Merge pull request #301 from nasa-fornax/add_runtime

Add runtime & explanatory text to light_curve_classifier.md

light_curves/light_curve_classifier.md

@@ -4,11 +4,11 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.15.2
+    jupytext_version: 1.16.0
 kernelspec:
-  display_name: Python 3 (ipykernel)
+  display_name: science_demo
   language: python
-  name: python3
+  name: conda-env-science_demo-py
 ---
 
 # Light Curve Classifier
@@ -63,6 +63,9 @@ Trained classifiers as well as estimates of their accuracy and plots of confusio
 - `scipy` for statistical analysis
 - `json` for storing intermediate files
 - `google_drive_downloader` to access files stored in google drive
+
+## Runtime
+As of 2024 August, this notebook takes ~170s to run to completion on Fornax using the 'Astrophysics Default Image' and the 'Large' server with 16GB RAM/ 4CPU. 
 
 ## Authors
 Jessica Krick, Shooby Hemmati, Troy Raen, Brigitta Sipocz, Andreas Faisst, Vandana Desai, Dave Shoop
@@ -117,20 +120,27 @@ pd.options.mode.copy_on_write = True
 ```
 
 ## 1. Read in a dataset of archival light curves
+ We use here a sample of AGN including known CLAGN & random SDSS AGN
+
+ If you want to use your own sample, you can use the code [light_curve_generator.md](https://nasa-fornax.github.io/fornax-demo-notebooks/light_curves/light_curve_generator.html) in this same repo to make the required pandas dataframe which you will need to run this notebook. 
 
 ```{code-cell} ipython3
-#access structure of light curves made in the light curve generator notebook
-# has known CLAGN & random SDSS small sample of targets, all bands
+# First we want to load light curves made in the light curve generator notebook
+
+# The data is on google drive, this will download it for you and read it into 
+# a pandas dataframe
 savename_df_lc = './data/small_CLAGN_SDSS_df_lc.parquet'
 gdd.download_file_from_google_drive(file_id='1DrB-CWdBBBYuO0WzNnMl5uQnnckL7MWH',
                                     dest_path=savename_df_lc,
                                     unzip=True)
 
+#load the data into a pandas dataframe
 df_lc = pd.read_parquet(savename_df_lc)
 ```
 
 ```{code-cell} ipython3
-#get rid of indices set in the light curve code and reset them as needed before sktime algorithms
+#get rid of indices set in the light curve code and reset them as needed 
+#before sktime algorithms
 df_lc = df_lc.reset_index()  
 
 #what does the dataset look like at the start?
@@ -151,8 +161,9 @@ is to remove the sparsest datasets.
 ##what are the unique set of bands included in our light curves
 df_lc.band.unique()
 
-# get rid of some of the bands that don't have enough data for all the sources
-#CLAGN are generall fainter targets, and therefore mostly not found in datasets like TESS & K2
+#get rid of some of the bands that don't have enough data for all the sources
+#CLAGN are generall fainter targets, and therefore mostly not found 
+#in datasets like TESS & K2
 
 bands_to_drop = ["IceCube", "TESS", "FERMIGTRIG", "K2"]
 df_lc = df_lc[~df_lc["band"].isin(bands_to_drop)]