Update README.md

README.md

@@ -55,17 +55,18 @@ those who prefer traditional storage formats, GeoTIFF files are available in our
 
 *CloudSEN12+ spatial coverage. The terms p509 and p2000 denote the patch size 509 × 509 and 2000 × 2000, respectively. ‘high’, ‘scribble’, and ‘nolabel’ refer to the types of expert-labeled annotations*
 
-**ML-STAC Snippet**
+**TACO Snippet**
 ```python
-import mlstac
-dataset = mlstac.load('isp-uv-es/CloudSEN12Plus')
+import tacoreader
+import rasterio as rio
+dataset = tacoreader.load("tacofoundation:cloudsen12-l1c")
 ```
 
 **Sensor: Sentinel2 - MSI**
 
-**ML-STAC Task: image-segmentation**
+**TACO Task: image-segmentation**
 
-**ML-STAC Dataset Version: 1.0.0**
+**TACO Dataset Version: 1.1.0**
 
 **Data raw repository:  [https://cloudsen12.github.io/](https://cloudsen12.github.io/)**
 
@@ -161,31 +162,39 @@ website [https://cloudsen12.github.io/](https://cloudsen12.github.io/).
 
 
 ```python
-import mlstac
-import matplotlib.pyplot as plt
-import numpy as np
-
-ds = mlstac.load(snippet="isp-uv-es/CloudSEN12Plus")
-subset = ds.metadata[(ds.metadata["split"] == "test") & (ds.metadata["label_type"] == "high") & (ds.metadata["proj_shape"] == 509)][10:14]
-datacube = mlstac.get_data(dataset=subset)
-```
-
-Make a plot of the data point downloaded
-
-```python
-datapoint = datacube[2]
-datapoint_rgb = np.moveaxis(datapoint[[3, 2, 1]], 0, -1) / 5_000
-fig, ax = plt.subplots(1, 3, figsize=(10, 5))
-ax[0].imshow(datapoint_rgb)
-ax[0].set_title("RGB")
-ax[1].imshow(datapoint[13], cmap="gray")
-ax[1].set_title("Human label")
-ax[2].imshow(datapoint[14], cmap="gray")
-ax[2].set_title("UnetMobV2 v1.0")
+import tacoreader
+import rasterio as rio
+
+print(tacoreader.__version__) # 0.5.3
+
+# Remotely load the Cloud-Optimized Dataset 
+dataset = tacoreader.load("tacofoundation:cloudsen12-l1c")
+#dataset = tacoreader.load("tacofoundation:cloudsen12-l2a")
+#dataset = tacoreader.load("tacofoundation:cloudsen12-extra")
+
+# Read a sample
+sample_idx = 2422
+s2_l1c = dataset.read(sample_idx).read(0)
+s2_label = dataset.read(sample_idx).read(1)
+
+# Retrieve the data
+with rio.open(s2_l1c) as src, rio.open(s2_label) as dst:    
+    s2_l1c_data = src.read([4, 3, 2], window=rio.windows.Window(0, 0, 512, 512))
+    s2_label_data = dst.read(window=rio.windows.Window(0, 0, 512, 512))
+
+# Display
+fig, ax = plt.subplots(1, 2, figsize=(10, 5))
+ax[0].imshow(s2_l1c_data.transpose(1, 2, 0) / 3000)
+ax[0].set_title("Sentinel-2 L1C")
+ax[1].imshow(s2_label_data[0])
+ax[1].set_title("Human Label")
+plt.tight_layout()
+plt.savefig("taco_check.png")
+plt.close(fig)
 ```
 
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/scVhZf3rkB3uWkZZ6Epmu.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/0nRv7sqMRMNY-TVkY2kh7.png)
 
 ## Citation