Source code for foxes.input.states.scan_ws

import numpy as np

from foxes.core import States
import foxes.variables as FV
import foxes.constants as FC



[docs]
class ScanWS(States):
    """
    A given list of wind speeds, all other variables are fixed.

    Parameters
    ----------
    wd: float
        The wind direction
    ti: float
        The TI value
    rho: float
        The air density

    :group: input.states

    """


[docs]
    def __init__(self, ws_list, wd, ti=None, rho=None):
        """
        Constructor.

        Parameters
        ----------
        ws_list: array_like
            The wind speed values
        wd: float
            The wind direction
        ti: float, optional
            The TI value
        rho: float, optional
            The air density

        """
        super().__init__()

        self._wsl = np.array(ws_list)
        self.N = len(ws_list)
        self.wd = wd
        self.ti = ti
        self.rho = rho



[docs]
    def load_data(self, algo, verbosity=0):
        """
        Load and/or create all model data that is subject to chunking.

        Such data should not be stored under self, for memory reasons. The
        data returned here will automatically be chunked and then provided
        as part of the mdata object during calculations.

        Parameters
        ----------
        algo: foxes.core.Algorithm
            The calculation algorithm
        verbosity: int
            The verbosity level, 0 = silent

        Returns
        -------
        idata: dict
            The dict has exactly two entries: `data_vars`,
            a dict with entries `name_str -> (dim_tuple, data_ndarray)`;
            and `coords`, a dict with entries `dim_name_str -> dim_array`

        """
        self.WS = self.var(FV.WS)

        idata = super().load_data(algo, verbosity)
        idata["data_vars"][self.WS] = ((FC.STATE,), self._wsl)

        return idata



[docs]
    def size(self):
        """
        The total number of states.

        Returns
        -------
        int:
            The total number of states

        """
        return self.N



[docs]
    def output_point_vars(self, algo):
        """
        The variables which are being modified by the model.

        Parameters
        ----------
        algo: foxes.core.Algorithm
            The calculation algorithm

        Returns
        -------
        output_vars: list of str
            The output variable names

        """
        pvars = [FV.WS]
        if self.wd is not None:
            pvars.append(FV.WD)
        if self.ti is not None:
            pvars.append(FV.TI)
        if self.rho is not None:
            pvars.append(FV.RHO)
        return pvars



[docs]
    def weights(self, algo):
        """
        The statistical weights of all states.

        Parameters
        ----------
        algo: foxes.core.Algorithm
            The calculation algorithm

        Returns
        -------
        weights: numpy.ndarray
            The weights, shape: (n_states, n_turbines)

        """
        return np.full((self.N, algo.n_turbines), 1.0 / self.N, dtype=FC.DTYPE)



[docs]
    def calculate(self, algo, mdata, fdata, tdata):
        """ "
        The main model calculation.

        This function is executed on a single chunk of data,
        all computations should be based on numpy arrays.

        Parameters
        ----------
        algo: foxes.core.Algorithm
            The calculation algorithm
        mdata: foxes.core.MData
            The model data
        fdata: foxes.core.FData
            The farm data
        tdata: foxes.core.TData
            The target point data

        Returns
        -------
        results: dict
            The resulting data, keys: output variable str.
            Values: numpy.ndarray with shape
            (n_states, n_targets, n_tpoints)

        """
        tdata[FV.WS] = np.zeros_like(tdata[FC.TARGETS][..., 0])
        tdata[FV.WS][:] = mdata[self.WS][:, None, None]

        if self.wd is not None:
            tdata[FV.WD] = np.full_like(tdata[FV.WS], self.wd)
        if self.ti is not None:
            tdata[FV.TI] = np.full_like(tdata[FV.WS], self.ti)
        if self.rho is not None:
            tdata[FV.RHO] = np.full_like(tdata[FV.WS], self.rho)

        return {v: tdata[v] for v in self.output_point_vars(algo)}