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# extending_distributions.pyx

    #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)

    np.import_array()

    @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