# Copyright (C) 2012 Alex Nitz
# 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
#
# =============================================================================
#
import numpy
from .simd_threshold_cython import parallel_thresh_cluster, parallel_threshold
from .eventmgr import _BaseThresholdCluster
from .. import opt
if opt.HAVE_GETCONF:
default_segsize = opt.LEVEL2_CACHE_SIZE / numpy.dtype('complex64').itemsize
else:
# Seems to work for Sandy Bridge/Ivy Bridge/Haswell, for now?
default_segsize = 32768
[docs]def threshold_numpy(series, value):
arr = series.data
locs = numpy.where(arr.real**2 + arr.imag**2 > value**2)[0]
vals = arr[locs]
return locs, vals
outl = None
outv = None
count = None
[docs]def threshold_inline(series, value):
arr = numpy.array(series.data, copy=False, dtype=numpy.complex64)
global outl, outv, count
if outl is None or len(outl) < len(series):
outl = numpy.zeros(len(series), dtype=numpy.uint32)
outv = numpy.zeros(len(series), dtype=numpy.complex64)
count = numpy.zeros(1, dtype=numpy.uint32)
N = len(series)
threshold = value**2.0
parallel_threshold(N, arr, outv, outl, count, threshold)
num = count[0]
if num > 0:
return outl[0:num], outv[0:num]
else:
return numpy.array([], numpy.uint32), numpy.array([], numpy.float32)
# threshold_numpy can also be used here, but for now we use the inline code
# in all instances. Not sure why we're defining threshold *and* threshold_only
# but we are, and I'm not going to change this at this point.
threshold = threshold_inline
threshold_only = threshold_inline
[docs]class CPUThresholdCluster(_BaseThresholdCluster):
def __init__(self, series):
self.series = numpy.array(series.data, copy=False,
dtype=numpy.complex64)
self.slen = numpy.uint32(len(series))
self.outv = numpy.zeros(self.slen, numpy.complex64)
self.outl = numpy.zeros(self.slen, numpy.uint32)
self.segsize = numpy.uint32(default_segsize)
[docs] def threshold_and_cluster(self, threshold, window):
self.count = parallel_thresh_cluster(self.series, self.slen,
self.outv, self.outl,
numpy.float32(threshold),
numpy.uint32(window),
self.segsize)
if self.count > 0:
return self.outv[0:self.count], self.outl[0:self.count]
else:
return numpy.array([], dtype = numpy.complex64), numpy.array([], dtype = numpy.uint32)
def _threshold_cluster_factory(series):
return CPUThresholdCluster