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Fix typos in documentation (#1635)

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* Always capitalize Eigen

* Fix spelling
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Darius Arnold 2019-06-10 21:57:00 +02:00 committed by Wenzel Jakob
parent 21bf16f5b8
commit 09330b94ea
1 changed files with 5 additions and 5 deletions
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10
docs/advanced/cast/eigen.rst
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@ -37,7 +37,7 @@ 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 data types are the same (e.g. ``dtype='float64'`` and ``MatrixType::Scalar`` is
``double``); and that the storage is layout compatible. The latter limitation ``double``); and that the storage is layout compatible. The latter limitation
is discussed in detail in the section below, and requires careful 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. compatible.
If the numpy matrix cannot be used as is (either because its types differ, e.g. If the numpy matrix cannot be used as is (either because its types differ, e.g.
@ -226,7 +226,7 @@ order.
Failing rather than copying 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 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 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 stride layout. If you want to explicitly avoid copying in such a case, you
@ -289,13 +289,13 @@ 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 will accept a row vector, it will be passed as a row vector. (The column
vector takes precedence 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 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. 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. 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 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 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 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 time--pybind11 converts to a 1D numpy array when returning the value. For