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      "meanOffsetM": 0,
      "datum": "mean-relative"
    }
  },
  "format": "marees-atlas-source",
  "formatVersion": 1,
  "amplitudeUnit": "m",
  "phaseUnit": "degrees Universal-Time Greenwich lag",
  "adaptation": "37 unchanged values per nearest finite wet cell; no spatial interpolation, datum offset, minor inference or benchmark fitting.",
  "sourceLicense": {
    "source": "Ifremer / PREVIMER / MARC",
    "dataset_title": "Atlas de composantes harmoniques de hauteurs et courants de marée en Manche et Atlantique",
    "catalogue_uuid": "1bc4a49d-d4cd-469e-827c-bd3bd0eeabba",
    "catalogue_url": "https://sextant.ifremer.fr/geonetwork/srv/api/records/1bc4a49d-d4cd-469e-827c-bd3bd0eeabba/formatters/xsl-view",
    "catalogue_xml_url": "https://sextant.ifremer.fr/geonetwork/srv/api/records/1bc4a49d-d4cd-469e-827c-bd3bd0eeabba/formatters/xml",
    "retrieved_at_utc": "2026-10-29T16:57:30.1121636Z",
    "catalogue_xml_sha256": "724c22341162d0a44c37aa55647f3be9d8844f198bf415a9124774976db73af9",
    "licence_name": "Creative Commons Attribution (CC BY)",
    "licence_version": null,
    "licence_url_as_published": "http://creativecommons.fr/licences/",
    "licence_version_note": "The catalogue does not specify a CC BY version. Do not silently label this atlas CC BY 4.0; TICON-4 is a separate dataset with its own explicit version.",
    "legal_constraints_as_published": [
      "Données sous Licence Creative Commons - Attribution (BY) : http://creativecommons.fr/licences/",
      "Données Ifremer. Accès gratuit, formulaire de demande d'accès sur http://marc.ifremer.fr/produits/atlas_de_composantes_harmoniques"
    ],
    "dataset_attribution": "Ifremer / PREVIMER, Atlas de composantes harmoniques de hauteurs et courants de marée en Manche et Atlantique, MARC V0 (février 2013). Source catalogue Sextant UUID 1bc4a49d-d4cd-469e-827c-bd3bd0eeabba. Licence CC BY (version non précisée dans le catalogue).",
    "validation_report": {
      "author": "Lucia Pineau-Guillou",
      "year": 2013,
      "publication_date": "2013-06-01",
      "title": "PREVIMER. Validation des atlas de composantes harmoniques de hauteurs et courants de marée",
      "url": "https://archimer.ifremer.fr/doc/00157/26801/",
      "pdf_url": "https://archimer.ifremer.fr/doc/00157/26801/24912.pdf"
    },
    "spatial_interpretation_note": "250 m is the model grid spacing, not an accuracy guarantee and not the distance of the sampled wet cells from each beach. State the actual extraction/interpolation method, coordinate and coastal distance per beach after validation.",
    "adaptation_attribution_note": "The final app should describe extraction of the 37 sea-level constituents and its own astronomical prediction/adaptation, retaining the source attribution. Coefficient and datum sources must remain separately identified."
  },
  "scientificImplementation": {
    "name": "NumPy reconstruction of the original TTB 2013 numerical convention",
    "license": "GPL-2.0-only",
    "upstreamAttribution": "SIROCCO/T-UGOm tools. Florent Lyard and Damien Allain, LEGOS/CNRS, Toulouse. Historical revision 2013-02-07.",
    "licenseFile": "TTB-COPYING.txt",
    "sourceFile": "atlas_predict.py",
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    "sourceFiles": [
      {
        "name": "atlas_predict.py",
        "content": "# SPDX-License-Identifier: GPL-2.0-only\n# Astronomical compatibility implementation after SIROCCO/T-UGOm 2013.\n# Upstream scientific work: Florent Lyard and Damien Allain, LEGOS/CNRS.\n# Original GPL-2.0 text: ../dist/data/TTB-COPYING.txt.\n\"\"\"Reconstruct the 2013 PREVIMER atlas in its original TTB convention.\n\nNo fitting, datum offset, inferred tide or network access is performed. The\nsource NetCDF identifies tools 2.2.3, Hg 1145:2c4ace44f605, 2013-02-07 17:32.\nThat exact historical revision is preserved in the producer's public Git repo:\nhttps://gitlab.in2p3.fr/sirocco/sirocco_tools/sirocco_tools/-/tree/119b33e963f7c508d87e4642c4d725201e573771\nWave definitions: src/tides.def; formulae/astronomy: src/tides.cpp.\n\nThese numerical definitions intentionally reproduce the ORIGINAL analysis,\nincluding M1, KJ2, KQ1 and MK4 conventions later changed by the producer. The\nmodern pyTMD/FES aliases are not equivalent. This is a compatibility model,\nnot a new astronomical theory or a correction to the historical atlas.\n\"\"\"\nfrom __future__ import annotations\n\nimport hashlib\nimport json\nimport sys\nfrom pathlib import Path\n\nROOT = Path(__file__).resolve().parents[2] / 'essai-calcul-local'\nsys.path.insert(0, str(ROOT / '.deps'))\nimport numpy as np\nimport xarray as xr\n\n\ndef configure_experiment_root(path):\n    \"\"\"Select local input NetCDF files; no network I/O or implicit downloads.\"\"\"\n    global ROOT\n    ROOT = Path(path).resolve()\n\nTTB_REVISION = '119b33e963f7c508d87e4642c4d725201e573771'\n# Each row: name, T,s,h,p,p1,shift_deg, xi,nu,nu_prime,nu_sec,R, f_formula.\n# The five fast arguments use the historic Darwin solar angle T=UT*15+180.\n_ROWS = '''\nMm    0  1  0 -1  0    0  0  0  0  0  0 73\nMf    0  2  0  0  0    0 -2  0  0  0  0 74\n2Q1   1 -4  1  2  0   90  2 -1  0  0  0 75\nSig1  1 -4  3  0  0   90  2 -1  0  0  0 75\nQ1    1 -3  1  1  0   90  2 -1  0  0  0 75\nRo1   1 -3  3 -1  0   90  2 -1  0  0  0 75\nO1    1 -2  1  0  0   90  2 -1  0  0  0 75\nMP1   1 -2  3  0  0  -90  0 -1  0  0  0 76\nM1    1 -1  1  1  0  -90  1 -1  0  0  0 144\nKi1   1 -1  3 -1  0  -90  0 -1  0  0  0 76\nPi1   1  0 -2  0  1   90  0  0  0  0  0 0\nP1    1  0 -1  0  0   90  0  0  0  0  0 0\nK1    1  0  1  0  0  -90  0  0 -1  0  0 227\nPsi1  1  0  2  0 -1  -90  0  0  0  0  0 0\nPhi1  1  0  3  0  0  -90  0  0  0  0  0 0\nTta1  1  1 -1  1  0  -90  0 -1  0  0  0 76\nJ1    1  1  1 -1  0  -90  0 -1  0  0  0 76\nOO1   1  2  1  0  0  -90 -2 -1  0  0  0 77\nKQ1   1  3  1 -1  0  -90 -2 -1  0  0  0 15\nE2    2 -5  4  1  0    0  2 -2  0  0  0 78\n2N2   2 -4  2  2  0    0  2 -2  0  0  0 78\nMu2   2 -4  4  0  0    0  2 -2  0  0  0 78\nN2    2 -3  2  1  0    0  2 -2  0  0  0 78\nNu2   2 -3  4 -1  0    0  2 -2  0  0  0 78\nM2    2 -2  2  0  0    0  2 -2  0  0  0 78\nLa2   2 -1  0  1  0  180  2 -2  0  0  0 78\nL2    2 -1  2 -1  0  180  2 -2  0  0 -1 215\nT2    2  0 -1  0  1    0  0  0  0  0  0 0\nS2    2  0  0  0  0    0  0  0  0  0  0 0\nR2    2  0  1  0 -1  180  0  0  0  0  0 0\nK2    2  0  2  0  0    0  0  0  0 -2  0 235\nKJ2   2  1  2 -1  0    0  0  0  0 -2  0 79\nMN4   4 -5  4  1  0    0  4 -4  