reformat

examples/contamination/contamination-efast.jl

@@ -11,5 +11,5 @@ Mads.wellon!(md, "w20a") # use well w20a
 if !isdefined(Mads, :efastresult)
 	efast_results = Mads.efast(md; N=1000, seed=2016)
 end
-Mads.plotobsSAresults(md, efastresult; filter=r"w13a", filename="w13a-efast.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]", separate_files=true)
-Mads.plotobsSAresults(md, efastresult; filter=r"w20a", filename="w20a-efast.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]", separate_files=true)
+Mads.plotobsSAresults(md, efastresult; filter=r"w13a", filename="w13a-efast.svg", xtitle="Time [a]", ytitle="Concentration [ppb]", separate_files=true)
+Mads.plotobsSAresults(md, efastresult; filter=r"w20a", filename="w20a-efast.svg", xtitle="Time [a]", ytitle="Concentration [ppb]", separate_files=true)

examples/contamination/contamination-saltelli.jl

@@ -11,5 +11,5 @@ Mads.wellon!(md, "w20a") # use well w20a
 if !isdefined(Mads, :saltelliresult)
 	saltelli_results = Mads.saltelli(md; N=10000, seed=2016)
 end
-Mads.plotobsSAresults(md, saltelliresult; filter=r"w13a", filename="w13a-saltelli.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]", separate_files=true)
-Mads.plotobsSAresults(md, saltelliresult; filter=r"w20a", filename="w20a-saltelli.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]", separate_files=true)
+Mads.plotobsSAresults(md, saltelliresult; filter=r"w13a", filename="w13a-saltelli.svg", xtitle="Time [a]", ytitle="Concentration [ppb]", separate_files=true)
+Mads.plotobsSAresults(md, saltelliresult; filter=r"w20a", filename="w20a-saltelli.svg", xtitle="Time [a]", ytitle="Concentration [ppb]", separate_files=true)

examples/contamination/contamination-w03-w13-svr-efast-mads.jl

@@ -11,7 +11,7 @@ svrexec, svrread, svrsave, svrclean = Mads.makesvrmodel(md, 1000)
 
 numberofsamples = 100
 paramdict = Mads.getparamrandom(md, numberofsamples)
-paramarray = hcat(map(i->collect(paramdict[i]), keys(paramdict))...)
+paramarray = hcat(map(i -> collect(paramdict[i]), keys(paramdict))...)
 @time predictions = Mads.forward(md, paramdict)'
 
 Mads.madsinfo("Model predictions ...")
@@ -34,10 +34,10 @@ mdsvr["Julia model"] = svrexec
 mdsvr["Filename"] = "$rootname-svr.mads"
 
 sasvr = Mads.efast(mdsvr)
-Mads.plotobsSAresults(mdsvr, sasvr, format="PNG")
+Mads.plotobsSAresults(mdsvr, sasvr; format="PNG")
 Mads.display("$rootname-svr-efast-385.png")
 
 svrsave()
 svrclean()
 svrread()
-svrclean()
+svrclean()

examples/contamination/contamination-w03-w13-svr-efast.jl

@@ -10,7 +10,7 @@ Mads.wellon!(md, "w13a") # use well w13a
 numberofsamples = 100
 if !isdefined(Mads, :predictions) || size(predictions) != numberofsamples
 	paramdict = Mads.getparamrandom(md, numberofsamples)
-	paramarray = hcat(map(i->collect(paramdict[i]), keys(paramdict))...)
+	paramarray = hcat(map(i -> collect(paramdict[i]), keys(paramdict))...)
 	predictions = Mads.forward(md, paramdict)'
 end
 
@@ -19,14 +19,14 @@ Mads.spaghettiplot(md, predictions; keyword="w13a-training-set", format="PNG")
 Mads.display("$rootname-w13a-training-set-$numberofsamples-spaghetti.png")
 
 svrpredictions = Array{Float64}(undef, 0, numberofsamples)
-for i=1:50
-	pmodel = SVR.train(predictions[i,:], paramarray'; eps=0.001, C=10000.);
-	y_pr = SVR.predict(pmodel, paramarray');
+for i = 1:50
+	pmodel = SVR.train(predictions[i, :], paramarray'; eps=0.001, C=10000.0)
+	y_pr = SVR.predict(pmodel, paramarray')
 	SVR.freemodel(pmodel)
 	svrpredictions = [svrpredictions; y_pr']
 end
 @show maximum(abs.(svrpredictions .- predictions))
 
