foxes.output.farm_results_eval.FarmResultsEval¶

class foxes.output.farm_results_eval.FarmResultsEval[source]¶

Bases: Output

Evaluates farm results data.

This sums over turbines and/or states, given the state-turbine farm_calc results.

Parameters:: farm_results (xarray.Dataset) – The farm results

results¶

The farm results

Type:: xarray.Dataset

__init__(farm_results)[source]¶

Methods

`__init__`(farm_results)
`add_capacity`([algo, P_nom, ambient, verbosity])	Adds capacity to the farm results
`add_efficiency`([verbosity])	Adds efficiency to the farm results
`calc_farm_efficiency`()	Calculates farm efficiency
`calc_farm_mean`(vars)	Calculates the mean over states and turbines.
`calc_farm_sum`(vars)	Calculates the sum over states and turbines.
`calc_farm_yield`([turbine_yield, power_uncert])	Calculates yield, P75 and P90 at the farm level
`calc_mean_farm_power`([ambient])	Calculates the mean total farm power.
`calc_states_mean`(vars)	Calculates the mean wrt states.
`calc_states_std`(vars)	Calculates the standard deviation wrt states.
`calc_states_sum`(vars)	Calculates the sum wrt states.
`calc_turbine_mean`(vars)	Calculates the mean wrt turbines.
`calc_turbine_sum`(vars)	Calculates the sum wrt turbines.
`calc_turbine_yield`([algo, annual, ambient, ...])	Calculates the yield per turbine
`new`(model_type, **kwargs)	Run-time output model factory.
`print_models`()	Prints all model names.
`reduce_all`(states_op, turbines_op)	Reduces states and turbine dimension by some operation
`reduce_states`(vars_op)	Reduces the states dimension by some operation
`reduce_turbines`(vars_op)	Reduces the turbine dimension by some operation
`weinsum`(rhs, *vars)	Calculates Einstein sum, adding weights as last argument to the given fields.
`write`(file_path, data[, format_col2var, ...])	Writes data to file via pandas.

__init__(farm_results)[source]¶

add_capacity(algo=None, P_nom=None, ambient=False, verbosity=1)[source]¶

Adds capacity to the farm results

Parameters:

algo (foxes.core.Algorithm, optional) – The algorithm, for nominal power calculation
P_nom (list of float, optional) – Nominal power values for each turbine, if algo not given
ambient (bool, optional) – Flag for calculating ambient capacity, by default False
verbosity (int) – The verbosity level, 0 = silent

add_efficiency(verbosity=1)[source]¶

Adds efficiency to the farm results

Parameters:: verbosity (int) – The verbosity level, 0 = silent

calc_farm_efficiency()[source]¶

Calculates farm efficiency

Returns:: eff – The farm efficiency
Return type:: float

calc_farm_mean(vars)[source]¶

Calculates the mean over states and turbines.

Parameters:: vars (list of str) – The variables
Returns:: data – The fully contracted results
Return type:: dict

calc_farm_sum(vars)[source]¶

Calculates the sum over states and turbines.

Parameters:: vars (list of str) – The variables
Returns:: data – The fully contracted results
Return type:: dict

calc_farm_yield(turbine_yield=None, power_uncert=None, **kwargs)[source]¶

Calculates yield, P75 and P90 at the farm level

Parameters:

turbine_yield (pandas.DataFrame, optional) – Yield values by turbine
power_uncert (float, optional) – Uncertainty in the power value. Triggers P75 and P90 outputs
kwargs (dict, optional) – Parameters for calc_turbine_yield(). Apply if turbine_yield is not given

Returns:

farm_yield (float) – Farm yield result, same unit as turbine yield
P75 (float, optional) – The P75 value, same unit as turbine yield
P90 (float, optional) – The P90 value, same unit as turbine yield

calc_mean_farm_power(ambient=False)[source]¶

Calculates the mean total farm power.

Parameters:: ambient (bool) – Flag for ambient power
Returns:: data – The mean wind farm power
Return type:: float

calc_states_mean(vars)[source]¶

Calculates the mean wrt states.

Parameters:: vars (list of str) – The variables
Returns:: data – The results per turbine
Return type:: pandas.DataFrame

calc_states_std(vars)[source]¶

Calculates the standard deviation wrt states.

Parameters:: vars (_type_) – _description_
Returns:: _description_
Return type:: _type_

calc_states_sum(vars)[source]¶

Calculates the sum wrt states.

Parameters:: vars (list of str) – The variables
Returns:: data – The results per turbine
Return type:: pandas.DataFrame

calc_turbine_mean(vars)[source]¶

Calculates the mean wrt turbines.

Parameters:: vars (list of str) – The variables
Returns:: data – The results per state
Return type:: pandas.DataFrame

calc_turbine_sum(vars)[source]¶

Calculates the sum wrt turbines.

Parameters:: vars (list of str) – The variables
Returns:: data – The results per state
Return type:: pandas.DataFrame

calc_turbine_yield(algo=None, annual=False, ambient=False, hours=None, delta_t=None, P_unit_W=None)[source]¶

Calculates the yield per turbine

Parameters:

algo (foxes.core.Algorithm, optional) – The algorithm, for P_nominal lookup
annual (bool, optional) – Flag for returing annual results, by default False
ambient (bool, optional) – Flag for ambient power, by default False
hours (int, optional) – The duration time in hours, if not timeseries states
delta_t (np.datetime64, optional) – The time delta step in case of time series data, by default automatically determined
P_unit_W (float) – The power unit in Watts, 1000 for kW. Looked up in algorithm if not given

Returns:

A dataframe of yield values by turbine in GWh

Return type:

pandas.DataFrame

classmethod new(model_type, **kwargs)¶

Run-time output model factory.

Parameters:: model_type (string) – The selected derived class name

classmethod print_models()¶: Prints all model names.

reduce_all(states_op, turbines_op)[source]¶

Reduces states and turbine dimension by some operation

Parameters:

states_op (dict) – The states contraction operations. Key: str, the variable name. Value: str, the operation, choices are: sum, mean, min, max.
turbines_op (dict) – The turbines contraction operations. Key: str, the variable name. Value: str, the operation, choices are: sum, mean, min, max.

Returns:

data – The fully contracted results

Return type:

dict

reduce_states(vars_op)[source]¶

Reduces the states dimension by some operation

Parameters:: vars_op (dict) – The operation per variable. Key: str, the variable name. Value: str, the operation, choices are: sum, mean, min, max.
Returns:: data – The results per turbine
Return type:: pandas.DataFrame

reduce_turbines(vars_op)[source]¶

Reduces the turbine dimension by some operation

Parameters:: vars_op (dict) – The operation per variable. Key: str, the variable name. Value: str, the operation, choices are: sum, mean, min, max.
Returns:: data – The results per state
Return type:: pandas.DataFrame

weinsum(rhs, *vars)[source]¶

Calculates Einstein sum, adding weights as last argument to the given fields.

It’s all about treating NaN values.

Parameters:

rhs (str) – The right-hand side of the einsum expression. Convention: ‘s’ for states, ‘t’ for turbines
vars (tuple of str or np.ndarray) – The variables mentioned in the expression, but without the obligatory weights that will be added at the end

Returns:

result – The results array

Return type:

np.ndarray

classmethod write(file_path, data, format_col2var={}, format_dict={}, **kwargs)¶

Writes data to file via pandas.

The kwargs are forwarded to the underlying pandas writing function.

Parameters:

file_path (string) – The path to the output file
data (pandas.DataFrame) – The data
format_col2var (dict) – Mapping from column names to flappy variables, for formatting
format_dict (dict) – Dictionary with format entries for columns, e.g. {FV.P: ‘{:.4f}’}. Note that the keys are flappy variables