Comments (3)
I have substantially rewritten the interface and feature generation now. It should now be able to handle that the case where the data is given as a dict of bands, e.g., {'B02': array, 'B03': array}
but also (my preferred) dense array style. Not that you guys are interested, but I've also totally reworked nrtpredict
so that it only loads the bands required by the model which makes it much faster.
from nrt-predict.
Looking at
Line 79 in 7684183
Line 151 in 7684183
data
would actually improve readability of the loading routines too!from nrt-predict.
I think people using it will appreciate fast!
from nrt-predict.
Related Issues (9)
- Implement Docker Container HOT 1
- Pytests not running within Docker HOT 3
- Implement Github Action for Continuous Integration HOT 2
- Implement automated linter HOT 5
- Improve developer feedback/documentation for basic test script HOT 2
- Configure Python module essential structure HOT 2
- Operate off global datasets HOT 1
- Configuration parameters for scaling datasets
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