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added ignore output and used remote data more appropraitely
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docs/alma/alma.rst

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@@ -22,7 +22,6 @@ supports object and region based querying and data staging and retrieval.
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You can get interactive help to find out what keywords to query for:
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.. code-block:: python
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.. doctest-remote-data::
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>>> from astroquery.alma import Alma
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>>> Alma.help()
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<BLANKLINE>
@@ -92,7 +91,6 @@ Users can log in to acquire proprietary data products. Login is performed
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via the ALMA CAS (central authentication server).
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.. code-block:: python
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.. doctest-remote-data::
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>>> from astroquery.alma import Alma
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>>> alma = Alma()
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>>> # First example: TEST is not a valid username, it will fail
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'scientific_category', 'lastModified']
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Please note that some of the column names are duplicated. First group of names
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(the ones containing "_") are column names as they appear in the ALMA ObsCore
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model while the second group are copies created to maintain backwards
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compatibility with previous version of the library.
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Region queries are just like any other in astroquery:
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.. code-block:: python
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.. doctest-remote-data::
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>>> from astropy import coordinates
@@ -171,14 +162,14 @@ As of version 0.3.4, you can also query other fields by keyword. For example,
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if you want to find all projects with a particular PI, you could do:
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.. code-block:: python
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.. doctest-remote-data::
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>>> rslt = Alma.query_object('W51', pi_name='*Ginsburg*', public=False)
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>>> rslt = Alma.query_object('W51', pi_name='*Ginsburg*', public=False) # doctest: +REMOTE_DATA
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The ''query_sia'' method offers another way to query ALMA using the IVOA SIA
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subset of keywords returning results in 'ObsCore' format.
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.. code-block:: python
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.. doctest-remote-data::
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>>> # Alma.query_sia(pol='XX')
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Finally, the ''query_tap'' method is the most general way of querying the ALMA
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their types.
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.. code-block:: python
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.. doctest-remote-data::
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>>> Alma.help_tap()
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>>> Alma.help_tap() # doctest: +REMOTE_DATA
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Table to query is "voa.ObsCore".
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For example: "select top 1 * from ivoa.ObsCore"
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The scheme of the table is as follows.
@@ -277,12 +267,12 @@ cycle 1 data sets tend to be >100 GB!
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.. code-block:: python
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.. doctest-remote-data::
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>>> import numpy as np
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>>> uids = np.unique(m83_data['Member ous id'])
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>>> print(uids)
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Member ous id
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>>> uids = np.unique(m83_data['member_ous_uid'])
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>>> print(uids) # doctest: +IGNORE_OUTPUT
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member_ous_uid
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-----------------------
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uid://A002/X3216af/X31
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uid://A002/X5a9a13/X689
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uid://A001/X11f/X30
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uid://A001/X122/Xf3
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New to most recent versions of the library is that data does not need to be
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staged any longer. The ```stage_data``` method has been deprecated, but the
@@ -360,7 +350,8 @@ You might want to look at the READMEs from a bunch of files so you know what kin
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.. code-block:: python
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.. doctest-remote-data::
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>>> filelist = Alma.download_and_extract_files(uid_url_table['URL'], regex='.*README$')
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>>> filelist = Alma.download_and_extract_files(uid_url_table['URL'], regex='.*README$') # doctest: +IGNORE_OUTPUT
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Further Examples

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