Resolved conflicts: merged all 6 module imports in eoc-chain-ui.js and index.js.
All moduleFactory cases now return real Faust WASM implementations — no stubs remain.
- Add totalOutputCount() helper: engine params + EOC params when nispsMode === 'shared'
- setActiveEngine() calls resizeMLP(totalOutputCount()) and rebuilds heatmap with combined paramMeta
- eoc:change listener handles nispsMode-changed and module-* events: resizes MLP, rebuilds heatmap, toggles .shared-mode CSS class
- routeOutputs() slices outputs: indices < engineParamCount go to engine, remainder to eocChain.setParam() in Shared mode
- CSS: .heatmap-strip.shared-mode adds green bottom border and '+ FX' label to signal combined output space
Faust DSP sources, compiled WASM + JSON descriptors, AudioWorklet processors,
and EOCModule subclasses for all three effects.
- meml-4b4: 4-band parametric EQ (fi.low_shelf, fi.peak_eq × 2, fi.high_shelf)
10 params: freq/gain per band, Q for the two mid bell bands
- meml-cpe: stereo feed-forward compressor (co.compressor_mono × 2)
7 params: threshold, ratio, attack, release, knee, makeup, mix (parallel)
- meml-wwc: zita reverb (re.zita_rev1_stereo) with pre-delay and M/S width
8 active params + mod_rate placeholder (zita internal mod not yet exposed)
All three DSPs compile cleanly under Faust 2.83.1. moduleFactory in
eoc-chain-ui.js now returns real instances for eq/compressor/reverb.
- Add AdditiveEngine (48 params) and FMEngine (55 params) wrapping FaustEngineBase
- Remove comingSoon flags from ENGINES descriptors
- Replace stub onSwitch handler with real engine construction + init + setActiveEngine
- Fix setActiveEngine EOC rewire to work with both C15 (_bridge.audioContext) and Faust (_audioCtx)
- Fix _startEocChain audioCtx resolution to support Faust engines
- Fix synth param send loop: use N_OUTPUTS instead of hardcoded SYNTH_PARAM_MAP.length
- Add rebuildHeatmap(paramMeta): resets rawParamValues/_lastSentParams and rebuilds
#heatmap-cells from engine paramMeta; colors from SYNTH_PARAM_COLORS for C15,
_colorFromGroup() hash for other engines
- Replace static SYNTH_PARAM_NAMES/COLORS lookups in buildHeatmap, setHeatmapValue,
and showParamPopup with activeEngine.paramMeta[index] lookups
- Scope MIDI CC localStorage key per engine via midiCCStorageKey()
- Add reloadMidiCCMap() helper; called in setActiveEngine() after engine switch
- loadCCMap/saveCCMap in midi-cc-map.js accept optional key parameter
- Wire EOCChain into audio graph after c15.start() (both start-btn and
quick-play paths); guarded by _eocInited flag so init happens once per
AudioContext lifetime
- getOutputNode() on C15Adapter now returns limiter (last node before
destination) so EOC inserts correctly between limiter and destination
- Expose limiterNode getter on C15Bridge
- Add getCurrentParamValue(i) to EOCModule base class, backed by _paramValues
array that setParam() writes to
- Add _buildParamSliders() to EOCChainUI: collapsible ▶ params toggle per
module row, one range slider per paramMeta entry, calls module.setParam()
on input; labelled "manual" when nispsMode=bypass
- eoc:change listener in a-app.js logs paramCount (hook point for future modes)
- saveState()/loadState() persist eocModules (id, enabled, params) and
eocNispsMode; restore re-adds modules via moduleFactory and re-applies values
- CSS: eoc-params-section, eoc-params-toggle, eoc-param-row/label/slider;
eoc-module-row gains flex-wrap to accommodate params section below controls
Adds EngineSwitcher module with clickable engine cards (active highlight,
coming-soon greyed state, loading bar animation, confirm-on-switch when
training data exists). Wired into the Engine drawer; C15 active,
Additive/FM marked coming-soon with toast. Dock title reflects active
engine; engineId persisted to/restored from localStorage.
- Add WASM ML Engine section documenting the dual-instance architecture
(main thread for inference, worker for training), all C bindings, and
the Dataset/computeWeights JS layer
- Add Debug Probe section documenting window.__nisps API
- Add Testing section with Playwright e2e test infrastructure
- Update output modes (now 4: visual, synth, MIDI CC, audio canvas)
- Document known issue: double loss scaling (meml-ues)
- Note batch inference support in input-heatmap.js
- input-pipeline.js: guard circular clamp against div-by-zero when
input is exactly at center (0.5, 0.5) — dist=0 produced NaN
- snapshot-stack.js: jumpTo() used slice(0, index) which excluded the
target snapshot; fixed to slice(0, index + 1)
- a-app.js: _lastSentParams not resized when MLP output count changes
during mode switch, causing stale throttle state and potential param
flood on first frame after switch
Adds the Faust DSP toolchain infrastructure: placeholder additive and FM DSP
files, build.sh (faust -lang wasm per .dsp), faustJsonToParamMeta() to convert
Faust JSON UI trees into the standard paramMeta format, FaustEngineBase
(SynthEngine subclass wiring init/setParam/noteOn/noteOff through AudioWorklet
messages), and FaustWorkletProcessor base class for concrete engine processors.
Add WasmIML.extractWeights() and WasmIML.createWithWarmStart() to preserve
learned joystick mappings across output-count changes; resizeMLP() now
transfers hidden-layer weights and shared output nodes instead of cold-starting.
