bump adcircpy version to v1.1.0
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…ing (#80) * adding sciki linear regression_model kwarg into surrogate from training, and use of LassoCV model in the example * add options for analysis in log scale and conversion from depths to elevations in the output * adding minimum allowable value (for adcirc this is depth of 0.8*h0) option for surrogate model * adding boxplot showing error distribution of surrogate model across the runs, change normalized bias/mean error to dimensional one * subsetting by wind swath in example * adding ability to handle sometimes wet sometimes dry nodes into the example correctly * adding option to use sklearn PCA tool for the Karhunen-loeve decomposition as this seems to be faster and can handle larger datasets. Switched matrix order for the KL mode to avoid transposes and be consistent with the sklearn output. * adding correlation coefficient for wet/dry logical switch, increasing height of surrogte KL fit plot where required * adding subset_dataset option to adcirc parser that renumbers and transfers over element table if present * correcting subsetting with elements inside, adding surface plotter to geometry * adding KL surface plot option and adding element table into sensitivities dataset * adding element_table into surrogate output netcdf/xarray.Datasets, and updating plotting for KL prediction * adding surface plot option for sensitivities and changing colorbar to plasma * redefining placement of colorbar for sensitivities plot * adding plot surface ability for validation and percentiles * change colormap extent to only consider model source * Adding an elevation extrapolator over dry nodes to avoid setting these to a small constant null depth value which was negatively affecting the surrogate generation. Adjusting some validation plotting axis and colormap limits * send kwargs to coastline plot function * build on latest Python * bump adcircpy version (#69) * change `storm_id` to `nhc_code` * change `sample_rule` to `sample-rule` * change `sample_rule` to `sample-rule` * internal refactoring * remove unused dependencies * tables is not named the same on Anaconda * formatting * update setup and package installation (#75) * use `gartersnake` * update build system * update build system * update build system * update build system * update build system * update Python testing version * update workflows * making water elevation dataarray output a deep copy * change handling of training/validation datasets for the conversion from log scale and depths when generating the surrogate so that the original datasets are kept for validation purposes and only the surrogate is converted back from log scale and from depths * instead of modifiying minimum_allowable_value for surrogate model evaluation first subtract off convert_from_depths which can be either numpy.ndarray or float * implementing headloss methodology for extrapolating water elevations over the floodplain * correcting p for idw_order in elevation extrapolation idw routine * adding capabilities for sparse quadrature and other user-defined options for generating the quadrature * correcting vortex attribute to and removing old unnecessary code * adding sciki linear regression_model kwarg into surrogate from training, and use of LassoCV model in the example * add options for analysis in log scale and conversion from depths to elevations in the output * adding minimum allowable value (for adcirc this is depth of 0.8*h0) option for surrogate model * adding boxplot showing error distribution of surrogate model across the runs, change normalized bias/mean error to dimensional one * subsetting by wind swath in example * adding ability to handle sometimes wet sometimes dry nodes into the example correctly * adding option to use sklearn PCA tool for the Karhunen-loeve decomposition as this seems to be faster and can handle larger datasets. Switched matrix order for the KL mode to avoid transposes and be consistent with the sklearn output. * adding correlation coefficient for wet/dry logical switch, increasing height of surrogte KL fit plot where required * adding subset_dataset option to adcirc parser that renumbers and transfers over element table if present * correcting subsetting with elements inside, adding surface plotter to geometry * adding KL surface plot option and adding element table into sensitivities dataset * adding element_table into surrogate output netcdf/xarray.Datasets, and updating plotting for KL prediction * adding surface plot option for sensitivities and changing colorbar to plasma * redefining placement of colorbar for sensitivities plot * adding plot surface ability for validation and percentiles * change colormap extent to only consider model source * Adding an elevation extrapolator over dry nodes to avoid setting these to a small constant null depth value which was negatively affecting the surrogate generation. Adjusting some validation plotting axis and colormap limits * making water elevation dataarray output a deep copy * change handling of training/validation datasets for the conversion from log scale and depths when generating the surrogate so that the original datasets are kept for validation purposes and only the surrogate is converted back from log scale and from depths * instead of modifiying minimum_allowable_value for surrogate model evaluation first subtract off convert_from_depths which can be either numpy.ndarray or float * implementing headloss methodology for extrapolating water elevations over the floodplain * correcting p for idw_order in elevation extrapolation idw routine * adding capabilities for sparse quadrature and other user-defined options for generating the quadrature * correcting vortex attribute to and removing old unnecessary code * updating KLPC example to fit new changes * deleting RunTimeError for try except on Version retrieval in setup.py which is not working * updating cyclone subsetting to new VortexTrack implementation and geopandas sjoin commands, and adding dask call * send warning for general exception when retrieving VCS-tagged version * apply `isort` and linter * use future-compatible import for vortextrack * pin this version below `stormevents` `1.2.3` * bumping stormevents to 1.2.5 version for working version with old API * introducing inhouse sampling method that equally divides the distribution within the given fraction of the distribution * changing Vmax bounds * adding maximum_depth subsetting ability, and correctly treating None node_status_mask * plotting changes: changing perturbation matrix to plot different sizes and gray colors for quadrature weights, adding extend and label options to colorbar_axis, adding color_map and kwarg options to node_map plot * adding option to specify log base, and correcting convert_from_depths for numpy array input * update reference files * formatting Co-authored-by: zacharyburnettNOAA <zachary.burnett@noaa.gov>
SorooshMani-NOAA
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Jun 21, 2022
