Some spelling corrections

Author: tdegeus

docs/source/changelog.rst

@@ -575,7 +575,7 @@ Other changes
   `#1908 <https://github.com/xtensor-stack/xtensor/pull/1908>`_
 - Added ``noexcept`` in ``svector``
   `#1919 <https://github.com/xtensor-stack/xtensor/pull/1919>`_
-- Add implementation of repeat (similar to numpy)
+- Add implementation of repeat (similar to NumPy)
   `#1896 <https://github.com/xtensor-stack/xtensor/pull/1896>`_
 - Fix initialization of out shape in ``xt::tile``
   `#1923 <https://github.com/xtensor-stack/xtensor/pull/1923>`_
@@ -768,7 +768,7 @@ Other changes
   `#1676 <https://github.com/xtensor-stack/xtensor/pull/1676>`_
 - Added missing coma
   `#1680 <https://github.com/xtensor-stack/xtensor/pull/1680>`_
-- Added Numpy-like parameter in ``load_csv``
+- Added NumPy-like parameter in ``load_csv``
   `#1682 <https://github.com/xtensor-stack/xtensor/pull/1682>`_
 - Added ``shape()`` method to ``xshape.hpp``
   `#1592 <https://github.com/xtensor-stack/xtensor/pull/1592>`_
@@ -1421,17 +1421,17 @@ Other changes
   `#1109 <https://github.com/xtensor-stack/xtensor/pull/1109>`_.
 - Added test case for ``setdiff1d``
   `#1110 <https://github.com/xtensor-stack/xtensor/pull/1110>`_.
-- Added missing reference to ``diff`` in ``From numpy to xtensor`` section
+- Added missing reference to ``diff`` in ``From NumPy to xtensor`` section
   `#1116 <https://github.com/xtensor-stack/xtensor/pull/1116>`_.
 - Add ``amax`` and ``amin`` to the documentation
   `#1121 <https://github.com/xtensor-stack/xtensor/pull/1121>`_.
 - ``histogram`` and ``histogram_bin_edges`` implementation
   `#1108 <https://github.com/xtensor-stack/xtensor/pull/1108>`_.
-- Added numpy comparison for interp
+- Added NumPy comparison for interp
   `#1111 <https://github.com/xtensor-stack/xtensor/pull/1111>`_.
 - Allow multiple return type reducer functions
   `#1113 <https://github.com/xtensor-stack/xtensor/pull/1113>`_.
-- Fixes ``average`` bug + adds Numpy based tests
+- Fixes ``average`` bug + adds NumPy based tests
   `#1118 <https://github.com/xtensor-stack/xtensor/pull/1118>`_.
 - Static ``xfunction`` cache for fixed sizes
   `#1105 <https://github.com/xtensor-stack/xtensor/pull/1105>`_.
@@ -2122,7 +2122,7 @@ Breaking changes
 
 - The API for ``xbuffer_adaptor`` has changed. The template parameter is the type of the buffer, not just the value type
   `#482 <https://github.com/xtensor-stack/xtensor/pull/482>`_.
-- Change ``edge_items`` print option to ``edgeitems`` for better numpy consistency
+- Change ``edge_items`` print option to ``edgeitems`` for better NumPy consistency
   `#489 <https://github.com/xtensor-stack/xtensor/pull/489>`_.
 - *xtensor* now depends on *xtl* version `~0.3.3`
   `#508 <https://github.com/xtensor-stack/xtensor/pull/508>`_.
@@ -2159,7 +2159,7 @@ Other changes
   `#492 <https://github.com/xtensor-stack/xtensor/pull/492>`_.
 - The ``size()`` method for containers now returns the total number of elements instead of the buffer size, which may differ when the smallest stride is greater than ``1``
   `#502 <https://github.com/xtensor-stack/xtensor/pull/502>`_.
-- The behavior of ``linspace`` with integral types has been made consistent with numpy
+- The behavior of ``linspace`` with integral types has been made consistent with NumPy
   `#510 <https://github.com/xtensor-stack/xtensor/pull/510>`_.
 
 0.12.1

docs/source/closure-semantics.rst

@@ -9,7 +9,7 @@
 Closure semantics
 =================
 
-The *xtensor* library is a tensor expression library implementing numpy-style broadcasting and universal functions but in a lazy fashion.
+The *xtensor* library is a tensor expression library implementing NumPy-style broadcasting and universal functions but in a lazy fashion.
 
 If ``x`` and ``y`` are two tensor expressions with compatible shapes, the result of ``x + y`` is not a tensor but an expression that does
 not hold any value. Values of ``x + y`` are computed upon access or when the result is assigned to a container such as :cpp:type:`xt::xtensor` or

docs/source/conf.py

@@ -41,7 +41,7 @@ def setup(app):
     'goatcounter.js'
 ]
 
-# Automatically link to numpy doc
+# Automatically link to NumPy doc
 extensions += ['sphinx.ext.intersphinx']
 intersphinx_mapping = {
     "numpy": ("https://numpy.org/doc/stable/", None),

docs/source/container.rst

@@ -79,7 +79,7 @@ Runtime vs Compile-time dimensionality
 Three container classes implementing multidimensional arrays are provided: :cpp:type:`xt::xarray` and
 :cpp:type:`xt::xtensor` and :cpp:type:`xt::xtensor_fixed`.
 
-- :cpp:type:`xt::xarray` can be reshaped dynamically to any number of dimensions. It is the container that is the most similar to numpy arrays.
+- :cpp:type:`xt::xarray` can be reshaped dynamically to any number of dimensions. It is the container that is the most similar to NumPy arrays.
 - :cpp:type:`xt::xtensor` has a dimension set at compilation time, which enables many optimizations.
   For example, shapes and strides of :cpp:type:`xt::xtensor` instances are allocated on the stack instead of the heap.
 - :cpp:type:`xt::xtensor_fixed` has a shape fixed at compile time.

docs/source/developer/concepts.rst

@@ -270,8 +270,8 @@ If you read the entire code of ``xcontainer``, you'll notice that two types are
 strides and backstrides: ``shape_type`` and ``inner_shape_type``, ``strides_type`` and
 ``inner_strides_type``, and ``backstrides_type`` and ``inner_backstrides_type``. The distinction
 between ``inner_shape_type`` and ``shape_type`` was motivated by the xtensor-python wrapper around
-numpy data structures, where the inner shape type is a proxy on the shape section of the numpy
-arrayobject. It cannot have a value semantics on its own as it is bound to the entire numpy array.
+NumPy data structures, where the inner shape type is a proxy on the shape section of the NumPy
+arrayobject. It cannot have a value semantics on its own as it is bound to the entire NumPy array.
 
 ``xstrided_container`` inherits from ``xcontainer``; it represents a container that holds its shape
 and strides. It provides methods for reshaping the container:

docs/source/expression.rst

@@ -90,7 +90,7 @@ Broadcasting
 
 The number of dimensions of an :cpp:type:`xt::xexpression` and the sizes of these dimensions are provided by the :cpp:func:`~xt::xexpression::shape` method, which returns a sequence of unsigned integers
 specifying the size of each dimension. We can operate on expressions of different shapes of dimensions in an elementwise fashion.
-Broadcasting rules of *xtensor* are similar to those of Numpy_ and libdynd_.
+Broadcasting rules of *xtensor* are similar to those of NumPy_ and libdynd_.
 
 In an operation involving two arrays of different dimensions, the array with the lesser dimensions is broadcast across the leading dimensions of the other.
 For example, if ``A`` has shape ``(2, 3)``, and ``B`` has shape ``(4, 2, 3)``, the result of a broadcast operation with ``A`` and ``B`` has shape ``(4, 2, 3)``.

docs/source/file_loading.rst

@@ -13,7 +13,7 @@ format.
 Please note that many more input and output formats are available in the `xtensor-io
 <https://github.com/xtensor-stack/xtensor-io>`_ package.
 `xtensor-io` offers functions to load and store from image files (``jpg``, ``gif``, ``png``...),
-sound files (``wav``, ``ogg``...), HDF5 files (``h5``, ``hdf5``, ...), and compressed numpy format (``npz``).
+sound files (``wav``, ``ogg``...), HDF5 files (``h5``, ``hdf5``, ...), and compressed NumPy format (``npz``).
 
 
 Loading CSV data into xtensor

docs/source/index.rst

@@ -25,8 +25,8 @@ Containers of *xtensor* are inspired by `NumPy`_, the Python array programming
 library. **Adaptors** for existing data structures to be plugged into the
 expression system can easily be written.
 
-In fact, *xtensor* can be used to **process numpy data structures in-place**
-using Python's `buffer protocol`_. For more details on the numpy bindings,
+In fact, *xtensor* can be used to **process NumPy data structures in-place**
+using Python's `buffer protocol`_. For more details on the NumPy bindings,
 check out the xtensor-python_ project. Language bindings for R and Julia are
 also available.
 

docs/source/numpy-differences.rst

@@ -15,12 +15,12 @@ xtensor and numpy are very different libraries in their internal semantics. Whil
 is a lazy expression system, numpy manipulates in-memory containers, however, similarities in
 APIs are obvious. See e.g. the numpy to xtensor cheat sheet.
 
-And this page tracks the subtle differences of behavior between numpy and xtensor.
+And this page tracks the subtle differences of behavior between NumPy and xtensor.
 
 Zero-dimensional arrays
 -----------------------
 
-With numpy, 0-D arrays are nearly indistinguishable from scalars. This led to some issues w.r.t.
+With NumPy, 0-D arrays are nearly indistinguishable from scalars. This led to some issues w.r.t.
 universal functions returning scalars with 0-D array inputs instead of actual arrays...
 
 In xtensor, 0-D expressions are not implicitly convertible to scalar values. Values held by 0-D
@@ -87,15 +87,15 @@ be assigned to a container such as xarray or xtensor.
 Missing values
 --------------
 
-Support of missing values in numpy can be emulated with the masked array module,
+Support of missing values in NumPy can be emulated with the masked array module,
 which provides a means to handle arrays that have missing or invalid data.
 
 Support of missing values in xtensor is done through a notion of optional values, implemented in ``xoptional<T, B>``, which serves both as a value type for container and as a reference proxy for optimized storage types. See the section of the documentation on :doc:`missing`.
 
 Strides
 -------
 
-Strided containers of xtensor and numpy having the same exact memory layout may have different strides when accessing them through the ``strides`` attribute.
+Strided containers of xtensor and NumPy having the same exact memory layout may have different strides when accessing them through the ``strides`` attribute.
 The reason is an optimization in xtensor, which is to set the strides to ``0`` in dimensions of length ``1``, which simplifies the implementation of broadcasting of universal functions.
 
 .. tip::
@@ -109,7 +109,7 @@ The reason is an optimization in xtensor, which is to set the strides to ``0`` i
 
         xt::strides(a, xt::stride_type::internal); // ``== a.strides()``
 
-        xt::strides(a, xt::stride_type::bytes) // strides in bytes, as in numpy
+        xt::strides(a, xt::stride_type::bytes) // strides in bytes, as in NumPy
 
 
 Array indices

docs/source/related.rst

@@ -25,10 +25,10 @@ xtensor-python
 
 The xtensor-python_ project provides the implementation of container types
 compatible with *xtensor*'s expression system, ``pyarray`` and ``pytensor``
-which effectively wrap numpy arrays, allowing operating on numpy arrays
+which effectively wrap NumPy arrays, allowing operating on NumPy arrays
 in-place.
 
-Example 1: Use an algorithm of the C++ library on a numpy array in-place
+Example 1: Use an algorithm of the C++ library on a NumPy array in-place
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 **C++ code**
@@ -38,8 +38,8 @@ Example 1: Use an algorithm of the C++ library on a numpy array in-place
     #include <numeric>                        // Standard library import for std::accumulate
     #include <pybind11/pybind11.h>            // Pybind11 import to define Python bindings
     #include <xtensor/xmath.hpp>              // xtensor import for the C++ universal functions
-    #define FORCE_IMPORT_ARRAY                // numpy C api loading
-    #include <xtensor-python/pyarray.hpp>     // Numpy bindings
+    #define FORCE_IMPORT_ARRAY                // NumPy C api loading
+    #include <xtensor-python/pyarray.hpp>     // NumPy bindings
 
     double sum_of_sines(xt::pyarray<double> &m)
     {
@@ -144,7 +144,7 @@ It takes care of the initial work of generating a project skeleton with
 A few examples included in the resulting project including
 
 - A universal function defined from C++
-- A function making use of an algorithm from the STL on a numpy array
+- A function making use of an algorithm from the STL on a NumPy array
 - Unit tests
 - The generation of the HTML documentation with sphinx
 
@@ -200,7 +200,7 @@ Example 1: Use an algorithm of the C++ library with a Julia array
 
    1.2853996391883833
 
-Example 2: Create a numpy-style universal function from a C++ scalar function
+Example 2: Create a NumPy-style universal function from a C++ scalar function
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 **C++ code**
@@ -257,8 +257,8 @@ It takes care of the initial work of generating a project skeleton with
 
 A few examples included in the resulting project including
 
-- A numpy-style universal function defined from C++
-- A function making use of an algorithm from the STL on a numpy array
+- A NumPy-style universal function defined from C++
+- A function making use of an algorithm from the STL on a NumPy array
 - Unit tests
 - The generation of the HTML documentation with sphinx
 
@@ -318,7 +318,7 @@ xtensor-blas
 The xtensor-blas_ project is an extension to the xtensor library, offering
 bindings to BLAS and LAPACK libraries through cxxblas and cxxlapack from the
 FLENS project. ``xtensor-blas`` powers the ``xt::linalg`` functionalities,
-which are the counterpart to numpy's ``linalg`` module.
+which are the counterpart to NumPy's ``linalg`` module.
 
 xtensor-fftw
 ------------
@@ -328,7 +328,7 @@ xtensor-fftw
 
 The xtensor-fftw_ project is an extension to the xtensor library, offering
 bindings to the fftw library.  ``xtensor-fftw`` powers the ``xt::fftw``
-functionalities, which are the counterpart to numpy's ``fft`` module.
+functionalities, which are the counterpart to NumPy's ``fft`` module.
 
 Example 1: Calculate a derivative in Fourier space
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~