mBLLL_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples using the ```mBLLL()``` class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## create some network\n",
    "\n",
    "First I want to  create some sort of quick SBM network for the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Gneiss/miniconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "szes=np.array([20,20,20])\n",
    "#create p matrix with expected 4 intraclass connections per node, 4 expected connections between classes\n",
    "pmat=[[4/szes[i]  if i==j else 4/szes[i]/szes[j] for i in range(3)] for j in range(3)]\n",
    "\n",
    "g1 = nx.stochastic_block_model(sizes=szes, p=pmat)\n",
    "while nx.degree_histogram(g1)[0] > 0:\n",
    "    g1 = nx.stochastic_block_model(sizes=szes, p=pmat)\n",
    "lay1 = nx.circular_layout([i for i in range(20)], center=np.array([0,0]))\n",
    "lay2 = nx.circular_layout([i for i in range(20,40)], center=np.array([2,2]))\n",
    "lay3 = nx.circular_layout([i for i in range(40,60)], center=np.array([4,0]))\n",
    "lay = {**lay1, **lay2, **lay3}\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1,2,1)\n",
    "nx.draw(g1, pos=lay, node_size=150)\n",
    "plt.subplot(1,2,2)\n",
    "plt.bar(np.arange(0,len(nx.degree_histogram(g1))), nx.degree_histogram(g1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the ```mBLLL()``` class:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### import the class from the file mBLLL.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mBLLL import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_algo = mBLLL(n_agents=50, network_nx=g1, cover_ranges=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(my_algo.agents_pos[0]).size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### initialize with chosen positions and coverage range\n",
    "\n",
    "We instantiate the mBLLL algo, by feeding it the network ```g1```, specifying two starting positions for the agents, and specifying the coverage ranges. In this case, we specify that the cover ranges is meant to be a constant and not the parameter of a poisson distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_algo = mBLLL(starting_positions=np.array([2,3]), network_nx=g1, cover_ranges=1, cover_ranges_distr=\"const\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List the attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_agents': 2,\n",
       " 'network_nx': <networkx.classes.graph.Graph at 0x117ec86d8>,\n",
       " 'n_nodes': 60,\n",
       " 'random_state': 42,\n",
       " 'timestamp': [],\n",
       " 'agents_pos': [array([2, 3])],\n",
       " 'cover_ranges': [1, 1],\n",
       " 'coverages': [array([ 2,  9, 10, 11, 12]), array([ 3, 16])],\n",
       " 'potentials': [7]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.__dict__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Access the attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([2, 3])]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.agents_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.potentials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make one cycle of the mBLLL algo, \"step by step\".\n",
    "\n",
    "One cycle of the mBLLL algo is in two phases, after (possibly randomly) selecting an agent:  \n",
    "1. the (attempted) movement of the agent, by calling ```.step()``` method.  \n",
    "2. the (attempted) infection of neighbors, by calling ```.infect()``` method.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start with the movement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_algo.step(agent=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the new position of the agents added to the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([2, 3]), array([11,  3])]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.agents_pos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then attempt infection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_algo.infect(agent=0, prob=0.2, cover_ranges=1, cover_ranges_distr=\"const\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now see the new positions of the new agents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.n_agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([2, 3]), array([11,  3, 11, 18])]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.agents_pos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After a cycle is completed, we need to add the new potential to the list of potentials:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_algo.add_potentials()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 11]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.potentials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make one cycle of the mBLLL algo all at once.\n",
    "\n",
    "These two phases are executed at once by the ```.run_mBLLL()``` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#instantiate algo\n",
    "my_algo = mBLLL(starting_positions=np.array([2,3]), network_nx=g1, cover_ranges=1, cover_ranges_distr=\"const\")\n",
    "#run 1 cycle\n",
    "my_algo.run_mBLLL(t_steps=1, prob=0.2, cover_ranges=1, cover_ranges_distr=\"const\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's access attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.n_agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([2, 3]), array([12,  3,  5, 15, 17])]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.agents_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 17]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.potentials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run 100 steps with allowing multiple agents on the same node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_algo.run_mBLLL(t_steps=100, prob=0.2, cover_ranges=1, cover_ranges_distr=\"const\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'n_agents')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "#plot potentials\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(my_algo.potentials)\n",
    "plt.xlabel(\"t_step\")\n",
    "plt.ylabel(\"potential\")\n",
    "#plot n_agents\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot([pos.size for pos in my_algo.agents_pos])\n",
    "plt.xlabel(\"t_step\")\n",
    "plt.ylabel(\"n_agents\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run 100 steps without allowing multiple agents on the same node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_algo = mBLLL(starting_positions=np.array([2,3]), network_nx=g1, cover_ranges=1, cover_ranges_distr=\"const\")\n",
    "my_algo.run_mBLLL(t_steps=100, prob=0.2, cover_ranges=1, cover_ranges_distr=\"const\", ghosts=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'n_agents')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "#plot potentials\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(my_algo.potentials)\n",
