resample cellarea continue

docs/Project.toml

@@ -11,18 +11,23 @@ DocumenterVitepress = "4710194d-e776-4893-9690-8d956a29c365"
 GBIF2 = "dedd4f52-e074-43bf-924d-d6bce14ad628"
 GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
 GeoInterface = "cf35fbd7-0cd7-5166-be24-54bfbe79505f"
+GeoMakie = "db073c08-6b98-4ee5-b6a4-5efafb3259c6"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJGLMInterface = "caf8df21-4939-456d-ac9c-5fefbfb04c0c"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 Maxnet = "81f79f80-22f2-4e41-ab86-00c11cf0f26f"
 NCDatasets = "85f8d34a-cbdd-5861-8df4-14fed0d494ab"
+NaNStatistics = "b946abbf-3ea7-4610-9019-9858bfdeaf2d"
+NaturalEarth = "436b0209-26ab-4e65-94a9-6526d86fea76"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Proj = "c94c279d-25a6-4763-9509-64d165bea63e"
 RasterDataSources = "3cb90ccd-e1b6-4867-9617-4276c8b2ca36"
 Rasters = "a3a2b9e3-a471-40c9-b274-f788e487c689"
 Shapefile = "8e980c4a-a4fe-5da2-b3a7-4b4b0353a2f4"
 SpeciesDistributionModels = "3ef73bbf-0321-4d3b-9a2e-5fbebc8e35da"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [sources]
 SpeciesDistributionModels = {url = "https://github.com/tiemvanderdeure/SpeciesDistributionModels.jl/"}

docs/src/.vitepress/config.mts

@@ -18,19 +18,46 @@ const navTemp = {
     { text: 'Get Started', link: '/get_started' },
     { text: 'Manual',
       items: [
-        { text: 'Methods', link: '/manual/methods' },
-        { text: 'Array Operations', link: '/manual/array_operations' },
-        { text: 'Data Sources', link: '/manual/data_sources' },
-        { text: 'Plots',
+        { text: 'Data Sources', link: '/manual/data_sources' }, 
+        { text: 'Methods',
           items: [
-            { text: 'Plots.jl', link: '/manual/plotting' },
-            { text: 'Makie.jl', link: 'manual/plot_makie' },
+            { text: 'Overview', link: '/manual/methods' },
+            { text: 'rasterize', link: '/api#Rasters.rasterize' },
+            { text: 'extract', link: '/api#Rasters.extract' },
+            { text: 'zonal', link: '/api#Rasters.zonal-Tuple{Any,%20Union{AbstractRaster,%20AbstractRasterStack}}'},
+            { text: 'aggregate', link: '/api#Rasters.aggregate' },
+            { text: 'disaggregate', link: '/api#Rasters.disaggregate' },
+            { text: 'cellarea', link: '/manual/cellarea' },
+            { text: 'mosaic', link: '/api#Rasters.mosaic-Tuple{Function,%20Union{AbstractRaster,%20AbstractRasterStack},%20Vararg{Union{AbstractRaster,%20AbstractRasterStack}}}' },
+            { text: 'crop', link: '/api#Rasters.crop' },
+            { text: 'extend', link: '/api#Rasters.extend' },
+            { text: 'trim', link: '/api#Rasters.trim-Tuple{Union{AbstractRaster,%20AbstractRasterStack}}' },
+            { text: 'resample', link: '/api#Rasters.resample-Tuple' },
+            { text: 'warp', link: '/api#Rasters.warp-Tuple' },
+            { text: 'classify', link: '/api#Rasters.classify' },
+            { text: 'mask', link: '/api#Rasters.mask-Tuple{Any}' },
+            { text: 'replace_missing', link: '/api#Rasters.replace_missing-Tuple{Any}' },
+            { text: 'modify', link: '/api#DimensionalData.modify-Tuple{Any,%20AbstractRasterSeries}' },
+            { text: 'read', link: '/api#Base.read-Tuple{Union{AbstractRaster,%20AbstractRasterSeries,%20AbstractRasterStack}}' },
+            { text: 'read!', link: '/api#Base.read!-Tuple{AbstractRaster,%20AbstractArray}' },
+            { text: 'open', link: '/api#Base.open-Tuple{Function,%20AbstractRaster}' },
+            { text: 'write', link: '/api#Base.write-Tuple{AbstractString,%20AbstractRasterSeries}' },
           ]
-        },  
+         },
       ]
     },
     { text: 'Tutorials',
       items: [
+        { text: 'Plotting',
+          collapsed: true,
+          items: [
+            { text: 'Plots.jl', link: '/tutorials/plotting' },
+            { text: 'Makie.jl', link: '/tutorials/plot_makie' },
+          ]
+        }, 
+        { text: 'Array Operations', link: '/tutorials/array_operations' },
+        { text: 'Spatial mean', link: '/tutorials/spatial_mean' },
+        { text: 'Reprojection and resampling', link: '/tutorials/resample'},
         { text: 'Species Distribution Modelling', link: '/tutorials/gbif_wflow' },
       ]
     },
@@ -98,19 +125,47 @@ export default defineConfig({
       { text: 'Get Started', link: '/get_started' },
       { text: 'Manual',
         items: [
-          { text: 'Methods', link: '/manual/methods' },
-          { text: 'Array Operations', link: '/manual/array_operations' },
           { text: 'Data Sources', link: '/manual/data_sources' },
-          { text: 'Plots',
+          { text: 'Methods',
+            collapsed: true,
             items: [
-              { text: 'Plots.jl', link: '/manual/plotting' },
-              { text: 'Makie.jl', link: '/manual/plot_makie' },
+              { text: 'Overview', link: '/manual/methods' },
+              { text: 'rasterize', link: '/api#Rasters.rasterize' },
+              { text: 'extract', link: '/api#Rasters.extract' },
+              { text: 'zonal', link: '/api#Rasters.zonal-Tuple{Any,%20Union{AbstractRaster,%20AbstractRasterStack}}'},
+              { text: 'aggregate', link: '/api#Rasters.aggregate' },
+              { text: 'disaggregate', link: '/api#Rasters.disaggregate' },
+              { text: 'cellarea', link: '/manual/cellarea' },
+              { text: 'mosaic', link: '/api#Rasters.mosaic-Tuple{Function,%20Union{AbstractRaster,%20AbstractRasterStack},%20Vararg{Union{AbstractRaster,%20AbstractRasterStack}}}' },
+              { text: 'crop', link: '/api#Rasters.crop' },
+              { text: 'extend', link: '/api#Rasters.extend' },
+              { text: 'trim', link: '/api#Rasters.trim-Tuple{Union{AbstractRaster,%20AbstractRasterStack}}' },
+              { text: 'resample', link: '/api#Rasters.resample-Tuple' },
+              { text: 'warp', link: '/api#Rasters.warp-Tuple' },
+              { text: 'classify', link: '/api#Rasters.classify' },
+              { text: 'mask', link: '/api#Rasters.mask-Tuple{Any}' },
+              { text: 'replace_missing', link: '/api#Rasters.replace_missing-Tuple{Any}' },
+              { text: 'modify', link: '/api#DimensionalData.modify-Tuple{Any,%20AbstractRasterSeries}' },
+              { text: 'read', link: '/api#Base.read-Tuple{Union{AbstractRaster,%20AbstractRasterSeries,%20AbstractRasterStack}}' },
+              { text: 'read!', link: '/api#Base.read!-Tuple{AbstractRaster,%20AbstractArray}' },
+              { text: 'open', link: '/api#Base.open-Tuple{Function,%20AbstractRaster}' },
+              { text: 'write', link: '/api#Base.write-Tuple{AbstractString,%20AbstractRasterSeries}' },
             ]
-          },  
+           },
         ]
       },
       { text: 'Tutorials',
         items: [
+          { text: 'Plotting',
+            collapsed: true,
+            items: [
+              { text: 'Plots.jl', link: '/tutorials/plotting' },
+              { text: 'Makie.jl', link: '/tutorials/plot_makie' },
+            ]
+          },
+          { text: 'Array Operations', link: '/tutorials/array_operations' },
+          { text: 'Spatial mean', link: '/tutorials/spatial_mean' },
+          { text: 'Reprojection and resampling', link: '/tutorials/resample'},
           { text: 'Species Distribution Modelling', link: '/tutorials/gbif_wflow' },
         ]
       },

docs/src/manual/cellarea.md

@@ -0,0 +1,67 @@
+## `cellarea`
+
+```@meta
+CollapsedDocStrings=true
+```
+
+```@docs; canonical=false
+cellarea
+```
+
+Computing the area of each cell in a raster is useful for a number of reasons - if you have a variable like 
+population per cell, or elevation ([spatially extensive variables](https://r-spatial.org/book/05-Attributes.html#sec-extensiveintensive)),
+you'll want to account for the fact that different cells have different areas.
+
+Let's construct a raster and see what this looks like!  We'll keep it in memory.
+
+The spherical method relies on the [Proj.jl](https://github.com/JuliaGeo/Proj.jl) package to perform coordinate transformation, so that has to be loaded explicitly.
+
+````@example cellarea
+using Rasters, Proj
+````
+
+To construct a raster, we'll need to specify the `x` and `y` dimensions.  These are called `lookups` in `Rasters.jl.`
+
+````@example cellarea
+using Rasters.Lookups
+````
+
+We can now construct the x and y lookups.  Here we'll use a start-at-one, step-by-five grid.
+Note that we're specifying that the "sampling", i.e., what the coordinates actually mean, 
+is `Intervals(Start())`, meaning that the coordinates are the starting point of each interval.
+
+This is in contrast to `Points()` sampling, where each index in the raster represents the value at a sampling point;
+here, each index represents a grid cell, which is defined by the coordinate being at the start.
+
+````@example cellarea
+x = X(1:5:30; sampling = Intervals(Start()), crs = EPSG(4326))
+y = Y(50:5:80; sampling = Intervals(Start()), crs = EPSG(4326));
+nothing # hide
+````
+
+I have chosen the y-range here specifically so we can show the difference between spherical and planar `cellarea`.
+
+````@ansi cellarea
+ras = Raster(ones(x, y); crs = EPSG(4326))
+````
+
+We can just call `cellarea` on this raster, which returns cell areas in meters, on Earth, assuming it's a sphere:
+
+````@ansi cellarea
+cellarea(ras)
+````
+
+and if we plot it, you can see the difference in cell area as we go from the equator to the poles:
+
+````@example cellarea
+using CairoMakie
+heatmap(cellarea(ras); axis = (; aspect = DataAspect()))
+````
+
+We can also try this using the planar method, which simply computes the area of the rectangle using `area = x_side_length * y_side_length`:
+
+````@ansi cellarea
+cellarea(Planar(), ras)
+````
+
+Note that this is of course wildly inaccurate for a geographic dataset - but if you're working in a projected coordinate system, like polar stereographic or Mercator, this can be very useful (and a _lot_ faster)!

docs/src/manual/data_sources.md

@@ -43,10 +43,6 @@ They are always 3 dimensional, and have `Y`, `X` and [`Band`](@ref) dimensions.
 using Rasters
 ````
 
-````@docs
-smapseries
-````
-
 ## Writing file formats to disk
 
 Files can be written to disk with ArchGDAL.jl and NCDatasets.jl backends using

docs/src/manual/array_operations.md → docs/src/tutorials/array_operations.md

@@ -1,3 +1,5 @@
+# Array operations
+
 Most base methods work as for regular julia `Array`s, such as `reverse` and
 rotations like `rotl90`. Base, statistics and linear algebra methods like `mean`
 that take a `dims` argument can also use the dimension name. 

docs/src/tutorials/gbif_wflow.md

@@ -2,7 +2,7 @@
 
 This example shows a full Species distribution modelling workflow, from loading data, to cleaning it, to fitting an ensemble and generating predictions.
 
-It uses GBIF and WorldClim data, which are common datasets in ecology.
+It uses GBIF and WorldClim data, which are common datasets in ecology. We'll load occurrences for the Mountain Pygmy Possum species using [GBIF2.jl](https://github.com/rafaqz/GBIF2.jl), an interface to the [Global Biodiversity Information Facility](https://www.gbif.org/), and extract environmental variables using BioClim data from [RasterDataSources.jl](https://github.com/EcoJulia/RasterDataSources.jl).
 
 ## Load Rasters, ArchGDAL, RasterDataSources and GBIF
 The GBIF2 library is used to download occurrence data, RasterDataSources to conveniently access Bioclim data. ArchGDAL is necessary to load in the Bioclim data.