The code is constituted by 3 main functions:
The input file (spk_wrapper, see below) or input DataFrame (spk_groups) must have the following columns:
| Column | Description |
|---|---|
| NAME | name of the star (numbers are allowed) |
| RA | right ascension in degrees |
| DEC | declination in degrees |
| PMRA | proper motion in right ascension (μαcos(δ)) in mas yr-1 |
| EPMRA | error of proper motion in right ascension in mas yr-1 |
| PMDEC | proper motion in declination in mas yr-1 |
| EPMDEC | error of proper motion in declination in mas yr-1 |
| RV | radial velocity in km s-1 |
| ERV | error of radial velocity in km s-1 |
| PLX (optional*) | parallax in mas |
| EPLX (optional*) | error of parallax in mas |
| d (optional*) | distance in pc |
| ed (optional*) | error of distance in pc |
* The user use spk_wrapper can choose between giving the parallax or the distance (and the corresponding error) of the stars (always one of them for each set analyzed at a time). If only distances and their error are given, the parallaxes and their errors are calculated from them. If both are provided, distance is ignored. However, if spk_groups is used independently, only PLX and EPLX are allowed.
The standard output file structure is as follows:
| Column | Description |
|---|---|
| NAME | That provided in the input |
| RV | The value provided in the input |
| ERV | The value provided in the input |
| U | U Galactic-space velocity component in km s-1 (positive towards the Galactic center) |
| eU | error in the U Galactic-space velocity component in km s-1 |
| V | V Galactic-space velocity component in km s-1 (positive towards the North Galactic Pole) |
| eV | error in the V Galactic-space velocity component in km s-1 |
| W | W Galactic-space velocity component in km s-1 |
| eW | error in the W Galactic-space velocity component in km s-1 |
| lab_YD | indicates if the stars belongs (YD) or not (NYD) to the young disk region defined by Eggen |
| lab_skg | kinematic moving group, association or range of them to which the star is candidate. Default values are Local Association (LA), Hyades Supercluster (HS), IC 2391 (IC), Ursa Major (UMA) and Castor (CAS), their overlaps (LA/IC, LA/CAS, LA/IC/CAS, and IC/CAS) or neither moving group nor association (NNN). |
| lab_kin | stellar population: thin disk (D), thick disk (TD), in between them (D-TD), or halo (H). |
"""
Parameters
----------
df_out : DataFrame
The DataFrame resulting from spk_groups or, at least, one containing
the following columns: U, V, and W.
file_name : str, optional
A string to be included in the names of the output figures. The default
value is empty.
dict_colors_groups : dict or dict-like str, optional
dict_colors_intergroup_stars : dict or dict-like str, optional
dict_colors_groups and dict_colors_intergroup_stars are python
dictionaries or dictionary-like strings containing the name (key) and
color (value) given by the user to the stars that fall in one
association/group or between several associations/groups apart from
those provided by default. To "remove" a default
association/group/intergroup from the graphics, the corresponding
dictionary must contain "default name": None (for example, to avoid
showing LA stars in red, dict_colors_groups must contain "LA": None).
autocomplete_colors : boolean, optional
In case the input DataFrame contains stars belonging to any new
association, group, or intergroup and it has not been defined in the
corresponding dictionary, the "autocomplete_colors" parameter sets
whether showing these stars in a randomly generated color (True) or
displaying them according to their stellar population if given (False).
Caution: autocomplete_colors only avoid repetition if the colors are
given by their hexadecimal name.
independent : boolean, optional
Different figures for each UV and WV plane? Default is False.
gf_uw2 : str, optional
The gf_uw2 parameter sets the aspect ratio of the Toomre diagram. The
values, circumferences (default) or ellipses, indicate how lines of
constant total velocity look in the resulting diagram.
"""Using the command line:
$ python spk_wrapper.py [-h] -i I [-e E] [-fn FN] [-dcg DCG] [-dci DCI][-ac AC] [-ind IND] [-g G]
optional arguments:
-h, --help show this help message and exit
-i I the input file containing the data (.txt or .csv)
-e E set how the code deals with zeros in the errors: drop (default)
or zeros
-fn FN set a string that all the output files must contain
-dcg DCG set colors for new groups or change those given by default (str-
like python dictionary)
-dci DCI set colors for new intergroup stars or change those given by
default (str-like python dictionary)
-ac AC should the stars candidates to any association, group, or
intergroup not defined in the dictionaries be shown in a random
color? Default is False
-ind IND Independent graphs for each plane? Default is False
-g G set the aspect ratio of the Toomre diagram: circumferences
(default) or ellipses
Using the Python interpreter:
- Using the wrapper:
>>> import spk_wrapper as spkw
>>> spkw.spk_wrapper(input_file, errors="drop", file_name="", dict_colors_groups=None,
dict_colors_intergroup_stars=None, autocomplete_colors=False,
independent=False, gf_uw2="circumferences")- Running spk_groups and spk_graphs independently (note that the input is now a DataFrame in both cases):
>>> import spk_groups as spkg
>>> spkg.spk_groups(df, file_name="")>>> import spk_graphs as spkgra
>>> spkgra.spk_groups(input_file, file_name="", dict_colors_groups=None,
dict_colors_intergroup_stars=None, autocomplete_colors=False,
independent=False, gf_uw2="circumferences")SteParKin is rather optimistic about the size in the UVW space of the stellar associations and moving groups considered as compared to other codes available in the literature (see References). We prefer this approach because allows to examine their actual limits, which are far from clear, and find members with peculiar kinematics. The cost, however, is to have a greater number of contaminators. We strongly recommend a full study of the properties of the candidates giving by the code to confirm or reject its membership in a particular group or association (see, for example, Cortes-Contreras et al. 2020). In any case, we note that the user can modify the definition of any group or association given by default or introduce a new one.
It is also worth mentioning that SteParKin does not resolve the overlapping between associations or/and moving groups. When a star falls inside two or more groups and/or associations, the star is labeled as "intergroup star", that is, lab_skg = Group1/Group2/... As for confirm a candidate to a single group/association and among others, the user can use the distance of the star (in the UVW space) to the centroids of the groups or associations, its Galactic-space position, and spectral characteristics to try to resolve the controversy. Using the default associations and moving groups, there are four overlaps: LA/IC, LA/CAS, LA/IC/CAS, IC/CAS.
The user should bear in mind that any new overlap (produced by either introducing a new association or moving group or modifying the parameters of a default one) must be defined in the dict_colors_intergroup_stars or the parameter autocomplete_colors must be True to these stars are plotted correctly. Otherwise, the stars in the new overlap will be represented according to their stellar population if given.
SteParKin is written in Python 3.7.0 and uses the following open-source packages (recommended version):
- Python Standard Library : os, io, math
- pandas (0.23.4)
- numpy (1.15.4)
- matplotlib (3.0.2)
- pycparser (2.19)
- urllib3 (1.24.1)
- json-c (0.13.1)
Using matplotlib 2.2.3 some plot features are badly drawn.
-
Test 1: This example shows how introduce a new group. The data are M-type stars from Cortés-Contreras et al. 2020.
-
Test 2: This example illustrates how even small variations in RV can result in different outcomes about the possibility of a star to be a candidate of one moving group or association. We took the different values of RV and ERV from the literature for a well-known young star (EK DRA). The rest of the data are from Simbad.
- [Bensby et al. 2003] (http://ui.adsabs.harvard.edu/abs/2003A%26A...410..527B/abstract)
- [Bensby et al. 2005] (http://ui.adsabs.harvard.edu/abs/2005A%26A...433..185B/abstract)
- [Cortés-Contreras et al. 2020] (https://ui.adsabs.harvard.edu/abs/2020sea..confE.131C/abstract)
- [Cortés-Contreras et al. 2023], A&A, in prep.
- [Crundall et al. 2019] (https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.3625C/abstract)
- [Eggen (1984e)] (https://ui.adsabs.harvard.edu/abs/1984AJ.....89.1350E/abstract)
- [Eggen (1989c)] (https://ui.adsabs.harvard.edu/abs/1989PASP..101..366E/abstract)
- [Gagné et al. 2018] (https://ui.adsabs.harvard.edu/abs/2018ApJ...856...23G/abstract)
- [Johnson & Soderblom (1987)] (https://ui.adsabs.harvard.edu/abs/1987AJ.....93..864J/abstract)
- [Montes et al. 2001] (http://ui.adsabs.harvard.edu/abs/2001MNRAS.328...45M/abstract)
- [Montes et al. 2023] JOSS, in prep.
- [Ridel 2016] (https://ui.adsabs.harvard.edu/abs/2016ascl.soft01011R/abstract)
Bug reports, feedback, and comments are very welcome, and they can be sent to dmontes@ucm.es
SteParKin was originally written in Fortran and later in IDL by D. Montes and H.M. Tabernero and was described in Montes et al. 2001. Since then, the code has been enhanced and rewritten in Python by D. Montes and A.J. Domínguez-Fernández.
If the user wants to use SteParKin as part of a scientific work, please, cite Montes et al. 2001 and Cortés-Contreras et al. 2020.
The authors wish to thank to M. Cortés-Contreras, J.A. Caballero, and C. Cifuentes their comments, which have improved significantly the quality of the code.