diff --git a/test/test_estimation.jl b/test/test_estimation.jl
index 69625581..16de11b6 100644
--- a/test/test_estimation.jl
+++ b/test/test_estimation.jl
@@ -1,4 +1,3 @@
-using Revise
 using MacroModelling
 import Turing, Pigeons, Zygote
 import Turing: NUTS, sample, logpdf, AutoZygote
@@ -9,7 +8,7 @@ import DynamicPPL
 include("../models/FS2000.jl")
 
 # load data
-dat = CSV.read("test/data/FS2000_data.csv", DataFrame)
+dat = CSV.read("data/FS2000_data.csv", DataFrame)
 data = KeyedArray(Array(dat)',Variable = Symbol.("log_".*names(dat)),Time = 1:size(dat)[1])
 data = log.(data)
 
@@ -37,7 +36,7 @@ Turing.@model function FS2000_loglikelihood_function(data, m)
     all_params ~ Turing.arraydist(dists)
 
     if DynamicPPL.leafcontext(__context__) !== DynamicPPL.PriorContext() 
-        Turing.@addlogprob! get_loglikelihood(m, data, all_params, verbose = false)
+        Turing.@addlogprob! get_loglikelihood(m, data, all_params)
     end
 end
 
@@ -48,15 +47,15 @@ n_samples = 1000
 
 # using Zygote
 # Turing.setadbackend(:zygote)
-# samps = @time sample(FS2000_loglikelihood, NUTS(), n_samples, progress = true, initial_params = FS2000.parameter_values)
+samps = @time sample(FS2000_loglikelihood, NUTS(), n_samples, progress = true, initial_params = FS2000.parameter_values)
 
-# println("Mean variable values (ForwardDiff): $(mean(samps).nt.mean)")
+println("Mean variable values (ForwardDiff): $(mean(samps).nt.mean)")
 
-# samps = @time sample(FS2000_loglikelihood, NUTS(adtype = Turing.AutoZygote()), n_samples, progress = true, initial_params = FS2000.parameter_values)
+samps = @time sample(FS2000_loglikelihood, NUTS(adtype = Turing.AutoZygote()), n_samples, progress = true, initial_params = FS2000.parameter_values)
 
-# println("Mean variable values (Zygote): $(mean(samps).nt.mean)")
+println("Mean variable values (Zygote): $(mean(samps).nt.mean)")
 
-# sample_nuts = mean(samps).nt.mean
+sample_nuts = mean(samps).nt.mean
 
 
 # generate a Pigeons log potential
@@ -84,27 +83,12 @@ end
 # define a specific initialization for this model
 Pigeons.initialization(::Pigeons.TuringLogPotential{typeof(FS2000_loglikelihood_function)}, ::AbstractRNG, ::Int64) = deepcopy(XMAX)
 
-Pigeons.pigeons(target = FS2000_lp,
+pt = @time Pigeons.pigeons(target = FS2000_lp,
             record = [Pigeons.traces; Pigeons.round_trip; Pigeons.record_default()],
             n_chains = 1,
             n_rounds = 10,
             multithreaded = true)
 
-pt = @profview Pigeons.pigeons(target = FS2000_lp,
-            record = [Pigeons.traces; Pigeons.round_trip; Pigeons.record_default()],
-            n_chains = 1,
-            n_rounds = 1,
-            multithreaded = false)
-        #     ────────────────────────────────────────────────────────────────────────────
-        #     scans     restarts    time(s)    allc(B)  log(Z₁/Z₀)   min(αₑ)   mean(αₑ) 
-        #   ────────── ────────── ────────── ────────── ────────── ────────── ──────────
-        #           2          0        4.8   3.21e+09          0      0.982      0.982 
-        #           4          0       7.17   4.99e+09          0          1          1 
-        #           8          0       12.5   8.73e+09          0          1          1 
-        #          16          0       28.5   1.97e+10          0          1          1 
-        #          32          0       56.2   3.89e+10          0          1          1 
-        #          64          0        109   7.47e+10          0          1          1 
-        #         128          0        230   1.57e+11          0          1          1 
 samps = MCMCChains.Chains(pt)
 
 println("Mean variable values (Pigeons): $(mean(samps).nt.mean)")