xugrid.create_snap_to_grid_dataframe#

xugrid.create_snap_to_grid_dataframe(lines: GeoDataFrame, grid: DataArray | UgridDataArray, max_snap_distance: float, tolerance: float = 1e-12) → DataFrame[source]#

Create a dataframe required to snap line geometries to a Ugrid2d topology.

A line is included and snapped to a grid edge when the line separates the centroid of the cell with the centroid of the edge.

Parameters:

  • lines (gpd.GeoDataFrame) – Line data. Geometry colum should contain exclusively LineStrings.

  • grid (xugrid.Ugrid2d) – Grid of cells to snap lines to.

  • max_snap_distance (float)

  • tolerance (float, optional, default value is 1e-12.) – Relative tolerance value to resolve edge cases. Increase the value if a line is unexpectedly not snapped to a grid cell edge.

Returns:

result – DataFrame with columns:

  • line_index: the index of the geodataframe geometry.

  • edge_index: the index of the

  • x0: start x-coordinate of edge segment.

  • y0: start y-coordinate of edge segment.

  • x1: end x-coordinate of edge segment.

  • y1: end y-coordinate of edge segment.

  • length: length of the edge.

Return type:

pd.DataFrame

Examples

First create data frame:

>>> snapping_df = create_snap_to_grid_dataframe(lines, grid2d, max_snap_distance=0.5)

Use the line_index column to assign values from lines to this new dataframe:

>>> snapping_df["my_variable"] = lines["my_variable"].iloc[snapping_df["line_index"]].to_numpy()

Run some reduction on the variable, to create an aggregated value per grid edge:

>>> aggregated = snapping_df.groupby("edge_index").sum()

Assign the aggregated values to a Ugrid2d topology:

>>> new = xu.full_like(edge_data, np.nan)
>>> new.data[aggregated.index] = aggregated["my_variable"]