GeneticAlgorithm.ipynb

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  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on: xxxx\n",
    "@author   : Anon\n",
    "\n",
    "NAME\n",
    "    Farm_Evalautor.py\n",
    "    \n",
    "PYTHON VERSION   \n",
    "    3.7.3 \n",
    "    \n",
    "DESCRIPTION\n",
    "    Calculates Annual Energy Production (AEP) of a Wind Farm\n",
    "    ============================================================    \n",
    "    \n",
    "    This is vectorzied version of Farm_Evalautor.py. \n",
    "    Farm_Evalautor_Vec.py is a python file that calculates AEP (GWh)\n",
    "    of a certain arrangement of wind turbines in a farm, under \n",
    "    given annual wind conditions. \n",
    "    \n",
    "    The code in this script for wake-effect modeling is based on\n",
    "    standard Jensen (PARK) model. \n",
    "    I. Katic, J. Hojstrup and N. Jensen, \"A simple model for cluster \n",
    "    efficiency,\" in European Wind Energy Association Conference and \n",
    "    Exhibition, 1986.\n",
    "    \n",
    "    As its inputs, the code takes three data files containing info \n",
    "    about:\n",
    "    - Turbine Locations\n",
    "    - Turbine Power Curve\n",
    "    - Annual Wind Conditions\n",
    "    \n",
    "PACKAGE LIST\n",
    "    You may need to install the package Shapely in your\n",
    "    python distribution. These are not pre-installed. \n",
    "    =============================================================\n",
    "    Packages Used:\n",
    "    Numpy\n",
    "    Pandas\n",
    "    Shapely\n",
    "    math (built-in)\n",
    "    \n",
    "OPTIMIZATION USAGE\n",
    "    This vectorized version is faster than unvectorized version\n",
    "    Farm_Evalautor.py. Due to speed benefits, we advise you to use \n",
    "    the function getAEP in this script while developing the optimizer. \n",
    "    =============================================================\n",
    "    \n",
    "    One way to use getAEP function as AEP evaluator while optimizing is:\n",
    "    - STEP 1. Import the relevant function from Farm_Evalautor_Vec. \n",
    "              from Farm_Evalautor_Vec import getTurbLoc, loadPowerCurve, \n",
    "              binWindResourceData, preProcessing, getAEP\n",
    "    - STEP 2. Set Turbine Radius to 50.0. First arg of getAEP\n",
    "    - STEP 3. Load Turbine Locations. Using function getTurbLoc\n",
    "    - STEP 4. Load Power Curve. Using function loadPowerCurve\n",
    "    - STEP 5. Load wind instance probabilities. \n",
    "              Using function binWindResourceData\n",
    "    - STEP 6. Perform Preprocessing by calling function preProcessing.\n",
    "              We do preprocessing to avoid same repeating calculations.\n",
    "              Do them once. \n",
    "    - STEP 7. Finally, call function getAEP\n",
    "    \n",
    "    This makes it easy to control the reloading of data and hence achieve\n",
    "    better performance.      \n",
    "\"\"\"\n",
    "\n",
    "# Module List\n",
    "import numpy  as np\n",
    "import pandas as pd                     \n",
    "from   math   import radians as DegToRad       # Degrees to radians Conversion\n",
    "\n",
    "from shapely.geometry import Point             # Imported for constraint checking\n",
    "from shapely.geometry.polygon import Polygon\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "def getTurbLoc(turb_loc_file_name):\n",
    "    \"\"\" \n",
    "    -**-THIS FUNCTION SHOULD NOT BE MODIFIED-**-\n",
    "    \n",
    "    Returns x,y turbine coordinates\n",
    "    \n",
    "    :Called from\n",
    "        main function\n",
    "    \n",
    "    :param\n",
    "        turb_loc_file_name - Turbine Loc csv file location\n",
    "        \n",
    "    :return\n",
    "        2D array\n",
    "    \"\"\"\n",
    "    \n",
    "    df = pd.read_csv(turb_loc_file_name, sep=',', dtype = np.float32)\n",
    "    turb_coords = df.to_numpy(dtype = np.float32)\n",
    "    return(turb_coords)\n",
    "\n",
    "\n",
    "def loadPowerCurve(power_curve_file_name):\n",
    "    \"\"\"\n",
    "    -**-THIS FUNCTION SHOULD NOT BE MODIFIED-**-\n",
    "    \n",
    "    Returns a 2D numpy array with information about\n",
    "    turbine thrust coeffecient and power curve of the \n",
    "    turbine for given wind speed\n",
    "    \n",
    "    :called_from\n",
    "        main function\n",
    "    \n",
    "    :param\n",
    "        power_curve_file_name - power curve csv file location\n",
    "        \n",
    "    :return\n",
    "        Returns a 2D numpy array with cols Wind Speed (m/s), \n",
    "        Thrust Coeffecient (non dimensional), Power (MW)\n",
    "    \"\"\"\n",
    "    powerCurve = pd.read_csv(power_curve_file_name, sep=',', dtype = np.float32)\n",
    "    powerCurve = powerCurve.to_numpy(dtype = np.float32)\n",
    "    return(powerCurve)\n",
    "    \n",
    "\n",
    "def binWindResourceData(wind_data_file_name):\n",
    "    r\"\"\"\n",
    "    -**-THIS FUNCTION SHOULD NOT BE MODIFIED-**-\n",
    "    \n",
    "    Loads the wind data. Returns a 2D array with shape (36,15). \n",
    "    Each cell in  array is a wind direction and speed 'instance'. \n",
    "    Values in a cell correspond to probability of instance\n",
    "    occurence.  \n",
    "    \n",
    "    :Called from\n",
    "        main function\n",
    "        \n",
    "    :param\n",
    "        wind_data_file_name - Wind Resource csv file  \n",
    "        \n",
    "    :return\n",
    "        1-D flattened array of the 2-D array shown below. Values \n",
    "        inside cells, rough probabilities of wind instance occurence. \n",
    "        Along: Row-direction (drct), Column-Speed (s). Array flattened\n",
    "        for vectorization purpose. \n",
    "        \n",
    "                      |0<=s<2|2<=s<4| ...  |26<=s<28|28<=s<30|\n",
    "        |_____________|______|______|______|________|________|\n",
    "        | drct = 360  |  --  |  --  |  --  |   --   |   --   |\n",
    "        | drct = 10   |  --  |  --  |  --  |   --   |   --   |\n",
    "        | drct = 20   |  --  |  --  |  --  |   --   |   --   |\n",
    "        |   ....      |  --  |  --  |  --  |   --   |   --   |\n",
    "        | drct = 340  |  --  |  --  |  --  |   --   |   --   |\n",
    "        | drct = 350  |  --  |  --  |  --  |   --   |   --   |        \n",
    "    \"\"\"\n",
    "    \n",
    "    # Load wind data. Then, extracts the 'drct', 'sped' columns\n",
    "    df = pd.read_csv(wind_data_file_name)\n",
    "    wind_resource = df[['drct', 'sped']].to_numpy(dtype = np.float32)\n",
    "    \n",
    "    # direction 'slices' in degrees\n",
    "    slices_drct   = np.roll(np.arange(10, 361, 10, dtype=np.float32), 1)\n",
    "    ## slices_drct   = [360, 10.0, 20.0.......340, 350]\n",
    "    n_slices_drct = slices_drct.shape[0]\n",
    "    \n",
    "    # speed 'slices'\n",
    "    slices_sped   = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, \n",
    "                        18.0, 20.0, 22.0, 24.0, 26.0, 28.0, 30.0]\n",
    "    n_slices_sped = len(slices_sped)-1\n",
    "\n",
    "    \n",
    "    # placeholder for binned wind\n",
    "    binned_wind = np.zeros((n_slices_drct, n_slices_sped), \n",
    "                           dtype = np.float32)\n",
    "    \n",
    "    # 'trap' data points inside the bins. \n",
    "    for i in range(n_slices_drct):\n",
    "        for j in range(n_slices_sped):     \n",
    "            \n",
    "            # because we already have drct in the multiples of 10\n",
    "            foo = wind_resource[(wind_resource[:,0] == slices_drct[i])] \n",
    "\n",
    "            foo = foo[(foo[:,1] >= slices_sped[j]) \n",
    "                          & (foo[:,1] <  slices_sped[j+1])]\n",
    "            \n",
    "            binned_wind[i,j] = foo.shape[0]  \n",
    "    \n",
    "    wind_inst_freq   = binned_wind/np.sum(binned_wind)\n",
    "    wind_inst_freq   = wind_inst_freq.ravel()\n",
    "    \n",
    "    return(wind_inst_freq)\n",
    "\n",
    "\n",
    "def searchSorted(lookup, sample_array):\n",
    "    \"\"\"\n",
    "    -**-THIS FUNCTION SHOULD NOT BE MODIFIED-**-\n",
    "    \n",
    "    Returns lookup indices for closest values w.r.t sample_array elements\n",
    "    \n",
    "    :called_from\n",
    "        preProcessing, getAEP\n",
    "    \n",
    "    :param\n",
    "        lookup       - The lookup array\n",
    "        sample_array - Array, whose elements need to be matched\n",
    "                       against lookup elements. \n",
    "        \n",
    "    :return\n",
    "        lookup indices for closest values w.r.t sample_array elements \n",
    "    \"\"\"\n",
    "    lookup_middles = lookup[1:] - np.diff(lookup.astype('f'))/2\n",
    "    idx1 = np.searchsorted(lookup_middles, sample_array)\n",
    "    indices = np.arange(lookup.shape[0])[idx1]\n",
    "    return indices\n",
    "\n",
    "   \n",
    "\n",
    "def preProcessing(power_curve):\n",
    "    \"\"\"\n",
    "    -**-THIS FUNCTION SHOULD NOT BE MODIFIED-**-\n",
    "    \n",
    "    Doing preprocessing to avoid the same repeating calculations.\n",
    "    Record the required data for calculations. Do that once.\n",
    "    Data are set up (shaped) to assist vectorization. Used later in\n",
    "    function totalAEP. \n",
    "    \n",
    "    :called_from\n",
    "        main function\n",
    "    \n",
    "    :param\n",
    "        power_curve - 2D numpy array with cols Wind Speed (m/s), \n",
    "                      Thrust Coeffecient (non dimensional), Power (MW)\n",
    "        \n",
    "    :return\n",
    "        n_wind_instances  - number of wind instances (int)\n",
    "        cos_dir           - For coordinate transformation \n",
    "                            2D Array. Shape (n_wind_instances,1)\n",
    "        sin_dir           - For coordinate transformation \n",
    "                            2D Array. Shape (n_wind_instances,1)\n",
    "        wind_sped_stacked - column staked all speed instances n_turb times. \n",
    "        C_t               - 3D array with shape (n_wind_instances, n_turbs, n_turbs)\n",
    "                            Value changing only along axis=0. C_t, thrust coeff.\n",
    "                            values for all speed instances. \n",
    "    \"\"\"\n",
    "    # number of turbines\n",
    "    n_turbs       =   50\n",
    "    \n",
    "    # direction 'slices' in degrees\n",
    "    slices_drct   = np.roll(np.arange(10, 361, 10, dtype=np.float32), 1)\n",
    "    ## slices_drct   = [360, 10.0, 20.0.......340, 350]\n",
    "    n_slices_drct = slices_drct.shape[0]\n",
    "    \n",
    "    # speed 'slices'\n",
    "    slices_sped   = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, \n",
    "                        18.0, 20.0, 22.0, 24.0, 26.0, 28.0, 30.0]\n",
    "    n_slices_sped = len(slices_sped)-1\n",
    "    \n",
    "    # number of wind instances\n",
    "    n_wind_instances = (n_slices_drct)*(n_slices_sped)\n",
    "    \n",
    "    # Create wind instances. There are two columns in the wind instance array\n",
    "    # First Column - Wind Speed. Second Column - Wind Direction\n",
    "    # Shape of wind_instances (n_wind_instances,2). \n",
    "    # Values [1.,360.],[3.,360.],[5.,360.]...[25.,350.],[27.,350.],29.,350.]\n",
