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foxes.output.SliceData.get_mean_data_xy(resolution, variables=
None, data_format='xarray', xmin=None, ymin=None, xmax=None, ymax=None, z=None, xspace=500.0, yspace=500.0, normalize_x=None, normalize_y=None, normalize_z=None, normalize_v={}, label_map={}, vmin={}, vmax={}, states_sel=None, states_isel=None, weight_turbine=0, to_file=None, write_pars={}, ret_states=False, ret_grid=False, verbosity=0, **kwargs)[source] Creates mean data of 2D farm flow slices in a horizontal xy-plane.
Parameters¶
- resolution: float
The resolution in m
- variables: list of str, optional
The variables, or None for all
- data_format: str
The output data format: numpy, pandas, xarray
- xmin: float, optional
The min x coordinate, or None for automatic
- ymin: float, optional
The min y coordinate, or None for automatic
- xmax: float, optional
The max x coordinate, or None for automatic
- ymax: float, optional
The max y coordinate, or None for automatic
- z: float, optional
The z coordinate of the plane
- xspace: float, optional
The extra space in x direction, before and after wind farm
- yspace: float, optional
The extra space in y direction, before and after wind farm
- normalize_x: float, optional
Divide x by this value
- normalize_y: float, optional
Divide y by this value
- normalize_z: float, optional
Divide z by this value
- normalize_v: dict, optional
Divide the variables by these values
- label_map: dict
The mapping from original to new field names
- vmin: dict
Minimal values for variables
- vmax: dict
Maximal values for variables
- states_sel: list, optional
Reduce to selected states
- states_isel: list, optional
Reduce to the selected states indices
- weight_turbine: int, optional
Index of the turbine from which to take the weight
- to_file: str, optional
Write data to this file name
- write_pars: dict
Additional write function parameters
- ret_states: bool
Flag for returning states indices
- ret_grid: bool
Flag for returning grid data
- verbosity: int, optional
The verbosity level, 0 = silent
- kwargs: dict, optional
Parameters forwarded to the algorithm’s calc_points function.
Returns¶
- data: dict or pandas.DataFrame or xarray.Dataset
The gridded data
- states: numpy.ndarray, optional
The states indices
- grid_data: tuple, optional
The grid data (x_pos, y_pos, z_pos, g_pts)