Update README.md

README.md

@@ -75,6 +75,45 @@ Each sample obtained by iterating through the WebDataset corresponds to one sola
 * `aia_1600.npy`: (numpy.ndarray) Image data for AIA 1600 Å. Shape: (512, 512). Dtype: float32.
 * `hmi_m.npy`: (numpy.ndarray) Line-of-sight magnetogram data from HMI. Shape: (512, 512). Dtype: float32.
 
+## Usage Example
+
+```python
+from datasets import load_dataset
+from datetime import datetime
+import numpy as np
+import matplotlib.pyplot as plt
+
+dataset = load_dataset("oxai4science/sdoml-lite", streaming=True)
+```
+```python
+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")
+    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)))
+    return timestamp, np.stack(d, axis=0)
+
+def plot(timestamp, data):
+    import matplotlib.pyplot as plt
+    _, axs = plt.subplots(2, 3, figsize=(15, 10))
+    axs = axs.flatten()
+    for i, c in enumerate(channels):
+        axs[i].imshow(data[i], cmap='gray')
+        axs[i].set_title(c)
+        axs[i].axis('off')
+    plt.tight_layout()
+    plt.suptitle(timestamp)
+    plt.subplots_adjust(top=0.94)
+    plt.show()
+```
+```python
+sample  = next(iter(dataset['train']))
+timestamp, data = process(sample)
+plot(timestamp, data)
+```
+
 ## 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.