ESO Queries (astroquery.eso)

Getting started

This is a python interface for querying the ESO archive web service. For now, it supports the following:

Requirements

The following packages are required for the use of this module:

  • keyring

  • lxml

  • requests >= 2.4.0

Authentication with ESO User Portal

Whereas querying the ESO database is fully open, accessing actual datasets requires authentication with the ESO User Portal (https://www.eso.org/sso/login). This authentication is performed directly with the provided login() command, as illustrated in the example below. This method uses your keyring to securely store the password in your operating system. As such you should have to enter your correct password only once, and later be able to use this package for automated interaction with the ESO archive.

>>> from astroquery.eso import Eso
>>> eso = Eso()
>>> # First example: TEST is not a valid username, it will fail
>>> eso.login("TEST")
TEST, enter your ESO password:

Authenticating TEST on www.eso.org...
Authentication failed!
>>> # Second example: pretend ICONDOR is a valid username
>>> eso.login("ICONDOR", store_password=True) 
ICONDOR, enter your ESO password:

Authenticating ICONDOR on www.eso.org...
Authentication successful!
>>> # After the first login, your password has been stored
>>> eso.login("ICONDOR") 
Authenticating ICONDOR on www.eso.org...
Authentication successful!

Automatic password

As shown above, your password can be stored by the keyring module, if you pass the argument store_password=True to Eso.login(). For security reason, storing the password is turned off by default.

MAKE SURE YOU TRUST THE MACHINE WHERE YOU USE THIS FUNCTIONALITY!!!

NB: You can delete your password later with the command keyring.delete_password('astroquery:www.eso.org', 'username').

Automatic login

You can further automate the authentication process by configuring a default username. The astroquery configuration file, which can be found following the procedure detailed in astropy.config, needs to be edited by adding username = ICONDOR in the [eso] section.

When configured, the username in the login() method call can be omitted as follows:

>>> from astroquery.eso import Eso
>>> eso = Eso()
>>> eso.login() 
ICONDOR, enter your ESO password:

NB: If an automatic login is configured, other Eso methods can log you in automatically when needed.

Query the ESO archive for raw data

Identifying available instrument-specific queries

The direct retrieval of datasets is better explained with a running example, continuing from the authentication example above. The first thing to do is to identify the instrument to query. The list of available instrument-specific queries can be obtained with the list_instruments() method.

>>> from astroquery.eso import Eso
>>> eso = Eso()
>>> eso.list_instruments()
['fors1', 'fors2', 'sphere', 'vimos', 'omegacam', 'hawki', 'isaac', 'naco', 'visir',
 'vircam', 'apex', 'giraffe', 'uves', 'xshooter', 'espresso', 'muse', 'crires',
 'kmos', 'sinfoni', 'amber', 'gravity', 'matisse', 'midi', 'pionier', 'wlgsu']

In the example above, 22 instruments are available, they correspond to the instruments listed on the following web page: http://archive.eso.org/cms/eso-data/instrument-specific-query-forms.html.

Inspecting available query options

Once an instrument is chosen, midi in our case, the query options for that instrument can be inspected by setting the help=True keyword of the query_instrument() method.

>>> eso.query_instrument('midi', help=True)  
List of the column_filters parameters accepted by the midi instrument query.
The presence of a column in the result table can be controlled if prefixed with a [ ] checkbox.
The default columns in the result table are shown as already ticked: [x].

