Hybrid_3objectives.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ea7f6c2d",
   "metadata": {},
   "source": [
    "# Loads packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa784e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "from scipy.spatial import ConvexHull\n",
    "from matplotlib.patches import Polygon\n",
    "import sys,os\n",
    "sys.path.append(os.path.realpath('./src/'))\n",
    "import pandas as pd\n",
    "from materialEncoder import MaterialEncoder\n",
    "import time\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5694514",
   "metadata": {},
   "source": [
    "# Defines functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fae8bda9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_np(x):\n",
    "  return x.detach().cpu().numpy()\n",
    "\n",
    "def to_torch(x):\n",
    "  return torch.tensor(x).float()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3716cc4b",
   "metadata": {},
   "source": [
    "# Loads the Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fd11d60f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luisy\\AppData\\Local\\Temp\\ipykernel_9788\\2149254349.py:8: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  trainingData = torch.tensor(normalizedData).float()\n"
     ]
    }
   ],
   "source": [
    "def preprocessData():\n",
    "  df = pd.read_excel('./data/MaterialDatabase_FULL2.xlsx')# # \n",
    "  dataIdentifier = {'name': df[df.columns[0]], 'className':df[df.columns[1]], 'classID':df[df.columns[2]]} # name of the material and type\n",
    "  trainInfo = np.log10(df[df.columns[3:]].to_numpy())\n",
    "  dataScaleMax = torch.tensor(np.max(trainInfo, axis = 0))\n",
    "  dataScaleMin = torch.tensor(np.min(trainInfo, axis = 0))\n",
    "  normalizedData = (torch.tensor(trainInfo) - dataScaleMin)/(dataScaleMax - dataScaleMin)\n",
    "  trainingData = torch.tensor(normalizedData).float()\n",
    "  dataInfo = {'youngsModulus':{'idx':0,'scaleMin':dataScaleMin[0], 'scaleMax':dataScaleMax[0]},\\\n",
    "              'costPerKg':{'idx':1,'scaleMin':dataScaleMin[1], 'scaleMax':dataScaleMax[1]},\\\n",
    "              'massDensity':{'idx':2,'scaleMin':dataScaleMin[2], 'scaleMax':dataScaleMax[2]},\\\n",
    "              'yieldStrength':{'idx':3,'scaleMin':dataScaleMin[3], 'scaleMax':dataScaleMax[3]},\\\n",
    "              'co2Kg':{'idx':4,'scaleMin':dataScaleMin[4], 'scaleMax':dataScaleMax[4]},\\\n",
    "              'energyKg':{'idx':5,'scaleMin':dataScaleMin[5], 'scaleMax':dataScaleMax[5]},\\\n",
    "              'waterKg':{'idx':6,'scaleMin':dataScaleMin[6], 'scaleMax':dataScaleMax[6]}}\n",
    "\n",
    "  return trainingData, dataInfo, dataIdentifier, trainInfo, df\n",
    "trainingData, dataInfo, dataIdentifier, trainInfo, df = preprocessData()\n",
    "numMaterialsInTrainingData, numFeatures = trainingData.shape\n",
    "props = df.columns[3:].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae194f0",
   "metadata": {},
   "source": [
    "# Trains the VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "688201b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter 0 reconLoss 4.92E+01 klLoss 5.75E-03 loss 4.93E+01\n",
      "Iter 500 reconLoss 1.48E+00 klLoss 6.09E-02 loss 1.55E+00\n",
      "Iter 1000 reconLoss 9.54E-01 klLoss 6.17E-02 loss 1.02E+00\n",
      "Iter 1500 reconLoss 6.27E-01 klLoss 6.42E-02 loss 6.91E-01\n",
      "Iter 2000 reconLoss 5.39E-01 klLoss 6.42E-02 loss 6.03E-01\n",
      "Iter 2500 reconLoss 5.00E-01 klLoss 6.37E-02 loss 5.63E-01\n",
      "Iter 3000 reconLoss 4.46E-01 klLoss 6.38E-02 loss 5.10E-01\n",
      "Iter 3500 reconLoss 4.20E-01 klLoss 6.40E-02 loss 4.84E-01\n",
      "Iter 4000 reconLoss 3.89E-01 klLoss 6.46E-02 loss 4.53E-01\n",
      "Iter 4500 reconLoss 3.21E-01 klLoss 6.46E-02 loss 3.85E-01\n",
      "Iter 5000 reconLoss 2.81E-01 klLoss 6.50E-02 loss 3.46E-01\n",
      "Iter 5500 reconLoss 2.42E-01 klLoss 6.58E-02 loss 3.08E-01\n",
      "Iter 6000 reconLoss 2.19E-01 klLoss 6.60E-02 loss 2.85E-01\n",
      "Iter 6500 reconLoss 1.78E-01 klLoss 6.70E-02 loss 2.45E-01\n",
      "Iter 7000 reconLoss 1.66E-01 klLoss 6.62E-02 loss 2.32E-01\n",
      "Iter 7500 reconLoss 1.57E-01 klLoss 6.65E-02 loss 2.23E-01\n",
      "Iter 8000 reconLoss 1.39E-01 klLoss 6.64E-02 loss 2.06E-01\n",
      "Iter 8500 reconLoss 1.33E-01 klLoss 6.59E-02 loss 1.99E-01\n",
      "Iter 9000 reconLoss 1.15E-01 klLoss 6.59E-02 loss 1.81E-01\n",
      "Iter 9500 reconLoss 1.12E-01 klLoss 6.51E-02 loss 1.77E-01\n",
      "Iter 10000 reconLoss 1.01E-01 klLoss 6.44E-02 loss 1.66E-01\n",
      "Iter 10500 reconLoss 1.08E-01 klLoss 6.39E-02 loss 1.72E-01\n",
      "Iter 11000 reconLoss 1.02E-01 klLoss 6.40E-02 loss 1.66E-01\n",
      "Iter 11500 reconLoss 8.52E-02 klLoss 6.43E-02 loss 1.49E-01\n",
      "Iter 12000 reconLoss 8.62E-02 klLoss 6.23E-02 loss 1.48E-01\n",
      "Iter 12500 reconLoss 8.23E-02 klLoss 6.21E-02 loss 1.44E-01\n",
      "Iter 13000 reconLoss 9.36E-02 klLoss 6.27E-02 loss 1.56E-01\n",
      "Iter 13500 reconLoss 8.22E-02 klLoss 6.17E-02 loss 1.44E-01\n",
      "Iter 14000 reconLoss 6.64E-02 klLoss 6.16E-02 loss 1.28E-01\n",
      "Iter 14500 reconLoss 6.46E-02 klLoss 6.11E-02 loss 1.26E-01\n",
      "Iter 15000 reconLoss 7.00E-02 klLoss 5.98E-02 loss 1.30E-01\n",
      "Iter 15500 reconLoss 6.19E-02 klLoss 5.93E-02 loss 1.21E-01\n",
      "Iter 16000 reconLoss 6.29E-02 klLoss 5.92E-02 loss 1.22E-01\n",
      "Iter 16500 reconLoss 5.94E-02 klLoss 5.95E-02 loss 1.19E-01\n",
      "Iter 17000 reconLoss 6.65E-02 klLoss 5.83E-02 loss 1.25E-01\n",
      "Iter 17500 reconLoss 6.24E-02 klLoss 5.95E-02 loss 1.22E-01\n",
      "Iter 18000 reconLoss 5.90E-02 klLoss 5.88E-02 loss 1.18E-01\n",
      "Iter 18500 reconLoss 5.00E-02 klLoss 5.85E-02 loss 1.08E-01\n",
      "Iter 19000 reconLoss 5.68E-02 klLoss 5.74E-02 loss 1.14E-01\n",
      "Iter 19500 reconLoss 4.85E-02 klLoss 5.76E-02 loss 1.06E-01\n",
      "Iter 20000 reconLoss 6.60E-02 klLoss 5.70E-02 loss 1.23E-01\n",
      "Iter 20500 reconLoss 4.95E-02 klLoss 5.69E-02 loss 1.06E-01\n",
      "Iter 21000 reconLoss 4.49E-02 klLoss 5.70E-02 loss 1.02E-01\n",
      "Iter 21500 reconLoss 4.27E-02 klLoss 5.63E-02 loss 9.90E-02\n",
      "Iter 22000 reconLoss 4.58E-02 klLoss 5.65E-02 loss 1.02E-01\n",
      "Iter 22500 reconLoss 5.01E-02 klLoss 5.61E-02 loss 1.06E-01\n",
      "Iter 23000 reconLoss 4.61E-02 klLoss 5.56E-02 loss 1.02E-01\n",
      "Iter 23500 reconLoss 3.78E-02 klLoss 5.57E-02 loss 9.35E-02\n",
      "Iter 24000 reconLoss 4.88E-02 klLoss 5.50E-02 loss 1.04E-01\n",
      "Iter 24500 reconLoss 3.85E-02 klLoss 5.50E-02 loss 9.35E-02\n",
      "Iter 25000 reconLoss 4.41E-02 klLoss 5.55E-02 loss 9.95E-02\n",
      "Iter 25500 reconLoss 4.15E-02 klLoss 5.46E-02 loss 9.61E-02\n",
      "Iter 26000 reconLoss 3.94E-02 klLoss 5.59E-02 loss 9.54E-02\n",
      "Iter 26500 reconLoss 4.27E-02 klLoss 5.45E-02 loss 9.71E-02\n",
      "Iter 27000 reconLoss 3.72E-02 klLoss 5.46E-02 loss 9.18E-02\n",
      "Iter 27500 reconLoss 4.04E-02 klLoss 5.44E-02 loss 9.48E-02\n",
      "Iter 28000 reconLoss 4.01E-02 klLoss 5.46E-02 loss 9.47E-02\n",
      "Iter 28500 reconLoss 3.55E-02 klLoss 5.43E-02 loss 8.98E-02\n",
      "Iter 29000 reconLoss 3.60E-02 klLoss 5.39E-02 loss 8.99E-02\n",
      "Iter 29500 reconLoss 4.85E-02 klLoss 5.37E-02 loss 1.02E-01\n",
      "Iter 30000 reconLoss 4.08E-02 klLoss 5.42E-02 loss 9.50E-02\n",
      "Iter 30500 reconLoss 4.11E-02 klLoss 5.33E-02 loss 9.44E-02\n",
      "Iter 31000 reconLoss 3.42E-02 klLoss 5.35E-02 loss 8.77E-02\n",
      "Iter 31500 reconLoss 3.55E-02 klLoss 5.36E-02 loss 8.91E-02\n",
      "Iter 32000 reconLoss 3.24E-02 klLoss 5.31E-02 loss 8.55E-02\n",
      "Iter 32500 reconLoss 3.75E-02 klLoss 5.37E-02 loss 9.12E-02\n",
      "Iter 33000 reconLoss 3.48E-02 klLoss 5.29E-02 loss 8.77E-02\n",
      "Iter 33500 reconLoss 4.13E-02 klLoss 5.30E-02 loss 9.42E-02\n",
      "Iter 34000 reconLoss 3.70E-02 klLoss 5.25E-02 loss 8.95E-02\n",
      "Iter 34500 reconLoss 3.02E-02 klLoss 5.25E-02 loss 8.26E-02\n",
      "Iter 35000 reconLoss 3.31E-02 klLoss 5.36E-02 loss 8.67E-02\n",
      "Iter 35500 reconLoss 4.12E-02 klLoss 5.24E-02 loss 9.37E-02\n",
      "Iter 36000 reconLoss 3.66E-02 klLoss 5.29E-02 loss 8.96E-02\n",
      "Iter 36500 reconLoss 4.42E-02 klLoss 5.28E-02 loss 9.70E-02\n",
      "Iter 37000 reconLoss 3.23E-02 klLoss 5.24E-02 loss 8.46E-02\n",
      "Iter 37500 reconLoss 3.61E-02 klLoss 5.26E-02 loss 8.86E-02\n",
      "Iter 38000 reconLoss 3.20E-02 klLoss 5.25E-02 loss 8.44E-02\n",
      "Iter 38500 reconLoss 3.23E-02 klLoss 5.23E-02 loss 8.46E-02\n",
      "Iter 39000 reconLoss 3.10E-02 klLoss 5.21E-02 loss 8.31E-02\n",
      "Iter 39500 reconLoss 2.86E-02 klLoss 5.21E-02 loss 8.07E-02\n",
      "Iter 40000 reconLoss 3.23E-02 klLoss 5.18E-02 loss 8.41E-02\n",
      "Iter 40500 reconLoss 3.01E-02 klLoss 5.22E-02 loss 8.24E-02\n",
      "Iter 41000 reconLoss 3.02E-02 klLoss 5.23E-02 loss 8.24E-02\n",
      "Iter 41500 reconLoss 3.09E-02 klLoss 5.15E-02 loss 8.24E-02\n",
      "Iter 42000 reconLoss 2.63E-02 klLoss 5.18E-02 loss 7.82E-02\n",
      "Iter 42500 reconLoss 3.15E-02 klLoss 5.16E-02 loss 8.31E-02\n",
      "Iter 43000 reconLoss 3.15E-02 klLoss 5.13E-02 loss 8.29E-02\n",
      "Iter 43500 reconLoss 2.55E-02 klLoss 5.15E-02 loss 7.70E-02\n",
      "Iter 44000 reconLoss 2.70E-02 klLoss 5.13E-02 loss 7.84E-02\n",
      "Iter 44500 reconLoss 2.90E-02 klLoss 5.10E-02 loss 8.00E-02\n",
      "Iter 45000 reconLoss 2.55E-02 klLoss 5.10E-02 loss 7.65E-02\n",
      "Iter 45500 reconLoss 2.93E-02 klLoss 5.06E-02 loss 7.99E-02\n",
      "Iter 46000 reconLoss 3.16E-02 klLoss 5.11E-02 loss 8.28E-02\n",
      "Iter 46500 reconLoss 2.54E-02 klLoss 5.10E-02 loss 7.64E-02\n",
      "Iter 47000 reconLoss 2.91E-02 klLoss 5.14E-02 loss 8.05E-02\n",
      "Iter 47500 reconLoss 2.64E-02 klLoss 5.10E-02 loss 7.74E-02\n",
      "Iter 48000 reconLoss 3.10E-02 klLoss 5.10E-02 loss 8.19E-02\n",
      "Iter 48500 reconLoss 2.56E-02 klLoss 5.10E-02 loss 7.66E-02\n",
      "Iter 49000 reconLoss 3.35E-02 klLoss 5.15E-02 loss 8.50E-02\n",
      "Iter 49500 reconLoss 2.35E-02 klLoss 5.13E-02 loss 7.48E-02\n",
      "training time : 167.47 \n"
     ]
    }
   ],
   "source": [
    "latentDim, hiddenDim = 2, 250\n",
    "numEpochs = 50000\n",
    "epsilon=4.2e-2\n",
    "epsilon=2e-2\n",
    "numEpochs = 50000\n",
    "klFactor = 5e-5\n",
    "learningRate = 2e-3\n",
    "savedNet = './data/vaeNet.nt'\n",
    "vaeSettings = {'encoder':{'inputDim':numFeatures, 'hiddenDim':hiddenDim,\\\n",
    "                                          'latentDim':latentDim},\\\n",
    "               'decoder':{'latentDim':latentDim, 'hiddenDim':hiddenDim,\\\n",
    "                                          'outputDim':numFeatures}}\n",
    "\n",
    "materialEncoder = MaterialEncoder(trainingData, dataInfo, dataIdentifier, vaeSettings)\n",
    "\n",
    "start = time.perf_counter()\n",
    "convgHistory = materialEncoder.trainAutoencoder(numEpochs, klFactor, savedNet, learningRate, epsilon)\n",
    "print('training time : {:.2F} '.format(time.perf_counter() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70968e2b",
   "metadata": {},
   "source": [
    "# Import the Truss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c84c26b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from examples import getExample\n",
    "from truss3DFE import Truss3DFE\n",
    "\n",
    "#Loads the example truss\n",
    "exampleName, nodeXY, connectivity, bc = getExample(19)\n",
    "#Changes the force\n",
    "bc['forces'] = {'nodes':np.array([8]), 'fx':1.E3*torch.tensor([0.]), 'fy':1E2*torch.tensor([200.]), 'fz':1E0*torch.tensor([0.])}\n",
    "\n",
    "\n",
    "n_bars=len(connectivity)\n",
    "\n",
    "\n",
    "\n",
    "#Loads the truss in truss\n",
    "truss = Truss3DFE(nodeXY, connectivity, bc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f45b989",
   "metadata": {},
   "source": [
    "# Gets the z coordinates of each material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3a0372f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "vae = materialEncoder.vaeNet\n",
    "#In decoded there are the decoded properties of each material\n",
    "#decoded = vae.decoder(vae.encoder.z)\n",
    "#In z_np are the coordinates of each real material in the latent space\n",
    "z_np = to_np(vae.encoder.z)\n",
    "#I get the minimum and maximum z to restrict my boundaries of latent space\n",
    "z0min, z1min = 0.95*np.min(z_np, axis=0)\n",
    "z0max, z1max = 1.05*np.max(z_np, axis=0)\n",
    "\n",
    "nprops=len(props)\n",
    "max_scale = np.empty(nprops)\n",
    "min_scale = np.empty(nprops)\n",
    "\n",
    "#Gets minimum and maximum of each property\n",
    "for p in props:\n",
    "    idx = materialEncoder.dataInfo[p]['idx']\n",
    "    max_scale[idx] = materialEncoder.dataInfo[p]['scaleMax']\n",
    "    min_scale[idx] = materialEncoder.dataInfo[p]['scaleMin']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "849b551f",
   "metadata": {},
   "source": [
    "# Defines objective and constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "00ba75a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: Two or more entries have isOn=True\n"
     ]
    }
   ],
   "source": [
    "#Defines constraints and objectives\n",
    "constraints = {'massCons': {'isOn':False, 'maxMass':5}, \\\n",
    "               'costCons': {'isOn':False, 'maxCost':5},\\\n",
    "               'tensionCons': {'isOn':True, 'FOS':4.},\\\n",
    "               'complianceCons': {'isOn':False, 'maxJ':35},\\\n",
    "               'compressionCons': {'isOn':True, 'FOS':4.},\\\n",
    "               'co2Cons': {'isOn':False, 'maxco2':30},\\\n",
    "               'energyCons': {'isOn':False, 'maxenergy' : 1e10},\\\n",
    "               'waterCons': {'isOn':False, 'maxwater':4e3},\\\n",
    "               'confidenceCons': {'isOn':True, 'minconf':93}}\n",
    "\n",
    "\n",
    "objectives = {'cost': {'isOn':False},\\\n",
    "              'mass': {'isOn':False}, \\\n",
    "              'co2': {'isOn':True},\\\n",
    "              'water': {'isOn':True},\\\n",
    "              'energy': {'isOn':True},\\\n",
    "              'compliance': {'isOn': False}}\n",
    "               \n",
    "# count the number of entries with 'isOn' set to True\n",
    "no = sum(1 for v in objectives.values() if v['isOn'])\n",
    "nc = sum(1 for v in constraints.values() if v['isOn'])\n",
    "\n",
    "# print an error message in red if there are 3 or more entries with 'isOn' set to True\n",
    "if no >= 3:\n",
    "    print('Error: Two or more entries have isOn=True')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4773dfa4",
   "metadata": {},
   "source": [
    "# Defines the problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aae90b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pymoo.core.problem import ElementwiseProblem\n",
    "\n",
    "class MyProblem(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self, truss, z_np, constraints, objectives, no, nc, n_bars):\n",
    "        super().__init__(n_var=n_bars+2,\n",
    "                         n_obj=no,\n",
    "                         n_ieq_constr=nc,\n",
    "                         xl = np.array([z0min, z1min] + [1e-7] * (n_bars)),\n",
    "                         xu = np.array([z0max, z1max] + [1e-3] * (n_bars)))\n",
    "\n",
    "        \n",
    "\n",
    "        self.truss=truss\n",
    "        self.z_np=z_np\n",
    "        self.constraints=constraints\n",
    "        self.objectives=objectives\n",
    "\n",
    "\n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "\n",
    "        Z = torch.zeros((1, vaeSettings['encoder']['latentDim']))\n",
    "        Z[:,0]=x[0]\n",
    "        Z[:,1]=x[1]\n",
    "\n",
    "        \n",
    "        matprop=np.empty(7)\n",
    "        #Decodes the properties of Z\n",
    "        decoded = vae.decoder(Z)\n",
    "        #Scales properties\n",
    "        for i in range(0,nprops): \n",
    "            matprop[i] = 10.**(min_scale[i] + decoded[:,i]*(max_scale[i] - min_scale[i]))\n",
    "        \n",
    "        x_tensor = torch.from_numpy(x[2:])\n",
    "        area = x_tensor * torch.ones((connectivity.shape[0]))   \n",
    "        u, _, _, _,_, internalForce = truss.solveFE(E = torch.tensor([matprop[0]]).float(), A=area)\n",
    "        compliance = truss.computeCompliance(u)\n",
    "        volume = truss.getVolume(area)\n",
    "        mass = matprop[2]*volume\n",
    "        cost = mass*matprop[1]\n",
    "        co2 = mass*matprop[4]\n",
    "        energy = mass*matprop[5]        \n",
    "        water = mass*matprop[6]\n",
    "\n",
    "\n",
    "        \n",
    "        con_values = []\n",
    "        \n",
    "        if(constraints['massCons']['isOn']):\n",
    "            constraints['massCons']['value'] = (mass/constraints['massCons']['maxMass']) - 1.\n",
    "            con_values.append(constraints['massCons']['value'])\n",
    "\n",
    "        if(constraints['costCons']['isOn']):\n",
    "            constraints['costCons']['value'] = (cost/constraints['costCons']['maxCost']) - 1.\n",
    "            con_values.append(constraints['costCons']['value'])\n",
    "\n",
    "        if(constraints['tensionCons']['isOn']):\n",
    "            tensileForce = 0.001+torch.relu(internalForce)\n",
    "            tensileStress = tensileForce/area\n",
    "            maxTensileStressExperienced = torch.pow(torch.sum(tensileStress**6), 1./6)*constraints['tensionCons']['FOS']\n",
    "            constraints['tensionCons']['value'] = (maxTensileStressExperienced/matprop[3]) - 1.\n",
    "            con_values.append(constraints['tensionCons']['value'])\n",
    "\n",
    "        if(constraints['complianceCons']['isOn']):\n",
    "            constraints['complianceCons']['value'] = (compliance/constraints['complianceCons']['maxJ']) - 1.\n",
    "            con_values.append(constraints['complianceCons']['value'])\n",
    "\n",
    "        if(constraints['compressionCons']['isOn']):\n",
    "            compressiveForce = 0.001+torch.relu(-internalForce)\n",
    "            k = 4. # used in geom factor... see buckling theory\n",
    "            geomFactor = k*(self.truss.barLength/(np.pi*area))**2\n",
    "            compressiveStress = compressiveForce*geomFactor\n",
    "            maxCompressiveStressExperienced = torch.pow(torch.sum(compressiveStress**6), 1./6)*constraints['compressionCons']['FOS']\n",
    "            constraints['compressionCons']['value'] = (maxCompressiveStressExperienced/matprop[0]) - 1.\n",
    "            con_values.append(constraints['compressionCons']['value'])\n",
    "\n",
    "        if(constraints['co2Cons']['isOn']):\n",
    "            constraints['co2Cons']['value'] = (co2/constraints['co2Cons']['maxco2']) - 1.\n",
    "            con_values.append(constraints['co2Cons']['value'])\n",
    "\n",
    "        if(constraints['energyCons']['isOn']):\n",
    "            constraints['energyCons']['value'] = (energy/constraints['energyCons']['maxenergy']) - 1.\n",
    "            con_values.append(constraints['energyCons']['value'])\n",
    "\n",
    "        if(constraints['waterCons']['isOn']):\n",
    "            constraints['waterCons']['value'] = (water/constraints['waterCons']['maxwater']) - 1.\n",
    "            con_values.append(constraints['waterCons']['value'])\n",
    "            \n",
    "        if(constraints['confidenceCons']['isOn']):\n",
    "            zData=x[0:2]\n",
    "            dist= np.linalg.norm(zData- z_np, axis = 1)\n",
    "            maxDist = np.max(dist)\n",
    "            distOrder = np.argsort(dist)\n",
    "            confidence_level = 100.*(1.- (dist[distOrder[0]]/maxDist))\n",
    "            constraints['confidenceCons']['value'] = 1 - (confidence_level/constraints['confidenceCons']['minconf'])\n",
    "            con_values.append(constraints['confidenceCons']['value'])        \n",
    "        \n",
    "        obj_values = []\n",
    "        \n",
    "        if(objectives['cost']['isOn']):\n",
    "            obj_values.append(cost)\n",
    "            \n",
    "        if(objectives['mass']['isOn']):\n",
    "            obj_values.append(mass)\n",
    "            \n",
    "        if(objectives['co2']['isOn']):\n",
    "            obj_values.append(co2)\n",
    "            \n",
    "        if(objectives['water']['isOn']):\n",
    "            obj_values.append(water)\n",
    "            \n",
    "        if(objectives['energy']['isOn']):\n",
    "            obj_values.append(energy)\n",
    "            \n",
    "        if(objectives['compliance']['isOn']):\n",
    "            obj_values.append(compliance)\n",
    "            \n",
    "\n",
    "\n",
    "        out[\"F\"] = np.array(obj_values)\n",
    "        out[\"G\"] = np.array(con_values)        \n",
    "        \n",
    "\n",
    "problem = MyProblem(truss, z_np, constraints, objectives, no, nc, n_bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd20368",
   "metadata": {},
   "source": [
    "# Defines the problem settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "abbcadb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
    "from pymoo.operators.crossover.sbx import SBX\n",
    "from pymoo.operators.mutation.pm import PM\n",
    "from pymoo.operators.sampling.rnd import FloatRandomSampling\n",
    "\n",
    "#pop_size: Size of initial population\n",
    "#n_offsprings: The number of offspring generated in each generation of the algorithm. \n",
    "#sampling: The sampling method used to generate new individuals in the offspring population.\n",
    "    #In this case, FloatRandomSampling is used, which generates random floating-point values\n",
    "    #in the search space to create new individuals.\n",
    "#crossover: The crossover operator used to create new individuals in the offspring population.\n",
    "#mutation: The mutation operator used to create new individuals in the offspring population.\n",
    "#eliminate_duplicates: A flag to indicate whether duplicate individuals should be eliminated from the population or not.\n",
    "algorithm = NSGA2(\n",
    "    pop_size=40,\n",
    "    n_offsprings=10,\n",
    "    sampling=FloatRandomSampling(),\n",
    "    crossover=SBX(prob=0.9, eta=15),\n",
    "    mutation=PM(eta=20),\n",
    "    eliminate_duplicates=True\n",
    ")\n",
    "from pymoo.termination import get_termination\n",
    "from pymoo.termination.default import DefaultMultiObjectiveTermination\n",
    "\n",
    "#termination = get_termination(\"n_gen\", 150)\n",
    "\n",
    "termination = DefaultMultiObjectiveTermination(\n",
    "    xtol=1e-2,\n",
    "    cvtol=1e-3,\n",
    "    ftol=0.1,\n",
    "    period=10,\n",
    "    n_max_gen=1500,\n",
    "    n_max_evals=50000\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79017bd0",
   "metadata": {},
   "source": [
    "# Runs the solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "58b8803f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "n_gen  |  n_eval  | n_nds  |     cv_min    |     cv_avg    |      eps      |   indicator  \n",
      "==========================================================================================\n",
      "     1 |       40 |      1 |  0.2556228351 |  3.824581E+02 |             - |             -\n",
      "     2 |       50 |      1 |  0.0839596387 |  3.010366E+01 |             - |             -\n",
      "     3 |       60 |      1 |  0.0839596387 |  8.4349477045 |             - |             -\n",
      "     4 |       70 |      1 |  0.0839596387 |  4.3318349263 |             - |             -\n",
      "     5 |       80 |      1 |  0.0839596387 |  2.6378035495 |             - |             -\n",
      "     6 |       90 |      1 |  0.000000E+00 |  1.8689129771 |             - |             -\n",
      "     7 |      100 |      1 |  0.000000E+00 |  1.3550173352 |  0.000000E+00 |             f\n",
      "     8 |      110 |      1 |  0.000000E+00 |  0.9084488661 |  0.000000E+00 |             f\n",
      "     9 |      120 |      1 |  0.000000E+00 |  0.5176302737 |  0.000000E+00 |             f\n",
      "    10 |      130 |      1 |  0.000000E+00 |  0.1996120364 |  0.000000E+00 |             f\n",
      "    11 |      140 |      1 |  0.000000E+00 |  0.1037231106 |  0.000000E+00 |             f\n",
      "    12 |      150 |      1 |  0.000000E+00 |  0.0668196902 |  8.359900E+08 |         ideal\n",
      "    13 |      160 |      2 |  0.000000E+00 |  0.0407379741 |  1.0000000000 |         ideal\n",
      "    14 |      170 |      2 |  0.000000E+00 |  0.0213016387 |  0.000000E+00 |             f\n",
      "    15 |      180 |      2 |  0.000000E+00 |  0.0077767926 |  1.0000000000 |         ideal\n",
      "    16 |      190 |      3 |  0.000000E+00 |  0.0020748900 |  0.1093388519 |         ideal\n",
      "    17 |      200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    18 |      210 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    19 |      220 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0795202370 |         ideal\n",
      "    20 |      230 |      2 |  0.000000E+00 |  0.000000E+00 |  0.6555579198 |         ideal\n",
      "    21 |      240 |      2 |  0.000000E+00 |  0.000000E+00 |  0.5892322267 |         ideal\n",
      "    22 |      250 |      2 |  0.000000E+00 |  0.000000E+00 |  0.3775850565 |         ideal\n",
      "    23 |      260 |      1 |  0.000000E+00 |  0.000000E+00 |  4.066506E+06 |         ideal\n",
      "    24 |      270 |      2 |  0.000000E+00 |  0.000000E+00 |  1.0000000000 |         ideal\n",
      "    25 |      280 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    26 |      290 |      2 |  0.000000E+00 |  0.000000E+00 |  1.0000000000 |         ideal\n",
      "    27 |      300 |      2 |  0.000000E+00 |  0.000000E+00 |  2.7435150293 |         ideal\n",
      "    28 |      310 |      1 |  0.000000E+00 |  0.000000E+00 |  1.784370E+06 |         ideal\n",
      "    29 |      320 |      3 |  0.000000E+00 |  0.000000E+00 |  1.0000000000 |         ideal\n",
      "    30 |      330 |      3 |  0.000000E+00 |  0.000000E+00 |  2.4242628492 |         nadir\n",
      "    31 |      340 |      3 |  0.000000E+00 |  0.000000E+00 |  0.9873297646 |         ideal\n",
      "    32 |      350 |      4 |  0.000000E+00 |  0.000000E+00 |  0.3659926171 |         ideal\n",
      "    33 |      360 |      4 |  0.000000E+00 |  0.000000E+00 |  0.4165705529 |         ideal\n",
      "    34 |      370 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    35 |      380 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    36 |      390 |      5 |  0.000000E+00 |  0.000000E+00 |  0.2958316413 |         ideal\n",
      "    37 |      400 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0694149424 |         ideal\n",
      "    38 |      410 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0166930588 |         ideal\n",
      "    39 |      420 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0420911424 |         ideal\n",
      "    40 |      430 |      5 |  0.000000E+00 |  0.000000E+00 |  0.1280314817 |         ideal\n",
      "    41 |      440 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0122136514 |         ideal\n",
      "    42 |      450 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    43 |      460 |      4 |  0.000000E+00 |  0.000000E+00 |  0.1837680553 |         ideal\n",
      "    44 |      470 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0660024335 |         ideal\n",
      "    45 |      480 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0194734227 |         ideal\n",
      "    46 |      490 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0358927644 |         ideal\n",
      "    47 |      500 |      5 |  0.000000E+00 |  0.000000E+00 |  0.1439020348 |         ideal\n",
      "    48 |      510 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0036271136 |         ideal\n",
      "    49 |      520 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0092500676 |         ideal\n",
      "    50 |      530 |      6 |  0.000000E+00 |  0.000000E+00 |  0.3086133952 |         nadir\n",
      "    51 |      540 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0087973097 |         ideal\n",
      "    52 |      550 |      7 |  0.000000E+00 |  0.000000E+00 |  0.1493365826 |         nadir\n",
      "    53 |      560 |      9 |  0.000000E+00 |  0.000000E+00 |  0.1594715912 |         ideal\n",
      "    54 |      570 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0204477290 |         nadir\n",
      "    55 |      580 |      8 |  0.000000E+00 |  0.000000E+00 |  0.1381075214 |         ideal\n",
      "    56 |      590 |      6 |  0.000000E+00 |  0.000000E+00 |  0.2560807792 |         ideal\n",
      "    57 |      600 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0184730836 |         ideal\n",
      "    58 |      610 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0058985477 |         ideal\n",
      "    59 |      620 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0644715066 |         ideal\n",
      "    60 |      630 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0704287406 |         ideal\n",
      "    61 |      640 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0197407866 |         ideal\n",
      "    62 |      650 |     10 |  0.000000E+00 |  0.000000E+00 |  0.1234620322 |         ideal\n",
      "    63 |      660 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0931765570 |         ideal\n",
      "    64 |      670 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0207258848 |         ideal\n",
      "    65 |      680 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0203060658 |         ideal\n",
      "    66 |      690 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0053662176 |         ideal\n",
      "    67 |      700 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0095501882 |             f\n",
      "    68 |      710 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0091053713 |         ideal\n",
      "    69 |      720 |     12 |  0.000000E+00 |  0.000000E+00 |  0.1556377901 |         ideal\n",
      "    70 |      730 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0062705242 |         ideal\n",
      "    71 |      740 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0347953494 |             f\n",
      "    72 |      750 |     11 |  0.000000E+00 |  0.000000E+00 |  0.2961173837 |         ideal\n",
      "    73 |      760 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0796883523 |         ideal\n",
      "    74 |      770 |     10 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    75 |      780 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0130845075 |             f\n",
      "    76 |      790 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0725379028 |         ideal\n",
      "    77 |      800 |     12 |  0.000000E+00 |  0.000000E+00 |  0.3312425622 |         nadir\n",
      "    78 |      810 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0606529574 |         ideal\n",
      "    79 |      820 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0702081301 |         ideal\n",
      "    80 |      830 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0603107478 |         ideal\n",
      "    81 |      840 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0164798471 |         ideal\n",
      "    82 |      850 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0865177984 |         ideal\n",
      "    83 |      860 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0138562581 |             f\n",
      "    84 |      870 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0570703600 |         ideal\n",
      "    85 |      880 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0893929243 |         ideal\n",
      "    86 |      890 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0598779896 |         ideal\n",
      "    87 |      900 |     11 |  0.000000E+00 |  0.000000E+00 |  0.1839541078 |         nadir\n",
      "    88 |      910 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0452041430 |             f\n",
      "    89 |      920 |     14 |  0.000000E+00 |  0.000000E+00 |  0.0039895655 |             f\n",
      "    90 |      930 |     11 |  0.000000E+00 |  0.000000E+00 |  0.1056450221 |         ideal\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    91 |      940 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0054724629 |             f\n",
      "    92 |      950 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0156025861 |         ideal\n",
      "    93 |      960 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0331356230 |         ideal\n",
      "    94 |      970 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0140939175 |             f\n",
      "    95 |      980 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0036820887 |         ideal\n",
      "    96 |      990 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0344708993 |         ideal\n",
      "    97 |     1000 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0119019252 |             f\n",
      "    98 |     1010 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0440516255 |         ideal\n",
      "    99 |     1020 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0149257151 |         ideal\n",
      "   100 |     1030 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0435132975 |         nadir\n",
      "   101 |     1040 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0524339331 |         ideal\n",
      "   102 |     1050 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0016827766 |             f\n",
      "   103 |     1060 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0889317826 |         ideal\n",
      "   104 |     1070 |     10 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   105 |     1080 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0195393287 |             f\n",
      "   106 |     1090 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0025358647 |         ideal\n",
      "   107 |     1100 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0360832681 |         ideal\n",
      "   108 |     1110 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0136618327 |             f\n",
      "   109 |     1120 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0370707010 |             f\n",
      "   110 |     1130 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0451737477 |         ideal\n",
      "   111 |     1140 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0859506758 |         ideal\n",
      "   112 |     1150 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0116973406 |             f\n",
      "   113 |     1160 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0170681914 |         nadir\n",
      "   114 |     1170 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0065613104 |             f\n",
      "   115 |     1180 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0547225431 |         ideal\n",
      "   116 |     1190 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0181678895 |             f\n",
      "   117 |     1200 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0121758975 |         ideal\n",
      "   118 |     1210 |      6 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   119 |     1220 |      7 |  0.000000E+00 |  0.000000E+00 |  0.1887381441 |         ideal\n",
      "   120 |     1230 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0007077810 |             f\n",
      "   121 |     1240 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0363438455 |             f\n",
      "   122 |     1250 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0055969397 |             f\n",
      "   123 |     1260 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0222820951 |             f\n",
      "   124 |     1270 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0094204315 |         ideal\n",
      "   125 |     1280 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0060985706 |             f\n",
      "   126 |     1290 |     11 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   127 |     1300 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0213271281 |             f\n",
      "   128 |     1310 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0174225170 |         ideal\n",
      "   129 |     1320 |     10 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   130 |     1330 |     10 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   131 |     1340 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0137484042 |             f\n",
      "   132 |     1350 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0191383819 |         ideal\n",
      "   133 |     1360 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0211965660 |             f\n",
      "   134 |     1370 |     15 |  0.000000E+00 |  0.000000E+00 |  0.1511151053 |         ideal\n",
      "   135 |     1380 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0238143195 |         nadir\n",
      "   136 |     1390 |     14 |  0.000000E+00 |  0.000000E+00 |  0.0432584909 |         ideal\n",
      "   137 |     1400 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0486940326 |         ideal\n",
      "   138 |     1410 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0803155391 |         ideal\n",
      "   139 |     1420 |     12 |  0.000000E+00 |  0.000000E+00 |  0.1433240842 |         nadir\n",
      "   140 |     1430 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0209530975 |         ideal\n",
      "   141 |     1440 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0358064125 |         ideal\n",
      "   142 |     1450 |     10 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   143 |     1460 |      9 |  0.000000E+00 |  0.000000E+00 |  0.2568983398 |         nadir\n",
      "   144 |     1470 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0486099907 |         ideal\n",
      "   145 |     1480 |      9 |  0.000000E+00 |  0.000000E+00 |  0.1408421315 |         ideal\n",
      "   146 |     1490 |      9 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   147 |     1500 |      9 |  0.000000E+00 |  0.000000E+00 |  0.1207243269 |         ideal\n",
      "   148 |     1510 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0938089741 |         nadir\n",
      "   149 |     1520 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0083081771 |             f\n",
      "   150 |     1530 |     11 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   151 |     1540 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0820634654 |         ideal\n",
      "   152 |     1550 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0857040443 |         ideal\n",
      "   153 |     1560 |      9 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   154 |     1570 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0114713106 |         ideal\n",
      "   155 |     1580 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0525563037 |         ideal\n",
      "   156 |     1590 |      8 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   157 |     1600 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0033699722 |             f\n",
      "   158 |     1610 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0383657271 |             f\n",
      "   159 |     1620 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0086103064 |         nadir\n",
      "   160 |     1630 |      8 |  0.000000E+00 |  0.000000E+00 |  0.4299034521 |         nadir\n",
      "   161 |     1640 |      9 |  0.000000E+00 |  0.000000E+00 |  0.2249420818 |         nadir\n",
      "   162 |     1650 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0856000514 |         ideal\n",
      "   163 |     1660 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0332804726 |         ideal\n",
      "   164 |     1670 |     12 |  0.000000E+00 |  0.000000E+00 |  0.1507918680 |         ideal\n",
      "   165 |     1680 |     11 |  0.000000E+00 |  0.000000E+00 |  0.1645993043 |         nadir\n",
      "   166 |     1690 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0052311601 |             f\n",
      "   167 |     1700 |     10 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   168 |     1710 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0019056977 |             f\n",
      "   169 |     1720 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0019056977 |             f\n",
      "   170 |     1730 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0019056977 |             f\n",
      "   171 |     1740 |     14 |  0.000000E+00 |  0.000000E+00 |  0.0260364715 |             f\n",
      "   172 |     1750 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0226331043 |         ideal\n",
      "   173 |     1760 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0096858339 |             f\n",
      "   174 |     1770 |     13 |  0.000000E+00 |  0.000000E+00 |  0.1058469113 |         ideal\n",
      "   175 |     1780 |     13 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   176 |     1790 |     14 |  0.000000E+00 |  0.000000E+00 |  0.0199442572 |             f\n",
      "   177 |     1800 |     14 |  0.000000E+00 |  0.000000E+00 |  0.0179383036 |         ideal\n",
      "   178 |     1810 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0067477547 |         ideal\n",
      "   179 |     1820 |     17 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   180 |     1830 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0088609363 |             f\n",
      "   181 |     1840 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0196105913 |         ideal\n",
      "   182 |     1850 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0076278366 |             f\n",
      "   183 |     1860 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0469115845 |         ideal\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   184 |     1870 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0134364356 |             f\n",
      "   185 |     1880 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0525656042 |         ideal\n",
      "   186 |     1890 |     15 |  0.000000E+00 |  0.000000E+00 |  0.0047879539 |             f\n",
      "   187 |     1900 |     15 |  0.000000E+00 |  0.000000E+00 |  0.0351592646 |         ideal\n",
      "   188 |     1910 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0079814954 |         ideal\n",
      "   189 |     1920 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0315026781 |         ideal\n",
      "   190 |     1930 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0097476717 |             f\n",
      "   191 |     1940 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0948075900 |         ideal\n",
      "   192 |     1950 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0115234756 |             f\n",
      "   193 |     1960 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0175240088 |         nadir\n",
      "   194 |     1970 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0100567906 |             f\n",
      "   195 |     1980 |     21 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   196 |     1990 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0113572386 |             f\n",
      "   197 |     2000 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0024090940 |             f\n",
      "   198 |     2010 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0081101969 |             f\n",
      "   199 |     2020 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0042056635 |             f\n",
      "   200 |     2030 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0256780163 |         ideal\n",
      "   201 |     2040 |     22 |  0.000000E+00 |  0.000000E+00 |  0.1148469461 |         ideal\n",
      "   202 |     2050 |     24 |  0.000000E+00 |  0.000000E+00 |  0.0078997261 |             f\n",
      "   203 |     2060 |     24 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   204 |     2070 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0095173361 |             f\n",
      "   205 |     2080 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0084084792 |         ideal\n",
      "   206 |     2090 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0087095536 |             f\n",
      "   207 |     2100 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0109886935 |             f\n",
      "   208 |     2110 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0036318421 |         ideal\n",
      "   209 |     2120 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0047665749 |             f\n",
      "   210 |     2130 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0222864660 |             f\n",
      "   211 |     2140 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0514108219 |         ideal\n",
      "   212 |     2150 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0007078241 |             f\n",
      "   213 |     2160 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0120310699 |             f\n",
      "   214 |     2170 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0098712343 |             f\n",
      "   215 |     2180 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0085040725 |         ideal\n",
      "   216 |     2190 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0109432417 |         ideal\n",
      "   217 |     2200 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0527204362 |         ideal\n",
      "   218 |     2210 |     22 |  0.000000E+00 |  0.000000E+00 |  0.0183961483 |             f\n",
      "   219 |     2220 |     24 |  0.000000E+00 |  0.000000E+00 |  0.0120050118 |             f\n",
      "   220 |     2230 |     25 |  0.000000E+00 |  0.000000E+00 |  0.0058681043 |             f\n",
      "   221 |     2240 |     28 |  0.000000E+00 |  0.000000E+00 |  0.0040199139 |         nadir\n",
      "   222 |     2250 |     24 |  0.000000E+00 |  0.000000E+00 |  0.0124622930 |             f\n",
      "   223 |     2260 |     25 |  0.000000E+00 |  0.000000E+00 |  0.0531758987 |         ideal\n",
      "   224 |     2270 |     28 |  0.000000E+00 |  0.000000E+00 |  0.0667184764 |         ideal\n",
      "   225 |     2280 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0094842372 |             f\n",
      "   226 |     2290 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0121803655 |             f\n",
      "   227 |     2300 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0048291537 |             f\n",
      "   228 |     2310 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0294682386 |         ideal\n",
      "   229 |     2320 |     14 |  0.000000E+00 |  0.000000E+00 |  0.0228635790 |         ideal\n",
      "   230 |     2330 |     14 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   231 |     2340 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0231470120 |         ideal\n",
      "   232 |     2350 |     15 |  0.000000E+00 |  0.000000E+00 |  0.0345009599 |             f\n",
      "   233 |     2360 |     15 |  0.000000E+00 |  0.000000E+00 |  0.0131230316 |         ideal\n",
      "   234 |     2370 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0194003028 |         nadir\n",
      "   235 |     2380 |     16 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   236 |     2390 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0119826003 |             f\n",
      "   237 |     2400 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0197841207 |         nadir\n",
      "   238 |     2410 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0067551539 |             f\n",
      "   239 |     2420 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0133746164 |             f\n",
      "   240 |     2430 |     18 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   241 |     2440 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0216439504 |         nadir\n",
      "   242 |     2450 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0168194581 |         ideal\n",
      "   243 |     2460 |     16 |  0.000000E+00 |  0.000000E+00 |  0.1384955881 |         nadir\n",
      "   244 |     2470 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0104615963 |         ideal\n",
      "   245 |     2480 |     15 |  0.000000E+00 |  0.000000E+00 |  0.0187857730 |         ideal\n",
      "   246 |     2490 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0851366625 |         ideal\n",
      "   247 |     2500 |     16 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   248 |     2510 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0389360896 |         ideal\n",
      "   249 |     2520 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0092900692 |             f\n",
      "   250 |     2530 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0016731244 |             f\n",
      "   251 |     2540 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0132426211 |             f\n",
      "   252 |     2550 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0021835877 |             f\n",
      "   253 |     2560 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0227133402 |         ideal\n",
      "   254 |     2570 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0253040039 |         nadir\n",
      "   255 |     2580 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0156395834 |         ideal\n",
      "   256 |     2590 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0097140175 |             f\n",
      "   257 |     2600 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0093497015 |             f\n",
      "   258 |     2610 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0068699940 |         ideal\n",
      "   259 |     2620 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0122425863 |             f\n",
      "   260 |     2630 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0004222901 |             f\n",
      "   261 |     2640 |     15 |  0.000000E+00 |  0.000000E+00 |  0.0191582750 |             f\n",
      "   262 |     2650 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0101247589 |             f\n",
      "   263 |     2660 |     14 |  0.000000E+00 |  0.000000E+00 |  0.0073689742 |             f\n",
      "   264 |     2670 |     14 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   265 |     2680 |     14 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   266 |     2690 |     15 |  0.000000E+00 |  0.000000E+00 |  0.1032878906 |         ideal\n",
      "   267 |     2700 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0015433036 |             f\n",
      "   268 |     2710 |     15 |  0.000000E+00 |  0.000000E+00 |  0.0297015655 |         ideal\n",
      "   269 |     2720 |     15 |  0.000000E+00 |  0.000000E+00 |  0.0026630082 |         ideal\n",
      "   270 |     2730 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0226216370 |         ideal\n",
      "   271 |     2740 |     18 |  0.000000E+00 |  0.000000E+00 |  0.1050021039 |         nadir\n",
      "   272 |     2750 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0068213328 |         ideal\n",
      "   273 |     2760 |     16 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   274 |     2770 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0228426903 |         ideal\n",
      "   275 |     2780 |     16 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   276 |     2790 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0075606561 |         nadir\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   277 |     2800 |     18 |  0.000000E+00 |  0.000000E+00 |  0.0089951776 |         nadir\n",
      "   278 |     2810 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0069777995 |             f\n",
      "   279 |     2820 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0538335112 |         ideal\n",
      "   280 |     2830 |     21 |  0.000000E+00 |  0.000000E+00 |  0.2204617490 |         nadir\n",
      "   281 |     2840 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0035658648 |             f\n",
      "   282 |     2850 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0100433308 |             f\n",
      "   283 |     2860 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0029329064 |             f\n",
      "   284 |     2870 |     22 |  0.000000E+00 |  0.000000E+00 |  0.0109291811 |             f\n",
      "   285 |     2880 |     25 |  0.000000E+00 |  0.000000E+00 |  0.0024836934 |             f\n",
      "   286 |     2890 |     26 |  0.000000E+00 |  0.000000E+00 |  0.0236142037 |         nadir\n",
      "   287 |     2900 |     24 |  0.000000E+00 |  0.000000E+00 |  0.0072548158 |             f\n",
      "   288 |     2910 |     23 |  0.000000E+00 |  0.000000E+00 |  0.4065149741 |         nadir\n",
      "   289 |     2920 |     25 |  0.000000E+00 |  0.000000E+00 |  0.0602936455 |         ideal\n",
      "   290 |     2930 |     22 |  0.000000E+00 |  0.000000E+00 |  0.0040290365 |         ideal\n",
      "   291 |     2940 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0061074774 |         ideal\n",
      "   292 |     2950 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0004749104 |             f\n",
      "   293 |     2960 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0004749104 |             f\n",
      "   294 |     2970 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0059158211 |             f\n",
      "   295 |     2980 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0073689174 |             f\n",
      "   296 |     2990 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0108943241 |             f\n",
      "   297 |     3000 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0103110852 |             f\n",
      "   298 |     3010 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0195668216 |         ideal\n",
      "   299 |     3020 |     22 |  0.000000E+00 |  0.000000E+00 |  0.0006564019 |             f\n",
      "   300 |     3030 |     22 |  0.000000E+00 |  0.000000E+00 |  0.0006564019 |             f\n",
      "   301 |     3040 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0030059309 |             f\n",
      "   302 |     3050 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0119562390 |         ideal\n",
      "   303 |     3060 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0517556160 |         ideal\n",
      "   304 |     3070 |     18 |  0.000000E+00 |  0.000000E+00 |  0.1269765724 |         ideal\n",
      "   305 |     3080 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0672564487 |         nadir\n",
      "   306 |     3090 |     16 |  0.000000E+00 |  0.000000E+00 |  0.0003686776 |             f\n",
      "   307 |     3100 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0032162028 |             f\n",
      "   308 |     3110 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0058933134 |             f\n",
      "   309 |     3120 |     19 |  0.000000E+00 |  0.000000E+00 |  0.0018576829 |             f\n",
      "   310 |     3130 |     20 |  0.000000E+00 |  0.000000E+00 |  0.0072982920 |             f\n",
      "   311 |     3140 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0066798625 |             f\n",
      "   312 |     3150 |     21 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   313 |     3160 |     23 |  0.000000E+00 |  0.000000E+00 |  0.0054961243 |             f\n",
      "   314 |     3170 |     23 |  0.000000E+00 |  0.000000E+00 |  0.0053335012 |             f\n",
      "   315 |     3180 |     23 |  0.000000E+00 |  0.000000E+00 |  0.0016057821 |             f\n",
      "   316 |     3190 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0063490416 |             f\n",
      "   317 |     3200 |     21 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "training time : 26.02 \n"
     ]
    }
   ],
   "source": [
    "from pymoo.optimize import minimize\n",
    "start = time.perf_counter()\n",
    "res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "\n",
    "print('training time : {:.2F} '.format(time.perf_counter() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1ee73177",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm', 'AISI 4340 norm']\n"
     ]
    },
    {
     "data": {
      "image/png": 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X3EG/ljs1FeuuvRyCkGCfHDMsLAxhYWH4/PPPsWDBAvD5fJP3T548idjYWHz44Yf43e9+R+drfffdd7j55puxdetWLFq0CM3NzfjjH/8IAHjiiSdAURSWL1+OyMhI7N27FxEREXj77bdx0UUXoaGhAZGRkT45n8kOY4kxMDBMWv796T40tHSavNbQ0omPPt3ns2NyuVxs374d//rXvyASibBw4UI88sgjqKioAADExMQA+K0EFPn7mWeewUMPPYT169djypQpuOSSS/D000/j7bffBgAcOHAAlZWV+OSTT1BYWIisrCy8+OKLEIlE2L17t8/OZ7LDiBgDA8OkZEAqR11zBwwUZfK6gaJQ19zhU9fitddei56eHnz55Ze47LLLcPDgQeTn52P79u02tzl9+jSeeuop2pILCwvD5s2b0dvbC6VSidOnT0OhUCAqKsrkM62trWhubvbZuUx2GHciAwPDpEQqG7b7vkQ2jJgosc+OHxwcjEsuuQSXXHIJHn/8cWzatAlPPPEEfv/731v9vMFgwN/+9jdcc801VvdlMBiQkJCAgwcPWrwvEom8O/hzCEbEGBgYJiVRkRF234928L63mT59Oh1Wz+Px6LJahPz8fNTX1yMzM9Pq9vn5+ejr6wOXyzUJBmGwDyNiDAwMk5LYKDFyp6aioaXTxKXIZrGQPSXFZ1aYVCrFddddh40bN2L27NkIDw/HqVOn8MILL2DlypUAgPT0dPz4449YuHAh+Hw+xGIxHn/8caxYsQIpKSm47rrrwGazUVFRgcrKSvz973/HxRdfjOLiYlx99dV4/vnnkZOTg56eHuzduxdXX301CgsLfXI+kx1mTYyBgWHSsu7ay5E9JcXktewpKVh37eU+O2ZYWBjmz5+Pl19+GYsXL8bMmTPx2GOPYfPmzXj99dcBAP/85z+xf/9+pKSkIC8vDwBw2WWX4euvv8b+/fsxb948LFiwAC+99BLS0tIAnE2Q3rt3LxYvXoyNGzciOzsb119/Pdra2hAXF+ez85nssCjKbFWUgYGBYQIYHx9Ha2srMjIyEBzsWTj8oFQOiWwY0ZERPl0HC3S8+Z1OFhh3IgMDw6QnJkp8XovX+QzjTmRgYGBgmLQwIsbAwMDAMGlh3IkMEwpFUdDpdBgfHweHwwGXywWHwwGbzT4vG/oxMDB4BiNiDBOGwWCAVquFXq+HVqulW6iz2Wyw2WxwuVxG1M5DmNgy73E+fpeMiDH4HIqioNVqMTY2huDgYLBYLFqoKIoCRVHQ6/XQ6/UYHx9nRO08gRTG1Wg0CAkJ8fNozg2USiWAs8nW5wuMiDH4FCJgg4ODqK6uxuLFi0FRFC1ILBYLLBYLbDab/ryxqKnVavp9Ho9HuyDJdgyTFy6XC4FAgMHBQfB4PPoaYHAdiqKgVCoxMDAAkUhEPyCcDzAixuAzjN2HxOpisVi0y8NYzAj2RE2n09HvEzEjlhojapMPFouFhIQEtLa2or293d/DOScglfPPJxgRY/A6xqJjMBisugKtCZg1bImaTqeDVqs1ETViqRH3I0PgExQUhKysLGg0Gn8PZdJDrv/zDUbEGLwKcR+S4qdEwIwtME9gRO3cg81mnzfVJRi8DyNiDF7DYDBAo9FYtb68JWLmOBI14Dch5fF4tLAxosbAcG7AiBiDxxDR0Ol0oCjKqvvQVyJmji1RKy8vR3R0NBISEsBisUzW0xhRY2CYvDAixuARBoMBOp3Own1ozkSJmLXjGrsYORwOHXBCLDVzUSPRjwwMDIEPI2IMbkFRFC0GJEjD3sRvLGL+EggyTuPFb+Pz0Gg09HkQUTOOfmRgYAg8GBFjcBlj9yEAhwJmvB0hUETBGVFjs9kWgSKBMn4GhvMdRsQYXMI498t47ckR/nInGh/f2c85I2rma2qMqDEw+AdGxBicguR+tbW1ISYmBnw+36WJe7JO8saiRkSYRGEaVxNhRI2BwT8wIsbgEOPcr9raWojFYpfzeogl5k9rzNNjE2FiRI2BIXBgRIzBLtZyv9wRg3NxErcmauQ/tVpNV6FgRI2BwXcwIsZgFeI+JNGHRMBIDURXIZO2s+WmvM1EHNM4wIXD4ZiI2sjICGpra5GXl2dRzJip0M/A4D6MiDFYYKt0FOB+gIa/RcwfGIsam83G2NgY/RAwPj5Of4aIGtN2hoHBdRgRYzCBWF+2Cvd6Q8T8RSA0DCTRnOaWmrGomVfoZ0SNgcE2jIgxALDM/fJ25Y1AEDF/Yu28bbkfDQYDI2oMDE7CiBgDnQdlMBgAwG7uF4vFoj/nCv4WsUCY9B2NwZ6oqdVqpus1A4MVGBE7jzFO5LXlPjTHG4EdDM5hXgmF6XrNwGAJI2LnKfaCN+zhaeUNiqLQ39+PhoYGhISEIDIyEmKxGKGhoef0xOvLXmpM12uG8xlGxM5DjEtHueqK8nRNrLGxEf39/Zg6dSp0Oh1kMhlaWlrAZrMhFovp/0JCQrw+8frbCvT2+bjSINTc/cjAcK7AiNh5BJngWlpakJCQgKCgIJcnVnfXxFQqFQBgeHgYxcXF4PF4oCgKaWlpMBgMGBkZgVwup620oKAgE1FjOv86xpkGoX19fUhMTERwcDDTS43hnIARsfMEY/dhXV0dYmNj3bIM3LHE+vr6UFVVBQCYO3cuBAIBPakCZ12ZIpEIIpEIGRkZ0Ov1GB4ehlwuR3d3N2prayEQCGhBE4lECAoKcnnc/sSfvdSISOn1ejQ2NiIqKsokiIdpEMowmWFE7DzAPPfL3eAMwLXADiKYvb29mDlzJioqKpwSEw6Hg8jISERGRgIAdDodhoaGIJPJ0NrairGxMYSFhZmIGpcb+Jeyv4WUwOVyaUuYPNzYK5HFiBpDIBP4dz6D29jK/fIkOMPZbcfGxlBWVgY2m42SkhIIBAJUVla6dVwul4vo6GhER0cDADQaDeRyOeRyORobGzE+Po7w8HBa1CIiIkzaqRDO90Rr86ak1tyPTNdrhskGI2LnKOa5X+YlkNxZ1yL7cTQh9/T0oLq6GikpKcjOzqYnSW/1FAsKCkJcXBzi4uIAAOPj47So1dTUQKfTQSgU0pGP4eHhHh/zXMBRZ22m6zXDZIQRsXMM44mH1Cn0Vukosq0tASStWvr7+zFnzhzExsZ67bj2CA4ORkJCAhISEkBRFJRKJS1qnZ2dMBgM4HA40Ov1iIyMRFhY2DlbhNgejkTMHKbrNcNkgBGxcwjz3C9b+UG+sMQUCgXKysrA5XJRUlKCkJAQm2P0JSwWC6GhoQgNDUVycjIoioJCoUBtbS2USiXOnDlDB5KIxWJERkb6JJzfnEB0J7oK0/WaIRBhROwcwZXcL3fD5AHrgR3d3d2oqalBamoqsrKybAYCmI9pIiras1gshIeH08KWmpqK0dFRyGQyDAwMoKmpCTweb0LC+f09mXsqYuYwXa8ZAgFGxCY5xhUbfF06CjC1xHQ6HWprazEwMIC5c+ciJibG6W39BZvNRkREBCIiIqyG89fV1SE4ONhE1FwN5w9UvC1ixjBdrxn8BSNikxh/lI4i246OjqK8vBw8Hg8LFy50ynrxp4jZ+l5shfPL5XK0t7ejuroaoaGhtOvR3XB+f4s3GcNECYY9Uevt7UV/fz+mT5/OiBqDxzAiNkkhT7jOWl/GeLImBgBDQ0Nobm5GWloaMjMznc4jCgRLzBHWwvmJqDU1NUGlUjkVzm8Nf0/O/mxIal6dX6vV0h4BtVptkqfGdL1mcAVGxCYZJPpOqVQiNDTUrZvcXTEhVoparUZ+fj490fv6uN7CnWMHBQUhNjaWjrQ0Duevra2FRqNBREQELWpCodCqqAeCeAdKV23jpHvAfoNQpus1gyMYEZtEGAwG6HQ69Pf3o7OzE/Pnz3frhnbHEhsdHUVZWRkMBgOSk5NdFjDA/yLmDczD+VUqFS1qXV1dMBgMdOSjWCz2Wzi/LQJhLBRFWQi9rV5qTNdrBkcwIjYJMM/9Im4YT0KlnRUTiqLQ1dWFuro6pKenQ6PROO0+8+S4kwEWiwWBQACBQICkpCRQFIWxsTHI5XK6RBaLxYJYLAafz6cnZX9NuoFkiTkahy1RY7peM5jDiFiAY146ity4nqxpOWuJ6XQ6VFVVQSaTIT8/H1FRUaitrfW4n5g/mIiJjcViISwsDGFhYUhJSYHBYMDo6CjkcjkGBgagVqtx9OhRi5YzE0UgiZir9RjtiRrT9fr8hhGxAMY498u4xp0neV5ke0diMjIygrKyMoSEhGDhwoXg8/lOb2sLYwvSHxPLRAuocTi/UChEfX09cnNzIZfL0dvbi/r6evD5fDry0dfh/IEiYtbcia5ifg3Z63rNiNq5DSNiAYij3C9PowvtbU9RFDo7O1FfX48pU6ZgypQpJsf2VEDPJXeiqxDXolgsBmA/nJ9U5+fxeF47fqCImDPuRFex1UuN6Xp97sOIWIDhTO6XJ8nKgG1rSqvVoqqqCkNDQygoKKBzp8y39aRk1fmKte/bPJxfq9XSQSLNzc1QKpUm4fwikcjt9UgyhkD4DbxhiTnClqgZd70mQSPkYYERtckJI2IBhLO5X76wxIaHh1FWVobQ0FAsXLjQplvLG4nS/iAQJiZHY+DxeCbh/Gq1mg4Sqaurczqc3xaBImK+sMQcYU3UxsfHcfLkSVxwwQW0+9G8mDHTSy3wYUQsACBuD+PoQ3s3uaciZiwmFEWhvb0djY2NmDp1KjIyMhwe2xsidj6siXkKn89HfHw84uPjrYbz6/V6k3D+8PBwu99rIImYJxalNyDXIovFQlBQkFVLjcViWVQTYUQt8GBEzM+4UzrK03UpIoJarRaVlZUYGRlBYWEhvVbj6NiT0RLzN56et71wfrlcjra2NrBYLIhEIjpIRCAQWAQ/BIKITYQ70RmMoyRtuR+ZrteBDyNifoRYX66WjvKGJaZSqXD06FGEh4ejpKTE6ag4b4nYRItZIEze3hyDtXB+hUIBmUyGwcFBNDU1gcvlmoTzB4qI+cOdaGsctixCa6LmqOs1I2r+gRExP2Ce++VO7UPAvXwbUrx3aGgI2dnZSE9Pd+nYjCUWmLDZbAiFQgiFQqSnp8NgMNDV+Uk4Pylc3NfXRydg+4NAtMQcQdbLCM6IGpfLDQixPtdhRGyCMRgMUKlUOHXqFPLz893KCzJ+OnQFjUaDyspKjI6OIjY2FhkZGS4f29PoxMlWO3GyHpvNZluE87e1taGvrw+dnZ2oqanxaTi/PQLFEiO999zBnqgxXa+dw1vr44yITRDGF7nBYMDQ0JDb+zK2xJxdIJfL5SgvL4dQKERycjJtBbqKp0J0Plti/pzAuFwuwsLCIBAIkJ+fD61Wi6GhIchkMp+E89vDHQ9CoI/DWVFj2s78hrfOnRGxCcA8eIO4dTzpruzs9hRFobW1Fc3NzcjKykJaWhqam5tpF4g7x2bciZMT4zUxHo+HmJgYupEpCeeXy+Wor6+HWq2GUCg0aTnjrQl/MroTXcVY1JgGodbZu3cvuru7MWvWLCxYsACdnZ04ePAgOBwOli5disTERKf2w4iYjzEuHWW89uWNpGFH22s0GlRUVGBsbAxFRUWIiIjwyrE9ETF/4e/JIRDE215gh3E4PwCTcP6enh7odDpERETQkY+OwvntESjuxIkK9Teu+QhYFzWNRoOxsTHEx8efF6K2a9cu3HPPPQgODkZERATeeecd3H777ejo6IBWqwWfz8ePP/6I2bNnO9wXI2I+wpnSUcQycxUSOWVPiGQyGcrLyyESiVBSUmKy3uEva8rfeWL+xt/n7Ep0YkhICEJCQpCYmGgRzt/e3g4AJjlqoaGhTu87UCwxT9bEPMGaqA0NDaG1tRVisdhqSD9wtredv68hb6DX6/H000/j9ttvx6233or33nsPq1evxsUXX4zS0lJoNBpcf/31ePTRR/Hll1863B8jYj7Amdwvb1SityYmFEWhpaUFLS0tyM7ORmpqqsWxPbXEmMCOyXVs4zG4Mwmah/OTCFe5XA6pVIrm5mZwOByL6vy2jhVIllggiCm5p0gQiHEvNWKlrVixAps2bcKGDRv8PVyPUSgU6OnpwYMPPgiBQIAHH3wQTzzxBJ566ilQFIWgoCBs2bIF11xzjVP7Y0TMyzib++WL0lFqtRoVFRVQqVQm7kNr2/rbEmOYeLyVJ8Zisehw/rS0NBgMBoyMjEAul6Ovrw8NDQ0ICgqiXY/m4fyBYokFiogBZ+cNYpkZ128kbWfGxsYQFhbmzyF6jfHxcQQFBaGzsxM5OTmoq6sDm82GSqWiz1upVDodfMaImJdwNffL2yImlUpRUVEBsViMvLw82gXh7WNP1rJTgfDk7+8x+CrZmc1mQyQSQSQSISMjA3q9nq7OT8L5BQIBLWj+cuOZEwjlrwjGImaNsbExhIaGTuCIfEdISAguuugi3HbbbVi3bh2+/vprzJ8/H1u3bsUzzzwDqVSKDz/8EEVFRU7tjxExL2AcOg9Y9jqyhjeqbhgMBlAUhaamJrS1tSE3NxfJyclOla3ytyVG+j4FwmQ2EQSCBTpRFTs4HA6ioqIQFRUFAHQ4v1wuR2trK1QqFerr6xEdHY3IyEhERETYfejyFYFqiVljbGwM4eHhEzgi3yEUCvGXv/wFd911Fx577DFcfPHFeOaZZ7B27VosWbIEo6Oj0Ov1+PTTT53aHyNiHmCcC+Jqs0dvWGKkCvf4+DgWLFjg9EXuz3UtiqIgl8tRVlYGjUZD1/qLjIxEWFiYTyfZQBASf+Kv8zcP5z9y5Aji4+MxPj5uNZxfKBROiIXkSDgmkvPJEgOAgoICHDp0CH19fUhOTgZw9rp46623IBQKccUVVyA1NdWpfTEi5ibmwRuuCBjguYgZDAbU1NQgNjYW+fn5Lj3J+jPXa2hoCO3t7cjMzIRQKMTw8DBkMhna2tpMqkxERkYiJCTE7eMEGoFQtzAQxkDGERUVBaFQCMB2OL9xdX5fWEwGg2HCqpQ4wp5rk6IoOhn9XIGiKHC5XLrwQmdnJyIiIvDggw/S7zsLI2JuoNfrMT4+TicsujMxuBtibzAY0NTUhPHxcaSmpmLatGkuH98TAXXXitPpdJDJZFCr1SgsLER4eDi0Wi3Cw8ORnJwMg8GA0dFRyGQyOjggODiYFjSxWBwwE85kJZBEzFiUzMP5lUolLWodHR2gKMok8tGVcH57BJo70VYJOo1GA51Od84EdgC/rQ9XVVVh165dOHDgAPLy8vDGG2+gq6sLx48fR3FxMZKSkhzuixExFyC5XwqFAocOHcKll17q9s3kToj9+Pg4ysvLodFoEBYWBrFY7HbItLvWlDtW3OjoKMrKymAwGJCQkACxWGwRecRmsxEREYGIiAhkZGRAp9PRZZFaW1tRVVWF8PBw2vXoagWJQIiM9LeABFJUoK3vgsViITQ0FKGhoUhOTgZFUVAoFHQ4f0tLi4nF7iic39E4AsmdaOu3USgUAHDOiBh5mGptbcXDDz8MmUwGPp+P+vp6AGfXUHfv3o329nbcf//9DvfHiJiTGLsPjStmuDspuGoNDQ4OoqKiArGxsSgoKMDp06cnRa5XT08PqqurkZaW5pLlyeVyER0djejoaABn0wdkMhnkcjmqq6uh0+lMemc5s57G5In5fwyA69Xjw8PDER4ejtTUVJNw/v7+fjqc31jUgoODndp3IAUW2VsTUygUYLHO9pM7FyAPD19++SVkMhmOHj2Kd999F5988gkAICMjAyKRCI2NjU7tjxExJyDlYcjNR9xa3s7zsnXsxsZGdHR0YPr06bR5HQhh8vYwGAyoq6tDb28v5syZg9jYWNTX17tdpYTP5yMhIQEJCQkmFSSIpcZms2lBi4yMdHoiO58IBHciuXY8efgzD+cnLWe6u7tRW1trEs4vEolsuukCzZ1oS8SUSqXXXKiBRFdXF10fsb6+nl4jBVwLZGFEzA7GuV/EFWPcKM/dCRlwToRUKhXKy8uh0+lQXFxs4k7wVIh8acWpVCqUlZWBoigUFxfTT5DeculZawg5MjICmUxG984KCQkxWU8LBPw9CQWCiBmnoXgDDodDu5iBs2uvZD2ttbWVThI2FjXjAtyB5E60Z4mdSyJGziM+Ph6nT58G8FvrIABoampCT08Pli9f7tT+GBGzgcFggE6ns1o6igiZLy2xgYEBVFZWIi4uDtOmTbO4wAPVEpNIJCgvL7c6bl+tSxk/nQOmE1lzczNUKhV4PB74fD7kcrlXK7I7SyC48gJhTcxTS8wRXC7XJJxfo9HQ10JjYyPGx8fpljOkRmEgYE/EzrXwevLbX3fddThw4AC2bNmCkydPIi4uDqdPn8YjjzwCFovFiJi7OJv75SsRMxgMaGhoQGdnJ2bMmGGzHYEn1pQnImavZmNzczNaW1tN3J7WPgf41ioxn8hIPpJSqURVVRUMBoPJetpEPeX6+0n6XLTEHBEUFIS4uDjExcUBOOslIInXarUa1dXV6O7u9nk4vyPOJ0sMOHstpqam4sknn8QDDzyAxsZGVFVVYffu3SguLsa2bduQk5Pj1L4YETPCvHSUvdwvDofjsTvRfHulUony8nIYDAaUlJTYffryR5g82dZcxEjLF6VSaTfp2hPx9ITg4GCEhYWBz+cjJycHY2NjkMlkdPFaLpdLux4jIyNN6vydSwSSiPnLIiTh/AkJCZDJZMjMzKQtdxLOb1yd39cJ+AR7rk2yJnYuwWKxoNPpUFhYiAMHDqC5uRk9PT1IT0+nlwichRGx/49x3y/jdS9beNJKhWxv3Jiyv78flZWVSEhIQG5urkNfvT9FDPhtQhwaGkJZWRkiIiJQXFzsMJfL32414/U0Eu1GEq5JYEBoaCgtasZrKJ7g7/MmY/C3iLla2cbXYxEIBHS3c+NwfrKmRlzVxgn4vhi7M5bYuYbxfTV16lRMnToVwNmH4jvvvBO33XYb8vLyHO/HZyOcJDjq+2ULb7RS0ev1MBgMqK+vR3d3N2bMmIGEhASnt/fUJejOpGacXtDd3Y36+npkZmYiPT3dpzUbPcVeNwHy1A2czVExX0MRCoW061EoFLptRfh74g4EEQuUNiyApfVjLZyfJOAPDAygqakJPB7PrXB+e5A5yNZ1dS5VsAeA4eFhAGfLkZHGn+Sa4HK5GB8fx//+9z/ccMMNTu3vvBYxZ/p+2cLTNTEOhwONRoPjx48DAEpKSlzKA/HUEgPcm9TI5ysrKyGXy1FQUEBHhjm7rfm/JwpnBJTH4yE2NhaxsbEAfiuJJJPJ0NXVBYPBYFIaSyAQBMyk7IhAELFACC4hOAqxN0/ANw/nr6uro6vKkP9shfM7GgcAu+7Ec0nEnnzySTQ0NCA0NBQhISG0Z4QkuXM4HAwPDzsdVXzeiph57pc7pZs8cScqFArIZDKkpKQgNzfX5Rvb3B3p6raAey4ulUoF4GywRElJiUvrR+aWmL8nVGcwL4lEfjeJRILm5mb6yZxYara+D8adeJZAscRIBwhX7jtr4fwkSKS9vR3V1dUIDQ2lrwVnXdFkHjlf3IkfffQRkpKSkJGRAalUitHRUSgUCiiVSqhUKjpqlBExG1AUBY1GA41GAw6H43btQ3fdiXq9HvX19ejp6UFoaCimT5/u8j4A761ruUJfXx8qKysBAHPnznU5ACIQSj95grG7iVQgIetppG8WmcTIepq5u8rf+HsMgWKJeSPAxLyqjEajoUulmYfzi8ViREREWBUqY0+QNcbGxmw2uJ2MZGdnY/369bj11lutvk+sMGet2vNKxIj7sL6+HlqtFjNmzHD7pnbHEhsbG0NZWRnYbDays7PR39/v1rHJ8T0JsQecrzhivG43a9YslJWVTXjNxkDE/MnceBIjLUZINXaSMO9PAsUSCyQR82ayc1BQkIkrenx8nF5fra2thUajManOT9ZXSVCHrd9GqVQ6VQh3snD11Vejv78f4+Pj4PF4Jtcli8XC4OAggoODmYod5uj1erpxJZfLhVqt9uiGdtUS6+3tRVVVFZKTk5GTk4OBgQGPA0M8SVgGnBOx8fFxlJWVQa/Xo6SkhG6P4s6x/R3Y4etjm09ipBo7CeenKAqVlZU+j3SzRaCImL/HQMYB+NYyDQ4ONimVZtxyhqyvikQi+jqw9fuMjY2dM3UTAeDPf/4zVCqVzaCY1NRUHD161OnWM+e8iJnnfrHZbI9zvMh+nNmHXq9HbW0t+vv76RqCZPuJqL1oDWfdiVKpFOXl5YiJicH06dNNnlrdOfa5Zok5QiAQQCAQICkpCe3t7ZDJZAgPD8fg4CAaGxsRFBREW3LuBgW4QiAISKC4E0k04ER9H6SAL7keSP1PEvmo0+lw+PBhkyAREjTkrejEn3/+Gf/4xz9w+vRp9Pb24rPPPsPVV19t8/N79uzBm2++ibKyMqjVasyYMQNPPvkkLrvsMo/GQb4HWwQFBTkVWk84p0WM5H6Z+7+9IWLOWGIKhQJlZWXgcDgmVgwZi69rL9qChLTaEhSKotDS0oKWlhZMmzaN7rxqvL27YnQ+iZgxLBYLPB4P6enpSE9Ph16vtwgKCAsLMwkK8EVdP3+LWCAIKRmHP8XUOF9RIBCgubkZubm5kMvlGBwcRFNTEw4cOICamhqo1WqMj497fMyxsTHMmTMHGzZswLXXXuvw8z///DMuueQSPPvssxCJRPjwww9x5ZVX4sSJEy6JjK85J0XMuHSUtejDibDEuru7UVNTg9TUVGRlZVncMJ7mmXkS2AHYFkGtVouKigooFArMnz/fpLK08baTzZ3ob8zPm8PhICoqClFRUQB+q/Enk8lQV1cHrVZLr59ERkYiPDzc48k/UNyJgWCJBco4gLNWIZfLpcP5yUMOj8eDQqHAN998gwcffBBvvvkmLrzwQqxYsQJXXXWVy8e5/PLLcfnllzv9+VdeecXk72effRZffPEFvvrqK0bEfIkzuV+euvLIPswbOwJnL8iamhoMDAxg7ty5dP0+a9v7y50IWBeU4eFhlJWVISwsDCUlJTarb7grRv6cQP09eTsag3GNP7J+IpPJIJPJ0NHRAQAmpbGMrXpnCQQRCxR3YqBVsLf2kLtw4UKUlJTgp59+wieffILg4GD89NNPOHbsmFsi5ikk+dvZvNCJ4pwSMePSUfb83d5yJ6rVapPXiPuQx+Nh4cKFdrP5vSFinlg1xsenKApdXV2oq6vD1KlTkZGRYXey80TEztfGlK4c23j9JDk52aRyBGkEyefzTdbTHJX7ImPwt4gFijtxsjTEBM7OK3FxcVi4cCGuuOKKCRyZKf/85z8xNjaGNWvW+G0M1jgnRMzV0lHeXhOjKIquu5eWlobMzEynai/62xIzGAzQ6/Worq6GRCJBfn4+7d5ytO1kFLHJinnlCOMk29bWVlRVVSE8PJwWNFv5SIEgYoFkiQXCOADHIhYIZad27dqFJ598El988QUdnBYoTHoRc6d0lDfXxHQ6HWpqaiCRSOy6D61tT6oGuDOxeEMEVSoVampq6MATZ+vAubsed76LmLcExDzJVq1W0+tpNTU10Ol0iIiIoC01Uok9EEQsUCyxySJiFEX5vYr9xx9/jD/84Q/45JNPcPHFF/ttHLaY1CJmnPvlSrist0RMo9Hg2LFj4PP5LokA2R5w/+nY08AOYoGlpKQgOzvbpRt6Mlpi/p44fXnefD4f8fHxiI+Ppyc9mUwGuVyOtrY2usixRqNxu1SZtwgU8Qi0NTFbY1GpVDAYDE7nTHmbXbt2YePGjdi1a5fTTSonmkkpYtZyv1yZpDy1giiKwtDQEIaHhzF16lRkZma6VXsRcP+mdtcSI003NRoNpkyZguzsbLeOPdlEDDg/wvtZLBZdSJX0ZRoZGaHrPdbW1qKtrc2kNJYz62negnEnWqLX622WcBsbGwMAr7gTFQoFmpqa6L9bW1tRVlaGyMhIpKam4uGHH0Z3dzd27NgB4KyA3XLLLXj11VexYMEC9PX1AThbTzSQymBNOhEzz/1ypzcRh8NxuxWJTqdDdXU1BgcHIRAIkJWV5dL2xmMA3EsaBtwTkvHxcZSXl0Or1SI0NBQikcitYzN5Yu7hD2uQ9MMSiUTo7++nr1eZTIbm5maoVCq6vl9kZCQiIiJ8OrkHijsxkAI77FmFCoUCHA7HKy1fTp06hWXLltF/33fffQCA9evXY/v27ejt7aUjYQHg7bffhk6nw5/+9Cf86U9/ol8nnw8UJo2Ikdyv/v5+dHZ2Ys6cOW7fDOSCcfVCHhkZQVlZGUJCQjB9+nS0tLS4dXzgtwnNXbemq5aYTCZDWVkZoqOjUVBQgJMnT3q1u7MvtzsXCITzpigKXC4XIpHIZD2NhPJXV1dDp9OZtJoJDQ31qugwlpgl9tyJSqXSa+1+li5davc6NBemgwcPenzMiWBSiJhx8AYJN/bkRyUXL0kodOb4nZ2dqK+vR0ZGBqZOnQqZTObRuhrpHu2JJebMthRFoa2tDU1NTcjJyUFKSorDih2OmIwiFghP//7GmueBz+eb1PcjpZBI5COHwzFpNeOpRRAolligrYnZElSFQkEH5jBYJ+BFzDz3i8vleiUog8ViObUfrVaL6upqiwaQnlbcIOPwxBpytK6n1WpRWVmJkZERFBUVmfixvXFsd7Y7X9fEAiEy0NEYjEshkc7G5k0gQ0JCaEETi8VO9csyH0MgiMdkscTGxsbOqV5iviBgRcxW7pc3IgsB50SIVLAQCAQWDSA9rX1I9uFpOxVbExNxfZKxmxeX9ZclxuA/XBVSEtUoFosxZcoUaLVautUMWU8TCoW0pUZai9gjUMTDWS/MRODInehtl+65RkCKmL3cLw6HY7Xck6vYE0OKotDR0YGGhgabFSz8bYnZ687c1dWF2tpa2vVp7Qbw9NiTzZ0YCPh7IvLUGuTxeIiJiaFzIcfHx+n1tO7ubrq1CIl8tLaWE0juxEAQU8C+iJ1rXZ19QcCJmMFggEajsZn7RSILPb0IbYmYVqtFVVUVhoeHUVhYaLNFtqdh+sb7cHdbwNS3b9z2JS8vj168t4annaEnm4j5e+IMBPH2tkszODgYiYmJSExMBEVRUCgUkMvlkEqlaG5uBpfLNSmNxefzAyqwIxDcmoBjd6K/q3UEOgEjYsR9qNVq6Qvd2g1HfPCehshaE7GhoSGUl5fTBXDt9XcyDpF392bwZndmpVJJd1w2b/tia3t/BXZQFIWmpiZ0dXWZPLl7I4yYwTa+FFIWi4Xw8HCEh4cjNTUVer2eXk/r7OxETU0NQkND6YrtOp3O5fU0bxJIlpijEHvGErNPQIiYwWCATqdzqnSUsdXhiU/beE3LOIIvMzMT6enpDp9YjSMcPRExd9fVjLszDwwMoKKiAomJicjNzXXq5vR0TczdwA6DwYAzZ85gbGwM2dnZGBsbQ09PD+rr6yEQCEyScL39pOxvaygQrMGJGgOHw6F/y6lTp0Kr1UIul6OxsRGDg4Po7e2FUCikPxMeHj6hohIoeWLk4d3RmhiDbfwqYsZ9v8gN5ugmI6Hp3ijgq9frodFoUFlZidHRUbvuQ3PMLSF3x+Bpd+aWlhb09PRg5syZSEhIcHp7T6MT3REEpVJJ/27z588HAMTExCAjI4Oe5GQyGerr66HRaBAREYGoqCif5CtNNP4WUDIGf32HPB4PsbGx6O7uRnx8PEQiER3K39nZCYqiTEL5vZUbZYtAscTIPci4E93HbyJmXjrKlcob3gju4HA4dBkWoVDo0H1oDrEW/VWJnrSBkUgkKC4udvlC98QSc8cV2dfXh8rKSgBAfn4+KIqCRqOhf3MyycXGxprU/5PJZGhpaTFZX4mMjHTptwL8bwX5ewzk9/L3xE0CO0JCQpCUlISkpCRQFEW3miFdjXk8Hi1o7vzezozD398F8FuxA3vuRGc6S5zP+EXESPShTqejLStX8DTMnjQdlEgkyMnJQVpamtuV5D0Zh7siJpfL6fWv2bNnu/WkNlGWGEVRaGhoQGdnJ3JyclBbW+twe2v1/4aHhyGVStHR0YGamhq69chElEo6FyDft7/F3FpgB4vFglAohFAopLsaDw8P0w1Ba2pqEBYWRguaN1zNgRLYodfr7T7AK5VKpKWlTfCoJhd+ETHyg7lauJfgScKzRqNBRUUFVCoVkpKSkJ6e7tZ+AM/D7F0VEoqi0N7ejsbGRmRnZ6O5udntSWki8sQ0Gg3Ky8sxPj6OBQsWuC00xvlKEvkwBiQysCkD1Go1qqurodfrLboeW/tezudk50BwZwLOhdgbr6cBZ68jY1ezWq02aTUTHh7u8ncbKGtijhr4jo2NQSAQTPCoJhd+cyd6Eh3nriUmk8lQXl4OkUiEhIQEj5MdJ9IS0+l0qKysxPDwMObNmweRSITW1laPohs9CSpx9NuNjIygtLQU4eHhKC4uBpfLhUqlcvs3V6rU2L33IJrau+nXMtOSsPqKJdDrtLQrqrGxke56HBUV5VZVCV8RCCIWiJaYI4KCghAXF4e4uDjai0LW00jBWuP1NFsPMcYEkjsx0BtiBjqBcXe7iKsiRlEUWlpa0NLSguzsbKSmpqK+vt7jZOWJssRGR0dRWlqKkJAQk7U7fwRnOHPcnp4eVFdXY8qUKZgyZQo9oZD/u3Pc3XsPormjx+S15o4e7N57CLdcexnCw8ORlpYGvV5PP7UbV5XgcrkmAUTnG4EiYp4mO7NYLAgEAggEAiQnJ5usp/X396OhoQF8Pt9E1KytpzEidu5wzouYWq2m3YfG9QM5HA4dHOEu3rDEHG1PBCE9Pd2ib5k/6h+Sba0JkcFgQH19Pbq7u612uTYWMVcmMol82MQCI1AUhab2bkjlw4gS//a7Gnc9Jk/t3d3dGBsbw+HDh00CRCYqN83f7rxAEjFvioe19TRSGqu9vR3V1dUWrWbIw2egrInZGwcTYu8Yv4mYJzeTsyImlUpRUVEBsViMvLw8E7eSN2ow+tISMxgMqK2tRV9fn1VBINtPZIQhwZqIqdVqlJeXQ6PRoLi42OqNZy5izl4DsqERu+9Lh0ZoETOHRMEBwMDAAKZMmQKZTDZhuWnGMO5E37di4XA4iIqKoiP6NBoNHeVaW1sLrVaLiIgIGAwGKJVKp1yPvsSeiJGuAv7q6jxZmLSWmL0Qe4qi0NzcjNbWVpP2I+b78EYBX08tMWvnoVKpUFZWBoqiUFxcbHNhdyJauVjDXMSGh4dRWloKkUiE/Px8m2tQ7roTI0VCu+9HOXjf+PgRERGIiIg4L3LTrOHv85no2olBQUGIj49HfHw8nbohlUohl8tRXV1tEjREgoImEkcW4bnmTvSFO9//TmE3sCdAarUaJ0+eRE9PD+bPn4/U1FSrX1ogWGLWth8cHMQvv/wCoVCIBQsW2I1M8kf9Q/PjdnV14ddff0VaWhrmzJljN4jCXRGLFkcgMy3J4ndksViIEYViakYaVq1aZbFde3s7hEIhKioq6Ne++OILLFu2DMnJyUhLS8OKFSuwY8cOFBcXY968eTh8+DAWLFiAU6dO4ejRo6ipqUFfXx80Go3F/tVqNRYuXGhxDKlUilWrViE7OxvR0dGYNm0aXnjhBSgUCqvn19zcjMTERKSkpFi8d+TIESxevBgxMTGYPXs23n//fae/N2OMO6H7E3/WTiSpG6QoQElJCWbPno3Q0FD09fXh+PHjOHbsGOrr6zEwMACtVuvzMTnjTjxXRMxYwEj5OW/gNxHzhTtRIpHg6NGj4PP5KCkpgVBo+wndE0vEeB/eik6kKAqNjY0oKytDbm4uZsyY4fBm9+eamMFgQE1NDerr65GXl2e10r8t3Ll4r1u+FFNTE01em5qaiIayX3Drrbfi+PHj6OzstDvmU6dOYcOGDbj66qtx4MABHDp0CI8//jiddB0aGoqoqChwOBwsXrwYM2bMQFBQEDo6OnDkyBGcPHkSzc3NkMvlMBgMeOyxxxAfH29xLDabjeXLl+O///0vzpw5gzfffBO//vornnnmGYvParVabNy4EcXFxRbvtbW1YfXq1SguLsaRI0dw//3348EHH8QXX3zh8vfn6ROwtyb0QKhib1wlg1jlBQUFWLRoEbKyssBisdDa2orDhw9b/Obexl6ov8FgOKf6ibFYLPzvf/9DY2OjyXKCp2I2Kd2JXC7XJCjDYDCgubkZbW1tmDZtGpKSLJ/azQkES4yIEMmnUqlUWLBggdM+cH+0UwF+q9fI5/PtujvN8WTyCgnm45ZrL4NUPnx2DUwkRHAQFw/ftREHDx5Ef38/du7ciYceesjmPn755RcUFxfj7rvvpl/LysrCihUrLD5r7GYCTNdWqqurceLECezbtw+vvfYa9u/fb/JdisVibNq0if47NTUV1157LXbu3GlxnKeffhrZ2dlYunQpfv31V5P3PvjgAyQnJ+P5558HAOTk5KC0tBRbt27FypUrrZ7j4cOHsXz5cnz55Zd44oknUFdXh1mzZuGf//ynyff/3nvvYevWreju7kZaWhoeeOAB3HDDDfT7QqEQL7/8Mvbv34+DBw/izjvvBIvFwjfffIPbbrsNzz33HORyOa6//nq8+OKLeO211/D666/DYDDg9ttvxwMPPGAxNvL07e+oQCKk5uPgcrkmQUFqtZoO5a+uroZOp6MLVovFYq90XHZUN5GiqHNqTezDDz/Etm3bcOWVV2LZsmWYM2eOx+vQk96dOD4+jpMnT6Kvrw8LFixAcnKyUxeWt9bEPBUxtVqNX375BVwuFyUlJS5dsJ4GZ7hbLaSrqwscDsehu9PaMQFgQCpHfXMHpPJhk/cl8mE0tHZavG5MlDgC2RkpiBJHYM+ePcjMzERWVhbWrl2LnTt32v0+IiMjUVtbi5qaGqfHTCBrK9OnT0dWVhbefvttPP/881AqlQCAiooK1NfXY3Bw0GKds7e3FwcPHkRBQYHJ64cOHcLnn3+Of/7zn1aP+euvv+LCCy80ee2iiy5CaWmpQ8voqaeewjPPPINDhw6By+Xivvvuo7//r776Clu2bMGdd96J48ePY+PGjbjjjjvw888/m+zj2WefxfLly3Hs2DGsW7cOANDa2or9+/djz549+OCDD/DRRx9h9erV6O7uxr59+/DUU0/h6aefthBkIHBKXzmb6Mzn85GQkIDp06dj4cKFKCwsRFRUFORyOc6cOYMjR46guroaPT09GB8fd3ss9uomAjhn3IkA8Nhjj2Hu3LnYvn077r77bjz22GM4ceIERkbsB2/ZY1JHJw4ODqKiogIxMTEoKChwKanV34EdFEVBLpdjaGgIubm5bpW+mmhLrLOzE3V1dYiMjASXy3X5CUo1rkZ5cx8Oln1GvzY1LQlXXlSMr388ZpHIfN3ypQgJ5lvbFQBgx44dWLt2LQDgkksugUKhwMGDB7Fs2TKrn7/mmmvQ1taGBQsWIDU1FYWFhbjooouwZs0ak67d9qAoCrfffjs2bdqEK664Au3t7QBA/37GuWnPP/88Dh48iPHxcSxatAhPPvkkvR+pVIrbb78d7777rk23d39/P2JjY01ei42NhU6ng1QqterKJDz++OO44IILAAD33nsvrrvuOlr4tm7diptuugmbN28GcNYaPXnyJLZu3YrFixfT+7juuuto8SIYDAa88cYbCA8PR25uLhYtWoSmpiZ8+umnYLPZyMrKwssvv4wjR46gqKjIYlvA/+ty7oT5s1gshIWFISwsjC6FNjIyYhLpGhISYpKf5sx8pNfrbV57SqUSXC7X6WtzMlBSUoKSkhJ0dXXh448/xu7du7F3714UFxdj3bp1mDlzpt1lIGtMSkuMzWZDoVDQ60ezZ892uSqDP92JOp0O5eXlGBwcRHh4uFOtX6wxUdGJBoMBVVVVaGxsREFBAcRisVsW4H+/+hHyUdMn1paOHrz9n6+sJjJ/8s1Bm/tqbGzE6dOnsXr1agBnXUHXXHMNPvroI6ufJ0Vnd+/ejbKyMjzwwAMICwvDX//6Vyxbtoy2qBzx1ltvYXR0FPfff7/J6yKRCNnZ2ViwYAEWLFiA+Ph43HHHHXj55Zfx6KOPor29Hc8++yz9xH7XXXfhuuuuw8KFC+0ez/y6cDZUfubMmfS/idgND5+1cOvr6+kuAoT58+ejoaHB5LW8vDyL/aamppp4C2JjY5GTk2MiCrGxsRgcHLTYNlAsMW/kqrHZbIhEIkyZMgWFhYVYtGgR3UW9ubkZhw8fxqlTp9DS0oKhoSGb95ozXZ39LfrehMy5ycnJuP/++/Hpp59i8eLFePvtt3HBBRdg9erV+PLLL13a56RbE1OpVGhpaYFGo0FJSYnbpra3AjusRa3ZQ6FQoLS0FHw+Hzk5OXaDERwxEdGJ4+PjKC0tBQAUFxcjJCQEw8PDLovYoGwIja1dFq9TFAXVuGXSubVEZmN27NgBnU6HnJwck214PB7kcrnVljpkzKSSyPr16/GXv/wF+fn52LNnD26++WarY5fIhyH7/+twP//8M06ePGnRNXvJkiVYs2YN3n77bZMK7QaDAaOjo4iKisK9996LlStXIjk5GQcOHMDevXuxdetWemwGgwFisRhbt27FunXrEBcXh/7+ftPvcXCQruhvD+OHOmuToC1xNMZaQIF5qTYWi2X1NWvXZSBZYt7OB+RyuYiJiaHzOcfHx+n1tMrKShgMBpP1NCJO51tDTA6Hg/r6epSWluL48eOorKxEe3s7VqxYgVWrVqG8vBw33ngj7rzzTjz33HNO7XNSuRMHBgZQWVlJJyt64ismlpgnUVuuWmK9vb2oqqpCWloaMjMzIZVK/dbKxRkBlMlkKCsrQ0xMDKZPn07fbO6E58vk7vm8rSUy63Q67Nq1C88++6zFmtHNN9+M//3vf7j11lud2n9aWhoEAgG9/mCMtXqNy1Zcjy0PPYwg3tlbp7e3F6tWrcL27dtRWFhosQ82m42IiAg6/2ju3LkIDw/H+++/D7lcDq1Wi7CwMJw5cwYffvgh9u/fj8TEs1GYRUVF2Ldvn8n+fvrpJ+Tl5blV95Nc5zk5OTh+/DhuvPFG+r1ff/3V5IHAF5Dr7VywxBwRHByMxMREJCYm0knLMpkMUqkUzc3N4HK5EIvFUCqVEIlEVvdxruWIKRQK/OlPf0J7eztkMhkiIyNx1VVXYc2aNUhOTqY/t2TJEvzxj38MfBFzBYPBQLfzmDFjBgQCAW0duAuZkD15KnN2Tcy4HNOcOXPodQ5vBIb4omIHRVHo6OhAQ0OD1WRxd44bKXbNz02wlsj87bffYmhoCOvWraPLiBGuvvpqfPTRR1ZF7MMPP8Rnn32GSy+9FCkpKRgeHsZbb70FrVZrIYaA9XqN3QMyVDQF45ZrLwPwm7WSkZFBVwb57rvvMDg4iPz8fISGhqKurg4vvPAC8vLyMHXqVADA5ZdfbtI3raGhgV4nZbFYUKvVWLduHd555x08/PDD+P3vf49ff/0VO3bswAcffODGN/kbd999N9avX485c+ZgyZIl+Pbbb/Hll1+67MZxlUCITAQmvoK98Xpaamoq3VrIuH9ab2+vSasZLpcLpVLpleagP//8M/7xj3/g9OnT6O3txWeffYarr77a7jaHDh3Cfffdh+rqaiQmJuLBBx/Ebbfd5tE4FAoF6urqsHLlSlxxxRWYO3cu/Z5OpwObzQabzcb06dOtppzYwq8i5swTPaleYTAYUFJSgtDQUIyOjnplPQtwnGzoaB+ORIi44yiKQklJiUk0nzdEzN38HVuWmF6vR3V1NaRSqc1O1+5YYjGRImRlJFu4FFksFoL5QRhXa0z2yWKxMDU10aYrcenSpRYCBgBXXXUVXnzxRZSVlVmMfe7cuTh48CBuvfVWDAwMQCQSYfbs2fj888+RlZVl8llX6jWaExISgu3bt+Phhx+GWq1GUlISFi5caCGsxn3TUlNTweFwMGPGDLpvmkKhwN///ne88847ePfddxEfH48XXnjBZni9I8hkuGLFCjz//PN49dVX8eCDDyItLQ3btm3DokWL3NqvswRCjhgZhz/F1KS1kERCr4nL5XI0NjZCJpPhueeew9SpU8Hj8aDVaj3quDE2NoY5c+Zgw4YNuPbaax1+vrW1FVdccQU2b96Mf//73zh69CjuuOMOxMTEOLW9LeLj43HixAmr7xm7v3Nyclx6oGJRfqxMqtFo7E6G/f39qKysREJCAnJzc2mxUSqVOHz4MC677DK3j01RFL777jssWbLE7VIz3d3d6OrqslgkJ0gkEpSXlyMuLg7Tpk2zEMvh4WGcPn3aqhXgDE1NTVCpVJg1a5bL28rlcpSXl2Pp0qX0ayqVCqWlpWCz2Zg7d67N4rhdXV3o7e3FvHnzXDqmclyN1z/4L2QjKvo1T6ITXaGvrw/d3d0WYe62aGjtxL8/22/z/ZtXXYLsDMsKG7aoqqqiK+07i3Fumkwmc7pvmjWItbdgwQKnj+9tRkdHUVZW5nOxdER3dzckEgnmzJnj13EAZ3MXp02bZvLAJZFI8J///AdfffUVvX6+bNkyXHzxxVi/fr3L0XvGsFgsh5bYli1b8OWXX6K2tpZ+7bbbbkN5eTmOHTvm9rH7+vqwe/duJCYmIjg4GCEhIQgNDaXd7Xw+HyKRyOVozIB0Jxq732bMmEGXiSFwOBx6IdzdJyqS7OiJRWfLEjNu/TJt2jQTf68xnlpi3gzskEqlKCsrQ3x8PKZNm2b3e3X3uIJgPgpykjElMwdqrR4R4QJEioRgsVgWicy2rJyJwlG9RjaLhYbWTp+O1bzun0KhsOibRuo8OgrpDoQWNIwlZok1T1B0dDTuuusu6PV6JCUl4dFHH8UPP/yAb775BuvXr/f5mI4dO4ZLL73U5LXLLrsM77//vkdW4cDAAJ555hlIpVL6mh0fH8fIyAhdDmz27Nm44447rEbG2iLg3IlKpRJlZWUAYLMaOvnRdTqd1V5BzuKNihvmIqjRaFBZWYmxsTHMnz/fp6WvvBFiT1EU2tra0NTUZFdwjfG07mKkKBxCoZDu70WIEkf4XbwIpF5jc0ePhZszmB+EHXu+p19z1mr0tI9WeHi4w75ppBmoebfjQBGxQBCPQOnqDDhOdg4LC0NeXh7y8vKsVkHxBX19fYiLizN5LS4uDjqdDhKJxMKocJbw8HAsW7YMIyMjWL16NYRCIQYHB/HFF19AIpFgxYoV+O9//4uff/4ZX3/9tYWL3xYBZYn19fWhqqoKiYmJyM3NtXmhGa9neYKnuWLmIkiquQuFQhQXFzt8YjEWEnfzxDwRE71ej/LycgwNDdHdoifiuP7ureUs1y1fik++MY1ODOYHWaQEkJw2EuxhDW+fs62+aTKZjE7bMO6bFggiFiiBHYEipsSbZE/E/BVi725+oj1Onz6NsbExfPDBByZJ/DfddBPuv/9+JCcno7KyEr/73e/w3nvv0eXWHBEQIqbX61FfX4+enh7MnDnTbiUC4OwX6a1kZW8U8KUoCp2dnaivr8fUqVOdLoZLLl5PRMxdS0ytVsNgMECtVqO4uNglP7Snlpg/RMyd71epUqO7X2Lymjs5bROBtdw042oSQUFBoCgKUqnU533TbBFI7sRAaYgJwK6IWesj6Gvi4+PR19dn8trAwAC4XC7dp80damtrIZfLLarQhIWFgcPh4Ntvv8WGDRuQkZGBnp4eG3uxxO/uxLGxMZSXl4PFYllE79kjUERMp9OhsrISEokE+fn5Lv3I5GnQXfcGEbFB2RBk8hFEiYWIjhQ53G5wcBDl5eUAgHnz5rlVgmeyiRjgujX0zq6vrIqWLew15wQmLsmX5KYZ901ra2tDf3+/X/umMZaY5TgA+yKWkZExkUMCcHYZ56uvvjJ57fvvv0dhYaFHUZLZ2dnYvn07Xn75Zdxyyy0ICgoCh8PBsWPHcOLECbqd0sjIiIXQ2cOvItbb24uKigokJSVZlK5xhL9rHwJn1790Oh1UKhVKSkpcbnVPztdta0qjxZHSRuz56Qz9WlZGMm5YeQkEVtZnjANOsrKyUFdX59bkNVlFDDCtvmFPcBrbulwSMMB+c05/njOPx0N4eDhGR0eRl5dH56bJZDK0tLTQFUDIf56sM9sjUCwxvV7vcpk6X43DWjV9glKp9Io7UaFQoKmpif67tbUVZWVliIyMRGpqKh5++GF0d3djx44dAM5GIr7++uu47777sHnzZhw7dgzvv/8+du3a5dE4rrnmGtTV1eGtt97Cjz/+iNjYWGi1Whw5cgQzZ87Epk2b0NDQgLGxMauFA2zh119Sp9Nh1qxZFouIzuDvVip9fX2orKwEABQWFrrlniA3tLtj+O7wGQzKR01ea2rrxq4v9uMPa03bixCLcXh4GEVFRQgODkZdXZ1brkx/NeP0BLVGi2OVLfjy59+aV9oLyOjqtaz9Zwt7OW3mn/MX5Hc2zk0jhWyHh4fp3LSamhqEh4fTghYREeE1qyWQLKBAGIejHFWFQuGVih2nTp0yKYp93333AQDWr1+P7du3o7e3Fx0dHfT7GRkZ2Lt3L+6991688cYbSExMxNatWz3KEQPOeoBuvPFG5Obm4rvvvkNPTw+Cg4Px8MMP4+abb4ZAIEBMTAz27Nnj0u/jVxFLTU21aFvhLP5yJ5LqIV1dXZg2bRqqqqrcnpzIU5g7gjAoG0J7z4DF6xRFobG1CxLZEO1aHBsbo/NNSkpKEBQURCdJu1vR21uW2EQFHHx/pBQSuWlnZXsBGckJzq9FTE1NxHXLl3o6RJ9i63t21DfNk9w0a2MIBPEIJBGzNw6lUumVXmJLly61e79u377d4rUlS5bgzJkzlh/2gKeeegozZ87En//8Z7r7hDnuGAP+/yXdhMPhuC2AxvtwRcRI7zKJRILi4mLagvRWd2dXcFSLUPr/3x8YGMCxY8fodjXEVeSJFehJdCIw8a41iXwYHb2DMD+qcUCGOVnpyTZD5kOC+bh7w7W4edUluHvDtbjl2sschtdPxDmfOHECIpGIXlswpqurC4sXL0ZFxW+W6BdffIFly5YhOTkZiYmJKCoqwpNPPkn3TWtra8NNN92EiIgIDA4O4sSJEzh27JjVvmlqtRoLFy6EUCg0OYZUKsWqVauQnZ2NWbNmYfXq1bj//vtt9o9qbm5GYmIiUlIsE8mPHDmCxYsXIyYmBrNnz8b777/v1vcUSIEd9sYxNjbmUr++QGdgYAADA789eHvrnpjUIjaRlphUKsUvv/yCkJAQOn/NuP6iu7grYo5qEUaKhGhqakJ5eTlmzJhhseZI/u3OheSpJTbRyIYcCL6N92+98UoLcQoJ5uPWG680ac7pLL4+d1Iz8vjx4xbdEcx/rwMHDmDDhg24+uqrceDAARw6dAiPP/64SVcG4n5MS0tDXl4eFi9ejOzsbJN2I6dPn0Zrayu2bNliNaqYzWZj+fLl+O9//4u9e/fir3/9Kw4ePIh77rnH4rNarRYbN260Wjevra0Nq1evRnFxMY4cOYL7778fDz74IL744guXvydP88TcLfVmbRy2RIwUDT6Xujpv3rwZLS0t+PbbbzE6Ogq1Wg2tVgu9Xu/RHOr/1U03majOzBRFobW1Fc3NzcjNzTXpHE3+7w9LLCZShBB+EFRqy1YwIcF8dLQ1Q6FQYMGCBVZvBDJ2f4gYaVMvkUjA5XIRERHh0wneUfUNWwEZkSIhHr7jJjS1daGzdxApCTHITHecDO4PxsbG8Nlnn+HgwYPo7+/Hzp078dBDD9Hvm/9e3377LYqLi3H33XfTr2VlZWHFCtO1VGNs5aZ98803+P777/HII49g//79GBwcxPj4OIKDgyEWi7Fp0yYAQHt7O0JDQ7Fp0ya6BY0xTz/9NLKzs7F06VKLztAffPABkpOT6dyhnJwclJaWYuvWrTZrSR4+fBjLly/Hl19+iSeeeAJ1dXWYNWsW7rjjDrpYMwC899572Lp1K7q7u5GWloYHHngAN9xwA/2+UCjEyy+/jP379+PgwYO48847wWKx8M033+C2227Dc889B7lcjuuvvx4vvvgiXnvtNbz++uswGAy4/fbbbSYpO2OJnUtV7L/55hv85z//wddff42FCxciISEBYWFhEAqFMBgMuPPOO92Kj/B7iL27cLlcn1tiWq0WlZWVGB0dRVFRkUXBWZKv5u2qH84wKBuyKmDA2VymkTEVLigpsRkSS56y3Rm7pyKm1+vptASyPkEqTfgiOi5aHIHUxBh09pi6FJ0NyMhMT/ZYvHztTtyzZw8yMzORlZWFtWvX4sEHH8SWLVtsPqzExcXhk08+QU1NDaZPn+7WMUNCQsDj8fDSSy9h586ddK7h4OAgjh07BoFAQK+liUQiGAwGSKVSfPXVVxbNQA8dOoTPP/8cR44csQjvBs62ijGvMXrRRRdhx44dDkshPfXUU3jmmWcQHR2Ne+65B//4xz/o+o1fffUVtmzZgv/7v//D0qVL8d1339EiZ9zl+tlnn8WTTz6J5557DhwOB//+97/R2tqK/fv3Y8+ePWhtbcW6devQ1taGzMxM7Nu3DydOnMAdd9yBJUuWWHS5BhyLmLeiEwOFlJQU3H777RgbG0NfXx/q6uowNjaG8fFxtLS04Oabb0ZcXJzL6+TntSXG4XBsNrUcGRlBaWkpwsLCUFxcbHNi9VX9RUc4WhNLSExxmNPhiYi5K9wURaGpqQk8Hg9FRUXgcDhQKBQm0XHG5ZOEQqFXrLTLLsjH598fxeDQb8EdEx2Q4Utrc8eOHfRi+SWXXAKFQoGDBw/SUWnmInbrrbfil19+wYIFC5CamorCwkJcdNFFWLNmjdOJ7xRF4bbbbsPGjRtRUFCA9vZ2AMD06dMxbdo0uixWfX09nnnmGfz6669Qq9W45JJL8Nprr9H7kUqluP322/Huu+/aLNPW399vkTsUGxsLnU4HqVRqt0DC448/jgsuuAAAcO+99+K6666jXYJbt27FTTfdhM2bNwM4a42ePHkSW7duNRGx6667DuvWrTPZr8FgwBtvvIHw8HDk5uZi0aJFaGpqwqeffgo2m42srCy8/PLLOHLkiFURs7c2p9froVQqzylLbMuWLU59ztX7ZFKLmKe+aVtC2NXVhdraWroDsL0v1RuWmC/WxKIjIxwmQbsboOHudkNDQxgdHYVIJEJBQQEMBgPd8VYkEmHq1KlQq9V088Curq6ztRb//9N8VFSU21ZaMD8I82emY2pWjl+KDPvSEmtsbMTp06exc+dOAGe9FNdccw0++ugjmyIWGhqK3bt3o6WlBYcPH8bJkyfx17/+FW+++SZ++OEHpwIK3nrrLYyOjuL++++3eI/H4yE2NhaxsbGgKAqvv/46ysvL0dTUhA8//BAbN27EY489hsjISFpYzK0zc9wthTRz5kz630Ts5HI5EhMTUV9fj9///vcmn58/fz7eeustk9esFaRNTU01cdXHxsaCw+GYrLfFxsZicNB6uoajuokAzqk1MeBs1Y6jR49Cq9XimmuuQWxsLLq7uxEWFuZ02TtzJq07kcPhYHx83KPjm4uYXq9HTU0N3dDQmeobnlpinqyJTU1NtGjayGKxMCUlAV/sP2LSu8taEvREuhN7enpQXV1Nl0eyJf58Ph8JCQlISEigyycRQautrUV4eDjtdhQKhS4v0AdSkWFvsWPHDuh0OpPOzBRFgcfjQS6XQywW2/y9yIPa+vXr8Ze//AX5+fnYs2cPbr75ZofH/fnnn3Hy5El6jYywZMkSrFmzBm+//TaAs9dLRkYGdDodZsyYgeLiYlx++eW46667oFAocODAAezbt49eJyM1BcViMbZu3Yp169YhLi4O/f39JscZHBykE7XtYZzYbG3OsSWOxlhz65l7OlgsltXXbN1jzojYuWSJlZWV4b777sPo6ChOnz6NoqIixMXF4d133wWLxcKjjz7qVhL6pLbEPA2xNxYQpVKJ0tJScDgcl6pv+MsSA4DLlxRi5+ffQz76m5hnpidBrzegqc20oaO1JGh3LSrj4AxHDyIURdFduefOnYu2tjZ6H462NS6fNGXKFDqHSSqVorKyEhRFmayl2XODBUKlCF+MQafTYdeuXXj22Wct1oxuvvlm/O9//8Ott97q1O+clpYGgUBAT6COeOGFF/DYY4/Rf/f29mLVqlXYvn271YoLxH1GvofExESkpaXhhx9+gEwmw/DwMIaHh/HLL79gz5492LVrF3JyckBRFIqKirBv3z6T/f3000/Iy8tzqxQSefjJycnB8ePHceONN9Lv/frrryYPBL7CnogplUoEBQV5VOYp0Hj00UcRFxeH9957D4WFhbSVuWjRItxzzz14/PHH3drvpBUxbwZ2kOab7pS/8lc7ld7eXtTWVGPO1HjMzS/E8KgSUWIhKAD/fOe/Fp+3lgTtboCGs5OxVqtFeXk5VCoVnZbQ1tbmtmvNvL/WyMgIZDIZuru7UVtbi7CwMFrQvFlpwhv4yp347bffYmhoCOvWrbMIPLr66qvpsHtznn32WahUKlx66aVISUnB8PAw3nrrLWi1WqebtJrnchFrJSMjg47+++6772jPRm9vL7q6urBt2zYsWLCAbhBq7O6jKArj4+P44osvIBKJUF9fj7a2NixduhRvv/02tmzZgo0bN+LXX3/Fjh078MEHHzj/ZRlBruG7774b69evx5w5c7BkyRJ8++23+PLLL13qLOwu9spfKRSKCatpORFotVocOnQIjY2NiI+Ph1arpR86U1JS0NHR4fb9Oqndid4IsVcqlaioqMDMmTPd6pMz0e5E44ohs2bNQllZGaLEEYiPPevSqW/usLu9VD5Ci5i7Amoc8WbrNxwbG8OZM2cgEAiwYMEC+onSW2WnWCyWSZFb40oTVVVVMBgMtJVG3MKTpQWMK+zYsQNLly61EDAAuOqqq/Diiy+irKzM4twvuOACvPvuu7j11lsxMDAAkUiE2bNn4/PPP3e6j5MzhISEYPv27Xj44YcxPj6OuLg4XHvttbj33nutfp7FYiEkJARsNht5eXkmfdOefvppbNu2De+99x5iYmLw1FNP4aqrrnJpPOR7IBbQihUr8Pzzz+PVV1/Fgw8+iLS0NGzbtm1Cuk/bs8SIiJ0rqFQq8Hg8UBQFtVoNFotFB/F0d3d7FJHMovx8Z6vVrhVZJQwMDKCxsdHhYrC94546dQoKhQILFy502/d86tQpxMbGIjU11a3ty8vLERYWhqlTpzr8rEajQVlZGTQaDfLy8hASEoLvv/8ey5Yt+y28WTZk1RIj/OWP19MidvjwYeTm5rrc7kGr1eLHH3/ExRdfbPVJklTJT0lJoZNjCadPn0Z0dDTS0tLoREdvW0wURdFraTKZDCMjI+Dz+dDr9Zg5c6ZfrLQzZ84gMTHRYZshX9HY2AiKopCdne2X4wNAVVUV3dTTXYz7psnlcgCmfdMcLQPodDr8/PPPWLRokd9ddeXl5YiKirLaiPb777/Ho48+itraWj+MzPuoVCrcfvvtAIA777wTl156KSoqKiCTyfDXv/4VkZGR2L59u1tl6PzuTnT3ydwTS0wmk9HiwePxPFo89XRNzNntScPNiIgI5Ofng8vl0t+b8fYxkSJkZSSjqa3boiNxYqxpoIona2KApWVj3CV6xowZSExMtLqtNxrsORqfUCiEUCikW5G0t7fTwSWkHiCx0lztPuAO/rYCz5WmmI76ppnnpplbOuReCQRXs6PAjnOp5FRISAjuuece3H333diyZQvkcjnuuecetLa2QqPR4PXXXwfg3pzgdxFzF3dEzHiSzcnJgUgkwsmTJz0ax0SsiXV3d6Ompsai4aathOUbVl6CXV/sN4lOpCgK3f0SvPjOf+lIRU/XxIy31ev1qK6uhlQqtdsl2h8TKY/Hg1gshlQqRVFREZ2X1tfXh4aGBnrii4qKgkgkCogJztsEQvFdb7disdY3jbge6+rqoNVqLfqmTRYR81YF+0Bi7ty5ePvtt/HBBx8gJiYGMpkMK1aswJ/+9CePmn9OahFzJTpRq9WiqqoKw8PD9CQ7Njbm955k9kTMYDCgrq4Ovb29yMvLswhltrW9IJiPP6xdAYlsCP/5Yj96+qUm75NIxRlp0R6viQFnCyOXlpYCONtQz55lYyycE22dEGskPDwc4eHhSE9Ppyc+qVSKmpoak6rtUVFRCAkJ8drx/W0J+fv4vq4eb56bZq1vGkme12q1Puub5iz2kp3HxsbOqTUxQm5uLl544QWv7tPvIuapO9EZN8no6ChKS0sREhJCtyIh+zAYDB65WrwRYm8taVutVqOsrAw6nQ7FxcU2XQv2RJACLAQM+C1SMT023GNLbHh4GGfOnEFUVBRmzJjhsDq4v5timmM+8Y2NjUEqldJrriEhIfSTvDX3lLP4+5wDoSHlRLo0bfVN6+vrA0VROHLkiM/6pjmLoxD7c03Eent78cUXX6C3t5d2+YvFYvD5fOTk5KCgoMCt/fpdxNyFBBQ4aqtAXHHp6enIzMw0uYnIdp50evWFJTY0NITS0lJERkZi5syZds/P3rqWo9JUynGt25YYi8VCX18fGhsbkZmZifT0dKcmqEATMWNYLBbCwsIQFhaGtLQ06HQ62koj7imylhYZGenymkUgNMX0J/7s40X6prHZbNqtTHIOSTSrcd+0iViPOp/ciR0dHXjooYfwww8/ICkpCXq9HmNjY9BoNOju7sYNN9yAnTt3QqfTuTwXT1oRMxYgaxeCXq9HXV0d+vr6MHfuXKs+V2+ImKdJ1+Yi1tnZibq6OmRlZSEtLc2phGBbQuSoNFVYaLBbgkISnRsbG21+t462n2jcWjDmchETE4OYmBjaSpPJZBgcHERjYyOCg4Pp4BBHVpq/hTsQRCxQ1uXYbLZFzqFCoTD5bfl8Pv2wIhaL3Z4f7GEvMvdccyfu378fx44dw65du+ialHq9Hnq9HhqNho4UnZQVO9y9sciPb80KUiqVKCsrA4vFQklJic11DW+1UvGGJWYwGFBTU4P+/n6nS14B9sva