cadCAD/tutorials/videos/robot-marbles-part-2/robot-marbles-part-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cadCAD Template: Robot and the Marbles - Part 2\n",
    "\n",
    "![](images/Overview.jpeg)\n",
    "![](images/Mech.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib \n",
    "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
    "import configBlank\n",
    "from cadCAD import configs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "exec_mode = ExecutionMode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "single_proc: [<cadCAD.configuration.Configuration object at 0x1167ee7b8>]\n",
      "[<cadCAD.configuration.Configuration object at 0x1167ee7b8>]\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>box_A</th>\n",
       "      <th>box_B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>run</th>\n",
       "      <th>timestep</th>\n",
       "      <th>substep</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      box_A  box_B\n",
       "run timestep substep              \n",
       "1   0        0           10      0\n",
       "    1        1            9      1\n",
       "    2        1            8      2\n",
       "    3        1            7      3\n",
       "    4        1            6      4\n",
       "    5        1            5      5\n",
       "    6        1            5      5\n",
       "    7        1            5      5\n",
       "    8        1            5      5\n",
       "    9        1            5      5\n",
       "    10       1            5      5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run Cad^2\n",
    "\n",
    "first_config = configs # only contains config1\n",
    "single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)\n",
    "run = Executor(exec_context=single_proc_ctx, configs=first_config)\n",
    "\n",
    "raw_result, tensor_field = run.execute()\n",
    "df = pd.DataFrame(raw_result)\n",
    "df.set_index(['run', 'timestep', 'substep'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot('timestep', ['box_A', 'box_B'], grid=True, \n",
    "        colormap = 'RdYlGn',\n",
    "        xticks=list(df['timestep'].drop_duplicates()), \n",
    "        yticks=list(range(1+(df['box_A']+df['box_B']).max())));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we have made it so that both robots read and update the state of the system at the same time, the equilibrium we had before (with 5 marbles in each box) is never reached. Instead, the system oscillates around that point."
   ]
  }
 ],
 "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",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}