def test_pick_models():
# download the needed files
vega_fname = download_rename("vega.hd5")
filename = download_rename("beast_example_phat_seds.grid.hd5")
filters = [
"HST_WFC3_F275W",
"HST_WFC3_F336W",
"HST_ACS_WFC_F475W",
"HST_ACS_WFC_F814W",
"HST_WFC3_F110W",
"HST_WFC3_F160W",
]
mag_cuts = [1.0]
make_ast_input_list.pick_models(
filename,
filters,
mag_cuts,
vega_fname=vega_fname,
outfile="/tmp/test_inputAST.txt",
ranseed=1234,
)
table_new = Table.read("/tmp/test_inputAST.txt", format="ascii")
# download cached version of the file and compare it to new file
cached_table_filename = download_rename("cache_inputAST.txt")
table_cache = Table.read(cached_table_filename,
format="csv",
delimiter=" ")
compare_tables(table_new, table_cache)
def test_ast_pick_models(self):
"""
Generate the artifial star test (AST) inputs using a cached version of
the sed grid and compare the result to a cached version.
"""
# download files specific to this test
cached_table_filename = download_rename(
"phat_small/cache_inputAST.txt")
mag_cuts = [1.0]
seds_fname = self.seds_fname_cache
if self.dset != "phat":
seds_fname = download_rename(
"phat_small/beast_example_phat_seds.grid.hd5")
else:
seds_fname = self.seds_fname_cache
outname = tempfile.NamedTemporaryFile(suffix=".txt").name
make_ast_input_list.pick_models(
seds_fname,
self.settings.filters,
mag_cuts,
outfile=outname,
ranseed=1234,
)
table_new = Table.read(outname, format="ascii")
# download cached version of the file and compare it to new file
table_cache = Table.read(cached_table_filename,
format="csv",
delimiter=" ")
compare_tables(table_new, table_cache)
def test_pick_models():
# download the needed files
vega_fname = download_rename('vega.hd5')
filename = download_rename('beast_example_phat_seds.grid.hd5')
filters = ['HST_WFC3_F275W', 'HST_WFC3_F336W', 'HST_ACS_WFC_F475W',
'HST_ACS_WFC_F814W', 'HST_WFC3_F110W', 'HST_WFC3_F160W']
mag_cuts = [1.]
table_out = make_ast_input_list.pick_models(filename, filters, mag_cuts, vega_fname=vega_fname,
outfile='/tmp/test_inputAST.txt', ranseed=1234)
table_new = Table.read('/tmp/test_inputAST.txt', format='ascii')
# download cached version of the file and compare it to new file
cached_table_filename = download_rename('cache_inputAST.txt')
table_cache = Table.read(cached_table_filename, format='csv', delimiter=' ')
compare_tables(table_new, table_cache)
modelsedgridfile,
settings.filters,
mag_cuts,
Nfilters,
N_fluxes,
min_N_per_flux,
outfile_chosenSEDs,
bins_outfile,
bright_cut=bright_cuts,
)
else:
chosen_seds = make_ast_input_list.pick_models(
modelsedgridfile,
settings.filters,
mag_cuts,
Nfilters,
N_per_age,
Nrealize,
outfile_chosenSEDs,
)
# Assign positions for ASTs
outfile_inputAST = os.path.join(outdir,
settings.project + "_inputAST.txt")
make_ast_xy_list.pick_positions_from_map(
chosen_seds,
input_map=settings.ast_bg_map_file,
N_bins=settings.ast_bg_nbins,
Npermodel=10,
outfile=outfile_inputAST,
refimage=settings.ast_reference_image,
def run_beast_production(basename,
physicsmodel=False,
ast=False,
observationmodel=False,
trim=False,
fitting=False,
resume=False,
source_density='',
sub_source_density=''):
"""
Turns the original command-line version of run_beast_production.py into
something callable from within a function
Parameters
----------
basename : string
name of the gst file (assuming it's located in ./data/)
For the info related to the other inputs, see the argparse info at the bottom
"""
