Repair actual moving-core Mangkhut pressure film and remove frozen ending

.gitattributes

@@ -193,3 +193,4 @@ evaluation/storm_structure/fung_wong_wind_radius.png filter=lfs diff=lfs merge=l
 evaluation/storm_structure/mangkhut_wind_radius.png filter=lfs diff=lfs merge=lfs -text
 evaluation/storm_structure/meranti_wind_radius.png filter=lfs diff=lfs merge=lfs -text
 evaluation/storm_structure/soudelor_wind_radius.png filter=lfs diff=lfs merge=lfs -text
+evaluation/release_data/pressure_core/mangkhut/common-pressure.npz filter=lfs diff=lfs merge=lfs -text

README.md

@@ -178,7 +178,9 @@ Native-detail input history was unavailable; the regional pressure reconstructio
 
 ## More historical pressure forecasts — MP4
 
-Each film shows a **Western Pacific pressure overview**, a larger **unshifted forecast-versus-observed route close-up**, and a central-pressure comparison with JMA best track. All three were freshly forecast on the Mac GPU using the frozen **Trackformer 1.2** release and **50 distinct seeded input perturbations**. The displayed physical pressure fields, routes and central pressures are separately averaged over all 50 members. Each 23-second MP4 contains 20 genuine six-hour forecast states through **+120 h**, with blue lows, red highs and 4 hPa isobars; no interpolated forecast states.
+Each film shows a **Western Pacific pressure overview**, a larger **unshifted forecast-versus-observed route close-up**, and a central-pressure comparison with JMA best track. All three were forecast on the Mac GPU using the frozen **Trackformer 1.2** release and **50 distinct seeded input perturbations**. The displayed physical fields, routes and central pressures are separately averaged over all 50 members. Each film contains 20 genuine six-hour forecast states through **+120 h**, with blue lows and red highs; no interpolated forecast states. Soudelor and Meranti retain their original 23-second films. **Mangkhut is now 20 seconds, without the extra frozen ending**, and uses 2 hPa isobars with 4 hPa labels.
+
+**Mangkhut pressure repair:** its actual moving model cores are now registered geographically for every member before averaging, rather than losing detailed pressure when the storm leaves the old fixed regional patch. The reconstruction uses the frozen model equation—evolving basin plus its tapered moving anomaly—with no scalar-pressure insertion or shift to the observed/forecast route. Original routes, central pressures, inputs and weights are unchanged. [Common-grid physical pressure arrays](evaluation/release_data/pressure_core/mangkhut/common-pressure.npz) · [Core and replay verification](evaluation/release_data/pressure_core/mangkhut/verification.json) · [Playback audit](evaluation/release_data/mangkhut_playback_verification.json).
 
 ### Soudelor (2015) — issue 5 August 2015, 00 UTC
 
@@ -198,10 +200,14 @@ Each film shows a **Western Pacific pressure overview**, a larger **unshifted fo
 
 [Play / download Meranti MP4](https://huggingface.co/euler314/typhoon-predict/resolve/main/docs/trackformer_1_2_meranti.mp4) · [Provenance](evaluation/release_data/meranti_video.json)
 
-**These are calendar-selected historical development examples, not untouched tests or representative skill claims.** Their dates were fixed before inference, without choosing the lowest-error starts, but may overlap the checkpoint's fitting/validation years. Inputs are nine exact six-hour retrospective NCEP analyses through issue time, current intensity and past motion. Future observations are comparison labels only. Native-detail history is not supplied to these runs and remains masked; the regional reconstruction has 0.25° sampling, while outside that fixed patch only the 2.5° basin field is shown. Meranti's issue-time wind was missing and remains masked, not filled from future data. No wind-radii forecast is implied.
+**These are calendar-selected historical development examples, not untouched tests or representative skill claims.** Their dates were fixed before inference, without choosing the lowest-error starts, but may overlap the checkpoint's fitting/validation years. Inputs are nine exact six-hour retrospective NCEP analyses through issue time, current intensity and past motion. Future observations are comparison labels only. Native-detail input history is not supplied and remains masked. Soudelor and Meranti retain a fixed regional reconstruction; beyond its coverage they show the 2.5° basin field only. Mangkhut uses the actual moving-core reconstruction: 0.1° close-up and 0.25° overview are interpolation/sampling, not additional native information; the core's information spacing is 20 km. Meranti's issue-time wind was missing and remains masked, not filled from future data. No validated wind-radii forecast is implied.
 
 [50-member mean and MP4 audit](evaluation/release_data/historical_video50_verification.json) · [Mac GPU forecast preparation](release_tools/forecast_historical_video50_mac.py) · [Video renderer](release_tools/build_fung_wong_video.py)
 
+### Public forecast and observation API
+
+[API guide](https://trackformer-weatherlab.rudin-euler-8253.chatgpt.site/history-api) · [Machine-readable resources](https://trackformer-weatherlab.rudin-euler-8253.chatgpt.site/api/history/v1/resources) · [All published data assets](https://trackformer-weatherlab.rudin-euler-8253.chatgpt.site/api/data/v1/catalog). Cross-origin, read-only JSON exposes routes, actual basin/core pressure fields, separate observed winds/radii and provenance for external pages. The original 32,230-issue Western Pacific archive and the still-progressing moving-core recovery have separate verification endpoints; missing core/wind/radius data remain unavailable, never zero. One-member historical issues are not relabelled 50-member ensembles.
+
 ### Illustrated technical paper
 
 The **[Trackformer 1.2 technical paper (PDF)](paper/trackformer.pdf)** explains the input tensors, multiscale attention, recurrent basin transport, moving pressure core, centre association, objectives and 50-member means. It includes a **model-structure diagram**, **1.2-versus-1.1 benchmark bars and lead-error curves**, and the selected **Fung-wong route, central-pressure timeline and model-generated isobar map**. Development-data limitations, short-lead regressions and pressure failures are reported alongside improvements. [Editable, self-contained LaTeX source](paper/trackformer.tex) · [Figure reproduction utility](release_tools/build_paper_figures.py). Historical material is recoverable from Git history and the [1.1 release](https://github.com/yu314-coder/typhoon-predict/releases/tag/trackformer-1.1).

docs/trackformer_1_2_mangkhut.mp4

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+size 8576997

evaluation/release_data/historical_video50_verification.json

@@ -1 +1 @@
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evaluation/release_data/mangkhut_playback_verification.json

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+{
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evaluation/release_data/mangkhut_video.json

