def polyFitSegmentTest(date):
order = 2
timeNormal = 100000000000
# timeFile = unicode('C:\Users\Levit\Experiment Data\德令哈测试\\20171226\零基线实验\\20171227015305-tdc2_4_filterN_coindence_filtered_250-350s.txt' , 'utf8')
timeFile = unicode(
'C:\Users\Levit\Experiment Data\双站数据\\20180121\\result\\synCoincidence-124-216--17-1-Coin-紫台WGS84-atm-factor-haiji_laser改正_filtered.txt',
'utf8')
timeList = fileToList.fileToList(timeFile)
xa = []
ya = []
# for i in range(len(timeList)):
# xa.append(timeList[i][0]-timeList[i][4])
# # ya.append((timeList[i][0] - timeList[i][1]))
# ya.append([(timeList[i][2])/1000000000000.0])
#
xa, ya, timeList, fitList, residual = filter.fitFilter(
timeList, 2500 / 1000000000000.0, 1, 2)
# fileToList.listToFile(timeList,timeFile[:-4] + '_filtered.txt')
# xa,ya,fitList, residual = polyFitSegment(xa, ya, 1, 0.1)
# xa, ya, filteredList, residual=filter.thresholdFilter(xa,ya,residual,timeList,0,0.000000002)
xa, ya, fitList, residual = polyFitSegment(xa, ya, order, 1000)
# xa, ya, filteredList, residual = filter.thresholdFilter(xa, ya, residual, timeList, 0, 0.0000001)
# residual=[]
# for item in timeList:
# # residual.append([(item[0]-item[1])/1000000000000.0])
# residual.append([(item[2] ) / 1000000000000.0])
print numpy.std(residual, ddof=1)
xa, residual = filter.normalByTime(timeList, residual, timeNormal)
fileToList.listToFileLong(
residual, timeFile[:-4] + '_residual-%s-0.1s-ps.txt' % order)
# fileToList.listToFile(filteredList,timeFile[:-4]+'_filtered.txt')
fig = plt.figure()
ax1 = fig.add_subplot(111)
# ax2= fig.add_subplot(212)
for i, item in enumerate(xa):
xa[i] = item / 1000000000000.0
# fitList[i][0]=fitList[i][0]*1000000000000.0
# ya[i]=[timeList[i][3]]
residual[i][0] = residual[i][0] * 1000000000000
# print '%s\t%s'%(xa[i],residual[i][0])
ax1.plot(xa, residual, color='g', linestyle='-', marker='*')
# xa, ya = filter.normalByTime(timeList, ya, timeNormal)
# ax1.plot(xa, ya, color='g', linestyle='-', marker='')
ax1.xaxis.grid(True, which='major') #x坐标轴的网格使用主刻度
ax1.yaxis.grid(True, which='major') #y坐标轴的网格使用次刻度show()
ax1.set_ylabel('Difference(Residual after %s order fitting) (ps)' % order,
fontsize=20)
ax1.set_xlabel('Time (s)', fontsize=20)
ax1.set_title('Time Compare', fontsize=24)
# ax2.plot(xa,ya,color='m',linestyle='',marker='.')
# ax2.plot(xa, fitList, color='g', linestyle='-', marker='')
# ax.legend()
plt.show()
def BKCSmoothTest():
paraFile = unicode(
'C:\Users\Levit\Experiment Data\阿里数据\\170829\\20170830031232_fineParse_1064_245-445_coincidence_0320_match_parameters_30_filtered.txt',
encoding='utf-8')
smoothNum = 30
saveFile = paraFile[:-4] + '_Smooth%s.txt' % smoothNum
paraList = fileToList.fileToList(paraFile)
smoothList = BKCSmooth(paraList, 80, 2, 50)
fileToList.listToFileLong(smoothList, saveFile)
def timeAnalysis(datafile):
timeList = fileToList.fileToList(dataFile)
length = len(timeList)
sum = 0.0
for i in range(length):
sum += timeList[i][1]
averTime = sum / length
for i in range(length):
timeList[i][1] = (timeList[i][1] - averTime) / 1000000000000.0
fileToList.listToFileLong(timeList, dataFile[:-4] + '_residual.txt')
def leastsqToSecTest():
timeFile = unicode(
'E:\Experiment Data\时频传输数据处理\双站数据处理\\3.2\\result\\synCoincidenceEM_0422_eff5_3.2_residual_segment_dotfilter0422.txt',
'utf8')
timeList = fileToList.fileToList(timeFile)
tau = 10000000000
R = leastsqToSec(timeList, tau, 1)
#R = interSec(R)
#print R
fileToList.listToFileLong(
R, timeFile[:-4] + '_%smilliSec0422.txt' % (tau / 1000000000))
def filterbyfitTest():
segment = 100
order = 2
windows = 180
rangeFile = unicode(
'C:\Users\Levit\Experiment Data\阿里数据\\170829\\20170830031232_fineParse_1064_245-445_coincidence_0320_match_parameters_30.txt',
encoding='utf-8')
rangeList = fileToList.fileToList(rangeFile)
saveFile = rangeFile[:-4] + '_filtered.txt'
filteredList = filterbyfit(rangeList, segment, order, windows, 2)
fileToList.listToFileLong(filteredList, saveFile)
def twoLocalTDCDataProcess(dataFile1,dataFile2,fineTimeFile1,fineTimeFile2,order,tau):
channel1=int(fineTimeFile1[-8])
channel2=int(fineTimeFile2[-8])
saveFile1 = dataFile1[:-4] + '_channel%s.txt'%channel1
dataList1 = TDCDataConvert.TDCDataParse(dataFile1, fineTimeFile1, 8, str(40+channel1-1))
saveFile2 = dataFile2[:-4] + '_channel%s.txt' % channel2
dataList2 = TDCDataConvert.TDCDataParse(dataFile2, fineTimeFile2, 8, str(40+channel2-1))
fileToList.listToFile(dataList1, saveFile1)
fileToList.listToFile(dataList2, saveFile2)
num1=len(dataList1)
num2=len(dataList2)
if num1!=num2:
print 'data lenght is not equal!'
return 0
xa=[]
xb=[]
ya=[]
for i in range(num1):
xa.append(dataList1[i][0])
xb.append([dataList1[i][0]])
ya.append(dataList1[i][0]-dataList2[i][0])
xa, ya, fitList, residual =fitting.polyFitSegment(xa, ya, order, 10000)
del xa[:]
del fitList[:]
xOneSec, residualOneSec = filter.normalByTime(xb, residual, 1000000000000)
fileToList.listToFileLong(residualOneSec, saveFile1[:-4] + '-%s_residual-%s-1s-ps.txt'%(channel2,order))
xTau,residualTau=filter.normalByTime(xb, residual, tau)
residualFile=saveFile1[:-4] + '-%s_residual-%s-%.3fs-ps.txt' % (channel2, order,tau/1000000000000.0)
fileToList.listToFileLong(residualTau, residualFile)
fig = plt.figure()
ax1 = fig.add_subplot(111)
for i,item in enumerate(xOneSec):
xOneSec[i]=item/1000000000000.0
residualOneSec[i][0]=residualOneSec[i][0]*1000000000000
ax1.plot(xOneSec,residualOneSec, color='g', linestyle='-', marker='*')
ax1.xaxis.grid(True, which='major') # x坐标轴的网格使用主刻度
ax1.yaxis.grid(True, which='major') # y坐标轴的网格使用次刻度show()
plt.show()
tdevFile = residualFile[:-4] + '_TDEV.txt'
tdev = varianceStatistics.TDEV(residualTau, tau/1000000000000.0)
fileToList.listToFileFloat(tdev, tdevFile)
print 'TDEV calculation finished!'
fig = plt.figure()
dataPlot.logPlotAx(tdev, fig, 'r','--','s', '11.25 tdev')
dataFile1 = unicode(
'C:\Users\Levit\Experiment Data\Rakon晶振测试数据\两TDC测试\\20171114200130-tdc2-13-2k-500s-3_residual-1-0.01s-ps_TDEV.txt',
'utf8')
List1 = fileToList.fileToList(dataFile1)
dataPlot.logPlotAx(List1, fig, 'g', '--', 'o', '11.14')
plt.show()
def aysnLaserBKCTest():
matchFile = unicode(
'C:\Users\Levit\Experiment Data\阿里数据\\170829\\20170830031232_fineParse_1064_245-445_coincidence_0320_match.txt',
encoding='utf-8')
groGPSFile = unicode(
'C:\Users\Levit\Experiment Data\阿里数据\\170829\\20170830031232_fineParse_GPS.txt',
encoding='utf-8')
satGPSFile = unicode(
'C:\Users\Levit\Experiment Data\阿里数据\\170829\\0829AliSatellite_channel_5_GPS.txt',
encoding='utf-8')
groGPSList = fileToList.fileToList(groGPSFile)
satGPSList = fileToList.fileToList(satGPSFile)
smoothNum = 30
saveFile = matchFile[:-4] + '_parameters_%s.txt' % smoothNum
matchList = fileToList.fileToList(matchFile)
parameterList = aysnLaserBKC(matchList, 240, smoothNum, groGPSList,
satGPSList)
fileToList.listToFileLong(parameterList, saveFile)
def fitting_segment(startTime,endTime,coindenceFile):
coindenceList=fileToList.fileToList(coindenceFile)
second=1000000000000.0
tau=100000000000
order=2
num = len(coindenceList)
xa = []
xb = []
ya = []
for i in range(num):
if coindenceList[i][0]/second>=startTime and coindenceList[i][0]/second<endTime:
xa.append(coindenceList[i][0])
ya.append(coindenceList[i][2])
# xa, ya, coindenceList, fitList, residual = filter.fitFilter(coindenceList, 2000 / 1000000000000.0, 2, 1)
xa, ya, fitList, residual = fitting.polyFitSegment(xa, ya, order, 10000)
for i in range(len(xa)):
xb.append([xa[i]])
xOneSec, residualOneSec = filter.normalByTime(xb, residual, second)
del xa[:]
del fitList[:]
del coindenceList[:]
fileToList.listToFileLong(residualOneSec, coindenceFile[:-4] + '_%s-%s-1s-ps.txt' % (startTime, endTime))
xTau, residualTau = filter.normalByTime(xb, residual, tau)
residualFile = coindenceFile[:-4] + '_%s-%s-%.3fs-ps.txt' % (startTime, endTime, tau / 1000000000000.0)
fileToList.listToFileLong(residualTau, residualFile)
fig = plt.figure()
ax1 = fig.add_subplot(111)
for i, item in enumerate(xOneSec):
xOneSec[i] = item / 1000000000000.0
residualOneSec[i][0] = residualOneSec[i][0] * 1000000000000
ax1.plot(xOneSec, residualOneSec, color='g', linestyle='-', marker='*')
ax1.xaxis.grid(True, which='major') # x坐标轴的网格使用主刻度
ax1.yaxis.grid(True, which='major') # y坐标轴的网格使用次刻度show()
plt.show()
tdevFile = residualFile[:-4] + '_TDEV.txt'
tdev = varianceStatistics.TDEV(residualTau, tau / 1000000000000.0)
fileToList.listToFileFloat(tdev, tdevFile)
print 'TDEV calculation finished!'
tdevName='TDEV %s-%s second'%(startTime,endTime)
fig = plt.figure()
dataPlot.logPlotAx(tdev, fig, 'r', '--', 's', tdevName)
plt.show()
def calculateGPS(groundDataFile1,groundDataFile2,GPSDistance1,GPSDistance2,coinfile):
freq,window,threshold=10000000, 30, 1000000
startSec, endSec, gpsShift=100,200,-19
dataFile1,gpsTime1,synTime1=dataPreprocessing.getFileNime(groundDataFile1)
dataPreprocessing.classifyData(dataFile1,gpsTime1,synTime1)
dataFile2,gpsTime2,synTime2=dataPreprocessing.getFileNime(groundDataFile2)
dataPreprocessing.classifyData(dataFile2,gpsTime2,synTime2)
gpsDelList=orbitDelayGps.gpsDisDelayEasyMode(GPSDistance1,GPSDistance2)
synTimeList1=fileToList.fileToList(synTime1)
synTimeList2=fileToList.fileToList(synTime2)
gpsTimeList1=fileToList.fileToList(gpsTime1)
gpsTimeList2=fileToList.fileToList(gpsTime2)
# synTimeList1=filter.freqFilter(synTimeList1,freq,window,threshold)
# synTimeList2=filter.freqFilter(synTimeList2,freq,window,threshold)
coincidenceList=timeDataCoincidence.timeCoincidenceEasyMode(synTimeList1,synTimeList2,gpsDelList,gpsTimeList1,gpsTimeList2,startSec,endSec,coinfile,gpsShift)
analysisList=timeAnalysis.adjacentAnalysis(coincidenceList,coinfile)
resultList=timeAnalysis.leastsqToSec(analysisList,100000000000)
#resultList=timeAnalysis.interSec(resultList)
#print resultList
fileToList.listToFileLong(resultList,coinfile[:-4]+'_result_100milliSec.txt')
def rangefilterTest():
segment = 400
order = 3
windows = 0.15
rangeFile = unicode(
'C:\Users\Levit\Experiment Data\阿里数据\\170829\\20170830031232_fineParse_1064_245-445_coincidence_0320_match_parameters_30_filtered_Smooth30.txt',
encoding='utf-8')
rangeList = fileToList.fileToList(rangeFile)
newList = []
count = 0
for item in rangeList:
count += 1
if count == 1:
# newList.append([item[1]/1000000000000.0,item[1]-item[0]-240000000000000])
newList.append([item[0], item[1]])
count = 0
# print newList[-1]
print 'newList finished'
saveFile = rangeFile[:-4] + '_%s_%s_residual_dis_1.txt' % (segment, order)
# saveFile = rangeFile[:-4] + '_filtered.txt'
filteredList, residualList = rangefilter(newList, segment, order, windows,
1)
fileToList.listToFileLong(residualList, saveFile)
def asynLaserRangingTest():
matchFile = unicode(
'C:\Users\Levit\Experiment Data\阿里数据\\170829\\20170830031232_fineParse_1064_245-445_coincidence_0320_match.txt',
encoding='utf-8')
atmFile = unicode('C:\Users\Levit\Experiment Data\阿里数据\\170829\\俯仰角.txt',
encoding='utf-8')
saveFile = matchFile[:-4] + '_ranging_Degnan_K_detSat_S100-1.txt'
saveResiFile = saveFile[:-4] + '_residual.txt'
saveRedFile = saveFile[:-4] + '_Sec.txt'
matchList = fileToList.fileToList(matchFile)
atmList = fileToList.fileToList(atmFile)
rangingList, residualList = asynLaserRanging(matchList, 240)
# for timeshift in range(0,1,5):
# timeshift=0.001*timeshift
# saveRedFile = matchFile[:-4] + '_rangingSec_NoIte-%s.txt'%timeshift
# reduceList=reduceSec(rangingList,5,timeshift,4,5)
# atmCorrect(reduceList,atmList)
# fileToList.listToFile(reduceList, saveRedFile)
reduceList = reduceSec(rangingList, 20, 0, 1, 3, 5)
atmCorrect(reduceList, atmList)
fileToList.listToFile(reduceList, saveRedFile)
# atmCorrect(rangingList, atmList)
fileToList.listToFileLong(residualList, saveResiFile)
fileToList.listToFileLong(rangingList, saveFile)
def nearMatchTest():
groCoinFile = unicode(
'C:\Users\Levit\Experiment Data\阿里数据\\170919\\20170920032416_fineParse_1_1064_10-220_coincidence_new.txt',
encoding='utf-8')
satCoinFile = unicode(
'C:\Users\Levit\Experiment Data\阿里数据\\170919\\20170920032416_fineParse_2_532_filtered_reflectFiltered_10-220_coincidence_new.txt',
