\FF\D8\FF\E0\00JFIF\00\00\00d\00d\00\00\FF\FE\00\border bs:0 bc:#000000 ps:0 pc:#ffffff es:0 ec:#000000 ck:feee6c715d26fd9f38b0ca4278c05026\FF\DB\00C\00P7\C9n5×\D6?\BDê\9Ds\EBp\9F[`8m\B7)o\B5\E8\E6I\99\FE3]]A2\BA\8Cw\D6E\93\\DEv\C8\009\F2\F1NI?uc\\F5\EA\96k\xN<~buv\EA\C8\D7 \8B\84\CEcxI\BBg\AE\9E=\D6+n\EC\80\C8A\8C\AE\EB\CF\D5\DA\E9"2\A4\B9j5\EB\F3W\B63\96\B30Yu\DA\FC8\ED\DF\E7Ms\FB\F1\8E\B3\FA\EA\E8\E6(\883zs\F2_\8DFk\8Bh \00\8C\DCw\D3R\B5+6X\BA\B2\C4j\AB0\B4\FCMw\C2I\8E\9B\E3\A9~9u\FA\D3l\80\C8%p\EE\FDn2€ \00 $\FEj\C4e\A9\DB\~\95\A7\A5\80EK\BB\8DDsP\00@@AD'k\CF\E8\DB\D2(\80\9AK\D3\85\D6lb\F2\BA\8C*\80\00)\95 59\A3R:\F3\CE"\B6\80\88\00\00i1u4ê\E9\F2á\A6\A2\FACM\93WMb*\E0*\00\00\00\00\00(\A8\80\00\00\80\00\00\00\00\00\00\00\00\00\FF\D9 C/// """A module for creating docstrings for sphinx ``data`` domains.""" import re import textwrap from ._array_like import NDArray _docstrings_list = [] def add_newdoc(name: str, value: str, doc: str) -> None: """Append ``_docstrings_list`` with a docstring for `name`. Parameters ---------- name : str The name of the object. value : str A string-representation of the object. doc : str The docstring of the object. """ _docstrings_list.append((name, value, doc)) def _parse_docstrings() -> str: """Convert all docstrings in ``_docstrings_list`` into a single sphinx-legible text block. """ type_list_ret = [] for name, value, doc in _docstrings_list: s = textwrap.dedent(doc).replace("\n", "\n ") # Replace sections by rubrics lines = s.split("\n") new_lines = [] indent = "" for line in lines: m = re.match(r'^(\s+)[-=]+\s*$', 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