v2.2.0

What's Changed

• Bump version from 2.1.1 to 2.2 by @rcjackson in #144
• VER: 2.2.0 by @rcjackson in #145
• FIX: GitHub Actions upload by @rcjackson in #146
• FIX: Trying to get the PyPI release to work again. by @rcjackson in #147
• FIX: Update artifact versions in PyPI build. by @rcjackson in #148

Full Changelog: v2.1.2...v2.2.0d

v2.2

What's Changed

• FIX: Tensorflow probability versions for CI by @rcjackson in #141
• Fix radar pair weighting being referenced by cur_array by @SunnysChan in #143
• Bump version from 2.1.1 to 2.2 by @rcjackson in #144
• VER: 2.2.0 by @rcjackson in #145

New Contributors

• @SunnysChan made their first contribution in #143

Full Changelog: v2.1.1...v2.2a

v2.1.1

Some minor fixes for deprecation warnings and changes to the unit testing suite.

What's Changed

• ADD: Nightly builds by @rcjackson in #138
• FIX: Remove python from actions environment, deprecation warnings and user warnings. by @rcjackson in #139

Full Changelog: v2.1.0...v2.1.1

v2.1.0

This new release updates the package to conform to the xarray version of DataTree. If you have used the nesting feature, it is important to re-read the nesting example on how to structure your nested grids in order to conform to the new standards.

v.2.0.3

This new release fixes errors with converting the origin_latitude to xarray when trying to read from a PyART Grid.

v2.0.2

Microrelease to fix automatic release to PyPI.

v2.0.1

Micro-release to fix KeyErrors when loading PyDDA grids and to fix errors with automatic release to PyPI.

2.0

PyDDA is moving to an Xarray-based data structure (a PyDDA Grid) for all of its retrievals. This will make it easier for PyDDA to take in any xarray-based dataset as inputs. In addition, we are now also using xarray DataTrees to support grid nesting using a tree structure. Documentation and examples on this nesting have been added to the user guide and example gallery.

The migration of your code to PyDDA 2.0 will involve small changes to the pipeline. Instead of using pyart.io.read_grid to read your grid, simply use pydda.io.read_grid! In addition, you can convert a PyART Grid to a PyDDA Grid) by using pydda.io.read_from_pyart_grid. In addition, PyDDA will automatically convert PyART grids that are specified as inputs to PyDDA grids in the get_dd_wind_field function, though it is recommended to use PyDDA grids as inputs since the initalization and constraint functions use PyDDA Grids.

1.4.0

PyDDA 1.4.0 has been released today! In this version of PyDDA we have:
* Moved to ruff for linting and added pre-commit hooks to better ensure code quality in PRs.
* Expanded unit testing of Jax/TensorFlow cost functions to resolve stability/syntax issues with these engines (i.e. issue #94)

v1.3.0: Bugfixes!

Today we have released PyDDA 1.3.0. This version has three major bugfixes:

• The weights from the final radar in the specified list were being set to zero by default. Now the coverage for the last radar is calculated correctly (Addressing #89 ).
• The yz-section plotting functions were not plotting the contours onto the specified axis, but rather whatever was the matplotlib current axis. This is now fixed.
• Smoothness constraints between the three engines are now consistent with each other (numpy engine was not dividing by grid size). I recommend setting Cx, Cy, and Cz to 1e-4 for best results.

New features:

• You can now specify the input initial state as u, v, and w fields in the first input Grid to get_dd_wind_field. The initialization procedures now dump the initial state into this variables in your Py-ART Grids automatically for you so that you don't have to specify these three parameters in get_dd_wind_field anymore. This reduces the number of minimum parameters for get_dd_wind_field to 1. In addition, this is useful if you want to save the state of the retrieval after many iterations, then apply low pass filters before resuming the retrieval to remove high frequency noise.
• get_dd_wind_field now returns 2 arguments, the list of grids and the parameters used for the retrieval. This allows the user to see the weights that PyDDA pre-calculates for each grid. Therefore, when you are using get_dd_wind_field, you'll want to call it using grids, params = get_dd_wind_field([grids], ...).