encoding='utf-8')
groList = fileToList.fileToList(groCoinFile)
satList = fileToList.fileToList(satCoinFile)
saveFile = groCoinFile[:-4] + '_nearMatch.txt'
result = nearMatch(groList, satList, 5, 90000000)
reduceVList = reduceSec(result, 30, 0, 1)
reduceKList = reduceSec(result, 30, 0, 2)
fileToList.listToFileLong(result, saveFile)
fileToList.listToFileLong(reduceVList, saveFile[:-4] + '_VSec.txt')
fileToList.listToFileLong(reduceKList, saveFile[:-4] + '_KSec.txt')
def twoLightTDCDataProcess(channel1,channel2,delay,window,dataFile1,dataFile2,fineTimeFile1,fineTimeFile2,order,tau,tdevName,tdevComp,tdevCompFile):
saveFile1 = dataFile1[:-4] + '_channel_%s.txt'%channel1
dataList1 = TDCDataConvert.TDCDataParse(dataFile1, fineTimeFile1, 8, str(40+channel1-1))
fileToList.listToFile(dataList1, saveFile1)
dataList1_filter=filter.freqFilter(dataList1,10000200,6,300000)
dataList1_filterN=filter.reflectNoiseFilter(dataList1_filter,1000000,0)
fileToList.listToFile(dataList1_filterN, saveFile1[:-4]+'_filterN.txt')
saveFile2 = dataFile2[:-4] + '_channel_%s.txt' % channel2
dataList2 = TDCDataConvert.TDCDataParse(dataFile2, fineTimeFile2, 8, str(40+channel2 - 1))
fileToList.listToFile(dataList2, saveFile2)
dataList2_filter = filter.freqFilter(dataList2, 10000200, 6, 300000)
dataList2_filterN = filter.reflectNoiseFilter(dataList2_filter, 1000000, 0)
TDCTest.countBySec(dataList2)
TDCTest.countBySec(dataList1)
TDCTest.countBySec(dataList2_filterN)
TDCTest.countBySec(dataList1_filterN)
fileToList.listToFile(dataList2_filterN, saveFile2[:-4] + '_filterN.txt')
saveCoinFile = dataFile1[:-4] + '_%s_filterN_coindence.txt'%channel1
coindenceList,averSecCount=TDCTest.coindenceTest(dataList1_filterN, dataList2_filterN, delay, window, saveCoinFile)
tdevName=tdevName+' %s'%averSecCount
del dataList1,dataList1_filter,dataList1_filterN
del dataList2,dataList2_filter,dataList2_filterN
num=len(coindenceList)
xa = []
xb = []
ya = []
for i in range(num):
xa.append(coindenceList[i][0])
ya.append(coindenceList[i][2])
xa, ya, coindenceList, fitList, residual = filter.fitFilter(coindenceList, 2000 / 1000000000000.0, 2, 1)
xa, ya, fitList, residual = fitting.polyFitSegment(xa, ya, order, 10000)
fileToList.listToFile(coindenceList,saveCoinFile[:-4]+'_filtered.txt')
for i in range(len(xa)):
xb.append([xa[i]])
xOneSec, residualOneSec = filter.normalByTime(xb, residual, 1000000000000)
del xa[:]
del fitList[:]
del coindenceList[:]
fileToList.listToFileLong(residualOneSec, saveFile1[:-4] + '-%s_residual-%s-1s-ps.txt' % (channel2, order))
xTau, residualTau = filter.normalByTime(xb, residual, tau)
residualFile = saveFile1[:-4] + '-%s_residual-%s-%.3fs-ps.txt' % (channel2, order, tau / 1000000000000.0)
fileToList.listToFileLong(residualTau, residualFile)
fig = plt.figure()
ax1 = fig.add_subplot(111)
for i, item in enumerate(xOneSec):
xOneSec[i] = item / 1000000000000.0
residualOneSec[i][0] = residualOneSec[i][0] * 1000000000000
ax1.plot(xOneSec, residualOneSec, color='g', linestyle='-', marker='*')
ax1.xaxis.grid(True, which='major') # x坐标轴的网格使用主刻度
ax1.yaxis.grid(True, which='major') # y坐标轴的网格使用次刻度show()
plt.show()
tdevFile = residualFile[:-4] + '_TDEV.txt'
tdev = varianceStatistics.TDEV(residualTau, tau / 1000000000000.0)
fileToList.listToFileFloat(tdev, tdevFile)
print 'TDEV calculation finished!'
fig = plt.figure()
dataPlot.logPlotAx(tdev, fig, 'r', '--', 's', tdevName)
List1 = fileToList.fileToList(tdevCompFile)
dataPlot.logPlotAx(List1, fig, 'g', '--', 'o', tdevComp)
plt.show()
