ruff lint and format

examples_artificial_data/01_raw_data_processing/00_filter_ws_power_curves.ipynb

@@ -722,7 +722,7 @@
     "        # In this case, we have an operational_status flag. If that has a 'False' value, then we\n",
     "        # mark those measurements as faulty.\n",
     "        ws_pow_filtering.filter_by_condition(\n",
-    "            condition=(ws_pow_filtering.df[\"is_operation_normal_{:03d}\".format(ti)] == False),\n",
+    "            condition=(ws_pow_filtering.df[\"is_operation_normal_{:03d}\".format(ti)] is False),\n",
     "            label=\"Self-flagged (is_operation_normal==False)\",\n",
     "            ti=ti,\n",
     "            verbose=True,\n",
@@ -842,7 +842,8 @@
     "        print(\"\\n\")\n",
     "\n",
     "    if plot_figures:\n",
-    "        # Plot farm-averaged power curve based on the data (useful to e.g., feed into FLORIS when not provided by OEM)\n",
+    "        # Plot farm-averaged power curve based on the data (useful to e.g., feed\n",
+    "        # into FLORIS when not provided by OEM)\n",
     "        fig, ax = ws_pow_filtering.plot_farm_mean_power_curve(fi=fi)\n",
     "        if save_figures:\n",
     "            plt.savefig(os.path.join(figure_save_path, \"median_farm_power_curves.png\"))\n",

examples_artificial_data/03_energy_ratio/00_demo_energy_ratio_syntax.ipynb

@@ -113,7 +113,8 @@
     }
    ],
    "source": [
-    "# Create a time history of points where the wind speed and wind direction step different combinations\n",
+    "# Create a time history of points where the wind speed and\n",
+    "# wind direction step different combinations\n",
     "ws_points = np.arange(5.0, 10.0, 1.0)\n",
     "wd_points = np.arange(\n",
     "    250.0,\n",
@@ -263,8 +264,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# erp.compute_energy_ratio(df_energy, [0], [2], df_names=['Baseline', 'WakeSteering'])\n",
-    "\n",
     "er_out = erp.compute_energy_ratio(\n",
     "    er_in,\n",
     "    test_turbines=[2],\n",

examples_artificial_data/03_energy_ratio/01_demo_energy_ratio_options.ipynb

@@ -111,7 +111,8 @@
     }
    ],
    "source": [
-    "# Create a time history of points where the wind speed and wind direction step different combinations\n",
+    "# Create a time history of points where the wind\n",
+    "# speed and wind direction step different combinations\n",
     "ws_points = np.arange(5.0, 10.0, 1.0)\n",
     "wd_points = np.arange(\n",
     "    250.0,\n",
@@ -235,7 +236,8 @@
     }
    ],
    "source": [
-    "# Calculate and plot the energy ratio of turbine 2 with respect to turbine 0, using turbine 0's measurements of wind speed and wind direction\n",
+    "# Calculate and plot the energy ratio of turbine 2 with respect to\n",
+    "# turbine 0, using turbine 0's measurements of wind speed and wind direction\n",
     "er_out = erp.compute_energy_ratio(\n",
     "    er_in, test_turbines=[2], ref_turbines=[0], ws_turbines=[0], wd_turbines=[0], N=50\n",
     ")\n",
@@ -272,7 +274,8 @@
     }
    ],
    "source": [
-    "# Reverse the above calculation showing the energy ratio of T0 / T2, letting T1 supply wind speed and direction\n",
+    "# Reverse the above calculation showing the energy ratio of T0 / T2,\n",
+    "# letting T1 supply wind speed and direction\n",
     "er_out = erp.compute_energy_ratio(\n",
     "    er_in, test_turbines=[0], ref_turbines=[2], ws_turbines=[1], wd_turbines=[1], N=50\n",
     ")\n",

examples_artificial_data/04_floris_tuning/00_tune_floris_to_artificial_data.ipynb

