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| author | Bobby R. Bruce <bbruce@ucdavis.edu> | 2019-09-23 13:52:58 -0700 |
|---|---|---|
| committer | Bobby R. Bruce <bbruce@ucdavis.edu> | 2019-09-24 21:40:15 +0000 |
| commit | f97cf54db7a6f7642cc9fd122f23c4396c39bcf0 (patch) | |
| tree | 17d2ed22a1114cb138500d46afddb3bafcc2b418 /ext/pybind11/docs/advanced/cast/eigen.rst | |
| parent | 9235ae56c282d5a02ada3ed9b4e0fe2ee5738bde (diff) | |
| download | gem5-f97cf54db7a6f7642cc9fd122f23c4396c39bcf0.tar.xz | |
ext: Updated Pybind11 to version 2.4.1.
This updates Pybind11 from version 2.2.1 to version 2.4.1. This fixes
warning/error received when "<experiment/optional>" is used when
compiling using c++14 with clang. It should be noted that
"ext/pybind11/include/pybind11/std.h" has been changed to include a fix
added by commit ba42457254cc362eddc099f22b60d469cc6369e0. This is
necessary to avoid build errors.
Built: Linux (gcc, c++11) and MacOS (clang, c++14).
Tested: Ran quick tests for X86, ARM, and RISC-V.
Deprecates: https://gem5-review.googlesource.com/c/public/gem5/+/21019
Change-Id: Ie9783511cb6be50136076a55330e645f4f36d075
Reviewed-on: https://gem5-review.googlesource.com/c/public/gem5/+/21119
Reviewed-by: Jason Lowe-Power <jason@lowepower.com>
Reviewed-by: Andreas Sandberg <andreas.sandberg@arm.com>
Maintainer: Jason Lowe-Power <jason@lowepower.com>
Maintainer: Andreas Sandberg <andreas.sandberg@arm.com>
Tested-by: kokoro <noreply+kokoro@google.com>
Diffstat (limited to 'ext/pybind11/docs/advanced/cast/eigen.rst')
| -rw-r--r-- | ext/pybind11/docs/advanced/cast/eigen.rst | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/ext/pybind11/docs/advanced/cast/eigen.rst b/ext/pybind11/docs/advanced/cast/eigen.rst index acdb51de6..59ba08c3c 100644 --- a/ext/pybind11/docs/advanced/cast/eigen.rst +++ b/ext/pybind11/docs/advanced/cast/eigen.rst @@ -37,11 +37,11 @@ that maps into the source ``numpy.ndarray`` data: this requires both that the data types are the same (e.g. ``dtype='float64'`` and ``MatrixType::Scalar`` is ``double``); and that the storage is layout compatible. The latter limitation is discussed in detail in the section below, and requires careful -consideration: by default, numpy matrices and eigen matrices are *not* storage +consideration: by default, numpy matrices and Eigen matrices are *not* storage compatible. If the numpy matrix cannot be used as is (either because its types differ, e.g. -passing an array of integers to an Eigen paramater requiring doubles, or +passing an array of integers to an Eigen parameter requiring doubles, or because the storage is incompatible), pybind11 makes a temporary copy and passes the copy instead. @@ -89,7 +89,7 @@ as dictated by the binding function's return value policy (see the documentation on :ref:`return_value_policies` for full details). That means, without an explicit return value policy, lvalue references will be copied and pointers will be managed by pybind11. In order to avoid copying, you should -explictly specify an appropriate return value policy, as in the following +explicitly specify an appropriate return value policy, as in the following example: .. code-block:: cpp @@ -226,7 +226,7 @@ order. Failing rather than copying =========================== -The default behaviour when binding ``Eigen::Ref<const MatrixType>`` eigen +The default behaviour when binding ``Eigen::Ref<const MatrixType>`` Eigen references is to copy matrix values when passed a numpy array that does not conform to the element type of ``MatrixType`` or does not have a compatible stride layout. If you want to explicitly avoid copying in such a case, you @@ -275,7 +275,7 @@ Vectors versus column/row matrices Eigen and numpy have fundamentally different notions of a vector. In Eigen, a vector is simply a matrix with the number of columns or rows set to 1 at compile time (for a column vector or row vector, respectively). Numpy, in -contast, has comparable 2-dimensional 1xN and Nx1 arrays, but *also* has +contrast, has comparable 2-dimensional 1xN and Nx1 arrays, but *also* has 1-dimensional arrays of size N. When passing a 2-dimensional 1xN or Nx1 array to Eigen, the Eigen type must @@ -287,15 +287,15 @@ On the other hand, pybind11 allows you to pass 1-dimensional arrays of length N as Eigen parameters. If the Eigen type can hold a column vector of length N it will be passed as such a column vector. If not, but the Eigen type constraints will accept a row vector, it will be passed as a row vector. (The column -vector takes precendence when both are supported, for example, when passing a +vector takes precedence when both are supported, for example, when passing a 1D numpy array to a MatrixXd argument). Note that the type need not be -expicitly a vector: it is permitted to pass a 1D numpy array of size 5 to an +explicitly a vector: it is permitted to pass a 1D numpy array of size 5 to an Eigen ``Matrix<double, Dynamic, 5>``: you would end up with a 1x5 Eigen matrix. Passing the same to an ``Eigen::MatrixXd`` would result in a 5x1 Eigen matrix. -When returning an eigen vector to numpy, the conversion is ambiguous: a row +When returning an Eigen vector to numpy, the conversion is ambiguous: a row vector of length 4 could be returned as either a 1D array of length 4, or as a -2D array of size 1x4. When encoutering such a situation, pybind11 compromises +2D array of size 1x4. When encountering such a situation, pybind11 compromises by considering the returned Eigen type: if it is a compile-time vector--that is, the type has either the number of rows or columns set to 1 at compile time--pybind11 converts to a 1D numpy array when returning the value. For |