Pydda

In this release, we have added an augmented Lagrangian solver to PyDDA that allows the user to be able to perform retrievals without having to select weights beforehand. Right now this solver has been tested using the mass continuity and radar observational cost functions. In order to enable this functionality, set the engine to "auglag" and then set the Co and Cm weights to 1.0.

We are glad to finally release PyDDA 1.0. In this version we have fixed the problems noted by issues 58 ,63 , and 64. In addition, we have been working hard to enhance the optimizer inside PyDDA. Therefore, we now provide the user with an option to use either Jax or TensorFlow (in addition to the current SciPy engine) to solve the optimization problem.

There are several advantages to the Jax and TensorFlow-based engines that we *highly* encourage our users to use. With Jax and TensorFlow, we are able to use automatic differentiation to calculate the gradients to the cost function. This makes the gradient calculation less susceptible to roundoff and boundary errors. In addition, Jax and TensorFlow both support CUDA-enabled GPUs. Therefore, if you are using a GPU, PyDDA is now capable of harnessing it in order to dramatically speed up the wind retrieval calculation. Even on a CPU-based system, the TensorFlow-based algorithm typically converges faster than the original SciPy-based algorithm. Therefore, we strongly encourage users to use these two other engines in their retrievals.

TensorFlow and Jax are optional dependencies. You need both TensorFlow 2.6.0 and tensorflow-probability in order to use the TensorFlow functionality.

In this new release we now support:
* Integration of point observations as a constraint
* Automatic download of ASOS data from the Iowa Mesonet Archive for a given grid as a source of point observations
* Fixes to the documentation
* Reorganization of the cost function/gradient code to make development easier

This fixes bugs in the calculation of the weights from the grid mask and also fixes the functionality behind throwing a warning for missing cartopy package.

I am proud to announce a new release of PyDDA. This release adds:
* A bug fix for the calculation of the radial velocity cost function to be more consistent with MultiDop. This will change the coefficients you use for this constraint.
* Improved the capability to adjust the thickness and sizes of quivers and streamline arrows for better visualizations.
* Support for integrating ERA-Interim data into retrieval. This feature can even automatically download and interpolate the ERA-Interim data to the analysis grid, but will require the ECMWF Web API to be installed in order to do so.

The ECMWF Web API, an optional dependency of PyDDA, can be downloaded here:

https://confluence.ecmwf.int/display/WEBAPI/Access+ECMWF+Public+Datasets

One will need to follow the instructions at the above link to set up an account with ECMWF and to use this account with the Web API.

This is the same as 0.3.0, but with a fix to the MANIFEST.in file for packaging.