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335 lines
12 KiB
335 lines
12 KiB
"""
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NumPy
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=====
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Provides
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1. An array object of arbitrary homogeneous items
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2. Fast mathematical operations over arrays
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3. Linear Algebra, Fourier Transforms, Random Number Generation
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How to use the documentation
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----------------------------
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Documentation is available in two forms: docstrings provided
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with the code, and a loose standing reference guide, available from
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`the NumPy homepage <https://www.scipy.org>`_.
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We recommend exploring the docstrings using
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`IPython <https://ipython.org>`_, an advanced Python shell with
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TAB-completion and introspection capabilities. See below for further
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instructions.
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The docstring examples assume that `numpy` has been imported as `np`::
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>>> import numpy as np
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Code snippets are indicated by three greater-than signs::
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>>> x = 42
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>>> x = x + 1
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Use the built-in ``help`` function to view a function's docstring::
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>>> help(np.sort)
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... # doctest: +SKIP
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For some objects, ``np.info(obj)`` may provide additional help. This is
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particularly true if you see the line "Help on ufunc object:" at the top
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of the help() page. Ufuncs are implemented in C, not Python, for speed.
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The native Python help() does not know how to view their help, but our
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np.info() function does.
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To search for documents containing a keyword, do::
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>>> np.lookfor('keyword')
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... # doctest: +SKIP
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General-purpose documents like a glossary and help on the basic concepts
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of numpy are available under the ``doc`` sub-module::
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>>> from numpy import doc
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>>> help(doc)
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... # doctest: +SKIP
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Available subpackages
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---------------------
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doc
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Topical documentation on broadcasting, indexing, etc.
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lib
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Basic functions used by several sub-packages.
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random
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Core Random Tools
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linalg
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Core Linear Algebra Tools
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fft
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Core FFT routines
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polynomial
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Polynomial tools
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testing
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NumPy testing tools
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f2py
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Fortran to Python Interface Generator.
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distutils
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Enhancements to distutils with support for
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Fortran compilers support and more.
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Utilities
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---------
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test
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Run numpy unittests
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show_config
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Show numpy build configuration
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dual
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Overwrite certain functions with high-performance Scipy tools
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matlib
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Make everything matrices.
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__version__
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NumPy version string
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Viewing documentation using IPython
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-----------------------------------
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Start IPython with the NumPy profile (``ipython -p numpy``), which will
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import `numpy` under the alias `np`. Then, use the ``cpaste`` command to
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paste examples into the shell. To see which functions are available in
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`numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
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``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
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down the list. To view the docstring for a function, use
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``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
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the source code).
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Copies vs. in-place operation
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-----------------------------
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Most of the functions in `numpy` return a copy of the array argument
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(e.g., `np.sort`). In-place versions of these functions are often
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available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
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Exceptions to this rule are documented.
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"""
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import sys
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import warnings
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from ._globals import ModuleDeprecationWarning, VisibleDeprecationWarning
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from ._globals import _NoValue
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# We first need to detect if we're being called as part of the numpy setup
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# procedure itself in a reliable manner.
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try:
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__NUMPY_SETUP__
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except NameError:
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__NUMPY_SETUP__ = False
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if __NUMPY_SETUP__:
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sys.stderr.write('Running from numpy source directory.\n')
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else:
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try:
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from numpy.__config__ import show as show_config
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except ImportError:
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msg = """Error importing numpy: you should not try to import numpy from
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its source directory; please exit the numpy source tree, and relaunch
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your python interpreter from there."""
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raise ImportError(msg)
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from .version import git_revision as __git_revision__
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from .version import version as __version__
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__all__ = ['ModuleDeprecationWarning',
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'VisibleDeprecationWarning']
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# Allow distributors to run custom init code
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from . import _distributor_init
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from . import core
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from .core import *
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from . import compat
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from . import lib
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# NOTE: to be revisited following future namespace cleanup.
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# See gh-14454 and gh-15672 for discussion.
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from .lib import *
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from . import linalg
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from . import fft
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from . import polynomial
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from . import random
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from . import ctypeslib
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from . import ma
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from . import matrixlib as _mat
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from .matrixlib import *
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# Make these accessible from numpy name-space
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# but not imported in from numpy import *
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# TODO[gh-6103]: Deprecate these
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from builtins import bool, int, float, complex, object, str
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from .compat import long, unicode
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from .core import round, abs, max, min
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# now that numpy modules are imported, can initialize limits
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core.getlimits._register_known_types()
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__all__.extend(['__version__', 'show_config'])
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__all__.extend(core.__all__)
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__all__.extend(_mat.__all__)
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__all__.extend(lib.__all__)
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__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
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# These are added by `from .core import *` and `core.__all__`, but we
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# overwrite them above with builtins we do _not_ want to export.
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__all__.remove('long')
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__all__.remove('unicode')
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# Remove things that are in the numpy.lib but not in the numpy namespace
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# Note that there is a test (numpy/tests/test_public_api.py:test_numpy_namespace)
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# that prevents adding more things to the main namespace by accident.
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# The list below will grow until the `from .lib import *` fixme above is
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# taken care of
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__all__.remove('Arrayterator')
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del Arrayterator
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# Filter out Cython harmless warnings
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warnings.filterwarnings("ignore", message="numpy.dtype size changed")
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warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
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warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
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# oldnumeric and numarray were removed in 1.9. In case some packages import
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# but do not use them, we define them here for backward compatibility.
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oldnumeric = 'removed'
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numarray = 'removed'
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if sys.version_info[:2] >= (3, 7):
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# Importing Tester requires importing all of UnitTest which is not a
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# cheap import Since it is mainly used in test suits, we lazy import it
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# here to save on the order of 10 ms of import time for most users
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#
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# The previous way Tester was imported also had a side effect of adding
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# the full `numpy.testing` namespace
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#
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# module level getattr is only supported in 3.7 onwards
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# https://www.python.org/dev/peps/pep-0562/
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def __getattr__(attr):
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if attr == 'testing':
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import numpy.testing as testing
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return testing
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elif attr == 'Tester':
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from .testing import Tester
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return Tester
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else:
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raise AttributeError("module {!r} has no attribute "
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"{!r}".format(__name__, attr))
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def __dir__():
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return list(globals().keys() | {'Tester', 'testing'})
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else:
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# We don't actually use this ourselves anymore, but I'm not 100% sure that
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# no-one else in the world is using it (though I hope not)
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from .testing import Tester
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# Pytest testing
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from numpy._pytesttester import PytestTester
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test = PytestTester(__name__)
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del PytestTester
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def _sanity_check():
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"""
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Quick sanity checks for common bugs caused by environment.
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There are some cases e.g. with wrong BLAS ABI that cause wrong
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results under specific runtime conditions that are not necessarily
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achieved during test suite runs, and it is useful to catch those early.
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See https://github.com/numpy/numpy/issues/8577 and other
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similar bug reports.
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"""
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try:
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x = ones(2, dtype=float32)
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if not abs(x.dot(x) - 2.0) < 1e-5:
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raise AssertionError()
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except AssertionError:
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msg = ("The current Numpy installation ({!r}) fails to "
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"pass simple sanity checks. This can be caused for example "
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"by incorrect BLAS library being linked in, or by mixing "
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"package managers (pip, conda, apt, ...). Search closed "
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"numpy issues for similar problems.")
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raise RuntimeError(msg.format(__file__))
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_sanity_check()
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del _sanity_check
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def _mac_os_check():
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"""
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Quick Sanity check for Mac OS look for accelerate build bugs.
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Testing numpy polyfit calls init_dgelsd(LAPACK)
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"""
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try:
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c = array([3., 2., 1.])
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x = linspace(0, 2, 5)
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y = polyval(c, x)
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_ = polyfit(x, y, 2, cov=True)
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except ValueError:
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pass
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import sys
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if sys.platform == "darwin":
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with warnings.catch_warnings(record=True) as w:
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_mac_os_check()
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# Throw runtime error, if the test failed Check for warning and error_message
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error_message = ""
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if len(w) > 0:
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error_message = "{}: {}".format(w[-1].category.__name__, str(w[-1].message))
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msg = (
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"Polyfit sanity test emitted a warning, most likely due "
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"to using a buggy Accelerate backend. "
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"If you compiled yourself, "
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"see site.cfg.example for information. "
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"Otherwise report this to the vendor "
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"that provided NumPy.\n{}\n".format(
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error_message))
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raise RuntimeError(msg)
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del _mac_os_check
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def _win_os_check():
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"""
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Quick Sanity check for Windows OS: look for fmod bug issue 16744.
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 """
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try:
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a = arange(13 * 13, dtype= float64).reshape(13, 13)
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a = a % 17 # calls fmod
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linalg.eig(a)
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except Exception:
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msg = ("The current Numpy installation ({!r}) fails to "
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"pass a sanity check due to a bug in the windows runtime. "
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"See this issue for more information: "
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"https://tinyurl.com/y3dm3h86")
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raise RuntimeError(msg.format(__file__)) from None
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if sys.platform == "win32" and sys.maxsize > 2**32:
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_win_os_check()
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del _win_os_check
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# We usually use madvise hugepages support, but on some old kernels it
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# is slow and thus better avoided.
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# Specifically kernel version 4.6 had a bug fix which probably fixed this:
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# https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
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import os
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use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
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if sys.platform == "linux" and use_hugepage is None:
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# If there is an issue with parsing the kernel version,
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# set use_hugepages to 0. Usage of LooseVersion will handle
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# the kernel version parsing better, but avoided since it
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# will increase the import time. See: #16679 for related discussion.
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try:
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use_hugepage = 1
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kernel_version = os.uname().release.split(".")[:2]
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kernel_version = tuple(int(v) for v in kernel_version)
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if kernel_version < (4, 6):
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use_hugepage = 0
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except ValueError:
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use_hugepages = 0
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elif use_hugepage is None:
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# This is not Linux, so it should not matter, just enable anyway
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use_hugepage = 1
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else:
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use_hugepage = int(use_hugepage)
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# Note that this will currently only make a difference on Linux
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core.multiarray._set_madvise_hugepage(use_hugepage)
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