420 lines
15 KiB
Python
420 lines
15 KiB
Python
|
"""
|
||
|
|
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://numpy.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
|
||
|
|
---------------------
|
||
|
|
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
|
||
|
|
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.
|
||
|
|
Note: `numpy.dual` is deprecated. Use the functions from NumPy or Scipy
|
||
|
|
directly instead of importing them from `numpy.dual`.
|
||
|
|
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.
|
||
|
|
|
||
|
|
"""
|
||
|
|
import sys
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
from ._globals import (
|
||
|
|
ModuleDeprecationWarning, VisibleDeprecationWarning,
|
||
|
|
_NoValue, _CopyMode
|
||
|
|
)
|
||
|
|
|
||
|
|
# 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 as e:
|
||
|
|
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 e
|
||
|
|
|
||
|
|
__all__ = ['ModuleDeprecationWarning',
|
||
|
|
'VisibleDeprecationWarning']
|
||
|
|
|
||
|
|
# mapping of {name: (value, deprecation_msg)}
|
||
|
|
__deprecated_attrs__ = {}
|
||
|
|
|
||
|
|
# Allow distributors to run custom init code
|
||
|
|
from . import _distributor_init
|
||
|
|
|
||
|
|
from . import core
|
||
|
|
from .core import *
|
||
|
|
from . import compat
|
||
|
|
from . import lib
|
||
|
|
# NOTE: to be revisited following future namespace cleanup.
|
||
|
|
# See gh-14454 and gh-15672 for discussion.
|
||
|
|
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 *
|
||
|
|
|
||
|
|
# Deprecations introduced in NumPy 1.20.0, 2020-06-06
|
||
|
|
import builtins as _builtins
|
||
|
|
|
||
|
|
_msg = (
|
||
|
|
"`np.{n}` is a deprecated alias for the builtin `{n}`. "
|
||
|
|
"To silence this warning, use `{n}` by itself. Doing this will not "
|
||
|
|
"modify any behavior and is safe. {extended_msg}\n"
|
||
|
|
"Deprecated in NumPy 1.20; for more details and guidance: "
|
||
|
|
"https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
|
||
|
|
|
||
|
|
_specific_msg = (
|
||
|
|
"If you specifically wanted the numpy scalar type, use `np.{}` here.")
|
||
|
|
|
||
|
|
_int_extended_msg = (
|
||
|
|
"When replacing `np.{}`, you may wish to use e.g. `np.int64` "
|
||
|
|
"or `np.int32` to specify the precision. If you wish to review "
|
||
|
|
"your current use, check the release note link for "
|
||
|
|
"additional information.")
|
||
|
|
|
||
|
|
_type_info = [
|
||
|
|
("object", ""), # The NumPy scalar only exists by name.
|
||
|
|
("bool", _specific_msg.format("bool_")),
|
||
|
|
("float", _specific_msg.format("float64")),
|
||
|
|
("complex", _specific_msg.format("complex128")),
|
||
|
|
("str", _specific_msg.format("str_")),
|
||
|
|
("int", _int_extended_msg.format("int"))]
|
||
|
|
|
||
|
|
__deprecated_attrs__.update({
|
||
|
|
n: (getattr(_builtins, n), _msg.format(n=n, extended_msg=extended_msg))
|
||
|
|
for n, extended_msg in _type_info
|
||
|
|
})
|
||
|
|
|
||
|
|
# Numpy 1.20.0, 2020-10-19
|
||
|
|
__deprecated_attrs__["typeDict"] = (
|
||
|
|
core.numerictypes.typeDict,
|
||
|
|
"`np.typeDict` is a deprecated alias for `np.sctypeDict`."
|
||
|
|
)
|
||
|
|
|
||
|
|
# NumPy 1.22, 2021-10-20
|
||
|
|
__deprecated_attrs__["MachAr"] = (
|
||
|
|
core._machar.MachAr,
|
||
|
|
"`np.MachAr` is deprecated (NumPy 1.22)."
