src.dynamic_boundary_conditions.tide.tide_slr_combine ===================================================== .. py:module:: src.dynamic_boundary_conditions.tide.tide_slr_combine .. autoapi-nested-parse:: Generates combined tide and sea level rise (SLR) data for a specific projection year, taking into account the provided confidence level, SSP scenario, inclusion of Vertical Land Motion (VLM), percentile, and more. Attributes ---------- .. autoapisummary:: src.dynamic_boundary_conditions.tide.tide_slr_combine.log Functions --------- .. autoapisummary:: src.dynamic_boundary_conditions.tide.tide_slr_combine.get_slr_scenario_data src.dynamic_boundary_conditions.tide.tide_slr_combine.get_interpolated_slr_scenario_data src.dynamic_boundary_conditions.tide.tide_slr_combine.add_slr_to_tide src.dynamic_boundary_conditions.tide.tide_slr_combine.get_combined_tide_slr_data Module Contents --------------- .. py:data:: log .. py:function:: get_slr_scenario_data(slr_data: geopandas.GeoDataFrame, confidence_level: str, ssp_scenario: str, add_vlm: bool, percentile: int) -> geopandas.GeoDataFrame Get sea level rise scenario data based on the specified confidence_level, ssp_scenario, add_vlm, and percentile. :param slr_data: A GeoDataFrame containing the sea level rise data. :type slr_data: gpd.GeoDataFrame :param confidence_level: The desired confidence level for the scenario data. Valid values are 'low' or 'medium'. :type confidence_level: str :param ssp_scenario: The desired Shared Socioeconomic Pathways (SSP) scenario for the scenario data. Valid options for both low and medium confidence are: 'SSP1-2.6', 'SSP2-4.5', or 'SSP5-8.5'. Additional options for medium confidence are: 'SSP1-1.9' or 'SSP3-7.0'. :type ssp_scenario: str :param add_vlm: Indicates whether to include Vertical Land Motion (VLM) in the scenario data. Set to True if VLM should be included, False otherwise. :type add_vlm: bool :param percentile: The desired percentile for the scenario data. Valid values are 17, 50, or 83. :type percentile: int :returns: A GeoDataFrame containing the sea level rise scenario data based on the specified confidence_level, ssp_scenario, add_vlm, and percentile. :rtype: gpd.GeoDataFrame :raises ValueError: - If an invalid 'confidence_level' value is provided. - If an invalid 'ssp_scenario' value is provided. - If an invalid 'add_vlm' value is provided. - If an invalid 'percentile' value is provided. .. py:function:: get_interpolated_slr_scenario_data(slr_scenario_data: geopandas.GeoDataFrame, increment_year: int = 1, interp_method: str = 'linear') -> geopandas.GeoDataFrame Interpolates sea level rise scenario data based on the specified year interval and interpolation method. :param slr_scenario_data: A GeoDataFrame containing the sea level rise scenario data. :type slr_scenario_data: gpd.GeoDataFrame :param increment_year: The year interval used for interpolation. Defaults to 1 year. :type increment_year: int = 1 :param interp_method: Temporal interpolation method to be used. Defaults to 'linear'. Available methods: 'linear', 'nearest', 'nearest-up', 'zero', 'slinear', 'quadratic', 'cubic', 'previous', 'next'. Refer to 'scipy.interpolate.interp1d()' for more details. :type interp_method: str = "linear" :returns: A GeoDataFrame containing the interpolated sea level rise scenario data. :rtype: gpd.GeoDataFrame :raises ValueError: - If the specified 'increment_year' is out of range. - If the specified 'interp_method' is not supported. .. py:function:: add_slr_to_tide(tide_data: geopandas.GeoDataFrame, slr_interp_scenario: geopandas.GeoDataFrame, proj_year: int) -> pandas.DataFrame Add sea level rise (SLR) data to the tide data for a specific projection year and return the combined tide and sea level rise value. :param tide_data: A GeoDataFrame containing tide data with added time information (seconds, minutes, hours) and location details. :type tide_data: gpd.GeoDataFrame :param slr_interp_scenario: A GeoDataFrame containing the interpolated sea level rise scenario data. :type slr_interp_scenario: gpd.GeoDataFrame :param proj_year: The projection year for which sea level rise data should be added to the tide data. :type proj_year: int :returns: A DataFrame that contains the combined tide and sea level rise data for the specified projection year. :rtype: pd.DataFrame :raises ValueError: If an invalid 'proj_year' value is provided. .. py:function:: get_combined_tide_slr_data(tide_data: geopandas.GeoDataFrame, slr_data: geopandas.GeoDataFrame, proj_year: int, confidence_level: str, ssp_scenario: str, add_vlm: bool, percentile: int, increment_year: int = 1, interp_method: str = 'linear') -> pandas.DataFrame Generate the combined tide and sea level rise (SLR) data for a specific projection year, considering the given confidence_level, ssp_scenario, add_vlm, percentile, and more. :param tide_data: A GeoDataFrame containing tide data with added time information (seconds, minutes, hours) and location details. :type tide_data: gpd.GeoDataFrame :param slr_data: A GeoDataFrame containing the sea level rise data. :type slr_data: gpd.GeoDataFrame :param proj_year: The projection year for which the combined tide and sea level rise data should be generated. :type proj_year: int :param confidence_level: The desired confidence level for the sea level rise data. :type confidence_level: str :param ssp_scenario: The desired Shared Socioeconomic Pathways (SSP) scenario for the sea level rise data. :type ssp_scenario: str :param add_vlm: Indicates whether Vertical Land Motion (VLM) should be included in the sea level rise data. :type add_vlm: bool :param percentile: The desired percentile for the sea level rise data. :type percentile: int :param increment_year: The year interval used for interpolating the sea level rise data. Defaults to 1 year. :type increment_year: int = 1 :param interp_method: Temporal interpolation method used for interpolating the sea level rise data. Defaults to 'linear'. Available methods: 'linear', 'nearest', 'nearest-up', 'zero', 'slinear', 'quadratic', 'cubic', 'previous', 'next'. Refer to 'scipy.interpolate.interp1d()' for more details. :type interp_method: str = "linear" :returns: A DataFrame containing the combined tide and sea level rise data for the specified projection year, taking into account the provided confidence_level, ssp_scenario, add_vlm, percentile, and more. :rtype: pd.DataFrame