Improve potential bottlenecks in `compute_Sv`

[lsetiawan]

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

Originally posted by @leewujung in https://github.com/OSOceanAcoustics/echopype/issues/1165#issuecomment-1776360813

[anantmittal]

pytest -vvrP echopype/tests/calibrate/test_cal_params.py::test_get_vend_cal_params_power
pytest -vvrP echopype/tests/calibrate/test_env_params.py::test_harmonize_env_param_time

[anantmittal]

@leewujung Could you explain a bit more about the potential bottleneck in get_vend_cal_params_power? Do we want to parallelize specific operations using dask? Or do we want to refactor the code using numpy/xarray itself to improve runtime?

[anantmittal]

@leewujung Similar question for improvement in harmonize_env_param_time. Are we looking into parallelizing specific (e.g., np.all) operations?

[leewujung]

Do we want to parallelize specific operations using dask? Or do we want to refactor the code using numpy/xarray itself to improve runtime?

I think it is both. We want to enable handling data with lazy-loaded arrays if it's not already, and ensure that the operations are distributed efficiently when dask supports them (and if not, see where dask is heading on the ops, since these ops are not very specific).

In get_vend_cal_params_power, I worry that expand_dims and to a lesser degree sortby is expensive.

In harmonize_env_param_time, the np.all in one of the conditions may be slow if time dimension is large, and .interp may also be slow.

[anantmittal]

For harmonize_env_param_time,

On running some experiments, np.all appears to be the fastest way when using just numpy. This link also shares the same thought.

[leewujung]

This harmonize_env_param_time component was addressed in #1235.

[leewujung]

We can close this now with #1235 and #1285 merged.