astrobase.varclass.starfeatures module

This calculates various features related to the color/proper-motion of stars.

All of the functions in this module require as input an ‘objectinfo’ dict. This should usually be taken from a light curve file read into an lcdict. The format and the minimum keys required are:

{'objectid': the name of the object,
 'ra': the right ascension of the object in decimal degrees,
 'decl': the declination of the object in decimal degrees,
 'ndet': the number of observations of this object}

You can also provide magnitudes and proper motions of the object using the following keys and the appropriate values in the objectinfo dict. These will be used to calculate colors, total and reduced proper motion, etc.:

'pmra' -> the proper motion in mas/yr in right ascension,
'pmdecl' -> the proper motion in mas/yr in declination,
'umag'  -> U mag             -> colors: U-B, U-V, U-g
'bmag'  -> B mag             -> colors: U-B, B-V
'vmag'  -> V mag             -> colors: U-V, B-V, V-R, V-I, V-K
'rmag'  -> R mag             -> colors: V-R, R-I
'imag'  -> I mag             -> colors: g-I, V-I, R-I, B-I
'jmag'  -> 2MASS J mag       -> colors: J-H, J-K, g-J, i-J
'hmag'  -> 2MASS H mag       -> colors: J-H, H-K
'kmag'  -> 2MASS Ks mag      -> colors: g-Ks, H-Ks, J-Ks, V-Ks
'sdssu' -> SDSS u mag        -> colors: u-g, u-V
'sdssg' -> SDSS g mag        -> colors: g-r, g-i, g-K, u-g, U-g, g-J
'sdssr' -> SDSS r mag        -> colors: r-i, g-r
'sdssi' -> SDSS i mag        -> colors: r-i, i-z, g-i, i-J, i-W1
'sdssz' -> SDSS z mag        -> colors: i-z, z-W2, g-z
'ujmag' -> UKIRT J mag       -> colors: J-H, H-K, J-K, g-J, i-J
'uhmag' -> UKIRT H mag       -> colors: J-H, H-K
'ukmag' -> UKIRT K mag       -> colors: g-K, H-K, J-K, V-K
'irac1' -> Spitzer IRAC1 mag -> colors: i-I1, I1-I2
'irac2' -> Spitzer IRAC2 mag -> colors: I1-I2, I2-I3
'irac3' -> Spitzer IRAC3 mag -> colors: I2-I3
'irac4' -> Spitzer IRAC4 mag -> colors: I3-I4
'wise1' -> WISE W1 mag       -> colors: i-W1, W1-W2
'wise2' -> WISE W2 mag       -> colors: W1-W2, W2-W3
'wise3' -> WISE W3 mag       -> colors: W2-W3
'wise4' -> WISE W4 mag       -> colors: W3-W4
astrobase.varclass.starfeatures.coord_features(objectinfo)[source]

Calculates object coordinates features, including:

  • galactic coordinates
  • total proper motion from pmra, pmdecl
  • reduced J proper motion from propermotion and Jmag
Parameters:objectinfo (dict) –

This is an objectinfo dict from a light curve file read into an lcdict. The format and the minimum keys required are:

{'ra': the right ascension of the object in decimal degrees,
 'decl': the declination of the object in decimal degrees,
 'pmra': the proper motion in right ascension in mas/yr,
 'pmdecl': the proper motion in declination in mas/yr,
 'jmag': the 2MASS J mag of this object}
Returns:A dict containing the total proper motion
Return type:dict
astrobase.varclass.starfeatures.color_features(in_objectinfo, deredden=True, custom_bandpasses=None, dust_timeout=10.0)[source]

Stellar colors and dereddened stellar colors using 2MASS DUST API:

http://irsa.ipac.caltech.edu/applications/DUST/docs/dustProgramInterface.html

Parameters:
  • in_objectinfo (dict) –

    This is a dict that contains the object’s magnitudes and positions. This requires at least ‘ra’, and ‘decl’ as keys which correspond to the right ascension and declination of the object, and one or more of the following keys for object magnitudes:

    'umag'  -> U mag             -> colors: U-B, U-V, U-g
'bmag'  -> B mag             -> colors: U-B, B-V
'vmag'  -> V mag             -> colors: U-V, B-V, V-R, V-I, V-K
'rmag'  -> R mag             -> colors: V-R, R-I
'imag'  -> I mag             -> colors: g-I, V-I, R-I, B-I
'jmag'  -> 2MASS J mag       -> colors: J-H, J-K, g-J, i-J
'hmag'  -> 2MASS H mag       -> colors: J-H, H-K
'kmag'  -> 2MASS Ks mag      -> colors: g-Ks, H-Ks, J-Ks, V-Ks
'sdssu' -> SDSS u mag        -> colors: u-g, u-V
'sdssg' -> SDSS g mag        -> colors: g-r, g-i, g-K, u-g, U-g, g-J
'sdssr' -> SDSS r mag        -> colors: r-i, g-r
'sdssi' -> SDSS i mag        -> colors: r-i, i-z, g-i, i-J, i-W1
'sdssz' -> SDSS z mag        -> colors: i-z, z-W2, g-z
'ujmag' -> UKIRT J mag       -> colors: J-H, H-K, J-K, g-J, i-J
'uhmag' -> UKIRT H mag       -> colors: J-H, H-K
'ukmag' -> UKIRT K mag       -> colors: g-K, H-K, J-K, V-K
'irac1' -> Spitzer IRAC1 mag -> colors: i-I1, I1-I2
'irac2' -> Spitzer IRAC2 mag -> colors: I1-I2, I2-I3
'irac3' -> Spitzer IRAC3 mag -> colors: I2-I3
'irac4' -> Spitzer IRAC4 mag -> colors: I3-I4
'wise1' -> WISE W1 mag       -> colors: i-W1, W1-W2
'wise2' -> WISE W2 mag       -> colors: W1-W2, W2-W3
'wise3' -> WISE W3 mag       -> colors: W2-W3
'wise4' -> WISE W4 mag       -> colors: W3-W4

    These are basically taken from the available reddening bandpasses from the 2MASS DUST service. If B, V, u, g, r, i, z aren’t provided but 2MASS J, H, Ks are all provided, the former will be calculated using the 2MASS JHKs -> BVugriz conversion functions in astrobase.magnitudes.

  • deredden (bool) – If True, will make sure all colors use dereddened mags where possible.
  • custom_bandpasses (dict) –

    This is a dict used to define any custom bandpasses in the in_objectinfo dict you want to make this function aware of and generate colors for. Use the format below for this dict:

    {
'<bandpass_key_1>':{'dustkey':'<twomass_dust_key_1>',
                    'label':'<band_label_1>'
                    'colors':[['<bandkey1>-<bandkey2>',
                               '<BAND1> - <BAND2>'],
                              ['<bandkey3>-<bandkey4>',
                               '<BAND3> - <BAND4>']]},
.
...
.
'<bandpass_key_N>':{'dustkey':'<twomass_dust_key_N>',
                    'label':'<band_label_N>'
                    'colors':[['<bandkey1>-<bandkey2>',
                               '<BAND1> - <BAND2>'],
                              ['<bandkey3>-<bandkey4>',
                               '<BAND3> - <BAND4>']]},
}

    Where:

    bandpass_key is a key to use to refer to this bandpass in the objectinfo dict, e.g. ‘sdssg’ for SDSS g band

    twomass_dust_key is the key to use in the 2MASS DUST result table for reddening per band-pass. For example, given the following DUST result table (using http://irsa.ipac.caltech.edu/applications/DUST/):

    |Filter_name|LamEff |A_over_E_B_V_SandF|A_SandF|A_over_E_B_V_SFD|A_SFD|
|char       |float  |float             |float  |float           |float|
|           |microns|                  |mags   |                |mags |
 CTIO U       0.3734              4.107   0.209            4.968 0.253
 CTIO B       0.4309              3.641   0.186            4.325 0.221
 CTIO V       0.5517              2.682   0.137            3.240 0.165
.
.
...

    The twomass_dust_key for ‘vmag’ would be ‘CTIO V’. If you want to skip DUST lookup and want to pass in a specific reddening magnitude for your bandpass, use a float for the value of twomass_dust_key. If you want to skip DUST lookup entirely for this bandpass, use None for the value of twomass_dust_key.

    band_label is the label to use for this bandpass, e.g. ‘W1’ for WISE-1 band, ‘u’ for SDSS u, etc.

    The ‘colors’ list contains color definitions for all colors you want to generate using this bandpass. this list contains elements of the form:

    ['<bandkey1>-<bandkey2>','<BAND1> - <BAND2>']

    where the the first item is the bandpass keys making up this color, and the second item is the label for this color to be used by the frontends. An example:

    ['sdssu-sdssg','u - g']
  • dust_timeout (float) – The timeout to use when contacting the 2MASS DUST web service.
Returns:

An objectinfo dict with all of the generated colors, dereddened magnitude,s dereddened colors, as specified in the input args is returned.
Return type:

dict
astrobase.varclass.starfeatures.mdwarf_subtype_from_sdsscolor(ri_color, iz_color)[source]

This calculates the M-dwarf subtype given SDSS r-i and i-z colors.

Parameters:
  • ri_color (float) – The SDSS r-i color of the object.
  • iz_color (float) – The SDSS i-z color of the object.
Returns:

(subtype, index1, index2) – subtype: if the star appears to be an M dwarf, will return an int between 0 and 9 indicating its subtype, e.g. will return 4 for an M4 dwarf. If the object isn’t an M dwarf, will return None

index1, index2: the M-dwarf color locus value and spread of this object calculated from the r-i and i-z colors.
Return type:

tuple
astrobase.varclass.starfeatures.color_classification(colorfeatures, pmfeatures)[source]

This calculates rough star type classifications based on star colors in the ugrizJHK bands.

Uses the output from color_features and coord_features. By default, color_features will use dereddened colors, as are expected by most relations here.

Based on the color cuts from:

  • SDSS SEGUE (Yanny+ 2009)
  • SDSS QSO catalog (Schneider+ 2007)
  • SDSS RR Lyrae catalog (Sesar+ 2011)
  • SDSS M-dwarf catalog (West+ 2008)
  • Helmi+ 2003
  • Bochanski+ 2014
Parameters:
  • colorfeatures (dict) – This is the dict produced by the color_features function.
  • pmfeatures (dict) – This is the dict produced by the coord_features function.
Returns:

A dict containing all of the possible classes this object can belong to as a list in the color_classes key, and values of the various color indices used to arrive to that conclusion as the other keys.
Return type:

dict
astrobase.varclass.starfeatures.neighbor_gaia_features(objectinfo, lclist_kdtree, neighbor_radius_arcsec, gaia_matchdist_arcsec=3.0, verbose=True, gaia_submit_timeout=10.0, gaia_submit_tries=3, gaia_max_timeout=180.0, gaia_mirror=None, gaia_data_release='dr2', complete_query_later=True, search_simbad=False)[source]

Gets several neighbor, GAIA, and SIMBAD features:

From the KD-Tree in the given light curve catalog the object is in: lclist_kdtree:

  • distance to closest neighbor in arcsec
  • total number of all neighbors within 2 x neighbor_radius_arcsec

From the GAIA DR2 catalog:

  • distance to closest neighbor in arcsec
  • total number of all neighbors within 2 x neighbor_radius_arcsec
  • gets the parallax for the object and neighbors
  • calculates the absolute GAIA mag and G-K color for use in CMDs
  • gets the proper motion in RA/Dec if available

From the SIMBAD catalog:

  • the name of the object
  • the type of the object
Parameters:
  • objectinfo (dict) –

    This is the objectinfo dict from an object’s light curve. This must contain at least the following keys:

    {'ra': the right ascension of the object,
 'decl': the declination of the object}
  • lclist_kdtree (scipy.spatial.cKDTree object) – This is a KD-Tree built on the Cartesian xyz coordinates from (ra, dec) of all objects in the same field as this object. It is similar to that produced by astrobase.lcproc.catalogs.make_lclist(), and is used to carry out the spatial search required to find neighbors for this object.
  • neighbor_radius_arcsec (float) – The maximum radius in arcseconds around this object to search for neighbors in both the light curve catalog and in the GAIA DR2 catalog.
  • gaia_matchdist_arcsec (float) – The maximum distance in arcseconds to use for a GAIA cross-match to this object.
  • verbose (bool) – If True, indicates progress and warns of problems.
  • gaia_submit_timeout (float) – Sets the timeout in seconds to use when submitting a request to look up the object’s information to the GAIA service. Note that if fast_mode is set, this is ignored.
  • gaia_submit_tries (int) – Sets the maximum number of times the GAIA services will be contacted to obtain this object’s information. If fast_mode is set, this is ignored, and the services will be contacted only once (meaning that a failure to respond will be silently ignored and no GAIA data will be added to the checkplot’s objectinfo dict).
  • gaia_max_timeout (float) – Sets the timeout in seconds to use when waiting for the GAIA service to respond to our request for the object’s information. Note that if fast_mode is set, this is ignored.
  • gaia_mirror (str) – This sets the GAIA mirror to use. This is a key in the services.gaia.GAIA_URLS dict which defines the URLs to hit for each mirror.
  • gaia_data_release ({'dr2', 'edr3'}) – The Gaia data release to use for the query. This provides hints for which table to use for the GAIA mirror being queried.
  • search_simbad (bool) – If this is True, searches for objects in SIMBAD at this object’s location and gets the object’s SIMBAD main ID, type, and stellar classification if available.
Returns:

Returns a dict with neighbor, GAIA, and SIMBAD features.
Return type:

dict