Current File : /opt/cloudlinux/venv/lib/python3.11/site-packages/astroid/brain//brain_numpy_ndarray.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 ndarray class."""
from __future__ import annotations
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
from astroid.nodes.node_classes import Attribute
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) -> bool:
return isinstance(node, Attribute) and node.attrname == "ndarray"
AstroidManager().register_transform(
Attribute,
inference_tip(infer_numpy_ndarray),
_looks_like_numpy_ndarray,
)
