eyelean-analysis 1.1.0

Analysis toolkit for EYE-LEAN (Locomotion, Exploration, Action, and Navigation with Eye Tracking) — Python half of the Eye_lean VR behavioral-research toolkit

eyelean_analysis — Python Researcher Guide

The Python half of Eye-LEAN. Loads the CSVs the Unity toolkit writes, then runs signal processing, eye-movement classification, attention and cognitive-load metrics, per-phase reports for the sample experiment, post-hoc calibration correction, and batch processing.

This is the "I have a recording, what do I do with it?" walkthrough. The Unity-side RESEARCHER_GUIDE.md covers recording and replay; this guide picks up where that one ends.

Contents

• Concepts at a glance
• Installation
• Quick start
• The data model
• Loading and inspection
• Scene-state and event sidecars (v1.2+)
• Eye-movement classification
• Pupillary cognitive load (LHIPA)
• Data quality metrics
• Per-task analysis recipes (sample experiment)
• Custom experiments
• Post-hoc calibration correction
• Batch processing
• Performance notes
• Troubleshooting
• API reference
• References

Concepts at a glance

A live Eye_lean session writes one to three CSV files into Logs/ (or Assets/StreamingAssets/ when running from the editor against the bundled sample):

File	Always present	Contents
EyeTracking_<session>.csv	yes	Per-frame gaze, pupil, head pose, vergence, validity flags.
EyeTracking_<session>_SceneState.csv	when scene recording is enabled	Per-frame transforms (pose + active flag) for every Recordable in the scene.
EyeTracking_<session>_SceneEvents.csv	when scene recording is enabled	Discrete events (Spawn/Despawn, Config*, ShowInstruction, CountingAnswerSelected, ChangeDetectionFeedback, RandomStateSnapshot, …).

Plus optional sidecar JSON:

• experiment_results_<session>.json — per-trial outcome records from the sample experiment (target acquisition times, counting answers, change-detection accuracy).
• <HMD>_<timestamp>.json — EyeTrackingProfile snapshot the calibrator saved. Used for post-hoc calibration correction.

The Python package treats the main CSV as authoritative and the sidecars as optional joins.

Installation

Python 3.9+ is supported.

# Stable release from PyPI (recommended for users):
pip install eyelean-analysis
pip install "eyelean-analysis[all]"          # + visualization, jupyter, batch, …

# Development install from a local clone (recommended for contributors):
pip install -e ./eyelean_analysis            # core
pip install -e "./eyelean_analysis[test]"    # core + pytest + PyWavelets
pip install -e "./eyelean_analysis[all]"     # everything

Optional extras declared in pyproject.toml:

Extra	Adds	Needed for
wavelet	PyWavelets	LHIPA
visualization	matplotlib	The plotting helpers
batch	joblib, tqdm	BatchProcessor parallel + progress
jupyter	jupyter, jupyterlab, ipywidgets	The example notebooks
export	pyreadstat	SPSS export
test	pytest, PyWavelets	Running the test suite

scipy, numpy, pandas are all in the core install.

Quick start

The fastest path is the 9-notebook plug-and-play suite under notebooks/examples/ — see the notebook index for what each one covers. Every notebook auto-discovers your data via one line:

ctx = ela.notebook_context()      # arg → env → Logs/ → bundled sample
print(ctx)                        # see what got resolved

Run on a fresh checkout:

jupyter lab eyelean_analysis/notebooks/examples/01_quickstart.ipynb

The bundled v1.2 sample at Eye_lean_Unity_Project/Eye_lean/Assets/StreamingAssets/ is the fallback, so every notebook works end-to-end before you've recorded anything yourself.

Programmatically without a notebook, five lines get you from a recording to a scanpath plot:

import eyelean_analysis as ela
import matplotlib.pyplot as plt

data = ela.load_eyetracking("Logs/EyeTracking_<session>.csv")
gaze = data.compute_gaze_points(distance=2.0)
ela.create_trajectory_plot(gaze["gaze_x"], gaze["gaze_z"])
plt.show()

The data model

load_eyetracking(path) returns an EyeLeanData wrapper around a pandas DataFrame. Two key conventions matter when interpreting columns:

Column aliasing

The Unity toolkit has emitted at least two CSV schemas across its lifetime (Flask-app era and the current Unity-direct era). The loader maps both to a single canonical schema before handing the DataFrame back. Canonical names are snake_case (combined_dir_x, head_pos_y, left_pupil_diameter, …); the full map is eyelean_analysis.COLUMN_ALIASES.

