Some code simplification and notebook cleanup (empty cells, metadata, etc)

Author: bsipocz

tutorials/euclid_access/4_Euclid_intro_PHZ_catalog.md

@@ -26,7 +26,6 @@ By the end of this tutorial, you will:
 - Pull and plot a spectrum of one of the galaxies in that catalog.
 - Learn how to upload images and catalogs to Firefly to inspect individual sources in greater detail.
 
-
 +++
 
 ## Introduction
@@ -41,10 +40,6 @@ The photometry of every source is processed through a photometric redshift fitti
 
 +++
 
-### NOTE to testers -- please log in to IPAC vpn to access the IRSAdev data. After the Q1 data release, we will not need to use the IPAC VPN or the irsadev site.
-
-+++
-
 ## Imports
 
 ```{code-cell} ipython3
@@ -79,16 +74,14 @@ import pyvo as vo
 
 ## 1. Find the MER Tile ID that corresponds to a given RA and Dec
 
-In this case, choose random coordinates to show a different MER mosaic image. Search a radius around these coordinates. 
+In this case, choose random coordinates to show a different MER mosaic image. Search a radius around these coordinates.
 
 ```{code-cell} ipython3
-## User selected inputs:
-ra=268
-dec=66
-search_radius= 10*units.arcsec
+ra = 268
+dec = 66
+search_radius= 10 * u.arcsec
 
-pos = SkyCoord(ra=ra, dec=dec, unit='deg')
-coord = SkyCoord(ra, dec, unit=(units.deg,units.deg), frame='icrs')
+coord = SkyCoord(ra, dec, unit='deg', frame='icrs')
 ```
 
 ### Use IRSA to search for all Euclid data on this target
@@ -98,22 +91,18 @@ This searches specifically in the euclid_DpdMerBksMosaic "collection" which is t
 ```{code-cell} ipython3
 irsa_service= vo.dal.sia2.SIA2Service('https://irsadev.ipac.caltech.edu/SIA')
 
-im_table = irsa_service.search(pos=(pos.ra.deg, pos.dec.deg, search_radius),
-                                collection='euclid_DpdMerBksMosaic')
+image_table = irsa_service.search(pos=(coord, search_radius), collection='euclid_DpdMerBksMosaic').to_table()
+```
 
-## Convert the table to pandas dataframe
-df_im_irsa=im_table.to_table().to_pandas()
+```{note}
+This table lists all MER mosaic images available in this 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
-## Change the settings so we can see all the columns in the dataframe and the full column width 
-## (to see the full long URL)
-pd.set_option('display.max_columns', None)
-pd.set_option('display.max_colwidth', None)
+# Convert the table to pandas dataframe
+df_im_irsa=image_table.to_pandas()
 ```
 
-#### This table lists all MER mosaic images available in this 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 here: https://euclid.caltech.edu/page/euclid-faq-tech/
-
 ```{code-cell} ipython3
 df_im_euclid=df_im_irsa[ (df_im_irsa['dataproduct_subtype']=='science') &  (df_im_irsa['facility_name']=='Euclid')]
 
@@ -176,17 +165,7 @@ We note that the phz_catalog on IRSA has more columns than it does on the ESA ar
 
 The fluxes are different from the fluxes derived in the MER catalog. The _unif fluxes are: "Unified flux recomputed after correction from galactic extinction and filter shifts"
 
-```{code-cell} ipython3
-## Change the settings so we can see all the columns in the dataframe and the full column width 
-## (to see the full long URL)
-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')
-```
++++
 
 ## Find some galaxies between 1.4 and 1.6 at a selected RA and Dec
 
@@ -197,9 +176,7 @@ We specify the following conditions on our search:
 - Using the phz_90_int1 and phz_90_int2, we select just the galaxies where the error on the photometric redshift is less than 15%
 - Select just the galaxies between a median redshift of 1.4 and 1.6
 
-```{code-cell} ipython3
-
-```
++++
 
 ### Search based on tileID
 
@@ -232,12 +209,6 @@ df_g_irsa = result.to_table().to_pandas()
 df_g_irsa.head()
 ```
 
-
-
-```{code-cell} ipython3
-
-```
-
 ## 3. Read in the MER image from IRSA directly
 
 ```{code-cell} ipython3
@@ -260,10 +231,6 @@ print(filename_dev)
 #####################
 ```
 
-```{code-cell} ipython3
-
-```
-
 ```{code-cell} ipython3
 ##Download the MER image -- note this file is about 1.46 GB
 
@@ -295,8 +262,6 @@ plt.imshow(im_mer_irsa, cmap='gray', origin='lower',
 colorbar = plt.colorbar()
 ```
 
-
-
 ## 4. Overplot the catalog on the MER mosaic image
 
 ```{code-cell} ipython3
@@ -338,10 +303,6 @@ plt.title('Galaxies between z = 1.4 and 1.6')
 plt.show()
 ```
 
-```{code-cell} ipython3
-
-```
-
 ## Pull the spectra on the top brightest source based on object ID
 
 ```{code-cell} ipython3
@@ -397,21 +358,21 @@ plt.ylabel('Flux (erg / (Angstrom s cm2))')
 plt.title('Object ID is '+str(obj_id))
 ```
 
-## Lets cut out a very small patch of the MER image to see what this galaxy looks like 
+## Lets cut out a very small patch of the MER image to see what this galaxy looks like
 
 ```{code-cell} ipython3
 df_g_irsa[df_g_irsa['object_id']==obj_id]['ra']
 ```
 
 ```{code-cell} ipython3
 ## How large do you want the image cutout to be?
-im_cutout= 2.0 * units.arcsec
+im_cutout= 2.0 * u.arcsec
 
 ## Use the ra and dec of the galaxy
 ra = df_g_irsa[df_g_irsa['object_id']==obj_id]['ra'].iloc[0]
 dec =  df_g_irsa[df_g_irsa['object_id']==obj_id]['dec'].iloc[0]
 
-coords_cutout = SkyCoord(ra, dec, unit=(units.deg,units.deg), frame='icrs')
+coords_cutout = SkyCoord(ra, dec, unit=(u.deg,u.deg), frame='icrs')
 ```
 
 ### Use fsspec to download a cutout of the fits file
@@ -444,18 +405,22 @@ colorbar = plt.colorbar()
 
 +++
 
-### Save the data locally if you have not already done so, in order to upload to IRSA viewer
+Save the data locally if you have not already done so, in order to upload to IRSA viewer.
 
 ```{code-cell} ipython3
-# download_path = '/your_path/'
-# hdu_mer_irsa.writeto(download_path+'./MER_image_VIS.fits', overwrite=True)
+download_path = "data"
+if os.path.exists(download_path):
+    print("Output directory already created.")
+else:
+    print("Creating data directory.")
+    os.mkdir(dowload_path)
 ```
 
 ### Vizualize the image with Firefly
 
 First initialize the client, then set the path to the image, upload it to firefly, load it and align with WCS.
 
-Note this can take a while to upload the full MER image. 
+Note this can take a while to upload the full MER image.
 
 ```{code-cell} ipython3
 fc = FireflyClient.make_client('https://irsa.ipac.caltech.edu/irsaviewer')
@@ -468,8 +433,8 @@ fc.align_images(lock_match=True)
 ### Save the table as a CSV for Firefly upload
 
 ```{code-cell} ipython3
-# csv_path = download_path+'/mer_df.csv'
-# df_g_irsa.to_csv(csv_path, index=False)
+csv_path = os.path.join(download_path, "mer_df.csv")
+df_g_irsa.to_csv(csv_path, index=False)
 ```
 
 ### Upload the CSV table to Firefly and display as an overlay on the FITS image
@@ -481,64 +446,10 @@ print(f"Uploaded Table URL: {uploaded_table}")
 fc.show_table(uploaded_table)
 ```
 
-```{code-cell} ipython3
-
-```
-
-```{code-cell} ipython3
-
-```
-
-### Optional -- Access the data from the ESA archive website directly
-
-+++
-
-#### 1. Download and read in data from the ESA archive
-
-- Go to https://easidr.esac.esa.int/sas/ and sign in with your credentials.
-- Click Search and go to the ADQL form and enter a query which matches the query above.
-
-+++
-
-Download the results, gunzip in the command line. You should now have a VOT table
-
-NOTE: Need to do the following in the command line: 
-
-mv original_cat.vot.gz to mer_cat.vot 
-
-to remove the .gz extension
-
-+++
-
-## Additional Resources
-
-If you have any issues accessing data from the archives, please contact the helpdesk directly: IRSA ([email protected]) and ESA (https://support.cosmos.esa.int/euclid).
-
-+++
-
 ## About this Notebook
 
-**Author(s)**: Tiffany Meshkat <br>
-**Keyword(s)**: Euclid, Q1, phz catalog <br>
-**First published**: March 19, 2025 <br>
-**Last updated**: March 19, 2025
+**Author**: Tiffany Meshkat (IPAC Scientist)
 
-+++
-
-
-
-```{code-cell} ipython3
-
-```
-
-```{code-cell} ipython3
+**Updated**: 2025-03-19
 
-```
-
-```{code-cell} ipython3
-
-```
-
-```{code-cell} ipython3
-
-```
+**Contact:** [the IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or reporting problems.