def makeDark(stackForDark=None, outPutFileName=None,verbose=True):
"""
takes a list or array of arrays and makes a dark. Return an array with the dark. If outPutFileName is provided, it saves the dark in an npz
"""
try:
dark = irUtils.medianStack(stackForDark)
print "Making dark hot pixel mask"
if verbose:
plotArray(dark,title='Dark',origin='upper')
except:
print "#### Error with the stack of dark. makeDark takes a list of array of arrays to make the dark ####"
sys.exit(0)
try:
np.savez(darkPath,dark=dark)
except:
if outPutFileName==None:
print "No output file name provided. Dark not saved"
else:
print "Wrong output path. Dark not saved"
return dark
def makeFlat(flatStack=None, dark=None, outPutFileName=None, badPixelMask=None, crop=False, cropColsForMedian=[(0,19),(60,79)], cropRowsForMedian=[(25,49)], verbose=True):
"""
Input:
flatStack: array or list of arrays to make the flat
dark: dark. If none the flat is not dark subtracteed
outPutFileName: if not provided returns the flat array without saving it
crop: if False uses the entire array to calculate the median, otherwise it crops the columns in the range specified by cropForMedian
cropColsForMedian, cropRowsForMedian: are tuples with each element being the range of columns/rows to be cropped when taking the median for the flat
10/1/2017: currently crops the high frequency boards (bad performances) and FL2 (not working on Faceless)
Returns:
dictionary with 'weights' and 'flat'
"""
try:
flat = irUtils.medianStack(flatStack)
print "Loading flat frame"
except:
print "No valid flat stack provided. Exiting"
sys.exit(0)
if verbose:
plotArray(flat,title='NotdarkSubFlat')
if dark==None:
#dark subtract the flat
print "Subtracting dark"
flatSub=flat-dark
else:
print "Dark=None, will make a flat without dark subtraction"
flatSub=flat
flatSub[np.where(flatSub < 0.0)]=np.nan
croppedFrame = np.copy(flatSub)
if crop:
if cropColsForMedian!=None:
cropColsForMedian=np.array(cropColsForMedian)
for iCrop, cropInd in enumerate(cropColsForMedian):
croppedFrame[:,cropInd[0]:cropInd[1]+1]=np.nan
if cropRowsForMedian!=None:
cropRowsForMedian=np.array(cropRowsForMedian)
for iCrop, cropInd in enumerate(cropRowsForMedian):
croppedFrame[cropInd[0]:cropInd[1]+1,:]=np.nan
#Apply bad pixel mask
if badPixelMask!=None:
print "Applying bad pixel mask"
croppedFrame[badPixelMask!=0] = np.nan
if verbose:
plotArray(croppedFrame,title='Cropped flat frame') #Plot the cropped flat frame
med = np.nanmedian(croppedFrame.flatten())
print 'median', med
flatSub[flatSub==0]=1
weights = med/flatSub #calculate the weights by dividing the median by the dark-subtracted flat frame
if verbose:
plotArray(weights,title='Weights') #Plot the weights
try:
np.savez(outPutFileName,weights = weights,flat=flatSub)
except:
if outPutFileName==None:
print 'No output file name provided. Not saving flat'
else:
print 'Output file name not valid. Not saving flat'
dict={}
dict['flat']=flat
dict['weights']=weights
return dict
originalPreciseYs >= 0) & (preciseYs <= 125)
ax.scatter(xs[goodMask],
ys[goodMask],
label=label,
color=colors[iLabel])
for (x, y) in zip(xs[goodMask], ys[goodMask]):
if 0 <= x and x < nCols and 0 <= y and y < nRows:
if grid[y, x] != 0:
grid[y, x] = 10
else:
grid[y, x] += iLabel + 1
ax.legend(loc='best')
ylims = ax.get_ylim()
ax.set_ylim(ylims[1], ylims[0])
plotArray(grid, origin='upper')
#now try to clean up overlapping pixels
for iLabel, (label, filename) in enumerate(zip(labels, filenames)):
print iLabel
yOffset = yOffsets[iLabel]
resIds = locationData[iLabel][:, 0]
