def loadObs(self,stackLabel):
timestampList = self.params[stackLabel+'Sequence']
run = self.params['run']
sunsetDate = self.params[stackLabel+'SunsetDate']
utcDate = self.params[stackLabel+'UtcDate']
intTime = self.params[stackLabel+'IntTime']
wvlLowerCutoff = self.params[stackLabel+'WvlLowerCutoff']
wvlUpperCutoff = self.params[stackLabel+'WvlUpperCutoff']
calTimestamp = self.params[stackLabel+'WvlTimestamp']
print stackLabel,calTimestamp
wvlSolnFileName = FileName(run=run,date=sunsetDate,tstamp=calTimestamp).calSoln()
wvlCalFileName = FileName(run=run,date=self.params[stackLabel+'WvlSunsetDate'],tstamp=calTimestamp).cal()
flatSolnFileName = FileName(run=run,date=self.params[stackLabel+'FlatCalSunsetDate'],tstamp=self.params[stackLabel+'FlatCalTimestamp']).flatSoln()
obsFileNames = [FileName(run=run,date=sunsetDate,tstamp=timestamp).obs() for timestamp in timestampList]
obList = [ObsFile(obsFn) for obsFn in obsFileNames]
for ob in obList:
ob.loadWvlCalFile(wvlSolnFileName)
ob.loadFlatCalFile(flatSolnFileName)
self.stackObsFileLists[stackLabel] = obList
cal = ObsFile(wvlCalFileName)
cal.loadWvlCalFile(wvlSolnFileName)
cal.loadFlatCalFile(flatSolnFileName)
self.stackWvlCals[stackLabel] = cal
def findv1(self):
populationMax=2000
ySum = np.zeros(populationMax)
frameSum = 'none'
seq5 = self.s['seq5'].split()
for seq in seq5:
print "seq=",seq
outfileName = "cosmicTimeList-"+seq+".pkl"
if not os.path.exists(outfileName):
fn = FileName(self.s['run'],
self.s['sundownDate'],
self.s['obsDate']+"-"+str(seq))
cosmic = Cosmic(fn,
beginTime=self.s['beginTime'],
endTime=self.s['endTime'],
loggingLevel = logging.INFO)
fc = cosmic.findCosmics(stride=int(self.s['stride']),
threshold=int(self.s['threshold']),
populationMax=populationMax,
nSigma=float(self.s['nSigma']))
outfile = open(outfileName, "wb")
pickle.dump(fc['cosmicTimeList'],outfile)
pickle.dump(fc['binContents'],outfile)
outfile.close()
cfn = "cosmicMask-%s.h5"%seq
ObsFile.writeCosmicIntervalToFile(fc['interval'],1.0e6, cfn,
self.s['beginTime'],
self.s['endTime'],
int(self.s['stride']),
int(self.s['threshold']),
float(self.s['nSigma']),
populationMax)
del cosmic
def testLoadBeammap():
'''
Test if a remapped beammap is actually remapped
'''
#open an obs file from PAL2012,the sky file for hr9087
#we'll use the embedded beammap file, which has some misplaced pixels
run = 'PAL2012'
date = '20121210'
obsTimestamp = '20121211-051650'
obsFN = FileName(run=run,date=date,tstamp=obsTimestamp)
obsFileName = obsFN.obs()
obs = ObsFile(obsFileName)
beammapFileName = obsFN.beammap()
#load the PixelMap for PAL2012 to know which pixels should be remapped
pixMap = remapPixels.PixelMap(obsFN.pixRemap())
pixMapSourceList,pixMapDestList = pixMap.getRemappedPix()
#load the corrected beammap into the obs
obs.loadBeammapFile(beammapFileName)
#check that each pixel that should be moved is moved
#by comparing the embedded beammap and the loaded corrected one
for source,dest in zip(pixMapSourceList,pixMapDestList):
assert obs.beamImage[dest] == obs.file.root.beammap.beamimage[source]
obs.file.close()
def main():
np.set_printoptions(threshold=np.nan)
testPixelRow = 24
testPixelCol = 17
#obs_20120919-131142.h5,obs_20120919-131346.h5
#create a cal file from a twilight flat
cal = FlatCal('../../params/flatCal.dict')
#open another twilight flat as an observation and apply a wavelength cal and the new flat cal
# run='LICK2012'
# obsFileName = FileName(run=run,date='20120918',tstamp='20120919-131142').flat()
# flatCalFileName = FileName(run=run,date='20120918',tstamp='20120919-131448').flatSoln()
# wvlCalFileName = FileName(run=run,date='20120916',tstamp='20120917-072537').calSoln()
run = 'PAL2012'
obsFileName = FileName(run=run,date='20121211',tstamp='20121212-140003').obs()
flatCalFileName = FileName(run=run,date='20121210',tstamp='').flatSoln()
wvlCalFileName = FileName(run=run,date='20121210',tstamp='20121211-133056').calSoln()
flatCalPath = os.path.dirname(flatCalFileName)
ob = ObsFile(obsFileName)#('obs_20120919-131142.h5')
ob.loadWvlCalFile(wvlCalFileName)#('calsol_20120917-072537.h5')
ob.loadFlatCalFile(flatCalFileName)#('flatsol_20120919-131142.h5')
#plot some uncalibrated and calibrated spectra for one pixel
fig = plt.figure()
ax = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
print ob.getPixelCount(testPixelRow,testPixelCol)
#flatSpectrum,wvlBinEdges = ob.getPixelSpectrum(testPixelRow,testPixelCol,weighted=False)
spectrum,wvlBinEdges = ob.getPixelSpectrum(testPixelRow,testPixelCol,wvlStart=cal.wvlStart,wvlStop=cal.wvlStop,wvlBinWidth=cal.wvlBinWidth,weighted=False,firstSec=0,integrationTime=-1)
weightedSpectrum,wvlBinEdges = ob.getPixelSpectrum(testPixelRow,testPixelCol,weighted=True)
#flatSpectrum,wvlBinEdges = cal.flatFile.getPixelSpectrum(testPixelRow,testPixelCol,wvlStart=cal.wvlStart,wvlStop=cal.wvlStop,wvlBinWidth=cal.wvlBinWidth,weighted=False,firstSec=0,integrationTime=-1)
flatSpectrum = cal.spectra[testPixelRow,testPixelCol]
x = wvlBinEdges[0:-1]
ax.plot(x,cal.wvlMedians,label='median spectrum',alpha=.5)
ax2.plot(x,cal.flatFactors[testPixelRow,testPixelCol,:],label='pixel weights',alpha=.5)
ax2.set_title('flat weights for pixel %d,%d'%(testPixelRow,testPixelCol))
ax.plot(x,spectrum+20,label='unweighted spectrum for pixel %d,%d'%(testPixelRow,testPixelCol),alpha=.5)
ax.plot(x,weightedSpectrum+10,label='weighted %d,%d'%(testPixelRow,testPixelCol),alpha=.5)
ax.plot(x,flatSpectrum+30,label='flatFile %d,%d'%(testPixelRow,testPixelCol),alpha=.5)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.3),fancybox=True,ncol=3)
plt.show()
#display a time-flattened image of the twilight flat as it is and after using itself as it's flat cal
#cal.flatFile.loadFlatCalFile(flatCalFileName)#('flatsol_20120919-131142.h5')
#cal.flatFile.displaySec(weighted=True,integrationTime=-1)
#ob.displaySec(integrationTime=-1)
#ob.displaySec(weighted=True,integrationTime=-1)
for idx in range(0,100,20):
factors10 = cal.flatFactors[:,:,idx]
plt.matshow(factors10,vmax=np.mean(factors10)+1.5*np.std(factors10))
plt.title('Flat weights at %d'%cal.wvlBinEdges[idx])
plt.colorbar()
plt.savefig('plots/factors%d.png'%idx)
plt.show()
def centroidObs(obsPath,centroidPath,centroidRa,centroidDec,haOffset,xGuess,yGuess,hotPath,flatPath):
obs = ObsFile(obsPath)
print obsPath,obs.getFromHeader('exptime'),obs
if not os.path.exists(hotPath):
hp.findHotPixels(obsFile=obs,outputFileName=hotPath)
obs.loadHotPixCalFile(hotPath,switchOnMask=False)
obs.loadBestWvlCalFile()
obs.loadFlatCalFile(flatPath)
obs.setWvlCutoffs(3000,8000)
cc.centroidCalc(obs,centroidRa,centroidDec,guessTime=300,integrationTime=30,secondMaxCountsForDisplay=2000,HA_offset=haOffset,xyapprox=[xGuess,yGuess],outputFileName=centroidPath)
print 'done centroid',centroidPath
del obs
def makemovie1(self):
run = self.s['run']
sundownDate = self.s['sundownDate']
obsDate = self.s['obsDate']
stride = int(self.s['stride'])
seq5 = self.s['seq5'].split()
for seq in seq5:
inFile = open("cosmicTimeList-%s.pkl"%(seq),"rb")
cosmicTimeList = pickle.load(inFile)
binContents = pickle.load(inFile)
cfn = "cosmicMask-%s.h5"%seq
intervals = ObsFile.readCosmicIntervalFromFile(cfn)
for interval in intervals:
print "interval=",interval
fn = FileName(run, sundownDate,obsDate+"-"+seq)
obsFile = ObsFile(fn.obs())
obsFile.loadTimeAdjustmentFile(fn.timeAdjustments())
i0=interval[0]
i1=interval[1]
intervalTime = i1-i0
dt = intervalTime/2
beginTime = max(0,i0-0.000200)
endTime = beginTime + 0.001
integrationTime = endTime-beginTime
nBins = int(np.round(obsFile.ticksPerSec*(endTime-beginTime)+1))
timeHgValues = np.zeros(nBins, dtype=np.int64)
ymax = sys.float_info.max/100.0
for iRow in range(obsFile.nRow):
for iCol in range(obsFile.nCol):
gtpl = obsFile.getTimedPacketList(iRow,iCol,
beginTime,integrationTime)
ts = (gtpl['timestamps'] - beginTime)*obsFile.ticksPerSec
ts64 = np.round(ts).astype(np.uint64)
tsBinner.tsBinner(ts64, timeHgValues)
plt.clf()
plt.plot(timeHgValues, label="data")
x0 = (i0-beginTime)*obsFile.ticksPerSec
x1 = (i1-beginTime)*obsFile.ticksPerSec
plt.fill_between((x0,x1),(0,0), (ymax,ymax), alpha=0.2, color='red')
plt.yscale("symlog",linthreshy=0.9)
plt.xlim(0,1000)
plt.ylim(-0.1,300)
tick0 = int(np.round(i0*obsFile.ticksPerSec))
plotfn = "cp-%05d-%s-%s-%s-%09d"%(timeHgValues.sum(),run,obsDate,seq,tick0)
plt.title(plotfn)
plt.legend()
plt.savefig(plotfn+".png")
plt.xlabel("nSigma=%d stride=%d threshold=%d"%(int(self.s['nSigma']),int(self.s['stride']),int(self.s['threshold'])))
print "plotfn=",plotfn
os.system("convert -delay 0 `ls -r cp*png` cp.gif")
def centroidObs(obsPath,centroidPath,centroidRa,centroidDec,haOffset,xGuess,yGuess,savePath,tstamp):
obs = ObsFile(obsPath)
# if not os.path.exists(hotPath):
# hp.findHotPixels(obsFile=obs,outputFileName=hotPath)
obs.loadAllCals()
# obs.loadHotPixCalFile(hotPath,switchOnMask=True)
# obs.loadBestWvlCalFile()
# obs.loadFlatCalFile(flatPath)
obs.setWvlCutoffs(3000,11000)
obs.loadCentroidListFile(centroidPath)
ctrdFile = obs.centroidListFile
sliceTimes = ctrdFile.root.centroidlist.times.read()
xPositions = ctrdFile.root.centroidlist.xPositions.read()
yPositions = ctrdFile.root.centroidlist.yPositions.read()
intTime = sliceTimes[1]-sliceTimes[0]
for iTime,time in enumerate(sliceTimes):
x = xPositions[iTime]
y = yPositions[iTime]
title='centroid_{}_{}s'.format(tstamp,time)
imgDict = obs.getPixelCountImage(firstSec=time,integrationTime=intTime,weighted=True)
imgPath=os.path.join(savePath,title+'.png')
pop = PopUp(showMe=False)
pop.plotArray(imgDict['image'],title=title)
pop.axes.plot(x,y,color='g',marker='d')
pop.fig.savefig(imgPath)
print 'saved to',imgPath
del obs
def openImage(self):
timestampList = [self.params['obsUtcDate']+'-'+ts for ts in self.params['obsSequence']]
run = self.params['run']
sunsetDate = self.params['obsSunsetDate']
utcDate = self.params['obsUtcDate']
self.intTime = self.params['intTime']
wvlLowerCutoff = self.params['wvlLowerCutoff']
wvlUpperCutoff = self.params['wvlUpperCutoff']
calTimestamp = self.params['wvlTimestamp']
wfn = FileName(run=run,date=sunsetDate,tstamp=calTimestamp).calSoln()
calfn = FileName(run=run,date=self.params['wvlSunsetDate'],tstamp=calTimestamp).cal()
ffn = FileName(run=run,date=self.params['flatCalSunsetDate'],tstamp='').flatSoln()
obsFns = [FileName(run=run,date=sunsetDate,tstamp=timestamp).obs() for timestamp in timestampList]
self.obList = [ObsFile(obsFn) for obsFn in obsFns]
for ob in self.obList:
print 'Loading ',ob.fullFileName
ob.loadWvlCalFile(wfn)
ob.loadFlatCalFile(ffn)
self.cal = ObsFile(calfn)
self.cal.loadWvlCalFile(wfn)
self.cal.loadFlatCalFile(ffn)
self.loadSpectra()
def __init__(self, fn, beginTime=0, endTime='exptime',
nBinsPerSec=10, flashMergeTime=1.0,
applyCosmicMask = False,
loggingLevel=logging.CRITICAL,
loggingHandler=logging.StreamHandler()):
"""
Opens fileName in MKID_RAW_PATH, sets roachList
endTime is exclusive
"""
self.logger = logging.getLogger("cosmic")
self.logger.setLevel(loggingLevel)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
loggingHandler.setFormatter(formatter)
self.logger.addHandler(loggingHandler)
self.logger.info("Cosmic: begin init for obsFile=%s"%fn.obs())
self.fn = fn
self.fileName = fn.obs();
self.file = ObsFile(self.fileName)
# apply Matt's time fix
timeAdjustments = self.fn.timeAdjustments()
if os.path.exists(timeAdjustments):
self.file.loadTimeAdjustmentFile(timeAdjustments)
# apply Julian's time masks
timeMaskFile = self.fn.timeMask();
if os.path.exists(timeMaskFile):
self.file.loadHotPixCalFile(timeMaskFile,switchOnMask=True)
# apply standard mask
if applyCosmicMask:
self.file.loadStandardCosmicMask()
self._setRoachList()
self._setAllSecs()
self.exptime = self.file.getFromHeader('exptime')
if endTime =='exptime':
self.endTime = float(self.exptime)
else:
self.endTime = float(endTime)
if ( (self.endTime > self.exptime) or (endTime < 0)):
raise RuntimeError("bad endTime: endTime=%s exptime=%s" % \
(str(endTime),str(self.exptime)))
self.beginTime = float(beginTime)
self.timeHgs = "none"
self.nBinsPerSec = nBinsPerSec
self.flashMergeTime = flashMergeTime
self.times = \
np.arange(self.beginTime, self.endTime, 1.0/self.nBinsPerSec)
# for measuring flashes, indexed by roach name
self.rMean = {} # mean from meanclip
self.rSigma = {} # sigma from meanclip
self.rNSurvived = {} # number of survivors from meanclip
self.rNormed = {} # (value-mean)/sigma
self.flashInterval = {}
self.logger.info("Cosmic: end init: beginTime=%s endTime=%s"%(str(self.beginTime),str(self.endTime)))
def timePL(tstamp,obsPath,centroidPath):
obs = ObsFile(obsPath)
obs.loadAllCals()
obs.setWvlCutoffs(3000,11000)
obs.loadCentroidListFile(centroidPath)
writePhotonList(obs,photListDescription=PulsarPhotonList,checkForExisting=False)
del obs
def main():
objectName = "hz21"
fileNum=0
energyBinWidth = 0.1
#make bins for 3000 to 13000
wvlStart = 3000
wvlStop = 13000
wvlBinEdges = ObsFile.makeWvlBins(energyBinWidth,wvlStart,wvlStop)
nWvlBins = len(wvlBinEdges)-1
binWidths = np.empty(nWvlBins)
print "Showing bin widths for %i bins"%(nWvlBins)
for i in xrange(nWvlBins):
binWidths[i] = wvlBinEdges[i+1]-wvlBinEdges[i]
print binWidths
nVirtPixX=250
nVirtPixY=250
cube = np.zeros((nVirtPixX,nVirtPixY,nWvlBins),dtype=float)
