Check to see if this masking `xr.where` parallelize

[lsetiawan]

This xr.where needs to be investigated to see if it can parallelize with the use of dask.

https://github.com/OSOceanAcoustics/echopype/blob/3ca028f04a4fd3643bb0ad350dc09a699883ee1e/echopype/mask/api.py#L500

[anantmittal]

Command to run the tests and capture the stdout: python .ci_helpers/run-test.py --local --pytest-args="-vvrP" echopype/mask/api.py

pytest -vvrP echopype/tests/mask/test_mask.py::test_frequency_differencing

[leewujung]

@lsetiawan : the code you pointed to above was actually not the one we were discussing 😬

Below are the 2 potential places where the calibration code (compute_Sv) code can have bottleneck:

• in get_vend_cal_params_power where a broadcasting is probably used to get a data variable into the "right" shape based on indexing: https://github.com/OSOceanAcoustics/echopype/blob/593a7e173dffd34858a7587cf2f31633a23a841c/echopype/calibrate/cal_params.py#L299-L304

• in harmonize_env_param_time where there is either a check for all the timestamps or an interpolation: https://github.com/OSOceanAcoustics/echopype/blob/593a7e173dffd34858a7587cf2f31633a23a841c/echopype/calibrate/env_params.py#L53-L66

[leewujung]

@anantmittal : Thanks for investigating the xr.where in the masking code though -- since the change is to use dask.where, the solution would probably be similarly useful for the get_vend_cal_params_power case above.

[leewujung]

I'll close this now, since my comment above is replicated in #1200.