{
  "protocol": {
    "date": "2026-10-25",
    "machine": "Apple M4 Pro; 10 performance + 4 efficiency cores; 48 GiB RAM",
    "python": "CPython 3.13.5",
    "numpy": "2.4.2",
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    "frames_per_batch": 32,
    "bin_count": 2000,
    "unit": "2theta degrees",
    "polarization_factor": 0.965,
    "solid_angle": true,
    "arrays": "Not measured in this paired-file cohort; see the earlier 80-process control cohort in results.json and protocol.json.",
    "files": "Each task reopens and decodes an uncompressed signed-int32 TIFF. Poisson off/on measured in the same process, in reversed mode order across trials; warm filesystem cache.",
    "geometry": "Same equivalent mirrored PONI geometry, masks, radial range and detector images for all backends.",
    "sampling_n": {
      "Pilatus1M": 15,
      "Eiger9M": 5
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    "pixel_split": "Geometric pixel splitting has no subpixel count.",
    "dioptrin": "Published PyPI 0.6.1 wheel, generic release build; fast_math=True. Native SHA-256 checked against the freshly installed release verification.",
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      "commit": "4f78cc3b1d8cab67128c9679f55de6340c68f191",
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      "version": "0.1.dev1+g4f78cc3b1",
      "source": "https://github.com/maxiv-science/azint"
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    "threading": "OMP_NUM_THREADS=1 for pyFAI and Dioptrin; equals worker count only for azint matrix construction. Numerical-library helper threads limited to one.",
    "qualification": "Every timing mode must have <=3.6 other busy logical cores. Rejected attempts retained separately; unrelated calculations were not suspended for this final run.",
    "order": "Variant and worker order reverse in the second fresh-process trial.",
    "uncertainty": "Bars are medians of two fresh-process trials; whiskers span their range, not confidence intervals.",
    "memory": "Whole-process RSS sampled every 10 ms. Both modes share a fresh process; mode order is reversed across trials. No preloaded image stack.",
    "dataset_images": {
      "Pilatus1M": "1043x981; 32 separate filenames containing copies of one image",
      "Eiger9M": "3262x3108; 16 distinct real images, each read twice per batch"
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    "headline_chart_methods": {
      "dioptrin": "dioptrin_ps",
      "pyfai": "pyfai_bbox",
      "azint": "azint_fast"
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    "headline_chart_workers": [
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    "headline_chart_workload": "Reopen and decode each TIFF and integrate; error estimation off. A second visible chart includes Poisson estimation.",
    "accepted_processes": 48,
    "rejected_attempts": 14,
    "tested_methods": {
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      "azint_fast": "azint \u00b7 CPU-tuned",
      "pyfai_bbox": "pyFAI 2026.9.0"
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    "mode_order": "Off then on in trial one; on then off in trial two.",
    "scope": "Published-library Python file pipelines; not a direct Dioptas application/GUI benchmark.",
    "setup": "Existing initialization creates engines with one Poisson-enabled first image in both cases; warm throughput then toggles error estimation.",
    "first_batch": "First batch in a mode may reuse workers/caches from the preceding mode; not an independent cold-start comparison.",
    "validation": "First batch and post-timing batch in both modes checked byte-for-byte against existing same-build/same-backend references.",
    "harness_sha256": "b08e3c88da7b0b4c7d9b73980a39c56e1147f96cf86d3958164e1c10568b9ed7",
    "imported_harness_sha256": {
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    "source_note": "file-benchmark.py records the measured driver; it imports the previous local harnesses and requires the original input files and backend builds. This data bundle is not a standalone runnable environment."
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