Update README.md

README.md

@@ -91,7 +91,7 @@ dataset = load_dataset("oxai4science/sdoml-lite", streaming=True)
 channels = ['hmi_m', 'aia_0131', 'aia_0171', 'aia_0193', 'aia_0211', 'aia_1600']
 
 def process(data):
-    timestamp = datetime.strptime(data['__key__'], "%Y/%m/%d/%H%M")
+    timestamp = data['__key__']
     d = []
     for c in map(lambda x: x+'.npy', channels):
         d.append(np.array(data[c]) if c in data else np.zeros((512, 512)))
@@ -119,6 +119,44 @@ plot(timestamp, data)
   <img src="https://huggingface.co/datasets/oxai4science/sdoml-lite/resolve/main/assets/usage_example_1.png">
 </div>
 
+Here is another example showing how to put together a minimal PyTorch dataset and dataloader to train with SDOML-lite.
+
+```python
+import torch
+from torch.utils.data import IterableDataset, DataLoader
+
+# PyTorch dataset
+class SDOMLlite(IterableDataset):
+    def __init__(self, dataset_iter):
+        self.dataset_iter = dataset_iter
+
+    def __iter__(self):
+        for sample in self.dataset_iter:
+            t, d = process(sample)
+            yield t, torch.tensor(d, dtype=torch.float32)
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+batch_size = 8
+
+# Create DataLoader
+dataset_iter = iter(dataset['train'])
+torch_dataset = SDOMLlite(dataset_iter)
+loader = DataLoader(torch_dataset, batch_size=batch_size)
+
+# Training loop
+num_epochs = 1
+for epoch in range(num_epochs):
+    for batch in loader:
+        times = batch[0]  # Timestamps
+        images = batch[1].to(device) # Images with shape (batch_size, 6, 512, 512)
+        print(f"Batch with shape: {images.shape}")
+        # ...
+        # training code
+        # ...
+        break
+    break
+```
+
 ## Data Generation and Processing
 
 The SDOML-lite dataset is generated using the pipeline detailed in the [sdoml-lite GitHub repository](https://github.com/oxai4science/sdoml-lite). The download and processing scripts were run in July 2024 using distributed computing resources provided by Google Cloud for FDL-X Heliolab 2024, which is a public-private partnership AI research initiative with NASA, Google Cloud and Nvidia and other leading research organizations.