Re: KD Tree with great circle distances

Hi Ed,
           I believe it is indeed very helpful and I may end up using
either the VP tree or the Ball tree. In order to give you few more details
I have data on a WGS 84 ellipsoid(we can assume this to be a sphere) from
66 N to 66 S and all the longitudes. The data is three dimensional but is
equidistant along the third dimension i.e. height. The data is also highly
sparse. From a flattened version of the 3d array I have 22163680 points and
out of that only 266111 points  have finite values(greater than zero). The
rest are all  NaNs.But the finite data(those values that are greater than
zero) is clustered i.e. possibly very close to each other. The resolution
horizontally is around 5 kms. What I intend to do is to 2D interpolation
for each height level.

Best regards,
Ashwin.
<https://scicomp.stackexchange.com/questions/tagged/python>


On Mon, May 21, 2018 at 1:27 AM, Edward Gryspeerdt <[email protected]>
wrote:

> Hi Ashwin,
>
> If you are using the whole globe, you might want to consider using
> something like a VP-tree instead.  A KD-tree needs left/right to be
> defined, which you can't do on a sphere.
>
> Scikit-learn has a BallTree, which you can use with a 'haversine' distance
> metric, which should allow you to do do nearest neighbours on a sphere
>
> http://scikit-learn.org/stable/modules/generated/
> sklearn.neighbors.BallTree.html
>
> Hope that helps,
> Ed
>
>
> On 20 May 2018 at 17:49, ashwin .D <[email protected]> wrote:
>
>> Hi David,
>>                 Many thanks for your prompt response and that maybe the
>> route that I may have to take eventually. I was just wondering whether
>> scipy has any equivalent for the stuff described in this paper -
>> http://www.soest.hawaii.edu/wessel/sphspline/Wessel+Becker_2008_GJI.pdf ?
>>
>> Regards,
>> Ashwin.
>>
>> On Sun, May 20, 2018 at 9:56 PM, David Hoese <[email protected]> wrote:
>>
>>> Hi Ashwin,
>>>
>>> As far as I know this is the easiest way to do it. I'm not a scipy
>>> developer, but you may be interested in the pyresample package:
>>>
>>> http://pyresample.readthedocs.io/en/latest/
>>>
>>> Which uses the pykdtree library:
>>>
>>> https://github.com/storpipfugl/pykdtree
>>>
>>> And can perform resampling of irregularly spaced data. For certain
>>> calculations/utilities it will also convert to XYZ space as described in
>>> that SO answer. Hope this helps a little, sorry if it isn't the answer
>>> you're looking for.
>>>
>>> Dave
>>>
>>>
>>> On 5/20/18 11:09 AM, ashwin .D wrote:
>>>
>>>> Hello,
>>>>             I am using - https://docs.scipy.org/doc/sci
>>>> py/reference/generated/scipy.spatial.KDTree.html#scipy.spatial.KDTree
>>>> which  I believe is using Euclidean distances underneath the hood. But I
>>>> want to be able to use great circle distances. Is this the only way to do
>>>> this - https://stackoverflow.com/questions/20654918/python-how-to-s
>>>> peed-up-calculation-of-distances-between-cities or is there some way I
>>>> can extend that class to use great circle distances(maybe somebody has
>>>> already done that ? ) I have a irregular shaped grid in a WGS 84
>>>> ellipsoid(can be assumed to be a sphere) and so I want to use great circle
>>>> distances or if somebody can convince me not to that would be great as well.
>>>>
>>>>
>>>> Best regards,
>>>> Ashwin.
>>>>
>>>>
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>>
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