    "    wind_instances = np.zeros((n_wind_instances,2), dtype=np.float32)\n",
    "    counter = 0\n",
    "    for i in range(n_slices_drct):\n",
    "        for j in range(n_slices_sped): \n",
    "            \n",
    "            wind_drct =  slices_drct[i]\n",
    "            wind_sped = (slices_sped[j] + slices_sped[j+1])/2\n",
    "            \n",
    "            wind_instances[counter,0] = wind_sped\n",
    "            wind_instances[counter,1] = wind_drct\n",
    "            counter += 1\n",
    "\n",
    "\t# So that the wind flow direction aligns with the +ve x-axis.\t\t\t\n",
    "    # Convert inflow wind direction from degrees to radians\n",
    "    wind_drcts =  np.radians(wind_instances[:,1] - 90)\n",
    "    # For coordinate transformation \n",
    "    cos_dir = np.cos(wind_drcts).reshape(n_wind_instances,1)\n",
    "    sin_dir = np.sin(wind_drcts).reshape(n_wind_instances,1)\n",
    "    \n",
    "    # create copies of n_wind_instances wind speeds from wind_instances\n",
    "    wind_sped_stacked = np.column_stack([wind_instances[:,0]]*n_turbs)\n",
    "   \n",
    "    # Pre-prepare matrix with stored thrust coeffecient C_t values for \n",
    "    # n_wind_instances shape (n_wind_instances, n_turbs, n_turbs). \n",
    "    # Value changing only along axis=0. C_t, thrust coeff. values for all \n",
    "    # speed instances.\n",
    "    # we use power_curve data as look up to estimate the thrust coeff.\n",
    "    # of the turbine for the corresponding closest matching wind speed\n",
    "    indices = searchSorted(power_curve[:,0], wind_instances[:,0])\n",
    "    C_t     = power_curve[indices,1]\n",
    "    # stacking and reshaping to assist vectorization\n",
    "    C_t     = np.column_stack([C_t]*(n_turbs*n_turbs))\n",
    "    C_t     = C_t.reshape(n_wind_instances, n_turbs, n_turbs)\n",
    "    \n",
    "    return(n_wind_instances, cos_dir, sin_dir, wind_sped_stacked, C_t)\n",
    "\n",
    "\n",
    "def getAEP(turb_coords):#turb_rad, turb_coords, power_curve, wind_inst_freq, \n",
    "            #n_wind_instances, cos_dir, sin_dir, wind_sped_stacked, C_t\n",
    "    turb_coord=turb_coords.reshape(78,78)\n",
    "    turb_coords=[]\n",
    "    for i in range(78):\n",
    "        for j in range(78):\n",
    "            if turb_coord[i][j]==1:\n",
    "                turb_coords.append([((i*50)+50),((j*50)+50)])\n",
    "    turb_coords=np.asarray(turb_coords)\n",
    "    print(turb_coords.shape)\n",
    "    # Turbine Specifications.\n",
    "    # -**-SHOULD NOT BE MODIFIED-**-\n",
    "    turb_specs    =  {   \n",
    "                         'Name': 'Anon Name',\n",
    "                         'Vendor': 'Anon Vendor',\n",
    "                         'Type': 'Anon Type',\n",
    "                         'Dia (m)': 100,\n",
    "                         'Rotor Area (m2)': 7853,\n",
    "                         'Hub Height (m)': 100,\n",
    "                         'Cut-in Wind Speed (m/s)': 3.5,\n",
    "                         'Cut-out Wind Speed (m/s)': 25,\n",
    "                         'Rated Wind Speed (m/s)': 15,\n",
    "                         'Rated Power (MW)': 3\n",
    "                     }\n",
    "    turb_diam      =  turb_specs['Dia (m)']\n",
    "    turb_rad       =  turb_diam/2 \n",
    "    \n",
    "    # Turbine x,y coordinates\n",
    "    #turb_coords   =  getTurbLoc(r'BResult1344.csv')#turbine_loc_test.csv')\n",
    "    #print(turb_coords.shape)\n",
    "    power_curve   =  loadPowerCurve('power_curve.csv')\n",
    "    \n",
    "    # Pass wind data csv file location to function binWindResourceData.\n",
    "    # Retrieve probabilities of wind instance occurence.\n",
    "    wind_inst_freq =  binWindResourceData(r'wind_data_2007.csv')   \n",
