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ðŸ“ ..
ðŸ“„ __init__.py(0 B)
ðŸ“ __pycache__
ðŸ“„ brain_argparse.py(1.52 KB)
ðŸ“„ brain_attrs.py(2.97 KB)
ðŸ“„ brain_boto3.py(1012 B)
ðŸ“„ brain_builtin_inference.py(33.45 KB)
ðŸ“„ brain_collections.py(4.31 KB)
ðŸ“„ brain_crypt.py(863 B)
ðŸ“„ brain_ctypes.py(2.6 KB)
ðŸ“„ brain_curses.py(3.4 KB)
ðŸ“„ brain_dataclasses.py(21.57 KB)
ðŸ“„ brain_dateutil.py(767 B)
ðŸ“„ brain_fstrings.py(2.41 KB)
ðŸ“„ brain_functools.py(5.85 KB)
ðŸ“„ brain_gi.py(7.37 KB)
ðŸ“„ brain_hashlib.py(2.75 KB)
ðŸ“„ brain_http.py(10.39 KB)
ðŸ“„ brain_hypothesis.py(1.69 KB)
ðŸ“„ brain_io.py(1.49 KB)
ðŸ“„ brain_mechanize.py(2.58 KB)
ðŸ“„ brain_multiprocessing.py(3.14 KB)
ðŸ“„ brain_namedtuple_enum.py(22.19 KB)
ðŸ“„ brain_nose.py(2.27 KB)
ðŸ“„ brain_numpy_core_einsumfunc.py(825 B)
ðŸ“„ brain_numpy_core_fromnumeric.py(732 B)
ðŸ“„ brain_numpy_core_function_base.py(1.27 KB)
ðŸ“„ brain_numpy_core_multiarray.py(4.14 KB)
ðŸ“„ brain_numpy_core_numeric.py(1.59 KB)
ðŸ“„ brain_numpy_core_numerictypes.py(8.35 KB)
ðŸ“„ brain_numpy_core_umath.py(4.78 KB)
ðŸ“„ brain_numpy_ma.py(896 B)
ðŸ“„ brain_numpy_ndarray.py(8.79 KB)
ðŸ“„ brain_numpy_random_mtrand.py(3.36 KB)
ðŸ“„ brain_numpy_utils.py(2.58 KB)
ðŸ“„ brain_pathlib.py(1.51 KB)
ðŸ“„ brain_pkg_resources.py(2.15 KB)
ðŸ“„ brain_pytest.py(2.17 KB)
ðŸ“„ brain_qt.py(2.74 KB)
ðŸ“„ brain_random.py(2.82 KB)
ðŸ“„ brain_re.py(2.8 KB)
ðŸ“„ brain_regex.py(3.28 KB)
ðŸ“„ brain_responses.py(1.82 KB)
ðŸ“„ brain_scipy_signal.py(2.22 KB)
ðŸ“„ brain_signal.py(3.79 KB)
ðŸ“„ brain_six.py(7.42 KB)
ðŸ“„ brain_sqlalchemy.py(1009 B)
ðŸ“„ brain_ssl.py(6.4 KB)
ðŸ“„ brain_subprocess.py(2.93 KB)
ðŸ“„ brain_threading.py(870 B)
ðŸ“„ brain_type.py(2.42 KB)
ðŸ“„ brain_typing.py(13.98 KB)
ðŸ“„ brain_unittest.py(1.12 KB)
ðŸ“„ brain_uuid.py(667 B)
ðŸ“„ helpers.py(908 B)

Editing: brain_numpy_utils.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

"""Different utilities for the numpy brains."""

from __future__ import annotations

from astroid.builder import extract_node
from astroid.context import InferenceContext
from astroid.nodes.node_classes import Attribute, Import, Name, NodeNG

# Class subscript is available in numpy starting with version 1.20.0
NUMPY_VERSION_TYPE_HINTS_SUPPORT = ("1", "20", "0")

def numpy_supports_type_hints() -> bool:
    """Returns True if numpy supports type hints."""
    np_ver = _get_numpy_version()
    return np_ver and np_ver > NUMPY_VERSION_TYPE_HINTS_SUPPORT

def _get_numpy_version() -> tuple[str, str, str]:
    """
    Return the numpy version number if numpy can be imported.

Otherwise returns ('0', '0', '0')
    """
    try:
        import numpy  # pylint: disable=import-outside-toplevel

return tuple(numpy.version.version.split("."))
    except (ImportError, AttributeError):
        return ("0", "0", "0")

def infer_numpy_member(src, node, context: InferenceContext | None = None):
    node = extract_node(src)
    return node.infer(context=context)

def _is_a_numpy_module(node: Name) -> bool:
    """
    Returns True if the node is a representation of a numpy module.

For example in :
        import numpy as np
        x = np.linspace(1, 2)
    The node <Name.np> is a representation of the numpy module.

:param node: node to test
    :return: True if the node is a representation of the numpy module.
    """
    module_nickname = node.name
    potential_import_target = [
        x for x in node.lookup(module_nickname)[1] if isinstance(x, Import)
    ]
    return any(
        ("numpy", module_nickname) in target.names or ("numpy", None) in target.names
        for target in potential_import_target
    )

def looks_like_numpy_member(member_name: str, node: NodeNG) -> bool:
    """
    Returns True if the node is a member of numpy whose
    name is member_name.

:param member_name: name of the member
    :param node: node to test
    :return: True if the node is a member of numpy
    """
    if (
        isinstance(node, Attribute)
        and node.attrname == member_name
        and isinstance(node.expr, Name)
        and _is_a_numpy_module(node.expr)
    ):
        return True
    if (
        isinstance(node, Name)
        and node.name == member_name
        and node.root().name.startswith("numpy")
    ):
        return True
    return False

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