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lightkurve_ext_tess.py
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lightkurve_ext_tess.py
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#
# Helpers to download TESS-specific non-lightcurve data: TOIs, TCEs, etc.
#
from collections.abc import Sequence
import pathlib
import re
from types import SimpleNamespace
import warnings
import numpy as np
import pandas as pd
from pandas.io.formats.style import Styler
import astropy
from astropy import coordinates as coord
from astropy.io import fits
from astropy.table import Table
from astropy.time import Time
import astropy.units as u
from astroquery.utils import TableList
import download_utils
import lightkurve as lk
import lightkurve_ext as lke
# Ues to resolve data files relative the the module (used by MomentumDumpsAccessor)
_MODULE_PATH_ = pathlib.Path(__file__).parent.resolve()
#
# Misc constants
#
R_earth = 6371000 # radius of the Earth [m]
R_jup = 69911000 # radius of Jupiter [m]
BTJD_REF = 2457000
#
# Generic CSV Download helper
#
def _get_csv(url, filename, download_dir, cache_policy_func, **kwargs):
local_filename = download_utils.download_file(
url, filename=filename, download_dir=download_dir, cache_policy_func=cache_policy_func
)
return pd.read_csv(local_filename, **kwargs)
def _single_row(df):
if len(df) > 0:
return df.iloc[0]
else:
return None
#
# TOIs / CTOIs
#
class TOIAccessor:
Headers = SimpleNamespace(
TIC="TIC ID",
TOI="TOI",
MASTER_PRIORITY="Master",
EPOCH_BJD="Epoch (BJD)",
EPOCH_BTJD="Epoch (BTJD)", # derived
PERIOD="Period (days)",
DURATION_HR="Duration (hours)",
DEPTH_PPM="Depth (ppm)",
DEPTH_PCT="Depth (percent)", # derived
PLANET_RADIUS_E="Planet Radius (R_Earth)",
PLANET_RADIUS_J="Planet Radius (R_Jupiter)", # derived
TESS_DISPOSITION="TESS Disposition",
TFOPWG_DISPOSITION="TFOPWG Disposition",
COMMENTS="Comments",
)
# TODO: in-memory cache (with @cached) needs to be redone to properly support cache_policy_func
@classmethod
def get_all_tois(cls, download_dir=None, cache_policy_func=None):
url = "https://exofop.ipac.caltech.edu/tess/download_toi.php?sort=toi&output=csv"
filename = "tess_tois.csv"
res = _get_csv(url, filename, download_dir, cache_policy_func=cache_policy_func, dtype={cls.Headers.TOI: str})
# add derived columns
res[cls.Headers.EPOCH_BTJD] = res[cls.Headers.EPOCH_BJD] - BTJD_REF
res[cls.Headers.PLANET_RADIUS_J] = res[cls.Headers.PLANET_RADIUS_E] * R_earth / R_jup
res[cls.Headers.DEPTH_PCT] = res[cls.Headers.DEPTH_PPM] / 10000
return res
def __init__(self, download_dir=None, cache_policy_func=None):
self._all = self.get_all_tois(download_dir=download_dir, cache_policy_func=cache_policy_func)
def all(self):
return self._all
def of_toi(self, toi):
tois = self._all[self._all[self.Headers.TOI] == str(toi)]
return _single_row(tois)
def of_tic(self, tic):
return self._all[self._all[self.Headers.TIC] == int(tic)]
def of_tics(self, tics):
return self._all[np.isin(self._all[self.Headers.TIC], tics)]
class CTOIAccessor:
Headers = SimpleNamespace(
TIC="TIC ID",
CTOI="CTOI",
TOI="Promoted to TOI",
EPOCH_BJD="Transit Epoch (BJD)",
EPOCH_BTJD="Transit Epoch (BTJD)", # derived
PERIOD="Period (days)",
DURATION_HR="Duration (hrs)",
DEPTH_PPM="Depth ppm",
DEPTH_PCT="Depth percent", # derived
PLANET_RADIUS_E="Planet Radius (R_Earth)",
PLANET_RADIUS_J="Planet Radius (R_Jupiter)", # derived
COMMENTS="Notes",
)
@classmethod
def get_all_ctois(cls, download_dir=None, cache_policy_func=None):
url = "https://exofop.ipac.caltech.edu/tess/download_ctoi.php?sort=ctoi&output=csv"
filename = "tess_ctois.csv"
res = _get_csv(
url,
filename,
download_dir,
cache_policy_func=cache_policy_func,
dtype={cls.Headers.CTOI: str, cls.Headers.TOI: str},
)
# add derived columns
res[cls.Headers.EPOCH_BTJD] = res[cls.Headers.EPOCH_BJD] - BTJD_REF
res[cls.Headers.PLANET_RADIUS_J] = res[cls.Headers.PLANET_RADIUS_E] * R_earth / R_jup
res[cls.Headers.DEPTH_PCT] = res[cls.Headers.DEPTH_PPM] / 10000
return res
def __init__(self, download_dir=None, cache_policy_func=None):
self._all = self.get_all_ctois(download_dir=download_dir, cache_policy_func=cache_policy_func)
def all(self):
return self._all
def of_ctoi(self, ctoi):
ctois = self._all[self._all[self.Headers.CTOI] == str(ctoi)]
return _single_row(ctois)
def of_tic(self, tic):
return self._all[self._all[self.Headers.TIC] == int(tic)]
def add_transit_as_codes_column_to_df(df, headers, label_value_func):
h = headers
# string interpolation does not work. So use old-school concatenation
# for single transit TOI/CTOIs, period returned is often nan or 0
# to make the codes (used in transit_specs) usable later on
# we substitute it with a large period
def handle_nan_or_zero(per):
if np.isnan(per) or per == 0.0:
return 9999.9
else:
return per
# somehow `period = pd.Series([handle_nan_or_zero(p) for p in df[h.PERIOD]])`
# does not work. I temporarily created a new column as a workaround
df["_period_nan_fixed"] = [handle_nan_or_zero(p) for p in df[h.PERIOD]]
df["Codes"] = (
"epoch="
+ df[h.EPOCH_BTJD].map("{:.4f}".format)
+ ", duration_hr="
+ df[h.DURATION_HR].map("{:.4f}".format)
+ ", period="
+ df["_period_nan_fixed"].map("{:.6f}".format)
+ ', label="'
+ label_value_func(df)
+ '", transit_depth_percent='
+ df[h.DEPTH_PCT].map("{:.4f}".format)
+ ","
)
df.drop(columns=["_period_nan_fixed"], inplace=True) # drop the temp column
return df
def _get_tois_in_html(tic, download_dir=None):
h = TOIAccessor.Headers
