def align_norm(fnlist, tolerance=5, thresh=3.5): """Aligns a set of images to each other, as well as normalizing the images to the same average brightness. Both the alignment and normalization are accomplished through stellar photometry using the IRAF routine 'daophot'. The centroids of a handful of stars are found and used to run the IRAF routine 'imalign'. The instrumental magnitudes of the stars are used to determine by how much each image must be scaled for the photometry to match across images. The images are simply updated with their rescaled, shifted selves. This overwrites the previous images and adds the header keyword 'fpphot' to the images. A handful of temporary files are created during this process, which should all be deleted by the routine at the end. But if it is interrupted, they might not be. If the uncertainty images exist, this routine also shifts them by the same amounts as the intensity images, as well as updating the uncertainty values for both the new normalization and the uncertainties in normalizing the images. Inputs: fnlist -> List of strings, each the path to a fits image. tolerance -> How close two objects can be and still be considered the same object. Default is 3 pixels. thresh -> Optional. Level above sky background variation to look for objs. Default is 3.5 (times SkySigma). Decrease if center positions aren't being found accurately. Increase for crowded fields to decrease computation time. """ # Get image FWHMs fwhm = np.empty(len(fnlist)) firstimage = FPImage(fnlist[0]) toggle = firstimage.fwhm axcen = firstimage.axcen aycen = firstimage.aycen arad = firstimage.arad firstimage.close() if axcen is None: print "Error! Images have not yet been aperture-masked! Do this first!" crash() if toggle is None: print "Warning! FWHMs have not been measured!" print "Assuming 5 pixel FWHM for all images." for i in range(len(fnlist)): fwhm[i] = 5 else: for i in range(len(fnlist)): image = FPImage(fnlist[i]) fwhm[i] = image.fwhm image.close() # Get sky background levels skyavg = np.empty(len(fnlist)) skysig = np.empty(len(fnlist)) for i in range(len(fnlist)): image = FPImage(fnlist[i]) skyavg[i], skysig[i], _skyvar = image.skybackground() image.close() # Identify the stars in each image xlists = [] ylists = [] print "Identifying stars in each image..." for i in range(len(fnlist)): xlists.append([]) ylists.append([]) image = FPImage(fnlist[i]) axcen = image.axcen aycen = image.aycen arad = image.arad sources = daofind(image.inty-skyavg[i], fwhm=fwhm[i], threshold=thresh*skysig[i]).as_array() for j in range(len(sources)): # If the source is not near the center or edge centermask = ((sources[j][1]-axcen)**2 + (sources[j][2]-aycen)**2 > (0.05*arad)**2) edgemask = ((sources[j][1]-axcen)**2 + (sources[j][2]-aycen)**2 < (0.95*arad)**2) if np.logical_and(centermask, edgemask): xlists[i].append(sources[j][1]) ylists[i].append(sources[j][2]) image.close() # Match objects between fields print "Matching objects between images..." xcoo = [] ycoo = [] for i in range(len(xlists[0])): # For each object in the first image accept = True for j in range(1, len(fnlist)): # For each other image dist2 = ((np.array(xlists[j])-xlists[0][i])**2 + (np.array(ylists[j])-ylists[0][i])**2) if (min(dist2) > tolerance**2): accept = False break if accept: # We found an object at that position in every image xcoo.append(xlists[0][i]) ycoo.append(ylists[0][i]) # Create coordinate arrays for the photometry and shifting x = np.zeros((len(fnlist), len(xcoo))) y = np.zeros_like(x) for i in range(len(xcoo)): # For every object found in the first image for j in range(len(fnlist)): # Find that object in every image dist2 = ((np.array(xlists[j])-xcoo[i])**2 + (np.array(ylists[j])-ycoo[i])**2) index = np.argmin(dist2) x[j, i] = xlists[j][index] y[j, i] = ylists[j][index] # Do aperture photometry on the matched objects print "Performing photometry on matched stars..." counts = np.zeros_like(x) dcounts = np.zeros_like(x) for i in range(len(fnlist)): image = FPImage(fnlist[i]) apertures = CircularAperture((x[i], y[i]), r=2*fwhm[i]) annuli = CircularAnnulus((x[i], y[i]), r_in=3*fwhm[i], r_out=4*fwhm[i]) phot_table = aperture_photometry(image.inty, apertures, error=np.sqrt(image.vari)) sky_phot_table = aperture_photometry(image.inty, annuli, error=np.sqrt(image.vari)) counts[i] = phot_table["aperture_sum"] / apertures.area() counts[i] -= sky_phot_table["aperture_sum"] / annuli.area() counts[i] *= apertures.area() dcounts[i] = phot_table["aperture_sum_err"] / apertures.area() image.close() # Calculate the shifts and normalizations norm, dnorm = calc_norm(counts, dcounts) for i in range(x.shape[1]): x[:, i] = -(x[:, i] - x[0, i]) y[:, i] = -(y[:, i] - y[0, i]) xshifts = np.average(x, axis=1) yshifts = np.average(y, axis=1) # Normalize the images and put shifts in the image headers for i in range(len(fnlist)): image = FPImage(fnlist[i], update=True) image.phottog = "True" image.dnorm = dnorm[i] image.inty /= norm[i] image.vari = image.vari/norm[i]**2 image.xshift = xshifts[i] image.yshift = yshifts[i] image.close() return