Source code for foxes.core.farm_controller

import numpy as np

from foxes.config import config
import foxes.constants as FC
from foxes.utils import new_instance

from .farm_data_model import FarmDataModelList, FarmDataModel
from .turbine_model import TurbineModel
from .turbine_type import TurbineType


[docs] class FarmController(FarmDataModel): """ Analyses selected turbine models and handles their call. Attributes ---------- turbine_types: list of foxes.core.TurbineType The turbine type of each turbine turbine_model_names: list of str Names of all turbine models found in the farm pre_rotor_models: foxes.core.FarmDataModelList The turbine models with pre-rotor flag post_rotor_models: foxes.core.FarmDataModelList The turbine models without pre-rotor flag pars: dict Parameters for the turbine models, stored under their respecitve name :group: core """
[docs] def __init__(self, pars={}): """ Constructor. Parameters ---------- pars: dict Parameters for the turbine models, stored under their respective name """ super().__init__() self.turbine_types = None self.turbine_model_names = None self.pre_rotor_models = None self.post_rotor_models = None self.pars = pars
[docs] def sub_models(self): """ List of all sub-models Returns ------- smdls: list of foxes.core.Model Names of all sub models """ return [ self.pre_rotor_models, self.post_rotor_models, ]
[docs] def set_pars(self, model_name, init_pars, calc_pars, final_pars): """ Set parameters for a turbine model Parameters ---------- model_name: str Name of the model init_pars: dict Parameters for initialization calc_pars: dict Parameters for calculation final_pars: dict Parameters for finalization """ self.pars[model_name] = { "init": init_pars, "calc": calc_pars, "final": final_pars, }
[docs] def needs_rews2(self): """ Returns flag for requiring REWS2 variable Returns ------- flag: bool True if REWS2 is required """ for tt in self.turbine_types: if tt.needs_rews2(): return True return False
[docs] def needs_rews3(self): """ Returns flag for requiring REWS3 variable Returns ------- flag: bool True if REWS3 is required """ for tt in self.turbine_types: if tt.needs_rews3(): return True return False
def _analyze_models(self, algo, pre_rotor, models): """ Helper function for model analysis """ tmodels = [] tmsels = [] mnames = [[m.name for m in mlist] for mlist in models] tmis = np.zeros(algo.n_turbines, dtype=config.dtype_int) news = True while news: news = False for ti, mlist in enumerate(models): if tmis[ti] < len(mlist): mname = mnames[ti][tmis[ti]] isnext = True for tj, jnames in enumerate(mnames): if ( tj != ti and mname in jnames and tmis[tj] < len(jnames) and jnames[tmis[tj]] != mname ): isnext = False break if isnext: m = models[ti][tmis[ti]] tmodels.append(m) tsel = np.zeros((algo.n_states, algo.n_turbines), dtype=bool) for tj, jnames in enumerate(mnames): mi = tmis[tj] if mi < len(jnames) and jnames[mi] == mname: ssel = algo.farm.turbines[tj].mstates_sel[mi] tsel[:, tj] = True if ssel is None else ssel tmis[tj] += 1 tmsels.append(tsel) news = True break if pre_rotor: self.pre_rotor_models = FarmDataModelList(models=tmodels) self.pre_rotor_models.name = f"{self.name}_prer" mtype = "pre-rotor" else: self.post_rotor_models = FarmDataModelList(models=tmodels) self.post_rotor_models.name = f"{self.name}_postr" mtype = "post-rotor" for ti, t in enumerate(algo.farm.turbines): if tmis[ti] != len(models[ti]): raise ValueError( f"Turbine {ti}, {t.name}: Could not find turbine model order that includes all {mtype} turbine models, missing {t.models[tmis[ti]:]}" ) return [m.name for m in tmodels], tmsels
[docs] def find_turbine_types(self, algo): """ Collects the turbine types. Parameters ---------- algo: foxes.core.Algorithm The algorithm """ # check turbine models, and find turbine types and pre/post-rotor models: self.turbine_types = [None for t in algo.farm.turbines] for ti, t in enumerate(algo.farm.turbines): for mname in t.models: if mname in algo.mbook.turbine_types: m = algo.mbook.turbine_types[mname] if not isinstance(m, TurbineType): raise TypeError( f"Model {mname} type {type(m).__name__} is not derived from {TurbineType.__name__}" ) if self.turbine_types[ti] is not None: raise TypeError( f"Two turbine type models found for turbine {ti}: {self.turbine_types[ti].name} and {mname}" ) m.name = mname self.turbine_types[ti] = m if self.turbine_types[ti] is None: raise ValueError( f"Turbine {ti}, {t.name}: Missing a turbine type model among models {t.models}" )
[docs] def collect_models(self, algo): """ Analyze and gather turbine models, based on the turbines of the wind farm. Parameters ---------- algo: foxes.core.Algorithm The calculation algorithm """ if self.turbine_types is None: self.find_turbine_types(algo) # check turbine models, and find turbine types and pre/post-rotor models: prer_models = [[] for t in algo.farm.turbines] postr_models = [[] for t in algo.farm.turbines] ttypes = {m.name: m for m in self.turbine_types} for ti, t in enumerate(algo.farm.turbines): prer = None for mi, mname in enumerate(t.models): if mname in ttypes: models = [ttypes[mname]] elif mname in algo.mbook.turbine_models: m = algo.mbook.turbine_models[mname] models = m.models if isinstance(m, FarmDataModelList) else [m] for mm in models: if not isinstance(mm, TurbineModel): raise TypeError( f"Model {mname} type {type(mm).__name__} is not derived from {TurbineModel.__name__}" ) m.name = mname else: raise KeyError( f"Model {mname} not found in model book types or models" ) prer = None for m in models: if prer is None: prer = m.pre_rotor elif not prer and m.pre_rotor: raise ValueError( f"Turbine {ti}, {t.name}: Model is classified as pre-rotor, but following the post-rotor model '{t.models[mi-1]}'" ) if m.pre_rotor: prer_models[ti].append(m) else: postr_models[ti].append(m) # analyze models: mnames_pre, tmsels_pre = self._analyze_models( algo, pre_rotor=True, models=prer_models ) mnames_post, tmsels_post = self._analyze_models( algo, pre_rotor=False, models=postr_models ) tmsels = tmsels_pre + tmsels_post self._tmall = [np.all(t) for t in tmsels] self.turbine_model_names = mnames_pre + mnames_post if len(self.turbine_model_names): self._tmsels = np.stack(tmsels, axis=2) else: raise ValueError(f"Controller '{self.name}': No turbine model found.")
def __get_pars(self, algo, models, ptype, mdata=None, downwind_index=None): """ Private helper function for gathering model parameters. """ pars = [] for m in models: mi = self.turbine_model_names.index(m.name) if self._tmall[mi]: s = np.s_[:, :] if downwind_index is None else np.s_[:, downwind_index] else: if downwind_index is None: s = mdata[FC.TMODEL_SELS][:, :, mi] else: s = np.s_[ mdata[FC.TMODEL_SELS][:, downwind_index, mi], downwind_index ] pars.append({"st_sel": s}) if m.name in self.pars: pars[-1].update(self.pars[m.name][ptype]) return pars
[docs] def initialize(self, algo, verbosity=0): """ Initializes the model. Parameters ---------- algo: foxes.core.Algorithm The calculation algorithm verbosity: int The verbosity level, 0 = silent """ self.collect_models(algo) super().initialize(algo, verbosity)
[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` """ idata = super().load_data(algo, verbosity) idata["coords"][FC.TMODELS] = self.turbine_model_names idata["data_vars"][FC.TMODEL_SELS] = ( (FC.STATE, FC.TURBINE, FC.TMODELS), self._tmsels, ) self._tmsels = None return idata
[docs] def output_farm_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 """ ovars = set(self.pre_rotor_models.output_farm_vars(algo)) ovars.update(self.post_rotor_models.output_farm_vars(algo)) return list(ovars)
[docs] def calculate(self, algo, mdata, fdata, pre_rotor, downwind_index=None): """ 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 pre_rotor: bool Flag for running pre-rotor or post-rotor models downwind_index: int, optional The index in the downwind order Returns ------- results: dict The resulting data, keys: output variable str. Values: numpy.ndarray with shape (n_states, n_turbines) """ s = self.pre_rotor_models if pre_rotor else self.post_rotor_models pars = self.__get_pars(algo, s.models, "calc", mdata, downwind_index) res = s.calculate(algo, mdata, fdata, parameters=pars) return res
[docs] def finalize(self, algo, verbosity=0): """ Finalizes the model. Parameters ---------- algo: foxes.core.Algorithm The calculation algorithm verbosity: int The verbosity level, 0 means silent """ super().finalize(algo, verbosity) self.turbine_model_names = None
[docs] @classmethod def new(cls, controller_type, *args, **kwargs): """ Run-time farm controller factory. Parameters ---------- controller_type: str The selected derived class name args: tuple, optional Additional parameters for the constructor kwargs: dict, optional Additional parameters for the constructor """ return new_instance(cls, controller_type, *args, **kwargs)