Content Comparison

def mesh2d_altitude_from_raster(self,
rasterpath, where='face', stat='mean', missing='default'):
 network,
   """ rasterpath,
   Method towhere: determineRasterStatPosition level within cell or on nodes. The values are determined by
    applying a statistic to the pixels within the cell bounds or around the node.

    In case of the option 'node' Voronoi polygons are drawn around the nodes. These cells
    are cut of at the edges of the the grid. This option might take a bit longer, since
    all the polygons need to be drawn and clipped at the edges.

    In case of msising values, which can occur:
    - due to no-data parts in the grid that is sampled
    - when the cell sizes within which the altitude is determined is smaller than a raster pixel.
    The missing data can be filled= "face",
    stat="mean",
    fill_option: FillOption = "fill_value",
    fill_value=None,
):
    """
    Method to determine level of nodes

    This function works faster for large amounts of cells, since it does not
    draw polygons but checks for nearest neighbours (voronoi) based
    on interpolation.

    Note that the raster is not clipped. Any values outside the bounds are
    also taken into account.
    
Parameters     ----------
    rasterpath : strArguments:
        Path to raster
    where network: strnetwork         Locations whereobject containing the altitudemesh
is determined. Can be on the faces,rasterpath: sopath withinto the rasterfile containing elevations
     cell boundaries, or node, on the cell edged. Default where: 'face' or 'node'; default = stat :face
str      stat: rasterstat  Statisticstatistic to determinedbe fromused values(default within polygon bounds. A string is required that
        describes a function that is known by numpy, such as 'mean'= mean)
      fill_option: "fill-value", 'nearest', or 'maxinterpolate',. withoutFill anymissing cells with a constant value, by   further arguments (quantile is not possible, since we'd need to specify which quantile)nearest-neighbour interpolation, or by linear interpolation.
        Default: 'mean'
    missing : str
        How fill_value: value to fill the missing values.
        - default: No filling, the missing values will have a NaN-value in the grid
        - nearest: Fill the missing data with the nearest cell value that has a value
        - interpolation: Interpolate the missing data with cell values that are present
            Default: 'default'
    """

cells with (also needed for interpolation in case that fails)
    """