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""" 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. """ from numpy.version import version as __version__ import os import warnings # 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