https://github.com/maurosilber/pawflim

Wavelet denoising of phasors.
https://github.com/maurosilber/pawflim

denoising flim phasors wavelets

Last synced: 12 months ago
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Wavelet denoising of phasors.

• Host: GitHub
• URL: https://github.com/maurosilber/pawflim
• Owner: maurosilber
• License: mit
• Created: 2023-05-09T18:57:04.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-07-01T17:00:28.000Z (almost 2 years ago)
• Last Synced: 2025-06-09T08:14:33.925Z (12 months ago)
• Topics: denoising, flim, phasors, wavelets
• Language: Python
• Homepage: https://doi.org/10.1088/2050-6120/aa72ab
• Size: 126 KB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

          
# pawFLIM: denoising via adaptive binning for FLIM datasets

![PyPi](https://img.shields.io/pypi/pyversions/pawflim.svg)

[![PyPi](https://img.shields.io/pypi/v/pawflim.svg)](https://pypi.python.org/pypi/pawflim)

[![License](https://img.shields.io/github/license/maurosilber/smo)](https://opensource.org/licenses/MIT)

[![Paper](https://img.shields.io/badge/DOI-10.1088%2F2050--6120%2Faa72ab-green)](https://doi.org/10.1088/2050-6120/aa72ab)

## Installation

pawFLIM can be installed from PyPI:

```

pip install pawflim

```

or conda-forge:

```

conda install -c conda-forge 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 (conjugated) Fourier coefficient

data[2] = ...  # 2n-th (conjugated) Fourier coefficient

denoised = pawflim(data, n_sigmas=2)

phasor = denoised[1] / denoised[0]

```

Note that we use the standard FLIM definition for the $n$-th phasor $r$:

$$ r_n = \\frac{R_n}{R_0} $$

where

$$ R_n = \\int I(t) , e^{i n \\omega t} dt $$

is the $n$-th (conjugated) Fourier coefficient.

See the notebook in

[examples](https://github.com/maurosilber/pawflim/blob/main/examples/simulated_data.ipynb)

for an example with simulated data.