Пример #1
0
def getSVMTrainingData(datas):
	trainingDatas = []
	for rawData, output in datas:
		channels = zip(*rawData)
		allBins = []
		for channel in channels:
			downSampledData = fftDataExtraction.downSample(channel, rawSps, constants.samplesPerSecond, interpolate = True)
			
			transforms = fftDataExtraction.applyTransformsToWindows(fftDataExtraction.getFFTWindows(downSampledData), magnitude = True, polyFitSubtraction = 2)
			
			bins = fftDataExtraction.DoFrequencyBinning(transforms)
			allBins.append(bins)
			
		trainingInputs = map(lambda x: x[0] + x[1], zip(*allBins))
		
		trainingDatas += [(input, output) for input in trainingInputs]
		
	return trainingDatas
Пример #2
0
import fftDataExtraction
import constants
from matplotlib import pylab


if __name__ == "__main__":
	filename = "Data/Mark/32kSPS_160kS_ExtensorRadialis_0%.xls"
	data = fftDataExtraction.getDownSampledData(filename)
	sps = constants.samplesPerSecond
	windows = fftDataExtraction.getFFTWindows(data[int(0.8*sps):])
	
	alltimes = [float(x) / sps for x in range(len(data))]
	times =  range(len(windows[0]))
	postData = fftDataExtraction.subtractPolynomialFit(windows[0], 5)
	#pylab.plot(alltimes, data)
	pylab.plot(times, windows[0])
	pylab.plot(times, postData)
	pylab.grid(True)
	pylab.show()