test copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mesa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MoneyAgent(mesa.Agent):\n",
    "    \"\"\"An agent with fixed initial wealth.\"\"\"\n",
    "\n",
    "    def __init__(self, unique_id, model):\n",
    "        super().__init__(unique_id, model)\n",
    "        self.wealth = 1\n",
    "    \n",
    "    def step(self):\n",
    "        # The agent's step will go here.\n",
    "        # For demonstration purposes we will print the agent's\n",
    "        if self.wealth == 0:\n",
    "            return\n",
    "        other_agent = self.random.choice(self.model.schedule.agents)\n",
    "        other_agent.wealth += 1\n",
    "        self.wealth -= 1\n",
    "        # print(\"Hi, I am agent \" + str(self.unique_id) + \" \" + \"with wealth \" + str(self.wealth))\n",
    "\n",
    "    def move(self):\n",
    "        possible_steps = self.model.grid.get_neighborhood(\n",
    "            self.pos,\n",
    "            moore=True,\n",
    "            include_center=False)\n",
    "        new_position = self.random.choice(possible_steps)\n",
    "        self.model.grid.move_agent(self, new_position)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MoneyModel(mesa.Model):\n",
    "    \"\"\"A model with some number of agents.\"\"\"\n",
    "\n",
    "    def __init__(self, N):\n",
    "        self.num_agents = N\n",
    "        self.schedule = mesa.time.RandomActivation(self)\n",
    "        # Create agents\n",
    "        for i in range(self.num_agents):\n",
    "            a = MoneyAgent(i, self)\n",
    "            self.schedule.add(a)\n",
    "    \n",
    "    def step(self):\n",
    "        \"\"\"Advance the model by one step.\"\"\"\n",
    "        self.schedule.step()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "from jupyter_dash import JupyterDash\n",
    "from dash import Dash, dcc, html, Input, Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "app = Dash(__name__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "empty_model = MoneyModel(10)\n",
    "empty_model.step()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MoneyModel(10)\n",
    "for i in range(10):\n",
    "    model.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4., 0., 4., 0., 0., 1., 0., 0., 0., 1.]),\n",
       " array([0. , 0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# The below is needed for both notebooks and scripts\n",
    "import matplotlib.pyplot as plt\n",
    "agent_wealth = [a.wealth for a in model.schedule.agents]\n",
    "plt.hist(agent_wealth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([414., 314., 174.,  64.,  27.,   7.]),\n",
       " array([0., 1., 2., 3., 4., 5., 6.]),\n",
       " <BarContainer object of 6 artists>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_wealth = []\n",
    "# This runs the model 100 times, each model executing 10 steps.\n",
    "for j in range(100):\n",
    "    # Run the model\n",
    "    model = MoneyModel(10)\n",
    "    for i in range(10):\n",
    "        model.step()\n",
    "\n",
    "    # Store the results\n",
    "    for agent in model.schedule.agents:\n",
    "        all_wealth.append(agent.wealth)\n",
    "\n",
    "plt.hist(all_wealth, bins=range(max(all_wealth) + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MoneyModel(mesa.Model):\n",
    "    \"\"\"A model with some number of agents.\"\"\"\n",
    "\n",
    "    def __init__(self, N, width, height):\n",
    "        self.num_agents = N\n",
    "        self.grid = mesa.space.MultiGrid(width, height, True)\n",
    "        self.schedule = mesa.time.RandomActivation(self)\n",
    "        # Create agents\n",
    "        for i in range(self.num_agents):\n",
    "            a = MoneyAgent(i, self)\n",
    "            self.schedule.add(a)\n",
    "            # Add the agent to a random grid cell\n",
    "            x = self.random.randrange(self.grid.width)\n",
    "            y = self.random.randrange(self.grid.height)\n",
    "            self.grid.place_agent(a, (x, y))\n",
    "\n",
    "    def step(self):\n",
    "        self.schedule.step()\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MoneyAgent(mesa.Agent):\n",
    "    \"\"\"An agent with fixed initial wealth.\"\"\"\n",
    "\n",
    "    def __init__(self, unique_id, model):\n",
    "        super().__init__(unique_id, model)\n",
    "        self.wealth = 1\n",
    "\n",
    "    def move(self):\n",
    "        possible_steps = self.model.grid.get_neighborhood(\n",
    "            self.pos, moore=True, include_center=False\n",
    "        )\n",
    "        new_position = self.random.choice(possible_steps)\n",
    "        self.model.grid.move_agent(self, new_position)\n",
    "\n",
    "    def give_money(self):\n",
    "        cellmates = self.model.grid.get_cell_list_contents([self.pos])\n",
