def test_simobs(self):
"""
Simulate observations using cached versions of the sed grid and noise model
and compare the result to a cached version.
"""
# download files specific to this test
simobs_fname_cache = download_rename("beast_example_phat_simobs.fits")
# get the physics model grid - includes priors
modelsedgrid = SEDGrid(self.seds_fname_cache)
# read in the noise model - includes bias, unc, and completeness
noisegrid = noisemodel.get_noisemodelcat(self.noise_fname_cache)
table_new = gen_SimObs_from_sedgrid(
modelsedgrid,
noisegrid,
nsim=100,
compl_filter="max",
ranseed=1234,
)
# check that the simobs files are exactly the same
table_cache = Table.read(simobs_fname_cache)
# to avoid issues with uppercase vs lowercase column names, make them all
# the same before comparing
for col in table_new.colnames:
table_new[col].name = col.upper()
for col in table_cache.colnames:
table_cache[col].name = col.upper()
compare_tables(table_cache, table_new)
def test_trim_grid():
# download the needed files
vega_fname = download_rename('vega.hd5')
seds_fname = download_rename('beast_example_phat_seds.grid.hd5')
noise_fname = download_rename('beast_example_phat_noisemodel.hd5')
# download cached version of noisemodel on the sed grid
simobs_fname_cache = download_rename('beast_example_phat_simobs.fits')
################
# get the physics model grid - includes priors
modelsedgrid = FileSEDGrid(seds_fname)
# read in the noise model - includes bias, unc, and completeness
noisegrid = noisemodel.get_noisemodelcat(noise_fname)
table_new = gen_SimObs_from_sedgrid(modelsedgrid,
noisegrid,
nsim=100,
compl_filter="f475w",
ranseed=1234,
vega_fname=vega_fname)
# check that the simobs files are exactly the same
table_cache = Table.read(simobs_fname_cache)
compare_tables(table_cache, table_new)
def test_simobs():
# download the needed files
vega_fname = download_rename("vega.hd5")
seds_fname = download_rename("beast_example_phat_seds.grid.hd5")
noise_fname = download_rename("beast_example_phat_noisemodel.grid.hd5")
# download cached version of noisemodel on the sed grid
simobs_fname_cache = download_rename("beast_example_phat_simobs.fits")
################
# get the physics model grid - includes priors
modelsedgrid = SEDGrid(seds_fname)
# read in the noise model - includes bias, unc, and completeness
noisegrid = noisemodel.get_noisemodelcat(noise_fname)
table_new = gen_SimObs_from_sedgrid(
modelsedgrid,
noisegrid,
nsim=100,
compl_filter="f475w",
ranseed=1234,
vega_fname=vega_fname,
)
# check that the simobs files are exactly the same
table_cache = Table.read(simobs_fname_cache)
# to avoid issues with uppercase vs lowercase column names, make them all
# the same before comparing
for col in table_new.colnames:
table_new[col].name = col.upper()
for col in table_cache.colnames:
table_cache[col].name = col.upper()
compare_tables(table_cache, table_new)
def simulate_obs(
physgrid_list,
noise_model_list,
output_catalog,
nsim=100,
compl_filter="F475W",
weight_to_use='weight',
ranseed=None,
):
"""
Wrapper for creating a simulated photometry.
Parameters
----------
physgrid_list : list of strings
Name of the physics model file. If there are multiple physics model
grids (i.e., if there are subgrids), list them all here, and they will
each be sampled nsim/len(physgrid_list) times.
noise_model_list : list of strings
Name of the noise model file. If there are multiple files for
physgrid_list (because of subgrids), list the noise model file
associated with each physics model file.
output_catalog : string
Name of the output simulated photometry catalog
n_sim : int (default=100)
Number of simulated objects to create. If nsim/len(physgrid_list) isn't
an integer, this will be increased so that each grid has the same
number of samples.
compl_filter : string (default='F475W')
filter name to use for completeness
weight_to_use : string (default='weight')
Set to either 'weight' (prior+grid), 'prior_weight', or 'grid_weight' to
choose the weighting for SED selection.
