| Name | nanopyx JSON |
| Version |
0.4.0
JSON |
| download |
| home_page | |
| Summary | Nanoscopy Python library (NanoPyx, the successor to NanoJ) - focused on light microscopy and super-resolution imaging |
| upload_time | 2024-03-07 16:35:21 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
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| keywords |
nanoj
super-resolution microscopy
fluorescence
bioimage analysis
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<html>
<img src="https://user-images.githubusercontent.com/7071808/259983552-ef7aa814-16ba-4947-a69f-1f096d70370e.png" alt="logo" width="400"/>
</html>
# Actively being developed with stable releases
[](https://pypi.org/project/nanopyx)
[](https://python.org)
[](https://pypi.org/project/nanopyx)
[](https://henriqueslab.github.io/NanoPyx)
[](https://github.com/HenriquesLab/NanoPyx/blob/main/LICENSE.txt)
[](https://github.com/HenriquesLab/NanoPyx/actions/workflows/nanopyx_oncall_mechanic.yml)
[](https://github.com/HenriquesLab/NanoPyx/graphs/contributors)
[](https://github.com/HenriquesLab/NanoPyx/)
[](https://github.com/HenriquesLab/NanoPyx/)
[](https://zenodo.org/badge/latestdoi/505388398)
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](https://github.com/HenriquesLab/NanoJ-Core), which is a Java library for the analysis of super-resolution microscopy data.
NanoPyx focuses on performance, by heavily exploiting cython aided multiprocessing and simplicity. It implements methods for the bioimage analysis field, with a special emphasis on those developed by the [Henriques Laboratory](https://henriqueslab.github.io/).
It will be distributed as a Python Library and also as [Codeless Jupyter Notebooks](https://github.com/HenriquesLab/NanoPyx#codeless-jupyter-notebooks-available), that can be run locally or on Google Colab, and as a [napari plugin](https://github.com/HenriquesLab/napari-NanoPyx).
You can read more about NanoPyx in our [preprint](https://www.biorxiv.org/content/10.1101/2023.08.13.553080v1).
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](https://www.biorxiv.org/content/10.1101/2023.08.13.553080v1) and [](https://zenodo.org/badge/latestdoi/505388398)
## Short Video Tutorials
| What is NanoPyx? | How to use NanoPyx in Google Colab? |
|:-:|:-:|
| [](https://youtu.be/iAdgusBAU0Q) | [](https://youtu.be/KD0RzolFnd4) |
### More specific tutorials [here](https://www.youtube.com/playlist?list=PLk5I3_KOhE7sdP2OBfD9ewoXm1cXon88R)!
## Codeless jupyter notebooks available:
| Category | Method | Last test | Notebook | Colab Link |
| --- | --- | --- | --- | --- |
| Denoising | Non-local Means | ✅ by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/NonLocalMeansDenoising.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/NonLocalMeansDenoising.ipynb) |
| Registration | Channel Registration | ✅ by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/ChannelRegistration.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/ChannelRegistration.ipynb) |
| Registration | Drift Correction | ✅ by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/DriftCorrection.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/DriftCorrection.ipynb) |
| Quality Control | Image fidelity and resolution metrics | ✅ by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/SRMetrics.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/SRMetrics.ipynb) |
| Super-resolution | SRRF | ✅ by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/SRRFandQC.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/SRRFandQC.ipynb) |
| Super-resolution | eSRRF | ✅ by BMS (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/eSRRFandQC.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/eSRRFandQC.ipynb) |
| Tutorial | Notebook with Example Dataset | ✅ by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/ExampleDataSRRFandQC.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/NanoPyxExampleTutorial.ipynb) |
## 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 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]:
```shell
pip install nanopyx
```
If you want to install with support for Jupyter notebooks:
```shell
pip install nanopyx[jupyter]
```
or if you want to install with all optional dependencies:
```shell
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
```shell
pip install cupy-cuda12x
```
To install latest development version:
```shell
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:
```shell
brew install gcc llvm libomp
```
## Run in jupyterlab within a docker container
```shell
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](https://youtu.be/KD0RzolFnd4) and [here](https://www.youtube.com/watch?v=Dx2lHoRB044)
- If you are using our [napari plugin](https://github.com/HenriquesLab/NanoPyx#napari-plugin) check out the official [napari tutorial](https://napari.org/stable/tutorials/index.html) 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](https://github.com/HenriquesLab/NanoPyx/wiki), our [cookiecutter](https://github.com/HenriquesLab/LiquidEngineCookieCutter) and our video tutorials [here](https://youtu.be/gRGEjdT8opY?si=o0ovP5B-235BM0hu) and [here](https://youtu.be/s2SY6IlsWQI?si=5goo0ZQ1Ynyz3yTF).
- Liquid engine template files for a simple example:
- Simple Liquid Engine templates [here](https://github.com/HenriquesLab/NanoPyx/blob/main/src/nanopyx/core/templates/_le_template_simple.pyx) and [here](https://github.com/HenriquesLab/NanoPyx/blob/main/src/nanopyx/core/templates/_le_template_simple_.py)
- Fully fledged Liquid Engine templates [here](https://github.com/HenriquesLab/NanoPyx/blob/main/src/nanopyx/core/templates/_le_template_advanced.pyx) and [here](https://github.com/HenriquesLab/NanoPyx/blob/main/src/nanopyx/core/templates/_le_template_advanced.cl)
## Wiki
If you want more in depth instructions on how to use nanopyx and its Liquid Engine please refer to our [wiki](https://github.com/HenriquesLab/NanoPyx/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](https://github.com/HenriquesLab/NanoPyx/blob/main/CONTRIBUTING.md) 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.
[CC-By v4.0]: https://creativecommons.org/licenses/by/4.0/
[file an issue]: https://github.com/HenriquesLab/NanoPyx/issues
[pip]: https://pypi.org/project/pip/
## Development at a glance
## [](https://repography.com) / Structure
[](https://github.com/HenriquesLab/NanoPyx)
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"description": "<html>\n<img src=\"https://user-images.githubusercontent.com/7071808/259983552-ef7aa814-16ba-4947-a69f-1f096d70370e.png\" alt=\"logo\" width=\"400\"/>\n</html>\n\n# Actively being developed with stable releases\n[](https://pypi.org/project/nanopyx)\n[](https://python.org)\n[](https://pypi.org/project/nanopyx)\n[](https://henriqueslab.github.io/NanoPyx)\n[](https://github.com/HenriquesLab/NanoPyx/blob/main/LICENSE.txt)\n[](https://github.com/HenriquesLab/NanoPyx/actions/workflows/nanopyx_oncall_mechanic.yml)\n[](https://github.com/HenriquesLab/NanoPyx/graphs/contributors)\n[](https://github.com/HenriquesLab/NanoPyx/)\n[](https://github.com/HenriquesLab/NanoPyx/)\n[](https://zenodo.org/badge/latestdoi/505388398)\n\nNanoscopy Python library (NanoPyx, the successor to NanoJ) - focused on light microscopy and super-resolution imaging\n\n---\n\n## What is the NanoPyx \ud83d\udd2c Library?\n\nNanoPyx is a library specialized in the analysis of light microscopy and super-resolution data.\nIt is a successor to [NanoJ](https://github.com/HenriquesLab/NanoJ-Core), which is a Java library for the analysis of super-resolution microscopy data.\n\nNanoPyx focuses on performance, by heavily exploiting cython aided multiprocessing and simplicity. It implements methods for the bioimage analysis field, with a special emphasis on those developed by the [Henriques Laboratory](https://henriqueslab.github.io/).\nIt will be distributed as a Python Library and also as [Codeless Jupyter Notebooks](https://github.com/HenriquesLab/NanoPyx#codeless-jupyter-notebooks-available), that can be run locally or on Google Colab, and as a [napari plugin](https://github.com/HenriquesLab/napari-NanoPyx).\n\nYou can read more about NanoPyx in our [preprint](https://www.biorxiv.org/content/10.1101/2023.08.13.553080v1).\n\nCurrently it implements the following approaches:\n- A reimplementation of the NanoJ image registration, SRRF and Super Resolution metrics\n- eSRRF\n- Non-local means denoising\n- More to come soon\u2122\n\nif you found this work useful, please cite: [preprint](https://www.biorxiv.org/content/10.1101/2023.08.13.553080v1) and [](https://zenodo.org/badge/latestdoi/505388398)\n\n## Short Video Tutorials\n| What is NanoPyx? | How to use NanoPyx in Google Colab? |\n|:-:|:-:|\n| [](https://youtu.be/iAdgusBAU0Q) | [](https://youtu.be/KD0RzolFnd4) |\n\n### More specific tutorials [here](https://www.youtube.com/playlist?list=PLk5I3_KOhE7sdP2OBfD9ewoXm1cXon88R)!\n\n## Codeless jupyter notebooks available:\n\n| Category | Method | Last test | Notebook | Colab Link |\n| --- | --- | --- | --- | --- |\n| Denoising | Non-local Means | \u2705 by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/NonLocalMeansDenoising.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/NonLocalMeansDenoising.ipynb) |\n| Registration | Channel Registration | \u2705 by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/ChannelRegistration.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/ChannelRegistration.ipynb) |\n| Registration | Drift Correction | \u2705 by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/DriftCorrection.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/DriftCorrection.ipynb) |\n| Quality Control | Image fidelity and resolution metrics | \u2705 by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/SRMetrics.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/SRMetrics.ipynb) |\n| Super-resolution | SRRF | \u2705 by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/SRRFandQC.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/SRRFandQC.ipynb) |\n| Super-resolution | eSRRF | \u2705 by BMS (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/eSRRFandQC.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/eSRRFandQC.ipynb) |\n| Tutorial | Notebook with Example Dataset | \u2705 by ADB (25/01/24) | [](https://github.com/HenriquesLab/NanoPyx/blob/main/notebooks/ExampleDataSRRFandQC.ipynb) | [](https://githubtocolab.com/HenriquesLab/NanoPyx/blob/main/notebooks/NanoPyxExampleTutorial.ipynb) |\n\n## napari plugin\n\nNanoPyx is also available as a napari plugin, which can be installed via pip:\n\n```pip install napari-nanopyx```\n\n## Installation\n\n`NanoPyx` is compatible and tested with Python 3.9, 3.10, 3.11 in MacOS, Windows and Linux. Installation time depends on your hardware and internet connection, but should take around 5 minutes.\n\nYou can install `NanoPyx` via [pip]:\n\n```shell\npip install nanopyx\n```\n\nIf you want to install with support for Jupyter notebooks:\n\n```shell\npip install nanopyx[jupyter]\n```\n\nor if you want to install with all optional dependencies:\n\n```shell\npip install nanopyx[all]\n```\n\nif 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\n\n```shell\npip install cupy-cuda12x\n```\n\nTo install latest development version:\n\n```shell\npip install git+https://github.com/HenriquesLab/NanoPyx.git\n```\n\n### Notes for Mac users\n\nIf you wish to compile the NanoPyx library from source, you will need to install the following dependencies:\n\n- Homebrew from <https://brew.sh/>\n- gcc, llvm and libomp from Homebrew through the command:\n\n```shell\nbrew install gcc llvm libomp\n```\n\n## Run in jupyterlab within a docker container\n\n```shell\ndocker run --name nanopyx1 -p 8888:8888 henriqueslab/nanopyx:latest\n```\n\n## Usage\n\nDepending on your preferences and coding proficiency you might be using NanoPyx differently. \n\n- If you are using Jupyter Notebooks or Google Colab notebooks check out our video tutorial [here](https://youtu.be/KD0RzolFnd4) and [here](https://www.youtube.com/watch?v=Dx2lHoRB044)\n- If you are using our [napari plugin](https://github.com/HenriquesLab/NanoPyx#napari-plugin) check out the official [napari tutorial](https://napari.org/stable/tutorials/index.html) and stay tuned for more!\n- If you prefer to use the Python library and take full advantage of the Liquid Engine flexibility check out our [wiki](https://github.com/HenriquesLab/NanoPyx/wiki), our [cookiecutter](https://github.com/HenriquesLab/LiquidEngineCookieCutter) and our video tutorials [here](https://youtu.be/gRGEjdT8opY?si=o0ovP5B-235BM0hu) and [here](https://youtu.be/s2SY6IlsWQI?si=5goo0ZQ1Ynyz3yTF).\n- Liquid engine template files for a simple example:\n - Simple Liquid Engine templates [here](https://github.com/HenriquesLab/NanoPyx/blob/main/src/nanopyx/core/templates/_le_template_simple.pyx) and [here](https://github.com/HenriquesLab/NanoPyx/blob/main/src/nanopyx/core/templates/_le_template_simple_.py)\n - Fully fledged Liquid Engine templates [here](https://github.com/HenriquesLab/NanoPyx/blob/main/src/nanopyx/core/templates/_le_template_advanced.pyx) and [here](https://github.com/HenriquesLab/NanoPyx/blob/main/src/nanopyx/core/templates/_le_template_advanced.cl)\n\n## Wiki\n\nIf you want more in depth instructions on how to use nanopyx and its Liquid Engine please refer to our [wiki](https://github.com/HenriquesLab/NanoPyx/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.\n\n## Contributing\n\nContributions are very welcome.\nPlease read our [Contribution Guidelines](https://github.com/HenriquesLab/NanoPyx/blob/main/CONTRIBUTING.md) to know how to proceed.\n\n## License\n\nDistributed under the terms of the [CC-By v4.0] license,\n\"NanoPyx\" is free and open source software\n\n## Issues\n\nIf you encounter any problems, please [file an issue] along with a detailed description.\n\n[CC-By v4.0]: https://creativecommons.org/licenses/by/4.0/\n[file an issue]: https://github.com/HenriquesLab/NanoPyx/issues\n[pip]: https://pypi.org/project/pip/\n\n## Development at a glance\n\n## [](https://repography.com) / Structure\n\n[](https://github.com/HenriquesLab/NanoPyx)\n",
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