InputHeatmap.update() now accepts options.inferBatchFn to evaluate
all grid points (plus the divergence center point) in a single WASM
call instead of 256 separate round-trips. The per-point inferFn path
is preserved as a fallback when inferBatchFn is not provided.
Add evalLoss, inferBatch, and getLayerStats to the window.__nisps
debug probe so Playwright tests and dev console can access the new
WasmIML capabilities. InputHeatmap (Phase 3) is not yet wired into
a-app.js, so batch inference heatmap integration is deferred.
Expose inferBatch, trainEx, moveWeightsEx, evalLoss, and getLayerStats
from the WASM binary into the JavaScript layer:
- inferBatch: batch inference for heatmap sampling
- trainEx: replaces train() with per-iteration loss history capture
- moveWeightsEx: native output pin mask support (removes save/restore hack)
- evalLoss: compute loss without updating weights
- getLayerStats: per-layer weight statistics (meanAbs, maxAbs, dead/sat fracs)
Worker also upgraded to trainEx, returning full lossHistory in payload.
Five new C functions for the WASM module:
- nisps_mlp_infer_batch: N-point batch inference in a single call
- nisps_mlp_train_ex: training with per-iteration loss history output
- nisps_mlp_move_weights_ex: moveWeights with output pin mask to skip pinned nodes
- nisps_mlp_eval_loss: compute MSE loss without updating weights
- nisps_mlp_get_layer_stats: per-layer weight magnitude, dead, and saturation stats
- Add window.__nisps debug probe (gated on ?debug=1) exposing iml state,
getOutputs/getLoss/getWeights/getExampleCount, and action triggers
(thumbsUp/thumbsDown/train/randomise/clearExamples/saveState)
- Fix WasmIML bug: this.dataset was a plain object; import Dataset and
use new Dataset(100) so computeWeights() is available for training
- Fix WasmIML.addExample/clearDataset to use Dataset API methods
- 44 Playwright e2e tests across 4 spec files:
- ml-engine.spec.js: WASM inference bounds, training loss, thumbs
up/down behavior, async training, example capture semantics
- ui-interactions.spec.js: drawer open/close, mode switching,
heatmap bar counts, preset chips, keyboard shortcuts (1/2/Z)
- input-pipeline.spec.js: input→output variation, clamping, joystick
drag, post-training output bounds across the full input space
- persistence.spec.js: URL params (?preset, ?spread), localStorage
round-trip, saveState probe
Add MIDI CC preset system for bundled device configurations:
- midi-cc-presets.js: listPresets/loadPreset/loadPresetFromFile API
- presets/polybrute.json: Arturia PolyBrute CC map
- Preset selector in quick controls bar and MIDI CC drawer
- File import button for loading JSON presets from disk
Bug fixes in a-app.js:
- Destroy IML instances before resizeMLP() to free WASM memory
- Fix randomiseWeights() call (was drawWeights())
- Fix thumbs-up to store rawParamValues instead of post-pipeline outputs
Add Dataset.computeWeights() with three modes:
- global: exponential recency decay (newest examples weighted higher)
- local: spatial suppression of older examples near the current input
- combined: both applied together
IML and WasmIML now compute weights on every train() call using the
active mode. Exposes recencyBias, weightingMode, localRadius properties.
WASM worker path passes sampleWeights through to C++ via the new binding.
Add optional sample_weights parameter to MLP::Train() and the WASM
nisps_mlp_train binding. When provided, weights replace the uniform
1/N scaling per sample — enabling recency, spatial, or any custom
importance weighting without changing the training interface.
Fixes from Opus 4.6 review (C1-C6, I1, I3, I4, I8):
C1: static lastOutputs → per-instance lastOutputsForDelta member
C2: add_example() now on audio thread only (owns iml); worker reads
from mutex-protected staging area (stagedFeatures/stagedLabels)
C3: Worker reads stagedWeightsForWorker (not iml.get_weights()),
eliminating concurrent read/write on iml's MLP
C4: Worker spins on weightsPending before writing pendingWeights,
preventing double-write race
C5: RAND and CLEAR now enqueue Randomize/Clear jobs through worker
instead of directly mutating iml on the audio thread
C6: OSC callbacks stage JSON into oscStagedJson + atomic flag;
audio thread applies in process() (no recv-thread mutation)
Also fixed:
- I4: Separate pendingJob field (enqueueJob no longer overwrites currentJob)
- I8: Removed redundant swapReady atomic
- noiseLevel, cachedNovelty, cachedConfidence now std::atomic<float>
- Worker syncs examples back to iml after training via load_examples()
Replace direct-mutation threading with shadow IML:
- Background thread clones weights from main → shadow IML
- Training and perturbation operate only on shadow instance
- New weights staged in pendingWeights, swapped atomically by audio thread
- Thumbs-down now enqueues Perturb job instead of calling move_weights directly
- Audio thread applies new weights via iml.set_weights() at safe point
- Examples copied to shadow for training, results copied back as weights only
Threading invariant now fully enforced: background thread never writes to
the inference IML. Audio thread applies staged weights between inference calls.
Standalone test exercising IML inference pipeline without VCV runtime:
- Basic inference: all 12 outputs valid in [0,1]
- Input responsiveness: 12/12 outputs change between corners
- Randomize: weights change produces different output mapping
- Spread parameter: measurably different distributions at 0 vs 1
- Expressiveness: 6/6 corner pairs distinct, full range utilized
- Smoothness: 100% of sweep steps are smooth (no binary jumps)
Finding: spread sigmoid saturation effect is architecture-dependent
with small [16,24,16] network (filed as meml-l5a for investigation).
