euler314 commited on 19 days ago

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Add verified 50-member Soudelor, Mangkhut and Meranti films; refresh Fung-wong

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.gitattributes +9 -0
README.md +27 -1
docs/trackformer_1_2_fung_wong.mp4 +2 -2
docs/trackformer_1_2_mangkhut.mp4 +3 -0
docs/trackformer_1_2_meranti.mp4 +3 -0
docs/trackformer_1_2_soudelor.mp4 +3 -0
evaluation/release_data/fung_wong_video.json +1 -1
evaluation/release_data/historical_video50_verification.json +1 -0
evaluation/release_data/mangkhut_coastlines.json +1 -0
evaluation/release_data/mangkhut_video.json +473 -0
evaluation/release_data/mangkhut_video.npz +3 -0
evaluation/release_data/meranti_coastlines.json +1 -0
evaluation/release_data/meranti_video.json +473 -0
evaluation/release_data/meranti_video.npz +3 -0
evaluation/release_data/soudelor_coastlines.json +1 -0
evaluation/release_data/soudelor_video.json +473 -0
evaluation/release_data/soudelor_video.npz +3 -0
evaluation/trackformer_1_2_fung_wong_video_poster.png +2 -2
evaluation/trackformer_1_2_mangkhut_video_poster.png +3 -0
evaluation/trackformer_1_2_meranti_video_poster.png +3 -0
evaluation/trackformer_1_2_soudelor_video_poster.png +3 -0
release_tools/build_fung_wong_video.py +84 -39
release_tools/forecast_historical_video50_mac.py +225 -0
release_tools/test_historical_video50_mac.py +45 -0
release_tools/verify_historical_video50.py +83 -0

.gitattributes CHANGED Viewed

@@ -176,3 +176,12 @@ evaluation/history-tick-inputs-1996-v2/weather-2024.npz filter=lfs diff=lfs merg
 evaluation/history-tick-inputs-1996-v2/weather-2025.npz filter=lfs diff=lfs merge=lfs -text
 evaluation/history-tick-inputs-1996-v2/weather-2026.npz filter=lfs diff=lfs merge=lfs -text
 evaluation/history-tick-inputs-1996-v2/weather-requested-1986228N19120.npz filter=lfs diff=lfs merge=lfs -text

 evaluation/history-tick-inputs-1996-v2/weather-2025.npz filter=lfs diff=lfs merge=lfs -text
 evaluation/history-tick-inputs-1996-v2/weather-2026.npz filter=lfs diff=lfs merge=lfs -text
 evaluation/history-tick-inputs-1996-v2/weather-requested-1986228N19120.npz filter=lfs diff=lfs merge=lfs -text
+docs/trackformer_1_2_soudelor.mp4 filter=lfs diff=lfs merge=lfs -text
+evaluation/trackformer_1_2_soudelor_video_poster.png filter=lfs diff=lfs merge=lfs -text
+evaluation/release_data/soudelor_video.npz filter=lfs diff=lfs merge=lfs -text
+docs/trackformer_1_2_mangkhut.mp4 filter=lfs diff=lfs merge=lfs -text
+evaluation/trackformer_1_2_mangkhut_video_poster.png filter=lfs diff=lfs merge=lfs -text
+evaluation/release_data/mangkhut_video.npz filter=lfs diff=lfs merge=lfs -text
+docs/trackformer_1_2_meranti.mp4 filter=lfs diff=lfs merge=lfs -text
+evaluation/trackformer_1_2_meranti_video_poster.png filter=lfs diff=lfs merge=lfs -text
+evaluation/release_data/meranti_video.npz filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -87,12 +87,38 @@ Mean track error is **41.0% lower** for 1.2 on this cohort. Direction, shape and
 **[Play or download the Fung-wong MP4](https://yu314-coder.github.io/typhoon-predict/trackformer_1_2_fung_wong.mp4)** · 23 seconds · +6 to +120 h · **50-member mean**. Click the preview above to play it; inline video playback depends on the Markdown host.
-The forecast starts **7 November 2025 at 00 UTC**. Actual saved model-generated pressure with **4 hPa isobars** is shown alongside the mean forecast route and observed best track, at matching valid times and on the same geographic map. Neither route nor field is shifted or rescaled to improve alignment. The central-pressure timeline compares the forecast to **JMA best-track pressure from IBTrACS TOKYO_PRES**, not a JMA forecast.
 This is the user-selected **Fung-wong route-and-pressure example**, not a claim of typical or untouched-test performance. The curves follow a similar broad path but do not perfectly overlap; timing and position differences remain. Central-pressure MAE is **7.36 hPa** across 20 valid labels, with an imperfect intensification and weakening cycle. For context, mean geographic position error is 130.9 km and +120 h error is 142.3 km; those distances alone do not establish route overlap. These scores use exact observed coordinates and great-circle distances, separately from the saved local-coordinate benchmark scores above.
 Native-detail input history was unavailable; the regional pressure reconstruction uses 0.25° output sampling, not native resolution. **From +66 h the forecast centre is outside the fixed regional patch**, so only the saved coarse basin field is displayed there. The video labels this coverage limit; it does not invent an extended detailed core. Member-mean central pressure still comes from each model member's moving-core readout, not the minimum of the displayed mean map. [Data, source hashes and video provenance](evaluation/release_data/fung_wong_video.json).
 ### 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).

 **[Play or download the Fung-wong MP4](https://yu314-coder.github.io/typhoon-predict/trackformer_1_2_fung_wong.mp4)** · 23 seconds · +6 to +120 h · **50-member mean**. Click the preview above to play it; inline video playback depends on the Markdown host.
+The forecast starts **7 November 2025 at 00 UTC**. The updated MP4 uses **blue for low pressure and red for high pressure**, with the unnecessary map grid and faint future-path overlays removed. Actual saved model-generated pressure with **4 hPa isobars** is shown alongside the mean forecast route and observed best track, at matching valid times and on the same geographic map. Neither route nor field is shifted or rescaled to improve alignment. The central-pressure timeline compares the forecast to **JMA best-track pressure from IBTrACS TOKYO_PRES**, not a JMA forecast.
 This is the user-selected **Fung-wong route-and-pressure example**, not a claim of typical or untouched-test performance. The curves follow a similar broad path but do not perfectly overlap; timing and position differences remain. Central-pressure MAE is **7.36 hPa** across 20 valid labels, with an imperfect intensification and weakening cycle. For context, mean geographic position error is 130.9 km and +120 h error is 142.3 km; those distances alone do not establish route overlap. These scores use exact observed coordinates and great-circle distances, separately from the saved local-coordinate benchmark scores above.
 Native-detail input history was unavailable; the regional pressure reconstruction uses 0.25° output sampling, not native resolution. **From +66 h the forecast centre is outside the fixed regional patch**, so only the saved coarse basin field is displayed there. The video labels this coverage limit; it does not invent an extended detailed core. Member-mean central pressure still comes from each model member's moving-core readout, not the minimum of the displayed mean map. [Data, source hashes and video provenance](evaluation/release_data/fung_wong_video.json).
+## 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.
+### Soudelor (2015) — issue 5 August 2015, 00 UTC
+<video controls playsinline preload="metadata" width="100%" poster="https://huggingface.co/euler314/typhoon-predict/resolve/main/evaluation/trackformer_1_2_soudelor_video_poster.png" src="https://huggingface.co/euler314/typhoon-predict/resolve/main/docs/trackformer_1_2_soudelor.mp4"></video>
+[Play / download Soudelor MP4](https://huggingface.co/euler314/typhoon-predict/resolve/main/docs/trackformer_1_2_soudelor.mp4) · [Provenance](evaluation/release_data/soudelor_video.json)
+### Mangkhut (2018) — issue 11 September 2018, 00 UTC
+<video controls playsinline preload="metadata" width="100%" poster="https://huggingface.co/euler314/typhoon-predict/resolve/main/evaluation/trackformer_1_2_mangkhut_video_poster.png" src="https://huggingface.co/euler314/typhoon-predict/resolve/main/docs/trackformer_1_2_mangkhut.mp4"></video>
+[Play / download Mangkhut MP4](https://huggingface.co/euler314/typhoon-predict/resolve/main/docs/trackformer_1_2_mangkhut.mp4) · [Provenance](evaluation/release_data/mangkhut_video.json)
+### Meranti (2016) — issue 10 September 2016, 00 UTC
+<video controls playsinline preload="metadata" width="100%" poster="https://huggingface.co/euler314/typhoon-predict/resolve/main/evaluation/trackformer_1_2_meranti_video_poster.png" src="https://huggingface.co/euler314/typhoon-predict/resolve/main/docs/trackformer_1_2_meranti.mp4"></video>
+[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.
+[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)
 ### 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).

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@@ -136,7 +136,7 @@
     "forecast_states": 20,
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     "playback": "Each six-hour state held for 1 second; final frame held 3 seconds extra. No interpolated forecast states."
   }
 }

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     "playback": "Each six-hour state held for 1 second; final frame held 3 seconds extra. No interpolated forecast states."
   }
 }

evaluation/release_data/historical_video50_verification.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"verified":true,"storms":{"soudelor":{"storm_id":"2015211N13162","issue_time_utc":"2015-08-05T00:00:00Z","model":"Trackformer 1.2","checkpoint_sha256":"f194a23d3f91ea76ad776dfad942fabd669eeae8b3fd665815463095367e9ee0","members":50,"distinct_inputs":50,"distinct_member_outputs":{"center":50,"pressure":50,"basin":50,"regional":50},"max_mean_difference":{"center":0.0,"pressure":0.0,"basin":0.0,"regional":0.0},"causal_nine_analyses_verified":true,"observed_plus_zero_alignment_verified":true,"future_observations_not_in_model_packet":true,"masked_native_history_verified":true,"native_history_available":false,"route_points":21,"pressure_states":20,"mp4_duration_seconds":23.0,"mp4_dimensions":[1600,1000],"files_sha256":{"mean_fields":"bed5425496abde931d81af06e1ef1438504c82468d5577c6bac5dc131a07e91f","mp4":"5fcc26588a75007c50255e92a55ff7442b405995072324850632258a40443bce","individual_members":"161e2a5d8def6ef6f555031ba6ba1b21f16ccf4390366d0360c5bbd06edcd31c","model_inputs":"2cad11d38a54c0ab41c8366793b2bae7febecae89609aeda3e016180df8b1ccd"},"evaluation_limit":"Calendar-selected historical development examples, not untouched holdouts."},"mangkhut":{"storm_id":"2018250N12170","issue_time_utc":"2018-09-11T00:00:00Z","model":"Trackformer 1.2","checkpoint_sha256":"f194a23d3f91ea76ad776dfad942fabd669eeae8b3fd665815463095367e9ee0","members":50,"distinct_inputs":50,"distinct_member_outputs":{"center":50,"pressure":50,"basin":50,"regional":50},"max_mean_difference":{"center":0.0,"pressure":0.0,"basin":0.0,"regional":0.0},"causal_nine_analyses_verified":true,"observed_plus_zero_alignment_verified":true,"future_observations_not_in_model_packet":true,"masked_native_history_verified":true,"native_history_available":false,"route_points":21,"pressure_states":20,"mp4_duration_seconds":23.0,"mp4_dimensions":[1600,1000],"files_sha256":{"mean_fields":"f06c857dc0e7edaa67a2e7262324fa0a9ebad684ce5a8ffa1610c27717c35b71","mp4":"a609f7dda0cb42ed5dc17e543c838489f7632317e40d7f29038ee69d691eacbb","individual_members":"5ba1e2325867bc71c48b9f94b8afa02925261e07c175d9c360a71b8334a9f43d","model_inputs":"7a180f3ec9f216b5cb8e9e5e1fdebb60bff779586d42054e10477edd23fdfccd"},"evaluation_limit":"Calendar-selected historical development examples, not untouched holdouts."},"meranti":{"storm_id":"2016253N13144","issue_time_utc":"2016-09-10T00:00:00Z","model":"Trackformer 1.2","checkpoint_sha256":"f194a23d3f91ea76ad776dfad942fabd669eeae8b3fd665815463095367e9ee0","members":50,"distinct_inputs":50,"distinct_member_outputs":{"center":50,"pressure":50,"basin":50,"regional":50},"max_mean_difference":{"center":0.0,"pressure":0.0,"basin":0.0,"regional":0.0},"causal_nine_analyses_verified":true,"observed_plus_zero_alignment_verified":true,"future_observations_not_in_model_packet":true,"masked_native_history_verified":true,"native_history_available":false,"route_points":21,"pressure_states":20,"mp4_duration_seconds":23.0,"mp4_dimensions":[1600,1000],"files_sha256":{"mean_fields":"565343166656155e8f227372cce2947536afe83593820c89790606e5ac909baf","mp4":"e12a83b0e129e5a5accb3a8d396481ea6f72979777d70d17fc9a18e6ad810a59","individual_members":"d04b729f31733e63f937ad6a523e8a034cc9e93c6b2775dc1d18ceeb2ad9987b","model_inputs":"5a65f65e765edd48adfa45938d4d452fbec001f7e567afe1b930b5d44e14f9e6"},"evaluation_limit":"Calendar-selected historical development examples, not untouched holdouts."}}}

