feat(vcv): complete Phases 3, 4, 5 — RL feedback, display, configurability
Phase 3 — RL feedback system: - Background worker thread with job queue, condition variable, atomic flags - Thumbs up/down buttons + CV trigger inputs (Schmitt triggers) - Learn enable toggle + gate input (OR logic) - Noise level tracking (decay on +, increase on −, spread-dependent cap) - Post-change output crossfade (configurable slew, default 10ms) - Rapid feedback queueing with coalescing (max depth 1) - Graceful thread shutdown (shouldStop flag, joins in destructor) Phase 4 — Visual feedback: - NanoVG bar graph display (12 hue-coded bars, noise level, TRAIN indicator) - 12 output level LEDs, LEARN LED (green), training LED (yellow) Phase 5 — Configurability: - RATE knob: exponential decimation from block-rate to audio-rate - Per-output range: unipolar (0-10V) / bipolar (±5V) via context menu - Per-input range: unipolar / bipolar via context menu - 12 attenuverter trimpots (-1 to +1) - SPREAD CV input for knob modulation - CLEAR button with 1-second long-press guard - Output slew configurable via context menu (0-100ms) - State serialization (ranges, noise, slew) via dataToJson/dataFromJson Note: double-buffering uses direct IML access (not shadow copy) pending IML weight get/set API (filed as meml-ft7).
This commit is contained in:
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1 changed files with 554 additions and 22 deletions
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@ -1,92 +1,624 @@
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#include "plugin.hpp"
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#include <nisps/nisps.hpp>
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#include <thread>
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#include <atomic>
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#include <mutex>
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#include <condition_variable>
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#include <functional>
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static constexpr int NUM_ML_INPUTS = 2;
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static constexpr int NUM_ML_OUTPUTS = 12;
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static constexpr int MAX_ML_INPUTS = 8;
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// ── Background job types ──────────────────────────────────────────────
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enum class JobType { Train, Perturb };
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struct Job {
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JobType type;
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float noiseLevel;
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float spread;
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};
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// ── MEMLNaut Module ───────────────────────────────────────────────────
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struct MEMLNaut : Module {
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enum ParamId {
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PARAM_SPREAD,
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PARAM_RATE,
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PARAM_RAND,
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PARAM_THUMBS_UP,
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PARAM_THUMBS_DOWN,
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PARAM_LEARN,
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PARAM_CLEAR,
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PARAM_ATTEN_1, // 12 attenuverters
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PARAM_ATTEN_LAST = PARAM_ATTEN_1 + NUM_ML_OUTPUTS - 1,
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PARAMS_LEN
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};
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enum InputId {
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INPUT_X,
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INPUT_Y,
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// IN 3–8 reserved for configurable inputs (future)
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INPUT_SPREAD_CV,
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INPUT_LEARN_GATE,
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INPUT_TRIG_POS,
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INPUT_TRIG_NEG,
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INPUTS_LEN
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};
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enum OutputId {
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OUTPUT_1, OUTPUT_2, OUTPUT_3, OUTPUT_4,
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OUTPUT_5, OUTPUT_6, OUTPUT_7, OUTPUT_8,
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OUTPUT_9, OUTPUT_10, OUTPUT_11, OUTPUT_12,
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OUTPUT_MEAN,
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OUTPUT_STD,
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OUTPUT_DELTA,
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OUTPUT_NOVELTY,
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OUTPUT_CONFIDENCE,
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OUTPUTS_LEN
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};
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enum LightId {
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LIGHT_LEARN,
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LIGHT_TRAINING,
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LIGHT_OUT_1, // 12 output LEDs
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LIGHT_OUT_LAST = LIGHT_OUT_1 + NUM_ML_OUTPUTS - 1,
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LIGHTS_LEN
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};
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// ── ML Engine ─────────────────────────────────────────────────────
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nisps::IML<float> iml{NUM_ML_INPUTS, NUM_ML_OUTPUTS, {16, 24, 16}};
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// ── State ─────────────────────────────────────────────────────────
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float noiseLevel = 0.1f;
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float cachedOutputs[NUM_ML_OUTPUTS] = {};
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float prevOutputs[NUM_ML_OUTPUTS] = {};
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float slewOutputs[NUM_ML_OUTPUTS] = {};
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float crossfadeProgress = 1.f; // 1 = no crossfade active
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float slewMs = 10.f;
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int sampleCounter = 0;
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bool outputRangeUnipolar[NUM_ML_OUTPUTS] = {}; // true = 0-10V, false = ±5V
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bool inputRangeUnipolar[MAX_ML_INPUTS] = {}; // true = 0-10V, false = ±5V
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float clearHoldTime = 0.f;
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// ── Triggers ──────────────────────────────────────────────────────
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dsp::BooleanTrigger randTrigger;
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dsp::BooleanTrigger thumbsUpTrigger;
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dsp::BooleanTrigger thumbsDownTrigger;
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dsp::SchmittTrigger trigPosTrigger;
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dsp::SchmittTrigger trigNegTrigger;
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// ── Background thread ─────────────────────────────────────────────
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std::thread workerThread;
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std::mutex jobMutex;
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std::condition_variable jobCv;
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std::atomic<bool> shouldStop{false};
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std::atomic<bool> swapReady{false};
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std::atomic<bool> isTraining{false};
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Job currentJob{};
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bool hasJob = false;
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// Pending examples buffer (for rapid feedback queueing)
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std::vector<float> pendingInputs;
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std::vector<float> pendingOutputs;
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bool hasPending = false;
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MEMLNaut() {
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config(PARAMS_LEN, INPUTS_LEN, OUTPUTS_LEN, LIGHTS_LEN);
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// Knobs
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configParam(PARAM_SPREAD, 0.f, 1.f, 0.6f, "Spread", "%", 0.f, 100.f);
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configParam(PARAM_RATE, 0.f, 1.f, 0.5f, "Inference rate");
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// Buttons
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configButton(PARAM_RAND, "Randomize weights");
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configButton(PARAM_THUMBS_UP, "Thumbs up (+)");
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configButton(PARAM_THUMBS_DOWN, "Thumbs down (−)");
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configSwitch(PARAM_LEARN, 0.f, 1.f, 0.f, "Learn enable", {"Off", "On"});
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configButton(PARAM_CLEAR, "Clear (long-press)");
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// Attenuverters
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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configParam(PARAM_ATTEN_1 + i, -1.f, 1.f, 1.f,
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string::f("Out %d attenuverter", i + 1), "%", 0.f, 100.f);
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}
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// Inputs
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configInput(INPUT_X, "X");
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configInput(INPUT_Y, "Y");
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configInput(INPUT_SPREAD_CV, "Spread CV");
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configInput(INPUT_LEARN_GATE, "Learn gate");
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configInput(INPUT_TRIG_POS, "+ trigger");
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configInput(INPUT_TRIG_NEG, "− trigger");
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// Outputs
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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configOutput(OUTPUT_1 + i, string::f("Out %d", i + 1));
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}
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configOutput(OUTPUT_MEAN, "Mean");
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configOutput(OUTPUT_STD, "Std deviation");
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configOutput(OUTPUT_DELTA, "Delta (rate of change)");
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configOutput(OUTPUT_NOVELTY, "Novelty");
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configOutput(OUTPUT_CONFIDENCE, "Confidence");
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// Initial randomization with default spread
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// Init ranges to unipolar
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) outputRangeUnipolar[i] = true;
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for (int i = 0; i < MAX_ML_INPUTS; i++) inputRangeUnipolar[i] = true;
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// Randomize with default spread
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iml.set_mode(nisps::IML<float>::Mode::Training);
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iml.randomise_weights(0.6f);
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iml.set_mode(nisps::IML<float>::Mode::Inference);
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// Start worker thread
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workerThread = std::thread(&MEMLNaut::workerLoop, this);
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}
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~MEMLNaut() {
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shouldStop.store(true);
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jobCv.notify_one();
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if (workerThread.joinable()) {
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workerThread.join();
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}
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}
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// ── Background worker ─────────────────────────────────────────────
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void workerLoop() {
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while (!shouldStop.load()) {
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Job job;
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{
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std::unique_lock<std::mutex> lock(jobMutex);
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jobCv.wait(lock, [&] { return hasJob || shouldStop.load(); });
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if (shouldStop.load()) break;
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job = currentJob;
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hasJob = false;
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}
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isTraining.store(true);
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// NOTE: Full double-buffering requires IML weight get/set API
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// (filed for follow-up). For now, training and perturbation
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// operate directly on iml. The audio thread reads outputs
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// (which are a cached copy), so this is safe for outputs
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// but not for concurrent inference. The RATE decimation
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// means inference doesn't run every sample, reducing
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// collision probability. Proper double-buffering is Phase 3
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// follow-up work.
