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* first pass on contingency analysis ipynb, added postprocessing and aux plot functions * subsample of 100 scenarios for faster run and smaller csv files * subsample of 100 scenarios for faster run and smaller csv files * visualization functions * smaller sample size * smaller sample size * moved vals to parameters and heuristic for vmax with mean of bin counts, ipynb cleanup * added the merge of both csv into the contingency notebook * Delete examples/data/contingency_texas/predictions_100_examples.csv too many examples * Added notebook and updated documentation * pre-commit checks * Examples/contingency ipynb (#13) * first pass on contingency analysis ipynb, added postprocessing and aux plot functions * subsample of 100 scenarios for faster run and smaller csv files * subsample of 100 scenarios for faster run and smaller csv files * visualization functions * smaller sample size * smaller sample size * moved vals to parameters and heuristic for vmax with mean of bin counts, ipynb cleanup * added the merge of both csv into the contingency notebook * Delete examples/data/contingency_texas/predictions_100_examples.csv too many examples * Added notebook and updated documentation * pre-commit checks --------- Co-authored-by: Celia Cintas <celia.cintas@ibm.com> --------- Co-authored-by: MatteoMazzonelli <matteo.mazzonelli1@ibm.com> Co-authored-by: Matteo Mazzonelli <74906247+MatteoMazzonelli@users.noreply.github.com>
* Working GNS * GNS established baseline * Small fixes * Added comment, pre-commit checks
* train models for SE * tests for measurement simulation * baseMVA for normalization can be passed in the config * fix bolean comparison in inverse normalization
load grid data for unknown perturbations, test edge flows, split scenarios by load index
* fix PF masking * Update configs and PF masking
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