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Releases: axiom-data-science/extract_model

v0.6

08 Jul 18:21
e7833e2

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extract_model can now be installed on Windows. If installing on Windows, horizontal interpolation will not work since xESMF will not work properly since ESMPy cannot be installed on Windows. Tests will run on Windows because those that use horizontal interpolation will not error out.

v0.5.3

07 Apr 17:26
3040061

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filter: can be used to easily filter a Dataset down to certain variables by their standard names, but also looks for the variables necessary to decode vertical depth coordinates.

sub_grid hadn't been written to account for both cases of dimensions and coordinates with the same name vs. not. Now it works for both scenarios.

Also cf-xarray is now available on conda-forge with the necessary changes so can integrate that.

v0.5.2: Improved subset function

05 Apr 18:55
a65179d

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Now subset function has been split into sub_bbox and sub_grid.

v0.5.1

01 Apr 21:50
6bb5f40

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Added another notebook of docs for time series examples.

v0.5: Added functionality for nearest point selection

01 Apr 17:20
238c2d1

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Can select nearest point to input lon/lat location, and do basic interpolation or selection in time and vertical.

Added preprocessing functions for ROMS, HYCOM, and POM, and placeholders for others.

Also added more docs for more examples.

v0.4

08 Sep 17:32
208132a

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With this release, Datasets are no longer input into extract_model — instead, DataArrays (in which the desired variable to be interpolated has already been selected) should be input. All of the docs and tests have been updated accordingly.

Package dependencies still

29 Jun 20:45
de6ddd6

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Pinned version required for cf-xarray which should fix the problem that has been on-going.

Fixed up package dependencies

29 Jun 19:20
dce0382

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Merge pull request #28 from kthyng/main

updated package listings

Removed pandas from reqs lists

28 Jun 17:53
ccfc78a

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Wasn't actually being used.

Improved logic

25 Jun 20:21
4463f2a

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This version integrates a new capability in cf-xarray to identify variables using custom attributes criteria. It cleans up the code in this package.