patchview 0.3.7

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

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.

• Free software: BSD 3-Clause license

• Documentation: https://patchview.readthedocs.io.

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 (Both ABF1 and ABF2). 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

To install PatchView from PyPI:

It is recommended to install Patchview in an virtual enviroment with Python3.10+. After activating your virtual environment, run this command in your terminal:

pip install git+https://github.com/ZeitgeberH/NeuroM@patchview#egg=NeuroM git+https://github.com/ZeitgeberH/dictdiffer#egg=dictdiffer git+https://github.com/jeremysanders/pyemf3#egg=pyemf3
pip install patchview

More details, please refer to the Installation page.

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