Session_19_Data_Collector_Agent.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "XAnII-29m3r2",
        "wp0VWkylOKr9",
        "BkX5GqjSnAu0"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SFIComplexityExplorer/Mesa-ABM-Tutorial/blob/main/Session_19_Data_Collector_Agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Dependencies"
      ],
      "metadata": {
        "id": "XAnII-29m3r2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "\n",
        "try:\n",
        "  import mesa\n",
        "except:\n",
        "  !pip install mesa --quiet\n",
        "import mesa\n",
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "ikSl2Mz0m252",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f16446f9-a9f6-4e6b-b533-aecc9494925d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Helper Functions"
      ],
      "metadata": {
        "id": "wp0VWkylOKr9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_distance(pos_1, pos_2):\n",
        "  '''\n",
        "  Calculate the Euclidean distance between two positions\n",
        "\n",
        "  used in trade.move()\n",
        "  '''\n",
        "\n",
        "  x1, y1 = pos_1\n",
        "  x2, y2 = pos_2\n",
        "  dx = x1 - x2\n",
        "  dy = y1 - y2\n",
        "  return math.sqrt(dx**2 + dy**2)\n",
        "\n",
        "def flatten(list_of_lists):\n",
        "  '''\n",
        "  helper function for model datacollector for trade price\n",
        "  collapses agent price list into one list\n",
        "  '''\n",
        "  return [item for sublist in list_of_lists for item in sublist]\n",
        "\n",
        "def geometric_mean(list_of_prices):\n",
        "  '''\n",
        "  find the geometric mean of a list of prices\n",
        "  '''\n",
        "  return np.exp(np.log(list_of_prices).mean())\n",
        "\n",
        "def get_trade(agent):\n",
        "  '''\n",
        "  For agent reportes in data collector\n",
        "\n",
        "  return list of trade partners and None for other agents\n",
        "  '''\n",
        "  if type(agent) == Trader:\n",
        "    return agent.trade_partners\n",
        "  else: return None"
      ],
      "metadata": {
        "id": "kg-JVcwzON_b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Resource Classes"
      ],
      "metadata": {
        "id": "BkX5GqjSnAu0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Sugar(mesa.Agent):\n",
        "  \"\"\"\n",
        "  Sugar:\n",
        "  - contains an amount of sugar\n",
        "  - grows 1 amount of sugar at each turn\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, unique_id, model, pos, max_sugar):\n",
        "    super().__init__(unique_id, model)\n",
        "    self.pos = pos\n",
        "    self.amount = max_sugar\n",
        "    self.max_sugar = max_sugar\n",
        "\n",
        "\n",
        "  def step(self):\n",
        "    '''\n",
        "    Sugar growth function, adds one unit of sugar each step until\n",
        "    max amount\n",
        "    '''\n",
        "    self.amount = min([self.max_sugar, self.amount+1])"
      ],
      "metadata": {
        "id": "o1EOk5KMnC55"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Spice(mesa.Agent):\n",
        "  \"\"\"\n",
        "  Spice:\n",
        "  - contains an amount of spice\n",
        "  - grows 1 amount of spice at each turn\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, unique_id, model, pos, max_spice):\n",
        "    super().__init__(unique_id, model)\n",
        "    self.pos = pos\n",
        "    self.amount = max_spice\n",
        "    self.max_spice = max_spice\n",
        "\n",
        "  def step(self):\n",
        "    '''\n",
        "    Spice growth function, adds one unit of spice each step until\n",
        "    max amout\n",
        "    '''\n",
        "    self.amount = min([self.max_spice, self.amount+1])"
      ],
      "metadata": {
        "id": "-oJ35Mx7nXmK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Trader Agent"
      ],
      "metadata": {
        "id": "XYcwNrHqnrPt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Trader(mesa.Agent):\n",
        "  \"\"\"\n",
        "  Trader:\n",
        "  - has a metabolism of sugar and spice\n",
        "  - harvest and trade sugar and spice to survive\n",
        "  \"\"\"\n",
        "\n",
        "\n",
        "  def __init__(self, unique_id, model, pos, moore=False, sugar=0,\n",
        "               spice=0, metabolism_sugar=0, metabolism_spice=0,\n",
        "               vision=0):\n",
        "    super().__init__(unique_id, model)\n",
        "    self.pos = pos\n",
        "    self.moore = moore\n",
        "    self.sugar = sugar\n",
        "    self.spice = spice\n",
        "    self.metabolism_sugar = metabolism_sugar\n",
        "    self.metabolism_spice = metabolism_spice\n",
        "    self.vision = vision\n",
        "    self.prices = []\n",
        "    self.trade_partners = []\n",
        "\n",
        "\n",
        "  def get_sugar(self, pos):\n",
        "    '''\n",
        "    used in self.get_sugar_amount()\n",
        "    '''\n",
        "\n",
        "    this_cell = self.model.grid.get_cell_list_contents(pos)\n",
        "    for agent in this_cell:\n",
        "      if type(agent) is Sugar:\n",
        "        return agent\n",
        "    return None\n",
        "\n",
        "\n",
        "  def get_sugar_amount(self,pos):\n",
        "    '''\n",
        "    used in self.move() as part of self.calculate_welfare()\n",
        "    '''\n",
        "\n",
        "    sugar_patch = self.get_sugar(pos)\n",
        "    if sugar_patch:\n",
        "      return sugar_patch.amount\n",
        "    return 0\n",
        "\n",
        "  def get_spice(self, pos):\n",
        "    '''\n",
        "    used in self.get_spice_amount()\n",
        "    '''\n",
        "\n",
        "    this_cell = self.model.grid.get_cell_list_contents(pos)\n",
        "    for agent in this_cell:\n",
        "      if type(agent) is Spice:\n",
        "        return agent\n",
        "    return None\n",
        "\n",
        "  def get_spice_amount(self, pos):\n",
        "    '''\n",
        "    used in self.move() as part of self.calculate_welfare()\n",
        "    '''\n",
        "\n",
        "    spice_patch = self.get_spice(pos)\n",
        "    if spice_patch:\n",
        "      return spice_patch.amount\n",
        "    return 0\n",
        "\n",
        "  def get_trader(self, pos):\n",
        "      '''\n",
        "      helper function used in self.trade_with_neighbors()\n",
        "      '''\n",
        "\n",
        "      this_cell = self.model.grid.get_cell_list_contents(pos)\n",
        "\n",
        "      for agent in this_cell:\n",
        "        if isinstance(agent, Trader):\n",
        "          return agent\n",
        "\n",
        "\n",
        "  def is_occupied_by_other(self,pos):\n",
        "    '''\n",
        "    helper function part 1 of self.move()\n",
        "    '''\n",
        "\n",
        "    if pos == self.pos:\n",
        "      # agent's position is considered unoccupied as agent can stay there\n",
        "      return False\n",
        "    # get contents of each cell in neighborhood\n",
        "    this_cell = self.model.grid.get_cell_list_contents(pos)\n",
        "    for a in this_cell:\n",
        "      # see if occupied by another agent\n",
        "      if isinstance(a, Trader):\n",
        "        return True\n",
        "    return False\n",
        "\n",
        "  def calculate_welfare(self, sugar, spice):\n",
        "    '''\n",
        "    helper function\n",
        "\n",
        "    part 2 self.move()\n",
        "    self.trade()\n",
        "    '''\n",
        "\n",
        "    # calculate total resources\n",
