def PlotSpillageVsNoiseFrequency(): frequencies = [x/100.0 for x in range(5980, 6020)] allData = [[] for _ in frequencies] sps = 768 xs =[] for i, noiseFreq in enumerate(frequencies): data = dataImport.generateSignal(rawSps, [(noiseFreq, 1.0)]) startOffset = 7 for offset in range(0, 2500, 100): 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) allData[i].append(leakage) pylab.subplot(211) for ys in zip(*allData): pylab.plot(frequencies, ys) pylab.grid(True) means = map(stats.mean, allData) pylab.subplot(212) pylab.grid(True) pylab.plot(frequencies, means, '-o') pylab.show()
for f in frequencies:
fStops.append(f - fRange)
fStops.append(f + fRange)
filter = signal.firwin(taps, fStops, nyq = sps/2)
result = signal.convolve(filter, data)
diff = len(result) - len(data)
result = result[diff/2:-(diff-diff/2)]
return result
if __name__ == "__main__":
sampleFreq = 8000.0
data = dataImport.generateSignal(sampleFreq, [(60.0, 1.0)], seconds = 2.0)
#data = dataImport.readADSFile("Data/Mark/32kSPS_160kS_FlexorRadialis_Transitions.xls")
#data = fftDataExtraction.downSample(data, 32000, sampleFreq, interpolate = False)
times = [float(x) / sampleFreq for x in range(len(data))]
fs = [60.0, 120.0, 180.0]
data2 = bandStopFilterData(data, sampleFreq, frequencies = fs, fRange = 2.0, log2Taps = 13)
pylab.subplot(211)
pylab.plot(times, data)
pylab.subplot(212)
pylab.plot(times, data2)
bestLeakage = getToneLeakage(bestBuffer) bestLeakages.append(bestLeakage) sampleRate = rawSps / sampleInterval sampleRates.append(sampleRate) bestRates.append(rawSps / bestInterval) lastSample = sample totalError = sum(leakages) print 'Total error through all time: %d' % totalError fig, ax = pylab.subplots() #pylab.subplot(211) ax.plot(range(len(sampleRates)), sampleRates, 'Blue') ax.plot(range(len(sampleRates)), bestRates, 'Red') ax.plot([0, 0], [767, 769]) #pylab.grid(True); pylab.subplot(212) ax.twinx().plot(range(len(leakages)), leakages, color = 'Blue') ax.twinx().plot(range(len(bestLeakages)), leakages, color = 'Red') pylab.grid(True); pylab.show() if __name__ == "__main__": #data = dataImport.readADSFile(filename) data = dataImport.generateSignal(rawSps, [(60.0, 1.0)], seconds = 10.0)#, (120.0, 0.5), (180.0, 0.8)]) ConvergeOnCoherentSampling(data, rawSps)
for i in range(n): for j in range(n): result[i] += timeData[j] * complex(cos(compression * 2.0 * pi * i / n*j), sin(compression * 2.0 * pi * i / n*j)) result = map(lambda x: x/n, result) return result if __name__ == "__main__": pylab.subplot(211) length = 1.0 / 6 sps = 768.0 compression = 0.2 data = dataImport.generateSignal(sps, [(60.0, 1.0)], length) f = map(absolute, dft(data, compression)) f = f[:len(f)/2] freqs = [compression / length * x for x in range(len(f))] pylab.plot(freqs, f) pylab.grid(True) pylab.subplot(212) length = 1.0 / 6 sps = 768.0 compression = 0.2 data = dataImport.generateSignal(sps, [(60.2, 1.0)], length) f = map(absolute, dft(data, compression)) f = f[:len(f)/2] freqs = [compression / length * x for x in range(len(f))]
else: print 'no update' pass lastSample = data if __name__ == "__main__": filename = "Data/Mark/32kSPS_160kS_FlexorRadialis_0%.xls"; rawSps = 32000 #filename = "Data/Mark/32kSPS_160kS_ExtensorRadialis_0%.xls"; rawSps = 32000 #filename = "NoiseData/6 Loops 8000SPS Volts.xls"; rawSps = 8000 #rawData = dataImport.readADSFile(filename) f = 60.0 sps = 8000 #rawData = fftDataExtraction.downSample(rawData, rawSps, sps, interpolate = False) rawData = dataImport.generateSignal(sps, [(f, 1.0)], seconds = 30.0) TestConvergence(rawData, sps) x, y = zip(*sorted(allErrors)) print len(x), len(y) pylab.plot(x, y, marker = 'o') #coefs = polyfit(x, y, 2) #x2 = [min(x) + i * (max(x) - min(x)) / len(x) for i in range(len(x))] #y2 = [sum([xi**(2.0-n) * c for n, c in enumerate(coefs)]) for xi in x2] #print len(x), len(y2) #pylab.plot(x2, y2, '-o') pylab.grid(True); pylab.show() #print stats.mean(centers) #print stats.stdDev(centers)
