RHEED Real-Time Analyzer

Python sidecar framework for real-time RHEED analytics in a TSST-centered PLD workflow.

Scope

• Keeps TSST as authoritative control and safety gate.
• Runs independent analytics and policy logic.
• Starts in advisory mode, with closed-loop hooks for later bounded control.

Installation

Option A: Conda environment (recommended)

conda create -n rheed-rt python=3.11 -y
conda activate rheed-rt
pip install -r requirements.txt
pip install -e .

Option B: Dev environment (tests + notebook + publishing tools)

conda create -n rheed-rt-dev python=3.11 -y
conda activate rheed-rt-dev
pip install -r requirements-dev.txt
pip install -e .[dev]

Quick Start

Run a short dummy sidecar session:

rheed-sidecar --duration 10

This writes JSONL events to logs/events.jsonl by default.

Test

pytest -q

Detailed test and demo notes are in tests/README.md.

Build And Publish

Detailed release instructions are in docs/PUBLISHING.md.

Automated release via GitHub Actions

This repo includes .github/workflows/main.yml with keyword-triggered release on pushes to main:

• #major -> bump X.0.0
• #minor -> bump x.Y.0
• #patch -> bump x.y.Z

Example commit message:

feat: add TSST adapter #major

When triggered, the workflow will:

1. bump versions in pyproject.toml and rheed_core/__init__.py
2. create/push a release commit and vX.Y.Z tag
3. build and publish to PyPI using pypa/gh-action-pypi-publish

Note: configure PyPI Trusted Publishing for this repository/workflow before first automated release.

Local package build check

python -m build
twine check dist/*

Current Capabilities

• Dummy real-time collector with noisy oscillatory intensity and synthetic image frames.
• Rolling preprocessing + feature extraction (signal + image proxies).
• Oscillation tracker with period/phase/amplitude/confidence estimation.
• Draft-integrated cycle analysis (peak segmentation + per-cycle relaxation tau estimate).
• Offline reusable analysis pipeline adapted from draft code:
  • range selection
  • denoise chain
  • peak-to-peak cycle splitting
  • tail/background cleanup
  • per-cycle relaxation fitting
• Advisory-first policy engine for drift, frame quality, period instability, and amplitude decay.
• Replayable JSONL event logging.

Draft Integration

Legacy analysis ideas from rheed_core/core/draft codes/ are bridged into the runtime path through:

• rheed_core/core/draft_bridge.py: FFT band-pass, median filtering, peak detection, cycle segmentation, and linearized tau fitting.
• rheed_core/core/preprocess.py: optional median + FFT filtering in streaming mode.
• rheed_core/core/features.py: Gaussian-moment-like image spread and drift estimation.
• rheed_core/core/state.py: latest cycle relaxation-time tracking.
• rheed_core/core/policy.py: advisory on unstable relaxation-time behavior.

Notebook Demos

• tests/demo_draft_bridge.ipynb: draft-derived signal processing + tau fitting utilities.
• tests/demo_pipeline_integration.ipynb: end-to-end sidecar usage with draft-aware config.
• tests/demo_offline_analysis.ipynb: second-pass offline flow mirroring real draft workflow.

Project Layout

rheed_core/
  main.py
  config.yaml
  core/
  io/
tests/
docs/

Roadmap

1. Replace dummy collector with TSST stream adapter.
2. Validate feature/state outputs against recorded experimental datasets.
3. Introduce bounded command outputs behind explicit safety constraints.