ESA Integral Science Legacy Archive (ISLA) (astroquery.esa.integral)#

INTEGRAL is the INTernational Gamma-Ray Astrophysics Laboratory of the European Space Agency. It observes the Universe in the X-ray and soft gamma-ray band. Since its launch, on October 17, 2002, the ISDC receives the spacecraft telemetry within seconds and provides alerts, processed data and analysis software to the worldwide scientific community.

Examples#

1. ADQL Queries to ISLA TAP#

The Query TAP functionality facilitates the execution of custom Table Access Protocol (TAP) queries within the Integral Science Legacy Archive. Results can be exported to a specified file in the chosen format, and queries may be executed asynchronously.

>>> from astroquery.esa.integral import IntegralClass
>>> isla = IntegralClass()
>>> isla.query_tap(query='select * from ivoa.obscore')
<Table length=6743>
access_estsize   access_format                                     access_url                                   calib_level dataproduct_type         em_max        ... t_exptime    t_max       t_min    t_resolution t_xel
    int64            object                                          object                                        int32         object             float64        ...  float64    float64     float64     float64    int64
-------------- ----------------- ------------------------------------------------------------------------------ ----------- ---------------- --------------------- ... --------- ----------- ----------- ------------ -----
         26923 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9MDA2MDAwMjAwMDE=           2            image 1.241528257871965e-13 ...  115497.0 52594.80985 52593.47308      6.1e-05     1
        488697 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9MDA2MDAwNDAwMDE=           2            image 1.241528257871965e-13 ...   15526.0 52596.64583 52596.46613      6.1e-05     1
       3459831 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9MDA2MDAwNjAwMDI=           2            image 1.241528257871965e-13 ...   93525.0 52600.54167  52599.4592      6.1e-05     1
        829304 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9MDA2MDAwNjAwMDM=           2            image 1.241528257871965e-13 ...   32400.0 52601.52083 52601.14583      6.1e-05     1
       2690370 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9MDA2MDAwNjAwMDU=           2            image 1.241528257871965e-13 ...  103739.0 52603.65248 52602.45179      6.1e-05     1
           ...               ...                                                                            ...         ...              ...                   ... ...       ...         ...         ...          ...   ...
        680110 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9ODg4MDExMzAwMDE=           2            image 1.241528257871965e-13 ...   29700.0 57181.66231 57181.28125      6.1e-05     1
        802534 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9ODg4MDExMzAwMDI=           2            image 1.241528257871965e-13 ...   30600.0 57183.65356 57183.28074      6.1e-05     1
        861813 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9ODg4MDExMzAwMDM=           2            image 1.241528257871965e-13 ...   30600.0 57184.66102 57184.28819      6.1e-05     1
        351253 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9ODg4MDExMzAwMDQ=           2            image 1.241528257871965e-13 ...   30600.0 57188.63542 57188.28125      6.1e-05     1
         10195 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9ODg4MDExMzAwMDU=           2            image 1.241528257871965e-13 ...   30600.0 57189.64284 57189.28867      6.1e-05     1
        817730 application/x-tar https://isla.esac.esa.int/tap/data?retrieval_type=SCW&b2JzaWQ9ODg5OTgwMTAzMDE=           2            image 1.241528257871965e-13 ...   24200.0 52636.82597 52636.49534      6.1e-05     1

2. Getting sources#

Users can utilize this method to retrieve a target from the Archive by specifying a target name. The output can be formatted and saved as needed.

>>> from astroquery.esa.integral import IntegralClass
>>> isla = IntegralClass()
>>> isla.get_sources(target_name='crab')
<Table length=1>
 name          ra               dec           source_id
object      float64           float64           object
------ ----------------- ----------------- ----------------
  Crab 83.63320922851562 22.01447105407715 J053432.0+220052

3. Getting metadata associated to sources#

By invoking this method, users gain access to detailed metadata for a given source, identified by its target name. The metadata provides in-depth information about the source’s archival.

>>> from astroquery.esa.integral import IntegralClass
>>> isla = IntegralClass()
>>> metadata = isla.get_source_metadata(target_name='Crab')
>>> metadata
[{'name': 'Integral', 'link': None, 'metadata': {'columns': ['description', 'value'], 'rows': [{'description': 'Name', 'value': 'Crab'}, {'description': 'Id', 'value': 'J053432.0+220052'}, {'description': 'Coordinates degrees', 'value': '83.6324 22.0174'}, {'description': 'Coordinates', 'value': '05:34:31.78 22:01:02.64'}, {'description': 'Galactic', 'value': '184.55 -5.78'}, {'description': 'Isgri flag2', 'value': 'very bright source'}, {'description': 'Jemx flag', 'value': 'detected'}, {'description': 'Spi flag', 'value': 'detected'}, {'description': 'Picsit flag', 'value': 'detected'}]}}, {'name': 'Simbad', 'link': 'https://simbad.cds.unistra.fr/simbad/sim-id?Ident=Crab', 'metadata': {'columns': ['description', 'value'], 'rows': [{'description': 'Id', 'value': 'NAME Crab'}, {'description': 'Type', 'value': 'SuperNova Remnant'}, {'description': 'Other types', 'value': 'gam|HII|IR|Psr|Rad|SNR|X'}]}}, {'name': 'Publications', 'link': None, 'metadata': None}]

