buhlmann.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Disclaimer\n",
    "If you use any of this for any other purpose than checking for my errors, you are insane as this is not tested.\n",
    "\n",
    "# Useful references\n",
    "\n",
    "* [Dmaziuk's Github page for ZHL](https://github.com/dmaziuk/diy-zhl/blob/master/diyzhl.ipynb)\n",
    "* [Introductory Deco Lessons, Eric C. Baker](https://www.shearwater.com/wp-content/uploads/2012/08/Introductory-Deco-Lessons.pdf)\n",
    "* [Understanding M-values, Eric C. Baker](https://www.frogkick.nl/files/understanding_m-values.pdf)\n",
    "* [Clearing up the Confusion over Deep Stops, Eric C. Baker](http://www.dive-tech.co.uk/resources/deepstops.pdf)\n",
    "* [DIY deco, Stuart Morrison](http://www.lizardland.co.uk/DIYDeco.html)\n",
    "* [Github page for SubSurface, core/deco.c](https://github.com/subsurface/subsurface/blob/master/core/deco.c)"
   ],
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     "name": "#%% md\n"
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  {
   "cell_type": "markdown",
   "source": [
    "# Schreiner Equation\n",
    "## Fundamental equation of the dissolved gas\n",
    "\n",
    "$\\frac{dP}{dt} = k(P_i - P)$\n",
    "\n",
    "For ascent or descent at a constant rate, this resolves:\n",
    "\n",
    "$P = P_{i,0}+R\\left(t-\\frac{1}{k}\\right)-\\left[P_{i,0}-P_0-\\frac{R}{k}\\right]e^{-kt}$\n",
    "- $P$ Final compartment inert gas pressure\n",
    "- $P_{i,0}$ Initial ambient pressure\n",
    "- $P_0$ Initial compartment inert gas pressure\n",
    "- $R$ rate of change in inspired gas pressure with change in ambient pressure\n",
    "- $t$ time\n",
    "- $k$ $\\frac{ln(2)}{\\lambda}$\n",
    "- $\\lambda$: half time\n",
    "\n",
    "This is implemented in `buhlman.get_partial_pressures()`:"
   ],
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    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from buhlmann import get_partial_pressures"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We define the ZHL-16C coefficients"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+-------------+--------+--------+-------------+--------+--------+\n",
      "| compartment | n2_halflife |  n2_a  |  n2_b  | he_halflife |  he_a  |  he_b  |\n",
      "+-------------+-------------+--------+--------+-------------+--------+--------+\n",
      "|     1.0     |     5.0     | 1.1696 | 0.5578 |     1.88    | 1.6189 | 0.477  |\n",
      "|     2.0     |     8.0     |  1.0   | 0.6514 |     3.02    | 1.383  | 0.5747 |\n",
      "|     3.0     |     12.5    | 0.8618 | 0.7222 |     4.72    | 1.1919 | 0.6527 |\n",
      "|     4.0     |     18.5    | 0.7562 | 0.7825 |     6.99    | 1.0458 | 0.7223 |\n",
      "|     5.0     |     27.0    |  0.62  | 0.8126 |    10.21    | 0.922  | 0.7582 |\n",
      "|     6.0     |     38.3    | 0.5043 | 0.8434 |    14.48    | 0.8205 | 0.7957 |\n",
      "|     7.0     |     54.3    | 0.441  | 0.8693 |    20.53    | 0.7305 | 0.8279 |\n",
      "|     8.0     |     77.0    |  0.4   | 0.891  |    29.11    | 0.6502 | 0.8553 |\n",
      "|     9.0     |    109.0    | 0.375  | 0.9092 |     41.2    | 0.595  | 0.8757 |\n",
      "|     10.0    |    146.0    |  0.35  | 0.9222 |    55.19    | 0.5545 | 0.8903 |\n",
      "|     11.0    |    187.0    | 0.3295 | 0.9319 |    70.69    | 0.5333 | 0.8997 |\n",
      "|     12.0    |    239.0    | 0.3065 | 0.9403 |    90.34    | 0.5189 | 0.9073 |\n",
      "|     13.0    |    305.0    | 0.2835 | 0.9477 |    115.29   | 0.5181 | 0.9122 |\n",
      "|     14.0    |    390.0    | 0.261  | 0.9544 |    147.42   | 0.5176 | 0.9171 |\n",
      "|     15.0    |    498.0    | 0.248  | 0.9602 |    188.24   | 0.5172 | 0.9217 |\n",
      "|     16.0    |    635.0    | 0.2327 | 0.9653 |    240.03   | 0.5119 | 0.9267 |\n",
      "+-------------+-------------+--------+--------+-------------+--------+--------+\n"
     ]
    },
    {
     "data": {
      "text/plain": "<prettytable.PrettyTable at 0x222e9cbbee0>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from buhlmann import ZHL_16C\n",
    "\n",
    "from prettytable import PrettyTable\n",
    "\n",
    "def generate_ascii_table(df):\n",
    "    x = PrettyTable()\n",
    "    x.field_names = df.columns.tolist()\n",
