def testGetImageDet( fileName=FileName(run="PAL2012", date="20121208", tstamp="20121209-120530").photonList(), firstSec=0, integrationTime=-1, newMethod=True, doWeighted=False, ): plFile = photlist.PhotList(fileName) try: tic = time.time() image = plFile.getImageDet( firstSec=firstSec, integrationTime=integrationTime, newMethod=newMethod, wvlMin=4000, wvlMax=6000, doWeighted=doWeighted, ) tElapsed = time.time() - tic finally: # plFile.close() print "Deleting file instance" del plFile plotArray(image) print "Done, time taken (s): ", tElapsed return image
def display(self,normMin=None,normMax=None,expWeight=True,pclip=None,colormap=mpl.cm.gnuplot2, image=None, logScale=False): ''' Display the current image. Currently just a short-cut to utils.plotArray, but needs updating to mark RA and Dec on the axes. ''' if expWeight: toDisplay = np.copy(self.image*self.expTimeWeights) else: toDisplay = np.copy(self.image) if logScale is True: toDisplay = np.log10(toDisplay) if image is not None: toDisplay = image if pclip: normMin = np.percentile(toDisplay[np.isfinite(toDisplay)],q=pclip) normMax = np.percentile(toDisplay[np.isfinite(toDisplay)],q=100.0-pclip) #Display NaNs as zeros so it looks better toDisplay[np.isnan(toDisplay)] = 0 #Find the coordinates of the centers of the virtual pixels in degrees #raMin = (self.gridRA[0:-1] + self.gridRA[1:])/2.0 / np.pi * 180. #dec = (self.gridDec[0:-1] + self.gridDec[1:])/2.0 / np.pi * 180. utils.plotArray(toDisplay,cbar=True,normMin=normMin,normMax=normMax,colormap=colormap)
def testPlotArray(self): "exercise the plotArray function and make the file testPlotArray.png" xyarray = np.arange(20).reshape((4,5)) - 5 fn1 = inspect.stack()[0][3]+".png" utils.plotArray(xyarray, showMe=False, cbar=True, cbarticks=[-4, 1,2,4,8,16], cbarlabels=['negative four', 'one','two','four','eight','sixteen'], plotTitle='This is the Plot Title!', colormap=mpl.cm.terrain, pixelsToMark=[(0,1)], pixelMarkColor='red', plotFileName=fn1, sigma=2.0)
def displayImageDet(self, firstSec=0,integrationTime=-1,wvlMin=-np.Inf, wvlMax=np.Inf, normMin=None, normMax=None, showHotPix=False): ''' Display an image built from the photon list in detector coordinate space ''' im = self.getImageDet(firstSec=firstSec, integrationTime=integrationTime, wvlMin=wvlMin, wvlMax=wvlMax) utils.plotArray(im, normMin=normMin, normMax=normMax, cbar=True, fignum=None) if showHotPix is True: if self.hotPixTimeMask is None: self.parseHotPixTimeMask() badPix = hp.getHotPixels(self.hotPixTimeMask, firstSec=firstSec, integrationTime=integrationTime) x = np.arange(self.nCol) y = np.arange(self.nRow) xx, yy = np.meshgrid(x, y) if np.sum(badPix) > 0: mpl.scatter(xx[badPix], yy[badPix], c='y')
def displayCentroidResult(obsFileIn, time): ''' To show an image with the location of the centroid measured by CentroidCalc marked on top. Use for diagnostic purposes. INPUTS: obsFileIn - either an ObsFile instance or the filename of an obsFile to load. If a filename, the file will be closed on completion; if an ObsFile instance, it'll be left alone. time - time since beginning of file (in seconds) to display the centroid for. OUTPUTS: A reconstructed image with the calculated centroid plotted on top. The image will be integrated over whatever time slice was used by CentroidCalc for calculating the centroid at the given input time. ''' if type(obsFileIn)=='str': obsFile = ObsFile.ObsFile(obsFileIn) else: obsFile = obsFileIn ctrdFileName = FileName.FileName(obsFile=obsFile).centroidList() ctrdFile = tables.openFile(ctrdFileName, mode='r') #Get the boundaries of the time slices used for centroiding #(in seconds from start of array.) sliceTimes = tables.root.centroidlist.times.read() xPositions = tables.root.centroidlist.xPositions.read() yPositions = tables.root.centroidlist.yPositions.read() iSliceEnd = np.searchsorted(ctrdtimes, time) iSliceStart = iSliceEnd-1 sliceStartTime = sliceTimes[iSliceStart] sliceEndTime = sliceTimes[iSliceEnd] sliceXpos,sliceYpos = xPositions[iSliceStart],ypositions[iSliceStart] #Integrate to get the corresponding image im = obsFile.getPixelCountImage(sliceStartTime, sliceEndTime-sliceStartTime, getRawCount=True) #And display the result.... utils.plotArray(im, sigma=3, cbar=True, plotTitle=os.path.basename(ctrdFileName)+', ' +str(sliceStartTime)+' - '+ str(sliceEndTime)+'sec', pixelsToMark=[sliceXpos,sliceYpos], fignum=None)
def testGetPixelCountImage(bins=250, integrationTime=1): ''' Do two runs of getPixelCountImage and compare the results to check the repeatability (i.e., test the degree of effect of the random dithering in the wavelength handling.) INPUTS: bins - set the number of bins for the output histogram OUTPUTS: Displays the two images in ds9, as well as image1 divided by image2. Also shows the latter in a regular plot, as well as a histogram of the image1/image2 ratios over all pixels. ''' obsfile = loadTestObsFile.loadTestObsFile() obsfile.setWvlCutoffs(4000,8000) #Get first image im1 = obsfile.getPixelCountImage(firstSec=0, integrationTime=30, weighted=True, fluxWeighted=False, getRawCount=False, scaleByEffInt=False)['image'] #Get supposedly identical image im2 = obsfile.getPixelCountImage(firstSec=0, integrationTime=30, weighted=True, fluxWeighted=False, getRawCount=False, scaleByEffInt=False)['image'] utils.ds9Array(im1,frame=1) utils.ds9Array(im2,frame=2) divim = im1/im2 utils.ds9Array(divim,frame=3) utils.plotArray(divim, colormap=pl.cm.hot, cbar=True, normMax=np.mean(divim)+2*np.std(divim)) toHist = divim.flatten() toHist = toHist[np.isfinite(toHist)] pl.figure() pl.hist(toHist,bins=bins) pl.title('Ratio of image1/image2, wavelength range '+str(obsfile.wvlLowerLimit) +'-'+str(obsfile.wvlUpperLimit)+'Ang') pl.xlabel('Ratio') pl.ylabel('Number of pixels') print 'Mean image1/image2: ',np.mean(toHist) print 'Std. dev image1/image2: ',np.std(toHist)
def makeImageStack(fileNames='photons_*.h5', dir=os.getenv('MKID_PROC_PATH', default="/Scratch")+'/photonLists/20121211', detImage=False, saveFileName='stackedImage.pkl', wvlMin=3500, wvlMax=12000, doWeighted=True, medCombine=False, vPlateScale=0.2, nPixRA=250,nPixDec=250,maxBadPixTimeFrac=0.2,integrationTime=-1, outputdir=''): ''' Create an image stack INPUTS: filenames - string, list of photon-list .h5 files. Can either use wildcards (e.g. 'mydirectory/*.h5') or if string starts with an @, supply a text file which contains a list of file names to stack. (e.g., 'mydirectory/@myfilelist.txt', where myfilelist.txt is a simple text file with one file name per line.) dir - to provide name of a directory in which to find the files detImage - if True, show the images in detector x,y coordinates instead of transforming to RA/dec space. saveFileName - name of output pickle file for saving final resulting object. doWeighted - boolean, if True, do the image flatfield weighting. medCombine - experimental, if True, do a median combine of the image stack instead of just adding them all.... Prob. should be implemented properly at some point, just a fudge for now. vPlateScale - (arcsec/virtual pixel) - to set the plate scale of the virtual pixels in the outputs image. nPixRA,nPixDec - size of virtual pixel grid in output image. maxBadPixTimeFrac - Maximum fraction of time which a pixel is allowed to be flagged as bad (e.g., hot) for before it is written off as permanently bad for the duration of a given image load (i.e., a given obs file). integrationTime - the integration time to use from each input obs file (from start of file). OUTPUTS: Returns a stacked image object, saves the same out to a pickle file, and (depending whether it's still set to or not) saves out the individual non- stacked images as it goes. ''' #Get the list of filenames if fileNames[0]=='@': #(Note, actually untested, but should be more or less right...) files=[] with open(fileNames[1:]) as f: for line in f: files.append(os.path.join(dir,line.strip())) else: files = glob.glob(os.path.join(dir, fileNames)) #Initialise empty image centered on Crab Pulsar virtualImage = rdi.RADecImage(nPixRA=nPixRA,nPixDec=nPixDec,vPlateScale=vPlateScale, cenRA=1.4596725441339724, cenDec=0.38422539085925933) imageStack = [] for eachFile in files: if os.path.exists(eachFile): print 'Loading: ',os.path.basename(eachFile) #fullFileName=os.path.join(dir,eachFile) phList = pl.PhotList(eachFile) baseSaveName,ext=os.path.splitext(os.path.basename(eachFile)) if detImage is True: imSaveName=os.path.join(outputdir,baseSaveName+'det.tif') im = phList.getImageDet(wvlMin=wvlMin,wvlMax=wvlMax) utils.plotArray(im) mpl.imsave(fname=imSaveName,arr=im,colormap=mpl.cm.gnuplot2,origin='lower') if eachFile==files[0]: virtualImage=im else: virtualImage+=im else: imSaveName=os.path.join(outputdir,baseSaveName+'.tif') virtualImage.loadImage(phList,doStack=not medCombine,savePreStackImage=imSaveName, wvlMin=wvlMin, wvlMax=wvlMax, doWeighted=doWeighted, maxBadPixTimeFrac=maxBadPixTimeFrac, integrationTime=integrationTime) imageStack.append(virtualImage.image*virtualImage.expTimeWeights) #Only makes sense if medCombine==True, otherwise will be ignored if medCombine==True: medComImage = scipy.stats.nanmedian(np.array(imageStack), axis=0) toDisplay = np.copy(medComImage) toDisplay[~np.isfinite(toDisplay)] = 0 utils.plotArray(toDisplay,pclip=0.1,cbar=True,colormap=mpl.cm.gray) else: virtualImage.display(pclip=0.5,colormap=mpl.cm.gray) medComImage = None mpl.show() else: print 'File doesn''t exist: ',eachFile #Save the results. #Note, if median combining, 'vim' will only contain one frame. If not, medComImage will be None. results = {'vim':virtualImage,'imstack':imageStack,'medim':medComImage} try: output = open(os.path(outputdir,saveFileName),'wb') pickle.dump(results,output,-1) output.close() except: warnings.warn('Unable to save results for some reason...') return results
ofs.setRm(degPerPix, math.degrees(theta), raArcsecPerSec, ) # # Make a FITS file of each frame of the cube for iFrame in range(66): frame = ofs.cubes[iFrame]['cube'].sum(axis=2) hdu = pyfits.PrimaryHDU(frame) fn = "%s-%02d.fit"%(ofs.name,iFrame) print "now make fn=",fn hdu.writeto(fn) # Whew! Now do the coaddition for all of the sequences. # This uses all wavelengths. The first improvement will be to # have it use a subset of the wavelength bins. mosaic = ofs.makeMosaicImage(range(66)) # Make a simple plot for now. You can also save data as a FITS file, or combine # the frames for three different wavelengths into a fabulous color picture! # But right now, let's just dump out a heat map so we something to show off. utils.plotArray(mosaic,cbar=True,plotTitle=ofs.name,showMe=False,plotFileName=ofs.name+"-all.png") # Write it out as a FITS file, too hdu = pyfits.PrimaryHDU(mosaic) fn = "%s-all.fit"%ofs.name hdu.writeto(fn) del ofs
for y in ally: if (np.abs(x-startpx))**2+(np.abs(y-startpy))**2 <= (r)**2 and 0 <= y and y < 46 and 0 <= x and x < 44: mask[y,x]=0. return mask def gaussian(height, center_x, center_y, width_x, width_y,offset): """Returns a gaussian function with the given parameters""" width_x = float(width_x) width_y = float(width_y) return lambda x,y: height*np.exp(-(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)+offset stackDict = np.load('nlttImageStackBlue15.npz') stack = stackDict['stack'] if len(sys.argv) == 1: print 'Useage: ',sys.argv[0],' iFrame' print """ set0 Frames 0-179 """ exit(1) iFrame = int(sys.argv[1]) frame = stack[:,:,iFrame] # plt.hist(np.ravel(frame),bins=100,range=(0,5000)) # plt.show() nanMask = np.isnan(frame) frame[nanMask] = 0 frame = np.ma.masked_array(frame,mask=nanMask) utils.plotArray(frame,cbar=True)
nlttTimesByPixels.append(timesInPixel) img[y,x] = sum(inPixel['FlatWeight']) rawImg[y,x] = len(inPixel) nlttPSF = np.concatenate(nlttPSFByPixels) nlttPSFTimes = np.concatenate(nlttTimesByPixels) secsInDay = 24*60*60. period = 0.2350606*secsInDay phases = (nlttPSFTimes % period)/(period) newtype=[('ArrivalTime', '<f8'),('Flag', '|u1'), ('Phase', '<f4'), ('PixelCol', '|u1'), ('PixelRow', '|u1'), ('Wavelength', '<f4'), ('Weight', '<f4')] nPhotons = len(nlttPSF) newTable = np.recarray(nPhotons,dtype=newtype) newTable['ArrivalTime'] = nlttPSFTimes newTable['Flag'] = nlttPSF['Flag'] newTable['Phase'] = phases timestream,timeEdges = np.histogram(nlttPSFTimes,weights=nlttPSF['FlatWeight'],bins=300*len(timestampList)) phaseTimestream,phaseTimeEdges = np.histogram(phases,weights=nlttPSF['FlatWeight'],bins=30*len(timestampList)) plt.plot(timeEdges[:-1],timestream) plt.show() counts = [len(pixelPhotons) for pixelPhotons in nlttPSFByPixels] utils.plotArray(img,cbar=True,normMax=800000) utils.plotArray(rawImg,cbar=True) print img[31,29] print rawImg[31,29] tbl = np.vstack([timeEdges[:-1],timestream]) np.savetxt(out,tbl.T)
def main(): obsSequence0 = """ 051516 052520 """ obsSequence1 = """ 033323 041843 045902 """ obsSequence2 = """ 050404 054424 """ obsSequence3 = """ 054926 062942 """ run = "PAL2012" obsSequences = [obsSequence1, obsSequence2, obsSequence3] wvlCals = ["063518", "063518", "063518"] flatCals = ["20121211", "20121211", "20121211"] fluxCalDates = ["20121206", "20121206", "20121206"] fluxCals = ["20121207-072055", "20121207-072055", "20121207-072055"] # Row coordinate of center of crab pulsar for each obsSequence centersRow = [29, 29, 10] # Col coordinate of center of crab pulsar for each obsSequence centersCol = [29, 30, 14] obsUtcDate = "20121212" obsUtcDates = ["20121212", "20121212", "20121212"] obsFileNames = [] obsFileNameTimestamps = [] wvlFileNames = [] flatFileNames = [] fluxFileNames = [] timeMaskFileNames = [] 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] obsFileNames.append([FileName(run=run, date=sunsetDate, tstamp=ts).obs() for ts in obsSequence]) timeMaskFileNames.append([FileName(run=run, date=sunsetDate, tstamp=ts).timeMask() for ts in obsSequence]) wvlCalTstamp = obsUtcDate + "-" + wvlCals[iSeq] wvlFileNames.append(FileName(run=run, date=sunsetDate, tstamp=wvlCalTstamp).calSoln()) fluxFileNames.append(FileName(run=run, date=fluxCalDates[iSeq], tstamp=fluxCals[iSeq]).fluxSoln()) flatFileNames.append(FileName(run=run, date=flatCals[iSeq], tstamp="").flatSoln()) for iSeq, obsSequence in enumerate(obsSequences): obsSequence = obsSequence.strip().split() print obsSequence for iOb, obs in enumerate(obsSequence): timeMaskFileName = timeMaskFileNames[iSeq][iOb] if not os.path.exists(timeMaskFileName): print "Running hotpix for ", obs hp.findHotPixels(obsFileNames[iSeq][iOb], timeMaskFileName) print "Flux file pixel mask saved to %s" % (timeMaskFileName) apertureRadius = 4 obLists = [[ObsFile(fn) for fn in seq] for seq in obsFileNames] tstampFormat = "%H:%M:%S" # print 'fileName','headerUnix','headerUTC','logUnix','packetReceivedUnixTime' for iSeq, obList in enumerate(obLists): for iOb, ob in enumerate(obList): print ob.fileName centerRow = centersRow[iSeq] centerCol = centersCol[iSeq] circCol, circRow = circ(centerCol, centerRow) ob.loadTimeAdjustmentFile(FileName(run="PAL2012").timeAdjustments()) ob.loadWvlCalFile(wvlFileNames[iSeq]) ob.loadFlatCalFile(flatFileNames[iSeq]) ob.loadFluxCalFile(fluxFileNames[iSeq]) timeMaskFileName = timeMaskFileNames[iSeq][iOb] ob.loadHotPixCalFile(timeMaskFileName) ob.setWvlCutoffs(None, None) for iSeq, obList in enumerate(obLists): for iOb, ob in enumerate(obList): print ob.fileName centerRow = centersRow[iSeq] centerCol = centersCol[iSeq] circCol, circRow = circ(centerCol, centerRow) imgDict = ob.getPixelCountImage() img = imgDict["image"] utils.plotArray(img, showMe=False) aperture = plt.Circle((centerCol, centerRow), rad, fill=False, color="g") aperture2 = plt.Circle((centerCol, centerRow), 2 * rad, fill=False, color="g") plt.gca().add_patch(aperture) plt.gca().add_patch(aperture2) plt.show()
def quantifyBadTime(inputFileName, startTime=0, endTime=-1, defaultTimeMaskFileName='./testTimeMask.h5', timeStep=1, fwhm=3.0, boxSize=5, nSigmaHot=3.0, nSigmaCold=2.5,maxIter=5,binWidth=3, dispToPickle=False, showHist=False, bkgdPercentile=50, weighted=False,fluxWeighted=False, useRawCounts=True): ''' Function to calculate various metrics for the degree of bad pixel behaviour in a raw raw obs file. Calculates the mean total hot/cold/dead time per good pixel (i.e., per pixel which is not permanently dead, hot, or cold). Makes a couple of heat maps showing time spent bad in each way for each pixel, as well as a histogram of times spent bad for the *temporarily* bad pixels. JvE Nov 20 2013. The parameters for the finding algorithm may need to be tuned a little, but the defaults should basically work, in principle. nSigmaHot and nSigmaCold are good places to start if things need playing around with. e.g. call, in principle: from hotpix import quantifyHotTime as qht qht.quantifyHotTime('/Users/vaneyken/Data/UCSB/ARCONS/turkDataCopy/ScienceData/PAL2012/20121208/obs_20121209-120530.h5') - should be all it needs.... INPUTS: inputFileName - either a raw obs. filename or a time mask file. If the former, runs a hot pixel search; otherwise uses time mask file instead. startTime, endTime - start and end times within the obs file to calculate the hot pixels for. (endTime =-1 means to end of file). defaultTimeMaskFileName - use this filename to output new time mask to (if inputFileName is an obs file) binWidth - width of time bins for plotting the bad-time histogram (seconds) dispToPickle - if not False, saves the data for the histogram plot to a pickle file. If a string, then uses that as the name for the pickle file. Otherwise saves to a default name. Saves a dictionary with four entries, the first three of which are each a flat array of total times spent bad for every pixel (in sec) ('hotTime','coldTime','deadTime'). The fourth, 'duration', is the duration of the input time-mask in seconds. showHist - if True, plot up a histogram of everything. Currently fails though if there are no bad-flagged intervals in any of the type categories (or their intersections, hot+cold, cold+dead). Parameters passed on to findHotPixels routine if called (see also documentation for that function): timeStep #Check for hot pixels every timeStep seconds fwhm #Expected full width half max of PSF in pixels. Any pixel #with flux much tighter than this will be flagged as bad. #Larger value => more sensitive hot pixel flagging. boxSize #Compare flux in each pixel with median flux in a #surrounding box of this size on a side. nSigmaHot #Require flux in a pixel to be > nSigmaHot std. deviations #above the max expected for a Gaussian PSF in order for it #to be flagged as hot. Larger value => less sensitive flagging. nSigmaCold #Require flux to be < nSigmaCold std. deviations below the median #in a surrounding box in order to be flagged as cold (where std. #deviation is estimated as the square root of the median flux). maxIter #Max num. of iterations for the bad pixel detection algorithm. bkdgPercentile, weighted, fluxWeighted - See findHotPixels() in hotpix.hotpixels.py OUTPUTS: A bunch of statistics on the different kinds of bad pixel behaviour, and a 'heat' plot showing how much time each pixel was bad in the array, for each type of behaviour. In theory it can plot a histogram of everything too, but currently it breaks if there are no bad intervals within any of the type categories (hot only, hot and cold, cold only, cold and dead, dead only...) ''' defaultPklFileName = 'badPixTimeHist.pickle' #Check whether the input file is a time mask or a regular obs file. absInputFileName = os.path.abspath(inputFileName) #To avoid weird issues with the way findHotPixels expands paths.... hdffile = tables.openFile(absInputFileName) inputIsTimeMaskFile = '/timeMasks' in hdffile hdffile.close() #Decide whether to generate a new time mask file or not if inputIsTimeMaskFile: print 'Input file is a time mask file' timeMaskFileName = absInputFileName else: print 'Assuming input file is an obs. file' timeMaskFileName = os.path.abspath(defaultTimeMaskFileName) if os.path.exists(timeMaskFileName): response='' while response != 'u' and response !='r': response = raw_input(timeMaskFileName+' already exists - "u" to use this (default) or "r" to regenerate? ') response = response.strip().lower() if response == '': response = 'u' else: response = 'r' #If the file *didn't already exist, pretend the user entered 'r' despite not having been asked. if response == 'r': #Make/regenerate the hot pixel file. print 'Making new hot pixel time mask file '+timeMaskFileName hp.findHotPixels(inputFileName=absInputFileName, outputFileName=timeMaskFileName, startTime=startTime, endTime=endTime, timeStep=timeStep, fwhm=fwhm, boxSize=boxSize, nSigmaHot=nSigmaHot, nSigmaCold=nSigmaCold, display=True, dispToPickle=dispToPickle, maxIter=maxIter, bkgdPercentile=bkgdPercentile, weighted=weighted,fluxWeighted=fluxWeighted,useRawCounts=useRawCounts) #Read in the time mask file and calculate hot, cold, and 'other' bad time per pixel. timeMask = hp.readHotPixels(timeMaskFileName) hotTime = np.zeros((timeMask.nRow,timeMask.nCol)) coldTime = np.zeros((timeMask.nRow,timeMask.nCol)) deadTime = np.zeros((timeMask.nRow,timeMask.nCol)) otherTime = np.zeros((timeMask.nRow,timeMask.nCol)) hotIntervals = np.array([]) coldIntervals = np.array([]) deadIntervals = np.array([]) otherIntervals = np.array([]) reasonStringMap = timeMask.reasonEnum for iRow in range(timeMask.nRow): for iCol in range(timeMask.nCol): for eachInterval,eachReasonCode in zip(timeMask.intervals[iRow,iCol], timeMask.reasons[iRow,iCol]): eachReasonString = reasonStringMap(eachReasonCode) #Convert integer code to human readable string intSize = utils.intervalSize(eachInterval) if eachReasonString == 'hot pixel': hotTime[iRow,iCol] += intSize hotIntervals = np.append(hotIntervals, intSize) elif eachReasonString == 'cold pixel': coldTime[iRow,iCol] += intSize coldIntervals = np.append(coldIntervals, intSize) elif eachReasonString == 'dead pixel': deadTime[iRow,iCol] += intSize deadIntervals = np.append(deadIntervals, intSize) else: otherTime[iRow,iCol] += intSize otherIntervals = np.append(otherIntervals, intSize) if np.size(hotIntervals) == 0: hotIntervals = np.array([-1]) if np.size(coldIntervals) == 0: coldIntervals = np.array([-1]) if np.size(deadIntervals) == 0: deadIntervals = np.array([-1]) if np.size(otherIntervals) == 0: otherIntervals = np.array([-1]) totBadTime = hotTime+coldTime+deadTime+otherTime maskDuration = timeMask.endTime-timeMask.startTime #Figure out which pixels are hot, cold, permanently hot, temporarily cold, etc. etc. nPix = timeMask.nRow * timeMask.nCol hotPix = hotTime > 0.1 coldPix = coldTime > 0.1 deadPix = deadTime > 0.1 otherPix = otherTime > 0.1 multiBehaviourPix = ( (np.array(hotPix,dtype=int)+np.array(coldPix,dtype=int) +np.array(deadPix,dtype=int)+np.array(otherPix,dtype=int)) > 1) #assert np.all(deadTime[deadPix] == maskDuration) #All dead pixels should be permanently dead.... tol = timeStep/10. #Tolerance for the comparison operators below. permHotPix = hotTime >= maskDuration-tol permColdPix = coldTime >= maskDuration-tol permDeadPix = deadTime >= maskDuration-tol permOtherPix = otherTime >= maskDuration-tol permGoodPix = (hotTime+coldTime+deadTime+otherTime < tol) permBadPix = permHotPix | permColdPix | permDeadPix | permOtherPix tempHotPix = (hotTime < maskDuration-tol) & (hotTime > tol) tempColdPix = (coldTime < maskDuration-tol) & (coldTime > tol) tempDeadPix = (deadTime < maskDuration-tol) & (deadTime > tol) tempOtherPix = (otherTime < maskDuration-tol) & (otherTime > tol) tempGoodPix = tempHotPix | tempColdPix | tempDeadPix | tempOtherPix #Bitwise or should work okay with boolean arrays tempBadPix = tempGoodPix #Just to be explicit about it.... nGoodPix = np.sum(permGoodPix | tempGoodPix) #A 'good pixel' is either temporarily or permanently good #assert np.sum(tempDeadPix) == 0 #Shouldn't be any temporarily dead pixels. APPARENTLY THERE ARE.... assert np.all((tempHotPix & permHotPix)==False) #A pixel can't be permanently AND temporarily hot assert np.all((tempColdPix & permColdPix)==False) #... etc. assert np.all((tempOtherPix & permOtherPix)==False) assert np.all((tempDeadPix & permDeadPix)==False) #Print out the results print '----------------------------------------------' print print '# pixels total: ', nPix print 'Mask duration (sec): ', maskDuration print print '% hot pixels: ', float(np.sum(permHotPix+tempHotPix))/nPix * 100. print '% cold pixels: ', float(np.sum(permColdPix+tempColdPix))/nPix * 100. print '% dead pixels: ', float(np.sum(permDeadPix+tempDeadPix))/nPix * 100. print '% other pixels: ', float(np.sum(permOtherPix+tempOtherPix))/nPix * 100. print print '% permanently hot pixels: ', float(np.sum(permHotPix))/nPix * 100. print '% permanently cold pixels: ', float(np.sum(permColdPix))/nPix * 100. print '% permanently dead pixels: ', float(np.sum(permDeadPix))/nPix * 100. print '% permanently "other" bad pixels: ', float(np.sum(permOtherPix))/nPix * 100. print print '% temporarily hot pixels: ', float(np.sum(tempHotPix))/nPix * 100. print '% temporarily cold pixels: ', float(np.sum(tempColdPix))/nPix * 100. print '% temporarily dead pixels: ', float(np.sum(tempDeadPix))/nPix * 100. print '% temporarily "other" bad pixels: ', float(np.sum(tempOtherPix))/nPix * 100. print print '% pixels showing multiple bad behaviours: ', float(np.sum(multiBehaviourPix))/nPix * 100. print print '% permanently bad pixels: ', float(np.sum(permBadPix))/nPix * 100. print '% temporarily bad pixels: ', float(np.sum(tempGoodPix))/nPix * 100. #Temp. good == temp. bad! print '% permanently good pixels: ', float(np.sum(permGoodPix))/nPix * 100. print print 'Mean temp. hot pixel time per good pixel: ', np.sum(hotTime[tempHotPix])/nGoodPix print 'Mean temp. hot pixel time per temp. hot pixel: ', np.sum(hotTime[tempHotPix])/np.sum(tempHotPix) print print 'Mean temp. cold pixel time per good pixel: ', np.sum(coldTime[tempColdPix])/nGoodPix print 'Mean temp. cold pixel time per temp. cold pixel: ', np.sum(coldTime[tempColdPix])/np.sum(tempColdPix) print print 'Mean temp. "other" bad pixel time per good pixel: ', np.sum(otherTime[tempOtherPix])/nGoodPix print 'Mean temp. "other" bad pixel time per temp. "other" pixel: ', np.sum(otherTime[tempOtherPix])/np.sum(tempOtherPix) print print '(All times in seconds)' print print 'Done.' print if np.sum(tempOtherPix) > 0 or np.sum(permOtherPix) > 0: print '--------------------------------------------------------' print 'WARNING: Pixels flagged for "other" reasons detected - ' print 'Histogram plot will not account for these!!' print '--------------------------------------------------------' #Display contour plots of the array of total bad times for each pixel for each type of behaviour utils.plotArray(hotTime, plotTitle='Hot time per pixel (sec)', fignum=None, cbar=True) utils.plotArray(coldTime, plotTitle='Cold time per pixel (sec)', fignum=None, cbar=True) utils.plotArray(otherTime, plotTitle='Other bad time per pixel (sec)', fignum=None, cbar=True) utils.plotArray(deadTime, plotTitle='Dead time per pixel (sec)', fignum=None, cbar=True) #Make histogram of time spent 'bad' for the temporarily bad pixels. #Ignore pixels flagged as bad for 'other' reasons (other than hot/cold/dead), #of which there should be none at the moment. assert np.all(otherPix == False) #Find total bad time for pixels which go only one of hot, cold, or dead onlyHotBadTime = totBadTime[hotPix & ~coldPix & ~deadPix] onlyColdBadTime = totBadTime[~hotPix & coldPix & ~deadPix] onlyDeadBadTime = totBadTime[~hotPix & ~coldPix & deadPix] #Find total bad time for pixels which alternate between more than one bad state hotAndColdBadTime = totBadTime[hotPix & coldPix & ~deadPix] hotAndDeadBadTime = totBadTime[hotPix & ~coldPix & deadPix] coldAndDeadBadTime = totBadTime[~hotPix & coldPix & deadPix] hotAndColdAndDeadBadTime = totBadTime[hotPix & coldPix & deadPix] allGoodBadTime = totBadTime[~hotPix & ~coldPix & ~deadPix] assert np.sum(allGoodBadTime) == 0 if dispToPickle is not False: #Save to pickle file to feed into a separate plotting script, primarily for #the pipeline paper. if type(dispToPickle) is str: pklFileName = dispToPickle else: pklFileName = defaultPklFileName pDict = {'hotTime':hotTime, 'coldTime':coldTime, 'deadTime':deadTime, 'onlyHotBadTime':onlyHotBadTime, 'onlyColdBadTime':onlyColdBadTime, 'onlyDeadBadTime':onlyDeadBadTime, 'hotAndColdBadTime':hotAndColdBadTime, 'hotAndDeadBadTime':hotAndDeadBadTime, 'coldAndDeadBadTime':coldAndDeadBadTime, 'hotAndColdAndDeadBadTime':hotAndColdAndDeadBadTime, 'maskDuration':maskDuration} #pDict = {"hotTime":hotTime.ravel(),"coldTime":coldTime.ravel(),"deadTime":deadTime.ravel(), # "duration":maskDuration} print 'Saving to file: ',pklFileName output = open(pklFileName, 'wb') pickle.dump(pDict,output) output.close() assert np.size(hotTime)==nPix and np.size(coldTime)==nPix and np.size(deadTime)==nPix assert (len(onlyHotBadTime)+len(onlyColdBadTime)+len(onlyDeadBadTime)+len(hotAndColdBadTime) +len(coldAndDeadBadTime)+len(hotAndDeadBadTime)+len(hotAndColdAndDeadBadTime) +len(allGoodBadTime))==nPix if showHist is True: #Be sure it's okay to leave hot+dead pixels out, and hot+cold+dead pixels. #assert len(hotAndDeadBadTime)==0 and len(hotAndColdAndDeadBadTime)==0 mpl.figure() norm = 1. #1./np.size(hotTime)*100. dataList = [onlyHotBadTime,hotAndColdBadTime,onlyColdBadTime,coldAndDeadBadTime,onlyDeadBadTime] dataList2 = [x if np.size(x)>0 else np.array([-1.0]) for x in dataList] #-1 is a dummy value for empty arrays, so that pyplot.hist doesn't barf. weights = [np.ones_like(x)*norm if np.size(x)>0 else np.array([0]) for x in dataList] mpl.hist(dataList2, range=None, #[-0.1,maskDuration+0.001], #Eliminate data at 0sec and maskDuration sec. (always good or permanently bad) weights=weights, label=['Hot only','Hot/cold','Cold only','Cold/dead','Dead only'], #,'Hot/dead','Hot/cold/dead'], color=['red','pink','lightgray','lightblue','blue'], #,'green','black'], bins=maskDuration/binWidth,histtype='stepfilled',stacked=True,log=False) mpl.title('Duration of Bad Pixel Behaviour - '+os.path.basename(inputFileName)) mpl.xlabel('Total time "bad" (sec)') mpl.ylabel('Percantage of pixels') mpl.legend() mpl.figure() mpl.hist(hotIntervals, bins=maskDuration/binWidth) print 'Median hot interval: ', np.median(hotIntervals) mpl.xlabel('Duration of hot intervals') mpl.ylabel('Number of intervals') mpl.figure() mpl.hist(coldIntervals, bins=maskDuration/binWidth) print 'Median cold interval: ', np.median(coldIntervals) mpl.xlabel('Duration of cold intervals') mpl.ylabel('Number of intervals') mpl.figure() mpl.hist(deadIntervals, bins=maskDuration/binWidth) print 'Median dead interval: ', np.median(deadIntervals) mpl.xlabel('Duration of dead intervals') mpl.ylabel('Number of intervals') mpl.figure() mpl.hist(otherIntervals, bins=maskDuration/binWidth) print 'Median "other" interval: ', np.median(otherIntervals) mpl.xlabel('Duration of "other" intervals') mpl.ylabel('Number of intervals') print 'Mask duration (s): ',maskDuration print 'Number of pixels: ',nPix print 'Fraction at 0s (hot,cold,dead): ', np.array([np.sum(hotTime<tol),np.sum(coldTime<tol), np.sum(deadTime<tol)]) / float(nPix) print 'Fraction at '+str(maskDuration)+'s (hot,cold,dead): ', np.array([np.sum(hotTime>maskDuration-tol), np.sum(coldTime>maskDuration-tol), np.sum(deadTime>maskDuration-tol)])/float(nPix)
def divideObsFiles(fileName1='/Users/vaneyken/Data/UCSB/ARCONS/turkDataCopy/ScienceData/PAL2012/20121211/flat_20121212-134024.h5', fileName2='/Users/vaneyken/Data/UCSB/ARCONS/turkDataCopy/ScienceData/PAL2012/20121211/flat_20121212-134637.h5', firstSec=0, integrationTime=10., nbins=None): ''' Divide the raw image from one obs file by another, and display and return the result. This works with raw counts, so the obs file need not be calibrated. INPUTS: fileName1, fileName2 -- names of two obs files to divide by each other, OR two ObsFile objects can be passed directly. firstSec - time during the obs files at which to start integration of images (sec) integrationTime - time to integrate for to make the images (sec) nbins - number of bins for histogram plot (if None, makes a semi-reasonable guess) OUTPUTS: Displays the divided result to the screen and to ds9. Returs a tuple of image arrays: divided image, input image 1, input image 2, obsFile 1, obsFile 2 ''' if type(fileName1) is str: obsf1 = ObsFile.ObsFile(fileName1) fn1 = fileName1 else: obsf1=fileName1 #Otherwise just assume it's an ObsFile instance. fn1=obsf1.fileName if type(fileName2) is str: obsf2 = ObsFile.ObsFile(fileName2) fn2 = fileName2 else: obsf2=fileName2 fn2=obsf2.fileName print 'Reading '+os.path.basename(fn1) pci1 = obsf1.getPixelCountImage(firstSec=firstSec,integrationTime=integrationTime,getRawCount=True) print 'Reading '+os.path.basename(fn2) pci2 = obsf2.getPixelCountImage(firstSec=firstSec,integrationTime=integrationTime,getRawCount=True) im1 = pci1['image'] im2 = pci2['image'] divIm = im1/im2 med = np.median(divIm[~np.isnan(divIm)]) #Approximate std. dev from median abs. dev. (more robust) sdev = astropy.stats.median_absolute_deviation(divIm[~np.isnan(divIm)]) *1.4826 badFlag = np.abs(divIm - med) > 3.0*sdev print 'Displaying' toDisplay = np.copy(divIm) toDisplay[np.isnan(toDisplay)]=0 utils.plotArray(toDisplay, cbar=True, normMin=med-4.*sdev, normMax=med+4.*sdev, colormap=mpl.cm.hot, fignum=None) yy,xx = np.indices(np.shape(divIm)) mpl.scatter(xx[badFlag], yy[badFlag], marker='o', c='r') utils.ds9Array(divIm) mpl.figure() if nbins is None: nbins=np.sqrt(np.sum(np.isfinite(divIm))) mpl.hist(divIm[np.isfinite(divIm)].flatten(),bins=nbins) print 'Median: ',med print 'Approx std. dev. (M.A.D * 1.4826): ',sdev print 'Done.' return divIm,im1,im2,obsf1,obsf2,badFlag
