I am currently working on a statistical model in which one step consists in finding the maximum in a netcdf over 1) all the time dimension and also over 2) disjoined areas (e.g. (longitude,1,10 and latitude,30,40) OR (longitude,12,30 and latitude,50,60) ).
The fact is hyperslabs are respective to one dimension so I assume that I can't reach such computation in one step.
Do you think about a "clever/nicer" and "faster" way than
1- split my origin ncfile according to all these hyperslab,
2- compute the maximum on each one
3- gather the results and finally extract the max of the max.
Thanks.
Romain
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Hi everybody,
I am currently working on a statistical model in which one step consists in finding the maximum in a netcdf over 1) all the time dimension and also over 2) disjoined areas (e.g. (longitude,1,10 and latitude,30,40) OR (longitude,12,30 and latitude,50,60) ).
The fact is hyperslabs are respective to one dimension so I assume that I can't reach such computation in one step.
Do you think about a "clever/nicer" and "faster" way than
1- split my origin ncfile according to all these hyperslab,
2- compute the maximum on each one
3- gather the results and finally extract the max of the max.
Thanks.
Romain
Your algorithm should work fine. I cannot think of a better one.
cz
Alright let's go then :)
Thanks !