Speed up wave.resource module

[akeeste]

@ssolson This is a follow-up to my other wave PRs and resolves #331. Handling the various edge cases robustly in pure numpy is difficult, so I want to first resolve #331 by using DataArrays throughout the wave resource functions instead of Datasets.

Similar to Ryan's testing mentioned in #331, I found that using DataArrays/Pandas has a 1000x speed up vs Datasets for very large input data. This should restore MHKiT's speed to it's previous state. Using a pure numpy base would have an additional 5-10x speed up from DataArrays, but I think the current work with DataArrays will:

• be sufficient for our users
• be easier to develop with
• be easier to handle edge cases

[akeeste]

@ssolson This PR is ready for review. Tests should pass now

With some modifications to the type handling functions, and an appropriate frequency_dimension input if required, the wave.resource functions should handle Pandas Series, Pandas DataFrames, and xarray DataArrays regardless of input shape, dimensions names, dimension order, etc. I largely moved away from converting data to xarraay Datasets because they are slow and more difficult to work with.

[akeeste]

mhkit.loads.graphics is unchanged, so I'm not sure why the pylint loads test is now failing on the number of positional arguments there. This branch is up to date with develop

[ssolson]

mhkit.loads.graphics is unchanged, so I'm not sure why the pylint loads test is now failing on the number of positional arguments there. This branch is up to date with develop

Pylint added new warnings around the way it wants users to handle positional arguments. I'll address it.

[ssolson]

mhkit.loads.graphics is unchanged, so I'm not sure why the pylint loads test is now failing on the number of positional arguments there. This branch is up to date with develop

Pylint added new warnings around the way it wants users to handle positional arguments. I'll address it.

Addressed in #357

Let's merge #357 then make sure the tests pass here.

[akeeste]

Thanks @ssolson. I'll merge that in here and fix a couple minor items with some examples

[akeeste]

@akeeste TODO:

• fix a couple last issues with example notebooks
• reduce time required for example notebooks to enforce speed back to previous benchmark

[akeeste]

@ssolson this PR is now ready for review and all tests are passing. I tightened up the timing on the environmental contours, 3 extreme response, and PacWave examples.

A straight forward test case on the difference in computational expense is using a wave resource function (e.g. energy_period) with a year of NDBC spectral data, or repeating Ryan's script in #331