multiple runs separate files

Preprocessing Multiple Runs Stored in Separate Files

Many studies save each run (block) of a task into its own .asc file – for example, because the EyeLink recording was stopped and restarted between runs, or because each phase of a session was exported separately. This vignette shows the recommended way to preprocess that kind of data with eyeris: loop over the files, preprocess each one independently, and let the BIDS run-<index> entity keep everything cleanly organized in your derivatives.

💡 Two different “multiple runs” situations. There are two ways multiple runs can show up, and they are handled differently:

This vignette: multiple runs, each in its own separate .asc file. You process each file with its own glassbox() → epoch() → bidsify() call.
The other case: several recording segments embedded inside a single .asc file. That is handled automatically by load_asc(block = “auto”) and a single bidsify() call – see the Complete Pipeline and Anatomy of an eyeris Object vignettes. We contrast it briefly at the end.

The one concept to know: `block` is `run`

In eyeris, the input knob is called block (the block argument to load_asc()), and the output BIDS entity is called run (the run-<index> token in your derivative filenames). They map one-to-one:

block N on input → run-0N on output

So when you set the block number for a file, you are setting its run number. “Block” is eyeris/experiment-design terminology; “run” is the BIDS acquisition entity. (There is no block entity in BIDS – a separately-acquired repetition of a task is a run.) This is why, in the workflow below, you set the run number once via load_asc(block = ...) and do not need to pass run_num to bidsify().

The pattern

Suppose participant AB01 completed three runs of an associative-memory task, each saved to its own file:

library(eyeris)

dl <- path.expand("~/Downloads")
asc_files <- file.path(dl, c(
  "sub-AB01_t1_2024-01-15_10h00.00.000.asc",
  "sub-AB01_t2_2024-01-15_10h30.00.000.asc",
  "sub-AB01_t3_2024-01-15_11h00.00.000.asc"
))
stopifnot(all(file.exists(asc_files)))

output_dir <- path.expand("~/Documents/eyeris")

for (i in seq_along(asc_files)) {
  glassbox(asc_files[i], load_asc = list(block = i), verbose = TRUE) |>
    epoch(
      events = "TST_trial-{trial}_{item}_{associate}",
      limits = c(0, 0.1),
      label  = "trialEpochs"
    ) |>
    bidsify(
      bids_dir       = output_dir,
      participant_id = "AB01",
      session_num    = "01",
      task_name      = "assocmem",
      save_raw       = TRUE,
      html_report    = TRUE,
      report_seed    = 0
    )
}

What each piece is doing:

load_asc = list(block = i) sets the run number for this file. On iteration i = 1 the file becomes block_1 → run-01, i = 2 → run-02, and so on. Passing a single numeric block also forces the entire file into one run, which is exactly what you want when one file = one run (more on this in Sanity check below).
epoch() extracts your trials. Here TST_trial-{trial}_{item}_{associate} matches each trial event and parses trial, item, and associate into columns of the epoched data frame.
bidsify() writes that run’s derivatives. participant_id, session_num, and task_name are the same on every iteration, so all three runs land in the same subject/session tree, distinguished only by their run-<index>.

ℹ️ Why is there no run_num in the bidsify() call? Because bidsify() inherits the run number from the eyeris object – i.e., from the block you set in load_asc(). Setting it again in bidsify() would be redundant. (run_num exists to relabel a single-run object – e.g. force a lone file to be saved as run-03 – and it is silently ignored for objects that already contain multiple blocks.)

What you get

After the loop finishes, your derivatives look like this (per-run data files shown for run-01; run-02 and run-03 follow the same pattern):

eyeris
└── derivatives
    └── sub-AB01
        └── ses-01
            ├── eye
            │   ├── sub-AB01_ses-01_task-assocmem_run-01_desc-timeseries.csv
            │   ├── sub-AB01_ses-01_task-assocmem_run-01_desc-blinks.csv
            │   ├── sub-AB01_ses-01_task-assocmem_run-01_desc-events.csv
            │   ├── sub-AB01_ses-01_task-assocmem_run-01_desc-epoch_summary.csv
            │   ├── sub-AB01_ses-01_task-assocmem_run-01_desc-preproc_pupil_epoch-trialepochs.csv
            │   ├── epoch_trialEpochs/   # per-trial confounds CSVs for run-01
            │   │   └── ...
            │   ├── sub-AB01_ses-01_task-assocmem_run-02_desc-timeseries.csv
            │   ├── ...
            │   ├── sub-AB01_ses-01_task-assocmem_run-03_desc-timeseries.csv
            │   └── ...
            ├── source
            │   ├── figures
            │   │   ├── task-assocmem_run-01
            │   │   │   └── ...
            │   │   ├── task-assocmem_run-02
            │   │   │   └── ...
            │   │   └── task-assocmem_run-03
            │   │       └── ...
            │   └── logs
            │       ├── task-assocmem_run-01_metadata.json
            │       ├── task-assocmem_run-02_metadata.json
            │       └── task-assocmem_run-03_metadata.json
            ├── sub-AB01_task-assocmem_epoch-trialEpochs_run-01.html
            ├── sub-AB01_task-assocmem_epoch-trialEpochs_run-02.html
            ├── sub-AB01_task-assocmem_epoch-trialEpochs_run-03.html
            └── sub-AB01_task-assocmem.html