    "plt.xlabel(\"t_step\")\n",
    "plt.ylabel(\"potential\")\n",
    "#plot n_agents\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot([pos.size for pos in my_algo.agents_pos])\n",
    "plt.xlabel(\"t_step\")\n",
    "plt.ylabel(\"n_agents\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run 5 steps with allowing multiple agents on the same node, and with defining a timestep as the activation of _all_ agents in random order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_algo = mBLLL(starting_positions=np.array([2,3]), network_nx=g1, cover_ranges=1, cover_ranges_distr=\"const\")\n",
    "my_algo.run_mBLLL(t_steps=5, prob=0.2, cover_ranges=1, cover_ranges_distr=\"const\", step_size=\"all\", ghosts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'n_agents')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "#get indices of ends of steps\n",
    "indices = np.concatenate((np.array([0]), np.where(np.diff(my_algo.timestamp) == 1)[0]+1, np.array([len(my_algo.timestamp)-1])))\n",
    "#plot potentials\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot([my_algo.potentials[i] for i in indices])\n",
    "plt.xlabel(\"t_step\")\n",
    "plt.ylabel(\"potential\")\n",
    "#plot n_agents\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot([pos.size for pos in [my_algo.agents_pos[i] for i in indices]])\n",
    "plt.xlabel(\"t_step\")\n",
    "plt.ylabel(\"n_agents\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run 5 steps without allowing multiple agents on the same node, and with defining a timestep as the activation of _all_ agents in random order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_algo = mBLLL(starting_positions=np.array([2,3]), network_nx=g1, cover_ranges=1, cover_ranges_distr=\"const\")\n",
    "my_algo.run_mBLLL(t_steps=5, prob=0.2, cover_ranges=1, cover_ranges_distr=\"const\", step_size=\"all\", ghosts=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'n_agents')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tgAmheWFVgEnuPs3MXjOzfpRN0l8NXBnBDCJx5a2Za3jh65VceHA7rjiiQ9BxRETKXXFJKTe9M4+6Natx/xnxOQy5SySvjpxP2SrTvz5+caS+UySeLczI4e73F3Jkl2bcd1rPuG6YRKTyGvPNKuan5/DcBQPifu9bXc8uEgN2FpZw7ZtzaFg7gafP60e1qvqrKyLxZ8n6bTz9+TJO6dOKU+J4GHKXiC5RISLl4/4PF7Fq43Zev/xgGtepHnQcEZFyV1RSyo2T5lG/ZgJ/O61n0HGiQqfTIhXcx2nrmfjTGkYf2YEhnZoGHUdEJCKe/2olCzO28cAZvWgS58OQu6gIE6nAMnN2ctuU+fRu04Abj+8adBwRkYhYmJHDszOWc1rf1pXqqm8VYSIVVEmpc8PbcyksLuXZEf21JZGIxKXC4lJuemc+DWtX575KMgy5i+aEiVRQL3y9kh9Xbeaxs/vQvmmdoOOIiETEc1+uYHHmNsZePJBGlWzOq06tRSqgOWu28ORnyxjWpxVnD0wMOo6ISESkrcvhuS9XMLx/G07o2TLoOFGnIkykgskrKOa6t+bSsn5N/j68t9YDE5G4VDYMOY/Gdapzz6k9go4TCA1HilQwd7+fRvqWHbx95aE0qJUQdBwRkYj4xxfLWbI+l5dHJtOwduUahtxFPWEiFcj7c9cxJXUdfz6mM4OSGgcdR0QkIuanb+VfX63krAGJHNu9RdBxAqMiTKSCWLt5B3e+l0byQY348zGdgo4jIhIRBcUl3DhpHk3rVufuSjoMuYuGI0UqgOKSUq59aw4YPH2+tiUSkfj19OfLWZ6Vx/hLB1X6KRcqwkQqgGdnLGfOmq08O6I/iY1qBx1HRCQi5q7dypivV3JuciJHd20edJzA6XRbJGA/rdrEP79cwdkDEzmtb+ug44iIRER+UQk3TppLi/o1uXNY5R6G3EU9YSIBytlRxA1vz6Vd49rcW8lWihaRysPdufPfaazM3s6EywZTv2blHobcRUWYSEDcndvfm09WbgGTrzqMujX011FE4tMTny7j3dnpXHdsZ/7QpVnQcSoMDUeKBOSdlHSmL1jPjSd0pW/bhkHHERGJiNd+/IV/frmCEYPbcv1xnYOOU6FErAgzs5pmNtPM5pnZQjO7L3S8sZl9ZmbLQ/eNIpVBpKJamZ3HPVMXcljHJlx5ZIeg44iIRMTHaeu5+/00juvenPtP76UdQH4lkj1hBcAx7t4X6AecZGaHALcBM9y9MzAj9Fyk0igoLuHaN+dQM6EKT57bjypV1CiJSPyZtXoz1741h35tG/KPEQO09M4eROw34mXyQk8TQjcHTgcmhI5PAM6IVAaRiuiJT5exMGMbj5zVh5YNagYdR0Sk3C3fkMvlr8wisWEtXh45iFrVqwYdqUKKaFlqZlXNbC6QBXzm7j8BLdw9EyB0r4VCpNL4z/Jsxn6ziosOaccJPVsGHUdEpNxl5uxk5LiZ1EioyoTLBtO4TuXcFzIcES3C3L3E3fsBicBgM+sV7mfNbLSZpZhZSnZ2duRCikTJprwC/jJpHp2b1+WvQ7VGjojEn5ydRYwaN4tt+cW8cukg2jbW4tO/JyoDtO6+FfgKOAnYYGatAEL3Wb/xmbHunuzuyc2a6XJWiW3uzi3vzidnZxHPjuivrnkRiTv5RSWMfjWFVRvzGHPxQHq2bhB0pAovkldHNjOzhqHHtYDjgCXAVGBk6G0jgfcjlUGkonjtx1+YsSSL20/uRvdW9YOOIyJSrkpLnRsnzeOnnzfz+Dl9GdKpadCRYkIkV4dsBUwws6qUFXuT3H2amf0ATDKzy4E1wDkRzCASuCXrt/HAh4s5umszRh2WFHQcEZFy5e7c/+EiPlyQyR1Du3F6vzZBR4oZESvC3H0+0H8PxzcBx0bqe0UqkvyisuUo6tdM4LFz+mqNnCgzs0eBB4CdwMdAX+B6d3890GAicWTsN6sY/91qLhvSniuO0LqH++J3izAz+8vvve7uT5ZvHJH48uD0xSzbkMeEywbTtG6NoONURie4+y1mNhxIp6zn/UtARZhIOXhvTjoPfbSEYX1acecp3XWiuY/21hNWLyopROLQ54s28OoPv/A/h7fXXmnB2bVL8FDgTXffrH8kRMrHf5Znc/M78zm0QxOeOLevFp7eD79bhLn7fdEKIhJPNmzL5+Z359GjVX1uPqlr0HEqsw/MbAllw5F/MrNmQH7AmURiXtq6HP742mw6Na/LmEsGUqOarvjeH2HNCTOzmsDlQE/gv0t8u/tlEcolErN2XSW0s6iEZ0f0V+MUrHuAR4Bt7l5iZjuA08L5oJmNA4YBWe7eK3TsXuAKYNfihXe4+/RyTy1Sga3ZtINR42fRsHZ1Jlw2mPo1E/b+IdmjcJeoeA1oCZwIfE3Z4qu5kQolEste+nYV367YyD2n9qRT87pBx6nsfnD3Le5eAuDu24GPwvzsK5StbfhrT7l7v9BNBZhUKpvyChg5fiZFJaVMuGwQLepr67UDEe7VkZ3c/RwzO93dJ5jZROCTSAYTiUUL0nN47JOlnNyrJecPaht0nErLzFoCbYBaZtYf2DVZpT4Q1hLe7v6NmSVFJKBIDNpRWMxlE1LI2LqTiVccTKfmmjZ+oMItwopC91tDWw+tB5IikkgkRm0vKObat+bQtG4NHjqzt64SCtaJwCjKeu13v4o7F7jjAH/2NWZ2CZAC3OjuW379BjMbDYwGaNeu3QF+nUjwiktKuWbiHBakb+WFiwYy8KDGQUeKC+EOR441s0bAXZSteL8IeDRiqURi0H0fLGT1pu08dV4/GtbWhrVBcvcJ7n40MMrdj97tdpq7TzmAH/080BHoB2QCT/zG92vbNYkb7s4d7y3giyVZ3H9GL07o2TLoSHEjrJ4wd38p9PBrQCuxifzKtPkZTEpJ55qjO3FIhyZBx5H/Nc3MLqCs5/6/7Z27/21/fpi7b9j12MxeBKYdaECRiu6pz5YxKSWda4/pxIUHHxR0nLiyt8VaL3L3139r0VYt1ioC6Vt2cPuUBfRr25DrjuscdBz5/70P5ACzgYID/WFm1srdM0NPhwNpB/ozRSqyN376hWe/WMF5yW254fguQceJO3vrCasTut/T7Dsv5ywiMaek1Lnh7bm4w7Pn9yehargj/BIlie6+pysc98rM3gSOApqaWTply10cZWb9KGv/VgNXllNOkQrnk4XruevfaRzdtRl/H95L81wjYG+LtY4JPfzc3b/b/TUzGxKxVCIx4rkvVzBr9RaePq8f7ZqEddGdRNf3Ztbb3Rfs6wfdfcQeDr9cDplEKryU1Zu59s059E5syHMXDqCaTjAjItzf6j/CPCZSacz+ZTPPzFjO8P5tOKN/m6DjyJ4dDsw2s6VmNt/MFpjZ/KBDiVRkK7JyuXxCCq0b1mLcyGRqVw93IQXZV3ubE3YocBjQ7FfzwuoDWgZcKq1t+UVc++ZcWjesyd9O7xl0HPltJwcdQCSWbNiWz8hxs0ioWoUJlw6mSd0aQUeKa3vrCasO1KWsWKu3220bcHZko4lUTO7OX99LY/22fJ45vz/1tGVHheXuvwBtgWNCj3cQ/giASKWyLb+IkeNmsnVHIa9cOkhTLKJgb3PCvga+NrNXQg2YSKU3JXUdH8zL4KYTujCgXaOg48jvMLN7gGSgKzAeSABeBzSnVWQ3BcUlXPnqbFZk5TH+0kH0atMg6EiVQrgDvTXMbCz/d62dYyIRSqSiWr1xO3e/n8bB7Rtz1VGdgo4jezcc6A+kArh7hplprxWR3ZSWOjdOmscPqzbx1Hl9OaKzFhiOlnCLsHeAF4CXgJLIxRGpuIpKSrnurTlUq1qFp87rR9Uqulw7BhS6u5uZA5hZnb19QKSy+fv0xUybn8ltJ3djeP/EoONUKuEWYcXu/vy+/GAzawu8CrQESoGx7v6Mmd0LXAFkh956h7tP35efLRKEpz5bxrz0HJ6/cACtG9YKOo6EZ5KZjQEamtkVwGXAiwFnEqkwXvxmFS9/+zOjDkviyiO1IU60hVuEfWBmfwLeY7dVp9198+98ppiyzW1TQ93/s83ss9BrT7n74/uVWCQA36/YyPNfr2TE4Lac3LtV0HEkTO7+uJkdT9nFRF2Bu939s718TKRSeH/uOv4+fTGn9G7F3cN6aDHWAIRbhI0M3d+82zHnd/aRDG3tkRl6nGtmiwEtpiQxZ8v2Qm6YNJf2Tetw17AeQceRfRQqulR4iezmuxUbuemdeRzcvjFPnNuXKppeEYiwLtV29/Z7uIXdb2lmSZRNjv0pdOia0MKJ48xsj5eXmdloM0sxs5Ts7Ow9vUUk4tydWyfPZ/P2Qp49v78WLYwxZpZrZtt+dVtrZu+ZmcZepFJamJHDla/NpmOzuoy9JJmaCVr2MyhhFWFmVtvM7gxdIYmZdTazYWF+ti4wGbje3bcBzwMdgX6U9ZQ9safPuftYd0929+RmzXSlhgRj4sw1fLpoA7ee1E2XbMemJynrwW8DJAI3UTYn7C1gXIC5RAKxdvMORo2fRf2a1Xjl0sE0qKV1DoMU7qKF44FCylbPB0gHHtjbh8wsgbIC7A13nwLg7hvcvcTdSylrDAfvc2qRKFi+IZf7py3iiM5NuWxI+6DjyP45yd3HuHuuu29z97HAUHd/G9Aib1KpbN5eyMhxMyksLmXCZYNp2aBm0JEqvXCLsI7u/ihQBODuO4HfHUC2shl+LwOL3f3J3Y7vPqt5OJC2T4lFoiC/qIRr35pLnerVNF8itpWa2blmViV0O3e31zywVCJRtrOwhMsnzCJ9605eGplM5xZaLq8iCHeCS6GZ1SLUaJlZR3a7SvI3DAEuBhaY2dzQsTuAEWbWL/SzVgNX7mtokUh79OOlLM7cxvhRg2heT2eLMexC4BngX5S1OT8CF4Xas2uCDCYSLcUlpVwzMZW5a7fy/IUDGJTUOOhIEhJuEXYv8DHQ1szeoKzAuvT3PuDu37Ln3jKtCSYV2pdLsxj3Xdm6OUd3ax50HDkA7r4KOPU3Xv42mllEguDu3PnvNGYsyeL+03tyUi8tsVORhFWEufunZjYbOISywuo6d98Y0WQiAcjOLeDmd+bRrWU9bju5W9Bx5ACZWU3gcqAn8N8uTXe/LLBQIlH09OfLeWvWWq4+uiMXH5oUdBz5lXCvjpzh7pvc/UN3n+buG81sRqTDiURTaalz0zvzyM0v5tkR/XXZdnx4jbJdO04EvqbsCsncQBOJRMnEn9bwzIzlnD0wkZtO6Bp0HNmD3y3CzKymmTUGmppZIzNrHLolAa2jEVAkWsZ/v5qvl2Vz57AedNGk1XjRyd3vAra7+wTgFKB3wJlEIu7zRRu4898LOKprMx46s7dWw6+g9jYceSVwPWUFV+pux7cBz0UqlEi0LczI4ZGPlnB8jxZcdHC7oONI+SkK3W81s17AeiApuDgikZe6ZgvXvJlKrzYNeO6CASRUDXchBIm23y3C3P0Z4Bkz+7O7/yNKmUSiamdhCde+OYdGdRJ45Kw+OmOML2NDu3LcCUwF6gJ3BRtJJHJWZudx+SuzaFm/JuNGDaJODe3yUZGF+39njJldCxwZev4VMMbdi377IyKx4f4PF7Fq43Zev/xgGtepHnQcKUfu/lLo4TfsYa9bMxsZGqYUiXlZ2/K55OWZVK1iTLhsME3r1gg6kuxFuH2U/wIGhu53PX4+UqFEouXjtEwm/rSGK4/syJBOTYOOI9F3XdABRMpDbn4RI8fPYsuOQsaNGsRBTeoEHUnCEG5P2CB377vb8y/MbF4kAolES2bOTm6dvIA+iQ34y/Fdgo4jwdDYs8S8wuJS/vj6bJZvyOXlUYPok9gw6EgSpnB7wkpCq+QDYGYdgJLIRBKJvJJS54a351JUUsoz5/enejVNXK2ktHWRxLRdS+t8t2ITj5zVhz90aRZ0JNkH4faE3Qx8aWarQs+T2MuK+SIVVUFxCc/OWM6Pqzbz2Nl9aN9U3faVmHrCJKY99NFips7L4JaTunLWwMSg48g+CrcI+w4YAxwbej4G+CEiiUQioLiklB9WbWLq3Aw+Xrie3PxiTu/XmrPVaMU1M6sBnEXZieN/2zt3/1vo4XcBxBIpFy/9ZxUv/udnRh7cvIYsAAAbmklEQVR6EFf9oePePyAVTrhF2KuUrQ12f+j5CMpWoj4nEqFEyoO7k7pmC1PnZvDhgkw25hVSt0Y1TuzZklP7tuKIzs20HEX8ex/IAWYDBb9+0d21ibfEpKnzMnjgw8Wc3Ksld5/aU21ZjAq3COv6q4n5X2pivlRE7s7izFymzsvgg3kZrNu6k+rVqnBc9+ac1rc1R3Vtru2IKpdEdz8p6BAi5emLJRu4cdJcBic15qnz+lG1igqwWBVuETbHzA5x9x8BzOxg1I0vFcjqjduZOi+DqfMyWJGVR9UqxhGdm3LjCV04vkcL6tVMCDqiBON7M+vt7guCDiJyoIpLSnnq82X866uVdGtZnxcvSdZJZYwLtwg7GLjEzNaEnrcDFpvZAsDdvU9E0on8jvU5+UybX1Z4zU/PAWBw+8Y8cEYvhvZupYVXBeBwYJSZ/UzZcKShNktiUGbOTq57cy4zV2/m/EFtuefUntSqrgIs1oVbhKk7XyqEzdsL+Sgtk6lzM5i5ejPu0LtNA/46tDvD+raiVYNaQUeUiuXkoAOIHKgvl2Txl0lzKSwu5Znz+3F6vzZBR5JyElYR5u6/RDqIyG/JKyjm04XrmTovg2+Xb6S41OnYrA7XH9uFU/u2okOzukFHlApKbZfEsqKSUh7/ZCljvllF91b1ee6C/mrv4ox29pQKKb+ohK+WZjF1XgYzFmdRUFxKm4a1uPyI9pzWtzU9WtXX1UAiErfWbd3JnyemkrpmKxce3I67hvXQ/K84FLEizMzaUra0RUugFBjr7s+YWWPgbcrW7VkNnOvuWyKVQ2JHcUkp360sW8vr04XryS0opkmd6pw3qC2n9W3NgHaNqKKrgEQkzn22aAM3vTOPklLnnxf0Z1if1kFHkgiJZE9YMXCju6eaWT1gtpl9BowCZrj7w2Z2G3AbcGsEc0gFVlrqzA6t5TV9QSabthdSr0Y1TuzVktP6tuawjk2oVlVbCkn0mdk4YBiQ5e69Qsd0EikRU1hcysMfLWHcdz/Tq019/jliAEna0SOuRawIc/dMIDP0ONfMFgNtgNOBo0JvmwB8hYqwSsXdWZixjQ/mZTBtfibrtu6kRrUqHNe9Baf2bc1RXZup210qgleAf1LWo7/LbegkUiJg7eYdXDMxlXnpOYw6LInbh3ajRjW1g/EuKnPCzCwJ6A/8BLQIFWi4e6aZNf+Nz4wGRgO0a9cuGjElwlZl5/13La9V2dupFlrL66YTu3B8j5bUraEpilJxuPs3obZrdzqJlHL3cVomN787H4AXLhrASb1aBZxIoiXi/+qZWV1gMnC9u28LdzK1u48FxgIkJyd75BJKJGVs3fnftbzS1m3DDAYnNebyw9tzci+t5SUxRyeRUm4Kikt48MPFTPjhF/omNuCfFwygbePaQceSKIpoEWZmCZQVYG+4+5TQ4Q1m1irUgLUCsiKZQaJvU14B09PW80FoLS+APokNuPOU7gzr05qWDWoGnFAksnQSKXuzeuN2rnkzlbR127j88PbcelI3qlfT/NfKJpJXRxrwMrDY3Z/c7aWpwEjg4dD9+5HKINGTm1/Epws3lK3ltWIjJaVOp+Z1+cvxXTi1b2vaa3KpxAedRMoB+2BeBrdPWUDVKsaLlyRzfI8WQUeSgESyJ2wIcDGwwMzmho7dQVnxNcnMLgfWAOdEMINEkLvz+eIspqSm88WS/13La/SRHTi1T2u6t6qntbwk3ugkUvZbflEJf5u2iIk/rWFAu4Y8O6I/iY00/FiZRfLqyG8p26dtT46N1PdKdCxIz+G+DxaS8ssWmtatwYjB7Ti1b2sGtGuowkvigpm9Sdkk/KZmlg7cg04iZT+tzM7j6jdSWbI+lyv/0IGbTuhKgpbfqfR0OZrsk+zcAh7/ZCmTZq+lSZ3qPHJWb84akKi1vCTuuPuI33hJJ5GyT96bk85f30ujRrUqjB81iKO77fF6DqmEVIRJWAqLS3nl+595dsYKCopLuOKIDlxzTCfq10wIOpqISIW0s7CEe6amMSklnUFJjXh2RH9aNagVdCypQFSEye9yd75cmsX90xbz88btHNOtOXee0l2byIqI/I7lG3K5emIqy7PyuProjtxwXBeNGMj/oSJMftOKrDzun7aIr5dl06FZHcZfOoiju6obXUTk97yTspa7319I7epVmXDpYI7s0izoSFJBqQiT/yNnZxHPfL6cV39YTa3qVbnzlO6MPCxJk0hFRH7H9oJi7no/jSmp6zikQ2OePb8/zetrXUT5bSrC5L9KSp23Z63l8U+XsmVHIecPaseNJ3Shad0aQUcTEanQlqzfxtVvpLJq43auO7Yz1x7bmapVdKW4/D4VYQLAT6s2cd8Hi1iUuY3BSY25+9Qe9GrTIOhYIiIVmrvz1qy13Dt1IfVrJfDG5QdzWKemQceSGKEirJJbt3UnD05fzIfzM2ndoCb/vKA/p/RupbW+RET2Iq+gmDumLGDqvAwO79SUp87rR7N6GjmQ8KkIq6R2FpbwwtcreeHrlZjBDcd1YfSRHahVvWrQ0UREKry0dTlcMzGVNZt3cNMJXbjqqE4afpR9piKsknF3PpifycPTF5ORk8+pfVtz28ndaNNQa9eIiOyNu/P6T2u4f9oiGtVO4M0rDuHgDk2CjiUxSkVYJZK2rmyroVmrt9CzdX2ePr8/g9s3DjqWiEhM2JZfxO2TF/Dhgkz+0KUZT57blya6cEkOgIqwSmBjXtlWQ2+nrKVx7eo8fGZvzkluq65zEZEwzU/fyjUT57Bu605uPakbVx7ZgSpqQ+UAqQiLY4XFpbz6w2qe+Xw5O4tK+J/D2/PnYztrqyERkTC5O698v5oHpy+mad0avD36EJKTNIIg5UNFWJz6ckkW909bxKqN2zm6azPuHNaDjtpqSEQkbDk7irj53Xl8umgDx3ZrzuPn9KVRnepBx5I4oiIszqzMzuOBaYv4cmk2HZrWYfyoQRzdTVsNiYjsizlrtnDNxDls2JbPnad05/LD22vpHil3KsLixLb8Ip79fDmvfL+aWgllWw1dcmgS1atpqyERkXC5Oy/952ce+XgJLerX5J0/Hkr/do2CjiVxSkVYjCspdd5JWctjnyxl845Czh/UlhtP6KqthkRE9tGW7YXc9M48ZizJ4oQeLXjs7L40qK05tBI5KsJi2MyfN3PfBwtZmLGNQUmNmHDqYG01JCKyH1JWb+baN+eQnVfAPaf2YNRhSRp+lIiLWBFmZuOAYUCWu/cKHbsXuALIDr3tDnefHqkM8Wrd1p08/NESPpiXQesGNfnHiP4M66OthkRE9lVpqfPCNyt54tNltGlYi8lXHUafxIZBx5JKIpI9Ya8A/wRe/dXxp9z98Qh+b9zaWVjCmG/Kthpyh+uO7cwf/9BRWw2JiOyHrNx8bn5nPl8vy2Zo75Y8fFYfLeEjURWxIszdvzGzpEj9/MrE3flwQSYPTV/Cuq07GdanFbcP7a6thkRE9lFRSSlfLslicmo6XyzJwsy4/4xeXHRwO40mSNQFMSfsGjO7BEgBbnT3LXt6k5mNBkYDtGvXLorxKpaFGTnc98EiZv68mR6t6vPUef201ZCIyD5wdxZmbGNyajrvz81g8/ZCmtatziWHJnHBwe20hqIEJtpF2PPA/YCH7p8ALtvTG919LDAWIDk52aMVsKLYlFfA458u461Za2hUuzoPndmbc7XVkIhI2LJzC3h/7jrenZ3OkvW5VK9ahWO7N+fsgYkc2aUZCVW1hI8EK6pFmLtv2PXYzF4EpkXz+2NBUUkpr/7wC09/voydhSVcNqQ91x7bmQa1NE9BRGRvCopLmLE4i8mz0/lqWTYlpU7fxAbcf3pPTu3bmoa1teK9VBxRLcLMrJW7Z4aeDgfSovn9Fd1XS8u2GlqZvZ0/dGnGXcN60Km5uslFRH6PuzM/PYd3Z6czdV4GOTuLaFG/Bv9zRHvOHpBI5xb1go4oskeRXKLiTeAooKmZpQP3AEeZWT/KhiNXA1dG6vtjyca8Am59dz4zlmRpqyERkTCtz8nnvTnrmJyazoqsPGpUq8IJPVty9sBEDu/UVNM3pMKL5NWRI/Zw+OVIfV+sSluXw+hXU9i8o5A7hnZj1GHttdWQiMhvyC8q4dNFG3h3djrfLs+m1GHgQY14cHhvTunTSlM3JKZoxfwAvT93Hbe8O5+mdWvw7h8P02r3IiJ74O6krtnCu7PXMW1+Brn5xbRuUJM/HdWJMwe0oYOubpQYpSIsACWlzqMfL2HMN6sY3L4x/7pwgPZ6FBH5lXVbd/JeajqTU9fx88bt1Eqoysm9WnLWwEQO7dCEKhpulBinIizKcnYUce1bc/h6WTYXH3IQd5/aQ5dJi4iE7Cgs5uO09UxOTef7lZtwh4PbN+aqozoytHcr6tbQP1sSP/SnOYpWZOVyxauzSd+yg4fO7M2IwZV3EVoRkV1KS52ZqzczeXY60xdksr2whLaNa3HdsZ05s38i7ZrUDjqiSESoCIuSzxdt4Pq351IzoSpvXnEIyUla9V5EKrc1m3YwOTWdKXPSWbt5J3WqV+WUPq04a0Aig5Iaa7hR4p6KsAhzd577cgVPfLaMXq0bMObigbTWno8iUknlFRQzfUEm785OZ+bPmzGDwzo24S/Hd+HEni2pXV3/LEnloT/tEbS9oJib353H9AXrGd6/DQ+d2ZuaCVWDjiUiElWlpc4Pqzbx7ux0Pk5bz86iEto3rcNNJ3Rh+IBE2ujEVCopFWERsnbzDq54NYVlG3L569Du/M8R7TFT17pIPDCz1UAuUAIUu3tysIkqpp83bmfy7HSmpKaTkZNPvZrVOKN/G84e2IYB7RqpTZRKT0VYBHy/YiNXT0ylpNR55dLBHNmlWdCRRKT8He3uG4MOUdHk7Cziw/mZvDt7LalrtlLF4IjOzbhtaHdO6NFCowEiu1ERVo7cnQnfr+b+DxfToWkdXrwkmaSmdYKOJSISUSWlzn+WZzM5dR2fLFxPYXEpnZvX5baTuzG8fxta1K8ZdESRCklFWDkpKC7hzvfSeGd2Osf3aMFT5/XTejYi8cuBT83MgTHuPjboQEFwdz5OW88jHy9h9aYdNKiVwPmD2nLWgET6JDbQcKPIXqhKKAdZ2/K58vXZzFmzlWuP7cz1x3bWpdUi8W2Iu2eYWXPgMzNb4u7f7HrRzEYDowHatYvP9QDnrd3KAx8uYtbqLXRuXpfnLhjAcT2aU6OahhtFwqUi7ADNWbOFP74+m9z8Yl64aAAn9WoVdCQRiTB3zwjdZ5nZe8Bg4JvdXh8LjAVITk72QEJGSPqWHTz2yVLen5tB07rVeXB4b85NTqSadv4Q2Wcqwg7AOylr+et7abRoUIMpfzqMbi3rBx1JRCLMzOoAVdw9N/T4BOBvAceKuNz8Iv711Upe/vZnDLj66I788Q8dqVczIehoIjFLRdh+KC4p5e/TFzP+u9UM6dSEf44YQKM61YOOJSLR0QJ4LzTfqRow0d0/DjZS5BSXlPLmrLU8/dkyNm0vZHj/Ntx0Ylet7SVSDlSE7aMt2wu55s1UvluxicuGtOeOod3UDS9Sibj7KqBv0Dkizd35amk2f5++mBVZeQxu35jxp3SnT2LDoKOJxA0VYftgyfptXPFqChu2FfD4OX05e2Bi0JFERMrdooxtPDh9Md+u2EhSk9qMuXggJ/RooasdRcpZxIowMxsHDAOy3L1X6Fhj4G0gCVgNnOvuWyKVoTx9tCCTG9+ZR90a1Xh79CH0b9co6EgiIuVqw7Z8nvh0Ke/MTqdBrQTuHtaDiw45iOrV1NsvEgmR7Al7Bfgn8Opux24DZrj7w2Z2W+j5rRHMcMBKS52nP1/Gs1+soH+7hrxw0UAtPCgicWVHYTFjv1nFmK9XUVxayuVD2vPnYzrToLYm3YtEUsSKMHf/xsySfnX4dOCo0OMJwFdU4CIsN7+IG96ex+eLN3BuciL3n9FLa+CISNwoKXUmp6bzxKdL2bCtgKG9W3LrSd04qIl2+hCJhmjPCWvh7pkA7p4ZWuhwj4Je7HD1xu1c8WoKqzZu577TenLJoQdpPoSIxI3vV2zkgQ8XsyhzG33bNuS5CwaQnNQ46FgilUqFnZgf5GKHXy/L5s8TU6laxXjtssEc1qlpNL9eRCRiVmTl8dD0xcxYkkWbhrV45vx+nNqntXb5EAlAtIuwDWbWKtQL1grIivL3/y5358X/rOLhj5bQpUU9XrwkmbaNawcdS0TkgG3KK+Dpz5czceYaaidU5daTunHpkCRqJmiKhUhQol2ETQVGAg+H7t+P8vf/pvyiEm6bPJ9/z81gaO+WPH5OX2pXr7AdhSIiYckvKuGV71fz3Bcr2FFUwgWD23H9cZ1pUrdG0NFEKr1ILlHxJmWT8JuaWTpwD2XF1yQzuxxYA5wTqe/fFxlbd3Lla7NJy8jhphO6cPXRnTT/S0RimrvzwfxMHvloCeu27uTYbs25fWg3OjWvF3Q0EQmJ5NWRI37jpWMj9Z37Y9bqzVz1+mzyi0p58eJkjuvRIuhIIiIHZPYvm7l/2mLmrt1K91b1efTsPgzR3FaRCqdSj7dN/GkN90xNI7FRbd4aPVBniCIS037ZtJ1HPl7C9AXraV6vBo+e3YezBiRSVZPuRSqkSlmEFRaX8rdpC3n9xzX8oUsznh3Rnwa1tCihiMSmnB1F/OOL5Uz4YTXVqlTh+uM6M/rIDprXKlLBVbq/oRvzCvjTG6nM/HkzV/6hA7ec2E1niSISkwqLS3n9x1949ovl5Ows4uwBidx0Ylft6iESIypVEZa2LofRr6awaXshz5zfj9P7tQk6kojIPnN3Pl20gYc/WsLPG7czpFMT7hjanZ6tGwQdTUT2QaUpwqbOy+CWd+fRuHZ13v3jYfROVGMlIrFnQXoOD3y4iJ9+3kyn5nUZNyqZo7s21xXdIjEo7ouwklLnsU+W8sLXKxmU1IjnLxpIU62PIyIxJmPrTh7/ZClT5qyjSZ3q3H9GL0YMaku1qlWCjiYi+ymui7CcnUVc99YcvlqazYUHt+OeU3tSvZoaLBGJHXkFxbzw1Upe/M8qHLjqqI5cdVRH6tfUxUQisS5ui7AVWXlc8WoKazfv4O/De3HhwQcFHUlEJGzFJaVMSknnyc+WsTGvgNP7tebmE7uS2EhbqYnEi7gswmYs3sD1b82lerUqTLziEAa3bxx0JBGRsH21NIsHpy9m2YY8kg9qxEsjk+nXtmHQsUSknMVVEebu/OurlTz+6VJ6tq7PmIuTadOwVtCxRETCsmT9Nv7+4WL+s3wjBzWpzfMXDuCkXi016V4kTsVVETZ9wXoe+2Qpp/drzcNn9qFW9apBRxIRCcvOwhLOG/Mj7s6dp3Tn4kMPokY1tWEi8SyuirCTe7XkhYsGcGJPnTmKSGypVb0q/7pwAD1a1adRnepBxxGRKIirIqxKFeOkXq2CjiEisl+0ybZI5aL1GkREREQCoCJMREREJAAqwkREREQCoCJMREREJAAqwkREREQCoCJMREREJAAqwkREREQCYO4edIa9MrNs4Jcw394U2BjBOJEUq9mVO7oqS+6D3L1ZpMJEyz62X1B5/v9WFModXZUld1jtV0wUYfvCzFLcPTnoHPsjVrMrd3Qpd3yL1d+TckeXckdXpHJrOFJEREQkACrCRERERAIQj0XY2KADHIBYza7c0aXc8S1Wf0/KHV3KHV0RyR13c8JEREREYkE89oSJiIiIVHgqwkREREQCEFdFmJmdZGZLzWyFmd0WdJ5wmdk4M8sys7Sgs4TLzNqa2ZdmttjMFprZdUFnCoeZ1TSzmWY2L5T7vqAz7Qszq2pmc8xsWtBZ9oWZrTazBWY218xSgs5TEan9ii61YcGIxTYsku1X3MwJM7OqwDLgeCAdmAWMcPdFgQYLg5kdCeQBr7p7r6DzhMPMWgGt3D3VzOoBs4EzKvrv28wMqOPueWaWAHwLXOfuPwYcLSxm9hcgGajv7sOCzhMuM1sNJLt7LC7SGHFqv6JPbVgwYrENi2T7FU89YYOBFe6+yt0LgbeA0wPOFBZ3/wbYHHSOfeHume6eGnqcCywG2gSbau+8TF7oaULoFhNnImaWCJwCvBR0Fil3ar+iTG1Y9KkN+7/iqQhrA6zd7Xk6MfAXKh6YWRLQH/gp2CThCXWHzwWygM/cPSZyA08DtwClQQfZDw58amazzWx00GEqILVfAVIbFjWx2oZFrP2KpyLM9nAsJs4OYpmZ1QUmA9e7+7ag84TD3UvcvR+QCAw2swo/hGJmw4Asd58ddJb9NMTdBwAnA1eHhrDkf6n9CojasOiI8TYsYu1XPBVh6UDb3Z4nAhkBZakUQvMRJgNvuPuUoPPsK3ffCnwFnBRwlHAMAU4LzU14CzjGzF4PNlL43D0jdJ8FvEfZ8Jv8L7VfAVAbFlUx24ZFsv2KpyJsFtDZzNqbWXXgfGBqwJniVmhy6MvAYnd/Mug84TKzZmbWMPS4FnAcsCTYVHvn7re7e6K7J1H2Z/sLd78o4FhhMbM6oYnPmFkd4AQgpq6kiwK1X1GmNiy6YrUNi3T7FTdFmLsXA9cAn1A2wXKSuy8MNlV4zOxN4Aegq5mlm9nlQWcKwxDgYsrOZuaGbkODDhWGVsCXZjafsn/4PnP3mLlUOka1AL41s3nATOBDd/844EwVitqvQKgNk3BEtP2KmyUqRERERGJJ3PSEiYiIiMQSFWEiIiIiAVARJiIiIhIAFWEiIiIiAVARJiIiIhIAFWEScWbW0Mz+tB+fuyMSeUREwqX2SyJJS1RIxIX2ZZvm7vu0tYaZ5bl73YiEEhEJg9oviST1hEk0PAx0DC2G+NivXzSzVmb2Tej1NDM7wsweBmqFjr0Ret9FZjYzdGyMmVUNHc8zsyfMLNXMZphZs+j+54lIHFP7JRGjnjCJuL2dSZrZjUBNd/97qGGq7e65u59Jmll34FHgTHcvMrN/AT+6+6tm5sBF7v6Gmd0NNHf3a6Lx3yYi8U3tl0RStaADiFC29ca40Ga6/3b3uXt4z7HAQGBW2ZZv1AKyQq+VAm+HHr8OxNxGvCISs9R+yX7TcKQEzt2/AY4E1gGvmdkle3ibARPcvV/o1tXd7/2tHxmhqCIi/x+1X3IgVIRJNOQC9X7rRTM7CMhy9xeBl4EBoZeKQmeXADOAs82seegzjUOfg7I/x2eHHl8AfFvO+UWk8lL7JRGj4UiJOHffZGbfmVka8JG73/yrtxwF3GxmRUAesOtMciww38xS3f1CM7sT+NTMqgBFwNXAL8B2oKeZzQZygPMi/18lIpWB2i+JJE3Ml5inS8FFJFap/arcNBwpIiIiEgD1hEnUmFlv4LVfHS5w94ODyCMiEi61XxIJKsJEREREAqDhSBEREZEAqAgTERERCYCKMBEREZEAqAgTERERCYCKMBEREZEA/D/NIG75nsnW+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "#get indices of ends of steps\n",
    "indices = np.concatenate((np.array([0]), np.where(np.diff(my_algo.timestamp) == 1)[0]+1, np.array([len(my_algo.timestamp)-1])))\n",
    "#plot potentials\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot([my_algo.potentials[i] for i in indices])\n",
    "plt.xlabel(\"t_step\")\n",
    "plt.ylabel(\"potential\")\n",
    "#plot n_agents\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot([pos.size for pos in [my_algo.agents_pos[i] for i in indices]])\n",
    "plt.xlabel(\"t_step\")\n",
    "plt.ylabel(\"n_agents\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### see last positions of agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 36, 16,  8,  5, 12, 11, 13,  3,  0, 14,  1, 48, 18,  7, 15, 34,\n",
       "       10, 17, 37,  4,  2, 23, 28, 29])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.agents_pos[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get coverage of an agent in position 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18,  5,  8, 11, 14, 16])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_algo.get_coverage(agent_position=18, network_nx=my_algo.network_nx, cover_range=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### save data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_algo.save_pickle(\"./demo_results.p\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "demo_results = pickle.load(open(\"./demo_results.