If standardize_columns=True (the default), the DataFrame columns are the canonical names. Pass standardize_columns=False to keep the original column casing.

Coordinate normalization

HeadPos_* and the per-eye *Origin_* columns are written normalized to the trial-start world position, not in absolute world space. The normalization origin is recorded in the metadata header as # CoordinateOrigin: x,y,z. To recover absolute world-space positions, add that origin back to the normalized columns:

meta = ela.read_csv_metadata("Logs/EyeTracking_<session>.csv")
ox, oy, oz = (float(c) for c in meta["CoordinateOrigin"].split(","))
abs_head_x = data.df["head_pos_x"] + ox

CombinedDir_* is a unit vector in world space, not normalized.

Timestamps

UnityTimestamp is Time.realtimeSinceStartup at sample acquisition, in seconds since process start. It's monotonic and dense at the eye tracker's native rate (90 Hz on VIVE Focus Vision). Use data.get_timestamps() to access it as a NumPy array; data.get_sample_rate() returns the median-derived rate.

For the full schema, see the top-level DATA_SCHEMA.md.

Loading and inspection

import eyelean_analysis as ela

data = ela.load_eyetracking("Logs/EyeTracking_<session>.csv")

print(data.summary())          # dict: n_samples, sample_rate, phases, …
print(data.duration, "s")
print(data.get_phases())       # ['Recording'] or ['FreeExploration', …]

# Direct DataFrame access for arbitrary slicing
df = data.df
free_explore = df[df["phase"] == "FreeExploration"]

The metadata header above the column row carries information that can't be re-derived from the data alone:

meta = ela.read_csv_metadata("Logs/EyeTracking_<session>.csv")
print(meta.get("Profile", "none"))           # which calibration profile
                                              # was active at recording
print(meta["CoordinateOrigin"])               # de-normalization origin
print(meta.get("FileVersion"))                # schema version

Use this — not the # Profile: header in the file content — to decide whether a recording needs post-hoc correction.

Scene-state and event sidecars (v1.2+)

When scene recording is enabled in Unity (default for the sample experiment), each session also writes two sidecars next to the main CSV. The package loads them with the same metadata-header tolerance:

state = ela.load_scene_state("Logs/EyeTracking_<session>_SceneState.csv")
events = ela.load_scene_events(
    "Logs/EyeTracking_<session>_SceneEvents.csv",
    decode_config=True,    # base64-decode the Detail of Config* events
)

Or load both at once given the main CSV path:

state, events = ela.load_scene_sidecars(
    "Logs/EyeTracking_<session>.csv",
    decode_config=True,
)

state is one row per (frame, recordable) pair. events is one row per discrete event; the Detail column is free-text for most types and base64-encoded JSON for Config* events (set decode_config=True to materialize a Config dict column).

To answer "which Recordable was the participant gazing at on a given frame?", join the gaze DataFrame with scene-state on Frame:

joined = ela.merge_gaze_with_scene_state(
    data.df, state,
    object_id=None,           # all recordables; or pass a stable id
)
# `joined` has gaze columns + Pos_X/Pos_Y/Pos_Z + ObjectId for every
# (sample, recordable-present-that-frame) pair.

ObjectId is a stable hash from Unity's seeded-id system: it's identical across replay runs of the same recording, so analyses that key on an object id are deterministic.

