From 773e3cad9a71cb9a7849d8e251fb8a99ab35d06b Mon Sep 17 00:00:00 2001 From: Pierre de Buyl Date: Mon, 5 Sep 2016 22:24:34 +0200 Subject: [PATCH 1/6] 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 --- doc/C_STYLE_GUIDE.rst.txt | 10 ++--- doc/HOWTO_BUILD_DOCS.rst.txt | 18 ++++----- doc/HOWTO_DOCUMENT.rst.txt | 8 ++-- doc/HOWTO_RELEASE.rst.txt | 2 +- doc/Py3K.rst.txt | 26 ++++++------ doc/TESTS.rst.txt | 2 +- doc/cdoc/README | 2 +- doc/neps/missing-data.rst | 2 +- doc/neps/new-iterator-ufunc.rst | 2 +- doc/neps/ufunc-overrides.rst | 6 +-- doc/source/_templates/indexcontent.html | 20 +++++----- doc/source/about.rst | 8 ++-- doc/source/bugs.rst | 2 +- doc/source/conf.py | 4 +- doc/source/contents.rst | 2 +- doc/source/dev/development_environment.rst | 6 +-- .../dev/gitwash/development_workflow.rst | 4 +- doc/source/dev/index.rst | 2 +- doc/source/f2py/distutils.rst | 2 +- doc/source/f2py/getting-started.rst | 4 +- doc/source/f2py/python-usage.rst | 12 +++--- doc/source/f2py/signature-file.rst | 6 +-- doc/source/license.rst | 2 +- doc/source/neps/index.rst | 6 +-- doc/source/reference/arrays.classes.rst | 6 +-- doc/source/reference/arrays.dtypes.rst | 6 +-- doc/source/reference/arrays.indexing.rst | 2 +- doc/source/reference/arrays.ndarray.rst | 8 ++-- doc/source/reference/arrays.rst | 2 +- doc/source/reference/arrays.scalars.rst | 4 +- doc/source/reference/c-api.array.rst | 2 +- doc/source/reference/c-api.config.rst | 8 ++-- doc/source/reference/c-api.coremath.rst | 4 +- .../reference/c-api.generalized-ufuncs.rst | 4 +- doc/source/reference/c-api.rst | 2 +- doc/source/reference/index.rst | 8 ++-- .../reference/internals.code-explanations.rst | 2 +- doc/source/reference/internals.rst | 2 +- doc/source/reference/routines.help.rst | 2 +- doc/source/reference/routines.io.rst | 2 +- doc/source/reference/routines.numarray.rst | 2 +- doc/source/reference/routines.oldnumeric.rst | 2 +- doc/source/reference/routines.other.rst | 2 +- .../routines.polynomials.classes.rst | 2 +- doc/source/reference/swig.interface-file.rst | 4 +- doc/source/reference/swig.rst | 2 +- doc/source/reference/ufuncs.rst | 4 +- doc/source/user/basics.io.genfromtxt.rst | 4 +- doc/source/user/basics.io.rst | 2 +- doc/source/user/basics.rst | 2 +- doc/source/user/building.rst | 6 +-- doc/source/user/c-info.rst | 2 +- doc/source/user/c-info.ufunc-tutorial.rst | 30 +++++++------- doc/source/user/numpy-for-matlab-users.rst | 40 +++++++++---------- doc/source/user/quickstart.rst | 6 +-- doc/summarize.py | 4 +- 56 files changed, 168 insertions(+), 168 deletions(-) diff --git a/doc/C_STYLE_GUIDE.rst.txt b/doc/C_STYLE_GUIDE.rst.txt index 4b89b3aea451..a5726f16fa36 100644 --- a/doc/C_STYLE_GUIDE.rst.txt +++ b/doc/C_STYLE_GUIDE.rst.txt @@ -6,7 +6,7 @@ The NumPy C coding conventions are based on Python PEP-0007 by Guido van Rossum with a few added strictures. There are many C coding conventions and it must be emphasized that the primary goal of the NumPy conventions isn't to choose the 'best', about which there is certain to be disagreement, but -to achieve uniformity. Because the Numpy conventions are very close to +to achieve uniformity. Because the NumPy conventions are very close to those in PEP-0007, that PEP is used as a template below with the NumPy additions and variations in the appropriate spots. @@ -17,7 +17,7 @@ Introduction ------------ This document gives coding conventions for the C code comprising -the C implementation of Numpy. Note, rules are there to be broken. +the C implementation of NumPy. Note, rules are there to be broken. Two good reasons to break a particular rule: 1. When applying the rule would make the code less readable, even @@ -53,7 +53,7 @@ C dialect Note: this will change with the proposed transition to C++. * No compiler warnings with major compilers (gcc, VC++, a few others). - Note: Numpy still produces compiler warnings that need to be addressed. + Note: NumPy still produces compiler warnings that need to be addressed. Code lay-out @@ -195,7 +195,7 @@ Code lay-out Naming conventions ------------------ -* There has been no consistent prefix for Numpy public functions, but +* There has been no consistent prefix for NumPy public functions, but they all begin with a prefix of some sort, followed by an underscore, and are in camel case: ``PyArray_DescrAlignConverter``, ``NpyIter_GetIterNext``. In the future the names should be of the form ``Npy*_PublicFunction``, @@ -213,7 +213,7 @@ Naming conventions Function documentation ---------------------- -Numpy doesn't have a C function documentation standard at this time, but +NumPy doesn't have a C function documentation standard at this time, but needs one. Most numpy functions are not documented in the code and that should change. One possibility is Doxygen with a plugin so that the same NumPy style used for Python functions can also be used for documenting diff --git a/doc/HOWTO_BUILD_DOCS.rst.txt b/doc/HOWTO_BUILD_DOCS.rst.txt index dc0145855282..5ce226ad4f6b 100644 --- a/doc/HOWTO_BUILD_DOCS.rst.txt +++ b/doc/HOWTO_BUILD_DOCS.rst.txt @@ -3,7 +3,7 @@ Building the NumPy API and reference docs ========================================= We currently use Sphinx_ for generating the API and reference -documentation for Numpy. You will need Sphinx 1.0.1 or newer. +documentation for NumPy. You will need Sphinx 1.0.1 or newer. If you only want to get the documentation, note that pre-built versions can be found at @@ -18,7 +18,7 @@ in several different formats. Instructions ------------ -If you obtained Numpy via git, get also the git submodules that contain +If you obtained NumPy via git, get also the git submodules that contain additional parts required for building the documentation:: git submodule init @@ -30,15 +30,15 @@ be installed with or without completely installing Matplotlib: see the Matplotlib documentation for more information. Since large parts of the main documentation are stored in -docstrings, you will need to first build Numpy, and install it so +docstrings, you will need to first build NumPy, and install it so that the correct version is imported by >>> import numpy -Note that you can eg. install Numpy to a temporary location and set +Note that you can eg. install NumPy to a temporary location and set the PYTHONPATH environment variable appropriately. -After Numpy is installed, write:: +After NumPy is installed, write:: make html @@ -59,11 +59,11 @@ Instead of the above, you can also do:: make dist -which will rebuild Numpy, install it to a temporary location, and +which will rebuild NumPy, install it to a temporary location, and build the documentation in all formats. This will most likely again only work on Unix platforms. -The documentation for Numpy distributed at http://docs.scipy.org in html and +The documentation for NumPy distributed at http://docs.scipy.org in html and pdf format is also built with ``make dist``. See `HOWTO RELEASE`_ for details on how to update http://docs.scipy.org. @@ -73,9 +73,9 @@ how to update http://docs.scipy.org. Sphinx extensions ----------------- -Numpy's documentation uses several custom extensions to Sphinx. These +NumPy's documentation uses several custom extensions to Sphinx. These are shipped in the ``sphinxext/`` directory (as git submodules, as discussed -above), and are automatically enabled when building Numpy's documentation. +above), and are automatically enabled when building NumPy's documentation. If you want to make use of these extensions in third-party projects, they are available on PyPi_ as the numpydoc_ package. diff --git a/doc/HOWTO_DOCUMENT.rst.txt b/doc/HOWTO_DOCUMENT.rst.txt index 954f331e8dd7..169b87df4150 100644 --- a/doc/HOWTO_DOCUMENT.rst.txt +++ b/doc/HOWTO_DOCUMENT.rst.txt @@ -141,7 +141,7 @@ The sections of the docstring are: A section (use if applicable) to warn users that the object is deprecated. Section contents should include: - * In what Numpy version the object was deprecated, and when it will be + * In what NumPy version the object was deprecated, and when it will be removed. * Reason for deprecation if this is useful information (e.g., object @@ -154,8 +154,8 @@ The sections of the docstring are: :: - .. note:: Deprecated in Numpy 1.6 - `ndobj_old` will be removed in Numpy 2.0, it is replaced by + .. note:: Deprecated in NumPy 1.6 + `ndobj_old` will be removed in NumPy 2.0, it is replaced by `ndobj_new` because the latter works also with array subclasses. 3. **Extended Summary** @@ -529,7 +529,7 @@ as appropriate. Documenting class instances --------------------------- -Instances of classes that are part of the Numpy API (for example `np.r_` +Instances of classes that are part of the NumPy API (for example `np.r_` `np,c_`, `np.index_exp`, etc.) may require some care. To give these instances a useful docstring, we do the following: diff --git a/doc/HOWTO_RELEASE.rst.txt b/doc/HOWTO_RELEASE.rst.txt index 8c180e1e01bc..6d0d54e89871 100644 --- a/doc/HOWTO_RELEASE.rst.txt +++ b/doc/HOWTO_RELEASE.rst.txt @@ -302,7 +302,7 @@ to public keyservers, with a command such as:: Apply patch to fix bogus strides -------------------------------- -NPY_RELAXED_STRIDE_CHECKING was made the default in Numpy 1.10 and bogus +NPY_RELAXED_STRIDE_CHECKING was made the default in NumPy 1.10 and bogus strides are used in the development branch to smoke out problems. The `patch `_ should be updated if necessary and applied to the release branch to rationalize the strides. diff --git a/doc/Py3K.rst.txt b/doc/Py3K.rst.txt index e06461794a64..7150430aca18 100644 --- a/doc/Py3K.rst.txt +++ b/doc/Py3K.rst.txt @@ -11,7 +11,7 @@ Developer notes on the transition to Python 3 General ======= -Numpy has now been ported to Python 3. +NumPy has now been ported to Python 3. Some glitches may still be present; however, we are not aware of any significant ones, the test suite passes. @@ -125,7 +125,7 @@ F2py is ported to Py3. Bytes vs. strings ----------------- -At many points in Numpy, bytes literals are needed. These can be created via +At many points in NumPy, bytes literals are needed. These can be created via numpy.compat.asbytes and asbytes_nested. @@ -245,7 +245,7 @@ Any new ones that need to be added should be added in this file. .. todo:: - Remove PyString_* eventually -- having a call to one of these in Numpy + Remove PyString_* eventually -- having a call to one of these in NumPy sources is a sign of an error... @@ -527,7 +527,7 @@ There are two issues related to the str/bytes change: This entry discusses return values etc. only, the 'S' dtype is a separate topic. -All uses of PyString in Numpy should be changed to one of +All uses of PyString in NumPy should be changed to one of - PyBytes: one-byte character strings in Py2 and Py3 - PyUString (defined in npy_3kconfig.h): PyString in Py2, PyUnicode in Py3 @@ -554,7 +554,7 @@ Some specific decisions that have been made so far: * descriptor.c: dtype field names are UString - At some places in Numpy code, there are some guards for Unicode field + At some places in NumPy code, there are some guards for Unicode field names. However, the dtype constructor accepts only strings as field names, so we should assume field names are *always* UString. @@ -604,7 +604,7 @@ PyUnicode PyUnicode in Py3 is pretty much as it was in Py2, except that it is now the only "real" string type. -In Py3, Unicode and Bytes are not comparable, ie., 'a' != b'a'. Numpy +In Py3, Unicode and Bytes are not comparable, ie., 'a' != b'a'. NumPy comparison routines were handled to act in the same way, leaving comparison between Unicode and Bytes undefined. @@ -627,17 +627,17 @@ PyInt ----- There is no limited-range integer type any more in Py3. It makes no -sense to inherit Numpy ints from Py3 ints. +sense to inherit NumPy ints from Py3 ints. Currently, the following is done: -1) Numpy's integer types no longer inherit from Python integer. +1) NumPy's integer types no longer inherit from Python integer. 2) int is taken dtype-equivalent to NPY_LONG 3) ints are converted to NPY_LONG PyInt methods are currently replaced by PyLong, via macros in npy_3kcompat.h. -Dtype decision rules were changed accordingly, so that Numpy understands +Dtype decision rules were changed accordingly, so that NumPy understands Py3 int translate to NPY_LONG as far as dtypes are concerned. array([1]).dtype will be the default NPY_LONG integer. @@ -645,7 +645,7 @@ array([1]).dtype will be the default NPY_LONG integer. .. todo:: Not inheriting from `int` on Python 3 makes the following not work: - ``np.intp("0xff", 16)`` -- because the Numpy type does not take + ``np.intp("0xff", 16)`` -- because the NumPy type does not take the second argument. This could perhaps be fixed... @@ -707,7 +707,7 @@ Module initialization The module initialization API changed in Python 3.1. -Most Numpy modules are now converted. +Most NumPy modules are now converted. PyTypeObject @@ -871,7 +871,7 @@ obtained must be closed with fclose after use. Should probably be done much later on... - Adapt all Numpy I/O to use the PyFile_* methods or the low-level + Adapt all NumPy I/O to use the PyFile_* methods or the low-level IO routines. In any case, it's unlikely that C stdio can be used any more. Perhaps using PyFile_* makes numpy.tofile e.g. to a gzip to work? @@ -914,4 +914,4 @@ PyCObject / PyCapsule The PyCObject API is removed in Python 3.2, so we need to rewrite it using PyCapsule. -Numpy was changed to use the Capsule API, using NpyCapsule* wrappers. +NumPy was changed to use the Capsule API, using NpyCapsule* wrappers. diff --git a/doc/TESTS.rst.txt b/doc/TESTS.rst.txt index 690baca3d14f..7028ca57a1af 100644 --- a/doc/TESTS.rst.txt +++ b/doc/TESTS.rst.txt @@ -382,5 +382,5 @@ Tests on random data are good, but since test failures are meant to expose new bugs or regressions, a test that passes most of the time but fails occasionally with no code changes is not helpful. Make the random data deterministic by setting the random number seed before generating it. Use -either Python's ``random.seed(some_number)`` or Numpy's +either Python's ``random.seed(some_number)`` or NumPy's ``numpy.random.seed(some_number)``, depending on the source of random numbers. diff --git a/doc/cdoc/README b/doc/cdoc/README index 98be85484294..a5363cfa1b11 100644 --- a/doc/cdoc/README +++ b/doc/cdoc/README @@ -1,7 +1,7 @@ cdoc ==== -This is a simple Doxygen project for building Numpy C code documentation, +This is a simple Doxygen project for building NumPy C code documentation, with docstrings extracted from the C sources themselves. The understood syntax for documentation in the C source is diff --git a/doc/neps/missing-data.rst b/doc/neps/missing-data.rst index 17fb06491e08..9dc509c53b57 100644 --- a/doc/neps/missing-data.rst +++ b/doc/neps/missing-data.rst @@ -884,7 +884,7 @@ a few different access patterns people use to get ahold of the numpy array data, here we examine a few of them to see what numpy can do. These examples are found from doing google searches of numpy C API array access. -Numpy Documentation - How to extend NumPy +NumPy Documentation - How to extend NumPy ----------------------------------------- http://docs.scipy.org/doc/numpy/user/c-info.how-to-extend.html#dealing-with-array-objects diff --git a/doc/neps/new-iterator-ufunc.rst b/doc/neps/new-iterator-ufunc.rst index 6d132f9a5dc8..e62c910cf023 100644 --- a/doc/neps/new-iterator-ufunc.rst +++ b/doc/neps/new-iterator-ufunc.rst @@ -51,7 +51,7 @@ NumPy defaults to returning C-contiguous arrays from UFuncs. This can result in extremely poor memory access patterns when dealing with data that is structured differently. A simple timing example illustrates this with a more than eight times performance hit from adding -Fortran-contiguous arrays together. All timings are done using Numpy +Fortran-contiguous arrays together. All timings are done using NumPy 2.0dev (Nov 22, 2010) on an Athlon 64 X2 4200+, with a 64-bit OS.:: In [1]: import numpy as np diff --git a/doc/neps/ufunc-overrides.rst b/doc/neps/ufunc-overrides.rst index be3b8b4aa2e7..98380ee974ba 100644 --- a/doc/neps/ufunc-overrides.rst +++ b/doc/neps/ufunc-overrides.rst @@ -176,7 +176,7 @@ The ``__numpy_ufunc__`` mechanism is fully independent of Python's standard operator override mechanism, and the two do not interact directly. -They however have indirect interactions, because Numpy's ``ndarray`` +They however have indirect interactions, because NumPy's ``ndarray`` type implements its binary operations via Ufuncs. Effectively, we have:: class ndarray(object): @@ -208,14 +208,14 @@ undesirable behavior. The reason why this occurs is: because ``MyObject`` is not an ndarray subclass, Python resolves the expression ``b * a`` by calling first -``b.__mul__``. Since Numpy implements this via an Ufunc, the call is +``b.__mul__``. Since NumPy implements this via an Ufunc, the call is forwarded to ``__numpy_ufunc__`` and not to ``__rmul__``. Note that if ``MyObject`` is a subclass of ``ndarray``, Python calls ``a.__rmul__`` first. The issue is therefore that ``__numpy_ufunc__`` implements "virtual subclassing" of ndarray behavior, without actual subclassing. This issue can be resolved by a modification of the binary operation -methods in Numpy:: +methods in NumPy:: class ndarray(object): ... diff --git a/doc/source/_templates/indexcontent.html b/doc/source/_templates/indexcontent.html index e3fe3ac9bc4e..3fbb616c646c 100644 --- a/doc/source/_templates/indexcontent.html +++ b/doc/source/_templates/indexcontent.html @@ -3,13 +3,13 @@