    "    \n",
    "    # Doing preprocessing to avoid the same repeating calculations. Record \n",
    "    # the required data for calculations. Do that once. Data are set up (shaped)\n",
    "    # to assist vectorization. Used later in function totalAEP.\n",
    "    n_wind_instances, cos_dir, sin_dir, wind_sped_stacked, C_t = preProcessing(power_curve)\n",
    "    \n",
    "    # check if there is any constraint is violated before we do anything. Comment \n",
    "    # out the function call to checkConstraints below if you desire. Note that \n",
    "    # this is just a check and the function does not quantifies the amount by \n",
    "    # which the constraints are violated if any. \n",
    "    #checkConstraints(turb_coords, turb_diam)\n",
    "    \"\"\"\n",
    "    -**-THIS FUNCTION SHOULD NOT BE MODIFIED-**-\n",
    "    \n",
    "    Calculates AEP of the wind farm. Vectorised version.\n",
    "    \n",
    "    :called from\n",
    "        main\n",
    "        \n",
    "    :param\n",
    "        turb_diam         - Radius of the turbine (m)\n",
    "        turb_coords       - 2D array turbine euclidean x,y coordinates\n",
    "        power_curve       - For estimating power. \n",
    "        wind_inst_freq    - 1-D flattened with rough probabilities of \n",
    "                            wind instance occurence.\n",
    "                            n_wind_instances  - number of wind instances (int)\n",
    "        cos_dir           - For coordinate transformation \n",
    "                            2D Array. Shape (n_wind_instances,1)\n",
    "        sin_dir           - For coordinate transformation \n",
    "                            2D Array. Shape (n_wind_instances,1)\n",
    "        wind_sped_stacked - column staked all speed instances n_turb times. \n",
    "        C_t               - 3D array with shape (n_wind_instances, n_turbs, n_turbs)\n",
    "                            Value changing only along axis=0. C_t, thrust coeff.\n",
    "                            values for all speed instances. \n",
    "    \n",
    "    :return\n",
    "        wind farm AEP in Gigawatt Hours, GWh (float)\n",
    "    \"\"\"\n",
    "    # number of turbines\n",
    "    #print(turb_coords.shape[0])\n",
    "    n_turbs        =   turb_coords.shape[0]\n",
    "    if n_turbs !=  50:\n",
    "        return 0\n",
    "        #, \"Error! Number of turbines is not 50.\"\n",
    "    \n",
    "    # Prepare the rotated coordinates wrt the wind direction i.e downwind(x) & crosswind(y) \n",
    "    # coordinates wrt to the wind direction for each direction in wind_instances array\n",
    "    rotate_coords   =  np.zeros((n_wind_instances, n_turbs, 2), dtype=np.float32)\n",
    "    # Coordinate Transformation. Rotate coordinates to downwind, crosswind coordinates\n",
    "    rotate_coords[:,:,0] =  np.matmul(cos_dir, np.transpose(turb_coords[:,0].reshape(n_turbs,1))) - \\\n",
    "                           np.matmul(sin_dir, np.transpose(turb_coords[:,1].reshape(n_turbs,1)))\n",
    "    rotate_coords[:,:,1] =  np.matmul(sin_dir, np.transpose(turb_coords[:,0].reshape(n_turbs,1))) +\\\n",
    "                           np.matmul(cos_dir, np.transpose(turb_coords[:,1].reshape(n_turbs,1)))\n",
    " \n",
    "    \n",
    "    # x_dist - x dist between turbine pairs wrt downwind/crosswind coordinates)\n",
    "    # for each wind instance\n",
    "    x_dist = np.zeros((n_wind_instances,n_turbs,n_turbs), dtype=np.float32)\n",
    "    for i in range(n_wind_instances):\n",
    "        tmp = rotate_coords[i,:,0].repeat(n_turbs).reshape(n_turbs, n_turbs)\n",
    "        x_dist[i] = tmp - tmp.transpose()\n",
    "    \n",
    "\n",
    "    # y_dist - y dist between turbine pairs wrt downwind/crosswind coordinates)\n",
