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@@ -357,3 +357,7 @@ tuning-competition-baseline/.venv/lib/python3.11/site-packages/nvidia/cudnn/lib/
 .venv/lib/python3.11/site-packages/numpy/linalg/_umath_linalg.cpython-311-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 .venv/lib/python3.11/site-packages/numpy/linalg/__pycache__/linalg.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
 .venv/lib/python3.11/site-packages/numpy/linalg/tests/__pycache__/test_linalg.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+.venv/lib/python3.11/site-packages/numpy/typing/tests/data/pass/__pycache__/random.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+.venv/lib/python3.11/site-packages/numpy/random/_mt19937.cpython-311-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+.venv/lib/python3.11/site-packages/numpy/random/_generator.cpython-311-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+.venv/lib/python3.11/site-packages/numpy/random/bit_generator.cpython-311-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text

.venv/lib/python3.11/site-packages/numpy/core/_internal.py

@@ -0,0 +1,935 @@
+"""
+A place for internal code
+
+Some things are more easily handled Python.
+
+"""
+import ast
+import re
+import sys
+import warnings
+
+from ..exceptions import DTypePromotionError
+from .multiarray import dtype, array, ndarray, promote_types
+try:
+    import ctypes
+except ImportError:
+    ctypes = None
+
+IS_PYPY = sys.implementation.name == 'pypy'
+
+if sys.byteorder == 'little':
+    _nbo = '<'
+else:
+    _nbo = '>'
+
+def _makenames_list(adict, align):
+    allfields = []
+
+    for fname, obj in adict.items():
+        n = len(obj)
+        if not isinstance(obj, tuple) or n not in (2, 3):
+            raise ValueError("entry not a 2- or 3- tuple")
+        if n > 2 and obj[2] == fname:
+            continue
+        num = int(obj[1])
+        if num < 0:
+            raise ValueError("invalid offset.")
+        format = dtype(obj[0], align=align)
+        if n > 2:
+            title = obj[2]
+        else:
+            title = None
+        allfields.append((fname, format, num, title))
+    # sort by offsets
+    allfields.sort(key=lambda x: x[2])
+    names = [x[0] for x in allfields]
+    formats = [x[1] for x in allfields]
+    offsets = [x[2] for x in allfields]
+    titles = [x[3] for x in allfields]
+
+    return names, formats, offsets, titles
+
+# Called in PyArray_DescrConverter function when
+#  a dictionary without "names" and "formats"
+#  fields is used as a data-type descriptor.
+def _usefields(adict, align):
+    try:
+        names = adict[-1]
+    except KeyError:
+        names = None
+    if names is None:
+        names, formats, offsets, titles = _makenames_list(adict, align)
+    else:
+        formats = []
+        offsets = []
+        titles = []
+        for name in names:
+            res = adict[name]
+            formats.append(res[0])
+            offsets.append(res[1])
+            if len(res) > 2:
+                titles.append(res[2])
+            else:
+                titles.append(None)
+
+    return dtype({"names": names,
+                  "formats": formats,
+                  "offsets": offsets,
+                  "titles": titles}, align)
+
+
+# construct an array_protocol descriptor list
+#  from the fields attribute of a descriptor
+# This calls itself recursively but should eventually hit
+#  a descriptor that has no fields and then return
+#  a simple typestring
+
+def _array_descr(descriptor):
+    fields = descriptor.fields
+    if fields is None:
+        subdtype = descriptor.subdtype
+        if subdtype is None:
+            if descriptor.metadata is None:
+                return descriptor.str
+            else:
+                new = descriptor.metadata.copy()
+                if new:
+                    return (descriptor.str, new)
+                else:
+                    return descriptor.str
+        else:
+            return (_array_descr(subdtype[0]), subdtype[1])
+
+    names = descriptor.names
+    ordered_fields = [fields[x] + (x,) for x in names]
+    result = []
+    offset = 0
+    for field in ordered_fields:
+        if field[1] > offset:
+            num = field[1] - offset
+            result.append(('', f'|V{num}'))
+            offset += num
+        elif field[1] < offset:
+            raise ValueError(
+                "dtype.descr is not defined for types with overlapping or "
+                "out-of-order fields")
+        if len(field) > 3:
+            name = (field[2], field[3])
+        else:
+            name = field[2]
+        if field[0].subdtype:
+            tup = (name, _array_descr(field[0].subdtype[0]),
+                   field[0].subdtype[1])
+        else:
+            tup = (name, _array_descr(field[0]))
+        offset += field[0].itemsize
+        result.append(tup)
+
+    if descriptor.itemsize > offset:
+        num = descriptor.itemsize - offset
+        result.append(('', f'|V{num}'))
+
+    return result
+
+# Build a new array from the information in a pickle.
+# Note that the name numpy.core._internal._reconstruct is embedded in
+# pickles of ndarrays made with NumPy before release 1.0
+# so don't remove the name here, or you'll
+# break backward compatibility.
+def _reconstruct(subtype, shape, dtype):
+    return ndarray.__new__(subtype, shape, dtype)
+
+
+# format_re was originally from numarray by J. Todd Miller
+
+format_re = re.compile(r'(?P<order1>[<>|=]?)'
+                       r'(?P<repeats> *[(]?[ ,0-9]*[)]? *)'
+                       r'(?P<order2>[<>|=]?)'
+                       r'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
+sep_re = re.compile(r'\s*,\s*')
+space_re = re.compile(r'\s+$')
+
+# astr is a string (perhaps comma separated)
+
+_convorder = {'=': _nbo}
+
+def _commastring(astr):
+    startindex = 0
+    result = []
+    while startindex < len(astr):
+        mo = format_re.match(astr, pos=startindex)
+        try:
+            (order1, repeats, order2, dtype) = mo.groups()
+        except (TypeError, AttributeError):
+            raise ValueError(
+                f'format number {len(result)+1} of "{astr}" is not recognized'
+                ) from None
+        startindex = mo.end()
+        # Separator or ending padding
+        if startindex < len(astr):
+            if space_re.match(astr, pos=startindex):
+                startindex = len(astr)
+            else:
+                mo = sep_re.match(astr, pos=startindex)
+                if not mo:
+                    raise ValueError(
+                        'format number %d of "%s" is not recognized' %
+                        (len(result)+1, astr))
+                startindex = mo.end()
+
+        if order2 == '':
+            order = order1
+        elif order1 == '':
+            order = order2
+        else:
+            order1 = _convorder.get(order1, order1)
+            order2 = _convorder.get(order2, order2)
+            if (order1 != order2):
+                raise ValueError(
+                    'inconsistent byte-order specification %s and %s' %
+                    (order1, order2))
+            order = order1
+
+        if order in ('|', '=', _nbo):
+            order = ''
+        dtype = order + dtype
+        if (repeats == ''):
+            newitem = dtype
+        else:
+            newitem = (dtype, ast.literal_eval(repeats))
+        result.append(newitem)
+
+    return result
+
+class dummy_ctype:
+    def __init__(self, cls):
+        self._cls = cls
+    def __mul__(self, other):
+        return self
+    def __call__(self, *other):
+        return self._cls(other)
+    def __eq__(self, other):
+        return self._cls == other._cls
+    def __ne__(self, other):
+        return self._cls != other._cls
+
+def _getintp_ctype():
+    val = _getintp_ctype.cache
+    if val is not None:
+        return val
+    if ctypes is None:
+        import numpy as np
+        val = dummy_ctype(np.intp)
+    else:
+        char = dtype('p').char
+        if char == 'i':
+            val = ctypes.c_int
+        elif char == 'l':
+            val = ctypes.c_long
+        elif char == 'q':
+            val = ctypes.c_longlong
+        else:
+            val = ctypes.c_long
+    _getintp_ctype.cache = val
+    return val
+_getintp_ctype.cache = None
+
+# Used for .ctypes attribute of ndarray
+
+class _missing_ctypes:
+    def cast(self, num, obj):
+        return num.value
+
+    class c_void_p:
+        def __init__(self, ptr):
+            self.value = ptr
+
+
+class _ctypes:
+    def __init__(self, array, ptr=None):
+        self._arr = array
+
+        if ctypes:
+            self._ctypes = ctypes
+            self._data = self._ctypes.c_void_p(ptr)
+        else:
+            # fake a pointer-like object that holds onto the reference
+            self._ctypes = _missing_ctypes()
+            self._data = self._ctypes.c_void_p(ptr)
+            self._data._objects = array
+
+        if self._arr.ndim == 0:
+            self._zerod = True
+        else:
+            self._zerod = False
+
+    def data_as(self, obj):
+        """
+        Return the data pointer cast to a particular c-types object.
+        For example, calling ``self._as_parameter_`` is equivalent to
+        ``self.data_as(ctypes.c_void_p)``. Perhaps you want to use the data as a
+        pointer to a ctypes array of floating-point data:
+        ``self.data_as(ctypes.POINTER(ctypes.c_double))``.
+
+        The returned pointer will keep a reference to the array.
+        """
+        # _ctypes.cast function causes a circular reference of self._data in
+        # self._data._objects. Attributes of self._data cannot be released
+        # until gc.collect is called. Make a copy of the pointer first then let
+        # it hold the array reference. This is a workaround to circumvent the
+        # CPython bug https://bugs.python.org/issue12836
+        ptr = self._ctypes.cast(self._data, obj)
+        ptr._arr = self._arr
+        return ptr
+
+    def shape_as(self, obj):
+        """
+        Return the shape tuple as an array of some other c-types
+        type. For example: ``self.shape_as(ctypes.c_short)``.
+        """
+        if self._zerod:
+            return None
+        return (obj*self._arr.ndim)(*self._arr.shape)
+
+    def strides_as(self, obj):
+        """
+        Return the strides tuple as an array of some other
+        c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
+        """
+        if self._zerod:
+            return None
+        return (obj*self._arr.ndim)(*self._arr.strides)
+
+    @property
+    def data(self):
+        """
+        A pointer to the memory area of the array as a Python integer.
+        This memory area may contain data that is not aligned, or not in correct
+        byte-order. The memory area may not even be writeable. The array
+        flags and data-type of this array should be respected when passing this
+        attribute to arbitrary C-code to avoid trouble that can include Python
+        crashing. User Beware! The value of this attribute is exactly the same
+        as ``self._array_interface_['data'][0]``.
+
+        Note that unlike ``data_as``, a reference will not be kept to the array:
+        code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
+        pointer to a deallocated array, and should be spelt
+        ``(a + b).ctypes.data_as(ctypes.c_void_p)``
+        """
+        return self._data.value
+
+    @property
+    def shape(self):
+        """
+        (c_intp*self.ndim): A ctypes array of length self.ndim where
+        the basetype is the C-integer corresponding to ``dtype('p')`` on this
+        platform (see `~numpy.ctypeslib.c_intp`). This base-type could be
+        `ctypes.c_int`, `ctypes.c_long`, or `ctypes.c_longlong` depending on
+        the platform. The ctypes array contains the shape of
+        the underlying array.
+        """
+        return self.shape_as(_getintp_ctype())
+
+    @property
+    def strides(self):
+        """
+        (c_intp*self.ndim): A ctypes array of length self.ndim where
+        the basetype is the same as for the shape attribute. This ctypes array
+        contains the strides information from the underlying array. This strides
+        information is important for showing how many bytes must be jumped to
+        get to the next element in the array.
+        """
+        return self.strides_as(_getintp_ctype())
+
+    @property
+    def _as_parameter_(self):
+        """
+        Overrides the ctypes semi-magic method
+
+        Enables `c_func(some_array.ctypes)`
+        """
+        return self.data_as(ctypes.c_void_p)
+
+    # Numpy 1.21.0, 2021-05-18
+
+    def get_data(self):
+        """Deprecated getter for the `_ctypes.data` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn('"get_data" is deprecated. Use "data" instead',
+                      DeprecationWarning, stacklevel=2)
+        return self.data
+
+    def get_shape(self):
+        """Deprecated getter for the `_ctypes.shape` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn('"get_shape" is deprecated. Use "shape" instead',
+                      DeprecationWarning, stacklevel=2)
+        return self.shape
+
+    def get_strides(self):
+        """Deprecated getter for the `_ctypes.strides` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn('"get_strides" is deprecated. Use "strides" instead',
+                      DeprecationWarning, stacklevel=2)
+        return self.strides
+
+    def get_as_parameter(self):
+        """Deprecated getter for the `_ctypes._as_parameter_` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn(
+            '"get_as_parameter" is deprecated. Use "_as_parameter_" instead',
+            DeprecationWarning, stacklevel=2,
+        )
+        return self._as_parameter_
+
+
+def _newnames(datatype, order):
+    """
+    Given a datatype and an order object, return a new names tuple, with the
+    order indicated
+    """
+    oldnames = datatype.names
+    nameslist = list(oldnames)
+    if isinstance(order, str):
+        order = [order]
+    seen = set()
+    if isinstance(order, (list, tuple)):
+        for name in order:
+            try:
+                nameslist.remove(name)
+            except ValueError:
+                if name in seen:
+                    raise ValueError(f"duplicate field name: {name}") from None
+                else:
+                    raise ValueError(f"unknown field name: {name}") from None
+            seen.add(name)
+        return tuple(list(order) + nameslist)
+    raise ValueError(f"unsupported order value: {order}")
+
+def _copy_fields(ary):
+    """Return copy of structured array with padding between fields removed.
+
+    Parameters
+    ----------
+    ary : ndarray
+       Structured array from which to remove padding bytes
+
+    Returns
+    -------
+    ary_copy : ndarray
+       Copy of ary with padding bytes removed
+    """
+    dt = ary.dtype
+    copy_dtype = {'names': dt.names,
+                  'formats': [dt.fields[name][0] for name in dt.names]}
