bottom = fit[0] * np.log10(flux - np.sqrt(flux)) delm = top - bottom return delm / 2 # User specifies filename information os.chdir(config.blazar_photometry) cat_list = glob.glob('*.cat') # ref_source_input = input('Enter ref_source: ') ref_source_input = 'ref_mrk501' year_input = input('Enter year: ') ref_source_data = np.copy(ref_cats.ref_mrk501) # loop to create .txt files containing all data(verbose_file) and data for plotting(data_file) for catalog in cat_list: os.chdir(config.blazar_photometry) head_info = read_fits.decode_fitshead(catalog) filter_input = read_fits.get_filter(head_info) verbose_file = open(ref_source_input + '_' + filter_input + '_' + year_input + '_verbose.txt', 'a') data_file = open(ref_source_input + '_' + filter_input + '_' + year_input + '.txt', 'a') cat_data_list = mag_fit(ref_source=ref_source_data, cat=catalog, filt=filter_input) if not cat_data_list: pass else: for x in cat_data_list: verbose_file.write(str(x)) verbose_file.write(',') verbose_file.write('\n') verbose_file.close() cat_name = cat_data_list[0]
target_dec = td.bytes_to_str(target_dec) RA_dict = td.target_dict(target_name, target_RA) dec_dict = td.target_dict(target_name, target_dec) obj_ra = float(RA_dict[config.obj.lower()]) * 15. obj_dec = float(dec_dict[config.obj.lower()]) print(obj_ra, obj_dec) # grab all catalogs with correct object os.chdir(config.catalogs) catalogs = np.asarray(glob.glob('*.cat'), dtype=str) good_cats = np.empty(len(catalogs), dtype=bool) for i in range(len(catalogs)): header = read_fits.decode_fitshead(catalogs[i]) obj = str(read_fits.get_info('OBJECT', header)) obj = obj.strip() obj = obj.replace('_', '') obj = obj.replace(' ', '') obj = obj.lower() if obj == config.obj.lower(): good_cats[i] = True else: good_cats[i] = False # catalogs = catalogs[good_cats] mega = [] # create 3D array containing source number, flux, ra and dec for each catalog. # fig1 = aplpy.FITSFigure(catalogs[0]) for i in catalogs: