Tipragot
628be439b8
Cela permet de ne pas avoir de problèmes de compatibilité car python est dans le git.
164 lines
8.9 KiB
Python
164 lines
8.9 KiB
Python
# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
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# For details: https://github.com/pylint-dev/astroid/blob/main/LICENSE
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# Copyright (c) https://github.com/pylint-dev/astroid/blob/main/CONTRIBUTORS.txt
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"""Astroid hooks for numpy ndarray class."""
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from __future__ import annotations
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from astroid.brain.brain_numpy_utils import numpy_supports_type_hints
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from astroid.builder import extract_node
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from astroid.context import InferenceContext
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from astroid.inference_tip import inference_tip
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from astroid.manager import AstroidManager
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from astroid.nodes.node_classes import Attribute
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def infer_numpy_ndarray(node, context: InferenceContext | None = None):
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ndarray = """
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class ndarray(object):
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def __init__(self, shape, dtype=float, buffer=None, offset=0,
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strides=None, order=None):
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self.T = numpy.ndarray([0, 0])
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self.base = None
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self.ctypes = None
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self.data = None
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self.dtype = None
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self.flags = None
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# Should be a numpy.flatiter instance but not available for now
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# Putting an array instead so that iteration and indexing are authorized
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self.flat = np.ndarray([0, 0])
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self.imag = np.ndarray([0, 0])
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self.itemsize = None
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self.nbytes = None
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self.ndim = None
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self.real = np.ndarray([0, 0])
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self.shape = numpy.ndarray([0, 0])
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self.size = None
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self.strides = None
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def __abs__(self): return numpy.ndarray([0, 0])
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def __add__(self, value): return numpy.ndarray([0, 0])
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def __and__(self, value): return numpy.ndarray([0, 0])
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def __array__(self, dtype=None): return numpy.ndarray([0, 0])
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def __array_wrap__(self, obj): return numpy.ndarray([0, 0])
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def __contains__(self, key): return True
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def __copy__(self): return numpy.ndarray([0, 0])
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def __deepcopy__(self, memo): return numpy.ndarray([0, 0])
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def __divmod__(self, value): return (numpy.ndarray([0, 0]), numpy.ndarray([0, 0]))
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def __eq__(self, value): return numpy.ndarray([0, 0])
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def __float__(self): return 0.
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def __floordiv__(self): return numpy.ndarray([0, 0])
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def __ge__(self, value): return numpy.ndarray([0, 0])
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def __getitem__(self, key): return uninferable
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def __gt__(self, value): return numpy.ndarray([0, 0])
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def __iadd__(self, value): return numpy.ndarray([0, 0])
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def __iand__(self, value): return numpy.ndarray([0, 0])
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def __ifloordiv__(self, value): return numpy.ndarray([0, 0])
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def __ilshift__(self, value): return numpy.ndarray([0, 0])
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def __imod__(self, value): return numpy.ndarray([0, 0])
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def __imul__(self, value): return numpy.ndarray([0, 0])
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def __int__(self): return 0
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def __invert__(self): return numpy.ndarray([0, 0])
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def __ior__(self, value): return numpy.ndarray([0, 0])
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def __ipow__(self, value): return numpy.ndarray([0, 0])
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def __irshift__(self, value): return numpy.ndarray([0, 0])
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def __isub__(self, value): return numpy.ndarray([0, 0])
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def __itruediv__(self, value): return numpy.ndarray([0, 0])
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def __ixor__(self, value): return numpy.ndarray([0, 0])
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def __le__(self, value): return numpy.ndarray([0, 0])
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def __len__(self): return 1
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def __lshift__(self, value): return numpy.ndarray([0, 0])
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def __lt__(self, value): return numpy.ndarray([0, 0])
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def __matmul__(self, value): return numpy.ndarray([0, 0])
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def __mod__(self, value): return numpy.ndarray([0, 0])
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def __mul__(self, value): return numpy.ndarray([0, 0])
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def __ne__(self, value): return numpy.ndarray([0, 0])
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def __neg__(self): return numpy.ndarray([0, 0])
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def __or__(self, value): return numpy.ndarray([0, 0])
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def __pos__(self): return numpy.ndarray([0, 0])
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def __pow__(self): return numpy.ndarray([0, 0])
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def __repr__(self): return str()
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def __rshift__(self): return numpy.ndarray([0, 0])
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def __setitem__(self, key, value): return uninferable
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def __str__(self): return str()
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def __sub__(self, value): return numpy.ndarray([0, 0])
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def __truediv__(self, value): return numpy.ndarray([0, 0])
