Add grid-index columns and reorder schema

- Add wind_speed_id, wave_hs_id, wave_tp_id to train/test/data CSVs
(split is reproducible from these integer grid IDs)
- Place each *_id column immediately before its value column
- data.csv now ships the raw table with grid IDs in place of the
is_train / wind_group / wave_group columns
- Track train_damage.csv via git-LFS (now exceeds HF 10 MiB limit)
- README: document the new column order

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.gitattributes

@@ -60,7 +60,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
 opt1/data.csv filter=lfs diff=lfs merge=lfs -text
 opt1/test_damage.csv filter=lfs diff=lfs merge=lfs -text
+opt1/train_damage.csv filter=lfs diff=lfs merge=lfs -text
 opt2/data.csv filter=lfs diff=lfs merge=lfs -text
 opt2/test_damage.csv filter=lfs diff=lfs merge=lfs -text
+opt2/train_damage.csv filter=lfs diff=lfs merge=lfs -text
 ref/data.csv filter=lfs diff=lfs merge=lfs -text
 ref/test_damage.csv filter=lfs diff=lfs merge=lfs -text
+ref/train_damage.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -46,9 +46,9 @@ turbine baseline (`ref`) and two FLOAT-derived re-designs (`opt1`,
 ```
 FLOATBench/
 ├── ref/                      IEA-22 reference turbine baseline
-│   ├── data.csv              194,040 rows × 16 cols (full table, is_train flag)
-│   ├── train_damage.csv       51,840 rows × 15 cols
-│   ├── test_damage.csv       142,200 rows × 15 cols (with regime labels)
+│   ├── data.csv              194,040 rows × 16 cols (raw, no split/regime labels)
+│   ├── train_damage.csv       51,840 rows × 18 cols (with regime labels)
+│   ├── test_damage.csv       142,200 rows × 18 cols (with regime labels)
 │   └── metadata.json         counts, split summary
 ├── opt1/                     FLOAT-derived re-design
 │   └── ...                   same files
@@ -58,12 +58,35 @@ FLOATBench/
 
 ## Schema
 
+Columns appear in the order below. Each `*_id` grid index sits
+immediately before the value it indexes (`wind_speed_id` before
+`wind_speed`, `wave_hs_id` before `wave_hs`, `wave_tp_id` before
+`wave_tp`).
+
+`data.csv` (16 cols):
+
+```
+sim_id, wind_speed_id, wind_speed, mean_wind_speed, std_wind_speed,
+wave_hs_id, wave_hs, wave_tp_id, wave_tp, wind_seed_id,
+section_id, section_height_m, section_radius_m, section_thickness_m,
+damage_weight, damage
+```
+
+`train_damage.csv` / `test_damage.csv` (18 cols): same order, with
+`wind_group, wave_group` inserted right before `damage_weight`.
+
+The tables below describe each column grouped by category.
+
 **Identifiers**
 
-| Column       | Type | Meaning                                                                |
-|--------------|------|------------------------------------------------------------------------|
-| `sim_id`     | int  | Unique simulation identifier (ties together the 30 sections of one run) |
-| `section_id` | int  | Tower section index ∈ {1,...,30}, 1 (base) to 30 (top)                  |
+| Column          | Type | Meaning                                                                |
+|-----------------|------|------------------------------------------------------------------------|
+| `sim_id`        | int  | Unique simulation identifier (ties the 30 sections of one run)         |
+| `section_id`    | int  | Tower section index ∈ {1,...,30}, 1 (base) to 30 (top)                 |
+| `wind_speed_id` | int  | Grid index ∈ {1,...,22}, ordered by `wind_speed` ascending             |
+| `wave_hs_id`    | int  | Grid index ∈ {1,...,7} within each `wind_speed`                        |
+| `wave_tp_id`    | int  | Grid index ∈ {1,...,7} within each (`wind_speed`, `wave_hs`)           |
+| `wind_seed_id`  | int  | Turbulence seed index ∈ {1,...,6}                                      |
 
 **Environmental features**
 
@@ -74,7 +97,6 @@ FLOATBench/
 | `std_wind_speed`  | float | Realised 10-min std of hub-height wind speed (m/s) |
 | `wave_hs`         | float | Significant wave height (m)                        |
 | `wave_tp`         | float | Wave peak period (s)                               |
-| `wind_seed_id`    | int   | Turbulence seed index ∈ {1,...,6}                  |
 
 **Tower section geometry**
 
@@ -84,19 +106,13 @@ FLOATBench/
 | `section_radius_m`    | float | Tower section outer radius (m)                     |
 | `section_thickness_m` | float | Tower section wall thickness (m)                   |
 
-**Regime labels**
+**Regime labels** (only in `train_damage.csv` and `test_damage.csv`)
 
 | Column       | Type | Meaning                                                                  |
 |--------------|------|--------------------------------------------------------------------------|
 | `wind_group` | str  | `In-train` / `Interpolate` / `Extrapolate` (all train rows are `In-train`) |
 | `wave_group` | str  | `In-train` / `Interpolate` / `Extrapolate` (all train rows are `In-train`) |
 
-**Split flag** (only in `data.csv`)
-
-| Column     | Type | Meaning                                       |
-|------------|------|-----------------------------------------------|
-| `is_train` | bool | `True` for train rows, `False` for test rows  |
-
 **Damage targets**
 
 | Column          | Type  | Meaning                                                                                |
@@ -107,21 +123,55 @@ FLOATBench/
 Lifetime damage at a section is recovered as
 `sum(damage_i * damage_weight_i)` over all conditions.
 
-## Split
+## Regime-aware split
+
+The recommended train/test partition is **regime-aware**: an
+alpha-shape over the joint wind/wave operating envelope partitions
+test points into `In-train` / `Interpolate` / `Extrapolate` regimes
+on both the wind and wave axes, populating all nine cells of the
+3×3 wind×wave regime grid. Per tower:
 
-The recommended train/test partition is **regime-aware**, an
-alpha-shape over the joint wind/wave operating envelope, populating
-all nine cells of the `In-train` / `Interpolate` / `Extrapolate` ×
-wind/wave grid. Per tower:
+| Subset | Rows    | Conditions | Description                                  |
+|--------|---------|------------|----------------------------------------------|
+| Train  | 51,840  | 288        | All `In-train`/`In-train` cell               |
+| Test   | 142,200 | 790        | Spans the remaining 8 wind×wave regime cells |
 
-| Subset | Rows    | Conditions | Description                                    |
-|--------|---------|------------|------------------------------------------------|
-| Train  | 51,840  | 288        | All `In-train`/`In-train` cell                 |
-| Test   | 142,200 | 790        | Spans the remaining 8 wind×wave regime cells   |
+Train rows carry `wind_group = wave_group = In-train` by
+construction. Test rows carry the assigned regime labels so the
+9-cell evaluation can be run directly.
+
+### Reproducing the split from grid IDs
+
+The partition is **fully determined by the integer grid IDs**
+(`wind_speed_id`, `wave_hs_id`, `wave_tp_id`) shipped on every row.
+A row is in train iff its three IDs all fall in the train sets:
+
+```python
+TRAIN_WS_IDS = {2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14,
+                16, 17, 18, 19, 20, 21}          # 18 of 22
+TRAIN_HS_IDS = {2, 3, 5, 6}                      # 4 of 7
+TRAIN_TP_IDS = {2, 3, 5, 6}                      # 4 of 7
+
+is_train = (df.wind_speed_id.isin(TRAIN_WS_IDS)
+            & df.wave_hs_id.isin(TRAIN_HS_IDS)
+            & df.wave_tp_id.isin(TRAIN_TP_IDS))
+```
+
+Train cells: 18 × 4 × 4 = 288. Total grid: 22 × 7 × 7 = 1,078.
+
+The alpha-shape regime labels (`wind_group`, `wave_group`) are
+derived from the train set's joint wind–wave envelope. Reproducing
+them and producing diagnostic plots requires the FLOATBench code
+repo: <https://github.com/Joao97ribeiro/FLOATBench>
+
+```bash
+python scripts/split/run.py --flagfile=scripts/split/config.cfg \
+       --dataset_dir=/path/to/FLOATBench-dataset
+```
 
-All rows carry per-row `wind_group` and `wave_group` labels (train
-rows are `In-train`/`In-train` by construction) so the regime-aware
-evaluation can be reproduced directly.
+This regenerates `train_damage.csv` / `test_damage.csv` byte-for-byte
+identical to the shipped files, plus a `split_metadata.json` and
+plots of the partition and train spacing.
 
 ## Quickstart
 

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