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Current Directory: /opt/imh-python/lib/python3.9/site-packages/numpy/lib
Viewing File: /opt/imh-python/lib/python3.9/site-packages/numpy/lib/utils.py
import os
import sys
import textwrap
import types
import re
import warnings
import functools
import platform

from .._utils import set_module
from numpy.core.numerictypes import issubclass_, issubsctype, issubdtype
from numpy.core import ndarray, ufunc, asarray
import numpy as np

__all__ = [
    'issubclass_', 'issubsctype', 'issubdtype', 'deprecate',
    'deprecate_with_doc', 'get_include', 'info', 'source', 'who',
    'lookfor', 'byte_bounds', 'safe_eval', 'show_runtime'
    ]


def show_runtime():
    """
    Print information about various resources in the system
    including available intrinsic support and BLAS/LAPACK library
    in use

    .. versionadded:: 1.24.0

    See Also
    --------
    show_config : Show libraries in the system on which NumPy was built.

    Notes
    -----
    1. Information is derived with the help of `threadpoolctl <https://pypi.org/project/threadpoolctl/>`_
       library if available.
    2. SIMD related information is derived from ``__cpu_features__``,
       ``__cpu_baseline__`` and ``__cpu_dispatch__``

    """
    from numpy.core._multiarray_umath import (
        __cpu_features__, __cpu_baseline__, __cpu_dispatch__
    )
    from pprint import pprint
    config_found = [{
        "numpy_version": np.__version__,
        "python": sys.version,
        "uname": platform.uname(),
        }]
    features_found, features_not_found = [], []
    for feature in __cpu_dispatch__:
        if __cpu_features__[feature]:
            features_found.append(feature)
        else:
            features_not_found.append(feature)
    config_found.append({
        "simd_extensions": {
            "baseline": __cpu_baseline__,
            "found": features_found,
            "not_found": features_not_found
        }
    })
    try:
        from threadpoolctl import threadpool_info
        config_found.extend(threadpool_info())
    except ImportError:
        print("WARNING: `threadpoolctl` not found in system!"
              " Install it by `pip install threadpoolctl`."
              " Once installed, try `np.show_runtime` again"
              " for more detailed build information")
    pprint(config_found)


def get_include():
    """
    Return the directory that contains the NumPy \\*.h header files.

    Extension modules that need to compile against NumPy should use this
    function to locate the appropriate include directory.

    Notes
    -----
    When using ``distutils``, for example in ``setup.py``::

        import numpy as np
        ...
        Extension('extension_name', ...
                include_dirs=[np.get_include()])
        ...

    """
    import numpy
    if numpy.show_config is None:
        # running from numpy source directory
        d = os.path.join(os.path.dirname(numpy.__file__), 'core', 'include')
    else:
        # using installed numpy core headers
        import numpy.core as core
        d = os.path.join(os.path.dirname(core.__file__), 'include')
    return d


class _Deprecate:
    """
    Decorator class to deprecate old functions.

    Refer to `deprecate` for details.

    See Also
    --------
    deprecate

    """

    def __init__(self, old_name=None, new_name=None, message=None):
        self.old_name = old_name
        self.new_name = new_name
        self.message = message

    def __call__(self, func, *args, **kwargs):
        """
        Decorator call.  Refer to ``decorate``.

        """
        old_name = self.old_name
        new_name = self.new_name
        message = self.message

        if old_name is None:
            old_name = func.__name__
        if new_name is None:
            depdoc = "`%s` is deprecated!" % old_name
        else:
            depdoc = "`%s` is deprecated, use `%s` instead!" % \
                     (old_name, new_name)

        if message is not None:
            depdoc += "\n" + message

        @functools.wraps(func)
        def newfunc(*args, **kwds):
            warnings.warn(depdoc, DeprecationWarning, stacklevel=2)
            return func(*args, **kwds)

        newfunc.__name__ = old_name
        doc = func.__doc__
        if doc is None:
            doc = depdoc
        else:
            lines = doc.expandtabs().split('\n')
            indent = _get_indent(lines[1:])
            if lines[0].lstrip():
                # Indent the original first line to let inspect.cleandoc()
                # dedent the docstring despite the deprecation notice.
                doc = indent * ' ' + doc
            else:
                # Remove the same leading blank lines as cleandoc() would.
                skip = len(lines[0]) + 1
                for line in lines[1:]:
                    if len(line) > indent:
                        break
                    skip += len(line) + 1
                doc = doc[skip:]
            depdoc = textwrap.indent(depdoc, ' ' * indent)
            doc = '\n\n'.join([depdoc, doc])
        newfunc.__doc__ = doc

        return newfunc


def _get_indent(lines):
    """
    Determines the leading whitespace that could be removed from all the lines.
    """
    indent = sys.maxsize
    for line in lines:
        content = len(line.lstrip())
        if content:
            indent = min(indent, len(line) - content)
    if indent == sys.maxsize:
        indent = 0
    return indent


def deprecate(*args, **kwargs):
    """
    Issues a DeprecationWarning, adds warning to `old_name`'s
    docstring, rebinds ``old_name.__name__`` and returns the new
    function object.

    This function may also be used as a decorator.

    Parameters
    ----------
    func : function
        The function to be deprecated.
    old_name : str, optional
        The name of the function to be deprecated. Default is None, in
        which case the name of `func` is used.
    new_name : str, optional
        The new name for the function. Default is None, in which case the
        deprecation message is that `old_name` is deprecated. If given, the
        deprecation message is that `old_name` is deprecated and `new_name`
        should be used instead.
    message : str, optional
        Additional explanation of the deprecation.  Displayed in the
        docstring after the warning.

    Returns
    -------
    old_func : function
        The deprecated function.

    Examples
    --------
    Note that ``olduint`` returns a value after printing Deprecation
    Warning:

    >>> olduint = np.deprecate(np.uint)
    DeprecationWarning: `uint64` is deprecated! # may vary
    >>> olduint(6)
    6

    """
    # Deprecate may be run as a function or as a decorator
    # If run as a function, we initialise the decorator class
    # and execute its __call__ method.

    if args:
        fn = args[0]
        args = args[1:]

        return _Deprecate(*args, **kwargs)(fn)
    else:
        return _Deprecate(*args, **kwargs)


def deprecate_with_doc(msg):
    """
    Deprecates a function and includes the deprecation in its docstring.

    This function is used as a decorator. It returns an object that can be
    used to issue a DeprecationWarning, by passing the to-be decorated
    function as argument, this adds warning to the to-be decorated function's
    docstring and returns the new function object.

    See Also
    --------
    deprecate : Decorate a function such that it issues a `DeprecationWarning`

    Parameters
    ----------
    msg : str
        Additional explanation of the deprecation. Displayed in the
        docstring after the warning.

    Returns
    -------
    obj : object

    """
    return _Deprecate(message=msg)


#--------------------------------------------
# Determine if two arrays can share memory
#--------------------------------------------

def byte_bounds(a):
    """
    Returns pointers to the end-points of an array.

    Parameters
    ----------
    a : ndarray
        Input array. It must conform to the Python-side of the array
        interface.

    Returns
    -------
    (low, high) : tuple of 2 integers
        The first integer is the first byte of the array, the second
        integer is just past the last byte of the array.  If `a` is not
        contiguous it will not use every byte between the (`low`, `high`)
        values.

    Examples
    --------
    >>> I = np.eye(2, dtype='f'); I.dtype
    dtype('float32')
    >>> low, high = np.byte_bounds(I)
    >>> high - low == I.size*I.itemsize
    True
    >>> I = np.eye(2); I.dtype
    dtype('float64')
    >>> low, high = np.byte_bounds(I)
    >>> high - low == I.size*I.itemsize
    True

    """
    ai = a.__array_interface__
    a_data = ai['data'][0]
    astrides = ai['strides']
    ashape = ai['shape']
    bytes_a = asarray(a).dtype.itemsize

    a_low = a_high = a_data
    if astrides is None:
        # contiguous case
        a_high += a.size * bytes_a
    else:
        for shape, stride in zip(ashape, astrides):
            if stride < 0:
                a_low += (shape-1)*stride
            else:
                a_high += (shape-1)*stride
        a_high += bytes_a
    return a_low, a_high


#-----------------------------------------------------------------------------
# Function for output and information on the variables used.
#-----------------------------------------------------------------------------


def who(vardict=None):
    """
    Print the NumPy arrays in the given dictionary.

