Remove VectorizedStatistics dependency

Project.toml

@@ -15,7 +15,6 @@ ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StatGeochemBase = "61e559cd-58b4-4257-8528-26bb26ff2b9a"
-VectorizedStatistics = "3b853605-1c98-4422-8364-4bd93ee0529e"
 
 [compat]
 DelimitedFiles = "1"
@@ -29,5 +28,4 @@ ProgressMeter = "1"
 Reexport = "0.2, 1.0"
 SpecialFunctions = "0.10, 1, 2"
 StatGeochemBase = "0.5.9"
-VectorizedStatistics = "0.5"
 julia = "1"

src/Chron.jl

@@ -21,7 +21,6 @@
 module Chron
 
     using Reexport
-    @reexport using VectorizedStatistics
     @reexport using NaNStatistics
     @reexport using StatGeochemBase
     @reexport using Isoplot

src/Fitplot.jl

@@ -219,7 +219,7 @@
                 smpl.Age_Distribution[i] = tmindist
                 Age_Summary[i] = "$terupt"
 
-                l, u = vminimum(tmindist), vmaximum(tmindist)
+                l, u = nanminimum(tmindist), nanmaximum(tmindist)
                 if isfinite(u-l)
                     # Fit custom many-parametric distribution function to histogram
                     binedges = range(l, u, length=101)
@@ -231,8 +231,8 @@
 
                     # Initial guess for parameters
                     p = ones(5)
-                    p[2] = vmean(tmindist)
-                    p[3] = vstd(tmindist)
+                    p[2] = nanmean(tmindist)
+                    p[3] = nanstd(tmindist)
 
                     # Fit nonlinear model
                     fobj = curve_fit(bilinear_exponential,bincenters,N,p)

src/Objects.jl

@@ -115,11 +115,11 @@
         agedistₜ = copy(agedist)
         StratAgeModel(
             heights, # Model heights
-            vmean(agedistₜ,dim=2), # Mean age
-            vstd(agedistₜ,dim=2), # Standard deviation
-            vmedian!(agedistₜ,dim=2), # Median age
-            vpercentile!(agedistₜ,2.5,dim=2), # 2.5th percentile
-            vpercentile!(agedistₜ,97.5,dim=2) # 97.5th percentile
+            nanmean(agedistₜ,dim=2), # Mean age
+            nanstd(agedistₜ,dim=2), # Standard deviation
+            nanmedian!(agedistₜ,dim=2), # Median age
+            nanpctile!(agedistₜ,2.5,dim=2), # 2.5th percentile
+            nanpctile!(agedistₜ,97.5,dim=2) # 97.5th percentile
         )
     end
 

test/testCoupled.jl

@@ -49,6 +49,7 @@ 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 all(!isnan, agedist)
 @test size(lldist) == (config.nsteps,)
 @test !isnan(mean(lldist))
 
@@ -69,6 +70,7 @@ 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 all(!isnan, agedist)
 @test size(lldist) == (config.nsteps,)
 @test !isnan(mean(lldist))
 
@@ -92,6 +94,7 @@ 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 all(!isnan, agedist)
 @test size(hiatusdist) == (nHiatuses, config.nsteps)
 @test mean(hiatusdist, dims=2) ≈ [0.064; 0.061;;] atol=0.3
 @test size(lldist) == (config.nsteps,)
@@ -115,6 +118,7 @@ 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 all(!isnan, agedist)
 @test size(lldist) == (config.nsteps,)
 @test !isnan(mean(lldist))
 
@@ -164,5 +168,6 @@ 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 all(!isnan, agedist)
 @test size(lldist) == (config.nsteps,)
 @test !isnan(mean(lldist))

test/testDist.jl

@@ -1,7 +1,7 @@
 ## --- Test simple metropolis_min and metropolis_minmax functions
 
     mu, sigma = collect(100:0.1:101), 0.01*ones(11);
-    dist = MeltsVolcanicZirconDistribution ./ vmean(MeltsVolcanicZirconDistribution)
+    dist = MeltsVolcanicZirconDistribution ./ nanmean(MeltsVolcanicZirconDistribution)
     @test Chron.Isoplot.dist_ll(dist, mu, sigma, 100,101) ≈ -3.6933357793356576
 
     tmindist = metropolis_min(2*10^5, dist, mu, sigma, burnin=10^5)

test/testRadiocarbon.jl

@@ -64,6 +64,7 @@ config.sieve = round(Int,npoints_approx) # Record one out of every nsieve steps
 @test mdl.Age_975CI ≈ [8405.0, 8386.1, 8347.1, 8163.5, 8151.0, 8123.9, 8029.5, 8016.3, 8003.0, 7985.9, 7958.2, 7916.3] atol=35
 # Test that all age-depth models are in stratigraphic order
 @test all([issorted(x, rev=true) for x in eachcol(agedist)])
+@test all(!isnan, agedist)
 @test -Inf < mean(lldist) < 0
 
 
@@ -85,6 +86,7 @@ hiatus.Duration_sigma = [  30.5,   20.0 ]
 @test mdl.Age_975CI ≈ [8408.06, 8394.94, 8231.34, 8159.62, 8147.76, 8123.97, 8054.25, 8039.57, 8025.91, 7918.86, 7907.9, 7889.46] atol=35
 # Test that all age-depth models are in stratigraphic order
 @test all([issorted(x, rev=true) for x in eachcol(agedist)])
+@test all(!isnan, agedist)
 @test size(hiatusdist) == (nHiatuses, config.nsteps)
 @test mean(hiatusdist, dims=2) ≈ [108.4; 189.0;;] atol=20
 @test -Inf < mean(lldist) < 0
@@ -136,6 +138,7 @@ config.sieve = round(Int,npoints_approx) # Record one out of every nsieve steps
 @test mdl.Age_975CI ≈ [8401.5, 8382.0, 8342.7, 8167.4, 8156.2, 8128.5, 8019.7, 8011.7, 8002.5, 7986.2, 7957.2, 7916.2] atol=40
 # Test that all age-depth models are in stratigraphic order
 @test all([issorted(x, rev=true) for x in eachcol(agedist)])
+@test all(!isnan, agedist)
 @test -Inf < mean(lldist) < 0
 
 
@@ -157,6 +160,7 @@ hiatus.Duration_sigma = [  30.5,   20.0 ]
 @test mdl.Age_975CI ≈ [8405.89, 8391.62, 8225.61, 8163.41, 8151.31, 8123.92, 8030.35, 8023.65, 8016.86, 7916.1, 7905.95, 7886.48] atol=40
 # Test that all age-depth models are in stratigraphic order
 @test all([issorted(x, rev=true) for x in eachcol(agedist)])
+@test all(!isnan, agedist)
 @test size(hiatusdist) == (nHiatuses, config.nsteps)
 @test mean(hiatusdist, dims=2) ≈ [110.5; 187.3;;]  atol=20
 @test -Inf < mean(lldist) < 0

test/testStratOnly.jl

@@ -30,6 +30,7 @@ config.sieve = round(Int,npoints_approx) # Record one out of every nsieve steps
 @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 all(!isnan, agedist)
 @test -Inf < mean(lldist) < 0
 
 @test NamedTuple(mdl) isa NamedTuple
@@ -52,6 +53,7 @@ hiatus.Duration_sigma = [   3.1,    2.0 ]
 @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 all(!isnan, agedist)
 @test size(hiatusdist) == (nHiatuses, config.nsteps)
 @test mean(hiatusdist, dims=2) ≈ [10.580012942504894; 18.96167245288326;;] atol=2
 @test -Inf < mean(lldist) < 0