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Jorge Fernandez HernandezJorge Fernandez Hernandez
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Revert "EUCLIDSWRQ-250 Fix problem in the documentation introduced in PR #3558"
This reverts commit 8397e08.
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docs/esa/euclid/euclid.rst

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@@ -329,8 +329,8 @@ and their sky coverage (in its "fov" field) is queried using ADQL_. Please note:
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>>> print("Downloading file:", file_name)
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Downloading file: EUC_MER_BGSUB-MOSAIC-VIS_TILE102158889-F95D3B_20241025T024806.508980Z_00.00.fits
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.. Skip testing as the example requires a lot of time to download a huge file
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.. doctest-skip::
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.. Skip testing as the example requires a lot of time to download a huge file
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>>> path = Euclid.get_product(file_name=file_name, output_file=file_name)
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@@ -430,8 +430,8 @@ This method...
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Download the cutout...
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.. Skip testing as the example requires a lot of time to download a huge file
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.. doctest-skip::
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>>> file_path = f"{res['file_path'][0]}/{res['file_name'][0]}"
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>>> cutout_out = Euclid.get_cutout(file_path=file_path, coordinate='NGC 6505', radius= 0.1 * u.arcmin, output_file='ngc6505_cutout_mer.fits')
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There are several ways to log in to the Euclid archive, as detailed below:
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.. doctest-skip::
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>>> from astroquery.esa.euclid import Euclid
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>>> Euclid.login_gui() # Login via graphic interface (pop-up window)
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All the asynchronous jobs launched by registered users are stored in the user area, which can store up to 10 GB of jobs. Therefore, it is recommended to remove unnecessary jobs to avoid filling up the user quota.
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The example below shows how to delete all the jobs in the user area using the list_async_jobs and remove_jobs_ methods.
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.. doctest-skip::
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>>> Euclid.login()
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>>> job_ids = [job.jobid for job in Euclid.list_async_jobs()]
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First, use the load_async_job_ method to download the metadata of the async jobs stored in the user space:
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>>> job_obj = [Euclid.load_async_job(jobid=jobid) for jobid in job_ids]
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>>> job_ids = [job.jobid for job in job_obj]
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>>> dates = [job.creationTime for job in job_obj]
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Second, create a dataframe that contains the jobid and date information:
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.. doctest-skip::
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>>> df = pd.DataFrame.from_dict({'job_id':job_ids, 'fulldate':dates})
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Finally, extract the job id's included in a given time range (in the example below, all the jobs stored since 2024-10-01 at 7 hours UTC) and delete them:
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>>> subset = df[(df['date'] == datetime.date(2024,10,1)) & (df['hour_UTC'].isin([7]))]
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>>> jobs_to_delete = subset['job_id'].to_list()

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