print "Find extinction coefficient from photometry of standards" standards_set = set(x for x,t in zip(list_images["objname"], list_images["type"]) if t == "standards") coefficient_list = [] for obj in standards_set: input_db = os.path.join(directory, obj+".db") airmasses, magnitudes, filters, SNR = extract.main(input_db) magnitude_errors_minus, magnitude_errors_plus = snr.snr_to_error(SNR) mag_errors = (-magnitude_errors_minus + magnitude_errors_plus) / 2. for filt in set(filters.flatten()): # find columns with the current filter columns = [ii for ii in range(len(filters[0, :])) if filters[0,ii] == filt] # if there are more than two images if len(airmasses[0,columns]) > 0 and len(airmasses[0,columns]) > 2: ext_coeff, serror_ext_coeff = calculate_extinction(airmasses[:, columns], magnitudes[:, columns], err_magnitudes=mag_errors[:,columns]) coefficient_list.append(ext_coeff) #plt.plot(airmasses[:, columns], magnitudes[:, columns], 'o') #plt.show() print "Extinction coefficient for ", obj, " for filter ", filt, ":", ext_coeff, "+/-", serror_ext_coeff else: print "Not used ", obj, " with filter " + filt + ". Too few elements." ext_coeff, serror_ext_coeff = np.mean(coefficient_list), np.std(coefficient_list) / np.sqrt(len(coefficient_list) - 1) print "Mean extinction = ", ext_coeff, "+/-", serror_ext_coeff # Equate PSFs for scientific objects SciObj_set = set(x for x,t in zip(list_images["objname"], list_images["type"]) if t in ["cig", "clusters"]) for obj in SciObj_set:
"--aperture", "4.", "--annulus", "6", "--dannulus", "2", "--individual-fwhm", "--objectk", objectk, "--filterk", filterk, "--datek", datek, "--expk", exptimek, "--fwhmk", seeingk, "--airmk", airmassk, "--coordinates", coord_file, obj_images[0]] + obj_images + [output_db]) print "Find extinction coefficient from photometry of standards" standards_set = set(x for x,t in zip(list_images["objname"], list_images["type"]) if t == "standards") coefficient_list = [] for obj in standards_set: input_db = os.path.join(directory, obj+".db") airmasses, magnitudes, filters = extract.main(input_db) for filt in set(filters.flatten()): # find columns with the current filter columns = [ii for ii in range(len(filters[0, :])) if filters[0,ii] == filt] # if there are more than two images if len(airmasses[0,columns]) > 0 and len(airmasses[0,columns]) > 2: ext_coeff, sigma_ext_coeff = calculate_extinction(airmasses[:, columns], magnitudes[:, columns]) coefficient_list.append(ext_coeff) plt.plot(airmasses[:, columns], magnitudes[:, columns], 'o') plt.show() print "Extinction coefficient for ", obj, " for filter ", filt, ":", ext_coeff, "+/-", sigma_ext_coeff else: print "Not used ", obj, " with filter " + filt + ". Too few elements." ext_coeff, sigma_ext_coeff = np.mean(coefficient_list), np.std(coefficient_list) print "Extinction = ", ext_coeff, "+/-", sigma_ext_coeff # Equate PSFs for scientific objects SciObj_set = set(x for x,t in zip(list_images["objname"], list_images["type"]) if t in ["cig", "clusters"]) for obj in SciObj_set: obj_images = list(list_images["filename"][np.where(list_images["objname"] == obj)]) stars_images = [utils.replace_extension(ii, "radec") for ii in obj_images]