 Mads.madsinfo("SVR predictions ...")
 Mads.spaghettiplot(md, svrpredictions; keyword="w13a-svr", format="PNG")
-Mads.display("$rootname-w13a-svr-$numberofsamples-spaghetti.png")
+Mads.display("$rootname-w13a-svr-$numberofsamples-spaghetti.png")

examples/contamination/contamination-w03.jl

@@ -12,9 +12,9 @@ Mads.wellon!(md, "w20a") # use well w20a
 
 numberofsamples = 10
 paramdict = Mads.getparamrandom(md, numberofsamples)
-paramarray = hcat(map(i->collect(paramdict[i]), keys(paramdict))...)
+paramarray = hcat(map(i -> collect(paramdict[i]), keys(paramdict))...)
 predictions = Mads.forward(md, paramdict)'
-wd = Mads.getwelldata(md, time=true)'
+wd = Mads.getwelldata(md; time=true)'
 
 # data = Array{Float64}(undef, 0, size(paramarray, 2) + size(wd, 2) + 1)
 # for i = 1:numberofsamples
@@ -34,4 +34,4 @@ Mads.scatterplotsamples(md, permutedims(mcmcchain.value), rootname * "-bayes.png
 
 posterior_predictions = Mads.forward(md, permutedims(mcmcchain.value))
 Mads.madsinfo("Posterior (Bayesian) spaghetti plot ...")
-Mads.spaghettiplot(md, posterior_predictions; keyword="w13a_w20a-posterior", format="PNG")
+Mads.spaghettiplot(md, posterior_predictions; keyword="w13a_w20a-posterior", format="PNG")

examples/contamination/contamination-w04-w13-svr-efast-mads.jl

@@ -11,18 +11,18 @@ svrexec, svrread, svrsave, svrclean = Mads.makesvrmodel(md, 100)
 
 numberofsamples = 100
 paramdict = Mads.getparamrandom(md, numberofsamples)
-paramarray = hcat(map(i->collect(paramdict[i]), keys(paramdict))...)
+paramarray = hcat(map(i -> collect(paramdict[i]), keys(paramdict))...)
 @time predictions = Mads.forward(md, paramdict)'
 
 Mads.madsinfo("Model predictions ...")
-Mads.spaghettiplot(md, predictions, keyword="w13a-model", format="PNG")
+Mads.spaghettiplot(md, predictions; keyword="w13a-model", format="PNG")
 Mads.display("$rootname-w13a-model-$numberofsamples-spaghetti.png")
 
 @time svrpredictions = svrexec(paramarray)
 @info("SVR discrepancy $(maximum(abs.(svrpredictions .- predictions)))")
 
 Mads.madsinfo("SVR predictions ...")
-Mads.spaghettiplot(md, svrpredictions, keyword="w13a-svr", format="PNG")
+Mads.spaghettiplot(md, svrpredictions; keyword="w13a-svr", format="PNG")
 Mads.display("$rootname-w13a-svr-$numberofsamples-spaghetti.png")
 
 sa = Mads.efast(md)
@@ -34,10 +34,10 @@ mdsvr["Julia model"] = svrexec
 mdsvr["Filename"] = "$rootname-svr.mads"
 
 sasvr = Mads.efast(mdsvr)
-Mads.plotobsSAresults(mdsvr, sasvr, format="PNG")
+Mads.plotobsSAresults(mdsvr, sasvr; format="PNG")
 Mads.display("$rootname-svr-efast-385.png")
 
 svrsave()
 svrclean()
 svrread()
-svrclean()
+svrclean()

examples/contamination/contamination-w04-w13-svr-efast.jl

@@ -10,7 +10,7 @@ Mads.wellon!(md, "w13a") # use well w13a
 numberofsamples = 100
 if !isdefined(Mads, :predictions) || size(predictions) != numberofsamples
 	paramdict = Mads.getparamrandom(md, numberofsamples)
-	paramarray = hcat(map(i->collect(paramdict[i]), keys(paramdict))...)
+	paramarray = hcat(map(i -> collect(paramdict[i]), keys(paramdict))...)
 	predictions = Mads.forward(md, paramdict)'
 end
 