Every data file carries its run-<index>, and the top-level sub-AB01_task-assocmem.html report aggregates all three runs. For a full breakdown of what each derivative file contains, see the Extracting Data Epochs and Exporting Pupil Data vignette.

💡 Note on the epoch label in filenames. Epoch labels are sanitized (lower-cased, punctuation removed, digits dropped) on their way to disk, so the label = “trialEpochs” you supply appears as epoch-trialepochs in the CSV filename. If you search your output by filename, search case-insensitively.

Handling missing or non-sequential runs

A common, important question:

“What if I have runs 1–3 but run 2 was never collected? Would run 3 get saved as run 2?”

No – eyeris never renumbers your runs. The run number written to disk is exactly the number you assign via block =. There is no global counter that re-sequences runs across files.

The one thing to watch is where you get the number from. In the loop above, block = i uses the loop position, which equals the true run number only if your files really are runs 1, 2, 3, … in order with none missing. If run 2 was never collected and you only have files for runs 1 and 3, then seq_along() would hand file #2 (the run-3 file) the index 2 and mislabel it as run-02.

The fix is to drive the run number from the true run identity, not the loop position. Two robust options:

Option A – an explicit vector of run numbers, paired with your files:

asc_files <- file.path(dl, c(
  "sub-AB01_t1_2024-01-15_10h00.00.000.asc", # run 1
  "sub-AB01_t3_2024-01-15_11h00.00.000.asc"  # run 3 (run 2 not collected)
))
run_nums <- c(1, 3) # the TRUE run numbers, in the same order as `asc_files`

for (i in seq_along(asc_files)) {
  glassbox(asc_files[i], load_asc = list(block = run_nums[i]), verbose = TRUE) |>
    epoch(
      events = "TST_trial-{trial}_{item}_{associate}",
      limits = c(0, 0.1),
      label  = "trialEpochs"
    ) |>
    bidsify(
      bids_dir       = output_dir,
      participant_id = "AB01",
      session_num    = "01",
      task_name      = "assocmem",
      save_raw       = TRUE,
      html_report    = TRUE,
      report_seed    = 0
    )
}

Option B – parse the run number out of the filename. Here the run is encoded as _t1_, _t2_, _t3_, so a small regex recovers it (and naturally handles gaps, since a missing run simply has no file):

for (f in asc_files) {
  run_n <- as.integer(sub(".*_t(\\d+)_.*", "\\1", basename(f)))

  glassbox(f, load_asc = list(block = run_n), verbose = TRUE) |>
    epoch(
      events = "TST_trial-{trial}_{item}_{associate}",
      limits = c(0, 0.1),
      label  = "trialEpochs"
    ) |>
    bidsify(
      bids_dir       = output_dir,
      participant_id = "AB01",
      session_num    = "01",
      task_name      = "assocmem",
      save_raw       = TRUE,
      html_report    = TRUE,
      report_seed    = 0
    )
}

With either approach, run 3 is saved as run-03 and the gap is preserved.

ℹ️ Gaps are valid BIDS. The BIDS specification does not require run indices to be consecutive – a dataset with run-01 and run-03 but no run-02 is perfectly valid, and it faithfully records what you actually collected. So there is no need to “compress” your run numbers to fill a gap.

Fixing or re-running a single run

Because every run is preprocessed by its own bidsify() call, you can fix or re-run just one run later without touching the others – which is the main practical advantage of the separate-file workflow. For example, if you tweak a preprocessing parameter for run 2:

glassbox(asc_files[2], load_asc = list(block = 2), verbose = TRUE) |>
  epoch(
    events = "TST_trial-{trial}_{item}_{associate}",
    limits = c(0, 0.1),
    label  = "trialEpochs"
  ) |>
  bidsify(
    bids_dir       = output_dir,
    participant_id = "AB01",
    session_num    = "01",
    task_name      = "assocmem",
    save_raw       = TRUE,
    html_report    = TRUE,
    report_seed    = 0
  )

bidsify() cleans out the existing derivatives for that specific subject + session + task + run before writing, so re-running run-02 replaces only run-02’s files and leaves run-01 and run-03 untouched.

