// This code is generated by generate_code.py, do not modify it manually.
//! This module contains all the known columns in the gaiadr2_astrophysical_parameters table.
use crate::traits::{Column, Table};
/// Astrophysical parameters from Gaia DR2, 2MASS, and AllWISE. This is a catalogue of astrophysical parameters for 123,076,271 stars derived by exploiting the power of multi-wavelength and multi-survey observations from Gaia DR2 parallaxes and integrated photometry along with 2MASS and AllWISE photometry. Provided are estimates of log age, log mass, log temperature, log luminosity, log surface gravity, distance modulus, dust extinction (A0), and average grain size (R0) along the lines of sight. In contrast to other catalogues, weakly informative priors were used instead of a Galactic model. The estimates and uncertainties are quantiles, so they are invariant under monotonic transformations (e.g., log, exp). This means that one can use the median estimate to obtain the median distance or temperature, for instance, and likewise for the uncertainties. Data replicated from the gdr2ap.main table at the GAVO Data Centre TAP service https://dc.g-vo.org/tap and TAP metadata as of January 2022. Reference paper: https://ui.adsabs.harvard.edu/abs/2022A%26A...662A.125F/abstract (DOI: 10.1051/0004-6361/202141828)
#[allow(non_camel_case_types)]
pub struct gaiadr2_astrophysical_parameters;
impl Table for gaiadr2_astrophysical_parameters {
fn string(&self) -> String {
"gaiadr2_astrophysical_parameters".to_string()
}
}
/// The columns in the gaiadr2_astrophysical_parameters table.
#[allow(non_camel_case_types)]
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, strum::Display)]
pub enum Col {
/// Unique source identifier. Note that this *cannot* be matched against the DR1 source_id.
source_id,
/// multivariate maximum posterior estimate for the dust exctinction A₀ towards this source.
a0_best,
/// median of the distribution of dust exctinction A₀ towards this source.
a0_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the dust exctinction A₀ towards this source. In ADQL, write a0_dist[1] for the minimum, a0_dist[2] for the 16th percentile, and so on.
a0_dist,
/// multivariate maximum posterior estimate for the average dust grain size extinction parameter.
r0_best,
/// median of the distribution of average dust grain size extinction parameter.
r0_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the average dust grain size extinction parameter. In ADQL, write r0_dist[1] for the minimum, r0_dist[2] for the 16th percentile, and so on.
r0_dist,
/// multivariate maximum posterior estimate for the log10 of the age.
loga_best,
/// median of the distribution of log10 of the age.
loga_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the log10 of the age. In ADQL, write loga_dist[1] for the minimum, loga_dist[2] for the 16th percentile, and so on.
loga_dist,
/// multivariate maximum posterior estimate for the log10 of the luminosity.
logl_best,
/// median of the distribution of log10 of the luminosity.
logl_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the log10 of the luminosity. In ADQL, write logl_dist[1] for the minimum, logl_dist[2] for the 16th percentile, and so on.
logl_dist,
/// multivariate maximum posterior estimate for the log10 of the mass.
logm_best,
/// median of the distribution of log10 of the mass.
logm_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the log10 of the mass. In ADQL, write logm_dist[1] for the minimum, logm_dist[2] for the 16th percentile, and so on.
logm_dist,
/// multivariate maximum posterior estimate for the log10 of the effective temperature.
logt_best,
/// median of the distribution of log10 of the effective temperature.
logt_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the log10 of the effective temperature. In ADQL, write logt_dist[1] for the minimum, logt_dist[2] for the 16th percentile, and so on.
logt_dist,
/// multivariate maximum posterior estimate for the log10 of the surface gravity.
logg_best,
/// median of the distribution of log10 of the surface gravity.
logg_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the log10 of the surface gravity. In ADQL, write logg_dist[1] for the minimum, logg_dist[2] for the 16th percentile, and so on.
logg_dist,
/// multivariate maximum posterior estimate for the attenuation in the Gaia BP band towards this source..
a_bp_best,
/// median of the distribution of attenuation in the Gaia BP band towards this source..
a_bp_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the attenuation in the Gaia BP band towards this source.. In ADQL, write a_bp_dist[1] for the minimum, a_bp_dist[2] for the 16th percentile, and so on.
a_bp_dist,
/// multivariate maximum posterior estimate for the attenuation in the Gaia G band towards this source..
a_g_best,
/// median of the distribution of attenuation in the Gaia G band towards this source..
a_g_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the attenuation in the Gaia G band towards this source.. In ADQL, write a_g_dist[1] for the minimum, a_g_dist[2] for the 16th percentile, and so on.
a_g_dist,
/// multivariate maximum posterior estimate for the attenuation in the Gaia RP band towards this source..
a_rp_best,
/// median of the distribution of attenuation in the Gaia RP band towards this source..
a_rp_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the attenuation in the Gaia RP band towards this source.. In ADQL, write a_rp_dist[1] for the minimum, a_rp_dist[2] for the 16th percentile, and so on.