# Consider cache TOIAccessor in some module global (keyed by download_dir) to avoid
# repeated loading/parsing the underlying TOI csv
tois = TOIAccessor(download_dir=download_dir).of_tic(tic)
if len(tois) < 1:
return "<p>No TOIs.</p>"
add_transit_as_codes_column_to_df(tois, h, label_value_func=lambda df: "TOI " + df[h.TOI])
report_view = tois[
[
h.TOI,
h.MASTER_PRIORITY,
h.TFOPWG_DISPOSITION,
h.PLANET_RADIUS_J,
h.EPOCH_BTJD,
h.DURATION_HR,
h.PERIOD,
h.DEPTH_PCT,
h.COMMENTS,
"Codes",
]
]
# tweak output styling
styler = Styler(report_view, cell_ids=False) # avoid unnecessary long cell ids
styler.hide(axis="index")
styler.format(
formatter={
(h.PLANET_RADIUS_J): "{:.3f}",
(h.EPOCH_BTJD, h.DURATION_HR): "{:.4f}",
(h.PERIOD): "{:.6f}",
(h.DEPTH_PCT): "{:.4f}",
}
)
styler.set_table_styles(
[
# make the TOI table align (roughly) with the TCE table
{"selector": "td.col0", "props": [("padding-left", "10px")]},
]
)
html = styler._repr_html_()
# make the headers to make them more compact
html = html.replace(h.MASTER_PRIORITY, "Master<br>priority", 1)
html = html.replace(h.TFOPWG_DISPOSITION, "TFOPWG<br>Dispo.", 1)
html = html.replace(h.PLANET_RADIUS_J, "R<sub>p</sub><br>R<sub>j</sub>", 1)
html = html.replace(h.EPOCH_BTJD, "Epoch<br>BTJD", 1)
html = html.replace(h.DURATION_HR, "Duration<br>hr", 1)
html = html.replace(h.PERIOD, "Period<br>day", 1)
html = html.replace(h.DEPTH_PCT, "Depth<br>%", 1)
# render nan as -- (as nan is really no value in our case)
# - styler.format()'s na_rep option seems to fix some but not all, so we do it ourselves
# - replace the pattern of <td class="..." >nan</td>
html = html.replace(">nan</td>", ">--</td>")
# turn Codes column into html input element (easier to be selected)
html = re.sub(
r"<td([^>]+)>(epoch=.+,)</td>",
r"""<td\1><input type="text" style="margin-left: 3ch; font-size: 90%; color: #666; width: 10ch;" onclick="this.select();" readonly="" value='\2'></td>""",
html,
)
return html
def _get_ctois_in_html(tic, download_dir=None):
# TODO: lots of codes similar to _get_tois_in_html(). factor them out
h = CTOIAccessor.Headers
# Consider cache TOIAccessor in some module global (keyed by download_dir) to avoid
# repeated loading/parsing the underlying TOI csv
ctois = CTOIAccessor(download_dir=download_dir).of_tic(tic)
if len(ctois) < 1:
return "<p>No CTOIs.</p>"
add_transit_as_codes_column_to_df(ctois, h, label_value_func=lambda df: "CTOI " + df[h.CTOI])
report_view = ctois[
[
h.CTOI,
h.TOI,
h.PLANET_RADIUS_J,
h.EPOCH_BTJD,
h.DURATION_HR,
h.PERIOD,
h.DEPTH_PCT,
h.COMMENTS,
"Codes",
]
]
# tweak output styling
styler = Styler(report_view, cell_ids=False) # avoid unnecessary long cell ids
styler.hide(axis="index")
styler.format(
formatter={
(h.PLANET_RADIUS_J): "{:.3f}",
(h.EPOCH_BTJD, h.DURATION_HR): "{:.4f}",
(h.PERIOD): "{:.6f}",
(h.DEPTH_PCT): "{:.4f}",
}
)
styler.set_table_styles(
[
# make the CTOI table align (roughly) with the TCE table
{"selector": "td.col0", "props": [("padding-left", "20px")]},
# min-width to ensure TOI column, often no value, are wide enough to hold typical TOI value
# so as to make alignment more consistent
{"selector": "td.col1", "props": [("min-width", "80px")]},
]
)
html = styler._repr_html_()
# make the headers to make them more compact
html = html.replace(h.TOI, "TOI?", 1)
html = html.replace(h.PLANET_RADIUS_J, "R<sub>p</sub><br>R<sub>j</sub>", 1)
html = html.replace(h.EPOCH_BTJD, "Epoch<br>BTJD", 1)
html = html.replace(h.DURATION_HR, "Duration<br>hr", 1)
html = html.replace(h.PERIOD, "Period<br>day", 1)
html = html.replace(h.DEPTH_PCT, "Depth<br>%", 1)
# render nan as -- (as nan is really no value in our case)
# - styler.format()'s na_rep option seems to fix some but not all, so we do it ourselves
# - replace the pattern of <td class="..." >nan</td>
html = html.replace(">nan</td>", ">--</td>")
# turn Codes column into html input element (easier to be selected)
html = re.sub(
r"<td([^>]+)>(epoch=.+,)</td>",
r"""<td\1><input type="text" style="margin-left: 3ch; font-size: 90%; color: #666; width: 10ch;" onclick="this.select();" readonly="" value='\2'></td>""",
html,
)
return html
def get_tic_meta_in_html(
lc_or_tic, a_subject_id=None, download_dir=None, tce_filter_func=None, include_transit_model_stellar_density=False
):
# tess_dv_fast.py is at https://github.com/orionlee/tess_dv_fast
# copy it over (or include it in sys.path)
import tess_dv_fast
# This function does not do the actual display,
# so that the caller can call it in background
# and display it wherever it's needed
def link(link_text, url):
return f"""<a href="{url}" target="_blank">{link_text}</a>"""
def prop(prop_name, prop_value):
return f""" <tr><td>{prop_name}</td><td>{prop_value}</td></tr>\n"""
# main logic
if isinstance(lc_or_tic, lk.LightCurve):
tic_id = str(lc_or_tic.meta.get("TICID"))
elif isinstance(lc_or_tic, (str, int)):
tic_id = lc_or_tic
else:
raise TypeError("lc_or_tic must be either a LightCurve object or a tic id (int/str)")
m = _to_stellar_meta(lc_or_tic)
def safe_m_get(key, default_val):
# in some meta, the key exists but the value is None
# this helper handles it
res = getattr(m, key, default_val)
return res if res is not None else default_val
html = f"""
<div id="tic_metadata_ctr">
<div id="tic_metadata_body">
<h3>TIC {tic_id}</h3>
"""
html += " " + link("ExoFOP", f"https://exofop.ipac.caltech.edu/tess/target.php?id={tic_id}")
html += "\n | "
html += link(
"PHT Talk",
f"https://www.zooniverse.org/projects/nora-dot-eisner/planet-hunters-tess/talk/search?query={tic_id}",
)
if a_subject_id is not None:
# note, a TIC can have multiple subjects, here is just one of them.
html += "\n , a subject: "
html += link(
a_subject_id,
f"https://www.zooniverse.org/projects/nora-dot-eisner/planet-hunters-tess/talk/subjects/{a_subject_id}",
)
# show the sector number (here we assume a_subject_id does correspond the the sector)
# the sector is useful to be included so that users can easily locate the TCE matching the sector.
html += f' (sector {safe_m_get("sector", "")})'
html += "<br>\n"
# stellar parameters
html += "<table>\n"
s_radius = safe_m_get("radius", -1)
s_mass = safe_m_get("mass", -1)
html += prop("R<sub>S</sub> (in R<sub>☉</sub>)", f"{s_radius:.3f}")
html += prop("M<sub>S</sub> (in M<sub>☉</sub>)", f"{s_mass:.3f}")
if s_radius > 0 and s_mass > 0:
s_rho = lke.estimate_rho(s_mass, s_radius, return_unit=u.g / u.cm**3).value
else:
s_rho = -1
if include_transit_model_stellar_density:
html += prop("rho<sub>S</sub> (in g/cm<sup>3</sup>)", f"{s_rho:.3f}")
html += prop("Magnitude (TESS)", f'{safe_m_get("tess_mag", -1):.2f}')
html += prop("T_eff (in K)", safe_m_get("teff", -1))
html += "</table>\n"
html += "<p>TCEs:</p>"
# TODO: not yet implemented in tess_dv_fast
# tce_filter_func=tce_filter_func,
# Note: tess_dv_fast cannot support
# include_transit_model_stellar_density=include_transit_model_stellar_density,
df_tces = tess_dv_fast.get_tce_infos_of_tic(tic_id)
html += tess_dv_fast.display_tce_infos(df_tces, return_as="html", no_tce_html="<p>No TCE.</p>")
# TOIs/CTOIs
html += "<p>TOIs / CTOIs:</p>"
html += _get_tois_in_html(tic_id, download_dir=download_dir)
html += _get_ctois_in_html(tic_id, download_dir=download_dir)
html += """
</div> <!-- id="tic_metadata_body" -->
</div> <!-- id="tic_metadata_ctr" -->
"""
return html
#
# TESS Momentum dump accessor
#
def get_momentum_dump_times(lcf):
"""Get the momentum dump times from the given lightcurve.
It is usually one from a sector.
The output can be added to data/tess_mom_dumps.txt for further plotting usage.
"""
# Note: momentum_dump signals are by default masked out in LightCurve objects.
# To access times marked as such, I need to access the raw LightCurveFile directly.
filename = lcf.meta.get("FILENAME", None)
if filename is None:
warnings.warn("get_momentum_dump_times(): No-Op, because there is the LightCurve object has no backing FITS file.")
return np.array([])
with fits.open(filename) as hdu:
if "TIME" not in hdu[1].columns.names:
# case the file has no TIME column, typically non SPOC-produced ones, e.g., CDIPS,
# the logic of finding momentum dump would not apply to such files anyway.
return np.array([])
# normal flow
time = hdu[1].data["TIME"]
mom_dumps_mask = np.bitwise_and(hdu[1].data["QUALITY"], lk.utils.TessQualityFlags.Desat) >= 1
time_mom_dumps = time[mom_dumps_mask]
return time_mom_dumps
class MomentumDumpsAccessor:
_mom_dumps_tab = None
@classmethod
def _load_mom_dumps_from_file(cls):
# data/tess_mom_dumps.txt is a tab-delimited list of all TESS momentum dumps
# (the same one used by LATTE).
# It can be generated by reading a sample of 2-minute cadence lightcurve FITS files (1 for each sector),
# looking for times where quality flag bit 6 (value 32) is 1.
# cls._mom_dumps_tab = pd.read_csv("data/tess_mom_dumps.txt", sep="\t")
# I use np.genfromtxt rather than pandas, as filtering from numpy array filtering is easier for the use case
cls._mom_dumps_tab = np.genfromtxt(f"{_MODULE_PATH_}/data/tess_mom_dumps.txt", delimiter="\t", names=True)
@classmethod
def refresh(cls):
cls._load_mom_dumps_from_file()
@classmethod
def get_all(cls, refresh=False):
if refresh or cls._mom_dumps_tab is None:
cls.refresh()
return cls._mom_dumps_tab
@classmethod
def get_in_range(cls, lc_or_tpf=None, start=None, end=None, refresh=False):
times = cls.get_all(refresh=refresh)["time"]
if lc_or_tpf is not None:
start, end = (
lc_or_tpf.time.value.min(),
lc_or_tpf.time.value.max() + 1e-6,
)
if start is not None:
times = times[times >= start]
if end is not None:
times = times[times < end]
return times
@classmethod
def exclude_around(cls, lc_or_tpf=None, window_before=15 / 60 / 24, window_after=15 / 60 / 24):
"""Exclude cadences of the given LC / TPF around momentum dumps.
Useful to exclude data points that are often skewed.
"""
def compress_as_exclude_ranges(mom_dumps, window_before, window_after):
"""Transform the list of momentum dumps to a list of range of time to exclude.
The function also compress the momentum dump list, by consolidating
multiple nearby timestamps to a single range.
This is done as an performance optimization, to reduce the number of actual lc/tpf truncation needed.
(In practice, it cuts the time or processing a typical TESS 2-minute cadence tpf from a few seconds to ~500ms)
"""
if len(mom_dumps) < 1:
return []
res = []
cur_range = [mom_dumps[0] - window_before, mom_dumps[0] + window_after]
for t in mom_dumps[1:]:
if t <= cur_range[1]:
cur_range[1] = t + window_after
else:
res.append(cur_range)
cur_range = [t - window_before, t + window_after]
res.append(cur_range)
return res
mom_dumps = cls.get_in_range(lc_or_tpf)
exclude_ranges = compress_as_exclude_ranges(mom_dumps, window_before, window_after)
res = lc_or_tpf
for an_exclude in exclude_ranges:
t = res.time.value
res = res[(t < an_exclude[0]) | (t >= an_exclude[1])]
return res
class WTVResultAccessor:
@classmethod
def get_all(cls, wtv_csv_path, add_sectors_summary=True, start_sector=1, end_sector_inclusive=69):
res = Table.read(wtv_csv_path)
if not add_sectors_summary:
return res
def to_sectors_summary(row):
summary = ""
for sector in np.arange(start_sector, end_sector_inclusive + 1):
if row[f"S{sector}"] > 0:
summary = f"{summary} {sector},"
return summary
summary_ary = []
for row in res:
summary_ary.append(to_sectors_summary(row))
res["Sectors"] = summary_ary
return res
#
# TESS Flux - Magnitude Conversion
#
def tess_flux_to_mag(flux):
"""Convert flux from TESS observation to magnitude."""
# Based on https://heasarc.gsfc.nasa.gov/docs/tess/observing-technical.html#saturation
# TESS CCDs produce 15000 e/s for magnitude 10 light source
if isinstance(flux, u.Quantity):
flux_raw = flux.to(u.electron / u.second).value
else:
flux_raw = flux
# np.log10 does not work on Quantity, unless it's dimensionless
res_raw = 10 - 2.5 * np.log10((flux_raw / 15000))
if isinstance(flux, u.Quantity):
return res_raw * u.mag
else:
return res_raw
def mag_to_tess_flux(mag):
"""Convert magnitude to flux in TESS observation."""
if isinstance(mag, u.Quantity):
mag_raw = mag.to(u.mag).value
else:
mag_raw = mag
flux_raw = (10 ** ((10 - mag_raw) / 2.5)) * 15000
if isinstance(mag, u.Quantity):
return flux_raw * ((u.electron / u.second))
else:
return flux_raw
def calc_flux_range(lcf_coll, flux_column="flux", accepted_authors=["SPOC", "TESS-SPOC"], stitched_lc_corrector=lambda lc: lc):
"""Derive flux range (in % and magnitude) from normalized lightcurve with mean TESS mag from TIC as the base"""
# the default SPOC, TESS-SPOC is to avoid inconsistency between SPOC and QLP
lcf_coll_filtered = lke.select(lcf_coll, lambda lc: lc.author in accepted_authors)
lc = lke.stitch(
lcf_coll_filtered,
corrector_func=lambda lc: (
lc.select_flux(flux_column).remove_nans()
# normalize on per-sector basis, it seems TESS calibration across sectors is not necessarily consistent
.normalize(unit="percent")
),
)
# optionally let caller tweak teh stitched LC, e.g., excluding some cadences, say, if flares are to be ignored.
lc = stitched_lc_corrector(lc)
flux_range_pct = np.asarray([lc.flux.max(), lc.flux.min()])
base_mag = lc.meta.get("TESSMAG")
flux_range_mag = lke.normalized_flux_val_to_mag(flux_range_pct, base_mag=base_mag)
return SimpleNamespace(
flux_range_pct=flux_range_pct,
flux_range_mag=flux_range_mag,
lc_stitched=lc,
lcf_coll_filtered=lcf_coll_filtered,
)
#
# Misc. TESS specific utilities
#
def display_crowdsap(lc):
"""Display `CROWDSAP` of a TESS SPOC lightcurve. Highlight it if it could be too low.
Warning that the field might be crowded using CROWDSAP header,
when CROWDSAP < 0.8 .
From section 4.1.2 of the paper TOI catalog from TESS primary mission
https://arxiv.org/pdf/2103.12538.pdf
"""
from IPython.display import display, HTML
if lc is not None and lc.meta.get("CROWDSAP") is not None:
display(
HTML(
f"""Fraction of flux in aperture attributed to the target, <span style="font-family: monospace;">CROWDSAP</span>:
<span style="background-color: {'red' if lc.meta.get("CROWDSAP") < 0.8 else 'transparent'}; padding: 2px;">{lc.meta.get("CROWDSAP")}</span>
 <span style="font-family: monospace;">FLFRCSAP</span>: {lc.meta.get('FLFRCSAP')}
 <a href="https://heasarc.gsfc.nasa.gov/docs/tess/UnderstandingCrowding.html" target="_crowdsap_tutorial">(Help)</a>
"""
)
)
def btjd_to_hjd_utc(time_val, position):
t_btjd = Time(time_val, format="btjd", scale="tdb")
t_bjd = t_btjd.copy("jd")
ra, dec = position.split(",")
sky_coord = coord.SkyCoord(ra, dec, unit=(u.deg, u.deg), frame="icrs")
return lke.to_hjd_utc(t_bjd, sky_coord).value
#
# TIC Metadata in catalogs (TIC / Gaia)
#
def catalog_info_of_tics(tic):
"""Return the info of a TIC in the TIC catalog"""
from astroquery.mast import Catalogs
return Catalogs.query_criteria(catalog="Tic", ID=tic)
def _to_stellar_meta(target):
if hasattr(target, "meta"): # case LC, TPF, etc.
