emcee fixes

src/MadsForward.jl

@@ -153,7 +153,13 @@ function forward(madsdata::AbstractDict, paramarray::AbstractArray; parallel::Bo
 	else
 		r = values(func_forward(paramarray))
 	end
-	return collect(r)
+	if isnothing(r)
+		@error("Model run failed! Check for errors in the model function and in the output above!")
+		throw(ErrorException("Model run failed!"))
+		return nothing
+	else
+		return collect(r)
+	end
 end
 
 @doc """

src/MadsFunc.jl

@@ -319,15 +319,13 @@ function makemadscommandfunction(madsdata_in::AbstractDict; obskeys::AbstractVec
 		parameterswithexpressions = evaluatemadsexpressions(madsdata, paramsnoexpressions)
 		local out = nothing
 		try
-			out = madscommandfunction(parameterswithexpressions)
+			out = Base.invokelatest(madscommandfunction, parameterswithexpressions)
 		catch errmsg
-			try
-				out = Base.invokelatest(madscommandfunction, parameterswithexpressions)
-			catch errmsg
-				printerrormsg(errmsg)
-				Mads.madswarn("Failed Dir: $(pwd())")
-				Mads.madserror("madscommandfunction in madscommandfunctionwithexpressions cannot be executed!")
+			printerrormsg(errmsg)
+			if pwd() != Mads.dir
+				Mads.madswarn("Execution failed in directory: $(pwd())")
 			end
+			Mads.madserror("madscommandfunction cannot be executed!")
 		end
 		return out
 	end

src/MadsMonteCarlo.jl

@@ -125,7 +125,7 @@ function emceesampling(madsdata::AbstractDict; filename::AbstractString="", load
 	chain, llhoods, observations = emceesampling(madsdata, p0; filename=filename, load=load, save=save, execute=execute, numwalkers=numwalkers, nexecutions=nexecutions, burnin=burnin, thinning=thinning, seed=seed, rng=rng, kw...)
 	return chain, llhoods, observations
 end
-function emceesampling(madsdata::AbstractDict, p0::AbstractMatrix; filename::AbstractString="", load::Bool=false, save::Bool=false, execute::Bool=true, numwalkers::Integer=10, nexecutions::Integer=100, burnin::Integer=numwalkers, thinning::Integer=10, seed::Integer=-1, weightfactor::Number=1.0, rng::Union{Nothing, Random.AbstractRNG, DataType}=nothing, distributed_function::Bool=false)
+function emceesampling(madsdata::AbstractDict, p0::AbstractMatrix; filename::AbstractString="", load::Bool=false, save::Bool=false, execute::Bool=true, numwalkers::Integer=10, nexecutions::Integer=100, burnin::Integer=numwalkers, thinning::Integer=10, seed::Integer=-1, weightfactor::Number=1.0, rng::Union{Nothing, Random.AbstractRNG, DataType}=nothing, distributed_function::Bool=false, type::DataType=Float64, checkoutputs::Bool=true)
 	if filename != ""
 		save = true
 	end
@@ -178,7 +178,6 @@ function emceesampling(madsdata::AbstractDict, p0::AbstractMatrix; filename::Abs
 	Mads.setseed(seed; rng=rng)
 	madsloglikelihood = makemadsloglikelihood(madsdata; weightfactor=weightfactor)
 	arrayloglikelihood = Mads.makearrayloglikelihood(madsdata, madsloglikelihood)
-	sleep(1) # rest to avoid a "method too new" error
 	if distributed_function
 		Distributed.@everywhere arrayloglikelihood_distributed = Mads.makearrayloglikelihood($madsdata, $madsloglikelihood)
 		arrayloglikelihood = (x) -> Core.eval(Main, :arrayloglikelihood_distributed)(x)
@@ -189,11 +188,10 @@ function emceesampling(madsdata::AbstractDict, p0::AbstractMatrix; filename::Abs
 	end
 	numsamples = numsamples_perwalker_burnin * numwalkers
 	@info("AffineInvariantMCMC burning stage (total number of executions $(numsamples), final burning chain size $(numsamples_perwalker_burnin * numwalkers))...")
-	sleep(5)
-	burninchain, _ = AffineInvariantMCMC.sample(arrayloglikelihood, numwalkers, p0, numsamples_perwalker_burnin, 1; filename="", load=false, save=false, rng=Mads.rng)
+	burninchain, _ = AffineInvariantMCMC.sample(arrayloglikelihood, numwalkers, p0, numsamples_perwalker_burnin, 1; filename="", load=false, save=false, rng=Mads.rng, type=type, checkoutputs=checkoutputs)
 	numsamples = numsamples_perwalker * numwalkers
 	@info("AffineInvariantMCMC exploration stage (total number of executions $(numsamples), final chain size $(div(numsamples_perwalker, thinning) * numwalkers))...")
-	chain, llhoods = AffineInvariantMCMC.sample(arrayloglikelihood, numwalkers, burninchain[:, :, end], numsamples_perwalker, thinning; filename="", load=false, save=false, rng=Mads.rng)
+	chain, llhoods = AffineInvariantMCMC.sample(arrayloglikelihood, numwalkers, burninchain[:, :, end], numsamples_perwalker, thinning; filename="", load=false, save=false, rng=Mads.rng, type=type, checkoutputs=checkoutputs)
 	chain, llhoods = AffineInvariantMCMC.flattenmcmcarray(chain, llhoods)
 	if save
 		madsinfo("Saving AffineInvariantMCMC results in $(filename) ...")