VAE_Mixed_Multi_pymoo.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": 3,
   "id": "fd11d60f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luisy\\AppData\\Local\\Temp\\ipykernel_9964\\405064810.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_FULL.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 4.68E+01 klLoss 5.67E-03 loss 4.68E+01\n",
      "Iter 500 reconLoss 2.33E-01 klLoss 6.02E-02 loss 2.93E-01\n",
      "Iter 1000 reconLoss 1.08E-01 klLoss 5.91E-02 loss 1.67E-01\n",
      "Iter 1500 reconLoss 7.33E-02 klLoss 5.82E-02 loss 1.32E-01\n",
      "Iter 2000 reconLoss 6.02E-02 klLoss 5.65E-02 loss 1.17E-01\n",
      "Iter 2500 reconLoss 4.35E-02 klLoss 5.46E-02 loss 9.81E-02\n",
      "Iter 3000 reconLoss 4.59E-02 klLoss 5.28E-02 loss 9.87E-02\n",
      "Iter 3500 reconLoss 3.41E-02 klLoss 5.15E-02 loss 8.56E-02\n",
      "Iter 4000 reconLoss 3.73E-02 klLoss 5.02E-02 loss 8.75E-02\n",
      "Iter 4500 reconLoss 3.03E-02 klLoss 4.89E-02 loss 7.91E-02\n",
      "Iter 5000 reconLoss 2.83E-02 klLoss 4.79E-02 loss 7.62E-02\n",
      "Iter 5500 reconLoss 2.79E-02 klLoss 4.69E-02 loss 7.48E-02\n",
      "Iter 6000 reconLoss 3.14E-02 klLoss 4.57E-02 loss 7.71E-02\n",
      "Iter 6500 reconLoss 2.66E-02 klLoss 4.51E-02 loss 7.17E-02\n",
      "Iter 7000 reconLoss 2.86E-02 klLoss 4.37E-02 loss 7.23E-02\n",
      "Iter 7500 reconLoss 2.24E-02 klLoss 4.34E-02 loss 6.58E-02\n",
      "Iter 8000 reconLoss 3.74E-02 klLoss 4.33E-02 loss 8.07E-02\n",
      "Iter 8500 reconLoss 2.49E-02 klLoss 4.23E-02 loss 6.71E-02\n",
      "Iter 9000 reconLoss 1.96E-02 klLoss 4.19E-02 loss 6.14E-02\n",
      "Iter 9500 reconLoss 2.12E-02 klLoss 4.16E-02 loss 6.28E-02\n",
      "Iter 10000 reconLoss 1.91E-02 klLoss 4.09E-02 loss 6.01E-02\n",
      "Iter 10500 reconLoss 1.75E-02 klLoss 4.14E-02 loss 5.88E-02\n",
      "Iter 11000 reconLoss 1.80E-02 klLoss 4.08E-02 loss 5.89E-02\n",
      "Iter 11500 reconLoss 1.82E-02 klLoss 4.12E-02 loss 5.95E-02\n",
      "Iter 12000 reconLoss 1.56E-02 klLoss 3.99E-02 loss 5.55E-02\n",
      "Iter 12500 reconLoss 1.30E-02 klLoss 3.94E-02 loss 5.23E-02\n",
      "Iter 13000 reconLoss 1.32E-02 klLoss 3.96E-02 loss 5.28E-02\n",
      "Iter 13500 reconLoss 1.27E-02 klLoss 4.00E-02 loss 5.27E-02\n",
      "Iter 14000 reconLoss 1.21E-02 klLoss 4.01E-02 loss 5.22E-02\n",
      "Iter 14500 reconLoss 1.27E-02 klLoss 3.95E-02 loss 5.22E-02\n",
      "Iter 15000 reconLoss 1.29E-02 klLoss 3.92E-02 loss 5.22E-02\n",
      "Iter 15500 reconLoss 1.33E-02 klLoss 3.91E-02 loss 5.24E-02\n",
      "Iter 16000 reconLoss 1.06E-02 klLoss 4.09E-02 loss 5.14E-02\n",
      "Iter 16500 reconLoss 1.32E-02 klLoss 4.00E-02 loss 5.32E-02\n",
      "Iter 17000 reconLoss 1.66E-02 klLoss 3.80E-02 loss 5.47E-02\n",
      "Iter 17500 reconLoss 1.14E-02 klLoss 3.87E-02 loss 5.01E-02\n",
      "Iter 18000 reconLoss 1.01E-02 klLoss 3.86E-02 loss 4.87E-02\n",
      "Iter 18500 reconLoss 1.26E-02 klLoss 3.86E-02 loss 5.12E-02\n",
      "Iter 19000 reconLoss 1.14E-02 klLoss 3.84E-02 loss 4.97E-02\n",
      "Iter 19500 reconLoss 1.27E-02 klLoss 3.80E-02 loss 5.07E-02\n",
      "Iter 20000 reconLoss 1.86E-02 klLoss 3.88E-02 loss 5.74E-02\n",
      "Iter 20500 reconLoss 1.38E-02 klLoss 3.95E-02 loss 5.33E-02\n",
      "Iter 21000 reconLoss 1.11E-02 klLoss 3.84E-02 loss 4.95E-02\n",
      "Iter 21500 reconLoss 1.26E-02 klLoss 3.79E-02 loss 5.05E-02\n",
      "Iter 22000 reconLoss 1.33E-02 klLoss 3.76E-02 loss 5.08E-02\n",
      "Iter 22500 reconLoss 1.11E-02 klLoss 3.75E-02 loss 4.86E-02\n",
      "Iter 23000 reconLoss 1.11E-02 klLoss 3.77E-02 loss 4.88E-02\n",
      "Iter 23500 reconLoss 1.15E-02 klLoss 3.79E-02 loss 4.94E-02\n",
      "Iter 24000 reconLoss 1.45E-02 klLoss 3.73E-02 loss 5.18E-02\n",
      "Iter 24500 reconLoss 1.04E-02 klLoss 3.70E-02 loss 4.74E-02\n",
      "Iter 25000 reconLoss 1.07E-02 klLoss 3.73E-02 loss 4.80E-02\n",
      "Iter 25500 reconLoss 1.00E-02 klLoss 3.73E-02 loss 4.73E-02\n",
      "Iter 26000 reconLoss 8.41E-03 klLoss 3.72E-02 loss 4.56E-02\n",
      "Iter 26500 reconLoss 9.64E-03 klLoss 3.73E-02 loss 4.70E-02\n",
      "Iter 27000 reconLoss 8.83E-03 klLoss 3.70E-02 loss 4.58E-02\n",
      "Iter 27500 reconLoss 1.16E-02 klLoss 3.67E-02 loss 4.84E-02\n",
      "Iter 28000 reconLoss 9.90E-03 klLoss 3.69E-02 loss 4.68E-02\n",
      "Iter 28500 reconLoss 1.06E-02 klLoss 3.66E-02 loss 4.71E-02\n",
      "Iter 29000 reconLoss 9.57E-03 klLoss 3.62E-02 loss 4.58E-02\n",
      "Iter 29500 reconLoss 1.04E-02 klLoss 3.63E-02 loss 4.67E-02\n",
      "Iter 30000 reconLoss 9.24E-03 klLoss 3.62E-02 loss 4.54E-02\n",
      "Iter 30500 reconLoss 9.15E-03 klLoss 3.65E-02 loss 4.56E-02\n",
      "Iter 31000 reconLoss 8.95E-03 klLoss 3.60E-02 loss 4.50E-02\n",
      "Iter 31500 reconLoss 9.03E-03 klLoss 3.59E-02 loss 4.49E-02\n",
      "Iter 32000 reconLoss 8.60E-03 klLoss 3.63E-02 loss 4.49E-02\n",
      "Iter 32500 reconLoss 9.99E-03 klLoss 3.67E-02 loss 4.66E-02\n",
      "Iter 33000 reconLoss 9.53E-03 klLoss 3.55E-02 loss 4.51E-02\n",
      "Iter 33500 reconLoss 8.19E-03 klLoss 3.61E-02 loss 4.43E-02\n",
      "Iter 34000 reconLoss 8.94E-03 klLoss 3.63E-02 loss 4.53E-02\n",
      "Iter 34500 reconLoss 8.30E-03 klLoss 3.55E-02 loss 4.38E-02\n",
      "Iter 35000 reconLoss 8.98E-03 klLoss 3.54E-02 loss 4.44E-02\n",
      "Iter 35500 reconLoss 1.19E-02 klLoss 3.56E-02 loss 4.75E-02\n",
      "Iter 36000 reconLoss 1.07E-02 klLoss 3.58E-02 loss 4.65E-02\n",
      "Iter 36500 reconLoss 9.07E-03 klLoss 3.55E-02 loss 4.46E-02\n",
      "Iter 37000 reconLoss 7.63E-03 klLoss 3.61E-02 loss 4.37E-02\n",
      "Iter 37500 reconLoss 1.14E-02 klLoss 3.84E-02 loss 4.98E-02\n",
      "Iter 38000 reconLoss 8.25E-03 klLoss 3.56E-02 loss 4.38E-02\n",
      "Iter 38500 reconLoss 9.46E-03 klLoss 3.53E-02 loss 4.48E-02\n",
      "Iter 39000 reconLoss 8.27E-03 klLoss 3.53E-02 loss 4.35E-02\n",
      "Iter 39500 reconLoss 9.05E-03 klLoss 3.54E-02 loss 4.45E-02\n",
      "Iter 40000 reconLoss 9.50E-03 klLoss 3.58E-02 loss 4.53E-02\n",
      "Iter 40500 reconLoss 9.88E-03 klLoss 3.56E-02 loss 4.55E-02\n",
      "Iter 41000 reconLoss 8.48E-03 klLoss 3.61E-02 loss 4.46E-02\n",
      "Iter 41500 reconLoss 9.97E-03 klLoss 3.49E-02 loss 4.49E-02\n",
      "Iter 42000 reconLoss 8.18E-03 klLoss 3.54E-02 loss 4.36E-02\n",
      "Iter 42500 reconLoss 9.85E-03 klLoss 3.58E-02 loss 4.57E-02\n",
      "Iter 43000 reconLoss 8.59E-03 klLoss 3.56E-02 loss 4.42E-02\n",
      "Iter 43500 reconLoss 9.03E-03 klLoss 3.52E-02 loss 4.42E-02\n",
      "Iter 44000 reconLoss 9.73E-03 klLoss 3.54E-02 loss 4.51E-02\n",
      "Iter 44500 reconLoss 1.25E-02 klLoss 3.54E-02 loss 4.79E-02\n",
      "Iter 45000 reconLoss 1.15E-02 klLoss 3.48E-02 loss 4.63E-02\n",
      "Iter 45500 reconLoss 9.55E-03 klLoss 3.53E-02 loss 4.48E-02\n",
      "Iter 46000 reconLoss 9.46E-03 klLoss 3.50E-02 loss 4.45E-02\n",
      "Iter 46500 reconLoss 1.12E-02 klLoss 3.52E-02 loss 4.63E-02\n",
      "Iter 47000 reconLoss 1.16E-02 klLoss 3.58E-02 loss 4.74E-02\n",
      "Iter 47500 reconLoss 8.07E-03 klLoss 3.49E-02 loss 4.30E-02\n",
      "Iter 48000 reconLoss 9.56E-03 klLoss 3.52E-02 loss 4.48E-02\n",
      "Iter 48500 reconLoss 1.09E-02 klLoss 3.52E-02 loss 4.61E-02\n",
      "Iter 49000 reconLoss 8.79E-03 klLoss 3.51E-02 loss 4.39E-02\n",
      "Iter 49500 reconLoss 7.84E-03 klLoss 3.48E-02 loss 4.26E-02\n",
      "training time : 184.46 \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": 22,
   "id": "c84c26b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from examples import getExample\n",
    "from trussFE import TrussFE\n",
    "\n",
    "#Loads the example truss\n",
    "exampleName, nodeXY, connectivity, bc = getExample(4)\n",
    "#Changes the force\n",
    "bc['forces'] = {'nodes':np.array([2]), 'fx':1.E3*torch.tensor([0.]), 'fy':1.E2*torch.tensor([125.])}\n",
    "\n",
    "#Loads the truss in truss\n",
    "truss = TrussFE(nodeXY, connectivity, bc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f45b989",
   "metadata": {},
   "source": [
    "# Gets the z coordinates of each material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "id": "00ba75a3",
   "metadata": {},
   "outputs": [],
   "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':True},\\\n",
    "              'mass': {'isOn':True}, \\\n",
    "              'co2': {'isOn':False},\\\n",
    "              'water': {'isOn':False},\\\n",
    "              'energy': {'isOn':False},\\\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": 25,
   "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",
    "        super().__init__(n_var=8,\n",
    "                         n_obj=no,\n",
    "                         n_ieq_constr=nc,\n",
    "                         xl=np.array([z0min, z1min, 2e-6, 2e-6, 2e-6, 2e-6, 2e-6, 2e-6]),\n",
    "                         xu=np.array([z0max, z1max, 1e-2, 1e-2, 1e-2, 1e-2, 1e-2, 1e-2]))\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",
    "        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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd20368",
   "metadata": {},
   "source": [
    "# Defines the problem settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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-9,\n",
    "    ftol=0.0001,\n",
    "    period=15,\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": 27,
   "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 |      3 |  0.000000E+00 |  3.220024E+02 |             - |             -\n",
      "     2 |       50 |      3 |  0.000000E+00 |  0.2417727709 |  0.000000E+00 |             f\n",
      "     3 |       60 |      3 |  0.000000E+00 |  0.0311418248 |  0.0308793366 |         ideal\n",
      "     4 |       70 |      3 |  0.000000E+00 |  0.0226938131 |  0.000000E+00 |             f\n",
      "     5 |       80 |      3 |  0.000000E+00 |  0.0150211350 |  0.0079018191 |         ideal\n",
      "     6 |       90 |      3 |  0.000000E+00 |  0.0044076741 |  0.0238793054 |         ideal\n",
      "     7 |      100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0039782759 |             f\n",
      "     8 |      110 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     9 |      120 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0200613137 |         ideal\n",
      "    10 |      130 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    11 |      140 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0381193516 |         ideal\n",
      "    12 |      150 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    13 |      160 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0044102984 |         ideal\n",
      "    14 |      170 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    15 |      180 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0154205612 |         ideal\n",
      "    16 |      190 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0056003854 |         ideal\n",
      "    17 |      200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0274659493 |         nadir\n",
      "    18 |      210 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0085386374 |         ideal\n",
      "    19 |      220 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0034548368 |         ideal\n",
      "    20 |      230 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0221199980 |         ideal\n",
      "    21 |      240 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0002113669 |             f\n",
      "    22 |      250 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0217908722 |         ideal\n",
      "    23 |      260 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0191692957 |         ideal\n",
      "    24 |      270 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    25 |      280 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    26 |      290 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    27 |      300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    28 |      310 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0038687054 |         ideal\n",
      "    29 |      320 |      6 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    30 |      330 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0070317514 |         ideal\n",
      "    31 |      340 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    32 |      350 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0052183273 |             f\n",
      "    33 |      360 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0124195755 |         ideal\n",
      "    34 |      370 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    35 |      380 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0575549998 |             f\n",
      "    36 |      390 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0323783460 |         nadir\n",
      "    37 |      400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    38 |      410 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    39 |      420 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0062716199 |         ideal\n",
      "    40 |      430 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0271880535 |             f\n",
      "    41 |      440 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0104074461 |         ideal\n",
      "    42 |      450 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0405538760 |         nadir\n",
      "    43 |      460 |      6 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    44 |      470 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0147926880 |         nadir\n",
      "    45 |      480 |      7 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    46 |      490 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0106882899 |         ideal\n",
      "    47 |      500 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0044824296 |         ideal\n",
      "    48 |      510 |      6 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    49 |      520 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0166072094 |             f\n",
      "    50 |      530 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0227324379 |         nadir\n",
      "    51 |      540 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0049203744 |             f\n",
      "    52 |      550 |      7 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    53 |      560 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0113554252 |         ideal\n",
      "    54 |      570 |      6 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    55 |      580 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0111130575 |         ideal\n",
      "    56 |      590 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0132122699 |             f\n",
      "    57 |      600 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0153049968 |         ideal\n",
      "    58 |      610 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0062955033 |         ideal\n",
      "    59 |      620 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    60 |      630 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0127836583 |         nadir\n",
      "    61 |      640 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    62 |      650 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0002198673 |             f\n",
      "    63 |      660 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0048929509 |             f\n",
      "    64 |      670 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    65 |      680 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0038794995 |         ideal\n",
      "    66 |      690 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0005968030 |             f\n",
      "    67 |      700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0005968030 |             f\n",
      "    68 |      710 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0092840959 |         ideal\n",
      "    69 |      720 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0004137668 |             f\n",