    "    for row in df.values:\n",
    "        x.add_row(row)\n",
    "    print(x)\n",
    "    return x\n",
    "\n",
    "generate_ascii_table(ZHL_16C)"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Surface pressure is assumed to be 1 bar\n",
    "Water Vapour Pressure in alveolis is expressed in bars"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# in bar\n",
    "SURFACE_PRESSURE = 1\n",
    "WATER_VAPOR_PRESSURE_ALVEOLI = 0.0567\n",
    "\n",
    "# in fsw\n",
    "# SURFACE_PRESSURE = 33\n",
    "# WATER_VAPOR_PRESSURE_ALVEOLI = 2.042\n",
    "\n",
    "\n",
    "# 1st dive so 0.79 N2 and 0 HE\n",
    "surface_n2_pp = 0.79\n"
   ],
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    "pycharm": {
     "name": "#%%\n"
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   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Define a gas for the dive"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial N2 partial pressure: <PandasArray>\n",
      "[0.7452070000000001, 0.7452070000000001, 0.7452070000000001,\n",
      " 0.7452070000000001, 0.7452070000000001, 0.7452070000000001,\n",
      " 0.7452070000000001, 0.7452070000000001, 0.7452070000000001,\n",
      " 0.7452070000000001, 0.7452070000000001, 0.7452070000000001,\n",
      " 0.7452070000000001, 0.7452070000000001, 0.7452070000000001,\n",
      " 0.7452070000000001]\n",
      "Length: 16, dtype: float64\n",
      "Initial He partial pressure: <IntegerArray>\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "Length: 16, dtype: Int64\n"
     ]
    }
   ],
   "source": [
    "from buhlmann import Gas, Tissues\n",
    "# gas = Gas(n2_pc=0.4, he_pc=0.45)\n",
    "gas = Gas(n2_pc=0.79, he_pc=0.0)\n",
    "\n",
    "initial_tissues = Tissues()\n",
    "print(\"Initial N2 partial pressure: {}\".format(initial_tissues.n2_p))\n",
    "print(\"Initial He partial pressure: {}\".format(initial_tissues.he_p))"
   ],
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     "name": "#%%\n"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "We define the ceiling using ZHL coefficients.\n",
    "Buhlamnn used the equation of type $y= mx + b$.\n",
    "\n",
    "But he named his coefficients such as the equation is:\n",
    "$y = \\frac{1}{b}x +a$\n",
    "\n",
    "And he chose to use:\n",
    " * $P_{tissue,tol}$ on the Y-axis, tolerated inert gas in tissue\n",
    " * $P_{amb tol}$ on the X-axis, tolerated ambient pressure (absolute)\n",
    "The equation is:\n",
    "$P_{tissue,tol} = \\frac{1}{b_{tissue}}P_{amb tol} +a_{tissue}$\n",
    "\n",
    "So the ceiling implied from the amount of inert gas in tissue is:\n",
    "$ceiling_{tissue}=P_{amb tol}=\\left(P_{tissue,tol} - a_{tissue}\\right) b_{tissue}$\n",
    "\n",
    "We take the deepest ceiling of any tissue as ceiling.\n",
    "$ceiling=max_{1<=tissue<=16}\\left(ceiling_{tissue}\\right)$\n",
    "\n",
    "We defined a function to compute this `buhlmann.ceiling(tissue)`:"
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ceiling in bar is 0.4947230071000001 at the start of the dive\n"
     ]
    }
   ],
   "source": [
    "from buhlmann import ceiling_pressure\n",
    "# this will return a negative number because we are a the beginning of the dive\n",
    "print(\"ceiling in bar is {} at the start of the dive\".format(ceiling_pressure(initial_tissues)))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "We have a function to convert this in depth:"
   ],
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ceiling in metres is -5.052769928999998 at the start of the dive\n"
     ]
    }
   ],
   "source": [
    "from buhlmann import ceiling_pressure, pressure_to_depth, depth_to_pressure\n",
    "print(\"ceiling in metres is {} at the start of the dive\".format(\n",
    "    pressure_to_depth(ceiling_pressure(initial_tissues))))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "This is how we can update the tissues across a dive:"
   ],
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:0, t_start=0, depth_start=0, t_end=2, depth_end=40\n",
      "ceiling: -5.019497451499991\n",
      "Step:1, t_start=2, depth_start=40, t_end=22, depth_end=40\n",
      "ceiling: 5.5835348059744145\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dive_plan = pd.DataFrame([\n",
    "    [0, 0],\n",
    "    [2, 40],\n",
    "    [22, 40]],\n",
    "    columns=['t', 'depth'])\n",
    "\n",
    "# create the segments from the plan\n",
    "dive_plan_tuples = [(t.t, t.depth) for t in dive_plan.itertuples()]\n",
    "dive_segments = zip(dive_plan_tuples[:-1], dive_plan_tuples[1:])\n",
    "\n",
    "tissues = initial_tissues\n",
    "for i_step, ((start_time, start_depth), (end_time, end_depth)) in enumerate(dive_segments):\n",
    "    print(\"Step:{}, t_start={}, depth_start={}, t_end={}, depth_end={}\".format(\n",
    "        i_step, start_time, start_depth, end_time, end_depth\n",
    "    ))\n",
    "    tissues = get_partial_pressures(\n",
    "            tissues,        # vector for compartments\n",
    "            gas,\n",
    "            depth_to_pressure(start_depth),    # for example 0 feet\n",
    "            depth_to_pressure(end_depth),      # for example 120 feet\n",
    "            end_time-start_time,              # time for depth change\n",
    "    )\n",
    "    print(\"ceiling: {}\".format(pressure_to_depth(ceiling_pressure(tissues))))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "For the sake of convenience, we will make a function to compute the tissues and ceilings at each step:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    t  depth                                          tissues   ceiling\n",
      "0   0      0  <buhlmann.Tissues object at 0x00000222D9D0A5E0> -5.052770\n",
      "1   2     40  <buhlmann.Tissues object at 0x00000222E9D1D400> -5.019497\n",
      "2  22     40  <buhlmann.Tissues object at 0x00000222E9D1D580>  5.583535\n"
     ]
    }
   ],
   "source": [
    "from buhlmann import run_dive\n",
    "updated_dive_plan = run_dive(dive_plan, initial_tissues, gas)\n",
    "print(updated_dive_plan)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We try to find the stops for the dive"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find stops for depth=40\n",
      "STOPS @ 40 metres\n",
      "    t  depth\n",
      "0  22     40\n",
      "1  26      6\n",
      "Find stops for depth=6\n",
      "STOPS @ 6 metres\n",
      "    t  depth\n",
      "0  26      6\n",
      "1  40      6\n",
      "2  41      0\n",
      "Final Dive Plan\n",
      "    t depth                                          tissues   ceiling\n",
      "0   0     0  <buhlmann.Tissues object at 0x00000222E9D541C0> -5.052770\n",
      "1   2    40  <buhlmann.Tissues object at 0x00000222E9D1E460> -5.019497\n",
      "2  22    40  <buhlmann.Tissues object at 0x00000222E9D46550>  5.583535\n",
      "1  26     6  <buhlmann.Tissues object at 0x00000222E9D466D0>  4.270459\n",
      "1  40     6  <buhlmann.Tissues object at 0x00000222E9D46FD0> -0.086336\n",
      "2  41     0  <buhlmann.Tissues object at 0x00000222E9D1D5E0> -0.263211\n"
     ]
    }
   ],
   "source": [
    "from buhlmann import find_next_stop, run_dive, Tissues, Gas\n",
    "import pandas as pd\n",
    "gas = Gas(n2_pc=0.79, he_pc=0.0)\n",
    "initial_tissues = Tissues()\n",
    "dive_plan = pd.DataFrame([\n",
    "    [0, 0],\n",
    "    [2, 40],\n",
    "    [22, 40]],\n",
    "    columns=['t', 'depth'])\n",
    "updated_dive_plan = run_dive(dive_plan, initial_tissues, gas)\n",
    "\n",
    "current_depth = updated_dive_plan.iloc[-1]['depth']\n",
    "current_time = updated_dive_plan.iloc[-1]['t']\n",
    "current_tissues = updated_dive_plan.iloc[-1]['tissues']\n",
    "allstops_schedule = pd.DataFrame(\n",
    "    columns=['t', 'depth', 'tissues', 'ceiling'])\n",
    "while current_depth != 0:\n",
    "    print(\"Find stops for depth={}\".format(current_depth))\n",
    "    stop_info = find_next_stop(current_tissues, current_depth, gas, 9)\n",
    "\n",
    "    stop_info['t'] += current_time\n",
    "    # # update the times\n",
    "    # stop_info = [(current_time, current_depth)] + \\\n",
    "    #             [ (t +current_time, d) for (t, d) in stop_info]\n",
    "\n",
    "    print(\"STOPS @ {} metres\\n{}\".format(current_depth, stop_info))\n",
    "\n",
    "    # update tissues\n",
    "    run_stop_schedule = run_dive(stop_info, current_tissues, gas)\n",
    "\n",
    "    allstops_schedule = allstops_schedule.append(run_stop_schedule[1:]) # don't repeat the first entry\n",