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'n_agents')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tgAmheWFVgEnuPs3MXjOzfpRN0l8NXBnBDCJx5a2Za3jh65VceHA7rjiiQ9BxRETKXXFJKTe9M4+6Natx/xnxOQy5SySvjpxP2SrTvz5+caS+UySeLczI4e73F3Jkl2bcd1rPuG6YRKTyGvPNKuan5/DcBQPifu9bXc8uEgN2FpZw7ZtzaFg7gafP60e1qvqrKyLxZ8n6bTz9+TJO6dOKU+J4GHKXiC5RISLl4/4PF7Fq43Zev/xgGtepHnQcEZFyV1RSyo2T5lG/ZgJ/O61n0HGiQqfTIhXcx2nrmfjTGkYf2YEhnZoGHUdEJCKe/2olCzO28cAZvWgS58OQu6gIE6nAMnN2ctuU+fRu04Abj+8adBwRkYhYmJHDszOWc1rf1pXqqm8VYSIVVEmpc8PbcyksLuXZEf21JZGIxKXC4lJuemc+DWtX575KMgy5i+aEiVRQL3y9kh9Xbeaxs/vQvmmdoOOIiETEc1+uYHHmNsZePJBGlWzOq06tRSqgOWu28ORnyxjWpxVnD0wMOo6ISESkrcvhuS9XMLx/G07o2TLoOFGnIkykgskrKOa6t+bSsn5N/j68t9YDE5G4VDYMOY/Gdapzz6k9go4TCA1HilQwd7+fRvqWHbx95aE0qJUQdBwRkYj4xxfLWbI+l5dHJtOwduUahtxFPWEiFcj7c9cxJXUdfz6mM4OSGgcdR0QkIuanb+VfX63krAGJHNu9RdBxAqMiTKSCWLt5B3e+l0byQY348zGdgo4jIhIRBcUl3DhpHk3rVufuSjoMuYuGI0UqgOKSUq59aw4YPH2+tiUSkfj19OfLWZ6Vx/hLB1X6KRcqwkQqgGdnLGfOmq08O6I/iY1qBx1HRCQi5q7dypivV3JuciJHd20edJzA6XRbJGA/rdrEP79cwdkDEzmtb+ug44iIRER+UQk3TppLi/o1uXNY5R6G3EU9YSIBytlRxA1vz6Vd49rcW8lWihaRysPdufPfaazM3s6EywZTv2blHobcRUWYSEDcndvfm09WbgGTrzqMujX011FE4tMTny7j3dnpXHdsZ/7QpVnQcSoMDUeKBOSdlHSmL1jPjSd0pW/bhkHHERGJiNd+/IV/frmCEYPbcv1xnYOOU6FErAgzs5pmNtPM5pnZQjO7L3S8sZl9ZmbLQ/eNIpVBpKJamZ3HPVMXcljHJlx5ZIeg44iIRMTHaeu5+/00juvenPtP76UdQH4lkj1hBcAx7t4X6AecZGaHALcBM9y9MzAj9Fyk0igoLuHaN+dQM6EKT57bjypV1CiJSPyZtXoz1741h35tG/KPEQO09M4eROw34mXyQk8TQjcHTgcmhI5PAM6IVAaRiuiJT5exMGMbj5zVh5YNagYdR0Sk3C3fkMvlr8wisWEtXh45iFrVqwYdqUKKaFlqZlXNbC6QBXzm7j8BLdw9EyB0r4VCpNL4z/Jsxn6ziosOaccJPVsGHUdEpNxl5uxk5LiZ1EioyoTLBtO4TuXcFzIcES3C3L3E3fsBicBgM+sV7mfNbLSZpZhZSnZ2duRCikTJprwC/jJpHp2b1+WvQ7VGjojEn5ydRYwaN4tt+cW8cukg2jbW4tO/JyoDtO6+FfgKOAnYYGatAEL3Wb/xmbHunuzuyc2a6XJWiW3uzi3vzidnZxHPjuivrnkRiTv5RSWMfjWFVRvzGHPxQHq2bhB0pAovkldHNjOzhqHHtYDjgCXAVGBk6G0jgfcjlUGkonjtx1+YsSSL20/uRvdW9YOOIyJSrkpLnRsnzeOnnzfz+Dl9GdKpadCRYkIkV4dsBUwws6qUFXuT3H2amf0ATDKzy4E1wDkRzCASuCXrt/HAh4s5umszRh2WFHQcEZFy5e7c/+EiPlyQyR1Du3F6vzZBR4oZESvC3H0+0H8PxzcBx0bqe0UqkvyisuUo6tdM4LFz+mqNnCgzs0eBB4CdwMdAX+B6d3890GAicWTsN6sY/91qLhvSniuO0LqH++J3izAz+8vvve7uT5ZvHJH48uD0xSzbkMeEywbTtG6NoONURie4+y1mNhxIp6zn/UtARZhIOXhvTjoPfbSEYX1acecp3XWiuY/21hNWLyopROLQ54s28OoPv/A/h7fXXmnB2bVL8FDgTXffrH8kRMrHf5Znc/M78zm0QxOeOLevFp7eD79bhLn7fdEKIhJPNmzL5+Z359GjVX1uPqlr0HEqsw/MbAllw5F/MrNmQH7AmURiXtq6HP742mw6Na/LmEsGUqOarvjeH2HNCTOzmsDlQE/gv0t8u/tlEcolErN2XSW0s6iEZ0f0V+MUrHuAR4Bt7l5iZjuA08L5oJmNA4YBWe7eK3TsXuAKYNfihXe4+/RyTy1Sga3ZtINR42fRsHZ1Jlw2mPo1E/b+IdmjcJeoeA1oCZwIfE3Z4qu5kQolEste+nYV367YyD2n9qRT87pBx6nsfnD3Le5eAuDu24GPwvzsK5StbfhrT7l7v9BNBZhUKpvyChg5fiZFJaVMuGwQLepr67UDEe7VkZ3c/RwzO93dJ5jZROCTSAYTiUUL0nN47JOlnNyrJecPaht0nErLzFoCbYBaZtYf2DVZpT4Q1hLe7v6NmSVFJKBIDNpRWMxlE1LI2LqTiVccTKfmmjZ+oMItwopC91tDWw+tB5IikkgkRm0vKObat+bQtG4NHjqzt64SCtaJwCjKeu13v4o7F7jjAH/2NWZ2CZAC3OjuW379BjMbDYwGaNeu3QF+nUjwiktKuWbiHBakb+WFiwYy8KDGQUeKC+EOR441s0bAXZSteL8IeDRiqURi0H0fLGT1pu08dV4/GtbWhrVBcvcJ7n40MMrdj97tdpq7TzmAH/080BHoB2QCT/zG92vbNYkb7s4d7y3giyVZ3H9GL07o2TLoSHEjrJ4wd38p9PBrQCuxifzKtPkZTEpJ55qjO3FIhyZBx5H/Nc3MLqCs5/6/7Z27/21/fpi7b9j12MxeBKYdaECRiu6pz5YxKSWda4/pxIUHHxR0nLiyt8VaL3L3139r0VYt1ioC6Vt2cPuUBfRr25DrjuscdBz5/70P5ACzgYID/WFm1srdM0NPhwNpB/ozRSqyN376hWe/WMF5yW254fguQceJO3vrCasTut/T7Dsv5ywiMaek1Lnh7bm4w7Pn9yehargj/BIlie6+pysc98rM3gSOApqaWTply10cZWb9KGv/VgNXllNOkQrnk4XruevfaRzdtRl/H95L81wjYG+LtY4JPfzc3b/b/TUzGxKxVCIx4rkvVzBr9RaePq8f7ZqEddGdRNf3Ztbb3Rfs6wfdfcQeDr9cDplEKryU1Zu59s059E5syHMXDqCaTjAjItzf6j/CPCZSacz+ZTPPzFjO8P5tOKN/m6DjyJ4dDsw2s6VmNt/MFpjZ/KBDiVRkK7JyuXxCCq0b1mLcyGRqVw93IQXZV3ubE3YocBjQ7FfzwuoDWgZcKq1t+UVc++ZcWjesyd9O7xl0HPltJwcdQCSWbNiWz8hxs0ioWoUJlw6mSd0aQUeKa3vrCasO1KWsWKu3220bcHZko4lUTO7OX99LY/22fJ45vz/1tGVHheXuvwBtgWNCj3cQ/giASKWyLb+IkeNmsnVHIa9cOkhTLKJgb3PCvga+NrNXQg2YSKU3JXUdH8zL4KYTujCgXaOg48jvMLN7gGSgKzAeSABeBzSnVWQ3BcUlXPnqbFZk5TH+0kH0atMg6EiVQrgDvTXMbCz/d62dYyIRSqSiWr1xO3e/n8bB7Rtz1VGdgo4jezcc6A+kArh7hplprxWR3ZSWOjdOmscPqzbx1Hl9OaKzFhiOlnCLsHeAF4CXgJLIxRGpuIpKSrnurTlUq1qFp87rR9Uqulw7BhS6u5uZA5hZnb19QKSy+fv0xUybn8ltJ3djeP/EoONUKuEWYcXu/vy+/GAzawu8CrQESoGx7v6Mmd0LXAFkh956h7tP35efLRKEpz5bxrz0HJ6/cACtG9YKOo6EZ5KZjQEamtkVwGXAiwFnEqkwXvxmFS9/+zOjDkviyiO1IU60hVuEfWBmfwLeY7dVp9198+98ppiyzW1TQ93/s83ss9BrT7n74/uVWCQA36/YyPNfr2TE4Lac3LtV0HEkTO7+uJkdT9nFRF2Bu939s718TKRSeH/uOv4+fTGn9G7F3cN6aDHWAIRbhI0M3d+82zHnd/aRDG3tkRl6nGtmiwEtpiQxZ8v2Qm6YNJf2Tetw17AeQceRfRQqulR4iezmuxUbuemdeRzcvjFPnNuXKppeEYiwLtV29/Z7uIXdb2lmSZRNjv0pdOia0MKJ48xsj5eXmdloM0sxs5Ts7Ow9vUUk4tydWyfPZ/P2Qp49v78WLYwxZpZrZtt+dVtrZu+ZmcZepFJamJHDla/NpmOzuoy9JJmaCVr2MyhhFWFmVtvM7gxdIYmZdTazYWF+ti4wGbje3bcBzwMdgX6U9ZQ9safPuftYd0929+RmzXSlhgRj4sw1fLpoA7ee1E2XbMemJynrwW8DJAI3UTYn7C1gXIC5RAKxdvMORo2fRf2a1Xjl0sE0qKV1DoMU7qKF44FCylbPB0gHHtjbh8wsgbIC7A13nwLg7hvcvcTdSylrDAfvc2qRKFi+IZf7py3iiM5NuWxI+6DjyP45yd3HuHuuu29z97HAUHd/G9Aib1KpbN5eyMhxMyksLmXCZYNp2aBm0JEqvXCLsI7u/ihQBODuO4HfHUC2shl+LwOL3f3J3Y7vPqt5OJC2T4lFoiC/qIRr35pLnerVNF8itpWa2blmViV0O3e31zywVCJRtrOwhMsnzCJ9605eGplM5xZaLq8iCHeCS6GZ1SLUaJlZR3a7SvI3DAEuBhaY2dzQsTuAEWbWL/SzVgNX7mtokUh79OOlLM7cxvhRg2heT2eLMexC4BngX5S1OT8CF4Xas2uCDCYSLcUlpVwzMZW5a7fy/IUDGJTUOOhIEhJuEXYv8DHQ1szeoKzAuvT3PuDu37Ln3jKtCSYV2pdLsxj3Xdm6OUd3ax50HDkA7r4KOPU3Xv42mllEguDu3PnvNGYsyeL+03tyUi8tsVORhFWEufunZjYbOISywuo6d98Y0WQiAcjOLeDmd+bRrWU9bju5W9Bx5ACZWU3gcqAn8N8uTXe/LLBQIlH09OfLeWvWWq4+uiMXH5oUdBz5lXCvjpzh7pvc/UN3n+buG81sRqTDiURTaalz0zvzyM0v5tkR/XXZdnx4jbJdO04EvqbsCsncQBOJRMnEn9bwzIzlnD0wkZtO6Bp0HNmD3y3CzKymmTUGmppZIzNrHLolAa2jEVAkWsZ/v5qvl2Vz57AedNGk1XjRyd3vAra7+wTgFKB3wJlEIu7zRRu4898LOKprMx46s7dWw6+g9jYceSVwPWUFV+pux7cBz0UqlEi0LczI4ZGPlnB8jxZcdHC7oONI+SkK3W81s17AeiApuDgikZe6ZgvXvJlKrzYNeO6CASRUDXchBIm23y3C3P0Z4Bkz+7O7/yNKmUSiamdhCde+OYdGdRJ45Kw+OmOML2NDu3LcCUwF6gJ3BRtJJHJWZudx+SuzaFm/JuNGDaJODe3yUZGF+39njJldCxwZev4VMMbdi377IyKx4f4PF7Fq43Zev/xgGtepHnQcKUfu/lLo4TfsYa9bMxsZGqYUiXlZ2/K55OWZVK1iTLhsME3r1gg6kuxFuH2U/wIGhu53PX4+UqFEouXjtEwm/rSGK4/syJBOTYOOI9F3XdABRMpDbn4RI8fPYsuOQsaNGsRBTeoEHUnCEG5P2CB377vb8y/MbF4kAolES2bOTm6dvIA+iQ34y/Fdgo4jwdDYs8S8wuJS/vj6bJZvyOXlUYPok9gw6EgSpnB7wkpCq+QDYGYdgJLIRBKJvJJS54a351JUUsoz5/enejVNXK2ktHWRxLRdS+t8t2ITj5zVhz90aRZ0JNkH4faE3Qx8aWarQs+T2MuK+SIVVUFxCc/OWM6Pqzbz2Nl9aN9U3faVmHrCJKY99NFips7L4JaTunLWwMSg48g+CrcI+w4YAxwbej4G+CEiiUQioLiklB9WbWLq3Aw+Xrie3PxiTu/XmrPVaMU1M6sBnEXZieN/2zt3/1vo4XcBxBIpFy/9ZxUv/udnRh7cvIYsAAAbmklEQVR6EFf9oePePyAVTrhF2KuUrQ12f+j5CMpWoj4nEqFEyoO7k7pmC1PnZvDhgkw25hVSt0Y1TuzZklP7tuKIzs20HEX8ex/IAWYDBb9+0d21ibfEpKnzMnjgw8Wc3Ksld5/aU21ZjAq3COv6q4n5X2pivlRE7s7izFymzsvgg3kZrNu6k+rVqnBc9+ac1rc1R3Vtru2IKpdEdz8p6BAi5emLJRu4cdJcBic15qnz+lG1igqwWBVuETbHzA5x9x8BzOxg1I0vFcjqjduZOi+DqfMyWJGVR9UqxhGdm3LjCV04vkcL6tVMCDqiBON7M+vt7guCDiJyoIpLSnnq82X866uVdGtZnxcvSdZJZYwLtwg7GLjEzNaEnrcDFpvZAsDdvU9E0on8jvU5+UybX1Z4zU/PAWBw+8Y8cEYvhvZupYVXBeBwYJSZ/UzZcKShNktiUGbOTq57cy4zV2/m/EFtuefUntSqrgIs1oVbhKk7XyqEzdsL+Sgtk6lzM5i5ejPu0LtNA/46tDvD+raiVYNaQUeUiuXkoAOIHKgvl2Txl0lzKSwu5Znz+3F6vzZBR5JyElYR5u6/RDqIyG/JKyjm04XrmTovg2+Xb6S41OnYrA7XH9uFU/u2okOzukFHlApKbZfEsqKSUh7/ZCljvllF91b1ee6C/mrv4ox29pQKKb+ohK+WZjF1XgYzFmdRUFxKm4a1uPyI9pzWtzU9WtXX1UAiErfWbd3JnyemkrpmKxce3I67hvXQ/K84FLEizMzaUra0RUugFBjr7s+YWWPgbcrW7VkNnOvuWyKVQ2JHcUkp360sW8vr04XryS0opkmd6pw3qC2n9W3NgHaNqKKrgEQkzn22aAM3vTOPklLnnxf0Z1if1kFHkgiJZE9YMXCju6eaWT1gtpl9BowCZrj7w2Z2G3AbcGsEc0gFVlrqzA6t5TV9QSabthdSr0Y1TuzVktP6tuawjk2oVlVbCkn0mdk4YBiQ5e69Qsd0EikRU1hcysMfLWHcdz/Tq019/jliAEna0SOuRawIc/dMIDP0ONfMFgNtgNOBo0JvmwB8hYqwSsXdWZixjQ/mZTBtfibrtu6kRrUqHNe9Baf2bc1RXZup210qgleAf1LWo7/LbegkUiJg7eYdXDMxlXnpOYw6LInbh3ajRjW1g/EuKnPCzCwJ6A/8BLQIFWi4e6aZNf+Nz4wGRgO0a9cuGjElwlZl5/13La9V2dupFlrL66YTu3B8j5bUraEpilJxuPs3obZrdzqJlHL3cVomN787H4AXLhrASb1aBZxIoiXi/+qZWV1gMnC9u28LdzK1u48FxgIkJyd75BJKJGVs3fnftbzS1m3DDAYnNebyw9tzci+t5SUxRyeRUm4Kikt48MPFTPjhF/omNuCfFwygbePaQceSKIpoEWZmCZQVYG+4+5TQ4Q1m1irUgLUCsiKZQaJvU14B09PW80FoLS+APokNuPOU7gzr05qWDWoGnFAksnQSKXuzeuN2rnkzlbR127j88PbcelI3qlfT/NfKJpJXRxrwMrDY3Z/c7aWpwEjg4dD9+5HKINGTm1/Epws3lK3ltWIjJaVOp+Z1+cvxXTi1b2vaa3KpxAedRMoB+2BeBrdPWUDVKsaLlyRzfI8WQUeSgESyJ2wIcDGwwMzmho7dQVnxNcnMLgfWAOdEMINEkLvz+eIspqSm88WS/13La/SRHTi1T2u6t6qntbwk3ugkUvZbflEJf5u2iIk/rWFAu4Y8O6I/iY00/FiZRfLqyG8p26dtT46N1PdKdCxIz+G+DxaS8ssWmtatwYjB7Ti1b2sGtGuowkvigpm9Sdkk/KZmlg7cg04iZT+tzM7j6jdSWbI+lyv/0IGbTuhKgpbfqfR0OZrsk+zcAh7/ZCmTZq+lSZ3qPHJWb84akKi1vCTuuPuI33hJJ5GyT96bk85f30ujRrUqjB81iKO77fF6DqmEVIRJWAqLS3nl+595dsYKCopLuOKIDlxzTCfq10wIOpqISIW0s7CEe6amMSklnUFJjXh2RH9aNagVdCypQFSEye9yd75cmsX90xbz88btHNOtOXee0l2byIqI/I7lG3K5emIqy7PyuProjtxwXBeNGMj/oSJMftOKrDzun7aIr5dl06FZHcZfOoiju6obXUTk97yTspa7319I7epVmXDpYI7s0izoSFJBqQiT/yNnZxHPfL6cV39YTa3qVbnzlO6MPCxJk0hFRH7H9oJi7no/jSmp6zikQ2OePb8/zetrXUT5bSrC5L9KSp23Z63l8U+XsmVHIecPaseNJ3Shad0aQUcTEanQlqzfxtVvpLJq43auO7Yz1x7bmapVdKW4/D4VYQLAT6s2cd8Hi1iUuY3BSY25+9Qe9GrTIOhYIiIVmrvz1qy13Dt1IfVrJfDG5QdzWKemQceSGKEirJJbt3UnD05fzIfzM2ndoCb/vKA/p/RupbW+RET2Iq+gmDumLGDqvAwO79SUp87rR7N6GjmQ8KkIq6R2FpbwwtcreeHrlZjBDcd1YfSRHahVvWrQ0UREKry0dTlcMzGVNZt3cNMJXbjqqE4afpR9piKsknF3PpifycPTF5ORk8+pfVtz28ndaNNQa9eIiOyNu/P6T2u4f9oiGtVO4M0rDuHgDk2CjiUxSkVYJZK2rmyroVmrt9CzdX2ePr8/g9s3DjqWiEhM2JZfxO2TF/Dhgkz+0KUZT57blya6cEkOgIqwSmBjXtlWQ2+nrKVx7eo8fGZvzkluq65zEZEwzU/fyjUT57Bu605uPakbVx7ZgSpqQ+UAqQiLY4XFpbz6w2qe+Xw5O4tK+J/D2/PnYztrqyERkTC5O698v5oHpy+mad0avD36EJKTNIIg5UNFWJz6ckkW909bxKqN2zm6azPuHNaDjtpqSEQkbDk7irj53Xl8umgDx3ZrzuPn9KVRnepBx5I4oiIszqzMzuOBaYv4cmk2HZrWYfyoQRzdTVsNiYjsizlrtnDNxDls2JbPnad05/LD22vpHil3KsLixLb8Ip79fDmvfL+aWgllWw1dcmgS1atpqyERkXC5Oy/952ce+XgJLerX5J0/Hkr/do2CjiVxSkVYjCspdd5JWctjnyxl845Czh/UlhtP6KqthkRE9tGW7YXc9M48ZizJ4oQeLXjs7L40qK05tBI5KsJi2MyfN3PfBwtZmLGNQUmNmHDqYG01JCKyH1JWb+baN+eQnVfAPaf2YNRhSRp+lIiLWBFmZuOAYUCWu/cKHbsXuALIDr3tDnefHqkM8Wrd1p08/NESPpiXQesGNfnHiP4M66OthkRE9lVpqfPCNyt54tNltGlYi8lXHUafxIZBx5JKIpI9Ya8A/wRe/dXxp9z98Qh+b9zaWVjCmG/Kthpyh+uO7cwf/9BRWw2JiOyHrNx8bn5nPl8vy2Zo75Y8fFYfLeEjURWxIszdvzGzpEj9/MrE3flwQSYPTV/Cuq07GdanFbcP7a6thkRE9lFRSSlfLslicmo6XyzJwsy4/4xeXHRwO40mSNQFMSfsGjO7BEgBbnT3LXt6k5mNBkYDtGvXLorxKpaFGTnc98EiZv68mR6t6vPUef201ZCIyD5wdxZmbGNyajrvz81g8/ZCmtatziWHJnHBwe20hqIEJtpF2PPA/YCH7p8ALtvTG919LDAWIDk52aMVsKLYlFfA458u461Za2hUuzoPndmbc7XVkIhI2LJzC3h/7jrenZ3OkvW5VK9ahWO7N+fsgYkc2aUZCVW1hI8EK6pFmLtv2PXYzF4EpkXz+2NBUUkpr/7wC09/voydhSVcNqQ91x7bmQa1NE9BRGRvCopLmLE4i8mz0/lqWTYlpU7fxAbcf3pPTu3bmoa1teK9VBxRLcLMrJW7Z4aeDgfSovn9Fd1XS8u2GlqZvZ0/dGnGXcN60Km5uslFRH6PuzM/PYd3Z6czdV4GOTuLaFG/Bv9zRHvOHpBI5xb1go4oskeRXKLiTeAooKmZpQP3AEeZWT/KhiNXA1dG6vtjyca8Am59dz4zlmRpqyERkTCtz8nnvTnrmJyazoqsPGpUq8IJPVty9sBEDu/UVNM3pMKL5NWRI/Zw+OVIfV+sSluXw+hXU9i8o5A7hnZj1GHttdWQiMhvyC8q4dNFG3h3djrfLs+m1GHgQY14cHhvTunTSlM3JKZoxfwAvT93Hbe8O5+mdWvw7h8P02r3IiJ74O6krtnCu7PXMW1+Brn5xbRuUJM/HdWJMwe0oYOubpQYpSIsACWlzqMfL2HMN6sY3L4x/7pwgPZ6FBH5lXVbd/JeajqTU9fx88bt1Eqoysm9WnLWwEQO7dCEKhpulBinIizKcnYUce1bc/h6WTYXH3IQd5/aQ5dJi4iE7Cgs5uO09UxOTef7lZtwh4PbN+aqozoytHcr6tbQP1sSP/SnOYpWZOVyxauzSd+yg4fO7M2IwZV3EVoRkV1KS52ZqzczeXY60xdksr2whLaNa3HdsZ05s38i7ZrUDjqiSESoCIuSzxdt4Pq351IzoSpvXnEIyUla9V5EKrc1m3YwOTWdKXPSWbt5J3WqV+WUPq04a0Aig5Iaa7hR4p6KsAhzd577cgVPfLaMXq0bMObigbTWno8iUknlFRQzfUEm785OZ+bPmzGDwzo24S/Hd+HEni2pXV3/LEnloT/tEbS9oJib353H9AXrGd6/DQ+d2ZuaCVWDjiUiElWlpc4Pqzbx7ux0Pk5bz86iEto3rcNNJ3Rh+IBE2ujEVCopFWERsnbzDq54NYVlG3L569Du/M8R7TFT17pIPDCz1UAuUAIUu3tysIkqpp83bmfy7HSmpKaTkZNPvZrVOKN/G84e2IYB7RqpTZRKT0VYBHy/YiNXT0ylpNR55dLBHNmlWdCRRKT8He3uG4MOUdHk7Cziw/mZvDt7LalrtlLF4IjOzbhtaHdO6NFCowEiu1ERVo7cnQnfr+b+DxfToWkdXrwkmaSmdYKOJSISUSWlzn+WZzM5dR2fLFxPYXEpnZvX5baTuzG8fxta1K8ZdESRCklFWDkpKC7hzvfSeGd2Osf3aMFT5/XTejYi8cuBT83MgTHuPjboQEFwdz5OW88jHy9h9aYdNKiVwPmD2nLWgET6JDbQcKPIXqhKKAdZ2/K58vXZzFmzlWuP7cz1x3bWpdUi8W2Iu2eYWXPgMzNb4u7f7HrRzEYDowHatYvP9QDnrd3KAx8uYtbqLXRuXpfnLhjAcT2aU6OahhtFwqUi7ADNWbOFP74+m9z8Yl64aAAn9WoVdCQRiTB3zwjdZ5nZe8Bg4JvdXh8LjAVITk72QEJGSPqWHTz2yVLen5tB07rVeXB4b85NTqSadv4Q2Wcqwg7AOylr+et7abRoUIMpfzqMbi3rBx1JRCLMzOoAVdw9N/T4BOBvAceKuNz8Iv711Upe/vZnDLj66I788Q8dqVczIehoIjFLRdh+KC4p5e/TFzP+u9UM6dSEf44YQKM61YOOJSLR0QJ4LzTfqRow0d0/DjZS5BSXlPLmrLU8/dkyNm0vZHj/Ntx0Ylet7SVSDlSE7aMt2wu55s1UvluxicuGtOeOod3UDS9Sibj7KqBv0Dkizd35amk2f5++mBVZeQxu35jxp3SnT2LDoKOJxA0VYftgyfptXPFqChu2FfD4OX05e2Bi0JFERMrdooxtPDh9Md+u2EhSk9qMuXggJ/RooasdRcpZxIowMxsHDAOy3L1X6Fhj4G0gCVgNnOvuWyKVoTx9tCCTG9+ZR90a1Xh79CH0b9co6EgiIuVqw7Z8nvh0Ke/MTqdBrQTuHtaDiw45iOrV1NsvEgmR7Al7Bfgn8Opux24DZrj7w2Z2W+j5rRHMcMBKS52nP1/Gs1+soH+7hrxw0UAtPCgicWVHYTFjv1nFmK9XUVxayuVD2vPnYzrToLYm3YtEUsSKMHf/xsySfnX4dOCo0OMJwFdU4CIsN7+IG96ex+eLN3BuciL3n9FLa+CISNwoKXUmp6bzxKdL2bCtgKG9W3LrSd04qIl2+hCJhmjPCWvh7pkA7p4ZWuhwj4Je7HD1xu1c8WoKqzZu577TenLJoQdpPoSIxI3vV2zkgQ8XsyhzG33bNuS5CwaQnNQ46FgilUqFnZgf5GKHXy/L5s8TU6laxXjtssEc1qlpNL9eRCRiVmTl8dD0xcxYkkWbhrV45vx+nNqntXb5EAlAtIuwDWbWKtQL1grIivL3/y5358X/rOLhj5bQpUU9XrwkmbaNawcdS0TkgG3KK+Dpz5czceYaaidU5daTunHpkCRqJmiKhUhQol2ETQVGAg+H7t+P8vf/pvyiEm6bPJ9/z81gaO+WPH5OX2pXr7AdhSIiYckvKuGV71fz3Bcr2FFUwgWD23H9cZ1pUrdG0NFEKr1ILlHxJmWT8JuaWTpwD2XF1yQzuxxYA5wTqe/fFxlbd3Lla7NJy8jhphO6cPXRnTT/S0RimrvzwfxMHvloCeu27uTYbs25fWg3OjWvF3Q0EQmJ5NWRI37jpWMj9Z37Y9bqzVz1+mzyi0p58eJkjuvRIuhIIiIHZPYvm7l/2mLmrt1K91b1efTsPgzR3FaRCqdSj7dN/GkN90xNI7FRbd4aPVBniCIS037ZtJ1HPl7C9AXraV6vBo+e3YezBiRSVZPuRSqkSlmEFRaX8rdpC3n9xzX8oUsznh3Rnwa1tCihiMSmnB1F/OOL5Uz4YTXVqlTh+uM6M/rIDprXKlLBVbq/oRvzCvjTG6nM/HkzV/6hA7ec2E1niSISkwqLS3n9x1949ovl5Ows4uwBidx0Ylft6iESIypVEZa2LofRr6awaXshz5zfj9P7tQk6kojIPnN3Pl20gYc/WsLPG7czpFMT7hjanZ6tGwQdTUT2QaUpwqbOy+CWd+fRuHZ13v3jYfROVGMlIrFnQXoOD3y4iJ9+3kyn5nUZNyqZo7s21xXdIjEo7ouwklLnsU+W8sLXKxmU1IjnLxpIU62PIyIxJmPrTh7/ZClT5qyjSZ3q3H9GL0YMaku1qlWCjiYi+ymui7CcnUVc99YcvlqazYUHt+OeU3tSvZoaLBGJHXkFxbzw1Upe/M8qHLjqqI5cdVRH6tfUxUQisS5ui7AVWXlc8WoKazfv4O/De3HhwQcFHUlEJGzFJaVMSknnyc+WsTGvgNP7tebmE7uS2EhbqYnEi7gswmYs3sD1b82lerUqTLziEAa3bxx0JBGRsH21NIsHpy9m2YY8kg9qxEsjk+nXtmHQsUSknMVVEebu/OurlTz+6VJ6tq7PmIuTadOwVtCxRETCsmT9Nv7+4WL+s3wjBzWpzfMXDuCkXi016V4kTsVVETZ9wXoe+2Qpp/drzcNn9qFW9apBRxIRCcvOwhLOG/Mj7s6dp3Tn4kMPokY1tWEi8SyuirCTe7XkhYsGcGJPnTmKSGypVb0q/7pwAD1a1adRnepBxxGRKIirIqxKFeOkXq2CjiEisl+0ybZI5aL1GkREREQCoCJMREREJAAqwkREREQCoCJMREREJAAqwkREREQCoCJMREREJAAqwkREREQCYO4edIa9MrNs4Jcw394U2BjBOJEUq9mVO7oqS+6D3L1ZpMJEyz62X1B5/v9WFModXZUld1jtV0wUYfvCzFLcPTnoHPsjVrMrd3Qpd3yL1d+TckeXckdXpHJrOFJEREQkACrCRERERAIQj0XY2KADHIBYza7c0aXc8S1Wf0/KHV3KHV0RyR13c8JEREREYkE89oSJiIiIVHgqwkREREQCEFdFmJmdZGZLzWyFmd0WdJ5wmdk4M8sys7Sgs4TLzNqa2ZdmttjMFprZdUFnCoeZ1TSzmWY2L5T7vqAz7Qszq2pmc8xsWtBZ9oWZrTazBWY218xSgs5TEan9ii61YcGIxTYsku1X3MwJM7OqwDLgeCAdmAWMcPdFgQYLg5kdCeQBr7p7r6DzhMPMWgGt3D3VzOoBs4EzKvrv28wMqOPueWaWAHwLXOfuPwYcLSxm9hcgGajv7sOCzhMuM1sNJLt7LC7SGHFqv6JPbVgwYrENi2T7FU89YYOBFe6+yt0LgbeA0wPOFBZ3/wbYHHSOfeHume6eGnqcCywG2gSbau+8TF7oaULoFhNnImaWCJwCvBR0Fil3ar+iTG1Y9KkN+7/iqQhrA6zd7Xk6MfAXKh6YWRLQH/gp2CThCXWHzwWygM/cPSZyA08DtwClQQfZDw58amazzWx00GEqILVfAVIbFjWx2oZFrP2KpyLM9nAsJs4OYpmZ1QUmA9e7+7ag84TD3UvcvR+QCAw2swo/hGJmw4Asd58ddJb9NMTdBwAnA1eHhrDkf6n9CojasOiI8TYsYu1XPBVh6UDb3Z4nAhkBZakUQvMRJgNvuPuUoPPsK3ffCnwFnBRwlHAMAU4LzU14CzjGzF4PNlL43D0jdJ8FvEfZ8Jv8L7VfAVAbFlUx24ZFsv2KpyJsFtDZzNqbWXXgfGBqwJniVmhy6MvAYnd/Mug84TKzZmbWMPS4FnAcsCTYVHvn7re7e6K7J1H2Z/sLd78o4FhhMbM6oYnPmFkd4AQgpq6kiwK1X1GmNiy6YrUNi3T7FTdFmLsXA9cAn1A2wXKSuy8MNlV4zOxN4Aegq5mlm9nlQWcKwxDgYsrOZuaGbkODDhWGVsCXZjafsn/4PnP3mLlUOka1AL41s3nATOBDd/844EwVitqvQKgNk3BEtP2KmyUqRERERGJJ3PSEiYiIiMQSFWEiIiIiAVARJiIiIhIAFWEiIiIiAVARJiIiIhIAFWEScWbW0Mz+tB+fuyMSeUREwqX2SyJJS1RIxIX2ZZvm7vu0tYaZ5bl73YiEEhEJg9oviST1hEk0PAx0DC2G+NivXzSzVmb2Tej1NDM7wsweBmqFjr0Ret9FZjYzdGyMmVUNHc8zsyfMLNXMZphZs+j+54lIHFP7JRGjnjCJuL2dSZrZjUBNd/97qGGq7e65u59Jmll34FHgTHcvMrN/AT+6+6tm5sBF7v6Gmd0NNHf3a6Lx3yYi8U3tl0RStaADiFC29ca40Ga6/3b3uXt4z7HAQGBW2ZZv1AKyQq+VAm+HHr8OxNxGvCISs9R+yX7TcKQEzt2/AY4E1gGvmdkle3ibARPcvV/o1tXd7/2tHxmhqCIi/x+1X3IgVIRJNOQC9X7rRTM7CMhy9xeBl4EBoZeKQmeXADOAs82seegzjUOfg7I/x2eHHl8AfFvO+UWk8lL7JRGj4UiJOHffZGbfmVka8JG73/yrtxwF3GxmRUAesOtMciww38xS3f1CM7sT+NTMqgBFwNXAL8B2oKeZzQZygPMi/18lIpWB2i+JJE3Ml5inS8FFJFap/arcNBwpIiIiEgD1hEnUmFlv4LVfHS5w94ODyCMiEi61XxIJKsJEREREAqDhSBEREZEAqAgTERERCYCKMBEREZEAqAgTERERCYCKMBEREZEA/D/NIG75nsnW+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "#get indices of ends of steps\n",
    "indices = np.concatenate((np.array([0]), np.where(np.diff(demo_results.timestamp) == 1)[0]+1, np.array([len(demo_results.timestamp)-1])))\n",
    "#plot potentials\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot([demo_results.potentials[i] for i in indices])\n",
    "plt.xlabel(\"t_step\")\n",
    "plt.ylabel(\"potential\")\n",
    "#plot n_agents\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot([pos.size for pos in [demo_results.agents_pos[i] for i in indices]])\n",
    "plt.xlabel(\"t_step\")\n",
    "plt.ylabel(\"n_agents\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### more help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access help pages of specific methods of the mBLLL class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function run_mBLLL in module mBLLL:\n",
      "\n",
      "run_mBLLL(self, t_steps=100, temperature=1, prob=0.5, cover_ranges=2, cover_ranges_distr='const', step_size='one', ghosts=True)\n",
      "    Method to run t_steps of the mBLLL algo.\n",
      "    \n",
      "    Run the mBLLL algo for t_steps.\n",
      "    \n",
      "    Args:\n",
      "        t_steps (int): number of steps for the simulation.\n",
      "        temperature (float): temperature of the simulation (see ref).\n",
      "        prob (float): probability of a covered node to be infected\n",
      "        cover_ranges (int): cover range of the new agents. depending on cover_ranges_distr, either will be the cover ranges or the new agents, or a parameter for a distribution from which the new cover ranges will be sampled\n",
      "        cover_ranges_distr (str): if \"const\", all new agents have cover range equal to cover_ranges. if \"poisson\", cover ranges for each new agent are sampled from a poisson distribution with rate=cover_ranges \n",
      "        step_size (str): if \"one\", only one (random) agent is activated in each step, if \"all\", all agents are activated in random order in each step\n",
      "        ghosts (bool): if True, multiple agents can occupy the same node. if False, moves and infections are restricted to empty nodes.\n",
      "    \n",
      "    Returns:\n",
      "        nothing\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mBLLL.run_mBLLL) #man page of the method BLLL.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access full help page of the class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class mBLLL in module mBLLL:\n",
      "\n",
      "class mBLLL(builtins.object)\n",
      " |  Template class for mBLLL algo, a modification of: \n",
      " |  Yazıcıoğlu, A.Y., Egerstedt, M., and Shamma, J.S. (2013). A Game Theoretic Approach to Distributed Coverage of Graphs by Heterogeneous Mobile Agents. IFAC Proceedings Volumes 46, 309–315.\n",
      " |  \n",
      " |  Attributes:\n",
      " |      agents_pos (list[numpy.array]): list of 1d-arrays of agent positions (matrix indices), of size (n_agents)\n",
      " |      potentials (list[int]): list of potentials \n",
      " |      \n",
      " |  Parameters:\n",
      " |      n_agents (int): number of agents. Corrected to number size of starting_positions if starting_positions is specified.\n",
      " |      starting_positions (np.array): 1-d array of starting positions. \n",
      " |          If None, starting positions are selected at random. \n",
      " |      cover_ranges (int or list): cover range of the agents. either an int to be applied to all agent, \n",
      " |          or a list of ints representing the cover ranges for each agent\n",
      " |      cover_ranges_distr (str): if \"const\", all agents have cover range equal to cover_ranges. if \"poisson\", cover ranges for each agent are sampled for a poisson distribution with rate cover_ranges \n",
      " |      network_nx (networkx.graph.Graph): networkx representation of the network\n",
      " |      random_state (int): random seed to be set during __init__()\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, n_agents=10, starting_positions=None, cover_ranges=2, cover_ranges_distr='const', network_nx=None, random_state=42)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  add_potentials(self)\n",
      " |      Method to add current potential to potential list.\n",
      " |      \n",
      " |      Calculates the current potential and adds it to self.potential.\n",
      " |      \n",
      " |      Args:\n",
      " |          nothing\n",
      " |      Returns:\n",
      " |          nothing\n",
      " |  \n",
      " |  get_coverage(self, agent_position, network_nx, cover_range)\n",
      " |      Find the nodes covered by an agent position.\n",
      " |      \n",
      " |      Method to return an array of the nodes covered by an agent position, with said cover_range, \n",
      " |      by computing the BFS tree. The method is called by .__init__() and .step() . \n",
      " |      \n",
      " |      Args:\n",
      " |          agent_position (int): position of the agent on the graph (index of the node)\n",
      " |          network_nx (networkx.graph.Graph): networkx representation of the network\n",
      " |          cover_range (int): coverage range of an agent, with:\n",
      " |              cover_range=0 -> agent only covers its own position,\n",
      " |              cover_range=1 -> agent only covers its own position + first neighbours, etc...\n",
      " |      Returns:\n",
