Calculate turbulent intensity dependent power and ct values, as given by two individual files.

The structure of each file is: ws,0.05,0.06,0.07,…

The first column represents wind speed in m/s and the subsequent columns are TI values (not neccessarily in order).

Attributes¶

source_P: str or pandas.DataFrame

The file path for the power curve, static name, or data

source_ct: str or pandas.DataFrame

The file path for the ct curve, static name, or data

WSCT: str

The wind speed variable for ct lookup

WSP: str

The wind speed variable for power lookup

rpars_P: dict, optional

Parameters for pandas power file reading

rpars_ct: dict, optional

Parameters for pandas ct file reading

ipars_P: dict, optional

Parameters for scipy.interpolate.interpn

ipars_ct: dict, optional

Parameters for scipy.interpolate.interpn

rho: float

The air density for which the data is valid or None for no correction

Public members¶

WsTI2PCtFromTwo(data_source_P, data_source_ct, rho=None, ...)[source]

Constructor.

__repr__()[source]

Return repr(self).

needs_rews2()[source]

Returns flag for requiring REWS2 variable

needs_rews3()[source]

Returns flag for requiring REWS3 variable

output_farm_vars(algo)[source]

The variables which are being modified by the model.

load_data(algo, verbosity=0)[source]

Load and/or create all model data that is subject to chunking.

calculate(algo, mdata, fdata, st_sel)[source]

The main model calculation.

finalize(algo, verbosity=0)[source]

Finalizes the model.

modify_cutin(modify_ct, modify_P)[source]

Modify the data such that a discontinuity at cutin wind speed is avoided

classmethod new(ttype_type, *args, **kwargs)[source]

Run-time turbine type factory.

output_coords()[source]

Gets the coordinates of all output arrays

ensure_variables(algo, mdata, fdata)[source]

Add variables to fdata, initialized with NaN

run_calculation(algo, *data, out_vars, **calc_pars)[source]

Starts the model calculation in parallel, via xarray’s apply_ufunc.

property model_id

Unique id based on the model type.

var(v)[source]

Creates a model specific variable name.

property initialized

Initialization flag.

sub_models()[source]

List of all sub-models

initialize(algo, verbosity=0, force=False)[source]

Initializes the model.

property running

Flag for currently running models

set_running(algo, data_stash, sel=None, isel=None, verbosity=0)[source]

Sets this model status to running, and moves all large data to stash.

unset_running(algo, data_stash, sel=None, isel=None, verbosity=0)[source]

Sets this model status to not running, recovering large data from stash

get_data(variable, target, lookup='smfp', mdata=None, ...)[source]

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