c5bb375dd0
src/curves/ loader + normalizer (with cross-pressure anomaly
detection) + reverseCurve helper
src/prediction/ predictors (predictFlow/Power/Ctrl) +
groupPredictors (lazy group-scope views) +
OperatingPoint (pressure-driven prediction setpoints)
src/drift/ DriftAssessor (per-metric drift) + PredictionHealth
(composes flow/power/pressure into HealthStatus +
confidence sibling — see OPEN_QUESTIONS 2026-05-10)
src/pressure/ VirtualPressureChildren (dashboard-sim) +
PressureInitialization (real-vs-virtual tracking) +
PressureRouter (dispatches by position)
src/state/ stateBindings (state.emitter listener helper) +
isOperationalState
src/measurement/ measurementHandlers (dispatcher for flow/power/temp/pressure)
src/flow/ flowController (handleInput body — execSequence,
execMovement, flowMovement, emergencystop)
src/display/ workingCurves (showWorkingCurves + showCoG admin)
src/commands/ canonical names: set.mode, cmd.startup/shutdown/estop,
set.setpoint, set.flow-setpoint,
data.simulate-measurement, query.curves, query.cog,
child.register. execSequence demuxes by payload.action
to canonical cmd.* handlers.
CONTRACT.md inputs/outputs/events/children surface
110 basic tests pass (100 new + 10 pre-existing).
specificClass.js / nodeClass.js untouched — integration in P5 wave 2.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
62 lines
2.0 KiB
JavaScript
62 lines
2.0 KiB
JavaScript
/**
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* Read-only snapshots of the active machine curves and the centre-of-gravity
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* statistics. These back the rotatingMachine admin endpoints used by the
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* editor (`/rotatingMachine/working-curves`, `/rotatingMachine/cog`).
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*
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* Both functions accept a single `predictors` argument — an object describing
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* the current curve state. By taking everything via that one parameter the
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* helpers stay pure and trivially testable with a plain fixture; the host
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* just passes itself (or a slim adapter) in.
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*
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* Expected shape of `predictors`:
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* {
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* hasCurve: boolean,
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* predictFlow, predictPower, // generalFunctions/predict instances
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* getCurrentCurves(): { powerCurve, flowCurve },
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* calcCog(): { cog, cogIndex, NCog, minEfficiency },
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* cog, cogIndex, NCog,
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* minEfficiency,
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* currentEfficiencyCurve,
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* absDistFromPeak, relDistFromPeak,
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* }
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*/
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const NO_CURVE_ERROR = 'No curve data available';
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function showCoG(predictors) {
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if (!predictors || !predictors.hasCurve) {
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return { error: NO_CURVE_ERROR, cog: 0, NCog: 0, cogIndex: 0 };
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}
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const { cog, cogIndex, NCog, minEfficiency } = predictors.calcCog();
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return {
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cog,
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cogIndex,
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NCog,
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NCogPercent: Math.round(NCog * 100 * 100) / 100,
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minEfficiency,
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currentEfficiencyCurve: predictors.currentEfficiencyCurve,
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absDistFromPeak: predictors.absDistFromPeak,
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relDistFromPeak: predictors.relDistFromPeak,
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};
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}
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function showWorkingCurves(predictors) {
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if (!predictors || !predictors.hasCurve) {
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return { error: NO_CURVE_ERROR };
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}
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const { powerCurve, flowCurve } = predictors.getCurrentCurves();
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return {
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powerCurve,
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flowCurve,
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cog: predictors.cog,
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cogIndex: predictors.cogIndex,
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NCog: predictors.NCog,
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minEfficiency: predictors.minEfficiency,
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currentEfficiencyCurve: predictors.currentEfficiencyCurve,
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absDistFromPeak: predictors.absDistFromPeak,
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relDistFromPeak: predictors.relDistFromPeak,
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};
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}
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module.exports = { showWorkingCurves, showCoG };
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