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| author | Alessandro Pietro Bardelli <apbard@users.noreply.github.com> | 2017-01-04 00:58:44 +0100 |
|---|---|---|
| committer | Alessandro Pietro Bardelli <apbard@users.noreply.github.com> | 2017-02-22 10:50:53 +0100 |
| commit | 9520de90837d0afaac3d1612047f4b952563b3d5 (patch) | |
| tree | 623938c6d79bda2affa5e1794f8512155d2d4241 /numpy/doc/subclassing.py | |
| parent | f6a07571df745f01eaccf4b05b8476da6f0b5833 (diff) | |
| download | numpy-9520de90837d0afaac3d1612047f4b952563b3d5.tar.gz | |
ENH: gradient support for unevenly spaced data
This somehow reverts #7618 and solves #6847, #7548 by implementing
support for unevenly spaced data. Now the behaviour is similar to
that of Matlab/Octave function. As argument it can take:
1. A single scalar to specify a sample distance for all dimensions.
2. N scalars to specify a constant sample distance for each dimension.
i.e. `dx`, `dy`, `dz`, ...
3. N arrays to specify the coordinates of the values along each
dimension of F. The length of the array must match the size of
the corresponding dimension
4. Any combination of N scalars/arrays with the meaning of 2. and 3.
Diffstat (limited to 'numpy/doc/subclassing.py')
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