obj_db = pd.read_csv(db_address_i, delim_whitespace=True, header=0, index_col=0) mask_df = pd.read_csv(mask_global_address_i, delim_whitespace=True, header=0, index_col=0) # Reset and measure the lines # lm = sr.LineMesurer(wave_rest, flux_voxel * norm_flux, normFlux=norm_flux, linesDF_address=mask_global_address_i) print(f'\n- {obj}: Cube dimensions {cube.shape}') # Get line region data lineFlux_dict, levelFlux_dict, levelText_dict = compute_line_flux_image( lineAreas, cube, z_objs[i], percent_array=pertil_array) # Declare voxels to analyse lineLabel = 'O3_5007A' percentil_array = pertil_array wcs_cube = WCS(cube.data_header) voxel_coord = (236, 132) idx_j, idx_i = voxel_coord fluxImage = lineFlux_dict[lineLabel] fluxLevels = levelFlux_dict[lineLabel] levels_text = levelText_dict[lineLabel] flux_voxel = cube[:, idx_j, idx_i].data.data plotConf = { 'image': { 'xlabel': r'RA',
for i, obj in enumerate(objList): # Load the data print(f'\n- {obj}') file_address_i = f'{dataFolder}/{fileList[i]}' wave, cube, header = sr.import_fits_data(file_address_i, instrument='MUSE') wave = wave / (1 + z_objs[i]) print(f'\n-- {header["OBJECT"]}') # Get astronomical coordinates one pixel coord_sky = cube.wcs.pix2sky(idx_voxel, unit=u.deg) dec, ra = deg2sexa(coord_sky)[0] wcs_cube = WCS(cube.data_header) lineFlux_dict, levelFlux_dict, levelText_dict = compute_line_flux_image( lineAreas, wave, cube) # Plot the line flux maps for lineLabel, lineLimits in lineAreas.items(): lineFlux_i, levelFlux_i, levelText_i = lineFlux_dict[ lineLabel], levelFlux_dict[lineLabel], levelText_dict[lineLabel] # Plot line image map with coordinates labelsDict = { 'xlabel': r'RA', 'ylabel': r'DEC', 'title': r'Galaxy {} {}'.format(obj, lineLabel) } # Plot Configuration