---
title: "babette: Step by Step"
author: "Richèl J.C. Bilderbeek"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{babette: Step by Step}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r check_empty_cache_at_start, include = FALSE}
beastier::remove_beaustier_folders()
beastier::check_empty_beaustier_folders()
```


## Introduction

![](babette_logo.png)

This step-by-step demo shows how to run the `babette` pipeline in detail.

First, load `babette`:

```{r load_babette, results='hide', warning=FALSE, error=FALSE, message=FALSE}
library(babette)
```

In all cases, this is done for a short MCMC chain length of 10K:

```{r}
inference_model <- create_inference_model()
inference_model$mcmc$chain_length <- 10000
inference_model$mcmc$tracelog$filename <- normalizePath(
  get_beautier_tempfilename(
    pattern = "tracelog_", fileext = ".log"
  ),
  mustWork = FALSE
)
inference_model$mcmc$treelog$filename <- normalizePath(
  get_beautier_tempfilename(
    pattern = "treelog_",
    fileext = ".trees"
  ),
  mustWork = FALSE
)
```

## Create a 'BEAST2' input file

This step is commonly done using BEAUti.
With `babette`, this can be done as follows:

```{r}
beast2_input_file <- tempfile(pattern = "beast2_", fileext = ".xml")
create_beast2_input_file_from_model(
  input_filename = get_babette_path("anthus_aco.fas"),
  inference_model = inference_model,
  output_filename = beast2_input_file
)
```

## Display (part of) the 'BEAST2' input file

```{r}
print(head(readLines(beast2_input_file)))
```

This file can both be loaded by BEAUti and be used by 'BEAST2'.

The file can be checked if it is indeed a valid input file:

```{r}
if (is_beast2_installed()) {
  is_beast2_input_file(beast2_input_file)
}
```

## Run MCMC

This step is commonly done using 'BEAST2' from the command-line or using its GUI.
With `babette`, this can be done as follows:

```{r}
if (is_beast2_installed()) {
  beast2_options <- create_beast2_options(
    input_filename = beast2_input_file
  )
  beastier::check_can_create_file(beast2_options$output_state_filename)
  beastier::check_can_create_treelog_file(beast2_options)
  run_beast2_from_options(
    beast2_options = beast2_options
  )
  if (!file.exists(beast2_options$output_state_filename)) {
    stop("the filename should have been created.")
  }
}
```

## Display (part of) the 'BEAST2' output files

The `.log` file contains the model parameters and parameter estimates:

```{r}
if (is_beast2_installed()) {
  print(head(readLines(inference_model$mcmc$tracelog$filename)))
  print(tail(readLines(inference_model$mcmc$tracelog$filename)))
}
```

The `.trees` file contains the alignment, taxa and posterior trees:

```{r}
if (is_beast2_installed()) {
  print(head(readLines(inference_model$mcmc$treelog$filename)))
  print(tail(readLines(inference_model$mcmc$treelog$filename)))
}
```

The `.xml.state` file contains the final state of the MCMC run and the
MCMC operator acceptances thus far:

```{r}
if (is_beast2_installed()) {
  print(head(readLines(beast2_options$output_state_filename)))
  print(tail(readLines(beast2_options$output_state_filename)))
}
```

## Parse output

This step is commonly done using Tracer.
With `babette`, this can be done as follows.

Parsing `.log` file to obtain the parameter estimates:

```{r}
if (is_beast2_installed()) {
  knitr::kable(
    head(parse_beast_tracelog_file(inference_model$mcmc$tracelog$filename))
  )
}
```

Parsing `.trees` file to obtain the posterior phylogenies:

```{r fig.width = 7, fig.height = 7}
if (is_beast2_installed()) {
  plot_densitree(parse_beast_trees(inference_model$mcmc$treelog$filename))
}
```

Parsing `.xml.state` file to obtain the MCMC operator acceptances:

```{r}
if (is_beast2_installed()) {
  knitr::kable(
    head(parse_beast_state_operators(beast2_options$output_state_filename))
  )
}
```

```{r cleunup, include = FALSE}
if (is_beast2_installed()) {
  bbt_delete_temp_files(
    inference_model = inference_model,
    beast2_options = beast2_options
  )
}
```

```{r check_empty_cache_at_end, include = FALSE}
beastier::remove_beaustier_folders()
beastier::check_empty_beaustier_folders()
```