def segment_signal(signal, numScales, deltaTau=None, kMin=None, linkType=None):
if deltaTau is None: deltaTau = DELTA_TAU
if kMin is None: kMin = pow((1 - exp(-2 * deltaTau)), -0.5)
signal = asarray(signal).flatten()
signalLength, maxDiff, numLeadingZeros, numTrailingZeros = _signal_properties(
signal)
# Calculate scale sigma's
scaleSigmaArray = exp(arange(0, numScales) * deltaTau)
# Setup initial node set
nodeIds = nonzero(diff(signal, axis=0))[0] + 1
if nodeIds[-1] != signalLength:
nodeIds = append(nodeIds, signalLength)
# Loop!
baseSignal = asarray(signal, dtype=np_double)
prevScaleSignal, prevSegmentEnds = baseSignal, nodeIds
nodeMapping, segmentEnds = {}, {0: nodeIds}
for scaleIndex in range(1, numScales):
scaleSignal = convolve(baseSignal, scaleSigmaArray[scaleIndex])
d, dcp, r = _search_volume(scaleSigmaArray, scaleIndex, kMin)
parents, scaleSegmentEnds = link(nodeIds, d, dcp, r, prevScaleSignal, scaleSignal, signalLength, \
maxDiff, prevSegmentEnds, numLeadingZeros, numTrailingZeros, linkType=linkType)
nodeMapping[scaleIndex] = zip(nodeIds, parents)
segmentEnds[scaleIndex] = scaleSegmentEnds
prevScaleSignal, prevSegmentEnds, nodeIds = scaleSignal, scaleSegmentEnds, unique(
parents)
return nodeMapping, segmentEnds
def segment_signal(signal, numScales, deltaTau=None, kMin=None, linkType=None):
if deltaTau is None: deltaTau = DELTA_TAU
if kMin is None: kMin = pow((1 - exp(-2 * deltaTau)), -0.5)
signal = asarray(signal).flatten()
signalLength, maxDiff, numLeadingZeros, numTrailingZeros = _signal_properties(signal)
# Calculate scale sigma's
scaleSigmaArray = exp(arange(0, numScales) * deltaTau)
# Setup initial node set
nodeIds = nonzero(diff(signal, axis=0))[0] + 1
if nodeIds[-1] != signalLength:
nodeIds = append(nodeIds, signalLength)
# Loop!
baseSignal = asarray(signal, dtype=np_double)
prevScaleSignal, prevSegmentEnds = baseSignal, nodeIds
nodeMapping, segmentEnds = {}, { 0: nodeIds }
for scaleIndex in range(1, numScales):
scaleSignal = convolve(baseSignal, scaleSigmaArray[scaleIndex])
d, dcp, r = _search_volume(scaleSigmaArray, scaleIndex, kMin)
parents, scaleSegmentEnds = link(nodeIds, d, dcp, r, prevScaleSignal, scaleSignal, signalLength, \
maxDiff, prevSegmentEnds, numLeadingZeros, numTrailingZeros, linkType=linkType)
nodeMapping[scaleIndex] = zip(nodeIds, parents)
segmentEnds[scaleIndex] = scaleSegmentEnds
prevScaleSignal, prevSegmentEnds, nodeIds = scaleSignal, scaleSegmentEnds, unique(parents)
return nodeMapping, segmentEnds
def segment_signal(signal, numScales, deltaTau=None, kMin=None, linkType=None, doNodeMapping=False, minNodeInterval=None):
assert type(signal) == list or (type(signal) == ndarray and signal.ndim == 1)
if deltaTau is None: deltaTau = DELTA_TAU
if kMin is None: kMin = pow((1 - exp(-2 * deltaTau)), -0.5)
if type(signal) == list: signal = asarray(signal).flatten()
signalLength, maxDiff, numLeadingZeros, numTrailingZeros = _signal_properties(signal)
# Calculate scale sigma's
scaleSigmaArray = exp(arange(0, numScales) * deltaTau)
# Setup initial node set
nodeIds = _initial_node_set(signal, signalLength, minNodeInterval)
# Setup initial loop vars
segmentEnds = { 0: nodeIds }
if doNodeMapping: nodeMapping = {}
# Loop!
baseSignal = asarray(signal, dtype=np_double)
prevScaleSignal, prevSegmentEnds = baseSignal, nodeIds
for scaleIndex in range(1, numScales):
scaleSignal = convolve(baseSignal, scaleSigmaArray[scaleIndex])
d, dcp, r = _search_volume(scaleSigmaArray, scaleIndex, kMin)
parents, scaleSegmentEnds = link(nodeIds, d, dcp, r, prevScaleSignal, scaleSignal, signalLength, \
maxDiff, prevSegmentEnds, numLeadingZeros, numTrailingZeros, linkType=linkType)
if doNodeMapping: nodeMapping[scaleIndex] = zip(nodeIds, parents)
segmentEnds[scaleIndex] = scaleSegmentEnds
prevScaleSignal, prevSegmentEnds, nodeIds = scaleSignal, scaleSegmentEnds, unique(parents)
return (segmentEnds, nodeMapping) if doNodeMapping else segmentEnds
