Am 30.06.16 um 17:49 schrieb Heli:
Dear all,
After a few tests, I think I will need to correct a bit my question. I will
give an example here.
I have file 1 with 250 lines:
X1,Y1,Z1
X2,Y2,Z2
....
Then I have file 2 with 3M lines:
X1,Y1,Z1,value11,value12, value13,....
X2,Y2,Z2,value21,value22, value23,...
....
I will need to interpolate values for the coordinates on file 1 from file 2.
(using nearest)
I am using the scipy.griddata for this.
scipy.interpolate.griddata(points, values, xi, method='linear', fill_value=nan,
rescale=False)
This constructs a Delaunay triangulation and no wonder takes some time
if you run it over 3M datapoints. You can probably save a factor of
three, because:
I need to repeat the griddata above to get interpolation for each of the column
of values.
I think this is wrong. It should, according to the docs, happily
interpolate from a 2D array of values. BTW, you stated you want nearest
interpolation, but you chose "linear". I think it won't make a big
difference on runtime, though. (nearest uses a KDtree, Linear uses QHull)
I was wondering if there are any ways to improve the time spent in
interpolation.
Are you sure you need the full generality of this algorithm? i.e., are
your values given on a scattered cloud of points in the 3D space, or
maybe the X,Y,Z in file2 are in fact on a rectangular grid? In the
former case, there is probably nothing you can really do. In the latter,
there should be a more efficient algorithm by looking up the nearest
index from X,Y,Z by index arithmetics. Or maybe even reshaping it into a
3D-array.
Christian
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