Target Information
------------------
    target:
    resolver: simbad (SIMBAD name), ned (NED name), none (OBJECT as specified by the observer)
    coord_sys: eq (Equatorial (FK5)), gal (Galactic)
    coord1:
    coord2:
    box:
    format: sexagesimal (Sexagesimal), decimal (Decimal)
[x] wdb_input_file:

Observation  and proposal parameters
------------------------------------
[ ] night:
    stime:
    starttime: 00 (00 hrs [UT]), 01 (01 hrs [UT]), 02 (02 hrs [UT]), 03 (03 hrs [UT]), 04 (04 hrs [UT]), 05 (05 hrs [UT]), 06 (06 hrs [UT]), 07 (07 hrs [UT]), 08 (08 hrs [UT]), 09 (09 hrs [UT]), 10 (10 hrs [UT]), 11 (11 hrs [UT]), 12 (12 hrs [UT]), 13 (13 hrs [UT]), 14 (14 hrs [UT]), 15 (15 hrs [UT]), 16 (16 hrs [UT]), 17 (17 hrs [UT]), 18 (18 hrs [UT]), 19 (19 hrs [UT]), 20 (20 hrs [UT]), 21 (21 hrs [UT]), 22 (22 hrs [UT]), 23 (23 hrs [UT]), 24 (24 hrs [UT])
    etime:
    endtime: 00 (00 hrs [UT]), 01 (01 hrs [UT]), 02 (02 hrs [UT]), 03 (03 hrs [UT]), 04 (04 hrs [UT]), 05 (05 hrs [UT]), 06 (06 hrs [UT]), 07 (07 hrs [UT]), 08 (08 hrs [UT]), 09 (09 hrs [UT]), 10 (10 hrs [UT]), 11 (11 hrs [UT]), 12 (12 hrs [UT]), 13 (13 hrs [UT]), 14 (14 hrs [UT]), 15 (15 hrs [UT]), 16 (16 hrs [UT]), 17 (17 hrs [UT]), 18 (18 hrs [UT]), 19 (19 hrs [UT]), 20 (20 hrs [UT]), 21 (21 hrs [UT]), 22 (22 hrs [UT]), 23 (23 hrs [UT]), 24 (24 hrs [UT])
[x] prog_id:
[ ] prog_type: % (Any), 0 (Normal), 1 (GTO), 2 (DDT), 3 (ToO), 4 (Large), 5 (Short), 6 (Calibration)
[ ] obs_mode: % (All modes), s (Service), v (Visitor)
[ ] pi_coi:
    pi_coi_name: PI_only (as PI only), none (as PI or CoI)
[ ] prog_title:
...

Only the first two sections, of the parameters accepted by the midi instrument query, are shown in the example above: Target Information and Observation and proposal parameters.

As stated at the beginning of the help message, the parameters accepted by the query are given just before the first : sign (e.g. target, resolver, stime, etime…). When a parameter is prefixed by [ ], the presence of the associated column in the query result can be controlled.

Note: the instrument query forms can be opened in your web browser directly using the open_form option of the query_instrument() method. This should also help with the identification of acceptable keywords.

Querying with constraints

It is now time to query the midi instrument for datasets. In the following example, observations of target NGC 4151 between 2007-01-01 and 2008-01-01 are searched, and the query is configured to return the observation date column.