a_rp_dist,
/// Recalibrated Gaia BP magnitude.
mag_bp,
/// Recalibrated error from original Gaia BP magnitude.
err_bp,
/// multivariate maximum posterior estimate for the Gaia BP magnitude.
bp_best,
/// median of the distribution of Gaia BP magnitude.
bp_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the Gaia BP magnitude. In ADQL, write bp_dist[1] for the minimum, bp_dist[2] for the 16th percentile, and so on.
bp_dist,
/// Recalibrated Gaia G magnitude.
mag_g,
/// Recalibrated error from original Gaia G magnitude.
err_g,
/// multivariate maximum posterior estimate for the Gaia G magnitude.
g_best,
/// median of the distribution of Gaia G magnitude.
g_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the Gaia G magnitude. In ADQL, write g_dist[1] for the minimum, g_dist[2] for the 16th percentile, and so on.
g_dist,
/// Recalibrated Gaia RP magnitude.
mag_rp,
/// Recalibrated error from original Gaia RP magnitude.
err_rp,
/// multivariate maximum posterior estimate for the Gaia RP magnitude.
rp_best,
/// median of the distribution of Gaia RP magnitude.
rp_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the Gaia RP magnitude. In ADQL, write rp_dist[1] for the minimum, rp_dist[2] for the 16th percentile, and so on.
rp_dist,
/// Recalibrated 2MASS J magnitude.
mag_j,
/// Recalibrated error from original 2MASS J magnitude.
err_j,
/// multivariate maximum posterior estimate for the 2MASS J magnitude.
j_best,
/// median of the distribution of 2MASS J magnitude.
j_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the 2MASS J magnitude. In ADQL, write j_dist[1] for the minimum, j_dist[2] for the 16th percentile, and so on.
j_dist,
/// Recalibrated 2MASS H magnitude.
mag_h,
/// Recalibrated error from original 2MASS H magnitude.
err_h,
/// multivariate maximum posterior estimate for the 2MASS H magnitude.
h_best,
/// median of the distribution of 2MASS H magnitude.
h_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the 2MASS H magnitude. In ADQL, write h_dist[1] for the minimum, h_dist[2] for the 16th percentile, and so on.
h_dist,
/// Recalibrated 2MASS Ks magnitude.
mag_ks,
/// Recalibrated error from original 2MASS Ks magnitude.
err_ks,
/// multivariate maximum posterior estimate for the 2MASS Ks magnitude.
ks_best,
/// median of the distribution of 2MASS Ks magnitude.
ks_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the 2MASS Ks magnitude. In ADQL, write ks_dist[1] for the minimum, ks_dist[2] for the 16th percentile, and so on.
ks_dist,
/// Recalibrated WISE W1 magnitude.
mag_w1,
/// Recalibrated error from original WISE W1 magnitude.
err_w1,
/// multivariate maximum posterior estimate for the WISE W1 magnitude.
w1_best,
/// median of the distribution of WISE W1 magnitude.
w1_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the WISE W1 magnitude. In ADQL, write w1_dist[1] for the minimum, w1_dist[2] for the 16th percentile, and so on.
w1_dist,
/// Recalibrated WISE W2 magnitude.
mag_w2,
/// Recalibrated error from original WISE W2 magnitude.
err_w2,
/// multivariate maximum posterior estimate for the WISE W2 magnitude.
w2_best,
/// median of the distribution of WISE W2 magnitude.
w2_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the WISE W2 magnitude. In ADQL, write w2_dist[1] for the minimum, w2_dist[2] for the 16th percentile, and so on.
w2_dist,
/// multivariate maximum posterior estimate for the distance modulus.
dmod_best,
/// median of the distribution of distance modulus.
dmod_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the distance modulus. In ADQL, write dmod_dist[1] for the minimum, dmod_dist[2] for the 16th percentile, and so on.
dmod_dist,
/// multivariate maximum posterior estimate for the log likelihood of the solution (paper Eq. 1).
lnlike_best,
/// median of the distribution of log likelihood of the solution (paper Eq. 1).
lnlike_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the log likelihood of the solution (paper Eq. 1). In ADQL, write lnlike_dist[1] for the minimum, lnlike_dist[2] for the 16th percentile, and so on.
lnlike_dist,
/// multivariate maximum posterior estimate for the log posterior of the solution (paper Eq. 2).
lnp_best,
/// median of the distribution of log posterior of the solution (paper Eq. 2).
lnp_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the log posterior of the solution (paper Eq. 2). In ADQL, write lnp_dist[1] for the minimum, lnp_dist[2] for the 16th percentile, and so on.
lnp_dist,
/// multivariate maximum posterior estimate for the log photometric likelihood jitter common to all bands.
log10jitter_best,
/// median of the distribution of log photometric likelihood jitter common to all bands.
log10jitter_p50,
/// Distribution (min, p16, p25, p50, p75, p84, max) of the log photometric likelihood jitter common to all bands. In ADQL, write log10jitter_dist[1] for the minimum, log10jitter_dist[2] for the 16th percentile, and so on.
