The pyABFauto project provides an analysis pipeline for ABF files that requires no human input.
pyABFauto's watcher module repeatedly scans analysis folders and automatically analyzes new ABFs when they appear (ideal for multi-user network drives). ABFs are read using pyABF, analyzed with numpy, and graphed with matplotlib.
When pyABFauto is combined with a dynamic web interface (such as FlaskABF) and made accessible to the experimenter, the scientist can rapidly assess neuron properties and the outcome of experiments in real time.
Uses are highly customized to the individual and experiment. Common analyses are available in pyABFauto/analyses and are called by protocols.py depending on information found in the ABF header (such as clamp-mode, experiment length, and whether or nor tag comments are present).
| Description | Analysis Output |
|---|---|
| Repeated voltage-clamp steps reveal information like membrane resistance and whole-cell capacitance |  |
| Action potential properties can be determined from current-clamp ramps |  |
| Action potential gain curves can be created from current-clamp steps |  |
| Voltage-clamp steps reveal current/voltage relationships and voltage-dependent tail currents |  |
| Evoked currents can be monitored over time |  |
This project is currently under development and is not intended to be used by the public. It is made public for reference and as an example application using the latest versions of pyABF.
An earlier version of byABFauto can be found here.
python -m venv env
env\Scripts\activate
python.exe -m pip install --upgrade pip
pip install -r requirements.txt