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⏱️ 30 Second Example

The UP42 Python package uses nine classes, representing the hierarchical structure of UP42:
Project > Workflow > Job > JobTask | JobCollection | Catalog > Order | Storage > Asset

In this example a new workflow consisting of Sentinel-2 L2A data and Sharpening Filter is created. The area of interest and workflow parameters are defined. After running the job, the results are downloaded and visualized.



The initial steps for installing and authentication were covered in the previous sections. After, we need to create a project to set up and configuring the workflow of the application.

import up42
up42.authenticate(project_id="project ID string", project_api_key="project-API-key") # inline authentication (also possible with json file)
project = up42.initialize_project()

Creating a workflow

Creating a workflow only consists of adding a workflow name, and adding some input tasks. The lists for all the input tasks can be found in the documentation processing blocks section.

# Construct workflow
workflow = project.create_workflow(name="30-seconds-workflow", use_existing=True)
input_tasks = ["Sentinel-2 L2A Visual (GeoTIFF)",
               "Sharpening Filter"]

Defining the input parameters

To further configure the workflow, we provide workflow input parameters, e.g. the AOI (area of interest), dates of analysis, among others. The AOI geometry can be read via various methods (e.g. up42.draw_aoi(), up42.read_vector_file()) and provided as multiple formats (e.g. GeoJSON, shapely Polygon, GeoDataFrame).

# Define the aoi and input parameters of the workflow to run it.
aoi = up42.get_example_aoi(as_dataframe=True)
# Or use up42.draw_aoi(), up42.read_vector_file(), FeatureCollection, GeoDataFrame etc.
input_parameters = workflow.construct_parameters(geometry=aoi, 

Price Estimation and Pre-testing

Price estimation is an important feature that allows to estimate the actual costs of your workflow before you run your application. Estimating costs are reported in credits units that you can check on your account dashboard.

# Price estimation

Prior a final run of our workflow we can test the configuration and availability of the tasks included in our pipeline. This is done by running test jobs before the final run.

# Run a test job to query data availability and check the configuration.
test_job = workflow.test_job(input_parameters, track_status=True)

Running the Workflow

Finally, the job run is added by passing the input parameters and enabling the tracking status feature for having status logs of our workflow. The last two lines download our resulting images and display them in the notebook interface.

# Run the actual job.
job = workflow.run_job(input_parameters, track_status=True)



After running our quick tutorial the expected result should be similar to the following.

Expected Result


If you were able to complete this tutorial you can continue with the detailed example! for further explanation of the basic flow of our SKD, or also continue with the with the Structure chapter!