    "\n",
    "    # update status variables\n",
    "    current_tissues = run_stop_schedule.iloc[-1]['tissues']\n",
    "    current_depth = run_stop_schedule.iloc[-1]['depth']\n",
    "    current_time = run_stop_schedule.iloc[-1]['t']\n",
    "\n",
    "\n",
    "updated_dive_plan = updated_dive_plan.append(allstops_schedule)\n",
    "print(\"Final Dive Plan\\n{}\".format(updated_dive_plan))\n",
    "# we put this functionality in buhlmann.get_stops_to_surface()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We provide this function to do all of this `buhlmann.get_stops_to_surface()`:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    t  depth                                          tissues   ceiling\n",
      "0   0      0  <buhlmann.Tissues object at 0x00000222E9D6DDF0> -5.052770\n",
      "1   2     40  <buhlmann.Tissues object at 0x00000222E9D1E670> -5.019497\n",
      "2  22     40  <buhlmann.Tissues object at 0x00000222E9D1EB80>  5.583535\n",
      "0  22     40  <buhlmann.Tissues object at 0x00000222E9D1EB80>  5.583535\n",
      "1  26      6  <buhlmann.Tissues object at 0x00000222E9D1EDC0>  4.270459\n",
      "1  40      6  <buhlmann.Tissues object at 0x00000222E9CF7F40> -0.086336\n",
      "2  41      0  <buhlmann.Tissues object at 0x00000222D9D04C40> -0.263211\n"
     ]
    }
   ],
   "source": [
    "from buhlmann import get_stops_to_surface\n",
    "from buhlmann import find_next_stop, run_dive, Tissues, Gas\n",
    "import pandas as pd\n",
    "gas = Gas(n2_pc=0.79, he_pc=0.0)\n",
    "initial_tissues = Tissues()\n",
    "dive_plan = pd.DataFrame([\n",
    "    [0, 0],\n",
    "    [2, 40],\n",
    "    [22, 40]],\n",
    "    columns=['t', 'depth'])\n",
    "run_dive_plan = run_dive(dive_plan, initial_tissues, gas)\n",
    "current_tissues = run_dive_plan.iloc[-1]['tissues']\n",
    "current_depth = run_dive_plan.iloc[-1]['depth']\n",
    "max_ascent_rate = 9 # metres/sec\n",
    "run_stops = get_stops_to_surface(current_tissues, current_depth, gas, max_ascent_rate)\n",
    "run_stops['t'] += run_dive_plan.iloc[-1]['t']\n",
    "run_dive_plan = run_dive_plan.append(run_stops)\n",
    "print(run_dive_plan)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "But to display a graph we will use the resolution parameter to run the dive"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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      "text/plain": "<Figure size 864x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from buhlmann import Gas, Tissues, run_dive, get_stops_to_surface\n",
    "gas = Gas(n2_pc=0.79, he_pc=0.0)\n",
    "initial_tissues = Tissues()\n",
    "dive_plan = pd.DataFrame([\n",
    "    [0, 0],\n",
    "    [2, 40],\n",
    "    [22, 40]],\n",
    "    columns=['t', 'depth'])\n",
    "run_dive_plan = run_dive(dive_plan, initial_tissues, gas)\n",
    "current_tissues = run_dive_plan.iloc[-1]['tissues']\n",
    "current_depth = run_dive_plan.iloc[-1]['depth']\n",
    "max_ascent_rate = 9  # metres/sec\n",
    "run_stops = get_stops_to_surface(current_tissues, current_depth, gas, max_ascent_rate)\n",
    "run_stops['t'] += run_dive_plan.iloc[-1]['t']\n",
    "run_dive_plan = run_dive_plan.append(run_stops)\n",
    "\n",
    "# print(run_dive_plan)\n",
    "dive_data = run_dive(run_dive_plan, initial_tissues, gas, resolution=1)\n",
    "# print(dive_data)\n",
    "\n",
    "from dive_plan_graph import gradient_fill\n",
    "\n",
    "%matplotlib inline\n",
    "import math\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "t = np.linspace(0, 2*math.pi, 800)\n",
    "a = np.sin(t)\n",
    "plt.figure(figsize=(12, 10), dpi=75)\n",
    "# plt.plot(dive_data['t'], dive_data['depth'], 'r')\n",
    "# plt.plot(dive_data['t'], dive_data['ceiling'], 'b')\n",
    "fig, ax = plt.subplots(figsize=(12,10))\n",
    "gradient_fill(dive_data['t'], dive_data['depth'], ax=ax)\n",
    "gradient_fill(dive_data['t'], dive_data['ceiling'], ax=ax)\n",
    "plt.gca().invert_yaxis()\n",
    "# ax.set_aspect('equal')\n",
    "ax.grid(True, which='both')\n",
    "\n",
    "ax.axhline(y=0, color='k')\n",
    "ax.axvline(x=0, color='k')\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "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": 0
}