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Releases: pangeo-data/xESMF

v0.5.0

28 Nov 15:50
bb29347

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New features

  • Create ESMF.Mesh objects from shapely.polygons (#24).
  • New class SpatialAverager offers user-friendly mechanism to average a 2-D field over a polygon. Includes support to handle interior holes and multi-part geometries. (#24)
  • Automatic detection of coordinates and computation of vertices based on cf-xarray. (#49)

Breaking changes

  • Deprecate esmf_grid in favor of Grid.from_xarray
  • Deprecate esmf_locstream in favor of LocStream.from_xarray
  • Installation requires numpy>=1.16 and cf-xarray>=0.3.1

Bug fixes

  • Fix serialization bug when using dask's distributed scheduler (#39).

Internal changes

  • Subclass ESMF.Mesh and create from_polygon method
  • Subclass ESMF.Grid and ESMF.LocStream and create from_xarray methods.
  • New BaseRegridder class, with support for Grid, LocStream and Mesh objects. Not all regridding methods are supported for Mesh objects.
  • Refactor Regridder to subclass BaseRegridder.

v0.4.0

30 Sep 20:20
6060004

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0.4.0 (01-10-2020)

The git repo is now hosted by pangeo-data (https://github.com/pangeo-data/xESMF)

Breaking changes

  • By default, weights are not written to disk, but instead kept in memory.
  • Installation requires ESMPy 8.0.0 and up.

New features

  • The Regridder object now takes a weights argument accepting a scipy.sparse COO matrix,
    a dictionary, an xarray.Dataset, or a path to a netCDF file created by ESMF. If None, weights
    are computed and can be written to disk using the to_netcdf method. This weights parameter
    replaces the filename and reuse_weights arguments, which are preserved for backward compatibility (#3).
    By @huard and @raphaeldussin
  • Added documentation discussion how to compute weights from a shell using MPI, and reuse from xESMF (#12 ).
    By @raphaeldussin
  • Add support for masks in :py:funcesmf_grid. This avoid NaNs to bleed into the interpolated values.
    When using a mask and the conservative regridding method, use a new method called
    conservative_normed to properly handle normalization (#1 ).
    By @raphaeldussin