RAVE 2.0 interactive modules and data pipelines for reproducible analysis and visualization of intracranial electroencephalography. This repository consists of built-in RAVE (https://rave.wiki/) modules and pipelines; currently contain the entire preprocessing pipelines. All presented as interactive modules.

RAVE 1.0 has the following limitations:

• Type of data to load is limited to power, phase, voltage. Custom data loading is complicated.
• The analysis code is often entangled with UI code, making it hard to just run the pipeline scripts offline (without GUI)
• The default input types are limited, the inputs UI can become very complicated when non-default arrangement is needed (which is often the case)
• The modules are designed to run simple analysis code. Users often need to run the whole pipeline in order to get results, even though partial pipeline code is needed.

The motivation of RAVE 2.0 is to overcome these limitations. A RAVE 2.0 pipeline module consists of two parts:

1. Data pipeline: well structured scripts that runs pipeline without graphical user interface;
2. UI module: R-Shiny application providing user interface. Users set the inputs from the UI, the module drives data pipeline, and visualize the pipeline results back to users.

Features include:

• Separate pipeline scripts and interactive UI modules. Users can run reproducible pipeline code without UI. This can speed up and scale up the pipeline when no UI is needed
• Module authors have 100% control over the UI code. They can choose to load any type of data, even for data not defined in RAVE
• The pipeline can run partially.
• All the intermediate key variables will be saved and read easily
• Supports Python

Notch Filter	Wavelet	Epoch Generator

Channel Reference	CT & MRI Co-registration	Electrode Localization

Check the installation guide to install RAVE. The pipeline will be downloaded automatically during installation/upgrade.

This repository forbids directly push. All changes must be made via pull-requests. Please fork this project and edit on your own before creating pull-requests. "At" our team members or join our slack channel to request for supports.

Before digging into each file, let's take a look on how to run RAVE notch filter without GUI:

# Load pipeline
pipeline <- raveio::pipeline("notch_filter")

# Set inputs
pipeline$set_settings(project_name = "devel", subject_code = "DemoSubject")

# Run pipeline
pipeline$run("apply_notch")
#> • start target settings_path
#> • built target settings_path [0 seconds]
#> • start target settings
#> • built target settings [0 seconds]
#> • start target notch_filter_lowerbound
#> • built target notch_filter_lowerbound [0 seconds]
#> • start target notch_filter_upperbound
#> • built target notch_filter_upperbound [0 seconds]
#> • start target subject_code
#> • built target subject_code [0.001 seconds]
#> • start target project_name
#> • built target project_name [0.001 seconds]
#> • start target filter_settings
#> • built target filter_settings [0 seconds]
#> • start target subject
#> • built target subject [0.003 seconds]
#> • start target imported_electrodes
#> • built target imported_electrodes [0 seconds]
#> • start target apply_notch
#> [Applying Notch filters]: Results collected                                                   
#> • built target apply_notch [1.888 seconds]                                                    
#> • end pipeline [1.967 seconds]

# Visualize pipeline
raveio::pipeline_visualize(pipeline$pipeline_path)

In 4 lines of R code, we load the pipeline, set inputs, apply the notch filters to subject devel/DemoSubject, and visualize the pipeline structure! Besides, all the intermediate data, such as imported_electrodes, subject, filter_settings, ... are stored and ready to be loaded:

pipeline$read(c("subject", "filter_settings"))
#> $subject
#> RAVE subject <devel/DemoSubject>
#> 
#> $filter_settings
#> $filter_settings$lb
#> [1]  59 118 178
#> 
#> $filter_settings$ub
#> [1]  61 122 182
#> 
#> $filter_settings$domain
#> [1] 1

Most data analysis scripts contain the following componets:

• Code to load external libraries, utility functions
• Set inputs, global data
• Run analyses and generate outputs, for example, data loading & cleaning, intermediate analysis, data & graphs export

RAVE data pipeline manages these three componets separately (see figure below).

• R/shared-xxx.R loads external libraries and defines reusable functions (optional)
• settings.yaml defines the input variables
• main.Rmd defines the analysis code

The RAVE internal workflow can be roughly described as: Load shared functions -> load settings.yaml as inputs -> run main.Rmd.

Using modules/notch_filter as an example,

• R/shared-diagnose_plot.R defines a reusable function diagnose_notch_filters
• settings.yaml specifies which RAVE subject to load and notch filter parameters (notch_filter_lowerbound and notch_filter_upperbound)
• main.Rmd contains R code blocks. Each code block starts with ```{rave ...}

In the previous example script,

1. When we run pipeline <- raveio::pipeline("notch_filter"), RAVE collects the pipeline directory information and wrap it in the variable pipeline.

2. pipeline$set_settings(project_name = "devel", subject_code = "DemoSubject") alters project_name and subject_code in the settings.yaml.

3. pipeline$run("apply_notch") analyzes code dependence within main.Rmd, and ONLY runs the code blocks needed to generate variable apply_notch. (if you check the dependence graph, diagnostic_plot is not needed, hence not evaluated.

4. raveio::pipeline_visualize(pipeline$pipeline_path) displays the dependency graph. Each node stands for an input in settings.yaml (e.g. project_name) or a code block in main.Rmd (e.g. apply_notch).

*(This sub-section might be ambiguous. Please ask the RAVE team to schedule a walk-through.)

To start, open rave-pipeline project in RStudio, and enter the R command (please change module_id and module_label accordingly)

raveio::module_add(module_id = "my_module_id", module_label = "My Module Label")

A sample module pipeline folder will be created under modules/ named after the specified module_id.

A settings.yaml is just a collection of key-value pairs. Module users are only allowed to set those inputs, hence consider carefully which inputs you want the users to alter. I recommend the RAVE function raveio::save_yaml to programmically write to this file to avoid yaml syntax errors.

main.Rmd is an R-markdown file that interleaves code (starting with ```) and descriptive paragraphs. RAVE does not care about descriptive paragraphs. You can remove them for now.

The first block should look like this:

```{r setup, include = FALSE}
# This code block sets up the engine environment
# Please do not remove me
raveio::pipeline_setup_rmd("my_module_id")
```

This is a special block that loads the inputs from settings.yaml to the memory for debug use. For example, if you set project_name to "demo" in settings.yaml, you can type project_name in the R console and it should return value "demo".

The subsequential code blocks start with ```{rave ...}. Each code block defines a pipeline "target" (a key intermediate variable). For example the following block defines a target variable subject.

```{rave load_subject, language = "R", export = "subject", format = "rave-subject", cue = "always"}
subject <- raveio::RAVESubject$new(
  project_name = project_name, 
  subject_code = subject_code,
  strict = FALSE
)
```

• ```{rave means this block is part of pipeline (all RAVE pipeline targets might start with it
• load_subject describes the goal of this block. Use _ to connect words
• language = "R" use R to run this block (alternative is Python, ask the RAVE team for more information. We will add instructions in the future)
• export = "subject" tells RAVE that this block will generate a variable subject
• format = "rave-subject" this is a special case, will talk about it later. You can ignore for now
• cue = "always" tells RAVE do not skip this code block, will talk about it later.

Within the ```, the script asks RAVE to generate a variable subject using project_name and subject_code. These variables are "undeclared" for this block (if you directly run this block, you will get variable not found errors).

Where do these undeclared vairables come from? The answer is they come from previous "targets" or from settings.yaml. In this case, project_name and subject_code are defined in settings.yaml.

(TODO: add more)

The last two code blocks should look like

```{r build, echo=FALSE, results='hide'}
build_pipeline(make_file = "make-electrode_localization.R")
```

```{r visualize, echo=FALSE}
Sys.setenv("RAVE_PIPELINE" = normalizePath("."))
raveio::pipeline_visualize()
```

Please leave them AS-IS. Now let's compile the pipeline script by pressing the following button:

RAVE will compile and translate the script into module/my_module_id/make-electrode_localization.R. You should be ready to run the pipeline now.

(TODO: add instruction)

Caveats

1. Each code block generates only one variable. RAVE pipeline will drop all the other temporary variables when exiting current block. Therefore, all the undefined variables must come from previous targets or pipline inputs only
2. A code block does not need to end with the target variable. However, the target variable must be declared within the block.

Take a look at the following case (containing 3 blocks). Suppose project_name and subject_code are defined in settings.yaml. Two of three contain errors.

```{rave load_subject, language = "R", export = "subject", format = "rave-subject", cue = "always"}
subject <- raveio::RAVESubject$new(
  project_name = project_name, 
  subject_code = subject_code,
  strict = FALSE
)
subject_id <- subject$subject_id
message("Loading subject: ", subject_id)
```

```{rave load_subject_power_data, language = "R", export = "repository", cue = "always"}
repository <- raveio::prepare_subject_power(subject_id)
```

```{rave calculate_baseline, language = "R", export = "baseline", cue = "always"}
raveio::power_baseline(repository, c(0,0.5))
```

• The first block does not end with variable subject, but it generates a variable subject within the code. There are two undefined variables for this block: project_name and subject_code, but they are defined in settings.yaml. Therefore, this block is valid.
• The second block contains repository. However the undeclared variable subject_id is temporary (generated in previous target subject). Since RAVE drops this variable when exiting previous block, this variable is unavailable, hence error will be raised. A valid modification will be:

repository <- raveio::prepare_subject_power(subject)

# or repository <- raveio::prepare_subject_power(subject$subject_id)

• The third block has an undeclared variable repository, which is generated by the second block. However, RAVE does not know which variable is baseline. You need to explicitly specify it. A revision will be

```{rave calculate_baseline, language = "R", export = "baseline", cue = "always"}
raveio::power_baseline(repository, c(0,0.5))
baseline <- repository$power$baselined
```