Add probability field calc and plots #127
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## main #127 +/- ##
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This is an example of its use. I got better result on a different dataset, but I don't have it now!
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| if minimum_allowable_value is not None: | ||
| too_small = (training_set + training_set['depth']).values < minimum_allowable_value | ||
| training_set.values[too_small] = numpy.nan | ||
| training_set = numpy.fmax(training_set, -training_set['depth']) |
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@WPringle could the difference in the model vs surrogate be how/when we adjust?
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That's a good point. What was the min_depth set and what are the depths in the SCHISM model on the overland points that get wet from NWM?
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I think the mindepth is 0.01, I need to check the depths on the overland... I'll get back to you on that. In the meantime, can you please also take a look at the newly added code as a whole and see if you spot any obvious/conceptual mistakes or not?
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@WPringle minimum allowable depth is set to 0.8 * h0 which is 0.8 * 0.01. The depths that are set as being too small are:
too_small = (maxelev+ depths).values < min_depthThese nodes are set to nan in the latest version of the code; in an earlier version, these values would then be set to be equal to -depth which resulted in the earlier plots above
Looking at the max and min of the depths and maxelev of the too_small region show:
depths[too_small.any(axis=0)].min(), depths[too_small.any(axis=0)].max(), depths[too_small.any(axis=0)].mean()
# (-44.28715, -0.01106, -5.5330863)
maxelev[too_small].min(), maxelev[too_small].max(), maxelev[too_small].mean()
# (0.010003523, 44.07152, 5.2620897)
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@WPringle swapping the order of set small elev to null and set null to depth for model calculation, (i.e. and ) resulted in these plots: 
I think it might make more sense to keep the swapped version. What do you think? |
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These are the first couple of probability fields for the 30 member Florence run with OFCL track (past forecast, no NWM)
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