Querying astronomical databases with Astroquery

ESOpy 3.0 @ Vitacura - 15-17 April 2019 - fvogt@eso.org

Before we start:

Premise:

  • lots of important online databases ... SIMBAD, NED, GAIA, NASA ADS, ESO archive ...

Astroquery class:

  • a powerful way to access a wide range of astro databases using a consistent format
  • wrapper around existing webforms !!!

Cost:

  • 1 astropy sub-packages

Benefits:

  • Get easy access to GAIA to anchor HST ACS images ...
  • Find telescope guide stars ...
  • Update your citation statistics (-> c.f. project)

A note for the installation:

  • pip install --pre astroquery

1) Getting started

In [1]:
# Import the packages
import astropy.units as u
from astropy.coordinates import SkyCoord
from astroquery.gaia import Gaia

Created TAP+ (v1.0.1) - Connection:
	Host: gea.esac.esa.int
	Use HTTPS: False
	Port: 80
	SSL Port: 443

2) Querying around a certain location

In [3]:
coord = SkyCoord(ra=280, dec=-60, unit=(u.degree, u.degree), frame='icrs')
radius = 30*u.arcsec
r = Gaia.query_object(coordinate=coord, radius=radius)
In [4]:
# Let's check the output
print(r.keys())
r.pprint()

['dist', 'solution_id', 'designation', 'source_id', 'random_index', 'ref_epoch', 'ra', 'ra_error', 'dec', 'dec_error', 'parallax', 'parallax_error', 'parallax_over_error', 'pmra', 'pmra_error', 'pmdec', 'pmdec_error', 'ra_dec_corr', 'ra_parallax_corr', 'ra_pmra_corr', 'ra_pmdec_corr', 'dec_parallax_corr', 'dec_pmra_corr', 'dec_pmdec_corr', 'parallax_pmra_corr', 'parallax_pmdec_corr', 'pmra_pmdec_corr', 'astrometric_n_obs_al', 'astrometric_n_obs_ac', 'astrometric_n_good_obs_al', 'astrometric_n_bad_obs_al', 'astrometric_gof_al', 'astrometric_chi2_al', 'astrometric_excess_noise', 'astrometric_excess_noise_sig', 'astrometric_params_solved', 'astrometric_primary_flag', 'astrometric_weight_al', 'astrometric_pseudo_colour', 'astrometric_pseudo_colour_error', 'mean_varpi_factor_al', 'astrometric_matched_observations', 'visibility_periods_used', 'astrometric_sigma5d_max', 'frame_rotator_object_type', 'matched_observations', 'duplicated_source', 'phot_g_n_obs', 'phot_g_mean_flux', 'phot_g_mean_flux_error', 'phot_g_mean_flux_over_error', 'phot_g_mean_mag', 'phot_bp_n_obs', 'phot_bp_mean_flux', 'phot_bp_mean_flux_error', 'phot_bp_mean_flux_over_error', 'phot_bp_mean_mag', 'phot_rp_n_obs', 'phot_rp_mean_flux', 'phot_rp_mean_flux_error', 'phot_rp_mean_flux_over_error', 'phot_rp_mean_mag', 'phot_bp_rp_excess_factor', 'phot_proc_mode', 'bp_rp', 'bp_g', 'g_rp', 'radial_velocity', 'radial_velocity_error', 'rv_nb_transits', 'rv_template_teff', 'rv_template_logg', 'rv_template_fe_h', 'phot_variable_flag', 'l', 'b', 'ecl_lon', 'ecl_lat', 'priam_flags', 'teff_val', 'teff_percentile_lower', 'teff_percentile_upper', 'a_g_val', 'a_g_percentile_lower', 'a_g_percentile_upper', 'e_bp_min_rp_val', 'e_bp_min_rp_percentile_lower', 'e_bp_min_rp_percentile_upper', 'flame_flags', 'radius_val', 'radius_percentile_lower', 'radius_percentile_upper', 'lum_val', 'lum_percentile_lower', 'lum_percentile_upper', 'datalink_url', 'epoch_photometry_url']
         dist             solution_id     ... epoch_photometry_url
                                          ...                     
--------------------- ------------------- ... --------------------
0.0026034636994048854 1635721458409799680 ...                   --
0.0038518741347606357 1635721458409799680 ...                   --
  0.00454542650096783 1635721458409799680 ...                   --
 0.005613919443965546 1635721458409799680 ...                   --
 0.005846434715822121 1635721458409799680 ...                   --
 0.006209042666371929 1635721458409799680 ...                   --
 0.007469463683838576 1635721458409799680 ...                   --
 0.008202004514524316 1635721458409799680 ...                   --

3) Practical break 1

Look at X1_Gaia-pm.py and try to fill the gaps!

4) Querying the ESO archive

In [9]:
import numpy as np
from astroquery.eso import Eso
In [11]:
# Connect to the archive
eso = Eso()
eso.ROW_LIMIT=-1 
eso.login("fvogt") 

WARNING: No password was found in the keychain for the provided username. [astroquery.query]

fvogt, enter your password:
········
INFO: Authenticating fvogt on www.eso.org... [astroquery.eso.core]
INFO: Authentication successful! [astroquery.eso.core]
In [12]:
# What can be queried ?
eso.list_instruments()
Out[12]:
['fors1',
 'fors2',
 'sphere',
 'vimos',
 'omegacam',
 'hawki',
 'isaac',
 'naco',
 'visir',
 'vircam',
 'apex',
 'giraffe',
 'uves',
 'xshooter',
 'espresso',
 'muse',
 'crires',
 'kmos',
 'sinfoni',
 'amber',
 'gravity',
 'midi',
 'pionier',
 'wlgsu']
In [17]:
# eso.query_instrument('muse', help=True)
table_muse = eso.query_instrument('muse', column_filters={'dp_cat':'SCIENCE',
                                                          'dp_type':'OBJECT',
                                                          #'dp_type':'SKY',
                                                          'stime':'2019-04-01', 
                                                          'etime':'2019-04-17',
                                                          #'prog_type':'1',
                                                          #'pi_coi':'VOGT',
                                                          },
                                                          #columns={},
                                                          )
table_muse.pprint()

Release Date      Object         RA     ... TPL EXPNO  INS MODE  DIMM Seeing-avg
------------ --------------- ---------- ... --------- ---------- ---------------
  2019-04-02    NGC5139_F136 201.712641 ...         1   NFM-AO-N     0.47 [0.01]
  2019-09-29           P1115 169.565833 ...         1   WFM-AO-N     0.57 [0.01]
  2019-09-29           P1115 169.575757 ...         2   WFM-AO-N     0.73 [0.01]
  2019-09-29           P1115 169.575755 ...         3   WFM-AO-N     0.74 [0.01]
  2020-04-02    SDSS J112023 170.099625 ...         1   WFM-AO-E     0.73 [0.01]
  2020-04-02    SDSS J112023 170.099769 ...         2   WFM-AO-E     0.70 [0.01]
  2020-04-02    SDSS J112023 170.099769 ...         3   WFM-AO-E     1.05 [0.30]
  2020-04-02        UGC 7342 184.580644 ...         2 WFM-NOAO-E     1.25 [0.57]
  2020-04-02        UGC 7342 184.580644 ...         3 WFM-NOAO-E     1.43 [0.79]
  2020-04-03       NGC  3201 154.519112 ...         1   WFM-AO-N     1.55 [0.94]
         ...             ...        ... ...       ...        ...             ...
  2020-04-15 J1510556-112847 227.731668 ...         1 WFM-NOAO-N     0.52 [0.01]
  2020-04-15 J1510556-112847 227.732919 ...         2 WFM-NOAO-N     0.46 [0.01]
  2020-04-15 J1510556-112847 227.731665 ...         3 WFM-NOAO-N     0.46 [0.01]
  2020-04-15 J1510556-112847 227.730414 ...         4 WFM-NOAO-N     0.46 [0.01]
  2020-04-15       SN2018ddr 209.660292 ...         1 WFM-NOAO-N     0.47 [0.01]
  2020-04-15       SN2018ddr 209.660992 ...         2 WFM-NOAO-N     0.51 [0.01]
  2020-04-15       SN2018ddr 209.659871 ...         3 WFM-NOAO-N     0.57 [0.01]
  2020-04-15       SN2018ddr 209.660152 ...         4 WFM-NOAO-N     0.56 [0.01]
  2020-04-15        NGC 5770  223.31243 ...         1 WFM-NOAO-N     0.49 [0.01]
  2020-04-15        NGC 5770 223.312652 ...         1 WFM-NOAO-N     0.43 [0.01]
  2020-04-15        NGC 5770  223.31257 ...         1 WFM-NOAO-N     0.41 [0.01]
Length = 330 rows
In [18]:
print('Number of files: %i' % (len(table_muse)))
print(table_muse.keys())