Eye-movement classification

detect_eye_movements runs an I-VT (velocity-threshold) classifier on angular gaze data. Inputs are angles in degrees, not direction vectors:

import numpy as np

dx = data.df["combined_dir_x"].to_numpy()
dy = data.df["combined_dir_y"].to_numpy()
dz = data.df["combined_dir_z"].to_numpy()
yaw_deg   = np.degrees(np.arctan2(dx, dz))
pitch_deg = -np.degrees(np.arcsin(np.clip(dy, -1.0, 1.0)))

mv = ela.detect_eye_movements(yaw_deg, pitch_deg, data.get_timestamps())
print(len(mv["fixations"]), "fixations,",
      len(mv["saccades"]),  "saccades")

Each fixation has start_time, end_time, duration, centroid_x, centroid_y. Each saccade has amplitude, peak_velocity, mean_velocity. Defaults follow the I-VT literature (50 deg/s threshold, 75 ms merge window, 100 ms minimum fixation); override via velocity_threshold=, min_fixation_duration=, merge_threshold= keyword args.

For attention typing, feed the classified movements into the K-coefficient calculator (Krejtz et al. 2016):

k = ela.calculate_k_coefficient(mv["fixations"], mv["saccades"])
print(k.k_coefficient, k.attention_type)  # K and FOCAL/AMBIENT/NEUTRAL

Pupillary cognitive load (LHIPA)

LHIPA (Duchowski et al. 2018) is a wavelet-based cognitive-load index computed on a continuous pupil-diameter signal. As of v1.3 the on-device live monitor uses RIPA2 (Jayawardena et al. 2025) instead of LHIPA; LHIPA remains available for offline analysis through this Python package. For per-frame on-device cognitive load, read the LiveLoadIndex column produced by RIPAMonitor (see docs/RIPA_MONITOR.md).

left  = data.df["left_pupil_diameter"].to_numpy()
right = data.df["right_pupil_diameter"].to_numpy()
pupil = np.nanmean([left, right], axis=0)

result = ela.calculate_lhipa(pupil, sample_rate=data.get_sample_rate())
print(result.lhipa, result.is_valid)

Higher LHIPA ≈ higher cognitive load. The metric needs at least ~5 seconds of continuous pupil data; shorter windows return is_valid=False.

Data quality metrics

calculate_quality_metrics produces a single struct summarizing the recording's signal quality — useful for batch reports and for filtering out bad sessions before downstream analysis:

q = ela.calculate_quality_metrics(
    timestamps   = data.get_timestamps(),
    validity     = data.df["is_tracking_valid"].to_numpy()
                   if "is_tracking_valid" in data.df.columns else None,
    left_pupil   = data.df["left_pupil_diameter"].to_numpy(),
    right_pupil  = data.df["right_pupil_diameter"].to_numpy(),
    left_openness  = data.df.get("left_openness"),
    right_openness = data.df.get("right_openness"),
)
print(q.validity_percent, q.n_gaps, q.n_blinks, q.quality_score)

quality_score is a 0-100 composite; validity_percent, sample_rate_stability, n_gaps, n_blinks, and pupil-range diagnostics are exposed individually.

Per-task analysis recipes (sample experiment)

The bundled SampleExperiment cycles four phases:

1. FreeExploration — open look-around in a research room.
2. VisualSearch — find target spheres among distractors.
3. CountingTask — enumerate target shapes.
4. ChangeDetection — flicker paradigm; spot which object moved.

The fastest way to a per-phase report:

report = ela.analyze_sample_experiment(
    "Logs/EyeTracking_<session>.csv",
    results_json_path="Logs/experiment_results_<session>.json",
)
df = report.to_dataframe()      # one row per phase

Missing columns and short phases populate missing_metrics rather than raising. To go deeper into one phase, slice the DataFrame on phase and run the per-phase tools.

FreeExploration → fixation map

free = data.df[data.df["phase"] == "FreeExploration"]
yaw   =  np.degrees(np.arctan2(free["combined_dir_x"], free["combined_dir_z"]))
pitch = -np.degrees(np.arcsin(np.clip(free["combined_dir_y"], -1, 1)))

mv = ela.detect_eye_movements(yaw.values, pitch.values,
                              free["timestamp"].values)
ela.create_fixation_plot(mv["fixations"])  # bubble plot, area ∝ duration

