├── .gitignore ├── .travis.yml ├── COPYING ├── README.md ├── build.xml ├── examples ├── README.md ├── smoothingpriors │ ├── OUPriorTests.Rmd │ ├── OUPriorTests.html │ ├── OUPriorTests.md │ ├── comparison │ │ └── hcv_bdsky.xml │ ├── figs │ │ ├── ouprior_test1-1.png │ │ ├── ouprior_test2-1.png │ │ ├── ouprior_test3-1.png │ │ ├── ouprior_test4-1.png │ │ └── ouprior_test5-1.png │ ├── ouprior_test1.xml │ ├── ouprior_test2.xml │ ├── ouprior_test3.xml │ ├── ouprior_test4.xml │ └── ouprior_test5.xml └── treeslicer │ ├── comparison │ ├── dengue4_bdsky_csc.xml │ ├── dengue4_bdsky_equidistant5.xml │ ├── dengue4_bdsky_equidistant5_rootcondition.xml │ └── dengue4_bdsky_fixeddates.xml │ ├── dengue4_bdsky_csc_treeslicer.xml │ ├── dengue4_bdsky_csc_treeslicer_joint.xml │ ├── dengue4_bdsky_datestrings_treeslicer.xml │ ├── dengue4_bdsky_equidistant5_treeslicer.xml │ ├── dengue4_bdsky_equidistant5_treeslicer_joint.xml │ └── dengue4_bdsky_fixeddates_treeslicer.xml ├── src ├── beast │ └── evolution │ │ └── speciation │ │ └── BirthDeathSkylineModelTestHelper.java └── skylinetools │ ├── distributions │ ├── BMPrior.java │ └── OUPrior.java │ ├── parameter │ ├── DateParser.java │ └── RealParameterSequence.java │ └── util │ ├── TreeDateLogger.java │ ├── TreeDateSlicer.java │ ├── TreeSliceDateLogger.java │ └── TreeSlicer.java ├── test ├── README.md ├── beast │ └── evolution │ │ └── speciation │ │ └── SkylineTimesTest.java ├── skylinetools │ ├── distributions │ │ └── OUPriorLkTest.java │ ├── parameter │ │ ├── DateParserTest.java │ │ └── RealParameterSequenceTest.java │ └── util │ │ ├── TreeDateSlicerTest.java │ │ └── TreeSlicerTest.java ├── tree.C1.MCC.trim.round.figtree.tree └── tree.C2.MCC.trim.round.figtree.tree └── version.xml /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | .Rhistory 3 | *.log 4 | *.trees 5 | *.state 6 | *.iml 7 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: java 2 | 3 | install: ant build 4 | 5 | before_script: sudo apt-get install ant-optional 6 | 7 | script: ant test 8 | -------------------------------------------------------------------------------- /COPYING: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Skyline Tools 2 | 3 | Package of useful utilities and distributions to use with skyline models in BEAST2. For now the package only supports the birth-death skyline (BDSKY) and multitype birth-death process (BDMM), but there is no reason these utilities cannot be used by other models. 4 | 5 | - [**Documentation**](https://github.com/laduplessis/skylinetools/wiki) 6 | - [**Examples**](examples/) 7 | - [**Unit tests**](test/) 8 | 9 | [![Build Status](https://travis-ci.com/laduplessis/skylinetools.svg?branch=master)](https://travis-ci.com/laduplessis/skylinetools) 10 | 11 | ## Package contents 12 | 13 | - *Parameters* 14 | - **RealParameterSequence:** Shortcut to create a linear sequence of real numbers with equal increments. 15 | - *Smoothing priors* 16 | - **Brownian-motion smoothing prior:** Time-aware smoothing prior for a skyline parameter based on a Brownian-motion process. (Similar to the GMRF used for the Skyride/Skygrid). _(not fully tested)_ 17 | - **Ornstein-Uhlenbeck smoothing prior:** Time-aware smoothing prior for a skyline parameter based on an Ornstein-Uhlenbeck process. _(not fullly tested)_ 18 | - *Utilities* 19 | - **TreeSlicer:** Interface for getting times on a tree. 20 | - **TreeSliceDateLogger:** Log dates of treeslice times. 21 | - **TreeDateLogger:** Log treeheight or other parameters as dates. 22 | 23 | 24 | ## Installation 25 | 26 | To install Skyline Tools: 27 | 28 | 1. Download and install [BEAST2](www.beast2.org). 29 | 2. Launch the BEAUti application distributed with BEAST. 30 | 3. From the File menu select "Manage Packages". 31 | 4. Click the "Package repositories" button at the bottom of the Package Manager dialog box. 32 | 5. Select "Add URL" and enter the following URL: `https://laduplessis.github.io/skylinetools/package.xml`. 33 | 7. Click the "Done" button, then select "skylinetools" from the list of packages. 34 | 8. Click the "Install/Upgrade" button. Once installation is complete, XML files using SkylineTools can be run from BEAST2. 35 | 36 | 37 | ## Citation 38 | 39 | If you use TreeSlicer in any of your XML files please add a link to this repository. 40 | 41 | ## License 42 | 43 | This package is distributed under the GNU General Public Licence version 3, which is contained in this directory in the file named COPYING. 44 | 45 | 46 | --- 47 | _Louis du Plessis, 2018_ 48 | -------------------------------------------------------------------------------- /build.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | ** Required file version.xml does not exist. ** 40 | If this is a new project, run "ant skeleton" from 41 | the command line to create the files required for 42 | your BEAST 2 package. 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | No local copy of the beast2 source found at ${beastDir}. 67 | Compiling against version ${beast-version} from GitHub. 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | Compiling against beast2 source found at ${beastDir}. 93 | 94 | 95 | 96 | 97 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | Downloading dependencies... 118 | 119 | BDSKY: 120 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | ** Package ${dist}/${fullName}.zip created successfuly! ** 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | ** This directory contains files besides the build script. ** 267 | You should run "ant skeleton" in a directory containing only the build script. 268 | 269 | 270 | =============================== 271 | Create skeleton BEAST 2 package 272 | =============================== 273 | 274 | First, we need some information... 275 | 276 | 277 | 278 | 279 | Enter package name 280 | Select open source software license 281 | Enter package version 282 | Enter minimum required BEAST 2 version 283 | 284 | Assembling files and directory structure... 285 | 286 | <addon name="${projName}" version="${projVersion}"> 287 | <depends on="BEAST" atleast="${beastVersionReq}"/> 288 | 289 | <!-- Add other dependencies as necessary. --> 290 | </addon> 291 | 292 | 293 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | Done. 319 | 320 | The directory structure is as follows: 321 | ${src} - your java source goes here 322 | ${test} - your junit tests go here (You _are_ going to write, those, aren't you!) 323 | ${doc} - your documentation goes here 324 | ${examples} - your example XML scripts go here 325 | ${templates} - your BEAUti templates go here 326 | 327 | To build your package, just type "ant" at the command line. 328 | 329 | To run unit tests, type "ant test". 330 | 331 | That's it! Happy coding! 332 | 333 | 334 | 335 | 336 | -------------------------------------------------------------------------------- /examples/README.md: -------------------------------------------------------------------------------- 1 | # Examples 2 | 3 | ## 1. OUPrior 4 | 5 | ### 1.1. OUPrior Tests 6 | 7 | `ouprior_test1.xml`-`ouprior_test5.xml`: Tests to check that the OU-prior implemented in BEAST2 is correct. Check that MCMC samples correct trajectories and that it is possible to estimate parameters given a trajectory. ([Results](smoothingpriors/OUPriorTests.md)) 8 | 9 | 10 | ### 1.2. OUPrior examples 11 | 12 | **With OUPrior** 13 | 14 | 1. `hcv_bdsky_ouprior.xml`: Egyptian HCV dataset (contemporaneous sampling only), with relative times (between 0 and 1). OUPrior on reproductive number with dimension 40. 15 | 2. Serial sampling and real times (Ebola) 16 | 17 | **Comparison xml files (without OUPrior)** 18 | 19 | 1. `hcv_bdsky.xml`: Egyptian HCV dataset (contemporaneous sampling only), reproductive number with dimension 40. 20 | 21 | 22 | 23 | --- 24 | 25 | 26 | ## 2. TreeSlicer 27 | 28 | ### 2.1. TreeSlicer Tests 29 | Tests to compare the result of using TreeSlicer to obtaining the same or similar results in a more complicated way without it. Note that some XMLs log more statistics than necessary and contain some unnecessary RPN calculators to check correctness of the results. 30 | 31 | **With TreeSlicer** 32 | 33 | 1. `dengue4_bdsky_equidistant5_treeslicer.xml`: 5 intervals for R_e, equally spaced between tmrca and present, sampling proportion 0 before oldest sample. 34 | 2. `dengue4_bdsky_fixeddates_treeslicer.xml`: 5 intervals for R_e at specific fixed dates, sampling proportion 0 before oldest sample. 35 | 3. `dengue4_bdsky_csc_treeslicer.xml`: 2 intervals for R_e, with the shift-date estimated. 36 | 4. `dengue4_bdsky_csc_treeslicer_hierarchical.xml`: 2 trees, each with 2 intervals for R_e, with a shared shift-date, which is estimated. 37 | 5. `dengue4_bdsky_equidistant5_treeslicer_hierarchical.xml`: 2 trees, each with the same 3 shared intervals for R_e. 38 | 39 | **Comparison xml files (without TreeSlicer)** 40 | 41 | 1. `dengue4_bdsky_equidistant5.xml`: 5 intervals for R_e, equally spaced between origin and present, sampling proportion 0 before oldest sample. 42 | 2. `dengue4_bdsky_equidistant5_rootcondition.xml`: 5 intervals for R_e, equally spaced between tmrca and present, sampling proportion 0 before oldest sample (no origin estimated). 43 | 3. `dengue4_bdsky_fixeddates.xml`: 5 intervals for R_e at specific fixed dates, sampling proportion 0 before oldest sample. 44 | 4. `dengue4_bdsky_csc.xml`: 2 intervals for R_e, with the shift-time estimated. 45 | 46 | 47 | 48 | ### 2.2 TreeSlicer Examples 49 | _Coming soon_ 50 | 51 | 52 | --- 53 | _Louis du Plessis, 2018_ -------------------------------------------------------------------------------- /examples/smoothingpriors/OUPriorTests.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Ornstein-Uhlenbeck smoothing prior tests" 3 | author: "Louis du Plessis" 4 | date: "7/19/2018" 5 | output: 6 | html_document: 7 | keep_md: true 8 | theme: paper 9 | self_contained: false 10 | --- 11 | 12 | Tests to check that the OU-prior implemented in BEAST2 is correct. In particular checking that sampling trajectories in BEAST2 with MCMC produces the correct theoretical distribution as well as the same distribution as simulating trajectories using the analytical solution (when drawing parameter values from prior distributions). Additionally, also test the ability of the BEAST2 implementation to estimate parameter values given a trajectory. 13 | 14 | 15 | ```{r setup, include=FALSE} 16 | knitr::opts_chunk$set(echo = TRUE, fig.path="figs/", fig.width=7.5, fig.height=5) 17 | 18 | rm(list = ls()) 19 | library(bdskytools) 20 | ``` 21 | 22 | 23 | 24 | ### Test 1: `ouprior_test1.xml` 25 | 26 | - OU-process with all parameters fixed. 27 | - Sample x using MCMC in BEAST2 and compare to theoretical quantiles. 28 | 29 | 30 | ```{r ouprior_test1} 31 | 32 | ##################### 33 | # ouprior_test1.xml # 34 | ##################### 35 | 36 | # Calculate density of OU-process and get percentiles (blue and red) 37 | t <- seq(0,9,by=1) 38 | x0 <- 10 39 | mu <- 3 40 | sigma <- 1 41 | nu <- 0.1 42 | X <- plotOUProcessHPD(x0, t, mu, sigma, nu, ylim = c(0,12), main='ouprior_test1.xml', xlab="", ylab="") 43 | sim_hpd <- getMatrixHPD(X) 44 | 45 | # Sample from posterior in BEAST2 and get HPD (orange and green) 46 | lf <- readLogfile("ouprior_test1.log") 47 | 48 | xsample <- getSkylineSubset(lf,"x") 49 | for (i in 1:nrow(xsample)) { 50 | lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5) 51 | } 52 | sample_hpd <- getMatrixHPD(xsample) 53 | lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2) 54 | lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2) 55 | lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2) 56 | ``` 57 | 58 | 59 | 60 | ### Test 2: `ouprior_test2.xml` 61 | 62 | - OU-process with all parameters fixed and x_0 sampled from an exponential prior. 63 | - Sample x using MCMC in BEAST2 and compare HPDs of x to 1000 simulated trajectories (using analytical solution from time-transformed Wiener process). 64 | 65 | 66 | ```{r ouprior_test2} 67 | 68 | ##################### 69 | # ouprior_test2.xml # 70 | ##################### 71 | 72 | # Simulate OU-trajectory and get HPD (blue and red) 73 | t <- seq(0,9,by=1) 74 | x0_p <- getPrior(rexp,1,rate=10) 75 | mu <- 3 76 | sigma <- 1 77 | nu <- 0.1 78 | X <- plotOUProcessHPDEmpirical(x0_p, t, mu, sigma, nu, ylim = c(-4,6), main='ouprior_test2.xml') 79 | sim_hpd <- getMatrixHPD(X) 80 | 81 | # Sample from posterior in BEAST2 and get HPD (orange and green) 82 | lf <- readLogfile("ouprior_test2.log") 83 | 84 | xsample <- getSkylineSubset(lf,"x") 85 | for (i in 1:nrow(xsample)) { 86 | lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5) 87 | } 88 | sample_hpd <- getMatrixHPD(xsample) 89 | lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2) 90 | lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2) 91 | lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2) 92 | 93 | ``` 94 | 95 | 96 | ### Test 3: `ouprior_test3.xml` 97 | 98 | - OU-process with all parameters drawn from priors. 99 | - Sample x using MCMC in BEAST2 and compare HPDs of x to 1000 simulated trajectories (using analytical solution from time-transformed Wiener process). 100 | - Compare posterior distributions for parameters to priors (without data posterior should equal prior). 101 | 102 | ```{r ouprior_test3, fig.height=8, results='hide'} 103 | 104 | ##################### 105 | # ouprior_test3.xml # 106 | ##################### 107 | 108 | layout(matrix(c(1,1,1,1,2,3,4,5), nrow=2, byrow=TRUE), heights = c(5,3)) 109 | 110 | # Simulate OU-trajectory and get HPD (blue and red) 111 | t <- seq(0,0.9,by=0.1) 112 | x0_p <- getPrior(runif, 1, min=1.5, max=2.5) 113 | mu_p <- getPrior(rlnorm, 1, meanlog=0, sdlog=0.5) 114 | sigma_p <- getPrior(rnorm, 1, mean=0.5, sd=0.1) 115 | nu_p <- getPrior(rgamma, 1, shape=1, scale=5) 116 | X <- plotOUProcessHPDEmpirical(x0_p, t, mu_p, sigma_p, nu_p, ylim = c(0,4), main='ouprior_test3.xml') 117 | sim_hpd <- getMatrixHPD(X) 118 | 119 | # Sample from posterior in BEAST2 and get HPD (orange and green) 120 | lf <- readLogfile("ouprior_test3.log") 121 | 122 | xsample <- getSkylineSubset(lf,"x") 123 | for (i in 1:nrow(xsample)) { 124 | lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5) 125 | } 126 | sample_hpd <- getMatrixHPD(xsample) 127 | lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2) 128 | lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2) 129 | lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2) 130 | 131 | # mu 132 | mu_density <- density(lf$mu) 133 | plotPrior('lnorm',list(meanlog=0, sdlog=0.5), main='mu') 134 | lines(mu_density$x, mu_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 135 | polygon(c(0, mu_density$x, 0), c(0, mu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 136 | 137 | # Sigma 138 | sigma_density <- density(lf$sigma) 139 | plotPrior('norm',list(mean=0.5, sd=0.1), main='sigma') 140 | lines(sigma_density$x, sigma_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 141 | polygon(c(0, sigma_density$x, 0), c(0, sigma_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 142 | 143 | # nu 144 | nu_density <- density(lf$nu, bw=0.3) 145 | plotPrior('gamma',list(shape=1, scale=5), main='nu') 146 | lines(nu_density$x, nu_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 147 | polygon(c(0, nu_density$x, 0), c(0, nu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 148 | 149 | #x0 150 | x0_density <- density(lf$x1, bw=0.05) 151 | plotPrior('unif',list(min=1.5, max=2.5), main=expression(x[0])) 152 | lines(x0_density$x, x0_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 153 | polygon(c(0, x0_density$x, 0), c(0, x0_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 154 | 155 | 156 | ``` 157 | 158 | 159 | 160 | ### Test 4: `ouprior_test4.xml` 161 | 162 | - OU-process with all parameters drawn from priors, except for the mean. 163 | - Run for 51 time points instead of just 10 as above. 164 | - Sample x using MCMC in BEAST2 and compare HPDs of x to 1000 simulated trajectories (using analytical solution from time-transformed Wiener process). 165 | - Compare posterior distributions for parameters to priors (without data posterior should equal prior). 166 | 167 | 168 | ```{r ouprior_test4, fig.height=8, results='hide'} 169 | 170 | ##################### 171 | # ouprior_test4.xml # 172 | ##################### 173 | 174 | layout(matrix(c(1,1,1,2,3,4), nrow=2, byrow=TRUE), heights=c(5,3)) 175 | 176 | # Simulate OU-trajectory and get HPD (blue and red) 177 | t <- seq(0,1,length.out=51) 178 | x0_p <- getPrior(rexp, 1, rate=1) 179 | mu <- 1 180 | sigma_p <- getPrior(rnorm, 1, mean=0.3, sd=0.1) 181 | nu_p <- getPrior(rgamma, 1, shape=1, scale=50) 182 | X <- plotOUProcessHPDEmpirical(x0_p, t, mu, sigma_p, nu_p, ylim = c(0.5,2.5), main='ouprior_test4.xml') 183 | sim_hpd <- getMatrixHPD(X) 184 | 185 | # Sample from posterior in BEAST2 and get HPD (orange and green) 186 | lf <- readLogfile("ouprior_test4.log") 187 | 188 | xsample <- getSkylineSubset(lf,"x") 189 | for (i in 1:nrow(xsample)) { 190 | lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5) 191 | } 192 | sample_hpd <- getMatrixHPD(xsample) 193 | lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2) 194 | lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2) 195 | lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2) 196 | 197 | # Sigma 198 | sigma_density <- density(lf$sigma) 199 | plotPrior('norm',list(mean=0.3, sd=0.1), main='sigma') 200 | lines(sigma_density$x, sigma_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 201 | polygon(c(0, sigma_density$x, 0), c(0, sigma_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 202 | 203 | # nu 204 | nu_density <- density(lf$nu, bw=5) 205 | plotPrior('gamma',list(shape=1, scale=50), main='nu') 206 | lines(nu_density$x, nu_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 207 | polygon(c(0, nu_density$x, 0), c(0, nu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 208 | 209 | #x0 210 | x0_density <- density(lf$x1) 211 | plotPrior('exp',list(rate=1), main=expression(x[0])) 212 | lines(x0_density$x, x0_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 213 | polygon(c(0, x0_density$x, 0), c(0, x0_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 214 | 215 | ``` 216 | 217 | 218 | ### Test 5: `ouprior_test5.xml` 219 | 220 | - OU-process with fixed parameters, simulate a single trajectory with 101 time points. 221 | - Fix trajectory in BEAST2 and attempt to estimate mean and variance (mean-reversion fixed to true value). 222 | 223 | ```{r ouprior_test5, results='hide'} 224 | 225 | ##################### 226 | # ouprior_test5.xml # 227 | ##################### 228 | 229 | set.seed(10) 230 | 231 | t <- seq(0,10,by=0.1) 232 | x0 <- 6 233 | mu <- 5 234 | sigma <- 0.1 235 | nu <- 1 236 | x <- simulateOU(x0, t, mu, sigma, nu) 237 | 238 | #plot(t,x,type='l',col=pal.dark(cblue)) 239 | print(paste(x,collapse = " ")) 240 | 241 | # Posterior estimates in BEAST2 and get HPD (orange and green) 242 | lf <- readLogfile("ouprior_test5.log") 243 | 244 | par(mfrow=c(1,2)) 245 | 246 | # mu 247 | mu_density <- density(lf$mu) 248 | plot(mu_density$x, mu_density$y, lwd=2, lty=2, col=pal.dark(cgreen), type='l',ylab="",xlab="mu estimate") 249 | polygon(c(0, mu_density$x, 0), c(0, mu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 250 | abline(v=mu,lty=2,lwd=2,col=pal.dark(cred)) 251 | title("ouprior_test5.xml") 252 | 253 | # sigma 254 | sigma_density <- density(lf$sigma) 255 | plot(sigma_density$x, sigma_density$y, lwd=2, lty=2, col=pal.dark(cgreen), type='l',ylab="",xlab="sigma estimate") 256 | polygon(c(0, sigma_density$x, 0), c(0, sigma_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 257 | abline(v=sigma,lty=2,lwd=2,col=pal.dark(cred)) 258 | 259 | ``` -------------------------------------------------------------------------------- /examples/smoothingpriors/OUPriorTests.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | Ornstein-Uhlenbeck smoothing prior tests 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 33 | 42 | 43 | 44 | 45 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 98 | 99 | 100 | 101 |
102 | 103 | 104 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 126 | 127 | 128 |

Tests to check that the OU-prior implemented in BEAST2 is correct. In particular checking that sampling trajectories in BEAST2 with MCMC produces the correct theoretical distribution as well as the same distribution as simulating trajectories using the analytical solution (when drawing parameter values from prior distributions). Additionally, also test the ability of the BEAST2 implementation to estimate parameter values given a trajectory.

129 |
130 |

Test 1: ouprior_test1.xml

131 |
    132 |
  • OU-process with all parameters fixed.
    • 133 |
  • Sample x using MCMC in BEAST2 and compare to theoretical quantiles.
    • 134 |
135 | 
    #####################
136 |     # ouprior_test1.xml #
137 |     #####################
138 |     
139 |     # Calculate density of OU-process and get percentiles (blue and red)
140 |     t     <- seq(0,9,by=1)
141 |     x0    <- 10
142 |     mu    <- 3
143 |     sigma <- 1
144 |     nu    <- 0.1
145 |     X     <- plotOUProcessHPD(x0, t, mu, sigma, nu, ylim = c(0,12), main='ouprior_test1.xml', xlab="", ylab="")
146 |     sim_hpd <- getMatrixHPD(X)
147 |     
148 |     # Sample from posterior in BEAST2 and get HPD (orange and green)
149 |     lf <- readLogfile("ouprior_test1.log")
150 |     
151 |     xsample <- getSkylineSubset(lf,"x")
152 |     for (i in 1:nrow(xsample)) {
153 |       lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5)
154 |     }
155 |     sample_hpd <- getMatrixHPD(xsample)
156 |     lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2)
157 |     lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2)
158 |     lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2)
159 |

160 |
161 |
162 |

Test 2: ouprior_test2.xml

163 |
    164 |
  • OU-process with all parameters fixed and x_0 sampled from an exponential prior.
    • 165 |
  • Sample x using MCMC in BEAST2 and compare HPDs of x to 1000 simulated trajectories (using analytical solution from time-transformed Wiener process).
    • 166 |
167 | 
    #####################
168 |     # ouprior_test2.xml #
169 |     #####################
170 |     
171 |     # Simulate OU-trajectory and get HPD (blue and red)
172 |     t     <- seq(0,9,by=1)
173 |     x0_p  <- getPrior(rexp,1,rate=10)
174 |     mu    <- 3
175 |     sigma <- 1
176 |     nu    <- 0.1
177 |     X     <- plotOUProcessHPDEmpirical(x0_p, t, mu, sigma, nu, ylim = c(-4,6), main='ouprior_test2.xml')
178 |     sim_hpd <- getMatrixHPD(X)
179 |     
180 |     # Sample from posterior in BEAST2 and get HPD (orange and green)
181 |     lf <- readLogfile("ouprior_test2.log")
182 |     
183 |     xsample <- getSkylineSubset(lf,"x")
184 |     for (i in 1:nrow(xsample)) {
185 |         lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5)
186 |     }
187 |     sample_hpd <- getMatrixHPD(xsample)
188 |     lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2)
189 |     lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2)
190 |     lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2)
191 |

192 |
193 |
194 |

Test 3: ouprior_test3.xml

195 |
    196 |
  • OU-process with all parameters drawn from priors.
    • 197 |
  • Sample x using MCMC in BEAST2 and compare HPDs of x to 1000 simulated trajectories (using analytical solution from time-transformed Wiener process).
    • 198 |
  • Compare posterior distributions for parameters to priors (without data posterior should equal prior).
    • 199 |
200 | 
    #####################
201 |     # ouprior_test3.xml #
202 |     #####################
203 |     
204 |     layout(matrix(c(1,1,1,1,2,3,4,5), nrow=2, byrow=TRUE), heights = c(5,3))
205 |     
206 |     # Simulate OU-trajectory and get HPD (blue and red)
207 |     t       <- seq(0,0.9,by=0.1)
208 |     x0_p    <- getPrior(runif,  1, min=1.5, max=2.5)     
209 |     mu_p    <- getPrior(rlnorm, 1, meanlog=0, sdlog=0.5)
210 |     sigma_p <- getPrior(rnorm,  1, mean=0.5, sd=0.1)
211 |     nu_p    <- getPrior(rgamma, 1, shape=1, scale=5)
212 |     X     <- plotOUProcessHPDEmpirical(x0_p, t, mu_p, sigma_p, nu_p, ylim = c(0,4), main='ouprior_test3.xml')
213 |     sim_hpd <- getMatrixHPD(X)
214 |     
215 |     # Sample from posterior in BEAST2 and get HPD (orange and green)
216 |     lf <- readLogfile("ouprior_test3.log")
217 |     
218 |     xsample <- getSkylineSubset(lf,"x")
219 |     for (i in 1:nrow(xsample)) {
220 |       lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5)
221 |     }
222 |     sample_hpd <- getMatrixHPD(xsample)
223 |     lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2)
224 |     lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2)
225 |     lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2)
226 |     
227 |     # mu
228 |     mu_density <- density(lf$mu)
229 |     plotPrior('lnorm',list(meanlog=0, sdlog=0.5), main='mu')
230 |     lines(mu_density$x, mu_density$y, lwd=2, lty=2, col=pal.dark(cgreen))
231 |     polygon(c(0, mu_density$x, 0), c(0, mu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA)
232 |     
233 |     # Sigma
234 |     sigma_density <- density(lf$sigma)
235 |     plotPrior('norm',list(mean=0.5, sd=0.1), main='sigma')
236 |     lines(sigma_density$x, sigma_density$y, lwd=2, lty=2, col=pal.dark(cgreen))
237 |     polygon(c(0, sigma_density$x, 0), c(0, sigma_density$y, 0), col=pal.dark(cgreen,0.5), border=NA)
238 |     
239 |     # nu
240 |     nu_density <- density(lf$nu, bw=0.3)
241 |     plotPrior('gamma',list(shape=1, scale=5), main='nu')
242 |     lines(nu_density$x, nu_density$y, lwd=2, lty=2, col=pal.dark(cgreen))
243 |     polygon(c(0, nu_density$x, 0), c(0, nu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA)
244 |     
245 |     #x0 
246 |     x0_density <- density(lf$x1, bw=0.05)
247 |     plotPrior('unif',list(min=1.5, max=2.5), main=expression(x[0]))
248 |     lines(x0_density$x, x0_density$y, lwd=2, lty=2, col=pal.dark(cgreen))
249 |     polygon(c(0, x0_density$x, 0), c(0, x0_density$y, 0), col=pal.dark(cgreen,0.5), border=NA)
250 |

251 |
252 |
253 |

Test 4: ouprior_test4.xml

254 |
    255 |
  • OU-process with all parameters drawn from priors, except for the mean.
    • 256 |
  • Run for 51 time points instead of just 10 as above.
    • 257 |
  • Sample x using MCMC in BEAST2 and compare HPDs of x to 1000 simulated trajectories (using analytical solution from time-transformed Wiener process).
    • 258 |
  • Compare posterior distributions for parameters to priors (without data posterior should equal prior).
    • 259 |
260 | 
    #####################
261 |     # ouprior_test4.xml #
262 |     #####################
263 |     
264 |     layout(matrix(c(1,1,1,2,3,4), nrow=2, byrow=TRUE), heights=c(5,3))
265 |     
266 |     # Simulate OU-trajectory and get HPD (blue and red)
267 |     t       <- seq(0,1,length.out=51)
268 |     x0_p    <- getPrior(rexp,  1, rate=1)     
269 |     mu      <- 1
270 |     sigma_p <- getPrior(rnorm,  1, mean=0.3, sd=0.1)
271 |     nu_p    <- getPrior(rgamma, 1, shape=1, scale=50)
272 |     X       <- plotOUProcessHPDEmpirical(x0_p, t, mu, sigma_p, nu_p, ylim = c(0.5,2.5), main='ouprior_test4.xml')
273 |     sim_hpd <- getMatrixHPD(X)
274 |     
275 |     # Sample from posterior in BEAST2 and get HPD (orange and green)
276 |     lf <- readLogfile("ouprior_test4.log")
277 |     
278 |     xsample <- getSkylineSubset(lf,"x")
279 |     for (i in 1:nrow(xsample)) {
280 |       lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5)
281 |     }
282 |     sample_hpd <- getMatrixHPD(xsample)
283 |     lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2)
284 |     lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2)
285 |     lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2)
286 |     
287 |     # Sigma
288 |     sigma_density <- density(lf$sigma)
289 |     plotPrior('norm',list(mean=0.3, sd=0.1), main='sigma')
290 |     lines(sigma_density$x, sigma_density$y, lwd=2, lty=2, col=pal.dark(cgreen))
291 |     polygon(c(0, sigma_density$x, 0), c(0, sigma_density$y, 0), col=pal.dark(cgreen,0.5), border=NA)
292 |     
293 |     # nu
294 |     nu_density <- density(lf$nu, bw=5)
295 |     plotPrior('gamma',list(shape=1, scale=50), main='nu')
296 |     lines(nu_density$x, nu_density$y, lwd=2, lty=2, col=pal.dark(cgreen))
297 |     polygon(c(0, nu_density$x, 0), c(0, nu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA)
298 |     
299 |     #x0 
300 |     x0_density <- density(lf$x1)
301 |     plotPrior('exp',list(rate=1), main=expression(x[0]))
302 |     lines(x0_density$x, x0_density$y, lwd=2, lty=2, col=pal.dark(cgreen))
303 |     polygon(c(0, x0_density$x, 0), c(0, x0_density$y, 0), col=pal.dark(cgreen,0.5), border=NA)
304 |

305 |
306 |
307 |

Test 5: ouprior_test5.xml

308 |
    309 |
  • OU-process with fixed parameters, simulate a single trajectory with 101 time points.
    • 310 |
  • Fix trajectory in BEAST2 and attempt to estimate mean and variance (mean-reversion fixed to true value).
    • 311 |
312 | 
    #####################
313 |     # ouprior_test5.xml #
314 |     #####################
315 |     
316 |     set.seed(10)
317 |     
318 |     t     <- seq(0,10,by=0.1)
319 |     x0    <- 6
320 |     mu    <- 5
321 |     sigma <- 0.1
322 |     nu    <- 1
323 |     x <- simulateOU(x0, t, mu, sigma, nu)
324 |     
325 |     #plot(t,x,type='l',col=pal.dark(cblue))
326 |     print(paste(x,collapse = " "))
327 |     
328 |     # Posterior estimates in BEAST2 and get HPD (orange and green)
329 |     lf <- readLogfile("ouprior_test5.log")
330 |     
331 |     par(mfrow=c(1,2))
332 |     
333 |     # mu
334 |     mu_density <- density(lf$mu)
335 |     plot(mu_density$x, mu_density$y, lwd=2, lty=2, col=pal.dark(cgreen), type='l',ylab="",xlab="mu estimate")
336 |     polygon(c(0, mu_density$x, 0), c(0, mu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA)
337 |     abline(v=mu,lty=2,lwd=2,col=pal.dark(cred))
338 |     title("ouprior_test5.xml")
339 |     
340 |     # sigma
341 |     sigma_density <- density(lf$sigma)
342 |     plot(sigma_density$x, sigma_density$y, lwd=2, lty=2, col=pal.dark(cgreen), type='l',ylab="",xlab="sigma estimate")
343 |     polygon(c(0, sigma_density$x, 0), c(0, sigma_density$y, 0), col=pal.dark(cgreen,0.5), border=NA)
344 |     abline(v=sigma,lty=2,lwd=2,col=pal.dark(cred))
345 |