@@ -131,7 +131,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Identify the indexes of the reference turbine, the control turbine, the single_wake turbine and the deep_wake turbine\n",
+    "# Identify the indexes of the reference turbine, the control turbine,\n",
+    "# the single_wake turbine and the deep_wake turbine\n",
     "ref_idx = 0\n",
     "control_idx = int(np.floor(n_row_y / 2))\n",
     "single_wake_idx = int(np.floor(n_row_y / 2) + n_row_y)\n",
@@ -234,7 +235,8 @@
     }
    ],
    "source": [
-    "# Create a time history of points where the wind speed and wind direction step different combinations\n",
+    "# Create a time history of points where the wind speed and wind\n",
+    "# direction step different combinations\n",
     "ws_points = np.arange(5.0, 10.0, 1.0)\n",
     "wd_points = np.arange(\n",
     "    250.0,\n",

examples_smarteole/02_download_and_format_dataset.ipynb

@@ -594,27 +594,31 @@
     "                ),  # We want to use the 'active' power production for our analysis in FLASC\n",
     "                \"wind_speed_{:1d}_avg\".format(ii + 1): \"ws_{:03d}\".format(\n",
     "                    ii\n",
-    "                ),  # Turbine-felt wind speed. Ideally, this should be the freestream-equivalent wind speed at this turbine.\n",
+    "                ),  # Turbine-felt wind speed. Ideally, this should be the freestream-equivalent\n",
+    "                # wind speed at this turbine.\n",
     "                \"wind_direction_{:1d}_avg\".format(ii + 1): \"wd_{:03d}\".format(\n",
     "                    ii\n",
-    "                ),  # Wind direction from the data. If this is not available, can approximate this with the nacelle heading.\n",
+    "                ),  # Wind direction from the data. If this is not available, can approximate\n",
+    "                # this with the nacelle heading.\n",
     "                \"derate_{:1d}\".format(ii + 1): \"is_operation_normal_{:03d}\".format(ii),\n",
     "            }\n",
     "        )\n",
     "\n",
-    "    df_list = []\n",
+    "    # df_list = []\n",
     "    print(\"formatting dataframe...\")\n",
     "    df_scada = df_scada.rename(columns=scada_dict)\n",
     "\n",
-    "    # Convert  is_operation_normal columns from integers in original format (0: normal, 1: not normal)\n",
+    "    # Convert  is_operation_normal columns from integers in\n",
+    "    #  original format (0: normal, 1: not normal)\n",
     "    # to boolean FLASC convention (True: normal, False: not normal)\n",
     "    for ii in range(len(turbine_names)):\n",
     "        df_scada[\"is_operation_normal_{:03d}\".format(ii)] = ~df_scada[\n",
     "            \"is_operation_normal_{:03d}\".format(ii)\n",
     "        ].astype(bool)\n",
     "\n",
     "    # We'll also save the wind vane angle of the turbine for which wake steering is implemented, the\n",
-    "    # target yaw offset from the wake steering controller, and the control mode (baseline or wake steering)\n",
+    "    # target yaw offset from the wake steering controller,\n",
+    "    #  and the control mode (baseline or wake steering)\n",
     "    scada_dict = {\n",
     "        \"wind_vane_6_avg\": \"wind_vane_005\",\n",
     "        \"control_log_offset_avg\": \"target_yaw_offset_005\",\n",
@@ -624,7 +628,8 @@
     "        columns=scada_dict\n",
     "    )  # Simplify names and use FLASC zero indexing convention\n",
     "\n",
-    "    # The control mode is indicated as 0 for baseline and 1 for wake steering. Let's change this to a column of\n",
+    "    # The control mode is indicated as 0 for baseline and 1 for wake steering.\n",
+    "    #  Let's change this to a column of\n",
     "    # strings with values of \"baseline\" or \"controlled\".\n",
     "    df_scada[\"control_mode\"] = df_scada[\"control_mode\"].round()\n",
     "    df_scada.loc[df_scada[\"control_mode\"] == 0.0, \"control_mode\"] = \"baseline\"\n",
@@ -746,7 +751,8 @@
     "            all turbines.\n",
     "\n",
     "    Returns:\n",
-    "        df [pd.DataFrame]: Dataframe with added wd_smarteole, ws_smarteole, and pow_ref_smarteole columns.\n",
+    "        df [pd.DataFrame]: Dataframe with added wd_smarteole,\n",
+    "            ws_smarteole, and pow_ref_smarteole columns.\n",
     "    \"\"\"\n",
     "\n",
     "    data_dir = os.path.join(\"data\", \"SMARTEOLE-WFC-open-dataset\")\n",