numpy — pyFormex 1.0.5 documentation

# Source code for numpy

    """
    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 <http://www.scipy.org>`_.

    We recommend exploring the docstrings using
    `IPython <http://ipython.scipy.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__

        from ._import_tools import PackageLoader

        def pkgload(*packages, **options):
            loader = PackageLoader(infunc=True)
            return loader(*packages, **options)

        from . import add_newdocs
        __all__ = ['add_newdocs',
                   'ModuleDeprecationWarning',
                   'VisibleDeprecationWarning']

        pkgload.__doc__ = PackageLoader.__call__.__doc__

        # Allow distributors to run custom init code
        from . import _distributor_init

        from . import core
        from .core import *
        from . import compat
        from . import lib
        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 *
        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

        __all__.extend(['__version__', 'pkgload', 'PackageLoader',
                   'show_config'])
        __all__.extend(core.__all__)
        __all__.extend(_mat.__all__)
        __all__.extend(lib.__all__)
        __all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])

        # Filter out Cython harmless warnings
        warnings.filterwarnings("ignore", message="numpy.dtype size changed")
        warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
        warnings.filterwarnings("ignore", message="numpy.ndarray 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'

        # 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.testing._private.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.")
                raise RuntimeError(msg.format(__file__))

        _sanity_check()
        del _sanity_check