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pawflim

Wavelet denoising of phasors.

https://github.com/maurosilber/pawflim

Science Score: 57.0%

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 2 DOI reference(s) in README
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    Low similarity (7.0%) to scientific vocabulary

Keywords

denoising flim phasors wavelets

Keywords from Contributors

energy-system-model
Last synced: 6 months ago · JSON representation ·

Repository

Wavelet denoising of phasors.

Basic Info
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Topics
denoising flim phasors wavelets
Created almost 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

pawFLIM: denoising via adaptive binning for FLIM datasets

PyPi PyPi License Paper

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 $r$:

$$ rn = \frac{Rn}{R_0} $$

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.

Owner

  • Name: Mauro Silberberg
  • Login: maurosilber
  • Kind: user
  • Location: Argentina

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  pawFLIM: reducing bias and uncertainty to enable lower photon count in FLIM experiments
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Mauro
    family-names: Silberberg
    email: maurosilber@df.uba.ar
    orcid: 'https://orcid.org/0000-0002-2402-1100'
    affiliation: >-
      Department of Physics, FCEN, University of
      Buenos Aires and IFIBA, CONICET, Buenos Aires.
      C1428EHA, Argentina
  - given-names: Hernán Edgardo
    family-names: Grecco
    email: hgrecco@df.uba.ar
    affiliation: >-
      Department of Physics, FCEN, University of
      Buenos Aires and IFIBA, CONICET, Buenos Aires.
      C1428EHA, Argentina; and, Department of
      Systemic Cell Biology, Max Planck Institute of
      Molecular Physiology, Dortmund, 44227, Germany
    orcid: 'https://orcid.org/0000-0002-1165-4320'
identifiers:
  - type: doi
    value: 10.1088/2050-6120/aa72ab
abstract: >-
  Förster resonant energy transfer measured by fluorescence lifetime imaging microscopy (FRET-FLIM)
  is the method of choice for monitoring the spatio-temporal dynamics of protein interactions in living cells.
  To obtain an accurate estimate of the molecular fraction of interacting proteins requires a large number of photons,
  which usually precludes the observation of a fast process,
  particularly with time correlated single photon counting (TCSPC) based FLIM.
  In this work, we propose a novel method named pawFLIM (phasor analysis via wavelets)
  that allows the denoising of FLIM datasets
  by adaptively and selectively adjusting the desired compromise between spatial and molecular resolution.
  The method operates by applying a weighted translational-invariant Haar-wavelet transform denoising algorithm to phasor images.
  This results in significantly less bias and mean square error than other existing methods.
  We also present a new lifetime estimator (named normal lifetime)
  with a smaller mean squared error and overall bias
  as compared to frequency domain phase and modulation lifetimes.
  Overall, we present an approach that will enable the observation of the dynamics of biological processes at the molecular level with better temporal and spatial resolution.
license: MIT

GitHub Events

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Last synced: 8 months ago

All Time
  • Total Commits: 16
  • Total Committers: 2
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.125
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Mauro Silberberg m****r@g****m 14
pre-commit-ci[bot] 6****] 2

Issues and Pull Requests

Last synced: 6 months ago

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  • Total issues: 0
  • Total pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: 30 days
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 2
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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  • pre-commit-ci[bot] (3)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 1,418 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
pypi.org: pawflim

Denoising via adaptive binning for FLIM datasets.

  • Homepage: https://github.com/maurosilber/pawflim
  • Documentation: https://pawflim.readthedocs.io/
  • 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 copies or substantial portions of the Software. 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 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  • Latest release: 1.0.4
    published about 2 years ago
  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 1,418 Last month
Rankings
Dependent packages count: 7.5%
Downloads: 13.4%
Forks count: 30.2%
Average: 32.0%
Stargazers count: 39.1%
Dependent repos count: 69.8%
Maintainers (1)
maurosilber
Last synced: 6 months ago

Dependencies

.github/workflows/ci.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v4 composite
  • pypa/gh-action-pypi-publish release/v1 composite
pyproject.toml pypi
requirements.test.txt pypi
  • hypothesis * test
  • pytest * test
requirements.txt pypi
  • binlets >=1
  • numpy *
  • scipy *