0  0  0 1\nM4    4 -4  4  0  0    0  4 -4  0  0  0 1\nMS4   4 -2  2  0  0    0  2 -2  0  0  0 2\nMK4   4 -2  4  0  0    0  2 -2  0 -2  0 4\nM6    6 -6  6  0  0    0  6 -6  0  0  0 4\n'''\nWAVES = []\nfor line in _ROWS.strip().splitlines():\n    parts = line.split()\n    x = [int(v) for v in parts[1:]]\n    WAVES.append({'name': parts[0], 'darwin': x[:6], 'nodal': x[6:11], 'formula': x[11]})\nNAMES = [wave['name'] for wave in WAVES]\nDARWIN = np.array([wave['darwin'][:5] for wave in WAVES])\nSHIFT = np.radians([wave['darwin'][5] for wave in WAVES])\nNODAL = np.array([wave['nodal'] for wave in WAVES])\nRATES_DEG_PER_CENTURY = np.array([13149000.0, 481267.892, 36000.76892, 4069.0322056, 1.719175])\nOMEGA = np.radians(DARWIN @ RATES_DEG_PER_CENTURY) / (36525 * 86400)\n\nPLACES = {\n    'billou': {'name': 'Plage du Bilou', 'targetLatitude': 48.704765, 'targetLongitude': -4.050046,\n               'targetSource': 'https://www.roscoff-tourisme.com/fr/fiche/equipements-de-loisirs/plage-du-billou-santec_TFOLOIBRE029V527UHR/',\n               'MANW': [117, 44]},\n    'theven': {'name': 'Le Théven', 'targetLatitude': 48.710312, 'targetLongitude': -4.035724,\n               'targetSource': 'https://www.roscoff-tourisme.com/fr/fiche/equipements-de-loisirs/plage-de-theven-santec_TFOLOIBRE000FS0004D/',\n               'MANW': [121, 51]},\n    'laber': {'name': 'Le Laber · Perharidy', 'targetLatitude': 48.72022, 'targetLongitude': -4.0066,\n              'targetSource': 'https://www.openstreetmap.org/way/163078411', 'MANW': [126, 57],\n              'targetNote': 'Western side of the Laber inlet, Perharidy; selected by the user. Nominal centre of the mapped beach, not a surveyed prediction point.'},\n    'roscoffDiagnostic': {'name': 'Roscoff tide-gauge diagnostic', 'targetLatitude': 48.718426,\n                         'targetLongitude': -3.965679, 'MANW': [121, 71]},\n}\n\n\ndef astronomical_basis(dates):\n    \"\"\"Return f and (V+u) radians under the atlas's historical 2013 convention.\"\"\"\n    days = (np.asarray(dates, dtype='datetime64[ns]') - np.datetime64('1900-01-01', 'ns')) / np.timedelta64(1, 'D')\n    centuries = np.asarray(days, dtype=float).reshape(-1) / 36525\n    angles_deg = np.array([180.0, 277.0256206, 280.1895015, 334.3837214, 281.2208568])[None, :] + centuries[:, None] * RATES_DEG_PER_CENTURY\n    angles = np.radians(np.remainder(angles_deg, 360))\n    node = np.remainder(np.radians(259.1560563 - 1934.1423972 * centuries), 2 * np.pi)\n    inclination = np.arccos(.913694997 - .035692561 * np.cos(node))\n    a = np.arctan(1.01883 * np.tan(node / 2))\n    b = np.arctan(.64412 * np.tan(node / 2))\n    xi = node - a - b\n    xi = np.where(node > np.pi, xi - 2 * np.pi, xi)\n    nu = a - b\n    t2 = np.tan(inclination / 2) ** 2\n    P = angles[:, 3] - xi\n    x1ra = np.sqrt(1 - 12 * t2 * np.cos(2 * P) + 36 * t2 ** 2)\n    R = np.arctan(np.sin(2 * P) / (1 / (6 * t2) - np.cos(2 * P)))\n    nu_prime = np.arctan(np.sin(2 * inclination) * np.sin(nu) / (np.sin(2 * inclination) * np.cos(nu) + .3347))\n    nu_sec = .5 * np.arctan(np.sin(inclination) ** 2 * np.sin(2 * nu) / (np.sin(inclination) ** 2 * np.cos(2 * nu) + .0727))\n    si, ch, sh = np.sin(inclination), np.cos(inclination / 2), np.sin(inclination / 2)\n    factors = {\n        0: np.ones(len(centuries)),\n        73: (2 / 3 - si ** 2) / .5021,\n        74: si ** 2 / .1578,\n        75: si * ch ** 2 / .3800,\n        76: np.sin(2 * inclination) / .7214,\n        77: si * sh ** 2 / .0164,\n        78: ch ** 4 / .9154,\n        79: si ** 2 / .1565,\n        144: (1 - 10 * sh ** 2 + 15 * sh ** 4) * ch / .5873,\n        227: np.sqrt(.8965 * np.sin(2 * inclination) ** 2 + .6001 * np.sin(2 * inclination) * np.cos(nu) + .1006),\n        235: np.sqrt(19.0444 * si ** 4 + 2.7702 * si ** 2 * np.cos(2 * nu) + .0981),\n    }\n    factors.update({1: factors[78] ** 2, 2: factors[78], 4: factors[78] ** 3,\n                    15: factors[235] * factors[75], 215: factors[78] * x1ra})\n    f = np.column_stack([factors[wave['formula']] for wave in WAVES])\n    nodal_angles = np.column_stack([xi, nu, nu_prime, nu_sec, R])\n    phase = angles @ DARWIN.T + SHIFT + nodal_angles @ NODAL.T\n    return f, phase\n\n\ndef haversine_m(latitude, longitude, target_lat, target_lon):\n    lat, lon, tlat, tlon = np.radians([latitude, longitude, target_lat, target_lon])\n    a = np.sin((lat - tlat) / 2) ** 2 + np.cos(lat) * np.cos(tlat) * np.sin((lon - tlon) / 2) ** 2\n    return float(6371000 * 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a)))\n\n\ndef extract_points(domain='MANW'):\n    \"\"\"Extract exact unmodified amplitudes/phases at nearest fully wet cells.\"\"\"\n    folder = ROOT / 'data' / 'ifremer' / domain\n    manifest = json.loads((folder / 'download-manifest.json').read_text(encoding='utf-8'))\n    paths = [folder / f'{name}-XE-{domain}-atlas.nc' for name in NAMES]\n    with xr.open_dataset(paths[NAMES.index('M2')], engine='scipy') as ds:\n        latitude = ds.latitude.values\n        longitude = ds.longitude.values\n        wet = np.isfinite(ds.XE_a.values) & np.isfinite(ds.XE_G.values)\n        shape = list(ds.XE_a.shape)\n        attrs = dict(ds.attrs)\n    arrays = []\n    for path in paths:\n        with xr.open_dataset(path, engine='scipy') as ds:\n            a, g = ds.XE_a.values.copy(), ds.XE_G.values.copy()\n            wet &= np.isfinite(a) & np.isfinite(g)\n            arrays.append((a, g))\n    points = {}\n    for key, place in PLACES.items():\n        target_lat, target_lon = place['targetLatitude'], place['targetLongitude']\n        la, lo = np.radians(latitude), np.radians(longitude)\n        tla, tlo = np.radians([target_lat, target_lon])\n        h = np.sin((la - tla) / 2) ** 2 + np.cos(la) * np.cos(tla) * np.sin((lo - tlo) / 2) ** 2\n        j, i = np.unravel_index(np.argmin(np.where(wet, h, np.inf)), wet.shape)\n        if domain in place and [int(j), int(i)] != place[domain]:\n            raise ValueError(f'Nearest wet point changed for {key}: {(j, i)}')\n        amp = np.array([a[j, i] for a, g in arrays], dtype=float)\n        phase = np.array([g[j, i] for a, g in arrays], dtype=float)\n        weights = amp * np.exp(-1j * np.radians(phase))\n        points[key] = {k: v for k, v in place.items() if k != 'MANW'}\n        points[key].update(latitude=float(latitude[j, i]), longitude=float(longitude[j, i]),\n                           distanceM=haversine_m(latitude[j, i], longitude[j, i], target_lat, target_lon),\n                           cell={'j': int(j), 'i': int(i), 'indexBase': 0},\n                           amplitudeM=amp.tolist(), phaseDegrees=phase.tolist(),\n                           weights=[[z.real, z.imag] for z in weights],\n                           meanOffsetM=0, datum='mean-relative')\n    return {'domain': domain, 'shape': shape, 'netcdfAttributes': attrs, 'files': manifest['files'],\n            'ftpDirectory': manifest['directory'], 'waves': WAVES, 'models': points}\n\n\ndef reconstruct(point, dates):\n    f, phase = astronomical_basis(dates)\n    weights = np.array([complex(*z) for z in point['weights']])\n    return np.real(f * np.exp(1j * phase) * weights[None, :]).sum(axis=1)\n\n\ndef main():\n    import argparse\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument('--domain', choices=['MANW', 'FINIS'], default='MANW')\n    parser.add_argument('--extract', type=Path)\n    parser.add_argument('--experiment-root', type=Path, default=ROOT)\n    args = parser.parse_args()\n    configure_experiment_root(args.experiment_root)\n    source = extract_points(args.domain)\n    if args.extract:\n        args.extract.write_text(json.dumps(source, indent=2, ensure_ascii=False) + '\\n', encoding='utf-8')\n    # Independent pre-existing event comparison against the 355 SHOM benchmarks.