tutorials/Scenario_Theta.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "# Scenario Theta\n",
    "\n",
    "Scenario Theta is a synthetic stormwater system included in pystorms for prototyping control algorithms. This scenario is used in the pystorms paper to demonstrate the use of pystorms to evaluate the performance of control algorithms.\n",
    "\n",
    "This notebook demonstrates the use of the equal-filling control algorithm for controlling outflows in the network. Please refer to the manuscript for more details of the approach. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pystorms\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn-v0_8-whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Uncontrolled Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " o  Retrieving project data"
     ]
    }
   ],
   "source": [
    "# Run simulation with gates open\n",
    "env_uncontrolled = pystorms.scenarios.theta()\n",
    "done = False\n",
    "while not done:\n",
    "    done = env_uncontrolled.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert flows as a dataframe for easy plotting and timeseries handling\n",
    "uncontrolled_flows = pd.DataFrame.from_dict(env_uncontrolled.data_log[\"flow\"])\n",
    "uncontrolled_flows.index = env_uncontrolled.data_log[\"simulation_time\"]\n",
    "uncontrolled_flows = uncontrolled_flows.resample(\"15min\").mean()\n",
    "uncontrolled_flows = uncontrolled_flows.rename(columns={\"8\": \"Uncontrolled\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def controller(\n",
    "    depths: np.ndarray,\n",
    "    tolerance: float = 0.50,\n",
    "    LAMBDA: float = 0.50,\n",
    "    MAX_DEPTH: float = 2.0,\n",
    ") -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Implementation of equal-filling controller\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    depths: np.ndarray\n",
    "        depths in the basins being controlled\n",
    "    tolerance: float\n",
    "        parameter to control oscillations in the actions\n",
    "    LAMBDA: float\n",
    "        parameter for tuning controller's response\n",
    "    MAX_DEPTH: float\n",
    "        max depth in the basins to compute filling degree\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    actions: np.ndarray\n",
    "        control actions\n",
    "    \"\"\"\n",
    "\n",
    "    # Compute the filling degree\n",
    "    f = depths / MAX_DEPTH\n",
    "\n",
    "    # Estimate the average filling degree\n",
    "    f_mean = np.mean(f)\n",
    "\n",
    "    # Compute psi\n",
    "    N = len(depths)\n",
    "    psi = np.zeros(N)\n",
    "    for i in range(0, N):\n",
    "        psi[i] = f[i] - f_mean\n",
    "        if psi[i] < 0.0 - tolerance:\n",
    "            psi[i] = 0.0\n",
    "        elif psi[i] >= 0.0 - tolerance and psi[i] <= 0.0 + tolerance:\n",
    "            psi[i] = f_mean\n",
    "\n",
    "    # Assign valve positions\n",
    "    actions = np.zeros(N)\n",
    "    for i in range(0, N):\n",
    "        if depths[i] > 0.0:\n",
    "            k = 1.0 / np.sqrt(2 * 9.81 * depths[i])\n",
    "            action = k * LAMBDA * psi[i] / np.sum(psi)\n",
    "            actions[i] = min(1.0, action)\n",
    "    return actions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " o  Retrieving project data"
     ]
    }
   ],
   "source": [
    "# Controlled simulation\n",
    "env_controlled = pystorms.scenarios.theta()\n",
    "done = False\n",
    "\n",
    "while not done:\n",
    "    state = env_controlled.state()\n",
    "    # Note the difference between controlled and uncontrolled simulation\n",
    "    actions = controller(state)\n",
    "    done = env_controlled.step(actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "controlled_flows = pd.DataFrame.from_dict(env_controlled.data_log[\"flow\"])\n",
    "controlled_flows.index = env_controlled.data_log[\"simulation_time\"]\n",
    "controlled_flows = controlled_flows.resample(\"15min\").mean()\n",
    "controlled_flows = controlled_flows.rename(columns={\"8\": \"Controlled\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "textstr = \"Controlled Performance = {:.2f} \\nUncontrolled Peformance = {:.2f}\".format(\n",
    "    env_controlled.performance(), env_uncontrolled.performance()\n",
    ")\n",
    "\n",
    "fig = plt.figure(num=1, figsize=(20, 5))\n",
    "ax = plt.gca()\n",
    "controlled_flows.plot(ax=ax)\n",
    "uncontrolled_flows.plot(ax=ax)\n",
    "ax.text(\n",
    "    0.20,\n",
    "    0.90,\n",
    "    textstr,\n",
    "    transform=ax.transAxes,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"center\",\n",
    "    bbox=dict(boxstyle=\"round\", facecolor=\"grey\", alpha=0.2),\n",
    ")\n",
    "ax.text(\n",
    "    0.80,\n",
    "    0.73,\n",
    "    \"Exceedance Threshold\",\n",
    "    transform=ax.transAxes,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"center\",\n",
    ")\n",
    "plt.axhline(y=0.50, color=\"red\")\n",
    "plt.title(\"Scenario Theta: Equal-filling Controller\", loc=\"left\")\n",
    "plt.ylabel(\"Flows\")\n",
    "plt.xlabel(\"Time\")"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "pystorms",
   "language": "python",
   "name": "pystorms"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}