Remove tester notes and uniforming author info with the rest of the notebooks

bsipocz · bsipocz · commit 1ddc1d914596 · 2025-03-12T21:52:55.000-07:00
diff --git a/tutorials/euclid_access/1_Euclid_intro_MER_images.md b/tutorials/euclid_access/1_Euclid_intro_MER_images.md
@@ -26,7 +26,6 @@ By the end of this tutorial, you will:
 - Use matplotlib to plot a grid of cutouts.
 - Identify sources in the cutouts and make basic measurements.
 
-
 +++
 
 ## Introduction
@@ -41,13 +40,9 @@ MER mosaic images are all the images from Level 2 images in different filters ma
 
 +++
 
-### 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.
-
-+++
-
 ## Data volume
 
-Each MER image is approximately 1.47 GB. On Caltech wifi this takes between 1-5 mins to download. 
+Each MER image is approximately 1.47 GB. Downloading can take some time.
 
 +++
 
@@ -140,12 +135,12 @@ 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 = 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
 
 print(filename)
 
-print('Please note this image is',filesize,'GB. With 230 Mbps internet download speed, it takes about 1 minute to download.')
+print(f'Please note this image is {filesize} GB. With 230 Mbps internet download speed, it takes about 1 minute to download.')
 ```
 
 ### For future notebooks, extract the tileID of this image from the filename and extract the tileID
@@ -233,7 +228,7 @@ print(urls)
 #####################
 ```
 
-Create an array with the instrument and filter name so we can add this to the plots. 
+Create an array with the instrument and filter name so we can add this to the plots.
 
 ```{code-cell} ipython3
 df_im_euclid.loc[:, "filters"] = df_im_euclid["instrument_name"] + "_" + df_im_euclid["energy_bandpassname"]
@@ -294,12 +289,11 @@ for url in urls:
 ## Combine all cutouts into a single HDUList and display information
 final_hdulist = fits.HDUList(cutout_list)
 final_hdulist.info()
-
 ```
 
 ## 3. Visualize multiwavelength Euclid Q1 MER cutouts
 
-Need to determine the number of images for the grid layout, then we iterate through the images and plot each one. 
+Need to determine the number of images for the grid layout, then we iterate through the images and plot each one.
 
 ```{code-cell} ipython3
 num_images = len(final_hdulist)
@@ -418,18 +412,6 @@ for i in range(len(sources_thr)):
     ax.add_artist(e)
 ```
 
-```{code-cell} ipython3
-
-```
-
-```{code-cell} ipython3
-
-```
-
-## Exercise
-
-+++
-
 ### Optional -- Access the data from the ESA archive website directly
 
 +++
@@ -441,27 +423,10 @@ for i in range(len(sources_thr)):
 
 +++
 
-## Additional Resources
-
-If you have any issues accessing data from the archives, please contact the helpdesk directly: IRSA (irsasupport@ipac.caltech.edu) and ESA (https://support.cosmos.esa.int/euclid).
-
-+++
-
 ## About this Notebook
 
-**Author(s)**: Tiffany Meshkat <br>
-**Keyword(s)**: Euclid, Q1, MER mosaics <br>
-**First published**: March 19, 2025 <br>
-**Last updated**: March 19, 2025
-
-```{code-cell} ipython3
+**Author**: Tiffany Meshkat (IPAC Scientist)
 
-```
+**Updated**: March 19, 2025
 
-```{code-cell} ipython3
-
-```
-
-```{code-cell} ipython3
-
-```
+**Contact:** [the IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or reporting problems.
diff --git a/tutorials/euclid_access/2_Euclid_intro_MER_catalog.md b/tutorials/euclid_access/2_Euclid_intro_MER_catalog.md
@@ -39,10 +39,6 @@ Each entry in the MER catalog is a single source containing all its photometry f
 
 +++
 
-### 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
@@ -77,11 +73,7 @@ for tablename in tables.keys():
 ### Choose the Euclid MER table
 
 ```{code-cell} ipython3
-table_mer= 'euclid_q1_mer_catalogue'
-```
-
-```{code-cell} ipython3
-
+table_mer = 'euclid_q1_mer_catalogue'
 ```
 