evaluation/release_data/mangkhut_coastlines.json ADDED Viewed

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+    "inference_seconds": 10.995
+  },
+  "leads_hours": [
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+  "display_bounds": [
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+  "source_inputs": {
+    "source": "Existing exact NOAA NCEP Reanalysis-1 basin atlas; retrospective analyses.",
+    "channels": [
+      "mslp",
+      "hgt500",
+      "uwnd850",
+      "vwnd850",
+      "uwnd500",
+      "vwnd500",
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+      "vwnd200"
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+    "units": [
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+      "m/s",
+      "m/s",
+      "m/s",
+      "m/s",
+      "m/s",
+      "m/s"
+    ],
+    "analysis_times_utc": [
+      "2018-09-09T00:00:00Z",
+      "2018-09-09T06:00:00Z",
+      "2018-09-09T12:00:00Z",
+      "2018-09-09T18:00:00Z",
+      "2018-09-10T00:00:00Z",
+      "2018-09-10T06:00:00Z",
+      "2018-09-10T12:00:00Z",
+      "2018-09-10T18:00:00Z",
+      "2018-09-11T00:00:00Z"
+    ],
+    "source_sha256": {
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+    },
+    "subset_sha256": "ae33a5f42b3342a41e6567c8bea6f12f8a1175ef62e2dd845a5efc6a2f73b358",
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+    "issue_observation": {
+      "name": "MANGKHUT",
+      "storm_id": "2018250N12170",
+      "issue_time_utc": "2018-09-11T00:00:00Z",
+      "lat": 14.0,
+      "lon": 141.2,
+      "motion": [
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+      ],
+      "pressure_hpa": 935.0,
+      "wind_kt": 95.0
+    },
+    "motion_note": "Only current and exact previous six-hour observed coordinates; east/north km per six hours.",
+    "causal": true,
+    "future_labels_used_for_inference": false
+  },
+  "observed_csv_sha256": "4348af2679b4be0d53ec630bccda759044663a1f57e2bbd5c1d19428aab562fc",
+  "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.",
+  "native_history_available": false,
+  "grid_note": "0.25-degree regional reconstruction sampling, not native effective resolution. Basin sampling is 2.5 degrees. Coarse basin only outside the fixed regional patch.",
+  "regional_bounds": [
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+    156.25,
+    -1.0,
+    29.0
+  ],
+  "first_forecast_lead_outside_regional_hours": 78,
+  "metrics": {
+    "mean_track_error_km": 77.47351113275661,
+    "track_error_120h_km": 191.70061661021862,
+    "central_pressure_mae_hpa": 7.39349365234375,
+    "pressure_valid_leads": 20,
+    "position_valid_leads": 20
+  },
+  "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",
+    "duration_seconds": 23,
+    "forecast_states": 20,
+    "codec": "H.264",
+    "encoder": "h264_videotoolbox",
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+      "142cb26496b5720e19d8c464b3249ff28dd44125010af5cb39b067aca3ef8d06"
+    ],
+    "mean_policy": "Equal-weight physical basin and fixed-issue regional grids; member-mean coordinates and central pressures separately. Moving-core grids are not directly averaged.",
+    "inference_seconds": 11.915
+  },
+  "leads_hours": [
+    6,
+    12,
+    18,
+    24,
+    30,
+    36,
+    42,
+    48,
+    54,
+    60,
+    66,
+    72,
+    78,
+    84,
+    90,
+    96,
+    102,
+    108,
+    114,
+    120
+  ],
+  "display_bounds": [
+    100,
+    180,
+    0,
+    60
+  ],
+  "source_inputs": {
+    "source": "Existing exact NOAA NCEP Reanalysis-1 basin atlas; retrospective analyses.",
+    "channels": [
+      "mslp",
+      "hgt500",
+      "uwnd850",
+      "vwnd850",
+      "uwnd500",
+      "vwnd500",
+      "uwnd200",
+      "vwnd200"
+    ],
+    "units": [
+      "hPa",
+      "m",
+      "m/s",
+      "m/s",
+      "m/s",
+      "m/s",
+      "m/s",
+      "m/s"
+    ],
+    "analysis_times_utc": [
+      "2015-08-03T00:00:00Z",
+      "2015-08-03T06:00:00Z",
+      "2015-08-03T12:00:00Z",
+      "2015-08-03T18:00:00Z",
+      "2015-08-04T00:00:00Z",
+      "2015-08-04T06:00:00Z",
+      "2015-08-04T12:00:00Z",
+      "2015-08-04T18:00:00Z",
+      "2015-08-05T00:00:00Z"
+    ],
+    "source_sha256": {
+      "track_build/basin_all_int8.npz": "71e5f1bb46c44e5936c506a3f8af021c1fe94c238ed030c05c66e8f342374b6d",
+      "track_build/basin_slp_atlas_float16.npy": "9f6a4ec7b2f6beadfa1075af9176d2a653e39a824d504d989e5e56e19c571521",
+      "output/automatic-forecast-cache/geography.npz": "f5471471da4a1e15f85fbfb2529065806281357c8e50cb272fcd2b02e8553b96"
+    },
+    "subset_sha256": "d4088aa71e5f9d9b39f160993c312ab2f59a59dd057c2ba0dc27e307a7ffa1b2",
+    "physical_weather_sha256": "24ef01ade0ecc400388444dd45efe9deaaad5f52ed97d871895a145c0c681c81",
+    "normalized_input_sha256": "2cad11d38a54c0ab41c8366793b2bae7febecae89609aeda3e016180df8b1ccd",
+    "issue_observation": {
+      "name": "SOUDELOR",
+      "storm_id": "2015211N13162",
+      "issue_time_utc": "2015-08-05T00:00:00Z",
+      "lat": 19.5,
+      "lon": 134.9,
+      "motion": [
+        -136.26825395228352,
+        22.23999999999992
+      ],
+      "pressure_hpa": 930.0,
+      "wind_kt": 95.0
+    },
+    "motion_note": "Only current and exact previous six-hour observed coordinates; east/north km per six hours.",
+    "causal": true,
+    "future_labels_used_for_inference": false
+  },
+  "observed_csv_sha256": "4348af2679b4be0d53ec630bccda759044663a1f57e2bbd5c1d19428aab562fc",
+  "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.",
+  "native_history_available": false,
+  "grid_note": "0.25-degree regional reconstruction sampling, not native effective resolution. Basin sampling is 2.5 degrees. Coarse basin only outside the fixed regional patch.",
+  "regional_bounds": [
+    120.0,
+    150.0,
+    4.5,
+    34.5
+  ],
+  "first_forecast_lead_outside_regional_hours": 90,
+  "metrics": {
+    "mean_track_error_km": 120.55338024247541,
+    "track_error_120h_km": 354.3977091200638,
+    "central_pressure_mae_hpa": 9.860003662109374,
+    "pressure_valid_leads": 20,
+    "position_valid_leads": 20
+  },
+  "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_soudelor.mp4",
+    "duration_seconds": 23,
+    "forecast_states": 20,
+    "codec": "H.264",
+    "encoder": "h264_videotoolbox",
+    "sha256": "5fcc26588a75007c50255e92a55ff7442b405995072324850632258a40443bce",
+    "playback": "Each six-hour state held for 1 second; final frame held 3 seconds extra. No interpolated forecast states."
+  },
+  "video_layout": {
+    "overview_bounds": [
+      100,
+      180,
+      0,
+      60
+    ],
+    "closeup_bounds": [
+      113.0,
+      138.0,
+      16.0,
+      33.0
+    ],
+    "pressure_colors": "Blue below 1013.25 hPa; red above. Fixed range throughout each film.",
+    "map_graticule": false,
+    "geographic_route_adjustment": false
+  }
+}

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	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:bed5425496abde931d81af06e1ef1438504c82468d5577c6bac5dc131a07e91f
+size 844519

evaluation/trackformer_1_2_fung_wong_video_poster.png CHANGED Viewed

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Pointer size: 131 Bytes
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Git LFS Details

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evaluation/trackformer_1_2_mangkhut_video_poster.png ADDED Viewed

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evaluation/trackformer_1_2_soudelor_video_poster.png ADDED Viewed

Git LFS Details

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release_tools/build_fung_wong_video.py CHANGED Viewed

@@ -1,6 +1,6 @@
-"""Render the selected saved Fung-wong 50-member forecast, without model inference.
-Requires NumPy, Matplotlib and ffmpeg; --source-root prepares the public subset.
 Never shift the model field or route to improve alignment with observations.
 """
 import argparse
@@ -15,7 +15,7 @@ import numpy as np
 import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
-from matplotlib.patches import Rectangle
 REPO = Path(__file__).resolve().parents[1]
 DATA = REPO / 'evaluation/release_data'
@@ -101,69 +101,109 @@ def prepare(root):
     print(json.dumps(meta['metrics'], indent=2), flush=True)
-def build(work):
     work.mkdir(parents=True, exist_ok=True)
-    meta = json.loads((DATA/'fung_wong_video.json').read_text())
-    assert sha(DATA/'fung_wong_video.npz') == meta['data_sha256']
-    with np.load(DATA/'fung_wong_video.npz', allow_pickle=False) as z:
         a = {k: z[k] for k in z.files}
-    rings = json.loads((DATA/'fung_wong_coastlines.json').read_text())
     f, o, lat, lon = (a[k] for k in ('forecast_lat_lon', 'observed_lat_lon', 'latitude', 'longitude'))
-    bounds = [115, 141, 8, 25]
-    levels, contours = np.arange(936, 1017, 2), np.arange(936, 1017, 4)
     glat, glon, leads = np.linspace(60, 0, 25), np.linspace(100, 180, 33), np.arange(6, 121, 6)
     start = datetime.fromisoformat(meta['issue_time_utc'].replace('Z', '+00:00'))
     plt.rcParams.update({'font.size': 12, 'axes.titlesize': 15})
     for k, lead in enumerate(leads):
         fig = plt.figure(figsize=(16, 10), dpi=100, facecolor='#f8fafb')
-        gs = fig.add_gridspec(2, 2, width_ratios=[3.9, 1.25], height_ratios=[4.5, 1.4],
-            left=.055, right=.93, bottom=.155, top=.85, wspace=.25, hspace=.38)
-        ax = fig.add_subplot(gs[0, :])
-        ax.contourf(glon, glat, a['basin_pressure_hpa'][k], levels=levels, cmap='RdYlBu_r', extend='both')
-        ax.contour(glon, glat, a['basin_pressure_hpa'][k], levels=contours, colors='#546971', linewidths=.45)
-        im = ax.contourf(lon, lat, a['regional_pressure_hpa'][k], levels=levels, cmap='RdYlBu_r', extend='both')
-        cs = ax.contour(lon, lat, a['regional_pressure_hpa'][k], levels=contours, colors='#354b58', linewidths=.65)
-        ax.clabel(cs, levels=contours[::2], fmt='%d', fontsize=9)
-        for ring in rings:
-            xy = np.asarray(ring); ax.plot(xy[:, 0], xy[:, 1], c='#66716b', lw=.8)
-        ax.add_patch(Rectangle((lon.min(), lat.min()), np.ptp(lon), np.ptp(lat), fill=False, edgecolor='#265b61', lw=1.2, ls=':'))
-        for points, color, label, style in [(o, '#182f42', 'Observed best track', '--'), (f, '#b31565', 'Trackformer 1.2 mean of 50', '-')]:
-            ax.plot(points[:, 1], points[:, 0], ls=style, c=color, alpha=.2, lw=1.6)
-            ax.plot(points[:k+2, 1], points[:k+2, 0], ls=style, c=color, lw=2.8, label=label)
-            ax.scatter(points[k+1, 1], points[k+1, 0], s=75, c=color, edgecolors='white', linewidths=1.8, zorder=6)
-        ax.plot([f[k+1, 1], o[k+1, 1]], [f[k+1, 0], o[k+1, 0]], ':', c='#7a5a85', lw=1.4)
-        ax.set(xlim=bounds[:2], ylim=bounds[2:], xlabel='Longitude °E', ylabel='Latitude °N')
-        ax.set_aspect(1/np.cos(np.deg2rad(16.5))); ax.grid(alpha=.13)
         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,
             ha='right', va='bottom', fontsize=10, bbox={'facecolor': 'white', 'alpha': .9, 'edgecolor': 'none', 'pad': 5})
-        fig.colorbar(im, cax=fig.add_axes([.952, .40, .014, .40]), label='Model mean sea-level pressure (hPa)')
-        curve = fig.add_subplot(gs[1, 0])
         curve.plot(leads, a['central_pressure_hpa'], c='#b31565', lw=2, label='Model')
         curve.plot(leads, a['observed_pressure_hpa'], '--', c='#182f42', lw=2, label='Observed (JMA best track)')
         curve.axvline(lead, c='#688e98', lw=1.5)
         curve.scatter([lead], [a['central_pressure_hpa'][k]], c='#b31565', s=40, zorder=4)
         curve.set(xlim=(6, 120), xlabel='Forecast lead (hours)', ylabel='Central pressure (hPa)', xticks=[6, 24, 48, 72, 96, 120])
         curve.grid(alpha=.2); curve.legend(loc='lower left', fontsize=10)
-        info = fig.add_subplot(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'
         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  /  FUNG-WONG  /  +{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, 'Dotted boundary: fixed regional reconstruction (no native-detail history). Outside: coarse basin field only.', fontsize=10, color='#47636d')
         fig.savefig(work/f'frame_{k:03d}.png', dpi=100)
-        if k == 9: fig.savefig(REPO/'evaluation/trackformer_1_2_fung_wong_video_poster.png', dpi=100)
         plt.close(fig)
-    output = REPO/'docs/trackformer_1_2_fung_wong.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': 'docs/trackformer_1_2_fung_wong.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.'}
-    (DATA/'fung_wong_video.json').write_text(json.dumps(meta, indent=2)+'\n')
     print('Saved', output, output.stat().st_size, 'bytes', flush=True)
@@ -171,6 +211,11 @@ if __name__ == '__main__':
     p = argparse.ArgumentParser(description=__doc__)
     p.add_argument('--source-root', type=Path)
     p.add_argument('--work', type=Path, required=True)
     args = p.parse_args()
-    if args.source_root: prepare(args.source_root)
-    build(args.work)

