#Pyfits getdata series#

INPUTS: flatImagesPath - Path to the flat field exposuresДЇlatDarkImagesPath - Path to the flat field darks

#Pyfits getdata series#

Make a master flat using a series of images taken at twilightД«y fitting the individual pixel intensities over time using least-squaresДЄnd use the intercept as the normalizing factor in the master flat. Im = np.zeros((3, 1174, 1174), dtype=np.uint8)Дef twilightFlatMaker(flatImagesPath,flatDarkImagesPath,masterFlatSavePath,plots=False): Print skypi, sigpi, skypv, sigpv, skypb, sigpb Default is one for combined cubes.ДЇits = 'slice_w minutes.'.format((end - start)/60.) PngImage.paste(createRGB(scaledImages),(0,0))Д¬ube : MUSE data cube name containing both data and stat extensions. PngImage = Image.new(mode, scaledImages.shape) ScaledImages = scaleAsinh(images, scales =) Images.append(pyfits.getdata(args.bFile,0)) Images.append(pyfits.getdata(args.gFile,0))

Images.append(pyfits.getdata(args.rFile,0)) Parser.add_argument('-outputFile', dest='outputFile', help='Output PNG file name', default='rgb.png') Parser.add_argument('-addNoise', dest='noise', nargs='+', type=float, help='Gaussian noise to add to each image', default=) Parser.add_argument('bFile', help='Input B image', default='b.fits') Parser.add_argument('gFile', help='Input G image', default='g.fits') Parser.add_argument('rFile', help='Input R image', default='r.fits')

Parser = argparse.ArgumentParser(description='Coadd R,G,B fits images and return color png') '''Input a set of R G B images and return a color image'''