177 lines
5.5 KiB
Python
177 lines
5.5 KiB
Python
"""
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Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
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Please note that this module is private. All functions and objects
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are available in the main ``numpy`` namespace - use that instead.
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"""
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from numpy.version import version as __version__
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import os
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import warnings
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# disables OpenBLAS affinity setting of the main thread that limits
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# python threads or processes to one core
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env_added = []
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for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
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if envkey not in os.environ:
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os.environ[envkey] = '1'
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env_added.append(envkey)
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try:
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from . import multiarray
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except ImportError as exc:
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import sys
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msg = """
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IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
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Importing the numpy C-extensions failed. This error can happen for
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many reasons, often due to issues with your setup or how NumPy was
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installed.
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We have compiled some common reasons and troubleshooting tips at:
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https://numpy.org/devdocs/user/troubleshooting-importerror.html
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Please note and check the following:
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* The Python version is: Python%d.%d from "%s"
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* The NumPy version is: "%s"
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and make sure that they are the versions you expect.
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Please carefully study the documentation linked above for further help.
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Original error was: %s
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""" % (sys.version_info[0], sys.version_info[1], sys.executable,
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__version__, exc)
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raise ImportError(msg)
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finally:
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for envkey in env_added:
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del os.environ[envkey]
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del envkey
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del env_added
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del os
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from . import umath
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# Check that multiarray,umath are pure python modules wrapping
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# _multiarray_umath and not either of the old c-extension modules
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if not (hasattr(multiarray, '_multiarray_umath') and
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hasattr(umath, '_multiarray_umath')):
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import sys
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path = sys.modules['numpy'].__path__
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msg = ("Something is wrong with the numpy installation. "
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"While importing we detected an older version of "
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"numpy in {}. One method of fixing this is to repeatedly uninstall "
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"numpy until none is found, then reinstall this version.")
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raise ImportError(msg.format(path))
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from . import numerictypes as nt
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multiarray.set_typeDict(nt.sctypeDict)
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from . import numeric
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from .numeric import *
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from . import fromnumeric
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from .fromnumeric import *
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from . import defchararray as char
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from . import records as rec
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from .records import record, recarray, format_parser
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from .memmap import *
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from .defchararray import chararray
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from . import function_base
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from .function_base import *
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from . import _machar
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from ._machar import *
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from . import getlimits
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from .getlimits import *
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from . import shape_base
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from .shape_base import *
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from . import einsumfunc
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from .einsumfunc import *
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del nt
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from .fromnumeric import amax as max, amin as min, round_ as round
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from .numeric import absolute as abs
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# do this after everything else, to minimize the chance of this misleadingly
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# appearing in an import-time traceback
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from . import _add_newdocs
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from . import _add_newdocs_scalars
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# add these for module-freeze analysis (like PyInstaller)
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from . import _dtype_ctypes
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from . import _internal
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from . import _dtype
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from . import _methods
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__all__ = ['char', 'rec', 'memmap']
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__all__ += numeric.__all__
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__all__ += ['record', 'recarray', 'format_parser']
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__all__ += ['chararray']
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__all__ += function_base.__all__
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__all__ += getlimits.__all__
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__all__ += shape_base.__all__
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__all__ += einsumfunc.__all__
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# We used to use `np.core._ufunc_reconstruct` to unpickle. This is unnecessary,
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# but old pickles saved before 1.20 will be using it, and there is no reason
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# to break loading them.
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def _ufunc_reconstruct(module, name):
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# The `fromlist` kwarg is required to ensure that `mod` points to the
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# inner-most module rather than the parent package when module name is
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# nested. This makes it possible to pickle non-toplevel ufuncs such as
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# scipy.special.expit for instance.
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mod = __import__(module, fromlist=[name])
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return getattr(mod, name)
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def _ufunc_reduce(func):
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# Report the `__name__`. pickle will try to find the module. Note that
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# pickle supports for this `__name__` to be a `__qualname__`. It may
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# make sense to add a `__qualname__` to ufuncs, to allow this more
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# explicitly (Numba has ufuncs as attributes).
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# See also: https://github.com/dask/distributed/issues/3450
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return func.__name__
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def _DType_reconstruct(scalar_type):
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# This is a work-around to pickle type(np.dtype(np.float64)), etc.
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# and it should eventually be replaced with a better solution, e.g. when
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# DTypes become HeapTypes.
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return type(dtype(scalar_type))
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def _DType_reduce(DType):
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# To pickle a DType without having to add top-level names, pickle the
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# scalar type for now (and assume that reconstruction will be possible).
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if DType is dtype:
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return "dtype" # must pickle `np.dtype` as a singleton.
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scalar_type = DType.type # pickle the scalar type for reconstruction
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return _DType_reconstruct, (scalar_type,)
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def __getattr__(name):
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# Deprecated 2021-10-20, NumPy 1.22
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if name == "machar":
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warnings.warn(
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"The `np.core.machar` module is deprecated (NumPy 1.22)",
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DeprecationWarning, stacklevel=2,
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)
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return _machar
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raise AttributeError(f"Module {__name__!r} has no attribute {name!r}")
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import copyreg
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copyreg.pickle(ufunc, _ufunc_reduce)
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copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
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# Unclutter namespace (must keep _*_reconstruct for unpickling)
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del copyreg
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del _ufunc_reduce
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del _DType_reduce
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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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