flags = locationData[iLabel][:, 1]
xs = np.floor(locationData[iLabel][:, 2])
ys = np.floor(locationData[iLabel][:, 3]) + yOffset
ys[xs == -1] = -1
preciseXs = locationData[iLabel][:, 2]
preciseYs = locationData[iLabel][:, 3] + yOffset
fails = np.array(locationData[iLabel][:, 1], dtype=np.bool)
originalPreciseYs = locationData[iLabel][:, 3]
goodMask = (~fails) & (preciseXs >= 0) & (originalPreciseYs >= 0)
preciseXs = segmentLocationData[:,2]
preciseYs = segmentLocationData[:,3]+yOffset
originalPreciseYs = segmentLocationData[:,3]
goodMask = (~fails) & (preciseXs >= 0) & (originalPreciseYs >= 0)
ax.scatter(xs[goodMask],ys[goodMask],label=label,color=colors[iLabel])
for (x,y) in zip(xs[goodMask],ys[goodMask]):
if 0 <= x and x < nCols and 0 <= y and y < nRows:
if grid[y,x] != 0:
grid[y,x] = 10
else:
grid[y,x] += iLabel+1
ax.legend(loc='best')
ylims = ax.get_ylim()
ax.set_ylim(ylims[1],ylims[0])
plotArray(grid,origin='upper')
#now try to clean up overlapping pixels
for iLabel,(label,filename) in enumerate(zip(labels,filenames)):
print iLabel
yOffset = yOffsets[iLabel]
resIds = locationData[iLabel][:,0]
xs = np.floor(locationData[iLabel][:,2])
ys = np.floor(locationData[iLabel][:,3]) + yOffset
ys[xs==-1] = -1
preciseXs = locationData[iLabel][:,2]
preciseYs = locationData[iLabel][:,3]+yOffset
fails = np.array(locationData[iLabel][:,1],dtype=np.bool)
originalPreciseYs = locationData[iLabel][:,3]
goodMask = (~fails) & (preciseXs >= 0) & (originalPreciseYs >= 0)
print 'good',np.sum(goodMask)
dPath = os.path.join(rPath,date)
flatPath = os.path.join(dPath,flatFileName)
baseFN = flatFileName.split('.')[0]
outFN = baseFN+'_weights.npz'
outPath = os.path.join(dPath,outFN)
try:
flatData = np.load(flatPath)
except:
print "couldn't find flat stack .npz file at path:"
print flatPath
sys.exit(0)
flatFrame = flatData['final']
plotArray(flatFrame,title='flat frame')
########
# This cropping assumes poor FL2 performance and
# poor high frequency performance, a la Faceless from 2017a,b.
# This is a good place to play around to change crappy weights
# at fringes of the array.
#######
croppedFrame = np.copy(flatFrame)
if cropFLs:
croppedFrame[25:50,::] = np.nan
if cropHF:
croppedFrame[::,:20] = np.nan
croppedFrame[::,59:] = np.nan
rWvls = np.array([808, 920, 980, 1120, 1310])
rmasks = np.array([
rdata['good_fits_808nm'], rdata['good_fits_920nm'],
rdata['good_fits_980nm'], rdata['good_fits_1120nm'],
rdata['good_fits_1310nm']
])
dataDir = '/mnt/data0/ScienceDataIMGs/LabData/20170712/'
for i in range(len(wvls)):
#find nearest "good pix" mask from Nick's R data
wvlDiffs = np.abs(rWvls - wvls[i])
nearestWvl = rWvls[np.where(wvlDiffs == np.min(wvlDiffs))][0]
print nearestWvl
nearestMask = rmasks[np.where(rWvls == nearestWvl)][0].transpose()
if i == 0: plotArray(nearestMask)
stack = loadIMGStack(dataDir,
light[i][0],
light[i][1],
nCols=80,
nRows=125)
darkstack = loadIMGStack(dataDir,
dark[i][0],
dark[i][1],
nCols=80,
nRows=125)
bpm = dhpm.makeMask(run='LabData',
date='20170712',
startTimeStamp=dark[i][0],
stopTimeStamp=dark[i][1],
def makeDHPMask(stack=None, outputFileName=None, verbose=False, sigma=3, maxCut=2450, coldCut=False, manualArray=None):
'''
MaxCut sets level for initial hot pixel cut. Everything with cps>maxCut -> np.nan
Sigma determines level for second cut. Everything with cps>mean+sigma*stdev -> np.nan
If coldCut=True, third cut where everything with cps<mean-sigma*stdev -> np.nan
manualArray is a way to give a list of bad pixels manually, in format [[row,col],[row,col],...]