VisualSearch → target acquisition time

The results JSON records per-trial acquisition times directly. To verify them against the gaze stream, use the events sidecar:

events = ela.load_scene_events(
    "Logs/EyeTracking_<session>_SceneEvents.csv",
)
# Each VisualSearch trial emits a Spawn for the target and a Despawn
# when the participant fixates on it long enough.
trial_starts = events[(events["EventType"] == "Spawn")
                      & events["ObjectId"].str.startswith("vs_target_")]
trial_ends   = events[(events["EventType"] == "Despawn")
                      & events["ObjectId"].str.startswith("vs_target_")]
durations = (trial_ends["T"].values - trial_starts["T"].values)

CountingTask → accuracy

answers = events[events["EventType"] == "CountingAnswerSelected"]
# Detail is the chosen number; correct answer is in the Config payload.
config_row = events[events["EventType"] == "ConfigCounting"].iloc[0]
# When loaded with decode_config=True, config_row["Config"] is a dict.

ChangeDetection → detection latency

feedback = events[events["EventType"] == "ChangeDetectionFeedback"]
# Detail = "Correct" or "Incorrect"; T = when the answer was finalized.
hide   = events[events["EventType"] == "ChangeDetectionHideScene"]
shown  = events[events["EventType"] == "ChangeDetectionShowChangedScene"]
# Latency: feedback["T"] - shown["T"], paired by trial order.

For more elaborate per-trial joins, scenes are spawned with stable ObjectIds; pair Spawn → Despawn pairs by id within each phase.

Custom experiments

Researchers building experiments on top of Eye_lean don't need to fork the analysis package. The patterns:

Filter to your phase names. phase is a free-text column written by your experiment controller; once you set it, this works:

mine = data.df[data.df["phase"].isin(["MyPhase1", "MyPhase2"])]

Consume custom CSV columns. SessionRecorder lets you append extra columns at session start; they reach the DataFrame untouched (no aliasing). Either reference them by their literal name or extend the alias map at load time:

loader = ela.EyeLeanLoader(custom_aliases={
    "my_metric": ["MyMetric", "myMetric_v2"],
})
data = loader.load("...csv")

Read your own event types. The events sidecar accepts arbitrary EventType strings; everything except Config* (base64+JSON) is stored as plain text in Detail. Filter on the type you care about:

mine = events[events["EventType"] == "MyExperimentLandmark"]

Align scene-state to gaze. The seeded-id system makes ObjectIds stable across runs of the same scene, so analyses keyed on a specific recordable are reproducible:

joined = ela.merge_gaze_with_scene_state(
    data.df, state, object_id="my_stable_id"
)

Post-hoc calibration correction

If a recording was made before a better calibration profile became available — or you want to compare the same recording under several profiles — re-apply a saved EyeTrackingProfile to the CSV:

stats = ela.apply_profile_to_csv(
    "Logs/HTC VIVE Focus Vision_<timestamp>.json",
    "Logs/EyeTracking_<session>.csv",
    "Logs/EyeTracking_<session>_corrected.csv",
)
print(stats["samples_corrected_combined"],
      stats["samples_skipped_invalid"])

The math, schema, and the compounding-offsets foot-gun (never apply correction to an already-corrected CSV — the metadata's # Profile: line is the canonical "was a profile active at recording" flag) are documented in docs/POST_HOC_CORRECTION.md.

Batch processing

BatchProcessor runs the same analysis pipeline across many CSVs, returning a flat per-file summary suitable for cross-participant analyses:

summary = ela.process_directory_batch(
    "data/recordings/",
    pattern="*.csv",
    output_path="data/results/summary.csv",
    n_workers=4,
)

For more control (custom analyzers, partial pipelines), use the class directly:

proc = ela.BatchProcessor(
    n_workers=4,
    compute_lhipa=True,
    compute_entropy=True,
    compute_fixations=True,
)
proc.add_analyzer(lambda d: {"my_metric": d.df["my_col"].mean()})
results = proc.process_directory("data/recordings/", pattern="*.csv")
df = ela.BatchProcessor.results_to_dataframe(results)

The full pipeline runs at ~1× wall-clock (a 5-minute recording takes ~5-10 seconds end-to-end, single-threaded).

Performance notes

• A typical 5-minute recording at 90 Hz is ~27,000 rows. At ~100 KB per row of CSV, that's ~3 MB on disk; pandas loads it in <1 s.
• Scene-state sidecars are larger (one row per recordable per frame). A 4-minute SampleExperiment recording with ~10 active recordables produces ~120,000 state rows; that loads in ~0.3 s.
• LHIPA dominates batch runtime: ~2-3 s per recording for the wavelet decomposition. Disable it (compute_lhipa=False) when you don't need it.
• Memory ceiling: a single recording's full DataFrame is well under 100 MB; you can hold dozens in memory before pressure becomes a concern. For larger sweeps, use process_directory_batch with n_workers > 1 and let it stream the per-file summary to disk.
• The fixation/saccade classifier and quality metrics are O(n) in sample count; nothing in the package is intentionally super-linear.

Troubleshooting

Loader warns about mixed-type columns. Some optional columns (per-frame phase strings, partial validity flags) only fill in once the experiment has actually started. The first few hundred rows are NaN, the rest are strings. Pass low_memory=False to silence:

data = ela.load_eyetracking("...csv", low_memory=False)

SceneState.csv exists but is empty. Check the metadata header: SampleEveryNthFrame controls write rate, and the recorder needs at least one Recordable in the scene. The bundled SampleExperimentController auto-registers the right Recordables; for custom scenes, attach a Recordable component to anything you want recorded.

Post-hoc correction produces near-zero offsets but the recording looks miscalibrated. The CSV may already be corrected. Read read_csv_metadata(path)["Profile"] — if it's anything other than none, that profile was applied live and a second correction will compound the offsets.

merge_gaze_with_scene_state produces more rows than the gaze DataFrame. That's expected when multiple recordables are present in the scene each frame: the merge is many-to-many on Frame. Pass object_id= to restrict to a single recordable.

Tests pass on dev machine but fail in CI. Confirm the CI runner has PyWavelets available — the LHIPA tests skip cleanly without it, but a few other paths log warnings.

API reference

Top-level eyelean_analysis module exports (all also available via the subpackages they live in):

Data loading

• load_eyetracking(path, **kw) → EyeLeanData
• EyeLeanLoader(custom_aliases=None) — class-based loader
• EyeLeanData — DataFrame wrapper with accessors
• read_csv_metadata(path) → dict of header # Key: value lines
• load_scene_state(path) → pd.DataFrame
• load_scene_events(path, decode_config=False) → pd.DataFrame
• load_scene_sidecars(main_csv_path, decode_config=False) → (state_df, events_df)
• merge_gaze_with_scene_state(gaze_df, state_df, object_id=None, frame_column="frame_number")
• COLUMN_ALIASES — the canonical→variants map

Notebook bootstrap

• notebook_context(csv=None, *, require_sidecars=False, require_profile=False, require_results=False, load=True) → NotebookContext
• NotebookContext — dataclass with csv_path, scene_state_path, scene_events_path, profile_path, results_path, repo_root, data, metadata, source

Validation

• DataValidator(...), ValidationResult, validate_file(path)

Filters

• butterworth_filter(data, cutoff_hz, sample_rate_hz, order=4)
• savgol_smooth(data, window, order=3)
• compute_gaze_velocity(x, y, timestamps)

Classification

• detect_eye_movements(x, y, t, **thresholds) → {"fixations": [...], "saccades": [...]}
• classify_fixations(...), classify_saccades(...)
• Fixation, Saccade — dataclasses
• calculate_k_coefficient(fixations, saccades) → KCoefficientResult
• classify_attention(k) → AttentionType
• k_coefficient_timeseries(...)