\n    from predict_local import extrema\n    from validate_local import compare, DATES, benchmarks\n    events = extrema(DATES, reconstruct(source['models']['roscoffDiagnostic'], DATES))\n    result = {'domain': args.domain, 'convention': 'TTB-2.2.3-exact-2013', 'ttbRevision': TTB_REVISION,\n              'sourceConstantsFitted': False, 'diagnostic': source['models']['roscoffDiagnostic'], 'sources': {}}\n    for reference in benchmarks['sources']:\n        timing_reference = {**reference, 'events': [{**event, 'coef': None} for event in reference['events']]}\n        _, summary = compare(events, timing_reference, [])\n        result['sources'][reference['id']] = summary\n    target = ROOT / 'results' / f'atlas-{args.domain.lower()}-validation.json'\n    target.write_text(json.dumps(result, indent=2, ensure_ascii=False) + '\\n', encoding='utf-8')\n    print(json.dumps({'report': str(target), 'domain': args.domain,\n                      'points': {key: {k: value for k, value in point.items() if k in ['cell', 'distanceM', 'latitude', 'longitude']} for key, point in source['models'].items()},\n                      'sources': result['sources']}, ensure_ascii=False))\n\n\nif __name__ == '__main__':\n    main()\n"
      },
      {
        "name": "export_atlas.py",
        "content": "# SPDX-License-Identifier: GPL-2.0-only\n# Scientific compatibility implementation after SIROCCO/T-UGOm 2013.\n# Upstream scientific work: Florent Lyard and Damien Allain, LEGOS/CNRS.\n\"\"\"Export the original 2013 Ifremer MANW tide atlas for local browser sums.\n\nRun with the bundled Python and the existing scientific experiment directory:\n  python scripts/export_atlas.py\n  python scripts/export_atlas.py --experiment-root ../essai-calcul-local\n\nRequires numpy/xarray in EXPERIMENT/.deps and the downloaded 37 MANW NetCDF files.\nThe scientific implementation is tracked next to this script in atlas_predict.py.\nThere is no network I/O. Outputs retain\nall 37 explicit constituents, with no inferred tide or mean-level correction.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport hashlib\nimport json\nimport sys\nfrom datetime import datetime, timezone\nfrom pathlib import Path\n\nREPO = Path(__file__).resolve().parents[1]\nparser = argparse.ArgumentParser(description=__doc__)\nparser.add_argument('--experiment-root', type=Path, default=REPO.parent / 'essai-calcul-local')\nparser.add_argument('--out-dist', type=Path, default=REPO / 'dist')\nparser.add_argument('--start', default='2025-01-01')\nparser.add_argument('--end', default='2037-01-01')\nargs = parser.parse_args()\nexperiment = args.experiment_root.resolve()\nsys.path.insert(0, str(experiment / '.deps'))\nsys.path.insert(0, str(REPO / 'scripts'))\nimport numpy as np\nfrom atlas_predict import extract_points, astronomical_basis, reconstruct, configure_experiment_root, OMEGA, NAMES, TTB_REVISION\nconfigure_experiment_root(experiment)\n\nMS_DAY = 86400000\n\n\ndef ms(value):\n    return int(np.datetime64(value, 'ms').astype(np.int64))\n\n\ndef iso(value):\n    return datetime.fromtimestamp(value / 1000, tz=timezone.utc).isoformat(timespec='seconds').replace('+00:00', 