Basic Floris v4 compatibility (#169)

* turbulence_intensity -> turbuelence_intensities. * Minor compatibility changes to calculate_florix_approx_table. Left TODOs for better integration with FLORIS v4. * Avoid use of ParallelInterface as not yet updated. May revert; however, fast as it. * Ruff * ruff format * Update "thrust" to "thrust_coefficient" per change in FLORIS * Update visualize_cut_plane syntax * ws_array and wd_array must be same length * Correct to absolute power * Update to v4 * Update to matplotlib 3.8 syntax * bump up matplotlib requirement * Update 06 * Update example 07 * Update tuning notebook * Update FLORIS tuning * Update tuner utilities * Remove extra hyphen * Update 06 example * Update 07 example * Update 01 notebook --------- Co-authored-by: Paul <paul.fleming@nrel.gov>
NREL · Feb 8, 2024 · 1d05ec6 · 1d05ec6
1 parent 9c7b004
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diff --git a/examples_artificial_data/00_setup_floris_model/01_visualize_flowfield.py b/examples_artificial_data/00_setup_floris_model/01_visualize_flowfield.py
@@ -24,7 +24,7 @@
     fi.reinitialize(
         wind_directions=[wind_direction],
         wind_speeds=[wind_speed],
-        turbulence_intensity=turbulence_intensity,
+        turbulence_intensities=[turbulence_intensity],
     )
     plot_floris_layout(fi, plot_terrain=False)
 
@@ -37,10 +37,9 @@
     )
 
     fig, ax = plt.subplots(figsize=(9, 6))
-    im = visualize_cut_plane(horizontal_plane, ax=ax, title=None)
+    im = visualize_cut_plane(horizontal_plane, ax=ax, title=None, color_bar=True)
     ax.set_xlabel("x coordinate (m)")
     ax.set_ylabel("y coordinate (m)")
-    plt.colorbar(im, ax=ax)
     fig.suptitle(
         "Inflow: {:.1f} m/s, {:.1f} deg, {:.1f} % turbulence.".format(
             wind_speed, wind_direction, turbulence_intensity * 100.0

diff --git a/examples_artificial_data/00_setup_floris_model/02_precalculate_floris_solutions.py b/examples_artificial_data/00_setup_floris_model/02_precalculate_floris_solutions.py
@@ -2,7 +2,6 @@
 from time import perf_counter as timerpc
 
 import numpy as np
-from floris.tools import ParallelComputingInterface
 
 from flasc.utilities import floris_tools as ftools
 from flasc.utilities.utilities_examples import load_floris_artificial as load_floris
@@ -22,14 +21,14 @@
         start_time = timerpc()
         print("Precalculating FLORIS table for '{:s}' model...".format(wake_model))
         fi, turbine_weights = load_floris(wake_model=wake_model)
-        fi_pci = ParallelComputingInterface(
-            fi=fi,
-            max_workers=max_workers,
-            n_wind_direction_splits=max_workers,
-            print_timings=True,
-        )
+        # fi_pci = ParallelComputingInterface(
+        #     fi=fi,
+        #     max_workers=max_workers,
+        #     n_wind_direction_splits=max_workers,
+        #     print_timings=True,
+        # )
         df_fi_approx = ftools.calc_floris_approx_table(
-            fi=fi_pci,
+            fi=fi,  # fi=fi_pci,
             wd_array=np.arange(0.0, 360.01, 3.0),
             ws_array=np.arange(1.0, 30.01, 1.0),
             ti_array=[0.03, 0.06, 0.09, 0.12, 0.15],

diff --git a/examples_artificial_data/03_energy_ratio/00_demo_energy_ratio_syntax.ipynb b/examples_artificial_data/03_energy_ratio/00_demo_energy_ratio_syntax.ipynb
diff --git a/examples_artificial_data/03_energy_ratio/01_demo_energy_ratio_options.ipynb b/examples_artificial_data/03_energy_ratio/01_demo_energy_ratio_options.ipynb
diff --git a/examples_artificial_data/03_energy_ratio/04_energy_ratio_in_turbine_array.py b/examples_artificial_data/03_energy_ratio/04_energy_ratio_in_turbine_array.py
@@ -120,7 +120,7 @@ def plot_energy_ratios(turbine_array, results_energy_ratio, ax=None, label=None)
         _, ax = plt.subplots(figsize=(7.0, 3.0))
 
     x = range(len(results_energy_ratio))
-    color = next(ax._get_lines.prop_cycler)["color"]
+    color = ax._get_lines.get_next_color()
     ax.fill_between(
         x,
         results_energy_ratio["baseline_lb"],

diff --git a/examples_artificial_data/03_energy_ratio/06_wake_steering_example.py b/examples_artificial_data/03_energy_ratio/06_wake_steering_example.py
@@ -66,14 +66,14 @@
     # Compute the power of the second turbine for two cases
     # Baseline: The front turbine is aligned to the wind
     # WakeSteering: The front turbine is yawed 25 deg
-    fi.reinitialize(wind_speeds=ws_array, wind_directions=wd_array, time_series=True)
+    fi.reinitialize(wind_speeds=ws_array, wind_directions=wd_array)
     fi.calculate_wake()
     power_baseline_ref = fi.get_turbine_powers().squeeze()[:, 0].flatten()
     power_baseline_control = fi.get_turbine_powers().squeeze()[:, 1].flatten()
     power_baseline_downstream = fi.get_turbine_powers().squeeze()[:, 2].flatten()
 
-    yaw_angles = np.zeros([len(t), 1, 3]) * 25
-    yaw_angles[:, :, 1] = 25  # Set control turbine yaw angles to 25 deg
+    yaw_angles = np.zeros([len(t), 3]) * 25
+    yaw_angles[:, 1] = 25  # Set control turbine yaw angles to 25 deg
     fi.calculate_wake(yaw_angles=yaw_angles)
     power_wakesteering_ref = fi.get_turbine_powers().squeeze()[:, 0].flatten()
     power_wakesteering_control = fi.get_turbine_powers().squeeze()[:, 1].flatten()

diff --git a/examples_artificial_data/03_energy_ratio/07_wake_steering_total_uplift.py b/examples_artificial_data/03_energy_ratio/07_wake_steering_total_uplift.py
@@ -67,14 +67,14 @@
     # Compute the power of the second turbine for two cases
     # Baseline: The front turbine is aligned to the wind
     # WakeSteering: The front turbine is yawed 25 deg
-    fi.reinitialize(wind_speeds=ws_array, wind_directions=wd_array, time_series=True)
+    fi.reinitialize(wind_speeds=ws_array, wind_directions=wd_array)
     fi.calculate_wake()
     power_baseline_ref = fi.get_turbine_powers().squeeze()[:, 0].flatten()
     power_baseline_control = fi.get_turbine_powers().squeeze()[:, 1].flatten()
     power_baseline_downstream = fi.get_turbine_powers().squeeze()[:, 2].flatten()
 
-    yaw_angles = np.zeros([len(t), 1, 3]) * 25
-    yaw_angles[:, :, 1] = 25  # Set control turbine yaw angles to 25 deg
+    yaw_angles = np.zeros([len(t), 3]) * 25
+    yaw_angles[:, 1] = 25  # Set control turbine yaw angles to 25 deg
     fi.calculate_wake(yaw_angles=yaw_angles)
     power_wakesteering_ref = fi.get_turbine_powers().squeeze()[:, 0].flatten()
     power_wakesteering_control = fi.get_turbine_powers().squeeze()[:, 1].flatten()

diff --git a/examples_artificial_data/04_floris_tuning/00_tune_floris_to_artificial_data.ipynb b/examples_artificial_data/04_floris_tuning/00_tune_floris_to_artificial_data.ipynb
diff --git a/examples_artificial_data/floris_input_artificial/cc.yaml b/examples_artificial_data/floris_input_artificial/cc.yaml
@@ -30,7 +30,8 @@ farm:
 flow_field:
   air_density: 1.225
   reference_wind_height: -1 # -1 is code for use the hub height
-  turbulence_intensity: 0.06
+  turbulence_intensities:
+  - 0.06
   wind_directions:
   - 270.0
   wind_shear: 0.12

diff --git a/examples_artificial_data/floris_input_artificial/emgauss.yaml b/examples_artificial_data/floris_input_artificial/emgauss.yaml
@@ -112,7 +112,8 @@ flow_field:
 
