Hybrid_antenna.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_4756\\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 : 172.77 \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": 5,
   "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(6)\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": "code",
   "execution_count": 6,
   "id": "bfb080e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.title(f\"Structure: {exampleName}\")\n",
    "\n",
    "# Plot nodes\n",
    "plt.plot(nodeXY[:, 0], nodeXY[:, 1], 'ro', markersize=8)\n",
    "\n",
    "# Plot elements\n",
    "for i in range(len(connectivity)):\n",
    "    node1 = connectivity[i, 0]\n",
    "    node2 = connectivity[i, 1]\n",
    "    x = [nodeXY[node1, 0], nodeXY[node2, 0]]\n",
    "    y = [nodeXY[node1, 1], nodeXY[node2, 1]]\n",
    "    plt.plot(x, y, 'b')\n",
    "\n",
    "\n",
    "# Set axis limits\n",
    "plt.xlim(-1, 2)\n",
    "plt.ylim(-1, 6)\n",
    "\n",
    "# Add labels\n",
    "plt.xlabel('X Position')\n",
    "plt.ylabel('Y Position')\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f45b989",
   "metadata": {},
   "source": [
    "# Gets the z coordinates of each material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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':False}, \\\n",
    "              'co2': {'isOn':True},\\\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": 9,
   "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=49,\n",
    "                         n_obj=no,\n",
    "                         n_ieq_constr=nc,\n",
    "                         xl = np.array([z0min, z1min] + [2e-8] * 47),\n",
    "                         xu = np.array([z0max, z1max] + [2e-3] * 47))\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": 64,
   "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=100,\n",
    "    n_offsprings=20,\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=0.1,\n",
    "    period=10,\n",
    "    n_max_gen=200,\n",
    "    n_max_evals=5000\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79017bd0",
   "metadata": {},
   "source": [
    "# Runs the solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "58b8803f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "n_gen  |  n_eval  | n_nds  |     cv_min    |     cv_avg    |      eps      |   indicator  \n",
      "==========================================================================================\n",
      "     1 |      100 |      1 |  0.0934313118 |  1.933195E+03 |             - |             -\n",
      "     2 |      120 |      1 |  0.000000E+00 |  1.006947E+02 |             - |             -\n",
      "     3 |      140 |      1 |  0.000000E+00 |  2.384514E+01 |  0.000000E+00 |             f\n",
      "     4 |      160 |      1 |  0.000000E+00 |  9.0853138169 |  0.000000E+00 |             f\n",
      "     5 |      180 |      1 |  0.000000E+00 |  4.7441932738 |  0.000000E+00 |             f\n",
      "     6 |      200 |      1 |  0.000000E+00 |  2.7713954503 |  0.000000E+00 |             f\n",
      "     7 |      220 |      1 |  0.000000E+00 |  1.7047645360 |  6.433471E+02 |         ideal\n",
      "     8 |      240 |      1 |  0.000000E+00 |  1.1114940702 |  1.814523E+01 |         ideal\n",
      "     9 |      260 |      1 |  0.000000E+00 |  0.6824985330 |  0.000000E+00 |             f\n",
      "    10 |      280 |      1 |  0.000000E+00 |  0.3867530581 |  2.952933E+01 |         ideal\n",
      "    11 |      300 |      1 |  0.000000E+00 |  0.1794785817 |  7.781217E+01 |         ideal\n",
      "    12 |      320 |      2 |  0.000000E+00 |  0.0809327776 |  1.5461140853 |         ideal\n",
      "    13 |      340 |      2 |  0.000000E+00 |  0.0450677511 |  0.000000E+00 |             f\n",
      "    14 |      360 |      2 |  0.000000E+00 |  0.0322782621 |  0.6400518758 |         ideal\n",
      "    15 |      380 |      2 |  0.000000E+00 |  0.0202838605 |  0.0896648981 |         ideal\n",
      "    16 |      400 |      2 |  0.000000E+00 |  0.0103335451 |  0.0318331593 |         ideal\n",
      "    17 |      420 |      2 |  0.000000E+00 |  0.0023069129 |  0.0256872055 |         ideal\n",
      "    18 |      440 |      2 |  0.000000E+00 |  0.0000274610 |  0.000000E+00 |             f\n",
      "    19 |      460 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0444904903 |         nadir\n",
      "    20 |      480 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    21 |      500 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    22 |      520 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0281747933 |         ideal\n",
      "    23 |      540 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    24 |      560 |      2 |  0.000000E+00 |  0.000000E+00 |  1.5315101543 |         ideal\n",
      "    25 |      580 |      2 |  0.000000E+00 |  0.000000E+00 |  1.0295684278 |         ideal\n",
      "    26 |      600 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    27 |      620 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    28 |      640 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    29 |      660 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    30 |      680 |      2 |  0.000000E+00 |  0.000000E+00 |  0.2033947515 |         ideal\n",
      "    31 |      700 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    32 |      720 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0579552649 |         ideal\n",
      "    33 |      740 |      2 |  0.000000E+00 |  0.000000E+00 |  0.2923473660 |         ideal\n",
      "    34 |      760 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0638543481 |         ideal\n",
      "    35 |      780 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0280467995 |         ideal\n",
      "    36 |      800 |      2 |  0.000000E+00 |  0.000000E+00 |  0.2550912877 |         ideal\n",
      "    37 |      820 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0171196783 |         ideal\n",
      "    38 |      840 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    39 |      860 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    40 |      880 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1737668723 |             f\n",
      "    41 |      900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1376217961 |         ideal\n",
      "    42 |      920 |      5 |  0.000000E+00 |  0.000000E+00 |  0.1606071145 |         ideal\n",
      "    43 |      940 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0788392620 |         ideal\n",
      "    44 |      960 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0872524571 |             f\n",
      "    45 |      980 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0440289981 |             f\n",
      "    46 |     1000 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0385590405 |         ideal\n",
      "    47 |     1020 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0524857638 |         ideal\n",
      "    48 |     1040 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    49 |     1060 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0511888982 |             f\n",
      "    50 |     1080 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0559312762 |         ideal\n",
      "    51 |     1100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0920940110 |         ideal\n",
      "    52 |     1120 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0666437572 |         ideal\n",
      "    53 |     1140 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0506837190 |         ideal\n",
      "    54 |     1160 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    55 |     1180 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0630527458 |             f\n",
      "    56 |     1200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0157043889 |         ideal\n",
      "    57 |     1220 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0296170274 |         ideal\n",
      "    58 |     1240 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0163713979 |         ideal\n",
      "    59 |     1260 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0706585828 |         ideal\n",
      "    60 |     1280 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0528135538 |         ideal\n",
      "    61 |     1300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0144792922 |             f\n",
      "    62 |     1320 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    63 |     1340 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0671559897 |         ideal\n",
      "    64 |     1360 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0331704689 |         ideal\n",
      "    65 |     1380 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0123107936 |         ideal\n",
      "    66 |     1400 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0040074210 |         nadir\n",
      "    67 |     1420 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0483678279 |         ideal\n",
      "    68 |     1440 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    69 |     1460 |      4 |  0.000000E+00 |  0.000000E+00 |  0.1018520571 |             f\n",
      "    70 |     1480 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0482410049 |         ideal\n",
      "    71 |     1500 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0079182719 |         ideal\n",
      "    72 |     1520 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0235788418 |         ideal\n",
      "    73 |     1540 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    74 |     1560 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0199970310 |         ideal\n",
      "    75 |     1580 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0895146509 |         ideal\n",
      "    76 |     1600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0137528440 |         ideal\n",
      "    77 |     1620 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0102751350 |         ideal\n",
      "    78 |     1640 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0441137211 |         ideal\n",
      "    79 |     1660 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0426240747 |             f\n",
      "    80 |     1680 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0858176693 |         ideal\n",
      "    81 |     1700 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0266291972 |         ideal\n",
      "    82 |     1720 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0269233256 |         ideal\n",
      "    83 |     1740 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0210043002 |             f\n",
      "    84 |     1760 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0061854235 |             f\n",
      "    85 |     1780 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0285884867 |             f\n",
      "    86 |     1800 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0182481931 |         ideal\n",
      "    87 |     1820 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0042079582 |         ideal\n",
      "    88 |     1840 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0939783334 |         ideal\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    89 |     1860 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0257021845 |         ideal\n",
      "    90 |     1880 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0057284968 |             f\n",
      "    91 |     1900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0223879660 |             f\n",
      "    92 |     1920 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0398077128 |             f\n",
      "    93 |     1940 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0018444042 |             f\n",
      "    94 |     1960 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0193102491 |         ideal\n",
      "    95 |     1980 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0062523462 |             f\n",
      "    96 |     2000 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0309231339 |         ideal\n",
      "    97 |     2020 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0061479535 |             f\n",
      "    98 |     2040 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0027845884 |             f\n",
      "    99 |     2060 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0089811295 |         ideal\n",
      "   100 |     2080 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0069357605 |             f\n",
      "   101 |     2100 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0214928784 |         ideal\n",
      "   102 |     2120 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0306302908 |         ideal\n",
      "   103 |     2140 |      5 |  0.000000E+00 |  0.000000E+00 |  0.1242008562 |         ideal\n",
      "   104 |     2160 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0114050810 |             f\n",
      "   105 |     2180 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   106 |     2200 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   107 |     2220 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   108 |     2240 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0276072308 |             f\n",
      "   109 |     2260 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0108133127 |             f\n",
      "   110 |     2280 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0079461933 |         ideal\n",
      "   111 |     2300 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0300085848 |             f\n",
      "   112 |     2320 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0036507984 |         ideal\n",
      "   113 |     2340 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0516567143 |         ideal\n",
      "   114 |     2360 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0239670505 |         ideal\n",