for n in xrange(nWvlBins):
print "Making image for wvls %i to %i"%(wvlBinEdges[n], wvlBinEdges[n+1])
virtualImage, imageStack, medImage = makeImageStack(fileNames='photons_*.h5', dir=os.getenv('MKID_PROC_PATH', default="/Scratch")+'/photonLists/20131209',
detImage=False, saveFileName='stackedImage.pkl', wvlMin=wvlBinEdges[n],
wvlMax=wvlBinEdges[n+1], doWeighted=True, medCombine=False, vPlateScale=0.2,
nPixRA=nVirtPixX,nPixDec=nVirtPixY)
print virtualImage
print virtualImage.image
print np.shape(virtualImage.image)
cube[:,:,n] = virtualImage.image
#calculate midpoints of wvl bins for plotting
wvls = np.empty((nWvlBins),dtype=float)
for n in xrange(nWvlBins):
binsize=wvlBinEdges[n+1]-wvlBinEdges[n]
wvls[n] = (wvlBinEdges[n]+(binsize/2.0))
print "wvls ",wvls
#reshape cube for makeMovie
movieCube = np.zeros((nWvlBins,np.shape(cube)[0],np.shape(cube)[1]),dtype=float)
for i in xrange(nWvlBins):
movieCube[i,:,:] = cube[:,:,i]
#show individual frames as they are made to debug
#plt.matshow(movieCube[i],vmin = 0, vmax = 100)
#plt.show()
print "movieCube shape ", np.shape(movieCube)
print "wvls shape ", np.shape(wvls)
#print cube
#print "--------------------------"
#print movieCube
np.savez('%s_raw_%s.npz'%(objectName,fileNum),stack=movieCube,wvls=wvls)
utils.makeMovie(movieCube,frameTitles=wvls,cbar=True,outName='%s_pl_raw_%s.gif'%(objectName,fileNum), normMin=0, normMax=1000)
'''
def centroidObs(obsPath,centroidPath,centroidRa,centroidDec,haOffset,xGuess,yGuess):
obs = ObsFile(obsPath)
print obsPath,obs.getFromHeader('exptime'),obs
obs.loadAllCals()
# obs.loadBestWvlCalFile()
# obs.loadFlatCalFile(flatPath)
obs.setWvlCutoffs(3000,11000)
# if not os.path.exists(hotPath):
# hp.findHotPixels(obsFile=obs,outputFileName=hotPath,display=True,fwhm=2.,boxSize=5, nSigmaHot=4.0,)
# obs.loadHotPixCalFile(hotPath,switchOnMask=True)
cc.centroidCalc(obs,centroidRa,centroidDec,guessTime=300,integrationTime=30,secondMaxCountsForDisplay=2000,HA_offset=haOffset,xyapprox=[xGuess,yGuess],outputFileName=centroidPath,usePsfFit=True,radiusOfSearch=8)
print 'done centroid',centroidPath
del obs
def main():
"""
params = []
paramfile = sys.argv[1]
f = open(paramfile,'r')
for line in f:
params.append(line)
f.close()
datadir = params[0].split('=')[1].strip()
flatdir = params[1].split('=')[1].strip()
fluxdir = params[2].split('=')[1].strip()
wvldir = params[3].split('=')[1].strip()
obsfile = params[4].split('=')[1].strip()
skyfile = params[5].split('=')[1].strip()
flatfile = params[6].split('=')[1].strip()
fluxfile = params[7].split('=')[1].strip()
wvlfile = params[8].split('=')[1].strip()
objectName = params[9].split('=')[1].strip()
fluxCalObject = params[10].split('=')[1].strip()
obsFileName = os.path.join(datadir, obsfile)
skyFileName = os.path.join(datadir, skyfile)
wvlCalFileName = os.path.join(wvldir, wvlfile)
flatCalFileName = os.path.join(flatdir, flatfile)
fluxCalFileName = os.path.join(fluxdir, fluxfile)
"""
if len(sys.argv) > 2:
fileNum = str(sys.argv[2])
else:
fileNum = "0"
# science object parameter file
params = []
paramfile = sys.argv[1]
f = open(paramfile, "r")
for line in f:
params.append(line)
f.close()
datadir = params[0].split("=")[1].strip()
flatdir = params[1].split("=")[1].strip()
wvldir = params[2].split("=")[1].strip()
obsfile = params[3].split("=")[1].strip()
skyfile = params[4].split("=")[1].strip()
flatfile = params[5].split("=")[1].strip()
wvlfile = params[6].split("=")[1].strip()
objectName = params[9].split("=")[1].strip()
# wvldir = "/Scratch/waveCalSolnFiles/oldbox_numbers/20121205"
# objectName = "crabNight1"
if len(params) > 10:
xpix = int(params[10].split("=")[1].strip())
ypix = int(params[11].split("=")[1].strip())
apertureRadius = int(params[12].split("=")[1].strip())
startTime = int(params[13].split("=")[1].strip())
intTime = int(params[14].split("=")[1].strip())
obsFileName = os.path.join(datadir, obsfile)
skyFileName = os.path.join(datadir, skyfile)
wvlCalFileName = os.path.join(wvldir, wvlfile)
flatCalFileName = os.path.join(flatdir, flatfile)
obs = ObsFile(obsFileName)
# obs.loadWvlCalFile(wvlCalFileName)
obs.loadBestWvlCalFile()
obs.loadFlatCalFile(flatCalFileName)
print "analyzing file %s" % (obsFileName)
print "loaded data file and calibrations\n---------------------\n"
nRow = obs.nRow
nCol = obs.nCol
obsTime = obs.getFromHeader("exptime")
# wvlBinEdges,obsSpectra = loadSpectra(obs,nCol,nRow)
# nWvlBins=len(wvlBinEdges)-1
# print np.shape(obsSpectra)
# print nRow
# print nCol
# print nWvlBins
# Apply Hot pixel masking before getting dead time correction
# HotPixFile = getTimeMaskFileName(obsFileName)
HotPixFile = FileName(obsFile=obs).timeMask()
print "making hot pixel file ", HotPixFile
if not os.path.exists(HotPixFile): # check if hot pix file already exists
hp.findHotPixels(inputFileName=obsFileName, outputFileName=HotPixFile)
print "Flux file pixel mask saved to %s" % (HotPixFile)
obs.loadHotPixCalFile(HotPixFile)
print "Hot pixel mask loaded %s" % (HotPixFile)
# GET RAW PIXEL COUNT IMAGE TO CALCULATE CORRECTION FACTORS
print "Making raw cube to get dead time correction"
cubeDict = obs.getSpectralCube(firstSec=startTime, integrationTime=intTime, weighted=False)
cube = np.array(cubeDict["cube"], dtype=np.double)
wvlBinEdges = cubeDict["wvlBinEdges"]
effIntTime = cubeDict["effIntTime"]
print "median effective integration time = ", np.median(effIntTime)
nWvlBins = len(wvlBinEdges) - 1
print "cube shape ", np.shape(cube)
print "effIntTime shape ", np.shape(effIntTime)
# add third dimension to effIntTime for broadcasting
effIntTime = np.reshape(effIntTime, np.shape(effIntTime) + (1,))
# put cube into counts/s in each pixel
cube /= effIntTime
# CALCULATE DEADTIME CORRECTION
# NEED TOTAL COUNTS PER SECOND FOR EACH PIXEL TO DO PROPERLY
# ASSUMES SAME CORRECTION FACTOR APPLIED FOR EACH WAVELENGTH, MEANING NO WL DEPENDANCE ON DEAD TIME EFFECT
DTCorr = np.zeros((np.shape(cube)[0], np.shape(cube)[1]), dtype=float)
for f in range(0, np.shape(cube)[2]):
print cube[:, :, f]
print "-----------------------"
DTCorr += cube[:, :, f]
print DTCorr
print "\n=====================\n"
# Correct for 100 us dead time
DTCorrNew = DTCorr / (1 - DTCorr * 100e-6)
CorrFactors = DTCorrNew / DTCorr # This is what the frames need to be multiplied by to get their true values
print "Dead time correction factors = "
print CorrFactors
# REMAKE CUBE WITH FLAT WEIGHTS AND APPLY DEAD TIME CORRECTION AS WELL
print "Making Weighted cube"
# load/generate hot pixel mask file
# HotPixFile = getTimeMaskFileName(obsFileName)
HotPixFile = FileName(obsFile=obs).timeMask()
if not os.path.exists(HotPixFile): # check if hot pix file already exists
hp.findHotPixels(inputFileName=obsFileName, outputFileName=HotPixFile)
print "Flux file pixel mask saved to %s" % (HotPixFile)
obs.loadHotPixCalFile(HotPixFile)
print "Hot pixel mask loaded %s" % (HotPixFile)
cubeDict = obs.getSpectralCube(firstSec=startTime, integrationTime=intTime, weighted=True, fluxWeighted=False)
# cubeDict = obs.getSpectralCube(firstSec=startTime, integrationTime=intTime, weighted=True, fluxWeighted=True)
cube = np.array(cubeDict["cube"], dtype=np.double)
wvlBinEdges = cubeDict["wvlBinEdges"]
effIntTime = cubeDict["effIntTime"]
print "median effective integration time = ", np.median(effIntTime)
nWvlBins = len(wvlBinEdges) - 1
print "cube shape ", np.shape(cube)
print "effIntTime shape ", np.shape(effIntTime)
# add third dimension to effIntTime for broadcasting
effIntTime = np.reshape(effIntTime, np.shape(effIntTime) + (1,))
# put cube into counts/s in each pixel
cube /= effIntTime
# add third dimension to CorrFactors for broadcasting
CorrFactors = np.reshape(CorrFactors, np.shape(CorrFactors) + (1,))
# apply dead time correction factors
cube *= CorrFactors
# calculate midpoints of wvl bins for plotting
wvls = np.empty((nWvlBins), dtype=float)
for n in xrange(nWvlBins):
binsize = wvlBinEdges[n + 1] - wvlBinEdges[n]
wvls[n] = wvlBinEdges[n] + (binsize / 2.0)
print "wvls ", wvls
# reshape cube for makeMovie
movieCube = np.zeros((nWvlBins, np.shape(cube)[0], np.shape(cube)[1]), dtype=float)
for i in xrange(nWvlBins):
movieCube[i, :, :] = cube[:, :, i]
# show individual frames as they are made to debug
# plt.matshow(movieCube[i],vmin = 0, vmax = 100)
# plt.show()
print "movieCube shape ", np.shape(movieCube)
print "wvls shape ", np.shape(wvls)
# print cube
# print "--------------------------"
# print movieCube
outdir = "/home/srmeeker/scratch/standards/"
np.savez(outdir + "%s_raw_%s.npz" % (objectName, fileNum), stack=movieCube, wvls=wvls)
utils.makeMovie(
movieCube,
frameTitles=wvls,
cbar=True,
outName=outdir + "%s_raw_%s.gif" % (objectName, fileNum),
normMin=0,
normMax=50,
)
"""
def main():
run = 'PAL2014'
year = '2014'
initialPath = '/Scratch'
packetMasterLogDir = '/LABTEST/PacketMasterLogs/'
initialPath = os.path.join(initialPath,run)
outPath = FileName(run=run,mkidDataDir='/Scratch/').timeAdjustments()
outFile = tables.openFile(outPath,'w')
timeAdjustGroup = outFile.createGroup('/','timeAdjust','Times to add to timestamps')
firmwareDelayTable = outFile.createTable(timeAdjustGroup,'firmwareDelay',firmwareDelayDescription,'Times to add to all timestamps taken with a firmware bof')
newFirmwareEntry = firmwareDelayTable.row
newFirmwareEntry['firmwareName']='chan_svf_2014_Aug_06_1839.bof'
newFirmwareEntry['firmwareDelay']=-41e-6 #s, subtract 41 us from timestamps
newFirmwareEntry.append()
firmwareDelayTable.flush()
firmwareDelayTable.close()
roachDelayTable = outFile.createTable(timeAdjustGroup,'roachDelays',roachDelaysDescription,'Times to add to each roach\'s timestamps')
for sunsetDatePath in sorted(glob.glob(os.path.join(initialPath,year+'*'))):
sunsetDate = os.path.basename(sunsetDatePath)
for fullObsPath in sorted(glob.glob(os.path.join(sunsetDatePath,'obs*.h5'))):
obsFileName = os.path.basename(fullObsPath)
obsTStamp = obsFileName.split('.')[0].split('_')[1]
print obsFileName
obsFN = FileName(run=run,date=sunsetDate,tstamp=obsTStamp,packetMasterLogDir=packetMasterLogDir)
pmLogFileName = obsFN.packetMasterLog()
try:
ob = ObsFile(fullObsPath)
except:
print 'can\'t open file'
continue
try:
if os.path.getsize(pmLogFileName) <= 0:
continue
except:
print 'can\'t open Packet Master Log ',pmLogFileName
continue
try:
f = open(pmLogFileName,'r')
except:
print 'can\'t open Packet Master Log ',pmLogFileName
continue
lastTstampLines = np.zeros(8)
firstTstampLines = np.zeros(8)
tstampLine = ''
for line in f:
if 'here' in line:
#skip over large log files with debug info
print 'skipping file with "here"'
continue
if line.split(' ')[0] == 'bundle':
tstampLine = line
break
if tstampLine == '':
print 'skipping file without "bundle"'
#didn't find lines with 'bundle' in them
continue
f.seek(0)
for line in f:
if line.split(' ')[0] == 'bundle':
try:
at = float(line.split('at')[1].split()[0])
except:
break
lastTstampLine = at
iRoach = int(line.split('roach')[1].split('took')[0].strip())
lastTstampLines[iRoach] = at
if firstTstampLines[iRoach] == 0:
firstTstampLines[iRoach] = at
packetReceivedUnixTimestamp = float((tstampLine.split('took')[1].split('total')[0].strip()))
firstPacketDelay = packetReceivedUnixTimestamp-int(ob.getFromHeader('unixtime'))
roachSecDelays =np.array(np.floor(lastTstampLines+firstPacketDelay-ob.getFromHeader('exptime')),dtype=np.int)
print 'roach delays',roachSecDelays
if np.all(roachSecDelays >= 0):
newEntry = roachDelayTable.row
newEntry['obsFileName'] = os.path.basename(fullObsPath)
newEntry['roachDelays'] = roachSecDelays
newEntry.append()
roachDelayTable.flush()
else:
print 'obs was aborted midway, delays cannot be calculated'
roachDelayTable.close()
outFile.close()
inCounts = []
for line in powermeter:
if '#' in line:
continue
inCounts.append(float(line.strip()))
inCounts = np.array(inCounts)
pixToUse = [[0,26],[2,25],[2,27],[2,31],[2,34],[2,39],[3,17],[3,26],[3,32],
[3,36],[4,26],[4,32],[5,32],[6,22],[6,29],[6,30],[7,25],[7,27],[7,28],
[7,32],[7,37],[8,30],[10,32],[10,42],[11,30],[12,29],[12,31],[13,29],[13,31],
[14,27],[14,32],[15,25],[15,26],[15,30],[15,31],[15,32],[16,23],[16,30],
[17,23],[17,25],[17,26],[17,39],[18,24],[18,28],[19,23],[19,26],[19,28],[19,30]]
cmap = matplotlib.cm.jet
obsFileName = '/Scratch/linearityTestData/obs_20130612-003423.h5'
obs = ObsFile(obsFileName)
row,col=pixToUse[0]
allResolutions = []
allModResolutions = []
allModResolutions2 = []
allCountRates = []
timeBinStarts = timeBinStarts[0:-3]
for (row,col) in pixToUse[0:3]:
firstSec=timeBinStarts[0]
intTime=timeBinWidth
resolutions = []
modResolutions = []
modResolutions2 = []
countRates = []
def findCosmics(self, stride=10, threshold=100,
populationMax=2000, nSigma=5, writeCosmicMask=False,
ppsStride=10000):
"""
Find cosmics ray suspects. Histogram the number of photons
recorded at each timeStamp. When the number of photons in a
group of stride timeStamps is greater than threshold in second
iSec, add (iSec,timeStamp) to cosmicTimeLists. Also keep
track of the histogram of the number of photons per stride
timeStamps.
return a dictionary of 'populationHg', 'cosmicTimeLists',
'binContents', 'timeHgValues', 'interval', 'frameSum', and 'pps'
populationHg is a histogram of the number of photons in each time bin.