4. Retrieving observations from ISLA#

Observation data can be extracted using this method, defining a criteria such as target name, coordinates, search radius, time range, or revolution number range. The data can be formatted and saved to a file, with the option to perform the operation asynchronously.

>>> from astroquery.esa.integral import IntegralClass
>>> isla = IntegralClass()
>>> isla.get_observations(target_name='crab', radius=12.0, start_revno='0290', end_revno='0599')
<Table length=28>
   dec     end_revno       endtime       exposure    obsid    prop_id     ra     ... scw_number  scw_size       srcname       start_revno      starttime       surname                               title
 float64     object         object        int64      object     str7   float64   ...   int64      int64          str20           object          object         str20                                str120
---------- --------- ------------------- -------- ----------- ------- ---------- ... ---------- ---------- ------------------ ----------- ------------------- ---------- --------------------------------------------------------------
26.3157778      0352 2005-09-02 21:20:59   200032 03200490001 0320049  84.727375 ...        110 8865617044          A 0535+26        0352 2005-08-31 11:07:06 Kretschmar Target of Opportunity Observations of an Outburst in A 0535+26
21.5003972      0301 2005-04-01 15:16:17    55000 03998020003 0399802 90.9793537 ...         47 2220095363                GPS        0301 2005-03-31 21:33:56       ISWT                    Galactic Plane Survey for AO 03, Pattern 02
    22.684      0300 2005-03-30 22:42:38    41400 88600690001 8860069     94.424 ...         45 1714289129 10 deg around Crab        0300 2005-03-30 10:34:41       ISWT                                 Crab spring/05 IBIS 10 deg arc
    20.611      0300 2005-03-31 13:35:11    41400 88600690002 8860069     73.003 ...         45 1881430682 10 deg around Crab        0300 2005-03-30 22:44:07       ISWT                                 Crab spring/05 IBIS 10 deg arc
17.1577222      0300 2005-03-30 01:19:34    45000 88600710001 8860071 80.5439167 ...         49 1981344397  Crab SPI/IBIS 5x5        0300 2005-03-29 12:01:31       ISWT                                    Crab spring/05 SPI/IBIS 5*5
       ...       ...                 ...      ...         ...     ...        ... ...        ...        ...                ...         ...                 ...        ...                                                            ...
22.0144722      0483 2006-09-29 16:31:27    45000 88601140004 8860114 83.6332083 ...         48 2048814449  Crab cal IBIS/SPI        0483 2006-09-29 01:41:20     Public                                   Crab calibration autumn 2006
22.0144444      0541 2007-03-20 18:38:03    10000 88601230001 8860123 83.6329167 ...          3  460762160     Crab cal JEM-X        0541 2007-03-20 15:51:22     Public                       Crab-2007 Spring: JEM-X grey filter test
22.0144444      0541 2007-03-20 21:29:01     8500 88601240001 8860124 83.6329167 ...          3  392713076     Crab cal JEM-X        0541 2007-03-20 18:39:32     Public                    Crab-2007 Spring: JEM-X anode segments test
22.0144444      0541 2007-03-21 00:08:52     9500 88601250001 8860125 83.6329167 ...          3  418415105     Crab cal JEM-X        0541 2007-03-20 21:30:30     Public                       Crab-2007 Spring: JEM-X VC settings test
22.0144444      0541 2007-03-22 03:16:24    96041 88601260001 8860126 83.6329167 ...         26 4305461837      Crab cal IBIS        0541 2007-03-21 00:10:22     Public                         Crab-2007 Spring: IBIS on-axis staring
22.0144444      0541 2007-03-20 15:35:22    90000 88601270001 8860127 83.6329167 ...         99 4305389452  Crab cal IBIS/SPI        0541 2007-03-19 12:53:21     Public                              Crab-2007 Spring: SPI 5x5 pattern

5. Downloading Science Windows#

Science window data can be downloaded using this method by providing only one identifier, such as science window IDs, observation ID, revolution number, or proposal ID. An additional parameter, read_fits (default value True) reads automatically the downloaded FITS files.