@@ -19,8 +19,8 @@ Mads.spaghettiplot(md, predictions; keyword="w13a-training-set", format="PNG")
 Mads.display("$rootname-w13a-training-set-$numberofsamples-spaghetti.png")
 
 svrpredictions = Array{Float64}(undef, 0, numberofsamples)
-for i=1:50
-	pmodel = SVR.train(predictions[i,:], paramarray'; eps=0.001, C=10000.)
+for i = 1:50
+	pmodel = SVR.train(predictions[i, :], paramarray'; eps=0.001, C=10000.0)
 	y_pr = SVR.predict(pmodel, paramarray')
 	SVR.freemodel(pmodel)
 	svrpredictions = [svrpredictions; y_pr']

examples/contamination/contamination.jl

@@ -17,18 +17,18 @@ Mads.showallparameters(md) # show all the model parameters
 Mads.showparameters(md) # show all the adjustable model parameters
 
 # use all wells
-Mads.plotmadsproblem(md; keyword = "all_wells") # display the well locations and the initial source location
+Mads.plotmadsproblem(md; keyword="all_wells") # display the well locations and the initial source location
 
 forward_predictions = Mads.forward(md) # execute forward model simulation based on initial parameter guesses
 
-Mads.plotmatches(md, forward_predictions; xtitle = "Time [a]", ytitle = "Concentration [ppb]") # plot initial matches
+Mads.plotmatches(md, forward_predictions; xtitle="Time [a]", ytitle="Concentration [ppb]") # plot initial matches
 Mads.display("w01-match.svg")
 
 inverse_parameters, inverse_results = Mads.calibrate(md) # perform model calibration
 
 inverse_predictions = Mads.forward(md, inverse_parameters) # execute forward model simulation based on calibrated values
 
-Mads.plotmatches(md, inverse_predictions; xtitle = "Time [a]", ytitle = "Concentration [ppb]") # plot calibrated matches
+Mads.plotmatches(md, inverse_predictions; xtitle="Time [a]", ytitle="Concentration [ppb]") # plot calibrated matches
 Mads.display("w01-match.svg")
 
 # use only two wells
@@ -37,28 +37,28 @@ Mads.wellon!(md, "w13a") # use well w13a
 Mads.wellon!(md, "w20a") # use well w20a
 Mads.showobservations(md) # show all the observations
 
-Mads.plotmadsproblem(md; keyword = "w13a_w20a") # display the well locations and the initial source location
+Mads.plotmadsproblem(md; keyword="w13a_w20a") # display the well locations and the initial source location
 
 forward_predictions = Mads.forward(md) # execute a forward model simulation based on the initial parameter guesses
 
-Mads.plotmatches(md, forward_predictions, filename = rootname * "-w13a_w20a-init-match.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]") # plot initial matches
+Mads.plotmatches(md, forward_predictions; filename=rootname * "-w13a_w20a-init-match.svg", xtitle="Time [a]", ytitle="Concentration [ppb]") # plot initial matches
 Mads.display("w01-w13a_w20a-init-match.svg")
 
 inverse_parameters, inverse_results = Mads.calibrate(md) # perform model calibration
 
 inverse_predictions = Mads.forward(md, inverse_parameters) # execute a forward model simulation based on the calibrated values
 
-Mads.plotmatches(md, inverse_predictions; filename = rootname * "-w13a_w20a-calib-match.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]") # plot calibrated matches
+Mads.plotmatches(md, inverse_predictions; filename=rootname * "-w13a_w20a-calib-match.svg", xtitle="Time [a]", ytitle="Concentration [ppb]") # plot calibrated matches
 Mads.display("w01-w13a_w20a-calib-match.svg")
 
 # Sensitivity analysis: spaghetti plots based on prior parameter uncertainty ranges
 Mads.madsinfo("Prior spaghetti plot ...")
 paramvalues = Mads.getparamrandom(md, 100)
-Mads.spaghettiplot(md, paramvalues; keyword = "w13a_w20a-prior", xtitle = "Time [a]", ytitle = "Concentration [ppb]")
+Mads.spaghettiplot(md, paramvalues; keyword="w13a_w20a-prior", xtitle="Time [a]", ytitle="Concentration [ppb]")
 Mads.display("w01-w13a_w20a-prior-100-spaghetti.svg")
 
 Mads.madsinfo("Bayesian sampling ...")
-mcmcchain = Mads.bayessampling(md, seed = 20151001)
+mcmcchain = Mads.bayessampling(md; seed=20151001)
 
 Mads.madsinfo("Bayesian scatter plots ...")
 Mads.scatterplotsamples(md, permutedims(mcmcchain.value), rootname * "-bayes.png")
@@ -68,7 +68,7 @@ Mads.display(rootname * "-bayes.png")
 mcmcvalues = Mads.paramarray2dict(md, permutedims(mcmcchain.value))
 
 Mads.madsinfo("Posterior (Bayesian) spaghetti plot ...")
-Mads.spaghettiplot(md, mcmcvalues, keyword = "w13a_w20a-posterior", format = "PNG", xtitle = "Time [a]", ytitle = "Concentration [ppb]")
+Mads.spaghettiplot(md, mcmcvalues; keyword="w13a_w20a-posterior", format="PNG", xtitle="Time [a]", ytitle="Concentration [ppb]")
 Mads.display("w01-w13a_w20a-posterior-1000-spaghetti.png")
 
 # Create a new problem (example)
@@ -90,27 +90,27 @@ md_new["Sources"][1]["gauss"]["dy"]["init"] = 0 # set a point source
 md_new["Sources"][1]["gauss"]["dz"]["init"] = 0 # set a point source
 new_forward_predictions = Mads.forward(md_new) # execute a forward model simulation
 Mads.setobservationtargets!(md_new, new_forward_predictions) # set calibration targets to match the forward model predictions
-Mads.plotmatches(md_new, new_forward_predictions; filename = rootname * "-new-problem.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]")
+Mads.plotmatches(md_new, new_forward_predictions; filename=rootname * "-new-problem.svg", xtitle="Time [a]", ytitle="Concentration [ppb]")
 Mads.display("w01-new-problem.svg")
 Mads.dumpyamlmadsfile(md_new, "w01-new-problem.mads") # write out a new mads input file
 
 # Calibrate with random initial guesses
 Mads.allwellsoff!(md_new) # turn off all wells
 Mads.wellon!(md_new, "w13a") # use well w13a
-Mads.calibraterandom(md_new, 10; seed = 20151001) # calibrate 10 times with random initial guesses
+Mads.calibraterandom(md_new, 10; seed=20151001) # calibrate 10 times with random initial guesses
 
 # Global sensitivity analysis using Saltelli's method
-saltelli_results = Mads.saltelli(md; N = 1000, seed = 2016)
-Mads.plotobsSAresults(md, saltelliresult; filter = r"w13a", filename = "w13a-saltelli.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]")
+saltelli_results = Mads.saltelli(md; N=1000, seed=2016)
+Mads.plotobsSAresults(md, saltelliresult; filter=r"w13a", filename="w13a-saltelli.svg", xtitle="Time [a]", ytitle="Concentration [ppb]")
 Mads.display("w13a-saltelli.svg")
-Mads.plotobsSAresults(md, saltelliresult; filter = r"w20a", filename = "w20a-saltelli.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]")
+Mads.plotobsSAresults(md, saltelliresult; filter=r"w20a", filename="w20a-saltelli.svg", xtitle="Time [a]", ytitle="Concentration [ppb]")
 Mads.display("w20a-saltelli.svg")
 
 # Global sensitivity analysis using Saltelli's eFAST method
-efast_results = Mads.efast(md; N = 1000, seed = 2016)
-Mads.plotobsSAresults(md, efastresult, filter = r"w13a", filename = "w13a-efast.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]")
+efast_results = Mads.efast(md; N=1000, seed=2016)
+Mads.plotobsSAresults(md, efastresult; filter=r"w13a", filename="w13a-efast.svg", xtitle="Time [a]", ytitle="Concentration [ppb]")
 Mads.display("w13a-efast.svg")
-Mads.plotobsSAresults(md, efastresult; filter = r"w20a", filename = "w20a-efast.svg", xtitle = "Time [a]", ytitle = "Concentration [ppb]")
+Mads.plotobsSAresults(md, efastresult; filter=r"w20a", filename="w20a-efast.svg", xtitle="Time [a]", ytitle="Concentration [ppb]")
 Mads.display("w20a-efast.svg")
 
