load one year by default

Author: troyraen

tutorials/parquet-catalog-demos/neowise-source-table-lightcurves.md

@@ -60,8 +60,8 @@ For example, a cone search radius of 1 arcsec will require about 10 CPUs, 65G RA
 50 minutes to load the data from all 10 NEOWISE years.
 Increasing the radius to 10 arcsec will return about 2.5x more rows using roughly the same resources.
 Increasing the target sample size can result in similar efficiency gains.
-To try out this notebook with fewer resources, use a subset of NEOWISE years.
-Using one year is expected to require about 5 CPUs, 20G RAM, and 10 minutes.
+For ease of use, the default in this notebook is to load only one NEOWISE year, which is
+expected to require about 5 CPUs, 20G RAM, and 10 minutes.
 These estimates are based on testing in science platform environments.
 Your numbers will vary based on many factors including compute power, bandwidth, and physical distance from the data.
 
@@ -104,11 +104,11 @@ Real use cases are likely to require all ten years but it can be helpful to star
 fewer while exploring to make things run faster.
 
 ```{code-cell} ipython3
-YEARS = list(range(1, 11))  # all years => about 11 CPU, 65G RAM, and 50 minutes runtime
+# Choose your own subset of years. By default, just year 10.
+YEARS = [10]  # one year => about 5 CPU, 20G RAM, and 10 minutes runtime
 
-# To try out a smaller version of the notebook,
-# uncomment the next line and choose your own subset of years.
-# YEARS = [10]  # one year => about 5 CPU, 20G RAM, and 10 minutes runtime
+# Uncomment the next line to use the full dataset, years 1-10.
+# YEARS = list(range(1, 11))  # all years => about 11 CPU, 65G RAM, and 50 minutes runtime
 ```
 
 ```{code-cell} ipython3