* adding sciki linear regression_model kwarg into surrogate from training, and use of LassoCV model in the example * add options for analysis in log scale and conversion from depths to elevations in the output * adding minimum allowable value (for adcirc this is depth of 0.8*h0) option for surrogate model * adding boxplot showing error distribution of surrogate model across the runs, change normalized bias/mean error to dimensional one * subsetting by wind swath in example * adding ability to handle sometimes wet sometimes dry nodes into the example correctly * adding option to use sklearn PCA tool for the Karhunen-loeve decomposition as this seems to be faster and can handle larger datasets. Switched matrix order for the KL mode to avoid transposes and be consistent with the sklearn output. * adding correlation coefficient for wet/dry logical switch, increasing height of surrogte KL fit plot where required * adding subset_dataset option to adcirc parser that renumbers and transfers over element table if present * correcting subsetting with elements inside, adding surface plotter to geometry * adding KL surface plot option and adding element table into sensitivities dataset * adding element_table into surrogate output netcdf/xarray.Datasets, and updating plotting for KL prediction * adding surface plot option for sensitivities and changing colorbar to plasma * redefining placement of colorbar for sensitivities plot * adding plot surface ability for validation and percentiles * change colormap extent to only consider model source * Adding an elevation extrapolator over dry nodes to avoid setting these to a small constant null depth value which was negatively affecting the surrogate generation. Adjusting some validation plotting axis and colormap limits * send kwargs to coastline plot function * build on latest Python * bump adcircpy version (#69) * change `storm_id` to `nhc_code` * change `sample_rule` to `sample-rule` * change `sample_rule` to `sample-rule` * internal refactoring * remove unused dependencies * tables is not named the same on Anaconda * formatting * update setup and package installation (#75) * use `gartersnake` * update build system * update build system * update build system * update build system * update build system * update Python testing version * update workflows * making water elevation dataarray output a deep copy * change handling of training/validation datasets for the conversion from log scale and depths when generating the surrogate so that the original datasets are kept for validation purposes and only the surrogate is converted back from log scale and from depths * instead of modifiying minimum_allowable_value for surrogate model evaluation first subtract off convert_from_depths which can be either numpy.ndarray or float * implementing headloss methodology for extrapolating water elevations over the floodplain * correcting p for idw_order in elevation extrapolation idw routine * adding capabilities for sparse quadrature and other user-defined options for generating the quadrature * correcting vortex attribute to and removing old unnecessary code * adding sciki linear regression_model kwarg into surrogate from training, and use of LassoCV model in the example * add options for analysis in log scale and conversion from depths to elevations in the output * adding minimum allowable value (for adcirc this is depth of 0.8*h0) option for surrogate model * adding boxplot showing error distribution of surrogate model across the runs, change normalized bias/mean error to dimensional one * subsetting by wind swath in example * adding ability to handle sometimes wet sometimes dry nodes into the example correctly * adding option to use sklearn PCA tool for the Karhunen-loeve decomposition as this seems to be faster and can handle larger datasets. Switched matrix order for the KL mode to avoid transposes and be consistent with the sklearn output. * adding correlation coefficient for wet/dry logical switch, increasing height of surrogte KL fit plot where required * adding subset_dataset option to adcirc parser that renumbers and transfers over element table if present * correcting subsetting with elements inside, adding surface plotter to geometry * adding KL surface plot option and adding element table into sensitivities dataset * adding element_table into surrogate output netcdf/xarray.Datasets, and updating plotting for KL prediction * adding surface plot option for sensitivities and changing colorbar to plasma * redefining placement of colorbar for sensitivities plot * adding plot surface ability for validation and percentiles * change colormap extent to only consider model source * Adding an elevation extrapolator over dry nodes to avoid setting these to a small constant null depth value which was negatively affecting the surrogate generation. Adjusting some validation plotting axis and colormap limits * making water elevation dataarray output a deep copy * change handling of training/validation datasets for the conversion from log scale and depths when generating the surrogate so that the original datasets are kept for validation purposes and only the surrogate is converted back from log scale and from depths * instead of modifiying minimum_allowable_value for surrogate model evaluation first subtract off convert_from_depths which can be either numpy.ndarray or float * implementing headloss methodology for extrapolating water elevations over the floodplain * correcting p for idw_order in elevation extrapolation idw routine * adding capabilities for sparse quadrature and other user-defined options for generating the quadrature * correcting vortex attribute to and removing old unnecessary code * updating KLPC example to fit new changes * deleting RunTimeError for try except on Version retrieval in setup.py which is not working * updating cyclone subsetting to new VortexTrack implementation and geopandas sjoin commands, and adding dask call * send warning for general exception when retrieving VCS-tagged version * apply `isort` and linter * use future-compatible import for vortextrack * pin this version below `stormevents` `1.2.3` * bumping stormevents to 1.2.5 version for working version with old API * introducing inhouse sampling method that equally divides the distribution within the given fraction of the distribution * changing Vmax bounds * adding maximum_depth subsetting ability, and correctly treating None node_status_mask * plotting changes: changing perturbation matrix to plot different sizes and gray colors for quadrature weights, adding extend and label options to colorbar_axis, adding color_map and kwarg options to node_map plot * adding option to specify log base, and correcting convert_from_depths for numpy array input * update reference files * formatting * adding both extrapolation and no extrapolation klpc examples * commenting out scatter validation plot due to takes a lot of memory and time to plot for large mesh and ensemble -- can move to density scatter plot in future * Auto fixing linting errors Co-authored-by: zacharyburnettNOAA <zachary.burnett@noaa.gov>
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