Make number of calls to consistent

src/core/mcs.jl

@@ -1,20 +1,21 @@
 function get_probdists_gtap_df(gtap, n=1000)
-    if gtap in ["highDF", "midDF", "lowDF"]
-        highDF = gtap_df_all[:, :, 3]
-        lowDF = gtap_df_all[:, :, 4]
-        midDF = gtap_df_all[:, :, 5]
+
+    highDF = gtap_df_all[:, :, 3]
+    lowDF = gtap_df_all[:, :, 4]
+    midDF = gtap_df_all[:, :, 5]
 
-        # For each region and temperature point we construct an interpolation where the x values are between 0 and 1
-        # and the y values are the values from the three scenarios.
-        dists = [LinearInterpolation([0.,0.5,1.], [lowDF[r,temp],midDF[r,temp], highDF[r,temp]]) for r in 1:16, temp in 1:3]
+    # For each region and temperature point we construct an interpolation where the x values are between 0 and 1
+    # and the y values are the values from the three scenarios.
+    dists = [LinearInterpolation([0.,0.5,1.], [lowDF[r,temp],midDF[r,temp], highDF[r,temp]]) for r in 1:16, temp in 1:3]
 
-        # We only sample one set of random numbers, as we want perfect correlation between all the individual
-        # parameter values.
-        samples = rand(TriangularDist(0., 1., 0.5), n)
+    # We only sample one set of random numbers, as we want perfect correlation between all the individual
+    # parameter values.
+    samples = rand(TriangularDist(0., 1., 0.5), n)
 
-        # Now evaluate the interpolated function we created above with the samples from the triangular distributions
-        sample_stores = [Mimi.SampleStore(dists[r,temp].(samples)) for r in 1:16, temp in 1:3]
+    # Now evaluate the interpolated function we created above with the samples from the triangular distributions
+    sample_stores = [Mimi.SampleStore(dists[r,temp].(samples)) for r in 1:16, temp in 1:3]
 
+    if gtap in ["highDF", "midDF", "lowDF"]
         return sample_stores
     else
         @warn "No probability distributions available for gtap damage function entered ($gtap), returning `nothing`."