Number of files: 330
['Release Date', 'Object', 'RA', 'DEC', 'Target Ra Dec', 'Target l b', 'ProgId', 'DP.ID', 'OB ID', 'OBS Target Name', 'EXPTIME [s]', 'DPR CATG', 'DPR TYPE', 'DPR TECH', 'TPL START', 'TPL NEXP', 'TPL EXPNO', 'INS MODE', 'DIMM Seeing-avg']
In [20]:
time_on_sky = np.sum(table_muse['EXPTIME [s]']) # Total time spent in that spot
print('Science time in P104: %.1f hours' % (time_on_sky/3600))

Science time in P104: 36.7 hours

5) Practical break 2

Look at X2_ESO-archive.py, and try to fill the gaps

6) Querying SIMBAD

In [48]:
from astroquery.simbad import Simbad

Simbad.add_votable_fields('flux(H)')

#Simbad.list_votable_fields()

result = Simbad.query_criteria('rah >15 & rah <16 & dec >-35 & dec < -29 & Hmag <15 & Hmag >10')
In [51]:
print(result.keys())
result['MAIN_ID', 'RA','DEC', 'FLUX_H'].pprint()

['MAIN_ID', 'RA', 'DEC', 'RA_PREC', 'DEC_PREC', 'COO_ERR_MAJA', 'COO_ERR_MINA', 'COO_ERR_ANGLE', 'COO_QUAL', 'COO_WAVELENGTH', 'COO_BIBCODE', 'FLUX_H']
         MAIN_ID                RA           DEC      FLUX_H
                             "h:m:s"       "d:m:s"     mag  
------------------------- ------------- ------------- ------
  2MASX J15025234-3425518 15 02 52.3397 -34 25 51.747 12.537
          TYC 7315-1707-1 15 05 38.0878 -33 38 16.126 10.411
           TYC 7319-178-1 15 01 50.0377 -34 57 00.986 10.587
           TYC 7319-187-1 15 07 33.3102 -34 18 00.728 10.089
           TYC 7319-267-1 15 07 23.8878 -33 50 07.801 10.778
           TYC 7319-346-1 15 07 57.0337 -34 59 26.341 10.289
           TYC 7319-357-1 15 04 51.4483 -34 13 54.980 11.046
           TYC 7319-771-1 15 02 41.5617 -34 16 28.389 10.242
           TYC 7319-836-1 15 07 39.6549 -34 42 37.044 10.124
               ESO 387-17  15 16 55.433  -34 28 21.77 11.473
                      ...           ...           ...    ...
WISEA J151924.12-340751.7 15 19 24.1016 -34 07 50.197 12.315
WISEA J152408.87-342324.1 15 24 08.9911 -34 23 23.859  11.14
WISEA J153621.37-320221.9 15 36 21.4545 -32 02 21.100 11.642
WISEA J154450.16-290159.3 15 44 50.2839 -29 01 57.874 11.846
WISEA J154737.37-312716.9 15 47 37.3988 -31 27 15.913 11.835
WISEA J155122.28-292514.3 15 51 22.3769 -29 25 14.926 12.382
WISEA J155255.81-332214.8 15 52 55.8421 -33 22 13.672 10.921
  2MASS J15044217-3018552 15 04 42.1711 -30 18 55.299 10.121
  2MASS J15433953-2909035 15 43 39.5368 -29 09 03.567 10.356
  2MASS J15593706-3255414 15 59 37.0651 -32 55 41.491 10.244
Length = 1760 rows
In [ ]: