# BrewPi

A Python-based controller for automating the mash/brewing process of beer: it
holds a pot of liquid at target temperatures (or ramps it at a target heating
rate) according to a configurable mash schedule, drives a heater and stirrer,
and exposes live control/telemetry over a WebSocket so a desktop GUI (or any
other client) can monitor and steer the brew.

The project began as a simulation/control-theory playground (Smith-predictor
temperature control, pot transport-delay model, see
[`docs/NonLinMPC.pdf`](docs/NonLinMPC.pdf)) and has grown real-hardware
backends for an induction hob and an RTD temperature probe.

## Architecture

```
server/brewpi.py            Server entry point: wires sensor, pot/plant,
                             heater, temperature controller and stirrer
                             together, runs them as asyncio tasks, and
                             serves state/commands over a WebSocket.

client/brewpi_gui.py        PyQt5 desktop client (brewpi.ui) that connects
                             to the server's WebSocket, displays live
                             temperature/power/state, and lets the user set
                             target temperature, heat rate, stirrer speed,
                             ambient temperature, and switch the heater/
                             stirrer/temperature controller on or off (plus
                             a "reset Pot to ambient" button, shown only
                             when the server's plant is simulated) - plus
                             Sud control (New/Load/Save/Start/Pause/Stop),
                             its forecast plot on the Automatic tab, and a
                             per-step Progress tab (see "Progress tab"
                             below).
client/user_config.py       Small JSON-backed key/value store
                             (~/.config/brewpi/gui.json) for GUI preferences
                             that should survive restarts (last Sud file
                             dialog directory, last ambient temperature) -
                             distinct from the server's config.json and
                             from sude/*.json schedules.

components/                 Pluggable building blocks behind factories:
  pid/                         temperature controllers: plain PID
                                ("Normal") or PID + Smith predictor
                                ("Smith", runs two internal pot models —
                                one with the plant's transport delay, one
                                without — to compensate for the dead time).
                                Both are a 4-state FSM (IDLE/HEAT/HOLD/COOL)
                                with a master enabled switch - IDLE means
                                disabled (output forced to 0); COOL handles
                                a target below the current temperature with
                                its own gains, output forced to 0 only by
                                the actuator that can't act on it (e.g. a
                                heat-only heater), not by the controller
                                assuming it can't cool
  plant/                       pot thermal model (transport-delay line)
                                used in simulation
  sensor/                      temperature sensors: simulated, or a real
                                MAX31865 RTD amplifier over SPI
  actor/                       heater and stirrer drivers: simulated, a
                                Hendi induction hob (serial protocol), or a
                                Pololu 1376 stirrer motor controller
  sud.py                       mash-schedule sequencer; starts out empty,
                                a client loads one of several sude/*.json
                                schedules onto it and it drives the
                                temperature controller from it
  sud_forecast.py              predicts how long a loaded schedule will
                                actually take by simulating it with the
                                same kind of plant/controller (and params)
                                as the real server - see "Forecast vs.
                                actual duration" below

tasks/                       Async tasks (one per component) that poll
                             hardware/sim state at a fixed interval and
                             publish changes through the message dispatcher.
                             sud_log.py's SudLogTask additionally records
                             each Sud run's measured data and forecast to
                             logs/*.json - see "Logging & analysis" below.

ws/                          Minimal WebSocket pub/sub layer: server
                             (single- and multi-user), client, and a
                             keyed message dispatcher (e.g. "Sensor",
                             "Heater", "TempCtrl", "Stirrer", "Pot", "Sud").

scripts/demos/               Standalone, eyeballed-plot demos (matplotlib)
                             exercising components/* in isolation — pid/,
                             plant/, and a full mash-schedule run in sud/.
                             Not used at runtime; run directly, e.g.
                             `python -m scripts.demos.sud.demo_sud`.

sude/                        Mash schedules ("Sud" = brew/wort), each a
                             JSON list of "ramp"/"hold" steps with target
                             temperature/heat rate or hold duration,
                             per-step stirrer timing, and optional pause
                             for user confirmation.

config.json.templ            Configuration template - the "sim" component
                             names (stirrer/plant) already default to
                             simulation; point Stirrer/Heater at real
                             serial ports and plant_name at a real
                             backend to switch to hardware.
```

### Data flow

The server (`server/brewpi.py`) loads `config.json`, builds the configured
stirrer/controller via factories (`*Factory.create(name, ...)`), and the
heater/pot/sensor trio together as one consistent rig via `PlantFactory`
(`components/plant/plant_factory.py`) - based on the `Controller` section
(`stirrer_name`, `pid_type`, `plant_name`, or `"sim"` for any of them) -
and connects their outputs to each other's inputs via `set_on_changed`
callbacks, e.g.:

- sensor temperature → temperature controller's `theta_ist`
- temperature controller output `y` → heater power
- heater effective power → pot model power (simulation only - PotReal, the
  real, unmodeled kettle, has no model to feed)

Each component runs inside its own `ATask` at a configurable interval
(`--dt`, scaled by `--sim-warp-factor`) and pushes state changes onto a
keyed WebSocket channel that any connected client can subscribe to and send
commands back on (e.g. `{"TempCtrl": {"Soll": {"Temp": 65}}}`).

### State replay on connect

A fundamental assumption baked into this protocol: a client never knows what
it's connecting to. The GUI might be the first thing to ever talk to a
freshly started server, or it might connect to one that's been running
unattended for hours, mid-brew, with a schedule already loaded and paused
partway through step 4. A client can also disconnect (network hiccup, the
user closing and reopening the GUI) and reconnect later, at any server state,
with no special "resume" handshake. In every one of these cases, the client
must end up with the *same* full picture of current state as one that's been
connected the whole time - not just whatever changes happen after it joins.

This is what `WsServerMultiUser.global_state` (`ws/server/ws_server_multi_user.py`)
exists for: every message ever broadcast over the dispatcher is merged into
it (`ws/user.py`'s `update()`), keyed exactly like the live channels are.
Subscribing to a channel (`{"+": "Sud"}`, sent automatically for every
channel on connect - see `MessageDispatcherSync(auto_subscribe=True)`)
doesn't just start a future stream of changes; the server immediately
replays the *entire* currently-known state for that channel in one message,
via `User.send()`'s `dpath` search over `global_state`. A reconnecting
client gets exactly the same replay a brand-new one would, because the
server doesn't distinguish between the two cases at all - there's no
"first connect" special-casing anywhere in this path.

The corollary, easy to miss: this mechanism has no concept of "transient" -
anything sent through it is implicitly durable state that will be replayed
to whoever connects next, even long after the fact. A one-shot *event*
(something that happened, rather than something that's currently true) has
to be modeled as a value that gets explicitly cleared back to neutral right
after sending, or it will linger in `global_state` and get handed to some
unrelated future client as if it just happened to them. `tasks/sud.py`'s
`SudTask.recv()` hit this directly: a `Load` rejected because a run is in
progress sends `{"Sud": {"Error": "..."}}` to explain why, immediately
followed by `{"Sud": {"Error": None}}` to clear it - without that second
send, every client connecting afterwards, no matter how much later or how
unrelated to the rejected `Load`, would immediately see that same stale
error on connect. The client mirrors this by treating a falsy `Error` as a
no-op rather than an empty dialog.

## Requirements

- Python 3.8+
- Server (`server/requirements.txt`): `numpy`, `scipy`, `matplotlib` (only
  for `scripts/demos/*`, not the running server itself), `websockets`,
  `dpath`, and `pyserial`/`spidev` if using real hardware backends.
- Client (`client/requirements.txt`): `PyQt5`.

Install with:

```bash
pip install -r server/requirements.txt
pip install -r client/requirements.txt
```

## Running

1. Copy `config.json.templ` to `config.json` next to `brewpi.py` - it
   already runs entirely in simulation (no hardware needed) as-is; switch
   `stirrer_name`/`plant_name`'s `"sim"` value(s) to a real backend, and
   set the corresponding serial port, to use real hardware. `plant_name`
   alone picks the heater/pot/sensor trio together (see `PlantFactory`) -
   `"sim"` gets `HeaterSim`/`Pot`/`TempSensorSim`, anything else gets
   `HeaterHendi`/`PotReal`/`TempSensor_max31865`.
2. Start the server:

   ```bash
   cd server
   ./brewpi.py
   ```

   This serves the WebSocket on `ws://0.0.0.0:8765`, ticking in real time
   (1 simulated second per tick, no speedup) by default. `--dt` (simulated
   seconds/tick) and `--sim-warp-factor` (how many of those fit into one
   real second) are run-mode knobs, not `config.json` material - a
   plant/hardware description shouldn't change just because of how a
   particular run happens to be invoked - so they're CLI-only:

   ```bash
   ./brewpi.py --sim-warp-factor 50   # 50x speedup, e.g. for dev/testing
   ```
3. Start the GUI client and connect to the server's URI:

   ```bash
   cd client
   ./brewpi_gui.py
   ```

`server/brewpi.sh` shows how this is wired up to run under a `virtualenvwrapper`
environment (`$WORKON_HOME`/`$BREWPI_HOME`) on a Raspberry Pi-style deployment.

## Mash schedules

Files under `sude/` describe a brew's mash schedule ("Sud"): `pot_mass`,
`pot_material`, `L` (pot energy loss coefficient), `Td` (transport
propagation delay) - all fixed for the whole brew - and a `steps` list.
Every step ramps toward its `temperature`, then optionally holds:

- ramping toward `temperature` at `"ramp": {"rate": ...}` (°C/min) is not
  opt-in - it happens for every step, for as long as the temperature
  controller's own gap-tracking FSM (`components/pid/temp_controller_base.py`)
  says the gap actually warrants it; a step omitting `temperature` simply
  has no new target of its own and keeps whatever the previous step left
  running, so the ramp resolves immediately. `ramp.rate` itself still comes
  from `default.step.ramp` unless a step overrides it.
- a hold — `"hold": {"duration": ...}` — hold the current target for
  `duration` minutes (omit/`0` for an immediate step) - this part stays
  opt-in: only a step that specifies `hold` gets a hold-duration phase
  after the ramp.

A step with both ramps to `temperature` and then holds there for `duration`
- useful for a mash rest ("ramp to 63°C, then hold 40 min") without needing
two separate schedule entries. `user_wait_for_continue`/`user_message`
(below) apply once the whole step is done, i.e. after the hold phase if
there is one.

Both `ramp` and `hold` carry a `stirrer` block (`speed`, `interval_time`,
`on_ratio`): `interval_time: 0` runs the stirrer continuously at `speed`;
`interval_time > 0` pulses it on a period of `interval_time` seconds, on for
`on_ratio * interval_time` of it. A step may also set
`user_wait_for_continue: true` (with an optional `user_message` to prompt
the user with) to pause for confirmation once the step completes, e.g. to
add malt or check gravity, instead of advancing immediately.

Each step also has its own `grain_mass`/`water_mass` (defaulted like
everything else from `default.step`), since both change over the course of
a brew — e.g. malt going in partway through, or water boiling off.
`Sud.derive_plant_params()` folds a step's `grain_mass`/`water_mass`
together with the brew-wide `pot_mass`/`pot_material`/`L`/`Td` into a full
`Pot` plant-params dict (`M`/`C`/`L`/`Td`) - `M`/`C` vary with the step,
`L`/`Td` come straight through unchanged. `tasks/sud.py`'s `SudTask.
apply_plant_params()` pushes the result onto both the real plant
(`Pot.set_plant_params()`) and the controller's Smith-predictor model
(`TempController.set_model_plant_params()`, where it applies) - once
immediately when a Sud is loaded (using its first step), and again on
every real step transition during a run, rather than deriving it once at
startup (`scripts/demos/sud/demo_sud.py` mirrors the same pattern
standalone).

Neither the real plant nor a model-based controller (Smith) ever gets
generic/placeholder params - they're only ever set from an actually
loaded Sud's own doc. Until one is loaded, both stay inert
(`Pot.is_configured()`/`TempControllerBase.is_configured()` are `False`,
so `tasks/pot.py`'s `PotTask` and `tasks/tempctrl.py`'s `TcTask` simply
skip `process()` rather than running on bogus values) - the temperature
controller's "Normal" variant has no plant-model dependency at all,
though, so it stays active regardless of whether a Sud is loaded.

The top-level `default.step` object gives every field above (including the
nested `ramp`/`hold`/`stirrer` blocks) a default value; a step in `steps`
only needs to specify the fields it overrides — anything it omits is filled
in from `default.step`, recursively. `ramp` is always defaulted in this way
(every step ramps); `hold` stays opt-in - only present if the step specifies
it. `temperature` is the one exception: it's never defaulted from
`default.step` (that's just inert template filler in every `sude/*.json`) -
a step either specifies its own, or has none and doesn't change the target.

