diff --git a/src/BeefBLUP.jl b/src/BeefBLUP.jl
index a2dbfe1..bcfb815 100755
--- a/src/BeefBLUP.jl
+++ b/src/BeefBLUP.jl
@@ -18,32 +18,32 @@ using Gtk
 # Main entry-level function - acts just like the script
 function beefblup()
 
-# Ask for an input spreadsheet
-path = open_dialog_native(
+    # Ask for an input spreadsheet
+    path = open_dialog_native(
     "Select a beefblup worksheet",
     GtkNullContainer(),
     ("*.csv", GtkFileFilter("*.csv", name="beefblup worksheet"))
-)
+    )
 
-# Ask for an output text filename
-savepath = save_dialog_native(
+    # Ask for an output text filename
+    savepath = save_dialog_native(
     "Save your beefblup results",
     GtkNullContainer(),
     (GtkFileFilter("*.txt", name="Results file"),
     "*.txt")
-)
+    )
 
-# Ask for heritability
-print("What is the heritability for this trait?> ")
-h2 = parse(Float64, readline(stdin))
+    # Ask for heritability
+    print("What is the heritability for this trait?> ")
+    h2 = parse(Float64, readline(stdin))
 
-beefblup(path, savepath, h2)
+    beefblup(path, savepath, h2)
 
 end
 
 function beefblup(datafile::String, h2::Float64)
     # Assume the data is named the same as the file without the trailing extension
-    dataname = join(split(datafile, ".")[1:end-1])
+    dataname = join(split(datafile, ".")[1:end - 1])
 
     # Create a new results name
     resultsfile = string(dataname, "_results.txt")
@@ -55,211 +55,210 @@ end
 # Main worker function, can perform all the work if given all the user input
 function beefblup(path::String, savepath::String, h2::Float64)
 
-# Import data from a suitable spreadsheet
-data = DataFrame(CSV.File(path))
+    # Import data from a suitable spreadsheet
+    data = DataFrame(CSV.File(path))
 
-# Sort the array by date
-sort!(data, :birthdate)
+    # Sort the array by date
+    sort!(data, :birthdate)
 
-# Define fields to hold id values for animals and their parents
-numanimals = length(data.id)
+    # Define fields to hold id values for animals and their parents
+    numanimals = length(data.id)
 
-# Find the index values for animals and their parents
-dam = indexin(data.dam, data.id)
-sire = indexin(data.sire, data.id)
+    # Find the index values for animals and their parents
+    dam = indexin(data.dam, data.id)
+    sire = indexin(data.sire, data.id)
 
-# Extract all of the fixed effects
-fixedfx = select(data, Not([:id, :birthdate, :sire, :dam]))[:,1:end-1]
+    # Extract all of the fixed effects
+    fixedfx = select(data, Not([:id, :birthdate, :sire, :dam]))[:,1:end - 1]
 
-# Find any columns that need to be deleted
-for i in 1:ncol(fixedfx)
-    if length(unique(fixedfx[:,i])) <= 1
-        @warn string("column '", names(fixedfx)[i], "' does not have any unique animals and will be removed from this analysis")
-        DataFrames.select!(fixedfx,Not(i))
-    end
-end
-
-# Determine how many contemporary groups there are
-numtraits = ncol(fixedfx)
-numgroups = ones(1, numtraits)
-for i in 1:numtraits
-    numgroups[i] = length(unique(fixedfx[:,i]))
-end
-
-# If there are more groups than animals, then the analysis cannot continue
-if sum(numgroups) >= numanimals
-    throw(ErrorException("there are more contemporary groups than animals"))
-end
-
-# Define a "normal" animal as one of the last in the groups, provided that
-# all traits do not have null values
-normal = Array{String}(undef,1,numtraits)
-for i in 1:numtraits
-    for j in numanimals:-1:1
-        if !ismissing(fixedfx[j,i])
-            normal[i] = string(fixedfx[j,i])
-            break
+    # Find any columns that need to be deleted
+    for i in 1:ncol(fixedfx)
+        if length(unique(fixedfx[:,i])) <= 1
+            @warn string("column '", names(fixedfx)[i], "' does not have any unique animals and will be removed from this analysis")
+            DataFrames.select!(fixedfx, Not(i))
         end
     end
-end
 
-# Form the fixed-effect matrix
-X = zeros(Int8, numanimals, floor(Int,sum(numgroups))-length(numgroups)+1)
-X[:,1] = ones(Int8, 1, numanimals)
+    # Determine how many contemporary groups there are
+    numtraits = ncol(fixedfx)
+    numgroups = ones(1, numtraits)
+    for i in 1:numtraits
+        numgroups[i] = length(unique(fixedfx[:,i]))
+    end
 
-# Create an external counter that will increment through both loops
-counter = 2
+    # If there are more groups than animals, then the analysis cannot continue
+    if sum(numgroups) >= numanimals
+        throw(ErrorException("there are more contemporary groups than animals"))
+    end
 
-# Store the traits in a string array
-adjustedtraits =
-Array{String}(undef,floor(Int,sum(numgroups))-length(numgroups))
-# Iterate through each group
-for i in 1:length(normal)
-    # Find the traits that are present in this trait
-    localdata = string.(fixedfx[:,i])
-    traits = unique(localdata)
-    # Remove the normal version from the analysis
-    effecttraits = traits[findall(x -> x != normal[i], traits)]
-    # Iterate inside of the group
-    for j in 1:(length(effecttraits))
+    # Define a "normal" animal as one of the last in the groups, provided that
+    # all traits do not have null values
+    normal = Array{String}(undef, 1, numtraits)
+    for i in 1:numtraits
+        for j in numanimals:-1:1
+            if !ismissing(fixedfx[j,i])
+                normal[i] = string(fixedfx[j,i])
+                break
+            end
+        end
+    end
+
+    # Form the fixed-effect matrix
+    X = zeros(Int8, numanimals, floor(Int, sum(numgroups)) - length(numgroups) + 1)
+    X[:,1] = ones(Int8, 1, numanimals)
+
+    # Create an external counter that will increment through both loops
+    counter = 2
+
+    # Store the traits in a string array
+    adjustedtraits =
+    Array{String}(undef,floor(Int, sum(numgroups)) - length(numgroups))
+    # Iterate through each group
+    for i in 1:length(normal)
+        # Find the traits that are present in this trait
+        localdata = string.(fixedfx[:,i])
+        traits = unique(localdata)
+        # Remove the normal version from the analysis
+        effecttraits = traits[findall(x -> x != normal[i], traits)]
+        # Iterate inside of the group
+        for j in 1:(length(effecttraits))
             matchedindex = findall(x -> x == effecttraits[j], localdata)
             X[matchedindex, counter] .= 1
             # Add this trait to the string
             adjustedtraits[counter - 1] = traits[j]
-        # Increment the big counter
-        counter = counter + 1
-    end
-end
-
-# Create an empty matrix for the additive relationship matrix
-A = zeros(numanimals, numanimals)
-
-# Create the additive relationship matrix by the FORTRAN method presented by
-# Henderson
-for i in 1:numanimals
-    if !isnothing(dam[i]) && !isnothing(sire[i])
-        for j in 1:(i-1)
-            A[j,i] = 0.5*(A[j,sire[i]] + A[j,dam[i]])
-            A[i,j] = A[j,i]
+            # Increment the big counter
+            counter = counter + 1
         end
-        A[i,i] = 1 + 0.5*A[sire[i], dam[i]]
-    elseif !isnothing(dam[i]) && isnothing(sire[i])
-        for j in 1:(i-1)
-            A[j,i] = 0.5*A[j,dam[i]]
+    end
+
+    # Create an empty matrix for the additive relationship matrix
+    A = zeros(numanimals, numanimals)
+
+    # Create the additive relationship matrix by the FORTRAN method presented by
+    # Henderson
+    for i in 1:numanimals
+        if !isnothing(dam[i]) && !isnothing(sire[i])
+            for j in 1:(i - 1)
+                A[j,i] = 0.5 * (A[j,sire[i]] + A[j,dam[i]])
+                A[i,j] = A[j,i]
+            end
+            A[i,i] = 1 + 0.5 * A[sire[i], dam[i]]
+        elseif !isnothing(dam[i]) && isnothing(sire[i])
+        for j in 1:(i - 1)
+            A[j,i] = 0.5 * A[j,dam[i]]
             A[i,j] = A[j,i]
         end
         A[i,i] = 1
     elseif isnothing(dam[i]) && !isnothing(sire[i])
-        for j in 1:(i-1)
-            A[j,i] = 0.5*A[j,sire[i]]
+        for j in 1:(i - 1)
+            A[j,i] = 0.5 * A[j,sire[i]]
             A[i,j] = A[j,i]
         end
         A[i,i] = 1
     else
-        for j in 1:(i-1)
+        for j in 1:(i - 1)
             A[j,i] = 0
             A[i,j] = 0
         end
         A[i,i] = 1
+        end
     end
-end
 
