- class foxes.models.turbine_types.PCtFromTwo(foxes.core.TurbineType)[source]
Calculate power and ct by interpolating from power curve and ct curve data files.
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
- col_ws: str
The wind speed column
- col_P: str
The power column
- col_ct: str
The ct column
- rho: float
The air density for which the data is valid or None for no correction
- 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
Public members¶
-
PCtFromTwo(data_source_P, data_source_ct, col_ws_P_file=
'ws'
, ...)[source] Constructor.
- 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.
-
modify_cutin(modify_ct, modify_P, steps=
20
, iterations=100
, ...)[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.
- 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