Complete through "Processing and formatting"

Julia/beefblup.jl

@@ -9,20 +9,112 @@
 using XLSX
 
 # Display stuff
-print("beefblup v 0.0.0.1\n")
+println("beefblup v 0.0.0.1")
-print("(C) 2019 Thomas A. Christensen II\n")
+println("(C) 2019 Thomas A. Christensen II")
-print("https://github.com/millironx/beefblup\n")
+println("https://github.com/millironx/beefblup")
 print("\n")
 
 ### Prompt User
 # Ask for an input spreadsheet
-print("Enter the full filename of a beefblup worksheet> ")
+# print("Enter the full filename of a beefblup worksheet> ")
-path = readline(stdin)
+# path = readline(stdin)
+path = "C:\\Users\\cclea\\source\\repos\\beefblup\\Excel\\Master BLUP Worksheet.xlsx"
 
 # Ask for an output text filename
-print("Enter the full filename of your desired results> ")
+# print("Enter the full filename of your desired results> ")
-savepath = readline(stdin)
+# savepath = readline(stdin)
+savepath = "C:\\Users\\cclea\\source\\repos\\beefblup\\results.txt"
 
 # Ask for heritability
-print("What is the heritability for this trait?> ")
+# print("What is the heritability for this trait?> ")
-h2 = parse(Float64, readline(stdin))
+# h2 = parse(Float64, readline(stdin))
+h2 = 0.4
+
+### Import input filename
+print("[🐮]: Importing Excel file...")
+
+# Import data from a suitable spreadsheet
+data = XLSX.readxlsx(path)[1][:]
+
+print("Done!\n")
+
+### Process input file
+print("[🐮]: Processing and formatting data ...")
+
+# Extract the headers into a separate array
+headers = data[1,:]
+data = data[2:end,:]
+
+# Sort the array by date
+data = sortslices(data, dims=1, lt=(x,y)->isless(x[2],y[2]))
+
+# Define fields to hold id values for animals and their parents
+ids = data[:,1]
+damids = data[:,3]
+sireids = data[:,4]
+numanimals = length(ids)
+
+# Find the index values for animals and their parents
+dam = indexin(damids, ids)
+sire = indexin(sireids, ids)
+
+# Store column numbers that need to be deleted
+# Column 6 contains an intermediate Excel calculation and always need to
+# be deleted
+colstokeep = [1, 2, 3, 4, 5]
+
+# Find any columns that need to be deleted
+for i in 7:length(headers)
+    if length(unique(data[:,i])) <= 1
+        colname = headers[i]
+        print("Column '")
+        print(colname)
+        print("' does not have any unique animals and will be removed from this analysis\n")
+    else
+        push!(colstokeep, i)
+    end
+end
+
+# Delete the appropriate columns from the datasheet and the headers
+data = data[:, colstokeep]
+headers = headers[colstokeep]
+
+# Determine how many contemporary groups there are
+numgroups = ones(1, length(headers)-5)
+for i in 6:length(headers)
+    numgroups[i-5] = length(unique(data[:,i]))
+end
+
+# If there are more groups than animals, then the analysis cannot continue
+if sum(numgroups) >= numanimals
+    println("There are more contemporary groups than animals. The analysis will
+    now abort.")
+    exit()
+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,length(headers)-5)
+for i in 6:length(headers)
+    for j in numanimals:-1:1
+        if !ismissing(data[j,i])
+            normal[i-5] = data[j,i]
+            break
+        end
+    end
+end
+
+# Print the results of the "normal" definition
+println(" ")
+println("For the purposes of this analysis, a 'normal' animal will be defined by")
+println("the following traits:")
+for i in 6:length(headers)
+    print(headers[i])
+    print(": ")
+    print(normal[i-5])
+    print("\n")
+end
+println("If no animal matching this description exists, the results may appear")
+println("outlandish, but are still as correct as the accuracy suggests")
+
+print("Done!\n")