pawFLIM: denoising via adaptive binning for FLIM datasets
Installation
pawFLIM can be installed from PyPI:
pip install pawflim
Usage
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 for an example with simulated data.
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Metadata
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Release files for pawflim 1.0.4
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Source distribution (sdist)
| File | Size | Uploaded | |
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| pawflim-1.0.4.tar.gz | 121.7 kB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pawflim-1.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 127.1 kB
Release files / pawflim-1.0.4.tar.gz
| Download URL | pawflim-1.0.4.tar.gz |
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| Size | 121.7 kB |
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Release files / pawflim-1.0.4-py3-none-any.whl
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| Size | 5.5 kB |
| Tags | Python 3 |
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