ranseed : int
seed for random number generator
"""
# numbers of samples to do
# (ensure there are enough for even sampling of multiple model grids)
n_phys = len(physgrid_list)
samples_per_grid = int(np.ceil(nsim / n_phys))
# list to hold all simulation tables
simtable_list = []
# make a table for each physics model + noise model
for physgrid, noise_model in zip(np.atleast_1d(physgrid_list),
np.atleast_1d(noise_model_list)):
# get the physics model grid - includes priors
modelsedgrid = SEDGrid(str(physgrid))
# read in the noise model - includes bias, unc, and completeness
noisegrid = noisemodel.get_noisemodelcat(str(noise_model))
# generate the table
simtable = gen_SimObs_from_sedgrid(
modelsedgrid,
noisegrid,
nsim=samples_per_grid,
compl_filter=compl_filter,
weight_to_use=weight_to_use,
ranseed=ranseed,
)
# append to the list
simtable_list.append(simtable)
# stack all the tables into one and write it out
vstack(simtable_list).write(output_catalog, overwrite=True)
parser.add_argument("obsgrid", help="filename of observation/nosie grid")
parser.add_argument("outfile", help="filename for simulated observations")
parser.add_argument("--nsim",
default=100,
type=int,
help="number of simulated objects")
parser.add_argument("--compl_filter",
default="F475W",
help="filter name to use for completeness")
parser.add_argument("--ranseed",
default=None,
type=int,
help="seed for random number generator")
args = parser.parse_args()
# get the physics model grid - includes priors
modelsedgrid = FileSEDGrid(args.physgrid)
# read in the noise model - includes bias, unc, and completeness
noisegrid = noisemodel.get_noisemodelcat(args.obsgrid)
simtable = gen_SimObs_from_sedgrid(
modelsedgrid,
noisegrid,
nsim=args.nsim,
compl_filter=args.compl_filter,
ranseed=args.ranseed,
)
simtable.write(args.outfile, overwrite=True)
def simulate_obs(
physgrid_list,
noise_model_list,
output_catalog,
beastinfo_list=None,
nsim=100,
compl_filter="F475W",
complcut=None,
magcut=None,
weight_to_use="weight",
ranseed=None,
):
"""
Simulate photometry based on a physicsmodel grid(s) and observation model(s).
Parameters
----------
physgrid_list : list of strings
Name of the physics model file. If there are multiple physics model
grids (i.e., if there are subgrids), list them all here, and they will
each be sampled nsim/len(physgrid_list) times.
noise_model_list : list of strings
Name of the noise model file. If there are multiple files for
physgrid_list (because of subgrids), list the noise model files
associated with each physics model file.
output_catalog : string
Name of the output simulated photometry catalog
beastinfo_list : list of strings (default=None)
Name of the beast info file. The mass and age prior models are read
from this model to use to compute the number of stars to simulate. If
there are multiple files for physgrid_list (because of subgrids), list
the beast info files associated with each physics model file.
n_sim : int (default=100)
Number of simulated objects to create if beastinfo_list is not given. If
nsim/len(physgrid_list) isn't an integer, this will be increased so that
each grid has the same number of samples.
compl_filter : str (default=F475W)
filter to use for completeness (required for toothpick model)
set to max to use the max value in all filters
complcut : float (defualt=None)
completeness cut for only including model seds above the given
completeness, where the cut ranges from 0 to 1.
magcut : float (defualt=None)
faint-end magnitude cut for only including model seds brighter
than the given magnitude in compl_filter.
weight_to_use : string (default='weight')
Set to either 'weight' (prior+grid), 'prior_weight', 'grid_weight',
or 'uniform' (this option is valid only when nsim is supplied) to
choose the weighting for SED selection.
ranseed : int
seed for random number generator
"""
# numbers of samples to do
# (ensure there are enough for even sampling of multiple model grids)
n_phys = len(np.atleast_1d(physgrid_list))
nsim = int(nsim)
samples_per_grid = int(np.ceil(nsim / n_phys))
if complcut is not None:
complcut = float(complcut)
if magcut is not None:
magcut = float(magcut)
if ranseed is not None:
ranseed = int(ranseed)
# list to hold all simulation tables
simtable_list = []
# make a table for each physics model + noise model
for k, physgrid in enumerate(np.atleast_1d(physgrid_list)):
noise_model = np.atleast_1d(noise_model_list)[k]
# get the physics model grid - includes priors
modelsedgrid = SEDGrid(str(physgrid))
# read in the noise model - includes bias, unc, and completeness
noisegrid = noisemodel.get_noisemodelcat(str(noise_model))
if beastinfo_list is not None:
with asdf.open(np.atleast_1d(beastinfo_list)[k]) as af:
binfo = af.tree
age_prior_model = binfo["age_prior_model"]
mass_prior_model = binfo["mass_prior_model"]
else:
age_prior_model = None
mass_prior_model = None
# generate the table
simtable = gen_SimObs_from_sedgrid(
modelsedgrid,
noisegrid,
age_prior_model=age_prior_model,
mass_prior_model=mass_prior_model,
nsim=samples_per_grid,
compl_filter=compl_filter,
complcut=complcut,
magcut=magcut,
weight_to_use=weight_to_use,
ranseed=ranseed,
)
# append to the list
simtable_list.append(simtable)
# stack all the tables into one and write it out
vstack(simtable_list).write(output_catalog, overwrite=True)
