def testPlotFrequencyDomain(): filename = baseFilename % 0 rawData = dataImport.readADSFile(filename) rawSps = 32000 downSampled = _downSample(rawData, rawSps) downSampledLinear = _downSampleLinearInterpolate(rawData, rawSps) rawTimes = range(len(rawData)) times = [float(x) * rawSps / samplesPerSecond for x in range(len(downSampled))] #pylab.plot(times, downSampled) #pylab.plot(rawTimes, rawData) #pylab.plot(times, downSampledLinear) pylab.show() index = 0 fdat = applyTransformsToWindows(getFFTWindows(downSampled), True)[index] fdatLin = applyTransformsToWindows(getFFTWindows(downSampledLinear), True)[index] #print [str(x) for x in zip(fdat, fdatLin)] frequencies = [i * samplesPerSecond / windowSize for i in range(len(fdat))] pylab.semilogy(frequencies, fdat) pylab.semilogy(frequencies, fdatLin) pylab.grid(True) pylab.show()
def createChangingTimeDomainDataPhaseMatch(baseFilename, low = 0, high = 10):
dat0 = dataImport.readADSFile(baseFilename % low)
dat1 = dataImport.readADSFile(baseFilename % high)
sps = dataImport.getSPS(baseFilename % low)
if dataImport.getSPS(baseFilename % high) != sps:
raise Exception("samples per second do not match - FAIL")
dataSources = [dat0, dat1]
indeces = [0, 0]
if dataLength < float(constants.windowSize) / constants.samplesPerSecond * 1.5:
raise Exception("Data length is too short - not getting full ffts of a single data source")
numSamples = int(dataLength * sps)
result = []
output = []
for i in range(numSignalChunks * 2):
newIndex = i % 2
if i == 0:
dataToAppend = centerAroundZero(dataSources[newIndex][indeces[newIndex] : indeces[newIndex] + numSamples])
indeces[newIndex] += numSamples
else:
#gotta phase match
newData = centerAroundZero(dataSources[newIndex][indeces[newIndex] : int(indeces[newIndex] + numSamples + sps * startIndexRange*2)])
startOffset = getBestStartIndex(result, newData, sps)
#print startOffset
dataToAppend = newData[startOffset: startOffset + numSamples]
indeces[newIndex] += numSamples + startOffset
if len(dataToAppend) != numSamples:
raise Exception("Data to be appended is not the correct length")
oldIndex = len(result)
result += dataToAppend
output += [newIndex] * numSamples
#down sample result and output
result = fftDataExtraction.downSample(result, sps, interpolate = True)
output = fftDataExtraction.downSample(output, sps,)
return result, output
def getDownSampledData(filename): rawData = dataImport.readADSFile(filename) rawSps = dataImport.getSPS(filename) result = downSample(rawData, rawSps) return result
import dataImport from matplotlib import pylab import fftDataExtraction if __name__ == "__main__": filename = "Data/Mark/32kSPS_160kS_FlexorRadialis_Transitions.xls" data = dataImport.readADSFile(filename) data = data[40000:60000] data = fftDataExtraction.subtractPolynomialFit(data, 3) pylab.plot(range(len(data)), data) pylab.show()
numOutputs = len(trainingData[0][1])
for outputIndex in range(numOutputs):
inputs = [input for (input, output) in trainingData]
outputs = [output[outputIndex] for (input, output) in trainingData]
prob = svmutil.svm_problem(outputs, inputs)
param = svmAccuracy.getSvmParam(cross_validation_only = True)
model = svmutil.svm_train(prob, param)
print 'output index: %d - %s\n' % (outputIndex, {0 : "Index", 1: "Middle"}[outputIndex])
if __name__ == "__main__":
datas = []
for f in files:
output = files[f]
filename = baseFilename % f
rawData = dataImport.readADSFile(filename, channels = len(output))
if filterData:
channels = zip(*rawData)
filteredChannels = []
for channel in channels:
filteredChannels.append(FirFilter.bandStopFilterData(channel, rawSps, [60.0, 120.0, 180.0], log2Taps = 13))
print 'filtered'
rawData = zip(*filteredChannels)
datas.append((rawData, output))
#plotTimeDomainSignals(datas)
trainingData = getSVMTrainingData(datas)
getSVMAccuracy(trainingData)
