457 lines
16 KiB
Python
457 lines
16 KiB
Python
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"""
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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://numpy.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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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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distutils
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Enhancements to distutils with support for
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Fortran compilers support and more (for Python <= 3.11).
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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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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 and import `numpy` usually under the alias ``np``: `import
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numpy as np`. Then, directly past or use the ``%cpaste`` magic to paste
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examples into the shell. To see which functions are available in `numpy`,
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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 _NoValue, _CopyMode
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# These exceptions were moved in 1.25 and are hidden from __dir__()
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from .exceptions import (
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ComplexWarning, ModuleDeprecationWarning, VisibleDeprecationWarning,
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TooHardError, AxisError)
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# If a version with git hash was stored, use that instead
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from . import version
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from .version import __version__
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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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# Allow distributors to run custom init code before importing numpy.core
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from . import _distributor_init
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try:
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from numpy.__config__ import show as show_config
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except ImportError as e:
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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) from e
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__all__ = [
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'exceptions', 'ModuleDeprecationWarning', 'VisibleDeprecationWarning',
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'ComplexWarning', 'TooHardError', 'AxisError']
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# mapping of {name: (value, deprecation_msg)}
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__deprecated_attrs__ = {}
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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 exceptions
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from . import dtypes
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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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# Deprecations introduced in NumPy 1.20.0, 2020-06-06
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import builtins as _builtins
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_msg = (
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"module 'numpy' has no attribute '{n}'.\n"
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"`np.{n}` was a deprecated alias for the builtin `{n}`. "
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"To avoid this error in existing code, use `{n}` by itself. "
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"Doing this will not modify any behavior and is safe. {extended_msg}\n"
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"The aliases was originally deprecated in NumPy 1.20; for more "
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"details and guidance see the original release note at:\n"
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" https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
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_specific_msg = (
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"If you specifically wanted the numpy scalar type, use `np.{}` here.")
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_int_extended_msg = (
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"When replacing `np.{}`, you may wish to use e.g. `np.int64` "
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"or `np.int32` to specify the precision. If you wish to review "
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"your current use, check the release note link for "
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"additional information.")
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_type_info = [
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("object", ""), # The NumPy scalar only exists by name.
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("bool", _specific_msg.format("bool_")),
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("float", _specific_msg.format("float64")),
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("complex", _specific_msg.format("complex128")),
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("str", _specific_msg.format("str_")),
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("int", _int_extended_msg.format("int"))]
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__former_attrs__ = {
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n: _msg.format(n=n, extended_msg=extended_msg)
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for n, extended_msg in _type_info
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}
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# Future warning introduced in NumPy 1.24.0, 2022-11-17
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_msg = (
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"`np.{n}` is a deprecated alias for `{an}`. (Deprecated NumPy 1.24)")
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# Some of these are awkward (since `np.str` may be preferable in the long
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# term), but overall the names ending in 0 seem undesirable
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_type_info = [
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("bool8", bool_, "np.bool_"),
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("int0", intp, "np.intp"),
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("uint0", uintp, "np.uintp"),
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("str0", str_, "np.str_"),
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("bytes0", bytes_, "np.bytes_"),
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("void0", void, "np.void"),
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("object0", object_,
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"`np.object0` is a deprecated alias for `np.object_`. "
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"`object` can be used instead. (Deprecated NumPy 1.24)")]
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# Some of these could be defined right away, but most were aliases to
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# the Python objects and only removed in NumPy 1.24. Defining them should
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# probably wait for NumPy 1.26 or 2.0.
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# When defined, these should possibly not be added to `__all__` to avoid
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# import with `from numpy import *`.
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__future_scalars__ = {"bool", "long", "ulong", "str", "bytes", "object"}
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__deprecated_attrs__.update({
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n: (alias, _msg.format(n=n, an=an)) for n, alias, an in _type_info})
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import math
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__deprecated_attrs__['math'] = (math,
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"`np.math` is a deprecated alias for the standard library `math` "
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"module (Deprecated Numpy 1.25). Replace usages of `np.math` with "
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"`math`")
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del math, _msg, _type_info
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from .core import abs
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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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# Remove min and max from __all__ to avoid `from numpy import *` override
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# the builtins min/max. Temporary fix for 1.25.x/1.26.x, see gh-24229.
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__all__.remove('min')
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__all__.remove('max')
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__all__.remove('round')
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# Remove one of the two occurrences of `issubdtype`, which is exposed as
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# both `numpy.core.issubdtype` and `numpy.lib.issubdtype`.
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__all__.remove('issubdtype')
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# These are exported by np.core, but are replaced by the builtins below
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# remove them to ensure that we don't end up with `np.long == np.int_`,
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# which would be a breaking change.
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del long, unicode
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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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# These names were removed in NumPy 1.20. For at least one release,
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# attempts to access these names in the numpy namespace will trigger
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# a warning, and calling the function will raise an exception.
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_financial_names = ['fv', 'ipmt', 'irr', 'mirr', 'nper', 'npv', 'pmt',
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'ppmt', 'pv', 'rate']
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__expired_functions__ = {
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name: (f'In accordance with NEP 32, the function {name} was removed '
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'from NumPy version 1.20. A replacement for this function '
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'is available in the numpy_financial library: '
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'https://pypi.org/project/numpy-financial')
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for name in _financial_names}
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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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def __getattr__(attr):
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# Warn for expired attributes, and return a dummy function
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# that always raises an exception.
