Zy Filemanager

📁 File Browser

Name	Size	Permissions	Actions
📁 __pycache__	DIR	0755	🗑️
📄 __init__.py (7,003 bytes)	6.84 KB	0644	✍️ ⬇️ 🗑️
📄 _add_docstring.py (3,922 bytes)	3.83 KB	0644	✍️ ⬇️ 🗑️
📄 _array_like.py (4,238 bytes)	4.14 KB	0644	✍️ ⬇️ 🗑️
📄 _callable.pyi (11,121 bytes)	10.86 KB	0644	✍️ ⬇️ 🗑️
📄 _char_codes.py (5,916 bytes)	5.78 KB	0644	✍️ ⬇️ 🗑️
📄 _dtype_like.py (5,661 bytes)	5.53 KB	0644	✍️ ⬇️ 🗑️
📄 _extended_precision.py (777 bytes)	777 B	0644	✍️ ⬇️ 🗑️
📄 _nbit.py (345 bytes)	345 B	0644	✍️ ⬇️ 🗑️
📄 _nested_sequence.py (2,724 bytes)	2.66 KB	0644	✍️ ⬇️ 🗑️
📄 _scalars.py (980 bytes)	980 B	0644	✍️ ⬇️ 🗑️
📄 _shape.py (211 bytes)	211 B	0644	✍️ ⬇️ 🗑️
📄 _ufunc.pyi (12,638 bytes)	12.34 KB	0644	✍️ ⬇️ 🗑️
📄 setup.py (337 bytes)	337 B	0644	✍️ ⬇️ 🗑️

✍️ Editing: _add_docstring.py

            File: /opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/_typing/_add_docstring.py

            Size: 3.83 KB | 
            Last Modified: 2026-01-20 20:01:48        

"""A module for creating docstrings for sphinx ``data`` domains."""

import re
import textwrap

from ._array_like import NDArray

_docstrings_list = []

def add_newdoc(name: str, value: str, doc: str) -> None:
    """Append ``_docstrings_list`` with a docstring for `name`.

Parameters
    ----------
    name : str
        The name of the object.
    value : str
        A string-representation of the object.
    doc : str
        The docstring of the object.

"""
    _docstrings_list.append((name, value, doc))

def _parse_docstrings() -> str:
    """Convert all docstrings in ``_docstrings_list`` into a single
    sphinx-legible text block.

"""
    type_list_ret = []
    for name, value, doc in _docstrings_list:
        s = textwrap.dedent(doc).replace("\n", "\n    ")

# Replace sections by rubrics
        lines = s.split("\n")
        new_lines = []
        indent = ""
        for line in lines:
            m = re.match(r'^(\s+)[-=]+\s*$', line)
            if m and new_lines:
                prev = textwrap.dedent(new_lines.pop())
                if prev == "Examples":
                    indent = ""
                    new_lines.append(f'{m.group(1)}.. rubric:: {prev}')
                else:
                    indent = 4 * " "
                    new_lines.append(f'{m.group(1)}.. admonition:: {prev}')
                new_lines.append("")
            else:
                new_lines.append(f"{indent}{line}")

s = "\n".join(new_lines)
        s_block = f""".. data:: {name}\n    :value: {value}\n    {s}"""
        type_list_ret.append(s_block)
    return "\n".join(type_list_ret)

add_newdoc('ArrayLike', 'typing.Union[...]',
    """
    A `~typing.Union` representing objects that can be coerced
    into an `~numpy.ndarray`.

Among others this includes the likes of:

* Scalars.
    * (Nested) sequences.
    * Objects implementing the `~class.__array__` protocol.

.. versionadded:: 1.20

See Also
    --------
    :term:`array_like`:
        Any scalar or sequence that can be interpreted as an ndarray.

Examples
    --------
    .. code-block:: python

>>> import numpy as np
        >>> import numpy.typing as npt

>>> def as_array(a: npt.ArrayLike) -> np.ndarray:
        ...     return np.array(a)

""")

add_newdoc('DTypeLike', 'typing.Union[...]',
    """
    A `~typing.Union` representing objects that can be coerced
    into a `~numpy.dtype`.

Among others this includes the likes of:

* :class:`type` objects.
    * Character codes or the names of :class:`type` objects.
    * Objects with the ``.dtype`` attribute.

.. versionadded:: 1.20

See Also
    --------
    :ref:`Specifying and constructing data types <arrays.dtypes.constructing>`
        A comprehensive overview of all objects that can be coerced
        into data types.

Examples
    --------
    .. code-block:: python

>>> import numpy as np
        >>> import numpy.typing as npt

>>> def as_dtype(d: npt.DTypeLike) -> np.dtype:
        ...     return np.dtype(d)

""")

add_newdoc('NDArray', repr(NDArray),
    """
    A :term:`generic <generic type>` version of
    `np.ndarray[Any, np.dtype[+ScalarType]] <numpy.ndarray>`.

Can be used during runtime for typing arrays with a given dtype
    and unspecified shape.

.. versionadded:: 1.21

Examples
    --------
    .. code-block:: python

>>> import numpy as np
        >>> import numpy.typing as npt

>>> print(npt.NDArray)
        numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]

>>> print(npt.NDArray[np.float64])
        numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]

>>> NDArrayInt = npt.NDArray[np.int_]
        >>> a: NDArrayInt = np.arange(10)

>>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
        ...     return np.array(a)

""")

_docstrings = _parse_docstrings()

↩️ Back

Zy Filemanager

📁 File Browser

📤 Upload Files

Single File

Multiple Files

ZIP Extract

➕ Create New

New Folder

New File

✍️ Editing: _add_docstring.py

Backup Management