meta = target.meta
ra, dec, equinox = meta.get("RA"), meta.get("DEC"), meta.get("EQUINOX")
pmra, pmdec = meta.get("PMRA"), meta.get("PMDEC")
tess_mag = meta.get("TESSMAG")
tic = meta.get("TICID")
if tic is not None:
label = f"{tic}"
else:
label = f"[{ra:4f} {dec:4f}]"
return SimpleNamespace(
# for Gaia DR3 query use case
ra=ra,
dec=dec,
equinox=equinox,
pmra=pmra,
pmdec=pmdec,
e_pmra=np.nan,
e_pmdec=np.nan,
tess_mag=tess_mag,
label=label,
# additional attributes for get_tic_meta_in_html() use case
sector=meta.get("SECTOR"),
radius=meta.get("RADIUS"),
teff=meta.get("TEFF"),
)
# case target is a tic id
if isinstance(target, (int, str)):
result = catalog_info_of_tics(target)
if len(result) < 1:
return None
row = result[0]
ra, dec, equinox = row["ra"], row["dec"], 2000
pmra, pmdec = row["pmRA"], row["pmDEC"]
e_pmra, e_pmdec = row["e_pmRA"], row["e_pmDEC"]
tess_mag = row["Tmag"]
tic = row["ID"]
if tic is not None:
label = f"{tic}"
else:
label = f"[{ra:4f} {dec:4f}]"
gaiadr2_id = row["GAIA"] # useful to crossmatch with Gaia data
return SimpleNamespace(
ra=ra,
dec=dec,
equinox=equinox,
pmra=pmra,
pmdec=pmdec,
e_pmra=e_pmra,
e_pmdec=e_pmdec,
tess_mag=tess_mag,
label=label,
gaiadr2_id=gaiadr2_id,
# additional attributes for get_tic_meta_in_html() use case
radius=row["rad"],
mass=row["mass"],
teff=row["Teff"],
)
raise TypeError(f"target, of type {type(target)} is not supported")
def decode_gaiadr3_nss_flag(nss_flag):
"""Decode NSS (NON_SINGLE_STAR) flag in Gaia DR3 Main.
Reference:
https://gea.esac.esa.int/archive/documentation/GDR3/Gaia_archive/chap_datamodel/sec_dm_main_source_catalogue/ssec_dm_gaia_source.html#p344
"""
flags = []
for mask, nss_type in [
(0b1, "AB"), # astrometric binary
(0b10, "SB"), # spectroscopic binary
(0b100, "EB"), # eclipsing binary
]:
if nss_flag & mask > 0:
flags.append(nss_type)
return flags
def decode_gaiadr3_nss_solution_flag(sol_flag):
"""Decode the flag for NSS solution in Gaia DR3 NSS 2 body orbit tables.
Not to be confused with the flag in Gaia DR3 Main table.
Reference:
https://gea.esac.esa.int/archive/documentation/GDR3/Gaia_archive/chap_datamodel/sec_dm_non--single_stars_tables/ssec_dm_nss_two_body_orbit.html#p155
"""
bits_meaning = {
# for AB
0: "AB_No_solution_searched",
1: "AB_No_stochastic_solution_searched",
2: "AB_Failure_to_compute_a_stochastic_solution",
6: "AB_RV_available",
7: "AB_RV_used_for_perspective_acceleration_correction",
# for SB
8: "SB_BAD_UNCHECKED_NUMBER_OF_TRANSITS",
9: "SB_NO_MORE_VARIABLE_AFTER_FILTERING",
10: "SB_BAD_CHECKED_NUMBER_OF_TRANSITS",
11: "SB2_REDIRECTED_TO_SB1_CHAIN_NOT_ENOUGH_COUPLE_MEASURES",
12: "SB2_REDIRECTED_TO_SB1_CHAIN_PERIODS_NOT_COHERENT",
13: "SB_NO_SIGNIFICANT_PERIODS_CAN_BE_FOUND",
14: "SB_REFINED_SOLUTION_DOES_NOT_CONVERGE",
15: "SB_REFINED_SOLUTION_SINGULAR_VARIANCE_COVARIANCE_MATRIX",
16: "SB_CIRCULAR_SOLUTION_SINGULAR_VARIANCE_COVARIANCE_MATRIX",
17: "SB_TREND_SOLUTION_SINGULAR_VARIANCE_COVARIANCE_MATRIX",
18: "SB_REFINED_SOLUTION_NEGATIVE_DIAGONAL_OF_VARIANCE_COVARIANCE_MATRIX",
19: "SB_CIRCULAR_SOLUTION_NEGATIVE_DIAGONAL_OF_VARIANCE_COVARIANCE_MATRIX",
20: "SB_TREND_SOLUTION_NEGATIVE_DIAGONAL_OF_VARIANCE_COVARIANCE_MATRIX",
21: "SB_CIRCULAR_SOLUTION_DOES_NOT_CONVERGE",
22: "SB_LUCY_TEST_APPLIED",
23: "SB_TREND_SOLUTION_NOT_APPLIED",
24: "SB_SOLUTION_OUTSIDE_E_LOGP_ENVELOP",
25: "SB_PERIOD_FOUND_IN_CU7_PERIODICITY",
26: "SB_FORTUITOUS_SB2",
# for EB
32: "EB_No_variance-covariance_matrix",
# for Combined solutions
48: "CO_NOCOMBINATION_FOUND",
49: "CO_BAD_GOF_COMBINATION",
50: "CO_WRONG_COMPONENT_COMBINATION",
51: "CO_SB2_TREATED_AS_SB1",
52: "CO_STOCHA_TO_ORBITAL",
53: "CO_STOCHA_TO_MULTIPLE",
54: "CO_ORBITALALTERNATIVE_TO_ORBITAL",
55: "CO_TRIPLE_COMBINATION",
56: "CO_TREND_COMBINATION",
57: "CO_DU434_INPUT_USED",
}
flags = []
for bit, meaning in bits_meaning.items():
if sol_flag & 2**bit > 0:
flags.append(meaning)
return flags
def _is_all_finite(num_or_num_list):
def is_one_finite(num):
return num is not None and np.isfinite(num)
if not isinstance(num_or_num_list, (list, tuple, np.ndarray)):
num_or_num_list = [num_or_num_list]
return np.all([is_one_finite(n) for n in num_or_num_list])
def linkify_gaiadr3_result_html(result: Table, max_num_rows=999):
"""Return Gaia DR3 Vizier Search Result in HtML, with links back to Vizier"""
