A way to inject data into graph with gpt/python?

[marpet]

You can define variables in your graph which you can set from the command line.
This is explained here:
Bulk Processing with GPT - SNAP - Confluence (atlassian.net)

In Python you can open the products you want to process and calculate the bounding box and then use it as parameter.

For doing this you can have a look at this example:
How to use the SNAP API from Python - SNAP - Confluence (atlassian.net)

And there is snapista. With this you can generate a graph and execute it as command line process.
But you would still need snappy to retrieve the geoinformation, or you read it from the metadata.

Introducing snapista, a GPT wrapper for Python

Hello! Let me introduce snapista, a GPT wrapper for Python. The goal is to provide an easy and pythonic way (to some extent) to write and run SNAP graphs using Python (a script or a Jupyter Notebook). To create a graph, one would write: from snapista import Graph from snapista import Operator g = Graph() g.add_node(operator=Operator('Read'), node_id='read_1') calibration = Operator('Calibration') calibration.createBetaBand = 'false' g.add_node(operator=calibration, …