def reduce(file_list, label, calib_files, recipe_name=None, save_to=None, user_pars=None): """ Helper function used to prevent replication of code. Parameters ---------- file_list : list List of files that will be reduced. label : str Labed used on log files name. calib_files : list List of calibration files properly formatted for DRAGONS Reduce(). recipe_name : str, optional Name of the recipe used to reduce the data. save_to : str, optional Stores the calibration files locally in a list. user_pars : list, optional List of user parameters Returns ------- str : Output reduced file. list : An updated list of calibration files. """ objgraph = pytest.importorskip("objgraph") logutils.get_logger().info("\n\n\n") logutils.config(file_name=f"test_image_{label}.log") r = Reduce() r.files = file_list r.ucals = normalize_ucals(r.files, calib_files) r.uparms = user_pars if recipe_name: r.recipename = recipe_name r.runr() output_file = r.output_filenames[0] if save_to: calib_files.append("{}:{}".format( save_to, os.path.join("calibrations", save_to, r.output_filenames[0]))) [os.remove(f) for f in r.output_filenames] # check that we are not leaking objects assert len(objgraph.by_type('NDAstroData')) == 0 return output_file, calib_files
def test_reduce_image(path_to_inputs): calib_files = [] all_files = glob.glob( os.path.join(path_to_inputs, 'GSAOI/test_reduce/', '*.fits')) all_files.sort() assert len(all_files) > 1 list_of_darks = dataselect.select_data(all_files, ['DARK'], []) list_of_darks.sort() list_of_kshort_flats = dataselect.select_data( all_files, ['FLAT'], [], dataselect.expr_parser('filter_name=="Kshort"')) list_of_kshort_flats.sort() list_of_h_flats = dataselect.select_data( all_files, ['FLAT'], [], dataselect.expr_parser('filter_name=="H"')) list_of_h_flats.sort() list_of_science_files = dataselect.select_data( all_files, [], [], dataselect.expr_parser( 'observation_class=="science" and exposure_time==60.')) list_of_science_files.sort() for darks in [list_of_darks]: reduce_darks = Reduce() assert len(reduce_darks.files) == 0 reduce_darks.files.extend(darks) assert len(reduce_darks.files) == len(darks) logutils.config(file_name='gsaoi_test_reduce_dark.log', mode='quiet') reduce_darks.runr() del reduce_darks logutils.config(file_name='gsaoi_test_reduce_bpm.log', mode='quiet') reduce_bpm = Reduce() reduce_bpm.files.extend(list_of_h_flats) reduce_bpm.files.extend(list_of_darks) reduce_bpm.recipename = 'makeProcessedBPM' reduce_bpm.runr() bpm_filename = reduce_bpm.output_filenames[0] del reduce_bpm logutils.config(file_name='gsaoi_test_reduce_flats.log', mode='quiet') reduce_flats = Reduce() reduce_flats.files.extend(list_of_kshort_flats) reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_flats.runr() calib_files.append('processed_flat:{}'.format( reduce_flats.output_filenames[0])) del reduce_flats logutils.config(file_name='gsaoi_test_reduce_science.log', mode='quiet') reduce_target = Reduce() reduce_target.files.extend(list_of_science_files) reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_target.ucals = normalize_ucals(reduce_target.files, calib_files) reduce_target.runr() del reduce_target
def test_reduce_image(test_path, caldb): logutils.config(file_name='gsaoi_test_reduce_image.log') caldb.init(wipe=True) all_files = glob.glob( os.path.join(test_path, 'GSAOI/test_reduce/', '*.fits')) assert len(all_files) > 1 list_of_darks = dataselect.select_data(all_files, ['DARK'], []) list_of_kshort_flats = dataselect.select_data( all_files, ['FLAT'], [], dataselect.expr_parser('filter_name=="Kshort"')) list_of_h_flats = dataselect.select_data( all_files, ['FLAT'], [], dataselect.expr_parser('filter_name=="H"')) list_of_std_LHS_2026 = dataselect.select_data( all_files, [], [], dataselect.expr_parser('object=="LHS 