{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Example 1: A Simple Neuroptimiser with a problem from IOH\n",
    "This example demonstrates how to use the Neuroptimiser library to solve an optimisation problem from the IOH framework. The problem is defined by its ID and instance, and the Neuroptimiser is configured with a set of parameters for the agents."
   ],
   "id": "8adf569f028839ad"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 1. Setup\n",
    "Import necessary libraries and set up the environment for plotting."
   ],
   "id": "fb33b2b07c748040"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:40.878994Z",
     "start_time": "2025-06-17T15:46:40.834333Z"
    }
   },
   "source": [
    "# Import necessary libraries\n",
    "import os\n",
    "import shutil\n",
    "from copy import deepcopy\n",
    "import random\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "from neuroptimiser import NeurOptimiser\n",
    "from ioh import get_problem\n",
    "\n",
    "# Set up the plotting style and parameters\n",
    "width = 6.5\n",
    "height = width * 0.618\n",
    "res_dpi = 333\n",
    "\n",
    "seed = 42\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "root_figures = \"./figures/\"\n",
    "\n",
    "plt.style.context('paper')\n",
    "sns.set_style(\"ticks\")\n",
    "plt.rcParams['font.family']         = 'serif'\n",
    "plt.rcParams['axes.labelsize']      = 11\n",
    "plt.rcParams['xtick.labelsize']     = 11\n",
    "plt.rcParams['ytick.labelsize']     = 11\n",
    "plt.rcParams['legend.fontsize']     = 11\n",
    "plt.rcParams['axes.grid']           = False\n",
    "plt.rcParams['axes.spines.top']     = True\n",
    "plt.rcParams['axes.spines.right']   = True\n",
    "plt.rcParams['axes.edgecolor']      = 'black'\n",
    "plt.rcParams['xtick.color']         = 'black'\n",
    "plt.rcParams['ytick.color']         = 'black'\n",
    "plt.rcParams['axes.labelcolor']     = 'black'\n",
    "\n",
    "if shutil.which(\"pdflatex\") is not None:\n",
    "    plt.rcParams['text.usetex']     = True\n",
    "    plt.rcParams['font.serif']      = ['Computer Modern Roman']\n",
    "else:\n",
    "    plt.rcParams['text.usetex']     = False\n",
    "\n",
    "def save_this(fig, _in=\"\"):\n",
    "    if is_saving:\n",
    "        os.makedirs(f\"{root_figures}/\" + _in, exist_ok=True)\n",
    "        fig.savefig(root_figures + _in + \"/\" + prefix_filename + \".png\", transparent=True, dpi=res_dpi, bbox_inches=\"tight\")\n",
    "    else:\n",
    "        plt.show()"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 2. Parameter definition\n",
    "In this section, we define the problem parameters, including the number of steps, problem ID, instance, dimensions. In addition, we set the custom parameters for the Neuroptimiser, which will replace the default parameters of the spiking core."
   ],
   "id": "f97a0bd88a8b9c7d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:40.998051Z",
     "start_time": "2025-06-17T15:46:40.994866Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Define the problem parameters\n",
    "num_steps       = 100       # Number of iterations\n",
    "problem_id      = 1         # Problem ID from the IOH framework\n",
    "problem_ins     = 1         # Problem instance\n",
    "num_dimensions  = 2         # Number of dimensions for the problem\n",
    "num_agents      = 30        # Number of agents/units in the Neuroptimiser\n",
    "num_neighbours  = 10        # Number of neighbours for each agent/unit\n",
    "\n",
    "# Define the Neuroptimiser parameters\n",
    "neuropt_name    = \"neuropt_Ex1-LinRand\"\n",
    "\n",
    "config_params = dict(\n",
    "    num_iterations  = num_steps,\n",
    "    num_agents      = num_agents,\n",
    "    spiking_core    = \"TwoDimSpikingCore\",\n",
    "    num_neighbours  = num_neighbours,\n",
    "    neuron_topology = \"2dr\",\n",
    "    unit_topology   = \"random\",\n",
    ")\n",
    "\n",
    "core_params = [   # Specify the custom parameters for the spiking core\n",
    "    {\"name\": \"izhikevich\",\n",
    "     \"coeffs\": \"random\",\n",
    "     \"spk_cond\": \"fixed\",\n",
    "     \"hs_operator\": \"differential\",\n",
    "     \"hs_variant\": \"current-to-rand\",\n",
    "     },\n",
    "]\n",
    "\n",
    "# Additional parameters\n",
    "is_saving       = True      # Whether to save the figures or not\n",
    "prefix_filename = (f\"{neuropt_name}_\"\n",
    "                   f\"{problem_id}p_\"\n",
    "                   f\"{problem_ins}i_\"\n",
    "                   f\"{num_dimensions}d_\"\n",
    "                   f\"{num_steps}s_\"\n",
    "                   f\"{num_agents}u\")\n",
    "plt.close()\n",
    "if is_saving:\n",
    "    print(f\"prefix_filename: '{prefix_filename}'\")"
   ],
   "id": "ffcbc9470bdb0f38",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prefix_filename: 'neuropt_Ex1-LinRand_1p_1i_2d_100s_30u'\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 3. Problem setup and optimisation\n",
    "We first retrieve the problem from the IOH framework."
