def OnNewBuffer(self, buffer, sampleInterval): power = map(lambda x: absolute(x) ** 2.0, fourier(buffer)) leakage = power[9] + power[11] if leakage < self.bestLeakage: self.bestLeakage = leakage self.bestInterval = sampleInterval self.xs.append(sampleInterval) self.ys.append(leakage) if len(self.xs) > 5: #fit quadratic to the leakages and sample intervals coefficients = polyfit(self.xs, self.ys, 2) nextInterval = stats.quadraticMinimum(*coefficients) self.bestInterval = nextInterval #print rawSps / nextInterval else: nextInterval = self.bestInterval * (1.0 + self.initialLeapSize*(random() - 0.5)) print rawSps / nextInterval return self.bestInterval, nextInterval
def PlotSpillageVsSampleFrequency(rawData, rawSps): startOffset = 7 allData = [] start = 748 end = 488 for offset in range(0, 2500, 100): xs =[] ys = [] for sps in range(748, 788, 1): #sps /= 10.0 downSampledData = fftDataExtraction.downSample(data, rawSps, sps)[offset+startOffset:offset+128+startOffset] #pylab.plot([x/float(sps) for x in range(len(downSampledData))], downSampledData); pylab.show() #print sps, offset, len(downSampledData) frequencies = [float(sps) * x / len(downSampledData) for x in range(len(downSampledData)/2+1)] leakage = getToneLeakage(downSampledData) #print 'leakage: %f' % leakage #plot(fData, frequencies) #print '%d - %f' % (sps, leakage) xs.append(sps) ys.append(leakage) allData.append(ys) pylab.subplot(211) for ys in allData: pylab.plot(xs, ys) means = map(stats.mean, zip(*allData)) pylab.subplot(212) pylab.plot(xs, means, '-o') pylab.grid(True) order = 2 coefficients = polyfit(xs, means, order) print coefficients, stats.quadraticMinimum(*coefficients) generated = [] for x in xs: y = sum([(float(x)**float(order - i)) * c for i, c in enumerate(coefficients)]) #print x,y generated.append(y) diff = sum([(x1-x2)**2.0 / x1 for x1, x2 in zip(generated, means)]) print start, end, diff #pylab.plot(xs, generated) pylab.show()
