Update spherex_qr -> spherex_qr2

tutorials/spherex/spherex_cutouts.md

@@ -44,7 +44,7 @@ The following packages must be installed to run this notebook.
 ```
 
 ```{code-cell} ipython3
-import concurrent
+import concurrent.futures
 import time
 
 import astropy.units as u
@@ -101,11 +101,11 @@ service = pyvo.dal.TAPService("https://irsa.ipac.caltech.edu/TAP")
 # Sort by observation time.
 query = f"""
 SELECT
-    'https://irsa.ipac.caltech.edu/' || a.uri || '?center={ra.to(u.degree).value},{dec.to(u.degree).value}d&size={size.to(u.degree).value}' AS uri,
+    'https://irsa.ipac.caltech.edu/' || a.uri || '?center={ra.value},{dec.value}d&size={size.value}' AS uri,
     p.time_bounds_lower
 FROM spherex.artifact a
 JOIN spherex.plane p ON a.planeid = p.planeid
-WHERE 1 = CONTAINS(POINT('ICRS', {ra.to(u.degree).value}, {dec.to(u.degree).value}), p.poly)
+WHERE 1 = CONTAINS(POINT('ICRS', {ra.value}, {dec.value}), p.poly)
         AND p.energy_bandpassname = '{bandpass}'
 ORDER BY p.time_bounds_lower
 """
@@ -117,6 +117,11 @@ print("Time to do TAP query: {:2.2f} seconds.".format(time.time() - t1))
 print("Number of images found: {}".format(len(results)))
 ```
 
+:::{note}
+SPHEREx data are also available via SIA which can provide a simpler interface for many queries, as demonstrated in {ref}`spherex-intro`.
+An advantage of the method shown above is that it provides access to data immediately after ingestion (which occurs weekly) and is not subject to the same ~1 day delay as SIA.
+:::
+
 ## 6. Define a function that processes a list of SPHEREx Spectral Image Cutouts
 
 This function takes in a row of the catalog that we created above and does the following:
@@ -158,7 +163,7 @@ def process_cutout(row, ra, dec, cache):
         # Compute wavelength at cutout position.
         spectral_wcs = WCS(header, fobj=hdulist, key="W")
         spectral_wcs.sip = None
-        wavelength, bandpass = spectral_wcs.pixel_to_world(x, y)
+        wavelength, bandwidth = spectral_wcs.pixel_to_world(x, y)
         row["central_wavelength"] = wavelength.to(u.micrometer).value
 
         # Collect the HDUs for this cutout and append the row's cutout_index to the EXTNAME.
@@ -327,7 +332,7 @@ plt.show()
 
 **Authors:** IRSA Data Science Team, including Vandana Desai, Andreas Faisst, Troy Raen, Brigitta Sipőcz, Jessica Krick, Shoubaneh Hemmati
 
-**Updated:** 2025-09-30
+**Updated:** 24 October 2025
 
 **Contact:** [IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or problems.
 

tutorials/spherex/spherex_intro.md

@@ -41,10 +41,11 @@ More information is available in the [SPHEREx Explanatory Supplement](https://ir
 +++
 
 ## 3. Requirements
-The following packages must be installed to run this notebook. Comment out the following lines if they are already installed.
+The following packages must be installed to run this notebook.
 
 ```{code-cell} ipython3
-# pip install numpy matplotlib astropy astroquery firefly-client
+# Uncomment the next line to install dependencies if needed.
+# %pip install numpy matplotlib astropy astroquery firefly-client
 ```
 
 ## 4. Imports
@@ -91,16 +92,22 @@ The IRSA SIA collections can be listed using using the ``list_collections`` meth
 +++
 
 The collections are documented at [SPHEREx Data Access: Application Program Interfaces (APIs)](https://caltech-ipac.github.io/spherex-archive-documentation/spherex-data-access#application-program-interfaces-apis)
-There are currently three collections available:
+There are currently three collections available for the second Quick Release:
 
-* `'spherex_qr'` -- Quick Release Spectral Image MEFs that are part of the SPHEREx **Wide Survey**
-* `'spherex_qr_cal'` -- Quick Release **Calibration files**
-* `'spherex_qr_deep'` -- Quick Release Spectral Image MEFs that are part of the SPHEREx **Deep Survey**
+* `'spherex_qr2'` -- Quick Release 2 Spectral Image MEFs that are part of the SPHEREx **Wide Survey**
+* `'spherex_qr2_cal'` -- Quick Release 2 **Calibration files**
+* `'spherex_qr2_deep'` -- Quick Release 2 Spectral Image MEFs that are part of the SPHEREx **Deep Survey**
 
 ```{code-cell} ipython3
-results = Irsa.query_sia(pos=(coord, search_radius), collection='spherex_qr')
+results = Irsa.query_sia(pos=(coord, search_radius), collection='spherex_qr2')
 ```
 
+:::{note}
+SPHEREx data are ingested on a weekly basis.
+Due to the nature of the ingestion process, availability via SIA will lag on the order of a day.
+To avoid this delay, users can access data through the browsable directories or the SPHEREx Data Explorer GUI (see [SPHEREx Data Access](https://caltech-ipac.github.io/spherex-archive-documentation/spherex-data-access)), or do a TAP query as shown in {ref}`spherex-cutouts`.
+:::
+
 Each row of the results of your query represents a different spectral image.
 Because SPHEREx data will be released on a weekly basis, the number of rows returned will change
 depending on when you submit the query.
@@ -168,7 +175,7 @@ fc.reinit_viewer()
 Visualize a spectral image MEF by sending its URL to the viewer.
 
 ```{code-cell} ipython3
-fc.show_fits(url=spectral_image_url,
+fc.show_fits_image(file_input=spectral_image_url,
              plot_id="spectral_image",
              Title="Spectral Image"
              )
@@ -219,7 +226,7 @@ The main WCS describes the astrometric registration of the image, including opti
 
 There are two alternative WCS systems:
 - WCSNAMEA describes zero-based pixel coordinates.
-- WCSNAMEW describes spectral coordinates 'Wavelength' and 'Bandpass'. This WCS contains a reference to the lookup table in the 'WCS-WAVE' extension.
+- WCSNAMEW describes spectral coordinates 'Wavelength' and 'Bandwidth'. This WCS contains a reference to the lookup table in the 'WCS-WAVE' extension.
 
 +++
 
@@ -260,7 +267,7 @@ Note: The previous line triggers an Astropy INFO printout,
 which implies that the SIP distortion coefficients from the main WCS are preserved in the alternative WCS.
 This is because the SIP convention, not formally part of the FITS standard,
 is ambiguous as to whether it is meant to apply to 'alternative' (lettered) WCSes in addition to the primary WCS.
-See [astropy/astropy#13105](https://github.com/astropy/astropy/issues/13105).)
+See [astropy/astropy#13105](https://github.com/astropy/astropy/issues/13105).
 
 The wavelength per pixel is a property of the detector-filter combination and is independent of optical distortion in the telescope,
 and is modeled accordingly in WCS 'W', so we turn the SIP distortion off for this WCS.
@@ -272,9 +279,9 @@ spectral_wcs.sip = None
 
 ```{code-cell} ipython3
 # The standard Astropy methods for converting pixel coordinates to world coordinates can also be used to obtain spectral coordinates.
-# Take the pixel coordinates that we determined for the image center and resolve them to the wavelength and bandpass for that pixel
-wl, bp = spectral_wcs.pixel_to_world(x, y)
-wl, bp
+# Take the pixel coordinates that we determined for the image center and resolve them to the wavelength and bandwidth for that pixel
+wl, bw = spectral_wcs.pixel_to_world(x, y)
+wl, bw
 ```
 
 ### 8c. How does wavelength vary across the detector?
@@ -290,10 +297,10 @@ spectral_image_data = spectral_image.data
 # Use the spectral WCS to convert these pixel coordinates to spectral coordinates.
 spectral_coords = spectral_wcs.pixel_to_world(x, y)
 
-# Break out the two spectral coordinates (wavelength and bandpass) from the spectral coordinates, and print the results.
-wavelength, bandpass = spectral_coords
+# Break out the two spectral coordinates (wavelength and bandwidth) from the spectral coordinates, and print the results.
+wavelength, bandwidth = spectral_coords
 print("Wavelength: \n", wavelength)
-print("Bandpass: \n", bandpass)
+print("Bandwidth: \n", bandwidth)
 ```
 
 ```{code-cell} ipython3
@@ -436,6 +443,6 @@ Andreas Faisst, Shoubaneh Hemmati, Vandana Desai
 **Contact:** [IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions
 or problems.
 
-**Updated:** June 2025
+**Updated:** 24 October 2025
 
 **Runtime:** approximately 30 seconds

tutorials/spherex/spherex_psf.md

@@ -70,7 +70,7 @@ Both should be defined using `astropy` units.
 The goal is to obtain the cutout and then extract the PSF corresponding to the coordinates of interest.
 
 ```{tip}
-To learn more about how to access SPHEREx spectral images and how to download cutouts, we refer to the [SPHEREx Intro Tutorial](spherex-intro) and the [SPHEREx Cutouts Tutorial](spherex-cutouts).
+To learn more about how to access SPHEREx spectral images and how to download cutouts, we refer to the [SPHEREx Intro Tutorial](#spherex-intro) and the [SPHEREx Cutouts Tutorial](#spherex-cutouts).
 ```
 
 ```{code-cell} ipython3
@@ -92,11 +92,11 @@ service = pyvo.dal.TAPService("https://irsa.ipac.caltech.edu/TAP")
 # Sort by observation time.
 query = f"""
 SELECT
-    'https://irsa.ipac.caltech.edu/' || a.uri || '?center={ra.to(u.degree).value},{dec.to(u.degree).value}d&size={size.to(u.degree).value}' AS uri,
+    'https://irsa.ipac.caltech.edu/' || a.uri || '?center={ra.value},{dec.value}d&size={size.value}' AS uri,
     p.time_bounds_lower
 FROM spherex.artifact a
 JOIN spherex.plane p ON a.planeid = p.planeid
-WHERE 1 = CONTAINS(POINT('ICRS', {ra.to(u.degree).value}, {dec.to(u.degree).value}), p.poly)
+WHERE 1 = CONTAINS(POINT('ICRS', {ra.value}, {dec.value}), p.poly)
 ORDER BY p.time_bounds_lower
 """
 
@@ -107,6 +107,11 @@ print("Time to do TAP query: {:2.2f} seconds.".format(time.time() - t1))
 print("Number of images found: {}".format(len(results)))
 ```
 
+:::{note}
+SPHEREx data are also available via SIA which can provide a simpler interface for many queries, as demonstrated in {ref}`spherex-intro`.
+An advantage of the method shown above is that it provides access to data immediately after ingestion (which occurs weekly) and is not subject to the same ~1 day delay as SIA.
+:::
+
 For this example, we focus on the first one of the retrieved SPHEREx spectral images.
 
 ```{code-cell} ipython3
@@ -176,7 +181,7 @@ We do this by first determining the pixel (x,y) coordinates of our coordinates o
 
 ```{code-cell} ipython3
 wcs = WCS(cutout_header)
-xpix_cutout, ypix_cutout = wcs.world_to_pixel(SkyCoord(ra=ra.to(u.degree), dec=dec.to(u.degree)))
+xpix_cutout, ypix_cutout = wcs.world_to_pixel(SkyCoord(ra=ra, dec=dec))
 
 print(f"Pixel values of coordinates of interest on cutout image: x = {xpix_cutout}, y = {ypix_cutout}")
 ```
@@ -269,7 +274,7 @@ plt.show()
 
 **Authors:** IRSA Data Science Team, including Vandana Desai, Andreas Faisst, Brigitta Sipőcz, Troy Raen
 
-**Updated:** 2025-09-30
+**Updated:** 24 October 2025
 
 **Contact:** Contact [IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or problems.