@@ -894,7 +894,11 @@ def list_variables(data: Union[netCDF4.Dataset, xr.Dataset, xr.DataArray]) -> Li
894894 "data must be a NetCDF4 Dataset, xarray Dataset, or "
895895 f"xarray DataArray. Got: { type (data )} "
896896 )
897- def calculate_grid_convergence_index (fine_grid , coarse_grid , refinement_ratio ,factor_of_safety = 1.25 , order = 2 ):
897+
898+
899+ def calculate_grid_convergence_index (
900+ fine_grid , coarse_grid , refinement_ratio , factor_of_safety = 1.25 , order = 2
901+ ):
898902 """
899903 Calculate the Grid Convergence Index (GCI) between two grid sizes. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html
900904
@@ -904,7 +908,7 @@ def calculate_grid_convergence_index(fine_grid, coarse_grid, refinement_ratio,fa
904908 Results from the finer grid.
905909 coarse_grid: numpy.ndarray
906910 Results from the coarser grid.
907- refinement_ratio: float
911+ refinement_ratio: float
908912 Refinement ratio between the grids.
909913 order: int
910914 Order of accuracy (default is 2).
@@ -919,4 +923,4 @@ def calculate_grid_convergence_index(fine_grid, coarse_grid, refinement_ratio,fa
919923
920924 # Calculate the GCI
921925 gci = (factor_of_safety * error ) / (refinement_ratio ** order - 1 )
922- return gci
926+ return gci
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