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def __xor__(self, value): return numpy.ndarray([0, 0])
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def all(self, axis=None, out=None, keepdims=False): return np.ndarray([0, 0])
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def any(self, axis=None, out=None, keepdims=False): return np.ndarray([0, 0])
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def argmax(self, axis=None, out=None): return np.ndarray([0, 0])
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def argmin(self, axis=None, out=None): return np.ndarray([0, 0])
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def argpartition(self, kth, axis=-1, kind='introselect', order=None): return np.ndarray([0, 0])
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def argsort(self, axis=-1, kind='quicksort', order=None): return np.ndarray([0, 0])
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def astype(self, dtype, order='K', casting='unsafe', subok=True, copy=True): return np.ndarray([0, 0])
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def byteswap(self, inplace=False): return np.ndarray([0, 0])
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def choose(self, choices, out=None, mode='raise'): return np.ndarray([0, 0])
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def clip(self, min=None, max=None, out=None): return np.ndarray([0, 0])
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def compress(self, condition, axis=None, out=None): return np.ndarray([0, 0])
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def conj(self): return np.ndarray([0, 0])
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def conjugate(self): return np.ndarray([0, 0])
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def copy(self, order='C'): return np.ndarray([0, 0])
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def cumprod(self, axis=None, dtype=None, out=None): return np.ndarray([0, 0])
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def cumsum(self, axis=None, dtype=None, out=None): return np.ndarray([0, 0])
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def diagonal(self, offset=0, axis1=0, axis2=1): return np.ndarray([0, 0])
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def dot(self, b, out=None): return np.ndarray([0, 0])
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def dump(self, file): return None
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def dumps(self): return str()
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def fill(self, value): return None
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def flatten(self, order='C'): return np.ndarray([0, 0])
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def getfield(self, dtype, offset=0): return np.ndarray([0, 0])
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def item(self, *args): return uninferable
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def itemset(self, *args): return None
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def max(self, axis=None, out=None): return np.ndarray([0, 0])
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def mean(self, axis=None, dtype=None, out=None, keepdims=False): return np.ndarray([0, 0])
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def min(self, axis=None, out=None, keepdims=False): return np.ndarray([0, 0])
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def newbyteorder(self, new_order='S'): return np.ndarray([0, 0])
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def nonzero(self): return (1,)
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def partition(self, kth, axis=-1, kind='introselect', order=None): return None
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def prod(self, axis=None, dtype=None, out=None, keepdims=False): return np.ndarray([0, 0])
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def ptp(self, axis=None, out=None): return np.ndarray([0, 0])
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def put(self, indices, values, mode='raise'): return None
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def ravel(self, order='C'): return np.ndarray([0, 0])
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def repeat(self, repeats, axis=None): return np.ndarray([0, 0])
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def reshape(self, shape, order='C'): return np.ndarray([0, 0])
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def resize(self, new_shape, refcheck=True): return None
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def round(self, decimals=0, out=None): return np.ndarray([0, 0])
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def searchsorted(self, v, side='left', sorter=None): return np.ndarray([0, 0])
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def setfield(self, val, dtype, offset=0): return None
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def setflags(self, write=None, align=None, uic=None): return None
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def sort(self, axis=-1, kind='quicksort', order=None): return None
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def squeeze(self, axis=None): return np.ndarray([0, 0])
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def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False): return np.ndarray([0, 0])
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def sum(self, axis=None, dtype=None, out=None, keepdims=False): return np.ndarray([0, 0])
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def swapaxes(self, axis1, axis2): return np.ndarray([0, 0])
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def take(self, indices, axis=None, out=None, mode='raise'): return np.ndarray([0, 0])
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def tobytes(self, order='C'): return b''
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def tofile(self, fid, sep="", format="%s"): return None
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def tolist(self, ): return []
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def tostring(self, order='C'): return b''
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def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None): return np.ndarray([0, 0])
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def transpose(self, *axes): return np.ndarray([0, 0])
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def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False): return np.ndarray([0, 0])
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def view(self, dtype=None, type=None): return np.ndarray([0, 0])
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"""
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if numpy_supports_type_hints():
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ndarray += """
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@classmethod
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def __class_getitem__(cls, value):
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return cls
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"""
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node = extract_node(ndarray)
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return node.infer(context=context)
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def _looks_like_numpy_ndarray(node) -> bool:
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return isinstance(node, Attribute) and node.attrname == "ndarray"
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def register(manager: AstroidManager) -> None:
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manager.register_transform(
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Attribute,
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inference_tip(infer_numpy_ndarray),
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_looks_like_numpy_ndarray,
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)
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