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* Update python versioning requirements * Remove numpy note; add python version note * Try strictly less than 3.15
…dation study (#256) * Add script to generate a fake LES timeseries (for time being) * Add initial script for direct LES comparison with SCADA * Expand example for dynamic model validation in relative wake loss and absolute AEP * First iteration of dynamic model validation by directly comparing two timeseries, even if they have different resource. Simply synced on time. * Add functionality to remap LES timeseries to SCADA using steady-state table * Add FLORIS as an option to the comparison / validation * Simplify and clean up code * Add the true LES timeseries and also the import script for it. Delete the legacy fake simulation timeseries. Update analysis code * Update jupyter notebook with last modifications, clean-up * Add separate library file with AEP and wake loss calculation functions * Merged everything from df_scada and df_models to simple df_list; and bug fix * Simplify, remove unnecessary variables * Rename from AEP to cumulative production. Add warnings when numbers are not a whole multiple of 12 months. * from AEP variables to cumprod (cumulative production) * Finalize important reformatting, enforce that pow_ref cannot be specified beforehand * Add option to filter by wind speed range. Also add more information in printing to console, i.e. data timespan and wind speed range * Fix units, all inputs in [W], all outputs in [MWh] * Add mirror_nans function in dataframe_manipulations library * rename aep_analysis to cumulative_production_analysis * Since gridded solutions do not always start at 0.0 deg, but can also start at half the step size, e.g. 1.5 or 2.5 deg, we need different manipulations to expand interpolation. * Simplify timeseries_to_grid_solutions * Clean-up and add plot title * Update jupyter notebook with extensive explanations, using library functions, cleaned up * Add docstrings * Add more docstrings * Small changes and clarification in markdown comments * Moved LES data to Zenodo; add download function and process functions to import_aspire_les_data.py * Reduce import statements. Check if LES timeseries file exists, if not, raise warning to user. * Apply comment from Robin * Apply formatting and linting requirements * Separated ii==0 scenario from the for loop for improved linting * Fix lint error: line too long in import_aspire_les_data * Resolve the test failure for interpolate_floris_from_df_approx. Clarify UserWarning so that its a lot easier for the user to understand. * More clarifications in UserWarning * Apply ruff linter * Update index .gitignore to avoid duplicate gitignore files * Lint formatting to the example 12 jupyter notebook * Split cumulative_and_wake_loss function into two separate functions * Fix precommit issues, small bug fixes * Update docstring * Ruff and pre-commit fixes; restore Github tests passing * Move NaN mirroring to cumulative_production_analysis * Add dependencies * No version on xarrary * Add note on ssl --------- Co-authored-by: misi9170 <michael.sinner@nrel.gov> Co-authored-by: paulf81 <paul.fleming@nrel.gov> Co-authored-by: paulf81 <paul.fleming@nlr.gov>
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v2.5 of FLASC brings new LES example for the Smarteole dataset, contributed by @Bartdoekemeijer and @Robin9697. We also discontinue support for python 3.9 and add explicit support for python 3.14.
New features
Package updates
New contributors
Full Changelog: v2.4.2...v2.5
For NLR release: