def buildGrowthCurve(name, center, distances, pa, ba, inputImage, mask, isoA_max):
band = Settings.getConstants().band
#masked input array
inputImageM = np.ma.masked_array(inputImage, mask=mask)
ellipseMask = np.zeros((inputImage.shape), dtype=np.uint8)
ellipseMaskM = ellipseMask.copy()
fluxData = Photometry.initFluxData(inputImage, center, distances)
#currentPixels = center
#currentFlux = inputImage[center]
Npix = 1 #init
growthSlope = 200 #init
md = np.max(distances)
#print md, 'MD'
for isoA in range(1, min(int(isoA_max), int(md))):
#draw ellipse for all pixels:
currentPixels = ellipse.draw_ellipse(inputImage.shape, center[0], center[1], pa, isoA, ba)
#Npix = inputImage[currentPixels].shape[0]
#currentFlux = np.sum(inputImage[currentPixels])
ellipseMask[currentPixels] = 1
Npix = inputImage[currentPixels].shape[0]
#draw ellipse for masked pixels only:
currentPixelsM = ellipse.draw_ellipse(inputImageM.shape, center[0], center[1], pa, isoA, ba)
ellipseMaskM[currentPixelsM] = 1
maskedPixels = inputImageM[np.where((ellipseMaskM == 1) & (mask == 0))]
#print np.sum(maskedPixels), np.sum(inputImage[np.where(ellipseMask == 1)])
fluxData[isoA, 0] = isoA
fluxData[isoA, 1] = np.sum(inputImage[np.where(ellipseMask == 1)])# cumulative flux
fluxData[isoA, 2] = inputImage[np.where(ellipseMask == 1)].shape[0]
fluxData[isoA, 3] = np.sum(maskedPixels)# - maskedPixels.shape[0]*sky# cumulative flux, without masked pixels
fluxData[isoA, 4] = maskedPixels.shape[0]
#print Npix, NpixM, fluxData[isoA, 2], fluxData[isoA, 4]
isoA = isoA +1
#gc_sky = np.mean(fluxData[isoA-width:isoA-1, 2])
#flux = np.sum(inputImage[np.where(ellipseMask == 1)]) - sky*inputImage[np.where(ellipseMask == 1)].shape[0]
fluxData = fluxData[0:isoA-1,:]
#fluxData[:, 3] = fluxData[:, 3] - fluxData[isoA-1, 4]*sky
#print fluxData[isoA-1, 4], 'no pix'
#fluxData[:, 3] = np.cumsum(fluxData[:, 3])
#fluxData[:, 1] = np.cumsum(fluxData[:, 1])
#the last isoA value was incremented, so it should be subtracted
#fluxData[:, 1] = fluxData[:, 1] - sky*fluxData[:, 5]#cumulative flux, _sky_subtracted
#fluxData[:, 3] = fluxData[:, 3] - sky*fluxData[:, 4]
#fluxData[:, 2] = fluxData[:, 2] - sky #sky-subtracted flux per pixel
#print inputImage[np.where(ellipseMask == 1)].shape[0], '***************************************'
# --------------------------------------- writing an ellipse of counted points, testing only
plotGrowthCurve.plotGrowthCurve(fluxData, Settings.getConstants().band, name)
#hdu = pyfits.PrimaryHDU(ellipseMask)
#hdu.writeto('vimos_masks/Mask'+name+"_"+Settings.getConstants().band+'.fits', clobber=True)
np.savetxt('vimos_growth_curves/el/'+Settings.getConstants().band+'/gc_profile_el_new_'+name+'.csv', fluxData)
def calculateGrowthCurve(listFile, dataDir, i):
CALIFA_ID = str(i+1)
inputImage = Photometry.getInputFile(listFile, dataDir, i)
dbDir = '../db/'
imgDir = 'img/'+setBand()+'/'
center = Photometry.getCenter(listFile, i, dataDir)
distances = Photometry.createDistanceArray(listFile, i, dataDir)
#hdu = pyfits.PrimaryHDU(distances)
#hdu.writeto('distances.fits')
sky = inputImage[np.where(distances > int(round(Photometry.iso25D)))]
skyMean = np.mean(sky)
skySD = np.std(sky)
