VAE_Single_pymoo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ea7f6c2d",
   "metadata": {},
   "source": [
    "# Loads packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa784e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "from scipy.spatial import ConvexHull\n",
    "from matplotlib.patches import Polygon\n",
    "import sys,os\n",
    "sys.path.append(os.path.realpath('./src/'))\n",
    "import pandas as pd\n",
    "from materialEncoder import MaterialEncoder\n",
    "import time\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5694514",
   "metadata": {},
   "source": [
    "# Defines functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fae8bda9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_np(x):\n",
    "  return x.detach().cpu().numpy()\n",
    "\n",
    "def to_torch(x):\n",
    "  return torch.tensor(x).float()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3716cc4b",
   "metadata": {},
   "source": [
    "# Loads the Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd11d60f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luisy\\AppData\\Local\\Temp\\ipykernel_14024\\405064810.py:8: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  trainingData = torch.tensor(normalizedData).float()\n"
     ]
    }
   ],
   "source": [
    "def preprocessData():\n",
    "  df = pd.read_excel('./data/MaterialDatabase_FULL.xlsx')# # \n",
    "  dataIdentifier = {'name': df[df.columns[0]], 'className':df[df.columns[1]], 'classID':df[df.columns[2]]} # name of the material and type\n",
    "  trainInfo = np.log10(df[df.columns[3:]].to_numpy())\n",
    "  dataScaleMax = torch.tensor(np.max(trainInfo, axis = 0))\n",
    "  dataScaleMin = torch.tensor(np.min(trainInfo, axis = 0))\n",
    "  normalizedData = (torch.tensor(trainInfo) - dataScaleMin)/(dataScaleMax - dataScaleMin)\n",
    "  trainingData = torch.tensor(normalizedData).float()\n",
    "  dataInfo = {'youngsModulus':{'idx':0,'scaleMin':dataScaleMin[0], 'scaleMax':dataScaleMax[0]},\\\n",
    "              'costPerKg':{'idx':1,'scaleMin':dataScaleMin[1], 'scaleMax':dataScaleMax[1]},\\\n",
    "              'massDensity':{'idx':2,'scaleMin':dataScaleMin[2], 'scaleMax':dataScaleMax[2]},\\\n",
    "              'yieldStrength':{'idx':3,'scaleMin':dataScaleMin[3], 'scaleMax':dataScaleMax[3]},\\\n",
    "              'co2Kg':{'idx':4,'scaleMin':dataScaleMin[4], 'scaleMax':dataScaleMax[4]},\\\n",
    "              'energyKg':{'idx':5,'scaleMin':dataScaleMin[5], 'scaleMax':dataScaleMax[5]},\\\n",
    "              'waterKg':{'idx':6,'scaleMin':dataScaleMin[6], 'scaleMax':dataScaleMax[6]}}\n",
    "\n",
    "  return trainingData, dataInfo, dataIdentifier, trainInfo, df\n",
    "trainingData, dataInfo, dataIdentifier, trainInfo, df = preprocessData()\n",
    "numMaterialsInTrainingData, numFeatures = trainingData.shape\n",
    "props = df.columns[3:].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae194f0",
   "metadata": {},
   "source": [
    "# Trains the VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "688201b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter 0 reconLoss 4.68E+01 klLoss 5.67E-03 loss 4.68E+01\n",
      "Iter 500 reconLoss 2.33E-01 klLoss 6.02E-02 loss 2.93E-01\n",
      "Iter 1000 reconLoss 1.08E-01 klLoss 5.91E-02 loss 1.67E-01\n",
      "Iter 1500 reconLoss 7.33E-02 klLoss 5.82E-02 loss 1.32E-01\n",
      "Iter 2000 reconLoss 6.02E-02 klLoss 5.65E-02 loss 1.17E-01\n",
      "Iter 2500 reconLoss 4.35E-02 klLoss 5.46E-02 loss 9.81E-02\n",
      "Iter 3000 reconLoss 4.59E-02 klLoss 5.28E-02 loss 9.87E-02\n",
      "Iter 3500 reconLoss 3.41E-02 klLoss 5.15E-02 loss 8.56E-02\n",
      "Iter 4000 reconLoss 3.73E-02 klLoss 5.02E-02 loss 8.75E-02\n",
      "Iter 4500 reconLoss 3.03E-02 klLoss 4.89E-02 loss 7.91E-02\n",
      "Iter 5000 reconLoss 2.83E-02 klLoss 4.79E-02 loss 7.62E-02\n",
      "Iter 5500 reconLoss 2.79E-02 klLoss 4.69E-02 loss 7.48E-02\n",
      "Iter 6000 reconLoss 3.14E-02 klLoss 4.57E-02 loss 7.71E-02\n",
      "Iter 6500 reconLoss 2.66E-02 klLoss 4.51E-02 loss 7.17E-02\n",
      "Iter 7000 reconLoss 2.86E-02 klLoss 4.37E-02 loss 7.23E-02\n",
      "Iter 7500 reconLoss 2.24E-02 klLoss 4.34E-02 loss 6.58E-02\n",
      "Iter 8000 reconLoss 3.74E-02 klLoss 4.33E-02 loss 8.07E-02\n",
      "Iter 8500 reconLoss 2.49E-02 klLoss 4.23E-02 loss 6.71E-02\n",
      "Iter 9000 reconLoss 1.96E-02 klLoss 4.19E-02 loss 6.14E-02\n",
      "Iter 9500 reconLoss 2.12E-02 klLoss 4.16E-02 loss 6.28E-02\n",
      "Iter 10000 reconLoss 1.91E-02 klLoss 4.09E-02 loss 6.01E-02\n",
      "Iter 10500 reconLoss 1.75E-02 klLoss 4.14E-02 loss 5.88E-02\n",
      "Iter 11000 reconLoss 1.80E-02 klLoss 4.08E-02 loss 5.89E-02\n",
      "Iter 11500 reconLoss 1.82E-02 klLoss 4.12E-02 loss 5.95E-02\n",
      "Iter 12000 reconLoss 1.56E-02 klLoss 3.99E-02 loss 5.55E-02\n",
      "Iter 12500 reconLoss 1.30E-02 klLoss 3.94E-02 loss 5.23E-02\n",
      "Iter 13000 reconLoss 1.32E-02 klLoss 3.96E-02 loss 5.28E-02\n",
      "Iter 13500 reconLoss 1.27E-02 klLoss 4.00E-02 loss 5.27E-02\n",
      "Iter 14000 reconLoss 1.21E-02 klLoss 4.01E-02 loss 5.22E-02\n",
      "Iter 14500 reconLoss 1.27E-02 klLoss 3.95E-02 loss 5.22E-02\n",
      "Iter 15000 reconLoss 1.29E-02 klLoss 3.92E-02 loss 5.22E-02\n",
      "Iter 15500 reconLoss 1.33E-02 klLoss 3.91E-02 loss 5.24E-02\n",
      "Iter 16000 reconLoss 1.06E-02 klLoss 4.09E-02 loss 5.14E-02\n",
      "Iter 16500 reconLoss 1.32E-02 klLoss 4.00E-02 loss 5.32E-02\n",
      "Iter 17000 reconLoss 1.66E-02 klLoss 3.80E-02 loss 5.47E-02\n",
      "Iter 17500 reconLoss 1.14E-02 klLoss 3.87E-02 loss 5.01E-02\n",
      "Iter 18000 reconLoss 1.01E-02 klLoss 3.86E-02 loss 4.87E-02\n",
      "Iter 18500 reconLoss 1.26E-02 klLoss 3.86E-02 loss 5.12E-02\n",
      "Iter 19000 reconLoss 1.14E-02 klLoss 3.84E-02 loss 4.97E-02\n",
      "Iter 19500 reconLoss 1.27E-02 klLoss 3.80E-02 loss 5.07E-02\n",
      "Iter 20000 reconLoss 1.86E-02 klLoss 3.88E-02 loss 5.74E-02\n",
      "Iter 20500 reconLoss 1.38E-02 klLoss 3.95E-02 loss 5.33E-02\n",
      "Iter 21000 reconLoss 1.11E-02 klLoss 3.84E-02 loss 4.95E-02\n",
      "Iter 21500 reconLoss 1.26E-02 klLoss 3.79E-02 loss 5.05E-02\n",
      "Iter 22000 reconLoss 1.33E-02 klLoss 3.76E-02 loss 5.08E-02\n",
      "Iter 22500 reconLoss 1.11E-02 klLoss 3.75E-02 loss 4.86E-02\n",
      "Iter 23000 reconLoss 1.11E-02 klLoss 3.77E-02 loss 4.88E-02\n",
      "Iter 23500 reconLoss 1.15E-02 klLoss 3.79E-02 loss 4.94E-02\n",
      "Iter 24000 reconLoss 1.45E-02 klLoss 3.73E-02 loss 5.18E-02\n",
      "Iter 24500 reconLoss 1.04E-02 klLoss 3.70E-02 loss 4.74E-02\n",
      "Iter 25000 reconLoss 1.07E-02 klLoss 3.73E-02 loss 4.80E-02\n",
      "Iter 25500 reconLoss 1.00E-02 klLoss 3.73E-02 loss 4.73E-02\n",
      "Iter 26000 reconLoss 8.41E-03 klLoss 3.72E-02 loss 4.56E-02\n",
      "Iter 26500 reconLoss 9.64E-03 klLoss 3.73E-02 loss 4.70E-02\n",
      "Iter 27000 reconLoss 8.83E-03 klLoss 3.70E-02 loss 4.58E-02\n",
      "Iter 27500 reconLoss 1.16E-02 klLoss 3.67E-02 loss 4.84E-02\n",
      "Iter 28000 reconLoss 9.90E-03 klLoss 3.69E-02 loss 4.68E-02\n",
      "Iter 28500 reconLoss 1.06E-02 klLoss 3.66E-02 loss 4.71E-02\n",
      "Iter 29000 reconLoss 9.57E-03 klLoss 3.62E-02 loss 4.58E-02\n",
      "Iter 29500 reconLoss 1.04E-02 klLoss 3.63E-02 loss 4.67E-02\n",
      "Iter 30000 reconLoss 9.24E-03 klLoss 3.62E-02 loss 4.54E-02\n",
      "Iter 30500 reconLoss 9.15E-03 klLoss 3.65E-02 loss 4.56E-02\n",
      "Iter 31000 reconLoss 8.95E-03 klLoss 3.60E-02 loss 4.50E-02\n",
      "Iter 31500 reconLoss 9.03E-03 klLoss 3.59E-02 loss 4.49E-02\n",
      "Iter 32000 reconLoss 8.60E-03 klLoss 3.63E-02 loss 4.49E-02\n",
      "Iter 32500 reconLoss 9.99E-03 klLoss 3.67E-02 loss 4.66E-02\n",
      "Iter 33000 reconLoss 9.53E-03 klLoss 3.55E-02 loss 4.51E-02\n",
      "Iter 33500 reconLoss 8.19E-03 klLoss 3.61E-02 loss 4.43E-02\n",
      "Iter 34000 reconLoss 8.94E-03 klLoss 3.63E-02 loss 4.53E-02\n",
      "Iter 34500 reconLoss 8.30E-03 klLoss 3.55E-02 loss 4.38E-02\n",
      "Iter 35000 reconLoss 8.98E-03 klLoss 3.54E-02 loss 4.44E-02\n",
      "Iter 35500 reconLoss 1.19E-02 klLoss 3.56E-02 loss 4.75E-02\n",
      "Iter 36000 reconLoss 1.07E-02 klLoss 3.58E-02 loss 4.65E-02\n",
      "Iter 36500 reconLoss 9.07E-03 klLoss 3.55E-02 loss 4.46E-02\n",
      "Iter 37000 reconLoss 7.63E-03 klLoss 3.61E-02 loss 4.37E-02\n",
      "Iter 37500 reconLoss 1.14E-02 klLoss 3.84E-02 loss 4.98E-02\n",
      "Iter 38000 reconLoss 8.25E-03 klLoss 3.56E-02 loss 4.38E-02\n",
      "Iter 38500 reconLoss 9.46E-03 klLoss 3.53E-02 loss 4.48E-02\n",
      "Iter 39000 reconLoss 8.27E-03 klLoss 3.53E-02 loss 4.35E-02\n",
      "Iter 39500 reconLoss 9.05E-03 klLoss 3.54E-02 loss 4.45E-02\n",
      "Iter 40000 reconLoss 9.50E-03 klLoss 3.58E-02 loss 4.53E-02\n",
      "Iter 40500 reconLoss 9.88E-03 klLoss 3.56E-02 loss 4.55E-02\n",
      "Iter 41000 reconLoss 8.48E-03 klLoss 3.61E-02 loss 4.46E-02\n",
      "Iter 41500 reconLoss 9.97E-03 klLoss 3.49E-02 loss 4.49E-02\n",
      "Iter 42000 reconLoss 8.18E-03 klLoss 3.54E-02 loss 4.36E-02\n",
      "Iter 42500 reconLoss 9.85E-03 klLoss 3.58E-02 loss 4.57E-02\n",
      "Iter 43000 reconLoss 8.59E-03 klLoss 3.56E-02 loss 4.42E-02\n",
      "Iter 43500 reconLoss 9.03E-03 klLoss 3.52E-02 loss 4.42E-02\n",
      "Iter 44000 reconLoss 9.73E-03 klLoss 3.54E-02 loss 4.51E-02\n",
      "Iter 44500 reconLoss 1.25E-02 klLoss 3.54E-02 loss 4.79E-02\n",
      "Iter 45000 reconLoss 1.15E-02 klLoss 3.48E-02 loss 4.63E-02\n",
      "Iter 45500 reconLoss 9.55E-03 klLoss 3.53E-02 loss 4.48E-02\n",
      "Iter 46000 reconLoss 9.46E-03 klLoss 3.50E-02 loss 4.45E-02\n",
      "Iter 46500 reconLoss 1.12E-02 klLoss 3.52E-02 loss 4.63E-02\n",
      "Iter 47000 reconLoss 1.16E-02 klLoss 3.58E-02 loss 4.74E-02\n",
      "Iter 47500 reconLoss 8.07E-03 klLoss 3.49E-02 loss 4.30E-02\n",
      "Iter 48000 reconLoss 9.56E-03 klLoss 3.52E-02 loss 4.48E-02\n",
      "Iter 48500 reconLoss 1.09E-02 klLoss 3.52E-02 loss 4.61E-02\n",
      "Iter 49000 reconLoss 8.79E-03 klLoss 3.51E-02 loss 4.39E-02\n",
      "Iter 49500 reconLoss 7.84E-03 klLoss 3.48E-02 loss 4.26E-02\n",
      "training time : 184.16 \n"
     ]
    }
   ],
   "source": [
    "latentDim, hiddenDim = 2, 250\n",
    "numEpochs = 50000\n",
    "epsilon=4.2e-2\n",
    "epsilon=2e-2\n",
    "numEpochs = 50000\n",
    "klFactor = 5e-5\n",
    "learningRate = 2e-3\n",
    "savedNet = './data/vaeNet.nt'\n",
    "vaeSettings = {'encoder':{'inputDim':numFeatures, 'hiddenDim':hiddenDim,\\\n",
    "                                          'latentDim':latentDim},\\\n",
    "               'decoder':{'latentDim':latentDim, 'hiddenDim':hiddenDim,\\\n",
    "                                          'outputDim':numFeatures}}\n",
    "\n",
    "materialEncoder = MaterialEncoder(trainingData, dataInfo, dataIdentifier, vaeSettings)\n",
    "\n",
    "start = time.perf_counter()\n",
    "convgHistory = materialEncoder.trainAutoencoder(numEpochs, klFactor, savedNet, learningRate, epsilon)\n",
    "print('training time : {:.2F} '.format(time.perf_counter() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70968e2b",
   "metadata": {},
   "source": [
    "# Import the Truss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c84c26b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from examples import getExample\n",
    "from trussFE import TrussFE\n",
    "\n",
    "#Loads the example truss\n",
    "exampleName, nodeXY, connectivity, bc = getExample(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": 6,
   "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": 7,
   "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':False, '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": 8,
   "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": 9,
   "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": 10,
   "id": "58b8803f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luisy\\Desktop\\SMSUDL-paper\\src\\trussFE.py:74: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\\cb\\pytorch_1000000000000\\work\\torch\\csrc\\utils\\tensor_new.cpp:233.)\n",
      "  Kasm = torch.sparse_coo_tensor(self.nodeIdx, sK,\\\n"
     ]
    },
    {
     "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 |  8.296225E+01 |             - |             -\n",
      "     2 |       50 |      1 |  0.000000E+00 |  0.0066625307 |  0.000000E+00 |             f\n",
      "     3 |       60 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     4 |       70 |      1 |  0.000000E+00 |  0.000000E+00 |  6.039120E+01 |         ideal\n",
      "     5 |       80 |      1 |  0.000000E+00 |  0.000000E+00 |  1.4436099233 |         ideal\n",
      "     6 |       90 |      1 |  0.000000E+00 |  0.000000E+00 |  0.4694563212 |         ideal\n",
      "     7 |      100 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     8 |      110 |      1 |  0.000000E+00 |  0.000000E+00 |  2.453125E+01 |         ideal\n",
      "     9 |      120 |      1 |  0.000000E+00 |  0.000000E+00 |  1.140682E+01 |         ideal\n",
      "    10 |      130 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    11 |      140 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    12 |      150 |      1 |  0.000000E+00 |  0.000000E+00 |  0.4246315286 |         ideal\n",
      "    13 |      160 |      1 |  0.000000E+00 |  0.000000E+00 |  1.716625E+01 |         ideal\n",
      "    14 |      170 |      1 |  0.000000E+00 |  0.000000E+00 |  4.581723E+01 |         ideal\n",
      "    15 |      180 |      1 |  0.000000E+00 |  0.000000E+00 |  2.933784E+01 |         ideal\n",
      "    16 |      190 |      1 |  0.000000E+00 |  0.000000E+00 |  5.217979E+01 |         ideal\n",
      "    17 |      200 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\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 |  0.000000E+00 |             f\n",
      "    22 |      250 |      1 |  0.000000E+00 |  0.000000E+00 |  2.6960299753 |         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 |  0.000000E+00 |             f\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 |  0.000000E+00 |             f\n",
      "    28 |      310 |      1 |  0.000000E+00 |  0.000000E+00 |  2.1508158987 |         ideal\n",
      "    29 |      320 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    30 |      330 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    31 |      340 |      1 |  0.000000E+00 |  0.000000E+00 |  3.9235225030 |         ideal\n",
      "    32 |      350 |      1 |  0.000000E+00 |  0.000000E+00 |  3.0777247499 |         ideal\n",
      "    33 |      360 |      1 |  0.000000E+00 |  0.000000E+00 |  1.7443332705 |         ideal\n",
      "    34 |      370 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    35 |      380 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    36 |      390 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    37 |      400 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0517766164 |         ideal\n",
      "    38 |      410 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    39 |      420 |      1 |  0.000000E+00 |  0.000000E+00 |  0.1245818453 |         ideal\n",
      "    40 |      430 |      1 |  0.000000E+00 |  0.000000E+00 |  4.6082554001 |         ideal\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 |  0.000000E+00 |             f\n",
      "    44 |      470 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    45 |      480 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\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 |  0.000000E+00 |             f\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 |  0.1845102846 |         ideal\n",
      "    52 |      550 |      1 |  0.000000E+00 |  0.000000E+00 |  0.3331483679 |         ideal\n",
      "    53 |      560 |      1 |  0.000000E+00 |  0.000000E+00 |  0.9721488285 |         ideal\n",
      "    54 |      570 |      1 |  0.000000E+00 |  0.000000E+00 |  1.6453056919 |         ideal\n",
      "    55 |      580 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    56 |      590 |      1 |  0.000000E+00 |  0.000000E+00 |  0.2178606361 |         ideal\n",
      "    57 |      600 |      1 |  0.000000E+00 |  0.000000E+00 |  2.7812445061 |         ideal\n",
      "    58 |      610 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\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 |  0.000000E+00 |             f\n",
      "    62 |      650 |      1 |  0.000000E+00 |  0.000000E+00 |  0.9160892275 |         ideal\n",
      "    63 |      660 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    64 |      670 |      1 |  0.000000E+00 |  0.000000E+00 |  0.9976312098 |         ideal\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 |  1.4012256778 |         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 |  0.4434529172 |         ideal\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 |  0.2377332159 |         ideal\n",
      "    80 |      830 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    81 |      840 |      1 |  0.000000E+00 |  0.000000E+00 |  0.7708314261 |         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 |  0.2629864013 |         ideal\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 |  0.000000E+00 |             f\n",
      "    87 |      900 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0403473316 |         ideal\n",
      "    88 |      910 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    89 |      920 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0041904217 |         ideal\n",
      "    90 |      930 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    91 |      940 |      1 |  0.000000E+00 |  0.000000E+00 |  0.5322022120 |         ideal\n",
      "    92 |      950 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    93 |      960 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0455552398 |         ideal\n",
      "    94 |      970 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    95 |      980 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    96 |      990 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    97 |     1000 |      1 |  0.000000E+00 |  0.000000E+00 |  0.4269954699 |         ideal\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 |  0.000000E+00 |             f\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 |  0.000000E+00 |             f\n",
      "   104 |     1070 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0013686463 |             f\n",
      "   105 |     1080 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0013686463 |             f\n",
      "   106 |     1090 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0013686463 |             f\n",
      "   107 |     1100 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0013686463 |             f\n",
      "   108 |     1110 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0013686463 |             f\n",
      "   109 |     1120 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0083008758 |         ideal\n",
      "   110 |     1130 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   111 |     1140 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0112787910 |         ideal\n",
      "   112 |     1150 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0046188485 |         ideal\n",
      "   113 |     1160 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   114 |     1170 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0188267112 |         ideal\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 |  0.000000E+00 |             f\n",
      "   118 |     1210 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0828402694 |         ideal\n",
      "   119 |     1220 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0780443333 |         ideal\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 |  0.1428561799 |         ideal\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 |  0.000000E+00 |             f\n",
      "   127 |     1300 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   128 |     1310 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   129 |     1320 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   130 |     1330 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   131 |     1340 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   132 |     1350 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   133 |     1360 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   134 |     1370 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   135 |     1380 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   136 |     1390 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   137 |     1400 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   138 |     1410 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0017056567 |             f\n",
      "   139 |     1420 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0044976295 |         ideal\n",
      "   140 |     1430 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   141 |     1440 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0192711225 |         ideal\n",
      "   142 |     1450 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016704530 |             f\n",
      "   143 |     1460 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0016704530 |             f\n",
      "   144 |     1470 |      1 |  0.000000E+00 |  0.000000E+00 |  0.3647514919 |         ideal\n",
      "   145 |     1480 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0999253424 |         ideal\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 |  0.0007720212 |             f\n",
      "   154 |     1570 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0007720212 |             f\n",
      "   155 |     1580 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0007720212 |             f\n",
      "   156 |     1590 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0007720212 |             f\n",
      "   157 |     1600 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0007720212 |             f\n",
      "   158 |     1610 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0007720212 |             f\n",
      "   159 |     1620 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0007720212 |             f\n",
      "   160 |     1630 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0035737121 |         ideal\n",
      "   161 |     1640 |      1 |  0.000000E+00 |  0.000000E+00 |  0.1338125180 |         ideal\n",
      "   162 |     1650 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   163 |     1660 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0326047610 |         ideal\n",
      "   164 |     1670 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0689212491 |         ideal\n",
      "   165 |     1680 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0341944430 |         ideal\n",
      "   166 |     1690 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   167 |     1700 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   168 |     1710 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   169 |     1720 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   170 |     1730 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0188453414 |         ideal\n",
      "   171 |     1740 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   172 |     1750 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0105906321 |         ideal\n",
      "   173 |     1760 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   174 |     1770 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   175 |     1780 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   176 |     1790 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   177 |     1800 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   178 |     1810 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0074746737 |         ideal\n",
      "   179 |     1820 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   180 |     1830 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   181 |     1840 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0960099587 |         ideal\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 |  0.0040780023 |         ideal\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   186 |     1890 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0097648498 |         ideal\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 |  0.0014146970 |             f\n",
      "   191 |     1940 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014146970 |             f\n",
      "   192 |     1950 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014146970 |             f\n",
      "   193 |     1960 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014146970 |             f\n",
      "   194 |     1970 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014146970 |             f\n",
      "   195 |     1980 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0014146970 |             f\n",
      "   196 |     1990 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0124524804 |         ideal\n",
      "   197 |     2000 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   198 |     2010 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   199 |     2020 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   200 |     2030 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   201 |     2040 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   202 |     2050 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   203 |     2060 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   204 |     2070 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   205 |     2080 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   206 |     2090 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   207 |     2100 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   208 |     2110 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   209 |     2120 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   210 |     2130 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   211 |     2140 |      1 |  0.000000E+00 |  0.000000E+00 |  5.104289E-06 |             f\n",
      "   212 |     2150 |      1 |  0.000000E+00 |  0.000000E+00 |  5.104289E-06 |             f\n",
      "   213 |     2160 |      1 |  0.000000E+00 |  0.000000E+00 |  5.104289E-06 |             f\n",
      "   214 |     2170 |      1 |  0.000000E+00 |  0.000000E+00 |  5.104289E-06 |             f\n",
      "   215 |     2180 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0026098801 |         ideal\n",
      "   216 |     2190 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   217 |     2200 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   218 |     2210 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   219 |     2220 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0004097362 |             f\n",
      "   220 |     2230 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0004097362 |             f\n",
      "   221 |     2240 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0004097362 |             f\n",
      "   222 |     2250 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0004097362 |             f\n",
      "   223 |     2260 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0004097362 |             f\n",
      "   224 |     2270 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0127928382 |         ideal\n",
      "   225 |     2280 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   226 |     2290 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   227 |     2300 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   228 |     2310 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   229 |     2320 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   230 |     2330 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0002315876 |             f\n",
      "   231 |     2340 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0002315876 |             f\n",
      "   232 |     2350 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0002315876 |             f\n",
      "   233 |     2360 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0002315876 |             f\n",
      "   234 |     2370 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0106934103 |         ideal\n",
      "   235 |     2380 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   236 |     2390 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   237 |     2400 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   238 |     2410 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   239 |     2420 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   240 |     2430 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   241 |     2440 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   242 |     2450 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   243 |     2460 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   244 |     2470 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   245 |     2480 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   246 |     2490 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   247 |     2500 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   248 |     2510 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   249 |     2520 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   250 |     2530 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0103686564 |         ideal\n",
      "   251 |     2540 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   252 |     2550 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   253 |     2560 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   254 |     2570 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   255 |     2580 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   256 |     2590 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   257 |     2600 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   258 |     2610 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   259 |     2620 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   260 |     2630 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   261 |     2640 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   262 |     2650 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   263 |     2660 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   264 |     2670 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   265 |     2680 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   266 |     2690 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   267 |     2700 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   268 |     2710 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   269 |     2720 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   270 |     2730 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   271 |     2740 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   272 |     2750 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   273 |     2760 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   274 |     2770 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   275 |     2780 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "solving time : 15.35 \n"
     ]
    }
   ],
   "source": [
    "from pymoo.optimize import minimize\n",
    "start = time.perf_counter()\n",
    "res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "print('solving time : {:.2F} '.format(time.perf_counter() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70dd546",
   "metadata": {},
   "source": [
    "# Analyse the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2230796a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.92102089]\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": 12,
   "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": 13,
   "id": "a4824385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "closest material 0 : AISI 1045 CD , confidence 95.44\n",
      "closest material 1 : AISI 4130 , confidence 88.99\n",
      "closest material 2 : AISI 4130 norm , confidence 86.93\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_Single/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": 14,
   "id": "a48cb451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import patheffects\n",
    "def plotLatentclose(dataIdentifier, vae, zData, ltnt1, ltnt2, plotHull, annotateHead, saveFileName):\n",
    "    clrs = ['slategrey', 'violet', 'lawngreen', 'indigo', 'chocolate', 'gold']\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)\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], 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=0.3, edgecolor='black')  # 'black'\n",
    "            poly.set_capstyle('round')\n",
    "            plt.gca().add_patch(poly)\n",
    "            ax.annotate(dataIdentifier['className'][i], (cent[0] + 0.1, cent[1] - 0.2), size=15, color='red',\n",
    "                        zorder=10)  # Higher zorder to bring the annotation to the front\n",
    "            txt_optimal = ax.annotate(dataIdentifier['className'][i], (cent[0] + 0.1, cent[1] - 0.2), size=15, color='red',\n",
    "                        zorder=10)  # Create a new text object for the annotation\n",
    "            txt_optimal.set_path_effects([patheffects.withStroke(linewidth=2, foreground=\"white\")])  # Apply white border\n",
    "\n",
    "\n",
    "\n",
    "    for i, txt in enumerate(ptLabel):\n",
    "        if (not annotateHead) or (annotateHead and i < np.max(colorcol) + 1):\n",
    "            ax.annotate(txt, (z[i, ltnt1], z[i, ltnt2]), size=8)\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",
    "\n",
    "    plt.plot(zData[0], zData[1], marker='*', color='maroon')\n",
    "    ax.annotate('Optimal Material', (zData[0] * 1.05, zData[1] * 1.05), color='maroon', size=10)\n",
    "\n",
    "    # Set the limits for x and y axes to cover the region ±1 unit from the optimal material point\n",
    "    xmin, xmax = zData[0] - 1, zData[0] + 1\n",
    "    ymin, ymax = zData[1] - 1, zData[1] + 1\n",
    "    plt.xlim(xmin, xmax)\n",
    "    plt.ylim(ymin, ymax)\n",
    "\n",
    "    plt.savefig(saveFileName, bbox_inches='tight', dpi=200)\n",
    "    return fig, ax\n",
    "\n",
    "\n",
    "fig, ax = plotLatentclose(dataIdentifier, vae, zData, 0, 1, plotHull = True, annotateHead = False,\\\n",
    "                                    saveFileName ='./figures_Single/optimalLatentclose.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7d0e5c",
   "metadata": {},
   "source": [
    "# Defines fixed problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "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": 17,
   "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",
      "solving time : 3.55 \n",
      "[6.37189096]\n"
     ]
    }
   ],
   "source": [
    "start = time.perf_counter()\n",
    "res = minimize(problem_fix,\n",
    "               algorithm_fix,\n",
    "               termination_fix,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "print('solving time : {:.2F} '.format(time.perf_counter() - start))\n",
    "X = res.X\n",
    "F = res.F\n",
    "G = res.G\n",
    "\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": 18,
   "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",
      "    89 |      940 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   181 |     1860 |      1 |  0.000000E+00 |  0.000000E+00 |  0.0191926119 |         ideal\n",
      "   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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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",
      "   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": []
  }
 ],
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