'logC': [],
'logA_std': [],
'ind_std': [],
'logB_std': [],
'logC_std': []
}
# for gal in ['M33', 'M31']:
for gal in ['M31']:
plot_folder = osjoin(data_path, "{}_plots".format(gal))
if not os.path.exists(plot_folder):
os.mkdir(plot_folder)
gal_obj = Galaxy(gal)
gal_obj.distance = fitinfo_dict[gal]['distance']
if gal == 'M31':
# Add 180 deg to the PA
gal_obj.position_angle += 180 * u.deg
filename = osjoin(data_path, gal, fitinfo_dict[gal]['filename'])
hdu_coldens = fits.open(filename)
proj_coldens = Projection.from_hdu(
fits.PrimaryHDU(hdu_coldens[0].data[0].squeeze(),
hdu_coldens[0].header))
# Get minimal size
proj_coldens = proj_coldens[nd.find_objects(np.isfinite(proj_coldens))[0]]
# The models from the peak velocity aren't as biased, based on comparing
# the VLA and VLA+GBT velocity curves. Using these as the defaults
folder_name = "diskfit_peakvels_noasymm_noradial_nowarp_output"
param_name = \
fourteenB_HI_data_path("{}/rad.out.params.csv".format(folder_name))
param_table = Table.read(param_name)
gal = Galaxy("M33")
update_galaxy_params(gal, param_table)
# Load in the model from the feathered data as well.
folder_name = "diskfit_peakvels_noasymm_noradial_nowarp_output"
param_name = \
fourteenB_HI_data_wGBT_path("{}/rad.out.params.csv".format(folder_name))
param_table = Table.read(param_name)
gal_feath = Galaxy("M33")
update_galaxy_params(gal_feath, param_table)
# Force 840 kpc for the distance
gal.distance = 840 * u.kpc
gal_feath.distance = 840 * u.kpc
