foxes.models.wake_superpositions.ws_pow¶

Classes¶

`WSPow`	Power superposition of wind deficit results.

Module Contents¶

class foxes.models.wake_superpositions.ws_pow.WSPow(pow: float, scale_amb: bool = False, scale_target: bool = False, lim_low: float | None = None, lim_high: float | None = None)[source]¶

Bases: foxes.core.WakeSuperposition

Power superposition of wind deficit results.

Parameters:¶

pow: Power to which wake results are raised.
scale_amb: Flag for selecting ambient instead of waked wind speed.
scale_target: Flag for selecting target instead of source turbine wind speed.
lim_low: Lower limit of the final waked wind speed.
lim_high: Upper limit of the final waked wind speed.

add_wake(algo: foxes.core.algorithm.Algorithm, mdata: foxes.core.data.MData, fdata: foxes.core.data.FData, tdata: foxes.core.data.TData, downwind_index: int, st_sel: numpy.ndarray, variable: str, wake_delta: numpy.ndarray, wake_model_result: numpy.ndarray) → numpy.ndarray[source]¶: Add a selected wake contribution to the accumulated deficit.

calc_final_wake_delta(algo: foxes.core.algorithm.Algorithm, mdata: foxes.core.data.MData, fdata: foxes.core.data.FData, tdata: foxes.core.data.TData, variable: str, wake_delta: numpy.ndarray) → numpy.ndarray[source]¶: Apply optional wind-speed limits to the accumulated deficit.

finalize(algo: foxes.core.algorithm.Algorithm, verbosity: int = 0) → None¶

Finalize the model.

Parameters:¶

algo: The calculation algorithm.
verbosity: The verbosity level; 0 is silent.

get_data(variable: str, target: str, lookup: str = 'smfp', mdata: foxes.core.data.MData | None = None, fdata: foxes.core.data.FData | None = None, tdata: foxes.core.data.TData | None = None, downwind_index: int | None = None, accept_none: False = False, accept_nan: bool = True, algo: foxes.core.algorithm.Algorithm | None = None, upcast: bool = False, selection: numpy.ndarray[Any, Any] | tuple[Any, ...] | list[Any] | None = None) → numpy.ndarray[Any, Any]¶

get_data(variable: str, target: str, lookup: str = 'smfp', mdata: foxes.core.data.MData | None = None, fdata: foxes.core.data.FData | None = None, tdata: foxes.core.data.TData | None = None, downwind_index: int | None = None, accept_none: True = True, accept_nan: bool = True, algo: foxes.core.algorithm.Algorithm | None = None, upcast: bool = False, selection: numpy.ndarray[Any, Any] | tuple[Any, ...] | list[Any] | None = None) → numpy.ndarray[Any, Any] | None

Getter for a data entry in the model object or provided data sources

Parameters:¶

variable: The variable name used as the data key.
target: The dimensions identifier for the output: FC.STATE_TURBINE, FC.STATE_TARGET, or FC.STATE_TARGET_TPOINT.
lookup: The order of data sources. Combination of: 's' for self, 'm' for mdata, 'f' for fdata, 't' for tdata, and 'w' for wake-modeling data.
mdata: The model data.
fdata: The farm data.
tdata: The target point data.
downwind_index: The index in the downwind order.
accept_none: Do not raise an error if the data entry is None.
accept_nan: Do not raise an error if the data entry is np.nan.
algo: The algorithm, needed for data from previous iterations.
upcast: Ensure the target dimensions are present. If expansion is needed, the result is a read-only broadcasted view.
selection: Apply this selection to the result, for state-turbine, state-target, or state-target-tpoint outputs.

initialize(algo: foxes.core.algorithm.Algorithm, loaded_data: LoadedData | None = None, force: bool = False, verbosity: int = 0) → LoadedData¶

Initialize the model.

Parameters:¶

algo: The calculation algorithm.
loaded_data: Data that has already been loaded, to be extended by this function. Keys are “coords”, a dict with entries dim_name_str -> dim_array; “data_vars”, a dict with entries name_str -> (dim_tuple, data_ndarray); and “extra_data”, a dict with non-array additional data.
force: Overwrite existing data.
verbosity: The verbosity level, where 0 is silent.

Returns:¶

loaded_data: The loaded data, containing the keys “coords”, “data_vars”, and “extra_data”.

input_farm_vars(algo: foxes.core.algorithm.Algorithm) → list[str][source]¶: Return farm variables required for wake scaling.

load_data(algo: foxes.core.algorithm.Algorithm, loaded_data: LoadedData, force: bool = False, verbosity: int = 0) → None¶

Load and/or create all data required for model calculations.

The function adds to loaded_data.

Parameters:¶

algo: The calculation algorithm.
loaded_data: Data that has already been loaded, to be extended by this function. Keys are “coords”, a dict with entries dim_name_str -> dim_array; “data_vars”, a dict with entries name_str -> (dim_tuple, data_ndarray); and “extra_data”, a dict with non-array additional data.
force: Overwrite existing data.
verbosity: The verbosity level, where 0 is silent.

classmethod new(superp_type: str, *args: Any, **kwargs: Any) → WakeSuperposition¶

Run-time wake superposition model factory.

Parameters:¶

superp_type: The selected derived class name
args: Additional parameters for constructor
kwargs: Additional parameters for constructor

set_running(algo: foxes.core.algorithm.Algorithm, data_stash: dict[str, dict[str, Any]] | None, sel: dict[str, Any] | None = None, isel: dict[str, Any] | None = None, verbosity: int = 0) → None¶

Set this model to the running state and move large data to the stash.

The stashed data is restored by unset_running after the calculation has finished.

Parameters:¶

algo: The calculation algorithm.
data_stash: The large-data stash. This function adds entries here when provided. Keys are model names and values are dictionaries of large model data.
sel: The subset selection dictionary.
isel: The index subset selection dictionary.
verbosity: The verbosity level; 0 is silent.

sub_models() → list[Model]¶

Return the list of all sub-models.

Returns:¶

smdls: All sub-models.

unset_running(algo: foxes.core.algorithm.Algorithm, data_stash: dict[str, dict[str, Any]] | None, sel: dict[str, Any] | None = None, isel: dict[str, Any] | None = None, verbosity: int = 0) → None¶

Set this model status to not running and recover large data from stash.

Parameters:¶

algo: The calculation algorithm.
data_stash: Reconstruct model data from this stash when provided. Keys are model names and values are dictionaries of large model data.
sel: The subset selection dictionary.
isel: The index subset selection dictionary.
verbosity: The verbosity level; 0 is silent.

unvar(vnm: str) → str | None¶

Translate a model-specific variable name to the original variable name.

Parameters:¶

vnm: The model-specific variable name.

Returns:¶

v: The original variable name.

var(v: str) → str¶

Create a model-specific variable name.

Parameters:¶

v: The variable name.

Returns:¶

vnm: The model-specific variable name.

property initialized : bool¶

Initialization flag.

Returns:¶

initialized: True if the model has been initialized.

lim_high = None¶

lim_low = None¶

property model_id : int¶

Unique id based on the model type.

Returns:¶

int: Unique id of the model object

name = 'Model'¶

pow¶

property running : bool¶

Flag for currently running models

Returns:¶

running: True if currently running

scale_amb = False¶

scale_target = False¶