width_x = float(width_x) width_y = float(width_y) return lambda x,y: height*np.exp(-(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)+offset stackDict = np.load('nlttImageStack.npz') stack = stackDict['stack'] if len(sys.argv) == 1: print 'Useage: ',sys.argv[0],' iFrame' print """ set0 Frames 0-89 set1 Frames 90-269 set2 Frames 270-359 """ exit(1) iFrame = int(sys.argv[1]) frame = stack[:,:,iFrame] # plt.hist(np.ravel(frame),bins=100,range=(0,5000)) # plt.show() nanMask = np.isnan(frame) frame[nanMask] = 0 #nanMask[frame>500]=1 #nanMask[frame<150]=1 frame = np.ma.masked_array(frame,mask=nanMask) sky = np.ma.masked_array(frame[0:20,20:40],mask=nanMask[0:20,20:40]) print sky.std(),np.ma.median(sky),np.ma.mean(sky) utils.plotArray(sky,cbar=True)
def makeImageStack(fileNames='photons_*.h5', dir=os.getenv('MKID_PROC_PATH', default="/Scratch")+'/photonLists/20131209', detImage=False, saveFileName='stackedImage.pkl', wvlMin=None, wvlMax=None, doWeighted=True, medCombine=False, vPlateScale=0.2, nPixRA=250,nPixDec=250): ''' Create an image stack INPUTS: filenames - string, list of photon-list .h5 files. Can either use wildcards (e.g. 'mydirectory/*.h5') or if string starts with an @, supply a text file which contains a list of file names to stack. (e.g., 'mydirectory/@myfilelist.txt', where myfilelist.txt is a simple text file with one file name per line.) dir - to provide name of a directory in which to find the files detImage - if True, show the images in detector x,y coordinates instead of transforming to RA/dec space. saveFileName - name of output pickle file for saving final resulting object. doWeighted - boolean, if True, do the image flatfield weighting. medCombine - experimental, if True, do a median combine of the image stack instead of just adding them all.... Prob. should be implemented properly at some point, just a fudge for now. vPlateScale - (arcsec/virtual pixel) - to set the plate scale of the virtual pixels in the outputs image. nPixRA,nPixDec - size of virtual pixel grid in output image. OUTPUTS: Returns a stacked image object, saves the same out to a pickle file, and (depending whether it's still set to or not) saves out the individual non- stacked images as it goes. ''' #Get the list of filenames if fileNames[0]=='@': #(Note, actually untested, but should be more or less right...) files=[] with open(fileNames[1:]) as f: for line in f: files.append(os.path.join(dir,line.strip())) else: files = glob.glob(os.path.join(dir, fileNames)) #Initialise empty image centered on Crab Pulsar virtualImage = rdi.RADecImage(nPixRA=nPixRA,nPixDec=nPixDec,vPlateScale=vPlateScale, cenRA=3.20238771, cenDec=0.574944617) imageStack = [] for eachFile in files: if os.path.exists(eachFile): print 'Loading: ',os.path.basename(eachFile) #fullFileName=os.path.join(dir,eachFile) phList = pl.PhotList(eachFile) baseSaveName,ext=os.path.splitext(os.path.basename(eachFile)) if detImage is True: imSaveName=baseSaveName+'det.tif' im = phList.getImageDet(wvlMin=wvlMin,wvlMax=wvlMax) utils.plotArray(im) mpl.imsave(fname=imSaveName,arr=im,colormap=mpl.cm.gnuplot2,origin='lower') if eachFile==files[0]: virtualImage=im else: virtualImage+=im else: imSaveName=baseSaveName+'.tif' virtualImage.loadImage(phList,doStack=not medCombine,savePreStackImage=imSaveName, wvlMin=wvlMin, wvlMax=wvlMax, doWeighted=doWeighted) imageStack.append(virtualImage.image*virtualImage.expTimeWeights) #Only makes sense if medCombine==True, otherwise will be ignored if medCombine==True: medComImage = scipy.stats.nanmedian(np.array(imageStack), axis=0) normMin = np.percentile(medComImage[np.isfinite(medComImage)],q=0.1) normMax = np.percentile(medComImage[np.isfinite(medComImage)],q=99.9) toDisplay = np.copy(medComImage) toDisplay[~np.isfinite(toDisplay)] = 0 #utils.plotArray(toDisplay,normMin=normMin,normMax=normMax) else: #virtualImage.display(pclip=0.1) medComImage = None else: print 'File doesn''t exist: ',eachFile #Save the results try: output = open(saveFileName,'wb') pickle.dump(virtualImage,output,-1) output.close() except: warnings.warn('Unable to save results for some reason...') return virtualImage, imageStack, medComImage