VAE_Single_pymoo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ea7f6c2d",
   "metadata": {},
   "source": [
    "# Loads packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": 105,
   "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": 106,
   "id": "fd11d60f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luisy\\AppData\\Local\\Temp\\ipykernel_16104\\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 : 155.75 \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": 107,
   "id": "c84c26b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from examples import getExample\n",
    "from trussFE import TrussFE\n",
    "\n",
    "#Loads the example truss\n",
    "exampleName, nodeXY, connectivity, bc = getExample(4)\n",
    "#Changes the force\n",
    "bc['forces'] = {'nodes':np.array([2]), 'fx':1.E3*torch.tensor([0.]), 'fy':1.E2*torch.tensor([125.])}\n",
    "\n",
    "#Loads the truss in truss\n",
    "truss = TrussFE(nodeXY, connectivity, bc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f45b989",
   "metadata": {},
   "source": [
    "# Gets the z coordinates of each material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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": 228,
   "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':95}}\n",
    "\n",
    "\n",
    "objectives = {'cost': {'isOn':True},\\\n",
    "              'mass': {'isOn':False}, \\\n",
    "              'co2': {'isOn':False},\\\n",
    "              'water': {'isOn':False},\\\n",
    "              'energy': {'isOn':False},\\\n",
    "              'compliance': {'isOn': False}}\n",
    "               \n",
    "# count the number of entries with 'isOn' set to True\n",
    "no = sum(1 for v in objectives.values() if v['isOn'])\n",
    "nc = sum(1 for v in constraints.values() if v['isOn'])\n",
    "\n",
    "# print an error message in red if there are 3 or more entries with 'isOn' set to True\n",
    "if no >= 2:\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": 229,
   "id": "aae90b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pymoo.core.problem import ElementwiseProblem\n",
    "\n",
    "class MyProblem(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self, truss, z_np, constraints, objectives, no, nc):\n",
    "        super().__init__(n_var=8,\n",
    "                         n_obj=no,\n",
    "                         n_ieq_constr=nc,\n",
    "                         xl=np.array([z0min, z1min, 2e-6, 2e-6, 2e-6, 2e-6, 2e-6, 2e-6]),\n",
    "                         xu=np.array([z0max, z1max, 2e-2, 2e-2, 2e-2, 2e-2, 2e-2, 2e-2]))\n",
    "\n",
    "        self.truss=truss\n",
    "        self.z_np=z_np\n",
    "        self.constraints=constraints\n",
    "        self.objectives=objectives\n",
    "\n",
    "    def _evaluate(self, x, out, *args, **kwargs):\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",
    "        \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": 230,
   "id": "abbcadb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
    "from pymoo.operators.crossover.sbx import SBX\n",
    "from pymoo.operators.mutation.pm import PM\n",
    "from pymoo.operators.sampling.rnd import FloatRandomSampling\n",
    "\n",
    "#pop_size: Size of initial population\n",
    "#n_offsprings: The number of offspring generated in each generation of the algorithm. \n",
    "#sampling: The sampling method used to generate new individuals in the offspring population.\n",
    "    #In this case, FloatRandomSampling is used, which generates random floating-point values\n",
    "    #in the search space to create new individuals.\n",
    "#crossover: The crossover operator used to create new individuals in the offspring population.\n",
    "#mutation: The mutation operator used to create new individuals in the offspring population.\n",
    "#eliminate_duplicates: A flag to indicate whether duplicate individuals should be eliminated from the population or not.\n",
    "algorithm = NSGA2(\n",
    "    pop_size=40,\n",
    "    n_offsprings=10,\n",
    "    sampling=FloatRandomSampling(),\n",
    "    crossover=SBX(prob=0.7, 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-4,\n",
    "    cvtol=1e-9,\n",
    "    ftol=0.0001,\n",
    "    period=20,\n",
    "    n_max_gen=500,\n",
    "    n_max_evals=50000\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79017bd0",
   "metadata": {},
   "source": [
    "# Runs the solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "58b8803f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "n_gen  |  n_eval  | n_nds  |     cv_min    |     cv_avg    |      eps      |   indicator  \n",
      "==========================================================================================\n",
      "     1 |       40 |      1 |  0.000000E+00 |  8.302839E+01 |             - |             -\n",
      "     2 |       50 |      1 |  0.000000E+00 |  0.0683867976 |  0.000000E+00 |             f\n",
      "     3 |       60 |      1 |  0.000000E+00 |  0.0384760993 |  1.081112E+01 |         ideal\n",
      "     4 |       70 |      1 |  0.000000E+00 |  0.0269438972 |  0.000000E+00 |             f\n",
      "     5 |       80 |      1 |  0.000000E+00 |  0.0130504187 |  0.000000E+00 |             f\n",
      "     6 |       90 |      1 |  0.000000E+00 |  0.0075799236 |  3.690673E+01 |         ideal\n",
      "     7 |      100 |      1 |  0.000000E+00 |  0.0030520953 |  4.701477E+01 |         ideal\n",
      "     8 |      110 |      1 |  0.000000E+00 |  0.0000914164 |  0.000000E+00 |             f\n",
      "     9 |      120 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    10 |      130 |      1 |  0.000000E+00 |  0.000000E+00 |  8.607667E+01 |         ideal\n",
      "    11 |      140 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    12 |      150 |      1 |  0.000000E+00 |  0.000000E+00 |  5.9771407081 |         ideal\n",
      "    13 |      160 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    14 |      170 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    15 |      180 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    16 |      190 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    17 |      200 |      1 |  0.000000E+00 |  0.000000E+00 |  3.6497274908 |         ideal\n",
      "    18 |      210 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    19 |      220 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    20 |      230 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    21 |      240 |      1 |  0.000000E+00 |  0.000000E+00 |  1.038106E+01 |         ideal\n",
      "    22 |      250 |      1 |  0.000000E+00 |  0.000000E+00 |  0.1488658671 |         ideal\n",
      "    23 |      260 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    24 |      270 |      1 |  0.000000E+00 |  0.000000E+00 |  0.9568159100 |         ideal\n",
      "    25 |      280 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    26 |      290 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    27 |      300 |      1 |  0.000000E+00 |  0.000000E+00 |  8.5630863559 |         ideal\n",
      "    28 |      310 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    29 |      320 |      1 |  0.000000E+00 |  0.000000E+00 |  2.5990061296 |         ideal\n",
      "    30 |      330 |      1 |  0.000000E+00 |  0.000000E+00 |  8.5986906686 |         ideal\n",
      "    31 |      340 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    32 |      350 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    33 |      360 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    34 |      370 |      1 |  0.000000E+00 |  0.000000E+00 |  0.7663668673 |         ideal\n",
      "    35 |      380 |      1 |  0.000000E+00 |  0.000000E+00 |  6.8323242998 |         ideal\n",
      "    36 |      390 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    37 |      400 |      1 |  0.000000E+00 |  0.000000E+00 |  2.5161829397 |         ideal\n",
      "    38 |      410 |      1 |  0.000000E+00 |  0.000000E+00 |  0.6019813668 |         ideal\n",
      "    39 |      420 |      1 |  0.000000E+00 |  0.000000E+00 |  1.095855E+01 |         ideal\n",
      "    40 |      430 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    41 |      440 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    42 |      450 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    43 |      460 |      1 |  0.000000E+00 |  0.000000E+00 |  0.5034359484 |         ideal\n",
      "    44 |      470 |      1 |  0.000000E+00 |  0.000000E+00 |  1.0517415218 |         ideal\n",
      "    45 |      480 |      1 |  0.000000E+00 |  0.000000E+00 |  1.430719E+01 |         ideal\n",
      "    46 |      490 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    47 |      500 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    48 |      510 |      1 |  0.000000E+00 |  0.000000E+00 |  1.4566452113 |         ideal\n",
      "    49 |      520 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    50 |      530 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    51 |      540 |      1 |  0.000000E+00 |  0.000000E+00 |  3.0155107115 |         ideal\n",
      "    52 |      550 |      1 |  0.000000E+00 |  0.000000E+00 |  0.7982428201 |         ideal\n",
      "    53 |      560 |      1 |  0.000000E+00 |  0.000000E+00 |  4.4444956910 |         ideal\n",
      "    54 |      570 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    55 |      580 |      1 |  0.000000E+00 |  0.000000E+00 |  3.4411335671 |         ideal\n",
      "    56 |      590 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    57 |      600 |      1 |  0.000000E+00 |  0.000000E+00 |  0.4790216299 |         ideal\n",
      "    58 |      610 |      1 |  0.000000E+00 |  0.000000E+00 |  1.497722E+01 |         ideal\n",
      "    59 |      620 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    60 |      630 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    61 |      640 |      1 |  0.000000E+00 |  0.000000E+00 |  0.2488070318 |         ideal\n",
      "    62 |      650 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    63 |      660 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    64 |      670 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    65 |      680 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    66 |      690 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    67 |      700 |      1 |  0.000000E+00 |  0.000000E+00 |  5.0985628399 |         ideal\n",
      "    68 |      710 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    69 |      720 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    70 |      730 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    71 |      740 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    72 |      750 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    73 |      760 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    74 |      770 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    75 |      780 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    76 |      790 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    77 |      800 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    78 |      810 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    79 |      820 |      1 |  0.000000E+00 |  0.000000E+00 |  0.1018728760 |         ideal\n",
      "    80 |      830 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0363620996 |         ideal\n",
      "    81 |      840 |      1 |  0.000000E+00 |  0.000000E+00 |  1.0755097196 |         ideal\n",
      "    82 |      850 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    83 |      860 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    84 |      870 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    85 |      880 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    86 |      890 |      1 |  0.000000E+00 |  0.000000E+00 |  1.5291878179 |         ideal\n",
      "    87 |      900 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    88 |      910 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    89 |      920 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    90 |      930 |      1 |  0.000000E+00 |  0.000000E+00 |  1.3601104347 |         ideal\n",
      "    91 |      940 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    92 |      950 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    93 |      960 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    94 |      970 |      1 |  0.000000E+00 |  0.000000E+00 |  1.1947986013 |         ideal\n",
      "    95 |      980 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0656607228 |         ideal\n",
      "    96 |      990 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0871747244 |         ideal\n",
      "    97 |     1000 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    98 |     1010 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    99 |     1020 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   100 |     1030 |      1 |  0.000000E+00 |  0.000000E+00 |  2.7815640872 |         ideal\n",
      "   101 |     1040 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   102 |     1050 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   103 |     1060 |      1 |  0.000000E+00 |  0.000000E+00 |  1.2312518769 |         ideal\n",
      "   104 |     1070 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0897737275 |         ideal\n",
      "   105 |     1080 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   106 |     1090 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   107 |     1100 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   108 |     1110 |      1 |  0.000000E+00 |  0.000000E+00 |  1.4356328185 |         ideal\n",
      "   109 |     1120 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   110 |     1130 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   111 |     1140 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0138778954 |         ideal\n",
      "   112 |     1150 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   113 |     1160 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   114 |     1170 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   115 |     1180 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   116 |     1190 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   117 |     1200 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0051296279 |         ideal\n",
      "   118 |     1210 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   119 |     1220 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   120 |     1230 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   121 |     1240 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   122 |     1250 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   123 |     1260 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   124 |     1270 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   125 |     1280 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   126 |     1290 |      1 |  0.000000E+00 |  0.000000E+00 |  0.3948007498 |         ideal\n",
      "   127 |     1300 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   128 |     1310 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   129 |     1320 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   130 |     1330 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   131 |     1340 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   132 |     1350 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   133 |     1360 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   134 |     1370 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   135 |     1380 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   136 |     1390 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   137 |     1400 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   138 |     1410 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   139 |     1420 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   140 |     1430 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0492689046 |         ideal\n",
      "   141 |     1440 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   142 |     1450 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0279226665 |         ideal\n",
      "   143 |     1460 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   144 |     1470 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   145 |     1480 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   146 |     1490 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   147 |     1500 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   148 |     1510 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   149 |     1520 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   150 |     1530 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   151 |     1540 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   152 |     1550 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   153 |     1560 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   154 |     1570 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   155 |     1580 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   156 |     1590 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   157 |     1600 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0190451667 |         ideal\n",
      "   158 |     1610 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   159 |     1620 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   160 |     1630 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0302795315 |         ideal\n",
      "   161 |     1640 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   162 |     1650 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0047473517 |         ideal\n",
      "   163 |     1660 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   164 |     1670 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0000341522 |             f\n",
      "   165 |     1680 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0000341522 |             f\n",
      "   166 |     1690 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0000341522 |             f\n",
      "   167 |     1700 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0000341522 |             f\n",
      "   168 |     1710 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0000341522 |             f\n",
      "   169 |     1720 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0000341522 |             f\n",
      "   170 |     1730 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0035428175 |         ideal\n",
      "   171 |     1740 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0249242575 |         ideal\n",
      "   172 |     1750 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023537155 |             f\n",
      "   173 |     1760 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023537155 |             f\n",
      "   174 |     1770 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023537155 |             f\n",
      "   175 |     1780 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023537155 |             f\n",
      "   176 |     1790 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0890247250 |         ideal\n",
      "   177 |     1800 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   178 |     1810 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   179 |     1820 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   180 |     1830 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   181 |     1840 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   182 |     1850 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   183 |     1860 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   184 |     1870 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   185 |     1880 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   186 |     1890 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   187 |     1900 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   188 |     1910 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   189 |     1920 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   190 |     1930 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   191 |     1940 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   192 |     1950 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   193 |     1960 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   194 |     1970 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   195 |     1980 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   196 |     1990 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n"
     ]
    }
   ],
   "source": [
    "from pymoo.optimize import minimize\n",
    "\n",
    "res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70dd546",
   "metadata": {},
   "source": [
    "# Analyse the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "2230796a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.87933606]\n"
     ]
    }
   ],
   "source": [
    "X = res.X\n",
    "F = res.F\n",
    "G = res.G\n",
    "\n",
    "\n",
    "print(F)\n",
    "\n",
    "\n",
    "Z = torch.zeros((1, vaeSettings['encoder']['latentDim']))\n",
    "Z[:,0]=X[0]\n",
    "Z[:,1]=X[1]\n",
    "\n",
    "\n",
    "\n",
    "matprop=np.empty(7)\n",
    "decoded = vae.decoder(Z)\n",
    "for i in range(0,nprops): \n",
    "    matprop[i] = 10.**(min_scale[i] + decoded[:,i]*(max_scale[i] - min_scale[i]))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "852c3922",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotLatent(dataIdentifier, vae, zData, ltnt1, ltnt2, plotHull, annotateHead, saveFileName):\n",
    "    clrs = ['purple', 'green', 'orange', 'pink', 'yellow', 'black', 'violet', 'cyan', 'red', 'blue']\n",
    "    colorcol = dataIdentifier['classID']\n",
    "    ptLabel = dataIdentifier['name']\n",
    "    autoencoder = vae\n",
    "    z = autoencoder.encoder.z.to('cpu').detach().numpy()\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    for i in range(np.max(colorcol)+1): \n",
    "      zMat = np.vstack((z[colorcol == i,ltnt1], z[colorcol == i,ltnt2])).T\n",
    "      ax.scatter(zMat[:, 0], zMat[:, 1], c = 'black', s = 4)#clrs[i]\n",
    "      if(i == np.max(colorcol)): #removed for last class TEST\n",
    "        break # END TEST\n",
    "      if(plotHull):\n",
    "        hull = ConvexHull(zMat)\n",
    "        cent = np.mean(zMat, 0)\n",
    "        pts = []\n",
    "        for pt in zMat[hull.simplices]:\n",
    "            pts.append(pt[0].tolist())\n",
    "            pts.append(pt[1].tolist())\n",
    "\n",
    "        pts.sort(key=lambda p: np.arctan2(p[1] - cent[1],\n",
    "                                        p[0] - cent[0]))\n",
    "        pts = pts[0::2]  # Deleting duplicates\n",
    "        pts.insert(len(pts), pts[0])\n",
    "        poly = Polygon(1.1*(np.array(pts)- cent) + cent,\n",
    "                       facecolor= clrs[i], alpha=0.1, edgecolor = 'black') #'black'\n",
    "        poly.set_capstyle('round')\n",
    "        plt.gca().add_patch(poly)\n",
    "        ax.annotate(self.dataIdentifier['className'][i], (cent[0], cent[1]), size = 12)\n",
    "    for i, txt in enumerate(ptLabel):\n",
    "      if(annotateHead == False or ( annotateHead == True and  i<np.max(colorcol)+1)):\n",
    "        ax.annotate(txt, (z[i,ltnt1], z[i,ltnt2]), size = 6)\n",
    "\n",
    "\n",
    "    plt.xlabel('z{:d}'.format(ltnt1), size = 18)\n",
    "    plt.ylabel('z{:d}'.format(ltnt2), size = 18)\n",
    "    # Hide the right and top spines\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    plt.plot(zData[0], zData[1], marker='*', color='red');\n",
    "    ax.annotate('Optimal Material', (zData[0]*1.05, zData[1]*1.05), color='red', size = 10)\n",
    "    plt.savefig(saveFileName, bbox_inches='tight', dpi = 200)\n",
    "\n",
    "\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379884e0",
   "metadata": {},
   "source": [
    "# Gets closer material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "a4824385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "closest material 0 : AISI 1045 CD , confidence 96.44\n",
      "closest material 1 : AISI 4130 , confidence 88.62\n",
      "closest material 2 : AISI 1020 , confidence 85.91\n",
      "AISI 1045 CD\n",
      "[2.05e+11 4.38e-01 7.85e+03 6.25e+08 2.50e+00 3.20e+07 5.00e+01]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#zData: Coordinates of the artificial material\n",
    "#z_np: Coordinates of the real materials\n",
    "zData=X[0:2]\n",
    "dist= np.linalg.norm(zData- z_np, axis = 1)\n",
    "meanDist = np.max(dist)\n",
    "distOrder = np.argsort(dist)\n",
    "\n",
    "matToUseFromDB = {'material':[], 'confidence':[]}\n",
    "for i in range(3):\n",
    "  mat = dataIdentifier['name'][distOrder[i]]\n",
    "  matToUseFromDB['material'].append(mat)\n",
    "  confidence = 100.*(1.- (dist[distOrder[i]]/meanDist))\n",
    "  matToUseFromDB['confidence'].append(confidence)\n",
    "  print(f\"closest material {i} : {mat} , confidence {confidence:.2F}\")\n",
    "\n",
    "print(matToUseFromDB['material'][0])\n",
    "\n",
    "\n",
    "\n",
    "#Plots materials in the latent space + the artifical material\n",
    "ltnt1, ltnt2 = 0, 1\n",
    "fig, ax = plotLatent(dataIdentifier, vae, zData, 0, 1, plotHull = False, annotateHead = False,\\\n",
    "                                    saveFileName ='./figures/optimalLatent.png')\n",
    "\n",
    "\n",
    "ind = np.where(dataIdentifier['name'] == matToUseFromDB['material'][0])\n",
    "ind2 = np.where(dataIdentifier['name'] == matToUseFromDB['material'][1])\n",
    "ind3 = np.where(dataIdentifier['name'] == matToUseFromDB['material'][2])\n",
    "\n",
    "matprops=10**trainInfo[ind,:]\n",
    "matprop=matprops[0,0,:]\n",
    "print(matprop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "a48cb451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAHACAYAAAD0lI8uAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABwEklEQVR4nO3de1zN9+MH8Nfpfj/pesqlXCaXLGSImYyJzXWGSMqS2WYtzAxfk93MZWZfZoZoLGLDF2PIVsbIpcQShSG6CNOpSNf374/z68zRPZHP6fV8PM6D8/m8P+/P+31OH16935+LTAghQERERESSoFPfDSAiIiKi6mN4IyIiIpIQhjciIiIiCWF4IyIiIpIQhjciIiIiCWF4IyIiIpIQhjciIiIiCWF4IyIiIpIQhjciIiIiCWF4IyIiIpIQrQ1vK1euRPPmzWFkZAR3d3ccPny40vKHDh2Cu7s7jIyM0KJFC6xatapMmW3btqFdu3YwNDREu3btsGPHjifVfCIiIqJyaWV427JlC4KDgzFnzhycPn0avXr1wsCBA5GSklJu+StXruDVV19Fr169cPr0acyePRtBQUHYtm2busyxY8cwevRo+Pr64syZM/D19cWoUaNw/Pjxp9UtIiIiIsi08cH03bp1Q+fOnfHdd9+pl7Vt2xbDhg3DggULypSfOXMmdu3ahfPnz6uXTZ48GWfOnMGxY8cAAKNHj0Z2djZ+/fVXdZkBAwagUaNG2Lx58xPsDREREdG/tG7kraCgALGxsejfv7/G8v79++Po0aPlbnPs2LEy5b28vHDq1CkUFhZWWqaiOgEgPz8f2dnZ6pdSqcStW7eghXmZiIiInhKtC2+3b99GcXEx7O3tNZbb29sjIyOj3G0yMjLKLV9UVITbt29XWqaiOgFgwYIFkMvl6pelpSXs7OyQk5NTm64RaZFdAGQA9P7/z1312xwiIgnRuvBWSiaTabwXQpRZVlX5R5fXtM5Zs2ZBqVSqX9evX692+4m02xAAOwEE/f+fQ+q3OUREEqJX3w2oazY2NtDV1S0zIpaZmVlm5KyUQqEot7yenh6sra0rLVNRnQBgaGgIQ0PD2nSDqAEYAoY2IqKa07qRNwMDA7i7uyMyMlJjeWRkJHr06FHuNh4eHmXKHzhwAF26dIG+vn6lZSqqk4iIiOhJ0LqRNwCYNm0afH190aVLF3h4eGD16tVISUnB5MmTAaimM1NTU7FhwwYAqitLV6xYgWnTpiEwMBDHjh1DaGioxlWk77//Pl566SUsXLgQQ4cOxc6dO3Hw4EEcOXKkXvpIREREDZNWhrfRo0fjzp07+OSTT5Ceng5XV1fs3bsXTk5OAID09HSNe741b94ce/fuxdSpU/Htt9/C0dER//3vfzFixAh1mR49eiAiIgL/+c9/MHfuXLRs2RJbtmxBt27dnnr/iIiIqOHSyvu8Pauys7Mhl8uhVCphYWFR380hIiIiCdK6c96IiIiItBnDGxEREZGEMLwRERERSQjDGxEREZGEMLwREZGap6cnZDIZDh48qLF88eLFkMlkCA4OrlW9V69ehUwmg5mZGczMzGBlZYVBgwbh6tWrj99oogaG4Y2IiDS4uLhg/fr1GsvCwsLQpk2bx677xo0byM3NxY0bN2BtbY3AwMAKyxYVFT32/oi0EcMbERFp8Pb2xq+//gqlUgkAOH78OIQQGve1LB1J27hxI1q1agVLS0v4+/ujsLCwWvswMTHB6NGjce7cOfUyf39/BAQEYNSoUbCwsMB3332HnJwcTJo0CQ4ODnBwcMDkyZNx7969arVh+PDh6pE+MzMz6Ovrw9/fHwBQWFiIjz/+GC1btoS1tTWGDBmCtLS0uvj4iJ44hjciItJgaWmJAQMGqJ8ys27dOkyYMKHcsnv27EFcXBwSExNx8OBBhIeHV2sfOTk52Lx5M3r16qWxfPPmzQgICEBWVhYCAgLw/vvv49KlS0hISMBff/2FCxcuYOrUqdVqw44dO5Cbm4vc3FycOHECcrkcY8aMAQDMmTMHf/75J44cOYL09HS0bt0a3t7eNfqciOoLwxsREZUxYcIErF+/Hnl5edi2bRt8fX3LLRcSEgILCws4Ojpi4MCBiI2NrbReJycnWFpawtLSEr///jvmzJmjsb5///7w8vKCjo4OjIyMsGnTJixYsADW1tawsbHBF198gQ0bNqCkpKTabcjMzMRrr72GTz/9FF5eXhBCYOXKlVi6dCkcHBxgYGCAzz77DH/++SeuX79ey0+M6OlheCMiojL69u2LjIwMfPrpp/Dw8IBCoSi33MPLTU1NkZOTU2m9165dQ1ZWFvLz8/HVV1/B09MTN2/eVK9v1qyZ+u+3bt1Cfn4+nJ2d1ctatGiB/Px83L59u1ptePDgAYYOHYphw4bh7bffBgDcvn0b9+7dw0svvaQOkgqFAgYGBgxvJAkMb0REBGAXgKkA7gAAdHR0MH78eHz55ZcVTpk+Dj09PXh7e0NHRwdHjhxRL9fR+fe/JVtbWxgYGGhckXrlyhUYGhrCxsamyn0IIeDv7w9bW1t89dVX6uXW1tYwMTHB8ePHkZWVpX7l5eWhR48eddNBoieI4Y2IqMHbBWAogOUAEv7/BUydOhUHDhzA4MGD63yPJSUl+Pnnn5GVlYV27dqVW0ZHRwdjx47FnDlz8M8//+DOnTuYM2cOfH19NUJeRT7++GMkJSVh8+bNGuV1dHQwefJkTJ8+XT3SdufOHWzZsqVuOkf0hDG8ERE1eFEAdAEU///7SwAAKysr9OvXD/r6+nW2pyZNmsDMzAyWlpb47LPPsHnzZrRt27bC8t988w2cnZ3Rrl07tG/fHq1atcLSpUurta+NGzfi/PnzsLe3V19xOnnyZADAggUL4OHhgZdffhnm5uZwd3fHgQMH6qSPRE+aTAgh6rsRDUV2djbkcjmUSiUsLCzquzlERP+vdOStNMDtBDCkXltERBXTq+8GEBFRfRsCVWCLBuAJBjeiZxvDGxERQRXYGNqIpIDnvBERERFJCMMbERERkYQwvBERERFJCMMbERERkYQwvBERERFJCMMbERERkYQwvBERERFJCMMbERERkYQwvBERERFJCMMbERERkYQwvBERERFJCMMbERERkYQwvBERERFJCMMbERERkYQwvBERERFJCMMbERERkYQwvBERkSS9+eabkMlkOH/+vMbyPXv24KWXXkKjRo1gZ2eHN954Azdu3NAos3btWrRu3Rrm5uZo06YNNm7cqLE+KysLEydOhI2NDSwsLNClSxfcv3+/3HZER0fD0tKyTvtGVBmGNyIikpzc3Fxs3boVVlZWCA0N1VinVCoxc+ZMXL9+HVeuXIGFhQVGjRqlXn/69Gm88847+P7775GdnY1vv/0WEydORGJiIgCgpKQEgwYNgr6+PpKTk5GVlYU1a9ZAX1+/Vm0tKiqqfUeJysHwRkREkhMREQFTU1MsXLgQGzZsQGFhoXrd2LFj8dprr8HMzAympqYIDg7G8ePH1SHqypUrcHZ2Rp8+fSCTydC3b180a9ZMHd5+/fVXpKSkYPny5bCysoKOjg46depU7fDm6emJDz/8EP3794epqSl+/fVX3Lx5E6NGjYKtrS2aNWuGOXPmqNtTOnK3du1aNG3aFNbW1vjwww/r+BMjbaJ14e3u3bvw9fWFXC6HXC6Hr68vsrKyKixfWFiImTNnokOHDjA1NYWjoyPGjx+PtLQ0jXKenp6QyWQaL29v7yfcGyIiKk9oaCh8fHzg7e2N+/fvY/fu3RWWPXToENq2bQs9PT0AgJeXF8zNzREZGYmSkhLs378fd+/eRc+ePTXKv/XWW7C2toarq2uZadWqhIWF4bPPPkNubi769euHsWPHQl9fH1euXMHhw4fxv//9D4sWLVKXz8nJwV9//YWLFy/iyJEj+PbbbxEdHV3zD4YaBqFlBgwYIFxdXcXRo0fF0aNHhaurqxg0aFCF5bOyskS/fv3Eli1bxIULF8SxY8dEt27dhLu7u0a53r17i8DAQJGenq5+ZWVl1ahtSqVSABBKpbJWfSMiIiHOnTsnAIj4+HghhBDjxo0Tr776arll4+LihFwuFwcOHFAvKykpEV999ZUwMjISurq6wsDAQISHh6vXBwQECABi+fLlIj8/Xxw5ckSYmZmJw4cPl7uPqKgoIZfL1e979+4t3n//ffX7GzduCAAiPT1dvSw8PFw899xz6u1lMpm4d++een2/fv3EkiVLqv+hUIOiV7/RsW6dP38e+/btQ0xMDLp16wYAWLNmDTw8PJCUlAQXF5cy28jlckRGRmosW758Obp27YqUlBQ0a9ZMvdzExAQKheLJdoKIiCoVGhoKNzc3uLm5AQD8/PwwYMAApKamonHjxupyf/31FwYMGIAVK1bglVdeUS9ft24dvvrqK8TExKBDhw7466+/MHjwYDRq1AgDBw6EmZkZmjRpgilTpgAAevbsiWHDhmHXrl148cUXq9XGh//vuHHjBoyMjDT+/2jRooXGRRQWFhYwMTFRvzc1NUVOTk4NPxlqKLRq2vTYsWOQy+Xq4AYA3bt3h1wux9GjR6tdj1KphEwmK3P1UHh4OGxsbNC+fXt88MEHVR5Y+fn5yM7O1ngREVHtFRYWYuPGjUhOToZCoYBCoYCPjw+Ki4sRFhamLpeQkIB+/frhyy+/xLhx4zTqOH36NAYOHAg3Nzfo6OjAzc0Nr7zyCvbs2QMAcHNzg0wme6x26uj8+99rkyZN8ODBA9y8eVO97MqVK2jSpMlj7YMaLq0KbxkZGbCzsyuz3M7ODhkZGdWq48GDB/joo48wduxYWFhYqJf7+Phg8+bNiI6Oxty5c7Ft2za8/vrrlda1YMEC9bl3crkcTZs2rVmHiIhILWlXEuYPnw9llhJxcXGIj49HfHw8zpw5g7lz52LdunUQQuDcuXPo27cvPv30U0yYMKFMPR4eHti/fz/OnTsHADh37hz279+PTp06AQCGDx+OvLw8rFq1CsXFxTh+/Dh27tyJIUOG1KrdjRs3Rp8+ffDBBx/g3r17SElJwRdffAE/P7/afxjUoEkivIWEhJS5WODR16lTpwCg3N+WhBDV+i2qsLAQ3t7eKCkpwcqVKzXWBQYGol+/fnB1dYW3tzd+/vlnHDx4EHFxcRXWN2vWLCiVSvXr+vXrNew5EREBquAWMTQCW/dsRbvCdpAly9QjbwqFAkFBQUhLS0NUVBSWLFmCW7duYdq0aTAzM1O/UlJSAKh+GX/nnXcwePBgmJmZ4dVXX8Wbb76JN998EwBgaWmJPXv2IDQ0FBYWFhg/fjy+/fbbak+ZlmfTpk3Iy8uDk5MTevbsiddee41XlFKtyYQQor4bUZXbt2/j9u3blZZxdnbGpk2bMG3atDJXl1paWuLrr78u9zewUoWFhRg1ahT+/vtv/P7777C2tq50f0IIGBoaYuPGjRg9enS1+pGdnQ25XA6lUqkxqkdERJXbN3UfTiw/AVEsINOVoVtQN3gt9arvZhHVC0lcsGBjYwMbG5sqy3l4eECpVOLEiRPo2rUrAOD48eNQKpXo0aNHhduVBreLFy8iKiqqyuAGqIbZCwsL4eDgUP2OEBFRrTTv0xzHlx2HTFcGUSzg7Olc300iqjeSGHmriYEDByItLQ3ff/89AGDSpElwcnLSuAdQmzZtsGDBAgwfPhxFRUUYMWIE4uLi8Msvv8De3l5dzsrKCgYGBrh8+TLCw8Px6quvwsbGBomJiZg+fTqMjY1x8uRJ6OrqVqttHHkjIqq9pF1JuBp9Fc6eznAZUvbuAUQNhdaFt3/++QdBQUHYtWsXAGDIkCFYsWKFxpWjMpkM69evh7+/P65evYrmzZuXW1dUVBQ8PT1x/fp1jBs3DgkJCcjNzUXTpk3x2muvYd68ebCysqp22xjeiIiI6HFpXXh7ljG8ERER0eOSxNWmRERERKTC8EZEREQkIQxvRERERBLC8EZEREQkIQxvRERERBLC8EZEREQkIQxvRERERBLC8EZEREQkIQxvRERERBLC8EZEREQkIQxvRERa4s0334RMJsP58+c1lkdHR2s83zk9PR1jx46FQqGAubk5WrRogalTp6rX+/v7Izg4uNx95Ofnw9PTE3Z2drCwsECbNm2wevVqjTKTJk2Ci4sLdHR0sGzZsjJ1CCGwYMECODs7w9TUFK1bt8bx48fV62UyGeLj46vV519//RVdu3aFXC5Ho0aN8MILL2Dv3r3VXk8kRXr13QAiInp8ubm52Lp1K6ysrBAaGoolS5ZUWNbX1xfNmjXDhQsXYGFhgStXruDIkSPV2o+enh6WL1+Otm3bQk9PD4mJiejTpw/atm2LXr16AQDc3NwwevRozJkzp9w65syZgz/++AMHDx5Ey5YtkZKSAgMDgxr3+fLlyxg5ciR+/PFHDBkyBAUFBTh+/Dh0dHSqtZ5IqvgTTESkBSIiImBqaoqFCxdiw4YNKCwsrLBsTEwMJkyYAEtLS+jo6KBly5bw8/Or1n50dXXRoUMH6OmpfveXyWSQyWS4dOmSusy7776Lvn37wsjIqMz2//zzD5YuXYp169ahVatWkMlkcHJygoODQw17DJw+fRr29vYYNmwYdHR0YGRkhN69e6tDZFXriaSK4Y2ISAuEhobCx8cH3t7euH//Pnbv3l1h2RdffBHBwcHYsGEDkpOTa7W/QYMGwcjICO3atYO9vT2GDx9ere1iYmJgaGiIPXv2oHHjxmjevDk++uijSsNmRdzd3ZGWloa3334b+/btwz///FOj9URSxfBGRCRxiYmJiImJgZ+fH8zMzDB8+HCEhoZWWH7r1q0YPHgwli1bhvbt28PJyQmbNm2q0T5/+eUX3Lt3D9HR0RgxYgSMjY2rtd0///yD7OxsxMbGIikpCYcOHcLevXuxaNGiGu0fAJo3b44///wTubm5mDhxImxtbfHKK6/g77//rtZ6IqlieCMikrjQ0FC4ubnBzc0NAODn54f9+/cjNTW13PIWFhYICQlBXFwc7t69i6CgIIwfP77MhQ5V0dXVRe/evXHz5k0sXry4WtuYmZkBAObPnw8zMzM0a9YM77//Pnbu3FmjfZfq3LkzNm7ciBs3biA5ORlCCIwbN67a64mkiOGNiEiiknYl4ZegXxAWGobk5GQoFAooFAr4+PiguLgYYWFhVdZhZmaG6dOnQy6XIzExsVbtKCwsxMWLF6tVtjRgymSyWu2rMi1btsT777+Pv/76q1briaSC4Y2ISIKSdiUhYmgEfvz2RyiVSmxbsg3x8fGIj4/HmTNnMHfuXKxbtw5CiDLbzpgxA/Hx8SgoKEBBQQHWrl2Le/fuwd3dvcr9xsfHIzIyEnl5eSgqKsKePXsQHh4OLy8vdZmCggI8ePAAJSUlKCoqwoMHD1BUVARANZXZr18/zJ8/H/fv30daWhqWL1+OoUOHauyntI7SV3nnxB0+fBgrV65EWloaACAjIwNr1qxBjx49qrWeSKoY3oiIJOhK1BXIdGWILYlFB1kH6FzSUY+8KRQKBAUFIS0tDVFRUWW2zc/Ph7e3N6ytraFQKLB+/Xrs3LkTzs7OVe63qKgIs2fPhr29PaytrTF79mwsXboUY8eOVZfp378/jI2NcfjwYcyYMQPGxsb47LPP1OvDw8OhVCphb2+PF154AV5eXvjwww819tOtWzcYGxurX4GBgWXa0qhRI+zfvx/u7u4wNTVF586d0ahRI/zwww/VWk8kVTJR3q9l9ERkZ2dDLpdDqVTCwsKivptDRBJWOvIm05VBFAt47/SGyxCX+m4WET0FDG9PEcMbEdWlpF1JuBp9Fc6ezgxuRA0Iw9tTxPBGREREj4vnvBERERFJCMMbERERkYQwvBERERFJCMMbEdEz5M0334RMJivztIPo6GhYWlqq36enp2Ps2LFQKBQwNzdHixYtMHXqVPV6f39/BAcHl7uP/Px8eHp6ws7ODhYWFmjTpg1Wr16tUSYtLQ2vvvoqTE1N0axZM6xZs6bcuhISEmBgYIBhw4bVqr9EVHMMb0REz4jc3Fxs3boVVlZWlT6bFAB8fX1hZGSECxcuQKlUIjIyEh07dqzWfvT09LB8+XKkpaUhOzsb27dvx9y5c3H48GF1mTFjxkChUCAzMxM//fQTZsyYgUOHDmnUU1JSgsDAQHh4eNS4r0RUewxvRETPiIiICJiammLhwoXYsGFDuU8VKBUTE4MJEybA0tISOjo6aNmyJfz8/Kq1H11dXXTo0AF6enoAVI+qkslkuHTpEgDg8uXLOHLkCBYsWABTU1N069YNPj4+WLdunUY9y5cvh4uLC/r06VPLHhNRbTC8ERE9I0JDQ+Hj4wNvb2/cv38fu3fvrrDsiy++iODgYGzYsAHJycm12t+gQYNgZGSEdu3awd7eHsOHDwcAnD17Fg4ODrC3t1eX7dixI86ePat+n5KSgmXLlmHJkiW12jcR1R7DGxHRMyAxMRExMTHw8/ODmZkZhg8fXunU6datWzF48GAsW7YM7du3h5OTEzZt2lSjff7yyy+4d+8eoqOjMWLECBgbGwNQTd8+fH4dAFhaWiInJ0f9fvLkyQgJCYGNjU2N9klEj4/hjYjoGRAaGgo3Nze4ubkBAPz8/LB//36kpqaWW97CwgIhISGIi4vD3bt3ERQUhPHjx5e50KEqurq66N27N27evInFixcDAMzMzKBUKjXKKZVKmJubAwA2bdqEBw8eVHualojqll59N4CIqKErLCzExo0bkZubC4VCAQAQQqC4uBhhYWGYM2dOpdubmZlh+vTp+OKLL5CYmIi2bdvWqg0XL14EADz//PNIS0tDZmYm7OzsAADx8fHo0KEDAODAgQOIjY1VtzU3NxdFRUVo0qQJbty4UeN9E1HNcOSNiKgeJe1Kwvzh86HMUiIuLg7x8fGIj4/HmTNnMHfuXKxbtw7lPcVwxowZiI+PR0FBAQoKCrB27Vrcu3cP7u7uVe4zPj4ekZGRyMvLQ1FREfbs2YPw8HB4eXkBAFq2bImePXti9uzZuH//Pk6cOIHw8HAEBAQAAL755hskJSWp2zp58mT06dMHsbGxdfvhEFG5OPJGRFRPknYlIWJoBLZiK9qhHWTJMiiGKNTrg4KCsHjxYkRFRUFHR/N37fz8fHh7eyM1NRX6+vpo27Ytdu7cCWdn5yr3W1RUhNmzZyMpKQkymQzOzs5YunQpxo4dqy6zefNmTJw4Eba2trCyssKiRYvQu3dvAIBcLodcLleXNTMzg6GhocYFDkT05PDB9E8RH0xPRA/bN3UfTiw/AVEsINOVoVtQN3gt9arvZhHRM07rpk3v3r0LX19f9W+Gvr6+yMrKqnQbf39/9X2OSl/du3fXKJOfn4/33nsPNjY2MDU1xZAhQ3huBxE9luZ9mquDmygWcPZ0ru8mEZEEaN3I28CBA3Hjxg31o14mTZoEZ2fnSu+X5O/vj5s3b2L9+vXqZQYGBrCyslK/f/vtt7F7926EhYXB2toa06dPxz///IPY2Fjo6upWq20ceSOiRyXtSsLV6Ktw9nSGyxCX+m4OEUmAVoW38+fPo127doiJiUG3bt0AqO5C7uHhgQsXLsDFpfx/GP39/ZGVlYX//e9/5a5XKpWwtbXFxo0bMXr0aACq5/41bdoUe/fuVZ/kWxWGNyIiInpcWjVteuzYMcjlcnVwA4Du3btDLpfj6NGjlW4bHR0NOzs7tG7dGoGBgcjMzFSvi42NRWFhIfr3769e5ujoCFdX1yrrJSIiIqpLWnW1aUZGhvqeRA+zs7NDRkZGhdsNHDgQI0eOhJOTE65cuYK5c+fi5ZdfRmxsLAwNDZGRkQEDAwM0atRIYzt7e/tK683Pz0d+fr76fXZ2di16RURERPQvSYy8hYSElLmg4NHXqVOnAKgesPwoIUS5y0uNHj0ar732GlxdXTF48GD8+uuvSE5Oxp49eyptV1X1LliwQH3hhFwuR9OmTavZYyIiIqLySWLkbcqUKfD29q60jLOzM86ePYubN2+WWXfr1q0a3X/IwcEBTk5O6ruNKxQKFBQU4O7duxqjb5mZmejRo0eF9cyaNQvTpk1Tv8/OzmaAIyIiosciifBmY2NTrYcfe3h4QKlU4sSJE+jatSsA4Pjx41AqlZWGrEfduXMH169fh4ODAwDA3d0d+vr6iIyMxKhRowAA6enpSEhIwKJFiyqsx9DQEIaGhtXeLxEREVFVJDFtWl1t27bFgAEDEBgYiJiYGMTExCAwMBCDBg3SuNK0TZs22LFjBwDVM/k++OADHDt2DFevXkV0dDQGDx4MGxsbDB8+HIDqbuIBAQGYPn06fvvtN5w+fRrjxo1Dhw4d0K9fv3rpKxERETVMkhh5q4nw8HAEBQWprwwdMmQIVqxYoVEmKSkJSqUSAKCrq4u//voLGzZsQFZWFhwcHNCnTx9s2bIF5ubm6m2+/vpr6OnpYdSoUcjLy0Pfvn0RFhZW7Xu8EREREdUFrbrP27OO93kjIiKix6VV06ZERERE2o7hjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCGN6IiIiIJIThjYiIiEhCtC683b17F76+vpDL5ZDL5fD19UVWVlal28hksnJfixcvVpfx9PQss97b2/sJ94aIiIhIk159N6CujR07Fjdu3MC+ffsAAJMmTYKvry92795d4Tbp6eka73/99VcEBARgxIgRGssDAwPxySefqN8bGxvXYcuJiIiIqqZV4e38+fPYt28fYmJi0K1bNwDAmjVr4OHhgaSkJLi4uJS7nUKh0Hi/c+dO9OnTBy1atNBYbmJiUqYsERER0dOkVdOmx44dg1wuVwc3AOjevTvkcjmOHj1arTpu3ryJPXv2ICAgoMy68PBw2NjYoH379vjggw+Qk5NTaV35+fnIzs7WeBERERE9Dq0aecvIyICdnV2Z5XZ2dsjIyKhWHT/88APMzc3x+uuvayz38fFB8+bNoVAokJCQgFmzZuHMmTOIjIyssK4FCxZg/vz5NesEERERUSUkMfIWEhJS4UUFpa9Tp04BUF188CghRLnLy7Nu3Tr4+PjAyMhIY3lgYCD69esHV1dXeHt74+eff8bBgwcRFxdXYV2zZs2CUqlUv65fv16DXhMRERGVJYmRtylTplR5ZaezszPOnj2Lmzdvlll369Yt2NvbV7mfw4cPIykpCVu2bKmybOfOnaGvr4+LFy+ic+fO5ZYxNDSEoaFhlXURERERVZckwpuNjQ1sbGyqLOfh4QGlUokTJ06ga9euAIDjx49DqVSiR48eVW4fGhoKd3d3uLm5VVn23LlzKCwshIODQ9UdICIiIqojkpg2ra62bdtiwIABCAwMRExMDGJiYhAYGIhBgwZpXGnapk0b7NixQ2Pb7Oxs/PTTT5g4cWKZei9fvoxPPvkEp06dwtWrV7F3716MHDkSnTp1Qs+ePZ94v4iIiIhKaVV4A1RXhHbo0AH9+/dH//798fzzz2Pjxo0aZZKSkqBUKjWWRUREQAiBMWPGlKnTwMAAv/32G7y8vODi4oKgoCD0798fBw8ehK6u7hPtDxEREdHDZEIIUd+NaCiys7Mhl8uhVCphYWFR380hIiIiCdK6kTciIiIibcbwRkRERCQhDG9EREREEsLwRkRERCQhDG9EREREEsLwRkRERCQhDG9EREREEsLwRkRERCQhDG9EREREEsLwRkRERCQhDG9EREREEsLwRkRERCQhDG9EREREEsLwRkRERCQhDG9EREREEsLwRkRERCQhDG9EREREEsLwRkRERCQhDG9EREREEsLwRkREde7NN9+ETCbD+fPnNZZHR0fD0tJS/T49PR1jx46FQqGAubk5WrRogalTp6rX+/v7Izg4uMr9JSQkwMDAAMOGDVMvy8/Ph6enJ+zs7GBhYYE2bdpg9erV1d4eANLS0vDqq6/C1NQUzZo1w5o1a6psC9GTxvBGRER1Kjc3F1u3boWVlRVCQ0MrLevr6wsjIyNcuHABSqUSkZGR6NixY432V1JSgsDAQHh4eGgs19PTw/Lly5GWlobs7Gxs374dc+fOxeHDh6u1PQCMGTMGCoUCmZmZ+OmnnzBjxgwcOnSoRu0jqmsMb0REVKciIiJgamqKhQsXYsOGDSgsLKywbExMDCZMmABLS0vo6OigZcuW8PPzq9H+li9fDhcXF/Tp00djua6uLjp06AA9PT0AgEwmg0wmw6VLl6q1/eXLl3HkyBEsWLAApqam6NatG3x8fLBu3boK2zJu3Dg4OjrCwsIC7u7uiIqKUq8LCwtDx44d8emnn8LOzg729vZYtmyZen1ISAgGDx6MKVOmwNLSEs2aNcOWLVtq9FlQw8DwRkREdSo0NBQ+Pj7w9vbG/fv3sXv37grLvvjiiwgODsaGDRuQnJxc432lpKRg2bJlWLJkSYVlBg0aBCMjI7Rr1w729vYYPnx4tbY/e/YsHBwcYG9vr17WsWNHnD17tsJ99e3bF+fPn8edO3fg7e2NN954Azk5Oer1586dg5GREVJTU7FlyxZ88MEHuHz5snr9/v370bNnT9y5cwefffYZJk6cqLE9EcDwRkREdSgxMRExMTHw8/ODmZkZhg8fXunU6datWzF48GAsW7YM7du3h5OTEzZt2lTt/U2ePBkhISGwsbGpsMwvv/yCe/fuITo6GiNGjICxsXG1ts/NzdU4Pw8ALC0tKw1TEyZMgFwuh76+PmbMmIGSkhKNsGdtbY0ZM2ZAX18fnp6eaN68OeLj49XrO3fujDFjxkBXVxe+vr4oKCioVaitCzxv8dnF8EZERHUmNDQUbm5ucHNzAwD4+flh//79SE1NLbe8hYUFQkJCEBcXh7t37yIoKAjjx48vExjKs2nTJjx48KBa06y6urro3bs3bt68icWLF1drezMzMyiVSo1lSqUS5ubm5ZYvKSnBnDlz8Nxzz8HCwgKWlpZQKpW4ffu2uoxCodDYxtTUVCMMPrxeJpPB2Ni4XkbeeN5i3SgqKnoi9TK8ERFRnSgsLMTGjRuRnJwMhUIBhUIBHx8fFBcXIywsrMrtzczMMH36dMjlciQmJlZZ/sCBA4iNjVXva8mSJdi3bx+aNGlSaRsvXrxYre2ff/55pKWlITMzU719fHw8OnToUG7dmzZtwqZNm7Bnzx4olUpkZWVBLpdDCFFlX541DfW8xatXr0Imk2Hjxo1o1aoVLC0t4e/vr9H/AwcOoFOnTpDL5ejcuTMOHjyoXufv74+AgACMGjUKFhYW+O677+Dp6YmZM2eib9++MDU1Rffu3ZGamoqQkBDY2tqiSZMm2LFjR40+L4Y3IiJ6bEm7kjB/+Hwos5SIi4tDfHw84uPjcebMGcydOxfr1q0rN8TMmDED8fHxKCgoQEFBAdauXYt79+7B3d29yn1+8803SEpKUu9r8uTJ6NOnD2JjYwGoglZkZCTy8vJQVFSEPXv2IDw8HF5eXtXavmXLlujZsydmz56N+/fv48SJEwgPD0dAQEC57cnOzoaBgQFsbGxQUFCATz75BNnZ2bX9SOtVQz5vEQD27NmDuLg4JCYm4uDBgwgPDwegCoNDhw7F3LlzcefOHcyePRtDhgzBlStX1Ntu3rwZAQEByMrKUv+shIeH45tvvsGdO3dgamqKXr16QS6XIz09HfPmzUNgYGClAflRDG9ERPRYknYlIWJoBLbu2Yp2he0gS5apR7MUCgWCgoKQlpamceVlqfz8fHh7e8Pa2hoKhQLr16/Hzp074ezsXOV+5XK5xn7MzMxgaGio/o+6qKgIs2fPhr29PaytrTF79mwsXboUY8eOrdb2gOo/4tTUVNja2mLEiBFYtGgRevfuXW57/Pz81OfttWjRAsbGxmjatGktPtH61dDPWwRUV/5aWFjA0dERAwcOVAf6iIgIeHp64vXXX4eenh7eeOMNvPjii9i8ebN62/79+8PLyws6OjowMTEBoLoK2dXVFUZGRhgxYgTy8vIwdepU6OnpwcfHB3fu3MG1a9eq/KxK6VW7JBERUTmuRF2BTFcGn2IfyHRluBp9FS5DXNTrbWxskJeXp36flZWl/vt///vfSuuuznRrqZCQEI33Xbp0wcmTJ2u9PQA0btwYv/76a7W2NzU1xfbt2zWWzZgxQ/13f39/+Pv7a6x/+GKF8vb/8Gf1tJR33uKAAQOQmpqKxo0blylfet5iSEgIcnNz8f3332P8+PHo1KkT2rZtW+m+anPe4tatW7F48WL85z//qfPzFks9fO6hqamp+nu4ceNGmV8sWrRogRs3bqjfN2vWrNL6TExMNH5BKA14ubm5lbbpYQxvRET0WJr3aY7jy45DpiuDKBZw9nSu7yZRLSTtSsLFgxcRtiEMeQV56sAhhFCftzhnzpxK6yg9b/GLL75AYmJileHt4fMOAVWAKSoqQpMmTTQC0cMqOm+xvO0fPm/Rzs4OQOXnLValSZMmOHLkiMayK1euaIzG6ug8+UnNpzZtmpWVhebNm6Nly5ZPa5dERPQUuAxxgfdOb3QL6gbvnd4ao24kDaVT3z9++yOUSiW2LdnWIM9brMro0aMRHR2NnTt3ori4GNu3b8fhw4fh7e1dq/pq66mNvBUXF+PatWuQyWRPa5dERPSUuAxxYWiTsNKp79jiWHSQdYDOJR2Nqb6goCAsXrwYUVFRZUaWSs9bTE1Nhb6+Ptq2bVuj8xblcrn6fUXnLSYlJUEmk8HZ2bnMeYuVbQ+ozlucOHEibG1tYWVlVel5i1Vp1aoVtm/fjlmzZsHX1xctWrTAjh070KJFi1rVV1sy8ZSuYb5z5w5sbW0hk8lQXFz8NHb5zMnOzoZcLodSqYSFhUV9N4eIiAjAvyNvpVPfHEF9tvGcNyIiogaudOr7avRVOHs6M7g942oU3l5++eVa76gm9y8hIiKip4tT39JRo/AWHR0NmUz2TN8t+vPPP8eePXsQHx8PAwODal1mLYTA/PnzsXr1aty9exfdunXDt99+i/bt26vL5Ofn44MPPsDmzZuRl5eHvn37YuXKlZXeyZuIiIiortUovOnq6qKkpAReXl5lns9Wlfz8fERERNRom9ooKCjAyJEj4eHhUeXz2EotWrQIS5cuRVhYGFq3bo3PPvsMr7zyCpKSktT3ggkODsbu3bsREREBa2trTJ8+HYMGDUJsbCx0dXWfZJeIiIiI1Gp0wUKHDh2QmJiINWvW4M0336zRjp72BQthYWEIDg6ucuRNCAFHR0cEBwdj5syZAFRB097eHgsXLsRbb70FpVIJW1tbbNy4EaNHjwYApKWloWnTpti7d6/6kuWq8IIFIiIielw1us9b6T1bSu+fog2uXLmCjIwM9O/fX73M0NAQvXv3xtGjRwGo+ltYWKhRxtHREa6uruoy5cnPz0d2drbGi6ihCgsLQ8eOHeu7GUREklej8Na5c2cIIXD69Okn1Z6nLiMjAwA07glT+r50XUZGBgwMDNCoUaMKy5RnwYIF6nvQyOVyST7jjurPkSNHMHDgQDRq1AiWlpZwc3PDokWLUFBQ8Fj1Ojs743//+1+V5Xbv3o2XXnoJ5ubmsLa2RteuXbFq1arH2vfTdPz4cfTp00f9+T3//PMaj1qSyWQajyYiIpKKGoc3ADh79myNL1owMjLC+PHjMX78+BptB6ie9yaTySp9nTp1qsb1PuzRmwcLIaq8oXBVZWbNmgWlUql+Xb9+/bHaSA3HL7/8goEDB8LLywsXL15EVlYWtmzZgsTERKSnpz/x/X/33Xfw8/NDYGAgbty4gdu3b2PlypX45ZdfalVfUVFRHbewcjk5ORgwYABGjx6NzMxM3Lp1C6GhoerH4xARSZqQgFu3bonz589X+srLy9PYZv369UIul1dZ9+XLlwUAERcXp7F8yJAhYvz48UIIIX777TcBQPzzzz8aZZ5//nnx8ccfV7sfSqVSABBKpbLa21DDU1JSIpo3by4+/fTTSsv5+PgIBwcHYW5uLjp37ix+//139bq///5b9O3bV1hYWIhGjRqJHj16iHv37ok33nhDyGQyYWRkJExNTcVbb71Vpt7s7Gxhbm4uNm7cWOG+c3JyxJAhQ4Stra2wsLAQvXr1EvHx8er18+bNE6+99pqYPHmyaNSokZg2bZpYv369cHNzE7NmzRJWVlaiadOm4ttvv9Xo95IlS0SLFi1Eo0aNhJeXl7h8+bJ6vZOTk1i4cKHo1q2bMDMzEy+99JJISUkpt30nT54U+vr6ori4uNz1L7zwggAgjI2Nhampqfj8888r/qCJiJ4xkghvtVHd8FZSUiIUCoVYuHChell+fr6Qy+Vi1apVQgghsrKyhL6+vtiyZYu6TFpamtDR0RH79u2rdpsY3qg6kpKSBABx6dKlSsutW7dOZGVliYKCArFo0SJhZWUlsrOzhRBCjBkzRrz11luioKBAFBQUiD///FPk5+cLIVQhaMeOHRXWu3//fqGjo6MuXx6lUikiIiJEbm6uyMvLE0FBQaJ169aipKRECKEKb7q6umL9+vWisLBQ3Lt3T6xfv17o6uqK2bNni/z8fHH06FFhbm4uDh06JIQQ4ocffhCOjo7i7NmzIi8vT0ybNk20bdtWFBYWqtvdvn17cfnyZZGXlycGDhwo/Pz8ym1fdna2sLW1FSNHjhT/+9//RHp6epkyAMTp06cr/YyJiJ5Fj/Vg+pSUFKSkpFT7BrypqalISUl5nF1Wq03x8fFISUlBcXGx+mG1ubm56jJt2rTBjh07AKimS4ODg/HFF19gx44dSEhIgL+/P0xMTDSenRYQEIDp06fjt99+w+nTpzFu3Dh06NAB/fr1e6L9obJqcuJ7SkoKzMzMoFQqn2yjauDq1auQyWQVXgl969YtAEDjxo0rrWfChAmQy+XQ19fHjBkzUFJSgrNnzwIA9PX1kZ6ejqtXr0JfXx89evSAgYFBtdp369Yt2NraVlrewsICo0ePhqmpKYyMjDB//nwkJycjLS1NXcbV1RX+/v7Q09ODiYkJAMDU1BQhISEwMDCAh4cHfHx8sGHDBgDAxo0bERQUhA4dOsDIyAhffPEFbty4gRMnTqjrnDJlClq0aAEjIyP4+PhUePGUubk5jh49CisrK0ybNg2Ojo7o1q0b4uLiqvUZEBE9yx4rvDk7O6N58+bo2bNntc7D6dKlyxN/eOvHH3+MTp06Yd68ecjNzUWnTp3QqVMnjXPikpKSNP4z//DDDxEcHIx33nkHXbp0QWpqKg4cOKC+xxsAfP311xg2bBhGjRqFnj17wsTEBLt37+Y93p4QT09PGBoawszMDI0aNULv3r1x8uTJGtfTrFkz5Obmajy4uKYSEhLwxhtvwNbWFhYWFmjbti3+85//PLFAaGNjA0D1y05FSkpKMGfOHDz33HOwsLCApaUllEolbt++DQBYvHgxGjdujH79+qFJkybo0KEDFAoFzM3NkZqainXr1qnr8vT0xLJlyzT2f/v27UovjMjLy8M777wDZ2dnWFhYqB9AXbp/QPXZP8rR0RH6+vrq905OTup+3rhxQ+NB1oaGhnB0dMSNGzfUyx6+v6SpqSlycnIqbGOrVq2watUqXL58GTdu3ECrVq0wZMiQZ/om40RE1fFY4Q1QnbR/6tQpdO/eHWfOnKlW+ScpLCwMQjUdrPHy9PTUaIO/v7/6vUwmQ0hICNLT0/HgwQMcOnQIrq6uGvUaGRlh+fLluHPnDu7fv4/du3fz6tEnbOHChcjNzUV6ejo6d+6MYcOGPfU2xMXFwcPDA23atMGZM2eQnZ2Nffv2IS8vTz3KVRNVnbiftCsJf3/3NxrbNa70ptabNm3Cpk2bsGfPHiiVSmRlZUEul6uPLzs7O6xcuRLXrl1DkyZNcOnSJSxatAhKpRIKhUIjJD2qR48eMDExwU8//VRhma+++gqxsbE4cuQIsrOzcfXqVQCax7eOTtl/XtLS0jRG6lNSUtQjjE2aNFHXA6huuJ2WllYnTzFxdHTERx99hNTUVPzzzz8Ayl6kREQkFY8d3kpHIq5fv45evXph9+7dddEuIjUjIyMEBAQgLS0Nd+7cKbN+6dKleO6552Bubo6WLVtixYoV6nWPTlH6+/sjMDAQ3t7eMDc3h4uLC6Kjoyvc9/Tp0zF69Gh89tlncHR0BKAaLfrqq6/Qq1cvAKqRWycnJ5ibm6Ndu3YaoSc6OhqWlpb47rvv0KxZM3h4eKjX/fTTT3B2doa1tTXeeecd/LXtL0QMjcDJFSfxXOZzmDt3LoyNjfH888/j4MGDSE5ORkBAAN544w189913uHPnDjp37gwXFxdMmDBB4z6CW7duRUpKCoQQ+Ouvv2Bqagq5XA4dHR00adIETk5O6v4dPnwYM2fOhJmZGQYOHAhzc3MsXLgQQUFBCA8PR3Z2NoQQiI+Px5AhQwCobjhtZGSERo0aITc3F7Nnz67Wd3nv3j18+umnKCgowPHjxxEeHg4fHx8AwLhx47BixQokJiYiPz8f//nPf9C4cWN07dq1WnU/7MKFC1i4cCGuXr2KkpISZGVlYcWKFWjdujWsra0BqG71c/ny5RrXTURU3x47vJmYmODYsWN4+eWXkZubi9dffx1ff/11XbSNCABw//59rF27Fk5OTur/eB/m5OSE33//HdnZ2Vi7di1mzJiBP//8s8L6IiIiMGnSJGRlZcHX11djFPbR/R4+fBhjxoyptH1ubm44efIksrKy8PHHH8PX1xdXrlxRr8/JycGZM2dw4cIFHDp0SL18x44diI+Px19//YWjR49i0ZJFkOnKcKf4Do7gCMZ7jEevXr1w+fJlvPLKKxgyZAjatGkDExMTnD17Fm5ubgBU9yHcuXOnxkhwbGwsevToATMzMxQXF0NfXx9ZWVlITk7G7NmzsWLFCjRq1Ah5eXno1auXepTz119/BQC8/fbbWL9+PVatWgVHR0fY2Nhg0qRJeO211wAA06ZNg66uLuzt7eHq6qoRSivj6uqKoqIiODg44I033sDnn3+OPn36AADGjx+P9957D4MGDYJCocCZM2ewe/du6OnV6Cl+AFTnvJ0+fRq9evWChYUFXFxccOvWLY1fLj/99FMEBQWhUaNG+PLLL2u8DyKievM4VzvIZDLh4OAghBCiqKhITJw4UchkMqGjoyPeeustUVRUpFFeoVAIHR2dx9mlpPFq0+rr3bu3MDIyEnK5XNjb2wsvLy9x5swZIYRQ33KiIkOHDhWfffaZEEKIK1euCADi7t27Qggh/Pz8xOjRo9Vlb9y4IQCI27dvl6mndN358+dr1HY3Nzfx448/CiGEiIqK0tj/w206fvy4ellERIRopmgmQhAiXpa9LFqhlbiw84J6/SuvvKK+nUVN+iCE6udu3rx5olOnTkJPT080a9ZMhIeHq9f37t1bfP311zXqIxER1Z/HHnkrpaurizVr1mDBggUAgDVr1mDAgAHP1FV+JC0LFixAVlYWMjIysG/fPjz//PPllgsPD0fnzp3Vd9Lfu3evxonzj3r0pHcA5Z743qhRI+jo6FR64QCgupilffv2kMvlsLS0REJCgsb+zc3NYWlpWWa70qnL0r9nZmXCe6c3jDsYo8uALnAZ4qJe36JFi0pP3K+oD4Dq1IaQkBDExcXh7t27CAoKwvjx43H+/PlK+0VERM+mOgtvpWbOnImtW7fCyMgIv//+Ozw8PDSmkIjqUkpKCvz8/LBo0SLcunULWVlZePXVV+vkwhgTExP06tWr0gsHjhw5gpCQEGzYsAF3795FVlYWXF1dqzxxHwCuXbum0Y/GjRvDZYgLPEZ5IAtZGmWvXLlSJyfum5mZYfr06ZDL5UhMTKy0fURE9Gx6Iv9qjxgxAlFRUbCzs8OFCxfQvXt3HD58+EnsirRM0q4k7Ju6D/fv3K9W+dzcXAghYGdnBx0dHezduxcHDhyos/Z89dVX2LJlC+bNm6d+ju2NGzcwc+ZMHD58GNnZ2dDT04OtrS1KSkqwbt06JCQkVKvuTz75BFlZWUhLS8OCBQvUJ+6PHj0a0dHR2LlzJ4qLi7F9+3YcPnwY3t7eterDjBkzEB8fj4KCAhQUFGDt2rW4d+8e3N3dAfDEfSIiqXliv3J37doVMTExaN++PW7duoX+/furL9EnKk/SriREDI3AieUnkJmQicyEzCq3adeuHebMmYOXX34Z1tbW2LJli/qKyLrg7u6OP//8EwkJCWjfvj0sLCzQr18/6Ovrw83NDQMGDMCIESPQoUMHODo64ty5c+jZs2e16h46dCg6duwIV1dXdOvWTX3FZqtWrbB9+3bMmzcPjRo1wieffIIdO3bU+h6J+fn58Pb2hrW1NRQKBdavX4+dO3eqbxcSHByMgwcPwtLSEoMGDarVPoiI6OmRiceYX9LR0YFCodC4q/qjcnJyMHLkSPVoiEwmQ3FxcW13KWnZ2dmQy+VQKpWwsLCo7+Y8c/ZN3YcTy09AFAvIdGXoFtQNXku96rtZREREz5SaX4P/kHnz5sHMzKzSMubm5tizZw+CgoKwd+/ex9kdabnmfZrj+LLjkOnKIIoFnD2d67tJREREz5zHmjadN28epk+fXmU5XV1dfPvtt7hy5QovXqAKuQxxgfdOb3QL6gbvnd4aV1sS0eN58803IZPJylxlXHoj6VLp6ekYO3as+nFqLVq0wNSpU9Xr/f39ERwcXOX+EhISYGBgUObJKJMmTYKLiwt0dHQ0HssGqKb4PT09YWdnBwsLC7Rp0warV6/WKJOWloZXX30VpqamaNasGdasWVOt/hNpk8caeSOqay5DXBjaiOpYbm4utm7dCisrK4SGhmLJkiUVlvX19UWzZs1w4cIFWFhY4MqVKzhy5EiN9ldSUoLAwMByb97s5uaG0aNHY86cOWXW6enpYfny5Wjbti309PSQmJiIPn36oG3btuonmowZMwYtW7ZEZmYmEhIS4OXlhdatW6N37941amNtFBUV1eqm0UR1jfcIICLSchERETA1NcXChQuxYcMGjefLPiomJgYTJkyApaUldHR00LJlS/j5+dVof8uXL4eLi4v66RkPe/fdd9G3b18YGRmVWaerq4sOHTqoA5JMJoNMJsOlS5cAAJcvX8aRI0ewYMECmJqaolu3bvDx8cG6devKbUfp4/E2btyIVq1awdLSEv7+/hr9P3DgADp16gS5XI7OnTvj4MGD6nX+/v4ICAjAqFGjYGFhge+++w6enp6YOXMm+vbtC1NTU3Tv3h2pqakICQmBra0tmjRpgh07dtTo8yKqKYY3ItJ6DX3KMDQ0FD4+PvD29sb9+/crfQb1iy++iODgYGzYsAHJycnVqv9hKSkpWLZsWaWje1UZNGgQjIyM0K5dO9jb22P48OEAgLNnz8LBwQH29vbqsh07dsTZs2crrW/Pnj2Ii4tDYmIiDh48iPDwcACqMDh06FDMnTsXd+7cwezZszFkyBCN03s2b96MgIAAZGVlISAgAIDqxuDffPMN7ty5A1NTU/Tq1QtyuRzp6emYN28eAgMDKw3IRI+L4Y2ItNqjU4aV8fX1hZGRES5cuAClUonIyEh07NixRvuraspw5cqV6Nq1a5l1pVOGaWlpyM7Oxvbt2zF37lyNe2SOGTMGCoUCmZmZ+OmnnzBjxgyN5+WWJzExETExMfDz84OZmRmGDx9e6eewdetWDB48GMuWLUP79u3h5OSETZs2Vbv/kydPRkhICGxsbKq9zaN++eUX3Lt3D9HR0RgxYgSMjY0BqL7LR59WYmlpWeHTRUqFhITAwsICjo6OGDhwIGJjYwGoRiQ9PT3x+uuvQ09PD2+88QZefPFFbN68Wb1t//794eXlBR0dHZiYmAAAxo0bB1dXVxgZGWHEiBHIy8vD1KlToaenBx8fH9y5c0fjJtxEdY3hjYi0WkOdMiwVGhoKNzc3uLm5AQD8/Pywf//+Ch/79jiPU9u0aRMePHhQ48+sPLq6uujduzdu3ryJxYsXA1A9IeTRRy4qlUqYm5tXWtejj5MrDXs3btxQ3++w1KOPomvWrFml9ZmYmGiMBJYGvNzc3ErbRPQ4GN6ISKs15CnDwsJCbNy4EcnJyVAoFFAoFPDx8UFxcTHCwsKqbEt5j1OrzIEDBxAbG6ve15IlS7Bv377HerRbYWEhLl68CAB4/vnnkZaWhszMf2/gHR8fjw4dOtSq7iZNmuDq1asayx59FB0fH0fPIv5UEpHWashThkm7kjB/+Hwos5SIi4tDfHw84uPjcebMGcydOxfr1q0r9xnAVT1OrTLffPMNkpKS1PuaPHky+vTpo56mBICCggI8ePAAJSUlKCoqwoMHD1BUVARAFcQiIyORl5eHoqIi7NmzB+Hh4fDyUt2su2XLlujZsydmz56N+/fv48SJEwgPD1efi1ZTdf0oOqKnheGNiLRWQ50yLH3U3NY9W9GusB1kyTL1aJhCoUBQUBDS0tIQFRVVZtuqHqdWGblcrrEfMzMzGBoaaowW9u/fH8bGxjh8+DBmzJgBY2NjfPbZZwBUt+KYPXs27O3tYW1tjdmzZ2Pp0qUYO3asevvNmzcjNTUVtra2GDFiBBYtWlTr24TU9aPoiJ6Wx3o8FtUMH49F9PQUFhaicePGyM3NVR9vQghkZmbis88+w5w5cxAdHY1hw4YhKyurwnqsra2xevVqjBgxAv7+/rC0tCxzpSiguhJ1x44dGiNlRUVFsLGx0TiHCgA8PT0xbNiwKq9cnTRpEvLz8/HDDz/g8uXLaN26NdLT02FnZwcAmDJlCrKzs7FhwwaN7fioOSLtxpE3ItI6DX3KsHmf5urgxkfNEWkf3iqaiLSKesoQW9EO/z9lOOTfqwODgoKwePFiREVFlTkZvXTKMDU1Ffr6+mjbtm2Npgzlcrn6fUVThqW39iidNpw3bx5CQkLUU4ZJSUmQyWRwdnYud8pw4sSJsLW1hZWVVYVThqWPmrsafRXOns58agmRluG06VPEaVOiJ49ThkSk7ThtSkRahVOGRKTtOPL2FHHkjejpSNqVxClDItJaDG9PEcMbERERPS5OmxIRERFJCMMbERERkYQwvBERERFJCMMbERERkYQwvBERERFJCMMbERERkYQwvBERERFJCMMbERERkYQwvBERERFJCMMbERERkYRoXXj7/PPP0aNHD5iYmMDS0rLK8oWFhZg5cyY6dOgAU1NTODo6Yvz48UhLS9Mo5+npCZlMpvHy9vZ+Qr0gIiIiKp/WhbeCggKMHDkSb7/9drXK379/H3FxcZg7dy7i4uKwfft2JCcnY8iQIWXKBgYGIj09Xf36/vvv67r5RERERJXSq+8G1LX58+cDAMLCwqpVXi6XIzIyUmPZ8uXL0bVrV6SkpKBZs2bq5SYmJlAoFHXWViIiIqKa0rqRt7qgVCohk8nKTLuGh4fDxsYG7du3xwcffICcnJxK68nPz0d2drbGi4iIiOhxaN3I2+N68OABPvroI4wdOxYWFhbq5T4+PmjevDkUCgUSEhIwa9YsnDlzpsyo3cMWLFigHgkkIiIiqgsyIYSo70ZUJSQkpMoQdPLkSXTp0kX9PiwsDMHBwcjKyqr2fgoLCzFy5EikpKQgOjpaI7w9KjY2Fl26dEFsbCw6d+5cbpn8/Hzk5+er32dnZ6Np06ZQKpWV1k1ERERUEUmMvE2ZMqXKKzudnZ0fax+FhYUYNWoUrly5gt9//73KcNW5c2fo6+vj4sWLFYY3Q0NDGBoaPla7iIiIiB4mifBmY2MDGxubJ1Z/aXC7ePEioqKiYG1tXeU2586dQ2FhIRwcHJ5Yu4iIiIgepXUXLKSkpCA+Ph4pKSkoLi5GfHw84uPjkZubqy7Tpk0b7NixAwBQVFSEN954A6dOnUJ4eDiKi4uRkZGBjIwMFBQUAAAuX76MTz75BKdOncLVq1exd+9ejBw5Ep06dULPnj3rpZ9ERETUMEli5K0mPv74Y/zwww/q9506dQIAREVFwdPTEwCQlJQEpVIJALhx4wZ27doFAOjYsaNGXaXbGBgY4LfffsM333yD3NxcNG3aFK+99hrmzZsHXV3dJ98pIiIiov8niQsWtEV2djbkcjkvWCAiIqJa07ppUyIiIiJtxvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMb0REREQSwvBGREREJCEMbxLz5ptvQiaT4fz58xrLo6OjYWlpqX6fnp6OsWPHQqFQwNzcHC1atMDUqVPV6/39/REcHFzhfiZNmgQXFxfo6Ohg2bJlZdanpaXh1VdfhampKZo1a4Y1a9bU6fakPaTyM1sqISEBBgYGGDZsmMZyZ2dnGBsbw8zMDGZmZhptr8j69evRpUsXmJmZwd7eHr1798ZPP/2k7r9MJoOZmRksLCxgb2+PgQMHYufOnVXWS0QNG8ObhOTm5mLr1q2wsrJCaGhopWV9fX1hZGSECxcuQKlUIjIyEh07dqz2vtzc3LBy5Up07dq13PVjxoyBQqFAZmYmfvrpJ8yYMQOHDh2qs+1JO0jpZxYASkpKEBgYCA8Pj3Lr2Lx5M3Jzc5Gbm4usrKxK2/PRRx/h448/xrx583Dz5k2kpaVh/vz52LFjh7qMXC5Hbm4usrOzcenSJfj6+iIgIAALFiyodr+JqAES9NQolUoBQCiVylptv2bNGmFnZyfWrFkjbG1tRUFBgXpdVFSUkMvl6vempqbijz/+qLAuPz8/8f7771e5z969e4uvv/5aY9mlS5eEjo6OyMjIUC975513xPjx4+t8e5I2qf3MLlu2TPj5+Yl58+aJoUOHaqxzcnISO3bsqHL/Qghx+fJloaurW2l/Hu1/qc2bNwsjIyNx586dau2LiBoejrxJSGhoKHx8fODt7Y379+9j9+7dFZZ98cUXERwcjA0bNiA5OblO23H27Fk4ODjA3t5evaxjx444e/bsU9leCtNwycnJGD58OBQKBSwtLdGzZ0/8+eef1d5eW0jpZzYlJQXLli3DkiVLKqznrbfego2NDTw8PLB3794Kyx08eBAODg7o1atXjdv6+uuvo6CgAMePH6/xtkTUMDC8SURiYiJiYmLg5+cHMzMzDB8+vNJpqK1bt2Lw4MFYtmwZ2rdvDycnJ2zatKlO2pKbm1vmfB9LS0vk5OQ88e2lMg2XlZWFgQMH4q+//sKdO3fg7++PV199Fbdv367W9tpAaj+zkydPRkhICGxsbMqtY+PGjbhy5QpSU1Px3nvvYcSIETh58mS5ZW/duoXGjRvXqq0GBgawsbHBP//8U6vtiUj7MbxJRGhoKNzc3ODm5gYA8PPzw/79+5GamlpueQsLC4SEhCAuLg53795FUFAQxo8fX2a0qjbMzMygVCo1limVSpibmz/x7SMiImBqaoqFCxdiw4YNKCwsrLBsTEwMJkyYAEtLS+jo6KBly5bw8/OrVhsB4N1330Xfvn1hZGRUZt3ly5dx5MgRLFiwAKampujWrRt8fHywbt06AEDXrl0xadIk2NraQldXF4GBgdDV1VWP9FS1vTaQ0s/spk2b8ODBg0p/Pnr16gUTExMYGhpi7NixGDx4MLZt21ZuWRsbmwr7WZWCggLcvn0bVlZWtdqeiLQfw9szLmlXEn4J+gVhoWFITk6GQqGAQqGAj48PiouLERYWVmUdZmZmmD59OuRyORITEx+7Tc8//zzS0tKQmZmpXhYfH48OHTo88e2lNA33sL/++gs5OTlo165drbaXEin+zB44cACxsbHqti5ZsgT79u1DkyZNKqxTR6fifz5feeUVpKenl5kqr47t27fDwMAA3bt3r/G2RNQwaF14+/zzz9GjRw+YmJhU61J+QHXuk0wm03g9+g9nfn4+3nvvPdjY2MDU1BRDhgzBjRs3nkAP/pW0KwkRQyPw47c/QqlUYtuSbYiPj0d8fDzOnDmDuXPnYt26dRBClNl2xowZiI+PR0FBAQoKCrB27Vrcu3cP7u7u1dp3QUEBHjx4gJKSEhQVFeHBgwcoKioCALRs2RI9e/bE7Nmzcf/+fZw4cQLh4eEICAio1faDBw/Gt99+i379+mm04dFz2A4dOoSYmBhs2LABDg4OkMlkmD59unr9l19+iby8PPX7R6fhzMzM4OjoWKtz2DIzMzXOYZs5cyYMDAw0ti8pKUFycnKZ7e/evQtvb2/Mnj0bCoUCwL/TeA/fSmLGjBm4cOFCubeSMDc3h5OTE2bNmoWSkpJqfYf1Qao/s9988w2SkpLUbZ08eTL69OmD2NhYAKrz4f744w/k5+ejsLAQW7duxc6dO8vcTqRUixYtMH36dIwdOxZ79uzB/fv3UVxcjCNHjmDcuHHlbpObm4stW7bgvffew9y5c9GoUaNq9ZuIGqD6vmKirn388cdi6dKlYtq0aeVeyVUePz8/MWDAAJGenq5+PXql1+TJk0Xjxo1FZGSkiIuLE3369BFubm6iqKio2m2r6dWmvwb/Kubrzhet0Ep0lHUU+6bu01h/69YtYWRkJH777bcyV6699957wsXFRZiZmYlGjRqJHj16iH37/t2+qiv3evfuLQBovObNm6def+PGDTFgwABhYmIimjRpIlavXl2r7Y2NjYVMJhOmpqZi+vTpGnU82qdmzZqJRo0aibt374ri4mLxww8/CJlMJm7cuCGEEMLLy0sYGBiU25+cnBwxfPhwoaOjI55//nnx9ddfl/kMXnrpJTFhwgSRm5srYmJihFwuF9HR0aJ3794iODhYfP/99yIzM1MUFRWJt99+W8hkMnHr1i319q1btxZWVlYa2//yyy+ia9euYtKkSaKkpERddvv27cLc3Fw0adJE7Nq1S+Tm5orvv/9etGzZUowZM6bc/icmJgqFQiFCQ0Mr/N7qm5R/Zh/26NWm586dE25ubsLU1FTI5XLxwgsviF27dlX5eYSGhgp3d3dhYmIibG1txUsvvSR+/vlnIYTq+wUgTE1Nhbm5ubCzsxNeXl7VvqKViBourQtvpdavX1+j8PbobQEelpWVJfT19UVERIR6WWpqqtDR0dH4z6UqNQ1vF3ZeECEIEfN154sQhIgLOy9Ue19SUZ1bSVzYeUHsfm+3ACAMDAyEvb29sLe3F3Z2dgKA+Oyzz4QQlYe3UlZWVqJdu3Zlwltlt5Ko6NYTANT/EV+6dEnIZDLxxhtvqMtMnDhR2NjYiAkTJmgENyGE+P333wUAsXPnTvWyd999V/j6+pbp/8PeeOMN8c4771Tax/rUEH5miYjqm9ZNm9ZWdHQ07Ozs0Lp1awQGBmqcGxMbG4vCwkL0799fvczR0RGurq44evRohXXm5+cjOztb41UTLkNc4L3TG92CusF7pzdchrjUvGPPuKrOYSspKlFPwwFAM/tmmD17NrZt21brabjyLowo7xw0V1dXnDlzptxpuPv370Mmk2Hbtm24f/+++sT1KVOmAACys7Px22+/obi4GGvXroVMJtPY38WLF2FgYIBdu3ZVOPX8qL/++gt//PEHWrduXZ2Ptl40hJ9ZIqL6xvAGYODAgQgPD8fvv/+Or776CidPnsTLL7+M/Px8AEBGRgYMDAzKnINib2+PjIyMCutdsGAB5HK5+tW0adMat81liAu8lnpp5X+C1bmVRElhCWS6MsSWxKIDOqBb424ICwuDp6cnunXrhsaNGyMtLQ1RUVFl6s/Pz4e3tzesra2hUCiwfv167Ny5s9yrR8u7lcSKFStw5swZHD58GDNmzICxsTE+++wz9Tls77//Pu7evQtbW1t8+eWXcHBwQO/evQEAO3bswJUrV5CVlQULCwv1I5XCw8MBqG4l0b59e6SmpsLW1hYjRozAokWL1NuXUiqVsLS0hLGxMZ5//nm89tpreOeddx7nY3/itPlnlojomVDfQ3/VMW/evDLnsjz6OnnypMY2NZk2fVRaWprQ19cX27ZtE0IIER4eXu50XL9+/cRbb71VYT0PHjwQSqVS/bp+/fpjPWFB20ybNk24ubmp30dGRgpdXV31OWxRUVHC3NS83Gm4nJwcsWTJEqGrqysSExOFEI93B/7t27eLJk2aaCxbs2aN6Nixo8ayrKysCs9hq872pVatWlWm/KMenjYtKioSq1evFq1atRJZWVmVbkdERNpNEiNvU6ZMwfnz5yt9ubq61tn+HBwc4OTkhIsXLwIAFAoFCgoKcPfuXY1ymZmZGtNsjzI0NISFhYXGi1QKCwuxcePGKm8loaOnU+40XH3c/iQ7OxteXl5o3749Vq1apTEVWtPbn9T0VhKl94pr164dQkJCatg7IiLSJpIIbzY2NmjTpk2lr/Kmwmrrzp07uH79OhwcHAAA7u7u0NfXR2RkpLpMeno6EhIS0KNHjzrbb0ORtCsJ84fPhzJLibi4uCpvJVE6Dbf28Np6u5VEaXBr3bp1ueewVef2KQ+rza0kAGDu3LlYtWpVrW8AS0REWqC+h/7q2rVr18Tp06fF/PnzhZmZmTh9+rQ4ffq0yMnJUZdxcXER27dvF0Kopt+mT58ujh49Kq5cuSKioqKEh4eHaNy4scjOzlZvM3nyZNGkSRNx8OBBERcXJ15++eUnfqsQbVR6NeJzeE50RMcyVyM+q7eSCAsLEwCEiYmJMDU1Vb9+/PHHam1fkapuJVHe1H///v3F22+/XWXdRESknWRClHOZnoT5+/vjhx9+KLM8KioKnp6eAACZTIb169fD398feXl5GDZsGE6fPo2srCw4ODigT58++PTTTzUuMHjw4AFmzJiBTZs2IS8vD3379sXKlStrdBFCdnY25HI5lEplg51C3Td1H04sPwFRLCDTlaFbUDd4LfWq72YRERFJhtaFt2cZw9u/d+CX6cogigVvJ0FERFRDDG9PEcObStKuJFyNvgpnT2cGNyIiohpieHuKGN6IiIjocUnialMiIiIiUmF4IyIiIpIQhjcikpZTp4CXX1b9SUTUADG8EZG0bNgAREUBGzfWd0uIiOqFXn03gIioSteuAbdvAzIZsGWLallEBODnBwgB2NgATk7120YioqeEV5s+RbzalKiWHn4cmUymCmylf5biP2VE1EBw2pSInn0//gjo/f9EQWlIK/1TT0+1noiogeC0KRE9+3x8gLZtAXf3suuOHwc6d376bSIiqicceSMiadHR0fyTiKiB4b9+RCQNdnaAQqEafVu1SvWnQqFaTkTUgDC8EZE0NGkCXL2qmiZ96y3Vn1evqpbXt5AQoGNH7dnPk+TsDCxbVv3yYWGApeWTaQuRRDG8EZF0GBr+e+WpTKZ6Xx3XrwMBAYCjI2BgoLqtyPvvA3fu1LwNMhnwv/9pLvvgA+C332peV127elXVPj09IDVVc116umq5TKYqV13+/sCwYXXXxpMngUmT6q4+ogaI4Y2ItNvffwNdugDJycDmzcClS6pp199+Azw8gH/+efx9mJkB1taPX09dcXRU3cz4YT/8ADRuXD/tAYCCAtWftraAiUn9tYNICzC8EZF2e/dd1WjbgQNA795As2bAwIHAwYOq0ak5c/4t6+wMfPopMHasKpA5OgLLl2uuB4Dhw1UjWKXvH53OLB2t+uILwN5eNe03fz5QVATMmAFYWamme9et02zrzJlA69aqcNOiBTB3LlBYWPM++/kB69drLgsLUy1/WHGxakSyeXPA2BhwcQG++ebf9SEhqtC3c6eqvzIZEB2tWpeaCoweDTRqpAquQ4dqjuiVfgYLFqg+x9atVcsfnTZduhTo0AEwNQWaNgXeeQfIza15n4kaEIY3ItJe//wD7N+vCgTGxprrFArVLUi2bNG8we/ixcDzzwNxccCsWcDUqUBkpGrdyZOqP9evV01Dlr4vz++/A2lpwB9/qAJKSAgwaJAq7Bw/DkyerHpdv/7vNubmqpCVmKgKUWvWAF9/XfN+DxkC3L0LHDmien/kiOqzGDxYs1xJiSpEbt2q2ufHHwOzZ6veA6rp4FGjgAEDVP1NTwd69ADu3wf69FEF3D/+UNVvZqYqVzrCBqhGN8+fV31+v/xSflt1dID//hdISFAFxd9/Bz78sOZ9JmpAeJ83ItJeFy+qglnbtuWvb9tWFXJu3fr3qtWePYGPPlL9vXVr4M8/VQHqlVdUU36AaiRNoah831ZWqlCio6Ma0Vq0SBV6Zs9WrZ81C/jyS1X93t6qZf/5z7/bOzsD06erwmVNw4y+PjBunGpk78UXVX+OG6da/mi5+fP/fd+8OXD0qCq8jRqlCmTGxkB+vmZ/f/xR1a+1a/89B3H9etXnEh0N9O+vWmZqqipjYFBxW4ODNff/6afA228DK1fWrM9EDQjDGxE1XKUjbg8/fsvDQ7OMh0fNro4s1b695r3o7O0BV9d/3+vqqqYbMzP/Xfbzz6p9XbqkmjosKgJq+yi9gABV27/4AvjpJ+DYMVV9j1q1ShWwrl0D8vJUI2dVXdEaG6tqo7m55vIHD4DLl/9936FD5cENAKKiVG1MTASys1VtfPAAuHdPFf6IqAxOmxKR9mrVShXMEhPLX3/hgmoa08am8noeDnfV9egol0xW/rKSEtXfY2JUI3ADB6qmGE+fVp2P9/A0ZE24ugJt2gBjxqhGGB8OjqW2blVNC7/5puqcwPh4YMKEqvdZUqK6z158vOYrOVl1vmCpqsLXtWvAq6+q2rZtmyoUfvutal1tzvUjaiA48kZE2svaWjXduXKlKqQ8fN5bRgYQHg6MH68ZzmJiNOuIiVGFoFL6+qoT/evan3+qbmHy8AUU1649Xp1vvqk63++778pff/iw6hy2d975d9nDI2eAauTs0f527qyazrWzq/3IIACcOqUaafvqq39HKUvPtyOiCnHkjYi024oVqnO2vLxUJ9dfvw7s26cKdY0bA59/rln+zz9V56clJ6tGgX76SXVPuFLOzqoT8TMyVOfL1ZVWrYCUFCAiQhWg/vtfYMeOx6szMFB1Pt/EiRXv89Qp1UUdycmqq1sfvQjD2Rk4exZISgJu31aNiPn4qEYrhw5VBcArV4BDh1Sf040b1W9fy5aq8LZ8ueqWLhs3qqZxiahSDG9EpN2ee04VUFq2VN3aomVL1U1i+/RRnQdmZaVZfvp01fRdp06qk+e/+koV/Ep99ZXq6smmTVVl6srQoarRwSlTVOecHT2qClOPQ09PFbL0KphkmTwZeP111efSrZvqpsUPj8IBqgDo4qK6V56trSrcmpiognCzZqrt27ZVjfLl5dVsJK5jR9WVuAsXqqZOw8NVtxYhokrJhHj4Gnl6krKzsyGXy6FUKmHxOFMNRPRkODurrn58+ApIIqJnDEfeiIiIiCSE4Y2IiIhIQni1KRFRqZo8sJ2IqJ5w5I2IiIhIQhjeiIiIiCSE4Y2IiIhIQhjeiIiIiCSE4Y2IiIhIQhjeiIiIiCSE4Y2IiIhIQhjeiIiIiCRE68Lb559/jh49esDExASWlpbV2kYmk5X7Wrx4sbqMp6dnmfXe3t5PqBdERERE5dO68FZQUICRI0fi7bffrvY26enpGq9169ZBJpNhxIgRGuUCAwM1yn3//fd13XwiIiKiSmnd47Hmz58PAAgLC6v2NgqFQuP9zp070adPH7Ro0UJjuYmJSZmyRERERE+T1o28Pa6bN29iz549CAgIKLMuPDwcNjY2aN++PT744APk5OTUQwuJiIioIdO6kbfH9cMPP8Dc3Byvv/66xnIfHx80b94cCoUCCQkJmDVrFs6cOYPIyMgK68rPz0d+fr76fXZ29hNrNxERETUMkhh5CwkJqfCigtLXqVOn6mRf69atg4+PD4yMjDSWBwYGol+/fnB1dYW3tzd+/vlnHDx4EHFxcRXWtWDBAsjlcvWradOmddJGIiIiarhkQghR342oyu3bt3H79u1Kyzg7O2sErrCwMAQHByMrK6va+zl8+DBeeuklxMfHw83NrdKyQggYGhpi48aNGD16dLllyht5a9q0KZRKJSwsLKrdLiIiIqJSkpg2tbGxgY2NzRPfT2hoKNzd3asMbgBw7tw5FBYWwsHBocIyhoaGMDQ0rMsmEhERUQMniWnTmkhJSUF8fDxSUlJQXFyM+Ph4xMfHIzc3V12mTZs22LFjh8Z22dnZ+OmnnzBx4sQydV6+fBmffPIJTp06hatXr2Lv3r0YOXIkOnXqhJ49ez7xPhERERGVksTIW018/PHH+OGHH9TvO3XqBACIioqCp6cnACApKQlKpVJju4iICAghMGbMmDJ1GhgY4LfffsM333yD3NxcNG3aFK+99hrmzZsHXV3dJ9cZIiIiokdI4pw3bZGdnQ25XM5z3oiIiKjWtG7alIiIiEibMbwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSYhWhberV68iICAAzZs3h7GxMVq2bIl58+ahoKCg0u2EEAgJCYGjoyOMjY3h6emJc+fOaZTJz8/He++9BxsbG5iammLIkCG4cePGk+wOERERURlaFd4uXLiAkpISfP/99zh37hy+/vprrFq1CrNnz650u0WLFmHp0qVYsWIFTp48CYVCgVdeeQU5OTnqMsHBwdixYwciIiJw5MgR5ObmYtCgQSguLn7S3SIiIiJSkwkhRH034klavHgxvvvuO/z999/lrhdCwNHREcHBwZg5cyYA1Sibvb09Fi5ciLfeegtKpRK2trbYuHEjRo8eDQBIS0tD06ZNsXfvXnh5eVWrLdnZ2ZDL5VAqlbCwsKibDhIREVGDolffDXjSlEolrKysKlx/5coVZGRkoH///uplhoaG6N27N44ePYq33noLsbGxKCws1Cjj6OgIV1dXHD16tMLwlp+fj/z8fI22AKoQR0RERNJibm4OmUxW383Q7vB2+fJlLF++HF999VWFZTIyMgAA9vb2Gsvt7e1x7do1dRkDAwM0atSoTJnS7cuzYMECzJ8/v8zypk2bVrsPRERE9GzIzMyEra1tfTdDGuEtJCSk3BD0sJMnT6JLly7q92lpaRgwYABGjhyJiRMnVrmPR5O0EKLKdF1VmVmzZmHatGnq91lZWXByckJKSgrkcnmVbdIW2dnZaNq0Ka5fv96gpovZb/a7IWC/2e+GoLTfBgYG9d0UABIJb1OmTIG3t3elZZydndV/T0tLQ58+feDh4YHVq1dXup1CoQCgGl1zcHBQL8/MzFSPxikUChQUFODu3bsao2+ZmZno0aNHhXUbGhrC0NCwzHK5XN6gfuhLWVhYsN8NCPvdsLDfDUtD7fezMGUKSCS82djYwMbGplplU1NT0adPH7i7u2P9+vXQ0an8gtrmzZtDoVAgMjISnTp1AgAUFBTg0KFDWLhwIQDA3d0d+vr6iIyMxKhRowAA6enpSEhIwKJFix6jZ0REREQ1o1W3CklLS4OnpyeaNm2KJUuW4NatW8jIyChzXlqbNm2wY8cOAKoUHRwcjC+++AI7duxAQkIC/P39YWJigrFjxwJQjZQFBARg+vTp+O2333D69GmMGzcOHTp0QL9+/Z56P4mIiKjhksTIW3UdOHAAly5dwqVLl9CkSRONdQ/fESUpKUl95ScAfPjhh8jLy8M777yDu3fvolu3bjhw4ADMzc3VZb7++mvo6elh1KhRyMvLQ9++fREWFgZdXd1qt8/Q0BDz5s0rdypVm7Hf7HdDwH6z3w0B+/1s9Fvr7/NGREREpE20atqUiIiISNsxvBERERFJCMMbERERkYQwvBERERFJCMPbY1i5ciWaN28OIyMjuLu74/Dhw5WWP3ToENzd3WFkZIQWLVpg1apVZcps27YN7dq1g6GhIdq1a6e+pcmzpCb93r59O1555RXY2trCwsICHh4e2L9/v0aZsLAwyGSyMq8HDx486a7USE36HR0dXW6fLly4oFFO275vf3//cvvdvn17dRkpfN9//PEHBg8eDEdHR8hkMvzvf/+rchttOL5r2m9tOb5r2m9tOb5r2m9tOL4XLFiAF154Aebm5rCzs8OwYcOQlJRU5XbP2vHN8FZLW7ZsQXBwMObMmYPTp0+jV69eGDhwIFJSUsotf+XKFbz66qvo1asXTp8+jdmzZyMoKAjbtm1Tlzl27BhGjx4NX19fnDlzBr6+vhg1ahSOHz/+tLpVpZr2+48//sArr7yCvXv3IjY2Fn369MHgwYNx+vRpjXIWFhZIT0/XeBkZGT2NLlVLTftdKikpSaNPzz33nHqdNn7f33zzjUZ/r1+/DisrK4wcOVKj3LP+fd+7dw9ubm5YsWJFtcpry/Fd035ry/Fd036XkvrxXdN+a8PxfejQIbz77ruIiYlBZGQkioqK0L9/f9y7d6/CbZ7J41tQrXTt2lVMnjxZY1mbNm3ERx99VG75Dz/8ULRp00Zj2VtvvSW6d++ufj9q1CgxYMAAjTJeXl7C29u7jlr9+Gra7/K0a9dOzJ8/X/1+/fr1Qi6X11UTn4ia9jsqKkoAEHfv3q2wzobwfe/YsUPIZDJx9epV9TIpfN8PAyB27NhRaRltOb4fVp1+l0eKx/fDqtNvbTm+H1ab71sbju/MzEwBQBw6dKjCMs/i8c2Rt1ooKChAbGws+vfvr7G8f//+OHr0aLnbHDt2rEx5Ly8vnDp1CoWFhZWWqajOp602/X5USUkJcnJyYGVlpbE8NzcXTk5OaNKkCQYNGlTmN/f69Dj97tSpExwcHNC3b19ERUVprGsI33doaCj69esHJycnjeXP8vddG9pwfNcFKR7fj0PKx3dd0Ibju/SG/Y/+zD7sWTy+Gd5q4fbt2yguLlY/uL6Uvb19mUdxlcrIyCi3fFFREW7fvl1pmYrqfNpq0+9HffXVV7h37576GbGA6nFlYWFh2LVrFzZv3gwjIyP07NkTFy9erNP211Zt+u3g4IDVq1dj27Zt2L59O1xcXNC3b1/88ccf6jLa/n2np6fj119/xcSJEzWWP+vfd21ow/FdF6R4fNeGNhzfj0sbjm8hBKZNm4YXX3wRrq6uFZZ7Fo9vrXo81tMmk8k03gshyiyrqvyjy2taZ32obRs3b96MkJAQ7Ny5E3Z2durl3bt3R/fu3dXve/bsic6dO2P58uX473//W3cNf0w16beLiwtcXFzU7z08PHD9+nUsWbIEL730Uq3qrC+1bWNYWBgsLS0xbNgwjeVS+b5rSluO79qS+vFdE9p0fNeWNhzfU6ZMwdmzZ3HkyJEqyz5rxzdH3mrBxsYGurq6ZRJ1ZmZmmeRdSqFQlFteT08P1tbWlZapqM6nrTb9LrVlyxYEBARg69at6NevX6VldXR08MILLzwzv6k9Tr8f1r17d40+afP3LYTAunXr4OvrCwMDg0rLPmvfd21ow/H9OKR8fNcVqR3fj0Mbju/33nsPu3btQlRUVJlnoT/qWTy+Gd5qwcDAAO7u7oiMjNRYHhkZiR49epS7jYeHR5nyBw4cQJcuXaCvr19pmYrqfNpq029A9Ru5v78/Nm3ahNdee63K/QghEB8fDwcHh8duc12obb8fdfr0aY0+aev3Daiu6Lp06RICAgKq3M+z9n3XhjYc37Ul9eO7rkjt+H4cUj6+hRCYMmUKtm/fjt9//x3Nmzevcptn8vh+IpdBNAARERFCX19fhIaGisTERBEcHCxMTU3VV9189NFHwtfXV13+77//FiYmJmLq1KkiMTFRhIaGCn19ffHzzz+ry/z5559CV1dXfPnll+L8+fPiyy+/FHp6eiImJuap968iNe33pk2bhJ6envj2229Fenq6+pWVlaUuExISIvbt2ycuX74sTp8+LSZMmCD09PTE8ePHn3r/KlLTfn/99ddix44dIjk5WSQkJIiPPvpIABDbtm1Tl9HG77vUuHHjRLdu3cqtUwrfd05Ojjh9+rQ4ffq0ACCWLl0qTp8+La5duyaE0N7ju6b91pbju6b91pbju6b9LiXl4/vtt98WcrlcREdHa/zM3r9/X11GCsc3w9tj+Pbbb4WTk5MwMDAQnTt31rjU2M/PT/Tu3VujfHR0tOjUqZMwMDAQzs7O4rvvvitT508//SRcXFyEvr6+aNOmjcY/Bs+KmvS7d+/eAkCZl5+fn7pMcHCwaNasmTAwMBC2traif//+4ujRo0+xR9VTk34vXLhQtGzZUhgZGYlGjRqJF198UezZs6dMndr2fQshRFZWljA2NharV68utz4pfN+lt4Ko6OdWW4/vmvZbW47vmvZbW47v2vycS/34Lq+/AMT69evVZaRwfMv+vzNEREREJAE8542IiIhIQhjeiIiIiCSE4Y2IiIhIQhjeiIiIiCSE4Y2IiIhIQhjeiIiIiCSE4Y2IiIhIQhjeiIiIiCSE4Y2IqA4JIRAaGopevXrBysoKJiYmaN26NaZNm4abN2/Wd/OISAvwCQtERHUkPz8fQ4cOxf79+wEAenp6MDIyQm5uLgDA2toa+/fvh7u7e302k4gkjiNvRER1ZOrUqdi/fz/09fWxYsUK3Lt3Dzk5OTh58iTatGmDO3fuYNCgQcjOzq7vphKRhDG8ERHVgeTkZKxevRoA8Mknn+Ddd9+FgYEBAKBLly7Ys2cPjI2NkZGRgcWLF9dnU4lI4hjeiIgeERISAplMVq3X1atXAQA//vgjiouLYWZmhvfee69MnS1atMDo0aMBABs3bnya3SEiLcPwRkT0CDMzM9jb21f4Mjc3L7NNZGQkAOCll16CqalpufUOHDgQAHDt2jUkJSU9uQ4QkVZjeCMiesQHH3yAjIyMcl9JSUlo0qQJAKBz585wcHAAAJw7dw4A4OrqWmG9D68rLU9EVFMMb0RE1VRUVIQRI0bg/PnzaNy4MXbt2gVDQ0Pk5OQgJycHANC4ceMKt394XVpa2hNvLxFpJ4Y3IqJqevvtt/Hbb7/B1NQUu3btUoex0uAGACYmJhVu//C6h7chIqoJhjciompYuHAh1q5dCx0dHfz444/o3LlzjeuQyWRPoGVE1NAwvBERVWHbtm2YNWsWAFWIGzZsmMb6hy9guH//foX13Lt3r9xtiIhqguGNiKgSJ06cgK+vL4QQmDhxIj744IMyZczNzdVhLDU1tcK6Hl7n6OhY940logaB4Y2IqALXrl3DkCFDkJeXhz59+mDlypUVlm3fvj0AICEhocIyD68rLU9EVFMMb0RE5cjOzsagQYNw8+ZNtG7dGtu2bYO+vn6F5V955RUAwOHDhyucOt23bx8AwMnJCS4uLnXfaCJqEBjeiIgeUVRUhFGjRiEhIQHW1tbYs2cPGjVqVOk248aNg66uLnJycrBixYoy669evYqIiAgAgK+v7xNpNxE1DAxvRESPeP/997F//34YGBhg27ZtaNWqVZXbtG7dGpMmTQIAzJ07F9999x0KCgoAALGxsXjttdeQl5cHhUJR7nlzRETVJRNCiPpuBBHRs8TZ2RnXrl2Dvr4+rKysKi178uRJNG3aFACQn5+PoUOHYv/+/QAAfX19GBkZqe/pZm1tjf3798Pd3f3JdoCItJpefTeAiOhZVVhYiJs3b1Zapri4WP13Q0ND/PrrrwgNDcUPP/yAhIQEPHjwAM899xwGDRqEmTNnwt7e/kk3m4i0HEfeiIiIiCSE57wRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGEMLwRERERSQjDGxEREZGE/B/ezuv2qj5e9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Set the limits for the plot\n",