log10jitter_dist,
/// Recalibrated parallax.
parallax,
/// Error in recalibrated parallax.
err_parallax,
}
impl Column for Col {}
#[cfg(test)]
/// Collects all the known columns in the gaiadr2_astrophysical_parameters table.
pub fn collect_known(map: &mut std::collections::HashMap<String, Vec<String>>) {
let mut col_strings = Vec::new();
col_strings.push(Col::source_id.to_string());
col_strings.push(Col::a0_best.to_string());
col_strings.push(Col::a0_p50.to_string());
col_strings.push(Col::a0_dist.to_string());
col_strings.push(Col::r0_best.to_string());
col_strings.push(Col::r0_p50.to_string());
col_strings.push(Col::r0_dist.to_string());
col_strings.push(Col::loga_best.to_string());
col_strings.push(Col::loga_p50.to_string());
col_strings.push(Col::loga_dist.to_string());
col_strings.push(Col::logl_best.to_string());
col_strings.push(Col::logl_p50.to_string());
col_strings.push(Col::logl_dist.to_string());
col_strings.push(Col::logm_best.to_string());
col_strings.push(Col::logm_p50.to_string());
col_strings.push(Col::logm_dist.to_string());
col_strings.push(Col::logt_best.to_string());
col_strings.push(Col::logt_p50.to_string());
col_strings.push(Col::logt_dist.to_string());
col_strings.push(Col::logg_best.to_string());
col_strings.push(Col::logg_p50.to_string());
col_strings.push(Col::logg_dist.to_string());
col_strings.push(Col::a_bp_best.to_string());
col_strings.push(Col::a_bp_p50.to_string());
col_strings.push(Col::a_bp_dist.to_string());
col_strings.push(Col::a_g_best.to_string());
col_strings.push(Col::a_g_p50.to_string());
col_strings.push(Col::a_g_dist.to_string());
col_strings.push(Col::a_rp_best.to_string());
col_strings.push(Col::a_rp_p50.to_string());
col_strings.push(Col::a_rp_dist.to_string());
col_strings.push(Col::mag_bp.to_string());
col_strings.push(Col::err_bp.to_string());
col_strings.push(Col::bp_best.to_string());
col_strings.push(Col::bp_p50.to_string());
col_strings.push(Col::bp_dist.to_string());
col_strings.push(Col::mag_g.to_string());
col_strings.push(Col::err_g.to_string());
col_strings.push(Col::g_best.to_string());
col_strings.push(Col::g_p50.to_string());
col_strings.push(Col::g_dist.to_string());
col_strings.push(Col::mag_rp.to_string());
col_strings.push(Col::err_rp.to_string());
col_strings.push(Col::rp_best.to_string());
col_strings.push(Col::rp_p50.to_string());
col_strings.push(Col::rp_dist.to_string());
col_strings.push(Col::mag_j.to_string());
col_strings.push(Col::err_j.to_string());
col_strings.push(Col::j_best.to_string());
col_strings.push(Col::j_p50.to_string());
col_strings.push(Col::j_dist.to_string());
col_strings.push(Col::mag_h.to_string());
col_strings.push(Col::err_h.to_string());
col_strings.push(Col::h_best.to_string());
col_strings.push(Col::h_p50.to_string());
col_strings.push(Col::h_dist.to_string());
col_strings.push(Col::mag_ks.to_string());
col_strings.push(Col::err_ks.to_string());
col_strings.push(Col::ks_best.to_string());
col_strings.push(Col::ks_p50.to_string());
col_strings.push(Col::ks_dist.to_string());
col_strings.push(Col::mag_w1.to_string());
col_strings.push(Col::err_w1.to_string());
col_strings.push(Col::w1_best.to_string());
col_strings.push(Col::w1_p50.to_string());
col_strings.push(Col::w1_dist.to_string());
col_strings.push(Col::mag_w2.to_string());
col_strings.push(Col::err_w2.to_string());
col_strings.push(Col::w2_best.to_string());
col_strings.push(Col::w2_p50.to_string());
col_strings.push(Col::w2_dist.to_string());
col_strings.push(Col::dmod_best.to_string());
col_strings.push(Col::dmod_p50.to_string());
col_strings.push(Col::dmod_dist.to_string());
col_strings.push(Col::lnlike_best.to_string());
col_strings.push(Col::lnlike_p50.to_string());
col_strings.push(Col::lnlike_dist.to_string());
col_strings.push(Col::lnp_best.to_string());
col_strings.push(Col::lnp_p50.to_string());
col_strings.push(Col::lnp_dist.to_string());
col_strings.push(Col::log10jitter_best.to_string());
col_strings.push(Col::log10jitter_p50.to_string());
col_strings.push(Col::log10jitter_dist.to_string());
col_strings.push(Col::parallax.to_string());
col_strings.push(Col::err_parallax.to_string());
map.insert(gaiadr2_astrophysical_parameters.string(), col_strings);
}