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Run Julia template engine for CI purposes
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8 changed files with 60 additions and 352 deletions
2
LICENSE
2
LICENSE
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@ -1,6 +1,6 @@
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BSD 3-Clause License
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Copyright (c) 2020, Thomas A. Christensen II
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Copyright (c) 2021, Thomas A. Christensen II <25492070+MillironX@users.noreply.github.com> and contributors
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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21
Project.toml
21
Project.toml
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@ -1,12 +1,13 @@
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name = "BeefBLUP"
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uuid = "03993faf-e476-444a-86c9-f31e8122fa24"
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authors = ["Thomas A. Christensen II <25492070+MillironX@users.noreply.github.com>"]
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version = "0.3.0"
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uuid = "4c4dd571-0773-455e-9a95-72727008e3f4"
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authors = ["Thomas A. Christensen II <25492070+MillironX@users.noreply.github.com> and contributors"]
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version = "0.1.0"
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[deps]
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ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
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CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
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DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
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Gtk = "4c0ca9eb-093a-5379-98c5-f87ac0bbbf44"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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[compat]
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julia = "1.3"
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[extras]
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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[targets]
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test = ["Test"]
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83
README.md
83
README.md
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@ -1,82 +1,3 @@
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# [:cow:]: beefblup
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# BeefBLUP
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[](https://github.com/MillironX/beefblup/blob/master/LICENSE.md)
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[](https://gitter.im/beefblup/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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[](https://GitHub.com/MillironX/beefblup/releases)
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beefblup is a program for ranchers to calculate expected breeding
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values (EBVs) for their own beef cattle. It is intended to be usable by anyone
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without requiring any prior knowledge of computer programming or linear algebra.
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Why? It's part of my effort to
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**\#KeepEPDsReal**
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## Installation
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1. [Download and install Julia](https://julialang.org/downloads/platform/)
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2. Download the [beefblup ZIP
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file](https://github.com/MillironX/beefblup/archive/refs/tags/v0.2.zip) and unzip it someplace memorable
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3. In your file explorer, copy the address of the "beefblup" folder
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4. Launch Julia
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5. Type `cd("<the address copied in step 5")` and press **Enter** (For example,
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`cd("C:\Users\MillironX\Documents\beefblup")`)
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6. Type the `]` key
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7. Type `activate .` and press **Enter**
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8. Type `instantiate` and press **Enter**
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9. Installation is done: you can close the Julia window
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## How to Use
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1. Make a copy of the "sample.csv" spreadsheet and replace the data with your own
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1. The trait you wish to calculate EBVs for always goes in the rightmost column
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2. If you wish to add more contemporary group traits to your analysis, include them before the rightmost column
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2. Save and close
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3. In your file explorer, copy the address of the "beefblup" folder
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4. Launch Julia
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5. Type `cd("<the address copied in step 5")` and press **Enter** (For example,
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`cd("C:\Users\MillironX\Documents\beefblup")`)
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6. Type the `]` key
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7. Type `activate .` and press **Enter**
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8. Press **Backspace**
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9. Type `include("src/beefblup.jl")` and press **Enter**
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10. Select the spreadsheet you created in steps 1-4
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11. Follow the on-screen prompts
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12. **#KeepEPDsReal!**
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## For Programmers
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> **Also Note:** beefblup was written on, and has only been tested with Julia
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> v1.2.0 and higher. While this shouldn't affect most everyday users, it might
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> affect you if you are stuck on the current LTS version of Julia (v1.0.5).