This is a poisson distribution with a long tail due to cosmic events
cosmicTimeLists is a numpy array of all the sequences that are
suspects for cosmic rays
binContents corresponds to cosmicTimeLists. For each time in
cosmicTimeLists, binContents is the number of photons detected
at that time.
timeHgValues is a histogram of the number of photons in each time
interval
frameSum is a two dimensional numpy array of the number of photons
detected by each pixel
interval is the interval of data to be masked out
pps is photons per second, calculated every ppsStride bins.
"""
self.logger.info("findCosmics: begin stride=%d threshold=%d populationMax=%d nSigma=%d writeCosmicMask=%s"%(stride,threshold,populationMax,nSigma,writeCosmicMask))
exptime = self.endTime-self.beginTime
nBins = int(np.round(self.file.ticksPerSec*exptime+1))
bins = np.arange(0, nBins, 1)
timeHgValues,frameSum = self.getTimeHgAndFrameSum(self.beginTime,self.endTime)
remainder = len(timeHgValues)%ppsStride
if remainder > 0:
temp = timeHgValues[:-remainder]
else:
temp = timeHgValues
ppsTime = (ppsStride*self.file.tickDuration)
pps = np.sum(temp.reshape(-1, ppsStride), axis=1)/ppsTime
self.logger.info("findCosmics: call populationFromTimeHgValues")
pfthgv = Cosmic.populationFromTimeHgValues\
(timeHgValues,populationMax,stride,threshold)
#now build up all of the intervals in seconds
self.logger.info("findCosmics: build up intervals: nCosmicTime=%d"%len(pfthgv['cosmicTimeList']))
i = interval()
iCount = 0
secondsPerTick = self.file.tickDuration
for cosmicTime in pfthgv['cosmicTimeList']:
#t0 = max(0,self.beginTime+(cosmicTime-50)/1.e6)
#t1 = min(self.endTime,self.beginTime+(cosmicTime+50)/1.e6)
#intTime = t1-t0
t0 = self.beginTime+cosmicTime*secondsPerTick
dt = stride*secondsPerTick
t1 = t0+dt
left = max(self.beginTime, t0-nSigma*dt)
right = min(self.endTime, t1+2*nSigma*dt)
i = i | interval[left,right]
self.logger.debug("findCosmics: iCount=%d t0=%f t1=%f left=%f right=%f"%(iCount,t0,t1,left,right))
iCount+=1
tMasked = Cosmic.countMaskedBins(i)
ppmMasked = 1000000*tMasked/(self.endTime-self.beginTime)
retval = {}
retval['timeHgValues'] = timeHgValues
retval['populationHg'] = pfthgv['populationHg']
retval['cosmicTimeList'] = pfthgv['cosmicTimeList']
retval['binContents'] = pfthgv['binContents']
retval['frameSum'] = frameSum
retval['interval'] = i
retval['ppmMasked'] = ppmMasked
retval['pps'] = pps
retval['ppsTime'] = ppsTime
if writeCosmicMask:
cfn = self.fn.cosmicMask()
self.logger.info("findCosmics: write masks to =%s"%cfn)
ObsFile.writeCosmicIntervalToFile(i, self.file.ticksPerSec,
cfn,self.beginTime, self.endTime,
stride, threshold, nSigma, populationMax)
self.logger.info("findCosmics: end with ppm masked=%d"%ppmMasked)
return retval
NumFrames = 31
#nFiles = 13 #j0926
#nFiles=25 #crab
nFiles = 1 #try with only 1 file
curves = np.zeros((nFiles,NumFrames),dtype=float)
for k in xrange(nFiles):
#FileName = '/home/srmeeker/scratch/standards/crabNight2_fit_%s.npz'%(fileNum)
FileName = '/home/srmeeker/scratch/standards/crabNight1_fit_%s.npz'%(fileNum)
print FileName
t = np.load(FileName)
energyBinWidth = 0.1
wvlStart = 3000
wvlStop = 13000
wvlBinEdges = ObsFile.makeWvlBins(energyBinWidth,wvlStart,wvlStop)
nWvlBins = len(wvlBinEdges)-1
binWidths = np.empty(nWvlBins)
for i in xrange(nWvlBins):
binWidths[i] = wvlBinEdges[i+1]-wvlBinEdges[i]
#print binWidths
params = t['params']
wvls = t['wvls']
amps = params[:,1]
widths = params[:,4]
xpos = params[:,2]
ypos = params[:,3]
#print len(wvls)
#print len(binWidths)
sunsetDate = '20121208'
utcDate = '20121209'
# Specify which wavelength calibration file to use.
calTimestamp = '20121209-131132'
# Create wavelength and flat cal file names
wvlCalFilename = FileName(run=run,date=sunsetDate,tstamp=calTimestamp).calSoln()
flatCalFilename = FileName(run=run,date=sunsetDate,tstamp=calTimestamp).flatSoln()
# No twilights taken on December 8, using December 7 twilights to do flat cal instead.
flatCalFilename = '/Scratch/flatCalSolnFiles/20121207/flatsol_20121207.h5'
# Run standard ObsFile functions.
# Create ObsFile instance.
tic = time()
print 'Loading obs file and performing calibrations ...'
obsFn = FileName(run=run,date=sunsetDate,tstamp=obsTimestamp).obs()
ob = ObsFile(obsFn)
# Load roach time delay corrections.
ob.loadTimeAdjustmentFile(FileName(run=run).timeAdjustments())
# Search for time mask for given observation file. If the time mask does not exist, create it.
index1 = obsFn.find('_')
hotPixFn = '/Scratch/timeMasks/timeMask' + obsFn[index1:]
if not os.path.exists(hotPixFn):
hp.findHotPixels(obsFn,hotPixFn)
print "Flux file pixel mask saved to %s"%(hotPixFn)
# Load time mask, wavelength calibration, and flat calibration and set wavelenth cutoffs.
ob.loadHotPixCalFile(hotPixFn,switchOnMask=True)
ob.loadWvlCalFile(wvlCalFilename)
ob.loadFlatCalFile(flatCalFilename)
ob.setWvlCutoffs(3000,5000)
print 'Total load time: ' + str(time()-tic) + 's'
sunsetDate = sunsetDates[iSeq]
for position in ObjPosFile:
if iSeq + 1 == position[0]:
print "finding aperture and sky masks"
guessX = position[1]
guessY = position[2]
apertureMask = circleAperture(guessX, guessY, radius1)
bigMask = circleAperture(guessX, guessY, radius2)
skyMask = bigMask - apertureMask
# print skyMask[0:20][5:25]
for i, ts in enumerate(timestampList):
print "loading", ts
obsFn = FileName(run=run, date=sunsetDate, tstamp=ts).obs()
ob = ObsFile(obsFn)
ob.loadTimeAdjustmentFile(FileName(run=run).timeAdjustments())
index1 = obsFn.find("_")
hotPixFn = "/Scratch/timeMasks/timeMask" + obsFn[index1:]
if not os.path.exists(hotPixFn):
hp.findHotPixels(obsFn, hotPixFn)
print "Flux file pixel mask saved to %s" % (hotPixFn)
ob.loadHotPixCalFile(hotPixFn, switchOnMask=True)
ob.loadWvlCalFile(wfn)
ob.loadFlatCalFile(ffn)
ob.setWvlCutoffs(wvlLowerCutoff, wvlUpperCutoff)
bad_solution_mask = np.zeros((46, 44))
bad_count = 0
for y in range(46):
for x in range(44):
#cosmic = Cosmic(fn, endTime='exptime')
cosmic = Cosmic(fn, beginTime=0)
fc = cosmic.findCosmics(stride=stride,
threshold=threshold,
populationMax=populationMax,
nSigma=nSigma)
if frameSum == 'none':
frameSum = fc['frameSum']
else:
frameSum += fc['frameSum']
outfile = open("cosmicTimeList-"+seq+".pkl", "wb")
pickle.dump(fc['cosmicTimeList'],outfile)
pickle.dump(fc['binContents'],outfile)
outfile.close()
cfn = "cosmicMax-%s.h5"%seq
ObsFile.writeCosmicIntervalToFile(fc['interval'],1.0e6, cfn)
populationHg = fc['populationHg']
yPlot = populationHg[0].astype(np.float)
ySum += yPlot
norm = yPlot.sum()
yPlot /= norm
yPlot[yPlot==0] = 1e-3/norm
xPlot = 0.5*(populationHg[1][1:]+populationHg[1][:-1])
plt.clf()
plt.plot(xPlot,yPlot,'-')
plt.yscale('log')
plt.ylim(ymin=0.5/norm)
plt.xlim(1,populationMax)
plt.xscale('log')
#poisson = []
#xValues = populationHg[1][1:]-0.5
def main():
obsSequence1="""
035332
035834
040336
040838
041341
041843
042346
042848
043351
043853
044355
044857
045359
045902
"""
obsSequence2="""
050404
050907
051409
051912
052414
052917
053419
053922
"""
obsSequence3="""
054926
055428
"""
pulseLabel = 1 #1 for interpulse, 2 for main pulse
verbose = True
run = 'PAL2012'
path = '/Scratch/dataProcessing/crabData2/'
nIdxToCheck = 81
nBins = 250
outFilePath = path+'indPulseProfiles_3sigma_P{}_KS.h5'.format(pulseLabel)
wvlBinEdges = ObsFile.makeWvlBins(wvlStart=4000,wvlStop=11000)
wvlBinWidths = np.diff(wvlBinEdges)
nWvlBins = len(wvlBinWidths)
peakIdx=167#the index of the phaseBin at the main pulse peak
obsSequences = [obsSequence1,obsSequence2,obsSequence3]
#obsSequences = [obsSequence1]
wvlCals = ['063518','063518','063518']
flatCals = ['20121211','20121211','20121211']
fluxCalDates = ['20121206','20121206','20121206']
fluxCals = ['20121207-072055','20121207-072055','20121207-072055']
obsUtcDates = ['20121212','20121212','20121212']
obsFileNames = []
obsFileNameTimestamps = []
wvlFileNames = []
flatFileNames = []
fluxFileNames = []
timeMaskFileNames = []
plFileNames = []
skyFileNames = []
for iSeq in range(len(obsSequences)):
obsSequence = obsSequences[iSeq]
obsSequence = obsSequence.strip().split()
obsFileNameTimestamps.append(obsSequence)
obsUtcDate = obsUtcDates[iSeq]
sunsetDate = str(int(obsUtcDate)-1)
obsSequence = [obsUtcDates[iSeq]+'-'+ts for ts in obsSequence]
plFileNames.append([FileName(run=run,date=sunsetDate,tstamp=ts).crabList() for ts in obsSequence])
skyFileNames.append([FileName(run=run,date=sunsetDate,tstamp=ts).crabSkyList() for ts in obsSequence])
np.set_printoptions(precision=11,threshold=np.nan)
labels=np.array('iBTOA pulseNumber BTOA Noise_Offset Noise_RMS Max Mean Index TestMax TestMean TestIndex'.split())
table = np.loadtxt(path+'giantPulseList_P{}_3sigma_indices.txt'.format(pulseLabel),skiprows=1,usecols=range(len(labels)))
peakDetectionMask = table[:,np.argmax('Max'==labels)]!=0
table = table[peakDetectionMask]#cut out test_P2 (false) detections
dimMask = np.ones(len(table))
#dimMask[13292:22401]=0
dimMask = dimMask==1
radioStrength = table[:,5]
allGiantPulseNumbers = table[:,1]
if pulseLabel == 2:
radioStrengthCutoff = 0.175
elif pulseLabel == 1:
radioStrengthCutoff = 0.01
radioCutoffMask = radioStrength >= radioStrengthCutoff
strongMask = np.logical_and(radioCutoffMask,dimMask)
table = table[strongMask]
giantDict = dict()
for iLabel,label in enumerate(labels):
giantDict[label] = table[:,np.argmax(label==labels)]
noiseStrengthLabel = 'TestMax'
#count number of detections in the test range and the P2 peak range
nNoiseDetections = np.sum(giantDict[noiseStrengthLabel]!=0)
print nNoiseDetections,'noise detections'
nPeakDetections = np.sum(giantDict['Max']!=0)
print nPeakDetections,'peak detections'
giantPulseNumbers = giantDict['pulseNumber']
radioMax = giantDict['Max']
radioMean = giantDict['Mean']
radioDetectedIndices = giantDict['Index']
nGRP = len(giantPulseNumbers)
if verbose:
print 'Number of GRPs',nGRP
counts = np.zeros((nGRP,nIdxToCheck))
skyCounts = np.zeros((nGRP,nIdxToCheck))
idxOffsets = np.array(np.linspace(-(nIdxToCheck//2),nIdxToCheck//2,nIdxToCheck),dtype=np.int)
nIdxOffsets = len(idxOffsets)
histStart = 0.
histEnd = 1.
pulsarPeriod = 33e-3 #s, approximately
indProfiles = np.zeros((nGRP,nIdxOffsets,nBins))
skyIndProfiles = np.zeros((nGRP,nIdxOffsets,nBins))
fullSpectra = np.zeros((nIdxOffsets,nWvlBins),dtype=np.double)
skyFullSpectra = np.zeros((nIdxOffsets,nWvlBins),dtype=np.double)
peakSpectra = np.zeros((nIdxOffsets,nWvlBins),dtype=np.double)
skyPeakSpectra = np.zeros((nIdxOffsets,nWvlBins),dtype=np.double)
plList = [PhotList(fn) for seq in plFileNames for fn in seq]
skyList = [PhotList(fn) for seq in skyFileNames for fn in seq]
plMins = []
plMaxs = []
for iPL,pl in enumerate(plList):
minPulseNumber = pl.photTable.cols.pulseNumber[pl.photTable.colindexes['pulseNumber'][0]]
maxPulseNumber = pl.photTable.cols.pulseNumber[pl.photTable.colindexes['pulseNumber'][-1]]
print pl.fileName,minPulseNumber,maxPulseNumber
plMins.append(minPulseNumber)
plMaxs.append(maxPulseNumber)
plMins = np.array(plMins)
plMaxs = np.array(plMaxs)
pulseNumberTable = np.array([gpn+idxOffsets for gpn in giantPulseNumbers])
giantPulseNumberMask = np.zeros(np.shape(pulseNumberTable))
validWvlCutoff = 11000 #angstroms
if verbose:
print 'filling giant pulse number mask'
for iGiantPN,giantPN in enumerate(allGiantPulseNumbers):
if verbose and iGiantPN % 500 == 0:
print 'mask',iGiantPN,'of',len(allGiantPulseNumbers)
giantMatches = (pulseNumberTable == giantPN)
giantPulseNumberMask = np.logical_or(giantPulseNumberMask,giantMatches)
outFile = tables.openFile(outFilePath,mode='w')
nLivePixels = []
for iPL,pl in enumerate(plList):
pixels = np.unique(pl.photTable.cols.xyPix[:])
print pixels
nPixels = len(pixels)
nLivePixels.append(nPixels)
nLivePixels = np.array(nLivePixels)
nSkyLivePixels = []
for iPL,pl in enumerate(skyList):
pixels = np.unique(pl.photTable.cols.xyPix[:])
print pixels
nPixels = len(pixels)
nSkyLivePixels.append(nPixels)
nSkyLivePixels = np.array(nSkyLivePixels)
pulseNumberList = np.unique(pulseNumberTable.ravel())
nPulseNumbers = len(pulseNumberList)
pulseNumberListMask = np.ones(nPulseNumbers)
floatAtom = tables.Float64Atom()
uint8Atom = tables.UInt8Atom()
nExpectedRows=1e7
#phaseArrays = [outFile.createEArray(outFile.root,'phases{:03d}'.format(iIdxOffset),floatAtom,(0,),title='phases at pulse number {} w.r.t. GRP'.format(idxOffset),expectedrows=nExpectedRows) for iIdxOffset,idxOffset in enumerate(idxOffsets)]
#skyPhaseArrays = [outFile.createEArray(outFile.root,'skyPhases{:03d}'.format(iIdxOffset),floatAtom,(0,),title='phases at pulse number {} w.r.t. GRP in sky'.format(idxOffset),expectedrows=nExpectedRows) for iIdxOffset,idxOffset in enumerate(idxOffsets)]
#wavelengthArrays = [outFile.createEArray(outFile.root,'wavelengths{:03d}'.format(iIdxOffset),floatAtom,(0,),title='wavelengths at pulse number {} w.r.t. GRP'.format(idxOffset),expectedrows=nExpectedRows) for iIdxOffset,idxOffset in enumerate(idxOffsets)]
#skyWavelengthArrays = [outFile.createEArray(outFile.root,'skyWavelengths{:03d}'.format(iIdxOffset),floatAtom,(0,),title='wavelengths at pulse number {} w.r.t. GRP in sky'.format(idxOffset),expectedrows=nExpectedRows) for iIdxOffset,idxOffset in enumerate(idxOffsets)]
grpPeakWavelengthArrays = outFile.createVLArray(outFile.root,'grpWavelengths',floatAtom,'photon wavelength list for GRPs at main peak',tables.Filters(complevel=1))
nongrpPeakWavelengthArrays = outFile.createVLArray(outFile.root,'nongrpWavelengths',floatAtom,'photon wavelength list for nonGRPs at main peak',tables.Filters(complevel=1))
for iGiantPN,giantPN in enumerate(giantPulseNumbers):
if verbose and iGiantPN % 100 == 0:
print iGiantPN
iPL = np.searchsorted(plMaxs,giantPN)
if iPL >= len(plMins):
if verbose:
print iGiantPN,'GRP not found in optical'
continue
if plMins[iPL] > giantPN:
if verbose:
print iGiantPN,'GRP not found in optical'
continue
if plMins[iPL] >= giantPN+idxOffsets[0] or plMaxs[iPL] <= giantPN+idxOffsets[-1]:
if verbose:
print iGiantPN,'optical pulses surrounding GRP not found'
continue
pl = plList[iPL]
skyPL = skyList[iPL]
#grab all photons in the pulseNumber range covered by all idxOffsets for this GRP
pulseSelectionIndices = pl.photTable.getWhereList('({} <= pulseNumber) & (pulseNumber <= {})'.format(giantPN+idxOffsets[0],giantPN+idxOffsets[-1]))
skyPulseSelectionIndices = skyPL.photTable.getWhereList('({} <= pulseNumber) & (pulseNumber <= {})'.format(giantPN+idxOffsets[0],giantPN+idxOffsets[-1]))
photonsInPulseSelection = pl.photTable.readCoordinates(pulseSelectionIndices)
skyPhotonsInPulseSelection = skyPL.photTable.readCoordinates(skyPulseSelectionIndices)
nPulsesInSelection = len(np.unique(photonsInPulseSelection['pulseNumber']))
nSkyPulsesInSelection = len(np.unique(skyPhotonsInPulseSelection['pulseNumber']))
if nPulsesInSelection < nIdxOffsets or nSkyPulsesInSelection < nIdxOffsets:
if verbose:
print 'only ',nPulsesInSelection,' pulses for ',iGiantPN,giantPN
continue
startIdx = 0
nPixels = 1.*nLivePixels[iPL]
nSkyPixels = 1.*nSkyLivePixels[iPL]
nongrpPeakWvls = np.array([])
for iIdxOffset,idxOffset in enumerate(idxOffsets):
pulseNumber = giantPN+idxOffset
if giantPulseNumberMask[iGiantPN,iIdxOffset] == False or idxOffset==0:
photons = photonsInPulseSelection[photonsInPulseSelection['pulseNumber']==pulseNumber]
skyPhotons = skyPhotonsInPulseSelection[skyPhotonsInPulseSelection['pulseNumber']==pulseNumber]
phases = photons['phase']
wavelengths = photons['wavelength']
skyPhases = skyPhotons['phase']
skyWavelengths = skyPhotons['wavelength']
count = 1.*len(photons)/nPixels
skyCount = 1.*len(skyPhotons)/nSkyPixels
counts[iGiantPN,iIdxOffset] = count
skyCounts[iGiantPN,iIdxOffset] = skyCount
profile,phaseBinEdges = np.histogram(phases,bins=nBins,range=(histStart,histEnd))
skyProfile,phaseBinEdges = np.histogram(skyPhases,bins=nBins,range=(histStart,histEnd))
indProfiles[iGiantPN,iIdxOffset] = profile/nPixels
skyIndProfiles[iGiantPN,iIdxOffset] = skyProfile/nSkyPixels
spectrum,_ = np.histogram(wavelengths,bins=wvlBinEdges)
spectrum = 1.0*spectrum/(nPixels*wvlBinWidths)#convert to counts per pixel per angstrom
fullSpectra[iIdxOffset] += spectrum
skySpectrum,_ = np.histogram(skyWavelengths,bins=wvlBinEdges)
skySpectrum = 1.0*skySpectrum/(nSkyPixels*wvlBinWidths)#convert to counts per pixel per angstrom
skyFullSpectra[iIdxOffset] += skySpectrum
phasesBinned = np.digitize(phases,phaseBinEdges)-1
peakPhaseMask = np.logical_or(phasesBinned==(peakIdx-1),phasesBinned==peakIdx)
peakPhaseMask = np.logical_or(peakPhaseMask,phasesBinned==(peakIdx+1))
peakWvls = wavelengths[peakPhaseMask]
if idxOffset == 0:
grpPeakWavelengthArrays.append(peakWvls)
else:
nongrpPeakWvls = np.append(nongrpPeakWvls,peakWvls)
peakSpectrum,_ = np.histogram(peakWvls,bins=wvlBinEdges)
peakSpectrum = 1.0*peakSpectrum/(nPixels*wvlBinWidths)#convert to counts per pixel per angstrom
peakSpectra[iIdxOffset] += peakSpectrum
skyPhasesBinned = np.digitize(skyPhases,phaseBinEdges)-1
skyPeakPhaseMask = np.logical_or(skyPhasesBinned==(peakIdx-1),skyPhasesBinned==peakIdx)
skyPeakPhaseMask = np.logical_or(skyPeakPhaseMask,skyPhasesBinned==(peakIdx+1))
skyPeakWvls = skyWavelengths[skyPeakPhaseMask]
skyPeakSpectrum,_ = np.histogram(skyPeakWvls,bins=wvlBinEdges)
skyPeakSpectrum = 1.0*skyPeakSpectrum/(nSkyPixels*wvlBinWidths)#convert to counts per pixel per angstrom
skyPeakSpectra[iIdxOffset] += skyPeakSpectrum
# phaseArrays[iIdxOffset].append(phases)
# skyPhaseArrays[iIdxOffset].append(skyPhases)
# wavelengthArrays[iIdxOffset].append(photons['wavelength'])
# skyWavelengthArrays[iIdxOffset].append(skyPhotons['wavelength'])
nongrpPeakWavelengthArrays.append(nongrpPeakWvls)
if verbose:
print 'done searching'
outFile.createArray(outFile.root,'counts',counts)
outFile.createArray(outFile.root,'skyCounts',skyCounts)
outFile.createArray(outFile.root,'idxOffsets',idxOffsets)
outFile.createArray(outFile.root,'radioMax',radioMax)
outFile.createArray(outFile.root,'radioMean',radioMean)
outFile.createArray(outFile.root,'indProfiles',indProfiles)
outFile.createArray(outFile.root,'skyIndProfiles',skyIndProfiles)
outFile.createArray(outFile.root,'phaseBinEdges',phaseBinEdges)
outFile.createArray(outFile.root,'giantPulseNumbers',giantPulseNumbers)
outFile.createArray(outFile.root,'giantPulseNumberMask',giantPulseNumberMask)
outFile.createArray(outFile.root,'pulseNumberTable',pulseNumberTable)
outFile.createArray(outFile.root,'radioIndices',radioDetectedIndices)
outFile.createArray(outFile.root,'nPixels',nLivePixels)
outFile.createArray(outFile.root,'nSkyPixels',nSkyLivePixels)
outFile.createArray(outFile.root,'fullSpectra',fullSpectra)
outFile.createArray(outFile.root,'skyFullSpectra',skyFullSpectra)
outFile.createArray(outFile.root,'peakSpectra',peakSpectra)
outFile.createArray(outFile.root,'skyPeakSpectra',skyPeakSpectra)
outFile.createArray(outFile.root,'wvlBinEdges',wvlBinEdges)
outFile.flush()
outFile.close()
if verbose:
print 'done saving'
def __init__(self,paramFile):
"""
opens flat file,sets wavelength binnning parameters, and calculates flat factors for the file
"""
self.params = readDict()
self.params.read_from_file(paramFile)
run = self.params['run']
sunsetDate = self.params['sunsetDate']
flatTstamp = self.params['flatTstamp']
wvlSunsetDate = self.params['wvlSunsetDate']
wvlTimestamp = self.params['wvlTimestamp']
obsSequence = self.params['obsSequence']
needTimeAdjust = self.params['needTimeAdjust']
self.deadtime = self.params['deadtime'] #from firmware pulse detection
self.intTime = self.params['intTime']
self.timeSpacingCut = self.params['timeSpacingCut']
self.nSigmaClip = self.params['nSigmaClip']
self.nNearest = self.params['nNearest']
obsFNs = [FileName(run=run,date=sunsetDate,tstamp=obsTstamp) for obsTstamp in obsSequence]
self.obsFileNames = [fn.obs() for fn in obsFNs]
self.obsList = [ObsFile(obsFileName) for obsFileName in self.obsFileNames]
timeMaskFileNames = [fn.timeMask() for fn in obsFNs]
timeAdjustFileName = FileName(run=run).timeAdjustments()
print len(self.obsFileNames), 'flat files to co-add'
self.flatCalFileName = FileName(run=run,date=sunsetDate,tstamp=flatTstamp).illumSoln()
if wvlSunsetDate != '':
wvlCalFileName = FileName(run=run,date=wvlSunsetDate,tstamp=wvlTimestamp).calSoln()
for iObs,obs in enumerate(self.obsList):
if wvlSunsetDate != '':
obs.loadWvlCalFile(wvlCalFileName)
else:
obs.loadBestWvlCalFile()
if needTimeAdjust:
obs.loadTimeAdjustmentFile(timeAdjustFileName)
timeMaskFileName = timeMaskFileNames[iObs]
print timeMaskFileName
#Temporary step, remove old hotpix file
#if os.path.exists(timeMaskFileName):
# os.remove(timeMaskFileName)
if not os.path.exists(timeMaskFileName):
print 'Running hotpix for ',obs
hp.findHotPixels(self.obsFileNames[iObs],timeMaskFileName,fwhm=np.inf,useLocalStdDev=True)
print "Flux file pixel mask saved to %s"%(timeMaskFileName)
obs.loadHotPixCalFile(timeMaskFileName)
self.wvlFlags = self.obsList[0].wvlFlagTable
self.nRow = self.obsList[0].nRow
self.nCol = self.obsList[0].nCol
print 'files opened'
#self.wvlBinWidth = params['wvlBinWidth'] #angstroms
self.energyBinWidth = self.params['energyBinWidth'] #eV
self.wvlStart = self.params['wvlStart'] #angstroms
self.wvlStop = self.params['wvlStop'] #angstroms
self.wvlBinEdges = ObsFile.makeWvlBins(self.energyBinWidth,self.wvlStart,self.wvlStop)
self.intTime = self.params['intTime']
self.countRateCutoff = self.params['countRateCutoff']
self.fractionOfChunksToTrim = self.params['fractionOfChunksToTrim']
#wvlBinEdges includes both lower and upper limits, so number of bins is 1 less than number of edges
self.nWvlBins = len(self.wvlBinEdges)-1
filt = sys.argv[1]
else:
filt='V'
# All examples shown here will use data from the W Uma binary, 1SWASP J0002, found here:
fileName = '/ScienceData/PAL2014/20140923/obs_20140924-080031.h5'
# We'll select a pixel in the middle of the target for our examples
col = 18
row = 16
# first, load the data into an ObsFile object
obs = ObsFile(fileName)
print "Loaded obsFile", fileName
# if we tried to simply load a filter now, it would fail as we haven't defined our spectrum's binning.
# Uncomment the following to see the error you would get.
'''
obs.loadFilter()
'''
# Wavelength bins can be provided during the filter loading, but the easiest way is to
# just load the flat calibration first, as that sets everything to the binning used for
# flat cal. This file already has all its calibrations associated
# with it in /Scratch/calLookup/lookup.h5. If a file doesn't have it's data written into
# this table the following will not work and calibrations must be loaded manually.
class FluxCal:
def __init__(self,paramFile,plots=False,verbose=False):
"""
Opens flux file, prepares standard spectrum, and calculates flux factors for the file.
Method is provided in param file. If 'relative' is selected, an obs file with standard star defocused over
the entire array is expected, with accompanying sky file to do sky subtraction.
If any other method is provided, 'absolute' will be done by default, wherein a point source is assumed
to be present. The obs file is then broken into spectral frames with photometry (psf or aper) performed
on each frame to generate the ARCONS observed spectrum.
"""
self.verbose=verbose
self.plots = plots
self.params = readDict()
self.params.read_from_file(paramFile)
run = self.params['run']
sunsetDate = self.params['fluxSunsetLocalDate']
self.fluxTstamp = self.params['fluxTimestamp']
skyTstamp = self.params['skyTimestamp']
wvlSunsetDate = self.params['wvlCalSunsetLocalDate']
wvlTimestamp = self.params['wvlCalTimestamp']
flatCalFileName = self.params['flatCalFileName']
needTimeAdjust = self.params['needTimeAdjust']
self.deadtime = float(self.params['deadtime']) #from firmware pulse detection
self.timeSpacingCut = self.params['timeSpacingCut']
bLoadBeammap = self.params.get('bLoadBeammap',False)
self.method = self.params['method']
self.objectName = self.params['object']
self.r = float(self.params['energyResolution'])
self.photometry = self.params['photometry']
self.centroidRow = self.params['centroidRow']
self.centroidCol = self.params['centroidCol']
self.aperture = self.params['apertureRad']
self.annulusInner = self.params['annulusInner']
self.annulusOuter = self.params['annulusOuter']
self.collectingArea = self.params['collectingArea']
self.startTime = self.params['startTime']
self.intTime = self.params['integrationTime']
fluxFN = FileName(run=run,date=sunsetDate,tstamp=self.fluxTstamp)
self.fluxFileName = fluxFN.obs()
self.fluxFile = ObsFile(self.fluxFileName)
if self.plots:
self.plotSavePath = os.environ['MKID_PROC_PATH']+os.sep+'fluxCalSolnFiles'+os.sep+run+os.sep+sunsetDate+os.sep+'plots'+os.sep
if not os.path.exists(self.plotSavePath): os.mkdir(self.plotSavePath)
if self.verbose: print "Created directory %s"%self.plotSavePath
obsFNs = [fluxFN]
self.obsList = [self.fluxFile]
if self.startTime in ['',None]: self.startTime=0
if self.intTime in ['',None]: self.intTime=-1
if self.method=="relative":
try:
print "performing Relative Flux Calibration"
skyFN = FileName(run=run,date=sunsetDate,tstamp=skyTstamp)
self.skyFileName = skyFN.obs()
self.skyFile = ObsFile(self.skyFileName)
obsFNs.append(skyFN)
self.obsList.append(self.skyFile)
except:
print "For relative flux calibration a sky file must be provided in param file"
self.__del__()
else:
self.method='absolute'
print "performing Absolute Flux Calibration"
if self.photometry not in ['aperture','PSF']: self.photometry='PSF' #default to PSF fitting if no valid photometry selected
timeMaskFileNames = [fn.timeMask() for fn in obsFNs]
timeAdjustFileName = FileName(run=run).timeAdjustments()
#make filename for output fluxCalSoln file
self.fluxCalFileName = FileName(run=run,date=sunsetDate,tstamp=self.fluxTstamp).fluxSoln()
print "Creating flux cal: %s"%self.fluxCalFileName
if wvlSunsetDate != '':
wvlCalFileName = FileName(run=run,date=wvlSunsetDate,tstamp=wvlTimestamp).calSoln()
if flatCalFileName =='':
flatCalFileName=FileName(obsFile=self.fluxFile).flatSoln()
#load cal files for flux file and, if necessary, sky file
for iObs,obs in enumerate(self.obsList):
if bLoadBeammap:
print 'loading beammap',os.environ['MKID_BEAMMAP_PATH']
obs.loadBeammapFile(os.environ['MKID_BEAMMAP_PATH'])
if wvlSunsetDate != '':
obs.loadWvlCalFile(wvlCalFileName)
else:
obs.loadBestWvlCalFile()
obs.loadFlatCalFile(flatCalFileName)
obs.setWvlCutoffs(-1,-1)
if needTimeAdjust:
obs.loadTimeAdjustmentFile(timeAdjustFileName)
timeMaskFileName = timeMaskFileNames[iObs]
print timeMaskFileName
if not os.path.exists(timeMaskFileName):
print 'Running hotpix for ',obs
hp.findHotPixels(obsFile=obs,outputFileName=timeMaskFileName,fwhm=np.inf,useLocalStdDev=True)
print "Flux cal/sky file pixel mask saved to %s"%(timeMaskFileName)
obs.loadHotPixCalFile(timeMaskFileName)
if self.verbose: print "Loaded hot pixel file %s"%timeMaskFileName
#get flat cal binning information since flux cal will need to match it
self.wvlBinEdges = self.fluxFile.flatCalFile.root.flatcal.wavelengthBins.read()
self.nWvlBins = self.fluxFile.flatWeights.shape[2]
self.binWidths = np.empty((self.nWvlBins),dtype=float)
self.binCenters = np.empty((self.nWvlBins),dtype=float)
for i in xrange(self.nWvlBins):
self.binWidths[i] = self.wvlBinEdges[i+1]-self.wvlBinEdges[i]
self.binCenters[i] = (self.wvlBinEdges[i]+(self.binWidths[i]/2.0))
if self.method=='relative':
print "Extracting ARCONS flux and sky spectra"
self.loadRelativeSpectrum()
print "Flux Spectrum loaded"
self.loadSkySpectrum()
print "Sky Spectrum loaded"
elif self.method=='absolute':
print "Extracting ARCONS point source spectrum"
self.loadAbsoluteSpectrum()
print "Loading standard spectrum"
try:
self.loadStdSpectrum(self.objectName)
except KeyError:
print "Invalid spectrum object name"
self.__del__()
sys.exit()
print "Generating sensitivity curve"
self.calculateFactors()
print "Sensitivity Curve calculated"
print "Writing fluxCal to file %s"%self.fluxCalFileName
self.writeFactors(self.fluxCalFileName)
if self.plots: self.makePlots()
print "Done"
def __del__(self):
try:
self.fluxFile.close()
self.calFile.close()
except AttributeError:#fluxFile was never defined
pass
def getDeadTimeCorrection(self, obs): #WRONG RIGHT NOW. NEEDS TO HAVE RAW COUNTS SUMMED, NOT CUBE WHICH EXCLUDES NOISE TAIL
if self.verbose: print "Making raw cube to get dead time correction"
cubeDict = obs.getSpectralCube(firstSec=self.startTime, integrationTime=self.intTime, weighted=False)
cube= np.array(cubeDict['cube'], dtype=np.double)
wvlBinEdges= cubeDict['wvlBinEdges']
effIntTime= cubeDict['effIntTime']
if self.verbose: print "median effective integration time = ", np.median(effIntTime)
nWvlBins=len(wvlBinEdges)-1
if self.verbose: print "cube shape ", np.shape(cube)
if self.verbose: print "effIntTime shape ", np.shape(effIntTime)
#add third dimension to effIntTime for broadcasting
effIntTime = np.reshape(effIntTime,np.shape(effIntTime)+(1,))
#put cube into counts/s in each pixel
cube /= effIntTime
#CALCULATE DEADTIME CORRECTION
#NEED TOTAL COUNTS PER SECOND FOR EACH PIXEL TO DO PROPERLY
#ASSUMES SAME CORRECTION FACTOR APPLIED FOR EACH WAVELENGTH, MEANING NO WL DEPENDANCE ON DEAD TIME EFFECT
DTCorr = np.zeros((np.shape(cube)[0],np.shape(cube)[1]),dtype=float)
for f in range(0,np.shape(cube)[2]):
#if self.verbose: print cube[:,:,f]
#if self.verbose: print '-----------------------'
DTCorr += cube[:,:,f]
#if self.verbose: print DTCorr
#if self.verbose: print '\n=====================\n'
#Correct for firmware dead time (100us in 2012 ARCONS firmware)
DTCorrNew=DTCorr/(1-DTCorr*self.deadtime)
CorrFactors = DTCorrNew/DTCorr #This is what the frames need to be multiplied by to get their true values
if self.verbose: print "Dead time correction factors: ", CorrFactors
#add third dimension to CorrFactors for broadcasting
CorrFactors = np.reshape(CorrFactors,np.shape(CorrFactors)+(1,))
return CorrFactors
def loadAbsoluteSpectrum(self):
'''
extract the ARCONS measured spectrum of the spectrophotometric standard by breaking data into spectral cube
and performing photometry (aper or psf) on each spectral frame
'''
if self.verbose:print "Making spectral cube"
cubeDict = self.fluxFile.getSpectralCube(firstSec=self.startTime, integrationTime=self.intTime, weighted=True, fluxWeighted=False)
cube= np.array(cubeDict['cube'], dtype=np.double)
effIntTime= cubeDict['effIntTime']
if self.verbose: print "median effective integration time in flux file cube = ", np.median(effIntTime)
if self.verbose: print "cube shape ", np.shape(cube)
if self.verbose: print "effIntTime shape ", np.shape(effIntTime)
#add third dimension to effIntTime for broadcasting
effIntTime = np.reshape(effIntTime,np.shape(effIntTime)+(1,))
#put cube into counts/s in each pixel
cube /= effIntTime
#get dead time correction factors
DTCorr = self.getDeadTimeCorrection(self.fluxFile)
cube*=DTCorr #cube now in units of counts/s and corrected for dead time
if self.plots and not 'figureHeader' in sys.modules:
if self.verbose: print "Saving spectral frames as movie..."
movieCube = np.zeros((self.nWvlBins,np.shape(cube)[0],np.shape(cube)[1]),dtype=float)
for i in xrange(self.nWvlBins):
movieCube[i,:,:] = cube[:,:,i]
makeMovie(movieCube,frameTitles=self.binCenters,cbar=True,outName=self.plotSavePath+'FluxCal_Cube_%s.gif'%(self.objectName), normMin=0, normMax=50)
if self.verbose: print "Movie saved in %s"%self.plotSavePath
LCplot=False #light curve pop-ups not compatible with FLuxCal plotting 2/18/15
#if self.photometry=='PSF': LCplot = False
LC = LightCurve.LightCurve(verbose=self.verbose, showPlot=LCplot)
self.fluxSpectrum=np.empty((self.nWvlBins),dtype=float)
self.skySpectrum=np.zeros((self.nWvlBins),dtype=float)
for i in xrange(self.nWvlBins):
frame = cube[:,:,i]
if self.verbose: print "%s photometry on frame %i of cube, central wvl = %f Angstroms"%(self.photometry,i,self.binCenters[i])
if self.photometry == 'aperture':
fDict = LC.performPhotometry(self.photometry,frame,[[self.centroidCol,self.centroidRow]],expTime=None,aper_radius = self.aperture, annulus_inner = self.annulusInner, annulus_outer = self.annulusOuter, interpolation="linear")
self.fluxSpectrum[i] = fDict['flux']
self.skySpectrum[i] = fDict['skyFlux']
print "Sky estimate = ", fDict['skyFlux']
else:
fDict = LC.performPhotometry(self.photometry,frame,[[self.centroidCol,self.centroidRow]],expTime=None,aper_radius = self.aperture)
self.fluxSpectrum[i] = fDict['flux']
self.fluxSpectrum=self.fluxSpectrum/self.binWidths/self.collectingArea #spectrum now in counts/s/Angs/cm^2
self.skySpectrum=self.skySpectrum/self.binWidths/self.collectingArea
return self.fluxSpectrum, self.skySpectrum
def loadRelativeSpectrum(self):
self.fluxSpectra = [[[] for i in xrange(self.nCol)] for j in xrange(self.nRow)]
self.fluxEffTime = [[[] for i in xrange(self.nCol)] for j in xrange(self.nRow)]
for iRow in xrange(self.nRow):
for iCol in xrange(self.nCol):
count = self.fluxFile.getPixelCount(iRow,iCol)
fluxDict = self.fluxFile.getPixelSpectrum(iRow,iCol,weighted=True,firstSec=0,integrationTime=-1)
self.fluxSpectra[iRow][iCol],self.fluxEffTime[iRow][iCol] = fluxDict['spectrum'],fluxDict['effIntTime']
self.fluxSpectra = np.array(self.fluxSpectra)
self.fluxEffTime = np.array(self.fluxEffTime)
DTCorr = self.getDeadTimeCorrection(self.fluxFile)
#print "Bin widths = ",self.binWidths
self.fluxSpectra = self.fluxSpectra/self.binWidths/self.fluxEffTime*DTCorr
self.fluxSpectrum = self.calculateMedian(self.fluxSpectra) #find median of subtracted spectra across whole array
return self.fluxSpectrum
def loadSkySpectrum(self):
self.skySpectra = [[[] for i in xrange(self.nCol)] for j in xrange(self.nRow)]
self.skyEffTime = [[[] for i in xrange(self.nCol)] for j in xrange(self.nRow)]
for iRow in xrange(self.nRow):
for iCol in xrange(self.nCol):
count = self.skyFile.getPixelCount(iRow,iCol)
skyDict = self.skyFile.getPixelSpectrum(iRow,iCol,weighted=True,firstSec=0,integrationTime=-1)
self.skySpectra[iRow][iCol],self.skyEffTime[iRow][iCol] = skyDict['spectrum'],skyDict['effIntTime']
self.skySpectra = np.array(self.skySpectra)
self.skyEffTime = np.array(self.skyEffTime)
DTCorr = self.getDeadTimeCorrection(self.skyFile)
self.skySpectra = self.skySpectra/self.binWidths/self.skyEffTime*DTCorr
self.skySpectrum = self.calculateMedian(self.skySpectra) #find median of subtracted spectra across whole array
return self.skySpectrum
def loadStdSpectrum(self, objectName="G158-100"):
#import the known spectrum of the calibrator and rebin to the histogram parameters given
#must be imported into array with dtype float so division later does not have error
std = MKIDStd.MKIDStd()
a = std.load(objectName)
a = std.countsToErgs(a) #convert std spectrum to ergs/s/Angs/cm^2 for BB fitting and cleaning
self.stdWvls = np.array(a[:,0])
self.stdFlux = np.array(a[:,1]) #std object spectrum in ergs/s/Angs/cm^2
if self.plots:
#create figure for plotting standard spectrum modifications
self.stdFig = plt.figure()
self.stdAx = self.stdFig.add_subplot(111)
plt.xlim(4000,11000)
plt.plot(self.stdWvls,self.stdFlux*1E15,linewidth=1,color='grey',alpha=0.75)
convX_rev,convY_rev = self.cleanSpectrum(self.stdWvls,self.stdFlux)
convX = convX_rev[::-1] #convolved spectrum comes back sorted backwards, from long wvls to low which screws up rebinning
convY = convY_rev[::-1]
#rebin cleaned spectrum to flat cal's wvlBinEdges
newa = rebin(convX,convY,self.wvlBinEdges)
rebinnedWvl = np.array(newa[:,0])
rebinnedFlux = np.array(newa[:,1])
if self.plots:
#plot final resampled spectrum
plt.plot(convX,convY*1E15,color='blue')
plt.step(rebinnedWvl,rebinnedFlux*1E15,color = 'black',where='mid')
plt.legend(['%s Spectrum'%self.objectName,'Blackbody Fit','Gaussian Convolved Spectrum','Rebinned Spectrum'],'upper right', numpoints=1)
plt.xlabel(ur"Wavelength (\r{A})")
plt.ylabel(ur"Flux (10$^{-15}$ ergs s$^{-1}$ cm$^{-2}$ \r{A}$^{-1}$)")
plt.ylim(1,5)
plt.savefig(self.plotSavePath+'FluxCal_StdSpectrum_%s.eps'%self.objectName,format='eps')
#convert standard spectrum back into counts/s/angstrom/cm^2
newa = std.ergsToCounts(newa)
self.binnedSpectrum = np.array(newa[:,1])
def cleanSpectrum(self,x,y):
##=============== BB Fit to extend spectrum beyond 11000 Angstroms ==================
fraction = 1.0/3.0
nirX = np.arange(int(x[(1.0-fraction)*len(x)]),20000)
T, nirY = fitBlackbody(x,y,fraction=fraction,newWvls=nirX,tempGuess=5600)
if self.plots: plt.plot(nirX,nirY*1E15,linestyle='--',linewidth=2, color="black",alpha=0.5)
extendedWvl = np.concatenate((x,nirX[nirX>max(x)]))
extendedFlux = np.concatenate((y,nirY[nirX>max(x)]))
##======= Gaussian convolution to smooth std spectrum to MKIDs median resolution ========
newX, newY = gaussianConvolution(extendedWvl,extendedFlux,xEnMin=0.005,xEnMax=6.0,xdE=0.001,fluxUnits = "lambda",r=self.r,plots=False)
return newX, newY
def calculateFactors(self):
"""
Calculate the sensitivity spectrum: the weighting factors that correct the flat calibrated spectra to the real spectra
For relative calibration:
First subtract sky spectrum from ARCONS observed spectrum. Then take median of this spectrum as it should be identical
across the array, assuming the flat cal has done its job. Then divide this into the known spectrum of the object.
For absolute calibration:
self.fluxSpectra already has sky subtraction included. Simply divide this spectrum into the known standard spectrum.
"""
self.subtractedSpectrum = self.fluxSpectrum - self.skySpectrum
self.subtractedSpectrum = np.array(self.subtractedSpectrum,dtype=float) #cast as floats so division does not fail later
if self.method=='relative':
normWvl = 5500 #Angstroms. Choose an arbitrary wvl to normalize the relative correction at
ind = np.where(self.wvlBinEdges >= normWvl)[0][0]-1
self.subtractedSpectrum = self.subtractedSpectrum/(self.subtractedSpectrum[ind]) #normalize
self.binnedSpectrum = self.binnedSpectrum/(self.binnedSpectrum[ind]) #normalize treated Std spectrum while we are at it
#Calculate FluxCal factors
self.fluxFactors = self.binnedSpectrum/self.subtractedSpectrum
#self.fluxFlags = np.zeros(np.shape(self.fluxFactors),dtype='int')
self.fluxFlags = np.empty(np.shape(self.fluxFactors),dtype='int')
self.fluxFlags.fill(pipelineFlags.fluxCal['good']) #Initialise flag array filled with 'good' flags. JvE 5/1/2013.