|
||
|
|
)
|
||
|
|
|
||
|
|
_msg = (
|
||
|
|
"`np.{n}` is a deprecated alias for `np.compat.{n}`. "
|
||
|
|
"To silence this warning, use `np.compat.{n}` by itself. "
|
||
|
|
"In the likely event your code does not need to work on Python 2 "
|
||
|
|
"you can use the builtin `{n2}` for which `np.compat.{n}` is itself "
|
||
|
|
"an alias. Doing this will not modify any behaviour and is safe. "
|
||
|
|
"{extended_msg}\n"
|
||
|
|
"Deprecated in NumPy 1.20; for more details and guidance: "
|
||
|
|
"https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
|
||
|
|
|
||
|
|
__deprecated_attrs__["long"] = (
|
||
|
|
getattr(compat, "long"),
|
||
|
|
_msg.format(n="long", n2="int",
|
||
|
|
extended_msg=_int_extended_msg.format("long")))
|
||
|
|
|
||
|
|
__deprecated_attrs__["unicode"] = (
|
||
|
|
getattr(compat, "unicode"),
|
||
|
|
_msg.format(n="unicode", n2="str",
|
||
|
|
extended_msg=_specific_msg.format("str_")))
|
||
|
|
|
||
|
|
del _msg, _specific_msg, _int_extended_msg, _type_info, _builtins
|
||
|
|
|
||
|
|
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'])
|
||
|
|
|
||
|
|
# Remove one of the two occurrences of `issubdtype`, which is exposed as
|
||
|
|
# both `numpy.core.issubdtype` and `numpy.lib.issubdtype`.
|
||
|
|
__all__.remove('issubdtype')
|
||
|
|
|
||
|
|
# These are exported by np.core, but are replaced by the builtins below
|
||
|
|
# remove them to ensure that we don't end up with `np.long == np.int_`,
|
||
|
|
# which would be a breaking change.
|
||
|
|
del long, unicode
|
||
|
|
__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
|
||
|
|
|
||
|
|
# These names were removed in NumPy 1.20. For at least one release,
|
||
|
|
# attempts to access these names in the numpy namespace will trigger
|
||
|
|
# a warning, and calling the function will raise an exception.
|
||
|
|
_financial_names = ['fv', 'ipmt', 'irr', 'mirr', 'nper', 'npv', 'pmt',
|
||
|
|
'ppmt', 'pv', 'rate']
|
||
|
|
__expired_functions__ = {
|
||
|
|
name: (f'In accordance with NEP 32, the function {name} was removed '
|
||
|
|
'from NumPy version 1.20. A replacement for this function '
|
||
|
|
'is available in the numpy_financial library: '
|
||
|
|
'https://pypi.org/project/numpy-financial')
|
||
|
|
for name in _financial_names}
|
||
|
|
|
||
|
|
# 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'
|
||
|
|
|
||
|
|
def __getattr__(attr):
|
||
|
|
# Warn for expired attributes, and return a dummy function
|
||
|
|
# that always raises an exception.
|
||
|
|
try:
|
||
|
|
msg = __expired_functions__[attr]
|
||
|
|
except KeyError:
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
warnings.warn(msg, DeprecationWarning, stacklevel=2)
|
||
|
|
|
||
|
|
def _expired(*args, **kwds):
|
||
|
|
raise RuntimeError(msg)
|
||
|
|
|
||
|
|
return _expired
|
||
|
|
|
||
|
|
# Emit warnings for deprecated attributes
|
||
|
|
try:
|
||
|
|
val, msg = __deprecated_attrs__[attr]
|
||
|
|
except KeyError:
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
warnings.warn(msg, DeprecationWarning, stacklevel=2)
|
||
|
|
return val
|
||
|
|
|
||
|
|
# 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
|
||
|
|
if attr == 'testing':
|
||
|
|
import numpy.testing as testing
|
||
|
|
return testing
|
||
|
|
elif attr == 'Tester':
|
||
|
|
from .testing import Tester
|
||
|
|
return Tester
|
||
|
|
|
||
|
|
raise AttributeError("module {!r} has no attribute "
|
||
|
|
"{!r}".format(__name__, attr))
|
||
|
|
|
||
|
|
def __dir__():
|
||
|
|
return list(globals().keys() | {'Tester', 'testing'})
|
||
|
|
|
||
|
|
# 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__)) 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
|
||
|
|
|
||
|
|
import sys
|
||
|
|
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)
|
||
|
|
|
||
|
|
# 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()
|
||
|
|
|
||
|
|
# Tell PyInstaller where to find hook-numpy.py
|
||
|
|
def _pyinstaller_hooks_dir():
|
||
|
|
from pathlib import Path
|
||
|
|
return [str(Path(__file__).with_name("_pyinstaller").resolve())]
|
||
|
|
|
||
|
|
|
||
|
|
# get the version using versioneer
|
||
|
|
from .version import __version__, git_revision as __git_version__
|