346 |
347 | 348 | 349 | 350 | 351 |
352 | 353 | 365 | 366 | 367 | 375 | 376 | 377 | 378 | -------------------------------------------------------------------------------- /examples/smoothingpriors/OUPriorTests.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Ornstein-Uhlenbeck smoothing prior tests" 3 | author: "Louis du Plessis" 4 | date: "7/19/2018" 5 | output: 6 | html_document: 7 | keep_md: true 8 | theme: cosmo 9 | self_contained: false 10 | --- 11 | 12 | Tests to check that the OU-prior implemented in BEAST2 is correct. In particular checking that sampling trajectories in BEAST2 with MCMC produces the correct theoretical distribution as well as the same distribution as simulating trajectories using the analytical solution (when drawing parameter values from prior distributions). Additionally, also test the ability of the BEAST2 implementation to estimate parameter values given a trajectory. 13 | 14 | 15 | 16 | 17 | 18 | 19 | ### Test 1: `ouprior_test1.xml` 20 | 21 | - OU-process with all parameters fixed. 22 | - Sample x using MCMC in BEAST2 and compare to theoretical quantiles. 23 | 24 | 25 | 26 | ```r 27 | ##################### 28 | # ouprior_test1.xml # 29 | ##################### 30 | 31 | # Calculate density of OU-process and get percentiles (blue and red) 32 | t <- seq(0,9,by=1) 33 | x0 <- 10 34 | mu <- 3 35 | sigma <- 1 36 | nu <- 0.1 37 | X <- plotOUProcessHPD(x0, t, mu, sigma, nu, ylim = c(0,12), main='ouprior_test1.xml', xlab="", ylab="") 38 | sim_hpd <- getMatrixHPD(X) 39 | 40 | # Sample from posterior in BEAST2 and get HPD (orange and green) 41 | lf <- readLogfile("ouprior_test1.log") 42 | 43 | xsample <- getSkylineSubset(lf,"x") 44 | for (i in 1:nrow(xsample)) { 45 | lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5) 46 | } 47 | sample_hpd <- getMatrixHPD(xsample) 48 | lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2) 49 | lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2) 50 | lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2) 51 | ``` 52 | 53 | ![](figs/ouprior_test1-1.png) 54 | 55 | 56 | 57 | ### Test 2: `ouprior_test2.xml` 58 | 59 | - OU-process with all parameters fixed and x_0 sampled from an exponential prior. 60 | - Sample x using MCMC in BEAST2 and compare HPDs of x to 1000 simulated trajectories (using analytical solution from time-transformed Wiener process). 61 | 62 | 63 | 64 | ```r 65 | ##################### 66 | # ouprior_test2.xml # 67 | ##################### 68 | 69 | # Simulate OU-trajectory and get HPD (blue and red) 70 | t <- seq(0,9,by=1) 71 | x0_p <- getPrior(rexp,1,rate=10) 72 | mu <- 3 73 | sigma <- 1 74 | nu <- 0.1 75 | X <- plotOUProcessHPDEmpirical(x0_p, t, mu, sigma, nu, ylim = c(-4,6), main='ouprior_test2.xml') 76 | sim_hpd <- getMatrixHPD(X) 77 | 78 | # Sample from posterior in BEAST2 and get HPD (orange and green) 79 | lf <- readLogfile("ouprior_test2.log") 80 | 81 | xsample <- getSkylineSubset(lf,"x") 82 | for (i in 1:nrow(xsample)) { 83 | lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5) 84 | } 85 | sample_hpd <- getMatrixHPD(xsample) 86 | lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2) 87 | lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2) 88 | lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2) 89 | ``` 90 | 91 | ![](figs/ouprior_test2-1.png) 92 | 93 | 94 | ### Test 3: `ouprior_test3.xml` 95 | 96 | - OU-process with all parameters drawn from priors. 97 | - Sample x using MCMC in BEAST2 and compare HPDs of x to 1000 simulated trajectories (using analytical solution from time-transformed Wiener process). 98 | - Compare posterior distributions for parameters to priors (without data posterior should equal prior). 99 | 100 | 101 | ```r 102 | ##################### 103 | # ouprior_test3.xml # 104 | ##################### 105 | 106 | layout(matrix(c(1,1,1,1,2,3,4,5), nrow=2, byrow=TRUE), heights = c(5,3)) 107 | 108 | # Simulate OU-trajectory and get HPD (blue and red) 109 | t <- seq(0,0.9,by=0.1) 110 | x0_p <- getPrior(runif, 1, min=1.5, max=2.5) 111 | mu_p <- getPrior(rlnorm, 1, meanlog=0, sdlog=0.5) 112 | sigma_p <- getPrior(rnorm, 1, mean=0.5, sd=0.1) 113 | nu_p <- getPrior(rgamma, 1, shape=1, scale=5) 114 | X <- plotOUProcessHPDEmpirical(x0_p, t, mu_p, sigma_p, nu_p, ylim = c(0,4), main='ouprior_test3.xml') 115 | sim_hpd <- getMatrixHPD(X) 116 | 117 | # Sample from posterior in BEAST2 and get HPD (orange and green) 118 | lf <- readLogfile("ouprior_test3.log") 119 | 120 | xsample <- getSkylineSubset(lf,"x") 121 | for (i in 1:nrow(xsample)) { 122 | lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5) 123 | } 124 | sample_hpd <- getMatrixHPD(xsample) 125 | lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2) 126 | lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2) 127 | lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2) 128 | 129 | # mu 130 | mu_density <- density(lf$mu) 131 | plotPrior('lnorm',list(meanlog=0, sdlog=0.5), main='mu') 132 | lines(mu_density$x, mu_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 133 | polygon(c(0, mu_density$x, 0), c(0, mu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 134 | 135 | # Sigma 136 | sigma_density <- density(lf$sigma) 137 | plotPrior('norm',list(mean=0.5, sd=0.1), main='sigma') 138 | lines(sigma_density$x, sigma_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 139 | polygon(c(0, sigma_density$x, 0), c(0, sigma_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 140 | 141 | # nu 142 | nu_density <- density(lf$nu, bw=0.3) 143 | plotPrior('gamma',list(shape=1, scale=5), main='nu') 144 | lines(nu_density$x, nu_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 145 | polygon(c(0, nu_density$x, 0), c(0, nu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 146 | 147 | #x0 148 | x0_density <- density(lf$x1, bw=0.05) 149 | plotPrior('unif',list(min=1.5, max=2.5), main=expression(x[0])) 150 | lines(x0_density$x, x0_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 151 | polygon(c(0, x0_density$x, 0), c(0, x0_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 152 | ``` 153 | 154 | ![](figs/ouprior_test3-1.png) 155 | 156 | 157 | 158 | ### Test 4: `ouprior_test4.xml` 159 | 160 | - OU-process with all parameters drawn from priors, except for the mean. 161 | - Run for 51 time points instead of just 10 as above. 162 | - Sample x using MCMC in BEAST2 and compare HPDs of x to 1000 simulated trajectories (using analytical solution from time-transformed Wiener process). 163 | - Compare posterior distributions for parameters to priors (without data posterior should equal prior). 164 | 165 | 166 | 167 | ```r 168 | ##################### 169 | # ouprior_test4.xml # 170 | ##################### 171 | 172 | layout(matrix(c(1,1,1,2,3,4), nrow=2, byrow=TRUE), heights=c(5,3)) 173 | 174 | # Simulate OU-trajectory and get HPD (blue and red) 175 | t <- seq(0,1,length.out=51) 176 | x0_p <- getPrior(rexp, 1, rate=1) 177 | mu <- 1 178 | sigma_p <- getPrior(rnorm, 1, mean=0.3, sd=0.1) 179 | nu_p <- getPrior(rgamma, 1, shape=1, scale=50) 180 | X <- plotOUProcessHPDEmpirical(x0_p, t, mu, sigma_p, nu_p, ylim = c(0.5,2.5), main='ouprior_test4.xml') 181 | sim_hpd <- getMatrixHPD(X) 182 | 183 | # Sample from posterior in BEAST2 and get HPD (orange and green) 184 | lf <- readLogfile("ouprior_test4.log") 185 | 186 | xsample <- getSkylineSubset(lf,"x") 187 | for (i in 1:nrow(xsample)) { 188 | lines(t,xsample[i,],col=pal.dark(corange,0.1),lwd=0.5) 189 | } 190 | sample_hpd <- getMatrixHPD(xsample) 191 | lines(t,sample_hpd[1,],col=pal.dark(cgreen),lty=2,lwd=2) 192 | lines(t,sample_hpd[2,],col=pal.dark(cgreen),lty=2,lwd=2) 193 | lines(t,sample_hpd[3,],col=pal.dark(cgreen),lty=2,lwd=2) 194 | 195 | # Sigma 196 | sigma_density <- density(lf$sigma) 197 | plotPrior('norm',list(mean=0.3, sd=0.1), main='sigma') 198 | lines(sigma_density$x, sigma_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 199 | polygon(c(0, sigma_density$x, 0), c(0, sigma_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 200 | 201 | # nu 202 | nu_density <- density(lf$nu, bw=5) 203 | plotPrior('gamma',list(shape=1, scale=50), main='nu') 204 | lines(nu_density$x, nu_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 205 | polygon(c(0, nu_density$x, 0), c(0, nu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 206 | 207 | #x0 208 | x0_density <- density(lf$x1) 209 | plotPrior('exp',list(rate=1), main=expression(x[0])) 210 | lines(x0_density$x, x0_density$y, lwd=2, lty=2, col=pal.dark(cgreen)) 211 | polygon(c(0, x0_density$x, 0), c(0, x0_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 212 | ``` 213 | 214 | ![](figs/ouprior_test4-1.png) 215 | 216 | 217 | ### Test 5: `ouprior_test5.xml` 218 | 219 | - OU-process with fixed parameters, simulate a single trajectory with 101 time points. 220 | - Fix trajectory in BEAST2 and attempt to estimate mean and variance (mean-reversion fixed to true value). 221 | 222 | 223 | ```r 224 | ##################### 225 | # ouprior_test5.xml # 226 | ##################### 227 | 228 | set.seed(10) 229 | 230 | t <- seq(0,10,by=0.1) 231 | x0 <- 6 232 | mu <- 5 233 | sigma <- 0.1 234 | nu <- 1 235 | x <- simulateOU(x0, t, mu, sigma, nu) 236 | 237 | #plot(t,x,type='l',col=pal.dark(cblue)) 238 | print(paste(x,collapse = " ")) 239 | 240 | # Posterior estimates in BEAST2 and get HPD (orange and green) 241 | lf <- readLogfile("ouprior_test5.log") 242 | 243 | par(mfrow=c(1,2)) 244 | 245 | # mu 246 | mu_density <- density(lf$mu) 247 | plot(mu_density$x, mu_density$y, lwd=2, lty=2, col=pal.dark(cgreen), type='l',ylab="",xlab="mu estimate") 248 | polygon(c(0, mu_density$x, 0), c(0, mu_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 249 | abline(v=mu,lty=2,lwd=2,col=pal.dark(cred)) 250 | title("ouprior_test5.xml") 251 | 252 | # sigma 253 | sigma_density <- density(lf$sigma) 254 | plot(sigma_density$x, sigma_density$y, lwd=2, lty=2, col=pal.dark(cgreen), type='l',ylab="",xlab="sigma estimate") 255 | polygon(c(0, sigma_density$x, 0), c(0, sigma_density$y, 0), col=pal.dark(cgreen,0.5), border=NA) 256 | abline(v=sigma,lty=2,lwd=2,col=pal.dark(cred)) 257 | ``` 258 | 259 | ![](figs/ouprior_test5-1.png) 260 | -------------------------------------------------------------------------------- /examples/smoothingpriors/figs/ouprior_test1-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/laduplessis/skylinetools/7993f64ca5699ad0da053af2f2a6d8e8a4c45fa8/examples/smoothingpriors/figs/ouprior_test1-1.png -------------------------------------------------------------------------------- /examples/smoothingpriors/figs/ouprior_test2-1.png: -------------------------------------------------------------------------------- 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/examples/smoothingpriors/figs/ouprior_test5-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/laduplessis/skylinetools/7993f64ca5699ad0da053af2f2a6d8e8a4c45fa8/examples/smoothingpriors/figs/ouprior_test5-1.png -------------------------------------------------------------------------------- /examples/smoothingpriors/ouprior_test1.xml: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /examples/smoothingpriors/ouprior_test2.xml: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | -------------------------------------------------------------------------------- /examples/smoothingpriors/ouprior_test3.xml: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | 6 | beast.math.distributions.Uniform 7 | beast.math.distributions.Exponential 8 | beast.math.distributions.LogNormalDistributionModel 9 | beast.math.distributions.Normal 10 | beast.math.distributions.Beta 11 | beast.math.distributions.Gamma 12 | beast.math.distributions.LaplaceDistribution 13 | beast.math.distributions.Prior 14 | beast.math.distributions.InverseGamma 15 | beast.math.distributions.OneOnX 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | -------------------------------------------------------------------------------- /examples/smoothingpriors/ouprior_test4.xml: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | 6 | beast.math.distributions.Uniform 7 | beast.math.distributions.Exponential 8 | beast.math.distributions.LogNormalDistributionModel 9 | beast.math.distributions.Normal 10 | beast.math.distributions.Beta 11 | beast.math.distributions.Gamma 12 | beast.math.distributions.LaplaceDistribution 13 | beast.math.distributions.Prior 14 | beast.math.distributions.InverseGamma 15 | beast.math.distributions.OneOnX 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | -------------------------------------------------------------------------------- /examples/smoothingpriors/ouprior_test5.xml: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | -------------------------------------------------------------------------------- /examples/treeslicer/comparison/dengue4_bdsky_fixeddates.xml: -------------------------------------------------------------------------------- 1 | 2 | 14 | 15 | 16 | 19 | 20 | beast.math.distributions.Uniform 21 | beast.math.distributions.Exponential 22 | beast.math.distributions.LogNormalDistributionModel 23 | beast.math.distributions.Normal 24 | beast.math.distributions.Beta 25 | beast.math.distributions.Gamma 26 | beast.math.distributions.LaplaceDistribution 27 | beast.math.distributions.Prior 28 | beast.math.distributions.InverseGamma 29 | beast.math.distributions.OneOnX 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | D4Brazi82=1982, 61 | D4ElSal83=1983, 62 | D4ElSal94=1994, 63 | D4Indon76=1976, 64 | D4Indon77=1977, 65 | D4Mexico84=1984, 66 | D4NewCal81=1981, 67 | D4Philip64=1964, 68 | D4Philip56=1956, 69 | D4Philip84=1984, 70 | D4PRico86=1986, 71 | D4SLanka78=1978, 72 | D4Tahiti79=1979, 73 | D4Tahiti85=1985, 74 | D4Thai63=1963, 75 | D4Thai78=1978, 76 | D4Thai84=1984 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 2.0 85 | 0.25 86 | 1.0 87 | 88 | 100.0 89 | 0 0.01 90 | 1.0 91 | 2.0 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 1.0 150 | 1.0 151 | 0.0 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | -------------------------------------------------------------------------------- /src/beast/evolution/speciation/BirthDeathSkylineModelTestHelper.java: -------------------------------------------------------------------------------- 1 | package beast.evolution.speciation; 2 | 3 | import java.util.List; 4 | 5 | /** 6 | * Created by dlouis on 06/04/16. 7 | */ 8 | public class BirthDeathSkylineModelTestHelper extends BirthDeathSkylineModel { 9 | 10 | public List getBirthRateChangeTimes() { return birthRateChangeTimes; } 11 | 12 | public List getDeathRateChangeTimes() { return deathRateChangeTimes; } 13 | 14 | public List getSamplingRateChangeTimes() { return samplingRateChangeTimes; } 15 | 16 | public List getrhoSamplingChangeTimes() { return rhoSamplingChangeTimes; } 17 | 18 | public List rChangeTimes() { return rChangeTimes; } 19 | 20 | } -------------------------------------------------------------------------------- /src/skylinetools/distributions/BMPrior.java: -------------------------------------------------------------------------------- 1 | package skylinetools.distributions; 2 | 3 | import beast.core.Distribution; 4 | import beast.core.Function; 5 | import beast.core.Input; 6 | import beast.core.State; 7 | import beast.core.parameter.RealParameter; 8 | import beast.math.distributions.ParametricDistribution; 9 | 10 | import java.util.List; 11 | import java.util.Random; 12 | 13 | /** 14 | * Implementation of the Brownian motion probability density for use as a prior on a sequence of (x_i,t_i) pairs. 15 | * 16 | * The density is *almost* identical to the GMRF smoothing prior used in the Skyride, Skygrid family of models, but with 17 | * a distribution placed on x0 (can be null, in which case density should be equal to GMRF density) 18 | * 19 | * GMRF implementation in Skyride/Skygrid models uses a matrix algebra library to calculate the density (as a MVN 20 | * distribution) and is paired with a GMRF block update operator. 21 | * 22 | * For now missing the same constant as the OUPrior 23 | * 24 | * @author Louis du Plessis 25 | * Date: 2018/07/23 26 | */ 27 | public class BMPrior extends Distribution { 28 | 29 | // the trajectory to compute Ornstein-Uhlenbeck prior of 30 | public Input xInput = 31 | new Input<>("x", "The x_i values", (Function) null); 32 | 33 | // the times associated with the x_i values 34 | public Input timeInput = 35 | new Input<>("times", "The times t_i specifying when x changes", (Function) null); 36 | 37 | // tau 38 | public Input tauInput = 39 | new Input("precision", "The precision parameter of the distribution (reciprocal of variance)", (RealParameter) null); 40 | 41 | // sigma 42 | public Input sigmaInput = 43 | new Input("sigma", "The standard deviation parameter of the distribution", (RealParameter) null, Input.Validate.XOR, tauInput); 44 | 45 | public Input x0PriorInput = 46 | new Input<>("x0Prior", "The prior to use on x0, or null if none.", (ParametricDistribution) null); 47 | 48 | public Input logSpace = new Input<>("logspace", "true if prior should be applied to log(x).", false); 49 | 50 | 51 | 52 | public double calculateLogP() { 53 | 54 | double tau; 55 | if (tauInput.get() == null) { 56 | double sigma = sigmaInput.get().getValue(); 57 | tau = 1/(sigma * sigma); 58 | } else { 59 | tau = tauInput.get().getValue(); 60 | } 61 | 62 | ParametricDistribution x0Prior = x0PriorInput.get(); 63 | 64 | double[] t = timeInput.get().getDoubleValues(); 65 | double[] x = xInput.get().getDoubleValues(); 66 | 67 | boolean logspace = logSpace.get(); 68 | if (logspace) { 69 | for (int i = 0; i < x.length; i++) { 70 | x[i] = Math.log(x[i]); 71 | } 72 | } 73 | 74 | int n = x.length - 1; 75 | 76 | double logL = n/2.0 * Math.log(tau); 77 | 78 | for (int i = 1; i <= n; i++) { 79 | 80 | double dt = t[i]-t[i-1]; 81 | double dx = x[i]-x[i-1]; 82 | 83 | logL -= 0.5*Math.log(dt); 84 | logL -= (0.5*tau*dx*dx)/dt; 85 | } 86 | 87 | if (x0Prior != null) logL += x0Prior.calcLogP(new Function() { 88 | @Override 89 | public int getDimension() { 90 | return 1; 91 | } 92 | 93 | @Override 94 | public double getArrayValue() { 95 | return x[0]; 96 | } 97 | 98 | @Override 99 | public double getArrayValue(int iDim) { 100 | return x[0]; 101 | } 102 | }); 103 | 104 | logP = logL; 105 | return logP; 106 | } 107 | 108 | @Override 109 | public List getArguments() { 110 | return null; 111 | } 112 | 113 | @Override 114 | public List getConditions() { 115 | return null; 116 | } 117 | 118 | @Override 119 | public void sample(State state, Random random) { 120 | 121 | } 122 | 123 | } 124 | -------------------------------------------------------------------------------- /src/skylinetools/distributions/OUPrior.java: -------------------------------------------------------------------------------- 1 | package skylinetools.distributions; 2 | 3 | import beast.core.Distribution; 4 | import beast.core.Function; 5 | import beast.core.Input; 6 | import beast.core.State; 7 | import beast.core.parameter.RealParameter; 8 | import beast.math.distributions.ParametricDistribution; 9 | 10 | import java.util.Arrays; 11 | import java.util.List; 12 | import java.util.Random; 13 | 14 | /** 15 | * Implementation of the Ornstein-Uhlenbeck probability density for use as a prior on a sequence of (x_i,t_i) pairs. 16 | * 17 | * TODO: 18 | * - Check that dimension of timeInput and xInput match 19 | * - Add constant to likelihood calculation 20 | * - Should sample method be implemented? Shouldn't be too difficult, but this is never used 21 | * 22 | * To ponder: Should normalization be included? I think not... 23 | * 24 | * @author Alexei Drummond. 25 | */ 26 | public class OUPrior extends Distribution { 27 | 28 | 29 | // the trajectory to compute Ornstein-Uhlenbeck prior of 30 | public Input xInput = 31 | new Input<>("x", "The x_i values", (Function) null); 32 | 33 | // the times associated with the x_i values 34 | public Input timeInput = 35 | new Input<>("times", "The times t_i specifying when x changes", (Function) null); 36 | 37 | // mean 38 | public Input meanInput = 39 | new Input("mean", "The mean of the equilibrium distribution", (RealParameter) null); 40 | 41 | // sigma 42 | public Input sigmaInput = 43 | new Input("sigma", "The standard deviation parameter of the equilibrium distribution", (RealParameter) null); 44 | 45 | // nu 46 | public Input nuInput = 47 | new Input("nu", "The reversion parameter of the Ornstein-Uhlenbeck mean reversion process", (RealParameter) null); 48 | 49 | public Input x0PriorInput = 50 | new Input<>("x0Prior", "The prior to use on x0, or null if none.", (ParametricDistribution) null); 51 | 52 | public Input logSpace = new Input<>("logspace", "true if prior should be applied to log(x).", false); 53 | 54 | public Input normalize = new Input<>("normalize", "true if times should be normalized such that t[n]-t[0]=1", false); 55 | 56 | 57 | public double calculateLogP() { 58 | 59 | double mu = meanInput.get().getValue(); 60 | double sigma = sigmaInput.get().getValue(); 61 | double sigsq = sigma * sigma; 62 | double nu = nuInput.get().getValue(); 63 | 64 | ParametricDistribution x0Prior = x0PriorInput.get(); 65 | 66 | double[] t = timeInput.get().getDoubleValues(); 67 | double[] x = xInput.get().getDoubleValues(); 68 | 69 | boolean logspace = logSpace.get(); 70 | if (logspace) { 71 | for (int i = 0; i < x.length; i++) { 72 | x[i] = Math.log(x[i]); 73 | } 74 | } 75 | 76 | int n = x.length - 1; 77 | 78 | 79 | double logL = -n/2.0 * Math.log(sigsq / (2.0*nu)); 80 | 81 | double period = 1; 82 | // Standardize differences between times 83 | // (so variance and mean-reversion are not dependent on time-scale) 84 | if (normalize.get() == true) { 85 | period = t[t.length-1]-t[0]; 86 | } 87 | System.out.println(Arrays.toString(t)); 88 | System.out.println(period); 89 | 90 | for (int i = 1; i <= n; i++) { 91 | 92 | double dt = (t[i]-t[i-1])/period; 93 | double relterm = 1.0-Math.exp(-2.0*nu*(dt)); 94 | 95 | logL -= Math.log(relterm)/2.0; 96 | 97 | double term = x[i] - mu - (x[i-1]-mu) * Math.exp(-nu*(dt)); 98 | 99 | logL -= nu / sigsq * (term*term / relterm); 100 | 101 | System.out.print(dt+" + "); 102 | } 103 | System.out.println(); 104 | 105 | if (x0Prior != null) logL += x0Prior.calcLogP(new Function() { 106 | @Override 107 | public int getDimension() { 108 | return 1; 109 | } 110 | 111 | @Override 112 | public double getArrayValue() { 113 | return x[0]; 114 | } 115 | 116 | @Override 117 | public double getArrayValue(int iDim) { 118 | return x[0]; 119 | } 120 | }); 121 | 122 | logP = logL; 123 | return logP; 124 | } 125 | 126 | @Override 127 | public List getArguments() { 128 | return null; 129 | } 130 | 131 | @Override 132 | public List getConditions() { 133 | return null; 134 | } 135 | 136 | @Override 137 | public void sample(State state, Random random) { 138 | 139 | } 140 | } 141 | -------------------------------------------------------------------------------- /src/skylinetools/parameter/DateParser.java: -------------------------------------------------------------------------------- 1 | package skylinetools.parameter; 2 | 3 | import beast.core.Input; 4 | import beast.core.parameter.RealParameter; 5 | import beast.core.util.Log; 6 | 7 | import java.time.LocalDate; 8 | import java.time.format.DateTimeFormatter; 9 | import java.time.format.DateTimeParseException; 10 | 11 | 12 | /** 13 | * Class for converting formatted date strings to decimal year dates, e.g. 01-Jan-2015 = 2015.0 14 | * 15 | * Can be used as input to TreeDateSlicer or any other class that needs a decimal date. 16 | * Heavily borrows from TimeParameter.java in feast by Tim Vaughan 17 | * 18 | * See https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html for the date format string. 19 | * 20 | * Note that the date strings are only converted on initialisation. Any operations on the values after that will leave 21 | * the input date strings unaffected. 22 | * 23 | * @author Louis du Plessis 24 | * Date: 2018/12/28 25 | */ 26 | public class DateParser extends RealParameter { 27 | 28 | public Input DateFormatInput = new Input<>("format","Date format (yyyy-MM-dd)"); 29 | public Input DatesInput = new Input<>("dates","Dates to be parsed"); 30 | 31 | 32 | public DateParser() { 33 | valuesInput.setRule(Input.Validate.OPTIONAL); 34 | } 35 | 36 | @Override 37 | public void initAndValidate() { 38 | 39 | for (String date : DatesInput.get().trim().split("\\s+")) { 40 | valuesInput.setValue(parseDate(date), this); 41 | } 42 | 43 | super.initAndValidate(); 44 | } 45 | 46 | 47 | protected double getYearDate(LocalDate date) { 48 | return date.getYear() + (date.getDayOfYear() - 1.0) / (date.isLeapYear() ? 366.0 : 365.0); 49 | } 50 | 51 | /** 52 | * 53 | * (Partly copied from TimeParameter.java in feast by Tim Vaughan) 54 | * 55 | * @param dateString 56 | * @return 57 | */ 58 | protected double parseDate(String dateString) { 59 | 60 | double yeardate; 61 | 62 | try { 63 | if (DateFormatInput.get() != null) { 64 | DateTimeFormatter formatter = DateTimeFormatter.ofPattern(DateFormatInput.get()); 65 | LocalDate date = LocalDate.parse(dateString, formatter); 66 | yeardate = getYearDate(date); 67 | 68 | Log.warning.println("Using format '" + DateFormatInput.get() + "' to parse '" + dateString + 69 | "' as: " + yeardate); 70 | } else { 71 | yeardate = Double.parseDouble(dateString); 72 | } 73 | } catch (NumberFormatException | DateTimeParseException ex) { 74 | throw new IllegalArgumentException("Error parsing input dates - " + 75 | "must be either a number or a properly formatted date string (" + dateString + " failed)."); 76 | } 77 | 78 | return yeardate; 79 | } 80 | 81 | 82 | 83 | } 84 | -------------------------------------------------------------------------------- /src/skylinetools/parameter/RealParameterSequence.java: -------------------------------------------------------------------------------- 1 | package skylinetools.parameter; 2 | 3 | import beast.core.Description; 4 | import beast.core.Input; 5 | import beast.core.parameter.RealParameter; 6 | 7 | import java.util.Arrays; 8 | 9 | 10 | /** 11 | * A parameter object to easily initialise a linear sequence of real numbers 12 | * Can either use the "by" input to specify the step size or simply use the "dimension" 13 | * input to specify the length of the sequence (the step size will be calculated). 14 | * 15 | * @author Louis du Plessis 16 | * Date: 2018/07/17 17 | */ 18 | @Description("A real-valued sequence of numbers with a fixed step-size between them (at initialisation).") 19 | public class RealParameterSequence extends RealParameter { 20 | 21 | 22 | public final Input fromInput = new Input<>("from", "Starting value of the sequence", 23 | Input.Validate.REQUIRED); 24 | 25 | public final Input toInput = new Input<>("to", "End value of the sequence", 26 | Input.Validate.REQUIRED); 27 | 28 | // Specify either byInput or dimensionInput 29 | public final Input byInput = new Input<>("by", "Increment of the sequence"); 30 | 31 | 32 | // Override input rule of RealParameter (base class) 33 | // (values are derived here) 34 | public RealParameterSequence() { 35 | valuesInput.setRule(Input.Validate.OPTIONAL); 36 | } 37 | 38 | 39 | @Override 40 | public void initAndValidate() { 41 | 42 | double from = fromInput.get().doubleValue(), 43 | to = toInput.get().doubleValue(); 44 | 45 | double step; 46 | int dim; 47 | 48 | if (from == to) { 49 | throw new IllegalArgumentException("from cannot be equal to to"); 50 | } 51 | 52 | // Set limits (from RealParameter) 53 | if (lowerValueInput.get() != null) { 54 | m_fLower = lowerValueInput.get(); 55 | } else { 56 | m_fLower = Double.NEGATIVE_INFINITY; 57 | } 58 | if (upperValueInput.get() != null) { 59 | m_fUpper = upperValueInput.get(); 60 | } else { 61 | m_fUpper = Double.POSITIVE_INFINITY; 62 | } 63 | 64 | // Calculate dimension and stepsize 65 | if (byInput.get() == null) { 66 | //System.out.println("Length"); 67 | 68 | dim = dimensionInput.get().intValue(); 69 | 70 | if (dim <= 0) { 71 | throw new IllegalArgumentException("Length needs to be bigger than 0"); 72 | } 73 | 74 | step = (to-from)/(dim - 1); 75 | } else { 76 | //System.out.println("By"); 77 | 78 | step = byInput.get().doubleValue(); 79 | 80 | if (step == 0) { 81 | throw new IllegalArgumentException("Step size cannot be 0"); 82 | } 83 | 84 | dim = (int) Math.floor((to-from)/step) + 1; 85 | 86 | if ((from > to && step > 0) || (to > from && step < 0)) { 87 | throw new IllegalArgumentException("No way to reach to from from with by input"); 88 | } 89 | 90 | } 91 | 92 | // Set values 93 | dimensionInput.setValue(dim, this); 94 | values = new Double[dim]; 95 | for (int i = 0; i < dim; i++) { 96 | values[i] = from + i*step; 97 | } 98 | //System.out.println(Arrays.toString(values)+": "+dim); 99 | 100 | // Accounting (from Parameter.Base) 101 | storedValues = new Double[dim]; 102 | m_bIsDirty = new boolean[dimensionInput.get()]; 103 | minorDimension = minorDimensionInput.get(); 104 | if (minorDimension > 0 && dimensionInput.get() % minorDimension > 0) { 105 | throw new IllegalArgumentException("Dimension must be divisible by stride"); 106 | } 107 | this.storedValues = values.clone(); 108 | 109 | } 110 | 111 | } 112 | -------------------------------------------------------------------------------- /src/skylinetools/util/TreeDateLogger.java: -------------------------------------------------------------------------------- 1 | package skylinetools.util; 2 | 3 | import beast.core.CalculationNode; 4 | import beast.core.Function; 5 | import beast.core.Input; 6 | import beast.core.Loggable; 7 | import beast.core.parameter.RealParameter; 8 | import beast.core.util.Log; 9 | import beast.evolution.tree.Tree; 10 | 11 | import java.io.PrintStream; 12 | 13 | /** 14 | * Log date of tMRCA or if parameter is supplied, logs dates of those parameters subtracted from most recent date in tree 15 | * E.g. log the date of the origin in a birth-death model. 16 | * 17 | * @author Louis du Plessis 18 | * Date: 2018/08/05 19 | * 20 | */ 21 | public class TreeDateLogger extends CalculationNode implements Loggable, Function { 22 | 23 | final public Input treeInput = new Input<>("tree", "Tree to report dates for.", Input.Validate.REQUIRED); 24 | 25 | final public Input parameterInput = new Input<>("parameter", "Parameter to report dates for on tree.", Input.Validate.OPTIONAL); 26 | 27 | int dim; 28 | 29 | @Override 30 | public void initAndValidate() { 31 | if (!treeInput.get().hasDateTrait()) { 32 | Log.warning.println("Tree "+treeInput.get().getID()+ " does not have a date trait. TreeDateLogger "+ getID() + " will log heights instead"); 33 | } 34 | 35 | if (parameterInput.get() != null) { 36 | dim = parameterInput.get().getDimension(); 37 | } else { 38 | dim = 1; 39 | } 40 | 41 | } 42 | 43 | @Override 44 | public void init(PrintStream out) { 45 | final Tree tree = treeInput.get(); 46 | 47 | if (parameterInput.get() != null) { 48 | if (dim == 1) { 49 | out.print(tree.getID() + "." + parameterInput.get().getID() + ".date\t"); 50 | } else { 51 | 52 | for (int i = 0; i < dim; i++) { 53 | out.print(tree.getID() + "." + parameterInput.get().getID() + ".dates" + (i+1) + "\t"); 54 | } 55 | 56 | } 57 | } else { 58 | out.print(tree.getID() + ".height.date\t"); 59 | } 60 | } 61 | 62 | @Override 63 | public void log(long sample, PrintStream out) { 64 | final Tree tree = treeInput.get(); 65 | 66 | if (parameterInput.get() != null) { 67 | for (int i = 0; i < dim; i++) { 68 | out.print(tree.getDate(parameterInput.get().getValue(i)) + "\t"); 69 | } 70 | } else { 71 | out.print(tree.getDate(tree.getRoot().getHeight()) + "\t"); 72 | } 73 | } 74 | 75 | @Override 76 | public void close(PrintStream out) { 77 | // nothing to do 78 | } 79 | 80 | @Override 81 | public int getDimension() { 82 | return dim; 83 | } 84 | 85 | @Override 86 | public double getArrayValue() { 87 | final Tree tree = treeInput.get(); 88 | 89 | if (parameterInput.get() != null) { 90 | return tree.getDate(parameterInput.get().getValue(0)); 91 | } else { 92 | return tree.getDate(tree.getRoot().getHeight()); 93 | } 94 | } 95 | 96 | @Override 97 | public double getArrayValue(int dim) { 98 | final Tree tree = treeInput.get(); 99 | 100 | if (parameterInput.get() != null) { 101 | return tree.getDate(parameterInput.get().getValue(dim)); 102 | } else { 103 | return tree.getDate(tree.getRoot().getHeight()); 104 | } 105 | } 106 | 107 | } 108 | -------------------------------------------------------------------------------- /src/skylinetools/util/TreeDateSlicer.java: -------------------------------------------------------------------------------- 1 | package skylinetools.util; 2 | 3 | import beast.core.Function; 4 | import beast.core.Input; 5 | import beast.evolution.tree.Tree; 6 | import beast.util.HeapSort; 7 | 8 | import java.util.Arrays; 9 | 10 | /** 11 | * TreeSlicer class that slices a tree at fixed calendar dates 12 | * 13 | * TODO: Make the class read dates in different formats and convert to decimal year date. (or use separate DateParser class) 14 | * 15 | * @author Louis du Plessis 16 | * Date: 2018/12/28 17 | * 18 | **/ 19 | public class TreeDateSlicer extends TreeSlicer { 20 | 21 | public Input datesInput = 22 | new Input<>("dates", "Dates at which to slice the tree"); 23 | 24 | protected double[] dates; 25 | 26 | @Override 27 | public void initAndValidate() { 28 | 29 | int dimension; 30 | 31 | /* Read tree */ 32 | tree = treeInput.get(); 33 | //updateAnchorTimes(tree); 34 | Anchor.update(tree); 35 | 36 | /* Read dates and set dimension accordingly (depends on whether present date is included or not) */ 37 | updateDates(); 38 | 39 | if (dates[dates.length-1] == Anchor.PRESENT.getDate()) { 40 | dimension = dates.length; 41 | } else 42 | dimension = dates.length+1; 43 | dimensionInput.setValue(dimension, this); 44 | 45 | 46 | /* Initialise arrays */ 47 | values = new Double[dimension]; 48 | storedValues = new Double[dimension]; 49 | calculateTimes(tree); 50 | // System.out.println(this.ID+"\t"+this.getDimension()); 51 | 52 | 53 | // Initialization accounting (not really used) 54 | // Don't want to use super.initAndValidate() because we're doing something else with the values 55 | // m_fLower = Double.NEGATIVE_INFINITY; 56 | m_fLower = 0.0; // Height cannot be negative 57 | m_fUpper = Double.POSITIVE_INFINITY; 58 | m_bIsDirty = new boolean[dimensionInput.get()]; 59 | minorDimension = minorDimensionInput.get(); 60 | if (minorDimension > 0 && dimensionInput.get() % minorDimension > 0) { 61 | throw new IllegalArgumentException("Error in "+this.getID()+": Dimension must be divisible by stride"); 62 | } 63 | this.storedValues = values.clone(); 64 | 65 | timesKnown = false; 66 | } 67 | 68 | 69 | 70 | /** 71 | * Update the dates and order them (in case they are sampled) 72 | */ 73 | protected void updateDates() { 74 | dates = datesInput.get().getDoubleValues(); 75 | HeapSort.sort(dates); 76 | } 77 | 78 | 79 | @Override 80 | protected void calculateTimes(Tree tree) { 81 | 82 | /* Update newest, oldest, tmrca */ 83 | //updateAnchorTimes(tree); 84 | Anchor.update(tree); 85 | //System.out.println(tmrca+"\t"+oldest+"\t"+heightToDate(oldest)+"\t"+newest+"\t"+presentDate); 86 | 87 | updateDates(); 88 | for (int i = 1; i < getDimension(); i++) 89 | values[values.length - i] = Math.max(0.0, dateToHeight(dates[i - 1])); 90 | values[0] = 0.0; // This has to be set to 0.0 for BDSKY to run 91 | 92 | timesKnown = true; 93 | } 94 | 95 | } -------------------------------------------------------------------------------- /src/skylinetools/util/TreeSliceDateLogger.java: -------------------------------------------------------------------------------- 1 | package skylinetools.util; 2 | 3 | import beast.core.CalculationNode; 4 | import beast.core.Function; 5 | import beast.core.Input; 6 | import beast.core.Loggable; 7 | 8 | import java.io.PrintStream; 9 | 10 | /** 11 | * Class to convert the interval times of a TreeSlicer object into calendar dates 12 | * Can be logged or just used as a CalculationNode 13 | * 14 | * @author Louis du Plessis 15 | * Date: 2018/02/12 16 | * 17 | */ 18 | public class TreeSliceDateLogger extends CalculationNode implements Loggable, Function { 19 | 20 | final public Input treeSliceInput = 21 | new Input<>("treeSlice", "TreeSlicer to log dates for", Input.Validate.REQUIRED); 22 | 23 | @Override 24 | public void initAndValidate() { 25 | // nothing to do 26 | } 27 | 28 | @Override 29 | public void init(PrintStream out) { 30 | final TreeSlicer treeslice = treeSliceInput.get(); 31 | final int valueCount = treeslice.getDimension(); 32 | 33 | if (valueCount == 1) { 34 | out.print(treeslice.getID()+".dates\t"); 35 | } else { 36 | for (int value = 0; value < valueCount; value++) { 37 | out.print(treeslice.getID()+ ".dates" + (value + 1) + "\t"); 38 | 39 | } 40 | } 41 | } 42 | 43 | @Override 44 | public void log(long sample, PrintStream out) { 45 | final TreeSlicer treeslice = treeSliceInput.get(); 46 | 47 | final int values = treeslice.getDimension(); 48 | for (int value = 0; value < values; value++) { 49 | out.print(treeslice.heightToDate(treeslice.getValue(value)) + "\t"); 50 | } 51 | } 52 | 53 | @Override 54 | public void close(PrintStream out) { 55 | // nothing to do 56 | } 57 | 58 | @Override 59 | public int getDimension() { 60 | final TreeSlicer treeslice = treeSliceInput.get(); 61 | return treeslice.getDimension(); 62 | } 63 | 64 | // Remember to convert height to date! 65 | @Override 66 | public double getArrayValue() { 67 | final TreeSlicer treeslice = treeSliceInput.get(); 68 | return treeslice.heightToDate(treeslice.getArrayValue()); 69 | } 70 | 71 | // Remember to convert height to date! 72 | @Override 73 | public double getArrayValue(int dim) { 74 | final TreeSlicer treeslice = treeSliceInput.get(); 75 | return treeslice.heightToDate(treeslice.getArrayValue(dim)); 76 | } 77 | 78 | 79 | 80 | 81 | } 82 | -------------------------------------------------------------------------------- /src/skylinetools/util/TreeSlicer.java: -------------------------------------------------------------------------------- 1 | package skylinetools.util; 2 | 3 | import beast.core.*; 4 | import beast.core.parameter.Parameter; 5 | import beast.core.parameter.RealParameter; 6 | import beast.evolution.tree.Node; 7 | import beast.evolution.tree.Tree; 8 | import beast.util.HeapSort; 9 | 10 | import java.util.ArrayList; 11 | import java.util.Arrays; 12 | import java.util.List; 13 | 14 | 15 | /** 16 | * Base TreeSlicer class 17 | * 18 | * Equidistant slices between present and an anchor point on the tree. 19 | * 20 | * Input dates always have dimension one less than the treeslicer itself (unless the last date is equal to the most recent tip). 21 | * (Since the last value in the slice has to be 0 (i.e. the most recent tip in the tree). 22 | * 23 | * 24 | * @author Louis du Plessis 25 | * Date: 2018/02/12 26 | * 27 | */ 28 | public class TreeSlicer extends RealParameter { 29 | 30 | public Input treeInput = 31 | new Input<>("tree", "Tree over which to calculate the slice", Input.Validate.REQUIRED); 32 | 33 | public Input toInput = 34 | new Input<>("to", "Anchor point to stop the slicing intervals (tmrca/oldestsample)", "tmrca"); 35 | 36 | public Input inclusiveInput = 37 | new Input<>("inclusive", "Include the final anchor point (to criterion) in the vector",true); 38 | 39 | 40 | 41 | /* Anchor times on the tree */ 42 | protected enum Anchor { 43 | 44 | PRESENT, // The present, or the time of the most recent sample, should be at height 0.0 45 | OLDESTSAMPLE, // The height of the oldest sample in the tree, 0 <= OLDESTSAMPLE <= TMRCA 46 | TMRCA; // The height of the tree (tMRCA) 47 | 48 | 49 | private double height, date; 50 | 51 | public double getHeight() { return this.height; } 52 | 53 | public double getDate() { return this.date; } 54 | 55 | 56 | /** 57 | * Update anchor times in the tree 58 | * 59 | * O(n) for n nodes 60 | * 61 | * Use getNodesAsArray() instead of getExternalNodes() because it only passes a pointer whereas getExterNodes() 62 | * requires constructing an ArrayList (so it is O(n) plus a lot of extra memory operations). 63 | * 64 | * Unfortunately tree.somethingIsDirty() does NOT appear to work to indicate if anchor times would have changed or 65 | * not, so this MUST be recalculated every time the times are updated. 66 | * 67 | * @param tree 68 | */ 69 | public static void update(Tree tree) { 70 | 71 | final double EPS = 1e-7; 72 | 73 | double height; 74 | 75 | TMRCA.height = tree.getRoot().getHeight(); 76 | TMRCA.date = tree.getRoot().getDate(); 77 | 78 | // This next part should only be necessary when tipdates are sampled (How can this be checked?) 79 | // May be optimised by skipping the first n-1 nodes, but this way is very secure, though slightly slower 80 | OLDESTSAMPLE.height = 0; 81 | PRESENT.height = TMRCA.height; 82 | for (Node N : tree.getNodesAsArray()) { 83 | if (N.isLeaf()) { 84 | height = N.getHeight(); 85 | 86 | if (height > OLDESTSAMPLE.height) { 87 | // Need to adjust so BDSKY likelihood nonzero 88 | OLDESTSAMPLE.height = height+EPS; 89 | OLDESTSAMPLE.date = N.getDate(); 90 | } 91 | 92 | if (height < PRESENT.height) { 93 | PRESENT.height = height; 94 | PRESENT.date = N.getDate(); 95 | } 96 | } 97 | } 98 | 99 | } 100 | 101 | } 102 | /* End Anchor times */ 103 | 104 | 105 | protected Tree tree; 106 | protected Anchor stop; 107 | protected boolean inclusive; 108 | protected boolean timesKnown; 109 | 110 | 111 | // Override input rule of RealParameter (base class) 112 | public TreeSlicer() { 113 | valuesInput.setRule(Input.Validate.OPTIONAL); 114 | } 115 | 116 | @Override 117 | public void initAndValidate() { 118 | 119 | int dimension; 120 | String stopStr; 121 | 122 | /* Read tree */ 123 | tree = treeInput.get(); 124 | Anchor.update(tree); 125 | 126 | 127 | /* Read dimension of the slice */ 128 | dimension = dimensionInput.get(); 129 | 130 | 131 | /* Read to input (where to end slice) */ 132 | if (toInput.get() != null) 133 | stopStr = toInput.get().toUpperCase().trim(); 134 | else 135 | stopStr = "TMRCA"; 136 | 137 | stop = null; 138 | for (Anchor a : Anchor.values()) { 139 | if (Anchor.valueOf(stopStr) == a) { 140 | stop = a; 141 | } 142 | } 143 | if (stop == null) { 144 | throw new IllegalArgumentException("Error in "+this.getID()+": Unknown anchor point ("+stopStr+") for to input."); 145 | } 146 | 147 | 148 | /* Include the final anchor point as a breakpoint */ 149 | inclusive = inclusiveInput.get(); 150 | 151 | 152 | /* Initialise arrays */ 153 | values = new Double[dimension]; 154 | storedValues = new Double[dimension]; 155 | calculateTimes(tree); 156 | // System.out.println(this.ID+"\t"+this.getDimension()); 157 | 158 | 159 | // Initialization accounting (not really used) 160 | // Don't want to use super.initAndValidate() because we're doing something else with the values 161 | // m_fLower = Double.NEGATIVE_INFINITY; 162 | m_fLower = 0.0; // Height cannot be negative 163 | m_fUpper = Double.POSITIVE_INFINITY; 164 | m_bIsDirty = new boolean[dimensionInput.get()]; 165 | minorDimension = minorDimensionInput.get(); 166 | if (minorDimension > 0 && dimensionInput.get() % minorDimension > 0) { 167 | throw new IllegalArgumentException("Error in "+this.getID()+": Dimension must be divisible by stride"); 168 | } 169 | this.storedValues = values.clone(); 170 | 171 | timesKnown = false; 172 | } 173 | 174 | 175 | 176 | /* Methods should only be called after anchor times have been updated */ 177 | 178 | protected double dateToHeight(double date) { 179 | Anchor.update(tree); 180 | return (Anchor.PRESENT.getDate() - date); 181 | } 182 | 183 | protected double heightToDate(double height) { 184 | Anchor.update(tree); 185 | return (Anchor.PRESENT.getDate() - height); 186 | } 187 | 188 | 189 | /** 190 | * Update the slice times 191 | * 192 | * Unfortunately tree.somethingIsDirty() does NOT appear to work to indicate if anchor times have changed so 193 | * updateAnchortimes() MUST be called. 