change all non-code instances of Numpy to NumPy

Instances remain for NumpyVersion and Numpy.rec.fromarrays that are
references to code.

Release notes were left unchanged.

see issue #7986

23 files changed, 41 insertions, 41 deletions

diff --git a/doc/source/reference/arrays.classes.rst b/doc/source/reference/arrays.classes.rst
index b82f7d33c..298e81717 100644
--- a/doc/source/reference/arrays.classes.rst
+++ b/doc/source/reference/arrays.classes.rst
@@ -37,14 +37,14 @@ Special attributes and methods
 
 .. seealso:: :ref:`Subclassing ndarray <basics.subclassing>`
 
-Numpy provides several hooks that classes can customize:
+NumPy provides several hooks that classes can customize:
 
 .. method:: class.__numpy_ufunc__(ufunc, method, i, inputs, **kwargs)
 
    .. versionadded:: 1.11
 
    Any class (ndarray subclass or not) can define this method to
-   override behavior of Numpy's ufuncs. This works quite similarly to
+   override behavior of NumPy's ufuncs. This works quite similarly to
    Python's ``__mul__`` and other binary operation routines.
 
    - *ufunc* is the ufunc object that was called.
@@ -336,7 +336,7 @@ Record arrays (:mod:`numpy.rec`)
 .. seealso:: :ref:`routines.array-creation.rec`, :ref:`routines.dtype`,
              :ref:`arrays.dtypes`.
 
-Numpy provides the :class:`recarray` class which allows accessing the
+NumPy provides the :class:`recarray` class which allows accessing the
 fields of a structured array as attributes, and a corresponding
 scalar data type object :class:`record`.
 
diff --git a/doc/source/reference/arrays.dtypes.rst b/doc/source/reference/arrays.dtypes.rst
index 2db32e30d..01a969826 100644
--- a/doc/source/reference/arrays.dtypes.rst
+++ b/doc/source/reference/arrays.dtypes.rst
@@ -29,14 +29,14 @@ following aspects of the data:
    pair: dtype; scalar
 
 To describe the type of scalar data, there are several :ref:`built-in
-scalar types <arrays.scalars.built-in>` in Numpy for various precision
+scalar types <arrays.scalars.built-in>` in NumPy for various precision
 of integers, floating-point numbers, *etc*. An item extracted from an
 array, *e.g.*, by indexing, will be a Python object whose type is the
 scalar type associated with the data type of the array.
 
 Note that the scalar types are not :class:`dtype` objects, even though
 they can be used in place of one whenever a data type specification is
-needed in Numpy.
+needed in NumPy.
 
 .. index::
    pair: dtype; field
@@ -459,7 +459,7 @@ Type strings
 :class:`dtype`
 ==============
 
-Numpy data type descriptions are instances of the :class:`dtype` class.
+NumPy data type descriptions are instances of the :class:`dtype` class.
 
 Attributes
 ----------
diff --git a/doc/source/reference/arrays.indexing.rst b/doc/source/reference/arrays.indexing.rst
index 6e9bb9276..b7bc3a655 100644
--- a/doc/source/reference/arrays.indexing.rst
+++ b/doc/source/reference/arrays.indexing.rst
@@ -3,7 +3,7 @@
 Indexing
 ========
 
-.. sectionauthor:: adapted from "Guide to Numpy" by Travis E. Oliphant
+.. sectionauthor:: adapted from "Guide to NumPy" by Travis E. Oliphant
 
 .. currentmodule:: numpy
 
diff --git a/doc/source/reference/arrays.ndarray.rst b/doc/source/reference/arrays.ndarray.rst
index b68e40e3f..bd6821b62 100644
--- a/doc/source/reference/arrays.ndarray.rst
+++ b/doc/source/reference/arrays.ndarray.rst
@@ -103,7 +103,7 @@ the bytes are interpreted is defined by the :ref:`data-type object
 