The server always runs a `Sud` (`components/sud.py`), starting out empty (no
schedule) - `sude/` can hold several schedule files, and a client loads one
of them onto the running `Sud` (`{"Sud": {"Load": <sud.json contents>}}`),
kept purely in memory until replaced by another Load or the server
restarts; nothing is ever read from or written to `sude/*.json` by the
server itself, that's on the client (e.g. the GUI's File menu). `Sud`
resolves each raw step against `default.step` and tracks the current
(resolved) step, while `tasks/sud.py`'s `SudTask` drives the temperature
controller's `theta_soll`/`heatrate_soll` toward each step's target
(advancing once the controller's own FSM reports the gap closed -
`TempControllerBase.is_holding()` - rather than a separate ad hoc
tolerance), counts down hold steps' `duration`, and applies each step's
`stirrer` block (`interval_time`/
`on_ratio` map directly onto the stirrer's cycle time/duty cycle). It
exposes progress (current step, remaining hold time, elapsed run time -
tick-counted, not derived from wall-clock time times the warp factor, since
the latter is only nominal and drifts under real scheduling load - state,
any `user_message`) on the `"Sud"` WebSocket channel and accepts
`{"Sud": {"Start": true}}` to begin the schedule (also restarts one that's
already finished), `{"Sud": {"Pause": true}}` to freeze progress without
losing it, `{"Sud": {"Confirm": true}}` to acknowledge a pause and move to
the next step, and `{"Sud": {"Load": <sud.json contents>}}` to replace the
running schedule - refused (with an explicit, one-shot `Error` reply - see
"State replay on connect" above) while a run is already in progress, rather
than the client trying to guess that from its own view of the state.

The temperature controller has a master enabled switch (off by default -
see `components/pid/temp_controller_base.py`): `SudTask` enables it for as
long as a run is in progress (any state other than idle/finished) and
disables it again - forcing the heater output to 0 - the moment it stops or
finishes, handing control back to manual mode. The GUI's Manual tab has an
"Enabled" checkbox for driving this directly outside of a Sud run; while
disabled, its temperature/heat-rate setpoint controls are inactive.

### Forecast vs. actual duration

The GUI's Automatic tab plots two things against the same time axis: a
*forecast* (faded, via alpha) and, once a run starts, the *actual*
measured trace (solid, full opacity) overlaid on top of it - a comparison
between predicted and real control behavior, not just a progress display.

The forecast is computed server-side (`components/sud_forecast.py`'s
`SudForecastEstimator`) by actually running the schedule through a
throwaway `Sud`/`Pot`/temperature-controller trio built from the same
params (`pid_type`, `TempCtrl` gains, ambient, heater max power) the real
server uses - plant params (`M`/`C`/`L`/`Td`) aren't a constructor
argument at all, since they come from the doc being forecast itself
(`Sud.derive_plant_params()`, same as the real run), not from any generic
default. A multi-hour brew simulates in well under a second since it's
pure CPU-bound iteration, no real time/IO involved.

It's anchored to the real current temperature
(`TempControllerBase.get_theta_ist_set()` - the raw sensor reading, valid
even before this controller's first `process()` tick, unlike
`get_theta_ist()`), not a cold start at ambient - `SudTask.send_forecast()`
computes it once on Load (a preview of what's coming), and again on every
fresh `Start` (not a Pause→resume, which keeps whatever forecast a run
already established rather than discarding it mid-run), since the real
temperature can have drifted in the meantime (time passing, manual heating
via the Manual tab in between).

`SudTask._send_forecast()` thins the curve to at most `MAX_FORECAST_POINTS`
(1000) before broadcasting it - a fine enough `dt` over a multi-hour brew
can otherwise produce a single `Forecast` message of several MB, large
enough to exceed the `websockets` library's default 1 MiB `max_size` and
get the connection closed outright (code 1009). `SudTask.forecast_t`/
`forecast_theta` themselves stay at full simulated resolution (used for
the exact-match truncation in `_reanchor_forecast()` below, and for
`SudLogTask`'s full-fidelity `logs/forecast_*.json` - see "Logging &
analysis") - only the copy actually sent to clients is thinned, which the
GUI's few-hundred-pixel-wide plot can't show the difference from anyway.

The one wrinkle is a step with `user_wait_for_continue`: a human's response
time genuinely can't be forecast. Rather than stall the whole estimate
there, `SudForecastEstimator.estimate()` models it as a zero-delay
auto-confirm and keeps simulating straight through to the schedule's actual
end - so the full projected curve is visible right away on Load, instead of
stopping at the first such step.

That optimistic guess - and, more generally, *any* divergence the original
simulation couldn't have predicted (a malt fill-in's actual cooldown, a
ramp that runs faster or slower than modeled, ...) - gets corrected the
moment the schedule actually reaches the next real step boundary, not just
at a user confirmation: `tasks/sud.py`'s `SudTask._reanchor_forecast()`,
called from `on_step_changed()` on *every* transition (a full step change
and a step's own ramp→hold phase switch alike), truncates the forecast
back to right now and splices in a freshly anchored simulation of the
rest of the schedule - anchored at the real elapsed time and the real
current temperature (`TempControllerBase.get_theta_ist()` - trustworthy
here, unlike at Load, since a real run has been actively ticking for a
while by the time this runs). The corrected forecast is sent in full each
time, so the GUI's `SudForecastPlot.show_forecast()` simply redraws the
(faded) forecast line outright rather than patching it up itself.

Two transitions can fire in close succession (a step whose hold duration
is already `0` advances right through it within a single tick, and a
fresh `Start` triggers both the explicit Load-time forecast *and* the
first step's own reanchor at once) - each spawns its own
`_reanchor_forecast()`/`send_forecast()` call awaiting a worker-thread
simulation, so whichever resumes second could otherwise blindly splice
its own (older) tail onto whatever the other already finished writing.
A monotonically increasing `SudTask._forecast_generation` counter, bumped
at the start of each such call and checked again after its simulation
returns, guards against this: only the very latest call's result is ever
committed, and an older one resuming after a newer one already has discards
itself instead.

Per-step start times (`SudTask.forecast_step_starts`, sent as `StepStarts`
on the `Forecast` message - the schedule's absolute step index mapped to
where it begins on the same timeline as `forecast_t`) power the Progress
tab's remaining-time countdown (see "Progress tab" below) - both the real
time for an already-passed step and the predicted time for one still
ahead. These are recorded *synchronously* in `on_step_changed()` itself,
not only as part of `_reanchor_forecast()`'s own (correct, but async, and
thus subject to the same race above) recomputation - an async
recomputation that loses that race is simply discarded before ever
committing, and without the synchronous copy, a later reanchor's "anything
before my own index is already real, leave it alone" filter would then
preserve whatever stale prediction was sitting there from before instead.

A client connecting (or reconnecting) mid-brew must not perturb any of
this: `{"Sud": {"Save": true}}` - sent unconditionally by every client on
connect, to fetch the currently-loaded schedule for preview - used to
always trigger `send_forecast()`'s cold, from-step-0 simulation, which has
no notion of "already mid-run" and would silently overwrite the
accurate, continuously-maintained forecast/`forecast_step_starts` with a
context-free "starting fresh right now" guess. It's now only taken for a
genuinely not-yet-started schedule (`IDLE`/`DONE`); an already-running one
just gets what's already there re-sent. The GUI mirrors this on its own
side - `update_sud_forecast()` only resets the displayed step/forecast
state when the incoming `Json` is actually a *different* schedule
(`sud_last_loaded_doc` equality check), not a re-fetch of the same running
one, since that standalone `Json`+`Forecast` reply (unlike the subscribe-
triggered replay, which carries `Step`/`State` alongside it) has nothing
in the same message to restore the wiped state afterward.

Comparing the two lines: real-world divergence from the forecast - more
than a transient blip during a ramp's settling - is worth investigating as
an actual control issue (see the `HoldCool` threshold note in
`components/pid/temp_controller_base.py` - too tight a threshold there once
caused exactly this kind of large, otherwise-unexplained gap, by making the
controller chatter in and out of `COOL` and never actually converge). The
GUI plots the actual trace using `Sud.elapsed` (tick-counted simulated
seconds, see "State replay on connect" above) rather than wall-clock time
times the configured `--sim-warp-factor` - the latter is only nominal, and
real asyncio/Python scheduling overhead means the actually-achieved
speedup runs measurably below it (confirmed: ~80x instead of a configured
100x), which on its own used to produce a divergence large enough to look
like a control problem.

Two other sources of spurious (non-control-related) divergence, both
fixed:

- `client/brewpi_gui.py`'s actual-trace history used to only get its first
  sample at `on_plot_timer()`'s next 1-second (real time) tick - under a
  real `sim_warp_factor`, simulated time has already moved measurably past
  `t=0` by then, leaving a small visible gap between the forecast's exact
  `t=0` anchor and where the actual line first appears. `on_sud_changed()`
  now seeds `forecast_history` with the real `(elapsed, plot_temp_ist)`
  point the moment the first `Elapsed` push of a *fresh* run arrives
  (gated on elapsed still being small, `FRESH_RUN_ELAPSED_THRESHOLD_S`, so
  reconnecting mid-brew doesn't get a bogus point plotted at `x=0`).
- Both `TempController` variants used to hardcode `last_theta_ist = 20` at
  construction, used to compute the very first `process()` tick's
  `heatrate_ist` - a wildly bogus rate spike whenever the real starting
  temperature wasn't actually 20 (invisible until a forecast actually got
  anchored to something other than 20, per the fix above), which then took
  several exponentially-smoothed ticks to decay out of the rate-PID loop,
  visibly distorting the trajectory during exactly that window. It now
  starts `None` and treats the first tick's rate as `0` (no real history
  yet) instead of computing it against a hardcoded, usually-wrong
  baseline - `SudForecastEstimator` is the case that actually surfaced
  this: it builds a fresh `TempController` on every call, so its first
  tick always hit this fresh, unlike the real controller, which only ever
  goes through it once near server startup and has long since
  self-corrected by the time any forecast comparison matters.

### Progress tab

Where the Automatic tab's forecast plot shows the brew as one curve, the
GUI's Progress tab shows it as a stack of `StepPlate`s, one per schedule
step (built entirely in code, not via `brewpi.ui`/`main_window.py` - its
content is already fully dynamic, so there's nothing for Designer to
usefully own). Each plate has:

- an LED: off (not yet entered), solid green (finished), or flashing -
  orange while actively ramping, green while actively holding. `WAIT_USER`
  and `PAUSED` don't change the step's `Type` (`Sud._finish_step()`/
  `pause()` leave it as whatever phase was actually last real), so the LED
  keeps flashing whichever color that was rather than going stale.
- target temperature, resolved through any step that doesn't set its own
  (inherits whatever the previous one left running, same as the real
  controller).
- actual temperature and stirrer state: live while this is the active
  step, frozen at their last value the moment a later `Step` push reports
  the schedule has moved past it (`Window.on_sud_changed()`), blank before
  it's ever been reached.
- grain/water mass and ramp rate, straight from the schedule.
- energy consumption (Wh, see below) and a remaining-time countdown,
  derived from `forecast_step_starts`/`StepStarts` (see "Forecast vs.
  actual duration" above) - this step's total span (next step's start
  minus this one's, or the forecast's own final point if it's the last
  step and finished) minus how much of it has elapsed so far.

Energy has no forecast equivalent to preview before a run starts - what's
actually used can only be measured, not predicted. `SudTask` integrates it
server-side every tick from the heater's own live effective power
(`Pot.get_power()`, already fed from `heater.power_eff` - see "Data flow"
above), in Joules, for whichever step is current; `on_step_changed()`
banks the finished total (converted to Wh) into `energy_by_step` on every
genuine step transition - including the final one, to `DONE` - the same
index-change check that drives `forecast_step_starts`' bookkeeping, so a
ramp→hold phase switch doesn't reset it mid-step. Counted through
`WAIT_USER`/`PAUSED` too: the controller stays enabled and may well still
be actively (and genuinely) drawing power even though the schedule itself
isn't progressing. Both `energy_by_step` and the running step's total are
reset on every fresh Load.

The status bar additionally sums these into running totals for the whole
brew so far: total process time (just `Sud.elapsed` - every step's time,
`WAIT_USER` dwell included, already adds up sequentially with no gaps) and
total energy in kWh (no single running counter exists server-side for that
one, since energy is banked per step rather than accumulated as a grand
total - summed client-side instead, from every finished step's own Wh
total plus whatever the active one has used so far).

## Logging & analysis

`tasks/sud_log.py`'s `SudLogTask` records every Sud run's measured data -
the same six signals the GUI's Automatic tab plots live (`temp_ist`/
`temp_soll`, `rate_ist`/`rate_soll`, `power_set`/`power_eff`), each sample
with both simulated-elapsed-seconds and a real wall-clock timestamp - to
`logs/log_<date>T<time>_<sud-name>.json`, plus the forecast it was being
compared against (its *final*, most-corrected version - see "Forecast vs.
actual duration" above) to a paired `logs/forecast_<date>T<time>_
<sud-name>.json`, sharing the same date-time token. A fresh pair starts the
moment a run actually starts (`Sud.state` leaves `IDLE`/`DONE`) and is
written out whole the moment it ends (`DONE`, or aborted via `Stop`) -
never partially mid-run, since a half-written file is of no use before
the run is over anyway.

`server/brewpi.py` also mirrors everything it prints (every component's
`print()`-based status/debug output) to a plain text log,
`logs/brewpi.<timestamp>.log`, alongside those JSON logs - useful for
post-mortems without needing to have been watching the console live.
