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")
modResolutions2 = []
countRates = []
fig = plt.figure()
ax = fig.add_subplot(111)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
fig3 = plt.figure()
ax3 = fig3.add_subplot(111)
print (row,col)
for iTimeBin,firstSec in enumerate(timeBinStarts):
colorFraction = (iTimeBin+1.)/len(timeBinStarts)
color=cmap(colorFraction)
packetListDict = obs.getTimedPacketList(row,col,firstSec=firstSec,integrationTime=intTime,useParabolaPeaks=True)
timestamps = packetListDict['timestamps']
rawPeakHeights = packetListDict['peakHeights']
baselines = packetListDict['baselines']
nCounts = len(timestamps)
countRate = nCounts/intTime
print '%.0f cps'%countRate,
modBaselines = smoothBaseline(baselines,timestamps)
phases = rawPeakHeights-baselines
modPhases = rawPeakHeights-modBaselines
ax.plot(timestamps,rawPeakHeights,'c,')
ax.plot(timestamps,baselines,'k,')
ax.plot(timestamps,modBaselines,'r-')
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))
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)
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,timeHgValues.max())
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)
def main():
#filenames = ['obs_20121212-033825.h5',
fn = FileName(run='PAL2012',date='20121211',tstamp='20121212-055428').obs()
ob = ObsFile(fn)
frame = ob.getPixelCountImage(0,30,weighted=False)
hotPixMask = hotPixels.findHotPixels(image=frame,firstSec=0,intTime=30,weighted=False)['badflag']
frame[hotPixMask == 1] = 0
def plotFrame(fig,axes):
hMat=axes.matshow(frame,cmap=matplotlib.cm.gnuplot,origin='lower',vmax=np.mean(frame)+3*np.std(frame))
fig.colorbar(hMat)
PopUp(plotFunc=plotFrame)
#d = datetime.date(2012,10,15)#date of table value
#d2 = datetime.date(2012,12,11)#date of observation
#dt=(d2-d).total_seconds()#seconds between obs and table value
##crabtime ephemeris
#nu=29.6957720714 #1/us
#pdot=4.2013878e-13#us/s
#period=(1.0/nu)+pdot*dt#us
#goldstone ephemeris
F0=29.7414493465249770#Hz
deltaF0=0.0000000055983574
F1=-3.669005118205e-10#df0/dt Hz/s
F2=-3.085573120457e-20#d^2f0/dt^2 Hz/s^2
pEpoch = 54781.604891 #Modified Julian Date corresponding to F0
pEpoch = pEpoch+2400000.5#convert mjd to jd
pEpoch *= 24*3600 #in seconds
#obsDate = ob.getFromHeader('jd')
unixEpochJD = 2440587.5
unixSecsInDay = 86400.
headerUnixtime = ob.getFromHeader('unixtime')
obsDate = headerUnixtime/unixSecsInDay+unixEpochJD
startTime = obsDate*24*3600#in seconds
dt = startTime-pEpoch#seconds since pepoch
#freq = F0+F1*dt+F2/2*dt**2
freq = F0+F1*dt
period = 1.0/freq
print 'period=',period,'s'
#period=0.03367660643405371
#period=0.03367664238573182
#period=0.03367662440988317
iRow = 10
iCol = 14
integrationTime = 30
circCol,circRow = circ(iCol,iRow,radius=5)
firstSec = 0
dt = startTime-pEpoch + firstSec
freq = F0+F1*dt
period = 1.0/freq
print 'period=',period,'s'
nPhaseBins = 200
#np.set_printoptions(threshold=np.nan)
jdTimes = np.array([],dtype=np.float64)
times = np.array([])
for i in range(len(circCol)):
iRow = circRow[i]
iCol = circCol[i]
timestamps,peaks,baselines = ob.getTimedPacketList(iRow,iCol,firstSec,integrationTime)
timestamps = np.array(timestamps,dtype=np.float64)
jdTimestamps = obsDate+timestamps /(24.*3600.)
jdTimes = np.append(jdTimes,jdTimestamps)
times = np.append(times,timestamps)
jdTimes -= 2400000.5 #convert to modified jd
np.savetxt('crabOpticalSample-20121212-055428.txt',jdTimes)
periodDays = period/(24.*3600.)
phaseOffset = .2
phases = (jdTimes % periodDays)/periodDays+.2
phases = phases % 1.0
print len(phases)
histPhases,phaseBinEdges = np.histogram(phases,bins=nPhaseBins)
print jdTimes[0:10]
print times[0:10]
plt.plot(phaseBinEdges[0:-1],histPhases)
plt.show()
countRates = []
fig = plt.figure()
ax = fig.add_subplot(111)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
fig3 = plt.figure()
ax3 = fig3.add_subplot(111)
print (row,col)
for iTimeBin,firstSec in enumerate(timeBinStarts):
print 'time bin',iTimeBin,firstSec
colorFraction = (iTimeBin+1.)/len(timeBinStarts)
color=cmap(colorFraction)
packetListDict = obs.getTimedPacketList(row,col,firstSec=firstSec,integrationTime=intTime)
timestamps = packetListDict['timestamps']
rawPeakHeights = packetListDict['peakHeights']
baselines = packetListDict['baselines']
nCounts = len(timestamps)
countRate = nCounts/intTime
print '%.0f cps'%countRate
modBaselines = smoothBaseline(baselines,timestamps)
phases = rawPeakHeights-baselines
modPhases = rawPeakHeights-modBaselines
ax.plot(timestamps,rawPeakHeights,'c,')
ax.plot(timestamps,baselines,'k,')
ax.plot(timestamps,modBaselines,'r-')
import os, sys
from util import FileName
from util.ObsFile import ObsFile
from util.readDict import readDict
param = readDict()
MKID_PROC_PATH = '.'
os.environ['MKID_PROC_PATH'] = MKID_PROC_PATH
run = 'PAL2012'
sunsetDate = '20121211'
obsSequence = '20121212-074700'
fn = FileName.FileName(run=run,date=sunsetDate,tstamp=obsSequence)
obsFile = ObsFile(fn.obs())
if len(sys.argv) > 1:
obsFile.loadStandardCosmicMask()
print " length of cosmicMask=",len(obsFile.cosmicMask)
firstSec = 0
integrationTime = 3
nPhoton = 0
for iRow in xrange(obsFile.nRow):
for iCol in xrange(obsFile.nCol):
gtpl = obsFile.getTimedPacketList(iRow,iCol,firstSec,integrationTime)
nPhoton += len(gtpl['timestamps'])
print "nPhoton=",nPhoton
del obsFile