Metrics

• calculate_lhipa(pupil, sample_rate) → LHIPAResult
• calculate_ripa2(pupil, sample_rate, ...) → RIPA2Result (per-sample cognitive-load index; byte-for-byte parity with the on-device LiveLoadIndex column written by RIPAMonitor)
• fixation_entropy(fixations, horizontal_bins=8, vertical_bins=8, ...) → FixationEntropyResult (Shiferaw 2019 stationary gaze entropy SGE + gaze transition entropy GTE over a fixation list, with normalised-by-log2(N) variants for cross-recording comparison)
• calculate_gaze_entropy(x, y, ...) → EntropyResult (legacy raw-sample Shannon entropy; prefer fixation_entropy for SGE per Shiferaw 2019)
• transition_entropy(...) (Krejtz 2015 GTE; canonical AOI mode or spatial-proxy mode for raw samples)
• calculate_quality_metrics(...) → QualityMetrics

Experiments

• SAMPLE_EXPERIMENT_PHASES
• analyze_sample_experiment(csv, results_json_path=None) → SampleExperimentReport
• PhaseReport, SampleExperimentReport

Calibration

• load_profile(json_path) → EyeTrackingProfile
• apply_combined_correction(world_dirs, head_quats, correction)
• apply_profile_to_csv(profile_path, csv_in, csv_out) → dict
• EyeTrackingProfile, GazeCorrection

Batch

• BatchProcessor(...), ProcessingResult
• process_batch(file_paths, output_path=None, **kw) → pd.DataFrame
• process_directory_batch(dir, pattern="*.csv", output_path=None, **kw) → pd.DataFrame

Visualization

• create_heatmap(x, y, ...) — generic 2D histogram
• gaze_heatmap_2d(yaw, pitch, ...) — Gaussian-smoothed angular gaze heatmap
• gaze_heatmap_3d_projections(x, y, z, ...) — top/front/side orthographic density plots of a 3D vergence-point cloud
• aoi_heatmap(df, object_name, ...) — angular heatmap restricted to rows where GazedObjectName == object_name
• list_gazed_objects(df, ...) — per-object dwell counts and seconds
• create_trajectory_plot(x, y, ...)
• create_timeseries_plot(timestamps, values, ...)
• create_fixation_plot(fixations, ...)
• create_pupil_plot(timestamps, left, right, ...)

References

• Duchowski, A. T., Krejtz, K., et al. (2018). The Index of Pupillary Activity: Measuring Cognitive Load vis-à-vis Task Difficulty with Pupil Oscillation. CHI.
• Duchowski, A. T., et al. (2020). The Low/High Index of Pupillary Activity. CHI '20. — LHIPA.
• Duchowski, A. T., et al. (2022). Vergence calculation algorithms.
• Jayawardena, G., Jayawardana, Y., & Gwizdka, J. (2025). RIPA2: Real-time Pupil-derived Index of Cognitive Activity. JEMR, 18(6), 70.
• Krejtz, K., Szmidt, T., Duchowski, A. T., & Krejtz, I. (2015). Entropy-based statistical analysis of eye movement transitions. ACM Trans. Applied Perception, 13(1), 4. — GTE formula and normalisation convention.
• Krejtz, K., Duchowski, A. T., et al. (2016). Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze. PLOS ONE. — K-coefficient.
• Salvucci, D. D., & Goldberg, J. H. (2000). Identifying fixations and saccades in eye-tracking protocols. ETRA. — I-VT classifier (with min-duration / merge-gap extensions from Olsson 2007 and Komogortsev 2010).
• Shiferaw, B., Downey, L., & Crewther, D. (2019). A review of gaze entropy as a measure of visual scanning efficiency. Neurosci. Biobehav. Rev., 96, 353–366. — SGE/GTE conventions (fixation-sequence input, log2(N) normalisation).

Citation

@software{eye_lean_toolkit,
  title     = {{EYE-LEAN} (Locomotion, Exploration, Action, and Navigation with Eye Tracking): a Behavioral Research Toolkit for Data Rich Virtual Reality Experiments},
  author    = {Suchojad, Jakub and Dalawella, Kavindya and DeStefani, Serena and Stromswold, Karin and Feldman, Jacob},
  year      = {2026},
  publisher = {Zenodo},
  version   = {v1.0.0},
  doi       = {10.5281/zenodo.20040453},
  url       = {https://doi.org/10.5281/zenodo.20040453}
}

See ../CITATION.cff and ../ACKNOWLEDGMENTS.md for the full citation table covering RIPA2 (Jayawardena 2025), LHIPA (Duchowski 2018), K-coefficient, and others.

License

MIT License. See ../LICENSE.