jensandClaude Sonnet 4.6
40d57dc68a
Both TODO.md backlogs and the README's architecture section still described the Kalman-filter/heat-diffusion design that's since been replaced by the delay-line Pot model and Kalman-free Smith predictor. Check off the items that rewrite already fixed (heat-loss units, transport delay vs. single-pole lag, three-Kalman-tuning), note how the kn/sensor-noise item was resolved differently than proposed, and add newly-spotted issues: brewpi.py wiring set_model_power unconditionally (breaks pid_type "Normal"), kalman.py being dead code in production, stale Kalman/kn keys in the config templates, and debug print()s left in Pot.process(). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
132 lines
5.6 KiB
Markdown
132 lines
5.6 KiB
Markdown
# BrewPi
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A Python-based controller for automating the mash/brewing process of beer: it
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holds a pot of liquid at target temperatures (or ramps it at a target heating
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rate) according to a configurable mash schedule, drives a heater and stirrer,
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and exposes live control/telemetry over a WebSocket so a desktop GUI (or any
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other client) can monitor and steer the brew.
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The project began as a simulation/control-theory playground (Smith-predictor
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temperature control, pot transport-delay model, see
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[`docs/NonLinMPC.pdf`](docs/NonLinMPC.pdf)) and has grown real-hardware
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backends for an induction hob and an RTD temperature probe.
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## Architecture
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```
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brewpi/brewpi.py Server entry point: wires sensor, pot/plant,
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heater, temperature controller and stirrer
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together, runs them as asyncio tasks, and
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serves state/commands over a WebSocket.
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client/brewpi_gui.py PyQt5 desktop client (brewpi.ui) that connects
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to the server's WebSocket, displays live
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temperature/power/state, and lets the user set
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target temperature, heat rate, stirrer speed,
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and switch the heater/stirrer on or off.
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components/ Pluggable building blocks behind factories:
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pid/ temperature controllers: plain PID
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("Normal") or PID + Smith predictor
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("Smith", runs two internal pot models —
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one with the plant's transport delay, one
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without — to compensate for the dead time)
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plant/ pot thermal model (transport-delay line)
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used in simulation
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sensor/ temperature sensors: simulated, or a real
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MAX31865 RTD amplifier over SPI
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actor/ heater and stirrer drivers: simulated, a
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Hendi induction hob (serial protocol), or a
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Pololu 1376 stirrer motor controller
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tasks/ Async tasks (one per component) that poll
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hardware/sim state at a fixed interval and
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publish changes through the message dispatcher.
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ws/ Minimal WebSocket pub/sub layer: server
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(single- and multi-user), client, and a
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keyed message dispatcher (e.g. "Sensor",
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"Heater", "TempCtrl", "Stirrer", "Pot").
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tracer.py Logs traced variables to .mat files (for
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offline analysis/tuning in MATLAB/Octave,
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see results.m / results_tc.m).
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sude/ Mash schedules ("Sud" = brew/wort), each a
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JSON list of temperature rests ("Rasten")
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with target temperature, heat rate, and
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optional pause for user confirmation.
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config.json.templ Configuration template (real hardware).
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config.json.sim Configuration template for simulation mode.
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```
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### Data flow
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The server (`brewpi/brewpi.py`) loads `config.json`, builds the configured
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sensor/heater/stirrer/controller via factories (`*Factory.create(name, ...)`)
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based on the `Controller` section (`sensor_name`, `heater_name`,
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`stirrer_name`, `pid_type`, or `"sim"` for any of them), and connects their
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outputs to each other's inputs via `set_on_changed` callbacks, e.g.:
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- sensor temperature → temperature controller's `theta_ist`
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- temperature controller output `y` → heater power
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- heater effective power → pot model power (simulation only)
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Each component runs inside its own `ATask` at a configurable interval
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(`Controller.dt`, scaled by `sim_warp_factor`) and pushes state changes onto a
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keyed WebSocket channel that any connected client can subscribe to and send
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commands back on (e.g. `{"TempCtrl": {"Soll": {"Temp": 65}}}`).
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## Requirements
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- Python 3.8+
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- Server (`brewpi/requirements.txt`): `numpy`, `scipy`, `websockets`, `dpath`,
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and `pyserial`/`spidev` if using real hardware backends.
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- Client (`client/requirements.txt`): `PyQt5`.
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Install with:
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```bash
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pip install -r brewpi/requirements.txt
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pip install -r client/requirements.txt
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```
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## Running
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1. Copy a config template to `config.json` next to `brewpi.py` and adjust it.
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Use `config.json.sim` to run entirely in simulation (no hardware needed),
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or `config.json.templ` as a starting point for real hardware (set the
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heater/stirrer serial ports and sensor type).
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2. Start the server:
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```bash
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cd brewpi
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./brewpi.py
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```
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This serves the WebSocket on `ws://0.0.0.0:8765`.
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3. Start the GUI client and connect to the server's URI:
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```bash
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cd client
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./brewpi_gui.py
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```
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`brewpi/brewpi.sh` shows how this is wired up to run under a `virtualenvwrapper`
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environment (`$WORKON_HOME`/`$BREWPI_HOME`) on a Raspberry Pi-style deployment.
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## Mash schedules
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Files under `sude/` describe a brew's mash schedule: pot weight, malt/water
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weights, stirrer speed/duty, and a list of temperature rests, each with a
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target temperature, heating rate, and whether to pause for user confirmation
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before continuing (e.g. to add malt or check gravity).
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## Logging & analysis
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`tracer.py` (and `tasks/tracer.py`) periodically dump traced signals
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(temperatures, heat rates, heater power) to `logs/*.mat` files, which can be
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loaded in MATLAB/Octave — see `results.m` and `results_tc.m` — to evaluate
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and tune the controller.
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