'''
medStack = irUtils.medianStack(stack)
if verbose:
try:
plotArray(medStack,title='Median Dark Stack')
except:
plt.matshow(medStack)
plt.show()
#initial masking, take out anything with cps > maxCut
mask = np.zeros(np.shape(medStack),dtype=int)
mask[np.where(medStack>=maxCut)]=1
if verbose:
try:
plotArray(mask, title='cps>%i == 1'%maxCut)
except:
plt.matshow(mask)
plt.show()
medStack[np.where(mask==1)]=np.nan
medStack[np.where(medStack==0)]=np.nan
if verbose:
try:
plotArray(medStack, title='Median Stack with mask 1')
except:
plt.matshow(medStack)
plt.show()
#second round of masking, where cps > mean+sigma*std
mask2 = np.zeros(np.shape(medStack),dtype=int)
mask2[np.where(medStack>=np.nanmean(medStack)+sigma*np.nanstd(medStack))]=1
#if coldCut is true, also mask cps < mean-sigma*std
if coldCut==True:
mask2[np.where(medStack<=np.nanmean(medStack)-sigma*np.nanstd(medStack))]=1
if verbose:
try:
plotArray(mask2, title='cps>mean+%i-sigma == 1'%sigma)
except:
plt.matshow(mask2)
plt.show()
medStack[np.where(mask2==1)]=np.nan
if verbose:
try:
plotArray(medStack, title='Median Stack with mask 2')
except:
plt.matshow(medStack)
plt.show()
finalMask = mask+mask2
# provide an easy means to pipe in an array of manually identified pixels
if manualArray is not None:
for pix in manualArray:
finalMask[pix[0],pix[1]]=1
if verbose:
try:
plotArray(finalMask, title='Final mask')
except:
plt.matshow(finalMask)
plt.show()
print("Number masked pixels = ", len(np.array(np.where(finalMask==1)).flatten()))
return finalMask
#fits reading test
import numpy as np
import pyfits
from arrayPopup import plotArray
def readFITS(filename):
f = pyfits.open(filename)
scidata = np.array(f[0].data)
return scidata
if __name__ == "__main__":
fname = '/mnt/data0/ProcessedData/seth/imageStacks/PAL2016b/SAO42642_8Dithers_3xSamp_allHPM_20161122.fits'
im = readFITS(fname)
plotArray(im,title='loaded fits image',origin='upper',vmin=0)
def makeMask(run=None, date=None, basePath=None,startTimeStamp=None, stopTimeStamp=None, verbose=False, sigma=3, maxCut=2450, coldCut=False, manualArray=None):
'''
MaxCut sets level for initial hot pixel cut. Everything with cps>maxCut -> np.nan
Sigma determines level for second cut. Everything with cps>mean+sigma*stdev -> np.nan
If coldCut=True, third cut where everything with cps<mean-sigma*stdev -> np.nan
manualArray is a way to give a list of bad pixels manually, in format [[row,col],[row,col],...]
'''
try:
dataPath = basePath+str(run)+os.path.sep+str(date)+os.path.sep
stack = loadIMGStack(dataPath, startTimeStamp, stopTimeStamp, nCols=nCols, nRows=nRows)
except:
print("Could not find dark data in ScienceData path, checking ramdisk")
dataPath = '/mnt/ramdisk/'
stack = loadIMGStack(dataPath, startTimeStamp, stopTimeStamp, nCols=nCols, nRows=nRows)
medStack = ir.utils.medianStack(stack)
if verbose:
try:
plotArray(medStack,title='Median Dark Stack')
except:
plt.matshow(medStack)
plt.show()
#initial masking, take out anything with cps > maxCut
mask = np.zeros(np.shape(medStack),dtype=int)
mask[np.where(medStack>=maxCut)]=1
if verbose:
try:
plotArray(mask, title='cps>%i == 1'%maxCut)
except:
plt.matshow(mask)
plt.show()
medStack[np.where(mask==1)]=np.nan
medStack[np.where(medStack==0)]=np.nan
if verbose:
try:
plotArray(medStack, title='Median Stack with mask 1')
except:
plt.matshow(medStack)
plt.show()
#second round of masking, where cps > mean+sigma*std
mask2 = np.zeros(np.shape(medStack),dtype=int)
mask2[np.where(medStack>=np.nanmean(medStack)+sigma*np.nanstd(medStack))]=1
#if coldCut is true, also mask cps < mean-sigma*std
if coldCut==True:
mask2[np.where(medStack<=np.nanmean(medStack)-sigma*np.nanstd(medStack))]=1
if verbose:
try:
plotArray(mask2, title='cps>mean+%i-sigma == 1'%sigma)
except:
plt.matshow(mask2)
plt.show()
medStack[np.where(mask2==1)]=np.nan
if verbose:
try:
plotArray(medStack, title='Median Stack with mask 2')
except:
plt.matshow(medStack)
plt.show()
finalMask = mask+mask2
# provide an easy means to pipe in an array of manually identified pixels
if manualArray is not None:
for pix in manualArray:
finalMask[pix[0],pix[1]]=1
if verbose:
try:
plotArray(finalMask, title='Final mask')
except:
plt.matshow(finalMask)
plt.show()
print("Number masked pixels = ", len(np.array(np.where(finalMask==1)).flatten()))
return finalMask
def plotMask(mask):
try:
plotArray(mask, title='hp Mask')
except:
plt.matshow(mask)
plt.show()
wvlRange=None)
print('Running getPixelCountImage on ', firstSec, 'seconds to ',
intTime, 'seconds of data on all wavelengths')
processedIm = np.transpose(img['image']) / intTime
print(np.shape(processedIm))
processedIm = processedIm[50:124, 0:80]
roughShiftsX.append(dXs[i])
roughShiftsY.append(dYs[i])
centroidsX.append(refPointX - dXs[i])
centroidsY.append(refPointY - dYs[i])
#plot an example of the UNmasked image for inspection
if i == 0:
plotArray(processedIm,
title='Dither Pos %i' % i,
origin='upper',
vmin=0)
#cut out cold/dead pixels
processedIm[np.where(processedIm <= coldCut)] = np.nan
#plot an example of the masked image for inspection
if i == 0:
plotArray(processedIm,
title='Dither Pos %i ColdPix Masked' % i,
origin='upper',
vmin=0)
#pad frame with margin for shifting and stacking
paddedFrame = irUtils.embedInLargerArray(processedIm,
frameSize=padFraction)
outfile = h5dir + outfileName + str(i)
print('Running getPixelCountImage on ',firstSec,'seconds to ',intTime,'seconds of data from wavelength ',wvlStart,'to ',wvlStop)
else:
img = obsfile.getPixelCountImage(firstSec =0, integrationTime=intTime,applyWeight=False,flagToUse = 0,wvlRange = None)
print('Running getPixelCountImage on ',firstSec,'seconds to ',intTime,'seconds of data on all wavelengths')
processedIm = np.transpose(img['image'])/intTime
print(np.shape(processedIm))
processedIm=processedIm[50:124, 0:80]
roughShiftsX.append(dXs[i])
roughShiftsY.append(dYs[i])
centroidsX.append(refPointX-dXs[i])
centroidsY.append(refPointY-dYs[i])
#plot an example of the UNmasked image for inspection
if i==0:
plotArray(processedIm,title='Dither Pos %i'%i,origin='upper',vmin=0)
#cut out cold/dead pixels
processedIm[np.where(processedIm<=coldCut)]=np.nan
#plot an example of the masked image for inspection
if i==0:
plotArray(processedIm,title='Dither Pos %i ColdPix Masked'%i,origin='upper',vmin=0)
#pad frame with margin for shifting and stacking
paddedFrame = irUtils.embedInLargerArray(processedIm,frameSize=padFraction)
outfile=h5dir+outfileName+str(i)
np.save(outfile, paddedFrame)
#apply rough dX and dY shift to frame
print("Shifting dither %i, frame %i by x=%i, y=%i"%(i,0,dXs[i], dYs[i]))
shiftedFrame = irUtils.rotateShiftImage(paddedFrame,0,dXs[i],dYs[i])
def stackCube(h5File, npzFile, verbose=True):
#load npz file with cubes and timestamps
npzDict = loadCubeStack(npzFile)
cubeTimes = npzDict['times']
cubeCubes = npzDict['cubes']
cubeWBEs = npzDict['wvlBinEdges']
#define bin center wavelengths (in nm)
wbWidths = np.diff(cubeWBEs)
centers = cubeWBEs[:-1] + wbWidths / 2.0
nWvlBins = len(centers)
if verbose: print "Loaded npz file..."
#load h5 file with centroids, image resampling info, dark frames, and timestamps
h5Dict = loadH5Stack(h5File)
h5Times = h5Dict['times']
centXs = h5Dict['centX']
centYs = h5Dict['centY']
hotPix = h5Dict['hpm']
#get image stacking info from params dictionary
paramsDict = h5Dict['params']
padFraction = np.float(paramsDict['padFraction'][0])
upSample = paramsDict['upSample'][0]
doHPM = bool(paramsDict['doHPM'][0])
coldCut = paramsDict['coldCut'][0]
if verbose: print "Loaded h5 file..."
if cubeTimes.all() == h5Times.all():
print "Timestamps match. Carrying on with stacking..."
else:
print "Timestamp mismatch between two files!!"
print cubeTimes
print h5Times
sys.exit(0)
if doHPM:
hpMask = hotPix[0]
cubeStack = []
for i in range(nWvlBins):
cubeStack.append([])
finalCube = []
finalTimes = []
for t in np.arange(len(cubeTimes)):
time = cubeTimes[t]
cube = cubeCubes[t]
centX = centXs[t]
centY = centYs[t]
finalTimes.append(cubeTimes[t])
for w in np.arange(nWvlBins):
im = np.array(cube[:, :, w], float)
im = np.transpose(im)
#apply hp mask to image
if doHPM:
im[np.where(hpMask == 1)] = np.nan
#cut out cold/dead pixels
im[np.where(im <= coldCut)] = np.nan
#pad frame with margin for shifting and stacking
paddedFrame = irUtils.embedInLargerArray(im, frameSize=padFraction)
#upSample frame for sub-pixel registration with fitting code
upSampledFrame = irUtils.upSampleIm(paddedFrame, upSample)
#conserve flux. Break each pixel into upSample^2 sub pixels,
#each subpixel should have 1/(upSample^2) the flux of the original pixel
upSampledFrame /= float(upSample * upSample)
### UPDATE WITH DX AND DY DETERMINED FROM ACTUAL STARTING POINT
### SHIFTS ALL TO 0 RIGHT NOW
#apply dX and dY shift to frame
dX = centX * -1. * upSample
dY = centY * -1. * upSample
if verbose:
print "Shifting timestamp %i, wvl %i by x=%2.2f, y=%2.2f" % (
t, w, dX, dY)
shiftedFrame = irUtils.rotateShiftImage(upSampledFrame, 0, dX, dY)
cubeStack[w].append(shiftedFrame)
cubeStack = np.array(cubeStack)
for n in np.arange(nWvlBins):
finalCube.append(irUtils.medianStack(cubeStack[n]))
if verbose:
plotArray(finalCube[n], title="%i nm" % centers[n])
finalCube = np.array(finalCube)
return {
'finalCube': finalCube,
'wvls': centers,
'cubeStack': cubeStack,
'times': finalTimes
}
tauArray[row,col]=np.nan
else:
tauArray[row,col]=tau_corr
#add another check here for hot pixel masks to force those to NAN as well
except:
print "Failed to get tau_corr, setting Tau to NAN"
tauArray[row,col]=np.nan
ax1.legend(loc=4)
ax2.legend()
ax3.legend(loc=0)
plt.show()
plotArray(tauArray*1000,title='Tau_C [ms]',origin='upper',vmin=0)
'''
#take light curve and bin into histogram of intensities
intHistBinEdges = None
nBinsPerUnitInt = 0.5
nBins = 30
pixCutOffIntensities=[]
listToHist = lc
intRange = np.array([np.min(listToHist),np.max(listToHist)])
intBins = (intRange[1]-intRange[0])//nBinsPerUnitInt
intHist,intHistBinEdges = np.histogram(listToHist,bins=nBins,range=intRange)
intHist = np.array(intHist)/float(len(lc))
print 'Usage: {} tstampStart tstampEnd'.format(sys.argv[0])
exit(0)
else:
startTstamp = int(sys.argv[1])
endTstamp = int(sys.argv[2])
darkStart = 1469354906
darkEnd = 1469354926
darkStack = loadImageStack(darkStart, darkEnd)
darkFrame = medianFrame(darkStack)
imageStack = loadImageStack(startTstamp, endTstamp)
#option to flatten stack to median combined frame
mf = medianFrame(imageStack)
FITSfile = str(startTstamp) + '_to_' + str(
endTstamp) + '_goodBeammapOnly.fits'
path = outpath + FITSfile
writeFits(imageStack, path)
if not mf == None:
medianFile = str(startTstamp) + '_to_' + str(
endTstamp) + '_goodBeammapOnly_median.fits'
path = outpath + medianFile
writeFits(mf, path)
if verbose:
plotArray(image=mf)
#form = PopUp(showMe=False,title='B')
#form.plotArray(np.arange(9).reshape(3,3))
#form.show()
#append upsampled, padded frame to array for storage into next part of code
ditherFrames.append(upSampledFrame)
print "Loaded dither position %i"%i
shiftedFrames = np.array(ditherFrames)
#if fitPos==True, do second round of shifting using mpfit correlation
#using x and y pos from earlier as guess
if fitPos==True:
reshiftedFrames=[]
if refFile!=None and os.path.exists(refFile):
refIm = readFITS(refFile)
print "Loaded %s for fitting"%refFile
plotArray(refIm,title='loaded reference FITS',origin='upper',vmin=0)
else:
refIm=shiftedFrames[0]
cnt=0
for im in shiftedFrames:
print "\n\n------------------------------\n"
print "Fitting frame %i of %i"%(cnt,len(shiftedFrames))
pGuess=[0,1,1]
pLowLimit=[-1,(dXs.min()-5)*upSample,(dYs.min()-5)*upSample]
pUpLimit=[1,(dXs.max()+5)*upSample,(dYs.max()+5)*upSample]
print "guess", pGuess, "limits", pLowLimit, pUpLimit
#mask out background structure, only fit on known object location
maskRad=apMaskRadPrim
pMask = aperture(xPos[0]*upSample,yPos[0]*upSample,maskRad*upSample, numRows*upSample, numCols*upSample)
fails = np.array(flags, dtype=np.bool)
#define mask of good pixels
goodMask = (~fails)
#look through goodMask positions and add each pixel to their corresponding location in the grid
#if a grid position already has
for (x, y) in zip(xs[goodMask], ys[goodMask]):
grid[y, x] += 1
#plot initial "good" grid with number of resonators assigned to each location
fig, ax = plt.subplots(1, 1)
ax.legend(loc='best')
ylims = ax.get_ylim()
ax.set_ylim(ylims[1], ylims[0])
plotArray(grid,
title='Original "good" flagged pixel locations',
origin='upper')
#setup dictionary for storing new output data
for i in range(len(ids)):
resId = ids[i]
distVector = (preciseXs[i] - (xs[i] + .5), preciseYs[i] - (ys[i] + .5))
distMag = np.sqrt(distVector[0]**2 + distVector[1]**2)
gridDicts.append({
'x': xs[i],
'y': ys[i],
'preciseX': preciseXs[i],
'preciseY': preciseYs[i],
'distMag': distMag,
'resId': resId,
'fail': flags[i]
def stackCube(h5File,npzFile, verbose=True):
#load npz file with cubes and timestamps
npzDict = loadCubeStack(npzFile)
cubeTimes = npzDict['times']
cubeCubes = npzDict['cubes']
cubeWBEs = npzDict['wvlBinEdges']
#define bin center wavelengths (in nm)
wbWidths = np.diff(cubeWBEs)
centers = cubeWBEs[:-1]+wbWidths/2.0
nWvlBins = len(centers)
if verbose: print "Loaded npz file..."
#load h5 file with centroids, image resampling info, dark frames, and timestamps
h5Dict = loadH5Stack(h5File)
h5Times = h5Dict['times']
centXs = h5Dict['centX']
centYs = h5Dict['centY']
hotPix = h5Dict['hpm']
#get image stacking info from params dictionary
paramsDict = h5Dict['params']
padFraction = np.float(paramsDict['padFraction'][0])
upSample = paramsDict['upSample'][0]
doHPM = bool(paramsDict['doHPM'][0])
coldCut = paramsDict['coldCut'][0]
if verbose: print "Loaded h5 file..."
if cubeTimes.all() == h5Times.all():
print "Timestamps match. Carrying on with stacking..."
else:
print "Timestamp mismatch between two files!!"
print cubeTimes
print h5Times
sys.exit(0)
if doHPM:
hpMask = hotPix[0]
cubeStack = []
for i in range(nWvlBins): cubeStack.append([])
finalCube = []
finalTimes = []
for t in np.arange(len(cubeTimes)):
time = cubeTimes[t]
cube = cubeCubes[t]
centX = centXs[t]
centY = centYs[t]
finalTimes.append(cubeTimes[t])
for w in np.arange(nWvlBins):
im = np.array(cube[:,:,w],float)
im = np.transpose(im)
#apply hp mask to image
if doHPM:
im[np.where(hpMask==1)]=np.nan
#cut out cold/dead pixels
im[np.where(im<=coldCut)]=np.nan
#pad frame with margin for shifting and stacking
paddedFrame = irUtils.embedInLargerArray(im,frameSize=padFraction)
#upSample frame for sub-pixel registration with fitting code
upSampledFrame = irUtils.upSampleIm(paddedFrame,upSample)
#conserve flux. Break each pixel into upSample^2 sub pixels,
#each subpixel should have 1/(upSample^2) the flux of the original pixel
upSampledFrame/=float(upSample*upSample)
### UPDATE WITH DX AND DY DETERMINED FROM ACTUAL STARTING POINT
### SHIFTS ALL TO 0 RIGHT NOW
#apply dX and dY shift to frame
dX = centX*-1.*upSample
dY = centY*-1.*upSample
if verbose:
print "Shifting timestamp %i, wvl %i by x=%2.2f, y=%2.2f"%(t,w, dX, dY)
shiftedFrame = irUtils.rotateShiftImage(upSampledFrame,0,dX,dY)
cubeStack[w].append(shiftedFrame)
cubeStack = np.array(cubeStack)
for n in np.arange(nWvlBins):
finalCube.append(irUtils.medianStack(cubeStack[n]))
if verbose:
plotArray(finalCube[n],title="%i nm"%centers[n])
finalCube = np.array(finalCube)
return {'finalCube':finalCube,'wvls':centers, 'cubeStack':cubeStack, 'times':finalTimes}
hpmfile=np.load(hpmPath)
darkHPM=hpmfile['darkHPM']
print "Found HPM File"
else:
print "Could not find existing hot pix mask, generating one from dark..."
#try:
print 'maxCut',maxCut
darkHPMImg = dhpm.makeMask(run=run, date=date, basePath=imgDir,startTimeStamp=darkSpanImg[0], stopTimeStamp=darkSpanImg[1], coldCut=True, maxCut=maxCut,sigma=sigma,manualArray=None)
hpFN='darkHPMImg_'+target+'.npz' #Save the hot pixel mask into the output directory
hpPath = os.path.join(outputDir,hpFN)
np.savez(hpPath,darkHPMImg = darkHPMImg)
print hpPath
#except:
# print "Failed to generate dark mask. Turning off hot pixel masking"
# doHPM = False
plotArray(darkHPMImg, title='Hot Pixel Mask Image', origin='upper')
if calibPath[0]!='0':
hpmPath=os.path.join(calibPath,"darkHPM_"+target+".npz")
hpmfile=np.load(hpmPath)
darkHPM=hpmfile['darkHPM']
print "Found HPM File"
else:
print "Could not find existing hot pix mask, generating one from dark..."
try:
darkHPMFlat = dhpm.makeMask(run=run, date=date, basePath=imgDir,startTimeStamp=darkSpanFlat[0], stopTimeStamp=darkSpanFlat[1], coldCut=True, maxCut=maxCut,sigma=sigma,manualArray=None)
hpFN='darkHPMFlat_'+target+'.npz' #Save the hot pixel mask into the output directory
hpPath = os.path.join(outputDir,hpFN)
np.savez(hpPath,darkHPMFlat = darkHPMFlat)