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"""
Template for each `dtype` helper function in `np.random.randint`.
"""
def _rand_bool(npy_bool low, npy_bool high, size, rngstate):
"""
_rand_bool(low, high, size, rngstate)
Return random np.bool_ integers between ``low`` and ``high``, inclusive.
Return random integers from the "discrete uniform" distribution in the
closed interval [``low``, ``high``). On entry the arguments are presumed
to have been validated for size and order for the np.bool_ type.
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution.
high : int
Highest (signed) integer to be drawn from the distribution.
size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
rngstate : encapsulated pointer to rk_state
The specific type depends on the python version. In Python 2 it is
a PyCObject, in Python 3 a PyCapsule object.
Returns
-------
out : python integer or ndarray of np.bool_
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
"""
cdef npy_bool off, rng, buf
cdef npy_bool *out
cdef ndarray array "arrayObject"
cdef npy_intp cnt
cdef rk_state *state = <rk_state *>PyCapsule_GetPointer(rngstate, NULL)
off = <npy_bool>(low)
rng = <npy_bool>(high) - <npy_bool>(low)
if size is None:
rk_random_bool(off, rng, 1, &buf, state)
return np.bool_(<npy_bool>buf)
else:
array = <ndarray>np.empty(size, np.bool_)
cnt = PyArray_SIZE(array)
array_data = <npy_bool *>PyArray_DATA(array)
with nogil:
rk_random_bool(off, rng, cnt, array_data, state)
return array
def _rand_int8(npy_int8 low, npy_int8 high, size, rngstate):
"""
_rand_int8(low, high, size, rngstate)
Return random np.int8 integers between ``low`` and ``high``, inclusive.
Return random integers from the "discrete uniform" distribution in the
closed interval [``low``, ``high``). On entry the arguments are presumed
to have been validated for size and order for the np.int8 type.
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution.
high : int
Highest (signed) integer to be drawn from the distribution.
size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
rngstate : encapsulated pointer to rk_state
The specific type depends on the python version. In Python 2 it is
a PyCObject, in Python 3 a PyCapsule object.
Returns
-------
out : python integer or ndarray of np.int8
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
"""
cdef npy_uint8 off, rng, buf
cdef npy_uint8 *out
cdef ndarray array "arrayObject"
cdef npy_intp cnt
cdef rk_state *state = <rk_state *>PyCapsule_GetPointer(rngstate, NULL)
off = <npy_uint8>(low)
rng = <npy_uint8>(high) - <npy_uint8>(low)
if size is None:
rk_random_uint8(off, rng, 1, &buf, state)
return np.int8(<npy_int8>buf)
else:
array = <ndarray>np.empty(size, np.int8)
cnt = PyArray_SIZE(array)
array_data = <npy_uint8 *>PyArray_DATA(array)
with nogil:
rk_random_uint8(off, rng, cnt, array_data, state)
return array
def _rand_int16(npy_int16 low, npy_int16 high, size, rngstate):
"""
_rand_int16(low, high, size, rngstate)
Return random np.int16 integers between ``low`` and ``high``, inclusive.
Return random integers from the "discrete uniform" distribution in the
closed interval [``low``, ``high``). On entry the arguments are presumed
to have been validated for size and order for the np.int16 type.
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution.
high : int
Highest (signed) integer to be drawn from the distribution.
size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
rngstate : encapsulated pointer to rk_state
The specific type depends on the python version. In Python 2 it is
a PyCObject, in Python 3 a PyCapsule object.
Returns
-------
out : python integer or ndarray of np.int16
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
"""
cdef npy_uint16 off, rng, buf
cdef npy_uint16 *out
cdef ndarray array "arrayObject"
cdef npy_intp cnt
cdef rk_state *state = <rk_state *>PyCapsule_GetPointer(rngstate, NULL)
off = <npy_uint16>(low)
rng = <npy_uint16>(high) - <npy_uint16>(low)
if size is None:
rk_random_uint16(off, rng, 1, &buf, state)
return np.int16(<npy_int16>buf)
else:
array = <ndarray>np.empty(size, np.int16)
cnt = PyArray_SIZE(array)
array_data = <npy_uint16 *>PyArray_DATA(array)
with nogil:
rk_random_uint16(off, rng, cnt, array_data, state)
return array
def _rand_int32(npy_int32 low, npy_int32 high, size, rngstate):
"""
_rand_int32(low, high, size, rngstate)
Return random np.int32 integers between ``low`` and ``high``, inclusive.
Return random integers from the "discrete uniform" distribution in the
closed interval [``low``, ``high``). On entry the arguments are presumed
to have been validated for size and order for the np.int32 type.
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution.
high : int
Highest (signed) integer to be drawn from the distribution.
size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
rngstate : encapsulated pointer to rk_state
The specific type depends on the python version. In Python 2 it is
a PyCObject, in Python 3 a PyCapsule object.
Returns
-------
out : python integer or ndarray of np.int32
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
"""
cdef npy_uint32 off, rng, buf
cdef npy_uint32 *out
cdef ndarray array "arrayObject"
cdef npy_intp cnt
cdef rk_state *state = <rk_state *>PyCapsule_GetPointer(rngstate, NULL)
off = <npy_uint32>(low)
rng = <npy_uint32>(high) - <npy_uint32>(low)
if size is None:
rk_random_uint32(off, rng, 1, &buf, state)
return np.int32(<npy_int32>buf)
else:
array = <ndarray>np.empty(size, np.int32)
cnt = PyArray_SIZE(array)
array_data = <npy_uint32 *>PyArray_DATA(array)
with nogil:
rk_random_uint32(off, rng, cnt, array_data, state)
return array
def _rand_int64(npy_int64 low, npy_int64 high, size, rngstate):
"""
_rand_int64(low, high, size, rngstate)
Return random np.int64 integers between ``low`` and ``high``, inclusive.
Return random integers from the "discrete uniform" distribution in the
closed interval [``low``, ``high``). On entry the arguments are presumed
to have been validated for size and order for the np.int64 type.
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution.
high : int
Highest (signed) integer to be drawn from the distribution.
size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
rngstate : encapsulated pointer to rk_state
The specific type depends on the python version. In Python 2 it is
a PyCObject, in Python 3 a PyCapsule object.
Returns
-------
out : python integer or ndarray of np.int64
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
"""
cdef npy_uint64 off, rng, buf
cdef npy_uint64 *out
cdef ndarray array "arrayObject"
cdef npy_intp cnt
cdef rk_state *state = <rk_state *>PyCapsule_GetPointer(rngstate, NULL)
off = <npy_uint64>(low)
rng = <npy_uint64>(high) - <npy_uint64>(low)
if size is None:
rk_random_uint64(off, rng, 1, &buf, state)
return np.int64(<npy_int64>buf)
else:
array = <ndarray>np.empty(size, np.int64)
cnt = PyArray_SIZE(array)
array_data = <npy_uint64 *>PyArray_DATA(array)
with nogil:
rk_random_uint64(off, rng, cnt, array_data, state)
return array
def _rand_uint8(npy_uint8 low, npy_uint8 high, size, rngstate):
"""
_rand_uint8(low, high, size, rngstate)
Return random np.uint8 integers between ``low`` and ``high``, inclusive.
Return random integers from the "discrete uniform" distribution in the
closed interval [``low``, ``high``). On entry the arguments are presumed
to have been validated for size and order for the np.uint8 type.
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution.
high : int
Highest (signed) integer to be drawn from the distribution.
size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
rngstate : encapsulated pointer to rk_state
The specific type depends on the python version. In Python 2 it is
a PyCObject, in Python 3 a PyCapsule object.
Returns
-------
out : python integer or ndarray of np.uint8
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
"""
cdef npy_uint8 off, rng, buf
cdef npy_uint8 *out
cdef ndarray array "arrayObject"
cdef npy_intp cnt
cdef rk_state *state = <rk_state *>PyCapsule_GetPointer(rngstate, NULL)
off = <npy_uint8>(low)
rng = <npy_uint8>(high) - <npy_uint8>(low)
if size is None:
rk_random_uint8(off, rng, 1, &buf, state)
return np.uint8(<npy_uint8>buf)
else:
array = <ndarray>np.empty(size, np.uint8)
cnt = PyArray_SIZE(array)
array_data = <npy_uint8 *>PyArray_DATA(array)
with nogil:
rk_random_uint8(off, rng, cnt, array_data, state)
return array
def _rand_uint16(npy_uint16 low, npy_uint16 high, size, rngstate):
"""
_rand_uint16(low, high, size, rngstate)
Return random np.uint16 integers between ``low`` and ``high``, inclusive.
Return random integers from the "discrete uniform" distribution in the
closed interval [``low``, ``high``). On entry the arguments are presumed
to have been validated for size and order for the np.uint16 type.
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution.
high : int
Highest (signed) integer to be drawn from the distribution.
size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
rngstate : encapsulated pointer to rk_state
The specific type depends on the python version. In Python 2 it is
a PyCObject, in Python 3 a PyCapsule object.
Returns
-------
out : python integer or ndarray of np.uint16
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
"""
cdef npy_uint16 off, rng, buf
cdef npy_uint16 *out
cdef ndarray array "arrayObject"
cdef npy_intp cnt
cdef rk_state *state = <rk_state *>PyCapsule_GetPointer(rngstate, NULL)
off = <npy_uint16>(low)
rng = <npy_uint16>(high) - <npy_uint16>(low)
if size is None:
rk_random_uint16(off, rng, 1, &buf, state)
return np.uint16(<npy_uint16>buf)
else:
array = <ndarray>np.empty(size, np.uint16)
cnt = PyArray_SIZE(array)
array_data = <npy_uint16 *>PyArray_DATA(array)
with nogil:
rk_random_uint16(off, rng, cnt, array_data, state)
return array
def _rand_uint32(npy_uint32 low, npy_uint32 high, size, rngstate):
"""
_rand_uint32(low, high, size, rngstate)
Return random np.uint32 integers between ``low`` and ``high``, inclusive.
Return random integers from the "discrete uniform" distribution in the
closed interval [``low``, ``high``). On entry the arguments are presumed
to have been validated for size and order for the np.uint32 type.
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution.
high : int
Highest (signed) integer to be drawn from the distribution.
size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
rngstate : encapsulated pointer to rk_state
The specific type depends on the python version. In Python 2 it is
a PyCObject, in Python 3 a PyCapsule object.
Returns
-------
out : python integer or ndarray of np.uint32
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
"""
cdef npy_uint32 off, rng, buf
cdef npy_uint32 *out
cdef ndarray array "arrayObject"
cdef npy_intp cnt
cdef rk_state *state = <rk_state *>PyCapsule_GetPointer(rngstate, NULL)
off = <npy_uint32>(low)
rng = <npy_uint32>(high) - <npy_uint32>(low)
if size is None:
rk_random_uint32(off, rng, 1, &buf, state)
return np.uint32(<npy_uint32>buf)
else:
array = <ndarray>np.empty(size, np.uint32)
cnt = PyArray_SIZE(array)
array_data = <npy_uint32 *>PyArray_DATA(array)
with nogil:
rk_random_uint32(off, rng, cnt, array_data, state)
return array
def _rand_uint64(npy_uint64 low, npy_uint64 high, size, rngstate):
"""
_rand_uint64(low, high, size, rngstate)
Return random np.uint64 integers between ``low`` and ``high``, inclusive.
Return random integers from the "discrete uniform" distribution in the
closed interval [``low``, ``high``). On entry the arguments are presumed
to have been validated for size and order for the np.uint64 type.
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution.
high : int
Highest (signed) integer to be drawn from the distribution.
size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
rngstate : encapsulated pointer to rk_state
The specific type depends on the python version. In Python 2 it is
a PyCObject, in Python 3 a PyCapsule object.
Returns
-------
out : python integer or ndarray of np.uint64
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
"""
cdef npy_uint64 off, rng, buf
cdef npy_uint64 *out
cdef ndarray array "arrayObject"
cdef npy_intp cnt
cdef rk_state *state = <rk_state *>PyCapsule_GetPointer(rngstate, NULL)
off = <npy_uint64>(low)
rng = <npy_uint64>(high) - <npy_uint64>(low)
if size is None:
rk_random_uint64(off, rng, 1, &buf, state)
return np.uint64(<npy_uint64>buf)
else:
array = <ndarray>np.empty(size, np.uint64)
cnt = PyArray_SIZE(array)
array_data = <npy_uint64 *>PyArray_DATA(array)
with nogil:
rk_random_uint64(off, rng, cnt, array_data, state)
return array
|
