HRL: High Resolution Luminance

(Python 3 version)

HRL is a library for running high resolution luminance experiments for psychophysics in Python. It is primarily a wrapper for a number of python libraries and hardware drivers, and serves to coordinate them in a purpose built, user friendly way.

HRL's core purpose is the presentation of static, greyscale images and the recording of subject responses with a high degree of temporal precision. If your experiment can be reduced broadly to this scenario, HRL could be the library for you.

HRL does not provide tools for generating stimuli, nor is it designed to deal with colour in any way. HRL is not designed to handle complex, dynamic stimuli, and if a stimulus cannot be easily broken down into a series of static images, HRL may be ill suited to the task. If any of these capabilites are essential, than other libraries may be called for.

HRL is designed to work with a number of optional hardware components. The inclusion of these hardware components is handled in a modular fashion, and HRL is designed so that experiments could be written on a home computer, and then transfered to a lab computer with scientific hardware as required.

The most important component to achieving HRL's full functionality is a DATAPixx device or ViewPixx device. The first version of HRL was designed to work with an CRT analogue monitor, so that the DATAPixx box could use the full 16-bit resolution by converting digital signals into 16 bit luminance values. In the current version we have also adapted HRL to work with the newest ViewPixx LCD monitor, which also allows achromatic 16-bit luminance presentation. ViewPixx and Datapixx also includes functionality for high precision temporal control.

Another component of HRL is that it is designed to work with certain luminance measurement devices. HRL comes with a suite of calibration scripts designed to help tune scientific hardware using these measurement devices.

Installation

HRL is a full Python 3 package, and can be installed using pip or conda. It is not (yet) on PyPI, so first clone the repository via

git clone https://github.com/computational-psychology/hrl

Then install (from the root of the repository) using

pip install .

or

pip install -e .

for an editable installation.

Dependencies

• Required: pygame, pyopengl, numpy
• Optional: pyoptical, pyserial, and pypixxlib, the propertary library from VPixx Technologies.

pip install . will automatically install the required dependencies.

Usage

Installing HRL installs both the libraries for developing experiments, as well as a CLI utility for running calibrations, tests, and other features. The libraries are installed under the base Python module hrl. The scripts can be accessed by running hrl-util at the command line.

Documentation

• HRL docstrings can be accessed via pydoc. Running pydoc hrl will display an introductory help file which in turn will explain where exactly to find the rest of the documentation.
• Running hrl-util at the command line without any arguments will display another introductory help file, overviewing the various scripts that can be run via hrl-util.
• Example experiments using HRL can be found in the templates repository. This is a good place to start for beginners using HRL.
• A heavily documented example experiment is available under examples/sacha.

Calibration

HRL comes with a CLI utility hrl-util for developing a gamma correction Lookup Table (LUT) as well as various tests for calibrating the scientific hardware:

hrl-util --help