    "        if len(cellmates) > 1:\n",
    "            other_agent = self.random.choice(cellmates)\n",
    "            other_agent.wealth += 1\n",
    "            self.wealth -= 1\n",
    "\n",
    "    def step(self):\n",
    "        self.move()\n",
    "        if self.wealth > 0:\n",
    "            self.give_money()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MoneyModel(50, 10, 20)\n",
    "for i in range(20):\n",
    "    model.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x15f80c6d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "agent_counts = np.zeros((model.grid.width, model.grid.height))\n",
    "for cell in model.grid.coord_iter():\n",
    "    cell_content, x, y = cell\n",
    "    agent_count = len(cell_content)\n",
    "    agent_counts[x][y] = agent_count\n",
    "plt.imshow(agent_counts, interpolation=\"nearest\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "coloraxis": "coloraxis",
         "hovertemplate": "x: %{x}<br>y: %{y}<br>color: %{z}<extra></extra>",
         "name": "0",
         "type": "heatmap",
         "xaxis": "x",
         "yaxis": "y",
         "z": [
          [
           0,
           1,
           0,
           0,
           0,
           1,
           1,
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           0,
           0,
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           0,
           0
          ],
          [
           0,
           0,
           0,
           1,
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           0,
           0,
           0,
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           1,
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           0,
           0,
           1,
           0,
           0,
           1
          ],
          [
           0,
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           1,
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          ],
          [
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           0,
           2,
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          ],
          [
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          ],
          [
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          ],
          [
           0,
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           1,
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          ],
          [
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          ],
          [
           0,
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          ],
          [
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           2
          ]
         ]
        }
       ],
       "layout": {
        "coloraxis": {
         "colorscale": [
          [
           0,
           "#0d0887"
          ],
          [
           0.1111111111111111,
           "#46039f"
          ],
          [
           0.2222222222222222,
           "#7201a8"
          ],
          [
           0.3333333333333333,
           "#9c179e"
          ],
          [
           0.4444444444444444,
           "#bd3786"
          ],
          [
           0.5555555555555556,
           "#d8576b"
          ],
          [
           0.6666666666666666,
           "#ed7953"
          ],
          [
           0.7777777777777778,
           "#fb9f3a"
          ],
          [
           0.8888888888888888,
           "#fdca26"
          ],
          [
           1,
           "#f0f921"
          ]
         ]
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
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     },
     "metadata": {},
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   ],
   "source": [
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "\n",
    "fig = px.imshow(agent_counts)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_gini(model):\n",
    "    agent_wealths = [agent.wealth for agent in model.schedule.agents]\n",
    "    x = sorted(agent_wealths)\n",
    "    N = model.num_agents\n",
    "    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))\n",
    "    return 1 + (1 / N) - 2 * B\n",
    "\n",
    "\n",
    "class MoneyAgent(mesa.Agent):\n",
    "    \"\"\"An agent with fixed initial wealth.\"\"\"\n",
    "\n",
    "    def __init__(self, unique_id, model):\n",
    "        super().__init__(unique_id, model)\n",
    "        self.wealth = 1\n",
    "\n",
    "    def move(self):\n",
    "        possible_steps = self.model.grid.get_neighborhood(\n",
    "            self.pos, moore=True, include_center=False\n",
    "        )\n",
    "        new_position = self.random.choice(possible_steps)\n",
    "        self.model.grid.move_agent(self, new_position)\n",
    "\n",
    "    def give_money(self):\n",
    "        cellmates = self.model.grid.get_cell_list_contents([self.pos])\n",
    "        if len(cellmates) > 1:\n",
    "            other = self.random.choice(cellmates)\n",
    "            other.wealth += 1\n",
    "            self.wealth -= 1\n",
    "\n",
    "    def step(self):\n",
    "        self.move()\n",
    "        if self.wealth > 0:\n",
    "            self.give_money()\n",
    "\n",
    "\n",
    "class MoneyModel(mesa.Model):\n",
    "    \"\"\"A model with some number of agents.\"\"\"\n",
    "\n",
    "    def __init__(self, N, width, height):\n",
    "        self.num_agents = N\n",
    "        self.grid = mesa.space.MultiGrid(width, height, True)\n",
    "        self.schedule = mesa.time.RandomActivation(self)\n",
    "        self.instant_gini=None\n",
    "        # Create agents\n",
    "        for i in range(self.num_agents):\n",
    "            a = MoneyAgent(i, self)\n",
    "            self.schedule.add(a)\n",
    "            # Add the agent to a random grid cell\n",
    "            x = self.random.randrange(self.grid.width)\n",
    "            y = self.random.randrange(self.grid.height)\n",
    "            self.grid.place_agent(a, (x, y))\n",
    "\n",
    "        self.datacollector = mesa.DataCollector(\n",
    "            model_reporters={\"Gini\": compute_gini}, agent_reporters={\"Wealth\": \"wealth\"}\n",
    "        )\n",
    "\n",
    "    def step(self):\n",
    "        self.datacollector.collect(self)\n",
    "        self.instant_gini = compute_gini(self)\n",
    "        self.schedule.step()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MoneyModel(50, 10, 10)\n",
    "for i in range(100):\n",
    "    model.step()\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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6XaMgkQDrDxRix+mSDl8rCAI+bJq5A6DVn53BZBLw5+9PAgDuHtvvmtNzneHmEaF4aEIkYvupEdWF4ceuYhghIoL5ofD8l4dQWduAUeFqPOXEcXuFXIoVs+OgkEux/VQJPtmT77T36olytFXYdbYMcqkEL93WuaGDuAh/zE2OBAAsXn8UtQZjh67bfa4cx4v0UMqlkEkl+OVMGU4U6Tt0bVFlLf654xwKK2o73M7P9xUgK7ccnh4yPJ3i/FoRWxbdGo0vHp/g8iDUEsMIERGA/+zOw47TpVB5SPG32XHwcPL+HUM0vnhhejQA4PXvjuNsyWWnvl9P8sW+AgDA1OEh6BfQ+Vkez00bhjC1CvnlNVi+JadD16z+xdwTcnd8P+usqdXt9I5cqjZg2cYTmPzmNvzpuxN45OO9MJra7+0qqarHso3mYaTf3TwU4f7irJWllMtEL6RmGCEit3fhUo31ofDircMxOMQ161M8NCESkwYHoa7BhLS1B9FgNLV/US/XYDRhw8FCAMA98V2bCeOjlONPs0YCMA+37G9aPbctuaXV2HpCB8Bcd/LwpIEAgK8OXkRJ0w64LVXXN+LtjNO4/i8/4f2fz8HQaIJUApzUVmHN3vZ7u1779jj0dY0YGe6HhyZE2vnpeheGESJye3/bchr1jSYkDQzs1EJSnSWVSvDmPaPhp5Lj0IVKvPPjGZe9d3e1LacEpZcNCPJRYvKw4C5/v6nDNbhrTDhMAvD8F4dQ19D2cM3Hu3IhCMANw4IxKNgH8QMCEBfhD4PRhE92t17HqqC8Brf8fQf+uuUUquobMTzMDx+ljsPLTWuh/PWHU6isbbjG5yzG14cuQioB0u8c3S130nUl9/70ROT2TumqsP6AeWXVRbcOd/kiWWFqT/zpzlEAgHe3ncE5Nx+usQzR3Dmmr8OGypbMjEGwrxJnS6qxYutpm+dU1jbg86b3fmRS8zTiR5p6Rz7ZnWcNMvllNbj3/d3IL69BX7UK/7hvDL57ahJuGBaCB8YPwOAQH5RXG/B2hu33qjE04qUN5kLphyYMxKh+aod8zp6MYYSI3Npbm3NgEsyrqsZF+IvShpmjwzBlWDAajAKWfn3MbWfXlF6ux48nzdNLHblYmb+XAq83Dde8//NZHCqouOqctXvNm85Fh/pi4uA+1uO3jAxFX7UKZdUGfH3wInJLq3Hv+5korKjFwCBvrHtyIm6P7Wtdi8RDJsVLM8xFtx/vyrUZLv++9TQuXKpFuL8nfnfzUId9zp6MYYSI3NaB/Ev44bgOUgnw3DTxHgoSiQR/nDkCCpkUO06XYtNRrWhtEdOGA4VoNAmI7afGUAcvh37ziFDcHtvXPFzz5SHUNzYP1zQaTfjXLvMwzMMTB7bqHZPLpJjbVM/x3vazuPf93bhYWYdBwd5Y+9j4q3bVBYApw0JwY3QIGk2CdQVZANDXNWD5Dzn4Z1NB7Kt3jIC3stesPdolvAtE5JYEQcBfNplnWPxqbD8MDnHeXiAdERnkjccnR+EfP57Ba98ex+Rhwd1me3dXEAQBX2abh8vuju/nlPf44+0jsOtsKU7pLuPBD7MQ1hQkKmoaUFhRiz7eCtwed/WKuPeO648VW0/jfGk1APMqup/NG49g37ZXdl08Yzh+PlWCjJPF+OGYFnllNXh32xlcqjHXkdw1NhxTh2uc8Cl7JvaMEJFb2nmmFJnnyqCQSfHMTd2jq/yJKYPRL8ATFyvr3K6Y9dhFPU5qq6CQS3F7bLhT3iPQW4HX7jAP12SdL8dXBy/iq4MXsf2UeVG0B5MHQOVx9Uqvai8PzB5nHjaKDvXFmseuHUQAYFCwj7VH5bH/ZOP1jSdwqaYBg4K98d79Y/HXe2Id+Ml6PveJ3URETVr2ijwwfoBo6ztcyVMhw9KZIzDv3/vwwY5z+FV8PwxywT4l3YGlcPXmGA3UXh5Oe59bRoXhgzkJyC+vaXXcRynDrDFth6A/TI9GbIQaN0ZroPbsWPuenjoE6w8UorzagL5qFZ5JGYq7xoa7/cwZWzoVRlauXIk333wTWq0WsbGxePvtt5GYmGjz3ClTpmD79u1XHb/11lvx3XffdebtiYi65PujWhwprIS3Qob5NwwSuzmtpAw31xv8eLIYf/z6GP79cKLLZ/i4Wn2jEV8dugjAeUM0Ld0UY//wiKdChjvH2Nc2tacH1j42Hie0Vbg5RmOz14XM7I5na9euRVpaGpYuXYr9+/cjNjYW06ZNQ3Gx7Q121q1bh6KiIuvr6NGjkMlkuOeee7rceCKizli1/SwA4NHrouze0dXZJBIJls6MgUJuLmZ19t4o3UHGiWJU1DQg1E+F64Z0fW2R7mSIxhe3x/ZlEGmH3WFk+fLlmDdvHlJTUxETE4NVq1bBy8sLq1evtnl+YGAgQkNDra8tW7bAy8uLYYR6tZKqeuSVVbd6VdW1vQASuU6OtgqHL1RCLpVgTrLrFjizx4A+3niuacrnn747gc/3FojcIufafa4MAHDrqDDIpL27F4hss2uYxmAwIDs7G4sWLbIek0qlSElJQWZmZoe+x4cffoh7770X3t5tb8hTX1+P+vrmpXf1+o5tUkTUHWw8UoQnP91/1XFfpRzfPDUJkSLujEmt9z3pbr0iLc27LgolVfX4YMd5vLDuMLyUMtw2+uqZHr3BmWLzWhzDw8Sd0UTisatnpLS0FEajERpN6/E2jUYDrbb9efFZWVk4evQoHn300Wuel56eDrVabX1FRDhu8RsiZ/tvlnlPCqVcCh+lHD5KORQyKarqG7F4wxG3XdCqO3DkvifOJpFI8OKtw3FfYgRMAvDMmoP48aRO7GY5hSWMuGpPIOp+XDqb5sMPP8SoUaPaLHa1WLRoEdLS0qx/1+v1DCTUI5RXG7DrrLnLefMz11t7QXJLqzFtxc/45UwZ1h8oxF1jnV+kR1dr3vdE4ZB9T5xNIpHgT7NGobreiK8PXcTjn+zHkttiMKCPFwK8FAjwVkDt6QHZFQWuKg9pjyl61dc1oLhpE7pBDCNuy64wEhQUBJlMBp2udTrX6XQIDQ295rXV1dVYs2YNXn311XbfR6lUQqnsvt2nRG3ZclwLo0lATJhfq+GYyCBvPD11CN7cnIM/fXcCU4aFINBbIWJL3VPzvifhDtv3xNlkUgn++utY1BgasfVEsXVPk2uJjfDHuicm9Ij6C0uvSIivEn4q503ppe7Nrv8bFQoF4uPjkZGRYT1mMpmQkZGB5OTka177xRdfoL6+Hg888EDnWkrUA3x3xDxcOWN02FVfe+z6KAzT+KK82mDdrp5cx1n7nriCh0yKd34zFo9dH4XxUYEYpvFFiK8SHjLbYeNQQQX25pa7uJWdc5ZDNIRODNOkpaVh7ty5SEhIQGJiIlasWIHq6mqkpqYCAObMmYPw8HCkp6e3uu7DDz/ErFmz0KdPH1vflqjHq6gxYNeZUgDmzbWu5CGTYtldo3D3ql34MvsC7hoTjgmDgxzejoLyGvxndx7mXRfV7iqR7uSrgxedtu+JK6g8ZHjx1uGtjgmCgLoGEwQ01yG9tP4o1h0oxPdHijA+qvv/vD1TwjBCnZjaO3v2bLz11ltYsmQJ4uLicPDgQWzatMla1Jqfn4+ioqJW1+Tk5GDnzp145JFHHNNqom7oh+M6NJoERIf6IqqNVTPjBwTg/qT+AIDFG45atyR3pJe/Oor3fz6HV7897vDv7Sgmk4BTuiqXFfMKgmAdorm7h/WKXItEIoGnQgYvhdz6ui3W3Cv3/VEtTKbuXyzNnhECOrk3zYIFC5CXl4f6+nrs2bMHSUlJ1q9t27YNH3/8cavzhw0bBkEQcNNNN3WpsUTd2cYj5hA+Y9TVQzQt/X56NEJ8lThfWo0X1x9BrcFxgSSvrNq6z8a3hy9ax+O7m9c3nsDNf/sZ/9qV65L3a7XvSS+dHmsxaXAwfFVyFFfVY1/eJVHb0mA0YcXWU5j2t59xtLDS5jnWmTRusuw92dYzKriIurnKmgb8YhmiaSeM+Kk88Nos82Zd6/YX4tZ/7EC2gx4an+3Jh6WzQRCAd3/qfput5ZVVW0PIym1n7eod2nOuDBsOFNrdo+KqfU+6A4Vcal3u3BKQneVsyWV88PM5m6E3R1uFWSt/wYqtp5Gjq7L+N2iprsFo3SOGM2ncG8MIkQP8cFyLBqOAYRrfDnU3TxsRin89nIhQPxXOl1bjnlW78OdNJ1Hf2PlekroGIz5v+oH/9NQhAIANBwuR27TteXexfMspNDYNH5RU1Vu3jW9PRY0BD320F8+sPYi1dqxI2nLfk55WuNpZt460DNUUOW2opr7RiNSP9uL1jSeQsnw77nz3F3y6Jw8VNQas2n4WM9/eiWMX9dYZPbZ6afLKamASzAsChrC+ya0xjBA5wPdHzbNobm2nV6SlyUODsfmZ63HXmHCYBOC9bWdx58pdqK5v7FQbNh4pwqWaBoT7e2Lh1CG4YVgwTALw7rbu0ztyokiPry3BoGlDtP/7+SwajaZ2r/0sKx+1Tb0of/zmGE7pqjr0nt8dLrLuezLJCQXD3dF1Q4Pgq5RDp6/H/nznDNWs3pmL/PIaeClkkEklOJBfgcXrj2LMa1vwxvcnYTCaMDU6BBuenAjA/N/+8hX/ti09KoNCfHrMuijkHAwjbmzlT2ew/IccsZvR41XWNmDHaXOdxq2jrr3ezpXUXh5YPjsOqx6Ih7+XB44X6fHD8fZXM7blP7vzAAC/SeoPmVSCp5p6R9btL0TBFduli+WtzTkQBPPU51fuGIEALw8UlNfiu3aGExqMJvx7l/nzhfgqUddgwoLP9rdbb2MyCXhvm3lTvDkTBvSIdTccQSmXIaVpqKa9e9sZxVV1eOfH0wCA1+4YicxFN2LxrcMxVOMDQQB8lHL85e7R+OfcBIzqp0a/AE+YBODAFcGIK6+SBcOImyoor8Gbm3Pwjx/PIL+sezyoeqqtx3VoMAoYEuKDIZ2cMjp9ZCjuatqe/EB+hd3XHy2sxIH8CnjIJPh101DE2P4BuG5IEBpNAt5r2qVWTPtyy5FxshgyqQS/u2kovBRypE4cCMDcK3StOpCNR4qg1dchyEeJDfMnIthXiVO6y+3OGNp6QofTxZfhq5TjgfHdc1M8Z7FML9/khFk1b23OQbXBiNgIf9w5JhwhvirMuz4Km5+5HpueuQ4/PTcFv06IsPZ2jIsMBADsy70ijHBaLzVhGHFTP+UUW/98UsuNCLvi+6Pm3zztGaKxZewAfwCdCyOfNPWK3DIyrNXaIpbakS/2FeBiRW2ra1y5R44gCPjzppMAgF8n9LNOfZ6bHAlvhQwntVWt/k1eee2HO88DAOYkD0Bff0/87ddxkEjM+wB9e/him9e929Qr8kDyALdb3fP6ocHwVshQVFmHAwUVDvu+Ry5U4oumOp8lt8VA2qK3SSKRIDrU76r1beIHBAAA9uW1XojNOkzDmTRuj2HETVlWogTMVe/UObUGI34+ZZ5F09UwMqa/+Qf2iSK9XdN9K2sbrJu/PZjc+rf/cZGBGB8ViAajgEXrjmDpV0fx4Id7MPGNHxH98iakfX4QJVX1tr6tQ23LKcHe3EtQyqXWgASYh6ksPRbv/mS792Zf3iUcvlAJhVxqXaNl0pAgPDllEABg0f+O2ByGyjxXhoMFFVDKpXi4qQfGnag8ZJg63LGzagRBwCvfHIMgAHfE9bWGjPYkRJrPO5BfYa0PMpkEnGPPCDVhGHFDNYZG62ZuAHCyg4WAdLUDBZdgMJoQ6qfCUE3XfqD2Vaug8VOi0STgSBtrMtjyv+wLqGswITrUFwk2Hg6Wh//2UyX4V2YedpwuRWFFLeobTVi3vxA3/nUb/rUrt0NFpJ1hMgn4y2ZzbdLcCZEIU3u2+vojkwZCIZdiX94lZJ2/egnzD3eYe0XuGhOOPj7Nv3E/kzIU8QMCUFXfiLkfZUGnr2t1naVW5NcJEW67Eq0lIH9/pOiaPWHZeeVIWrbV2gPVlm8PF2Ff3iV4esjwwi3RHW7H0BBf+KrkqDEYcaLI/PPG8m9QIZMiIsCzne9AvR3DiBvadaYMhsbmBw97RjrPMgY+bmBgl2cDSCQSjIkwh4mOzoAoqqy1Fq4+MH6AzTYkR/XB01OH4OYYDX47OQp/+dVofPF4MtY8Nh6jwtWoqmvE0q+P4fZ3frmqwNARPt9XgBNFevgq5Xhi8qCrvh7ip8LdTTNr/rblVKvZRAXlNdaC3ocnte7d8JBJ8Y/7xqCvWoVzJdW49/3d0FaaA8mRC5XYcboUMqkEj10f5fDP1FNMGRYML4UMFyvrcLCNoZoaQyOeXXsIOn09/vpDDsqrDTbPqzUY8cb35qG2xycPuipUXotUKrlqqMYyRDMwyBvyHrJpITkP/wW4oR+bxuanjTB34Z4vrXbKsuTuwLIZ2bjIjnVXt6e5buTaoeBEkR5paw/iuj//hPOl1fBVyTFrTLjNcyUSCdJuGor35yRg0S3D8etxEU3DN32wYf5EvDZrJPxUchwv0uO+D3ajqLLW5vfpjEvVBmutyMKUIQhoY6fi314fBZlUgsxzZZj85k/4+JfzqG804qNfcmESgOuGBNncTybc3xNrf5uMcH9PnC+txuz3M1FYUYv3tpunM98e2xcRgV4O+zw9jcpDhhujQwCYZ88ZbRSy/mVTjnXhsRqDEavb6B15/+dzKKyoRV+1qlMB78oiVs6koZYYRtyMIAj4qale5N7E/lB7esBoErrtsuHdWaPRhP1NCzlZftB2laVuZH9+hc1u9byyajz44R7c8vcdWHegEI0mAYkDA/GvhxPho7R730vIpBI8OH4AfnpuCoaH+aGuwYQfjum6/Dks3vwhB5dqGjBM44u5EyLbPG9AH298MCcekX28UHrZgD9+cxw3vrUda/fmAzAP5bQlItALa387Hv0DvZBXVoN73ttlXffliSlX98S4m3nXRUEhk2LriWK89u3xVv+u9uaW41+ZuQCAB5tqd/61KxeVNQ2tvkdRZS1WNc3IeuHW4fBUyOxuR8ueEUEQWq0xQsQw4mZOFFWhqLIOnh4yJEf1wbBQ82+bHKqx34miKlQbjPBVyR22C+yocDXkUglKqupRWHF1D8XvPj+EHadLIZUAt40Ow1fzJ+Lz3yZjbP+u9cz08VFiVpx5z5atJ9oOI8cv6vHZnvxWw3xtOVRQgf9mmcPEq3eMgEc7XfE3RmuwJW0ylt05Cho/JQoralFtMGJQsDeuHxJ8zWv7BXhhzWPjEdnHCxcr6yAIQMpwTY/cndfRYiP8sXx2LADg4125+GdTDU6twYjff3kYgmCe4fTK7SMwTOOLqvpGfLSrde/In78/idoGIxIGBGDm6M4Vasf284eHTAKdvh4XLtXibIllJo13Fz4d9RYMI27GMn1y4uA+UHnIEG0JIyxitZtliCZ+QIDDFtNSecgQ09cPgLl3pKXc0mrsy7sEqQT44dnr8c5vxiI2wt8h7wvAOvNiz7nyq1bKBMyFqI9/ko0X1x/Bgx/uwaU2agsAwGgS8PJXRyEIwJ1jwpHUwa3sPWRS/CapP7Y/fwMW3RKN2H5qvHL7yFbTR9vS198Tax5LRlSwN+RSCRbcOLhD7+kObhvdFy/NGA7AvEnhN4cu4q8/5OB8aTU0fkosnmGeovvUVPM9W73zPKrqzL0j2XmXsOHgRUgkwNKZIzpdG+WpkGFEXzUAc+8I1xihlhhG3IxlSu8NTePI0aHmB99J9ozYzVKI56ghGgtLL8eVdSPrDpin7143JBiDQxz/G/+gYG9E9vGCwWjCjqadf1vam1turS3Yc74cd6z8BafbCLFr9xbg8IVK+CrlWHRrx2ddWKg8ZPjt5EH4asEkTBrS8SXcQ9UqbHz6Omx7fgriHBjUeoNHJg1E6sRIAOYetg9/Mfd+pN81CmpP8xost4wMw6Bgb+jrGvHvzDyYTAJe/eYYAODusf0wqp+6S22wzPbafFSHipoGSCRcY4TMGEbcSHm1wTpL44Zh5jDSPEzDhc/sIQgCss47tl7EYkx/fwCte0ZMJgHr9psXmrprrO1C1a6SSCTW3pGtJ65egGzdfnMYun5oMCICPZFfXoM7391lrUGyKLtcj79sNhetPnvTUIT4qpzS3raoPGToF+C+RattkUgkeGlGDKaPCIXBaIIgmP8t3RitsZ4jk0rw1I3mqeD/3HEOn2bl49CFSngrZHh++rAutyGh6f8Vy1BgvwBPqDzsrz+h3sf+ijfqsbafKoYgANGhvujrb56WZwkjOn09KmoM8PeyPduBWssrq0Hp5XooZFKM7uJvi1ey9Iwcv1iJugYjVB4y7Mu7hAuXauGjlOPmGPv2v7HH1OEh+HDnefyUUwyjSbAOP9U1GK17nMyfMghDNL54/JNsZJ0vxyP/2osRfdWorG3ApWoDqpqGeKJDfTHnikXYSFwyqQQr7o1D2ucHodPXY8ltMVedc9voMKzYegq5ZTVY8tVRAMCCG4c4JFRailgtuzYPZq8INWHPiBv58aS5633q8BDrMR+lHP2aFhziUE3HZTXVi4zup3b4b3b9AjwR5KNAg1HAsYvmxc8svSK3jgrt1EyGjhoXGQhflRzl1QYcLGgeJvrhuA6X6xvRL8AT4yIDEeitwCePJOHecREwCcCRwkrkl9dYg4iPUo5ld43i+hHdkMpDhnfvj8f/nphg85cPuUyK+TeYa0cEAegf6IWHJ0U65L2DfZUYGNRcsMohGrJgz4ibaDSasL2peNWy7oBFdKgvLlyqxckiPcZfUWiYtvYgDhdW4rN5SS7vbu/O9lnWFxno2CEaoGnxs/4B2HJchwP5FRjRV43vDpt7Je4a28/h79eSh0yKKcNC8M2hi9h6ohjxA8yf739Ne5HcNSbcWkyqkEuRftco3B3fDxU1DQjw9oC/lwIBXgqoPT3cZofc3mjWmHC889MZ5JXVYPGM4VDKHReA4wcE4HxpNQAWr1Iz/triJrLzLkFf14gALw/ERbSeBjqsjRk1Z0suY92BQpwpvow3Np50WVt7gr2WlVcdtNjZlSx1IwfyK/DDcR2qmnolEh1cn2JLSlPPWUbTuH6xvg47Tpt71e68IgxJJBIkRAYiJUaD+AGBGBTsg0BvBYNID+chk+K/88Zj7WPjMW2EY4cFW/4/wzBCFuwZcRMZTUWGk4cGX/WgaGtGzZdNvw0D5pkcs8dFdHiKZm9WUlWP86XVkEiA+P7OCQdj+zcvC19tMA99tOyVcKYpQ0Mgk0pwSncZBeU15i3oBfNvtC272Kl36+vvaa0tcyRLbxvAMELN2DPiBgRBsHbz32zjtxzLWiOntFUwNRWWGVvM3hjS9ANjyVfH0OCkzdR6kuymKb3DNL5QezlnW/rR/dSQSSUoqqzD9lO2eyWcRe3lYf3tdesJHf7n5Fk85F4GBXvjySmD8NzNQ1kwT1YMI27gQEEFCitq4aWQWaf0thQZ5A2FTIpqg9G66ufPp0ug09cjwMsDnz6ahAAvD+ToqvDvzDxXN7/bcdaU3pa8FHJrSBQEYGx/f5f2SqQ0TfFd/ct5nNRWQSGT4rZRfV32/tR7SSQS/H56NBY0TSEmAhhG3IKlVyRluMbmTAwPmdS6P4RlqObLfebfhu+IC0eInwp/mG5euOpvW06h+Iqt2t2NZbGzBCfVi1hY6kYA5xeuXsmy3khBuTmcpsSEOK0XiIiIYaSXM5mah2huu8aeEtEtFj+rqDFgy3Fz8eI9CeaH4K8TIhAb4Y/L9Y1YtvGEk1vdfVXXN+LYRfMCcYlOmEnTkqVuRCGTXvO/nTMMDPJGVIs9Q+4a49owRETuhQWsvVx2/iVo9XXwVcoxeVjbm41ZwsgJbRW+PnQRBqMJw8P8rHtJSKUS/OmOkbh95U5sOHgR/ft4w9+z+TflED8lZowK6/S+FT3FgfwKGE0Cwv09EaZ2fHFfS1OHa5AwIACThwaLMraeMlyD90vOIdBbcc1/O0REXcUw0st9e+giAOCmEZprrhXQcvfe/DLz/iP3xLf+bXhUPzXuT+qPT3bn4x8Zp6/6Hl4PyVotLd0bWRY7c3avCACoPT3w5RMTnP4+bflNYn9knNDhoQmR7e64S0TUFQwjPUBD0z4SLSnk7T8cjCYBG49qAQAzR1+7+NAyvfdMsXknTQ+ZBLPGXD174g/ToyGVSHCppsF67EzxZZwo0uPbQ0W9PozsOlMKwDVhRGyRQd7I+N0UsZtBRG6AYaSbe+P7k1i1/exVx28bHYa37xtzzWGRPefLUFJVD7WnByYOvvbOpxo/JdSeHqisNYeMqdEaBHpfPTTgq/LAq3eMbHUs63w5fv1/mdh6QgdDo6lDQaknqqprwIGCCgDApHbuJxERdVzvfGr0EkaTgM/22J5K++3hInzdNATTFkvh6rQRmnYDgkQisQ7VAMDd8R0vWIwfEIAgHyX0dY3IPFfW4et6msyzZTCaBAwM8kZEIHeFJSJyFIaRbuxEkR76ukb4KOU4uOQmHP7jzTj8x5uxcKp5fv5r3x5HRY3B5rWNRhM2NQ3R3NbOEI2FpYg1yEeJKXYULMqkEtw8wjw8Y3nP3mhn0xANe0WIiByLYaQbyzxr7mVIHBgIfy8F/FQe8FN5YP4NgzEkxAellw1443vbe8ZknitDWbUBgd4KTBjUsSXcLXtQ/Pb6KLt3W71lpPnaLce1MJqEds7umXaebgojQxhGiIgciWGkG7MMeSRfsR+MQi7FsrtGAQDW7C3AHhtDI98eMg/RTB8Z2uFgMXFwEHL+NB3zro+yu63jo/pA7emB0ssG7G2aceIINYZGax2LmAoranGutBoyqQTJHQx3RETUMZ0KIytXrkRkZCRUKhWSkpKQlZV1zfMrKiowf/58hIWFQalUYujQodi4cWOnGuwuGo0mZJ03P9RtPfzGRQbivsT+AIBF64+gvtEIwFxnsumoFhuPtr/QmS2d3SrcQya1LiHuiKEaQRCwJisf4/60FTct347yatvDUa6ys2nX2th+avipuBIpEZEj2R1G1q5di7S0NCxduhT79+9HbGwspk2bhuLiYpvnGwwG3HTTTcjNzcWXX36JnJwcfPDBBwgP56Zb13KksBKX6xvhp5JjeJifzXNemB6NIB8lzpVUY8XW0/jP7jxM/es2PP5JNqrqGjEo2BtJA133W7xlqGbzMa11w73O0Onr8PDHe/HCuiOoNhhRXFWPd38646hmdsoO6xANF/8iInI0u8PI8uXLMW/ePKSmpiImJgarVq2Cl5cXVq9ebfP81atXo7y8HBs2bMDEiRMRGRmJyZMnIzY2tsuN780sQzTjo/pA1sa28WovD/zx9hgAwHvbzuLlDUeRW1YDtacH5t8wCGt/m9zmtc4waUgQvBUyFFXW4XBhpd3XC4KArw4W4ua//YyfckqgkEmtC6/9e3ceLjZt4udqJpOAX5qKV69jvQgRkcPZFUYMBgOys7ORkpLS/A2kUqSkpCAzM9PmNV9//TWSk5Mxf/58aDQajBw5EsuWLYPRaGzzferr66HX61u93I2leLW9+oQZo8KswyMRgZ545fYRyFx0I56fZu41cSWVhww3RJt3Bf6+aZioPQ1GE3adKcUr3xzD5De3YeGag6isbcCocDW+fXoS/nL3aCQNDISh0YS/b7161VdXOHZRj0s1DfBRyhEX4S9KG4iIejO7Fj0rLS2F0WiERtN6lU2NRoOTJ23P6jh37hx+/PFH3H///di4cSPOnDmDJ598Eg0NDVi6dKnNa9LT0/HKK6/Y07RexdBowr5c8zb17YURiUSClfePwcmiKowMV7u0J8SWW0aG4dvDRdh8VIsXpke3uShbbmk13vnpDDYf06KqrtF6XCmX4okpgzD/hsHWJch/Pz0av3pvF77ILsC866MwuGmHYVfZccZcLzI+KpDLohMROYHTV2A1mUwICQnB+++/D5lMhvj4eBQWFuLNN99sM4wsWrQIaWlp1r/r9XpEREQ4u6ndxuELFahtMCLQW4GhIb7tnq+UyxDbTX5jnzIsGEq5FLllNTiprbqq3kWnr8M/Mk5j7d4CNDbVlfTxVuDG6BBMHa7BdUOC4K1s/c8yfkAAUoZrsPWEDsu35ODd++Nd9nmAFlN6ub4IEZFT2BVGgoKCIJPJoNPpWh3X6XQIDQ21eU1YWBg8PDwgkzXP0hg+fDi0Wi0MBgMUiquXHFcqlVAqXTvE0J3sOmupFwmEVOSeDnt5K+W4fmgwthzX4Z87zuOmmOZetIMFFfh413nUNZgAmIPL/BsGY2z/gHZ7dJ6fNgwZJ3XYeESLwxcqMLqfvzM/hlWtwWjtpbpuKItXiYicwa4+Z4VCgfj4eGRkZFiPmUwmZGRkIDk52eY1EydOxJkzZ2AymazHTp06hbCwMJtBhFrUi0T1zPUspjctnva//Rfw+CfZ1teq7WdR12BC/IAArH1sPD5OTcS4yMAODS0NC/XFnXHmGVhvbs5xavtbysoth8FoQl+1ClFB3i57XyIid2L3ME1aWhrmzp2LhIQEJCYmYsWKFaiurkZqaioAYM6cOQgPD0d6ejoA4IknnsA777yDhQsX4qmnnsLp06exbNkyPP300479JL1EXYMR2fkdqxfprmaMDsPPp0tQeKn17BdvpRxzkgfgxuiQa27w15ZnbxqKbw5fxI7TpfjlTGm7m/85gmV9kUlDgjrVZiIiap/dYWT27NkoKSnBkiVLoNVqERcXh02bNlmLWvPz8yGVNne4REREYPPmzXj22WcxevRohIeHY+HChfjDH/7guE/RixzIr4Ch0YRgXyUGBbu2UNNRVB4y/P3eMQ7/vhGBXvhNYn/8KzMPr317HN8+NcnuZeuvpbK2AedKLqPlCik/5VjCCIdoiIicRSIIQrffSESv10OtVqOyshJ+frYXAOstlm85hX9knMbtsX3xj/sc/0Dv6cqrDbjhrW2orG3A0pkxSJ04sNPf68KlGmSeLcP+/EvIzruEU7rLbZ6b/VIK+rh4qjQRUU/X0ee302fTkH12d3B9EXcV6K3A76cPw+L1R7H8h1OYMToMIb4qu76HySTgve1n8dcfcnDlQrGhfioo5K17W24ZGcogQkTkRAwj3UitwYgDBU31Ij20eNUV7h3XH2v3FuDwhUq88f1JLP91XIevLa824Nm1B7H9lHn4JS7CH0lRgYjvH4CxAwJcvlAcERExjHQru8+VocEoIEytwoA+XmI3p9uSSSV47Y6RmPXuL1i3vxD3juuPxIGB7V63L7ccT/33AIoq66CUS/HaHSNxT0I/FqYSEYmMy0l2I6t/OQ8AuClGwwdkO2Ij/HHvOPOuxUu+OopGo6nNc0/rqvDat8cx+/3dKKqsQ1SQNzbMn4hfj4vgfSYi6gbYM9JNHLlQiR2nSyGTSjDvuiixm9Mj/H7aMHx/tAgntVX4eFcu5iRHWr9WXd+I744U4YvsCzhUUGE9PjO2L9LvGgUfJf/pExF1F/yJ3E28u+0MAGDm6DBEBHKIpiMCvBX4w/RoLFp3BH/67gT+9N0Jm+fJpBLcMCwE946LwNThnVvjhIiInIdhpBs4U3wZm45pAQBPTBkscmt6ltkJEfjm0EXrEvotDdP44p6EfrgjLhzBvixMJSLqrhhGXEQQBBwv0mNIiO9VU0f/b/tZCAKQMjwEw0Lb3xiPmkmlEnz6aBKq6htbH5dIOBRDRNRD8Ke1i/xvfyGe++IQRvdT4/0HExCqNq+NcbGiFusPFAJgr0hnSSQS+Kk8xG4GERF1EmfTuMiGpsBx+EIlbn9np7Wo8oMd59BoEpA0MBDxAwJEbCEREZE4GEZcoLKmAbvPmWsaBvTxQnFVPX79f5n4z+48rMkqAAA8eQN7RYiIyD0xjLjAjzk6NJoEDNX44NunJuGGYcGobzTh5Q1HUdtgxIi+frh+iPN3oCUiIuqOGEZcYPNRHQBg2ohQ+Ko88M+54/DY9c1riTw5ZTCnmxIRkdtiAauT1TUYrfugTBsRCsC87sWLtw5H0sBAFFXW4dZRoWI2kYiISFQMI06243QpahuMCPf3xIi+rbdPnjpcI1KriIiIug8O0zjZ5qbFzLjfDBERkW0MI07UaDQh44S5XuTmEewFISIisoVhxIn25l7CpZoGBHh5IDGy/S3uiYiI3BHDiBNZhmimDtdALuOtJiIisoVPSCcRBAFbjjcN0cRwiIaIiKgtDCMOIAgCLlbUwmgSrMeOXdSjsKIWnh4yXD80WMTWERERdW+c2usA/9xxHq9vPIFAbwVuGBaClOEhyM67BACYPDQYKg+ZyC0kIiLqvhhGuqiuwYhV288CAMqrDfjf/gv43/4L1q9zFg0REdG1cZimi74+eBFl1Qb0Vavw2bwkPDppICL7eAEA1J4emBrNMEJERHQt7BnpAkEQsPqX8wCAuRMiMWFQECYMCsLiGcORW1YDTw8Z1F4eIreSiIioe2MY6YJfzpThpLYKXgoZ7k3sbz0ukUgwMMhbxJYRERH1HBym6YIPd54DANwT3w9qT/aAEBERdQbDSCedKb6Mn3JKIJEAD00cKHZziIiIeiyGkU76qKlWZGp0CIdkiIiIuoBhpBMuNU3hBYCHJ7FXhIiIqCsYRjrhs6x81DWYMDzMD8lRfcRuDhERUY/GMGKnRqMJ/87MBQA8MmkgJBKJuA0iIiLq4ToVRlauXInIyEioVCokJSUhKyurzXM//vhjSCSSVi+VStXpBovtlO4ydPp6+CrlmBkbJnZziIiIejy7w8jatWuRlpaGpUuXYv/+/YiNjcW0adNQXFzc5jV+fn4oKiqyvvLy8rrUaDGd0lUBAKLDfKGUc88ZIiKirrI7jCxfvhzz5s1DamoqYmJisGrVKnh5eWH16tVtXiORSBAaGmp9aTQ9d4n0k1pzGBkW6ityS4iIiHoHu8KIwWBAdnY2UlJSmr+BVIqUlBRkZma2ed3ly5cxYMAARERE4I477sCxY8eu+T719fXQ6/WtXt1FjtbclmEahhEiIiJHsCuMlJaWwmg0XtWzodFooNVqbV4zbNgwrF69Gl999RU++eQTmEwmTJgwARcuXLB5PgCkp6dDrVZbXxEREfY006lO6S4DAIaF+oncEiIiot7B6bNpkpOTMWfOHMTFxWHy5MlYt24dgoOD8X//939tXrNo0SJUVlZaXwUFBc5uZofo6xpQWFELgD0jREREjmLXRnlBQUGQyWTQ6XStjut0OoSGhnboe3h4eGDMmDE4c+ZMm+colUoolUp7muYSp5uKV0P9VNyNl4iIyEHs6hlRKBSIj49HRkaG9ZjJZEJGRgaSk5M79D2MRiOOHDmCsLCeNy2WxatERESOZ1fPCACkpaVh7ty5SEhIQGJiIlasWIHq6mqkpqYCAObMmYPw8HCkp6cDAF599VWMHz8egwcPRkVFBd58803k5eXh0UcfdewncYFTDCNEREQOZ3cYmT17NkpKSrBkyRJotVrExcVh06ZN1qLW/Px8SKXNHS6XLl3CvHnzoNVqERAQgPj4eOzatQsxMTGO+xQuYu0ZYb0IERGRw0gEQRDEbkR79Ho91Go1Kisr4ecnziwWQRAw5rUtqKhpwLdPTcLIcLUo7SAiIuopOvr85t40HVRSVY+KmgZIJcDgEB+xm0NERNRrMIx0kGWIJjLIGyoPLgNPRETkKAwjHWTZk4b1IkRERI7FMNJBnNZLRETkHAwjHcSeESIiIudgGOkAo0loDiPsGSEiInIohpEOKCivQV2DCUq5FAP6eIvdHCIiol6FYaQDLPUiQzQ+kEklIreGiIiod2EY6YAc68qr4iy4RkRE1JsxjHRAc70IFzsjIiJyNIaRDjip1QMAhoWyZ4SIiMjRGEbaUddgRG5ZDQBO6yUiInIGhpF2nC25DKNJgNrTAxo/pdjNISIi6nUYRtrRcrEziYQzaYiIiByNYaQdXAaeiIjIuRhG2pFXaq4XGRTMxc6IiIicgWGkHZW1DQCAAG+FyC0hIiLqnRhG2qGvM4cRP08PkVtCRETUOzGMtMMaRlQMI0RERM7AMNIOfW0jAEDtKRe5JURERL0Tw8g1CIKAqqaeEV/2jBARETkFw8g1VBuMMAnmP3OYhoiIyDkYRq5B3zSTxkMmgcqDt4qIiMgZ+IS9hpbFq1x9lYiIyDkYRq7BUrzKab1ERETOwzByDZZhGj8VZ9IQERE5C8PINXDBMyIiIudjGLmG5p4RhhEiIiJnYRi5Bn2dpWaEwzRERETOwjByDewZISIicj6GkWvQW1dfZc8IERGRszCMXENVHaf2EhEROVunwsjKlSsRGRkJlUqFpKQkZGVldei6NWvWQCKRYNasWZ15W5fjjr1ERETOZ3cYWbt2LdLS0rB06VLs378fsbGxmDZtGoqLi695XW5uLp577jlcd911nW6sqzUvesZhGiIiImexO4wsX74c8+bNQ2pqKmJiYrBq1Sp4eXlh9erVbV5jNBpx//3345VXXkFUVFSXGuxK7BkhIiJyPrvCiMFgQHZ2NlJSUpq/gVSKlJQUZGZmtnndq6++ipCQEDzyyCMdep/6+nro9fpWLzFYZ9OwZoSIiMhp7AojpaWlMBqN0Gg0rY5rNBpotVqb1+zcuRMffvghPvjggw6/T3p6OtRqtfUVERFhTzMdQhCE5nVG2DNCRETkNE6dTVNVVYUHH3wQH3zwAYKCgjp83aJFi1BZWWl9FRQUOLGVttUYjDCaBACsGSEiInImu56yQUFBkMlk0Ol0rY7rdDqEhoZedf7Zs2eRm5uLmTNnWo+ZTCbzG8vlyMnJwaBBg666TqlUQqlU2tM0h7PUi8ilEnh6yERtCxERUW9mV8+IQqFAfHw8MjIyrMdMJhMyMjKQnJx81fnR0dE4cuQIDh48aH3dfvvtuOGGG3Dw4EFRhl86qnkmjQckEonIrSEiIuq97B5/SEtLw9y5c5GQkIDExESsWLEC1dXVSE1NBQDMmTMH4eHhSE9Ph0qlwsiRI1td7+/vDwBXHe9uqqwzaThEQ0RE5Ex2P2lnz56NkpISLFmyBFqtFnFxcdi0aZO1qDU/Px9Sac9f2LV5KXgWrxIRETmTRBAEQexGtEev10OtVqOyshJ+fn4uec8NBwrxzNqDmDi4Dz59dLxL3pOIiKg36ejzu+d3YTgJFzwjIiJyDYaRNlgXPGMYISIiciqGkTZYFzzjGiNEREROxTDSBvaMEBERuQbDSBusNSPcl4aIiMipGEba0LzoGYdpiIiInIlhpA2cTUNEROQaDCNtqKprXg6eiIiInIdhpA2WAlZfLgdPRETkVAwjNgiCwGEaIiIiF2EYsaGuwYQGo3mVfA7TEBERORfDiA2WXhGpBPBWyERuDRERUe/GMGKDdcEzTw9IJBKRW0NERNS7MYzYwHoRIiIi12EYsYELnhEREbkOw4gN7BkhIiJyHYYRG7hJHhERkeswjNigr+MwDRERkaswjNhgGabxZc8IERGR0zGM2GAtYGUYISIicjqGERusBawcpiEiInI6hhEbWMBKRETkOgwjNjQXsDKMEBERORvDiA1V1p4RDtMQERE5G8OIDc01I+wZISIicjaGkSsIgtBiOXiGESIiImdjGLlCfaMJBqMJAIdpiIiIXIFh5AqWIRqpBPBWMIwQERE5G8PIFSxDNL4qD0ilEpFbQ0RE1PsxjFyheSl49ooQERG5AsPIFbjgGRERkWsxjFyBO/YSERG5VqfCyMqVKxEZGQmVSoWkpCRkZWW1ee66deuQkJAAf39/eHt7Iy4uDv/5z3863WBnY88IERGRa9kdRtauXYu0tDQsXboU+/fvR2xsLKZNm4bi4mKb5wcGBmLx4sXIzMzE4cOHkZqaitTUVGzevLnLjXcGLnhGRETkWnaHkeXLl2PevHlITU1FTEwMVq1aBS8vL6xevdrm+VOmTMGdd96J4cOHY9CgQVi4cCFGjx6NnTt3drnxzmBd8Iw9I0RERC5hVxgxGAzIzs5GSkpK8zeQSpGSkoLMzMx2rxcEARkZGcjJycH111/f5nn19fXQ6/WtXq7S3DPCmhEiIiJXsCuMlJaWwmg0QqPRtDqu0Wig1WrbvK6yshI+Pj5QKBSYMWMG3n77bdx0001tnp+eng61Wm19RURE2NPMLmHNCBERkWu5ZDaNr68vDh48iL179+L1119HWloatm3b1ub5ixYtQmVlpfVVUFDgimYCAKrquC8NERGRK9k1FhEUFASZTAadTtfquE6nQ2hoaJvXSaVSDB48GAAQFxeHEydOID09HVOmTLF5vlKphFKptKdpDmMdpuGiZ0RERC5hV8+IQqFAfHw8MjIyrMdMJhMyMjKQnJzc4e9jMplQX19vz1u7jGWYxpfDNERERC5h96//aWlpmDt3LhISEpCYmIgVK1aguroaqampAIA5c+YgPDwc6enpAMz1HwkJCRg0aBDq6+uxceNG/Oc//8F7773n2E/iIFz0jIiIyLXsfuLOnj0bJSUlWLJkCbRaLeLi4rBp0yZrUWt+fj6k0uYOl+rqajz55JO4cOECPD09ER0djU8++QSzZ8923KdwIBawEhERuZZEEARB7Ea0R6/XQ61Wo7KyEn5+fk57n7oGI6Jf3gQAOLT0ZqhZxEpERNRpHX1+c2+aFiwzaSQSwFfJYRoiIiJXYBhpwTKTxkcph1QqEbk1RERE7oFhpAXrGiOsFyEiInIZhpEWagzmMOKlkIncEiIiIvfBMNJCXYMRAODJMEJEROQyDCMt1BpMAACVnGGEiIjIVRhGWrD0jKjYM0JEROQyDCMt1FqGaTx4W4iIiFyFT90WrDUjHuwZISIichWGkRZqDU3DNAwjRERELsMw0kJdI8MIERGRqzGMtGCZTcOpvURERK7DMNKCpYCVU3uJiIhch2GkhXrrome8LURERK7Cp24LtZxNQ0RE5HIMIy1Yh2kYRoiIiFyGYaQFTu0lIiJyPYaRFuoam2bTMIwQERG5DMNIC3UG7tpLRETkagwjLbBmhIiIyPUYRlpoDiO8LURERK7Cp24L3CiPiIjI9RhGWrCGEdaMEBERuQzDSJMGowkNRgEAe0aIiIhciWGkiaVXBGABKxERkSsxjDSpazBZ/6yU87YQERG5Cp+6TVoWr0okEpFbQ0RE5D4YRppwWi8REZE4+ORtwmm9RERE4mAYaWLdJI/TeomIiFyKYaRJLXtGiIiIRMEw0qSO+9IQERGJolNhZOXKlYiMjIRKpUJSUhKysrLaPPeDDz7Addddh4CAAAQEBCAlJeWa54vFMrWXPSNERESuZXcYWbt2LdLS0rB06VLs378fsbGxmDZtGoqLi22ev23bNtx333346aefkJmZiYiICNx8880oLCzscuMdiTv2EhERicPuMLJ8+XLMmzcPqampiImJwapVq+Dl5YXVq1fbPP/TTz/Fk08+ibi4OERHR+Of//wnTCYTMjIyutx4R7IUsHJfGiIiIteyK4wYDAZkZ2cjJSWl+RtIpUhJSUFmZmaHvkdNTQ0aGhoQGBjY5jn19fXQ6/WtXs5m7Rnh6qtEREQuZdeTt7S0FEajERqNptVxjUYDrVbboe/xhz/8AX379m0VaK6Unp4OtVptfUVERNjTzE6p5469REREonBpN8Abb7yBNWvWYP369VCpVG2et2jRIlRWVlpfBQUFTm8bp/YSERGJQ27PyUFBQZDJZNDpdK2O63Q6hIaGXvPat956C2+88Qa2bt2K0aNHX/NcpVIJpVJpT9O6jAWsRERE4rCrZ0ShUCA+Pr5V8amlGDU5ObnN6/7yl7/gtddew6ZNm5CQkND51jqRZWovwwgREZFr2dUzAgBpaWmYO3cuEhISkJiYiBUrVqC6uhqpqakAgDlz5iA8PBzp6ekAgD//+c9YsmQJPvvsM0RGRlprS3x8fODj4+PAj9I1zcM0LGAlIiJyJbvDyOzZs1FSUoIlS5ZAq9UiLi4OmzZtsha15ufnQyptfqC/9957MBgMuPvuu1t9n6VLl+KPf/xj11rvQHWc2ktERCQKu8MIACxYsAALFiyw+bVt27a1+ntubm5n3sLlWDNCREQkDo5JNOHeNEREROJgGGlSy71piIiIRMEw0qSOi54RERGJgmGkiWVvGpWcYYSIiMiVGEaa1DVaekZ4S4iIiFyJT94m1p4R1owQERG5FMMIAJNJQH0jC1iJiIjEwDCC5iEagD0jRERErsYwguZ9aQCGESIiIldjGEHz6qsKuRQyqUTk1hAREbkXhhE0F6+yXoSIiMj1GEbQcil43g4iIiJX49MXLVZfZc8IERGRyzGMgDv2EhERiYlhBFzwjIiISEwMIwDquOAZERGRaBhGANQZuGMvERGRWBhG0Fwzwp4RIiIi12MYQXMYUXJqLxERkcvx6QtO7SUiIhITwwg4TENERCQmhhGwgJWIiEhMDCNo3rWX64wQERG5HsMIuAIrERGRmBhGwJoRIiIiMTGMoMVsGgVvBxERkavx6YvmMKKSs2eEiIjI1RhG0KJmhLNpiIiIXI5hBM279rJmhIiIyPUYRsCpvURERGJiGAGXgyciIhITwwg4tZeIiEhMnQojK1euRGRkJFQqFZKSkpCVldXmuceOHcOvfvUrREZGQiKRYMWKFZ1tq1MIgtCigJXZjIiIyNXsfvquXbsWaWlpWLp0Kfbv34/Y2FhMmzYNxcXFNs+vqalBVFQU3njjDYSGhna5wY5mMJogCOY/s2aEiIjI9ewOI8uXL8e8efOQmpqKmJgYrFq1Cl5eXli9erXN88eNG4c333wT9957L5RKZZcb7Gh1BpP1zxymISIicj27wojBYEB2djZSUlKav4FUipSUFGRmZjqsUfX19dDr9a1ezmIZopFLJfCQcZiGiIjI1ex6+paWlsJoNEKj0bQ6rtFooNVqHdao9PR0qNVq6ysiIsJh3/tKLF4lIiISV7fsCli0aBEqKyutr4KCAqe9l2Var5JhhIiISBRye04OCgqCTCaDTqdrdVyn0zm0OFWpVLqsvqSWm+QRERGJyq4nsEKhQHx8PDIyMqzHTCYTMjIykJyc7PDGuUIdl4InIiISlV09IwCQlpaGuXPnIiEhAYmJiVixYgWqq6uRmpoKAJgzZw7Cw8ORnp4OwFz0evz4ceufCwsLcfDgQfj4+GDw4MEO/CidY11jhGGEiIhIFHaHkdmzZ6OkpARLliyBVqtFXFwcNm3aZC1qzc/Ph1Ta3OFy8eJFjBkzxvr3t956C2+99RYmT56Mbdu2df0TdBH3pSEiIhKX3WEEABYsWIAFCxbY/NqVASMyMhKCZVWxboizaYiIiMTl9lWbDCNERETicvswYilgVXm4/a0gIiIShds/geusU3vZM0JERCQGtw8jnE1DREQkLoYR1owQERGJyu3DCKf2EhERiYthhD0jREREonL7MFJrmU3DAlYiIiJRMIywZ4SIiEhUbh9G6hq4zggREZGY3P4JzJoRIiIicbl9GOEwDRERkbgYRprCiJJhhIiISBRuH0Ys64ywZ4SIiEgcDCMG7k1DREQkJrcPI6wZISIiEpdbh5EGowmNJgEAp/YSERGJxa2fwJZpvQD3piEiIhKLW4cRyxCNRAIo5W59K4iIiETj1k/gOkPzTBqJRCJya4iIiNyTe4eRRstS8ByiISIiEotbhxHLjr2cSUNERCQe9w4j3CSPiIhIdG79FLauMcIFz4iIiETj1mGk3tIzImcYISIiEotbhxH2jBAREYnPvcNI09RezqYhIiISj3uHkQZO7SUiIhKbW4eROusmeW59G4iIiETl1k/hOu7YS0REJDq3DiOWRc9ULGAlIiISjVuHEety8JzaS0REJJpOhZGVK1ciMjISKpUKSUlJyMrKuub5X3zxBaKjo6FSqTBq1Chs3LixU411NMtsGk7tJSIiEo/dYWTt2rVIS0vD0qVLsX//fsTGxmLatGkoLi62ef6uXbtw33334ZFHHsGBAwcwa9YszJo1C0ePHu1y47uKNSNERETiszuMLF++HPPmzUNqaipiYmKwatUqeHl5YfXq1TbP//vf/47p06fj+eefx/Dhw/Haa69h7NixeOedd7rc+K6qZRghIiISnV1hxGAwIDs7GykpKc3fQCpFSkoKMjMzbV6TmZnZ6nwAmDZtWpvnA0B9fT30en2rlzNYekaUnNpLREQkGruewqWlpTAajdBoNK2OazQaaLVam9dotVq7zgeA9PR0qNVq6ysiIsKeZnYYe0aIiIjE1y27BBYtWoTKykrrq6CgwCnvc098BJ6YMghRwT5O+f5ERETUPrk9JwcFBUEmk0Gn07U6rtPpEBoaavOa0NBQu84HAKVSCaVSaU/TOuU3Sf2d/h5ERER0bXb1jCgUCsTHxyMjI8N6zGQyISMjA8nJyTavSU5ObnU+AGzZsqXN84mIiMi92NUzAgBpaWmYO3cuEhISkJiYiBUrVqC6uhqpqakAgDlz5iA8PBzp6ekAgIULF2Ly5Mn461//ihkzZmDNmjXYt28f3n//fcd+EiIiIuqR7A4js2fPRklJCZYsWQKtVou4uDhs2rTJWqSan58PqbS5w2XChAn47LPP8NJLL+HFF1/EkCFDsGHDBowcOdJxn4KIiIh6LIkgCILYjWiPXq+HWq1GZWUl/Pz8xG4OERERdUBHn9/dcjYNERERuQ+GESIiIhIVwwgRERGJimGEiIiIRMUwQkRERKJiGCEiIiJRMYwQERGRqBhGiIiISFQMI0RERCQqu5eDF4NlkVi9Xi9yS4iIiKijLM/t9hZ77xFhpKqqCgAQEREhckuIiIjIXlVVVVCr1W1+vUfsTWMymXDx4kX4+vpCIpE47Pvq9XpERESgoKCAe944Ge+16/Beuxbvt+vwXruOo+61IAioqqpC3759W22ie6Ue0TMilUrRr18/p31/Pz8//sN2Ed5r1+G9di3eb9fhvXYdR9zra/WIWLCAlYiIiETFMEJERESicuswolQqsXTpUiiVSrGb0uvxXrsO77Vr8X67Du+167j6XveIAlYiIiLqvdy6Z4SIiIjExzBCREREomIYISIiIlExjBAREZGo3DqMrFy5EpGRkVCpVEhKSkJWVpbYTerx0tPTMW7cOPj6+iIkJASzZs1CTk5Oq3Pq6uowf/589OnTBz4+PvjVr34FnU4nUot7hzfeeAMSiQTPPPOM9Rjvs2MVFhbigQceQJ8+feDp6YlRo0Zh37591q8LgoAlS5YgLCwMnp6eSElJwenTp0Vscc9kNBrx8ssvY+DAgfD09MSgQYPw2muvtdrbhPe6c37++WfMnDkTffv2hUQiwYYNG1p9vSP3tby8HPfffz/8/Pzg7++PRx55BJcvX+564wQ3tWbNGkGhUAirV68Wjh07JsybN0/w9/cXdDqd2E3r0aZNmyZ89NFHwtGjR4WDBw8Kt956q9C/f3/h8uXL1nMef/xxISIiQsjIyBD27dsnjB8/XpgwYYKIre7ZsrKyhMjISGH06NHCwoULrcd5nx2nvLxcGDBggPDQQw8Je/bsEc6dOyds3rxZOHPmjPWcN954Q1Cr1cKGDRuEQ4cOCbfffrswcOBAoba2VsSW9zyvv/660KdPH+Hbb78Vzp8/L3zxxReCj4+P8Pe//916Du9152zcuFFYvHixsG7dOgGAsH79+lZf78h9nT59uhAbGyvs3r1b2LFjhzB48GDhvvvu63Lb3DaMJCYmCvPnz7f+3Wg0Cn379hXS09NFbFXvU1xcLAAQtm/fLgiCIFRUVAgeHh7CF198YT3nxIkTAgAhMzNTrGb2WFVVVcKQIUOELVu2CJMnT7aGEd5nx/rDH/4gTJo0qc2vm0wmITQ0VHjzzTetxyoqKgSlUin897//dUUTe40ZM2YIDz/8cKtjd911l3D//fcLgsB77ShXhpGO3Nfjx48LAIS9e/daz/n+++8FiUQiFBYWdqk9bjlMYzAYkJ2djZSUFOsxqVSKlJQUZGZmitiy3qeyshIAEBgYCADIzs5GQ0NDq3sfHR2N/v378953wvz58zFjxoxW9xPgfXa0r7/+GgkJCbjnnnsQEhKCMWPG4IMPPrB+/fz589Bqta3ut1qtRlJSEu+3nSZMmICMjAycOnUKAHDo0CHs3LkTt9xyCwDea2fpyH3NzMyEv78/EhISrOekpKRAKpViz549XXr/HrFRnqOVlpbCaDRCo9G0Oq7RaHDy5EmRWtX7mEwmPPPMM5g4cSJGjhwJANBqtVAoFPD39291rkajgVarFaGVPdeaNWuwf/9+7N2796qv8T471rlz5/Dee+8hLS0NL774Ivbu3Yunn34aCoUCc+fOtd5TWz9TeL/t88ILL0Cv1yM6OhoymQxGoxGvv/467r//fgDgvXaSjtxXrVaLkJCQVl+Xy+UIDAzs8r13yzBCrjF//nwcPXoUO3fuFLspvU5BQQEWLlyILVu2QKVSid2cXs9kMiEhIQHLli0DAIwZMwZHjx7FqlWrMHfuXJFb17t8/vnn+PTTT/HZZ59hxIgROHjwIJ555hn07duX97oXc8thmqCgIMhksqtmFuh0OoSGhorUqt5lwYIF+Pbbb/HTTz+hX79+1uOhoaEwGAyoqKhodT7vvX2ys7NRXFyMsWPHQi6XQy6XY/v27fjHP/4BuVwOjUbD++xAYWFhiImJaXVs+PDhyM/PBwDrPeXPlK57/vnn8cILL+Dee+/FqFGj8OCDD+LZZ59Feno6AN5rZ+nIfQ0NDUVxcXGrrzc2NqK8vLzL994tw4hCoUB8fDwyMjKsx0wmEzIyMpCcnCxiy3o+QRCwYMECrF+/Hj/++CMGDhzY6uvx8fHw8PBode9zcnKQn5/Pe2+HqVOn4siRIzh48KD1lZCQgPvvv9/6Z95nx5k4ceJVU9RPnTqFAQMGAAAGDhyI0NDQVvdbr9djz549vN92qqmpgVTa+tEkk8lgMpkA8F47S0fua3JyMioqKpCdnW0958cff4TJZEJSUlLXGtCl8tcebM2aNYJSqRQ+/vhj4fjx48Jjjz0m+Pv7C1qtVuym9WhPPPGEoFarhW3btglFRUXWV01NjfWcxx9/XOjfv7/w448/Cvv27ROSk5OF5ORkEVvdO7ScTSMIvM+OlJWVJcjlcuH1118XTp8+LXz66aeCl5eX8Mknn1jPeeONNwR/f3/hq6++Eg4fPizccccdnG7aCXPnzhXCw8OtU3vXrVsnBAUFCb///e+t5/Bed05VVZVw4MAB4cCBAwIAYfny5cKBAweEvLw8QRA6dl+nT58ujBkzRtizZ4+wc+dOYciQIZza21Vvv/220L9/f0GhUAiJiYnC7t27xW5SjwfA5uujjz6ynlNbWys8+eSTQkBAgODl5SXceeedQlFRkXiN7iWuDCO8z471zTffCCNHjhSUSqUQHR0tvP/++62+bjKZhJdfflnQaDSCUqkUpk6dKuTk5IjU2p5Lr9cLCxcuFPr37y+oVCohKipKWLx4sVBfX289h/e6c3766SebP5/nzp0rCELH7mtZWZlw3333CT4+PoKfn5+QmpoqVFVVdbltEkFosawdERERkYu5Zc0IERERdR8MI0RERCQqhhEiIiISFcMIERERiYphhIiIiETFMEJERESiYhghIiIiUTGMEBERkagYRoiIiEhUDCNEREQkKoYRIiIiEhXDCBEREYnq/wHhMA6WSmJ44wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gini = model.datacollector.get_model_vars_dataframe()\n",
    "gini.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1220706129.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[20], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    model.datacollector.model_vars[]\u001b[0m\n\u001b[0m                                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "model.datacollector.model_vars[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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></th>\n",
       "      <th>Wealth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Step</th>\n",
       "      <th>AgentID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Wealth\n",
       "Step AgentID        \n",
       "0    0             1\n",
       "     1             1\n",
       "     2             1\n",
       "     3             1\n",
       "     4             1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_wealth = model.datacollector.get_agent_vars_dataframe()\n",
    "agent_wealth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "end_wealth = agent_wealth.xs(99, level=\"Step\")[\"Wealth\"]\n",
    "end_wealth.hist(bins=range(agent_wealth.Wealth.max() + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Step'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_agent_wealth = agent_wealth.xs(14, level=\"AgentID\")\n",
    "one_agent_wealth.Wealth.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.asarray([1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_rows = np.random.randn(1, 1)\n",
    "new_rows = last_rows[-1, :] + np.random.randn(5, 1).cumsum(axis=0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}