Function _lookfor_generate_cache

numpy/lib/utils.py:878–1015

Generate docstring cache for given module. Parameters ---------- module : str, None, module Module for which to generate docstring cache import_modules : bool Whether to import sub-modules in packages. regenerate : bool Re-generate the docstring cach

(module, import_modules, regenerate)

        print("\n".join(help_text))

def _lookfor_generate_cache(module, import_modules, regenerate):
    """
    Generate docstring cache for given module.

    Parameters
    ----------
    module : str, None, module
        Module for which to generate docstring cache
    import_modules : bool
        Whether to import sub-modules in packages.
    regenerate : bool
        Re-generate the docstring cache

    Returns
    -------
    cache : dict {obj_full_name: (docstring, kind, index), ...}
        Docstring cache for the module, either cached one (regenerate=False)
        or newly generated.

    """
    # Local import to speed up numpy's import time.
    import inspect

    from io import StringIO

    if module is None:
        module = "numpy"

    if isinstance(module, str):
        try:
            __import__(module)
        except ImportError:
            return {}
        module = sys.modules[module]
    elif isinstance(module, list) or isinstance(module, tuple):
        cache = {}
        for mod in module:
            cache.update(_lookfor_generate_cache(mod, import_modules,
                                                 regenerate))
        return cache

    if id(module) in _lookfor_caches and not regenerate:
        return _lookfor_caches[id(module)]

    # walk items and collect docstrings
    cache = {}
    _lookfor_caches[id(module)] = cache
    seen = {}
    index = 0
    stack = [(module.__name__, module)]
    while stack:
        name, item = stack.pop(0)
        if id(item) in seen:
            continue
        seen[id(item)] = True

        index += 1
        kind = "object"