> > > the basic idea is in "polyfit on multiple data points" on > numpy-disscusion mailing list April 2009 > > In this case, calculations have to be done by groups > > subtract mean (this needs to be replaced by group demeaning) > modeldm = model - model.mean() > obsdm = obs - obs.mean() > > xx = np.histogram2d( > xx, xedges, yedges = np.histogram2d(lat, lon, weights=modeldm*modeldm, > bins=(latedges,lonedges)) > xy, xedges, yedges = np.histogram2d(lat, lon, weights=obsdm*obsdm, > bins=(latedges,lonedges)) > > > slopes = xy/xx # slopes by group > > expand slopes to length of original array > predicted = model - obs * slopes_expanded > ... > > the main point is to get the group functions, for demeaning, ... for > the 2d labels (and get the labels out of histogramdd) > > I'm out of time (off to the airport soon), but I can look into it next > weekend. > > Josef > > Thanks Josef, I will chase up the April list...
If I understand what you have done above, this returns the slope of best fit lines forced through the origin, is that right? Have a great trip! Tom
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