Metadata-Version: 2.1
Name: opticalmapping
Version: 0.1.2
Summary: A toolbox for analyzing optical mapping and fluorescence imaging data.
Home-page: https://github.com/cardiacvision/optimap
Author: Jan Lebert, Jan Christoph
Author-email: jan.lebert@ucsf.edu
License: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: scipy
Requires-Dist: scikit-image
Requires-Dist: scikit-video
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# optimap
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### optimap: An open-source library for the processing of fluorescence video data

``optimap`` is an open-source Python toolbox for exploring, visualizing, and analyzing high-speed fluorescence imaging data with a focus on cardiac optical mapping data. It includes modules for loading, processing and exporting videos, extracting and measuring optical traces, visualizing action potential or calcium waves, computing activation maps, performing motion compensation and inhibiting motion artifacts, measuring contractility and further post-processing, analyzing and visualizing the results. Refer to the documentation and tutorials for a more detailed list of features.

> ⚠️ optimap is currently in early development, expect breaking changes and bugs.

## Installation
optimap is available for Mac OSX, Windows and Linux, see [Installing optimap](https://optimap.readthedocs.io/en/latest/chapters/getting_started/#installing-optimap) for more detailed information.

### Installing pre-built binaries (Mac OSX, Windows, Linux)

Pre-built binaries can be installed using pip:

```bash
pip install opticalmapping[all]
```

The above command will install optimap and all recommended dependencies (including OpenCV and PySide2). If you wish to install your own version of OpenCV (e.g. for CUDA support) or Qt implementation use:

```bash
pip install opticalmapping
```

## Getting Started
optimap is a script-based software, which means that you run Python-based analysis scripts rather than using a graphical user interface. We provide several very simple example scripts, which explain the usage of optimap, see [Tutorials](https://optimap.readthedocs.io/en/latest/tutorials/basics/). The example scripts can also be downloaded directly here: example script 1, example script 2. See the [Getting Started](https://optimap.readthedocs.io/en/latest/chapters/getting_started/) guide for installation instructions and a quick introduction to optimap. optimap relies on other packages such as numpy, matplotlib and OpenCV.

## Links

* [Documentation](https://optimap.readthedocs.io)
* [Issue tracker](https://github.com/cardiacvision/optimap/issues)
* [Source code](https://github.com/cardiacvision/optimap)

## Contributing

We welcome bug reports, questions, ideas for new features and pull-requests to fix issues or add new features to optimap. See [Contributing](https://optimap.readthedocs.io/en/latest/chapters/contributing/) for more information.

## License

optimap is licensed under the [MIT License](https://github.com/cardiacvision/optimap/blob/main/LICENSE.md).