2026"')) list_of_std_cskd8 = dataselect.select_data( all_files, [], [], dataselect.expr_parser('object=="cskd-8"')) list_of_science_files = dataselect.select_data( all_files, [], [], dataselect.expr_parser( 'observation_class=="science" and exposure_time==60.')) for darks in [list_of_darks]: reduce_darks = Reduce() assert len(reduce_darks.files) == 0 reduce_darks.files.extend(darks) assert len(reduce_darks.files) == len(darks) reduce_darks.runr() caldb.add_cal(reduce_darks.output_filenames[0]) reduce_bpm = Reduce() reduce_bpm.files.extend(list_of_h_flats) reduce_bpm.files.extend(list_of_darks) reduce_bpm.recipename = 'makeProcessedBPM' reduce_bpm.runr() bpm_filename = reduce_bpm.output_filenames[0] reduce_flats = Reduce() reduce_flats.files.extend(list_of_kshort_flats) reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_flats.runr() caldb.add_cal(reduce_flats.output_filenames[0]) reduce_target = Reduce() reduce_target.files.extend(list_of_science_files) reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_target.runr() for f in caldb.list_files(): print(f)
def test_reduce_image(change_working_dir): with change_working_dir(): calib_files = [] all_files = [download_from_archive(f) for f in datasets] all_files.sort() assert len(all_files) > 1 darks_3s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==3')) darks_3s.sort() darks_20s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==20')) darks_20s.sort() darks_120s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==120')) darks_120s.sort() flats = dataselect.select_data( all_files, ['F2', 'FLAT', 'RAW'], [], dataselect.expr_parser('filter_name=="Y"')) flats.sort() science = dataselect.select_data( all_files, ['F2', 'RAW'], ['CAL'], dataselect.expr_parser('filter_name=="Y"')) science.sort() for darks in [darks_3s, darks_20s, darks_120s]: reduce_darks = Reduce() assert len(reduce_darks.files) == 0 reduce_darks.files.extend(darks) assert len(reduce_darks.files) == len(darks) logutils.config(file_name='f2_test_reduce_darks.log', mode='quiet') reduce_darks.runr() calib_files.append('processed_dark:{}'.format( reduce_darks.output_filenames[0])) logutils.config(file_name='f2_test_reduce_bpm.log', mode='quiet') reduce_bpm = Reduce() reduce_bpm.files.extend(flats) assert len(reduce_bpm.files) == len(flats) reduce_bpm.files.extend(darks_3s) assert len(reduce_bpm.files) == len(flats) + len(darks_3s) reduce_bpm.recipename = 'makeProcessedBPM' reduce_bpm.runr() bpm_filename = reduce_bpm.output_filenames[0] logutils.config(file_name='f2_test_reduce_flats.log', mode='quiet') reduce_flats = Reduce() reduce_flats.files.extend(flats) reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_flats.runr() calib_files.append('processed_flat:{}'.format( reduce_flats.output_filenames[0])) logutils.config(file_name='f2_test_reduce_science.log', mode='quiet') reduce_target = Reduce() reduce_target.files.extend(science) reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_target.ucals = normalize_ucals(reduce_target.files, calib_files) reduce_target.runr()
def test_reduce_image(test_path, caldb): logutils.config(file_name='gsaoi_test_reduce_image.log') caldb.init(wipe=True) all_files = glob.glob( os.path.join(test_path, 'GSAOI/test_reduce/', '*.fits')) assert len(all_files) > 1 list_of_darks = dataselect.select_data( all_files, ['DARK'], []) list_of_kshort_flats = dataselect.select_data( all_files, ['FLAT'], [], dataselect.expr_parser('filter_name=="Kshort"')) list_of_h_flats = dataselect.select_data( all_files, ['FLAT'], [], dataselect.expr_parser('filter_name=="H"')) list_of_std_LHS_2026 = dataselect.select_data( all_files, [], [], dataselect.expr_parser('object=="LHS 2026"')) list_of_std_cskd8 = dataselect.select_data( all_files, [], [], dataselect.expr_parser('object=="cskd-8"')) list_of_science_files = dataselect.select_data( all_files, [], [], dataselect.expr_parser('observation_class=="science" and exposure_time==60.')) for darks in [list_of_darks]: reduce_darks = Reduce() assert len(reduce_darks.files) == 0 reduce_darks.files.extend(darks) assert len(reduce_darks.files) == len(darks) reduce_darks.runr() caldb.add_cal(reduce_darks.output_filenames[0]) reduce_bpm = Reduce() reduce_bpm.files.extend(list_of_h_flats) reduce_bpm.files.extend(list_of_darks) reduce_bpm.recipename = 'makeProcessedBPM' reduce_bpm.runr() bpm_filename = reduce_bpm.output_filenames[0] reduce_flats = Reduce() reduce_flats.files.extend(list_of_kshort_flats) reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_flats.runr() caldb.add_cal(reduce_flats.output_filenames[0]) reduce_target = Reduce() reduce_target.files.extend(list_of_science_files) reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_target.runr() for f in caldb.list_files(): print(f)
def test_reduce_image(test_path, caldb): logutils.config(file_name='f2_test_reduce_image.log') caldb.init(wipe=True) all_files = glob.glob( os.path.join(test_path, 'F2/test_reduce/', '*.fits')) assert len(all_files) > 1 darks_3s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==3')) darks_20s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==20')) darks_120s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==120')) flats = dataselect.select_data( all_files, ['F2', 'FLAT', 'RAW'], [], dataselect.expr_parser('filter_name=="Y"')) science = dataselect.select_data( all_files, ['F2', 'RAW'], ['CAL'], dataselect.expr_parser('filter_name=="Y"')) for darks in [darks_3s, darks_20s, darks_120s]: reduce_darks = Reduce() assert len(reduce_darks.files) == 0 reduce_darks.files.extend(darks) assert len(reduce_darks.files) == len(darks) reduce_darks.runr() caldb.add_cal(reduce_darks.output_filenames[0]) reduce_bpm = Reduce() reduce_bpm.files.extend(flats) reduce_bpm.files.extend(darks_3s) reduce_bpm.recipename = 'makeProcessedBPM' reduce_bpm.runr() bpm_filename = reduce_bpm.output_filenames[0] reduce_flats = Reduce() reduce_flats.files.extend(flats) reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_flats.runr() caldb.add_cal(reduce_flats.output_filenames[0]) reduce_target = Reduce() reduce_target.files.extend(science) reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_target.runr() for f in caldb.list_files(): print(f)
def test_reduce_image(path_to_inputs): calib_files = [] all_files = glob.glob( os.path.join(path_to_inputs, 'F2/test_reduce/', '*.fits')) all_files.sort() assert len(all_files) > 1 darks_3s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==3')) darks_3s.sort() darks_20s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==20')) darks_20s.sort() darks_120s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==120')) darks_120s.sort() flats = dataselect.select_data(all_files, ['F2', 'FLAT', 'RAW'], [], dataselect.expr_parser('filter_name=="Y"')) flats.sort() science = dataselect.select_data( all_files, ['F2', 'RAW'], ['CAL'], dataselect.expr_parser('filter_name=="Y"')) science.sort() for darks in [darks_3s, darks_20s, darks_120s]: reduce_darks = Reduce() assert len(reduce_darks.files) == 0 reduce_darks.files.extend(darks) assert len(reduce_darks.files) == len(darks) logutils.config(file_name='f2_test_reduce_darks.log', mode='quiet') reduce_darks.runr() calib_files.append('processed_dark:{}'.format( reduce_darks.output_filenames[0])) logutils.config(file_name='f2_test_reduce_bpm.log', mode='quiet') reduce_bpm = Reduce() reduce_bpm.files.extend(flats) reduce_bpm.files.extend(darks_3s) reduce_bpm.recipename = 'makeProcessedBPM' reduce_bpm.runr() bpm_filename = reduce_bpm.output_filenames[0] logutils.config(file_name='f2_test_reduce_flats.log', mode='quiet') reduce_flats = Reduce() reduce_flats.files.extend(flats) reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_flats.runr() calib_files.append('processed_flat:{}'.format( reduce_flats.output_filenames[0])) logutils.config(file_name='f2_test_reduce_science.log', mode='quiet') reduce_target = Reduce() reduce_target.files.extend(science) reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_target.ucals = normalize_ucals(reduce_target.files, calib_files) reduce_target.runr()
def test_reduce_image_GS_HAM_2x2_i_std(path_to_inputs): logutils.config(file_name='gmos_test_reduce_image_GS_HAM_1x1_i.log') calib_files = [] raw_subdir = 'GMOS/GS-2017B-Q-6' all_files = sorted(glob.glob( os.path.join(path_to_inputs, raw_subdir, '*.fits'))) assert len(all_files) > 1 list_of_sci_bias = dataselect.select_data( all_files, ['BIAS'], [], dataselect.expr_parser('detector_x_bin==2 and detector_y_bin==2') ) list_of_sci_flats = dataselect.select_data( all_files, ['TWILIGHT'], [], dataselect.expr_parser( 'filter_name=="i" and detector_x_bin==2 and detector_y_bin==2' ) ) list_of_science_files = dataselect.select_data( all_files, [], [], dataselect.expr_parser( 'observation_class=="partnerCal" and filter_name=="i"' ) ) reduce_bias = Reduce() assert len(reduce_bias.files) == 0 reduce_bias.files.extend(list_of_sci_bias) assert len(reduce_bias.files) == len(list_of_sci_bias) reduce_bias.runr() calib_files.append( 'processed_bias:{}'.format(reduce_bias.output_filenames[0]) ) reduce_flats = Reduce() reduce_flats.files.extend(list_of_sci_flats) # reduce_flats.uparms = [('addDQ:user_bpm', 'fixed_bpm_2x2_FullFrame.fits')] reduce_flats.ucals = normalize_ucals(reduce_flats.files, calib_files) reduce_flats.runr() calib_files.append( 'processed_flat:{}'.format(reduce_flats.output_filenames[0]) ) reduce_target = Reduce() reduce_target.files.extend(list_of_science_files) reduce_target.ucals = normalize_ucals(reduce_target.files, calib_files) reduce_target.uparms = [ ('stackFrames:memory', 1), # ('addDQ:user_bpm', 'fixed_bpm_2x2_FullFrame.fits'), ('resampleToCommonFrame:interpolator', 'spline3') ] reduce_target.runr()
def test_reduce_image(test_path, caldb): logutils.config(file_name='f2_test_reduce_image.log') caldb.init(wipe=True) all_files = glob.glob(os.path.join(test_path, 'F2/test_reduce/', '*.fits')) assert len(all_files) > 1 darks_3s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==3')) darks_20s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==20')) darks_120s = dataselect.select_data( all_files, ['F2', 'DARK', 'RAW'], [], dataselect.expr_parser('exposure_time==120')) flats = dataselect.select_data(all_files, ['F2', 'FLAT', 'RAW'], [], dataselect.expr_parser('filter_name=="Y"')) science = dataselect.select_data( all_files, ['F2', 'RAW'], ['CAL'], dataselect.expr_parser('filter_name=="Y"')) for darks in [darks_3s, darks_20s, darks_120s]: reduce_darks = Reduce() assert len(reduce_darks.files) == 0 reduce_darks.files.extend(darks) assert len(reduce_darks.files) == len(darks) reduce_darks.runr() caldb.add_cal(reduce_darks.output_filenames[0]) reduce_bpm = Reduce() reduce_bpm.files.extend(flats) reduce_bpm.files.extend(darks_3s) reduce_bpm.recipename = 'makeProcessedBPM' reduce_bpm.runr() bpm_filename = reduce_bpm.output_filenames[0] reduce_flats = Reduce() reduce_flats.files.extend(flats) reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_flats.runr() caldb.add_cal(reduce_flats.output_filenames[0]) reduce_target = Reduce() reduce_target.files.extend(science) reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)] reduce_target.runr() for f in caldb.list_files(): print(f)