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### Development Roadmap
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| Version | Feature | Status |
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| ------- | ----------------------------------------------------------------------------------- | ------------------ |
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| v0.1 | Julia port of original MATLAB script | :heavy_check_mark: |
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| v0.2 | Spreadsheet format redesign | :heavy_check_mark: |
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| v0.3 | API rewrite (change to function calls and package format instead of script running) | |
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| v0.4 | Add GUI for all options | |
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| v0.5 | Automatically calculated Age-Of-Dam, Year, and Season fixed-effects | |
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| v0.6 | Repeated measurement BLUP (aka dairyblup) | |
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| v0.7 | Multiple trait BLUP | |
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| v0.8 | Maternal effects BLUP | |
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| v0.9 | Genomic BLUP | |
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| v0.10 | beefblup binaries | |
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| v1.0 | [Finally, RELEASE!!!](https://youtu.be/1CBjxGdgC1w?t=282) | |
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### Bug Reports
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For every bug report, please include at least the following:
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- Platform (Windows, Mac, Fedora, etc)
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- Julia version
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- beefblup version
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- How you are running Julia (From PowerShell, via the REPL, etc.)
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- A beefblup spreadsheet that can be used to recreate the issue
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- Description of the problem
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- Expected behavior
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- A screenshot and/or REPL printout
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## License
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Distributed under the 3-Clause BSD License
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[](https://MillironX.github.io/BeefBLUP.jl/stable) [](https://MillironX.github.io/BeefBLUP.jl/dev) [](https://travis-ci.com/MillironX/BeefBLUP.jl) [](https://codecov.io/gh/MillironX/BeefBLUP.jl) [](https://github.com/invenia/BlueStyle)
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3
docs/Project.toml
Normal file
3
docs/Project.toml
Normal file
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@ -0,0 +1,3 @@
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[deps]
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BeefBLUP = "4c4dd571-0773-455e-9a95-72727008e3f4"
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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24
docs/make.jl
Normal file
24
docs/make.jl
Normal file
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@ -0,0 +1,24 @@
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using BeefBLUP
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using Documenter
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DocMeta.setdocmeta!(BeefBLUP, :DocTestSetup, :(using BeefBLUP); recursive=true)
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makedocs(;
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modules=[BeefBLUP],
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authors="Thomas A. Christensen II <25492070+MillironX@users.noreply.github.com> and contributors",
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repo="https://github.com/MillironX/BeefBLUP.jl/blob/{commit}{path}#{line}",
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sitename="BeefBLUP.jl",
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format=Documenter.HTML(;
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prettyurls=get(ENV, "CI", "false") == "true",
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canonical="https://MillironX.github.io/BeefBLUP.jl",
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assets=String[],
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),
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pages=[
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"Home" => "index.md",
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],
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)
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deploydocs(;
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repo="github.com/MillironX/BeefBLUP.jl",
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devbranch="develop",
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)
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14
docs/src/index.md
Normal file
14
docs/src/index.md
Normal file
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@ -0,0 +1,14 @@
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```@meta
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CurrentModule = BeefBLUP
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```
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# BeefBLUP
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Documentation for [BeefBLUP](https://github.com/MillironX/BeefBLUP.jl).
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```@index
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```
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```@autodocs
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Modules = [BeefBLUP]
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```
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261
src/BeefBLUP.jl
Executable file → Normal file
261
src/BeefBLUP.jl
Executable file → Normal file
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@ -1,264 +1,5 @@
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# beefblup
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# Julia package for performing single-variate BLUP to find beef cattle
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# breeding values
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# (C) 2021 Thomas A. Christensen II
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# Licensed under BSD-3-Clause License
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# cSpell:includeRegExp #.*
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# cSpell:includeRegExp ("""|''')[^\1]*\1
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module BeefBLUP
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# Import the required packages
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using CSV
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using DataFrames
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using LinearAlgebra
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using Dates
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using Gtk
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# Main entry-level function - acts just like the script
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function beefblup()
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# Ask for an input spreadsheet
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path = open_dialog_native(
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"Select a beefblup worksheet",
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GtkNullContainer(),
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("*.csv", GtkFileFilter("*.csv", name="beefblup worksheet"))
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)
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# Ask for an output text filename
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savepath = save_dialog_native(
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"Save your beefblup results",
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GtkNullContainer(),
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(GtkFileFilter("*.txt", name="Results file"),
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"*.txt")
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)
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# Ask for heritability
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print("What is the heritability for this trait?> ")
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h2 = parse(Float64, readline(stdin))
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beefblup(path, savepath, h2)
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# Write your package code here.
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end
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function beefblup(datafile::String, h2::Float64)
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# Assume the data is named the same as the file without the trailing extension
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dataname = join(split(datafile, ".")[1:end - 1])
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# Create a new results name
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resultsfile = string(dataname, "_results.txt")
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# Pass this info on to the worker
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beefblup(datafile, resultsfile, h2)
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end
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# Main worker function, can perform all the work if given all the user input
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function beefblup(path::String, savepath::String, h2::Float64)
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# Import data from a suitable spreadsheet
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data = DataFrame(CSV.File(path))
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# Sort the array by date
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sort!(data, :birthdate)
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# Define fields to hold id values for animals and their parents
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numanimals = length(data.id)
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# Find the index values for animals and their parents
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dam = indexin(data.dam, data.id)
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sire = indexin(data.sire, data.id)
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# Extract all of the fixed effects
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fixedfx = select(data, Not([:id, :birthdate, :sire, :dam]))[:,1:end - 1]
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# Find any columns that need to be deleted
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for i in 1:ncol(fixedfx)
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if length(unique(fixedfx[:,i])) <= 1
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@warn string("column '", names(fixedfx)[i], "' does not have any unique animals and will be removed from this analysis")
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DataFrames.select!(fixedfx, Not(i))
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end
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end
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# Determine how many contemporary groups there are
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numtraits = ncol(fixedfx)
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numgroups = ones(1, numtraits)
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for i in 1:numtraits
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numgroups[i] = length(unique(fixedfx[:,i]))
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end
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# If there are more groups than animals, then the analysis cannot continue
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if sum(numgroups) >= numanimals
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throw(ErrorException("there are more contemporary groups than animals"))
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end
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# Define a "normal" animal as one of the last in the groups, provided that
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# all traits do not have null values
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normal = Array{String}(undef, 1, numtraits)
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for i in 1:numtraits
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for j in numanimals:-1:1
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if !ismissing(fixedfx[j,i])
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normal[i] = string(fixedfx[j,i])
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break
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end
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end
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end
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# Form the fixed-effect matrix
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X = zeros(Int8, numanimals, floor(Int, sum(numgroups)) - length(numgroups) + 1)
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X[:,1] = ones(Int8, 1, numanimals)
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# Create an external counter that will increment through both loops
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counter = 2
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# Store the traits in a string array
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adjustedtraits =
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Array{String}(undef,floor(Int, sum(numgroups)) - length(numgroups))
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# Iterate through each group
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for i in 1:length(normal)
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# Find the traits that are present in this trait
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localdata = string.(fixedfx[:,i])
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traits = unique(localdata)
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# Remove the normal version from the analysis
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effecttraits = traits[findall(x -> x != normal[i], traits)]
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# Iterate inside of the group
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for j in 1:(length(effecttraits))
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matchedindex = findall(x -> x == effecttraits[j], localdata)
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X[matchedindex, counter] .= 1
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# Add this trait to the string
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adjustedtraits[counter - 1] = traits[j]
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# Increment the big counter
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counter = counter + 1
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end
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end
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# Create an empty matrix for the additive relationship matrix
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A = zeros(numanimals, numanimals)
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# Create the additive relationship matrix by the FORTRAN method presented by
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# Henderson
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for i in 1:numanimals
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if !isnothing(dam[i]) && !isnothing(sire[i])
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for j in 1:(i - 1)
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A[j,i] = 0.5 * (A[j,sire[i]] + A[j,dam[i]])
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A[i,j] = A[j,i]
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end
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A[i,i] = 1 + 0.5 * A[sire[i], dam[i]]
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elseif !isnothing(dam[i]) && isnothing(sire[i])
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for j in 1:(i - 1)
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A[j,i] = 0.5 * A[j,dam[i]]
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A[i,j] = A[j,i]
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end
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A[i,i] = 1
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elseif isnothing(dam[i]) && !isnothing(sire[i])
