import { synthesizeMicrophoneSignals } from "/packages/acoustics-core/propagation.js";
import { computeDasWithBackend } from "/packages/webgpu-kernels/compute.js";

function post(type, jobId, payload = {}, transfer = []) {
  self.postMessage({ type, jobId, ...payload }, transfer);
}

function serializeError(error) {
  return {
    name: error?.name ?? "Error",
    code: error?.code ?? "",
    message: error instanceof Error ? error.message : String(error),
    stack: error?.stack ?? "",
    causes: error instanceof AggregateError
      ? error.errors.map((cause) => ({ name: cause?.name, code: cause?.code, message: cause?.message }))
      : [],
  };
}

self.addEventListener("message", async (event) => {
  if (event.data?.type !== "run") return;
  const { jobId, scene, arrayDefinition, experiment, assetBuffers } = event.data;
  const totalStartedAt = performance.now();
  try {
    post("progress", jobId, { stage: "signal", message: "192채널 자유음장 신호를 합성하고 있습니다…" });
    const signalStartedAt = performance.now();
    const synthesis = synthesizeMicrophoneSignals({
      scene,
      arrayDefinition,
      assetBuffers,
      minimumDistance: experiment.beamforming.minimumDistance,
    });
    const signalGeneration = performance.now() - signalStartedAt;
    post("progress", jobId, {
      stage: "signal-complete",
      message: `마이크 신호 합성 완료 · ${signalGeneration.toFixed(0)} ms`,
    });

    const beamformingStartedAt = performance.now();
    const das = await computeDasWithBackend({
      channels: synthesis.channels,
      sampleRate: synthesis.sampleRate,
      arrayDefinition,
      scene,
      experiment,
      onFallback: ({ error }) =>
        post("fallback", jobId, {
          code: error.code,
          message: error.message,
        }),
      onProgress: (progress) => post("progress", jobId, { stage: progress.stage, progress }),
    });
    const beamforming = performance.now() - beamformingStartedAt;
    const transfer = [];
    for (const band of das.bands) {
      transfer.push(band.rawMap.buffer, band.displayMap.buffer);
    }
    post(
      "result",
      jobId,
      {
        run: {
          ...das,
          signalStats: {
            preNormalizationPeak: synthesis.preNormalizationPeak,
            appliedScale: synthesis.appliedScale,
            clippedSampleCount: synthesis.clippedSampleCount,
            peak: synthesis.peak,
          },
          timingMs: {
            total: performance.now() - totalStartedAt,
            signalGeneration,
            beamforming,
          },
        },
      },
      transfer,
    );
  } catch (error) {
    post("error", jobId, { error: serializeError(error) });
  }
});