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| @testset "Empirical" begin | ||
| @testset "Variogram" begin | ||
| # homogeneous field has zero variogram | ||
| sdata = georef((z=ones(3),), [(1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)]) | ||
| g = EmpiricalVariogram(sdata, :z, nlags=2, maxlag=2.0) | ||
| @test g.abscissas ≈ [1 / 2, √2] * u"m" | ||
| @test g.ordinates[2] == 0.0 | ||
| @test g.counts == [0, 3] | ||
| @testset "EmpiricalVariogram" begin | ||
| # homogeneous field has zero variogram | ||
| sdata = georef((z=ones(3),), [(1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)]) | ||
| g = EmpiricalVariogram(sdata, :z, nlags=2, maxlag=2.0) | ||
| @test g.abscissas ≈ [1 / 2, √2] * u"m" | ||
| @test g.ordinates[2] == 0.0 | ||
| @test g.counts == [0, 3] | ||
|
|
||
| # basic test on number of lags | ||
| sdata = georef((z=[1.0, 0.0, 1.0],), [(25.0, 25.0), (50.0, 75.0), (75.0, 50.0)]) | ||
| g = EmpiricalVariogram(sdata, :z, nlags=20, maxlag=1.0) | ||
| @test length(g.abscissas) == 20 | ||
| @test length(g.ordinates) == 20 | ||
| @test length(g.counts) == 20 | ||
| # basic test on number of lags | ||
| sdata = georef((z=[1.0, 0.0, 1.0],), [(25.0, 25.0), (50.0, 75.0), (75.0, 50.0)]) | ||
| g = EmpiricalVariogram(sdata, :z, nlags=20, maxlag=1.0) | ||
| @test length(g.abscissas) == 20 | ||
| @test length(g.ordinates) == 20 | ||
| @test length(g.counts) == 20 | ||
|
|
||
| # empirical variogram on integer coordinates | ||
| sdata = georef((z=ones(3),), [(1, 0, 0), (0, 1, 0), (0, 0, 1)]) | ||
| g = EmpiricalVariogram(sdata, :z, nlags=2, maxlag=2, algorithm=:full) | ||
| @test g.abscissas ≈ [1 / 2, √2] * u"m" | ||
| @test g.ordinates[2] == 0.0 | ||
| @test g.counts == [0, 3] | ||
| # empirical variogram on integer coordinates | ||
| sdata = georef((z=ones(3),), [(1, 0, 0), (0, 1, 0), (0, 0, 1)]) | ||
| g = EmpiricalVariogram(sdata, :z, nlags=2, maxlag=2, algorithm=:full) | ||
| @test g.abscissas ≈ [1 / 2, √2] * u"m" | ||
| @test g.ordinates[2] == 0.0 | ||
| @test g.counts == [0, 3] | ||
|
|
||
| # empirical variogram with only missing data | ||
| z = Union{Float64,Missing}[missing, missing, missing] | ||
| 𝒟 = georef((z=z,), rand(Point, 3)) | ||
| g = EmpiricalVariogram(𝒟, :z, maxlag=1.0, nlags=5) | ||
| @test g.abscissas == [0.1, 0.3, 0.5, 0.7, 0.9] * u"m" | ||
| @test all(iszero, g.counts) | ||
| # empirical variogram with only missing data | ||
| z = Union{Float64,Missing}[missing, missing, missing] | ||
| 𝒟 = georef((z=z,), rand(Point, 3)) | ||
| g = EmpiricalVariogram(𝒟, :z, maxlag=1.0, nlags=5) | ||
| @test g.abscissas == [0.1, 0.3, 0.5, 0.7, 0.9] * u"m" | ||
| @test all(iszero, g.counts) | ||
|
|
||
| # accumulation algorithms give the same result | ||
| rng = StableRNG(123) | ||
| sdata = georef((z=rand(rng, 1000),), rand(rng, Point, 1000)) | ||
| g₁ = EmpiricalVariogram(sdata, :z, maxlag=0.01, algorithm=:full) | ||
| g₂ = EmpiricalVariogram(sdata, :z, maxlag=0.01, algorithm=:ball) | ||
| @test isequal(g₁.abscissas, g₂.abscissas) | ||
| @test isequal(g₁.ordinates, g₂.ordinates) | ||
| @test isequal(g₁.counts, g₂.counts) | ||
| # accumulation algorithms give the same result | ||
| rng = StableRNG(123) | ||
| sdata = georef((z=rand(rng, 1000),), rand(rng, Point, 1000)) | ||
| g₁ = EmpiricalVariogram(sdata, :z, maxlag=0.01, algorithm=:full) | ||
| g₂ = EmpiricalVariogram(sdata, :z, maxlag=0.01, algorithm=:ball) | ||
| @test isequal(g₁.abscissas, g₂.abscissas) | ||
| @test isequal(g₁.ordinates, g₂.ordinates) | ||
| @test isequal(g₁.counts, g₂.counts) | ||
|
|
||
| # custom distance is recorded | ||
| rng = StableRNG(123) | ||
| sdata = georef((z=rand(rng, 2),), [Point(LatLon(0.0, 0.0)), Point(LatLon(0.0, 90.0))]) | ||
| g = EmpiricalVariogram(sdata, :z, distance=Haversine(6371.0), algorithm=:full) | ||
| @test g.distance == Haversine(6371.0) | ||
| # custom distance is recorded | ||
| rng = StableRNG(123) | ||
| sdata = georef((z=rand(rng, 2),), [Point(LatLon(0.0, 0.0)), Point(LatLon(0.0, 90.0))]) | ||
| g = EmpiricalVariogram(sdata, :z, distance=Haversine(6371.0), algorithm=:full) | ||
| @test g.distance == Haversine(6371.0) | ||
|
|
||
| # print methods | ||
| rng = StableRNG(123) | ||
| d = georef((z=rand(rng, 100, 100),)) | ||
| g = EmpiricalVariogram(d, :z) | ||
| @test sprint(show, g) == | ||
| "EmpiricalVariogram(abscissas: [0.25 m, ..., 9.93304 m], ordinates: [0.0, ..., 0.0841979], distance: Euclidean(0.0), estimator: MatheronEstimator(), npairs: 1447200)" | ||
| @test sprint(show, MIME"text/plain"(), g) == """ | ||
| EmpiricalVariogram | ||
| ├─ abscissas: [0.25 m, 1.0 m, 1.41421 m, ..., 8.7407 m, 9.28182 m, 9.93304 m] | ||
| ├─ ordinates: [0.0, 0.0843099, 0.0845995, ..., 0.0838336, 0.0839823, 0.0841979] | ||
| ├─ distance: Euclidean(0.0) | ||
| ├─ estimator: MatheronEstimator() | ||
| └─ npairs: 1447200""" | ||
| # print methods | ||
| rng = StableRNG(123) | ||
| d = georef((z=rand(rng, 100, 100),)) | ||
| g = EmpiricalVariogram(d, :z) | ||
| @test sprint(show, g) == | ||
| "EmpiricalVariogram(abscissas: [0.25 m, ..., 9.93304 m], ordinates: [0.0, ..., 0.0841979], distance: Euclidean(0.0), estimator: MatheronEstimator(), npairs: 1447200)" | ||
| @test sprint(show, MIME"text/plain"(), g) == """ | ||
| EmpiricalVariogram | ||
| ├─ abscissas: [0.25 m, 1.0 m, 1.41421 m, ..., 8.7407 m, 9.28182 m, 9.93304 m] | ||
| ├─ ordinates: [0.0, 0.0843099, 0.0845995, ..., 0.0838336, 0.0839823, 0.0841979] | ||
| ├─ distance: Euclidean(0.0) | ||
| ├─ estimator: MatheronEstimator() | ||
| └─ npairs: 1447200""" | ||
|
|
||
| # test variography with compositional data | ||
| data = georef((z=rand(Composition{3}, 100),), rand(Point, 100)) | ||
| g = EmpiricalVariogram(data, :z, maxlag=1.0, algorithm=:full) | ||
| @test all(≥(0u"m"), g.abscissas) | ||
| @test all(≥(0), g.ordinates) | ||
| @test all(≥(0), g.counts) | ||
| # test variography with compositional data | ||
| data = georef((z=rand(Composition{3}, 100),), rand(Point, 100)) | ||
| g = EmpiricalVariogram(data, :z, maxlag=1.0, algorithm=:full) | ||
| @test all(≥(0u"m"), g.abscissas) | ||
| @test all(≥(0), g.ordinates) | ||
| @test all(≥(0), g.counts) | ||
|
|
||
| # test variography with unitful data | ||
| data = georef((z=[1 * u"K" for i in 1:100],), rand(Point, 100)) | ||
| g = EmpiricalVariogram(data, :z, nlags=20) | ||
| @test all(≥(0u"m"), g.abscissas) | ||
| @test g.ordinates == fill(0.0 * u"K^2", 20) | ||
| # test variography with unitful data | ||
| data = georef((z=[1 * u"K" for i in 1:100],), rand(Point, 100)) | ||
| g = EmpiricalVariogram(data, :z, nlags=20) | ||
| @test all(≥(0u"m"), g.abscissas) | ||
| @test g.ordinates == fill(0.0 * u"K^2", 20) | ||
|
|
||
| # Matheron's vs Cressie's estimator | ||
| img = readdlm(joinpath(datadir, "Gaussian30x10.txt")) | ||
| data = georef((; Z=img)) | ||
| g₁ = EmpiricalVariogram(data, :Z, maxlag=50.0, estimator=:matheron) | ||
| g₂ = EmpiricalVariogram(data, :Z, maxlag=50.0, estimator=:cressie) | ||
| @test g₁.abscissas == g₂.abscissas | ||
| @test all(isapprox.(g₁.ordinates, g₂.ordinates, atol=0.1)) | ||
| @test g₁.counts == g₂.counts | ||
| # Matheron's vs Cressie's estimator | ||
| img = readdlm(joinpath(datadir, "Gaussian30x10.txt")) | ||
| data = georef((; Z=img)) | ||
| g₁ = EmpiricalVariogram(data, :Z, maxlag=50.0, estimator=:matheron) | ||
| g₂ = EmpiricalVariogram(data, :Z, maxlag=50.0, estimator=:cressie) | ||
| @test g₁.abscissas == g₂.abscissas | ||
| @test all(isapprox.(g₁.ordinates, g₂.ordinates, atol=0.1)) | ||
| @test g₁.counts == g₂.counts | ||
|
|
||
| # specify variables as strings | ||
| img = readdlm(joinpath(datadir, "Gaussian30x10.txt")) | ||
| data = georef((; Z=img)) | ||
| g = EmpiricalVariogram(data, "Z", maxlag=50.0) | ||
| @test all(≥(0u"m"), g.abscissas) | ||
| @test all(>(0.8), g.ordinates[11:end]) | ||
| @test all(≥(0), g.counts) | ||
| end | ||
| # specify variables as strings | ||
| img = readdlm(joinpath(datadir, "Gaussian30x10.txt")) | ||
| data = georef((; Z=img)) | ||
| g = EmpiricalVariogram(data, "Z", maxlag=50.0) | ||
| @test all(≥(0u"m"), g.abscissas) | ||
| @test all(>(0.8), g.ordinates[11:end]) | ||
| @test all(≥(0), g.counts) | ||
| end | ||
|
|
||
| @testset "Varioplane" begin | ||
| img = readdlm(joinpath(datadir, "anisotropic.tsv")) | ||
| data = georef((z=img,)) | ||
| g = EmpiricalVarioplane(data, :z, maxlag=50.0) | ||
| @test sprint(show, g) == "EmpiricalVarioplane" | ||
| @test sprint(show, MIME"text/plain"(), g) == """ | ||
| EmpiricalVarioplane | ||
| 50 angles | ||
| └─0.00° | ||
| └─3.67° | ||
| └─7.35° | ||
| └─11.02° | ||
| └─14.69° | ||
| ⋮ | ||
| └─165.31° | ||
| └─168.98° | ||
| └─172.65° | ||
| └─176.33° | ||
| └─180.00°""" | ||
| end | ||
| @testset "DirectionalVariogram" begin | ||
| # merge operation does not produce NaN | ||
| dir = (0.286788, -0.496732, -0.819152) | ||
| 𝒟 = georef(CSV.File(joinpath(datadir, "nanlags.csv")), (:X, :Y, :Z)) | ||
| g = DirectionalVariogram(dir, 𝒟, :Cu, dtol=45, maxlag=150, nlags=20) | ||
| @test !any(isnan.(g.abscissas)) | ||
| @test !any(isnan.(g.ordinates)) | ||
| @test !any(isnan.(g.counts)) | ||
|
|
||
| @testset "Directional" begin | ||
| # merge operation does not produce NaN | ||
| dir = (0.286788, -0.496732, -0.819152) | ||
| 𝒟 = georef(CSV.File(joinpath(datadir, "nanlags.csv")), (:X, :Y, :Z)) | ||
| g = DirectionalVariogram(dir, 𝒟, :Cu, dtol=45, maxlag=150, nlags=20) | ||
| @test !any(isnan.(g.abscissas)) | ||
| @test !any(isnan.(g.ordinates)) | ||
| @test !any(isnan.(g.counts)) | ||
| # directional variogram and known anisotropy ratio | ||
| img = readdlm(joinpath(datadir, "anisotropic.tsv")) | ||
| sdata = georef((z=img,)) | ||
| gₕ = DirectionalVariogram((1.0, 0.0), sdata, :z, maxlag=50.0) | ||
| gᵥ = DirectionalVariogram((0.0, 1.0), sdata, :z, maxlag=50.0) | ||
| γₕ = GeoStatsFunctions.fit(GaussianVariogram, gₕ) | ||
| γᵥ = GeoStatsFunctions.fit(GaussianVariogram, gᵥ) | ||
| @test range(γₕ) / range(γᵥ) ≈ 3.0 atol = 0.1 | ||
| end | ||
|
|
||
| # directional variogram and known anisotropy ratio | ||
| img = readdlm(joinpath(datadir, "anisotropic.tsv")) | ||
| sdata = georef((z=img,)) | ||
| gₕ = DirectionalVariogram((1.0, 0.0), sdata, :z, maxlag=50.0) | ||
| gᵥ = DirectionalVariogram((0.0, 1.0), sdata, :z, maxlag=50.0) | ||
| γₕ = GeoStatsFunctions.fit(GaussianVariogram, gₕ) | ||
| γᵥ = GeoStatsFunctions.fit(GaussianVariogram, gᵥ) | ||
| @test range(γₕ) / range(γᵥ) ≈ 3.0 atol = 0.1 | ||
| end | ||
| @testset "PlanarVariogram" begin | ||
| # directional equals planar rotated by 90 degrees in 2D | ||
| img = readdlm(joinpath(datadir, "anisotropic.tsv")) | ||
| sdata = georef((z=img,)) | ||
| g₁ = PlanarVariogram((0.0, 1.0), sdata, :z, maxlag=50.0) | ||
| g₂ = DirectionalVariogram((1.0, 0.0), sdata, :z, maxlag=50.0) | ||
| @test g₁.abscissas == g₂.abscissas | ||
| @test g₁.ordinates ≈ g₂.ordinates | ||
| @test g₁.counts == g₂.counts | ||
| g₁ = PlanarVariogram((1.0, 0.0), sdata, :z, maxlag=50.0) | ||
| g₂ = DirectionalVariogram((0.0, 1.0), sdata, :z, maxlag=50.0) | ||
| @test g₁.abscissas == g₂.abscissas | ||
| @test g₁.ordinates ≈ g₂.ordinates | ||
| @test g₁.counts == g₂.counts | ||
|
|
||
| @testset "Planar" begin | ||
| # directional equals planar rotated by 90 degrees in 2D | ||
| img = readdlm(joinpath(datadir, "anisotropic.tsv")) | ||
| sdata = georef((z=img,)) | ||
| g₁ = PlanarVariogram((0.0, 1.0), sdata, :z, maxlag=50.0) | ||
| g₂ = DirectionalVariogram((1.0, 0.0), sdata, :z, maxlag=50.0) | ||
| @test g₁.abscissas == g₂.abscissas | ||
| @test g₁.ordinates ≈ g₂.ordinates | ||
| @test g₁.counts == g₂.counts | ||
| g₁ = PlanarVariogram((1.0, 0.0), sdata, :z, maxlag=50.0) | ||
| g₂ = DirectionalVariogram((0.0, 1.0), sdata, :z, maxlag=50.0) | ||
| @test g₁.abscissas == g₂.abscissas | ||
| @test g₁.ordinates ≈ g₂.ordinates | ||
| @test g₁.counts == g₂.counts | ||
| # planar variogram and known anisotropy ratio | ||
| gₕ = PlanarVariogram((0.0, 1.0), sdata, :z, maxlag=50.0) | ||
| gᵥ = PlanarVariogram((1.0, 0.0), sdata, :z, maxlag=50.0) | ||
| γₕ = GeoStatsFunctions.fit(GaussianVariogram, gₕ) | ||
| γᵥ = GeoStatsFunctions.fit(GaussianVariogram, gᵥ) | ||
| @test range(γₕ) / range(γᵥ) ≈ 3.0 atol = 0.1 | ||
| end | ||
|
|
||
| # planar variogram and known anisotropy ratio | ||
| gₕ = PlanarVariogram((0.0, 1.0), sdata, :z, maxlag=50.0) | ||
| gᵥ = PlanarVariogram((1.0, 0.0), sdata, :z, maxlag=50.0) | ||
| γₕ = GeoStatsFunctions.fit(GaussianVariogram, gₕ) | ||
| γᵥ = GeoStatsFunctions.fit(GaussianVariogram, gᵥ) | ||
| @test range(γₕ) / range(γᵥ) ≈ 3.0 atol = 0.1 | ||
| end | ||
| @testset "EmpiricalVarioplane" begin | ||
| img = readdlm(joinpath(datadir, "anisotropic.tsv")) | ||
| data = georef((z=img,)) | ||
| g = EmpiricalVarioplane(data, :z, maxlag=50.0) | ||
| @test sprint(show, g) == "EmpiricalVarioplane" | ||
| @test sprint(show, MIME"text/plain"(), g) == """ | ||
| EmpiricalVarioplane | ||
| 50 angles | ||
| └─0.00° | ||
| └─3.67° | ||
| └─7.35° | ||
| └─11.02° | ||
| └─14.69° | ||
| ⋮ | ||
| └─165.31° | ||
| └─168.98° | ||
| └─172.65° | ||
| └─176.33° | ||
| └─180.00°""" | ||
| end |
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