    "ax.set_xlim([0, 2])\n",
    "ax.set_ylim([-2, 0])\n",
    "\n",
    "for i in range(np.max(colorcol)+1): \n",
    "  zMat = np.vstack((z[colorcol == i,ltnt1], z[colorcol == i,ltnt2])).T\n",
    "  # Use the color corresponding to the class ID\n",
    "  ax.scatter(zMat[:, 0], zMat[:, 1], c=clrs[i], s=4)\n",
    "  if i == np.max(colorcol):\n",
    "    break\n",
    "for i, txt in enumerate(ptLabel):\n",
    "    ax.annotate(txt, (z[i,ltnt1], z[i,ltnt2]), size=9)\n",
    "\n",
    "plt.xlabel('z{:d}'.format(ltnt1), size=18)\n",
    "plt.ylabel('z{:d}'.format(ltnt2), size=18)\n",
    "\n",
    "# Hide the right and top spines\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "# Plot the optimal material point\n",
    "plt.plot(zData[0], zData[1], marker='*', color='red')\n",
    "ax.annotate('Optimal Material', (zData[0]*1.05, zData[1]*1.05), color='red', size=10)\n",
    "\n",
    "# Save the plot\n",
    "plt.savefig('./figures/optimalLatent_zoom.png', bbox_inches='tight', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7d0e5c",
   "metadata": {},
   "source": [
    "# Defines fixed problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "42932446",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Problem of minimizing mass with the tensile and compression constraints. We have 2 variables of the latent space constrained\n",
    "#to the limits in the map and 6 areas with limits.\n",
    "class MyProblem_fix(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self, truss, matprop, constraints, objectives, no, nc):\n",
    "        super().__init__(n_var=6,\n",
    "                         n_obj=no,\n",
    "                         n_ieq_constr=nc,\n",
    "                         xl=np.array([2e-6, 2e-6, 2e-6, 2e-6, 2e-6, 2e-6]),\n",
    "                         xu=np.array([2e-2, 2e-2, 2e-2, 2e-2, 2e-2, 2e-2]))\n",
    "        self.truss = truss\n",
    "        self.matprop = matprop\n",
    "        self.constraints = constraints\n",
    "        self.objectives = objectives\n",
    "\n",
    "\n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        x_tensor = torch.from_numpy(x)\n",
    "        area = x_tensor * torch.ones((connectivity.shape[0]))   \n",
    "        u, _, _, _, internalForce = truss.solveFE(E = torch.tensor([self.matprop[0]]).float(), A=area)\n",
    "        compliance = truss.computeCompliance(u)\n",
    "        volume = truss.getVolume(area)\n",
    "        mass = self.matprop[2]*volume\n",
    "        cost = mass*self.matprop[1]\n",
    "        co2 = mass*self.matprop[4]\n",
    "        energy = mass*self.matprop[5]        \n",
    "        water = mass*self.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/self.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/self.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['maxenergy']['maxMass']) - 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",
    "        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",
    "problem_fix = MyProblem_fix(truss, matprop, constraints, objectives, no, ncons)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5eca1b3",
   "metadata": {},
   "source": [
    "# Defines fixed problem settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "47aaff22",
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithm_fix = NSGA2(\n",
    "    pop_size=60,\n",
    "    n_offsprings=10,\n",
    "    sampling=FloatRandomSampling(),\n",
    "    crossover=SBX(prob=0.9, eta=15),\n",
    "    mutation=PM(eta=20),\n",
    "    eliminate_duplicates=True\n",
    ")\n",
    "\n",
    "\n",
    "termination_fix = DefaultMultiObjectiveTermination(\n",
    "    xtol=1e-8,\n",
    "    cvtol=1e-6,\n",
    "    ftol=0.00025,\n",
    "    period=20,\n",
    "    n_max_gen=1000,\n",
    "    n_max_evals=100000\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22b7943",
   "metadata": {},
   "source": [
    "# Runs the solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "38c1bb9d",
   "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 |      1 |  0.000000E+00 |  1.407596E+02 |             - |             -\n",
      "     2 |       70 |      1 |  0.000000E+00 |  0.000000E+00 |  3.797843E+01 |         ideal\n",
      "     3 |       80 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     4 |       90 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     5 |      100 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     6 |      110 |      1 |  0.000000E+00 |  0.000000E+00 |  2.398682E+01 |         ideal\n",
      "     7 |      120 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     8 |      130 |      1 |  0.000000E+00 |  0.000000E+00 |  7.2965823299 |         ideal\n",
      "     9 |      140 |      1 |  0.000000E+00 |  0.000000E+00 |  2.0082408077 |         ideal\n",
      "    10 |      150 |      1 |  0.000000E+00 |  0.000000E+00 |  4.8100977190 |         ideal\n",
      "    11 |      160 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    12 |      170 |      1 |  0.000000E+00 |  0.000000E+00 |  2.055883E+01 |         ideal\n",
      "    13 |      180 |      1 |  0.000000E+00 |  0.000000E+00 |  5.4009630885 |         ideal\n",
      "    14 |      190 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    15 |      200 |      1 |  0.000000E+00 |  0.000000E+00 |  1.7366290632 |         ideal\n",
      "    16 |      210 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    17 |      220 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    18 |      230 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    19 |      240 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    20 |      250 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    21 |      260 |      1 |  0.000000E+00 |  0.000000E+00 |  1.099346E+01 |         ideal\n",
      "    22 |      270 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    23 |      280 |      1 |  0.000000E+00 |  0.000000E+00 |  1.4742395007 |         ideal\n",
      "    24 |      290 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    25 |      300 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    26 |      310 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    27 |      320 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    28 |      330 |      1 |  0.000000E+00 |  0.000000E+00 |  3.9700030786 |         ideal\n",
      "    29 |      340 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    30 |      350 |      1 |  0.000000E+00 |  0.000000E+00 |  0.4339469821 |         ideal\n",
      "    31 |      360 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    32 |      370 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    33 |      380 |      1 |  0.000000E+00 |  0.000000E+00 |  4.9459941450 |         ideal\n",
      "    34 |      390 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    35 |      400 |      1 |  0.000000E+00 |  0.000000E+00 |  0.5803105814 |         ideal\n",
      "    36 |      410 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    37 |      420 |      1 |  0.000000E+00 |  0.000000E+00 |  3.5593823961 |         ideal\n",
      "    38 |      430 |      1 |  0.000000E+00 |  0.000000E+00 |  1.8072037706 |         ideal\n",
      "    39 |      440 |      1 |  0.000000E+00 |  0.000000E+00 |  3.2072350554 |         ideal\n",
      "    40 |      450 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    41 |      460 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    42 |      470 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    43 |      480 |      1 |  0.000000E+00 |  0.000000E+00 |  1.9376159791 |         ideal\n",
      "    44 |      490 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    45 |      500 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    46 |      510 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    47 |      520 |      1 |  0.000000E+00 |  0.000000E+00 |  1.6746329892 |         ideal\n",
      "    48 |      530 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0088219349 |         ideal\n",
      "    49 |      540 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    50 |      550 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    51 |      560 |      1 |  0.000000E+00 |  0.000000E+00 |  1.3674387543 |         ideal\n",
      "    52 |      570 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    53 |      580 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    54 |      590 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    55 |      600 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    56 |      610 |      1 |  0.000000E+00 |  0.000000E+00 |  2.2068743291 |         ideal\n",
      "    57 |      620 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    58 |      630 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    59 |      640 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    60 |      650 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    61 |      660 |      1 |  0.000000E+00 |  0.000000E+00 |  1.4700125466 |         ideal\n",
      "    62 |      670 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    63 |      680 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    64 |      690 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    65 |      700 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    66 |      710 |      1 |  0.000000E+00 |  0.000000E+00 |  0.5400954205 |         ideal\n",
      "    67 |      720 |      1 |  0.000000E+00 |  0.000000E+00 |  1.5489452387 |         ideal\n",
      "    68 |      730 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    69 |      740 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    70 |      750 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    71 |      760 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    72 |      770 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    73 |      780 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    74 |      790 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    75 |      800 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    76 |      810 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    77 |      820 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    78 |      830 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    79 |      840 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    80 |      850 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    81 |      860 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    82 |      870 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    83 |      880 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    84 |      890 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    85 |      900 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    86 |      910 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    87 |      920 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    88 |      930 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    89 |      940 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    90 |      950 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    91 |      960 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    92 |      970 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "[6.37189096]\n"
     ]
    }
   ],
   "source": [
    "res = minimize(problem_fix,\n",
    "               algorithm_fix,\n",
    "               termination_fix,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "\n",
    "X = res.X\n",
    "F = res.F\n",
    "G = res.G\n",
    "\n",
    "\n",
    "print(F)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24b4edc",
   "metadata": {},
   "source": [
    "# --------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7aa001",
   "metadata": {},
   "source": [