        "    m_total = self.metabolism_sugar + self.metabolism_spice\n",
        "    # Cobb-Douglas functional form; starting on p. 97 on Growing Artificial Societies\n",
        "    return sugar**(self.metabolism_sugar/m_total) * spice**(\n",
        "        self.metabolism_spice/m_total)\n",
        "\n",
        "\n",
        "  def is_starved(self):\n",
        "    '''\n",
        "    Helper function for self.maybe_die()\n",
        "    '''\n",
        "\n",
        "    return (self.sugar <= 0) or (self.spice <= 0)\n",
        "\n",
        "  def calculate_MRS(self, sugar, spice):\n",
        "    '''\n",
        "    Helper function for self.trade()\n",
        "\n",
        "    Determines what trader agent is needs and can give up\n",
        "    '''\n",
        "\n",
        "    return (spice/self.metabolism_spice) / (sugar/self.metabolism_sugar)\n",
        "\n",
        "  def calculate_sell_spice_amount(self, price):\n",
        "    '''\n",
        "    helper function for self.maybe_sell_spice() which is called from\n",
        "    self.trade()\n",
        "    '''\n",
        "\n",
        "    if price >= 1:\n",
        "      sugar = 1\n",
        "      spice = int(price)\n",
        "    else:\n",
        "      sugar = int(1/price)\n",
        "      spice = 1\n",
        "    return sugar, spice\n",
        "\n",
        "  def sell_spice(self, other, sugar, spice):\n",
        "    '''\n",
        "    used in self.maybe_sell_spice()\n",
        "\n",
        "    exchanges sugar and spice between traders\n",
        "    '''\n",
        "\n",
        "    self.sugar += sugar\n",
        "    other.sugar -= sugar\n",
        "    self.spice -= spice\n",
        "    other.spice += spice\n",
        "\n",
        "\n",
        "  def maybe_sell_spice(self, other, price, welfare_self, welfare_other):\n",
        "    '''\n",
        "    helper function for self.trade()\n",
        "    '''\n",
        "\n",
        "    sugar_exchanged, spice_exchanged = self.calculate_sell_spice_amount(price)\n",
        "\n",
        "    # Assess new sugar and spice amount - what if change did occur\n",
        "    self_sugar = self.sugar + sugar_exchanged\n",
        "    other_sugar = other.sugar - sugar_exchanged\n",
        "    self_spice = self.spice - spice_exchanged\n",
        "    other_spice = other.spice + spice_exchanged\n",
        "\n",
        "    # double check to ensure agents have resources\n",
        "\n",
        "    if ((self_sugar <= 0) or\n",
        "        (other_sugar <=0) or\n",
        "        (self_spice <= 0) or\n",
        "        (other_spice <= 0)):\n",
        "      return False\n",
        "\n",
        "    # trade criteria #1 - are both agents better off?\n",
        "    both_agents_better_off = (\n",
        "        (welfare_self < self.calculate_welfare(self_sugar, self_spice)) and\n",
        "        (welfare_other < other.calculate_welfare(other_sugar, other_spice)))\n",
        "\n",
        "    # trade criteria #2 is their mrs crossing\n",
        "    mrs_not_crossing = self.calculate_MRS(self_sugar, self_spice\n",
        "                                          ) > other.calculate_MRS(\n",
        "                                              other_sugar, other_spice\n",
        "                                          )\n",
        "\n",
        "    if not (both_agents_better_off and mrs_not_crossing):\n",
        "      return False\n",
        "\n",
        "    # criteria met, execute trade\n",
        "    self.sell_spice(other, sugar_exchanged, spice_exchanged)\n",
        "\n",
        "    return True\n",
        "\n",
        "\n",
        "  def trade(self, other):\n",
        "    '''\n",
        "    helper function used in trade_with_neighbors()\n",
        "\n",
        "    other is a trader agent object\n",
        "    '''\n",
        "\n",
        "    # sanity check to verify code is working as expected\n",
        "    assert self.sugar > 0\n",
        "    assert self.spice > 0\n",
        "    assert other.sugar > 0\n",
        "    assert other.spice > 0\n",
        "\n",
        "    # calculate marginal rate of subsitution in Growing Artificial Socieites p. 101\n",
        "    mrs_self = self.calculate_MRS(self.sugar, self.spice)\n",
        "    mrs_other = other.calculate_MRS(other.sugar, other.spice)\n",
        "\n",
        "    #calculate each agents welfare\n",
        "    welfare_self = self.calculate_welfare(self.sugar, self.spice)\n",
        "    welfare_other = other.calculate_welfare(other.sugar, other.spice)\n",
        "\n",
        "    if math.isclose(mrs_self, mrs_other):\n",
        "      return\n",
        "\n",
        "    # calcualte price\n",
        "    price = math.sqrt(mrs_self*mrs_other)\n",
        "\n",
        "    if mrs_self > mrs_other:\n",
        "      # self is a sugar buyer, spice seller\n",
        "      sold = self.maybe_sell_spice(other, price, welfare_self, welfare_other)\n",
        "      # no trade - criteria not met\n",
        "      if not sold:\n",
        "        return\n",
        "    else:\n",
        "      # self is a spice buyer, sugar seller\n",
        "      sold = other.maybe_sell_spice(self, price, welfare_other, welfare_self)\n",
        "      # no trade - criteria not met\n",
        "      if not sold:\n",
        "        return\n",
        "\n",
        "    # Capture data\n",
        "    self.prices.append(price)\n",
        "    self.trade_partners.append(other.unique_id)\n",
        "\n",
        "    # continue trading\n",
        "    self.trade(other)\n",
        "\n",
        "  ######################################################################\n",
        "  #                                                                    #\n",
        "  #                      MAIN TRADE FUNCTIONS                          #\n",
        "  #                                                                    #\n",
        "  ######################################################################\n",
        "\n",
        "\n",
        "  def move(self):\n",
        "    '''\n",
        "    Function for trader agent to identify optimal move for each step in 4 parts\n",
        "    1 - identify all possible moves\n",
        "    2 - determine which move maximizes welfare\n",
        "    3 - find closest best option\n",
        "    4 - move\n",
        "    '''\n",
        "\n",
        "    # 1. identify all possible moves\n",
        "\n",
        "    neighbors = [i\n",
        "                 for i in self.model.grid.get_neighborhood(\n",
        "                   self.pos, self.moore, True, self.vision\n",
        "                 ) if not self.is_occupied_by_other(i)]\n",
        "\n",
        "    # 2. determine which move maximizes welfare\n",
        "\n",
        "    welfares = [\n",
        "        self.calculate_welfare(\n",
        "            self.sugar + self.get_sugar_amount(pos),\n",
        "            self.spice + self.get_spice_amount(pos))\n",
        "        for pos in neighbors\n",
        "    ]\n",
        "\n",
        "    # 3. Find closest best option\n",
        "\n",
        "    # find the highest welfare in welfares\n",
        "    max_welfare = max(welfares)\n",
        "    # get the index of max welfare cells\n",
        "    candidate_indices = [i for i in range(len(welfares))\n",
        "                        if math.isclose(welfares[i], max_welfare)]\n",
        "\n",
        "    # convert index to positions of those cells\n",
        "    candidates = [neighbors[i] for i in candidate_indices]\n",
        "\n",
        "    min_dist = min(get_distance(self.pos, pos) for pos in candidates)\n",