+    return array(ary, dtype=copy_dtype, copy=True)
+
+def _promote_fields(dt1, dt2):
+    """ Perform type promotion for two structured dtypes.
+
+    Parameters
+    ----------
+    dt1 : structured dtype
+        First dtype.
+    dt2 : structured dtype
+        Second dtype.
+
+    Returns
+    -------
+    out : dtype
+        The promoted dtype
+
+    Notes
+    -----
+    If one of the inputs is aligned, the result will be.  The titles of
+    both descriptors must match (point to the same field).
+    """
+    # Both must be structured and have the same names in the same order
+    if (dt1.names is None or dt2.names is None) or dt1.names != dt2.names:
+        raise DTypePromotionError(
+                f"field names `{dt1.names}` and `{dt2.names}` mismatch.")
+
+    # if both are identical, we can (maybe!) just return the same dtype.
+    identical = dt1 is dt2
+    new_fields = []
+    for name in dt1.names:
+        field1 = dt1.fields[name]
+        field2 = dt2.fields[name]
+        new_descr = promote_types(field1[0], field2[0])
+        identical = identical and new_descr is field1[0]
+
+        # Check that the titles match (if given):
+        if field1[2:] != field2[2:]:
+            raise DTypePromotionError(
+                    f"field titles of field '{name}' mismatch")
+        if len(field1) == 2:
+            new_fields.append((name, new_descr))
+        else:
+            new_fields.append(((field1[2], name), new_descr))
+
+    res = dtype(new_fields, align=dt1.isalignedstruct or dt2.isalignedstruct)
+
+    # Might as well preserve identity (and metadata) if the dtype is identical
+    # and the itemsize, offsets are also unmodified.  This could probably be
+    # sped up, but also probably just be removed entirely.
+    if identical and res.itemsize == dt1.itemsize:
+        for name in dt1.names:
+            if dt1.fields[name][1] != res.fields[name][1]:
+                return res  # the dtype changed.
+        return dt1
+
+    return res
+
+
+def _getfield_is_safe(oldtype, newtype, offset):
+    """ Checks safety of getfield for object arrays.
+
+    As in _view_is_safe, we need to check that memory containing objects is not
+    reinterpreted as a non-object datatype and vice versa.
+
+    Parameters
+    ----------
+    oldtype : data-type
+        Data type of the original ndarray.
+    newtype : data-type
+        Data type of the field being accessed by ndarray.getfield
+    offset : int
+        Offset of the field being accessed by ndarray.getfield
+
+    Raises
+    ------
+    TypeError
+        If the field access is invalid
+
+    """
+    if newtype.hasobject or oldtype.hasobject:
+        if offset == 0 and newtype == oldtype:
+            return
+        if oldtype.names is not None:
+            for name in oldtype.names:
+                if (oldtype.fields[name][1] == offset and
+                        oldtype.fields[name][0] == newtype):
+                    return
+        raise TypeError("Cannot get/set field of an object array")
+    return
+
+def _view_is_safe(oldtype, newtype):
+    """ Checks safety of a view involving object arrays, for example when
+    doing::
+
+        np.zeros(10, dtype=oldtype).view(newtype)
+
+    Parameters
+    ----------
+    oldtype : data-type
+        Data type of original ndarray
+    newtype : data-type
+        Data type of the view
+
+    Raises
+    ------
+    TypeError
+        If the new type is incompatible with the old type.
+
+    """
+
+    # if the types are equivalent, there is no problem.
+    # for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
+    if oldtype == newtype:
+        return
+
+    if newtype.hasobject or oldtype.hasobject:
+        raise TypeError("Cannot change data-type for object array.")
+    return
+
+# Given a string containing a PEP 3118 format specifier,
+# construct a NumPy dtype
+
+_pep3118_native_map = {
+    '?': '?',
+    'c': 'S1',
+    'b': 'b',
+    'B': 'B',
+    'h': 'h',
+    'H': 'H',
+    'i': 'i',
+    'I': 'I',
+    'l': 'l',
+    'L': 'L',
+    'q': 'q',
+    'Q': 'Q',
+    'e': 'e',
+    'f': 'f',
+    'd': 'd',
+    'g': 'g',
+    'Zf': 'F',
+    'Zd': 'D',
+    'Zg': 'G',
+    's': 'S',
+    'w': 'U',
+    'O': 'O',
+    'x': 'V',  # padding
+}
+_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
+
+_pep3118_standard_map = {
+    '?': '?',
+    'c': 'S1',
+    'b': 'b',
+    'B': 'B',
+    'h': 'i2',
+    'H': 'u2',
+    'i': 'i4',
+    'I': 'u4',
+    'l': 'i4',
+    'L': 'u4',
+    'q': 'i8',
+    'Q': 'u8',
+    'e': 'f2',
+    'f': 'f',
+    'd': 'd',
+    'Zf': 'F',
+    'Zd': 'D',
+    's': 'S',
+    'w': 'U',
+    'O': 'O',
+    'x': 'V',  # padding
+}
+_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
+
+_pep3118_unsupported_map = {
+    'u': 'UCS-2 strings',
+    '&': 'pointers',
+    't': 'bitfields',
+    'X': 'function pointers',
+}
+
+class _Stream:
+    def __init__(self, s):
+        self.s = s
+        self.byteorder = '@'
+
+    def advance(self, n):
+        res = self.s[:n]
+        self.s = self.s[n:]
+        return res
+
+    def consume(self, c):
+        if self.s[:len(c)] == c:
+            self.advance(len(c))
+            return True
+        return False
+
+    def consume_until(self, c):
+        if callable(c):
+            i = 0
+            while i < len(self.s) and not c(self.s[i]):
+                i = i + 1
+            return self.advance(i)
+        else:
+            i = self.s.index(c)
+            res = self.advance(i)
+            self.advance(len(c))
+            return res
+
+    @property
+    def next(self):
+        return self.s[0]
+
+    def __bool__(self):
+        return bool(self.s)
+
+
+def _dtype_from_pep3118(spec):
+    stream = _Stream(spec)
+    dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
+    return dtype
+
+def __dtype_from_pep3118(stream, is_subdtype):
+    field_spec = dict(
+        names=[],
+        formats=[],
+        offsets=[],
+        itemsize=0
+    )
+    offset = 0
+    common_alignment = 1
+    is_padding = False
+
+    # Parse spec
+    while stream:
+        value = None
+
+        # End of structure, bail out to upper level
+        if stream.consume('}'):
+            break
+
+        # Sub-arrays (1)
+        shape = None
+        if stream.consume('('):
+            shape = stream.consume_until(')')
+            shape = tuple(map(int, shape.split(',')))
+
+        # Byte order
+        if stream.next in ('@', '=', '<', '>', '^', '!'):
+            byteorder = stream.advance(1)
+            if byteorder == '!':
+                byteorder = '>'
+            stream.byteorder = byteorder
+
+        # Byte order characters also control native vs. standard type sizes
+        if stream.byteorder in ('@', '^'):
+            type_map = _pep3118_native_map
+            type_map_chars = _pep3118_native_typechars
+        else:
+            type_map = _pep3118_standard_map
+            type_map_chars = _pep3118_standard_typechars
+
+        # Item sizes
+        itemsize_str = stream.consume_until(lambda c: not c.isdigit())
+        if itemsize_str:
+            itemsize = int(itemsize_str)
+        else:
+            itemsize = 1
+
+        # Data types
+        is_padding = False
+
+        if stream.consume('T{'):
+            value, align = __dtype_from_pep3118(
+                stream, is_subdtype=True)
+        elif stream.next in type_map_chars:
+            if stream.next == 'Z':
+                typechar = stream.advance(2)
+            else:
+                typechar = stream.advance(1)
+
+            is_padding = (typechar == 'x')
+            dtypechar = type_map[typechar]
+            if dtypechar in 'USV':
+                dtypechar += '%d' % itemsize
+                itemsize = 1
+            numpy_byteorder = {'@': '=', '^': '='}.get(
+                stream.byteorder, stream.byteorder)
+            value = dtype(numpy_byteorder + dtypechar)
+            align = value.alignment
+        elif stream.next in _pep3118_unsupported_map:
+            desc = _pep3118_unsupported_map[stream.next]
+            raise NotImplementedError(
+                "Unrepresentable PEP 3118 data type {!r} ({})"
+                .format(stream.next, desc))
+        else:
+            raise ValueError("Unknown PEP 3118 data type specifier %r" % stream.s)
+
+        #
+        # Native alignment may require padding
+        #
+        # Here we assume that the presence of a '@' character implicitly implies
+        # that the start of the array is *already* aligned.
+        #
+        extra_offset = 0
+        if stream.byteorder == '@':
+            start_padding = (-offset) % align
+            intra_padding = (-value.itemsize) % align
+
+            offset += start_padding
+
+            if intra_padding != 0:
+                if itemsize > 1 or (shape is not None and _prod(shape) > 1):
+                    # Inject internal padding to the end of the sub-item
+                    value = _add_trailing_padding(value, intra_padding)
+                else:
+                    # We can postpone the injection of internal padding,
+                    # as the item appears at most once
+                    extra_offset += intra_padding
+
+            # Update common alignment
+            common_alignment = _lcm(align, common_alignment)
+
+        # Convert itemsize to sub-array
+        if itemsize != 1:
+            value = dtype((value, (itemsize,)))
+
+        # Sub-arrays (2)
+        if shape is not None:
+            value = dtype((value, shape))
+
+        # Field name
+        if stream.consume(':'):
+            name = stream.consume_until(':')
+        else:
+            name = None
+
+        if not (is_padding and name is None):
+            if name is not None and name in field_spec['names']:
+                raise RuntimeError(f"Duplicate field name '{name}' in PEP3118 format")
+            field_spec['names'].append(name)
+            field_spec['formats'].append(value)
+            field_spec['offsets'].append(offset)
+
+        offset += value.itemsize
+        offset += extra_offset
+
+        field_spec['itemsize'] = offset
+
+    # extra final padding for aligned types
+    if stream.byteorder == '@':
+        field_spec['itemsize'] += (-offset) % common_alignment
+
+    # Check if this was a simple 1-item type, and unwrap it
+    if (field_spec['names'] == [None]
+            and field_spec['offsets'][0] == 0
+            and field_spec['itemsize'] == field_spec['formats'][0].itemsize
+            and not is_subdtype):
+        ret = field_spec['formats'][0]
+    else:
+        _fix_names(field_spec)
+        ret = dtype(field_spec)
+
+    # Finished
+    return ret, common_alignment
+
+def _fix_names(field_spec):
+    """ Replace names which are None with the next unused f%d name """
+    names = field_spec['names']
+    for i, name in enumerate(names):
+        if name is not None:
+            continue
+
+        j = 0
+        while True:
+            name = f'f{j}'
+            if name not in names:
+                break
+            j = j + 1
+        names[i] = name
+
+def _add_trailing_padding(value, padding):
+    """Inject the specified number of padding bytes at the end of a dtype"""
+    if value.fields is None:
+        field_spec = dict(
+            names=['f0'],
+            formats=[value],
+            offsets=[0],
+            itemsize=value.itemsize
+        )
+    else:
+        fields = value.fields
+        names = value.names
+        field_spec = dict(
+            names=names,
+            formats=[fields[name][0] for name in names],
+            offsets=[fields[name][1] for name in names],
+            itemsize=value.itemsize
+        )
+
+    field_spec['itemsize'] += padding
+    return dtype(field_spec)
+
+def _prod(a):
+    p = 1
+    for x in a:
+        p *= x
+    return p
+
+def _gcd(a, b):
+    """Calculate the greatest common divisor of a and b"""
+    while b:
+        a, b = b, a % b
+    return a
+
+def _lcm(a, b):
+    return a // _gcd(a, b) * b
+
+def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
+    """ Format the error message for when __array_ufunc__ gives up. """
+    args_string = ', '.join(['{!r}'.format(arg) for arg in inputs] +
+                            ['{}={!r}'.format(k, v)
+                             for k, v in kwargs.items()])
+    args = inputs + kwargs.get('out', ())
+    types_string = ', '.join(repr(type(arg).__name__) for arg in args)
+    return ('operand type(s) all returned NotImplemented from '
+            '__array_ufunc__({!r}, {!r}, {}): {}'
+            .format(ufunc, method, args_string, types_string))
+
+
+def array_function_errmsg_formatter(public_api, types):
+    """ Format the error message for when __array_ufunc__ gives up. """
+    func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
+    return ("no implementation found for '{}' on types that implement "
+            '__array_function__: {}'.format(func_name, list(types)))
+
+
+def _ufunc_doc_signature_formatter(ufunc):
+    """
+    Builds a signature string which resembles PEP 457
+
+    This is used to construct the first line of the docstring
+    """
+
+    # input arguments are simple
+    if ufunc.nin == 1:
+        in_args = 'x'
+    else:
+        in_args = ', '.join(f'x{i+1}' for i in range(ufunc.nin))
+
+    # output arguments are both keyword or positional
+    if ufunc.nout == 0:
+        out_args = ', /, out=()'
+    elif ufunc.nout == 1:
+        out_args = ', /, out=None'
+    else:
+        out_args = '[, {positional}], / [, out={default}]'.format(
+            positional=', '.join(
+                'out{}'.format(i+1) for i in range(ufunc.nout)),
+            default=repr((None,)*ufunc.nout)
+        )
+
+    # keyword only args depend on whether this is a gufunc
+    kwargs = (
+        ", casting='same_kind'"
+        ", order='K'"
+        ", dtype=None"
+        ", subok=True"
+    )
+
+    # NOTE: gufuncs may or may not support the `axis` parameter
+    if ufunc.signature is None:
+        kwargs = f", where=True{kwargs}[, signature, extobj]"
+    else:
+        kwargs += "[, signature, extobj, axes, axis]"
+
+    # join all the parts together
+    return '{name}({in_args}{out_args}, *{kwargs})'.format(
+        name=ufunc.__name__,
+        in_args=in_args,
+        out_args=out_args,
+        kwargs=kwargs
+    )
+
+
+def npy_ctypes_check(cls):
+    # determine if a class comes from ctypes, in order to work around
+    # a bug in the buffer protocol for those objects, bpo-10746
+    try:
+        # ctypes class are new-style, so have an __mro__. This probably fails
+        # for ctypes classes with multiple inheritance.
+        if IS_PYPY:
+            # (..., _ctypes.basics._CData, Bufferable, object)
+            ctype_base = cls.__mro__[-3]
+        else:
+            # # (..., _ctypes._CData, object)
+            ctype_base = cls.__mro__[-2]
+        # right now, they're part of the _ctypes module
+        return '_ctypes' in ctype_base.__module__
+    except Exception:
+        return False