# before doing ANYTHING, force datamodel to re-import (otherwise, any
# changes within this python session will not be loaded!)
importlib.reload(datamodel)
# check input parameters, print what is the problem, stop run_beast
verify_params.verify_input_format(datamodel)
# update the filenames as needed for production
# - photometry sub-file
datamodel.obsfile = basename.replace(
'.fits',
'_with_sourceden' + '_SD_' + source_density.replace('_', '-') +
'_sub' + sub_source_density + '.fits')
# - stats files
stats_filebase = "%s/%s"%(datamodel.project,datamodel.project) \
+ '_sd' + source_density.replace('_','-') \
+ '_sub' + sub_source_density
sed_trimname = stats_filebase + '_sed_trim.grid.hd5'
# - trimmed noise model
noisemodel_trimname = stats_filebase + '_noisemodel_trim.hd5'
# - SED grid
#modelsedgrid_filename = "%s/%s_seds.grid.hd5"%(datamodel.project,
# datamodel.project)
modelsedgrid_filename = "METAL_seds.grid.hd5"
print("***run information***")
print(" project = " + datamodel.project)
print(" obsfile = " + datamodel.obsfile)
print(" astfile = " + datamodel.astfile)
print(" noisefile = " + datamodel.noisefile)
print(" trimmed sedfile = " + sed_trimname)
print("trimmed noisefiles = " + noisemodel_trimname)
print(" stats filebase = " + stats_filebase)
# make sure the project directory exists
pdir = create_project_dir(datamodel.project)
if physicsmodel:
# download and load the isochrones
(iso_fname, oiso) = make_iso_table(datamodel.project,
oiso=datamodel.oiso,
logtmin=datamodel.logt[0],
logtmax=datamodel.logt[1],
dlogt=datamodel.logt[2],
z=datamodel.z)
if hasattr(datamodel, 'add_spectral_properties_kwargs'):
extra_kwargs = datamodel.add_spectral_properties_kwargs
else:
extra_kwargs = None
if hasattr(datamodel, 'velocity'):
redshift = (datamodel.velocity / const.c).decompose().value
else:
redshift = 0
# generate the spectral library (no dust extinction)
(spec_fname, g_spec) = make_spectral_grid(
datamodel.project,
oiso,
osl=datamodel.osl,
redshift=redshift,
distance=datamodel.distances,
distance_unit=datamodel.distance_unit,
add_spectral_properties_kwargs=extra_kwargs)
# add the stellar priors as weights
# also computes the grid weights for the stellar part
(pspec_fname, g_pspec) = add_stellar_priors(datamodel.project, g_spec)
# generate the SED grid by integrating the filter response functions
# effect of dust extinction applied before filter integration
# also computes the dust priors as weights
(seds_fname, g_seds) = make_extinguished_sed_grid(
datamodel.project,
g_pspec,
datamodel.filters,
extLaw=datamodel.extLaw,
av=datamodel.avs,
rv=datamodel.rvs,
fA=datamodel.fAs,
rv_prior_model=datamodel.rv_prior_model,
av_prior_model=datamodel.av_prior_model,
fA_prior_model=datamodel.fA_prior_model,
spec_fname=modelsedgrid_filename,
add_spectral_properties_kwargs=extra_kwargs)
if ast:
N_models = datamodel.ast_models_selected_per_age
Nfilters = datamodel.ast_bands_above_maglimit
Nrealize = datamodel.ast_realization_per_model
mag_cuts = datamodel.ast_maglimit
obsdata = datamodel.get_obscat(basename, datamodel.filters)
if len(mag_cuts) == 1:
tmp_cuts = mag_cuts
min_mags = np.zeros(len(datamodel.filters))
for k, filtername in enumerate(obsdata.filters):
sfiltername = obsdata.data.resolve_alias(filtername)
sfiltername = sfiltername.replace('rate', 'vega')
sfiltername = sfiltername.replace('RATE', 'VEGA')
keep, = np.where(obsdata[sfiltername] < 99.)
min_mags[k] = np.percentile(obsdata[keep][sfiltername], 90.)
# max. mags from the gst observation cat.
mag_cuts = min_mags + tmp_cuts
outfile = './' + datamodel.project + '/' + datamodel.project + '_inputAST.txt'
outfile_params = './' + datamodel.project + '/' + datamodel.project + '_ASTparams.fits'
chosen_seds = pick_models(modelsedgrid_filename,
datamodel.filters,
mag_cuts,
Nfilter=Nfilters,
N_stars=N_models,
Nrealize=Nrealize,
outfile=outfile,
outfile_params=outfile_params)
if datamodel.ast_with_positions == True:
separation = datamodel.ast_pixel_distribution
filename = datamodel.project + '/' + datamodel.project + '_inputAST.txt'
if datamodel.ast_reference_image is not None:
# With reference image, use the background or source density map if available
if datamodel.ast_density_table is not None:
pick_positions_from_map(
obsdata,
chosen_seds,
datamodel.ast_density_table,
datamodel.ast_N_bins,
datamodel.ast_realization_per_model,
outfile=filename,
refimage=datamodel.ast_reference_image,
refimage_hdu=0,
Nrealize=1,
set_coord_boundary=datamodel.ast_coord_boundary)
else:
pick_positions(obsdata,
filename,
separation,
refimage=datamodel.ast_reference_image)
else:
# Without reference image, we can only use this function
if datamodel.ast_density_table is None:
pick_positions(obsdata, filename, separation)
else:
print(
"To use ast_density_table, ast_reference_image must be specified."
)
if observationmodel:
print('Generating noise model from ASTs and absflux A matrix')
# get the modesedgrid on which to generate the noisemodel
modelsedgrid = FileSEDGrid(modelsedgrid_filename)
# generate the AST noise model
noisemodel.make_toothpick_noise_model( \
datamodel.noisefile,
datamodel.astfile,
modelsedgrid,
use_rate=True,
absflux_a_matrix=datamodel.absflux_a_matrix)
if trim:
print('Trimming the model and noise grids')
# read in the observed data
obsdata = datamodel.get_obscat(basename, datamodel.filters)
# get the modesedgrid on which to generate the noisemodel
modelsedgrid = FileSEDGrid(modelsedgrid_filename)
# read in the noise model just created
noisemodel_vals = noisemodel.get_noisemodelcat(datamodel.noisefile)
# trim the model sedgrid
trim_grid.trim_models(modelsedgrid,
noisemodel_vals,
obsdata,
sed_trimname,
noisemodel_trimname,
sigma_fac=3.)
if fitting:
start_time = time.clock()
# read in the the AST noise model
noisemodel_vals = noisemodel.get_noisemodelcat(noisemodel_trimname)
# read in the observed data
obsdata = datamodel.get_obscat(datamodel.obsfile, datamodel.filters)
# output files
statsfile = stats_filebase + '_stats.fits'
pdf1dfile = statsfile.replace('stats.fits', 'pdf1d.fits')
lnpfile = statsfile.replace('stats.fits', 'lnp.hd5')
fit.summary_table_memory(obsdata,
noisemodel_vals,
sed_trimname,
resume=resume,
threshold=-10.,
save_every_npts=100,
lnp_npts=500,
stats_outname=statsfile,
pdf1d_outname=pdf1dfile,
lnp_outname=lnpfile,
surveyname=datamodel.surveyname)
new_time = time.clock()
print('time to fit: ', (new_time - start_time) / 60., ' min')
def make_ast_inputs(flux_bin_method=True):
"""
Make the list of artificial stars to be run through the photometry pipeline
Parameters
----------
flux_bin_method : boolean (default=True)
If True, use the flux bin method to select SEDs. If False, randomly
select SEDs from the model grid.