    "    # for each wind instance    \n",
    "    y_dist = np.zeros((n_wind_instances,n_turbs,n_turbs), dtype=np.float32)\n",
    "    for i in range(n_wind_instances):\n",
    "        tmp = rotate_coords[i,:,1].repeat(n_turbs).reshape(n_turbs, n_turbs)\n",
    "        y_dist[i] = tmp - tmp.transpose()\n",
    "    y_dist = np.abs(y_dist) \n",
    "     \n",
    "\n",
    "    # Now use element wise operations to calculate speed deficit.\n",
    "    # kw, wake decay constant presetted to 0.05\n",
    "    # use the jensen's model formula. \n",
    "    # no wake effect of turbine on itself. either j not an upstream or wake \n",
    "    # not happening on i because its outside of the wake region of j\n",
    "    # For some values of x_dist here RuntimeWarning: divide by zero may occur\n",
    "    # That occurs for negative x_dist. Those we anyway mark as zeros. \n",
    "    sped_deficit = (1-np.sqrt(1-C_t))*((turb_rad/(turb_rad + 0.05*x_dist))**2) \n",
    "    sped_deficit[((x_dist <= 0) | ((x_dist > 0) & (y_dist > (turb_rad + 0.05*x_dist))))] = 0.0\n",
    "    \n",
    "    \n",
    "    # Calculate Total speed deficit from all upstream turbs, using sqrt of sum of sqrs\n",
    "    sped_deficit_eff  = np.sqrt(np.sum(np.square(sped_deficit), axis = 2))\n",
    "\n",
    "    \n",
    "    # Element wise multiply the above with (1- sped_deficit_eff) to get\n",
    "    # effective windspeed due to the happening wake\n",
    "    wind_sped_eff     = wind_sped_stacked*(1.0-sped_deficit_eff)\n",
    "\n",
    "    \n",
    "    # Estimate power from power_curve look up for wind_sped_eff\n",
    "    indices = searchSorted(power_curve[:,0], wind_sped_eff.ravel())\n",
    "    power   = power_curve[indices,2]\n",
    "    power   = power.reshape(n_wind_instances,n_turbs)\n",
    "    #print(power.shape)\n",
    "    # Farm power for single wind instance \n",
    "    power   = np.sum(power, axis=1)\n",
    "    \n",
    "    # multiply the respective values with the wind instance probabilities \n",
    "    # year_hours = 8760.0\n",
    "    AEP = 8760.0*np.sum(power*wind_inst_freq)\n",
    "    \n",
    "    # Convert MWh to GWh\n",
    "    AEP = AEP/1e3\n",
    "    #print(AEP)\n",
    "    flag= checkConstraints(turb_coords, turb_diam)\n",
    "    if flag==0:\n",
    "        return 0\n",
    "    else:\n",
    "        return -AEP\n",
    "    \n",
    "\n",
    "    \n",
    "def checkConstraints(turb_coords, turb_diam):\n",
    "    \"\"\"\n",
    "    -**-THIS FUNCTION SHOULD NOT BE MODIFIED-**-\n",
    "    \n",
    "    Checks if the turbine configuration satisfies the two\n",
    "    constraints:(i) perimeter constraint,(ii) proximity constraint \n",
    "    Prints which constraints are violated if any. Note that this \n",
    "    function does not quantifies the amount by which the constraints \n",
    "    are violated if any. \n",
    "    \n",
    "    :called from\n",
    "        main \n",
    "        \n",
    "    :param\n",
    "        turb_coords - 2d np array containing turbine x,y coordinates\n",
    "        turb_diam   - Diameter of the turbine (m)\n",
    "    \n",
    "    :return\n",
    "        None. Prints messages.   \n",
    "    \"\"\"\n",
    "    bound_clrnc      = 50\n",
    "    prox_constr_viol = False\n",
    "    peri_constr_viol = False\n",
    "    \n",
    "    # create a shapely polygon object of the wind farm\n",
    "    farm_peri = [(0, 0), (0, 4000), (4000, 4000), (4000, 0)]\n",
    "    farm_poly = Polygon(farm_peri)\n",
    "    \n",
    "    # checks if for every turbine perimeter constraint is satisfied. \n",
    "    # breaks out if False anywhere\n",
    "    for turb in turb_coords:\n",
    "        turb = Point(turb)\n",
    "        inside_farm   = farm_poly.contains(turb)\n",
