Further spelling adaptions

Author: SabineHaas

example/further_example.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -64,8 +64,8 @@
     "weather = basic_example.get_weather_data('weather.csv')\n",
     "print(weather[['wind_speed', 'temperature', 'pressure']][0:3])\n",
     "\n",
-    "# Initialise wind turbines\n",
-    "my_turbine, e126 = basic_example.initialise_wind_turbines()\n",
+    "# Initialize wind turbines\n",
+    "my_turbine, e126 = basic_example.initialize_wind_turbines()\n",
     "print()\n",
     "print('nominal power of my_turbine: {}'.format(my_turbine.nominal_power))"
    ]
@@ -74,16 +74,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Initialise wind farm\n",
+    "### Initialize wind farm\n",
     "\n",
-    "To initialise a specific wind farm you can use a dictionary that contains the basic parameters. A wind farm is defined by its name, wind turbine fleet, and optionally also by a wind farm efficiency and the wind farm's location.\n",
+    "To initialize a specific wind farm you can use a dictionary that contains the basic parameters. A wind farm is defined by its name, wind turbine fleet, and optionally also by a wind farm efficiency and the wind farm's location.\n",
     "\n",
     "A wind farm efficiency can be a constant value or be dependent on the wind speed. The coordinates are not being used here but are necessary if you need to assign your find farm to a certain weather data point."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,13 +95,13 @@
     "                           {'wind_turbine': e126,\n",
     "                            'number_of_turbines': 3}\n",
     "                           ]}\n",
-    "# initialise WindFarm object\n",
+    "# initialize WindFarm object\n",
     "example_farm = WindFarm(**example_farm_data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -124,7 +124,7 @@
     "    'efficiency': 0.9,\n",
     "    'coordinates': [52.2, 13.1]}\n",
     "\n",
-    "# initialise WindFarm object\n",
+    "# initialize WindFarm object\n",
     "example_farm_2 = WindFarm(**example_farm_2_data)\n",
     "\n",
     "print('nominal power of first turbine type of example_farm_2: {}'.format(\n",
@@ -135,14 +135,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Initialise wind turbine cluster\n",
+    "### Initialize wind turbine cluster\n",
     "\n",
     "Like for a wind farm for the initialization of a wind turbine cluster you can use a dictionary that contains the basic parameters. A wind turbine cluster is defined by its name, wind farms and optionally by its location."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,7 +151,7 @@
     "    'name': 'example_cluster',\n",
     "    'wind_farms': [example_farm, example_farm_2]}\n",
     "\n",
-    "# initialise WindTurbineCluster object\n",
+    "# initialize WindTurbineCluster object\n",
     "example_cluster = WindTurbineCluster(**example_cluster_data)\n"
    ]
   },
@@ -170,21 +170,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "DEBUG:root:Wake losses considered by dena_mean wind efficiency curve.\n",
+      "DEBUG:root:Aggregated power curve smoothed by method: turbulence_intensity\n",
       "DEBUG:root:Calculating wind speed using logarithmic wind profile.\n",
       "DEBUG:root:Calculating power output using power curve.\n"
      ]
     }
    ],
    "source": [
     "# power output calculation for example_farm\n",
-    "# initialise TurbineClusterModelChain with default parameters and use\n",
+    "# initialize TurbineClusterModelChain with default parameters and use\n",
     "# run_model method to calculate power output\n",
     "mc_example_farm = TurbineClusterModelChain(example_farm).run_model(weather)\n",
     "# write power output time series to WindFarm object\n",
@@ -193,13 +195,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "DEBUG:root:Wake losses considered with constant_efficiency.\n",
+      "DEBUG:root:Aggregated power curve smoothed by method: Staffell_Pfenninger\n",
       "DEBUG:root:Calculating wind speed using logarithmic wind profile.\n",
       "DEBUG:root:Calculating temperature using temperature gradient.\n",
       "DEBUG:root:Calculating density using ideal gas equation.\n",
@@ -240,7 +244,7 @@
     "        'density_correction': True,  # False (default) or True\n",
     "        'obstacle_height': 0,  # default: 0\n",
     "        'hellman_exp': None}  # None (default) or None\n",
-    "# initialise TurbineClusterModelChain with own specifications and use\n",
+    "# initialize TurbineClusterModelChain with own specifications and use\n",
     "# run_model method to calculate power output\n",
     "mc_example_cluster = TurbineClusterModelChain(\n",
     "        example_cluster, **modelchain_data).run_model(weather)\n",
@@ -254,12 +258,12 @@
    "source": [
     "### Plot results\n",
     "\n",
-    "If you have matplotlib installed you can visualise the calculated power output."
+    "If you have matplotlib installed you can visualize the calculated power output."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -282,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -316,13 +320,6 @@
     "    example_farm.power_output.plot(legend=True, label='example farm')\n",
     "    plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {