diff --git a/wsim-gldas-vis.qmd b/wsim-gldas-vis.qmd
index 6b34644..1f9866a 100644
--- a/wsim-gldas-vis.qmd
+++ b/wsim-gldas-vis.qmd
@@ -196,8 +196,13 @@ df_wsim_gldas_1mo <-as.data.frame(wsim_gldas_1mo, xy = TRUE)
 
 ggplot2::ggplot(data = df_wsim_gldas_1mo) +
   ggplot2::geom_raster(ggplot2::aes(x = x, y = y, fill = deficit)) +
-  ggplot2::scale_color_gradientn(
-  colors = c('darkred', 'red', 'darkorange', 'orange', 'gold', 'yellow', 'white')) + 
+  #ggplot2::scale_color_gradientn(
+  #colors = c('darkred', 'red', 'darkorange', 'orange', 'gold', 'yellow', 'white')) +
+  scale_fill_viridis_c() +
+  theme_void() +
+  theme(
+    legend.position = "bottom"
+  )
   ggplot2::facet_wrap(time ~ .)
   
 
@@ -219,11 +224,10 @@ Visualize an individual cell with particular extreme or maybe volatile values. U
 Create a vector with the point location.
 
 ```{r}
-# coordinates for Austin, Texas
+# vector of coordinates for Austin, Texas
 coords <- c(-97.7431, 30.2672)
 
-# create a stars object with point coordinates where
-# st_sfc(), st_points(), st_crs(),st_dimensions() 
+# create a stars object with point coordinates where 'sf::st_sfc()' is used to create a geometry list column and add a coordinate reference system. 'sf::st_points()' is within that function to turn the coords vector into a point. 'stars:: st_crs()' and 'stars::st_dimensions()'  respectively retrieves the coordinate reference system and the dimensions of wsim_gldas_1mo to ensure that the final values in point_stars is the same reference system and shape. 
 point_stars <- sf::st_sfc(st_point(coords), crs = stars::st_crs(wsim_gldas_1mo),
                       dim = names(stars::st_dimensions(wsim_gldas_1mo)))