 A segment of memory is inherently 1-dimensional, and there are many
 different schemes for arranging the items of an *N*-dimensional array
-in a 1-dimensional block. Numpy is flexible, and :class:`ndarray`
+in a 1-dimensional block. NumPy is flexible, and :class:`ndarray`
 objects can accommodate any *strided indexing scheme*. In a strided
 scheme, the N-dimensional index :math:`(n_0, n_1, ..., n_{N-1})`
 corresponds to the offset (in bytes):
@@ -155,7 +155,7 @@ base offset itself is a multiple of `self.itemsize`.
 .. note::
 
     Points (1) and (2) are not yet applied by default. Beginning with
-    Numpy 1.8.0, they are applied consistently only if the environment
+    NumPy 1.8.0, they are applied consistently only if the environment
     variable ``NPY_RELAXED_STRIDES_CHECKING=1`` was defined when NumPy
     was built. Eventually this will become the default.
 
@@ -440,7 +440,7 @@ Each of the arithmetic operations (``+``, ``-``, ``*``, ``/``, ``//``,
 ``%``, ``divmod()``, ``**`` or ``pow()``, ``<<``, ``>>``, ``&``,
 ``^``, ``|``, ``~``) and the comparisons (``==``, ``<``, ``>``,
 ``<=``, ``>=``, ``!=``) is equivalent to the corresponding
-:term:`universal function` (or :term:`ufunc` for short) in Numpy.  For
+:term:`universal function` (or :term:`ufunc` for short) in NumPy.  For
 more information, see the section on :ref:`Universal Functions
 <ufuncs>`.
 
@@ -560,7 +560,7 @@ Matrix Multiplication:
 .. note::
 
    Matrix operators ``@`` and ``@=`` were introduced in Python 3.5
-   following PEP465. Numpy 1.10 has a preliminary implementation of ``@``
+   following PEP465. NumPy 1.10 has a preliminary implementation of ``@``
    for testing purposes. Further documentation can be found in the
    :func:`matmul` documentation.
 
diff --git a/doc/source/reference/arrays.rst b/doc/source/reference/arrays.rst
index 40c9f755d..faa91a389 100644
--- a/doc/source/reference/arrays.rst
+++ b/doc/source/reference/arrays.rst
@@ -20,7 +20,7 @@ with every array. In addition to basic types (integers, floats,
 
 An item extracted from an array, *e.g.*, by indexing, is represented
 by a Python object whose type is one of the :ref:`array scalar types
-<arrays.scalars>` built in Numpy. The array scalars allow easy manipulation
+<arrays.scalars>` built in NumPy. The array scalars allow easy manipulation
 of also more complicated arrangements of data.
 
 .. figure:: figures/threefundamental.png
diff --git a/doc/source/reference/arrays.scalars.rst b/doc/source/reference/arrays.scalars.rst
index f8fad0095..4acaf1b3b 100644
--- a/doc/source/reference/arrays.scalars.rst
+++ b/doc/source/reference/arrays.scalars.rst
@@ -94,7 +94,7 @@ Python Boolean scalar.
    :class:`int` built-in under Python 3, because type :class:`int` is no
    longer a fixed-width integer type.
 
-.. tip:: The default data type in Numpy is :class:`float_`.
+.. tip:: The default data type in NumPy is :class:`float_`.
 
 In the tables below, ``platform?`` means that the type may not be
 available on all platforms. Compatibility with different C or Python
@@ -288,4 +288,4 @@ the built-in scalar types): One way is to simply subclass the
 a degree, but internally certain behaviors are fixed by the data type of 
 the array.  To fully customize the data type of an array you need to 
 define a new data-type, and register it with NumPy. Such new types can only 
-be defined in C, using the :ref:`Numpy C-API <c-api>`.
+be defined in C, using the :ref:`NumPy C-API <c-api>`.
diff --git a/doc/source/reference/c-api.array.rst b/doc/source/reference/c-api.array.rst
index 049c7b537..4ab276d9a 100644
--- a/doc/source/reference/c-api.array.rst
+++ b/doc/source/reference/c-api.array.rst
@@ -2291,7 +2291,7 @@ an element copier function as a primitive.::
 Array Iterators
 ---------------
 
-As of Numpy 1.6, these array iterators are superceded by
+As of NumPy 1.6, these array iterators are superceded by
 the new array iterator, :c:type:`NpyIter`.
 