2ItUzExPQkSYwGVLTKvV0k0P58yZ47KA+dMS8+S4xlYa6UhuXOBWo9FAJBLRouZJB2RfEAgiFgguTWueG+N10rS0NJPctObmZqhUKpOi1OHh4R6fBymtZc+d6K90DF9AjImLLroIwG9l0QDrJb1cwe8i5i6kDYq5gAwODqKiogIJCQnIzc21++TnjVYq3loTO3HiBCiKsiu6tra3d3xrkYqZ6Um4YeUlqK+tcWliJwnMISEh4HA4bgU9BLI70RXMrTSlUgmpVAqpVIrm5mYEBQXRgiYWiwH4P7DC38cPJEvMHrb6pslkMrrZqCu5adYg89b5Ethx9dVXY3h4GAcPHsTSpUu9ei1OWhEDTMPsSYuPtrY2mzlKjvbhDp6G2KtUKrqi9vTp010OHHB0fONIRalZRXtXCgBLJBKUl5fTpbl++uknj4sH+3tS9RbGQQSpqan0k7xUKkVDQwPUajXYbDYGBwchEAigVGshHx6d0Er6jCV2FneS6z3NTbM1DsC+iJ1La2L19fX48MMPsWfPHqxduxYJCQl0egSPx8PcuXPdqpgEBICIeVp6SqfTQaPRoLy8HOPj4zY7Gdvbhz/ciRRFobOzE+3t7QgODsbMmTPd+i6cTViOjhRZtGNxZluKotDe3o7GxkZMnz6dronnaaL0RDORFqD5k7xSqcSZM2cgkw/h+1/KIRtR0Z+dmpqINSuWISTYuR5e7hIoIjYZLDF7uJObZu171+v1YLFYNsdyrgV2dHd3Y9q0aYiJicGRI0cwODiIoaEhsFgs9Pb24u2338bmzZsdNgq1ht9FzBO4XC6Gh4dRUVEBsViMvLw8lxcGPRUxd7Y3bvkydepU9Pf3e7Q26K4l6Ghbg8GA6upqSCQSiwRmTxKlzwV3oisIBAIEBQWhoqXfpOMAADR39OD9XV/iyguLEBUV5bOir4EgYpPFnegKzuSmGbseSXqPo6jqc60h5p133onNmzfjzJkzaG1txaxZs5CSkoKBgQFoNBqkp6cDsG2Z2mPSihhFUdDpdGhqakJ2drZTkXzWmOjOzCqVCqWlpXTQyejoKHp7e90+viciZk9Q1Go1SktLQVGU1QRmb4iYvyfViWRUOY6OHuvNEQfko+js6UNrayt4PJ5JexlvRcUFgogFgjvRkXh4gq3cNOOu5SQ3jc/n2xXTc21NTKvV4oUXXsDJkydx4MABvPzyy5g9ezaOHz+OlpYWJCQkuN0A1O+1V9y5qHU6He0+TE9PdzpHyRoT2Q9MKpXil19+QUREBObPn4/g4GCP19TcbWwJ2B778PAwjh07hpCQEBQVFVlduLYmYoOyIdQ3d0AiGzL5t6PtzgeU4xq778fGJ2LRokWYNm0auFwu3XH5zJkzaGtrw+joqEffWyCIWCBYYr4oOG0LkpuWmZmJoqIiXHDBBUhJScH4+DhaWlqg0WhQUVGBrq4uKJVKejuSzuGNrs7btm1DRkYGgoODUVBQgMOHD9v9/M6dOzFnzhwIBAIkJCRgw4YNkEotCya4yltvvYVPPvkE11xzDeLi4ugCD2KxGDt27EBzczMA9yKIJ50lplAoUFpaSudx2GuE6AwTsSZmvK6Um5tr0ofJX/3IAOuC0tvbi6qqKos6jda2pRuKqsax68sfTCIgjTHuKu0vEfP3BC4Itl/iKEokBIfDod1OWVlZdFScVCpFe3s7OByOScSjK1XY/S1iJKTc37+DP9fljHPTBgYG0NLSgoiICJPctNraWgQHB0Ov13tsiX388ce45557sG3bNixcuBBvv/02Lr/8ctTU1FjtunHkyBHccsstePnll3HllVeiu7sbt912GzZt2oTPPvvMo7G8//77uP3227Fhwwa8/vrrtKs0Pz8fEonEo1zbSSVipAp5WloasrKyUFFR4bXGmJ5sb09E9Ho9qqqqIJPJrBbG9YaIuSsKLBbLJLqzsbERHR0dTiUwG4vRri9/sOgkbYxxV2lzEZtIQfOnBRgaHITUxBh09koscvampiZajVI0j4obGhqCTCZDa2srqqurIRQKaVELCwuzKxCBIGKA/wvvBkJwCRkHj8dDWlqaSW7aDz/8gP/+97/o6+vD7bffjlWrVuGyyy5Dfn6+y27Ql156CX/4wx+wadMmAMArr7yC7777Dm+++Saee+45i88fP34c6enpuOuuuwCcbW566623eqXWoUKhoKMPh4aGaIHW6/UYGRnxSLD9/2s6AUkErqmpwZw5c+geVZ5WywB8a4kplUocP34c4+PjKCkpsVrZfaKbappvS9YWz5w5g76+PixYsMCpBGay7aBsCI2tXXYFwrirNPnb+P/nC1csmYepqaapH1NTE3Hd8qUOt2Wz2YiMjERmZibmz5+P4uJixMfHY3R0FGfOnMHRo0fpZHlrtTgZETuLL9fEXME8Co9EtD7++OMoLy8Hi8XCmjVrUFZWhksuuQR///vfXdq/RqPB6dOncemll5q8fumll+KXX36xuk1JSQm6urqwd+9eUBSF/v5+7N69G8uXL3f9BM246KKLsGvXLgBnrwUyx7z11ltITk5GXFwcgEnaisXRoFUqFd2ZtqSkxKSmmbe6O/si2ZnkVTlKuvanO5HNZkOj0eD48ePg8/koLi522kVFLCpH9RmNkcpHwDkP1sQk8mHIhkYs8sCC+UG45drLIJUPQ2rlfVcIDg42sdJIAEF7ezsdQECstPDwcL+LGLlG/e1O1Ov1fq9eT8Zhr26iXq/HH//4R2zZsgU6nQ7j4+NWP2sLiUQCvV5PiwMhLi4OfX19VrcpKSnBzp07sXbtWoyPj0On0+Gqq67Ca6+95tKxrbFlyxb8/ve/xy233IK2tjZ8/PHH2L17Nz7++GM888wzFuN0Bb+LmD2IEMTFxWHatGkWPzqXy3W7MzTBG5YY8feTf7e2tqK5udkkr8re8UktQnducE8CO1QqFQYHB5GWlobs7GyXnpKJiDmqz2hMlFiIYbnpIrGjfBlvMRGTp1Klxu69B9HU/ptrNTMtCdctX2oi3FHiCK8mOZMAAlIZRK1W09VDOjs76d9qaGgIERERfpnEA8kS8/cYAMcV7AHQ60ZcLtftcHvz697ePFNTU4O77roLjz/+OC677DL09vbigQcewG233Yb333/freMTpkyZgpdeeglbt27FJZdcgoMHD0IgEOD//u//sGHDBo/2HZAiRlEUmpub0draalcIvGGJcTgcq+4XV7YHQNc/rKysxPDwMIqKihAR4XiiIjeUu24OdywxEmjS3d2N8PBw5ObmunxcMjHaqs9o/tnM9CRER4owMiSjvyvy2xGXMJvNpr8PX0w0vrYAd+89iOaOHpPXmjt68Mk3B5GVKJwwK4TP5yMxMRGJiYkwGAwYGRlBZWUlJBIJOjs7TeoAkv5aviZQLLHJIGJjY2MIDg72yO0ZHR0NDodjYXUNDAzYtHqee+45LFy4EA888AAAYPbs2QgNDcWiRYvw97//3e2KGoTCwkLs2LEDGo0GBoPBax3V/S5i5hc1CTtVKpUOq294S8RcNdWNITeEQqFAVVUVgoKCTHqWObu9JyLmyndA1hcHBgaQmpoKlUrleCMrGFuA1uozGkNqNRIoiqJDnYOCgmhRI+tzZP9sNntCrDRvIJEPm1hgBIqi0NTejQSRbyty2ILNZkMkEoHH4yE7OxuhoaF0xGNX19nfiwhaVFSUz6w0EpnIiNhv47DnTvQ06T0oKAgFBQXYv38/Vq1aRb++f/9+rFy50uo2SqXSIi+RjNGbD4Devsb8LmLAb0/1w8PDKC0thVAodGp9xlsi5qk7EQBOnjyJlJQUl91yxiLm7vGd3ZYkMBsMBpSUlGBgYABjY2NuHdc4ytBafUYAFrUaiStDJpOhsbERMTEx9AQL/GbNmltq5DyNLbVAQzbkqHeb/TwxX0O+ez6fj4SEBCQkJNB1AImg1dbW0sm4xErz1vcdCDligH0LaKLHYWt+IyLmKffddx/WrVuHwsJCFBcX45133kFHRwduu+02AMDDDz+M7u5u7NixAwBw5ZVXYvPmzXjzzTdpd+I999yDoqIip2vR+oOAEDGKotDR0YH6+nqXGix6IzrRk8AI4vYEgMzMTGRkZLi8D3KevhaxkZERnDlzBmKxmG606Ul4vrVtzeszGv+brBsmJCSAz+dDIpGgsrISer0eUVFRdK1BkvdHLDNvWWm+tgAiRfbXBgXBQX6PDjQ/vnEdwClTpkCj0dBWWmVl5dk1z/+ft+ZpTmYg5IiRcQS6mJKuzp5+X2vXroVUKsVTTz2F3t5ezJw5E3v37kVaWhqAszmhHR0d9Od///vfY3R0FK+//jruv/9+iEQiXHjhhXj++ec9Goev8buIGQwGVFRUQCqVoqCgAJGRkU5v609LTKvVorKyEqOjo+ByuU73/zKHTMTunoczgR22Eph9VbLKHOI+NBgM4HK5iIuLQ1xcHCiKwujoKCQSCbq7u1FbW4uwsDBER0cjJiYGQqHQYs3REyvNl2ti0eIIZKYlobmjx2oemCDY+cRkX+BM4JB5o0hipfX09KCurg5hYWF0OayIiAiXxCBQLLFAErGJaIh5xx134I477rD63vbt2y1eu/POO3HnnXd65dgThd9FjDwN5uTkuPyk5y8RI1VDQkJCUFxcjKNHj/otYdmeEJH2NO3t7ZgzZw5iY2NN3vekeoaz2xoLD7GejPchFAohFAppS0AqlWJwcBBnzpwBi8WiLbSoqCgTtyMRRhLZGQhradctX4pPvjGNTiR5YGdOnwo4S8wexr9NRkYGbaXJZDJUVVXBYDCY1Hh0tEjPWGKmOAqxP5eK//oav4sYcDYz3B0R4HK5Ey5i/f39qKiooKuGeGpJuTMGY2wJoE6nQ0VFBRQKBRYsWGD1pvAkPN+RiBFxIedlLmDWCAoKMlmvGR4ehkQiQWtrK6qqqhAREUGLWlhYmImVRgTNlpU2EYQE872WB+ZtPM0Ts2Wl9fb20j21SHCINSstkCyxQFkTc+ROZHCOgBAxd5lIS4yUZWpvb8esWbNMWodPZBFhZ7Yl/av4fD4WLFhgMxrI05JV9ixAIirkOK5inPuUlZWF8fFxSCQSSCQStLS0ICgoiBa0yMhIq1aasRVILLWJyEuzlgfm7wRvbyY7m1tpWq2WXkurrq6GXq+HWCymRS04ODigLKBAGcf50tXZ15wTIubJDeqMAGm1WpSXl0OpVKK4uNjCqvF36SjjbaVSKcrKypCYmIicnBy7N6wvLDHjYAxnrC9nCQ4ORnJyMpKTk6HX6zE0NITBwUHU19dDrVZDLBbToiYQCEystPHxcbS2tiI0NJT+nfzhdpxM7kRX4PF4JuucCoUCUqkUfX19aGhogEAgQEhICP1g4U8R8ffxCY5EjHEnOk9AiJgnAgR45iJwJECjo6MoLS1FaGiozbB/f1piRIhIhGdDQwOmTZuG5ORkp47rzehEYwvImwJmjnE1d9KIUCKRYHBwEA0NDQgJCTGJdiwvL0doaChmzJhB/96TLYTfUybKEmSxWAgPD0d4eDjS09Ppzsfd3d1Qq9U4fPgwxGIxvZ4WEhIyIeMCfnvACoTf2Jk8MQbnCAgRcxdyEXiS+2HPnUii+jIyMjB16lSbk7K/LTG9Xo/q6moMDAygsLCQLj/kCG9aYvYCOHyJcSPCtLQ06HQ6yGQyOoRfq9XSokYqh5uvpU1EovW55E50BdL5mHzXubm5kEqlGBgYQGNjI0JCQujgEJFI5NP1KvIbTIY1MWfvYYZJLmJkotHpdG5ngZPahcZPaAaDAY2Njejs7LQa1WdtH/6yxPR6PVQqFTgcDoqLi116svV0TYwEb7gawOFLuFwuPWn29vYiPT0dHA7HJEycWGkRERETmmh9rroTnT0+h8NBWFgYwsLC6AcOuVwOqVSKuro6aLVaEyvNuNi3NyD3mL8tMTLfMO5E7xAQIubuzUXasXgaGQj8tuCr0WjortHFxcVOmfX+ssRGRkZQW1sLFouF+fPnu/yE6aklZhwVSF7zdxg1RVFoa2tDa2srZs+eTbd8MA7hl0gkKCsrAwC7IfznQjksIDCK71pz43G5XMTExCAmJobuZkys6KamJgQHB9NWmlgs9tiCMn7Q8idkHPZEzBtdnc8XJsddaAdvlY0izdl++eUXcLlcpwWMjMFTS8zVc+jr68OJEycQFxcHDofjdt1FT6MTSf4PmdiPHz+O8PBwq/XZ2tvbERoaivLycvq1zz//HEuWLEFCQgLi4uJQWFiIhx56iH7/o48+crrkjUqlQl5eHmbOnImQkBBawKRSKVauXIlp06Zh+vTpuPbaa/HFF19g6tSpCA4ORmtrKw4dOoSTJ0/S4fyJiYlIS0sDn89HUFAQuFwuOBwOjh49iiVLliAuLg6zZs3Cu+++69Rv7093or9dmYDjPDEWi4WwsDCkpqYiLy8PixYtQmZmJiiKQkNDAw4fPoyysjJ0dnZibGzMrXMKlCLEjsSUiU50jYCwxDzBGyLGYrHQ29uLpqYmTJkyBVOmTHHpQp9IS4wkMLe1tWHOnDng8/k2+wM5wl1LjKIo8Hg8dHR0QKlUIiYmBtHR0QgJCcGOHTtw22234V//+hc6OzuRkpJicz8//fQT1q9fjyeffBLLly8Hi8VCXV0dDh486PKYtFotbr31VnotwdgVxWazsWLFCjzxxBOIjo5Gc3Mz7rvvPsjlcmzfvh2ZmZl0CH9fXx/+8Ic/IDc3F3V1dRgYGKCttLa2Nlx//fVYv3493nnnHRw/fhwPPPAAIiMjceWVV9LXki0rzZuTp1ardbr3m7Gl7C9czRMzt9KUSiXdXqa5uRlBQUF0cI+zVhpx4QWCiNmz5JVKJeNOdIGAsMQ8uag8FTEyiTc3N2Pu3Ll2Azhs4WljS2ctOZ1Oh7KyMvT09GDBggWIjY31OCjE1Sda4kJMTk7G/PnzERkZif7+fhw9ehQ//fQTdu/ejbVr1+J3v/sd/v3vf9vd1759+1BSUoJ7770X2dnZyMrKwpVXXol//vOfLo1JpVLhjTfewKlTp6w28BOLxdi8eTPy8/ORmpqKZcuWYfPmzSYdbkkI/+eff47CwkLccMMNAIDGxkYcOHAAZ86cwUsvvYSkpCS8+OKLmDVrFjZv3ox169Zh27Zt9NqqXq+HTqeDVquFTqfDoUOHIBQKUVZWht/97neIi4vDxRdfjMbGRpMxvvfee5g9ezaioqKQn59Pd8ElCIVCvP/++7j++usRHx+PF154Ac8++ywWLlyIjz76CNOnT0dCQgLuvfde6PV6vPLKK8jMzMSUKVPw4osvAvCviHlSsYME76SmpmLu3LlYtGgRsrOzAQANDQ34+eefUVpaio6ODrtWWqBEJjoKRGOiE13D/7+oh3hSBFitVuPkyZMAgJkzZ9LuJ3fG4GtLTKlU4vjx49BqtSguLqZ95t4KznCEcTUM40X69PR0FBYWYsmSJaiurkZqaioUCgVmzpyJ999/H729vTb7tcXFxaGmpgbV1dVujR8AhoeH8d133+HVV1/Fjh07nFpL6O3txZdffokLLrjA5PWDBw/is88+w8svv4zQ0FBwOBwsXLgQxcXFiI6OxsmTJ5GTk4OjR4+ivr4eUqkUF110EUpLS8FisRAUFISgoCCL6EfgrFv0sccew4EDB8Dlck3q2X311VfYsmUL7rzzThw/fhwbN27EHXfcgZ9//tlkfM8++yyWL1+OY8eOYd26dQCA1tZW7N+/H3v27MEHH3yAjz76CKtXr0Z3dzf27duHp556Cs888wzq6+snlSVmDw6Hg+joaOTk5KCkpATz589HdHQ0ZDIZTp48iWPHjqGurg6Dg4Mmc0OgJDrbC+oga4PMmpjznLfuxKGhIZSWlkIsFiM4ONhp14ytMfgyOpEkMCckJCA3N9fkRiTbuhN9RvbjaFvj/C/iLjP/PI/Hw+eff44NGzZgyZIlmDlzJrZu3YpPP/0Uubm5iIiIgEZj2o7k9ttvxy+//IKioiKkpqaiqKgIF154Ia6//nqn6mgODAygsrISb775Jm699VYUFhaivb3d5ufXr1+Pb775BiqVCldccQW2bdtGvyeVSnHrrbfi/fffh1BoWpGehPArlUrk5eUhKysLEokE1dXV6O7uhk6nQ2VlJaZPn27SzNB4slq3bh0uuOAC8Hg83HXXXbj++uuhVCohEAiwdetW3HTTTdi8eTMAICsrCydPnsTWrVuxePFiehzXXXcdLV4Eg8GAN954g25uumjRIjQ1NeHTTz8Fm81GVlYWXnrpJVRVVXldxCTyYcicLK/lSytIIBBAIBAgJSWFToQnbkeVSoWIiAhERUWBy+X63ZUInPWo2LPEyHXB4BwBIWKeXFju1E8kvZNI25ejR4/6Lc/L0fakRU1ubq7V9SVnhcgaxm1gbE0w5gJm63MNDQ04deoU/vOf/9CFe9esWYNTp07hD3/4A523BQBlZWXg8/mIiYnB7t270dbWhp9//hm//vorHnnkEWzbtg0HDhyweSOTxO7m5macOnUKOp2O7kZrj+effx6PPPIIGhsb8eSTT+Khhx7CK6+8AgD485//jDVr1lhYZ+ZwOBzExsYiNjYWFEXRVubAwABkMplJCL9QKERPz9lOzytXrqQrhpDOun19fUhOTkZ9fT3Wr19vcpz58+fjrbfeMnktLy/PYjypqakmT+2xsbF0mx1CTEwMhoeHvTaBK1Vq7N5rWug4My0J1y1fipBg6w8fE1UA2DgRHjjraiZraTKZDABQW1tLRz2aN4GcCBwVZ2AsMdcICBHzBFcsMYPBgNraWvT19SE/P5++0P0RXWi+vbnLjYy1v7/fbgKzM0Jk77iA7eg1Vypw/Otf/4JOp0NWVpbJ9jweDy+//DJSUlLo7zgtLQ0URaG6uho6nQ6RkZG45JJLcMMNN+DBBx/EnDlzsHv3btxyyy0WxzEYDKivr8fAwAAKCgrw9ttv49dff7X4fhYtWoS1a9fi3XffpV8jBWxzcnLoY27ZsgUJCQk4dOgQvvnmG7z66qv02A0GA4RCIV577TWsX78ecXFx6O/vp/fHYrEwNjYGLpeLiy66CBRF0SH8paWl0Ov1kEqlAIDw8HA6ipTkNBo/FFAUBY1GQ79m7Xq0tk5i7kFgsVhWX/OmiOzeexDNHT0mrzV39OCTbw7ilmsvs7qNvwoAh4SE0OXK+vr60NbWBi6Xi5aWFlRXVyMiIoLOSwsLC5sQobW3JqbRaKDRaBgRc4HzRsTGx8dRVlYGg+FsV2PjpGBv5Jp50xLTaDQoLS2FTqdzmMBsnKDtKuSGtVUD0VkB0+l0+M9//oPnnnsOF110kcl7N910Ez7++GO6myxwNtBi2rRpyM3NhUKhMOknFhoaiuDgYEilUotxEbedSqVCUVERQkJC8OKLL+Lxxx+nP9Pb24uVK1dix44dmDdvns0xk30TF+dPP/1kcg188803eOmll/Djjz/SIf5FRUXYt2+fyX5+/PFH5Ofn08KRkJCA6Oho+veLjo4GABw9ehRJSUmIjo6GSqUCcFaA+Hw+cnJy8Ouvv+LGG2+k19FOnDiBrKws6HQ6r03+3pigJfJhEwuMQFEUmtq7IZUPW3UtBkL1eIqiEBQUhKysLGRlZUGlUtGFi9vb202sOLFY7NESgz0c1U0EwEQnusB5IWJyuRxlZWWIiorCjBkzLC4gbwRmeGv70dFRnD59GiKRCAUFBQ7dHY6sKWe2NRZAdypw7Nu3D0NDQ1i/fj0iIkwnsKuvvhr/+te/TESM8Oyzz0KpVOKyyy5Damoq2Gw2Xn31VWi1WkRHR+Pnn39GT08PDAYDFAoFKisrERQUhHnz5tETjLmLldz8GRkZSEpKAgB8++23tOUWFhaG2tpaPProoyguLqa73Obm5prs58yZM2Cz2ZgxYwb92qZNm/D2229jy5Yt2LBhA06cOIF//etfJs0FVSoVSktLIRAIUFBQQAcWFBcXQ6vV0mtpAOiyS3fddRd+//vfIz8/H4sXL8a+ffvw9ddf47PPPjNJtNbpdG5V4fdGtQ6y/jWiGLP7OenQiFURC4RWLObeipCQECQlJSEpKQkGgwFDQ0OQyWRobW1FdXU1hEIhLWretNIcNcQErFvdDNYJCBHzNMRerVZbfY+iKHR2dqK+vh7Z2dlITU21eqxAscT6+vpQWVnpUq6asTvRVcy3Na5SQd53Zgz/+te/sGzZMgsBA86uBf3jH/9AaWmpRdfuCy64AO+88w42b96MgYEBiEQizJkzB19//TUWLFiAoaEhnDx5EgaDAceOHQOfz0dCQoJLOVLA2clq+/bteOihh6BWq5GcnIyrrroK999/v9P7AID09HTs2bMHW7ZswTvvvIOEhAS8+OKLuPrqqwGcfQA5c+YMYmNjkZuba/Ld8fl8xMXFmRRm5nA4aGxsRFhYGO688068+OKL+Mtf/oL09HS89dZbuOiiiyxKYBn/mwT0+BJr61/2iBIJrb4eCE0x7bnc2Ww2IiMjERkZSecNknU0YqUZNwH1xEpzZIkJBAK/C/5kgkUFQjo/zrp13BlKS0sLRkZGMHfuXJPX9Xo9ampqMDg4iLlz51pMoMZUVFRAIBAgMzPT5eMDwODgIOrq6rBo0SK3tm9vb0d7ezvUajVmz55NL/w7y/fff4+FCxe69fRGthUIBB73APMFg4ODqKysRFJSEoKDgyGRSCCXy+mKHNHR0RCJRH4fr1QqRUVFBcRRMQgJFSIqMgLRTjbEJFX4JRIJZDKZSRV+sVhsYjEbu3mN7xd7idZjY2M4deoUlixZ4vJ57fj0OzR39Di8N1ksFqamJtpcE6usrERERARSU1NdHoO3aG9vx+joKGbOnOnSdqRBKxE1hUJhYqWFh4e7JNBNTU3Q6/XIycmxeO/UqVNYu3Yt+vv7/S76k4WAsMQ8wZoVpVKp6Np4JSUlDlune2pJebK9TqdDd3c3XavRnQVdb9VAnOgK9I7o7OxEY2Mjpk+fTjchNa5UTwTOYDAgKiqKnvjdLQbtLj09PaiorELboBKdp5ro17PSk7H2ygttRuwRBAIBUlNTkZqaalKF3zjwhZybeQi/saAZW9DG9R3ddSfaWv+yxtTURFy3fKnN9wPBnehutwvjBq3A2fxSEvHY2dkJFotlYqU5uv6Yrs7eZdKLmHmIvUwmQ1lZGWJjYzF9+nSnbpyJSFa2hkqlwpkzZ2AwGBAWFuZ2RJKnVTt0Ol1ACRipl9fb24v8/HyIRCKT90mlehLmPjIyAolEgo6ODtTU1EAoFNJWmi8jzkix4ba2NnRIVejqk5i839TejY+/+gm/v+5yp/dpfm4k8KW3txd1dXUIDQ2lz828Cr+xlWZ8PWu1WvphxRUhkQ2N2H1/5SULER4mcDpPzN/Xlrdy1fh8PhITE5GYmAiDwYCRkRFIpVL6+gsPD6etNKFQaHHeer3eptApFAoIBAK/f1eTiYARMVudgh1h3N25vb0djY2NNnOq7O3D1rqaK2NwBZlMhtLSUiQkJCAyMhItLS1uH9+dqh0kgIPH46GyspKuUycSifx6A+n1elRWVmJsbAxFRUUOkz5ZLBYiIiIQERGBqVOnQq1W0w0yW1tb6Rp80dHRiIyM9FqEHEVRqK+vR39/PzKmZuOHU3utfqaxrQsS+bDTrkVjjBtMZmRkQKvVWlThJxZoVFSU1WohBoMBMpmMrmzjShX+SBvrW4T05HiH4kUIBEvMFwnXbDYbIpEIIpGIvv5IxGNXVxcA0FYa+Y2YNizeJWBEzF3IzVlRUQGZTOZSU0jjfUxkdGJnZyfq6uqQk5OD1NRUDA4OTmg/MuMAjnnz5tHuK1Jd3lp7kolArVajrKwMbDYbRUVFbh2bz+ebRJzJ5XIMDg6ivr4earXaxDXnbldhvV6PqqoqWmg7eiV2Py+Vj7glYubweDw6142iKAwPD0MikaC9vZ2OpiPnFh4eDi6Xi8bGRvT09GDOnDm029vZXmnR4ghkpiVZrImR9S9nBQwIHEvM19czCT5KSEiAwWDA6OgoLWi1tbUIDw+HTqcDn8+3KqpM3UTXmfQiptPpoFQqERQUhJKSEqfKFZnjjQK+xLKxd6MaDAbU1dWht7cXBQUFdLCJLyt+mENcTmRSCgoKspgYBwcH0dLSgqqqKojFYkRHRyMmJsanpXAUCgVdBsxZN7Aj2Gw2/QRMatJJJBL09/ejvr4eoaGh9LlFREQ4NclqNBqUlZWBxWLRof5RIvtu4CixfYvGHVgsFm0BGEfTSSQSOqGXw+FAq9UiPz+fLqXlaq+065YvxSffmEYnOlr/soYtS8yV0lWeotfrHa6PexM2m017CUg/O5lMRj9Y9Pb20hGRUVFR4PP5XlsT27ZtG/7xj3+gt7cXM2bMwCuvvGI38EytVuOpp57Cv//9b7qSzF//+lds3LjR47H4mkktYmTxm0wo7k583upJZs9NQCY/UsDXvFXIRKzJOUpgNp4YSTLo4OAgBgcH0djYCIFAYDLpe8s1QyL7UlNTXW6D4yykXxUpWmzNNefIAiVrmGFhYZg5cyb9W0dHipCVnoym9m4LiyUzLckrVpgjgoODaQtUq9WitLQUSqUSXC6XrmhCzo8UNwYcd7TmB/Fwy7WXQSofPpsD5obYSOTD6JMMQxSpBGmS7k7pKk/xdxV78sDY2dmJtLQ0Oqm/p6cHP//8M55//nm6h5qraSTGfPzxx7jnnnuwbds2LFy4EG+//TYuv/xy1NTU2IwOXbNmDfr7+/H+++8jMzMTAwMDbhdWn2gCRsRcmbgoikJrayuam5sxZcoUNDc3e3RxerOxpjURI/lDQqEQ+fn5FgnMnlpizkQnGltgzgZwhISEmETNSaVSDA4O0m7HqKgoxMTEeOR27O7uRl1dHaZNm+Z080tvYM01R9bRqqqqIBKJTCZ9hUKBM2fOIC4uDjk5ORbf39orL8THX/2ExrYu+rXMtCSsvfLCCTsn4GwQBxHlhQsXgsfjmYTwNzU1gc/n0w8jIpGIvh6Ng0OIZ4FMZMIwAURC14JkzIXq15p2WqjcKV3lKf4WMQKZJ4RCIYRCITIyMjBlyhQoFAp8/PHHqKioQHR0NC6++GJcfvnl2Lhxo0vjfumll/CHP/wBmzZtAgC88sor+O677/Dmm2/iueees/j8t99+i0OHDqGlpYX2DqWnp3vlXCeCgBExZ9HpdKiqqsLQ0BCKiorA5/PR2NjoUVUCbyQ7A9YTjvv7+1FRUYGMjAybvcp8XQXf+Enb3QhELpeLuLg4xMXFWZ30XXU7kuaeXV1dyMvLs5vH52usWaBk0m9ubgaXy4VWq0VCQgKysrKsfn8hwXz8/rrLIZEPQyofQZRYOCEWmDFqtRqlpaXg8/mYPXs2fV0ah/Dr9Xo6PaGmpgYajcYkPcFaCL9xGx6CvbU0gi2h2vnFD+jo7rf4vKPSVZ4SaCJmTGRkJDZt2oS6ujosWLAA69atw759+/Djjz/SYuQMGo0Gp0+fNumODgCXXnqpSf88Y7788ksUFhbihRdewEcffYTQ0FBcddVVePrpp91eN55IJpWIjY2NobS0lF7/CgoKogvn6vV6tytSeypiJMnUeB8URaGlpQUtLS2YNWsWnedkDW+sidmqf2hcgcNbIfS23I4SicQpt6Ner0d1dTVGRkZQVFQUcAvZISEhSElJQUpKCrq6ulBXVweRSASpVIpDhw6ZTPrma7DRYueTnL2JUqnEmTNnIBKJ7K4pcjgck47J1kL4ybnZC+G3dl0Zr6XZq7FoTcCMsVW6ylMCWcQIY2NjiI6ORkFBAQoKClzet0QiMemUQIiLi7PZAb6lpQVHjhxBcHAwPvvsM0gkEtxxxx2QyWT44IMPXB7DRBMwIuZociVurOTkZGRnZ9MXI7kY/CliZB/kpja2FufPn2/Rn8ocIkLuWpPWRNA8gMOXN681tyOJdqQoihY0EmBB3F1FRUUTnpjsLMY5YHl5efTYFQoFBgcH6YLF4eHhJq1X/BGBR9zV8fHxyM7OdnoM9kL4yW9n7DK2FcJvLdFaKht2+3xsla7yFHeTnb3NRITYm18D9uYWEjm6c+dOunTcSy+9hNWrV+ONN94IeGssYETMFsYWzYwZMyzWTchToD/7gZF96PV6evGfy+XS1qIz2wLuV/o2H7+xBTbRCczW3I4SiYR2O7JYLAgEAsycOTOgBYzkgBUWFtJJ6MaTPok2I27Hjo4OsNlsWrAnqlcVKW6dlpaGjIwMj35r83XCkZERDA4O2gzht5doLQyz71JOTYxDZ++Ax6H7rhAIlhi5L+2JmCdtWKKjo8HhcCysroGBAZvl7BISEpCUlGRS+3TatGmgKApdXV0m7ZUCkYAWMZL/NTo6atei8caaljcsseHhYTQ3NyMuLg7Tpk1z+obxVMSMAztcaaHia4zdjpGRkSgrK6ND2U+cOBFw9Q8Byxwwe0+hQUFBJpUbhoaGaJeqSqWCWCymz88X6Qmk7FZ2drZJYWFvYJxEnpmZSSeRkxB+DodjEs1JQvqBs9dxXEwkpqYmoqWz10KopqQmYM3yZV4J3XeFQBExADbvc0+7OgcFBaGgoAD79+/HqlWr6Nf379+PlStXWt1m4cKF+OSTT6BQKGgrsKGhAWw22+vXlS8IGBEzn2xJ3lBwcDCKi4vtPrWThGd3Ma764e6kr9fr6Q7MrhY59aQnGNnePFTa3wJmTG9vL2pqapCTk0PfFMZuR1L/kEyK0dHRE5pkTbCWA+YsxlXQs7Oz6YjAwcFBNDQ00OuE3hLsnp4e1NbWYubMmS4XjHYH8yRyItjNzc2orKw0CeEXCATg8Xi4YeXF2PXFDybBHRkp8bj6koXgsFm4ceVFkA8rIB8ZnZA8sUDoaUYelm2NQ6FQeNwQ87777sO6detQWFiI4uJivPPOO+jo6KDbIT388MPo7u7Gjh07AAA33ngjnn76aWzYsAF/+9vfIJFI8MADD2Djxo0B70oEAkjEjOnv70dlZSVSUlKc8vF7M7rQ1YucJDBrtVpMnTrVrSrdxiH67kBEzJUeYBMBcQV3dHRg7ty5dCdtwLbbsa2tDdXV1RCJRLQVMxGBH7ZywNzFvKivuWB7UrC4vb0dzc3NFt/pRGFLsM1D+CMiIpCbIkZ2SjSi4+IRJRIiUiQ0SbSOCBcgIlxALwm42ivNFQLBEiO9xKzdnxRFeSXZee3atZBKpXjqqafQ29uLmTNnYu/evXTvvN7eXnR0dNCfDwsLw/79+3HnnXeisLAQUVFRWLNmDf7+9797NI6JIqBEjIRdt7W1OYzoM8a8CLCrGAeHuDJ5kSd3jUaDsLAwj9wA7obZE+txcHAQAoFgwktF2cJgMKCmpgZyuRzz5s2zu1htXn2ChLiTJGtfux1HRkZQWlpqMwfMU8wF292CxeT+6O7uRkFBgdX+bf7AWgh/X18f3fwzKioKoUFshAnOhu97K4TfVew1o5woHI3BG5YYANxxxx244447rL5n3MSVkJubi/3793t8XH8QMCKm0+lw5swZjI2NYcGCBS79kN6suOEs5gnMpaWlE1r/EPgtgCM5ORm9vb1obm5GdXU1XR8wJiZmQsvsELRaLcrLy6HT6ehcPlcwDnG31nbFm25HqVSK8vJyZGRkID093ecWrHnB4vHxcdqKaW1tBY/Ho8/NuGAxRVGora2FVCrFvHnzAi4tgcDhcBAaGoqhoSEkJiYiJSUFUqkUfX19JqW+7IXwW2vO6mzRYluQ6N9AEDF7D8qeromdjwSMiGk0GnC5XBQXF7s8MXkjz8uV0k8DAwOoqKhAWloaMjMz6e0nuogvCeAICwtDdnY2srOzMTY2hsHBQXrSCAsLo/OCXG3e5w5KpRKlpaUIDQ1FXl6ex245a21XBgcHTdyORLBdndjJutL06dORkJDg0TjdJTg4GMnJyUhOToZer4dcLodEIjEpWBwVFQWJRILx8XHMmzfPLw8mzqJQKHD69GmTxPDw8HCLUl/GIfzGblVHVppxPporgmSc0+ZP7IkYqe/pDUvsfCJgRCw0NBRz5sxxa1tvRRc62oe9BOaJqn8I2K/AERoaitDQUKSnp9Mh4CRMmjzlkxBwb9/QQ0NDKCsrQ0JCgkv5Ss5iHjFn7HZsbm5GcHCwSTklW+dnnAPmr3UlaxhH/JEJbWBgAM3NzdDpdAgNDUVXVxdtxQTCuqcxw8PDKC0tRWpqqtVwf2sh/BKJBJ2dnbRbleSl2Qvhd5RobQ1HUYEThT0R02g00Ol0jIi5SMCImCc35ESIGOlzZSuB2delo4DfXCLOBnCYh4CTtiS1tbXQarX0hOGNbsh9fX2oqalBZmbmhLWgd+R2ND4/MiFSFIW6ujoMDAyY5IAFGiwWC0FBQRgYGEBERASmT5+OoaEhDA4OOl2weCIh+WpTpkyhAwjsYa0PHKnNaZxzZyuE35mO1sYY3zP+xFG1DgBMPzEXCRgR8wRPQ+wB+5aUSqVCaWkpOBwOiouLra7x+NoSM6/AQZ4+Xdk/aUuSk5MDhUKBgYEBOrggIiKCdju64pYjVk1raytmzZqFmJgYp7f1Jo7cjhEREYiOjoZUKoVarXaYA+ZvSLSkUCjEjBkzwGazaSuGdBO2VrCY1K6cSCuNrCt6kq9m3i3ZPITfvCCzMx2tjYNDSFUKf1uv9iKgFQoFXQyAwXkCSsTc7e7saXQiYNuSksvlKC0tRWxsrMOadL6yxLydwGxcecI4uMDYLUcEzV7LFZJeIJFIUFhY6LC81kRh7nYcHx9HX18fWltbodPpEBISgo6ODoduR39BKubHxsZajZY07iZsrWCxcZV6sVjs0/MbGBhAVVUVpk2b5rV1RfMQflvnFx0dDbFYTEc8GnsqzHulaTQa+h7z5+/tyBILDQ31u9BONgJKxNyFw+FAo9F4vA9zISTdWLOzs5Gammr34mKz2R5Zg7ZEbCIqcBgHFxi75Yw7PZP6eaSUklarRUVFBTQaDYqKigI62ICiKHR3dyMyMhLTpk2j3XL23I7+YmhoiF5Xcra3mrFb1bhKfXV1NXQ6nd2CxZ7Q29tLJ1zHkkZhPsDa+UkkEtTW1kKj0Vh06zaONibWWX9/P/h8Pn2P+iKE3xnsiRjp6syImGucMyLmzTUxg8GA+vp69PT0ID8/36mFf0+F1Jo70h8VOMzdcqTlCnHrREZGQiQSoaenBwKBAPPmzZuQGoHuYi0HzJrbkdQHNHarTrRbTiKRoKKiAllZWUhJSXFrH9aq1FsrWOxptGpXVxcaGhowZ86cCQ2MMT8/827dAoGAfuASiUTgcDhoaWmBRCLB3LlzweVynepo7SvsiRgTXu8eATX7uOtO9KaIaTQalJeXQ61WW3Rgtoena2LG7khXAzh8hXnLFaVSic7OTrS0tICiKHA4HLS3t09Y+L6rkLUaEmxgrZO1uduRtJQhbisyYfra7UhKc82YMcPpJH9H+KpgMVkDzc/Ph0gk8spY3cFat25ipRErm7Rrmjt3rom7e6ITrQmMJeZ9AkrE3MUbIsZms6FUKnH8+HGEhYVhwYIFLlkY3loTs5boGSgXtUKhQHd3N7KyspCQkGASTUaekM2TdP2FOzlgwcHBJm4ra6WijNuSeIuOjg40NTX5PNzfWsFiUhVFpVKZuOWsPbyRFJPOzk4UFBQEzBoogcfj0ZVRDAYDKisrIZPJEBwcjFOnTlm0zfFmCL+z6PV6m9eOt9qwnG+cMyLmaXSiRqPBwMAAMjIy6ARmV/BGdKJer6dvpIlwbTgLRVHo6OhAc3MzZsyYQRecTUhIQEJCgkn4Pqkj6c3wfVfH6o0cMA6HY+F2lEgkFm5HEi3nzoMGRVFobm5GV1fXhJeRMg6eyMnJod1ytgoWs1gsNDQ0oK+vD4WFhQE92ZI0itHRUSxYsAAhISF0CL+5FUoeung8nkch/M7iTGAHg2sElIi5a3F4YolRFIXW1lbI5XJER0e73TvHG5aYTqcLuAr0BoMBDQ0N6O/vtznRWgvfHxwcNEliNQ7f99W5+SoHzFapKLJWSNyOJFrOmQmOjHVwcDAgRIEkyaelpVktWMzj8aDT6ZCXl+f3sdqDoii6ZmdhYSEdcGQthF8qlbocwj8gkUM+MorIiHBER/7mYnZW1Bz1EmNEzHUCSsTcxd0Qe9I7Si6XIyEhwaMABU/KTlEUheDgYDQ3N2NoaAixsbF0YIE/0el0qKyshEqlcjqvynwdRq1WY3BwEIODg2hpafHZOhNJRlcqlT7PATMvFUWiAauqqpyqUG8wGFBVVYXR0VHMmzcv4PLVjAsW6/V6lJWVYWRkBMHBwTh58qTTBYsnGoqiUF1djeHhYRMBM8fYCrWWohAUFGRRv1Kt0eJ/Xx9AY1sXvZ+pqYm4+tKFCAnmO22lObLEAvkBIVA5J0TMHUtsfHwcZ86cAZvNRnFxMTo7O6FSqSZ0DCSAw2AwIDU1FfHx8ZBIJBgYGEBjYyNCQ0PpCV8oFE7oZDE+Po6ysjLweDyXe2sZw+fzTSZ8so5mXMyXrDO5ewxP+oB5inm03OjoKL1OSJLIjWs76vV6ujjyvHnzAra7NQB6XUmj0WDhwoUICgoyKVjc0tJidcL311irq6sxOjqKwsJCl1IJzEP4Sf3Kuro6OoT/eFUbuvulJtu1dPbi8/2/4OarL3Y6OMRRYAcjYq5zXooYycWJiYmhE5i9UQnf1QK+5gvIxjcTKZY6MDCAM2fOmEyWvqh7aMzo6ChKS0sRFRXlUodqR5ivM5HwfVJ1gnRCjomJcdo68XYfME9gsVgQCoUQCoUWbkcy4RsMBvD5fOTn5we0gBELTK/Xo7CwkH4wsFWw2HjCJ1baROUOEstWoVCgoKDAo1w44/qVZK2wqbUdXX0Si89SFIXm9m6MjKkQGXHWdW0t0drYSnMUYh+oZdACmYASMU/WxMiF42gfthKYvVGF3lkRNC8hZU0kjIulWqt7SJ7uvZ2gS3KVfN2axFr4vnFggTNWqK/7gHmK8YRPqnCQfMKjR4961BjTl2i1Wtqyzc/Pt+lmtzbhSyQS9Pb2oq6ujv4NfVmwmFiLSqUShYWFXv0eSQh/SKj9KMzGplbkz54GPp9vkWhtnHBNhI08wJrf92NjY37rpjCZCSgRcxfjppa2bjhHCcze6A7tjAi6U4HDPHCCuKxIpJw7Fow1Ojs70dDQ4JfWJMaNFYkVOjg4SLt8ja1QDofjMAcskCACFhMTg9zcXABw6Hb01/loNBqcOXMGfD4fs2fPdtqytZazRdyOpaWlYLFYXi9YbDAYUFFRgfHxcRQUFPjsQSBKZN86Uo2N4vDhwxYh/GQuIkLW1NQE4Kz70pqVxgR2uMc5JWI6nc6qiJEnS3sJzN5wJzrTyoU8kbkbgWjuslKpVHTghLEFExsb63QCMkVRaGxspAVeLBa7PC5vYm6Fknwm0mMrNDQUCoUCOTk5ble2mChIe5KUlBSTMlL23I4TWfvQGLVajdOnT9OuWU+Oy+PxTFIwhoeH6caf3ihYbDAY6KIEBQUFPl0HjY4UISs9GU3t3SbFGFgsFjLTknDh0kVWE8mJpR0ZGYmOjg709/ejsLAQISEhVhOtOzo6sGDBAp+dx7lKQImYu0+fxv5mc8hTcGhoqN0EZm9ZYtZcmsYBHN4OoQ8JCTGxYMhkeOrUKXC5XFrQbE2GJEJToVCgqKjI7xGR5phHkjU0NKC7uxsCgQD19fXo7e2dkPB9dyDWoqP2NLaiHY1rH/o6506lUuH06dMQi8WYPn26V79HNpsNsVgMsVhsEg3obooCCY7RarU+FzDC2isvxMdf/WQSnZiZloS1V14IwDKR3Fi0KysrwWKxkJqaSs8P5iH8X331FaqqqrB69Wqfn8u5RkCJmCdYC7MnRWxTU1PpLrO28IYlBljmgVgL4PDVRGv+9Gs8Ger1eotCt2q1GmVlZWCz2QEfKUdRFOrr6zEwMICioiKEh4dDrVZbWDATVSbKEX19faiurnbZNWsr2tGXOXdjY2M4ffq0zar53sY8GpDkpDlTsNg44CQ/P3/CIlFDgvn4/XWXQyIfhlQ+giixENFi68npxqLN4XAwPj6OlJQUjI6O4sSJE3REZ0dHB0pKSnD06FFs3rwZO3fuxJo1aybkfM4lzhkRMxYhksBMKkwkJiY63N7TwA7jbH/yb2cCOHyFcUWC3NxcjI6OYmBggO6vJRQKMTY2BrFYjNmzZwdMdRBr2MoB4/P5SEpKQlJSkokF483wfXfo7OxEY2Pj/2vvzOOiLPf3fyH7voMgsqsosmu2aGquKAhqHbOTX9MWO6apHc1s01IztVzKOh49dSyXNkFSc8MFUzOTVTYRBAURZoadmWGY7f794e9+zgzOwDDMBj7v16s/ghHuB4bn89yf+/pcF6KiouDh4aH111GndlSUt9OCpm3bsbW1FdnZ2fD19dXKqaandFSstra2oq6uTqVhsZ2dHfLy8iCXyzsVnOgTD1dntcWrIxUVFaisrFQavKeKTg6Hg2XLloHD4cDW1hbPPfccRowYoc+l91nMiDaOu3qCEKK1E/ylS5cwdOhQuLq6oqCgAA0NDYiNjdXYyqelpQXXr1/HhAkTtPr+crkcZ86cwbhx42BjY6PU7zYlBw7gf76CVlZWaG9vh4ODA3MzNDUjX8UZsOjoaI2KkaI7PY/HY4o13YXqq2VKvQUrKysRExOjV3NcxUgSHo+nVduxubkZ2dnZCAwMRFBQkN7Wqi30nInH46G+vp5xDhk8eDA8PT1NOj3h7t27KC8v79Q5JiMjA4sXL8aoUaNQV1eHS5cuITQ0FOnp6RgwYICBV9x7Md13QTcxNzdHW1sbSktLYWZmhieffLJb8yK6aCeamZk9ZFNjagWsuroaN2/exNChQ+Hr68uco3G5XNy9exeWlpY9frrXFdrOgHV0p+8ofqFxHTT0Uxe/H8V258iRI/U+tKrYdqQ77bq6uofajupcNRoaGpCbm9vleZ0xoedMXl5eyM7Ohlwuh4uLC8rLy1FYWNilYbGxqKysRHl5OeLi4tQWsCtXruD555/Hli1bsGjRIpiZmaGlpQXnzp1jZfbdpM/sxK5cuYK2tjZ4e3szce7dQSQSISMjA1OmTNH6ppaeno5Ro0bBzs7O5AoYNZutqqpCVFQU3NzcHnoNbXVwuVzweDyllpyHh4dBn3z1NQOmKN+vq6tTiiNxd3fXalha0UYqNjbW6DZS1Oqrrq4O9fX1D7Ud6+vrkZ+fj7CwMI1a7cZEIpEgJycHFhYWiIqKYn4/dCatrq4OjY2NjGFxV2nk+qaqqgplZWWddoH++usvJCUlYcOGDViyZInJ3CN6KyZVxIAHf4Ddpbq6Gvn5+fD29kZ0dLRWbwqJRIJz585h4sSJWt2sCSE4f/48wsPD4e7ublIRKjKZDIWFhWhpaUF0dLRGuwTFlhyXy4VQKGTcGDw9PfXqxkAHrvU9A6Yo3+fxeGhvb1e6Rk128lQpJxaLTdKFo2PbUSKRQC6Xw8/PDyEhISa3XkUkEgmys7NhZWXV6cyaYo4Yj8cDIURvsTmdQYNCO8tZy87ORmJiIt5//3289dZbJnOP6M306iJGWzj37t2Do6MjvLy8tO7t0zOtZ555pttveto+LCwsRG1tLXPG5OXlZXSDVHqmBADR0dFa/0ELhULmZt/U1KS3a9QmB0wX0JRguoNpbm6Go6MjU9BUXSP92ZqbmyMqKsqkz2iA/7WSvby8IBQK0draarJmvhKJBFlZWbC2tkZUVJTGOyvFh6+6ujrw+XxmkFyf11hdXY2SkhLExMSonbO8ceMGpk2bhlWrVuGdd94xmZ91b8fkiphYLNYo3VkikSAvLw9tbW2IjY1FeXk5bG1tERoaqtX3JYTg9OnTGDt2bLfaQR0FHFKplDljqq+vh6WlJeNKb2jZt0AgQE5ODhwdHXXqK9jxwJ2eo3l5eWl9jYo5YOranYZE8Rrr6uoeaslRZwt7e3ujezZqAlVMRkdHMz9bxREFxbYjHdA1VktOLBYjKysLdnZ2iIiI6NE6FBWd9Bp1bVh8//593Lx5U+ln25GioiLEx8djyZIl+PDDD9kCpkN6ZRFTHGCOjIyEpaUlioqKYG5ujiFDhmj9vdPT0/HEE09ofCjflYBDUfZN2xz0Rqjt+YumNDY2Ii8vDwMGDNCrdLrjNdJzNC8vL7i7u2u0O1HM1oqJiTE5E1R6VkivkfrfOTs7IzIy0qRbcsADqfedO3c6PadRvMa6ujom2JSeMxnqGmkBow8HuiykiobFPB5PJ4bFNTU1KC4uRlRUlNoA1pKSEsTHx2PhwoXYuHEjW8B0TK8rYnSAeeDAgRg8eDDzhigpKYFMJsOwYcO0/t7nz5/XOGFX0dxTEwEHdW3ncrngcrlob29XEk3o8iZRU1ODoqIiDBkyBH5+fjr7ul2h6EzP4/E0OkdTnAGLiYkxuiiiK2gCgoODA6RSKQQCAVxcXJhrNCWVXMfkaE0fDgghTLBpXV0dWlpa4OTkxLxf9dWS06XtVVeoah87ODgwuzRNVKu1tbUoKirqtICVlZUhPj4ec+fOxZYtW0x6HrO3YnJFjB48d4S2m8rKylQOMJeVlUEoFCIyMlLr752RkYHIyMhOW1nUQorK8bURcNA/IFrQ+Hw+XFxcmLajtjdyxTmliIiIHg3a6gJ6k+DxeEpnTF5eXrC3t1dyS9d0BsyYUBupkJAQBAQEAICShVJDQ4Ne5PvaoCj5j4uL65GxrGLbsaGhAZaWlkrejrroKIhEImRlZcHZ2ZmJRzIkiobFdXV1jGExNZ3u+N7kcDgoKChAZGQkPD09VX7NO3fuYOrUqUhKSsLOnTvZAqYnekURo6KJ+vp6tS2RiooKNDU1ISYmRuvvfenSJYSFhal9U3Z04NCVAlEkEjGy9sbGRq1EE3K5nIllj46ONrmWHD1jomeFVlZWkEqlcHBw6BUFjN606HydKqRSqZJ8H4CS+76hhB+EEOa9EBcXp9Pdraq2o2JLTpssL1rAXFxcdO7bqA2K3oe0o0B32x4eHhAIBMjPz++0gFVVVWHKlCmYOnUqvv76a7aA6RGTL2IikQg5OTkAgJiYGLV968rKSnC53B5Zt/zxxx8ICQmBt7f3Q58zlAOH4vBxXV0d4wdIRROqvi8VuUilUkRHRxssjFBbaEvO2toaEokEhBClWS1TU/lR6XRERITam1ZH6I2Q7kRFIhEznKvPEQXFgMjY2Fi9vhdUtR27UnR2hBoPu7m5YejQoUYvYKqgw/J1dXVoaGhg3q/+/v4qDQFqamowZcoUPP3009i7d6/Ji356OyZXxKRSKdOqo7Y47u7uCA8P7/TNcP/+fVRVVWHUqFFaf+9r165h4MCBDz1pG8uBg4om6C4NAFPQqLJKKBQiNzcXtra23cp/MhYdZ8AAMDd7LpfL3Oy7M6ulL6gH5927dxEdHd2jiJqOrVV9jCjIZDLcuHED7e3tRplZo21HqgTsqu3Y1taGzMxMxt/TFAuYInV1dcx5PFUhU7uvlpYWBAYGwsrKCvHx8RgxYgS+++47k/977AuYbBG7f/8+CgsLERoaqlHCMIfDwe3bt/Hkk09q/b0zMzPh7e2tlFHVXQGHviCEoKmpiSlo7e3tcHZ2RktLC/r372+yT7GKaDIDRm/2XC6XERQYI2qFnilxOBzExsbqtD2rakRB8fxFm9aTVCpFbm4uCCEm0Z6lKQqKQ9aKbUeZTIasrCx4enqaZCp3R+h56LBhw9C/f38AUDIsXrt2LdLS0hhV7rfffouRI0ea/HX1BUyuiEkkEhQXFzP2SJq2b+rq6lBcXIwxY8Zo/b1zcnLg6uqKwMBAnQg49AUhBHfv3kVZWRmsrKwgFosZg1svLy+TaydqOwPWcY7J2tpaaeZOn04ehYWFaG5u1vmZkqrvpTiioI2RL7VmMjc3R3R0tMk9/dO2I/1dNjc3Mw79YWFhJmc63RHqMzl06FC1D18NDQ1ISkqCjY0N+vfvj/T0dDg4OGDbtm14/vnnDbziRwuTK2KFhYWoqalBbGxstxRVdC5q3LhxWn/vvLw8ODg4IDg4WC8CDl1AC1h5eTlzRqNocEuFITTewthBkbqaAaO5U/Q6ASjNo+nqxq1oIxUTE2PQdqZifhiPx2PcJhR3oh2hc1W2traIiIgwuQLWEYFAgOvXr8PJyQn9+vVTUjvqcgBZVzQ2NiInJ6dTn8mmpiYkJibCx8cHKSkpsLa2hlgsxuXLl+Hj44OhQ4caeNWPFiZXxIRCIRO50B16GqUCAAUFBbCyskJwcDDkcjnMzMxMSlUkl8uVCoKTk9NDr5FIJMxNkApDDLF7UYW+ZsBoa7WjaKKn52h0R9OvXz9ERUUZvSUnEomYa2xoaICtra2SfJ+6hjg6Omplem1o+Hw+srKyMGDAAISEhDCpD4qD5GKxWGknaswz0aamJmRnZ2Pw4MFq5y1bWlqQlJQEFxcX/PrrrybXBXkUMLkiJpPJIJVKu/3vBAIBLl++jClTpmj9vYuKikAIwaBBg0zKgR54cIPNz89He3t7pypNRTruXszMzJQk3/p84tUmB0wbFIdWeTye0jka3YlqAlXBmuqORiqVPuT+QqNJqGuNKcPn85GZmYmBAwciODhY5d+Wqt8lVTt6eHgYtO1IRWWhoaFKZ+SK8Pl8zJo1C1ZWVjh+/LhJDbo/SphcEZPL5ZBIJN3+d+3t7bhw4QImT56stXffvXv3UFRUZFLtOOCBiis3NxfW1taIjIzUSoJOJd90wJraCnl5ecHDw0OnN0GhUMi4WhjaV5DGkNDdi42NDVPQ1A0fCwQCZGdnMzJvU9/RtLa2IjMzE3Z2dpDJZAZNGNCG1tZWZGVlwd/fH8HBwRr/u44CGAsLCyVvR329r2gBCwkJUZu1JhQKMXv2bBBCcOLECb3nx7Gop88UMalUirNnz2LChAnduiF3FHB0NPC1sbFhCpqTk5PBCxrN1aLhh7q4wdKDdqp05PP5cHV1ZdqOPbkJ0vX2799fyRbMGNCdKJ25A/43fEzP0VpaWpCdna13j0ldQderuKOh2VodEwZMIambrjcgIKBH6dHq2o5U1amrtmNLSwuysrKURkA60tbWhjlz5kAoFOLUqVMq2/o95ffff8fWrVuRlZWFmpoaHDlyBMnJySpfu2jRIuzZswfbt2/H8uXLH/o8IQTTpk3DqVOnOv06vRXTmirtAfSpTCaTaVzEaEuGDlebmZkxabK+vr5KN8Hs7GwmTdfLy8sgqcdcLhcFBQU6z9UyMzODo6MjHB0dERISwghDOBwOSkpKHrKH0vT7GioHTFPMzc2ZBxDFneitW7fQ3t4OR0dHtLa2IiAgQOv0A0NCh8SDgoIQGBjIfNze3h729vYICAhghuV5PB7u3r1rsN2LKuiOpuN6taFfv35wd3eHu7s7hgwZwrQdqYO8o6MjU9C0Ldytra3MetUVsPb2drz44otobm5Genq6XgoY8KA7EBUVhQULFmD27NlqX5eWloZr1651Gm66Y8cOo/8t6pM+U8SoCIPuqLqio4WUqoLU8SZIU48LCgogl8uZG70+HOkrKysZn0hVDiK6xNbWFv7+/vD391eyh6qoqNCoHQcYLwdMU/r16wdXV1e4urpi8ODBqKqqwq1bt2BjY4M7d+6gsbGxUxWgsaFzSp2JDADA0tISPj4+8PHxUdq93Lx5k2kh68N0uiO04Ha2o9EWMzMzODg4wMHBAUFBQUptR1q4FefuNPnbpKKTgIAAtQVXLBZj/vz5qK2txdmzZ9UGX+qC+Ph4xMfHd/qa6upqLFmyBKdPn8b06dNVviYvLw/btm3D9evXTfLvUheYXBHryRODubm5RkVMGwcOxSfBsLCwh57qqdy7p+dLikO2mjrq6xJVO1Eej8eINDoKQxRdLWJiYoyeA6YJ1dXVKCsrQ2RkJLy8vJjMKS6Xi7KyMpMx8aXQB6fO5pRU0XH3Qi2iqqqqUFRUxIRF6nqQnBawzkQRukTxPatYuEtKSpTSutXFrdACNnDgQLUtT4lEgldeeQUVFRU4f/68Wtd6QyGXyzFv3jysWrUK4eHhKl8jFAoxd+5c7Nq1ixnQ7ouYXBHrCZoUMV1YSJmZmcHFxQUuLi4YNGgQc7509+5dFBYWMn80Xl5e3erVS6VS5Ofno62tDY899pjRY0k67kSprF3xqV4ikUAgEGDEiBEmZzrcEcWha8UEXhsbG/j5+cHPz0/JxJfK7ekDijFmmGpra1FYWIiIiAh4eXlp/XUUW8jBwcFM4ebxeCgvL2c8Onsa3krnqrraMeoLxcKtqHasqanBzZs3HzovFAqFSrJ/VUilUrz++usoKirC+fPnNTZg0CebN2+GhYUF3nzzTbWvWbFiBZ588kkkJSUZcGWGp08VMQsLC7XyfCrg0LWFlKrzJS6Xi9raWpSUlMDJyYkpBJ1JcEUiEXJzc2FhYYGRI0eanGS6X79+cHNzg5ubGwYPHozm5mYUFhZCJBKBEIJbt27pRBiiL+gaa2trOy24FhYW8Pb2hre3t1Lhpk/1iopOfXsTUuPhqKgoncfqKBZuxR13fn4+E2xKBTCavheps8WQIUMwYMAAna5XG9S1Hevq6nD37l2Ym5tDKpXCw8NDbQtRJpNh6dKlyMrKwoULF0xiR5OVlYWdO3ciOztb7T3s6NGjOH/+PGOe3pcxOXUi8ODwVBuuXr2KoKCgh95oqgQchmgRUbk3l8tFQ0MD7O3tmYKmaPra2tqKnJwcuLu79wqJd8cZMKlUyigdm5qa4OjoyBQ0UxhRoDE1TU1NiI2N1WqeR9GxncfjobW1lXHT6OoBRRuoK0tPjYe7i6pgU2pp1lnWHT2z68zZwpSgc2s2NjaQSqVKbUdXV1fY29tDLpdj+fLlOH/+PDIyMtTK7fWNmZmZkqpwx44deOutt5TuEzRdY+DAgbhz5w6WL1+OL774QuVrxowZg4yMDANfhf7oU0Xsr7/+woABA5SeAjURcBgCxYgVmqfl5eUFKysrlJeXIzAwEEFBQUa/4XdFVzNgHXPD6IiCsc6XqLO7SCRCbGyszqTYHd006DlaT0cx6BljZWUlYmJiDH4m2hGhUMi0HRsbG2Fvb88UNHqdVJXa3TM7Y0Hd8728vDB48GAAUBpT2LRpEyoqKjBgwAAUFxfj0qVLRlWvdixi9fX1qKmpUXrNlClTMG/ePCxYsABDhgxBbW0tM1ZCiYiIwM6dO5GYmNijcQdTwySLmFgshjbLoq7Y9InJWBEqXUEjVioqKtDc3My0sOi5i6nuxLo7A6Y4osDj8dCvXz+lKBl9XydNjgagV9cQOltIrb7odXbXGYUQgtLSUtTU1CAuLs7kBmgV5fv19fXo168fHB0d0djY2GlYqCkhEomY+Bd17vnV1dV49913ceXKFbS2tsLR0REJCQlYsGABnnjiCYOsk8/no6ysDMCDHMVt27Zh/PjxcHNzU7kjDAwMxPLly1XOiVE6FsO+Qp86E1MUdhBCmPMxUypgwIP1NDY2QigUIi4uDsADBVpRURFkMpmS0tFU7I/o03ZISIjGkmlVwhAul4vi4mJIJBIlA19dF5j29nZkZ2fDxsZG7zlrFhYW6N+/P/r376+1rJ0aJdfV1WHEiBEmKfPvKN+vqKhAeXk5LC0tUVxcDC6XaxKeh+qgBczNzU1tASOEYO/evbh8+TIuXLiA4OBgXLp0CceOHUN5ebnBilhmZibGjx/P/P9bb70FAJg/fz727dtnkDX0FvrUTiw/Px82NjYICQkxiQwwVchkMhQUFKC1tRUxMTFKNytCCFpaWhhrKJFIxAgJPD09jSb2oDNg4eHhOjnYVnRr53K5EAgEOrVNEgqFyM7OZuLujbWzVeWMQmPuPT09mXM0embX3NyM2NhYo6tSNYHD4aCgoIBJUlDnX2kq56Lt7e3IzMxk3hPqCtimTZuwZ88enD9/HsOHDzfCSlm6i0kWMYlEwogwukNRURHMzMyY/rUpRagAD/6QcnNzGZf0rtRtVEjA4XAYayhDZoYpzoB1JwesuwiFQqagNTc3a2XgS6GuCz4+Phg0aJBJ/f7pORqXy2XOlzw8PNDc3AyxWIy4uDiT3MF0hBawyMhIlXJzVTlwupDva0t7ezuysrLg5OSE8PBwtQXs888/x86dO3Hu3DlER0cbdI0s2tNnihiVUHM4HAwcOBDe3t4mdUPg8/nIyclhngS7296i1lBcLldJAajNjV4TaOxLXV1dj3LAuotYLFZSdHbHu7KxsRG5ubkIDAzUKA3cmNDInNLSUojFYkbo05N0Z0NQU1OD4uJiREZGaiT7p+e/dJemrXxfW8RiMTIzM+Ho6Ijhw4erLWBffPEFtm7ditOnT2PkyJF6XROLbukTRYxK6Nvb21FTU8Okxzo7OzM3QGO2aOrr63Hjxg0MHDiQyVHqCYo3+vr6etjZ2THXqQvDV6roa2trQ2xsrNHmvhQHj6kwRN2NnrpamMqMUldIpVJmhicqKkopDJOeF9LzJVOZGaQ+hVFRUVo5VtB2Ob1OgUDAdBc8PDx0PqZAA0Pt7e0xfPhwlQ8GhBD8+9//xscff4yTJ0/q7cyrp4a+DQ0NWLt2Lc6cOYOqqip4eHggOTkZ69evN7qC1diYpLCjOzdhRQWilZUV8xTe3t7OnC2Vlpbqfeeijurqaty8eRNhYWE6u7laWVkxowSKrvuZmZmwtLRkbvSurq7dLmiKM2DGHrruOHhMBRNUAEPPCyUSCUpLSzF8+PAeuVoYCrFYjJycHFhaWiIqKgrm5uZK9lC0oFEHGE3mtPRNdXU1SkpKtC5gwIO/a2dnZzg7OyM0NFQpkfzWrVtMe1UX4xgSiQTZ2dmws7PrtID997//xbp16/Dbb7/pVbTRU0Pf+/fv4/79+/jss88wbNgw3L17F6+//jru37+Pw4cP623dvQGT3IlJpVKNPBCpeKMrAUfHnYu6oWNdQgjB7du3UVVVpdfzJEXkcjmzc+FyuQCgJGnvqoVJZ8BoUrCpKCM7QoUhXC4X1dXVEIvFcHJywoABA3Qay6EPqGrSzs4OERERXbYNFW/0inNautp1awJ1DomOjtbb+1gikTDvXTqmoNh27M57kRYwmr+nroDt378fq1atwtGjR5WUgPpGndS9uroao0aNYgx9u5LM//LLL3jxxRchEAi0yhjsK/TKK++YAdaVAlHVzoXD4eDOnTuwtrZmZrR0lRcmk8kYh4iRI0cabN5HcT5p6NChjKSdSr0Vpfsd3/SmlAPWFdTqq7a2FoQQREVFQSgUMv541OrL1Bzp29rakJWV1S3VpGLCgOKcVmZmJhOzos9ooKqqKpSWlip5TeoDS0tLpTEFavdFDbYV1audPaTQNq2VlVWnBeynn37CypUrkZKSYtACpg5NDH07QkVQj3IBA0y0iHV2A+2pA4fiTI+qvDC6Q9OmFQc82PXl5eVBLpfjscceM9quwMzMTCl6hO5cysvLUVBQoCTdb2lp6fYMmDGRy+UoLi5GQ0MDRo4cyRQq2kam7dXbt2/D1tZWJ04aPYWmR3t4eCAsLEyrdXSc06KCicLCQqa9qstztMrKSty+fRuxsbF6jR3pSEefTlXZYbSgKXZSpFIpsrOzYWFhobaAAcCRI0ewdOlS/Pzzz5gyZYrBrqszNDH0VaS+vh7r16/HokWL9Lwy08ck24kymUylkS8VcFAPMF3ekOhNgZ6jAWAKmqZqMYFAwLTjVFkymQoCgYCZXWpubgYA+Pj4ICQkxORnlGQyGfLz8yEUCrsUnVBhCE12NnSoKYXK/n19ffWSHt2ZYELbczTq3RgbG2tSwgF6NEDl+1ZWVkzLsaKiAubm5oiOjlb7t3fs2DEsXLgQBw8eNJpzRcd2YlZWFqZPn868R4DOHThaWlowefJkuLq64ujRoyYj/DEWvaaIGdJCihDCBGByuVzIZLIuAzAbGxuRl5cHX19fk5tPUgWdAbtz5w58fX0hEAjQ2NgIBwcHJQGMKV0HtZEihCAmJqZbf7yKwhD6O1V0DNFXS6a5uRk5OTnw9/c3mDdmx3GMjvEjXa3hzp07qKioQFxcnN6Si3UBle9zuVzU1NSAEMK8d1XtRk+ePIn/+7//w759+/Dcc88ZadXaGfpSWltbMWXKFNjZ2eH48eMmmRhhaEyyiMnlckgkEqX/N5YDh6KLBofDUQrA9PT0hIWFBWpqalBUVITBgwcbJASwp6ibAaOzSx3Ne43digMeCCLoWQdV9GmL4s6Fy+VCKBQyrThdCkPo3Jo+0o01paPxtKWlJXOdqnajdLg9Li7O5PPhgAc3+9zcXMhkMgwaNIhpsVJ3lBs3buCxxx4Dh8PB3Llz8e9//xsvvPCCUd/L2hj6Ag92YFOmTIG1tTVOnDih85GE3opJF7GOAg5jO3Ao2ghRuyRbW1uIRCKdWTLpG01nwGQyGSMi4PF4zHmhupufPqE2Us7OzggPD9f596ZnLlwul7FM6qkwhHpNmtLcmkwmYzoMHQePPTw8cPfuXVRVVfWqApaXlwepVIrY2Fil3TTdjb7++uu4cuUKrK2tMXbsWLz77rt4/PHHDd7q76mhb2trKyZNmgShUIgjR44ovS89PT1N9ujCEJhsEROLxUoCDmMXsI7I5XLk5+czOxaau0R3LqYo86YzYNT2StN2HG3F0eJNCFFqxenzD4ieJxlKNUkz4OiZCx0kV4we6Qpqy2TKDzYdd6MCgQBmZmYIDAyEn5+fybep5HI58vLyIJFIHipgily5cgVz587Fs88+i7a2Nhw/fhzm5ub45ZdfMHbsWIOtNyMjQ6UKUp2hb8cipu7fAw92z+pCPR8FTLKICQQCZhdmaga+wIMWDX0CjI6Oho2NDZPoTP3/TMUthKI4A6Zu+FMTaGgivVbF9qqu3SVoOy4gIMAoWWuqhCFd7Uapgo4a45o6hBCUlZWhuroavr6+aGlpUTpH0+cspbbI5XLcuHED7e3tiI2NVfueu3btGpKTk7Fx40a88cYbMDMzg1QqxdWrVzFs2DCth7ZZTAuTLGLPP/888vLyMGPGDMycObNHN11d09bWhpycHNja2iIiIkLlEyB9mudwOIxYgs6iGWNuqbm5Gbm5uTrfzahqr7q5uTE3+p7sRnk8HvLz8zF48GD4+fnpZL09QXE3yuPxVApD6ExVT1wtDAnNL6utrUVcXBzz3qTBpnQ3Ss/RvLy8jGLgqwjtgLS1tSEuLk5tAcvOzkZiYiI++OADrFixwqSKMItuMcki1traimPHjiElJQWnTp2Cj48PU9BiYmKM9kdElWb9+/dXm0fUkY5iCVtbW3h5ecHb29sgT7ja5IBpi1AoVJLuOzs7Mze/7hxC0+iX4cOHw9vbW48r1g5FsQ+Px0NbWxtsbGwgEonUOrubGtQwm8vlIi4uTu3vR52Br75VnaqQy+UoKCiAQCBAXFyc2hSIGzduYNq0aXj77bexevVqtoD1cUyyiCnC5/Nx8uRJpKam4rfffoObmxsSExMxc+ZMjBw50mAHmhwOB4WFhQgNDVV5EKsJij6HdXV1jHO5l5dXj73iVEF9G41xNkN3o9SNXlOrr7t37+L27du9ajdTXFyMmpoa2NnZQSAQaF28DQUhBCUlJeDxeBgxYoTG7W7aSqYFTSgU6jQHrqvvXVBQAD6f32kBKyoqwtSpU7F06VJ8+OGHbAF7BDD5IqZIW1sbTp8+jdTUVBw7dgx2dnaYMWMGkpOT8cQTT+jlqZAQwgx+6tJgVtEtRFH9p4uWjaFywDRFUeZdV1cHa2vrh4q34tlMTEyMSQ3YqkMx/ic2NhYODg4qi7ehvQ67WjN1O4mLi+vRea1iEGZzczPjpKHrGUNCCAoLC9HS0oIRI0aoLWA3b95EfHw8XnnlFWzYsEFvP+ueOtIDDx7yVq5ciR9++AFtbW2YMGECvv76a5Nonfc2elURU0QkEuHcuXNITU3Fr7/+CnNzcyQmJiI5ORljxozRicCAzlPxeDzExMTobfCTnrdwOBzweDwQQpSGq7tT0IyVA6YpisW7rq4OZmZm8PT0hEgkYp6yTcnvUB20GNTX16ttx3XceXc1o2WINRcVFaGxsREjRozQ6c6po5OGroIw6ZqbmpowYsQIteesZWVlmDp1Kl544QVs2bJFrz/bkydP4sqVK4iNjcXs2bPVFrG0tDSsW7cOPB4Pq1atUipi//jHP3Ds2DHs27cP7u7u+Oc//4mGhgZkZWU90nJ5bei1RUwRiUSCixcv4vDhw0hLS4NEIkFiYiKSkpIwbtw4rQQGUqmUUUBFR0cbTGFICGGMe7lcLqRSqZL6r7M3uKnkgGkKdd0vKSmBSCRiDIw1uVZjIpfLmZ1BXFycRj9nRa9DLpcLuVzO3OQNca10N9Pc3KzxmrVF0Umjrq6OGcmg9lCadkwUd42dFd2KigrEx8cjOTkZO3bsMOjDgTaO9M3NzfD09MT+/fsxZ84cAA/OgQcOHIgTJ06YjJ9jb6FPFDFFpFIpLl++zBQ0Pp+PadOmITk5GRMmTNCoGIlEIuTk5DBRDsZyiVYUEHC5XIhEIrVydm1nwIyJVCpFbm4u5HI5oqKiIBKJlK5V0aTYVK6HejdSdZy61lZnqPq9Kqo6tfmanUGLbmtrK+Li4gw6w6h4jqZ4rV25oxBCcPPmTdTX13dawCorKzF16lRMnToVX3/9tcF3t6qKmFwux8SJE5GUlIRly5Y9NPN1/vx5TJgwAQ0NDUrJAFFRUUhOTsZHH31k0Gvo7Ziki31PsLCwwLhx4zBu3Djs3LkTf/75J1JSUrB69WrU19djypQpSE5OxuTJk1W2rWgkiaenJ8LCwowqJ+4YIigQCJgImcLCQri5uTEqx/z8fDg5OZnUOEJniMViZGdnw8rKCtHR0bCwsIC1tbXStXK5XFRWVqKoqIgZJNe3gKAzpFIp8vLyIJPJMGLECK0Lq+LvddCgQcy1VldXo7i4mJkx9PT07LEwpKOiz9BD+GZmZnBxcYGLiwtzrYqO9E5OTkxBo+doVHhSV1fXaQG7f/8+EhISMGHCBHz11Vcm877vypG+trYWVlZWD0XbeHt7o7a21hBL7FP0uSKmiLm5OZ566ik89dRT+Oyzz5CZmYmUlBSsW7cOr732GiZNmoTk5GRMnToVTk5O+PHHH9HW1obx48cjICDA6IfwipiZmcHBwQEODg4ICQlh5Ox3796FQCCAtbU1XFxcIBaLTb6N2NbWhuzsbLWD14rXGhwczFgIcTgclJSUMDc+Q87dSSQS5OTkwNzcvFOHCG2wt7dHUFAQgoKCIBKJmLOl0tJSRtWpqXmvInSmSigUar1r1DX29vawt7d/KDanvLycOUcTiURobm7GyJEj1XZOamtrMX36dDzxxBPYs2ePybSes7KysHPnTmRnZ3f7/kEIMal7Tm+hz7UTNYFa1qSkpCA1NZWJnMjJycHWrVuxYMGCXvFmojNg/v7+sLKyYlzLqfefKUq8+Xw+srOz4eXlpfGsnSIdU7qpLZQ+1X9010jby4a6YdK0447CEE0UrNTVQiQSITY21iQKWGdQr87y8nLw+XylwM+O1mY8Hg/Tpk1DREQEDhw4YNRQSG0c6dl2om55JIuYIlKpFPPnz0daWhrCw8ORm5uL8ePHIykpCQkJCXB3dzfJglZdXY2SkhIMGzZMaQZMLBYzZy0NDQ1K0SqGSphWR1NTExNLEhwc3OOfqyr1n+KYgi5+byKRCNnZ2XBwcDBqq1Yx744qWNX5V9KHNLFY3KktkylBCMHt27dRXV2NuLg4xiSAx+Mxu9OamhpMmjQJr7zyCkJDQ/HTTz8Z/dq0caSnwo4DBw7gb3/7GwCgpqYGfn5+rLBDCx7pIkYIwezZs1FcXIwTJ04gMDAQpaWlOHz4MFJTU5GXl4cxY8YgKSkJiYmJ8Pb2NnpB684MmDq3EGPMLNFd46BBg/QSV0OVjvQmD4Bpw3V3TIHS1taGrKwsuLq6YtiwYUb/3VNUiSVolIybmxuKi4shlUq7nblmTG7fvo179+5hxIgRSi1iQggEAgHS0tKwY8cOlJSUwNnZGStXrsSsWbMQFhZm8LX21JEeeCCxP378OPbt2wc3NzesXLkS9fX1rMReCx7pIgYAv/76K55++umHDllpsUhJScGRI0dw/fp1PP7440hKSkJSUhJ8fX0NflNTnAGjw7WaQs1sORyO0q7F29tbL24hitC8NUM5h3QcU5BIJEqqTk3aTwKBAFlZWVq3PQ0FvckrRsmYm5sjKCgI/fv3Nwnz6a6gD2UjRoxQ+55uaWlBUlISHB0dMWvWLJw4cQJnz57F+PHjcfLkSYOut6eO9MCDHf6qVatw6NAhpWHn3pBHaGo88kVMEwghqKqqQmpqKo4cOYIrV65gxIgRSEpKQnJyMvz9/fV+k6MzYCKRCDExMT0SbyjO8fB4PJiZmTE7NF0P4VZWVqKsrMxoNlKEELS2tjIFra2trUs5e2trK7KysuDn54eQkBCTLWCK0HBIqVSK/v37o76+vlt2X8bizp07uHPnTqcZZnw+HzNnzoS1tTV+++03pjALBAJUVFRg+PDhhlwyi4nBFrFuQghhrGZSU1Px+++/IzIyEsnJyUhKStLLTU8sFjPKOF3PgKnKCtPWLUQResZx7949k7KRonJ2LpeL1tZWuLi4MAXN1taWObcLDAxEUFCQsZerEVKpFDk5OTAzM0NMTAzTjqJ2XzweT2n33VMXDV1B7dzi4uLUuuEIhULMnj0bAPDbb78Z/VyXxfRgi1gPIISgrq4OaWlpSElJwfnz5xEWFsYUtLCwsB4XNJpqbIgZMHrWwuFwlNpw3t7e3XZaoHZd3W17GhIqGOByuWhsbIStrS3a2toQGBiI0NBQYy9PI2gB69evH6Kjo9Wep3R0ozdksKkqKisrcfv2bcTGxqp9wGlra8OcOXMgFApx6tQpvdm+sfRu2CKmIwghaGxsxNGjR5GSkoL09HQEBwczETLh4eHdLkA0+sXHx8cgqcaKKLbhOByOxg4adLi2tbUVsbGxveJMBnhwbldYWAgHBwcIBALY2NgwbThNE50NDZ1ds7CwQFRUlMaFSFWwKf3denh46F2OX1VVhbKysk4LWHt7O1544QXU19fjzJkzcHFx0euaWHovbBHTE83NzTh+/DhSU1OZTLSkpCTMnDkT0dHRXRY0GgppiBwwTVAMv+Tz+cy5kpeXF3PTo44WVBln6rNJFA6Hg4KCAia/jM4sdUx0NoVQSIpEImEcT3oyu0aFIfR8VFWLVZfcu3cPt27dQmxsrNrCJBaLMW/ePNy7dw/nzp3TawpDV47069atw48//oiqqipYWVkhLi4OGzduxKhRo5jX1NbWYtWqVUhPT0drayuGDBmCd999F88++6ze1s3yP9giZgD4fD5OnDiB1NRUnDhxAm5ubkyEjKpMNGPmgGkCdQuhajgXFxe4ubmBw+HAysoKUVFRRh1A7Q503i4iIkJlmKWq+Sxqk2SMNhzwoIBlZWXBxsYGkZGROi2qHVusDg4OzBlpT4Uh9GcdExPzkBqYIpFIsHDhQty6dQvnz5/Xe8BoV470hw4dgpeXF+Mcs337dvzyyy8oKytj1jZp0iQ0Nzdj165d8PDwwKFDh7B27VpkZmYiJiZGr+tnYYuYwREKhThz5gxSUlJw/Phx2NvbMwVt1KhRePfdd+Hk5IQ33njD6DlgmiASiVBdXY07d+5ALpfDyckJ3t7eJukW0hGqnIyOjtboZ92xDScWi5XacIaYyRKLxcjKyoKdnR0iIiL0uitUzIGrr6+HlZWVkmNIdwoa9Urs7GctlUrx2muv4caNG7hw4YLBU73VOdIr0tLSAmdnZ5w9exYTJkwAADg4OOBf//oX5s2bx7zO3d0dW7Zswcsvv6zvZT/y9I7H5T6EnZ0dkpOTkZycDJFIhLNnzyI1NRXPP/88rKysIJFI8PHHH5tcDpg6pFIpqqur4evri+DgYOamV1ZWxsi7vb29dRqSqAsqKioYabemysmOZra0xapoyEzbcPow2qUFzN7e3iDuIZaWlvDx8YGPj4+SMCQvLw8ANN6R1tTU4ObNm50O58tkMixZsgTZ2dnIyMgweAHTBLFYjD179sDZ2RlRUVHMx0ePHo2ffvoJ06dPh4uLC37++We0t7dj3LhxxlvsIwS7EzMBBAIB/va3v6GoqAhjxozB6dOnIZPJkJCQgOTkZIwbN84kz5eo8ETVPFXHNGdTEUrQBOn79+8jNjZWZw8LHVus1Iney8tLJ+dK7e3tyMrKgqOjo1YiIV1Ch8lp27G9vZ3JC+so+qHnjVFRUfDw8FD59eRyOZYtW4aMjAxcuHBBpeuFIVC3Ezt+/Dief/55CIVC+Pj4IC0tDSNHjmQ+39zcjDlz5uD06dOwsLCAnZ0dDh8+jEmTJhn4Ch5N2CJmAiQkJIDP5yMtLQ0uLi5MJtovv/yCtLQ0CIVCTJs2DUlJSZg4caJJuNTX19cjLy8PoaGhXd50OgolLCwsdO5xqAk04oPL5eo1Qbq9vZ05Q9PFwLFIJEJWVhacnZ0RHh5uUjtaRWEIFf1QYYiZmRlu3bqFyMhItWdbcrkcq1atwsmTJ3HhwgWjzuapK2ICgQA1NTWoq6vD3r17cf78eVy7dg1eXl4AgKVLl+Kvv/7CJ598Ag8PD6SlpWH79u24dOkSIiIijHAljxZsETMBiouLERwcrLIFJZPJcPXqVcb+qqGhAVOnTkVycjImTZpksCgSRWpra1FYWIhhw4bBx8enW/+WCiU4HA7jFuLp6Qlvb2+du4V0/L7FxcVobGxEXFycwaT/HXek1tbWTEHTxO6LFjAXFxeT8m9UB43Nqa6uBp/Ph62tLXx9feHp6flQAZfL5Xj33XeRmpqKjIwMo8/maXImBgCDBg3CwoULsWbNGty+fRuhoaEoKChAeHg485qJEyciNDQUu3fv1vOqWdgzMRNg6NChaj9nbm6O0aNHY/To0fj8889x/fp1pKSk4MMPP8Srr76KyZMnIykpCfHx8QY5R6uqqkJpaWmn7aHO6NevHzw8PODh4QG5XM54HBYWFkImk6mN3+gJNFdLIBBg5MiRBg2G7HiuRE2K6YByZ3Zf1IDYzc0NQ4cONfkCBgC2trawtbWFUChkbuo8Hg8VFRWwtraGtbU1Ghoa8Mwzz2DDhg345ZdfcOHCBaMXsO5ACEF7ezuAB21kAA/97szNzSGXyw2+tkcRdifWS5HL5cjNzWUy0e7cuYOJEydixowZmD59us5NfQkhKC8vR2VlJWJiYnQ+fKpK+ddd015VUM/J9vZ2k8rVonZf9FyJFnBPT094eHhALBYjMzMTHh4eOnF+MRQ0rSA8PFxJnEGFIWfOnMHKlSsBPBAFbdmyBQsXLjTaUHxnjvTu7u7YuHEjZsyYAR8fH9TX1+Prr7/GgQMHkJWVhfDwcEgkEqYj8dlnn8Hd3R1paWlYtWoVjh8/jmnTphnluh4l2CLWByCEoLCwkImQKSkpUcpEc3Nz69FNUPEsyRA2UoQQ8Pl8xv6qra1NI7eQjkilUuTm5oIQgujoaJONJSGEoKWlhSngIpEIAODi4oKIiAiTKbxdQc9Jhw4dqrbNTAjB1q1bcfDgQTzxxBO4fPkyuFwupk6dikOHDhn8WjtzpN+9ezdeeOEFXLt2DXV1dXB3d8fIkSPx/vvvKwk7SktL8c477+Dy5cvg8/kIDQ3FypUrlST3LPqDLWJ9DEIIbt26xezQaCZacnIyEhMTmQN3TTEFGykqHOBwOODz+XB1dYW3t3enUnbqaGFhYdGpp6CpwefzkZmZCTs7O8jlcuZ6aQE3BVGPKhoaGpCbm4uwsDD4+vqqfA0hBF988QW2bt2KM2fOYMSIESCEoKCgAFeuXMHrr79u4FWz9AXYItaHoS1AKgrJzMzEE088gaSkJMyYMaPLTDSZTKaUEGwKO4K2tjZmx9Lc3KxSyt7e3o7s7GzY2trq3NFCnwgEAmRmZsLX1xehoaEwMzN76HqdnJyYgmYMUY8qGhsbkZOTgyFDhmDAgAEqX0MIwe7du7F+/XqcOnUKjz/+uIFXydJXYYvYI4JiJlpqair++OMPjBw5kgn57JiJJhaLkZuby7ijm6KNFJWyU4skR0dHuLm5oba2Fi4uLkafp+oOfD4fWVlZGDBggNo4H7FYzEj36+vrYW9vzwhhDJ3UTWlqakJ2djYGDx4MPz8/la8hhODbb7/Fe++9h99++w1jxowx8CpZ+jJsEXsEUcxES0lJwaVLlxAVFcUUNDMzM7z22mv48MMPMXr06F7RihOLxaiurkZ5eTnkcjns7e0Z+ytTDINUhLYQBw4ciODgYI3WKpVKlaT7NCvMkLN3zc3NyM7ORmhoqNpEYkII9u/fj1WrVuHYsWOsiwWLzukdj6ksOsXMzAy+vr544403cO7cOVRXV+O1117D5cuXERcXhzFjxsDS0rJHoZiGRiwWo7KyEgMHDsS4ceMQHBwMPp+Pv/76C1euXEFpaSmam5thas9sra2tyMzMhL+/f7cCVS0sLNC/f39ERkZi7NixCAsLY1IEfv/9dxQVFaGurk5vMm9awEJCQjotYD/++CNWrlyJlJQUvRaw33//HYmJiUyLPC0tTenz69atQ1hYGOzt7eHq6oqJEyfi2rVrD32dq1ev4plnnoG9vT1cXFwwbtw4tLW16WydMpkMwP+k+Sw9h92JsTBcv34d8fHxePzxx0EIwblz5xASEsJkog0bNswki1pLSwuys7NV7mQUZ7N4PJ5SrIqrq6tRd2h03QEBATpzqlCcvePxeFoHm3ZGS0sLsrKyEBwc3GlMUEpKCl5//XX8/PPPmD59eo+/b2fowo3+6tWrmDp1KtasWYPExERYWVkhLy8PiYmJOpktJITAzMwMDQ0NmD59Oj799FOMHTu2x1/3UadPFLHAwEDcvXv3oY8vXrwYX331Ffh8Pt555x2kpaWhvr4egYGBePPNN/GPf/zDCKs1TSorKzF8+HB89NFHWLFiBYAHT9vHjh1jMtEGDBjAZKJFRUWZREFrampCTk4OgoKCEBgY2OlrFWNVuFwuADAFzc3NzaDXQ3cymqxbWxSDTemogqJJsTZCndbWVmRlZSEwMLDTdR89ehQvv/wyDh482KUDhq7R1o3+8ccfx6RJk7B+/Xqdr0kmk8Hc3BxSqRRLly4Fn8/H7t27TUac05sxvdN6Lbh+/TqzTQeAgoICTJo0Cc899xwAYMWKFbhw4QIOHDiAwMBAnDlzBosXL4avry+SkpKMtWyTwt/fHxkZGYiNjWU+5uzsjBdffBEvvvgiWltbmUy0qVOnwsPDQykTzRgFjc4ldSYqUETRLWTo0KFobGwEl8tFUVGR3txCVEELb0hIiF7Nbs3MzODk5AQnJyeEhoYyowr37t1DcXEx43Ho5eWlkXSfik8CAgI6LWAnTpzAyy+/jH379hm8gGmCKjd6LpeLa9eu4e9//zuefPJJ3L59G2FhYdi4cSNGjx7d4+9pbm4OsViM3bt3o6amBqtWrWILmI7oEzuxjixfvhzHjx9HaWkpzMzMMHz4cMyZMwcffPAB85q4uDhMmzZNL09dfR2hUIjTp08zmWiOjo6YMWMGkpKS8MQTTxhECMLlcpGfn6+Vf2NHFIeNORwO48ru7e3dI7cQVdAC1pkYwhCIRCKm5UiVnXSHpmqYnRYwmligjrNnz2Lu3LnYu3cvXnjhBX1eglq0caP/888/8cQTT8DNzQ2fffYZoqOj8f333+Prr79GQUEBBg0a1ON1/fDDD1ixYgVaWlqQkpKC+Pj4Hn9Nlj5YxMRiMXx9ffHWW2/h3XffBQC8/vrryMrKQlpaGnx9fZGRkYEZM2bg5MmTOnnKepQRiURIT09Hamoqjh49CisrKyQkJGDmzJl46qmn9OKSUVNTg6KiIkRERDBO4rqCuoXQFpxAIFByC+nJrBydp9J052goxGKxUvilra0tsyt1cnKCUChEZmZmp/J/ALh48SKee+457Nq1C/PnzzfaeaM2bvR//PEHnnrqKaxZswaffPIJ828iIyMxffp0bNq0qdvrkMvlD3Uovv32W6xfvx7Dhw/Hpk2bMHz4cK2ukeV/9Lki9vPPP+OFF15AZWUl4xwgFovx6quv4vvvv4eFhQX69euH//znP6wtjI6RSCS4cOECDh8+jF9//RVyuRzTp0/HzJkzMXbsWJ0MS9+7dw+3bt1CVFQU3N3ddbDqzlGMGWltbWXcM7y8vLp12E8dLTobCDYFpFIpI4Spq6tDv379IJPJ4OHhgfDwcLW77CtXrmDWrFn4/PPP8eqrrxpVMKONG31FRQWCg4Oxf/9+vPjii8xr5syZAwsLCxw8eLBba5BKpcwOnu52Bw8eDADYt28fdu7ciZEjR2LZsmVK7vcs3adPnIkp8s033yA+Pl7J+uaLL77An3/+iaNHjyIgIAC///47Fi9eDB8fH0ycONGIq+1bWFpaYvLkyZg8eTK+/vprXLp0Cb/88gsWL14MoVCI6dOnIykpCRMmTNDKPunu3bsoLy9HTEwMXF1d9XAFD2Nvb4+goCAEBQUxMSO1tbUoKSnROPiSnt11ZslkKlhYWMDb2xve3t4QCAS4fv06bG1t0djYiEuXLjE7NDc3N6agXbt2Dc8++yw2bdpk9ALWHRTd6AMDA+Hr64uSkhKl19y6davbbT+ZTMYUsDlz5uD27dtoaGhAcHAwdu7ciZdeeglyuRxfffUVdu7ciTfeeEMpKZqle/Spndjdu3cRHByM1NRURrDR1tYGZ2dnHDlyREnm+8orr+DevXs4deqUsZb7yCCTyfDHH38w9ldNTU1KmWh2dnad/ntqn1VVVYXY2Fg4OTkZaOXqaW9vZxzoGxoa4ODgwAxXKx7YU1f3zkxxTZG2tjZkZmbC09MTQ4YMAQAl6f6ZM2eQk5ODUaNG4euvv8batWuxfPlyoxWwnrrRA8COHTuwdu1afPPNN4iOjsZ3332Hzz77DAUFBZ2eA6pjwoQJkMlkWL9+PYKCguDv74/58+djz549sLS0xP79+7F161YMGTIEX3/9tdrgUJbO6VM7sf/+97/w8vJSKlYSiQQSiYTN+zEi5ubmGDNmDMaMGYNt27bhr7/+QkpKCj744AOlTLSpU6c+lIlGCEFpaSlqamowYsQIvTvoa4q1tTX8/Pzg5+cHiUTCFLTy8nLY2trCy8sLlpaWKCsrQ3h4OPr372/sJWsMDeL08PDAkCFDmMLk6uoKV1dXDB48GA4ODuBwONi7dy/4fD7S09Ph4OCAGTNmKEWwGIrMzEwlN/q33noLwP/c6G/evInvvvtOyY3+0qVLSq285cuXQyQSYcWKFWhoaEBUVBTS09O1KmCXL19GS0sLjh49Ch8fH6xZswZ+fn5YsWIFc048b948iEQi2NjYsAWsB/SZnZhcLkdQUBDmzp2LTz/9VOlz48aNQ11dHXbt2oWAgABcvHgR//jHP7Bt2zZ2VsyI0Ew0GiFz9+5dTJw4EUlJSZg2bRocHBywYsUKjB8/HlOnTu1yx2YKUDuoqqoqNDU1wcrKCj4+PhonORsbkUiEzMzMLoM4CwsLER8fjzfffBNz585FWloajhw5gubmZhQWFhp41caHzoFRfvnlF3z88cfIz8/HqlWr8MMPP+D48eOIjo5GXl4efvvtN0Z4xtIz+kwRO3PmDKZMmYKSkhLmAJVSW1uLNWvW4MyZM2hoaEBAQABee+01rFixwuRvKo8KNJJDMRMtNDQUTU1N+OmnnxAbG9trflccDoeJqzc3NweHw3nILcTFxcUkhsUVaW9vR2ZmJlxcXDBs2DC1P++bN28iPj4er776KtavX6/0ura2NqMFXBqL6upqRqxz/PhxJCQk4NatW/i///s/+Pj44OrVqzh16hSio6MBAMeOHcOWLVuwZ8+eTlPdWTSjzxQxlr6DSCRCQkICCgoKEBAQgJycHKVMNE9PT5MtaBwOB4WFhYiIiFBqEdEkZ1rQCCHw9PSEt7e3wd1CVNHe3o6srCw4OTkhPDxc7c+3tLQU8fHx+Pvf/47Nmzcbfd3G5uDBgzhy5Ag+/vhjLF++HBUVFcjLywOfz8eyZctw6tQpvP3221izZg0AoLi4GNOmTcNLL72EtWvXGnn1fQO2iOkIqVSKdevW4eDBg6itrYWPjw9eeuklvP/++8wfOiEEH330Efbs2YPGxkaMGjUKX331FSuxVUAqlWL69OloamrCyZMn4erqivLychw+fBhHjhxBVlYWnnjiCSQnJzMH9aZS0GpqalBcXIzIyEh4eHiofR0hhBFJcLlcSKVSeHh4wMvLCx4eHgZPDRCLxcjKyoKDgwOGDx+u9udZUVGBqVOnYtasWdi+ffsjX8AA4PDhw9i8eTO4XC4sLCxQUFDA7ERzcnLw9ttvg8vlIiAgAO7u7sjIyMDEiROxd+9eI6+878C+C3XE5s2bsXv3buzatQvFxcXYsmULtm7dii+//JJ5zZYtW7Bt2zbs2rUL169fR//+/TFp0iS0trYaceWmhYWFBebNm4f09HS4ubnBzMwMISEhWL16Na5evYqysjLMnDkTR44cQVhYGCZNmoQvv/wSlZWVRnWov3//PoqLixEVFdVpAQMezDG5urpiyJAhGD16NGJjY2FjY4OysjJkZGQgLy8PNTU1kEgkel83LWD29vad7sAqKysxffp0JCQk6L2A6cqRHnjwwBAfH6/y6+iCZ599FsHBwaipqUFERASKioqYz8XExODLL7/E0qVLGVuzNWvWsAVMx7A7MR2RkJAAb29vfPPNN8zHZs+eDTs7O+zfvx+EEPj6+mL58uVYvXo1gActHG9vb2zevBmLFi0y1tJ7JYQQ3L9/n8lEu3z5MqKjo5lMNE1zuXRBdXU1SkpKEB0dDTc3N62/DiEEAoEAHA5H524hqpBIJMjKyoKtrS0iIiLUFqb79+9jypQpGDduHPbs2aP3naIuHOkp27dvR3p6Ok6ePKnRAHR3oGIOaqLw1VdfwdfXF0uWLGHd6Q0IW8R0xKeffordu3fjzJkzGDx4MPLy8jB58mTs2LEDc+fORXl5OUJCQpCdnY2YmBjm3yUlJcHFxQXfffedEVffuyGEgMvlIi0tDSkpKcjIyMCwYcOQlJSE5ORkDB48WG8FjTqI9LSAqUIoFDItx5aWFri4uMDb2xuenp5aDYsrIpFIkJ2dDWtra0RGRqotYLW1tYiPj8eoUaPw3//+1+CtTm0d6QEgLy8PCQkJuH79Onx8fHRSxBStpDraSp04cQIbNmxA//798eabb2LcuHHg8/n49ttvsXTpUpNpe/c1+tScmDFZvXo1mpubERYWBnNzc8hkMmzcuBFz584F8OBmAOChGRpvb2+VMTIsmmNmZgZvb28sWrQIr732GhoaGvDrr78iJSUFmzdvRkhICBMhM3ToUJ21wqqqqlBWVqY3BxE7Ozsm8oQa9nI4HJSUlMDJyYlROnZ39EAqlSInJwdWVladFjAej4fExETExMTg22+/NcmEb1WO9MCDB4C5c+di165dOpvRUyxae/fuRX5+PgghmDp1KuLj4zFt2jRYWlpi/fr12LRpE/744w8cOnQIbm5uePPNN3WyBpaHYYuYjvjpp59w4MABHDp0COHh4cjNzcXy5cvh6+uL+fPnM6/r+DRGg/JYdIOZmRnc3d2xcOFCLFy4EE1NTUwm2o4dO+Dn58cUtM5u4F1RWVmJ27dvIyYmBi4uLrq9CBXY2NjA398f/v7+EIvFzA6trKwMDg4OTEHrahhcKpUiOzsbFhYWnV5/fX09EhMTMWTIEOzfv1+nTv66oKMjfXp6utJZ5IoVK/Dkk0/qNGqJ/qz++c9/4uDBg5g1axZu3bqFv/76C1lZWVizZg0mTZoEGxsb/Pvf/8aJEycwatQopSMGFt1jWu/MXsyqVavwzjvv4PnnnwcARERE4O7du9i0aRPmz5/PPA1S5SKFy+UaxeHgUcHFxQXz5s3DvHnz0Nrait9++w2pqamYMmUKPDw8mJbjiBEjNC5o1MMxNjYWzs7Oer6Ch7GyslJyC6mrqwOHw0FFRQXjFuLl5QVHR8eHUq5zcnJgbm6OqKgotTurpqYmJCUlISAgAD/++KNekgh6yvjx45Gbm8s40v/tb39jHOmPHj2K8+fPIycnR+ffd8+ePUhLS8PJkycRExOD/fv3Y8GCBWhra4NAIMDHH3+MMWPGMGeMpmCR1tdh1Yk6QigUdmptFRQUhP79+yM9PZ35vFgsxsWLF/Hkk08adK2PKo6Ojnj++efx888/g8Ph4PPPPwePx0NycjKGDRuGt99+G1euXFEKWO3InTt3UF5ejri4OKMUsI5YWlrCx8cH0dHRGDduHEJCQhjfw8uXL6OkpARNTU1MC9HMzAzR0dFqC1hLSwuSk5Ph5eWFX375RediEl1hb2+P0NBQPP744/jmm29gYWHB7HjOnz+P27dvw8XFBRYWFswucvbs2Rg3bpzW37O9vR1SqRTz589HTEwMDh8+jGXLlmHnzp146qmn8N///hfr1q2DQCCAi4sLW8AMBLsT0xGJiYnYuHEj/P39ER4ejpycHGzbtg0LFy4E8KDNtXz5cnzyyScYNGgQBg0ahE8++QR2dnZGCw98lLGzs8OsWbMwa9YsJhMtJSUFc+bMgbW1NRITE5lMNHoTPH78OBwcHDBixIiHPB5NAXNzc8aBXiaToaGhAVwuFzk5OZDJZLC0tOzUiYPP52PWrFlwdHTEkSNHeiweMSSKjvTvvPMOXnnlFaXPR0REYPv27UhMTNTq66empiIzMxNLly4F8OA89KOPPsLatWvxxhtvoKSkBKmpqTh06BD8/f1ZOzsDwhYxHfHll1/igw8+wOLFi8HlcuHr64tFixbhww8/ZF7z9ttvo62tDYsXL2aGnc+cOWOSN8RHCRsbGyQmJiIxMRFisZjJRKNnmdOnT4dIJMKpU6dw7dq1XvH7Mjc3h6enJ9zc3CASidDe3g4XFxcUFRUxbiFeXl5wdXWFhYUFBAIBnn32WVhaWuLXX381qnWUoiM98GDIOjc3t1NH+nv37uG5554DAPTv31+lmMPf3x9BQUHdWgshBDKZDH/++Sf++OMPvPbaawgMDMTp06fR3t7OFMXGxkaMGzcO8fHxeOmll7S/eJZuw0rsWVjUIJVKcfHiRbz99tsoKCjAwIEDGbeQ8ePHm/xORS6XIy8vDxKJBLGxsbCwsAAhBM3NzeBwOKipqcHLL7+M4cOHQygUQiqVIj093ehtsIyMDCVHegp1pH/hhRdw7do1JUf6999/HyNHjlT7NTUNygSUxVbUC7KlpQUjR45ETEwMfvzxR1y9ehUvvfQSFi9ejISEBCxduhR+fn7Ys2eP1tfNoh1sEetDdGV9JZFI8P777+PEiRMoLy+Hs7MzJk6ciE8//dTkwxqNASEE7777Lvbt24f09HQ0NDQwmWjNzc2Ij49HcnIyJk6caHIO+3K5HDdu3EB7eztiY2NVijPkcjkuXryITz/9FLm5uZBKpZg6dSpmz56NhIQEg6guTZmDBw/i999/x8svv4zHHnsMmZmZmDlzJj788EPMnz8fr7/+Oi5cuID29nYMHDgQV69eZa24jABbxPoQGzduxPbt2/Hdd98hPDwcmZmZWLBgATZs2IBly5ahubkZzz77LF599VVERUWhsbERy5cvh1QqRWZmprGXb3LweDwkJCRg//79SskIcrkc165dYwoah8PBlClTmEw0Y2eeyeVy5Ofno62tDXFxcWrVhWKxGPPmzUN1dTXOnj0LDoeDlJQUpKamYuXKlY/0Wa1EIkF8fDzOnz8Pd3d3rF27FhMnTsR3332HiooK7NixA/b29rh58yb4fD7GjBljcmMIjwpsEetDdGV9pYrr16/jsccew927d+Hv72+opfYauprjk8vlyMnJYSJkqqqqlDLRnJycDDoHKJfLUVBQAIFAgLi4OLXqQolEggULFqC0tBQXLlx4yO/xUZxfVLxmQghOnz6N/fv3Y9CgQTh+/DhGjx4NHo+HzMxMLF++nBVvmAjs3rcPMXr0aJw7dw63bt0C8MB25/Lly5g2bZraf9Pc3AwzM7NHvnWkjq5u5P369UNcXBw2bdqEmzdv4tq1a4iOjsb27dsRGBiIZ599Ft9//z0aGhr0blBMCEFhYSH4fH6nBUwqlWLRokW4efMmzp49q9Kw+FErYMCDaz579izOnj0LMzMzPP3004yf5dmzZzFo0CBYWFigtLQUb7zxBm7cuGHsJbOA3Yn1KegZzubNm5Wsr2iWUUdEIhFGjx6NsLAwHDhwwMCr7dsQQnDz5k0mQqagoABPP/00kpOTkZCQoPNMNFrAWlpaEBcXB2tra5Wvk8lkWLx4Ma5du4aMjAz2LFQBsViMFStW4F//+heWL1+OBQsWICgoCFFRUVi+fDmWLl2K1tZWrFq1CteuXcPFixeNLoJhAUBY+gw//PAD8fPzIz/88AO5ceMG+f7774mbmxvZt2/fQ68Vi8UkKSmJxMTEkObmZiOs9tFBLpeT0tJSsmnTJvLYY48RCwsL8vTTT5Nt27aRsrIywufziUAg0Po/Pp9P/vrrL3LmzBnS0NCg9nWtra1kwYIFJDg4mNy9e1fv133x4kWSkJBAfHx8CABy5MgRpc+vXbuWDBkyhNjZ2REXFxcyYcIE8ueffzKfr6+vJ0uWLCGDBw8mtra2ZODAgWTp0qWkqalJr+s+deoUGTNmDBk9ejT58MMPyZEjR8iMGTPI9evXmde0tbXpdQ0smsMWsT6En58f2bVrl9LH1q9fT4YMGaL0MbFYTJKTk0lkZCSpq6sz5BIfeeRyOamoqCCff/45eeqpp4i5uTl58sknyebNm8nNmze7XdD4fD65fv06OX36NKmvr++0gC1atIgEBASQ8vJyg1zriRMnyHvvvUdSUlJUFrGDBw+S9PR0cvv2bVJQUEBefvll4uTkRLhcLiGEkPz8fDJr1ixy9OhRUlZWRs6dO0cGDRpEZs+erfe1l5eXkz179pDBgwcTZ2dnEhAQQD766CO9f1+W7sMWsT6Em5sb+frrr5U+9sknn5BBgwYx/08LWHh4OHOzYDEOcrmc3Lt3j3zxxRdk7NixxMLCgowcOZJs2LCB5Ofnd1nQ+Hw+ycrK0qiALVmyhAwYMICUlpYa5VpVFbGONDc3EwDk7Nmzal/z888/EysrKyKRSHS8QtW0tbWRN954g1hYWJCgoCB2B2aCsMKOPgS1vvrtt99w584dHDlyBNu2bcPMmTMBPDjQf/bZZ5GZmYmDBw9CJpOhtrYWtbW1EIvFRl79o4eZmRkGDBiApUuX4sKFC6iqqsLChQuRkZGB2NhYjB49Glu2bEFJSclDohBCCG7dugUej4e4uDi1g9dyuRzr1q3D4cOHce7cOYSGhhri0rqNukiVjjQ3N8PJyckgcnZCCGxsbLBr1y6cPn0aV65cMfkB90cSIxdRFh3S0tJCli1bRvz9/YmNjQ0JDg4m7733HmlvbyeEEFJRUUEAqPzvwoULxl08C4NcLid1dXXkP//5D4mPjydWVlYkPDycvPvuu+T69eukpaWFLFy4kGzYsIHweLxOd2rvvvsu8fT0JAUFBUa9JqjZiR07dozY29sTMzMz4uvrS/766y+1X6Ouro74+/uT9957T48rVUYulxvse7FoB6tOZGExYcj/t4k6evQoUlNTcerUKQwYMACNjY346quvkJiYqNIlghCCzz77DF988QXOnz/f6e7GEKizfRIIBKipqWEiVc6fP89EqijS0tKCyZMnw9XVFUePHjXJeBgW48AWMRaWXsTq1auxZ88ejB49GhcuXICXlxeTiRYXF4d+/fqBEIIvvvgCW7duxZkzZzBixAhjL1tj78JBgwZh4cKFSmMhra2tmDJlCuzs7HD8+HG2pceiBHsmxqJzpFIp3n//fQQFBcHW1hbBwcH4+OOPmWy1jixatAhmZmbYsWOHYRfay/j000+xb98+XLlyBceOHQOHw8HWrVvB4XAwY8YMhIeHY/Xq1XjnnXewefNmnDhxwiQKWHcgCpEqwP92YFZWVjh69ChbwFgegjX7YtE5mzdvxu7dux/ycHR2dsayZcuUXpuWloZr166xQ7caMHr0aCQkJGDYsGEAHgRDzp49G7Nnz0ZbWxvS09Nx8OBBHD58GEePHsXjjz9u1PX2NFKltbUVkydPhlAoxIEDB9DS0oKWlhYAgKenp9pgT5ZHDOMdx7H0VaZPn04WLlyo9LFZs2aRF198Uelj9+7dIwMGDCAFBQUkICCAbN++3YCr7LsIhUJjL4EQQsiFCxdUiojmz59P2trayMyZM4mvry+xsrIiPj4+ZMaMGUrCDnX/HgCpqKgw3oWxmBTsToxF54wePRq7d+/GrVu3MHjwYMbDUbFdKJfLMW/ePKxatQrh4eHGW2wfxJiBloqMGzeuU7/I1NTUHv17FhaAbSey6IHVq1ejubkZYWFhSh6Oc+fOZV6zefNmWFhY4M033zTiSllYWHo7bBFj0Tk//fQTDhw4gEOHDiE8PBy5ublYvnw5fH19MX/+fGRlZWHnzp3Izs5+JN3SWVhYdAcrsWfROQMHDsQ777yDN954g/nYhg0bcODAAdy8eRM7duzAW2+9pTTfJJPJ0K9fPwwcOBB37twxwqpZWFh6I+xOjEXnCIXChwZwzc3NGYn9vHnzMHHiRKXPT5kyBfPmzcOCBQsMtk4WFpbeDzsnxqJzuvJwdHd3x/Dhw5X+s7S0RP/+/TFkyBAjr55Fkd9//x2JiYnw9fWFmZkZ0tLSlD6/bt06hIWFwd7eHq6urpg4cSKuXbum9Jr29nYsXboUHh4esLe3x4wZM3Dv3j0DXgVLX4YtYiw658svv8Szzz6LxYsXY+jQoVi5ciUWLVqE9evXG3tpLN1EIBAgKioKu3btUvn5wYMHY9euXcjPz8fly5cRGBiIyZMng8fjMa9Zvnw5jhw5gh9//BGXL18Gn89HQkICZDKZoS6DpQ/Dnok9Auzduxfff/89CgoKAABxcXH45JNP8Nhjjxl5ZSy9CU2so1paWuDs7IyzZ89iwoQJaG5uhqenJ/bv3485c+YAAO7fv4+BAwfixIkTmDJlioFWz9JXYXdijwAZGRmYO3cuLly4gKtXr8Lf3x+TJ09GdXW1sZemdzS1wCouLsaMGTPg7OwMR0dHPP7446isrDTSqnsnquJUsrKyIJFIMHnyZOZ1vr6+GD58OP744w9jLZWlD8EWsV6CXC7H5s2bERoaCmtra/j7+2Pjxo0AgPz8fDzzzDOwtbWFu7s7XnvtNfD5fObfHjx4EIsXL0Z0dDTCwsKwd+9eyOVynDt3zliXYzCoBdauXbtQXFyMLVu2YOvWrfjyyy+Z19y+fRujR49GWFgYMjIykJeXhw8++ID16dOQ48ePw8HBATY2Nti+fTvS09Ph4eEBAKitrYWVlRVcXV2V/o23tzdqa2uNsVyWPgarTuwlrFmzBnv37sX27dsxevRo1NTU4ObNmxAKhZg6dSoef/xxXL9+HVwuF6+88gqWLFmCffv2qfxaQqEQEokEbm5uhr0II3D16lUkJSVh+vTpAIDAwED88MMPyMzMZF7z3nvvYdq0adiyZQvzseDgYIOvtbcyfvx45ObmMnEqf/vb31TGqShCCGFnBFl0gxEtr1g0pKWlhVhbW5O9e/c+9Lk9e/YQV1dXwufzmY/99ttvpF+/fqS2tlbl11u8eDEJCQl5JKLWN23aRAICAkhJSQkhhJDc3Fzi5eVFDh06RAghRCaTEQcHB/Lxxx+TyZMnE09PT/LYY4+pDHB81IGaYMuOhIaGkk8++YQQQsi5c+cIANLQ0KD0msjISPLhhx/qY5ksjxhsO7EXUFxcjPb2dkyYMEHl56KiomBvb8987KmnnoJcLkdJSclDr9+yZQt++OEHpKamPhLtstWrV2Pu3LkICwuDpaUlYmJisHz5csYCi8vlgs/n49NPP8XUqVNx5swZzJw5E7NmzcLFixeNvPreCVGIU4mLi4OlpSXS09OZz9fU1KCgoABPPvmksZbI0odg24m9gM4MXUknbZmOH//ss8/wySef4OzZs4iMjNTpGk2VriywqMAjKSkJK1asAABER0fjjz/+wO7duzF27FhjLt/o9DROxdnZGS+//DL++c9/wt3dHW5ubli5ciUiIiIeGnhnYdEKY28FWbqmra2N2Nra9qiduGXLFuLk5ESuXr1qkDWbCn5+fmTXrl1KH1u/fj0ZMmQIIYSQ9vZ2YmFhQdavX6/0mrfffps8+eSTBlunqdLTOBVCHrx/lyxZQtzc3IitrS1JSEgglZWVRroilr4GuxPrBdjY2GD16tV4++23YWVlhaeeego8Hg+FhYX4+9//jrVr12L+/PlYt24deDweli5dinnz5sHb2xvAgxbiBx98gEOHDiEwMJBRhTk4OMDBwcGYl6Z3urLAsrKywsiRIx9qvd66dQsBAQEGW6ep0tM4FeDB+/fLL79UUoSysOgMY1dRFs2QyWRkw4YNJCAggFhaWhJ/f3/m8PzGjRtk/PjxxMbGhri5uZFXX32VtLa2Mv82ICBA5dP02rVrjXQ1hmP+/PlkwIAB5Pjx46SiooKkpqYSDw8P8vbbbzOvSU1NJZaWlmTPnj2ktLSUfPnll8Tc3JxcunTJiCtnYWHRBNaxg6VP09raig8++ABHjhwBl8uFr68v5s6diw8//BBWVlbM67799lts2rQJ9+7dw5AhQ/DRRx8hKSnJiCtnYWHRBLaIsbCwsLD0WliJPQsLCwtLr4UtYiwsBkQTL0c+n48lS5bAz88Ptra2GDp0KP71r38ZcdUsLKYLq05kYTEg1Mvxu+++Q3h4ODIzM7FgwQI4Oztj2bJlAIAVK1bgwoULOHDgAAIDA3HmzBksXrwYvr6+7DkdC0sH2DMxFhYDkpCQAG9vb3zzzTfMx2bPng07Ozvs378fADB8+HDMmTMHH3zwAfOauLg4TJs2jc1kY2HpANtOZGExIKNHj8a5c+dw69YtAEBeXh4uX76MadOmKb3m6NGjqK6uBiEEFy5cwK1bt9jsLRYWFbDtRBYWA7J69Wo0NzcjLCwM5ubmkMlk2LhxI+PlCABffPEFXn31Vfj5+cHCwgL9+vXDf/7zH4wePdqIK2dhMU3YIsbCYkC68nIEHhSxP//8E0ePHkVAQAB+//13LF68GD4+PqzfIAtLB9gzMRYWAzJw4EC88847eOONN5iPbdiwAQcOHMDNmzfR1tYGZ2dnHDlyhMlAA4BXXnkF9+7dw6lTp4yxbBYWk4U9E2NhMSBdeTlKJBJIJJJOX8PCwvI/2HYiC4sBSUxMxMaNG+Hv74/w8HDk5ORg27ZtWLhwIQDAyckJY8eOxapVq2Bra4uAgABcvHgR33//PbZt22bk1bOwmB5sO5GFxYBo4uVYW1uLNWvW4MyZM2hoaEBAQABee+01rFixQm12HAvLowpbxFhYWFhYei3smRgLCwsLS6+FLWIsLCwsLL0WtoixsLCwsPRa2CLGwsLCwtJrYYsYCwsLC0uvhS1iLCwsLCy9FraIsbCwsLD0WtgixsLCwsLSa2GLGAsLCwtLr4UtYiwsLCwsvRa2iLGwsLCw9FrYIsbCwsLC0mv5fzmmMS/ttXMaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "X = res.X\n",
    "F = res.F\n",
    "G = res.G\n",
    "\n",
    "\n",
    "Z_res=X[:,0:2]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "names = []\n",
    "types = []\n",
    "\n",
    "legend_dict = {}\n",
    "\n",
    "\n",
    "for zData in Z_res:\n",
    "    dist = np.linalg.norm(zData - z_np, axis=1)\n",
    "    meanDist = np.max(dist)\n",
    "    distOrder = np.argsort(dist)\n",
    "    matname = dataIdentifier['name'][distOrder[0]]\n",
    "    mattype = dataIdentifier['className'][distOrder[0]]\n",
    "    names.append(matname)\n",
    "    types.append(mattype)\n",
    "\n",
    "unique_labels = list(set(types))\n",
    "\n",
    "\n",
    "\n",
    "# Define specific colors for each material type\n",
    "mat_colors = {\n",
    "    'Steel': 'slategrey',\n",
    "    'Aluminium Alloys': 'violet',\n",
    "    'Plastics': 'lawngreen',\n",
    "    'Titanium Alloys': 'indigo',\n",
    "    'Copper Alloys': 'chocolate',\n",
    "    'Composites': 'gold',\n",
    "    # Add more material types and corresponding colors as needed\n",
    "}\n",
    "\n",
    "\n",
    "print(names)        \n",
    "\n",
    "\n",
    "\n",
    "# 3D Scatter Plot\n",
    "fig = plt.figure()\n",
    "ax3d = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# add scatter plot for each unique label\n",
    "for i, (x, y, z) in enumerate(F):\n",
    "    material = names[i]\n",
    "    mattype = types[i]\n",
    "    color = mat_colors[mattype]\n",
    "    label = mattype\n",
    "    ax3d.scatter(x, y, z, color=color, label=label)\n",
    "    #ax3d.text(x + 0.1, y + 0.1, z + 0.1, material)\n",
    "    if mattype not in legend_dict:\n",
    "        legend_dict[mattype] = ax3d.scatter(x, y, z, color=color, label=label)\n",
    "\n",
    "   \n",
    "\n",
    "ax3d.text(80, 138, 1e8, material)\n",
    "ax3d.text(84, 138, 1e8, material)\n",
    "ax3d.text(82, 130, 1e8, material)\n",
    "\n",
    "\n",
    "# set legend colors and remove duplicates\n",
    "handles, labels = [], []\n",
    "for label in legend_dict:\n",
    "    handles.append(legend_dict[label])\n",
    "    labels.append(label)\n",
    "    legend_dict[label].set_color(mat_colors[label])\n",
    "    \n",
    "ax.legend(handles, labels, loc=\"upper right\")\n",
    "\n",
    "\n",
    "# show plot\n",
    "\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy', 'compliance']\n",
    "enabled_entries = [entry for entry in entry_order if objectives[entry]['isOn']]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax3d.legend(handles, labels, loc=\"upper right\")\n",
    "ax3d.set_xlabel(enabled_entries[0])\n",
    "ax3d.set_ylabel(enabled_entries[1])\n",
    "ax3d.set_zlabel(enabled_entries[2])\n",
    "ax3d.set_title(\"Objective space\")\n",
    "\n",
    "plt.savefig('./figures_Advanced/Preliminary_Pareto.png', bbox_inches='tight', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70dd546",
   "metadata": {},
   "source": [
    "# Re-runs a solver with real materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "94d744c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduces the database to the groups imvolved\n",
    "candidate_types1 = list(set(types))\n",
    "\n",
    "mask = df['class'].isin(candidate_types1)\n",
    "\n",
    "# Filter the DataFrame using the mask\n",
    "df2 = df[mask]\n",
    "df2 = df2.reset_index(drop=True)\n",
    "\n",
    "dataIdentifier2 = {'name': df2[df2.columns[0]], 'className':df2[df2.columns[1]], 'classID':df2[df2.columns[2]]} # name of the material and type\n",
    "trainInfo2 = torch.tensor((df2[df2.columns[3:]].to_numpy())).float()\n",
    "\n",
    "\n",
    "props = df2.columns[3:].tolist()\n",
    "nmat, nprop = trainInfo2.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "69bd5642",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pymoo.core.problem import ElementwiseProblem\n",
    "from pymoo.core.variable import Real, Integer, Choice, Binary\n",
    "from pymoo.termination.default import DefaultMultiObjectiveTermination\n",
    "\n",
    "class MixedVarsZDT1(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self, truss, trainInfo2 , constraints, objectives, nc, no, nmat, n_bars, **kwargs):\n",
    "\n",
    "        variables = dict()\n",
    "\n",
    "        variables[f\"x01\"] = Integer(bounds=(0, nmat-1))\n",
    "\n",
    "        for k in range(2, n_bars+2):\n",
    "            variables[f\"x{k:02}\"] = Real(bounds=(1e-7, 1e-3))\n",
    "\n",
    "        super().__init__(vars=variables, n_obj=no, n_ieq_constr=nc, **kwargs)\n",
    "        \n",
    "        self.truss=truss\n",
    "        self.trainInfo2=trainInfo2\n",
    "        self.constraints=constraints\n",
    "        self.objectives=objectives\n",
    "        \n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        x = np.array([x[f\"x{k:02}\"] for k in range(1, n_bars+2)])\n",
    "        \n",
    "        matprop=trainInfo2[int(x[0]),:]  \n",
    "        \n",
    "        x_tensor = torch.from_numpy(x[1:])\n",
    "        area = x_tensor * torch.ones((connectivity.shape[0]))   \n",
    "        u, _, _, _, _, internalForce = truss.solveFE(E = torch.tensor([matprop[0]]).float(), A=area)\n",
    "        compliance = truss.computeCompliance(u)\n",
    "        volume = truss.getVolume(area)\n",
    "        mass = matprop[2]*volume\n",
    "        cost = mass*matprop[1]\n",
    "        co2 = mass*matprop[4]\n",
    "        energy = mass*matprop[5]        \n",
    "        water = mass*matprop[6] \n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        con_values = []\n",
    "        \n",
    "        if(constraints['massCons']['isOn']):\n",
    "            constraints['massCons']['value'] = (mass/constraints['massCons']['maxMass']) - 1.\n",
    "            con_values.append(constraints['massCons']['value'])\n",
    "\n",
    "        if(constraints['costCons']['isOn']):\n",
    "            constraints['costCons']['value'] = (cost/constraints['costCons']['maxCost']) - 1.\n",
    "            con_values.append(constraints['costCons']['value'])\n",
    "\n",
    "        if(constraints['tensionCons']['isOn']):\n",
    "            tensileForce = 0.001+torch.relu(internalForce)\n",
    "            tensileStress = tensileForce/area\n",
    "            maxTensileStressExperienced = torch.pow(torch.sum(tensileStress**6), 1./6)*constraints['tensionCons']['FOS']\n",
    "            constraints['tensionCons']['value'] = (maxTensileStressExperienced/matprop[3]) - 1.\n",
    "            con_values.append(constraints['tensionCons']['value'])\n",
    "\n",
    "        if(constraints['complianceCons']['isOn']):\n",
    "            constraints['complianceCons']['value'] = (compliance/constraints['complianceCons']['maxJ']) - 1.\n",
    "            con_values.append(constraints['complianceCons']['value'])\n",
    "\n",
    "        if(constraints['compressionCons']['isOn']):\n",
    "            compressiveForce = 0.001+torch.relu(-internalForce)\n",
    "            k = 4. # used in geom factor... see buckling theory\n",
    "            geomFactor = k*(self.truss.barLength/(np.pi*area))**2\n",
    "            compressiveStress = compressiveForce*geomFactor\n",
    "            maxCompressiveStressExperienced = torch.pow(torch.sum(compressiveStress**6), 1./6)*constraints['compressionCons']['FOS']\n",
    "            constraints['compressionCons']['value'] = (maxCompressiveStressExperienced/matprop[0]) - 1.\n",
    "            con_values.append(constraints['compressionCons']['value'])\n",
    "\n",
    "        if(constraints['co2Cons']['isOn']):\n",
    "            constraints['co2Cons']['value'] = (co2/constraints['co2Cons']['maxco2']) - 1.\n",
    "            con_values.append(constraints['co2Cons']['value'])\n",
    "\n",
    "        if(constraints['energyCons']['isOn']):\n",
    "            constraints['energyCons']['value'] = (energy/constraints['energyCons']['maxenergy']) - 1.\n",
    "            con_values.append(constraints['energyCons']['value'])\n",
    "\n",
    "        if(constraints['waterCons']['isOn']):\n",
    "            constraints['waterCons']['value'] = (water/constraints['waterCons']['maxwater']) - 1.\n",
    "            con_values.append(constraints['waterCons']['value'])\n",
    "            \n",
    "        \n",
    "        \n",
    "        obj_values = []\n",
    "        \n",
    "        if(objectives['cost']['isOn']):\n",
    "            obj_values.append(cost)\n",
    "            \n",
    "        if(objectives['mass']['isOn']):\n",
    "            obj_values.append(mass)\n",
    "            \n",
    "        if(objectives['co2']['isOn']):\n",
    "            obj_values.append(co2)\n",
    "            \n",
    "        if(objectives['water']['isOn']):\n",
    "            obj_values.append(water)\n",
    "            \n",
    "        if(objectives['energy']['isOn']):\n",
    "            obj_values.append(energy)\n",
    "            \n",
    "        if(objectives['compliance']['isOn']):\n",
    "            obj_values.append(compliance)\n",
    "  \n",
    "            \n",
    "  \n",
    "\n",
    "        out[\"F\"] = np.array(obj_values)\n",
    "        out[\"G\"] = np.array(con_values)\n",
    "\n",
    "if(constraints['confidenceCons']['isOn']):\n",
    "    ncons=nc-1\n",
    "else:\n",
    "    ncons=nc\n",
    "\n",
    "  \n",
    "\n",
    "problem2 = MixedVarsZDT1(truss, trainInfo2, constraints, objectives, ncons, no, nmat, n_bars)\n",
    "\n",
    "    \n",
    "from pymoo.visualization.scatter import Scatter\n",
    "from pymoo.algorithms.moo.nsga2 import NSGA2, RankAndCrowdingSurvival\n",
    "from pymoo.core.mixed import MixedVariableMating, MixedVariableGA, MixedVariableSampling, MixedVariableDuplicateElimination\n",
    "from pymoo.optimize import minimize\n",
    "\n",
    "algorithm = NSGA2(pop_size=50,\n",
    "                  sampling=MixedVariableSampling(),\n",
    "                  mating=MixedVariableMating(eliminate_duplicates=MixedVariableDuplicateElimination()),\n",
    "                  eliminate_duplicates=MixedVariableDuplicateElimination(),\n",
    "                  )\n",
    "\n",
    "termination=('n_gen', 150)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "318df8d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "n_gen  |  n_eval  | n_nds  |     cv_min    |     cv_avg    |      eps      |   indicator  \n",
      "==========================================================================================\n",
      "     1 |       50 |      1 |  0.000000E+00 |  1.508056E+02 |             - |             -\n",
      "     2 |      100 |      1 |  0.000000E+00 |  5.2720013027 |  1.201983E+09 |         ideal\n",
      "     3 |      150 |      2 |  0.000000E+00 |  1.4973712788 |  1.0000000000 |         ideal\n",
      "     4 |      200 |      2 |  0.000000E+00 |  0.5757362925 |  0.000000E+00 |             f\n",
      "     5 |      250 |      3 |  0.000000E+00 |  0.1334441186 |  0.0447491139 |         ideal\n",
      "     6 |      300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.2924113619 |         ideal\n",
      "     7 |      350 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0114699190 |         ideal\n",
      "     8 |      400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0264182105 |         ideal\n",
      "     9 |      450 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0698369773 |         ideal\n",
      "    10 |      500 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0083654497 |         ideal\n",
      "    11 |      550 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0475030611 |         ideal\n",
      "    12 |      600 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0579653099 |         ideal\n",
      "    13 |      650 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    14 |      700 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0254006754 |         ideal\n",
      "    15 |      750 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0103246760 |             f\n",
      "    16 |      800 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0172544927 |         ideal\n",
      "    17 |      850 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0055979189 |             f\n",
      "    18 |      900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0165255533 |             f\n",
      "    19 |      950 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0028468763 |         ideal\n",
      "    20 |     1000 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0131889263 |         ideal\n",