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   "observed_route_source": "Exact matching-timestamp IBTrACS LAT/LON; display verification only.",
   "observed_pressure_source": "IBTrACS TOKYO_PRES JMA best track, not JMA forecasts; missing labels remain missing.",
-  "field_source": "Equal-weight physical means of all 50 basin and fixed-issue regional fields; no directly averaged moving grids.",
+  "field_source": "Original evolving basin plus each actual tapered moving anomaly, geographically registered before 50-member physical averaging. No scalar insertion or route/truth shift.",
   "native_history_available": false,
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   "metrics": {
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@@ -446,12 +446,15 @@
   "selection": "Calendar issue chosen before running these forecasts, without screening errors. Historical development showcase; dates may overlap checkpoint fitting/validation years. Not an untouched test or evidence of no overfitting.",
   "video": {
     "file": "docs/trackformer_1_2_mangkhut.mp4",
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   "video_layout": {
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@@ -469,5 +472,1640 @@
     "pressure_colors": "Blue below 1013.25 hPa; red above. Fixed range throughout each film.",
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+      "wind_estimation_method": "pressure-gradient-experimental-v1",
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+            "wind_averaging_period": "not calibrated to an agency averaging period",
+            "land_friction_asymmetry_and_translation": "not modelled",
+            "calibration": "none; fixed physical assumptions, not fitted to benchmarks"
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+            "azimuth_samples": 72,
+            "radial_smoothing": "three-point pressure average",
+            "geometry": "azimuthal mean; circular equivalent radii, not quadrants",
+            "wind_averaging_period": "not calibrated to an agency averaging period",
+            "land_friction_asymmetry_and_translation": "not modelled",
+            "calibration": "none; fixed physical assumptions, not fitted to benchmarks"
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+          "azimuth_samples": 72,
+          "radial_smoothing": "three-point pressure average",
+          "geometry": "azimuthal mean; circular equivalent radii, not quadrants",
+          "wind_averaging_period": "not calibrated to an agency averaging period",
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+          "azimuth_samples": 72,
+          "radial_smoothing": "three-point pressure average",
+          "geometry": "azimuthal mean; circular equivalent radii, not quadrants",
+          "wind_averaging_period": "not calibrated to an agency averaging period",
+          "land_friction_asymmetry_and_translation": "not modelled",
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+          "wind_averaging_period": "not calibrated to an agency averaging period",
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+        "members": 50,
+        "aggregation": "diagnose each member, then equal-weight mean; never diagnose the mean pressure",
+        "assumptions": {
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+          "maximum_radius_km": 480,
+          "azimuth_samples": 72,
+          "radial_smoothing": "three-point pressure average",
+          "geometry": "azimuthal mean; circular equivalent radii, not quadrants",
+          "wind_averaging_period": "not calibrated to an agency averaging period",
+          "land_friction_asymmetry_and_translation": "not modelled",
+          "calibration": "none; fixed physical assumptions, not fitted to benchmarks"
+        },
+        "estimates": {
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+        },
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+      },
+      {
+        "method": "pressure-gradient-experimental-v1",
+        "experimental": true,
+        "members": 50,
+        "aggregation": "diagnose each member, then equal-weight mean; never diagnose the mean pressure",
+        "assumptions": {
+          "air_density_kg_m3": 1.15,
+          "ocean_surface_reduction": 0.8,
+          "maximum_radius_km": 480,
+          "azimuth_samples": 72,
+          "radial_smoothing": "three-point pressure average",
+          "geometry": "azimuthal mean; circular equivalent radii, not quadrants",
+          "wind_averaging_period": "not calibrated to an agency averaging period",
+          "land_friction_asymmetry_and_translation": "not modelled",
+          "calibration": "none; fixed physical assumptions, not fitted to benchmarks"
+        },
+        "estimates": {
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+      },
+      {
+        "method": "pressure-gradient-experimental-v1",
+        "experimental": true,
+        "members": 50,
+        "aggregation": "diagnose each member, then equal-weight mean; never diagnose the mean pressure",
+        "assumptions": {
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+          "ocean_surface_reduction": 0.8,
+          "maximum_radius_km": 480,
+          "azimuth_samples": 72,
+          "radial_smoothing": "three-point pressure average",
+          "geometry": "azimuthal mean; circular equivalent radii, not quadrants",
+          "wind_averaging_period": "not calibrated to an agency averaging period",
+          "land_friction_asymmetry_and_translation": "not modelled",
+          "calibration": "none; fixed physical assumptions, not fitted to benchmarks"
+        },
+        "estimates": {
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+          },
+          "rmw_estimate_km": {
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+          },
+          "r34_estimate_km": {
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+            "valid_members": 50,
+            "total_members": 50
+          },
+          "r50_estimate_km": {
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+            "p10": 220.6652662699374,
+            "p90": 227.85334658458692,
+            "valid_members": 50,
+            "total_members": 50
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+          "r64_estimate_km": {
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+          }
+        },
+        "status_counts": {
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+        }
+      },
+      {
+        "method": "pressure-gradient-experimental-v1",
+        "experimental": true,
+        "members": 50,
+        "aggregation": "diagnose each member, then equal-weight mean; never diagnose the mean pressure",
+        "assumptions": {
+          "air_density_kg_m3": 1.15,
+          "ocean_surface_reduction": 0.8,
+          "maximum_radius_km": 480,
+          "azimuth_samples": 72,
+          "radial_smoothing": "three-point pressure average",
+          "geometry": "azimuthal mean; circular equivalent radii, not quadrants",
+          "wind_averaging_period": "not calibrated to an agency averaging period",
+          "land_friction_asymmetry_and_translation": "not modelled",
+          "calibration": "none; fixed physical assumptions, not fitted to benchmarks"
+        },
+        "estimates": {
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+            "p10": null,
+            "p90": null,
+            "valid_members": 20,
+            "total_members": 50
+          }
+        },
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+        }
+      },
+      {
+        "method": "pressure-gradient-experimental-v1",
+        "experimental": true,
+        "members": 50,
+        "aggregation": "diagnose each member, then equal-weight mean; never diagnose the mean pressure",
+        "assumptions": {
+          "air_density_kg_m3": 1.15,
+          "ocean_surface_reduction": 0.8,
+          "maximum_radius_km": 480,
+          "azimuth_samples": 72,
+          "radial_smoothing": "three-point pressure average",
+          "geometry": "azimuthal mean; circular equivalent radii, not quadrants",
+          "wind_averaging_period": "not calibrated to an agency averaging period",
+          "land_friction_asymmetry_and_translation": "not modelled",
+          "calibration": "none; fixed physical assumptions, not fitted to benchmarks"
+        },
+        "estimates": {
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+          },
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+            "p90": null,
+            "valid_members": 0,
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+          }
+        },
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+        }
+      },
+      {
+        "method": "pressure-gradient-experimental-v1",
+        "experimental": true,
+        "members": 50,
+        "aggregation": "diagnose each member, then equal-weight mean; never diagnose the mean pressure",
+        "assumptions": {
+          "air_density_kg_m3": 1.15,
+          "ocean_surface_reduction": 0.8,
+          "maximum_radius_km": 480,
+          "azimuth_samples": 72,
+          "radial_smoothing": "three-point pressure average",
+          "geometry": "azimuthal mean; circular equivalent radii, not quadrants",
+          "wind_averaging_period": "not calibrated to an agency averaging period",
+          "land_friction_asymmetry_and_translation": "not modelled",
+          "calibration": "none; fixed physical assumptions, not fitted to benchmarks"
+        },
+        "estimates": {
+          "maximum_wind_auxiliary_kt": {
+            "mean": 178.62585235595702,
+            "p10": 177.64862060546875,
+            "p90": 179.59320068359375,
+            "valid_members": 50,
+            "total_members": 50
+          },
+          "pressure_wind_estimate_kt": {
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+            "p10": 60.72983180675786,
+            "p90": 61.907323189394795,
+            "valid_members": 50,
+            "total_members": 50
+          },
+          "rmw_estimate_km": {
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+            "p10": 60.078455694854924,
+            "p90": 60.092307903408916,
+            "valid_members": 50,
+            "total_members": 50
+          },
+          "r34_estimate_km": {
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+            "p10": 249.4091393803782,
+            "p90": 255.59958414029367,
+            "valid_members": 50,
+            "total_members": 50
+          },
+          "r50_estimate_km": {
+            "mean": 155.4694924332101,
+            "p10": 149.96250368125305,
+            "p90": 160.97504668341705,
+            "valid_members": 50,
+            "total_members": 50
+          },
+          "r64_estimate_km": {
+            "mean": null,
+            "p10": null,
+            "p90": null,
+            "valid_members": 0,
+            "total_members": 50
+          }
+        },
+        "status_counts": {
+          "experimental_estimate": 50
+        }
+      }
+    ],
+    "inference_seconds": 23.216
+  },
+  "method": "Original model equation: bilinear basin + georeferenced model tapered moving anomaly; physical member fields registered before equal-weight mean.",
+  "scalar_pressure_inserted": false,
+  "route_or_truth_alignment": false,
+  "native_detail_history_available": false,
+  "model_core_information_spacing_km": 20,
+  "sampling_note": "0.1-degree core export and 0.25-degree basin reconstruction are sampling, not additional observations.",
+  "files_sha256": {
+    "common-pressure.npz": "5a9183d758fce8294bf854412edb9185cf4fcc2f386e8af74f4759a60bb16bdc",
+    "core-reconstruction.json": "fce45ac6395b867f092e31d8cb0ad7160ffb3fe1f76dbe489095b14385eee0eb",
+    "captured-core-members.npz": "e65def9a4508c311a0778ebb12b8ddf7ea680dc6133c3f2467fb792d259dba09"
+  }
+}

release_tools/build_fung_wong_video.py

@@ -12,10 +12,6 @@ from datetime import datetime, timedelta
 from pathlib import Path
 
 import numpy as np
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-from matplotlib.colors import TwoSlopeNorm
 
 REPO = Path(__file__).resolve().parents[1]
 DATA = REPO / 'evaluation/release_data'
@@ -27,6 +23,33 @@ def sha(path):
         return hashlib.file_digest(stream, 'sha256').hexdigest()
 
 
+def encode_video(work, output, forecast_states=20):
+    """One second per actual six-hour state, with no appended frozen tail."""
+    frames = sorted(work.glob('frame_*.png'))
+    expected = [work/f'frame_{i:03d}.png' for i in range(forecast_states)]
+    if frames != expected:
+        raise ValueError('Missing or unexpected forecast video frames')
+    subprocess.run(['ffmpeg', '-y', '-hide_banner', '-loglevel', 'warning',
+        '-framerate', '1', '-i', str(work/'frame_%03d.png'), '-vf', 'fps=30',
+        '-frames:v', str(forecast_states*30), '-c:v', 'h264_videotoolbox',
+        '-b:v', '4M', '-pix_fmt', 'yuv420p', '-movflags', '+faststart',
+        '-an', str(output)], check=True)
+    probe = json.loads(subprocess.check_output(['ffprobe', '-v', 'error',
+        '-select_streams', 'v:0', '-show_entries',
+        'stream=codec_name,width,height,nb_frames:format=duration',
+        '-of', 'json', str(output)]))
+    stream = probe['streams'][0]
+    if (stream['codec_name'], stream['width'], stream['height'], int(stream['nb_frames'])) != ('h264', 1600, 1000, forecast_states*30):
+        raise ValueError('Unexpected encoded video dimensions or frame count')
+    if abs(float(probe['format']['duration'])-forecast_states) > .05:
+        raise ValueError('Unexpected video duration or frozen tail')
+    return {'duration_seconds': float(probe['format']['duration']),
+        'forecast_states': forecast_states, 'encoded_frames': int(stream['nb_frames']),
+        'state_duration_seconds': 1, 'extra_final_hold_seconds': 0,
+        'codec': 'H.264', 'encoder': 'h264_videotoolbox', 'sha256': sha(output),
+        'playback': 'Each genuine six-hour state held for 1 second; no extra final hold and no interpolated forecast states.'}
+
+
 def distances(a, b):
     a, b = np.deg2rad(a), np.deg2rad(b)
     d = b - a
@@ -101,7 +124,14 @@ def prepare(root):
     print(json.dumps(meta['metrics'], indent=2), flush=True)
 
 
-def build(work, stem='fung_wong', data=DATA):
+def build(work, stem='fung_wong', data=DATA, core_export=None, output=None):
+    # CPU forecast/archive tests import the data helpers, not this renderer.
+    # Keep optional video dependencies out of those scheduled forecast jobs.
+    import matplotlib
+    matplotlib.use('Agg')
+    import matplotlib.pyplot as plt
+    from matplotlib.colors import TwoSlopeNorm
+
     work.mkdir(parents=True, exist_ok=True)
     meta = json.loads((data/f'{stem}_video.json').read_text())
     assert meta['model'] == 'Trackformer 1.2' and meta['members'] == 50
@@ -109,6 +139,17 @@ def build(work, stem='fung_wong', data=DATA):
     assert sha(data/f'{stem}_video.npz') == meta['data_sha256']
     with np.load(data/f'{stem}_video.npz', allow_pickle=False) as z:
         a = {k: z[k] for k in z.files}
+    if core_export is not None:
+        receipt = json.loads((core_export/'verification.json').read_text())
+        if (receipt['checkpoint_sha256'] != CHECKPOINT or receipt['members'] != 50
+                or receipt['issue_time_utc'] != meta['issue_time_utc']
+                or receipt['storm_id'] != meta['storm_id']):
+            raise ValueError('Core pressure identity mismatch')
+        if sha(core_export/'common-pressure.npz') != receipt['files_sha256']['common-pressure.npz']:
+            raise ValueError('Core pressure export changed')
+        with np.load(core_export/'common-pressure.npz', allow_pickle=False) as z:
+            common_pressure, common_lat, common_lon = z['pressure_hpa'], z['latitude'], z['longitude']
+        meta['pressure_reconstruction'] = receipt
     rings = json.loads((data/f'{stem}_coastlines.json').read_text())
     f, o, lat, lon = (a[k] for k in ('forecast_lat_lon', 'observed_lat_lon', 'latitude', 'longitude'))
     if f.shape != (21, 2) or a['regional_pressure_hpa'].shape != (20, 121, 121) or a['basin_pressure_hpa'].shape != (20, 25, 33):
@@ -116,10 +157,10 @@ def build(work, stem='fung_wong', data=DATA):
     bounds = meta.get('display_bounds', [115, 141, 8, 25])
     vmin, vmax = 936, 1016
     if stem != 'fung_wong':
-        field_min = min(a['basin_pressure_hpa'].min(), a['regional_pressure_hpa'].min())
-        field_max = max(a['basin_pressure_hpa'].max(), a['regional_pressure_hpa'].max())
+        field_min = common_pressure.min() if core_export else min(a['basin_pressure_hpa'].min(), a['regional_pressure_hpa'].min())
+        field_max = common_pressure.max() if core_export else max(a['basin_pressure_hpa'].max(), a['regional_pressure_hpa'].max())
         vmin, vmax = min(1008, 4*np.floor(field_min/4)), max(1020, 4*np.ceil(field_max/4))
-    levels, contours = np.arange(vmin, vmax+1, 2), np.arange(vmin, vmax+1, 4)
+    levels, contours = np.arange(vmin, vmax+1, 2), np.arange(vmin, vmax+1, 2)
     # Conventional diverging pressure colors: lows blue, highs red. The
     # numeric colorbar remains explicit; this changes rendering, never fields.
     pressure_norm = TwoSlopeNorm(vmin=vmin, vcenter=1013.25, vmax=vmax)
@@ -152,12 +193,17 @@ def build(work, stem='fung_wong', data=DATA):
             ax = fig.add_subplot(gs[0, :])
             maps = [(ax, bounds, False)]
         for axis, extent, is_small in maps:
-            axis.contourf(glon, glat, a['basin_pressure_hpa'][k], levels=levels, cmap='RdYlBu_r', norm=pressure_norm, extend='both')
-            axis.contour(glon, glat, a['basin_pressure_hpa'][k], levels=contours, colors='#546971', linewidths=.4)
-            im = axis.contourf(lon, lat, regional[k], levels=levels, cmap='RdYlBu_r', norm=pressure_norm, extend='both')
-            cs = axis.contour(lon, lat, regional[k], levels=contours, colors='#354b58', linewidths=.6)
+            if core_export:
+                im = axis.contourf(common_lon, common_lat, common_pressure[k], levels=levels, cmap='RdYlBu_r', norm=pressure_norm, extend='both')
+                cs = axis.contour(common_lon, common_lat, common_pressure[k], levels=contours, colors='#354b58',
+                                  linewidths=[.65 if p % 4 == 0 else .3 for p in contours])
+            else:
+                axis.contourf(glon, glat, a['basin_pressure_hpa'][k], levels=levels, cmap='RdYlBu_r', norm=pressure_norm, extend='both')
+                axis.contour(glon, glat, a['basin_pressure_hpa'][k], levels=contours, colors='#546971', linewidths=.4)
+                im = axis.contourf(lon, lat, regional[k], levels=levels, cmap='RdYlBu_r', norm=pressure_norm, extend='both')
+                cs = axis.contour(lon, lat, regional[k], levels=contours, colors='#354b58', linewidths=.6)
             if not is_small:
-                axis.clabel(cs, levels=contours[::2], fmt='%d', fontsize=9)
+                axis.clabel(cs, levels=contours[contours % 4 == 0], fmt='%d', fontsize=9)
             for ring in rings:
                 xy = np.asarray(ring); axis.plot(xy[:, 0], xy[:, 1], c='#66716b', lw=.65 if is_small else .8)
             for path, color, label, style in [(o, '#182f42', 'Observed best track', '--'), (f, '#b31565', 'Trackformer 1.2 mean of 50', '-')]:
@@ -170,7 +216,7 @@ def build(work, stem='fung_wong', data=DATA):
             if is_small:
                 axis.tick_params(labelsize=9)
                 axis.set_xticks([100, 140, 180])
-                axis.text(0, -.24, 'Same 50-member pressure mean\nBasin + fixed regional reconstruction\nNo decorative grid / no route shift',
+                axis.text(0, -.24, 'Same 50-member pressure mean\nBasin + georeferenced moving core\nNo decorative grid / no route shift' if core_export else 'Same 50-member pressure mean\nBasin + fixed regional reconstruction\nNo decorative grid / no route shift',
                     transform=axis.transAxes, fontsize=10, color='#47636d', va='top')
         ax.legend(loc='lower left', fontsize=11, framealpha=.95)
         ax.text(.985, .035, 'Same map / same valid time\nNo route shifting or rescaling', transform=ax.transAxes,
@@ -186,23 +232,20 @@ def build(work, stem='fung_wong', data=DATA):
         curve.grid(alpha=.2); curve.legend(loc='lower left', fontsize=10)
         info = fig.add_subplot(gs[1, 2] if overview_layout else gs[1, 1]); info.axis('off')
         inside = lon.min() <= f[k+1, 1] <= lon.max() and lat.min() <= f[k+1, 0] <= lat.max()
-        coverage = 'Centre within regional field' if inside else 'Centre outside regional field\nCoarse pressure only here'
+        coverage = 'Actual model moving core\n50 fields registered before mean' if core_export else 'Centre within regional field' if inside else 'Centre outside regional field\nCoarse pressure only here'
         info.text(0, 1, f"Position error: {a['track_error_km'][k]:.0f} km\nModel: {a['central_pressure_hpa'][k]:.1f} hPa\nObserved: {a['observed_pressure_hpa'][k]:.0f} hPa\n\n{coverage}", va='top', fontsize=12, color='#244754')
         valid = start+timedelta(hours=int(lead))
         fig.suptitle(f"Trackformer 1.2  /  {meta['storm']}  /  +{lead:03d} h", x=.055, y=.97, ha='left', fontsize=24, fontweight='bold', color='#173947')
         fig.text(.055, .918, f'Issue: {start:%d %b %Y %H:%M} UTC     |     Valid: {valid:%d %b %Y %H:%M} UTC     |     50-member mean', fontsize=14, color='#385865')
-        fig.text(.055, .067, 'Actual model pressure with 4 hPa isobars. Observations are verification only; selected example, not typical skill.', fontsize=11, color='#47636d')
-        fig.text(.055, .040, 'Regional reconstruction has no native-detail history. Outside its coverage: saved coarse basin field only.', fontsize=10, color='#47636d')
+        fig.text(.055, .067, 'Actual model pressure: 2 hPa isobars, bold/labeled 4 hPa. Observations are verification only; selected example, not typical skill.', fontsize=11, color='#47636d')
+        fig.text(.055, .040, 'Model basin + actual moving pressure anomalies on a common geographic grid. Mean core readout need not equal the mean-field minimum.' if core_export else 'Regional reconstruction has no native-detail history. Outside its coverage: saved coarse basin field only.', fontsize=10, color='#47636d')
         fig.text(.93, .91, 'Blue: low (L)  /  Red: high (H)', ha='right', fontsize=12, color='#385865')
         fig.savefig(work/f'frame_{k:03d}.png', dpi=100)
-        if k == 9: fig.savefig(REPO/f'evaluation/trackformer_1_2_{stem}_video_poster.png', dpi=100)
+        if k == 9: fig.savefig(work/'poster.png' if output else REPO/f'evaluation/trackformer_1_2_{stem}_video_poster.png', dpi=100)
+        print(f'Rendered +{lead:03d}h', flush=True)
         plt.close(fig)
-    output = REPO/f'docs/trackformer_1_2_{stem}.mp4'
-    subprocess.run(['ffmpeg', '-y', '-hide_banner', '-loglevel', 'warning', '-framerate', '1', '-i', str(work/'frame_%03d.png'),
-        '-vf', 'fps=30,tpad=stop_mode=clone:stop_duration=3', '-c:v', 'h264_videotoolbox', '-b:v', '4M', '-pix_fmt', 'yuv420p', '-movflags', '+faststart', '-an', str(output)], check=True)
-    meta['video'] = {'file': f'docs/trackformer_1_2_{stem}.mp4', 'duration_seconds': 23, 'forecast_states': 20,
-        'codec': 'H.264', 'encoder': 'h264_videotoolbox', 'sha256': sha(output),
-        'playback': 'Each six-hour state held for 1 second; final frame held 3 seconds extra. No interpolated forecast states.'}
+    output = output or REPO/f'docs/trackformer_1_2_{stem}.mp4'
+    meta['video'] = {'file': f'docs/trackformer_1_2_{stem}.mp4', **encode_video(work, output)}
     (data/f'{stem}_video.json').write_text(json.dumps(meta, indent=2)+'\n')
     print('Saved', output, output.stat().st_size, 'bytes', flush=True)
 
@@ -213,9 +256,11 @@ if __name__ == '__main__':
     p.add_argument('--work', type=Path, required=True)
     p.add_argument('--stem', choices=['fung_wong', 'soudelor', 'mangkhut', 'meranti'], default='fung_wong')
     p.add_argument('--data', type=Path, default=DATA)
+    p.add_argument('--core-export', type=Path)
+    p.add_argument('--output', type=Path)
     args = p.parse_args()
     if args.source_root:
         if args.stem != 'fung_wong' or args.data != DATA:
             p.error('--source-root prepares only the original saved Fung-wong dataset')
         prepare(args.source_root)
-    build(args.work, args.stem, args.data)
+    build(args.work, args.stem, args.data, args.core_export, args.output)

release_tools/publish_mangkhut_video_revision.py

@@ -0,0 +1,84 @@
+"""Scoped HF video revision: preserve the remote card and all other model assets."""
+import argparse
+import hashlib
+import json
+from pathlib import Path
+
+from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download
+
+ROOT = Path(__file__).resolve().parents[1]
+REPO = 'euler314/typhoon-predict'
+CACHE = '/Volumes/D/typhoon_predict/.cache/huggingface-intensity'
+START = '## More historical pressure forecasts — MP4\n'
+END = '### Illustrated technical paper\n'
+
+
+def sha(path):
+    with Path(path).open('rb') as stream:
+        return hashlib.file_digest(stream, 'sha256').hexdigest()
+
+
+def merge_card(card, github):
+    """Only replace shared descriptions; keep HF front matter and inline films."""
+    for text in (card, github):
+        if text.count(START) != 1 or text.count(END) != 1:
+            raise ValueError('Unexpected card anchors; preserve remote content')
+    old = card.split(START, 1)[1].split(END, 1)[0]
+    new = github.split(START, 1)[1].split(END, 1)[0]
+    lead = new.split('\n\n| Soudelor', 1)[0]
+    tail = new.split('**These are calendar-selected', 1)[1]
+    if old.count('### Soudelor') != 1 or old.count('**These are calendar-selected') != 1:
+        raise ValueError('Unexpected HF video section')
+    videos = '### Soudelor'+old.split('### Soudelor', 1)[1].split('**These are calendar-selected', 1)[0]
+    result = card.split(START, 1)[0]+START+'\n'+lead.strip()+'\n\n'+videos+'**These are calendar-selected'+tail+END+card.split(END, 1)[1]
+    if not result.startswith('---\n') or result.count('<video ') != card.count('<video ') or result.count('<video ') != 4:
+        raise ValueError('Lost HF metadata or an inline film')
+    return result
+
+
+def run(output, publish):
+    if not str(output.resolve()).startswith('/Volumes/D/'):
+        raise ValueError('All publication artifacts must stay on D')
+    output.mkdir(parents=True, exist_ok=True)
+    api = HfApi()
+    parent = api.model_info(REPO).sha
+    original = Path(hf_hub_download(REPO, 'README.md', revision=parent, cache_dir=CACHE))
+    card = output/'README.md'
+    card.write_text(merge_card(original.read_text(), (ROOT/'README.md').read_text()))
+    files = {'README.md': card}
+    for name in ('docs/trackformer_1_2_mangkhut.mp4', 'evaluation/trackformer_1_2_mangkhut_video_poster.png',
+                 'evaluation/release_data/mangkhut_video.json', 'evaluation/release_data/mangkhut_playback_verification.json',
+                 'evaluation/release_data/historical_video50_verification.json',
+                 'release_tools/build_fung_wong_video.py', 'release_tools/verify_historical_video50.py',
+                 'release_tools/test_historical_video_playback.py', 'release_tools/publish_mangkhut_video_revision.py'):
+        files[name] = ROOT/name
+    for name in ('common-pressure.npz', 'verification.json', 'core-reconstruction.json'):
+        relative = 'evaluation/release_data/pressure_core/mangkhut/'+name
+        files[relative] = ROOT/relative
+    meta = json.loads(files['evaluation/release_data/mangkhut_video.json'].read_text())
+    check = json.loads(files['evaluation/release_data/mangkhut_playback_verification.json'].read_text())
+    if meta['members'] != 50 or meta['video']['duration_seconds'] != 20 or not check['verified']:
+        raise ValueError('Unverified video revision')
+    if sha(files['docs/trackformer_1_2_mangkhut.mp4']) != meta['video']['sha256'] or check['video_sha256'] != meta['video']['sha256']:
+        raise ValueError('Video hash mismatch')
+    receipt = {'status':'prepared', 'repo_id': REPO, 'parent_commit': parent,
+               'files':{p:sha(v) for p,v in files.items()}, 'weights_changed':False, 'other_films_changed':False}
+    if publish:
+        result = api.create_commit(repo_id=REPO, repo_type='model', parent_commit=parent,
+            commit_message='Repair actual moving-core Mangkhut pressure film and remove frozen ending',
+            operations=[CommitOperationAdd(path_in_repo=p,path_or_fileobj=str(v)) for p,v in files.items()])
+        for name, expected in receipt['files'].items():
+            path = hf_hub_download(REPO, name, revision=result.oid, cache_dir=CACHE)
+            if sha(path) != expected:
+                raise ValueError('Remote file SHA mismatch: '+name)
+        receipt.update(status='published_verified', commit=result.oid, commit_url=str(result.commit_url), all_remote_files_sha256_verified=True)
+    (output/'publication.json').write_text(json.dumps(receipt, indent=2)+'\n')
+    print(json.dumps({k:v for k,v in receipt.items() if k!='files'}))
+
+
+if __name__ == '__main__':
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument('--output', type=Path, required=True)
+    p.add_argument('--publish', action='store_true')
+    args = p.parse_args()
+    run(args.output, args.publish)