+"""Render genuine saved Trackformer 1.2 50-member videos, without model inference.
+Requires NumPy, Matplotlib and ffmpeg; --source-root prepares the Fung-wong subset.
 Never shift the model field or route to improve alignment with observations.
 """
 import argparse
 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'
     print(json.dumps(meta['metrics'], indent=2), flush=True)
+def build(work, stem='fung_wong', data=DATA):
     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
+    assert meta['checkpoint_sha256'] == CHECKPOINT
+    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}
+    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):
+        raise ValueError('Video requires exact +6 through +120-hour fields and route')
+    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())
+        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)
+    # 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)
+    regional = np.ma.masked_where(np.broadcast_to(~a.get('regional_valid_mask', np.ones((121, 121), dtype=bool)), (20, 121, 121)), a['regional_pressure_hpa'])
     glat, glon, leads = np.linspace(60, 0, 25), np.linspace(100, 180, 33), np.arange(6, 121, 6)
     start = datetime.fromisoformat(meta['issue_time_utc'].replace('Z', '+00:00'))
+    overview_layout = stem != 'fung_wong'
+    if overview_layout:
+        # One fixed geographic close-up for the entire film, including both
+        # unshifted paths. Its crop affects display only, never model inference.
+        points = np.concatenate([f, o])
+        points = points[np.isfinite(points).all(axis=1)]
+        closeup = [max(100, np.floor(points[:, 1].min()-3)), min(180, np.ceil(points[:, 1].max()+3)),
+                   max(0, np.floor(points[:, 0].min()-3)), min(60, np.ceil(points[:, 0].max()+3))]
+        meta['video_layout'] = {'overview_bounds': bounds, 'closeup_bounds': [float(v) for v in closeup],
+            'pressure_colors': 'Blue below 1013.25 hPa; red above. Fixed range throughout each film.',
+            'map_graticule': False, 'geographic_route_adjustment': False}
     plt.rcParams.update({'font.size': 12, 'axes.titlesize': 15})
     for k, lead in enumerate(leads):
         fig = plt.figure(figsize=(16, 10), dpi=100, facecolor='#f8fafb')
+        if overview_layout:
+            gs = fig.add_gridspec(2, 3, width_ratios=[2.6, 4.6, 1.4], height_ratios=[4.5, 1.4],
+                left=.065, right=.91, bottom=.16, top=.83, wspace=.34, hspace=.40)
+            small = fig.add_subplot(gs[0, 0])
+            ax = fig.add_subplot(gs[0, 1:])
+            maps = [(small, bounds, True), (ax, closeup, False)]
+        else:
+            gs = fig.add_gridspec(2, 2, width_ratios=[3.9, 1.25], height_ratios=[4.5, 1.4],
+                left=.055, right=.93, bottom=.155, top=.85, wspace=.25, hspace=.38)
+            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 not is_small:
+                axis.clabel(cs, levels=contours[::2], 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', '-')]:
+                axis.plot(path[:k+2, 1], path[:k+2, 0], ls=style, c=color, lw=1.8 if is_small else 2.8, label=label)
+                axis.scatter(path[k+1, 1], path[k+1, 0], s=28 if is_small else 75, c=color, edgecolors='white', linewidths=1.2, zorder=6)
+            axis.set(xlim=extent[:2], ylim=extent[2:], xlabel='Longitude °E', ylabel='Latitude °N')
+            axis.set_aspect(1/np.cos(np.deg2rad((extent[2]+extent[3])/2))); axis.grid(False)
+            if overview_layout:
+                axis.set_title('Western Pacific overview' if is_small else 'Route and model-pressure close-up', loc='left', pad=12)
+            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',
+                    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,
             ha='right', va='bottom', fontsize=10, bbox={'facecolor': 'white', 'alpha': .9, 'edgecolor': 'none', 'pad': 5})
+        bar = fig.colorbar(im, cax=fig.add_axes([.943 if overview_layout else .952, .40, .011, .40]), label='Model mean MSLP (hPa)')
+        bar.ax.yaxis.set_label_position('left')
+        curve = fig.add_subplot(gs[1, :2] if overview_layout else gs[1, 0])
         curve.plot(leads, a['central_pressure_hpa'], c='#b31565', lw=2, label='Model')
         curve.plot(leads, a['observed_pressure_hpa'], '--', c='#182f42', lw=2, label='Observed (JMA best track)')
         curve.axvline(lead, c='#688e98', lw=1.5)
         curve.scatter([lead], [a['central_pressure_hpa'][k]], c='#b31565', s=40, zorder=4)
         curve.set(xlim=(6, 120), xlabel='Forecast lead (hours)', ylabel='Central pressure (hPa)', xticks=[6, 24, 48, 72, 96, 120])
         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'
         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(.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)
         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.'}
+    (data/f'{stem}_video.json').write_text(json.dumps(meta, indent=2)+'\n')
     print('Saved', output, output.stat().st_size, 'bytes', flush=True)
     p = argparse.ArgumentParser(description=__doc__)
     p.add_argument('--source-root', type=Path)
     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)
     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)

