/usr/lib/python2.7/dist-packages/pyopencl/clmath.py is in python-pyopencl 2015.1-2build3.
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__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in 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:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
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
AUTHORS 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 IN
THE SOFTWARE.
"""
import pyopencl.array as cl_array
import pyopencl.elementwise as elementwise
from pyopencl.array import _get_common_dtype
import numpy as np
def _make_unary_array_func(name):
@cl_array.elwise_kernel_runner
def knl_runner(result, arg):
if arg.dtype.kind == "c":
from pyopencl.elementwise import complex_dtype_to_name
fname = "%s_%s" % (complex_dtype_to_name(arg.dtype), name)
else:
fname = name
return elementwise.get_unary_func_kernel(
result.context, fname, arg.dtype)
def f(array, queue=None):
result = array._new_like_me(queue=queue)
knl_runner(result, array, queue=queue)
return result
return f
# See table 6.8 in the CL 1.1 spec
acos = _make_unary_array_func("acos")
acosh = _make_unary_array_func("acosh")
acospi = _make_unary_array_func("acospi")
asin = _make_unary_array_func("asin")
asinh = _make_unary_array_func("asinh")
asinpi = _make_unary_array_func("asinpi")
@cl_array.elwise_kernel_runner
def _atan2(result, arg1, arg2):
return elementwise.get_float_binary_func_kernel(
result.context, "atan2", arg1.dtype, arg2.dtype, result.dtype)
@cl_array.elwise_kernel_runner
def _atan2pi(result, arg1, arg2):
return elementwise.get_float_binary_func_kernel(
result.context, "atan2pi", arg1.dtype, arg2.dtype, result.dtype)
atan = _make_unary_array_func("atan")
def atan2(y, x, queue=None):
"""
.. versionadded:: 2013.1
"""
queue = queue or y.queue
result = y._new_like_me(_get_common_dtype(y, x, queue))
_atan2(result, y, x, queue=queue)
return result
atanh = _make_unary_array_func("atanh")
atanpi = _make_unary_array_func("atanpi")
def atan2pi(y, x, queue=None):
"""
.. versionadded:: 2013.1
"""
queue = queue or y.queue
result = y._new_like_me(_get_common_dtype(y, x, queue))
_atan2pi(result, y, x, queue=queue)
return result
cbrt = _make_unary_array_func("cbrt")
ceil = _make_unary_array_func("ceil")
# TODO: copysign
cos = _make_unary_array_func("cos")
cosh = _make_unary_array_func("cosh")
cospi = _make_unary_array_func("cospi")
erfc = _make_unary_array_func("erfc")
erf = _make_unary_array_func("erf")
exp = _make_unary_array_func("exp")
exp2 = _make_unary_array_func("exp2")
exp10 = _make_unary_array_func("exp10")
expm1 = _make_unary_array_func("expm1")
fabs = _make_unary_array_func("fabs")
# TODO: fdim
floor = _make_unary_array_func("floor")
# TODO: fma
# TODO: fmax
# TODO: fmin
@cl_array.elwise_kernel_runner
def _fmod(result, arg, mod):
return elementwise.get_fmod_kernel(result.context, result.dtype,
arg.dtype, mod.dtype)
def fmod(arg, mod, queue=None):
"""Return the floating point remainder of the division `arg/mod`,
for each element in `arg` and `mod`."""
queue = (queue or arg.queue) or mod.queue
result = arg._new_like_me(_get_common_dtype(arg, mod, queue))
_fmod(result, arg, mod, queue=queue)
return result
# TODO: fract
@cl_array.elwise_kernel_runner
def _frexp(sig, expt, arg):
return elementwise.get_frexp_kernel(sig.context, sig.dtype,
expt.dtype, arg.dtype)
def frexp(arg, queue=None):
"""Return a tuple `(significands, exponents)` such that
`arg == significand * 2**exponent`.
"""
sig = arg._new_like_me(queue=queue)
expt = arg._new_like_me(queue=queue)
_frexp(sig, expt, arg, queue=queue)
return sig, expt
# TODO: hypot
ilogb = _make_unary_array_func("ilogb")
@cl_array.elwise_kernel_runner
def _ldexp(result, sig, exp):
return elementwise.get_ldexp_kernel(result.context, result.dtype,
sig.dtype, exp.dtype)
def ldexp(significand, exponent, queue=None):
"""Return a new array of floating point values composed from the
entries of `significand` and `exponent`, paired together as
`result = significand * 2**exponent`.
"""
result = significand._new_like_me(queue=queue)
_ldexp(result, significand, exponent)
return result
lgamma = _make_unary_array_func("lgamma")
# TODO: lgamma_r
log = _make_unary_array_func("log")
log2 = _make_unary_array_func("log2")
log10 = _make_unary_array_func("log10")
log1p = _make_unary_array_func("log1p")
logb = _make_unary_array_func("logb")
# TODO: mad
# TODO: maxmag
# TODO: minmag
@cl_array.elwise_kernel_runner
def _modf(intpart, fracpart, arg):
return elementwise.get_modf_kernel(intpart.context, intpart.dtype,
fracpart.dtype, arg.dtype)
def modf(arg, queue=None):
"""Return a tuple `(fracpart, intpart)` of arrays containing the
integer and fractional parts of `arg`.
"""
intpart = arg._new_like_me(queue=queue)
fracpart = arg._new_like_me(queue=queue)
_modf(intpart, fracpart, arg, queue=queue)
return fracpart, intpart
nan = _make_unary_array_func("nan")
# TODO: nextafter
# TODO: remainder
# TODO: remquo
rint = _make_unary_array_func("rint")
# TODO: rootn
round = _make_unary_array_func("round")
sin = _make_unary_array_func("sin")
# TODO: sincos
sinh = _make_unary_array_func("sinh")
sinpi = _make_unary_array_func("sinpi")
sqrt = _make_unary_array_func("sqrt")
tan = _make_unary_array_func("tan")
tanh = _make_unary_array_func("tanh")
tanpi = _make_unary_array_func("tanpi")
tgamma = _make_unary_array_func("tgamma")
trunc = _make_unary_array_func("trunc")
# no point wrapping half_ or native_
# TODO: table 6.10, integer functions
# TODO: table 6.12, clamp et al
@cl_array.elwise_kernel_runner
def _bessel_jn(result, n, x):
return elementwise.get_bessel_kernel(result.context, "j", result.dtype,
np.dtype(type(n)), x.dtype)
@cl_array.elwise_kernel_runner
def _bessel_yn(result, n, x):
return elementwise.get_bessel_kernel(result.context, "y", result.dtype,
np.dtype(type(n)), x.dtype)
def bessel_jn(n, x, queue=None):
result = x._new_like_me(queue=queue)
_bessel_jn(result, n, x)
return result
def bessel_yn(n, x, queue=None):
result = x._new_like_me(queue=queue)
_bessel_yn(result, n, x)
return result
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