    "        correct_clrnc = farm_poly.boundary.distance(turb) >= bound_clrnc\n",
    "        if (inside_farm == False or correct_clrnc == False):\n",
    "            #print(turb)\n",
    "            peri_constr_viol = True\n",
    "            break\n",
    "    \n",
    "    # checks if for every turbines proximity constraint is satisfied. \n",
    "    # breaks out if False anywhere\n",
    "    for i,turb1 in enumerate(turb_coords):\n",
    "        for turb2 in np.delete(turb_coords, i, axis=0):\n",
    "            if  np.linalg.norm(turb1 - turb2) < 4*turb_diam:\n",
    "                #print(turb1,end=\" \")\n",
    "                #print(turb2)\n",
    "                prox_constr_viol = True\n",
    "                break\n",
    "    flag=0\n",
    "    # print messages\n",
    "    if  peri_constr_viol  == True  and prox_constr_viol == True:\n",
    "        pass\n",
    "          #print('Somewhere both perimeter constraint and proximity constraint are violated\\n')\n",
    "    elif peri_constr_viol == True  and prox_constr_viol == False:\n",
    "        pass\n",
    "          #print('Somewhere perimeter constraint is violated\\n')\n",
    "    elif peri_constr_viol == False and prox_constr_viol == True:\n",
    "        pass\n",
    "          #print('Somewhere proximity constraint is violated\\n')\n",
    "    else: \n",
    "        #print('Both perimeter and proximity constraints are satisfied !!\\n')\n",
    "        flag=1\n",
    "        \n",
    "    return flag\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mchgenalg import GeneticAlgorithm\n",
    "import numpy as np\n",
    "ga = GeneticAlgorithm(getAEP)\n",
    "# First, define function that will be used to evaluate the fitness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "def fitness_function(genome):\n",
    "    # let's count the number of one-values in the genome\n",
    "    # this will be our fitness\n",
    "    sum = np.sum(genome)\n",
    "    return sum\n",
    "# Configure the algorithm:\n",
    "population_size = 10\n",
    "genome_length = 6084\n",
    "ga = GeneticAlgorithm(getAEP)\n",
    "ga.generate_binary_population(size=population_size, genome_length=genome_length)\n",
    "# How many pairs of individuals should be picked to mate\n",
    "ga.number_of_pairs = 5\n",
    "# Selective pressure from interval [1.0, 2.0]\n",
    "# the lower value, the less will the fitness play role\n",
    "ga.selective_pressure = 1.5\n",
    "ga.mutation_rate = 0.1\n",
    "# If two parents have the same genotype, ignore them and generate TWO random parents\n",
    "# This helps preventing premature convergence\n",
    "ga.allow_random_parent = True # default True\n",
    "# Use single point crossover instead of uniform crossover\n",
    "ga.single_point_cross_over = False # default False\n",
    "\n",
    "# Run 1000 iteration of the algorithm\n",
    "# You can call the method several times and adjust some parameters\n",
    "# (e.g. number_of_pairs, selective_pressure, mutation_rate,\n",
    "# allow_random_parent, single_point_cross_over)\n",
    "ga.run(1000)\n",
    "\n",
    "best_genome, best_fitness = ga.get_best_genome()\n",
    "\n",
    "# If you want, you can have a look at the population:\n",
    "population = ga.population\n",
    "# and the fitness of each element:\n",
    "fitness_vector = ga.get_fitness_vector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6084\n"
     ]
    }
   ],
   "source": [
    "print(len(best_genome))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3425\n"
     ]
    }
   ],
   "source": [
    "num=0\n",
    "for i in best_genome:\n",
    "    if i==True:\n",
    "        num+=1\n",
    "print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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