gaia_access 0.1.1

Access the Gaia ESA Archive for astronomical data.
Documentation 
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// This code is generated by generate_code.py, do not modify it manually.

//! This module contains all the known columns in the gaiaedr3_distance table.

use crate::traits::{Column, Table};

///
/// Bailer-Jones et al. (2021) EDR3 distances. <p> Estimating distances from parallaxes. V: Geometric and photogeometric distances to 1.47 billion stars in Gaia Early Data Release 3.
/// Bailer-Jones et al. 2021 AJ 161 147. Data replicated from gedr3dist.main table at GAVO Data Center TAP service https://dc.g-vo.org/tap and TAP metadata as of December 2020.
/// <p>
///     Original table description.
/// <p>
///     We estimate the distance from the Sun to sources in Gaia EDR3 that have
///     parallaxes. We provide two types of distance estimate, together with
///     their corresponding asymmetric uncertainties, using Bayesian posterior
///     density functions that we sample for each source. Our prior is based
///     on a detailed model of the 3D spatial, colour, and magnitude
///     distribution of stars in our Galaxy that includes a 3D map of
///     interstellar extinction.
/// <p>
///     The first type of distance estimate is purely geometric, in that it only
///     makes use of the Gaia parallax and parallax uncertainty. This uses a
///     direction-dependent distance prior derived from our Galaxy model. The
///     second type of distance estimate is photogeometric: in addition to
///     parallax it also uses the source's G-band magnitude and BP-RP
///     colour. This type of estimate uses the geometric prior together with a
///     direction-dependent and colour-dependent prior on the absolute magnitude
///     of the star.
/// <p>
///     Our distance estimate and uncertainties are quantiles, so are invariant
///     under logarithmic transformations. This means that our median estimate
///     of the distance can be used to give the median estimate of the distance
///     modulus, and likewise for the uncertainties.
#[allow(non_camel_case_types)]
pub struct gaiaedr3_distance;

impl Table for gaiaedr3_distance {
    fn string(&self) -> String {
        "gaiaedr3_distance".to_string()
    }
}

/// The columns in the gaiaedr3_distance 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 or DR2 source_ids.
    source_id,
    /// The median of the geometric distance posterior. The geometric distance estimate.
    r_med_geo,
    /// The 16th percentile of the geometric distance posterior. The lower 1-sigma-like bound on the confidence interval.
    r_lo_geo,
    /// The 84th percentile of the geometric distance posterior. The upper 1-sigma-like bound on the confidence interval.
    r_hi_geo,
    /// The median of the photogeometric distance posterior. The photogeometric distance estimate.
    r_med_photogeo,
    /// The 16th percentile of the photogeometric distance posterior. The lower 1-sigma-like bound on the confidence interval.
    r_lo_photogeo,
    /// The 84th percentile of the photogeometric distance posterior. The upper 1-sigma-like bound on the confidence interval.
    r_hi_photogeo,
    /// Additional information on the solution. Do not use for filtering (see table note in the reference URL).
    flag,
}

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::source_id.to_string());
    col_strings.push(Col::r_med_geo.to_string());
    col_strings.push(Col::r_lo_geo.to_string());
    col_strings.push(Col::r_hi_geo.to_string());
    col_strings.push(Col::r_med_photogeo.to_string());
    col_strings.push(Col::r_lo_photogeo.to_string());
    col_strings.push(Col::r_hi_photogeo.to_string());
    col_strings.push(Col::flag.to_string());
    map.insert(gaiaedr3_distance.string(), col_strings);
}