181 lines
5.6 KiB
Python
181 lines
5.6 KiB
Python
"""
|
|
Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
|
|
|
|
Please note that this module is private. All functions and objects
|
|
are available in the main ``numpy`` namespace - use that instead.
|
|
|
|
"""
|
|
|
|
import os
|
|
import warnings
|
|
|
|
from numpy.version import version as __version__
|
|
|
|
|
|
# disables OpenBLAS affinity setting of the main thread that limits
|
|
# python threads or processes to one core
|
|
env_added = []
|
|
for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
|
|
if envkey not in os.environ:
|
|
os.environ[envkey] = '1'
|
|
env_added.append(envkey)
|
|
|
|
try:
|
|
from . import multiarray
|
|
except ImportError as exc:
|
|
import sys
|
|
msg = """
|
|
|
|
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
|
|
|
|
Importing the numpy C-extensions failed. This error can happen for
|
|
many reasons, often due to issues with your setup or how NumPy was
|
|
installed.
|
|
|
|
We have compiled some common reasons and troubleshooting tips at:
|
|
|
|
https://numpy.org/devdocs/user/troubleshooting-importerror.html
|
|
|
|
Please note and check the following:
|
|
|
|
* The Python version is: Python%d.%d from "%s"
|
|
* The NumPy version is: "%s"
|
|
|
|
and make sure that they are the versions you expect.
|
|
Please carefully study the documentation linked above for further help.
|
|
|
|
Original error was: %s
|
|
""" % (sys.version_info[0], sys.version_info[1], sys.executable,
|
|
__version__, exc)
|
|
raise ImportError(msg)
|
|
finally:
|
|
for envkey in env_added:
|
|
del os.environ[envkey]
|
|
del envkey
|
|
del env_added
|
|
del os
|
|
|
|
from . import umath
|
|
|
|
# Check that multiarray,umath are pure python modules wrapping
|
|
# _multiarray_umath and not either of the old c-extension modules
|
|
if not (hasattr(multiarray, '_multiarray_umath') and
|
|
hasattr(umath, '_multiarray_umath')):
|
|
import sys
|
|
path = sys.modules['numpy'].__path__
|
|
msg = ("Something is wrong with the numpy installation. "
|
|
"While importing we detected an older version of "
|
|
"numpy in {}. One method of fixing this is to repeatedly uninstall "
|
|
"numpy until none is found, then reinstall this version.")
|
|
raise ImportError(msg.format(path))
|
|
|
|
from . import numerictypes as nt
|
|
multiarray.set_typeDict(nt.sctypeDict)
|
|
from . import numeric
|
|
from .numeric import *
|
|
from . import fromnumeric
|
|
from .fromnumeric import *
|
|
from . import defchararray as char
|
|
from . import records
|
|
from . import records as rec
|
|
from .records import record, recarray, format_parser
|
|
# Note: module name memmap is overwritten by a class with same name
|
|
from .memmap import *
|
|
from .defchararray import chararray
|
|
from . import function_base
|
|
from .function_base import *
|
|
from . import _machar
|
|
from . import getlimits
|
|
from .getlimits import *
|
|
from . import shape_base
|
|
from .shape_base import *
|
|
from . import einsumfunc
|
|
from .einsumfunc import *
|
|
del nt
|
|
|
|
from .numeric import absolute as abs
|
|
|
|
# do this after everything else, to minimize the chance of this misleadingly
|
|
# appearing in an import-time traceback
|
|
from . import _add_newdocs
|
|
from . import _add_newdocs_scalars
|
|
# add these for module-freeze analysis (like PyInstaller)
|
|
from . import _dtype_ctypes
|
|
from . import _internal
|
|
from . import _dtype
|
|
from . import _methods
|
|
|
|
__all__ = ['char', 'rec', 'memmap']
|
|
__all__ += numeric.__all__
|
|
__all__ += ['record', 'recarray', 'format_parser']
|
|
__all__ += ['chararray']
|
|
__all__ += function_base.__all__
|
|
__all__ += getlimits.__all__
|
|
__all__ += shape_base.__all__
|
|
__all__ += einsumfunc.__all__
|
|
|
|
# We used to use `np.core._ufunc_reconstruct` to unpickle. This is unnecessary,
|
|
# but old pickles saved before 1.20 will be using it, and there is no reason
|
|
# to break loading them.
|
|
def _ufunc_reconstruct(module, name):
|
|
# The `fromlist` kwarg is required to ensure that `mod` points to the
|
|
# inner-most module rather than the parent package when module name is
|
|
# nested. This makes it possible to pickle non-toplevel ufuncs such as
|
|
# scipy.special.expit for instance.
|
|
mod = __import__(module, fromlist=[name])
|
|
return getattr(mod, name)
|
|
|
|
|
|
def _ufunc_reduce(func):
|
|
# Report the `__name__`. pickle will try to find the module. Note that
|
|
# pickle supports for this `__name__` to be a `__qualname__`. It may
|
|
# make sense to add a `__qualname__` to ufuncs, to allow this more
|
|
# explicitly (Numba has ufuncs as attributes).
|
|
# See also: https://github.com/dask/distributed/issues/3450
|
|
return func.__name__
|
|
|
|
|
|
def _DType_reconstruct(scalar_type):
|
|
# This is a work-around to pickle type(np.dtype(np.float64)), etc.
|
|
# and it should eventually be replaced with a better solution, e.g. when
|
|
# DTypes become HeapTypes.
|
|
return type(dtype(scalar_type))
|
|
|
|
|
|
def _DType_reduce(DType):
|
|
# As types/classes, most DTypes can simply be pickled by their name:
|
|
if not DType._legacy or DType.__module__ == "numpy.dtypes":
|
|
return DType.__name__
|
|
|
|
# However, user defined legacy dtypes (like rational) do not end up in
|
|
# `numpy.dtypes` as module and do not have a public class at all.
|
|
# For these, we pickle them by reconstructing them from the scalar type:
|
|
scalar_type = DType.type
|
|
return _DType_reconstruct, (scalar_type,)
|
|
|
|
|
|
def __getattr__(name):
|
|
# Deprecated 2022-11-22, NumPy 1.25.
|
|
if name == "MachAr":
|
|
warnings.warn(
|
|
"The `np.core.MachAr` is considered private API (NumPy 1.24)",
|
|
DeprecationWarning, stacklevel=2,
|
|
)
|
|
return _machar.MachAr
|
|
raise AttributeError(f"Module {__name__!r} has no attribute {name!r}")
|
|
|
|
|
|
import copyreg
|
|
|
|
copyreg.pickle(ufunc, _ufunc_reduce)
|
|
copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
|
|
|
|
# Unclutter namespace (must keep _*_reconstruct for unpickling)
|
|
del copyreg
|
|
del _ufunc_reduce
|
|
del _DType_reduce
|
|
|
|
from numpy._pytesttester import PytestTester
|
|
test = PytestTester(__name__)
|
|
del PytestTester
|