"""Example 9: Parallel Models
This example demonstrates how to use the ParFlorisModel class to parallelize the
calculation of the FLORIS model. ParFlorisModel inherits from the FlorisModel
and so can be used in the same way with a consistent interface. ParFlorisModel
replaces the ParallelFlorisModel, which will be deprecated in a future release.
"""
import numpy as np
from floris import (
FlorisModel,
ParFlorisModel,
TimeSeries,
UncertainFlorisModel,
)
# When using parallel optimization it is important the "root" script include this
# if __name__ == "__main__": block to avoid problems
if __name__ == "__main__":
# Instantiate the FlorisModel
fmodel = FlorisModel("inputs/gch.yaml")
# The ParFlorisModel can be instantiated either from a FlorisModel or from
# the input file.
pfmodel_1 = ParFlorisModel("inputs/gch.yaml") # Via input file
pfmodel_2 = ParFlorisModel(fmodel) # Via FlorisModel
# The ParFlorisModel has additional inputs which define the parallelization
# but don't affect the output.
pfmodel_3 = ParFlorisModel(
fmodel,
interface="multiprocessing", # Default
max_workers=2, # Defaults to num_cpu
n_wind_condition_splits=2, # Defaults to max_workers
)
# Define a simple inflow
time_series = TimeSeries(
wind_speeds=np.arange(1, 25, 0.5), wind_directions=270.0, turbulence_intensities=0.06
)
# Demonstrate that interface and results are the same
fmodel.set(wind_data=time_series)
pfmodel_1.set(wind_data=time_series)
pfmodel_2.set(wind_data=time_series)
pfmodel_3.set(wind_data=time_series)
fmodel.run()
pfmodel_1.run()
pfmodel_2.run()
pfmodel_3.run()
# Compare the results
powers_fmodel = fmodel.get_turbine_powers()
powers_pfmodel_1 = pfmodel_1.get_turbine_powers()
powers_pfmodel_2 = pfmodel_2.get_turbine_powers()
powers_pfmodel_3 = pfmodel_3.get_turbine_powers()
print(
f"Testing if outputs of fmodel and pfmodel_1 are "
f"close: {np.allclose(powers_fmodel, powers_pfmodel_1)}"
)
print(
f"Testing if outputs of fmodel and pfmodel_2 are "
f"close: {np.allclose(powers_fmodel, powers_pfmodel_2)}"
)
print(
f"Testing if outputs of fmodel and pfmodel_3 are "
f"close: {np.allclose(powers_fmodel, powers_pfmodel_3)}"
)
# Because ParFlorisModel is a subclass of FlorisModel, it can also be used as
# an input to the UncertainFlorisModel class. This allows for parallelization of
# the uncertainty calculations.
ufmodel = UncertainFlorisModel(fmodel)
pufmodel = UncertainFlorisModel(pfmodel_1)
# Demonstrate matched results
ufmodel.set(wind_data=time_series)
pufmodel.set(wind_data=time_series)
ufmodel.run()
pufmodel.run()
powers_ufmodel = ufmodel.get_turbine_powers()
powers_pufmodel = pufmodel.get_turbine_powers()
print("--------------------")
print(
f"Testing if outputs of ufmodel and pufmodel are "
f"close: {np.allclose(powers_ufmodel, powers_pufmodel)}"
)
import warnings
warnings.filterwarnings('ignore')
