Update environment

03-geoio.qmd

@@ -94,7 +94,7 @@ geotable = GeoIO.load("data/earth.tif")
 ```
 
 If we save the geotable to a `.geojson` file on disk, and then load it back,
-we observe that the `CartesianGrid` gets replaced by a `GeometrySet`:
+we observe that the grid gets replaced by a geometry set:
 
 ```{julia}
 fname = tempname() * ".geojson"

05-transforms.qmd

@@ -362,8 +362,6 @@ are the `Continuous` and the `Categorical` scientific traits. To convert (or coe
 scientific traits of columns in a geotable, we can use the `Coerce` transform:
 
 ```{julia}
-using GeoStats.DataScienceTraits: Continuous
-
 st = georef((a=[1,2,2,2,3,3], b=[1,2,3,4,5,6])) |> Coerce("b" => Continuous)
 ```
 

10-correlation.qmd

@@ -180,12 +180,15 @@ n = 0.1
 m = GaussianVariogram
 g = m(range=r, sill=s, nugget=n)
 
+xs = range(1.0e-6, 30.0, length=100)
+ys = g.(xs)
+
 fig = Mke.Figure()
-Mke.Axis(fig[1,1], xlabel="h", ylabel="γ(h)", limits = (nothing, nothing, 0, 1.1))
-Mke.plot!(g, maxlag=25, label="model")
-Mke.vlines!(r, color="black", linestyle=:dash, label="range")
-Mke.hlines!(s, color="teal", linestyle=:dash, label="sill")
-Mke.hlines!(n, color="teal", linestyle=:dot, label="nugget")
+ax = Mke.Axis(fig[1,1], xlabel="h", ylabel="γ(h)", limits = (nothing, nothing, 0, 1.1))
+Mke.lines!(ax, xs, ys, color="slategray", label="model")
+Mke.vlines!(ax, r, color="black", linestyle=:dash, label="range")
+Mke.hlines!(ax, s, color="teal", linestyle=:dash, label="sill")
+Mke.hlines!(ax, n, color="teal", linestyle=:dot, label="nugget")
 Mke.axislegend("Elements", position = :rc)
 fig
 ```
@@ -314,9 +317,9 @@ geospatial correlation. The most widely used are the `GaussianVariogram`, the
 
 fig = Mke.Figure()
 Mke.Axis(fig[1,1])
-Mke.plot!(γ1, maxlag=2, vcolor = "teal", label = "Gaussian")
-Mke.plot!(γ2, maxlag=2, vcolor = "slategray3", label = "Spherical")
-Mke.plot!(γ3, maxlag=2, vcolor = "brown", label = "Exponential")
+varioplot!(γ1, maxlag=2.0, color = "teal", label = "Gaussian")
+varioplot!(γ2, maxlag=2.0, color = "slategray3", label = "Spherical")
+varioplot!(γ3, maxlag=2.0, color = "brown", label = "Exponential")
 Mke.axislegend("Model", position = :rb)
 fig
 ```
@@ -406,7 +409,7 @@ g = EmpiricalVariogram(img, :Z, maxlag = 50.0)
 ```
 
 ```{julia}
-Mke.plot(g)
+varioplot(g)
 ```
 
 ::: {.callout-note}
@@ -424,8 +427,8 @@ specific directions:
 gₕ = DirectionalVariogram((1.0, 0.0), img, :Z, maxlag = 50.0)
 gᵥ = DirectionalVariogram((0.0, 1.0), img, :Z, maxlag = 50.0)
 
-Mke.plot(gₕ, hshow = false, vcolor = "maroon")
-Mke.plot!(gᵥ, hshow = false, vcolor = "slategray")
+varioplot(gₕ, showhist = false, color = "maroon")
+varioplot!(gᵥ, showhist = false, color = "slategray")
 Mke.current_figure()
 ```
 
@@ -447,7 +450,7 @@ solid curve over the heatmap:
 ```{julia}
 fig = Mke.Figure()
 ax = Mke.PolarAxis(fig[1,1])
-Mke.plot!(ax, gₚ)
+varioplot!(ax, gₚ)
 fig
 ```
 

11-interpolation.qmd

@@ -220,7 +220,7 @@ g = EmpiricalVariogram(samples, "Z", maxlag = 100.0)
 ```
 
 ```{julia}
-Mke.plot(g)
+varioplot(g)
 ```
 
 A better alternative in this case is to use the robust `:cressie`
@@ -231,7 +231,7 @@ g = EmpiricalVariogram(samples, "Z", maxlag = 100.0, estimator = :cressie)
 ```
 
 ```{julia}
-Mke.plot(g)
+varioplot(g)
 ```
 
 After estimating the empirical variogram, the next step consists of fitting
@@ -242,7 +242,7 @@ a theoretical model. The behavior near the origin resembles a `SphericalVariogra
 ```
 
 ```{julia}
-Mke.plot(γ, maxlag = 100.0)
+varioplot(γ, maxlag = 100.0)
 ```
 
 Now that we extracted the geospatial correlation from the samples, we can

12-mining.qmd

@@ -155,18 +155,11 @@ elements, we visualize the `values` of the drill hole samples with the
 `pairplot`:
 
 ```{julia}
-dtable |> Select("Cu", "Au", "Ag", "S") |> DropUnits() |> values |> pairplot
+dtable |> Select("Cu", "Au", "Ag", "S") |> values |> pairplot
 ```
 
 We can observe that the distribution is very skewed.
 
-::: {.callout-note}
-
-The `DropUnits` transform can be useful to drop units from the columns of a
-table before calling functions that do not support units yet (e.g., `pairplot`).
-
-:::
-
 ### Domain of interpolation
 
 Before we can interpolate these variables, we need to define our domain of interpolation.
@@ -274,7 +267,7 @@ In order to remove compositional data constraints, we will perform the centered
 transform (`CLR`) from the [CoDa.jl](https://github.com/JuliaEarth/CoDa.jl) module:
 
 ```{julia}
-grades = dtable |> Select("Cu", "Au", "Ag", "S") |> DropUnits()
+grades = dtable |> Select("Cu", "Au", "Ag", "S")
 
 grades |> CLR() |> values |> pairplot
 ```
@@ -306,12 +299,15 @@ samples
 
 #### Geospatial correlation
 
-Let's fit a theoretical variogram for all four (independent) variables:
+Let's fit a theoretical variogram for all four (independent) variables
+up to a given maximum lag:
 
 ```{julia}
+maxlag = 300.0u"m"
+
 ns = setdiff(names(samples), ["geometry"])
 
-gs = [EmpiricalVariogram(samples, n, estimator = :cressie) for n in ns]
+gs = [EmpiricalVariogram(samples, n, maxlag = maxlag) for n in ns]
 
 γs = [GeoStatsFunctions.fit(Variogram, g, h -> 1 / h^2) for g in gs]
 ```
@@ -327,8 +323,8 @@ model. The constant `100` was chosen based on visual inspection of the
 
 ```{julia}
 function gammaplot(n, g, γ)
-  Mke.plot(g, axis = (; title = n))
-  Mke.plot!(γ, maxlag = 300, vcolor = "teal")
+  varioplot(g, axis = (; title = n))
+  varioplot!(γ, maxlag = maxlag, color = "teal")
   Mke.current_figure()
 end