Load in SMLM data and annotate within napari

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Load in SMLM data and annotate within napari

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.


You can install napari-locpix via pip:

pip install napari-locpix

To install latest development version :

pip install git+


This plugin allows a user to

  1. Read in SMLM data
  2. Visualise SMLM data in a histogram
  3. Add segmentations to the data
  4. Extract the underlying localisations from the segmentations


The input data can be in the form of a .csv or .parquet.

We expect there to be 4 columns at least, which should he identified inthe file column selection:

  • X coordinate
  • Y coordinate
  • Frame
  • Channel

If the data has been annotated with this software we can also load this in. Note however we currently only support loading in annotated data saved as a .parquet folder. Therefore, we recommend always keeping a .parquet copy until loading in an annotated .csv is supported.

The data can be outputted to a .parquet or a .csv

Drop localisations with zero label, gives you the option to only save the localisations which have been annotated i.e. labels 1 and above.

Channels labels allows you to give a real name label to each of the channels e.g. Chan 0 label: 'Alexa 647'


Using the render button you can render the loaded in data according to the histogram settings

X/Y bins defines the number of bins for the histogram

Vis interpolation defines how to interpolate the image before viewing


Annotations can be added using Napari's viewer.

Simply click the add Labels.

Note that this software will expect the labels to be called "Labels"


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.


Distributed under the terms of the MIT license, "napari-locpix" is free and open source software


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


  • 0.0.6

Last updated:

  • 08 February 2024

First released:

  • 16 January 2023


Supported data:

  • Information not submitted

Plugin type:

GitHub activity:

  • Stars: 1
  • Forks: 0
  • Issues + PRs: 7

Python versions supported:

Operating system:


  • numpy
  • qtpy
  • polars
  • pyarrow