#set factors that will cause trouble to 1
#self.fluxFlags[self.fluxFactors == np.inf] = 1
self.fluxFlags[self.fluxFactors == np.inf] = pipelineFlags.fluxCal['infWeight'] #Modified to use flag dictionary - JvE 5/1/2013
self.fluxFactors[self.fluxFactors == np.inf]=1.0
self.fluxFlags[np.isnan(self.fluxFactors)] = pipelineFlags.fluxCal['nanWeight'] #Modified to use flag dictionary - JvE 5/1/2013
self.fluxFactors[np.isnan(self.fluxFactors)]=1.0
self.fluxFlags[self.fluxFactors <= 0]=pipelineFlags.fluxCal['LEzeroWeight'] #Modified to use flag dictionary - JvE 5/1/2013
self.fluxFactors[self.fluxFactors <= 0]=1.0
def calculateMedian(self, spectra):
spectra2d = np.reshape(spectra,[self.nRow*self.nCol,self.nWvlBins])
wvlMedian = np.empty(self.nWvlBins,dtype=float)
for iWvl in xrange(self.nWvlBins):
spectrum = spectra2d[:,iWvl]
goodSpectrum = spectrum[spectrum != 0]#dead pixels need to be taken out before calculating medians
wvlMedian[iWvl] = np.median(goodSpectrum)
return wvlMedian
def makePlots(self):
"""
Output all debugging plots of ARCONS sky and object spectra, known calibrator spectrum, and sensitivity curve
"""
scratchDir = os.getenv('MKID_PROC_PATH')
fluxDir = self.plotSavePath
fluxCalBase = 'FluxCal_%s'%self.objectName
plotFileName = fluxCalBase+".pdf"
fullFluxPlotFileName = os.path.join(fluxDir,plotFileName)
#uncomment to make some plots for the paper. Proper formatting Will also require figureheader to be imported and for movie making to be turned off
self.paperFig = plt.figure()
self.paperAx = self.paperFig.add_subplot(111)
plt.xlim(4000,11000)
plt.plot(self.binCenters,self.fluxFactors,linewidth=3,color='black')
plt.xlabel(ur"Wavelength (\r{A})")
plt.ylabel(ur"Spectral Calibration Curve")
plt.ylim(0,150)
plt.savefig(self.plotSavePath+'FluxCal_Sensitivity_%s.eps'%self.objectName,format='eps')
#save throughput as a .npz file that other code uses when making paper plots
np.savez(self.plotSavePath+'%s_%s_throughput.npz'%(self.objectName.strip(),self.fluxTstamp),throughput=1.0/self.fluxFactors,wvls=self.binCenters)
pp = PdfPages(fullFluxPlotFileName)
#plt.rcParams['font.size'] = 2
wvls = self.binCenters
plt.figure()
ax1 = plt.subplot(111)
ax1.set_title('ARCONS median flat cal\'d flux in counts')
plt.plot(wvls,self.fluxSpectrum)
pp.savefig()
plt.figure()
ax2 = plt.subplot(111)
ax2.set_title('ARCONS median flat cal\'d sky in counts')
plt.plot(wvls,self.skySpectrum)
pp.savefig()
plt.figure()
ax3 = plt.subplot(111)
ax3.set_title('Flux data minus sky in counts')
plt.plot(wvls,self.subtractedSpectrum)
pp.savefig()
plt.figure()
ax4 = plt.subplot(111)
ax4.set_title('Std Spectrum of %s'%(self.objectName))
plt.plot(self.stdWvls,self.stdFlux)
pp.savefig()
plt.figure()
ax5 = plt.subplot(111)
ax5.set_title('Binned Std Spectrum')
plt.plot(wvls,self.binnedSpectrum)
pp.savefig()
plt.figure()
ax6 = plt.subplot(111)
ax6.set_title('Median Sensitivity Spectrum')
ax6.set_xlim((3000,13000))
#ax6.set_ylim((0,5))
plt.plot(wvls,self.fluxFactors)
pp.savefig()
plt.figure()
ax7 = plt.subplot(111)
ax7.set_title('1/Sensitivity (Throughput)')
ax7.set_xlim((4000,11000))
plt.plot(wvls,1.0/self.fluxFactors)
pp.savefig()
plt.figure()
ax8 = plt.subplot(111)
ax8.set_title('Flux Cal\'d ARCONS Spectrum of Std')
plt.plot(wvls,self.fluxFactors*self.subtractedSpectrum)
pp.savefig()
pp.close()
print "Saved Flux Cal plots to %s"%(fullFluxPlotFileName)
def writeFactors(self,fluxCalFileName):
"""
Write flux cal weights to h5 file
"""
if os.path.isabs(fluxCalFileName) == True:
fullFluxCalFileName = fluxCalFileName
else:
scratchDir = os.getenv('MKID_PROC_PATH')
fluxDir = os.path.join(scratchDir,'fluxCalSolnFiles')
fullFluxCalFileName = os.path.join(fluxDir,fluxCalFileName)
try:
fluxCalFile = tables.openFile(fullFluxCalFileName,mode='w')
except:
print 'Error: Couldn\'t create flux cal file, ',fullFluxCalFileName
return
calgroup = fluxCalFile.createGroup(fluxCalFile.root,'fluxcal','Table of flux calibration weights by wavelength')
caltable = tables.Array(calgroup,'weights',object=self.fluxFactors,title='Flux calibration Weights indexed by wavelengthBin')
flagtable = tables.Array(calgroup,'flags',object=self.fluxFlags,title='Flux cal flags indexed by wavelengthBin. 0 is Good')
bintable = tables.Array(calgroup,'wavelengthBins',object=self.wvlBinEdges,title='Wavelength bin edges corresponding to third dimension of weights array')
fluxCalFile.flush()
fluxCalFile.close()
print "Finished Flux Cal, written to %s"%(fullFluxCalFileName)
def cleanSpectrum_old(self,x,y,objectName):
'''
function to take high resolution spectrum of standard star, extend IR coverage with
an exponential tail, then rebin down to ARCONS resolution. This function has since been
deprecated with the current cleanSpectrum which uses a BB fit to extend IR coverage,
and does the rebinning using a gaussian convolution. This is left in for reference.
'''
#locations and widths of absorption features in Angstroms
#features = [3890,3970,4099,4340,4860,6564,6883,7619]
#widths = [50,50,50,50,50,50,50,50]
#for i in xrange(len(features)):
# #check for absorption feature in std spectrum
# ind = np.where((x<(features[i]+15)) & (x>(features[i]-15)))[0]
# if len(ind)!=0:
# ind = ind[len(ind)/2]
# #if feature is found (flux is higher on both sides of the specified wavelength where the feature should be)
# if y[ind]<y[ind+1] and y[ind]<y[ind-1]:
# #cut out width[i] around feature[i]
# inds = np.where((x >= features[i]+widths[i]) | (x <= features[i]-widths[i]))
# x = x[inds]
# y = y[inds]
#fit a tail to the end of the spectrum to interpolate out to desired wavelength in angstroms
fraction = 3.0/4.0
newx = np.arange(int(x[fraction*len(x)]),20000)
slopeguess = (np.log(y[-1])-np.log(y[fraction*len(x)]))/(x[-1]-x[fraction*len(x)])
print "Guess at exponential slope is %f"%(slopeguess)
guess_a, guess_b, guess_c = float(y[fraction*len(x)]), x[fraction*len(x)], slopeguess
guess = [guess_a, guess_b, guess_c]
fitx = x[fraction*len(x):]
fity = y[fraction*len(x):]
exp_decay = lambda fx, A, x0, t: A * np.exp((fx-x0) * t)
params, cov = curve_fit(exp_decay, fitx, fity, p0=guess, maxfev=2000)
A, x0, t= params
print "A = %s\nx0 = %s\nt = %s\n"%(A, x0, t)
best_fit = lambda fx: A * np.exp((fx-x0)*t)
calcx = np.array(newx,dtype=float)
newy = best_fit(calcx)
#func = interpolate.splrep(x[fration*len(x):],y[fraction*len(x):],s=smooth)
#newx = np.arange(int(x[fraction*len(x)]),self.wvlBinEdges[-1])
#newy = interpolate.splev(newx,func)
wl = np.concatenate((x,newx[newx>max(x)]))
flux = np.concatenate((y,newy[newx>max(x)]))
#new method, rebin data to grid of wavelengths generated from a grid of evenly spaced energy bins
#R=7.0 at 4500
#R=E/dE -> dE = R/E
dE = 0.3936 #eV
start = 1000 #Angs
stop = 20000 #Angs
enBins = ObsFile.makeWvlBins(dE,start,stop)
rebinned = rebin(wl,flux,enBins)
re_wl = rebinned[:,0]
re_flux = rebinned[:,1]
#plt.plot(re_wl,re_flux,color='r')
re_wl = re_wl[np.isnan(re_flux)==False]
re_flux = re_flux[np.isnan(re_flux)==False]
start1 = self.wvlBinEdges[0]
stop1 = self.wvlBinEdges[-1]
#regrid downsampled data
new_wl = np.arange(start1,stop1)
#print re_wl
#print re_flux
#print new_wl
#weight=1.0/(re_flux)**(2/1.00)
print len(re_flux)
weight = np.ones(len(re_flux))
#decrease weights near peak
ind = np.where(re_flux == max(re_flux))[0]
weight[ind] = 0.3
for p in [1,2,3]:
if p==1:
wt = 0.3
elif p==2:
wt = 0.6
elif p==3:
wt = 0.7
try:
weight[ind+p] = wt
except IndexError:
pass
try:
if ind-p >= 0:
weight[ind-p] = wt
except IndexError:
pass
weight[-4:] = 1.0
#weight = [0.7,1,0.3,0.3,0.5,0.7,1,1,1]
#print len(weight)
#weight = re_flux/min(re_flux)
#weight = 1.0/weight
#weight = weight/max(weight)
#print weight
f = interpolate.splrep(re_wl,re_flux,w=weight,k=3,s=max(re_flux)**1.71)
new_flux = interpolate.splev(new_wl,f,der=0)
return new_wl, new_flux
def main():
"""
params = []
paramfile = sys.argv[1]
f = open(paramfile,'r')
for line in f:
params.append(line)
f.close()
datadir = params[0].split('=')[1].strip()
flatdir = params[1].split('=')[1].strip()
fluxdir = params[2].split('=')[1].strip()
wvldir = params[3].split('=')[1].strip()
obsfile = params[4].split('=')[1].strip()
skyfile = params[5].split('=')[1].strip()
flatfile = params[6].split('=')[1].strip()
fluxfile = params[7].split('=')[1].strip()
wvlfile = params[8].split('=')[1].strip()
objectName = params[9].split('=')[1].strip()
fluxCalObject = params[10].split('=')[1].strip()
obsFileName = os.path.join(datadir, obsfile)
skyFileName = os.path.join(datadir, skyfile)
wvlCalFileName = os.path.join(wvldir, wvlfile)
flatCalFileName = os.path.join(flatdir, flatfile)
fluxCalFileName = os.path.join(fluxdir, fluxfile)
"""
if len(sys.argv) >2:
fileNum = str(sys.argv[2])
else:
fileNum = '0'
#science object parameter file
params = []
paramfile = sys.argv[1]
f = open(paramfile,'r')
for line in f:
params.append(line)
f.close()
datadir = params[0].split('=')[1].strip()
flatdir = params[1].split('=')[1].strip()
wvldir = params[2].split('=')[1].strip()
obsfile = params[3].split('=')[1].strip()
skyfile = params[4].split('=')[1].strip()
flatfile = params[5].split('=')[1].strip()
wvlfile = params[6].split('=')[1].strip()
objectName = params[9].split('=')[1].strip()
wvldir = "/Scratch/waveCalSolnFiles/oldbox_numbers/20121206"
if len(params)>10:
xpix = int(params[10].split('=')[1].strip())
ypix = int(params[11].split('=')[1].strip())
apertureRadius = int(params[12].split('=')[1].strip())
startTime = int(params[13].split('=')[1].strip())
intTime =int(params[14].split('=')[1].strip())
obsFileName = os.path.join(datadir, obsfile)
skyFileName = os.path.join(datadir, skyfile)
wvlCalFileName = os.path.join(wvldir, wvlfile)
flatCalFileName = os.path.join(flatdir, flatfile)
obs = ObsFile(obsFileName)
obs.loadWvlCalFile(wvlCalFileName)
obs.loadFlatCalFile(flatCalFileName)
print "analyzing file %s"%(obsFileName)
print "loaded data file and calibrations\n---------------------\n"
nRow = obs.nRow
nCol = obs.nCol
obsTime = obs.getFromHeader("exptime")
#wvlBinEdges,obsSpectra = loadSpectra(obs,nCol,nRow)
#nWvlBins=len(wvlBinEdges)-1
#print np.shape(obsSpectra)
#print nRow
#print nCol
#print nWvlBins
#load/generate hot pixel mask file
HotPixFile = getTimeMaskFileName(obsFileName)
if not os.path.exists(HotPixFile):
hp.findHotPixels(obsFileName,HotPixFile)
print "Flux file pixel mask saved to %s"%(HotPixFile)
obs.loadHotPixCalFile(HotPixFile)
print "Hot pixel mask loaded %s"%(HotPixFile)
#######
#EVERYTHING BEFORE HERE IS STANDARD FILE/CALFILE LOADING
startWvl = 3000
#stopWvl = 7000 #for V-band
stopWvl = 9000 #for R-band
print "Making spectral cube"
#for pg0220 first sec should be 80 since object is moving around before this
#for pg0220A first sec should be 70, integration time is 140
#for landolt 9542 first sec should be 20, int time is -1
cubeDict = obs.getSpectralCube(firstSec=startTime, integrationTime=intTime, wvlStart = startWvl, wvlStop = stopWvl, wvlBinEdges = [startWvl,stopWvl], weighted=False)
cube= np.array(cubeDict['cube'], dtype=np.double)
wvlBinEdges= cubeDict['wvlBinEdges']
effIntTime= cubeDict['effIntTime']
print "median effective integration time = ", np.median(effIntTime)
nWvlBins=len(wvlBinEdges)-1
print "cube shape ", np.shape(cube)
print "effIntTime shape ", np.shape(effIntTime)
#add third dimension to effIntTime for broadcasting
effIntTime = np.reshape(effIntTime,np.shape(effIntTime)+(1,))
cube /= effIntTime #put cube into counts/s
#calculate midpoints of wvl bins for plotting
wvls = np.empty((nWvlBins),dtype=float)
for n in xrange(nWvlBins):
binsize=wvlBinEdges[n+1]-wvlBinEdges[n]
wvls[n] = (wvlBinEdges[n]+(binsize/2.0))
print "wvls ",wvls
#reshape cube for makeMovie
movieCube = np.zeros((nWvlBins,np.shape(cube)[0],np.shape(cube)[1]),dtype=float)
for i in xrange(nWvlBins):
movieCube[i,:,:] = cube[:,:,i]
#show individual frames as they are made to debug
#plt.matshow(movieCube[i],vmin = 0, vmax = 100)
#plt.show()
print "movieCube shape ", np.shape(movieCube)
print "wvls shape ", np.shape(wvls)
#print cube
#print "--------------------------"
#print movieCube
print "adding frames with wvl below ", stopWvl
finalframe = np.zeros((1,np.shape(movieCube)[1],np.shape(movieCube)[2]))
for f in xrange(len(wvls[wvls<stopWvl])):
print wvls[f]
finalframe[0]+=movieCube[f]
plt.matshow(movieCube[f],vmin=0,vmax = 40)
plt.show()
movieCube = finalframe
np.savez('%s_%s.npz'%(objectName,fileNum),stack=movieCube,wvls=wvls)
print "Saved frame to .npz file"
plt.matshow(movieCube[0],vmin=0,vmax = 40)
plt.show()
def __init__(self,paramFile,plots=False,verbose=False):
"""
Opens flux file, prepares standard spectrum, and calculates flux factors for the file.
Method is provided in param file. If 'relative' is selected, an obs file with standard star defocused over
the entire array is expected, with accompanying sky file to do sky subtraction.
If any other method is provided, 'absolute' will be done by default, wherein a point source is assumed
to be present. The obs file is then broken into spectral frames with photometry (psf or aper) performed
on each frame to generate the ARCONS observed spectrum.