      "    21 |     1050 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0350560189 |         ideal\n",
      "    22 |     1100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0035157630 |         ideal\n",
      "    23 |     1150 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0082500336 |         ideal\n",
      "    24 |     1200 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    25 |     1250 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0402851295 |         ideal\n",
      "    26 |     1300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.1171973612 |         nadir\n",
      "    27 |     1350 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0021061457 |             f\n",
      "    28 |     1400 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0902725551 |         nadir\n",
      "    29 |     1450 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0473488648 |         nadir\n",
      "    30 |     1500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0352485954 |         ideal\n",
      "    31 |     1550 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0038490661 |         ideal\n",
      "    32 |     1600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0025273285 |         ideal\n",
      "    33 |     1650 |      4 |  0.000000E+00 |  0.000000E+00 |  0.1023847249 |         ideal\n",
      "    34 |     1700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0068296961 |         nadir\n",
      "    35 |     1750 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    36 |     1800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0028846780 |         ideal\n",
      "    37 |     1850 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    38 |     1900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0086943828 |         nadir\n",
      "    39 |     1950 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0183513764 |         ideal\n",
      "    40 |     2000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0033945518 |         ideal\n",
      "    41 |     2050 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0003144695 |             f\n",
      "    42 |     2100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0099116235 |         nadir\n",
      "    43 |     2150 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0097356811 |         ideal\n",
      "    44 |     2200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0235025530 |         ideal\n",
      "    45 |     2250 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0025180127 |         ideal\n",
      "    46 |     2300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0159484036 |         ideal\n",
      "    47 |     2350 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0060282326 |         ideal\n",
      "    48 |     2400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    49 |     2450 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0077040409 |             f\n",
      "    50 |     2500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0047948510 |         ideal\n",
      "    51 |     2550 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0039571705 |         ideal\n",
      "    52 |     2600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    53 |     2650 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0060837256 |         ideal\n",
      "    54 |     2700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    55 |     2750 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    56 |     2800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0116771488 |         ideal\n",
      "    57 |     2850 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0050504363 |             f\n",
      "    58 |     2900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0209402656 |         ideal\n",
      "    59 |     2950 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    60 |     3000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    61 |     3050 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    62 |     3100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    63 |     3150 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0026912078 |         nadir\n",
      "    64 |     3200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0002386759 |             f\n",
      "    65 |     3250 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0052033225 |         ideal\n",
      "    66 |     3300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0068739705 |             f\n",
      "    67 |     3350 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    68 |     3400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0046587680 |         nadir\n",
      "    69 |     3450 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0111858620 |         ideal\n",
      "    70 |     3500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0089768161 |         ideal\n",
      "    71 |     3550 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    72 |     3600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0027000091 |         ideal\n",
      "    73 |     3650 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    74 |     3700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    75 |     3750 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    76 |     3800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    77 |     3850 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0013258932 |             f\n",
      "    78 |     3900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0013258932 |             f\n",
      "    79 |     3950 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0373787011 |         ideal\n",
      "    80 |     4000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    81 |     4050 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0288810441 |         nadir\n",
      "    82 |     4100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0008194282 |             f\n",
      "    83 |     4150 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0008194282 |             f\n",
      "    84 |     4200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0008194282 |             f\n",
      "    85 |     4250 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0008194282 |             f\n",
      "    86 |     4300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0008194282 |             f\n",
      "    87 |     4350 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0008194282 |             f\n",
      "    88 |     4400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0008194282 |             f\n",
      "    89 |     4450 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0008194282 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    90 |     4500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0012076904 |             f\n",
      "    91 |     4550 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0012076904 |             f\n",
      "    92 |     4600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0082149432 |         nadir\n",
      "    93 |     4650 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0006460483 |             f\n",
      "    94 |     4700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0111454858 |         nadir\n",
      "    95 |     4750 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0032930379 |             f\n",
      "    96 |     4800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0004656597 |             f\n",
      "    97 |     4850 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0025770013 |         nadir\n",
      "    98 |     4900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    99 |     4950 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   100 |     5000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   101 |     5050 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   102 |     5100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   103 |     5150 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0012034415 |             f\n",
      "   104 |     5200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0241620885 |         nadir\n",
      "   105 |     5250 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0230203352 |         nadir\n",
      "   106 |     5300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0018943819 |             f\n",
      "   107 |     5350 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0019851391 |             f\n",
      "   108 |     5400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0019851391 |             f\n",
      "   109 |     5450 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0019851391 |             f\n",
      "   110 |     5500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0072135025 |         ideal\n",
      "   111 |     5550 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   112 |     5600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0163210021 |         ideal\n",
      "   113 |     5650 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0011426755 |             f\n",
      "   114 |     5700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0011426755 |             f\n",
      "   115 |     5750 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0011426755 |             f\n",
      "   116 |     5800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0159836297 |         nadir\n",
      "   117 |     5850 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0119891659 |         ideal\n",
      "   118 |     5900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0054920913 |         nadir\n",
      "   119 |     5950 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0025872919 |         nadir\n",
      "   120 |     6000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0032320267 |         ideal\n",
      "   121 |     6050 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0052297128 |         ideal\n",
      "   122 |     6100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   123 |     6150 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   124 |     6200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0046581566 |         nadir\n",
      "   125 |     6250 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0036181930 |         nadir\n",
      "   126 |     6300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0042281244 |         ideal\n",
      "   127 |     6350 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0002747709 |             f\n",
      "   128 |     6400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0013316900 |             f\n",
      "   129 |     6450 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0042345944 |         nadir\n",
      "   130 |     6500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0038518387 |         ideal\n",
      "   131 |     6550 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   132 |     6600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   133 |     6650 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0050456457 |         nadir\n",
      "   134 |     6700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   135 |     6750 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0116630802 |         ideal\n",
      "   136 |     6800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   137 |     6850 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   138 |     6900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   139 |     6950 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   140 |     7000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0046646340 |         nadir\n",
      "   141 |     7050 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0168533573 |         nadir\n",
      "   142 |     7100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   143 |     7150 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   144 |     7200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   145 |     7250 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   146 |     7300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   147 |     7350 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   148 |     7400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   149 |     7450 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   150 |     7500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "Solver time : 26.41\n"
     ]
    }
   ],
   "source": [
    "start = time.perf_counter()\n",
    "res = minimize(problem2,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "print('Solver time : {:.2F}'.format(time.perf_counter() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e147dc84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AISI 4340 norm', 'AISI 1045 CD', 'Cast Carbon St', 'AISI 1010']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = res.X\n",
    "F = res.F\n",
    "G = res.G\n",
    "Z_res = [[d['x01']] for d in X]\n",
    "\n",
    "\n",
    "\n",
    "legend_dict = {}\n",
    "\n",
    "\n",
    "names = []\n",
    "types = []\n",
    "\n",
    "for xData in Z_res:\n",
    "    matname = dataIdentifier2['name'][int(xData[0])]\n",
    "    mattype = dataIdentifier2['className'][int(xData[0])]\n",
    "    names.append(matname)\n",
    "    types.append(mattype)\n",
    "    \n",
    "unique_labels = list(set(types))\n",
    "\n",
    "\n",
    "\n",
    "# Define specific colors for each material type\n",
    "mat_colors = {\n",
    "    'Steel': 'slategrey',\n",
    "    'Aluminium Alloys': 'violet',\n",
    "    'Plastics': 'lawngreen',\n",
    "    'Titanium Alloys': 'indigo',\n",
    "    'Copper Alloys': 'chocolate',\n",
    "    'Composites': 'gold',\n",
    "    # Add more material types and corresponding colors as needed\n",
    "}\n",
    "\n",
    "\n",
    "print(names)        \n",
    "\n",
    "\n",
    "\n",
    "# 3D Scatter Plot\n",
    "fig = plt.figure()\n",
    "ax3d = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# add scatter plot for each unique label\n",
    "for i, (x, y, z) in enumerate(F):\n",
    "    material = names[i]\n",
    "    mattype = types[i]\n",
    "    color = mat_colors[mattype]\n",
    "    label = mattype\n",
    "    ax3d.scatter(x, y, z, color=color, label=label)\n",
    "    ax3d.text(x + 0.1, y + 0.1, z + 0.1, material)\n",
    "    if mattype not in legend_dict:\n",
    "        legend_dict[mattype] = ax3d.scatter(x, y, z, color=color, label=label)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# set legend colors and remove duplicates\n",
    "handles, labels = [], []\n",
    "for label in legend_dict:\n",
    "    handles.append(legend_dict[label])\n",
    "    labels.append(label)\n",
    "    legend_dict[label].set_color(mat_colors[label])\n",
    "    \n",
    "ax.legend(handles, labels, loc=\"upper right\")\n",
    "\n",
    "\n",
    "# show plot\n",
    "\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy', 'compliance']\n",
    "enabled_entries = [entry for entry in entry_order if objectives[entry]['isOn']]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax3d.legend(handles, labels, loc=\"upper right\")\n",
    "ax3d.set_xlabel(enabled_entries[0])\n",
    "ax3d.set_ylabel(enabled_entries[1])\n",
    "ax3d.set_zlabel(enabled_entries[2])\n",
    "ax3d.set_title(\"Objective space\")\n",
    "\n",
    "plt.savefig('./figures_Advanced/Final_Pareto.png', bbox_inches='tight', dpi=200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "89d966f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 81.16668394 106.59546754]\n",
      "[200.14948893 124.94711156]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter Plot for X and Y\n",
    "# Find Pareto front\n",
    "num_points = F.shape[0]\n",
    "pareto_front = []\n",
    "\n",
    "for i in range(num_points):\n",
    "    is_pareto = True\n",
    "    for j in range(num_points):\n",
    "        if F[j, 0] < F[i, 0] and F[j, 1] < F[i, 1]:\n",
    "            is_pareto = False\n",
    "            break\n",
    "    if is_pareto:\n",
    "        pareto_front.append(i)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "pareto_front_x = F[pareto_front, 0]\n",
    "pareto_front_y = F[pareto_front, 1]\n",
    "\n",
    "print(pareto_front_x)\n",
    "print(pareto_front_y)\n",
    "\n",
    "scatter_points = []\n",
    "for i, (x, y) in enumerate(zip(pareto_front_x, pareto_front_y)):\n",
    "    material = names[pareto_front[i]]\n",
    "    mattype = types[pareto_front[i]]\n",
    "    color = mat_colors[mattype]\n",
    "    label = mattype\n",
    "    scatter = ax.scatter(x, y, color=color, label=label)\n",
    "    ax.text(x + 0.1, y + 0.1, material)\n",
    "    scatter_points.append(scatter)\n",
    "\n",
    "# Create legend handles and labels for displayed points only\n",
    "handles, labels = [], []\n",
    "for scatter in scatter_points:\n",
    "    if scatter.get_label() not in labels:\n",
    "        handles.append(scatter)\n",
    "        labels.append(scatter.get_label())\n",
    "\n",
    "ax.legend(handles, labels, loc=\"upper right\")\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "ax.set_title(\"Pareto Front: \" + str(enabled_entries[0]) + \" vs \" + str(enabled_entries[1]))\n",
    "\n",
    "plt.savefig('./figures/Scatter_Plot_X_vs_Y.png', bbox_inches='tight', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "62008cae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 81.16668394 139.41588711]\n",
      "[1.60352338e+08 2.39487112e+07]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter Plot for X and Z\n",
    "# Find Pareto front\n",
    "num_points = F.shape[0]\n",
    "pareto_front = []\n",
    "\n",
    "for i in range(num_points):\n",
    "    is_pareto = True\n",
    "    for j in range(num_points):\n",
    "        if F[j, 0] < F[i, 0] and F[j, 2] < F[i, 2]:\n",
    "            is_pareto = False\n",
    "            break\n",
    "    if is_pareto:\n",
    "        pareto_front.append(i)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "pareto_front_x = F[pareto_front, 0]\n",
    "pareto_front_y = F[pareto_front, 2]\n",
    "\n",
    "print(pareto_front_x)\n",
    "print(pareto_front_y)\n",
    "\n",
    "scatter_points = []\n",
    "for i, (x, y) in enumerate(zip(pareto_front_x, pareto_front_y)):\n",
    "    material = names[pareto_front[i]]\n",
    "    mattype = types[pareto_front[i]]\n",
    "    color = mat_colors[mattype]\n",
    "    label = mattype\n",
    "    scatter = ax.scatter(x, y, color=color, label=label)\n",
    "    ax.text(x + 0.1, y + 0.1, material)\n",
    "    scatter_points.append(scatter)\n",
    "\n",
    "# Create legend handles and labels for displayed points only\n",
    "handles, labels = [], []\n",
    "for scatter in scatter_points:\n",
    "    if scatter.get_label() not in labels:\n",
    "        handles.append(scatter)\n",
    "        labels.append(scatter.get_label())\n",
    "\n",
    "ax.legend(handles, labels, loc=\"upper right\")\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[2])\n",
    "ax.set_title(\"Pareto Front: \" + str(enabled_entries[0]) + \" vs \" + str(enabled_entries[2]))\n",
    "\n",
    "plt.savefig('./figures/Scatter_Plot_X_vs_Z.png', bbox_inches='tight', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "64012c5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[200.14948893 124.94711156 188.11008366 685.02291616]\n",
      "[1.60352338e+08 6.29960324e+08 5.38261758e+08 2.39487112e+07]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter Plot for Y and Z\n",
    "# Find Pareto front\n",
    "num_points = F.shape[0]\n",
    "pareto_front = []\n",
    "\n",
    "for i in range(num_points):\n",
    "    is_pareto = True\n",
    "    for j in range(num_points):\n",
    "        if F[j, 1] < F[i, 1] and F[j, 2] < F[i, 2]:\n",
    "            is_pareto = False\n",
    "            break\n",
    "    if is_pareto:\n",
    "        pareto_front.append(i)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "pareto_front_x = F[pareto_front, 1]\n",
    "pareto_front_y = F[pareto_front, 2]\n",
    "\n",
    "print(pareto_front_x)\n",
    "print(pareto_front_y)\n",
    "\n",
    "scatter_points = []\n",
    "for i, (x, y) in enumerate(zip(pareto_front_x, pareto_front_y)):\n",
    "    material = names[pareto_front[i]]\n",
    "    mattype = types[pareto_front[i]]\n",
    "    color = mat_colors[mattype]\n",
    "    label = mattype\n",
    "    scatter = ax.scatter(x, y, color=color, label=label)\n",
    "    ax.text(x + 0.1, y + 0.1, material)\n",
    "    scatter_points.append(scatter)\n",
    "\n",
    "# Create legend handles and labels for displayed points only\n",
    "handles, labels = [], []\n",
    "for scatter in scatter_points:\n",
    "    if scatter.get_label() not in labels:\n",
    "        handles.append(scatter)\n",
    "        labels.append(scatter.get_label())\n",
    "\n",
    "ax.legend(handles, labels, loc=\"upper right\")\n",
    "ax.set_xlabel(enabled_entries[1])\n",
    "ax.set_ylabel(enabled_entries[2])\n",
    "ax.set_title(\"Pareto Front: \" + str(enabled_entries[1]) + \" vs \" + str(enabled_entries[2]))\n",
    "\n",
    "plt.savefig('./figures/Scatter_Plot_Y_vs_Z.png', bbox_inches='tight', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f039f2fe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}