>>> table = eso.query_instrument('midi', column_filters={'target':'NGC 4151',
...                                                      'stime':'2007-01-01',
...                                                      'etime':'2008-01-01'},
...                              columns=['night'])
>>> print(len(table))
38
>>> print(table.columns)
<TableColumns names=('Release Date','Object','RA','DEC','Target Ra Dec','Target l b','DATE OBS','ProgId','DP.ID','OB.ID','OBS.TARG.NAME','DPR.CATG','DPR.TYPE','DPR.TECH','INS.MODE','DIMM Seeing-avg')>
>>> table.pprint(max_width=100)
Release Date          Object             RA     ...       DPR.TECH       INS.MODE DIMM Seeing-avg
------------ ----------------------- ---------- ... -------------------- -------- ---------------
  2008-02-07                 NGC4151 182.635969 ...         IMAGE,WINDOW STARINTF     0.69 [0.01]
  2008-02-07                 NGC4151 182.635969 ...         IMAGE,WINDOW STARINTF     0.68 [0.01]
  2008-02-07                 NGC4151 182.635969 ...         IMAGE,WINDOW STARINTF     0.68 [0.01]
  2008-02-07                 NGC4151 182.635969 ...         IMAGE,WINDOW STARINTF     0.69 [0.01]
  2008-02-07                 NGC4151 182.635969 ...         IMAGE,WINDOW STARINTF     0.69 [0.01]
  2008-02-07                 NGC4151 182.635969 ...         IMAGE,WINDOW STARINTF     0.74 [0.01]
  2008-02-07                 NGC4151 182.635969 ...         IMAGE,WINDOW STARINTF     0.69 [0.01]
  2008-02-07                 NGC4151 182.635969 ...         IMAGE,WINDOW STARINTF     0.66 [0.01]
  2008-02-07                 NGC4151 182.635969 ...         IMAGE,WINDOW STARINTF     0.64 [0.01]
  2008-02-07                 NGC4151 182.635969 ...         IMAGE,WINDOW STARINTF     0.60 [0.01]
         ...                     ...        ... ...                  ...      ...             ...
  2007-02-07  TRACK,OBJECT,DISPERSED 182.635969 ...       INTERFEROMETRY STARINTF     0.72 [0.01]
  2007-02-07 SEARCH,OBJECT,DISPERSED 182.635969 ...       INTERFEROMETRY STARINTF     0.62 [0.01]
  2007-02-07 SEARCH,OBJECT,DISPERSED 182.635969 ...       INTERFEROMETRY STARINTF     0.61 [0.01]
  2007-02-07 SEARCH,OBJECT,DISPERSED 182.635969 ...       INTERFEROMETRY STARINTF     0.54 [0.01]
  2007-02-07 SEARCH,OBJECT,DISPERSED 182.635969 ...       INTERFEROMETRY STARINTF     0.53 [0.01]
  2007-02-07  TRACK,OBJECT,DISPERSED 182.635969 ...       INTERFEROMETRY STARINTF     0.51 [0.01]
  2007-02-07  TRACK,OBJECT,DISPERSED 182.635969 ...       INTERFEROMETRY STARINTF     0.51 [0.01]
  2007-02-07  TRACK,OBJECT,DISPERSED 182.635969 ...       INTERFEROMETRY STARINTF     0.51 [0.01]
  2007-02-07       PHOTOMETRY,OBJECT 182.635969 ... IMAGE,WINDOW,CHOPNOD STARINTF     0.54 [0.01]
  2007-02-07       PHOTOMETRY,OBJECT 182.635969 ... IMAGE,WINDOW,CHOPNOD STARINTF     0.54 [0.01]
Length = 38 rows

And indeed, 38 datasets are found, and the DATE OBS column is in the result table.

Querying all instruments

The ESO database can also be queried without a specific instrument in mind. This is what the method query_main() is for. The associated query form on the ESO archive website is http://archive.eso.org/wdb/wdb/eso/eso_archive_main/form. Except for the keyword specifying the instrument the behaviour of query_main() is identical to query_instrument().

ESO instruments without a specific query interface can be queried with query_main(), specifying the instrument constraint. This is the case of e.g. harps, feros or the all sky cameras APICAM and MASCOT. Here is an example to query all-sky images from APICAM with luminance filter.