Parts of the documentation:

-

Numpy User Guide
+

NumPy User Guide
start here

-

Numpy Reference
+

NumPy Reference
reference documentation

F2Py Guide
f2py documentation

-

Numpy Developer Guide
+

NumPy Developer Guide
contributing to NumPy

@@ -34,28 +34,28 @@

Reporting bugs

About NumPy

-

Numpy Enhancement Proposals
+

NumPy Enhancement Proposals

Release Notes

-

License of Numpy

+

License of NumPy

Acknowledgements

Large parts of this manual originate from Travis E. Oliphant's book - "Guide to Numpy" (which generously entered + "Guide to NumPy" (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.

The preferred way to update the documentation is by submitting a pull request on Github (see the Developer Guide. - The Numpy Documentation Wiki + The NumPy Documentation Wiki can also still be used to submit documentation fixes. - Please help us to further improve the Numpy documentation! + Please help us to further improve the NumPy documentation!

{% endblock %} diff --git a/doc/source/about.rst b/doc/source/about.rst index 9485b514253f..0f585950ac90 100644 --- a/doc/source/about.rst +++ b/doc/source/about.rst @@ -25,7 +25,7 @@ by *Numarray* and can also be used to replace *Numarray*. NumPy community --------------- -Numpy is a distributed, volunteer, open-source project. *You* can help +NumPy is a distributed, volunteer, open-source project. *You* can help us make it better; if you believe something should be improved either in functionality or in documentation, don't hesitate to contact us --- or even better, contact us and participate in fixing the problem. @@ -36,11 +36,11 @@ Our main means of communication are: - `Mailing lists `__ -- `Numpy Issues `__ (bug reports go here) +- `NumPy Issues `__ (bug reports go here) -- `Old Numpy Trac `__ (no longer used) +- `Old NumPy Trac `__ (no longer used) -More information about the development of Numpy can be found at +More information about the development of NumPy can be found at http://scipy.org/Developer_Zone If you want to fix issues in this documentation, the easiest way diff --git a/doc/source/bugs.rst b/doc/source/bugs.rst index b55be432961a..950934b14dcf 100644 --- a/doc/source/bugs.rst +++ b/doc/source/bugs.rst @@ -5,7 +5,7 @@ Reporting bugs File bug reports or feature requests, and make contributions (e.g. code patches), by opening a "new issue" on GitHub: -- Numpy Issues: http://github.com/numpy/numpy/issues +- NumPy Issues: http://github.com/numpy/numpy/issues Please give as much information as you can in the ticket. It is extremely useful if you can supply a small self-contained code snippet that reproduces diff --git a/doc/source/conf.py b/doc/source/conf.py index 3324982c7cf3..11841c1bb8dd 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -189,7 +189,7 @@ # ----------------------------------------------------------------------------- texinfo_documents = [ - ("contents", 'numpy', 'Numpy Documentation', _stdauthor, 'Numpy', + ("contents", 'numpy', 'NumPy Documentation', _stdauthor, 'NumPy', "NumPy: array processing for numbers, strings, records, and objects.", 'Programming', 1), @@ -203,7 +203,7 @@ # ----------------------------------------------------------------------------- -# Numpy extensions +# NumPy extensions # ----------------------------------------------------------------------------- # If we want to do a phantom import from an XML file for all autodocs diff --git a/doc/source/contents.rst b/doc/source/contents.rst index 1d46e8c7b667..61c0037fc65b 100644 --- a/doc/source/contents.rst +++ b/doc/source/contents.rst @@ -1,5 +1,5 @@ ##################### -Numpy manual contents +NumPy manual contents ##################### .. toctree:: diff --git a/doc/source/dev/development_environment.rst b/doc/source/dev/development_environment.rst index 41e8743628b7..e6df9803cb1e 100644 --- a/doc/source/dev/development_environment.rst +++ b/doc/source/dev/development_environment.rst @@ -11,7 +11,7 @@ Since NumPy contains parts written in C and Cython that need to be compiled before use, make sure you have the necessary compilers and Python development headers installed - see :ref:`building-from-source`. -Having compiled code also means that importing Numpy from the development +Having compiled code also means that importing NumPy from the development sources needs some additional steps, which are explained below. For the rest of this chapter we assume that you have set up your git repo as described in :ref:`using-git`. @@ -28,7 +28,7 @@ do one of:: $ python runtests.py --bench $ python runtests.py -g -m full -This builds Numpy first, so the first time it may take a few minutes. If +This builds NumPy first, so the first time it may take a few minutes. If you specify ``-n``, the tests are run against the version of NumPy (if any) found on current PYTHONPATH. @@ -169,7 +169,7 @@ repo, use one of:: Debugging --------- -Another frequently asked question is "How do I debug C code inside Numpy?". +Another frequently asked question is "How do I debug C code inside NumPy?". The easiest way to do this is to first write a Python script that invokes the C code whose execution you want to debug. For instance ``mytest.py``:: diff --git a/doc/source/dev/gitwash/development_workflow.rst b/doc/source/dev/gitwash/development_workflow.rst index b6b544455281..b788a042c89f 100644 --- a/doc/source/dev/gitwash/development_workflow.rst +++ b/doc/source/dev/gitwash/development_workflow.rst @@ -470,10 +470,10 @@ backport. Pushing changes to the main repo ================================ -*This is only relevant if you have commit rights to the main Numpy repo.* +*This is only relevant if you have commit rights to the main NumPy repo.* When you have a set of "ready" changes in a feature branch ready for -Numpy's ``master`` or ``maintenance`` branches, you can push +NumPy's ``master`` or ``maintenance`` branches, you can push them to ``upstream`` as follows: 1. First, merge or rebase on the target branch. diff --git a/doc/source/dev/index.rst b/doc/source/dev/index.rst index cb71a3e5cb6a..543194119541 100644 --- a/doc/source/dev/index.rst +++ b/doc/source/dev/index.rst @@ -1,5 +1,5 @@ ##################### -Contributing to Numpy +Contributing to NumPy ##################### .. toctree:: diff --git a/doc/source/f2py/distutils.rst b/doc/source/f2py/distutils.rst index 4a318452e5b4..fdcd38468d01 100644 --- a/doc/source/f2py/distutils.rst +++ b/doc/source/f2py/distutils.rst @@ -2,7 +2,7 @@ Using via `numpy.distutils` ============================= -:mod:`numpy.distutils` is part of Numpy extending standard Python ``distutils`` +:mod:`numpy.distutils` is part of NumPy extending standard Python ``distutils`` to deal with Fortran sources and F2PY signature files, e.g. compile Fortran sources, call F2PY to construct extension modules, etc. diff --git a/doc/source/f2py/getting-started.rst b/doc/source/f2py/getting-started.rst index ae01d204e3f3..b54d1aba808a 100644 --- a/doc/source/f2py/getting-started.rst +++ b/doc/source/f2py/getting-started.rst @@ -95,7 +95,7 @@ Python the Fortran subroutine ``FIB`` is accessible via ``fib1.fib``:: F2PY implements basic compatibility checks between related arguments in order to avoid any unexpected crashes. - * When a Numpy array, that is Fortran contiguous and has a dtype + * When a NumPy array, that is Fortran contiguous and has a dtype corresponding to presumed Fortran type, is used as an input array argument, then its C pointer is directly passed to Fortran. @@ -209,7 +209,7 @@ In Python:: * Clearly, the signature of ``fib2.fib`` now corresponds to the intention of Fortran subroutine ``FIB`` more closely: given the number ``n``, ``fib2.fib`` returns the first ``n`` Fibonacci numbers - as a Numpy array. Also, the new Python signature ``fib2.fib`` + as a NumPy array. Also, the new Python signature ``fib2.fib`` rules out any surprises that we experienced with ``fib1.fib``. * Note that by default using single ``intent(out)`` also implies diff --git a/doc/source/f2py/python-usage.rst b/doc/source/f2py/python-usage.rst index c7c7e3fd06dc..f5f1d23043ba 100644 --- a/doc/source/f2py/python-usage.rst +++ b/doc/source/f2py/python-usage.rst @@ -66,7 +66,7 @@ String arguments F2PY generated wrapper functions accept (almost) any Python object as a string argument, ``str`` is applied for non-string objects. -Exceptions are Numpy arrays that must have type code ``'c'`` or +Exceptions are NumPy arrays that must have type code ``'c'`` or ``'1'`` when used as string arguments. A string can have arbitrary length when using it as a string argument @@ -95,7 +95,7 @@ Array arguments ================ In general, array arguments of F2PY generated wrapper functions accept -arbitrary sequences that can be transformed to Numpy array objects. +arbitrary sequences that can be transformed to NumPy array objects. An exception is ``intent(inout)`` array arguments that always must be proper-contiguous and have proper type, otherwise an exception is raised. Another exception is ``intent(inplace)`` array arguments that @@ -103,13 +103,13 @@ attributes will be changed in-situ if the argument has different type than expected (see ``intent(inplace)`` attribute for more information). -In general, if a Numpy array is proper-contiguous and has a proper +In general, if a NumPy array is proper-contiguous and has a proper type then it is directly passed to wrapped Fortran/C function. Otherwise, an element-wise copy of an input array is made and the copy, being proper-contiguous and with proper type, is used as an array argument. -There are two types of proper-contiguous Numpy arrays: +There are two types of proper-contiguous NumPy arrays: * Fortran-contiguous arrays when data is stored column-wise, i.e. indexing of data as stored in memory starts from the lowest @@ -130,7 +130,7 @@ and C-contiguous if the order is as follows:: A[0,0] A[0,1] A[1,0] A[1,1] To test whether an array is C-contiguous, use ``.iscontiguous()`` -method of Numpy arrays. To test for Fortran contiguity, all +method of NumPy arrays. To test for Fortran contiguity, all F2PY generated extension modules provide a function ``has_column_major_storage()``. This function is equivalent to ``.flags.f_contiguous`` but more efficient. @@ -313,7 +313,7 @@ with the current extension module, but not to other extension modules (this restriction is due to how Python imports shared libraries). In Python, the F2PY wrappers to ``common`` blocks are ``fortran`` type objects that have (dynamic) attributes related to data members of -common blocks. When accessed, these attributes return as Numpy array +common blocks. When accessed, these attributes return as NumPy array objects (multidimensional arrays are Fortran-contiguous) that directly link to data members in common blocks. Data members can be changed by direct assignment or by in-place changes to the diff --git a/doc/source/f2py/signature-file.rst b/doc/source/f2py/signature-file.rst index a8924712ff17..6afcdeb8c5b0 100644 --- a/doc/source/f2py/signature-file.rst +++ b/doc/source/f2py/signature-file.rst @@ -348,9 +348,9 @@ The following attributes are used by F2PY: + ``inout`` The argument is considered as an input/output or *in situ* output argument. ``intent(inout)`` arguments can be only - "contiguous" Numpy arrays with proper type and size. Here + "contiguous" NumPy arrays with proper type and size. Here "contiguous" can be either in Fortran or C sense. The latter one - coincides with the contiguous concept used in Numpy and is + coincides with the contiguous concept used in NumPy and is effective only if ``intent(c)`` is used. Fortran contiguity is assumed by default. @@ -360,7 +360,7 @@ The following attributes are used by F2PY: + ``inplace`` The argument is considered as an input/output or *in situ* output argument. ``intent(inplace)`` arguments must be - Numpy arrays with proper size. If the type of an array is + NumPy arrays with proper size. If the type of an array is not "proper" or the array is non-contiguous then the array will be changed in-place to fix the type and make it contiguous. diff --git a/doc/source/license.rst b/doc/source/license.rst index 2b3b7ebd33ec..8f360af8830e 100644 --- a/doc/source/license.rst +++ b/doc/source/license.rst @@ -1,5 +1,5 @@ ************* -Numpy License +NumPy License ************* Copyright (c) 2005, NumPy Developers diff --git a/doc/source/neps/index.rst b/doc/source/neps/index.rst index 94cf563a4211..de4cd64b6f6f 100644 --- a/doc/source/neps/index.rst +++ b/doc/source/neps/index.rst @@ -1,10 +1,10 @@ =========================== -Numpy Enhancement Proposals +NumPy Enhancement Proposals =========================== -Numpy Enhancement Proposals (NEPs) describe proposed changes to Numpy. +NumPy Enhancement Proposals (NEPs) describe proposed changes to NumPy. NEPs are modeled on Python Enhancement Proposals (PEPs), and are typically -written up when large changes to Numpy are proposed. +written up when large changes to NumPy are proposed. This page provides an overview of all NEPs, making only a distinction between the ones that have been implemented and those that have not been implemented. diff --git a/doc/source/reference/arrays.classes.rst b/doc/source/reference/arrays.classes.rst index b82f7d33c114..298e81717578 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 ` -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 2db32e30df7f..01a9698262fb 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 ` in Numpy for various precision +scalar types ` 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 6e9bb9276e1a..b7bc3a6555ce 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 b68e40e3f2f1..bd6821b62896 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 `. @@ -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 40c9f755d103..faa91a389562 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 -` built in Numpy. The array scalars allow easy manipulation +` 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 f8fad009529c..4acaf1b3b708 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 `. +be defined in C, using the :ref:`NumPy C-API `. diff --git a/doc/source/reference/c-api.array.rst b/doc/source/reference/c-api.array.rst index 049c7b5379f1..4ab276d9a0b4 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 17d7f557dbc7..60bf61a32666 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 08b1adb3aa2c..9027a4e0d56a 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 92dc8aec0881..d34a9d4d4f68 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 b1a5eb477f2c..b8cbe97b2162 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 9e0ef68db60d..f74816d6fa7f 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 `__ (which generously entered +`Guide to NumPy `__ (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 `__. 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 f946d0420290..fca87f260f4f 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 c9716813d11f..e1d6644a6cf4 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 a41563ccea3a..9b6eb4ad307c 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 ff8c05c165ef..6747f60bd15d 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 713f99d303ce..3bbc413d73f2 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 e83331d01dc2..2120fc69e1d7 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 b7a924eba78e..4a027b5a1e7b 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 c40795434be9..0db77eb7c169 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 1d6fbe04dba7..36b226b9b35b 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 3931b8e110d1..6865cc96a495 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 7f38135e94dc..62e90b83c1e5 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. diff --git a/doc/source/user/basics.io.genfromtxt.rst b/doc/source/user/basics.io.genfromtxt.rst index 1fd7e7b65e19..1fed3fe8eb00 100644 --- a/doc/source/user/basics.io.genfromtxt.rst +++ b/doc/source/user/basics.io.genfromtxt.rst @@ -4,7 +4,7 @@ Importing data with :func:`~numpy.genfromtxt` ********************************************* -Numpy provides several functions to create arrays from tabular data. +NumPy provides several functions to create arrays from tabular data. We focus here on the :func:`~numpy.genfromtxt` function. In a nutshell, :func:`~numpy.genfromtxt` runs two main loops. The first @@ -325,7 +325,7 @@ takes any format string:: Validating names ---------------- -Numpy arrays with a structured dtype can also be viewed as +NumPy arrays with a structured dtype can also be viewed as :class:`~numpy.recarray`, where a field can be accessed as if it were an attribute. For that reason, we may need to make sure that the field name doesn't contain any space or invalid character, or that it does not diff --git a/doc/source/user/basics.io.rst b/doc/source/user/basics.io.rst index 54a65662b9a3..c0c96dceccbd 100644 --- a/doc/source/user/basics.io.rst +++ b/doc/source/user/basics.io.rst @@ -1,5 +1,5 @@ ************** -I/O with Numpy +I/O with NumPy ************** .. toctree:: diff --git a/doc/source/user/basics.rst b/doc/source/user/basics.rst index 1d91cc55cd64..7875aff6e680 100644 --- a/doc/source/user/basics.rst +++ b/doc/source/user/basics.rst @@ -1,5 +1,5 @@ ************ -Numpy basics +NumPy basics ************ .. toctree:: diff --git a/doc/source/user/building.rst b/doc/source/user/building.rst index 5cb143f381d9..2e7e1823cb7e 100644 --- a/doc/source/user/building.rst +++ b/doc/source/user/building.rst @@ -47,8 +47,8 @@ Building NumPy requires the following software installed: 4) Cython - To build development versions of Numpy, you'll need a recent version of - Cython. Released Numpy sources on PyPi include the C files generated from + To build development versions of NumPy, you'll need a recent version of + Cython. Released NumPy sources on PyPi include the C files generated from Cython code, so for released versions having Cython installed isn't needed. Basic Installation @@ -121,7 +121,7 @@ is almost always a very bad idea. Disabling ATLAS and other accelerated libraries ----------------------------------------------- -Usage of ATLAS and other accelerated libraries in Numpy can be disabled +Usage of ATLAS and other accelerated libraries in NumPy can be disabled via:: BLAS=None LAPACK=None ATLAS=None python setup.py build diff --git a/doc/source/user/c-info.rst b/doc/source/user/c-info.rst index 2fb50c5efb50..0ac0ed26d27e 100644 --- a/doc/source/user/c-info.rst +++ b/doc/source/user/c-info.rst @@ -1,5 +1,5 @@ ################# -Using Numpy C-API +Using NumPy C-API ################# .. toctree:: diff --git a/doc/source/user/c-info.ufunc-tutorial.rst b/doc/source/user/c-info.ufunc-tutorial.rst index f064fbcc9490..ba481a544c49 100644 --- a/doc/source/user/c-info.ufunc-tutorial.rst +++ b/doc/source/user/c-info.ufunc-tutorial.rst @@ -18,7 +18,7 @@ Before reading this, it may help to familiarize yourself with the basics of C extensions for Python by reading/skimming the tutorials in Section 1 of `Extending and Embedding the Python Interpreter `_ and in `How to extend -Numpy `_ +NumPy `_ The umath module is a computer-generated C-module that creates many ufuncs. It provides a great many examples of how to create a universal @@ -245,9 +245,9 @@ logit ufuncs constructed below, the logit ufuncs were almost exactly depending on the nature of the function. -.. _`sec:Numpy-one-loop`: +.. _`sec:NumPy-one-loop`: -Example Numpy ufunc for one dtype +Example NumPy ufunc for one dtype ================================= .. index:: @@ -273,7 +273,7 @@ the primary thing that must be changed to create your own ufunc. /* * single_type_logit.c * This is the C code for creating your own - * Numpy ufunc for a logit function. + * NumPy ufunc for a logit function. * * In this code we only define the ufunc for * a single dtype. The computations that must @@ -443,9 +443,9 @@ array([ -inf, -1.09861229, 0. , 1.09861229, inf]) -.. _`sec:Numpy-many-loop`: +.. _`sec:NumPy-many-loop`: -Example Numpy ufunc with multiple dtypes +Example NumPy ufunc with multiple dtypes ======================================== .. index:: @@ -473,7 +473,7 @@ the primary thing that must be changed to create your own ufunc. /* * multi_type_logit.c * This is the C code for creating your own - * Numpy ufunc for a logit function. + * NumPy ufunc for a logit function. * * Each function of the form type_logit defines the * logit function for a different numpy dtype. Each @@ -728,9 +728,9 @@ array([ -inf, -1.09861229, 0. , 1.09861229, inf]) -.. _`sec:Numpy-many-arg`: +.. _`sec:NumPy-many-arg`: -Example Numpy ufunc with multiple arguments/return values +Example NumPy ufunc with multiple arguments/return values ========================================================= Our final example is a ufunc with multiple arguments. It is a modification @@ -738,7 +738,7 @@ of the code for a logit ufunc for data with a single dtype. We compute (A*B, logit(A*B)). We only give the C code as the setup.py file is exactly the same as -the setup.py file in `Example Numpy ufunc for one dtype`_, except that +the setup.py file in `Example NumPy ufunc for one dtype`_, except that the line .. code-block:: python @@ -767,7 +767,7 @@ as well as all other properties of a ufunc. /* * multi_arg_logit.c * This is the C code for creating your own - * Numpy ufunc for a multiple argument, multiple + * NumPy ufunc for a multiple argument, multiple * return value ufunc. The places where the * ufunc computation is carried out are marked * with comments. @@ -885,9 +885,9 @@ as well as all other properties of a ufunc. #endif -.. _`sec:Numpy-struct-dtype`: +.. _`sec:NumPy-struct-dtype`: -Example Numpy ufunc with structured array dtype arguments +Example NumPy ufunc with structured array dtype arguments ========================================================= This example shows how to create a ufunc for a structured array dtype. @@ -898,7 +898,7 @@ custom dtypes and structured array dtypes. We need to also call PyUFunc_RegisterLoopForDescr to finish setting up the ufunc. We only give the C code as the setup.py file is exactly the same as -the setup.py file in `Example Numpy ufunc for one dtype`_, except that +the setup.py file in `Example NumPy ufunc for one dtype`_, except that the line .. code-block:: python @@ -925,7 +925,7 @@ The C file is given below. /* * add_triplet.c * This is the C code for creating your own - * Numpy ufunc for a structured array dtype. + * NumPy ufunc for a structured array dtype. * * Details explaining the Python-C API can be found under * 'Extending and Embedding' and 'Python/C API' at diff --git a/doc/source/user/numpy-for-matlab-users.rst b/doc/source/user/numpy-for-matlab-users.rst index c3179b18267d..cf019f630554 100644 --- a/doc/source/user/numpy-for-matlab-users.rst +++ b/doc/source/user/numpy-for-matlab-users.rst @@ -1,7 +1,7 @@ .. _numpy-for-matlab-users: ====================== -Numpy for Matlab users +NumPy for Matlab users ====================== Introduction @@ -63,7 +63,7 @@ Some Key Differences 'array' or 'matrix'? Which should I use? ======================================== -Numpy provides, in addition to ``np.ndarray``, an additional matrix type +NumPy provides, in addition to ``np.ndarray``, an additional matrix type that you may see used in some existing code. Which one to use? Short answer @@ -84,7 +84,7 @@ product, matrix vector multiplication etc.). Long answer ----------- -Numpy contains both an ``array`` class and a ``matrix`` class. The +NumPy contains both an ``array`` class and a ``matrix`` class. The ``array`` class is intended to be a general-purpose n-dimensional array for many kinds of numerical computing, while ``matrix`` is intended to facilitate linear algebra computations specifically. In practice there @@ -171,7 +171,7 @@ The ``array`` is thus much more advisable to use. Facilities for Matrix Users =========================== -Numpy has some features that facilitate the use of the ``matrix`` type, +NumPy has some features that facilitate the use of the ``matrix`` type, which hopefully make things easier for Matlab converts. - A ``matlib`` module has been added that contains matrix versions of @@ -521,21 +521,21 @@ Linear Algebra Equivalents * - ``a & b`` - ``logical_and(a,b)`` - - element-by-element AND operator (Numpy ufunc) :ref:`See note + - element-by-element AND operator (NumPy ufunc) :ref:`See note LOGICOPS ` * - ``a | b`` - ``logical_or(a,b)`` - - element-by-element OR operator (Numpy ufunc) :ref:`See note LOGICOPS + - element-by-element OR operator (NumPy ufunc) :ref:`See note LOGICOPS ` * - ``bitand(a,b)`` - ``a & b`` - - bitwise AND operator (Python native and Numpy ufunc) + - bitwise AND operator (Python native and NumPy ufunc) * - ``bitor(a,b)`` - ``a | b`` - - bitwise OR operator (Python native and Numpy ufunc) + - bitwise OR operator (Python native and NumPy ufunc) * - ``inv(a)`` - ``linalg.inv(a)`` @@ -657,41 +657,41 @@ numpy to have a similarly terse range construction mechanism. Note that *indexed* using square brackets, which allows the use of Python's slice syntax in the arguments. -\ **LOGICOPS**: & or \| in Numpy is bitwise AND/OR, while in Matlab & +\ **LOGICOPS**: & or \| in NumPy is bitwise AND/OR, while in Matlab & and \| are logical AND/OR. The difference should be clear to anyone with significant programming experience. The two can appear to work the same, but there are important differences. If you would have used Matlab's & -or \| operators, you should use the Numpy ufuncs +or \| operators, you should use the NumPy ufuncs logical\_and/logical\_or. The notable differences between Matlab's and -Numpy's & and \| operators are: +NumPy's & and \| operators are: -- Non-logical {0,1} inputs: Numpy's output is the bitwise AND of the +- Non-logical {0,1} inputs: NumPy's output is the bitwise AND of the inputs. Matlab treats any non-zero value as 1 and returns the logical - AND. For example (3 & 4) in Numpy is 0, while in Matlab both 3 and 4 + AND. For example (3 & 4) in NumPy is 0, while in Matlab both 3 and 4 are considered logical true and (3 & 4) returns 1. -- Precedence: Numpy's & operator is higher precedence than logical +- Precedence: NumPy's & operator is higher precedence than logical operators like < and >; Matlab's is the reverse. If you know you have boolean arguments, you can get away with using -Numpy's bitwise operators, but be careful with parentheses, like this: z -= (x > 1) & (x < 2). The absence of Numpy operator forms of logical\_and +NumPy's bitwise operators, but be careful with parentheses, like this: z += (x > 1) & (x < 2). The absence of NumPy operator forms of logical\_and and logical\_or is an unfortunate consequence of Python's design. **RESHAPE and LINEAR INDEXING**: Matlab always allows multi-dimensional -arrays to be accessed using scalar or linear indices, Numpy does not. +arrays to be accessed using scalar or linear indices, NumPy does not. Linear indices are common in Matlab programs, e.g. find() on a matrix -returns them, whereas Numpy's find behaves differently. When converting +returns them, whereas NumPy's find behaves differently. When converting Matlab code it might be necessary to first reshape a matrix to a linear sequence, perform some indexing operations and then reshape back. As reshape (usually) produces views onto the same storage, it should be possible to do this fairly efficiently. Note that the scan order used by -reshape in Numpy defaults to the 'C' order, whereas Matlab uses the +reshape in NumPy defaults to the 'C' order, whereas Matlab uses the Fortran order. If you are simply converting to a linear sequence and back this doesn't matter. But if you are converting reshapes from Matlab code which relies on the scan order, then this Matlab code: z = reshape(x,3,4); should become z = x.reshape(3,4,order='F').copy() in -Numpy. +NumPy. Customizing Your Environment ============================ diff --git a/doc/source/user/quickstart.rst b/doc/source/user/quickstart.rst index 034ac223a340..5ab8cdad7590 100644 --- a/doc/source/user/quickstart.rst +++ b/doc/source/user/quickstart.rst @@ -25,7 +25,7 @@ The Basics NumPy's main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a -tuple of positive integers. In Numpy dimensions are called *axes*. The +tuple of positive integers. In NumPy dimensions are called *axes*. The number of axes is *rank*. For example, the coordinates of a point in 3D space ``[1, 2, 1]`` is an @@ -39,7 +39,7 @@ length of 3. [[ 1., 0., 0.], [ 0., 1., 2.]] -Numpy's array class is called ``ndarray``. It is also known by the alias +NumPy's array class is called ``ndarray``. It is also known by the alias ``array``. Note that ``numpy.array`` is not the same as the Standard Python Library class ``array.array``, which only handles one-dimensional arrays and offers less functionality. The more important attributes of @@ -612,7 +612,7 @@ The shape of an array can be changed with various commands: The order of the elements in the array resulting from ravel() is normally "C-style", that is, the rightmost index "changes the fastest", so the element after a[0,0] is a[0,1]. If the array is reshaped to some -other shape, again the array is treated as "C-style". Numpy normally +other shape, again the array is treated as "C-style". NumPy normally creates arrays stored in this order, so ravel() will usually not need to copy its argument, but if the array was made by taking slices of another array or created with unusual options, it may need to be copied. The diff --git a/doc/summarize.py b/doc/summarize.py index 833436cee0f2..dbadb30b326b 100755 --- a/doc/summarize.py +++ b/doc/summarize.py @@ -2,7 +2,7 @@ """ summarize.py -Show a summary about which Numpy functions are documented and which are not. +Show a summary about which NumPy functions are documented and which are not. """ from __future__ import division, absolute_import, print_function @@ -114,7 +114,7 @@ def check_numpy(): def get_undocumented(documented, module, module_name=None, skip=[]): """ - Find out which items in Numpy are not documented. + Find out which items in NumPy are not documented. Returns ------- From 2a55233b81a6ea18a57d1dd4f7bc5fff9f2fb681 Mon Sep 17 00:00:00 2001 From: Pierre de Buyl Date: Tue, 6 Sep 2016 14:42:08 +0200 Subject: [PATCH 2/6] DOC: change Numpy to NumPy in dosctrings and comments The strings in error messages were left untouched --- numpy/__init__.py | 4 ++-- numpy/add_newdocs.py | 20 ++++++++--------- numpy/core/_internal.py | 2 +- numpy/core/code_generators/cversions.txt | 2 +- .../code_generators/generate_numpy_api.py | 2 +- .../code_generators/generate_ufunc_api.py | 2 +- .../core/code_generators/ufunc_docstrings.py | 6 ++--- numpy/core/defchararray.py | 14 ++++++------ numpy/core/fromnumeric.py | 10 ++++----- numpy/core/include/numpy/npy_common.h | 6 ++--- numpy/core/memmap.py | 2 +- numpy/core/numeric.py | 22 +++++++++---------- numpy/core/src/multiarray/buffer.c | 2 +- numpy/core/src/multiarray/methods.c | 2 +- numpy/core/src/multiarray/multiarraymodule.c | 2 +- numpy/core/src/umath/struct_ufunc_test.c.src | 2 +- numpy/core/src/umath/test_rational.c.src | 2 +- numpy/core/src/umath/ufunc_object.c | 2 +- numpy/core/tests/test_indexing.py | 4 ++-- numpy/core/tests/test_regression.py | 4 ++-- numpy/ctypeslib.py | 2 +- numpy/doc/basics.py | 10 ++++----- numpy/doc/constants.py | 12 +++++----- numpy/doc/creation.py | 6 ++--- numpy/doc/glossary.py | 2 +- numpy/doc/indexing.py | 4 ++-- numpy/doc/internals.py | 8 +++---- numpy/doc/structured_arrays.py | 4 ++-- numpy/doc/subclassing.py | 4 ++-- numpy/doc/ufuncs.py | 2 +- numpy/dual.py | 2 +- numpy/lib/_version.py | 6 ++--- numpy/lib/function_base.py | 6 ++--- numpy/lib/nanfunctions.py | 6 ++--- numpy/lib/npyio.py | 10 ++++----- numpy/lib/type_check.py | 2 +- numpy/lib/ufunclike.py | 4 ++-- numpy/lib/utils.py | 6 ++--- numpy/linalg/linalg.py | 2 +- numpy/polynomial/_polybase.py | 2 +- numpy/polynomial/chebyshev.py | 2 +- numpy/polynomial/hermite.py | 2 +- numpy/polynomial/hermite_e.py | 2 +- numpy/polynomial/laguerre.py | 2 +- numpy/polynomial/legendre.py | 2 +- numpy/polynomial/polynomial.py | 2 +- numpy/random/mtrand/mtrand.pyx | 2 +- 47 files changed, 113 insertions(+), 113 deletions(-) diff --git a/numpy/__init__.py b/numpy/__init__.py index 5384d61ab05a..0f1bcf766d82 100644 --- a/numpy/__init__.py +++ b/numpy/__init__.py @@ -65,7 +65,7 @@ polynomial Polynomial tools testing - Numpy testing tools + NumPy testing tools f2py Fortran to Python Interface Generator. distutils @@ -83,7 +83,7 @@ matlib Make everything matrices. __version__ - Numpy version string + NumPy version string Viewing documentation using IPython ----------------------------------- diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index 41267b797349..adeaff90c6e3 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -296,7 +296,7 @@ Notes ----- `nditer` supersedes `flatiter`. The iterator implementation behind - `nditer` is also exposed by the Numpy C API. + `nditer` is also exposed by the NumPy C API. The Python exposure supplies two iteration interfaces, one which follows the Python iterator protocol, and another which mirrors the C-style @@ -570,7 +570,7 @@ def luf(lamdaexpr, *args, **kwargs): add_newdoc('numpy.core', 'broadcast', ('nd', """ - Number of dimensions of broadcasted result. For code intended for Numpy + Number of dimensions of broadcasted result. For code intended for NumPy 1.12.0 and later the more consistent `ndim` is preferred. Examples @@ -2014,7 +2014,7 @@ def luf(lamdaexpr, *args, **kwargs): were elements. .. warning:: - This function is preliminary and included in Numpy 1.10 for testing + This function is preliminary and included in NumPy 1.10 for testing and documentation. Its semantics will not change, but the number and order of the optional arguments will. @@ -4841,7 +4841,7 @@ def luf(lamdaexpr, *args, **kwargs): """ frompyfunc(func, nin, nout) - Takes an arbitrary Python function and returns a Numpy ufunc. + Takes an arbitrary Python function and returns a NumPy ufunc. Can be used, for example, to add broadcasting to a built-in Python function (see Examples section). @@ -4858,7 +4858,7 @@ def luf(lamdaexpr, *args, **kwargs): Returns ------- out : ufunc - Returns a Numpy universal function (``ufunc``) object. + Returns a NumPy universal function (``ufunc``) object. See Also -------- @@ -4888,7 +4888,7 @@ def luf(lamdaexpr, *args, **kwargs): Return the current object that defines floating-point error handling. The error object contains all information that defines the error handling - behavior in Numpy. `geterrobj` is used internally by the other + behavior in NumPy. `geterrobj` is used internally by the other functions that get and set error handling behavior (`geterr`, `seterr`, `geterrcall`, `seterrcall`). @@ -4952,7 +4952,7 @@ def luf(lamdaexpr, *args, **kwargs): Set the object that defines floating-point error handling. The error object contains all information that defines the error handling - behavior in Numpy. `seterrobj` is used internally by the other + behavior in NumPy. `seterrobj` is used internally by the other functions that set error handling behavior (`seterr`, `seterrcall`). Parameters @@ -5028,7 +5028,7 @@ def luf(lamdaexpr, *args, **kwargs): Parameters ---------- x : array_like - Input array to be binned. Prior to Numpy 1.10.0, this array had to + Input array to be binned. Prior to NumPy 1.10.0, this array had to be 1-dimensional, but can now have any shape. bins : array_like Array of bins. It has to be 1-dimensional and monotonic. @@ -6235,7 +6235,7 @@ def luf(lamdaexpr, *args, **kwargs): 2 if the dtype is for a user-defined numpy type A user-defined type uses the numpy C-API machinery to extend numpy to handle a new array type. See - :ref:`user.user-defined-data-types` in the Numpy manual. + :ref:`user.user-defined-data-types` in the NumPy manual. = ======================================================================== Examples @@ -7623,7 +7623,7 @@ def luf(lamdaexpr, *args, **kwargs): ############################################################################## add_newdoc('numpy.core.numerictypes', 'bool_', - """Numpy's Boolean type. Character code: ``?``. Alias: bool8""") + """NumPy's Boolean type. Character code: ``?``. Alias: bool8""") add_newdoc('numpy.core.numerictypes', 'complex64', """ diff --git a/numpy/core/_internal.py b/numpy/core/_internal.py index 85be66d06ee0..741c8bb5fbcb 100644 --- a/numpy/core/_internal.py +++ b/numpy/core/_internal.py @@ -366,7 +366,7 @@ def _view_is_safe(oldtype, newtype): return # Given a string containing a PEP 3118 format specifier, -# construct a Numpy dtype +# construct a NumPy dtype _pep3118_native_map = { '?': '?', diff --git a/numpy/core/code_generators/cversions.txt b/numpy/core/code_generators/cversions.txt index c66947fafe7c..0726fc85e8ca 100644 --- a/numpy/core/code_generators/cversions.txt +++ b/numpy/core/code_generators/cversions.txt @@ -22,7 +22,7 @@ # Version 9 (NumPy 1.8) Added interface for partition functions, # PyArray_NEW_ZEROED, commented out as the hash changed in -# Numpy 1.9 due to annotation. +# NumPy 1.9 due to annotation. #0x00000009 = 327bd114df09c2eb7a0bcc6901e2a3ed # Version 9 (NumPy 1.9) Added function annotations. diff --git a/numpy/core/code_generators/generate_numpy_api.py b/numpy/core/code_generators/generate_numpy_api.py index d376ffd29286..d6d6ab21fa53 100644 --- a/numpy/core/code_generators/generate_numpy_api.py +++ b/numpy/core/code_generators/generate_numpy_api.py @@ -151,7 +151,7 @@ c_api_header = """ =========== -Numpy C-API +NumPy C-API =========== """ diff --git a/numpy/core/code_generators/generate_ufunc_api.py b/numpy/core/code_generators/generate_ufunc_api.py index bb2ad78540b3..b6034bbe7980 100644 --- a/numpy/core/code_generators/generate_ufunc_api.py +++ b/numpy/core/code_generators/generate_ufunc_api.py @@ -204,7 +204,7 @@ def do_generate_api(targets, sources): fid = open(doc_file, 'w') fid.write(''' ================= -Numpy Ufunc C-API +NumPy Ufunc C-API ================= ''') for func in ufunc_api_list: diff --git a/numpy/core/code_generators/ufunc_docstrings.py b/numpy/core/code_generators/ufunc_docstrings.py index a017ca26d43e..0d506102f09a 100644 --- a/numpy/core/code_generators/ufunc_docstrings.py +++ b/numpy/core/code_generators/ufunc_docstrings.py @@ -1530,7 +1530,7 @@ def add_newdoc(place, name, doc): Not a Number, positive infinity and negative infinity are considered to be non-finite. - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity. Errors result if the @@ -1597,7 +1597,7 @@ def add_newdoc(place, name, doc): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Errors result if the second argument is supplied when the first @@ -1650,7 +1650,7 @@ def add_newdoc(place, name, doc): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Examples diff --git a/numpy/core/defchararray.py b/numpy/core/defchararray.py index e18f912d60d6..3e01aaa8e3c4 100644 --- a/numpy/core/defchararray.py +++ b/numpy/core/defchararray.py @@ -1680,7 +1680,7 @@ class chararray(ndarray): `dtype` `object_`, `string_` or `unicode_`, and use the free functions in the `numpy.char` module for fast vectorized string operations. - Versus a regular Numpy array of type `str` or `unicode`, this + Versus a regular NumPy array of type `str` or `unicode`, this class adds the following functionality: 1) values automatically have whitespace removed from the end @@ -1817,7 +1817,7 @@ def __new__(subtype, shape, itemsize=1, unicode=False, buffer=None, else: dtype = string_ - # force itemsize to be a Python long, since using Numpy integer + # force itemsize to be a Python long, since using NumPy integer # types results in itemsize.itemsize being used as the size of # strings in the new array. itemsize = long(itemsize) @@ -2486,7 +2486,7 @@ def array(obj, itemsize=None, copy=True, unicode=None, order=None): in :mod:`numpy.char ` for fast vectorized string operations instead. - Versus a regular Numpy array of type `str` or `unicode`, this + Versus a regular NumPy array of type `str` or `unicode`, this class adds the following functionality: 1) values automatically have whitespace removed from the end @@ -2552,12 +2552,12 @@ class adds the following functionality: if sys.maxunicode == 0xffff: # On a narrow Python build, the buffer for Unicode # strings is UCS2, which doesn't match the buffer for - # Numpy Unicode types, which is ALWAYS UCS4. + # NumPy Unicode types, which is ALWAYS UCS4. # Therefore, we need to convert the buffer. On Python # 2.6 and later, we can use the utf_32 codec. Earlier # versions don't have that codec, so we convert to a # numerical array that matches the input buffer, and - # then use Numpy to convert it to UCS4. All of this + # then use NumPy to convert it to UCS4. All of this # should happen in native endianness. if sys.hexversion >= 0x2060000: obj = obj.encode('utf_32') @@ -2593,7 +2593,7 @@ class adds the following functionality: itemsize = obj.itemsize # itemsize is in 8-bit chars, so for Unicode, we need # to divide by the size of a single Unicode character, - # which for Numpy is always 4 + # which for NumPy is always 4 if issubclass(obj.dtype.type, unicode_): itemsize //= 4 @@ -2642,7 +2642,7 @@ def asarray(obj, itemsize=None, unicode=None, order=None): Convert the input to a `chararray`, copying the data only if necessary. - Versus a regular Numpy array of type `str` or `unicode`, this + Versus a regular NumPy array of type `str` or `unicode`, this class adds the following functionality: 1) values automatically have whitespace removed from the end diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index 99173d105fb6..8a56a6e1f356 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -471,8 +471,8 @@ def swapaxes(a, axis1, axis2): Returns ------- a_swapped : ndarray - For Numpy >= 1.10, if `a` is an ndarray, then a view of `a` is - returned; otherwise a new array is created. For earlier Numpy + For NumPy >= 1.10, if `a` is an ndarray, then a view of `a` is + returned; otherwise a new array is created. For earlier NumPy versions a view of `a` is returned only if the order of the axes is changed, otherwise the input array is returned. @@ -1059,7 +1059,7 @@ def searchsorted(a, v, side='left', sorter=None): ----- Binary search is used to find the required insertion points. - As of Numpy 1.4.0 `searchsorted` works with real/complex arrays containing + As of NumPy 1.4.0 `searchsorted` works with real/complex arrays containing `nan` values. The enhanced sort order is documented in `sort`. Examples @@ -2631,7 +2631,7 @@ def rank(a): Notes ----- In the old Numeric package, `rank` was the term used for the number of - dimensions, but in Numpy `ndim` is used instead. + dimensions, but in NumPy `ndim` is used instead. Examples -------- @@ -2737,7 +2737,7 @@ def around(a, decimals=0, out=None): Notes ----- - For values exactly halfway between rounded decimal values, Numpy + For values exactly halfway between rounded decimal values, NumPy rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0, -0.5 and 0.5 round to 0.0, etc. Results may also be surprising due to the inexact representation of decimal fractions in the IEEE diff --git a/numpy/core/include/numpy/npy_common.h b/numpy/core/include/numpy/npy_common.h index baf5549d970f..4509f21647db 100644 --- a/numpy/core/include/numpy/npy_common.h +++ b/numpy/core/include/numpy/npy_common.h @@ -372,21 +372,21 @@ typedef struct {npy_longdouble real, imag;} npy_clongdouble; #endif #if NPY_SIZEOF_COMPLEX_DOUBLE != 2 * NPY_SIZEOF_DOUBLE #error npy_cdouble definition is not compatible with C99 complex definition ! \ - Please contact Numpy maintainers and give detailed information about your \ + Please contact NumPy maintainers and give detailed information about your \ compiler and platform #endif typedef struct { double real, imag; } npy_cdouble; #if NPY_SIZEOF_COMPLEX_FLOAT != 2 * NPY_SIZEOF_FLOAT #error npy_cfloat definition is not compatible with C99 complex definition ! \ - Please contact Numpy maintainers and give detailed information about your \ + Please contact NumPy maintainers and give detailed information about your \ compiler and platform #endif typedef struct { float real, imag; } npy_cfloat; #if NPY_SIZEOF_COMPLEX_LONGDOUBLE != 2 * NPY_SIZEOF_LONGDOUBLE #error npy_clongdouble definition is not compatible with C99 complex definition ! \ - Please contact Numpy maintainers and give detailed information about your \ + Please contact NumPy maintainers and give detailed information about your \ compiler and platform #endif typedef struct { npy_longdouble real, imag; } npy_clongdouble; diff --git a/numpy/core/memmap.py b/numpy/core/memmap.py index 6fa845277765..2d15f0a74e0e 100644 --- a/numpy/core/memmap.py +++ b/numpy/core/memmap.py @@ -21,7 +21,7 @@ class memmap(ndarray): """Create a memory-map to an array stored in a *binary* file on disk. Memory-mapped files are used for accessing small segments of large files - on disk, without reading the entire file into memory. Numpy's + on disk, without reading the entire file into memory. NumPy's memmap's are array-like objects. This differs from Python's ``mmap`` module, which uses file-like objects. diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index 81ed0178a08e..51f8dd034706 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -759,7 +759,7 @@ def isfortran(a): This function is obsolete and, because of changes due to relaxed stride checking, its return value for the same array may differ for versions - of Numpy >= 1.10 and previous versions. If you only want to check if an + of NumPy >= 1.10 and previous versions. If you only want to check if an array is Fortran contiguous use ``a.flags.f_contiguous`` instead. Parameters @@ -1158,11 +1158,11 @@ def alterdot(): """ Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions. - Typically, as a user of Numpy, you do not explicitly call this - function. If Numpy is built with an accelerated BLAS, this function is - automatically called when Numpy is imported. + Typically, as a user of NumPy, you do not explicitly call this + function. If NumPy is built with an accelerated BLAS, this function is + automatically called when NumPy is imported. - When Numpy is built with an accelerated BLAS like ATLAS, these + When NumPy is built with an accelerated BLAS like ATLAS, these functions are replaced to make use of the faster implementations. The faster implementations only affect float32, float64, complex64, and complex128 arrays. Furthermore, the BLAS API only includes @@ -1170,10 +1170,10 @@ def alterdot(): arrays with larger dimensionalities use the built in functions and are not accelerated. - .. note:: Deprecated in Numpy 1.10 + .. note:: Deprecated in NumPy 1.10 The cblas functions have been integrated into the multarray module and alterdot now longer does anything. It will be - removed in Numpy 1.11.0. + removed in NumPy 1.11.0. See Also -------- @@ -1195,10 +1195,10 @@ def restoredot(): an accelerated BLAS, or when being very careful about benchmarking linear algebra operations. - .. note:: Deprecated in Numpy 1.10 + .. note:: Deprecated in NumPy 1.10 The cblas functions have been integrated into the multarray module and restoredot now longer does anything. It will be - removed in Numpy 1.11.0. + removed in NumPy 1.11.0. See Also -------- @@ -1505,8 +1505,8 @@ def rollaxis(a, axis, start=0): Returns ------- res : ndarray - For Numpy >= 1.10 a view of `a` is always returned. For earlier - Numpy versions a view of `a` is returned only if the order of the + For NumPy >= 1.10 a view of `a` is always returned. For earlier + NumPy versions a view of `a` is returned only if the order of the axes is changed, otherwise the input array is returned. See Also diff --git a/numpy/core/src/multiarray/buffer.c b/numpy/core/src/multiarray/buffer.c index 5fa3ba95b7ec..e76d406deebf 100644 --- a/numpy/core/src/multiarray/buffer.c +++ b/numpy/core/src/multiarray/buffer.c @@ -387,7 +387,7 @@ _buffer_format_string(PyArray_Descr *descr, _tmp_string_t *str, break; } case NPY_UNICODE: { - /* Numpy Unicode is always 4-byte */ + /* NumPy Unicode is always 4-byte */ char buf[128]; assert(descr->elsize % 4 == 0); PyOS_snprintf(buf, sizeof(buf), "%dw", descr->elsize / 4); diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 6346906489de..cb8fea2134ef 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -1646,7 +1646,7 @@ array_setstate(PyArrayObject *self, PyObject *args) Py_INCREF(rawdata); #if defined(NPY_PY3K) - /* Backward compatibility with Python 2 Numpy pickles */ + /* Backward compatibility with Python 2 NumPy pickles */ if (PyUnicode_Check(rawdata)) { PyObject *tmp; tmp = PyUnicode_AsLatin1String(rawdata); diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index 39acf87faff7..7c3c95b245dd 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -1895,7 +1895,7 @@ array_scalar(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *kwds) } else { #if defined(NPY_PY3K) - /* Backward compatibility with Python 2 Numpy pickles */ + /* Backward compatibility with Python 2 NumPy pickles */ if (PyUnicode_Check(obj)) { tmpobj = PyUnicode_AsLatin1String(obj); obj = tmpobj; diff --git a/numpy/core/src/umath/struct_ufunc_test.c.src b/numpy/core/src/umath/struct_ufunc_test.c.src index de8838b90689..9a6318f47256 100644 --- a/numpy/core/src/umath/struct_ufunc_test.c.src +++ b/numpy/core/src/umath/struct_ufunc_test.c.src @@ -10,7 +10,7 @@ /* * struct_ufunc_test.c * This is the C code for creating your own - * Numpy ufunc for a structured array dtype. + * NumPy ufunc for a structured array dtype. * * Details explaining the Python-C API can be found under * 'Extending and Embedding' and 'Python/C API' at diff --git a/numpy/core/src/umath/test_rational.c.src b/numpy/core/src/umath/test_rational.c.src index b0c06e1ace7e..01ded5bbd2bd 100644 --- a/numpy/core/src/umath/test_rational.c.src +++ b/numpy/core/src/umath/test_rational.c.src @@ -731,7 +731,7 @@ static PyTypeObject PyRational_Type = { 0, /* tp_version_tag */ }; -/* Numpy support */ +/* NumPy support */ static PyObject* npyrational_getitem(void* data, void* arr) { diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c index 28c59f890158..ebe03b360c3c 100644 --- a/numpy/core/src/umath/ufunc_object.c +++ b/numpy/core/src/umath/ufunc_object.c @@ -2047,7 +2047,7 @@ PyUFunc_GeneralizedFunction(PyUFuncObject *ufunc, * Validate the core dimensions of all the operands, and collect all of * the labelled core dimensions into 'core_dim_sizes'. * - * The behavior has been changed in Numpy 1.10.0, and the following + * The behavior has been changed in NumPy 1.10.0, and the following * requirements must be fulfilled or an error will be raised: * * Arguments, both input and output, must have at least as many * dimensions as the corresponding number of core dimensions. In diff --git a/numpy/core/tests/test_indexing.py b/numpy/core/tests/test_indexing.py index dcce2c30016e..f16756221e7d 100644 --- a/numpy/core/tests/test_indexing.py +++ b/numpy/core/tests/test_indexing.py @@ -296,7 +296,7 @@ def test_too_many_fancy_indices_special_case(self): assert_raises(IndexError, a.__getitem__, (np.array([0]),) * 32) def test_scalar_array_bool(self): - # Numpy bools can be used as boolean index (python ones as of yet not) + # NumPy bools can be used as boolean index (python ones as of yet not) a = np.array(1) assert_equal(a[np.bool_(True)], a[np.array(True)]) assert_equal(a[np.bool_(False)], a[np.array(False)]) @@ -1133,7 +1133,7 @@ def test_non_integer_argument_errors(self): assert_raises(TypeError, np.take, a, [0], np.float64(1.)) def test_non_integer_sequence_multiplication(self): - # Numpy scalar sequence multiply should not work with non-integers + # NumPy scalar sequence multiply should not work with non-integers def mult(a, b): return a * b diff --git a/numpy/core/tests/test_regression.py b/numpy/core/tests/test_regression.py index 5966ff688652..51d0447e3f5c 100644 --- a/numpy/core/tests/test_regression.py +++ b/numpy/core/tests/test_regression.py @@ -1734,7 +1734,7 @@ def test_object_array_self_reference(self): b = np.array(a, dtype=object) a[()] = b assert_raises(TypeError, int, a) - # Numpy has no tp_traverse currently, so circular references + # NumPy has no tp_traverse currently, so circular references # cannot be detected. So resolve it: a[()] = 0 @@ -1983,7 +1983,7 @@ def test_string_truncation(self): def test_string_truncation_ucs2(self): # Ticket #2081. Python compiled with two byte unicode # can lead to truncation if itemsize is not properly - # adjusted for Numpy's four byte unicode. + # adjusted for NumPy's four byte unicode. if sys.version_info[0] >= 3: a = np.array(['abcd']) else: diff --git a/numpy/ctypeslib.py b/numpy/ctypeslib.py index bf34ce38b8fc..73328224e5cc 100644 --- a/numpy/ctypeslib.py +++ b/numpy/ctypeslib.py @@ -95,7 +95,7 @@ def load_library(libname, loader_path): But there are cross-platform considerations, such as library file extensions, plus the fact Windows will just load the first library it finds with that name. - Numpy supplies the load_library function as a convenience. + NumPy supplies the load_library function as a convenience. Parameters ---------- diff --git a/numpy/doc/basics.py b/numpy/doc/basics.py index 745bff15a611..dac236644a04 100644 --- a/numpy/doc/basics.py +++ b/numpy/doc/basics.py @@ -6,7 +6,7 @@ Array types and conversions between types ========================================= -Numpy supports a much greater variety of numerical types than Python does. +NumPy supports a much greater variety of numerical types than Python does. This section shows which are available, and how to modify an array's data-type. ========== ========================================================== @@ -43,7 +43,7 @@ Additionally to ``intc`` the platform dependent C integer types ``short``, ``long``, ``longlong`` and their unsigned versions are defined. -Numpy numerical types are instances of ``dtype`` (data-type) objects, each +NumPy numerical types are instances of ``dtype`` (data-type) objects, each having unique characteristics. Once you have imported NumPy using :: @@ -124,7 +124,7 @@ Array Scalars ============= -Numpy generally returns elements of arrays as array scalars (a scalar +NumPy generally returns elements of arrays as array scalars (a scalar with an associated dtype). Array scalars differ from Python scalars, but for the most part they