How to use Scikit-learn's affinity propagation clustering with my own datasets?
Clustering algorithms usually assume that you have positions of objects, and want to find dense groups of observations.

If I understand you correctly, you have a 2d grid of sensor readings, and you want to segment them into regions. That is a slightly different problem. If you'd just put your sensor readings into a clustering, then the clusters will not be spatially coherent: clustering assumes there is no particular order to the points.

So you'll need to look into segmentation.

A naive way would be to use (sensor.x, sensor.y, sensor.value) tuples. Including the sensor positions will cause the results to be somewhat spatially coherent. But that makes it very sensitive to scaling, and there is no "correct" way of scaling this. There is a trade-off between spatial coherence and measurement coherence.