def main(obj, test, mag_min, mag_max, increment, instrument):
properties = p.object_params_instrument(obj, instrument=instrument)
output = p.object_output_params(obj=obj, instrument=instrument)
paths = p.object_output_paths(obj=obj, instrument=instrument)
synth_path = properties['data_dir'] + 'synthetic/'
u.mkdir_check(synth_path)
synth_path = synth_path + 'frb_position/'
u.mkdir_check(synth_path)
now = time.Time.now()
now.format = 'isot'
synth_path += f'range_{now}/'
u.mkdir_check(synth_path)
filters = output['filters']
for magnitude in np.arange(mag_min, mag_max, increment):
magnitudes = []
for i in range(len(filters)):
magnitudes.append(magnitude)
test_spec = test + '_' + str(u.round_to_sig_fig(magnitude, 4))
test_path = synth_path + test_spec + '/'
ph.insert_synthetic_at_frb(obj=properties,
test_path=test_path,
filters=filters,
magnitudes=magnitudes,
add_path=False,
psf=True,
output_properties=output,
instrument=instrument,
paths=paths)
p.add_output_path(obj=obj,
key='subtraction_image_synth_frb_range',
path=synth_path,
instrument=instrument)
def main(obj, test, n, filter_dist, instrument, limit):
properties = p.object_params_instrument(obj, instrument=instrument)
burst_properties = p.object_params_frb(obj=obj[:-2])
output = p.object_output_params(obj=obj, instrument=instrument)
paths = p.object_output_paths(obj=obj, instrument=instrument)
z = burst_properties['z']
mjd_burst = burst_properties['mjd_burst']
ebv_mw = burst_properties['dust_ebv']
mjd_obs = properties['mjd']
synth_path = properties['data_dir'] + 'synthetic/'
u.mkdir_check(synth_path)
synth_path = synth_path + 'sn_random/'
u.mkdir_check(synth_path)
epoch = mjd_obs - mjd_burst
f_0 = filter_dist[0]
hg_ra = burst_properties['hg_ra']
hg_dec = burst_properties['hg_dec']
burst_ra = burst_properties['burst_ra']
burst_dec = burst_properties['burst_dec']
image_path = paths[f_0 + '_' + properties['subtraction_image']]
image = fits.open(image_path)
wcs_info = wcs.WCS(image[0].header)
burst_x, burst_y = wcs_info.all_world2pix(burst_ra, burst_dec, 0)
now = time.Time.now()
now.format = 'isot'
synth_path += test + '_' + str(now) + '/'
u.mkdir_check(synth_path)
filters = output['filters']
psf_models = []
for i, f in enumerate(filters):
sn.register_filter(f=f, instrument=instrument)
psf_model = fits.open(paths[f[0] + '_psf_model'])
psf_models.append(psf_model)
for i in range(n):
test_spec = test + '_' + str(i)
test_path = synth_path + test_spec + '/'
magnitudes = []
mags_filters, model, x, y, tbl = sn.random_light_curves_type_ia(
filters=filters,
image=image,
hg_ra=hg_ra,
hg_dec=hg_dec,
z=z,
ebv_mw=ebv_mw,
output_path=test_path,
output_title=test,
limit=limit,
x=burst_x,
y=burst_y,
ra=burst_ra,
dec=burst_dec)
days = mags_filters['days']
for f in filters:
magnitude = sn.magnitude_at_epoch(epoch=epoch,
days=days,
mags=mags_filters[f])
print(f, 'mag:', magnitude)
magnitudes.append(magnitude)
ph.insert_synthetic(x=float(x),
y=float(y),
obj=properties,
test_path=test_path,
filters=filters,
magnitudes=magnitudes,
suffix='sn_random_random_ia',
extra_values=tbl,
paths=paths,
output_properties=output,
psf_models=psf_models,
instrument=instrument)
p.add_output_path(obj=obj,
key='subtraction_image_synth_sn_random_ia',
path=synth_path,
instrument=instrument)
def main(field, destination, epoch, instrument, template_instrument,
comparison_type):
if destination[-1] != '/':
destination = destination + '/'
p.refresh_params_frbs()
types = ['normal', 'synth_random', 'synth_frb']
if comparison_type not in types:
raise ValueError(comparison_type +
' is not a valid synthetic argument; choose from ' +
str(types))
if comparison_type == 'normal':
comparison_type = ''
else:
comparison_type = '_' + comparison_type
params = p.object_params_frb(field)
u.mkdir_check(f'{params["data_dir"]}subtraction/')
destination_path = f'{params["data_dir"]}subtraction/{destination}/'
u.mkdir_check(destination_path)
comparison_title = f'{field}_{epoch}'
comparison_paths = p.object_output_paths(obj=comparison_title,
instrument=instrument)
comparison_params = p.object_params_instrument(obj=comparison_title,
instrument=instrument)
params = p.object_params_frb(field)
template_epoch = params['template_epoch_' + template_instrument.lower()]
template_title = f'{field}_{template_epoch}'
template_paths = p.object_output_paths(obj=template_title,
instrument=template_instrument)
template_outputs = p.object_output_params(obj=template_title,
instrument=template_instrument)
template_params = p.object_params_instrument(
obj=template_title, instrument=template_instrument)
filters = params['filters']
for f in filters:
values = {}
f_0 = f[0]
destination_path_filter = f'{destination_path}{f}/'
u.mkdir_check(destination_path_filter)
# COMPARISON IMAGE:
comparison_image_name = comparison_params[
'subtraction_image'] + comparison_type
# Get path to comparison image from parameter .yaml file
if f'{f_0}_{comparison_image_name}' in comparison_paths:
comparison_origin = comparison_paths[
f'{f_0}_{comparison_image_name}']
elif f'{f_0.lower()}_{comparison_image_name}' in comparison_paths:
comparison_origin = comparison_paths[
f'{f_0.lower()}_{comparison_image_name}']
else:
raise ValueError(
f'{f_0.lower()}_{comparison_image_name} not found in {comparison_title} paths'
)
comparison_destination = f'{comparison_title}_comparison.fits'
if comparison_type != '':
shutil.copyfile(
comparison_origin.replace('.fits',
'.csv'), destination_path_filter +
comparison_destination.replace('.fits', '.csv'))
print('Copying comparison image')
print('From:')
print('\t', comparison_origin)
print('To:')
print(f'\t {destination_path}{f}/{comparison_destination}')
shutil.copy(
comparison_params['data_dir'] + 'output_values.yaml',
f'{destination_path}{f}/{comparison_title}_comparison_output_values.yaml'
)
shutil.copy(
comparison_params['data_dir'] + 'output_values.json',
f'{destination_path}{f}/{comparison_title}_comparison_output_values.json'
)
shutil.copy(comparison_origin,
f'{destination_path}{f}/{comparison_destination}')
values['comparison_file'] = comparison_origin
# TEMPLATE IMAGE
if template_instrument != 'FORS2' and template_instrument != 'XSHOOTER':
f_0 = f_0.lower()
template_image_name = template_params[
'subtraction_image'] + comparison_type
if f'{f_0}_{template_image_name}' in template_paths:
template_origin = template_paths[f'{f_0}_{template_image_name}']
elif f'{f_0.lower()}_{template_image_name}' in template_paths:
template_origin = template_paths[
f'{f_0.lower()}_{template_image_name}']
else:
raise ValueError(
f'{f_0.lower()}_{template_image_name} not found in {template_title} paths'
)
fwhm_template = template_outputs[f_0 + '_fwhm_pix']
template_destination = f'{template_title}_template.fits'
print('Copying template')
print('From:')
