gaia_access 0.1.1

Access the Gaia ESA Archive for astronomical data.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171

// This code is generated by generate_code.py, do not modify it manually.

//! This module contains all the known columns in the panstarrs1_original_valid table.

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

/// The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is
/// a system for wide-field astronomical imaging developed and operated by
/// the Institute for Astronomy at the University of Hawaii. Pan-STARRS1
/// (PS1) is the first part of Pan-STARRS to be completed and is the basis
/// for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and
/// its 1.4 Gigapixel camera to image the sky in five broadband filters (g,
/// r, i, z, y).
///
/// The current table contains a filtered subsample of the 10 723 304 629
/// entries listed in the original ObjectThin table.
/// We used only ObjectThin and MeanObject tables to extract
/// panstarrs1OriginalValid table, this means that objects detected only in
/// stack images are not included here. The main reason for us to avoid the
/// use of objects detected in stack images is that their astrometry is not
/// as good as the mean objects astrometry: “The stack positions (raStack,
/// decStack) have considerably larger systematic astrometric errors than
/// the mean epoch positions (raMean, decMean).” The astrometry for the
/// MeanObject positions uses Gaia DR1 as a reference catalog, while the
/// stack positions use 2MASS as a reference catalog.
///
/// In details, we filtered out all objects where:
///
/// -   nDetections = 1
///
/// -   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set
///
/// -   mean astrometry could not be measured, objInfoFlag 524288 set
///
/// -   stack position used for mean astrometry, objInfoFlag 1048576 set
///
/// -   error on all magnitudes equal to 0 or to -999;
///
/// -   all magnitudes set to -999;
///
/// -   error on RA or DEC greater than 1 arcsec.
///
/// The number of objects in panstarrs1OriginalValid is 2 264 263 282.
///
/// The panstarrs1OriginalValid table contains only a subset of the columns
/// available in the combined ObjectThin and MeanObject tables. A
/// description of the original ObjectThin and MeanObjects tables can be
/// found at:
/// https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables
///
/// Download:
/// http://mastweb.stsci.edu/ps1casjobs/home.aspx
/// Documentation:
/// https://outerspace.stsci.edu/display/PANSTARRS
/// http://pswww.ifa.hawaii.edu/pswww/
/// References:
/// The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560
/// Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,
/// arXiv:1612.05240
/// Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.
/// Z., et al. 2016, arXiv:1612.05245
/// Pan-STARRS Pixel Analysis: Source Detection and Characterization,
/// Magnier, E. A., et al. 2016, arXiv:1612.05244
/// Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et
/// al. 2016, arXiv:1612.05242
/// The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.
/// 2016, arXiv:1612.05243
///
/// Catalogue curator:
/// SSDC - ASI Space Science Data Center
/// https://www.ssdc.asi.it/
#[allow(non_camel_case_types)]
pub struct panstarrs1_original_valid;

impl Table for panstarrs1_original_valid {
    fn string(&self) -> String {
        "panstarrs1_original_valid".to_string()
    }
}

/// The columns in the panstarrs1_original_valid table.
#[allow(non_camel_case_types)]
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, strum::Display)]
pub enum Col {
    /// IAU name
    obj_name,
    /// Unique Object Identifier
    obj_id,
    /// RA Mean
    ra,
    /// DEC Mean
    dec,
    /// RA Mean standard deviation
    ra_error,
    /// DEC Mean standard deviation
    dec_error,
    /// Epoch Mean
    epoch_mean,
    /// Mean magnitude in g filter
    g_mean_psf_mag,
    /// Error in Mean magnitude in g filter
    g_mean_psf_mag_error,
    /// g filter flags
    g_flags,
    /// Mean magnitude in r filter
    r_mean_psf_mag,
    /// Error in Mean magnitude in r filter
    r_mean_psf_mag_error,
    /// r filter flags
    r_flags,
    /// Mean magnitude in i filter
    i_mean_psf_mag,
    /// Error in Mean magnitude in i filter
    i_mean_psf_mag_error,
    /// i filter flags
    i_flags,
    /// Mean magnitude in z filter
    z_mean_psf_mag,
    /// Error in Mean magnitude in z filter
    z_mean_psf_mag_error,
    /// z filter flags
    z_flags,
    /// Mean magnitude in y filter
    y_mean_psf_mag,
    /// Error in Mean magnitude in y filter
    y_mean_psf_mag_error,
    /// y filter flags
    y_flags,
    /// Number of detections
    n_detections,
    /// Local zone index
    zone_id,
    /// Object information flags
    obj_info_flag,
    /// Object quality flags
    quality_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::obj_name.to_string());
    col_strings.push(Col::obj_id.to_string());
    col_strings.push(Col::ra.to_string());
    col_strings.push(Col::dec.to_string());
    col_strings.push(Col::ra_error.to_string());
    col_strings.push(Col::dec_error.to_string());
    col_strings.push(Col::epoch_mean.to_string());
    col_strings.push(Col::g_mean_psf_mag.to_string());
    col_strings.push(Col::g_mean_psf_mag_error.to_string());
    col_strings.push(Col::g_flags.to_string());
    col_strings.push(Col::r_mean_psf_mag.to_string());
    col_strings.push(Col::r_mean_psf_mag_error.to_string());
    col_strings.push(Col::r_flags.to_string());
    col_strings.push(Col::i_mean_psf_mag.to_string());
    col_strings.push(Col::i_mean_psf_mag_error.to_string());
    col_strings.push(Col::i_flags.to_string());
    col_strings.push(Col::z_mean_psf_mag.to_string());
    col_strings.push(Col::z_mean_psf_mag_error.to_string());
    col_strings.push(Col::z_flags.to_string());
    col_strings.push(Col::y_mean_psf_mag.to_string());
    col_strings.push(Col::y_mean_psf_mag_error.to_string());
    col_strings.push(Col::y_flags.to_string());
    col_strings.push(Col::n_detections.to_string());
    col_strings.push(Col::zone_id.to_string());
    col_strings.push(Col::obj_info_flag.to_string());
    col_strings.push(Col::quality_flag.to_string());
    map.insert(panstarrs1_original_valid.string(), col_strings);
}