"""
self.verbose=verbose
self.plots = plots
self.params = readDict()
self.params.read_from_file(paramFile)
run = self.params['run']
sunsetDate = self.params['fluxSunsetLocalDate']
self.fluxTstamp = self.params['fluxTimestamp']
skyTstamp = self.params['skyTimestamp']
wvlSunsetDate = self.params['wvlCalSunsetLocalDate']
wvlTimestamp = self.params['wvlCalTimestamp']
flatCalFileName = self.params['flatCalFileName']
needTimeAdjust = self.params['needTimeAdjust']
self.deadtime = float(self.params['deadtime']) #from firmware pulse detection
self.timeSpacingCut = self.params['timeSpacingCut']
bLoadBeammap = self.params.get('bLoadBeammap',False)
self.method = self.params['method']
self.objectName = self.params['object']
self.r = float(self.params['energyResolution'])
self.photometry = self.params['photometry']
self.centroidRow = self.params['centroidRow']
self.centroidCol = self.params['centroidCol']
self.aperture = self.params['apertureRad']
self.annulusInner = self.params['annulusInner']
self.annulusOuter = self.params['annulusOuter']
self.collectingArea = self.params['collectingArea']
self.startTime = self.params['startTime']
self.intTime = self.params['integrationTime']
fluxFN = FileName(run=run,date=sunsetDate,tstamp=self.fluxTstamp)
self.fluxFileName = fluxFN.obs()
self.fluxFile = ObsFile(self.fluxFileName)
if self.plots:
self.plotSavePath = os.environ['MKID_PROC_PATH']+os.sep+'fluxCalSolnFiles'+os.sep+run+os.sep+sunsetDate+os.sep+'plots'+os.sep
if not os.path.exists(self.plotSavePath): os.mkdir(self.plotSavePath)
if self.verbose: print "Created directory %s"%self.plotSavePath
obsFNs = [fluxFN]
self.obsList = [self.fluxFile]
if self.startTime in ['',None]: self.startTime=0
if self.intTime in ['',None]: self.intTime=-1
if self.method=="relative":
try:
print "performing Relative Flux Calibration"
skyFN = FileName(run=run,date=sunsetDate,tstamp=skyTstamp)
self.skyFileName = skyFN.obs()
self.skyFile = ObsFile(self.skyFileName)
obsFNs.append(skyFN)
self.obsList.append(self.skyFile)
except:
print "For relative flux calibration a sky file must be provided in param file"
self.__del__()
else:
self.method='absolute'
print "performing Absolute Flux Calibration"
if self.photometry not in ['aperture','PSF']: self.photometry='PSF' #default to PSF fitting if no valid photometry selected
timeMaskFileNames = [fn.timeMask() for fn in obsFNs]
timeAdjustFileName = FileName(run=run).timeAdjustments()
#make filename for output fluxCalSoln file
self.fluxCalFileName = FileName(run=run,date=sunsetDate,tstamp=self.fluxTstamp).fluxSoln()
print "Creating flux cal: %s"%self.fluxCalFileName
if wvlSunsetDate != '':
wvlCalFileName = FileName(run=run,date=wvlSunsetDate,tstamp=wvlTimestamp).calSoln()
if flatCalFileName =='':
flatCalFileName=FileName(obsFile=self.fluxFile).flatSoln()
#load cal files for flux file and, if necessary, sky file
for iObs,obs in enumerate(self.obsList):
if bLoadBeammap:
print 'loading beammap',os.environ['MKID_BEAMMAP_PATH']
obs.loadBeammapFile(os.environ['MKID_BEAMMAP_PATH'])
if wvlSunsetDate != '':
obs.loadWvlCalFile(wvlCalFileName)
else:
obs.loadBestWvlCalFile()
obs.loadFlatCalFile(flatCalFileName)
obs.setWvlCutoffs(-1,-1)
if needTimeAdjust:
obs.loadTimeAdjustmentFile(timeAdjustFileName)
timeMaskFileName = timeMaskFileNames[iObs]
print timeMaskFileName
if not os.path.exists(timeMaskFileName):
print 'Running hotpix for ',obs
hp.findHotPixels(obsFile=obs,outputFileName=timeMaskFileName,fwhm=np.inf,useLocalStdDev=True)
print "Flux cal/sky file pixel mask saved to %s"%(timeMaskFileName)
obs.loadHotPixCalFile(timeMaskFileName)
if self.verbose: print "Loaded hot pixel file %s"%timeMaskFileName
#get flat cal binning information since flux cal will need to match it
self.wvlBinEdges = self.fluxFile.flatCalFile.root.flatcal.wavelengthBins.read()
self.nWvlBins = self.fluxFile.flatWeights.shape[2]
self.binWidths = np.empty((self.nWvlBins),dtype=float)
self.binCenters = np.empty((self.nWvlBins),dtype=float)
for i in xrange(self.nWvlBins):
self.binWidths[i] = self.wvlBinEdges[i+1]-self.wvlBinEdges[i]
self.binCenters[i] = (self.wvlBinEdges[i]+(self.binWidths[i]/2.0))
if self.method=='relative':
print "Extracting ARCONS flux and sky spectra"
self.loadRelativeSpectrum()
print "Flux Spectrum loaded"
self.loadSkySpectrum()
print "Sky Spectrum loaded"
elif self.method=='absolute':
print "Extracting ARCONS point source spectrum"
self.loadAbsoluteSpectrum()
print "Loading standard spectrum"
try:
self.loadStdSpectrum(self.objectName)
except KeyError:
print "Invalid spectrum object name"
self.__del__()
sys.exit()
print "Generating sensitivity curve"
self.calculateFactors()
print "Sensitivity Curve calculated"
print "Writing fluxCal to file %s"%self.fluxCalFileName
self.writeFactors(self.fluxCalFileName)
if self.plots: self.makePlots()
print "Done"
for seq in seqs:
NumFiles.append(len(seq))
NumFiles = sum(NumFiles)
print (NumFiles)*expTime/integrationTime,'frames to make'
for iSeq in range(len(seqs)):
timestampList = timestampLists[iSeq]
print timestampList
wfn = wvlCalFilenames[iSeq]
ffn = flatCalFilenames[iSeq]
sunsetDate = sunsetDates[iSeq]
for i,ts in enumerate(timestampList):
print 'loading',ts
obsFn = FileName(run=run,date=sunsetDate,tstamp=ts).obs()
ob = ObsFile(obsFn)
ob.loadTimeAdjustmentFile(FileName(run=run).timeAdjustments())
index1 = obsFn.find('_')
hotPixFn = '/Scratch/timeMasks/timeMask' + obsFn[index1:]
if not os.path.exists(hotPixFn):
hp.findHotPixels(obsFn,hotPixFn)
print "Flux file pixel mask saved to %s"%(hotPixFn)
ob.loadHotPixCalFile(hotPixFn,switchOnMask=True)
ob.loadWvlCalFile(wfn)
ob.loadFlatCalFile(ffn)
ob.setWvlCutoffs(wvlLowerCutoff,wvlUpperCutoff)
bad_solution_mask=np.zeros((46,44))
bad_count=0;
for y in range(46):
for x in range(44):
stride = 10
threshold = 100
nAboveThreshold = 0
npList = []
sigList = []
run = 'PAL2012'
sundownDate = '20121211'
obsDate = '20121212'
for seq in seq5:
inFile = open("cosmicTimeList-%s.pkl"%(seq),"rb")
cosmicTimeList = pickle.load(inFile)
binContents = pickle.load(inFile)
cfn = "cosmicMax-%s.h5"%seq
intervals = ObsFile.readCosmicIntervalFromFile(cfn)
for interval in intervals:
print "interval=",interval
fn = FileName(run, sundownDate,obsDate+"-"+seq)
obsFile = ObsFile(fn.obs())
obsFile.loadTimeAdjustmentFile(fn.timeAdjustments())
i0=interval[0]
i1=interval[1]
intervalTime = i1-i0
dt = intervalTime/2
beginTime = max(0,i0-0.000200)
endTime = beginTime + 0.001
integrationTime = endTime-beginTime
nBins = int(np.round(obsFile.ticksPerSec*(endTime-beginTime)+1))
timeHgValues = np.zeros(nBins, dtype=np.int64)
ymax = sys.float_info.max/100.0
def cleanSpectrum(x,y,objectName, wvlBinEdges):
#locations and widths of absorption features in Angstroms
#features = [3890,3970,4099,4340,4860,6564,6883,7619]
#widths = [50,50,50,50,50,50,50,50]
#for i in xrange(len(features)):
# #check for absorption feature in std spectrum
# ind = np.where((x<(features[i]+15)) & (x>(features[i]-15)))[0]
# if len(ind)!=0:
# ind = ind[len(ind)/2]
# #if feature is found (flux is higher on both sides of the specified wavelength where the feature should be)
# if y[ind]<y[ind+1] and y[ind]<y[ind-1]:
# #cut out width[i] around feature[i]
# inds = np.where((x >= features[i]+widths[i]) | (x <= features[i]-widths[i]))
# x = x[inds]
# y = y[inds]
#fit a tail to the end of the spectrum to interpolate out to desired wavelength in angstroms
fraction = 2.1/3
newx = np.arange(int(x[fraction*len(x)]),20000)
slopeguess = (np.log(y[-1])-np.log(y[fraction*len(x)]))/(x[-1]-x[fraction*len(x)])
print "Guess at exponential slope is %f"%(slopeguess)
guess_a, guess_b, guess_c = float(y[fraction*len(x)]), x[fraction*len(x)], slopeguess
guess = [guess_a, guess_b, guess_c]
fitx = x[fraction*len(x):]
fity = y[fraction*len(x):]
exp_decay = lambda fx, A, x0, t: A * np.exp((fx-x0) * t)
params, cov = curve_fit(exp_decay, fitx, fity, p0=guess, maxfev=2000)
A, x0, t= params
print "A = %s\nx0 = %s\nt = %s\n"%(A, x0, t)
best_fit = lambda fx: A * np.exp((fx-x0)*t)
calcx = np.array(newx,dtype=float)
newy = best_fit(calcx)
#func = interpolate.splrep(x[fration*len(x):],y[fraction*len(x):],s=smooth)
#newx = np.arange(int(x[fraction*len(x)]),self.wvlBinEdges[-1])
#newy = interpolate.splev(newx,func)
wl = np.concatenate((x,newx[newx>max(x)]))
flux = np.concatenate((y,newy[newx>max(x)]))
#new method, rebin data to grid of wavelengths generated from a grid of evenly spaced energy bins
#R=7.0 at 4500
#R=E/dE -> dE = R/E
dE = 0.3936 #eV
start = 1000 #Angs
stop = 25000 #Angs
enBins = ObsFile.makeWvlBins(dE,start,stop)
rebinned = rebin(wl,flux,enBins)
re_wl = rebinned[:,0]
re_flux = rebinned[:,1]
#plt.plot(re_wl,re_flux,color='r')
re_wl = re_wl[np.isnan(re_flux)==False]
re_flux = re_flux[np.isnan(re_flux)==False]
start1 = wvlBinEdges[0]
stop1 = wvlBinEdges[-1]
#regrid downsampled data
new_wl = np.arange(start1,stop1)
#print re_wl
#print re_flux
#print new_wl
#weight=1.0/(re_flux)**(2/1.00)
print len(re_flux)
weight = np.ones(len(re_flux))
#decrease weights near peak
ind = np.where(re_flux == max(re_flux))[0]
weight[ind] = 0.3
for p in [1,2,3]:
if p==1:
wt = 0.3
elif p==2:
wt = 0.6
elif p==3:
wt = 0.7
try:
weight[ind+p] = wt
except IndexError:
pass
try:
if ind-p >= 0:
weight[ind-p] = wt
except IndexError:
pass
#change weights to set how tightly fit must match data points
#weight[-4:] = 1.0
weight = [0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7]
#print len(weight)
#weight = re_flux/min(re_flux)
#weight = 1.0/weight
#weight = weight/max(weight)
print weight
f = interpolate.splrep(re_wl,re_flux,w=weight,k=3,s=max(re_flux)**200)
new_flux = interpolate.splev(new_wl,f,der=0)
return new_wl, new_flux
class Cosmic:
def __init__(self, fn, beginTime=0, endTime='exptime',
nBinsPerSec=10, flashMergeTime=1.0,
applyCosmicMask = False,
loggingLevel=logging.CRITICAL,
loggingHandler=logging.StreamHandler()):
"""
Opens fileName in MKID_RAW_PATH, sets roachList
endTime is exclusive
"""
self.logger = logging.getLogger("cosmic")
self.logger.setLevel(loggingLevel)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
loggingHandler.setFormatter(formatter)
self.logger.addHandler(loggingHandler)
self.logger.info("Cosmic: begin init for obsFile=%s"%fn.obs())
self.fn = fn
self.fileName = fn.obs();
self.file = ObsFile(self.fileName)
# apply Matt's time fix
timeAdjustments = self.fn.timeAdjustments()
if os.path.exists(timeAdjustments):
self.file.loadTimeAdjustmentFile(timeAdjustments)
# apply Julian's time masks
timeMaskFile = self.fn.timeMask();
if os.path.exists(timeMaskFile):
self.file.loadHotPixCalFile(timeMaskFile,switchOnMask=True)
# apply standard mask
if applyCosmicMask:
self.file.loadStandardCosmicMask()
self._setRoachList()
self._setAllSecs()
self.exptime = self.file.getFromHeader('exptime')
if endTime =='exptime':
self.endTime = float(self.exptime)
else:
self.endTime = float(endTime)
if ( (self.endTime > self.exptime) or (endTime < 0)):
raise RuntimeError("bad endTime: endTime=%s exptime=%s" % \
(str(endTime),str(self.exptime)))
self.beginTime = float(beginTime)
self.timeHgs = "none"
self.nBinsPerSec = nBinsPerSec
self.flashMergeTime = flashMergeTime
self.times = \
np.arange(self.beginTime, self.endTime, 1.0/self.nBinsPerSec)
# for measuring flashes, indexed by roach name
self.rMean = {} # mean from meanclip
self.rSigma = {} # sigma from meanclip
self.rNSurvived = {} # number of survivors from meanclip
self.rNormed = {} # (value-mean)/sigma
self.flashInterval = {}
self.logger.info("Cosmic: end init: beginTime=%s endTime=%s"%(str(self.beginTime),str(self.endTime)))
def __del__(self):
"""
Close any open files
"""
# print "now in Cosmic.__del__ for ",self.fileName
try:
del self.file
except:
pass
def _setRoachList(self):
self.roachList = []
for row in self.file.beamImage:
for name in row:
roachName = name.split("/")[1]
if roachName not in self.roachList:
self.roachList.append(roachName)
self.roachList.sort()
def _setAllSecs(self):
nRow = self.file.nRow
nCol = self.file.nCol
self.allSecs = \
dict( ((i,j),None) for i in range(nRow) for j in range(nCol))
for iRow in np.arange(nRow):
for iCol in np.arange(nCol):
self.allSecs[iRow,iCol] = \
self.file.file.getNode(self.file.beamImage[iRow][iCol])
def nPhoton(self, beginTime=0, endTime='expTime'):
"""
trivial example of counting the number of photons in a file
"""
nPhoton = 0
for iRow in range(self.file.nRow):
for iCol in range(self.file.nCol):
for iSec in range(self.beginTime, self.endTime):
sec = self.allSecs[iRow,iCol][iSec]
nPhoton += len(sec)
return nPhoton
def findFlashes(self, clipsig=3.0, maxiter=5,\
converge_num=0.05, verbose=0, flashsig=6):
"""
find flashes by looking at the time histograms. Calculate the
mean,sigma using meanclip and the parameters clipsig, maxiter, converge_num
A time bin has a flash if the normalized signal (measured-mean)/sigma
is larger than flashsig.
"""
# make the blank data structures
if self.timeHgs == "none":
self.makeTimeHgs()
self.flashInterval["all"] = []
# find the flashes in each roach
for roach in self.roachList:
self.rMean[roach],self.rSigma[roach],self.rNSurvived[roach] = \
meanclip.meanclip(\
np.array(self.timeHgs[roach]), clipsig, maxiter, converge_num,\
verbose)
self.rNormed[roach] = \
(self.timeHgs[roach]-self.rMean[roach])/self.rSigma[roach]
self.flashInterval[roach] = interval()
prev = 0
a = self.rNormed[roach]
for i in range(len(a)):
this = a[i] > flashsig
if (this != prev):
if (this):
iBegin = i
else:
self.flashInterval[roach] = \
self.flashInterval[roach] | interval[iBegin,i]
prev = this
if (prev):
self.flashInterval[roach] = \
self.flashInterval[roach] | interval[iBegin,i]
# union of all flashes
self.flashInterval["all"] = interval()
for roach in self.roachList:
self.flashInterval["all"] = \
self.flashInterval["all"] | self.flashInterval[roach]
# look for gaps smaller than self.flashMergeTime and plug them
dMax = self.nBinsPerSec*self.flashMergeTime
extrema = self.flashInterval["all"].extrema
for i in range(len(extrema)/2-1):
i0 = extrema[2*i+1][0]
i1 = extrema[2*(i+1)][0]
if (i1-i0) <= dMax:
t0 = self.beginTime + float(i0)/self.nBinsPerSec
t1 = self.beginTime + float(i1)/self.nBinsPerSec
self.flashInterval["all"] = \
self.flashInterval["all"] | interval[i0,i1]
# convert to ticks since the beginning of the data file
rlAll = list(self.roachList)
rlAll.append("all")
ticksPerSecond = int(1.0/self.file.tickDuration)
offset = self.beginTime*ticksPerSecond
scale = 1.0/(self.file.tickDuration*self.nBinsPerSec)
for roach in rlAll:
self.flashInterval[roach] = offset+scale*self.flashInterval[roach]
def writeFlashesToHdf5(self,overwrite=1):
"""
write intervals with flashes to the timeMask file
"""
# get the output file name, and make the directory if you need to
cmFileName = self.fn.cosmicMask()
(cmDir,name) = os.path.split(cmFileName)
if not os.path.exists(cmDir):
os.makedirs(cmDir)
# write parameters used to find flashes
h5f = tables.openFile(cmFileName, 'w')
fnode = filenode.newNode(h5f, where='/', name='timeMaskHdr')
fnode.attrs.beginTime = self.beginTime
fnode.attrs.endTime = self.endTime
fnode.attrs.nBinsPerSec = self.nBinsPerSec
fnode.attrs.flashMergeTime = self.flashMergeTime
fnode.close();
# write the times where flashes are located
tbl = h5f.createTable('/','timeMask',TimeMask.TimeMask,"Time Mask")
rlAll = list(self.roachList)
rlAll.append("all")
for roach in rlAll:
extrema = self.flashInterval[roach].extrema
for i in range(len(extrema)/2):
row = tbl.row
row['tBegin'] = int(extrema[2*i][0])
row['tEnd'] = int(extrema[2*i+1][0])
if (roach == "all"):
reason = "Merged Flash"
else:
reason = "Flash in %s" % roach
row['reason'] = TimeMask.timeMaskReason[reason]
row.append()
tbl.flush()
tbl.close()
h5f.close()
def makeTimeHgs(self):
"""
Fill in the timeHgs variable
This is a dictionary, indexed by the roach name, of the time histograms
"""
self.timeHgs = {}
for iSec in range(self.beginTime, self.endTime):
self.logger.info("in makeTimeHgs iSec=%4d / %4d" % (iSec,self.endTime))
hgsThisSec = {}
for iRow in range(self.file.nRow):
for iCol in range(self.file.nCol):
sec = self.allSecs[iRow,iCol][iSec]
if len(sec) > 0:
times = sec & self.file.timestampMask
hg,edges = \
np.histogram(times,bins=self.nBinsPerSec, \
range=(0,1.0/self.file.tickDuration))
roachName = \
self.file.beamImage[iRow][iCol].split("/")[1]
if not hgsThisSec.has_key(roachName):
hgsThisSec[roachName] = \
np.zeros(self.nBinsPerSec,dtype=np.int64)
hgsThisSec[roachName] += hg
for roachName in hgsThisSec.keys():
if not self.timeHgs.has_key(roachName):
self.timeHgs[roachName] = []
self.timeHgs[roachName] += list(hgsThisSec[roachName])
def plotTimeHgs(self):
"""
Plot the time HGS in plt structure, with legend
"""
plt.clf()
plt.figure(1)
keys = self.timeHgs.keys()
keys.sort()
plt.subplot(211)
for roachName in keys:
hg = self.timeHgs[roachName]
plt.plot(self.times, hg,label=roachName)
plt.legend()
dt = 1.0/self.nBinsPerSec
plt.ylabel("photons/%.2f sec" % dt)
plt.title("Cosmic timeHgs "+ self.fileName)
plt.subplot(212)
for roachName in keys:
plt.plot(self.times, \
self.rNormed[roachName],label=roachName)
plt.xlabel("time (sec)")
plt.ylim(-23,30)
dt = 1.0/self.nBinsPerSec
plt.ylabel("normalized photons/%.2f sec" % dt)
y = -5
x0 = self.beginTime + 0.1*(self.endTime-self.beginTime)
xmax = plt.xlim()[1]
rlAll = list(self.roachList)
rlAll.append("all")
for roach in rlAll:
print "plot for roach=",roach
plt.plot([x0,xmax],[y,y], linestyle=":", color="gray")
plt.text(x0, y, roach, fontsize=8, va="center")
extrema = self.flashInterval[roach].extrema
for i in range(len(extrema)/2):
t0 = (extrema[2*i][0] - 0.5)*self.file.tickDuration
t1 = (extrema[2*i+1][0] - 0.5)*self.file.tickDuration
plt.plot([t0,t1],[y,y],'r', linewidth=4)
y -= 2
def findCosmics(self, stride=10, threshold=100,
populationMax=2000, nSigma=5, writeCosmicMask=False,
ppsStride=10000):
"""
Find cosmics ray suspects. Histogram the number of photons
recorded at each timeStamp. When the number of photons in a
group of stride timeStamps is greater than threshold in second
iSec, add (iSec,timeStamp) to cosmicTimeLists. Also keep
track of the histogram of the number of photons per stride
timeStamps.
return a dictionary of 'populationHg', 'cosmicTimeLists',
'binContents', 'timeHgValues', 'interval', 'frameSum', and 'pps'
populationHg is a histogram of the number of photons in each time bin.
This is a poisson distribution with a long tail due to cosmic events
cosmicTimeLists is a numpy array of all the sequences that are
suspects for cosmic rays
binContents corresponds to cosmicTimeLists. For each time in
cosmicTimeLists, binContents is the number of photons detected
at that time.
timeHgValues is a histogram of the number of photons in each time
interval
frameSum is a two dimensional numpy array of the number of photons
detected by each pixel
interval is the interval of data to be masked out
pps is photons per second, calculated every ppsStride bins.
"""
self.logger.info("findCosmics: begin stride=%d threshold=%d populationMax=%d nSigma=%d writeCosmicMask=%s"%(stride,threshold,populationMax,nSigma,writeCosmicMask))
exptime = self.endTime-self.beginTime
nBins = int(np.round(self.file.ticksPerSec*exptime+1))
bins = np.arange(0, nBins, 1)
timeHgValues,frameSum = self.getTimeHgAndFrameSum(self.beginTime,self.endTime)
remainder = len(timeHgValues)%ppsStride
if remainder > 0:
temp = timeHgValues[:-remainder]
else:
temp = timeHgValues
ppsTime = (ppsStride*self.file.tickDuration)
pps = np.sum(temp.reshape(-1, ppsStride), axis=1)/ppsTime
self.logger.info("findCosmics: call populationFromTimeHgValues")
pfthgv = Cosmic.populationFromTimeHgValues\
(timeHgValues,populationMax,stride,threshold)
#now build up all of the intervals in seconds
self.logger.info("findCosmics: build up intervals: nCosmicTime=%d"%len(pfthgv['cosmicTimeList']))
i = interval()
iCount = 0
secondsPerTick = self.file.tickDuration
for cosmicTime in pfthgv['cosmicTimeList']:
#t0 = max(0,self.beginTime+(cosmicTime-50)/1.e6)
#t1 = min(self.endTime,self.beginTime+(cosmicTime+50)/1.e6)
#intTime = t1-t0
t0 = self.beginTime+cosmicTime*secondsPerTick
dt = stride*secondsPerTick
t1 = t0+dt
left = max(self.beginTime, t0-nSigma*dt)
right = min(self.endTime, t1+2*nSigma*dt)
i = i | interval[left,right]
self.logger.debug("findCosmics: iCount=%d t0=%f t1=%f left=%f right=%f"%(iCount,t0,t1,left,right))
iCount+=1
tMasked = Cosmic.countMaskedBins(i)
ppmMasked = 1000000*tMasked/(self.endTime-self.beginTime)
retval = {}
retval['timeHgValues'] = timeHgValues
retval['populationHg'] = pfthgv['populationHg']
retval['cosmicTimeList'] = pfthgv['cosmicTimeList']
retval['binContents'] = pfthgv['binContents']
retval['frameSum'] = frameSum
retval['interval'] = i
retval['ppmMasked'] = ppmMasked
retval['pps'] = pps
retval['ppsTime'] = ppsTime
if writeCosmicMask:
cfn = self.fn.cosmicMask()
self.logger.info("findCosmics: write masks to =%s"%cfn)
ObsFile.writeCosmicIntervalToFile(i, self.file.ticksPerSec,
cfn,self.beginTime, self.endTime,
stride, threshold, nSigma, populationMax)
self.logger.info("findCosmics: end with ppm masked=%d"%ppmMasked)
return retval
def getTimeHgAndFrameSum(self, beginTime, endTime):
integrationTime = endTime - beginTime
nBins = int(np.round(self.file.ticksPerSec*integrationTime+1))
timeHgValues = np.zeros(nBins, dtype=np.int64)
frameSum = np.zeros((self.file.nRow,self.file.nCol))
self.logger.info("get all time stamps for integrationTime=%f"%integrationTime)
for iRow in range(self.file.nRow):
#print "Cosmic.findCosmics: iRow=",iRow
for iCol in range(self.file.nCol):
# getTimedPacketList is slow. Use getPackets instead.
#gtpl = self.file.getTimedPacketList(iRow,iCol,beginTime,
# integrationTime)
gtpl = self.file.getPackets(iRow,iCol,
beginTime,integrationTime)
timestamps = gtpl['timestamps']
if timestamps.size > 0:
timestamps = \
(timestamps - beginTime)*self.file.ticksPerSec
# per Matt S. suggestion 2013-07-09
ts32 = np.round(timestamps).astype(np.uint32)
tsBinner.tsBinner32(ts32, timeHgValues)
frameSum[iRow,iCol] += ts32.size
return timeHgValues,frameSum
@staticmethod
def countMaskedBins(maskInterval):
retval = 0
for x in maskInterval:
retval += x[1]-x[0]
return retval
@staticmethod
def populationFromTimeHgValues(timeHgValues,populationMax,stride,threshold):
"""
Rebin the timgHgValues histogram by combining stride bins. If
stride > 1, then bin a second time after shifting by stride/2
Create populationHg, a histogram of the number of photons in
the large bins. Also, create (and then sort) a list
cosmicTimeList of the start of bins (in original time units)
of overpopulated bins that have more than threshold number of
photons.
return a dictionary containing populationHg and cosmicTimeList
"""
popRange = (-0.5,populationMax-0.5)
if stride==1:
populationHg = np.histogram(\
timeHgValues, populationMax, range=popRange)
cosmicTimeList = np.where(timeHgValues > threshold)[0]
binContents = np.extract(timeHgValues > threshold, timeHgValues)
else:
# rebin the timeHgValues before counting the populations
length = timeHgValues.size
remainder = length%stride
if remainder == 0:
end = length
else:
end = -remainder
timeHgValuesTrimmed = timeHgValues[0:end]
timeHgValuesRebinned0 = np.reshape(
timeHgValuesTrimmed, [length/stride, stride]).sum(axis=1)
populationHg0 = np.histogram(
timeHgValuesRebinned0, populationMax, range=popRange)
cosmicTimeList0 = stride*np.where(\
timeHgValuesRebinned0 > threshold)[0]
binContents0 = np.extract(timeHgValuesRebinned0 > threshold,
timeHgValuesRebinned0)
timeHgValuesRebinned1 = np.reshape(
timeHgValuesTrimmed[stride/2:-stride/2],
[(length-stride)/stride, stride]).sum(axis=1)
populationHg1 = np.histogram(\
timeHgValuesRebinned1, populationMax, range=popRange)
cosmicTimeList1 = (stride/2)+stride*np.where(\
timeHgValuesRebinned1 > threshold)[0]
binContents1 = np.extract(timeHgValuesRebinned1 > threshold,
timeHgValuesRebinned1)
populationHg = (populationHg0[0]+populationHg1[0],\
populationHg0[1])
cosmicTimeList = np.concatenate((cosmicTimeList0,cosmicTimeList1))
binContents = np.concatenate((binContents0, binContents1))
args = np.argsort(cosmicTimeList)
cosmicTimeList = cosmicTimeList[args]
binContents = binContents[args]
cosmicTimeList.sort()
retval = {}
retval['populationHg'] = populationHg
retval['cosmicTimeList'] = cosmicTimeList
retval['binContents'] = binContents
return retval
def makeMovies(self,beginTick, endTick, backgroundFrame, accumulate=False):
tick0 = np.uint64(beginTick)
tick1 = np.uint64(endTick)
for iRow in range(cosmic.file.nRow):
for iCol in range(cosmic.file.nCol):
gtpl = self.getTimedPacketList(iRow,iCol,sec0,1)
timestamps = gtpl['timestamps']
timestamps *= cosmic.file.ticksPerSec
ts32 = timestamps.astype(np.uint32)
for ts in ts32:
tindex = ts-t0
try:
listOfPixelsToMark[tindex].append((iRow,iCol))
except IndexError:
pass
for tick in range(t0,t1):
frames.append(frameSum)
title = makeTitle(tick,t0,t1)
titles.append(title)
mfn0 = "m-%s-%s-%s-%s-%010d-%010d-i.gif"%(run,sundownDate,obsDate,seq,t0,t1)
utils.makeMovie(frames, titles, outName=mfn0, delay=0.1, colormap=mpl.cm.gray,
listOfPixelsToMark=listOfPixelsToMark,
pixelMarkColor='red')
for i in range(len(listOfPixelsToMark)-1):
listOfPixelsToMark[i+1].extend(listOfPixelsToMark[i])
mfn1 = "m-%s-%s-%s-%s-%010d-%010d-a.gif"%(run,sundownDate,obsDate,seq,t0,t1)
utils.makeMovie(frames, titles, outName=mfn1, delay=0.1, colormap=mpl.cm.gray,
listOfPixelsToMark=listOfPixelsToMark,
pixelMarkColor='green')
def fitDecayTime(self,t0Sec,lengthSec=200,plotFileName='none'):
print "hello from fitDecayTime"
timedPacketList = self.file.getTimedPacketList(
iRow, iCol, sec0, lengthSec)
def fitExpon(self, t0, t1):
"""
Fit an exponential to all photons from time t0 to time t1
t0 and t1 are in ticks, 1e6 ticks per second
return a dictionary of: timeStamps,fitParams,chi2
"""
firstSec = int(t0/1e6) # in seconds
integrationTime = 1+int((t1-t0)/1e6) # in seconds
nBins = integrationTime*1e6 # number of microseconds; one bin per microsecond
timeHgValues = np.zeros(nBins, dtype=np.int64)
print "firstSec=",firstSec," integrationTime=",integrationTime
for iRow in range(self.file.nRow):
for iCol in range(self.file.nCol):
timedPacketList = self.file.getTimedPacketList(
iRow, iCol, firstSec=firstSec,
integrationTime=integrationTime)
timeStamps = timedPacketList['timestamps']
if (len(timeStamps) > 0):
# covert the time values to microseconds, and
# make it the type np.uint64
# round per Matt S. suggestion 2013-07-09
#ts64 = (timeStamps).astype(np.uint64)
ts32round = np.round(timeStamps).astype(np.uint32)
tsBinner.tsBinner(ts32round, timeHgValues)
temp = 1e6*(timeStamps-firstSec)
for i in range(len(timeStamps)):
ts32 = ((timeStamps-firstSec)*1e6).astype(np.uint32)
# add these timestamps to the histogram timeHgValues
remain0 = int(t0%1e6)
remain1 = int(t1%1e6)
timeHgValues = timeHgValues[remain0:remain1]
x = np.arange(len(timeHgValues))
y = timeHgValues
xArray = np.arange(0, dtype=np.int64)
yArray = np.arange(0, dtype=np.int64)
for i in range(len(x)):
if y[i] > 2:
xArray = np.append(xArray,i)
yArray = np.append(yArray,y[i])
ySigma = np.sqrt(yArray)
mean = (x*y).sum()/float(y.sum())
bExponGuess = 1/mean
aExponGuess = bExponGuess*timeHgValues.sum()
cExponGuess = 0
dExponGuess = 0
pExponGuess = [aExponGuess, bExponGuess, cExponGuess, dExponGuess]
bGaussGuess = mean
avgx2 = (x*x*y).sum()/float(y.sum())
cGaussGuess = np.sqrt(avgx2-bGaussGuess*bGaussGuess)
aGaussGuess = (timeHgValues.sum()/(cGaussGuess*np.sqrt(2*np.pi)))
pGaussGuess = [aGaussGuess, bGaussGuess, cGaussGuess]
xLimit = [bGaussGuess-4*cGaussGuess, bGaussGuess+4*cGaussGuess]
retval = {'timeHgValues':timeHgValues, 'pExponFit':pExponGuess,
'pGaussFit':pGaussGuess, 'xLimit':xLimit,
'cGaussGuess':cGaussGuess, 'timeStamps':timeStamps}
return retval
@staticmethod
def intervalTime(intervals):
"""
return the time (in seconds) masked by the intervals
"""
time = 0
for interval in intervals:
time += interval[1]-interval[0]
return time
@staticmethod
def funcExpon(x, a, b, c, d):
retval = a*np.exp(-b*(x-d)) + c
retval[x < d] = 0
return retval
@staticmethod
def funcGauss(x, a, b, c):
return a*np.exp(-(x-b)**2/(2.*c**2))