 cd(currentdir)

examples/contamination/runtests.jl

@@ -82,8 +82,8 @@ if !haskey(ENV, "MADS_NO_GADFLY")
 	Mads.spaghettiplots(md, paramvalues; keyword="w13a_w20a")
 	Mads.spaghettiplot(md, paramvalues; keyword="w13a_w20a")
 	s = splitdir(rootname)
-	for filesinadir in Mads.searchdir(Regex(string(s[2], "-w13a_w20a", "[.]*", "spaghetti.svg")), path=s[1])
-		Mads.rmfile(filesinadir, path=s[1])
+	for filesinadir in Mads.searchdir(Regex(string(s[2], "-w13a_w20a", "[.]*", "spaghetti.svg")); path=s[1])
+		Mads.rmfile(filesinadir; path=s[1])
 	end
 
 	sa_results = Mads.saltelli(md; N=5, seed=2015)
@@ -108,43 +108,42 @@ Test.@testset "Contamination" begin
 	Test.@test forward_predictions_source == forward_predictions
 	Test.@test isapprox(sum(abs.(forward_predictions_vector .- good_forward_predictions)), 0, atol=1e-4)
 	Test.@test isapprox(sum(abs.(jacobian .- good_jacobian)), 0, atol=1e-4)
-	Test.@test isapprox(Statistics.mean([abs.(param_values[i] - [40.0,4.0,15.0][i]) for i=1:3]), 0, atol=1e-4)
+	Test.@test isapprox(Statistics.mean([abs.(param_values[i] - [40.0, 4.0, 15.0][i]) for i = 1:3]), 0, atol=1e-4)
 	Test.@test all(Mads.getwelldata(md) .== [1608.0 2113.0; 1491.0 1479.0; 3.0 3.0])
 
-	t = isapprox(Statistics.mean([abs.(samples[i] - good_samples[i]) for i=1:size(good_samples)[1]+20]), 0, atol=1e-4)
+	t = isapprox(Statistics.mean([abs.(samples[i] - good_samples[i]) for i = 1:(size(good_samples)[1] + 20)]), 0; atol=1e-4)
 	if t
-		Test.@test isapprox(Statistics.mean([abs.(samples[i] - good_samples[i]) for i=1:size(good_samples)[1]+20]), 0, atol=1e-4)
+		Test.@test isapprox(Statistics.mean([abs.(samples[i] - good_samples[i]) for i = 1:(size(good_samples)[1] + 20)]), 0, atol=1e-4)
 	else
 		@show samples
 	end
 
-	t = isapprox(Statistics.mean([abs.(llhoods[i] - good_llhoods[i]) for i=1:size(good_llhoods)[1]]), 0, atol=1e-4)
+	t = isapprox(Statistics.mean([abs.(llhoods[i] - good_llhoods[i]) for i = 1:size(good_llhoods)[1]]), 0; atol=1e-4)
 	if t
-		Test.@test isapprox(Statistics.mean([abs.(llhoods[i] - good_llhoods[i]) for i=1:size(good_llhoods)[1]]), 0, atol=1e-4)
+		Test.@test isapprox(Statistics.mean([abs.(llhoods[i] - good_llhoods[i]) for i = 1:size(good_llhoods)[1]]), 0, atol=1e-4)
 	else
 		@show good_llhoods
 		@show llhoods
 	end
 
-
-	t = isapprox(Statistics.mean([abs.(newllhoods[i] - good_newllhoods[i]) for i=1:size(newllhoods)[1]]), 0, atol=1e-3)
+	t = isapprox(Statistics.mean([abs.(newllhoods[i] - good_newllhoods[i]) for i = 1:size(newllhoods)[1]]), 0; atol=1e-3)
 	if t
-		Test.@test isapprox(Statistics.mean([abs.(newllhoods[i] - good_newllhoods[i]) for i=1:size(newllhoods)[1]]), 0, atol=1e-3)
+		Test.@test isapprox(Statistics.mean([abs.(newllhoods[i] - good_newllhoods[i]) for i = 1:size(newllhoods)[1]]), 0, atol=1e-3)
 	else
 		@show good_newllhoods
 		@show newllhoods
 	end
 
