Update README.md

README.md

@@ -1,22 +1,69 @@
----
-license: cc-by-4.0
-configs:
-- config_name: default
-  data_files:
-  - split: all_samples
-    path: data/all_samples-*
-dataset_info:
-  features:
-  - name: Base_2_2/Zone/CellData/diffusion_coefficient
-    list: float32
-  - name: Base_2_2/Zone/CellData/flow
-    list: float32
-  - name: Global/forcing_magnitude
-    list: float32
-  splits:
-  - name: all_samples
-    num_bytes: 6554400000
-    num_examples: 50000
-  download_size: 3321884222
-  dataset_size: 6554400000
----
+---
+license: cc-by-4.0
+configs:
+- config_name: default
+  data_files:
+  - split: all_samples
+    path: data/all_samples-*
+dataset_info:
+  features:
+  - name: Base_2_2/Zone/CellData/diffusion_coefficient
+    list: float32
+  - name: Base_2_2/Zone/CellData/flow
+    list: float32
+  - name: Global/forcing_magnitude
+    list: float32
+  splits:
+  - name: all_samples
+    num_bytes: 6554400000
+    num_examples: 50000
+  download_size: 3321884222
+  dataset_size: 6554400000
+---
+
+Example of usage:
+
+```python
+import torch
+from plaid.bridges import huggingface_bridge as hfb
+from torch.utils.data import DataLoader
+
+
+def reshape_all(batch: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
+    """Helper function that reshapes the flattened fields into images of sizes (128, 128)."""
+    batch["diffusion_coefficient"] = batch["diffusion_coefficient"].reshape(
+        -1, 128, 128
+    )
+
+    batch["flow"] = batch["flow"].reshape(-1, 128, 128)
+
+    return batch
+
+
+# Load the dataset from the hub
+ds = hfb.load_dataset_from_hub(
+    repo_id="Nionio/PDEBench_2D_DarcyFlow", split="all_samples"
+)
+
+# Rename the features
+ds = ds.rename_columns(
+    {
+        "Base_2_2/Zone/CellData/diffusion_coefficient": "diffusion_coefficient",
+        "Base_2_2/Zone/CellData/flow": "flow",
+        "Global/forcing_magnitude": "forcing",
+    }
+)
+
+# Convert to torch
+ds = ds.with_format("torch")
+
+# Reshape fields
+ds = ds.map(reshape_all, batched=True)
+
+# Example of usage with a DataLoader
+dl = DataLoader(ds, batch_size=32, shuffle=True)
+for batch in dl:
+    for k, v in batch.items():
+        print(k, v.shape)
+        break
+```