.venv/lib/python3.11/site-packages/numpy/core/_type_aliases.pyi

@@ -0,0 +1,13 @@
+from typing import Any, TypedDict
+
+from numpy import generic, signedinteger, unsignedinteger, floating, complexfloating
+
+class _SCTypes(TypedDict):
+    int: list[type[signedinteger[Any]]]
+    uint: list[type[unsignedinteger[Any]]]
+    float: list[type[floating[Any]]]
+    complex: list[type[complexfloating[Any, Any]]]
+    others: list[type]
+
+sctypeDict: dict[int | str, type[generic]]
+sctypes: _SCTypes

.venv/lib/python3.11/site-packages/numpy/core/cversions.py

@@ -0,0 +1,13 @@
+"""Simple script to compute the api hash of the current API.
+
+The API has is defined by numpy_api_order and ufunc_api_order.
+
+"""
+from os.path import dirname
+
+from code_generators.genapi import fullapi_hash
+from code_generators.numpy_api import full_api
+
+if __name__ == '__main__':
+    curdir = dirname(__file__)
+    print(fullapi_hash(full_api))

.venv/lib/python3.11/site-packages/numpy/core/numerictypes.py

@@ -0,0 +1,689 @@
+"""
+numerictypes: Define the numeric type objects
+
+This module is designed so "from numerictypes import \\*" is safe.
+Exported symbols include:
+
+  Dictionary with all registered number types (including aliases):
+    sctypeDict
+
+  Type objects (not all will be available, depends on platform):
+      see variable sctypes for which ones you have
+
+    Bit-width names
+
+    int8 int16 int32 int64 int128
+    uint8 uint16 uint32 uint64 uint128
+    float16 float32 float64 float96 float128 float256
+    complex32 complex64 complex128 complex192 complex256 complex512
+    datetime64 timedelta64
+
+    c-based names
+
+    bool_
+
+    object_
+
+    void, str_, unicode_
+
+    byte, ubyte,
+    short, ushort
+    intc, uintc,
+    intp, uintp,
+    int_, uint,
+    longlong, ulonglong,
+
+    single, csingle,
+    float_, complex_,
+    longfloat, clongfloat,
+
+   As part of the type-hierarchy:    xx -- is bit-width
+
+   generic
+     +-> bool_                                  (kind=b)
+     +-> number
+     |   +-> integer
+     |   |   +-> signedinteger     (intxx)      (kind=i)
+     |   |   |     byte
+     |   |   |     short
+     |   |   |     intc
+     |   |   |     intp
+     |   |   |     int_
+     |   |   |     longlong
+     |   |   \\-> unsignedinteger  (uintxx)     (kind=u)
+     |   |         ubyte
+     |   |         ushort
+     |   |         uintc
+     |   |         uintp
+     |   |         uint_
+     |   |         ulonglong
+     |   +-> inexact
+     |       +-> floating          (floatxx)    (kind=f)
+     |       |     half
+     |       |     single
+     |       |     float_          (double)
+     |       |     longfloat
+     |       \\-> complexfloating  (complexxx)  (kind=c)
+     |             csingle         (singlecomplex)
+     |             complex_        (cfloat, cdouble)
+     |             clongfloat      (longcomplex)
+     +-> flexible
+     |   +-> character
+     |   |     str_     (string_, bytes_)       (kind=S)    [Python 2]
+     |   |     unicode_                         (kind=U)    [Python 2]
+     |   |
+     |   |     bytes_   (string_)               (kind=S)    [Python 3]
+     |   |     str_     (unicode_)              (kind=U)    [Python 3]
+     |   |
+     |   \\-> void                              (kind=V)
+     \\-> object_ (not used much)               (kind=O)
+
+"""
+import numbers
+import warnings
+
+from .multiarray import (
+        ndarray, array, dtype, datetime_data, datetime_as_string,
+        busday_offset, busday_count, is_busday, busdaycalendar
+        )
+from .._utils import set_module
+
+# we add more at the bottom
+__all__ = ['sctypeDict', 'sctypes',
+           'ScalarType', 'obj2sctype', 'cast', 'nbytes', 'sctype2char',
+           'maximum_sctype', 'issctype', 'typecodes', 'find_common_type',
+           'issubdtype', 'datetime_data', 'datetime_as_string',
+           'busday_offset', 'busday_count', 'is_busday', 'busdaycalendar',
+           ]
+
+# we don't need all these imports, but we need to keep them for compatibility
+# for users using np.core.numerictypes.UPPER_TABLE
+from ._string_helpers import (
+    english_lower, english_upper, english_capitalize, LOWER_TABLE, UPPER_TABLE
+)
+
+from ._type_aliases import (
+    sctypeDict,
+    allTypes,
+    bitname,
+    sctypes,
+    _concrete_types,
+    _concrete_typeinfo,
+    _bits_of,
+)
+from ._dtype import _kind_name
+
+# we don't export these for import *, but we do want them accessible
+# as numerictypes.bool, etc.
+from builtins import bool, int, float, complex, object, str, bytes
+from numpy.compat import long, unicode
+
+
+# We use this later
+generic = allTypes['generic']
+
+genericTypeRank = ['bool', 'int8', 'uint8', 'int16', 'uint16',
+                   'int32', 'uint32', 'int64', 'uint64', 'int128',
+                   'uint128', 'float16',
+                   'float32', 'float64', 'float80', 'float96', 'float128',
+                   'float256',
+                   'complex32', 'complex64', 'complex128', 'complex160',
+                   'complex192', 'complex256', 'complex512', 'object']
+
+@set_module('numpy')
+def maximum_sctype(t):
+    """
+    Return the scalar type of highest precision of the same kind as the input.
+
+    Parameters
+    ----------
+    t : dtype or dtype specifier
+        The input data type. This can be a `dtype` object or an object that
+        is convertible to a `dtype`.
+
+    Returns
+    -------
+    out : dtype
+        The highest precision data type of the same kind (`dtype.kind`) as `t`.
+
+    See Also
+    --------
+    obj2sctype, mintypecode, sctype2char
+    dtype
+
+    Examples
+    --------
+    >>> np.maximum_sctype(int)
+    <class 'numpy.int64'>
+    >>> np.maximum_sctype(np.uint8)
+    <class 'numpy.uint64'>
+    >>> np.maximum_sctype(complex)
+    <class 'numpy.complex256'> # may vary
+
+    >>> np.maximum_sctype(str)
+    <class 'numpy.str_'>
+
+    >>> np.maximum_sctype('i2')
+    <class 'numpy.int64'>
+    >>> np.maximum_sctype('f4')
+    <class 'numpy.float128'> # may vary
+
+    """
+    g = obj2sctype(t)
+    if g is None:
+        return t
+    t = g
+    base = _kind_name(dtype(t))
+    if base in sctypes:
+        return sctypes[base][-1]
+    else:
+        return t
+
+
+@set_module('numpy')
+def issctype(rep):
+    """
+    Determines whether the given object represents a scalar data-type.
+
+    Parameters
+    ----------
+    rep : any
+        If `rep` is an instance of a scalar dtype, True is returned. If not,
+        False is returned.
+
+    Returns
+    -------
+    out : bool
+        Boolean result of check whether `rep` is a scalar dtype.
+
+    See Also
+    --------
+    issubsctype, issubdtype, obj2sctype, sctype2char
+
+    Examples
+    --------
+    >>> np.issctype(np.int32)
+    True
+    >>> np.issctype(list)
+    False
+    >>> np.issctype(1.1)
+    False
+
+    Strings are also a scalar type:
+
+    >>> np.issctype(np.dtype('str'))
+    True
+
+    """
+    if not isinstance(rep, (type, dtype)):
+        return False
+    try:
+        res = obj2sctype(rep)
+        if res and res != object_:
+            return True
+        return False
+    except Exception:
+        return False
+
+
+@set_module('numpy')
+def obj2sctype(rep, default=None):
+    """
+    Return the scalar dtype or NumPy equivalent of Python type of an object.
+
+    Parameters
+    ----------
+    rep : any
+        The object of which the type is returned.
+    default : any, optional
+        If given, this is returned for objects whose types can not be
+        determined. If not given, None is returned for those objects.
+
+    Returns
+    -------
+    dtype : dtype or Python type
+        The data type of `rep`.
+
+    See Also
+    --------
+    sctype2char, issctype, issubsctype, issubdtype, maximum_sctype
+
+    Examples
+    --------
+    >>> np.obj2sctype(np.int32)
+    <class 'numpy.int32'>
+    >>> np.obj2sctype(np.array([1., 2.]))
+    <class 'numpy.float64'>
+    >>> np.obj2sctype(np.array([1.j]))
+    <class 'numpy.complex128'>
+
+    >>> np.obj2sctype(dict)
+    <class 'numpy.object_'>
+    >>> np.obj2sctype('string')
+
+    >>> np.obj2sctype(1, default=list)
+    <class 'list'>
+
+    """
+    # prevent abstract classes being upcast
+    if isinstance(rep, type) and issubclass(rep, generic):
+        return rep
+    # extract dtype from arrays
+    if isinstance(rep, ndarray):
+        return rep.dtype.type
+    # fall back on dtype to convert
+    try:
+        res = dtype(rep)
+    except Exception:
+        return default
+    else:
+        return res.type
+
+
+@set_module('numpy')
+def issubclass_(arg1, arg2):
+    """
+    Determine if a class is a subclass of a second class.
+
+    `issubclass_` is equivalent to the Python built-in ``issubclass``,
+    except that it returns False instead of raising a TypeError if one
+    of the arguments is not a class.
+
+    Parameters
+    ----------
+    arg1 : class
+        Input class. True is returned if `arg1` is a subclass of `arg2`.
+    arg2 : class or tuple of classes.
+        Input class. If a tuple of classes, True is returned if `arg1` is a
+        subclass of any of the tuple elements.
+
+    Returns
+    -------
+    out : bool
+        Whether `arg1` is a subclass of `arg2` or not.
+
+    See Also
+    --------
+    issubsctype, issubdtype, issctype
+
+    Examples
+    --------
+    >>> np.issubclass_(np.int32, int)
+    False
+    >>> np.issubclass_(np.int32, float)
+    False
+    >>> np.issubclass_(np.float64, float)
+    True
+
+    """
+    try:
+        return issubclass(arg1, arg2)
+    except TypeError:
+        return False
+
+
+@set_module('numpy')
+def issubsctype(arg1, arg2):
+    """
+    Determine if the first argument is a subclass of the second argument.
+
+    Parameters
+    ----------
+    arg1, arg2 : dtype or dtype specifier
+        Data-types.
+
+    Returns
+    -------
+    out : bool
+        The result.
+
+    See Also
+    --------
+    issctype, issubdtype, obj2sctype
+
+    Examples
+    --------
+    >>> np.issubsctype('S8', str)
+    False
+    >>> np.issubsctype(np.array([1]), int)
+    True
+    >>> np.issubsctype(np.array([1]), float)
+    False
+
+    """
+    return issubclass(obj2sctype(arg1), obj2sctype(arg2))
+
+
+@set_module('numpy')
+def issubdtype(arg1, arg2):
+    r"""
+    Returns True if first argument is a typecode lower/equal in type hierarchy.
+
+    This is like the builtin :func:`issubclass`, but for `dtype`\ s.
+
+    Parameters
+    ----------
+    arg1, arg2 : dtype_like
+        `dtype` or object coercible to one
+
+    Returns
+    -------
+    out : bool
+
+    See Also
+    --------
+    :ref:`arrays.scalars` : Overview of the numpy type hierarchy.
+    issubsctype, issubclass_
+
+    Examples
+    --------
+    `issubdtype` can be used to check the type of arrays:
+
+    >>> ints = np.array([1, 2, 3], dtype=np.int32)
+    >>> np.issubdtype(ints.dtype, np.integer)
+    True
+    >>> np.issubdtype(ints.dtype, np.floating)
+    False
+
+    >>> floats = np.array([1, 2, 3], dtype=np.float32)
+    >>> np.issubdtype(floats.dtype, np.integer)
+    False
+    >>> np.issubdtype(floats.dtype, np.floating)
+    True
+
+    Similar types of different sizes are not subdtypes of each other:
+
+    >>> np.issubdtype(np.float64, np.float32)
+    False
+    >>> np.issubdtype(np.float32, np.float64)
+    False
+
+    but both are subtypes of `floating`:
+
+    >>> np.issubdtype(np.float64, np.floating)
+    True
+    >>> np.issubdtype(np.float32, np.floating)
+    True
+
+    For convenience, dtype-like objects are allowed too:
+
+    >>> np.issubdtype('S1', np.string_)
+    True
+    >>> np.issubdtype('i4', np.signedinteger)
+    True
+
+    """
+    if not issubclass_(arg1, generic):
+        arg1 = dtype(arg1).type
+    if not issubclass_(arg2, generic):
+        arg2 = dtype(arg2).type
+
+    return issubclass(arg1, arg2)
+
+
+# This dictionary allows look up based on any alias for an array data-type
+class _typedict(dict):
+    """
+    Base object for a dictionary for look-up with any alias for an array dtype.
+
+    Instances of `_typedict` can not be used as dictionaries directly,
+    first they have to be populated.
+
+    """
+
+    def __getitem__(self, obj):
+        return dict.__getitem__(self, obj2sctype(obj))
+
+nbytes = _typedict()
+_alignment = _typedict()
+_maxvals = _typedict()
+_minvals = _typedict()
+def _construct_lookups():
+    for name, info in _concrete_typeinfo.items():
+        obj = info.type
+        nbytes[obj] = info.bits // 8
+        _alignment[obj] = info.alignment
+        if len(info) > 5:
+            _maxvals[obj] = info.max
+            _minvals[obj] = info.min
+        else:
+            _maxvals[obj] = None
+            _minvals[obj] = None
+
+_construct_lookups()
+
+
+@set_module('numpy')
+def sctype2char(sctype):
+    """
+    Return the string representation of a scalar dtype.
+
+    Parameters
+    ----------
+    sctype : scalar dtype or object
+        If a scalar dtype, the corresponding string character is
+        returned. If an object, `sctype2char` tries to infer its scalar type
+        and then return the corresponding string character.
+
+    Returns
+    -------
+    typechar : str
+        The string character corresponding to the scalar type.
+
+    Raises
+    ------
+    ValueError
+        If `sctype` is an object for which the type can not be inferred.
+
+    See Also
+    --------
+    obj2sctype, issctype, issubsctype, mintypecode
+
+    Examples
+    --------
+    >>> for sctype in [np.int32, np.double, np.complex_, np.string_, np.ndarray]:
+    ...     print(np.sctype2char(sctype))
+    l # may vary
+    d
+    D
+    S
+    O
+
+    >>> x = np.array([1., 2-1.j])
+    >>> np.sctype2char(x)
+    'D'
+    >>> np.sctype2char(list)
+    'O'
+
+    """
+    sctype = obj2sctype(sctype)
+    if sctype is None:
+        raise ValueError("unrecognized type")
+    if sctype not in _concrete_types:
+        # for compatibility
+        raise KeyError(sctype)
+    return dtype(sctype).char
+
+# Create dictionary of casting functions that wrap sequences
+# indexed by type or type character
+cast = _typedict()
+for key in _concrete_types:
+    cast[key] = lambda x, k=key: array(x, copy=False).astype(k)
+
+
+def _scalar_type_key(typ):
+    """A ``key`` function for `sorted`."""
+    dt = dtype(typ)
+    return (dt.kind.lower(), dt.itemsize)
+
+
+ScalarType = [int, float, complex, bool, bytes, str, memoryview]
+ScalarType += sorted(_concrete_types, key=_scalar_type_key)
+ScalarType = tuple(ScalarType)
+
+
+# Now add the types we've determined to this module
+for key in allTypes:
+    globals()[key] = allTypes[key]
+    __all__.append(key)
+
+del key
+
+typecodes = {'Character':'c',
+             'Integer':'bhilqp',
+             'UnsignedInteger':'BHILQP',
+             'Float':'efdg',
+             'Complex':'FDG',
+             'AllInteger':'bBhHiIlLqQpP',
+             'AllFloat':'efdgFDG',
+             'Datetime': 'Mm',
+             'All':'?bhilqpBHILQPefdgFDGSUVOMm'}
+
+# backwards compatibility --- deprecated name
+# Formal deprecation: Numpy 1.20.0, 2020-10-19 (see numpy/__init__.py)
+typeDict = sctypeDict
+
+# b -> boolean
+# u -> unsigned integer
+# i -> signed integer
+# f -> floating point
+# c -> complex
+# M -> datetime
+# m -> timedelta
+# S -> string
+# U -> Unicode string
+# V -> record
+# O -> Python object
+_kind_list = ['b', 'u', 'i', 'f', 'c', 'S', 'U', 'V', 'O', 'M', 'm']
+
+__test_types = '?'+typecodes['AllInteger'][:-2]+typecodes['AllFloat']+'O'
+__len_test_types = len(__test_types)
+
+# Keep incrementing until a common type both can be coerced to
+#  is found.  Otherwise, return None
+def _find_common_coerce(a, b):
+    if a > b:
+        return a
+    try:
+        thisind = __test_types.index(a.char)
+    except ValueError:
+        return None
+    return _can_coerce_all([a, b], start=thisind)
+
+# Find a data-type that all data-types in a list can be coerced to
+def _can_coerce_all(dtypelist, start=0):
+    N = len(dtypelist)
+    if N == 0:
+        return None
+    if N == 1:
+        return dtypelist[0]
+    thisind = start
+    while thisind < __len_test_types:
+        newdtype = dtype(__test_types[thisind])
+        numcoerce = len([x for x in dtypelist if newdtype >= x])
+        if numcoerce == N:
+            return newdtype
+        thisind += 1
+    return None
+
+def _register_types():
+    numbers.Integral.register(integer)
+    numbers.Complex.register(inexact)
+    numbers.Real.register(floating)
+    numbers.Number.register(number)
+
+_register_types()
+
+
+@set_module('numpy')
+def find_common_type(array_types, scalar_types):
+    """
+    Determine common type following standard coercion rules.
+
+    .. deprecated:: NumPy 1.25
+
+        This function is deprecated, use `numpy.promote_types` or
+        `numpy.result_type` instead.  To achieve semantics for the
+        `scalar_types` argument, use `numpy.result_type` and pass the Python
+        values `0`, `0.0`, or `0j`.
+        This will give the same results in almost all cases.
+        More information and rare exception can be found in the
+        `NumPy 1.25 release notes <https://numpy.org/devdocs/release/1.25.0-notes.html>`_.
+
+    Parameters
+    ----------
+    array_types : sequence
+        A list of dtypes or dtype convertible objects representing arrays.
+    scalar_types : sequence
+        A list of dtypes or dtype convertible objects representing scalars.
+
+    Returns
+    -------
+    datatype : dtype
+        The common data type, which is the maximum of `array_types` ignoring
+        `scalar_types`, unless the maximum of `scalar_types` is of a
+        different kind (`dtype.kind`). If the kind is not understood, then
+        None is returned.
+
+    See Also
+    --------
+    dtype, common_type, can_cast, mintypecode
+
+    Examples
+    --------
+    >>> np.find_common_type([], [np.int64, np.float32, complex])
+    dtype('complex128')
+    >>> np.find_common_type([np.int64, np.float32], [])
+    dtype('float64')
+
+    The standard casting rules ensure that a scalar cannot up-cast an
+    array unless the scalar is of a fundamentally different kind of data
+    (i.e. under a different hierarchy in the data type hierarchy) then
+    the array:
+
+    >>> np.find_common_type([np.float32], [np.int64, np.float64])
+    dtype('float32')
+
+    Complex is of a different type, so it up-casts the float in the
+    `array_types` argument:
+
+    >>> np.find_common_type([np.float32], [complex])
+    dtype('complex128')
+
+    Type specifier strings are convertible to dtypes and can therefore
+    be used instead of dtypes:
+
+    >>> np.find_common_type(['f4', 'f4', 'i4'], ['c8'])
+    dtype('complex128')
+
+    """
+    # Deprecated 2022-11-07, NumPy 1.25
+    warnings.warn(
+            "np.find_common_type is deprecated.  Please use `np.result_type` "
+            "or `np.promote_types`.\n"
+            "See https://numpy.org/devdocs/release/1.25.0-notes.html and the "
+            "docs for more information.  (Deprecated NumPy 1.25)",
+            DeprecationWarning, stacklevel=2)
+
+    array_types = [dtype(x) for x in array_types]
+    scalar_types = [dtype(x) for x in scalar_types]
+
+    maxa = _can_coerce_all(array_types)
+    maxsc = _can_coerce_all(scalar_types)
+
+    if maxa is None:
+        return maxsc
+
+    if maxsc is None:
+        return maxa
+
+    try:
+        index_a = _kind_list.index(maxa.kind)
+        index_sc = _kind_list.index(maxsc.kind)
+    except ValueError:
+        return None
+
+    if index_sc > index_a:
+        return _find_common_coerce(maxsc, maxa)
+    else:
+        return maxa

