pyBer

pyBer is a desktop application for fiber photometry analysis. It helps you load Doric, HDF5, or CSV recordings, clean artifacts, preprocess traces, align signals to behavior or DIO events, inspect PSTHs and heatmaps, detect transients, and export results for Python, MATLAB, R, or Prism.

The app is built for users who want an interactive workflow first, with deterministic processing code underneath.

Quick Install

Install Miniforge or Anaconda first, then run:

cd C:\Analysis\app_project\pyBer
conda env create -f environment.yml
conda activate pyBer
Rscript -e "install.packages('fastFMM', repos='https://cloud.r-project.org')"
python .\pyBer\main.py

The fastFMM step is only needed for the FLMM temporal modeling panel. The rest of pyBer works without it.

Launch From VS Code

1. Open the repository folder in VS Code.
2. Select the interpreter from the pyBer conda environment.
3. Open pyBer/main.py.
4. Press Run, or use:

conda activate pyBer
python .\pyBer\main.py

If VS Code launches the wrong Python, run Python: Select Interpreter and choose the environment created from environment.yml.

What You Can Do

• Preprocess raw photometry traces with filtering, resampling, baseline correction, motion correction, and artifact handling.
• Detect and inspect artifacts with interpolation, cutout, local low-pass filtering, or no-op handling.
• Export processed CSV or HDF5 files with selectable fields and metadata.
• Align processed signals to DIO, behavior states, behavior onsets, or behavior transitions.
• Detect signal events and compare transient amplitude with baseline-prominence normalized metrics.
• Build individual or group PSTHs, heatmaps, event duration plots, and metrics.
• Fit temporal models with continuous GLM or trial-level FLMM.
• Rank GLM/FLMM feature contribution with leave-one-feature-out summaries.

Documentation

The full user guide is here:

• pyBer Documentation

It includes installation, first launch, preprocessing, postprocessing, transient detection, temporal modeling, group workflows, export, and troubleshooting.

Repository Layout

• pyBer/main.py: application entry point.
• pyBer/analysis_core.py: preprocessing and signal processing backend.
• pyBer/gui_preprocessing.py: preprocessing panels.
• pyBer/gui_postprocessing.py: postprocessing, PSTH, metrics, and export panels.
• pyBer/temporal_modeling.py: GLM and FLMM modeling panel.
• environment.yml: conda environment for development and user installs.
• pyBer.spec: PyInstaller build configuration.

Build The Executable

From an activated environment:

conda activate pyBer
python -m PyInstaller --noconfirm --clean pyBer.spec

The executable is written to dist/pyBer.exe.

Notes

pyBer sets PYTHONNOUSERSITE=1 in the environment so old packages from the user Python folder do not interfere with the conda environment. This is important for Qt, pyqtgraph, numpy, and rpy2 stability on Windows.