def compareToLinear(): xs = [0, 1, 2, 3, 4, 5] ys = [y * 100.0 for y in [1, 3, 5, 7, 9, 11]] coefs, C = polynomialFit(xs, ys, 2, returnErrors = True) print coefs print C print stats.lineOfBestFit(xs, ys, returnErrors = True)
def getSignalCoherenceError(downSampledFrequency, rawData = rawData): downSampled = fftDataExtraction.downSample(rawData, rawSps, downSampledFrequency, interpolate = True)#[int(random()*100):] #downSampled = downSampled[:int(downSampledFrequency)/2] #downSampled = downSampled[:40] highSamples = downSampled[::2] lowSamples = downSampled[1::2] times = map(lambda x: x/downSampledFrequency, range(len(downSampled))) highTimes = times[::2] lowTimes = times[1::2] differences = map(lambda x: (x[0]-x[1])**1.0, zip(highSamples, lowSamples)) slope, yint = stats.lineOfBestFit(highTimes, differences) #return slope #return abs(slope) #return 1.0 / slope #return max(differences) - min(differences) #derivSquared = sum([(differences[i+1] - differences[i])**2.0 for i in range(len(differences)-1)]) #return derivSquared return stats.variance(differences)
def centerAroundZero(timeData): #line of best fit, then subtract that xValues = range(len(timeData)) slope, yint = stats.lineOfBestFit(xValues, timeData) result = [y-yint-x*slope for x, y in zip(xValues, timeData)] return result