      "    70 |      730 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0203244794 |             f\n",
      "    71 |      740 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    72 |      750 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0061646444 |         ideal\n",
      "    73 |      760 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    74 |      770 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0065559760 |         ideal\n",
      "    75 |      780 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0300620237 |             f\n",
      "    76 |      790 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    77 |      800 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0035298643 |         ideal\n",
      "    78 |      810 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0054965503 |         ideal\n",
      "    79 |      820 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0052545667 |         ideal\n",
      "    80 |      830 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0206693735 |             f\n",
      "    81 |      840 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    82 |      850 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0122886320 |             f\n",
      "    83 |      860 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    84 |      870 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0166151657 |         ideal\n",
      "    85 |      880 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0071794330 |         ideal\n",
      "    86 |      890 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    87 |      900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    88 |      910 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0250707142 |         nadir\n",
      "    89 |      920 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0005482247 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    90 |      930 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0086158704 |         ideal\n",
      "    91 |      940 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0007080360 |             f\n",
      "    92 |      950 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0091348539 |             f\n",
      "    93 |      960 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0042878961 |             f\n",
      "    94 |      970 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0089771785 |         ideal\n",
      "    95 |      980 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    96 |      990 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    97 |     1000 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0065870743 |         ideal\n",
      "    98 |     1010 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0066406737 |         ideal\n",
      "    99 |     1020 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   100 |     1030 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0161796325 |         ideal\n",
      "   101 |     1040 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0000357797 |             f\n",
      "   102 |     1050 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0048221908 |             f\n",
      "   103 |     1060 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0287386228 |             f\n",
      "   104 |     1070 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0077704520 |         ideal\n",
      "   105 |     1080 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0103278342 |         ideal\n",
      "   106 |     1090 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   107 |     1100 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0144546952 |         nadir\n",
      "   108 |     1110 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0217010754 |         nadir\n",
      "   109 |     1120 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0234814601 |         ideal\n",
      "   110 |     1130 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0137417359 |             f\n",
      "   111 |     1140 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0285345640 |             f\n",
      "   112 |     1150 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0128567645 |             f\n",
      "   113 |     1160 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   114 |     1170 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0136661114 |         nadir\n",
      "   115 |     1180 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0021892046 |             f\n",
      "   116 |     1190 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0033453351 |             f\n",
      "   117 |     1200 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0123022959 |         nadir\n",
      "   118 |     1210 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0061544212 |         ideal\n",
      "   119 |     1220 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0085746479 |             f\n",
      "   120 |     1230 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   121 |     1240 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   122 |     1250 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0259600154 |         ideal\n",
      "   123 |     1260 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   124 |     1270 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   125 |     1280 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0130137529 |         ideal\n",
      "   126 |     1290 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   127 |     1300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   128 |     1310 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   129 |     1320 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   130 |     1330 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0094485284 |         ideal\n",
      "   131 |     1340 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0309329686 |         ideal\n",
      "   132 |     1350 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   133 |     1360 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   134 |     1370 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   135 |     1380 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0004032560 |             f\n",
      "   136 |     1390 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0004032560 |             f\n",
      "   137 |     1400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0004032560 |             f\n",
      "   138 |     1410 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0523983579 |             f\n",
      "   139 |     1420 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   140 |     1430 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   141 |     1440 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0223686235 |         ideal\n",
      "   142 |     1450 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   143 |     1460 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   144 |     1470 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   145 |     1480 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   146 |     1490 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   147 |     1500 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0038088503 |         ideal\n",
      "   148 |     1510 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0079676301 |         ideal\n",
      "   149 |     1520 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   150 |     1530 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   151 |     1540 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   152 |     1550 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   153 |     1560 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   154 |     1570 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   155 |     1580 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0085562190 |             f\n",
      "   156 |     1590 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0158721479 |         ideal\n",
      "   157 |     1600 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0079918860 |         ideal\n",
      "   158 |     1610 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0038132406 |         ideal\n",
      "   159 |     1620 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0110306760 |         ideal\n",
      "   160 |     1630 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0046964855 |             f\n",
      "   161 |     1640 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0056530471 |         ideal\n",
      "   162 |     1650 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0120473666 |         ideal\n",
      "   163 |     1660 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   164 |     1670 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   165 |     1680 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0016731856 |             f\n",
      "   166 |     1690 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0016731856 |             f\n",
      "   167 |     1700 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0016731856 |             f\n",
      "   168 |     1710 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0016731856 |             f\n",
      "   169 |     1720 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0285738044 |             f\n",
      "   170 |     1730 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0080531971 |             f\n",
      "   171 |     1740 |      6 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   172 |     1750 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0111053704 |             f\n",
      "   173 |     1760 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   174 |     1770 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   175 |     1780 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   176 |     1790 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   177 |     1800 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   178 |     1810 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0133904771 |         ideal\n",
      "   179 |     1820 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   180 |     1830 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   181 |     1840 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   182 |     1850 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   183 |     1860 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   184 |     1870 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0150006964 |             f\n",
      "   185 |     1880 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0063076714 |         ideal\n",
      "   186 |     1890 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0060705701 |             f\n",
      "   187 |     1900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0133125396 |             f\n",
      "   188 |     1910 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   189 |     1920 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000369362 |             f\n",
      "   190 |     1930 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000369362 |             f\n",
      "   191 |     1940 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000369362 |             f\n",
      "   192 |     1950 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0004636032 |             f\n",
      "   193 |     1960 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0026175019 |             f\n",
      "   194 |     1970 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   195 |     1980 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   196 |     1990 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   197 |     2000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   198 |     2010 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0050818539 |             f\n",
      "   199 |     2020 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0061703606 |         ideal\n",
      "   200 |     2030 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   201 |     2040 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   202 |     2050 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   203 |     2060 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   204 |     2070 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   205 |     2080 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   206 |     2090 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   207 |     2100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   208 |     2110 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   209 |     2120 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   210 |     2130 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0004862334 |             f\n",
      "   211 |     2140 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0004862334 |             f\n",
      "   212 |     2150 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0062644717 |             f\n",
      "   213 |     2160 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0068138391 |         nadir\n",
      "   214 |     2170 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   215 |     2180 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0013429869 |             f\n",
      "   216 |     2190 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0122031323 |             f\n",
      "   217 |     2200 |      6 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   218 |     2210 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0151722504 |         ideal\n",
      "   219 |     2220 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   220 |     2230 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   221 |     2240 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   222 |     2250 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   223 |     2260 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   224 |     2270 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   225 |     2280 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   226 |     2290 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   227 |     2300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   228 |     2310 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0093179073 |             f\n",
      "   229 |     2320 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   230 |     2330 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   231 |     2340 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0001123113 |             f\n",
      "   232 |     2350 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0001123113 |             f\n",
      "   233 |     2360 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0014877676 |             f\n",
      "   234 |     2370 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0014877676 |             f\n",
      "   235 |     2380 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0014877676 |             f\n",
      "   236 |     2390 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0014877676 |             f\n",
      "training time : 11.53 \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": 28,
   "id": "10d4e846",
   "metadata": {},
   "outputs": [
    {
     "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",
    "\n",
    "Z_res=X[:,0:2]\n",
    "\n",
    "names = []\n",
    "types = []\n",
    "fig, ax = plt.subplots()\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",
    "\n",
    "\n",
    "unique_labels = list(set(types))\n",
    "\n",
    "mat_colors = {}\n",
    "for i, mattype in enumerate(types):\n",
    "    if mattype not in mat_colors:\n",
    "        mat_colors[mattype] = plt.cm.get_cmap('jet')(i / len(set(types)))\n",
    "\n",
    "# add scatter plot for each unique label\n",
    "for i, (x, y) in enumerate(F):\n",
    "    material = names[i]\n",
    "    mattype = types[i]\n",
    "    color = mat_colors[mattype]\n",
    "    label = mattype\n",
    "    ax.scatter(x, y, color=color, label=label)\n",
    "    ax.text(x + 0.1, y + 0.1, material)\n",
    "    if mattype not in legend_dict:\n",
    "        legend_dict[mattype] = ax.scatter(x, y, color=color, label=label)\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",
    "# show plot\n",
    "plt.title(\"Objective Space\")\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy', 'compliance']\n",
    "enabled_entries = [entry for entry in entry_order if objectives[entry]['isOn']]\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures/Integer_sol_aprox.png',bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70dd546",
   "metadata": {},
   "source": [
    "# Re-runs a solver with real materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "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, **kwargs):\n",
    "\n",
    "        variables = dict()\n",
    "\n",
    "        variables[f\"x01\"] = Integer(bounds=(0, nmat-1))\n",
    "\n",
    "        for k in range(2, 8):\n",
    "            variables[f\"x{k:02}\"] = Real(bounds=(2e-6, 2e-2))\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, 8)])\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",
    "        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",
    "        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",
    "\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=60,\n",
    "                  sampling=MixedVariableSampling(),\n",
    "                  mating=MixedVariableMating(eliminate_duplicates=MixedVariableDuplicateElimination()),\n",
    "                  eliminate_duplicates=MixedVariableDuplicateElimination(),\n",
    "                  )\n",
    "\n",
    "termination=('n_gen', 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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 |       60 |      2 |  0.000000E+00 |  1.3951241726 |             - |             -\n",
      "     2 |      120 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     3 |      180 |      2 |  0.000000E+00 |  0.000000E+00 |  0.2832297788 |         ideal\n",
      "     4 |      240 |      3 |  0.000000E+00 |  0.000000E+00 |  0.3051965874 |         ideal\n",
      "     5 |      300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0779176234 |         ideal\n",
      "     6 |      360 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1214751830 |         ideal\n",
      "     7 |      420 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0407365647 |         ideal\n",
      "     8 |      480 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0304730869 |         nadir\n",
      "     9 |      540 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0758810113 |         ideal\n",
      "    10 |      600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0808060611 |         ideal\n",
      "    11 |      660 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0139099894 |         nadir\n",
      "    12 |      720 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0163683432 |         ideal\n",
      "    13 |      780 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0763002211 |         ideal\n",
      "    14 |      840 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0204660171 |         ideal\n",
      "    15 |      900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0561882666 |         ideal\n",
      "    16 |      960 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0314169298 |             f\n",
      "    17 |     1020 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0320585131 |         ideal\n",
      "    18 |     1080 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0354452611 |             f\n",
      "    19 |     1140 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    20 |     1200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0203598123 |         ideal\n",
      "    21 |     1260 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0084004653 |         nadir\n",
      "    22 |     1320 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    23 |     1380 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0700883855 |         ideal\n",
      "    24 |     1440 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0100913391 |         ideal\n",
      "    25 |     1500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0071862044 |         ideal\n",
      "    26 |     1560 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0265964227 |             f\n",
      "    27 |     1620 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0054063420 |         ideal\n",
      "    28 |     1680 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0165828635 |         ideal\n",
      "    29 |     1740 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0393961061 |         nadir\n",
      "    30 |     1800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0668074710 |         nadir\n",
      "    31 |     1860 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0175646199 |             f\n",
      "    32 |     1920 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    33 |     1980 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0335412839 |         nadir\n",
      "    34 |     2040 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    35 |     2100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0033937309 |         ideal\n",
      "    36 |     2160 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0193169031 |         ideal\n",
      "    37 |     2220 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    38 |     2280 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0041518428 |         nadir\n",
      "    39 |     2340 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0033196243 |         ideal\n",
      "    40 |     2400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0107677102 |         ideal\n",
      "    41 |     2460 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0654378032 |         nadir\n",
      "    42 |     2520 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0028868081 |             f\n",
      "    43 |     2580 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    44 |     2640 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    45 |     2700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0723174320 |         nadir\n",
      "    46 |     2760 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0000843920 |             f\n",
      "    47 |     2820 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0200152604 |             f\n",
      "    48 |     2880 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0182354213 |         ideal\n",
      "    49 |     2940 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    50 |     3000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0148652923 |         ideal\n",
      "    51 |     3060 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0029414664 |             f\n",
      "    52 |     3120 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0602438700 |         nadir\n",
      "    53 |     3180 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0301173408 |             f\n",
      "    54 |     3240 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0074586979 |         ideal\n",
      "    55 |     3300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0004758025 |             f\n",
      "    56 |     3360 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0004758025 |             f\n",
      "    57 |     3420 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0053995721 |         ideal\n",
      "    58 |     3480 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0016834276 |             f\n",
      "    59 |     3540 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0413424162 |         ideal\n",
      "    60 |     3600 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0139813673 |         nadir\n",
      "    61 |     3660 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0046219366 |         ideal\n",
      "    62 |     3720 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    63 |     3780 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    64 |     3840 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    65 |     3900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0020354550 |             f\n",
      "    66 |     3960 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0020354550 |             f\n",
      "    67 |     4020 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0154937071 |             f\n",
      "    68 |     4080 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    69 |     4140 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0025538688 |             f\n",
      "    70 |     4200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0002334230 |             f\n",
      "    71 |     4260 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0160979141 |         nadir\n",
      "    72 |     4320 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0027811215 |             f\n",
      "    73 |     4380 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0514134749 |         nadir\n",
      "    74 |     4440 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0067596853 |         ideal\n",
      "    75 |     4500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    76 |     4560 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    77 |     4620 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    78 |     4680 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    79 |     4740 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    80 |     4800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0133838752 |         nadir\n",
      "    81 |     4860 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    82 |     4920 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    83 |     4980 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0021762997 |             f\n",
      "    84 |     5040 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0021762997 |             f\n",
      "    85 |     5100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0046513746 |             f\n",
      "    86 |     5160 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0266283621 |         nadir\n",
      "    87 |     5220 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    88 |     5280 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0010699631 |             f\n",
      "    89 |     5340 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0010699631 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    90 |     5400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0011094563 |             f\n",
      "    91 |     5460 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0012081114 |             f\n",
      "    92 |     5520 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0061435777 |         nadir\n",
      "    93 |     5580 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    94 |     5640 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0002370519 |             f\n",
      "    95 |     5700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0026905491 |         nadir\n",
      "    96 |     5760 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0000226727 |             f\n",
      "    97 |     5820 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0000226727 |             f\n",
      "    98 |     5880 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0000971152 |             f\n",
      "    99 |     5940 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0000971152 |             f\n",
      "   100 |     6000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0020150851 |             f\n",
      "   101 |     6060 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0020161239 |             f\n",
      "   102 |     6120 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0027866662 |             f\n",
      "   103 |     6180 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0003791179 |             f\n",
      "   104 |     6240 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0003791179 |             f\n",
      "   105 |     6300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0024701944 |             f\n",
      "   106 |     6360 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0035223307 |             f\n",
      "   107 |     6420 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   108 |     6480 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0035768865 |         ideal\n",
      "   109 |     6540 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0000567183 |             f\n",
      "   110 |     6600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0063109499 |         nadir\n",
      "   111 |     6660 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0003648033 |             f\n",
      "   112 |     6720 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0058853293 |         nadir\n",
      "   113 |     6780 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0211624586 |         nadir\n",
      "   114 |     6840 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0187758877 |         nadir\n",
      "   115 |     6900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0045790815 |         nadir\n",
      "   116 |     6960 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   117 |     7020 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0011245139 |             f\n",
      "   118 |     7080 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0011245139 |             f\n",
      "   119 |     7140 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0011245139 |             f\n",
      "   120 |     7200 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0011245139 |             f\n",
      "   121 |     7260 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0011245139 |             f\n",
      "   122 |     7320 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0011245139 |             f\n",
      "   123 |     7380 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012740622 |             f\n",
      "   124 |     7440 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012740622 |             f\n",
      "   125 |     7500 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012740622 |             f\n",
      "   126 |     7560 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012740622 |             f\n",
      "   127 |     7620 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012740622 |             f\n",
      "   128 |     7680 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0023709953 |             f\n",
      "   129 |     7740 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0023709953 |             f\n",
      "   130 |     7800 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0023709953 |             f\n",
      "   131 |     7860 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0029344546 |             f\n",
      "   132 |     7920 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0002035969 |             f\n",
      "   133 |     7980 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0002035969 |             f\n",
      "   134 |     8040 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0002035969 |             f\n",
      "   135 |     8100 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0007757138 |             f\n",
      "   136 |     8160 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0007757138 |             f\n",
      "   137 |     8220 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0035268074 |             f\n",
      "   138 |     8280 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   139 |     8340 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0025743140 |             f\n",
      "   140 |     8400 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   141 |     8460 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0038063816 |         ideal\n",
      "   142 |     8520 |      5 |  0.000000E+00 |  0.000000E+00 |  9.367959E-06 |             f\n",
      "   143 |     8580 |      5 |  0.000000E+00 |  0.000000E+00 |  9.367959E-06 |             f\n",
      "   144 |     8640 |      5 |  0.000000E+00 |  0.000000E+00 |  9.367959E-06 |             f\n",
      "   145 |     8700 |      5 |  0.000000E+00 |  0.000000E+00 |  9.367959E-06 |             f\n",
      "   146 |     8760 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012187708 |             f\n",
      "   147 |     8820 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012187708 |             f\n",
      "   148 |     8880 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022233837 |             f\n",
      "   149 |     8940 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022991516 |             f\n",
      "   150 |     9000 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0026363018 |             f\n",
      "Solver time : 22.61\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": 32,
   "id": "e147dc84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CQsI7t09ERPS+im0AGjhwIC5duoTIyEjpZWtri6lTp+LQoUPaLi9b69evx4QJE3Dq1Cncv38/175HjhxBnz590KNHD5w5cwYRERFYsGBBjsEkJ6mpqejZsyfGjBmT7XK1Wo1OnTrh5cuXOHXqFLZt24Zdu3ZpBK1MaWlp6Nu3L1q0aJHj/m7cuIHY2FjpVbVq1VzrmzVrFnr37o2GDRviwIEDuHLlCpYvX46LFy/il19+kfqZmZkhNjYWDx48QFhYGD799FNs3LgR9erVw6NHj/L4aRAREeWNVq8BevHiBW7fvi29j46ORmRkJMqUKQMHBwdYWVlp9NfV1YWNjQ1cXFyKutR3evnyJX799VecPXsWcXFxCAwMxFdffZVj/99++w3NmzfH1KlTpbZq1aqhW7du77XfzOudAgMDs11++PBhXLt2DTExMdI0AsuXL8eQIUOwYMECjSNks2fPRvXq1dGqVSuEhYVlu73y5cu/86hUpjNnzmDhwoXw8/PDpEmTpHYnJye0adMG//77r9SmUChgY2MDAKhQoQJq1KiBLl26oGbNmpg2bRo2bdqUp30SUVZqtRppaWnaLoNIg66uLpRKpdb2r9UAdO7cOXh7e0vvJ0+eDAAYPHhwjl/oxdX27dvh4uICFxcXDBgwABMmTMCXX36Z4/VLNjY22LJlC65cuYJatWoVWl3h4eGoVauWxhxK7dq1Q0pKCiIiIqTP/9ixY9ixYwciIyMRFBSU4/bq16+P5ORkuLq6Yvbs2Rp/fm/bvHkzTExMcnyo5ruCVPny5dG/f3+sX78earVaq/9QiEoiIQTi4uI0ftkgKk4sLCxgY2OjlXn6tBqAvLy88D7PYr17927hFfOB/P39MWDAAABA+/bt8eLFCxw9ehStW7fOtv+ECRMQGhqK2rVrw9HREU2aNEHbtm3Rv39/6OvrF1hdcXFxsLa21miztLSEnp4e4uLiAABPnjzBkCFDsGnTphyvmapQoQJ++uknuLm5ISUlBb/88gtatWqFkJAQtGzZMtt1bt26hUqVKkFXVzff9VevXh3Pnz/HkydPUL58+Xxvh0iOMsNP+fLlYWRkxMlgqdgQQiApKQnx8fEAXn/HFLVifRt8SXHjxg2cOXNGOnKiUqnQu3dvrF+/PscAZGxsjN9//x137tzB8ePHcfr0aUyZMgUrV65EeHg4jIyMCqy+7H7oCSGk9pEjR6Jfv345BhkA0tGtTE2bNkVMTAyWLVuW43pv7iO/MgMyf3ATvR+1Wi2Fn7cvJyAqDgwNDQEA8fHxKF++fJEf5S+2F0GXJP7+/khPT0fFihWlO9bWrVuHoKAgPHv2LNd1K1eujBEjRuD//u//cP78eVy7dg3bt28vsNpsbGykIz2Znj17hrS0NOnI0LFjx7Bs2TKp9uHDhyMhIQEqlQrr16/PcdtNmjTBrVu3clxerVo13Llz54OuPYiKioKZmRl/gBO9p8x/dwX5yxRRQcv8+6mNa9QYgD5Qeno6Nm7ciOXLl2vcsXbx4kU4Ojpi8+bNed6Wk5MTjIyM8PLlywKrr2nTprhy5QpiY2OltsOHD0NfXx9ubm4AXl8n9Gbt8+bNg6mpKSIjI/HJJ5/kuO0LFy7ketiyX79+ePHiBdauXZvt8nddlxAfH48tW7agW7duhfIcGCI54NFTKs60+feTp8DyKTU1HWvXnkNw8O/455+nGDhwMMqV0zxK0aNHD/j7+2P8+PFZ1p87dy6SkpLQsWNHODo64t9//8WqVauQlpaGNm3a5LmO+/fv4+nTp7h//z7UarU0z1CVKlVgYmKCtm3bwtXVFQMHDsTSpUvx9OlTfP755xg5cqR0vU+NGjU0tnnu3Dno6OhoXJzt5+cHJycn1KxZE6mpqdi0aRN27dqFXbt25Vhb48aNMW3aNEyZMgUPHz7EJ598AltbW9y+fRs//PADmjdvLt0dlnmxphAC//77L8LDw7Fw4UKYm5vj22+/zfPnQURElCeilEtISBAAREJCQoFtc+rUw0Kp/FoAcwVQTQBVhVL5tZg69bBGv4iICAFAREREiOjoaAFAXLhwQQghxLFjx4SPj4+wt7cXenp6wtraWrRv316EhoZK6wcEBAhzc/Ncaxk8eLAAkOV1/Phxqc+9e/dEp06dhKGhoShTpowYP368SE5OznGb2e138eLFonLlysLAwEBYWlqK5s2bi99//z1Pn9f27dtFy5YthampqTA2NhZ16tQR8+bNE8+ePZP2l1m3QqEQ5ubmolGjRmLevHkF+udGJCevXr0S165dE69evdJ2KSXKnDlzRN26dbVdhmzk9ve0ML6/38QA9J6mTj383+CT/SszBP3xxx9CR0dHtGvXLss23g5DQgixc+dO0ahRI2FmZiZMTEyEq6urmDx5srQ8L2EoU3Jysqhbt26WfQghxMSJE0WDBg2Enp5etv/Ir1+/Lry8vET58uWFvr6+cHZ2FrNmzRKpqaka/UJCQkSDBg2kPuvWrctTbURUNEp6AHr8+LH49NNPNX5JbNu2rQgLCxNCCAFA7N69u8D3ywBUtLQZgHhhxXtITU3HihXhufZZsSIcqanpWpkVOtO0adM05v15kxACw4YNQ+/evbNdrquri0GDBuHw4cO4ceMG/Pz88PPPP2POnDlSn+joaHTs2BEtWrTAhQsX8MUXX2DixIm5ng4rLJzcjajwqdUZCAm5i61bLyMk5C7U6oxC36ePjw8uXryIDRs24ObNm9i7dy+8vLykR+gQfbBCiVXFSEEmyO++C8/16E/m69tvjwlTU1Nx/fp10bt3b/H1119rbOftI0CTJk0SXl5eue47r0eA9u/fL6pXry6uXr2a7RGgTO/zW85nn30mmjdvLr2fNm2aqF69ukafUaNGiSZNmuS4jePHjwsA4siRI8LNzU0YGhqKpk2biuvXr2v0W7t2rahUqZLQ1dUV1apVExs3btRYDkCsW7dOfPzxx8LIyEh89dVX0lj8/f2Fvb29MDY2FqNHjxbp6eli8eLFwtraWpQrV07Mnz8/T+MlKg0K6gjQrl3XhJ3dCo2fcXZ2K8SuXdcKqNKsnj17JgCIkJCQbJc7OjpqnPJ3dHSUlu3du1fj6PTcuXNFWlqatPzff/8VI0eOFOXKlROmpqbC29tbREZGSst5BKho8QhQCXHnTt5+8zh8+D8as0IHBATkOuGjjY0Nrl69iitXrnxQfY8fP8bIkSPxyy+/FNitr7dv38bBgwfh6ekptYWHh6Nt27Ya/dq1a4dz586984jMrFmzsHz5cpw7dw4qlQrDhg2Tlu3evRuTJk3ClClTcOXKFYwaNQpDhw7F8ePHNbYxZ84cdO3aFZcvX5bWv3PnDg4cOICDBw9i69atWL9+PTp16oQHDx7gxIkTWLx4MWbPno3Tp09/6EdCJBtBQVHo0eNXPHiQqNH+8GEievT4FUFBUYWyXxMTE5iYmGDPnj1ISUnJsvzs2bMAgICAAMTGxkrvDx06hAEDBmDixIm4du0afvzxRwQGBmLBggUAXh8B79SpE+Li4rB//35ERESgQYMGaNWqFY8syRAD0HuoXLlMnvr99dexbGeFzsmECRPQsGFD1K5dG05OTujTpw/Wr1+f7T/8nAghMGTIEIwePRru7u55Xi8nzZo1g4GBAapWrYoWLVpg3rx50rLsZpe2trZGeno6/vnnn1y3u2DBAnh6esLV1RUzZsxAWFgYkpOTAQDLli3DkCFDMHbsWFSrVg2TJ09G9+7dsWzZMo1t9OvXD8OGDUOlSpXg6OgIAMjIyMD69evh6uqKLl26wNvbWzqF5+LigqFDh8LFxQUhISEf/NkQyYFanYFJkw4iu9/dMtt8fQ8WyukwlUqFwMBAbNiwARYWFvDw8MAXX3yBS5cuAQDKlSsH4H+PUch8v2DBAsyYMQODBw9GpUqV0KZNG3zzzTf48ccfAQDHjx/H5cuXsWPHDri7u6Nq1apYtmwZLCwssHPnzgIfBxVvDEDvYexYdyiVuc9ZoKPzBA8eRKFPnz4ANGeFzknmrNC3b9/G7NmzYWJigilTpqBRo0ZISkrKU22rV69GYmIiZs6cmfcB5WL79u04f/48tmzZgt9//z1LCHl77gaRxxmb69SpI/1/5hxCmVOhR0VFwcPDQ6O/h4cHoqI0f8vMLuA5OTnB1NRUem9tbQ1XV1eN+YOsra2lfRFR7kJD72c58vMmIYCYmESEhuZ+jWN++fj44NGjR9i7dy/atWuHkJAQNGjQINfnREZERGDevHnSESQTExOMHDkSsbGxSEpKQkREBF68eAErKyuNPtHR0bhz506hjIOKL84D9B709FSYPLkpli7N/knpAODmFoezZ1/PCp1JCAFdXV08e/YMlpaWOa5buXJlaWboWbNmoVq1ati+fTuGDh36ztqOHTuG06dPZ3mOmLu7O/r3748NGzbkYYT/Y29vDwBwdXWFWq3Gp59+iilTpkCpVGY7u3R8fDxUKtU7Z2x+87lgmWEpIyMjS1smkc3jNIyNjXPdbuZ2smt7c19ElLPY2OcF2i8/DAwM0KZNG7Rp0wZfffUVRowYgTlz5mDIkCHZ9s/IyMDXX3+N7t27Z7utjIwMVKhQIdsjwe96ODOVPgxA72nJkteTFK5YEQ61+n/HhpVKBXx9G2HTpnVYvnx5lmtkfHx8sHnz5mwnRczO+84KvWrVKsyfP196/+jRI7Rr1w7bt29H48aN87SNnAghkJaWJh3ladq0Kfbt26fR5/Dhw3B3d/+gB5/WqFEDp06dwqBBg6S2sLCwLBM1ElHhq1DB9N2d3qNfQXB1dcWePXsAvP6lR61Wayxv0KABbty4gSpVqmS7foMGDRAXFweVSgUnJ6dCrpaKOwagfFiypA3mz/fG2rXncOvWEygUCjRuXBH37v2JZ8+eYfjw4TA3N9dYp7BnhXZwcNB4b2JiAuD1USU7Ozup/fbt23jx4gXi4uLw6tUraeZoV1dX6OnpYfPmzdDV1UXt2rWhr6+PiIgIzJw5E71794ZK9fqvy+jRo7FmzRpMnjwZI0eORHh4OPz9/bF169Y8f4bZmTp1Knr16iVdlLhv3z4EBQXhyJEjH7RdInp/LVo4wM7ODA8fJmZ7HZBCAdjZmaFFC4esCz/QkydP0LNnTwwbNgx16tSBqakpzp07hyVLlqBr164AXv+SePToUXh4eEBfXx+Wlpb46quv0LlzZ9jb26Nnz57Q0dHBpUuXcPnyZcyfPx+tW7dG06ZN0a1bNyxevBguLi549OgR9u/fj27duhXI9ZNUcjAA5ZOengoODuZYvjwcDx4k4vvvzwLYAgODyjh69BG6d9cMQD4+Pli4cCHOnz+PMmU0L6b29PTE999/j0GDBuHx48ewtLRE/fr1cfjwYY0nsBeEESNG4MSJE9L7+vXrA3g9t4+TkxNUKhUWL16MmzdvQggBR0dHjBs3Dp999pm0jrOzM/bv34/PPvsM33//PWxtbbFq1Sr4+Ph8UG3dunXDypUrsXTpUkycOBHOzs4ICAiAl5fXB22XiN6fUqmDlSvbo0ePX6FQQCMEZZ6V9vNrD6Wy4C8lNTExQePGjfHdd99JD1S2t7fHyJEj8cUXXwAAli9fjsmTJ+Pnn39GxYoVcffuXbRr1w6//fYb5s2bhyVLlkBXVxfVq1fHiBEj/lu3Avv378esWbMwbNgw/P3337CxsUHLli2z3NhBpZ9C5HZ/dimQmJgIc3NzJCQkSM++KgiZt4e+/ell/mDYubMXunfnqRsi0o7k5GRER0fD2dkZBgYG+d5OUFAUJk06qHFBtL29Gfz82vNnHH2w3P6eFtb3dyYeAcqHd90eqlC8vj20a1eXQvntiIioqHTvXgNdu7ogNPQ+YmOfo0IFU7Ro4cCfbVTiMQDlw/vcHurl5VR0hRERFQKlUoc/y6jUYYTPh+JweygRERHlHwNQPhTH20OJiIgo7xiA8iHz9tCcJj1WKF5fJFgYt4cSERHRh2MAyofM20MBZAlBhX17KBEREX04fkPnU/fuNbBzZy9UrKh5a56dnRlvgSciIirmGIA+QPfuNXD37iQcPz4YW7Z0x/HjgxEdPSlf4ScsLAxKpRLt27fPsuzu3btQKBTSrM0AsGvXLjRu3Bjm5uYwNTVFzZo1MWXKFGl5YGBgnp9tk5KSgnr16mXZx5MnT9C+fXvY2tpCX18f9vb2GD9+PBITs78D7vbt2zA1Nc12vydOnICbmxsMDAxQqVIl/PDDD3mqjYiIqDAwAH2gzNtD+/atDS8vp3yf9lq/fj0mTJiAU6dO4f793J+ufOTIEfTp0wc9evTAmTNnEBERgQULFiA1NTVf+542bRpsbW2ztOvo6KBr167Yu3cvbt68icDAQBw5cgSjR4/O0jctLQ19+/ZFixYtsiyLjo5Gx44d0aJFC1y4cAFffPEFJk6ciF27duWr3g+RlpZW5PskIqLihwGoGHj58iV+/fVXjBkzBp07d0ZgYGCu/X/77Tc0b94cU6dOhYuLC6pVq4Zu3bph9erV773vAwcO4PDhw1i2bFmWZZaWlhgzZgzc3d3h6OiIVq1aYezYsQgNDc3Sd/bs2ahevTp69eqVZdkPP/wABwcH+Pn5oUaNGhgxYgSGDRuW7T4zhYSEQKFQ4OjRo3B3d4eRkRGaNWuGGzduaPRbt24dKleuDD09Pbi4uOCXX37RWK5QKPDDDz+ga9euMDY2xvz58zF37lzUq1cP69evh4ODA0xMTDBmzBio1WosWbIENjY2KF++PBYsWJDXj5GIKE8yf/6Q9jEAFQPbt2+Hi4sLXFxcMGDAAAQEBCC3J5TY2Njg6tWruHLlygft9/Hjxxg5ciR++eUXGBkZvbP/o0ePEBQUBE9PT432Y8eOYceOHfj++++zXS88PBxt27bVaGvXrh3OnTv3ziMys2bNwvLly3Hu3DmoVCoMGzZMWrZ7925MmjQJU6ZMwZUrVzBq1CgMHToUx48f19jGnDlz0LVrV1y+fFla/86dOzhw4AAOHjyIrVu3Yv369ejUqRMePHiAEydOYPHixZg9ezZOnz79zs+FiApeXFwcJkyYgEqVKkmn4Lt06YKjR49qu7QP8vnnn2uMYciQIejWrZv2CpIxBqBiwN/fHwMGDAAAtG/fHi9evMj1H/mECRPQsGFD1K5dG05OTujTpw/Wr1+PlJSUPO9TCIEhQ4Zg9OjR73wCct++fWFkZISKFSvCzMwM//d//ycte/LkCYYMGYLAwMAcn9USFxeX5UGD1tbWSE9Pxz///JPrvhcsWABPT0+4urpixowZCAsLQ3JyMgBg2bJlGDJkCMaOHYtq1aph8uTJ6N69e5YjS/369cOwYcNQqVIlODo6AgAyMjKwfv16uLq6okuXLvD29saNGzfg5+cHFxcXDB06FC4uLggJCcm1PiI5UAsg5CWwNeH1f9WF/ATJu3fvws3NDceOHcOSJUtw+fJlHDx4EN7e3hg3blzh7ryQmZiYwMrKSttlEBiAtO7GjRs4c+YM+vTpAwBQqVTo3bs31q9fn+M6xsbG+P3333H79m3Mnj0bJiYmmDJlCho1aoSkpKQ87Xf16tVITEzEzJkz39n3u+++w/nz57Fnzx7cuXMHkydPlpaNHDkS/fr1Q8uWLXPdhuKt+QIyj3C93f62OnXqSP9foUIFAEB8fDwAICoqCh4eHhr9PTw8EBUVpdGWXcBzcnKCqen/Jqq0traGq6srdHR0NNoy90UkV0GJgNMtwPse0O/h6/863XrdXljGjh0LhUKBM2fOoEePHqhWrRpq1qyJyZMnS0dl79+/j65du8LExARmZmbo1asXHj9+LG0jv6e6FQoF1q1bhw4dOsDQ0BDOzs7YsWOHRp/Lly/jo48+gqGhIaysrPDpp5/ixYsX0vKQkBA0atQIxsbGsLCwgIeHB+7du6dRV+b/b9iwAf/5z3+gUCigUCikX7oePnyI3r17w9LSElZWVujatSvu3r2bp31Q3jAAaZm/vz/S09NRsWJFqFQqqFQqrFu3DkFBQXj27Fmu61auXBkjRozA//3f/+H8+fO4du0atm/fnqf9Hjt2DKdPn4a+vj5UKhWqVKkC4HVYGDx4sEZfGxsbVK9eHV27dsWPP/6IdevWITY2VtrOsmXLpNqHDx+OhIQEqFQqKcTZ2NggLi5OY5vx8fFQqVTv/E1IV1dX+v/MsJSRkZGlLZMQIkubsbFxrtvN3E52bW/ui0hughKBHg+AB+ma7Q/TX7cXRgh6+vQpDh48iHHjxmX7b9fCwgJCCHTr1g1Pnz7FiRMnEBwcjDt37qB3794affN7qvvLL7+Ej48PLl68iAEDBqBv377SL1ZJSUlo3749LC0tcfbsWezYsQNHjhzB+PHjAQDp6eno1q0bPD09cenSJYSHh+PTTz/N9pe9zz//HL169UL79u0RGxuL2NhYNGvWDElJSfD29oaJiQlOnjyJU6dOwcTEBO3bt0dqaup77YNyxoehalF6ejo2btyI5cuXZ7lGxsfHB5s3b5b+Ub2Lk5MTjIyM8PLlyzz1X7VqFebPny+9f/ToEdq1a4ft27ejcePGOa6XeeQm83RbeHg41Gq1tPw///kPFi9ejLCwMFSsWBEA0LRpU+zbt09jO4cPH4a7u3uW0PE+atSogVOnTmHQoEFSW1hYGGrU4BxMRB9KLYBJcUB2Z7sEAAUA3zigqymgLMDv3du3b0MIgerVq+fY58iRI7h06RKio6Nhb28PAPjll19Qs2ZNnD17Fg0bNgTwv1PdpqamcHV1lU5179+/Hzo6OnBxccHixYsREhKCJk2aSNvv2bMnRowYAQD45ptvEBwcjNWrV2Pt2rXYvHkzXr16hY0bN0oBbc2aNejSpQsWL14MXV1dJCQkoHPnzqhcuTIA5PgzycTEBIaGhkhJSYGNjY3UvmnTJujo6OD//u//pFATEBAACwsLhISEwN3dPc/7oJwxABUxtToDoaH3ERv7HHfuhOPZs2cYPnw4zM3NNfr16NED/v7+2QaguXPnIikpCR07doSjoyP+/fdfrFq1CmlpaWjTpk2e6nBw0HxMh4mJCYDXR5Xs7OwAAPv378fjx4/RsGFDmJiY4Nq1a5g2bRo8PDzg5OQEIOs/unPnzkFHRwe1atWS2kaPHo01a9Zg8uTJGDlyJMLDw+Hv74+tW7fmqdacTJ06Fb169UKDBg3QqlUr7Nu3D0FBQThy5MgHbZeIgNCkrEd+3iQAxKS/7ueV9UBNvuXl9HhUVBTs7e2l8AMArq6usLCwQFRUlBSAsjvVrVQq33mqu2nTplneZ86RFhUVhbp162ocnfLw8EBGRgZu3LiBli1bYsiQIWjXrh3atGmD1q1bo1evXtIp/LyIiIiQ5lV7U3JyMu7cuYO2bdt+8D6Ip8CKVFBQFJycVsLbewP69QvCl18uh45OZRw9+ihLXx8fH0RGRuL8+fNZlnl6euKvv/7CoEGDUL16dXTo0AFxcXE4fPgwXFxcCqxeQ0ND/Pzzz2jevDlq1KgBX19fdO7cGb/99tt7bcfZ2Rn79+9HSEgI6tWrh2+++QarVq2Cj4/PB9XXrVs3rFy5EkuXLkXNmjXx448/IiAgAF5eXh+0XSICYnMJP/npl1dVq1aFQqHIci3fm7I71Z1de0Ge6s7cbk77frNPQEAAwsPD0axZM2zfvh3VqlV7rztKMzIy4ObmhsjISI3XzZs30a9fvwLZBwEQpVxCQoIAIBISErRax65d14RCMVcAmi+F4vVr165rWq2PiEqXV69eiWvXrolXr17la/3jL4TA1Xe/jr8o4MKFEO3btxcVK1YUL15k3fizZ8/E4cOHhVKpFPfv35far169KgCIs2fPCiGEmDNnjqhbt67GuoMHDxZdu3bVaPP09BSTJk2S3gMQY8aM0ejTpEkTqe2nn34SlpaWGrX9/vvvQkdHR8TFxWU7niZNmogJEyZkW9fIkSNF586dNfpn7uN9vrfe3EdJktvf08L+/uYRoCKgVmdg0qSDyG5qn8w2X9+DUKt5wS0RFQ8tjAA71etrfbKjAGCvet2voK1duxZqtRqNGjXCrl27cOvWLURFRWHVqlVo2rQpWrdujTp16qB///44f/48zpw5g0GDBsHT0/Od03rkxY4dO7B+/XrcvHkTc+bMwZkzZ6TLEfr37w8DAwMMHjwYV65cwfHjxzFhwgQMHDgQ1tbWiI6OxsyZMxEeHo579+7h8OHDuHnzZo7X6Dg5OeHSpUu4ceMG/vnnH6SlpaF///4oW7YsunbtitDQUERHR+PEiROYNGkSHjx48N77oOwxABWB0ND7ePAg59slhABiYhIRGpr7IzCIiIqKUgGs/O91uW+HoMz3fjYFewF0JmdnZ5w/fx7e3t6YMmUKatWqhTZt2uDo0aNYt24dFAoF9uzZA0tLS7Rs2RKtW7dGpUqV8nwX7Lt8/fXX2LZtG+rUqYMNGzZg8+bNcHV1BQAYGRnh0KFDePr0KRo2bIgePXqgVatWWLNmjbT8+vXr8PHxQbVq1fDpp59i/PjxGDVqVLb7GjlyJFxcXODu7o5y5crhjz/+gJGREU6ePAkHBwd0794dNWrUwLBhw/Dq1SuYmZm99z4oewohcplyuBRITEyEubk5EhIScpyor7Bt3XoZ/foFvbPfli3d0bdv7SKoiIhKu+TkZERHR8PZ2RkGBgb53k5Q4uu7wd68INpe9Tr8dNfOj9RCpVAosHv3bs7OXERy+3ta2N/fvAusCFSoYPruTu/Rj4ioqHQ3e32re2jS6wueK/z3tFdhHPkhKkoMQEWgRQsH2NmZ4eHDxGyvA1IoADs7M7Ro4ZB1IRGRlikVBXurO1FxwGuAioBSqYOVK9sDeB123pT53s+vPZRK/nEQEWmT+O8s01T68Ru3iHTvXgM7d/ZCxYqa5zHt7Mywc2cvdO/Oq/eJiIiKCk+BFaHu3Wuga1cXaSboChVM0aKFA4/8EBERFTEGoCKmVOrAy8tJ22UQERHJGg89EBERkewwABEREZHsMAARERGR7DAAERFRiRMSEgKFQoF///23UPdz9+5dKBQKREZG5nmduXPnol69eoVW04d6u74hQ4bI8tZ/BiAiIiqWwsLCoFQq0b59e63VYG9vj9jYWNSqVSvP63z++ec4evRoIVb1bgsXLoRSqcS3336r1TqKMwYgIiLKVYZajbshIbi8dSvuhoQgQ60ukv2uX78eEyZMwKlTp3D/vnYeFq1UKmFjYwOVKu83TZuYmMDKyqoQq3q3gIAATJs2DevXr9dqHcUZAxAREeUoKigIK52csMHbG0H9+mGDtzdWOjkhKujdD3j+EC9fvsSvv/6KMWPGoHPnzggMDMy1f3annfz8/ODk5CS9zzzVs3DhQlhbW8PCwgJff/010tPTMXXqVJQpUwZ2dnYaoeHtU2CZp96OHj0Kd3d3GBkZoVmzZrhx40aOtXh5ecHX11ejtm7dumHIkCHSeycnJ8yfPx+DBg2CiYkJHB0d8Z///Ad///03unbtChMTE9SuXRvnzp1752d34sQJvHr1CvPmzcPLly9x8uTJd67zppSUFEycOBHly5eHgYEBmjdvjrNnzwJ4PVN2lSpVsGzZMo11rly5Ah0dHdy5c0f6DBwcHKCvrw9bW1tMnDjxvWooCgxARESUraigIPzaowcSHzzQaE98+BC/9uhRqCFo+/btcHFxgYuLCwYMGICAgACI7B6m+J6OHTuGR48e4eTJk1ixYgXmzp2Lzp07w9LSEn/++SdGjx6N0aNHIyYmJtftzJo1C8uXL8e5c+egUqkwbNiwD67tu+++g4eHBy5cuIBOnTph4MCBGDRoEAYMGIDz58+jSpUqGDRo0Ds/B39/f/Tt2xe6urro27cv/P3936uOadOmYdeuXdiwYYO033bt2uHp06dQKBQYNmwYAgICNNZZv349WrRogcqVK2Pnzp347rvv8OOPP+LWrVvYs2cPateu/d6fR2FjACIioiwy1GocnDQJ2T7B+b9tB319C+10mL+/PwYMGAAAaN++PV68eFEg19WUKVMGq1atgouLC4YNGwYXFxckJSXhiy++QNWqVTFz5kzo6enhjz/+yHU7CxYsgKenJ1xdXTFjxgyEhYUhOTn5g2rr2LEjRo0ahapVq+Krr77C8+fP0bBhQ/Ts2RPVqlXD9OnTERUVhcePH+e4jcTEROzatUv67AYMGICdO3ciMTExTzW8fPkS69atw9KlS9GhQwe4urri559/hqGhoRSkhg4dihs3buDMmTMAgLS0NGzatEkKgffv34eNjQ1at24NBwcHNGrUCCNHjvyQj6ZQMAAREVEW90NDsxz50SAEEmNicD80tMD3nfnl2qdPHwCASqVC7969C+R6lpo1a0JH539ffdbW1hpHJ5RKJaysrBAfH5/rdurUqSP9f4UKFQDgneu8y5vbtLa2BgCN2jLbctvPli1bUKlSJdStWxcAUK9ePVSqVAnbtm3LUw137txBWloaPDw8pDZdXV00atQIUVFRAF6Pt1OnTtKfx2+//Ybk5GT07NkTANCzZ0+8evUKlSpVwsiRI7F7926kp6fnaf9FiQGIiIiyeB4bW6D93oe/vz/S09NRsWJFqFQqqFQqrFu3DkFBQXj27Fm26+jo6GQ5NZSWlpaln66ursZ7hUKRbVtGRkauNb65jkKhAIAc18lPbZnbfJ/9AK9PRV29elX63FQqFa5evZrn02CZdWbu6832N9tGjBiBbdu24dWrVwgICEDv3r1hZGQE4PWdczdu3MD3338PQ0NDjB07Fi1btsx2zNrEAERERFmY/veoRkH1y6v09HRs3LgRy5cvR2RkpPS6ePEiHB0dsXnz5mzXK1euHOLi4jSCxvvM3VOYypUrh9g3gqJarcaVK1cKfD+XL1/GuXPnEBISovHZnTx5EmfPns3TPqtUqQI9PT2cOnVKaktLS8O5c+dQo0YNqa1jx44wNjbGunXrcODAgSzXQBkaGuLjjz/GqlWrEBISgvDwcFy+fLngBlsA+DBUIiLKwqFFC5jZ2SHx4cPsrwNSKGBmZweHFi0KdL+//fYbnj17huHDh8Pc3FxjWY8ePeDv74/x48dnWc/Lywt///03lixZgh49euDgwYM4cOAAzMzMCrS+/Pjoo48wefJk/P7776hcuTK+++67QpnA0d/fH40aNULLli2zLGvatCn8/f3x3Xff5boNY2NjjBkzRrorzsHBAUuWLEFSUhKGDx8u9VMqlRgyZAhmzpyJKlWqoGnTptKywMBAqNVqNG7cGEZGRvjll19gaGgIR0fHghtsAdDqEaCTJ0+iS5cusLW1hUKhwJ49e6RlaWlpmD59OmrXrg1jY2PY2tpi0KBBePTokfYKJiKSCR2lEu1Xrnz95q3TIZnv2/v5QUepLND9+vv7o3Xr1lnCDwD4+PggMjIS58+fz7KsRo0aWLt2Lb7//nvUrVsXZ86cweeff16gteXXsGHDMHjwYAwaNAienp5wdnaGt7d3ge4jNTUVmzZtgo+PT7bLfXx8sGnTJqSmpr5zW99++y18fHwwcOBANGjQALdv38ahQ4dgaWmp0W/48OFITU3NcvTHwsICP//8Mzw8PFCnTh0cPXoU+/bt0/rcSG9TiIK4rzCfDhw4gD/++AMNGjSAj48Pdu/eLU3HnZCQgB49emDkyJGoW7cunj17Bl9fX6Snp+dpHoRMiYmJMDc3R0JCQrH4TYCIqCgkJycjOjoazs7OMDAwyPd2ooKCcHDSJI0Los3s7dHezw81uncviFKphPrjjz/g5eWFBw8eSBdov6/c/p4W9ve3Vk+BdejQAR06dMh2mbm5OYKDgzXaVq9ejUaNGuH+/ftwcHAoihKJiGStRvfucOnaFfdDQ/E8NhamFSrAoUWLAj/yQyVHSkoKYmJi8OWXX6JXr175Dj/aVqKuAUpISIBCoYCFhUWOfVJSUpCSkiK9z+vcB0RElD0dpRJOXl7aLoOKia1bt2L48OGoV68efvnlF22Xk28l5i6w5ORkzJgxA/369cv1UNiiRYtgbm4uvezt7YuwSiIiotJtyJAhUKvViIiIQMWKFbVdTr6ViACUlpaGPn36ICMjA2vXrs2178yZM5GQkCC93jWdOREREclPsT8FlpaWhl69eiE6OhrHjh1754VQ+vr60NfXL6LqiIiKNy3e50L0Ttr8+1msjwBlhp9bt27hyJEjxe4WOiKi4ipzBuGkpCQtV0KUs8y/n2/Pxl0UtHoE6MWLF7h9+7b0Pjo6GpGRkShTpgxsbW3Ro0cPnD9/Hr/99hvUajXi4uIAvH6YnZ6enrbKJiIq9pRKJSwsLKTnRhkZGWV5vAGRtgghkJSUhPj4eFhYWECphbsKtToPUEhISLaTQQ0ePBhz586Fs7NztusdP34cXnm8I4HzABGRXAkhEBcXVyizDhMVBAsLC9jY2GQbzkv1PEBeXl65nv/juWsiovxTKBSoUKECypcvX+weREmkq6urlSM/mYr9RdBERPRhlEqlVr9oiIqjYn0RNBEREVFhYAAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZ0WoAOnnyJLp06QJbW1soFArs2bNHY7kQAnPnzoWtrS0MDQ3h5eWFq1evaqdYIiIiKjW0GoBevnyJunXrYs2aNdkuX7JkCVasWIE1a9bg7NmzsLGxQZs2bfD8+fMirpSIiIhKE5U2d96hQwd06NAh22VCCPj5+WHWrFno3r07AGDDhg2wtrbGli1bMGrUqKIslYiIiEqRYnsNUHR0NOLi4tC2bVupTV9fH56enggLC9NiZURERFTSafUIUG7i4uIAANbW1hrt1tbWuHfvXo7rpaSkICUlRXqfmJhYOAUSERFRiVVsjwBlUigUGu+FEFna3rRo0SKYm5tLL3t7+8IukYiIiEqYYhuAbGxsAPzvSFCm+Pj4LEeF3jRz5kwkJCRIr5iYmEKtk4iIiEqeYhuAnJ2dYWNjg+DgYKktNTUVJ06cQLNmzXJcT19fH2ZmZhovIiIiojdp9RqgFy9e4Pbt29L76OhoREZGokyZMnBwcICvry8WLlyIqlWromrVqli4cCGMjIzQr18/LVZNREREJZ1WA9C5c+fg7e0tvZ88eTIAYPDgwQgMDMS0adPw6tUrjB07Fs+ePUPjxo1x+PBhmJqaaqtkIiIiKgUUQgih7SIKU2JiIszNzZGQkMDTYURERCVEYX9/F9trgIiIiIgKCwMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREclOvgLQ+fPncfnyZen9f/7zH3Tr1g1ffPEFUlNTC6w4IiIiosKQrwA0atQo3Lx5EwDw119/oU+fPjAyMsKOHTswbdq0Ai2QiIiIqKDlKwDdvHkT9erVAwDs2LEDLVu2xJYtWxAYGIhdu3YVZH1EREREBS5fAUgIgYyMDADAkSNH0LFjRwCAvb09/vnnn4KrjoiIiKgQ5CsAubu7Y/78+fjll19w4sQJdOrUCQAQHR0Na2vrAi2QiIiIqKDlKwD5+fnh/PnzGD9+PGbNmoUqVaoAAHbu3IlmzZoVaIFEREREBU0hhBAFtbHk5GQolUro6uoW1CY/WGJiIszNzZGQkAAzMzNtl0NERER5UNjf3/k6AhQTE4MHDx5I78+cOQNfX19s3LixWIUfIiIiouzkKwD169cPx48fBwDExcWhTZs2OHPmDL744gvMmzevQAskIiIiKmj5CkBXrlxBo0aNAAC//voratWqhbCwMOlWeCIiIqLiLF8BKC0tDfr6+gBe3wb/8ccfAwCqV6+O2NjYgquOiIiIqBDkKwDVrFkTP/zwA0JDQxEcHIz27dsDAB49egQrK6sCLZCIiIiooOUrAC1evBg//vgjvLy80LdvX9StWxcAsHfvXunUGBEREVFxle/b4NVqNRITE2FpaSm13b17F0ZGRihfvnyBFfiheBs8ERFRyVPY39+q/K6oVCo1wg8AODk5fWg9RERERIUu3wFo586d+PXXX3H//n2kpqZqLDt//vwHF0ZERERUWPJ1DdCqVaswdOhQlC9fHhcuXECjRo1gZWWFv/76Cx06dCjoGomIiIgKVL4C0Nq1a/HTTz9hzZo10NPTw7Rp0xAcHIyJEyciISGhoGskIiIiKlD5CkD379+XHnpqaGiI58+fAwAGDhyIrVu3Flx1RERERIUgXwHIxsYGT548AQA4Ojri9OnTAIDo6GgU4LNViYiIiApFvgLQRx99hH379gEAhg8fjs8++wxt2rRB79698cknnxRogUREREQFLV/zAGVkZCAjIwMq1eubyHbs2IHQ0FBUqVIFY8aMKVZPhOc8QERERCVPYX9/53sixOTkZFy6dAnx8fHIyMj43wYVCnTp0qXACvxQDEBEREQlT7GcCPHgwYMYOHCgdB3QmxQKBdRq9QcXRkRERFRY8nUN0Pjx49GrVy/ExsZKp8MyXww/REREVNzlKwDFx8dj8uTJsLa2Luh6iIiIiApdvgJQjx49EBISUsClEBERERWNfF0EnZSUhJ49e6JcuXKoXbt2lru+Jk6cWGAFfiheBE1ERFTyFMuLoLds2YJDhw7B0NAQISEhUCgU0jKFQlGsAhARERHR2/IVgGbPno158+ZhxowZ0NHJ11k0IiIiIq3JV3pJTU1F7969Cz38pKenY/bs2XB2doahoSEqVaqEefPmacw7RERERPS+8pVgBg8ejO3btxd0LVksXrwYP/zwA9asWYOoqCgsWbIES5cuxerVqwt936XBokWLoFAo4OvrK7UJITB37lzY2trC0NAQXl5euHr1qsZ6o0aNQuXKlWFoaIhy5cqha9euuH79urT87t27GD58uBRMK1eujDlz5iA1NTXbOpycnKBQKHJ8eXl5SX3Dw8Px0UcfwdjYGBYWFvDy8sKrV68K9HMhIiLK1ykwtVqNJUuW4NChQ6hTp06Wi6BXrFhRIMWFh4eja9eu6NSpE4DXX6Rbt27FuXPnCmT7pdnZs2fx008/oU6dOhrtS5YswYoVKxAYGIhq1aph/vz5aNOmDW7cuAFTU1MAgJubG/r37w8HBwc8ffoUc+fORdu2bREdHQ2lUonr168jIyMDP/74I6pUqYIrV65g5MiRePnyJZYtW5ZtLZnzQ4WFhcHHxwc3btyQLmrT09MD8PrPu3379pg5cyZWr14NPT09XLx4kadZiYiowOXrLjBvb++cN6hQ4NixYx9UVKZvv/0WP/zwAw4fPoxq1arh4sWLaNu2Lfz8/NC3b99s10lJSUFKSor0PjExEfb29rK6C+zFixdo0KAB1q5di/nz56NevXrw8/ODEAK2trbw9fXF9OnTAbz+vKytrbF48WKMGjUq2+1dunQJdevWxe3bt1G5cuVs+yxduhTr1q3DX3/9lWttISEh8Pb2xrNnz2BhYaGxrEmTJmjTpg2++eab9x80ERGVKsXyLrDjx48XdB3Zmj59OhISElC9enUolUqo1WosWLAgx/ADvD7t8/XXXxdJfcXVuHHj0KlTJ7Ru3Rrz58+X2qOjoxEXF4e2bdtKbfr6+vD09ERYWFi2Aejly5cICAiAs7Mz7O3tc9xnQkICypQpk++a4+Pj8eeff6J///5o1qwZ7ty5g+rVq2PBggVo3rx5vrdLRESUnWJ9bmH79u3YtGkTtmzZgvPnz2PDhg1YtmwZNmzYkOM6M2fOREJCgvSKiYkpwoq1b9u2bYiIiMCiRYuyLIuLiwOALDN4W1tbS8syrV27FiYmJjAxMcHBgwcRHBwsnap62507d7B69WqMHj0633VnHjmaO3cuRo4ciYMHD6JBgwZo1aoVbt26le/tEhERZadYB6CpU6dixowZ6NOnD2rXro2BAwfis88+y/bLPZO+vj7MzMw0XnIRExODSZMmYfPmzTAwMMix35vzNgGvL4x+u61///64cOECTpw4gapVq6JXr15ITk7Osq1Hjx6hffv26NmzJ0aMGCG116xZUwpQHTp0eGftmXf2jRo1CkOHDkX9+vXx3XffwcXFBevXr3/n+kRERO8jX6fAikpSUlKWC2CVSiVvg89BREQE4uPj4ebmJrWp1WqcPHkSa9aswY0bNwC8PhJUoUIFqU98fHyWo0Lm5uYwNzdH1apV0aRJE1haWmL37t0apx8fPXoEb29vNG3aFD/99JPG+vv370daWhoAwNDQ8J21Z9bj6uqq0V6jRg3cv38/L8MnIiLKs2IdgLp06YIFCxbAwcEBNWvWxIULF7BixQoMGzZM26UVS61atcLly5c12oYOHYrq1atj+vTpqFSpEmxsbBAcHIz69esDeD2n04kTJ7B48eJcty2E0Li4/OHDh/D29oabmxsCAgKyBFVHR8f3qt3JyQm2trZSSMt08+bNPB1BIiIieh/FOgCtXr0aX375JcaOHYv4+HjY2tpi1KhR+Oqrr7RdWrFkamqKWrVqabQZGxvDyspKavf19cXChQtRtWpVVK1aFQsXLoSRkRH69esH4PW1ONu3b0fbtm1Rrlw5PHz4EIsXL4ahoSE6duwI4PWRHy8vLzg4OGDZsmX4+++/pf3Z2Njkq3aFQoGpU6dizpw5qFu3LurVq4cNGzbg+vXr2LlzZ762SURElJNiHYBMTU3h5+cHPz8/bZdSbKWnpuLc2rV4eucOylSuDPexY6HK4WJlAJg2bRpevXqFsWPH4tmzZ2jcuDEOHz4szQFkYGCA0NBQ+Pn54dmzZ7C2tkbLli0RFhaG8uXLAwAOHz6M27dv4/bt27Czs9PYfj5mVZD4+voiOTkZn332GZ4+fYq6desiODg4x1vviYiI8itf8wCVJKX5afDB06YhfMUKiP9OMggACqUSTSdPRpslS7RYGRER0YcplvMAkfYFT5uGsKVLs7QLtVpqZwgiIiLKXrG+DZ6yl56aivB3PG4kfMUKpOfwbC4iIiK5YwAqgc6tXatx2is7Qq3GubVri6giIiKikoUBqAR6eudOgfYjIiKSGwagEqhMHu+Kyms/IiIiuWEAKoHcx46FQqnMtY9CqYT72LFFVBEREVHJwgBUAqn09NB08uRc+zSdPDnX+YCIiIjkjLfBl1CZt7hzHiAiIqL3x4kQS7j3nQmaiIioJOBEiJQrlZ4emvj6arsMIiKiEoXXABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewU+wD08OFDDBgwAFZWVjAyMkK9evUQERGh7bKIiIioBFNpu4DcPHv2DB4eHvD29saBAwdQvnx53LlzBxYWFtoujYiIiEqwYh2AFi9eDHt7ewQEBEhtTk5O2iuIiIiISoVifQps7969cHd3R8+ePVG+fHnUr18fP//8c67rpKSkIDExUeNFRERE9KZiHYD++usvrFu3DlWrVsWhQ4cwevRoTJw4ERs3bsxxnUWLFsHc3Fx62dvbF2HFREREVBIohBBC20XkRE9PD+7u7ggLC5PaJk6ciLNnzyI8PDzbdVJSUpCSkiK9T0xMhL29PRISEmBmZlboNRMREdGHS0xMhLm5eaF9fxfrI0AVKlSAq6urRluNGjVw//79HNfR19eHmZmZxouIiIjoTcU6AHl4eODGjRsabTdv3oSjo6OWKiIiIqLSoFgHoM8++wynT5/GwoULcfv2bWzZsgU//fQTxo0bp+3SiIiIqAQr1gGoYcOG2L17N7Zu3YpatWrhm2++gZ+fH/r376/t0oiIiKgEK9YXQReEwr6IioiIiAqerC+CJiIiIioMDEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOyUqAC1atAgKhQK+vr7aLoWIiIhKsBITgM6ePYuffvoJderU0XYpREREVMKViAD04sUL9O/fHz///DMsLS21XQ4RERGVcCUiAI0bNw6dOnVC69attV0KERERlQIqbRfwLtu2bUNERATOnTuXp/4pKSlISUmR3icmJhZWaURERFRCFesjQDExMZg0aRI2b94MAwODPK2zaNEimJubSy97e/tCrpKIiIhKmmIdgCIiIhAfHw83NzeoVCqoVCqcOHECq1atgkqlglqtzrLOzJkzkZCQIL1iYmK0UDkREZE8xcXFYcKECahUqRL09fVhb2+PLl264OjRowAAJycnKBQKjZednZ20fuZyc3NzAIC7uzuWLl0KIYTU5+7duxrrW1paomXLljhx4kSe6yzWAahVq1a4fPkyIiMjpZe7uzv69++PyMhIKJXKLOvo6+vDzMxM40VERESF7+7du3Bzc8OxY8ewZMkSXL58GQcPHoS3tzfGjRsn9Zs3bx5iY2Ol14ULFzS2M2/ePNy8eRMAMGHCBHzxxRf46aefsuzvyJEjiI2NxYkTJ2BmZoaOHTsiOjo6T7UW62uATE1NUatWLY02Y2NjWFlZZWknIiIi7Ro7diwUCgXOnDkDY2Njqb1mzZoYNmyY9N7U1BQ2NjY5bsfU1BTW1tYAgMGDByMwMBCHDx/GqFGjNPpZWVnBxsYGNjY2+PHHH2FnZ5dtv+wU6yNAREREVDI8ffoUBw8exLhx4zTCTyYLC4t8bTc0NBRRUVHQ1dXNtZ+RkREAIC0tLU/bLXEBKCQkBH5+ftoug4iIiN5w+/ZtCCFQvXr1d/adPn06TExMpNeqVauyLLe1tQUAdO7cGUIITJw4McftvXz5EjNnzoRSqYSnp2ee6i3Wp8CIiIioZMi8SFmhULyz79SpUzFkyBDpfdmyZbMs9/HxQYMGDdCiRQu0bdsWzZo1y7KdZs2aQUdHB0lJSahQoQICAwNRu3btPNXLAEREREQfrGrVqlAoFIiKikK3bt1y7Vu2bFlUqVIl1+WVK1cGAPzyyy9o0KABmjRpkmVC5O3bt8PV1RUWFhawsrJ6r3pL3CkwIiIiKn7KlCmDdu3a4fvvv8fLly+zLP/333/ztV1LS0tMmDABn3/+ucat8ABgb2+PypUrv3f4ARiAiIiIKB9SMwC/J8CE2Nf/Tc0A1q5dC7VajUaNGmHXrl24desWoqKisGrVKjRt2jTf+xo3bhxu3LiBXbt2FVj9PAVGRERE72XaY2DFE+DN6Yg/fwxMtnLG+fPnsWDBAkyZMgWxsbEoV64c3NzcsG7dunzvr1y5chg4cCDmzp2L7t27f/gAACjE28eTSpnExESYm5sjISGBkyISERF9oGmPgaVPcl4+1QpYYv3h+yns72+eAiMiIqI8Sc14feQnNyv+ezqsuGMAIiIiojxZ+0zztFd21P/tV9wxABEREVGe3Ekt2H7axABEREREeVJZr2D7aRMDEBEREeXJWEtA+Y4+yv/2K+4YgIiIiChP9HSAye+Yc3Cy1et+xR3nASIiIqI8y7zF/e15gJR4HX4K4hb4osAARERERO9liTUwv9zru73upL6+5mesZck48pOJAYiIiIjem54O4Pv+j+AqNkpQViMiIiIqGAxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7DEBEREQkOwxAREREJDsMQERERCQ7pX4maCEEACAxMVHLlRAREVFeZX5vZ36PF7RSH4CeP38OALC3t9dyJURERPS+nj9/DnNz8wLfrkIUVrQqJjIyMvDo0SOYmppCoVAgMTER9vb2iImJgZmZmbbLK1JyHTvHLa9xA/Idu1zHDch37KV53EIIPH/+HLa2ttDRKfgrdkr9ESAdHR3Y2dllaTczMyt1f1nySq5j57jlR65jl+u4AfmOvbSOuzCO/GTiRdBEREQkOwxAREREJDuyC0D6+vqYM2cO9PX1tV1KkZPr2DlueY0bkO/Y5TpuQL5jl+u4C0KpvwiaiIiI6G2yOwJERERExABEREREssMARERERLLDAERERESyI6sAtHbtWjg7O8PAwABubm4IDQ3VdkkFbtGiRWjYsCFMTU1Rvnx5dOvWDTdu3NDoI4TA3LlzYWtrC0NDQ3h5eeHq1ataqrhwLFq0CAqFAr6+vlJbaR73w4cPMWDAAFhZWcHIyAj16tVDRESEtLw0jj09PR2zZ8+Gs7MzDA0NUalSJcybNw8ZGRlSn9Iy7pMnT6JLly6wtbWFQqHAnj17NJbnZZwpKSmYMGECypYtC2NjY3z88cd48OBBEY7i/eU27rS0NEyfPh21a9eGsbExbG1tMWjQIDx69EhjGyVx3MC7/8zfNGrUKCgUCvj5+Wm0l9SxFxXZBKDt27fD19cXs2bNwoULF9CiRQt06NAB9+/f13ZpBerEiRMYN24cTp8+jeDgYKSnp6Nt27Z4+fKl1GfJkiVYsWIF1qxZg7Nnz8LGxgZt2rSRnptW0p09exY//fQT6tSpo9FeWsf97NkzeHh4QFdXFwcOHMC1a9ewfPlyWFhYSH1K49gXL16MH374AWvWrEFUVBSWLFmCpUuXYvXq1VKf0jLuly9fom7dulizZk22y/MyTl9fX+zevRvbtm3DqVOn8OLFC3Tu3BlqtbqohvHecht3UlISzp8/jy+//BLnz59HUFAQbt68iY8//lijX0kcN/DuP/NMe/bswZ9//glbW9ssy0rq2IuMkIlGjRqJ0aNHa7RVr15dzJgxQ0sVFY34+HgBQJw4cUIIIURGRoawsbER3377rdQnOTlZmJubix9++EFbZRaY58+fi6pVq4rg4GDh6ekpJk2aJIQo3eOePn26aN68eY7LS+vYO3XqJIYNG6bR1r17dzFgwAAhROkdNwCxe/du6X1exvnvv/8KXV1dsW3bNqnPw4cPhY6Ojjh48GCR1f4h3h53ds6cOSMAiHv37gkhSse4hch57A8ePBAVK1YUV65cEY6OjuK7776TlpWWsRcmWRwBSk1NRUREBNq2bavR3rZtW4SFhWmpqqKRkJAAAChTpgwAIDo6GnFxcRqfhb6+Pjw9PUvFZzFu3Dh06tQJrVu31mgvzePeu3cv3N3d0bNnT5QvXx7169fHzz//LC0vrWNv3rw5jh49ips3bwIALl68iFOnTqFjx44ASu+435aXcUZERCAtLU2jj62tLWrVqlWqPouEhAQoFArp6GdpHndGRgYGDhyIqVOnombNmlmWl+axF5RS/zBUAPjnn3+gVqthbW2t0W5tbY24uDgtVVX4hBCYPHkymjdvjlq1agGANN7sPot79+4VeY0Fadu2bYiIiMC5c+eyLCvN4/7rr7+wbt06TJ48GV988QXOnDmDiRMnQl9fH4MGDSq1Y58+fToSEhJQvXp1KJVKqNVqLFiwAH379gVQuv/M35SXccbFxUFPTw+WlpZZ+pSWn4HJycmYMWMG+vXrJz0UtDSPe/HixVCpVJg4cWK2y0vz2AuKLAJQJoVCofFeCJGlrTQZP348Ll26hFOnTmVZVto+i5iYGEyaNAmHDx+GgYFBjv1K27iB178Juru7Y+HChQCA+vXr4+rVq1i3bh0GDRok9SttY9++fTs2bdqELVu2oGbNmoiMjISvry9sbW0xePBgqV9pG3dO8jPO0vJZpKWloU+fPsjIyMDatWvf2b+kjzsiIgIrV67E+fPn33scJX3sBUkWp8DKli0LpVKZJfXGx8dn+a2ptJgwYQL27t2L48ePw87OTmq3sbEBgFL3WURERCA+Ph5ubm5QqVRQqVQ4ceIEVq1aBZVKJY2ttI0bACpUqABXV1eNtho1akgX+JfWP/OpU6dixowZ6NOnD2rXro2BAwfis88+w6JFiwCU3nG/LS/jtLGxQWpqKp49e5Zjn5IqLS0NvXr1QnR0NIKDg6WjP0DpHXdoaCji4+Ph4OAg/by7d+8epkyZAicnJwCld+wFSRYBSE9PD25ubggODtZoDw4ORrNmzbRUVeEQQmD8+PEICgrCsWPH4OzsrLHc2dkZNjY2Gp9FamoqTpw4UaI/i1atWuHy5cuIjIyUXu7u7ujfvz8iIyNRqVKlUjluAPDw8Mgy1cHNmzfh6OgIoPT+mSclJUFHR/NHmFKplG6DL63jfltexunm5gZdXV2NPrGxsbhy5UqJ/iwyw8+tW7dw5MgRWFlZaSwvreMeOHAgLl26pPHzztbWFlOnTsWhQ4cAlN6xFygtXXxd5LZt2yZ0dXWFv7+/uHbtmvD19RXGxsbi7t272i6tQI0ZM0aYm5uLkJAQERsbK72SkpKkPt9++60wNzcXQUFB4vLly6Jv376iQoUKIjExUYuVF7w37wITovSO+8yZM0KlUokFCxaIW7duic2bNwsjIyOxadMmqU9pHPvgwYNFxYoVxW+//Saio6NFUFCQKFu2rJg2bZrUp7SM+/nz5+LChQviwoULAoBYsWKFuHDhgnS3U17GOXr0aGFnZyeOHDkizp8/Lz766CNRt25dkZ6erq1hvVNu405LSxMff/yxsLOzE5GRkRo/71JSUqRtlMRxC/HuP/O3vX0XmBAld+xFRTYBSAghvv/+e+Ho6Cj09PREgwYNpFvDSxMA2b4CAgKkPhkZGWLOnDnCxsZG6Ovri5YtW4rLly9rr+hC8nYAKs3j3rdvn6hVq5bQ19cX1atXFz/99JPG8tI49sTERDFp0iTh4OAgDAwMRKVKlcSsWbM0vvxKy7iPHz+e7b/rwYMHCyHyNs5Xr16J8ePHizJlyghDQ0PRuXNncf/+fS2MJu9yG3d0dHSOP++OHz8ubaMkjluId/+Zvy27AFRSx15UFEIIURRHmoiIiIiKC1lcA0RERET0JgYgIiIikh0GICIiIpIdBiAiIiKSHQYgIiIikh0GICIiIpIdBiAiIiKSHQYgIiIikh0GICIq9ebOnYt69eppuwwiKkYYgIiIiEh2GICIqNjIyMjA4sWLUaVKFejr68PBwQELFiwAAFy+fBkfffQRDA0NYWVlhU8//RQvXryQ1g0JCUGjRo1gbGwMCwsLeHh44N69ewgMDMTXX3+NixcvQqFQQKFQIDAwUEsjJKLiQqXtAoiIMs2cORM///wzvvvuOzRv3hyxsbG4fv06kpKS0L59ezRp0gRnz55FfHw8RowYgfHjxyMwMBDp6eno1q0bRo4cia1btyI1NRVnzpyBQqFA7969ceXKFRw8eBBHjhwBAJibm2t5pESkbXwYKhEVC8+fP0e5cuWwZs0ajBgxQmPZzz//jOnTpyMmJgbGxsYAgP3796NLly549OgRdHV1YWVlhZCQEHh6embZ9ty5c7Fnzx5ERkYWxVCIqATgKTAiKhaioqKQkpKCVq1aZbusbt26UvgBAA8PD2RkZODGjRsoU6YMhgwZgnbt2qFLly5YuXIlYmNji7J8IiphGICIqFgwNDTMcZkQAgqFIttlme0BAQEIDw9Hs2bNsH37dlSrVg2nT58ulFqJqORjACKiYqFq1aowNDTE0aNHsyxzdXVFZGQkXr58KbX98ccf0NHRQbVq1aS2+vXrY+bMmQgLC0OtWrWwZcsWAICenh7UanXhD4KISgwGICIqFgwMDDB9+nRMmzYNGzduxJ07d3D69Gn4+/ujf//+MDAwwODBg3HlyhUcP34cEyZMwMCBA2FtbY3o6GjMnDkT4eHhuHfvHg4fPoybN2+iRo0aAAAnJydER0cjMjIS//zzD1JSUrQ8WiLSNt4FRkTFxpdffgmVSoWvvvoKjx49QoUKFTB69GgYGRnh0KFDmDRpEho2bAgjIyP4+PhgxYoVAAAjIyNcv34dGzZswJMnT1ChQgWMHz8eo0aNAgD4+PggKCgI3t7e+PfffxEQEIAhQ4ZocaREpG28C4yIiIhkh6fAiIiISHYYgIiIiEh2GICIiIhIdhiAiIiISHYYgIiIiEh2GICIiIhIdhiAiIiISHYYgIiIiEh2GICIiIhIdhiAiIiISHYYgIiIiEh2GICIiIhIdv4fGdVpWzlDhdkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymoo.visualization.pcp import PCP\n",
    "X = res.X\n",
    "F = res.F\n",
    "G = res.G\n",
    "Z_res = [[d['x01']] for d in X]\n",
    "\n",
    "\n",
    "# Uncomment this to plot the PCP\n",
    "'''\n",
    "labels = list(res.opt[0].X.keys())\n",
    "labels_x = ['matID', 'A1', 'A2', 'A3', 'A4', 'A5', 'A6']\n",
    "bounds = np.array([problem.vars[name].bounds for name in labels]).T\n",
    "\n",
    "\n",
    "X = np.array([[sol.X[name] for name in labels] for sol in res.opt])\n",
    "\n",
    "plot1 = PCP(labels=labels_x, bounds=bounds)\n",
    "plot1.set_axis_style(color=\"grey\", alpha=1)\n",
    "plot1.add(X)\n",
    "plot1.show()\n",
    "\n",
    "\n",
    "# Maybe the solution is limited by the limits in areas. Example, you want to minimize the compliance but\n",
    "# you have a maximum area, so you can't reduce the compliance further\n",
    "\n",
    "upper_bounds = bounds[1, 1:]*0.999\n",
    "lower_bounds = bounds[0, 1:]*1.001\n",
    "\n",
    "\n",
    "\n",
    "# Check if each row of X is within the limits\n",
    "is_within_limits = np.logical_and(np.all(X[:,1:] >= lower_bounds, axis=1), np.all(X[:,1:] <= upper_bounds, axis=1))\n",
    "\n",
    "# Print the results\n",
    "if np.all(is_within_limits):\n",
    "    print(\"Solution not limited by the areas\")\n",
    "else:\n",
    "    print(\"Solution limited by the areas\")\n",
    "\n",
    "\n",
    "'''\n",
    "\n",
    "names = []\n",
    "types = []\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "legend_dict = {}\n",
    "\n",
    "\n",
    "\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",
    "mat_colors = {}\n",
    "for i, mattype in enumerate(types):\n",
    "    if mattype not in mat_colors:\n",
    "        mat_colors[mattype] = plt.cm.get_cmap('jet')(i / len(set(types)))\n",
    "\n",
    "# add scatter plot for each unique label\n",
    "for i, (x, y) in enumerate(F):\n",
    "    material = names[i]\n",
    "    mattype = types[i]\n",
    "    color = mat_colors[mattype]\n",
    "    label = mattype\n",
    "    ax.scatter(x, y, color=color, label=label)\n",
    "    ax.text(x + 0.1, y + 0.1, material)\n",
    "    if mattype not in legend_dict:\n",
    "        legend_dict[mattype] = ax.scatter(x, y, color=color, label=label)\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",
    "# show plot\n",
    "plt.title(\"Objective Space\")\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy', 'compliance']\n",
    "enabled_entries = [entry for entry in entry_order if objectives[entry]['isOn']]\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures/Integer_Mixed_def.png',bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4028ac",
   "metadata": {},
   "source": [
    "# Defines function to plot the structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bf389685",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_truss_structure(nodeXY, connectivity, Areas, name):\n",
    "    # Set maximum thickness for truss members\n",
    "    max_thickness = 0.02\n",
    "\n",
    "    # Create plot and axes\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    # Plot nodes\n",
    "    x = [n[0] for n in nodeXY]\n",
    "    y = [n[1] for n in nodeXY]\n",
    "    #ax.scatter(x, y, color='black', s=40)\n",
    "\n",
    "    # Plot truss members\n",
    "    for i, (n1, n2) in enumerate(connectivity):\n",
    "        #thickness = min(Areas[i], max_thickness)\n",
    "        thickness = Areas[i]/max_thickness\n",
    "        x1, y1 = nodeXY[n1]\n",
    "        x2, y2 = nodeXY[n2]\n",
    "        ax.plot([x1, x2], [y1, y2], linewidth=thickness*400, color='black', alpha=0.7)\n",
    "    \n",
    "    ax.scatter(x, y, color='gray', s=40, zorder=20)\n",
    "    # Set axis limits and labels\n",
    "    ax.set_xlim(min(x)-0.1, max(x)+0.1)\n",
    "    ax.set_ylim(min(y)-0.1, max(y)+0.1)\n",
    "    ax.set_xlabel('X position (m)')\n",
    "    ax.set_ylabel('Y position (m)')\n",
    "    ax.set_title(f'Truss Structure for {name} solution')\n",
    "\n",
    "    # Show plot\n",
    "    plt.savefig(f'./figures/Truss/{name}.png',bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "32cfacff",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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21vhTpkzBDz/8gKlTp2LJkiUoLCzETz/9VOmPMgD069cPjo6O8PPzQ2pqKsaMGQMVFRU8fPgQ6urqwvEqHTp0wC+//IKjR4/C2toaqqqq6NChA3r06IG2bdvCy8sLAoEAenp6OHnyJG7evCnyz2Px4sX47bff0L9/f7i7u6Njx44oKytDQkIC/vjjD3h6elbpAqpL27ZtMXv2bGzduhUKCgoYOXKkcGZQ8+bN4e7uXq94FXR1dYVTtz89LhAIqtwXGBiIL7/8El9//TXmzZuHtLQ0eHt7w87ODi4uLsLzVq1ahdTUVPTv3x/m5ubIzMzEhQsXsHv3bnz99dfo1q2b8FwHBwf8/vvv8PHxgYGBQaXFFLW1tWFra/tZz+3T57N27Vp4eXkJC5mUlBSsXLkSFhYWmDdvXq3X1/W7AwAbN27EjBkzMGbMGMyZMwdFRUXYtGkTMjMzq6zC/ilHR0fMnDkT8+bNw1dffYXXr19j48aNMDIyqnRey5YtoaamhkOHDsHGxgaampowMzOr9GGjgrheM0SK8DyAm5AGqWk2WXWL4LEsyxYVFbFLly5lmzdvzqqpqbEDBgxgHz16VGWGire3N9u9e3dWT0+PVVFRYa2trVl3d3c2PT2dZdnyxe+cnZ3Zdu3asRoaGqympibbsWNHNiQkhBUIBLXmvHPnTnbSpEmstbU1q66uziorK7MtW7Zk586dyyYmJlY699GjR+wXX3zBqqurswCEs2UqZtl8Ogurwvnz59nOnTuzampqrLW1NRsaGlplBg3Lls/sCQkJYe3s7FhlZWVWR0eH7d27N3v27FnhOfHx8eywYcNYLS0tFkClmWrPnz9nhw0bxmpra7NGRkbs999/z547d67a2WQ1/Uxyc3NZX19ftm3btsIcOnTowLq7u7MpKSm1fi+rm01W8bwCAwPZNm3asE2aNGENDQ3ZmTNnVvn+1paXqGqL8ccff7C9evViVVVVWX19fXbWrFlVFk48c+YMO3ToUNbExIRVUlJiNTU1WXt7e/ann36qtGAjy7LC2WbVfdVnYUxRFivdvXu38HVhYGDAzpgxo8r3rzp1/e5UOHXqFNuzZ09WVVWV1dDQYIcMGcL+9ddflc6pbjZZWVkZu3HjRtba2ppVVVVlu3fvzl6+fLnKbDKWZdkjR46w7dq1Y5s0aVJpJmlNvwsNec3UNIuTSAfajoMQQgghco3GDBFCCCFErlExRAghhBC5RsUQIYQQQuSa1BVD27ZtE24A2K1bN9y4caPW84uKiuDj44MWLVpARUUFLVu2rNeidoQQQgiRbVI1tf7o0aNYvHgxtm3bhi+++AI7d+7EyJEjERUVBQsLi2qvmTx5MlJTU7F37160atUKaWlpEAgEjZw5IYQQQiSVVM0m69mzJ7p27VppkTgbGxtMmDABAQEBVc6/cOECpk6diri4uGqXvyeEEEIIkZqWoeLiYty/f7/KgnbDhg3DrVu3qr3mzJkz6N69OzZu3IiDBw9CQ0MD48aNw7p166CmplbtNUVFRZWWei8rK8P79+9hYGAg8rL9hBBCCOEXy7LIycmBmZlZndvISE0xlJ6ejtLSUpiYmFQ6bmJiUuPeSnFxcbh58yZUVVVx8uRJpKenY968eXj//n2N44YCAgKwZs0azvMnhBBCSONLTExEs2bNaj1HaoqhCp+2zrAsW2OLTVlZGRiGwaFDh4TbLQQHB8PBwQE///xzta1Dy5cvh4eHh/B2VlYWLCwskJiYCG1tbQ6fCSGEEELEJTs7G82bN4eWllad50pNMWRoaAhFRcUqrUBpaWlVWosqmJqawtzcvNK+UzY2NmBZFm/evEHr1q2rXKOioiLc4O9j2traVAwRQgghUkaUIS5SM7VeWVkZ3bp1Q2RkZKXjkZGR6NOnT7XXfPHFF0hKSqq0I/nz58+hoKBQZ5MZIYQQQuSD1BRDAODh4YE9e/Zg3759iI6Ohru7OxISEjB37lwA5V1cs2bNEp4/ffp0GBgYwMXFBVFRUbh+/TqWLFkCV1fXGgdQE0IIIUS+SE03GQBMmTIFGRkZWLt2LZKTk2FnZ4fz58+jRYsWAIDk5GQkJCQIz9fU1ERkZCS+//57dO/eHQYGBpg8eTL8/Pz4egqEEEIIkTBStc4QH7Kzs6Gjo4OsrCwaM0QIIYRIifr8/ZaqbjJCCCGEEK5RMUQIIYQQuUbFECGEEELkGhVDhBBCCJFrVAwRQgghRK5RMUQIIYQQuUbFECGEEELkGhVDhBBCCJFrVAwRQgghRK5RMUQIIYQQuUbFECGEEELkGhVDhBBCCJFrVAyJUVFREd8pEEIIIaQOSnwnIMvmzp2LoqIiNG3aFE2bNoWpqWmlf3V1dXH27FmMHj0aysrKfKdLCCGEyCUqhsSkuLgYGRkZYFkWOTk5iI2NrXS/QCDAq1evUFhYiJ07d2LSpEkwNTUVfhkbG6NJkyY8ZU8IIYTIDyqGxCQ1NRUsy1Z7X35+PmJjY4XdaA8fPkR6ejpMTU2F5zAMA0NDwyqtSRX/amhoNMrzIIQQQmQdFUNikpKSUu3xDx8+IC4uDqWlpZWOv3nzBmpqatDV1QUAsCyLd+/e4d27d3j8+HGVOFpaWpUKpI//r6enB4ZhOH9OhBBCiCyiYkhMkpOTK91mWRZJSUl4+/ZtteezLIu4uDjY2NhATU2tzvg5OTnIycnB8+fPq9ynrKxcbWtSRfebkhL92AkhhJAK9FdRTD5uGSotLUVcXBw+fPhQ6zUCgQAvXryAra0tFBUVP/uxi4uLkZCQgISEhCr3MQwDIyOjKkVSxf/V1dU/+3EJIYQQaUTFkJhUtAwVFhYiNjYWBQUFle7X0tJCmzZtoKamhoKCAjx//hw5OTkoKCjAy5cv0bp1a7F0dbEsi7S0NKSlpVXb/aatrV3jOCXqfiOEECKLqBgSk+TkZGRnZ+PFixcQCATC4woKCujXrx9sbW3BsixYlgXDMLC3t0dUVBRu3LiBzMxMvH37Fs2aNWv0vLOzs5GdnY2YmJgq96moqFRaJuDjYsnIyIi63wghhEgl+uslBqWlpXj06BHi4+OrzCirKIQYhqnSymJrawsAuHbtGpKSkqCmpgYDA4NGy7suRUVFeP36NV6/fl3lPgUFBRgZGVXbqmRqagpVVVUeMiaEEELqRsUQx1iWRWBgIF69elXlPm1tbWEhVB2GYWBra4sHDx4gJycHr169gqqqqlRMoy8rK0NqaipSU1OrvV9HR6dKa1LFvzo6OtT9RgghhDdUDHGMYRgoKFS/y0nr1q2F3WI1YVkWbdq0wf3791FWViYcUC3tCzBmZWUhKysLz549q3KfqqpqpQLp4/8bGxs3aDA5IYQQUhcqhsSga9eu0NPTqzJ7TE1NrcaFGCuwLFtpan1RURFevHiBtm3b1lhkSbvCwkLEx8cjPj6+yn0KCgowNjaudpxS06ZNqfuNEEJIg1ExJAYpKSmwtrZGVFRUpVlkBQUFdXYHMQxTZeZZTk4OEhISYGlpKY50JVpZWRlSUlKQkpKCR48eVblfV1e3xnFK2tra1P1GCCGkTlQMiUFycjIUFRXRunVrREVFCWeTxcbGwt7evtZrGYapdiHFtLQ0qKurw9jYWCw5S6vMzExkZmYiOjq6yn2qqqo1jlMyNDSk7jdCCCEAqBgSi4oFF1VVVdGqVSvExMSAZVlkZ2cjKiqqxkHULMsiKioKOTk51cZ9/fo11NTUoKWlJdb8ZUVhYSFevXpV7WB2RUVFGBsbV7vwZNOmTaGiosJDxoQQQvhAxZAYfLwVh7a2Npo3by5cDfrGjRsAUGWdIYZhhOsM1YRlWeGAavpj3TClpaVITk6usm1KBX19fZiYmFTbskTdb4QQIlsYtq4RvXIuOzsbOjo6yMrKgra2dp3n5+TkYPr06ZWOsSyLV69eIT09XXisphWoRaGurg4bGxvq5uGJurp6lVlvFUWToaGhzA50J4QQaVKfv9/UMsSx6narZxgGlpaWKCwsRG5uLoDyoun+/fuf9Rj5+fmIj4+HtbU1tVDwID8/H3FxcYiLi6tyn5KSUpXut4//VVZW5iFjQgghtaFiiGM1dbsoKCigVatWiIqKQnFxcYMfJyMjA2pqajAzM2twLMIdgUCApKQkJCUlVXu/vr5+teOUTE1NoampScUtIYTwgLrJ6lDfbrKkpCTcv38fKSkpSE5OFk4LLykpAQDk5ubi2bNnKCsra3BuDMOgdevW0NXVbXAswj8NDY1qW5NMTU1hYGBA3W+EEFIP9fn7TcVQHepbDFWHZVlkZGQIC6TLly/j9OnTKCwsRFFRUaWNXOtLUVERtra2lRZqJLJHSUkJJiYm1Y5TMjExoe43Qgj5BBVDHOKiGKrO3r17cerUKQDlXStFRUUoKioSFkgV/5aUlNS5arWqqipsbW1p13g5ZmBgUOM4JVqKgRAij6gY4pC4iqHS0lKsXr262lWVP1ZWViYslKorlip+fDo6OmjTpg2NOSFVaGho1DhOycDAgF4zhBCZRMUQh8RVDAHlM8o8PT1rHHRdF5ZlUVJSIiyMOnbsiJYtWwq74/Ly8jjNl8ieJk2aCNdT+rRVycTEROo3CCaEyC8qhjgkzmIIABISEuDp6YnCwkJO4i1ZsgT9+/cHUF5sfTyQ++N/MzIyOHk8IrsYhqnS/fbx/zU0NPhOkRBCakTFEIfEXQwBwN27d+Hn58dJLGVlZQQGBqJVq1a1nldcXIzU1FThKswfF0upqakNGtRN5IOWlla1i082bdqUut8IIbyjYohDjVEMAcDRo0cRERHBSSxDQ0OEhIR89pT7srIypKenV2lNqvh/fn4+J3kS2aWsrFzj7DdjY2PqfiOEiB0VQxxqrGKIZVkEBgbir7/+4iSejY0N/P39OZ9hxrIscnJyqu16S0lJwfv37zl9PCJ7GIaBoaFhjbPfxNn9dvnyZRQUFGD48OE0+5IQGUfFEIcaqxgCyndZX7JkCeLj4zmJN3z4cMyfP79RuyuKioqE3W+fFktpaWnU/UbqpKWlVeM4JT09vc9+PRcUFMDNzQ1ZWVkwMzODk5MTevfuTd15hMgoKoY41JjFEACkpqbC3d1d5E1b6/Ldd99h1KhRnMRqqIrut+rGKSUnJ6OgoIDvFImEU1ZWrnGVbmNj41pbew4dOoRffvml0rG2bdvC1dUVtra24k6dENLIqBjiUGMXQwDw+PFjrFy5kpMtOxQVFeHn5wc7OzsOMhMflmWRnZ1d4zilDx8+8J0ikXAMw8DIyKjacUrKyspYvHgxioqKqr22Z8+ecHZ2RrNmzRo5a0KIuFAxxCE+iiEAOHfuHHbs2MFJLG1tbYSEhMDY2JiTeHwoLCwU7vP2aatSWloaSktL+U6RSLD4+Hi8f/8eqqqqUFFRgYqKSqX/N2nSBIqKihg2bBimT58OPT09vlMmhDQQFUMc4qsYYlkWoaGh+OOPPziJZ2VlhY0bN0JVVZWTeJKktLQU7969q9KaVPEvV2s4EelUUFCAp0+f1rqtjYKCgrBA0tTUxODBgzFx4kRYWlrCyMiIBlsTIoWoGOIQX8UQAJSUlMDHxwfR0dGcxOvbty+WLl0qVwNGWZZFVlZWjYtPZmZm8p0iEbPnz59/1s+5SZMmMDc3h4mJCYyNjWFqaoo1a9ZAQUGB+yQJIZyrz99v+rgjwZo0aYLly5fDw8MD6enpDY538+ZNWFlZYfLkyRxkJx0YhoGuri50dXXRrl27KvdXdL9VN04pLS2Nk3FbhD85OTmfXfCWlJQgPj4eKSkpaN68OUpKSqgQIkRGUTEk4fT09LBixQp4e3ujuLi4wfEiIiJgaWkJe3t7DrKTfqqqqrC0tISlpWWV+wQCQaXut0//rWkwLpEciYmJDY5RWFiI2NhYlJSUIDo6GjY2NhxkRgiRJNRNVgc+u8k+dvXqVQQFBXESS01NDUFBQWjevDkn8eQRy7LIzMyscZxSVlYW3ynKvffv3+PFixecxTM0NIS1tTX69OmDWbNmwdzcnLPYhBDuyfSYoW3btmHTpk1ITk5G+/bt8eOPP6Jfv351XvfXX39hwIABsLOzw6NHj0R+PEkphgAgLCwMJ06c4CSWqakpgoODoampyUk8UllBQUG1rUnJycl49+5drYN5ScOxLIsnT57UOnheS0sLbdq0gZqaGgoKCvD8+fNa1/dq1qwZzMzMAJQPuB4xYgSmTZv22dveEELES2aLoaNHj8LR0RHbtm3DF198gZ07d2LPnj2IioqChYVFjddlZWWha9euaNWqFVJTU6W2GCorK8PatWtx//59TuJ16dIFq1atgqKiIifxiGgqut+qG6eUnJzMSXeovEtNTcXr16+rvU9BQQH9+vWDra0tWJYFy7JgGAYMwyAqKgo3btyodqxYy5YtYWBgUOmYqqoqvvrqK0yYMEEmZ2oSIs1kthjq2bMnunbtiu3btwuP2djYYMKECQgICKjxuqlTp6J169ZQVFTEqVOnpLYYAoDc3Fx4enoiKSmJk3gTJ06Eq6srJ7FIw7Esiw8fPtQ4Tik7O5vvFCVeaWkp/v333xq3fhkwYABsbW2rnVXJsiyioqJw7dq1KvfZ2trW2JKqp6eH6dOn48svv6QPF4RICJmcTVZcXIz79+/D29u70vFhw4bh1q1bNV4XFhaGly9fIiIiAn5+fuJOU+w0NTWxcuVKeHp6crJ7/MmTJ2FlZYVBgwZxkB1pKIZhoK+vD319/Wq3iMjPz69xnBJ1v5VLTk6usRDS1tausRACyr//tra2ePDgQZUus9pafj58+ICff/4Zp0+fhrOzM+zt7eVqCQtCpJ3UFEPp6ekoLS2FiYlJpeMmJiZISUmp9prY2Fh4e3vjxo0bIi+aVlRUVGmWkCR+Em/WrBm8vLywbt06Tv74bd26Fc2aNUPr1q05yI6Ik7q6OqytrWFtbV3lPoFAgNTU1GrHKaWmpspF91txcXGN7wcA0Lp1a2G3WE1YlkWbNm0qdUcrKSmJ1OLz5s0b+Pn5oX379nBxcUHbtm3r9wQIIbyQmmKowqdvYjW9sZWWlmL69OlYs2YN2rRpI3L8gIAArFmzpsF5iluPHj3g6OiIAwcONDhWSUkJ/Pz8EBISAn19fQ6yI3xQUlKCubl5tbOcWJbF+/fvq7QmVfyfq42B+fb27dta14ZSU1Or8wMEy7JQU1OrdExFRaVeLT3//fcfvLy80LdvX8yaNQumpqYiX0sIaXxSM2aouLgY6urqOHbsGCZOnCg8vmjRIjx69KhKH39mZib09PQqfZorKysDy7JQVFTEH3/8gcGDB1d5nOpahpo3by4xY4Y+xrIsNm3ahBs3bnASr127dvD390eTJk04iUekR15eXo3jlNLT06Wi+02UbTe6deuGHj161Lp4YllZGe7du1epZUhfXx+tWrX6rLwUFRUxatQoTJkyBTo6Op8VgxBSfzI5ZkhZWRndunVDZGRkpWIoMjIS48ePr3K+trY2njx5UunYtm3bcPnyZRw/fhxWVlbVPk7Fxo3SgGEYLFy4EG/fvkVcXFyD4z179gzbt2/H999/T+Md5IyGhgZatmyJli1bVrmvpKQEaWlp1Y5TSklJQUlJCQ8ZV5WYmFhn0RYTE1PngqMMw+D58+eVjjXkPaG0tBRnz57Fn3/+CQcHB4wfP15q3mMIkRdSUwwBgIeHBxwdHdG9e3f07t0bu3btQkJCAubOnQsAWL58Od6+fYsDBw5AQUEBdnZ2la43NjaGqqpqlePSTFVVFb6+vnB3d+dkob/IyEhYWVlh7NixHGRHZEHFHl01db9lZGTU2KqUm5vbKDlmZ2fXue0Gy7JITk7Go0eP0Llz51pnk9Vn8LSoCgoKcPDgQZw7dw4zZ87EkCFDaHsPQiSEVBVDU6ZMQUZGBtauXYvk5GTY2dnh/PnzaNGiBYDyWSQJCQk8Z9n4jIyM4O3tDV9fX5SWljY43p49e9CiRQt07NiRg+yILGMYBoaGhjA0NKz2Q0Zubm61RVJKSgoyMjI4635TV1eHqakpUlJSqo3JsiwKCgpQWlqKixcvAgA6d+5c4zpDn+KyJef9+/f46aefcOrUKTg7O6N79+7UEksIz6RmzBBfJG2dodr8/vvv2LZtGyextLS0EBISUmX2HiFcKS4urjL77eP/1zQ9vjZFRUV4+/ZtlY2NCwsLhatRMwwDTU1N6OrqirwCdadOncTWtdWhQwe4uLjQbE5COCaziy7yQZqKIQD4+eefceHCBU5iWVpaYtOmTbSyLml0Fd1vNa3SnZeXV+v1+fn5SEhIQHZ2NkpKSiqdr6GhUa9JAgzDNErrTf/+/eHo6IimTZuK9XEIkRdUDHFI2oohgUAAX19f/Pfff5zE69OnD7y9vakZn0iUnJycGscpZWRkCM9LS0vD06dPha1Mqqqq9S7uVVVVG63LWElJCaNHj8aUKVOgpaXVKI9JiKyiYohD0lYMAeV7sS1evLhKV8HnmjFjBqZOncpJLELEraL7LS4uDgEBAXj79i0yMzNRWFj4WQOWK7rTGpOGhgYcHBwwbtw4KCsrN+pjEyIr6vP3m6YyyCAdHR34+vpy9iZ66NAh3Llzh5NYhIibsrIyzM3Nce3aNTRp0gSWlpaYMGECXr58idDQUPTu3Rvt2rWDpaUlTE1Noa+vD3V19RpXmOZjGnxeXh7279+POXPm4NKlS7UuJEkIaThqGaqDNLYMVbh+/To2bdrESSxVVVVs3rxZOHOPEEl24MABHDt2DEDVyQA5OTk4evQozp07V2mQNsuyEAgEwoVXi4qKUFhYCF1dXd5XZre0tISzszO6du1KXdaEiIi6yTgkzcUQAOzfvx/Hjx/nJFbTpk0RHBxMYxmIRPv4Q4CCggLWrVtX7Zif1NRUHDhwANevX2/sFD9bp06d4OLiUu3imISQyqgY4pC0F0NlZWXw8/PDvXv3OInXuXNnrF69WqRNKwlpbC9fvsTSpUuFm9LOmTMHY8aMqfWaFy9eICwsDI8fP26MFDkxcOBAODo6wtjYmO9UCJFYVAxxSNqLIaB8/IGnpyfevn3LSbzx48fj22+/5SQWIVz5dOLAsGHDsGDBApG6lViWxYMHDxAWFobXr1+LO1VOKCkpYcyYMZg8eTK11hJSDSqGOCQLxRBQvpu3p6dnneuziGrx4sUYMmQIJ7EIaahPl5SwsbHB+vXr673pcFlZGS5fvoyIiIhKU/QlmYaGBiZPnowxY8bQzDNCPkKzyUgV5ubmWLJkCWeDL0NDQxETE8NJLEIaaufOncJCyMDAAMuXL693IQSUjzEaOnQodu7ciVmzZkFdXZ3rVDmXl5eHsLAwzJ07F1euXOFsixNC5Am1DNVBVlqGKvz2228IDw/nJJa+vj5CQkJ4n2lD5NvH29AoKytjw4YNnG1tkZ2djaNHj+L8+fOftT0IH6ytreHi4oLOnTvznQohvKJuMg7JWjHEsiyCgoJw7do1TuK1adMGAQEB1DxPePH06dNKGxR7enpi4MCBnD9OcnIyDh48WO0mrpKqS5cucHZ2hrW1Nd+pEMILKoY4JGvFEFC+Qu/SpUvx8uVLTuINGTIEixYtovVPSKNKS0uDh4cHsrKyAACTJk2Ci4uLWB/z+fPnCAsLw9OnT8X6OFxhGAYDBw7EzJkzaeYZkTtUDHFIFoshAEhPT8fixYuFf0ga6ttvv8X48eM5iUVIXYqKirB06VLExcUBALp164Yffvjhs7bbqC+WZfHPP/8gLCwMiYmJYn88LjRp0gRjx47F119/DU1NTb7TIaRR0ABqUidDQ0OsWLECSkpKnMTbu3cvHj16xEksQmrDsiy2bNkiLITMzMzg5eXVKIUQUN7a0qNHD2zduhXff/+9VIyZKykpwYkTJ+Dm5oZTp06hpKSE75QIkShUDMkxW1tbzJ07l5NYLMsiMDAQycnJnMQjpCbHjx8Xjt1RU1ODr68vL60dioqKGDZsGHbu3ImZM2dCTU2t0XOor9zcXOzduxdz587FtWvXaOYZIf8fFUNybvjw4Rg9ejQnsXJzc7Fu3ToUFBRwEo+QT927dw8HDx4EUN5Cs2TJEjRv3pzXnFRVVTFlyhTs3r0bY8aMkYrV2dPS0rB582a4u7vj33//5TsdQnhHY4bqIKtjhj4mEAiwcuVKzgaF9uzZEz4+PjSgmnAqMTERnp6ewmJ71qxZ+Prrr3nOqqqkpCQcOHAAf/31F9+piKxbt25wdnaGpaUl36kQwhkaQM0heSiGgPKtDDw8PJCWlsZJvKlTp2LGjBmcxCIkLy8PHh4eSEpKAgD069eP00VExSEmJgb79u1DVFQU36mIhGEYDB48GDNnzoShoSHf6RDSYFQMcUheiiEAiIuLw9KlS1FUVMRJvOXLl6NPnz6cxCLyq6ysDGvXrsX9+/cBlC8qGBgYCFVVVZ4zqxvLsvj7778RFhbG2d6A4qasrIxx48bBwcEBGhoafKdDyGejYohD8lQMAcDNmzcRGBjISSwVFRVs3ryZmt5Jg4SFheHEiRMAAG1tbYSEhEjdmjmlpaWIjIzEoUOHkJmZyXc6ItHS0sLUqVMxatQozmadEtKYaGo9+Wx9+/bFlClTOIlVVFSEdevWITs7m5N4RP5cvXpVWAgpKipi+fLlUlcIAeW5jxgxArt378aMGTOkolUrJycHu3fvxnfffYcbN27QzDMi06gYIlXMmDED9vb2nMRKS0tDYGCg1OzrRCRHbGwstm7dKrw9Z84c2NnZ8ZhRw6mqqmLq1KnYtWsXRo0a1WhrIzVESkoKNm7cCE9PTzx58oTvdAgRC+omq4O8dZNVyM/Ph5eXF2cr7I4dOxazZ8/mJBaRfR8+fIC7uzsyMjIAACNGjMD8+fN5zop7b9++xf79+3H79m2+UxFZjx494OzsDAsLC75TIaRWNGaIQ/JaDAHlU4Q9PDyQl5fHSbyFCxfiyy+/5CQWkV0lJSXw8fFBdHQ0gPLFQdevXy/T41aio6Oxb98+PHv2jO9URMIwDIYOHYoZM2bAwMCA73QIqRYVQxyS52IIAB4+fIhVq1ZxMl5ASUkJ/v7+sLGx4SAzIotYlsXWrVsRGRkJoHzbmB9//BE6Ojo8ZyZ+LMvi9u3b2L9/v3AJAUmnrKyMCRMm4KuvvoK6ujrf6RBSCQ2gJpzp0qULXF1dOYklEAjg7++P9PR0TuIR2XP+/HlhIaSsrAxfX1+5KISA8taWPn364Oeff8Z3330nFc+7uLgYv/76K9zc3PC///2PxgYSqUXFEKnT+PHjMWjQIE5iZWZmwt/fH8XFxZzEI7LjyZMn2LVrl/D2okWL0LJlSx4z4oeSkhJGjRqF3bt3Y+rUqVBRUeE7pTplZ2dj586dmDdvHm7evEkzz4jUoW6yOsh7N1mF4uJieHt7IzY2lpN4gwYNgru7u0SvIEwaT2pqKtzd3ZGTkwMAcHBwgJOTE89ZSYb379/jyJEjuHjxotQUGW3btoWLiwvat2/PdypEjtGYIQ5RMfR/MjIy4O7ujg8fPnASz9XVFRMnTuQkFpFehYWFWLJkCeLj4wEA3bt3x8qVK6Vi2nljSkxMxP79+3H37l2+UxFZz5494eTkxPtmukQ+UTHEISqGKnv27BmWL1/OydgAhmGwevVqdO3alYPMiDRiWRYbNmzArVu3AADm5uYICgqibSBq8fTpU4SFheH58+d8pyIShmEwfPhwTJs2Dfr6+nynQ+QIFUMcomKoqj///BNbtmzhJJaGhgaCgoJgbm7OSTwiXY4ePYqIiAgA9FqoD5ZlcevWLezfvx/Jycl8pyMSFRUVTJw4EZMmTYKamhrf6RA5QLPJiFgNHToUY8eO5SRWXl4e/Pz8kJ+fz0k8Ij3u3r0rLIQYhsGSJUuoEBIRwzD44osvsG3bNsyZM0cqPqgVFRXhl19+gZubG86fP08zz4hEoWKIfBZXV1d07NiRk1hv3rzB5s2bpWZwKGm4hIQEbN68WXjbyckJ3bp14zEj6aSkpIQxY8Zg9+7dmDx5MpSVlflOqU5ZWVnYvn07FixYgFu3btHvPZEI1E1WB+omq1lOTg7c3d2RmprKSbzJkyfD0dGRk1hEcuXk5MDT01PYvdO/f394eXnRzEIOZGRk4NChQ/jzzz+lpsho164dXF1daTFWwjkaM8QhKoZqFx8fjyVLlqCwsJCTeMuWLUPfvn05iUUkT2lpKVavXo1Hjx4BAFq2bInAwECpWEtHmiQkJCA8PBz37t3jOxWR9e7dG05OTtRVSjhDxRCHqBiq261btxAQEMBJLGVlZWzatAnW1tacxCOSZe/evTh16hQAQEdHBz/++CMMDQ35TUqGPXnyBGFhYZytDyZuCgoKGDFiBKZNmwZdXV2+0yFSjoohDlExJJrDhw/jyJEjnMQyMjJCSEiIVGxHQER3+fJlhISEACgf67J+/XrY2trynJXsY1kWN2/exP79+znr0hY3VVVVTJo0CRMnToSqqirf6RApRbPJSKObNm0aevXqxUmsd+/eYcOGDTTbRIbExMQgNDRUeHvu3LlUCDUShmHQr18/bN++HW5ubtDS0uI7pToVFhbi8OHDcHNzw4ULF1BaWsp3SkTGUctQHahlSHQFBQXw8vJCQkICJ/FGjRqF7777jpNYhD/v37+Hu7s73r9/D4B+rnzLy8vD8ePHcebMGanZI7BZs2ZwdnaGvb09DbQnIqNuMg5RMVQ/KSkp8PDwEO4x1VDz58/HiBEjOIlFGl9xcTFWrFiBmJgYAICdnR3WrVsHJSUlnjMj6enpOHToEC5duiQ1M89sbW3h6uqKtm3b8p0KkQJUDHGIiqH6+/fff/HDDz+grKyswbEUFRWxfv162vBRCrEsiy1btuDSpUsAAGNjYwQHB9NYMAkTHx+P8PBw3L9/n+9URPbFF19g1qxZMDMz4zsVIsGoGOIQFUOf58yZM9i9ezcnsXR0dBASEgIjIyNO4pHG8fFrQEVFBRs3bqRZghLs33//RVhYGF6+fMl3KiJRVFQUzjyjAptUhwZQE96NHTsWQ4YM4SRWVlYW1q9fj6KiIk7iEfF79OgR9u7dK7y9ePFiKoQkXKdOnRASEgIvLy8YGxvznU6dSktLce7cOXz77bc4evQoZ2udEflExRARC4ZhMH/+fM769l++fImffvpJasY2yLPk5GQEBgYKu0knT55MC2lKCYZhMGDAAOzYsQPffPMNNDU1+U6pToWFhYiIiMCcOXPwxx9/0Mwz8lmom6wO1E3WMJ/OJGooJycnODg4cBKLcO/TGYX29vbw9fWlGUBSKjc3VzjzrKSkhO90RNK8eXO4uLige/fu9LqTczRmiENUDDXc8+fP4e3tzcmbKcMw+OGHH9C9e3cOMiNcYlkWAQEBuH37NoDyP0qbN2+Guro6z5mRhnr37h0iIiJw5coVqWmdtbOzg4uLC9q0acN3KoQnVAxxiIohbny8+nBDqaurIzg4mPYwkjAfr0KuoaGB4OBgmu0jY+Li4hAeHo6HDx/ynYrI+vXrB0dHR5iamvKdCmlkVAxxiIoh7uzZswenT5/mJJa5uTmCgoKgoaHBSTzSMB/vT8cwDFavXo2uXbvynBURl4cPHyIsLAyvXr3iOxWRKCkpYeTIkZg6dSq9j8sRmk1GJJKLiws6d+7MSay3b99i06ZNnKxlRBomPj6+Uqufi4sLFUIyrkuXLtiyZQs8PDykYqNdgUCAs2fPws3NDceOHaOZqaQKKoZIo1FUVMTSpUs5a66+f/8+Dh48yEks8nlycnLg5+cnnNY8aNAgTJgwgd+kSKNgGAaDBg3Czp074eLiIhWttPn5+Thw4ADmzJmDP//8kz5MESGpK4a2bdsGKysrqKqqolu3brhx40aN5544cQJffvkljIyMoK2tjd69e+PixYuNmC35lJaWFnx9fTnbifr48eO4fv06J7FI/ZSWlmLDhg3CndBbt26NBQsW0AweOaOsrIxJkyZh9+7dmDhxolRstZKRkYEtW7Zg4cKFuH//vtQMCifiI1XF0NGjR7F48WL4+Pjg4cOH6NevH0aOHFnjxqDXr1/Hl19+ifPnz+P+/fsYNGgQxo4dK1WD/2SRhYUFvLy8OIu3ZcsWvHjxgrN4RDR79+7F48ePAQB6enrw8fGBsrIyz1kRvmhpacHV1RU7d+7EwIED+U5HJK9fv8bq1avh6+tL7yFyTqoGUPfs2RNdu3bF9u3bhcdsbGwwYcIE4eDNurRv3x5TpkzBDz/8INL5NIBafH755RccOnSIk1iGhoYICQmBrq4uJ/FI7SIjI/HTTz8BKB+cGhAQgHbt2vGcFZEkL168QFhYmLBglgb9+/fHrFmzYGJiwncqhAMyOYC6uLgY9+/fx7BhwyodHzZsGG7duiVSjLKyMuTk5EBfX7/Gc4qKipCdnV3pi4jHlClT8MUXX3ASKz09Hf7+/hAIBJzEIzV79uwZtm3bJrw9f/58KoRIFa1atYKfnx9Wr14NS0tLvtMRyfXr1zF37lzs2bMHOTk5fKdDGpHUFEPp6ekoLS2tUrGbmJggJSVFpBhBQUHIy8vD5MmTazwnICAAOjo6wq/mzZs3KG9SM4ZhsHjxYs7eKKOjo7Fjxw7q/xejjIyMSkXn2LFjMXToUJ6zIpKKYRh069YNW7ZsweLFi6Vm5tnp06fh5uaG3377DcXFxXynRBqB1BRDFT4dnMmyrEgDNo8cOYLVq1fj6NGjtW5CuHz5cmRlZQm/EhMTG5wzqZmqqip8fX2hpaXFSbyLFy/i999/5yQWqay4uBjr16/Hhw8fAAAdO3aEq6srz1kRaaCgoIAhQ4Zg586dcHJykopVyfPy8hAeHo45c+bg0qVLNPNMxklNMWRoaAhFRcUqrUBpaWl19u8ePXoU33zzDX799dc6P8WqqKhAW1u70hcRLxMTE3h7e0NBgZuX465du/D06VNOYpFyLMsiNDQUsbGxAP7vZyYNM4eI5FBWVoaDgwN2796N8ePHS8XrJz09HT/++CMWLVqEBw8e8J0OEROpKYaUlZXRrVs3REZGVjoeGRmJPn361HjdkSNH4OzsjMOHD2P06NHiTpN8po4dO8LNzY2TWKWlpQgICEBaWhon8Qhw+vRpXLlyBQD3rXlE/mhra+Pbb7/F9u3b0b9/f77TEUl8fDxWrVqFlStXIi4uju90CMekphgCAA8PD+zZswf79u1DdHQ03N3dkZCQgLlz5wIo7+KaNWuW8PwjR45g1qxZCAoKQq9evZCSkoKUlBRkZWXx9RRILUaPHl1lgPznys7OrrQYIPl8Dx8+xL59+4S33d3dpWZALJFsTZs2xZIlSxAcHIwOHTrwnY5IHj16hEWLFiE4OJg+cMkQqZpaD5Qvurhx40YkJyfDzs4OISEhwk8Wzs7OiI+Px9WrVwEAAwcOxLVr16rEcHJyQnh4uEiPR1PrG1dJSQl8fHwQHR3NSby+ffti6dKltBDgZ0pOToa7uzvy8vIAANOmTcP06dN5zorIIpZlcf/+fYSFhdW4dpykUVJSwtixYzF58mRoamrynQ75BG3UyiEqhhrfhw8f4O7ujoyMDE7iOTo61jqDkFQvPz8fXl5ewkkEvXr1wooVK6iwJGJVVlaGS5cuISIiAu/fv+c7HZFoampi8uTJGD16NC08KkFkcp0hIj+4Xs04IiICf//9Nyex5AXLsggKChIWQhYWFvDw8KBCiIidgoICvvzyS+zatQuOjo5QU1PjO6U65ebmYt++fZg7dy6uXLlCy3tIIWoZqgO1DPHn6tWrCAoK4iSWmpoagoKCaN0oEUVERODo0aMAyrdZCAoK4myDXULqIysrC0ePHsX58+dRWlrKdzoisba2houLCzp37sx3KnKNusk4RMUQv8LCwnDixAlOYpmamiI4OJj69utw8+ZNBAYGAij/lL527Vp06tSJ56yIvEtOTsaBAwdw8+ZNvlMRWdeuXeHs7AwrKyu+U5FLVAxxiIohfpWVlWHt2rW4f/8+J/G6dOmCVatWQVFRkZN4subVq1dYsmQJioqKAABubm4YN24cz1kR8n9iYmIQFhaG//77j+9URMIwDAYPHoyZM2dKxQrcsoSKIQ5RMcS/3NxceHp6IikpiZN4EydOpJWTq5GVlQUPDw/hdOEhQ4Zg0aJFNE6ISByWZXHv3j2Eh4dLzS4BTZo0wfjx4+Hg4AANDQ2+05ELNICayBRNTU34+vpyNpDy5MmTwgUESTmBQIANGzYIC6G2bdti3rx5VAgRicQwDOzt7bF161YsWLAAenp6fKdUp5KSEhw/fhxubm44ffo0SkpK+E6JfISKISIVmjdvjiVLlnD2x3nr1q3CrSUIsGfPHuEWJvr6+lixYgVNESYST1FREcOHD8euXbswY8YMqKqq8p1SnXJycrBnzx7MnTsX169fp5lnEoK6yepA3WSS5dixYzhw4AAnsfT19RESEgJ9fX1O4kmrixcvIjQ0FEB5U/6GDRvQpk0bnrMipP4yMzNx5MgRXLhwQWo2Vm3VqhVcXFzQsWNHvlOROWIfM5SYmIj4+Hjk5+fDyMgI7du3h4qKymcnLMmoGJIsLMti06ZNuHHjBifx2rVrB39/fzRp0oSTeNImKioKPj4+EAgEAMq32hg8eDDPWRHSMG/fvsWBAwdw69YtvlMRWffu3eHs7IwWLVrwnYrMEEsx9Pr1a+zYsQNHjhxBYmJipaY9ZWVl9OvXD7Nnz8ZXX33F2e7jkoCKIclTWFiIZcuWcbZZ4pdffonvv/9e7sbHpKenY/HixcK9+saPH49vv/2W56wI4U50dDTCwsI4295H3BiGwdChQzF9+nSaecYBzgdQL1q0CB06dEBsbCzWrl2L//77D1lZWSguLkZKSgrOnz+Pvn37YuXKlejYsSPu3bvHyRMhpDqqqqrw8fGBjo4OJ/EiIyNx7tw5TmJJi6KiIvj5+QkLoc6dO8PFxYXnrAjhlo2NDQIDA+Hj4wNzc3O+06kTy7KIjIzEnDlzcPDgQeGegET8RGoZWrJkCZYuXQojI6M6A54/fx75+flwcHDgJEG+UcuQ5Hr69Cl8fX05WZVWQUEB69atk4t++4qtNio2MTY1NUVQUBC0tLR4zowQ8REIBIiMjMShQ4eEHwIknZaWFqZNm4aRI0dCSUmJ73SkDq0zxCEqhiTb77//jm3btnESS0tLCyEhITAxMeEknqQ6ceIEwsLCAJS3sgUFBcHCwoLnrAhpHAUFBTh58iROnjyJwsJCvtMRSdOmTeHk5IQvvvhC7rrzG4LWGSJyY+TIkRgxYgQnsXJycuDn5yc1b5Cf4/79+wgPDxfe9vLyokKIyBU1NTVMnz4dO3fuxIgRI6RijGtKSgoCAwPh5eUlXAKDcKveLUMZGRn44YcfcOXKFaSlpVWZvvj+/XtOE+QbtQxJPoFAAB8fH0RFRXESr0+fPvD29pa5T2Bv376Fp6encBzCjBkzMHXqVJ6zIoRfb968wf79+3Hnzh2+UxGZvb09nJ2daePpOoi1m2zkyJF4+fIlvvnmG5iYmFT5g+Hk5FT/jCUYFUPSISsrC4sXL0Z6ejon8WStUMjLy4Onpyfevn0LQHYLPkI+V1RUFPbt24eYmBi+UxEJwzD48ssvMWPGDLlfK60mYi2GtLS0cPPmTbnZxZqKIenx8uVLLF26FMXFxZzE8/HxQa9evTiJxaeysjL4+fkJZ3laWlpi06ZNUrFaLyGNiWVZ3L59G+Hh4UhOTuY7HZEoKytj4sSJmDRpEtTV1flOR6KIdcxQu3btUFBQ8NnJESIuLVu2xKJFiziLFxQUhNevX3MWjy8RERHCQkhLSwu+vr5UCBFSDYZh0KdPH2zbtg1z587lbPkOcSouLsbRo0cxe/ZsnDt3TriAKqmfehdD27Ztg4+PD65du4aMjAxkZ2dX+iKET/379+dsWYfCwkL4+fkhJyeHk3h8uH79Oo4dOwagfPkAb29vmZ8tR0hDKSkpYfTo0di1axemTJkiFTssZGVlYceOHZg/fz5u3bpFe57VU72LIV1dXWRlZWHw4MEwNjaGnp4e9PT0oKurKxU7BxPZ5+joiO7du3MSKyUlBRs3buRkLaPG9vLlS2zZskV4283NTS7WUSKEK+rq6pg5cyZ27dqF4cOHS8UYu6SkJAQEBGDJkiWcTSqRB/UeM2Rvbw8lJSUsWrSo2gHUAwYM4DRBvtGYIen06YDhhpK2rSo+HVA+bNgwLFiwQCrezAmRVImJiQgPD8fff//Ndyoi69mzJ5ydndGsWTO+U2l0Yh1Ara6ujocPH6Jt27YNSlJaUDEkvT6dSt5QixcvxpAhQziJJU4CgQC+vr7477//AJRvSbB+/Xq53YyWEK49ffoU+/btQ2xsLN+piERBQQHDhg3D9OnT5aoHR6wDqLt3747ExMTPTo6QxmJubo4lS5Zw1hoSGhoqFdNud+7cKSyEDAwMsHz5ciqECOGQnZ0dgoKCsGzZMjRt2pTvdOpUVlaGCxcuYPbs2Th8+LBMLyz7uerdMnTs2DGsXr0aS5YsQYcOHaq8ycramARqGZJ+v/32W6VVlxtCX18fISEhEruux8fbkygrK2PDhg1o3bo1z1kRIrsEAgF+//13HDlyRGomW+jq6mL69On48ssvZXrPM7F2k1W3dDnDMGBZFgzDSOVA09pQMST9Pt2YtKHatm0Lf39/KCsrcxKPK59uXOvp6YmBAwfymxQhciIvLw8nTpzAqVOnOFvrTNzMzc3h7OyMnj17yuR4QrEWQ3Wtu9KiRYv6hJN4VAzJhuLiYixduhQvX77kJN6QIUOwaNEiiXkDSUtLg4eHh3A37kmTJsHFxYXnrAiRP+np6Th8+DD+/PNPqZnebmNjAxcXF9jY2PCdCqdo13oOUTEkO9LT07F48WJhwdBQ3377LcaPH89JrIYoKirC0qVLERcXBwDo1q0bfvjhB6nYgJIQWRUfH4/9+/fjn3/+4TsVkfXp0wezZs2Cubk536lwgvMB1Ldv3xb5wfPy8oSDNwmRJIaGhlixYgVnfeR79+7Fo0ePOIn1uViWxZYtW4SFkJmZGby8vKgQIoRnlpaWWLVqFdavX49WrVrxnY5Ibt26hXnz5mH79u3IzMzkO51GJdI75qxZs/Dll1/i119/RW5ubrXnREVFYcWKFWjVqhUePHjAaZKEcMXW1hZz587lJBbLsggMDOR1D6Pjx4/jxo0bAAA1NTX4+vpCU1OTt3wIIZV17NgRwcHBWLJkiVSs/l5WVobz58/Dzc0Nv/zyi9zMPBOpm6ykpAQ7d+5EaGgoXr58iTZt2sDMzAyqqqr48OEDnj17hry8PEyaNAnLly+HnZ1dY+TeKKibTDZt374d58+f5ySWhYUFNm/eDDU1NU7iierevXtYt26dcPLCypUr0aNHj0bNgRAiupKSEpw/fx5Hjx6Vmplnenp6mDFjBoYOHQpFRUW+06kXsY4ZevDgAW7cuIH4+HgUFBTA0NAQXbp0waBBgyR2unFDUDEkmwQCAVauXImnT59yEq9nz57w8fFptAHViYmJ8PT0FG6aPGvWLHz99deN8tiEkIbJy8vD8ePHcfr0aZSUlPCdjkiaN28OJycn2NvbS8zEkbrQAGoOUTEku7KysuDh4YG0tDRO4k2dOhUzZszgJFZt8vLy4OHhgaSkJABAv379OF1ckhDSON69e4dDhw7h8uXLUjPzrH379nBxcZGKXSioGOIQFUOyLS4uDkuXLkVRUREn8ZYvX44+ffpwEqs6ZWVlWLt2Le7fvw8AsLa2RmBgIFRVVcX2mIQQ8Xr16hXCw8Olarxt3759MWvWLJiamvKdSo2oGOIQFUOy7+bNmwgMDOQklqqqKjZt2gRLS0tO4n0qLCwMJ06cAADo6OggODgYxsbGYnksQkjjevToEcLCwoSzQyWdoqIiRo0ahSlTpkBHR4fvdKqgYohDVAzJh4MHD+LXX3/lJJaxsTFCQkI4f71cvXoVQUFBAMrfhPz8/GRqsgIhpHyW6rVr13DgwAG8e/eO73REoqamBgcHB4wfPx4qKip8pyMk1o1aCZFFM2fOhL29PSex0tLSEBgYCIFAwEk8AIiNjcXWrVuFt+fMmUOFECEyiGEYDBw4EDt27ICrqys0NDT4TqlOBQUFOHjwIGbPno3IyEiUlZXxnVK9UctQHahlSH7k5+fDy8sLiYmJnMQbO3YsZs+e3eA4Hz58gLu7OzIyMgAAI0aMwPz58xsclxAi+XJycnDs2DGcPXuW0w9Y4mRhYQFnZ2d0796d14kdYu8mu3TpEi5duoS0tLQqFeC+ffvqG06iUTEkX5KSkuDh4YG8vDxO4i1cuBBffvnlZ19fUlICHx8fREdHAyhfNHL9+vUyvdM0IaSqtLQ0RERE4MqVK3ynIrIOHTrAxcUFrVu35uXxxdpNtmbNGgwbNgyXLl1Ceno6Pnz4UOmLEGlmZmaGpUuXcvZpZtu2bcJCpr5YlsX27duF13O9nQghRHoYGxvDw8MDW7ZsQadOnfhORyRPnjyBh4cHNm3ahJSUFL7TqVW9W4ZMTU2xceNGODo6iisniUItQ/Lp5MmTnLVy6urqIiQkBIaGhvW67ty5c9ixYwcAQFlZGRs3bkTLli05yYkQIr1YlsXDhw8RFhaG+Ph4vtMRiZKSEkaPHo0pU6ZAS0urUR5TrC1DxcXFYl1HhRBJMGHCBAwaNIiTWJmZmfD390dxcbHI1zx58gS7du0S3l60aBEVQoQQAOWDrLt27YotW7bA3d293h+0+CAQCHD69Gm4ubnh+PHj9Xo/bAz1Loa+/fZbHD58WBy5ECIxGIbBggULOOvrjo2NRWhoqEirzKampiIgIEA4Hs/BwQH9+/fnJA9CiOxQUFDA4MGDsXPnTjg7O0vFzLO8vDzs378fc+bMwaVLlyRm5lm9u8kWLVqEAwcOoGPHjujYsSOaNGlS6f7g4GBOE+QbdZPJt4yMDLi7u3M2Hs7V1RUTJ06s8f7CwkIsWbJE2PTdvXt3rFy5EgoKtAoGIaR2OTk5OHr0KM6dOyc1M88sLS3h7OyMrl27cj7zTKyzyWrrOmAYBpcvX65POIlHxRB59uwZli9fzsmbC8MwWL16Nbp27VrlPpZlsWHDBty6dQsAYG5ujqCgIKn4tEcIkRypqak4cOAArl+/zncqIuvUqRNcXFw4HQ5AK1BziIohAgB//vkntmzZwkksDQ0NBAcHw8zMrNLxo0ePIiIiQnhOUFAQzM3NOXlMQoj8efHiBcLCwvD48WO+UxHZwIED4ejoyMk2Q41WDL158wYMw8j0GzYVQ6TCrl27cPbsWU5iNWvWDEFBQVBXVwcA3L17F35+fgDKW49WrVqFbt26cfJYhBD5xbIsHjx4gLCwMLx+/ZrvdESipKSEMWPGYPLkyQ2aeSbW2WQVu2br6OigRYsWsLCwgK6uLtatWycxA6EIEQdXV1d07NiRk1hv3rzB5s2bwbIsEhISsHnzZuF9Tk5OVAgRQjjBMAy6deuGn376CYsWLYKBgQHfKdVJIBDg1KlTcHNzw4kTJxpl5lm9W4aWL1+OvXv3Ys2aNfjiiy/Asiz++usvrF69Gm5ubli/fr24cuUFtQyRj+Xk5MDd3R2pqamcxBs7diz++ecfJCcnAwAGDBgAT09PXpewJ4TIrqKiIpw5cwbHjx9Hfn4+3+mIxMjICI6Ojhg4cGC93hvF2k1mZmaGHTt2YNy4cZWOnz59GvPmzcPbt2/rE07iUTFEPhUfH48lS5agsLCwQXFYlsXz589hZGQEfX19tGzZEhs3boSysjJHmRJCSPWys7Nx9OhRnD9/XmpmnllbW8PFxQWdO3cW6XyxFkOqqqp4/Pgx2rRpU+l4TEwMOnfujIKCgvqEk3hUDJHq3Lp1CwEBAQ2KkZCQgJSUFCgoKMDe3h579+6VisXTCCGyIzk5GQcPHsSNGzf4TkVkXbp0gbOzM6ytrWs9T6xjhjp16oTQ0NAqx0NDQ6VmvxRCGqpPnz6YNm3aZ1+fnp4u3KuHZVkoKipWWbOLEELEzdTUFEuXLkVQUBDs7Owa9bGVlZVhamqK5s2bw9TUVORW8YcPH2Lx4sXYunWrSAvZiqLexdDGjRuxb98+2Nra4ptvvsG3334LW1tbhIeHY9OmTZwkVZtt27bBysoKqqqq6NatW53V7LVr19CtWzeoqqrC2tpauNcTIQ01bdo09OrVq97X5ebmVtpPyNLSEiUlJdiwYYPUNFcTQmRLmzZt4O/vjx9++AHNmzcX62MxDIMWLVqgQ4cOMDMzg5GREUxNTdGhQwe0aNFCpHFBLMtCVVWVs/GV9S6GBgwYgOfPn2PixInIzMzE+/fvMWnSJMTExKBfv36cJFWTo0ePYvHixfDx8cHDhw/Rr18/jBw5EgkJCdWe/+rVK4waNQr9+vXDw4cPsWLFCixcuBC//fabWPMk8oFhGHh4eMDCwkLka0pKSvDixQvhzEtjY2MYGRkBAJ4+fYrdu3eLJVdCCKkLwzDo0aMHtm7diu+//x76+vpieRwLCwsYGhqCYRgwDAMFBQUoKCiAYRgYGhqK9J6qrq6OKVOmcJaTVC262LNnT3Tt2hXbt28XHrOxscGECROqHb+xbNkynDlzBtHR0cJjc+fOxb///ovbt2+L9Jg0ZojUJTk5GZ6ensjJyan1vLKyMjx79gy5ubkAAC0tLbRr167KJ5v58+djxIgRYsuXEEJEUVhYiNOnT+O3337jbDywsrIyOnToUGuLDsuyePLkSa1T6p2cnODg4FDrY3E+Zujx48fCT7KPHz+u9UtciouLcf/+fQwbNqzS8WHDhgm3L/jU7du3q5w/fPhw/PPPPygpKan2mqKiImRnZ1f6IqQ2pqamWLZsWZ37h8XHxwsLIRUVFbRq1araN4QdO3bgv//+E0uuhBAiKlVVVUyZMgW7d+/GmDFjoKio2KB4xcXFYBhGpHE+ta2HZGhoWGVGe0OJVAx17twZ6enpwv936dIFnTt3rvLVpUsXTpP7WHp6OkpLS2FiYlLpuImJiXAg6qdSUlKqPV8gEAifz6cCAgKgo6Mj/BJ33ymRDZ06dcI333xT4/2pqanC15yCggJat25d44Dp0tJSBAQE4N27d2LJlRBC6kNHRwdz5szBtm3b8MUXX9T7+tLSUiQmJuLx48dgWbbOYohlWSgpKdV4/8yZMzlfgqTmR/vIq1evhOMaXr16xWkC9fXpJ2mWZWttbqvu/OqOV1i+fDk8PDyEt7Ozs6kgIiIZO3Ys4uLicOnSpUrHs7OzK41rs7a2Fm7DUZOsrCysX78egYGBUFFREUu+hBBSH2ZmZvD29kZMTAz27duHqKioWs8vKytDWloakpKShJNDCgoK6hz0zDBMjZNJLC0ta90w/nOJ1DL08eju169fw9zcHC1atKj0ZW5uLtZ9TwwNDaGoqFilFSgtLa1K60+Fpk2bVnu+kpJSjU1wKioq0NbWrvRFiCgYhsG8efPQtm1b4bHCwkK8ePFCWISbmZmJPCjx5cuX+OmnnzibOkoIIVxo27YtNmzYAF9f3xr3Js3IyMCTJ0+QkJBQqbCJjY0VaQZYRkZGtcednZ3rHJLwOeodcdCgQXj//n2V41lZWWKp1iooKyujW7duiIyMrHQ8MjISffr0qfaa3r17Vzn/jz/+QPfu3WlNFyIWysrKWLFiBfT19VFaWorY2FjhG4Guri6aNWtWr3jXr1+n2Y+EEInDMAx69uyJn3/+GfPnz4euri6A8pbw//77Dy9fvkRRUVGV67KzsxEVFVXjhzyWZZGenl7t4OlOnTqha9eunD6PCvUuhmrqlsrIyICGhgYnSdXEw8MDe/bswb59+xAdHQ13d3ckJCRg7ty5AMq7uGbNmiU8f+7cuXj9+jU8PDwQHR2Nffv2Ye/evfDy8hJrnkS+6evrY8WKFXj9+rVwBoaamhpatmz5WfEOHDiAf/75h8sUCSGEE4qKihgxYgR8fX2hoqKC2NhY5OXl1XrNjRs38Pz5c+H4obKyMuH/09PTa1wux8XFRWz7Noo0ZggAJk2aBKC8GnR2dq40jqG0tBSPHz+usYWGK1OmTEFGRgbWrl2L5ORk2NnZ4fz582jRogWA8inOH38TrayscP78ebi7u+Pnn3+GmZkZfvrpJ3z11VdizZOQ+/fvQ1tbG+np6VBSUkLr1q0/eyYGy7LYtGkTgoODa2ySJoQQPqSnp+Pw4cP4888/wbIsOnTogKSkJLx7967a1h9lZWU0a9YMOTk5ePLkCQwMDKCkpASBQICMjIwap9MPGDDgsz9QikLkdYZcXFwAAPv378fkyZOhpqYmvE9ZWRmWlpZwc3OTub2VaJ0hUl8f71uWkJAgnJnYUObm5ggKChJ7CywhhNQlLy8PJ06cwKlTp6otYAoKCvDmzRt8+PABAKCkpARTU1OYmJjUe8yPkpISduzYUeP44JrU5++3yC1DYWFhAMpHcnt5edEbMiHViI+PR0hIiPD2Dz/8gEePHuHRo0cNjv327Vts3rwZK1euFMsAQkIIqYtAIMDvv/+OI0eO1LrQrJqaGlq3bo2CggIoKSmBYZhap8vXZsyYMfUuhOpLqlag5gO1DBFR5eTkwN3dHampqQDKJxu4u7sjNzcXnp6eSE5O5uRxHBwc4OTkxEksQggRBcuy+Ouvv7B///4a1/b71MCBA+Ho6AgjIyPcvn0b+/fvR1JSUr0eV0NDA7t374aWlla9c+a8Zahr1664dOkS9PT00KVLl1oHMD148KB+2RIiAwQCATZs2CAshFq3bo0FCxaAYRhoaWnB19cXnp6eKCwsbPBjHT9+HFZWVujfv3+DYxFCSF2ePn2KsLAwPH/+XKTzO3bsCBcXF7Rq1Up4rE+fPrC3t8cff/yBw4cPIysrS6RYkydP/qxCqL5EKobGjx8vHDA9YcIEceZDiFTat2+fcDsaPT09+Pj4VFoh1cLCAl5eXvDz8+Pk8bZs2QIzM7NKbzaEEMKlxMREhIeH4++//xbpfEtLSzg7O6Nr167VNpooKSlh1KhRGDRoEE6cOIGTJ09WO/2+gpGREcaMGfPZ+dcHdZPVgbrJSF0iIyPx008/ASj/ZQ8ICEC7du2qPfeXX37BoUOHOHlcQ0NDhISECNf3IIQQLrx//x6HDh1CZGSkyPuIOTo6YtCgQfUaz/j+/XscOXIEFy9erPZxPDw8GrR+YX3+fte7GEpMTATDMMLF4/7++28cPnwYtra2mD179mcnLamoGCK1efbsGZYvXy5cWHHRokUYOnRojeezLIsNGzbUuLlwfdna2mL9+vWfPTCREEIq5OfnC1tsatsxvoK6ujq+/vprjBs3rkF7hSUmJmL//v24e/eu8JiVlRW2bNnSoHWFON+1/mPTp0/HlStXAJRvhDp06FD8/fffWLFiBdauXft5GRMihTIyMuDv7y8shMaOHVtrIQSUr9Pl7u4OS0tLTnKIiorCzp07acsOQshnEwgEOHfuHGbPno2jR4/WWQgpKSlh/Pjx2L17NxwcHBq8aWrz5s3h6+uLgIAAtGnTBoB4F1isTr1bhvT09HDnzh20bdsWP/30E44ePYq//voLf/zxB+bOnYu4uDhx5coLahki1SkuLoa3tzdiY2MBlA8YXLNmjcgtNKmpqXB3d691amp9fPfddxg1ahQnsQgh8oFl2XrP8urfvz8cHR3RtGlTseX05MkTdOzYscGxxLLOUIWSkhLhYOo///wT48aNAwC0a9eOs6nDhEgylmURGhoqLIRMTEzg7e1dr66qimtWrlyJsrKyBue0a9cuWFhYwM7OrsGxCCGyLyoqCvv27UNMTIxI59vZ2cHV1RWtW7cWa14Mw3BSCNVXvbvJ2rdvjx07duDGjRuIjIzEiBEjAABJSUk17gRPiCw5ffq0sKtYVVUVvr6+nzX1s2PHjnBzc+Mkp9LSUgQEBCAtLY2TeIQQ2fTmzRv4+flh2bJlIhVCFhYW+OGHH+Dv7y/2QohP9W4ZCgwMxMSJE7Fp0yY4OTmhU6dOAIAzZ87A3t6e8wQJkSQPHz7Evn37hLcbOv5n9OjRiIuLQ2RkZINzy87Ohp+fHzZu3AhVVdUGxyOEyI4PHz4IZ26J0hqtr6+PmTNnYsiQIXKx4v1nTa0vLS1FdnY29PT0hMfi4+Ohrq4OY2NjThPkG40ZIhWSk5Ph7u4u3JF52rRpmD59eoPjlpSUwMfHB9HR0Q2OBQB9+/bF0qVLG3XwISFEMhUWFgpniImy6KuamhocHBwqrS8orcQ6ZggAFBUVIRAIcPPmTTAMgzZt2nA2O4YQSZSfn49169YJC6FevXph2rRpnMRu0qQJli9fDnd3d2RkZDQ43s2bN2FlZYXJkydzkB0hRBoJBAJERkbi8OHDyMzMrPN8RUVFjBo1ClOmTOFkY2lpU+9iKC8vD99//z0OHDggbGpTVFTErFmzsHXrVqirq3OeJCF8YlkWQUFBSExMBFDeh+7h4cFpy0vFqtXe3t4ire9Rl4iICFhaWlLXNSFyhmVZ3L17F+Hh4Xj79q1I1/Tt2xezZs2CqampmLOTXPXuCPTw8MC1a9dw9uxZZGZmIjMzE6dPn8a1a9fg6ekpjhwJ4dWhQ4eEy9FX7DOmpqbG+eNU7GfGBZZlsXnzZmEBRwiRfc+ePcOyZcuwfv16kQqh9u3bY/PmzVi2bJlcF0LAZ4wZMjQ0xPHjxzFw4MBKx69cuYLJkyfj3bt3XObHOxozJN9u3ryJwMBAAICCggLWrl0rnDQgLvv27cPJkyc5iWVqaorg4GBoampyEo8QInnevn2LAwcOiLyyffPmzeHk5AR7e3uZHlso1jFD+fn5MDExqXLc2NgY+fn59Q1HiMSKi4vDjz/+KLz9zTffiL0QAgBnZ2ckJCTg/v37DY6VnJyMjRs3YtWqVVBUVOQgO0KIpMjMzMSRI0dw4cIFkWaI6enpYcaMGRg6dCi9H3yi3t1kvXv3xqpVqyqNSi8oKMCaNWvQu3dvTpMjhC9ZWVlYv369cEflIUOGYOzYsY3y2AoKCvDy8oKZmRkn8R4+fIj9+/dzEosQwr/CwkL88ssvcHNzw/nz5+sshFRVVTFjxgzs2rULw4cPp0KoGvVuGdqyZQtGjBiBZs2aoVOnTmAYBo8ePYKqqiouXrwojhwJaVQCgQAbNmwQLmDYtm1bzJs3r1GbkzU1NeHr6wtPT08UFBQ0ON7JkydhZWXVoB2gCSH8Ki0txZ9//olDhw7hw4cPdZ6voKCAESNGYNq0adDV1RV/glLss9YZKigoQEREBJ49ewaWZWFra4sZM2aIZVAp32jMkPzZsWMHzp07B6B84bGQkBDo6+vzksu9e/ewbt06TjZibdKkCQIDA2V6FVlCZBHLsvj777+xf/9+kSdF9OnTB7NmzYK5ubmYs5Nc9fn7/VnFkDyhYki+XLx4EaGhoQDKi4cNGzYId1Hmy7Fjx3DgwAFOYhkYGCA4OJi34o4QUj8xMTEICwvDf//9J9L5NjY2cHFxgY2NjZgzk3xiX3QxJiYGW7duRXR0NBiGQbt27bBgwQK0a9fusxImRBJERUVhx44dwtsLFizgvRACAAcHB7x69Qo3btxocKyMjAwEBATA398fTZo04SA7Qog4JCcn48CBA7h586ZI55ubm8PZ2Rk9e/aU6Rli4lLvAdTHjx+HnZ0d7t+/j06dOqFjx4548OABOnTogGPHjokjR0LELj09Hf7+/hAIBACA8ePHY/DgwTxnVY5hGCxcuBDW1tacxHv27Bm2b9/OSdcbIYRbWVlZ2LVrF7777juRCiEdHR3MmzcPoaGh6NWrFxVCn6ne3WTW1taYOXMm1q5dW+n4qlWrcPDgQcTFxXGaIN+om0z2FRUVYdmyZXj58iUAoHPnzli9erXEzbhIS0uDu7s7srOzOYk3Z84cjBkzhpNYhJCGKSoqwunTp3H8+HGRJk2oqKhg0qRJmDhxokyO1+WCWMcMqaur4/Hjx2jVqlWl47GxsejUqZPMrTVExZBsq9hq49q1awDKFykMCgqClpYWz5lV7+nTp/D19UVpaWmDYykoKGDdunXo2LEjB5kRQj5HWVkZLl26hIiICLx//77O8xUUFDBs2DBMmzaNxv7VoT5/v+vdTTZw4MBqxy7cvHkT/fr1q284Qnh18uRJYSGkqqoKX19fiS2EAMDOzg5z5szhJFZZWRk2bNiA1NRUTuIRQkTHsizu3buH77//Hj/99JNIhVDPnj0RGhqK+fPnUyHEsXoPoB43bhyWLVuG+/fvo1evXgCAO3fu4NixY1izZg3OnDlT6VxCJNX9+/cRHh4uvO3l5QULCwv+EhLRyJEjERcXhwsXLjQ4Vk5ODvz8/LBp0yaoqqpykB0hpC6xsbEICwvDkydPRDq/bdu2cHV1ha2trZgzk1/17iZTUBCtMYlhGE6a8vlG3WSy6e3bt/D09EReXh4AYMaMGZg6dSrPWYlOIBDAx8cHUVFRnMTr06cPvL29afAlIWKUkpKCgwcP4vr16yKdb2pqCmdnZ/Tu3Zt+Nz8DrTPEISqGZE9eXh48PT2FuzpLayGQmZkJd3d3pKencxJP2gpCQqRFTk4Ojh49inPnzglnrNZGR0cH06ZNw/Dhw6Gk9Fkr4BA0wjpDhEirsrIyBAUFCQshS0tLuLu7S10hBAC6urrw9fXF0qVLUVxc3OB4hw4dgpWVFXr27MlBdoSQ4uJinDlzBsePHxe2QtdGWVkZEydOxKRJk6Curt4IGZIK9R5ATYg0O3jwIO7duwcA0NLSgq+vr1SPlWnZsiUWLVrEWbzNmzfj9evXnMUjRB5VzBCbM2cO9u/fX2chxDAMhg0bht27d2PmzJlUCPGAWoaI3Lh+/TqOHz8OoHzsm7e3N0xMTHjOquH69++PV69eCZ9bQxQWFsLPzw/BwcESPauOEEnEsiwePHiA8PBwxMfHi3SNvb09nJ2d0bx5c/EmR2olcjH05s0bNGvWTJy5ECI2L1++xJYtW4S33dzcZGp9HUdHR8THx+Off/5pcKyUlBRs3LhRIheeJERSvXz5EmFhYfj3339FOr9169ZwdXWFnZ2dmDMjohC5m8zOzg4HDx4UZy6EiEVWVhb8/PyE42qGDRuG0aNH85wVtxQUFODl5cXZDtWPHj1CWFgYJ7EIkWVpaWkICgrC4sWLRSqEmjZtimXLliEoKIgKIQkicjHk7++P+fPn46uvvkJGRoY4cyKEMwKBAAEBAcIZVzY2Npg7d65UDpiui4aGBlauXAkNDQ1O4p0+fRqXLl3iJBYhsiYnJwd79+7FnDlzcPXq1TrP19LSwuzZs7F9+3b07dtXJt+DpJnIxdC8efPw77//4sOHD2jfvn2lxRUJkVQ7d+7Ef//9BwAwMDDA8uXLZXq3dnNzcyxZsoSzN9rQ0FDExMRwEosQWVBcXIwTJ07Azc0Np06dqnOqvLKyMr7++mvs3r0bY8eOpanyEuqz1hkKDQ2Fu7s7bGxsqvxgHzx4wFlykoDWGZJev//+O7Zt2wag/A1pw4YNaN26Nc9ZNY7ffvut0uraDaGvr4+QkBBa/p/INZZlcfXqVRw8eBDv3r2r83yGYTBkyBDMmDEDhoaGjZAh+ZRY1xl6/fo1fvvtN+jr62P8+PFU5RKJ9PTpU+zcuVN4+/vvv5ebQggAJk2ahLi4OJFXuq3N+/fv4e/vD39/fygrK3OQHSHSpWIMXVxcnEjnd+/eHU5OTrC0tBRvYoQz9apkdu/eDU9PTwwdOhRPnz6FkZGRuPIi5LOlpaVhw4YNwu1gJk2ahIEDB/KbVCNjGAYLFy7E27dv8fLlywbHi4mJwbZt27Bo0SIa60DkRlxcHMLDw/Hw4UORzm/ZsiVcXV1laqaqvBC5GBoxYgT+/vtvhIaGYtasWeLMiZDPVlRUhPXr1yMrKwsA0K1bNzg5OfGcFT9UVFTg6+uLxYsXC78fDXHp0iVYWVlh/PjxHGRHiOR69+4dIiIicOXKFYgyksTY2BhOTk7o168ffViQUiIXQ6WlpXj8+DGtNUQkFsuy2LJli7Ap28zMDF5eXiJvLiyLDA0NsWLFCvj4+Ii0J1Jd9u7dixYtWqBz584NT44QCZObm4tjx47h7NmzKCkpqfN8TU1NTJ06FaNGjZLpiRnygDZqrQMNoJYex44dw4EDBwAAampqCAoKolVd/7+LFy8iNDSUk1iampoIDg6GqakpJ/EI4VtJSQnOnTuHo0ePIjc3t87zmzRpgnHjxsHBwQGampqNkCH5HLRRK5E79+7dEy4KyjAMlixZQoXQR4YPH464uDicP3++wbFyc3Ph5+eHzZs3Q01NjYPsCOEHy7K4fv06Dhw4gLS0tDrPZxgGgwcPxowZM2jMrIyhYohIvcTERGzatEnYt+/o6IgePXrwnJXkcXNzQ0JCAp4+fdrgWAkJCQgODsaKFStojASRSo8fP8a+fftEnmDQtWtXODs7w8rKSsyZET5QN1kdqJtMsuXl5cHDwwNJSUkAgH79+nG66KCsycrKgoeHh0ifgkUxdepUzJgxg5NYhDSG+Ph4hIeH4/79+yKdb21tDRcXFxonJ4Wom4zIhbKyMmzatElYCFlbW2PhwoVUCNVCR0cHPj4+WLp0KYqKihoc75dffoGVlRX69OnDQXaEiE96ejoiIiJw+fJlkWaIGRkZwdHREQMHDqT3FDlAxRCRWvv37xd+uqv4I6+qqspzVpLP2toaixcvRmBgICfxQkJCYGZmRgvMEYmUl5eH48eP48yZM8LNmmujoaGBKVOmYPTo0bTIqByhYohIpatXr+LEiRMAAEVFRXh7e8PY2JjnrKRH37598erVK/z6668NjlVYWIh169YhJCSEupKJxBAIBDh//jx++eUX5OTk1Hm+kpISxo4di6+//hpaWlqNkCGRJFQMEakTGxuLrVu3Cm/PmTMHdnZ2PGYknWbOnIn4+Hj8/fffDY6VlpaGwMBArFmzhrboIbxiWRY3b97EgQMHkJKSItI1gwYNwsyZM+kDlRyjAdR1oAHUkuXDhw9wd3dHRkYGgPKV0efPn89zVtIrPz8fXl5eSExM5CTe2LFjMXv2bE5iEVJfT548QVhYGGJjY0U6v1OnTnBxcUHLli3FnBnhQ33+fkvN0rwfPnyAo6MjdHR0oKOjA0dHR2RmZtZ4fklJCZYtW4YOHTpAQ0MDZmZmmDVrlnCwLZE+JSUlCAgIEBZCtra2mDNnDs9ZSTd1dXX4+vpCQ0ODk3hnz55FZGQkJ7EIEVVCQgLWrl2LFStWiFQIWVpaYs2aNVi3bh0VQgSAFLUMjRw5Em/evMGuXbsAALNnz4alpSXOnj1b7flZWVlwcHCAm5sbOnXqhA8fPmDx4sUQCAT4559/RH5cahmSDCzLYuvWrcI/tIaGhvjxxx+ho6PDc2ay4cGDB1i9erVIs2zqoqSkBH9/f9jY2HCQGSE1y8jIwKFDh/Dnn3+K9No1NDTEzJkzMWjQILnepkde1Ofvt1QUQ9HR0bC1tcWdO3fQs2dPAMCdO3fQu3dvPHv2DG3bthUpzr1792Bvb4/Xr1/DwsJCpGuoGJIM586dw44dOwAAysrK2LhxI32i49jJkyexb98+TmLp6uoiJCQEhoaGnMQj5GP5+fn47bffcOrUKZFniDk4OGDcuHE0Q0yOyFw32e3bt6GjoyMshACgV69e0NHRwa1bt0SOk5WVBYZhoKurK4Ysibg8efJE2CIIAIsWLaJCSAwmTJiAQYMGcRIrMzMT/v7+Iv2hIkRUAoEA//vf/+Dm5oZff/21zteXkpISxo8fj927d8PBwYEKIVIjqZj2kZKSUu0of2NjY5FnCxQWFsLb2xvTp0+vtUIsKiqqtBhddnZ2/RMmnElNTUVAQADKysoAAA4ODujfvz/PWckmhmGwYMECvHnzRuQBqLWJjY1FaGgo3N3dadE60iAsy+Kvv/7CgQMHkJycLNI1/fv3h6OjI5o2bSrm7Igs4LVlaPXq1WAYptavivE91b2Zsiwr0ptsSUkJpk6dirKyMmzbtq3WcwMCAoSDtHV0dGizTx4VFhbCz89PuEZI9+7d4ejoyHNWsk1ZWRk+Pj7Q09PjJN6VK1dw6tQpTmIR+fTff/9hyZIlCAwMFKkQ6tChA4KDg7FkyRIqhIjIeB0zlJ6ejvT09FrPsbS0xOHDh+Hh4VFl9ljFuAQXF5cary8pKcHkyZMRFxeHy5cvw8DAoNbHq65lqHnz5jRmqJGxLIsNGzYIu0HNzc0RFBTE2awnUrtnz55h+fLlEAgEDY7FMAxWr16Nrl27cpAZkReJiYnYv38/7t69K9L5FhYWcHFxQbdu3aglkgCQ4QHUd+/ehb29PQDg7t276NWrV60DqCsKodjYWFy5cgVGRkb1fmwaQM2Po0ePIiIiAkD54MegoCCYm5vznJV8iYyMxE8//cRJLA0NDQQHB8PMzIyTeER2vX//HkeOHMHFixdFmiGmr68PR0dHDB48mGaIkUpkrhgCyqfWJyUlYefOnQDKp9a3aNGi0tT6du3aISAgABMnToRAIMBXX32FBw8e4H//+x9MTEyE5+nr64s8kI6KocZ39+5d+Pn5AShvVVi1ahW6devGc1byadeuXTUuX1FfzZs3x+bNm6Gurs5JPCJbCgoKcOLECZw8eVKkTYTV1NTw9ddfY9y4cVBRUWmEDIm0kcld6w8dOoSFCxdi2LBhAIBx48YhNDS00jkxMTHIysoCALx58wZnzpwBAHTu3LnSeVeuXMHAgQPFnjOpv4SEBGzevFl428nJiQohHrm6uuL169d4/Phxg2MlJiZi8+bNWLlyJXVjECGBQIA//vgDhw8fFr5/10ZRURGjRo3ClClTaJ0xwhmpaRniC7UMNZ6cnBx4enoKB0kOGDAAnp6e9IeTZzk5OXB3d0dqaion8SZPnkwD4QlYlsXt27dx4MABvH37VqRr+vbti1mzZsHU1FTM2RFZIJMtQ0S2lZaWYuPGjcJCqGXLlli4cCEVQhJAS0sLvr6+WLJkCQoLCxsc79dff4WVlRX69u3LQXZEGkVHR2Pfvn149uyZSOfb2dnB2dlZ5AV2CakvKoaIRAgPD8ejR48AADo6OvD19aUF0iSIpaUl3N3dERAQwEm8H3/8EWZmZrC2tuYkHpEOb9++xf79+3H79m2Rzm/evDmcnZ3Ro0cP+mBExIqKIcK7y5cvC9eiUVJSwooVK2gbBwnUp08fTJs2DUeOHGlwrKKiIqxfvx7BwcE07kMOZGZm4siRI7hw4YJwAdXa6OvrY8aMGRgyZAgUFRUbIUMi76gYIryKiYmpNBB+7ty5sLW15TEjUptp06bh1atXuHPnToNjpaWlYcOGDVi3bh2UlOitSBYVFhbi5MmTOHHihEhdrKqqqnBwcMD48eOhqqraCBkSUo4WZSC8ef/+Pfz9/VFSUgIAGDVqFIYPH85zVqQ2DMPAw8ND5I2O6/L06VPs3r2bk1hEcpSWluLChQtwc3PD4cOH6yyEFBUVMXr0aOzZswdTpkyhQog0Ovo4RnhRXFwMf39/vH//HkD5AEk3NzeesyKiUFNTg6+vLzw8PJCbm9vgeOfPn4eVlRVGjBjBQXaETyzL4u+//0Z4eDjevHkj0jV9+vTBrFmzaFFVwisqhkijY1kW27ZtQ0xMDIDyDXe9vb2pq0SKmJqaYtmyZVi1apVIY0DqsnPnTlhYWFAXqRSLiYnBvn37EBUVJdL5NjY2cHV1Rbt27cScGSF1o3WG6kDrDHHvzJkzwq4RFRUVbNy4kWYVSamPf5YNpaOjg5CQkM/aNofwJykpCQcOHMBff/0l0vnm5uZwcXGBvb09zRAjYkXrDBGJ9ejRI+zdu1d4e/HixVQISbGxY8ciLi4Oly5danCsrKwsrF+/HoGBgbS9ghTIysrCL7/8gt9//x2lpaV1nq+rq4sZM2bgyy+/pBliROJQMUQaTXJyMgIDA4XdKpMnT6aF96QcwzCYN28e3rx5I+z2bIiXL1/ip59+gpeXF7UaSKjCwkKcPn0ax48fF3mG2KRJkzBx4kQaGE0kFhVDpFEUFBTAz89POODW3t4eM2fO5DkrwgVlZWWsWLEC7u7uwgHxDXH9+nVYW1vjq6++4iA7wpXS0lJcunQJhw4dEunnrKCggOHDh2PatGnQ09NrhAwJ+XxUDBGxY1kWISEhSEhIAFC+qiztOSZb9PX1sWLFCixfvly4VEJD7N+/Hy1atED37t05yI40BMuy+OeffxAWFobExESRrunVqxecnJzQrFkzMWdHCDdoAHUdaAB1wx0+fFi4arGGhgaCg4NhZmbGc1ZEHC5fvoyQkBBOYqmrqyM4OJimXPPo+fPnCAsLw9OnT0U6v127dnB1dYWNjY2YMyOkbjSAmkiMW7duCQshhmGwbNkyKoRk2ODBgxEXF4fTp083OFZ+fj7WrVuHoKAgaGhocJAdEVVycjIOHjyIGzduiHS+mZkZnJyc0Lt3b2rxJVKJiiEiNvHx8ZVaCVxdXdGlSxceMyKNwcXFBa9fvxZuvNsQb9++xebNm7Fy5UooKNCC+eKWnZ0tnCEmEAjqPF9HRwfTp0/HsGHDaJ0wItXo3YWIRU5ODvz8/ISzTQYNGoTx48fznBVpDIqKili6dCmaNm3KSbx//vkHBw8e5CQWqV5RURGOHTsGNzc3nD17ts5CSEVFBVOnTsXu3bsxatQoKoSI1KNXMOGcQCDAhg0bkJqaCgBo3bo1FixYQM3nckRLSwsrV66Ep6enSNOv63L8+HFYWVmhf//+HGRHKpSVleHy5cuIiIhARkZGneczDINhw4Zh+vTp0NfXb4QMCWkcVAwRzu3btw+PHz8GAOjp6cHHxwfKyso8Z0Uam4WFBby8vODn58dJvC1btsDMzAytWrXiJJ48Y1kWDx48QFhYGF6/fi3SNT179oSTkxOaN28u5uwIaXxUDBFORUZG4uzZswAAJSUlrFixAgYGBjxnRfjSs2dPzJgxA4cOHWpwrOLiYqxfvx4hISHQ1dVteHJy6sWLFwgLCxN+YKlLmzZt4OLiAjs7OzFnRgh/qBginHn27Bm2bdsmvD1//nzahJFgypQpePXqFW7dutXgWOnp6QgICMD69etpnEo9paam4sCBA7h+/bpI55uammLWrFn44osvqIubyDx6NyGcyMjIgL+/v3Dg5dixYzF06FCesyKSgGEYuLu7IykpCfHx8Q2OFxUVhZ07d2L+/PkNT04O5OTk4OjRozh37pxIM8S0tbUxdepUjBw5kgpOIjfolU4arKL74sOHDwCAjh07wtXVleesiCRRVVWFr68v3N3dkZOT0+B4Fy5cgJWVFUaNGsVBdrKpuLgYZ8+exbFjx5CXl1fn+crKypgwYQImTZpE6zoRuUPFEGkQlmURGhqK2NhYAICJiQm8vb3pEyWpouK1sXLlSuFmvQ2xa9cuWFhY0FiWT5SVleHKlSuIiIhAenp6neczDIOhQ4dixowZNL6PyC3ajqMOtB1H7U6dOoW9e/cCKP/0v2nTJlhaWvKbFJFo//vf/7Bz505OYmlrayMkJATGxsacxJN2FTPERO2O7NGjB5ycnNCiRQvxJkYID2g7DtIoHj58iH379glvu7u7UyFE6jR69GjExcUhMjKywbGys7Oxfv16BAYGQlVVlYPspFNcXBzCwsJEXvW7VatWcHFxQceOHcWbGCFSgooh8lmSk5MRGBiIiobFadOmoU+fPjxnRaQBwzD47rvv8ObNG0RHRzc4XlxcHLZs2YKlS5fK3ayntLQ0RERE4MqVKyKdb2JiglmzZqFfv35y970ipDZUDJF6q9hAs2JQZq9evTBt2jSesyLSpEmTJli+fDnc3d1FWvm4Ljdv3oSVlRUmT57MQXaSLzc3F7/++qtIW2cA5SuCT5kyBaNGjUKTJk0aIUNCpAsVQ6ReWJZFUFAQEhMTAZSvMuzh4UGfMkm9VaxO7u3tjeLi4gbHi4iIgKWlJezt7TnITjIVFxfj3Llz+PXXX5Gbm1vn+crKyhg3bhwcHBxohhghtaAB1HWgAdSVRURE4OjRowDKP20GBwdztiEnkU9XrlxBcHAwJ7HU1NQQFBQkc1tGsCyLq1ev4uDBg3j37l2d5zMMg8GDB2PmzJkwNDRshAwJkTw0gJqIxc2bN4WFkIKCApYtW0aFEGmwQYMG4dWrVzh58mSDYxUUFMDPzw9BQUHQ1NTkIDv+PXr0CGFhYYiLixPp/G7dusHZ2ZkmMxBSD1QMEZHExcXhxx9/FN7+5ptv0KlTJ/4SIjLF2dkZr1+/xoMHDxocKykpCZs2bcKqVaugoKDAQXb8ePXqFcLDw0X+nlhbW8PFxQWdO3cWb2KEyCDqJqsDdZMBWVlZ8PDwQFpaGgBgyJAhWLRoEY0TIpzKzc2Fp6cnkpKSOIk3ceJEqVwJPT09HREREbh8+TJEeXs2NjaGo6MjBgwYQL+ThHyEuskIZwQCATZs2CAshNq2bYt58+bRmy7hnKamJnx9feHp6YmCgoIGxzt58iSsrKwwaNAgDrITv7y8PBw7dgxnzpxBSUlJnedramoKZ4gpKys3QoaEyC4qhkit9uzZg6dPnwIA9PX1sWLFCnrjJWLTvHlzeHl5wc/PT6RWkbps3boVzZo1Q+vWrTnITjxKSkpw/vx5HD16VKR925o0aYKxY8fi66+/lplxUYTwTXo71InYXbx4EefOnQNQ/gbs4+MDfX19nrMiss7e3h4zZ87kJFZJSUmlTYQlCcuyuH79OubOnYs9e/bUWQgxDINBgwZhx44dcHFxoUKIEA5RyxCpVlRUFHbs2CG8vWDBArRp04bHjIg8+frrr/Hq1SvcvHmzwbEyMjLg7+8Pf39/iVlw8PHjxwgLC8OLFy9EOr9z585wcXGBtbW1mDMjRD5RMUSqSE9Ph7+/v3Bl2/Hjx2Pw4ME8Z0XkCcMwWLRoEZKSkkSeUl6bZ8+eYfv27fj+++95He/2+vVrhIeH459//hHpfCsrK7i4uKBLly5izowQ+UbFEKmkqKgIfn5+yMrKAvB/n0gJaWyqqqrw8fGBu7s7srOzGxwvMjIS1tbWGDNmDAfZ1U96ejoOHz6MP//8U6SxUIaGhnB0dMTAgQOlenkAQqQFFUNEiGVZbN26FS9fvgQAmJqaYunSpVBUVOQ5MyKvjI2NsXz5cvj6+qK0tLTB8Xbv3g0LC4tG2609Ly8PJ06cwKlTp0TackRDQwNff/01xo4dSxMVCGlE9JGDCJ08eRLXrl0DUP6p3NfXF1paWjxnReSdnZ0dZs+ezUmssrIybNiwAampqZzEq4lAIMDZs2fh5uaGX3/9tc5CSElJCRMmTMDu3bvx1VdfUSFESCOjliECALh//z7Cw8OFt728vGBhYcFfQoR8ZNSoUXj16hUuXLjQ4Fg5OTnw8/PDpk2boKqqykF2/4dlWfz111/Yv38/UlJSRLpmwIABcHR0hImJCae5EEJER8UQwdu3b7Fp0ybhWIYZM2agZ8+ePGdFSGVz5sxBQkICoqKiGhwrPj4eISEh8Pb25mxA9dOnTxEWFobnz5+LdH7Hjh3h4uKCVq1acfL4hJDPR9tx1EHWt+PIy8uDp6cn3r59CwD44osvsGzZMlphmkikzMxMuLu7Iz09nZN4M2bMwNSpUxsUIzExEeHh4fj7779FOr9FixZwcXFB165d6feMEDGi7TiISMrKyhAUFCQshCwtLbF48WJ6gyYSS1dXF76+vli6dKlIA5LrcujQIVhZWX1WS+j79+9x6NAhREZGijRDzMDAAI6Ojhg0aBDNECNEwtBvpBw7ePAg7t27BwDQ0tKCr68v52MoCOFay5YtsWjRIs7ibd68GQkJCSKfn5+fj4iICLi5ueGPP/6osxBSV1eHk5MTdu7ciSFDhlAhRIgEopYhOXX9+nUcP34cAKCgoABvb28awEmkRv/+/fHq1Svha7ghCgsL4efnh6CgoFpnTwoEAly8eBFHjhwRrsNVGyUlJYwaNQpTpkyRyS52QmQJFUNy6OXLl9iyZYvwtpubW6Otu0IIVxwdHREfHy/yas61SU5OxsaNG7F69eoq62qxLIvbt29j//79SEpKEilev3794OjoCFNT0wbnRggRPxpAXQdZG0CdlZWFxYsXCwegDhs2DAsWLKBxQkQqfToBoKHGjx+Pb7/9Vng7KioK+/btQ0xMjEjX29nZwdXVFa1bt+YkH0LI56MB1KRaAoEAAQEBwkLIxsYGc+fOpUKISC0NDQ2sXLkSHh4eyM/Pb3C806dPw8rKCm3btkV4eDju3r0r0nXNmzeHi4sLunfvTr9PhEghahmqgyy1DP3888/CResMDAwQEhICPT09nrMipOH++ecfrF27VqRZXbUpKSlBSkoKDAwMoK6uXuf5+vr6mDlzJgYPHkzb1hAiYahliFTx+++/CwshZWVl+Pj4UCFEZEb37t3h5ORUaRX1uigrK8PAwABKSkooKSlBdHQ04uLiUFpaioyMDNja2ta4LYaamhocHBwwbtw4moFJiAyQmjmeHz58gKOjI3R0dKCjowNHR0dkZmaKfP2cOXPAMAx+/PFHseUoqZ4+fYqdO3cKb3///fc0poHInEmTJqF///51nscwDFq0aIEOHTrAzMwMRkZGMDMzw9ChQ9G3b18oKCiguLgYL168QFlZWaVrFRUVMWbMGOzevRuTJ0+mQogQGSE1LUPTp0/HmzdvhK0bs2fPhqOjI86ePVvntadOncLdu3dhZmYm7jQlTlpaGjZs2CDc8XvSpEkYOHAgv0kRIgYMw2DhwoV4+/YtXr58CaC826uoqAiFhYXCf7t06QJDQ0Ph2J6Px/jY2toCAK5du4bc3FzEx8fDysoKDMPgiy++wKxZs+TyfYQQWScVxVB0dDQuXLiAO3fuCFeK3b17N3r37o2YmBi0bdu2xmvfvn2LBQsW4OLFixg9enRjpSwRioqKsH79euGaKN26dYOTkxPPWRHCHYFAgHfv3iElJQXJyclISUmBuro6nj9/jpycHOGHgAra2tpo3bp1jYOcGYaBra0tHjx4gJycHKSnp6Nt27YICAio9X2GECLdpKIYun37NnR0dCotmd+rVy/o6Ojg1q1bNb5JlZWVwdHREUuWLEH79u1FeqyioiIUFRUJb2dnZzcseZ6wLIstW7YgLi4OAGBmZgYvLy9a/ZZIncLCwkrFTnJysvD/aWlpVbqyAMDU1LTahRFbt24NlmVrnfHFsizatGmDqKgoNGvWDKWlpSgoKOD0ORFCJItUFEMpKSkwNjauctzY2BgpKSk1XhcYGAglJSUsXLhQ5McKCAjAmjVrPitPSXL8+HHcuHEDQPlgT19fX2hqavKcFSFVsSyL7OzsSkXOx//WZ2xgBS0tLbRo0QLx8fGVjqupqdU544xlWZiYmEBBQQEMw6CsrAyBgYEIDg6mRRQJkVG8FkOrV6+us/Co2Duruk9ytX3Cu3//PrZs2YIHDx7Ua92P5cuXw8PDQ3g7OzsbzZs3F/l6SXDv3j0cPHgQQPn3bcmSJVL3HIhsKS0trdSdVVHsVNwuLCzk/DGNjY2Rn5+PtLQ04bGCgoI63w8UFBTQpEmTSueVlJTg7du3VAwRIqN4LYYWLFiAqVOn1nqOpaUlHj9+jNTU1Cr3vXv3rsb9tG7cuIG0tDRYWFgIj5WWlsLT0xM//vhjlU+MFVRUVKCioiL6k5AwiYmJ2LRpk/DTr6OjI3r06MFzVkQeVHRnfdqyU9Gd9en4ncZgYWGBgoIC5OTkAABiY2Nhb29f53UZGRnC/xsZGcHX1xfW1tZiy5MQwi9eiyFDQ0MYGhrWeV7v3r2RlZWFv//+W/hGdvfuXWRlZaFPnz7VXuPo6IihQ4dWOjZ8+HA4OjrCxcWl4clLoNzcXPj5+QnHN/Tr1w8ODg48Z0VkRUV3Vk3jdz58+MB3ilUoKCigVatWiIqKQlFREbKzsxEVFQVbW9saW5vT09NRXFwMoHx7DW9vb+jo6DR26oSQRiQVY4ZsbGwwYsQIuLm5CdfLmT17NsaMGVNp8HS7du0QEBCAiRMnwsDAAAYGBpXiNGnSBE2bNpXJWSFlZWXYtGmTcCNJa2trLFy4kLYGIPVSVlaG9PT0GsfvSMtAYg0NDTRt2hSmpqZo2rQpACAiIgIKCgooKChAenq68IPYx93t6enpSEhIAACMGjUKbm5uUFKSirdJQkgDSM1v+aFDh7Bw4UIMGzYMADBu3DiEhoZWOicmJqbaGSTyYP/+/Xjw4AEAQEdHBz4+PrQgHKlWcXFxtV1ZycnJSEtLg0Ag4DtFkejr6wuLHVNT00r/19TUrPJBoGXLlggMDAQAvH79GsnJycIVqAUCATIyMlBcXAwlJSXMmTMHI0aM4ONpEUJ4IDXFkL6+PiIiImo9p65ZIjWNE5J2V69exYkTJwCUr5Dr7e1d7ew7Ih9YlkVOTk61xU5KSgrev3/Pd4oiUVJSgrGxcaUip+JfExOTeo/t69u3L169eoVff/0VQHlRmJycXOkcHR0drFixQrj4IiFEPkhNMUSqFxsbi61btwpvz5kzB3Z2djxmRBpDRXdWTeN3uNjBvTGoq6tXadmpuG1oaMj5ulgzZ85EfHw8/v777yr3tWzZEj4+PjAyMuL0MQkhko+KISn24cMHrF+/XjjYc8SIERg5ciTPWRGuFBcXIzU1tdrxO6mpqVLVnfVpy07Fv1paWo06ro1hGHh6esLLywuJiYnC4/3798fChQuleiYpIeTzUTEkpUpKShAQECCcAmxra4s5c+bwnBWpr5ycnBrH73w8vVuSKSoqVunO+riVR9IKDHV1dfj6+sLDwwP5+flwcnLCpEmTaLIBIXKMiiEpxLIstm/fjujoaADlSxSsWLGCZr1IoIqp2snJydW28uTl5fGdokhUVVWrDFL+uDtLUVGR7xTrxczMDN7e3hAIBOjevTvf6RBCeEZ/PaXQ+fPnERkZCQBQVlaGr68vrYPCo4rurOrG70hTd5aurm61xY6pqSm0tbVlruWkc+fOfKdACJEQVAxJmSdPnmDXrl3C24sWLULLli15zEg+5Obm1jg7KyMjo86ZjJJAQUEBJiYmlQYpf/wvLcVACJFXVAxJkdTUVAQEBAh36XZwcED//v15zko2sCyLjIyMGsfv5Obm8p2iSFRVVatt2WnatCmMjIykrjuLEEIaAxVDUqKwsBB+fn7CPZa6d+8OR0dHnrOSLiUlJdV2Z1Xsp1VSUsJ3iiLR0dGpcfyOjo6OzHVnEUKIuFExJAVYlkVISIhw0Uhzc3N4eXlxvgaLLMjLy6t23Z2K2VnS0p1lZGRUbbHTtGlTqKmp8Z0iIYTIFCqGpMCvv/6KW7duASjfc2nlypXQ0NDgOSt+sCyL9+/f1zh+p6LlTNKpqKhUGrvzccFjZGREMwMJIaQR0TuuhLt7965wGxKGYbBkyRKYm5vznJV4CQSCWmdnVSwyKem0tbVrbN3R09Oj7ixCCJEQVAxJsISEBGzevFl428nJCd26deMxI+7k5+dX25WVkpKCd+/eSUV3FsMwwu6sT4udpk2bQl1dne8UCSGEiICKIQmVk5MDPz8/FBYWAgAGDBiASZMm8ZyV6FiWxYcPHyoNUP646MnOzuY7RZEoKyvXuJWEsbExdWcRQogMoHdyCVRaWoqNGzcKd9Ru2bIlFi5cKHHdKgKBAGlpaTWO35GW7iwtLa1qp6KbmppSdxYhhMgBKoYkUHh4OB49egSgfBq1r68vlJWVecmloKCg2mInOTlZqrqzDA0Naxy/I6+D0QkhhJSjYkjCXL58GadOnQIAKCkpYcWKFTA0NBTb47Esi8zMzBrH72RlZYntsbnUpEmTaldWrujOatKkCd8pEkIIkVBUDEmQmJgYhIaGCm/PnTsXtra2DY4rEAjw7t27GhcbrBiXJOm0tLRqHL+jr69P3VmEEEI+CxVDEuL9+/fw9/cXroI8atQoDB8+XOTrCwsLq23ZSUlJQVpamnALD0nGMAwMDAxqHL9D3VmEEELEgYohCVBcXAx/f3+8f/8eAGBnZwc3N7dK57Asi6ysrBrH70hTd5aJiUmNs7P4GhtFCCFEflExxJMPHz7gyZMnyM3NxePHj/Hq1SuwLAttbW2MGjUKkZGRVQofaenO0tDQqLF1x8DAgLqzCCGESBSGlYbpQDzKzs6Gjo4OsrKyoK2t3eB4eXl5OH36NGJjYwGUt/iwLAuGYRAdHY309HSpWKzv0+6sj//V0tLiOz1CCCFyrj5/v6llqBGlpqZi3bp1MDQ0FLaOMAwj/L+NjQ3S09Px+vVrPtMEUD6TraI769Nix8TEhLqzCCGEyAwqhhqRgoJCpULoUxXr4SQnJzfKgoUaGho1zs4yMDCAgoKC2HMghBBC+EbFUCOKjo4W6TwDAwPh6tMNpa+vX+P4HU1NTRq/QwghRO5RMdSIcnNz6zyHZdl67XelpKQEY2Pjalt3TExMoKKi0pCUCSGEEJlHxVAjEqUlhmEYCASCSsfU1NSqLXZMTU1haGhI3VmEEEJIA1Ax1Ig6dOiAK1eu1HoOwzDo27cvLCwshIWPtrY2dWcRQgghYkLFUCPS09ODtbU14uLiajyna9euGDt2bCNmRQghhMg36l9pZNOnT0fXrl0BlLcCKSgoCFt9unbtilGjRvGZHiGEECJ3aNHFOnC96GKFj1eg1tTURIcOHaCnp8dZfEIIIUSe0aKLUkBPTw/9+/fnOw1CCCFE7lE3GSGEEELkGhVDhBBCCJFrVAwRQgghRK5RMUQIIYQQuUbFECGEEELkGhVDhBBCCJFrVAwRQgghRK5RMUQIIYQQuUbFECGEEELkGhVDhBBCCJFrVAwRQgghRK5RMUQIIYQQuUbFECGEEELkGhVDhBBCCJFrVAwRQgghRK5RMUQIIYQQuUbFECGEEELkGhVDhBBCCJFrVAwRQgghRK5JTTH04cMHODo6QkdHBzo6OnB0dERmZmad10VHR2PcuHHQ0dGBlpYWevXqhYSEBPEnTAghhBCpIDXF0PTp0/Ho0SNcuHABFy5cwKNHj+Do6FjrNS9fvkTfvn3Rrl07XL16Ff/++y9WrlwJVVXVRsqaEEIIIZKOYVmW5TuJukRHR8PW1hZ37txBz549AQB37txB79698ezZM7Rt27ba66ZOnYomTZrg4MGDn/3Y2dnZ0NHRQVZWFrS1tT87DiGEEEIaT33+fktFy9Dt27eho6MjLIQAoFevXtDR0cGtW7eqvaasrAznzp1DmzZtMHz4cBgbG6Nnz544depUI2VNCCGEEGmgxHcCokhJSYGxsXGV48bGxkhJSan2mrS0NOTm5mLDhg3w8/NDYGAgLly4gEmTJuHKlSsYMGBAtdcVFRWhqKhIeDsrKwtAeYVJCCGEEOlQ8XdblA4wXouh1atXY82aNbWec+/ePQAAwzBV7mNZttrjQHnLEACMHz8e7u7uAIDOnTvj1q1b2LFjR43FUEBAQLU5NW/evNY8CSGEECJ5cnJyoKOjU+s5vBZDCxYswNSpU2s9x9LSEo8fP0ZqamqV+969ewcTE5NqrzM0NISSkhJsbW0rHbexscHNmzdrfLzly5fDw8NDeLusrAzv37+HgYFBjYXX58rOzkbz5s2RmJgok+OR6PlJP1l/jvT8pJ+sP0d6fp+PZVnk5OTAzMysznN5LYYMDQ1haGhY53m9e/dGVlYW/v77b9jb2wMA7t69i6ysLPTp06faa5SVldGjRw/ExMRUOv78+XO0aNGixsdSUVGBiopKpWO6urp15tgQ2traMvkir0DPT/rJ+nOk5yf9ZP050vP7PHW1CFWQigHUNjY2GDFiBNzc3HDnzh3cuXMHbm5uGDNmTKWZZO3atcPJkyeFt5csWYKjR49i9+7dePHiBUJDQ3H27FnMmzePj6dBCCGEEAkkFcUQABw6dAgdOnTAsGHDMGzYMHTs2LHKlPmYmBjhgGcAmDhxInbs2IGNGzeiQ4cO2LNnD3777Tf07du3sdMnhBBCiISSitlkAKCvr4+IiIhaz6luxLirqytcXV3FlVaDqKioYNWqVVW65WQFPT/pJ+vPkZ6f9JP150jPr3FIxaKLhBBCCCHiIjXdZIQQQggh4kDFECGEEELkGhVDhBBCCJFrVAwRQgghRK5RMcShbdu2wcrKCqqqqujWrRtu3LhR6/nXrl1Dt27doKqqCmtra+zYsaPKOb/99htsbW2hoqICW1vbSusoNbb6PL8TJ07gyy+/hJGREbS1tdG7d29cvHix0jnh4eFgGKbKV2FhobifSo3q8xyvXr1abf7Pnj2rdJ60/gydnZ2rfX7t27cXniNJP8Pr169j7NixMDMzA8MwIm3KLE2/g/V9ftL4O1jf5yhtv4P1fX7S9jsYEBCAHj16QEtL6/+1d+9BUdXvH8Dfy2XloiIoIA0K4YiECeGluHiJIC5pymSCowEaIjVCgU4NlmMw/pGWxqQwjUzI0kwgkSJOlokjYiIK4kKpjCSh2AgyhJpcLC7P9w9/nB/L7gJ7Ufe0z2uGGc7nPOdzPg9nH33Ys8vCwcEBERERSn/4WBVDqENuhvSksLAQycnJ+PjjjyGXy7Fo0SKEh4ejublZZXxTUxNee+01LFq0CHK5HB999BHee+89HDp0SIiprKxEVFQUoqOjUVdXh+joaERGRuLChQtPKi2BpvmdOXMGr776Kn788UfU1NQgMDAQr7/+OuRyuULcxIkT0dLSovBlYWHxJFJSommOg65du6aw/pkzZwr7xHwNv/zyS4W8bt26BTs7O6xatUohzlCuYVdXF7y9vZGZmTmmeLHVoKb5ibEGNc1xkFhqUNP8xFaD5eXl2LRpE86fP4/S0lL09fUhJCQEXV1dao8xmDokphcvvvgivfPOOwpjHh4elJqaqjL+ww8/JA8PD4WxhIQE8vX1FbYjIyMpLCxMISY0NJRWr16tp1WPnab5qeLp6Unp6enCdm5uLtnY2OhriTrTNMeysjICQHfv3lU753/pGhYXF5NEIqEbN24IY4Z2DQcBoOLi4hFjxFaDQ40lP1UMvQaHGkuOYqvBobS5hmKqQSKitrY2AkDl5eVqYwylDvmZIT34999/UVNTg5CQEIXxkJAQnDt3TuUxlZWVSvGhoaG4ePEient7R4xRN+fjok1+ww0MDODBgwews7NTGO/s7ISLiwucnZ2xbNkypd9anxRdcvTx8YGTkxOCgoJQVlamsO+/dA1zcnIQHBys9Nl+hnINNSWmGtQHQ69BXYihBvVBbDU4+IkQwx9zQxlKHXIzpAft7e3o7++Ho6OjwrijoyNaW1tVHtPa2qoyvq+vD+3t7SPGqJvzcdEmv+H27NmDrq4uREZGCmMeHh6QyWQ4evQoCgoKYGFhgYCAAPz+++96Xf9YaJOjk5MTsrOzcejQIRw+fBizZs1CUFAQzpw5I8T8V65hS0sLfvrpJ2zYsEFh3JCuoabEVIP6YOg1qA0x1aCuxFaDRITNmzdj4cKFeP7559XGGUodiubjOMRAIpEobBOR0tho8cPHNZ3zcdJ2LQUFBUhLS0NJSQkcHByEcV9fX/j6+grbAQEBmDt3Lvbt24e9e/fqb+Ea0CTHWbNmKXxQsJ+fH27duoXdu3dj8eLFWs35uGm7FplMhkmTJiEiIkJh3BCvoSbEVoPaElMNakKMNagtsdVgYmIifv31V5w9e3bUWEOoQ35mSA+mTJkCU1NTpS61ra1NqZsdNHXqVJXxZmZmmDx58ogx6uZ8XLTJb1BhYSHi4uLw3XffITg4eMRYExMTLFiw4Kn8RqNLjkP5+voqrP+/cA2JCAcOHEB0dDSkUumIsU/zGmpKTDWoC7HUoL4Yag3qQmw1mJSUhKNHj6KsrAzOzs4jxhpKHXIzpAdSqRTz5s1DaWmpwnhpaSn8/f1VHuPn56cUf+LECcyfPx/m5uYjxqib83HRJj/g0W+j69atQ35+PpYuXTrqeYgItbW1cHJy0nnNmtI2x+HkcrnC+sV+DYFH7xC5fv064uLiRj3P07yGmhJTDWpLTDWoL4Zag7oQSw0SERITE3H48GGcOnUKzz777KjHGEwd6u2l2Ebu4MGDZG5uTjk5OXT16lVKTk4ma2tr4VX/qampFB0dLcT/8ccfZGVlRSkpKXT16lXKyckhc3Nz+v7774WYiooKMjU1pZ07d1J9fT3t3LmTzMzM6Pz58wafX35+PpmZmVFWVha1tLQIX/fu3RNi0tLS6Pjx49TY2EhyuZzWr19PZmZmdOHChSeeH5HmOWZkZFBxcTE1NDTQ5cuXKTU1lQDQoUOHhBgxX8NBb731Fr300ksq5zSka/jgwQOSy+Ukl8sJAH3xxRckl8vp5s2bRCT+GtQ0PzHWoKY5iq0GNc1vkFhq8N133yUbGxs6ffq0wmOuu7tbiDHUOuRmSI+ysrLIxcWFpFIpzZ07V+HthLGxsbRkyRKF+NOnT5OPjw9JpVJydXWlr776SmnOoqIimjVrFpmbm5OHh4dCkT9pmuS3ZMkSAqD0FRsbK8QkJyfT9OnTSSqVkr29PYWEhNC5c+eeYEbKNMlx165dNGPGDLKwsCBbW1tauHAhHTt2TGlOsV5DIqJ79+6RpaUlZWdnq5zPkK7h4Nus1T3mxF6DmuYnxhrUNEex1aA2j1Ex1aCq3ABQbm6uEGOodSj5vwQYY4wxxowSv2aIMcYYY0aNmyHGGGOMGTVuhhhjjDFm1LgZYowxxphR42aIMcYYY0aNmyHGGGOMGTVuhhhjjDFm1LgZYoz9Jw1+sOVoJBIJjhw58tjXAwCLFy9Gfn6+TnNkZmZi+fLleloRYwzgZogxpoP+/n74+/tj5cqVCuP379/HtGnTsG3btqe0MiAqKgoNDQ3CdlpaGl544QWluJaWFoSHhz/29fzwww9obW3F6tWrdZonPj4e1dXVY/o0cMbY2HAzxBjTmqmpKfLy8nD8+HF8++23wnhSUhLs7Oywffv2p7Y2S0tLODg4jBo3depUjBs37rGvZ+/evVi/fj1MTHT7Z3fcuHFYs2YN9u3bp6eVMca4GWKM6WTmzJn49NNPkZSUhNu3b6OkpAQHDx5EXl4epFKp2uNcXV2xY8cOrFmzBuPHj8czzzyj9B98c3MzVqxYgfHjx2PixImIjIzEnTt3hP11dXUIDAzEhAkTMHHiRMybNw8XL14EoHibTCaTIT09HXV1dZBIJJBIJJDJZACUb5P99ttveOWVV2BpaYnJkydj48aN6OzsFPavW7cOERER2L17N5ycnDB58mRs2rQJvb29anNtb2/HyZMnlW5vSSQS7N+/H8uWLYOVlRWee+45VFZW4vr163j55ZdhbW0NPz8/NDY2Khy3fPlyHDlyBD09PWrPyRgbO26GGGM6S0pKgre3N2JiYrBx40Zs375d5S2p4T7//HN4eXnh0qVL2Lp1K1JSUlBaWgoAICJERESgo6MD5eXlKC0tRWNjI6KiooTj165dC2dnZ1RXV6OmpgapqakwNzdXOk9UVBS2bNmC2bNno6WlBS0tLQrzDOru7kZYWBhsbW1RXV2NoqIinDx5EomJiQpxZWVlaGxsRFlZGfLy8iCTyYTmSpWzZ88Kzc5wO3bsQExMDGpra+Hh4YE1a9YgISEBW7duFRq74eefP38+ent7UVVVpf6HyxgbO71+7CtjzGjV19cTAJozZw719vaOGu/i4kJhYWEKY1FRURQeHk5ERCdOnCBTU1Nqbm4W9l+5coUAUFVVFRERTZgwgWQymcr5c3NzycbGRtj+5JNPyNvbWykOABUXFxMRUXZ2Ntna2lJnZ6ew/9ixY2RiYkKtra1E9OhTt11cXKivr0+IWbVqFUVFRanNNSMjg9zc3FSee9u2bcJ2ZWUlAaCcnBxhrKCggCwsLJSOtbW1VZs7Y0wz/MwQY0wvDhw4ACsrKzQ1NeHPP/8c0zF+fn5K2/X19QCA+vp6TJs2DdOmTRP2e3p6YtKkSULM5s2bsWHDBgQHB2Pnzp1Kt5M0VV9fD29vb1hbWwtjAQEBGBgYwLVr14Sx2bNnw9TUVNh2cnJCW1ub2nl7enpgYWGhcp+Xl5fwvaOjIwBgzpw5CmMPHz7E33//rXCcpaUluru7x5gZY2wk3AwxxnRWWVmJjIwMlJSUwM/PD3FxcSAireaSSCQAHt0mG/x+qKHjaWlpuHLlCpYuXYpTp07B09MTxcXFWueh7pxD1wVA6VacRCLBwMCA2nmnTJmCu3fvqtw3dK7Bc6gaGz5/R0cH7O3t1Z6TMTZ23AwxxnTS09OD2NhYJCQkIDg4GF9//TWqq6uxf//+UY89f/680raHhweAR88CNTc349atW8L+q1ev4v79+wqvvXF3d0dKSgpOnDiBN954A7m5uSrPJZVK0d/fP+J6PD09UVtbi66uLmGsoqICJiYmcHd3HzUfdXx8fNDa2qq2IdJUY2MjHj58CB8fH73Mx5ix42aIMaaT1NRUDAwMYNeuXQCA6dOnY8+ePfjggw9w48aNEY+tqKjAZ599hoaGBmRlZaGoqAjvv/8+ACA4OBheXl5Yu3YtLl26hKqqKsTExGDJkiWYP38+enp6kJiYiNOnT+PmzZuoqKhAdXW1yhcpA4/evdbU1ITa2lq0t7fjn3/+UYpZu3YtLCwsEBsbi8uXL6OsrAxJSUmIjo4WbmFpw8fHB/b29qioqNB6jqF++eUXuLm5YcaMGXqZjzFjx80QY0xr5eXlyMrKgkwmU3idTXx8PPz9/Ue9XbZlyxbU1NTAx8cHO3bswJ49exAaGgrg/9/ybmtri8WLFyM4OBhubm4oLCwE8OhvHP3111+IiYmBu7s7IiMjER4ejvT0dJXnWrlyJcLCwhAYGAh7e3sUFBQoxVhZWeHnn39GR0cHFixYgDfffBNBQUHIzMzU5ccEU1NTvP322wp/i0kXBQUFiI+P18tcjDFAQtre2GeMMR24uroiOTkZycnJT3spT8SdO3cwe/Zs1NTUwMXFRet5Ll++jKCgIDQ0NMDGxkaPK2TMePEzQ4wx9gQ4OjoiJycHzc3NOs1z+/ZtfPPNN9wIMaZHZk97AYwxZixWrFih8xwhISF6WAljbCi+TcYYY4wxo8a3yRhjjDFm1LgZYowxxphR42aIMcYYY0aNmyHGGGOMGTVuhhhjjDFm1LgZYowxxphR42aIMcYYY0aNmyHGGGOMGTVuhhhjjDFm1P4HVhxGKcGe5YEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for x in X:\n",
    "    Areas=[x[f'x0{j}'] for j in range(2, 8)]\n",
    "    Material=dataIdentifier2['name'][x[f'x0{1}']]\n",
    "    plot_truss_structure(nodeXY, connectivity, Areas, Material)"
   ]
  },
  {
   "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
}