   ###
   # The level of turbulence intensity level in the wind.
-  turbulence_intensity: 0.06
+  turbulence_intensities:
+  - 0.06
 
   ###
   # The wind directions to include in the simulation.

diff --git a/examples_artificial_data/floris_input_artificial/gch.yaml b/examples_artificial_data/floris_input_artificial/gch.yaml
@@ -112,7 +112,8 @@ flow_field:
 
   ###
   # The level of turbulence intensity level in the wind.
-  turbulence_intensity: 0.06
+  turbulence_intensities:
+  - 0.06
 
   ###
   # The wind directions to include in the simulation.

diff --git a/examples_artificial_data/floris_input_artificial/jensen.yaml b/examples_artificial_data/floris_input_artificial/jensen.yaml
@@ -30,7 +30,8 @@ farm:
 flow_field:
   air_density: 1.225
   reference_wind_height: -1 # -1 is code for use the hub height
-  turbulence_intensity: 0.06
+  turbulence_intensities:
+  - 0.06
   wind_directions:
   - 270.0
   wind_shear: 0.12

diff --git a/examples_artificial_data/floris_input_artificial/turbopark.yaml b/examples_artificial_data/floris_input_artificial/turbopark.yaml
@@ -30,7 +30,8 @@ farm:
 flow_field:
   air_density: 1.225
   reference_wind_height: 90.0
-  turbulence_intensity: 0.06
+  turbulence_intensities:
+  - 0.06
   wind_directions:
   - 270.0
   wind_shear: 0.12

diff --git a/examples_smarteole/01_precalculate_floris_solutions.ipynb b/examples_smarteole/01_precalculate_floris_solutions.ipynb
@@ -1,45 +1,61 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Precalculate FLORIS Solutions\n",
     "\n",
     "Use FLASC tools to precalculate the FLORIS solutions across wind speeds and directions without wake steering to enable faster comparisons in later steps. The FLORIS solutions based on four different wake models are then saved in the \"/precalculated_floris_solutions\" folder. "
-   ],
-   "metadata": {},
-   "attachments": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "from time import perf_counter as timerpc\n",
     "\n",
     "import numpy as np\n",
-    "from floris.tools import ParallelComputingInterface\n",
     "\n",
+    "# from floris.tools import ParallelComputingInterface\n",
     "from flasc.utilities import floris_tools as ftools\n",
     "from flasc.utilities.utilities_examples import load_floris_smarteole as load_floris"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# User settings\n",
     "max_workers = 16\n",
     "wake_models = [\"jensen\", \"turbopark\", \"gch\", \"cc\"]"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precalculating FLORIS table for 'jensen' model...\n",
+      "Generating a df_approx table of FLORIS solutions covering a total of 18150 cases.\n",
+      "Finished calculating the FLORIS solutions for the dataframe.\n",
+      "Computation time: 5.62 s\n",
+      "FLORIS table for 'turbopark' model exists. Skipping...\n",
+      "FLORIS table for 'gch' model exists. Skipping...\n",
+      "FLORIS table for 'cc' model exists. Skipping...\n"
+     ]
+    }
+   ],
    "source": [
     "# Precalculate FLORIS solutions\n",
     "root_path = Path.cwd() / \"precalculated_floris_solutions\"\n",
@@ -54,69 +70,31 @@
     "    start_time = timerpc()\n",
     "    print(\"Precalculating FLORIS table for '{:s}' model...\".format(wake_model))\n",
     "    fi, _ = load_floris(wake_model=wake_model)\n",
-    "    fi_pci = ParallelComputingInterface(\n",
-    "        fi=fi,\n",
-    "        max_workers=max_workers,\n",
-    "        n_wind_direction_splits=max_workers,\n",
-    "        print_timings=True,\n",
-    "    )\n",
+    "    # fi_pci = ParallelComputingInterface(\n",
+    "    #     fi=fi,\n",
+    "    #     max_workers=max_workers,\n",
+    "    #     n_wind_direction_splits=max_workers,\n",
+    "    #     print_timings=True,\n",
+    "    # )\n",
     "    df_fi_approx = ftools.calc_floris_approx_table(\n",
-    "        fi=fi_pci,\n",
+    "        fi=fi,  # fi_pci,\n",
     "        wd_array=np.arange(0.0, 360.01, 3.0),\n",
     "        ws_array=np.arange(1.0, 30.01, 1.0),\n",
     "        ti_array=[0.03, 0.06, 0.09, 0.12, 0.15],\n",
     "    )\n",
     "    end_time = timerpc()\n",
     "    print(\"Computation time: {:.2f} s\".format(end_time - start_time))\n",
     "    df_fi_approx.to_feather(fn)"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Precalculating FLORIS table for 'jensen' model...\n",
-      "Generating a df_approx table of FLORIS solutions covering a total of 18150 cases.\n",
-      "===============================================================================\n",
-      "Total time spent for parallel calculation (16 workers): 6.492 s\n",
-      "  Time spent in parallel preprocessing: 0.005 s\n",
-      "  Time spent in parallel loop execution: 6.486 s.\n",
-      "  Time spent in parallel postprocessing: 0.000 s\n",
-      "===============================================================================\n",
-      "Total time spent for parallel calculation (16 workers): 6.612 s\n",
-      "  Time spent in parallel preprocessing: 0.005 s\n",
-      "  Time spent in parallel loop execution: 6.607 s.\n",
-      "  Time spent in parallel postprocessing: 0.000 s\n",
-      "===============================================================================\n",
-      "Total time spent for parallel calculation (16 workers): 7.267 s\n",
-      "  Time spent in parallel preprocessing: 0.005 s\n",
-      "  Time spent in parallel loop execution: 7.261 s.\n",
-      "  Time spent in parallel postprocessing: 0.000 s\n",
-      "===============================================================================\n",
-      "Total time spent for parallel calculation (16 workers): 7.577 s\n",
-      "  Time spent in parallel preprocessing: 0.005 s\n",
-      "  Time spent in parallel loop execution: 7.572 s.\n",
-      "  Time spent in parallel postprocessing: 0.000 s\n",
-      "===============================================================================\n",
-      "Total time spent for parallel calculation (16 workers): 8.281 s\n",
-      "  Time spent in parallel preprocessing: 0.005 s\n",
-      "  Time spent in parallel loop execution: 8.275 s.\n",
-      "  Time spent in parallel postprocessing: 0.000 s\n",
-      "Finished calculating the FLORIS solutions for the dataframe.\n",
-      "Computation time: 36.74 s\n",
-      "FLORIS table for 'turbopark' model exists. Skipping...\n",
-      "FLORIS table for 'gch' model exists. Skipping...\n",
-      "FLORIS table for 'cc' model exists. Skipping...\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "96c53852a1e56d9fbc8381f88ff3256056a2f574c5e86cd3dfe6ce1bc9d68e6a"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.10.4 64-bit ('flasc-reqs': conda)"
+   "display_name": "Python 3.10.4 64-bit ('flasc-reqs': conda)",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -142,9 +120,6 @@
    "toc_position": {},
    "toc_section_display": true,
    "toc_window_display": false
-  },
-  "interpreter": {
-   "hash": "96c53852a1e56d9fbc8381f88ff3256056a2f574c5e86cd3dfe6ce1bc9d68e6a"
   }
  },
  "nbformat": 4,