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Patchview perform data analysis and visualization on whole-cell recording data, including firing pattern analysis, event analysis, synatpic connection detection, morphorlocial analysis and more.

Project description

https://img.shields.io/pypi/v/patchview.svg Python3.8 Documentation Status https://img.shields.io/badge/code%20style-black-000000.svg BSD-3-Clause https://joss.theoj.org/papers/10.21105/joss.04706/status.svg https://img.shields.io/pypi/dm/patchview?label=pypi%20downloads docs/resources/images/patchview_ads.png

PatchView perform data analysis and visualization on multi channel whole-cell recording (multi-patch) data, including firing pattern analysis, event analysis, synaptic connection detection, morphological analysis and more.

Features

PatchView integrates multiple open-source tools (see credit page) and wrap them using an intuitive graphic user interface (GUI). Thus users can perform most analysis quickly for the data collected in a typical patch-clamp experiment without installing Python and these tools or writing any Python scripts.

  • Importing both Heka data and Axon Instruments data. Exporting to Python pickle file or NWB (Neurodata Without Borders) file format.

  • Visualizing single and multiple traces with zoom, pan operations.

  • Automatically sorting experiments data according to predefined labels.

  • Performing analysis on intrinsic membrane properties, action potential detection, firing pattern analysis.

  • Synaptic connection analysis.

  • Visualizing and quantification of neuron’s morphological reconstruction from Neurolucida

Citation

If you find our work useful for your research, please cite:

Hu et al., (2022). PatchView: A Python Package for Patch-clamp Data Analysis and Visualization. Journal of Open Source Software, 7(78), 4706, https://doi.org/10.21105/joss.04706

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