resample: Resample an irregularly-sampled pupil timeseries onto a...

In eyeris: Flexible, Extensible, & Reproducible Pupillometry Preprocessing

Resample an irregularly-sampled pupil timeseries onto a uniform grid

Description

Places each recording segment onto the expected uniform (consistently-spaced) sampling grid: local sub-period timing jitter is smoothed by linear interpolation, and longer-than-expected gaps (dropped samples) become explicit NA rows for the later interpolate() step to handle.

Usage

resample(eyeris, verbose = TRUE, call_info = NULL)

Arguments

eyeris	An object of class eyeris derived from load_asc()
verbose	A flag to indicate whether to print detailed logging messages. Defaults to TRUE
call_info	A list of call information and parameters. If not provided, it will be generated from the function call

Details

Most of the eyeris pipeline (e.g., detransient(), lpfilt(), downsample()) assumes a fixed sampling rate. EyeLink trackers honor that assumption by zero-filling missing pupil samples, but some hardware instead drops samples entirely when pupil data is missing, leaving holes in the otherwise evenly-spaced time vector. Those holes silently distort any rate-dependent step.

resample() repairs the time axis in two stages. Whether a block needs repair is decided by the robust check_uniform_sampling_intervals() detector, which distinguishes genuine dropped samples from data that only looks irregular – notably high-rate trackers that report integer-millisecond timestamps for sub-millisecond samples (these are left untouched, so genuine samples are never collapsed). For blocks it does repair:

1. Build the target grid. The uniform grid is anchored on the first reliable regular interval – the first observed interval that matches the expected sampling period – rather than on the first timestamp. This keeps early sub-period jitter (e.g., intervals of 3, 3, 4, 4 ms at a 4 ms period) from offsetting the whole grid. The grid is then extended in both directions at the expected period so that it spans every observed timestamp, including any samples that precede the anchor (back-extension).

2. Resample onto the grid. Observed samples are placed on the grid by linear interpolation: samples that land on a grid point are kept verbatim, and short/jittered intervals are interpolated across so their values contribute to the regular grid. Any observed interval longer than the expected period is treated as a real gap: the missing grid sample(s) inside it are inserted as NA rather than interpolated across, and flagged in a new logical is_resampled column so they can be tracked downstream.

resample() does not fill the inserted NA values itself; that is the job of interpolate(), which decides how much of a missing span to fill according to its own missing-data policy. Running resample() therefore turns the "dropped-sample" problem into the ordinary "missing-value" (NA) problem that the rest of the pipeline already handles.

For data that is already uniformly sampled (e.g., EyeLink), resample() is a guaranteed no-op: no rows are inserted, no is_resampled column is added, and the data is returned unchanged. The same is true of a block whose surviving samples already form a uniform (coarser) grid – e.g., pure systematic decimation – where there is nothing to insert without fabricating data.

Value

An eyeris object whose timeseries blocks have been placed on a uniform time grid, with a new logical is_resampled column marking inserted (gap) rows.

Note

This step is part of the glassbox() preprocessing pipeline and runs automatically by default (it is a no-op unless irregular sampling is detected). Opt out with glassbox(resample = FALSE). Advanced users may call it directly if needed.

See Also

interpolate() for filling the gaps left by dropped samples, and glassbox() for the recommended way to run this step as part of the full eyeris glassbox preprocessing pipeline.

eyeris documentation built on Aug. 1, 2026, 1:07 a.m.