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""" 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 matlib Make everything matrices. __version__ NumPy version string Viewing documentation using IPython ----------------------------------- Start IPython and import `numpy` usually under the alias ``np``: `import numpy as np`. Then, directly past or use the ``%cpaste`` magic 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 _NoValue, _CopyMode # These exceptions were moved in 1.25 and are hidden from __dir__() from .exceptions import ( ComplexWarning, ModuleDeprecationWarning, VisibleDeprecationWarning, TooHardError, AxisError) # 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: # Allow distributors to run custom init code before importing numpy.core from . import _distributor_init 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__ = [ 'exceptions', 'ModuleDeprecationWarning', 'VisibleDeprecationWarning', 'ComplexWarning', 'TooHardError', 'AxisError'] # mapping of {name: (value, deprecation_msg)} __deprecated_attrs__ = {} from . import core from .core import * from . import compat from . import exceptions from . import dtypes 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 = ( "module 'numpy' has no attribute '{n}'.\n" "`np.{n}` was a deprecated alias for the builtin `{n}`. " "To avoid this error in existing code, use `{n}` by itself. " "Doing this will not modify any behavior and is safe. {extended_msg}\n" "The aliases was originally deprecated in NumPy 1.20; for more " "details and guidance see the original release note at:\n" " 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"))] __former_attrs__ = { n: _msg.format(n=n, extended_msg=extended_msg) for n, extended_msg in _type_info } # Future warning introduced in NumPy 1.24.0, 2022-11-17 _msg = ( "`np.{n}` is a deprecated alias for `{an}`. (Deprecated NumPy 1.24)") # Some of these are awkward (since `np.str` may be preferable in the long # term), but overall the names ending in 0 seem undesirable _type_info = [ ("bool8", bool_, "np.bool_"), ("int0", intp, "np.intp"), ("uint0", uintp, "np.uintp"), ("str0", str_, "np.str_"), ("bytes0", bytes_, "np.bytes_"), ("void0", void, "np.void"), ("object0", object_, "`np.object0` is a deprecated alias for `np.object_`. " "`object` can be used instead. (Deprecated NumPy 1.24)")] # Some of these could be defined right away, but most were aliases to # the Python objects and only removed in NumPy 1.24. Defining them should # probably wait for NumPy 1.26 or 2.0. # When defined, these should possibly not be added to `__all__` to avoid # import with `from numpy import *`. __future_scalars__ = {"bool", "long", "ulong", "str", "bytes", "object"} __deprecated_attrs__.update({ n: (alias, _msg.format(n=n, an=an)) for n, alias, an in _type_info}) import math __deprecated_attrs__['math'] = (math, "`np.math` is a deprecated alias for the standard library `math` " "module (Deprecated Numpy 1.25). Replace usages of `np.math` with " "`math`") del math, _msg, _type_info from .core import abs # 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. import warnings import math 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 if attr in __future_scalars__: # And future warnings for those that will change, but also give # the AttributeError warnings.warn( f"In the future `np.{attr}` will be defined as the " "corresponding NumPy scalar.", FutureWarning, stacklevel=2) if attr in __former_attrs__: raise AttributeError(__former_attrs__[attr]) if attr == 'testing': import numpy.testing as testing return testing elif attr == 'Tester': "Removed in NumPy 1.25.0" raise RuntimeError("Tester was removed in NumPy 1.25.") raise AttributeError("module {!r} has no attribute " "{!r}".format(__name__, attr)) def __dir__(): public_symbols = globals().keys() | {'testing'} public_symbols -= { "core", "matrixlib", # These were moved in 1.25 and may be deprecated eventually: "ModuleDeprecationWarning", "VisibleDeprecationWarning", "ComplexWarning", "TooHardError", "AxisError" } return list(public_symbols) # 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) - float32(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 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 # get the version using versioneer from .version import __version__, git_revision as __git_version__ # Remove symbols imported for internal use del sys, warnings