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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>numpy — Matplotlib 3.3.3 documentation</title>

title="Search within Matplotlib 3.3.3 documentation"

href="../_static/opensearch.xml"/>

</head><body>

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<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px">Version 3.3.3</div>

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<li><a href="index.html">Module code</a><ul>

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<h1>Source code for numpy</h1><div class="highlight"><pre>

"""

NumPy

=====

Provides

1. An array object of arbitrary homogeneous items

2. Fast mathematical operations over arrays

3. Linear Algebra, Fourier Transforms, Random Number Generation

How to use the documentation

----------------------------

Documentation is available in two forms: docstrings provided

with the code, and a loose standing reference guide, available from

`the NumPy homepage <https://www.scipy.org>`_.

We recommend exploring the docstrings using

`IPython <https://ipython.org>`_, an advanced Python shell with

TAB-completion and introspection capabilities. See below for further

instructions.

The docstring examples assume that `numpy` has been imported as `np`::

>>> import numpy as np

Code snippets are indicated by three greater-than signs::

>>> x = 42

>>> x = x + 1

Use the built-in ``help`` function to view a function's docstring::

>>> help(np.sort)

... # doctest: +SKIP

For some objects, ``np.info(obj)`` may provide additional help. This is

particularly true if you see the line "Help on ufunc object:" at the top

of the help() page. Ufuncs are implemented in C, not Python, for speed.

The native Python help() does not know how to view their help, but our

np.info() function does.

To search for documents containing a keyword, do::

>>> np.lookfor('keyword')

... # doctest: +SKIP

General-purpose documents like a glossary and help on the basic concepts

of numpy are available under the ``doc`` sub-module::

>>> from numpy import doc

>>> help(doc)

... # doctest: +SKIP

Available subpackages

---------------------

doc

Topical documentation on broadcasting, indexing, etc.

lib

Basic functions used by several sub-packages.

random

Core Random Tools

linalg

Core Linear Algebra Tools

fft

Core FFT routines

polynomial

Polynomial tools

testing

NumPy testing tools

f2py

Fortran to Python Interface Generator.

distutils

Enhancements to distutils with support for

Fortran compilers support and more.

Utilities

---------

test

Run numpy unittests

show_config

Show numpy build configuration

dual

Overwrite certain functions with high-performance Scipy tools

matlib

Make everything matrices.

__version__

NumPy version string

Viewing documentation using IPython

-----------------------------------

Start IPython with the NumPy profile (``ipython -p numpy``), which will

import `numpy` under the alias `np`. Then, use the ``cpaste`` command to

paste examples into the shell. To see which functions are available in

`numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use

``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow

down the list. To view the docstring for a function, use

``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view

the source code).

Copies vs. in-place operation

-----------------------------

Most of the functions in `numpy` return a copy of the array argument

(e.g., `np.sort`). In-place versions of these functions are often

available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.

Exceptions to this rule are documented.

"""

from __future__ import division, absolute_import, print_function

import sys

import warnings

from ._globals import ModuleDeprecationWarning, VisibleDeprecationWarning

from ._globals import _NoValue

# We first need to detect if we're being called as part of the numpy setup

# procedure itself in a reliable manner.

try:

__NUMPY_SETUP__

except NameError:

__NUMPY_SETUP__ = False

if __NUMPY_SETUP__:

sys.stderr.write('Running from numpy source directory.\n')

else:

try:

from numpy.__config__ import show as show_config

except ImportError:

msg = """Error importing numpy: you should not try to import numpy from

its source directory; please exit the numpy source tree, and relaunch

your python interpreter from there."""

raise ImportError(msg)

from .version import git_revision as __git_revision__

from .version import version as __version__

__all__ = ['ModuleDeprecationWarning',

'VisibleDeprecationWarning']

# Allow distributors to run custom init code

from . import _distributor_init

from . import core

from .core import *

from . import compat

from . import lib

# FIXME: why have numpy.lib if everything is imported here??

from .lib import *

from . import linalg

from . import fft

from . import polynomial

from . import random

from . import ctypeslib

from . import ma

from . import matrixlib as _mat

from .matrixlib import *

from .compat import long

# Make these accessible from numpy name-space

# but not imported in from numpy import *

# TODO[gh-6103]: Deprecate these

if sys.version_info[0] >= 3:

from builtins import bool, int, float, complex, object, str

unicode = str

else:

from __builtin__ import bool, int, float, complex, object, unicode, str

from .core import round, abs, max, min

# now that numpy modules are imported, can initialize limits

core.getlimits._register_known_types()

__all__.extend(['__version__', 'show_config'])

__all__.extend(core.__all__)

__all__.extend(_mat.__all__)

__all__.extend(lib.__all__)

__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])

# These are added by `from .core import *` and `core.__all__`, but we

# overwrite them above with builtins we do _not_ want to export.

__all__.remove('long')

__all__.remove('unicode')

# Remove things that are in the numpy.lib but not in the numpy namespace

# Note that there is a test (numpy/tests/test_public_api.py:test_numpy_namespace)

# that prevents adding more things to the main namespace by accident.

# The list below will grow until the `from .lib import *` fixme above is

# taken care of

__all__.remove('Arrayterator')

del Arrayterator

# Filter out Cython harmless warnings

warnings.filterwarnings("ignore", message="numpy.dtype size changed")

# oldnumeric and numarray were removed in 1.9. In case some packages import

# but do not use them, we define them here for backward compatibility.

oldnumeric = 'removed'

numarray = 'removed'

if sys.version_info[:2] >= (3, 7):

# Importing Tester requires importing all of UnitTest which is not a

# cheap import Since it is mainly used in test suits, we lazy import it

# here to save on the order of 10 ms of import time for most users

#

# The previous way Tester was imported also had a side effect of adding

# the full `numpy.testing` namespace

#

# module level getattr is only supported in 3.7 onwards

# https://www.python.org/dev/peps/pep-0562/

def __getattr__(attr):

if attr == 'testing':

import numpy.testing as testing

return testing

elif attr == 'Tester':

from .testing import Tester

return Tester

else:

raise AttributeError("module {!r} has no attribute "

"{!r}".format(__name__, attr))

def __dir__():

return list(globals().keys()) + ['Tester', 'testing']

else:

# We don't actually use this ourselves anymore, but I'm not 100% sure that

# no-one else in the world is using it (though I hope not)

from .testing import Tester

# Pytest testing

from numpy._pytesttester import PytestTester

test = PytestTester(__name__)

del PytestTester

def _sanity_check():

"""

Quick sanity checks for common bugs caused by environment.

There are some cases e.g. with wrong BLAS ABI that cause wrong

results under specific runtime conditions that are not necessarily

achieved during test suite runs, and it is useful to catch those early.

See https://github.com/numpy/numpy/issues/8577 and other

similar bug reports.

"""

try:

x = ones(2, dtype=float32)

if not abs(x.dot(x) - 2.0) < 1e-5:

raise AssertionError()

except AssertionError:

msg = ("The current Numpy installation ({!r}) fails to "

"pass simple sanity checks. This can be caused for example "

"by incorrect BLAS library being linked in, or by mixing "

"package managers (pip, conda, apt, ...). Search closed "

"numpy issues for similar problems.")

raise RuntimeError(msg.format(__file__))

_sanity_check()

del _sanity_check

</pre></div>

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