.venv/lib/python3.11/site-packages/numpy/core/overrides.py

@@ -0,0 +1,181 @@
+"""Implementation of __array_function__ overrides from NEP-18."""
+import collections
+import functools
+import os
+
+from .._utils import set_module
+from .._utils._inspect import getargspec
+from numpy.core._multiarray_umath import (
+    add_docstring,  _get_implementing_args, _ArrayFunctionDispatcher)
+
+
+ARRAY_FUNCTIONS = set()
+
+array_function_like_doc = (
+    """like : array_like, optional
+        Reference object to allow the creation of arrays which are not
+        NumPy arrays. If an array-like passed in as ``like`` supports
+        the ``__array_function__`` protocol, the result will be defined
+        by it. In this case, it ensures the creation of an array object
+        compatible with that passed in via this argument."""
+)
+
+def set_array_function_like_doc(public_api):
+    if public_api.__doc__ is not None:
+        public_api.__doc__ = public_api.__doc__.replace(
+            "${ARRAY_FUNCTION_LIKE}",
+            array_function_like_doc,
+        )
+    return public_api
+
+
+add_docstring(
+    _ArrayFunctionDispatcher,
+    """
+    Class to wrap functions with checks for __array_function__ overrides.
+
+    All arguments are required, and can only be passed by position.
+
+    Parameters
+    ----------
+    dispatcher : function or None
+        The dispatcher function that returns a single sequence-like object
+        of all arguments relevant.  It must have the same signature (except
+        the default values) as the actual implementation.
+        If ``None``, this is a ``like=`` dispatcher and the
+        ``_ArrayFunctionDispatcher`` must be called with ``like`` as the
+        first (additional and positional) argument.
+    implementation : function
+        Function that implements the operation on NumPy arrays without
+        overrides.  Arguments passed calling the ``_ArrayFunctionDispatcher``
+        will be forwarded to this (and the ``dispatcher``) as if using
+        ``*args, **kwargs``.
+
+    Attributes
+    ----------
+    _implementation : function
+        The original implementation passed in.
+    """)
+
+
+# exposed for testing purposes; used internally by _ArrayFunctionDispatcher
+add_docstring(
+    _get_implementing_args,
+    """
+    Collect arguments on which to call __array_function__.
+
+    Parameters
+    ----------
+    relevant_args : iterable of array-like
+        Iterable of possibly array-like arguments to check for
+        __array_function__ methods.
+
+    Returns
+    -------
+    Sequence of arguments with __array_function__ methods, in the order in
+    which they should be called.
+    """)
+
+
+ArgSpec = collections.namedtuple('ArgSpec', 'args varargs keywords defaults')
+
+
+def verify_matching_signatures(implementation, dispatcher):
+    """Verify that a dispatcher function has the right signature."""
+    implementation_spec = ArgSpec(*getargspec(implementation))
+    dispatcher_spec = ArgSpec(*getargspec(dispatcher))
+
+    if (implementation_spec.args != dispatcher_spec.args or
+            implementation_spec.varargs != dispatcher_spec.varargs or
+            implementation_spec.keywords != dispatcher_spec.keywords or
+            (bool(implementation_spec.defaults) !=
+             bool(dispatcher_spec.defaults)) or
+            (implementation_spec.defaults is not None and
+             len(implementation_spec.defaults) !=
+             len(dispatcher_spec.defaults))):
+        raise RuntimeError('implementation and dispatcher for %s have '
+                           'different function signatures' % implementation)
+
+    if implementation_spec.defaults is not None:
+        if dispatcher_spec.defaults != (None,) * len(dispatcher_spec.defaults):
+            raise RuntimeError('dispatcher functions can only use None for '
+                               'default argument values')
+
+
+def array_function_dispatch(dispatcher=None, module=None, verify=True,
+                            docs_from_dispatcher=False):
+    """Decorator for adding dispatch with the __array_function__ protocol.
+
+    See NEP-18 for example usage.
+
+    Parameters
+    ----------
+    dispatcher : callable or None
+        Function that when called like ``dispatcher(*args, **kwargs)`` with
+        arguments from the NumPy function call returns an iterable of
+        array-like arguments to check for ``__array_function__``.
+
+        If `None`, the first argument is used as the single `like=` argument
+        and not passed on.  A function implementing `like=` must call its
+        dispatcher with `like` as the first non-keyword argument.
+    module : str, optional
+        __module__ attribute to set on new function, e.g., ``module='numpy'``.
+        By default, module is copied from the decorated function.
+    verify : bool, optional
+        If True, verify the that the signature of the dispatcher and decorated
+        function signatures match exactly: all required and optional arguments
+        should appear in order with the same names, but the default values for
+        all optional arguments should be ``None``. Only disable verification
+        if the dispatcher's signature needs to deviate for some particular
+        reason, e.g., because the function has a signature like
+        ``func(*args, **kwargs)``.
+    docs_from_dispatcher : bool, optional
+        If True, copy docs from the dispatcher function onto the dispatched
+        function, rather than from the implementation. This is useful for
+        functions defined in C, which otherwise don't have docstrings.
+
+    Returns
+    -------
+    Function suitable for decorating the implementation of a NumPy function.
+
+    """
+    def decorator(implementation):
+        if verify:
+            if dispatcher is not None:
+                verify_matching_signatures(implementation, dispatcher)
+            else:
+                # Using __code__ directly similar to verify_matching_signature
+                co = implementation.__code__
+                last_arg = co.co_argcount + co.co_kwonlyargcount - 1
+                last_arg = co.co_varnames[last_arg]
+                if last_arg != "like" or co.co_kwonlyargcount == 0:
+                    raise RuntimeError(
+                        "__array_function__ expects `like=` to be the last "
+                        "argument and a keyword-only argument. "
+                        f"{implementation} does not seem to comply.")
+
+        if docs_from_dispatcher:
+            add_docstring(implementation, dispatcher.__doc__)
+
+        public_api = _ArrayFunctionDispatcher(dispatcher, implementation)
+        public_api = functools.wraps(implementation)(public_api)
+
+        if module is not None:
+            public_api.__module__ = module
+
+        ARRAY_FUNCTIONS.add(public_api)
+
+        return public_api
+
+    return decorator
+
+
+def array_function_from_dispatcher(
+        implementation, module=None, verify=True, docs_from_dispatcher=True):
+    """Like array_function_dispatcher, but with function arguments flipped."""
+
+    def decorator(dispatcher):
+        return array_function_dispatch(
+            dispatcher, module, verify=verify,
+            docs_from_dispatcher=docs_from_dispatcher)(implementation)
+    return decorator

.venv/lib/python3.11/site-packages/numpy/fft/tests/test_helper.py

@@ -0,0 +1,167 @@
+"""Test functions for fftpack.helper module
+
+Copied from fftpack.helper by Pearu Peterson, October 2005
+
+"""
+import numpy as np
+from numpy.testing import assert_array_almost_equal
+from numpy import fft, pi
+
+
+class TestFFTShift:
+
+    def test_definition(self):
+        x = [0, 1, 2, 3, 4, -4, -3, -2, -1]
+        y = [-4, -3, -2, -1, 0, 1, 2, 3, 4]
+        assert_array_almost_equal(fft.fftshift(x), y)
+        assert_array_almost_equal(fft.ifftshift(y), x)
+        x = [0, 1, 2, 3, 4, -5, -4, -3, -2, -1]
+        y = [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
+        assert_array_almost_equal(fft.fftshift(x), y)
+        assert_array_almost_equal(fft.ifftshift(y), x)
+
+    def test_inverse(self):
+        for n in [1, 4, 9, 100, 211]:
+            x = np.random.random((n,))
+            assert_array_almost_equal(fft.ifftshift(fft.fftshift(x)), x)
+
+    def test_axes_keyword(self):
+        freqs = [[0, 1, 2], [3, 4, -4], [-3, -2, -1]]
+        shifted = [[-1, -3, -2], [2, 0, 1], [-4, 3, 4]]
+        assert_array_almost_equal(fft.fftshift(freqs, axes=(0, 1)), shifted)
+        assert_array_almost_equal(fft.fftshift(freqs, axes=0),
+                                  fft.fftshift(freqs, axes=(0,)))
+        assert_array_almost_equal(fft.ifftshift(shifted, axes=(0, 1)), freqs)
+        assert_array_almost_equal(fft.ifftshift(shifted, axes=0),
+                                  fft.ifftshift(shifted, axes=(0,)))
+
+        assert_array_almost_equal(fft.fftshift(freqs), shifted)
+        assert_array_almost_equal(fft.ifftshift(shifted), freqs)
+
+    def test_uneven_dims(self):
+        """ Test 2D input, which has uneven dimension sizes """
+        freqs = [
+            [0, 1],
+            [2, 3],
+            [4, 5]
+        ]
+
+        # shift in dimension 0
+        shift_dim0 = [
+            [4, 5],
+            [0, 1],
+            [2, 3]
+        ]
+        assert_array_almost_equal(fft.fftshift(freqs, axes=0), shift_dim0)
+        assert_array_almost_equal(fft.ifftshift(shift_dim0, axes=0), freqs)
+        assert_array_almost_equal(fft.fftshift(freqs, axes=(0,)), shift_dim0)
+        assert_array_almost_equal(fft.ifftshift(shift_dim0, axes=[0]), freqs)
+
+        # shift in dimension 1
+        shift_dim1 = [
+            [1, 0],
+            [3, 2],
+            [5, 4]
+        ]
+        assert_array_almost_equal(fft.fftshift(freqs, axes=1), shift_dim1)
+        assert_array_almost_equal(fft.ifftshift(shift_dim1, axes=1), freqs)
+
+        # shift in both dimensions
+        shift_dim_both = [
+            [5, 4],
+            [1, 0],
+            [3, 2]
+        ]
+        assert_array_almost_equal(fft.fftshift(freqs, axes=(0, 1)), shift_dim_both)
+        assert_array_almost_equal(fft.ifftshift(shift_dim_both, axes=(0, 1)), freqs)
+        assert_array_almost_equal(fft.fftshift(freqs, axes=[0, 1]), shift_dim_both)
+        assert_array_almost_equal(fft.ifftshift(shift_dim_both, axes=[0, 1]), freqs)
+
+        # axes=None (default) shift in all dimensions
+        assert_array_almost_equal(fft.fftshift(freqs, axes=None), shift_dim_both)
+        assert_array_almost_equal(fft.ifftshift(shift_dim_both, axes=None), freqs)
+        assert_array_almost_equal(fft.fftshift(freqs), shift_dim_both)
+        assert_array_almost_equal(fft.ifftshift(shift_dim_both), freqs)
+
+    def test_equal_to_original(self):
+        """ Test that the new (>=v1.15) implementation (see #10073) is equal to the original (<=v1.14) """
+        from numpy.core import asarray, concatenate, arange, take
+
+        def original_fftshift(x, axes=None):
+            """ How fftshift was implemented in v1.14"""
+            tmp = asarray(x)
+            ndim = tmp.ndim
+            if axes is None:
+                axes = list(range(ndim))
+            elif isinstance(axes, int):
+                axes = (axes,)
+            y = tmp
+            for k in axes:
+                n = tmp.shape[k]
+                p2 = (n + 1) // 2
+                mylist = concatenate((arange(p2, n), arange(p2)))
+                y = take(y, mylist, k)
+            return y
+
+        def original_ifftshift(x, axes=None):
+            """ How ifftshift was implemented in v1.14 """
+            tmp = asarray(x)
+            ndim = tmp.ndim
+            if axes is None:
+                axes = list(range(ndim))
+            elif isinstance(axes, int):
+                axes = (axes,)
+            y = tmp
+            for k in axes:
+                n = tmp.shape[k]
+                p2 = n - (n + 1) // 2
+                mylist = concatenate((arange(p2, n), arange(p2)))
+                y = take(y, mylist, k)
+            return y
+
+        # create possible 2d array combinations and try all possible keywords
+        # compare output to original functions
+        for i in range(16):
+            for j in range(16):
+                for axes_keyword in [0, 1, None, (0,), (0, 1)]:
+                    inp = np.random.rand(i, j)
+
+                    assert_array_almost_equal(fft.fftshift(inp, axes_keyword),
+                                              original_fftshift(inp, axes_keyword))
+
+                    assert_array_almost_equal(fft.ifftshift(inp, axes_keyword),
+                                              original_ifftshift(inp, axes_keyword))
+
+
+class TestFFTFreq:
+
+    def test_definition(self):
+        x = [0, 1, 2, 3, 4, -4, -3, -2, -1]
+        assert_array_almost_equal(9*fft.fftfreq(9), x)
+        assert_array_almost_equal(9*pi*fft.fftfreq(9, pi), x)
+        x = [0, 1, 2, 3, 4, -5, -4, -3, -2, -1]
+        assert_array_almost_equal(10*fft.fftfreq(10), x)
+        assert_array_almost_equal(10*pi*fft.fftfreq(10, pi), x)
+
+
+class TestRFFTFreq:
+
+    def test_definition(self):
+        x = [0, 1, 2, 3, 4]
+        assert_array_almost_equal(9*fft.rfftfreq(9), x)
+        assert_array_almost_equal(9*pi*fft.rfftfreq(9, pi), x)
+        x = [0, 1, 2, 3, 4, 5]
+        assert_array_almost_equal(10*fft.rfftfreq(10), x)
+        assert_array_almost_equal(10*pi*fft.rfftfreq(10, pi), x)
+
+
+class TestIRFFTN:
+
+    def test_not_last_axis_success(self):
+        ar, ai = np.random.random((2, 16, 8, 32))
+        a = ar + 1j*ai
+
+        axes = (-2,)
+
+        # Should not raise error
+        fft.irfftn(a, axes=axes)

.venv/lib/python3.11/site-packages/numpy/random/LICENSE.md

@@ -0,0 +1,71 @@
+**This software is dual-licensed under the The University of Illinois/NCSA
+Open Source License (NCSA) and The 3-Clause BSD License**
+
+# NCSA Open Source License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+Developed by: Kevin Sheppard (<kevin.sheppard@economics.ox.ac.uk>,
+<kevin.k.sheppard@gmail.com>)
+[http://www.kevinsheppard.com](http://www.kevinsheppard.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal with
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimers.
+
+Redistributions in binary form must reproduce the above copyright notice, this
+list of conditions and the following disclaimers in the documentation and/or
+other materials provided with the distribution.
+
+Neither the names of Kevin Sheppard, nor the names of any contributors may be
+used to endorse or promote products derived from this Software without specific
+prior written permission.
+
+**THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH
+THE SOFTWARE.**
+
+
+# 3-Clause BSD License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice,
+   this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software
+   without specific prior written permission.
+
+**THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+THE POSSIBILITY OF SUCH DAMAGE.**
+
+# Components
+
+Many parts of this module have been derived from original sources, 
+often the algorithm's designer. Component licenses are located with 
+the component code.

.venv/lib/python3.11/site-packages/numpy/random/__init__.pxd

@@ -0,0 +1,14 @@
+cimport numpy as np
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "numpy/random/bitgen.h":
+    struct bitgen:
+        void *state
+        uint64_t (*next_uint64)(void *st) nogil
+        uint32_t (*next_uint32)(void *st) nogil
+        double (*next_double)(void *st) nogil
+        uint64_t (*next_raw)(void *st) nogil
+
+    ctypedef bitgen bitgen_t
+
+from numpy.random.bit_generator cimport BitGenerator, SeedSequence

.venv/lib/python3.11/site-packages/numpy/random/__init__.py

@@ -0,0 +1,215 @@
+"""
+========================
+Random Number Generation
+========================
+
+Use ``default_rng()`` to create a `Generator` and call its methods.
+
+=============== =========================================================
+Generator
+--------------- ---------------------------------------------------------
+Generator       Class implementing all of the random number distributions
+default_rng     Default constructor for ``Generator``
+=============== =========================================================
+
+============================================= ===
+BitGenerator Streams that work with Generator
+--------------------------------------------- ---
+MT19937
+PCG64
+PCG64DXSM
+Philox
+SFC64
+============================================= ===
+
+============================================= ===
+Getting entropy to initialize a BitGenerator
+--------------------------------------------- ---
+SeedSequence
+============================================= ===
+
+
+Legacy
+------
+
+For backwards compatibility with previous versions of numpy before 1.17, the
+various aliases to the global `RandomState` methods are left alone and do not
+use the new `Generator` API.
+
+==================== =========================================================
+Utility functions
+-------------------- ---------------------------------------------------------
+random               Uniformly distributed floats over ``[0, 1)``
+bytes                Uniformly distributed random bytes.
+permutation          Randomly permute a sequence / generate a random sequence.
+shuffle              Randomly permute a sequence in place.
+choice               Random sample from 1-D array.
+==================== =========================================================
+
+==================== =========================================================
+Compatibility
+functions - removed
+in the new API
+-------------------- ---------------------------------------------------------
+rand                 Uniformly distributed values.
+randn                Normally distributed values.
+ranf                 Uniformly distributed floating point numbers.
+random_integers      Uniformly distributed integers in a given range.
+                     (deprecated, use ``integers(..., closed=True)`` instead)
+random_sample        Alias for `random_sample`
+randint              Uniformly distributed integers in a given range
+seed                 Seed the legacy random number generator.
+==================== =========================================================
+
+==================== =========================================================
+Univariate
+distributions
+-------------------- ---------------------------------------------------------
+beta                 Beta distribution over ``[0, 1]``.
+binomial             Binomial distribution.
+chisquare            :math:`\\chi^2` distribution.
+exponential          Exponential distribution.
+f                    F (Fisher-Snedecor) distribution.
+gamma                Gamma distribution.
+geometric            Geometric distribution.
+gumbel               Gumbel distribution.
+hypergeometric       Hypergeometric distribution.
+laplace              Laplace distribution.
+logistic             Logistic distribution.
+lognormal            Log-normal distribution.
+logseries            Logarithmic series distribution.
+negative_binomial    Negative binomial distribution.
+noncentral_chisquare Non-central chi-square distribution.
+noncentral_f         Non-central F distribution.
+normal               Normal / Gaussian distribution.
+pareto               Pareto distribution.
+poisson              Poisson distribution.
+power                Power distribution.
+rayleigh             Rayleigh distribution.
+triangular           Triangular distribution.
+uniform              Uniform distribution.
+vonmises             Von Mises circular distribution.
+wald                 Wald (inverse Gaussian) distribution.
+weibull              Weibull distribution.
+zipf                 Zipf's distribution over ranked data.
+==================== =========================================================
+
+==================== ==========================================================
+Multivariate
+distributions
+-------------------- ----------------------------------------------------------
+dirichlet            Multivariate generalization of Beta distribution.
+multinomial          Multivariate generalization of the binomial distribution.
+multivariate_normal  Multivariate generalization of the normal distribution.
+==================== ==========================================================
+
+==================== =========================================================
+Standard
+distributions
+-------------------- ---------------------------------------------------------
+standard_cauchy      Standard Cauchy-Lorentz distribution.
+standard_exponential Standard exponential distribution.
+standard_gamma       Standard Gamma distribution.
+standard_normal      Standard normal distribution.
+standard_t           Standard Student's t-distribution.
+==================== =========================================================
+
+==================== =========================================================
+Internal functions
+-------------------- ---------------------------------------------------------
+get_state            Get tuple representing internal state of generator.
+set_state            Set state of generator.
+==================== =========================================================
+
+
+"""
+__all__ = [
+    'beta',
+    'binomial',
+    'bytes',
+    'chisquare',
+    'choice',
+    'dirichlet',
+    'exponential',
+    'f',
+    'gamma',
+    'geometric',
+    'get_state',
+    'gumbel',
+    'hypergeometric',
+    'laplace',
+    'logistic',
+    'lognormal',
+    'logseries',
+    'multinomial',
+    'multivariate_normal',
+    'negative_binomial',
+    'noncentral_chisquare',
+    'noncentral_f',
+    'normal',
+    'pareto',
+    'permutation',
+    'poisson',
+    'power',
+    'rand',
+    'randint',
+    'randn',
+    'random',
+    'random_integers',
+    'random_sample',
+    'ranf',
+    'rayleigh',
+    'sample',
+    'seed',
+    'set_state',
+    'shuffle',
+    'standard_cauchy',
+    'standard_exponential',
+    'standard_gamma',
+    'standard_normal',
+    'standard_t',
+    'triangular',
+    'uniform',
+    'vonmises',
+    'wald',
+    'weibull',
+    'zipf',
+]
+
+# add these for module-freeze analysis (like PyInstaller)
+from . import _pickle
+from . import _common
+from . import _bounded_integers
+
+from ._generator import Generator, default_rng
+from .bit_generator import SeedSequence, BitGenerator
+from ._mt19937 import MT19937
+from ._pcg64 import PCG64, PCG64DXSM
+from ._philox import Philox
+from ._sfc64 import SFC64
+from .mtrand import *
+
+__all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937',
+            'Philox', 'PCG64', 'PCG64DXSM', 'SFC64', 'default_rng',
+            'BitGenerator']
+
+
+def __RandomState_ctor():
+    """Return a RandomState instance.
+
+    This function exists solely to assist (un)pickling.
+
+    Note that the state of the RandomState returned here is irrelevant, as this
+    function's entire purpose is to return a newly allocated RandomState whose
+    state pickle can set.  Consequently the RandomState returned by this function
+    is a freshly allocated copy with a seed=0.
+
+    See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
+
+    """
+    return RandomState(seed=0)
+
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester

.venv/lib/python3.11/site-packages/numpy/random/__init__.pyi

@@ -0,0 +1,72 @@
+from numpy._pytesttester import PytestTester
+
+from numpy.random._generator import Generator as Generator
+from numpy.random._generator import default_rng as default_rng
+from numpy.random._mt19937 import MT19937 as MT19937
+from numpy.random._pcg64 import (
+    PCG64 as PCG64,
+    PCG64DXSM as PCG64DXSM,
+)
+from numpy.random._philox import Philox as Philox
+from numpy.random._sfc64 import SFC64 as SFC64
+from numpy.random.bit_generator import BitGenerator as BitGenerator
+from numpy.random.bit_generator import SeedSequence as SeedSequence
+from numpy.random.mtrand import (
+    RandomState as RandomState,
+    beta as beta,
+    binomial as binomial,
+    bytes as bytes,
+    chisquare as chisquare,
+    choice as choice,
+    dirichlet as dirichlet,
+    exponential as exponential,
+    f as f,
+    gamma as gamma,
+    geometric as geometric,
+    get_bit_generator as get_bit_generator,
+    get_state as get_state,
+    gumbel as gumbel,
+    hypergeometric as hypergeometric,
+    laplace as laplace,
+    logistic as logistic,
+    lognormal as lognormal,
+    logseries as logseries,
+    multinomial as multinomial,
+    multivariate_normal as multivariate_normal,
+    negative_binomial as negative_binomial,
+    noncentral_chisquare as noncentral_chisquare,
+    noncentral_f as noncentral_f,
+    normal as normal,
+    pareto as pareto,
+    permutation as permutation,
+    poisson as poisson,
+    power as power,
+    rand as rand,
+    randint as randint,
+    randn as randn,
+    random as random,
+    random_integers as random_integers,
+    random_sample as random_sample,
+    ranf as ranf,
+    rayleigh as rayleigh,
+    sample as sample,
+    seed as seed,
+    set_bit_generator as set_bit_generator,
+    set_state as set_state,
+    shuffle as shuffle,
+    standard_cauchy as standard_cauchy,
+    standard_exponential as standard_exponential,
+    standard_gamma as standard_gamma,
+    standard_normal as standard_normal,
+    standard_t as standard_t,
+    triangular as triangular,
+    uniform as uniform,
+    vonmises as vonmises,
+    wald as wald,
+    weibull as weibull,
+    zipf as zipf,
+)
+
+__all__: list[str]
+__path__: list[str]
+test: PytestTester

.venv/lib/python3.11/site-packages/numpy/random/_bounded_integers.pxd

@@ -0,0 +1,29 @@
+from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t,
+                          int8_t, int16_t, int32_t, int64_t, intptr_t)
+import numpy as np
+cimport numpy as np
+ctypedef np.npy_bool bool_t
+
+from numpy.random cimport bitgen_t
+
+cdef inline uint64_t _gen_mask(uint64_t max_val) nogil:
+    """Mask generator for use in bounded random numbers"""
+    # Smallest bit mask >= max
+    cdef uint64_t mask = max_val
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+    mask |= mask >> 32
+    return mask
+
+cdef object _rand_uint64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_bool(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)

.venv/lib/python3.11/site-packages/numpy/random/_common.pxd

@@ -0,0 +1,106 @@
+#cython: language_level=3
+
+from libc.stdint cimport uint32_t, uint64_t, int32_t, int64_t
+
+import numpy as np
+cimport numpy as np
+
+from numpy.random cimport bitgen_t
+
+cdef double POISSON_LAM_MAX
+cdef double LEGACY_POISSON_LAM_MAX
+cdef uint64_t MAXSIZE
+
+cdef enum ConstraintType:
+    CONS_NONE
+    CONS_NON_NEGATIVE
+    CONS_POSITIVE
+    CONS_POSITIVE_NOT_NAN
+    CONS_BOUNDED_0_1
+    CONS_BOUNDED_GT_0_1
+    CONS_BOUNDED_LT_0_1
+    CONS_GT_1
+    CONS_GTE_1
+    CONS_POISSON
+    LEGACY_CONS_POISSON
+
+ctypedef ConstraintType constraint_type
+
+cdef object benchmark(bitgen_t *bitgen, object lock, Py_ssize_t cnt, object method)
+cdef object random_raw(bitgen_t *bitgen, object lock, object size, object output)
+cdef object prepare_cffi(bitgen_t *bitgen)
+cdef object prepare_ctypes(bitgen_t *bitgen)
+cdef int check_constraint(double val, object name, constraint_type cons) except -1
+cdef int check_array_constraint(np.ndarray val, object name, constraint_type cons) except -1
+
+cdef extern from "include/aligned_malloc.h":
+    cdef void *PyArray_realloc_aligned(void *p, size_t n)
+    cdef void *PyArray_malloc_aligned(size_t n)
+    cdef void *PyArray_calloc_aligned(size_t n, size_t s)
+    cdef void PyArray_free_aligned(void *p)
+
+ctypedef void (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out)  noexcept nogil
+ctypedef double (*random_double_0)(void *state)  noexcept nogil
+ctypedef double (*random_double_1)(void *state, double a)  noexcept nogil
+ctypedef double (*random_double_2)(void *state, double a, double b)  noexcept nogil
+ctypedef double (*random_double_3)(void *state, double a, double b, double c)  noexcept nogil
+
+ctypedef void (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out)  noexcept nogil
+ctypedef float (*random_float_0)(bitgen_t *state)  noexcept nogil
+ctypedef float (*random_float_1)(bitgen_t *state, float a)  noexcept nogil
+
+ctypedef int64_t (*random_uint_0)(void *state)  noexcept nogil
+ctypedef int64_t (*random_uint_d)(void *state, double a)  noexcept nogil
+ctypedef int64_t (*random_uint_dd)(void *state, double a, double b)  noexcept nogil
+ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b)  noexcept nogil
+ctypedef int64_t (*random_uint_i)(void *state, int64_t a)  noexcept nogil
+ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c)  noexcept nogil
+
+ctypedef uint32_t (*random_uint_0_32)(bitgen_t *state)  noexcept nogil
+ctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a)  noexcept nogil
+
+ctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b)  noexcept nogil
+ctypedef int64_t (*random_int_2_i)(bitgen_t *state, int64_t a, int64_t b)  noexcept nogil
+
+cdef double kahan_sum(double *darr, np.npy_intp n) noexcept
+
+cdef inline double uint64_to_double(uint64_t rnd) noexcept nogil:
+    return (rnd >> 11) * (1.0 / 9007199254740992.0)
+
+cdef object double_fill(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object float_fill(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object float_fill_from_double(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object wrap_int(object val, object bits)
+
+cdef np.ndarray int_to_array(object value, object name, object bits, object uint_size)
+
+cdef validate_output_shape(iter_shape, np.ndarray output)
+
+cdef object cont(void *func, void *state, object size, object lock, int narg,
+                 object a, object a_name, constraint_type a_constraint,
+                 object b, object b_name, constraint_type b_constraint,
+                 object c, object c_name, constraint_type c_constraint,
+                 object out)
+
+cdef object disc(void *func, void *state, object size, object lock,
+                 int narg_double, int narg_int64,
+                 object a, object a_name, constraint_type a_constraint,
+                 object b, object b_name, constraint_type b_constraint,
+                 object c, object c_name, constraint_type c_constraint)
+
+cdef object cont_f(void *func, bitgen_t *state, object size, object lock,
+                   object a, object a_name, constraint_type a_constraint,
+                   object out)
+
+cdef object cont_broadcast_3(void *func, void *state, object size, object lock,
+                             np.ndarray a_arr, object a_name, constraint_type a_constraint,
+                             np.ndarray b_arr, object b_name, constraint_type b_constraint,
+                             np.ndarray c_arr, object c_name, constraint_type c_constraint)
+
+cdef object discrete_broadcast_iii(void *func, void *state, object size, object lock,
+                                   np.ndarray a_arr, object a_name, constraint_type a_constraint,
+                                   np.ndarray b_arr, object b_name, constraint_type b_constraint,
+                                   np.ndarray c_arr, object c_name, constraint_type c_constraint)

.venv/lib/python3.11/site-packages/numpy/random/_examples/cffi/extending.py

@@ -0,0 +1,40 @@
+"""
+Use cffi to access any of the underlying C functions from distributions.h
+"""
+import os
+import numpy as np
+import cffi
+from .parse import parse_distributions_h
+ffi = cffi.FFI()
+
+inc_dir = os.path.join(np.get_include(), 'numpy')
+
+# Basic numpy types
+ffi.cdef('''
+    typedef intptr_t npy_intp;
+    typedef unsigned char npy_bool;
+
+''')
+
+parse_distributions_h(ffi, inc_dir)
+
+lib = ffi.dlopen(np.random._generator.__file__)
+
+# Compare the distributions.h random_standard_normal_fill to
+# Generator.standard_random
+bit_gen = np.random.PCG64()
+rng = np.random.Generator(bit_gen)
+state = bit_gen.state
+
+interface = rng.bit_generator.cffi
+n = 100
+vals_cffi = ffi.new('double[%d]' % n)
+lib.random_standard_normal_fill(interface.bit_generator, n, vals_cffi)
+
+# reset the state
+bit_gen.state = state
+
+vals = rng.standard_normal(n)
+
+for i in range(n):
+    assert vals[i] == vals_cffi[i]

.venv/lib/python3.11/site-packages/numpy/random/_examples/cffi/parse.py

@@ -0,0 +1,54 @@
+import os
+
+
+def parse_distributions_h(ffi, inc_dir):
+    """
+    Parse distributions.h located in inc_dir for CFFI, filling in the ffi.cdef
+
+    Read the function declarations without the "#define ..." macros that will
+    be filled in when loading the library.
+    """
+
+    with open(os.path.join(inc_dir, 'random', 'bitgen.h')) as fid:
+        s = []
+        for line in fid:
+            # massage the include file
+            if line.strip().startswith('#'):
+                continue
+            s.append(line)
+        ffi.cdef('\n'.join(s))
+
+    with open(os.path.join(inc_dir, 'random', 'distributions.h')) as fid:
+        s = []
+        in_skip = 0
+        ignoring = False
+        for line in fid:
+            # check for and remove extern "C" guards
+            if ignoring:
+                if line.strip().startswith('#endif'):
+                    ignoring = False
+                continue
+            if line.strip().startswith('#ifdef __cplusplus'):
+                ignoring = True
+            
+            # massage the include file
+            if line.strip().startswith('#'):
+                continue
+    
+            # skip any inlined function definition
+            # which starts with 'static inline xxx(...) {'
+            # and ends with a closing '}'
+            if line.strip().startswith('static inline'):
+                in_skip += line.count('{')
+                continue
+            elif in_skip > 0:
+                in_skip += line.count('{')
+                in_skip -= line.count('}')
+                continue
+    
+            # replace defines with their value or remove them
+            line = line.replace('DECLDIR', '')
+            line = line.replace('RAND_INT_TYPE', 'int64_t')
+            s.append(line)
+        ffi.cdef('\n'.join(s))
+

.venv/lib/python3.11/site-packages/numpy/random/_examples/cython/extending.pyx

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+#cython: language_level=3
+
+from libc.stdint cimport uint32_t
+from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
+
+import numpy as np
+cimport numpy as np
+cimport cython
+
+from numpy.random cimport bitgen_t
+from numpy.random import PCG64
+
+np.import_array()
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uniform_mean(Py_ssize_t n):
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef double[::1] random_values
+    cdef np.ndarray randoms
+
+    x = PCG64()
+    capsule = x.capsule
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n)
+    # Best practice is to acquire the lock whenever generating random values.
+    # This prevents other threads from modifying the state. Acquiring the lock
+    # is only necessary if the GIL is also released, as in this example.
+    with x.lock, nogil:
+        for i in range(n):
+            random_values[i] = rng.next_double(rng.state)
+    randoms = np.asarray(random_values)
+    return randoms.mean()
+
+
+# This function is declared nogil so it can be used without the GIL below
+cdef uint32_t bounded_uint(uint32_t lb, uint32_t ub, bitgen_t *rng) nogil:
+    cdef uint32_t mask, delta, val
+    mask = delta = ub - lb
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+
+    val = rng.next_uint32(rng.state) & mask
+    while val > delta:
+        val = rng.next_uint32(rng.state) & mask
+
+    return lb + val
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def bounded_uints(uint32_t lb, uint32_t ub, Py_ssize_t n):
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef uint32_t[::1] out
+    cdef const char *capsule_name = "BitGenerator"
+
+    x = PCG64()
+    out = np.empty(n, dtype=np.uint32)
+    capsule = x.capsule
+
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    rng = <bitgen_t *>PyCapsule_GetPointer(capsule, capsule_name)
+
+    with x.lock, nogil:
+        for i in range(n):
+            out[i] = bounded_uint(lb, ub, rng)
+    return np.asarray(out)

.venv/lib/python3.11/site-packages/numpy/random/_examples/cython/extending_distributions.pyx

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+#cython: language_level=3
+"""
+This file shows how the to use a BitGenerator to create a distribution.
+"""
+import numpy as np
+cimport numpy as np
+cimport cython
+from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
+from libc.stdint cimport uint16_t, uint64_t
+from numpy.random cimport bitgen_t
+from numpy.random import PCG64
+from numpy.random.c_distributions cimport (
+      random_standard_uniform_fill, random_standard_uniform_fill_f)
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uniforms(Py_ssize_t n):
+    """
+    Create an array of `n` uniformly distributed doubles.
+    A 'real' distribution would want to process the values into
+    some non-uniform distribution
+    """
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef double[::1] random_values
+
+    x = PCG64()
+    capsule = x.capsule
+    # Optional check that the capsule if from a BitGenerator
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    # Cast the pointer
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n, dtype='float64')
+    with x.lock, nogil:
+        for i in range(n):
+            # Call the function
+            random_values[i] = rng.next_double(rng.state)
+    randoms = np.asarray(random_values)
+
+    return randoms
+
+# cython example 2
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uint10_uniforms(Py_ssize_t n):
+    """Uniform 10 bit integers stored as 16-bit unsigned integers"""
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef uint16_t[::1] random_values
+    cdef int bits_remaining
+    cdef int width = 10
+    cdef uint64_t buff, mask = 0x3FF
+
+    x = PCG64()
+    capsule = x.capsule
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n, dtype='uint16')
+    # Best practice is to release GIL and acquire the lock
+    bits_remaining = 0
+    with x.lock, nogil:
+        for i in range(n):
+            if bits_remaining < width:
+                buff = rng.next_uint64(rng.state)
+            random_values[i] = buff & mask
+            buff >>= width
+
+    randoms = np.asarray(random_values)
+    return randoms
+
+# cython example 3
+def uniforms_ex(bit_generator, Py_ssize_t n, dtype=np.float64):
+    """
+    Create an array of `n` uniformly distributed doubles via a "fill" function.
+
+    A 'real' distribution would want to process the values into
+    some non-uniform distribution
+
+    Parameters
+    ----------
+    bit_generator: BitGenerator instance
+    n: int
+        Output vector length
+    dtype: {str, dtype}, optional
+        Desired dtype, either 'd' (or 'float64') or 'f' (or 'float32'). The
+        default dtype value is 'd'
+    """
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef np.ndarray randoms
+
+    capsule = bit_generator.capsule
+    # Optional check that the capsule if from a BitGenerator
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    # Cast the pointer
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+
+    _dtype = np.dtype(dtype)
+    randoms = np.empty(n, dtype=_dtype)
+    if _dtype == np.float32:
+        with bit_generator.lock:
+            random_standard_uniform_fill_f(rng, n, <float*>np.PyArray_DATA(randoms))
+    elif _dtype == np.float64:
+        with bit_generator.lock:
+            random_standard_uniform_fill(rng, n, <double*>np.PyArray_DATA(randoms))
+    else:
+        raise TypeError('Unsupported dtype %r for random' % _dtype)
+    return randoms
+

.venv/lib/python3.11/site-packages/numpy/random/_examples/cython/meson.build

@@ -0,0 +1,45 @@
+project('random-build-examples', 'c', 'cpp', 'cython')
+
+py_mod = import('python')
+py3 = py_mod.find_installation(pure: false)
+
+cc = meson.get_compiler('c')
+cy = meson.get_compiler('cython')
+
+if not cy.version().version_compare('>=0.29.35')
+  error('tests requires Cython >= 0.29.35')
+endif
+
+_numpy_abs = run_command(py3, ['-c',
+               'import os; os.chdir(".."); import numpy; print(os.path.abspath(numpy.get_include() + "../../.."))'],
+                         check: true).stdout().strip()
+
+npymath_path = _numpy_abs / 'core' / 'lib'
+npy_include_path = _numpy_abs / 'core' / 'include'
+npyrandom_path = _numpy_abs / 'random' / 'lib'
+npymath_lib = cc.find_library('npymath', dirs: npymath_path)
+npyrandom_lib = cc.find_library('npyrandom', dirs: npyrandom_path)
+
+py3.extension_module(
+    'extending_distributions',
+    'extending_distributions.pyx',
+    install: false,
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+)
+py3.extension_module(
+    'extending',
+    'extending.pyx',
+    install: false,
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+)
+py3.extension_module(
+    'extending_cpp',
+    'extending_distributions.pyx',
+    install: false,
+    override_options : ['cython_language=cpp'],
+    cython_args: ['--module-name', 'extending_cpp'],
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+)

.venv/lib/python3.11/site-packages/numpy/random/_examples/numba/extending.py

@@ -0,0 +1,84 @@
+import numpy as np
+import numba as nb
+
+from numpy.random import PCG64
+from timeit import timeit
+
+bit_gen = PCG64()
+next_d = bit_gen.cffi.next_double
+state_addr = bit_gen.cffi.state_address
+
+def normals(n, state):
+    out = np.empty(n)
+    for i in range((n + 1) // 2):
+        x1 = 2.0 * next_d(state) - 1.0
+        x2 = 2.0 * next_d(state) - 1.0
+        r2 = x1 * x1 + x2 * x2
+        while r2 >= 1.0 or r2 == 0.0:
+            x1 = 2.0 * next_d(state) - 1.0
+            x2 = 2.0 * next_d(state) - 1.0
+            r2 = x1 * x1 + x2 * x2
+        f = np.sqrt(-2.0 * np.log(r2) / r2)
+        out[2 * i] = f * x1
+        if 2 * i + 1 < n:
+            out[2 * i + 1] = f * x2
+    return out
+
+# Compile using Numba
+normalsj = nb.jit(normals, nopython=True)
+# Must use state address not state with numba
+n = 10000
+
+def numbacall():
+    return normalsj(n, state_addr)
+
+rg = np.random.Generator(PCG64())
+
+def numpycall():
+    return rg.normal(size=n)
+
+# Check that the functions work
+r1 = numbacall()
+r2 = numpycall()
+assert r1.shape == (n,)
+assert r1.shape == r2.shape
+
+t1 = timeit(numbacall, number=1000)
+print(f'{t1:.2f} secs for {n} PCG64 (Numba/PCG64) gaussian randoms')
+t2 = timeit(numpycall, number=1000)
+print(f'{t2:.2f} secs for {n} PCG64 (NumPy/PCG64) gaussian randoms')
+
+# example 2
+
+next_u32 = bit_gen.ctypes.next_uint32
+ctypes_state = bit_gen.ctypes.state
+
+@nb.jit(nopython=True)
+def bounded_uint(lb, ub, state):
+    mask = delta = ub - lb
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+
+    val = next_u32(state) & mask
+    while val > delta:
+        val = next_u32(state) & mask
+
+    return lb + val
+
+
+print(bounded_uint(323, 2394691, ctypes_state.value))
+
+
+@nb.jit(nopython=True)
+def bounded_uints(lb, ub, n, state):
+    out = np.empty(n, dtype=np.uint32)
+    for i in range(n):
+        out[i] = bounded_uint(lb, ub, state)
+
+
+bounded_uints(323, 2394691, 10000000, ctypes_state.value)
+
+

.venv/lib/python3.11/site-packages/numpy/random/_examples/numba/extending_distributions.py