  • If read_fits=True, a list of objects containing filename, path and the FITS file opened is returned.

  • If read_fits=False, the file name and path where the file has been downloaded is provided.

>>> from astroquery.esa.integral import IntegralClass
>>> isla = IntegralClass()
>>> results = isla.download_science_windows(science_windows=['008100430010', '033400230030'], output_file=None, read_fits=False)

6. Timeline retrieval#

This method enables the exploration of the observation timeline for a specific region in the sky. Users can provide right ascension (RA) and declination (Dec) coordinates and adjust the radius to refine their search.

>>> from astroquery.esa.integral import IntegralClass
>>> from astropy.coordinates import SkyCoord
>>> isla = IntegralClass()
>>> coordinates = SkyCoord(83.63320922851562, 22.01447105407715, unit="deg")
>>> timeline = isla.get_timeline(coordinates=coordinates)
>>> timeline
{'total_items': 8714, 'fraFC': 0.8510442965343126, 'totEffExpo': 16416293.994214607, 'timeline': <Table length=8714>
     scwExpo            scwRevs                scwTimes                scwOffAxis
     float64            float64                 object                  float64
------------------ ------------------ -------------------------- ---------------------
 5147.824025240096  39.07800595179441 2003-02-07 07:01:25.500000 0.0025430719729003476
 4212.920636876789  39.09690979681126 2003-02-07 08:21:21.500000 0.0025430719729003476
 4212.920651101999  39.11392562227784 2003-02-07 09:33:18.500000 0.0025430719729003476
               ...                ...                        ...                   ...
1675.7455103834102 2767.3106645083526 2024-04-17 13:39:13.500000     4.729406754053243
1815.2853971426027   2767.31963717696 2024-04-17 14:13:35.500000     4.203559877974954
  2014.34083657953 2767.3294779577823        2024-04-17 14:51:17     4.731196416616404}

7. Retrieving epochs#

A list of observation epochs can be retrieved using this method, focusing on periods when data for a specific target, instrument, or energy band is available.

>>> from astroquery.esa.integral import IntegralClass
>>> isla = IntegralClass()
>>> isla.get_epochs(target_name='J011705.1-732636', band='28_40')
<Table length=50>
     epoch
     object
----------------
0152_01200360001
0745_06340000001
0746_06340000001
             ...
1618_12200430006
1618_12200430009
1618_12200430011

This will perform an ADQL search to the Integral database and will return the output.

8. Data Download#

For each of the following features, an additional parameter, read_fits, is available.

  • If read_fits=True, a list of objects containing filename, path and the FITS file opened is returned.

  • If read_fits=False, the file names and paths where the files have been downloaded is provided.

8.1. Retrieving Long-Term Timeseries#

This method provides access to long-term timeseries data for a specified target. Users can refine their results by selecting an instrument or energy band.

>>> from astroquery.esa.integral import IntegralClass
>>> isla = IntegralClass()
>>> ltt = isla.get_long_term_timeseries(target_name='J174537.0-290107', instrument='jem-x', read_fits=False)

8.2. Retrieving Short-Term Timeseries#

This method allows for the download of short-term time series data for a target and epoch of interest. Users can refine their search using instrument or energy band filters and the results are saved to a file for detailed examination.

>>> from astroquery.esa.integral import IntegralClass
>>> isla = IntegralClass()
>>> stt = isla.get_short_term_timeseries(target_name='J011705.1-732636', band='28_40', epoch='0745_06340000001', read_fits=False)

8.3. Retrieving spectra#

This method allows users to download spectral data for a target and epoch. Users can apply filters, such as instrument or energy band, and save the results to an output file for further processing.

>>> from astroquery.esa.integral import IntegralClass
>>> isla = IntegralClass()
>>> spectra = isla.get_spectra(target_name='J011705.1-732636', instrument='ibis', epoch='0745_06340000001', read_fits=False)

8.4. Retrieving mosaics#

Mosaic images corresponding to a specified epoch can be downloaded using this method. Users can filter by instrument or energy band and save the resulting image to a file for later use.

>>> from astroquery.esa.integral import IntegralClass
>>> isla = IntegralClass()
>>> mosaics = isla.get_mosaic(epoch='0727_88601650001', instrument='ibis', read_fits=False)
Reference/API#
astroquery.esa.integral Package#

European Space Astronomy Centre (ESAC) European Space Agency (ESA)

Classes#

IntegralClass([auth_session, tap_url])

This module connects with ESA Integral TAP

Conf()

Configuration parameters for astroquery.esa.integral.