Session_20_Batch_Runner.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "XAnII-29m3r2",
        "wp0VWkylOKr9",
        "BkX5GqjSnAu0",
        "XYcwNrHqnrPt"
      ],
      "authorship_tag": "ABX9TyPStyOepLlVZHt4Wn02RvpH",
      "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_20_Batch_Runner.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": "34029c10-bb11-4014-ae94-d66fa44d57be"
      },
      "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[31m16.2 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[31m3.6 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\n",
        "    - self.trade()\n",
        "    - self.maybe_sell_spice()\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(\n",
        "        self_sugar, self_spice\n",
        "    ) > other.calculate_MRS(other_sugar, other_spice)\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",
        "    self.running = True\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": "b392101e-7899-4c23-d68a-2705fafa5a67"
      },
      "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": "44e52ae2-95c1-494d-ec3f-3458c6710f72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Trader  Trade Volume     Price\n",
              "0       200           701  0.954813\n",
              "1       200           137  0.859638\n",
              "2       200           153  0.935586\n",
              "3       200           123  0.798584\n",
              "4       200           157  0.902948\n",
              "..      ...           ...       ...\n",
              "995      67             6  0.899610\n",
              "996      67             3  1.001948\n",
              "997      67            17  0.783423\n",
              "998      67             4  0.931170\n",
              "999      67             1  1.016155\n",
              "\n",
              "[1000 rows x 3 columns]"
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              "      <td>1.016155</td>\n",
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        "# retrieve agent level results\n",
        "agent_results = model.datacollector.get_agent_vars_dataframe()\n",
        "# filter out Nones from non-trader agents\n",
        "agent_results"
      ],
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              "                                                  Trade Network\n",
              "Step AgentID                                                   \n",
              "1    4138                                                [4223]\n",
              "     4139                                                    []\n",
              "     4140     [4240, 4240, 4240, 4240, 4240, 4240, 4240, 424...\n",
              "     4141                                                    []\n",
              "     4142                                                    []\n",
              "...                                                         ...\n",
              "1000 4329                                                    []\n",
              "     4334                                                    []\n",
              "     4335                                                    []\n",
              "     4336                                                    []\n",
              "     4337                                                    []\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8c09cd3d-14ac-4bbb-9e44-688fe7b679ea 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-8c09cd3d-14ac-4bbb-9e44-688fe7b679ea');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot number of agents per time step\n",
        "results.plot(y = \"Trader\", use_index=True)"
      ],
      "metadata": {
        "id": "lr0rYpIpovMg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "41f094b0-59b0-4324-9ce3-8ace802b6591"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "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": "ff2424cb-4938-4799-e2d4-99298798783e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f60c72f4ee0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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1NDSkq666SsePH89W+gAAsIJ1QXIjo66Qjz76SNOmTdPOnTt12WWXhfobukKAcBitDti1ZMMOHeob1Mymer1w2xX5Tk7RycmskP7+fknS5MmTA7eJxWIaGBhI+AGQGqPVERZP4uEwUDM30g4sRkZGdPPNN2vJkiWaO3du4Hbr169XY2Nj/KelpSXdQwJlhUIQYRGEhtO+qFUv3HYFLYBZlnZXSEdHh7Zu3arnn39eZ555ZuB2sVhMsVgs/u+BgQG1tLTQFZIETeAAoqDMQC5ktSvkxhtv1FNPPaVnn302aVAhSXV1dWpoaEj4KWU2miR5+gCiK+fuAJ7EUUgiBRaO4+jGG2/U5s2btWPHDs2ePTtb6SpaNoICmsCB6AjIgcJQHWXjNWvW6NFHH9UTTzyhiRMn6siRI5KkxsZG1dfXZyWBxaZjaVu8STJd7YtaefIAIrJx7wHIXKQxFhUVFb6/f+ihh/Stb30r1D6Ybgqg3DAGAqUgK2MsHMfx/QkbVADIvnIea1Co6KZBOeElZECJoRIrPIybQjkhsChTPNWWLiqxwsOsDZRTmUtgUaZ4qi1dVGJA4SmnMpfAokzxVAsAuWPK3AWtk0q+5SKjl5Clg1khAIByVcwvQsvJS8iAfCinvkoApaUcWotpsUDRKeaIHwCKFS0WKFnlEPEDQLGixQLIM1ZlBFAMaLFA0Sq3MRTlNA0NQOkjsEDBKbeKlq4dAKWEwAIFp9wq2mQLWpVb6w3yi+sNNhBYoKAw3iBRubXeIL+43mADgQUKCgVbonJrvUF+cb3BBmaFpIGn6uwhbwGgMIWtvwks0sACTQCAcsN00yyiubCwMOAMAAoHgUUaeC11eLmo9BmXAQCFg8AC1rmDiVxU+rQgoVDQegYQWCAL3MFELip9WpCSo7LLHVrPAAILKyi4E7mDCduVPnkdHZVd7tB6BhBYJEi30kpWcJdjRZhpMJEsz7JRSZb6d0Rllzu0ngEEFgnu3bZfh/oGde+2/ZH+LlnBHVQRlnpllolkwUM2KslSf6JnyXAAuURgYUGygjuoIiz1yiwKb+WWLHiI8kTo3W9QJRp0vEKpdLOZDq5DALYRWLjcsnyOZjbV65blc6ztM6gipHn6FG/llk5zsl8QcdcT+xL2G1SJBh2vUCrdbKaD6zB/CiVwBWwjsHDJZf8ofbGn+FVuUQtdb+Xb2dWjYUeqqlB8v1Er0VTb56piyGblXwzXYalWwIUSuOZCqX6H8MeS3jnA+y+ii7psujePs53npkVk2BFLu2dZqS6hn+oaLaVyo1C/w1LK41xgSe8sCdtv71ZOTya2RH1K9z55Z/tJ3K9FBNlRqt01qa7RUio3CvU7LKU8LiQEFgGCAoh1T7+ZMHMkzIVZqDeVDbaaOL37KfQmevOd3n3t3IJNY6ko9GshW3JdbmSzu6JQv8NSLpvzia6QAN6mO/PvCkmOpKb6Gu296yrfprRsNa8VYrNduk2c3nMJs59CPH+gVBRqdwUKB10hEfhF6t5I1vz7mgubE2aOeCNxv9kIthRis126Eb/3XMLsx+/83d8dA8QyQ/6VN57eYQstFrIbqZt9VVXIejN5LgYk5qpFIMyxwgzIdH93knjiykAunlhpdQKKFy0WEdiK1Dd29+p47KSa6muy0vcepp8yk6fOXLaIhDkXk567ntgXP5/jsZO6d9t+38W0yuWJK1stCyb/FrROYkGuEkfrFLKJwEKjlVzH0jZ1dvX43mhhb8LOrh71DQ5pfF111loUsjkDpdAGi3UsbVNVhTTsjJ6Xyd++wSHfxbQKdYCYbdmqnE3+7e79hAW5CgArrmZXsvwl8MoMgcXnvDea+8IKexNmu9DM9gyUXFfMqc6nfVGr7r52bkKLRFN9jZrqa3JSMRVq4eL9jk06127aYyW95b4gV6FgxdXsSpa/BF6ZIbD4nPdGc19Y5jPJ0Rdue1pf+9HzvvtI1fIhZVZZBVUo2az40lm3I+x+TbdRsvPxtkjsvesq7b3rqpxUTH5dMYXAWzmbdD792mErhSGVf3ZEvXfyFeAVakBtW7L8JfDKDIHF57w3mnk6Ph47KUl64bYrdKjvM0nSa4f6A/eTKtJN9XmymzqoQnHvy3ak7bdUto39+3UbpZr14SebhaC3K6YQJJvBdH5zo6oqpAWtk/KYQgSJeu+EDfBs3wPl8rSeLH+zFVyXS9BGYBGgfVGrxtdVx/vzN3b3qqayQpI0b2ajJP+pjgtaJyWNdFO9FyPKTe032M52pB007TbT/fulfUHrpDEVo8mPe7ft970hM3kt/cbuXv323f+p3777PwMDOXdXTDK5KjD8ztcUgh8fP6FhR9rd+4nVY5ZLYZht2XoKTtaNm45k6SyFayGf51AuQVtZTjcNO+XNvZ25INxT8aJOdQw6rns/5lhRpuMFTRPMx9S+qMdMlYdmf8djJ9U3OBT/zPx+Qesk7e79JGmeBn0fv333f6pvcEiSxuw3ap6Z4zXV12h8XXXWFkcLOl/3NraPzcJJhS2dxebSVQrXQjaXBEil2KdbM900ibBRo7s5zC+Kd3eXeFsq/KLioKdv977Taf4MesKwFR2nerJP95jecRbu/HQv7W3Oyz0ewxxnd+8nY/Ir7PgNo0KKt5zcu21/Wk9/p8bhKKuLo/mdr5Gt5lv6mwubXzdutr6vUrgW8tnFWS7jl8oysDAV2NHjsaSVpbtClTTmgnB3lzz92mEtaJ0U7zbxq2CDKp90Ljb3/oP+3lYh4DfN09jY3atz79yq2Z8PavVW6N5t3ZW0e5yF+bekMcfxG4+R7NzCjt+4ZfkczWyq1z0r58anWEpK6KLxBhp+3E8hZp82C16/QCnK32ba7FsuhWGpMN+XJOtN/qVwLUTp4kR6yrIrRDrVHCYpsPnavY1f09/G7l7du22/+geH5EjxKDhVl4aN5rBcr5K57uk39dnQiK65sFn3r5of/8ydR0aFpMb6Gt2yfI5vfpr8djfrm4rf77uIeq5+26faR1BzcqqujY3dvbpzy76E98fY5m66XTGvObArxO98vF1IQeeL0lMK3RYoLGHr77INLExQYAQVvmYbbyUpneqjr6+pVF11lWInh1VXXeW7bSbpjFIhprN/v3MM22/rDjoumNmoNw73a/jzK8o7VuLebfsVOzmsz4ZG5Ph8HnbcS7LvxJa1m/bo6dcOa8W8xEDKyxugZiOw2Ng9+v4Z85p2E7wmqyxSBUZUOqWP4BG2McYiBfeaCEHN18nWTdjY3av+zwf+1VVXaXxdtQaHRtJedXPtpj1qu/1prd20J+H3mU5fTSWom8O736DZLJ1dPbpjxXm6Z+VcHTj6G9VWV6q+pnJMs705zomTI/HWHff4B78CMGiciklv0EwRt6B8TWV37yehZli4F+0yL6azzd10u2Jec8omXHfXyS3L52S1mwx2ZGOmQil0W9iSSf5mexZJKcy08SrbwMLNfQOGrYg6u3riFeQty+dkvBri068d1rAz+l+3VBWA34DHKIJWs/Qe16+QcgcfpsIfHBrR5PF1Y4Ixsz9TMbpHYwcFR0HjVEx6JSWdiioF52uYfAnz3oz2Ra26Zfmc+DiRZPymJ4ddn8Pk//2r5vsuwuadsuy3tLx7Gyqd/Ek2sNv2YMJSrLTS4c7fqHmS7e8mzDiuYhM5sHjuued0zTXXqLm5WRUVFdqyZUsWkpU/YSsiU/GYCjLT1RBXzGuO96G7hakAjn02+gQftEJkshspqGIMOm7QbJSgAMVs//J7RyVJF8+ePKZiDAqe/Cp3v5YmKXgmhsnX85sbI71ePcp7M5IVPEFrlGTSEpVqYbSg1qW7ntgXqQBLt7UHye85v3Un0h2cm0q5rJuQivueCJsn5jtMtTZRukw6JPner8UcEEYeY7F161a98MILWrBggb7+9a9r8+bNWrlyZei/L5QxFoa3Gd6vbz2dvkr3mALb4y7cvIMn/eZm+/Wn+40xCTOvO+occG/6TJ9/mP79VAMQvdt1LG3Ty+8d9R0bEXbNEe93Hea7d28jKWG9CXfa3QN6zXbpjJ0x1+j5zY36+PiJUPtLZ+5+2+1Px8d19KxfkXTbcu3PT7U2jd9A5myuOxGmPCs2tq+tVOO0wpY7NtLgrh+kU/ewCTrClpG5uu+yNsbi6quv1g9+8ANdd911GSWwUHij1/tXzVfP+hUJN6HfE0aYJ95Mx12EYSLxr13YPGZutpkue/R4TPU1lToeOxnvmrl32/74WAXp1KBAd7eC9zzNk1WFws8BN3PG/dKc6gkgWUTv5p5e9+SrY1uc/NbL8Nuf31O9d+qeu2vL5I+k+Dbm701LlaQxS8P7tXAlOyfv8Z576yMNO6NLy5uuoGTbu1uXoiwIFNSK5qcYnozd08fDdkEF/c4IOm9z3TtKnD7tVxHYHO/iTY+5Vp5766OU52lTlP2n2jYoj833ee6dW0OtsWO2N0FF0DitsOVOJuflHnPWNzikO7fs0//bsi9+P0ctI915UwitHRnNCqmoqEjZYhGLxRSLxeL/HhgYUEtLS9ZbLJJF6t4n3J++eljjaqp0x4pzfZ9S033CyNbTQrLBju4nYjOTwI8ZoyApIVp2R+mSEmYWmM/CrC7pPndJ8Tz+ynnTE6ZL+j3th/nMj7t15GuuabF+K5suaJ0UL2zNU0uyp3r3Z+YpfuK4moQnGvc25zePzpAx/w0zk8PvKcb95OL+Po59NhT/biskXXPh6DTUo8djGhwaidQyFJXf9Wd+Z1pqvP/N1RNVsic47/Rx97XgnfbszjMz+8sMhk11ntLotTplfK1eP9Sv6soKVVdVqK66SpIS7iHb+eM9f/fqsvU1o8+RJ4cdDY04CfngfYIP81Sf7HP3rCQjqNXWe99JSti3NLZccE/zN7zlkjeN7oAhqEzzKxf88tYvTd7rzn3dmNli7vvkubc+SpglZ9RUVmjEcbRiXrMunj05aVnvvX7Mw1GY8iYdOZluGiaw+P73v6+77757zO+zHVgka8b1axaXNKaCSNb0HmbKo61lnsMENkHbVEjyfsFB60xIowGBCQLOmjZBrx/qj/+9XyERdIG7839GY31gN0TYLoowwVyYgMsULiZAkE5972GC0QWtk/TTVw/L0WhBPXl8nW/h4j1OlG4mw7tuhaSEgs20Opnv1HH9110J2q7Uk30XfgFYtgq5sGnzFubS2IpGSqxkTXB32dlT49+3uW9Mt6H7e3V/3ybgdF9jRn1NpU6cHFFtdZUGh4aznj/uisbPD1bOTcgH733p/p2b+3O/a9vbnRC0nTeN3jLZr1J279Oor6lSXXWlb6BvziEogHLvM2gtIkm+afQGKEEBqTkHv/LOlKvmmnOvi+QuO93fQdA9mO3lygtmuuntt9+u/v7++M/Bgwezchxv849fM67fYBy/QYepmqFMN4ffSpRuZj/S6AWU7uu3vc1dfukz29z1xD6t3bQn3vR/zYXNcvdEVFWcKhz9ms+ee+sjOZIGh4b1xuFTQYWZ/eI976BBqu789w7yDHqpWbJ8D9M0GNS94P69e3aK+d7N4NDtbx4JbDY2Bcz9q+ar8fOCoK66KrA7I9ksmCAdS9viT5WS4tNd/bpPzCBWUyiNq6nUzKZ6XXPh6PEuO3tq1vpek82Y8Z53mOmxtgQNgjTX6O7eTxKmj3csbYvfG2ZZd3P9Dg6NNlGboEI6FaCb83J3PZquD3ewarYx+2+qr1FddZWGHemzoWE11dekzJ+ozdre7c1UZfdDgUnP1y5sjueDtwxc0DpJFRqtsM3vvF1rTfU18W5Rb9lm9iuNBlPJuk/d06lNOVAxZivFZ0+YtYPcs8Mmj68ds2yA97z8lg8w95T5W/f34S537922X8POaL65yypJY4IXw+zzluVzxtQ9C1onJUwJ33vXVfrVPVdr711X6ZoL/ctO93cQNKA0nS7PbMh6i4VXtgZv+q1O6H2KDtuFEXXAXqov0BuRRxnEFyU9fosoSYq3XIz7fCGvy86eGtgN4C6AvNtJY5sozb8vO3tqVpt0k33uTVfQYKxULyxzP+27F7pKtmKo336iNHN7uz8khe5uSpZPuVgAq9AW2fIbMCn5Xxsm391Pvu7v34idHNbg0IhqKit0csSJ1GUqje0Odd+n3nT6lQnulhB3ZRH1e/drZQ3TbeTeT1BrUFDTu18XZNjywe+edX9f5liZDF5M9rcbuxNXG37urY8SFkOUxpaPft+NlNjFbL5Hcy5hF9XLxYDSMAqmKyTdhEXlV7F6mxnDXoSZ3BDJ0uctLNx95TaWXPa7GSX/kcZSYrdDmON5C5YwXUbp5l2qfafqzvLbNqjZOVXgYSqHysoKDQ07gYWB2Y8Z52AqqjBdVoatt6OGzftU527jGNkQNM7D3ezv12QdlO9GUAXubn6P0h2XqjKWFH86N9eKSbMpf/weSvwqGL8xOuY8wgYKqfLX7/t2z1I6cPQ3kvzHRiQT1L3sLYvdlb0Zf5Dq2k0n+A4aj+Pt2kkVUJntTReY2d49tsPb1ZMs0AsqH6KOR0tX1gKLTz/9VO+8844kaf78+brvvvt0+eWXa/LkyZo1a5a1hKXDe2Ol+xTtFwRkEiEGPdm4o1hvK0uYyDTKze/9TIo+3TFKK4GU2ROt7RaLTKb+pmrR8HIPmGvyPJH6vf49m9OSU32P3rEg2QgSM02v3zbJWgD9Bv4F5bs09snTb8py1HFVqQJ2vwGI7sGLfoMVjaAWrWQBdNiyIlV6vefv90AnJb+O/L6fo8dPaHBoOCEf2he1xsd8VVdWxNfa8Q4uT3XtJmvFSTYY0+98/cagmVYssw+/VhbvwF+/lg5vOt2tXUGDOA1T5rjHARXV4M2uri5dfvnlY35//fXX6+GHH7aWsHSFaW2I2o2RaYEa5sL2DiTzG+HsTUfUp5BU5xZU4fj116UzmNJ25RRlf2G+g7DdFakqf7/m4bADeaO2NERpffNeI031NfFR6RfMPLUmRpQgMVUrmc2WKr+A3D1wMuy16A0cvHkZZf2OoOP4PUUHVeRRAhbz5Os3wNhvfRNb+R80mNPbBW1mOrjvk6ByzttV666ITPCerEVPGluRJ7unkm0XtnwIail1tzoFdXElK7f9jmWuQ2l0kK03L937dncT2Wr19FO2LyELUznmum84bOVl3pJZIemelYmVedCUN2/kG7agksaOIHYX0N4m2DBNpanYzvcowUKmYxFSBaypgqgwi1ol669Ode7JnjS9BZG3VUzyf2Gc5N8f7063NyD262bzEya97uMuaJ0UXxXXO4YlVReWtxL2Bg7eoG/K+Fq9cbg/cJp41G7DZN0XUfLF3cKRrPk+yv3l18rrV6ase/pNDQ6NqN41xiQovd5z97Yu+bVYmO/Q3Y14z8pTU0+jBPUmrdLoAFXztJ8s/8OWD34tpd4ZKkamrX9rN+3Rk6+OrsfjN/uotroyPrX8luVzcvJyxoKZFZJL3i+sY2lbwsjtjd3ZXaI1SPuisYsXmfSaf7cvao3PNmisr0l6YZiLyywC5Z6l0b7o1IyVVEt8L2idNGZku8mX9kWJI7VtcO/ffe429udm8sfkiTR2xkjUa8F9LL/9e3/nPZ55qdkbh/vj23n/prOrJ17hRUmPCUi8afKmw33ddCxt0y3L54yZDWD25X0xnd9+TIVgmHQHfS+pviO/fHOn2eSNebHac299pEN9g9r+5hHfY5i/+emrhxMWDautHp2lYGaMdSwdnT3QPzikQ32D+vj4iTGL5LmZ2QmmIPe7lv2uF+nUYmlB132yfBlfVz3mBX5uYfLd71zMO376Boe0u/cT35lfpqKePL42oWzylm3uGWnm3M1T94LWSWpf1Bpfjr+zqydhqf8XbrtCd6w4L76wWGdXT3x7M2MiVYXpTqs0eg14W0n88sd73YXJ0/ZFo69DcM8smjez0Xc2jXthtqBjed2/ar5+sHJu4Oyjzz4/z9jJYXV29cRnl+R6vJOf1G9OKiLum7J9UWtC4eS9wfMxotabPvPve7ftV2dXT8KYEC/3mgSSxgxSc/+N+ynBHMsvHdKpKNgEE95CI+gi9Z5LGO79mSeBKH+fbH9u3j73ZOmXwl0L3mN595/qmObzoC4D7z7CdJO489IbkPiNIYidHE6Y9textG1Mk6mpCExzbtD+3OmVxg569Bvn4g36vfnlbbJ2T8kz94Z7SqgxODSScB+ZljzTFFtdVRH/3D1Y2gQO5l403StRA2m/eyHoekl232zsPjVddsr4WrXd/nRCq0my6yPqeCs/7gDz3m374wHQgtZJOtw3qHE1VfEpxt59mvM63DcYb3U1aTZlkXlTsLtl7kj/YEI55S2zU/G7ZtwtaGHGJ0ThV4abYG/FvGY9/dro9GR3AGauLfP/UdLQvqg1vojjhwOf6d5t+8eMxzt6PBa/vgshqJBKrCskVfOq3yplti64dNLn7TOO0v8fZXBj1L9N51xy/feZyvfx0xGl68e7gJEJQt39r6aQ9Ou3DmoSNvszQYQ34PAbV5CqCd97DO9iX+5maPdAwRXzmuNNxebc/Bam8i5ilGxBp1TdlX7liLebK1lffrLjuL9fdwX93obU4zz8muz9utXCjnsKWrjO/f+me8pdebsHYQZ1H7in25op8FGa7/26u8214p4OGrVLwK8L0G9V0KD8c6fFfV5+qztH4R1n4r2H/BbiypayHGMRtR89l2MtgvqopeAZDX7pK4WXCuGUbA0kTjbrwftEG6bv2bQC+AUIyQq+VCs0+h3L9LNLSmit8K646F063Ux1vOzsqdr+5pExYwKiBpPJBu65Ww/c/d6ZrDbqPt66p3+lwaFh1ddU6Vf3/F7aafWu3mrSmmqcgeS/rob7/91BnhnkmmqMlzt4lMZOxw7D73tINsU76n4l/2A21T6995MU7aWLyfabbJxJLh+SynKMhbtrwd3nafpQvX2b6fRJZpo2b9+6aSbze1GZX/rCvtY9iI2xDYV4rCjHt5EuW+cW1KfuJ2zfrNnWrDJ4/6r5Y1YcdO/vsrOnqqpCmjK+dsw5mfTt7v1Etyyfo0bPmAxp7Ivm3OMuzL3XVF8Tf+JLNs6oY2mb6qqr4hXP+Lrq+KvrpdHC3f3CPTMe4uPjJ+L30f2r5mvy+DpJUl11pTq7eiL1bXvP3Xw3QeNszO/Pb26MBzvplCvu9N2x4lzNbKqPT2NMdS1LSjg3k6a7r50bzz93Wv3S5m3mD1rxdUHrJN31xD7NaBw3ZnVj93UX1E05s6lejfU18S4EvxVck/GOBTNjM2Y2nVp5Np0y3Vyr5oWN5poJO9bMpMU97sKb3iDJvl8zfuKOFefFp9x67xvz/eS7zDVKKrAwX6Ck+A1ivhgp9VsG3TL5glIN5gr6fy+/gjDKGyf9BFVkUc437LZRKs1sCDq+jXTZOrdcBrdB/AaW+qXPGwS7K7W7r50bDy5MX7o0tqLx5pv3YcAMJjQBzPHYSU0ZX6sKSbGTI+pYOrqsurkPTBDjzUe/siAM97VtBjebY7jvR/dnJiAyL5v7+PiJSAGM3/GlxEDBDBi9c8u+hKDMO2jX7++9FbH5Lr33r9+16M4P8/8//fwNwkf6P0s6yNWvnHBXvn6BT6p8CQoQzXn5dcWFLdfMtTp5fF3gwOUw2he16p6Vo8GIaV1I9fdhyiq///c+ROe7zDVKKrAwF5ekhFHJfiOCU30BmXxBfn+b7Akg7EW7sbtXu3s/0d3Xzk27GySoIotyvmG3zXelGXT8TNIVdTZJKlELrWww+WHen+Ju2XOnz5tv3ifcZE92ZmT80eOxhCdCU0HHTo4OwOz/fJVCo29wKP7OmsGh4fg1Z4IhE8R489FbgZmnYvdr7/24z8l7DDfvZ51d4Wf0JJPq3jKzJYKOmarsSXYMv2vRrzIbV1MZ6uHGexxz76zdtCfhoS7M/RimzPHbJp1yPJMWBiPqfe09pl854/dAKiW+hyrfZa5RUmMspPTm90cd/JhKlIFgYQZ4hekLz1S2+voLTaZpL7T3Y0QVtg882YA/977C5KW7n1pKfCeH6X9297mbbg73Wx8lBS66FOZ7TLWsu985SWMHsmZ7IHiyvA7zro+g9PkdI+y7bJLlR5Rz8X4HYRYBTJUvqbbJVlmVrXIgVVnvVzdk8zXpXmU5eFMKP+o538KsU+8dzZ7NFdXKRaYFQqFeT2GlGkzpNzg4SrDh95l7wF5jfY1iJ0c0+PnbPW9ZPideMPq9vM08cWZaaEapTJOdXyEElmGuwTDpTGf13EzS5P0OvJVntitrm0GflJ33caRaoTfqvWhb2QYWXqmmC+VLqoLZPUWPgMKeTJ++cyGbafFeW96WPb8gVho7KyBZ65k7eKmQxiwXHiX4z9f3kskS8FFFbT0Nszx8pq2mRjor0yZrKU52/EJvXchFUJnNlnQbCCw+520qkvyXG873F+blDYgKIU2lLMw7ZvKRllwVYEGtZEFTtFNVau6ujzDv3Ci0lsZcHj/V9x1UadvsGrXR3eV3PRVCl6GtICvf12QhpKMsp5v68Q4qszF4MRNhRymbdBJU5Ib7uvAu2ZzPtGSLd3CZOaZZLtsMegwa+Gq287s2zT1XX5O4dLZ3hsG5d27V7Nuejg/m85st4r0fbU2nszH4zgxINUs1pyto4J7Zp/dzkzYzTdg9vTddfvkdpQILup4yuYZtfdeZzMqIup9cyPa9YUPJt1h45bspvJAi+VKV6XeZy5XsyknQmyHdL1hK1WJRSM3afgsqZSJqS4TNssQvv6Pu33YZmq8FDPMdOKSSz/E/tFgEiDLVKhsKZTpQKcu09ck8rZu3L6arkJ4gpNykJ9kxvNPlTIvG+c2NYwrKoPsx6P6Jem427sOOpacW/7JxP5vrVgp+WZb3+LbKEr/8jrp/262+3nVEsslMe+3sGru+R6GJem/kAy0WKDmF8h0XWutULtKT7BjZfNIqtLxORzrXbaFc69lIS66/01K4hrKNFgsP80QjqSD6yQpZpk+2+X5SL5S+0EJ6gpBykx73MbzXQdATra3Wg0LK63Skc93mamxYGLbvO5vfaZgyqRSuoUJRNi0WRKPhZZpX5ZTXhfTEWGjSmYKIaMjTcMqpTMomWiw8iEbDyzSvyimvM3lizHfLTrYFzWagArSHPA2nnMqkQlA2LRZANmTyxGhz5UMgl7guyxMtFkAOZPLEGOYpqpD60MtZqbcuRcV1iWQILAocBVrpChOU0IRbGAqtIs13uZDOdZnvNBeDUskjukIKHIOOgPwrtKb/YiwXijHNuVboeURXSIngiRXIv0IbJFmM5UIxpjnXSiWPaLEAkDeF1hJQDMgz5AstFgAKXqGNXSgG5BkKHYEFgLwplabfXCLPUOjoCgEAACnRFQIAAHKOwAIAAFhDYAEAAKwhsAAAANYQWAAAAGsILAAAgDUEFgAAwBoCCwAAYA2BBQAAsIbAAgAAWENgAQAArCGwAAAA1hBYAAAAa6pzfUDzMtWBgYFcHxoAAKTJ1NupXoqe88Di2LFjkqSWlpZcHxoAAGTo2LFjamxsDPy8wkkVelg2MjKiw4cPa+LEiaqoqLC234GBAbW0tOjgwYNJ3xOPzJDPuUNe5wb5nBvkc+5kK68dx9GxY8fU3NysysrgkRQ5b7GorKzUmWeembX9NzQ0cNHmAPmcO+R1bpDPuUE+50428jpZS4XB4E0AAGANgQUAALCmZAKLuro63XXXXaqrq8t3Ukoa+Zw75HVukM+5QT7nTr7zOueDNwEAQOkqmRYLAACQfwQWAADAGgILAABgDYEFAACwpmQCi3/4h3/QF77wBY0bN06XXHKJXn755XwnqWisX79eF110kSZOnKhp06Zp5cqV2r9/f8I2n332mdasWaMpU6ZowoQJ+v3f/3198MEHCdscOHBAK1as0GmnnaZp06bpe9/7nk6ePJnLUykqGzZsUEVFhW6++eb478hnew4dOqT29nZNmTJF9fX1uuCCC/TKK6/EP3ccR3/1V3+lM844Q/X19Vq2bJnefvvthH0cPXpUq1evVkNDg5qamvSd73xHn376aa5PpWANDw/rzjvv1OzZs1VfX6+2tjbdc889Ce+SIJ/T89xzz+maa65Rc3OzKioqtGXLloTPbeXra6+9pi9/+csaN26cWlpa9Dd/8zeZJ94pAY899phTW1vr/Ou//qvzxhtvOH/8x3/sNDU1OR988EG+k1YUli9f7jz00EPOvn37nL179zpf/epXnVmzZjmffvppfJsbbrjBaWlpcZ555hnnlVdecRYtWuR86Utfin9+8uRJZ+7cuc6yZcucPXv2OD/72c+c008/3bn99tvzcUoF7+WXX3a+8IUvOPPmzXNuuumm+O/JZzuOHj3qtLa2Ot/61recl156yXn33Xedbdu2Oe+88058mw0bNjiNjY3Oli1bnFdffdX52te+5syePdsZHByMb/N7v/d7zoUXXuh0d3c7//3f/+381m/9lrNq1ap8nFJBWrdunTNlyhTnqaeect577z3n8ccfdyZMmOD8/d//fXwb8jk9P/vZz5w77rjD+clPfuJIcjZv3pzwuY187e/vd6ZPn+6sXr3a2bdvn7Np0yanvr7eeeCBBzJKe0kEFhdffLGzZs2a+L+Hh4ed5uZmZ/369XlMVfH68MMPHUnOzp07HcdxnL6+PqempsZ5/PHH49v86le/ciQ5L774ouM4ozdBZWWlc+TIkfg2nZ2dTkNDgxOLxXJ7AgXu2LFjzllnneVs377d+d3f/d14YEE+23Prrbc6l156aeDnIyMjzowZM5y//du/jf+ur6/PqaurczZt2uQ4juO8+eabjiRn165d8W22bt3qVFRUOIcOHcpe4ovIihUrnD/6oz9K+N3Xv/51Z/Xq1Y7jkM+2eAMLW/n6j//4j86kSZMSyo5bb73VmTNnTkbpLfqukBMnTmj37t1atmxZ/HeVlZVatmyZXnzxxTymrHj19/dLkiZPnixJ2r17t4aGhhLy+JxzztGsWbPiefziiy/qggsu0PTp0+PbLF++XAMDA3rjjTdymPrCt2bNGq1YsSIhPyXy2aYnn3xSCxcu1De+8Q1NmzZN8+fP14MPPhj//L333tORI0cS8rqxsVGXXHJJQl43NTVp4cKF8W2WLVumyspKvfTSS7k7mQL2pS99Sc8884zeeustSdKrr76q559/XldffbUk8jlbbOXriy++qMsuu0y1tbXxbZYvX679+/frk08+STt9OX8JmW3/93//p+Hh4YSCVpKmT5+u//mf/8lTqorXyMiIbr75Zi1ZskRz586VJB05ckS1tbVqampK2Hb69Ok6cuRIfBu/78B8hlGPPfaYfvnLX2rXrl1jPiOf7Xn33XfV2dmpP/uzP9Nf/uVfateuXVq7dq1qa2t1/fXXx/PKLy/deT1t2rSEz6urqzV58mTy+nO33XabBgYGdM4556iqqkrDw8Nat26dVq9eLUnkc5bYytcjR45o9uzZY/ZhPps0aVJa6Sv6wAJ2rVmzRvv27dPzzz+f76SUnIMHD+qmm27S9u3bNW7cuHwnp6SNjIxo4cKF+uEPfyhJmj9/vvbt26d/+qd/0vXXX5/n1JWOf//3f9cjjzyiRx99VOeff7727t2rm2++Wc3NzeRzGSv6rpDTTz9dVVVVY0bOf/DBB5oxY0aeUlWcbrzxRj311FN69tlnE15tP2PGDJ04cUJ9fX0J27vzeMaMGb7fgfkMo10dH374oX7nd35H1dXVqq6u1s6dO3X//ferurpa06dPJ58tOeOMM3Teeecl/O7cc8/VgQMHJJ3Kq2TlxowZM/Thhx8mfH7y5EkdPXqUvP7c9773Pd122236gz/4A11wwQX65je/qe9+97tav369JPI5W2zla7bKk6IPLGpra7VgwQI988wz8d+NjIzomWee0eLFi/OYsuLhOI5uvPFGbd68WTt27BjTNLZgwQLV1NQk5PH+/ft14MCBeB4vXrxYr7/+esKFvH37djU0NIwp4MvVlVdeqddff1179+6N/yxcuFCrV6+O/z/5bMeSJUvGTJl+66231NraKkmaPXu2ZsyYkZDXAwMDeumllxLyuq+vT7t3745vs2PHDo2MjOiSSy7JwVkUvt/85jeqrEysRqqqqjQyMiKJfM4WW/m6ePFiPffccxoaGopvs337ds2ZMyftbhBJpTPdtK6uznn44YedN9980/mTP/kTp6mpKWHkPIJ1dHQ4jY2NTldXl/P+++/Hf37zm9/Et7nhhhucWbNmOTt27HBeeeUVZ/Hixc7ixYvjn5tpkFdddZWzd+9e5+c//7kzdepUpkGm4J4V4jjksy0vv/yyU11d7axbt855++23nUceecQ57bTTnI0bN8a32bBhg9PU1OQ88cQTzmuvveZce+21vtP15s+f77z00kvO888/75x11lllPw3S7frrr3dmzpwZn276k5/8xDn99NOdv/iLv4hvQz6n59ixY86ePXucPXv2OJKc++67z9mzZ4/T29vrOI6dfO3r63OmT5/ufPOb33T27dvnPPbYY85pp53GdFPjRz/6kTNr1iyntrbWufjii53u7u58J6loSPL9eeihh+LbDA4OOn/6p3/qTJo0yTnttNOc6667znn//fcT9vPrX//aufrqq536+nrn9NNPd/78z//cGRoayvHZFBdvYEE+2/PTn/7UmTt3rlNXV+ecc845zo9//OOEz0dGRpw777zTmT59ulNXV+dceeWVzv79+xO2+fjjj51Vq1Y5EyZMcBoaGpxvf/vbzrFjx3J5GgVtYGDAuemmm5xZs2Y548aNc774xS86d9xxR8L0RfI5Pc8++6xvuXz99dc7jmMvX1999VXn0ksvderq6pyZM2c6GzZsyDjtvDYdAABYU/RjLAAAQOEgsAAAANYQWAAAAGsILAAAgDUEFgAAwBoCCwAAYA2BBQAAsIbAAgAAWENgAQAArCGwAAAA1hBYAAAAawgsAACANf8fZJAaPpDPEXcAAAAASUVORK5CYII=\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": "a5a8320e-6d28-4561-c11d-6b2db9fb22e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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peuihh/TUU0+ppaUlMmsFAABiWo8CSmFhoXJzc5WdnR0yv6qqSq2trSHzx40bp4yMDFVUVEiSKioqNGHCBHm9XqdOTk6OgsGg9u3b1+XympubFQwGQyYAADBwJYT7gvXr1+v3v/+9du7ceUpZIBBQYmKikpOTQ+Z7vV4FAgGnzonhpLO8s6wrJSUl+tnPfhZuUwEAQIwKawSltrZW999/v1544QUNGTKkr9p0iqKiIjU1NTlTbW1tvy0bAAD0v7ACSlVVlRoaGnTllVcqISFBCQkJ2rp1q1asWKGEhAR5vV61tLSosbEx5HX19fXy+XySJJ/Pd8pdPZ2/d9Y5mcvlktvtDpkAAMDAFVZAmTJlivbs2aPdu3c706RJk5Sfn+/8PHjwYJWXlzuvqa6uVk1Njfx+vyTJ7/drz549amhocOqUlZXJ7XYrMzMzQqsFAABiWVjXoIwYMUKXXHJJyLxhw4Zp5MiRzvyZM2dqwYIFSklJkdvt1ty5c+X3+zV58mRJ0tSpU5WZmakZM2Zo+fLlCgQCWrJkiQoLC+VyuSK0WgAAIJaFfZHs2Tz++OOKj49XXl6empublZOTo5UrVzrlgwYNUmlpqWbPni2/369hw4apoKBAS5cujXRTAABAjIozxphoNyJcwWBQHo9HTU1NEb0e5VhLmzKLN0uS9i/NUVJixPMbAADnrHCO3/wvHgAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWCSugrFq1Spdeeqncbrfcbrf8fr82btzolB8/flyFhYUaOXKkhg8frry8PNXX14e8R01NjXJzc5WUlKTU1FQtXLhQbW1tkVkbAAAwIIQVUM4//3wtW7ZMVVVV2rVrl2644Qbdeuut2rdvnyRp/vz5evXVV7VhwwZt3bpVdXV1mj59uvP69vZ25ebmqqWlRdu2bdNzzz2ntWvXqri4OLJrBQAAYlqcMcb05g1SUlL02GOP6fbbb9fo0aO1bt063X777ZKkDz/8UOPHj1dFRYUmT56sjRs36uabb1ZdXZ28Xq8kafXq1Vq0aJE+//xzJSYmdmuZwWBQHo9HTU1NcrvdvWl+iGMtbcos3ixJ2r80R0mJCRF7bwAAznXhHL97fA1Ke3u71q9fr6NHj8rv96uqqkqtra3Kzs526owbN04ZGRmqqKiQJFVUVGjChAlOOJGknJwcBYNBZxSmK83NzQoGgyETAAAYuMIOKHv27NHw4cPlcrl033336aWXXlJmZqYCgYASExOVnJwcUt/r9SoQCEiSAoFASDjpLO8sO52SkhJ5PB5nSk9PD7fZAAAghoQdUC6++GLt3r1blZWVmj17tgoKCrR///6+aJujqKhITU1NzlRbW9unywMAANEV9kUWiYmJ+sY3viFJmjhxonbu3Klf/OIX+s53vqOWlhY1NjaGjKLU19fL5/NJknw+n3bs2BHyfp13+XTW6YrL5ZLL5Qq3qQAAIEb1+jkoHR0dam5u1sSJEzV48GCVl5c7ZdXV1aqpqZHf75ck+f1+7dmzRw0NDU6dsrIyud1uZWZm9rYpAABggAhrBKWoqEjTpk1TRkaGDh8+rHXr1umtt97S5s2b5fF4NHPmTC1YsEApKSlyu92aO3eu/H6/Jk+eLEmaOnWqMjMzNWPGDC1fvlyBQEBLlixRYWEhIyQAAMARVkBpaGjQ9773PX322WfyeDy69NJLtXnzZv3DP/yDJOnxxx9XfHy88vLy1NzcrJycHK1cudJ5/aBBg1RaWqrZs2fL7/dr2LBhKigo0NKlSyO7VgAAIKb1+jko0cBzUAAAiD398hwUAACAvkJAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1wgooJSUluuqqqzRixAilpqbqtttuU3V1dUid48ePq7CwUCNHjtTw4cOVl5en+vr6kDo1NTXKzc1VUlKSUlNTtXDhQrW1tfV+bQAAwIAQVkDZunWrCgsLtX37dpWVlam1tVVTp07V0aNHnTrz58/Xq6++qg0bNmjr1q2qq6vT9OnTnfL29nbl5uaqpaVF27Zt03PPPae1a9equLg4cmsFAABiWpwxxvT0xZ9//rlSU1O1detWfetb31JTU5NGjx6tdevW6fbbb5ckffjhhxo/frwqKio0efJkbdy4UTfffLPq6urk9XolSatXr9aiRYv0+eefKzEx8azLDQaD8ng8ampqktvt7mnzT3GspU2ZxZslSfuX5igpMSFi7w0AwLkunON3r65BaWpqkiSlpKRIkqqqqtTa2qrs7Gynzrhx45SRkaGKigpJUkVFhSZMmOCEE0nKyclRMBjUvn37etMcAAAwQPR4iKCjo0Pz5s3Ttddeq0suuUSSFAgElJiYqOTk5JC6Xq9XgUDAqXNiOOks7yzrSnNzs5qbm53fg8FgT5sNAABiQI9HUAoLC7V3716tX78+ku3pUklJiTwejzOlp6f3+TIBAED09CigzJkzR6WlpXrzzTd1/vnnO/N9Pp9aWlrU2NgYUr++vl4+n8+pc/JdPZ2/d9Y5WVFRkZqampyptra2J80GAAAxIqyAYozRnDlz9NJLL2nLli0aO3ZsSPnEiRM1ePBglZeXO/Oqq6tVU1Mjv98vSfL7/dqzZ48aGhqcOmVlZXK73crMzOxyuS6XS263O2QCAAADV1jXoBQWFmrdunV65ZVXNGLECOeaEY/Ho6FDh8rj8WjmzJlasGCBUlJS5Ha7NXfuXPn9fk2ePFmSNHXqVGVmZmrGjBlavny5AoGAlixZosLCQrlcrsivIQAAiDlhBZRVq1ZJkr797W+HzF+zZo3uvvtuSdLjjz+u+Ph45eXlqbm5WTk5OVq5cqVTd9CgQSotLdXs2bPl9/s1bNgwFRQUaOnSpb1bEwAAMGD06jko0cJzUAAAiD399hwUAACAvkBAAQAA1iGgAAAA6xBQAACIoGMtbbpw8Wu6cPFrOtbSFu3mxCwCCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAMA5hAs4ESsIKAAAwDoEFAAAYB0CCgAAsA4BBQCAPpJZvJlrfXqIgAIAAKxDQDkNUi8AANFDQAEAANYhoJwBIygAAEQHAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAGDP5TLzBwEFAAAIB1CCgn4C8uAADsQEABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAeUMvmxpj3YTAAA4JxFQzuD65W+FPBuFp1QCANA/CCgAAMA6BBQAAGAdAgoAALBOQrQbYLvM4s2SpP1Lc6LcEgAAzh1hj6C8/fbbuuWWW5SWlqa4uDi9/PLLIeXGGBUXF2vMmDEaOnSosrOzdeDAgZA6hw4dUn5+vtxut5KTkzVz5kwdOXKkVysCAAAGjrADytGjR3XZZZfpqaee6rJ8+fLlWrFihVavXq3KykoNGzZMOTk5On78uFMnPz9f+/btU1lZmUpLS/X2229r1qxZPV+LfpBZvJk7d4Be4C44AOEI+xTPtGnTNG3atC7LjDF64okntGTJEt16662SpP/6r/+S1+vVyy+/rDvvvFMffPCBNm3apJ07d2rSpEmSpCeffFI33XST/u3f/k1paWm9WB0AADAQRPQi2YMHDyoQCCg7O9uZ5/F4lJWVpYqKCklSRUWFkpOTnXAiSdnZ2YqPj1dlZWUkmwMAAGJURC+SDQQCkiSv1xsy3+v1OmWBQECpqamhjUhIUEpKilPnZM3NzWpubnZ+DwaDkWw2AACwTEzcZlxSUiKPx+NM6enpUWnHpIfLo7JcAED4uO4ptkU0oPh8PklSfX19yPz6+nqnzOfzqaGhIaS8ra1Nhw4dcuqcrKioSE1NTc5UW1sbyWb3CBs+0D2d35XOW/YBoDsiGlDGjh0rn8+n8vKvRhqCwaAqKyvl9/slSX6/X42NjaqqqnLqbNmyRR0dHcrKyuryfV0ul9xud8gEAAAGrrCvQTly5Ig+/vhj5/eDBw9q9+7dSklJUUZGhubNm6eHH35YF110kcaOHasHH3xQaWlpuu222yRJ48eP14033qh7771Xq1evVmtrq+bMmaM777yTO3gAAICkHgSUXbt26e///u+d3xcsWCBJKigo0Nq1a/XAAw/o6NGjmjVrlhobG3Xddddp06ZNGjJkiPOaF154QXPmzNGUKVMUHx+vvLw8rVixIgKrA8A2nAIF0BNhB5Rvf/vbMsactjwuLk5Lly7V0qVLT1snJSVF69atC3fRAADgHBETd/EAAIBzCwEFp8WdSgAQiv1i/yGgnAP4QgEAYg0BpYd4aBsAAH2HgAIAAKxDQAEAANYhoEQA13UAABBZBBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoETApIfL9cWR49FuBgAAAwYB5RzGI/ABALYioACWIDACwFcIKBEy6eFyDioAAEQIASWC+AsYAIDIIKBEAUEGQCyI5X3VsZY2ZRZvjnYz0AsEFFgplneMfY2+wdmwjWAgIKAAgMUIGzhXEVAAAIB1CCj9hL+CAADoPgJKBE16uDzaTeg2LiCLHYRbdMos3sw2gF6Jpf0JAQUAAFiHgNIPzvXRilhK7AAAOxBQ+ggHZQCInBP3qV8cOX5O7V/P1eNJQrQbcC6KpWtVYJdwRuJOHLnbvzRHSYl83aOJzyP6+AxiC59OHzkxhJwpkGQWbw75onR1OogvEoCBhKCA7uAUD6x04jDmuTSk2Vcyizefc8PDQH+w7fSLbe3pDQIKECabdgA2tQVA1/hu9gzjagAAnMVAvRvT5tNtjKBYhqR9bjndCAinZHA65+K2ESvh4Fz8bPqSPVEJ1oiVnQH6j81/ZUmn32ZPnm9j26Mp3M/U9u0gHLG6LiffWDGQMYJigc7HVx9raevzW5A7/2KPtQASyWstYum6jWg92vzE50x8ceR4vy8/Uk7Xd7G0DcQ6+vrsItFHkdq32/R5DfwIFiPOtFH1NuWfuJF92dIefuO68f4ntlHSKb+H8/pz4S+DaDnW0hZ2/37Z0t7t4ByJz7I779HVNhfue/eFaG/LXa1fTz7zvsLoLMJhx1aLXuvtjrE7w4YnLuOdB76t65e/5fzc07ZK0q4lU8J6fbjLONN69fZgEu6B9MQ63d1ZdzccZBZv1q4lUzRq+JBu1e9uH3V+zicv5+R2hRMUuivSB/yB/JDEs21PAykcxPrneKaA3dX2HqmRjFjbBggoMaQzRHT+fKZ6PXGmv7ROfs8TD1on/nxyvc6DWW+c/Bf86UZrujponux0X9BI/tXfnXZEyskjYpMeLu+Xv9y7MxJ3Ypg9WX+MNGQWb45IeD7xs+xNW09+tk8k17k7B57+Hq4P52B4umuFwj2gnvw97Gl7u7tvicT+7XTONELY1bGgr9oRTVENKE899ZQee+wxBQIBXXbZZXryySd19dVXR7NJ1uvtju1MB5bOL2CkN/RwDtan++s80ss50/K7cmKbuuqfE/v1dO2w4eK2L1vadeHi1/rkvU93DdXJIzAnO1Pw7iqYdvX60+nuKc1IHEzPVO/kg0znenV1SjTc7eNsbe8MrWfS1YG4O6+JxPbc1XZz4jZxNqdb9744nd1TZ9o39fTaw+5srydvV12F1D8faQ6pb1PQidqe8r//+7+1YMECrV69WllZWXriiSeUk5Oj6upqpaamRqtZ1uvtQXjqE+/0+TLOpKsvyMnLi/SOJRIjG2cLIGc7CJ/clkjparln+ovqTO08+RReuMvtzXbTV8PO3dnez3ZwP1vds4WQzjqn69O+HFk5cfmvz7s+7Nd1p222njbo6tTkiU78Tp/8+YQTGrqqF84+rDujvt1xpmWG8/nYdPoszhhjorHgrKwsXXXVVfqP//gPSVJHR4fS09M1d+5cLV68+IyvDQaD8ng8ampqktvtjlibvjhy3KoPB0DsOPG6rEjpz9OFUt+sQ6w407q/UniNbn1qW/826P97fd713QraPdEZyk633n0x4hvO8TsqAaWlpUVJSUn6n//5H912223O/IKCAjU2NuqVV14Jqd/c3Kzm5q+GoYLBoNLT0wkoAAD0kXAuuu+ucAJKVE7xfPHFF2pvb5fX6w2Z7/V69eGHH55Sv6SkRD/72c/6vF2jhg/R/y3L7fPlAACAM4uJB7UVFRWpqanJmWpra6PdJAAA0IeiMoIyatQoDRo0SPX19SHz6+vr5fP5Tqnvcrnkcrn6q3kAACDKojKCkpiYqIkTJ6q8/KvrPTo6OlReXi6/3x+NJgEAAItE7TbjBQsWqKCgQJMmTdLVV1+tJ554QkePHtX3v//9aDUJAABYImoB5Tvf+Y4+//xzFRcXKxAI6PLLL9emTZtOuXAWAACce6L2HJTe6KvnoAAAgL4TzvE7Ju7iAQAA5xYCCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgnag96r43Oh9+GwwGo9wSAADQXZ3H7e48xD4mA8rhw4clSenp6VFuCQAACNfhw4fl8XjOWCcm/xdPR0eH6urqNGLECMXFxUX0vYPBoNLT01VbW8v/+elD9HP/oJ/7B/3cf+jr/tFX/WyM0eHDh5WWlqb4+DNfZRKTIyjx8fE6//zz+3QZbrebjb8f0M/9g37uH/Rz/6Gv+0df9PPZRk46cZEsAACwDgEFAABYh4ByEpfLpZ/85CdyuVzRbsqARj/3D/q5f9DP/Ye+7h829HNMXiQLAAAGNkZQAACAdQgoAADAOgQUAABgHQIKAACwDgHlBE899ZQuvPBCDRkyRFlZWdqxY0e0mxRTSkpKdNVVV2nEiBFKTU3Vbbfdpurq6pA6x48fV2FhoUaOHKnhw4crLy9P9fX1IXVqamqUm5urpKQkpaamauHChWpra+vPVYkpy5YtU1xcnObNm+fMo58j49NPP9V3v/tdjRw5UkOHDtWECRO0a9cup9wYo+LiYo0ZM0ZDhw5Vdna2Dhw4EPIehw4dUn5+vtxut5KTkzVz5kwdOXKkv1fFau3t7XrwwQc1duxYDR06VH/zN3+jhx56KOT/tdDX4Xv77bd1yy23KC0tTXFxcXr55ZdDyiPVp3/4wx90/fXXa8iQIUpPT9fy5csjswIGxhhj1q9fbxITE81//ud/mn379pl7773XJCcnm/r6+mg3LWbk5OSYNWvWmL1795rdu3ebm266yWRkZJgjR444de677z6Tnp5uysvLza5du8zkyZPNNddc45S3tbWZSy65xGRnZ5v33nvP/Pa3vzWjRo0yRUVF0Vgl6+3YscNceOGF5tJLLzX333+/M59+7r1Dhw6ZCy64wNx9992msrLSfPLJJ2bz5s3m448/duosW7bMeDwe8/LLL5v333/f/OM//qMZO3as+fLLL506N954o7nsssvM9u3bzTvvvGO+8Y1vmLvuuisaq2StRx55xIwcOdKUlpaagwcPmg0bNpjhw4ebX/ziF04d+jp8v/3tb82Pf/xj8+KLLxpJ5qWXXgopj0SfNjU1Ga/Xa/Lz883evXvNr371KzN06FDzy1/+stftJ6D8f1dffbUpLCx0fm9vbzdpaWmmpKQkiq2KbQ0NDUaS2bp1qzHGmMbGRjN48GCzYcMGp84HH3xgJJmKigpjzF+/UPHx8SYQCDh1Vq1aZdxut2lubu7fFbDc4cOHzUUXXWTKysrM3/3d3zkBhX6OjEWLFpnrrrvutOUdHR3G5/OZxx57zJnX2NhoXC6X+dWvfmWMMWb//v1Gktm5c6dTZ+PGjSYuLs58+umnfdf4GJObm2vuueeekHnTp083+fn5xhj6OhJODiiR6tOVK1ea8847L2S/sWjRInPxxRf3us2c4pHU0tKiqqoqZWdnO/Pi4+OVnZ2tioqKKLYstjU1NUmSUlJSJElVVVVqbW0N6edx48YpIyPD6eeKigpNmDBBXq/XqZOTk6NgMKh9+/b1Y+vtV1hYqNzc3JD+lOjnSPnNb36jSZMm6Y477lBqaqquuOIKPfPMM075wYMHFQgEQvrZ4/EoKysrpJ+Tk5M1adIkp052drbi4+NVWVnZfytjuWuuuUbl5eX66KOPJEnvv/++3n33XU2bNk0Sfd0XItWnFRUV+ta3vqXExESnTk5Ojqqrq/WXv/ylV22MyX8WGGlffPGF2tvbQ3bWkuT1evXhhx9GqVWxraOjQ/PmzdO1116rSy65RJIUCASUmJio5OTkkLper1eBQMCp09Xn0FmGv1q/fr1+//vfa+fOnaeU0c+R8cknn2jVqlVasGCB/vVf/1U7d+7UD37wAyUmJqqgoMDpp6768cR+Tk1NDSlPSEhQSkoK/XyCxYsXKxgMaty4cRo0aJDa29v1yCOPKD8/X5Lo6z4QqT4NBAIaO3bsKe/RWXbeeef1uI0EFPSJwsJC7d27V++++260mzLg1NbW6v7771dZWZmGDBkS7eYMWB0dHZo0aZIeffRRSdIVV1yhvXv3avXq1SooKIhy6waWX//613rhhRe0bt06ffOb39Tu3bs1b948paWl0dfnME7xSBo1apQGDRp0yl0O9fX18vl8UWpV7JozZ45KS0v15ptv6vzzz3fm+3w+tbS0qLGxMaT+if3s8/m6/Bw6y/DXUzgNDQ268sorlZCQoISEBG3dulUrVqxQQkKCvF4v/RwBY8aMUWZmZsi88ePHq6amRtJX/XSm/YbP51NDQ0NIeVtbmw4dOkQ/n2DhwoVavHix7rzzTk2YMEEzZszQ/PnzVVJSIom+7guR6tO+3JcQUCQlJiZq4sSJKi8vd+Z1dHSovLxcfr8/ii2LLcYYzZkzRy+99JK2bNlyyrDfxIkTNXjw4JB+rq6uVk1NjdPPfr9fe/bsCflSlJWVye12n3KwOFdNmTJFe/bs0e7du51p0qRJys/Pd36mn3vv2muvPeU2+Y8++kgXXHCBJGns2LHy+Xwh/RwMBlVZWRnSz42NjaqqqnLqbNmyRR0dHcrKyuqHtYgNx44dU3x86OFo0KBB6ujokERf94VI9anf79fbb7+t1tZWp05ZWZkuvvjiXp3ekcRtxp3Wr19vXC6XWbt2rdm/f7+ZNWuWSU5ODrnLAWc2e/Zs4/F4zFtvvWU+++wzZzp27JhT57777jMZGRlmy5YtZteuXcbv9xu/3++Ud97+OnXqVLN7926zadMmM3r0aG5/PYsT7+Ixhn6OhB07dpiEhATzyCOPmAMHDpgXXnjBJCUlmeeff96ps2zZMpOcnGxeeeUV84c//MHceuutXd6mecUVV5jKykrz7rvvmosuuuicvvW1KwUFBeZrX/uac5vxiy++aEaNGmUeeOABpw59Hb7Dhw+b9957z7z33ntGkvn5z39u3nvvPfPHP/7RGBOZPm1sbDRer9fMmDHD7N2716xfv94kJSVxm3GkPfnkkyYjI8MkJiaaq6++2mzfvj3aTYopkrqc1qxZ49T58ssvzb/8y7+Y8847zyQlJZl/+qd/Mp999lnI+/zf//2fmTZtmhk6dKgZNWqU+eEPf2haW1v7eW1iy8kBhX6OjFdffdVccsklxuVymXHjxpmnn346pLyjo8M8+OCDxuv1GpfLZaZMmWKqq6tD6vz5z382d911lxk+fLhxu93m+9//vjl8+HB/rob1gsGguf/++01GRoYZMmSI+frXv25+/OMfh9y6Sl+H78033+xyn1xQUGCMiVyfvv/+++a6664zLpfLfO1rXzPLli2LSPvjjDnhUX0AAAAW4BoUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKzz/wBU3Y6Au481GgAAAABJRU5ErkJggg==\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\")\n"
      ],
      "metadata": {
        "id": "-OZb4MUb40jO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c624f98-150c-4802-b3b3-67f25c0fdab7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Connectivity:  0 Clustering:  0.3969109262646445 Efficiency:  0.4460739033346986\n",
            "Some traders not connected\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "degree = [d for n,d in G.degree()]\n",
        "plt.hist(degree)"
      ],
      "metadata": {
        "id": "s5pJwG705mMA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "outputId": "bc362231-0c43-48ad-9446-8ce67cea6df2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([62., 43., 20.,  9.,  3.,  9., 15., 18., 11.,  7.]),\n",
              " array([ 1. ,  6.9, 12.8, 18.7, 24.6, 30.5, 36.4, 42.3, 48.2, 54.1, 60. ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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": {
        "id": "xLwzO1WP55vS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "55ae76b1-5ff4-4c73-a040-76ee9e09b046"
      },
      "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": "markdown",
      "source": [
        "# Batch Run and Analysis"
      ],
      "metadata": {
        "id": "azz_uiGGakvw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "params = { \"width\": 50, \"height\": 50,\n",
        "          \"vision_min\": range(1,3),\n",
        "          \"metabolism_max\": [3,5]}\n",
        "\n",
        "results_batch = mesa.batch_run(\n",
        "    SugarscapeG1mt,\n",
        "    parameters = params,\n",
        "    iterations = 1,\n",
        "    number_processes = 1,\n",
        "    data_collection_period = 1,\n",
        "    display_progress = True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diPBWOafao4R",
        "outputId": "49fa7ca0-218e-4d74-8435-96b4a3c773ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 25%|██▌       | 1/4 [01:20<04:00, 80.28s/it]<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",
            "100%|██████████| 4/4 [04:13<00:00, 63.31s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "results_df = pd.DataFrame(results_batch)\n",
        "results_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "b_6hoS8HcyAO",
        "outputId": "7af87e8d-9d11-47e8-d6a1-443cc3aabf7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        RunId  iteration  Step  width  height  vision_min  metabolism_max  \\\n",
              "0           0          0     0     50      50           1               3   \n",
              "1           0          0     1     50      50           1               3   \n",
              "2           0          0     1     50      50           1               3   \n",
              "3           0          0     1     50      50           1               3   \n",
              "4           0          0     1     50      50           1               3   \n",
              "...       ...        ...   ...    ...     ...         ...             ...   \n",
              "442900      3          0  1000     50      50           2               5   \n",
              "442901      3          0  1000     50      50           2               5   \n",
              "442902      3          0  1000     50      50           2               5   \n",
              "442903      3          0  1000     50      50           2               5   \n",
              "442904      3          0  1000     50      50           2               5   \n",
              "\n",
              "        Trader  Trade Volume     Price  AgentID  \\\n",
              "0          200           614  0.967595      NaN   \n",
              "1          200           159  1.024258   4138.0   \n",
              "2          200           159  1.024258   4139.0   \n",
              "3          200           159  1.024258   4140.0   \n",
              "4          200           159  1.024258   4141.0   \n",
              "...        ...           ...       ...      ...   \n",
              "442900      59             3  1.001749   4299.0   \n",
              "442901      59             3  1.001749   4315.0   \n",
              "442902      59             3  1.001749   4323.0   \n",
              "442903      59             3  1.001749   4327.0   \n",
              "442904      59             3  1.001749   4335.0   \n",
              "\n",
              "                               Trade Network  \n",
              "0                                        NaN  \n",
              "1                                         []  \n",
              "2                                         []  \n",
              "3       [4167, 4167, 4167, 4167, 4167, 4167]  \n",
              "4                                     [4303]  \n",
              "...                                      ...  \n",
              "442900                                    []  \n",
              "442901                                    []  \n",
              "442902                                    []  \n",
              "442903                                    []  \n",
              "442904                                    []  \n",
              "\n",
              "[442905 rows x 12 columns]"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(results_df[\"Step\"], results_df[\"Price\"], s=0.75)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "YYjtaWEEc7_9",
        "outputId": "ecc62e63-d9cb-4ca6-b380-d7ba4a5cb978"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f60c8f2c940>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "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": [
        "results_explore = results_df[results_df[\"metabolism_max\"]== 3]\n",
        "results_explore"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "wjLv_D84dWqL",
        "outputId": "24f943a0-6baa-4a3b-8d25-082a7363058a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        RunId  iteration  Step  width  height  vision_min  metabolism_max  \\\n",
              "0           0          0     0     50      50           1               3   \n",
              "1           0          0     1     50      50           1               3   \n",
              "2           0          0     1     50      50           1               3   \n",
              "3           0          0     1     50      50           1               3   \n",
              "4           0          0     1     50      50           1               3   \n",
              "...       ...        ...   ...    ...     ...         ...             ...   \n",
              "378971      2          0  1000     50      50           2               3   \n",
              "378972      2          0  1000     50      50           2               3   \n",
              "378973      2          0  1000     50      50           2               3   \n",
              "378974      2          0  1000     50      50           2               3   \n",
              "378975      2          0  1000     50      50           2               3   \n",
              "\n",
              "        Trader  Trade Volume     Price  AgentID  \\\n",
              "0          200           614  0.967595      NaN   \n",
              "1          200           159  1.024258   4138.0   \n",
              "2          200           159  1.024258   4139.0   \n",
              "3          200           159  1.024258   4140.0   \n",
              "4          200           159  1.024258   4141.0   \n",
              "...        ...           ...       ...      ...   \n",
              "378971     143            15  1.061647   4331.0   \n",
              "378972     143            15  1.061647   4333.0   \n",
              "378973     143            15  1.061647   4335.0   \n",
              "378974     143            15  1.061647   4336.0   \n",
              "378975     143            15  1.061647   4337.0   \n",
              "\n",
              "                               Trade Network  \n",
              "0                                        NaN  \n",
              "1                                         []  \n",
              "2                                         []  \n",
              "3       [4167, 4167, 4167, 4167, 4167, 4167]  \n",
              "4                                     [4303]  \n",
              "...                                      ...  \n",
              "378971                                    []  \n",
              "378972                                    []  \n",
              "378973                                    []  \n",
              "378974                                    []  \n",
              "378975                                    []  \n",
              "\n",
              "[306267 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8bb2c5e7-6f6b-4748-bd2a-c1a595d441d4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RunId</th>\n",
              "      <th>iteration</th>\n",
              "      <th>Step</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>vision_min</th>\n",
              "      <th>metabolism_max</th>\n",
              "      <th>Trader</th>\n",
              "      <th>Trade Volume</th>\n",
              "      <th>Price</th>\n",
              "      <th>AgentID</th>\n",
              "      <th>Trade Network</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>614</td>\n",
              "      <td>0.967595</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>159</td>\n",
              "      <td>1.024258</td>\n",
              "      <td>4138.0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>159</td>\n",
              "      <td>1.024258</td>\n",
              "      <td>4139.0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>159</td>\n",
              "      <td>1.024258</td>\n",
              "      <td>4140.0</td>\n",
              "      <td>[4167, 4167, 4167, 4167, 4167, 4167]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>159</td>\n",
              "      <td>1.024258</td>\n",
              "      <td>4141.0</td>\n",
              "      <td>[4303]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378971</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>143</td>\n",
              "      <td>15</td>\n",
              "      <td>1.061647</td>\n",
              "      <td>4331.0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378972</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>143</td>\n",
              "      <td>15</td>\n",
              "      <td>1.061647</td>\n",
              "      <td>4333.0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378973</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>143</td>\n",
              "      <td>15</td>\n",
              "      <td>1.061647</td>\n",
              "      <td>4335.0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378974</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>143</td>\n",
              "      <td>15</td>\n",
              "      <td>1.061647</td>\n",
              "      <td>4336.0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378975</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>143</td>\n",
              "      <td>15</td>\n",
              "      <td>1.061647</td>\n",
              "      <td>4337.0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>306267 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8bb2c5e7-6f6b-4748-bd2a-c1a595d441d4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8bb2c5e7-6f6b-4748-bd2a-c1a595d441d4 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-8bb2c5e7-6f6b-4748-bd2a-c1a595d441d4');\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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(results_explore[\"Step\"], results_explore[\"Price\"], s=0.75)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "eDGVWE5mdqEq",
        "outputId": "8fb76092-af4f-463b-9f70-7fadf973e866"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f60c4d88880>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "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": [
        "for i in range(4):\n",
        "  results_explore = results_df[results_df[\"RunId\"] ==i]\n",
        "  plt.plot(results_explore[\"Step\"], results_explore[\"Trader\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "YsdGJ9LEd2gz",
        "outputId": "79717caf-0463-4c60-c433-17de948f5fec"
      },
      "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": [
        "#Agent results\n",
        "G = nx.Graph()\n",
        "\n",
        "trade = results_df.dropna(subset=[\"Trade Network\"])\n",
        "# add agent keys to make initial node set\n",
        "G.add_nodes_from(list(trade[\"AgentID\"].unique()))\n",
        "\n",
        "# create edge list\n",
        "for idx, row in trade.iterrows():\n",
        "    if len(row[\"Trade Network\"]) > 0:\n",
        "        for agent in row[\"Trade Network\"]:\n",
        "            G.add_edge(row[\"AgentID\"], agent)"
      ],
      "metadata": {
        "id": "rkgxjbhefZ6u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Get some statistics\n",
        "\n",
        "print(f\"Node Connectivity {nx.node_connectivity(G)}\")\n",
        "print(f\"Average Clustering {nx.average_clustering(G)}\")\n",
        "print(f\"Global Efficiency {nx.global_efficiency(G)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtClxAjx_g3y",
        "outputId": "2f82bc8d-71cc-4956-96a3-c450482f8852"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Node Connectivity 14\n",
            "Average Clustering 0.6825679633407337\n",
            "Global Efficiency 0.8045393634840872\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot a histogram of number of links\n",
        "\n",
        "degree_sequence = sorted((d for n, d in G.degree()), reverse=True)\n",
        "degree_sequence = [d for n, d in G.degree()]\n",
        "plt.hist(degree_sequence)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "p74gGrmZ_lTD",
        "outputId": "40cc397d-4d05-482c-9aa4-a98369164941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 2.,  2.,  4.,  8., 12., 26., 45., 43., 39., 19.]),\n",
              " array([ 14. ,  29.7,  45.4,  61.1,  76.8,  92.5, 108.2, 123.9, 139.6,\n",
              "        155.3, 171. ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "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 network\n",
        "nx.draw(G)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "vLEairFy_6tg",
        "outputId": "3096799b-6e09-4d3b-c6b2-e48b6fd2514c"
      },
      "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": {}
        }
      ]
    }
  ]
}