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 =
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 extension is
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
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
