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public_repos/numpy/doc | public_repos/numpy/doc/source/getting_started.rst | :orphan:
Getting started
=============== | 0 |
public_repos/numpy/doc | public_repos/numpy/doc/source/release.rst | *************
Release notes
*************
.. toctree::
:maxdepth: 3
2.0.0 <release/2.0.0-notes>
1.26.2 <release/1.26.2-notes>
1.26.1 <release/1.26.1-notes>
1.26.0 <release/1.26.0-notes>
1.25.2 <release/1.25.2-notes>
1.25.1 <release/1.25.1-notes>
1.25.0 <release/1.25.0-notes>
1.24.4... | 0 |
public_repos/numpy/doc | public_repos/numpy/doc/source/conf.py | import os
import re
import sys
import importlib
from docutils import nodes
from docutils.parsers.rst import Directive
# Minimum version, enforced by sphinx
needs_sphinx = '4.3'
# This is a nasty hack to use platform-agnostic names for types in the
# documentation.
# must be kept alive to hold the patched names
_nam... | 0 |
public_repos/numpy/doc | public_repos/numpy/doc/source/doxyfile | # Doxyfile 1.8.18
#---------------------------------------------------------------------------
# Project related configuration options
#---------------------------------------------------------------------------
DOXYFILE_ENCODING = UTF-8
PROJECT_NAME = NumPy
PROJECT_NUMBER =
PROJECT_BRIEF ... | 0 |
public_repos/numpy/doc | public_repos/numpy/doc/source/license.rst | *************
NumPy license
*************
.. include:: ../../LICENSE.txt
:literal:
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/array_api.rst | .. _array_api:
********************************
Array API standard compatibility
********************************
.. note::
The ``numpy.array_api`` module is still experimental. See `NEP 47
<https://numpy.org/neps/nep-0047-array-api-standard.html>`__.
NumPy includes a reference implementation of the `array AP... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.char.rst | String operations
=================
.. currentmodule:: numpy.char
.. module:: numpy.char
The `numpy.char` module provides a set of vectorized string
operations for arrays of type `numpy.str_` or `numpy.bytes_`. For example
>>> np.char.capitalize(["python", "numpy"])
array(['Python', 'Numpy'], dtype='<U6... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/global_state.rst | .. _global_state:
************
Global state
************
NumPy has a few import-time, compile-time, or runtime options
which change the global behaviour.
Most of these are related to performance or for debugging
purposes and will not be interesting to the vast majority
of users.
Performance-related options
========... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.datetime.rst | .. _routines.datetime:
Datetime support functions
**************************
.. currentmodule:: numpy
.. autosummary::
:toctree: generated/
datetime_as_string
datetime_data
Business day functions
======================
.. currentmodule:: numpy
.. autosummary::
:toctree: generated/
busdaycalendar... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/typing.rst | .. _typing:
.. automodule:: numpy.typing
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/arrays.scalars.rst | .. _arrays.scalars:
*******
Scalars
*******
.. currentmodule:: numpy
Python defines only one type of a particular data class (there is only
one integer type, one floating-point type, etc.). This can be
convenient in applications that don't need to be concerned with all
the ways data can be represented in a computer.... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.bitwise.rst | Binary operations
=================
.. currentmodule:: numpy
Elementwise bit operations
--------------------------
.. autosummary::
:toctree: generated/
bitwise_and
bitwise_or
bitwise_xor
invert
left_shift
right_shift
Bit packing
-----------
.. autosummary::
:toctree: generated/
packbits... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/distutils.rst | **********************************
Packaging (:mod:`numpy.distutils`)
**********************************
.. module:: numpy.distutils
.. warning::
``numpy.distutils`` is deprecated, and will be removed for
Python >= 3.12. For more details, see :ref:`distutils-status-migration`
.. warning::
Note that ``setu... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.chebyshev.rst | .. versionadded:: 1.4.0
.. automodule:: numpy.polynomial.chebyshev
:no-members:
:no-inherited-members:
:no-special-members:
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/swig.rst | **************
NumPy and SWIG
**************
.. sectionauthor:: Bill Spotz
.. toctree::
:maxdepth: 2
swig.interface-file
swig.testing
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/testing.rst | .. _testing-guidelines:
Testing guidelines
==================
.. include:: ../../TESTS.rst
:start-line: 6
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/arrays.dtypes.rst | .. currentmodule:: numpy
.. _arrays.dtypes:
**********************************
Data type objects (:class:`dtype`)
**********************************
A data type object (an instance of :class:`numpy.dtype` class)
describes how the bytes in the fixed-size block of memory
corresponding to an array item should be interp... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.err.rst | Floating point error handling
=============================
.. currentmodule:: numpy
Setting and getting error handling
----------------------------------
.. autosummary::
:toctree: generated/
seterr
geterr
seterrcall
geterrcall
errstate
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.padding.rst | Padding arrays
==============
.. currentmodule:: numpy
.. autosummary::
:toctree: generated/
pad
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/maskedarray.rst | .. _maskedarray:
*************
Masked arrays
*************
Masked arrays are arrays that may have missing or invalid entries.
The :mod:`numpy.ma` module provides a nearly work-alike replacement for numpy
that supports data arrays with masks.
.. index::
single: masked arrays
.. toctree::
:maxdepth: 2
maske... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.classes.rst | Using the convenience classes
=============================
The convenience classes provided by the polynomial package are:
============ ================
Name Provides
============ ================
Polynomial Power series
Chebyshev Chebyshev series
Legendre Legendre series
Laguerre ... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/maskedarray.generic.rst | .. currentmodule:: numpy.ma
.. _maskedarray.generic:
.. module:: numpy.ma
The :mod:`numpy.ma` module
==========================
Rationale
---------
Masked arrays are arrays that may have missing or invalid entries.
The :mod:`numpy.ma` module provides a nearly work-alike replacement for numpy
that supports data arr... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.dtype.rst | .. _routines.dtype:
Data type routines
==================
.. currentmodule:: numpy
.. autosummary::
:toctree: generated/
can_cast
promote_types
min_scalar_type
result_type
common_type
Creating data types
-------------------
.. autosummary::
:toctree: generated/
dtype
rec.format_parser
... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.poly1d.rst | Poly1d
======
.. currentmodule:: numpy
Basics
------
.. autosummary::
:toctree: generated/
poly1d
polyval
poly
roots
Fitting
-------
.. autosummary::
:toctree: generated/
polyfit
Calculus
--------
.. autosummary::
:toctree: generated/
polyder
polyint
Arithmetic
----------
.. autosu... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.ctypeslib.rst | .. module:: numpy.ctypeslib
**********************************************************
ctypes foreign function interface (:mod:`numpy.ctypeslib`)
**********************************************************
.. currentmodule:: numpy.ctypeslib
.. autofunction:: as_array
.. autofunction:: as_ctypes
.. autofunction:: as_c... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.laguerre.rst | .. versionadded:: 1.6.0
.. automodule:: numpy.polynomial.laguerre
:no-members:
:no-inherited-members:
:no-special-members:
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.array-creation.rst | .. _routines.array-creation:
Array creation routines
=======================
.. seealso:: :ref:`Array creation <arrays.creation>`
.. currentmodule:: numpy
From shape or value
-------------------
.. autosummary::
:toctree: generated/
empty
empty_like
eye
identity
ones
ones_like
zeros
zero... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/alignment.rst | :orphan:
****************
Memory alignment
****************
.. This document has been moved to ../dev/alignment.rst.
This document has been moved to :ref:`alignment`.
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.window.rst | Window functions
================
.. currentmodule:: numpy
Various windows
---------------
.. autosummary::
:toctree: generated/
bartlett
blackman
hamming
hanning
kaiser
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.statistics.rst | Statistics
==========
.. currentmodule:: numpy
Order statistics
----------------
.. autosummary::
:toctree: generated/
ptp
percentile
nanpercentile
quantile
nanquantile
Averages and variances
----------------------
.. autosummary::
:toctree: generated/
median
average
mean
std... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.linalg.rst | .. _routines.linalg:
.. module:: numpy.linalg
Linear algebra (:mod:`numpy.linalg`)
====================================
The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient
low level implementations of standard linear algebra algorithms. Those
libraries may be provided by NumPy itself usin... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.legendre.rst | .. versionadded:: 1.6.0
.. automodule:: numpy.polynomial.legendre
:no-members:
:no-inherited-members:
:no-special-members:
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.rst | .. _routines.polynomial:
Polynomials
===========
Polynomials in NumPy can be *created*, *manipulated*, and even *fitted* using
the :doc:`convenience classes <routines.polynomials.classes>`
of the `numpy.polynomial` package, introduced in NumPy 1.4.