# the linkify depends on Source column.
# so simply return html if Source is not there
if "Source" not in result.colnames:
with astropy.conf.set_temp("max_lines", max_num_rows):
return result._repr_html_()
# the result table content is to be tweaked for linkify implementation
# so we use a copy.
result = result.copy()
# Include the source for variable and NSS when applicable
# They will be used for reformatting as link.
if "VarFlag" in result.colnames:
c = np.char.add(result["VarFlag"], result["Source"].astype(str))
c[result["VarFlag"] != "VARIABLE"] = "NOT_AVAILABLE"
result["VarFlag"] = c
if "NSS" in result.colnames:
c = np.char.add(result["NSS"].astype(str), result["Source"].astype(str))
c = np.char.add(np.full_like(c, "NSS"), c)
c[result["NSS"] == 0] = "0"
result["NSS"] = c
with astropy.conf.set_temp("max_lines", max_num_rows):
html = result._repr_html_()
# linkify Gaia DR3 Source, Variable and NSS
for id in result["Source"]:
# the long URL includes both Gaia DR3 Main and Astrophysical, with frequently used columns included.
gaiadr3_url = f"https://vizier.cds.unistra.fr/viz-bin/VizieR-4?-ref=VIZ6578bb1b54eda&-to=-4b&-from=-4&-this=-4&%2F%2Fsource=I%2F355%2Fgaiadr3&%2F%2Ftables=I%2F355%2Fgaiadr3&%2F%2Ftables=I%2F355%2Fparamp&-out.max=50&%2F%2FCDSportal=http%3A%2F%2Fcdsportal.u-strasbg.fr%2FStoreVizierData.html&-out.form=HTML+Table&%2F%2Foutaddvalue=default&-order=I&-oc.form=sexa&-out.src=I%2F355%2Fgaiadr3%2CI%2F355%2Fparamp&-nav=cat%3AI%2F355%26tab%3A%7BI%2F355%2Fgaiadr3%7D%26tab%3A%7BI%2F355%2Fparamp%7D%26key%3Asource%3DI%2F355%2Fgaiadr3%26HTTPPRM%3A&-c=&-c.eq=J2000&-c.r=++2&-c.u=arcmin&-c.geom=r&-source=&-x.rs=10&-source=I%2F355%2Fgaiadr3+I%2F355%2Fparamp&-out.orig=standard&-out=RA_ICRS&-out=DE_ICRS&-out=Source&Source={id}&-out=Plx&-out=PM&-out=pmRA&-out=pmDE&-out=sepsi&-out=IPDfmp&-out=RUWE&-out=Dup&-out=Gmag&-out=BPmag&-out=RPmag&-out=BP-RP&-out=RV&-out=e_RV&-out=VarFlag&-out=NSS&-out=XPcont&-out=XPsamp&-out=RVS&-out=EpochPh&-out=EpochRV&-out=MCMCGSP&-out=MCMCMSC&-out=Teff&-out=logg&-out=%5BFe%2FH%5D&-out=Dist&-out=A0&-out=HIP&-out=PS1&-out=SDSS13&-out=SKYM2&-out=TYC2&-out=URAT1&-out=AllWISE&-out=APASS9&-out=GSC23&-out=RAVE5&-out=2MASS&-out=RAVE6&-out=RAJ2000&-out=DEJ2000&-out=Pstar&-out=PWD&-out=Pbin&-out=ABP&-out=ARP&-out=GMAG&-out=Rad&-out=SpType-ELS&-out=Rad-Flame&-out=Lum-Flame&-out=Mass-Flame&-out=Age-Flame&-out=Flags-Flame&-out=Evol&-out=z-Flame&-meta.ucd=0&-meta=0&-usenav=1&-bmark=GET"
html = html.replace(
f">{id}<",
f"><a target='vizier_gaia_dr3' href='{gaiadr3_url}'>{id}</a><",
)
gaiadr3_var_url = f"https://vizier.cds.unistra.fr/viz-bin/VizieR-4?-ref=VIZ65ac1f481b91d6&-to=-4b&-from=-3&-this=-4&%2F%2Fsource=%2BI%2F358%2Fvarisum%2BI%2F358%2Fvclassre%2BI%2F358%2Fveb%2BI%2F358%2Fvcc%2BI%2F358%2Fvst&%2F%2Fc=06%3A59%3A36.3+%2B23%3A28%3A51.14&%2F%2Ftables=I%2F358%2Fvarisum&%2F%2Ftables=I%2F358%2Fvclassre&%2F%2Ftables=I%2F358%2Fvcc&%2F%2Ftables=I%2F358%2Fveb&%2F%2Ftables=I%2F358%2Fvst&-out.max=50&%2F%2FCDSportal=http%3A%2F%2Fcdsportal.u-strasbg.fr%2FStoreVizierData.html&-out.form=HTML+Table&-out.add=_r&%2F%2Foutaddvalue=default&-sort=_r&-order=I&-oc.form=sexa&-out.src=I%2F358%2Fvarisum%2CI%2F358%2Fvclassre%2CI%2F358%2Fveb%2CI%2F358%2Fvcc%2CI%2F358%2Fvst&-nav=cat%3AI%2F358%26tab%3A%7BI%2F358%2Fvarisum%7D%26tab%3A%7BI%2F358%2Fvclassre%7D%26tab%3A%7BI%2F358%2Fvcc%7D%26tab%3A%7BI%2F358%2Fveb%7D%26tab%3A%7BI%2F358%2Fvst%7D%26key%3Asource%3D%2BI%2F358%2Fvarisum%2BI%2F358%2Fvclassre%2BI%2F358%2Fveb%2BI%2F358%2Fvcc%2BI%2F358%2Fvst%26key%3Ac%3D06%3A59%3A36.3+%2B23%3A28%3A51.14%26pos%3A06%3A59%3A36.3+%2B23%3A28%3A51.14%28+60+arcsec%29%26HTTPPRM%3A&-c=&-c.eq=J2000&-c.r=+60&-c.u=arcsec&-c.geom=r&-source=&-x.rs=10&-source=I%2F358%2Fvarisum+I%2F358%2Fvclassre+I%2F358%2Fveb+I%2F358%2Fvcc+I%2F358%2Fvst&-out.orig=standard&-out=Source&Source={id}&-out=RA_ICRS&-out=DE_ICRS&-out=TimeG&-out=DurG&-out=Gmagmean&-out=TimeBP&-out=DurBP&-out=BPmagmean&-out=TimeRP&-out=DurRP&-out=RPmagmean&-out=VCR&-out=VRRLyr&-out=VCep&-out=VPN&-out=VST&-out=VLPV&-out=VEB&-out=VRM&-out=VMSO&-out=VAGN&-out=Vmicro&-out=VCC&-out=SolID&-out=Classifier&-out=Class&-out=ClassSc&-out=Rank&-out=TimeRef&-out=Freq&-out=magModRef&-out=PhaseGauss1&-out=sigPhaseGauss1&-out=DepthGauss1&-out=PhaseGauss2&-out=sigPhaseGauss2&-out=DepthGauss2&-out=AmpCHP&-out=PhaseCHP&-out=ModelType&-out=Nparam&-out=rchi2&-out=PhaseE1&-out=DurE1&-out=DepthE1&-out=PhaseE2&-out=DurE2&-out=DepthE2&-out=Per&-out=T0G&-out=T0BP&-out=T0RP&-out=HG0&-out=HG1&-out=HG2&-out=HG3&-out=HG4&-out=HG5&-out=HBP0&-out=HBP1&-out=HBP2&-out=HBP3&-out=HBP4&-out=HBP5&-out=HRP0&-out=HRP1&-out=HRP2&-out=HRP3&-out=HRP4&-out=HRP5&-out=Gmodmean&-out=BPmodmean&-out=RPmodmean&-out=Mratiomin&-out=alpha&-out=Ampl&-out=NfoVTrans&-out=FoVAbbemean&-out=NTimeScale&-out=TimeScale&-out=Variogram&-meta.ucd=2&-meta=1&-meta.foot=1&-usenav=1&-bmark=GET"
html = html.replace(
f">VARIABLE{id}<",
(
f"><a target='vizier_gaia_dr3_var' href='{gaiadr3_var_url}' "
"style='background-color: rgba(255, 255, 0, 0.5); font-weight: bold;'>VARIABLE</a><"
),
)
gaiadr3_nss_url = f"https://vizier.cds.unistra.fr/viz-bin/VizieR-4?-ref=VIZ65a1a2351812e4&-to=-4b&-from=-3&-this=-4&%2F%2Fsource=I%2F357&%2F%2Ftables=I%2F357%2Ftboasb1c&%2F%2Ftables=I%2F357%2Ftboeb&%2F%2Ftables=I%2F357%2Ftboes&%2F%2Ftables=I%2F357%2Ftbooc&%2F%2Ftables=I%2F357%2Ftbooac&%2F%2Ftables=I%2F357%2Ftbooavc&%2F%2Ftables=I%2F357%2Ftbootsc&%2F%2Ftables=I%2F357%2Ftbootsvc&%2F%2Ftables=I%2F357%2Ftbosb1&%2F%2Ftables=I%2F357%2Ftbosb1c&%2F%2Ftables=I%2F357%2Ftbosb2&%2F%2Ftables=I%2F357%2Ftbosb2c&%2F%2Ftables=I%2F357%2Facc7&%2F%2Ftables=I%2F357%2Facc9&%2F%2Ftables=I%2F357%2Flinspec1&%2F%2Ftables=I%2F357%2Flinspec2&%2F%2Ftables=I%2F357%2Fvimfl&-out.max=50&%2F%2FCDSportal=http%3A%2F%2Fcdsportal.u-strasbg.fr%2FStoreVizierData.html&-out.form=HTML+Table&-out.add=_r&%2F%2Foutaddvalue=default&-sort=_r&-order=I&-oc.form=sexa&-out.src=I%2F357%2Ftboasb1c%2CI%2F357%2Ftboeb%2CI%2F357%2Ftboes%2CI%2F357%2Ftbooc%2CI%2F357%2Ftbooac%2CI%2F357%2Ftbooavc%2CI%2F357%2Ftbootsc%2CI%2F357%2Ftbootsvc%2CI%2F357%2Ftbosb1%2CI%2F357%2Ftbosb1c%2CI%2F357%2Ftbosb2%2CI%2F357%2Ftbosb2c%2CI%2F357%2Facc7%2CI%2F357%2Facc9%2CI%2F357%2Flinspec1%2CI%2F357%2Flinspec2%2CI%2F357%2Fvimfl&-nav=cat%3AI%2F357%26tab%3A%7BI%2F357%2Ftboasb1c%7D%26tab%3A%7BI%2F357%2Ftboeb%7D%26tab%3A%7BI%2F357%2Ftboes%7D%26tab%3A%7BI%2F357%2Ftbooc%7D%26tab%3A%7BI%2F357%2Ftbooac%7D%26tab%3A%7BI%2F357%2Ftbooavc%7D%26tab%3A%7BI%2F357%2Ftbootsc%7D%26tab%3A%7BI%2F357%2Ftbootsvc%7D%26tab%3A%7BI%2F357%2Ftbosb1%7D%26tab%3A%7BI%2F357%2Ftbosb1c%7D%26tab%3A%7BI%2F357%2Ftbosb2%7D%26tab%3A%7BI%2F357%2Ftbosb2c%7D%26tab%3A%7BI%2F357%2Facc7%7D%26tab%3A%7BI%2F357%2Facc9%7D%26tab%3A%7BI%2F357%2Flinspec1%7D%26tab%3A%7BI%2F357%2Flinspec2%7D%26tab%3A%7BI%2F357%2Fvimfl%7D%26key%3Asource%3DI%2F357%26HTTPPRM%3A&-c=&-c.eq=J2000&-c.r=++2&-c.u=arcmin&-c.geom=r&-source=&-x.rs=10&-source=I%2F357%2Ftboasb1c+I%2F357%2Ftboeb+I%2F357%2Ftboes+I%2F357%2Ftbooc+I%2F357%2Ftbooac+I%2F357%2Ftbooavc+I%2F357%2Ftbootsc+I%2F357%2Ftbootsvc+I%2F357%2Ftbosb1+I%2F357%2Ftbosb1c+I%2F357%2Ftbosb2+I%2F357%2Ftbosb2c+I%2F357%2Facc7+I%2F357%2Facc9+I%2F357%2Flinspec1+I%2F357%2Flinspec2+I%2F357%2Fvimfl&-out.orig=standard&-out=Source&Source={id}&-out=NSSmodel&-out=RA_ICRS&-out=DE_ICRS&-out=Plx&-out=pmRA&-out=pmDE&-out=ATI&-out=BTI&-out=FTI&-out=GTI&-out=CTI&-out=HTI&-out=Per&-out=Tperi&-out=ecc&-out=Vcm&-out=Flags&-out=_RA.icrs&-out=_DE.icrs&-out=ffactp&-out=ffacts&-out=inc&-out=Tratio&-out=Teclp&-out=Tecls&-out=Durp&-out=Durs&-out=K1&-out=MassRatio&-out=K2&-out=dpmRA&-out=dpmDE&-out=ddpmRA&-out=ddpmDE&-out=Velmean&-out=dVel%2Fdt&-out=dVel%2Fdt2&-out=RAVIM&-out=DEVIM&-meta.ucd=2&-meta=1&-meta.foot=1&-usenav=1&-bmark=GET"
html = re.sub(
rf">NSS(\d){id}<",
(
rf"><a target='vizier_gaia_dr3_nss' href='{gaiadr3_nss_url}' "
rf"style='background-color: rgba(255, 255, 0, 0.5); font-weight: bold;'> \1 </a><"
),
html,
)
return html
def search_gaiadr3_of_tics(
targets,
radius_arcsec=15,
magnitude_range=2.5,
pm_error_factor=None, # e.g., 3
pm_range_fraction=0.25,
pm_range_minimum=1.0,
warn_if_all_filtered=True,
compact_columns=True,
also_return_html=True,
also_return_astrophysical=False, # defaulted to False for backward compatibility
verbose_html=True,
include_nss_summary_in_html=True,
):
"""Locate the lightcurve target's correspond entry in GaiaDR3.
The match is by an heuristics based on coordinate and magnitude.
Parameters
----------
target : int, LightCurve, TargetPixelFile, or a list of them
targets to be searched. Either the TIC, or LightCurve/TargetPixelFile of a TIC.
pm_error_factor, pm_range_fraction, pm_range_minimum
range of proper motion to include in the search result,
with the pmRA / pmDEC of the target as the reference.
`pm_error_factor` is used if e_pmRA, e_pmDEC is present (case the target is from TIC catalog).
The pmRA range will be `e_pmRA` * `pm_error_factor` (ditto for pmDEC)
`pm_range_fraction` is used if e_pmRA, e_pmDEC is not present.
The pmRA range will be `pmRA` * `pm_range_fraction` (ditto for pmDEC)
In all cases if `pm_range_minimum` is defined, the range will have a minimum of
`+/- pm_range_minimum`.
It is useful to handle the case the derived range is very small and overly restrictive.
"""
# OPEN:
# Consider alternative by crossmatching Gaia DR2 of the TIC (available on MAST) with Gaia EDR3
# https://gea.esac.esa.int/archive/documentation/GEDR3/Catalogue_consolidation/chap_cu9dr2xm/sec_cu9dr2xm_adql_queries/sec_cu9dr2xm_closest_edr3_neighbour_to_each_dr2_source_10m.html
# some suggestion: limit cross match result by comparing GMag (difference < 0.1 was suggested in some doc)
# Other Gaia crossmatch tips:
# https://www.cosmos.esa.int/web/gaia-users/archive/combine-with-other-data
if isinstance(targets, (Sequence, np.ndarray, lk.collections.Collection)):
add_target_as_col = True
else:
add_target_as_col = False
targets = [targets]
# _paramp: Gaia DR3 Astrophysical, "I/355/paramp"
result_list, result_paramp_list = [], []
targets = np.asarray([_to_stellar_meta(t) for t in targets])
targets = targets[targets != None]
for t in targets:
gaiadr2_id = getattr(t, "gaiadr2_id", np.nan)
if magnitude_range is not None:
lower_limit, upper_limit = t.tess_mag - magnitude_range, t.tess_mag + magnitude_range
else:
lower_limit, upper_limit = None, None
pmra_lower, pmra_upper, pmdec_lower, pmdec_upper = None, None, None, None
if _is_all_finite([pm_error_factor, t.pmra, t.e_pmra]):
pmra_range = t.e_pmra * pm_error_factor
if pm_range_minimum is not None:
pmra_range = max(pmra_range, pm_range_minimum)
pmra_lower, pmra_upper = t.pmra - pmra_range, t.pmra + pmra_range
pmdec_range = t.e_pmdec * pm_error_factor
if pm_range_minimum is not None:
pmdec_range = max(pmdec_range, pm_range_minimum)
pmdec_lower, pmdec_upper = t.pmdec - pmdec_range, t.pmdec + pmdec_range
elif _is_all_finite([pm_range_fraction, t.pmra]):
pmra_range = np.abs(t.pmra) * pm_range_fraction
if pm_range_minimum is not None:
pmra_range = max(pmra_range, pm_range_minimum)
pmra_lower, pmra_upper = t.pmra - pmra_range, t.pmra + pmra_range
pmdec_range = np.abs(t.pmdec) * pm_range_fraction
if pm_range_minimum is not None:
pmdec_range = max(pmdec_range, pm_range_minimum)
pmdec_lower, pmdec_upper = t.pmdec - pmdec_range, t.pmdec + pmdec_range
# print("DBG pm range for filter - ra:", t.pmra, pmra_lower, pmra_upper, "dec: ", t.pmdec, pmdec_lower, pmdec_upper)
a_result, a_result_paramp = lke.search_nearby(
t.ra,
t.dec,
equinox=f"J{t.equinox}",
radius_arcsec=radius_arcsec,
magnitude_limit_column="RPmag",
magnitude_lower_limit=lower_limit,
magnitude_upper_limit=upper_limit,
pmra=t.pmra,
pmdec=t.pmdec,
pmra_lower=pmra_lower,
pmra_upper=pmra_upper,
pmdec_lower=pmdec_lower,
pmdec_upper=pmdec_upper,
include_gaiadr3_astrophysical=True,
warn_if_all_filtered=warn_if_all_filtered,
)
if a_result is not None:
a_result["target"] = [t.label for i in range(0, len(a_result))]
a_result["target_gaia_dr2_source"] = [gaiadr2_id for i in range(0, len(a_result))]
result_list.append(a_result)
result_paramp_list.append(a_result_paramp)
with warnings.catch_warnings():
# Avoid spurious "MergeConflictWarning: Cannot merge meta key 'null' types <class 'float'>
# and <class 'float'>, choosing null=nan [astropy.utils.metadata]"
result = astropy.table.vstack(result_list) if len(result_list) > 0 else Table()
result_paramp = astropy.table.vstack(result_paramp_list) if len(result_paramp_list) > 0 else Table()
if len(result) < 1:
if also_return_html:
return None, None, ""
else:
return (
None,
None,
)
# flag entries (that could indicate binary systems, etc.)
flag_column_values = []
for row in result:
flag = ""
# RUWE > 1.4 cutoff source:
# https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_ruwe.html
if row["RUWE"] > 1.4:
flag += "!"
# astrometric excess noise sig > 2 cutoff source
# https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html
# https://web.archive.org/web/20211121142803/https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html
if row["sepsi"] > 2:
flag += "!"