Code for regenerating the random-number-based contrast values applied in experiments with white-noise stimulation in the Gollisch Lab. Used for both temporal and spatiotemporal stimuli, and for both binary and Gaussian white noise.
The random-number sequences are needed for analyzing the spike responses of recorded cells under white-noise stimulation.
A full installation of the package is done from command-line using pip with
pip install retinawhitenoise
If the pip package is not available for a given operating system or Python version, the package and Cython code can be compiled manually instead.
pip install https://github.com/gollischlab/RecreateWhiteNoiseStimuliWithPython/archive/main.tar.gz
Windows might require the Microsoft C++ Build Tools to be installed (including "Windows SDK" and "MSVC ... C++ x64/x86 build tools"). For details, see here.
The Python package retinawhitenoise offers two modules binarystimulus and gaussianstimulus,
each offering routines to recreate, save (to disk), and load stimulus sequences.
Instructions on how to use them with example code and explanations is included in the jupyter notebook examples.ipynb.
FAQs
Recreate white-noise stimuli
We found that retinawhitenoise demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
Security News
Malicious Go packages are impersonating popular libraries to install hidden loader malware on Linux and macOS, targeting developers with obfuscated payloads.
Security News
Bybit's $1.46B hack by North Korea's Lazarus Group pushes 2025 crypto losses to $1.6B in just two months, already surpassing all of 2024's $1.49B total.
Security News
OpenSSF has published OSPS Baseline, an initiative designed to establish a minimum set of security-related best practices for open source software projects.