   ],
   "id": "f3a7caa4f24c16a2"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:41.011251Z",
     "start_time": "2025-06-17T15:46:41.008037Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Get the problem from the IOH framework\n",
    "problem = get_problem(fid=problem_id,\n",
    "                      instance=problem_ins,\n",
    "                      dimension=num_dimensions,\n",
    "                      )\n",
    "problem.reset()\n",
    "print(problem)"
   ],
   "id": "bf463a0cd2859a27",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<RealSingleObjectiveProblem 1. Sphere (iid=1 dim=2)>\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Then, we instantiate the Neuroptimiser with the defined parameters and solve the problem. The `debug_mode` is set to `True` to enable detailed logging of the optimisation process.",
   "id": "f3bc216156714f0c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:50.074374Z",
     "start_time": "2025-06-17T15:46:41.031354Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Instantiation\n",
    "optimiser = NeurOptimiser(config_params, core_params)\n",
    "\n",
    "# Solve the problem\n",
    "optimiser.solve(problem, debug_mode=True)"
   ],
   "id": "8c6f19c161e212f2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[neuropt:log] Debug mode is enabled. Monitoring will be activated.\n",
      "[neuropt:log] Parameters are set up.\n",
      "[neuropt:log] Initial positions and topologies are set up.\n",
      "[neuropt:log] Tensor contraction layer, neighbourhood manager, and high-level selection unit are created.\n",
      "[neuropt:log] Population of nheuristic units is created.\n",
      "[neuropt:log] Connections between nheuristic units and auxiliary processes are established.\n",
      "[neuropt:log] Monitors are set up.\n",
      "[neuropt:log] Starting simulation with 100 iterations...\n",
      "... step: 0, best fitness: 81.21773529052734\n",
      "... step: 10, best fitness: 79.9867172241211\n",
      "... step: 20, best fitness: 79.5005111694336\n",
      "... step: 30, best fitness: 79.5005111694336\n",
      "... step: 40, best fitness: 79.48989868164062\n",
      "... step: 50, best fitness: 79.4843978881836\n",
      "... step: 60, best fitness: 79.4843978881836\n",
      "... step: 70, best fitness: 79.4843978881836\n",
      "... step: 80, best fitness: 79.48188018798828\n",
      "... step: 90, best fitness: 79.48102569580078\n",
      "... step: 99, best fitness: 79.48017120361328\n",
      "[neuropt:log] Simulation completed. Fetching monitor data... done\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0.24347829, -1.14769556]), array([79.4801712]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:50.095294Z",
     "start_time": "2025-06-17T15:46:50.091968Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Show the overall configuration parameters of the optimiser\n",
    "print(optimiser.config_params)"
   ],
   "id": "f604a4864ae20733",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'num_iterations': 100, 'num_agents': 30, 'spiking_core': 'TwoDimSpikingCore', 'num_neighbours': 10, 'neuron_topology': '2dr', 'unit_topology': 'random', 'function': <function AbstractSolver._rescale_problem.<locals>.scaled_problem at 0x165002cb0>, 'num_dimensions': 2, 'seed': 69, 'search_space': array([[-5.,  5.],\n",
      "       [-5.,  5.]]), 'core_params': {}}\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:50.135958Z",
     "start_time": "2025-06-17T15:46:50.110293Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Show the core parameters used in the optimisation\n",
    "pd.DataFrame(optimiser.core_params)"
   ],
   "id": "572f03fc993f4bac",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          name  coeffs spk_cond   hs_operator       hs_variant  thr_k  \\\n",
       "0   izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "1   izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "2   izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "3   izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "4   izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "5   izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "6   izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "7   izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "8   izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "9   izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "10  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "11  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "12  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "13  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "14  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "15  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "16  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "17  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "18  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "19  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "20  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "21  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "22  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "23  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "24  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "25  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "26  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "27  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "28  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "29  izhikevich  random    fixed  differential  current-to-rand   0.05   \n",
       "\n",
       "    is_bounded    dt  max_steps   thr_min  spk_alpha thr_mode  thr_alpha  \\\n",
       "0        False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "1        False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "2        False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "3        False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "4        False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "5        False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "6        False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "7        False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "8        False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "9        False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "10       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "11       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "12       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "13       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "14       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "15       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "16       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "17       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "18       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "19       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "20       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "21       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "22       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "23       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "24       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "25       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "26       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "27       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "28       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "29       False  0.01        100  0.000001       0.25  diff_pg        1.0   \n",
       "\n",
       "    alpha approx  noise_std  seed  thr_max ref_mode  \\\n",
       "0     1.0    rk4        0.1    69      1.0       pg   \n",
       "1     1.0    rk4        0.1    70      1.0       pg   \n",
       "2     1.0    rk4        0.1    71      1.0       pg   \n",
       "3     1.0    rk4        0.1    72      1.0       pg   \n",
       "4     1.0    rk4        0.1    73      1.0       pg   \n",
       "5     1.0    rk4        0.1    74      1.0       pg   \n",
       "6     1.0    rk4        0.1    75      1.0       pg   \n",
       "7     1.0    rk4        0.1    76      1.0       pg   \n",
       "8     1.0    rk4        0.1    77      1.0       pg   \n",
       "9     1.0    rk4        0.1    78      1.0       pg   \n",
       "10    1.0    rk4        0.1    79      1.0       pg   \n",
       "11    1.0    rk4        0.1    80      1.0       pg   \n",
       "12    1.0    rk4        0.1    81      1.0       pg   \n",
       "13    1.0    rk4        0.1    82      1.0       pg   \n",
       "14    1.0    rk4        0.1    83      1.0       pg   \n",
       "15    1.0    rk4        0.1    84      1.0       pg   \n",
       "16    1.0    rk4        0.1    85      1.0       pg   \n",
       "17    1.0    rk4        0.1    86      1.0       pg   \n",
       "18    1.0    rk4        0.1    87      1.0       pg   \n",
       "19    1.0    rk4        0.1    88      1.0       pg   \n",
       "20    1.0    rk4        0.1    89      1.0       pg   \n",
       "21    1.0    rk4        0.1    90      1.0       pg   \n",
       "22    1.0    rk4        0.1    91      1.0       pg   \n",
       "23    1.0    rk4        0.1    92      1.0       pg   \n",
       "24    1.0    rk4        0.1    93      1.0       pg   \n",
       "25    1.0    rk4        0.1    94      1.0       pg   \n",
       "26    1.0    rk4        0.1    95      1.0       pg   \n",
       "27    1.0    rk4        0.1    96      1.0       pg   \n",
       "28    1.0    rk4        0.1    97      1.0       pg   \n",
       "29    1.0    rk4        0.1    98      1.0       pg   \n",
       "\n",
       "                                  init_position  \n",
       "0      [-0.250919762305275, 0.9014286128198323]  \n",
       "1      [0.4639878836228102, 0.1973169683940732]  \n",
       "2     [-0.687962719115127, -0.6880109593275947]  \n",
       "3     [-0.8838327756636011, 0.7323522915498704]  \n",
       "4       [0.2022300234864176, 0.416145155592091]  \n",
       "5     [-0.9588310114083951, 0.9398197043239886]  \n",
       "6     [0.6648852816008435, -0.5753217786434477]  \n",
       "7    [-0.6363500655857988, -0.6331909802931324]  \n",
       "8   [-0.39151551408092455, 0.04951286326447568]  \n",
       "9   [-0.13610996271576847, -0.4175417196039162]  \n",
       "10   [0.22370578944475894, -0.7210122786959163]  \n",
       "11   [-0.4157107029295637, -0.2672763134126166]  \n",
       "12   [-0.08786003156592814, 0.5703519227860272]  \n",
       "13   [-0.6006524356832805, 0.02846887682722321]  \n",
       "14   [0.18482913772408494, -0.9070991745600046]  \n",
       "15   [0.21508970380287673, -0.6589517526254169]  \n",
       "16     [-0.869896814029441, 0.8977710745066665]  \n",
       "17     [0.9312640661491187, 0.6167946962329223]  \n",
       "18  [-0.39077246165325863, -0.8046557719872323]  \n",
       "19    [0.3684660530243138, -0.1196950125207974]  \n",
       "20  [-0.7559235303104423, -0.00964617977745963]  \n",
       "21    [-0.9312229577695632, 0.8186408041575641]  \n",
       "22  [-0.48244003679996617, 0.32504456870796394]  \n",
       "23  [-0.3765778478211781, 0.040136042355621626]  \n",
       "24    [0.0934205586865593, -0.6302910889489459]  \n",
       "25     [0.9391692555291171, 0.5502656467222291]  \n",
       "26     [0.8789978831283782, 0.7896547008552977]  \n",
       "27    [0.19579995762217028, 0.8437484700462337]  \n",
       "28    [-0.823014995896161, -0.6080342751617096]  \n",
       "29   [-0.9095454221789239, -0.3493393384734713]  "
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>coeffs</th>\n",
       "      <th>spk_cond</th>\n",
       "      <th>hs_operator</th>\n",
       "      <th>hs_variant</th>\n",
       "      <th>thr_k</th>\n",
       "      <th>is_bounded</th>\n",
       "      <th>dt</th>\n",
       "      <th>max_steps</th>\n",
       "      <th>thr_min</th>\n",
       "      <th>spk_alpha</th>\n",
       "      <th>thr_mode</th>\n",
       "      <th>thr_alpha</th>\n",
       "      <th>alpha</th>\n",
       "      <th>approx</th>\n",
       "      <th>noise_std</th>\n",
       "      <th>seed</th>\n",
       "      <th>thr_max</th>\n",
       "      <th>ref_mode</th>\n",
       "      <th>init_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>69</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.250919762305275, 0.9014286128198323]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.4639878836228102, 0.1973169683940732]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>71</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.687962719115127, -0.6880109593275947]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>72</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.8838327756636011, 0.7323522915498704]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>73</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.2022300234864176, 0.416145155592091]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.9588310114083951, 0.9398197043239886]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.6648852816008435, -0.5753217786434477]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>76</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.6363500655857988, -0.6331909802931324]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>77</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.39151551408092455, 0.04951286326447568]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.13610996271576847, -0.4175417196039162]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.22370578944475894, -0.7210122786959163]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.4157107029295637, -0.2672763134126166]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>81</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.08786003156592814, 0.5703519227860272]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.6006524356832805, 0.02846887682722321]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>83</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.18482913772408494, -0.9070991745600046]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>84</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.21508970380287673, -0.6589517526254169]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>85</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.869896814029441, 0.8977710745066665]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>86</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.9312640661491187, 0.6167946962329223]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>87</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.39077246165325863, -0.8046557719872323]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>88</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.3684660530243138, -0.1196950125207974]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>89</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.7559235303104423, -0.00964617977745963]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.9312229577695632, 0.8186408041575641]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.48244003679996617, 0.32504456870796394]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.3765778478211781, 0.040136042355621626]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>93</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.0934205586865593, -0.6302910889489459]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.9391692555291171, 0.5502656467222291]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.8789978831283782, 0.7896547008552977]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>96</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[0.19579995762217028, 0.8437484700462337]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.823014995896161, -0.6080342751617096]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>izhikevich</td>\n",
       "      <td>random</td>\n",
       "      <td>fixed</td>\n",
       "      <td>differential</td>\n",
       "      <td>current-to-rand</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.25</td>\n",
       "      <td>diff_pg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>rk4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pg</td>\n",
       "      <td>[-0.9095454221789239, -0.3493393384734713]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 4. Results processing and visualisation\n",
    "In this section, we process the results of the optimisation and visualise them using various plots. The results include the fitness values, agent positions, and phase portraits."
   ],
   "id": "8b985328ea61fba7"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:50.186324Z",
     "start_time": "2025-06-17T15:46:50.181987Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Recover the results from the optimiser\n",
    "fp              = optimiser.results[\"fp\"]\n",
    "fg              = optimiser.results[\"fg\"]\n",
    "positions       = np.array(optimiser.results[\"p\"])\n",
    "best_position   = np.array(optimiser.results[\"g\"])\n",
    "v1              = np.array(optimiser.results[\"v1\"])\n",
    "v2              = np.array(optimiser.results[\"v2\"])\n",
    "\n",
    "# Calculate the absolute error in fitness values\n",
    "efp             = np.abs(np.array(fp) - problem.optimum.y)\n",
    "efg             = np.abs(np.array(fg) - problem.optimum.y)\n",
    "\n",
    "# Convert the spikes to integer type\n",
    "spikes          = np.array(optimiser.results[\"s\"]).astype(int)\n",
    "\n",
    "# Print some minimal information about the results\n",
    "print(f\"fg: {fg[-1][0]:.4f}, f*: {problem.optimum.y:.4f}, error: {efg[-1][0]:.4e}\")\n",
    "print(f\"norm2(g - x*): {np.linalg.norm(best_position[-1] - problem.optimum.x):.4e}\")\n",
    "print(f\"{v1.min():.4f} <= v1 <= {v1.max():.4f}\")\n",
    "print(f\"{v2.min():.4f} <= v2 <= {v2.max():.4f}\")"
   ],
   "id": "2ce73f98f2651bcb",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fg: 79.4802, f*: 79.4800, error: 1.7120e-04\n",
      "norm2(g - x*): 1.3030e-02\n",
      "-1.9109 <= v1 <= 2.6796\n",
      "-2.1922 <= v2 <= 2.8139\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Error convergence\n",
    "This plot shows the convergence of the absolute error in fitness values over the iterations of the optimisation process."
   ],
   "id": "319d5950c8c802c4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:51.041975Z",
     "start_time": "2025-06-17T15:46:50.243162Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(figsize=(width*0.9, height/1.8))\n",
    "\n",
    "plt.plot(efp, color=\"silver\", alpha=0.5)\n",
    "plt.plot(np.max(efp, axis=1), '--', color=\"red\", label=r\"Max.\")\n",
    "plt.plot(np.average(efp, axis=1), '--', color=\"black\", label=r\"Mean\")\n",
    "plt.plot(np.median(efp, axis=1), '--', color=\"blue\", label=r\"Median\")\n",
    "plt.plot(efg, '--', color=\"green\", label=r\"Min.\")\n",
    "\n",
    "plt.xlabel(r\"Step, $t$\")\n",
    "plt.ylabel(r\"Abs. Error, $\\varepsilon_f$\")\n",
    "\n",
    "lgd = plt.legend(ncol=2, loc=\"lower left\")\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "\n",
    "ax.patch.set_alpha(0)\n",
    "fig.tight_layout()\n",
    "\n",
    "save_this(fig, _in=\"fitness\")"
   ],
   "id": "d2dcb6286e0a6e8d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 585x223.167 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Position evolution in 2D\n",
    "This plot shows the evolution of the unit positions in the 2D space over the iterations of the optimisation process."
   ],
   "id": "5960f7acea606757"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:51.356839Z",
     "start_time": "2025-06-17T15:46:51.059432Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(figsize=(width/2, width/2))\n",
    "\n",
    "cmap = plt.get_cmap('viridis', num_agents)\n",
    "color = cmap(np.linspace(0, 1, num_agents))\n",
    "\n",
    "plt.plot(problem.optimum.x[0], problem.optimum.x[1], \"*\",\n",
    "         color=\"black\", markeredgecolor=\"cyan\", markersize=12, label=\"Optimum\")\n",
    "\n",
    "for agent, c in enumerate(color):\n",
    "    plt.plot(positions[:, agent, 0], positions[:,agent, 1], \"--o\",\n",
    "             color=c, alpha=0.9, markersize=2, linewidth=1,\n",
    "             label=f\"Agent {agent}\")\n",
    "\n",
    "plt.plot(best_position[:, 0], best_position[:, 1], \"--*\",\n",
    "         color=\"red\", markersize=3, label=\"Best position\")\n",
    "\n",
    "# plt.legend()\n",
    "plt.xlabel(r\"$x_1$\")\n",
    "plt.ylabel(r\"$x_2$\")\n",
    "\n",
    "ax.patch.set_alpha(0)\n",
    "fig.tight_layout()\n",
    "\n",
    "save_this(fig, _in=\"positions_2d\")"
   ],
   "id": "ab337214980e288f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 325x325 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Position evolution in 3D\n",
    "This plot shows the evolution of the unit positions in the 3D space over the iterations of the optimisation process."
   ],
   "id": "4bf2c8f7a422712b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:51.531133Z",
     "start_time": "2025-06-17T15:46:51.369592Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig = plt.figure(figsize=(width/2, width/2))\n",
    "\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.set_proj_type('ortho')\n",
    "\n",
    "cmap = plt.get_cmap('viridis', num_agents)\n",
    "color = cmap(np.linspace(0, 1, num_agents))\n",
    "\n",
    "steps = np.arange(optimiser._num_iterations) + 1\n",
    "\n",
    "for agent, c in enumerate(color):\n",
    "    ax.plot3D(positions[:, agent, 0], positions[:, agent, 1], steps,\n",
    "              \"--o\", color=c, alpha=0.9, markersize=2, linewidth=1,\n",
    "              label=f\"Agent {agent}\")\n",
    "\n",
    "ax.plot3D(best_position[:, 0], best_position[:, 1], steps,\n",
    "          \"-\", color=\"red\", markersize=2,  linewidth=1.5,\n",
    "          label=\"Best position\")\n",
    "ax.plot3D(problem.optimum.x[0], problem.optimum.x[1], steps,\n",
    "          \":\", color=\"black\", markersize=2,  linewidth=1.5,\n",
    "          label=\"Optimum\")\n",
    "\n",
    "for axis in [ax.xaxis, ax.yaxis, ax.zaxis]:\n",
    "    axis.pane.set_edgecolor('black')\n",
    "    axis.pane.set_linewidth(1.0)\n",
    "\n",
    "# ax.viewfig, _init(elev=35, azim=135)\n",
    "ax.view_init(elev=30, azim=100)\n",
    "# ax.legend()\n",
    "ax.set_xlabel(r\"$x_1$\", labelpad=1)\n",
    "ax.set_ylabel(r\"$x_2$\", labelpad=1)\n",
    "ax.set_zlabel(r\"$t$\", labelpad=0)\n",
    "ax.set_box_aspect([1, 1, 0.8])\n",
    "\n",
    "ax.set_zlim(1, num_steps)\n",
    "\n",
    "ax.grid(False)\n",
    "ax.xaxis.pane.fill = False\n",
    "ax.yaxis.pane.fill = False\n",
    "ax.zaxis.pane.fill = False\n",
    "\n",
    "ax.patch.set_alpha(0)\n",
    "fig.subplots_adjust(left=0.1, right=0.95, top=0.95, bottom=0.1)\n",
    "# fig.tight_layout()\n",
    "\n",
    "save_this(fig, _in=\"positions_3d\")"
   ],
   "id": "5c70a20f91423513",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 325x325 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Phase portrait\n",
    "This section visualises the phase portrait of the optimisation process in 2D. The phase portrait shows the trajectory of each agent in the 2D space over the iterations."
   ],
   "id": "f25148756c32fcac"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:51.827803Z",
     "start_time": "2025-06-17T15:46:51.544716Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, axs = plt.subplots(nrows=np.ceil(num_dimensions / 2).astype(int),\n",
    "                        ncols=2, figsize=(width, width/2))\n",
    "\n",
    "steps = np.arange(optimiser._num_iterations) + 1\n",
    "cmap = plt.get_cmap('Spectral', num_agents)\n",
    "color = cmap(np.linspace(0, 1, num_agents))\n",
    "\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "\n",
    "    for agent, c in enumerate(color):\n",
    "        ax.plot(v1[agent, :, i], v2[agent, :, i], \"-o\",\n",
    "                color=c, alpha=0.5, markersize=1, linewidth=1,\n",
    "                label=f\"Agent {agent}\")\n",
    "        ax.plot(v1[agent, 0, i], v2[agent, 0, i], \"-s\",\n",
    "                color=c, alpha=0.5, markersize=1, linewidth=1,\n",
    "                label=f\"Agent {agent}\")\n",
    "\n",
    "    ax.set_xlabel(r\"$v_{}$\".format(\"{1,\" + str(i+1) + \"}\"))\n",
    "    ax.set_ylabel(r\"$v_{}$\".format(\"{2,\" + str(i+1) + \"}\"))\n",
    "\n",
    "    ax.set_xlim(v1.min(), v1.max())\n",
    "    ax.set_ylim(v2.min(), v2.max())\n",
    "\n",
    "    ax.patch.set_alpha(0)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "save_this(fig, _in=\"portrait_2d\")"
   ],
   "id": "9733d0fe53945d83",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 650x325 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Phase portrait in 3D\n",
    "This section visualises the phase portrait of the optimisation process in 3D. The phase portrait shows the trajectory of each agent in the 3D space over the iterations."
