Save WindData onto FlorisModel and simplify post-run() calls

examples/07_calc_aep_from_rose.py

@@ -55,26 +55,15 @@
     wind_speeds=wind_speeds,
     turbulence_intensities=turbulence_intensities,
 )
+fmodel.run()
 
 # Compute the AEP using the default settings
 aep = fmodel.get_farm_AEP(freq=freq)
-print("Farm AEP (default options): {:.3f} GWh".format(aep / 1.0e9))
-
-# Compute the AEP again while specifying a cut-in and cut-out wind speed.
-# The wake calculations are skipped for any wind speed below respectively
-# above the cut-in and cut-out wind speed. This can speed up computation and
-# prevent unexpected behavior for zero/negative and very high wind speeds.
-# In this example, the results should not change between this and the default
-# call to 'get_farm_AEP()'.
-aep = fmodel.get_farm_AEP(
-    freq=freq,
-    cut_in_wind_speed=3.0,  # Wakes are not evaluated below this wind speed
-    cut_out_wind_speed=25.0,  # Wakes are not evaluated above this wind speed
-)
-print("Farm AEP (with cut_in/out specified): {:.3f} GWh".format(aep / 1.0e9))
+print("Farm AEP: {:.3f} GWh".format(aep / 1.0e9))
 
 # Finally, we can also compute the AEP while ignoring all wake calculations.
 # This can be useful to quantity the annual wake losses in the farm. Such
-# calculations can be facilitated by enabling the 'no_wake' handle.
-aep_no_wake = fmodel.get_farm_AEP(freq, no_wake=True)
-print("Farm AEP (no_wake=True): {:.3f} GWh".format(aep_no_wake / 1.0e9))
+# calculations can be facilitated by first running with run_no_wake().
+fmodel.run_no_wake()
+aep_no_wake = fmodel.get_farm_AEP(freq=freq)
+print("Farm AEP (no wakes): {:.3f} GWh".format(aep_no_wake / 1.0e9))

examples/13_optimize_yaw_with_neighboring_farm.py

@@ -202,6 +202,7 @@ def yaw_opt_interpolant(wd, ws):
     print(" ")
     print("===========================================================")
     print("Calculating baseline annual energy production (AEP)...")
+    fmodel_aep.run()
     aep_bl_subset = 1.0e-9 * fmodel_aep.get_farm_AEP(
         freq=freq_windrose,
         turbine_weights=turbine_weights
@@ -247,11 +248,13 @@ def yaw_opt_interpolant(wd, ws):
     print("===========================================================")
     print("Calculating annual energy production with wake steering (AEP)...")
     fmodel_aep.set(yaw_angles=yaw_angles_opt_nonb_AEP)
+    fmodel_aep.run()
     aep_opt_subset_nonb = 1.0e-9 * fmodel_aep.get_farm_AEP(
         freq=freq_windrose,
         turbine_weights=turbine_weights,
     )
     fmodel_aep.set(yaw_angles=yaw_angles_opt_AEP)
+    fmodel_aep.run()
     aep_opt_subset = 1.0e-9 * fmodel_aep.get_farm_AEP(
         freq=freq_windrose,
         turbine_weights=turbine_weights,

examples/15_optimize_layout.py

@@ -54,18 +54,18 @@
 fmodel.set(layout_x=layout_x, layout_y=layout_y)
 
 # Setup the optimization problem
-layout_opt = LayoutOptimizationScipy(fmodel, boundaries, wind_data=wind_rose)
+layout_opt = LayoutOptimizationScipy(fmodel, boundaries)
 
 # Run the optimization
 sol = layout_opt.optimize()
 
 # Get the resulting improvement in AEP
 print('... calcuating improvement in AEP')
 fmodel.run()
-base_aep = fmodel.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6
+base_aep = fmodel.get_farm_AEP() / 1e6
 fmodel.set(layout_x=sol[0], layout_y=sol[1])
 fmodel.run()
-opt_aep = fmodel.get_farm_AEP_with_wind_data(wind_data=wind_rose)  / 1e6
+opt_aep = fmodel.get_farm_AEP() / 1e6
 
 percent_gain = 100 * (opt_aep - base_aep) / base_aep
 

examples/16c_optimize_layout_with_heterogeneity.py

@@ -87,7 +87,6 @@
 layout_opt = LayoutOptimizationScipy(
     fmodel,
     boundaries,
-    wind_data=wind_rose,
     min_dist=2*D,
     optOptions={"maxiter":maxiter}
 )
@@ -100,10 +99,10 @@
 print('... calcuating improvement in AEP')
 
 fmodel.run()
-base_aep = fmodel.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6
+base_aep = fmodel.get_farm_AEP() / 1e6
 fmodel.set(layout_x=sol[0], layout_y=sol[1])
 fmodel.run()
-opt_aep = fmodel.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6
+opt_aep = fmodel.get_farm_AEP() / 1e6
 
 percent_gain = 100 * (opt_aep - base_aep) / base_aep
 
@@ -128,7 +127,6 @@
 layout_opt = LayoutOptimizationScipy(
     fmodel,
     boundaries,
-    wind_data=wind_rose,
     min_dist=2*D,
     enable_geometric_yaw=True,
     optOptions={"maxiter":maxiter}
@@ -142,12 +140,11 @@
 print('... calcuating improvement in AEP')
 
 fmodel.set(yaw_angles=np.zeros_like(layout_opt.yaw_angles))
-base_aep = fmodel.get_farm_AEP_with_wind_data(wind_data=wind_rose) / 1e6
+fmodel.run()
+base_aep = fmodel.get_farm_AEP() / 1e6
 fmodel.set(layout_x=sol[0], layout_y=sol[1], yaw_angles=layout_opt.yaw_angles)
 fmodel.run()
-opt_aep = fmodel.get_farm_AEP_with_wind_data(
-    wind_data=wind_rose
-) / 1e6
+opt_aep = fmodel.get_farm_AEP() / 1e6
 
 percent_gain = 100 * (opt_aep - base_aep) / base_aep
 

examples/29_floating_vs_fixedbottom_farm.py

@@ -124,6 +124,7 @@
         wind_speeds= ws_grid.flatten(),
         turbulence_intensities=0.06 * np.ones_like(wd_grid.flatten())
     )
+    fmodel.run()
 
 # Compute the AEP
 aep_fixed = fmodel_fixed.get_farm_AEP(freq=freq)

examples/34_wind_data.py

@@ -39,15 +39,15 @@
 time_series = TimeSeries(wd_array, ws_array, turbulence_intensities=ti_array)
 
 # Now build the wind rose
-wind_rose = time_series.to_wind_rose()
+wind_rose = time_series.to_WindRose()
 
 # Plot the wind rose
 fig, ax = plt.subplots(subplot_kw={"polar": True})
 wind_rose.plot_wind_rose(ax=ax,legend_kwargs={"title": "WS"})
 fig.suptitle("WindRose Plot")
 
 # Now build a wind rose with turbulence intensity
-wind_ti_rose = time_series.to_wind_ti_rose()
+wind_ti_rose = time_series.to_WindTIRose()
 
 # Plot the wind rose with TI
 fig, axs = plt.subplots(2, 1, figsize=(6,8), subplot_kw={"polar": True})
@@ -78,9 +78,9 @@
 wind_rose_power = fmodel_wind_rose.get_farm_power()
 wind_ti_rose_power = fmodel_wind_ti_rose.get_farm_power()
 
-time_series_aep = fmodel_time_series.get_farm_AEP_with_wind_data(time_series)
-wind_rose_aep = fmodel_wind_rose.get_farm_AEP_with_wind_data(wind_rose)
-wind_ti_rose_aep = fmodel_wind_ti_rose.get_farm_AEP_with_wind_data(wind_ti_rose)
+time_series_aep = fmodel_time_series.get_farm_AEP()
+wind_rose_aep = fmodel_wind_rose.get_farm_AEP()
+wind_ti_rose_aep = fmodel_wind_ti_rose.get_farm_AEP()
 
 print(f"AEP from TimeSeries {time_series_aep / 1e9:.2f} GWh")
 print(f"AEP from WindRose {wind_rose_aep / 1e9:.2f} GWh")