'Z')\n\n\ndef digest(path):\n    return hashlib.sha256(path.read_bytes()).hexdigest()\n\n\ndef main():\n    start_ms, end_ms = ms(args.start), ms(args.end)\n    if end_ms <= start_ms:\n        raise ValueError('End must follow start')\n    data_dir = args.out_dist.resolve() / 'data'\n    data_dir.mkdir(parents=True, exist_ok=True)\n    scientific_source = (REPO / 'scripts' / 'atlas_predict.py').read_bytes()\n    (data_dir / 'atlas_predict.py').write_bytes(scientific_source)\n    license_path = REPO / 'dist' / 'data' / 'TTB-COPYING.txt'\n    if data_dir != license_path.parent:\n        (data_dir / license_path.name).write_bytes(license_path.read_bytes())\n    source = extract_points('MANW')\n    licensing = json.loads((experiment / 'data' / 'ifremer' / 'catalog-license.json').read_text(encoding='utf-8'))\n    validation = json.loads((experiment / 'results' / 'atlas-manw-validation.json').read_text(encoding='utf-8'))\n    source.update({\n        'format': 'marees-atlas-source', 'formatVersion': 1,\n        'amplitudeUnit': 'm', 'phaseUnit': 'degrees Universal-Time Greenwich lag',\n        'adaptation': '37 unchanged values per nearest finite wet cell; no spatial interpolation, datum offset, minor inference or benchmark fitting.',\n        'sourceLicense': licensing,\n        'scientificImplementation': {\n            'name': 'NumPy reconstruction of the original TTB 2013 numerical convention',\n            'license': 'GPL-2.0-only',\n            'upstreamAttribution': 'SIROCCO/T-UGOm tools. Florent Lyard and Damien Allain, LEGOS/CNRS, Toulouse. Historical revision 2013-02-07.',\n            'licenseFile': 'TTB-COPYING.txt',\n            'sourceFile': 'atlas_predict.py',\n            'licenseText': license_path.read_text(encoding='utf-8'),\n            'sourceFiles': [\n                {'name': 'atlas_predict.py', 'content': scientific_source.decode('utf-8')},\n                {'name': 'export_atlas.py', 'content': Path(__file__).read_text(encoding='utf-8')},\n            ],\n            'runtimeNote': 'No upstream TTB program code is embedded in the browser runtime. Its local model asset stores numerical astronomical envelopes; the browser engine performs a generic harmonic sum.',\n        },\n        'astronomicalConvention': {\n            'name': 'TTB 2.2.3 original 2013 analysis convention',\n            'gitRevision': TTB_REVISION, 'mercurialRevision': '1145:2c4ace44f605',\n            'definitions': f'https://gitlab.in2p3.fr/sirocco/sirocco_tools/sirocco_tools/-/blob/{TTB_REVISION}/src/tides.def',\n            'formulae': f'https://gitlab.in2p3.fr/sirocco/sirocco_tools/sirocco_tools/-/blob/{TTB_REVISION}/src/tides.cpp',\n            'epoch': '1900-01-01T00:00:00Z', 'julianCenturyDays': 36525,\n            'linearRatesDegreesPerCentury': {'T': 13149000.0, 's': 481267.892, 'h': 36000.76892, 'p': 4069.0322056, 'p1': 1.719175, 'N': -1934.1423972},\n            'notes': [\n                'Reproduce the original historical convention, including its then-current rare-wave definitions, so coefficients retain their original meaning.',\n                'Modern pyTMD/FES aliases are not used: M1 f144 and xi-nu; MP1 f76; KQ1 f235*f75; KJ2 f79 and -2nu_sec; MK4 f78 cubed.',\n                'No 18.6-year or beach accuracy claim follows from the numerical support horizon.',\n            ],\n        },\n    })\n    source_path = data_dir / 'atlas-source.json'\n    source_path.write_text(json.dumps(source, indent=2, ensure_ascii=False) + '\\n', encoding='utf-8')\n    sample_start_ms = start_ms - 2 * MS_DAY\n    sample_ms = np.arange(sample_start_ms, end_ms + 2 * MS_DAY + MS_DAY, MS_DAY, dtype=np.int64)\n    dates = sample_ms.astype('datetime64[ms]').astype('datetime64[ns]')\n    f, phase = astronomical_basis(dates)\n    elapsed = (sample_ms - sample_start_ms) / 1000\n    envelope = f * np.exp(1j * (phase - elapsed[:, None] * OMEGA[None, :]))\n    packed = np.stack((envelope.real, envelope.imag), axis=-1).astype('<f4')\n    if not np.all(np.isfinite(packed)):\n        raise ValueError('Non-finite astronomical envelope')\n\n    def reconstruct_export(instants, weights):\n        position = (instants - sample_start_ms) / MS_DAY\n        lower = np.floor(position).astype(int)\n        alpha = (position - lower)[:, None]\n        z0 = packed[lower, :, 0].astype(float) + 1j * packed[lower, :, 1].astype(float)\n        z1 = packed[lower + 1, :, 0].astype(float) + 1j * packed[lower + 1, :, 1].astype(float)\n        z = z0 + alpha * (z1 - z0)\n        rotation = np.exp(1j * ((instants - sample_start_ms) / 1000)[:, None] * OMEGA[None, :])\n        return np.real(z * rotation * weights).sum(axis=1)\n\n    rng = np.random.default_rng(144852)\n    checks_ms = np.unique(np.r_[start_ms, start_ms + 1, end_ms - 1, rng.integers(start_ms, end_ms, 1024)])\n    checks_dates = checks_ms.astype('datetime64[ms]').astype('datetime64[ns]')\n    errors = {}\n    for key, point in source['models'].items():\n        weights = np.array([complex(*z) for z in point['weights']])\n        direct = reconstruct(point, checks_dates)\n        errors[key] = float(np.max(np.abs(reconstruct_export(checks_ms, weights) - direct)))\n    if max(errors.values()) > .002:\n        raise ValueError(f'Export differs from direct reconstruction by more than 2 mm: {errors}')\n    binary = data_dir / 'atlas-v1.bin'\n    binary.write_bytes(packed.tobytes(order='C'))\n    manifest = {\n        'format': 'marees-atlas-envelope', 'formatVersion': 1,\n        'modelVersion': 'ifremer-previmer-manw37-ttb-2.2.3-original-2013-v1',\n        'createdAt': datetime.now(timezone.utc).isoformat().replace('+00:00', 'Z'),\n        'start': iso(start_ms), 'end': iso(end_ms), 'startMs': start_ms, 'endMs': end_ms,\n        'sampleStartMs': int(sample_ms[0]), 'sampleStepMs': MS_DAY, 'sampleCount': len(sample_ms),\n        'timeStandard': 'UTC', 'predictionUnit': 'm', 'datum': 'mean-relative', 'meanOffsetM': 0,\n        'binary': {'file': binary.name, 'bytes': binary.stat().st_size, 'sha256': digest(binary),\n                   'type': 'float32-le', 'layout': 'sample,constituent,real-imag',\n                   'envelope': 'f * exp(i * (V + u - omega * seconds_since_sample_start))'},\n        'constituents': NAMES, 'omegaRadPerSecond': OMEGA.tolist(),\n        'models': {key: point for key, point in source['models'].items() if key != 'roscoffDiagnostic'},\n        'sourceAsset': {'file': source_path.name, 'sha256': digest(source_path)},\n        'numerics': {'nodalCorrections': 'Original TTB 2.2.3 (2013) Schureman factors, historical wave definitions retained',\n                      'astronomicalArguments': 'Original linear Darwin UT arguments, epoch 1900-01-01',\n                      'minorInference': False, 'sourceDataFittedToBenchmarks': False,\n                      'interpolation': 'Daily complex slow-envelope interpolation with exact frequency rotation and fresh harmonic sum per instant',\n                      'events': '5-minute UTC grid, three-point parabolic refinement, same-kind separation 3 hours',\n                      'exportCheck': {'sampleCount': len(checks_ms), 'randomSeed': 144852, 'maxDifferenceM': errors}},\n        'validation': {'location': 'Nearest wet MANW cell 74 m from Roscoff tide gauge; not the three