Adding openuniverse workshop requirements

.gitmodules

@@ -1,3 +0,0 @@
-[submodule "tutorials/simulated-data/OpenUniverse2024/fsps"]
-	path = tutorials/simulated-data/OpenUniverse2024/fsps
-	url = https://github.com/cconroy20/fsps.git

tox.ini

@@ -58,7 +58,7 @@ install_command =
     # Adding back the default install command; commented out version for clear cases, more complex one if we need to add more conditional skips
     # !oldestdeps: python -I -m pip install {opts} {packages}
     # fsps has issues installing on windows; dealing with it here
-    !oldestdeps: bash -c "if python -c 'import platform; print(platform.platform())' | grep -i win; then sed -i -e 's|fsps|\#fsps|g' tutorial_requirements.txt; elif [[ $CIRCLECI == true ]]; then sed -i -e 's|fsps|\#fsps|g' tutorial_requirements.txt; fi && python -I -m pip install $@"
+    !oldestdeps: bash -c "if python -c 'import platform; print(platform.platform())' | grep -i win; then sed -i -e 's|fsps|\#fsps|g' tutorial_requirements.txt; fi && python -I -m pip install $@"
 
 
 commands =

tutorial_requirements.txt

@@ -30,6 +30,8 @@ scikit-learn
 gdown
 astro-sedpy
 astro-prospector
+dynesty<2.0.0
+fsps
 seaborn
 regions
 # For supporting myst-based notebooks, lower pin as we need a bugfix from that release

tutorials/simulated-data/OpenUniverse2024/openuniverse2024_SED_fit.md

@@ -69,7 +69,7 @@ starttime = time.time()
 # %pip install numpy pandas h5py matplotlib seaborn pyarrow gdown
 # FSPS + Prospector + SEDPY+ dynasty are required for SED modeling
 # If you have trouble installing FSPS, you can skip SED modeling.
-# %pip install astro-prospector astro-sedpy "dynesty<2.0.0" fsps
+# %pip install astro-prospector astro-sedpy "dynesty<2.0.0" fsps pyarrow gdown
 ```
 
 ```{code-cell} ipython3
@@ -194,7 +194,7 @@ def assemble_SN_data(sn_flux_file, galaxy_flux_file, galaxy_info_file, *, region
     galaxy_info_file : str
         S3 path to the corresponding galaxy info parquet (physical parameters).
     region : str, optional
-        S3 region, default to 'us-east-1' where Fornax is located.        
+        S3 region, default to 'us-east-1' where Fornax is located.
 
     Returns
     -------
@@ -738,8 +738,8 @@ Choose this option if you:
 - do not need to change the fitting configuration
 ---
 
-Both paths produce the same variables — `df_small`, `obs_list`, and `outputs` — so all of the downstream analysis and plotting cells will work identically.  
-If you are following this notebook for the first time, start with **Option B** by leaving `RUN_FITS = FALSE`to get a sense of the workflow, and come back later to experiment with **Option A** when you are ready to explore the full fitting process.
+Both paths produce the same variables — `df_small`, `obs_list`, and `outputs` — so all of the downstream analysis and plotting cells will work identically.
+If you are following this notebook for the first time, start with **Option B** by leaving `RUN_FITS = False`to get a sense of the workflow, and come back later to experiment with **Option A** when you are ready to explore the full fitting process.
 
 ---
 
@@ -1200,40 +1200,51 @@ obs_list[0]
 +++
 
 :::{caution}
-This step runs Prospector on a small subset of galaxies and writes a new `sn_fits.h5` file.  It can take **30+ minutes** depending on machine and number of CPUs.  If you only want to explore results, **skip this step** and go to **3B** below.  If you are sure you want to run this section yourself, change the RUN_FITS variable below to "True"
+This step runs Prospector on a small subset of galaxies and writes a new `sn_fits.h5` file.
+
+It can take **30+ minutes** depending on machine and number of CPUs.
+If you only want to explore results, **skip this step** and go to **3B** below.
+
+If you are sure you want to run this section yourself, change the RUN_FITS variable "True".
 :::
 
-```{code-cell} ipython3
-# This notebook uses [FSPS](https://github.com/cconroy20/fsps) to fit SEDs.
-#FSPS requires installation which includes cloning that repo to get the data files.
-#This repo ([irsa-tutorials](https://github.com/Caltech-IPAC/irsa-tutorials/)) includes FSPS as a submodule to make things a little easier.
-#If you have cloned this repo, running the following cell will complete the setup.
-#If not, either clone this repo and then run the cell or else follow the full instructions at the FSPS link instead.
+#### FSPS installation and setup
 
-if RUN_FITS:
+This notebook uses [FSPS](https://github.com/cconroy20/fsps) to fit SEDs.
+You will need to download this repo and set the path correcly. The full size will a few GBs.
+The following cell will have some simple commands to do the downloading and basic setup. 
+Please uncomment these lines or otherwise follow the instructions on the FSPS link instead.
 
-    # Clone FSPS if not already available (only needed once).
-    # FSPS contains stellar population synthesis libraries used by Prospector.
-    # The first time this is run it will clone the FSPS repo and
-    # download more than 1 GB of code and data files.
-    # Idempotent unless there is a new FSPS commit in this repo (expect rarely).
-    !git submodule update --init
+Fornax users can ignore this cell altogether, both the data and the environment variable is set up for the IRSA environment. 
 
-    # Set the environment variable pointing to the cloned fsps directory.
-    from pathlib import Path
-    os.environ["SPS_HOME"] = f"{Path().cwd() / 'fsps'}"
+```{code-cell} ipython3
+#if RUN_FITS:
+#
+#    # Download the FSPS repository if not already available (only needed once).
+#    !git clone --depth=1 https://github.com/cconroy20/fsps
+#
+#    # Set the environment variable pointing to the cloned fsps directory.
+#    from pathlib import Path
+#    os.environ["SPS_HOME"] = f"{Path().cwd() / 'fsps'}"
+```
+
+Now that the the data is downloaded and path is properly set, do the import and verifications:
 
-    # Now import fsps cleanly
-    import fsps, prospect.sources.galaxy_basis as gb  #some hack required because of a bug in prospector
+```{code-cell} ipython3
+if RUN_FITS:
+    import fsps
+    import prospect.sources.galaxy_basis as gb
     gb.fsps = fsps  # inject fsps into the module namespace
 
-    #verify this setup worked
+    # verify this setup worked
     sp = fsps.StellarPopulation()
     print("Available FSPS libraries:", sp.libraries)
     from prospect.models import SpecModel
     from prospect.fitting import lnprobfn, fit_model
 ```
 
+#### Running the fits
+
 ```{code-cell} ipython3
 if RUN_FITS: