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Require Chron.jl v0.5
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@brenhinkeller
brenhinkeller committed Jun 13, 2024
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4 changes: 2 additions & 2 deletions Project.toml
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@@ -1,7 +1,7 @@
name = "SubsidenceChron"
uuid = "f7321fe0-76de-4184-b262-f3439fe138fd"
authors = ["Tianran Zhang", "C. Brenhin Keller"]
version = "0.3.2"
version = "0.4.0"

[deps]
Chron = "68885b1f-77b5-52a7-b2e7-6a8014c56b98"
Expand All @@ -15,7 +15,7 @@ StatGeochemBase = "61e559cd-58b4-4257-8528-26bb26ff2b9a"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[compat]
Chron = "0.4.15, 0.5"
Chron = "0.5"
Distributions = "0.15 - 0.25"
IfElse = "0.1"
LoopVectorization = "0.12"
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12 changes: 12 additions & 0 deletions test/testCoupled.jl
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Expand Up @@ -49,6 +49,8 @@ println("StratMetropolisDist:")
@test mdl.Age_975CI ≈ [66.1, 66.09, 66.09, 66.08, 66.08, 66.07, 66.06, 66.05, 66.03, 66.01, 65.97, 65.97, 65.96, 65.96, 65.95] atol=0.15
# Test that all age-depth models are in stratigraphic order
@test all([issorted(x, rev=true) for x in eachcol(agedist)])
@test size(lldist) == (config.nsteps,)
@test !isnan(mean(lldist))

# Try adding systematic uncertainties too
smpl.Chronometer = (:UPb, :UPb, :ArAr, :UPb, :UPb)
Expand All @@ -67,6 +69,8 @@ println("StratMetropolisDist with systematic uncertainties:")
@test mdl.Age_975CI ≈ [66.1, 66.09, 66.09, 66.08, 66.08, 66.07, 66.06, 66.05, 66.03, 66.01, 65.97, 65.97, 65.96, 65.96, 65.95] atol=0.15
# Test that all age-depth models are in stratigraphic order
@test all([issorted(x, rev=true) for x in eachcol(agedist)])
@test size(lldist) == (config.nsteps,)
@test !isnan(mean(lldist))

## --- As above, but with hiata

Expand All @@ -88,6 +92,10 @@ println("StratMetropolisDist with hiata:")
@test mdl.Age_975CI ≈ [66.11, 66.1, 66.1, 66.1, 66.1, 66.08, 66.08, 66.08, 66.08, 65.98, 65.96, 65.96, 65.96, 65.96, 65.95] atol=0.15
# Test that all age-depth models are in stratigraphic order
@test all([issorted(x, rev=true) for x in eachcol(agedist)])
@test size(hiatusdist) == (nHiatuses, config.nsteps)
@test mean(hiatusdist, dims=2) ≈ [0.064; 0.061;;] atol=0.3
@test size(lldist) == (config.nsteps,)
@test !isnan(mean(lldist))

## --- As above, but treat everything as a gaussian/weighted mean

Expand All @@ -107,6 +115,8 @@ println("StratMetropolisDist with fitted Gaussians:")
@test mdl.Age_975CI ≈ [66.09, 66.09, 66.08, 66.08, 66.08, 66.07, 66.06, 66.05, 66.04, 66.02, 66.0, 65.99, 65.98, 65.97, 65.96] atol=0.15
# Test that all age-depth models are in stratigraphic order
@test all([issorted(x, rev=true) for x in eachcol(agedist)])
@test size(lldist) == (config.nsteps,)
@test !isnan(mean(lldist))

## -- Test coupled with Isoplot.jl Pb-loss-aware eruption ages

Expand Down Expand Up @@ -154,3 +164,5 @@ println("StratMetropolisDist, Pb-loss-aware:")
@test mdl.Age_975CI ≈ [752.51, 752.47, 752.43, 752.37, 752.29, 752.2, 752.08, 751.85, 750.99] atol=0.7
# Test that all age-depth models are in stratigraphic order
@test all([issorted(x, rev=true) for x in eachcol(agedist)])
@test size(lldist) == (config.nsteps,)
@test !isnan(mean(lldist))
16 changes: 9 additions & 7 deletions test/testStratOnly.jl
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@@ -1,5 +1,3 @@
## --- Age-depth modelling only

# Make an instance of a ChronAgeData object for nSamples
nSamples = 4
smpl = ChronAgeData(nSamples)
Expand Down Expand Up @@ -28,11 +26,13 @@ config.sieve = round(Int,npoints_approx) # Record one out of every nsieve steps
# Test that results match expectation, within some tolerance
@test mdl.Age isa Vector{Float64}
@test mdl.Age ≈ [751.87, 742.76, 733.65, 724.51, 720.15, 715.96, 711.8, 709.15, 706.6, 704.04, 701.42, 698.67] atol=1
@test mdl.Age_025CI ≈ [742.48, 723.81, 718.7, 715.73, 707.42, 703.73, 701.34, 698.2, 696.35, 695.04, 694.0, 693.05] atol=2
@test mdl.Age_025CI ≈ [742.48, 723.81, 718.7, 715.73, 707.42, 703.73, 701.34, 698.2, 696.35, 695.04, 694.0, 693.05] atol=3
@test mdl.Age_975CI ≈ [761.17, 757.98, 752.64, 733.57, 731.21, 728.07, 722.24, 720.59, 718.6, 716.11, 712.48, 704.28] atol=3
# Test that all age-depth models are in stratigraphic order
@test all([issorted(x, rev=true) for x in eachcol(agedist)])
@test -Inf < mean(lldist) < 0

@test NamedTuple(mdl) isa NamedTuple

# Data about hiatuses
nHiatuses = 2 # The number of hiatuses you have data for
Expand All @@ -45,11 +45,13 @@ hiatus.Duration_sigma = [ 3.1, 2.0 ]
# Run the model. Note the additional `hiatus` arguments
@time (mdl, agedist, hiatusdist, lldist) = StratMetropolis(smpl, hiatus, config); sleep(0.5)


# Test that results match expectation, within some tolerance
@test mdl.Age isa Vector{Float64}
@test mdl.Age ≈ [753.23, 748.14, 729.73, 724.3, 721.09, 718.04, 714.96, 713.54, 711.82, 701.99, 699.95, 698.21] atol=1
@test mdl.Age_025CI ≈ [744.19, 734.06, 718.01, 715.91, 710.5, 707.73, 705.75, 703.97, 701.87, 694.01, 693.25, 692.56] atol=3
@test mdl.Age_975CI ≈ [762.31, 760.19, 743.82, 732.92, 730.96, 728.45, 724.28, 723.32, 722.16, 712.29, 708.49, 703.75] atol=4
@test mdl.Age ≈ [752.76, 747.88, 728.92, 724.14, 720.8, 717.62, 714.44, 712.95, 711.41, 700.82, 699.4, 697.99] atol=1
@test mdl.Age_025CI ≈ [743.41, 734.96, 717.54, 715.41, 709.67, 707.05, 705.26, 703.72, 702.49, 693.27, 692.65, 692.06] atol=3
@test mdl.Age_975CI ≈ [761.77, 759.23, 741.37, 732.59, 730.64, 728.05, 723.52, 722.34, 720.9, 709.03, 706.78, 703.46] atol=3
# Test that all age-depth models are in stratigraphic order
@test all([issorted(x, rev=true) for x in eachcol(agedist)])
@test size(hiatusdist) == (nHiatuses, config.nsteps)
@test mean(hiatusdist, dims=2) ≈ [10.580012942504894; 18.96167245288326;;] atol=2
@test -Inf < mean(lldist) < 0
8 changes: 4 additions & 4 deletions test/testSubsidence.jl
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Expand Up @@ -107,7 +107,7 @@ config.sieve = round(Int,npoints_approx) # Record one out of every nsieve steps
mean_ages = nanmean(agedist_test, dim=2)
expected_mean_ages = [398.51, 397.72, 396.96, 396.19, 395.43, 394.67, 393.92, 393.17, 392.41, 391.65, 390.9, 390.15, 389.4, 388.65, 387.8, 386.92, 386.06, 385.19, 384.3, 383.42, 382.53, 381.65, 380.77, 379.89, 379.02, 378.14, 377.26, 376.37, 375.49, 374.61, 373.73, 372.85, 371.97, 371.08, 370.21, 369.33, 368.45, 367.56, 366.68, 365.8, 364.93, 364.05, 363.18, 362.31, 360.6, 358.87, 357.17, 355.45, 353.74, 352.0, 350.31, 348.61, 346.93, 345.24, 343.53, 341.79, 340.07, 338.35, 336.61, 334.87, 333.13, 331.4, 329.66, 327.94, 324.34, 320.83, 317.26, 313.63, 310.12, 306.46, 302.86, 299.29, 295.63, 291.97, 288.41, 284.76, 281.0, 277.38, 273.77, 270.23, 266.64, 262.96, 259.28, 255.54, 251.75, 248.12, 244.46, 240.89, 237.42, 233.89, 230.45, 227.02, 223.57, 220.26]
@test all(isapprox.(mean_ages, expected_mean_ages, atol=20))
# println(round.(mean_ages, digits=2))
println(round.(mean_ages, digits=2))

# Test subsidence parameters
@test isapprox(only(subsmdl_test.Beta), 1.385317084366247, atol=0.1)
Expand Down Expand Up @@ -176,14 +176,14 @@ smpl.Height_Unit = "m"

# Test mean age-depth model
mean_ages = nanmean(agedist_test, dim=2)
expected_mean_ages = [398.51, 397.72, 396.96, 396.19, 395.43, 394.67, 393.92, 393.17, 392.41, 391.65, 390.9, 390.15, 389.4, 388.65, 387.8, 386.92, 386.06, 385.19, 384.3, 383.42, 382.53, 381.65, 380.77, 379.89, 379.02, 378.14, 377.26, 376.37, 375.49, 374.61, 373.73, 372.85, 371.97, 371.08, 370.21, 369.33, 368.45, 367.56, 366.68, 365.8, 364.93, 364.05, 363.18, 362.31, 360.6, 358.87, 357.17, 355.45, 353.74, 352.0, 350.31, 348.61, 346.93, 345.24, 343.53, 341.79, 340.07, 338.35, 336.61, 334.87, 333.13, 331.4, 329.66, 327.94, 324.34, 320.83, 317.26, 313.63, 310.12, 306.46, 302.86, 299.29, 295.63, 291.97, 288.41, 284.76, 281.0, 277.38, 273.77, 270.23, 266.64, 262.96, 259.28, 255.54, 251.75, 248.12, 244.46, 240.89, 237.42, 233.89, 230.45, 227.02, 223.57, 220.26]
expected_mean_ages = [398.61, 397.78, 396.99, 396.26, 395.55, 394.87, 394.18, 393.38, 392.74, 392.04, 391.27, 390.53, 389.65, 388.74, 387.93, 387.15, 386.38, 385.6, 384.81, 384.06, 383.33, 382.53, 381.73, 380.95, 380.14, 379.3, 378.42, 377.62, 376.79, 375.94, 375.09, 374.1, 373.26, 372.27, 371.32, 370.25, 369.26, 368.4, 367.37, 366.26, 365.2, 364.1, 363.21, 362.3, 360.66, 358.53, 356.82, 354.9, 352.73, 350.94, 348.94, 347.02, 345.05, 343.42, 341.77, 340.15, 338.74, 337.24, 335.72, 334.24, 332.81, 331.04, 329.31, 327.89, 324.6, 320.52, 316.51, 313.65, 309.71, 306.69, 303.72, 300.81, 297.91, 294.51, 291.23, 288.37, 285.97, 282.59, 278.41, 275.78, 273.22, 270.78, 267.98, 264.7, 261.29, 258.09, 254.56, 250.22, 246.32, 242.24, 237.7, 232.23, 226.76, 220.52]
@test all(isapprox.(mean_ages, expected_mean_ages, atol=25))
# println(round.(mean_ages, digits=2))

# Test subsidence parameters
@test isapprox(only(subsmdl_test.Beta), 1.1247054117222945, atol=0.1)
@test isapprox(only(subsmdl_test.Beta_025CI), 1.0148599420923872, atol=0.1)
@test isapprox(only(subsmdl_test.Beta_975CI), 1.3342992791049268, atol=0.1)
@test isapprox(only(subsmdl_test.Beta_025CI), 1.0148599420923872, atol=0.15)
@test isapprox(only(subsmdl_test.Beta_975CI), 1.3509359022349554, atol=0.15)
@test isapprox(only(subsmdl_test.T0), 388.6046712104339, atol=5)
@test isapprox(only(subsmdl_test.T0_025CI), 338.7980819566939, atol=10)
@test isapprox(only(subsmdl_test.T0_975CI), 499.6217634635351, atol=10)

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Registration pull request created: JuliaRegistries/General/108936

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## Breaking changes

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Tagging

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git tag -a v0.4.0 -m "<description of version>" f9880e209390aaee5e5623c4d45e784a94b2c946
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