def make_iso_table(
project,
oiso=None,
logtmin=6.0,
logtmax=10.13,
dlogt=0.05,
z=[0.0152],
iso_fname=None,
):
"""
The isochrone tables are loaded (downloading if necessary)
Parameters
----------
project: str
project name
oiso: isochrone.Isochrone object
contains the full isochrones information
logtmin: float
log-age min
logtmax: float
log-age max
dlogt: float
log-age step to request
z: float or sequence
list of metalicity values, where default (Z=0.152) is adopted Z_sun
for PARSEC/COLIBRI models
Returns
-------
fname: str
name of saved file
oiso: isochrone.Isochrone object
contains the full isochrones information
"""
if iso_fname is None:
iso_fname = "%s/%s_iso.csv" % (project, project)
if not os.path.isfile(iso_fname):
if oiso is None:
oiso = isochrone.PadovaWeb()
t = oiso._get_t_isochrones(max(5.0, logtmin), min(10.13, logtmax),
dlogt, z)
t.header["NAME"] = "{0} Isochrones".format("_".join(
iso_fname.split("_")[:-1]))
print("{0} Isochrones".format("_".join(iso_fname.split("_")[:-1])))
t.write(iso_fname)
# read in the isochrone data from the file
# not sure why this is needed, but reproduces previous ezpipe method
oiso = ezIsoch(iso_fname)
return (iso_fname, oiso)
def test_make_kurucz_tlusty_spectral_grid(self):
"""
Generate the spectral grid based on Kurucz and Tlusty stellar atmosphere
models based on a cached set of isochrones and compare the result to a cached
version.
"""
# read in the cached isochrones
oiso = ezIsoch(self.iso_fname_cache)
# calculate the redshift
redshift = (self.settings.velocity / const.c).decompose().value
# make the spectral grid
spec_fname = tempfile.NamedTemporaryFile(suffix=".hd5").name
(spec_fname, g) = make_spectral_grid(
"test",
oiso,
osl=self.settings.osl,
redshift=redshift,
distance=self.settings.distances,
distance_unit=self.settings.distance_unit,
spec_fname=spec_fname,
# filterLib=filter_fname,
extLaw=self.settings.extLaw,
add_spectral_properties_kwargs=self.settings.
add_spectral_properties_kwargs,
)
# compare the new to the cached version
compare_hdf5(self.spec_fname_cache, spec_fname)
def test_make_kurucz_tlusty_spectral_grid():
# download the needed files
kurucz_fname = download_rename("kurucz2004.grid.fits")
tlusty_fname = download_rename("tlusty.lowres.grid.fits")
filter_fname = download_rename("filters.hd5")
iso_fname = download_rename("beast_example_phat_iso.csv")
# download cached version of spectral grid
spec_fname_cache = download_rename("beast_example_phat_spec_grid.hd5")
################
# generate the same spectral grid from the code
# read in the cached isochrones
oiso = ezIsoch(iso_fname)
# define the distance
distances = [24.47]
distance_unit = units.mag
velocity = -300 * units.km / units.s
redshift = (velocity / const.c).decompose().value
# define the spectral libraries to use
osl = stellib.Tlusty(filename=tlusty_fname) + stellib.Kurucz(
filename=kurucz_fname)
# define the extinction curve to use
extLaw = extinction.Gordon16_RvFALaw()
filters = [
"HST_WFC3_F275W",
"HST_WFC3_F336W",
"HST_ACS_WFC_F475W",
"HST_ACS_WFC_F814W",
"HST_WFC3_F110W",
"HST_WFC3_F160W",
]
add_spectral_properties_kwargs = dict(filternames=filters)
spec_fname = "/tmp/beast_example_phat_spec_grid.hd5"
spec_fname, g = make_spectral_grid(
"test",
oiso,
osl=osl,
redshift=redshift,
distance=distances,
distance_unit=distance_unit,
spec_fname=spec_fname,
filterLib=filter_fname,
extLaw=extLaw,
add_spectral_properties_kwargs=add_spectral_properties_kwargs,
)
# compare the new to the cached version
compare_hdf5(spec_fname_cache, spec_fname)
def create_physicsmodel(nsubs=1, nprocs=1, subset=[None, None]):
"""
Create the physics model grid. If nsubs > 1, this will make sub-grids.
Parameters
----------
nsubs : int (default=1)
number of subgrids to split the physics model into
nprocs : int (default=1)
Number of parallel processes to use
(currently only implemented for subgrids)
subset : list of two ints (default=[None,None])
Only process subgrids in the range [start,stop].
(only relevant if nsubs > 1)
"""
# 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)
# filename for the SED grid
modelsedgrid_filename = "%s/%s_seds.grid.hd5" % (
datamodel.project,
datamodel.project,
)
# grab the current subgrid slice
subset_slice = slice(subset[0], subset[1])
# make sure the project directory exists
create_project_dir(datamodel.project)
# 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,
)
# remove the isochrone points with logL=-9.999
oiso = ezIsoch(oiso.selectWhere("*", "logL > -9"))
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,
extLaw=datamodel.extLaw,
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,
age_prior_model=datamodel.age_prior_model,
mass_prior_model=datamodel.mass_prior_model,
met_prior_model=datamodel.met_prior_model,
)
# --------------------
# no subgrids
# --------------------
if nsubs == 1:
# 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
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,
)
# --------------------
# use subgrids
# --------------------
if nsubs > 1:
# Work with the whole grid up to there (otherwise, priors need a
# rework - they don't like having only a subset of the parameter
# space, especially when there's only one age for example)
# Make subgrids, by splitting the spectral grid into equal sized pieces
custom_sub_pspec = subgridding_tools.split_grid(pspec_fname, nsubs)
file_prefix = "{0}/{0}_".format(datamodel.project)
# function to process the subgrids individually
def gen_subgrid(i, sub_name):
sub_g_pspec = FileSEDGrid(sub_name)
sub_seds_fname = "{}seds.gridsub{}.hd5".format(file_prefix, i)
# 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
(sub_seds_fname, sub_g_seds) = make_extinguished_sed_grid(
datamodel.project,
sub_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,
add_spectral_properties_kwargs=extra_kwargs,
seds_fname=sub_seds_fname,
)
return sub_seds_fname
# run the above function
par_tuples = [
(i, sub_name) for i, sub_name in enumerate(custom_sub_pspec)
][subset_slice]
parallel_wrapper(gen_subgrid, par_tuples, nprocs=nprocs)
# Save a list of subgrid names that we expect to see
required_names = [
"{}seds.gridsub{}.hd5".format(file_prefix, i) for i in range(nsubs)
]
outdir = os.path.join(".", datamodel.project)
subgrid_names_file = os.path.join(outdir, "subgrid_fnames.txt")
with open(subgrid_names_file, "w") as fname_file:
for fname in required_names:
fname_file.write(fname + "\n")
