soil/examples/markov_chains/MarkovChains.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "id": "7641396c-a602-477e-bf03-09e1191ff549",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f12285c-78db-4ee8-b9c6-7799d34f10f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%autoreload 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7710bb03-0cb9-413a-a407-fe48855ff917",
   "metadata": {},
   "outputs": [],
   "source": [
    "%aimport markov_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2dffca0f-da9e-4f69-ac43-7afe52ad2d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "%aimport soil\n",
    "%aimport soil.visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "12871006-70ca-4c6f-8a3e-0aae1d0bce31",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = markov_sim.load_city_graph(\"Chamberi, Madrid\", network_type=\"drive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "31e96cc5-b703-4d2a-a006-7b9a2cedc365",
   "metadata": {},
   "outputs": [],
   "source": [
    "# env = markov_sim.CityEnv(G=G, n_assets=20, side=10, max_weight=1, seed=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e070b36-0ba6-4780-8fd4-3c72fa3bb240",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(2):\n",
    "#     env.step()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 35,
   "id": "56f8b997-65b0-431d-9517-b93edb1cfcd8",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "/home/j/.cache/pypoetry/virtualenvs/soil-cCX5yKRx-py3.10/lib/python3.10/site-packages/osmnx/plot.py:955: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  plt.show()\n",
      "/home/j/.cache/pypoetry/virtualenvs/soil-cCX5yKRx-py3.10/lib/python3.10/site-packages/osmnx/plot.py:955: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  plt.show()\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86e45bd44e434674b11805fd94e98414",
       "version_major": 2,
       "version_minor": 0
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      "text/html": [
       "Cannot show widget. You probably want to rerun the code cell above (<i>Click in the code cell, and press Shift+Enter <kbd>⇧</kbd>+<kbd>↩</kbd></i>)."
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      "text/plain": [
       "Cannot show ipywidgets in text"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from soil.visualization import JupyterViz, GeoNetworkDrawer, Controller\n",
    "from soil import visualization\n",
    "from matplotlib import colors\n",
    "from matplotlib import colormaps\n",
    "plasma = colormaps.get_cmap('plasma')\n",
    "model_params = {\n",
    "    \"n_assets\": {\n",
    "        \"type\": \"SliderInt\",\n",
    "        \"value\": 100,\n",
    "        \"label\": \"Number of assets:\",\n",
    "        \"min\": 1,\n",
    "        \"max\": 1000,\n",
    "        \"step\": 1,\n",
    "    },\n",
    "    \"max_weight\": {\n",
    "        \"type\": \"SliderInt\",\n",
    "        \"value\": 3,\n",
    "        \"label\": \"Maximum edge weight:\",\n",
    "        \"min\": 1,\n",
    "        \"max\": 20,\n",
    "        \"step\": 1,\n",
    "    },\n",
    "    \"ratio_lazy\": {\n",
    "        \"type\": \"SliderFloat\",\n",
    "        \"value\": 0,\n",
    "        \"label\": \"Ratio of lazy agents (they prefer shorter streets):\",\n",
    "        \"min\": 0,\n",
    "        \"max\": 1,\n",
    "        \"step\": 0.05,\n",
    "    },\n",
    "    \"side\": {\n",
    "        \"type\": \"SliderInt\",\n",
    "        \"value\": 10,\n",
    "        \"label\": \"Size of the side:\",\n",
    "        \"min\": 2,\n",
    "        \"max\": 20,\n",
    "        \"step\": 1,\n",
    "    },\n",
    "    \"gradual_move\": {\n",
    "        \"type\": \"Checkbox\",\n",
    "        \"value\": True,\n",
    "        \"label\": \"Use gradual movement\",\n",
    "    }, \n",
    "    \"lockstep\": {\n",
    "        \"type\": \"Checkbox\",\n",
    "        \"value\": True,\n",
    "        \"label\": \"Run in locksteps\",\n",
    "    },\n",
    "    \"G\": G,\n",
    "    # \"width\": 10,\n",
    "}\n",
    "\n",
    "def colorize(d):\n",
    "    # print(d)\n",
    "    if any(a.waiting for a in d):\n",
    "        return 'red'\n",
    "    else:\n",
    "        return 'blue'\n",
    "\n",
    "def network_portrayal(graph, spring=True):\n",
    "    global pos, l\n",
    "    node_size = [10*(len(node[1][\"agent\"])) for node in graph.nodes(data=True)]\n",
    "    node_color = [colorize(d[\"agent\"]) for (k, d) in graph.nodes(data=True)]\n",
    "    # pos = {node: (d[\"x\"], d[\"y\"]) for node, d in graph.nodes(data=True)}\n",
    "    edge_width = [graph.edges[k]['travel_time']/100 for k in graph.edges]\n",
    "    # print(edge_width)\n",
    "    weights = [graph.edges[k]['occupation'] for k in graph.edges]\n",
    "    norm = colors.Normalize(vmin=0, vmax=max(weights))\n",
    "    color = plasma(norm(weights))\n",
    "    # print(color)\n",
    "    return dict(node_size=node_size, node_color=node_color, edge_linewidth=edge_width, edge_color=color)\n",
    "\n",
    "page = visualization.JupyterViz(\n",
    "    markov_sim.CityEnv,\n",
    "    model_params,\n",
    "    measures=[\"NodeGini\", \"EdgeGini\", \"EdgeOccupation\"],\n",
    "    name=\"City Environment\",\n",
    "    space_drawer=GeoNetworkDrawer,\n",
    "    agent_portrayal=network_portrayal,\n",
    "    columns=3,\n",
    ")\n",
    "# This is required to render the visualization in the Jupyter notebook\n",
    "page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "70da18d7-66bd-4710-89a6-aca14707c56e",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NodeGini</th>\n",
       "      <th>EdgeGini</th>\n",
       "      <th>EdgeOccupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.866567</td>\n",
       "      <td>0.927276</td>\n",
       "      <td>0.087624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.866567</td>\n",
       "      <td>0.933494</td>\n",
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       "      <td>0.866567</td>\n",
       "      <td>0.929943</td>\n",
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       "      <th>127</th>\n",
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       "      <td>0.934185</td>\n",
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       "      <th>128</th>\n",
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       "      <th>129</th>\n",
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       "      <td>0.936299</td>\n",
       "      <td>0.078591</td>\n",
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       "      <th>130</th>\n",
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       "      <td>0.929784</td>\n",
       "      <td>0.086721</td>\n",
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       "<p>132 rows × 3 columns</p>\n",
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       "     NodeGini  EdgeGini  EdgeOccupation\n",
       "0    0.866567  0.927276        0.087624\n",
       "1    0.866567  0.933494        0.081301\n",
       "2    0.863867  0.933163        0.078591\n",
       "3    0.866567  0.929943        0.084914\n",
       "4    0.869433  0.934949        0.076784\n",
       "..        ...       ...             ...\n",
       "127  0.880367  0.934185        0.075881\n",
       "128  0.881400  0.933038        0.078591\n",
       "129  0.881400  0.936299        0.078591\n",
       "130  0.881400  0.929784        0.086721\n",
       "131  0.876733  0.932746        0.082204\n",
       "\n",
       "[132 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page.controller.model.datacollector.get_model_vars_dataframe()"
   ]
  },
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   "execution_count": null,
   "id": "d9a7d3c8-2f87-47d5-8d27-a7387ea3457d",
   "metadata": {},
   "outputs": [],
   "source": []
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