0.3.0

New features

• Added new eo_tides.utils.clip_models function for clipping tide models to a smaller spatial extent. This can have a major positive impact on performance, sometimes producing more than a 10 x speedup. This function identifies all NetCDF-format tide models in a given input directory, including "ATLAS-netcdf" (e.g. TPXO9-atlas-nc), "FES-netcdf" (e.g. FES2022, EOT20), and "GOT-netcdf" (e.g. GOT5.5) format files. Files for each model are then clipped to the extent of the provided bounding box, handling model-specific file structures. After each model is clipped, the result is exported to the output directory and verified with pyTMD to ensure the clipped data is suitable for tide modelling.

Major changes

• The parallel_splits parameter that controls the number of chunks data is broken into for parallel analysis has been refactored to use a new default of "auto". This now attempts to automatically determine a sensible value based on available CPU, number of points, and number of models being run. All CPUs will be used where possible, unless this will produce splits with less than 1000 points in each (which would increase overhead). Parallel splits will be reduced if multiple models are requested, as these are run in parallel too and will compete for the same resources.
• Changed the default interpolation method from "spline" to "linear". This appears to produce the same results, but works considerably faster.
• Updates to enable correct cropping, recently resolved in PyTMD 2.1.8

Breaking changes

• The list_models function has been relocated to eo_tides.utils (from eo_tides.model)

PRs

• Improve parallelisation, add clip_models functionality by @robbibt in #25

Full Changelog: 0.2.0...0.2.1