    "# Run solver for a given matprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "23d67d6c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([17], dtype=int64),)\n",
      "[[[2.10000e+11 6.14000e-01 7.80000e+03 3.99826e+08 2.50000e+00\n",
      "   3.20000e+07 5.00000e+01]]]\n",
      "==========================================================================================\n",
      "n_gen  |  n_eval  | n_nds  |     cv_min    |     cv_avg    |      eps      |   indicator  \n",
      "==========================================================================================\n",
      "     1 |       60 |      1 |  0.000000E+00 |  1.374440E+02 |             - |             -\n",
      "     2 |       70 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     3 |       80 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     4 |       90 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     5 |      100 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     6 |      110 |      1 |  0.000000E+00 |  0.000000E+00 |  4.164393E+01 |         ideal\n",
      "     7 |      120 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     8 |      130 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     9 |      140 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    10 |      150 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    11 |      160 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    12 |      170 |      1 |  0.000000E+00 |  0.000000E+00 |  9.6457548513 |         ideal\n",
      "    13 |      180 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    14 |      190 |      1 |  0.000000E+00 |  0.000000E+00 |  1.114554E+01 |         ideal\n",
      "    15 |      200 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    16 |      210 |      1 |  0.000000E+00 |  0.000000E+00 |  3.510762E+01 |         ideal\n",
      "    17 |      220 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    18 |      230 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    19 |      240 |      1 |  0.000000E+00 |  0.000000E+00 |  2.092087E+01 |         ideal\n",
      "    20 |      250 |      1 |  0.000000E+00 |  0.000000E+00 |  8.1273294175 |         ideal\n",
      "    21 |      260 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    22 |      270 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    23 |      280 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    24 |      290 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    25 |      300 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    26 |      310 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    27 |      320 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    28 |      330 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    29 |      340 |      1 |  0.000000E+00 |  0.000000E+00 |  1.187175E+01 |         ideal\n",
      "    30 |      350 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    31 |      360 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    32 |      370 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    33 |      380 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    34 |      390 |      1 |  0.000000E+00 |  0.000000E+00 |  4.0920888232 |         ideal\n",
      "    35 |      400 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    36 |      410 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    37 |      420 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    38 |      430 |      1 |  0.000000E+00 |  0.000000E+00 |  0.7224185720 |         ideal\n",
      "    39 |      440 |      1 |  0.000000E+00 |  0.000000E+00 |  5.3685166441 |         ideal\n",
      "    40 |      450 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    41 |      460 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    42 |      470 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    43 |      480 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    44 |      490 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    45 |      500 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    46 |      510 |      1 |  0.000000E+00 |  0.000000E+00 |  3.4836078444 |         ideal\n",
      "    47 |      520 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    48 |      530 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    49 |      540 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    50 |      550 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    51 |      560 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    52 |      570 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    53 |      580 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    54 |      590 |      1 |  0.000000E+00 |  0.000000E+00 |  1.9335659180 |         ideal\n",
      "    55 |      600 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    56 |      610 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    57 |      620 |      1 |  0.000000E+00 |  0.000000E+00 |  1.1849992609 |         ideal\n",
      "    58 |      630 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    59 |      640 |      1 |  0.000000E+00 |  0.000000E+00 |  1.4510272403 |         ideal\n",
      "    60 |      650 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    61 |      660 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    62 |      670 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    63 |      680 |      1 |  0.000000E+00 |  0.000000E+00 |  0.9946315973 |         ideal\n",
      "    64 |      690 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    65 |      700 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    66 |      710 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    67 |      720 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    68 |      730 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    69 |      740 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    70 |      750 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    71 |      760 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    72 |      770 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    73 |      780 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    74 |      790 |      1 |  0.000000E+00 |  0.000000E+00 |  0.9035036654 |         ideal\n",
      "    75 |      800 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    76 |      810 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    77 |      820 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    78 |      830 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    79 |      840 |      1 |  0.000000E+00 |  0.000000E+00 |  0.3087519497 |         ideal\n",
      "    80 |      850 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    81 |      860 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    82 |      870 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    83 |      880 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    84 |      890 |      1 |  0.000000E+00 |  0.000000E+00 |  0.1570276384 |         ideal\n",
      "    85 |      900 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0743766184 |         ideal\n",
      "    86 |      910 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    87 |      920 |      1 |  0.000000E+00 |  0.000000E+00 |  0.1864721341 |         ideal\n",
      "    88 |      930 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    89 |      940 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    90 |      950 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0299039148 |         ideal\n",
      "    91 |      960 |      1 |  0.000000E+00 |  0.000000E+00 |  0.4882409824 |         ideal\n",
      "    92 |      970 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    93 |      980 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    94 |      990 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    95 |     1000 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    96 |     1010 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    97 |     1020 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    98 |     1030 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0705989418 |         ideal\n",
      "    99 |     1040 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   100 |     1050 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   101 |     1060 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   102 |     1070 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0085128905 |         ideal\n",
      "   103 |     1080 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   104 |     1090 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   105 |     1100 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   106 |     1110 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   107 |     1120 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0296697557 |         ideal\n",
      "   108 |     1130 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   109 |     1140 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   110 |     1150 |      1 |  0.000000E+00 |  0.000000E+00 |  0.3549802359 |         ideal\n",
      "   111 |     1160 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   112 |     1170 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   113 |     1180 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   114 |     1190 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   115 |     1200 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   116 |     1210 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0990965671 |         ideal\n",
      "   117 |     1220 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   118 |     1230 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   119 |     1240 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   120 |     1250 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   121 |     1260 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   122 |     1270 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   123 |     1280 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   124 |     1290 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   125 |     1300 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   126 |     1310 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   127 |     1320 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   128 |     1330 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   129 |     1340 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   130 |     1350 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   131 |     1360 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   132 |     1370 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   133 |     1380 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0359242719 |         ideal\n",
      "   134 |     1390 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   135 |     1400 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   136 |     1410 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   137 |     1420 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   138 |     1430 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   139 |     1440 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   140 |     1450 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0145343902 |         ideal\n",
      "   141 |     1460 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   142 |     1470 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0100695045 |         ideal\n",
      "   143 |     1480 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0861512033 |         ideal\n",
      "   144 |     1490 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   145 |     1500 |      1 |  0.000000E+00 |  0.000000E+00 |  0.1173641058 |         ideal\n",
      "   146 |     1510 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   147 |     1520 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   148 |     1530 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   149 |     1540 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   150 |     1550 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   151 |     1560 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   152 |     1570 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   153 |     1580 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   154 |     1590 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   155 |     1600 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   156 |     1610 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   157 |     1620 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   158 |     1630 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   159 |     1640 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   160 |     1650 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   161 |     1660 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   162 |     1670 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   163 |     1680 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0480777918 |         ideal\n",
