>
>
> >
> What exactly are trying to fit because it is rather bad practice to fit
> a model to some summarized data as you lose the uncertainty in the
> original data?
> If you define your boxes, you can loop through directly on each box and
> even fit the equation:
>
> model=mu +beta1*obs
>
> The
>
>
> 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 =
>
> are you doing something like np.polyfit(model, obs, 1) ?
>
> If you are using polyfit with deg=1, i.e. fitting a straight line,
> then this could be also calculated using the weights in histogram2d.
>
> histogram2d (histogramdd) uses np.digitize and np.bincount, so I'm
> surprised if the hi
Hi All,
I have a problem involving lat/lon data. Basically, I am evaluating
numerical weather model data against satellite data, and trying to produce
gridded plots of various statistics. There are various steps involved with
this, but basically, I get to the point where I have four arrays of th