Metadata-Version: 2.4
Name: fastddm
Version: 0.3.14
Summary: A Python/C++ library for the analysis of Differential Dynamic Microscopy experiments
Home-page: https://github.com/somexlab/fastddm
Author: Enrico Lattuada, Fabian Krautgasser, Maxime Lavaudd, Roberto Cerbino
Author-email: cerbino@gmail.com
Maintainer-email: Enrico Lattuada <lattuada.enrico@gmail.com>, Maxime Lavaud <lavaudmaxime.ml@gmail.com>
License: GNU GPL 3.0
Project-URL: Source, https://github.com/somexlab/fastddm
Project-URL: Documentation, https://fastddm.readthedocs.io
Project-URL: Issues, https://github.com/somexlab/fastddm/issues
Project-URL: Changelog, https://github.com/somexlab/fastddm/blob/main/CHANGELOG.rst
Keywords: cuda,differential-dynamic-microscopy,digital-fourier-microscopy,gpu,image-analysis,microscopy,python
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: C++
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: psutil
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: scikit-image
Requires-Dist: tifffile
Requires-Dist: nd2reader
Requires-Dist: pandas
Requires-Dist: lmfit
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Requires-Dist: pytest-regtest; extra == "test"
Provides-Extra: doc
Requires-Dist: sphinx; extra == "doc"
Requires-Dist: sphinx-tabs; extra == "doc"
Requires-Dist: nbsphinx; extra == "doc"
Requires-Dist: furo; extra == "doc"
Requires-Dist: ipython; extra == "doc"
Provides-Extra: dev
Requires-Dist: fastddm[doc,test]; extra == "dev"
Dynamic: home-page
Dynamic: license
Dynamic: license-file

![FastDDM](docs/source/fastddm-logo-horizontal.svg)

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**FastDDM** is a Python package for the analysis of microscopy image sequences using Differential Dynamic Microscopy on CPU and GPU.
The features implemented are targeted at the experimental soft matter research community dealing with inert and active/biological samples.

## Resources
- [Documentation](https://fastddm.readthedocs.io/):
  Python API description, installation, examples, and usage information.
- [Example notebooks](https://github.com/somexlab/fastddm-tutorials):
  Jupyter notebooks with practical examples.

## Example scripts
These examples demonstrate some of the Python API.

Calculation of the image structure function and its azimuthal average:

```python
import fastddm as fddm

file_names = [...]  # define here your list of image file names 
images = fddm.read_images(file_names)

pixel_size = 0.3    # um
frame_rate = 50     # frames per second
    
# compute image structure function and set experimental parameters
dqt = fddm.ddm(img_seq, range(1, len(img_seq)))
dqt.pixel_size = pixel_size
dqt.set_frame_rate(frame_rate)

# compute the azimuthal average
aa = fddm.azimuthal_average(dqt, bins=dqt.shape[-1] - 1, range=(0.0, dqt.ky[-1]))
```

## Contributing to FastDDM
Contributions are welcome via [pull requests](https://github.com/somexlab/fastddm/pulls) after
agreeing to the [Contributors' Agreement](https://github.com/somexlab/fastddm/CONTRIBUTING.rst).
Please, refer to the [Developers'](https://fastddm.readthedocs.io/en/latest/contributing.html) section in the documentation.

Please, report bugs and suggest features via the [issue tracker](https://github.com/somexlab/fastddm/issues).

## Citing FastDDM
Please, cite this publication in every work that uses FastDDM:

    E. Lattuada, F. Krautgasser, F. Giavazzi, and R. Cerbino.
    The Hitchhiker’s Guide to Differential Dynamic Microscopy.
    (In preparation.)

## License
FastDDM is available under the [GNU GPL-3.0 license](LICENSE).

## Acknowledgements
* The [fftw-3.3.10](https://www.fftw.org/) and [pybind11 2.12.0](https://github.com/pybind/pybind11) libraries are included in the source tree.
* This project was funded by the Austrian Science Fund (FWF), Grant Number M 3250-N.
