File Content: brain_numpy_core_multiarray.py # Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html # For details: https://github.com/PyCQA/astroid/blob/main/LICENSE # Copyright (c) https://github.com/PyCQA/astroid/blob/main/CONTRIBUTORS.txt """Astroid hooks for numpy.core.multiarray module.""" import functools from astroid.brain.brain_numpy_utils import infer_numpy_member, looks_like_numpy_member from astroid.brain.helpers import register_module_extender from astroid.builder import parse from astroid.inference_tip import inference_tip from astroid.manager import AstroidManager from astroid.nodes.node_classes import Attribute, Name def numpy_core_multiarray_transform(): return parse( """ # different functions defined in multiarray.py def inner(a, b): return numpy.ndarray([0, 0]) def vdot(a, b): return numpy.ndarray([0, 0]) """ ) register_module_extender( AstroidManager(), "numpy.core.multiarray", numpy_core_multiarray_transform ) METHODS_TO_BE_INFERRED = { "array": """def array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0): return numpy.ndarray([0, 0])""", "dot": """def dot(a, b, out=None): return numpy.ndarray([0, 0])""", "empty_like": """def empty_like(a, dtype=None, order='K', subok=True): return numpy.ndarray((0, 0))""", "concatenate": """def concatenate(arrays, axis=None, out=None): return numpy.ndarray((0, 0))""", "where": """def where(condition, x=None, y=None): return numpy.ndarray([0, 0])""", "empty": """def empty(shape, dtype=float, order='C'): return numpy.ndarray([0, 0])""", "bincount": """def bincount(x, weights=None, minlength=0): return numpy.ndarray([0, 0])""", "busday_count": """def busday_count( begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None ): return numpy.ndarray([0, 0])""", "busday_offset": """def busday_offset( dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None ): return numpy.ndarray([0, 0])""", "can_cast": """def can_cast(from_, to, casting='safe'): return True""", "copyto": """def copyto(dst, src, casting='same_kind', where=True): return None""", "datetime_as_string": """def datetime_as_string(arr, unit=None, timezone='naive', casting='same_kind'): return numpy.ndarray([0, 0])""", "is_busday": """def is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None): return numpy.ndarray([0, 0])""", "lexsort": """def lexsort(keys, axis=-1): return numpy.ndarray([0, 0])""", "may_share_memory": """def may_share_memory(a, b, max_work=None): return True""", # Not yet available because dtype is not yet present in those brains # "min_scalar_type": """def min_scalar_type(a): # return numpy.dtype('int16')""", "packbits": """def packbits(a, axis=None, bitorder='big'): return numpy.ndarray([0, 0])""", # Not yet available because dtype is not yet present in those brains # "result_type": """def result_type(*arrays_and_dtypes): # return numpy.dtype('int16')""", "shares_memory": """def shares_memory(a, b, max_work=None): return True""", "unpackbits": """def unpackbits(a, axis=None, count=None, bitorder='big'): return numpy.ndarray([0, 0])""", "unravel_index": """def unravel_index(indices, shape, order='C'): return (numpy.ndarray([0, 0]),)""", "zeros": """def zeros(shape, dtype=float, order='C'): return numpy.ndarray([0, 0])""", } for method_name, function_src in METHODS_TO_BE_INFERRED.items(): inference_function = functools.partial(infer_numpy_member, function_src) AstroidManager().register_transform( Attribute, inference_tip(inference_function), functools.partial(looks_like_numpy_member, method_name), ) AstroidManager().register_transform( Name, inference_tip(inference_function), functools.partial(looks_like_numpy_member, method_name), ) Submit