// This code is generated by generate_code.py, do not modify it manually.
//! This module contains all the known columns in the catwise2020 table.
use crate::traits::{Column, Table};
/// The CatWISE2020 Catalogue consists of 1,890,715,640 sources over the entire sky selected from Wide-field Infrared Survey Explorer (WISE) and NEOWISE survey data at 3.4 and 4.6 micrometer (W1 and W2) collected from 7 January 2010 to 13 December 2018. This data set adds two years to that used for the CatWISE Preliminary Catalogue (Eisenhardt+, 2020ApJS..247...69E, 2020ApJS..247...69E), bringing the total to six times as many exposures spanning over sixteen times as large a time baseline as the AllWISE catalogue. The other major change from the CatWISE Preliminary Catalogue is that the detection list for the CatWISE2020 Catalogue was generated using "crowdsource" software (Schlafly+, 2019ApJS..240...30S, 2019ApJS..240...30S), while the CatWISE Preliminary Catalogue used the detection software used for AllWISE. These two factors result in roughly twice as many sources in the CatWISE2020 Catalogue. The 90% completeness depth for the CatWISE2020 Catalogue is at W1 = 17.7 mag and W2 = 17.5 mag, 1.7 mag deeper than in the CatWISE Preliminary Catalogue. This table has been created in May 2023 based on the VizieR version of the CatWISE2020 Catalogue (see <a href="https://vizier.cfa.harvard.edu/viz-bin/VizieR?-source=II/365" class="external-link" target="_blank" rel="nofollow noopener" title="Follow link">https://vizier.cfa.harvard.edu/viz-bin/VizieR?-source=II/365</a>). The corrections to the astrometry (positions and proper motions) identified in Marocco+ (2021ApJS..253....8M) have been included by CDS/VizieR and are therefore also included in this table.
#[allow(non_camel_case_types)]
pub struct catwise2020;
impl Table for catwise2020 {
fn string(&self) -> String {
"catwise2020".to_string()
}
}
/// The columns in the catwise2020 table.
#[allow(non_camel_case_types)]
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, strum::Display)]
pub enum Col {
/// Tile name + processing code + wphot index (source_id)
obj_id,
/// Right ascension (ICRS) (1)
ra_icrs,
/// [0/74.3] Uncertainty in ra (sigra)
e_ra_icrs,
/// Declination (ICRS) (1)
de_icrs,
/// [0/83.3] Uncertainty in dec (sigdec)
e_de_icrs,
/// Source name (JHHMMSS.ss+DDMMSS.s; <CWISE JHHMMSS.ss+DDMMSS.s> in Simbad) (source_name)
name,
/// [-25.7/44.2] Uncertainty cross-term (sigradec)
e_pos,
/// X-pixel coordinate in the unWISE full depth coadd (wx)
xpos,
/// Y-pixel coordinate in the unWISE full depth coadd (wy)
ypos,
/// [-98.2/4997]? Frame sky background value, band-1; in dn units (w1sky) (2)
sky_w1,
/// [0.4/999]? Frame sky background value uncertainty; band-1 (w1sigsk) (2)
e_sky_w1,
/// [0/999]? Frame sky confusion based on the UNC images; in dn units (w1conf) (2)
conf_w1,
/// [-99/5000]? Frame sky background value, band-2; in dn (w2sky) (2)
sky_w2,
/// [0.7/999]? Frame sky background value uncertainty; band-2 (w2sigsk) (2)
e_sky_w2,
/// [0/999]? Frame sky confusion based on the UNC images; in dn units (w2conf) (2)
conf_w2,
/// [0/221]? Number of profile-fit flux measurements for source with SNR>=3, band 1 (w1NM)
n_w1,
/// [0/221]? Number of profile-fit flux measurements for source, band-1 (w1M)
m_w1,
/// [0/221]? Number of profile-fit flux measurements for source with SNR>=3, band-2 (w2NM)
