Validating the model during creation, and before it goes to MODFLOW

[deltamarnix]

MODFLOW validates models before running them, but we foresee that we can aid the user in creating models by validating the input that the user provides. We can define multiple moments where validation can be useful:

• When reading MODFLOW input. The moment that the flopy internal model is constructed during a read, we can do checks to see if the data is in the correct format, or even more rigorous checks to validate the data itself. This can be useful when users have manually edited MODFLOW input files.
• When creating flopy models from script. Any time that a script initializes a package / model / simulation, we can check the input data that comes into the parameters.
• When writing MODFLOW input files. We can consider to add more extensive validation checks when writing the MODFLOW input files to disk. Perhaps check if all data exists, or some reference files that need to be in the right place. Some checks could be on a very specific part, but we could also think of post-write checks where dependencies between files need to be validated.
• When reading MODFLOW output. Just like input, output could be altered by hand. So we need to check if the data is still correct while reading it.

Since we were thinking of using attrs as our data class package, we can leverage the validation functionality that is built into the package. But it is likely that not all checks can be built within this structure. attrs states that initializers should remain simple. And therefore I'm not sure if we should validate a whole data grid with attrs the moment that we read it. Especially since we want to do lazy loading as much as possible with packages like Dask or XArray.
I do think that validation of more simple parameters can already help a lot, especially on input types and perhaps some extra range checks.

It would also be possible to disable the checks system while creating and setting attributes, and only check input validation via attr.validate() manually. We could give the user a simple handle to let them validate their model model.validate().

There are some built-in validators that help set up simple checks, also for lists and dicts.