def get_fixed_compstars(
star_descriptions: List[StarDescription], comparison_stars: List[str]
):
logging.info(f"Using fixed compstars {comparison_stars}")
ucac4 = UCAC4()
star_ids_1 = []
star_desc_result = []
star_catalog = do_calibration.create_star_descriptions_catalog(star_descriptions)
for ucac_id in comparison_stars:
# getting star_id_1
ucacsd = ucac4.get_star_description_from_id(ucac_id)
ra, dec = ucacsd.coords.ra, ucacsd.coords.dec
star_id_1 = do_calibration.get_starid_1_for_radec([ra], [dec], star_catalog)
star_ids_1.append(star_id_1)
# adding info to star_description
star = star_descriptions[star_id_1 - 1]
logging.info(
f"Compstar match: {ucacsd.aavso_id} with {star.local_id} ({ra}, {dec})"
)
do_calibration.add_catalog_data_to_sd(
star,
ucacsd.vmag,
ucacsd.vmag_err,
ucacsd.aavso_id,
"UCAC4",
SkyCoord(ra, dec, unit="deg"),
)
star_desc_result.append(star)
logging.debug(
f"Using fixed compstar '{ucac_id}' with Vmag: '{ucacsd.vmag}' and star id: {star_id_1}"
)
return [x.local_id for x in star_desc_result], star_desc_result
def main(vastdir, file):
ucac4 = UCAC4()
# construct star descriptions
sds = utils_sd.construct_star_descriptions(vastdir, None)
star_catalog = do_calibration.create_star_descriptions_catalog(sds)
df = main_vast.read_localid(file)
for idx, row in df.iterrows():
rowucac4 = row["ucac4_name"]
result = ucac4_to_localid(ucac4, rowucac4, star_catalog, sds)
row["local_id"] = result
print(df.to_csv())
print(results)
def inspect(vastdir, resultdir, fitsdir, apikey, stars):
"""generate image+txt file to inspect a certain star on wrong ucac/localid nrs """
reading.trash_and_recreate_dir(Path(resultdir) / "inspect")
ref_jd, _, _, reference_frame = reading.extract_reference_frame(vastdir)
_, shapex, shapey = reading.get_fits_data(Path(fitsdir, Path(reference_frame).name))
refframes: List[RefFrame] = [
RefFrame(
ref_jd,
Path(vastdir) / "new-image.fits",
Path(fitsdir, Path(reference_frame).name),
)
]
# construct star descriptions
sds = utils_sd.construct_star_descriptions(vastdir, None)
star_catalog = do_calibration.create_star_descriptions_catalog(sds)
if args.radecs:
main_vast.read_and_tag_radec(args.radecs, sds)
if args.localids:
stardict = main_vast.get_localid_to_sd_dict(sds)
main_vast.read_and_tag_localid(args.localids, stardict)
if args.stars:
stars = sorted(list(map(lambda x: int(x), stars)))
else:
stars = sorted([x.local_id for x in sds if x.get_metadata("SELECTEDTAG")])
for starid in stars:
process(
vastdir,
resultdir,
fitsdir,
apikey,
shapex,
shapey,
starid,
ref_jd,
reference_frame,
sds,
star_catalog,
refframes,
)
def main(vastdir, star):
ucac4 = UCAC4()
# get UCAC4 sd for search later
ucac_details = ucac4.get_ucactuple_from_id(utils.get_full_ucac4_id(star))
ucacsd = ucac4.get_star_description_from_tuple(ucac_details)
logging.info(f"Found UCAC4: {ucacsd}")
# construct star descriptions
sds = utils_sd.construct_star_descriptions(vastdir, None)
star_catalog = do_calibration.create_star_descriptions_catalog(sds)
# Get the 10 closest neighbours
sd_dict = utils.get_localid_to_sd_dict(sds)
neighbours = []
for neigh in range(1, 22):
idx, d2d, _ = match_coordinates_sky(ucacsd.coords,
star_catalog,
nthneighbor=neigh)
neighbours.append(sds[idx])
ucac4.add_sd_metadatas(neighbours)
logging.info("\n" +
"\n".join([f"Star {x.local_id}: {x}" for x in neighbours]))
def get_fixed_compstars(star_descriptions=None):
logging.info(f"Using fixed compstars {init.comparison_stars}")
if star_descriptions is None:
star_descriptions = do_calibration.get_star_descriptions(init.star_list)
star_ids_1 = []
star_desc_result = []
star_catalog = do_calibration.create_star_descriptions_catalog(star_descriptions)
for ucac_id in init.comparison_stars:
# getting star_id_1
df = do_calibration.get_ucac4_id_as_dataframe(ucac_id)
star_id_1 = do_calibration.get_starid_1_for_radec(df['RAJ2000'], df['DEJ2000'], star_catalog)
star_ids_1.append(star_id_1)
# adding info to star_description
star = star_descriptions[star_id_1 - 1]
coord_catalog = SkyCoord(df['RAJ2000'], df['DEJ2000'], unit='deg')
logging.info(df.info())
# df['e_Vmag'] is not present !!!
logging.info(df['UCAC4'].iloc(0)[0])
vmag = df['Vmag'].iloc(0)[0]
do_calibration.add_info_to_star_description(star, vmag, np.nan, df['UCAC4'].iloc(0)[0].decode("utf-8"),
"UCAC4", coord_catalog)
star_desc_result.append(star)
logging.debug(f"Using fixed compstar '{ucac_id}' with Vmag: '{vmag}' and star id: {star_id_1}")
return star_ids_1, star_desc_result
def read_and_tag_radec(radec_catalog: str, stars: List[StarDescription]):
""" used for the -o or --radeccatalog cli argument """
try:
# read the CSV file from disk
logging.info(f"Using radec catalog: {radec_catalog}")
df = pd.read_csv(
radec_catalog,
sep=",",
comment="#",
names=[
"our_name",
"ra",
"dec",
"ucac4_name",
"ucac4_force",
"minmax",
"min",
"max",
"var_type",
"period",
"period_err",
"epoch",
"comments",
],
dtype={
"ra": float,
"dec": float,
"ucac4_name": str,
"minmax": str,
"epoch": float,
},
skipinitialspace=True,
warn_bad_lines=True,
)
df["chosenRA"] = df["ra"]
df["chosenDEC"] = df["dec"]
# process DataFrame row per row
for idx, row in df.iterrows():
ucac_ra, ucac_dec = ucac4.get_ra_dec_from_id(
ucac4_id=row["ucac4_name"])
# override 'our' ra/dec with ucac4 ra/dec if flag is set
if (row["ucac4_force"]):
row["chosenRA"], row["chosenDEC"] = ucac_ra, ucac_dec
logging.warning(
f"Forcing ucac: {row['chosenRA']}, {row['ra']}")
df.iloc[idx] = row
logging.info(f"Selecting {len(df)} stars added by {radec_catalog}")
# logging.info(f"--radeccatalog : {df}")
ra, dec = (df["chosenRA"], df["chosenDEC"])
df = df.replace({np.nan: None})
skycoord: SkyCoord = do_calibration.create_generic_astropy_catalog(
ra, dec)
star_catalog = do_calibration.create_star_descriptions_catalog(stars)
idx, d2d, d3d = match_coordinates_sky(skycoord,
star_catalog,
nthneighbor=1)
for count, index in enumerate(idx):
row = df.iloc[count]
the_star = stars[index]
logging.info(
f"Matching {row['our_name']} to {the_star.local_id} with sep {d2d[count].degree} "
f"at coords {the_star.coords}")
# add sitedata for the star in this row
add_site_metadata(the_star, row, separation=d2d[count].degree)
# add selected, add UCAC4, override UCAC4
postprocess_csv_reads(the_star, row)
if d2d[count].degree > 0.01:
logging.warning(
f"Separation between {df.iloc[count]['our_name']} "
f"and {stars[index].local_id} is {d2d[count]}")
except Exception as ex:
template = "An exception of type {0} occurred. Arguments:\n{1!r}"
message = template.format(type(ex).__name__, ex.args)
import traceback
print(traceback.print_exc())
logging.error(message)
logging.error(f"Could not read {radec_catalog}")