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High performance computing in Python

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High performance computing in Python
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57
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CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production PlaceWageningen

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Software requirements: opengeohub/py-geo docker image (gdal, rasterio, eumap, scikit-learn) What are the possibilities to improve the performance of computation in Python? This tutorial shows how to performe Numpy operations using multicore processing, how to accelerate python functions using Numba, how to calculate fast numerical expression using NumExpr, how to use the TilingProcessing to distribute raster operations in multiple cores.
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