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#!/usr/bin/env python
"""
PyCUDA-based special functions.
"""
import os
import pycuda.gpuarray as gpuarray
import pycuda.elementwise as elementwise
from pycuda.tools import context_dependent_memoize
import numpy as np
from . import misc
from .misc import init
# Get installation location of C headers:
from . import install_headers
@context_dependent_memoize
def _get_sici_kernel(dtype):
if dtype == np.float32:
args = 'float *x, float *si, float *ci'
op = 'sicif(x[i], &si[i], &ci[i])'
elif dtype == np.float64:
args = 'double *x, double *si, double *ci'
op = 'sici(x[i], &si[i], &ci[i])'
else:
raise ValueError('unsupported type')
return elementwise.ElementwiseKernel(args, op,
options=["-I", install_headers],
preamble='#include "cuSpecialFuncs.h"')
def sici(x_gpu):
"""
Sine/Cosine integral.
Computes the sine and cosine integral of every element in the
input matrix.
Parameters
----------
x_gpu : GPUArray
Input matrix of shape `(m, n)`.
Returns
-------
(si_gpu, ci_gpu) : tuple of GPUArrays
Tuple of GPUarrays containing the sine integrals and cosine
integrals of the entries of `x_gpu`.
Examples
--------
>>> import pycuda.gpuarray as gpuarray
>>> import pycuda.autoinit
>>> import numpy as np
>>> import scipy.special
>>> import special
>>> x = np.array([[1, 2], [3, 4]], np.float32)
>>> x_gpu = gpuarray.to_gpu(x)
>>> (si_gpu, ci_gpu) = sici(x_gpu)
>>> (si, ci) = scipy.special.sici(x)
>>> np.allclose(si, si_gpu.get())
True
>>> np.allclose(ci, ci_gpu.get())
True
"""
si_gpu = gpuarray.empty_like(x_gpu)
ci_gpu = gpuarray.empty_like(x_gpu)
func = _get_sici_kernel(x_gpu.dtype)
func(x_gpu, si_gpu, ci_gpu)
return (si_gpu, ci_gpu)
@context_dependent_memoize
def _get_exp1_kernel(dtype):
if dtype == np.complex64:
args = 'pycuda::complex<float> *z, pycuda::complex<float> *e'
elif dtype == np.complex128:
args = 'pycuda::complex<double> *z, pycuda::complex<double> *e'
else:
raise ValueError('unsupported type')
op = 'e[i] = exp1(z[i])'
return elementwise.ElementwiseKernel(args, op,
options=["-I", install_headers],
preamble='#include "cuSpecialFuncs.h"')
def exp1(z_gpu):
"""
Exponential integral with `n = 1` of complex arguments.
Parameters
----------
z_gpu : GPUArray
Input matrix of shape `(m, n)`.
Returns
-------
e_gpu : GPUArray
GPUarrays containing the exponential integrals of
the entries of `z_gpu`.
Examples
--------
>>> import pycuda.gpuarray as gpuarray
>>> import pycuda.autoinit
>>> import numpy as np
>>> import scipy.special
>>> import special
>>> z = np.asarray(np.random.rand(4, 4)+1j*np.random.rand(4, 4), np.complex64)
>>> z_gpu = gpuarray.to_gpu(z)
>>> e_gpu = exp1(z_gpu)
>>> e_sp = scipy.special.exp1(z)
>>> np.allclose(e_sp, e_gpu.get())
True
"""
e_gpu = gpuarray.empty_like(z_gpu)
func = _get_exp1_kernel(z_gpu.dtype)
func(z_gpu, e_gpu)
return e_gpu
exp1.cache = {}
@context_dependent_memoize
def _get_expi_kernel(dtype):
if dtype == np.complex64:
args = 'pycuda::complex<float> *z, pycuda::complex<float> *e'
elif dtype == np.complex128:
args = 'pycuda::complex<double> *z, pycuda::complex<double> *e'
else:
raise ValueError('unsupported type')
op = 'e[i] = expi(z[i])'
return elementwise.ElementwiseKernel(args, op,
options=["-I", install_headers],
preamble='#include "cuSpecialFuncs.h"')
def expi(z_gpu):
"""
Exponential integral of complex arguments.
Parameters
----------
z_gpu : GPUArray
Input matrix of shape `(m, n)`.
Returns
-------
e_gpu : GPUArray
GPUarrays containing the exponential integrals of
the entries of `z_gpu`.
Examples
--------
>>> import pycuda.gpuarray as gpuarray
>>> import pycuda.autoinit
>>> import numpy as np
>>> import scipy.special
>>> import special
>>> z = np.asarray(np.random.rand(4, 4)+1j*np.random.rand(4, 4), np.complex64)
>>> z_gpu = gpuarray.to_gpu(z)
>>> e_gpu = expi(z_gpu)
>>> e_sp = scipy.special.expi(z)
>>> np.allclose(e_sp, e_gpu.get())
True
"""
e_gpu = gpuarray.empty_like(z_gpu)
func = _get_expi_kernel(z_gpu.dtype)
func(z_gpu, e_gpu)
return e_gpu
if __name__ == "__main__":
import doctest
doctest.testmod()
|
