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@leouieda leouieda released this 06 Apr 16:37
v1.4.0
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Learn about vigilant mode.

DOI: https://doi.org/10.5281/zenodo.3739449

Bug fixes:

  • Profile distances are now returned in projected (Cartesian) coordinates by the profile method of gridders if a projection is given. The method has the option to apply a projection to the coordinates before predicting so we can pass geographic coordinates to Cartesian gridders. In these cases, the distance along the profile is calculated by the profile_coordinates function with the unprojected coordinates (in the geographic case it would be degrees). The profile point calculation is also done assuming that coordinates are Cartesian, which is clearly wrong if inputs are longitude and latitude. To fix this, we now project the input points prior to passing them to profile_coordinates. This means that the distances are Cartesian and generation of profile points is also Cartesian (as is assumed by the function). The generated coordinates are projected back so that the user gets longitude and latitude but distances are still projected Cartesian meters. (#231)
  • Function verde.grid_to_table now sets the correct order for coordinates. We were relying on the order of the coords attribute of the xarray.Dataset for the order of the coordinates. This is wrong because xarray takes the coordinate order from the dims attribute instead, which is what we should also have been doing. (#229)

Documentation:

  • Generalize coordinate system specifications in verde.base.BaseGridder docstrings. Most methods don't really depend on the coordinate system so use a more generic language to allow derived classes to specify their coordinate systems without having to overload the base methods just to rewrite the docstrings. (#240)

New features:

  • New function verde.convexhul_mask to mask points in a grid that fall outside the convex hull defined by data points. (#237)
  • New function verde.project_grid that transforms 2D gridded data using a given projection. It re-samples the data using ScipyGridder (by default) and runs a blocked mean (optional) to avoid aliasing when the points aren't evenly distributed in the projected coordinates (like in polar projections). Finally, it applies a convexhul_mask to the grid to avoid extrapolation to points that had no original data. (#246)
  • New function verde.expanding_window for selecting data that falls inside of an expanding window around a central point. (#238)
  • New function verde.rolling_window for rolling window selections of irregularly sampled data. (#236)

Improvements:

  • Allow verde.grid_to_table to take xarray.DataArray as input. (#235)

Maintenance:

  • Use newer MacOS images on Azure Pipelines. (#234)

This release contains contributions from:

  • Leonardo Uieda
  • Santiago Soler
  • Jesse Pisel
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