects Functions returning specialized Eigen matrices like Eigen::DiagonalMatrix and Eigen::SelfAdjointView--which inherit from EigenBase but not DenseBase--isn't currently allowed; such classes are explicitly copyable into a Matrix (by definition), and so we can support functions that return them by copying the value into a Matrix then casting that resulting dense Matrix into a numpy.ndarray. This commit does exactly that.
2016-08-04 19:24:41 +00:00
incr_diag OK
symmetric_lower OK
symmetric_upper OK
Adopt PEP 484 type hints for C++ types exported to Python
2016-08-03 23:40:40 +00:00
double_col(arg0: numpy.ndarray[float32[m, 1]]) -> numpy.ndarray[float32[m, 1]]
double_row(arg0: numpy.ndarray[float32[1, n]]) -> numpy.ndarray[float32[1, n]]
double_mat_rm(arg0: numpy.ndarray[float32[m, n]]) -> numpy.ndarray[float32[m, n]]
sparse_passthrough_r(arg0: scipy.sparse.csr_matrix[float32]) -> scipy.sparse.csr_matrix[float32]
sparse_passthrough_c(arg0: scipy.sparse.csc_matrix[float32]) -> scipy.sparse.csc_matrix[float32]
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