# how can you easily do that with the Data object?
# 1) calculate the weights ...
# these are dependent on the number of days which you get as ...
ml = air._get_days_per_month()
print ml
w = ml / float(sum(ml)) # relative weights
print w
print 'The sum of the weights should be 1.: ', sum(w)
# 2) now we need to multiply the data with the weights and then
# sum up over time
new = air.mul_tvec(w,
copy=True) # check also the other options of this routine!
res = new.timsum(return_object=True) # gives a 2D array with weighted results
print res.shape
map_plot(air, title='classical unweighted temporal mean', show_stat=True)
map_plot(res, title='weighted temporal mean', show_stat=True)
map_plot(air.sub(res),
title='difference',
show_stat=True,
cmap_data='RdBu_r',
vmin=-0.1,
vmax=0.1)
# you will see that the differences are marginal, but they are nevertheless there
# for this example.
# how can you easily do that with the Data object? # 1) calculate the weights ... # these are dependent on the number of days which you get as ... ml = air._get_days_per_month() print ml w = ml / float(sum(ml)) # relative weights print w print 'The sum of the weights should be 1.: ', sum(w) # 2) now we need to multiply the data with the weights and then # sum up over time new = air.mul_tvec(w, copy=True) # check also the other options of this routine! res = new.timsum(return_object=True) # gives a 2D array with weighted results print res.shape map_plot(air, title='classical unweighted temporal mean', show_stat=True) map_plot(res, title='weighted temporal mean', show_stat=True) map_plot(air.sub(res), title='difference', show_stat=True, cmap_data='RdBu_r', vmin=-0.1, vmax=0.1) # you will see that the differences are marginal, but they are nevertheless there # for this example. plt.show()