release_tools/forecast_historical_video50_mac.py ADDED Viewed

	@@ -0,0 +1,225 @@

+"""Prepare honest 50-member historical video data using the frozen 1.2 on MPS.
+Reads existing verified local analyses only; no downloads or model fitting.
+Future best-track coordinates/pressures are attached AFTER model inference.
+Each calendar-selected example is development evidence, not a holdout test.
+"""
+import argparse
+import csv
+import hashlib
+import json
+from datetime import timedelta
+from pathlib import Path
+import numpy as np
+import torch
+import automatic_forecasts as auto
+from ensemble_forecast import array_hash, run_ensemble, mean_outputs
+from build_fung_wong_video import distances
+CASES = {
+    'soudelor': ('SOUDELOR', '2015211N13162', '2015-08-05T00:00:00Z'),
+    'mangkhut': ('MANGKHUT', '2018250N12170', '2018-09-11T00:00:00Z'),
+    'meranti': ('MERANTI', '2016253N13144', '2016-09-10T00:00:00Z'),
+}
+SOURCE_HASHES = {
+    'track_build/basin_all_int8.npz': '71e5f1bb46c44e5936c506a3f8af021c1fe94c238ed030c05c66e8f342374b6d',
+    'track_build/basin_slp_atlas_float16.npy': '9f6a4ec7b2f6beadfa1075af9176d2a653e39a824d504d989e5e56e19c571521',
+    'output/automatic-forecast-cache/geography.npz': 'f5471471da4a1e15f85fbfb2529065806281357c8e50cb272fcd2b02e8553b96',
+}
+def sha(path):
+    h = hashlib.sha256()
+    with Path(path).open('rb') as stream:
+        for block in iter(lambda: stream.read(8 * 1024**2), b''):
+            h.update(block)
+    return h.hexdigest()
+def exact_history_indices(times, issue):
+    wanted = auto.ns(issue) + np.arange(-8, 1, dtype='int64') * 6 * auto.HOUR
+    indices = np.searchsorted(times, wanted)
+    if np.any(indices >= len(times)) or not np.array_equal(times[indices], wanted):
+        raise ValueError('Missing exact causal six-hour analysis; no nearest-date fill')
+    return indices
+def number(row, key):
+    value = row.get(key, '').strip()
+    return float(value) if value else None
+def issue_row(rows, name, sid, issue):
+    now = auto.parse(issue)
+    stamp = now.strftime('%Y-%m-%d %H:%M:%S')
+    p = rows[stamp]
+    lat, lon = float(p['LAT']), float(p['LON'])
+    prior = rows.get((now-timedelta(hours=6)).strftime('%Y-%m-%d %H:%M:%S'))
+    motion = [0., 0.] if prior is None else [
+        (lon-float(prior['LON']))*111.2*np.cos(np.deg2rad(lat)),
+        (lat-float(prior['LAT']))*111.2]
+    # Construct an allowlisted input row. No future display labels are attached.
+    return {'name': name, 'storm_id': sid, 'issue_time_utc': issue,
+            'lat': lat, 'lon': lon, 'motion': motion,
+            'pressure_hpa': number(p, 'TOKYO_PRES'), 'wind_kt': number(p, 'TOKYO_WIND')}
+def observations(rows, issue):
+    route, pressure = [], []
+    now = auto.parse(issue)
+    for k in range(21):
+        row = rows.get((now+timedelta(hours=k*6)).strftime('%Y-%m-%d %H:%M:%S'))
+        route.append([float(row['LAT']), float(row['LON'])] if row else [np.nan, np.nan])
+        value = number(row, 'TOKYO_PRES') if row else None
+        pressure.append(value if value is not None and 800 < value < 1100 else np.nan)
+    return np.asarray(route), np.asarray(pressure[1:])
+def main():
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument('--project', type=Path, required=True)
+    ap.add_argument('--output', type=Path, required=True)
+    ap.add_argument('--storm', choices=list(CASES), action='append', required=True)
+    ap.add_argument('--chunk', type=int, default=5)
+    args = ap.parse_args()
+    if not str(args.output.resolve()).startswith('/Volumes/D/'):
+        raise ValueError('New artifacts must stay on /Volumes/D')
+    if not torch.backends.mps.is_available():
+        raise RuntimeError('MPS GPU required; no silent CPU fallback')
+    args.output.mkdir(parents=True, exist_ok=True)
+    contract_meta = json.loads((auto.MODEL/'manifest.json').read_text())
+    contract = contract_meta['data_contract']
+    if contract_meta['public_version'] != '1.2' or contract_meta['source_checkpoint_sha256'] != auto.CHECKPOINT:
+        raise ValueError('Wrong release identity')
+    for filename, expected in SOURCE_HASHES.items():
+        if sha(args.project/filename) != expected:
+            raise ValueError('Local analysis/geography identity mismatch: '+filename)
+    for name, expected in contract_meta['source_module_sha256'].items():
+        if sha(auto.MODEL/name) != expected:
+            raise ValueError('Frozen release code changed: '+name)
+    weights = args.project/'output/automatic-forecast-cache/weights.pt'
+    if sha(weights) != contract_meta['inference_weights_sha256']:
+        raise ValueError('Wrong frozen model weights')
+    with np.load(args.project/'track_build/basin_all_int8.npz', allow_pickle=False) as z:
+        times = z['time'].astype('int64')
+        scale, offset = z['scale'].copy(), z['offset'].copy()
+        if list(z['channels']) != contract['channels'][1:]:
+            raise ValueError('Channel order mismatch')
+        if not np.array_equal(z['lat'], contract['global_lat']) or not np.array_equal(z['lon'], contract['global_lon']):
+            raise ValueError('Analysis grid mismatch')
+        # The mmap is only a decoded-cache optimization. Compare all requested
+        # subsets to the hash-verified original encoded source before inference.
+        q_mmap = np.load(args.project/'data/v164_reuse/basin_q_verified.npy', mmap_mode='r')
+        q_original = z['q']
+        for slug in args.storm:
+            idx = exact_history_indices(times, CASES[slug][2])
+            if not np.array_equal(q_mmap[idx], q_original[idx]):
+                raise ValueError('Decoded weather cache differs from original')
+        del q_original
+    slp = np.load(args.project/'track_build/basin_slp_atlas_float16.npy', mmap_mode='r')
+    csvpath = args.project/'data/ibtracs/ibtracs.WP.list.v04r01.csv'
+    csvhash = sha(csvpath)
+    with csvpath.open() as stream:
+        all_rows = [r for r in csv.DictReader(stream) if r['SID'] in {CASES[s][1] for s in args.storm}]
+    coastpath = args.project/'trackformer-weatherlab-site/public/data/history/coastlines.json'
+    coastlines = json.loads(coastpath.read_text())
+    geo = np.load(args.project/'output/automatic-forecast-cache/geography.npz', allow_pickle=False)
+    torch.set_num_threads(4)
+    model = auto.CoreForecaster(contract).eval()
+    model.load_state_dict(torch.load(weights, map_location='cpu', weights_only=True), strict=True)
+    for slug in args.storm:
+        dest = args.output/slug
+        # A finished issue is immutable, even when rerunning the command.
+        if (dest/'verification.json').exists():
+            receipt = json.loads((dest/'verification.json').read_text())
+            for filename, expected in receipt['files_sha256'].items():
+                if sha(dest/filename) != expected:
+                    raise ValueError('Completed artifact changed: '+filename)
+            print(json.dumps({'storm': slug, 'reused_verified_complete': True}), flush=True)
+            continue
+        dest.mkdir(parents=True, exist_ok=True)
+        name, sid, issue = CASES[slug]
+        rows = {r['ISO_TIME']: r for r in all_rows if r['SID'] == sid}
+        if len(rows) != sum(r['SID'] == sid for r in all_rows):
+            raise ValueError('Duplicate storm observation timestamp')
+        row = issue_row(rows, name, sid, issue)
+        idx = exact_history_indices(times, issue)
+        weather = np.concatenate((np.asarray(slp[idx], dtype='float32')[:, None],
+            np.asarray(q_mmap[idx], dtype='float32')*scale[None, :, None, None]+offset[None, :, None, None]), axis=1)
+        x = auto.inputs(weather, times[idx], row, contract, geo)
+        if not np.array_equal(x['center'][0].numpy(), np.asarray([row['lat'], row['lon']], dtype='float32')):
+            raise ValueError('+0 observation alignment failed')
+        np.savez_compressed(dest/'weather-history.npz', physical_weather=weather, time_ns=times[idx])
+        np.savez_compressed(dest/'model-inputs.npz', **{k: v.numpy() for k, v in x.items()}, history_time_ns=times[idx])
+        print(json.dumps({'storm': slug, 'starting': issue, 'device': 'mps', 'members': 50}), flush=True)
+        output, policy = run_ensemble(model, x, contract, 'mps', args.chunk,
+            lambda done, total, elapsed: print(json.dumps({'storm': slug, 'members_done': done,
+                'members': total, 'elapsed_seconds': elapsed}), flush=True))
+        means = mean_outputs(output)
+        for key in ('center', 'pressure', 'basin', 'regional'):
+            if not np.allclose(means[key], output[key].mean(axis=0, dtype=np.float64), rtol=0, atol=1e-4):
+                raise ValueError('Mean aggregation failed: '+key)
+        np.savez_compressed(dest/'ensemble-members.npz', **output, seeds=np.asarray(policy['seeds']))
+        # Attach future labels only after all forecast members have finished.
+        obs, truth_pressure = observations(rows, issue)
+        origin = np.array([row['lat'], row['lon']])
+        anchor = np.round(origin*4)/4
+        lat = anchor[0]+np.linspace(15, -15, 121)
+        lon = anchor[1]+np.linspace(-15, 15, 121)
+        route = np.vstack([origin, means['center']])
+        errors = distances(means['center'], obs[1:])
+        pressure_mask = np.isfinite(truth_pressure)
+        npz = dest/f'{slug}_video.npz'
+        np.savez_compressed(npz, forecast_lat_lon=route, observed_lat_lon=obs,
+            central_pressure_hpa=means['pressure'], observed_pressure_hpa=truth_pressure,
+            regional_pressure_hpa=means['regional'], basin_pressure_hpa=means['basin'],
+            latitude=lat, longitude=lon, track_error_km=errors,
+            regional_valid_mask=(lat[:, None] >= 0)&(lat[:, None] <= 60)&(lon[None, :] >= 100)&(lon[None, :] <= 180),
+            valid_member_counts=output['track_valid'].sum(axis=0))
+        rings = [r for r in coastlines if any(100 <= p[0] <= 180 and 0 <= p[1] <= 60 for p in r)]
+        (dest/f'{slug}_coastlines.json').write_text(json.dumps(rings, separators=(',', ':'))+'\n')
+        local_source = {name: value for name, value in SOURCE_HASHES.items()}
+        meta = {'model': 'Trackformer 1.2', 'storm': name, 'storm_id': sid, 'issue_time_utc': issue,
+            'members': 50, 'device': 'mps', 'checkpoint_sha256': auto.CHECKPOINT,
+            'inference_weights_sha256': contract_meta['inference_weights_sha256'],
+            'source_module_sha256': contract_meta['source_module_sha256'],
+            'data_sha256': sha(npz), 'ensemble_policy': policy,
+            'leads_hours': list(range(6, 121, 6)), 'display_bounds': [100, 180, 0, 60],
+            'source_inputs': {'source': 'Existing exact NOAA NCEP Reanalysis-1 basin atlas; retrospective analyses.',
+                'channels': contract['channels'], 'units': contract['units'],
+                'analysis_times_utc': [str(np.datetime64(int(t), 'ns').astype('datetime64[s]'))+'Z' for t in times[idx]],
+                'source_sha256': local_source, 'subset_sha256': sha(dest/'weather-history.npz'),
+                'physical_weather_sha256': array_hash(weather),
+                'normalized_input_sha256': sha(dest/'model-inputs.npz'),
+                'issue_observation': row,
+                'motion_note': 'Only current and exact previous six-hour observed coordinates; east/north km per six hours.',
+                'causal': True, 'future_labels_used_for_inference': False},
+            'observed_csv_sha256': csvhash,
+            '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.',
+            'native_history_available': False,
+            'grid_note': '0.25-degree regional reconstruction sampling, not native effective resolution. Basin sampling is 2.5 degrees. Coarse basin only outside the fixed regional patch.',
+            'regional_bounds': [float(lon.min()), float(lon.max()), float(lat.min()), float(lat.max())],
+            'first_forecast_lead_outside_regional_hours': next((6*(i+1) for i, point in enumerate(means['center'])
+                if not (lon.min() <= point[1] <= lon.max() and lat.min() <= point[0] <= lat.max())), None),
+            'metrics': {'mean_track_error_km': float(np.nanmean(errors)),
+                'track_error_120h_km': float(errors[-1]) if np.isfinite(errors[-1]) else None,
+                'central_pressure_mae_hpa': float(np.abs(means['pressure'][pressure_mask]-truth_pressure[pressure_mask]).mean()) if pressure_mask.any() else None,
+                'pressure_valid_leads': int(pressure_mask.sum()), 'position_valid_leads': int(np.isfinite(errors).sum())},
+            '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.'}
+        auto.write(dest/f'{slug}_video.json', meta)
+        names = ['weather-history.npz', 'model-inputs.npz', 'ensemble-members.npz',
+                 f'{slug}_video.npz', f'{slug}_video.json', f'{slug}_coastlines.json']
+        auto.write(dest/'verification.json', {'model': 'Trackformer 1.2', 'device': 'mps', 'members': 50,
+            'distinct_inputs': len(set(policy['input_sha256'])), 'distinct_routes': len(set(policy['route_sha256'])),
+            'distinct_basin_fields': len(set(policy['basin_field_sha256'])), 'route_points': 21,
+            'pressure_frames': 20, 'physical_means_verified': True, 'observed_plus_zero_verified': True,
+            'checkpoint_sha256': auto.CHECKPOINT, 'files_sha256': {n: sha(dest/n) for n in names}})
+        print(json.dumps({'complete': slug, 'output': str(dest), 'metrics': meta['metrics']}), flush=True)
+if __name__ == '__main__':
+    main()