 An array iterator is a simple way to access the elements of an
diff --git a/doc/source/reference/c-api.config.rst b/doc/source/reference/c-api.config.rst
index 17d7f557d..60bf61a32 100644
--- a/doc/source/reference/c-api.config.rst
+++ b/doc/source/reference/c-api.config.rst
@@ -4,14 +4,14 @@ System configuration
 .. sectionauthor:: Travis E. Oliphant
 
 When NumPy is built, information about system configuration is
-recorded, and is made available for extension modules using Numpy's C
+recorded, and is made available for extension modules using NumPy's C
 API.  These are mostly defined in ``numpyconfig.h`` (included in
 ``ndarrayobject.h``). The public symbols are prefixed by ``NPY_*``.
-Numpy also offers some functions for querying information about the
+NumPy also offers some functions for querying information about the
 platform in use.
 
-For private use, Numpy also constructs a ``config.h`` in the NumPy
-include directory, which is not exported by Numpy (that is a python
+For private use, NumPy also constructs a ``config.h`` in the NumPy
+include directory, which is not exported by NumPy (that is a python
 extension which use the numpy C API will not see those symbols), to
 avoid namespace pollution.
 
diff --git a/doc/source/reference/c-api.coremath.rst b/doc/source/reference/c-api.coremath.rst
index 08b1adb3a..9027a4e0d 100644
--- a/doc/source/reference/c-api.coremath.rst
+++ b/doc/source/reference/c-api.coremath.rst
@@ -1,4 +1,4 @@
-Numpy core libraries
+NumPy core libraries
 ====================
 
 .. sectionauthor:: David Cournapeau
@@ -10,7 +10,7 @@ Starting from numpy 1.3.0, we are working on separating the pure C,
 making the code cleaner, and enabling code reuse by other extensions outside
 numpy (scipy, etc...).
 
-Numpy core math library
+NumPy core math library
 -----------------------
 
 The numpy core math library ('npymath') is a first step in this direction. This
diff --git a/doc/source/reference/c-api.generalized-ufuncs.rst b/doc/source/reference/c-api.generalized-ufuncs.rst
index 92dc8aec0..d34a9d4d4 100644
--- a/doc/source/reference/c-api.generalized-ufuncs.rst
+++ b/doc/source/reference/c-api.generalized-ufuncs.rst
@@ -4,7 +4,7 @@ Generalized Universal Function API
 
 There is a general need for looping over not only functions on scalars
 but also over functions on vectors (or arrays).
-This concept is realized in Numpy by generalizing the universal functions
+This concept is realized in NumPy by generalizing the universal functions
 (ufuncs).  In regular ufuncs, the elementary function is limited to
 element-by-element operations, whereas the generalized version (gufuncs)
 supports "sub-array" by "sub-array" operations.  The Perl vector library PDL
@@ -60,7 +60,7 @@ output array of the right size. If the size of a core dimension of an output
 cannot be determined from a passed in input or output array, an error will be
 raised.
 
