Merge pull request #1956 from tinumide/nrao-others-doc-cleanup

Nvas, Oac Documentation cleanup

Author: bsipocz

docs/nvas/nvas.rst

@@ -1,5 +1,3 @@
-.. doctest-skip-all
-
 .. _astroquery.nvas:
 
 ********************************
@@ -19,20 +17,15 @@ coordinates can also be entered directly as a string that conforms to the
 format specified by `astropy.coordinates`. Some other parameters you may
 optionally specify are the ``radius`` and the frequency band for which the image
 must be fetched. You can also specify the maximum allowable noise level in mJy
-via the ``max_rms`` keyword parameter. By default this is set to 10000 mJy 
+via the ``max_rms`` keyword parameter. By default this is set to 10000 mJy
+
+.. doctest-remote-data::
 
-.. code-block:: python
-    
     >>> from astroquery.nvas import Nvas
     >>> import astropy.units as u
-    >>> images = Nvas.get_images("3c 273", radius=2*u.arcsec, band="K", max_rms=500)
-
+    >>> images = Nvas.get_images("3c 273", radius=2*u.arcsec, band="K", max_rms=500)  # doctest: +IGNORE_WARNINGS
     1 images found.
-    Downloading http://www.vla.nvas.edu/astro/archive/pipeline/position/J122906.7+020308/22.4I0.37_TEST_1995NOV15_1_352.U55.6S.imfits
-    |===========================================|  10M/ 10M (100.00%)     19m37s
-
-    >>> images
-
+    >>> images      # doctest: +IGNORE_OUTPUT
     [[<astropy.io.fits.hdu.image.PrimaryHDU at 0x3376150>]]
 
 
@@ -47,16 +40,15 @@ the bands. Here's a list of the valid values that this parameter can take. ::
 Let's look at an example that uses coordinates for specifying the search
 centre.
 
-.. code-block:: python
+.. doctest-remote-data::
 
     >>> from astroquery.nvas import Nvas
     >>> import astropy.coordinates as coord
     >>> import astropy.units as u
     >>> images = Nvas.get_images(coord.SkyCoord(49.489, -0.37,
-    ...                          unit=(u.deg, u.deg), frame='galactic'),
-    ...                          band="K")
+    ...                          unit=(u.deg, u.deg), frame='galactic'), band="K")  # doctest: +IGNORE_WARNINGS
+    2 images found.
 
-                                 
 You may also fetch UVfits files rather than the IMfits files which is the
 default. To do this simply set the ``get_uvfits`` to ``True``, in any of the query
 methods. You can also fetch the URLs to the downloadable images rather than the
@@ -65,20 +57,15 @@ actual images themselves. To do this use the
 arguments as :meth:`~astroquery.nvas.NvasClass.get_images` above except for the
 ``verbose`` argument which isn't relevant in this case.
 
-.. code-block:: python
-  
+.. doctest-remote-data::
+
     >>> from astroquery.nvas import Nvas
     >>> import astropy.coordinates as coord
     >>> import astropy.units as u
     >>> image_urls = Nvas.get_image_list("05h34m31.94s 22d00m52.2s",
-    ...                                  radius='0d0m0.6s', max_rms=500)
-
-    WARNING: Coordinate string is being interpreted as an ICRS
-    coordinate. [astroquery.utils.commons]
-
+    ...                               radius='0d0m0.6s', max_rms=500) # doctest: +IGNORE_WARNINGS
     >>> image_urls
-    
-     ['http://www.vla.nrao.edu/astro/archive/pipeline/position/J053431.5+220114/1.51I4.12_T75_1986AUG12_1_118.U3.06M.imfits',
+    ['http://www.vla.nrao.edu/astro/archive/pipeline/position/J053431.5+220114/1.51I4.12_T75_1986AUG12_1_118.U3.06M.imfits',
      'http://www.vla.nrao.edu/astro/archive/pipeline/position/J053431.5+220114/1.51I3.92_T75_1986AUG20_1_373.U2.85M.imfits',
      'http://www.vla.nrao.edu/astro/archive/pipeline/position/J053431.5+220114/4.89I1.22_T75_1986AUG12_1_84.8U2.73M.imfits',
      'http://www.vla.nrao.edu/astro/archive/pipeline/position/J053431.9+220052/1.44I1.26_AH0336_1989FEB03_1_197.U8.29M.imfits',

docs/oac/oac.rst

@@ -1,5 +1,3 @@
-.. doctest-skip-all
-
 .. _astroquery.oac:
 
 **********************************
@@ -16,37 +14,38 @@ at the `OAC API Github Repository <https://github.com/astrocatalogs/OACAPI>`_
 
 Primary Methods
 ===============
+
 There are two primary methods for submitting API queries. The first is query_object
 which can be used to search the OAC based on an event name. Multiple events can
 be retrieved by submitting a list of event names.
 
 The default behavior returns a top-level list of all available metadata for the
 queried event(s).
 
-.. code-block:: python
+.. doctest-remote-data::
 
     >>> from astroquery.oac import OAC
     >>> metadata = OAC.query_object("GW170817")
 
 The query can be further refined by using the available QUANTITY and ATTRIBUTE
 options. For example, to retrieve the light curve for an object:
 
-.. code-block:: python
+.. doctest-remote-data::
 
     >>> photometry = OAC.query_object("GW170817", quantity="photometry",
-                                      attribute=["time", "magnitude",
-                                                 "e_magnitude", "band",
-                                                 "instrument"])
+    ...                               attribute=["time", "magnitude",
+    ...                                          "e_magnitude", "band",
+    ...                                          "instrument"])
 
 The results of a query can be further refined by using the ARGUMENT option
 
-.. code-block:: python
+.. doctest-remote-data::
 
     >>> photometry = OAC.query_object("GW170817", quantity="photometry",
-                                      attribute=["time", "magnitude",
-                                                 "e_magnitude", "band",
-                                                 "instrument"],
-                                      argument=["band=i"])
+    ...                               attribute=["time", "magnitude",
+    ...                                          "e_magnitude", "band",
+    ...                                          "instrument"],
+    ...                               argument=["band=i"])
 
 The second method available is query_region which performs a cone or box
 search for a given set of coordinates. Coordinates can be submitted as an Astropy
@@ -56,7 +55,7 @@ a single set of coordinates.
 
 For this example, we first establish coordinates and search parameters:
 
-.. code-block:: python
+.. doctest-remote-data::
 
     >>> import astropy.coordinates as coord
     >>> import astropy.units as u
@@ -71,25 +70,22 @@ For this example, we first establish coordinates and search parameters:
 
 An example cone search:
 
-.. code-block:: python
+.. doctest-remote-data::
 
-    >>> photometry = OAC.query_region(coordinates=test_coords,
-                                      radius=test_radius,
-                                      quantity="photometry",
-                                      attribute=["time", "magnitude",
-                                                 "e_magnitude", "band",
-                                                 "instrument"])
+    >>> photometry = OAC.query_region(coordinates=test_coords, radius=test_radius,
+    ...                               quantity="photometry",
+    ...                               attribute=["time", "magnitude",
+    ...                                          "e_magnitude", "band", "instrument"])
 
 An example box search:
 
-.. code-block:: python
+.. doctest-remote-data::
 
     >>> photometry = OAC.query_region(coordinates=test_coords,
-                                      width=test_width, height=test_height,
-                                      quantity="photometry",
-                                      attribute=["time", "magnitude",
-                                                 "e_magnitude", "band",
-                                                 "instrument"])
+    ...                               width=test_width, height=test_height,
+    ...                               quantity="photometry",
+    ...                               attribute=["time", "magnitude",
+    ...                                          "e_magnitude", "band", "instrument"])
 
 As with the query_object method, searches using query_region can be refined
 using the QUANTITY, ATTRIBUTE, and ARGUMENT options. The complete list of
@@ -101,13 +97,14 @@ data_format option.
 
 Tailored Methods
 ================
+
 There are three tailed search methods available to users to facilitate quick
 retrieval of common data products.
 
 The method get_photometry is designed to quickly return the photometry for a
 given event or list of events.
 
-.. code-block:: python
+.. doctest-remote-data::
 
     >>> from astroquery.oac import OAC
     >>> photometry = OAC.get_photometry("SN2014J")
@@ -117,7 +114,7 @@ query_object. More complex queries should use the base query_object method.
 
 For example, to retrieve only R-band photometry:
 
-.. code-block:: python
+.. doctest-remote-data::
 
     >>> photometry = OAC.get_photometry("SN2014J", argument="band=R")
 
@@ -127,7 +124,7 @@ The method get_single_spectrum is designed to retrieve a single spectrum for a
 single object at a specified time. The time should be given in MJD, but does
 not have to be exact. The query will return the spectrum that is closest in time.
 
-.. code-block:: python
+.. doctest-remote-data::
 
     >>> from astroquery.oac import OAC
     >>> test_time = 57740
@@ -138,7 +135,7 @@ The output is an Astropy table.
 The method get_spectra is designed to return all available spectra for an event
 or list of events.
 
-.. code-block:: python
+.. doctest-remote-data::
 
     >>> from astroquery.oac import OAC
     >>> spectra = OAC.get_spectra("SN2014J")