{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wind rose data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we demonstrate how mean results over wind rose data are calculated in `foxes`. We need the following imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import foxes\n",
    "import foxes.variables as FV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we initialize the engine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = foxes.Engine.new(\"process\", chunk_size_states=1000, chunk_size_points=3000)\n",
    "engine.initialize()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's setup the wind farm. We choose 6 x 5 turbines on a regular grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "farm = foxes.WindFarm()\n",
    "foxes.input.farm_layout.add_grid(\n",
    "    farm=farm,\n",
    "    xy_base=[0.0, 0.0],\n",
    "    step_vectors=[[900.0, 50.0], [-80.0, 500.0]],\n",
    "    steps=[6, 5],\n",
    "    turbine_models=[\"NREL5MW\", \"kTI_05\"],\n",
    "    verbosity=0,\n",
    ")\n",
    "\n",
    "ax = foxes.output.FarmLayoutOutput(farm).get_figure(figsize=(4, 3))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create the states based on the static data file `wind_rose_bremen.csv.gz`. The data represents a (coarse) wind rose with 216 states. Each of them consists of the wind direction and wind speed bin centres, and the respective statistical weight of the bin (normalized such that 1 represents 100%):\n",
    "```\n",
    "state,wd,ws,weight\n",
    "0,0.0,3.5,0.00158\n",
    "1,0.0,6.0,0.00244\n",
    "2,0.0,8.5,0.00319\n",
    "3,0.0,12.5,0.0036700002\n",
    "4,0.0,17.5,0.00042\n",
    "...\n",
    "```\n",
    "Let's create the states object and have a look at the wind rose:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "states = foxes.input.states.StatesTable(\n",
    "    data_source=\"wind_rose_bremen.csv\",\n",
    "    output_vars=[FV.WS, FV.WD, FV.TI, FV.RHO],\n",
    "    var2col={FV.WS: \"ws\", FV.WD: \"wd\", FV.WEIGHT: \"weight\"},\n",
    "    fixed_vars={FV.RHO: 1.225, FV.TI: 0.05},\n",
    ")\n",
    "\n",
    "o = foxes.output.StatesRosePlotOutput(states, point=[0.0, 0.0, 100.0])\n",
    "o.get_figure(16, FV.AMB_WS, [0, 3.5, 6, 10, 15, 20], figsize=(6, 6))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now setup our algorithm. In this example, we invoke one wake model for the wind deficit, `Bastankhah_linear` (with linear wake superposition), and another for the turbulence intensity wake effect, `CrespoHernandez_max` (with maximum wake superposition). Both obtain the wake growth parameter `k` by a relation `k = 0.5 * TI`, see `turbine_models` choice in the wind farm setup. We use default partial wakes for both models, indicated py `partial_wakes=None`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "algo = foxes.algorithms.Downwind(\n",
    "    farm,\n",
    "    states,\n",
    "    rotor_model=\"centre\",\n",
    "    wake_models=[\"Bastankhah2014_linear\", \"CrespoHernandez_max\"],\n",
    "    partial_wakes=None,\n",
    "    verbosity=0,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now calculate the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "farm_results = algo.calc_farm()\n",
    "\n",
    "o = foxes.output.FarmResultsEval(farm_results)\n",
    "o.add_efficiency()\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, figsize=(6, 7))\n",
    "o = foxes.output.FarmLayoutOutput(farm, farm_results)\n",
    "o.get_figure(\n",
    "    fig=fig,\n",
    "    ax=axs[0],\n",
    "    color_by=\"mean_REWS\",\n",
    "    title=\"Mean REWS [m/s]\",\n",
    "    s=150,\n",
    "    annotate=0,\n",
    ")\n",
    "o.get_figure(\n",
    "    fig=fig,\n",
    "    ax=axs[1],\n",
    "    color_by=\"mean_EFF\",\n",
    "    title=\"Mean efficiency [%]\",\n",
    "    s=150,\n",
    "    annotate=0,\n",
    ")\n",
    "plt.show()\n",
    "\n",
    "o = foxes.output.FarmResultsEval(farm_results)\n",
    "P0 = o.calc_mean_farm_power(ambient=True)\n",
    "P = o.calc_mean_farm_power()\n",
    "print(f\"\\nFarm power        : {P / 1000:.1f} MW\")\n",
    "print(f\"Farm ambient power: {P0 / 1000:.1f} MW\")\n",
    "print(f\"Farm efficiency   : {o.calc_farm_efficiency() * 100:.2f} %\")\n",
    "print(f\"Annual farm yield : {o.calc_farm_yield(algo=algo):.2f} GWh\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we display the mean wind speed field as a 2D plot, including wake effects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o = foxes.output.FlowPlots2D(algo, farm_results)\n",
    "o.get_mean_fig_xy(FV.WS, resolution=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After all work is down, let's shutdown the engine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine.finalize()"
   ]
  }
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