Reproducibility Guide

This document describes how to reproduce Synaptipy’s published analysis results exactly.

Pinned Environment

For day-to-day development, use the cross-platform install recipe:

conda env create -f environment.yml
conda activate synaptipy
pip install -e ".[dev]"

For exact reproduction of paper outputs on the original paper platform, use the frozen files in paper/envs/:

conda env create -f paper/envs/conda_env_macos_arm64.yml
conda activate synaptipy

Docker Container

For full system-level reproducibility:

docker build -t synaptipy .
docker run synaptipy validation/ -v

The Docker image copies src/, tests/, validation/, examples/, and paper/ so validation commands operate on the same tree used by reviewers.

Paper Reproduction

The paper pipeline is manifest-driven. The fixed Allen Institute validation cohort is listed in paper/data_manifest.json; NWB files are downloaded into paper/data/allen_cache/, which is not committed to git.

Run lightweight checks before downloading data:

python paper/scripts/paper_figures/generate_paper_figures.py --check-only
python paper/scripts/generate_paper_tables.py --check-only

Regenerate tables and figures:

python paper/scripts/paper_figures/generate_paper_figures.py --run-analysis --force

Generated tables include companion provenance JSON files containing the manifest, software versions, command-line options, and git commit.

Random Seed Policy

Synaptipy’s analysis algorithms are fully deterministic — no stochastic elements are used in:

  • Spike detection (threshold-based, not stochastic)

  • Curve fitting (deterministic initial conditions from data statistics)

  • Event detection (deterministic matched filter)

  • Signal processing (IIR/FIR filters)

The only source of non-determinism is floating-point ordering in parallel operations (disabled by default). All curve_fit initial parameter estimates are derived deterministically from the input data (e.g., initial tau estimate = time to 63% of steady-state voltage).

Verification

After installing, verify your environment produces correct results:

python validation/validate_algorithms.py

All checks should pass with tolerances specified in the validation scripts.

Version Pinning Rationale

Dependency

Pin

Reason

NumPy >= 2.0.0

Uses new copy semantics; array API changes in 2.0

SciPy >= 1.14.0

sosfiltfilt stability improvements

PySide6 == 6.7.3

Known crashes in 6.8.0 (QTBUG-130070) and 6.10.x signal-connection changes

Neo >= 0.14.0

ABF2 reader fixes for multi-protocol files

PyNWB >= 3.1.0

IcephysFile schema corrections

h5py >= 3.14.0

Thread-safety improvements for concurrent reads