{
 "metadata": {
  "name": "Hello distributed world"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 'Hello distributed world'\n",
      "\n",
      "The goal of this tutorial is to setup a simulation and run it by using minimal example."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pymote import *\n",
      "net_gen = NetworkGenerator(100)\n",
      "net = net_gen.generate_random_network()\n",
      "from pymote.algorithms.broadcast import Flood\n",
      "net.algorithms = ( (Flood, {'informationKey':'I'}), )\n",
      "some_node = net.nodes()[0]\n",
      "some_node.memory['I'] = 'Hello distributed world'\n",
      "sim = Simulation(net)\n",
      "sim.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "WARNING  [__init__.py]: Environment variable PYMOTE_SETTINGS_MODULE is undefined, using global_settings.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: Simulation 0x686af80 created successfully.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 1 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 2 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 3 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 4 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 5 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 6 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 7 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 8 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 9 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 10 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 11 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 12 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 13 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 14 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 15 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 16 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 17 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 18 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 19 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 20 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 21 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 22 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 23 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 24 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 25 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Algorithm finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "INFO     [simulation.py]: Simulation has finished. There are no algorithms left to run. To run it from the start use sim.reset().\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That's it! For more elaborate description please continue reading."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the example given above, the goal was to simulate distributed algorithm `Flood` on arbitrary network. Algorithm `Flood` solves *Broadcast* problem, i.e. the goal of the algorithm is to share information that is present in one or more nodes to all other nodes in the network."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this example the IPython interactive console is started with `ipymote` or `ipython --profile=pymote`. This way certain names from Pymote package should already be available such as `Node` or `Network`."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Generating network\n",
      "\n",
      "Networks are fundamental objects in the Pymote and they can be created by instantiating `Network` class or by using  `NetworkGenerator` class. `NetworkGenerator` can receive different parameters such as number of nodes (exact, min, max), average number of neighbors per node etc.\n",
      "\n",
      "In this example the only parametar that we want to have in control is number of nodes. which is set to 100."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net_gen = NetworkGenerator(100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now generator's methods can be used to generate new networks with specified parameters, i.e. for generating network with random placement of nodes there is method `generate_random_network()`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net = net_gen.generate_random_network()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Note:* One of the `NetworkGenerator` parameters control if the generated network should be connected (which means  there are no isolated segments of the network). This parameter `connected` is by default set to `True` so if the number of nodes is set too low then some generator's methods  might not be able to produce the network."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The result is instance of the Network class:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<pymote.network.Network at 0x671ac90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "which can be visualized with its `show()` method:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Fi0tUVNTr62whkJycLLNnz5YVK1bof2o/PI/x888/i5GRkRw9elRERBITE6Vjx45ibW0t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6d+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\nogAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Algorithm\n",
      "\n",
      "Algorithm `Flood` is part of the `broadcast` module so it should be imported:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pymote.algorithms.broadcast import Flood"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "and then assigned to the generated network:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net.algorithms = ( (Flood, {'informationKey':'I'}), )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Two things should be noted:\n",
      "\n",
      "* there can be multiple algorithms that are being assigned to the network so specific algorithms are elements of `tuple`. Since in this example there is only one algorithm we must append `,` so that Python knows it's a one element `tuple` i.e. `(1)` is `int`, but `(1,)` is `tuple`.\n",
      "* every algorithm element is `tuple` itself, consisting of two elements. Former is an algorithm class, in this example `Flood`. Later is a `dict` of keyword parameters, i.e. `Flood` must be given 'informationKey' as a required parameter: that is the name of the key under which the nodes should store broadcasted information in their memory.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This algorithm demands that at least one node knows information to be broadcasted, so in this example at least one node should have some value stored under `I` key:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "some_node = net.nodes()[0]                         # take the first node, it can be any node\n",
      "some_node.memory['I'] = 'Hello distributed world'  # under key 'I' write the information to be boradcasted"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      " This way `some_node` has become algorithm initiator."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Simulation\n",
      "\n",
      "After network creation and setup all that is left to do is to create and run simulation. Every simulation has its own network (sent as parameter) that it runs on:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim = Simulation(net)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: Simulation 0x6a12788 created successfully.\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, run simulation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 1 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 2 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 3 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 4 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 5 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 6 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 7 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 8 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 9 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 10 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 11 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 12 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 13 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 14 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 15 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 16 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 17 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 18 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 19 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 20 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 21 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 22 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 23 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 24 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 25 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 26 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 27 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 28 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Algorithm finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "INFO     [simulation.py]: Simulation has finished. There are no algorithms left to run. To run it from the start use sim.reset().\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After the algorithm(s) execution is done we can check if the resullt is as expected, that is every node should have information in their memory:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for node in net.nodes():\n",
      "    print node.memory['I'],"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world Hello distributed world\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "During simulation execution, network and its nodes are changing their status, memory content etc. To memorize the currently running algorithm and current step of the simulation, network has a special attribute `algorithmsState`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net.algorithmState"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "{'finished': True, 'index': 0, 'step': 29}"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If simulation is going to be run again, it must be reset:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.reset()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "INFO     [simulation.py]: Resetting simulation.\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "whereby:\n",
      "\n",
      "* `algorithmState` is returned to the initial value:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net.algorithmState"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "{'finished': False, 'index': 0, 'step': 1}"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* memory content of all node's is deleted (as long as some other attributes)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for node in net.nodes():\n",
      "    print node.memory,"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {} {}\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "some_node.memory['I'] = 'Hello again'\n",
      "sim.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 1 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 2 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 3 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 4 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 5 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 6 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 7 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 8 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 9 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 10 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 11 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 12 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 13 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 14 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 15 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 16 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 17 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 18 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 19 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 20 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 21 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 22 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 23 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 24 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 25 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 26 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 27 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Step 28 finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DEBUG    [simulation.py]: [Flood] Algorithm finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "INFO     [simulation.py]: Simulation has finished. There are no algorithms left to run. To run it from the start use sim.reset().\n"
       ]
      }
     ],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}