Bruteforce_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "747e58db",
   "metadata": {},
   "source": [
    "# Loads packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80d8907d",
   "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": "ccb7b898",
   "metadata": {},
   "source": [
    "# Loads the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60593859",
   "metadata": {},
   "outputs": [],
   "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 = torch.tensor((df[df.columns[3:]].to_numpy())).float()\n",
    "\n",
    "  return dataIdentifier, trainInfo, df\n",
    "dataIdentifier, trainInfo, df = preprocessData()\n",
    "\n",
    "props = df.columns[3:].tolist()\n",
    "nmat, nprop = trainInfo.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8abacbe1",
   "metadata": {},
   "source": [
    "# Loads the truss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "152f0b88",
   "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",
    "\n",
    "#Loads the truss in truss\n",
    "truss = TrussFE(nodeXY, connectivity, bc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d76e8f",
   "metadata": {},
   "source": [
    "# Sets the constraints and objectives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85991eb5",
   "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",
    "\n",
    "\n",
    "objectives = {'cost': {'isOn':False},\\\n",
    "              'mass': {'isOn':True}, \\\n",
    "              'co2': {'isOn':False},\\\n",
    "              'water': {'isOn':False},\\\n",
    "              'energy': {'isOn':False},\\\n",
    "              'compliance': {'isOn': False}}\n",
    "               \n",
    "# count the number of entries with 'isOn' set to True\n",
    "no = sum(1 for v in objectives.values() if v['isOn'])\n",
    "nc = sum(1 for v in constraints.values() if v['isOn'])\n",
    "\n",
    "# print an error message in red if there are 3 or more entries with 'isOn' set to True\n",
    "if no >= 3:\n",
    "    print('Error: Three or more entries have isOn=True')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbb78a4",
   "metadata": {},
   "source": [
    "# Defines the problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82f9e266",
   "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",
    "from pymoo.termination import get_termination\n",
    "from pymoo.termination.default import DefaultMultiObjectiveTermination\n",
    "from pymoo.optimize import minimize\n",
    "from pymoo.core.problem import ElementwiseProblem\n",
    "\n",
    "\n",
    "\n",
    "#Problem of minimizing mass with the tensile and compressio 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, nc, no):\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",
    "\n",
    "        self.truss=truss\n",
    "        self.matprop=matprop\n",
    "        self.constraints=constraints\n",
    "        self.objectives=objectives\n",
    "\n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "\n",
    "        x_tensor = torch.from_numpy(x)\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",
    "        \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",
    "        out[\"F\"] = np.array(obj_values)\n",
    "        out[\"G\"] = np.array(con_values)\n",
    "\n",
    "\n",
    "\n",
    "algorithm_fix = NSGA2(\n",
    "    pop_size=40,\n",
    "    n_offsprings=20,\n",
    "    sampling=FloatRandomSampling(),\n",
    "    crossover=SBX(prob=0.9, eta=15),\n",
    "    mutation=PM(eta=20),\n",
    "    eliminate_duplicates=True\n",
    ")\n",
    "\n",
    "\n",
    "termination_fix = DefaultMultiObjectiveTermination(\n",
    "    xtol=1e-7,\n",
    "    cvtol=1e-6,\n",
    "    ftol=0.01,\n",
    "    period=10,\n",
    "    n_max_gen=1000,\n",
    "    n_max_evals=50000\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d5aa07",
   "metadata": {},
   "source": [
    "# Runs the solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f1533362",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "50\n",
      "51\n",
      "52\n",
      "53\n",
      "54\n",
      "55\n",
      "56\n",
      "57\n",
      "58\n",
      "59\n",
      "60\n",
      "61\n",
      "62\n",
      "63\n",
      "64\n",
      "65\n",
      "66\n",
      "67\n",
      "68\n",
      "69\n",
      "70\n",
      "71\n",
      "72\n",
      "73\n",
      "74\n",
      "75\n",
      "76\n",
      "77\n",
      "78\n",
      "79\n",
      "80\n",
      "81\n",
      "82\n",
      "83\n",
      "84\n",
      "85\n",
      "86\n",
      "87\n",
      "88\n",
      "89\n",
      "90\n",
      "91\n",
      "92\n",
      "Solver time : 321.14\n"
     ]
    }
   ],
   "source": [
    "N=nmat\n",
    "results = np.empty((N, 5))\n",
    "areas = np.empty((N, 6))\n",
    "names = []\n",
    "types = []\n",
    "\n",
    "\n",
    "start = time.perf_counter()\n",
    "for i in range(N):\n",
    "\n",
    "    matlist=[i for i in range(N)]\n",
    "    matprop=trainInfo[i,:]\n",
    "    print(i)\n",
    "    res = minimize(MyProblem_fix(truss, matprop, constraints, objectives, nc, no),\n",
    "               algorithm_fix,\n",
    "               termination_fix,\n",
    "               seed=1,\n",
    "               save_history=False,\n",
    "               verbose=False)\n",
    "    results[i,1] = np.hstack(res.F) #mass\n",
    "    results[i,0] = results[i,1]*matprop[1] #cost\n",
    "    results[i,2] = results[i,1]*matprop[4] #co2\n",
    "    results[i,3] = results[i,1]*matprop[5] #energy\n",
    "    results[i,4] = results[i,1]*matprop[6] #water\n",
    "    areas[i] = np.hstack(res.X)\n",
    "    \n",
    "    \n",
    "    matname = dataIdentifier['name'][i]\n",
    "    mattype = dataIdentifier['className'][i]\n",
    "    names.append(matname)\n",
    "    types.append(mattype)\n",
    "print('Solver time : {:.2F}'.format(time.perf_counter() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7420c5",
   "metadata": {},
   "source": [
    "# Cost vs mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11e9bd2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "objectivesx = {'cost': {'isOn':True}, \\\n",
    "              'mass': {'isOn':True}, \\\n",
    "              'co2': {'isOn':False}, \\\n",
    "              'water': {'isOn':False}, \\\n",
    "              'energy': {'isOn':False}, \\\n",
    "              'compliance': {'isOn': False}}\n",
    "\n",
    "# Define specific colors for each material type\n",
    "mat_colors = {\n",
    "    'Steel': 'slategrey',\n",
    "    'Aluminium Alloys': 'violet',\n",
    "    'Plastics': 'lawngreen',\n",
    "    'Titanium Alloys': 'indigo',\n",
    "    'Copper Alloys': 'chocolate',\n",
    "    'Composites': 'gold',\n",
    "    # Add more material types and corresponding colors as needed\n",
    "}\n",
    "\n",
    "# Create scatter plot\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(len(results)):\n",
    "    x = results[i][0]\n",
    "    y = results[i][1]\n",
    "    label = names[i]\n",
    "    material_type = types[i]\n",
    "    color = mat_colors.get(material_type, (0.8, 0.8, 0.8))\n",
    "    ax.scatter(x, y, color=color, label=material_type)\n",
    "    #ax.text(x + 0.1, y + 0.1, label)\n",
    "\n",
    "# Add legend and axis labels\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "unique_labels = list(set(labels))\n",
    "unique_handles = [handles[labels.index(label)] for label in unique_labels]\n",
    "ax.legend(unique_handles, unique_labels)\n",
    "\n",
    "# show plot\n",
    "plt.title(\"Objective Space\")\n",
    "\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy']\n",
    "enabled_entries = []\n",
    "\n",
    "for entry in entry_order:\n",
    "    if objectivesx[entry]['isOn']:\n",
    "        enabled_entries.append(entry)\n",
    "\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures_Bruteforce/brute_costvsmass.png', bbox_inches='tight', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ed23c1",
   "metadata": {},
   "source": [
    "# Plots Pareto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fe5d742a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 1: Extract x (cost) and y (mass) values from results\n",
    "x_values = [result[0] for result in results]\n",
    "y_values = [result[1] for result in results]\n",
    "\n",
    "# Step 2: Identify non-dominated points to get Pareto front\n",
    "pareto_front_indices = []\n",
    "for i in range(len(x_values)):\n",
    "    is_dominated = False\n",
    "    for j in range(len(x_values)):\n",
    "        if i == j:\n",
    "            continue\n",
    "        if x_values[i] >= x_values[j] and y_values[i] >= y_values[j]:\n",
    "            is_dominated = True\n",
    "            break\n",
    "    if not is_dominated:\n",
    "        pareto_front_indices.append(i)\n",
    "\n",
    "# Step 3: Plot the Pareto front materials in a scatter plot\n",
    "fig, ax = plt.subplots()\n",
    "for i in pareto_front_indices:\n",
    "    x = x_values[i]\n",
    "    y = y_values[i]\n",
    "    label = names[i]\n",
    "    material_type = types[i]\n",
    "    color = mat_colors.get(material_type, (0.8, 0.8, 0.8))\n",
    "    ax.scatter(x, y, color=color, label=material_type)\n",
    "    ax.text(x + 0.1, y + 0.1, label)\n",
    "\n",
    "# Add legend and axis labels\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "unique_labels = list(set(labels))\n",