Prior to NumPy 1.4, `numpy.poly1d` was the class of choice and it is... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.testing.rst | .. module:: numpy.testing
Test support (:mod:`numpy.testing`)
===================================
.. currentmodule:: numpy.testing
Common test support for all numpy test scripts.
This single module should provide all the common functionality for numpy
tests in a single location, so that :ref:`test scripts
<developm... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.logic.rst | Logic functions
===============
.. currentmodule:: numpy
Truth value testing
-------------------
.. autosummary::
:toctree: generated/
all
any
Array contents
--------------
.. autosummary::
:toctree: generated/
isfinite
isinf
isnan
isnat
isneginf
isposinf
Array type testing
---------... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.other.rst | Miscellaneous routines
======================
.. toctree::
.. currentmodule:: numpy
Performance tuning
------------------
.. autosummary::
:toctree: generated/
setbufsize
getbufsize
Memory ranges
-------------
.. autosummary::
:toctree: generated/
shares_memory
may_share_memory
lib.array_uti... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/index.rst | .. module:: numpy
.. _reference:
###############
NumPy reference
###############
:Release: |version|
:Date: |today|
This reference manual details functions, modules, and objects
included in NumPy, describing what they are and what they do.
For learning how to use NumPy, see the :ref:`complete documentation <numpy_d... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.emath.rst | Mathematical functions with automatic domain
********************************************
.. currentmodule:: numpy
.. note:: :mod:`numpy.emath` is a preferred alias for ``numpy.lib.scimath``,
available after :mod:`numpy` is imported.
.. automodule:: numpy.emath
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/distutils_status_migration.rst | .. _distutils-status-migration:
Status of ``numpy.distutils`` and migration advice
==================================================
`numpy.distutils` has been deprecated in NumPy ``1.23.0``. It will be removed
for Python 3.12; for Python <= 3.11 it will not be removed until 2 years after
the Python 3.12 release (Oc... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.package.rst | :orphan:
.. automodule:: numpy.polynomial
:no-members:
:no-inherited-members:
:no-special-members:
Configuration
-------------
.. autosummary::
:toctree: generated/
numpy.polynomial.set_default_printstyle
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/internals.rst | :orphan:
***************
NumPy internals
***************
.. This document has been moved to ../dev/internals.rst.
This document has been moved to :ref:`numpy-internals`.
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.fft.rst | .. _routines.fft:
.. automodule:: numpy.fft
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/arrays.indexing.rst | .. _routines.indexing:
.. _arrays.indexing:
Indexing routines
=================
.. seealso:: :ref:`basics.indexing`
.. currentmodule:: numpy
Generating index arrays
-----------------------
.. autosummary::
:toctree: generated/
c_
r_
s_
nonzero
where
indices
ix_
ogrid
ravel_multi_index... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.hermite.rst | .. versionadded:: 1.6.0
.. automodule:: numpy.polynomial.hermite
:no-members:
:no-inherited-members:
:no-special-members:
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.io.rst | .. _routines.io:
Input and output
================
.. currentmodule:: numpy
NumPy binary files (npy, npz)
-----------------------------
.. autosummary::
:toctree: generated/
load
save
savez
savez_compressed
lib.npyio.NpzFile
The format of these binary file types is documented in
:py:mod:`numpy.li... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/distutils_guide.rst | .. _distutils-user-guide:
NumPy distutils - users guide
=============================
.. warning::
``numpy.distutils`` is deprecated, and will be removed for
Python >= 3.12. For more details, see :ref:`distutils-status-migration`
.. include:: ../../DISTUTILS.rst
:start-line: 6
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/security.rst | NumPy security
==============
Security issues can be reported privately as described in the project README
and when opening a `new issue on the issue tracker <https://github.com/numpy/numpy/issues/new/choose>`_.
The `Python security reporting guidelines <https://www.python.org/dev/security/>`_
are a good resource and ... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.set.rst | Set routines
============
.. currentmodule:: numpy
Making proper sets
------------------
.. autosummary::
:toctree: generated/
unique
Boolean operations
------------------
.. autosummary::
:toctree: generated/
in1d
intersect1d
isin
setdiff1d
setxor1d
union1d
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/arrays.ndarray.rst | .. currentmodule:: numpy
.. _arrays.ndarray:
******************************************
The N-dimensional array (:class:`ndarray`)
******************************************
An :class:`ndarray` is a (usually fixed-size) multidimensional
container of items of the same type and size. The number of dimensions
and items... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/arrays.nditer.cython.rst | Putting the inner loop in Cython
================================
Those who want really good performance out of their low level operations
should strongly consider directly using the iteration API provided
in C, but for those who are not comfortable with C or C++, Cython
is a good middle ground with reasonable perform... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.matlib.rst | .. module:: numpy.matlib
Matrix library (:mod:`numpy.matlib`)
************************************
.. currentmodule:: numpy
This module contains all functions in the :mod:`numpy` namespace, with
the following replacement functions that return :class:`matrices
<matrix>` instead of :class:`ndarrays <ndarray>`.
.. cur... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.polynomial.rst | .. versionadded:: 1.4.0
.. automodule:: numpy.polynomial.polynomial
:no-members:
:no-inherited-members:
:no-special-members:
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.help.rst | .. _routines.help:
NumPy-specific help functions
=============================
.. currentmodule:: numpy
.. autosummary::
:toctree: generated/
info
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.rst | .. _routines:
********
Routines
********
In this chapter routine docstrings are presented, grouped by functionality.
Many docstrings contain example code, which demonstrates basic usage
of the routine. The examples assume that NumPy is imported with::
>>> import numpy as np
A convenient way to execute examples is... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.math.rst | Mathematical functions
======================
.. currentmodule:: numpy
Trigonometric functions
-----------------------
.. autosummary::
:toctree: generated/
sin
cos
tan
arcsin
arccos
arctan
hypot
arctan2
degrees
radians
unwrap
deg2rad
rad2deg
Hyperbolic functions
----------... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/swig.testing.rst | Testing the numpy.i typemaps
============================
Introduction
------------
Writing tests for the ``numpy.i`` `SWIG <https://www.swig.org/>`_
interface file is a combinatorial headache. At present, 12 different
data types are supported, each with 74 different argument signatures,
for a total of 888 typemaps ... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/ufuncs.rst | .. sectionauthor:: adapted from "Guide to NumPy" by Travis E. Oliphant
.. currentmodule:: numpy
.. _ufuncs:
************************************
Universal functions (:class:`ufunc`)
************************************
.. seealso:: :ref:`ufuncs-basics`
A universal function (or :term:`ufunc` for short) is a functio... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/arrays.rst | .. _arrays:
*************
Array objects
*************
.. currentmodule:: numpy
NumPy provides an N-dimensional array type, the :ref:`ndarray
<arrays.ndarray>`, which describes a collection of "items" of the same
type. The items can be :ref:`indexed <arrays.indexing>` using for
example N integers.
