A new workflow is created and filled with tasks (Sentinel-2 data, image sharpening). The area of interest and workflow parameters are defined. After running the job, the results are downloaded and visualized.
import up42
up42.authenticate(project_id=12345, project_api_key=12345)
#up42.authenticate(cfg_file="config.json")
project = up42.initialize_project()
project
# Add blocks/tasks to the workflow.
workflow = project.create_workflow(name="30-seconds-workflow",
use_existing=True)
print(up42.get_blocks(basic=True))
input_tasks= ['sobloo-s2-l1c-aoiclipped', 'sharpening']
workflow.add_workflow_tasks(input_tasks=input_tasks)
# Define the aoi and input parameters of the workflow to run it.
aoi = workflow.get_example_aoi(as_dataframe=True)
#aoi = workflow.read_vector_file("data/aoi_berlin.geojson", as_dataframe=True)
input_parameters = workflow.construct_parameters(geometry=aoi,
geometry_operation="bbox",
start_date="2018-01-01",
end_date="2020-12-31",
limit=1)
input_parameters["sobloo-s2-l1c-aoiclipped:1"].update({"max_cloud_cover":60})
input_parameters
# Run a test job to query data availability and check the configuration.
test_job = workflow.test_job(input_parameters=input_parameters, track_status=True)
test_results = test_job.get_results_json()
print(test_results)
# Run the actual job.
job = workflow.run_job(input_parameters=input_parameters, track_status=True)
job.download_results()
job.plot_results()