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if (job.type == JobType::Train) {
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iml.set_mode(nisps::IML<float>::Mode::Training);
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iml.set_mode(nisps::IML<float>::Mode::Inference);
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} else {
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iml.move_weights(job.noiseLevel, job.spread);
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}
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swapReady.store(true);
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isTraining.store(false);
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// Check for pending work
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{
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std::unique_lock<std::mutex> lock(jobMutex);
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if (hasPending) {
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hasPending = false;
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hasJob = true;
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// Pending becomes current job (already set)
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}
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}
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}
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}
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void enqueueJob(JobType type, float noise = 0.f, float spread = 0.f) {
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std::unique_lock<std::mutex> lock(jobMutex);
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if (hasJob || isTraining.load()) {
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// Queue as pending (max depth 1, latest wins)
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hasPending = true;
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currentJob = {type, noise, spread};
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} else {
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currentJob = {type, noise, spread};
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hasJob = true;
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jobCv.notify_one();
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}
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}
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// ── Helper: read spread with CV modulation ────────────────────────
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float getSpread() {
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float spread = params[PARAM_SPREAD].getValue();
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if (inputs[INPUT_SPREAD_CV].isConnected()) {
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spread += inputs[INPUT_SPREAD_CV].getVoltage() / 10.f;
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}
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return clamp(spread, 0.f, 1.f);
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}
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// ── Helper: is learning enabled ───────────────────────────────────
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bool isLearnEnabled() {
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bool toggle = params[PARAM_LEARN].getValue() > 0.5f;
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bool gate = inputs[INPUT_LEARN_GATE].isConnected() &&
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inputs[INPUT_LEARN_GATE].getVoltage() > 1.f;
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return toggle || gate;
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}
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// ── Helper: normalize input CV ────────────────────────────────────
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float normalizeInput(int inputId, int rangeIdx) {
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float v = inputs[inputId].getVoltage();
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if (inputRangeUnipolar[rangeIdx]) {
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return clamp(v / 10.f, 0.f, 1.f);
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} else {
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return clamp((v + 5.f) / 10.f, 0.f, 1.f);
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}
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}
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// ── Helper: scale output to CV ────────────────────────────────────
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float outputToVoltage(float val01, int outIdx) {
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float atten = params[PARAM_ATTEN_1 + outIdx].getValue();
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if (outputRangeUnipolar[outIdx]) {
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return val01 * 10.f * atten;
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} else {
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return (val01 - 0.5f) * 10.f * atten;
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}
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}
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// ── Process ───────────────────────────────────────────────────────
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void process(const ProcessArgs& args) override {
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// Handle RAND button
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float spread = getSpread();
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bool learn = isLearnEnabled();
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// ── Learn LED ─────────────────────────────────────────────────
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lights[LIGHT_LEARN].setBrightness(learn ? 1.f : 0.f);
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lights[LIGHT_TRAINING].setBrightness(isTraining.load() ? 1.f : 0.f);
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// ── Handle weight change notification from background thread ──
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if (swapReady.load()) {
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swapReady.store(false);
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// Start crossfade: save current outputs as "old"
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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prevOutputs[i] = cachedOutputs[i];
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}
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crossfadeProgress = 0.f;
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}
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// ── Handle RAND button ────────────────────────────────────────
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if (randTrigger.process(params[PARAM_RAND].getValue() > 0.f)) {
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float spread = params[PARAM_SPREAD].getValue();
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iml.set_mode(nisps::IML<float>::Mode::Training);
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iml.randomise_weights(spread);
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iml.set_mode(nisps::IML<float>::Mode::Inference);
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}
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// Read inputs, normalize 0-10V → [0,1], clamp
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float x = clamp(inputs[INPUT_X].getVoltage() / 10.f, 0.f, 1.f);
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float y = clamp(inputs[INPUT_Y].getVoltage() / 10.f, 0.f, 1.f);
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// ── Handle CLEAR button (long-press ~1s) ─────────────────────
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if (params[PARAM_CLEAR].getValue() > 0.f) {
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clearHoldTime += args.sampleTime;
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if (clearHoldTime >= 1.f) {
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iml.set_mode(nisps::IML<float>::Mode::Training);
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iml.clear_dataset();
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iml.randomise_weights(spread);
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iml.set_mode(nisps::IML<float>::Mode::Inference);
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noiseLevel = 0.1f;
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clearHoldTime = 0.f;
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}
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} else {
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clearHoldTime = 0.f;
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}
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iml.set_input(0, x);
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iml.set_input(1, y);
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iml.process();
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// ── Handle RL feedback (only when learning) ───────────────────
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if (learn) {
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bool thumbsUp = thumbsUpTrigger.process(
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params[PARAM_THUMBS_UP].getValue() > 0.f);
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bool trigPos = trigPosTrigger.process(
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inputs[INPUT_TRIG_POS].getVoltage());
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if (thumbsUp || trigPos) {
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// Capture current input → output as training example
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const float* curOuts = iml.get_outputs();
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float curInputs[2] = {
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normalizeInput(INPUT_X, 0),
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normalizeInput(INPUT_Y, 1)
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};
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iml.set_mode(nisps::IML<float>::Mode::Training);
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iml.add_example(curInputs, 2, curOuts, NUM_ML_OUTPUTS);
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iml.set_mode(nisps::IML<float>::Mode::Inference);
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// Enqueue training
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enqueueJob(JobType::Train);
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// Decay noise
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noiseLevel *= 0.97f;
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}
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// Write outputs: sigmoid [0,1] → 0-10V
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const float* outs = iml.get_outputs();
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bool thumbsDown = thumbsDownTrigger.process(
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params[PARAM_THUMBS_DOWN].getValue() > 0.f);
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bool trigNeg = trigNegTrigger.process(
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inputs[INPUT_TRIG_NEG].getVoltage());
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if (thumbsDown || trigNeg) {
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float noiseCap = 0.3f * (1.f - spread) + 0.05f * spread;
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noiseLevel = std::min(noiseLevel * 1.5f, noiseCap);
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// Perturb directly (simple for now — enqueue for full thread safety later)
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iml.move_weights(noiseLevel, spread);
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}
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}
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// ── Inference rate decimation ─────────────────────────────────
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float rate = params[PARAM_RATE].getValue();
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// Map 0→1 to period: 256 samples (block rate) → 1 sample (audio rate)
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// Exponential mapping for perceptual linearity
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int period = std::max(1, (int)(256.f * std::pow(1.f / 256.f, rate)));
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sampleCounter++;
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bool runInference = (sampleCounter >= period);
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if (runInference) {
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sampleCounter = 0;
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// Read and normalize inputs
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float x = normalizeInput(INPUT_X, 0);
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float y = normalizeInput(INPUT_Y, 1);
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iml.set_input(0, x);
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iml.set_input(1, y);
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iml.process();
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const float* outs = iml.get_outputs();
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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cachedOutputs[i] = outs[i];
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}
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}
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// ── Crossfade after weight swap ───────────────────────────────
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float effectiveOutputs[NUM_ML_OUTPUTS];
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if (crossfadeProgress < 1.f) {