@@ -0,0 +1,67 @@
+r"""
+Building the required library in this example requires a source distribution
+of NumPy or clone of the NumPy git repository since distributions.c is not
+included in binary distributions.
+
+On *nix, execute in numpy/random/src/distributions
+
+export ${PYTHON_VERSION}=3.8 # Python version
+export PYTHON_INCLUDE=#path to Python's include folder, usually \
+    ${PYTHON_HOME}/include/python${PYTHON_VERSION}m
+export NUMPY_INCLUDE=#path to numpy's include folder, usually \
+    ${PYTHON_HOME}/lib/python${PYTHON_VERSION}/site-packages/numpy/core/include
+gcc -shared -o libdistributions.so -fPIC distributions.c \
+    -I${NUMPY_INCLUDE} -I${PYTHON_INCLUDE}
+mv libdistributions.so ../../_examples/numba/
+
+On Windows
+
+rem PYTHON_HOME and PYTHON_VERSION are setup dependent, this is an example
+set PYTHON_HOME=c:\Anaconda
+set PYTHON_VERSION=38
+cl.exe /LD .\distributions.c -DDLL_EXPORT \
+    -I%PYTHON_HOME%\lib\site-packages\numpy\core\include \
+    -I%PYTHON_HOME%\include %PYTHON_HOME%\libs\python%PYTHON_VERSION%.lib
+move distributions.dll ../../_examples/numba/
+"""
+import os
+
+import numba as nb
+import numpy as np
+from cffi import FFI
+
+from numpy.random import PCG64
+
+ffi = FFI()
+if os.path.exists('./distributions.dll'):
+    lib = ffi.dlopen('./distributions.dll')
+elif os.path.exists('./libdistributions.so'):
+    lib = ffi.dlopen('./libdistributions.so')
+else:
+    raise RuntimeError('Required DLL/so file was not found.')
+
+ffi.cdef("""
+double random_standard_normal(void *bitgen_state);
+""")
+x = PCG64()
+xffi = x.cffi
+bit_generator = xffi.bit_generator
+
+random_standard_normal = lib.random_standard_normal
+
+
+def normals(n, bit_generator):
+    out = np.empty(n)
+    for i in range(n):
+        out[i] = random_standard_normal(bit_generator)
+    return out
+
+
+normalsj = nb.jit(normals, nopython=True)
+
+# Numba requires a memory address for void *
+# Can also get address from x.ctypes.bit_generator.value
+bit_generator_address = int(ffi.cast('uintptr_t', bit_generator))
+
+norm = normalsj(1000, bit_generator_address)
+print(norm[:12])

.venv/lib/python3.11/site-packages/numpy/random/_generator.cpython-311-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c23f8e487f474185f11f28fc7b8eb2ebbc60712f68c3eb5f3cf014f2c7ec46c
+size 980520

.venv/lib/python3.11/site-packages/numpy/random/_generator.pyi

@@ -0,0 +1,681 @@
+from collections.abc import Callable
+from typing import Any, Union, overload, TypeVar, Literal
+
+from numpy import (
+    bool_,
+    dtype,
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    int_,
+    ndarray,
+    uint,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+from numpy.random import BitGenerator, SeedSequence
+from numpy._typing import (
+    ArrayLike,
+    _ArrayLikeFloat_co,
+    _ArrayLikeInt_co,
+    _DoubleCodes,
+    _DTypeLikeBool,
+    _DTypeLikeInt,
+    _DTypeLikeUInt,
+    _Float32Codes,
+    _Float64Codes,
+    _FloatLike_co,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _IntCodes,
+    _ShapeLike,
+    _SingleCodes,
+    _SupportsDType,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UIntCodes,
+)
+
+_ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any])
+
+_DTypeLikeFloat32 = Union[
+    dtype[float32],
+    _SupportsDType[dtype[float32]],
+    type[float32],
+    _Float32Codes,
+    _SingleCodes,
+]
+
+_DTypeLikeFloat64 = Union[
+    dtype[float64],
+    _SupportsDType[dtype[float64]],
+    type[float],
+    type[float64],
+    _Float64Codes,
+    _DoubleCodes,
+]
+
+class Generator:
+    def __init__(self, bit_generator: BitGenerator) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def __getstate__(self) -> dict[str, Any]: ...
+    def __setstate__(self, state: dict[str, Any]) -> None: ...
+    def __reduce__(self) -> tuple[Callable[[str], Generator], tuple[str], dict[str, Any]]: ...
+    @property
+    def bit_generator(self) -> BitGenerator: ...
+    def spawn(self, n_children: int) -> list[Generator]: ...
+    def bytes(self, length: int) -> bytes: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def permutation(self, x: int, axis: int = ...) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def permutation(self, x: ArrayLike, axis: int = ...) -> ndarray[Any, Any]: ...
+    @overload
+    def standard_exponential(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        *,
+        method: Literal["zig", "inv"] = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def random(
+        self,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        *,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def beta(
+        self,
+        a: _FloatLike_co,
+        b: _FloatLike_co,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def beta(
+        self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def exponential(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+    ) -> int: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: _DTypeLikeBool = ...,
+        endpoint: bool = ...,
+    ) -> bool: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: _DTypeLikeInt | _DTypeLikeUInt = ...,
+        endpoint: bool = ...,
+    ) -> int: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeBool = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[bool_]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int8]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int16]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int32]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint8]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint16]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint32]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint]]: ...
+    # TODO: Use a TypeVar _T here to get away from Any output?  Should be int->ndarray[Any,dtype[int64]], ArrayLike[_T] -> _T | ndarray[Any,Any]
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> int: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> Any: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> ndarray[Any, Any]: ...
+    @overload
+    def uniform(
+        self,
+        low: _FloatLike_co = ...,
+        high: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def uniform(
+        self,
+        low: _ArrayLikeFloat_co = ...,
+        high: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def normal(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def normal(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(  # type: ignore[misc]
+        self,
+        shape: _FloatLike_co,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def gamma(self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def f(
+        self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def noncentral_f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def chisquare(
+        self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def noncentral_chisquare(self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_chisquare(
+        self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_t(self, df: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: None = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def vonmises(self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def vonmises(
+        self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def pareto(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def pareto(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def weibull(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def weibull(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def power(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def power(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_cauchy(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def laplace(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def laplace(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def gumbel(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def gumbel(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def logistic(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def logistic(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def lognormal(
+        self,
+        mean: _FloatLike_co = ...,
+        sigma: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def lognormal(
+        self,
+        mean: _ArrayLikeFloat_co = ...,
+        sigma: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def rayleigh(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def wald(self, mean: _FloatLike_co, scale: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def wald(
+        self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def triangular(
+        self,
+        left: _FloatLike_co,
+        mode: _FloatLike_co,
+        right: _FloatLike_co,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def triangular(
+        self,
+        left: _ArrayLikeFloat_co,
+        mode: _ArrayLikeFloat_co,
+        right: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def binomial(self, n: int, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def binomial(
+        self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def negative_binomial(self, n: _FloatLike_co, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def negative_binomial(
+        self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def poisson(self, lam: _FloatLike_co = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def poisson(
+        self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def zipf(self, a: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def zipf(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def geometric(self, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def geometric(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def hypergeometric(
+        self,
+        ngood: _ArrayLikeInt_co,
+        nbad: _ArrayLikeInt_co,
+        nsample: _ArrayLikeInt_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def logseries(self, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def logseries(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    def multivariate_normal(
+        self,
+        mean: _ArrayLikeFloat_co,
+        cov: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        check_valid: Literal["warn", "raise", "ignore"] = ...,
+        tol: float = ...,
+        *,
+        method: Literal["svd", "eigh", "cholesky"] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    def multinomial(
+        self, n: _ArrayLikeInt_co,
+            pvals: _ArrayLikeFloat_co,
+            size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    def multivariate_hypergeometric(
+        self,
+        colors: _ArrayLikeInt_co,
+        nsample: int,
+        size: None | _ShapeLike = ...,
+        method: Literal["marginals", "count"] = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    def dirichlet(
+        self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    def permuted(
+        self, x: ArrayLike, *, axis: None | int = ..., out: None | ndarray[Any, Any] = ...
+    ) -> ndarray[Any, Any]: ...
+    def shuffle(self, x: ArrayLike, axis: int = ...) -> None: ...
+
+def default_rng(
+    seed: None | _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator = ...
+) -> Generator: ...

.venv/lib/python3.11/site-packages/numpy/random/_mt19937.cpython-311-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d39b0e5a9e90613deede597857f8abec29cfb2ccccaff6ed6f11cbc10f684a82
+size 120312

.venv/lib/python3.11/site-packages/numpy/random/_mt19937.pyi

@@ -0,0 +1,22 @@
+from typing import Any, TypedDict
+
+from numpy import dtype, ndarray, uint32
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _MT19937Internal(TypedDict):
+    key: ndarray[Any, dtype[uint32]]
+    pos: int
+
+class _MT19937State(TypedDict):
+    bit_generator: str
+    state: _MT19937Internal
+
+class MT19937(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def _legacy_seeding(self, seed: _ArrayLikeInt_co) -> None: ...
+    def jumped(self, jumps: int = ...) -> MT19937: ...
+    @property
+    def state(self) -> _MT19937State: ...
+    @state.setter
+    def state(self, value: _MT19937State) -> None: ...

.venv/lib/python3.11/site-packages/numpy/random/_pcg64.pyi

@@ -0,0 +1,42 @@
+from typing import TypedDict
+
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _PCG64Internal(TypedDict):
+    state: int
+    inc: int
+
+class _PCG64State(TypedDict):
+    bit_generator: str
+    state: _PCG64Internal
+    has_uint32: int
+    uinteger: int
+
+class PCG64(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def jumped(self, jumps: int = ...) -> PCG64: ...
+    @property
+    def state(
+        self,
+    ) -> _PCG64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _PCG64State,
+    ) -> None: ...
+    def advance(self, delta: int) -> PCG64: ...
+
+class PCG64DXSM(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def jumped(self, jumps: int = ...) -> PCG64DXSM: ...
+    @property
+    def state(
+        self,
+    ) -> _PCG64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _PCG64State,
+    ) -> None: ...
+    def advance(self, delta: int) -> PCG64DXSM: ...

.venv/lib/python3.11/site-packages/numpy/random/_philox.pyi

@@ -0,0 +1,36 @@
+from typing import Any, TypedDict
+
+from numpy import dtype, ndarray, uint64
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _PhiloxInternal(TypedDict):
+    counter: ndarray[Any, dtype[uint64]]
+    key: ndarray[Any, dtype[uint64]]
+
+class _PhiloxState(TypedDict):
+    bit_generator: str
+    state: _PhiloxInternal
+    buffer: ndarray[Any, dtype[uint64]]
+    buffer_pos: int
+    has_uint32: int
+    uinteger: int
+
+class Philox(BitGenerator):
+    def __init__(
+        self,
+        seed: None | _ArrayLikeInt_co | SeedSequence = ...,
+        counter: None | _ArrayLikeInt_co = ...,
+        key: None | _ArrayLikeInt_co = ...,
+    ) -> None: ...
+    @property
+    def state(
+        self,
+    ) -> _PhiloxState: ...
+    @state.setter
+    def state(
+        self,
+        value: _PhiloxState,
+    ) -> None: ...
+    def jumped(self, jumps: int = ...) -> Philox: ...
+    def advance(self, delta: int) -> Philox: ...

.venv/lib/python3.11/site-packages/numpy/random/_pickle.py

@@ -0,0 +1,80 @@
+from .mtrand import RandomState
+from ._philox import Philox
+from ._pcg64 import PCG64, PCG64DXSM
+from ._sfc64 import SFC64
+
+from ._generator import Generator
+from ._mt19937 import MT19937
+
+BitGenerators = {'MT19937': MT19937,
+                 'PCG64': PCG64,
+                 'PCG64DXSM': PCG64DXSM,
+                 'Philox': Philox,
+                 'SFC64': SFC64,
+                 }
+
+
+def __bit_generator_ctor(bit_generator_name='MT19937'):
+    """
+    Pickling helper function that returns a bit generator object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the name of the BitGenerator
+
+    Returns
+    -------
+    bit_generator : BitGenerator
+        BitGenerator instance
+    """
+    if bit_generator_name in BitGenerators:
+        bit_generator = BitGenerators[bit_generator_name]
+    else:
+        raise ValueError(str(bit_generator_name) + ' is not a known '
+                                                   'BitGenerator module.')
+
+    return bit_generator()
+
+
+def __generator_ctor(bit_generator_name="MT19937",
+                     bit_generator_ctor=__bit_generator_ctor):
+    """
+    Pickling helper function that returns a Generator object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the core BitGenerator's name
+    bit_generator_ctor : callable, optional
+        Callable function that takes bit_generator_name as its only argument
+        and returns an instantized bit generator.
+
+    Returns
+    -------
+    rg : Generator
+        Generator using the named core BitGenerator
+    """
+    return Generator(bit_generator_ctor(bit_generator_name))
+
+
+def __randomstate_ctor(bit_generator_name="MT19937",
+                       bit_generator_ctor=__bit_generator_ctor):
+    """
+    Pickling helper function that returns a legacy RandomState-like object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the core BitGenerator's name
+    bit_generator_ctor : callable, optional
+        Callable function that takes bit_generator_name as its only argument
+        and returns an instantized bit generator.
+
+    Returns
+    -------
+    rs : RandomState
+        Legacy RandomState using the named core BitGenerator
+    """
+
+    return RandomState(bit_generator_ctor(bit_generator_name))

.venv/lib/python3.11/site-packages/numpy/random/_sfc64.pyi

@@ -0,0 +1,28 @@
+from typing import Any, TypedDict
+
+from numpy import dtype as dtype
+from numpy import ndarray as ndarray
+from numpy import uint64
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _SFC64Internal(TypedDict):
+    state: ndarray[Any, dtype[uint64]]
+
+class _SFC64State(TypedDict):
+    bit_generator: str
+    state: _SFC64Internal
+    has_uint32: int
+    uinteger: int
+
+class SFC64(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    @property
+    def state(
+        self,
+    ) -> _SFC64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _SFC64State,
+    ) -> None: ...

.venv/lib/python3.11/site-packages/numpy/random/bit_generator.cpython-311-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4498c107393d71027b88b81505ba7b00591b1085dab86f08262ca675407a990
+size 242584

.venv/lib/python3.11/site-packages/numpy/random/bit_generator.pxd

@@ -0,0 +1,35 @@
+cimport numpy as np
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "numpy/random/bitgen.h":
+    struct bitgen:
+        void *state
+        uint64_t (*next_uint64)(void *st) nogil
+        uint32_t (*next_uint32)(void *st) nogil
+        double (*next_double)(void *st) nogil
+        uint64_t (*next_raw)(void *st) nogil
+
+    ctypedef bitgen bitgen_t
+
+cdef class BitGenerator():
+    cdef readonly object _seed_seq
+    cdef readonly object lock
+    cdef bitgen_t _bitgen
+    cdef readonly object _ctypes
+    cdef readonly object _cffi
+    cdef readonly object capsule
+
+
+cdef class SeedSequence():
+    cdef readonly object entropy
+    cdef readonly tuple spawn_key
+    cdef readonly Py_ssize_t pool_size
+    cdef readonly object pool
+    cdef readonly uint32_t n_children_spawned
+
+    cdef mix_entropy(self, np.ndarray[np.npy_uint32, ndim=1] mixer,
+                     np.ndarray[np.npy_uint32, ndim=1] entropy_array)
+    cdef get_assembled_entropy(self)
+
+cdef class SeedlessSequence():
+    pass

.venv/lib/python3.11/site-packages/numpy/random/bit_generator.pyi

@@ -0,0 +1,112 @@
+import abc
+from threading import Lock
+from collections.abc import Callable, Mapping, Sequence
+from typing import (
+    Any,
+    NamedTuple,
+    TypedDict,
+    TypeVar,
+    Union,
+    overload,
+    Literal,
+)
+
+from numpy import dtype, ndarray, uint32, uint64
+from numpy._typing import _ArrayLikeInt_co, _ShapeLike, _SupportsDType, _UInt32Codes, _UInt64Codes
+
+_T = TypeVar("_T")
+
+_DTypeLikeUint32 = Union[
+    dtype[uint32],
+    _SupportsDType[dtype[uint32]],
+    type[uint32],
+    _UInt32Codes,
+]
+_DTypeLikeUint64 = Union[
+    dtype[uint64],
+    _SupportsDType[dtype[uint64]],
+    type[uint64],
+    _UInt64Codes,
+]
+
+class _SeedSeqState(TypedDict):
+    entropy: None | int | Sequence[int]
+    spawn_key: tuple[int, ...]
+    pool_size: int
+    n_children_spawned: int
+
+class _Interface(NamedTuple):
+    state_address: Any
+    state: Any
+    next_uint64: Any
+    next_uint32: Any
+    next_double: Any
+    bit_generator: Any
+
+class ISeedSequence(abc.ABC):
+    @abc.abstractmethod
+    def generate_state(
+        self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...
+    ) -> ndarray[Any, dtype[uint32 | uint64]]: ...
+
+class ISpawnableSeedSequence(ISeedSequence):
+    @abc.abstractmethod
+    def spawn(self: _T, n_children: int) -> list[_T]: ...
+
+class SeedlessSeedSequence(ISpawnableSeedSequence):
+    def generate_state(
+        self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...
+    ) -> ndarray[Any, dtype[uint32 | uint64]]: ...
+    def spawn(self: _T, n_children: int) -> list[_T]: ...
+
+class SeedSequence(ISpawnableSeedSequence):
+    entropy: None | int | Sequence[int]
+    spawn_key: tuple[int, ...]
+    pool_size: int
+    n_children_spawned: int
+    pool: ndarray[Any, dtype[uint32]]
+    def __init__(
+        self,
+        entropy: None | int | Sequence[int] | _ArrayLikeInt_co = ...,
+        *,
+        spawn_key: Sequence[int] = ...,
+        pool_size: int = ...,
+        n_children_spawned: int = ...,
+    ) -> None: ...
+    def __repr__(self) -> str: ...
+    @property
+    def state(
+        self,
+    ) -> _SeedSeqState: ...
+    def generate_state(
+        self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...
+    ) -> ndarray[Any, dtype[uint32 | uint64]]: ...
+    def spawn(self, n_children: int) -> list[SeedSequence]: ...
+
+class BitGenerator(abc.ABC):
+    lock: Lock
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def __getstate__(self) -> dict[str, Any]: ...
+    def __setstate__(self, state: dict[str, Any]) -> None: ...
+    def __reduce__(
+        self,
+    ) -> tuple[Callable[[str], BitGenerator], tuple[str], tuple[dict[str, Any]]]: ...
+    @abc.abstractmethod
+    @property
+    def state(self) -> Mapping[str, Any]: ...
+    @state.setter
+    def state(self, value: Mapping[str, Any]) -> None: ...
+    @property
+    def seed_seq(self) -> ISeedSequence: ...
+    def spawn(self, n_children: int) -> list[BitGenerator]: ...
+    @overload
+    def random_raw(self, size: None = ..., output: Literal[True] = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def random_raw(self, size: _ShapeLike = ..., output: Literal[True] = ...) -> ndarray[Any, dtype[uint64]]: ...  # type: ignore[misc]
+    @overload
+    def random_raw(self, size: None | _ShapeLike = ..., output: Literal[False] = ...) -> None: ...  # type: ignore[misc]
+    def _benchmark(self, cnt: int, method: str = ...) -> None: ...
+    @property
+    def ctypes(self) -> _Interface: ...
+    @property
+    def cffi(self) -> _Interface: ...

.venv/lib/python3.11/site-packages/numpy/random/c_distributions.pxd

@@ -0,0 +1,120 @@
+#!python
+#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3
+from numpy cimport npy_intp
+
+from libc.stdint cimport (uint64_t, int32_t, int64_t)
+from numpy.random cimport bitgen_t
+
+cdef extern from "numpy/random/distributions.h":
+
+    struct s_binomial_t:
+        int has_binomial
+        double psave
+        int64_t nsave
+        double r
+        double q
+        double fm
+        int64_t m
+        double p1
+        double xm
+        double xl
+        double xr
+        double c
+        double laml
+        double lamr
+        double p2
+        double p3
+        double p4
+
+    ctypedef s_binomial_t binomial_t
+
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
+    double random_standard_uniform(bitgen_t *bitgen_state) nogil
+    void random_standard_uniform_fill(bitgen_t* bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_uniform_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
+    double random_standard_exponential(bitgen_t *bitgen_state) nogil
+    float random_standard_exponential_f(bitgen_t *bitgen_state) nogil
+    void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    void random_standard_exponential_inv_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_inv_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
+    double random_standard_normal(bitgen_t* bitgen_state) nogil
+    float random_standard_normal_f(bitgen_t *bitgen_state) nogil
+    void random_standard_normal_fill(bitgen_t *bitgen_state, npy_intp count, double *out) nogil
+    void random_standard_normal_fill_f(bitgen_t *bitgen_state, npy_intp count, float *out) nogil
+    double random_standard_gamma(bitgen_t *bitgen_state, double shape) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
+
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
+    void random_standard_uniform_fill_f(bitgen_t* bitgen_state, npy_intp cnt, float *out) nogil
+    float random_standard_normal_f(bitgen_t* bitgen_state) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
+
+    int64_t random_positive_int64(bitgen_t *bitgen_state) nogil
+    int32_t random_positive_int32(bitgen_t *bitgen_state) nogil
+    int64_t random_positive_int(bitgen_t *bitgen_state) nogil
+    uint64_t random_uint(bitgen_t *bitgen_state) nogil
+
+    double random_normal(bitgen_t *bitgen_state, double loc, double scale) nogil
+
+    double random_gamma(bitgen_t *bitgen_state, double shape, double scale) nogil
+    float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale) nogil
+
+    double random_exponential(bitgen_t *bitgen_state, double scale) nogil
+    double random_uniform(bitgen_t *bitgen_state, double lower, double range) nogil
+    double random_beta(bitgen_t *bitgen_state, double a, double b) nogil
+    double random_chisquare(bitgen_t *bitgen_state, double df) nogil
+    double random_f(bitgen_t *bitgen_state, double dfnum, double dfden) nogil
+    double random_standard_cauchy(bitgen_t *bitgen_state) nogil
+    double random_pareto(bitgen_t *bitgen_state, double a) nogil
+    double random_weibull(bitgen_t *bitgen_state, double a) nogil
+    double random_power(bitgen_t *bitgen_state, double a) nogil
+    double random_laplace(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_gumbel(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_logistic(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma) nogil
+    double random_rayleigh(bitgen_t *bitgen_state, double mode) nogil
+    double random_standard_t(bitgen_t *bitgen_state, double df) nogil
+    double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,
+                                       double nonc) nogil
+    double random_noncentral_f(bitgen_t *bitgen_state, double dfnum,
+                               double dfden, double nonc) nogil
+    double random_wald(bitgen_t *bitgen_state, double mean, double scale) nogil
+    double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa) nogil
+    double random_triangular(bitgen_t *bitgen_state, double left, double mode,
+                             double right) nogil
+
+    int64_t random_poisson(bitgen_t *bitgen_state, double lam) nogil
+    int64_t random_negative_binomial(bitgen_t *bitgen_state, double n, double p) nogil
+    int64_t random_binomial(bitgen_t *bitgen_state, double p, int64_t n, binomial_t *binomial) nogil
+    int64_t random_logseries(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric_search(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric_inversion(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_zipf(bitgen_t *bitgen_state, double a) nogil
+    int64_t random_hypergeometric(bitgen_t *bitgen_state, int64_t good, int64_t bad,
+                                    int64_t sample) nogil
+
+    uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max) nogil
+
+    # Generate random uint64 numbers in closed interval [off, off + rng].
+    uint64_t random_bounded_uint64(bitgen_t *bitgen_state,
+                                   uint64_t off, uint64_t rng,
+                                   uint64_t mask, bint use_masked) nogil
+
+    void random_multinomial(bitgen_t *bitgen_state, int64_t n, int64_t *mnix,
+                            double *pix, npy_intp d, binomial_t *binomial) nogil
+
+    int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state,
+                          int64_t total,
+                          size_t num_colors, int64_t *colors,
+                          int64_t nsample,
+                          size_t num_variates, int64_t *variates) nogil
+    void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state,
+                               int64_t total,
+                               size_t num_colors, int64_t *colors,
+                               int64_t nsample,
+                               size_t num_variates, int64_t *variates) nogil
+

.venv/lib/python3.11/site-packages/numpy/random/mtrand.pyi

@@ -0,0 +1,571 @@
+import builtins
+from collections.abc import Callable
+from typing import Any, Union, overload, Literal
+
+from numpy import (
+    bool_,
+    dtype,
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    int_,
+    ndarray,
+    uint,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+from numpy.random.bit_generator import BitGenerator
+from numpy._typing import (
+    ArrayLike,
+    _ArrayLikeFloat_co,
+    _ArrayLikeInt_co,
+    _DoubleCodes,
+    _DTypeLikeBool,
+    _DTypeLikeInt,
+    _DTypeLikeUInt,
+    _Float32Codes,
+    _Float64Codes,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _IntCodes,
+    _ShapeLike,
+    _SingleCodes,
+    _SupportsDType,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UIntCodes,
+)
+
+_DTypeLikeFloat32 = Union[
+    dtype[float32],
+    _SupportsDType[dtype[float32]],
+    type[float32],
+    _Float32Codes,
+    _SingleCodes,
+]
+
+_DTypeLikeFloat64 = Union[
+    dtype[float64],
+    _SupportsDType[dtype[float64]],
+    type[float],
+    type[float64],
+    _Float64Codes,
+    _DoubleCodes,
+]
+
+class RandomState:
+    _bit_generator: BitGenerator
+    def __init__(self, seed: None | _ArrayLikeInt_co | BitGenerator = ...) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def __getstate__(self) -> dict[str, Any]: ...
+    def __setstate__(self, state: dict[str, Any]) -> None: ...
+    def __reduce__(self) -> tuple[Callable[[str], RandomState], tuple[str], dict[str, Any]]: ...
+    def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None: ...
+    @overload
+    def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]: ...
+    @overload
+    def get_state(
+        self, legacy: Literal[True] = ...
+    ) -> dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]: ...
+    def set_state(
+        self, state: dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]
+    ) -> None: ...
+    @overload
+    def random_sample(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def random_sample(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def random(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def beta(self, a: float, b: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def beta(
+        self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def exponential(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def exponential(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_exponential(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def tomaxint(self, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def tomaxint(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+    ) -> int: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: _DTypeLikeBool = ...,
+    ) -> bool: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: _DTypeLikeInt | _DTypeLikeUInt = ...,
+    ) -> int: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeBool = ...,
+    ) -> ndarray[Any, dtype[bool_]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
+    ) -> ndarray[Any, dtype[int8]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
+    ) -> ndarray[Any, dtype[int16]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
+    ) -> ndarray[Any, dtype[int32]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
+    ) -> ndarray[Any, dtype[uint8]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
+    ) -> ndarray[Any, dtype[uint16]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
+    ) -> ndarray[Any, dtype[uint32]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
+    ) -> ndarray[Any, dtype[uint64]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...,
+    ) -> ndarray[Any, dtype[uint]]: ...
+    def bytes(self, length: int) -> builtins.bytes: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> int: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> Any: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> ndarray[Any, Any]: ...
+    @overload
+    def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def uniform(
+        self,
+        low: _ArrayLikeFloat_co = ...,
+        high: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def rand(self) -> float: ...
+    @overload
+    def rand(self, *args: int) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def randn(self) -> float: ...
+    @overload
+    def randn(self, *args: int) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def random_integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def standard_normal(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self, size: _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def normal(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(  # type: ignore[misc]
+        self,
+        shape: float,
+        size: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def f(
+        self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def chisquare(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def chisquare(
+        self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_chisquare(
+        self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_t(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: None = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def vonmises(
+        self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def pareto(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def pareto(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def weibull(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def weibull(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def power(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def power(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_cauchy(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def laplace(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gumbel(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def logistic(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def lognormal(
+        self,
+        mean: _ArrayLikeFloat_co = ...,
+        sigma: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def rayleigh(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def rayleigh(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def wald(self, mean: float, scale: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def wald(
+        self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def triangular(
+        self,
+        left: _ArrayLikeFloat_co,
+        mode: _ArrayLikeFloat_co,
+        right: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def binomial(self, n: int, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def binomial(
+        self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def negative_binomial(
+        self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def poisson(self, lam: float = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def poisson(
+        self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def zipf(self, a: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def zipf(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def geometric(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def geometric(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def hypergeometric(
+        self,
+        ngood: _ArrayLikeInt_co,
+        nbad: _ArrayLikeInt_co,
+        nsample: _ArrayLikeInt_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def logseries(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def logseries(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    def multivariate_normal(
+        self,
+        mean: _ArrayLikeFloat_co,
+        cov: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        check_valid: Literal["warn", "raise", "ignore"] = ...,
+        tol: float = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    def multinomial(
+        self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    def dirichlet(
+        self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    def shuffle(self, x: ArrayLike) -> None: ...
+    @overload
+    def permutation(self, x: int) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def permutation(self, x: ArrayLike) -> ndarray[Any, Any]: ...
+
+_rand: RandomState
+
+beta = _rand.beta
+binomial = _rand.binomial
+bytes = _rand.bytes
+chisquare = _rand.chisquare
+choice = _rand.choice
+dirichlet = _rand.dirichlet
+exponential = _rand.exponential
+f = _rand.f
+gamma = _rand.gamma
+get_state = _rand.get_state
+geometric = _rand.geometric
+gumbel = _rand.gumbel
+hypergeometric = _rand.hypergeometric
+laplace = _rand.laplace
+logistic = _rand.logistic
+lognormal = _rand.lognormal
+logseries = _rand.logseries
+multinomial = _rand.multinomial
+multivariate_normal = _rand.multivariate_normal
+negative_binomial = _rand.negative_binomial
+noncentral_chisquare = _rand.noncentral_chisquare
+noncentral_f = _rand.noncentral_f
+normal = _rand.normal
+pareto = _rand.pareto
+permutation = _rand.permutation
+poisson = _rand.poisson
+power = _rand.power
+rand = _rand.rand
+randint = _rand.randint
+randn = _rand.randn
+random = _rand.random
+random_integers = _rand.random_integers
+random_sample = _rand.random_sample
+rayleigh = _rand.rayleigh
+seed = _rand.seed
+set_state = _rand.set_state
+shuffle = _rand.shuffle
+standard_cauchy = _rand.standard_cauchy
+standard_exponential = _rand.standard_exponential
+standard_gamma = _rand.standard_gamma
+standard_normal = _rand.standard_normal
+standard_t = _rand.standard_t
+triangular = _rand.triangular
+uniform = _rand.uniform
+vonmises = _rand.vonmises
+wald = _rand.wald
+weibull = _rand.weibull
+zipf = _rand.zipf
+# Two legacy that are trivial wrappers around random_sample
+sample = _rand.random_sample
+ranf = _rand.random_sample
+
+def set_bit_generator(bitgen: BitGenerator) -> None:
+    ...
+
+def get_bit_generator() -> BitGenerator:
+    ...