diff options
Diffstat (limited to 'venv/Lib/site-packages/astroid/brain/brain_numpy_ndarray.py')
| -rw-r--r-- | venv/Lib/site-packages/astroid/brain/brain_numpy_ndarray.py | 153 |
1 files changed, 0 insertions, 153 deletions
diff --git a/venv/Lib/site-packages/astroid/brain/brain_numpy_ndarray.py b/venv/Lib/site-packages/astroid/brain/brain_numpy_ndarray.py deleted file mode 100644 index 8c231a3..0000000 --- a/venv/Lib/site-packages/astroid/brain/brain_numpy_ndarray.py +++ /dev/null @@ -1,153 +0,0 @@ -# Copyright (c) 2015-2016, 2018 Claudiu Popa <pcmanticore@gmail.com> -# Copyright (c) 2016 Ceridwen <ceridwenv@gmail.com> -# Copyright (c) 2017-2018 hippo91 <guillaume.peillex@gmail.com> - -# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html -# For details: https://github.com/PyCQA/astroid/blob/master/COPYING.LESSER - - -"""Astroid hooks for numpy ndarray class.""" - -import functools -import astroid - - -def infer_numpy_ndarray(node, context=None): - ndarray = """ - class ndarray(object): - def __init__(self, shape, dtype=float, buffer=None, offset=0, - strides=None, order=None): - self.T = None - self.base = None - self.ctypes = None - self.data = None - self.dtype = None - self.flags = None - self.flat = None - self.imag = None - self.itemsize = None - self.nbytes = None - self.ndim = None - self.real = None - self.shape = None - 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): 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]) - """ - node = astroid.extract_node(ndarray) - return node.infer(context=context) - - -def _looks_like_numpy_ndarray(node): - return isinstance(node, astroid.Attribute) and node.attrname == "ndarray" - - -astroid.MANAGER.register_transform( - astroid.Attribute, - astroid.inference_tip(infer_numpy_ndarray), - _looks_like_numpy_ndarray, -) |