def test_star_type_probability_all_params(self):
"""
Test for star_type_probability. Inputs and expected outputs created by
running generate_files_for_tests.py in beast-examples/metal_small.
In this version, all required parameters are present.
"""
# download cached file
star_prob_fname = download_rename(
f"{self.basename}_star_type_probability.asdf")
with asdf.open(star_prob_fname) as af:
star_prob_info = copy.deepcopy(af.tree)
# run star_type_probability
star_prob = star_type_probability.star_type_probability(
self.pdf1d_fname_cache,
self.pdf2d_fname_cache,
**star_prob_info["input"],
)
# expected output table
expected_star_prob = Table(star_prob_info["output"])
# compare to new table
compare_tables(expected_star_prob, Table(star_prob))
def test_star_type_probability_no_Av(self):
"""
Test for star_type_probability.py
"""
# download the needed files
pdf2d_fname = download_rename("beast_example_phat_pdf2d_no_Av.fits")
star_prob_fname = download_rename(
"beast_example_phat_startype_no_Av.fits")
# run star_type_probability
star_prob = star_type_probability.star_type_probability(
self.pdf1d_fname_cache,
pdf2d_fname,
output_filebase=None,
ext_O_star_params={
"min_M_ini": 10,
"min_Av": 0.5,
"max_Av": 5
},
)
# expected output table
expected_star_prob = Table.read(star_prob_fname)
# compare to new table
compare_tables(expected_star_prob, Table(star_prob))
def test_star_type_probability_all_params():
"""
Test for star_type_probability.py
"""
# download the needed files
pdf1d_fname = download_rename("beast_example_phat_pdf1d.fits")
pdf2d_fname = download_rename("beast_example_phat_pdf2d.fits")
star_prob_fname = download_rename("beast_example_phat_startype.fits")
# run star_type_probability
star_prob = star_type_probability.star_type_probability(
pdf1d_fname,
pdf2d_fname,
output_filebase=None,
ext_O_star_params={
'min_M_ini': 10,
'min_Av': 0.5,
'max_Av': 5
})
# expected output table
expected_star_prob = Table.read(star_prob_fname)
# compare to new table
compare_tables(expected_star_prob, Table(star_prob))
def test_star_type_probability_no_Av(self):
"""
Test for star_type_probability.
In this version, A_V was not saved in the 2D PDFs.
"""
# download cached file
star_prob_fname = download_rename(
f"{self.basename}_star_type_probability.asdf")
with asdf.open(star_prob_fname) as af:
star_prob_info = copy.deepcopy(af.tree)
# edit the 2D PDF file to not have A_V info
temp_pdf2d_fname = tempfile.NamedTemporaryFile(suffix=".fits").name
temp_hdu_list = []
with fits.open(self.pdf2d_fname_cache) as hdu:
for ext in hdu:
if "Av+" in ext.name or "+Av" in ext.name:
continue
temp_hdu_list.append(ext)
fits.HDUList(temp_hdu_list).writeto(temp_pdf2d_fname)
# edit the expected output to have NaNs in columns that require A_V
# (currently, that's all columns)
expected_star_prob = Table(star_prob_info["output"])
for col in expected_star_prob.colnames:
if col == "ext_O_star":
expected_star_prob[col] = np.nan
if col == "dusty_agb":
expected_star_prob[col] = np.nan
# run star_type_probability
star_prob = star_type_probability.star_type_probability(
self.pdf1d_fname_cache,
temp_pdf2d_fname,
**star_prob_info["input"],
)
# compare to expected table
compare_tables(expected_star_prob, Table(star_prob))
def generate_files_for_tests(run_beast=True, run_tools=True):
"""
Use the metal_small example to generate a full set of files for the BEAST
regression tests.
