Update README.md

README.md

@@ -40,7 +40,6 @@ MDOF/
     └── Data/
         └── fno/
             ├── Blg_F6_18m_IM7_st0.h5.h5       # Floor acc. response
-            └── ...
 ```
 
 ### Ground Motion File (`GMs_knet_3474_AF_57.h5`)
@@ -65,69 +64,7 @@ Each file stores the simulated structural response for all GM × scale combinati
 
 ## Loading
 
-### Option 1 — Direct HDF5 access (h5py)
-
-```python
-import h5py
-import numpy as np
-
-GM_FILE = "path/to/MDOF/All_GMs/GMs_knet_3474_AF_57.h5"
-
-with h5py.File(GM_FILE, "r") as f:
-    # Load ground motion i=0, amplitude factor j=0
-    gm = f["gm_0/af_0/data"][:]          # shape (3000,)
-    pga = f["gm_0/af_0/pga"][()]
-
-print(f"GM shape: {gm.shape}, PGA: {pga:.4f} m/s²")
-```
-
-```python
-import h5py
-import numpy as np
-
-BLG_FILE = "path/to/MDOF/knet-250/Data/fno/building_0001.h5"
-
-with h5py.File(BLG_FILE, "r") as f:
-    # Floor acceleration response for GM i=0, AF j=0
-    floor_acc = f["response/gm_0/af_0/floor_acc"][:]   # (n_floors, 3000)
-    damage_state = f["building/damage_state/gm_0/af_0"][()]
-
-print(f"Floor acc shape: {floor_acc.shape}, Damage state: {damage_state}")
-```
-
-### Option 2 — PyTorch Dataset (recommended for training)
-
-Clone the [SeismicFNO](https://github.com/HKUJasonJiang/Seismic-FNO) repository and use `DynamicDataset`:
-
-```python
-import numpy as np
-from torch.utils.data import DataLoader
-from module.dataprep_v2 import DynamicDataset
-
-GM_FILE      = "path/to/MDOF/All_GMs/GMs_knet_3474_AF_57.h5"
-BUILDING_DIR = "path/to/MDOF/knet-250/Data/fno/"
-
-# Full dataset (all 3474 × 57 combinations)
-dataset = DynamicDataset(
-    gm_file_path       = GM_FILE,
-    building_files_dir = BUILDING_DIR,
-)
-
-# Or pass a pre-computed index array for train/val/test splits
-rng     = np.random.default_rng(42)
-indices = rng.permutation(len(dataset))
-train_ds = DynamicDataset(GM_FILE, BUILDING_DIR, gm_indices=indices[:int(0.7 * len(indices))])
-
-loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4)
-
-# Each batch: (gm, building_attributes, floor_acc_response, damage_state)
-gm, attr, resp, ds = next(iter(loader))
-print(gm.shape, resp.shape)   # (64, 3000, 1), (64, 3000, 1)
-```
-
-## Citation
-
-If you use this dataset, please cite the K-NET strong-motion network and the associated SeismicFNO paper (forthcoming).
+Refer to github repo and codes.
 
 ## License