Skip to main content
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 (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

Metadata

Release files for patchview 0.3.7

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for patchview 0.3.7
File Size Uploaded Provenance
patchview-0.3.7.tar.gz 9.4 MB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for patchview 0.3.7
File Interpreter ABI Platform
patchview-0.3.7-py2.py3-none-any.whl Python 2, Python 3 none any Details

Total release size: 9.7 MB

Release files / patchview-0.3.7.tar.gz

Download URL patchview-0.3.7.tar.gz
Size 9.4 MB
Tags Source
SHA-256 checksum
How to use checksums
2ba65a1c237a0d7edf07f737a7d10f8850decbebcc92c384d205c7d9f6919118
BLAKE2b-256 checksum
How to use checksums
cd92e90dd12f6dc09fb52ccc5bddca1d57c6f15b594ac089d3df3bc5952c6e44
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.10.14

Release files / patchview-0.3.7-py2.py3-none-any.whl

Download URL patchview-0.3.7-py2.py3-none-any.whl
Size 331.5 kB
Tags Python 2 Python 3
SHA-256 checksum
How to use checksums
c2f9f541bea2ec2fd3dd109fc9a2c4f84881ba948d0ccff8b88204a27ed6095b
BLAKE2b-256 checksum
How to use checksums
e56edabc510fc346840f685e09da5051534dea07c608dedbce23b6c254a2e923
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.10.14

Security for patchview 0.3.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page