    If there is no dictionary passed in or `vardict` is None then returns
    NumPy arrays in the globals() dictionary (all NumPy arrays in the
    namespace).

    Parameters
    ----------
    vardict : dict, optional
        A dictionary possibly containing ndarrays.  Default is globals().

    Returns
    -------
    out : None
        Returns 'None'.

    Notes
    -----
    Prints out the name, shape, bytes and type of all of the ndarrays
    present in `vardict`.

    Examples
    --------
    >>> a = np.arange(10)
    >>> b = np.ones(20)
    >>> np.who()
    Name            Shape            Bytes            Type
    ===========================================================
    a               10               80               int64
    b               20               160              float64
    Upper bound on total bytes  =       240

    >>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str',
    ... 'idx':5}
    >>> np.who(d)
    Name            Shape            Bytes            Type
    ===========================================================
    x               2                16               float64
    y               3                24               float64
    Upper bound on total bytes  =       40

    """
    if vardict is None:
        frame = sys._getframe().f_back
        vardict = frame.f_globals
    sta = []
    cache = {}
    for name in vardict.keys():
        if isinstance(vardict[name], ndarray):
            var = vardict[name]
            idv = id(var)
            if idv in cache.keys():
                namestr = name + " (%s)" % cache[idv]
                original = 0
            else:
                cache[idv] = name
                namestr = name
                original = 1
            shapestr = " x ".join(map(str, var.shape))
            bytestr = str(var.nbytes)
            sta.append([namestr, shapestr, bytestr, var.dtype.name,
                        original])

    maxname = 0
    maxshape = 0
    maxbyte = 0
    totalbytes = 0
    for val in sta:
        if maxname < len(val[0]):
            maxname = len(val[0])
        if maxshape < len(val[1]):
            maxshape = len(val[1])
        if maxbyte < len(val[2]):
            maxbyte = len(val[2])
        if val[4]:
            totalbytes += int(val[2])

    if len(sta) > 0:
        sp1 = max(10, maxname)
        sp2 = max(10, maxshape)
        sp3 = max(10, maxbyte)
        prval = "Name %s Shape %s Bytes %s Type" % (sp1*' ', sp2*' ', sp3*' ')
        print(prval + "\n" + "="*(len(prval)+5) + "\n")

    for val in sta:
        print("%s %s %s %s %s %s %s" % (val[0], ' '*(sp1-len(val[0])+4),
                                        val[1], ' '*(sp2-len(val[1])+5),
                                        val[2], ' '*(sp3-len(val[2])+5),
                                        val[3]))
    print("\nUpper bound on total bytes  =       %d" % totalbytes)
    return

#-----------------------------------------------------------------------------


# NOTE:  pydoc defines a help function which works similarly to this
#  except it uses a pager to take over the screen.

# combine name and arguments and split to multiple lines of width
# characters.  End lines on a comma and begin argument list indented with
# the rest of the arguments.
def _split_line(name, arguments, width):
    firstwidth = len(name)
    k = firstwidth
    newstr = name
    sepstr = ", "
    arglist = arguments.split(sepstr)
    for argument in arglist:
        if k == firstwidth:
            addstr = ""
        else:
            addstr = sepstr
        k = k + len(argument) + len(addstr)
        if k > width:
            k = firstwidth + 1 + len(argument)
            newstr = newstr + ",\n" + " "*(firstwidth+2) + argument
        else:
            newstr = newstr + addstr + argument
    return newstr

_namedict = None
_dictlist = None

# Traverse all module directories underneath globals
# to see if something is defined
def _makenamedict(module='numpy'):
    module = __import__(module, globals(), locals(), [])
    thedict = {module.__name__:module.__dict__}
    dictlist = [module.__name__]
    totraverse = [module.__dict__]
    while True:
        if len(totraverse) == 0:
            break
        thisdict = totraverse.pop(0)
        for x in thisdict.keys():
            if isinstance(thisdict[x], types.ModuleType):
                modname = thisdict[x].__name__
                if modname not in dictlist:
                    moddict = thisdict[x].__dict__
                    dictlist.append(modname)
                    totraverse.append(moddict)
                    thedict[modname] = moddict
    return thedict, dictlist


def _info(obj, output=None):
    """Provide information about ndarray obj.

    Parameters
    ----------
    obj : ndarray
        Must be ndarray, not checked.
    output
        Where printed output goes.

    Notes
    -----
    Copied over from the numarray module prior to its removal.
    Adapted somewhat as only numpy is an option now.

    Called by info.

    """
    extra = ""
    tic = ""
    bp = lambda x: x
    cls = getattr(obj, '__class__', type(obj))
    nm = getattr(cls, '__name__', cls)
    strides = obj.strides
    endian = obj.dtype.byteorder

    if output is None:
        output = sys.stdout

    print("class: ", nm, file=output)
    print("shape: ", obj.shape, file=output)
    print("strides: ", strides, file=output)
    print("itemsize: ", obj.itemsize, file=output)
    print("aligned: ", bp(obj.flags.aligned), file=output)
    print("contiguous: ", bp(obj.flags.contiguous), file=output)
    print("fortran: ", obj.flags.fortran, file=output)
    print(
        "data pointer: %s%s" % (hex(obj.ctypes._as_parameter_.value), extra),
        file=output
        )
    print("byteorder: ", end=' ', file=output)
    if endian in ['|', '=']:
        print("%s%s%s" % (tic, sys.byteorder, tic), file=output)
        byteswap = False
    elif endian == '>':
        print("%sbig%s" % (tic, tic), file=output)
        byteswap = sys.byteorder != "big"
    else:
        print("%slittle%s" % (tic, tic), file=output)
        byteswap = sys.byteorder != "little"
    print("byteswap: ", bp(byteswap), file=output)
    print("type: %s" % obj.dtype, file=output)


@set_module('numpy')
def info(object=None, maxwidth=76, output=None, toplevel='numpy'):
    """
    Get help information for an array, function, class, or module.

    Parameters
    ----------
    object : object or str, optional
        Input object or name to get information about. If `object` is
        an `ndarray` instance, information about the array is printed.
        If `object` is a numpy object, its docstring is given. If it is
        a string, available modules are searched for matching objects.
        If None, information about `info` itself is returned.
    maxwidth : int, optional
        Printing width.
    output : file like object, optional
        File like object that the output is written to, default is
        ``None``, in which case ``sys.stdout`` will be used.
        The object has to be opened in 'w' or 'a' mode.
    toplevel : str, optional
        Start search at this level.

    See Also
    --------
    source, lookfor

    Notes
    -----
    When used interactively with an object, ``np.info(obj)`` is equivalent
    to ``help(obj)`` on the Python prompt or ``obj?`` on the IPython
    prompt.

    Examples
    --------
    >>> np.info(np.polyval) # doctest: +SKIP
       polyval(p, x)
         Evaluate the polynomial p at x.
         ...

    When using a string for `object` it is possible to get multiple results.

    >>> np.info('fft') # doctest: +SKIP
         *** Found in numpy ***
    Core FFT routines
    ...
         *** Found in numpy.fft ***
     fft(a, n=None, axis=-1)
    ...
         *** Repeat reference found in numpy.fft.fftpack ***
         *** Total of 3 references found. ***

    When the argument is an array, information about the array is printed.

