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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
``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.
@ -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
@ -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
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