dec-03-task-assigning/python/dec-3-task-assigning-python.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# OR Advent 2024 December 3: task assigning (Python)\n",
    "\n",
    "## Assignment\n",
    "\n",
    "Hey, ORville here\n",
    "\n",
    "I'm a logistics manager overseeing the allocation of tasks to employees in our company, FurnitORe.\n",
    "\n",
    "Right now we have 100 tasks that need to be completed, and we also have 100 employees available to handle them.\n",
    "\n",
    "The catch is that assigning a task to an employee has a cost.\n",
    "\n",
    "These costs vary depending on the difficulty of the task, the expertise of the employee, and other factors.\n",
    "\n",
    "I need your help to figure out the most cost-effective way to assign these tasks to employees.\n",
    "\n",
    "You can assume that each task is done by just one employee, and one employee is assigned to just one task.\n",
    "\n",
    "Can you help me solve this problem?\n",
    "\n",
    "## Notes\n",
    "\n",
    "**This implementation requires Python 3.12, not 3.13.**\n",
    "\n",
    "## Dependencies\n",
    "\n",
    "This Jupyter Notebook solves an OR Advent problem with [Timefold Solver](https://timefold.ai/open-source-solver), the open source planning solver AI. We add it as a dependency:"
   ],
   "id": "6dec503fd3832d94"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:10.074806Z",
     "start_time": "2024-12-03T20:34:09.707733Z"
    }
   },
   "cell_type": "code",
   "source": "pip install timefold==1.16.0b0",
   "id": "cba987b8c96efaf9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: timefold==1.16.0b0 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (1.16.0b0)\r\n",
      "Requirement already satisfied: JPype1>=1.5.0 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from timefold==1.16.0b0) (1.5.0)\r\n",
      "Requirement already satisfied: packaging in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from JPype1>=1.5.0->timefold==1.16.0b0) (24.1)\r\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "\n",
    "## Domain\n",
    "\n",
    "We need to assign tasks to employees:"
   ],
   "id": "f7db1507e84978a4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:10.078038Z",
     "start_time": "2024-12-03T20:34:10.075878Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "@dataclass\n",
    "class Employee:\n",
    "    id: int"
   ],
   "id": "757137072877cecd",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "This class changes during planning. Timefold Solver fills in the `@PlanningVariable` annotated fields for all instances:",
   "id": "aecc532d51aa902b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:10.546028Z",
     "start_time": "2024-12-03T20:34:10.078700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from timefold.solver.domain import (planning_entity, PlanningVariable, PlanningId)\n",
    "from dataclasses import dataclass, field\n",
    "from typing import (Annotated, Dict)\n",
    "\n",
    "@planning_entity\n",
    "@dataclass\n",
    "class Task:\n",
    "    id: Annotated[int, PlanningId]\n",
    "    employee_cost_map: Dict[int, int] = field(default_factory=dict)\n",
    "    employee: Annotated[Employee | None, PlanningVariable] = field(default=None)\n",
    "\n",
    "\n",
    "    def calc_cost(self) -> int:\n",
    "        if self.employee is None:\n",
    "            return 0\n",
    "        return self.employee_cost_map.get(self.employee.id)"
   ],
   "id": "e0b7bdd54c095a8d",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Constraints\n",
    "\n",
    "The solver takes into account hard and soft constraints:"
   ],
   "id": "5de0f51c16a9a40d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:11.787872Z",
     "start_time": "2024-12-03T20:34:10.547187Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from timefold.solver.score import (constraint_provider, HardSoftScore, Joiners,\n",
    "                                   ConstraintFactory, Constraint)\n",
    "\n",
    "@constraint_provider\n",
    "def define_constraints(constraint_factory: ConstraintFactory):\n",
    "    return [\n",
    "        # Hard constraints\n",
    "        employee_conflict(constraint_factory),\n",
    "        # Soft constraints\n",
    "        minimize_cost(constraint_factory),\n",
    "    ]\n",
    "\n",
    "\n",
    "def employee_conflict(constraint_factory: ConstraintFactory) -> Constraint:\n",
    "    return (constraint_factory\n",
    "            .for_each_unique_pair(Task,\n",
    "                                  Joiners.equal(lambda task: task.employee))\n",
    "            .penalize(HardSoftScore.ONE_HARD)\n",
    "            .as_constraint(\"Employee conflict\"))\n",
    "\n",
    "\n",
    "def minimize_cost(constraint_factory: ConstraintFactory) -> Constraint:\n",
    "    return (constraint_factory\n",
    "            .for_each(Task)\n",
    "            .penalize(HardSoftScore.ONE_SOFT, lambda task: task.calc_cost())\n",
    "            .as_constraint(\"Minimize cost\"))\n"
   ],
   "id": "fed72b2424334a18",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### The dataset class\n",
    "\n",
    "This class represents the dataset to solve:"
   ],
   "id": "824dc3b974444a79"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:11.792289Z",
     "start_time": "2024-12-03T20:34:11.788482Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from timefold.solver.domain import (planning_solution, \n",
    "                                    PlanningEntityCollectionProperty,\n",
    "                                    ValueRangeProvider,\n",
    "                                    PlanningScore)\n",
    "from timefold.solver.score import HardSoftScore\n",
    "from dataclasses import dataclass, field\n",
    "from typing import Annotated\n",
    "\n",
    "@planning_solution\n",
    "@dataclass\n",
    "class OrAdventDataset:\n",
    "    employees: Annotated[list[Employee], ValueRangeProvider]\n",
    "    tasks: Annotated[list[Task],  PlanningEntityCollectionProperty]\n",
    "    score: Annotated[HardSoftScore, PlanningScore] = field(default=None)"
   ],
   "id": "f62624e265cbf78a",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Input reader",
   "id": "11a5e12693108afc"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:11.795246Z",
     "start_time": "2024-12-03T20:34:11.793012Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def read_dataset() -> OrAdventDataset:\n",
    "    \n",
    "    with open(\"../instance.txt\", 'r') as file:\n",
    "        lines = file.readlines()\n",
    "        \n",
    "    data_lines = [line.strip() for line in lines if not line.strip().startswith('#') and line.strip()]\n",
    "\n",
    "    number_of_tasks = 100\n",
    "    employees = [Employee(id=i) for i in range(0, number_of_tasks)]\n",
    "\n",
    "    tasks = []\n",
    "    for task_id, line in enumerate(data_lines):\n",
    "        employee_costs = list(map(int, line.split()))\n",
    "        employee_cost_map = {employee_id: cost for employee_id, cost in enumerate(employee_costs)}\n",
    "        tasks.append(Task(id=task_id, employee_cost_map=employee_cost_map))\n",
    "\n",
    "    return OrAdventDataset(employees, tasks)"
   ],
   "id": "f12e88884314eab9",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Solve it\n",
    "\n",
    "Configure and run the solver:"
   ],
   "id": "59d1a178f2e52c55"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:40.899701Z",
     "start_time": "2024-12-03T20:34:11.795821Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from timefold.solver.config import (SolverConfig, ScoreDirectorFactoryConfig,\n",
    "                                    TerminationConfig, Duration)\n",
    "from timefold.solver import SolverFactory\n",
    "\n",
    "solver_factory = SolverFactory.create(\n",
    "    SolverConfig(\n",
    "        solution_class=OrAdventDataset,\n",
    "        entity_class_list=[Task],\n",
    "        score_director_factory_config=ScoreDirectorFactoryConfig(\n",
    "            constraint_provider_function=define_constraints\n",
    "        ),\n",
    "        termination_config=TerminationConfig(\n",
    "            unimproved_spent_limit=Duration(seconds=5)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "problem = read_dataset()\n",
    "\n",
    "print(f'Solving the problem ...')\n",
    "solver = solver_factory.build_solver()\n",
    "solution = solver.solve(problem)\n",
    "print(f'Solving finished with score ({solution.score}).')"
   ],
   "id": "1ff2655f5e7cb952",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving the problem ...\n",
      "Solving finished with score (0hard/-441soft).\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Visualize the schedule",
   "id": "1507377c5b4202eb"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:40.903632Z",
     "start_time": "2024-12-03T20:34:40.900526Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from IPython.core.display import HTML\n",
    "\n",
    "html_content = \"<table><tr><th>Task</th><th>Employee</th><th>Cost</th></tr>\"\n",
    "for task in solution.tasks:\n",
    "    html_content += f\"<tr><td>{task.id}</td><td>{task.employee.id}</td><td>{task.calc_cost()}</td></tr>\"\n",
    "html_content += \"</table>\"\n",
    "\n",
    "HTML(html_content)"
   ],
   "id": "c07ec007586d9b1f",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "<table><tr><th>Task</th><th>Employee</th><th>Cost</th></tr><tr><td>0</td><td>68</td><td>2</td></tr><tr><td>1</td><td>41</td><td>9</td></tr><tr><td>2</td><td>77</td><td>4</td></tr><tr><td>3</td><td>28</td><td>3</td></tr><tr><td>4</td><td>61</td><td>3</td></tr><tr><td>5</td><td>17</td><td>4</td></tr><tr><td>6</td><td>45</td><td>6</td></tr><tr><td>7</td><td>16</td><td>5</td></tr><tr><td>8</td><td>92</td><td>7</td></tr><tr><td>9</td><td>85</td><td>2</td></tr><tr><td>10</td><td>57</td><td>4</td></tr><tr><td>11</td><td>88</td><td>3</td></tr><tr><td>12</td><td>40</td><td>5</td></tr><tr><td>13</td><td>30</td><td>4</td></tr><tr><td>14</td><td>6</td><td>4</td></tr><tr><td>15</td><td>66</td><td>4</td></tr><tr><td>16</td><td>43</td><td>4</td></tr><tr><td>17</td><td>0</td><td>3</td></tr><tr><td>18</td><td>72</td><td>2</td></tr><tr><td>19</td><td>94</td><td>2</td></tr><tr><td>20</td><td>10</td><td>3</td></tr><tr><td>21</td><td>64</td><td>2</td></tr><tr><td>22</td><td>12</td><td>3</td></tr><tr><td>23</td><td>55</td><td>2</td></tr><tr><td>24</td><td>24</td><td>5</td></tr><tr><td>25</td><td>63</td><td>5</td></tr><tr><td>26</td><td>69</td><td>3</td></tr><tr><td>27</td><td>42</td><td>5</td></tr><tr><td>28</td><td>74</td><td>2</td></tr><tr><td>29</td><td>21</td><td>5</td></tr><tr><td>30</td><td>9</td><td>6</td></tr><tr><td>31</td><td>65</td><td>2</td></tr><tr><td>32</td><td>29</td><td>5</td></tr><tr><td>33</td><td>4</td><td>2</td></tr><tr><td>34</td><td>27</td><td>8</td></tr><tr><td>35</td><td>1</td><td>3</td></tr><tr><td>36</td><td>62</td><td>2</td></tr><tr><td>37</td><td>7</td><td>3</td></tr><tr><td>38</td><td>98</td><td>4</td></tr><tr><td>39</td><td>39</td><td>3</td></tr><tr><td>40</td><td>97</td><td>6</td></tr><tr><td>41</td><td>76</td><td>4</td></tr><tr><td>42</td><td>19</td><td>9</td></tr><tr><td>43</td><td>86</td><td>7</td></tr><tr><td>44</td><td>32</td><td>8</td></tr><tr><td>45</td><td>82</td><td>3</td></tr><tr><td>46</td><td>25</td><td>3</td></tr><tr><td>47</td><td>31</td><td>5</td></tr><tr><td>48</td><td>2</td><td>2</td></tr><tr><td>49</td><td>48</td><td>5</td></tr><tr><td>50</td><td>14</td><td>6</td></tr><tr><td>51</td><td>53</td><td>3</td></tr><tr><td>52</td><td>13</td><td>8</td></tr><tr><td>53</td><td>37</td><td>3</td></tr><tr><td>54</td><td>58</td><td>4</td></tr><tr><td>55</td><td>73</td><td>1</td></tr><tr><td>56</td><td>50</td><td>3</td></tr><tr><td>57</td><td>78</td><td>8</td></tr><tr><td>58</td><td>83</td><td>7</td></tr><tr><td>59</td><td>20</td><td>4</td></tr><tr><td>60</td><td>90</td><td>7</td></tr><tr><td>61</td><td>75</td><td>4</td></tr><tr><td>62</td><td>22</td><td>2</td></tr><tr><td>63</td><td>96</td><td>8</td></tr><tr><td>64</td><td>87</td><td>5</td></tr><tr><td>65</td><td>11</td><td>3</td></tr><tr><td>66</td><td>56</td><td>7</td></tr><tr><td>67</td><td>38</td><td>2</td></tr><tr><td>68</td><td>3</td><td>4</td></tr><tr><td>69</td><td>15</td><td>4</td></tr><tr><td>70</td><td>34</td><td>3</td></tr><tr><td>71</td><td>91</td><td>2</td></tr><tr><td>72</td><td>46</td><td>9</td></tr><tr><td>73</td><td>89</td><td>3</td></tr><tr><td>74</td><td>26</td><td>4</td></tr><tr><td>75</td><td>81</td><td>4</td></tr><tr><td>76</td><td>35</td><td>6</td></tr><tr><td>77</td><td>5</td><td>3</td></tr><tr><td>78</td><td>95</td><td>10</td></tr><tr><td>79</td><td>79</td><td>5</td></tr><tr><td>80</td><td>60</td><td>5</td></tr><tr><td>81</td><td>71</td><td>4</td></tr><tr><td>82</td><td>23</td><td>2</td></tr><tr><td>83</td><td>54</td><td>4</td></tr><tr><td>84</td><td>8</td><td>7</td></tr><tr><td>85</td><td>80</td><td>6</td></tr><tr><td>86</td><td>84</td><td>2</td></tr><tr><td>87</td><td>70</td><td>3</td></tr><tr><td>88</td><td>18</td><td>4</td></tr><tr><td>89</td><td>47</td><td>3</td></tr><tr><td>90</td><td>51</td><td>2</td></tr><tr><td>91</td><td>52</td><td>5</td></tr><tr><td>92</td><td>49</td><td>8</td></tr><tr><td>93</td><td>33</td><td>2</td></tr><tr><td>94</td><td>44</td><td>3</td></tr><tr><td>95</td><td>36</td><td>13</td></tr><tr><td>96</td><td>93</td><td>6</td></tr><tr><td>97</td><td>59</td><td>7</td></tr><tr><td>98</td><td>67</td><td>4</td></tr><tr><td>99</td><td>99</td><td>4</td></tr></table>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Statistics\n",
