Swapping some pyvo calls for astroquery for 1_euclid

Author: bsipocz

tutorials/euclid_access/1_Euclid_intro_MER_images.md

@@ -58,7 +58,7 @@ Each MER image is approximately 1.47 GB. Downloading can take some time.
 
 ```{code-cell} ipython3
 # Uncomment the next line to install dependencies if needed.
-# !pip install numpy 'astropy>=5.3' matplotlib pyvo 'sep>=1.4' fsspec pandas
+# !pip install numpy 'astropy>=5.3' matplotlib 'astroquery>=0.4.10' 'sep>=1.4' fsspec
 ```
 
 ```{code-cell} ipython3
@@ -78,7 +78,7 @@ from astropy.visualization import ImageNormalize, PercentileInterval, AsinhStret
 from astropy.wcs import WCS
 from astropy import units as u
 
-import pyvo as vo
+from astroquery.ipac.irsa import Irsa
 import sep
 
 # Copy-on-write is more performant and avoids unexpected modifications of the original DataFrame.
@@ -95,44 +95,23 @@ coord = SkyCoord.from_name('HD 168151')
 
 Use IRSA's Simple Image Access (SIA) API to search for all Euclid MER mosaics that overlap with the search region you have specified. We specify the euclid_DpdMerBksMosaic "collection" because it lists all of the multiwavelength MER mosaics, along with their associated catalogs.
 
-```{code-cell} ipython3
-irsa_service= vo.dal.sia2.SIA2Service('https://irsa.ipac.caltech.edu/SIA')
-
-image_table = irsa_service.search(pos=(coord, search_radius), collection='euclid_DpdMerBksMosaic')
-```
-
-Convert the table to pandas dataframe
-
-```{code-cell} ipython3
-df_im_irsa=image_table.to_table().to_pandas().reset_index()
-```
+```{tip}
+The IRSA SIA collections can be listed using using the ``list_collections`` method, we can filter on the ones containing "euclid" in the collection name:
 
-Change the settings so we can see all the columns in the dataframe and the full column width (to see the full long URL)
-
-```{code-cell} ipython3
-pd.set_option('display.max_columns', None)
-pd.set_option('display.max_colwidth', None)
-
-
-## Can use the following lines to reset the max columns and column width of pandas
-# pd.reset_option('display.max_columns')
-# pd.reset_option('display.max_colwidth')
+    Irsa.list_collections(filter='euclid')
 ```
 
 ```{code-cell} ipython3
-df_im_irsa
+image_table = Irsa.query_sia(pos=(coord, search_radius), collection='euclid_DpdMerBksMosaic')
 ```
 
-This dataframe contains lots of datasets that have been "Euclidized", which means that they have been put on a common pixel scale chosen for the Euclid mission. Choose "science" as the data product subtype to see all science images of this tile.
+This table lists all MER mosaic images available in this search position. These mosaics include the Euclid VIS, Y, J, H images, as well as ground-based telescopes which have been put on the same pixel scale. For more information, see the [Euclid documentation at IPAC](https://euclid.caltech.edu/page/euclid-faq-tech/).
 
-```{code-cell} ipython3
-df_im_euclid=df_im_irsa[ (df_im_irsa['dataproduct_subtype']=='science')].reset_index()
-
-df_im_euclid
-```
+Note that there are various image types are returned as well, we filter out the `science` images from these:
 
 ```{code-cell} ipython3
-print('There are',len(df_im_euclid),'MER images of this object/MER tile.')
+science_images = image_table[image_table['dataproduct_subtype'] == 'science']
+science_images
 ```
 
 ## 2. Retrieve a Euclid Q1 MER mosaic image in the VIS bandpass
@@ -144,8 +123,8 @@ print('There are',len(df_im_euclid),'MER images of this object/MER tile.')
 Note that 'access_estsize' is in units of kb
 
 ```{code-cell} ipython3
-filename = df_im_euclid[df_im_euclid['energy_bandpassname']=='VIS']['access_url'].to_list()[0]
-filesize = df_im_euclid[df_im_euclid['energy_bandpassname']=='VIS']['access_estsize'].to_list()[0]/1000000
+filename = science_images[science_images['energy_bandpassname']=='VIS']['access_url'][0]
+filesize = science_images[science_images['energy_bandpassname']=='VIS']['access_estsize'][0]/1000000
 
 print(filename)
 
@@ -216,7 +195,7 @@ We'd like to take a look at the multiwavelength images of our object, but the fu
 ```
 
 ```{code-cell} ipython3
-urls = df_im_euclid['access_url'].to_list()
+urls = science_images['access_url']
 
 urls
 ```