h5 = ObsFile(obsfile)
print "ObsFile loaded"
h5.loadWvlCalFile(calfile)
print "WvlCal loaded"
nypix = h5.nRow
nxpix = h5.nCol
image = np.empty((nypix,nxpix),dtype=int)
colors = np.empty((nypix,nxpix),dtype = int)
print "Making image"
for i in xrange(nypix):
for j in xrange(nxpix):
photons = h5.getPixelWvlList(i,j,starttime,endtime)
ind = np.where(np.logical_and((photons > startwl),(photons < stopwl)))
#print "uncut counts = ", len(photons)
photons = photons[ind]
image[i,j] = len(photons)
#print "cut counts = ", image[i,j]
if color == True:
try:
colors[i,j] = np.median(photons)
except:
colors[i,j] = 10000
print "..."
print "Finished constructing image"
if n==1:
totalimage = image
# 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'
tic = time()
print 'Retrieving photon timestamps...'
# Define the area of an observation containing the object.
appMask = aperture(15,8,8)
yValues,xValues = np.where(appMask==0)
# Get a list of timestamps with photons from each pixel in the aperture.
timestamps=[]
for j in range(len(xValues)):
x=ob.getPixelWvlList(iRow=yValues[j],iCol=xValues[j])
timestamps = np.append(timestamps,x['timestamps'])
print 'Total retrieval time: ' + str(time()-tic) + 's'
# Use timestamp data to perform periodograms.
timestep = 5.0*10**-4
averagingTime = 2.0
exptime = ob.getFromHeader('exptime')
totalAverageBins = int(exptime/averagingTime)
timestepsPerBin = int(averagingTime/timestep)
# Specify frequencies for which the periodogram algorithm should find transform components.
freqs = np.linspace(1,1000,num=10**4)
angularFreqs=2*np.pi*freqs
binnedCounts, binEdges = np.histogram(timestamps,range=[0,exptime],bins = exptime/timestep)
times = binEdges[0:timestepsPerBin]
exptime = ob.getFromHeader('exptime')
unixtime= ob.getFromHeader('unixtime')
if i == 0:
unixOffset = unixtime
exptimes[i]=exptime
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(3000,8000)
for j in range(len(x_values)):
x=ob.getPixelWvlList(iRow=y_values[j],iCol=x_values[j])
timestamps = np.append(timestamps,unixtime - unixOffset + (x['timestamps']))
print 'Loaded files in',time()-tic, 'seconds.'
# Put photon in events in small time bins ~ 1 microsecond goal
tic = time()
print 'Binning photon events...'
total_seconds = np.sum(exptimes)
bins_per_second = 10**6
#time_bins = np.linspace(0,total_seconds,total_seconds*bins_per_second+1)
#jd = time_bins/86400
#counts_per_timestep = np.zeros(len(time_bins)-1)
#for j in range(len(time_bins)-1):
#counts_per_timestep[j]=len(np.where(np.logical_and(timestamps>=time_bins[j],timestamps<time_bins[j+1]))[:][0])
counts_per_timestep, bin_edges = np.histogram(timestamps,bins = bins_per_second*total_seconds)
jd =bin_edges[0:-1]