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