"""
# before doing ANYTHING, force datamodel to re-import (otherwise, any
# changes within this python session will not be loaded!)
importlib.reload(datamodel)
# check input parameters
verify_params.verify_input_format(datamodel)
# read in the photometry catalog
obsdata = datamodel.get_obscat(datamodel.obsfile, datamodel.filters)
# --------------------
# select SEDs
# --------------------
Nrealize = datamodel.ast_realization_per_model
Nfilters = datamodel.ast_bands_above_maglimit
# file names for stars and corresponding SED parameters
outfile_seds = "./{0}/{0}_inputAST_seds.txt".format(datamodel.project)
outfile_params = "./{0}/{0}_ASTparams.fits".format(datamodel.project)
# if the SED file doesn't exist, create SEDs
if not os.path.isfile(outfile_seds):
print("Selecting SEDs for ASTs")
if flux_bin_method:
N_fluxes = datamodel.ast_n_flux_bins
min_N_per_flux = datamodel.ast_n_per_flux_bin
bins_outfile = "./{0}/{0}_ASTfluxbins.txt".format(datamodel.project)
modelsedgrid_filename = "./{0}/{0}_seds.grid.hd5".format(datamodel.project)
chosen_seds = pick_models_toothpick_style(
modelsedgrid_filename,
datamodel.filters,
Nfilters,
N_fluxes,
min_N_per_flux,
outfile=outfile_seds,
outfile_params=outfile_params,
bins_outfile=bins_outfile,
)
else:
# construct magnitude cuts
mag_cuts = datamodel.ast_maglimit
if len(mag_cuts) == 1:
tmp_cuts = mag_cuts
min_mags = np.zeros(len(datamodel.filters))
for k, filtername in enumerate(obsdata.filters):
sfiltername = obsdata.data.resolve_alias(filtername)
sfiltername = sfiltername.replace("rate", "vega")
sfiltername = sfiltername.replace("RATE", "VEGA")
(keep,) = np.where(obsdata[sfiltername] < 99.0)
min_mags[k] = np.percentile(obsdata[keep][sfiltername], 90.0)
# max. mags from the gst observation cat.
mag_cuts = min_mags + tmp_cuts
N_models = datamodel.ast_models_selected_per_age
chosen_seds = pick_models(
modelsedgrid_filename,
datamodel.filters,
mag_cuts,
Nfilter=Nfilters,
N_stars=N_models,
Nrealize=Nrealize,
outfile=outfile_seds,
outfile_params=outfile_params,
)
# if the SED file does exist, read them in
else:
print("Reading existing AST SEDs")
chosen_seds = Table.read(outfile_seds, format="ascii")
# --------------------
# assign positions
# --------------------
# if we want ASTs with positions included (rather than just the fluxes from
# the section above)
if datamodel.ast_with_positions:
print("Assigning positions to artifical stars")
outfile = "./{0}/{0}_inputAST.txt".format(datamodel.project)
# if we're replicating SEDs across source density or background bins
if datamodel.ast_density_table is not None:
make_ast_xy_list.pick_positions_from_map(
obsdata,
chosen_seds,
datamodel.ast_density_table,
datamodel.ast_N_bins,
datamodel.ast_realization_per_model,
outfile=outfile,
refimage=datamodel.ast_reference_image,
refimage_hdu=1,
wcs_origin=1,
Nrealize=1,
set_coord_boundary=datamodel.ast_coord_boundary,
region_from_filters="all",
)
# if we're not using SD/background maps, SEDs will be distributed
# based on catalog sources
else:
make_ast_xy_list.pick_positions(
obsdata,
outfile,
datamodel.ast_pixel_distribution,
refimage=datamodel.ast_reference_image,
)
def make_ast_inputs(beast_settings_info, pick_method="flux_bin_method"):
"""
Make the list of artificial stars to be run through the photometry pipeline
Parameters
----------
beast_settings_info : string or beast.tools.beast_settings.beast_settings instance
if string: file name with beast settings
if class: beast.tools.beast_settings.beast_settings instance
pick_method : string (default = "flux_bin_method")
By default, use the flux bin method to select SEDs.
If set to "random_seds", randomly select SEDs from the model grid.
If set to "suppl_seds", supplement the existing input ASTs by randomly
selecting additional SEDs from the list of non-selected models.
"""
# process beast settings info
if isinstance(beast_settings_info, str):
settings = beast_settings.beast_settings(beast_settings_info)
elif isinstance(beast_settings_info, beast_settings.beast_settings):
settings = beast_settings_info
else:
raise TypeError(
"beast_settings_info must be string or beast.tools.beast_settings.beast_settings instance"
)
# read in the photometry catalog
obsdata = Observations(settings.obsfile,
settings.filters,
obs_colnames=settings.obs_colnames)
# --------------------
# select SEDs
# --------------------
modelsedgrid_filename = "./{0}/{0}_seds.grid.hd5".format(settings.project)
Nrealize = settings.ast_realization_per_model
# file names for stars and corresponding SED parameters
if pick_method == "suppl_seds":
outfile_seds = "./{0}/{0}_inputAST_seds_suppl.txt".format(
settings.project)
outfile_params = "./{0}/{0}_ASTparams_suppl.fits".format(
settings.project)
else:
outfile_seds = "./{0}/{0}_inputAST_seds.txt".format(settings.project)
outfile_params = "./{0}/{0}_ASTparams.fits".format(settings.project)
# if the SED file doesn't exist, create SEDs
if not os.path.isfile(outfile_seds):
print("Selecting SEDs for ASTs")
if pick_method == "flux_bin_method":
N_fluxes = settings.ast_n_flux_bins
min_N_per_flux = settings.ast_n_per_flux_bin
bins_outfile = "./{0}/{0}_ASTfluxbins.txt".format(settings.project)
chosen_seds = pick_models_toothpick_style(
modelsedgrid_filename,
settings.filters,
N_fluxes,
min_N_per_flux,
outfile=outfile_seds,
outfile_params=outfile_params,
bins_outfile=bins_outfile,
)
if pick_method == "random_pick":
# construct magnitude cuts
mag_cuts = settings.ast_maglimit
Nfilters = settings.ast_bands_above_maglimit
if len(mag_cuts) == 1:
tmp_cuts = mag_cuts
min_mags = np.zeros(len(settings.filters))
for k, filtername in enumerate(obsdata.filters):
sfiltername = obsdata.filter_aliases[filtername]
sfiltername = sfiltername.replace("rate", "vega")
sfiltername = sfiltername.replace("RATE", "VEGA")
(keep, ) = np.where(obsdata[sfiltername] < 99.0)
min_mags[k] = np.percentile(obsdata[keep][sfiltername],
90.0)
# max. mags from the gst observation cat.
mag_cuts = min_mags + tmp_cuts
N_models = settings.ast_models_selected_per_age
chosen_seds = pick_models(
modelsedgrid_filename,
settings.filters,
mag_cuts,
Nfilter=Nfilters,
N_stars=N_models,
Nrealize=Nrealize,
outfile=outfile_seds,
outfile_params=outfile_params,
)
if pick_method == "suppl_seds":
print("Supplementing ASTs")
nAST = settings.ast_N_supplement
existingASTfile = settings.ast_existing_file
mag_cuts = settings.ast_suppl_maglimit
color_cuts = settings.ast_suppl_colorlimit
chosen_seds = supplement_ast(
modelsedgrid_filename,
settings.filters,
nAST=nAST,
existingASTfile=existingASTfile,
outASTfile=outfile_seds,
outASTfile_params=outfile_params,
mag_cuts=mag_cuts,
color_cuts=color_cuts,
)
# if the SED file does exist, read them in
else:
print("Reading existing AST SEDs")
chosen_seds = Table.read(outfile_seds, format="ascii")
# --------------------
# assign positions
# --------------------
# if we want ASTs with positions included (rather than just the fluxes from
# the section above)
if settings.ast_with_positions:
print("Assigning positions to artifical stars")
outfile = "./{0}/{0}_inputAST.txt".format(settings.project)
if pick_method == "suppl_seds":
outfile = "./{0}/{0}_inputAST_suppl.txt".format(settings.project)
# if we're replicating SEDs across source density or background bins
if settings.ast_density_table is not None:
if hasattr(settings, "ast_reference_image_hdu_extension"):
hdu_ext = settings.ast_reference_image_hdu_extension
else:
hdu_ext = 1
make_ast_xy_list.pick_positions_from_map(
obsdata,
chosen_seds,
settings.ast_density_table,
settings.sd_binmode,
settings.sd_Nbins,
settings.sd_binwidth,
settings.sd_custom,
settings.ast_realization_per_model,
outfile=outfile,
refimage=settings.ast_reference_image,
refimage_hdu=hdu_ext,
wcs_origin=1,
Nrealize=1,
set_coord_boundary=settings.ast_coord_boundary,
region_from_filters="all",
erode_boundary=settings.ast_erode_selection_region,
)
# if we're not using SD/background maps, SEDs will be distributed
# based on catalog sources
else:
make_ast_xy_list.pick_positions(
obsdata,
outfile_seds,
outfile,
settings.ast_pixel_distribution,
refimage=settings.ast_reference_image,
)
datamodel.filters)
min_mags = np.zeros(len(datamodel.filters))
for k, filtername in enumerate(obsdata.filters):
sfiltername = obsdata.data.resolve_alias(filtername)
sfiltername = sfiltername.replace('rate', 'vega')
sfiltername = sfiltername.replace('RATE', 'VEGA')
keep, = np.where(obsdata[sfiltername] < 99.)