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for j in 1:(i - 1)
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A[j,i] = 0.5 * A[j,sire[i]]
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A[i,j] = A[j,i]
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end
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A[i,i] = 1
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else
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for j in 1:(i - 1)
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A[j,i] = 0
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A[i,j] = 0
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end
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A[i,i] = 1
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end
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end
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# Extract the observed data
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Y = convert(Array{Float64}, data[:,end])
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# The random effects matrix
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Z = Matrix{Int}(I, numanimals, numanimals)
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# Remove items where there is no data
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nullobs = findall(isnothing, Y)
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Z[nullobs, nullobs] .= 0
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# Calculate heritability
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λ = (1 - h2) / h2
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# Use the mixed-model equations
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MME = [X' * X X' * Z; Z' * X (Z' * Z) + (inv(A) .* λ)]
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MMY = [X' * Y; Z' * Y]
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solutions = MME \ MMY
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# Find the accuracies
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diaginv = diag(inv(MME))
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reliability = ones(Float64, length(diaginv)) - diaginv .* λ
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# Find how many traits we found BLUE for
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numgroups = numgroups .- 1
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# Extract the names of the traits
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fixedfxnames = names(fixedfx)
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traitname = names(data)[end]
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# Start printing results to output
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fileID = open(savepath, "w")
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write(fileID, "beefblup Results Report\n")
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write(fileID, "Produced using beefblup (")
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write(fileID, "https://github.com/millironx/beefblup")
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write(fileID, ")\n\n")
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write(fileID, "Input:\t")
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write(fileID, path)
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write(fileID, "\nAnalysis performed:\t")
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write(fileID, string(Dates.today()))
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write(fileID, "\nTrait examined:\t")
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write(fileID, traitname)
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write(fileID, "\n\n")
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# Print base population stats
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write(fileID, "Base Population:\n")
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for i in 1:length(normal)
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write(fileID, "\t")
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write(fileID, fixedfxnames[i])
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write(fileID, ":\t")
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write(fileID, normal[i])
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write(fileID, "\n")
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end
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write(fileID, "\tMean ")
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write(fileID, traitname)
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write(fileID, ":\t")
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write(fileID, string(solutions[1]))
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write(fileID, "\n\n")
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# Contemporary group adjustments
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counter = 2
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write(fileID, "Contemporary Group Effects:\n")
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for i in 1:length(numgroups)
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write(fileID, "\t")
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write(fileID, fixedfxnames[i])
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write(fileID, "\tEffect\tReliability\n")
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for j in 1:numgroups[i]
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write(fileID, "\t")
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write(fileID, adjustedtraits[counter - 1])
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write(fileID, "\t")
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write(fileID, string(solutions[counter]))
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write(fileID, "\t")
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write(fileID, string(reliability[counter]))
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write(fileID, "\n")
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counter = counter + 1
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end
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write(fileID, "\n")
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end
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write(fileID, "\n")
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# Expected breeding values
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write(fileID, "Expected Breeding Values:\n")
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write(fileID, "\tID\tEBV\tReliability\n")
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for i in 1:numanimals
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write(fileID, "\t")
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write(fileID, string(data.id[i]))
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write(fileID, "\t")
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write(fileID, string(solutions[i + counter - 1]))
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write(fileID, "\t")
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write(fileID, string(reliability[i + counter - 1]))
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write(fileID, "\n")
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end
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write(fileID, "\n - END REPORT -")
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close(fileID)
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end
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end
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|
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@ -1,2 +1,6 @@
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using BeefBLUP
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using Test
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@testset "BeefBLUP.jl" begin
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# Write your tests here.
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end
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Reference in a new issue