issues: 1024582327
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| id | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at | closed_at | author_association | active_lock_reason | draft | pull_request | body | reactions | performed_via_github_app | state_reason | repo | type |
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| 1024582327 | I_kwDOAMm_X849EeK3 | 5861 | Xarray's interpolator behavior compared to scipy with a numpy array: new keyword behavior requested | 49281118 | open | 0 | 0 | 2021-10-12T23:08:34Z | 2021-10-12T23:09:40Z | NONE | I'm having trouble with the differing behavior between giving an xarray object to scipy's interpolating functions (particularly the RegularGridInterpolator and the one xarray's interpn is based on) versus giving a numpy array. When giving the interpolator a numpy array, I get a 1D array returned with one value for every point given. When an xarray object is given instead, I get an N dimensional array, as if a np.meshgrid statement is executed on the given points. I have provided more detail at the link below. This differing return behavior and the additional demand for the calculation for a grid made from the points (rather than the points themselves) is much slower than the numpy approach, but I can't use numpy arrays for medium data (because it won't all fit in my memory). Can a feature be added, maybe a 'numpy-like' keyword, to xarray's version of the scipy interpolator to only execute for the points given rather than a grid made from the points? Such a keyword would enable backwards-compatibility and reduce the computational demand for those interested in interpolating along a curved trajectory (such as in my case). Note: the same differing behavior occurs when I give scipy's RegularGridInterpolator an xarray object. https://github.com/scipy/scipy/issues/14824#issue-1021424672 |
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13221727 | issue |