8000 MAINT: Remove duplicate randint helpers code. by gfyoung · Pull Request #8088 · numpy/numpy · GitHub
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MAINT: Remove duplicate randint helpers code. #8088

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Merged
merged 1 commit into from
Sep 23, 2016
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MAINT: Remove duplicate randint helpers code. #8088

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277 changes: 0 additions & 277 deletions numpy/random/mtrand/mtrand.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -573,283 +573,6 @@ def _shape_from_size(size, d):
shape = tuple(size) + (d,)
return shape


# Set up dictionary of integer types and relevant functions.
#
# The dictionary is keyed by dtype(...).name and the values
# are a tuple (low, high, function), where low and high are
# the bounds of the largest half open interval `[low, high)`
# and the function is the relevant function to call for
# that precision.
#
# The functions are all the same except for changed types in
# a few places. It would be easy to template them.

def _rand_bool(low, high, size, rngstate):
"""
_rand_bool(low, high, size, rngstate)

See `_rand_int32` for documentation, only the return type changes.

"""
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)

rng = <npy_bool>(high - low)
off = <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)
out = <npy_bool *>PyArray_DATA(array)
with nogil:
rk_random_bool(off, rng, cnt, out, state)
return array


def _rand_int8(low, high, size, rngstate):
"""
_rand_int8(low, high, size, rngstate)

See `_rand_int32` for documentation, only the return type changes.

"""
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)

rng = <npy_uint8>(high - low)
off = <npy_uint8>(<npy_int8>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)
out = <npy_uint8 *>PyArray_DATA(array)
with nogil:
rk_random_uint8(off, rng, cnt, out, state)
return array


def _rand_int16(low, high, size, rngstate):
"""
_rand_int16(low, high, size, rngstate)

See `_rand_int32` for documentation, only the return type changes.

"""
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)

rng = <npy_uint16>(high - low)
off = <npy_uint16>(<npy_int16>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)
out = <npy_uint16 *>PyArray_DATA(array)
with nogil:
rk_random_uint16(off, rng, cnt, out, state)
return array


def _rand_int32(low, high, size, rngstate):
"""
_rand_int32(self, 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 scalar 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)

rng = <npy_uint32>(high - low)
off = <npy_uint32>(<npy_int32>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)
out = <npy_uint32 *>PyArray_DATA(array)
with nogil:
rk_random_uint32(off, rng, cnt, out, state)
return array


def _rand_int64(low, high, size, rngstate):
"""
_rand_int64(low, high, size, rngstate)

See `_rand_int32` for documentation, only the return type changes.

"""
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)

rng = <npy_uint64>(high - low)
off = <npy_uint64>(<npy_int64>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)
out = <npy_uint64 *>PyArray_DATA(array)
with nogil:
rk_random_uint64(off, rng, cnt, out, state)
return array

def _rand_uint8(low, high, size, rngstate):
"""
_rand_uint8(low, high, size, rngstate)

See `_rand_int32` for documentation, only the return type changes.

"""
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)

rng = <npy_uint8>(high - low)
off = <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)
out = <npy_uint8 *>PyArray_DATA(array)
with nogil:
rk_random_uint8(off, rng, cnt, out, state)
return array


def _rand_uint16(low, high, size, rngstate):
"""
_rand_uint16(low, high, size, rngstate)

See `_rand_int32` for documentation, only the return type changes.

"""
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)

rng = <npy_uint16>(high - low)
off = <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)
out = <npy_uint16 *>PyArray_DATA(array)
with nogil:
rk_random_uint16(off, rng, cnt, out, state)
return array


def _rand_uint32(low, high, size, rngstate):
"""
_rand_uint32(self, low, high, size, rngstate)

See `_rand_int32` for documentation, only the return type changes.

"""
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)

rng = <npy_uint32>(high - low)
off = <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)
out = <npy_uint32 *>PyArray_DATA(array)
with nogil:
rk_random_uint32(off, rng, cnt, out, state)
return array


def _rand_uint64(low, high, size, rngstate):
"""
_rand_uint64(low, high, size, rngstate)

See `_rand_int32` for documentation, only the return type changes.

"""
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)

rng = <npy_uint64>(high - low)
off = <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)
out = <npy_uint64 *>PyArray_DATA(array)
with nogil:
rk_random_uint64(off, rng, cnt, out, state)
return array

# Look up table for randint functions keyed by type name. The stored data
# is a tuple (lbnd, ubnd, func), where lbnd is the smallest value for the
# type, ubnd is one greater than the largest value, and func is the
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