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examples/02-ndvi/Python-Client-ndvi-amazonia.py

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+import openeo
+import time
+
+# Connect to the back-end
+#on localhost con = openeo.connect("http://localhost:8000")
+#con = openeo.connect("http://<AWS-IPv4-ADDRESS>:8000")
+connection = openeo.connect("http://localhost:8000")
+
+# Basic login with default params
+connection.authenticate_basic(username="user", password="password")
+
+# Get the collection list
+collections = connection.list_collections()
+
+# Check description of a collection
+#print(collections)
+
+# Check that required processes are available
+processes = connection.list_processes()
+#print(processes)
+
+
+# Load the initial data collection and limit the amount of data loaded
+datacube= connection.load_collection(
+    'sentinel-s2-l2a-cogs',
+    spatial_extent= {'west': -66.27866,'south': -9.34489,'east': -66.26212, 'north': -9.33131},
+    temporal_extent=['2021-05-01', '2022-06-30']
+    )
+
+
+# Filter the data cube for the desired bands
+datacube = datacube.process(
+        process_id="filter_bands", 
+        arguments={
+        "data": datacube, 
+        "bands": ["B08", "B04"]}
+        )
+
+
+
+# NDVI calculation
+datacube = datacube.process(
+        process_id="ndvi", 
+        arguments={
+        "data": datacube, 
+        "nir": "B08", 
+        "red": "B04"}
+        )
+
+
+# Save as GeoTiff or NetCDF
+result = datacube.save_result("GTiff")
+
+# Process and download data synchronously
+start_time = time.time()
+
+# Creating a new job at the back-end by sending the datacube information.
+job = result.save_result(format='GTiff').create_job()
+# Starts the job and waits until it finished to download the result.
+job.start_and_wait().get_results().download_file("./amazonia_2022_ndvi.tif")
+end_time = time.time()
+time_taken = end_time - start_time
+print("Time taken:", time_taken)
+print("End of processes")