-	t = isapprox(Statistics.mean([abs.(Mads.computemass(md; time=50.0)[i] - (550.0,0)[i]) for i=1:2]), 0, atol=1e-5)
+	t = isapprox(Statistics.mean([abs.(Mads.computemass(md; time=50.0)[i] - (550.0, 0)[i]) for i = 1:2]), 0; atol=1e-5)
 	if t
-		Test.@test isapprox(Statistics.mean([abs.(Mads.computemass(md; time=50.0)[i] - (550.0,0)[i]) for i=1:2]), 0, atol=1e-5)
+		Test.@test isapprox(Statistics.mean([abs.(Mads.computemass(md; time=50.0)[i] - (550.0, 0)[i]) for i = 1:2]), 0, atol=1e-5)
 	else
 		@show Mads.computemass(md; time=50.0)
 	end
 
-	t = isapprox(Statistics.mean([abs.(Mads.computemass(md)[i] - (550.0,0)[i]) for i=1:2]), 0, atol=1e-5)
+	t = isapprox(Statistics.mean([abs.(Mads.computemass(md)[i] - (550.0, 0)[i]) for i = 1:2]), 0; atol=1e-5)
 	if t
-		Test.@test isapprox(Statistics.mean([abs.(Mads.computemass(md)[i] - (550.0,0)[i]) for i=1:2]), 0, atol=1e-5)
+		Test.@test isapprox(Statistics.mean([abs.(Mads.computemass(md)[i] - (550.0, 0)[i]) for i = 1:2]), 0, atol=1e-5)
 	else
 		@show Mads.computemass(md)
 	end
@@ -153,31 +152,31 @@ Test.@testset "Contamination" begin
 	good_inverse_preds = JLD2.load(joinpath(workdir, "test_results", "inverse_predictions.jld2"), "inverse_predictions")
 
 	# Testing time-based well data against itself
-	ssr = 0.
-	for i=1:size(good_welldata_time)[1]
-		for j=1:size(good_welldata_time)[2]
-			ssr += (welldata_time[i*j] - good_welldata_time[i*j])^2
+	ssr = 0.0
+	for i = 1:size(good_welldata_time)[1]
+		for j = 1:size(good_welldata_time)[2]
+			ssr += (welldata_time[i * j] - good_welldata_time[i * j])^2
 		end
 	end
 
-	t = isapprox(ssr, 0., atol=1e-8)
+	t = isapprox(ssr, 0.0; atol=1e-8)
 	if t
-		Test.@test isapprox(ssr, 0., atol=1e-8)
+		Test.@test isapprox(ssr, 0.0, atol=1e-8)
 	else
 		@show welldata_time
 		@show good_welldata_time
 		@show ssr
 	end
 
 	# Testing inverse predictions against itself
-	ssr = 0.
+	ssr = 0.0
 	for obskey in union(Set(keys(inverse_predictions)), Set(keys(good_inverse_preds)))
 		ssr += (inverse_predictions[obskey] - good_inverse_preds[obskey])^2
 	end
 
-	t = isapprox(ssr, 0., atol=1e-8)
+	t = isapprox(ssr, 0.0; atol=1e-8)
 	if t
-		Test.@test isapprox(ssr, 0., atol=1e-8)
+		Test.@test isapprox(ssr, 0.0, atol=1e-8)
 	else
 		@show inverse_predictions
 		@show good_inverse_preds
@@ -189,10 +188,10 @@ Mads.@stdouterrcapture Mads.addsource!(md)
 Mads.@stdouterrcapture Mads.removesource!(md)
 
 Mads.rmfile(joinpath(workdir, "w01-w13a_w20a-match.svg"))
-Mads.rmfile(joinpath(workdir,"w01-w13a_w20a.initialresults"))
-Mads.rmfile(joinpath(workdir,"w01-w13a_w20a_saltelli_5.jld2"))
+Mads.rmfile(joinpath(workdir, "w01-w13a_w20a.initialresults"))
+Mads.rmfile(joinpath(workdir, "w01-w13a_w20a_saltelli_5.jld2"))
 
 Mads.veryquietoff()
 Mads.graphon()
 
-:passed
+:passed