NanoPyx

Nanoscopy Python library (NanoPyx, the successor to NanoJ) - focused on light microscopy and super-resolution imaging

What is the NanoPyx 🔬 Library?

NanoPyx is a library specialized in the analysis of light microscopy and super-resolution data. It is a successor to NanoJ, which is a Java library for the analysis of super-resolution microscopy data.

NanoPyx focuses on performance, by using the Liquid Engine at its core. It implements methods for the bioimage analysis field, with a special emphasis on those developed by the Henriques Laboratory. It will be distributed as a Python Library and also as Codeless Jupyter Notebooks, that can be run locally or on Google Colab, and as a napari plugin.

You can read more about NanoPyx in our preprint.

Currently it implements the following approaches:

A reimplementation of the NanoJ image registration, SRRF and Super Resolution metrics
eSRRF
Non-local means denoising
More to come soon™

if you found this work useful, please cite: preprint and

Short Video Tutorials

What is NanoPyx?	How to use NanoPyx in Google Colab?

Codeless jupyter notebooks available:

Category	Method	Last test
Denoising	Non-local Means	✅ by ADB (25/01/24)
Registration	Channel Registration	✅ by BMS (18/04/24)
Registration	Drift Correction	✅ by BMS (18/04/24)
Quality Control	Image fidelity and resolution metrics	✅ by ADB (25/01/24)
Super-resolution	SRRF	✅ by ADB (25/01/24)
Super-resolution	eSRRF	✅ by BMS (25/01/24)
Tutorial	Notebook with Example Dataset	✅ by ADB (25/01/24)

napari plugin

NanoPyx is also available as a napari plugin, which can be installed via pip:

pip install napari-nanopyx

Installation

NanoPyx is compatible and tested with Python 3.9, 3.10, 3.11 and 3.12 in MacOS, Windows and Linux. Installation time depends on your hardware and internet connection, but should take around 5 minutes.

You can install NanoPyx via pip:

pip install nanopyx

If you want to install with support for Jupyter notebooks:

pip install nanopyx[jupyter]

or if you want to install with all optional dependencies:

pip install nanopyx[all]

if you want access to the cupy implementation of 2D convolution you need to install the package version corresponding to your local CUDA installation. Please check the official documentation of cupy for further details. As an example if you wanted to install cupy for CUDA v12.X

pip install cupy-cuda12x

To install latest development version:

pip install git+https://github.com/HenriquesLab/NanoPyx.git

Notes for Mac users

If you wish to compile the NanoPyx library from source, you will need to install the following dependencies:

Homebrew from https://brew.sh/
gcc, llvm and libomp from Homebrew through the command:

brew install gcc llvm libomp

Run in jupyterlab within a docker container

docker run --name nanopyx1 -p 8888:8888 henriqueslab/nanopyx:latest

Usage

Depending on your preferences and coding proficiency you might be using NanoPyx differently.

If you are using Jupyter Notebooks or Google Colab notebooks check out our video tutorial here and here
If you are using our napari plugin check out the official napari tutorial and stay tuned for more!
If you prefer to use the Python library and take full advantage of the Liquid Engine flexibility check out our wiki, our cookiecutter and our video tutorials here and here.
Liquid engine template files for a simple example:
- Simple Liquid Engine templates here and here
- Fully fledged Liquid Engine templates here and here

Wiki

If you want more in depth instructions on how to use nanopyx and its Liquid Engine please refer to our wiki. In the wiki you can find step by step tutorials describing how each methods works and how to implement your own Liquid Engine methods.

Contributing

Contributions are very welcome. Please read our Contribution Guidelines to know how to proceed.

License

Distributed under the terms of the CC-By v4.0 license, "NanoPyx" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.

HenriquesLab/NanoPyx