jens
194156f244
A step's target temperature applies to the whole step (it's what a
ramp ramps to and what a hold then holds at), not just its ramp phase
- promote it from a nested "ramp": {"temp": ...} to a step-level
"temperature" field, defaulted like descr/grain_mass/etc. Updates all
consumers (Sud._build_step, SudTask, the GUI's forecast estimator,
demo_sud.py) and migrates sude/sud_0010.json and the README.
193 lines
9.2 KiB
Markdown
193 lines
9.2 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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sud.py optional mash-schedule sequencer that steps
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through a sude/*.json schedule and drives
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the temperature controller from it
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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", "Sud").
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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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scripts/demos/ Standalone, eyeballed-plot demos (matplotlib)
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exercising components/* in isolation — pid/,
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plant/, and a full mash-schedule run in sud/.
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Not used at runtime; run directly, e.g.
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`python -m scripts.demos.sud.demo_sud`.
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sude/ Mash schedules ("Sud" = brew/wort), each a
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JSON list of "heat"/"hold" steps with target
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temperature/heat rate or hold duration,
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per-step stirrer timing, and optional pause
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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 ("Sud"): `pot_mass`,
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`pot_material` (fixed for the whole brew), and a `steps` list, each a ramp, a
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hold, or both, plus a step-level `temperature` (the target a ramp step ramps
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to, and what a hold step holds at):
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- a ramp — `"ramp": {"rate": ...}` — ramp to `temperature` at `rate`
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(°C/min), and/or
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- a hold — `"hold": {"duration": ...}` — hold the current target for
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`duration` minutes (omit/`0` for an immediate step).
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A step with both ramps to `temperature` and then holds there for `duration`
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- useful for a mash rest ("ramp to 63°C, then hold 40 min") without needing
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two separate schedule entries. `user_wait_for_continue`/`user_message`
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(below) apply once the whole step is done, i.e. after the hold phase if
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there is one.
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Both `ramp` and `hold` carry a `stirrer` block (`speed`, `interval_time`,
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`on_ratio`): `interval_time: 0` runs the stirrer continuously at `speed`;
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`interval_time > 0` pulses it on a period of `interval_time` seconds, on for
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`on_ratio * interval_time` of it. A step may also set
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`user_wait_for_continue: true` (with an optional `user_message` to prompt
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the user with) to pause for confirmation once the step completes, e.g. to
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add malt or check gravity, instead of advancing immediately.
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Each step also has its own `grain_mass`/`water_mass` (defaulted like
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everything else from `default.step`), since both change over the course of
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a brew — e.g. malt going in partway through, or water boiling off. Pass a
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step's `grain_mass`/`water_mass` to `Sud.derive_plant_params()` (together
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with the brew-wide `pot_mass`/`pot_material`) to get that step's lumped
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`Pot` `M`/`C`; `scripts/demos/sud/demo_sud.py` recomputes and re-applies
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these (via `Pot.set_thermal_params()`/`TempController.set_model_params()`,
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on both the real plant and the controller's Smith-predictor model) every
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time the current step changes, instead of deriving them once at startup.
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The top-level `default.step` object gives every field above (including the
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nested `ramp`/`hold`/`stirrer` blocks) a default value; a step in `steps`
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only needs to specify the fields it overrides — anything it omits is filled
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in from `default.step`, recursively. A step is a ramp or a hold depending on
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which of those two keys it specifies; the other is not defaulted in.
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Set the top-level `sud` config key to a path under `sude/` (see
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`config.json.sim`/`config.json.templ`) to have the server step through it
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automatically: `components/sud.py`'s `Sud` resolves each raw step against
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`default.step` and tracks the current (resolved) step, while
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`tasks/sud.py`'s `SudTask` drives the temperature controller's
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`theta_soll`/`heatrate_soll` from ramp steps (advancing once `theta_ist`
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settles close to the target), counts down hold steps' `duration`, and
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applies each step's `stirrer` block (`interval_time`/`on_ratio` map directly
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onto the stirrer's cycle time/duty cycle). It exposes progress (current
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step, remaining hold time, state, any `user_message`) on the `"Sud"`
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WebSocket channel and accepts `{"Sud": {"Start": true}}` to begin the
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schedule and `{"Sud": {"Confirm": true}}` to acknowledge a pause and move to
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the next step. Omit `sud` from the config to run without schedule
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automation (manual `theta_soll`/`heatrate_soll` control via the GUI, as
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before).
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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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