Sanity check: confirm one run per file

EyeLink .asc files sometimes contain more than one recording segment (for instance, when the tracker was stopped and restarted for a recalibration or drift correct). If eyeris auto-detects multiple segments in a file, it treats that file as multi-block – and in that case run_num is ignored and the runs are numbered from the embedded block numbers instead of from your block = i.

Passing a single numeric block (as we do above) forces the whole file into one run, which is what you want for one-file-per-run data. You can confirm a file resolves to exactly one run by checking the length of $timeseries:

library(eyeris)

# the bundled demo file stands in for one of your per-run .asc files
demo_file <- eyelink_asc_demo_dataset()

eye <- glassbox(demo_file, load_asc = list(block = 1), verbose = FALSE)

length(eye$timeseries) # 1 -> exactly one run in this file
#> [1] 1
names(eye$timeseries)  # "block_1" -> will be written as run-01
#> [1] "block_1"

If length(eye$timeseries) comes back greater than 1 for a file you expected to be a single run, that file is being split into multiple blocks; forcing block = <run number> (a single numeric, as above) collapses it back into one run.

The other case: multiple runs inside one file

If instead all of your runs were recorded into a single continuous .asc file, you don’t loop – you let eyeris split the embedded segments for you and write them in one bidsify() call:

glassbox(one_file_with_all_runs, load_asc = list(block = "auto")) |>
  epoch(
    events = "TST_trial-{trial}_{item}_{associate}",
    limits = c(0, 0.1),
    label  = "trialEpochs"
  ) |>
  bidsify(
    bids_dir       = output_dir,
    participant_id = "AB01",
    session_num    = "01",
    task_name      = "assocmem"
  )

Here block = "auto" (the default) detects each recording segment and numbers them run-01, run-02, … from their embedded block numbers. See the Complete Pipeline and Anatomy of an eyeris Object vignettes for more on that path.

✨ Summary

When each run lives in its own .asc file, loop over the files and give each one its own glassbox() → epoch() → bidsify() call.
In eyeris, block is run: set the run number once via load_asc(block = ...); bidsify() inherits it, so you don’t pass run_num.
eyeris never renumbers runs – drive the block number from the true run identity (an explicit vector or a filename parse), not the loop position, and missing runs leave valid, faithful gaps (e.g. run-01, run-03).
Each run is processed independently, so you can re-run a single run later without disturbing the others.
Use length(eye$timeseries) to confirm a file resolves to a single run; pass a numeric block to force it.

📚 Citing `eyeris`

If you use the eyeris package in your research, please cite it!

Run the following in R to get the citation:

citation("eyeris")
#> To cite package 'eyeris' in publications use:
#> 
#>   Schwartz ST, Yang H, Xue AM, He M (2025). "eyeris: A flexible,
#>   extensible, and reproducible pupillometry preprocessing framework in
#>   R." _bioRxiv_, 1-37. doi:10.1101/2025.06.01.657312
#>   <https://doi.org/10.1101/2025.06.01.657312>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {eyeris: A flexible, extensible, and reproducible pupillometry preprocessing framework in R},
#>     author = {Shawn T Schwartz and Haopei Yang and Alice M Xue and Mingjian He},
#>     journal = {bioRxiv},
#>     year = {2025},
#>     pages = {1--37},
#>     doi = {10.1101/2025.06.01.657312},
#>   }

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multiple runs separate files

Preprocessing Multiple Runs Stored in Separate Files

The one concept to know: `block` is `run`

The pattern

What you get

Handling missing or non-sequential runs

Fixing or re-running a single run

Sanity check: confirm one run per file

The other case: multiple runs inside one file

✨ Summary

📚 Citing `eyeris`

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multiple runs separate files

Preprocessing Multiple Runs Stored in Separate Files

The one concept to know: block is run

The pattern

What you get

Handling missing or non-sequential runs

Fixing or re-running a single run

Sanity check: confirm one run per file

The other case: multiple runs inside one file

✨ Summary

📚 Citing eyeris

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Uh oh!

Uh oh!

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Clone this wiki locally

The one concept to know: `block` is `run`

📚 Citing `eyeris`