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README.md

@@ -19,10 +19,6 @@ task_categories:
 
 # RealPDEBench
 
-> **⚠️ Data Update Notice (2026-01-13)**
-> 
-> We are updating the dataset format to support dynamic `N_autoregressive` values. The new V2 format will be available before **January 15, 2026**. Please wait for the update before downloading.
-
 [![HF Dataset](https://img.shields.io/badge/HF%20Dataset-RealPDEBench-FFD21E?logo=huggingface)](https://huggingface.co/datasets/AI4Science-WestlakeU/RealPDEBench)
 [![arXiv](https://img.shields.io/badge/arXiv-2601.01829-b31b1b?logo=arxiv)](https://arxiv.org/abs/2601.01829)
 [![Website & Docs](https://img.shields.io/badge/Website%20%26%20Docs-realpdebench.github.io-1f6feb?logo=google-chrome)](https://realpdebench.github.io/)
@@ -82,34 +78,14 @@ This Hub repository (`AI4Science-WestlakeU/RealPDEBench`) is the **release repo*
 
 ## Data format on the Hub
 
-Each split is stored as a Hugging Face `datasets.Dataset` saved with `Dataset.save_to_disk()`. Concretely, each split is a directory containing:
-- `data-*.arrow` (sharded Arrow files, float32 payloads stored as bytes)
-- `dataset_info.json`
-- `state.json`
-
-### test_mode metadata (JSON)
-
-RealPDEBench supports `test_mode` evaluation splits (`in_dist`, `out_dist`, `seen`, `unseen`). The group definitions are shipped as JSON dicts per scenario:
-
-- `in_dist_test_params_{type}.json`
-- `out_dist_test_params_{type}.json`
-- `remain_params_{type}.json`
-
-where `{type}` is `real` or `numerical`.
+RealPDEBench stores **complete trajectories** in HuggingFace Arrow format, with separate JSON index files for train/val/test splits. This enables dynamic `N_autoregressive` support at runtime.
 
-### Temporal windowing (what an “example” means)
+Each scenario contains:
+- **Trajectory data**: `hf_dataset/{real,numerical}/` — Arrow files with complete time series
+- **Index files**: `hf_dataset/{split}_index_{type}.json` — maps sample indices to `(sim_id, time_id)`
+- **test_mode metadata**: `{in_dist,out_dist,remain}_params_{type}.json`
 
-RealPDEBench is stored as **sliding windows** cut from longer trajectories. Each row corresponds to `(sim_id, time_id)`:
-
-- `sim_id`: which trajectory (HDF5 file)
-- `time_id`: start index of the window
-
-Typical window lengths \(T\):
-- **40 frames** for `cylinder`, `fsi`, `foil`, `combustion` (often used as 20‑step input + 20‑step output)
-- **20 frames** for `controlled_cylinder` (often 10 + 10)
-- **20 frames** for `combustion/surrogate_train` (surrogate model training data)
-
-**Intended layout for the full release** (mirrors the on-disk structure used by RealPDEBench loaders):
+**Repository layout**:
 
 ```
 {repo_root}/
@@ -121,31 +97,28 @@ Typical window lengths \(T\):
     out_dist_test_params_numerical.json
     remain_params_numerical.json
     hf_dataset/
-      real_train/  real_val/  real_test/
-      numerical_train/  numerical_val/  numerical_test/
+      real/                           # Arrow: complete trajectories (92 files)
+        data-*.arrow
+        dataset_info.json
+        state.json
+      numerical/                      # Arrow: complete trajectories
+        data-*.arrow
+        dataset_info.json
+        state.json
+      train_index_real.json           # Index: [{"sim_id": "xxx.h5", "time_id": 0}, ...]
+      val_index_real.json
+      test_index_real.json
+      train_index_numerical.json
+      val_index_numerical.json
+      test_index_numerical.json
   fsi/
-    in_dist_test_params_real.json
-    out_dist_test_params_real.json
-    remain_params_real.json
-    in_dist_test_params_numerical.json
-    out_dist_test_params_numerical.json
-    remain_params_numerical.json
-    hf_dataset/
-      ...
+    ...  (same structure)
+  controlled_cylinder/
+    ...  (same structure)
+  foil/
+    ...  (same structure)
   combustion/
-    in_dist_test_params_real.json
-    out_dist_test_params_real.json
-    remain_params_real.json
-    in_dist_test_params_numerical.json
-    out_dist_test_params_numerical.json
-    remain_params_numerical.json
-    hf_dataset/
-      real_train/  real_val/  real_test/
-      numerical_train/              # (val/test intentionally empty)
-      surrogate_train/              # combustion-only (surrogate model training)
-      surrogate_train_sim_ids.txt
-      surrogate_train_meta.json
-  ...
+    ...  (same structure)
 ```
 
 ### How to download only what you need
@@ -166,58 +139,82 @@ local_dir = snapshot_download(
     endpoint="https://hf-mirror.com",
 )
 
-ds = load_from_disk(os.path.join(local_dir, "fsi", "hf_dataset", "numerical_val"))
-row = ds[0]
-print(row.keys())
+# Load trajectory data
+trajectories = load_from_disk(os.path.join(local_dir, "fsi", "hf_dataset", "real"))
+print(f"Loaded {len(trajectories)} trajectories")
+print(trajectories[0].keys())  # sim_id, u, v, shape_t, shape_h, shape_w
+```
+
+### Using the RealPDEBench loaders (recommended)
+
+For automatic train/val/test splitting and dynamic `N_autoregressive` support, use the provided dataset loaders:
+
+```python
+from realpdebench.data.fluid_hf_dataset import FSIHFDataset
+
+dataset = FSIHFDataset(
+    dataset_name="fsi",
+    dataset_root="/path/to/data",
+    dataset_type="real",
+    mode="test",
+    N_autoregressive=10,  # Dynamic! Works with any value
+)
+
+input_tensor, output_tensor = dataset[0]
+print(f"Input shape: {input_tensor.shape}")   # (20, H, W, 2)
+print(f"Output shape: {output_tensor.shape}") # (200, H, W, 2) = 20 × 10
 ```
 
 ## Schema (columns)
 
 ### Fluid datasets (`cylinder`, `controlled_cylinder`, `fsi`, `foil`)
 
-- **Keys**:
+- **Keys** (each row = one complete trajectory):
   - `sim_id` (string): trajectory file name (e.g., `10031.h5`)
-  - `time_id` (int): start frame index of the window
-  - `u`, `v` (bytes): float32 arrays of shape `(T, H, W)`
-  - `p` (bytes): float32 array `(T, H, W)` *(numerical splits only)*
-  - `shape_t`, `shape_h`, `shape_w` (int): shapes for decoding
+  - `u`, `v` (bytes): float32 arrays of shape `(T_full, H, W)` — **complete time series**
+  - `p` (bytes): float32 array `(T_full, H, W)` *(numerical splits only)*
+  - `shape_t` (int): **complete trajectory length** (e.g., 3990, 2173)
+  - `shape_h`, `shape_w` (int): spatial dimensions
 
 ### Combustion dataset (`combustion`)
 
-- **Keys**:
+- **Keys** (each row = one complete trajectory):
   - `sim_id` (string): e.g., `40NH3_1.1.h5`
-  - `time_id` (int): start frame index of the window
-  - `observed` (bytes): float32 array `(T, H, W)` (real: measured intensity; numerical: surrogate intensity)
-  - `numerical` (bytes): float32 array `(T, H, W, 15)` *(numerical splits only)*
+  - `observed` (bytes): float32 array `(T_full, H, W)` — **complete time series**
+  - `numerical` (bytes): float32 array `(T_full, H, W, 15)` *(numerical splits only)*
   - `numerical_channels` (int): number of numerical channels (15)
-  - `shape_t`, `shape_h`, `shape_w` (int): shapes for decoding
-
-### Combustion surrogate-train (`combustion/surrogate_train`)
+  - `shape_t` (int): **complete trajectory length** (e.g., 2001)
+  - `shape_h`, `shape_w` (int): spatial dimensions
 
-Used to train a surrogate model mapping simulated modalities → real modality (combustion only).
+### Index files (JSON)
 
-- **Keys**:
-  - `real` (bytes): float32 array `(T, H, W)` (target intensity)
-  - `numerical` (bytes): float32 array `(T, H, W, C)` (input fields)
-  - plus shapes (`*_shape_*`) and `numerical_channels`
+Each split has an index file mapping sample indices to trajectory positions:
 
-## Current converted data size (local conversion; full release target)
+```json
+[
+  {"sim_id": "10031.h5", "time_id": 0},
+  {"sim_id": "10031.h5", "time_id": 20},
+  {"sim_id": "10031.h5", "time_id": 40},
+  ...
+]
+```
 
-These numbers refer to our current HF Arrow conversion outputs (not all uploaded to this test repo yet):
+## Data size
 
-- **Total**: ~**954GB** across all scenarios
-- **Largest shard file**: ~**0.47GB** (well below the Hub’s recommended **<50GB per file**)
-- **Total file count**: ~**2.1k files** (well below the Hub’s recommended **<100k files per repo**)
+- **Total**: ~**210GB** across all scenarios
+- **Largest shard file**: ~**0.5GB** (well below the Hub's recommended **<50GB per file**)
+- **Total file count**: ~**550 files** (well below the Hub's recommended **<100k files per repo**)
 
-Per-scenario totals (HF Arrow):
+Per-scenario totals:
 
-| Scenario | Total size |
-|---|---:|
-| combustion | 622GB |
-| cylinder | 116GB |
-| fsi | 34GB |
-| controlled_cylinder | 61GB |
-| foil | 124GB |
+| Scenario | real | numerical | Total |
+|---|---:|---:|---:|
+| cylinder | 23GB | 34GB | 57GB |
+| controlled_cylinder | 24GB | 36GB | 59GB |
+| fsi | 6GB | 11GB | 17GB |
+| foil | 24GB | 37GB | 61GB |
+| combustion | 1GB | 15GB | 16GB |
+| **Total** | **78GB** | **133GB** | **~210GB** |
 
 ## Recommended benchmark protocols