All ndarrays are :... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.hermite_e.rst | .. versionadded:: 1.6.0
.. automodule:: numpy.polynomial.hermite_e
:no-members:
:no-inherited-members:
:no-special-members:
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.sort.rst | Sorting, searching, and counting
================================
.. currentmodule:: numpy
Sorting
-------
.. autosummary::
:toctree: generated/
sort
lexsort
argsort
ndarray.sort
sort_complex
partition
argpartition
Searching
---------
.. autosummary::
:toctree: generated/
argmax
na... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.polynomials.polyutils.rst | Polyutils
=========
.. automodule:: numpy.polynomial.polyutils
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/arrays.classes.rst | .. _arrays.classes:
#########################
Standard array subclasses
#########################
.. currentmodule:: numpy
.. for doctests
>>> np.random.seed(1)
.. note::
Subclassing a ``numpy.ndarray`` is possible but if your goal is to create
an array with *modified* behavior, as do dask arrays for di... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/arrays.nditer.rst | .. currentmodule:: numpy
.. _arrays.nditer:
*********************
Iterating over arrays
*********************
.. note::
Arrays support the iterator protocol and can be iterated over like Python
lists. See the :ref:`quickstart.indexing-slicing-and-iterating` section in
the Quickstart guide for basic usage a... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.ma.rst | .. _routines.ma:
Masked array operations
=======================
.. currentmodule:: numpy
Constants
---------
.. autosummary::
:toctree: generated/
ma.MaskType
Creation
--------
From existing data
~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated/
ma.masked_array
ma.array
ma.copy
m... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.functional.rst | Functional programming
**********************
.. currentmodule:: numpy
.. autosummary::
:toctree: generated/
apply_along_axis
apply_over_axes
vectorize
frompyfunc
piecewise
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/maskedarray.baseclass.rst | .. currentmodule:: numpy.ma
.. for doctests
>>> from numpy import ma
.. _numpy.ma.constants:
Constants of the :mod:`numpy.ma` module
=======================================
In addition to the :class:`MaskedArray` class, the :mod:`numpy.ma` module
defines several constants.
.. data:: masked
The :attr:`masked... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/constants.rst | .. currentmodule:: numpy
*********
Constants
*********
NumPy includes several constants:
.. data:: e
Euler's constant, base of natural logarithms, Napier's constant.
``e = 2.71828182845904523536028747135266249775724709369995...``
.. rubric:: See Also
exp : Exponential function
log : Natural l... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/arrays.interface.rst | .. index::
pair: array; interface
pair: array; protocol
.. _arrays.interface:
****************************
The array interface protocol
****************************
.. note::
This page describes the NumPy-specific API for accessing the contents of
a NumPy array from other C extensions. :pep:`3118` --
... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/swig.interface-file.rst | numpy.i: a SWIG interface file for NumPy
========================================
Introduction
------------
The Simple Wrapper and Interface Generator (or `SWIG
<https://www.swig.org>`_) is a powerful tool for generating wrapper
code for interfacing to a wide variety of scripting languages.
`SWIG`_ can parse header f... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/arrays.datetime.rst | .. currentmodule:: numpy
.. _arrays.datetime:
************************
Datetimes and timedeltas
************************
.. versionadded:: 1.7.0
Starting in NumPy 1.7, there are core array data types which natively
support datetime functionality. The data type is called :class:`datetime64`,
so named because :class:... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/internals.code-explanations.rst | :orphan:
*************************
NumPy C code explanations
*************************
.. This document has been moved to ../dev/internals.code-explanations.rst.
This document has been moved to :ref:`c-code-explanations`. | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.array-manipulation.rst | Array manipulation routines
***************************
.. currentmodule:: numpy
Basic operations
================
.. autosummary::
:toctree: generated/
copyto
shape
Changing array shape
====================
.. autosummary::
:toctree: generated/
reshape
ravel
ndarray.flat
ndarray.flatten... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/reference/routines.testing.overrides.rst | .. module:: numpy.testing.overrides
Support for testing overrides (:mod:`numpy.testing.overrides`)
==============================================================
.. currentmodule:: numpy.testing.overrides
Support for testing custom array container implementations.
Utility functions
-----------------
.. autosummary:... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/c-api.rst | C API for random
----------------
.. currentmodule:: numpy.random
.. versionadded:: 1.19.0
Access to various distributions below is available via Cython or C-wrapper
libraries like CFFI. All the functions accept a :c:type:`bitgen_t` as their
first argument. To access these from Cython or C, you must link with the
`... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/upgrading-pcg64.rst | .. _upgrading-pcg64:
.. currentmodule:: numpy.random
Upgrading ``PCG64`` with ``PCG64DXSM``
======================================
Uses of the `PCG64` `BitGenerator` in a massively-parallel context have been
shown to have statistical weaknesses that were not apparent at the first
release in numpy 1.17. Most users wi... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/legacy.rst | .. currentmodule:: numpy.random
.. _legacy:
Legacy random generation
------------------------
The `RandomState` provides access to
legacy generators. This generator is considered frozen and will have
no further improvements. It is guaranteed to produce the same values
as the final point release of NumPy v1.16. These... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/performance.py | from timeit import repeat
import pandas as pd
import numpy as np
from numpy.random import MT19937, PCG64, PCG64DXSM, Philox, SFC64
PRNGS = [MT19937, PCG64, PCG64DXSM, Philox, SFC64]
funcs = {}
integers = 'integers(0, 2**{bits},size=1000000, dtype="uint{bits}")'
funcs['32-bit Unsigned Ints'] = integers.format(bits=3... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/extending.rst | .. currentmodule:: numpy.random
.. _extending:
Extending
=========
The BitGenerators have been designed to be extendable using standard tools for
high-performance Python -- numba and Cython. The `~Generator` object can also
be used with user-provided BitGenerators as long as these export a small set of
required func... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/multithreading.rst | Multithreaded generation
========================
The four core distributions (:meth:`~.Generator.random`,
:meth:`~.Generator.standard_normal`, :meth:`~.Generator.standard_exponential`,
and :meth:`~.Generator.standard_gamma`) all allow existing arrays to be filled
using the ``out`` keyword argument. Existing arrays ne... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/index.rst | .. _numpyrandom:
.. py:module:: numpy.random
.. currentmodule:: numpy.random
Random sampling (:mod:`numpy.random`)
=====================================
.. _random-quick-start:
Quick start
-----------
The :mod:`numpy.random` module implements pseudo-random number generators
(PRNGs or RNGs, for short) with the abi... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/performance.rst | Performance
===========
.. currentmodule:: numpy.random
Recommendation
--------------
The recommended generator for general use is `PCG64` or its upgraded variant
`PCG64DXSM` for heavily-parallel use cases. They are statistically high quality,
full-featured, and fast on most platforms, but somewhat slow when compile... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/parallel.rst | Parallel random number generation
=================================
There are four main strategies implemented that can be used to produce
repeatable pseudo-random numbers across multiple processes (local
or distributed).
.. currentmodule:: numpy.random
.. _seedsequence-spawn:
`~SeedSequence` spawning
-------------... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/generator.rst | .. currentmodule:: numpy.random
Random ``Generator``
====================
The `~Generator` provides access to
a wide range of distributions, and served as a replacement for
:class:`~numpy.random.RandomState`. The main difference between
the two is that ``Generator`` relies on an additional BitGenerator to
manage stat... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/compatibility.rst | .. _random-compatibility:
.. currentmodule:: numpy.random
Compatibility policy
====================
`numpy.random` has a somewhat stricter compatibility policy than the rest of
NumPy. Users of pseudorandomness often have use cases for being able to
reproduce runs in fine detail given the same seed (so-called "stream... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/random/new-or-different.rst | .. _new-or-different:
.. currentmodule:: numpy.random
What's new or different
-----------------------
NumPy 1.17.0 introduced `Generator` as an improved replacement for
the :ref:`legacy <legacy>` `RandomState`. Here is a quick comparison of the two
implementations.
================== ==================== ==========... | 0 |
public_repos/numpy/doc/source/reference/random | public_repos/numpy/doc/source/reference/random/bit_generators/sfc64.rst | SFC64 Small Fast Chaotic PRNG
=============================
.. currentmodule:: numpy.random
.. autoclass:: SFC64
:members: __init__
:exclude-members: __init__
State
-----
.. autosummary::
:toctree: generated/
~SFC64.state
Extending