min_mags[k] = np.percentile(obsdata[keep][sfiltername], 90.)
# max. mags from the gst observation cat.
mag_cuts = min_mags + tmp_cuts
pick_models(modelsedgrid,
mag_cuts,
Nfilter=Nfilters,
N_stars=N_models,
Nrealize=Nrealize)
if datamodel.ast_with_positions == True:
separation = datamodel.ast_pixel_distribution
filename = datamodel.project + '/' + datamodel.project + '_inputAST.txt'
if datamodel.ast_reference_image is not None:
pick_positions(filename,
separation,
refimage=datamodel.ast_reference_image)
else:
pick_positions(filename, separation)
if args.observationmodel:
min_mags = np.zeros(len(datamodel.filters))
for k, filtername in enumerate(obsdata.filters):
sfiltername = obsdata.data.resolve_alias(filtername)
sfiltername = sfiltername.replace('rate', 'vega')
sfiltername = sfiltername.replace('RATE', 'VEGA')
keep, = np.where(obsdata[sfiltername] < 99.)
min_mags[k] = np.percentile(obsdata[keep][sfiltername], 90.)
# max. mags from the gst observation cat.
mag_cuts = min_mags + tmp_cuts
outfile = './' + datamodel.project + '/' + datamodel.project + '_inputAST.txt'
pick_models(modelsedgridfile,
datamodel.filters,
mag_cuts,
Nfilter=Nfilters,
N_stars=N_models,
Nrealize=Nrealize,
outfile=outfile)
if datamodel.ast_with_positions == True:
separation = datamodel.ast_pixel_distribution
filename = datamodel.project + '/' + datamodel.project + '_inputAST.txt'
if datamodel.ast_reference_image is not None:
pick_positions(obsdata,
filename,
separation,
refimage=datamodel.ast_reference_image)
else:
pick_positions(obsdata, filename, separation)
min_mags = np.zeros(len(datamodel.filters))
for k, filtername in enumerate(obsdata.filters):
sfiltername = obsdata.data.resolve_alias(filtername)
sfiltername = sfiltername.replace('rate', 'vega')
sfiltername = sfiltername.replace('RATE', 'VEGA')
keep, = np.where(obsdata[sfiltername] < 99.)
min_mags[k] = np.percentile(obsdata[keep][sfiltername], 90.)
# max. mags from the gst observation cat.
mag_cuts = min_mags + tmp_cuts
print(modelsedgrid)
outfile = './' + datamodel.project + '/' + datamodel.project + '_inputAST.txt'
outfile_params = './' + datamodel.project + '/' + datamodel.project + '_ASTparams.fits'
chosen_seds = pick_models(modelsedgridfile, datamodel.filters,
mag_cuts, Nfilter=Nfilters, N_stars=N_models, Nrealize=Nrealize,
outfile=outfile, outfile_params=outfile_params)
if datamodel.ast_with_positions == True:
separation = datamodel.ast_pixel_distribution
filename = datamodel.project + '/' + datamodel.project + '_inputAST.txt'
if datamodel.ast_reference_image is not None:
# With reference image, use the background or source density map if available
if datamodel.ast_density_table is not None:
pick_positions_from_map(obsdata,
chosen_seds,
datamodel.ast_density_table,
datamodel.ast_N_bins,
datamodel.ast_realization_per_model,
outfile=filename,
def make_ast_inputs(flux_bin_method=True):
"""
Make the list of artificial stars to be run through the photometry pipeline
Parameters
----------
flux_bin_method : boolean (default=True)
If True, use the flux bin method to select SEDs. If False, randomly
select SEDs from the model grid.
"""
# before doing ANYTHING, force datamodel to re-import (otherwise, any
# changes within this python session will not be loaded!)
importlib.reload(datamodel)
# check input parameters
verify_params.verify_input_format(datamodel)
# construct magnitude cuts
mag_cuts = datamodel.ast_maglimit
obsdata = datamodel.get_obscat(datamodel.obsfile, datamodel.filters)
if len(mag_cuts) == 1:
tmp_cuts = mag_cuts
min_mags = np.zeros(len(datamodel.filters))
for k, filtername in enumerate(obsdata.filters):
sfiltername = obsdata.data.resolve_alias(filtername)
sfiltername = sfiltername.replace("rate", "vega")
sfiltername = sfiltername.replace("RATE", "VEGA")
keep, = np.where(obsdata[sfiltername] < 99.0)
min_mags[k] = np.percentile(obsdata[keep][sfiltername], 90.0)
# max. mags from the gst observation cat.
mag_cuts = min_mags + tmp_cuts
# --------------------
# select SEDs
# --------------------
Nrealize = datamodel.ast_realization_per_model
Nfilters = datamodel.ast_bands_above_maglimit
# file names for stars and corresponding SED parameters
outfile = "./" + datamodel.project + "/" + datamodel.project + "_inputAST.txt"
outfile_params = (
"./" + datamodel.project + "/" + datamodel.project + "_ASTparams.fits"
)
if flux_bin_method:
N_fluxes = datamodel.ast_n_flux_bins
min_N_per_flux = datamodel.ast_n_per_flux_bin
bins_outfile = (
"./" + datamodel.project + "/" + datamodel.project + "_ASTfluxbins.txt"
)
chosen_seds = pick_models_toothpick_style(
modelsedgrid_filename,
datamodel.filters,
mag_cuts,
Nfilters,
N_fluxes,
min_N_per_flux,
outfile=outfile,
outfile_params=outfile_params,
bins_outfile=bins_outfile,
)
else:
N_models = datamodel.ast_models_selected_per_age
chosen_seds = pick_models(
modelsedgrid_filename,
datamodel.filters,
mag_cuts,
Nfilter=Nfilters,
N_stars=N_models,
Nrealize=Nrealize,
outfile=outfile,
outfile_params=outfile_params,
)
# --------------------
# assign positions
# --------------------
if datamodel.ast_with_positions:
separation = datamodel.ast_pixel_distribution
filename = datamodel.project + "/" + datamodel.project + "_inputAST.txt"
if datamodel.ast_reference_image is not None:
# With reference image, use one of these options
if datamodel.ast_source_density_table is not None:
pick_positions_from_map(
obsdata,
chosen_seds,
datamodel.ast_source_density_table,
datamodel.ast_N_bins,
datamodel.ast_realization_per_model,
outfile=filename,
refimage=datamodel.ast_reference_image,
refimage_hdu=0,
Nrealize=1,
set_coord_boundary=datamodel.ast_coord_boundary,
)
elif datamodel.ast_background_table is not None:
pick_positions_from_map(
obsdata,
chosen_seds,
datamodel.ast_background_table,
datamodel.ast_N_bins,
datamodel.ast_realization_per_model,
outfile=filename,
refimage=datamodel.ast_reference_image,
refimage_hdu=0,
Nrealize=1,
set_coord_boundary=datamodel.ast_coord_boundary,
)
else:
pick_positions(
obsdata,
filename,
separation,
refimage=datamodel.ast_reference_image,
)
else:
# Without reference image, we can only use this function
if (
datamodel.ast_source_density_table is None
and datamodel.ast_background_table is None
):
pick_positions(obsdata, filename, separation)
else:
print(
"To use ast_source_density_table or ast_background_table, ast_reference_image must be specified."
)
