[Numpy-discussion] 2D binning

[Mathew Yeates]

I guess it's as fast as I'm going to get. I don't really see any other way.
BTW, the lat/lons are integers)

-Mathew

On Tue, Jun 1, 2010 at 1:49 PM, Zachary Pincus <zachary.pincus at yale.edu>wrote:

> > Hi
> > Can anyone think of a clever (non-lopping) solution to the following?
> >
> > A have a list of latitudes, a list of longitudes, and list of data
> > values. All lists are the same length.
> >
> > I want to compute an average  of data values for each lat/lon pair.
> > e.g. if lat[1001] lon[1001] = lat[2001] [lon [2001] then
> > data[1001] = (data[1001] + data[2001])/2
> >
> > Looping is going to take wayyyy to long.
>
> As a start, are the "equal" lat/lon pairs exactly equal (i.e. either
> not floating-point, or floats that will always compare equal, that is,
> the floating-point bit-patterns will be guaranteed to be identical) or
> approximately equal to float tolerance?
>
> If you're in the approx-equal case, then look at the KD-tree in scipy
> for doing near-neighbors queries.
>
> If you're in the exact-equal case, you could consider hashing the lat/
> lon pairs or something. At least then the looping is O(N) and not
> O(N^2):
>
> import collections
> grouped = collections.defaultdict(list)
> for lt, ln, da in zip(lat, lon, data):
>   grouped[(lt, ln)].append(da)
>
> averaged = dict((ltln, numpy.mean(da)) for ltln, da in grouped.items())
>
> Is that fast enough?
>
> Zach
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