---------
.. autosummary::
:toctree: generated/
~SFC64.cffi
... | 0 |
public_repos/numpy/doc/source/reference/random | public_repos/numpy/doc/source/reference/random/bit_generators/pcg64.rst | Permuted congruential generator (64-bit, PCG64)
===============================================
.. currentmodule:: numpy.random
.. autoclass:: PCG64
:members: __init__
:exclude-members: __init__
State
-----
.. autosummary::
:toctree: generated/
~PCG64.state
Parallel generation
-------------------
..... | 0 |
public_repos/numpy/doc/source/reference/random | public_repos/numpy/doc/source/reference/random/bit_generators/philox.rst | Philox counter-based RNG
========================
.. currentmodule:: numpy.random
.. autoclass:: Philox
:members: __init__
:exclude-members: __init__
State
-----
.. autosummary::
:toctree: generated/
~Philox.state
Parallel generation
-------------------
.. autosummary::
:toctree: generated/
~... | 0 |
public_repos/numpy/doc/source/reference/random | public_repos/numpy/doc/source/reference/random/bit_generators/index.rst | .. currentmodule:: numpy.random
.. _random-bit-generators:
Bit generators
==============
The random values produced by :class:`~Generator`
originate in a BitGenerator. The BitGenerators do not directly provide
random numbers and only contains methods used for seeding, getting or
setting the state, jumping or advanc... | 0 |
public_repos/numpy/doc/source/reference/random | public_repos/numpy/doc/source/reference/random/bit_generators/mt19937.rst | Mersenne Twister (MT19937)
==========================
.. currentmodule:: numpy.random
.. autoclass:: MT19937
:members: __init__
:exclude-members: __init__
State
-----
.. autosummary::
:toctree: generated/
~MT19937.state
Parallel generation
-------------------
.. autosummary::
:toctree: generated/... | 0 |
public_repos/numpy/doc/source/reference/random | public_repos/numpy/doc/source/reference/random/bit_generators/pcg64dxsm.rst | Permuted congruential generator (64-bit, PCG64 DXSM)
====================================================
.. currentmodule:: numpy.random
.. autoclass:: PCG64DXSM
:members: __init__
:exclude-members: __init__
State
-----
.. autosummary::
:toctree: generated/
~PCG64DXSM.state
Parallel generation
----... | 0 |
public_repos/numpy/doc/source/reference/random | public_repos/numpy/doc/source/reference/random/examples/numba_cffi.rst | Extending via Numba and CFFI
----------------------------
.. literalinclude:: ../../../../../numpy/random/_examples/numba/extending_distributions.py
:language: python
| 0 |
public_repos/numpy/doc/source/reference/random | public_repos/numpy/doc/source/reference/random/examples/cffi.rst | Extending via CFFI
------------------
.. literalinclude:: ../../../../../numpy/random/_examples/cffi/extending.py
:language: python
| 0 |
public_repos/numpy/doc/source/reference/random | public_repos/numpy/doc/source/reference/random/examples/numba.rst | Extending via Numba
-------------------
.. literalinclude:: ../../../../../numpy/random/_examples/numba/extending.py
:language: python
| 0 |
public_repos/numpy/doc/source/reference/random/examples | public_repos/numpy/doc/source/reference/random/examples/cython/extending.pyx.rst | extending.pyx
-------------
.. literalinclude:: ../../../../../../numpy/random/_examples/cython/extending.pyx
:language: cython
| 0 |
public_repos/numpy/doc/source/reference/random/examples | public_repos/numpy/doc/source/reference/random/examples/cython/extending.pyx | extending.pyx
-------------
.. include:: ../../../../../../numpy/random/examples/extending.pyx
| 0 |
public_repos/numpy/doc/source/reference/random/examples | public_repos/numpy/doc/source/reference/random/examples/cython/index.rst |
.. _extending_cython_example:
Extending `numpy.random` via Cython
-----------------------------------
.. _note:
Starting with NumPy 1.26.0, Meson is the default build system for NumPy.
See :ref:`distutils-status-migration`.
.. toctree::
meson.build.rst
extending.pyx
extending_distributions.pyx
| 0 |
public_repos/numpy/doc/source/reference/random/examples | public_repos/numpy/doc/source/reference/random/examples/cython/meson.build.rst | meson.build
-----------
.. literalinclude:: ../../../../../../numpy/random/_examples/cython/meson.build
:language: python
| 0 |
public_repos/numpy/doc/source/reference/random/examples | public_repos/numpy/doc/source/reference/random/examples/cython/extending_distributions.pyx.rst | extending_distributions.pyx
---------------------------
.. literalinclude:: ../../../../../../numpy/random/_examples/cython/extending_distributions.pyx
:language: cython
| 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/figures/threefundamental.fig | #FIG 3.2
Landscape
Center
Inches
Letter
100.00
Single
-2
1200 2
6 1950 2850 4350 3450
2 2 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 5
1950 2850 4350 2850 4350 3450 1950 3450 1950 2850
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
2550 2850 2550 3450
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
3150 2850 3150 3450
2 1 0 1 0 7 50 -... | 0 |
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