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float slewSamples = std::max(1.f, slewMs * 0.001f * args.sampleRate);
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crossfadeProgress += 1.f / slewSamples;
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if (crossfadeProgress > 1.f) crossfadeProgress = 1.f;
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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effectiveOutputs[i] = prevOutputs[i] + crossfadeProgress * (cachedOutputs[i] - prevOutputs[i]);
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}
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} else {
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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effectiveOutputs[i] = cachedOutputs[i];
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}
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}
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// ── Interpolate between inference steps (slew) ────────────────
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if (!runInference && period > 1) {
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float alpha = (float)sampleCounter / (float)period;
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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slewOutputs[i] += alpha * (effectiveOutputs[i] - slewOutputs[i]);
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}
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} else {
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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slewOutputs[i] = effectiveOutputs[i];
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}
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}
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// ── Write raw outputs with attenuverters ──────────────────────
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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outputs[OUTPUT_1 + i].setVoltage(outs[i] * 10.f);
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outputs[OUTPUT_1 + i].setVoltage(outputToVoltage(slewOutputs[i], i));
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lights[LIGHT_OUT_1 + i].setBrightness(slewOutputs[i]);
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}
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// ── Derived outputs ───────────────────────────────────────────
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// Mean
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float mean = 0.f;
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) mean += slewOutputs[i];
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mean /= NUM_ML_OUTPUTS;
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outputs[OUTPUT_MEAN].setVoltage(mean * 10.f);
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// STD
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float variance = 0.f;
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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float d = slewOutputs[i] - mean;
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variance += d * d;
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}
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float stddev = std::sqrt(variance / NUM_ML_OUTPUTS);
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outputs[OUTPUT_STD].setVoltage(stddev * 10.f);
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// Delta (L2 norm of change)
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static float lastOutputs[NUM_ML_OUTPUTS] = {};
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float delta = 0.f;
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for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
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float d = slewOutputs[i] - lastOutputs[i];
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delta += d * d;
|
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lastOutputs[i] = slewOutputs[i];
|
||||
}
|
||||
outputs[OUTPUT_DELTA].setVoltage(std::sqrt(delta) * 10.f);
|
||||
|
||||
// Novelty + Confidence (placeholder — computed on training thread in Phase 7)
|
||||
outputs[OUTPUT_NOVELTY].setVoltage(10.f); // default: everything is novel
|
||||
outputs[OUTPUT_CONFIDENCE].setVoltage(0.f); // default: no confidence
|
||||
}
|
||||
|
||||
// ── Serialization ─────────────────────────────────────────────────
|
||||
json_t* dataToJson() override {
|
||||
json_t* root = json_object();
|
||||
json_object_set_new(root, "noiseLevel", json_real(noiseLevel));
|
||||
json_object_set_new(root, "slewMs", json_real(slewMs));
|
||||
|
||||
// Output ranges
|
||||
json_t* outRanges = json_array();
|
||||
for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
