Add spherex_cutouts and spherex_psf

deployed_notebooks_manifest.in

@@ -24,3 +24,5 @@ tutorials/parquet-catalog-demos/neowise-source-table-strategies.md
 tutorials/parquet-catalog-demos/wise-allwise-catalog-demo.md
 tutorials/roman_simulations/roman_hlss_number_density.md
 tutorials/spherex/spherex_intro.md
+tutorials/spherex/spherex_cutouts.md
+tutorials/spherex/spherex_psf.md

toc.yml

@@ -6,6 +6,8 @@ project:
     - title: SPHEREx
       children:
         - file: tutorials/spherex/spherex_intro.md
+        - file: tutorials/spherex/spherex_cutouts.md
+        - file: tutorials/spherex/spherex_psf.md
     - title: Euclid
       children:
         - title: Euclid Early Release Observations

tutorials/spherex/spherex_cutouts.md

@@ -0,0 +1,334 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.17.3
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+(spherex-cutouts)=
+# Download a collection of SPHEREx Spectral Image cutouts as a multi-extension FITS file
+
+## 1. Learning Goals
+
+- Perform a query for the list of SPHEREx Spectral Image Multi-Extension FITS files (MEFs) that overlap a given coordinate.
+- Retrieve cutouts for every entry in this list and package the cutouts as a new MEF.
+- Learn how to use parallel or serial processing to retrieve the cutouts.
+
+## 2. SPHEREx Overview
+
+SPHEREx is a NASA Astrophysics Medium Explorer mission that launched in March 2025.
+During its planned two-year mission, SPHEREx will obtain 0.75-5 micron spectroscopy over the entire sky, with deeper data in the SPHEREx Deep Fields.
+SPHEREx data will be used to:
+
+* **constrain the physics of inflation** by measuring its imprints on the three-dimensional large-scale distribution of matter,
+* **trace the history of galactic light production** through a deep multi-band measurement of large-scale clustering,
+* **investigate the abundance and composition of water and biogenic ices** in the early phases of star and planetary disk formation.
+
+The community will also mine SPHEREx data and combine it with synergistic data sets to address a variety of additional topics in astrophysics.
+
+More information is available in the [SPHEREx Explanatory Supplement](https://irsa.ipac.caltech.edu/data/SPHEREx/docs/SPHEREx_Expsupp_QR.pdf).
+
+## 3. Imports
+
+The following packages must be installed to run this notebook.
+
+```{code-cell} ipython3
+# Uncomment the next line to install dependencies if needed.
+# !pip install numpy astropy pyvo matplotlib
+```
+
+```{code-cell} ipython3
+import concurrent
+import time
+
+import astropy.units as u
+import matplotlib.pyplot as plt
+import numpy as np
+import pyvo
+from astropy.coordinates import SkyCoord
+from astropy.io import fits
+from astropy.table import Table
+from astropy.wcs import WCS
+
+# Suppress logging temporarily to prevent astropy
+# from repeatedly printing out warning notices related to alternate WCSs
+import logging
+logging.getLogger('astropy').setLevel(logging.ERROR)
+```
+
+## 4. Specify inputs and outputs
+
+Specify a right ascension, declination, cutout size, and a SPHEREx bandpass (e.g., 'SPHEREx-D2').
+
+In this example, we are creating cutouts of the Pinwheel galaxy (M101) for the SPHEREx detector D2.
+
+```{code-cell} ipython3
+# Choose a position.
+ra = 210.80227 * u.degree
+dec = 54.34895 * u.degree
+
+# Choose a cutout size.
+size = 0.1 * u.degree
+
+# Choose the bandpass of interest.
+bandpass = 'SPHEREx-D2'
+
+# Choose an output filename root for the output MEF file.
+output_filename = 'spherex_cutouts_mef.fits'
+```
+
+## 5. Query IRSA for a list of cutouts that satisfy the criteria specified above.
+
+Here we show how to use the `pyvo` TAP SQL query to retrieve all images that overlap with the position defined above.
+This query will retrieve a table of URLs that link to the MEF cutouts.
+Each row in the table corresponds to a single cutout and includes the data access URL and an observation timestamp.
+The results are sorted from oldest to newest.
+
+```{code-cell} ipython3
+# Define the service endpoint for IRSA's Table Access Protocol (TAP)
+# so that we can query SPHEREx metadata tables.
+service = pyvo.dal.TAPService("https://irsa.ipac.caltech.edu/TAP")
+
+# Define a query that will search the appropriate SPHEREx metadata tables
+# for spectral images that cover the chosen coordinates and match the
+# specified bandpass. Return the cutout data access URL and the time of observation.
+# 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,
+    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)
+        AND p.energy_bandpassname = '{bandpass}'
+ORDER BY p.time_bounds_lower
+"""
+
+# Execute the query and return as an astropy Table.
+t1 = time.time()
+results = service.search(query)
+print("Time to do TAP query: {:2.2f} seconds.".format(time.time() - t1))
+print("Number of images found: {}".format(len(results)))
+```
+
+## 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:
+
+- It downloads the cutout
+- It computes the wavelength of the center pixel of the cutout (in micro-meters)
+- It combines the image HDUs into a new HDU and adds it to the table row.
+
+Note that the values of the rows are being added in place.
+
+```{code-cell} ipython3
+def process_cutout(row, ra, dec, cache):
+    '''
+    Downloads the cutouts given in a row of the table including all SPHEREx images overlapping with a position.
+
+    Parameters:
+    ===========
+
+    row : astropy.table row
+        Row of a table that will be changed in place by this function. The table
+        is created by the SQL TAP query.
+    ra,dec : coordinates (astropy units)
+        Ra and Dec coordinates (same as used for the TAP query) with attached astropy units
+    cache : bool
+        If set to `True`, the output of cached and the cutout processing will run faster next time.
+        Turn this feature off by setting `cache = False`.
+    '''
+
+    with fits.open(row["uri"], cache=cache) as hdulist:
+        # There are seven HDUs:
+        # 0 contains minimal metadata in the header and no data.
+        # 1 through 6 are: IMAGE, FLAGS, VARIANCE, ZODI, PSF, WCS-WAVE
+        header = hdulist[1].header
+
+        # Compute pixel coordinates corresponding to cutout position.
+        spatial_wcs = WCS(header)
+        x, y = spatial_wcs.world_to_pixel(SkyCoord(ra=ra, dec=dec, unit="deg", frame="icrs"))
+
+        # 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)
+        row["central_wavelength"] = wavelength.to(u.micrometer).value
+
+        # Collect the HDUs for this cutout and append the row's cutout_index to the EXTNAME.
+        hdus = []
+        for hdu in hdulist[1:]:  # skip the primary header
+            hdu.header["EXTNAME"] = f"{hdu.header['EXTNAME']}{row['cutout_index']}"
+            hdus.append(hdu.copy())  # Copy so the data is available after the file is closed
+        row["hdus"] = hdus
+```
+
+## 7. Download the Cutouts
+
+This process can take a while.
+If run in series, it can take about 5 minutes for 700 images on a typical laptop machine.
+Here, we therefore exploit two different methods.
+First we show the serial approach and next we show how to parallelize the methods.
+The latter can be run on many CPUs and is therefore significantly faster.
+
+### 7.1 Serial Approach
+
+First, we implement the serial approach -- a simple `for` loop.
+Before that, we turn the results into an astropy table and add some place holders that will be filled in by the `process_cutout()` function.
+
+```{warning}
+Running the cell below may take a while for a large number of cutouts.
+Approximately 5-7 minutes for 700 images of cutout size 0.01 degree on a typical machine.
+```
+
+```{tip}
+The astropy `fits.open()` supports a caching argument.
+This can be passed through in the `process_cutout()` function.
+If `cache=True` is set, the images are cached and the cutout creation is sped up next time the code is run (even if the Jupyter kernel is restarted!).
+The downside is that the images are saved on the machine where this notebook is run (usually in `~/.astropy/cache/`).
+If many cutouts are created, this can sum up to a large cached data volume, in which case `cache=False` is preferred.
+
+To learn more about the cache please read the [astropy cache management documentation](https://docs.astropy.org/en/stable/utils/data.html#cache-management).
+```
+
+```{code-cell} ipython3
+results_table_serial = results.to_table()
+results_table_serial["cutout_index"] = range(1, len(results_table_serial) + 1)
+results_table_serial["central_wavelength"] = np.full(len(results_table_serial), np.nan)
+results_table_serial["hdus"] = np.full(len(results_table_serial), None)
+
+t1 = time.time()
+for row in results_table_serial:
+    process_cutout(row, ra, dec, cache=False)
+print("Time to create cutouts in serial mode: {:2.2f} minutes.".format((time.time() - t1) / 60))
+```
+
+### 7.2 Parallel Approach
+
+Next, we implement parallel processing, which will make the cutout creation faster.
+The maximal number of workers can be limited by setting the `max_workers` argument.
+The choice of this value depends on the number of cores but also on the number of parallel calls that can be digested by the IRSA server.
+
+```{tip}
+A good value for the maximum number of workers is between 7 and 12 for a machine with 8 cores.
+```
+
+```{tip}
+The astropy `fits.open()` supports a caching argument.
+This can be passed through in the `process_cutout()` function.
+If `cache=True` is set, the images are cached and the cutout creation is sped up next time the code is run (even if the Jupyter kernel is restarted!).
+The downside is that the images are saved on the machine where this notebook is run (usually in `~/.astropy/cache/`).
+If many cutouts are created, this can sum up to a large cached data volume, in which case `cache=False` is preferred.
+
+To learn more about the cache please read the [astropy cache management documentation](https://docs.astropy.org/en/stable/utils/data.html#cache-management).
+```
+
+Again, before running the cutout processing we define some place holders.
+
+```{code-cell} ipython3
+results_table_parallel = results.to_table()
+results_table_parallel["cutout_index"] = range(1, len(results_table_parallel) + 1)
+results_table_parallel["central_wavelength"] = np.full(len(results_table_parallel), np.nan)
+results_table_parallel["hdus"] = np.full(len(results_table_parallel), None)
+
+t1 = time.time()
+with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
+    futures = [executor.submit(process_cutout, row, ra, dec, False) for row in results_table_parallel]
+    concurrent.futures.wait(futures)
+print("Time to create cutouts in parallel mode: {:2.2f} minutes.".format((time.time() - t1) / 60))
+```
+
+## 8. Create a summary table HDU with renamed columns
+
+In the following, we continue to use the output of the parallel mode.
+The following cell does the following:
+
+- Create a summary FITS table.
+- Create the final FITS HDU including the summary table.
+
+```{code-cell} ipython3
+# Create a summary table HDU with renamed columns
+cols = fits.ColDefs([
+    fits.Column(name="cutout_index", format="J", array=results_table_parallel["cutout_index"], unit=""),
+    fits.Column(name="observation_date", format="D", array=results_table_parallel["time_bounds_lower"], unit="d"),
+    fits.Column(name="central_wavelength", format="D", array=results_table_parallel["central_wavelength"], unit="um"),
+    fits.Column(name="access_url", format="A200", array=results_table_parallel["uri"], unit=""),
+])
+table_hdu = fits.BinTableHDU.from_columns(cols)
+table_hdu.header["EXTNAME"] = "CUTOUT_INFO"
+```
+
+## 9. Create the final MEF
+
+Now, we create a primary HDU and combine it with the summary table HDU and the cutout image and wavelength HDUs to a final HDU list.
+
+```{code-cell} ipython3
+primary_hdu = fits.PrimaryHDU()
+hdulist_list = [primary_hdu, table_hdu]
+hdulist_list.extend(hdu for fits_hdulist in results_table_parallel["hdus"] for hdu in fits_hdulist)
+combined_hdulist = fits.HDUList(hdulist_list)
+```
+
+## 10. Write the final MEF
+
+Finally, we save the full HDU list to a multi-extension FITS file to disk.
+
+```{code-cell} ipython3
+# Write the final MEF
+combined_hdulist.writeto(output_filename, overwrite=True)
+```
+
+## 11. Test and visualize the final result
+
+We can now open the new MEF FITS file that we have created above.
+
+Loading the summary table (including wavelength information of each cutout) is straight forward:
+
+```{code-cell} ipython3
+summary_table = Table.read(output_filename , hdu=1)
+```
+
+Let's also extract the first 10 images (or all of the extracted cutouts if less than 10).
+
+```{code-cell} ipython3
+nbr_images = np.nanmin([10, len(summary_table)])
+imgs = []
+with fits.open(output_filename) as hdul:
+    for ii in range(nbr_images):
+        extname = "IMAGE{}".format(summary_table["cutout_index"][ii])
+        imgs.append(hdul[extname].data)
+```
+
+Plot the images with the wavelength corresponding to their central pixel.
+
+```{code-cell} ipython3
+fig = plt.figure(figsize=(15, 5))
+axs = [fig.add_subplot(2, 5, ii + 1) for ii in range(10)]
+
+for ii, img in enumerate(imgs):
+    axs[ii].imshow(imgs[ii], norm="log", origin="lower")
+    axs[ii].text(0.05, 0.05, r"$\lambda_{\rm center} = %2.4g \,{\rm \mu m}$" % summary_table["central_wavelength"][ii],
+                 va="bottom", ha="left", color="white", transform=axs[ii].transAxes)
+
+plt.show()
+```
+
+## Acknowledgements
+
+- [Caltech/IPAC-IRSA](https://irsa.ipac.caltech.edu/)
+
+## About this notebook
+
+**Authors:** IRSA Data Science Team, including Vandana Desai, Andreas Faisst, Troy Raen, Brigitta Sipőcz, Jessica Krick, Shoubaneh Hemmati
+
+**Updated:** 2025-09-30
+
+**Contact:** [IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or problems.
+
+**Runtime:** As of the date above, this notebook takes about 3 minutes to run to completion on a machine with 8GB RAM and 4 CPU.

tutorials/spherex/spherex_intro.md

@@ -12,6 +12,7 @@ kernelspec:
   name: python3
 ---
 
+(spherex-intro)=
 # Introduction to SPHEREx Spectral Images
 
 +++