Parameters
----------
run_beast : boolean (default=True)
if True, run the BEAST
run_tools : boolean (default=True)
if True, run the code to generate things for tools
"""
# read in BEAST settings
settings_orig = beast_settings.beast_settings("beast_settings.txt")
# also make a version with subgrids
settings_subgrids = copy.deepcopy(settings_orig)
settings_subgrids.n_subgrid = 2
settings_subgrids.project = f"{settings_orig.project}_subgrids"
# ==========================================
# run the beast for each set of settings
# ==========================================
if run_beast:
for settings in [settings_orig, settings_subgrids]:
# -----------------
# physics model
# -----------------
create_physicsmodel.create_physicsmodel(
settings,
nsubs=settings.n_subgrid,
nprocs=1,
)
# -----------------
# ASTs
# -----------------
# currently only works for no subgrids
if settings.n_subgrid == 1:
make_ast_inputs.make_ast_inputs(settings,
pick_method="flux_bin_method")
# -----------------
# obs model
# -----------------
create_obsmodel.create_obsmodel(
settings,
use_sd=False,
nsubs=settings.n_subgrid,
nprocs=1,
use_rate=True,
)
# -----------------
# trimming
# -----------------
# make file names
file_dict = create_filenames.create_filenames(
settings, use_sd=False, nsubs=settings.n_subgrid)
# read in the observed data
obsdata = Observations(settings.obsfile, settings.filters,
settings.obs_colnames)
for i in range(settings.n_subgrid):
# get the modesedgrid on which to generate the noisemodel
modelsedgridfile = file_dict["modelsedgrid_files"][i]
modelsedgrid = SEDGrid(modelsedgridfile)
# read in the noise model just created
noisemodel_vals = noisemodel.get_noisemodelcat(
file_dict["noise_files"][i])
# trim the model sedgrid
sed_trimname = file_dict["modelsedgrid_trim_files"][i]
noisemodel_trimname = file_dict["noise_trim_files"][i]
trim_grid.trim_models(
modelsedgrid,
noisemodel_vals,
obsdata,
sed_trimname,
noisemodel_trimname,
sigma_fac=3.0,
)
# -----------------
# fitting
# -----------------
run_fitting.run_fitting(
settings,
use_sd=False,
nsubs=settings.n_subgrid,
nprocs=1,
pdf2d_param_list=["Av", "M_ini", "logT"],
pdf_max_nbins=200,
)
# -----------------
# merging
# -----------------
# it'll automatically skip for no subgrids
merge_files.merge_files(settings,
use_sd=False,
nsubs=settings.n_subgrid)
print("\n\n")
# ==========================================
# reference files for assorted tools
# ==========================================
if run_tools:
# -----------------
# compare_spec_type
# -----------------
# the input settings
input = {
"spec_ra": [72.67213351],
"spec_dec": [-67.71720515],
"spec_type": ["A"],
"spec_subtype": [0],
"lumin_class": ["IV"],
"match_radius": 0.2,
}
# run it
output = compare_spec_type.compare_spec_type(
settings_orig.obsfile,
"{0}/{0}_stats.fits".format(settings_orig.project),
**input,
)
# save the inputs and outputs
asdf.AsdfFile({
"input": input,
"output": output
}).write_to("{0}/{0}_compare_spec_type.asdf".format(
settings_orig.project))
# -----------------
# star_type_probability
# -----------------
# input settings
input = {
"output_filebase": None,
"ext_O_star_params": {
"min_M_ini": 10,
"min_Av": 0.5,
"max_Av": 5
},
}
# run it
output = star_type_probability.star_type_probability(
"{0}/{0}_pdf1d.fits".format(settings_orig.project),
"{0}/{0}_pdf2d.fits".format(settings_orig.project),
**input,
)
# save the inputs and outputs
asdf.AsdfFile({
"input": input,
"output": output
}).write_to("{0}/{0}_star_type_probability.asdf".format(
settings_orig.project))
# ==========================================
# asdf file permissions
# ==========================================
# for unknown reasons, asdf currently writes files with permissions set
# to -rw-------. This changes it to -rw-r--r-- (like the rest of the
# BEAST files) so Karl can easily copy them over to the cached file
# website.
# list of asdf files
asdf_files = glob.glob("*/*.asdf")
# go through each one to change permissions
for fname in asdf_files:
os.chmod(fname,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IRGRP | stat.S_IROTH)
