Source code for

# 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
# 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
# =============================================================================
from __future__ import absolute_import
import numpy
from .simd_threshold_cython import parallel_thresh_cluster, parallel_threshold
from .eventmgr import _BaseThresholdCluster
from .. import opt

    default_segsize = opt.LEVEL2_CACHE_SIZE / numpy.dtype('complex64').itemsize
    # Seems to work for Sandy Bridge/Ivy Bridge/Haswell, for now?
    default_segsize = 32768

[docs]def threshold_numpy(series, value): arr = 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(, 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(, 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