      " |          agent_coverages (np.array): 1-d array of the nodes covered by the agent.\n",
      " |  \n",
      " |  infect(self, agent=None, prob=0.5, cover_ranges=2, cover_ranges_distr='const', ghosts=True, random_state=None)\n",
      " |      Method to make the infection step of the mBLLL algo.\n",
      " |      \n",
      " |      Infects each node in coverage with some probability.\n",
      " |      \n",
      " |      Args:\n",
      " |          agent (int): index of the agent for to move. If None, the agent is randomly chosen. Normally, for the full algorithm the agent should be the same as in the step() function.\n",
      " |          prob (float): probability of a covered node to be infected\n",
      " |          cover_ranges (int): cover range of the new agents. depending on cover_ranges_distr, either will be the cover ranges or the new agents, or a parameter for a distribution from which the new cover ranges will be sampled\n",
      " |          cover_ranges_distr (str): if \"const\", all new agents have cover range equal to cover_ranges. if \"poisson\", cover ranges for each new agent are sampled from a poisson distribution with rate=cover_ranges \n",
      " |          ghosts (bool): if True, multiple agents can occupy the same node. if False, infections are restricted to empty nodes.\n",
      " |          random_state (int): if not None, random seed to be set at the beginning of the infection step\n",
      " |      Returns:\n",
      " |          nothing\n",
      " |  \n",
      " |  run_BLLL(self, t_steps=100, temperature=1)\n",
      " |      Method to run t_steps of the BLLL algo.\n",
      " |      \n",
      " |      Run the BLLL algo for t_steps (see ref).\n",
      " |      \n",
      " |      Args:\n",
      " |          t_steps (int): number of steps for the simulation.\n",
      " |          temperature (float): temperature of the simulation (see ref).\n",
      " |      Returns:\n",
      " |          nothing\n",
      " |  \n",
      " |  run_BLLL_annealing(self, t_steps=100, temperature_start=10, temperature_stop=0.1, rate='geometric')\n",
      " |      Method to run t_steps of the BLLL algo in a simulated annealing fashion.\n",
      " |  \n",
      " |  run_mBLLL(self, t_steps=100, temperature=1, prob=0.5, cover_ranges=2, cover_ranges_distr='const', step_size='one', ghosts=True)\n",
      " |      Method to run t_steps of the mBLLL algo.\n",
      " |      \n",
      " |      Run the mBLLL algo for t_steps.\n",
      " |      \n",
      " |      Args:\n",
      " |          t_steps (int): number of steps for the simulation.\n",
      " |          temperature (float): temperature of the simulation (see ref).\n",
      " |          prob (float): probability of a covered node to be infected\n",
      " |          cover_ranges (int): cover range of the new agents. depending on cover_ranges_distr, either will be the cover ranges or the new agents, or a parameter for a distribution from which the new cover ranges will be sampled\n",
      " |          cover_ranges_distr (str): if \"const\", all new agents have cover range equal to cover_ranges. if \"poisson\", cover ranges for each new agent are sampled from a poisson distribution with rate=cover_ranges \n",
      " |          step_size (str): if \"one\", only one (random) agent is activated in each step, if \"all\", all agents are activated in random order in each step\n",
      " |          ghosts (bool): if True, multiple agents can occupy the same node. if False, moves and infections are restricted to empty nodes.\n",
      " |      \n",
      " |      Returns:\n",
      " |          nothing\n",
      " |  \n",
      " |  save_pickle(self, file_path, overwrite=False)\n",
      " |      Save object to a pickle file. \n",
      " |      \n",
      " |      Saves the whole BLLL() object to a pickle file.\n",
      " |      \n",
      " |      Args:\n",
      " |          file_path (str): path where to save the file\n",
      " |          overwrite (bool): should files be written over? default=False.\n",
      " |      \n",
      " |      Returns:\n",
      " |          nothing\n",
      " |  \n",
      " |  save_results(self, file_path, overwrite=False)\n",
      " |      Save attributes to a file. \n",
      " |      \n",
      " |      Attributes are stored in an array with n_steps+1 lines. \n",
      " |      First column stores the potentials, next n_agents columns store the positions of the agents at each time step. \n",
      " |      Array is saved as a .npy file at file_path.\n",
      " |      \n",
      " |      Args:\n",
      " |          file_path (str): path where to save the file\n",
      " |          overwrite (bool): should files be written over? default=False.\n",
      " |      \n",
      " |      Returns:\n",
      " |          nothing\n",
      " |  \n",
      " |  step(self, agent=None, temperature=1, trick=True, random_state=None, ghosts=True)\n",
      " |      Method to make one step of the BLLL algo.\n",
      " |      \n",
      " |      Propose a random move for an agent, accept the move with some probability (see ref).\n",
      " |      \n",
      " |      Args:\n",
      " |          agent (int): index of the agent for to move. If None, the agent is randomly chosen.\n",
      " |          temperature (float): temperature of the simulation (see ref).\n",
      " |          trick (bool): compare probs in log-space to avoid float64 overflow\n",
      " |          random_state (int): if not None, random seed to be set at the beginning of the step\n",
      " |          ghosts (bool): if True, multiple agents can occupy the same node. if False, moves are restricted to empty nodes.\n",
      " |      Returns:\n",
      " |          nothing\n",
      " |  \n",
      " |  utility(self, agent, coverage_list)\n",
      " |      Method to compute an agent's marginal utility.\n",
      " |      \n",
      " |      The method return the number of nodes uniquely covered by the agent. \n",
      " |      The method is called by .__init__() and .step() . \n",
      " |      \n",
      " |      Args:\n",
      " |          agent (int): index of the agent for which to compute the utility.\n",
      " |          coverage_list (list[np.array]): list of size n_agents,\n",
      " |              containing np.arrays representing the coverage of each agent\n",
      " |      Returns:\n",
      " |          utility (int): number of nodes uniquely covered by the agent.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __slotnames__ = []\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mBLLL) #complete man page of the class BLLL"
   ]
  }
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