beaches',\n                       'period': '2026-10-01 through 2026-12-31 for SHOM; 2026-10/11 for InfosVent',\n                       'sources': {key: {'time_minutes': value['time_minutes'], 'missed_events': value['missed_events'],\n                                          'extra_events': value['extra_events']} for key, value in validation['sources'].items()},\n                       'beachValidation': False},\n        'sources': [\n            {'name': licensing['dataset_title'], 'url': licensing['catalogue_url'],\n             'license': licensing['licence_name'], 'licenseVersion': None, 'licenseUrl': licensing['licence_url_as_published'],\n             'attribution': licensing['dataset_attribution'], 'adaptation': source['adaptation']},\n            {'name': 'PREVIMER validation report, Pineau-Guillou 2013', 'url': licensing['validation_report']['url']},\n            {'name': 'Original TTB astronomical convention', 'url': source['astronomicalConvention']['definitions'],\n             'revision': TTB_REVISION, 'use': 'Numerical definitions read to preserve the source analysis convention; no third-party runtime code is embedded.'},\n        ],\n        'limitations': [\n            'Nominal beach coordinates are masked. Each place uses the nearest fully finite offshore grid cell, 781 to 1213 m away; no values are invented on dry cells.',\n            '250 m is model grid spacing, not an accuracy guarantee. Port comparisons do not validate timing or heights inside the beach/inlet.',\n            'Heights are relative to the atlas mean sea level only. No chart datum conversion is applied and no depth or safe access can be inferred.',\n            'Source model and bathymetry date from 2013. Atmospheric surge, waves and morphological change are excluded.',\n            'The 2025–2036 numerical support range is not an accuracy claim over that horizon.',\n        ],\n    }\n    target = data_dir / 'atlas-v1.json'\n    target.write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + '\\n', encoding='utf-8')\n    print(json.dumps({'metadata': str(target), 'binaryBytes': binary.stat().st_size,\n                      'source': str(source_path), 'checks': manifest['numerics']['exportCheck']}, ensure_ascii=False))\n\n\nif __name__ == '__main__':\n    main()\n"
      }
    ],
    "runtimeNote": "No upstream TTB program code is embedded in the browser runtime. Its local model asset stores numerical astronomical envelopes; the browser engine performs a generic harmonic sum."
  },
  "astronomicalConvention": {
    "name": "TTB 2.2.3 original 2013 analysis convention",
    "gitRevision": "119b33e963f7c508d87e4642c4d725201e573771",
    "mercurialRevision": "1145:2c4ace44f605",
    "definitions": "https://gitlab.in2p3.fr/sirocco/sirocco_tools/sirocco_tools/-/blob/119b33e963f7c508d87e4642c4d725201e573771/src/tides.def",
    "formulae": "https://gitlab.in2p3.fr/sirocco/sirocco_tools/sirocco_tools/-/blob/119b33e963f7c508d87e4642c4d725201e573771/src/tides.cpp",
    "epoch": "1900-01-01T00:00:00Z",
    "julianCenturyDays": 36525,
    "linearRatesDegreesPerCentury": {
      "T": 13149000.0,
      "s": 481267.892,
      "h": 36000.76892,
      "p": 4069.0322056,
      "p1": 1.719175,
      "N": -1934.1423972
    },
    "notes": [
      "Reproduce the original historical convention, including its then-current rare-wave definitions, so coefficients retain their original meaning.",
      "Modern pyTMD/FES aliases are not used: M1 f144 and xi-nu; MP1 f76; KQ1 f235*f75; KJ2 f79 and -2nu_sec; MK4 f78 cubed.",
      "No 18.6-year or beach accuracy claim follows from the numerical support horizon."
    ]
  }
}