    "unique_handles = [handles[labels.index(label)] for label in unique_labels]\n",
    "ax.legend(unique_handles, unique_labels)\n",
    "\n",
    "# Show plot with only the Pareto front materials\n",
    "plt.title(\"Objective Space\")\n",
    "\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy']\n",
    "enabled_entries = [entry for entry in entry_order if objectivesx[entry]['isOn']]\n",
    "\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures_Bruteforce/brute_costvsmass_pareto.png', bbox_inches='tight', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69968c0b",
   "metadata": {},
   "source": [
    "# Cost vs CO2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5930e7fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "objectivesx = {'cost': {'isOn':True}, \\\n",
    "              'mass': {'isOn':False}, \\\n",
    "              'co2': {'isOn':True}, \\\n",
    "              'water': {'isOn':False}, \\\n",
    "              'energy': {'isOn':False}, \\\n",
    "              'compliance': {'isOn': False}}\n",
    "\n",
    "# Define specific colors for each material type\n",
    "mat_colors = {\n",
    "    'Steel': 'slategrey',\n",
    "    'Aluminium Alloys': 'violet',\n",
    "    'Plastics': 'lawngreen',\n",
    "    'Titanium Alloys': 'indigo',\n",
    "    'Copper Alloys': 'chocolate',\n",
    "    'Composites': 'gold',\n",
    "    # Add more material types and corresponding colors as needed\n",
    "}\n",
    "\n",
    "# Create scatter plot\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(len(results)):\n",
    "    x = results[i][0]\n",
    "    y = results[i][2]\n",
    "    label = names[i]\n",
    "    material_type = types[i]\n",
    "    color = mat_colors.get(material_type, (0.8, 0.8, 0.8))\n",
    "    ax.scatter(x, y, color=color, label=material_type)\n",
    "    #ax.text(x + 0.1, y + 0.1, label)\n",
    "\n",
    "# Add legend and axis labels\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "unique_labels = list(set(labels))\n",
    "unique_handles = [handles[labels.index(label)] for label in unique_labels]\n",
    "ax.legend(unique_handles, unique_labels)\n",
    "\n",
    "# show plot\n",
    "plt.title(\"Objective Space\")\n",
    "\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy']\n",
    "enabled_entries = []\n",
    "\n",
    "for entry in entry_order:\n",
    "    if objectivesx[entry]['isOn']:\n",
    "        enabled_entries.append(entry)\n",
    "\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures_Bruteforce/brute_costvsco2.png', bbox_inches='tight', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a318641",
   "metadata": {},
   "source": [
    "# Plots Pareto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c565296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 1: Extract x (cost) and y (mass) values from results\n",
    "x_values = [result[0] for result in results]\n",
    "y_values = [result[2] for result in results]\n",
    "\n",
    "# Step 2: Identify non-dominated points to get Pareto front\n",
    "pareto_front_indices = []\n",
    "for i in range(len(x_values)):\n",
    "    is_dominated = False\n",
    "    for j in range(len(x_values)):\n",
    "        if i == j:\n",
    "            continue\n",
    "        if x_values[i] >= x_values[j] and y_values[i] >= y_values[j]:\n",
    "            is_dominated = True\n",
    "            break\n",
    "    if not is_dominated:\n",
    "        pareto_front_indices.append(i)\n",
    "\n",
    "# Step 3: Plot the Pareto front materials in a scatter plot\n",
    "fig, ax = plt.subplots()\n",
    "for i in pareto_front_indices:\n",
    "    x = x_values[i]\n",
    "    y = y_values[i]\n",
    "    label = names[i]\n",
    "    material_type = types[i]\n",
    "    color = mat_colors.get(material_type, (0.8, 0.8, 0.8))\n",
    "    ax.scatter(x, y, color=color, label=material_type)\n",
    "    ax.text(x + 0.1, y + 0.1, label)\n",
    "\n",
    "# Add legend and axis labels\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "unique_labels = list(set(labels))\n",
    "unique_handles = [handles[labels.index(label)] for label in unique_labels]\n",
    "ax.legend(unique_handles, unique_labels)\n",
    "\n",
    "# Show plot with only the Pareto front materials\n",
    "plt.title(\"Objective Space\")\n",
    "\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy']\n",
    "enabled_entries = [entry for entry in entry_order if objectivesx[entry]['isOn']]\n",
    "\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures_Bruteforce/brute_costvsco2_pareto.png', bbox_inches='tight', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00043ff2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}