emcee fix

src/MadsMonteCarlo.jl

@@ -32,14 +32,14 @@ function loadecmeeresults(madsdata::AbstractDict, filename::AbstractString)
 			return nothing, nothing, nothing
 		end
 		chain, llhoods, params, obs = JLD2.load(filename, "chain",  "llhoods", "params", "obs")
-		@info("AffineInvariantMCMC results loaded: number of parameters = $(size(chain, 1)); number of realizations = $(size(chain, 2))")
+		@info("AffineInvariantMCMC results loaded: number of parameters = $(size(chain, 1)); number of observation = $(size(obs, 1)); number of realizations = $(size(chain, 2))")
 		if !Mads.jld2haskey(filename, "observations", "params", "obs"; quiet=false)
 			@warn("$(filename) does not contain AffineInvariantMCMC observation results!")
 			return chain, llhoods, nothing
 		end
 		observations, params, obs = JLD2.load(filename, "observations", "params", "obs")
 		flag_bad_data = false
-		if  size(observations, 1) != length(Mads.getobskeys(madsdata))
+		if size(observations, 1) != length(Mads.getobskeys(madsdata))
 			@warn("Different number of observations (Mads $(length(Mads.getobskeys(madsdata))) vs Input $(size(observations, 1)))!")
 			flag_bad_data = true
 		end
@@ -81,7 +81,7 @@ function loadecmeeresults(madsdata::AbstractDict, filename::AbstractString)
 	end
 end
 
-function emceesampling(madsdata::AbstractDict; filename::AbstractString="", load::Bool=false, save::Bool=false, execute::Bool=true, numwalkers::Integer=10, sigma::Number=0.01, seed::Integer=-1, rng::Union{Nothing,Random.AbstractRNG,DataType}=nothing, kw...)
+function emceesampling(madsdata::AbstractDict; filename::AbstractString="", load::Bool=false, save::Bool=false, execute::Bool=true, numwalkers::Integer=10, nexecutions::Integer=100, burnin::Integer=numwalkers, thinning::Integer=10, sigma::Number=0.01, seed::Integer=-1, rng::Union{Nothing,Random.AbstractRNG,DataType}=nothing, kw...)
 	if filename != ""
 		load = save = true
 	end
@@ -122,7 +122,7 @@ function emceesampling(madsdata::AbstractDict; filename::AbstractString="", load
 			p0[i, j] = pmin[i] + rand(Mads.rng, d) * (pmax[i] - pmin[i])
 		end
 	end
-	chain, llhoods, observations = emceesampling(madsdata, p0; filename=filename, load=load, save=save, execute=execute, numwalkers=numwalkers, seed=seed, rng=rng, kw...)
+	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)
@@ -144,12 +144,19 @@ function emceesampling(madsdata::AbstractDict, p0::AbstractMatrix; filename::Abs
 			@warn("Empty!")
 			bad_data = true
 		else
-			if size(chain, 2) != div(numsamples_perwalker, thinning) * numwalkers
+			if size(chain, 2) != div(nexecutions, thinning)
 				@warn("Preexisting data does not match the number of walkers and steps!")
 				bad_data = true
-			elseif size(chain, 1) != no
+				poop
+			elseif size(chain, 1) != np
 				@warn("Preexisting data does not match the number of parameters!")
 				bad_data = true
+			elseif size(observations, 1) != no
+				@warn("Preexisting data does not match the number of observations!")
+				bad_data = true
+			elseif size(observations, 2) != size(chain, 2) != length(llhoods)
+				@warn("Preexisting data dimension do not match!")
+				bad_data = true
 			end
 		end
 		if bad_data
@@ -179,7 +186,7 @@ function emceesampling(madsdata::AbstractDict, p0::AbstractMatrix; filename::Abs
 	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.flattenmcmcarray(chain, llhoods)
+	chain, llhoods = AffineInvariantMCMC.flattenmcmcarray(chain, llhoods)
 	if save
 		madsinfo("Saving AffineInvariantMCMC results in $(filename) ...")
 		JLD2.save(filename, "chain", chain, "llhoods", llhoods, "params", Mads.getoptparamkeys(madsdata), "obs", Mads.getobskeys(madsdata))
@@ -358,4 +365,4 @@ Returns:
 """
 function paramdict2array(dict::AbstractDict)
 	return permutedims(hcat(map(i->collect(dict[i]), collect(keys(dict)))...))
-end
+end