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WRF output can easily have a couple hundred data variables in a dataset, which is not ideal for interactive exploration of a dataset's contents. With DataTree, we would have a tree-like hierarchical data structure for xarray which could be used for this.
This would likely become a new accessor method, such as .xwrf.organize().
Tests
After xwrf.postprocess(), we have a post processed dataset (with likely many data variables). Then, after xwrf.organize(), we would have a DataTree with (a yet to be decided) tree-like grouping of data variables. Calling xwrf.organize() without xwrf.postprocess() would fail.
Questions
What form of heirarchy would we want to have and how deep?
2d_variables vs. 3d_variables?
semantic grouping of variables, such as thermodynamic, grid_metrics, kinematic, accumulated, etc.?
Parse the WRF Registry somehow and assign groups based on that?
some other strategy?
The text was updated successfully, but these errors were encountered:
Description
WRF output can easily have a couple hundred data variables in a dataset, which is not ideal for interactive exploration of a dataset's contents. With DataTree, we would have a tree-like hierarchical data structure for xarray which could be used for this.
From @lpilz in #10:
Implementation
This would likely become a new accessor method, such as
.xwrf.organize()
.Tests
After
xwrf.postprocess()
, we have a post processed dataset (with likely many data variables). Then, afterxwrf.organize()
, we would have a DataTree with (a yet to be decided) tree-like grouping of data variables. Callingxwrf.organize()
withoutxwrf.postprocess()
would fail.Questions
What form of heirarchy would we want to have and how deep?
The text was updated successfully, but these errors were encountered: