diff --git a/src/chains.jl b/src/chains.jl
index 9583fec5..0fee7e9e 100644
--- a/src/chains.jl
+++ b/src/chains.jl
@@ -324,7 +324,7 @@ function header(c::AbstractChains; section=missing)
 
     # Return header.
     return string(
-        # "Log model evidence = $(c.logevidence)\n", #FIXME: Uncomment.
+        "Log evidence      = $(c.logevidence)\n",
         "Iterations        = $(first(c)):$(last(c))\n",
         "Thinning interval = $(step(c))\n",
         "Chains            = $(join(map(string, chains(c)), ", "))\n",
diff --git a/test/demo-plot.png b/test/demo-plot.png
deleted file mode 100644
index 154e0c1e..00000000
Binary files a/test/demo-plot.png and /dev/null differ
diff --git a/test/plot_test.jl b/test/plot_test.jl
index 37460981..283e0480 100644
--- a/test/plot_test.jl
+++ b/test/plot_test.jl
@@ -53,4 +53,6 @@ chn = Chains(val)
 
     p_subset_colordim = plot(chn, 2, colordim = :parameter)
     @test isa(p_subset_colordim, Plots.Plot)
+
+    rm("demo-plot.png")
 end
diff --git a/test/sections_test.txt b/test/sections_test.txt
deleted file mode 100644
index 086ec014..00000000
--- a/test/sections_test.txt
+++ /dev/null
@@ -1,70 +0,0 @@
-Iterations        = 1:100
-Thinning interval = 1
-Chains            = Chain1, Chain2, Chain3, Chain4
-Samples per chain = 100
-parameters        = sigma, mu
-
-Empirical Posterior Estimates:
-=============================================
-parameters
-       Mean      SD    Naive SE   MCSE    ESS
-   mu 2200.5 1007.9052  50.3953 580.9475 3.01
-sigma 2100.5 1007.9052  50.3953 580.9475 3.01
-
-Quantiles:
-=============================================
-parameters
-      2.5%  25.0%   50.0%  75.0%  97.5%
-   mu  801 1500.75 2200.5 2900.25  3600
-sigma  701 1400.75 2100.5 2800.25  3500
-
-Iterations        = 1:100
-Thinning interval = 1
-Chains            = Chain1, Chain2, Chain3, Chain4
-Samples per chain = 100
-internals         = lp__, accept_stat__, stepsize__, treedepth__, n_leapfrog__, divergent__, energy__
-
-Empirical Posterior Estimates:
-=====================================================
-internals
-               Mean      SD    Naive SE   MCSE    ESS
-accept_stat__ 1500.5 1007.9052  50.3953 580.9475 3.01
-  divergent__ 1900.5 1007.9052  50.3953 580.9475 3.01
-     energy__ 2000.5 1007.9052  50.3953 580.9475 3.01
-         lp__ 1400.5 1007.9052  50.3953 580.9475 3.01
- n_leapfrog__ 1800.5 1007.9052  50.3953 580.9475 3.01
-   stepsize__ 1600.5 1007.9052  50.3953 580.9475 3.01
-  treedepth__ 1700.5 1007.9052  50.3953 580.9475 3.01
-
-Quantiles:
-=====================================================
-internals
-              2.5%  25.0%   50.0%  75.0%  97.5%
-accept_stat__  101  800.75 1500.5 2200.25  2900
-  divergent__  501 1200.75 1900.5 2600.25  3300
-     energy__  601 1300.75 2000.5 2700.25  3400
-         lp__    1  700.75 1400.5 2100.25  2800
- n_leapfrog__  401 1100.75 1800.5 2500.25  3200
-   stepsize__  201  900.75 1600.5 2300.25  3000
-  treedepth__  301 1000.75 1700.5 2400.25  3100
-
-Iterations        = 1:100
-Thinning interval = 1
-Chains            = Chain1, Chain2, Chain3, Chain4
-Samples per chain = 100
-parameters        = sigma, mu
-
-Empirical Posterior Estimates:
-=============================================
-parameters
-       Mean      SD    Naive SE   MCSE    ESS
-   mu 2200.5 1007.9052  50.3953 580.9475 3.01
-sigma 2100.5 1007.9052  50.3953 580.9475 3.01
-
-Quantiles:
-=============================================
-parameters
-      2.5%  25.0%   50.0%  75.0%  97.5%
-   mu  801 1500.75 2200.5 2900.25  3600
-sigma  701 1400.75 2100.5 2800.25  3500
-
diff --git a/test/sections_tests.jl b/test/sections_tests.jl
index df41214e..407f272f 100644
--- a/test/sections_tests.jl
+++ b/test/sections_tests.jl
@@ -1,34 +1,45 @@
 using  MCMCChains, Test
 
 @testset "describe sections" begin
-  
-  a3d = reshape(1:3600, 100, 9, 4)
-
-  cnames = ["lp__"  , "accept_stat__", "stepsize__" , "treedepth__" ,
-    "n_leapfrog__" , "divergent__"  , "energy__", "sigma", "mu" ]
-
-  pi = filter(p -> length(p) > 2 && p[end-1:end] == "__", cnames)
-  p = filter(p -> !(p in  pi), cnames)
-
-  c = Chains(a3d,
-    Symbol.(cnames),
-    Dict(
-      :parameters => Symbol.(p),
-      :internals => Symbol.(pi)
-    )
-  )
-  
-  tmpdir = tempdir()
-  open(joinpath(tmpdir, "sections_test.txt"), "w") do io
-    describe(io, c, section=:parameters);
-    describe(io, c, section=:internals);
-    describe(io, c);
-  end
-
-  
-  @test open(f->read(f, String), joinpath(tmpdir, "sections_test.txt")) ==
-      open(f->read(f, String), joinpath(@__DIR__, "sections_test.txt"))
-    
-  #println(open(f->read(f, String), joinpath(tmpdir, "sections_test.txt")))
-  
+
+    a3d = reshape(1:3600, 100, 9, 4)
+
+    cnames = ["lp__"  , "accept_stat__", "stepsize__" , "treedepth__" ,
+        "n_leapfrog__" , "divergent__"  , "energy__", "sigma", "mu" ]
+
+    pi = filter(p -> length(p) > 2 && p[end-1:end] == "__", cnames)
+    p = filter(p -> !(p in  pi), cnames)
+
+    c = Chains(a3d,
+        Symbol.(cnames),
+            Dict(
+                :parameters => Symbol.(p),
+                :internals => Symbol.(pi)
+                )
+        )
+
+    c2 = Chains(a3d,
+        Symbol.(cnames),
+            Dict(
+                :parameters => Symbol.(p),
+                :internals => Symbol.(pi)
+            )
+        )
+
+    tmpdir = tempdir()
+    open(joinpath(tmpdir, "sections_test1.txt"), "w") do io
+        describe(io, c, section=:parameters);
+        describe(io, c, section=:internals);
+        describe(io, c);
+    end
+
+    open(joinpath(tmpdir, "sections_test2.txt"), "w") do io
+        describe(io, c2, section=:parameters);
+        describe(io, c2, section=:internals);
+        describe(io, c2);
+    end
+
+
+    @test open(f->read(f, String), joinpath(tmpdir, "sections_test1.txt")) ==
+        open(f->read(f, String), joinpath(tmpdir, "sections_test2.txt"))
 end