print('\t', template_origin)
print('To:')
print(f'\t {destination_path}{f}/{template_destination}')
shutil.copy(
template_params['data_dir'] + 'output_values.yaml',
f'{destination_path}{f}/{template_title}_template_output_values.yaml'
)
shutil.copy(
template_params['data_dir'] + 'output_values.json',
f'{destination_path}{f}/{template_title}_template_output_values.json'
)
shutil.copy(template_origin,
f'{destination_path}{f}/{template_destination}')
values['template_file'] = comparison_origin
p.add_params(f'{destination_path}{f}/output_values.yaml', values)
def plot_galaxy(fig: plt.figure, data_title: str, instrument: str, f: str, ra: float, dec: float,
frame: Union[int, float], world_frame: bool = False,
n: int = 1, n_x: int = 1, n_y: int = 1,
cmap: str = 'viridis', show_cbar: bool = False, stretch: str = 'sqrt', vmin: float = None,
vmax: float = None,
show_filter: bool = True, show_instrument: bool = False,
show_grid: bool = False,
image_name: str = 'astrometry_image',
object_name: str = None, ticks: int = None, interval: str = 'minmax',
show_coords=True,
reverse_y=False):
instrument = instrument.lower()
instruments = {'fors2': 'FORS2', 'imacs': 'IMACS', 'xshooter': 'X-shooter', 'gmos': 'GMOS'}
if instrument == 'imacs':
f_0 = f
else:
f_0 = f[0]
epoch_properties = p.object_params_instrument(obj=data_title, instrument=instrument)
paths = p.object_output_paths(obj=data_title, instrument=instrument)
if 'sdss' in image_name:
instrument_formal = 'SDSS'
elif 'des' in image_name:
instrument_formal = 'DES'
else:
instrument_formal = instruments[instrument]
print(f_0)
print(image_name)
print(f_0 + '_' + image_name)
if f_0 + '_' + image_name in paths:
path = paths[f_0 + '_' + image_name]
elif f_0 + '_' + image_name in epoch_properties:
path = epoch_properties[f_0 + '_' + image_name]
else:
raise ValueError(f'No path for {f_0}_{image_name} found.')
title = object_name
if title is None:
title = ''
if show_instrument:
title += f'({instrument_formal}'
if show_filter:
if instrument == 'imacs':
title += f'${f[-1]}$-band'
else:
title += f'${f_0}$-band'
else:
if show_filter:
if instrument == 'imacs':
title += f' (${f[-1]}$-band)'
else:
title += f' (${f_0}$-band)'
if show_instrument:
title = title.replace('(', f'({instrument_formal}, ')
plot, hdu_cut = plot_subimage(fig=fig, hdu=path, ra=ra, dec=dec,
frame=frame, world_frame=world_frame, title=title,
n=n, n_x=n_x, n_y=n_y,
cmap=cmap, show_cbar=show_cbar, stretch=stretch, vmin=vmin,
vmax=vmax,
show_grid=show_grid,
ticks=ticks, interval=interval,
show_coords=show_coords,
reverse_y=reverse_y)
return plot, hdu_cut
def main(obj, test, n, mag_lower, mag_upper, colour_upper, colour_lower):
properties = p.object_params_fors2(obj)
output = p.object_output_params(obj=obj, instrument='FORS2')
paths = p.object_output_paths(obj)
burst_properties = p.object_params_frb(obj[:-2])
synth_path = properties['data_dir'] + 'synthetic/'
u.mkdir_check(synth_path)
synth_path = synth_path + 'random/'
u.mkdir_check(synth_path)
now = time.Time.now()
now.format = 'isot'
test = str(now) + '_' + test
test_path = synth_path + test + '/'
u.mkdir_check(test_path)
filters = {}
bluest = None
bluest_lambda = np.inf
for f in output['filters']:
filter_properties = p.filter_params(f=f, instrument='FORS2')
filters[f] = filter_properties
lambda_eff = filter_properties['lambda_eff']
if lambda_eff < bluest_lambda:
bluest_lambda = lambda_eff
bluest = f
# Insert random sources in the bluest filter.
f_0 = bluest[0]
output_properties = p.object_output_params(obj)
fwhm = output_properties[f_0 + '_fwhm_pix']
zeropoint, _, airmass, _ = ph.select_zeropoint(obj,
bluest,
instrument='fors2')
base_path = paths[f_0 + '_subtraction_image']
output_path = test_path + f_0 + '_random_sources.fits'
_, sources = ph.insert_random_point_sources_to_file(file=base_path,
fwhm=fwhm,
output=output_path,
n=n,
airmass=airmass,
zeropoint=zeropoint)
p.add_output_path(obj=obj,
key=f_0 + '_subtraction_image_synth_random',
path=output_path)
# Now insert sources at the same positions in other filters, but with magnitudes randomised.
for f in filters:
if f != bluest:
f_0 = f[0]
output_properties = p.object_output_params(obj)
fwhm = output_properties[f_0 + '_fwhm_pix']
zeropoint, _, airmass, _ = ph.select_zeropoint(obj,
f,
instrument='fors2')
base_path = paths[f_0 + '_subtraction_image']
mag = np.random.uniform(mag_lower, mag_upper, size=n)
output_path = test_path + f_0 + '_random_sources.fits'
ph.insert_point_sources_to_file(file=base_path,
fwhm=fwhm,
output=output_path,
x=sources['x_0'],
y=sources['y_0'],
mag=mag,
airmass=airmass,
zeropoint=zeropoint)
p.add_output_path(obj=obj,
key=f_0 + '_subtraction_image_synth_random',
path=output_path)
def main(obj, test, n, filter_dist, sn_type, instrument, limit):
properties = p.object_params_instrument(obj, instrument=instrument)
burst_properties = p.object_params_frb(obj=obj[:-2])
output = p.object_output_params(obj=obj, instrument=instrument)
paths = p.object_output_paths(obj=obj, instrument=instrument)
z = burst_properties['z']
mjd_burst = burst_properties['mjd_burst']
ebv_mw = burst_properties['dust_ebv']
mjd_obs = properties['mjd']
synth_path = properties['data_dir'] + 'synthetic/'
u.mkdir_check(synth_path)
synth_path = synth_path + 'sn_random/'
u.mkdir_check(synth_path)
epoch = mjd_obs - mjd_burst
f_0 = filter_dist[0]
hg_ra = burst_properties['hg_ra']
hg_dec = burst_properties['hg_dec']
image_path = paths[f_0 + '_' + properties['subtraction_image']]
image = fits.open(image_path)
now = time.Time.now()
now.format = 'isot'
test += sn_type + '_'
synth_path += test + str(now) + '/'
u.mkdir_check(synth_path)
filters = output['filters']
if 'FRB190608' in obj:
limit = 45
psf_models = []
for f in filters:
sn.register_filter(f=f)
psf_model = fits.open(paths[f[0] + '_psf_model'])
psf_models.append(psf_model)
for i in range(n):
test_spec = test + str(i)
test_path = synth_path + test_spec + '/'
magnitudes = []
mags_filters, model, x, y, tbl = sn.random_light_curves(
sn_type=sn_type,
filters=filters,
image=image,
hg_ra=hg_ra,
hg_dec=hg_dec,
z=z,
ebv_mw=ebv_mw,
output_path=test_path,
output_title=test,
limit=limit)
days = mags_filters['days']
for f in filters:
magnitude = sn.magnitude_at_epoch(epoch=epoch,
days=days,
mags=mags_filters[f])
print(f, 'mag:', magnitude)
magnitudes.append(magnitude)
ph.insert_synthetic(x=x,
y=y,
obj=properties,
test_path=test_path,
filters=filters,
magnitudes=magnitudes,
suffix='sn_random_random_' + sn_type,
extra_values=tbl,
paths=paths,
output_properties=output,
psf_models=psf_models,
instrument=instrument)
p.add_output_path(obj=obj,
key='subtraction_image_synth_sn_random_' + sn_type,
path=synth_path,
instrument=instrument)
def main(field, destination, epoch, instrument, template_instrument,
comparison_type):
if destination[-1] != '/':