        "\n",
        "    final_candidates = [ pos for pos in candidates\n",
        "                       if math.isclose(get_distance(self.pos, pos), min_dist, rel_tol=1e-02\n",
        "                                       )]\n",
        "    self.random.shuffle(final_candidates)\n",
        "\n",
        "    # 4. Move Agent\n",
        "    self.model.grid.move_agent(self, final_candidates[0])\n",
        "\n",
        "  def eat(self):\n",
        "    # get sugar\n",
        "    sugar_patch = self.get_sugar(self.pos)\n",
        "\n",
        "    if sugar_patch:\n",
        "        self.sugar += sugar_patch.amount\n",
        "        sugar_patch.amount = 0\n",
        "    self.sugar -= self.metabolism_sugar\n",
        "\n",
        "    # get_spice\n",
        "    spice_patch = self.get_spice(self.pos)\n",
        "\n",
        "    if spice_patch:\n",
        "        self.spice += spice_patch.amount\n",
        "        spice_patch.amount = 0\n",
        "    self.spice -= self.metabolism_spice\n",
        "\n",
        "  def maybe_die(self):\n",
        "    '''\n",
        "    Function to remove Traders who have consumed all their sugar or spice\n",
        "    '''\n",
        "\n",
        "    if self.is_starved():\n",
        "      self.model.grid.remove_agent(self)\n",
        "      self.model.schedule.remove(self)\n",
        "\n",
        "  def trade_with_neighbors(self):\n",
        "    '''\n",
        "    Function for trader agents to decide who to trade with in three parts\n",
        "\n",
        "    1- identify neighbors who can trade\n",
        "    2- trade (2 sessions)\n",
        "    3- collect data\n",
        "    '''\n",
        "\n",
        "    neighbor_agents = [self.get_trader(pos) for pos in self.model.grid.get_neighborhood(\n",
        "        self.pos, self.moore, False, self.vision) if self.is_occupied_by_other(pos)\n",
        "    ]\n",
        "\n",
        "    if len(neighbor_agents) == 0:\n",
        "      return\n",
        "\n",
        "\n",
        "    # iterate through traders in neighboring cells and trade\n",
        "    for a in neighbor_agents:\n",
        "      if a:\n",
        "        self.trade(a)\n",
        "\n",
        "    return"
      ],
      "metadata": {
        "id": "t51BNfwUnt53"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Class"
      ],
      "metadata": {
        "id": "SRrU2ABBoF0i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class SugarscapeG1mt(mesa.Model):\n",
        "  \"\"\"\n",
        "  Manager class to run Sugarscape with Traders\n",
        "  \"\"\"\n",
        "\n",
        "\n",
        "  def __init__(self, width=50,height=50, initial_population=200,\n",
        "               endowment_min=25, endowment_max=50, metabolism_min=1,\n",
        "               metabolism_max=5, vision_min=1, vision_max=5):\n",
        "\n",
        "    #Initiate width and heigh of sugarscape\n",
        "    self.width = width\n",
        "    self.height = height\n",
        "    #Initiate population attributes\n",
        "    self.initial_population = initial_population\n",
        "    self.endowment_min = endowment_min\n",
        "    self.endowment_max = endowment_max\n",
        "    self.metabolism_min = metabolism_min\n",
        "    self.metabolism_max = metabolism_max\n",
        "    self.vision_min = vision_min\n",
        "    self.vision_max = vision_max\n",
        "\n",
        "    # initiate activation schedule\n",
        "    self.schedule = mesa.time.RandomActivationByType(self)\n",
        "    # initiate mesa grid class\n",
        "    self.grid = mesa.space.MultiGrid(self.width, self.height, torus=False)\n",
        "    # initiate datacollector\n",
        "    self.datacollector = mesa.DataCollector(\n",
        "        model_reporters = {\"Trader\": lambda m: m.schedule.get_type_count(Trader),\n",
        "         \"Trade Volume\": lambda m: sum(len(a.trade_partners)\n",
        "         for a in m.schedule.agents_by_type[Trader].values()),\n",
        "         \"Price\": lambda m: geometric_mean(flatten([a.prices for a in m.schedule.agents_by_type[Trader].values()]))},\n",
        "       agent_reporters = {\"Trade Network\": lambda a: get_trade(a)}\n",
        "    )\n",
        "\n",
        "\n",
        "    #read in landscape file from supplmentary material\n",
        "    sugar_distribution =np.genfromtxt(\"/content/drive/MyDrive/sugar-map.txt\")\n",
        "    spice_distribution = np.flip(sugar_distribution, 1)\n",
        "\n",
        "    agent_id = 0\n",
        "    for _,(x,y) in self.grid.coord_iter():\n",
        "      max_sugar = sugar_distribution[x,y]\n",
        "      if max_sugar > 0:\n",
        "        sugar = Sugar(agent_id, self, (x,y), max_sugar)\n",
        "        self.schedule.add(sugar)\n",
        "        self.grid.place_agent(sugar, (x,y))\n",
        "        agent_id += 1\n",
        "\n",
        "      max_spice = spice_distribution[x,y]\n",
        "      if max_spice > 0:\n",
        "        spice = Spice(agent_id, self, (x,y), max_spice)\n",
        "        self.schedule.add(spice)\n",
        "        self.grid.place_agent(spice, (x,y))\n",
        "        agent_id += 1\n",
        "\n",
        "    for i in range(self.initial_population):\n",
        "      #get agent position\n",
        "      x = self.random.randrange(self.width)\n",
        "      y = self.random.randrange(self.height)\n",
        "      #see Growing Artificial Societies p. 108 for initialization\n",
        "      #give agents initial endowment\n",
        "      sugar = int(self.random.uniform(self.endowment_min, self.endowment_max+1))\n",
        "      spice = int(self.random.uniform(self.endowment_min, self.endowment_max+1))\n",
        "      #give agents initial metabolism\n",
        "      metabolism_sugar = int(self.random.uniform(self.metabolism_min, self.metabolism_max+1))\n",
        "      metabolism_spice = int(self.random.uniform(self.metabolism_min, self.metabolism_max+1))\n",
        "      #give agents vision\n",
        "      vision = int(self.random.uniform(self.vision_min, self.vision_max+1))\n",
        "      #create Trader object\n",
        "      trader = Trader(agent_id,\n",
        "                      self,\n",
        "                      (x,y),\n",
        "                      moore = False,\n",
        "                      sugar = sugar,\n",
        "                      spice = spice,\n",
        "                      metabolism_sugar = metabolism_sugar,\n",
        "                      metabolism_spice = metabolism_spice,\n",
        "                      vision = vision)\n",
        "      #place agent\n",
        "      self.grid.place_agent(trader, (x,y))\n",
        "      self.schedule.add(trader)\n",
        "      agent_id += 1\n",
        "\n",
        "  def randomize_traders(self):\n",
        "    '''\n",
        "    helper function for self.step()\n",
        "\n",
        "    puts traders in randomized list for step function\n",
        "    '''\n",
        "\n",
        "    trader_shuffle = list(self.schedule.agents_by_type[Trader].values())\n",
        "    self.random.shuffle(trader_shuffle)\n",
        "\n",
        "    return trader_shuffle\n",
        "\n",
        "\n",
        "  def step(self):\n",
        "    '''\n",
        "    Unique step function that does staged activation of sugar and spice\n",
        "    and then randomly activates traders\n",
        "    '''\n",
        "    # step Sugar agents\n",
        "    for sugar in self.schedule.agents_by_type[Sugar].values():\n",
        "      sugar.step()\n",
        "\n",
        "    # step Spice agents\n",
        "    for spice in self.schedule.agents_by_type[Spice].values():\n",
        "      spice.step()\n",
        "\n",
        "    # step trader agents\n",
        "    # to account for agent death and removal we need a seperate data strcuture to\n",
        "    # iterate\n",
        "    trader_shuffle = self.randomize_traders()\n",
        "\n",
        "    for agent in trader_shuffle:\n",
        "      agent.prices = []\n",
        "      agent.trade_partners = []\n",
        "      agent.move()\n",
        "      agent.eat()\n",
        "      agent.maybe_die()\n",
        "\n",
        "    trader_shuffle = self.randomize_traders()\n",
        "\n",
        "    for agent in trader_shuffle:\n",
        "      agent.trade_with_neighbors()\n",
        "\n",
        "    self.schedule.steps += 1 #important for data collector to track number of steps\n",
        "\n",
        "    # collect model level data\n",
        "    self.datacollector.collect(self)\n",
        "\n",
        "    #Need to remove excess data, as Mesa does not yet have a datacollection\n",
        "    #by agent type feature we will for into datacollector to update its data\n",
        "    #structures\n",
        "    agent_trades = self.datacollector._agent_records[self.schedule.steps]\n",
        "    #leverage None aspect of no data in data collector\n",
        "    agent_trades = [agent for agent in agent_trades if agent[2] is not None]\n",
        "    #reassign that step in the dictionary with lean trade data\n",
        "    self.datacollector._agent_records[self.schedule.steps] = agent_trades\n",
        "\n",
        "  def run_model(self, step_count=1000):\n",
        "\n",
        "    for i in range(step_count):\n",
        "      self.step()"
      ],
      "metadata": {
        "id": "7DTonjahn2BT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run Sugarscape"
      ],
      "metadata": {
        "id": "_KHKfeD9oiYo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = SugarscapeG1mt()\n",
        "model.run_model()\n"
      ],
      "metadata": {
        "id": "at455urSol7b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56199f34-bef8-4faa-b1e4-e45a5826e3e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-d4e043c58af1>:25: RuntimeWarning: Mean of empty slice.\n",
            "  return np.exp(np.log(list_of_prices).mean())\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analyze Data"
      ],
      "metadata": {
        "id": "XQ7ykQMWoRyU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = model.datacollector.get_model_vars_dataframe()\n",
        "\n",
        "results"
      ],
      "metadata": {
        "id": "vemquyVptT3Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "83775a13-9136-4fc6-a720-a380236305f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Trader  Trade Volume     Price\n",
              "0       200           612  0.982439\n",
              "1       200           131  0.965145\n",
              "2       200           146  1.051640\n",
              "3       200           117  0.993983\n",
              "4       200           140  1.036779\n",
              "..      ...           ...       ...\n",
              "995      67             5  1.016208\n",
              "996      67            45  1.306432\n",
              "997      67             4  1.152561\n",
              "998      67             4  1.020134\n",
              "999      67             3  1.003125\n",
              "\n",
              "[1000 rows x 3 columns]"
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              "      <th></th>\n",
              "      <th>Trader</th>\n",
              "      <th>Trade Volume</th>\n",
              "      <th>Price</th>\n",
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              "      <th>0</th>\n",
              "      <td>200</td>\n",
              "      <td>612</td>\n",
              "      <td>0.982439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>200</td>\n",
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              "      <td>0.965145</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>200</td>\n",
              "      <td>146</td>\n",
              "      <td>1.051640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>200</td>\n",
              "      <td>117</td>\n",
              "      <td>0.993983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>200</td>\n",
              "      <td>140</td>\n",
              "      <td>1.036779</td>\n",
              "    </tr>\n",
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              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>67</td>\n",
              "      <td>45</td>\n",
              "      <td>1.306432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>67</td>\n",
              "      <td>4</td>\n",
              "      <td>1.152561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>67</td>\n",
              "      <td>4</td>\n",
              "      <td>1.020134</td>\n",
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              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>67</td>\n",
              "      <td>3</td>\n",
              "      <td>1.003125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1000 rows × 3 columns</p>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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        "# retrieve agent level results\n",
        "agent_results = model.datacollector.get_agent_vars_dataframe()\n",
        "\n",
        "agent_results"
      ],
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            "text/plain": [
              "                                     Trade Network\n",
              "Step AgentID                                      \n",
              "1    4138                                       []\n",
              "     4139                                       []\n",
              "     4140                                       []\n",
              "     4141                             [4293, 4293]\n",
              "     4142     [4219, 4219, 4219, 4219, 4219, 4219]\n",
              "...                                            ...\n",
              "1000 4324                                       []\n",
              "     4326                                       []\n",
              "     4329                                       []\n",
              "     4333                                       []\n",
              "     4334                                       []\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-910c86da-f23f-448d-9266-b71b4803e519 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-910c86da-f23f-448d-9266-b71b4803e519');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot number of agents per time step\n",
        "results.plot(y = \"Trader\", use_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "lr0rYpIpovMg",
        "outputId": "6005b627-aaf4-40e0-f3c7-97f0b95c6205"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot trade price per step\n",
        "\n",
        "y = list(results[\"Price\"])\n",
        "x = range(1000)\n",
        "\n",
        "plt.scatter(x,y, s=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "MIAxl_Oqo7M4",
        "outputId": "1e3dba1a-d07c-4374-b0f8-8092a0836542"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f59b4278640>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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vXr3QtWvXTr8nIiKi5GQ7GNm3bx9uuumm8L8XLlwIAJgxYwZefPFFnDp1CseOHXOvhERERJTQ+G4aIiIi8gTfTUNERKQIDrWPjsEIERGRxzjUPjoGI2SIUTyphuckBdmccYPQLyeTQ+1NMBghQ4ziSTU8JynIpo3Kx64lN3O4vQkGIwnIjSdIRvGkGp6TRImLo2kS0NhV23Givhn9cjKxa0nnGW6JiIj8wNE0SYxPkEREFCRsGSGKU1llTfgdMOwPJiL6CltGiHwSxMRKjkwhIpUwGCGKUxC7xYIYQBFR4mIwQhQnFYfsxWr5CGIARUSJizkjRAmII6qISAXMGaEw5gckD+1YD8/vwZYPIgoMBiNJIBHzAxhgGdOOdVXN58p1HRERmWEwYiKRbnZ28wOCsO+JGGC5gbkgRGSHKtd75oyYSOY+9yDsO+f2ICKKn9fXe+aMxCmZnzCDsO8qjmAhIgoaVa73bBlRWFllDR7feggAsKhwMG+8REQUKGwZSQClFYdR39yK+uZW5kYQESUBVXI4/MZgRGFzxg1CTmYX5GR2kd6ERoktWS+ApA6egxcka3I+gxGFTRuVj+oVt6F6xW3soiFPJesFkNTBc/ACVXI4/MZghIiS9gJI6uA5eEGyJuczgZWIiIg8wQRWIiIiCgQGI0RE5AomocoX1GPAYCRggnqiEVHiYxKqfEE9BgxGAsbpicYghii5+XENYBKqfEE9BkxgDRin72QJwvtmiMg7vAaQDExgTVBOh30FNVpOVGypIr9FXgN4DiY31Y4/g5Ekkaxj153y+osqq19XtQsQ+SfyGhDU3IIgU+n7p9rxZzBCZMDrL6qslirVLkBBpdJNxalkay1V4Zip9P1T7fgzZ4TIgNPcHNUl6n75jfkXwaM/ZnPGDZLyPUjG75/V+zeDESIim5LxphJ0+mOmtVAEOZgMyjnIYISIiMhAUG7k0QSldY6jacgyFfpSibzC85s02rkAwDShPyjni2o5H/GyHYzs3LkTRUVFyM3NRUpKCjZs2BB1+VdffRW33norLrvsMmRlZWH06NHYunWr0/KSB1RIqgrKBSCIkr1uVTi/SQ1WzoWgnC+JNkLSdjDS1NSEYcOG4amnnrK0/M6dO3Hrrbdi8+bNqKqqwk033YSioiLs37/fdmEpOqc3HRUi7KBcAFRg9zgnQt3GE1CpcH6b0e+Xm0FjsgegZqycCyqfL4ksrpyRlJQUrF+/HpMmTbL1uWuvvRaTJ0/G8uXLLS3PnBFrgtKHaCQR+nC9FE/yXSLUrZ1zO0j7q98vAK59f926FgSpLgHz8gZtP/ziR70omzPS3t6OM2fOoGfPnqbLtLS0oLGxscOPX4L8RBHkiD7Rmhzdpm/dsHucg1q3+u+inX0OUkuQfr+cfH/NrlduXQuCVJeAcXnLKmuwYuPBQO2Hm6Ld01Q6vr4HI48//jjOnj2L733ve6bLlJSUIDs7O/yTl5fnW/lUOjh2BfWmQ7Hpby7Jcpz130U7+xyEoNwokdLJcTW7Xrl1jgShLvWMyltacRhtAkhLQWD2w03R7mkqHV9fu2lefvll3Hfffdi4cSPGjx9vulxLSwtaWlrC/25sbEReXp4v3TRszqNEE9RzOqjltiJZu1FkSPY6kr3/vswzYicYWbt2LWbNmoVXXnkFEydOtLWdRM0ZkX2SeCVR9yuogpxLlKiS5TuSLPtJ5pTKGVmzZg1mzpyJNWvW2A5EEpmXXUIyc1+C3NWViFRqitULcn5WvJKxq40oGtvByNmzZ1FdXY3q6moAwNGjR1FdXY1jx44BAIqLizF9+vTw8i+//DKmT5+OJ554AiNHjkRtbS1qa2vR0NDgzh4EmJc3CZkXAVVvfslK1Rsfb1Ry+BkEqnQtSObgNwhsd9NUVFTgpptu6vT7GTNm4MUXX8Q999yDTz75BBUVFQCAcePGYceOHabLW5Go3TReCnLzaJDLTtYl83GWue/J2m2n2n77cQ6o8B3ju2kosFS7aBC5TeY5rsINSgbV9tuPc0CFa6lSOSNB4VUzHpsH7VGpaZfICzLPcVW77bym2n77cQ4E6VrKlhEdr6JIFaJTSk6qPQ1SdG4eLx57UgFbRhzwKooMUnRK7lClNSxWkqgq5TTjdflU2383k3qZIGyfaudDMmEwohOrGc/piapa8yB5z4sbgZPzL1YgHFlOty/G8a7P6xuqfv0q3IjcfHAJ+kOQjOPBAE4eBiN/Y+XEt3KievkFKquswfUr/4DrV/6BkbvivLgROLlQxgqEI8vp9sU43vV5fUPVr9/vG5HRtcLOg0usa03QH4JkBAZBD+CcUCEIBxiMhJm9YEl/kKycqF5+gUorDqO+uRX1za2BjdxVOfHNuFU+L24EXlwoI8vp9jbiXV9k+dw+f/Tr9/tGFO+1ItGf4mUEBkEP4JxQ5TxiAuvfGCV7OUk89TJprKyyBo9vPQQAWFQ42JP1e53wpnoyr+rlS3aJdHzi/b4ZfZ5Jq6Sxei54fc5wnpE4aAdneH4PVNV8njRfbD8u9LFOfNkXU9nbj0eQy25VIuyjl/uQSMFaLIlwLljhdD9VORc4miYOWrNVVc3nSdVkF0+zqNXm81jNoFabDL3q7glyM60qza1esptToWKOlZfHyew7rHr3qBPJcL4DzvczaOcCgxEDw/N7IC3lwn+TSTw3YrcuDFYDokS+EDm9WHjdx263XLIveqrmWHl5nMy+w4n4fUmWZFOn+2l0LpRV1mDFxoNKngsMRgxU1XyONnHhv0Eg+6IPuHdhsBoQJfKFyGnrkNetOnZvaLJvgHPGDUJOZhfkZHZR6jyR0fqm1UVTy3nlnoj17FzLotWj2Xq8uFZ6ff1183wprTiMNgGkpUCp7wTAYMRQtBudnzd+q9uSfdEHEB6NoM3X4Mf2gtqdEkvk+Wd2HmjHfcXGg0q+gVV2wDhtVD6qV9yG6hW3xTxPVAjovTRtVD66h9KVayWK5Na1zGw9Xlwrzdap4jmlfSdX3jFEuWsngxED0W50ft74rW7L74t+rJujKhNIBVXk+Wd2HswZNwhpKUCbgC/no90A0OuA0c1zTIWA3muyg0Mr3Cqj2Xq8qAOzdbp9Trlxvqv8EMdgxCY/v9BWt+X3CRbt5ihrAilZ/Ai6zM6DaaPysfKOIcrfYJyKVbdunGPaNobn90iIeoxWZ2bXCZkPDl51NZqtx81rpVZ2AIbrdPtekejXVAYjNvl543e6LTsXFzenGJc5gZQs8Vwg3BiBJOtJx48bWKy6deMcU3HkXDx16+R8lNnNoOoN1o0ZubXvJgBXEr8T/ZrKYCTArHSXxOLFFONWl/GC30958VwgVL0QW+FH2WPVrRvnmIoX+Hjq1sn++NXNYGfbslnZd69G/pktr3IXiytEADQ0NAgAoqGhQXZRlDKmpFzkL35djCkp7/D73+z+RIwpKRe/2f1JzHXYWTYIzOrEb1bq1a1lZIi3XG7tl6r1Y5d+P1TZJ1XKYcbL8rm5brvrcrJtlY+V1fs3Z2ANoESbIdbNabEBKDEFsluzH8ZajypTPtvlV/0EhZv7EdRp4u2WUauznMwu6B5KV3rfvKZyXXAG1gSmYj93PNx8YZjVpkyvm6C9HhWgsbofqnUJ+VU/QeHmfhgda9WOvxG7ZdTqDIDy+2bEzS7loNcFwGAkkBLlAqyJd3/c7Cd3i9WgKN7XwFvdD/1y2jbnr9kvbRSF16MmgsbN/TA6J4x+p9rwe7vfSa3OFhUODuT10EmAGHnMIkf0BLUuAL4oz5ZE6x5xU7I0DbtNRjeDtk1tjhIr25ZdT6qUwQtu7JeTdejPPW04vp91G5Tj6VU54z1mu5bcbPv6IaPO2U3jAS2S3XTgpLJNYbKedrxoGk6GoYVWnwb1dRFvvWjbnDg019JMr4D8epJRBr++S27sl5N1yJ4XSIVzygqvyumkNSzyemF2/XBjpKXfGIzYYHYRV4msC7bRpFFOu0K0dT6+9VDCDy2MvCBZuYho///41kOObpbaNldPKbA00ysgv55klMGv75JRF5r+mFoJiqLVjdnn3Z4XyG7w5qTMMqhw7msiX7th98WIKu1LJHbT6NhpwlK1idHvckU2E7qxfZUzw71m1uxqNGKoqeU86ptbozbR+nVOx/qsqt+XaGSU2ej4x9uVZ/R5L/bNzS5HL7svo+273XpR5RxRoVxm2E3jgJPJwpw+nSaKyEjbjadJbZ2LCge7kgQaJGZPLvonIDuJe3aORzwz/sZ6Lbm+HFaOlwrHVNbbdWO1MJolMZrVldE63exCtTulfuRnr1/5B1y/8g8dyu/lE3y0fXdrgjI7zOrAjJW6iWxBUeH7FAtbRnTsRMzav608nfpJ9rwLqj4pJCs/joc+IdbsbaD6cmgX8GjHy0oLkZ39UelJMV6xkhjLKmvw+NZDAIBFhYNNj0esZayWAYCt75/RZwHjRGqzcppdj+NtAbS7Xqf1aPR9MKsDp+I5Rm5iy4gD0Z6EIiNgVYeVye4TVOVpMlIQngy8KKMfx0Orfy0QMdoPu/kJZss4fRJ1K//Dr/Mo2nZiJTGWVhxGfXMr6ptbTfd32qh8dA+ld1jGzr7pt2n3mqMtPzy/B5paziOzSypyMrsYJmGu2HjQcF8ij6eWX6YFBtHYeddTrPPGqB6t0LesR6uDeMQ6RqpdE5O+ZcRqRJ1IT1aREnnfNDJaT+zWa6LMtuqkrr3eN7eenK3kM5l93k4Zrl/5B9Q3tyInswuqV9xmeT+17ThpGfH7OxJre9rfUwBkZ3aJ2jIST31FY+WYOTknVWhZ9+t4s2XEIqtPTFrEDNh7A2MQ+DFqQHYULqPFyG69xiqj1fWZLefXMYg1OsSI3e+h3SDLzuesjCoCzGe6NPt8tPXGqqdoeSKRf5s2Kh/VK25D9YrbTPdXaz3pHkoPL+P3dyTW07r2959OGtJpXyKP56LCwcjJ7BJeh1usnDdOzsl4W9bd+C7LbkWPlPTBiNkBCeI4baecnJR2vwyy603V7iM9fRmN6tfq+ux0cdg9jlaW1++H1eOu0oXRSlm+ddVlpsuYfT7aeiPrSbtJLSocbPh3o6HeyzYcjCsJ0s/viFlrgr77wm5LFgDb3SVWyullAB/rO2/GjeupjGtiNEnfTWPGbHZCwNqL2BKdk5n/4kmYSzZeNKEa3QDsbkfGcZfd7aTf/uNbD3nWHRCtnqIlVgLAio0H0fa3K3kQErljJSjruy/011+j42/UdQbEd5122o0SeVzsJMPa+W7J/k7YwW4aG2I9heqjUL+jSdndG2acPPU7SfRKVrHq18l5YXTuOk0+dHLcV2w86Og8dmv4pJX6Mur+sDP5XrTuk2jl0NeT0XQBkccucqj3yjuGICezi+tJkE7Fqm+z88io+yLy+EeuW1vXosLB4eXjnTAxcoSL1To1arEyas2KVifD83vYan1MFGwZQeyhiV4lMdkpWxCedowYPSnw3T7xC9J5oY2KsPoeHKPPO00Q1D5jtb6MhkNqT9zD83tg54efATBv5Yk1nDJaouX8Nfux6cBJZKSnobm1zZN3xtgZ1upkHRo3z09te1r9NzS3QsD4XNK3kmictMjFmyhtdK5YXWeQvttWsGXEhjnjBoVfGmb09GYlCvUqJ0KlvnQnjFqVqmo+j1lXqrYIqWJ4fg+kpVz4r+q0J3c3z+P5a/ZjUPEmzF+zP/w7/TkT+X10km+jf+LWztvIpM9Yn8/J7ILTTS2dcjkamls7ndtVNZ+jTQCh9FTP3hkTbX1OE6TjyW+KFG1Y+M4PP0P93wKRtBREzdfRcm3qm1vx6Kb3bOdFOQl+tbfnzhk3CJsOnOx0rlhtzbBSd2WV9iZK05dR1WsqgxF8dbHUAhI7X3ztAFudfVD/GSsnRdCb44y+WFa+bF4Ed259GY1uhG5vIxbtxlVV87mr641W/nj2zUqintWk8bLKGrz29km0CWDTgZPh3y3bcDCc/Bh5jkVu3+xCHtn9of/uWek609/EtK6X5tb2cPfkosLBSEsBBDpfZyKDH6tzspgxqs9oQazTBGmj76rT0YdWvvcpsNeC/WVru63RZU6uPZHdM23CPGCKJdY1X2tp1OZfsToLuL6MKgYmDEb+Rt/v2tRy3vaXp6rmc1eGDlrh9Ynk5vqNvlhWAiwvWoSc1LtRXWw60PFGqF821rTobvGqxSzaU6/2N6e5H2bbiPX7yL50/cRWE4fmhutd398c7RzThrVqF3KrYp23RuXXWkdyMrtgeH4PlFYcNn3RptPvihmj8rgRxNoJ0ux+56KtS8sjKRqWG76hRtuWfnk7E+g5ydcyalEzm404XlqwkwKEu6PsjljTj1oau2o75q/ZLz04sR2M7Ny5E0VFRcjNzUVKSgo2bNgQ8zMVFRX4+te/jlAohCuuuAIvvviig6J6zyjJ0mkiVjTaBcpO0KPnVZeQX+s3o69rL+Z1sXuszIKLiUNzkZZy4b968T4R2eH0JmX3fNafC/ruzHjODf029OWJldSode8BF/IFvj0sF1U1n+PRTe+HR5Lk/G1yrGicdm05uRbo5/zQyq/lS2k3VLstRVZZaZWM1rXllJVjaibaea3vrjGabTUyaAVg+HZq/fJG12EnQWe0FrV4aHWpDxa0/dTmX9En+0aeM0bXVH2LG3AhkNl04KT0KStsJ7D+/ve/x65duzB8+HDceeedWL9+PSZNmmS6/NGjRzFkyBDMnj0b3//+91FeXo77778fmzZtQmFhoaVt+jm012nim9XEMMD6G1edbMuNz3mVjBuLUV3LHO5m5Z0rXm7fzjZibVv7u3beWX0jcuR6vapjO8dXS4DW9iUFF7o9tKRQo++bvrx2j6tZWSPLY6cutRtatPeGmCW7unkM9HUxcWiuo8Rys3fl2KlfO9emZRsOho/3osLBnY6Bvm5jnVNWzr9o3wEg9vDdeI6Xvi6jJYCXVV4YGh6Z3Gt1VmcvBxV4lsA6YcIEPPLII/jOd75jaflnnnkGAwcOxBNPPIFrrrkG8+bNw3e/+138+7//u91N+8JuP7HGamKY9v+AvSFj0coIWHuKsvrkIytPxWl+icbtFh27za1O6s3u06+VLg6jdUaedwCiLm+0T14EW9Ge1M3KEtlCojXDG00QZtZ14qQZ3azV6LW3oz9VajlGe4+eNswFsdt64OZ5rm/tstLVHO1JXSu/UQtarPPcaj5DacXhcAKrFohEPtlbrc+yyhqcbmpBCqK3lmnDhLVEWG0fSys6DyE2OjbxHC9tX8y69vTb0JJ7tc+VVdagqeV8uItQle4YM+leb2D37t0YP358h98VFhbi/vvvN/1MS0sLWlpawv9ubGz0qngxac1usWgnp1mGd7QnNTfoT/homf5mZdTIahUBjOta+512gYpWLiv75xWn9WbluOmZ7aP+90br1P89MrAwWt5of+yW1YrIY+7kPI4sp9nfzbbptKxzxg0KP6Wn4Kuuuflr9uN3b59EemoKuofSw0+rr719Icdo9ZSC8HqiTXSm3Wz1gZr2FAsgfIOJ56lWf2ztBPy1Dc3hYMMozyVynbHOsVjnr0b7u5Z/c0n3DNQ2NOPa3Gz8telcuA6s5u41t7YDsJZDE5kIqw0hjgzEIusy2nUp1nXDbF8iW2eaWs532jd9K6iW56Y/btNG5RseT7+v+5q45hlJSUmJ2U1z1VVXYebMmSguLg7/bvPmzZg4cSK++OILZGZmdvrMww8/jJUrV3b6vZ8zsLrFzk0qnkDArSDCrClaRtdDtHJZ/ZyTbUXbphvL6sthZe4Ku+zWi9Hy8dS3na6keLuBvHpBWmSZzW74RtsfVLwpnMMSKS0FOFwyEcBXdQxYm38lcltWmvBjld9O/WtdAcCF6fCraj63FAgZfS5ad4qd64HZvlvptnx86yG0nG9DKD0t6nfPqA4Baw+V2twxE4fmYvWUAtP9sNpNqc+T0c6FM1+2ok0AmV1Sce58e3ieGq0rVgtKtK44/fVG249AdtP4obi4GA0NDeGf48ePe7o9L0en2Gmis7NsZJmjdREYNa2aMWuKjncERTRW9tuo6dVJ86edz9hpPnfa1F5V83nMmWntnp92u4uMljfaH6vrtdKVZPa7eLsI3f4uG3UD6LcR+Q4ZoHNiM3DhRhGZ9Dxn3CDLs6aWVdagobkVwFdzlFhpwjcqv9HfjboZIutS6wroHkoPJ4VamTNI/zlt+ce3HurUhWDlWqavu2j7Hu17riWm1ze3omf3UPglfGbnjlYefSKs1a5yrUXid2+fNFy3dg40tZyPeX3W6rH+b+eB1t2qJc0vnfg1HC6ZiKUTrwmPspkzblD4HF15xxCsnlLQ4XpjtG+yeB6M9OnTB3V1dR1+V1dXh6ysLMNWEQAIhULIysrq8OMlN/tg9fR9dm7f0JwEOVYypo1yZuIZQWHl5mBlv63eMGOx8hmtzAAsf0G1PADtIm6nHLHK5NX5GU08QYHZ/hj9Pta+xzp/or1Qzo3ARCvftbnZ4fk5IrsRIutp9ZSC8I1Ca8bXbhT6p+Npo2K/XVdTWnE4nA8gAMs3klg3baN8D7MJ1yKXtXp9MzrXAXQKUMwCB6sBQrT9iqzLyFFvWoCiL0fkfEJW8170835oI+/S01IMRwBNG/XVCM5Y12d98KrNRaMPNLQ60K/T6Bx1ct30g+fdNIsXL8bmzZvxzjvvhH9311134fTp09iyZYul7Xg1msbrTGIvp/V10v3jdD/jyRB3YzSS15yOoIpcRzxTntspnx/c6Opxg9Pur2jdAPGWIzL/Rr9d7Tvm9jVFW/8l3TPw7skG02Z//bLxbjvWepx2MegDx1hdHvrp3a2M/rKyD9G6JfUjgLTuNq1rLdr+GnWh6JeL1p3oRZet26N64mH1/m07GDl79iw+/vhjAEBBQQF+8Ytf4KabbkLPnj3Rv39/FBcX48SJE/j1r38N4KuhvXPnzsWsWbOwfft2zJ8/X4mhvV6/A0DmDdYr2j5ZHZpsN2ixO9zSDW7kyVgtv4rnhNWckWi8+i7FExQB7iWLxyqHdrPRhhm7WQ9GAVa0m3OswMmtsgDW69dufkzk9pxOhRDPw1Bkvke0c8BqsOrk4c2rfEC/eJYzsm/fPhQUFKCg4EJUvnDhQhQUFGD58uUAgFOnTuHYsWPh5QcOHIhNmzZh27ZtGDZsGJ544gn893//t+VAxEteN1fF2/8tU2STpPZvbSgb8NXQ5GjNl1brIN7uoHhEngdOjpu2jliBlFmXi1EdepnLFKtMRk3ybk/+Z4XdY6Gdn49vPWS5Xz+ecmjraznfBgDo2iXV9XrQHx99N4dZk360rhYzVuslsosqsmvSbD2x8mNidcd866rLHL2LyWo3INC5S3b1lIIOXWuR54C+zPpp9rXlgK8mbLRzHXQjN87qulXCt/bGEG+2v9PteCGekSTRmkvdiritZMDbrSPVWiLMyhPvhG/R1u3G5+I5xn4eA6PmcK+6boD4uxGsiNbkHqs7yOvuVLPrhD4Yiiyjk5Y4r57q41mv/rNA54nrrLRyWDk+ql3D7Ar0aBo/WU1KMso2d5NR8pPb7JTdLLFtUeHgTk9DbkXcsZ4etPLbGdkTuc92n4ztjESywmwfrSZ3Riu/03210oJg5Ribbc/PBFyj0S1GLQtuPR3qvxdetYIaHR/td7FGs1htHbJaL7GSIY1aZSITM620xEVyetxifX+19UaO6Im2Pu0li/qXo1r9/jq5nwS5hd2OpG8ZsTpdbrwtI1af+o36Rt1MSIs2yZJVXvZJR+MkSTTe5NR45zNwW6wkungTca1sx+pn7CZOe93q6FVSqdetplbLAFjL4fCrlcus3v2sJ6tzsVi9ruhzX6LNbWO2j2bnjJfzfMjmWQKrDF4GI359MeJJpHKzidKNdXnZ7G1n206Ol9OESLOLhZcJZ/GWP55yuNElpkoSrNP1Wx1NEjm5lFvlt3sM/Bi5JisJ0ikrN3s7yfNllR3fjWMWjKh27kfyMyC0fP8WAdDQ0CAAiIaGBtlFcew3uz8RY0rKxW92f+L6Z+2sO55ljT4bz34lAqP9H1NSLvIXvy7GlJTHXFZl8ZbX7ue9rh+76zc7jpHrG/bwVpG/+HUx7OGtrpY/1vbNyhPv9qOtx49z2O/viRfnqd06tHKtdZPdcyseVu/fSd8ykgj8Su4K0lORV0M87W47ViuK1c96mZxqxk6XUCKyuo9BTmo3YrW1z6vyBek6oxdPV3ykRJp2ggmsScSrIVvRktP0/BqCamdb+sQwPxMoAXtJqpHMyup0H+LZ92jldbpev4Ywu7FOq4mDXiUYykpc1B93rR4j304LeJeYrPIQVDsJ5GZ/B2K/sd3rOlAxKZYtIxQ3P4Y3Rm7LTr84YK1lRIWnfZVaRqKt02kitNUhzPGW2+w8UeEYqywyx0L/5tfI73Yy1mU8rYXJWF8AE1il8LMpUyWxmh/dvJF6WZ92R1YlK7dGYwDGbwx1Mmoq2nbsdpVF+3yiixx9or1wzcrbbROZm6Ne3BrVGBTsppHAqJnO7y6CeDht2taa/LQ5HiKbFt3sYvCyeTFW06jZfvjZTeU3p3NSmNWJ/vhp9am9y2XFxoP49n++EQ5E9C8ysyuerjJAne+t3+eWVj/ai/UWFQ5G91A6mlvbo75ZOpagf0e0bqqdH37WaXZVu0orvnr7ruzzSyUMRlxkdeIbv9i9AMR7AY73BhBJtb5js/LYqbegXZTtBIT6fbNSJ/r61F61fuBEQzgQ8eIdRVaDWVXOPVn5Tvq34c4ZF30q90hG57i2H3YmLFRZvLlYVuozaNeKeLGbJoHFGj+vwqRNTqiWxW+lPHYy6Z1QYURHPJPhaS8luzY3G39tOqf8OeiXoHwn9cxygJx0vfm9/3YnK/OyXNHe9BskzBlRmFdJhUb5FdEuALJmUo2XF+XW+nGj9Y2XVdbg0U3v4cvWdhQNM3+Fe7Qye5UIqMJwyCDeODVBLrvbvJpY0Ml6/T6vVfgeabRgJLNLKnp2DwX23GTOiML0TXxuNcWZNaevvGOIaXOzvilalT5yK5yUO1Y9a/24586b942XVhxGc2s7BIBNB05aKuv8NfsxqHgTLumeYfoOEzfqXoVuBa1JXzuvgyTaMUi25nKvumud5Hv5fV6r8D3SLCocjJzMLviytT0w1+Z4MBiRIN4gwGlSYST9xcHJ5+O9SMebMGun3LHqWXsF+LW52VGDt8wuqUgBMHForqWyarkQ75xoMF3GjQtgvIm90Y6FneMUpKBWz4s5VYJKpRuyH/Nh6M9vlebfmDYqH91D6RCIL5k7KNhNE4Xbcz5Y3YbVd2JYaUqMt9kxWlnincvBzybRWMcyMpfDrWOs5UJkpKehubVNieZfI9GOhZ3jpO3vxKH2urFUxi6cxKZS14zGzaHEsrGbxgVuz4ZpxCgSj7V+O08u8T7lRCuL0bq1PBUr9ePnE5jZE4/ZrIhudaWtnlKAwyUTsXTiNZb3VUa3QLRjYec4VdV8jjZx4b+A+Yyr2mvYg9D1odLTctCp2OWlUkuQRj/sPVnOPbaMROFHy4id7cpgtyx23oCpAivHWLswyEiiC1JiMWDtzb3617Cr9DQaBCpdG5xQsRVCJYnUIqLhaBoFWL1wyLjAmJ30XmXSq0iV42M0bFCboRGAJ0OB4ylfvJ/1YgbKIJ138ZBxM/e6W5q+kojBGoMRyeyMq5dxAkZO+xztzbyJegGR/cWPzFfRtyb5+b6fWGTXkxVuTVGv+vkto6xBOP6JIkjnolXMGZGstOKw5SmtZfRZarMAZqSndpgJ0KgsiTqaQHZfsT5fRQsKtTrWymY0FNhtsfrxZdeTFfGMBtO/kdZKToPMvAcn+Svxller2+H5PaKuR5V8EFXK4UQy5yexZSQGp5FqECLcZHtpWDz9sdG6Upx2NejXCVh7s7AXkvXJ16j1yUp+UNDqy63yxlqPKvWiSjnoAraMuMRpq4DTCFeL6uev2d8huvci2tc/TUZbf6JE69qx3HTgpO1jGnkeaJOkxfOyK329yqzjILR8eMGo9clKXQStvtwqb6z1qFIvqpSD7GHLSAx+twoYvcK7eyjds3eaRG5XhRwFPS+S52K1jFiZ+8WLJEy7EjHzXpMorXFEyY4JrD7w8h0z2g1GC0K8DhK8fpGbU15O2mYmKC+oMktCTgRBHzVCRBewm8YHXiR2Rr7Ce1HhYF8SGbXtatvzsonTrCvKiNUmV7NupkRNvgW+qpuJQ3OlNUtH1rtb3Ykymtq9eGcUEVnDlpE4qPYkpVp5zHjxRB/v1PR6QalHr9iZfyVy+HqQkwf1rZLaO4VU67YkChq2jPhAa00AYOkpys2nLaPWBb/fPOp0nV480ZsNP3SSGBq0hF2zKdedHm+rrUlGw9e9atHwo6VCO+7alPZpKRd+n6gta0QqYTBiQawLoTZPgX7WTCfL2bngajeM3719MrzOaDcCL7or4h1ppHVFuXHT199IYpUp8t0osepd9SZ7o+MQz/G2GlBoy+mn/fcqkItnf+wGa/r98qPbkogYjFjiV96Bne1oF8yuXb46hNFuBPE+sRpdvKOtU9YN3Mp+Rg7LjVXvquedGO1zPMfbakDh1gRcVvJO4tkfu8GaKkOuiZIJgxELYl0I9UmmGqMLqtFydrajp10kl078WtR1Ri7v9KJqdPGOtk43b+B2Ahsr+6nNPqvNPBuU+RPMGO2zqjdRK4GB3XMtFreDtWSgemsgJR4msHokyIl8RuzOaRErCdJOkqg21DazSyp6dg8xmVARbiUHG83hkswJxCpItOsXycN5RiRSYUIsr/g1tbSeFoykABBIrPk0gow3rMTFgJDcwtE0Emk5Cd1D6Qn3RfZramk9rXuraJi8+TT8FJQmcnZ1JC5Vu/kocbFlxAN8qggGVY+TKi0OqtYPEQUHW0Y85uaL5fTr8uOpOAhP3tHK6Fb5VR0lI7vFQatfbSi6avVDRImHwYhDsW5kduau0K/Lj4nLVL0JA9ZuhG6VX/ZN34zsJnKtfgEoWT9ElHgcBSNPPfUUBgwYgK5du2LkyJHYu3dv1OV/+ctfYvDgwcjMzEReXh4eeOABfPnll44KrAqzG5nVp0r9DVW/Lj8mLlPpJhwZYFm5EbpVftk3fVVp9ev1+5CIiMKETWvXrhUZGRni+eefF++++6647777RE5OjqirqzNc/qWXXhKhUEi89NJL4ujRo2Lr1q2ib9++4oEHHrC8zYaGBgFANDQ02C2uL36z+xMxpqQ8/N/8xa+LYQ9vDf8u1mecbCdofrP7EzHs4a1i2MNbO5Rfq68xJeXh5YKwj07LqcL+uV0GFfaJiNRk9f5tO4F15MiRuOGGG/Dkk08CANrb25GXl4cf/ehHWLJkSafl582bh/fffx/l5eXh3z344IPYs2cP3njjDUvbVD2BVZ9wOGfcICb9GdDqCOg4NDeoSZJOk0y9TE61Wpdul0GVhFsiUo8nCaznzp1DVVUVxo8f/9UKUlMxfvx47N692/AzY8aMQVVVVbgr58iRI9i8eTNuv/120+20tLSgsbGxw4/K9N0GZk3/MpJGZSeq6rcfOeupJrK+ZJfZKqddRV52kVntxnO7DCp1+xFRMNlqGTl58iT69euHP//5zxg9enT49z/+8Y+xY8cO7Nmzx/Bzq1evxqJFiyCEwPnz5zF79myUlpaabufhhx/GypUrO/1e1ZYRTbRZSmU8Pcp+YnWyfdllDjIZrUxBbdkCgl12oqBQZmhvRUUFHnvsMTz99NN466238Oqrr2LTpk346U9/avqZ4uJiNDQ0hH+OHz/udTFdoT2ZbjpwstMTqoynR6Nt+tny4GSf+ZTtnIyEXJVHZsUS5LITJZp0OwtfeumlSEtLQ11dXYff19XVoU+fPoafWbZsGe6++258//vfBwBcd911aGpqwg9+8AMsXboUqamd46FQKIRQKGSnaErQ8kX0LSMa7Q2gkey+88Uqs6c+/QXY65uW2T67/RkZ+FR9gT5HKmiCXHaiRGOrZSQjIwPDhw/vkIza3t6O8vLyDt02el988UWngCMtLQ0AYDN3Vnnak+nqKQWWnlDLKmuwYuNB09aUWKK1cpg99SVqy4OVV9O7yax+g5Lz4pYgD48OctmJEo3tbpqFCxfiueeew69+9Su8//77mDNnDpqamjBz5kwAwPTp01FcXBxevqioCKWlpVi7di2OHj2Kbdu2YdmyZSgqKgoHJcmqtOIw2gSQlgJMHGr/vSvRmpnNgo5EvQDr68KPGUTN6pdN/0RE9tnqpgGAyZMn47PPPsPy5ctRW1uL66+/Hlu2bEHv3r0BAMeOHevQEvKTn/wEKSkp+MlPfoITJ07gsssuQ1FRER599FH39iKg7AwDNuoWiNbMHJTuDiusdIno60ILCHIyu7jWChRZBrP6nTNuEB7feghNLedRVlmTMMeAiMhLfFFeQCTzKBO7++5FPoedMrh1rJiXQkRBp8xoGnJHouZ6WGF3373oirJTBreOFbt8iChZsGWESFFetYywxYWI/MKWEUUFcbSFl2UOYn34xatkY7a4EJFqGIz4LIg3Ai/LHMT6CLpk7vIjIjUxGImDk6f6IN4IIstsdb+tLBfE+gg6P4Z3s8WLiOxgzkgcknWEi9X9jlyOuQrJI2jfDZ6bRN5gzogPkvWp3up+Ry7nRZcMn8DVFLTvBrsLieRiywj5Rv/0CcCVJ9EgPIHzqVttZZU1eHzrIQDAosLBPEZELmLLiGL4BN8xV8GNJ9Gyyho0tZxHTmYXDM/voWz98qlbbaUVh1Hf3IruoXQGIkSSMBjxCW9IHbnRjK+/iVTVfK5s/QatyyLZ8PgQycduGp+wqd59sbp9WOekx/OByH9W798MRihh+Z1Pwpud2oKQX0SUaJgzQknP7+Z3dsWpjd0xROpiywhJlUitCYm0L0REbmDLCLnOixFBiTT3iB8zmxIRJSIGI2SZF4GDF03n7C4hIgoWBiMBoMocJV4EDl60JjA3gIgoWJgzEgAcBeCM1zkczBEhIoqOOSMJhE/6znjdXRP07iBVWtyIiBiMBAATI53xOogLepAY9GCKiBIHu2koENgl4j7WKRF5jTOwUkJh3gwRUfAwZ4QSStC7RIiIyBxbRiiQ2MVARKQ+toxQQmPyJRFR4mAwQoEU5G4bDqklIuqI3TREPmMyLhElC3bTBBifnP0jo66D3KpDROQFtowoSHtyTksBVt4xhAmaHmIrBRGRd9gyEmBzxg1CWgrQJsAETY+xlYKISL502QWgzrSWEG3oKlnjZLjvtFH5bHkiIpKM3TSUMNjlQkSkFnbTUFR+Jm76tS19lwuTgImIgoPBSJJye9KwaDd/vyYo07/dmJOiEREFB4ORJOV24ma0m7+MJFEmphIRBQdzRiRKpPerJNK+EBGROzzNGXnqqacwYMAAdO3aFSNHjsTevXujLl9fX4+5c+eib9++CIVCuOqqq7B582Ynm04oidSVoO8iIWPMYyEiMmY7GPntb3+LhQsXYsWKFXjrrbcwbNgwFBYW4tNPPzVc/ty5c7j11lvxySefYN26dTh06BCee+459OvXL+7CBx27EpJLIgWfRERust1NM3LkSNxwww148sknAQDt7e3Iy8vDj370IyxZsqTT8s888wx+/vOf44MPPkCXLl0cFTJRu2koubAri4iSjdX7t61g5Ny5c+jWrRvWrVuHSZMmhX8/Y8YM1NfXY+PGjZ0+c/vtt6Nnz57o1q0bNm7ciMsuuwx33XUXFi9ejLS0NMPttLS0oKWlpcPO5OXlMRghIiIKEE9yRv7yl7+gra0NvXv37vD73r17o7a21vAzR44cwbp169DW1obNmzdj2bJleOKJJ/DII4+YbqekpATZ2dnhn7y8PDvFJCIiogDxfGhve3s7evXqhWeffRbDhw/H5MmTsXTpUjzzzDOmnykuLkZDQ0P45/jx414Xkwww4ZKIiPxg6900l156KdLS0lBXV9fh93V1dejTp4/hZ/r27YsuXbp06JK55pprUFtbi3PnziEjI6PTZ0KhEEKhkJ2ikQf0CZfMcSAiIq/YahnJyMjA8OHDUV5eHv5de3s7ysvLMXr0aMPPjB07Fh9//DHa29vDv/vwww/Rt29fw0CE1MHRPkRE5Afb3TQLFy7Ec889h1/96ld4//33MWfOHDQ1NWHmzJkAgOnTp6O4uDi8/Jw5c3D69GksWLAAH374ITZt2oTHHnsMc+fOdW8vyBOJOHcIu56IiNRjq5sGACZPnozPPvsMy5cvR21tLa6//nps2bIlnNR67NgxpKZ+FePk5eVh69ateOCBBzB06FD069cPCxYswOLFi93bCyKL2PVERKQeTgefoDinhTHWCxGRfzyZZ0QWBiP2jV21HSfqm9EvJxO7ltwsuzhERJSEPH03DakvWvIp8yaIiEglbBlJQmw1ISIiP7BlhExxyC4REamELSNERETkCbaMEJEnmHNERG5jMEJEtujnaiEicgODESKyhTlHROQ25owQERGRJ5gzQkRERIHAYISIiIikYjBCREREUjEYISIiIqkYjBAREZFUDEaIiIhIKgYjREREJBWDESIiIpKKwQgRERFJxWCEiIiIpGIwQkRERFIxGCEiIiKpGIwQERGRVOmyC2CF9mLhxsZGySUhIiIiq7T7tnYfNxOIYOTMmTMAgLy8PMklISIiIrvOnDmD7Oxs07+niFjhigLa29tx8uRJXHzxxUhJSXFtvY2NjcjLy8Px48eRlZXl2nqpM9a1P1jP/mA9+4P17B+v6loIgTNnziA3NxepqeaZIYFoGUlNTcXll1/u2fqzsrJ4ovuEde0P1rM/WM/+YD37x4u6jtYiomECKxEREUnFYISIiIikSupgJBQKYcWKFQiFQrKLkvBY1/5gPfuD9ewP1rN/ZNd1IBJYiYiIKHEldcsIERERycdghIiIiKRiMEJERERSMRghIiIiqZI6GHnqqacwYMAAdO3aFSNHjsTevXtlFylQSkpKcMMNN+Diiy9Gr169MGnSJBw6dKjDMl9++SXmzp2LSy65BBdddBH+8R//EXV1dR2WOXbsGCZOnIhu3bqhV69eeOihh3D+/Hk/dyUwVq1ahZSUFNx///3h37GO3XPixAlMmzYNl1xyCTIzM3Hddddh37594b8LIbB8+XL07dsXmZmZGD9+PD766KMO6zh9+jSmTp2KrKws5OTk4N5778XZs2f93hVltbW1YdmyZRg4cCAyMzMxaNAg/PSnP+3w7hLWszM7d+5EUVERcnNzkZKSgg0bNnT4u1v1euDAAXzzm99E165dkZeXh5/97GfxF14kqbVr14qMjAzx/PPPi3fffVfcd999IicnR9TV1ckuWmAUFhaKF154QRw8eFBUV1eL22+/XfTv31+cPXs2vMzs2bNFXl6eKC8vF/v27ROjRo0SY8aMCf/9/PnzYsiQIWL8+PFi//79YvPmzeLSSy8VxcXFMnZJaXv37hUDBgwQQ4cOFQsWLAj/nnXsjtOnT4v8/Hxxzz33iD179ogjR46IrVu3io8//ji8zKpVq0R2drbYsGGDePvtt8W3v/1tMXDgQNHc3Bxe5h/+4R/EsGHDRGVlpfjTn/4krrjiCjFlyhQZu6SkRx99VFxyySXi9ddfF0ePHhWvvPKKuOiii8R//Md/hJdhPTuzefNmsXTpUvHqq68KAGL9+vUd/u5GvTY0NIjevXuLqVOnioMHD4o1a9aIzMxM8V//9V9xlT1pg5Ebb7xRzJ07N/zvtrY2kZubK0pKSiSWKtg+/fRTAUDs2LFDCCFEfX296NKli3jllVfCy7z//vsCgNi9e7cQ4sKXJzU1VdTW1oaXKS0tFVlZWaKlpcXfHVDYmTNnxJVXXim2bdsm/v7v/z4cjLCO3bN48WLxjW98w/Tv7e3tok+fPuLnP/95+Hf19fUiFAqJNWvWCCGEeO+99wQA8eabb4aX+f3vfy9SUlLEiRMnvCt8gEycOFHMmjWrw+/uvPNOMXXqVCEE69ktkcGIW/X69NNPix49enS4dixevFgMHjw4rvImZTfNuXPnUFVVhfHjx4d/l5qaivHjx2P37t0SSxZsDQ0NAICePXsCAKqqqtDa2tqhnq+++mr0798/XM+7d+/Gddddh969e4eXKSwsRGNjI959910fS6+2uXPnYuLEiR3qEmAdu+m1117DiBEj8E//9E/o1asXCgoK8Nxzz4X/fvToUdTW1nao6+zsbIwcObJDXefk5GDEiBHhZcaPH4/U1FTs2bPHv51R2JgxY1BeXo4PP/wQAPD222/jjTfewIQJEwCwnr3iVr3u3r0b3/rWt5CRkRFeprCwEIcOHcLnn3/uuHyBeFGe2/7yl7+gra2tw8UZAHr37o0PPvhAUqmCrb29Hffffz/Gjh2LIUOGAABqa2uRkZGBnJycDsv27t0btbW14WWMjoP2NwLWrl2Lt956C2+++Wanv7GO3XPkyBGUlpZi4cKF+Nd//Ve8+eabmD9/PjIyMjBjxoxwXRnVpb6ue/Xq1eHv6enp6NmzJ+v6b5YsWYLGxkZcffXVSEtLQ1tbGx599FFMnToVAFjPHnGrXmtrazFw4MBO69D+1qNHD0flS8pghNw3d+5cHDx4EG+88YbsoiSU48ePY8GCBdi2bRu6du0quzgJrb29HSNGjMBjjz0GACgoKMDBgwfxzDPPYMaMGZJLlzj+93//Fy+99BJefvllXHvttaiursb999+P3Nxc1nMSS8pumksvvRRpaWmdRhzU1dWhT58+kkoVXPPmzcPrr7+OP/7xj7j88svDv+/Tpw/OnTuH+vr6Dsvr67lPnz6Gx0H7W7KrqqrCp59+iq9//etIT09Heno6duzYgdWrVyM9PR29e/dmHbukb9+++NrXvtbhd9dccw2OHTsG4Ku6inbd6NOnDz799NMOfz9//jxOnz7Nuv6bhx56CEuWLME///M/47rrrsPdd9+NBx54ACUlJQBYz15xq169up4kZTCSkZGB4cOHo7y8PPy79vZ2lJeXY/To0RJLFixCCMybNw/r16/H9u3bOzXdDR8+HF26dOlQz4cOHcKxY8fC9Tx69Gi88847Hb4A27ZtQ1ZWVqcbQzK65ZZb8M4776C6ujr8M2LECEydOjX8/6xjd4wdO7bT0PQPP/wQ+fn5AICBAweiT58+Heq6sbERe/bs6VDX9fX1qKqqCi+zfft2tLe3Y+TIkT7shfq++OILpKZ2vPWkpaWhvb0dAOvZK27V6+jRo7Fz5060traGl9m2bRsGDx7suIsGQHIP7Q2FQuLFF18U7733nvjBD34gcnJyOow4oOjmzJkjsrOzRUVFhTh16lT454svvggvM3v2bNG/f3+xfft2sW/fPjF69GgxevTo8N+1Yae33XabqK6uFlu2bBGXXXYZh51GoR9NIwTr2C179+4V6enp4tFHHxUfffSReOmll0S3bt1EWVlZeJlVq1aJnJwcsXHjRnHgwAFxxx13GA6NLCgoEHv27BFvvPGGuPLKK5N+yKnejBkzRL9+/cJDe1999VVx6aWXih//+MfhZVjPzpw5c0bs379f7N+/XwAQv/jFL8T+/ftFTU2NEMKdeq2vrxe9e/cWd999tzh48KBYu3at6NatG4f2xuM///M/Rf/+/UVGRoa48cYbRWVlpewiBQoAw58XXnghvExzc7P44Q9/KHr06CG6desmvvOd74hTp051WM8nn3wiJkyYIDIzM8Wll14qHnzwQdHa2urz3gRHZDDCOnbP7373OzFkyBARCoXE1VdfLZ599tkOf29vbxfLli0TvXv3FqFQSNxyyy3i0KFDHZb561//KqZMmSIuuugikZWVJWbOnCnOnDnj524orbGxUSxYsED0799fdO3aVfzd3/2dWLp0aYehoqxnZ/74xz8aXpNnzJghhHCvXt9++23xjW98Q4RCIdGvXz+xatWquMueIoRu2jsiIiIinyVlzggRERGpg8EIERERScVghIiIiKRiMEJERERSMRghIiIiqRiMEBERkVQMRoiIiEgqBiNEREQkFYMRIiIikorBCBEREUnFYISIiIikYjBCREREUv0/ayinK3cjoSgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot trade volume improved\n",
        "\n",
        "for i in range(1000):\n",
        "  plt.vlines(i,0,results[\"Trade Volume\"][i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "fJL9UPatpgOO",
        "outputId": "fa2cd8e9-dc22-46b0-8a31-10e51862d5a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Kykrl5eUpMTFRKSkpWrp0qRobG8PTGwAA0CuEFFBGjBih1atXq7y8XEeOHNFdd92l++67TydOnJAkLV68WK+88oq2b9+uffv26ezZs5oxY4bz+qamJuXl5am+vl4HDhzQSy+9pM2bN6uwsDC8vQIAAFEtxhhjurKA5ORkPfPMM3rggQc0bNgwbd26VQ888IAk6Z133tGYMWNUWlqqyZMna9euXfrqV7+qs2fPyuPxSJI2btyoZcuW6aOPPlJ8fHyH1hkIBOR2u1VbWyuXy9WV5ge5VN+ozMI9kqSTq3KVGB8XtmUDAHCjC2X/3elrUJqamrRt2zZdvHhRPp9P5eXlamhoUE5OjlNn9OjRSk9PV2lpqSSptLRU48aNc8KJJOXm5ioQCDhHYdpSV1enQCAQNAEAgN4r5IBy7NgxDRw4UAkJCXrkkUe0Y8cOZWZmyu/3Kz4+XklJSUH1PR6P/H6/JMnv9weFk5bylrJrKSoqktvtdqa0tLRQmw0AAKJIyAHl5ptv1tGjR1VWVqb58+drzpw5OnnyZHe0zbFixQrV1tY605kzZ7p1fQAAILJCvsgiPj5eX/jCFyRJEydO1OHDh/XjH/9YX//611VfX6+ampqgoyhVVVXyer2SJK/Xq0OHDgUtr+Uun5Y6bUlISFBCQkKoTQUAAFGqy89BaW5uVl1dnSZOnKi+ffuqpKTEKauoqFBlZaV8Pp8kyefz6dixY6qurnbqFBcXy+VyKTMzs6tNAQAAvURIR1BWrFih6dOnKz09XefPn9fWrVv1+9//Xnv27JHb7dbcuXO1ZMkSJScny+VyaeHChfL5fJo8ebIkadq0acrMzNTs2bO1Zs0a+f1+rVy5UgUFBRwhAQAAjpACSnV1tR566CF9+OGHcrvdGj9+vPbs2aO///u/lyQ9++yzio2N1cyZM1VXV6fc3FytX7/eeX2fPn20c+dOzZ8/Xz6fTwMGDNCcOXO0atWq8PYKAABEtS4/ByUSeA4KAADRp0eegwIAANBdCCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgnZACSlFRkSZNmqRBgwYpJSVF999/vyoqKoLqXL58WQUFBRoyZIgGDhyomTNnqqqqKqhOZWWl8vLylJiYqJSUFC1dulSNjY1d7w0AAOgVQgoo+/btU0FBgQ4ePKji4mI1NDRo2rRpunjxolNn8eLFeuWVV7R9+3bt27dPZ8+e1YwZM5zypqYm5eXlqb6+XgcOHNBLL72kzZs3q7CwMHy9AgAAUS3GGGM6++KPPvpIKSkp2rdvn7785S+rtrZWw4YN09atW/XAAw9Ikt555x2NGTNGpaWlmjx5snbt2qWvfvWrOnv2rDwejyRp48aNWrZsmT766CPFx8dfd72BQEBut1u1tbVyuVydbf5VLtU3KrNwjyTp5KpcJcbHhW3ZAADc6ELZf3fpGpTa2lpJUnJysiSpvLxcDQ0NysnJceqMHj1a6enpKi0tlSSVlpZq3LhxTjiRpNzcXAUCAZ04caIrzQEAAL1Epw8RNDc3a9GiRZoyZYrGjh0rSfL7/YqPj1dSUlJQXY/HI7/f79RpHU5aylvK2lJXV6e6ujrn90Ag0NlmAwCAKNDpIygFBQU6fvy4tm3bFs72tKmoqEhut9uZ0tLSun2dAAAgcjoVUBYsWKCdO3fqtdde04gRI5z5Xq9X9fX1qqmpCapfVVUlr9fr1Lnyrp6W31vqXGnFihWqra11pjNnznSm2QAAIEqEFFCMMVqwYIF27NihvXv3KiMjI6h84sSJ6tu3r0pKSpx5FRUVqqyslM/nkyT5fD4dO3ZM1dXVTp3i4mK5XC5lZma2ud6EhAS5XK6gCQAA9F4hXYNSUFCgrVu36le/+pUGDRrkXDPidrvVv39/ud1uzZ07V0uWLFFycrJcLpcWLlwon8+nyZMnS5KmTZumzMxMzZ49W2vWrJHf79fKlStVUFCghISE8PcQAABEnZACyoYNGyRJX/nKV4Lmb9q0Sd/85jclSc8++6xiY2M1c+ZM1dXVKTc3V+vXr3fq9unTRzt37tT8+fPl8/k0YMAAzZkzR6tWrepaTwAAQK/RpeegRArPQQEAIPr02HNQAAAAugMBBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoElGvILNyjS/WNkW4GAAA3JAIKAACwDgEFAABYh4ACAACsQ0BpB9egAAAQGQQU3PAu1TfqpuWv6qblrxJKAcASBBQAAGAdAgoAALAOAQUAAFiHgNIK1x8AAGAHAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUNrxaX0TTxgFACACCCjt+LS+yfk5s3APIQUAgB5CQAEAANYhoAAAAOsQUEKQWbiH61EAAOgBBJR2TFv7eqSbAADADSnkgLJ//37de++9Sk1NVUxMjF5++eWgcmOMCgsLNXz4cPXv3185OTk6depUUJ1z584pPz9fLpdLSUlJmjt3ri5cuNCljgAAgN4j5IBy8eJF3XLLLXr++efbLF+zZo3WrVunjRs3qqysTAMGDFBubq4uX77s1MnPz9eJEydUXFysnTt3av/+/Zo3b17newEAAHqVuFBfMH36dE2fPr3NMmOM1q5dq5UrV+q+++6TJP3Xf/2XPB6PXn75Zc2aNUtvv/22du/ercOHDysrK0uS9Nxzz+mee+7Rv/3bvyk1NbUL3QEAAL1BWK9BOX36tPx+v3Jycpx5brdb2dnZKi0tlSSVlpYqKSnJCSeSlJOTo9jYWJWVlYWzOQAAIEqFfASlPX6/X5Lk8XiC5ns8HqfM7/crJSUluBFxcUpOTnbqXKmurk51dXXO74FAIJzNBgAAlomKu3iKiorkdrudKS0tLdJNAgAA3SisAcXr9UqSqqqqguZXVVU5ZV6vV9XV1UHljY2NOnfunFPnSitWrFBtba0znTlzJpzNBgAAlglrQMnIyJDX61VJSYkzLxAIqKysTD6fT5Lk8/lUU1Oj8vJyp87evXvV3Nys7OzsNpebkJAgl8sVNAEAgN4r5GtQLly4oPfee8/5/fTp0zp69KiSk5OVnp6uRYsW6cknn9SoUaOUkZGhxx9/XKmpqbr//vslSWPGjNHdd9+thx9+WBs3blRDQ4MWLFigWbNmcQcPAACQ1IkjKEeOHNGECRM0YcIESdKSJUs0YcIEFRYWSpIee