   ],
   "id": "ba0fd1e9d8c29ffc"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:52.183148Z",
     "start_time": "2025-06-17T15:46:51.841587Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig = plt.figure(figsize=(width, width/2))\n",
    "\n",
    "num_rows = np.ceil(num_dimensions / 2).astype(int)\n",
    "num_cols = 2\n",
    "\n",
    "axs = [\n",
    "    fig.add_subplot(\n",
    "        num_rows, num_cols, i + 1, projection='3d'\n",
    "    ) for i in range(num_dimensions)\n",
    "]\n",
    "\n",
    "steps = np.arange(optimiser._num_iterations) + 1\n",
    "cmap = plt.get_cmap('Spectral', num_agents)\n",
    "color = cmap(np.linspace(0, 1, num_agents))\n",
    "\n",
    "for i, ax in enumerate(axs):\n",
    "    ax.set_proj_type('ortho')\n",
    "    ax.set_box_aspect([1, 1, 0.8])\n",
    "    ax.view_init(elev=35, azim=110)\n",
    "\n",
    "    for agent, c in enumerate(color):\n",
    "        ax.plot3D(v1[agent, :, i], v2[agent, :, i], steps, \"-\",\n",
    "                  color=c, alpha=0.9,\n",
    "                  markersize=1, linewidth=1,\n",
    "                  label=f\"Agent {agent}\")\n",
    "\n",
    "        ax.plot3D(v1[agent, 0, i], v2[agent, 0, i], 0, \"-s\",\n",
    "                  color=c, alpha=0.9,\n",
    "                  markersize=1, linewidth=1,\n",
    "                  label=f\"Agent {agent}\")\n",
    "\n",
    "    ax.set_xlabel(r\"$v_{}$\".format(\"{\" + str(i+1) + \",1}\"))\n",
    "    ax.set_ylabel(r\"$v_{}$\".format(\"{\" + str(i+1) + \",2}\"))\n",
    "    ax.set_zlabel(r\"$t$\", labelpad=0.1)\n",
    "\n",
    "    ax.set_xlim(v1.min(), v1.max())\n",
    "    ax.set_ylim(v2.min(), v2.max())\n",
    "\n",
    "    for axis in [ax.xaxis, ax.yaxis, ax.zaxis]:\n",
    "        axis.pane.set_edgecolor('black')\n",
    "        axis.pane.set_linewidth(1.0)\n",
    "\n",
    "    ax.grid(False)\n",
    "    ax.xaxis.pane.fill = False\n",
    "    ax.yaxis.pane.fill = False\n",
    "    ax.zaxis.pane.fill = False\n",
    "    ax.patch.set_alpha(0)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "save_this(fig, _in=\"portrait_3d\")"
   ],
   "id": "5fbf4d7d202c8c6d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 650x325 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Spike activity heatmap\n",
    "This section visualises the spike activity of the agents over the iterations of the optimisation process. The heatmap shows the summed spike counts across all dimensions for each agent at each step."
   ],
   "id": "ea719c14462bef3b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-17T15:46:52.424853Z",
     "start_time": "2025-06-17T15:46:52.197115Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(figsize=(width*0.9, height * 0.6))\n",
    "\n",
    "spikes_sum = np.sum(spikes, axis=2)\n",
    "cmap = plt.get_cmap(\"YlGnBu_r\", 3)\n",
    "im = ax.imshow(spikes_sum.T, aspect='auto', origin='lower',\n",
    "               cmap=cmap, vmin=0, vmax=2)\n",
    "\n",
    "ax.set_xlabel(r\"Step, $t$\")\n",
    "ax.set_ylabel(r\"Agent index, $i$\")\n",
    "\n",
    "cbar = fig.colorbar(im, ax=ax, pad=0.02, ticks=[0, 1, 2])\n",
    "cbar.set_label(r\"Spike count, $s_{1}+s_{2}$\")\n",
    "\n",
    "ax.patch.set_alpha(0)\n",
    "fig.tight_layout()\n",
    "\n",
    "save_this(fig, _in=\"spikes_hm\")"
   ],
   "id": "335758800b0c272e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 585x241.02 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}