https://github.com/gwastro/pycbc
Tip revision: bb57a39edbb8e0a66ac0d9b514eaa41afaa2a5ca authored by Ian Harry on 17 June 2019, 14:58:05 UTC
Prep for new release (#2779)
Prep for new release (#2779)
Tip revision: bb57a39
matchedfilter_cpu.pyx
# Copyright (C) 2018 Alex Nitz, Josh Willis
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# =============================================================================
#
# Preamble
#
# =============================================================================
#
# cython: embedsignature=True
from __future__ import absolute_import
import numpy
from .matchedfilter import _BaseCorrelator
cimport numpy, cython
from cython.parallel import prange
ctypedef fused COMPLEXTYPE:
float complex
double complex
def _batch_correlate(numpy.ndarray [long, ndim=1] x,
numpy.ndarray [float complex, ndim=1] y,
numpy.ndarray [long, ndim=1] z,
size, num_vectors):
cdef unsigned int nvec = num_vectors
cdef unsigned int vsize = size
cdef float complex* xp
cdef float complex* zp
for i in range(nvec):
xp = <float complex*> x[i]
zp = <float complex*> z[i]
for j in range(vsize):
zp[j] = xp[j].conjugate() * y[j]
def batch_correlate_execute(self, y):
num_vectors = self.num_vectors # pylint:disable=unused-variable
size = self.size # pylint:disable=unused-variable
_batch_correlate(self.x.data, y.data, self.z.data, size, num_vectors)
def correlate_numpy(x, y, z):
z.data[:] = numpy.conjugate(x.data)[:]
z *= y
@cython.boundscheck(False)
@cython.wraparound(False)
def _correlate(COMPLEXTYPE[:] x,
COMPLEXTYPE[:] y,
COMPLEXTYPE[:] z):
cdef unsigned int xmax = x.shape[0]
cdef unsigned int i
for i in prange(xmax, nogil=True):
z[i] = x[i].conjugate() * y[i]
def correlate(x, y, z):
_correlate(x.data, y.data, z.data)
class CPUCorrelator(_BaseCorrelator):
def __init__(self, x, y, z):
self.x = numpy.array(x.data, copy=False)
self.y = numpy.array(y.data, copy=False)
self.z = numpy.array(z.data, copy=False)
def correlate(self):
_correlate(self.x, self.y, self.z)
def _correlate_factory(x, y, z):
return CPUCorrelator