    >>> a = np.array([[1 + 2j, 3, -4], [-5j, 6, 0]], dtype=np.complex64)
    >>> np.info(a)
    class:  ndarray
    shape:  (2, 3)
    strides:  (24, 8)
    itemsize:  8
    aligned:  True
    contiguous:  True
    fortran:  False
    data pointer: 0x562b6e0d2860  # may vary
    byteorder:  little
    byteswap:  False
    type: complex64

    """
    global _namedict, _dictlist
    # Local import to speed up numpy's import time.
    import pydoc
    import inspect

    if (hasattr(object, '_ppimport_importer') or
           hasattr(object, '_ppimport_module')):
        object = object._ppimport_module
    elif hasattr(object, '_ppimport_attr'):
        object = object._ppimport_attr

    if output is None:
        output = sys.stdout

    if object is None:
        info(info)
    elif isinstance(object, ndarray):
        _info(object, output=output)
    elif isinstance(object, str):
        if _namedict is None:
            _namedict, _dictlist = _makenamedict(toplevel)
        numfound = 0
        objlist = []
        for namestr in _dictlist:
            try:
                obj = _namedict[namestr][object]
                if id(obj) in objlist:
                    print("\n     "
                          "*** Repeat reference found in %s *** " % namestr,
                          file=output
                          )
                else:
                    objlist.append(id(obj))
                    print("     *** Found in %s ***" % namestr, file=output)
                    info(obj)
                    print("-"*maxwidth, file=output)
                numfound += 1
            except KeyError:
                pass
        if numfound == 0:
            print("Help for %s not found." % object, file=output)
        else:
            print("\n     "
                  "*** Total of %d references found. ***" % numfound,
                  file=output
                  )

    elif inspect.isfunction(object) or inspect.ismethod(object):
        name = object.__name__
        try:
            arguments = str(inspect.signature(object))
        except Exception:
            arguments = "()"

        if len(name+arguments) > maxwidth:
            argstr = _split_line(name, arguments, maxwidth)
        else:
            argstr = name + arguments

        print(" " + argstr + "\n", file=output)
        print(inspect.getdoc(object), file=output)

    elif inspect.isclass(object):
        name = object.__name__
        try:
            arguments = str(inspect.signature(object))
        except Exception:
            arguments = "()"

        if len(name+arguments) > maxwidth:
            argstr = _split_line(name, arguments, maxwidth)
        else:
            argstr = name + arguments

        print(" " + argstr + "\n", file=output)
        doc1 = inspect.getdoc(object)
        if doc1 is None:
            if hasattr(object, '__init__'):
                print(inspect.getdoc(object.__init__), file=output)
        else:
            print(inspect.getdoc(object), file=output)

        methods = pydoc.allmethods(object)

        public_methods = [meth for meth in methods if meth[0] != '_']
        if public_methods:
            print("\n\nMethods:\n", file=output)
            for meth in public_methods:
                thisobj = getattr(object, meth, None)
                if thisobj is not None:
                    methstr, other = pydoc.splitdoc(
                            inspect.getdoc(thisobj) or "None"
                            )
                print("  %s  --  %s" % (meth, methstr), file=output)

    elif hasattr(object, '__doc__'):
        print(inspect.getdoc(object), file=output)


@set_module('numpy')
def source(object, output=sys.stdout):
    """
    Print or write to a file the source code for a NumPy object.

    The source code is only returned for objects written in Python. Many
    functions and classes are defined in C and will therefore not return
    useful information.

    Parameters
    ----------
    object : numpy object
        Input object. This can be any object (function, class, module,
        ...).
    output : file object, optional
        If `output` not supplied then source code is printed to screen
        (sys.stdout).  File object must be created with either write 'w' or
        append 'a' modes.

    See Also
    --------
    lookfor, info

    Examples
    --------
    >>> np.source(np.interp)                        #doctest: +SKIP
    In file: /usr/lib/python2.6/dist-packages/numpy/lib/function_base.py
    def interp(x, xp, fp, left=None, right=None):
        \"\"\".... (full docstring printed)\"\"\"
        if isinstance(x, (float, int, number)):
            return compiled_interp([x], xp, fp, left, right).item()
        else:
            return compiled_interp(x, xp, fp, left, right)

    The source code is only returned for objects written in Python.