release_tools/test_historical_video_playback.py

@@ -0,0 +1,37 @@
+"""Regression: never add an extra frozen forecast tail to new encodes."""
+import json
+import tempfile
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+from build_fung_wong_video import encode_video
+
+
+class VideoPlaybackTest(unittest.TestCase):
+    def test_all_twenty_states_without_padding(self):
+        with tempfile.TemporaryDirectory(dir='/Volumes/D/typhoon_predict/output') as name:
+            work = Path(name)
+            for i in range(20):
+                (work/f'frame_{i:03d}.png').touch()
+            video = work/'video.mp4'
+            video.touch()
+            probe = {'streams': [{'codec_name': 'h264', 'width': 1600,
+                      'height': 1000, 'nb_frames': '600'}], 'format': {'duration': '20'}}
+            with patch('build_fung_wong_video.subprocess.run') as run, patch(
+                    'build_fung_wong_video.subprocess.check_output', return_value=json.dumps(probe).encode()):
+                result = encode_video(work, video)
+                command = run.call_args.args[0]
+                self.assertNotIn('tpad', ' '.join(command))
+                self.assertEqual(command[command.index('-frames:v')+1], '600')
+                self.assertEqual(result['extra_final_hold_seconds'], 0)
+                self.assertEqual(result['duration_seconds'], 20)
+
+    def test_missing_state_is_not_repeated(self):
+        with tempfile.TemporaryDirectory(dir='/Volumes/D/typhoon_predict/output') as name:
+            with self.assertRaisesRegex(ValueError, 'Missing or unexpected'):
+                encode_video(Path(name), Path(name)/'video.mp4')
+
+
+if __name__ == '__main__':
+    unittest.main()

release_tools/verify_historical_video50.py

@@ -11,9 +11,11 @@ from ensemble_forecast import array_hash
 from forecast_historical_video50_mac import CASES, sha
 
 
-def verify(forecasts, repo):
+def verify(forecasts, repo, stems=None):
     report = {}
     for slug, (name, sid, issue) in CASES.items():
+        if stems is not None and slug not in stems:
+            continue
         saved = forecasts/slug
         data = repo/'evaluation/release_data'
         meta = json.loads((data/f'{slug}_video.json').read_text())
@@ -52,8 +54,10 @@ def verify(forecasts, repo):
         probe = json.loads(subprocess.check_output(['ffprobe', '-v', 'error', '-select_streams', 'v:0',
             '-show_entries', 'stream=codec_name,width,height,nb_frames:format=duration', '-of', 'json', str(movie)]))
         stream = probe['streams'][0]
-        assert (stream['codec_name'], stream['width'], stream['height'], int(stream['nb_frames'])) == ('h264', 1600, 1000, 690)
-        assert abs(float(probe['format']['duration'])-23) < .1
+        duration = meta['video']['duration_seconds']
+        assert duration in (20, 23)  # Preserve older published films; new encodes have no extra hold.
+        assert (stream['codec_name'], stream['width'], stream['height'], int(stream['nb_frames'])) == ('h264', 1600, 1000, int(duration*30))
+        assert abs(float(probe['format']['duration'])-duration) < .1
         report[slug] = {'storm_id': sid, 'issue_time_utc': issue, 'model': 'Trackformer 1.2',
             'checkpoint_sha256': meta['checkpoint_sha256'], 'members': 50,
             'distinct_inputs': len(set(meta['ensemble_policy']['input_sha256'])),
@@ -74,10 +78,14 @@ if __name__ == '__main__':
     ap.add_argument('--forecasts', type=Path, required=True)
     ap.add_argument('--repo', type=Path, default=Path(__file__).resolve().parents[1])
     ap.add_argument('--receipt', type=Path, required=True)
+    ap.add_argument('--stems', nargs='+', choices=list(CASES))
+    ap.add_argument('--merge-receipt', action='store_true', help='Preserve previously verified unselected storms')
     args = ap.parse_args()
     if not str(args.receipt.resolve()).startswith('/Volumes/D/'):
         raise ValueError('Verification receipt must stay on /Volumes/D')
-    report = verify(args.forecasts, args.repo)
-    auto.write(args.receipt, {'verified': True, 'storms': report})
+    report = verify(args.forecasts, args.repo, args.stems)
+    previous = json.loads(args.receipt.read_text()) if args.merge_receipt and args.receipt.exists() else {'verified':True,'storms':{}}
+    previous['storms'].update(report)
+    auto.write(args.receipt, previous)
     print(json.dumps({'verified': list(report), 'members_per_storm': 50, 'mean_differences': 0,
                       'receipt': str(args.receipt)}))