>>> eso.ROW_LIMIT = -1   # Return all results
>>> table = eso.query_main(column_filters={'instrument': 'APICAM', 'filter_path': 'LUMINANCE',
...                                        'stime':'2019-04-26', 'etime':'2019-04-27'}, cache=False)
>>> print(len(table))
207
>>> print(table.columns)
<TableColumns names=('OBJECT','RA','DEC','Program_ID','Instrument','Category','Type','Mode','Dataset ID','Release_Date','TPL ID','TPL START','Exptime','Exposure','filter_lambda_min','filter_lambda_max','MJD-OBS','Airmass','DIMM Seeing at Start')>
>>> table.pprint(max_width=100)
 OBJECT      RA         DEC      Program_ID  ...   MJD-OBS    Airmass DIMM Seeing at Start
------- ----------- ----------- ------------ ... ------------ ------- --------------------
ALL SKY 09:18:37.39 -24:32:32.7 60.A-9008(A) ... 58599.987766     1.0                  N/A
ALL SKY 09:21:07.68 -24:32:30.1 60.A-9008(A) ... 58599.989502     1.0                  N/A
ALL SKY 09:23:38.98 -24:32:27.5 60.A-9008(A) ...  58599.99125     1.0                  N/A
ALL SKY 09:26:10.28 -24:32:24.9 60.A-9008(A) ... 58599.992998     1.0                  N/A
ALL SKY 09:28:40.58 -24:32:22.4 60.A-9008(A) ... 58599.994734     1.0                  N/A
ALL SKY 09:31:43.93 -24:32:19.4 60.A-9008(A) ... 58599.996852     1.0                  N/A
ALL SKY 09:34:15.23 -24:32:17.0 60.A-9008(A) ...   58599.9986     1.0                  N/A
ALL SKY 09:36:47.53 -24:32:14.5 60.A-9008(A) ... 58600.000359     1.0                  N/A
ALL SKY 09:39:18.82 -24:32:12.2 60.A-9008(A) ... 58600.002106     1.0                  N/A
ALL SKY 09:41:49.11 -24:32:09.9 60.A-9008(A) ... 58600.003843     1.0                  N/A
    ...         ...         ...          ... ...          ...     ...                  ...
ALL SKY 19:07:39.21 -24:39:35.1 60.A-9008(A) ... 58600.395914     1.0                  N/A
ALL SKY 19:10:11.68 -24:39:39.1 60.A-9008(A) ... 58600.397674     1.0                  N/A
ALL SKY 19:12:44.15 -24:39:43.2 60.A-9008(A) ... 58600.399433     1.0                  N/A
ALL SKY 19:15:15.62 -24:39:47.1 60.A-9008(A) ... 58600.401181     1.0                  N/A
ALL SKY 19:17:46.09 -24:39:51.1 60.A-9008(A) ... 58600.402917     1.0                  N/A
ALL SKY 19:20:46.65 -24:39:55.8 60.A-9008(A) ...    58600.405     1.0                  N/A
ALL SKY 19:23:18.12 -24:39:59.7 60.A-9008(A) ... 58600.406748     1.0                  N/A
ALL SKY 19:25:51.60 -24:40:03.7 60.A-9008(A) ... 58600.408519     1.0                  N/A
ALL SKY 19:28:22.08 -24:40:07.6 60.A-9008(A) ... 58600.410255     1.0                  N/A
ALL SKY 19:30:52.55 -24:40:11.4 60.A-9008(A) ... 58600.411991     1.0                  N/A
Length = 207 rows

Query the ESO archive for reduced data

In addition to raw data, ESO makes available processed data. In this section, we show how to obtain these processed survey data from the archive.

Identify available surveys

The list of available surveys can be obtained with astroquery.eso.EsoClass.list_surveys() as follows:

>>> surveys = eso.list_surveys()

Query a specific survey with constraints

Let’s assume that we work with the HARPS survey, and that we are interested in target HD203608. The archive can be queried as follows:

>>> table = eso.query_surveys('HARPS', cache=False, target="HD203608")

The returned table has an ARCFILE column. It can be used to retrieve the datasets with astroquery.eso.EsoClass.retrieve_data() (see next section).

Obtaining extended information on data products

Only a small subset of the keywords presents in the data products can be obtained with query_instrument() or query_main(). There is however a way to get the full primary header of the FITS data products, using get_headers(). This method is detailed in the example below.

>>> table = eso.query_instrument('midi', column_filters={'target':'NGC 4151',
...                                                      'stime':'2007-01-01',
...                                                      'etime':'2008-01-01'},
...                              columns=['night'])
>>> table_headers = eso.get_headers(table['DP.ID'])
>>> table_headers.pprint()  
           DP.ID             SIMPLE BITPIX ... HIERARCH ESO OCS TPL NFILE   HIERARCH ESO OCS EXPO1 FNAME3
---------------------------- ------ ------ ... -------------------------- ---------------------------------
MIDI.2007-02-07T07:01:51.000   True     16 ...                          0
MIDI.2007-02-07T07:02:49.000   True     16 ...                          0
MIDI.2007-02-07T07:03:30.695   True     16 ...                          0
MIDI.2007-02-07T07:05:47.000   True     16 ...                          0
MIDI.2007-02-07T07:06:28.695   True     16 ...                          0
MIDI.2007-02-07T07:09:03.000   True     16 ...                          0
MIDI.2007-02-07T07:09:44.695   True     16 ...                          0
MIDI.2007-02-07T07:13:09.000   True     16 ...                          0
MIDI.2007-02-07T07:13:50.695   True     16 ...                          0
MIDI.2007-02-07T07:15:55.000   True     16 ...                          0
                         ...    ...    ... ...                        ...                               ...
MIDI.2007-02-07T07:52:27.992   True     16 ...                          8 MIDI.2007-02-07T07:52:27.992.fits
MIDI.2007-02-07T07:56:21.000   True     16 ...                          0
MIDI.2007-02-07T07:57:35.485   True     16 ...                          0
MIDI.2007-02-07T07:59:46.000   True     16 ...                          0
MIDI.2007-02-07T08:01:00.486   True     16 ...                          0
MIDI.2007-02-07T08:03:42.000   True     16 ...                          8
MIDI.2007-02-07T08:04:56.506   True     16 ...                          8
MIDI.2007-02-07T08:06:11.013   True     16 ...                          8 MIDI.2007-02-07T08:06:11.013.fits
MIDI.2007-02-07T08:08:19.000   True     16 ...                          8 MIDI.2007-02-07T08:06:11.013.fits
MIDI.2007-02-07T08:09:33.506   True     16 ...                          8 MIDI.2007-02-07T08:06:11.013.fits
Length = 38 rows
>>> len(table_headers.columns)
340

As shown above, for each data product ID (DP.ID), the full header (570 columns in our case) of the archive FITS file is collected. In the above table table_headers, there are as many rows as in the column table['DP.ID'].

Downloading datasets from the archive

Continuing from the query with constraints example, the first two datasets are selected, using their data product IDs DP.ID (or ARCFILE for surveys), and retrieved from the ESO archive.

>>> data_files = eso.retrieve_data(table['DP.ID'][:2])
Staging request...
Downloading files...
Downloading MIDI.2007-02-07T07:01:51.000.fits.Z...
Downloading MIDI.2007-02-07T07:02:49.000.fits.Z...
Done!

The file names, returned in data_files, points to the decompressed datasets (without the .Z extension) that have been locally downloaded. They are ready to be used with fits.

The default location (in the astropy cache) of the decompressed datasets can be adjusted by providing a location keyword in the call to retrieve_data().

In all cases, if a requested dataset is already found, it is not downloaded again from the archive.

By default, calling eso.retrieve_data submits a new archive request through the web form to stage and download the requested datasets. If you would like to download datasets from an existing request, either submitted through the functions here or externally, call retrieve_data with the request_id option:

>>> data_files = eso.retrieve_data(table['DP.ID'][:2], request_id=999999)

The request_id can be found in the automatic email sent by the archive after staging the initial request, i.e., https://dataportal.eso.org/rh/requests/[USERNAME]/{request_id}. A summary of your available requests is shown at https://dataportal.eso.org/rh/requests/[USERNAME]/recentRequests.

Note: The function does check that the specified retrieval URL based on request_id is valid and then that the datasets indicated there are consistent with the user-specified datasets, but there is currently no reverse checking that the specified datasets are provided in request_id.

Reference/API

astroquery.eso Package

ESO service.

Classes

EsoClass()

Conf()

Configuration parameters for astroquery.eso.