destination = destination + '/'
# p.refresh_params_frbs()
# types = ['multi_frb_range', 'multi_sn_models', 'multi_sn_random', 'multi_sn_random_ia', 'multi_sn_random_ib']
# if comparison_type not in types:
# raise ValueError(comparison_type + ' is not a valid multi-synthetic argument; choose from ' + str(types))
comparison_type = '_' + comparison_type
type_suffix = comparison_type[7:]
params = p.object_params_frb(field)
u.mkdir_check(f'{params["data_dir"]}subtraction/')
destination_path = f'{params["data_dir"]}subtraction/{destination}/'
u.mkdir_check(destination_path)
comparison_title = f'{field}_{epoch}'
comparison_paths = p.object_output_paths(obj=comparison_title,
instrument=instrument)
comparison_origin_top = comparison_paths['subtraction_image_synth_' +
type_suffix]
comparison_tests = os.listdir(comparison_origin_top)
comparison_params = p.object_params_instrument(obj=comparison_title,
instrument=instrument)
params = p.object_params_frb(field)
template_epoch = params['template_epoch_' + template_instrument.lower()]
template_title = f'{field}_{template_epoch}'
template_paths = p.object_output_paths(obj=template_title,
instrument=template_instrument)
template_outputs = p.object_output_params(obj=template_title,
instrument=template_instrument)
template_params = p.object_params_instrument(
obj=template_title, instrument=template_instrument)
filters = params['filters']
for test in comparison_tests:
destination_test = destination_path + test + '/'
origin_test = comparison_origin_top + test + '/'
u.mkdir_check(destination_test)
test_files = os.listdir(origin_test)
for f in filters:
for file in filter(lambda fil: fil[:2] != f + '_',
os.listdir(origin_test)):
shutil.copyfile(origin_test + file, destination_test + file)
values = {}
f_0 = f[0]
destination_path_filter = f'{destination_test}{f}/'
u.mkdir_check(destination_path_filter)
# COMPARISON IMAGE:
# Get path to comparison image from parameter .yaml file
comparison_origin = origin_test + filter(
lambda file: file[0] == f_0 and file[-5:] == '.fits',
test_files).__next__()
comparison_destination = f'{comparison_title}_comparison.fits'
shutil.copyfile(comparison_origin,
destination_path_filter + comparison_destination)
shutil.copyfile(
comparison_origin.replace('.fits',
'.csv'), destination_path_filter +
comparison_destination.replace('.fits', '.csv'))
print('Copying comparison image from:')
print('\t', comparison_origin)
print('To:')
print(f'\t {destination_test}{f}/{comparison_destination}')
shutil.copy(
comparison_params['data_dir'] + 'output_values.yaml',
f'{destination_test}{f}/{comparison_title}_comparison_output_values.yaml'
)
shutil.copy(
comparison_params['data_dir'] + 'output_values.json',
f'{destination_test}{f}/{comparison_title}_comparison_output_values.json'
)
shutil.copy(comparison_origin,
f'{destination_test}{f}/{comparison_destination}')
values['comparison_file'] = comparison_origin
if template_instrument != 'FORS2':
# TEMPLATE IMAGE
if template_instrument != 'XSHOOTER':
f_0 = f_0.lower()
template_image_name = template_params['subtraction_image']
if f'{f_0}_{template_image_name}' in template_paths:
template_origin = template_paths[
f'{f_0}_{template_image_name}']
elif f'{f_0.lower()}_{template_image_name}' in template_paths:
template_origin = template_paths[
f'{f_0.lower()}_{template_image_name}']
else:
raise ValueError(
f'{f_0.lower()}_{template_image_name} not found in {template_title} paths'
)
fwhm_template = template_outputs[f_0 + '_fwhm_pix']
template_destination = f'{template_title}_template.fits'
print('Copying template from:')
print('\t', template_origin)
print('To:')
print(f'\t {destination_test}{f}/{template_destination}')
shutil.copy(
template_params['data_dir'] + 'output_values.yaml',
f'{destination_test}{f}/{template_title}_template_output_values.yaml'
)
shutil.copy(
template_params['data_dir'] + 'output_values.json',
f'{destination_test}{f}/{template_title}_template_output_values.json'
)
shutil.copy(template_origin,
f'{destination_test}{f}/{template_destination}')
values['template_file'] = comparison_origin
p.add_params(f'{destination_test}{f}/output_values.yaml',
values)
def plot_galaxy(fig: plt.Figure, data_title: str, instrument: str, f: str, ra: float, dec: float,
frame: Union[int, float], world_frame: bool = False,
n: int = 1, n_x: int = 1, n_y: int = 1,
cmap: str = 'viridis', show_cbar: bool = False, stretch: str = 'sqrt', vmin: float = None,
vmax: float = None,
show_filter: bool = True, show_instrument: bool = False,
show_grid: bool = False,
image_name: str = 'astrometry_image',
object_name: str = None, ticks: int = None, interval: str = 'minmax',
show_coords=True,
reverse_y=False,
show_frb=False, ellipse_colour: str = 'white',
line_width=1.):
instrument = instrument.lower()
instruments = {'fors2': 'FORS2', 'imacs': 'IMACS', 'xshooter': 'X-shooter', 'gmos': 'GMOS',
'hst': 'Hubble Space Telescope'}
if instrument == 'imacs':
f_0 = f
else:
f_0 = f[0]
epoch_properties = p.object_params_instrument(obj=data_title, instrument=instrument)
paths = p.object_output_paths(obj=data_title, instrument=instrument)
if 'sdss' in image_name:
instrument_formal = 'SDSS'
elif 'des' in image_name:
instrument_formal = 'DES'
else:
instrument_formal = instruments[instrument]
print(f_0)
print(image_name)
print(f_0 + '_' + image_name)
if f_0 + '_' + image_name in paths:
path = paths[f_0 + '_' + image_name]
elif f_0 + '_' + image_name in epoch_properties:
path = epoch_properties[f_0 + '_' + image_name]
else:
raise ValueError(f'No path for {f_0}_{image_name} found.')
title = object_name
if title is None:
title = ''
if show_instrument:
title += f'({instrument_formal}'
if show_filter:
if instrument == 'imacs':
title += f'${f[-1]}$-band'
else:
title += f'${f_0}$-band'
else:
if show_filter:
if instrument == 'imacs':
title += f' (${f[-1]}$-band)'
else:
title += f' (${f_0}$-band)'
if show_instrument:
title = title.replace('(', f'({instrument_formal}, ')
plot, hdu_cut = plot_subimage(fig=fig, hdu=path, ra=ra, dec=dec,
frame=frame, world_frame=world_frame, title=title,
n=n, n_x=n_x, n_y=n_y,
cmap=cmap, show_cbar=show_cbar, stretch=stretch, vmin=vmin,
vmax=vmax,
show_grid=show_grid,
ticks=ticks, interval=interval,
show_coords=show_coords,
reverse_y=reverse_y,
)
burst_name = data_title[:data_title.find("_")]
name = burst_name[3:]
burst_properties = p.object_params_frb(burst_name)
burst_ra = burst_properties['burst_ra']
burst_dec = burst_properties['burst_dec']
if show_frb is True:
show_frb = 'quadrature'
if show_frb == 'all':
# Statistical
a, b, theta = am.calculate_error_ellipse(burst_properties, error='statistical')
plot_gal_params(hdu=hdu_cut, ras=[burst_ra], decs=[burst_dec], a=[a],
b=[b],
theta=[-theta], colour=ellipse_colour, ls='-')
# Systematic
a, b, theta = am.calculate_error_ellipse(burst_properties, error='systematic')
