gapipes

gapipes is a pandas-centric lightweight collection of routines to fetch and work with the Gaia data.

It provides

• sensible and tested Gaia TAP+ client to fetch the data

• custom accessors to pandas DataFrame and Series to do common operations on the Gaia data such as

  • making astropy coordinates objects
  • calculating renormalized unit weight error
  • making covariance matrix from errors and correlation coefficients.

A quick tour

Say you have some Gaia data. Let's quickly fetch some from the Gaia archive.

import gapipes as gp

df = gp.gaia.query("""
select top 50 *
from gaiadr2.gaia_source
where
1=contains(point('', ra, dec),
           circle('', 130.226, 19.665, 1))
and parallax between 4.613 and 7.312
""")

df is a pandas Dataframe containing gaia sources.

print(type(df), len(df), 'rows')

<class 'pandas.core.frame.DataFrame'> 50 rows

When you import gapipes, it registers a custom accessor g, under which there is a collection of common operations. Let's just work with first 5 rows for display purpose.

d = df.head()

• Make astropy ICRS coordinate instance with proper motion and radial velocity:

d.g.icrs

<ICRS Coordinate: (ra, dec, distance) in (deg, deg, pc)
    [(130.12157181, 18.79627847, 181.87376145),
     (130.05526312, 18.72393993, 190.20501526),
     (130.13982678, 18.67443893, 188.26622276),
     (130.24423701, 18.67500043, 152.59152456),
     (130.06723764, 18.79161546, 209.60959948)]
 (pm_ra_cosdec, pm_dec, radial_velocity) in (mas / yr, mas / yr, km / s)
    [(-60.7075949 , -52.76753264,         nan),
     (-35.80150307, -12.40964744,         nan),
     (-37.06980874, -11.88689953, 29.54114051),
     (-33.0232434 ,  -5.58953023, 36.23858577),
     (-22.05408719, -19.82573781,  9.80905085)]>

• Make astropy Galactic coordinate instance:

d.g.galactic

<Galactic Coordinate: (l, b, distance) in (deg, deg, pc)
    [(206.89277308, 32.19810551, 181.87376145),
     (206.94629392, 32.1137144 , 190.20501526),
     (207.03459301, 32.17095501, 188.26622276),
     (207.07558656, 32.26359033, 152.59152456),
     (206.87635259, 32.14836265, 209.60959948)]
 (pm_l_cosb, pm_b, radial_velocity) in (mas / yr, mas / yr, km / s)
    [(        nan,          nan,         nan),
     (        nan,          nan,         nan),
     (-2.08990742, -38.87288757, 29.54114051),
     (-6.52731056, -32.85074839, 36.23858577),
     (10.70417661, -27.65616106,  9.80905085)]>

• Make [parallax, pmra, pmdec] 3x3 covariance matrix (using "*_error" and "parallax_pmra_corr", ... columns)

d.g.make_cov()

array([[[ 0.03407061,  0.03978523, -0.01119716],
        [ 0.03978523,  0.0693536 , -0.01736009],
        [-0.01119716, -0.01736009,  0.01528202]],

       [[ 0.05322305,  0.04000269, -0.01155556],
        [ 0.04000269,  0.1277317 , -0.03569367],
        [-0.01155556, -0.03569367,  0.04880749]],

       [[ 0.00363713,  0.00340469, -0.00060153],
        [ 0.00340469,  0.00880243, -0.00189437],
        [-0.00060153, -0.00189437,  0.0036257 ]],

       [[ 0.06099286,  0.06141351, -0.01816473],
        [ 0.06141351,  0.13105763, -0.03839579],
        [-0.01816473, -0.03839579,  0.04169299]],

       [[ 0.00185897,  0.00182671, -0.00045218],
        [ 0.00182671,  0.00437075, -0.00137452],
        [-0.00045218, -0.00137452,  0.00190053]]])

The same accessor is available for pandas.Series that contains one row of gaia_source table, i.e., data for a single source.

s = d.iloc[0]
s

solution_id                                           1635721458409799680
designation                                   Gaia DR2 659494740858299264
source_id                                              659494740858299264
random_index                                                    642953719
ref_epoch                                                          2015.5
                                              ...                        
lum_val                                                               NaN
lum_percentile_lower                                                  NaN
lum_percentile_upper                                                  NaN
datalink_url            https://gea.esac.esa.int/data-server/datalink/...
epoch_photometry_url                                                  NaN
Name: 0, Length: 96, dtype: object

• Make astropy ICRS coordinate object

s.g.icrs

<ICRS Coordinate: (ra, dec, distance) in (deg, deg, pc)
    (130.12157181, 18.79627847, 181.87376145)
 (pm_ra_cosdec, pm_dec, radial_velocity) in (mas / yr, mas / yr, km / s)
    (-60.7075949, -52.76753264, nan)>

• Open Simbad coordinate search for this source in default web browser.

s.g.open_simbad() # will open the page in your browser

Installation

pip install git+https://github.com/smoh/gapipes.git@master