-Note: Prior to Numpy 1.10.0, less strict checks were in place: missing core
+Note: Prior to NumPy 1.10.0, less strict checks were in place: missing core
 dimensions were created by prepending 1's to the shape as necessary, core
 dimensions with the same label were broadcast together, and undetermined
 dimensions were created with size 1.
diff --git a/doc/source/reference/c-api.rst b/doc/source/reference/c-api.rst
index b1a5eb477..b8cbe97b2 100644
--- a/doc/source/reference/c-api.rst
+++ b/doc/source/reference/c-api.rst
@@ -1,7 +1,7 @@
 .. _c-api:
 
 ###########
-Numpy C-API
+NumPy C-API
 ###########
 
 .. sectionauthor:: Travis E. Oliphant
diff --git a/doc/source/reference/index.rst b/doc/source/reference/index.rst
index 9e0ef68db..f74816d6f 100644
--- a/doc/source/reference/index.rst
+++ b/doc/source/reference/index.rst
@@ -11,7 +11,7 @@ NumPy Reference
 .. module:: numpy
 
 This reference manual details functions, modules, and objects
-included in Numpy, describing what they are and what they do.
+included in NumPy, describing what they are and what they do.
 For learning how to use NumPy, see also :ref:`user`.
 
 
@@ -31,11 +31,11 @@ Acknowledgements
 ================
 
 Large parts of this manual originate from Travis E. Oliphant's book
-`Guide to Numpy <http://www.tramy.us/>`__ (which generously entered
+`Guide to NumPy <http://www.tramy.us/>`__ (which generously entered
 Public Domain in August 2008). The reference documentation for many of
 the functions are written by numerous contributors and developers of
-Numpy, both prior to and during the
-`Numpy Documentation Marathon
+NumPy, both prior to and during the
+`NumPy Documentation Marathon
 <http://scipy.org/Developer_Zone/DocMarathon2008>`__.
 
 Please help to improve NumPy's documentation! Instructions on how to
diff --git a/doc/source/reference/internals.code-explanations.rst b/doc/source/reference/internals.code-explanations.rst
index f946d0420..fca87f260 100644
--- a/doc/source/reference/internals.code-explanations.rst
+++ b/doc/source/reference/internals.code-explanations.rst
@@ -1,7 +1,7 @@
 .. currentmodule:: numpy
 
 *************************
-Numpy C Code Explanations
+NumPy C Code Explanations
 *************************
 
     Fanaticism consists of redoubling your efforts when you have forgotten
diff --git a/doc/source/reference/internals.rst b/doc/source/reference/internals.rst
index c9716813d..e1d6644a6 100644
--- a/doc/source/reference/internals.rst
+++ b/doc/source/reference/internals.rst
@@ -1,5 +1,5 @@
 ***************
-Numpy internals
+NumPy internals
 ***************
 
 .. toctree::
diff --git a/doc/source/reference/routines.help.rst b/doc/source/reference/routines.help.rst
index a41563cce..9b6eb4ad3 100644
--- a/doc/source/reference/routines.help.rst
+++ b/doc/source/reference/routines.help.rst
@@ -1,6 +1,6 @@
 .. _routines.help:
 
-Numpy-specific help functions
+NumPy-specific help functions
 =============================
 
 .. currentmodule:: numpy
diff --git a/doc/source/reference/routines.io.rst b/doc/source/reference/routines.io.rst
index ff8c05c16..6747f60bd 100644
--- a/doc/source/reference/routines.io.rst
+++ b/doc/source/reference/routines.io.rst
@@ -3,7 +3,7 @@ Input and output
 
 .. currentmodule:: numpy
 
-Numpy binary files (NPY, NPZ)
+NumPy binary files (NPY, NPZ)
 -----------------------------
 .. autosummary::
    :toctree: generated/
diff --git a/doc/source/reference/routines.numarray.rst b/doc/source/reference/routines.numarray.rst
index 713f99d30..3bbc413d7 100644
--- a/doc/source/reference/routines.numarray.rst
+++ b/doc/source/reference/routines.numarray.rst
@@ -2,4 +2,4 @@
 Numarray compatibility
 **********************
 
