mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-11-30 10:44:14 +01:00
153 lines
8.3 KiB
Python
153 lines
8.3 KiB
Python
# Copyright (c) 2015-2016, 2018-2019 Claudiu Popa <pcmanticore@gmail.com>
|
|
# Copyright (c) 2016 Ceridwen <ceridwenv@gmail.com>
|
|
# Copyright (c) 2017-2020 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 = 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): 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,
|
|
)
|