+wxLVy4UPPmzdOkSZN04cIF7d69W/369XOWsWXLFo0ePVpTp07VPffcozvvvFMvvPBCmLrU/fjPxgAAdK+Qj6B85StfkTHmmuUxMTFatWqVVq1adc06ycnJ2rp1a6irBgAAN4iouIsHAADcWAgoAADAOgQUAABgHQJKJ3GRLAAA3YeAAgAArENAAQAA1iGgdFLWkyWc5gEAoJsQUAAAVrtU36iblr+qm5a/yhfDGwgBpQt4oiwAAN2DgNJFhBQAAMKPgAIAAKxDQAEAANYhoIRBZuEeLt4CACCMCCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgBJG13omCk9BBHCjYzuIUBFQAACAdQgoAKzHt2/gxkNAiTA2vAAAXI2AYhH+rw8AAJ+Ji3QDerNL9Y3KLNwTNK/l95OrcpUYz/ADANAWjqAAUYBTgQBuNAQUdEh37CDbWiY7YgCIDNu2v5xjCLMrT+mg8zIL93AqDABuUBxBAQAA1iGgoMtsOyyI0PD+Ad2Lv7HOIaAAABBlboTQw8l94Aqtbw/nGhhEMxs+y209bgHoCI6gREhXH8pmyx0wbHx6l8zCPb36Gxk+0xPbipZ1sH1AZ/HVMILC+YfbXlC41rcoG75d4TO8FzeucL33nV0OnzvYiiMoiBo3wjlXAMBnCCiWudYhdnbO4RPKWHbHKY/e9F7a2Bcb2wS0xme0YwgoiFrR9Efeuq2fXKgLmg/0BtH094joQECxVOuLaHv6QlQ2NL1DT7+PveW/cfP5B+xAQLFYuO706Ui46a67N67c2Efrxr8j43OtvoUaMNnRt72stn6+nu4ey2j9PN8IInVXY6jr7C1/792BgNJLdfZD/8mFuuvuTHvzraiZhXt65W2RPbWx7o0b29Zj9/GFy9ccx97Sd0JXdGr9vl3vsxotCCg3mN4SLjp72ovntvSM640zO8Ho0Fu2F9HqRv874aZ3y7W1kb9U3xj0vIJL9Y3KerKkW9bf3o7mejugUINAW6+51jK6q7+d0R2h58r3uDOv72qbwvlslvaW093P4eAZM59pPQ5HVk6NcGvsZ/PnJpzbHJs/F/aMODrs3IV6Za29/g66vZ34tT7cX1rz+84267ra2+l210PrIr1h6eiGpK1vRx9fuOy8h68/9pVwNy0swvmtLrNwT8Tfr3Cz6bPYVT3Rl66s48rXhqMtrX++XlsifXS2Nx5hid6/FjjOXaiPdBOu0pE/lnAeBWn9DaD1cq+30wt1g3Kt9XRW1pMlYduYtteXUNt65Qa59bJb2tvekbVrfRO7Xjuv3MG0/N46oH1a36Sblr/aZr2ujmW4x7Gr2tthd9cOsSd3dJ0JFeEIsuEMQtEcOm3HyEahaWtf77F1fVrfFFL9SG/c21pHJL7VtGxE23LlUaqOnKJr/T607EBa9yvUIyytN7JtvfbT+qZrtqkrp59sPTXX3o6mMzvs7ggPrT9T3fGZbu/zkFm4J6xH8TpySiGUUw9dGe+2PgcdPW0erve5rfV35F+XhCIag5X9LcR1dWdg6c5TPtGsozvazn4bvXL5rd/jrCdLrtpgh/I+dWTj297yOtJ3m4JIV9rSkaM9V87rylGcSJ8mkK793rcOyT19Oi7Sn6fWR+06ep1Ge2N0re2CrdeCREpEA8rzzz+vZ555Rn6/X7fccouee+453X777ZFsEhA2Hd3RdGbj25UN9pWvtTGEXjl24Wpj6+W23gm0nJYKx46wOwNGR5fdVr3WfYv0Dr/FlaGntY5eQ9dWILzyiE/LUb+2AuCVp4Sl9gNCKGN3rRB7vaNR1zoSHEr4bW9sW1w5HqEeMe9uMcYYE4kV//d//7ceeughbdy4UdnZ2Vq7dq22b9+uiooKpaSktPvaQCAgt9ut2tpauVyusLWp9UWJAKLPkZVTlRgfF/GjEPjsVJGN4bcjwhVWo1l3HSELZf8dsYCSnZ2tSZMm6T/+4z8kSc3NzUpLS9PChQu1fPnydl9LQAEAoHsdWTlVQwf2C+syQ9l/R+RBbfX19SovL1dOTs5fGxIbq5ycHJWWll5Vv66uToFAIGgCAADdJ+vJkojevhyRa1A+/vhjNTU1yePxBM33eDx65513rqpfVFSkH/7wh93erqED++l/V+d1+3oAAED7ouJR9ytWrFBtba0znTlzJtJNAgAA3SgiR1CGDh2qPn36qKqqKmh+VVWVvF7vVfUTEhKUkJDQU80DAAARFpEjKPHx8Zo4caJKSv56QWpzc7NKSkrk8/ki0SQAAGCRiD0HZcmSJZozZ46ysrJ0++23a+3atbp48aK+9a1vRapJAADAEhELKF//+tf10UcfqbCwUH6/X7feeqt279591YWzAADgxhOx56B0RXc9BwUAAHQf65+DAgAA0B4CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgnYg96r4rWh5+GwgEItwSAADQUS377Y48xD4qA8r58+clSWlpaRFuCQAACNX58+fldrvbrROV/4unublZZ8+e1aBBgxQTExPWZQcCAaWlpenMmTP8n59uxDj3DMa5ZzDOPYex7hndNc7GGJ0/f16pqamKjW3/KpOoPIISGxurESNGdOs6XC4XH/4ewDj3DMa5ZzDOPYex7hndMc7XO3LSgotkAQCAdQgoAADAOgSUKyQkJOj73/++EhISIt2UXo1x7hmMc89gnHsOY90zbBjnqLxIFgAA9G4cQQEAANYhoAAAAOsQUAAAgHUIKAAAwDoElFaef/553XTTTerXr5+ys7N16NChSDcpqhQVFWnSpEkaNGiQUlJSdP/996uioiKozuXLl1VQUKAhQ4Zo4MCBmjlzpqqqqoLqVFZWKi8vT4mJiUpJSdHSpUvV2NjYk12JKqtXr1ZMTIwWLVrkzGOcw+ODDz7QN77xDQ0ZMkT9+/fXuHHjdOTIEafcGKPCwkINHz5c/fv3V05Ojk6dOhW0jHPnzik/P18ul0tJSUmaO3euLly40NNdsVpTU5Mef/xxZWRkqH///vqbv/kbPfHEE0H/r4WxDt3+/ft17733KjU1VTExMXr55ZeDysM1pn/605/0pS99Sf369VNaWprWrFkTng4YGGOM2bZtm4mPjzf/+Z//aU6cOGEefvhhk5SUZKqqqiLdtKiRm5trNm3aZI4fP26OHj1q7rnnHpOenm4uXLjg1HnkkUdMWlqaKSkpMUeOHDGTJ082d9xxh1Pe2Nhoxo4da3Jycsybb75pfvOb35ihQ4eaFStWRKJL1jt06JC56aabzPjx482jjz7qzGecu+7cuXNm5MiR5pvf/KYpKysz77//vtmzZ4957733nDqrV682brfbvPzyy+att94y//AP/2AyMjLMp59+6tS5++67zS233GIOHjxoXn/9dfOFL3zBPPjgg5HokrWeeuopM2TIELNz505z+vRps337djNw4EDz4x//2KnDWIfuN7/5jfne975nfvnLXxpJZseOHUHl4RjT2tpa4/F4TH5+vjl+/Lj5+c9/bvr3729+8pOfdLn9BJT/7/bbbzcFBQXO701NTSY1NdUUFRVFsFXRrbq62kgy+/btM8YYU1NTY/r27Wu2b9/u1Hn77beNJFNaWmqM+ewPKjY21vj9fqfOhg0bjMvlMnV1dT3bAcudP3/ejBo1yhQXF5u/+7u/cwIK4xwey5YtM3feeec1y5ubm43X6zXPPPOMM6+mpsYkJCSYn//858YYY06ePGkkmcOHDzt1du3aZWJiYswHH3zQfY2PMnl5eeaf//mfg+bNmDHD5OfnG2MY63C4MqCEa0zXr19vBg8eHLTdWLZsmbn55pu73GZO8Uiqr69XeXm5cnJynHmxsbHKyclRaWlpBFsW3WprayVJycnJkqTy8nI1NDQEjfPo0aOVnp7ujHNpaanGjRsnj8fj1MnNzVUgENCJEyd6sPX2KygoUF5eXtB4SoxzuPz6179WVlaWvva1ryklJUUTJkzQiy++6JSfPn1afr8/aJzdbreys7ODxjkpKUlZWVlOnZycHMXGxqqsrKznOmO5O+64QyUlJXr33XclSW+99ZbeeOMNTZ8+XRJj3R3CNaalpaX68pe/rPj4eKdObm6uKioq9Je//KVLbYzKfxYYbh9//LGampqCNtaS5PF49M4770SoVdGtublZixYt0pQpUzR27FhJkt/vV3x8vJKSkoLqejwe+f1+p05b70NLGT6zbds2/fGPf9Thw4evKmOcw+P999/Xhg0btGTJEv3rv/6rDh8+rG9/+9uKj4/XnDlznHFqaxxbj3NKSkpQeVxcnJKTkxnnVpYvX65AIKDRo0erT58+ampq0lNPPaX8/HxJYqy7QbjG1O/3KyMj46pltJQNHjy4020koKBbFBQU6Pjx43rjjTci3ZRe58yZM3r00UdVXFysfv36Rbo5vVZzc7OysrL09NNPS5ImTJig48ePa+PGjZozZ06EW9e7/OIXv9CWLVu0detWffGLX9TRo0e1aNEipaamMtY3ME7xSBo6dKj69Olz1V0OVVVV8nq9EWpV9FqwYIF27typ1157TSNGjHDme71e1dfXq6amJqh+63H2er1tvg8tZfjsFE51dbVuu+02xcXFKS4uTvv27dO6desUFxcnj8fDOIfB8OHDlZmZGTRvzJgxqqyslPTXcWpvu+H1elVdXR1U3tjYqHPnzjHOrSxdulTLly/XrFmzNG7cOM2ePVuLFy9WUVGRJMa6O4RrTLtzW0JAkRQfH6+JEyeqpKTEmdfc3KySkhL5fL4Itiy6GGO0YMEC7dixQ3v37r3qsN/EiRPVt2/foHGuqKhQZWWlM84+n0/Hjh0L+qMoLi6Wy+W6amdxo5o6daqOHTumo0ePOlNWVpby8/OdnxnnrpsyZcpVt8m/++67GjlypCQpIyNDXq83aJwDgYDKysqCxrmmpkbl5eVOnb1796q5uVnZ2dk90IvocOnSJcXGBu+O+vTpo+bmZkmMdXcI15j6fD7t379fDQ0NTp3i4mLdfPPNXTq9I4nbjFts27bNJCQkmM2bN5uTJ0+aefPmmaSkpKC7HNC++fPnG7fbbX7/+9+bDz/80JkuXbrk1HnkkUdMenq62bt3rzly5Ijx+XzG5/M55S23v06bNs0cPXrU7N692wwbNozbX6+j9V08xjDO4XDo0CETFxdnnnrqKXPq1CmzZcsWk5iYaH72s585dVavXm2SkpLMr371K/OnP/3J3HfffW3epjlhwgRTVlZm3njjDTNq1Kgb+tbXtsyZM8d87nOfc24z/uUvf2mGDh1qHnvsMacOYx268+fPmzfffNO8+eabRpL50Y9+ZN58803zf//3f8aY8IxpTU2N8Xg8Zvbs2eb48eNm27ZtJjExkduMw+25554z6enpJj4+3tx+++3m4MGDkW5SVJHU5rRp0yanzqeffmr+5V/+xQwePNgkJiaaf/zHfzQffvhh0HL+93//10yfPt3079/fDB061HznO98xDQ0NPdyb6HJlQGGcw+OVV14xY8eONQkJCWb06NHmhRdeCCpvbm42jz/+uPF4PCYhIcFMnTrVVFRUBNX55JNPzIMPPmgGDhxoXC6X+da3vmXOnz/fk92wXiAQMI8++qhJT083/fr1M5///OfN9773vaBbVxnr0L322mttbpPnzJljjAnfmL711lvmzjvvNAkJCeZzn/ucWb16dVjaH2NMq0f1AQAAWIBrUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwzv8DZLe9rY2bW+YAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  create graph object\n",
        "G = nx.Graph()\n",
        "\n",
        "# add agent keys to make initial node set\n",
        "G.add_nodes_from(list(model.schedule.agents_by_type[Trader].keys()))\n",
        "\n",
        "# create edge list\n",
        "for idx, row in agent_results.iterrows():\n",
        "  if len(row[\"Trade Network\"]) > 0:\n",
        "    for agent in row[\"Trade Network\"]:\n",
        "      G.add_edge(idx[1], agent)\n",
        "\n"
      ],
      "metadata": {
        "id": "ql0x0DcEqMVF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Connectivity: \", nx.node_connectivity(G), \"Clustering: \",\n",
        "      nx.average_clustering(G), \"Efficiency: \", nx.global_efficiency(G))\n",
        "\n",
        "try:\n",
        "  print(\"Diameter: \", nx.diameter(G))\n",
        "except:\n",
        "  print(\"Some traders not connected\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-OZb4MUb40jO",
        "outputId": "22481149-e220-4f33-a306-afc694eeb854"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Connectivity:  1 Clustering:  0.42994965317446054 Efficiency:  0.46981831854933115\n",
            "Diameter:  6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "degree = [d for n,d in G.degree()]\n",
        "plt.hist(degree)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "s5pJwG705mMA",
        "outputId": "621c902a-ddbd-4f43-e4e9-a90ea342d400"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([65., 36., 27., 12., 12., 10., 20., 11.,  3.,  2.]),\n",
              " array([ 1. ,  8.2, 15.4, 22.6, 29.8, 37. , 44.2, 51.4, 58.6, 65.8, 73. ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nx.draw(G)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "xLwzO1WP55vS",
        "outputId": "fe6cafae-3886-416b-e81b-2059f40ace44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wA57Rer-6CtV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}