      "   115 |     2380 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0105092226 |             f\n",
      "   116 |     2400 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0149129383 |         ideal\n",
      "   117 |     2420 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0025571153 |         ideal\n",
      "   118 |     2440 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0342804038 |             f\n",
      "   119 |     2460 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0246651246 |         ideal\n",
      "   120 |     2480 |      8 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   121 |     2500 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0760133858 |         ideal\n",
      "   122 |     2520 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0034748767 |         ideal\n",
      "   123 |     2540 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0190716971 |             f\n",
      "   124 |     2560 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0042563461 |             f\n",
      "   125 |     2580 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0100243215 |         ideal\n",
      "   126 |     2600 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0496757946 |         ideal\n",
      "   127 |     2620 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0120331892 |             f\n",
      "   128 |     2640 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0087151574 |             f\n",
      "   129 |     2660 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0100473774 |             f\n",
      "   130 |     2680 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0016176178 |             f\n",
      "   131 |     2700 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0166229724 |             f\n",
      "   132 |     2720 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0168471861 |             f\n",
      "   133 |     2740 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0688932349 |         ideal\n",
      "   134 |     2760 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0066420880 |             f\n",
      "   135 |     2780 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0229686983 |         ideal\n",
      "   136 |     2800 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0066786734 |         ideal\n",
      "   137 |     2820 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0033035562 |             f\n",
      "   138 |     2840 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0126071788 |             f\n",
      "   139 |     2860 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0141550912 |         ideal\n",
      "   140 |     2880 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0133394239 |             f\n",
      "   141 |     2900 |     10 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   142 |     2920 |      9 |  0.000000E+00 |  0.000000E+00 |  0.1487607275 |         ideal\n",
      "   143 |     2940 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0242457724 |         ideal\n",
      "   144 |     2960 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0083181590 |             f\n",
      "   145 |     2980 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0006026268 |             f\n",
      "   146 |     3000 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0017799254 |             f\n",
      "   147 |     3020 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0362126812 |             f\n",
      "   148 |     3040 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0375288162 |             f\n",
      "   149 |     3060 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0385296375 |             f\n",
      "   150 |     3080 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0160077459 |             f\n",
      "   151 |     3100 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0123034504 |             f\n",
      "   152 |     3120 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0419638999 |         ideal\n",
      "   153 |     3140 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0257104915 |             f\n",
      "   154 |     3160 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0292705666 |         ideal\n",
      "   155 |     3180 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0012682375 |             f\n",
      "   156 |     3200 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0167688891 |         ideal\n",
      "   157 |     3220 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0103016993 |             f\n",
      "   158 |     3240 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0106747856 |         ideal\n",
      "   159 |     3260 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0511991128 |         ideal\n",
      "   160 |     3280 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0873048530 |         ideal\n",
      "   161 |     3300 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0030224510 |             f\n",
      "   162 |     3320 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0102406914 |             f\n",
      "   163 |     3340 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0508235287 |         ideal\n",
      "   164 |     3360 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0197374082 |             f\n",
      "   165 |     3380 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0085505394 |             f\n",
      "   166 |     3400 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0365463844 |         ideal\n",
      "   167 |     3420 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0096456176 |             f\n",
      "   168 |     3440 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0924372921 |         ideal\n",
      "   169 |     3460 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   170 |     3480 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0575949946 |         ideal\n",
      "   171 |     3500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   172 |     3520 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0214180987 |             f\n",
      "   173 |     3540 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0547979754 |             f\n",
      "   174 |     3560 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0129285021 |             f\n",
      "   175 |     3580 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0480737843 |             f\n",
      "   176 |     3600 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0462265412 |         ideal\n",
      "   177 |     3620 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   178 |     3640 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0087790916 |         ideal\n",
      "   179 |     3660 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0554289101 |         ideal\n",
      "   180 |     3680 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0088334613 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   181 |     3700 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0037623621 |             f\n",
      "   182 |     3720 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0028774926 |             f\n",
      "   183 |     3740 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0022778650 |             f\n",
      "   184 |     3760 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0149761261 |         ideal\n",
      "   185 |     3780 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0549846988 |         ideal\n",
      "   186 |     3800 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0086964069 |             f\n",
      "   187 |     3820 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0150259886 |         ideal\n",
      "   188 |     3840 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0304540220 |         ideal\n",
      "   189 |     3860 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0011501652 |             f\n",
      "   190 |     3880 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0480755077 |         ideal\n",
      "   191 |     3900 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0027145097 |             f\n",
      "   192 |     3920 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0107288273 |             f\n",
      "   193 |     3940 |      7 |  0.000000E+00 |  0.000000E+00 |  0.1101156797 |         nadir\n",
      "   194 |     3960 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0220254618 |             f\n",
      "   195 |     3980 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0136859012 |         ideal\n",
      "   196 |     4000 |     11 |  0.000000E+00 |  0.000000E+00 |  0.0151637148 |         ideal\n",
      "   197 |     4020 |     12 |  0.000000E+00 |  0.000000E+00 |  0.0322292508 |         ideal\n",
      "   198 |     4040 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0018121386 |             f\n",
      "   199 |     4060 |     10 |  0.000000E+00 |  0.000000E+00 |  0.0160270988 |             f\n",
      "   200 |     4080 |      9 |  0.000000E+00 |  0.000000E+00 |  0.0079329058 |             f\n",
      "training time : 17.81 \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": 66,
   "id": "10d4e846",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AISI 4340 norm', 'AISI 1045 CD', 'AISI 4340 norm', 'AISI 1045 CD', 'AISI 4340 norm', 'AISI 1045 CD', 'AISI 1045 CD', 'AISI 4130', 'AISI 4130']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = res.X\n",
    "F = res.F\n",
    "G = res.G\n",
    "\n",
    "\n",
    "Z_res=X[:,0:2]\n",
    "\n",
    "names = []\n",
    "types = []\n",
    "fig, ax = plt.subplots()\n",
    "legend_dict = {}\n",
    "\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",
    "\n",
    "# Define specific colors for each material type\n",
    "mat_colors = {\n",
    "    'Steel': 'slategrey',\n",
    "    'Aluminium Alloys': 'violet',\n",
    "    'Plastics': 'lawngreen',\n",
    "    'Titanium Alloys': 'indigo',\n",
    "    'Copper Alloys': 'chocolate',\n",
    "    'Composites': 'gold',\n",
    "    # Add more material types and corresponding colors as needed\n",
    "}\n",
    "\n",
    "print(names)        \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",
    "#ax.text(20, 19, 'Gray Cast Iron')\n",
    "#ax.text(25, 7, '1060-H14')\n",
    "#ax.text(50, 6, 'CFRP')\n",
    "#ax.text(100, 5, 'CFRP')\n",
    "        \n",
    "        \n",
    "# set legend colors and remove duplicates\n",
    "handles, labels = [], []\n",
    "for label in legend_dict:\n",
    "    handles.append(legend_dict[label])\n",
    "    labels.append(label)\n",
    "    legend_dict[label].set_color(mat_colors[label])\n",
    "    \n",
    "ax.legend(handles, labels, loc=\"upper right\")\n",
    "\n",
    "# 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_Hybrid/Preliminary.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": 67,
   "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",
    "#print(dataIdentifier2)\n",
    "\n",
    "props = df2.columns[3:].tolist()\n",
    "nmat, nprop = trainInfo2.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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, 49):\n",
    "            variables[f\"x{k:02}\"] = Real(bounds=(2e-8, 2e-3))\n",
    "\n",
    "        super().__init__(vars=variables, n_obj=no, n_ieq_constr=nc, **kwargs)\n",
    "        \n",
    "        self.truss=truss\n",
    "        self.trainInfo2=trainInfo2\n",
    "        self.constraints=constraints\n",
    "        self.objectives=objectives\n",
    "        \n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        x = np.array([x[f\"x{k:02}\"] for k in range(1, 49)])\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",
    "    \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', 350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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 |  1.060488E+02 |             - |             -\n",
      "     2 |      120 |      3 |  0.000000E+00 |  3.5412700126 |  0.5449591077 |         ideal\n",
      "     3 |      180 |      3 |  0.000000E+00 |  0.5849298314 |  0.0787863372 |         ideal\n",
      "     4 |      240 |      3 |  0.000000E+00 |  0.0156236258 |  0.4183762822 |         ideal\n",
      "     5 |      300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.2064133627 |         ideal\n",
      "     6 |      360 |      3 |  0.000000E+00 |  0.000000E+00 |  0.4941890059 |         ideal\n",
      "     7 |      420 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0108467685 |             f\n",
      "     8 |      480 |      2 |  0.000000E+00 |  0.000000E+00 |  7.2773729317 |         ideal\n",
      "     9 |      540 |      2 |  0.000000E+00 |  0.000000E+00 |  0.8815458101 |         ideal\n",
      "    10 |      600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1609447178 |         ideal\n",
      "    11 |      660 |      2 |  0.000000E+00 |  0.000000E+00 |  1.9251717981 |         ideal\n",
      "    12 |      720 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0939025580 |         ideal\n",
      "    13 |      780 |      1 |  0.000000E+00 |  0.000000E+00 |  4.697757E+01 |         ideal\n",
      "    14 |      840 |      2 |  0.000000E+00 |  0.000000E+00 |  1.0000000000 |         ideal\n",
      "    15 |      900 |      1 |  0.000000E+00 |  0.000000E+00 |  5.536331E+01 |         ideal\n",
      "    16 |      960 |      2 |  0.000000E+00 |  0.000000E+00 |  1.0000000000 |         ideal\n",
      "    17 |     1020 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    18 |     1080 |      2 |  0.000000E+00 |  0.000000E+00 |  2.1266745644 |         ideal\n",
      "    19 |     1140 |      2 |  0.000000E+00 |  0.000000E+00 |  0.9248754435 |         ideal\n",
      "    20 |     1200 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    21 |     1260 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1208106512 |         ideal\n",
      "    22 |     1320 |      2 |  0.000000E+00 |  0.000000E+00 |  0.7790231061 |         ideal\n",
      "    23 |     1380 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1461866742 |         ideal\n",
      "    24 |     1440 |      2 |  0.000000E+00 |  0.000000E+00 |  0.5169721613 |         ideal\n",
      "    25 |     1500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.2312091530 |         ideal\n",
      "    26 |     1560 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0266292499 |             f\n",
      "    27 |     1620 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0041720991 |         ideal\n",
      "    28 |     1680 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1105606950 |         ideal\n",
      "    29 |     1740 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1190542085 |         ideal\n",
      "    30 |     1800 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0735058020 |         ideal\n",
      "    31 |     1860 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1469040863 |         ideal\n",
      "    32 |     1920 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0605621076 |         ideal\n",
      "    33 |     1980 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    34 |     2040 |      2 |  0.000000E+00 |  0.000000E+00 |  0.2701041027 |         ideal\n",
      "    35 |     2100 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    36 |     2160 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1405257051 |         ideal\n",
      "    37 |     2220 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1076328451 |         ideal\n",
      "    38 |     2280 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0069484239 |         ideal\n",
      "    39 |     2340 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    40 |     2400 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    41 |     2460 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0545001677 |         ideal\n",
      "    42 |     2520 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1399011931 |         ideal\n",
      "    43 |     2580 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0576805296 |         ideal\n",
      "    44 |     2640 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0155689686 |         ideal\n",
      "    45 |     2700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    46 |     2760 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0028268165 |             f\n",
      "    47 |     2820 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    48 |     2880 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1007549813 |         ideal\n",
      "    49 |     2940 |      2 |  0.000000E+00 |  0.000000E+00 |  4.8392024099 |         nadir\n",
      "    50 |     3000 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1063825232 |         ideal\n",
      "    51 |     3060 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0419590240 |         ideal\n",
      "    52 |     3120 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    53 |     3180 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1306691906 |         ideal\n",
      "    54 |     3240 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    55 |     3300 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0850724176 |         ideal\n",
      "    56 |     3360 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0501935893 |         ideal\n",
      "    57 |     3420 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0231080869 |         ideal\n",
      "    58 |     3480 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0141601857 |         ideal\n",
      "    59 |     3540 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0772629714 |         ideal\n",
      "    60 |     3600 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0153953800 |         ideal\n",
      "    61 |     3660 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0541423921 |         ideal\n",
      "    62 |     3720 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0514245235 |         ideal\n",
      "    63 |     3780 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    64 |     3840 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    65 |     3900 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0344296724 |         ideal\n",
      "    66 |     3960 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0752942198 |         ideal\n",
      "    67 |     4020 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1341159381 |         ideal\n",
      "    68 |     4080 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1180354097 |         ideal\n",
      "    69 |     4140 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1845847693 |         ideal\n",
      "    70 |     4200 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0245779518 |         ideal\n",
      "    71 |     4260 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    72 |     4320 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    73 |     4380 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0133134349 |         ideal\n",
      "    74 |     4440 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1469046916 |         ideal\n",
      "    75 |     4500 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1830120194 |         ideal\n",
      "    76 |     4560 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1908026439 |         ideal\n",
      "    77 |     4620 |      3 |  0.000000E+00 |  0.000000E+00 |  0.2094214666 |         ideal\n",
      "    78 |     4680 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0959803103 |         ideal\n",
      "    79 |     4740 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0152135093 |         ideal\n",
      "    80 |     4800 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1520125560 |         ideal\n",
      "    81 |     4860 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0946057836 |         ideal\n",
      "    82 |     4920 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1402365423 |         ideal\n",
      "    83 |     4980 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0276057739 |         ideal\n",
      "    84 |     5040 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1537884624 |         ideal\n",
      "    85 |     5100 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    86 |     5160 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0756499192 |         ideal\n",
      "    87 |     5220 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1313458732 |         ideal\n",
      "    88 |     5280 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    89 |     5340 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0287059751 |         ideal\n",
      "    90 |     5400 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0322018721 |         ideal\n",
      "    91 |     5460 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1010111344 |         ideal\n",
      "    92 |     5520 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0110582859 |             f\n",
      "    93 |     5580 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    94 |     5640 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0437721661 |         ideal\n",
      "    95 |     5700 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0482075456 |         ideal\n",
      "    96 |     5760 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0182406035 |         ideal\n",
      "    97 |     5820 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0012882507 |             f\n",
      "    98 |     5880 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0234050623 |         ideal\n",
      "    99 |     5940 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0485770483 |         ideal\n",
      "   100 |     6000 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   101 |     6060 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0475882047 |         ideal\n",
      "   102 |     6120 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0445155445 |         ideal\n",
      "   103 |     6180 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0033235753 |         ideal\n",
      "   104 |     6240 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0690501431 |         ideal\n",
      "   105 |     6300 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1462432991 |         ideal\n",
      "   106 |     6360 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0227567323 |         ideal\n",
      "   107 |     6420 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   108 |     6480 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0388122356 |         ideal\n",
      "   109 |     6540 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0592844497 |         ideal\n",
      "   110 |     6600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   111 |     6660 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   112 |     6720 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0720627631 |         ideal\n",
      "   113 |     6780 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0143279172 |             f\n",
      "   114 |     6840 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0227359392 |         ideal\n",
      "   115 |     6900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0445709649 |         ideal\n",
      "   116 |     6960 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0322238301 |         ideal\n",
      "   117 |     7020 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0358483160 |         ideal\n",
      "   118 |     7080 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1031309750 |         ideal\n",
      "   119 |     7140 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0717374163 |         ideal\n",
      "   120 |     7200 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   121 |     7260 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   122 |     7320 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   123 |     7380 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0430123584 |         ideal\n",
      "   124 |     7440 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0534446569 |         ideal\n",
      "   125 |     7500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0089164016 |         ideal\n",
      "   126 |     7560 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0592578355 |         ideal\n",
      "   127 |     7620 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   128 |     7680 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   129 |     7740 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0294584751 |         ideal\n",
      "   130 |     7800 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0309335950 |         ideal\n",
      "   131 |     7860 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0208661479 |         ideal\n",
      "   132 |     7920 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0184200305 |         ideal\n",
      "   133 |     7980 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0491363858 |         ideal\n",
      "   134 |     8040 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0229542152 |         ideal\n",
      "   135 |     8100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0002212491 |             f\n",
      "   136 |     8160 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0002212491 |             f\n",
      "   137 |     8220 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0002212491 |             f\n",
      "   138 |     8280 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0253837394 |         ideal\n",
      "   139 |     8340 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   140 |     8400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0643829914 |         ideal\n",
      "   141 |     8460 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0053557539 |         ideal\n",
      "   142 |     8520 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0073608356 |         ideal\n",
      "   143 |     8580 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0082497735 |             f\n",
      "   144 |     8640 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   145 |     8700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0061511020 |         ideal\n",
      "   146 |     8760 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0196644352 |         ideal\n",
      "   147 |     8820 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0303841703 |         ideal\n",
      "   148 |     8880 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0026228020 |             f\n",
      "   149 |     8940 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   150 |     9000 |      2 |  0.000000E+00 |  0.000000E+00 |  4.5255021573 |         nadir\n",
      "   151 |     9060 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0344269030 |         ideal\n",
      "   152 |     9120 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   153 |     9180 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0025689599 |         ideal\n",
      "   154 |     9240 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0074646683 |         ideal\n",
      "   155 |     9300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0388538649 |         ideal\n",
      "   156 |     9360 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0066921411 |         ideal\n",
      "   157 |     9420 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0046201110 |         nadir\n",
      "   158 |     9480 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   159 |     9540 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0072570836 |         ideal\n",
      "   160 |     9600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0015770041 |             f\n",
      "   161 |     9660 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0080413543 |         ideal\n",
      "   162 |     9720 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0109497842 |         ideal\n",
      "   163 |     9780 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0041456573 |         ideal\n",
      "   164 |     9840 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0035084204 |         ideal\n",
      "   165 |     9900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0052565387 |         ideal\n",
      "   166 |     9960 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0113604943 |         ideal\n",
      "   167 |    10020 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0113704873 |         nadir\n",
      "   168 |    10080 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0263101781 |         ideal\n",
      "   169 |    10140 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   170 |    10200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0147736618 |         ideal\n",
      "   171 |    10260 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0067520167 |         ideal\n",
      "   172 |    10320 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1046741247 |         ideal\n",
      "   173 |    10380 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0105645870 |         ideal\n",
      "   174 |    10440 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0820081226 |         ideal\n",
      "   175 |    10500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0062954977 |         ideal\n",
      "   176 |    10560 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   177 |    10620 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   178 |    10680 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0053473364 |         ideal\n",
      "   179 |    10740 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   180 |    10800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0082532429 |         ideal\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   181 |    10860 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   182 |    10920 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0483350105 |         ideal\n",
      "   183 |    10980 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0182193110 |         ideal\n",
      "   184 |    11040 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0429001959 |         ideal\n",
      "   185 |    11100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0070017717 |         ideal\n",
      "   186 |    11160 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0065746004 |         ideal\n",
      "   187 |    11220 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0562184529 |         ideal\n",
      "   188 |    11280 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   189 |    11340 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0038176534 |         nadir\n",
      "   190 |    11400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1200003428 |         ideal\n",
      "   191 |    11460 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   192 |    11520 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0318162802 |         ideal\n",
      "   193 |    11580 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0381182720 |             f\n",
      "   194 |    11640 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0781657662 |         ideal\n",
      "   195 |    11700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0112141829 |         ideal\n",
      "   196 |    11760 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   197 |    11820 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   198 |    11880 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   199 |    11940 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0075738730 |         ideal\n",
      "   200 |    12000 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0219040147 |         ideal\n",
      "   201 |    12060 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   202 |    12120 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0120462951 |         ideal\n",
      "   203 |    12180 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0081678122 |         nadir\n",
      "   204 |    12240 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0354468149 |         ideal\n",
      "   205 |    12300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   206 |    12360 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0228325646 |         ideal\n",
      "   207 |    12420 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0139787591 |             f\n",
      "   208 |    12480 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0064253844 |             f\n",
      "   209 |    12540 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   210 |    12600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0324610120 |         ideal\n",
      "   211 |    12660 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   212 |    12720 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   213 |    12780 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   214 |    12840 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0006912388 |             f\n",
      "   215 |    12900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0086925783 |         nadir\n",
      "   216 |    12960 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0087427834 |             f\n",
      "   217 |    13020 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0043389151 |         ideal\n",
      "   218 |    13080 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   219 |    13140 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0020497620 |             f\n",
      "   220 |    13200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0083913016 |         ideal\n",
      "   221 |    13260 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   222 |    13320 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0056335383 |         ideal\n",
      "   223 |    13380 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0205628136 |         ideal\n",
      "   224 |    13440 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   225 |    13500 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0161491414 |         ideal\n",
      "   226 |    13560 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0460183062 |         ideal\n",
      "   227 |    13620 |      3 |  0.000000E+00 |  0.000000E+00 |  0.8214234527 |         nadir\n",
      "   228 |    13680 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   229 |    13740 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   230 |    13800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0136493594 |         ideal\n",
      "   231 |    13860 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   232 |    13920 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0030959623 |             f\n",
      "   233 |    13980 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   234 |    14040 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   235 |    14100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   236 |    14160 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0166068168 |         ideal\n",
      "   237 |    14220 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0274354968 |         ideal\n",
      "   238 |    14280 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0063305705 |         nadir\n",
      "   239 |    14340 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0044922534 |         ideal\n",
      "   240 |    14400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   241 |    14460 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0138570329 |             f\n",
      "   242 |    14520 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   243 |    14580 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0042340307 |         ideal\n",
      "   244 |    14640 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   245 |    14700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   246 |    14760 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0027830401 |         ideal\n",
      "   247 |    14820 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   248 |    14880 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0108806944 |         ideal\n",
      "   249 |    14940 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0027764502 |         ideal\n",
      "   250 |    15000 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0071506448 |         ideal\n",
      "   251 |    15060 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   252 |    15120 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   253 |    15180 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   254 |    15240 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0054224336 |         ideal\n",
      "   255 |    15300 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   256 |    15360 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0125465599 |         ideal\n",
      "   257 |    15420 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0005469048 |             f\n",
      "   258 |    15480 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0021011623 |             f\n",
      "   259 |    15540 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0047575841 |         ideal\n",
      "   260 |    15600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0038281225 |         ideal\n",
      "   261 |    15660 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   262 |    15720 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0070751439 |         ideal\n",
      "   263 |    15780 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0014834456 |             f\n",
      "   264 |    15840 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0014834456 |             f\n",
      "   265 |    15900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0037948190 |         ideal\n",
      "   266 |    15960 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0096278609 |         ideal\n",
      "   267 |    16020 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0028075152 |         ideal\n",
      "   268 |    16080 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0004580635 |             f\n",
      "   269 |    16140 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0114035974 |         nadir\n",
      "   270 |    16200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0059834923 |             f\n",
      "   271 |    16260 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0128888942 |         ideal\n",
      "   272 |    16320 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0029522250 |         nadir\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   273 |    16380 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0006749740 |             f\n",
      "   274 |    16440 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0006749740 |             f\n",
      "   275 |    16500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0010840821 |             f\n",
      "   276 |    16560 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0010840821 |             f\n",
      "   277 |    16620 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0015731619 |             f\n",
      "   278 |    16680 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0029700640 |         ideal\n",
      "   279 |    16740 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0064825085 |         ideal\n",
      "   280 |    16800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   281 |    16860 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0046922259 |         ideal\n",
      "   282 |    16920 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0031653890 |         nadir\n",
      "   283 |    16980 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   284 |    17040 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0057298673 |         nadir\n",
      "   285 |    17100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0070998806 |         ideal\n",
      "   286 |    17160 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   287 |    17220 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0033652587 |             f\n",
      "   288 |    17280 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0005447422 |             f\n",
      "   289 |    17340 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0038214936 |         nadir\n",
      "   290 |    17400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   291 |    17460 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   292 |    17520 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0050789046 |         ideal\n",
      "   293 |    17580 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   294 |    17640 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0046981591 |         ideal\n",
      "   295 |    17700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0094332722 |         ideal\n",
      "   296 |    17760 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   297 |    17820 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0051765905 |         nadir\n",
      "   298 |    17880 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   299 |    17940 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   300 |    18000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   301 |    18060 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   302 |    18120 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   303 |    18180 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0007122359 |             f\n",
      "   304 |    18240 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0007122359 |             f\n",
      "   305 |    18300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0015570064 |             f\n",
      "   306 |    18360 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0040044123 |             f\n",
      "   307 |    18420 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   308 |    18480 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0000960016 |             f\n",
      "   309 |    18540 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0000960016 |             f\n",
      "   310 |    18600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0000960016 |             f\n",
      "   311 |    18660 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0075558390 |         ideal\n",
      "   312 |    18720 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0006812087 |             f\n",
      "   313 |    18780 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0007201677 |             f\n",
      "   314 |    18840 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0010811009 |             f\n",
      "   315 |    18900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0077854557 |             f\n",
      "   316 |    18960 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0022239119 |             f\n",
      "   317 |    19020 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0078369868 |         ideal\n",
      "   318 |    19080 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   319 |    19140 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0038650613 |             f\n",
      "   320 |    19200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0002454717 |             f\n",
      "   321 |    19260 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0034291657 |         ideal\n",
      "   322 |    19320 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0092476405 |         ideal\n",
      "   323 |    19380 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0149011465 |         ideal\n",
      "   324 |    19440 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   325 |    19500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0289758073 |         ideal\n",
      "   326 |    19560 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   327 |    19620 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   328 |    19680 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   329 |    19740 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   330 |    19800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   331 |    19860 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   332 |    19920 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   333 |    19980 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0005050697 |             f\n",
      "   334 |    20040 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0012071316 |             f\n",
      "   335 |    20100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0012071316 |             f\n",
      "   336 |    20160 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0012071316 |             f\n",
      "   337 |    20220 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0012071316 |             f\n",
      "   338 |    20280 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0012071316 |             f\n",
      "   339 |    20340 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0015302574 |             f\n",
      "   340 |    20400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0090927969 |         ideal\n",
      "   341 |    20460 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   342 |    20520 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   343 |    20580 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0004487012 |             f\n",
      "   344 |    20640 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0004487012 |             f\n",
      "   345 |    20700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0014773902 |             f\n",
      "   346 |    20760 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0083753967 |         nadir\n",
      "   347 |    20820 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0026129380 |             f\n",
      "   348 |    20880 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0001223426 |             f\n",
      "   349 |    20940 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0001223426 |             f\n",
      "   350 |    21000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0001223426 |             f\n",
      "Solver time : 59.95\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": 76,
   "id": "e147dc84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'x01': 4, 'x02': 0.0003595742957426794, 'x03': 8.625854162109533e-05, 'x04': 9.266375972286782e-05, 'x05': 0.000348008612339741, 'x06': 1.9539480059188097e-06, 'x07': 0.0003443846893225863, 'x08': 2.1042895516316422e-05, 'x09': 2.450209103933298e-05, 'x10': 0.00034379403133112706, 'x11': 1.3185059167250012e-07, 'x12': 0.00033604126702317964, 'x13': 3.822374723302222e-07, 'x14': 1.6315381845036005e-06, 'x15': 0.00033320851410554274, 'x16': 5.266878770733916e-05, 'x17': 0.00022244196848620617, 'x18': 4.5533661482157034e-07, 'x19': 4.792527998636438e-06, 'x20': 0.00022349217854076385, 'x21': 0.00014103562644369308, 'x22': 0.0002084798126430805, 'x23': 9.44825335759698e-05, 'x24': 8.712469891140874e-05, 'x25': 0.00022878018820759912, 'x26': 3.897929336221882e-05, 'x27': 0.00016123063292735096, 'x28': 0.00017347510794069862, 'x29': 0.00017953865225744248, 'x30': 0.00014353760352379074, 'x31': 0.00026206622911286874, 'x32': 0.0003149695549948577, 'x33': 6.965833712111021e-06, 'x34': 2.8046674083616695e-07, 'x35': 2.7767884761418775e-07, 'x36': 6.7109986040046e-06, 'x37': 0.00031196054622337465, 'x38': 0.00029980174019943146, 'x39': 4.0399685756956555e-07, 'x40': 8.660245643626726e-07, 'x41': 9.513245582966106e-08, 'x42': 4.158926997795659e-06, 'x43': 0.00030662367933020066, 'x44': 0.0001736980952103295, 'x45': 0.0001668942940911396, 'x46': 0.00019605495208530017, 'x47': 0.00017186829829749075, 'x48': 0.00016114145208809567}\n",
      " {'x01': 7, 'x02': 0.00035957451798789274, 'x03': 8.573163784924888e-05, 'x04': 9.521148431111901e-05, 'x05': 0.0003480084792656363, 'x06': 1.2540021655002317e-06, 'x07': 0.00034437165179721527, 'x08': 2.104286181517005e-05, 'x09': 2.4501753298950448e-05, 'x10': 0.00034381239535032825, 'x11': 1.3204193515330615e-07, 'x12': 0.0003384915795414237, 'x13': 3.822098576274494e-07, 'x14': 2.2716528522382944e-06, 'x15': 0.00033320854715776304, 'x16': 5.3193219488149414e-05, 'x17': 0.00022243088965111054, 'x18': 2.895475496104148e-07, 'x19': 4.792518267463063e-06, 'x20': 0.00022747400043347373, 'x21': 0.00014105939790307184, 'x22': 0.00020722312995566822, 'x23': 9.450034169836191e-05, 'x24': 8.713572268778438e-05, 'x25': 0.00022858626088388952, 'x26': 4.5178142381228e-05, 'x27': 0.00015440548713584404, 'x28': 0.00017719966632048342, 'x29': 0.00017973675374552246, 'x30': 0.0001435376568968995, 'x31': 0.00026200630863803566, 'x32': 0.00031552989703322184, 'x33': 6.75004507539439e-06, 'x34': 3.536029644405893e-06, 'x35': 2.776353738595232e-07, 'x36': 6.72092801556037e-06, 'x37': 0.000311960191224918, 'x38': 0.00029804020429052635, 'x39': 3.836861169960629e-06, 'x40': 3.900183706160832e-06, 'x41': 9.507515357583993e-08, 'x42': 2.4146237609457333e-05, 'x43': 0.0003067570287305123, 'x44': 0.00012473093520144757, 'x45': 0.00015508984826314253, 'x46': 0.00019379341566982535, 'x47': 0.00017186767101236712, 'x48': 0.00014380691089209289}\n",
      " {'x01': 9, 'x02': 0.00035956510130508016, 'x03': 0.00011149108741649426, 'x04': 9.50186927107358e-05, 'x05': 0.00034802220058240515, 'x06': 2.027094144744552e-06, 'x07': 0.0003443413736021953, 'x08': 2.1061808819730715e-05, 'x09': 2.4529916183391914e-05, 'x10': 0.00034380115255471346, 'x11': 1.3171244746045033e-07, 'x12': 0.00033904493648028106, 'x13': 3.823706600615781e-07, 'x14': 1.6311087614003114e-06, 'x15': 0.000333212324565316, 'x16': 5.2653139883607084e-05, 'x17': 0.00022243007181335303, 'x18': 1.7637930027269318e-07, 'x19': 4.824787565477811e-06, 'x20': 0.00022342103645389875, 'x21': 0.00014107089591186588, 'x22': 0.00020693507123483666, 'x23': 9.473661046460872e-05, 'x24': 8.712257434370368e-05, 'x25': 0.00022876163053570496, 'x26': 4.519462034580065e-05, 'x27': 0.0001551941091862571, 'x28': 0.0001776663127221225, 'x29': 0.00018162776556551743, 'x30': 0.00014353631208000988, 'x31': 0.00026201090431917853, 'x32': 0.000314967436944834, 'x33': 6.721982114447009e-06, 'x34': 3.0687447798169767e-07, 'x35': 3.052321933837294e-07, 'x36': 4.926855584446437e-06, 'x37': 0.0003119734268944233, 'x38': 0.00029710607777513344, 'x39': 4.3125413280952226e-07, 'x40': 1.8297516605456023e-06, 'x41': 1.319738012419164e-07, 'x42': 6.865145388421815e-06, 'x43': 0.0003066188894856797, 'x44': 7.956638244022833e-05, 'x45': 0.0001537399738331792, 'x46': 0.00015100931814041523, 'x47': 0.0001718406480105813, 'x48': 0.00010979395707759873}]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = res.X\n",
    "F = res.F\n",
    "G = res.G\n",
    "Z_res = [[d['x01']] for d in X]\n",
    "\n",
    "print(X)\n",
    "\n",
    "\n",
    "names = []\n",
    "types = []\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "legend_dict = {}\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",
    "# Define specific colors for each material type\n",
    "mat_colors = {\n",
    "    'Steel': 'slategrey',\n",
    "    'Aluminium Alloys': 'violet',\n",
    "    'Plastics': 'lawngreen',\n",
    "    'Titanium Alloys': 'indigo',\n",
    "    'Copper Alloys': 'chocolate',\n",
    "    'Composites': 'gold',\n",
    "    # Add more material types and corresponding colors as needed\n",
    "}\n",
    "\n",
    "\n",
    "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",
    "\n",
    "\n",
    "# add scatter plot for each unique label\n",
    "for i, (x, y) in enumerate(F):\n",
    "\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",
    "\n",
    "# set legend colors and remove duplicates\n",
    "handles, labels = [], []\n",
    "for label in legend_dict:\n",
    "    handles.append(legend_dict[label])\n",
    "    labels.append(label)\n",
    "    legend_dict[label].set_color(mat_colors[label])\n",
    "    \n",
    "ax.legend(handles, labels, loc=\"upper right\")\n",
    "\n",
    "\n",
    "# show plot\n",
    "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_Hybrid/Final',bbox_inches='tight', dpi = 200)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4028ac",
   "metadata": {},
   "source": [
    "# Defines function to plot the structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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": 78,
   "id": "32cfacff",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tba31tx9/f3/U1NTg2rVr/Lbu7m78/vvvasfNnTsXwPXZD4MZKAbVrIYbz6dqxpAm5s+fj0uXLsHNzQ0zZszo89PfzImhqLpxVOucqOTn5+PMmTNIS0vT+j4BoLGxETt27MDs2bORnZ3d52fZsmXIz88fNNkY6G9/9epVNDc3D7lWyc3w9fXFrFmz8NVXX6kN4D506BDOnTuHxYsX89v6e34rV64EAOzcuROfffbZoI916NAhXLhwAbGxsYMeN3bsWDz00EP4/fff8cUXX/R7zKVLl/pcY/3p6OjAQw89BLlcjhdeeGHI41Xs7e0xd+5c/PnPf0Z3dze/aFxaWhqysrL4xETliy++gJ2d3aDPTdPXIKD5azw4OBi+vr7Ytm2bWvdWW1sbtm/fzs8EIsZj8FSZkAEsX74c//d//4dXX30VSUlJKC4uxocffggnJye142JiYjB//nxERETAxcUFZ86cwZdffsm/GZw8eRJPPvkkli5diqCgIAiFQmRlZeHkyZN46aWXBo0hNDQUaWlpmDt3LgIDA9HZ2YnDhw/jn//8J7y8vPiZLMD1mRQ5OTn4+eef4e3tDUdHRwQHBw96/3fffTdeffVV3HPPPfjTn/6Ezs5OfPDBB31mFyUkJGD58uV48803ce3aNcyfPx/W1tY4fvw47Ozs8NRTT/ExfPPNN/j222/51VTDw8Mxc+ZMBAcH449//CPkcjlcXFywY8eOfmcfDOSZZ57B9u3bkZiYiGeffRYRERFQKpUoKyuDRCLB888/j5iYGI3vD7j+hr5mzRr85z//gUAgwNy5c1FaWor/+7//g5+f37BnPWzduhWdnZ14+umn+Smkvbm5uWHr1q3YuHEj3n///X7vY82aNWhqasKSJUsQFhYGCwsLnD17Fu+//z4EAgFefPHFYcWmWiiwpKQEAHD06FF+CvSdd97JH/fOO+9AJBJh6dKlePzxx1FTU4OXXnoJYWFheOCBB/jj+nt+qvFCs2fPVmtNiIyMxP3334+pU6fCxsYGR44cwbvvvouxY8dqlDC89957KCkpwapVq/D777/jjjvugJeXF+rq6iCVSrFp0yZ88803arOVysrKcOjQISiVSshkMhw/fpzvyvnnP/8JsVg86GM+/PDDsLW1xezZs+Ht7Y3q6mqsXbsWTk5OmDlzJgDgtddewy+//IKUlBS8+uqrcHV1xdatW/Hrr79i3bp1fd4zetP0NQho/hoXCARYt24dli1bhvnz5+ORRx5BV1cX3n33XTQ1NfVZ1ZoYAUOO5CXGY6BZP6Ghof0e39XVxV544QXm5+fHbG1tWVJSEissLOwz6+ell15iM2bMYC4uLsza2poFBASwZ599ltXV1THGGLt27RpbtWoVmzJlCrO3t2cODg4sIiKCvf/++0wulw8a8/r169nixYtZQEAAs7OzY0KhkAUGBrJHH32UlZeXqx1bWFjIZs+ezezs7BgAfvaNasbNjbNlVHbv3s2mTZvGbG1tWUBAAPvwww/7nXGgUCjY+++/z8LCwphQKGROTk4sLi6O/fzzz/wxpaWlTCwWM0dHRwZAbTbD+fPnmVgsZmPGjGEeHh7sqaeeYr/++mu/s34G+pu0trayv/zlLyw4OJiPITw8nD377LOsurp60HM50EwUhULB3nnnHTZ58mRmZWXF3N3d2f3339/n/A4W142mTZvGPD09WVdX14DHxMbGMnd3d9bV1dXvDJXff/+drV69moWEhDAnJydmaWnJvL292eLFi/vMxtFm1g/+O4uov58bSSQSFhsby2xsbJirqytbsWKFRgvmDXSu77nnHjZp0iRmb2/PrKys2IQJE9ijjz7Krl69qlHsjDEml8vZli1bWGpqKnN1dWWWlpbMw8ODzZ07l23bto2f5aI6p+g1a8rFxYVFR0ezZ555hp0+fVqjx9uyZQtLSUlhXl5eTCgUMh8fH3bXXXexkydPqh1XVFTEbr/9dubk5MSEQiGLjIzsM6NqoAXfNH0NDvQa72/2HGOM7dy5k8XExDAbGxtmb2/P0tLS2P79+9WOGehvNdBMPaIbHGMaDO0mhBBCCDEAGqNCCCGEEKNFiQohhBBCjBYlKoQQQggxWpSoEEIIIcRoUaJCCCGEEKNFiQohhBBCjJZJL/imVCpx9epVODo6arV8NyGEEEIMhzGGlpYW+Pj4DFkiwqQTlatXr/apyEkIIYQQ01BeXo5x48YNeoxJJyqOjo4Arj/RMWPGGDgaQgghhGiiubkZfn5+/Of4YEw6UVF194wZM2ZYico333wDJycneHl5QSaToaOjAw4ODggPD6fqmTrS2NiIoqIitLa20rnWAzrf+kXnW7/ofOtXY2MjCgoKUFVVhfb2drS0tODOO+8cVtFTFU2GbRh0Cf3XX38db7zxhto2Ly8vVFdXa3T75uZmODk5QSaTaZ2oyOVyLF26FL6+vmqFwVQnzd/fH4sXL9Yo2yNDUygU2L17N44dOwaO48BxHBhjYIwhKioK8+bNg4WFhaHDNBt0vvWLzrd+0fnWj6amJly6dAkXL17E+fPn+WKQjDH+s9LT0xOPPPKI1udbm89vg7eohIaGIiMjg/9dXxdXWVkZn6T0l9FdvnwZL7/8Mrq7uxEYGIjAwEAEBAQgMDBw0GqfpH+qNxUA/BuKimr77bffbpDYzNFQ57upqYmvbktuXn5+Pl9xma5v3Rvq+m5sbER0dLShwjM5jDE0NDSgvLwc5eXlqKioQHl5OWQyGQBg8uTJ8PHx4T8re39m1tbWYvfu3Tq9vg2eqFhaWmLs2LF6f9zTp08PmKQA1/8Q7u7u+Pbbb7F79261fdbW1nBwcICDgwMcHR3h4OAAGxsbfYRtkiwtLeHv7z9oE19BQQEyMjKgVCr1GJl5EggEcHFxGfR8X7p0CTt37kR3d7ceIzNPQqEQ4eHhQ17fu3btglwu12Nk5snS0hK+vr6Dnu+SkhL89NNPdH33gzGGrq4utLW1ob29nf93oGtzzJgxSE5OHvR8Hzt2DHPmzNFZt5vBE5ULFy7Ax8cH1tbWiImJwVtvvYWAgIB+j+3q6kJXVxf/e3Nz87Af99y5c0MewxjDxIkTUVBQoLa9p6cHra2tatssLS1hb28POzs72NnZwd7enpKX//L29tbouI6ODlRVVek4GvPn7e2t0RuGm5sbne8R4ObmptFxHMehrq5Ox9GYP03fT+j6vv4Z1tHRwScjqh9VF44mgoKC1Lp6+sNxHIqKipCYmDgSYfdh0EQlJiYGX3zxBSZPnoxr167hzTffRHx8PE6fPt3vi3/t2rV9xrQMV2NjIywtLQc9+Ywx2NraanR/crkcMpmMbyoDrndj9U5c7OzsYGtrO+rWfLG0tBzyQmeMwdLS4HmzWaDzrV90vvWLznf/lEoln4j0Tkxudhiqra3tkPfBcVyfL+8jyaB/yblz5/L/Dw8PR1xcHAIDA7FlyxY899xzfY5/+eWX1barpjdpS6lUoqGhAV5eXoMex3EcOjo6tL5/FYVCgZaWFrS0tPDbBAIBbG1t1Vpf7OzshlzwxpTJ5fIhkzOO46hZfJhu7C7r6emh861HdH3rF53v658tN3bddHZ23nRS0p+Ojo4hz7dSqYSDg8OIP7aKUaWc9vb2CA8Px4ULF/rdb21tDWtr65t+nMrKStTU1GiUqFy+fBlWVlY3/Zi9dXd3o7u7G42Njfw2Ozs7ODo68mNeHBwczOYbgab9xLa2tjc1zc3c3ThoUEU160FF06S3vr5+xGIbzerr6+Hj4zPkcYwxtRmGZHg0+TDmOA4PPfQQ7O3t9RCRbslkMn5wq+qnvr4eHMfB3t5e589RqVRq1AsQHh6usxiM6pOwq6sLZ86cQUJCgk4fR6FQYPr06WhqaoKDg0O/fwTGGOrq6jBp0iSdxjKQtrY2eHt78zONAgICMGnSJJOdcfTzzz/zo/T7Ex0dTbMiemGM9duPrGnyOtT5DgwMxD333MM/lspo65bU1kDnqvesn/5ER0fj9ddf1+r+e6O/i7qhru+oqCjMmjVLjxHdPMYYamtrcfHiRZSUlODSpUsoKSlBQ0NDn2P1vWxGXV3dgJNPGGNwdnbW6fo1Bl1H5Y9//CNuv/12jB8/HjU1NXjzzTeRm5uLoqIiTJgwYcjb38w6KsD1hGXnzp04deoUv011Ourq6lBWVqaTprSb4ebmxicvqp/BZi8ZC1r3YGBKpbLf2U4306I2nPN947Vu7NeUvmhyXnR5fVPy0pepv58olUpUVlbyCYkqKdHlOI+bwXEcJkyYADc3N7UW3Js539p8fhs0UbnnnnuQl5eHuro6eHh4IDY2Fn/7298QEhKi0e1vNlFRuXF1w0mTJqGxsVHtAiorKzPaqbOOjo5q67wEBgaqzXk3Jo2NjTh58iTa2tpG5UqScrm8z7dyjuN09qba2NiII0eOoL29HW5ubhqfb0pahtfSNNzzfTOx9Tba/k6qlVLb29vh7OxslO8ncrkcZWVl/OfJpUuXcPnyZbUZrMZGKBRi4sSJap8r48ePR1tb24itBGwyicrNGqlERRPd3d24cuWK2sVWWlqKnp4enT7ucNnY2GDixImYNGkSf6H5+fkZxbgX1SVn7m+qNyYlwPWZYPoeOK1qOnZ1dR3W7UdT19BIJGiqsWf6/sAc7K3cnP9ubW1tAGAU41E6OztRWlqq9jlRVlZm1AN77ezs+rTS+/r66rxFyqRWpjUVQqEQQUFBCAoK4rfJ5XJUVFSo9SdeunTppmYKjZTOzk6cOXMGZ86c4bepFl7rnSX7+/uPyADl0Ywx1mcmgmp6pDl8QPRu5jXHpMVcWo8GinuwQdhk+FpbW/t03VRUVBjdcIHenJyc1L68BgQEYOzYsUZ/LVCLyghjjKG6uloto7506dJNLU6nSxzHwc/PT61MQEBAgE6/nZhyi8pAg1wtLCyM9vncbItKf8whYdHVczBUi4o2zKnrSB8tKg0NDWpfSC9evIiamhqdPd5I8PT07NNSMtSK1fpEXT9GhjGG+vp6tVaXS5cuGfUqlWPHju0z7sXZ2XlE7ttUEhVVS0lvHMcZRfeZNnSRqKiYWmuEPuI1hUSlP6aavIxkosIYQ01NjdqXzJKSErWlJIwNx3Hw8fFRS0gCAgKMvqAudf0YGVXdIHd3d8TExPDbm5ub+exclcBcvXrVgJH+T3V1Naqrq7F//35+m6ura58Xg4eHh9G/kWlCqVT2aSnhOG7E19AxNzd2dxlrS4uxxmVMBpp6aq7dRqqZNzd+gVQlPsbIwsIC48ePV3sfnjhxotmXa6FExYDGjBmDadOmYdq0afy29vZ2XL58We2FYywzjhoaGtDQ0ID8/Hx+m6OjY5/mRWOdcaTS3yBXgUBASclNGihpMdS1YGqtPcbIXJKXnp4elJWVqX0xNKWZN6ovhhMmTBiV71OUqBgZOzs7hIaGIjQ0lN/We8aRKoEpLS01isqgLS0tOHHiBE6cOMFvU8046v0CU1U7FQqFeo1PlZTcuHKrOZcsMAaGHIBLrSe6pUny0vvvr8+/AWMMbW1tKC8vV3u/NPaZN/b29n0W9/Tx8THqtWD0iRIVE9DfjCOFQoGKigq1F2NJSQna29sNGOl1/c046uzsRElJCQIDAxEWFgYvLy/4+/sjISFBo+XHh9LfeBIAZjPzxlTpq5WFWk8Ma6gCgdocrynGGAoLC5GZmYmamhrU1taisLAQFhYWGldYNgRnZ+c+XeheXl50zQ6CBtOakRtnHKkSmN4VnQ2ltrYW48ePR0hICP+BpVrsrKKiAt3d3RCJRAgLC0NAQMCgpQIoKdGOLgfTDsdIJRXGmpyY6mBafRnOoF3VF7MLFy7g+++/x7lz5+Dj44PJkyf3eT9RvecZw0ebp6dnn0kJxvI6NDSa9UN4jDE0NDSoTasrKSlBbW2tXuNwdXXFxIkTB2w2Li4uhkwm47uG3N3d+YFi/v7+CAgIUFu+2dRm3hiSsSUqvQ2nm8bYu3YoUdFe779pT08Prly5wr9XlZSUqHV1FxYWIi4uDiEhIYPWabty5Yre4uc4Dr6+vny3jWqZB2OfeWNINOuH8DiOg5ubG9zc3NSKdKlmHPVueamsrNRJDKpBYQN9qHAch5CQEJw8eZIf3FZTU4OamhocPHiQH08yZswY/o1A9S3F29vbKD+siGY0HctirK0n5OaoJg/cOJ5EqVT2O76FMQYPD48BkxTgf7Msq6qqdDKOz9LSkp95o2opGQ0zbwyJEpVRqr8ZRx0dHfybhuqnvLy83wXOtOHq6jrkoDrGGNzc3FBdXT3gMc3NzSgsLERhYSG/zdbWVm0QWmBgIMaNG0ctLiZmoLEsva8bSk5Mm0wm67OS62DLMQz09w4KCtJokK6bmxuqqqpuKmahUMi3jqhaSsaPHz8qZ94YEr2bE56trS1CQkLUikKqZhz1bn25fPmyVt9UVJVNhzKcF39HRwdOnz6N06dPq92PqlSAKoHx9/fX+4wjQkYjXS5wyXEcHBwchnw/UZWw0Ia9vX2f8SS+vr40Q9AIUKJCBjXQjCPVQkm9vx0NNOOora1tyG8/HMeN2PTBnp4eXLhwARcuXOC3CQQCvlRA7xYYOzu7EXlMcnMG6tox9zpDpo4xhqqqKrWERNclQzSd8jzY+8mNM28CAwPh6elJ15WRokSFaE21OuL48eORkpIC4H8zjm58w1I19/ZOdAZSX1+vs5iVSiWuXLmCK1euICsri9/u7e2t1vISGBg46IwjMrI0TT6MbTG50UihUPRZn6SkpETvRVhlMhnGjx8/6DEcx/HvJ56enn0K8RnjwHIyMJr1Q3RGNePo66+/xvnz5+Hq6jrgKP3a2lqUlZUZIMq+3Nzc+nzbUs04MkXGNuuHpicbv+7ubpSWlqp98SgtLUVPT4+hQwPwvyrAA72fdHd34+6776aZN0aMpicTo6NQKLB7924cO3aM36a69EpKStDU1GQU6x4MxNHRsc8iTcZeKkDFWBIVXXbdGFPSYmqJSnt7e58ZgOXl5UZRtmMglZWV/JpLAPhlCxhjiIqKwrx582hVVyNHiQoxWo2NjSgqKkJrayscHBwQHh4OKysrfr0E1be3iooKo05cgOulAm6sc2SMM44MmagYIoEw9FgWY05UZDJZn0GuNzszRh98fX37jC8bM2ZMv+8nxnjeSV+UqBCT19nZyTc9qxZ+unLlilHX6wD+N+Oo9xvqxIkTDTrjyBCJiqGThRtj0GccxpCo9J550/tHl+PARoKFhQU/6L13dWBbW1tDh0ZGGC34RkyejY0NpkyZgilTpvDb5HI5ysrK1L4RXr58GZ2dnQaMVN1AM47GjRvX583X3t7egJGOPGPqfrnx8c15AC5jDFevXu0zkL2lpcXQoQ1KtRBk76R+woQJtIwA6YNaVIhJUyqV/Ju0quXl0qVLaG1tNXRoQ1LNOOrdfaSLGUe6bFExtuRkKPqIV5ctKnK5nC9G2ntpAGNK1vtjZ2fXp5vU19eXxpGMYtT1Q0Y11SyiG/viVR/YxszNza3PG7q7u/tNfaDqIlExhq6dm3Xj6rcj9TxGKlHp7u7G5cuX1Qa6GtPMm4E4OTn1GXg+duxYk71OiG5QokJIP5qamvp8Ex1syX5joZpx1DuB0WbG0UglKr2r1AKmm6DcaKRbWYaTqLS1tfUpX1FRUWHUM28AwMPDo09SMtAyBIT0RokKIRpqa2tT+8Z68eJFk5lxNHHiRLWFrPz8/PqdcXSziYo5tJ5oaiSSlqESFdXMm94tfsY+84bjOPj4+PRZHJHWKCHDRYkKITehq6sLpaWlah8mpaWlRj/jyNLSUm3GUWBgIPz9/dHW1gZAu0TF1Mae6MJwEzRVouLs7Iy6uro+rXimMPOmd3XgSZMmUXVgMuIoUSFkhMnlcrXlwy9evGh0M476Y21tDT8/P7i4uMDLywuRkZEIDw/vd8YRJSf90+S8qGbenDp1CidOnIBMJoNMJkNVVZVWBTz1TTXzpndLyYQJE6g6MNE5SlQI0QOlUomqqqo+zfjGMi3U19cXY8eO7TMtt7q6Gi0tLXjssccQGBioNqiUKsUOrr9WlnXr1uHSpUvw8fHB2LFj+eN6n+/Kykr9B3sDe3t7BAQE8C1ukyZNgo+PD828IQZBiQohBsIY69Pcb4gZR6okZaBv/8XFxWhsbIS1tTUAaj3RVu+3zeLiYsyYMQMhISEDnm99JyuqWji9x5N4eXnR35kYDVrwjRAD4TgOHh4e8PDwQGxsLL9dNeOo98BdXQ2gFAqFg04H5TgOISEhOHnypNFPdTVWvc+tm5vbgEmK6tixY8eitrZWJ91Anp6efWaFubi4UFJCzAYlKoTogbOzM6KjoxEdHc1v629Kanl5+U3POHJzcxvyGMYY5HI5rl69Cnd3d1oNdBhkMhlqa2sxfvx4ta6egbi5ud1UcspxHHx9ffkBrqpuHJp5Q8wdJSqEGIi9vT3CwsIQFhbGb1PNOOrd8nL58mWtZhxZWloO+cHJGIOVlRUqKipQWVkJJycneHh4wNnZmb6JD6Krqwt1dXWoq6tDV1cXAGDq1KlDJpeMMa2KVVpYWGDChAlqrSQ084aMVpSoEGJErK2tERwcjODgYH6btsumy+XyIZMNjuPQ0dEB4PqHaFNTE5qammBlZQV3d3e4u7tTIbj/UiqVaGpqQm1tLZqbm/skJR0dHRqd74GSzRtn3gQGBmL8+PE084aQ/6JEhRAjp1ofxd/fH2lpaQD+Nx32xjIBLS0tqK+vh4+Pz6D3yXEczp8/32d7T08PqqqqUFVVBQcHB3h4eMDNzW1UzgZqb29HbW0t6uvrB23RunjxImJiYoa8v/r6etjb2/cZT+Lr6zsqzy8hmqJZP4SYCdWMo5KSEmRnZ6Ozs3PQWT+5ubka3a+FhQVcXV3h4eEBBweHkQ7bqCgUCtTX16O2tpZfKE8TiYmJCA0NHfB8u7u7Y+nSpfD09KSuNUJA05MJGfW6urqwefNmfvBm77VUGGNYsmQJDhw4gLy8PNTW1mp8vz4+PkhMTMScOXPM5jXHGMO5c+eQl5eHw4cPazwTytXVFQkJCUhMTMT58+dx4sQJWFhYqJ1rjuMwZcoULFq0iJ8KTgihRIUQAuD06dN49dVX4ebmBktLS8jlcjz00EOYNWsWfwxjDEVFRZBIJDhw4IDGH9IWFhaIiYmBSCRCVFSUSXZdNDQ0ICsrC1KpFFevXtXoNpaWlvzznj59ep/nfeedd8LV1ZU/3++8885NV1EmxBzROiqEEABAd3e32pTYG5fO5zgOERERiIiIQGtrK3JzcyGRSFBSUjLo/SoUChw4cAAHDhyAm5sb0tLSIBKJ+JVZjZVcLkdBQQEkEgmOHj2qcXXi8ePHQywWIzk5GU5OTgMe1/t8cxxHSQohI4ASFUIIAMDBwQG33XYbbrvtNpSUlEAikSAnJ2fIsRr19fX47rvv8N133yEiIgIikQjx8fFGtTZLZWUlpFIpMjMz0dTUpNFtbGxskJSUBJFIhMmTJ9PYEkIMhBIVQkgfAQEBePTRR7F69WocPHgQEokEJ0+eHPJ2J0+exMmTJ/HJJ58gKSkJYrEYgYGBeoi4r87OTuzfvx8SiQTFxcUa327q1Km45ZZbMHv2bFq3hBAjQIkKIWRAQqEQSUlJSEpKQnV1NTIyMpCRkYH6+vpBb9fW1obdu3dj9+7dCAgIgEgkQnJyss5nDTHGcP78eUilUuTl5fFrxQzFycmJ774aN26cTmMkhGiHEhVCiEbGjh2L+++/H/fddx+OHTsGqVSKw4cPQ6FQDHq7kpISrF+/Hp9//jni4uIgFosRERExol0pzc3NyM7OhkQiQVlZmUa34TgOM2bMgFgsxowZM7RaOZYQoj/0yiSEaEUgEGDGjBmYMWMGZDIZP3OmvLx80Nv19PQgLy8PeXl58PLygkgkQlpaGtzd3YcVh1KpRGFhISQSCQ4fPqxxmQFvb2/+sV1dXYf12IQQ/aFEhRAybE5OTrjjjjuwaNEinDt3DhKJBHv37h1weX+Va9eu4auvvsLWrVsRFRUFsViMWbNmadSqUVNTA6lUioyMDNTV1WkUp1AoxOzZsyEWiwdcmI0QYpwoUSGE3DTVwmZTpkzBmjVrsHfvXkilUpw5c2bQ2zHGUFBQgIKCAjg5OSElJQVisRh+fn5qx3V3d+PQoUOQSqU4ceKExhWmg4KCIBKJkJiY2GdqNiHENFCiQggZUTY2NhCJRBCJRCgvL4dUKkVWVhZkMtmgt5PJZNi5cyd27tyJ4OBgPmHZu3cvsrOz0draqtHjOzo6Ijk5GSKRCBMnThyJp0QIMSBKVAghOuPn54fVq1djxYoVyM/Ph1QqxdGjRwdtEVEoFNi7dy9+/PFHdHZ2wsXFha8zNFCXDcdxiIyMhFgsRkxMjFGt4UIIuTmUqBBCdM7S0hJxcXGIi4tDfX09MjMzkZGRobZqbnNzM+rq6tDQ0KC2YmxdXR3q6upga2sLd3d3uLu7w8rKCgDg7u6O9PR0iEQieHp66v15EUJ0jxIVQoheubm54a677sLSpUuxb98+bNiwAfv27UN7e/ugt+vo6EB5eTkqKysRERGB+++/H/fccw+ftBBCzBMlKoQQvZLL5X26gaZMmYL6+nrU1dUNuGS/nZ0d36JiaWmJ7777DhkZGfxCbd7e3np+JoQQfaBEhRCiFxUVFZBIJP0OrLW0tISXlxe8vLzQ1taGuro61NfXgzEGNzc3eHh49Dtrp6GhAd9//z2+//57hIeHQyQSYfbs2TRGhRAzQokKIURnOjs7sW/fPkgkkiGnKqvY29tj1qxZSE5OhqWlJXJycnDixIkhb1dUVISioiKsX7+eLyYYGBhIa6YQYuIoUSGEjChVvR2JRIK8vLwhF39TcXFxQVpaGtLT0+Hr68tvT09Px7Vr1/g6Q0Mt8ta7ztDEiRP5OkOOjo439bwIIYZBiQohZETIZDK+3s5Qy+mrCAQCzJw5EyKRCNHR0QOuTOvl5YVly5bh3nvvxfHjx/k6Q0Mtm3/58mVs2LABmzZtQlxcHEQiESIjI6mVhRATQokKIWTYlEoljh8/DolEgiNHjmhcb8fHxwcikQipqala1dsRCASIjo5GdHQ0ZDIZcnJyNCpE2LvOkKenJ0QiEdLT04ddZ4gQoj+UqBBCtHbt2jVIpVJkZmZqVW8nISEBIpEIISEhN92q4eTkhIULF2LBggU4f/48pFIpcnNzh+xqqqmpwdatW7Ft2zZMnz4dIpEIMTExNM2ZECNFiQohRCPd3d04ePAgJBIJTp48qfHtgoKCIBaLkZCQoJN6OxzHITg4GMHBwXjooYewf/9+SCQSFBcXD3o7xhiOHTuGY8eOYcyYMUhJSYFIJMKECRNGPEZCyPBRokIIGVRJSQkkEglycnIGXOPkRo6OjvwHv7+/v24D7MXGxgZpaWlIS0tDZWUlX2V5qDpDzc3N+Omnn/DTTz9h8uTJfCFDOzs7PUVOCBkIJSqEkD5aW1uRm5sLiUSCkpISjW7DcRymTZvG19sxdFeKr68vVq1ahfvvvx9Hjx6FRCIZss4QAJw/fx7nz5/HZ599hjlz5kAsFmPq1Kk0AJcQA6FEhRAC4HpXSFFRESQSCQ4ePIju7m6Nbufp6Yn09HSkpaUZZb0dS0tLxMbGIjY2Fg0NDcjMzIRUKlWrM9Sfrq4uZGZmIjMzE76+vvzgXxcXFz1FTggBKFEhZNRTFQmUSqWorq7W6DaqIoNisdikpvu6urpi6dKluPPOO3H69GlIJBLs379/yKSssrISmzdvxhdffIGZM2dCLBYjOjoaFhYWeoqckNGLEhVCzJhQKISbmxssLS0hl8v5MSZyuRxHjhyBVCpFQUHBkN0hKv7+/hCLxSa/gBrHcQgLC0NYWBgeeeQR5OXlQSqV4sKFC4PeTqlU4vDhwzh8+DBcXV2RmpoKkUgEHx8fANfPt6urK3++GxsbqQWGkJvEMU3foXRs7dq1eOWVV/CHP/wB//rXvzS6TXNzM5ycnCCTyTBmzBjdBkiICVEoFNi6dSs/voQxBo7jwHEc7O3tUVhYOOQAUxU7Ozt+SfpJkyaZTOvJcJSWlvIDh1taWjS+3dSpU+Ht7c2f097ne9q0aZg/fz61vhDSizaf30bRopKfn48NGzYgIiLC0KEQYhZ2796NkpISPqnonVy0trbC2dl5yEQlLCyML/JnbW2t03iNhb+/P9asWYNVq1bh8OHDkEgkOHHixJAtTs3NzbCzs+v3fBcWFkIgEOD222/XaeyEmCuDJyqtra1YtmwZPv30U7z55puGDocQk9fY2Ihjx44N2PLBcRzc3d1RVVXVZ2xGf90Zo5FqcbqEhATU1NTwdYZqa2v7PXbs2LGDtjQdO3YMc+bMoW4gQobB4InKE088gdtuuw3p6elDJipdXV3o6urif29ubtZ1eISYnKKiInAcN2QrgKurK65evcovS5+WloaoqCi+i0LTYoLmbsyYMVi8eDEWLVqEkydPIiMjA0eOHIFCoQAAjUoAcByHoqIiJCYm6jpcQsyOQROVb775BgUFBTh69KhGx69duxZvvPGGjqMixLS1trYOmagolUrIZDJcu3YNbm5uKCgoQEFBgR6jNG09PT2or69HbW0thEIhvL29Bx2DwnEcWltb9RghIebDYIlKeXk5/vCHP0AikcDGxkaj27z88st47rnn+N+bm5vh5+enqxAJMUkODg5DtqYIBAK4u7sjKSkJ6enptGy8lq5cuYKsrCxkZ2fD1tZ2yAHGjDE4ODjoKTpCzIvBEpWCggLU1NQgOjqa36ZQKJCXl4cPP/wQXV1dfb6hWFtbj5pBfYQMV3h4OLKzs4c87tq1aygvL8eePXsQFBTELxuvi3o85qC9vZ2fxnz+/Hl+u0Kh0ChRCQ8P13WIhJglgyUqaWlpKCoqUtv2wAMPYMqUKXjxxRdpKh8hw+Ti4oKoqCgUFBQM+AHq7+8Pe3t7HDx4ED09Pbhw4QIuXLiAzz77DLNnz4ZYLEZoaKhZT0XWBGMMxcXFkEql2Ldvn9oYOeD6qrwikQgdHR24cuXKgOcrKiqKBtISMkwGS1QcHR0RFhamts3e3h5ubm59thNCtDNv3jzk5OTwi7Kp1vUAgOjoaMybNw8WFhZobW1FTk4OpFIpSkpK0N3djezsbGRnZ8Pb2xsikQhpaWkaDRg1J42NjcjMzERGRgYqKyvV9llZWfGr8kZERIDjOJSUlOC9996Du7s7APXzPXnyZMybN0/vz4EQc2HwWT+EkJFnYWGBuro6lJaWqq1Mq1Ao8Prrr/PHOTg4YP78+Zg/fz4uXboEiUSC3NxctLW1oaqqCl988QW++uorzJgxg1823tLSPN82FAoFjh49CqlUivz8fCiVSrX9AQEBEIlESEpK6rMqr0AgwJUrV1BVVaV2vuvr67Fq1SpqISbkJhjVO05OTo6hQyDErHR3d6sV3xusZSQwMBCPPfYYHnzwQRw4cABSqRQnT56EUqnEkSNHcOTIETg7OyM1NRVisRi+vr76eAo6d/XqVUilUmRmZqKxsVFtn729PZKSkiAWixEYGDjkfd14vgkhN8+oEhVCiOEJhUIkJycjOTkZVVVVyMjIQGZmJurr69HU1IQff/wRP/74I0JCQiAWizF79myNZ+4Zi87OTuzfvx9SqRSnT5/usz8iIgIikQjx8fEQCoUGiJAQokKJCiFkQN7e3li+fDmWLVuGY8eOQSqV4vDhw1AoFCguLkZxcTHWr1+PxMREiEQiTJ482WgH4DLGcPHiRb57q6OjQ22/m5sb0tLSkJ6eDm9vbwNFSQi5ESUqhJAhCQQCzJgxAzNmzIBMJkNWVhakUinKy8vR0dGB33//Hb///jvGjx8PsViMlJQUoykU2tLSguzsbEilUpSWlqrts7CwQExMDEQiEaKioiAQCAwTJCFkQJSoEEK04uTkhDvuuAOLFi3CuXPnIJFIsHfvXnR2dqKsrAyfffYZNm/ezCcA06dP13sCwBhDYWEhJBIJDh06BLlcrrbfz88PIpEIqampcHJy0mtshBDtUKJCCBkWjuMwZcoUTJkyBQ8//DD27dsHiUSCs2fPQi6XY//+/di/fz/c3d2Rnp6O9PR0eHl56TSmwQoI2tjYICEhAWKxGMHBwUbbRUUIUUeJCiHkptna2kIkEkEkEqG8vBxSqRRZWVmQyWSoq6vDN998g2+++QaRkZEQi8WIjY0dsUGqPT09OHToECQSCU6cONGnfMCUKVMgFosxZ84c2NrajshjEkL0hxIVQsiI8vPzw+rVq7FixQocOXIEUqkUBQUFYIzhxIkTOHHiBBwcHJCcnAyxWIyJEycO63FKS0shkUiQk5ODlpYWtX1OTk5ITU2FSCSiemCEmDhKVAghOmFpaYn4+HjEx8ejrq6OH4BbXV2N1tZW/PLLL/jll18wadIkfiG1oeoMtbW18fV2Lly4oLaP4zhER0dDJBJh1qxZZrswHSGjDb2SCSE65+7ujrvuugtLly5FUVERpFIpDhw4gO7ubly8eBEXL17Exo0bMXv2bIhEIoSFhfFjSBhjOH36NF9vp7u7W+2+x44dyw+MVS1hTwgxH5SoEEL0huM4REREICIiAo8++ihyc3MhkUhw6dKlPnWGYmNjwXEcDh06hKtXr6rdj1AoRHx8PEQiEcLDw2lgLCFmjBIVQohB2NvbY968eZg3bx5KSkr4AbiVlZU4f/48du3aBeD6eBMPDw84OTkhKCgIYrFYo24iQoh5oNWNCCEGZ21tDWtra1hYWECpVEKpVIIxBsYYWltb0dzcDCsrK741hpIUQkYPalEhhBiEqt6ORCJBcXExv93NzQ1ubm4IDAyEo6MjysrK0NDQAIVCgR07dmDHjh2YOnUqRCIREhISTK7OECFEO5SoEEL0hjGG8+fPQyqVIi8vr0+9nf4Wh1MqlTh+/DgkEgmOHDkCuVyOM2fO4MyZM9iwYQMt4kaImaNEhRCic83NzcjOzoZEIkFZWZnaPktLS8yaNQtisbjf5fYFAgGio6MRHR0NmUzG3095eTk6OzshlUohlUppWXxCzBQlKoQQnVC1hKgqLvdXb0dVwFDTxMLJyQmLFi3CwoULcf78eUgkEuTl5aGzsxPl5eX4/PPPsWXLFsTExAyY+BBCTAslKoSQEVVTUwOpVIqMjAzU1dWp7bOxsUFiYiLEYjEmT5487K4ajuMQHByM4OBgtTpDZ86cgUKhwIEDB3DgwAG4u7sjLS0N6enpGDt27Eg8PUKInlGiQgi5ad3d3Th06BCkUmm/9XamTp3K19sZ6cGvNjY2/LiWiooKSKVSZGZm8nWGvv32W3z77beIiIiAWCxGXFzciNUZIoToHiUqhJBhu3z5MqRSKbKzs9Ha2qq2z8nJCWlpaRCJRBg3bpxe4hk3bhweeOABLF++HEePHoVEIsHRo0fBGMPJkydx8uRJ2Nvb83WGAgIC9BIXIWT4KFEhhGilra0Nubm5kEqluHjxoto+juMwY8YMiMVizJgxw2D1diwtLREbG4vY2Fg0NDQgMzMTUqkUVVVVaGtrw6+//opff/0VAQEB/AJyDg4OBomVEDI4SlQIIUNijOHUqVOQSqXYv39/n3o73t7eEIlESEtLg6urq4Gi7J+rqyuWLl2KO++8E6dPn4ZEIuGfQ0lJCT755BN8/vnniIuLg1gspiX5CTEylKgQQgZUX1+PzMxMZGRkoKqqSm2fUCjE7NmzIRaLERoaavQf7hzHISwsDGFhYXjkkUfUqjB3d3cjNzcXubm5GDt2LNLT05GWlkZFDgkxApSoEELUyOVy5OfnQyqV8uM7eps0aRLEYjESExNNdil7e3t7zJ07F3Pnzu0zzqa6uhpfffUVtm7diqioKIjFYsyaNctg3ViEjHb0yiOEAAAqKiogkUiQlZUFmUymts/BwQEpKSkQiUSYOHGigSLUjYkTJ2LNmjVYtWoVP3OpsLAQjDEUFBSgoKAATk5OSElJgVgshp+fn6FDJmRUoUSFEDMmFArh5ubGt4oolUq1/Z2dndi7dy+kUinOnDnT5/bTpk2DSCRCbGys2U/pFQqFSExMRGJiYp+1YGQyGXbu3ImdO3ciODgYYrEYCQkJsLW17XMfDg4OsLe3h1wuR319vYGeDSHmg2M3tuuakObmZjg5OUEmk2HMmDGGDocQo6FQKPDCCy/A0dGRr0LMcRw4jkN0dDQmTZqEjIwMflXX3lT1dkQiETw9PQ30DIyDUqlEYWEhpFIpDh06pLa6ro2NDRISEiASiSAUCvH+++/D3d2dP9+qFXEnT56Mu+++GxYWFoZ6GoQYHW0+vylRIcQM/fzzzygoKOh3gCtjDHV1dbhy5Qq/TTWdVyQSYdq0abTsfD9U9YqkUqnauQOuj9txcnIacEBxVFQUbr/9dn2ESYhJ0Obzm7p+CDEzjY2NOHbs2IAfmhzHwd3dHRKJBAqFAl5eXvDw8EB+fj7y8/P1HK3pYYyhu7sbNTU1qK2thZ2dHaKjowed9XTs2DHMmTMHLi4ueoyUEPNAiQohZoQxhh9//JHv6hnsuPj4eNTV1YHjOCiVSnR1dekxUtMmFAoxbtw4eHt7w8XFZcjzDQDHjx9HamqqniIkxHxQ+y4hZqKrqwv/+te/UFBQ0GdKcX/s7OyMfu0TY2dhYcGPAxqMUqnE77//jvLycj1FRoj5oBYVQszA1atXsXbtWpSWlsLb23vIBITjOPT09PAfsBYWFrCystJHqGahd2LCcdyQY3o4jkNjYyOee+45PP3000hISNB1iISYDUpUCDFxhw4dwvvvv4/29nYA11eT9fHxGfQ2HMdh3bp1/JiJtrY2NDc38/sdHR2p9s1/McagUCj45O/GxKSxsREffPDBkPdTX1+P7u5urFu3DmfPnsUDDzxAi8gRogF6lRBiohQKBb788kts375dbXt3dzfq6urg7u4+6CyU3gM77e3t1VaZbW5uVlsy38XFBTY2NiP8DIyTKjFR4Thu0ITCxcUF06ZNQ2Fh4YD3V1dXp1YfadeuXbhw4QJefPFFuLm5jVjshJgjmp5MiAlqbGzEunXrcOrUqX73cxyH8ePH88mKQCDg1/eIiorCvHnztFrXo7GxUW29FTc3N7NaAE7VYtK7K0zb2+/evZufbcVxHJ/s1NXVoaysrN9xLE5OTnjhhRcQERFx80+CEBNC66gQYsaKi4vxzjvvoKGhYdDjJkyYgCeffBI1NTVobW2Fg4MDwsPDb3qKLGMM9fX16Onp4bd5enqa1IJmvVtMAEAgEIzIwOLGxkYUFRWhtbUVNjY2yM/Px759+wa9DcdxWLFiBZYsWUKDm8moQYkKIWaIMYZdu3bh888/77MU/o1SUlLw+OOP66W7hjGG2tpa/sNfIBDA09PTqD50lUqlWovGSCUmQ2GM4ddff8XGjRvVVrXtT0xMDJ599lmTLfRIiDYoUSHEzHR0dODf//439u/fP+hxlpaWePjhhzF37lyDJQoKhQK1tbV8YmBpaTnoeBldMFRiMpCzZ8/i7bffHrL2j7e3N1566SUEBAToKTJCDIMSFULMSFlZGd566y1UVlYOepy7uztefvllTJ48WU+Raaanpwd1dXX879bW1nB1dR3Rx2CMqbUyGTox6Y9MJsO7776LEydODHqcUCjE448/jrS0ND1FRoj+UaJCiJnIy8vDBx98MOSqsdOnT8cf//hHk3gddHV1qY2vsbOzg5OTk1b3cWNioslaJsZAqVRi69at+O6774Y89tZbb8XDDz9sVoOWCVGhRIUQEyeXy7Fx40b88ssvQx57zz334N577zWJD+r+aLKGi6kmJgPJz8/HP//5T7S1tQ163KRJk/Dyyy+P+irWxPxQokKICaurq8Pbb7+Nc+fODXqcg4MDnn/+ecyYMUNPkelHS0sLWlpa+MTE2dkZtra2JjWrSBPV1dVYu3YtSkpKBj3OwcEBf/zjHxEdHa2nyAjRPUpUCDFRJ06cwLp169RaGPpjjt+0b5wyrEpMzHkNl+7ubnzyySeQSqWDHsdxHO6++26TbjkjpDdKVAgxMYwxfP/99/jqq6+GLHBnLmMXhrOWSX9ruHh4eJj8UvRSqRQff/yx2vPqjymNRSJkMJSoEGJCWltb8d577yE/P3/Q40x9Nogupgybwhoumrp06RLWrl2La9euDXqcsc7uIkQblKgQYiJKSkrw1ltvDfnhZIrraxhiLROlUomamhqDruFyMzRNWo1hvRxCbgYlKoSYgIyMDHz88cdqxer6YyorlhrjWiZyuRy1tbX877pYw2WkadMNmJKSgieeeALW1tZ6io6QkUGJCiFGrLu7G+vXr4dEIhn0OGOvAWOKU4ZHYg0XfSksLMS777475MBqf39/vPzyy/Dx8dFTZITcPEpUCDFS165dw9q1a3Hp0qVBjzPGqrqmmJgMpb29HTKZjP/dwcEBjo6OBoxInaZT1e3s7PDMM88gLi5OT5ERcnMoUSHECGm6yNeUKVPw0ksvwc3NTU+R9Y8xxv+omNtaJjdqaWlBa2sr/7tqDRdD0mbxv8WLF2PFihVm/3cipo8SFUKMiFKpxLZt2/Dtt98OeeyCBQvwwAMPGGy6rUKh4LuZGGOj/gOvqakJHR0d/O+GXMMlNzcX//nPf4YspxAWFoYXXngBLi4ueoqMEO1RokKIkZDJZPjHP/6BwsLCQY+zsbHB008/jYSEBP0E9l+9u3KA6905xjgexhgYwxoumhaodHFxwYsvvojQ0FA9RUaIdihRIcQInDt3Dm+//bZa5eD+jBs3Dq+88gr8/Px0HpMhpgybqxvXcOE4Dp6enjoft9Pe3o4PPvgA+/fvH/Q4gUCA1atXY8GCBfQ3JkaHEhVCDIgxht9++w2ffvop5HL5oMfOmTMHTz/9tM7GQVBioj9KpRK1tbV8K5WFhQU8PDx0cr4ZY9i1axc+//zzPq1iN5o9ezaefvpp2NnZjXgchAyXzhOV8vJylJaWor29HR4eHggNDTXIPH5KVIix6ezsxP/7f/8POTk5gx5nYWGB1atX4/bbbx/RDzJjXMtktLpxDRehUAhXV9cR/XucPn0a77zzDhobGwc9ztfXF6+88grGjx8/Yo9NyM3QSaJy5coVfPLJJ/j6669RXl6u9i1NKBQiISEBa9aswZIlS/Q2ZZESFWJMKisr8dZbb6GsrGzQ41xdXfHSSy9h6tSpN/2Y5jhl2Fx1d3ejvr6e/32k1nBpbGzEunXrcOrUqUGPs7a2xlNPPYWkpKSbfkxCbtaIJyp/+MMfsGnTJojFYixYsACzZs2Cr68vbG1t0dDQgFOnTmHv3r34+uuvYWlpiU2bNmHmzJkj9oQGQokKMRYHDhzAv/71L7UZIv2JiIjAn/70Jzg7Ow/rcSgxMR8juYaLQqHAF198gR9//HHIY+fPn48HH3zQ5As5EtM24onKn/70J7zwwgvw8PAY8sF3796N9vZ23HnnnZpHPEyUqBBDk8vl+OKLL7Bjx44hj73zzjtx//33azXl98Z1TABQYmKmblzDxcnJSetxJZomzMHBwXjppZfg7u4+rFgJuVk0mJYQPWhoaMC6detw+vTpQY+zt7fHs88+i5iYGI3ut/daJgAlJqOVTCZDe3s7/7urq6tGYwErKyuxdu1aXLlyZdDjxowZgz/96U+YNm3azYZKiNYoUSFEx06dOoV33nkHTU1Ngx7n7++PV155Bd7e3gMeQ2uZkKEwxtDQ0KBWwHKwNVw6Ozvx0UcfITs7e9D75TgO999/P5YuXUrXHNErnSYq9fX1ePXVV5GdnY2ampo+b7K9C37pGiUqRJe6u7tRXV0N4Hqz/KVLl9DR0YGKigrk5OTwzevW1tb9tnqkpqbi8ccf7/MtmBITcrM0WcOFMYY9e/Zgw4YN/U6TZ4zxic/06dMRFxcHhUIBW1tbBAUF8e+pbm5uNJ6FjDidJipz587FpUuX8OCDD8LLy6vPG+zKlSs1vq+PP/4YH3/8MUpLSwEAoaGhePXVVzF37lyNbk+JCtGlsrIyPPnkkxg/fjzfl696uXAch+LiYuzduxehoaFq66BYWlrikUcewS233AKO49TWMlElJZSYkJE02Bou58+fx9q1a/ssPNjV1YWioiIkJCQgJCRE7RoFrhdELCsrw6effgovLy/9PiFi9rT5/NY6Td63bx/27duHyMjIYQeoMm7cOLz99tuYNGkSAGDLli1YuHAhjh8/Tks/E6OgSlJUb969E4yQkBAA6q2Inp6eePHFFxEYGMh/aAgEAhpnQnRKIBCoJRNyuZxvDXR0dMRrr72GjRs39inloEpS+kueaaAtMRZav3tOmTJlyBHlmrr99tsxb948TJ48GZMnT8bf//53ODg44NChQyNy/4TcjJaWFrUk5UYcxyEkJATW1tZgjGH69On45z//iaCgIFhYWPA/1HpC9M3S0hLe3t78j4+PDx599FHceuut6OzsRE9PD6ytrfkkpT8cx8Hd3R3Nzc16jp4QdVq3qHz00Ud46aWX8OqrryIsLAxWVlZq+4fbBaNQKPD999+jra0NcXFx/R7T1dWlVjmUXkBEly5dugTG2KCJBmMMzs7OiIqKwty5c1FVVYWqqio9RkmI5lJTU+Hp6YmNGzfCwcFhyOsbAC5cuICgoCA9RUhIX1onKs7OzpDJZEhNTVXbrrrgVYO7NFVUVIS4uDh0dnbCwcEBO3bs4JvUb7R27Vq88cYb2oZMyLB0dnYOeQxjDEKhEDk5OUMum0+IsVAqlX3qQPWHMTZiLeiEDJfWicqyZcsgFAqxbdu2fgfTais4OBiFhYVoamrC9u3bsXLlSuTm5vabrLz88st47rnn+N+bm5v1UnGWjE42NjZDXt8CgQCJiYmIjY3VU1SEjIxDhw7xExkGwnGczgpmEqIprROVU6dO4fjx4wgODh6RAIRCIT+YdsaMGcjPz8e///1vrF+/vs+x1tbWBil+SEanwMBAHDt2bMjjIiMjMWHCBNja2tJ4FGL0VC0pHMcNuSgcAOr2IQan9WDaGTNmoLy8XBexALje1Nh7HAohhuLo6Ii6uroBm8cZY6irq4O7uzvs7OzQ1dWFzs5OtUW5CDEGqhpRSqUSHMfBwsICLi4uGl3ftPQDMTStW1Seeuop/OEPf8Cf/vQnhIeH9xlMGxERofF9vfLKK5g7dy78/PzQ0tKCb775Bjk5OdizZ4+2YRGiE6pKyO7u7gOuM6FiY2MD4PrU0M7OTjDGBlwMjhB96F2Oob/rsPf1DUBtcO2N1zchhqJ1onL33XcDAFavXs1v4zhuWINpr127huXLl6OqqgpOTk6IiIjAnj17IBKJtA2LEJ1gjOHKlSuoqqriV+iUy+Wor68fsOXE0tKSX8mzq6uLf21QtyXRh96FLAUCwZCz1rS9vgnRN60TlcuXL4/Yg2/cuHHE7osQXeru7h7WtGNVcqJUKvlZRL0TGUJGQu/khOM4rVvxhnt9E6IPWr9bTpgwQRdxEGLWBAIB3zXU3d3Ndw1pMrOIkIH0rhtFXYzEXGl0ZR88eFDjO2xraxuy7D0ho5lQKISNjQ1sbGzQ2dmJjo4OamYnGmOMQaFQ8ONPqEQDMXcaXd0rVqyASCTCd999h9bW1n6PKS4uxiuvvIJJkyZpNKWTkNFOtUaFra0tBAIBOjo60NHRMeQiXGR0UiUnjDEqz0BGFY26foqLi7F+/Xq8+uqrWLZsGSZPngwfHx/Y2NigsbERZ8+eRVtbGxYvXgypVIqwsDBdx02IWek9bkXVLSQQCGgA7iinmlYMDD0wlhBzpVGiYmVlhSeffBJPPvkkjh07hr1796K0tBQdHR2IjIzEs88+i5SUFLi6uuo6XkLMnmosi0Kh4AfgWllZwcLCwpBhET25cWAs/d3JaKf1YNqoqChERUXpIhZCSC+q5n3g+gDcnp4eAP9LZIh5USUoqq4dQsh1NEeSEBMgFAoBXP8wo2nO5uNmpxUTMhrQuxwhJoTjOL5Fpaenh09arK2tafyCCaFpxYRojhIVQkyUlZUVX8JClbAIBAK+9YUYlxtbTyixJEQzlKgQYgZ61xnq6OgAcL27iMY6GB61nhBycyhRIcSM3FhnqLu7W627iOgHTSsmZOQMK1HJzMxEZmYmampq1L4tAMDnn38+IoERQm5O7zpDqlYWKysrGoCrIzcmJ9SaRcjI0Pod64033sBf//pXzJgxA97e3vRNgRAjJxAIYGtrC+D6NOeOjg6+mjO9fm+eUqnkx55QckLIyNM6Ufnkk0+wefNmLF++XBfxEEJ0qPc0566uLgA0AHc4aFoxIfqjdaLS3d2N+Ph4XcRCCNGT3uNW5HI5P2tIKBTSh+4gaGAsIfqn9SvtoYcewrZt23QRCyHEACwtLflqzt3d3ejs7ORbW8j/qhUrlUqqVkyIAWjdotLZ2YkNGzYgIyMDERER/DoOKu+9996IBUcI0S9VK4tSqRzVK+D2HhhL9XYIMSyt331OnjyJadOmAQBOnTqlto8G5hFiHgQCAZ+0qFpZAPOvM0QDYwkxPlonKtnZ2bqIgxBipPqrM2RhYdGnNdVU3TitmLp1CDEuN9WeW1FRAY7j4OvrO1LxEEKM1I0DcFVrs9jY2Jhka6qq9YS6dggxblp/dVAqlfjrX/8KJycnTJgwAePHj4ezszP+9re/9Vn8jRBiniwtLWFrawtbW1t0dXWhs7MT3d3dhg5rSKrWE4VCwSco1IJCiHHTukXlz3/+MzZu3Ii3334bs2fPBmMM+/fvx+uvv47Ozk78/e9/10WchBAjpWplUSgUfNeQlZWVUbVSqBITWvOEENOjdaKyZcsWfPbZZ1iwYAG/LTIyEr6+vnj88ccpUSFklLKwsOCTk+7ubvT09AAw3ADc3ouyUb0dQkyX1olKQ0MDpkyZ0mf7lClT0NDQMCJBEUJMW38DcPUxzZlWjCXE/Gj9Ko6MjMSHH37YZ/uHH36IyMjIEQmKEGIeVANwbWxs+KSlo6ODTyZGimrcCWOMn7lDLSiEmAetv96sW7cOt912GzIyMhAXFweO43DgwAGUl5dj9+7duoiREGIGrKysYGVlNWJ1hmhaMSGjg9av7KSkJJw/fx533HEHmpqa0NDQgMWLF+PcuXNISEjQRYyEEDPSu5VFIBCgo6MDHR0dGs8aVCgUfOuJalwMtZ4QYr6G1WHs4+NDg2YJITet97iVrq4ufl0Ta2trteNoYCwho5dGicrJkycRFhYGgUCAkydPDnpsRETEiARGCBldVMmJqs6QqsXE0tKSBsYSMopplKhMmzYN1dXV8PT0xLRp08BxXL+D4TiOg0KhGPEgCSGji1Ao5KsWqxaSM/c6Q4SQ/mmUqFy+fBkeHh78/wkhZCTdOK1Y1bWjWpfFXOsMEUKGplGiMmHCBP7/V65cQXx8fJ/1EORyOQ4cOKB2LCGEDKb3ANrBunZurDOkSlqsra1pvAohZk7rTt+UlJR+F3aTyWRISUkZkaAIIeZL1aWjWtZe26nFlpaW/KwhU6ozRAgZHq1n/ahG5d+ovr4e9vb2IxIUIcT8qMavjWS1YlOoM0QIuTkaJyqLFy8GcP1NZtWqVWrTBxUKBU6ePIn4+PiRj5AQYrJuXJRNV900xlZniBAycjROVJycnABcf+NxdHSEra0tv08oFCI2NhYPP/zwyEdICDEpNw6M1XfrhqHqDBFCdEPjV+6mTZsAAP7+/vjjH/9I3TyEEDWaDozVl94DcHt6emgALiEmSuuvGK+99pou4iCEmKCBphUbm5GuM0QI0R+NEpWoqChkZmbCxcUF06dPH/TN6NixYyMWHCHEOBlb64mmBprmLBQKTep5EDKaaJSoLFy4kB88u2jRIl3GQwgxUqbSeqIpTesMEUIMS6NEpXd3D3X9EDJ63JicmGurw411hgAagEuIsdD6Xae8vBwVFRX870eOHMEzzzyDDRs2jGhghBDDUSqVUCgUUCqV/IJspt6CogmBQMAvJqdKWlQFEgkhhqF1onLfffchOzsbAFBdXY309HQcOXIEr7zyCv7617+OeICEEP24ccXY3muTjEZCoRA2NjawtrbmV8BVrc9CCNEfrROVU6dOYdasWQCA7777DuHh4Thw4AC2bduGzZs3j3R8hBAdU7WeMMb45GQ0tJ5oSjUA18bGBhzHUSsLIXqmdQdsT08P35+bkZGBBQsWAACmTJmCqqqqkY2OEKITo2XsyUjrbwAuTXMmRLe0fncKDQ3FJ598gr1790IqleLWW28FAFy9ehVubm4jHiAhZGSounZ6L2k/Wsae6IK1tTVsbGxgYWHBt7Ko6hkRQkaO1onKO++8g/Xr1yM5ORn33nsvIiMjAQC7du3iu4QIIcajd9cOJScjz8LCgu8aUhVHVM0cIoTcPK27fpKTk1FXV4fm5ma4uLjw29esWQM7O7sRDY4QMjw3FgOkrh39oDpDhIy8Yb16LCwsIJfLsW/fPnAch8mTJ8Pf33+EQyOEaEupVPJjT0bzjB1D66/OEGOMH5BLCNGc1l+z2trasHr1anh7eyMxMREJCQnw8fHBgw8+iPb2dl3ESAgZBE0rNm5WVlZ815BqmnN3d7ehwyLEZGidqDz33HPIzc3Fzz//jKamJjQ1NeGnn35Cbm4unn/+eV3ESAjphyo5oWnFpqH3NGeBQMCPZeldN4kQ0pfWXT/bt2/HDz/8gOTkZH7bvHnzYGtri7vuugsff/zxSMZHCOlFNa1YNTCWEhPTRHWGCNGc1olKe3s7vLy8+mz39PSkrh9CdIDWPDFvVGeIkMFp/Y4XFxeH1157TW36XUdHB9544w3ExcWNaHCEjGZKpZIfHEvTis1f7zpDqllDtAIuIcNoUfn3v/+NW2+9FePGjUNkZCQ4jkNhYSFsbGzw+++/6yJGQkaNG1tPKDEZnaysrGBlZQXGGLq6ugBcn8VlZWVl4MgI0T+tE5WwsDBcuHABX331Fc6ePQvGGO655x4sW7YMtra2uoiRELOnWtGUunZIb72nOcvlcr4l29rampJYMmoMqxPU1tYWDz/88EjHQsiocuOibPTBQwbTe9yKKmGhOkNkNBhWonLu3Dn85z//wZkzZ8BxHKZMmYInn3wSU6ZMGen4CDErvZMT1ZonhGhL1cqiWrIfuN5dRNcTMUdatzH/8MMPCAsLQ0FBASIjIxEREYFjx44hPDwc33//vS5iJMTkqRIU1cBYCwsL6uIhN43qDJHRQOsWlRdeeAEvv/wy/vrXv6ptf+211/Diiy9i6dKlIxYcIYb0+eefo7i4GADg4OCAiRMnwtbWFh0dHbh8+TJaW1sHHZdF04qJPmlbZ2jjxo1DXt9Uv40YA60TlerqaqxYsaLP9vvvvx/vvvuuVve1du1a/Pjjjzh79ixsbW0RHx+Pd955B8HBwdqGRciIu3r1Ktrb25GQkICQkBA+8eA4DtOmTUNxcTGOHTvW53a9Vxql5ITom6YDcCsqKoZ1fROib8Oqnrx3715MmjRJbfu+ffuQkJCg1X3l5ubiiSeewMyZMyGXy/HnP/8ZYrEYxcXFsLe31zY0Qkac6k28v6nCISEh/HRRmlZMjNFQA3A1vb4JMSStE5UFCxbgxRdfREFBAWJjYwEAhw4dwvfff4833ngDu3btUjt2MHv27FH7fdOmTfD09ERBQQESExO1DY2QEWVhYcG/ifeH4zgEBQVBJpNh3Lhx1HpCjNqNA3AZYxpd383Nzf2uRk6IvmidqDz++OMAgI8++ggfffRRv/uA6xe5am0ITclkMgCAq6trv/u7urr4xY8AoLm5Wav7J0QbdnZ2fFP4QBhjuHz5MsLDw/UYGSHDpypg6eDgoNH1feHCBQQFBekxQkLUaf0VULWs91A/2iYpjDE899xzmDNnDsLCwvo9Zu3atXBycuJ//Pz8tA2fEI0JBIIhly/vPXCREFOi6fXd0dGhp4gI6Z/RtFU/+eSTOHnyJL7++usBj3n55Zchk8n4n/Lycj1GSEYbpVI55FgTqnhLTFVXV5dG1zetOE4MzSjKcz711FPYtWsX8vLyMG7cuAGPs7a2pg8Fojft7e1wdHQc9BiO45CTk4PW1lakp6dTXz4xaj09PTh06BAKCgpw8eJFzJgxY9DjVeNUCDEkgyYqjDE89dRT2LFjB3JycjBx4kRDhkOIGoVCgeLi4gEHHKr679966y20t7fju+++Q3NzMxISEhAbG0tLmxOjUVpaiqysLLS1tSE2NhZPPfUUzp8/r9H1PWbMGANETMj/aJyoVFRUDNraMRxPPPEEtm3bhp9++gmOjo6orq4GADg5OVFzIzEKe/fuBYA+60xwHMevM8FxHKZOnYqpU6eio6MD+/btw9tvvw0vLy+IRCIEBAQY+FmQ0aitrQ15eXk4e/Ys/P39sXjxYjg7O6sdo8n1TYihcWyo0VT/5ezsjP/85z9Yvnz5yD34AP2jmzZtwqpVq4a8fXNzM5ycnCCTySjrJyPu2WefRVFREYDr3Y6enp4QCoXo7u5GTU0Nurq6EBAQgA8//LDf1pPy8nJIpVKUlZVh1qxZSEpKovWBiE4xxnD69Gns3bsXAoEAiYmJmDJlSr/vtc8//7za9e3h4QErKyv09PSgtrYWXV1dmDRpEj744IMBV7clZLi0+fzW+Op766238MQTT2Dnzp3YsGED3NzcbjpQDXMkQgzCwsJCrWWvrq6O/79AIICtrS3GjBkzYBePn58fVq9eDblcjiNHjuCDDz6AjY0N0tLSEB4eTovCkRHT0NCAzMxMVFZWIjQ0FKtWrRqyVVogEKiN+autrVXbb21tDXt7e0pSiMFpfAU+/vjjmDt3Lh588EGEhoZiw4YNQy7oRgi5vjpofHw84uPjUVdXh6ysLL64Z1pa2ogk/WT0kcvlOHr0KI4cOQInJyekpqbSkg3ELGmVKk+cOBFZWVn48MMPsWTJEkydOrVPtk19moQMzN3dHXfddReWLl2KoqIifPHFF+js7ERycjJmzpxJ317JkCorK5GRkQGZTIYZM2bg8ccfp+uGmDWtr+4rV65g+/btcHV1xcKFC+kFQsgwcByHiIgIREREoLW1Fbm5uXjzzTcxfvx4iMXiER+4TkxbZ2cn9u3bh6KiIvj6+uK2226Du7u7ocMiRC+0yjI+/fRTPP/880hPT8epU6fg4eGhq7gIGTUcHBxw22234bbbbkNJSQl+/fVXXLt2DXFxcUhISOBrtJDRhTGG8+fPIzc3FwqFArNnz8YzzzxDY5vIqKNxonLrrbfiyJEj+PDDD7FixQpdxkTIqBUQEIBHHnkE3d3dOHjwIP7xj3/A2dkZ6enpCA4Opg+pUaC5uRnZ2dm4fPkyJk+ejPvuuw8ODg6GDosQg9E4UVEoFDh58iQ1SROiB0KhEElJSUhKSkJ1dTUyMjKwbds2TJ8+HampqXBycjJ0iGQEKZVKHD9+HIcOHYKNjQ1SUlKwcOFCQ4dFiFHQOFGRSqW6jIMQMoCxY8fi/vvv5z/MNmzYAABISUlBVFQUBAKjKdlFtHTt2jVkZmaipqYG06dPx8MPP0wrGhNyAxoJS4iJEAgEiI6ORnR0NGQyGbKzs7Fr1y4EBQVBJBJh7Nixhg6RaKC7uxuHDh3CsWPH4OHhgbS0NPrbETIISlQIMUFOTk5YtGgRFi5ciPPnz2P79u1obGzEnDlzEB8fT9/KjVBJSQmys7PR0dGBuLg4PP3009QaRogGKFEhxIRxHIfg4GAEBwfzU1jffvtteHp6QiwWU50hA2tra0Nubi7Onj2LgIAA3HnnnTS+iBAtUaJCiJmwsbFBeno60tPTUVFRAYlEgi+++AIzZ85EUlISzRzRE8YYTp06hX379sHCwgJJSUmYO3cuzdgiZJgoUSHEDI0bN46vM5Sfn48PP/wQ1tbWVGdIh+rr65GVlYXKykqEhYXhgQceoDVwCBkBlKgQYsYsLS0RFxeHuLg41NfXIzMzEz/88ANCQ0ORlpZGq5veJFUimJ+fD2dnZ6SmptISDoSMMEpUCBkl3Nzc+DpDp06dwldffYWOjg4kJSVh1qxZVA5DCxUVFcjMzERzczNmzpxJ9XYI0SF6ZREyynAch/DwcISHh/ODPd988034+flBLBZTBd4BdHZ2Yu/evTh16hTGjRuH+fPnU+VrQvSAEhVCRjF7e3vMmzcP8+bNw+XLl/Hbb7+huroasbGxSEhIgK2traFDNCjGGM6dO4e8vDwoFArMmTMH6enpNMaHED2iRIUQAgCYOHEi1qxZwy9I9t5778HR0REikQhTpkwZVR/OMpkMWVlZKC0tRXBwMJYtWwZ7e3tDh0XIqESJCiFEjVAoRGJiIhITE1FTUwOpVIqvv/4a06ZNQ2pqKpydnQ0dok6oShQcPHgQdnZ2SElJwR133GHosAgZ9ShRIYQMyNPTE8uWLeM/xD/99FMwxvg6QxYWFoYO8aZVV1cjMzMTdXV1mD59OtasWUMr+xJiRChRIYQM6cY6Qzk5Ofjll18wadIkpKenw9vb29AhaqW7uxsHDx7E8ePH4eXlBZFIBE9PT0OHRQjpByUqhBCtODk5YeHChViwYAHOnz+PH3/8EY2NjZg9ezZmz55t1K0Rqno7nZ2dVG+HEBNBiQohZFhurDO0f/9+vP322/Dw8IBIJEJgYKBRDMBtbW1Fbm4uzp8/T/V2CDFBlKgQQm6ajY0N0tLSkJaWhsrKSkilUnz11VeIjo5GcnIyHB0d9RoPYwxFRUXYt28fLC0tkZSUhHnz5hlF4kQI0Q4lKoSQEeXr64tVq1ZBLpejoKAAH330EaysrJCamorIyEidJgt1dXXIysrC1atXER4ejtWrV1O9HUJMHCUqhBCdsLS0RExMDGJiYtDQ0ICsrCz8+OOPCAkJQXp6+ojVGZLL5Thy5AiOHj0KZ2dnpKWlwdfXd0TumxBieJSoEEJ0ztXVFXfeeSeWLFmC06dPY+vWrWhvb0dCQgJiYmJgZWWl9X2Wl5cjKysLzc3NmDVrFtXbIcRM0auaEKI3HMchLCwMYWFhaGtrw969e/HWW2/B19cXYrEY48ePH/T2HR0d2LdvH06fPo1x48bh9ttvh6urq56iJ4QYAiUqhBCDsLe3x6233opbb70VpaWl2LNnD6qqqvg6Q3Z2dgCuD4w9e/Ys9u7dC6VSSfV2CBllKFEhhBicv78/1qxZg56eHhw6dAj/+te/YGFhAVtbWygUCkydOpXq7RAyStFKR4QQoyEQCGBjY4MxY8bAxsYGjDE0Njaira0N3d3dhg6PEGIA1KJCCDG4qqoqZGZmor6+HtHR0XjkkUf4AbZKpRInTpzA559/DrlcjpSUFERHR5tFnSFCyNAoUSGEGER3dzf279+PEydOwMvLC2KxuN96OwKBANOnT8f06dPR0tKC7Oxs/PWvf0VAQABEIhF8fHwMED0hRF8oUSGE6NWlS5eQnZ2Nrq4uxMfHa1Vvx9HREQsWLMDtt9+OixcvYufOnaivr8ecOXMQHx8Pa2trHUdPCNE3SlQIITrX0tKCnJwcXLx4EYGBgbj77rtvall9juMQFBSEoKAgdHV1Yf/+/Xj33Xfh5uaG9PR0TJo0iWYFEWImKFEhhOgEYwwnT57E/v37YWVlheTkZMyfP3/EEwhra2ukpqYiNTUVV69ehVQqxdatWw1WZ4gQMrIoUSGEjKi6ujpkZmaiqqoKERERePDBB/XWJePj44OVK1dCoVDovc4QIUQ3KFEhhNw0uVyOw4cPo6CgAC4uLkhLSzPoIFcLCwvMmjULs2bNUqszNHXqVKSnp8PDw8NgsRFCtEOJCiFk2MrKypCVlYWWlhbExMTgiSeeMLppw73rDBUXF2Pbtm1oa2tDYmLisOsMEUL0hxIVQohWOjo6kJeXhzNnzsDPzw8LFy6Ei4uLocMaEsdxCA0NRWhoKNrb25GXl4e33noLPj4+EIlE8Pf3N3SIhJB+UKJCCBkSYwxnzpxBXl4eACAxMRFisdhkx3zY2dnxdYauXLkCqVSKyspKxMTEIDExka8zRAgxPEpUCCEDampqQmZmJsrKyjB16lSsWLHC7D7EJ0yYgIceegg9PT04fPgw/v3vf8POzg7p6ekICQkx2WSMEHNBiQohRI1CocCxY8dw+PBh2NvbIzU1FUuWLDF0WDpnZWWFOXPmYM6cOaitrUVmZia+/fZbREREIC0tzSS6twgxR5SoEEIA/K/eTkNDA6KiotTq7Yw2Hh4euOeee8AYw4kTJ7Bp0yb09PQgOTkZM2bMMLoBw4SYM0pUCBnFurq6cODAAZw4cQJjx47FLbfcQlN3e+E4DtOmTcO0adP41XX/9re/YeLEiUhPT4evr6+hQyTE7FGiQsgowxjDxYsXkZubi+7ubsTHx+MPf/gDjcUYgqOjI26//XbMnz8fly5dwq5du1BXV4f4+HjMnj0bNjY2hg6RELNEiQoho0TvejuTJk266Xo7oxXHcZg0aRImTZrEt0j94x//gIuLC0QiEYKCgijpI2QEUaJCiBlTjbHYv38/rK2tdVZvZ7SytrZGSkoKUlJSUFVVxdcZioqKQkpKCsaMGWPoEAkxeZSoEGKGVLNWrl27hoiICDz00EN6q7czWnl7e2PFihX8rKlPPvkEAoEAqampmDZtGgQCgaFDJMQkUaJCiJlQrQNSUFAANzc3pKWlwdvb29BhjToWFhaYOXMmZs6ciaamJmRlZeGnn35CcHAw0tPT4enpaegQCTEplKgQYuKuXLmCrKwstLW1ISYmBk8++SRNnzUSzs7OWLx4Me644w6cPXsW33zzDVpbW5GQkIDY2NhRO/2bEG1QokKICVLVqjl79izGjx+PO+64A87OzoYOiwyA4zhMnToVU6dORUdHB/bu3Yu1a9di7NixEIvFVGeIkEFQokKIiWCMobi4GHv37gXHcUhMTMQtt9xCA2NNjK2tLcRiMcRiMcrKyiCVSlFRUYFZs2YhMTER9vb2hg6REKNCiQohRq6xsRGZmZmoqKhASEgIVq5cCVtbW0OHRUbA+PHj8eCDD0Iul+Pw4cP44IMPYGtri/T0dISGhlISSggoUSHEKCkUChw9ehRHjhzBmDFjkJKSgjvvvNPQYREdsbS0xOzZszF79mzU1dUhIyMD3333HcLDw5GWlgZXV1dDh0iIwVCiQogRuXr1KjIzM9HY2IgZM2bgscceg6UlvUxHE3d3d77O0MmTJ7F582Z0d3fzdYboeiCjDV3xhBhYZ2cn9u/fj6KiIvj4+GDu3Llwd3c3dFjEwDiOQ2RkJCIjI9HS0oLc3Fy8+eabmDBhAsRiMdUZIqMGJSqEGICq3k5OTg56enowe/ZsqrdDBuTo6Ij58+fzdYZ+/vln1NTUID4+HnPmzKE6Q8SsUaJCiB61tLQgOzsbJSUlmDRpEu699144ODgYOixiQgIDAxEYGIju7m61OkPp6emYPHkyJbvE7FCiQoiOMcZQWFiIAwcO8LVhFixYYOiwiIkTCoVITk5GcnIyqqqqkJGRgW3btlGdIWJ2KFEhREdqamqQmZmJmpoaREZG4uGHH4ZQKDR0WMQMeXt7Y/ny5VAqlTh27BjWr18PjuOQkpKC6dOnU50hYtI4xhgz1IPn5eXh3XffRUFBAaqqqrBjxw4sWrRI49s3NzfDyckJMpmMvj2QERcYGIi2tjYA18cIBAUFwc7ODu3t7bhw4QJaWlpgb2+PS5cu8bfp6enBoUOHcOzYMbi7uyM1NZXq7RCDkMlkyMrKQmFhISZPnoz09HR4eXnx++fMmYOKigoAgIODAwIDA2FjY4POzk5cunQJra2tGDduHPbt22eop0DMmDaf3wZtUWlra0NkZCQeeOABLFmyxJChENIvjuOQkJCAkJAQMMbAGAPHcZg1axaKi4tx7NgxAEBpaSmysrLQ0dGBmJgYPPXUU/QtlhiUk5MT7rjjDixatAhnz57Fd999h+bmZr7OUGtrK7q6uvq9vqdPn47i4mJcuHDB0E+DEMMmKnPnzsXcuXMNGQIhg1K9iXMc12eQomr7+++/D39/fyxZsgROTk4GipSQ/t1YZ2jfvn14++23UV9fjzlz5gx6fdvZ2RkoakL+h8aoEDIABwcH/k28P6oPgJUrV9LKocQk2NraQiQSQSQS4Zdffhl0mX6O4+Dv74/Gxka4uLjoOVJC/sekEpWuri50dXXxvzc3NxswGmLuJk6cyDeFD4QxhhMnTmDOnDl6jIyQm+fv76/R9V1UVITExEQ9RkaIOpPqRF+7di2cnJz4Hz8/P0OHRMyYra0thhprzhjD7t27sXPnTshkMj1FRsjwqCpwf/DBB+ju7h7y+gaA1tZWPURGyMBMqkXl5ZdfxnPPPcf/3tzcTMkK0ZmOjo4hF8/iOA4ymQxffPEFtm7dipiYGIhEIkRFRdFgWmI0GhsbkZWVBalUisrKSgDXW6g1WRyOFiQkhmZSiYq1tTWsra0NHQYZJS5fvoxp06YNegzHceju7gZwveLxgQMHcODAAbi5uSEtLQ0ikQhjx47VQ7SEqFMoFCgoKIBEIkF+fj6USiW/z9HREZcuXdIoEQ8PD9d1qIQMyqCJSmtrKy5evMj/fvnyZRQWFsLV1RXjx483YGSEXL8+i4uLBxxQyxjDhQsXsGXLFuTn50MqleLo0aNgjKG+vh7fffcdvvvuO0REREAkEiE+Pp4WfCM6d/XqVUilUmRlZaGhoYHfripyKBaLERsbi5kzZw55fZeWltJAWmJwBl3wLScnBykpKX22r1y5Eps3bx7y9rTgG9GlsLAwdHZ2IiYmBkFBQWrrTHAchwsXLuDkyZMoKirib1NfX4/MzExkZGSgqqpK7f7s7e2RnJwMsViMgIAAfT8dYsa6urpw4MABSCQSnDp1Sm2fh4cH0tPTkZ6eDk9PT357QkICqqqqBr2+y8vLkZeXp++nQ0YBbT6/DZqo3CxKVIguLViwgB9sKBQK4ebmBktLS8jlctTX16O7uxscx2HXrl19bssYw6lTpyCVSrF//36+e0glICAAYrEYSUlJNAaADIuqArdUKkVubi7a29v5fZaWloiNjYVYLEZkZGS/46Uef/xxlJeXAxj4+vbz88NHH32kt+dERg+TWZmWEFPR3d3dp4VkMKq+/fDwcDzyyCPIzc2FVCrluzpLSkrwySefYOPGjYiPj4dYLEZ4eDhVviVDamlpQU5ODiQSCUpLS9X2+fv7QyQSISUlBY6Ojhrfp7bXNyH6RIkKITpmb2+PefPmYd68eSgpKYFUKkVOTg5aW1vR09OD3Nxc5ObmYuzYsUhPT0daWhrc3d0NHTYxIqr1eiQSCQ4ePAi5XM7vs7OzQ2JiIsRiMSZNmkTJLjE7lKgQokcBAQF45JFH8MADD+DQoUOQSCQ4ceIEAKC6uhpfffUVtm7diqioKIjFYsyaNQuWlvQyHa1qa2uRkZGBjIwM1NTUqO0LCwuDSCTC7NmzaTYkMWv0DkiIAQiFQiQmJiIxMRHXrl3jP4zq6urAGENBQQEKCgrg5OSElJQUiMViWjNolOjp6cHhw4chkUhQWFiotiibi4sLP+3dx8fHgFESoj+UqBBiYF5eXli2bBnuvfdeHD9+HFKpFIcPH4ZcLodMJsPOnTuxc+dOBAcHQywWIyEhAba2toYOm4yw0tJSSKVSZGdno6Wlhd8uEAgwc+ZMiMViREdHw8LCwoBREqJ/lKgQYiQEAgGio6MRHR0NmUzGD5gsKysDAJw7dw7nzp3Dp59+ijlz5kAsFmPKlCk0JsGEtbe3Iy8vDxKJBBcuXFDb5+vri/T0dKSmplLRSzKqUaJCiBFycnLCwoULsWDBApw/f56fgtrZ2YnOzk6+q2jcuHEQi8VITU2Fk5OTocMmGlDV25FKpdi7d6/a1HVra2vMmTMHIpFo0MrdhIwmlKgQYsQ4jkNwcDCCg4Px0EMPYf/+/ZBIJCguLgYAVFRU4PPPP8eWLVswa9Ysvs4QdQ8Yn8bGRn4xQFW9HZXJkydDJBIhMTERdnZ2BoqQEONEiQohJsLGxgZpaWlIS0tDZWUlpFIpMjMz0dTUBIVCgYMHD+LgwYNwdXXlVyL19vY2dNijmkKhwNGjRyGVSvutt5OamgqRSIQJEyYYMEpCjBslKoSYIF9fX6xatQr333+/WuE5xhgaGhr4OkPh4eH8FFaqM6Q/lZWVyMjIQGZmJhobG/ntHMdh+vTpEIlEiImJgZWVlQGjJMQ0UKJCiAmztLRETEwMYmJi0NDQgMzMTEilUn6V0aKiIhQVFWH9+vVISkqCSCRCYGAgjX3Qgc7OTuzfvx9SqRSnT59W2+fp6QmRSIS0tDR4eHgYKEJCTBMlKoSYCVdXVyxduhR33nknTp8+DYlEwtcZamtrw+7du7F7925MnDgRIpEIycnJWi2zTvpSVdBWDXbu6Ojg91laWiI+Ph4ikQiRkZGUHBIyTJSoEGJmOI5DWFgYwsLC8MgjjyAvLw9SqZSf/nr58mVs2LABmzZtQlxcHH2QDkNzczOys7MhlUpx5coVtX2UCBIysihRIcSM2dvbY+7cuZg7dy5KS0shkUiQk5ODlpYW9PT0IC8vD3l5eXzXRHp6OtUZGoBSqURhYSGkUikOHTrUp95OUlISxGIxda0RMsIoUSFklPD398eaNWuwatUqfon2EydOgDGGmpoabN26Fdu2bUNUVBQ/2JPqDAE1NTX8DKva2lq1fWFhYRCLxYiPj6d6O4ToCL0LETLKCIVCJCQkICEhATU1NfzicbW1tWp1hsaMGcPXGRo/fryhw9arnp4etaKRvevtuLq68vV2aPo3IbpHiQoho5inpyfuu+8+3HPPPThx4gQkEgnfrdHc3IyffvoJP/30E4KDg/kFycy5ztCN3WMqFhYWfL0dWlCPEP2iRIUQAoFAgOnTp2P69Olobm5GTk4OpFIpSktLAfRfZ2jq1KlmMRajra2tz4BjFV9fX75EgbOzs2ECJGSUo0SFEKJmzJgxWLBgAW6//XZcvHgREomEn3rb1dWFzMxMZGZmwtfXFyKRCKmpqXBxcTF02FphjPWZwq1ibW2NhIQEKvpIiJGgRIUQ0i+O4xAUFISgoCA8+OCDfRYzq6ysxObNm/HFF19g5syZuOWWW4y+W6S/RfFUVN1bCQkJVG+HECNCiQohZEg31hnqvTy8UqnE4cOHcfjwYaMcaCqXy3H06FFIJBIUFBSo1dsZzQOGCTEVlKgQQrTi6+uLlStX4v777+9TcK+hoQHff/89vv/+e4NP3a2srIREIkFWVhaampr47RzH0RRsQkwIvUIJIcNiYWGhVmcoKysLUqkUV69eBQCcOnUKp06dwieffMLXGZo0aZJOx3x0dnZi3759kEgkOHPmjNo+WtSOENNEiQoh5Ka5urrizjvvxJIlS1BcXAypVIp9+/ahq6sL7e3t+O233/Dbb7/B398fYrF4RJeXZ4zh/PnzkEqlyMvLU6u3Y2Vlhbi4OIjFYkRERNDAWEJMECUqhJARw3EcQkNDERoaijVr1vDTfs+fPw/g+jolGzZswOeff84nEMOtM6SqtyORSFBWVqa2LyAgACKRCElJSVRvhxATR4kKIUQn7OzscOutt+LWW29FaWkpMjIykJWVhZaWFsjlcuzduxd79+6Fp6cn0tPTkZ6eDg8Pj0HvU6lU4vjx45BKpTh8+LBavR17e3u1ejuEEPNAiQohROf8/f3x0EMPYeXKlTh8+DCkUimOHz/O1xnatm0bvv76a0ybNg1isRgxMTGwsrLib3/t2jV+qf+6ujq1+46IiIBIJEJ8fDyEQqG+nxohRMcoUSGE6I2VlRXmzJmDOXPmoLa2lk8+ampqwBjD8ePHcfz4cTg6OiIxMRGurq44efIkTpw4oXY/rq6ufCuMsUyDJoToBiUqhBCD8PDwwL333qtWZ+jgwYNobm7GlStXkJubC7lcDgcHB7i7u8PDwwPx8fEQiURGv7AcIWTkCAwdACFkdFOtgBsWFgZvb2+0tLTw41gAQKFQQKlUwsLCAnZ2drCzs4NAQG9dhIwW1KJCCDEIxhhOnToFqVSqVm/Hy8sL48ePx5QpUyAQCHDu3Dm0t7dDqVSq1RlKT09HWlqaydUZIoRohxIVQohe1dfXIzMzExkZGX3q7UyZMgVisRhz5syBra0tAKCrqwsHDhyARCLBqVOnAFxfdXbLli348ssvMXPmTIhEIsyYMYO6gwgxQ5SoEEJ0Ti6XIz8/H1KpFEePHgVjjN/n5OSE1NRUiEQi+Pn59bmttbU1UlJSkJKSgqqqKkilUmRmZqKhoUGtzpCLiwtfZ8jHx0efT48QokOUqBBCdKaiooKvtyOTyfjtHMchOjoaIpEIs2bN0rjejre3N1asWIFly5bh2LFjkEgkOHLkCJRKJRobG/HDDz/ghx9+QGhoKEQiEWbPng0bGxtdPT1CiB5QokIIGVGdnZ3Yu3cvpFJpn3o7Y8eOhUgkQmpq6k3V27GwsMDMmTMxc+ZMNDY28nWGKisrAQCnT5/G6dOnsX79er7OUFBQEC2hT4gJokSFEHLTGGM4d+4cJBIJ9u7di87OTn6flZUV4uPjIRaLER4ePuLJgouLC5YsWYLFixfjzJkzkEgkfJ2hjo4O7NmzB3v27IG/vz9EIhFSUlJoWX1CTAglKoSQYZPJZHxrRnl5udq+gIAAiMViJCUlwcHBQeexcByHkJAQhISEYM2aNXyrzrlz5wBcrzP06aefYtOmTYiNjYVYLMa0adOolYUQI0eJCiFEK0qlEseOHePr7SgUCn6fvb09kpOTIRaLERAQYLAY7ezscMstt+CWW25BWVkZJBIJsrOz0dzcDLlcjn379mHfvn3w8PDgV7j19PQ0WLyEkIFRokII0Uh1dTW/5H19fb3avoiICIjFYsTFxRldvZ3x48fjoYcewqpVq/g6Q8eOHQNjDLW1tfj666/xzTffIDIyEmKxGLGxsWp1hgghhkWJCiFkQN3d3Th48CAkEglOnjypts/NzY1vjRg7dqyBItScpaUlZs+ejdmzZ6Ourg4ZGRmQSqV8naHCwkIUFhbC0dGRbxXy9/c3dNiEjHqUqBBC+igpKYFEIkFOTg7a2tr47ZaWlpg1axbEYjGmT59uskvZu7u745577sHdd9+NkydP8nWGenp60NLSgp9//hk///wzgoKCIBKJkJiYCHt7e0OHTcioRIkKIQQA0NraitzcXEgkEpSUlKjt8/Pzg1gsRkpKCpycnAwU4cjjOA6RkZGIjIxES0sLcnNzIZVK+ed/4cIFXLhwAZ999hnmzJkDkUiE0NBQGoBLiB5RokLIKMYYQ1FREd+ioKq3AwA2NjZITEyEWCzG5MmTzf7D2dHREfPnz8f8+fNx6dIlSCQS5Obmoq2tDd3d3cjKykJWVhZ8fHz4tWBcXV0NHTYhZo8SFUJGobq6Or7eTnV1tdq+qVOn8vV2RuuqroGBgXjsscfw4IMP4sCBA5BKpfwYnatXr/J1hmbMmAGxWIzo6GiNV9clhGiHXlmEjBJyuRxHjhyBRCLhZ72oqOrtiMVijBs3zoBRGhehUIjk5GQkJyejqqqKn/WkqjN05MgRHDlyBM7OznydIV9fX0OHTYhZoUSFEDNXXl7OryNyY72dGTNmQCQSYebMmdQiMARvb28sX74c9913H7+OzJEjR6BQKNDU1ITt27dj+/btCAkJgVgspjpDhIwQemcixAx1dHRg7969kEgk/MqsKt7e3khPT0daWhrc3NwMFKHp6l1nqKmpCdnZ2ZBIJKioqAAAFBcXo7i4GOvXr0diYiJEItGoGONDiK5QokKImWCM4ezZs5BKpX3q7QiFQsyePRsikQhhYWH0oTlCnJ2dcccdd2DRokV9zn1HRwd+//13/P777xg/fjw/a2rMmDGGDpsQk0KJCiEmTlVvp/e3epVJkyZBJBIhKSmJ1gHRIY7jMHXqVEydOhUPP/ww9u3bB4lEgrNnzwIAysrK8Nlnn2Hz5s2IiYnh6wyZ6jo0hOgTJSqEmCCFQtFnnISKg4MDUlJSIBKJMHHiRANGOTrZ2tpCJBJBJBKhvLwcUqkUWVlZkMlkkMvl2L9/P/bv3w93d3ekp6dDJBJRnSFCBkGJCiEm5MaZJ71NmzYNIpEIsbGxRldvZ7Ty8/PD6tWrsWLFChw5cgRSqRQFBQVgjKGurg7ffPMNvv32W0RGRtLfjpABUKJCiJHr7u7G/v37IZVKUVRUpLZP9a08PT0dXl5eBoqQDMXS0hLx8fGIj49HXV0dsrKyIJVKUV1drVZniFrDCOmLEhVCjBBjDJcuXYJUKuVXR1WxtLRETEwMRCKRSdfbGa3c3d1x1113YenSpfyqwAcOHEBPTw9aW1v5OkM0voiQ6yhRIcSItLS0ICcnB1KpFJcvX1bbp5o5kpycbFb1dkYrjuMQERGBiIiIfussXbx4ERcvXsTGjRtpxhYZ1ShRIcTAGGM4ceIEpFIpX8FXxdbWltbiGAUcHBxw22234bbbbutTubq7uxvZ2dnIzs7m18BJT0+nOkNk1KBEhRADqaurQ0ZGBqRSKWpqatT20eqmo1dAQAAeffRRrF69GgcPHoREIuHrDFVVVeHLL7/EV199RasKk1GDrm5C9Egul+Pw4cOQSqV96u1QvRjSm1AoRFJSEpKSklBdXc3P9qqvrwdjDPn5+cjPz4eTkxN/3VCdJmKOKFEhRA/Kysr4ejvNzc38doFAQBV4yZDGjh2L+++/H/fddx+OHz8OiUSCI0eOQC6XQyaT4ccff8SPP/5Ila+JWaJ3RUJ0pL29HXv37oVUKu233o5YLEZqaiqNNSAaEwgEiI6ORnR0NGQyGV9nqLy8HABw5swZnDlzhq8zJBaLaWwTMXmUqBAyghhjOHPmDCQSCfbt24euri5+n6rejlgsRmhoKH14kJvi5OSERYsWYeHChTh//jwkEgny8vLQ2dmJzs5OSCQSSCQS+Pn58XWGaLYYMUUGT1Q++ugjvPvuu6iqqkJoaCj+9a9/ISEhwdBhEaJGKBTCzc0NlpaWkMvlqK+vR3d3N7+/sbGRX8SrsrJS7bZBQUEQiURITEyk9TDIiOM4DsHBwQgODlarM3TmzBkAQHl5OTZu3IgtW7Zg1qxZEIvFmD59utp9DHV9E2JIHOs9mk/Pvv32WyxfvhwfffQRZs+ejfXr1+Ozzz5DcXExxo8fP+Ttm5ub4eTkBJlMRhVJyYhbsGABgOvrl7i7uwO43mKiagmpra1FUVERxGIx8vPzoVQq+ds6OjoiOTkZYrEY/v7+eo+dkIqKCkgkEr7OUG/u7u4oLy+HhYUFgoKC+r2+6+rqoFQq8dFHH+k9dmL+tPn8NmiiEhMTg6ioKHz88cf8tqlTp2LRokVYu3btkLenRIXo0oIFC/gkpb9uGsYYiouL0dHRAeD6N9vIyEiIxWLExMRQzRZiFORyOfLz8yGVSnH06FF+pllRURFmzZqFkJCQAa/vjo4OvPPOO/oOmYwC2nx+G6zrp7u7GwUFBXjppZfUtovFYhw4cMBAURHyP0KhcMAkBbiemISEhODq1atITk5Geno6VcElRsfS0hJxcXGIi4tDfX09MjMzkZGRgcuXLw+YpADXr29bW1s0NjbCxcVFz1ET8j8GKxJSV1cHhULRp5Cal5cXqqur+71NV1cXmpub1X4I0RVNZ+MsW7YM9913HyUpxOi5ubnhrrvuwvr165GUlARNGtRvLIRJiL4ZvJrZjdl87z7SG61duxZOTk78j5+fnz5CJKOUlZXVkG/kjDG1goGEmAKO4+Dg4KDRsa2trTqOhpDBGSxRcXd3h4WFRZ/Wk5qamgHL1b/88suQyWT8j2rtAEJ0oaenZ8gpxNq84RNiTJRKpUZT5On6JoZmsERFKBQiOjoaUqlUbbtUKkV8fHy/t7G2tsaYMWPUfgjRlYaGBo2OCw8P13EkhIy8zs5OjY6j65sYmkHXUXnuueewfPlyzJgxA3FxcdiwYQPKysrw6KOPGjIsQgBcH/BdV1c36Kyfuro6GmhITJJCoRjy+u7o6KDrmxicQROVu+++G/X19fjrX/+KqqoqhIWFYffu3ZgwYYIhwyKEV1ZWBgADrjOh2k+IqfH29kZVVRU6Ojpga2vbZ39HRwctUEiMgkHXUblZtI4K0ZfGxkYUFRWhtbUVDg4OCA8Pp2+axGzQ9U30zWQWfLtZMpkMzs7OKC8vp0SFEEIIMRHNzc3w8/NDU1PTkDWoDF7r52a0tLQAAE1TJoQQQkxQS0vLkImKSbeoKJVKXL16FY6OjqOyEq0qI6UWJf2g861fdL71i863fo32880YQ0tLC3x8fCAQDD4B2aRbVAQCAcaNG2foMAyOpmrrF51v/aLzrV90vvVrNJ/voVpSVAy+Mi0hhBBCyEAoUSGEEEKI0aJExYRZW1vjtddeg7W1taFDGRXofOsXnW/9ovOtX3S+NWfSg2kJIYQQYt6oRYUQQgghRosSFUIIIYQYLUpUCCGEEGK0KFEhhBBCiNGiRMXENDY2Yvny5XBycoKTkxOWL1+OpqamQW+zatUqcByn9hMbG6ufgE3MRx99hIkTJ8LGxgbR0dHYu3fvoMfn5uYiOjoaNjY2CAgIwCeffKKnSM2DNuc7Jyenz3XMcRzOnj2rx4hNV15eHm6//Xb4+PiA4zjs3LlzyNvQ9T082p5rurYHR4mKibnvvvtQWFiIPXv2YM+ePSgsLMTy5cuHvN2tt96Kqqoq/mf37t16iNa0fPvtt3jmmWfw5z//GcePH0dCQgLmzp2LsrKyfo+/fPky5s2bh4SEBBw/fhyvvPIKnn76aWzfvl3PkZsmbc+3yrlz59Su5aCgID1FbNra2toQGRmJDz/8UKPj6foePm3PtQpd2wNgxGQUFxczAOzQoUP8toMHDzIA7OzZswPebuXKlWzhwoV6iNC0zZo1iz366KNq26ZMmcJeeumlfo9/4YUX2JQpU9S2PfLIIyw2NlZnMZoTbc93dnY2A8AaGxv1EJ15A8B27Ngx6DF0fY8MTc41XduDoxYVE3Lw4EE4OTkhJiaG3xYbGwsnJyccOHBg0Nvm5OTA09MTkydPxsMPP4yamhpdh2tSuru7UVBQALFYrLZdLBYPeG4PHjzY5/hbbrkFR48eRU9Pj85iNQfDOd8q06dPh7e3N9LS0pCdna3LMEc1ur71j67t/lGiYkKqq6vh6enZZ7unpyeqq6sHvN3cuXOxdetWZGVl4Z///Cfy8/ORmpqKrq4uXYZrUurq6qBQKODl5aW23cvLa8BzW11d3e/xcrkcdXV1OovVHAznfHt7e2PDhg3Yvn07fvzxRwQHByMtLQ15eXn6CHnUoetbf+jaHpxJV082F6+//jreeOONQY/Jz88HAHAc12cfY6zf7Sp33303//+wsDDMmDEDEyZMwK+//orFixcPM2rzdON5HOrc9nd8f9tJ/7Q538HBwQgODuZ/j4uLQ3l5Of7xj38gMTFRp3GOVnR96wdd24OjRMUIPPnkk7jnnnsGPcbf3x8nT57EtWvX+uyrra3t881nMN7e3pgwYQIuXLigdazmyt3dHRYWFn2+zdfU1Ax4bseOHdvv8ZaWlnBzc9NZrOZgOOe7P7Gxsfjqq69GOjwCur4Nja7t/6FExQi4u7vD3d19yOPi4uIgk8lw5MgRzJo1CwBw+PBhyGQyxMfHa/x49fX1KC8vh7e397BjNjdCoRDR0dGQSqW44447+O1SqRQLFy7s9zZxcXH4+eef1bZJJBLMmDEDVlZWOo3X1A3nfPfn+PHjdB3rCF3fhkXXdi8GHcpLtHbrrbeyiIgIdvDgQXbw4EEWHh7O5s+fr3ZMcHAw+/HHHxljjLW0tLDnn3+eHThwgF2+fJllZ2ezuLg45uvry5qbmw3xFIzWN998w6ysrNjGjRtZcXExe+aZZ5i9vT0rLS1ljDH20ksvseXLl/PHl5SUMDs7O/bss8+y4uJitnHjRmZlZcV++OEHQz0Fk6Lt+X7//ffZjh072Pnz59mpU6fYSy+9xACw7du3G+opmJSWlhZ2/Phxdvz4cQaAvffee+z48ePsypUrjDG6vkeStuearu3BUaJiYurr69myZcuYo6Mjc3R0ZMuWLeszpQ0A27RpE2OMsfb2diYWi5mHhwezsrJi48ePZytXrmRlZWX6D94E/L//9//YhAkTmFAoZFFRUSw3N5fft3LlSpaUlKR2fE5ODps+fToTCoXM39+fffzxx3qO2LRpc77feecdFhgYyGxsbJiLiwubM2cO+/XXXw0QtWlSTYG98WflypWMMbq+R5K255qu7cFxjP13dBQhhBBCiJGh6cmEEEIIMVqUqBBCCCHEaFGiQgghhBCjRYkKIYQQQowWJSqEEEIIMVqUqBBCCCHEaFGiQgghhBCjRYkKIUTvNm/eDGdn5yGP4zgOO3fu1Hk8AJCYmIht27bd1H18+OGHWLBgwQhFRAgBKFEhxGwpFArEx8djyZIlattlMhn8/Pzwl7/8xUCRXa/off78ef73119/HdOmTetzXFVVFebOnavzeH755RdUV1cPWRx0KA8//DDy8/Oxb9++EYqMEEKJCiFmysLCAlu2bMGePXuwdetWfvtTTz0FV1dXvPrqqwaLzdbWFp6enkMeN3bsWFhbW+s8ng8++AAPPPAABIKbe0u0trbGfffdh//85z8jFBkhhBIVQsxYUFAQ1q5di6eeegpXr17FTz/9hG+++QZbtmyBUCgc8Hb+/v7429/+hvvuuw8ODg7w8fHp8+FbVlaGhQsXwsHBAWPGjMFdd92Fa9eu8ftPnDiBlJQUODo6YsyYMYiOjsbRo0cBqHf9bN68GW+88QZOnDgBjuPAcRw2b94MoG/XT1FREVJTU2Fraws3NzesWbMGra2t/P5Vq1Zh0aJF+Mc//gFvb2+4ubnhiSeeQE9Pz4DPta6uDhkZGX26bDiOw/r16zF//nzY2dlh6tSpOHjwIC5evIjk5GTY29sjLi4Oly5dUrvdggULsHPnTnR0dAz4mIQQLRi62BAhRLeUSiVLTk5maWlpzNPTk/3tb38b8jYTJkxgjo6ObO3atezcuXPsgw8+YBYWFkwikfD3OX36dDZnzhx29OhRdujQIRYVFaVWaC00NJTdf//97MyZM+z8+fPsu+++Y4WFhYwxxjZt2sScnJwYY9cLZz7//PMsNDSUVVVVsaqqKtbe3s4Yu15gc8eOHYwxxtra2piPjw9bvHgxKyoqYpmZmWzixIl8oTfGrhd7GzNmDHv00UfZmTNn2M8//8zs7OzYhg0bBnyuO3bsYPb29kyhUKhtB8B8fX3Zt99+y86dO8cWLVrE/P39WWpqKtuzZw8rLi5msbGx7NZbb1W7XWtrK+M4juXk5Ax5ngkhQ6NEhZBR4MyZMwwACw8PZz09PUMeP2HChD4fwHfffTebO3cuY4wxiUTCLCws1Kpwnz59mgFgR44cYYwx5ujoyDZv3tzv/fdOVBhj7LXXXmORkZF9juudqGzYsIG5uLiw1tZWfv+vv/7KBAIBq66uZoxdT1QmTJjA5HI5f8zSpUvZ3XffPeBzff/991lAQEC/j/2Xv/yF//3gwYMMANu4cSO/7euvv2Y2NjZ9buvi4jLgcyeEaIe6fggZBT7//HPY2dnh8uXLqKio0Og2cXFxfX4/c+YMAODMmTPw8/ODn58fvz8kJATOzs78Mc899xweeughpKen4+233+7TRaKtM2fOIDIyEvb29vy22bNnQ6lU4ty5c/y20NBQWFhY8L97e3ujpqZmwPvt6OiAjY1Nv/siIiL4/3t5eQEAwsPD1bZ1dnaiublZ7Xa2trZob2/X8JkRQgZDiQohZu7gwYN4//338dNPPyEuLg4PPvggGGPDui+O4wAAjDH+/7313v7666/j9OnTuO2225CVlYWQkBDs2LFj2M9joMfsHRcAWFlZ9dmnVCoHvF93d3c0Njb2u6/3fakeo79tN95/Q0MDPDw8BnxMQojmKFEhxIx1dHRg5cqVeOSRR5Ceno7PPvsM+fn5WL9+/ZC3PXToUJ/fp0yZAuB660lZWRnKy8v5/cXFxZDJZJg6dSq/bfLkyXj22WchkUiwePFibNq0qd/HEgqFUCgUg8YTEhKCwsJCtLW18dv2798PgUCAyZMnD/l8BvL/27l3l0aiMAzjz+IWYiMWwU5DBBtBGNDGQpEEL5WFYhOMIKRTDIhgK1YK6UwpiE0Q/wIrFZlGiVikCoiXQhTUVm3cLRZ2FffCxrA76POrz7xzmOplvpkTBAFXV1e/LCt/6+TkhIeHB4IgqEme9NFZVKR3bGFhgaenJ5aXlwFoaWkhn88zPz/P2dnZb68Nw5CVlRUqlQqFQoGtrS1mZ2cBSKVSdHZ2kk6nOTo64uDggEwmQ19fH11dXdzf3zM9Pc3u7i7n5+eEYcjh4eGLEvNcPB7n9PSU4+Njbm5ueHx8fLUmnU5TX1/P5OQk5XKZnZ0dZmZmmJiY+D6WqUYQBMRiMcIwrDrjuf39fRKJBG1tbTXJkz46i4r0Tu3t7VEoFFhfX3/xXUc2m6Wnp+ePI6C5uTlKpRJBELC0tEQ+n2dwcBD48dtwU1MTvb29pFIpEokEm5ubwLczXG5vb8lkMrS3tzM+Ps7w8DCLi4s/vdfo6ChDQ0P09/cTi8UoFouv1jQ0NLC9vc3d3R3d3d2MjY2RTCZZXV19y2Oirq6OqampF2fNvEWxWCSbzdYkSxJ8+lLtsFrSuxWPx8nlcuRyuf+9lX/i+vqajo4OSqUSra2tVeeUy2WSySSVSoXGxsYa7lD6uHyjIunDa25uZm1tjYuLizflXF5esrGxYUmRaujz/96AJEXByMjImzMGBgZqsBNJzzn6kSRJkeXoR5IkRZZFRZIkRZZFRZIkRZZFRZIkRZZFRZIkRZZFRZIkRZZFRZIkRZZFRZIkRZZFRZIkRdZX9fBbRxJ0Ca8AAAAASUVORK5CYII=\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"
    }
   ],
   "source": [
    "for x in X:\n",
    "    Areas = [x[f'x{j:02}'] for j in range(2, 49)]\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
}