can be used interchangeably (the primary exception is for versions of Python older than v2.x, where integer array @@ -152,12 +152,12 @@ environment: specifically, x86 machines provide hardware floating-point with 80-bit precision, and while most C compilers provide this as their ``long double`` type, MSVC (standard for Windows builds) makes -``long double`` identical to ``double`` (64 bits). Numpy makes the +``long double`` identical to ``double`` (64 bits). NumPy makes the compiler's ``long double`` available as ``np.longdouble`` (and ``np.clongdouble`` for the complex numbers). You can find out what your numpy provides with``np.finfo(np.longdouble)``. -Numpy does not provide a dtype with more precision than C +NumPy does not provide a dtype with more precision than C ``long double``s; in particular, the 128-bit IEEE quad precision data type (FORTRAN's ``REAL*16``) is not available. diff --git a/numpy/doc/constants.py b/numpy/doc/constants.py index 36f94d307051..f9fccabfb659 100644 --- a/numpy/doc/constants.py +++ b/numpy/doc/constants.py @@ -3,7 +3,7 @@ Constants ========= -Numpy includes several constants: +NumPy includes several constants: %(constant_list)s """ @@ -82,7 +82,7 @@ def add_newdoc(module, name, doc): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity. @@ -122,7 +122,7 @@ def add_newdoc(module, name, doc): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Negative zero is considered to be a finite number. Examples @@ -193,7 +193,7 @@ def add_newdoc(module, name, doc): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Positive zero is considered to be a finite number. Examples @@ -253,7 +253,7 @@ def add_newdoc(module, name, doc): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity. @@ -298,7 +298,7 @@ def add_newdoc(module, name, doc): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. `NaN` and `NAN` are aliases of `nan`. diff --git a/numpy/doc/creation.py b/numpy/doc/creation.py index b10d45d48198..8480858d4629 100644 --- a/numpy/doc/creation.py +++ b/numpy/doc/creation.py @@ -19,7 +19,7 @@ expanding or mutating existing arrays. Nor will it cover creating object arrays or structured arrays. Both of those are covered in their own sections. -Converting Python array_like Objects to Numpy Arrays +Converting Python array_like Objects to NumPy Arrays ==================================================== In general, numerical data arranged in an array-like structure in Python can @@ -38,10 +38,10 @@ and types >>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]]) -Intrinsic Numpy Array Creation +Intrinsic NumPy Array Creation ============================== -Numpy has built-in functions for creating arrays from scratch: +NumPy has built-in functions for creating arrays from scratch: zeros(shape) will create an array filled with 0 values with the specified shape. The default dtype is float64. diff --git a/numpy/doc/glossary.py b/numpy/doc/glossary.py index 91cd7465185d..97b7b33629d8 100644 --- a/numpy/doc/glossary.py +++ b/numpy/doc/glossary.py @@ -305,7 +305,7 @@ [1, 2, 3, 4, 5, 6] Row-major order is also known as the C order, as the C programming - language uses it. New Numpy arrays are by default in row-major order. + language uses it. New NumPy arrays are by default in row-major order. self Often seen in method signatures, ``self`` refers to the instance diff --git a/numpy/doc/indexing.py b/numpy/doc/indexing.py index 33e9de3c4110..3e3e95641adc 100644 --- a/numpy/doc/indexing.py +++ b/numpy/doc/indexing.py @@ -65,7 +65,7 @@ that is subsequently indexed by 2. Note to those used to IDL or Fortran memory order as it relates to -indexing. Numpy uses C-order indexing. That means that the last +indexing. NumPy uses C-order indexing. That means that the last index usually represents the most rapidly changing memory location, unlike Fortran or IDL, where the first index represents the most rapidly changing location in memory. This difference represents a @@ -111,7 +111,7 @@ Index arrays ============ -Numpy arrays may be indexed with other arrays (or any other sequence- +NumPy arrays may be indexed with other arrays (or any other sequence- like object that can be converted to an array, such as lists, with the exception of tuples; see the end of this document for why this is). The use of index arrays ranges from simple, straightforward cases to diff --git a/numpy/doc/internals.py b/numpy/doc/internals.py index c25872bc02ea..a14fee7c2fff 100644 --- a/numpy/doc/internals.py +++ b/numpy/doc/internals.py @@ -6,13 +6,13 @@ Internal organization of numpy arrays ===================================== -It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to Numpy". +It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to NumPy". -Numpy arrays consist of two major components, the raw array data (from now on, +NumPy arrays consist of two major components, the raw array data (from now on, referred to as the data buffer), and the information about the raw array data. The data buffer is typically what people think of as arrays in C or Fortran, a contiguous (and fixed) block of memory containing fixed sized data items. -Numpy also contains a significant set of data that describes how to interpret +NumPy also contains a significant set of data that describes how to interpret the data in the data buffer. This extra information contains (among other things): 1) The basic data element's size in bytes @@ -109,7 +109,7 @@ numpy makes this issue yet more complicated. The internal machinery of numpy arrays is flexible enough to accept any ordering of indices. One can simply reorder indices by manipulating the internal stride information for arrays -without reordering the data at all. Numpy will know how to map the new index +without reordering the data at all. NumPy will know how to map the new index order to the data without moving the data. So if this is true, why not choose diff --git a/numpy/doc/structured_arrays.py b/numpy/doc/structured_arrays.py index 1135c1395cc7..5289e6d0bd85 100644 --- a/numpy/doc/structured_arrays.py +++ b/numpy/doc/structured_arrays.py @@ -6,7 +6,7 @@ Introduction ============ -Numpy provides powerful capabilities to create arrays of structured datatype. +NumPy provides powerful capabilities to create arrays of structured datatype. These arrays permit one to manipulate the data by named fields. A simple example will show what is meant.: :: @@ -79,7 +79,7 @@ Don't use these in new code! d) Single character type specifiers (e.g H for unsigned short ints). Avoid using these unless you must. Details can be found in the - Numpy book + NumPy book These different styles can be mixed within the same string (but why would you want to do that?). Furthermore, each type specifier can be prefixed diff --git a/numpy/doc/subclassing.py b/numpy/doc/subclassing.py index 40522bb9c700..9a339430bf51 100644 --- a/numpy/doc/subclassing.py +++ b/numpy/doc/subclassing.py @@ -97,7 +97,7 @@ If we subclass ndarray, we need to deal not only with explicit construction of our array type, but also :ref:`view-casting` or -:ref:`new-from-template`. Numpy has the machinery to do this, and this +:ref:`new-from-template`. NumPy has the machinery to do this, and this machinery that makes subclassing slightly non-standard. There are two aspects to the machinery that ndarray uses to support @@ -523,7 +523,7 @@ def __array_wrap__(self, arr, context=None): One of the problems that ndarray solves is keeping track of memory ownership of ndarrays and their views. Consider the case where we have created an ndarray, ``arr`` and have taken a slice with ``v = arr[1:]``. -The two objects are looking at the same memory. Numpy keeps track of +The two objects are looking at the same memory. NumPy keeps track of where the data came from for a particular array or view, with the ``base`` attribute: diff --git a/numpy/doc/ufuncs.py b/numpy/doc/ufuncs.py index 0132202adc55..a112e559cb05 100644 --- a/numpy/doc/ufuncs.py +++ b/numpy/doc/ufuncs.py @@ -6,7 +6,7 @@ Ufuncs are, generally speaking, mathematical functions or operations that are applied element-by-element to the contents of an array. That is, the result in each output array element only depends on the value in the corresponding -input array (or arrays) and on no other array elements. Numpy comes with a +input array (or arrays) and on no other array elements. NumPy comes with a large suite of ufuncs, and scipy extends that suite substantially. The simplest example is the addition operator: :: diff --git a/numpy/dual.py b/numpy/dual.py index 1517d8421345..8b91da262037 100644 --- a/numpy/dual.py +++ b/numpy/dual.py @@ -5,7 +5,7 @@ for FFTs, linear algebra, and special functions. This module allows developers to transparently support these accelerated functions when scipy is available but still support users who have only installed -Numpy. +NumPy. .. _Scipy : http://www.scipy.org diff --git a/numpy/lib/_version.py b/numpy/lib/_version.py index 54b9c1dc7812..0019c5607ea7 100644 --- a/numpy/lib/_version.py +++ b/numpy/lib/_version.py @@ -1,4 +1,4 @@ -"""Utility to compare (Numpy) version strings. +"""Utility to compare (NumPy) version strings. The NumpyVersion class allows properly comparing numpy version strings. The LooseVersion and StrictVersion classes that distutils provides don't @@ -18,7 +18,7 @@ class NumpyVersion(): """Parse and compare numpy version strings. - Numpy has the following versioning scheme (numbers given are examples; they + NumPy has the following versioning scheme (numbers given are examples; they can be > 9) in principle): - Released version: '1.8.0', '1.8.1', etc. @@ -40,7 +40,7 @@ class NumpyVersion(): Parameters ---------- vstring : str - Numpy version string (``np.__version__``). + NumPy version string (``np.__version__``). Examples -------- diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index b74e04028210..11cfb2c995a9 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -489,8 +489,8 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, based on the actual data within `range`, the bin count will fill the entire range including portions containing no data. normed : bool, optional - This keyword is deprecated in Numpy 1.6 due to confusing/buggy - behavior. It will be removed in Numpy 2.0. Use the ``density`` + This keyword is deprecated in NumPy 1.6 due to confusing/buggy + behavior. It will be removed in NumPy 2.0. Use the ``density`` keyword instead. If ``False``, the result will contain the number of samples in each bin. If ``True``, the result is the value of the probability *density* function at the bin, @@ -809,7 +809,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, else: return n, bins else: - # deprecated, buggy behavior. Remove for Numpy 2.0 + # deprecated, buggy behavior. Remove for NumPy 2.0 if normed: db = array(np.diff(bins), float) return n/(n*db).sum(), bins diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 7f7aea9bc042..ab88c6a37cbe 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -204,7 +204,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number. @@ -311,7 +311,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number. @@ -453,7 +453,7 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. - In Numpy versions <= 1.8 Nan is returned for slices that are all-NaN or + In NumPy versions <= 1.8 Nan is returned for slices that are all-NaN or empty. In later versions zero is returned. Parameters diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index 0b2fdfaba88e..376d34b918d0 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -376,9 +376,9 @@ def load(file, mmap_mode=None, allow_pickle=True, fix_imports=True, if encoding not in ('ASCII', 'latin1', 'bytes'): # The 'encoding' value for pickle also affects what encoding - # the serialized binary data of Numpy arrays is loaded + # the serialized binary data of NumPy arrays is loaded # in. Pickle does not pass on the encoding information to - # Numpy. The unpickling code in numpy.core.multiarray is + # NumPy. The unpickling code in numpy.core.multiarray is # written to assume that unicode data appearing where binary # should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'. # @@ -466,7 +466,7 @@ def save(file, arr, allow_pickle=True, fix_imports=True): Notes ----- For a description of the ``.npy`` format, see the module docstring - of `numpy.lib.format` or the Numpy Enhancement Proposal + of `numpy.lib.format` or the NumPy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html Examples @@ -552,7 +552,7 @@ def savez(file, *args, **kwds): variables they contain. The archive is not compressed and each file in the archive contains one variable in ``.npy`` format. For a description of the ``.npy`` format, see `numpy.lib.format` or the - Numpy Enhancement Proposal + NumPy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html When opening the saved ``.npz`` file with `load` a `NpzFile` object is @@ -1428,7 +1428,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, References ---------- - .. [1] Numpy User Guide, section `I/O with Numpy + .. [1] NumPy User Guide, section `I/O with NumPy `_. Examples diff --git a/numpy/lib/type_check.py b/numpy/lib/type_check.py index f620d49d5ced..07f4463c5a4c 100644 --- a/numpy/lib/type_check.py +++ b/numpy/lib/type_check.py @@ -350,7 +350,7 @@ def nan_to_num(x): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. diff --git a/numpy/lib/ufunclike.py b/numpy/lib/ufunclike.py index e91f64d0ef92..b6c017b968d3 100644 --- a/numpy/lib/ufunclike.py +++ b/numpy/lib/ufunclike.py @@ -82,7 +82,7 @@ def isposinf(x, y=None): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Errors result if the second argument is also supplied when `x` is a @@ -145,7 +145,7 @@ def isneginf(x, y=None): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Errors result if the second argument is also supplied when x is a scalar diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py index 133704d131ea..97b93cace27d 100644 --- a/numpy/lib/utils.py +++ b/numpy/lib/utils.py @@ -241,10 +241,10 @@ def byte_bounds(a): def who(vardict=None): """ - Print the Numpy arrays in the given dictionary. + Print the NumPy arrays in the given dictionary. If there is no dictionary passed in or `vardict` is None then returns - Numpy arrays in the globals() dictionary (all Numpy arrays in the + NumPy arrays in the globals() dictionary (all NumPy arrays in the namespace). Parameters @@ -646,7 +646,7 @@ def info(object=None, maxwidth=76, output=sys.stdout, toplevel='numpy'): def source(object, output=sys.stdout): """ - Print or write to a file the source code for a Numpy object. + Print or write to a file the source code for a NumPy object. The source code is only returned for objects written in Python. Many functions and classes are defined in C and will therefore not return diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index a05b0ee8e7bc..15c72fa02ca0 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -677,7 +677,7 @@ def qr(a, mode='reduced'): `a` is of type `matrix`, all the return values will be matrices too. New 'reduced', 'complete', and 'raw' options for mode were added in - Numpy 1.8 and the old option 'full' was made an alias of 'reduced'. In + NumPy 1.8 and the old option 'full' was made an alias of 'reduced'. In addition the options 'full' and 'economic' were deprecated. Because 'full' was the previous default and 'reduced' is the new default, backward compatibility can be maintained by letting `mode` default. diff --git a/numpy/polynomial/_polybase.py b/numpy/polynomial/_polybase.py index 09190ce4eb9d..be5cd3888da7 100644 --- a/numpy/polynomial/_polybase.py +++ b/numpy/polynomial/_polybase.py @@ -742,7 +742,7 @@ def fit(cls, x, y, deg, domain=None, rcond=None, full=False, w=None, deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For Numpy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11 a list of integers specifying the degrees of the terms to include may be used instead. domain : {None, [beg, end], []}, optional Domain to use for the returned series. If ``None``, diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py index dd346fbda155..f3f491c29cb5 100644 --- a/numpy/polynomial/chebyshev.py +++ b/numpy/polynomial/chebyshev.py @@ -1620,7 +1620,7 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For Numpy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py index 06d68668f700..3172b41d0956 100644 --- a/numpy/polynomial/hermite.py +++ b/numpy/polynomial/hermite.py @@ -1391,7 +1391,7 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For Numpy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py index a846b167c0a7..7cc85de5d0b3 100644 --- a/numpy/polynomial/hermite_e.py +++ b/numpy/polynomial/hermite_e.py @@ -1388,7 +1388,7 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For Numpy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py index 60728c5f997c..6aa08635610a 100644 --- a/numpy/polynomial/laguerre.py +++ b/numpy/polynomial/laguerre.py @@ -1390,7 +1390,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For Numpy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than diff --git a/numpy/polynomial/legendre.py b/numpy/polynomial/legendre.py index ec6c2f8bf5c7..29e9a0897d96 100644 --- a/numpy/polynomial/legendre.py +++ b/numpy/polynomial/legendre.py @@ -1421,7 +1421,7 @@ def legfit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For Numpy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py index 3ed6b67a474f..edefeed6c27d 100644 --- a/numpy/polynomial/polynomial.py +++ b/numpy/polynomial/polynomial.py @@ -1309,7 +1309,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For Numpy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index abf7a4102436..24c8bd9518f6 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -2547,7 +2547,7 @@ cdef class RandomState: Parameters ---------- df : int or array_like of ints - Degrees of freedom, should be > 0 as of Numpy 1.10, + Degrees of freedom, should be > 0 as of NumPy 1.10, should be > 1 for earlier versions. nonc : float or array_like of floats Non-centrality, should be non-negative. From 3f6672a30cd06d273e0b2160084a9abadcf9c315 Mon Sep 17 00:00:00 2001 From: Pierre de Buyl Date: Tue, 6 Sep 2016 14:54:08 +0200 Subject: [PATCH 3/6] DOC: change Numpy to NumPy in remaining files the files in doc/ and numpy/ were covered in previous commits --- INSTALL.rst.txt | 22 +++++++++---------- benchmarks/README.rst | 12 +++++----- runtests.py | 2 +- setup.py | 8 +++---- site.cfg.example | 8 +++---- tools/c_coverage/HOWTO_C_COVERAGE.txt | 8 +++---- .../art/dmgbackground.svg | 4 ++-- tools/travis-test.sh | 2 +- .../nsis_scripts/numpy-superinstaller.nsi.in | 2 +- 9 files changed, 34 insertions(+), 34 deletions(-) diff --git a/INSTALL.rst.txt b/INSTALL.rst.txt index f4e96fc1f05a..426105929ff6 100644 --- a/INSTALL.rst.txt +++ b/INSTALL.rst.txt @@ -1,8 +1,8 @@ Building and installing NumPy +++++++++++++++++++++++++++++ -**IMPORTANT**: the below notes are about building Numpy, which for most users -is *not* the recommended way to install Numpy. Instead, use either a complete +**IMPORTANT**: the below notes are about building NumPy, which for most users +is *not* the recommended way to install NumPy. Instead, use either a complete scientific Python distribution (recommended) or a binary installer - see http://scipy.org/install.html. @@ -39,13 +39,13 @@ nose__ http://somethingaboutorange.com/mrl/projects/nose/ .. note:: - If you want to build Numpy in order to work on Numpy itself, use + If you want to build NumPy in order to work on NumPy itself, use ``runtests.py``. For more details, see http://docs.scipy.org/doc/numpy-dev/dev/development_environment.html .. note:: - More extensive information on building Numpy (and Scipy) is maintained at + More extensive information on building NumPy (and Scipy) is maintained at http://scipy.org/scipylib/building/index.html @@ -68,8 +68,8 @@ NPY_NUM_BUILD_JOBS. Choosing compilers ================== -Numpy needs a C compiler, and for development versions also Cython. A Fortran -compiler isn't needed to build Numpy itself; the ``numpy.f2py`` tests will be +NumPy needs a C compiler, and for development versions also Cython. A Fortran +compiler isn't needed to build NumPy itself; the ``numpy.f2py`` tests will be skipped when running the test suite if no Fortran compiler is available. For building Scipy a Fortran compiler is needed though, so we include some details on Fortran compilers in the rest of this section. @@ -91,7 +91,7 @@ good `application note `_ is the best -place to ask. If the issue is clearly a bug in Numpy, please file an issue (or +place to ask. If the issue is clearly a bug in NumPy, please file an issue (or even better, a pull request) at https://github.com/numpy/numpy. diff --git a/benchmarks/README.rst b/benchmarks/README.rst index c03d42ecebc9..2ed5d150f816 100644 --- a/benchmarks/README.rst +++ b/benchmarks/README.rst @@ -4,7 +4,7 @@ NumPy benchmarks ================ -Benchmarking Numpy with Airspeed Velocity. +Benchmarking NumPy with Airspeed Velocity. Usage @@ -12,11 +12,11 @@ Usage Airspeed Velocity manages building and Python virtualenvs by itself, unless told otherwise. Some of the benchmarking features in -``runtests.py`` also tell ASV to use the Numpy compiled by +``runtests.py`` also tell ASV to use the NumPy compiled by ``runtests.py``. To run the benchmarks, you do not need to install a -development version of Numpy to your current Python environment. +development version of NumPy to your current Python environment. -Run a benchmark against currently checked out Numpy version (don't +Run a benchmark against currently checked out NumPy version (don't record the result):: python runtests.py --bench bench_core @@ -46,9 +46,9 @@ See `ASV documentation`_ for basics on how to write benchmarks. Some things to consider: -- The benchmark suite should be importable with any Numpy version. +- The benchmark suite should be importable with any NumPy version. -- The benchmark parameters etc. should not depend on which Numpy version +- The benchmark parameters etc. should not depend on which NumPy version is installed. - Try to keep the runtime of the benchmark reasonable. diff --git a/runtests.py b/runtests.py index 7be76e46de6b..0fc4412931d6 100755 --- a/runtests.py +++ b/runtests.py @@ -28,7 +28,7 @@ from __future__ import division, print_function # -# This is a generic test runner script for projects using Numpy's test +# This is a generic test runner script for projects using NumPy's test # framework. Change the following values to adapt to your project: # diff --git a/setup.py b/setup.py index 664992451595..7f2b24572b6d 100755 --- a/setup.py +++ b/setup.py @@ -255,18 +255,18 @@ def parse_setuppy_commands(): - `pip install .` (from a git repo or downloaded source release) - - `pip install numpy` (last Numpy release on PyPi) + - `pip install numpy` (last NumPy release on PyPi) """)) return True if '--help' in sys.argv[1:] or '-h' in sys.argv[1]: print(textwrap.dedent(""" - Numpy-specific help + NumPy-specific help ------------------- - To install Numpy from here with reliable uninstall, we recommend - that you use `pip install .`. To install the latest Numpy release + To install NumPy from here with reliable uninstall, we recommend + that you use `pip install .`. To install the latest NumPy release from PyPi, use `pip install numpy`. For help with build/installation issues, please ask on the diff --git a/site.cfg.example b/site.cfg.example index 05b49e5071f8..8e043cfb3901 100644 --- a/site.cfg.example +++ b/site.cfg.example @@ -90,7 +90,7 @@ # Atlas # ----- # Atlas is an open source optimized implementation of the BLAS and Lapack -# routines. Numpy will try to build against Atlas by default when available in +# routines. NumPy will try to build against Atlas by default when available in # the system library dirs. To build numpy against a custom installation of # Atlas you can add an explicit section such as the following. Here we assume # that Atlas was configured with ``prefix=/opt/atlas``. @@ -135,14 +135,14 @@ # ---- # BLIS (https://github.com/flame/blis) also provides a BLAS interface. It's a # relatively new library, its performance in some cases seems to match that of -# MKL and OpenBLAS, but it hasn't been benchmarked with Numpy or Scipy yet. +# MKL and OpenBLAS, but it hasn't been benchmarked with NumPy or Scipy yet. # # Notes on compiling BLIS itself: -# - the CBLAS interface (needed by Numpy) isn't built by default; define +# - the CBLAS interface (needed by NumPy) isn't built by default; define # BLIS_ENABLE_CBLAS to build it. # - ``./configure auto`` doesn't support 32-bit builds, see gh-7294 for # details. -# Notes on compiling Numpy against BLIS: +# Notes on compiling NumPy against BLIS: # - ``include_dirs`` below should be the directory where the BLIS cblas.h # header is installed. # diff --git a/tools/c_coverage/HOWTO_C_COVERAGE.txt b/tools/c_coverage/HOWTO_C_COVERAGE.txt index 320d9b0deaef..8822dd715421 100644 --- a/tools/c_coverage/HOWTO_C_COVERAGE.txt +++ b/tools/c_coverage/HOWTO_C_COVERAGE.txt @@ -27,7 +27,7 @@ For most cases, it is good enough to do:: > c_coverage_collect.sh python -c "import numpy; numpy.test()" > c_coverage_report.py callgrind.out.pid -which will run all of the Numpy unit tests, create a directory called +which will run all of the NumPy unit tests, create a directory called `coverage` and place the coverage results there. In a more advanced scenario, you may wish to run individual unit tests @@ -41,7 +41,7 @@ To collect coverage results, you merely run the python interpreter under valgrind's callgrind tool. The `c_coverage_collect.sh` helper script will pass all of the required arguments to valgrind. -For example, in typical usage, you may want to run all of the Numpy +For example, in typical usage, you may want to run all of the NumPy unit tests:: > c_coverage_collect.sh python -c "import numpy; numpy.test()" @@ -99,7 +99,7 @@ HTML reports The HTML report highlights the code that was run in green. The HTML report has special support for the "generated" functions in -Numpy. Each run line of code also contains a number in square +NumPy. Each run line of code also contains a number in square brackets indicating the number of different generated functions the line was run in. Hovering the mouse over the line will display a list of the versions of the function in which the line was run. These @@ -112,6 +112,6 @@ Caveats The coverage results occasionally misses lines that clearly must have been run. This usually can be traced back to the compiler optimizer removing lines because they are tautologically impossible or to -combine lines together. Compiling Numpy without optimizations helps, +combine lines together. Compiling NumPy without optimizations helps, but not completely. Even despite this flaw, this tool is still helpful in identifying large missed blocks or functions. diff --git a/tools/numpy-macosx-installer/art/dmgbackground.svg b/tools/numpy-macosx-installer/art/dmgbackground.svg index 308de1150610..ea092d78b13d 100644 --- a/tools/numpy-macosx-installer/art/dmgbackground.svg +++ b/tools/numpy-macosx-installer/art/dmgbackground.svg @@ -11649,7 +11649,7 @@ vU/54W6oo0GROeM/veMNBRRE7rWB9Q4H8mdDTv+nwe+evyS3TH57xxtzjOZOx9uueWP+5o735/+U id="tspan7191" x="223.51202" y="46.072498" - style="font-size:28.66733551px;font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;text-align:center;line-height:100%;writing-mode:lr-tb;text-anchor:middle;font-family:Utopia;-inkscape-font-specification:Utopia">Numpy + style="font-size:28.66733551px;font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;text-align:center;line-height:100%;writing-mode:lr-tb;text-anchor:middle;font-family:Utopia;-inkscape-font-specification:Utopia">NumPy @@ -11737,4 +11737,4 @@ vU/54W6oo0GROeM/veMNBRRE7rWB9Q4H8mdDTv+nwe+evyS3TH57xxtzjOZOx9uueWP+5o735/+U id="tspan2431" /> - \ No newline at end of file + diff --git a/tools/travis-test.sh b/tools/travis-test.sh index be9830e6ed5e..be841c8fa5db 100755 --- a/tools/travis-test.sh +++ b/tools/travis-test.sh @@ -71,7 +71,7 @@ setup_chroot() # linux32 python setup.py build # when travis updates to ubuntu 14.04 # - # Numpy may not distinguish between 64 and 32 bit ATLAS in the + # NumPy may not distinguish between 64 and 32 bit ATLAS in the # configuration stage. DIR=$1 set -u diff --git a/tools/win32build/nsis_scripts/numpy-superinstaller.nsi.in b/tools/win32build/nsis_scripts/numpy-superinstaller.nsi.in index add0ec69888a..4086df735ad1 100644 --- a/tools/win32build/nsis_scripts/numpy-superinstaller.nsi.in +++ b/tools/win32build/nsis_scripts/numpy-superinstaller.nsi.in @@ -15,7 +15,7 @@ SetCompressor /Solid LZMA ; Useful to disable compression under development ;General ;Name and file -Name "Numpy super installer" +Name "NumPy super installer" OutFile "@NUMPY_INSTALLER_NAME@" ;Default installation folder From 0780721328a25389844181fd7e38f56e1a865698 Mon Sep 17 00:00:00 2001 From: Pierre de Buyl Date: Tue, 6 Sep 2016 17:18:03 +0200 Subject: [PATCH 4/6] DOC: fix page title for swig --- doc/source/reference/swig.interface-file.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/reference/swig.interface-file.rst b/doc/source/reference/swig.interface-file.rst index 36b226b9b35b..94fe83d36ba9 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 From db0b231f17fb2148772181626235e2644eb67c3e Mon Sep 17 00:00:00 2001 From: Pierre de Buyl Date: Wed, 7 Sep 2016 09:48:40 +0200 Subject: [PATCH 5/6] DOC: change version references from x.y to x.y.z --- doc/source/reference/routines.numarray.rst | 2 +- numpy/add_newdocs.py | 2 +- numpy/core/code_generators/cversions.txt | 2 +- numpy/core/numeric.py | 6 +++--- numpy/lib/function_base.py | 6 +++--- numpy/lib/nanfunctions.py | 2 +- numpy/linalg/linalg.py | 2 +- numpy/polynomial/_polybase.py | 2 +- numpy/polynomial/chebyshev.py | 2 +- numpy/polynomial/hermite.py | 2 +- numpy/polynomial/hermite_e.py | 2 +- numpy/polynomial/laguerre.py | 2 +- numpy/polynomial/legendre.py | 2 +- numpy/polynomial/polynomial.py | 2 +- numpy/random/mtrand/mtrand.pyx | 2 +- 15 files changed, 19 insertions(+), 19 deletions(-) diff --git a/doc/source/reference/routines.numarray.rst b/doc/source/reference/routines.numarray.rst index 3bbc413d73f2..9e84f49b98bd 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 1.9.0. diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index adeaff90c6e3..1cf0315463a8 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -2014,7 +2014,7 @@ def luf(lamdaexpr, *args, **kwargs): were elements. .. warning:: - This function is preliminary and included in NumPy 1.10 for testing + This function is preliminary and included in NumPy 1.10.0 for testing and documentation. Its semantics will not change, but the number and order of the optional arguments will. diff --git a/numpy/core/code_generators/cversions.txt b/numpy/core/code_generators/cversions.txt index 0726fc85e8ca..47781add8fbc 100644 --- a/numpy/core/code_generators/cversions.txt +++ b/numpy/core/code_generators/cversions.txt @@ -22,7 +22,7 @@ # Version 9 (NumPy 1.8) Added interface for partition functions, # PyArray_NEW_ZEROED, commented out as the hash changed in -# NumPy 1.9 due to annotation. +# NumPy 1.9.0 due to annotation. #0x00000009 = 327bd114df09c2eb7a0bcc6901e2a3ed # Version 9 (NumPy 1.9) Added function annotations. diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index 51f8dd034706..ba99a1e42c43 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -759,7 +759,7 @@ def isfortran(a): This function is obsolete and, because of changes due to relaxed stride checking, its return value for the same array may differ for versions - of NumPy >= 1.10 and previous versions. If you only want to check if an + of NumPy >= 1.10.0 and previous versions. If you only want to check if an array is Fortran contiguous use ``a.flags.f_contiguous`` instead. Parameters @@ -1170,7 +1170,7 @@ def alterdot(): arrays with larger dimensionalities use the built in functions and are not accelerated. - .. note:: Deprecated in NumPy 1.10 + .. note:: Deprecated in NumPy 1.10.0 The cblas functions have been integrated into the multarray module and alterdot now longer does anything. It will be removed in NumPy 1.11.0. @@ -1195,7 +1195,7 @@ def restoredot(): an accelerated BLAS, or when being very careful about benchmarking linear algebra operations. - .. note:: Deprecated in NumPy 1.10 + .. note:: Deprecated in NumPy 1.10.0 The cblas functions have been integrated into the multarray module and restoredot now longer does anything. It will be removed in NumPy 1.11.0. diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 11cfb2c995a9..cb1a04f47b54 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -489,8 +489,8 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, based on the actual data within `range`, the bin count will fill the entire range including portions containing no data. normed : bool, optional - This keyword is deprecated in NumPy 1.6 due to confusing/buggy - behavior. It will be removed in NumPy 2.0. Use the ``density`` + This keyword is deprecated in NumPy 1.6.0 due to confusing/buggy + behavior. It will be removed in NumPy 2.0.0. Use the ``density`` keyword instead. If ``False``, the result will contain the number of samples in each bin. If ``True``, the result is the value of the probability *density* function at the bin, @@ -809,7 +809,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, else: return n, bins else: - # deprecated, buggy behavior. Remove for NumPy 2.0 + # deprecated, buggy behavior. Remove for NumPy 2.0.0 if normed: db = array(np.diff(bins), float) return n/(n*db).sum(), bins diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index ab88c6a37cbe..c024055bae0b 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -453,7 +453,7 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. - In NumPy versions <= 1.8 Nan is returned for slices that are all-NaN or + In NumPy versions <= 1.8.0 Nan is returned for slices that are all-NaN or empty. In later versions zero is returned. Parameters diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index 15c72fa02ca0..3576d382433e 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -677,7 +677,7 @@ def qr(a, mode='reduced'): `a` is of type `matrix`, all the return values will be matrices too. New 'reduced', 'complete', and 'raw' options for mode were added in - NumPy 1.8 and the old option 'full' was made an alias of 'reduced'. In + NumPy 1.8.0 and the old option 'full' was made an alias of 'reduced'. In addition the options 'full' and 'economic' were deprecated. Because 'full' was the previous default and 'reduced' is the new default, backward compatibility can be maintained by letting `mode` default. diff --git a/numpy/polynomial/_polybase.py b/numpy/polynomial/_polybase.py index be5cd3888da7..aad14738850f 100644 --- a/numpy/polynomial/_polybase.py +++ b/numpy/polynomial/_polybase.py @@ -742,7 +742,7 @@ def fit(cls, x, y, deg, domain=None, rcond=None, full=False, w=None, deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For NumPy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. domain : {None, [beg, end], []}, optional Domain to use for the returned series. If ``None``, diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py index f3f491c29cb5..82b3dc9a69fb 100644 --- a/numpy/polynomial/chebyshev.py +++ b/numpy/polynomial/chebyshev.py @@ -1620,7 +1620,7 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For NumPy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py index 3172b41d0956..d7038e54d005 100644 --- a/numpy/polynomial/hermite.py +++ b/numpy/polynomial/hermite.py @@ -1391,7 +1391,7 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For NumPy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py index 7cc85de5d0b3..8a70acfa2fbb 100644 --- a/numpy/polynomial/hermite_e.py +++ b/numpy/polynomial/hermite_e.py @@ -1388,7 +1388,7 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For NumPy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py index 6aa08635610a..ffd032883b9f 100644 --- a/numpy/polynomial/laguerre.py +++ b/numpy/polynomial/laguerre.py @@ -1390,7 +1390,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For NumPy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than diff --git a/numpy/polynomial/legendre.py b/numpy/polynomial/legendre.py index 29e9a0897d96..4886605455fd 100644 --- a/numpy/polynomial/legendre.py +++ b/numpy/polynomial/legendre.py @@ -1421,7 +1421,7 @@ def legfit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For NumPy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py index edefeed6c27d..95f044a2df27 100644 --- a/numpy/polynomial/polynomial.py +++ b/numpy/polynomial/polynomial.py @@ -1309,7 +1309,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): deg : int or 1-D array_like Degree(s) of the fitting polynomials. If `deg` is a single integer all terms up to and including the `deg`'th term are included in the - fit. For NumPy versions >= 1.11 a list of integers specifying the + fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index 24c8bd9518f6..138f0e39a07a 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -2547,7 +2547,7 @@ cdef class RandomState: Parameters ---------- df : int or array_like of ints - Degrees of freedom, should be > 0 as of NumPy 1.10, + Degrees of freedom, should be > 0 as of NumPy 1.10.0, should be > 1 for earlier versions. nonc : float or array_like of floats Non-centrality, should be non-negative. From 0a9fb7b7ea94b3d7554ca29c3a40057d22ec1f01 Mon Sep 17 00:00:00 2001 From: Pierre de Buyl Date: Wed, 7 Sep 2016 09:55:40 +0200 Subject: [PATCH 6/6] DOC: change version references from x.y to x.y.z --- doc/HOWTO_DOCUMENT.rst.txt | 4 ++-- doc/HOWTO_RELEASE.rst.txt | 2 +- doc/source/reference/arrays.ndarray.rst | 2 +- doc/source/reference/c-api.array.rst | 2 +- numpy/core/fromnumeric.py | 2 +- numpy/core/numeric.py | 2 +- 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/HOWTO_DOCUMENT.rst.txt b/doc/HOWTO_DOCUMENT.rst.txt index 169b87df4150..272dd6bf2a0a 100644 --- a/doc/HOWTO_DOCUMENT.rst.txt +++ b/doc/HOWTO_DOCUMENT.rst.txt @@ -154,8 +154,8 @@ The sections of the docstring are: :: - .. note:: Deprecated in NumPy 1.6 - `ndobj_old` will be removed in NumPy 2.0, it is replaced by + .. note:: Deprecated in NumPy 1.6.0 + `ndobj_old` will be removed in NumPy 2.0.0, it is replaced by `ndobj_new` because the latter works also with array subclasses. 3. **Extended Summary** diff --git a/doc/HOWTO_RELEASE.rst.txt b/doc/HOWTO_RELEASE.rst.txt index 6d0d54e89871..bad3e22d8118 100644 --- a/doc/HOWTO_RELEASE.rst.txt +++ b/doc/HOWTO_RELEASE.rst.txt @@ -302,7 +302,7 @@ to public keyservers, with a command such as:: Apply patch to fix bogus strides -------------------------------- -NPY_RELAXED_STRIDE_CHECKING was made the default in NumPy 1.10 and bogus +NPY_RELAXED_STRIDE_CHECKING was made the default in NumPy 1.10.0 and bogus strides are used in the development branch to smoke out problems. The `patch `_ should be updated if necessary and applied to the release branch to rationalize the strides. diff --git a/doc/source/reference/arrays.ndarray.rst b/doc/source/reference/arrays.ndarray.rst index bd6821b62896..14d35271ebfa 100644 --- a/doc/source/reference/arrays.ndarray.rst +++ b/doc/source/reference/arrays.ndarray.rst @@ -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 1.10.0 has a preliminary implementation of ``@`` for testing purposes. Further documentation can be found in the :func:`matmul` documentation. diff --git a/doc/source/reference/c-api.array.rst b/doc/source/reference/c-api.array.rst index 4ab276d9a0b4..45de1896c05a 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 1.6.0, 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/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index 8a56a6e1f356..84fdab7cca92 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -471,7 +471,7 @@ def swapaxes(a, axis1, axis2): Returns ------- a_swapped : ndarray - For NumPy >= 1.10, if `a` is an ndarray, then a view of `a` is + For NumPy >= 1.10.0, if `a` is an ndarray, then a view of `a` is returned; otherwise a new array is created. For earlier NumPy versions a view of `a` is returned only if the order of the axes is changed, otherwise the input array is returned. diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index ba99a1e42c43..ad2dcc291f42 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -1505,7 +1505,7 @@ def rollaxis(a, axis, start=0): Returns ------- res : ndarray - For NumPy >= 1.10 a view of `a` is always returned. For earlier + For NumPy >= 1.10.0 a view of `a` is always returned. For earlier NumPy versions a view of `a` is returned only if the order of the axes is changed, otherwise the input array is returned.