demo/demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## InfraSim Demo\n",
    "\n",
    "In this demonstration, we will do the following:\n",
    "\n",
    "- Construct an InfraSim.Thames model\n",
    "- Run the model\n",
    "- Look at the results\n",
    "- Create simple plots of flows and storage volumes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using license file /Users/amanmajid/gurobi.lic\n",
      "Academic license - for non-commercial use only\n",
      "Warning: variable name \"arcflow[SWOX GW,SWOX,water,1]\" has a space\n",
      "Warning: constraint name \"upper_bound[SWOX GW,SWOX,water,1]\" has a space\n",
      "Warning: to let Gurobi read it back, use rlp format\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "from infrasim.thames import *\n",
    "from infrasim.plotting import *\n",
    "\n",
    "# Define file paths\n",
    "nodes       = '../data/demo/spatial/nodes-demo.shp'\n",
    "edges       = '../data/demo/spatial/edges-demo.shp'\n",
    "flows       = '../data/demo/csv/demo-flows.csv'\n",
    "\n",
    "# initiate\n",
    "thames = Model(nodes,edges,flows,timesteps=1000,super_source=True,super_sink=True)\n",
    "\n",
    "# build\n",
    "thames.build()\n",
    "\n",
    "# run\n",
    "try:\n",
    "    thames.run(pprint=False)    #pprint will print to console\n",
    "except:\n",
    "    thames.debug()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>Commodity</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Timestep</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SWOX GW</td>\n",
       "      <td>SWOX</td>\n",
       "      <td>water</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Upper Thames</td>\n",
       "      <td>Farmoor</td>\n",
       "      <td>water</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>264.3083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Farmoor</td>\n",
       "      <td>SWOX</td>\n",
       "      <td>water</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>264.3083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gatehampton GW</td>\n",
       "      <td>J2</td>\n",
       "      <td>water</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Upper Thames</td>\n",
       "      <td>J1</td>\n",
       "      <td>water</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7708.6837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start      End Commodity  Day  Month    Year  Timestep      Value\n",
       "0         SWOX GW     SWOX     water  1.0      1  2020.0         1     0.0000\n",
       "1    Upper Thames  Farmoor     water  1.0      1  2020.0         1   264.3083\n",
       "2         Farmoor     SWOX     water  1.0      1  2020.0         1   264.3083\n",
       "3  Gatehampton GW       J2     water  1.0      1  2020.0         1     0.0000\n",
       "4    Upper Thames       J1     water  1.0      1  2020.0         1  7708.6837"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get results of flows along each arc\n",
    "thames.results_arcflows.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>Node</th>\n",
       "      <th>Commodity</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Timestep</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Farmoor</td>\n",
       "      <td>water</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>London Storages</td>\n",
       "      <td>water</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>202828.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Farmoor</td>\n",
       "      <td>water</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>London Storages</td>\n",
       "      <td>water</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2</td>\n",
       "      <td>202828.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Farmoor</td>\n",
       "      <td>water</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13822.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Node Commodity  Day  Month    Year  Timestep     Value\n",
       "0           Farmoor     water  1.0      1  2020.0         1   13822.0\n",
       "1   London Storages     water  1.0      1  2020.0         1  202828.0\n",
       "36          Farmoor     water  2.0      1  2020.0         2   13822.0\n",
       "37  London Storages     water  2.0      1  2020.0         2  202828.0\n",
       "72          Farmoor     water  3.0      1  2020.0         3   13822.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get results of storage volumes\n",
    "thames.results_storage_volumes.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot storage volume of Farmoor reservoir\n",
    "plot_storage_volume(thames.results_storage_volumes,node='Farmoor',figsize=(8,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot inflow and outflow at Farmoor reservoir\n",
    "plot_inflow_outflow(thames.results_arcflows,node='Farmoor',figsize=(8,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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