    >>> np.source(np.array)                         #doctest: +SKIP
    Not available for this object.

    """
    # Local import to speed up numpy's import time.
    import inspect
    try:
        print("In file: %s\n" % inspect.getsourcefile(object), file=output)
        print(inspect.getsource(object), file=output)
    except Exception:
        print("Not available for this object.", file=output)


# Cache for lookfor: {id(module): {name: (docstring, kind, index), ...}...}
# where kind: "func", "class", "module", "object"
# and index: index in breadth-first namespace traversal
_lookfor_caches = {}

# regexp whose match indicates that the string may contain a function
# signature
_function_signature_re = re.compile(r"[a-z0-9_]+\(.*[,=].*\)", re.I)


@set_module('numpy')
def lookfor(what, module=None, import_modules=True, regenerate=False,
            output=None):
    """
    Do a keyword search on docstrings.

    A list of objects that matched the search is displayed,
    sorted by relevance. All given keywords need to be found in the
    docstring for it to be returned as a result, but the order does
    not matter.

    Parameters
    ----------
    what : str
        String containing words to look for.
    module : str or list, optional
        Name of module(s) whose docstrings to go through.
    import_modules : bool, optional
        Whether to import sub-modules in packages. Default is True.
    regenerate : bool, optional
        Whether to re-generate the docstring cache. Default is False.
    output : file-like, optional
        File-like object to write the output to. If omitted, use a pager.

    See Also
    --------
    source, info

    Notes
    -----
    Relevance is determined only roughly, by checking if the keywords occur
    in the function name, at the start of a docstring, etc.

    Examples
    --------
    >>> np.lookfor('binary representation') # doctest: +SKIP
    Search results for 'binary representation'
    ------------------------------------------
    numpy.binary_repr
        Return the binary representation of the input number as a string.
    numpy.core.setup_common.long_double_representation
        Given a binary dump as given by GNU od -b, look for long double
    numpy.base_repr
        Return a string representation of a number in the given base system.
    ...

    """
    import pydoc

    # Cache
    cache = _lookfor_generate_cache(module, import_modules, regenerate)

    # Search
    # XXX: maybe using a real stemming search engine would be better?
    found = []
    whats = str(what).lower().split()
    if not whats:
        return

    for name, (docstring, kind, index) in cache.items():
        if kind in ('module', 'object'):
            # don't show modules or objects
            continue
        doc = docstring.lower()
        if all(w in doc for w in whats):
            found.append(name)

    # Relevance sort
    # XXX: this is full Harrison-Stetson heuristics now,
    # XXX: it probably could be improved

    kind_relevance = {'func': 1000, 'class': 1000,
                      'module': -1000, 'object': -1000}

    def relevance(name, docstr, kind, index):
        r = 0
        # do the keywords occur within the start of the docstring?
        first_doc = "\n".join(docstr.lower().strip().split("\n")[:3])
        r += sum([200 for w in whats if w in first_doc])
        # do the keywords occur in the function name?
        r += sum([30 for w in whats if w in name])
        # is the full name long?
        r += -len(name) * 5
        # is the object of bad type?
        r += kind_relevance.get(kind, -1000)
        # is the object deep in namespace hierarchy?
        r += -name.count('.') * 10
        r += max(-index / 100, -100)
        return r

    def relevance_value(a):
        return relevance(a, *cache[a])
    found.sort(key=relevance_value)

    # Pretty-print
    s = "Search results for '%s'" % (' '.join(whats))
    help_text = [s, "-"*len(s)]
    for name in found[::-1]:
        doc, kind, ix = cache[name]

        doclines = [line.strip() for line in doc.strip().split("\n")
                    if line.strip()]

        # find a suitable short description
        try:
            first_doc = doclines[0].strip()
            if _function_signature_re.search(first_doc):
                first_doc = doclines[1].strip()
        except IndexError:
            first_doc = ""
        help_text.append("%s\n    %s" % (name, first_doc))

    if not found:
        help_text.append("Nothing found.")

    # Output
    if output is not None:
        output.write("\n".join(help_text))
    elif len(help_text) > 10:
        pager = pydoc.getpager()
        pager("\n".join(help_text))
    else:
        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"

        if inspect.ismodule(item):
            kind = "module"
            try:
                _all = item.__all__
            except AttributeError:
                _all = None

            # import sub-packages
            if import_modules and hasattr(item, '__path__'):
                for pth in item.__path__:
                    for mod_path in os.listdir(pth):
                        this_py = os.path.join(pth, mod_path)
                        init_py = os.path.join(pth, mod_path, '__init__.py')
                        if (os.path.isfile(this_py) and
                                mod_path.endswith('.py')):
                            to_import = mod_path[:-3]
                        elif os.path.isfile(init_py):
                            to_import = mod_path
                        else:
                            continue
                        if to_import == '__init__':
                            continue

                        try:
                            old_stdout = sys.stdout
                            old_stderr = sys.stderr
                            try:
                                sys.stdout = StringIO()
                                sys.stderr = StringIO()
                                __import__("%s.%s" % (name, to_import))
                            finally:
                                sys.stdout = old_stdout
                                sys.stderr = old_stderr
                        except KeyboardInterrupt:
                            # Assume keyboard interrupt came from a user
                            raise
                        except BaseException:
                            # Ignore also SystemExit and pytests.importorskip
                            # `Skipped` (these are BaseExceptions; gh-22345)
                            continue

            for n, v in _getmembers(item):
                try:
                    item_name = getattr(v, '__name__', "%s.%s" % (name, n))
                    mod_name = getattr(v, '__module__', None)
                except NameError:
                    # ref. SWIG's global cvars
                    #    NameError: Unknown C global variable
                    item_name = "%s.%s" % (name, n)
                    mod_name = None
                if '.' not in item_name and mod_name:
                    item_name = "%s.%s" % (mod_name, item_name)

                if not item_name.startswith(name + '.'):
                    # don't crawl "foreign" objects
                    if isinstance(v, ufunc):
                        # ... unless they are ufuncs
                        pass
                    else:
                        continue
                elif not (inspect.ismodule(v) or _all is None or n in _all):
                    continue
                stack.append(("%s.%s" % (name, n), v))
        elif inspect.isclass(item):
            kind = "class"
            for n, v in _getmembers(item):
                stack.append(("%s.%s" % (name, n), v))
        elif hasattr(item, "__call__"):
            kind = "func"

        try:
            doc = inspect.getdoc(item)
        except NameError:
            # ref SWIG's NameError: Unknown C global variable
            doc = None
        if doc is not None:
            cache[name] = (doc, kind, index)

    return cache

def _getmembers(item):
    import inspect
    try:
        members = inspect.getmembers(item)
    except Exception:
        members = [(x, getattr(item, x)) for x in dir(item)
                   if hasattr(item, x)]
    return members


def safe_eval(source):
    """
    Protected string evaluation.

    Evaluate a string containing a Python literal expression without
    allowing the execution of arbitrary non-literal code.

    .. warning::

        This function is identical to :py:meth:`ast.literal_eval` and
        has the same security implications.  It may not always be safe
        to evaluate large input strings.

    Parameters
    ----------
    source : str
        The string to evaluate.

    Returns
    -------
    obj : object
       The result of evaluating `source`.

    Raises
    ------
    SyntaxError
        If the code has invalid Python syntax, or if it contains
        non-literal code.

    Examples
    --------
    >>> np.safe_eval('1')
    1
    >>> np.safe_eval('[1, 2, 3]')
    [1, 2, 3]
    >>> np.safe_eval('{"foo": ("bar", 10.0)}')
    {'foo': ('bar', 10.0)}

    >>> np.safe_eval('import os')
    Traceback (most recent call last):
      ...
    SyntaxError: invalid syntax

    >>> np.safe_eval('open("/home/user/.ssh/id_dsa").read()')
    Traceback (most recent call last):
      ...
    ValueError: malformed node or string: <_ast.Call object at 0x...>

    """
    # Local import to speed up numpy's import time.
    import ast
    return ast.literal_eval(source)


def _median_nancheck(data, result, axis):
    """
    Utility function to check median result from data for NaN values at the end
    and return NaN in that case. Input result can also be a MaskedArray.

    Parameters
    ----------
    data : array
        Sorted input data to median function
    result : Array or MaskedArray
        Result of median function.
    axis : int
        Axis along which the median was computed.

    Returns
    -------
    result : scalar or ndarray
        Median or NaN in axes which contained NaN in the input.  If the input
        was an array, NaN will be inserted in-place.  If a scalar, either the
        input itself or a scalar NaN.
    """
    if data.size == 0:
        return result
    potential_nans = data.take(-1, axis=axis)
    n = np.isnan(potential_nans)
    # masked NaN values are ok, although for masked the copyto may fail for
    # unmasked ones (this was always broken) when the result is a scalar.
    if np.ma.isMaskedArray(n):
        n = n.filled(False)

    if not n.any():
        return result

    # Without given output, it is possible that the current result is a
    # numpy scalar, which is not writeable.  If so, just return nan.
    if isinstance(result, np.generic):
        return potential_nans

    # Otherwise copy NaNs (if there are any)
    np.copyto(result, potential_nans, where=n)
    return result

def _opt_info():
    """
    Returns a string contains the supported CPU features by the current build.

    The string format can be explained as follows:
        - dispatched features that are supported by the running machine
          end with `*`.
        - dispatched features that are "not" supported by the running machine
          end with `?`.
        - remained features are representing the baseline.
    """
    from numpy.core._multiarray_umath import (
        __cpu_features__, __cpu_baseline__, __cpu_dispatch__
    )

    if len(__cpu_baseline__) == 0 and len(__cpu_dispatch__) == 0:
        return ''

    enabled_features = ' '.join(__cpu_baseline__)
    for feature in __cpu_dispatch__:
        if __cpu_features__[feature]:
            enabled_features += f" {feature}*"
        else:
            enabled_features += f" {feature}?"

    return enabled_features


def drop_metadata(dtype, /):
    """
    Returns the dtype unchanged if it contained no metadata or a copy of the
    dtype if it (or any of its structure dtypes) contained metadata.

    This utility is used by `np.save` and `np.savez` to drop metadata before
    saving.

    .. note::

        Due to its limitation this function may move to a more appropriate
        home or change in the future and is considered semi-public API only.

    .. warning::

        This function does not preserve more strange things like record dtypes
        and user dtypes may simply return the wrong thing.  If you need to be
        sure about the latter, check the result with:
        ``np.can_cast(new_dtype, dtype, casting="no")``.

    """
    if dtype.fields is not None:
        found_metadata = dtype.metadata is not None

        names = []
        formats = []
        offsets = []
        titles = []
        for name, field in dtype.fields.items():
            field_dt = drop_metadata(field[0])
            if field_dt is not field[0]:
                found_metadata = True

            names.append(name)
            formats.append(field_dt)
            offsets.append(field[1])
            titles.append(None if len(field) < 3 else field[2])

        if not found_metadata:
            return dtype

        structure = dict(
            names=names, formats=formats, offsets=offsets, titles=titles,
            itemsize=dtype.itemsize)

        # NOTE: Could pass (dtype.type, structure) to preserve record dtypes...
        return np.dtype(structure, align=dtype.isalignedstruct)
    elif dtype.subdtype is not None:
        # subarray dtype
        subdtype, shape = dtype.subdtype
        new_subdtype = drop_metadata(subdtype)
        if dtype.metadata is None and new_subdtype is subdtype:
            return dtype

        return np.dtype((new_subdtype, shape))
    else:
        # Normal unstructured dtype
        if dtype.metadata is None:
            return dtype
        # Note that `dt.str` doesn't round-trip e.g. for user-dtypes.
        return np.dtype(dtype.str)
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