n_w2,
/// [0/221]? Number of profile-fit flux measurements for source, band-2 (w2M)
m_w2,
/// [55205/58465] Mean observation epoch (MeanObsMJD)
mjd,
/// Right ascension (ICRS) at Ep=2015.4 (ra_pm) (3)
ra_pm_deg,
/// [1e-4/99.94]? Uncertainty in ra_pm (sigra_pm)
e_ra_pm_deg,
/// Declination (ICRS) at Ep=2015.4 (dec_pm) (3)
de_pm_deg,
/// [0/99.8]? Uncertainty in dec_pm (sigdec_pm)
e_de_pm_deg,
/// [-99.2/99.9]? Uncertainty cross-term (sigradec_pm)
e_pos_pm,
/// [-100/100] Proper motion in ra (PMRA) (1)
pm_ra,
/// [7e-4/1000] Uncertainty in PMRA (sigPMRA)
e_pm_ra,
/// [-100/100] Proper motion in dec (PMDec) (1)
pm_de,
/// [7e-4/1000] Uncertainty in PMDec (sigPMDec)
e_pm_de,
/// [0/1000]? Flux S/N ratio; band-1 (w1snr_pm)
snr_w1pm,
/// [0/1000]? Flux S/N ratio; band-2 (w2snr_pm)
snr_w2pm,
/// [-1.2e8/2e14]? WPRO raw flux, band-1 (W1, 3.35um) in dn units (w1flux_pm)
fw1pm,
/// [3.7e-4/3.5e12]? WPRO raw flux uncertainty; band-1 (w1sigflux_pm)
e_fw1pm,
/// [-4.8e8/2e10]? WPRO raw flux, band-2 (W2, 4.6um); in dn units (w2flux_pm)
fw2pm,
/// [1e-4/3.2e13]? Fit WPRO raw flux uncertainty; band-2 (w2sigflux_pm)
e_fw2pm,
/// [-10/27]? WPRO magnitude in band-1 (W1: 3.35um) (w1mpro_pm) (4)
w1mpro_pm,
/// [0/0.6]? W1mproPM uncertainty (w1sigmpro_pm)
e_w1mpro_pm,
/// [8.3e-7/6.4e21]? WPRO reduced chi^2^; band-1 (w1rchi2_pm)
chi2_w1pm,
/// [-10/25.5]? WPRO magnitude in band-2 (W2: 4.6um) (w2mpro_pm) (4)
w2mpro_pm,
/// [0/0.6]? W2mproPM uncertainty (w2sigmpro_pm)
e_w2mpro_pm,
/// [2.6e-6/375600]? WPRO reduced chi^2^; band-2 (w2rchi2_pm)
chi2_w2pm,
/// [8.3e-7/92260]? Reduced chi squared; total (rchi2_pm)
chi2pm,
/// Quality of the PM solution (pmcode) (5)
pm_qual,
/// [0/20]? Radial distance between apparitions (dist)
dist,
/// [-9.2/9.3]? W1mpro difference (dw1mag)
d_w1mpro,
/// [0/100]? Chi-square for dw1mag (1 DF) (rch2w1)
chi2d_w1mpro,
/// [-9.4/10.2]? W2mpro difference (dw2mag)
d_w2mpro,
/// [0/100]? Chi-square for dw2mag (1 DF) (rch2w2)
chi2d_w2mpro,
/// ? Averaged ecliptic longitude (elon_avg)
elon_avg,
/// [0.001/1.6]? One-sigma uncertainty in elon (elonSig)
e_elon_avg,
/// ? Averaged ecliptic latitude (elat_avg)
elat_avg,
/// [0.001/1.7]? One-sigma uncertainty in elat (elatSig)
e_elat_avg,
/// ? Descending-ascending ecliptic longitude (Delon) (6)
d_elon,
/// [0.001/101.5]? One-sigma uncertainty in Delon (DelonSig)
e_d_elon,
/// ? Descending-ascending ecliptic latitude (Delat)
d_elat,
/// [0.001/105.8]? One-sigma uncertainty in Delat (DelatSig)
e_d_elat,
/// [0/1065]? |Delon|/DelonSig (DelonSNR)
snrd_elon,
/// [0/475]? |Delat|/DelatSig (DelatSNR)
snrd_elat,
/// [0/7.3e7]? Chi-square for PMRA difference (1 DF) (chi2pmra)
chi2pm_ra,
/// [0/2.2e8]? Chi-square for PMRA difference (1 DF) (chi2pmdec)
chi2pm_de,
/// [0123n] Astrometry usage code (7)
ka,
/// [0123n] W1 photometry usage code (7)
k1,
/// [0123n] W2 photometry usage code (7)
k2,
/// [0123n] Proper motion usage code (7)
km,
/// [-831/569]? Parallax from PM desc-asce elon (par_pm)
plx1,
/// [0.001/53]? One-sigma uncertainty in par_pm (par_pmSig)
e_plx1,
/// [-831/504]? Parallax estimate from stationary solution (par_stat)
plx2,
/// [0.001/189]? One-sigma uncertainty in par_stat (par_sigma)
e_plx2,
/// [0/2.75]? Distance between CatWISE and AllWISE source (dist_x)
sep,
/// [0DHOPdhop] Worst case 4 character cc_flag from AllWISE (cc_flags) (8)
ccf,