#i+1 in the next line reflects the fact that CALIFA id's start with 1
pa = db.dbUtils.getFromDB('PA', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+ CALIFA_ID)[0][0] #parsing tuples
ba = db.dbUtils.getFromDB('ba', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+ CALIFA_ID)[0][0]#parsing tuples
#ba = 1
#r_mag = db.dbUtils.getFromDB('r_mag', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+ CALIFA_ID)[0][0]#parsing tuples
#r_e = db.dbUtils.getFromDB('re', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+ CALIFA_ID)[0][0]#parsing tuples
#lucy_re = db.dbUtils.getFromDB('re', dbDir+'CALIFA.sqlite', 'lucie', ' where id = '+ CALIFA_ID)[0][0]#parsing tuples
#l_SkyMean = db.dbUtils.getFromDB('sky', dbDir+'CALIFA.sqlite', 'lucie', ' where id = '+ CALIFA_ID)[0][0] - 1000#parsing tuples
# print 'ba', ba
#fluxData = Photometry.buildGrowthCurve(inputImage, center, distances, skyMean, pa, ba)
#isoA = fluxData.shape[0]
# --------------------------------------- starting GC photometry in circular annuli
print 'CIRCULAR APERTURE'
#circFlux, circFluxData = Photometry.circularFlux(inputImage, center, distances, skyMean)
circFlux, circFluxData, gc_sky = Photometry.buildGrowthCurve(inputImage, center, distances, skyMean, pa, 1, str(i+1))
circRadius = circFluxData.shape[0]
#print circRadius, 'circle radius'
#otherFlux = circFluxData[-1, 1]
#print 'fluxes: mask:', circFlux, 'sum', otherFlux
try:
circHLR = circFluxData[np.where(np.floor(circFlux/circFluxData[:,1]) == 1)][0][0] - 1 #Floor() -1 -- last element where the ratio is 2
except IndexError as e:
circHLR = str(e)
circMag = Photometry.calculateFlux(circFlux, listFile, i)
# --------------------------------------- starting ellipse GC photometry
print 'ELLIPTICAL APERTURE'
totalFlux, fluxData, gc_sky = Photometry.buildGrowthCurve(inputImage, center, distances, skyMean, pa, ba, CALIFA_ID, e=True)
otherFlux = fluxData[fluxData.shape[0]-1, 6]
elMajAxis = fluxData.shape[0]
#print totalFlux - otherFlux, 'flux diff'
#print 't', totalFlux, 'o', otherFlux
diff = [CALIFA_ID, totalFlux - otherFlux]
utils.writeOut(diff, 'fluxdiff.txt')
elMag = Photometry.calculateFlux(totalFlux, listFile, i)
try:
elHLR = fluxData[np.where(np.floor(totalFlux/fluxData[:, 1]) == 1)][0][0] - 1 #Floor() -1 -- last element where the ratio is 2
print elHLR
except IndexError as e:
print 'err'
elHLR = e
plotGrowthCurve.plotGrowthCurve(fluxData, CALIFA_ID)
# --------------------- writing output jpg file with both outermost annuli
outputImage = inputImage
circpix = ellipse.draw_ellipse(inputImage.shape, center[0], center[1], pa, circRadius, 1)
elPix = ellipse.draw_ellipse(inputImage.shape, center[0], center[1], pa, elMajAxis, ba)
outputImage[circpix] = 0
outputImage[elPix] = 0
outputImage, cdf = imtools.histeq(outputImage)
#scipy.misc.imsave('img/output/'+CALIFA_ID+'.jpg', outputImage)
scipy.misc.imsave(imgDir+'snapshots/'+CALIFA_ID+'_gc.jpg', outputImage)
#hdu = pyfits.PrimaryHDU(outputImage)
#outputName = 'CALIFA'+CALIFA_ID+'.fits'
#hdu.writeto(outputName)
# ------------------------------------- formatting output row
output = [CALIFA_ID, elMag, elHLR, circMag, circHLR, np.mean(sky), gc_sky]
print output
#print skyMean, oldSky, 'sky'
return output