-The numarray module was removed in Numpy 1.9.
+The numarray module was removed in NumPy 1.9.
diff --git a/doc/source/reference/routines.oldnumeric.rst b/doc/source/reference/routines.oldnumeric.rst
index e83331d01..2120fc69e 100644
--- a/doc/source/reference/routines.oldnumeric.rst
+++ b/doc/source/reference/routines.oldnumeric.rst
@@ -4,4 +4,4 @@ Old Numeric compatibility
 
 .. currentmodule:: numpy
 
-The oldnumeric module was removed in Numpy 1.9.0.
+The oldnumeric module was removed in NumPy 1.9.0.
diff --git a/doc/source/reference/routines.other.rst b/doc/source/reference/routines.other.rst
index b7a924eba..4a027b5a1 100644
--- a/doc/source/reference/routines.other.rst
+++ b/doc/source/reference/routines.other.rst
@@ -32,7 +32,7 @@ Memory ranges
    shares_memory
    may_share_memory
 
-Numpy version comparison
+NumPy version comparison
 ------------------------
 .. autosummary::
    :toctree: generated/
diff --git a/doc/source/reference/routines.polynomials.classes.rst b/doc/source/reference/routines.polynomials.classes.rst
index c40795434..0db77eb7c 100644
--- a/doc/source/reference/routines.polynomials.classes.rst
+++ b/doc/source/reference/routines.polynomials.classes.rst
@@ -39,7 +39,7 @@ All of the classes have the same methods, and especially they implement the
 Python numeric operators +, -, \*, //, %, divmod, \*\*, ==,
 and !=. The last two can be a bit problematic due to floating point
 roundoff errors. We now give a quick demonstration of the various
-operations using Numpy version 1.7.0.
+operations using NumPy version 1.7.0.
 
 Basics
 ------
diff --git a/doc/source/reference/swig.interface-file.rst b/doc/source/reference/swig.interface-file.rst
index 1d6fbe04d..36b226b9b 100644
--- a/doc/source/reference/swig.interface-file.rst
+++ b/doc/source/reference/swig.interface-file.rst
@@ -1,4 +1,4 @@
-Numpy.i: a SWIG Interface File for NumPy
+NumPy.i: a SWIG Interface File for NumPy
 ========================================
 
 Introduction
@@ -555,7 +555,7 @@ If you get a Python error that looks like the following::
 and the argument you are passing is an integer extracted from a
 NumPy array, then you have stumbled upon this problem.  The
 solution is to modify the `SWIG`_ type conversion system to accept
-Numpy array scalars in addition to the standard integer types.
+NumPy array scalars in addition to the standard integer types.
 Fortunately, this capabilitiy has been provided for you.  Simply copy
 the file::
 
diff --git a/doc/source/reference/swig.rst b/doc/source/reference/swig.rst
index 3931b8e11..6865cc96a 100644
--- a/doc/source/reference/swig.rst
+++ b/doc/source/reference/swig.rst
@@ -1,5 +1,5 @@
 **************
-Numpy and SWIG
+NumPy and SWIG
 **************
 
 .. sectionauthor:: Bill Spotz
diff --git a/doc/source/reference/ufuncs.rst b/doc/source/reference/ufuncs.rst
index 7f38135e9..62e90b83c 100644
--- a/doc/source/reference/ufuncs.rst
+++ b/doc/source/reference/ufuncs.rst
@@ -1,4 +1,4 @@
-.. sectionauthor:: adapted from "Guide to Numpy" by Travis E. Oliphant
+.. sectionauthor:: adapted from "Guide to NumPy" by Travis E. Oliphant
 
 .. _ufuncs:
 
@@ -20,7 +20,7 @@ is, a ufunc is a ":term:`vectorized`" wrapper for a function that
 takes a fixed number of scalar inputs and produces a fixed number of
 scalar outputs.
 
-In Numpy, universal functions are instances of the
+In NumPy, universal functions are instances of the
 :class:`numpy.ufunc` class. Many of the built-in functions are
 implemented in compiled C code, but :class:`ufunc` instances can also
 be produced using the :func:`frompyfunc` factory function.