Improve raster example

examples/raster_warping_masking.jl

@@ -4,6 +4,12 @@ Description = "Semi involved example showing raster warping and masking, and plo
 Cover = fig
 ```
 
+#=
+
+In this example, we want to show how you can create manipulate rasters for the purpose of visualization.
+
+=#
+
 using CairoMakie, GeoMakie
 using Rasters, ArchGDAL,NaturalEarth, FileIO
 
@@ -25,25 +31,37 @@ blue_marble_raster = Raster(
 )
 # Construct a mask of land using Natural Earth land polygons
 land_mask = Rasters.boolmask(NaturalEarth.naturalearth("land", 10); to = blue_marble_raster)
-#
+# Now, create a new raster as a copy of the background image, 
+# and set the values of all sea areas to be transparent.
 land_raster = deepcopy(blue_marble_raster)
 land_raster[(!).(land_mask)] .= RGBA{Makie.Colors.N0f8}(0.0, 0.0, 0.0, 0.0)
 land_raster
-
+# Do the same for the sea.
 sea_raster = deepcopy(blue_marble_raster)
 sea_raster[land_mask] .= RGBA{Makie.Colors.N0f8}(0.0, 0.0, 0.0, 0.0)
 sea_raster
 
 # ## Constructing fake data
 
-# Now, we construct fake data.
+# Now, we construct fake data.  
+# First, we create a matrix of values.
 field = [exp(cosd(x)) + 3(y/90) for x in -180:180, y in -90:90]
-
+# Then, we wrap it in a Raster.  This is now gridded data, along the specified `X` and `Y` dimensions.
+# Note the `missingval = NaN` keyword argument.  This is used to indicate that if the raster does have missing values,
+# they should be represented as `NaN`, and not 0, for example.
 ras = Raster(field, (X(LinRange(-30, 30, size(field, 1))), Y(LinRange(-5, 5, size(field, 2)))); missingval = NaN)
 
 # We rotate the raster and then translate it to approximate the position in the image.
 rotmat = Makie.rotmatrix2d(-π/5)
 
+# We warp the raster by the parameters below.  This is in total basically
+# rotating the raster by the rotation matrix `rotmat`, and then translating it 
+# by 30 units in the x direction and -36 in the y direction.
+# 
+# This is where the `missingval` keyword argument comes in.  If we didn't have it,
+# the values that are now `NaN` because they are outside the original raster would be filled by `0`, which is not what we want.
+#
+# Rasters.warp is pretty much a wrapper around [GDAL's `gdalwarp` program](https://gdal.org/en/latest/programs/gdalwarp.html), so all of the options are available.  It's incredibly versatile and flexible but is a bit tricky to use.
 transformed = Rasters.warp(
     ras,
     Dict(
@@ -58,14 +76,15 @@ transformed = Rasters.warp(
 )
 
 # ## Plotting the data
-
-fig = Figure(size = (1000, 1000))
+# We use an orthographic GeoAxis to show the data.
+# First, we create the figure and the axis,
+fig = Figure()
 ax = GeoAxis(fig[1, 1]; dest = "+proj=ortho +lon_0=0 +lat_0=0")
-
+# Then, we plot the sea and land rasters.  These are our background images.
 sea_plot = meshimage!(ax, sea_raster; source = "+proj=longlat +datum=WGS84 +type=crs", npoints = 300)
 
 land_plot = meshimage!(ax, land_raster; source = "+proj=longlat +datum=WGS84 +type=crs", npoints = 300)
-
+# Finally, we plot the transformed data.
 data_plot = surface!(ax, transformed; shading = NoShading)
 # Get the land plot above all other plots in the geoaxis.
 translate!(land_plot, 0, 0, 1)
@@ -74,13 +93,15 @@ fig
 
 # For extra credit, we'll find the center of the raster and transform the orthographic projection to be centered on it:
 
+using Statistics
 xy_matrix = Rasters.DimensionalData.DimPoints(transformed) |> collect
 center_x = mean(first.(xy_matrix))
 center_y = mean(last.(xy_matrix))
 
 # We'll use the `ortho` projection, which is centered on the point we just found.
-ax.dest = "+proj=ortho +lon_0=$(center_x) +lat_0=$(center_y)"
-
+ax.dest[] = "+proj=ortho +lon_0=$(center_x) +lat_0=$(center_y)"
+# Somehow, the limits are set to the wrong thing, so we'll set them manually and let the axis take care of it.
+ax.limits[] = (-180, 180, -90, 90)
 fig
 
 # ## Plotting on the 3D globe
@@ -90,20 +111,20 @@ fig
 using Geodesy
 
 # fig = Figure(size = (1000, 1000));
-fig = Figure()
+fig2 = Figure()
 
-ax = LScene(fig[1, 1])
+ax = LScene(fig2[1, 1])
 
 sea_plot = meshimage!(ax, sea_raster; npoints = 300)
 
 land_plot = meshimage!(ax, land_raster; npoints = 300)
 
 data_plot = surface!(ax, transformed; shading = NoShading)
 
-land_plot.z_level[] = 1000
+land_plot.z_level[] = 1000 # this means raising land by 1 km over the sea!
 
 sea_plot.transformation.transform_func[] = Geodesy.ECEFfromLLA(Geodesy.WGS84())
 land_plot.transformation.transform_func[] = Geodesy.ECEFfromLLA(Geodesy.WGS84())
 data_plot.transformation.transform_func[] = Geodesy.ECEFfromLLA(Geodesy.WGS84())
 
-fig
+fig2