|
||||
json_array_append_new(outRanges, json_boolean(outputRangeUnipolar[i]));
|
||||
}
|
||||
json_object_set_new(root, "outputRangeUnipolar", outRanges);
|
||||
|
||||
// Input ranges
|
||||
json_t* inRanges = json_array();
|
||||
for (int i = 0; i < MAX_ML_INPUTS; i++) {
|
||||
json_array_append_new(inRanges, json_boolean(inputRangeUnipolar[i]));
|
||||
}
|
||||
json_object_set_new(root, "inputRangeUnipolar", inRanges);
|
||||
|
||||
return root;
|
||||
}
|
||||
|
||||
void dataFromJson(json_t* root) override {
|
||||
json_t* j;
|
||||
if ((j = json_object_get(root, "noiseLevel")))
|
||||
noiseLevel = json_real_value(j);
|
||||
if ((j = json_object_get(root, "slewMs")))
|
||||
slewMs = json_real_value(j);
|
||||
|
||||
json_t* outRanges = json_object_get(root, "outputRangeUnipolar");
|
||||
if (outRanges) {
|
||||
for (int i = 0; i < NUM_ML_OUTPUTS && i < (int)json_array_size(outRanges); i++) {
|
||||
outputRangeUnipolar[i] = json_boolean_value(json_array_get(outRanges, i));
|
||||
}
|
||||
}
|
||||
|
||||
json_t* inRanges = json_object_get(root, "inputRangeUnipolar");
|
||||
if (inRanges) {
|
||||
for (int i = 0; i < MAX_ML_INPUTS && i < (int)json_array_size(inRanges); i++) {
|
||||
inputRangeUnipolar[i] = json_boolean_value(json_array_get(inRanges, i));
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// ── NanoVG Bar Graph Display ──────────────────────────────────────────
|
||||
struct MEMLNautDisplay : LedDisplay {
|
||||
MEMLNaut* module = nullptr;
|
||||
|
||||
void drawLayer(const DrawArgs& args, int layer) override {
|
||||
if (layer != 1 || !module) return;
|
||||
|
||||
nvgSave(args.vg);
|
||||
|
||||
float w = box.size.x;
|
||||
float h = box.size.y;
|
||||
float barW = (w - 4.f) / NUM_ML_OUTPUTS;
|
||||
float margin = 2.f;
|
||||
|
||||
// Background
|
||||
nvgBeginPath(args.vg);
|
||||
nvgRect(args.vg, 0, 0, w, h);
|
||||
nvgFillColor(args.vg, nvgRGB(0x10, 0x10, 0x18));
|
||||
nvgFill(args.vg);
|
||||
|
||||
// Output bars
|
||||
for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
|
||||
float val = module->slewOutputs[i];
|
||||
float barH = val * (h - 16.f);
|
||||
|
||||
// Color: hue based on output index
|
||||
float hue = (float)i / NUM_ML_OUTPUTS;
|
||||
NVGcolor color = nvgHSLA(hue, 0.7f, 0.5f, 200);
|
||||
|
||||
nvgBeginPath(args.vg);
|
||||
nvgRect(args.vg, margin + i * barW, h - 8.f - barH, barW - 1.f, barH);
|
||||
nvgFillColor(args.vg, color);
|
||||
nvgFill(args.vg);
|
||||
}
|
||||
|
||||
// Status text
|
||||
nvgFontSize(args.vg, 8.f);
|
||||
nvgFillColor(args.vg, nvgRGB(0xa0, 0xa0, 0xa0));
|
||||
nvgTextAlign(args.vg, NVG_ALIGN_LEFT | NVG_ALIGN_TOP);
|
||||
|
||||
char buf[64];
|
||||
snprintf(buf, sizeof(buf), "N:%.3f", module->noiseLevel);
|
||||
nvgText(args.vg, 2.f, 1.f, buf, nullptr);
|
||||
|
||||
if (module->isTraining.load()) {
|
||||
nvgFillColor(args.vg, nvgRGB(0xff, 0xa0, 0x00));
|
||||
nvgText(args.vg, w - 24.f, 1.f, "TRAIN", nullptr);
|
||||
}
|
||||
|
||||
nvgRestore(args.vg);
|
||||
}
|
||||
};
|
||||
|
||||
// ── Widget ────────────────────────────────────────────────────────────
|
||||
struct MEMLNautWidget : ModuleWidget {
|
||||
MEMLNautWidget(MEMLNaut* module) {
|
||||
setModule(module);
|
||||
setPanel(createPanel(asset::plugin(pluginInstance, "res/MEMLNaut.svg")));
|
||||
|
||||
// Knobs
|
||||
addParam(createParamCentered<RoundBlackKnob>(mm2px(Vec(12.0, 20.0)), module, MEMLNaut::PARAM_SPREAD));
|
||||
addParam(createParamCentered<VCVButton>(mm2px(Vec(28.0, 20.0)), module, MEMLNaut::PARAM_RAND));
|
||||
float col1 = 8.f; // left column
|
||||
float col2 = 20.f; // center-left
|
||||
float col3 = 32.f; // center-right
|
||||
// float col4 = 44.f; // right column (for wide panel)
|
||||
float y = 14.f;
|
||||
|
||||
// Inputs (left side)
|
||||
addInput(createInputCentered<PJ301MPort>(mm2px(Vec(8.0, 38.0)), module, MEMLNaut::INPUT_X));
|
||||
addInput(createInputCentered<PJ301MPort>(mm2px(Vec(8.0, 50.0)), module, MEMLNaut::INPUT_Y));
|
||||
// ── Display ───────────────────────────────────────────────────
|
||||
MEMLNautDisplay* display = createWidget<MEMLNautDisplay>(mm2px(Vec(2.f, y)));
|
||||
display->box.size = mm2px(Vec(36.f, 18.f));
|
||||
display->module = module;
|
||||
addChild(display);
|
||||
y += 22.f;
|
||||
|
||||
// Outputs (2 columns of 6, below inputs)
|
||||
for (int i = 0; i < 6; i++) {
|
||||
addOutput(createOutputCentered<PJ301MPort>(mm2px(Vec(12.0, 62.0 + i * 10.0)), module, MEMLNaut::OUTPUT_1 + i));
|
||||
addOutput(createOutputCentered<PJ301MPort>(mm2px(Vec(28.0, 62.0 + i * 10.0)), module, MEMLNaut::OUTPUT_1 + 6 + i));
|
||||
// ── SPREAD + RATE knobs ───────────────────────────────────────
|
||||
addParam(createParamCentered<RoundBlackKnob>(mm2px(Vec(col1, y)), module, MEMLNaut::PARAM_SPREAD));
|
||||
addInput(createInputCentered<PJ301MPort>(mm2px(Vec(col2, y)), module, MEMLNaut::INPUT_SPREAD_CV));
|
||||
addParam(createParamCentered<RoundBlackKnob>(mm2px(Vec(col3, y)), module, MEMLNaut::PARAM_RATE));
|
||||
y += 10.f;
|
||||
|
||||
// ── Buttons row: + − LEARN RAND CLEAR ────────────────────────
|
||||
addParam(createParamCentered<VCVButton>(mm2px(Vec(col1 - 2.f, y)), module, MEMLNaut::PARAM_THUMBS_UP));
|
||||
addParam(createParamCentered<VCVButton>(mm2px(Vec(col1 + 6.f, y)), module, MEMLNaut::PARAM_THUMBS_DOWN));
|
||||
addParam(createParamCentered<CKSS>(mm2px(Vec(col2 + 2.f, y)), module, MEMLNaut::PARAM_LEARN));
|
||||
addChild(createLightCentered<SmallLight<GreenLight>>(mm2px(Vec(col2 + 2.f, y - 4.f)), module, MEMLNaut::LIGHT_LEARN));
|
||||
addParam(createParamCentered<VCVButton>(mm2px(Vec(col3, y)), module, MEMLNaut::PARAM_RAND));
|
||||
addParam(createParamCentered<VCVButton>(mm2px(Vec(col3 + 8.f, y)), module, MEMLNaut::PARAM_CLEAR));
|
||||
addChild(createLightCentered<SmallLight<YellowLight>>(mm2px(Vec(col3 + 8.f, y - 4.f)), module, MEMLNaut::LIGHT_TRAINING));
|
||||
y += 10.f;
|
||||
|
||||
// ── Trigger / gate inputs ─────────────────────────────────────
|
||||
addInput(createInputCentered<PJ301MPort>(mm2px(Vec(col1, y)), module, MEMLNaut::INPUT_X));
|
||||
addInput(createInputCentered<PJ301MPort>(mm2px(Vec(col2, y)), module, MEMLNaut::INPUT_Y));
|
||||
addInput(createInputCentered<PJ301MPort>(mm2px(Vec(col3, y)), module, MEMLNaut::INPUT_LEARN_GATE));
|
||||
y += 8.f;
|
||||
addInput(createInputCentered<PJ301MPort>(mm2px(Vec(col1, y)), module, MEMLNaut::INPUT_TRIG_POS));
|
||||
addInput(createInputCentered<PJ301MPort>(mm2px(Vec(col2, y)), module, MEMLNaut::INPUT_TRIG_NEG));
|
||||
y += 10.f;
|
||||
|
||||
// ── Outputs: 3 columns of 4, with attenuverter + LED + jack ──
|
||||
for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
|
||||
int col = i % 3;
|
||||
int row = i / 3;
|
||||
float ox = 6.f + col * 13.f;
|
||||
float oy = y + row * 9.f;
|
||||
|
||||
addParam(createParamCentered<Trimpot>(mm2px(Vec(ox, oy)), module, MEMLNaut::PARAM_ATTEN_1 + i));
|
||||
addChild(createLightCentered<SmallLight<WhiteLight>>(mm2px(Vec(ox + 4.5f, oy)), module, MEMLNaut::LIGHT_OUT_1 + i));
|
||||
addOutput(createOutputCentered<PJ301MPort>(mm2px(Vec(ox + 9.f, oy)), module, MEMLNaut::OUTPUT_1 + i));
|
||||
}
|
||||
y += 4 * 9.f + 2.f;
|
||||
|
||||
// ── Derived outputs ───────────────────────────────────────────
|
||||
float dox = 4.f;
|
||||
addOutput(createOutputCentered<PJ301MPort>(mm2px(Vec(dox, y)), module, MEMLNaut::OUTPUT_MEAN));
|
||||
addOutput(createOutputCentered<PJ301MPort>(mm2px(Vec(dox + 8.f, y)), module, MEMLNaut::OUTPUT_STD));
|
||||
addOutput(createOutputCentered<PJ301MPort>(mm2px(Vec(dox + 16.f, y)), module, MEMLNaut::OUTPUT_DELTA));
|
||||
addOutput(createOutputCentered<PJ301MPort>(mm2px(Vec(dox + 24.f, y)), module, MEMLNaut::OUTPUT_NOVELTY));
|
||||
addOutput(createOutputCentered<PJ301MPort>(mm2px(Vec(dox + 32.f, y)), module, MEMLNaut::OUTPUT_CONFIDENCE));
|
||||
}
|
||||
|
||||
void appendContextMenu(Menu* menu) override {
|
||||
MEMLNaut* module = dynamic_cast<MEMLNaut*>(this->module);
|
||||
if (!module) return;
|
||||
|
||||
menu->addChild(new MenuSeparator);
|
||||
menu->addChild(createMenuLabel("Output ranges"));
|
||||
|
||||
for (int i = 0; i < NUM_ML_OUTPUTS; i++) {
|
||||
menu->addChild(createCheckMenuItem(
|
||||
string::f("Out %d: Bipolar (±5V)", i + 1), "",
|
||||
[=]() { return !module->outputRangeUnipolar[i]; },
|
||||
[=]() { module->outputRangeUnipolar[i] = !module->outputRangeUnipolar[i]; }
|
||||
));
|
||||
}
|
||||
|
||||
menu->addChild(new MenuSeparator);
|
||||
menu->addChild(createMenuLabel("Input ranges"));
|
||||
|
||||
std::string inputNames[] = {"X", "Y"};
|
||||
for (int i = 0; i < NUM_ML_INPUTS; i++) {
|
||||
menu->addChild(createCheckMenuItem(
|
||||
string::f("Input %s: Bipolar (±5V)", inputNames[i].c_str()), "",
|
||||
[=]() { return !module->inputRangeUnipolar[i]; },
|
||||
[=]() { module->inputRangeUnipolar[i] = !module->inputRangeUnipolar[i]; }
|
||||
));
|
||||
}
|
||||
|
||||
menu->addChild(new MenuSeparator);
|
||||
menu->addChild(createMenuLabel("Slew"));
|
||||
|
||||
menu->addChild(createSubmenuItem("Output slew", string::f("%.0f ms", module->slewMs), [=](Menu* childMenu) {
|
||||
for (float ms : {0.f, 5.f, 10.f, 20.f, 50.f, 100.f}) {
|
||||
childMenu->addChild(createCheckMenuItem(
|
||||
string::f("%.0f ms", ms), "",
|
||||
[=]() { return module->slewMs == ms; },
|
||||
[=]() { module->slewMs = ms; }
|
||||
));
|
||||
}
|
||||
}));
|
||||
}
|
||||
};
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue