Add instructions to reproduce the book locally

This also sets the paths in the notebooks to move one folder up, so that the output and data folder are project wise. This can be removed once execute-dir works.

README.md

@@ -3,3 +3,27 @@
 # Geocomputation with Julia
 
 [![Render](https://github.com/geocompx/geocompjl/actions/workflows/main.yaml/badge.svg)](https://github.com/geocompx/geocompjl/actions/workflows/main.yaml)
+
+Geocomputation with Julia is an open source book project. We are developing it in the open and publishing an up-to-date online version at https://jl.geocompx.org/.
+Geocomputation with Julia is part of the [geocompx](https://geocompx.org/) series providing geocomputation resources in different languages.
+
+## Reproducing the book locally
+
+To run the code that is part of the Geocomputation with Julia book requires the following dependencies:
+
+1. Julia: To install julia on your machine we recommend to use juliaup which can be installed follwing these [installation instructions](https://julialang.org/downloads/)
+For now we need to restrict to julia 1.10 because quarto 1.5 does not work with julia 1.11
+To restrict the julia version for this project folder run
+```
+juliaup override set 1.10
+```
+
+2. [Quarto](https://quarto.org/docs/get-started/), which is used to
+    render the book. This needs quarto 1.5.30 or higher
+3. Julia Dependencies:
+    To install the julia dependencies run the following in the main folder of this project:
+    ```
+    julia --project -e "using Pkg; Pkg.instantiate()"
+    ```
+
+

chapters/01-spatial-data.qmd

@@ -1,7 +1,5 @@
 ---
 engine: julia
-project:
-  execute-dir: project
 ---
 
 # Geographic data in Julia {#sec-spatial-class}
@@ -20,7 +18,7 @@ mkpath("output")
 ## Introduction
 ```{julia}
 using GeoDataFrames
-df = GeoDataFrames.read("data/world.gpkg")
+df = GeoDataFrames.read("../data/world.gpkg")
 ```
 
 ```{julia}
@@ -130,15 +128,15 @@ For now, for completeness, and also to use these rasters in subsequent chapters
 In the case of `elev`, we do it as follows with the `Rasters.write` functions and methods of the **Rasters.jl** package.
 
 ```{julia}
-write("output/elev.tif", elev_raster; force = true)
+write("../output/elev.tif", elev_raster; force = true)
 ```
 
 Note that the CRS we (arbitrarily) set for the `elev` raster is WGS84, defined using `crs=4326` according to the EPSG code.
 
 Exporting the `grain` raster is done in the same way, with the only differences being the file name and the array we write into the connection.
 
 ```{julia}
-write("output/grain.tif", Raster(grain, (new_x, new_y); crs = GFT.EPSG(4326)); force = true)
+write("../output/grain.tif", Raster(grain, (new_x, new_y); crs = GFT.EPSG(4326)); force = true)
 ```
 
 As a result, the files `elev.tif` and `grain.tif` are written into the `output` directory.

chapters/05-raster-vector.qmd

@@ -33,17 +33,17 @@ Makie.set_theme!(
 It also relies on the following data files:
 
 ```{julia}
-src_srtm = Raster("data/srtm.tif")
-src_nlcd = Raster("data/nlcd.tif")
-src_grain = Raster("output/grain.tif")
-src_elev = Raster("output/elev.tif")
-src_dem = Raster("data/dem.tif")
-zion = GeoDataFrames.read("data/zion.gpkg")
-zion_points = GeoDataFrames.read("data/zion_points.gpkg")
-cycle_hire_osm = GeoDataFrames.read("data/cycle_hire_osm.gpkg")
-us_states = GeoDataFrames.read("data/us_states.gpkg")
-nz = GeoDataFrames.read("data/nz.gpkg")
-src_nz_elev = Raster("data/nz_elev.tif")
+src_srtm = Raster("../data/srtm.tif")
+src_nlcd = Raster("../data/nlcd.tif")
+src_grain = Raster("../output/grain.tif")
+src_elev = Raster("../output/elev.tif")
+src_dem = Raster("../data/dem.tif")
+zion = GeoDataFrames.read("../data/zion.gpkg")
+zion_points = GeoDataFrames.read("../data/zion_points.gpkg")
+cycle_hire_osm = GeoDataFrames.read("../data/cycle_hire_osm.gpkg")
+us_states = GeoDataFrames.read("../data/us_states.gpkg")
+nz = GeoDataFrames.read("../data/nz.gpkg")
+src_nz_elev = Raster("../data/nz_elev.tif")
 ```
 
 ## Introduction
@@ -146,7 +146,7 @@ out_image_mask = Rasters.mask(src_srtm; with = zion, missingval = 9999)
 We can write this masked raster to file with `Rasters.write`:
 
 ```{julia}
-Rasters.write("output/srtm_masked.tif", out_image_mask; force = true
+Rasters.write("../output/srtm_masked.tif", out_image_mask; force = true
 )
 ```
 
@@ -170,7 +170,7 @@ out_image_mask_crop = Rasters.crop(out_image_mask; to = zion, touches = true)
 and we write it to file using `Rasters.write`:
 
 ```{julia}
-Rasters.write("output/srtm_masked_cropped.tif", out_image_mask_crop; force = true)
+Rasters.write("../output/srtm_masked_cropped.tif", out_image_mask_crop; force = true)
 ```
 
 @fig-raster-crop shows the original raster, and the three masking and/or cropping results.
@@ -750,15 +750,15 @@ One [suggestion](https://gis.stackexchange.com/questions/455980/vectorizing-all-
 To transform a raster to points, Rasters.jl provides the `Rasters.DimTable` constructor, which converts a raster into a lazy, table-like form.  This can be converted directly to a `DataFrame`, or operated on independently.
 
 ```{julia}
-dt = DimTable(Raster("output/elev.tif"))
+dt = DimTable(Raster("../output/elev.tif"))
 ```
 
 Notice that this has three columns, `:X`, `:Y`, and `:layer1`, corresponding to the pixel centroids and elevation values.  But what if we want to treat the X and Y dimensionas as point geometries?
 
 `DimTable` has a `mergedims` keyword argument for this, which allows us to merge the X and Y dimensions into a single dimension.
 
 ```{julia}
-dt = DimTable(Raster("output/elev.tif"), mergedims = (X, Y))
+dt = DimTable(Raster("../output/elev.tif"), mergedims = (X, Y))
 ```
 
 This has created a `DimTable` with a column `:XY`, which contains the pixel centroids as point-like objects.  We can convert this to a `DataFrame`, set some metadata to indicate that geometry is in `:XY`, and plot the result.
@@ -776,8 +776,8 @@ scatter(df.XY; color = df.layer1)
 We can even save this to a file trivially easily:
 
 ```{julia}
-GeoDataFrames.write("output/elev.gpkg", df)
-GeoDataFrames.read("output/elev.gpkg")
+GeoDataFrames.write("../output/elev.gpkg", df)
+GeoDataFrames.read("../output/elev.gpkg")
 ```