/// [0DHOP] Two character (W1 W2) artifact flag (ab_flags) (8)
abf,
}
impl Column for Col {}
#[cfg(test)]
pub fn collect_known(map: &mut std::collections::HashMap<String, Vec<String>>) {
let mut col_strings = Vec::new();
col_strings.push(Col::obj_id.to_string());
col_strings.push(Col::ra_icrs.to_string());
col_strings.push(Col::e_ra_icrs.to_string());
col_strings.push(Col::de_icrs.to_string());
col_strings.push(Col::e_de_icrs.to_string());
col_strings.push(Col::name.to_string());
col_strings.push(Col::e_pos.to_string());
col_strings.push(Col::xpos.to_string());
col_strings.push(Col::ypos.to_string());
col_strings.push(Col::sky_w1.to_string());
col_strings.push(Col::e_sky_w1.to_string());
col_strings.push(Col::conf_w1.to_string());
col_strings.push(Col::sky_w2.to_string());
col_strings.push(Col::e_sky_w2.to_string());
col_strings.push(Col::conf_w2.to_string());
col_strings.push(Col::n_w1.to_string());
col_strings.push(Col::m_w1.to_string());
col_strings.push(Col::n_w2.to_string());
col_strings.push(Col::m_w2.to_string());
col_strings.push(Col::mjd.to_string());
col_strings.push(Col::ra_pm_deg.to_string());
col_strings.push(Col::e_ra_pm_deg.to_string());
col_strings.push(Col::de_pm_deg.to_string());
col_strings.push(Col::e_de_pm_deg.to_string());
col_strings.push(Col::e_pos_pm.to_string());
col_strings.push(Col::pm_ra.to_string());
col_strings.push(Col::e_pm_ra.to_string());
col_strings.push(Col::pm_de.to_string());
col_strings.push(Col::e_pm_de.to_string());
col_strings.push(Col::snr_w1pm.to_string());
col_strings.push(Col::snr_w2pm.to_string());
col_strings.push(Col::fw1pm.to_string());
col_strings.push(Col::e_fw1pm.to_string());
col_strings.push(Col::fw2pm.to_string());
col_strings.push(Col::e_fw2pm.to_string());
col_strings.push(Col::w1mpro_pm.to_string());
col_strings.push(Col::e_w1mpro_pm.to_string());
col_strings.push(Col::chi2_w1pm.to_string());
col_strings.push(Col::w2mpro_pm.to_string());
col_strings.push(Col::e_w2mpro_pm.to_string());
col_strings.push(Col::chi2_w2pm.to_string());
col_strings.push(Col::chi2pm.to_string());
col_strings.push(Col::pm_qual.to_string());
col_strings.push(Col::dist.to_string());
col_strings.push(Col::d_w1mpro.to_string());
col_strings.push(Col::chi2d_w1mpro.to_string());
col_strings.push(Col::d_w2mpro.to_string());
col_strings.push(Col::chi2d_w2mpro.to_string());
col_strings.push(Col::elon_avg.to_string());
col_strings.push(Col::e_elon_avg.to_string());
col_strings.push(Col::elat_avg.to_string());
col_strings.push(Col::e_elat_avg.to_string());
col_strings.push(Col::d_elon.to_string());
col_strings.push(Col::e_d_elon.to_string());
col_strings.push(Col::d_elat.to_string());
col_strings.push(Col::e_d_elat.to_string());
col_strings.push(Col::snrd_elon.to_string());
col_strings.push(Col::snrd_elat.to_string());
col_strings.push(Col::chi2pm_ra.to_string());
col_strings.push(Col::chi2pm_de.to_string());
col_strings.push(Col::ka.to_string());
col_strings.push(Col::k1.to_string());
col_strings.push(Col::k2.to_string());
col_strings.push(Col::km.to_string());
col_strings.push(Col::plx1.to_string());
col_strings.push(Col::e_plx1.to_string());
col_strings.push(Col::plx2.to_string());
col_strings.push(Col::e_plx2.to_string());
col_strings.push(Col::sep.to_string());
col_strings.push(Col::ccf.to_string());
col_strings.push(Col::abf.to_string());
map.insert(catwise2020.string(), col_strings);
}