Metadata-Version: 2.1
Name: pawflim
Version: 1.0.0
Summary: Denoising via adaptive binning for FLIM datasets.
Author-email: Mauro Silberberg <maurosilber@gmail.com>
License: MIT License
        
        Copyright (c) 2023 Mauro Silberberg
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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        SOFTWARE.
        
Project-URL: Homepage, https://github.com/maurosilber/pawflim
Project-URL: Bug Tracker, https://github.com/maurosilber/pawflim/issues
Keywords: FLIM,pawFLIM,binlets,denoising,adaptive,wavelets
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: binlets (>=1)
Requires-Dist: numpy
Requires-Dist: scipy
Provides-Extra: test
Requires-Dist: pytest ; extra == 'test'
Requires-Dist: hypothesis ; extra == 'test'

# pawFLIM: denoising via adaptive binning for FLIM datasets

## Installation

pawFLIM can be installed from PyPI:

```
pip install pawflim
```

## Usage

```python
import numpy as np
from pawflim import pawflim

data = np.empty((3, *shape), dtype=complex)
data[0] = ... # number of photons
data[1] = ... # n-th fourier coefficient
data[2] = ... # 2n-th fourier coefficient

denoised = pawflim(data, n_sigmas=2)

phasor = denoised[1] / denoised[0]
```

See the notebook in
[examples](https://github.com/maurosilber/binlets-paper/blob/main/examples/simulated_data.ipynb)
for an example with simulated data.