    "\n",
    "For a big dataset, a schedule visualization is often too verbose.\n",
    "Let's visualize the solution through statistics:"
   ],
   "id": "af3104e744aaab5c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:41.436561Z",
     "start_time": "2024-12-03T20:34:40.904145Z"
    }
   },
   "cell_type": "code",
   "source": [
    "!pip install matplotlib\n",
    "%matplotlib inline"
   ],
   "id": "17a41b0c9d844b70",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (3.9.3)\r\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from matplotlib) (1.3.1)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from matplotlib) (0.12.1)\r\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from matplotlib) (4.55.1)\r\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from matplotlib) (1.4.7)\r\n",
      "Requirement already satisfied: numpy>=1.23 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from matplotlib) (2.1.3)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from matplotlib) (24.1)\r\n",
      "Requirement already satisfied: pillow>=8 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from matplotlib) (11.0.0)\r\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from matplotlib) (3.2.0)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from matplotlib) (2.9.0.post0)\r\n",
      "Requirement already satisfied: six>=1.5 in /Users/geoffreydesmet/projects/demo/timefold-notebooks/python/.venv/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\r\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:41.503804Z",
     "start_time": "2024-12-03T20:34:41.439716Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "costs = [task.calc_cost() for task in solution.tasks]\n",
    "cost_counts = Counter(costs)\n",
    "\n",
    "plt.bar(cost_counts.keys(), cost_counts.values())\n",
    "plt.xlabel('Cost')\n",
    "plt.ylabel('Number of tasks')\n",
    "plt.title('Number of tasks per cost')\n",
    "plt.show()"
   ],
   "id": "2cfe5c17a0d3b4a8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Analyze the score\n",
    "\n",
    "Let's break down the score per constraint:"
   ],
   "id": "2ef86c5b9b2f3d70"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:41.674968Z",
     "start_time": "2024-12-03T20:34:41.504297Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from timefold.solver import SolutionManager\n",
    "\n",
    "solution_manager = SolutionManager.create(solver_factory)\n",
    "score_analysis = solution_manager.analyze(solution)"
   ],
   "id": "21360362608c6152",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "And visualize it:",
   "id": "2c9dd9ef8ac27fff"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-03T20:34:41.679562Z",
     "start_time": "2024-12-03T20:34:41.675512Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from IPython.core.display import HTML\n",
    "\n",
    "html_content = f\"<p style='font-size: x-large'>Score: {score_analysis.score}</p>\"\n",
    "html_content += \"<ul>\"\n",
    "for constraint in score_analysis.constraint_map.values():\n",
    "    html_content += f\"<li>{constraint.constraint_name}: {constraint.score}</li>\"\n",
    "html_content += \"</ul>\"\n",
    "\n",
    "HTML(html_content)"
   ],
   "id": "cc07c2e07d3ba32",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "<p style='font-size: x-large'>Score: 0hard/-441soft</p><ul><li>Minimize cost: 0hard/-441soft</li><li>Employee conflict: 0hard/0soft</li></ul>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Notice\n",
    "\n",
    "This code isn't optimized for benchmarking or scaling.\n",
    "\n",
    "To learn more about planning optimization, visit [docs.timefold.ai](https://docs.timefold.ai)."
   ],
   "id": "ed22948f6b1cbd8c"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}