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import warnings
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import math
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try:
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msg = __expired_functions__[attr]
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except KeyError:
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pass
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else:
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warnings.warn(msg, DeprecationWarning, stacklevel=2)
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def _expired(*args, **kwds):
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raise RuntimeError(msg)
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return _expired
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# Emit warnings for deprecated attributes
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try:
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val, msg = __deprecated_attrs__[attr]
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except KeyError:
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pass
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else:
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warnings.warn(msg, DeprecationWarning, stacklevel=2)
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return val
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if attr in __future_scalars__:
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# And future warnings for those that will change, but also give
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# the AttributeError
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warnings.warn(
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f"In the future `np.{attr}` will be defined as the "
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"corresponding NumPy scalar.", FutureWarning, stacklevel=2)
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if attr in __former_attrs__:
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raise AttributeError(__former_attrs__[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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"Removed in NumPy 1.25.0"
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raise RuntimeError("Tester was removed in NumPy 1.25.")
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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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public_symbols = globals().keys() | {'testing'}
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public_symbols -= {
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"core", "matrixlib",
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# These were moved in 1.25 and may be deprecated eventually:
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"ModuleDeprecationWarning", "VisibleDeprecationWarning",
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"ComplexWarning", "TooHardError", "AxisError"
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}
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return list(public_symbols)
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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) - float32(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 "
|
||
|
|
"numpy issues for similar problems.")
|
||
|
|
raise RuntimeError(msg.format(__file__)) from None
|
||
|
|
|
||
|
|
_sanity_check()
|
||
|
|
del _sanity_check
|
||
|
|
|
||
|
|
def _mac_os_check():
|
||
|
|
"""
|
||
|
|
Quick Sanity check for Mac OS look for accelerate build bugs.
|
||
|
|
Testing numpy polyfit calls init_dgelsd(LAPACK)
|
||
|
|
"""
|
||
|
|
try:
|
||
|
|
c = array([3., 2., 1.])
|
||
|
|
x = linspace(0, 2, 5)
|
||
|
|
y = polyval(c, x)
|
||
|
|
_ = polyfit(x, y, 2, cov=True)
|
||
|
|
except ValueError:
|
||
|
|
pass
|
||
|
|
|
||
|
|
if sys.platform == "darwin":
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
_mac_os_check()
|
||
|
|
# Throw runtime error, if the test failed Check for warning and error_message
|
||
|
|
error_message = ""
|
||
|
|
if len(w) > 0:
|
||
|
|
error_message = "{}: {}".format(w[-1].category.__name__, str(w[-1].message))
|
||
|
|
msg = (
|
||
|
|
"Polyfit sanity test emitted a warning, most likely due "
|
||
|
|
"to using a buggy Accelerate backend."
|
||
|
|
"\nIf you compiled yourself, more information is available at:"
|
||
|
|
"\nhttps://numpy.org/doc/stable/user/building.html#accelerated-blas-lapack-libraries"
|
||
|
|
"\nOtherwise report this to the vendor "
|
||
|
|
"that provided NumPy.\n{}\n".format(error_message))
|
||
|
|
raise RuntimeError(msg)
|
||
|
|
del _mac_os_check
|
||
|
|
|
||
|
|
# We usually use madvise hugepages support, but on some old kernels it
|
||
|
|
# is slow and thus better avoided.
|
||
|
|
# Specifically kernel version 4.6 had a bug fix which probably fixed this:
|
||
|
|
# https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
|
||
|
|
import os
|
||
|
|
use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
|
||
|
|
if sys.platform == "linux" and use_hugepage is None:
|
||
|
|
# If there is an issue with parsing the kernel version,
|
||
|
|
# set use_hugepages to 0. Usage of LooseVersion will handle
|
||
|
|
# the kernel version parsing better, but avoided since it
|
||
|
|
# will increase the import time. See: #16679 for related discussion.
|
||
|
|
try:
|
||
|
|
use_hugepage = 1
|
||
|
|
kernel_version = os.uname().release.split(".")[:2]
|
||
|
|
kernel_version = tuple(int(v) for v in kernel_version)
|
||
|
|
if kernel_version < (4, 6):
|
||
|
|
use_hugepage = 0
|
||
|
|
except ValueError:
|
||
|
|
use_hugepages = 0
|
||
|
|
elif use_hugepage is None:
|
||
|
|
# This is not Linux, so it should not matter, just enable anyway
|
||
|
|
use_hugepage = 1
|
||
|
|
else:
|
||
|
|
use_hugepage = int(use_hugepage)
|
||
|
|
|
||
|
|
# Note that this will currently only make a difference on Linux
|
||
|
|
core.multiarray._set_madvise_hugepage(use_hugepage)
|
||
|
|
del use_hugepage
|
||
|
|
|
||
|
|
# Give a warning if NumPy is reloaded or imported on a sub-interpreter
|
||
|
|
# We do this from python, since the C-module may not be reloaded and
|
||
|
|
# it is tidier organized.
|
||
|
|
core.multiarray._multiarray_umath._reload_guard()
|
||
|
|
|
||
|
|
# default to "weak" promotion for "NumPy 2".
|
||
|
|
core._set_promotion_state(
|
||
|
|
os.environ.get("NPY_PROMOTION_STATE",
|
||
|
|
"weak" if _using_numpy2_behavior() else "legacy"))
|
||
|
|
|
||
|
|
# Tell PyInstaller where to find hook-numpy.py
|
||
|
|
def _pyinstaller_hooks_dir():
|
||
|
|
from pathlib import Path
|
||
|
|
return [str(Path(__file__).with_name("_pyinstaller").resolve())]
|
||
|
|
|
||
|
|
# Remove symbols imported for internal use
|
||
|
|
del os
|
||
|
|
|
||
|
|
|
||
|
|
# Remove symbols imported for internal use
|
||
|
|
del sys, warnings
|