def filter_comparison_stars(
star: StarDescription, comp_stars: ComparisonStars
) -> Tuple[ComparisonStars, ComparisonStars]:
compstar_match: star_description.CompStarData = star.get_metadata("COMPSTARS")
sd_ids = compstar_match.compstar_ids
# skip part of the work if the list is equal. Biggest set first otherwise diff is always empty
if len(set(comp_stars.ids).difference(set(sd_ids))) == 0:
return comp_stars
filtered_compstars = comp_stars.get_filtered_comparison_stars(sd_ids)
extra_compstar = comp_stars.get_filtered_comparison_stars([compstar_match.extra_id])
logging.debug(
f"get star compstars from catalog: {len(filtered_compstars.ids)}, {filtered_compstars.ids}"
)
return filtered_compstars, extra_compstar
def setUp(self) -> None:
logging.getLogger().setLevel(logging.DEBUG)
logging.basicConfig(format="%(asctime)s %(levelname)s %(message)s")
self.df = reading.read_lightcurve_vast("./tests/data/out02391.dat")
self.sd = stardesc(2391, 10, 10)
do_calibration.add_catalog_data_to_sd(self.sd, 10, 0.1,
"UCAC4-Numero2391", "UCAC4",
self.sd.coords)
self.sd_comp1 = stardesc(1, 10, 10)
do_calibration.add_catalog_data_to_sd(self.sd_comp1, 12.1, 0.1,
"UCAC4-Numero1", "UCAC4",
self.sd.coords)
self.sd_comp5 = stardesc(5, 10, 10)
do_calibration.add_catalog_data_to_sd(self.sd_comp5, 11.1, 0.1,
"UCAC4-Numero5", "UCAC4",
self.sd.coords)
self.sd.set_metadata(star_metadata.CompStarData(compstar_ids=[1, 5]))
# ComparisonStars(comparison_stars_ids, comparison_stars_1_sds, comp_observations, comp_catalogmags,
# comp_catalogerr)
observations = [
{
"2457236.66302": (12.2, 0.01)
},
{
"2457236.66302": (11.2, 0.01)
},
]
self.comp_stars = ComparisonStars([1, 5],
[self.sd_comp1, self.sd_comp5],
observations, [12, 11], [0.1, 0.1])
import toml
self.settings = toml.load("./tests/data/testsettings.txt")
def test_calculate_weighted_value_ensemble_photometry(self):
data = {"JD": ["1"], "Vrel": [15.414], "err": [0.012]}
df = DataFrame(data, columns=["JD", "Vrel", "err"])
# {JD, (mag, magerr)}
observations_1 = {"1": (11.775, 0.001)}
observations_2 = {"1": (12.220, 0.0012)}
observations_3 = {"1": (13.114, 0.0021)}
observations = [observations_1, observations_2, observations_3]
catalogmags = [11.8, 12.2, 13.1]
comp_stars = ComparisonStars(None, None, observations, catalogmags, None)
realV, realErr = do_compstars.calculate_ensemble_photometry(
df, comp_stars, do_compstars.weighted_value_ensemble_method
)
self.assertEqual(15.415, round(realV[0], 3))
self.assertEqual(0.0121, round(realErr[0], 4))
def set_comp_stars_and_ucac4(
star_descriptions: List[StarDescription],
selectedstars: List[StarDescription],
checkstarfile: str,
vastdir: str,
stardict: StarDict,
ref_jd: str,
) -> ComparisonStars:
if checkstarfile:
# load comparison stars
checkstars = read_checkstars(checkstarfile)
ucac4.add_sd_metadatas(selectedstars)
comparison_stars_ids, comparison_stars_1_sds = do_compstars.get_fixed_compstars(
star_descriptions, checkstars)
else:
ucac4.add_sd_metadatas(star_descriptions)
(
comparison_stars_ids,
comparison_stars_1_sds,
) = do_compstars.get_calculated_compstars(vastdir, stardict, ref_jd)
# get all observations for the comparison stars
comp_observations = []
for star in comparison_stars_ids:
comp_magdict = reading.read_magdict_for_star(vastdir, star)
# logging.info(f"Read comp magdict for {star}: {read_comp_magdict}")
comp_observations.append(comp_magdict)
comp_catalogmags = []
comp_catalogerr = []
for star in comparison_stars_1_sds:
star_ucac4_catalog = star.get_metadata("UCAC4")
comp_catalogmags.append(star_ucac4_catalog.vmag)
comp_catalogerr.append(star_ucac4_catalog.vmag_err)
comp_stars = ComparisonStars(
comparison_stars_ids,
comparison_stars_1_sds,
comp_observations,
comp_catalogmags,
comp_catalogerr,
)
logging.info(
f"Using {len(comparison_stars_ids)} comparison stars with on average "
f"{np.array(list(map(len, comp_observations))).mean()} observations")
return comp_stars
def test_closest_compstar_ids(self):
stars = [
self.stardesc(1, 1, 1, 10, 0.01, 10),
self.stardesc(4283, 3, 3, 12, 0.02, 10),
self.stardesc(132, 10.24496, 9.96736, 13, 0.02, 10),
self.stardesc(2, 10.24490, 9.96730, 12, 0.01, 10),
self.stardesc(3, 10.24490, 9.96730, 12, 0.01, 10),
] # not there
result = do_compstars._closest_compstar_ids(
stars[0],
ComparisonStars(
[x.local_id for x in stars[1:]],
[x for x in stars[1:]],
None,
None,
None,
),
)
self.assertEqual([4283, 2, 3, 132], result)