release_tools/test_historical_video50_mac.py ADDED Viewed

	@@ -0,0 +1,45 @@

+import unittest
+import numpy as np
+import automatic_forecasts as auto
+from forecast_historical_video50_mac import exact_history_indices, issue_row, observations
+class HistoricalVideoInputs(unittest.TestCase):
+    def setUp(self):
+        self.issue = '2016-09-10T00:00:00Z'
+        self.end = auto.ns(self.issue)
+        self.times = self.end + np.arange(-10, 3, dtype='int64')*6*auto.HOUR
+        self.rows = {
+            '2016-09-09 18:00:00': {'LAT': '14.1', 'LON': '140.5', 'TOKYO_PRES': '1006', 'TOKYO_WIND': ''},
+            '2016-09-10 00:00:00': {'LAT': '14.5', 'LON': '140.0', 'TOKYO_PRES': '1004', 'TOKYO_WIND': ' '},
+            '2016-09-10 06:00:00': {'LAT': '14.8', 'LON': '139.0', 'TOKYO_PRES': '1000', 'TOKYO_WIND': '35'},
+        }
+    def test_exact_history_ends_at_issue(self):
+        indices = exact_history_indices(self.times, self.issue)
+        np.testing.assert_array_equal(self.times[indices], self.end+np.arange(-8, 1)*6*auto.HOUR)
+    def test_missing_time_cannot_use_future_or_nearest(self):
+        with self.assertRaises(ValueError):
+            exact_history_indices(np.delete(self.times, 6), self.issue)
+    def test_future_labels_do_not_change_inputs(self):
+        initial = issue_row(self.rows, 'MERANTI', 'test', self.issue)
+        self.rows['2016-09-10 06:00:00'].update(LAT='50', LON='179', TOKYO_PRES='850')
+        self.assertEqual(initial, issue_row(self.rows, 'MERANTI', 'test', self.issue))
+        self.assertNotIn('observed', initial)
+    def test_missing_wind_remains_missing(self):
+        self.assertIsNone(issue_row(self.rows, 'MERANTI', 'test', self.issue)['wind_kt'])
+    def test_observations_are_exact_and_missing_stays_missing(self):
+        route, pressure = observations(self.rows, self.issue)
+        np.testing.assert_allclose(route[0], [14.5, 140.0])
+        self.assertEqual(pressure[0], 1000)
+        self.assertTrue(np.isnan(route[2:]).all())
+        self.assertTrue(np.isnan(pressure[1:]).all())
+if __name__ == '__main__':
+    unittest.main()

release_tools/verify_historical_video50.py ADDED Viewed

	@@ -0,0 +1,83 @@

+"""Check video identity, actual distinct member means, causal inputs and +0."""
+import argparse
+import json
+import subprocess
+from pathlib import Path
+import numpy as np
+import automatic_forecasts as auto
+from ensemble_forecast import array_hash
+from forecast_historical_video50_mac import CASES, sha
+def verify(forecasts, repo):
+    report = {}
+    for slug, (name, sid, issue) in CASES.items():
+        saved = forecasts/slug
+        data = repo/'evaluation/release_data'
+        meta = json.loads((data/f'{slug}_video.json').read_text())
+        original = json.loads((saved/'verification.json').read_text())
+        for filename, expected in original['files_sha256'].items():
+            if sha(saved/filename) != expected:
+                raise ValueError('Original inference artifact changed: '+filename)
+        assert meta['checkpoint_sha256'] == auto.CHECKPOINT
+        assert (meta['storm'], meta['storm_id'], meta['issue_time_utc']) == (name, sid, issue)
+        assert meta['members'] == 50 and meta['leads_hours'] == list(range(6, 121, 6))
+        assert sha(data/f'{slug}_video.npz') == meta['data_sha256']
+        with np.load(saved/'model-inputs.npz', allow_pickle=False) as x:
+            np.testing.assert_array_equal(x['history_time_ns'], auto.ns(issue)+np.arange(-8, 1)*6*auto.HOUR)
+            assert not x['detail_available'].any()
+            origin = x['center'][0].copy()
+        counts, deltas = {}, {}
+        with np.load(saved/'ensemble-members.npz', allow_pickle=False) as members, np.load(data/f'{slug}_video.npz', allow_pickle=False) as mean:
+            np.testing.assert_array_equal(members['seeds'], np.arange(2043, 2093))
+            np.testing.assert_allclose(mean['forecast_lat_lon'][0], origin, atol=1e-5)
+            np.testing.assert_allclose(mean['observed_lat_lon'][0], origin, atol=1e-5)
+            for raw_key, mean_key in [('center', 'forecast_lat_lon'), ('pressure', 'central_pressure_hpa'),
+                                      ('basin', 'basin_pressure_hpa'), ('regional', 'regional_pressure_hpa')]:
+                raw = members[raw_key]
+                assert raw.shape[0] == 50 and raw.shape[1] == 20 and np.isfinite(raw).all()
+                if raw_key != 'center':
+                    assert raw.min() >= 800 and raw.max() <= 1100
+                actual = mean[mean_key][1:] if raw_key == 'center' else mean[mean_key]
+                expected = raw.mean(axis=0, dtype=np.float64).astype('float32')
+                np.testing.assert_array_equal(actual, expected)
+                counts[raw_key] = len({array_hash(v) for v in raw})
+                assert counts[raw_key] == 50
+                deltas[raw_key] = float(np.max(np.abs(actual-expected)))
+        movie = repo/meta['video']['file']
+        if sha(movie) != meta['video']['sha256']:
+            raise ValueError('Video file SHA mismatch')
+        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
+        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'])),
+            'distinct_member_outputs': counts, 'max_mean_difference': deltas,
+            'causal_nine_analyses_verified': True, 'observed_plus_zero_alignment_verified': True,
+            'future_observations_not_in_model_packet': True, 'masked_native_history_verified': True,
+            'native_history_available': False, 'route_points': 21, 'pressure_states': 20,
+            'mp4_duration_seconds': float(probe['format']['duration']), 'mp4_dimensions': [1600, 1000],
+            'files_sha256': {'mean_fields': meta['data_sha256'], 'mp4': meta['video']['sha256'],
+                'individual_members': sha(saved/'ensemble-members.npz'), 'model_inputs': sha(saved/'model-inputs.npz')},
+            'evaluation_limit': 'Calendar-selected historical development examples, not untouched holdouts.'}
+        assert report[slug]['distinct_inputs'] == 50
+    return report
+if __name__ == '__main__':
+    ap = argparse.ArgumentParser(description=__doc__)
+    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)
+    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})
+    print(json.dumps({'verified': list(report), 'members_per_storm': 50, 'mean_differences': 0,
+                      'receipt': str(args.receipt)}))