NeonDataset class#

class pyneon.NeonDataset(dataset_dir: str | Path)#

Holder for multiple recordings. It reads from a directory containing a project downloaded from Pupil Cloud with the Timeseries CSV or Timeseries CSV and Scene Video option. For example, a dataset with 2 recordings would have the following folder structure:

dataset_dir/
├── recording_dir_1/
│   ├── info.json
│   ├── gaze.csv
|   └── ...
├── recording_dir_2/
│   ├── info.json
│   ├── gaze.csv
|   └── ...
├── ...
├── enrichment_info.txt
└── sections.csv

Individual recordings will be read into NeonRecording objects based on sections.csv. They are accessible through the recordings attribute.

Parameters:: dataset_dir (str or pathlib.Path) – Path to the directory containing the dataset.

dataset_dir#

Path to the directory containing the dataset.

Type:: pathlib.Path

recordings#

List of NeonRecording objects for each recording in the dataset.

Type:: list of NeonRecording

sections#

DataFrame containing the sections of the dataset.

Type:: pandas.DataFrame

load_enrichment(enrichment_dir: str | Path)#

Load enrichment information from an enrichment directory. The directory must contain an enrichment_info.txt file. Enrichment data will be parsed for each recording ID and added to NeonRecording object in the dataset.

The method is currently being developed and is not yet implemented.

Parameters:: enrichment_dir (str or pathlib.Path) – Path to the directory containing the enrichment information.