---
title: "Anatomy of an eyeris Object"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Anatomy of an eyeris Object}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

## 📦 Key Components

When preprocessing `.asc` EyeLink files with `eyeris`, returned objects will be
of the class `eyeris`, and will contain key components used throughout the
package's backend.

The key components are:

- `file`: the original file path of the source `.asc` file
- `timeseries`: a list of data frames (1 df per identified recording block per file) which contains the following columns:
  - `block`: block number
  - `time_orig`: raw tracker time (ms)
  - `eye_x`: eye position x-coordinate
  - `eye_y`: eye position y-coordinate
  - `eye`: which eye (Left or Right) the recorded data are sourced from
  - `hz`: tracker sampling rate (hz)
  - `type`: whether source data were recorded using the `diameter` or `area` method
  - `pupil_raw`: raw recorded pupil source data in arbitrary units (a.u.) 

You'll notice that for each preprocessing step run, a new column will be added after
the `pupil_raw` column; these new columns follow a structure where each subsequent
step is appended to the previous columns name 
(i.e., `pupil_raw_{previous steps}_{current_step}`). To illustrate:

  - `pupil_raw` -> `pupil_raw_deblink` -> `pupil_raw_deblink_detransient` -> and so on...
  
- `events`: a list of data frames containing trial event messages and timestamps
- `blinks`: a list of data frames containing start/stop/durations for blinks
- `info`: EyeLink EDF header data parsed into a data frame
- `latest`: internal tracker used for assessing which steps have been run so far
- `params`: detailed list of steps run and parameters passed to each step
- `epoch_{name}`: list of data frames for any given epoched timeseries

Now that we've explained what you can expect to see after running the `eyeris`
`glassbox()` function, we'll demonstrate what the `glassbox()` wrapper is
generally comprised of in terms of the steps and defaults that are implemented.

## 🧱 Building Blocks Under the Hood

While we strongly recommend against manually constructing the pipeline as will
be shown below (given that using the `glassbox()` will provide maximum
opportunities for reproducibility and reduction of accidental errors), more
advanced users may want to see how the individual steps can be used like
building blocks to iteratively test out parameters, switch steps around / remove
steps
(**_again, we strongly recommend against doing this unless you know what_**
**_you're doing_**), etc.

### The Default `glassbox()` Steps and Parameters, Deconstructed:

```{r, eval=FALSE}
system.file("extdata", "memory.asc", package = "eyeris") |>
  eyeris::load_asc(block = "auto") |>
  eyeris::deblink(extend = 50) |>
  eyeris::detransient(n = 16) |>
  eyeris::interpolate() |>
  eyeris::lpfilt(wp = 4, ws = 8, rp = 1, rs = 35, plot_freqz = TRUE) |>
  eyeris::zscore()
```

<div class="alert alert-light">
  💡 **For more detailed information on the implementation of functions within**
     **the `glassbox()` and thus how to create your own custom pipeline**
     **extensions that conform to the `eyeris` protocol, see the:**
     [🧩 Building your own Custom Pipeline Extensions vignette](custom-extensions.html).
</div>

---

## 📚 Citing `eyeris`

<div class="alert alert-light" style="margin-top: 1rem; margin-bottom: 1rem; padding-bottom: 0.5rem;">
  If you use the `eyeris` package in your research, please cite it!
  
  Run the following in R to get the citation:
</div>

```{r}
citation("eyeris")
```