-# Extract the observed data
-Y = convert(Array{Float64}, data[:,end])
+    # Extract the observed data
+    Y = convert(Array{Float64}, data[:,end])
 
-# The random effects matrix
-Z = Matrix{Int}(I, numanimals, numanimals)
+    # The random effects matrix
+    Z = Matrix{Int}(I, numanimals, numanimals)
 
-# Remove items where there is no data
-nullobs = findall(isnothing, Y)
-Z[nullobs, nullobs] .= 0
+    # Remove items where there is no data
+    nullobs = findall(isnothing, Y)
+    Z[nullobs, nullobs] .= 0
 
-# Calculate heritability
-λ = (1-h2)/h2
+    # Calculate heritability
+    λ = (1 - h2) / h2
 
-# Use the mixed-model equations
-MME = [X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*λ)]
-MMY = [X'*Y; Z'*Y]
-solutions = MME\MMY
+    # Use the mixed-model equations
+    MME = [X' * X X' * Z; Z' * X (Z' * Z) + (inv(A) .* λ)]
+    MMY = [X' * Y; Z' * Y]
+    solutions = MME \ MMY
 
-# Find the accuracies
-diaginv = diag(inv(MME))
-reliability = ones(Float64, length(diaginv)) - diaginv.*λ
+    # Find the accuracies
+    diaginv = diag(inv(MME))
+    reliability = ones(Float64, length(diaginv)) - diaginv .* λ
 
-# Find how many traits we found BLUE for
-numgroups = numgroups .- 1
+    # Find how many traits we found BLUE for
+    numgroups = numgroups .- 1
 
-# Extract the names of the traits
-fixedfxnames = names(fixedfx)
-traitname = names(data)[end]
+    # Extract the names of the traits
+    fixedfxnames = names(fixedfx)
+    traitname = names(data)[end]
 
-# Start printing results to output
-fileID = open(savepath, "w")
-write(fileID, "beefblup Results Report\n")
-write(fileID, "Produced using beefblup (")
-write(fileID, "https://github.com/millironx/beefblup")
-write(fileID, ")\n\n")
-write(fileID, "Input:\t")
-write(fileID, path)
-write(fileID, "\nAnalysis performed:\t")
-write(fileID, string(Dates.today()))
-write(fileID, "\nTrait examined:\t")
-write(fileID, traitname)
-write(fileID, "\n\n")
+    # Start printing results to output
+    fileID = open(savepath, "w")
+    write(fileID, "beefblup Results Report\n")
+    write(fileID, "Produced using beefblup (")
+    write(fileID, "https://github.com/millironx/beefblup")
+    write(fileID, ")\n\n")
+    write(fileID, "Input:\t")
+    write(fileID, path)
+    write(fileID, "\nAnalysis performed:\t")
+    write(fileID, string(Dates.today()))
+    write(fileID, "\nTrait examined:\t")
+    write(fileID, traitname)
+    write(fileID, "\n\n")
 
-# Print base population stats
-write(fileID, "Base Population:\n")
-for i in 1:length(normal)
-    write(fileID, "\t")
-    write(fileID, fixedfxnames[i])
-    write(fileID, ":\t")
-    write(fileID, normal[i])
-    write(fileID, "\n")
-end
-write(fileID, "\tMean ")
-write(fileID, traitname)
-write(fileID, ":\t")
-write(fileID, string(solutions[1]))
-write(fileID, "\n\n")
-
-# Contemporary group adjustments
-counter = 2
-write(fileID, "Contemporary Group Effects:\n")
-for i in 1:length(numgroups)
-    write(fileID, "\t")
-    write(fileID, fixedfxnames[i])
-    write(fileID, "\tEffect\tReliability\n")
-    for j in 1:numgroups[i]
+    # Print base population stats
+    write(fileID, "Base Population:\n")
+    for i in 1:length(normal)
         write(fileID, "\t")
-        write(fileID, adjustedtraits[counter - 1])
-        write(fileID, "\t")
-        write(fileID, string(solutions[counter]))
-        write(fileID, "\t")
-        write(fileID, string(reliability[counter]))
+        write(fileID, fixedfxnames[i])
+        write(fileID, ":\t")
+        write(fileID, normal[i])
         write(fileID, "\n")
+    end
+    write(fileID, "\tMean ")
+    write(fileID, traitname)
+    write(fileID, ":\t")
+    write(fileID, string(solutions[1]))
+    write(fileID, "\n\n")
 
-        counter = counter + 1
+    # Contemporary group adjustments
+    counter = 2
+    write(fileID, "Contemporary Group Effects:\n")
+    for i in 1:length(numgroups)
+        write(fileID, "\t")
+        write(fileID, fixedfxnames[i])
+        write(fileID, "\tEffect\tReliability\n")
+        for j in 1:numgroups[i]
+            write(fileID, "\t")
+            write(fileID, adjustedtraits[counter - 1])
+            write(fileID, "\t")
+            write(fileID, string(solutions[counter]))
+            write(fileID, "\t")
+            write(fileID, string(reliability[counter]))
+            write(fileID, "\n")
+
+            counter = counter + 1
+        end
+        write(fileID, "\n")
     end
     write(fileID, "\n")
-end
-write(fileID, "\n")
 
-# Expected breeding values
-write(fileID, "Expected Breeding Values:\n")
-write(fileID, "\tID\tEBV\tReliability\n")
-for i in 1:numanimals
-    write(fileID, "\t")
-    write(fileID, string(data.id[i]))
-    write(fileID, "\t")
-    write(fileID, string(solutions[i+counter-1]))
-    write(fileID, "\t")
-    write(fileID, string(reliability[i+counter-1]))
-    write(fileID, "\n")
-end
-
-write(fileID, "\n - END REPORT -")
-close(fileID)
+    # Expected breeding values
+    write(fileID, "Expected Breeding Values:\n")
+    write(fileID, "\tID\tEBV\tReliability\n")
+    for i in 1:numanimals
+        write(fileID, "\t")
+        write(fileID, string(data.id[i]))
+        write(fileID, "\t")
+        write(fileID, string(solutions[i + counter - 1]))
+        write(fileID, "\t")
+        write(fileID, string(reliability[i + counter - 1]))
+        write(fileID, "\n")
+    end
 
+    write(fileID, "\n - END REPORT -")
+    close(fileID)
 
 end
 end