## Step-by-step guide

If we have a fieldset / GRIB file containing values and we want to not only find the min/max values at each point, but also which fields those min/max values come from, we can compute a field where the value at each grid point is the index of the field that contains the min or max value for that point. The following Python code shows a way to do this. The code itself is quite short, but it has lots of explanation!

import metview as mv # read the GRIB file and compute the field of max values; we could # instead use min() here, and the script will compute the indexes # where the min values occur t = mv.read('t2.grib') m = t.max() n = int(t.count()) # duplicate the field of min/max values n times mxn = m.duplicate(n) # create a new fieldset where each value is 1 if it equals # the min/max value for that point; comparison is performed per field tmask = (t == mxn) # multiply each field by its index (1-based) tmask_times_index = mv.Fieldset() for i in range(0, n): tmask_times_index.append(tmask[i]*(i+1)) # each field now contains a mixture of zeros and j, where j # is the index of the field; j appears at grid points where # this field contains the min/max value for that point; take # the max field of this fieldset to obtain a field of indexes # where the min/max values appear. The significance of using max() # here is that if two or more fields contain the min/max value # for a given point, the higher index of those will be used; # use min() if you prefer to use the lowest index in this case. max_indexes = tmask_times_index.max() # max_indexes is a Fieldset like any other, but the values are 1-n # where n is the number of fields in the original data. # This is standard fieldset, so we can, for example, plot it. mv.plot(max_indexes)