tutorials/robots_and_marbles/robot-marbles-part-5/robot-marbles-part-5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cadCAD-org/demos/blob/master/tutorials/robots_and_marbles/robot-marbles-part-5/robot-marbles-part-5.ipynb)\n",
    "\n",
    "# cadCAD Tutorials: The Robot and the Marbles Part 5 - Networks\n",
    "\n",
    "To expand upon our previous examples, we will introduce the concept of using a graph network object that is updated during each state update. The ability to essential embed a graph 'database' into a state is a game changer for scalability, allowing increased complexity with multiple agents or components is represented, easily updated. Below, building upon our previous examples, we will represent the Robots and Marbles example with n boxes, and a variable number of marbles. \n",
    "\n",
    "## Behavior and Mechanisms:\n",
    "* A network of robotic arms is capable of taking a marble from one of their boxes and dropping it into the other one. \n",
    "* Each robotic arm in the network only controls two boxes and they act by moving a marble from one box to the other.\n",
    "* Each robotic arm is programmed to take one marble at a time from the box containing the most significant number of marbles and drop it in the other box. It repeats that process until the boxes contain an equal number of marbles.\n",
    "* For our analysis of this system, suppose we are only interested in monitoring the number of marbles in their two boxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "# Only run this cell if you need to install the libraries\n",
    "# If running in google colab, this is needed.\n",
    "\n",
    "!pip install cadcad matplotlib pandas numpy networkx scipy seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Import dependancies\n",
    "\n",
    "# Data processing and plotting libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from random import normalvariate\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "# cadCAD specific libraries\n",
    "from cadCAD.configuration.utils import config_sim\n",
    "from cadCAD.configuration import Experiment\n",
    "from cadCAD.engine import ExecutionContext, Executor\n",
    "from cadCAD import configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# define global variables\n",
    "BOXES=5 #number of boxes in our network\n",
    "m= 2 #for barabasi graph type number of edges is (n-2)*m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We create a [Barabási–Albert](https://en.wikipedia.org/wiki/Barab%C3%A1si%E2%80%93Albert_model) graph and then fill the 5 boxes with between 1 and 10 balls. You can create as many different nodes or types of nodes as needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# create graph object with the number of boxes as nodes\n",
    "network = nx.barabasi_albert_graph(BOXES, m)\n",
    "\n",
    "# add balls to box nodes\n",
    "for node in network.nodes:\n",
    "    network.nodes[node]['balls'] = np.random.randint(1,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now we will plot the network of boxes and with their labels showing how many balls are in each box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of boxes and balls\n",
    "nx.draw_kamada_kawai(network,labels=nx.get_node_attributes(network,'balls'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# we initialize the cadCAD state as a network object\n",
    "initial_state = {'network':network}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Behavior: node by edge dimensional operator: input the states of the boxes, output the deltas along the edges\n",
    "\n",
    "Find the difference that each robot sees between their two boxes, ie, find which of the two nodes connected to an edge has the greater value for each edge that exists.\n",
    "\n",
    "We specify the robotic networks logic in a Policy/Behavior function. Unlike previous examples, our policy controls a vector valued action, defined over the edges of our network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def p_robotic_network(params, substep, state_history, previous_state):\n",
    "    network = previous_state['network']\n",
    "    delta_balls = {}\n",
    "    for e in network.edges:\n",
    "        src = e[0]\n",
    "        dst = e[1]\n",
    "        #transfer one ball across the edge in the direction of more balls to less\n",
    "        delta_balls[e] = np.sign(network.nodes[src]['balls']-network.nodes[dst]['balls'])\n",
    "    return({'delta': delta_balls})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Mechanism: edge by node dimensional operator : input the deltas along the edges and update the boxes\n",
    "\n",
    "We make the state update functions less \"intelligent\", ie. they simply add the number of marbles specified in `policy_input` (which, per the policy function definition, may be negative)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def s_update_network(params, substep, state_history,  previous_state, policy_input):\n",
    "    network = previous_state['network'] \n",
    "    delta_balls = policy_input['delta']\n",
    "    for e in network.edges:\n",
    "        move_ball = delta_balls[e]\n",
    "        src = e[0]\n",
    "        dst = e[1]\n",
    "        if (network.nodes[src]['balls'] >= move_ball) and (network.nodes[dst]['balls'] >= -move_ball):\n",
    "            network.nodes[src]['balls'] = network.nodes[src]['balls']-move_ball\n",
    "            network.nodes[dst]['balls'] = network.nodes[dst]['balls']+move_ball\n",
    "            \n",
    "    return ('network', network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# wire up the mechanisms and states\n",
    "partial_state_update_blocks = [\n",
    "    { \n",
    "        'policies': { # The following policy functions will be evaluated and their returns will be passed to the state update functions\n",
    "            'action': p_robotic_network\n",
    "        },\n",
    "        'variables': { # The following state variables will be updated simultaneously\n",
    "            'network': s_update_network\n",
    "            \n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Settings of general simulation parameters, unrelated to the system itself\n",
    "* `N` is the number of times the simulation will be run (Monte Carlo runs)\n",
    "* `T` is a range with the number of discrete units of time the simulation will run for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "MONTE_CARLO_RUNS = 1\n",
    "SIMULATION_TIMESTEPS = 25 # Timesteps / iterations of the simulation\n",
    "\n",
    "sim_config = config_sim(\n",
    "    {\n",
    "        'N': MONTE_CARLO_RUNS,\n",
    "        'T': range(SIMULATION_TIMESTEPS),\n",
    "        #'M': {} # This will be explained in later tutorials\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [],
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                  ___________    ____\n",
      "  ________ __ ___/ / ____/   |  / __ \\\n",
      " / ___/ __` / __  / /   / /| | / / / /\n",
      "/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n",
      "\\___/\\__,_/\\__,_/\\____/_/  |_/_____/\n",
      "by cadCAD\n",
      "\n",
      "cadCAD Version: 0.4.28\n",
      "Execution Mode: local_proc\n",
      "Simulation Dimensions:\n",
      "Entire Simulation: (Models, Unique Timesteps, Params, Total Runs, Sub-States) = (1, 25, 0, 1, 1)\n",
      "     Simulation 0: (Timesteps, Params, Runs, Sub-States) = (25, 0, 1, 1)\n",
      "Execution Method: local_simulations\n",
      "Execution Mode: single_threaded\n",
      "Total execution time: 0.01s\n"
     ]
    }
   ],
   "source": [
    "del configs[:]\n",
    "\n",
    "experiment = Experiment()\n",
    "\n",
    "experiment.append_configs(\n",
    "    sim_configs=sim_config,\n",
    "    initial_state=initial_state,\n",
    "    partial_state_update_blocks=partial_state_update_blocks,\n",
    ")\n",
    "\n",
    "\n",
    "exec_context = ExecutionContext()\n",
    "run = Executor(exec_context=exec_context, configs=experiment.configs)\n",
    "\n",
    "(system_events, tensor_field, sessions) = run.execute()\n",
    "df = pd.DataFrame(system_events)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We create a helper functions to extract the networkx graph object from the Pandas dataframe and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#NetworkX helper function\n",
    "def get_nodes(g):\n",
    "    return [node for node in g.nodes if g.nodes[node]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Using our helper function get_nodes() we pull out the boxes ball quantity and save it to a new dataframe column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['Balls'] = df.network.apply(lambda g: np.array([g.nodes[j]['balls'] for j in get_nodes(g)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data = np.stack(df['Balls'].values)\n",
    "balls_df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Next we will plot the number of balls in each box over the simulation time period. We can see an oscillation occurs never reaching an equilibrium due to the uneven nature of the boxes and balls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = balls_df.plot( marker='o', markersize=10, markeredgewidth=2, alpha=0.9, markerfacecolor='black',\n",
    "                linewidth=3, figsize=(12,8),  title=\"Marbles in each box as a function of time\", \n",
    "                ylabel='Number of Marbles', grid=True, fillstyle='none',  \n",
    "                xticks=list(range(SIMULATION_TIMESTEPS)),\n",
    "                yticks=list(range(10)))\n",
    "ax.legend( title='Box #');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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id=\"T_f606a_row0_col7\" class=\"data row0 col7\" >8</td>\n      <td id=\"T_f606a_row0_col8\" class=\"data row0 col8\" >4</td>\n      <td id=\"T_f606a_row0_col9\" class=\"data row0 col9\" >8</td>\n      <td id=\"T_f606a_row0_col10\" class=\"data row0 col10\" >4</td>\n      <td id=\"T_f606a_row0_col11\" class=\"data row0 col11\" >8</td>\n      <td id=\"T_f606a_row0_col12\" class=\"data row0 col12\" >4</td>\n      <td id=\"T_f606a_row0_col13\" class=\"data row0 col13\" >8</td>\n      <td id=\"T_f606a_row0_col14\" class=\"data row0 col14\" >4</td>\n      <td id=\"T_f606a_row0_col15\" class=\"data row0 col15\" >8</td>\n      <td id=\"T_f606a_row0_col16\" class=\"data row0 col16\" >4</td>\n      <td id=\"T_f606a_row0_col17\" class=\"data row0 col17\" >8</td>\n      <td id=\"T_f606a_row0_col18\" class=\"data row0 col18\" >4</td>\n      <td id=\"T_f606a_row0_col19\" class=\"data row0 col19\" >8</td>\n      <td id=\"T_f606a_row0_col20\" class=\"data row0 col20\" >4</td>\n      <td 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id=\"T_f606a_row1_col22\" class=\"data row1 col22\" >9</td>\n      <td id=\"T_f606a_row1_col23\" class=\"data row1 col23\" >6</td>\n      <td id=\"T_f606a_row1_col24\" class=\"data row1 col24\" >9</td>\n      <td id=\"T_f606a_row1_col25\" class=\"data row1 col25\" >6</td>\n    </tr>\n    <tr>\n      <th id=\"T_f606a_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n      <td id=\"T_f606a_row2_col0\" class=\"data row2 col0\" >7</td>\n      <td id=\"T_f606a_row2_col1\" class=\"data row2 col1\" >6</td>\n      <td id=\"T_f606a_row2_col2\" class=\"data row2 col2\" >7</td>\n      <td id=\"T_f606a_row2_col3\" class=\"data row2 col3\" >6</td>\n      <td id=\"T_f606a_row2_col4\" class=\"data row2 col4\" >7</td>\n      <td id=\"T_f606a_row2_col5\" class=\"data row2 col5\" >6</td>\n      <td id=\"T_f606a_row2_col6\" class=\"data row2 col6\" >7</td>\n      <td id=\"T_f606a_row2_col7\" class=\"data row2 col7\" >6</td>\n      <td id=\"T_f606a_row2_col8\" class=\"data row2 col8\" >7</td>\n     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id=\"T_f606a_row2_col23\" class=\"data row2 col23\" >6</td>\n      <td id=\"T_f606a_row2_col24\" class=\"data row2 col24\" >7</td>\n      <td id=\"T_f606a_row2_col25\" class=\"data row2 col25\" >6</td>\n    </tr>\n    <tr>\n      <th id=\"T_f606a_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n      <td id=\"T_f606a_row3_col0\" class=\"data row3 col0\" >7</td>\n      <td id=\"T_f606a_row3_col1\" class=\"data row3 col1\" >7</td>\n      <td id=\"T_f606a_row3_col2\" class=\"data row3 col2\" >7</td>\n      <td id=\"T_f606a_row3_col3\" class=\"data row3 col3\" >7</td>\n      <td id=\"T_f606a_row3_col4\" class=\"data row3 col4\" >7</td>\n      <td id=\"T_f606a_row3_col5\" class=\"data row3 col5\" >7</td>\n      <td id=\"T_f606a_row3_col6\" class=\"data row3 col6\" >7</td>\n      <td id=\"T_f606a_row3_col7\" class=\"data row3 col7\" >7</td>\n      <td id=\"T_f606a_row3_col8\" class=\"data row3 col8\" >7</td>\n      <td id=\"T_f606a_row3_col9\" class=\"data row3 col9\" >7</td>\n      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id=\"T_f606a_row3_col24\" class=\"data row3 col24\" >7</td>\n      <td id=\"T_f606a_row3_col25\" class=\"data row3 col25\" >7</td>\n    </tr>\n    <tr>\n      <th id=\"T_f606a_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n      <td id=\"T_f606a_row4_col0\" class=\"data row4 col0\" >8</td>\n      <td id=\"T_f606a_row4_col1\" class=\"data row4 col1\" >7</td>\n      <td id=\"T_f606a_row4_col2\" class=\"data row4 col2\" >7</td>\n      <td id=\"T_f606a_row4_col3\" class=\"data row4 col3\" >7</td>\n      <td id=\"T_f606a_row4_col4\" class=\"data row4 col4\" >7</td>\n      <td id=\"T_f606a_row4_col5\" class=\"data row4 col5\" >7</td>\n      <td id=\"T_f606a_row4_col6\" class=\"data row4 col6\" >7</td>\n      <td id=\"T_f606a_row4_col7\" class=\"data row4 col7\" >7</td>\n      <td id=\"T_f606a_row4_col8\" class=\"data row4 col8\" >7</td>\n      <td id=\"T_f606a_row4_col9\" class=\"data row4 col9\" >7</td>\n      <td id=\"T_f606a_row4_col10\" class=\"data row4 col10\" >7</td>\n      <td id=\"T_f606a_row4_col11\" class=\"data row4 col11\" >7</td>\n      <td id=\"T_f606a_row4_col12\" class=\"data row4 col12\" >7</td>\n      <td id=\"T_f606a_row4_col13\" class=\"data row4 col13\" >7</td>\n      <td id=\"T_f606a_row4_col14\" class=\"data row4 col14\" >7</td>\n      <td id=\"T_f606a_row4_col15\" class=\"data row4 col15\" >7</td>\n      <td id=\"T_f606a_row4_col16\" class=\"data row4 col16\" >7</td>\n      <td id=\"T_f606a_row4_col17\" class=\"data row4 col17\" >7</td>\n      <td id=\"T_f606a_row4_col18\" class=\"data row4 col18\" >7</td>\n      <td id=\"T_f606a_row4_col19\" class=\"data row4 col19\" >7</td>\n      <td id=\"T_f606a_row4_col20\" class=\"data row4 col20\" >7</td>\n      <td id=\"T_f606a_row4_col21\" class=\"data row4 col21\" >7</td>\n      <td id=\"T_f606a_row4_col22\" class=\"data row4 col22\" >7</td>\n      <td id=\"T_f606a_row4_col23\" class=\"data row4 col23\" >7</td>\n      <td id=\"T_f606a_row4_col24\" class=\"data row4 col24\" >7</td>\n      <td id=\"T_f606a_row4_col25\" class=\"data row4 col25\" >7</td>\n    </tr>\n  </tbody>\n</table>\n"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balls_df.T.style.background_gradient(cmap ='viridis').set_properties(**{'font-size': '20px'}) "
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "In summary, we created a graph network of boxes and robotic arms to transfer balls between the boxes, striving for an unachievable equilibrium state. The ability to embed a graph, virtually a graph database, into a cadCAD state allows for tremendous scalability and flexibility as a modeling tool.  "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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 "nbformat": 4,
 "nbformat_minor": 4
}