194 | * 195 | * @param tree 196 | */ 197 | protected void calculateTimes(Tree tree) { 198 | 199 | double endTime, stepSize; 200 | 201 | /* Update anchor times */ 202 | Anchor.update(tree); 203 | //for (Anchor a : Anchor.values()) { 204 | // System.out.println(a.toString()+"\t"+a.getHeight()+"\t"+a.getDate()); 205 | //} 206 | 207 | endTime = stop.getHeight(); 208 | if (inclusive) 209 | stepSize = endTime / (getDimension() - 1); 210 | else 211 | stepSize = endTime / (getDimension()); 212 | 213 | 214 | for (int i = 0; i < getDimension(); i++) { 215 | values[i] = i * stepSize; 216 | } 217 | 218 | timesKnown = true; 219 | } 220 | 221 | 222 | @Override 223 | protected boolean requiresRecalculation() { 224 | // Tree is a stateNode so should always use somethingIsDirty() and NOT isDirtyCalculation! 225 | //System.out.println("Checking recalculation "+this.ID+" "+tree.isDirtyCalculation()+" "+tree.somethingIsDirty()); 226 | timesKnown = false; 227 | return true; 228 | //return tree.somethingIsDirty(); 229 | } 230 | 231 | 232 | /* Override methods to make sure times get recalculated whenever times are accessed */ 233 | 234 | @Override 235 | public Double getValue() { 236 | if (!timesKnown) { 237 | calculateTimes(tree); 238 | } 239 | return values[0]; 240 | } 241 | 242 | @Override 243 | public Double getValue(final int index) { 244 | if (!timesKnown) { 245 | calculateTimes(tree); 246 | } 247 | return values[index]; 248 | } 249 | 250 | @Override 251 | public double getArrayValue() { 252 | if (!timesKnown) { 253 | calculateTimes(tree); 254 | } 255 | return values[0]; 256 | } 257 | 258 | @Override 259 | public double getArrayValue(final int index) { 260 | if (!timesKnown) { 261 | calculateTimes(tree); 262 | } 263 | return values[index]; 264 | } 265 | 266 | @Override 267 | public Double [] getValues() { 268 | if (!timesKnown) { 269 | calculateTimes(tree); 270 | } 271 | return Arrays.copyOf(values, values.length); 272 | } 273 | 274 | } 275 | -------------------------------------------------------------------------------- /test/README.md: -------------------------------------------------------------------------------- 1 | # Tests 2 | 3 | Tests implemented 4 | 5 | 6 | ## TreeSlicer 7 | - Check on two fixed trees of tractable size that slices are correct. 8 | 9 | 10 | ## OUPrior 11 | 12 | - Likelihood of single point calculated correctly 13 | - Likelihood of trajectory calculated correctly (all values equal) 14 | - Likelihood of trajectory calculated correctly (different values) 15 | 16 | 17 | ## RealParameterSequence 18 | - Using by input with different positive and negative increments 19 | - Using dimension input with positive and negative increments 20 | 21 | 22 | --- 23 | _Louis du Plessis, 2018_ -------------------------------------------------------------------------------- /test/beast/evolution/speciation/SkylineTimesTest.java: -------------------------------------------------------------------------------- 1 | package beast.evolution.speciation; 2 | 3 | import beast.core.parameter.BooleanParameter; 4 | import beast.core.parameter.RealParameter; 5 | import beast.evolution.speciation.BirthDeathSkylineModelTestHelper; 6 | import beast.evolution.tree.Tree; 7 | import beast.util.TreeParser; 8 | import org.junit.Test; 9 | import junit.framework.TestCase; 10 | 11 | import java.util.List; 12 | 13 | /** 14 | * This test is to check how bdsky (beast.evolution.speciation.BirthDeathSkylineModel) sets change-point times, depending 15 | * on the input. 16 | * 17 | * There are 5 different ways to set the change-point times: 18 | * 19 | * 1.) Equally spaced between origin and present 20 | * 21 | * 2.) Time from the origin to the present: 22 | * a) Absolute times (in the same units as the tree branches) 23 | * b) Relative times (between 0 and 1) 24 | * 25 | * 3.) Time from the present to the origin (reverse-time): 26 | * a) Absolute times (in the same units as the tree branches) 27 | * b) Relative times (between 0 and 1) 28 | * 29 | * The bdsky package itself never tests that this actually works in the expected way. 30 | * 31 | * 32 | * Created by dlouis on 06/04/16. 33 | */ 34 | public class SkylineTimesTest extends TestCase { 35 | 36 | 37 | /* 38 | * Tree used simulated by: 39 | * 40 | * library(TreeSim) 41 | * set.seed(1) 42 | * tree <- sim.bd.taxa(n=10, numbsim=1, lambda=2, mu=0.5, frac=1, complete=FALSE, stochsampling=TRUE) 43 | * 44 | * Tree MRCA = 2.55557351 45 | * 46 | */ 47 | 48 | 49 | /** 50 | * Expect (1, 2, 3, 4, 5.0) 51 | * 52 | * 53 | * @throws Exception 54 | */ 55 | @Test 56 | public void testSkylineTimesEquallySpaced() throws Exception { 57 | 58 | Tree tree = new TreeParser(); 59 | 60 | tree.initByName("newick", "(((((t8:0.7222085766,(t5:0.04762441103,t1:0.6054274304):0.6506888754):0.5517904073,(t2:0.514380477,t10:0.4632931469):0.5324084692):0.3951336571,t6:0.7816201891):0.3005419225,t4:0.1858745544):0.05199122007,((t7:0.1588048418,t9:0.03676356945):0.5750080554,t3:1.336592522):0.02329921031):0.2517272777;", 61 | "IsLabelledNewick", true, "adjustTipHeights", false); 62 | 63 | BirthDeathSkylineModelTestHelper bdsky = new BirthDeathSkylineModelTestHelper(); 64 | 65 | RealParameter origin = new RealParameter("5"); 66 | RealParameter Re = new RealParameter("1 1 1 1 1"); 67 | RealParameter delta = new RealParameter("1 1 1 1 1"); 68 | RealParameter s = new RealParameter("1 1 1 1 1"); 69 | BooleanParameter reverse = new BooleanParameter("true true true true true"); 70 | 71 | 72 | bdsky.initByName("tree", tree, "origin", origin, 73 | "reproductiveNumber", Re, "becomeUninfectiousRate", delta, "samplingProportion", s, "reverseTimeArrays", reverse); 74 | 75 | List birthRateTimes = bdsky.getBirthRateChangeTimes(); 76 | double [] expected = new double [] {1, 2, 3, 4, 5.0}; 77 | int i = 0; 78 | for (Double t : birthRateTimes) { 79 | //System.out.println(t); 80 | assertEquals(t, expected[i]); 81 | i++; 82 | } 83 | 84 | //double logP = bdsky.calculateTreeLogLikelihood(tree); 85 | } 86 | 87 | 88 | /** 89 | * Expect (1.0, 2.0, 3.0, 4.0, 5.0) 90 | * 91 | * 92 | * @throws Exception 93 | */ 94 | @Test 95 | public void testSkylineTimesRelative() throws Exception { 96 | 97 | Tree tree = new TreeParser(); 98 | 99 | tree.initByName("newick", "(((((t8:0.7222085766,(t5:0.04762441103,t1:0.6054274304):0.6506888754):0.5517904073,(t2:0.514380477,t10:0.4632931469):0.5324084692):0.3951336571,t6:0.7816201891):0.3005419225,t4:0.1858745544):0.05199122007,((t7:0.1588048418,t9:0.03676356945):0.5750080554,t3:1.336592522):0.02329921031):0.2517272777;", 100 | "IsLabelledNewick", true, "adjustTipHeights", false); 101 | 102 | BirthDeathSkylineModelTestHelper bdsky = new BirthDeathSkylineModelTestHelper(); 103 | 104 | RealParameter origin = new RealParameter("5"); 105 | RealParameter Re = new RealParameter("1 1 1 1 1"); 106 | RealParameter delta = new RealParameter("1 1 1 1 1"); 107 | RealParameter s = new RealParameter("1 1 1 1 1"); 108 | RealParameter birthtimes = new RealParameter("0.0 0.2 0.4 0.6 0.8"); 109 | BooleanParameter reverse = new BooleanParameter("false false false false false"); 110 | 111 | bdsky.initByName("tree", tree, "origin", origin, 112 | "reproductiveNumber", Re, "becomeUninfectiousRate", delta, "samplingProportion", s, 113 | "birthRateTimesRelative", true, "birthRateChangeTimes", birthtimes, 114 | "reverseTimeArrays", reverse); 115 | 116 | List birthRateTimes = bdsky.getBirthRateChangeTimes(); 117 | double [] expected = new double [] {1.0, 2.0, 3.0, 4.0, 5.0}; 118 | int i = 0; 119 | for (Double t : birthRateTimes) { 120 | //System.out.println(t); 121 | assertEquals(t, expected[i]); 122 | i++; 123 | } 124 | 125 | //double logP = bdsky.calculateTreeLogLikelihood(tree); 126 | } 127 | 128 | /** 129 | * Expect (0.2, 0.4, 0.6, 0.8, 5.0) 130 | * 131 | * 132 | * @throws Exception 133 | */ 134 | @Test 135 | public void testSkylineTimes() throws Exception { 136 | 137 | Tree tree = new TreeParser(); 138 | 139 | tree.initByName("newick", "(((((t8:0.7222085766,(t5:0.04762441103,t1:0.6054274304):0.6506888754):0.5517904073,(t2:0.514380477,t10:0.4632931469):0.5324084692):0.3951336571,t6:0.7816201891):0.3005419225,t4:0.1858745544):0.05199122007,((t7:0.1588048418,t9:0.03676356945):0.5750080554,t3:1.336592522):0.02329921031):0.2517272777;", 140 | "IsLabelledNewick", true, "adjustTipHeights", false); 141 | 142 | BirthDeathSkylineModelTestHelper bdsky = new BirthDeathSkylineModelTestHelper(); 143 | 144 | RealParameter origin = new RealParameter("5"); 145 | RealParameter Re = new RealParameter("1 1 1 1 1"); 146 | RealParameter delta = new RealParameter("1 1 1 1 1"); 147 | RealParameter s = new RealParameter("1 1 1 1 1"); 148 | RealParameter birthtimes = new RealParameter("0 0.2 0.4 0.6 0.8"); 149 | BooleanParameter reverse = new BooleanParameter("false false false false false"); 150 | 151 | 152 | bdsky.initByName("tree", tree, "origin", origin, 153 | "reproductiveNumber", Re, "becomeUninfectiousRate", delta, "samplingProportion", s, 154 | "birthRateTimesRelative", false, "birthRateChangeTimes", birthtimes, 155 | "reverseTimeArrays", reverse); 156 | 157 | List birthRateTimes = bdsky.getBirthRateChangeTimes(); 158 | double [] expected = new double [] {0.2, 0.4, 0.6, 0.8, 5.0}; 159 | int i = 0; 160 | for (Double t : birthRateTimes) { 161 | //System.out.println(t); 162 | assertEquals(t, expected[i]); 163 | i++; 164 | } 165 | 166 | } 167 | 168 | 169 | /** 170 | * Expect (1.0, 2.0, 3.0, 4.0, 5.0) 171 | * 172 | * 173 | * @throws Exception 174 | */ 175 | @Test 176 | public void testSkylineTimesRelativeReverse() throws Exception { 177 | 178 | Tree tree = new TreeParser(); 179 | 180 | tree.initByName("newick", "(((((t8:0.7222085766,(t5:0.04762441103,t1:0.6054274304):0.6506888754):0.5517904073,(t2:0.514380477,t10:0.4632931469):0.5324084692):0.3951336571,t6:0.7816201891):0.3005419225,t4:0.1858745544):0.05199122007,((t7:0.1588048418,t9:0.03676356945):0.5750080554,t3:1.336592522):0.02329921031):0.2517272777;", 181 | "IsLabelledNewick", true, "adjustTipHeights", false); 182 | 183 | BirthDeathSkylineModelTestHelper bdsky = new BirthDeathSkylineModelTestHelper(); 184 | 185 | RealParameter origin = new RealParameter("5"); 186 | RealParameter Re = new RealParameter("1 1 1 1 1"); 187 | RealParameter delta = new RealParameter("1 1 1 1 1"); 188 | RealParameter s = new RealParameter("1 1 1 1 1"); 189 | RealParameter birthtimes = new RealParameter("0 0.2 0.4 0.6 0.8"); 190 | BooleanParameter reverse = new BooleanParameter("true true true true true"); 191 | 192 | 193 | bdsky.initByName("tree", tree, "origin", origin, 194 | "reproductiveNumber", Re, "becomeUninfectiousRate", delta, "samplingProportion", s, 195 | "birthRateTimesRelative", true, "birthRateChangeTimes", birthtimes, 196 | "reverseTimeArrays", reverse); 197 | 198 | List birthRateTimes = bdsky.getBirthRateChangeTimes(); 199 | double [] expected = new double [] {1.0, 2.0, 3.0, 4.0, 5.0}; 200 | int i = 0; 201 | for (Double t : birthRateTimes) { 202 | //System.out.println(t); 203 | assertEquals(t, expected[i], 1E-4); 204 | i++; 205 | } 206 | 207 | } 208 | 209 | 210 | /** 211 | * Expect (4.2, 4.4, 4.6, 4.8, 5.0) 212 | * 213 | * 214 | * @throws Exception 215 | */ 216 | @Test 217 | public void testSkylineTimesReverse() throws Exception { 218 | 219 | Tree tree = new TreeParser(); 220 | 221 | tree.initByName("newick", "(((((t8:0.7222085766,(t5:0.04762441103,t1:0.6054274304):0.6506888754):0.5517904073,(t2:0.514380477,t10:0.4632931469):0.5324084692):0.3951336571,t6:0.7816201891):0.3005419225,t4:0.1858745544):0.05199122007,((t7:0.1588048418,t9:0.03676356945):0.5750080554,t3:1.336592522):0.02329921031):0.2517272777;", 222 | "IsLabelledNewick", true, "adjustTipHeights", false); 223 | 224 | BirthDeathSkylineModelTestHelper bdsky = new BirthDeathSkylineModelTestHelper(); 225 | 226 | RealParameter origin = new RealParameter("5"); 227 | RealParameter Re = new RealParameter("1 1 1 1 1"); 228 | RealParameter delta = new RealParameter("1 1 1 1 1"); 229 | RealParameter s = new RealParameter("1 1 1 1 1"); 230 | RealParameter birthtimes = new RealParameter("0 0.2 0.4 0.6 0.8"); 231 | BooleanParameter reverse = new BooleanParameter("true true true true true"); 232 | 233 | 234 | bdsky.initByName("tree", tree, "origin", origin, 235 | "reproductiveNumber", Re, "becomeUninfectiousRate", delta, "samplingProportion", s, 236 | "birthRateTimesRelative", false, "birthRateChangeTimes", birthtimes, 237 | "reverseTimeArrays", reverse); 238 | 239 | List birthRateTimes = bdsky.getBirthRateChangeTimes(); 240 | double [] expected = new double [] {4.2, 4.4, 4.6, 4.8, 5.0}; 241 | int i = 0; 242 | for (Double t : birthRateTimes) { 243 | System.out.println(t); 244 | assertEquals(t, expected[i]); 245 | i++; 246 | } 247 | 248 | //double logP = bdsky.calculateTreeLogLikelihood(tree); 249 | } 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | } 261 | -------------------------------------------------------------------------------- /test/skylinetools/distributions/OUPriorLkTest.java: -------------------------------------------------------------------------------- 1 | package skylinetools.distributions; 2 | 3 | import beast.core.parameter.RealParameter; 4 | import junit.framework.TestCase; 5 | import org.junit.Test; 6 | import skylinetools.distributions.OUPrior; 7 | import test.beast.BEASTTestCase; 8 | 9 | /** 10 | * @author Louis du Plessis 11 | * Date: 2018/07/09 12 | */ 13 | public class OUPriorLkTest extends TestCase { 14 | 15 | 16 | /** 17 | * Test that log-likelihood is calculated correctly for a single point 18 | */ 19 | @Test 20 | public void testCalculateLogPSingle1() { 21 | 22 | System.out.println("OUPrior log-likelihood (single point 1)"); 23 | 24 | // Parameters of the OU-process 25 | RealParameter x = new RealParameter(); 26 | RealParameter t = new RealParameter(); 27 | RealParameter mu = new RealParameter(); 28 | RealParameter sigma = new RealParameter(); 29 | RealParameter nu = new RealParameter(); 30 | 31 | // Initialise values 32 | x.initByName("value","3.0 1.0"); 33 | t.initByName("value","0.0 1.0"); 34 | mu.initByName("value","1"); 35 | sigma.initByName("value","1"); 36 | nu.initByName("value","5"); 37 | 38 | // Set up OUPrior instance and evaluate likelihood 39 | OUPrior likelihood = new OUPrior(); 40 | likelihood.initByName("x",x,"times",t,"mean",mu,"sigma",sigma,"nu",nu); 41 | 42 | double expected = 1.150407; // Calculated in R 43 | double result = likelihood.calculateLogP(); 44 | 45 | System.out.println(expected+"\t"+result); 46 | assertEquals(expected, result, BEASTTestCase.PRECISION); 47 | } 48 | 49 | 50 | /** 51 | * Test that log-likelihood is calculated correctly for a trajectory of points (all equal) 52 | */ 53 | @Test 54 | public void testCalculateLogPTrajectory1() { 55 | 56 | System.out.println("OUPrior log-likelihood test (trajectory 1: all equal)"); 57 | 58 | // Parameters of the OU-process 59 | RealParameter x = new RealParameter(); 60 | RealParameter t = new RealParameter(); 61 | RealParameter mu = new RealParameter(); 62 | RealParameter sigma = new RealParameter(); 63 | RealParameter nu = new RealParameter(); 64 | 65 | // Initialise values 66 | x.initByName("value","5.0","dimension","11"); 67 | t.initByName("value","0 1 2 3 4 5 6 7 8 9 10"); 68 | mu.initByName("value","5"); 69 | sigma.initByName("value","1"); 70 | nu.initByName("value","2"); 71 | 72 | // Set up OUPrior instance and evaluate likelihood 73 | OUPrior likelihood = new OUPrior(); 74 | likelihood.initByName("x",x,"times",t,"mean",mu,"sigma",sigma,"nu",nu); 75 | 76 | double expected = 7.02389903972889; // Calculated in R 77 | double result = likelihood.calculateLogP(); 78 | 79 | System.out.println(expected+"\t"+result); 80 | assertEquals(expected, result, BEASTTestCase.PRECISION); 81 | } 82 | 83 | /** 84 | * Test that log-likelihood is calculated correctly for a trajectory of points (not all equal) 85 | */ 86 | @Test 87 | public void testCalculateLogPTrajectory2() { 88 | 89 | System.out.println("OUPrior log-likelihood test (trajectory 2: evolving)"); 90 | 91 | // Parameters of the OU-process 92 | RealParameter x = new RealParameter(); 93 | RealParameter t = new RealParameter(); 94 | RealParameter mu = new RealParameter(); 95 | RealParameter sigma = new RealParameter(); 96 | RealParameter nu = new RealParameter(); 97 | 98 | // Initialise values 99 | x.initByName("value","10 9 8 7 6 5 5 5 5 6 5"); 100 | t.initByName("value","0 1 2 3 4 5 6 7 8 9 10"); 101 | mu.initByName("value","5"); 102 | sigma.initByName("value","1"); 103 | nu.initByName("value","2"); 104 | 105 | // Set up OUPrior instance and evaluate likelihood 106 | OUPrior likelihood = new OUPrior(); 107 | likelihood.initByName("x",x,"times",t,"mean",mu,"sigma",sigma,"nu",nu); 108 | 109 | double expected = -36.1648364818251; // Calculated in R 110 | double result = likelihood.calculateLogP(); 111 | 112 | System.out.println(expected+"\t"+result); 113 | assertEquals(expected, result, BEASTTestCase.PRECISION); 114 | } 115 | 116 | /** 117 | * Test that log-likelihood is calculated correctly for a trajectory of points (not all equal + times normalised) 118 | */ 119 | @Test 120 | public void testCalculateLogPTrajectory3() { 121 | 122 | System.out.println("OUPrior log-likelihood test (trajectory 3: evolving + times normalised)"); 123 | 124 | // Parameters of the OU-process 125 | RealParameter x = new RealParameter(); 126 | RealParameter t = new RealParameter(); 127 | RealParameter mu = new RealParameter(); 128 | RealParameter sigma = new RealParameter(); 129 | RealParameter nu = new RealParameter(); 130 | 131 | // Initialise values 132 | x.initByName("value","10 9 8 7 6 5 5 5 5 6 5"); 133 | t.initByName("value","0 1 2 3 4 5 6 7 8 9 10"); 134 | mu.initByName("value","5"); 135 | sigma.initByName("value","1"); 136 | nu.initByName("value","2"); 137 | 138 | // Set up OUPrior instance and evaluate likelihood 139 | OUPrior likelihood = new OUPrior(); 140 | likelihood.initByName("x",x,"times",t,"mean",mu,"sigma",sigma,"nu",nu,"normalize","true"); 141 | 142 | double expected = -5.95923005052868; // Calculated in R 143 | double result = likelihood.calculateLogP(); 144 | 145 | System.out.println(expected+"\t"+result); 146 | assertEquals(expected, result, BEASTTestCase.PRECISION); 147 | } 148 | 149 | } -------------------------------------------------------------------------------- /test/skylinetools/parameter/DateParserTest.java: -------------------------------------------------------------------------------- 1 | package skylinetools.parameter; 2 | 3 | import junit.framework.TestCase; 4 | import org.junit.Test; 5 | import skylinetools.parameter.DateParser; 6 | import test.beast.BEASTTestCase; 7 | 8 | 9 | /** 10 | * @author Louis du Plessis 11 | * Date: 2018/12/28 12 | */ 13 | public class DateParserTest extends TestCase { 14 | 15 | @Test 16 | public void testDateParser1() { 17 | 18 | System.out.println("DateParser: Dates in different formats"); 19 | 20 | DateParser parser1 = new DateParser(), 21 | parser2 = new DateParser(), 22 | parser3 = new DateParser(); 23 | 24 | parser1.initByName("dates","10/02/2005 22/02/2000 15/11/1999 01/01/2004 31/12/2004 01/01/2015 31/12/2015","format","dd/MM/yyyy"); 25 | parser2.initByName("dates", "10-Feb-2005 22-Feb-2000 15-Nov-1999 1-Jan-2004 31-Dec-2004 1-Jan-2015 31-Dec-2015", "format","d-MMM-yyyy"); 26 | parser3.initByName("dates", " 2005.1095890411 2000.1420765027 1999.8712328767 2004.0000000000 2004.9972677596 2015.0000000000 2015.9972602740"); 27 | 28 | Double [] values1 = parser1.getValues(); 29 | Double [] values2 = parser2.getValues(); 30 | Double [] values3 = parser3.getValues(); 31 | Double [] expected = {2005.1095890411, 2000.1420765027, 1999.8712328767, 2004.0000000000, 32 | 2004.9972677596, 2015.0000000000, 2015.9972602740}; 33 | 34 | for (int i = 0; i < values2.length; i++) { 35 | assertEquals(expected[i], values1[i], BEASTTestCase.PRECISION); 36 | assertEquals(expected[i], values2[i], BEASTTestCase.PRECISION); 37 | assertEquals(expected[i], values3[i], BEASTTestCase.PRECISION); 38 | } 39 | //System.out.println(Arrays.toString(values1)); 40 | //System.out.println(Arrays.toString(values2)); 41 | //System.out.println(Arrays.toString(values3)); 42 | } 43 | 44 | } 45 | -------------------------------------------------------------------------------- /test/skylinetools/parameter/RealParameterSequenceTest.java: -------------------------------------------------------------------------------- 1 | package skylinetools.parameter; 2 | 3 | 4 | import junit.framework.TestCase; 5 | import org.junit.Test; 6 | import skylinetools.parameter.RealParameterSequence; 7 | import test.beast.BEASTTestCase; 8 | 9 | /** 10 | * @author Louis du Plessis 11 | * Date: 2018/07/17 12 | */ 13 | public class RealParameterSequenceTest extends TestCase { 14 | 15 | @Test 16 | public void testSeqBy1() { 17 | 18 | System.out.println("Test sequence from 5 to 15 with integer increments"); 19 | 20 | RealParameterSequence seq = new RealParameterSequence(); 21 | seq.initByName("from","5","to","15","by","1"); 22 | 23 | double [] vals = seq.getDoubleValues(), 24 | expected = {5,6,7,8,9,10,11,12,13,14,15}; 25 | 26 | // Check dimension 27 | assertEquals(expected.length, seq.getDimension()); 28 | 29 | // Check values 30 | for (int i = 0; i < vals.length; i++) { 31 | assertEquals(expected[i], vals[i]); 32 | } 33 | } 34 | 35 | @Test 36 | public void testSeqBy2() { 37 | 38 | System.out.println("Test sequence from 5 to 15 with integer increments (non-divisible)"); 39 | 40 | RealParameterSequence seq = new RealParameterSequence(); 41 | seq.initByName("from","5","to","15","by","4"); 42 | 43 | double [] vals = seq.getDoubleValues(), 44 | expected = {5,9,13}; 45 | 46 | // Check dimension 47 | assertEquals(expected.length, seq.getDimension()); 48 | assertEquals(expected.length, vals.length); 49 | 50 | 51 | // Check values 52 | for (int i = 0; i < vals.length; i++) { 53 | assertEquals(expected[i], vals[i]); 54 | } 55 | } 56 | 57 | @Test 58 | public void testSeqBy3() { 59 | 60 | System.out.println("Test sequence from 1 to 0 with negative increments"); 61 | 62 | RealParameterSequence seq = new RealParameterSequence(); 63 | seq.initByName("from","1","to","0","by","-0.1"); 64 | 65 | double [] vals = seq.getDoubleValues(), 66 | expected = {1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0}; 67 | 68 | // Check dimension 69 | assertEquals(expected.length, seq.getDimension()); 70 | assertEquals(expected.length, vals.length); 71 | 72 | 73 | // Check values 74 | for (int i = 0; i < vals.length; i++) { 75 | assertEquals(expected[i], vals[i], BEASTTestCase.PRECISION); 76 | } 77 | } 78 | 79 | @Test 80 | public void testSeqBy4() { 81 | 82 | System.out.println("Test sequence from 1 to 0 with negative increments (non-divisible)"); 83 | 84 | RealParameterSequence seq = new RealParameterSequence(); 85 | seq.initByName("from","1","to","0","by","-0.3"); 86 | 87 | double [] vals = seq.getDoubleValues(), 88 | expected = {1, 0.7, 0.4, 0.1}; 89 | 90 | // Check dimension 91 | assertEquals(expected.length, seq.getDimension()); 92 | assertEquals(expected.length, vals.length); 93 | 94 | // Check values 95 | for (int i = 0; i < vals.length; i++) { 96 | assertEquals(expected[i], vals[i], BEASTTestCase.PRECISION); 97 | } 98 | } 99 | 100 | 101 | 102 | @Test 103 | public void testSeqLength1() { 104 | 105 | System.out.println("Test sequence from 5 to 15 with length 11"); 106 | 107 | RealParameterSequence seq = new RealParameterSequence(); 108 | seq.initByName("from","5","to","15","dimension","11"); 109 | 110 | double [] vals = seq.getDoubleValues(), 111 | expected = {5,6,7,8,9,10,11,12,13,14,15}; 112 | 113 | // Check dimension 114 | assertEquals(expected.length, seq.getDimension()); 115 | 116 | // Check values 117 | for (int i = 0; i < vals.length; i++) { 118 | assertEquals(expected[i], vals[i]); 119 | } 120 | } 121 | 122 | @Test 123 | public void testSeqLength2() { 124 | 125 | System.out.println("Test sequence from 5.5 to 14.3 with length 3"); 126 | 127 | RealParameterSequence seq = new RealParameterSequence(); 128 | seq.initByName("from","5.5","to","14.3","dimension","3"); 129 | 130 | double [] vals = seq.getDoubleValues(), 131 | expected = {5.5, 9.9, 14.3}; 132 | 133 | // Check dimension 134 | assertEquals(expected.length, seq.getDimension()); 135 | 136 | // Check values 137 | for (int i = 0; i < vals.length; i++) { 138 | assertEquals(expected[i], vals[i]); 139 | } 140 | } 141 | 142 | @Test 143 | public void testSeqLength3() { 144 | 145 | System.out.println("Test sequence from 10 to 0 with length 5"); 146 | 147 | RealParameterSequence seq = new RealParameterSequence(); 148 | seq.initByName("from","10","to","0","dimension","5"); 149 | 150 | double [] vals = seq.getDoubleValues(), 151 | expected = {10, 7.5, 5, 2.5, 0.0}; 152 | 153 | // Check dimension 154 | assertEquals(expected.length, seq.getDimension()); 155 | 156 | // Check values 157 | for (int i = 0; i < vals.length; i++) { 158 | assertEquals(expected[i], vals[i]); 159 | } 160 | } 161 | 162 | } 163 | -------------------------------------------------------------------------------- /test/skylinetools/util/TreeDateSlicerTest.java: -------------------------------------------------------------------------------- 1 | package skylinetools.util; 2 | 3 | import beast.evolution.alignment.Taxon; 4 | import beast.evolution.alignment.TaxonSet; 5 | import beast.evolution.tree.TraitSet; 6 | import beast.evolution.tree.Tree; 7 | import beast.util.TreeParser; 8 | import junit.framework.TestCase; 9 | import org.junit.Test; 10 | import skylinetools.parameter.DateParser; 11 | import test.beast.BEASTTestCase; 12 | 13 | import java.util.ArrayList; 14 | import java.util.Arrays; 15 | import java.util.List; 16 | 17 | /** 18 | * @author Louis du Plessis 19 | * Date: 2018/07/10 20 | */ 21 | public class TreeDateSlicerTest extends TestCase { 22 | 23 | @Test 24 | public void testSliceTMRCA1() { 25 | 26 | System.out.println("TreeDateSlicer: Slice at specific years (tree 1)"); 27 | 28 | 29 | 30 | String [] taxa = {"D4Philip56", 31 | "D4Philip64", 32 | "D4Philip84", 33 | "D4SLanka78", 34 | "D4Thai78", 35 | "D4Thai84"}; 36 | List taxalist = Taxon.createTaxonList(new ArrayList(Arrays.asList(taxa))); 37 | Tree tree = new TreeParser(); 38 | TaxonSet taxonSet = new TaxonSet(taxalist); 39 | TraitSet dateTrait = new TraitSet(); 40 | 41 | // Years are not as specified in the names because I modified the trees... 42 | dateTrait.initByName("traitname","date","taxa",taxonSet,"value","D4Philip56=1956,"+ 43 | "D4Philip64=1964,"+ 44 | "D4Philip84=1984,"+ 45 | "D4SLanka78=1978,"+ 46 | "D4Thai78=1978,"+ 47 | "D4Thai84=1984"); 48 | 49 | tree.initByName("newick","((D4Philip56:2.0,(D4Philip64:3.0,D4Philip84:23.0):7.0):10.0,(D4SLanka78:19.0,(D4Thai78:5.0,D4Thai84:11.0):14.0):15.0);", 50 | "adjustTipHeights","false", "trait",dateTrait, "taxonset",taxonSet); 51 | 52 | TreeSlicer treeSlicer = new TreeDateSlicer(); 53 | treeSlicer.initByName("tree",tree,"dates","1950 1965 1970 1980"); 54 | 55 | 56 | /* tmrca = 40 / 1944, most recent tip = 1984 57 | 58 | 4 intervals at 1980, 1970, 1965, 1950: (4, 14, 19, 34) 59 | 60 | */ 61 | 62 | Double [] values = treeSlicer.getValues(); 63 | Double [] expected = {0.0, 4.0, 14.0, 19.0, 34.0}; 64 | 65 | for (int i = 0; i < values.length; i++) { 66 | //System.out.print(values[i]+"\t"); 67 | assertEquals(expected[i], values[i]); 68 | } 69 | //System.out.println(); 70 | } 71 | 72 | 73 | @Test 74 | public void testSliceTMRCA2() { 75 | 76 | System.out.println("TreeDateSlicer: Slice at specific dates as formatted strings (tree 1)"); 77 | 78 | String [] taxa = {"D4Philip56", 79 | "D4Philip64", 80 | "D4Philip84", 81 | "D4SLanka78", 82 | "D4Thai78", 83 | "D4Thai84"}; 84 | List taxalist = Taxon.createTaxonList(new ArrayList(Arrays.asList(taxa))); 85 | Tree tree = new TreeParser(); 86 | TaxonSet taxonSet = new TaxonSet(taxalist); 87 | TraitSet dateTrait = new TraitSet(); 88 | 89 | // Years are not as specified in the names because I modified the trees... 90 | dateTrait.initByName("traitname","date","taxa",taxonSet,"value","D4Philip56=1956,"+ 91 | "D4Philip64=1964,"+ 92 | "D4Philip84=1984,"+ 93 | "D4SLanka78=1978,"+ 94 | "D4Thai78=1978,"+ 95 | "D4Thai84=1984"); 96 | 97 | tree.initByName("newick","((D4Philip56:2.0,(D4Philip64:3.0,D4Philip84:23.0):7.0):10.0,(D4SLanka78:19.0,(D4Thai78:5.0,D4Thai84:11.0):14.0):15.0);", 98 | "adjustTipHeights","false", "trait",dateTrait, "taxonset",taxonSet); 99 | 100 | DateParser dates = new DateParser(); 101 | dates.initByName("dates", "21-Jun-1980 15-Sep-1966", "format", "dd-MMM-yyyy"); 102 | 103 | TreeSlicer treeSlicer = new TreeDateSlicer(); 104 | treeSlicer.initByName("tree",tree,"dates",dates); 105 | 106 | Double [] values = treeSlicer.getValues(); 107 | Double [] expected = {0.0, 3.5300546448088426, 17.29589041095892}; 108 | 109 | for (int i = 0; i < values.length; i++) { 110 | assertEquals(expected[i], values[i]); 111 | } 112 | // System.out.println(Arrays.toString(dates.getValues())); 113 | // System.out.println(Arrays.toString(values)); 114 | } 115 | 116 | 117 | 118 | 119 | 120 | 121 | } 122 | -------------------------------------------------------------------------------- /test/skylinetools/util/TreeSlicerTest.java: -------------------------------------------------------------------------------- 1 | package skylinetools.util; 2 | 3 | import beast.evolution.tree.Tree; 4 | import beast.util.TreeParser; 5 | import junit.framework.TestCase; 6 | import org.junit.Test; 7 | import test.beast.BEASTTestCase; 8 | 9 | /** 10 | * @author Louis du Plessis 11 | * Date: 2018/07/10 12 | */ 13 | public class TreeSlicerTest extends TestCase { 14 | 15 | 16 | /** 17 | * Test slice that divides in equidistant intervals between present and tmrca 18 | */ 19 | @Test 20 | public void testSliceTMRCA1() { 21 | 22 | System.out.println("TreeSlicer: Equidistant between present and tmrca (tree 1)"); 23 | 24 | Tree tree = new TreeParser("((D4Philip56:2.0,(D4Philip64:3.0,D4Philip84:23.0):7.0):10.0,(D4SLanka78:19.0,(D4Thai78:5.0,D4Thai84:11.0):14.0):15.0);",false); 25 | 26 | TreeSlicer treeSlicer = new TreeSlicer(); 27 | treeSlicer.initByName("tree",tree,"to","tmrca","inclusive","false","dimension","4"); 28 | 29 | 30 | /* tmrca = 40 / 1944, most recent tip = 1984 31 | 32 | 4 intervals: 33 | 40/4 = 10.00 34 | (0.0, 10, 20, 30) 35 | */ 36 | 37 | Double [] values = treeSlicer.getValues(); 38 | Double [] expected = {0.0, 10.0, 20.0, 30.0}; 39 | 40 | for (int i = 0; i < values.length; i++) { 41 | //System.out.print(values[i]+"\t"); 42 | assertEquals(expected[i], values[i]); } 43 | //System.out.println(); 44 | } 45 | 46 | /** 47 | * Test slice that divides in equidistant intervals between present and tmrca 48 | */ 49 | @Test 50 | public void testSliceTMRCA2() { 51 | 52 | System.out.println("TreeSlicer: Equidistant between present and tmrca (tree 2)"); 53 | 54 | Tree tree = new TreeParser("((((D4Mexico84:5.0,D4ElSal94:15.0):1.0,D4PRico86:8.0):1.0,D4Tahiti79:2.0):5.0,D4Indon77:5.0);",false); 55 | 56 | TreeSlicer treeSlicer = new TreeSlicer(); 57 | treeSlicer.initByName("tree",tree,"to","tmrca","inclusive","false","dimension","4"); 58 | 59 | /* tmrca = 22 / 1972, most recent tip = 1994 60 | 61 | 4 intervals: 62 | 22/4 = 5.5 63 | (0.0, 5.5, 11, 16.5) 64 | */ 65 | 66 | Double [] values = treeSlicer.getValues(); 67 | Double [] expected = {0.0, 5.5, 11.0, 16.5}; 68 | 69 | for (int i = 0; i < values.length; i++) { 70 | //System.out.print(values[i]+"\t"); 71 | assertEquals(expected[i], values[i]); 72 | } 73 | //System.out.println(); 74 | } 75 | 76 | 77 | /** 78 | * Test slice that divides in equidistant intervals between present and tmrca 79 | * Stop time of the slice is included in the slice 80 | */ 81 | @Test 82 | public void testSliceTMRCAincludeLast1() { 83 | 84 | System.out.println("TreeSlicer: Equidistant between present and tmrca, includeLast (tree 1)"); 85 | 86 | Tree tree = new TreeParser("((D4Philip56:2.0,(D4Philip64:3.0,D4Philip84:23.0):7.0):10.0,(D4SLanka78:19.0,(D4Thai78:5.0,D4Thai84:11.0):14.0):15.0);",false); 87 | 88 | TreeSlicer treeSlicer = new TreeSlicer(); 89 | treeSlicer.initByName("tree",tree,"to","tmrca","inclusive","true","dimension","4"); 90 | 91 | 92 | /* tmrca = 40 / 1944, most recent tip = 1984 93 | 94 | 4 intervals: 95 | 40/3 = 13.334 96 | (0.0, 13.334, 26.667, 40) 97 | */ 98 | 99 | Double [] values = treeSlicer.getValues(); 100 | Double [] expected = {0.0, 13.333333333, 26.666666666, 40.0}; 101 | 102 | for (int i = 0; i < values.length; i++) { 103 | //System.out.print(values[i]+"\t"); 104 | assertEquals(expected[i], values[i], BEASTTestCase.PRECISION); 105 | } 106 | //System.out.println(); 107 | } 108 | 109 | /** 110 | * Test slice that divides in equidistant intervals between present and tmrca 111 | * Stop time of the slice is included in the slice 112 | */ 113 | @Test 114 | public void testSliceTMRCAincludeLast2() { 115 | 116 | System.out.println("TreeSlicer: Equidistant between present and tmrca, includeLast (tree 2)"); 117 | 118 | Tree tree = new TreeParser("((((D4Mexico84:5.0,D4ElSal94:15.0):1.0,D4PRico86:8.0):1.0,D4Tahiti79:2.0):5.0,D4Indon77:5.0);",false); 119 | 120 | TreeSlicer treeSlicer = new TreeSlicer(); 121 | treeSlicer.initByName("tree",tree,"to","tmrca","inclusive","true","dimension","4"); 122 | 123 | /* tmrca = 22 / 1972, most recent tip = 1994 124 | 125 | 4 intervals: 126 | 22/3 = 5.5 127 | (0.0, 7.334, 14.667, 22) 128 | */ 129 | 130 | Double [] values = treeSlicer.getValues(); 131 | Double [] expected = {0.0, 7.333333, 14.6666666, 22.0}; 132 | 133 | for (int i = 0; i < values.length; i++) { 134 | //System.out.print(values[i]+"\t"); 135 | assertEquals(expected[i], values[i], BEASTTestCase.PRECISION); 136 | } 137 | //System.out.println(); 138 | } 139 | 140 | /** 141 | * Test slice that divides in equidistant intervals between present and oldest sample 142 | * Stop time of the slice is included in the slice 143 | */ 144 | @Test 145 | public void testSliceOldestSample1() { 146 | 147 | System.out.println("TreeSlicer: Equidistant between present and oldest sample (tree 1)"); 148 | 149 | Tree tree = new TreeParser("((D4Philip56:2.0,(D4Philip64:3.0,D4Philip84:23.0):7.0):10.0,(D4SLanka78:19.0,(D4Thai78:5.0,D4Thai84:11.0):14.0):15.0);",false); 150 | 151 | TreeSlicer treeSlicer = new TreeSlicer(); 152 | treeSlicer.initByName("tree",tree,"to","oldestSample","inclusive","true","dimension","5"); 153 | 154 | 155 | /* oldest tip = 28 / 1956, most recent tip = 0 / 1984 156 | 157 | 4 intervals: 158 | 28/4 = 7 159 | (0.0, 7, 14, 21, 28) 160 | */ 161 | 162 | Double [] values = treeSlicer.getValues(); 163 | Double [] expected = {0.0, 7.0, 14.0, 21.0, 28.0}; 164 | 165 | for (int i = 0; i < values.length; i++) { 166 | //System.out.print(values[i]+"\t"); 167 | assertEquals(expected[i], values[i], BEASTTestCase.PRECISION); 168 | } 169 | //System.out.println(); 170 | } 171 | 172 | /** 173 | * Test slice that divides in equidistant intervals between present and tmrca 174 | * Stop time of the slice is included in the slice 175 | */ 176 | @Test 177 | public void testSliceOldestSample2() { 178 | 179 | System.out.println("TreeSlicer: Equidistant between present and oldest sample (tree 2)"); 180 | 181 | Tree tree = new TreeParser("((((D4Mexico84:5.0,D4ElSal94:15.0):1.0,D4PRico86:8.0):1.0,D4Tahiti79:2.0):5.0,D4Indon77:5.0);",false); 182 | 183 | TreeSlicer treeSlicer = new TreeSlicer(); 184 | treeSlicer.initByName("tree",tree,"to","oldestSample","inclusive","true","dimension","5"); 185 | 186 | /* tmrca = 17 / 1977, most recent tip = 1994 187 | 188 | 4 intervals: 189 | 17/4 = 4.25 190 | (0.0, 4.25, 8.5, 12.75, 17) 191 | */ 192 | 193 | Double [] values = treeSlicer.getValues(); 194 | Double [] expected = {0.0, 4.25, 8.5, 12.75, 17.0}; 195 | 196 | for (int i = 0; i < values.length; i++) { 197 | //System.out.print(values[i]+"\t"); 198 | assertEquals(expected[i], values[i], BEASTTestCase.PRECISION); 199 | } 200 | //System.out.println(); 201 | } 202 | 203 | } 204 | -------------------------------------------------------------------------------- /test/tree.C1.MCC.trim.round.figtree.tree: -------------------------------------------------------------------------------- 1 | #NEXUS 2 | begin taxa; 3 | dimensions ntax=6; 4 | taxlabels 5 | D4Philip56 6 | D4Philip64 7 | D4Philip84 8 | D4SLanka78 9 | D4Thai78 10 | D4Thai84 11 | ; 12 | end; 13 | 14 | begin trees; 15 | tree tree_1 = [&R] ((D4Philip56:2.0,(D4Philip64:3.0,D4Philip84:23.0):7.0):10.0,(D4SLanka78:19.0,(D4Thai78:5.0,D4Thai84:11.0):14.0):15.0); 16 | end; 17 | 18 | begin figtree; 19 | set appearance.backgroundColorAttribute="Default"; 20 | set appearance.backgroundColour=#ffffff; 21 | set appearance.branchColorAttribute="User selection"; 22 | set appearance.branchColorGradient=false; 23 | set appearance.branchLineWidth=2.0; 24 | set appearance.branchMinLineWidth=0.0; 25 | set appearance.branchWidthAttribute="Fixed"; 26 | set appearance.foregroundColour=#000000; 27 | set appearance.hilightingGradient=false; 28 | set appearance.selectionColour=#2d3680; 29 | set branchLabels.colorAttribute="User selection"; 30 | set branchLabels.displayAttribute="Branch lengths (raw)"; 31 | set branchLabels.fontName="sansserif"; 32 | set branchLabels.fontSize=8; 33 | set branchLabels.fontStyle=0; 34 | set branchLabels.isShown=true; 35 | set branchLabels.significantDigits=4; 36 | set layout.expansion=0; 37 | set layout.layoutType="RECTILINEAR"; 38 | set layout.zoom=0; 39 | set legend.attribute=null; 40 | set legend.fontSize=10.0; 41 | set legend.isShown=false; 42 | set legend.significantDigits=4; 43 | set nodeBars.barWidth=4.0; 44 | set nodeBars.displayAttribute=null; 45 | set nodeBars.isShown=false; 46 | set nodeLabels.colorAttribute="User selection"; 47 | set nodeLabels.displayAttribute="Node heights (raw)"; 48 | set nodeLabels.fontName="sansserif"; 49 | set nodeLabels.fontSize=10; 50 | set nodeLabels.fontStyle=0; 51 | set nodeLabels.isShown=true; 52 | set nodeLabels.significantDigits=4; 53 | set nodeShapeExternal.colourAttribute="User selection"; 54 | set nodeShapeExternal.isShown=true; 55 | set nodeShapeExternal.minSize=10.0; 56 | set nodeShapeExternal.scaleType=Width; 57 | set nodeShapeExternal.shapeType=Circle; 58 | set nodeShapeExternal.size=10.0; 59 | set nodeShapeExternal.sizeAttribute="Fixed"; 60 | set nodeShapeInternal.colourAttribute="User selection"; 61 | set nodeShapeInternal.isShown=false; 62 | set nodeShapeInternal.minSize=10.0; 63 | set nodeShapeInternal.scaleType=Width; 64 | set nodeShapeInternal.shapeType=Circle; 65 | set nodeShapeInternal.size=4.0; 66 | set nodeShapeInternal.sizeAttribute="Fixed"; 67 | set polarLayout.alignTipLabels=false; 68 | set polarLayout.angularRange=0; 69 | set polarLayout.rootAngle=0; 70 | set polarLayout.rootLength=100; 71 | set polarLayout.showRoot=true; 72 | set radialLayout.spread=0.0; 73 | set rectilinearLayout.alignTipLabels=true; 74 | set rectilinearLayout.curvature=0; 75 | set rectilinearLayout.rootLength=100; 76 | set scale.offsetAge=1984.0; 77 | set scale.rootAge=1.0; 78 | set scale.scaleFactor=1.0; 79 | set scale.scaleRoot=false; 80 | set scaleAxis.automaticScale=false; 81 | set scaleAxis.fontSize=12.0; 82 | set scaleAxis.isShown=true; 83 | set scaleAxis.lineWidth=1.0; 84 | set scaleAxis.majorTicks=5.0; 85 | set scaleAxis.minorTicks=1.0; 86 | set scaleAxis.origin=0.0; 87 | set scaleAxis.reverseAxis=true; 88 | set scaleAxis.showGrid=true; 89 | set scaleBar.automaticScale=true; 90 | set scaleBar.fontSize=10.0; 91 | set scaleBar.isShown=false; 92 | set scaleBar.lineWidth=1.0; 93 | set scaleBar.scaleRange=5.0; 94 | set tipLabels.colorAttribute="User selection"; 95 | set tipLabels.displayAttribute="Names"; 96 | set tipLabels.fontName="sansserif"; 97 | set tipLabels.fontSize=12; 98 | set tipLabels.fontStyle=0; 99 | set tipLabels.isShown=true; 100 | set tipLabels.significantDigits=4; 101 | set trees.order=false; 102 | set trees.orderType="increasing"; 103 | set trees.rooting=false; 104 | set trees.rootingType="User Selection"; 105 | set trees.transform=false; 106 | set trees.transformType="cladogram"; 107 | end; 108 | 109 | -------------------------------------------------------------------------------- /test/tree.C2.MCC.trim.round.figtree.tree: -------------------------------------------------------------------------------- 1 | #NEXUS 2 | begin taxa; 3 | dimensions ntax=5; 4 | taxlabels 5 | D4ElSal94 6 | D4Indon77 7 | D4Mexico84 8 | D4PRico86 9 | D4Tahiti79 10 | ; 11 | end; 12 | 13 | begin trees; 14 | tree tree_1 = [&R] ((((D4Mexico84:5.0,D4ElSal94:15.0):1.0,D4PRico86:8.0):1.0,D4Tahiti79:2.0):5.0,D4Indon77:5.0); 15 | end; 16 | 17 | begin figtree; 18 | set appearance.backgroundColorAttribute="Default"; 19 | set appearance.backgroundColour=#ffffff; 20 | set appearance.branchColorAttribute="User selection"; 21 | set appearance.branchColorGradient=false; 22 | set appearance.branchLineWidth=2.0; 23 | set appearance.branchMinLineWidth=0.0; 24 | set appearance.branchWidthAttribute="Fixed"; 25 | set appearance.foregroundColour=#000000; 26 | set appearance.hilightingGradient=false; 27 | set appearance.selectionColour=#2d3680; 28 | set branchLabels.colorAttribute="User selection"; 29 | set branchLabels.displayAttribute="Branch lengths (raw)"; 30 | set branchLabels.fontName="sansserif"; 31 | set branchLabels.fontSize=8; 32 | set branchLabels.fontStyle=0; 33 | set branchLabels.isShown=true; 34 | set branchLabels.significantDigits=4; 35 | set layout.expansion=0; 36 | set layout.layoutType="RECTILINEAR"; 37 | set layout.zoom=0; 38 | set legend.attribute=null; 39 | set legend.fontSize=10.0; 40 | set legend.isShown=false; 41 | set legend.significantDigits=4; 42 | set nodeBars.barWidth=4.0; 43 | set nodeBars.displayAttribute=null; 44 | set nodeBars.isShown=false; 45 | set nodeLabels.colorAttribute="User selection"; 46 | set nodeLabels.displayAttribute="Node heights (raw)"; 47 | set nodeLabels.fontName="sansserif"; 48 | set nodeLabels.fontSize=10; 49 | set nodeLabels.fontStyle=0; 50 | set nodeLabels.isShown=true; 51 | set nodeLabels.significantDigits=4; 52 | set nodeShapeExternal.colourAttribute=null; 53 | set nodeShapeExternal.isShown=true; 54 | set nodeShapeExternal.minSize=10.0; 55 | set nodeShapeExternal.scaleType=Width; 56 | set nodeShapeExternal.shapeType=Circle; 57 | set nodeShapeExternal.size=10.0; 58 | set nodeShapeExternal.sizeAttribute=null; 59 | set nodeShapeInternal.colourAttribute=null; 60 | set nodeShapeInternal.isShown=false; 61 | set nodeShapeInternal.minSize=10.0; 62 | set nodeShapeInternal.scaleType=Width; 63 | set nodeShapeInternal.shapeType=Circle; 64 | set nodeShapeInternal.size=4.0; 65 | set nodeShapeInternal.sizeAttribute=null; 66 | set polarLayout.alignTipLabels=false; 67 | set polarLayout.angularRange=0; 68 | set polarLayout.rootAngle=0; 69 | set polarLayout.rootLength=100; 70 | set polarLayout.showRoot=true; 71 | set radialLayout.spread=0.0; 72 | set rectilinearLayout.alignTipLabels=true; 73 | set rectilinearLayout.curvature=0; 74 | set rectilinearLayout.rootLength=100; 75 | set scale.offsetAge=1994.0; 76 | set scale.rootAge=1.0; 77 | set scale.scaleFactor=1.0; 78 | set scale.scaleRoot=false; 79 | set scaleAxis.automaticScale=false; 80 | set scaleAxis.fontSize=12.0; 81 | set scaleAxis.isShown=true; 82 | set scaleAxis.lineWidth=1.0; 83 | set scaleAxis.majorTicks=5.0; 84 | set scaleAxis.minorTicks=1.0; 85 | set scaleAxis.origin=0.0; 86 | set scaleAxis.reverseAxis=true; 87 | set scaleAxis.showGrid=true; 88 | set scaleBar.automaticScale=true; 89 | set scaleBar.fontSize=10.0; 90 | set scaleBar.isShown=false; 91 | set scaleBar.lineWidth=1.0; 92 | set scaleBar.scaleRange=3.0; 93 | set tipLabels.colorAttribute="User selection"; 94 | set tipLabels.displayAttribute="Names"; 95 | set tipLabels.fontName="sansserif"; 96 | set tipLabels.fontSize=12; 97 | set tipLabels.fontStyle=0; 98 | set tipLabels.isShown=true; 99 | set tipLabels.significantDigits=4; 100 | set trees.order=true; 101 | set trees.orderType="decreasing"; 102 | set trees.rooting=false; 103 | set trees.rootingType="User Selection"; 104 | set trees.transform=false; 105 | set trees.transformType="cladogram"; 106 | end; 107 | 108 | -------------------------------------------------------------------------------- /version.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | --------------------------------------------------------------------------------