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Since October 1, 2024, this version is no longer actively maintained and will not be updated. New plugins and plugin updates will continue to be listed.

Napari Phasors

napari-phasors

A simple plugin to use phasor analysis

Workflow step:
Image annotation
Image segmentation

License BSD-3 PyPI Python Version tests codecov napari hub

A simple plugin to do phasor analysis in napari. Based on the phasorpy library.

Jump to Intallation


Usage

napari-phasors is composed of a few widgets that allow reading a few specific FLIM and hyperspectral file formats, perform phasor analysis, and display and export the results of manual phasor selections.

Sample Data

Two sample datasets for FLIM are provided, along with their corresponding calibration images. Additionally, a paramecium image is included as sample data for hyperspectral analysis.

sample_data

Phasor Analysis

Plot FLIM Data

FLIM phasor data can be plotted as a 2D histogram or scatter plot. The colormap, the number of bins and the scale of the colors can be customized. Filtering and thresholding can also be applied to process phasor data and the mean intensity image.

phasors_flim

Plot Hyperspectral Data

Hyperspectral phasor data can also be plotted as a 2D histogram or scatter plot and visualized in the full universal circle.

phasors_hyperspectral

Apparent Lifetime Display

A FLIM image can be colormapped according to the phase or modulation apparent lifetime. A histogram is also created for visualization of the distribution of apparent lifetimes of the FLIM image.

lifetimes

Phasor Calibration

FLIM images can be calibrated using a reference image acquired under the same experimental parameters. This reference image should consist of a homogeneous solution of a fluorophore with a known fluorescence lifetime and the laser frequency used in the experiment. This ensures accuracy and consistency in lifetime measurements.

calibration

Phasor Custom Import

Supported file formats (.tif, .ptu, .sdt, .fbd, .lsm, .ome.tif) can be read and transformed to the phasor space. Additional options, such as the harmonics, channels and frames, can be specified depending on the file format to be read.

custom_import

Phasor Export

The average intensity image and the phasor coordinates can be exported as OME-TIF files that can be read by napari-phasors and PhasorPy. Alternatively, the phasor coordinates, as well as the selections (cursors) can be exported as a CSV file.

export_phasors

Installation

You can install napari-phasors via pip. Follow these steps from a terminal.

We recommend using miniforge whenever possible. Click here to choose the right download option for your OS. If you do not use miniforge, but rather Anaconda or Miniconda, replace the mamba term whenever you see it below with conda.

Create a conda environment with napari by typing :

mamba create -n napari-phasors-env napari pyqt python=3.10

Activate the environment :

mamba activate napari-phasors-env

Install napari-phasors via pip :

pip install napari-phasors

Alternatively, install latest development version with :

pip install git+https://github.com/napari-phasors/napari-phasors.git

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the BSD-3 license, "napari-phasors" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.

Version:

  • 0.0.2

Last updated:

  • 29 November 2024

First released:

  • 22 November 2024

License:

Supported data:

  • Information not submitted

Save extension:

  • Information not submitted

Save layers:

GitHub activity:

  • Stars: 6
  • Forks: 3
  • Issues + PRs: 14

Python versions supported:

Operating system:

Requirements:

  • phasorpy
  • qtpy
  • scikit-image
  • biaplotter>=0.0.5a2
  • lfdfiles
  • sdtfile
  • ptufile
  • tifffile
  • pandas