 ### Learn some information about the table:
@@ -95,7 +87,7 @@ print(len(columns))
 
 ```{code-cell} ipython3
 for col in columns:
-    print(f'{f"{col.name}":30s}  {col.unit}  {col.description}') ## Currently no descriptions
+    print(f'{f"{col.name}":30s}  {col.unit}  {col.description}') 
 ```
 
 ### Define the following ADQL query to find the first 10k stars in the MER catalog
@@ -105,18 +97,16 @@ Since we are just using the MER catalog alone, it does not have a column for cla
 Set all the fluxes to be greater than 0 so the object is detected in all four Euclid MER mosaic images
 
 ```{code-cell} ipython3
-adql_stars = f"SELECT TOP 10000 mer.ra, mer.dec, mer.flux_vis_psf, mer.fluxerr_vis_psf, mer.flux_y_templfit,mer.fluxerr_y_templfit, \
-mer.flux_j_templfit, mer.fluxerr_j_templfit, mer.flux_h_templfit, mer.fluxerr_h_templfit, mer.point_like_prob, mer.extended_prob \
-FROM {table_mer} AS mer \
-WHERE  mer.flux_vis_psf > 0 \
-AND mer.flux_y_templfit > 0 \
-AND mer.flux_j_templfit > 0 \
-AND mer.flux_h_templfit > 0 \
-AND mer.point_like_flag = 1  \
-"
-
+adql_stars = ("SELECT TOP 10000 mer.ra, mer.dec, mer.flux_vis_psf, mer.fluxerr_vis_psf, mer.flux_y_templfit,mer.fluxerr_y_templfit, "
+    "mer.flux_j_templfit, mer.fluxerr_j_templfit, mer.flux_h_templfit, mer.fluxerr_h_templfit, mer.point_like_prob, mer.extended_prob "
+    f"FROM {table_mer} AS mer "
+    "WHERE  mer.flux_vis_psf > 0 "
+    "AND mer.flux_y_templfit > 0 "
+    "AND mer.flux_j_templfit > 0 "
+    "AND mer.flux_h_templfit > 0 "
+    "AND mer.point_like_flag = 1 ")
 
-## Run the query
+# Run the query
 
 result_stars = service.search(adql_stars)
 ```
@@ -152,7 +142,6 @@ plt.ylabel('Y')
 plt.xlim(-10,10)
 plt.ylim(10,35)
 plt.title('10k Stars in MER catalog -- IRSA')
-
 ```
 
 ## Exercise
@@ -249,27 +238,10 @@ mer.DEBLENDED_FLAG, mer.BLENDED_PROB, mer.BINARY_FLAG, mer. POINT_LIKE_FLAG, mer
 ____________________________
 ```
 
-```{code-cell} ipython3
-
-```
-
-```{code-cell} ipython3
-
-```
-
-## Additional Resources
-
-If you have any issues accessing data from the archives, please contact the helpdesk directly: IRSA (irsasupport@ipac.caltech.edu) and ESA (https://support.cosmos.esa.int/euclid).
-
-+++
-
 ## About this Notebook
 
-**Author(s)**: Tiffany Meshkat <br>
-**Keyword(s)**: Euclid, Q1, MER catalog <br>
-**First published**: March 19, 2025 <br>
-**Last updated**: March 19, 2025
+**Author**: Tiffany Meshkat (IPAC Scientist)
 
-```{code-cell} ipython3
+**Updated**: March 19, 2025
 
-```
+**Contact:** [the IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or reporting problems.
diff --git a/tutorials/euclid_access/3_Euclid_intro_1D_spectra.md b/tutorials/euclid_access/3_Euclid_intro_1D_spectra.md
@@ -43,10 +43,6 @@ This notebook provides an introduction to the SIR 1D spectra released as part of
 
 +++
 
-### 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
@@ -78,7 +74,6 @@ Search for all tables in IRSA labeled as euclid
 ```{code-cell} ipython3
 service = vo.dal.TAPService("https://irsadev.ipac.caltech.edu/TAP")
 
-
 tables = service.tables
 for tablename in tables.keys():
     if "tap_schema" not in tablename and "euclid" in tablename:
@@ -144,7 +139,6 @@ Currently IRSA has the spectra stored in very large files containing multiple (1
 
 # hdul = fits.open(BytesIO(response.content))  # Open FITS file from memory
 # hdul.info()  # Show file info
-
 ```
 
 ### Open the large FITS file without loading it entirely into memory, pulling out just the extension we want for the 1D spectra of our object
@@ -173,10 +167,6 @@ plt.ylabel('Flux'+dat['SIGNAL'].unit.to_string('latex_inline'))
 plt.title(obj_id)
 ```
 
-```{code-cell} ipython3
-
-```
-
 ## Exercise
 
 +++
@@ -198,31 +188,19 @@ ON phz_class.object_id=spec.source_id
 WHERE phz_class.phz_classification = 2
 ```
 
-
 - This shows the first 10 sources with spectra in the list.
 - Click the link/chain
 - Make sure "source_id" is selected under "IDs columns"
 - Click "show data" then click the download button next to "Spectra -- source_id"
 
 NOTE: You need to unzip the file to get a fits file with the combined spectra. On a mac you can do this by just double clicking the file to unzip.
 
-```{code-cell} ipython3
-
-```
-
-## Additional Resources
-
-If you have any issues accessing data from the archives, please contact the helpdesk directly: IRSA (irsasupport@ipac.caltech.edu) and ESA (https://support.cosmos.esa.int/euclid).
-
 +++
 
 ## About this Notebook
 
-**Author(s)**: Tiffany Meshkat <br>
-**Keyword(s)**: Euclid, Q1, 1D spectra <br>
-**First published**: March 19, 2025 <br>
-**Last updated**: March 19, 2025
+**Author**: Tiffany Meshkat (IPAC Scientist)
 
-```{code-cell} ipython3
+**Updated**: March 19, 2025
 
-```
+**Contact:** [the IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or reporting problems.
diff --git a/tutorials/euclid_access/5_Euclid_intro_SPE_catalog.md b/tutorials/euclid_access/5_Euclid_intro_SPE_catalog.md
@@ -37,9 +37,6 @@ These notebooks focus on how to access, download, and process Euclid Q1 data fro
 
 Every one dimensional spectrum is processed through a template and line fitting pipeline, producing several different 'SPE' catalogs. This notebook provides an introduction to the SPE catalogs released as part of Euclid Q1. Other Euclid notebooks show how to use other data products released as part of Euclid Q1.
 
-
-### 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
@@ -69,7 +66,7 @@ import pyvo as vo
 
 ## 1. Find the MER Tile ID that corresponds to a given RA and Dec
 
-In this case, choose the coordinates from the first notebook to save time downloading the MER mosaic. Search a radius of 1.5 arcminutes around these coordinates. 
+In this case, choose the coordinates from the first notebook to save time downloading the MER mosaic. Search a radius of 1.5 arcminutes around these coordinates.
 
 ```{code-cell} ipython3
 ra = 273.474451
@@ -121,14 +118,6 @@ tileID=re.search(r'TILE\s*(\d{9})', filename).group(1)
 print('The MER tile ID for this object is :',tileID)
 ```
 
-```{code-cell} ipython3
-
-```
-
-```{code-cell} ipython3
-
-```
-
 ## 2. Read in the MER image from IRSA directly
 
 ```{code-cell} ipython3
@@ -322,15 +311,10 @@ plt.ylabel('Flux (erg / (Angstrom s cm2))')
 plt.title(obj_id)
 ```
 
-## Additional Resources
-
-If you have any issues accessing data from the archives, please contact the helpdesk directly: IRSA (irsasupport@ipac.caltech.edu) and ESA (https://support.cosmos.esa.int/euclid).
+## About this Notebook
 
-+++
+**Author**: Tiffany Meshkat (IPAC Scientist)
 
-## About this Notebook
+**Updated**: March 19, 2025
 
-**Author(s)**: Tiffany Meshkat <br>
-**Keyword(s)**: Euclid, Q1, spe catalog <br>
-**First published**: March 19, 2025 <br>
-**Last updated**: March 19, 2025
+**Contact:** [the IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or reporting problems.