plot_gal_params(hdu=hdu_cut, ras=[burst_ra], decs=[burst_dec], a=[a],
b=[b],
theta=[-theta], colour=ellipse_colour, ls='--')
# Quadrature
a, b, theta = am.calculate_error_ellipse(burst_properties, error='quadrature')
plot_gal_params(hdu=hdu_cut, ras=[burst_ra], decs=[burst_dec], a=[a],
b=[b],
theta=[-theta], colour=ellipse_colour, ls=':')
elif show_frb is not False:
a, b, theta = am.calculate_error_ellipse(burst_properties, error=show_frb)
plot_gal_params(hdu=hdu_cut, ras=[burst_ra], decs=[burst_dec], a=[a],
b=[b],
theta=[-theta], colour=ellipse_colour, ls='-', lw=line_width)
return plot, hdu_cut
def main(obj, test_name, sex_x_col, sex_y_col, sex_ra_col, sex_dec_col,
sex_flux_col, get_sextractor_names, mag_tol, stars_only,
star_class_col, star_class_tol, show_plots, mag_range_sex_lower,
mag_range_sex_upper, pix_tol, separate_chips, write):
print(separate_chips)
sextractor_names = p.sextractor_names_psf() # None to auto-detect
properties = p.object_params_des(obj=obj)
outputs = p.object_output_params(obj=obj, instrument='des')
paths = p.object_output_paths(obj=obj)
cat_name = 'DES'
cat_path = properties['data_dir'] + 'des_objects.csv'
output = properties['data_dir'] + '/3-zeropoint/'
mkdir_check(output)
output = output + 'field/'
mkdir_check(output)
for fil in properties['filters']:
f_0 = fil[0]
f_up = f_0.upper()
output_path = output + f_0
cat_zeropoint = 0.
cat_zeropoint_err = 0.
now = time.Time.now()
now.format = 'isot'
test_name = str(now) + '_' + test_name
mkdir_check(output_path)
output_path = output_path + '/' + f_0 + '/'
mkdir_check(output_path)
output_path = output_path + '/' + test_name + '/'
mkdir_check(output_path)
if not os.path.isdir(output_path):
os.mkdir(output_path)
output_path = output_path + test_name + '/'
if not os.path.isdir(output_path):
os.mkdir(output_path)
image_path = properties[
'data_dir'] + '2-sextractor/' + f_0 + '_cutout.fits'
sextractor_path = properties[
'data_dir'] + '2-sextractor/' + f_0 + '_psf-fit.cat'
# We override here because there's no need to separate the chips if we're using the DES image.
separate_chips = False
# pix_tol = 1.
mag_range_lower = 16
mag_range_upper = 25
exp_time = 1.
print('SExtractor catalogue path:', sextractor_path)
print('Image path:', image_path)
print('Catalogue path:', cat_path)
print('Output:', output_path + test_name)
print()
print(cat_zeropoint)
up = photometry.determine_zeropoint_sextractor(
sextractor_cat=sextractor_path,
image=image_path,
image_name='DES',
cat_path=cat_path,
cat_name=cat_name,
output_path=output_path + "/chip_1/",
show=show_plots,
cat_ra_col='RA',
cat_dec_col='DEC',
cat_mag_col='WAVG_MAG_PSF_' + f_up,
sex_ra_col=sex_ra_col,
sex_dec_col=sex_dec_col,
sex_x_col=sex_x_col,
sex_y_col=sex_y_col,
dist_tol=pix_tol,
mag_tol=mag_tol,
flux_column=sex_flux_col,
mag_range_cat_upper=mag_range_upper,
mag_range_cat_lower=mag_range_lower,
mag_range_sex_upper=mag_range_sex_upper,
mag_range_sex_lower=mag_range_sex_lower,
stars_only=stars_only,
star_class_tol=star_class_tol,
star_class_col=star_class_col,
exp_time=exp_time,
get_sextractor_names=get_sextractor_names,
sextractor_names=sextractor_names,
cat_type='csv',
cat_zeropoint=cat_zeropoint,
cat_zeropoint_err=cat_zeropoint_err,
)
if write:
update_dict = {
f_0 + '_zeropoint_derived':
float(up['zeropoint_sub_outliers']),
f_0 + '_zeropoint_derived_err': float(up['zeropoint_err'])
}
p.add_output_values(obj=obj, params=update_dict, instrument='des')