      "   164 |     1690 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   165 |     1700 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   166 |     1710 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   167 |     1720 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0123786004 |         ideal\n",
      "   168 |     1730 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   169 |     1740 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   170 |     1750 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   171 |     1760 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   172 |     1770 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0673797691 |         ideal\n",
      "   173 |     1780 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   174 |     1790 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   175 |     1800 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   176 |     1810 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0000161441 |             f\n",
      "   177 |     1820 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0024467883 |             f\n",
      "   178 |     1830 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0024467883 |             f\n",
      "   179 |     1840 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0024467883 |             f\n",
      "   180 |     1850 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0024467883 |             f\n",
      "   181 |     1860 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0191926119 |         ideal\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   182 |     1870 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   183 |     1880 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   184 |     1890 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   185 |     1900 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   186 |     1910 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   187 |     1920 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0067794907 |         ideal\n",
      "   188 |     1930 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0046998156 |         ideal\n",
      "   189 |     1940 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   190 |     1950 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   191 |     1960 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   192 |     1970 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   193 |     1980 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   194 |     1990 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   195 |     2000 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   196 |     2010 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   197 |     2020 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   198 |     2030 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   199 |     2040 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   200 |     2050 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   201 |     2060 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   202 |     2070 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014583476 |             f\n",
      "   203 |     2080 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0026144197 |         ideal\n",
      "   204 |     2090 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   205 |     2100 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   206 |     2110 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   207 |     2120 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0004963833 |             f\n",
      "   208 |     2130 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0004963833 |             f\n",
      "   209 |     2140 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0004963833 |             f\n",
      "   210 |     2150 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0004963833 |             f\n",
      "   211 |     2160 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0027314243 |         ideal\n",
      "   212 |     2170 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   213 |     2180 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   214 |     2190 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   215 |     2200 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   216 |     2210 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   217 |     2220 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   218 |     2230 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   219 |     2240 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   220 |     2250 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   221 |     2260 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   222 |     2270 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   223 |     2280 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   224 |     2290 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   225 |     2300 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   226 |     2310 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   227 |     2320 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   228 |     2330 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   229 |     2340 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   230 |     2350 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   231 |     2360 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   232 |     2370 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   233 |     2380 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   234 |     2390 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   235 |     2400 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   236 |     2410 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   237 |     2420 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   238 |     2430 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   239 |     2440 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0010729793 |             f\n",
      "   240 |     2450 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   241 |     2460 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   242 |     2470 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   243 |     2480 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   244 |     2490 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   245 |     2500 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   246 |     2510 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   247 |     2520 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   248 |     2530 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   249 |     2540 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   250 |     2550 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016047971 |             f\n",
      "   251 |     2560 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016290203 |             f\n",
      "   252 |     2570 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016290203 |             f\n",
      "   253 |     2580 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016290203 |             f\n",
      "   254 |     2590 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0018799343 |             f\n",
      "   255 |     2600 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0018799343 |             f\n",
      "   256 |     2610 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0018799343 |             f\n",
      "   257 |     2620 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0018799343 |             f\n",
      "   258 |     2630 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0018799343 |             f\n",
      "   259 |     2640 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0018799343 |             f\n",
      "   260 |     2650 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0018799343 |             f\n",
      "   261 |     2660 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0018799343 |             f\n",
      "   262 |     2670 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0018850920 |             f\n",
      "   263 |     2680 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0018850920 |             f\n",
      "   264 |     2690 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0020329897 |             f\n",
      "   265 |     2700 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0020329897 |             f\n",
      "   266 |     2710 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0021908894 |             f\n",
      "   267 |     2720 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0021908894 |             f\n",
      "   268 |     2730 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0022128060 |             f\n",
      "   269 |     2740 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0022128060 |             f\n",
      "   270 |     2750 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0022128060 |             f\n",
      "   271 |     2760 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0022128060 |             f\n",
      "   272 |     2770 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0022128060 |             f\n",
      "   273 |     2780 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0022134958 |             f\n",
      "   274 |     2790 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0022547313 |             f\n",
      "   275 |     2800 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0022547313 |             f\n",
      "   276 |     2810 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0022547313 |             f\n",
      "   277 |     2820 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0022547313 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   278 |     2830 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   279 |     2840 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   280 |     2850 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   281 |     2860 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   282 |     2870 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   283 |     2880 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   284 |     2890 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   285 |     2900 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   286 |     2910 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   287 |     2920 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   288 |     2930 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   289 |     2940 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   290 |     2950 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023947493 |             f\n",
      "   291 |     2960 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0023993216 |             f\n",
      "[9.16397426]\n",
      "[-1.07763331e-04 -4.09737966e-06]\n",
      "[2.66048634e-04 1.92756005e-04 4.23117254e-04 4.48017601e-04\n",
      " 7.51281486e-05 3.20693161e-04]\n"
     ]
    }
   ],
   "source": [
    "mat_ind = np.where(dataIdentifier['name'] == 'Plain Carbon St')\n",
    "print(mat_ind)\n",
    "matprops2=10**trainInfo[mat_ind,:]\n",
    "print(matprops2)\n",
    "matprop2=matprops2[0,0,:]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "problem_matprop2 = MyProblem_fix(truss, matprop2, constraints, objectives, no, ncons)\n",
    "\n",
    "\n",
    "\n",
    "res2 = minimize(problem_matprop2,\n",
    "               algorithm_fix,\n",
    "               termination_fix,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "\n",
    "X = res2.X\n",
    "F = res2.F\n",
    "G = res2.G\n",
    "\n",
    "\n",
    "print(F)\n",
    "print(G)\n",
    "print(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87d078c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af505c64",
   "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
}