Zy Filemanager

📁 File Browser

Name	Size	Permissions	Actions
📁 __pycache__	DIR	0755	🗑️
📄 __init__.py (0 bytes)	0 B	0644	✍️ ⬇️ 🗑️
📄 brain_argparse.py (1,557 bytes)	1.52 KB	0644	✍️ ⬇️ 🗑️
📄 brain_attrs.py (3,038 bytes)	2.97 KB	0644	✍️ ⬇️ 🗑️
📄 brain_boto3.py (1,012 bytes)	1012 B	0644	✍️ ⬇️ 🗑️
📄 brain_builtin_inference.py (34,254 bytes)	33.45 KB	0644	✍️ ⬇️ 🗑️
📄 brain_collections.py (4,416 bytes)	4.31 KB	0644	✍️ ⬇️ 🗑️
📄 brain_crypt.py (863 bytes)	863 B	0644	✍️ ⬇️ 🗑️
📄 brain_ctypes.py (2,660 bytes)	2.6 KB	0644	✍️ ⬇️ 🗑️
📄 brain_curses.py (3,477 bytes)	3.4 KB	0644	✍️ ⬇️ 🗑️
📄 brain_dataclasses.py (22,089 bytes)	21.57 KB	0644	✍️ ⬇️ 🗑️
📄 brain_dateutil.py (767 bytes)	767 B	0644	✍️ ⬇️ 🗑️
📄 brain_fstrings.py (2,471 bytes)	2.41 KB	0644	✍️ ⬇️ 🗑️
📄 brain_functools.py (5,986 bytes)	5.85 KB	0644	✍️ ⬇️ 🗑️
📄 brain_gi.py (7,543 bytes)	7.37 KB	0644	✍️ ⬇️ 🗑️
📄 brain_hashlib.py (2,821 bytes)	2.75 KB	0644	✍️ ⬇️ 🗑️
📄 brain_http.py (10,640 bytes)	10.39 KB	0644	✍️ ⬇️ 🗑️
📄 brain_hypothesis.py (1,732 bytes)	1.69 KB	0644	✍️ ⬇️ 🗑️
📄 brain_io.py (1,526 bytes)	1.49 KB	0644	✍️ ⬇️ 🗑️
📄 brain_mechanize.py (2,646 bytes)	2.58 KB	0644	✍️ ⬇️ 🗑️
📄 brain_multiprocessing.py (3,211 bytes)	3.14 KB	0644	✍️ ⬇️ 🗑️
📄 brain_namedtuple_enum.py (22,725 bytes)	22.19 KB	0644	✍️ ⬇️ 🗑️
📄 brain_nose.py (2,320 bytes)	2.27 KB	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_core_einsumfunc.py (825 bytes)	825 B	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_core_fromnumeric.py (732 bytes)	732 B	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_core_function_base.py (1,298 bytes)	1.27 KB	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_core_multiarray.py (4,238 bytes)	4.14 KB	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_core_numeric.py (1,629 bytes)	1.59 KB	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_core_numerictypes.py (8,546 bytes)	8.35 KB	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_core_umath.py (4,893 bytes)	4.78 KB	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_ma.py (896 bytes)	896 B	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_ndarray.py (8,998 bytes)	8.79 KB	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_random_mtrand.py (3,436 bytes)	3.36 KB	0644	✍️ ⬇️ 🗑️
📄 brain_numpy_utils.py (2,637 bytes)	2.58 KB	0644	✍️ ⬇️ 🗑️
📄 brain_pathlib.py (1,542 bytes)	1.51 KB	0644	✍️ ⬇️ 🗑️
📄 brain_pkg_resources.py (2,200 bytes)	2.15 KB	0644	✍️ ⬇️ 🗑️
📄 brain_pytest.py (2,223 bytes)	2.17 KB	0644	✍️ ⬇️ 🗑️
📄 brain_qt.py (2,808 bytes)	2.74 KB	0644	✍️ ⬇️ 🗑️
📄 brain_random.py (2,890 bytes)	2.82 KB	0644	✍️ ⬇️ 🗑️
📄 brain_re.py (2,870 bytes)	2.8 KB	0644	✍️ ⬇️ 🗑️
📄 brain_regex.py (3,362 bytes)	3.28 KB	0644	✍️ ⬇️ 🗑️
📄 brain_responses.py (1,868 bytes)	1.82 KB	0644	✍️ ⬇️ 🗑️
📄 brain_scipy_signal.py (2,276 bytes)	2.22 KB	0644	✍️ ⬇️ 🗑️
📄 brain_signal.py (3,880 bytes)	3.79 KB	0644	✍️ ⬇️ 🗑️
📄 brain_six.py (7,594 bytes)	7.42 KB	0644	✍️ ⬇️ 🗑️
📄 brain_sqlalchemy.py (1,009 bytes)	1009 B	0644	✍️ ⬇️ 🗑️
📄 brain_ssl.py (6,554 bytes)	6.4 KB	0644	✍️ ⬇️ 🗑️
📄 brain_subprocess.py (2,996 bytes)	2.93 KB	0644	✍️ ⬇️ 🗑️
📄 brain_threading.py (870 bytes)	870 B	0644	✍️ ⬇️ 🗑️
📄 brain_type.py (2,483 bytes)	2.42 KB	0644	✍️ ⬇️ 🗑️
📄 brain_typing.py (14,315 bytes)	13.98 KB	0644	✍️ ⬇️ 🗑️
📄 brain_unittest.py (1,147 bytes)	1.12 KB	0644	✍️ ⬇️ 🗑️
📄 brain_uuid.py (667 bytes)	667 B	0644	✍️ ⬇️ 🗑️
📄 helpers.py (908 bytes)	908 B	0644	✍️ ⬇️ 🗑️

✍️ Editing: brain_numpy_ndarray.py

            File: /opt/cloudlinux/venv/lib/python3.11/site-packages/astroid/brain/brain_numpy_ndarray.py

            Size: 8.79 KB | 
            Last Modified: 2026-01-20 20:01:48        

# 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,
)

↩️ Back

Zy Filemanager

📁 File Browser

📤 Upload Files

Single File

Multiple Files

ZIP Extract

➕ Create New

New Folder

New File

✍️ Editing: brain_numpy_ndarray.py

Backup Management