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
